[{"Question":"A researcher has created a small simulation in MATLAB and we want to make it accessible to others. My plan is to take the simulation, clean up a few things and turn it into a set of functions. Then I plan to compile it into a C library and use SWIG to create a Python wrapper. At that point, I should be able to call the simulation from a small Django app. At least I hope so.\nDo I have the right plan? Has anyone else done something similar? Can you let me know if there are any serious pitfalls that I'm not aware of at the moment?","AnswerCount":4,"Available Count":3,"Score":0.2449186624,"is_accepted":false,"ViewCount":2313,"Q_Id":5136,"Users Score":5,"Answer":"One thing to remember is that the Matlab compiler does not actually compile the Matlab code into native machine instructions.  It simply wraps it into a standalone executable or a library with its own runtime engine that runs it.  You would be able to run your code without Matlab installed, and you would be able to interface it with other languages, but it will still be interpreted Matlab code, so there would be no speedup.","Q_Score":14,"Tags":"python,c,matlab","A_Id":1659332,"CreationDate":"2008-08-07T18:47:00.000","Title":"Does anyone have experience creating a shared library in MATLAB?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":1,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"A researcher has created a small simulation in MATLAB and we want to make it accessible to others. My plan is to take the simulation, clean up a few things and turn it into a set of functions. Then I plan to compile it into a C library and use SWIG to create a Python wrapper. At that point, I should be able to call the simulation from a small Django app. At least I hope so.\nDo I have the right plan? Has anyone else done something similar? Can you let me know if there are any serious pitfalls that I'm not aware of at the moment?","AnswerCount":4,"Available Count":3,"Score":0.0996679946,"is_accepted":false,"ViewCount":2313,"Q_Id":5136,"Users Score":2,"Answer":"I'd also try ctypes first. \n\nUse the Matlab compiler to compile the code into C. \nCompile the C code into a DLL.\nUse ctypes to load and call code from this DLL\n\nThe hardest step is probably 1, but if you already know Matlab and have used the Matlab compiler, you should not have serious problems with it.","Q_Score":14,"Tags":"python,c,matlab","A_Id":138534,"CreationDate":"2008-08-07T18:47:00.000","Title":"Does anyone have experience creating a shared library in MATLAB?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":1,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"A researcher has created a small simulation in MATLAB and we want to make it accessible to others. My plan is to take the simulation, clean up a few things and turn it into a set of functions. Then I plan to compile it into a C library and use SWIG to create a Python wrapper. At that point, I should be able to call the simulation from a small Django app. At least I hope so.\nDo I have the right plan? Has anyone else done something similar? Can you let me know if there are any serious pitfalls that I'm not aware of at the moment?","AnswerCount":4,"Available Count":3,"Score":1.2,"is_accepted":true,"ViewCount":2313,"Q_Id":5136,"Users Score":3,"Answer":"I won't help much but I remember that I was able to wrap a MATLAB simulation into DLL and then call it from a Delphi app. It worked really well.","Q_Score":14,"Tags":"python,c,matlab","A_Id":5302,"CreationDate":"2008-08-07T18:47:00.000","Title":"Does anyone have experience creating a shared library in MATLAB?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":1,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Using the zip function, Python allows for loops to traverse multiple sequences in parallel.  \nfor (x,y) in zip(List1, List2):\nDoes MATLAB have an equivalent syntax? If not, what is the best way to iterate over two parallel arrays at the same time using MATLAB?","AnswerCount":7,"Available Count":1,"Score":-0.057080742,"is_accepted":false,"ViewCount":7417,"Q_Id":49307,"Users Score":-2,"Answer":"for loops in MATLAB used to be slow, but this is not true anymore.\nSo vectorizing is not always the miracle solution. Just use the profiler, and tic and toc functions to help you identify possible bottlenecks.","Q_Score":14,"Tags":"python,arrays,matlab,for-loop","A_Id":138886,"CreationDate":"2008-09-08T08:25:00.000","Title":"Can parallel traversals be done in MATLAB just as in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I understand that these methods are for pickling\/unpickling and have no relation to the reduce built-in function, but what's the difference between the 2 and why do we need both?","AnswerCount":2,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":9908,"Q_Id":150284,"Users Score":9,"Answer":"__reduce_ex__ is what __reduce__ should have been but never became.  __reduce_ex__ works like __reduce__ but the pickle protocol is passed.","Q_Score":19,"Tags":"python,pickle","A_Id":150318,"CreationDate":"2008-09-29T19:31:00.000","Title":"What is the difference between __reduce__ and __reduce_ex__?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm writing a simulator in Python, and am curious about options and opinions regarding basic 2D animations. By animation, I'm referring to rendering on the fly, not displaying prerendered images.\nI'm currently using matplotlib (Wxagg backend), and it's possible that I'll be able to continue using it, but I suspect it won't be able to sufficiently scale in terms of performance or capabilities.\nRequirements are:\n\nCross-platform (Linux, MacOS X,\nWindows)\nLow complexity overhead\nPlays well with wxpython (at least won't step on each other's toes unduly)\nInteractivity. Detect when objects are clicked on, moused over, etc. \n\nNote that high performance isn't on the list, but the ability to handle ~100 bitmap objects on the screen would be good.\nYour thoughts?","AnswerCount":3,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":29957,"Q_Id":169810,"Users Score":3,"Answer":"You can try pygame, its very easy to handle and similar to SDL under c++","Q_Score":12,"Tags":"python,animation,2d","A_Id":1568711,"CreationDate":"2008-10-04T05:36:00.000","Title":"2D animation in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":1,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been recently asked to learn some MATLAB basics for a class.\nWhat does make it so cool for researchers and people that works in university?\nI saw it's cool to work with matrices and plotting things... (things that can be done easily in Python using some libraries).\nWriting a function or parsing a file is just painful. I'm still at the start, what am I missing?\nIn the \"real\" world, what should I think to use it for? When should it can do better than Python? For better I mean: easy way to write something performing.\n\nUPDATE 1: One of the things I'd like to know the most is \"Am I missing something?\" :D\nUPDATE 2: Thank you for your answers. My question is not about buy or not to buy MATLAB. The university has the possibility to give me a copy of an old version of MATLAB (MATLAB 5 I guess) for free, without breaking the license. I'm interested in its capabilities and if it deserves a deeper study (I won't need anything more than basic MATLAB in oder to pass the exam :P ) it will really be better than Python for a specific kind of task in the real world.","AnswerCount":21,"Available Count":13,"Score":0.047583087,"is_accepted":false,"ViewCount":200558,"Q_Id":179904,"Users Score":5,"Answer":"Seems to be pure inertia.  Where it is in use, everyone is too busy to learn IDL or numpy in sufficient detail to switch, and don't want to rewrite good working programs.  Luckily that's not strictly true, but true enough in enough places that Matlab will be around a long time.   Like Fortran (in active use where i work!)","Q_Score":53,"Tags":"python,matlab","A_Id":181127,"CreationDate":"2008-10-07T19:11:00.000","Title":"What is MATLAB good for? Why is it so used by universities? When is it better than Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been recently asked to learn some MATLAB basics for a class.\nWhat does make it so cool for researchers and people that works in university?\nI saw it's cool to work with matrices and plotting things... (things that can be done easily in Python using some libraries).\nWriting a function or parsing a file is just painful. I'm still at the start, what am I missing?\nIn the \"real\" world, what should I think to use it for? When should it can do better than Python? For better I mean: easy way to write something performing.\n\nUPDATE 1: One of the things I'd like to know the most is \"Am I missing something?\" :D\nUPDATE 2: Thank you for your answers. My question is not about buy or not to buy MATLAB. The university has the possibility to give me a copy of an old version of MATLAB (MATLAB 5 I guess) for free, without breaking the license. I'm interested in its capabilities and if it deserves a deeper study (I won't need anything more than basic MATLAB in oder to pass the exam :P ) it will really be better than Python for a specific kind of task in the real world.","AnswerCount":21,"Available Count":13,"Score":0.0190453158,"is_accepted":false,"ViewCount":200558,"Q_Id":179904,"Users Score":2,"Answer":"Matlab is good at doing number crunching. Also Matrix and matrix manipulation. It has many helpful built in libraries(depends on the what version) I think it is easier to use than python if you are going to be calculating equations.","Q_Score":53,"Tags":"python,matlab","A_Id":1890839,"CreationDate":"2008-10-07T19:11:00.000","Title":"What is MATLAB good for? Why is it so used by universities? When is it better than Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been recently asked to learn some MATLAB basics for a class.\nWhat does make it so cool for researchers and people that works in university?\nI saw it's cool to work with matrices and plotting things... (things that can be done easily in Python using some libraries).\nWriting a function or parsing a file is just painful. I'm still at the start, what am I missing?\nIn the \"real\" world, what should I think to use it for? When should it can do better than Python? For better I mean: easy way to write something performing.\n\nUPDATE 1: One of the things I'd like to know the most is \"Am I missing something?\" :D\nUPDATE 2: Thank you for your answers. My question is not about buy or not to buy MATLAB. The university has the possibility to give me a copy of an old version of MATLAB (MATLAB 5 I guess) for free, without breaking the license. I'm interested in its capabilities and if it deserves a deeper study (I won't need anything more than basic MATLAB in oder to pass the exam :P ) it will really be better than Python for a specific kind of task in the real world.","AnswerCount":21,"Available Count":13,"Score":1.0,"is_accepted":false,"ViewCount":200558,"Q_Id":179904,"Users Score":13,"Answer":"MATLAB is great for doing array manipulation, doing specialized math functions, and for creating nice plots quick.\nI'd probably only use it for large programs if I could use a lot of array\/matrix manipulation.\nYou don't have to worry about the IDE as much as in more formal packages, so it's easier for students without a lot of programming experience to pick up.","Q_Score":53,"Tags":"python,matlab","A_Id":179910,"CreationDate":"2008-10-07T19:11:00.000","Title":"What is MATLAB good for? Why is it so used by universities? When is it better than Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been recently asked to learn some MATLAB basics for a class.\nWhat does make it so cool for researchers and people that works in university?\nI saw it's cool to work with matrices and plotting things... (things that can be done easily in Python using some libraries).\nWriting a function or parsing a file is just painful. I'm still at the start, what am I missing?\nIn the \"real\" world, what should I think to use it for? When should it can do better than Python? For better I mean: easy way to write something performing.\n\nUPDATE 1: One of the things I'd like to know the most is \"Am I missing something?\" :D\nUPDATE 2: Thank you for your answers. My question is not about buy or not to buy MATLAB. The university has the possibility to give me a copy of an old version of MATLAB (MATLAB 5 I guess) for free, without breaking the license. I'm interested in its capabilities and if it deserves a deeper study (I won't need anything more than basic MATLAB in oder to pass the exam :P ) it will really be better than Python for a specific kind of task in the real world.","AnswerCount":21,"Available Count":13,"Score":0.0380768203,"is_accepted":false,"ViewCount":200558,"Q_Id":179904,"Users Score":4,"Answer":"The main reason it is useful in industry is the plug-ins built on top of the core functionality. Almost all active Matlab development for the last few years has focused on these.  \nUnfortunately, you won't have much opportunity to use these in an academic environment.","Q_Score":53,"Tags":"python,matlab","A_Id":179932,"CreationDate":"2008-10-07T19:11:00.000","Title":"What is MATLAB good for? Why is it so used by universities? When is it better than Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been recently asked to learn some MATLAB basics for a class.\nWhat does make it so cool for researchers and people that works in university?\nI saw it's cool to work with matrices and plotting things... (things that can be done easily in Python using some libraries).\nWriting a function or parsing a file is just painful. I'm still at the start, what am I missing?\nIn the \"real\" world, what should I think to use it for? When should it can do better than Python? For better I mean: easy way to write something performing.\n\nUPDATE 1: One of the things I'd like to know the most is \"Am I missing something?\" :D\nUPDATE 2: Thank you for your answers. My question is not about buy or not to buy MATLAB. The university has the possibility to give me a copy of an old version of MATLAB (MATLAB 5 I guess) for free, without breaking the license. I'm interested in its capabilities and if it deserves a deeper study (I won't need anything more than basic MATLAB in oder to pass the exam :P ) it will really be better than Python for a specific kind of task in the real world.","AnswerCount":21,"Available Count":13,"Score":0.0380768203,"is_accepted":false,"ViewCount":200558,"Q_Id":179904,"Users Score":4,"Answer":"One reason MATLAB is popular with universities is the same reason a lot of things are popular with universities: there's a lot of professors familiar with it, and it's fairly robust.\nI've spoken to a lot of folks who are especially interested in MATLAB's nascent ability to tap into the GPU instead of working serially.  Having used Python in grad school, I kind of wish I had the licks to work with MATLAB in that case.  It sure would make vector space calculations a breeze.","Q_Score":53,"Tags":"python,matlab","A_Id":180012,"CreationDate":"2008-10-07T19:11:00.000","Title":"What is MATLAB good for? Why is it so used by universities? When is it better than Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been recently asked to learn some MATLAB basics for a class.\nWhat does make it so cool for researchers and people that works in university?\nI saw it's cool to work with matrices and plotting things... (things that can be done easily in Python using some libraries).\nWriting a function or parsing a file is just painful. I'm still at the start, what am I missing?\nIn the \"real\" world, what should I think to use it for? When should it can do better than Python? For better I mean: easy way to write something performing.\n\nUPDATE 1: One of the things I'd like to know the most is \"Am I missing something?\" :D\nUPDATE 2: Thank you for your answers. My question is not about buy or not to buy MATLAB. The university has the possibility to give me a copy of an old version of MATLAB (MATLAB 5 I guess) for free, without breaking the license. I'm interested in its capabilities and if it deserves a deeper study (I won't need anything more than basic MATLAB in oder to pass the exam :P ) it will really be better than Python for a specific kind of task in the real world.","AnswerCount":21,"Available Count":13,"Score":0.0285636566,"is_accepted":false,"ViewCount":200558,"Q_Id":179904,"Users Score":3,"Answer":"I think you answered your own question when you noted that Matlab is \"cool to work with matrixes and plotting things\". Any application that requires a lot of matrix maths and visualisation will probably be easiest to do in Matlab.\nThat said, Matlab's syntax feels awkward and shows the language's age. In contrast, Python is a much nicer general purpose programming language and, with the right libraries can do much of what Matlab does. However, Matlab is always going to have a more concise syntax than Python for vector and matrix manipulation. \nIf much of your programming involves these sorts of manipulations, such as in signal processing and some statistical techniques, then Matlab will be a better choice.","Q_Score":53,"Tags":"python,matlab","A_Id":181295,"CreationDate":"2008-10-07T19:11:00.000","Title":"What is MATLAB good for? Why is it so used by universities? When is it better than Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been recently asked to learn some MATLAB basics for a class.\nWhat does make it so cool for researchers and people that works in university?\nI saw it's cool to work with matrices and plotting things... (things that can be done easily in Python using some libraries).\nWriting a function or parsing a file is just painful. I'm still at the start, what am I missing?\nIn the \"real\" world, what should I think to use it for? When should it can do better than Python? For better I mean: easy way to write something performing.\n\nUPDATE 1: One of the things I'd like to know the most is \"Am I missing something?\" :D\nUPDATE 2: Thank you for your answers. My question is not about buy or not to buy MATLAB. The university has the possibility to give me a copy of an old version of MATLAB (MATLAB 5 I guess) for free, without breaking the license. I'm interested in its capabilities and if it deserves a deeper study (I won't need anything more than basic MATLAB in oder to pass the exam :P ) it will really be better than Python for a specific kind of task in the real world.","AnswerCount":21,"Available Count":13,"Score":1.0,"is_accepted":false,"ViewCount":200558,"Q_Id":179904,"Users Score":15,"Answer":"Hold everything. When's the last time you programed your calculator to play tetris? Did you actually think you could write anything you want in those 128k of RAM? Likely not. MATLAB is not for programming unless you're dealing with huge matrices. It's the graphing calculator you whip out when you've got Megabytes to Gigabytes of data to crunch and\/or plot. Learn just basic stuff, but also don't kill yourself trying to make Python be a graphing calculator.\nYou'll quickly get a feel for when you want to crunch, plot or explore in MATLAB and when you want to have all that Python offers. Lots of engineers turn to pre and post processing in Python or Perl. Occasionally even just calling out to MATLAB for the hard bits.\nThey are such completely different tools that you should learn their basic strengths first without trying to replace one with the other. Granted for saving money I'd either use Octave or skimp on ease and learn to work with sparse matrices in Perl or Python.","Q_Score":53,"Tags":"python,matlab","A_Id":181492,"CreationDate":"2008-10-07T19:11:00.000","Title":"What is MATLAB good for? Why is it so used by universities? When is it better than Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been recently asked to learn some MATLAB basics for a class.\nWhat does make it so cool for researchers and people that works in university?\nI saw it's cool to work with matrices and plotting things... (things that can be done easily in Python using some libraries).\nWriting a function or parsing a file is just painful. I'm still at the start, what am I missing?\nIn the \"real\" world, what should I think to use it for? When should it can do better than Python? For better I mean: easy way to write something performing.\n\nUPDATE 1: One of the things I'd like to know the most is \"Am I missing something?\" :D\nUPDATE 2: Thank you for your answers. My question is not about buy or not to buy MATLAB. The university has the possibility to give me a copy of an old version of MATLAB (MATLAB 5 I guess) for free, without breaking the license. I'm interested in its capabilities and if it deserves a deeper study (I won't need anything more than basic MATLAB in oder to pass the exam :P ) it will really be better than Python for a specific kind of task in the real world.","AnswerCount":21,"Available Count":13,"Score":1.0,"is_accepted":false,"ViewCount":200558,"Q_Id":179904,"Users Score":34,"Answer":"I've been using matlab for many years in my research.  It's great for linear algebra and has a large set of well-written toolboxes.  The most recent versions are starting to push it into being closer to a general-purpose language (better optimizers, a much better object model, richer scoping rules, etc.).  \nThis past summer, I had a job where I used Python + numpy instead of Matlab.  I enjoyed the change of pace.  It's a \"real\" language (and all that entails), and it has some great numeric features like broadcasting arrays.  I also really like the ipython environment.  \nHere are some things that I prefer about Matlab:\n\nconsistency: MathWorks has spent a lot of effort making the toolboxes look and work like each other.  They haven't done a perfect job, but it's one of the best I've seen for a codebase that's decades old.\ndocumentation: I find it very frustrating to figure out some things in numpy and\/or python because the documentation quality is spotty: some things are documented very well, some not at all.  It's often most frustrating when I see things that appear to mimic Matlab, but don't quite work the same.  Being able to grab the source is invaluable (to be fair, most of the Matlab toolboxes ship with source too)\ncompactness: for what I do, Matlab's syntax is often more compact (but not always)\nmomentum: I have too much Matlab code to change now\n\nIf I didn't have such a large existing codebase, I'd seriously consider switching to Python + numpy.","Q_Score":53,"Tags":"python,matlab","A_Id":193386,"CreationDate":"2008-10-07T19:11:00.000","Title":"What is MATLAB good for? Why is it so used by universities? When is it better than Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been recently asked to learn some MATLAB basics for a class.\nWhat does make it so cool for researchers and people that works in university?\nI saw it's cool to work with matrices and plotting things... (things that can be done easily in Python using some libraries).\nWriting a function or parsing a file is just painful. I'm still at the start, what am I missing?\nIn the \"real\" world, what should I think to use it for? When should it can do better than Python? For better I mean: easy way to write something performing.\n\nUPDATE 1: One of the things I'd like to know the most is \"Am I missing something?\" :D\nUPDATE 2: Thank you for your answers. My question is not about buy or not to buy MATLAB. The university has the possibility to give me a copy of an old version of MATLAB (MATLAB 5 I guess) for free, without breaking the license. I'm interested in its capabilities and if it deserves a deeper study (I won't need anything more than basic MATLAB in oder to pass the exam :P ) it will really be better than Python for a specific kind of task in the real world.","AnswerCount":21,"Available Count":13,"Score":0.0285636566,"is_accepted":false,"ViewCount":200558,"Q_Id":179904,"Users Score":3,"Answer":"It's been some time since I've used Matlab, but from memory it does provide (albeit with extra plugins) the ability to generate source to allow you to realise your algorithm on a DSP.\nSince python is a general purpose programming language there is no reason why you couldn't do everything in python that you can do in matlab. However, matlab does provide a number of other tools - eg. a very broad array of dsp features, a broad array of S and Z domain features.\nAll of these could be hand coded in python (since it's a general purpose language), but if all you're after is the results perhaps spending the money on Matlab is the cheaper option?\nThese features have also been tuned for performance. eg. The documentation for Numpy specifies that their Fourier transform is optimised for power of 2 point data sets. As I understand Matlab has been written to use the most efficient Fourier transform to suit the size of the data set, not just power of 2.\nedit: Oh, and in Matlab you can produce some sensational looking plots very easily, which is important when you're presenting your data. Again, certainly not impossible using other tools.","Q_Score":53,"Tags":"python,matlab","A_Id":180736,"CreationDate":"2008-10-07T19:11:00.000","Title":"What is MATLAB good for? Why is it so used by universities? When is it better than Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been recently asked to learn some MATLAB basics for a class.\nWhat does make it so cool for researchers and people that works in university?\nI saw it's cool to work with matrices and plotting things... (things that can be done easily in Python using some libraries).\nWriting a function or parsing a file is just painful. I'm still at the start, what am I missing?\nIn the \"real\" world, what should I think to use it for? When should it can do better than Python? For better I mean: easy way to write something performing.\n\nUPDATE 1: One of the things I'd like to know the most is \"Am I missing something?\" :D\nUPDATE 2: Thank you for your answers. My question is not about buy or not to buy MATLAB. The university has the possibility to give me a copy of an old version of MATLAB (MATLAB 5 I guess) for free, without breaking the license. I'm interested in its capabilities and if it deserves a deeper study (I won't need anything more than basic MATLAB in oder to pass the exam :P ) it will really be better than Python for a specific kind of task in the real world.","AnswerCount":21,"Available Count":13,"Score":1.0,"is_accepted":false,"ViewCount":200558,"Q_Id":179904,"Users Score":7,"Answer":"Personally, I tend to think of Matlab as an interactive matrix calculator and plotting tool with a few scripting capabilities, rather than as a full-fledged programming language like Python or C.  The reason for its success is that matrix stuff and plotting work out of the box, and you can do a few very specific things in it with virtually no actual programming knowledge.  The language is, as you point out, extremely frustrating to use for more general-purpose tasks, such as even the simplest string processing.  Its syntax is quirky, and it wasn't created with the abstractions necessary for projects of more than 100 lines or so in mind.\nI think the reason why people try to use Matlab as a serious programming language is that most engineers (there are exceptions; my degree is in biomedical engineering and I like programming) are horrible programmers and hate to program.  They're taught Matlab in college mostly for the matrix math, and they learn some rudimentary programming as part of learning Matlab, and just assume that Matlab is good enough.  I can't think of anyone I know who knows any language besides Matlab, but still uses Matlab for anything other than a few pure number crunching applications.","Q_Score":53,"Tags":"python,matlab","A_Id":181274,"CreationDate":"2008-10-07T19:11:00.000","Title":"What is MATLAB good for? Why is it so used by universities? When is it better than Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been recently asked to learn some MATLAB basics for a class.\nWhat does make it so cool for researchers and people that works in university?\nI saw it's cool to work with matrices and plotting things... (things that can be done easily in Python using some libraries).\nWriting a function or parsing a file is just painful. I'm still at the start, what am I missing?\nIn the \"real\" world, what should I think to use it for? When should it can do better than Python? For better I mean: easy way to write something performing.\n\nUPDATE 1: One of the things I'd like to know the most is \"Am I missing something?\" :D\nUPDATE 2: Thank you for your answers. My question is not about buy or not to buy MATLAB. The university has the possibility to give me a copy of an old version of MATLAB (MATLAB 5 I guess) for free, without breaking the license. I'm interested in its capabilities and if it deserves a deeper study (I won't need anything more than basic MATLAB in oder to pass the exam :P ) it will really be better than Python for a specific kind of task in the real world.","AnswerCount":21,"Available Count":13,"Score":1.0,"is_accepted":false,"ViewCount":200558,"Q_Id":179904,"Users Score":6,"Answer":"I believe you have a very good point and it's one that has been raised in the company where I work. The company is limited in it's ability to apply matlab because of the licensing costs involved. One developer proved that Python was a very suitable replacement but it fell on ignorant ears because to the owners of those ears...\n\nNo-one in the company knew Python although many of us wanted to use it. \nMatLab has a name, a company, and task force behind it to solve any problems.\nThere were some (but not a lot) of legacy MatLab projects that would need to be re-written.\n\nIf it's worth \u00a310,000 (??) it's gotta be worth it!! \nI'm with you here. Python is a very good replacement for MatLab.\nI should point out that I've been told the company uses maybe 5% to 10% of MatLabs capabilities and that is the basis for my agreement with the original poster","Q_Score":53,"Tags":"python,matlab","A_Id":180017,"CreationDate":"2008-10-07T19:11:00.000","Title":"What is MATLAB good for? Why is it so used by universities? When is it better than Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been recently asked to learn some MATLAB basics for a class.\nWhat does make it so cool for researchers and people that works in university?\nI saw it's cool to work with matrices and plotting things... (things that can be done easily in Python using some libraries).\nWriting a function or parsing a file is just painful. I'm still at the start, what am I missing?\nIn the \"real\" world, what should I think to use it for? When should it can do better than Python? For better I mean: easy way to write something performing.\n\nUPDATE 1: One of the things I'd like to know the most is \"Am I missing something?\" :D\nUPDATE 2: Thank you for your answers. My question is not about buy or not to buy MATLAB. The university has the possibility to give me a copy of an old version of MATLAB (MATLAB 5 I guess) for free, without breaking the license. I'm interested in its capabilities and if it deserves a deeper study (I won't need anything more than basic MATLAB in oder to pass the exam :P ) it will really be better than Python for a specific kind of task in the real world.","AnswerCount":21,"Available Count":13,"Score":1.0,"is_accepted":false,"ViewCount":200558,"Q_Id":179904,"Users Score":6,"Answer":"The most likely reason that it's used so much in universities is that the mathematics faculty are used to it, understand it, and know how to incorporate it into their curriculum.","Q_Score":53,"Tags":"python,matlab","A_Id":179912,"CreationDate":"2008-10-07T19:11:00.000","Title":"What is MATLAB good for? Why is it so used by universities? When is it better than Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been recently asked to learn some MATLAB basics for a class.\nWhat does make it so cool for researchers and people that works in university?\nI saw it's cool to work with matrices and plotting things... (things that can be done easily in Python using some libraries).\nWriting a function or parsing a file is just painful. I'm still at the start, what am I missing?\nIn the \"real\" world, what should I think to use it for? When should it can do better than Python? For better I mean: easy way to write something performing.\n\nUPDATE 1: One of the things I'd like to know the most is \"Am I missing something?\" :D\nUPDATE 2: Thank you for your answers. My question is not about buy or not to buy MATLAB. The university has the possibility to give me a copy of an old version of MATLAB (MATLAB 5 I guess) for free, without breaking the license. I'm interested in its capabilities and if it deserves a deeper study (I won't need anything more than basic MATLAB in oder to pass the exam :P ) it will really be better than Python for a specific kind of task in the real world.","AnswerCount":21,"Available Count":13,"Score":0.0380768203,"is_accepted":false,"ViewCount":200558,"Q_Id":179904,"Users Score":4,"Answer":"I know this question is old, and therefore may no longer be\nwatched, but I felt it was necessary to comment. As an\naerospace engineer at Georgia Tech, I can say, with no\nqualms, that MATLAB is awesome. You can have it quickly\ninterface with your Excel spreadsheets to pull in data about\nhow high and fast rockets are flying, how the wind affects\nthose same rockets, and how different engines matter. Beyond\nrocketry, similar concepts come into play for cars, trucks,\naircraft, spacecraft, and even athletics. You can pull in\nlarge amounts of data, manipulate all of it, and make sure\nyour results are as they should be. In the event something is\noff, you can add a line break where an error occurs to debug\nyour program without having to recompile every time you want\nto run your program. Is it slower than some other programs?\nWell, technically. I'm sure if you want to do the number\ncrunching it's great for on an NVIDIA graphics processor, it\nwould probably be faster, but it requires a lot more effort\nwith harder debugging.\nAs a general programming language, MATLAB is weak. It's not\nmeant to work against Python, Java, ActionScript, C\/C++ or\nany other general purpose language. It's meant for the\nengineering and mathematics niche the name implies, and it\ndoes so fantastically.","Q_Score":53,"Tags":"python,matlab","A_Id":1113065,"CreationDate":"2008-10-07T19:11:00.000","Title":"What is MATLAB good for? Why is it so used by universities? When is it better than Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"How do I get the inverse of a matrix in python? I've implemented it myself, but it's pure python, and I suspect there are faster modules out there to do it.","AnswerCount":7,"Available Count":1,"Score":0.0285636566,"is_accepted":false,"ViewCount":125109,"Q_Id":211160,"Users Score":1,"Answer":"If you hate numpy, get out RPy and your local copy of R, and use it instead.\n(I would also echo to make you you really need to invert the matrix.  In R, for example, linalg.solve and the solve() function don't actually do a full inversion, since it is unnecessary.)","Q_Score":62,"Tags":"python,algorithm,matrix,linear-algebra,matrix-inverse","A_Id":213717,"CreationDate":"2008-10-17T05:30:00.000","Title":"Python Inverse of a Matrix","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to figure out the following problem.\nI am building Yet another math function grapher, The function is drawn on its predefined x,y range, that's all good.\nNow I am working on the background and the ticking of X, Y axes (if any axes are shown).\nI worked out the following.\nI have a fixed width  of 250 p\nThe tick gap should be between 12.5 and 50p.\nThe ticks should indicate either unit or half unit range, by that i mean the following.\nx range (-5, 5): one tick = 1\nx range (-1, 1): one tick = 0.5 or 0.1 depending on the gap that each of this option would generate. \nx range (0.1, 0.3): 0.05 \nGiven a Xrange\nHow would you get the number of ticks between either full or half unit range ?\nOr maybe there are other way to approach this type of problems.","AnswerCount":5,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":239,"Q_Id":346823,"Users Score":0,"Answer":"Using deltaX\nif deltax between 2 and 10 half increment\nif deltax between 10 and 20 unit increment\nif smaller than 2 we multiply by 10 and test again\nif larger than 20 we divide \nThen we get the position of the first unit or half increment on the width using xmin.\nI still need to test this solution.","Q_Score":2,"Tags":"python,algorithm,math","A_Id":346873,"CreationDate":"2008-12-06T21:40:00.000","Title":"Ticking function grapher","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Recently I needed to do weighted random selection of elements from a list, both with and without replacement. While there are well known and good algorithms for unweighted selection, and some for weighted selection without replacement (such as modifications of the resevoir algorithm), I couldn't find any good algorithms for weighted selection with replacement. I also wanted to avoid the resevoir method, as I was selecting a significant fraction of the list, which is small enough to hold in memory.\nDoes anyone have any suggestions on the best approach in this situation? I have my own solutions, but I'm hoping to find something more efficient, simpler, or both.","AnswerCount":9,"Available Count":2,"Score":0.0886555158,"is_accepted":false,"ViewCount":32149,"Q_Id":352670,"Users Score":4,"Answer":"The following is a description of random weighted selection of an element of a \nset (or multiset, if repeats are allowed), both with and without replacement in O(n) space \nand O(log n) time.\nIt consists of implementing a binary search tree, sorted by the elements to be \nselected, where each node of the tree contains:\n\nthe element itself (element)\nthe un-normalized weight of the element (elementweight), and\nthe sum of all the un-normalized weights of the left-child node and all of \nits children (leftbranchweight).\nthe sum of all the un-normalized weights of the right-child node and all of\nits chilren (rightbranchweight).\n\nThen we randomly select an element from the BST by descending down the tree. A \nrough description of the algorithm follows. The algorithm is given a node of\nthe tree. Then the values of leftbranchweight, rightbranchweight, \nand elementweight of node is summed, and the weights are divided by this \nsum, resulting in the values leftbranchprobability, \nrightbranchprobability, and elementprobability, respectively. Then a \nrandom number between 0 and 1 (randomnumber) is obtained.\n\nif the number is less than elementprobability,\n\n\nremove the element from the BST as normal, updating leftbranchweight\nand rightbranchweight of all the necessary nodes, and return the \nelement.\n\nelse if the number is less than (elementprobability + leftbranchweight)\n\n\nrecurse on leftchild (run the algorithm using leftchild as node)\n\nelse \n\n\nrecurse on rightchild\n\n\nWhen we finally find, using these weights, which element is to be returned, we either simply return it (with replacement) or we remove it and update relevant weights in the tree (without replacement).\nDISCLAIMER: The algorithm is rough, and a treatise on the proper implementation \nof a BST is not attempted here; rather, it is hoped that this answer will help \nthose who really need fast weighted selection without replacement (like I do).","Q_Score":52,"Tags":"python,algorithm,random,random-sample","A_Id":9827070,"CreationDate":"2008-12-09T13:15:00.000","Title":"Weighted random selection with and without replacement","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Recently I needed to do weighted random selection of elements from a list, both with and without replacement. While there are well known and good algorithms for unweighted selection, and some for weighted selection without replacement (such as modifications of the resevoir algorithm), I couldn't find any good algorithms for weighted selection with replacement. I also wanted to avoid the resevoir method, as I was selecting a significant fraction of the list, which is small enough to hold in memory.\nDoes anyone have any suggestions on the best approach in this situation? I have my own solutions, but I'm hoping to find something more efficient, simpler, or both.","AnswerCount":9,"Available Count":2,"Score":0.022218565,"is_accepted":false,"ViewCount":32149,"Q_Id":352670,"Users Score":1,"Answer":"This is an old question for which numpy now offers an easy solution so I thought I would mention it. Current version of numpy is version 1.2 and numpy.random.choice allows the sampling to be done with or without replacement and with given weights.","Q_Score":52,"Tags":"python,algorithm,random,random-sample","A_Id":66553611,"CreationDate":"2008-12-09T13:15:00.000","Title":"Weighted random selection with and without replacement","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"As per the title. I am trying to create a simple scater plot, but haven't found any Python 3.0 libraries that can do it. Note, this isn't for a website, so the web ones are a bit useless.","AnswerCount":5,"Available Count":1,"Score":0.0399786803,"is_accepted":false,"ViewCount":1540,"Q_Id":418835,"Users Score":1,"Answer":"Maybe you can use Python Imaging Library (PIL).\nAlso have a look at PyX, but this library is meant to output to PDF, ...","Q_Score":3,"Tags":"python,python-3.x,plot,graphing,scatter-plot","A_Id":421947,"CreationDate":"2009-01-07T01:12:00.000","Title":"Are there any graph\/plotting\/anything-like-that libraries for Python 3.0?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"What's the easiest way to shuffle an array with python?","AnswerCount":11,"Available Count":1,"Score":0.072599319,"is_accepted":false,"ViewCount":285289,"Q_Id":473973,"Users Score":4,"Answer":"In addition to the previous replies, I would like to introduce another function.\nnumpy.random.shuffle as well as random.shuffle perform in-place shuffling. However, if you want to return a shuffled array numpy.random.permutation is the function to use.","Q_Score":325,"Tags":"python,arrays,random,shuffle","A_Id":40674024,"CreationDate":"2009-01-23T18:34:00.000","Title":"Shuffle an array with python, randomize array item order with python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"edit: Initially I was trying to be general but it came out vague. I've included more detail below. \nI'm writing a script that pulls in data from two large CSV files, one of people's schedules and the other of information about their schedules. The data is mined and combined to eventually create pajek format graphs for Monday-Sat of peoples connections, with a seventh graph representing all connections over the week with a string of 1's and 0's to indicate which days of the week the connections are made. This last graph is a break from the pajek format and is used by a seperate program written by another researcher.\nPajek format has a large header, and then lists connections as (vertex1 vertex2) unordered pairs. It's difficult to store these pairs in a dictionary, because there are often multiple connections on the same day between two pairs.\nI'm wondering what the best way to output to these graphs are. Should I make the large single graph and have a second script deconstruct it into several smaller graphs? Should I keep seven streams open and as I determine a connection write to them, or should I keep some other data structure for each and output them when I can (like a queue)?","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":593,"Q_Id":555146,"Users Score":2,"Answer":"I would open seven file streams as accumulating them might be quite memory extensive if it's a lot of data. Of course that is only an option if you can sort them live and don't first need all data read to do the sorting.","Q_Score":0,"Tags":"python,file-io","A_Id":555159,"CreationDate":"2009-02-17T00:29:00.000","Title":"Multiple output files","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to load (de-serialize) a pre-computed list of integers from a file in a Python script (into a Python list). The list is large (upto millions of items), and I can choose the format I store it in, as long as loading is fastest.\nWhich is the fastest method, and why?\n\nUsing import on a .py file that just contains the list assigned to a variable\nUsing cPickle's load\nSome other method (perhaps numpy?)\n\nAlso, how can one benchmark such things reliably?\nAddendum: measuring this reliably is difficult, because import is cached so it can't be executed multiple times in a test. The loading with pickle also gets faster after the first time probably because page-precaching by the OS. Loading 1 million numbers with cPickle takes 1.1 sec the first time run, and 0.2 sec on subsequent executions of the script.\nIntuitively I feel cPickle should be faster, but I'd appreciate numbers (this is quite a challenge to measure, I think). \nAnd yes, it's important for me that this performs quickly.\nThanks","AnswerCount":6,"Available Count":1,"Score":0.0333209931,"is_accepted":false,"ViewCount":8359,"Q_Id":556730,"Users Score":1,"Answer":"cPickle will be the fastest since it is saved in binary and no real python code has to be parsed.\nOther advantates are that it is more secure (since it does not execute commands) and you have no problems with setting $PYTHONPATH correctly.","Q_Score":11,"Tags":"python,serialization,caching","A_Id":556961,"CreationDate":"2009-02-17T13:16:00.000","Title":"Python list serialization - fastest method","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working at some plots and statistics for work and I am not sure how I can do some statistics using numpy: I have a list of prices and another one of basePrices. And I want to know how many prices are with X percent above basePrice, how many are with Y percent above basePrice.\nIs there a simple way to do that using numpy?","AnswerCount":4,"Available Count":1,"Score":0.049958375,"is_accepted":false,"ViewCount":1834,"Q_Id":570137,"Users Score":1,"Answer":"In addition to df's answer, if you want to know the specific prices that are above the base prices, you can do:\nprices[prices > (1.10 * base_prices)]","Q_Score":1,"Tags":"python,numpy","A_Id":570197,"CreationDate":"2009-02-20T16:04:00.000","Title":"Statistics with numpy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Does anybody know if Python has an equivalent to Java's SortedSet interface?\nHeres what I'm looking for: lets say I have an object of type foo, and I know how to compare two objects of type foo to see whether foo1 is \"greater than\" or \"less than\" foo2. I want a way of storing many objects of type foo in a list L, so that whenever I traverse the list L, I get the objects in order, according to the comparison method I define.\nEdit:\nI guess I can use a dictionary or a list and sort() it every time I modify it, but is this the best way?","AnswerCount":7,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":9278,"Q_Id":628192,"Users Score":0,"Answer":"Do you have the possibility of using Jython? I just mention it because using TreeMap, TreeSet, etc. is trivial.  Also if you're coming from a Java background and you want to head in a Pythonic direction Jython is wonderful for making the transition easier.  Though I recognise that use of TreeSet in this case would not be part of such a \"transition\".\nFor Jython superusers I have a question myself: the blist package can't be imported because it uses a C file which must be imported. But would there be any advantage of using blist instead of TreeSet? Can we generally assume the JVM uses algorithms which are essentially as good as those of CPython stuff?","Q_Score":22,"Tags":"python,data-structures","A_Id":20666684,"CreationDate":"2009-03-09T21:58:00.000","Title":"Python equivalent to java.util.SortedSet?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":1},{"Question":"I had been interested in neural networks for a bit and thought about using one in python for a light project that compares various minimization techniques in a time domain (which is fastest).\nThen I realized I didn't even know if a NN is good for minimization. What do you think?","AnswerCount":8,"Available Count":4,"Score":0.0748596907,"is_accepted":false,"ViewCount":7231,"Q_Id":652283,"Users Score":3,"Answer":"Back-propagation works by minimizing the error.  However, you can really minimize whatever you want.  So, you could use back-prop-like update rules to find the Artificial Neural Network inputs that minimize the output.\nThis is a big question, sorry for the short answer.  I should also add that my suggested approach sounds pretty inefficient compared to more established methods and would only find a local minima.","Q_Score":10,"Tags":"python,artificial-intelligence,neural-network,minimization","A_Id":13611588,"CreationDate":"2009-03-16T21:53:00.000","Title":"Can a neural network be used to find a functions minimum(a)?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I had been interested in neural networks for a bit and thought about using one in python for a light project that compares various minimization techniques in a time domain (which is fastest).\nThen I realized I didn't even know if a NN is good for minimization. What do you think?","AnswerCount":8,"Available Count":4,"Score":0.0,"is_accepted":false,"ViewCount":7231,"Q_Id":652283,"Users Score":0,"Answer":"They're pretty bad for the purpose; one of the big problems of neural networks is that they get stuck in local minima.  You might want to look into support vector machines instead.","Q_Score":10,"Tags":"python,artificial-intelligence,neural-network,minimization","A_Id":652348,"CreationDate":"2009-03-16T21:53:00.000","Title":"Can a neural network be used to find a functions minimum(a)?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I had been interested in neural networks for a bit and thought about using one in python for a light project that compares various minimization techniques in a time domain (which is fastest).\nThen I realized I didn't even know if a NN is good for minimization. What do you think?","AnswerCount":8,"Available Count":4,"Score":0.024994793,"is_accepted":false,"ViewCount":7231,"Q_Id":652283,"Users Score":1,"Answer":"The training process of a back-propagation neural network works by minimizing the error from the optimal result. But having a trained neural network finding the minimum of an unknown function would be pretty hard.\nIf you restrict the problem to a specific function class, it could work, and be pretty quick too. Neural networks are good at finding patterns, if there are any.","Q_Score":10,"Tags":"python,artificial-intelligence,neural-network,minimization","A_Id":652327,"CreationDate":"2009-03-16T21:53:00.000","Title":"Can a neural network be used to find a functions minimum(a)?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I had been interested in neural networks for a bit and thought about using one in python for a light project that compares various minimization techniques in a time domain (which is fastest).\nThen I realized I didn't even know if a NN is good for minimization. What do you think?","AnswerCount":8,"Available Count":4,"Score":1.2,"is_accepted":true,"ViewCount":7231,"Q_Id":652283,"Users Score":-5,"Answer":"Neural networks are classifiers. They separate two classes of data elements. They learn this separation (usually) by preclassified data elements. Thus, I say: No, unless you do a major stretch beyond breakage.","Q_Score":10,"Tags":"python,artificial-intelligence,neural-network,minimization","A_Id":652362,"CreationDate":"2009-03-16T21:53:00.000","Title":"Can a neural network be used to find a functions minimum(a)?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have binary files no larger than 20Mb in size that have a header section and then a data section containing sequences of uchars. I have Numpy, SciPy, etc. and each library has different ways of loading in the data. Any suggestions for the most efficient methods I should use?","AnswerCount":4,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":450,"Q_Id":703262,"Users Score":0,"Answer":"I found that array.fromfile is the fastest methods for homogeneous data.","Q_Score":6,"Tags":"python,input,binaryfiles","A_Id":703588,"CreationDate":"2009-03-31T22:03:00.000","Title":"Most efficient way of loading formatted binary files in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"What ready available algorithms could I use to data mine twitter to find out the degrees of separation between 2 people on twitter.\nHow does it change when the social graph keeps changing and updating constantly.\nAnd then, is there any dump of twitter social graph data which I could use rather than making so many API calls to start over.","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2436,"Q_Id":785327,"Users Score":0,"Answer":"There was a company offering a dump of the social graph, but it was taken down and no longer available. As you already realized - it is kind of hard, as it is changing all the time.\nI would recommend checking out their social_graph api methods as they give the most info with the least API calls.","Q_Score":3,"Tags":"python,twitter,dump,social-graph","A_Id":817451,"CreationDate":"2009-04-24T10:30:00.000","Title":"Twitter Data Mining: Degrees of separation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I quickly checked numPy but it looks like it's using arrays as vectors? I am looking for a proper Vector3 type that I can instance and work on.","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":12341,"Q_Id":786691,"Users Score":2,"Answer":"I don't believe there is anything standard (but I could be wrong, I don't keep up with python that closely).\nIt's very easy to implement though, and you may want to build on top of the numpy array as a container for it anyway, which gives you lots of good (and efficient) bits and pieces.","Q_Score":6,"Tags":"python,vector","A_Id":786758,"CreationDate":"2009-04-24T16:46:00.000","Title":"Is there a Vector3 type in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it possible to read binary MATLAB .mat files in Python?\nI've seen that SciPy has alleged support for reading .mat files, but I'm unsuccessful with it. I installed SciPy version 0.7.0, and I can't find the loadmat() method.","AnswerCount":12,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":566507,"Q_Id":874461,"Users Score":12,"Answer":"There is a great library for this task called: pymatreader.\nJust do as follows:\n\nInstall the package: pip install pymatreader\n\nImport the relevant function of this package: from pymatreader import read_mat\n\nUse the function to read the matlab struct: data = read_mat('matlab_struct.mat')\n\nuse data.keys() to locate where the data is actually stored.\n\n\n\nThe keys will usually look like: dict_keys(['__header__', '__version__', '__globals__', 'data_opp']). Where data_opp will be the actual key which stores the data. The name of this key can ofcourse be changed between different files.\n\n\nLast step - Create your dataframe: my_df = pd.DataFrame(data['data_opp'])\n\nThat's it :)","Q_Score":508,"Tags":"python,matlab,file-io,scipy,mat-file","A_Id":66453257,"CreationDate":"2009-05-17T12:02:00.000","Title":"Read .mat files in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm looking for a simple solution using Python to store data as a flat file, such that each line is a string representation of an array that can be easily parsed.\nI'm sure python has library for doing such a task easily but so far all the approaches I have found seemed like it would have been sloppy to get it to work and I'm sure there is a better approach. So far I've tried: \n\nthe array.toFile() method but couldn't figure out how to get it to work with nested arrays of strings, it seemed geared towards integer data.\nLists and sets do not have a toFile method built in, so I would have had to parse and encode it manually. \nCSV seemed like a good approach but this would also require manually parsing it, and did not allow me to simply append new lines at the end - so any new calls the the CSVWriter would overwrite the file existing data.\n\nI'm really trying to avoid using databases (maybe SQLite but it seems a bit overkill) because I'm trying to develop this to have no software prerequisites besides Python.","AnswerCount":5,"Available Count":1,"Score":0.0798297691,"is_accepted":false,"ViewCount":5460,"Q_Id":875228,"Users Score":2,"Answer":"I'm looking for a simple solution using Python to store data as a flat file, such that each line is a string representation of an array that can be easily parsed.\n\nIs the data only ever going to be parsed by Python programs? If not, then I'd avoid pickle et al (shelve and marshal) since they're very Python specific. JSON and YAML have the important advantage that parsers are easily available for most any language.","Q_Score":3,"Tags":"python,file-io,csv,multidimensional-array,fileparsing","A_Id":875525,"CreationDate":"2009-05-17T19:00:00.000","Title":"Simple data storing in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a matrix in the type of a Numpy array.  How would I write it to disk it as an image?  Any format works (png, jpeg, bmp...).  One important constraint is that PIL is not present.","AnswerCount":21,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":767103,"Q_Id":902761,"Users Score":0,"Answer":"I attach an simple routine to convert a npy to an image. Works 100% and it is a piece of cake!\nfrom PIL import Image\nimport matplotlib\nimg = np.load('flair1_slice75.npy')\nmatplotlib.image.imsave(\"G1_flair_75.jpeg\", img)","Q_Score":370,"Tags":"python,image,numpy","A_Id":72331083,"CreationDate":"2009-05-24T00:08:00.000","Title":"Saving a Numpy array as an image","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using the python OpenCV bindings and at the moment I try to isolate a colorrange. That means I want to filter out everything that is not reddish. \nI tried to take only the red color channel but this includes the white spaces in the Image too. \nWhat is a good way to do that?","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":2798,"Q_Id":968317,"Users Score":0,"Answer":"How about using a formular like r' = r-(g+b)?","Q_Score":1,"Tags":"python,image-processing,opencv,color-space","A_Id":968351,"CreationDate":"2009-06-09T05:36:00.000","Title":"How to isolate a single color in an image","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using the python OpenCV bindings and at the moment I try to isolate a colorrange. That means I want to filter out everything that is not reddish. \nI tried to take only the red color channel but this includes the white spaces in the Image too. \nWhat is a good way to do that?","AnswerCount":3,"Available Count":2,"Score":0.0665680765,"is_accepted":false,"ViewCount":2798,"Q_Id":968317,"Users Score":1,"Answer":"Use the HSV colorspace.  Select pixels that have an H value in the range that you consider to contain \"red,\" and an S value large enough that you do not consider it to be neutral, maroon, brown, or pink.  You might also need to throw out pixels with low V's.  The H dimension is a circle, and red is right where the circle is split, so your H range will be in two parts, one near 255, the other near 0.","Q_Score":1,"Tags":"python,image-processing,opencv,color-space","A_Id":2204755,"CreationDate":"2009-06-09T05:36:00.000","Title":"How to isolate a single color in an image","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to use named entity recognition (NER) to find adequate tags for texts in a database.\nI know there is a Wikipedia article about this and lots of other pages describing NER, I would preferably hear something about this topic from you:\n\nWhat experiences did you make with the various algorithms?\nWhich algorithm would you recommend?\nWhich algorithm is the easiest to implement (PHP\/Python)?\nHow to the algorithms work? Is manual training necessary?\n\nExample:\n\"Last year, I was in London where I saw Barack Obama.\" => Tags: London, Barack Obama\nI hope you can help me. Thank you very much in advance!","AnswerCount":6,"Available Count":1,"Score":-1.0,"is_accepted":false,"ViewCount":9656,"Q_Id":1026925,"Users Score":-11,"Answer":"I don't really know about NER, but judging from that example, you could make an algorithm that searched for capital letters in the words or something like that. For that I would recommend regex as the most easy to implement solution if you're thinking small.\nAnother option is to compare the texts with a database, wich yould match string pre-identified as Tags of interest.\nmy 5 cents.","Q_Score":22,"Tags":"php,python,extract,analysis,named-entity-recognition","A_Id":1026976,"CreationDate":"2009-06-22T12:26:00.000","Title":"Algorithms for named entity recognition","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to process uploaded photos with PIL and determine some \"soft\" image metrics like:\n\nis the image contrastful or dull?\ncolorful or monochrome?\nbright or dark?\nis the image warm or cold (regarding light temperature)?\nis there a dominant hue?\n\nthe metrics should be measured in a rating-style, e.g. colorful++++ for a very colorful photo, colorful+ for a rather monochrome image.\nI already noticed PIL's ImageStat Module, that calculates some interesting values for my metrics, e.g. RMS of histogram etc. However, this module is rather poorly documented, so I'm looking for more concrete algorithms to determine these metrics.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":609,"Q_Id":1037090,"Users Score":1,"Answer":"I don't think there are methods that give you a metric exactly for what you want, but the methods that it has, like RMS, takes you a long way there. To do things with color, you can split the image into one layer per color, and get the RMS on each layer, which tells you some of the things you want to know. You can also convert the image in different ways so that you only retain color information, etc.","Q_Score":4,"Tags":"python,python-imaging-library","A_Id":1037217,"CreationDate":"2009-06-24T08:30:00.000","Title":"Simple Image Metrics with PIL","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"i'm just testing out the csv component in python, and i am having some trouble with it. \nI have a fairly standard csv string, and the default options all seems to fit with my test, but the result shouldn't group 1, 2, 3, 4 in a row and 5, 6, 7, 8 in a row?\nThanks a lot for any enlightenment provided!\n\nPython 2.6.2 (r262:71600, Apr 16 2009, 09:17:39) \n[GCC 4.0.1 (Apple Computer, Inc. build 5250)] on darwin\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\n>>> import csv\n>>> c = \"1, 2, 3, 4\\n 5, 6, 7, 8\\n\"\n>>> test = csv.reader(c)\n>>> for t in test:\n...     print t\n... \n['1']\n['', '']\n[' ']\n['2']\n['', '']\n[' ']\n['3']\n['', '']\n[' ']\n['4']\n[]\n[' ']\n['5']\n['', '']\n[' ']\n['6']\n['', '']\n[' ']\n['7']\n['', '']\n[' ']\n['8']\n[]\n>>>","AnswerCount":4,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":850,"Q_Id":1083364,"Users Score":2,"Answer":"test = csv.reader(c.split('\\n'))","Q_Score":7,"Tags":"python,csv","A_Id":1083367,"CreationDate":"2009-07-05T02:49:00.000","Title":"python csv question","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"i'm just testing out the csv component in python, and i am having some trouble with it. \nI have a fairly standard csv string, and the default options all seems to fit with my test, but the result shouldn't group 1, 2, 3, 4 in a row and 5, 6, 7, 8 in a row?\nThanks a lot for any enlightenment provided!\n\nPython 2.6.2 (r262:71600, Apr 16 2009, 09:17:39) \n[GCC 4.0.1 (Apple Computer, Inc. build 5250)] on darwin\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\n>>> import csv\n>>> c = \"1, 2, 3, 4\\n 5, 6, 7, 8\\n\"\n>>> test = csv.reader(c)\n>>> for t in test:\n...     print t\n... \n['1']\n['', '']\n[' ']\n['2']\n['', '']\n[' ']\n['3']\n['', '']\n[' ']\n['4']\n[]\n[' ']\n['5']\n['', '']\n[' ']\n['6']\n['', '']\n[' ']\n['7']\n['', '']\n[' ']\n['8']\n[]\n>>>","AnswerCount":4,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":850,"Q_Id":1083364,"Users Score":8,"Answer":"csv.reader expects an iterable. You gave it \"1, 2, 3, 4\\n 5, 6, 7, 8\\n\"; iteration produces characters. Try giving it [\"1, 2, 3, 4\\n\", \"5, 6, 7, 8\\n\"] -- iteration will produce lines.","Q_Score":7,"Tags":"python,csv","A_Id":1083376,"CreationDate":"2009-07-05T02:49:00.000","Title":"python csv question","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm working with Python and MATLAB right now and I have a 2D array in Python that I need to write to a file and then be able to read it into MATLAB as a matrix. Any ideas on how to do this? \nThanks!","AnswerCount":7,"Available Count":2,"Score":0.1137907297,"is_accepted":false,"ViewCount":69405,"Q_Id":1095265,"Users Score":4,"Answer":"You could write the matrix in Python to a CSV file and read it in MATLAB using csvread.","Q_Score":51,"Tags":"python,matlab,file-io,import,matrix","A_Id":1095296,"CreationDate":"2009-07-07T22:55:00.000","Title":"Matrix from Python to MATLAB","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm working with Python and MATLAB right now and I have a 2D array in Python that I need to write to a file and then be able to read it into MATLAB as a matrix. Any ideas on how to do this? \nThanks!","AnswerCount":7,"Available Count":2,"Score":0.1418931938,"is_accepted":false,"ViewCount":69405,"Q_Id":1095265,"Users Score":5,"Answer":"I would probably use numpy.savetxt('yourfile.mat',yourarray) in Python\nand then yourarray = load('yourfile.mat') in MATLAB.","Q_Score":51,"Tags":"python,matlab,file-io,import,matrix","A_Id":7737622,"CreationDate":"2009-07-07T22:55:00.000","Title":"Matrix from Python to MATLAB","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How would you create an algorithm to solve the following puzzle, \"Mastermind\"?\nYour opponent has chosen four different colours from a set of six (yellow, blue, green, red, orange, purple). You must guess which they have chosen, and in what order. After each guess, your opponent tells you how many (but not which) of the colours you guessed were the right colour in the right place [\"blacks\"] and how many (but not which) were the right colour but in the wrong place [\"whites\"]. The game ends when you guess correctly (4 blacks, 0 whites).\nFor example, if your opponent has chosen (blue, green, orange, red), and you guess (yellow, blue, green, red), you will get one \"black\" (for the red), and two whites (for the blue and green). You would get the same score for guessing (blue, orange, red, purple).\nI'm interested in what algorithm you would choose, and (optionally) how you translate that into code (preferably Python). I'm interested in coded solutions that are:\n\nClear (easily understood)\nConcise\nEfficient (fast in making a guess)\nEffective (least number of guesses to solve the puzzle)\nFlexible (can easily answer questions about the algorithm, e.g. what is its worst case?)\nGeneral (can be easily adapted to other types of puzzle than Mastermind)\n\nI'm happy with an algorithm that's very effective but not very efficient (provided it's not just poorly implemented!); however, a very efficient and effective algorithm implemented inflexibly and impenetrably is not of use.\nI have my own (detailed) solution in Python which I have posted, but this is by no means the only or best approach, so please post more! I'm not expecting an essay ;)","AnswerCount":9,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":37167,"Q_Id":1185634,"Users Score":0,"Answer":"To work out the \"worst\" case, instead of using entropic I am looking to the partition that has the maximum number of elements, then select the try that is a minimum for this maximum => This will give me the minimum number of remaining possibility when I am not lucky (which happens in the worst case).\nThis always solve standard case in 5 attempts, but it is not a full proof that 5 attempts are really needed because it could happen that for next step a bigger set possibilities would have given a better result than a smaller one (because easier to distinguish between).\nThough for the \"Standard game\" with 1680 I have a simple formal proof:\nFor the first step the try that gives the minimum for the partition with the maximum number is 0,0,1,1: 256. Playing 0,0,1,2 is not as good: 276.\nFor each subsequent try there are 14 outcomes (1 not placed and 3 placed is impossible) and 4 placed is giving a partition of 1. This means that in the best case (all partition same size) we will get a maximum partition that is a minimum of (number of possibilities - 1)\/13 (rounded up because we have integer so necessarily some will be less and other more, so that the maximum is rounded up).\nIf I apply this:\nAfter first play (0,0,1,1) I am getting 256 left.\nAfter second try: 20 = (256-1)\/13\nAfter third try : 2 = (20-1)\/13\nThen I have no choice but to try one of the two left for the 4th try. \nIf I am unlucky a fifth try is needed.\nThis proves we need at least 5 tries (but not that this is enough).","Q_Score":38,"Tags":"python,algorithm","A_Id":9515347,"CreationDate":"2009-07-26T21:43:00.000","Title":"How to solve the \"Mastermind\" guessing game?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Hierarchical Bayes models are commonly used in Marketing, Political Science, and Econometrics. Yet, the only package I know of is bayesm, which is really a companion to a book (Bayesian Statistics and Marketing, by Rossi, et al.) Am I missing something? Is there a software package for R or Python doing the job out there, and\/or a worked-out example in the associated language?","AnswerCount":7,"Available Count":3,"Score":0.057080742,"is_accepted":false,"ViewCount":8690,"Q_Id":1191689,"Users Score":2,"Answer":"I apply hierarchical Bayes models in R in combination with JAGS (Linux) or sometimes WinBUGS (Windows, or Wine).  Check out the book of Andrew Gelman, as referred to above.","Q_Score":12,"Tags":"python,r,statistics","A_Id":1832314,"CreationDate":"2009-07-28T02:43:00.000","Title":"Hierarchical Bayes for R or Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Hierarchical Bayes models are commonly used in Marketing, Political Science, and Econometrics. Yet, the only package I know of is bayesm, which is really a companion to a book (Bayesian Statistics and Marketing, by Rossi, et al.) Am I missing something? Is there a software package for R or Python doing the job out there, and\/or a worked-out example in the associated language?","AnswerCount":7,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":8690,"Q_Id":1191689,"Users Score":0,"Answer":"This answer comes almost ten years late, but it will hopefully help someone in the future.\nThe brms package in R is a very good option for Bayesian hierarchical\/multilevel models, using a syntax very similar to the lme4 package.\nThe brms package uses the probabilistic programming language Stan in the back to do the inferences. Stan uses more advanced sampling methods than JAGS and BUGS, such as Hamiltonian Monte Carlo, which provides more efficient and reliable samples from the posterior distribution.\nIf you wish to model more complicated phenomena, then you can use the rstanpackage to compile Stan models from R. There is also the Python alternative PyStan. However, in order to do this, you must learn how to use Stan.","Q_Score":12,"Tags":"python,r,statistics","A_Id":55978470,"CreationDate":"2009-07-28T02:43:00.000","Title":"Hierarchical Bayes for R or Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Hierarchical Bayes models are commonly used in Marketing, Political Science, and Econometrics. Yet, the only package I know of is bayesm, which is really a companion to a book (Bayesian Statistics and Marketing, by Rossi, et al.) Am I missing something? Is there a software package for R or Python doing the job out there, and\/or a worked-out example in the associated language?","AnswerCount":7,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":8690,"Q_Id":1191689,"Users Score":0,"Answer":"The lme4 package, which estimates hierarchical models using frequentist methods, has a function called mcmcsamp that allows you to sample from the posterior distribution of the model using MCMC. This currently  works only for linear models, quite unfortunately.","Q_Score":12,"Tags":"python,r,statistics","A_Id":1197766,"CreationDate":"2009-07-28T02:43:00.000","Title":"Hierarchical Bayes for R or Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a CSV file which I am processing and putting the processed data into a text file.\nThe entire data that goes into the text file is one big table(comma separated instead of space). My problem is How do I remember the column into which a piece of data goes in the text file?\nFor eg. Assume there is a column called 'col'.\nI just put some data under col. Now after a few iterations, I want to put some other piece of data under col again (In a different row). How do I know where exactly col comes? (And there are a lot of columns like this.)\nHope I am not too vague...","AnswerCount":6,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":798,"Q_Id":1199350,"Users Score":0,"Answer":"Probably either a dict of list or a list of dict. Personally, I'd go with the former. So, parse the heading row of the CSV to get a dict from column heading to column index. Then when you're reading through each row, work out what index you're at, grab the column heading, and then append to the end of the list for that column heading.","Q_Score":2,"Tags":"python,file,csv","A_Id":1199371,"CreationDate":"2009-07-29T10:44:00.000","Title":"Whats the best way of putting tabular data into python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a CSV file which I am processing and putting the processed data into a text file.\nThe entire data that goes into the text file is one big table(comma separated instead of space). My problem is How do I remember the column into which a piece of data goes in the text file?\nFor eg. Assume there is a column called 'col'.\nI just put some data under col. Now after a few iterations, I want to put some other piece of data under col again (In a different row). How do I know where exactly col comes? (And there are a lot of columns like this.)\nHope I am not too vague...","AnswerCount":6,"Available Count":2,"Score":0.0333209931,"is_accepted":false,"ViewCount":798,"Q_Id":1199350,"Users Score":1,"Answer":"Is SQLite an option for you? I know that you have CSV input and output. However, you can import all the data into the SQLite database. Then do all the necessary processing with the power of SQL. Then you can export the results as CSV.","Q_Score":2,"Tags":"python,file,csv","A_Id":1199409,"CreationDate":"2009-07-29T10:44:00.000","Title":"Whats the best way of putting tabular data into python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"What is the cleanest way to add a field to a structured numpy array?  Can it be done destructively, or is it necessary to create a new array and copy over the existing fields?  Are the contents of each field stored contiguously in memory so that such copying can be done efficiently?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":9033,"Q_Id":1201817,"Users Score":20,"Answer":"If you're using numpy 1.3, there's also numpy.lib.recfunctions.append_fields(). \nFor many installations, you'll need to import numpy.lib.recfunctions to access this. import numpy will not allow one to see the numpy.lib.recfunctions","Q_Score":22,"Tags":"python,numpy","A_Id":1208039,"CreationDate":"2009-07-29T17:24:00.000","Title":"Adding a field to a structured numpy array","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Can anyone explain to me how to do more complex data sets like team stats, weather, dice, complex number types \ni understand all the math and how everything works i just dont know how to input more complex data, and then how to read the data it spits out \nif someone could provide examples in python that would be a big help","AnswerCount":4,"Available Count":4,"Score":1.2,"is_accepted":true,"ViewCount":671,"Q_Id":1205449,"Users Score":3,"Answer":"You have to encode your input and your output to something that can be represented by the neural network units. ( for example 1 for \"x has a certain property p\" -1 for \"x doesn't have the property p\" if your units' range is in [-1, 1])\nThe way you encode your input and the way you decode your output depends on what you want to train the neural network for.  \nMoreover, there are many \"neural networks\" algoritms and learning rules for different tasks( Back propagation, boltzman machines, self organizing maps).","Q_Score":3,"Tags":"python,neural-network","A_Id":1205509,"CreationDate":"2009-07-30T09:22:00.000","Title":"Neural net input\/output","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Can anyone explain to me how to do more complex data sets like team stats, weather, dice, complex number types \ni understand all the math and how everything works i just dont know how to input more complex data, and then how to read the data it spits out \nif someone could provide examples in python that would be a big help","AnswerCount":4,"Available Count":4,"Score":0.0,"is_accepted":false,"ViewCount":671,"Q_Id":1205449,"Users Score":0,"Answer":"You have to add the number of units for input and output you need for the problem. If the unknown function to approximate depends on n parameter, you will have n input units. The number of output units depends on the nature of the funcion. For real functions with n real parameters you will have one output unit.\nSome problems, for example in forecasting of time series, you will have m output units for the m succesive values of the function. The encoding is important and depends on the choosen algorithm. For example, in backpropagation for feedforward nets, is better to transform, if possible, the greater number of features in discrete inputs, as for classification tasks.\nOther aspect of the encoding is that you have to evaluate the number of input and hidden units in function of the amount of data. Too many units related to data may give poor approximation due the course ff dimensionality problem. In some cases, you may to aggregate some of the input data in some way to avoid that problem or use some reduction mechanism as PCA.","Q_Score":3,"Tags":"python,neural-network","A_Id":20683280,"CreationDate":"2009-07-30T09:22:00.000","Title":"Neural net input\/output","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Can anyone explain to me how to do more complex data sets like team stats, weather, dice, complex number types \ni understand all the math and how everything works i just dont know how to input more complex data, and then how to read the data it spits out \nif someone could provide examples in python that would be a big help","AnswerCount":4,"Available Count":4,"Score":0.0996679946,"is_accepted":false,"ViewCount":671,"Q_Id":1205449,"Users Score":2,"Answer":"Your features must be decomposed into parts that can be represented as real numbers. The magic of a Neural Net is it's a black box, the correct associations will be made (with internal weights) during the training\n\nInputs\nChoose as few features as are needed to accurately describe the situation, then decompose each into a set of real valued numbers.\n\nWeather: [temp today, humidity today, temp yesterday, humidity yesterday...] the association between today's temp and today's humidity is made internally\nTeam stats: [ave height, ave weight, max height, top score,...]\nDice: not sure I understand this one, do you mean how to encode discrete values?*\nComplex number: [a,ai,b,bi,...]\n\n* Discrete valued features are tricky, but can still still be encoded as (0.0,1.0). The problem is they don't provide a gradient to learn the threshold on.\n\nOutputs\nYou decide what you want the output to mean, and then encode your training examples in that format. The fewer output values, the easier to train.\n\nWeather: [tomorrow's chance of rain, tomorrow's temp,...] **\nTeam stats: [chance of winning, chance of winning by more than 20,...]\nComplex number: [x,xi,...]\n\n** Here your training vectors would be: 1.0 if it rained the next day, 0.0 if it didn't\n\nOf course, whether or not the problem can actually be modeled by a neural net is a different question.","Q_Score":3,"Tags":"python,neural-network","A_Id":1207505,"CreationDate":"2009-07-30T09:22:00.000","Title":"Neural net input\/output","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Can anyone explain to me how to do more complex data sets like team stats, weather, dice, complex number types \ni understand all the math and how everything works i just dont know how to input more complex data, and then how to read the data it spits out \nif someone could provide examples in python that would be a big help","AnswerCount":4,"Available Count":4,"Score":0.0,"is_accepted":false,"ViewCount":671,"Q_Id":1205449,"Users Score":0,"Answer":"More complex data usually means adding more neurons in the input and output layers.\nYou can feed each \"field\" of your register, properly encoded as a real value (normalized, etc.) to each input neuron, or maybe you can even decompose even further into bit fields, assigning saturated inputs of 1 or 0 to the neurons... for the output, it depends on how you train the neural network, it will try to mimic the training set outputs.","Q_Score":3,"Tags":"python,neural-network","A_Id":1206597,"CreationDate":"2009-07-30T09:22:00.000","Title":"Neural net input\/output","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I will have to implement a convolution of two functions in Python, but SciPy\/Numpy appear to have functions only for the convolution of two arrays.\nBefore I try to implement this by using the the regular integration expression of convolution, I would like to ask if someone knows of an already available module that performs these operations.\nFailing that, which of the several kinds of integration that SciPy provides is the best suited for this?\nThanks!","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":8768,"Q_Id":1222147,"Users Score":1,"Answer":"Yes, SciPy\/Numpy is mostly concerned about arrays.\nIf you can tolerate an approximate solution, and your functions only operate over a range of value (not infinite) you can fill an array with the values and convolve the arrays.\nIf you want something more \"correct\" calculus-wise you would probably need a powerful solver (mathmatica, maple...)","Q_Score":2,"Tags":"python,convolution","A_Id":1226509,"CreationDate":"2009-08-03T12:42:00.000","Title":"Convolution of two functions in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm developing an app that handle sets of financial series data (input as csv or open document), one set could be say 10's x 1000's up to double precision numbers (Simplifying, but thats what matters).\nI plan to do operations on that data (eg. sum, difference, averages etc.) as well including generation of say another column based on computations on the input. This will be between columns (row level operations) on one set and also between columns on many (potentially all) sets at the row level also. I plan to write it in Python and it will eventually need a intranet facing interface to display the results\/graphs etc. for now, csv output based on some input parameters will suffice.\nWhat is the best way to store the data and manipulate? So far I see my choices as being either (1) to write csv files to disk and trawl through them to do the math or (2) I could put them into a database and rely on the database to handle the math. My main concern is speed\/performance as the number of datasets grows as there will be inter-dataset row level math that needs to be done.\n-Has anyone had experience going down either path and what are the pitfalls\/gotchas that I should be aware of?\n-What are the reasons why one should be chosen over another? \n-Are there any potential speed\/performance pitfalls\/boosts that I need to be aware of before I start that could influence the design?\n-Is there any project or framework out there to help with this type of task? \n-Edit-\nMore info:\nThe rows will all read all in order, BUT I may need to do some resampling\/interpolation to match the differing input lengths as well as differing timestamps for each row. Since each dataset will always have a differing length that is not fixed, I'll have some scratch table\/memory somewhere to hold the interpolated\/resampled versions. I'm not sure if it makes more sense to try to store this (and try to upsample\/interploate to a common higher length) or just regenerate it each time its needed.","AnswerCount":4,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":581,"Q_Id":1241758,"Users Score":0,"Answer":"Are you likely to need all rows in order or will you want only specific known rows?\nIf you need to read all the data there isn't much advantage to having it in a database.\nedit: If the code fits in memory then a simple CSV is fine. Plain text data formats are always easier to deal with than opaque ones if you can use them.","Q_Score":0,"Tags":"python,database,database-design,file-io","A_Id":1241784,"CreationDate":"2009-08-06T21:58:00.000","Title":"Store data series in file or database if I want to do row level math operations?","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm developing an app that handle sets of financial series data (input as csv or open document), one set could be say 10's x 1000's up to double precision numbers (Simplifying, but thats what matters).\nI plan to do operations on that data (eg. sum, difference, averages etc.) as well including generation of say another column based on computations on the input. This will be between columns (row level operations) on one set and also between columns on many (potentially all) sets at the row level also. I plan to write it in Python and it will eventually need a intranet facing interface to display the results\/graphs etc. for now, csv output based on some input parameters will suffice.\nWhat is the best way to store the data and manipulate? So far I see my choices as being either (1) to write csv files to disk and trawl through them to do the math or (2) I could put them into a database and rely on the database to handle the math. My main concern is speed\/performance as the number of datasets grows as there will be inter-dataset row level math that needs to be done.\n-Has anyone had experience going down either path and what are the pitfalls\/gotchas that I should be aware of?\n-What are the reasons why one should be chosen over another? \n-Are there any potential speed\/performance pitfalls\/boosts that I need to be aware of before I start that could influence the design?\n-Is there any project or framework out there to help with this type of task? \n-Edit-\nMore info:\nThe rows will all read all in order, BUT I may need to do some resampling\/interpolation to match the differing input lengths as well as differing timestamps for each row. Since each dataset will always have a differing length that is not fixed, I'll have some scratch table\/memory somewhere to hold the interpolated\/resampled versions. I'm not sure if it makes more sense to try to store this (and try to upsample\/interploate to a common higher length) or just regenerate it each time its needed.","AnswerCount":4,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":581,"Q_Id":1241758,"Users Score":0,"Answer":"What matters most if all data will fit simultaneously into memory. From the size that you give, it seems that this is easily the case (a few megabytes at worst).\nIf so, I would discourage using a relational database, and do all operations directly in Python. Depending on what other processing you need, I would probably rather use binary pickles, than CSV.","Q_Score":0,"Tags":"python,database,database-design,file-io","A_Id":1241787,"CreationDate":"2009-08-06T21:58:00.000","Title":"Store data series in file or database if I want to do row level math operations?","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm developing an app that handle sets of financial series data (input as csv or open document), one set could be say 10's x 1000's up to double precision numbers (Simplifying, but thats what matters).\nI plan to do operations on that data (eg. sum, difference, averages etc.) as well including generation of say another column based on computations on the input. This will be between columns (row level operations) on one set and also between columns on many (potentially all) sets at the row level also. I plan to write it in Python and it will eventually need a intranet facing interface to display the results\/graphs etc. for now, csv output based on some input parameters will suffice.\nWhat is the best way to store the data and manipulate? So far I see my choices as being either (1) to write csv files to disk and trawl through them to do the math or (2) I could put them into a database and rely on the database to handle the math. My main concern is speed\/performance as the number of datasets grows as there will be inter-dataset row level math that needs to be done.\n-Has anyone had experience going down either path and what are the pitfalls\/gotchas that I should be aware of?\n-What are the reasons why one should be chosen over another? \n-Are there any potential speed\/performance pitfalls\/boosts that I need to be aware of before I start that could influence the design?\n-Is there any project or framework out there to help with this type of task? \n-Edit-\nMore info:\nThe rows will all read all in order, BUT I may need to do some resampling\/interpolation to match the differing input lengths as well as differing timestamps for each row. Since each dataset will always have a differing length that is not fixed, I'll have some scratch table\/memory somewhere to hold the interpolated\/resampled versions. I'm not sure if it makes more sense to try to store this (and try to upsample\/interploate to a common higher length) or just regenerate it each time its needed.","AnswerCount":4,"Available Count":3,"Score":1.2,"is_accepted":true,"ViewCount":581,"Q_Id":1241758,"Users Score":2,"Answer":"\"I plan to do operations on that data (eg. sum, difference, averages etc.) as well including generation of say another column based on computations on the input.\"\nThis is the standard use case for a data warehouse star-schema design.  Buy Kimball's The Data Warehouse Toolkit.  Read (and understand) the star schema before doing anything else.\n\"What is the best way to store the data and manipulate?\"  \nA Star Schema.\nYou can implement this as flat files (CSV is fine) or RDBMS.  If you use flat files, you write simple loops to do the math.  If you use an RDBMS you write simple SQL and simple loops.  \n\"My main concern is speed\/performance as the number of datasets grows\" \nNothing is as fast as a flat file.  Period.  RDBMS is slower.  \nThe RDBMS value proposition stems from SQL being a relatively simple way to specify SELECT SUM(), COUNT() FROM fact JOIN dimension WHERE filter GROUP BY dimension attribute.  Python isn't as terse as SQL, but it's just as fast and just as flexible.  Python competes against SQL.\n\"pitfalls\/gotchas that I should be aware of?\"\nDB design.  If you don't get the star schema and how to separate facts from dimensions, all approaches are doomed.  Once you separate facts from dimensions, all approaches are approximately equal.\n\"What are the reasons why one should be chosen over another?\"\nRDBMS slow and flexible.  Flat files fast and (sometimes) less flexible.  Python levels the playing field.\n\"Are there any potential speed\/performance pitfalls\/boosts that I need to be aware of before I start that could influence the design?\"\nStar Schema:  central fact table surrounded by dimension tables.  Nothing beats it.\n\"Is there any project or framework out there to help with this type of task?\"\nNot really.","Q_Score":0,"Tags":"python,database,database-design,file-io","A_Id":1245169,"CreationDate":"2009-08-06T21:58:00.000","Title":"Store data series in file or database if I want to do row level math operations?","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am looking for a solid implementation of an ordered associative array, that is, an ordered dictionary.  I want the ordering in terms of keys, not of insertion order.\nMore precisely, I am looking for a space-efficent implementation of a int-to-float (or string-to-float for another use case) mapping structure for which:\n\nOrdered iteration is O(n)\nRandom access is O(1)\n\nThe best I came up with was gluing a dict and a list of keys, keeping the last one ordered with bisect and insert.\nAny better ideas?","AnswerCount":10,"Available Count":1,"Score":0.0798297691,"is_accepted":false,"ViewCount":12867,"Q_Id":1319763,"Users Score":4,"Answer":"An ordered tree is usually better for this cases, but random access is going to be log(n). You should keep into account also insertion and removal costs...","Q_Score":36,"Tags":"python,data-structures,collections,dictionary","A_Id":1319790,"CreationDate":"2009-08-23T22:33:00.000","Title":"Key-ordered dict in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Given a set of phrases, i would like to filter the set of all phrases that contain any of the other phrases. Contained here means that if a phrase contains all the words of another phrase it should be filtered out. Order of the words within the phrase does not matter.\nWhat i have so far is this:\n\nSort the set by the number of words in each phrase. \nFor each phrase X in the set:\n\n\nFor each phrase Y in the rest of the set:\n\n\nIf all the words in X are in Y then X is contained in Y, discard Y.\n\n\n\nThis is slow given a list of about 10k phrases.\nAny better options?","AnswerCount":4,"Available Count":2,"Score":0.049958375,"is_accepted":false,"ViewCount":709,"Q_Id":1372531,"Users Score":1,"Answer":"You could build an index which maps words to phrases and do something like:\n\nlet matched = set of all phrases\nfor each word in the searched phrase\n    let wordMatch = all phrases containing the current word\n    let matched = intersection of matched and wordMatch\n\nAfter this, matched would contain all phrases matching all words in the target phrase. It could be pretty well optimized by initializing matched to the set of all phrases containing only words[0], and then only iterating over words[1..words.length]. Filtering phrases which are too short to match the target phrase may improve performance, too.\nUnless I'm mistaken, a simple implementation has a worst case complexity (when the search phrase matches all phrases) of O(n\u00b7m), where n is the number of words in the search phrase, and m is  the number of phrases.","Q_Score":1,"Tags":"c#,java,c++,python,algorithm","A_Id":1372627,"CreationDate":"2009-09-03T10:02:00.000","Title":"Algorithm to filter a set of all phrases containing in other phrase","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Given a set of phrases, i would like to filter the set of all phrases that contain any of the other phrases. Contained here means that if a phrase contains all the words of another phrase it should be filtered out. Order of the words within the phrase does not matter.\nWhat i have so far is this:\n\nSort the set by the number of words in each phrase. \nFor each phrase X in the set:\n\n\nFor each phrase Y in the rest of the set:\n\n\nIf all the words in X are in Y then X is contained in Y, discard Y.\n\n\n\nThis is slow given a list of about 10k phrases.\nAny better options?","AnswerCount":4,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":709,"Q_Id":1372531,"Users Score":0,"Answer":"sort phrases by their contents, i.e., 'Z A' -> 'A Z', then eliminating phrases is easy going from shortest to longer ones.","Q_Score":1,"Tags":"c#,java,c++,python,algorithm","A_Id":1372585,"CreationDate":"2009-09-03T10:02:00.000","Title":"Algorithm to filter a set of all phrases containing in other phrase","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"One of the things I deal with most in data cleaning is missing values. R deals with this well using its \"NA\" missing data label. In python, it appears that I'll have to deal with masked arrays which seem to be a major pain to set up and don't seem to be well documented. Any suggestions on making this process easier in Python? This is becoming a deal-breaker in moving into Python for data analysis. Thanks\nUpdate It's obviously been a while since I've looked at the methods in the numpy.ma module. It appears that at least the basic analysis functions are available for masked arrays, and the examples provided helped me understand how to create masked arrays (thanks to the authors). I would like to see if some of the newer statistical methods in Python (being developed in this year's GSoC) incorporates this aspect, and at least does the complete case analysis.","AnswerCount":4,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":10010,"Q_Id":1377130,"Users Score":4,"Answer":"If you are willing to consider a library, pandas (http:\/\/pandas.pydata.org\/) is a library built on top of numpy which amongst many other things provides:\n\nIntelligent data alignment and integrated handling of missing data: gain automatic label-based alignment in computations and easily manipulate messy data into an orderly form\n\nI've been using it for almost one year in the financial industry where missing and badly aligned data is the norm and it really made my life easier.","Q_Score":11,"Tags":"python,numpy,data-analysis","A_Id":11086822,"CreationDate":"2009-09-04T03:44:00.000","Title":"How do you deal with missing data using numpy\/scipy?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have functions that contribute to small parts of a figure generation. I'm trying to use these functions to generate multiple figures? So something like this:\n\nwork with Figure 1\ndo something else\nwork with Figure 2\ndo something else\nwork with Figure 1\ndo something else\nwork with Figure 2\n\nIf anyone could help, that'd be great!","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":43360,"Q_Id":1401102,"Users Score":0,"Answer":"The best way to show multiple figures is use matplotlib or pylab. (for windows)\nwith matplotlib you can prepare the figures and then when you finish the process with them you can show with the comand \"matplotlib.show()\" and all figures should be shown.\n(on linux) you don\u00b4t have problems adding changes to figures because the interactive mode is enable (on windows the interactive mode don't work OK).","Q_Score":32,"Tags":"python,matplotlib,figures","A_Id":14591411,"CreationDate":"2009-09-09T17:57:00.000","Title":"Python with matplotlib - drawing multiple figures in parallel","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm removing an item from an array if it exists.\nTwo ways I can think of to do this\nWay #1\n\n# x array, r item to remove\nif r in x :\n  x.remove( r )\n\nWay #2\n\ntry :\n  x.remove( r )\nexcept :\n  pass\n\nTiming it shows the try\/except way can be faster\n(some times i'm getting:)\n\n1.16225508968e-06\n8.80804972547e-07\n\n1.14314196588e-06\n8.73752536492e-07\n\n\nimport timeit\n\nruns = 10000\nx = [ '101', '102', '103', '104', '105', 'a', 'b', 'c',\n  'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', '111', '112', '113',\n  'x', 'y', 'z', 'w', 'wwwwwww', 'aeiojwaef', 'iweojfoigj', 'oiowow',\n  'oiweoiwioeiowe', 'oiwjaoigjoaigjaowig',\n]\nr = 'a'\n\ncode1 =\"\"\"\nx = [ '101', '102', '103', '104', '105', 'a', 'b', 'c',\n  'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', '111', '112', '113',\n  'x', 'y', 'z', 'w', 'wwwwwww', 'aeiojwaef', 'iweojfoigj', 'oiowow',\n  'oiweoiwioeiowe', 'oiwjaoigjoaigjaowig',\n]\nr = 'a'\n\nif r in x :\n  x.remove(r)\n\"\"\"\nprint timeit.Timer( code1 ).timeit( runs ) \/ runs\n\ncode2 =\"\"\"\nx = [ '101', '102', '103', '104', '105', 'a', 'b', 'c',\n  'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', '111', '112', '113',\n  'x', 'y', 'z', 'w', 'wwwwwww', 'aeiojwaef', 'iweojfoigj', 'oiowow',\n  'oiweoiwioeiowe', 'oiwjaoigjoaigjaowig',\n]\nr = 'a'\n\ntry :\n  x.remove( r )\nexcept :\n  pass\n\"\"\"\nprint timeit.Timer( code2 ).timeit( runs ) \/ runs\n\n\nWhich is more pythonic?","AnswerCount":5,"Available Count":3,"Score":0.1194272985,"is_accepted":false,"ViewCount":267,"Q_Id":1418266,"Users Score":3,"Answer":"Speed depends on the ratio of hits to misses. To be pythonic choose the clearer method.\nPersonally I think way#1 is clearer (It takes less lines to have an 'if' block rather than an exception block and also uses less brain space). It will also be faster when there are more hits than misses (an exception is more expensive than skipping a if block).","Q_Score":2,"Tags":"python","A_Id":1418321,"CreationDate":"2009-09-13T17:15:00.000","Title":"Which is more pythonic for array removal?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm removing an item from an array if it exists.\nTwo ways I can think of to do this\nWay #1\n\n# x array, r item to remove\nif r in x :\n  x.remove( r )\n\nWay #2\n\ntry :\n  x.remove( r )\nexcept :\n  pass\n\nTiming it shows the try\/except way can be faster\n(some times i'm getting:)\n\n1.16225508968e-06\n8.80804972547e-07\n\n1.14314196588e-06\n8.73752536492e-07\n\n\nimport timeit\n\nruns = 10000\nx = [ '101', '102', '103', '104', '105', 'a', 'b', 'c',\n  'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', '111', '112', '113',\n  'x', 'y', 'z', 'w', 'wwwwwww', 'aeiojwaef', 'iweojfoigj', 'oiowow',\n  'oiweoiwioeiowe', 'oiwjaoigjoaigjaowig',\n]\nr = 'a'\n\ncode1 =\"\"\"\nx = [ '101', '102', '103', '104', '105', 'a', 'b', 'c',\n  'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', '111', '112', '113',\n  'x', 'y', 'z', 'w', 'wwwwwww', 'aeiojwaef', 'iweojfoigj', 'oiowow',\n  'oiweoiwioeiowe', 'oiwjaoigjoaigjaowig',\n]\nr = 'a'\n\nif r in x :\n  x.remove(r)\n\"\"\"\nprint timeit.Timer( code1 ).timeit( runs ) \/ runs\n\ncode2 =\"\"\"\nx = [ '101', '102', '103', '104', '105', 'a', 'b', 'c',\n  'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', '111', '112', '113',\n  'x', 'y', 'z', 'w', 'wwwwwww', 'aeiojwaef', 'iweojfoigj', 'oiowow',\n  'oiweoiwioeiowe', 'oiwjaoigjoaigjaowig',\n]\nr = 'a'\n\ntry :\n  x.remove( r )\nexcept :\n  pass\n\"\"\"\nprint timeit.Timer( code2 ).timeit( runs ) \/ runs\n\n\nWhich is more pythonic?","AnswerCount":5,"Available Count":3,"Score":0.0798297691,"is_accepted":false,"ViewCount":267,"Q_Id":1418266,"Users Score":2,"Answer":"The try\/except way","Q_Score":2,"Tags":"python","A_Id":1418275,"CreationDate":"2009-09-13T17:15:00.000","Title":"Which is more pythonic for array removal?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm removing an item from an array if it exists.\nTwo ways I can think of to do this\nWay #1\n\n# x array, r item to remove\nif r in x :\n  x.remove( r )\n\nWay #2\n\ntry :\n  x.remove( r )\nexcept :\n  pass\n\nTiming it shows the try\/except way can be faster\n(some times i'm getting:)\n\n1.16225508968e-06\n8.80804972547e-07\n\n1.14314196588e-06\n8.73752536492e-07\n\n\nimport timeit\n\nruns = 10000\nx = [ '101', '102', '103', '104', '105', 'a', 'b', 'c',\n  'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', '111', '112', '113',\n  'x', 'y', 'z', 'w', 'wwwwwww', 'aeiojwaef', 'iweojfoigj', 'oiowow',\n  'oiweoiwioeiowe', 'oiwjaoigjoaigjaowig',\n]\nr = 'a'\n\ncode1 =\"\"\"\nx = [ '101', '102', '103', '104', '105', 'a', 'b', 'c',\n  'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', '111', '112', '113',\n  'x', 'y', 'z', 'w', 'wwwwwww', 'aeiojwaef', 'iweojfoigj', 'oiowow',\n  'oiweoiwioeiowe', 'oiwjaoigjoaigjaowig',\n]\nr = 'a'\n\nif r in x :\n  x.remove(r)\n\"\"\"\nprint timeit.Timer( code1 ).timeit( runs ) \/ runs\n\ncode2 =\"\"\"\nx = [ '101', '102', '103', '104', '105', 'a', 'b', 'c',\n  'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', '111', '112', '113',\n  'x', 'y', 'z', 'w', 'wwwwwww', 'aeiojwaef', 'iweojfoigj', 'oiowow',\n  'oiweoiwioeiowe', 'oiwjaoigjoaigjaowig',\n]\nr = 'a'\n\ntry :\n  x.remove( r )\nexcept :\n  pass\n\"\"\"\nprint timeit.Timer( code2 ).timeit( runs ) \/ runs\n\n\nWhich is more pythonic?","AnswerCount":5,"Available Count":3,"Score":1.2,"is_accepted":true,"ViewCount":267,"Q_Id":1418266,"Users Score":6,"Answer":"I've always gone with the first method.  if in reads far more clearly than exception handling does.","Q_Score":2,"Tags":"python","A_Id":1418310,"CreationDate":"2009-09-13T17:15:00.000","Title":"Which is more pythonic for array removal?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm looking to create a 2d matrix of integers with symmetric addressing ( i.e. matrix[2,3] and matrix[3,2] will return the same value ) in python. The integers will have addition and subtraction done on them, and be used for logical comparisons. My initial idea was to create the integer objects up front and try to fill a list of lists with some python equivalent of pointers. I'm not sure how to do it, though. What is the best way to implement this, and should I be using lists or another data structure?","AnswerCount":4,"Available Count":2,"Score":0.049958375,"is_accepted":false,"ViewCount":247,"Q_Id":1425162,"Users Score":1,"Answer":"You only need to store the lower triangle of the matrix. Typically this is done with one n(n+1)\/2 length list. You'll need to overload the __getitem__ method to interpret what the entry means.","Q_Score":1,"Tags":"python,data-structures,matrix","A_Id":1425181,"CreationDate":"2009-09-15T04:48:00.000","Title":"Symmetrically adressable matrix","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm looking to create a 2d matrix of integers with symmetric addressing ( i.e. matrix[2,3] and matrix[3,2] will return the same value ) in python. The integers will have addition and subtraction done on them, and be used for logical comparisons. My initial idea was to create the integer objects up front and try to fill a list of lists with some python equivalent of pointers. I'm not sure how to do it, though. What is the best way to implement this, and should I be using lists or another data structure?","AnswerCount":4,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":247,"Q_Id":1425162,"Users Score":2,"Answer":"You're probably better off using a full square numpy matrix. Yes, it wastes half the memory storing redundant values, but rolling your own symmetric matrix in Python will waste even more memory and CPU by storing and processing the integers as Python objects.","Q_Score":1,"Tags":"python,data-structures,matrix","A_Id":1425305,"CreationDate":"2009-09-15T04:48:00.000","Title":"Symmetrically adressable matrix","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm attempting to shorten the memory footprint of 10B sequential integers by referencing them as indexes in a boolean array. In other words, I need to create an array of 10,000,000,000 elements, but that's well into the \"Long\" range. When I try to reference an array index greater than sys.maxint the array blows up:\n\nx = [False] * 10000000000\nTraceback (most recent call last):\n  File \"\", line 1, in \n    x = [0] * 10000000000\nOverflowError: cannot fit 'long' into an index-sized integer\n\nAnything I can do? I can't seem to find anyone on the net having this problem... Presumably the answer is \"python can't handle arrays bigger than 2B.\"","AnswerCount":6,"Available Count":2,"Score":0.0665680765,"is_accepted":false,"ViewCount":4919,"Q_Id":1436411,"Users Score":2,"Answer":"10B booleans (1.25MB of memory, assuming Python is sane)\n\nI think you have your arithmetic wrong -- stored supercompactly, 10B booleans would be 1.25 GIGA, _not__ MEGA, bytes.\nA list takes at least 4 bytes per item, so you'd need 40GB to do it the way you want.\nYou can store an array (see the array module in the standard library) in much less memory than that, so it might possibly fit.","Q_Score":2,"Tags":"python","A_Id":1436482,"CreationDate":"2009-09-17T02:18:00.000","Title":"long-index arrays in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm attempting to shorten the memory footprint of 10B sequential integers by referencing them as indexes in a boolean array. In other words, I need to create an array of 10,000,000,000 elements, but that's well into the \"Long\" range. When I try to reference an array index greater than sys.maxint the array blows up:\n\nx = [False] * 10000000000\nTraceback (most recent call last):\n  File \"\", line 1, in \n    x = [0] * 10000000000\nOverflowError: cannot fit 'long' into an index-sized integer\n\nAnything I can do? I can't seem to find anyone on the net having this problem... Presumably the answer is \"python can't handle arrays bigger than 2B.\"","AnswerCount":6,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":4919,"Q_Id":1436411,"Users Score":3,"Answer":"A dense bit vector is plausible but it won't be optimal unless you know you won't have more than about 10**10 elements, all clustered near each other, with a reasonably randomized distribution.  If you have a different distribution, then a different structure will be better.  \nFor instance, if you know that in that range, [0,10**10), only a few members are present, use a set(), or if the reverse is true, with nearly every element present except for a fraction, use a negated set, ie element not in mySet.  \nIf the elements tend to cluster around small ranges, you could use a run length encoding, something like [xrange(0,10),xrange(10,15),xrange(15,100)], which you lookup into by bisecting until you find a matching range, and if the index is even, then the element is in the set.  inserts and removals involve shuffling the ranges a bit.\nIf your distribution really is dense, but you need a little more than what fits in memory (seems to be typical in practice) then you can manage memory by using mmap and wrapping the mapped file with an adaptor that uses a similar mechanism to the suggested array('I') solution already suggested.\nTo get an idea of just how compressible you can possibly get, try building a plain file with a reasonable corpus of data in packed form and then apply a general compression algorithm (such as gzip) to see how much reduction you see.  If there is much reduction, then you can probably use some sort of space optimization in your code as well.","Q_Score":2,"Tags":"python","A_Id":1436547,"CreationDate":"2009-09-17T02:18:00.000","Title":"long-index arrays in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I know that __builtin__ sorted() function works on any iterable. But can someone explain this huge (10x) performance difference between anylist.sort() vs sorted(anylist) ? Also, please point out if I am doing anything wrong with way this is measured. \n\n\"\"\"\nExample Output:\n$ python list_sort_timeit.py \nUsing sort method: 20.0662879944\nUsing sorted builin method: 259.009809017\n\"\"\"\n\nimport random\nimport timeit\n\nprint 'Using sort method:',\nx = min(timeit.Timer(\"test_list1.sort()\",\"import random;test_list1=random.sample(xrange(1000),1000)\").repeat())\nprint x\n\nprint 'Using sorted builin method:',\nx =  min(timeit.Timer(\"sorted(test_list2)\",\"import random;test_list2=random.sample(xrange(1000),1000)\").repeat())\nprint x\n\n\nAs the title says, I was interested in comparing list.sort() vs sorted(list). The above snippet showed something interesting that, python's sort function behaves very well for already sorted data. As pointed out by Anurag, in the first case, the sort method is working on already sorted data and while in second sorted it is working on fresh piece to do work again and again. \nSo I wrote this one to test and yes, they are very close.\n\n\"\"\"\nExample Output:\n$ python list_sort_timeit.py \nUsing sort method: 19.0166599751\nUsing sorted builin method: 23.203567028\n\"\"\"\n\nimport random\nimport timeit\n\nprint 'Using sort method:',\nx = min(timeit.Timer(\"test_list1.sort()\",\"import random;test_list1=random.sample(xrange(1000),1000);test_list1.sort()\").repeat())\nprint x\n\nprint 'Using sorted builin method:',\nx =  min(timeit.Timer(\"sorted(test_list2)\",\"import random;test_list2=random.sample(xrange(1000),1000);test_list2.sort()\").repeat())\nprint x\n\nOh, I see Alex Martelli with a response, as I was typing this one.. ( I shall leave the edit, as it might be useful).","AnswerCount":3,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":63711,"Q_Id":1436962,"Users Score":9,"Answer":"Well, the .sort() method of lists sorts the list in place, while sorted() creates a new list.  So if you have a large list, part of your performance difference will be due to copying.\nStill, an order of magnitude difference seems larger than I'd expect.  Perhaps list.sort() has some special-cased optimization that sorted() can't make use of.  For example, since the list class already has an internal Py_Object*[] array of the right size, perhaps it can perform swaps more efficiently.\nEdit: Alex and Anurag are right, the order of magnitude difference is due to you accidentally sorting an already-sorted list in your test case.  However, as Alex's benchmarks show, list.sort() is about 2% faster than sorted(), which would make sense due to the copying overhead.","Q_Score":40,"Tags":"python,sorting","A_Id":1436981,"CreationDate":"2009-09-17T06:07:00.000","Title":"Python sort() method on list vs builtin sorted() function","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to plot some data in ipy.\nThe data consists of three variables alpha,beta and delta. The alpha and beta values are the coordinates of the data points that I wish to plot using a hammer projection. I want to scale the colour of the markers according to the delta values, preferably in a rainbow scale colormap i.e. from red to blue. The delta values range from 0-13 and I want a linear colour correlation.\nCan anyone please help me, I am getting very frustrated.\nMany thanks\nAngela","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":279,"Q_Id":1448582,"Users Score":0,"Answer":"Well i can\\t quite get exactly what are you looking for, whole solution or maybe you've got problem with colors, if that second thing below you got some ideas\nThe most simple solution would be to create 14-elements array with color that you write from hand with help of some graphic software and than simply fetch element with delta number while drawing\nYou can also search some algorithm to draw gradient, but instead of drawing store its color to array, if you wil genrate 130 values then you can get your color like that \nmycolors[delta*10]\nBe more specific with your question, maybe then more people will abe bale to help you\ni hope that my answer in some way helps\nMTH","Q_Score":0,"Tags":"ironpython","A_Id":1448617,"CreationDate":"2009-09-19T13:47:00.000","Title":"Colour map in ipython","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to:\n\nOpen a video file\nIterate over the frames of the file as images\nDo some analysis in this image frame of the video\nDraw in this image of the video\nCreate a new video with these changes\n\nOpenCV isn't working for my webcam, but python-gst is working. Is this possible using python-gst?\nThank you!","AnswerCount":4,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":12979,"Q_Id":1480431,"Users Score":7,"Answer":"Do you mean opencv can't connect to your webcam or can't read video files recorded by it?\nHave you tried saving the video in an other format?\nOpenCV is probably the best supported python image processing tool","Q_Score":13,"Tags":"python,image-processing,video-processing","A_Id":1480450,"CreationDate":"2009-09-26T04:23:00.000","Title":"Most used Python module for video processing?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to:\n\nOpen a video file\nIterate over the frames of the file as images\nDo some analysis in this image frame of the video\nDraw in this image of the video\nCreate a new video with these changes\n\nOpenCV isn't working for my webcam, but python-gst is working. Is this possible using python-gst?\nThank you!","AnswerCount":4,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":12979,"Q_Id":1480431,"Users Score":0,"Answer":"Just build a C\/C++ wrapper for your webcam and then use SWIG or SIP to access these functions from Python. Then use OpenCV in Python that's the best open sourced computer vision library in the wild.\nIf you worry for performance and you work under Linux, you could download free versions of Intel Performance Primitives (IPP) that could be loaded at runtime with zero effort from OpenCV. For certain algorithms you could get a 200% boost of performances, plus automatic multicore support for most of time consuming functions.","Q_Score":13,"Tags":"python,image-processing,video-processing","A_Id":1497418,"CreationDate":"2009-09-26T04:23:00.000","Title":"Most used Python module for video processing?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a plot of two boxplots in the same figure. For style reasons, the axis should have the same length, so that the graphic box is square. I tried to use the set_aspect method, but the axes are too different because of their range\nand the result is terrible.\nIs it possible to have 1:1 axes even if they do not have the same number of points?","AnswerCount":3,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":8361,"Q_Id":1506647,"Users Score":3,"Answer":"Try axis('equal'). It's been a while since I worked with matplotlib, but I seem to remember typing that command a lot.","Q_Score":4,"Tags":"python,matplotlib,boxplot","A_Id":1506741,"CreationDate":"2009-10-01T21:37:00.000","Title":"Matplotlib square boxplot","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"What algorithm is the built in sort() method in Python using? Is it possible to have a look at the code for that method?","AnswerCount":3,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":71971,"Q_Id":1517347,"Users Score":10,"Answer":"In early python-versions, the sort function implemented a modified version of quicksort.\nHowever, it was deemed unstable and as of 2.3 they switched to using an adaptive mergesort algorithm.","Q_Score":128,"Tags":"python,algorithm,sorting,python-internals","A_Id":1517357,"CreationDate":"2009-10-04T20:48:00.000","Title":"About Python's built in sort() method","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"How can I check which version of NumPy I'm using?","AnswerCount":17,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":514245,"Q_Id":1520234,"Users Score":0,"Answer":"It is good to know the version of numpy you run, but strictly speaking if you just need to have specific version on your system you can write like this:\npip install numpy==1.14.3  and this will install the version you need and uninstall other versions of numpy.","Q_Score":340,"Tags":"python,numpy,version","A_Id":53898417,"CreationDate":"2009-10-05T13:56:00.000","Title":"How do I check which version of NumPy I'm using?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How can I check which version of NumPy I'm using?","AnswerCount":17,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":514245,"Q_Id":1520234,"Users Score":11,"Answer":"You can try this:\n\npip show numpy","Q_Score":340,"Tags":"python,numpy,version","A_Id":46330631,"CreationDate":"2009-10-05T13:56:00.000","Title":"How do I check which version of NumPy I'm using?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a series of large text files (up to 1 gig) that are output from an experiment that need to be analysed in Python. They would be best loaded into a 2D numpy array, which presents the first question:\n\nAs the number of rows is unknown at the beginning of the loading, how can\na very large numpy array be most efficiently built, row by row?\n\nSimply adding the row to the array would be inefficient in memory terms, as two large arrays would momentarily co-exist. The same problem would seem to be occur if you use numpy.append. The stack functions are promising, but ideally I would want to grow the array in place.\nThis leads to the second question:\n\nWhat is the best way to observe the memory usage of a Python program that heavily\nuses numpy arrays?\n\nTo study the above problem, I've used the usual memory profiling tools - heapy and pympler - but am only getting the size of the outer array objects (80 bytes) and not the data they are containing. Asides from a crude measuring of how much memory the Python process is using, how can I get at the \"full\" size of the arrays as they grow?\nLocal details: OSX 10.6, Python 2.6, but general solutions are welcome.","AnswerCount":4,"Available Count":1,"Score":0.049958375,"is_accepted":false,"ViewCount":6464,"Q_Id":1530960,"Users Score":1,"Answer":"On possible option is to do a single pass through the file first to count the number of rows, without loading them.\nThe other option is to double your table size each time, which has two benefits:\n\nYou will only re-alloc memory log(n) times where n is the number of rows.\nYou only need 50% more ram than your largest table size\n\nIf you take the dynamic route, you could measure the length of the first row in bytes, then guess the number of rows by calculating (num bytes in file \/ num bytes in first row). Start with a table of this size.","Q_Score":11,"Tags":"python,numpy,memory-management","A_Id":1534340,"CreationDate":"2009-10-07T11:08:00.000","Title":"Incrementally building a numpy array and measuring memory usage","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am looking for Python implementation of k-means algorithm with examples to cluster and cache my database of coordinates.","AnswerCount":8,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":89635,"Q_Id":1545606,"Users Score":0,"Answer":"You can also use GDAL, which has many many functions to work with spatial data.","Q_Score":49,"Tags":"python,algorithm,cluster-analysis,k-means","A_Id":1545672,"CreationDate":"2009-10-09T19:16:00.000","Title":"Python k-means algorithm","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'd like to profile some VCS software, and to do so I want to generate a set of random files, in randomly arranged directories. I'm writing the script in Python, but my question is briefly: how do I generate a random directory tree with an average number of sub-directories per directory and some broad distribution of files per directory?\nClarification: I'm not comparing different VCS repo formats (eg. SVN vs Git vs Hg), but profiling software that deals with SVN (and eventually other) working copies and repos.\nThe constraints I'd like are to specify the total number of files (call it 'N', probably ~10k-100k) and the maximum depth of the directory structure ('L', probably 2-10). I don't care how many directories are generated at each level, and I don't want to end up with 1 file per dir, or 100k all in one dir.\nThe distribution is something I'm not sure about, since I don't know whether VCS' (SVN in particular) would perform better or worse with a very uniform structure or a very skewed structure. Nonetheless, it would be nice if I could come up with an algorithm that didn't \"even out\" for large numbers.\nMy first thoughts were: generate the directory tree using some method, and then uniformly populate the tree with files (treating each dir equally, with no regard as to nesting). My back-of-the-envelope calcs tell me that if there are 'L' levels, with 'D' subdirs per dir, and about sqrt(N) files per dir, then there will be about D^L dirs, so N =~ sqrt(N)*(D^L) => D =~ N^(1\/2L). So now I have an approximate value for 'D', how can I generate the tree? How do I populate the files?\nI'd be grateful just for some pointers to good resources on algorithms I could use.  My searching only found pretty applets\/flash.","AnswerCount":4,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":3603,"Q_Id":1553114,"Users Score":5,"Answer":"Why not download some real open source repos and use those?\nHave you thought about what goes into the files? is that random data too?","Q_Score":2,"Tags":"python,algorithm","A_Id":1553126,"CreationDate":"2009-10-12T07:06:00.000","Title":"Generate random directories\/files given number of files and depth","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a list. It contains x lists, each with y elements.\nI want to pair each element with all the other elements, just once, (a,b = b,a)\nEDIT: this has been criticized as being too vague.So I'll describe the history.\nMy function produces random equations and using genetic techniques, mutates and crossbreeds them, selecting for fitness.\nAfter a number of iterations, it returns a list of 12 objects, sorted by fitness of their 'equation' attribute.\nUsing the 'parallel python' module to run this function 8 times, a list containing 8 lists of 12 objects (each with an equation attribute) each is returned.\nNow,  within each list, the 12 objects have already been cross-bread with each other.\nI want to cross-breed each object in a list with all the other objects in all the other lists, but not with the objects within it's own list with which it has already been cross-bread. (whew!)","AnswerCount":3,"Available Count":2,"Score":0.0665680765,"is_accepted":false,"ViewCount":598,"Q_Id":1591762,"Users Score":1,"Answer":"You haven't made it completely clear what you need. It sounds like itertools should have what you need. Perhaps what you wish is an itertools.combinations of the itertools.product of the lists in your big list. \n@fortran: you can't have a set of sets. You can have a set of frozensets, but depending on what it really means to have duplicates here, that might not be what is needed.","Q_Score":2,"Tags":"python","A_Id":1592512,"CreationDate":"2009-10-19T23:51:00.000","Title":"How to make all combinations of the elements in an array?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a list. It contains x lists, each with y elements.\nI want to pair each element with all the other elements, just once, (a,b = b,a)\nEDIT: this has been criticized as being too vague.So I'll describe the history.\nMy function produces random equations and using genetic techniques, mutates and crossbreeds them, selecting for fitness.\nAfter a number of iterations, it returns a list of 12 objects, sorted by fitness of their 'equation' attribute.\nUsing the 'parallel python' module to run this function 8 times, a list containing 8 lists of 12 objects (each with an equation attribute) each is returned.\nNow,  within each list, the 12 objects have already been cross-bread with each other.\nI want to cross-breed each object in a list with all the other objects in all the other lists, but not with the objects within it's own list with which it has already been cross-bread. (whew!)","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":598,"Q_Id":1591762,"Users Score":0,"Answer":"First of all, please don't refer to this as an \"array\". You are using a list of lists. In Python, an array is a different type of data structure, provided by the array module. \nAlso, your application sounds suspiciously like a matrix. If you are really doing matrix manipulations, you should investigate the Numpy package.\nAt first glance your problem sounded like something that the zip() function would solve or itertools.izip(). You should definitely read through the docs for the itertools module because it has various list manipulations and they will run faster than anything you could write yourself in Python.","Q_Score":2,"Tags":"python","A_Id":1591802,"CreationDate":"2009-10-19T23:51:00.000","Title":"How to make all combinations of the elements in an array?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a python program that does something like this:\n\nRead a row from a csv file.\nDo some transformations on it.\nBreak it up into the actual rows as they would be written to the database.\nWrite those rows to individual csv files.\nGo back to step 1 unless the file has been totally read.\nRun SQL*Loader and load those files into the database.\n\nStep 6 isn't really taking much time at all.  It seems to be step 4 that's taking up most of the time.  For the most part, I'd like to optimize this for handling a set of records in the low millions running on a quad-core server with a RAID setup of some kind.\nThere are a few ideas that I have to solve this:\n\nRead the entire file from step one (or at least read it in very large chunks) and write the file to disk as a whole or in very large chunks.  The idea being that the hard disk would spend less time going back and forth between files.  Would this do anything that buffering wouldn't?\nParallelize steps 1, 2&3, and 4 into separate processes.  This would make steps 1, 2, and 3 not have to wait on 4 to complete.\nBreak the load file up into separate chunks and process them in parallel.  The rows don't need to be handled in any sequential order.  This would likely need to be combined with step 2 somehow.\n\nOf course, the correct answer to this question is \"do what you find to be the fastest by testing.\"  However, I'm mainly trying to get an idea of where I should spend my time first.  Does anyone with more experience in these matters have any advice?","AnswerCount":7,"Available Count":5,"Score":0.0855049882,"is_accepted":false,"ViewCount":2504,"Q_Id":1594604,"Users Score":3,"Answer":"If you are I\/O bound, the best way I have found to optimize is to read or write the entire file into\/out of memory at once, then operate out of RAM from there on.\nWith extensive testing I found that my runtime eded up bound not by the amount of data I read from\/wrote to disk, but by the number of I\/O operations I used to do it. That is what you need to optimize.\nI don't know Python, but if there is a way to tell it to write the whole file out of RAM in one go, rather than issuing a separate I\/O for each byte, that's what you need to do.\nOf course the drawback to this is that files can be considerably larger than available RAM. There are lots of ways to deal with that, but that is another question for another time.","Q_Score":3,"Tags":"python,performance,optimization,file-io","A_Id":1594704,"CreationDate":"2009-10-20T13:27:00.000","Title":"How should I optimize this filesystem I\/O bound program?","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a python program that does something like this:\n\nRead a row from a csv file.\nDo some transformations on it.\nBreak it up into the actual rows as they would be written to the database.\nWrite those rows to individual csv files.\nGo back to step 1 unless the file has been totally read.\nRun SQL*Loader and load those files into the database.\n\nStep 6 isn't really taking much time at all.  It seems to be step 4 that's taking up most of the time.  For the most part, I'd like to optimize this for handling a set of records in the low millions running on a quad-core server with a RAID setup of some kind.\nThere are a few ideas that I have to solve this:\n\nRead the entire file from step one (or at least read it in very large chunks) and write the file to disk as a whole or in very large chunks.  The idea being that the hard disk would spend less time going back and forth between files.  Would this do anything that buffering wouldn't?\nParallelize steps 1, 2&3, and 4 into separate processes.  This would make steps 1, 2, and 3 not have to wait on 4 to complete.\nBreak the load file up into separate chunks and process them in parallel.  The rows don't need to be handled in any sequential order.  This would likely need to be combined with step 2 somehow.\n\nOf course, the correct answer to this question is \"do what you find to be the fastest by testing.\"  However, I'm mainly trying to get an idea of where I should spend my time first.  Does anyone with more experience in these matters have any advice?","AnswerCount":7,"Available Count":5,"Score":0.0285636566,"is_accepted":false,"ViewCount":2504,"Q_Id":1594604,"Users Score":1,"Answer":"Use buffered writes for step 4.\nWrite a simple function that simply appends the output onto a string, checks the string length, and only writes when you have enough which should be some multiple of 4k bytes. I would say start with 32k buffers and time it.\nYou would have one buffer per file, so that most \"writes\" won't actually hit the disk.","Q_Score":3,"Tags":"python,performance,optimization,file-io","A_Id":1595358,"CreationDate":"2009-10-20T13:27:00.000","Title":"How should I optimize this filesystem I\/O bound program?","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a python program that does something like this:\n\nRead a row from a csv file.\nDo some transformations on it.\nBreak it up into the actual rows as they would be written to the database.\nWrite those rows to individual csv files.\nGo back to step 1 unless the file has been totally read.\nRun SQL*Loader and load those files into the database.\n\nStep 6 isn't really taking much time at all.  It seems to be step 4 that's taking up most of the time.  For the most part, I'd like to optimize this for handling a set of records in the low millions running on a quad-core server with a RAID setup of some kind.\nThere are a few ideas that I have to solve this:\n\nRead the entire file from step one (or at least read it in very large chunks) and write the file to disk as a whole or in very large chunks.  The idea being that the hard disk would spend less time going back and forth between files.  Would this do anything that buffering wouldn't?\nParallelize steps 1, 2&3, and 4 into separate processes.  This would make steps 1, 2, and 3 not have to wait on 4 to complete.\nBreak the load file up into separate chunks and process them in parallel.  The rows don't need to be handled in any sequential order.  This would likely need to be combined with step 2 somehow.\n\nOf course, the correct answer to this question is \"do what you find to be the fastest by testing.\"  However, I'm mainly trying to get an idea of where I should spend my time first.  Does anyone with more experience in these matters have any advice?","AnswerCount":7,"Available Count":5,"Score":1.2,"is_accepted":true,"ViewCount":2504,"Q_Id":1594604,"Users Score":3,"Answer":"Python already does IO buffering and the OS should handle both prefetching the input file and delaying writes until it needs the RAM for something else or just gets uneasy about having dirty data in RAM for too long.  Unless you force the OS to write them immediately, like closing the file after each write or opening the file in O_SYNC mode.\nIf the OS isn't doing the right thing, you can try raising the buffer size (third parameter to open()). For some guidance on appropriate values given a 100MB\/s 10ms latency IO system a 1MB IO size will result in approximately 50% latency overhead, while a 10MB IO size will result in 9% overhead. If its still IO bound, you probably just need more bandwidth. Use your OS specific tools to check what kind of bandwidth you are getting to\/from the disks.\nAlso useful is to check if step 4 is taking a lot of time executing or waiting on IO. If it's executing you'll need to spend more time checking which part is the culprit and optimize that, or split out the work to different processes.","Q_Score":3,"Tags":"python,performance,optimization,file-io","A_Id":1595626,"CreationDate":"2009-10-20T13:27:00.000","Title":"How should I optimize this filesystem I\/O bound program?","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a python program that does something like this:\n\nRead a row from a csv file.\nDo some transformations on it.\nBreak it up into the actual rows as they would be written to the database.\nWrite those rows to individual csv files.\nGo back to step 1 unless the file has been totally read.\nRun SQL*Loader and load those files into the database.\n\nStep 6 isn't really taking much time at all.  It seems to be step 4 that's taking up most of the time.  For the most part, I'd like to optimize this for handling a set of records in the low millions running on a quad-core server with a RAID setup of some kind.\nThere are a few ideas that I have to solve this:\n\nRead the entire file from step one (or at least read it in very large chunks) and write the file to disk as a whole or in very large chunks.  The idea being that the hard disk would spend less time going back and forth between files.  Would this do anything that buffering wouldn't?\nParallelize steps 1, 2&3, and 4 into separate processes.  This would make steps 1, 2, and 3 not have to wait on 4 to complete.\nBreak the load file up into separate chunks and process them in parallel.  The rows don't need to be handled in any sequential order.  This would likely need to be combined with step 2 somehow.\n\nOf course, the correct answer to this question is \"do what you find to be the fastest by testing.\"  However, I'm mainly trying to get an idea of where I should spend my time first.  Does anyone with more experience in these matters have any advice?","AnswerCount":7,"Available Count":5,"Score":0.057080742,"is_accepted":false,"ViewCount":2504,"Q_Id":1594604,"Users Score":2,"Answer":"Can you use a ramdisk for step 4? Low millions sounds doable if the rows are less than a couple of kB or so.","Q_Score":3,"Tags":"python,performance,optimization,file-io","A_Id":1597062,"CreationDate":"2009-10-20T13:27:00.000","Title":"How should I optimize this filesystem I\/O bound program?","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a python program that does something like this:\n\nRead a row from a csv file.\nDo some transformations on it.\nBreak it up into the actual rows as they would be written to the database.\nWrite those rows to individual csv files.\nGo back to step 1 unless the file has been totally read.\nRun SQL*Loader and load those files into the database.\n\nStep 6 isn't really taking much time at all.  It seems to be step 4 that's taking up most of the time.  For the most part, I'd like to optimize this for handling a set of records in the low millions running on a quad-core server with a RAID setup of some kind.\nThere are a few ideas that I have to solve this:\n\nRead the entire file from step one (or at least read it in very large chunks) and write the file to disk as a whole or in very large chunks.  The idea being that the hard disk would spend less time going back and forth between files.  Would this do anything that buffering wouldn't?\nParallelize steps 1, 2&3, and 4 into separate processes.  This would make steps 1, 2, and 3 not have to wait on 4 to complete.\nBreak the load file up into separate chunks and process them in parallel.  The rows don't need to be handled in any sequential order.  This would likely need to be combined with step 2 somehow.\n\nOf course, the correct answer to this question is \"do what you find to be the fastest by testing.\"  However, I'm mainly trying to get an idea of where I should spend my time first.  Does anyone with more experience in these matters have any advice?","AnswerCount":7,"Available Count":5,"Score":0.0285636566,"is_accepted":false,"ViewCount":2504,"Q_Id":1594604,"Users Score":1,"Answer":"Isn't it possible to collect a few thousand rows in ram, then go directly to the database server and execute them? \nThis would remove the save to and load from the disk that step 4 entails.\nIf the database server is transactional, this is also a safe way to do it - just have the database begin before your first row and commit after the last.","Q_Score":3,"Tags":"python,performance,optimization,file-io","A_Id":1597281,"CreationDate":"2009-10-20T13:27:00.000","Title":"How should I optimize this filesystem I\/O bound program?","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Suppose my attempt to write a pickle object out to disk is incomplete due to a crash. Will an attempt to unpickle the object always lead to an exception or is it possible that the fragment that was written out may be interpreted as valid pickle and the error go unnoticed?","AnswerCount":5,"Available Count":4,"Score":1.0,"is_accepted":false,"ViewCount":1351,"Q_Id":1653897,"Users Score":7,"Answer":"Contra the other answers offered, I believe that we can make a strong argument about the recoverability of a pickle. That answer is: \"Yes, an incomplete pickle always leads to an exception.\"\nWhy are we able to do this? Because the \"pickle\" format is in fact a small stack-based language. In a stack-based language you write code that pushes item after item on a stack, then invoke an operator that does something with the data you've accumulated. And it just so happens that a pickle has to end with the command \".\", which says: \"take the item now at the bottom of the stack and return it as the value of this pickle.\" If your pickle is chopped off early, it will not end with this command, and you will get an EOF error.\nIf you want to try recovering some of the data, you might have to write your own interpreter, or call into pickle.py somewhere that gets around its wanting to raise EOFError when done interpreting the stack without finding a \".\". The main thing to keep in mind is that, as in most stack-based languages, big data structures are built \"backwards\": first you put lots of little strings or numbers on the stack, then you invoke an operation that says \"put those together into a list\" or \"grab pairs of items on the stack and make a dictionary\". So, if a pickle is interrupted, you'll find the stack full of pieces of the object that was going to be built, but you'll be missing that final code that tells you what was going to be built from the pieces.","Q_Score":4,"Tags":"python","A_Id":1654390,"CreationDate":"2009-10-31T09:38:00.000","Title":"If pickling was interrupted, will unpickling necessarily always fail? - Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Suppose my attempt to write a pickle object out to disk is incomplete due to a crash. Will an attempt to unpickle the object always lead to an exception or is it possible that the fragment that was written out may be interpreted as valid pickle and the error go unnoticed?","AnswerCount":5,"Available Count":4,"Score":0.0399786803,"is_accepted":false,"ViewCount":1351,"Q_Id":1653897,"Users Score":1,"Answer":"I doubt you could make a claim that it will always lead to an exception.  Pickles are actually programs written in a specialized stack language.  The internal details of pickles change from version to version, and new pickle protocols are added occasionally.  The state of the pickle after a crash, and the resulting effects on the unpickler, would be very difficult to summarize in a simple statement like \"it will always lead to an exception\".","Q_Score":4,"Tags":"python","A_Id":1654321,"CreationDate":"2009-10-31T09:38:00.000","Title":"If pickling was interrupted, will unpickling necessarily always fail? - Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Suppose my attempt to write a pickle object out to disk is incomplete due to a crash. Will an attempt to unpickle the object always lead to an exception or is it possible that the fragment that was written out may be interpreted as valid pickle and the error go unnoticed?","AnswerCount":5,"Available Count":4,"Score":0.0399786803,"is_accepted":false,"ViewCount":1351,"Q_Id":1653897,"Users Score":1,"Answer":"To be sure that you have a \"complete\" pickle file, you need to pickle three things.\n\nPickle a header of some kind that claims how many objects and what the end-of-file flag will look like.  A tuple of an integer and the EOF string, for example.\nPickle the objects you actually care about.  The count is given by the header.\nPickle a tail object that you don't actually care about, but which simply matches the claim made in the header.  This can be simply a string that matches what was in the header.\n\nWhen you unpickle this file, you have to unpickle three things:\n\nThe header.  You care about the count and the form of the tail.\nThe objects you actually care about.\nThe tail object.  Check that it matches the header.  Other than that, it doesn't convey much except that the file was written in it's entirety.","Q_Score":4,"Tags":"python","A_Id":1654503,"CreationDate":"2009-10-31T09:38:00.000","Title":"If pickling was interrupted, will unpickling necessarily always fail? - Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Suppose my attempt to write a pickle object out to disk is incomplete due to a crash. Will an attempt to unpickle the object always lead to an exception or is it possible that the fragment that was written out may be interpreted as valid pickle and the error go unnoticed?","AnswerCount":5,"Available Count":4,"Score":0.0798297691,"is_accepted":false,"ViewCount":1351,"Q_Id":1653897,"Users Score":2,"Answer":"Pickling an object returns an str object, or writes an str object to a file ... it doesn't modify the original object. If a \"crash\" (exception) happens inside a pickling call, the result won't be returned to the caller, so you don't have anything that you could try to unpickle. Besides, why would you want to unpickle some dud rubbish left over after an exception?","Q_Score":4,"Tags":"python","A_Id":1654329,"CreationDate":"2009-10-31T09:38:00.000","Title":"If pickling was interrupted, will unpickling necessarily always fail? - Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I regularly make figures (the exploratory data analysis type) in R. I also program in Python and was wondering if there are features or concepts in matplotlib that would be worth learning. For instance, I am quite happy with R - but its image() function will produce large files with pixelated output, whereas Matlab's equivalent figure (I also program regularly in Matlab) seems to be manageable in file size and also 'smoothed' - does matplotlib also provide such reductions...? But more generally, I wonder what other advantages matplotlib might confer. I don't mean this to be a trolling question. Thanks.","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":10755,"Q_Id":1661479,"Users Score":13,"Answer":"This is a tough one to answer.  \nI recently switched some of my graphing workload from R to matplotlib.  In my humble opinion, I find matplotlib's graphs to be prettier (better default colors, they look crisper and more modern).  I also think matplotlib renders PNGs a whole lot better.\nThe real motivation for me though, was that I wanted to work with my underlying data in Python (and numpy) and not R. I think this is the big question to ask, in which language do you want to load, parse and manipulate your data?\nOn the other hand, a bonus for R is that the plotting defaults just work (there's a function for everything).  I find myself frequently digging through the matplotlib docs (they are thick) looking for some obscure way to adjust a border or increase a line thickness.  R's plotting routines have some maturity behind them.","Q_Score":11,"Tags":"python,r,matplotlib,scipy,data-visualization","A_Id":1662207,"CreationDate":"2009-11-02T14:01:00.000","Title":"matplotlib for R user?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I regularly make figures (the exploratory data analysis type) in R. I also program in Python and was wondering if there are features or concepts in matplotlib that would be worth learning. For instance, I am quite happy with R - but its image() function will produce large files with pixelated output, whereas Matlab's equivalent figure (I also program regularly in Matlab) seems to be manageable in file size and also 'smoothed' - does matplotlib also provide such reductions...? But more generally, I wonder what other advantages matplotlib might confer. I don't mean this to be a trolling question. Thanks.","AnswerCount":2,"Available Count":2,"Score":0.3799489623,"is_accepted":false,"ViewCount":10755,"Q_Id":1661479,"Users Score":4,"Answer":"I think that the largest advantage is that matplotlib is based on Python, which you say you already know. So, this is one language less to learn. Just spend the time mastering Python, and you'll benefit both directly for the plotting task at hand and indirectly for your other Python needs. \nBesides, IMHO Python is an overall richer language than R, with far more libraries that can help for various tasks. You have to access data for plotting, and data comes in many forms. In whatever form it comes I'm sure Python has an efficient library for it.\nAnd how about embedding those plots in more complete programs, say simple GUIs? matplotlib binds easily with Python's GUI libs (like PyQT) and you can make stuff that only your imagination limits.","Q_Score":11,"Tags":"python,r,matplotlib,scipy,data-visualization","A_Id":1662225,"CreationDate":"2009-11-02T14:01:00.000","Title":"matplotlib for R user?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using their default POS tagging and default tokenization..and it seems sufficient.  I'd like their default chunker too.\nI am reading the NLTK toolkit book, but it does not seem like they have a default chunker?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":4560,"Q_Id":1687510,"Users Score":9,"Answer":"You can get out of the box named entity chunking with the nltk.ne_chunk() method.  It takes a list of POS tagged tuples:\nnltk.ne_chunk([('Barack', 'NNP'), ('Obama', 'NNP'), ('lives', 'NNS'), ('in', 'IN'), ('Washington', 'NNP')])\nresults in:\nTree('S', [Tree('PERSON', [('Barack', 'NNP')]), Tree('ORGANIZATION', [('Obama', 'NNP')]), ('lives', 'NNS'), ('in', 'IN'), Tree('GPE', [('Washington', 'NNP')])])\nIt identifies Barack as a person, but Obama as an organization.  So, not perfect.","Q_Score":9,"Tags":"python,nlp,nltk,chunking","A_Id":1687712,"CreationDate":"2009-11-06T13:10:00.000","Title":"What is the default chunker for NLTK toolkit in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"For example...\nChicken is an animal.\nBurrito is a food.\nWordNet allows you to do \"is-a\"...the hiearchy feature.\nHowever, how do I know when to stop travelling up the tree? I want a LEVEL.\nThat is consistent.\nFor example, if presented with a bunch of words, I want wordNet to categorize all of them, but at a certain level, so it doesn't go too far up.  Categorizing \"burrito\" as a \"thing\" is too broad, yet \"mexican wrapped food\" is too specific.  I want to go up the hiearchy or down..until the right LEVEL.","AnswerCount":5,"Available Count":4,"Score":0.0,"is_accepted":false,"ViewCount":2585,"Q_Id":1695971,"Users Score":0,"Answer":"sorry, may I ask which tool could judge \"difficulty level\" of sentences?\nI wish to find out \"similar difficulty level\" of sentences for user to read.","Q_Score":6,"Tags":"python,text,nlp,words,wordnet","A_Id":58050062,"CreationDate":"2009-11-08T10:29:00.000","Title":"Does WordNet have \"levels\"? (NLP)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"For example...\nChicken is an animal.\nBurrito is a food.\nWordNet allows you to do \"is-a\"...the hiearchy feature.\nHowever, how do I know when to stop travelling up the tree? I want a LEVEL.\nThat is consistent.\nFor example, if presented with a bunch of words, I want wordNet to categorize all of them, but at a certain level, so it doesn't go too far up.  Categorizing \"burrito\" as a \"thing\" is too broad, yet \"mexican wrapped food\" is too specific.  I want to go up the hiearchy or down..until the right LEVEL.","AnswerCount":5,"Available Count":4,"Score":0.0798297691,"is_accepted":false,"ViewCount":2585,"Q_Id":1695971,"Users Score":2,"Answer":"In order to get levels, you need to predefine the content of each level. An ontology often defines these as the immediate IS_A children of a specific concept, but if that is absent, you need to develop a method of that yourself.\nThe next step is to put a priority on each concept, in case you want to present only one category for each word. The priority can be done in multiple ways, for instance as the count of IS_A relations between the category and the word, or manually selected priorities for each category. For each word, you can then pick the category with the highest priority. For instance, you may want meat to be \"food\" rather than chemical substance.\nYou may also want to pick some words, that change priority if they are in the path. For instance, if you want some chemicals which are also food, to be announced as chemicals, but others should still be food.","Q_Score":6,"Tags":"python,text,nlp,words,wordnet","A_Id":1696133,"CreationDate":"2009-11-08T10:29:00.000","Title":"Does WordNet have \"levels\"? (NLP)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"For example...\nChicken is an animal.\nBurrito is a food.\nWordNet allows you to do \"is-a\"...the hiearchy feature.\nHowever, how do I know when to stop travelling up the tree? I want a LEVEL.\nThat is consistent.\nFor example, if presented with a bunch of words, I want wordNet to categorize all of them, but at a certain level, so it doesn't go too far up.  Categorizing \"burrito\" as a \"thing\" is too broad, yet \"mexican wrapped food\" is too specific.  I want to go up the hiearchy or down..until the right LEVEL.","AnswerCount":5,"Available Count":4,"Score":0.0,"is_accepted":false,"ViewCount":2585,"Q_Id":1695971,"Users Score":0,"Answer":"WordNet's hypernym tree ends with a single root synset for the word \"entity\". If you are using WordNet's C library, then you can get a while recursive structure for a synset's ancestors using traceptrs_ds, and you can get the whole synset tree by recursively following nextss and ptrlst pointers until you hit null pointers.","Q_Score":6,"Tags":"python,text,nlp,words,wordnet","A_Id":1717952,"CreationDate":"2009-11-08T10:29:00.000","Title":"Does WordNet have \"levels\"? (NLP)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"For example...\nChicken is an animal.\nBurrito is a food.\nWordNet allows you to do \"is-a\"...the hiearchy feature.\nHowever, how do I know when to stop travelling up the tree? I want a LEVEL.\nThat is consistent.\nFor example, if presented with a bunch of words, I want wordNet to categorize all of them, but at a certain level, so it doesn't go too far up.  Categorizing \"burrito\" as a \"thing\" is too broad, yet \"mexican wrapped food\" is too specific.  I want to go up the hiearchy or down..until the right LEVEL.","AnswerCount":5,"Available Count":4,"Score":1.0,"is_accepted":false,"ViewCount":2585,"Q_Id":1695971,"Users Score":6,"Answer":"[Please credit Pete Kirkham, he first came with the reference to SUMO which may well answer the question asked by Alex, the OP]\n(I'm just providing a complement of information here; I started in a comment field but soon ran out of space and layout capabilites...)\nAlex: Most of SUMO is science or engineering? It does not contain every-day words like foods, people, cars, jobs, etc?\nPete K: SUMO is an upper ontology. The mid-level ontologies (where you would find concepts between 'thing' and 'beef burrito') listed on the page don't include food, but reflect the sorts of organisations which fund the project. There is a mid-level ontology for people. There's also one for industries (and hence jobs), including food suppliers, but no mention of burritos if you grep it.\nMy two cents\n100% of WordNet (3.0 i.e. the latest, as well as older versions) is mapped to SUMO, and that may just be what Alex need. The mid-level ontologies associated with SUMO (or rather with MILO) are effectively in specific domains, and do not, at this time, include Foodstuff, but since WordNet does (include all -well, many of- these everyday things) you do not need to leverage any formal ontology \"under\" SUMO, but instead use Sumo's WordNet mapping (possibly in addition to WordNet, which, again, is not an ontology but with its informal and loose \"hierarchy\" may also help.\nSome difficulty may arise, however, from two area (and then some ;-) ?):\n\nthe SUMO ontology's \"level\" may not be the level you'd have in mind for your particular application.  For example while \"Burrito\" brings \"Food\", at top level entity in SUMO  \"Chicken\" brings well \"Chicken\" which only through a long chain finds \"Animal\"  (specifically: Chicken->Poultry->Bird->Warm_Blooded_Vertebrae->Vertebrae->Animal).\nWordnet's coverage and metadata is impressive, but with regards to the mid-level concepts can be a bit inconsistent.  For example \"our\" Burrito's hypernym is appropriately \"Dish\", which provides it with circa 140 food dishes, which includes generics such as \"Soup\" or \"Casserole\" as well as \"Chicken Marengo\" (but omitting say \"Chicken Cacciatore\")\n\nMy point, in bringing up these issues, is not to criticize WordNet or SUMO and its related ontologies, but rather to illustrate simply some of the challenges associated with building ontology, particularly at the mid-level.\nRegardless of some possible flaws and lackings of a solution based on SUMO and WordNet, a pragmatic use of these frameworks may well \"fit the bill\" (85% of the time)","Q_Score":6,"Tags":"python,text,nlp,words,wordnet","A_Id":1698380,"CreationDate":"2009-11-08T10:29:00.000","Title":"Does WordNet have \"levels\"? (NLP)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to write a code in python to solve a sudoku puzzle. Do you guys have any idea about a good algorithm for this purpose. I read somewhere in net about a algorithm which solves it by filling the whole box with all possible numbers, then inserts known values into the corresponding boxes.From the row and coloumn of known values the known value is removed.If you guys know any better algorithm than this please help me to write one. Also I am confused that how i should read the known values from the user. It is really hard to enter the values one by one through console. Any easy way for this other than using gui?","AnswerCount":11,"Available Count":2,"Score":0.0181798149,"is_accepted":false,"ViewCount":147751,"Q_Id":1697334,"Users Score":1,"Answer":"Not gonna write full code, but I did a sudoku solver a long time ago. I found that it didn't always solve it (the thing people do when they have a newspaper is incomplete!), but now think I know how to do it.\n\nSetup: for each square, have a set of flags for each number showing the allowed numbers.\nCrossing out: just like when people on the train are solving it on paper, you can iteratively cross out known numbers. Any square left with just one number will trigger another crossing out. This will either result in solving the whole puzzle, or it will run out of triggers. This is where I stalled last time.\nPermutations: there's only 9! = 362880 ways to arrange 9 numbers, easily precomputed on a modern system. All of the rows, columns, and 3x3 squares must be one of these permutations. Once you have a bunch of numbers in there, you can do what you did with the crossing out. For each row\/column\/3x3, you can cross out 1\/9 of the 9! permutations if you have one number, 1\/(8*9) if you have 2, and so forth.\nCross permutations: Now you have a bunch of rows and columns with sets of potential permutations. But there's another constraint: once you set a row, the columns and 3x3s are vastly reduced in what they might be. You can do a tree search from here to find a solution.","Q_Score":22,"Tags":"python,algorithm,sudoku","A_Id":35500598,"CreationDate":"2009-11-08T17:54:00.000","Title":"Algorithm for solving Sudoku","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to write a code in python to solve a sudoku puzzle. Do you guys have any idea about a good algorithm for this purpose. I read somewhere in net about a algorithm which solves it by filling the whole box with all possible numbers, then inserts known values into the corresponding boxes.From the row and coloumn of known values the known value is removed.If you guys know any better algorithm than this please help me to write one. Also I am confused that how i should read the known values from the user. It is really hard to enter the values one by one through console. Any easy way for this other than using gui?","AnswerCount":11,"Available Count":2,"Score":0.0906594778,"is_accepted":false,"ViewCount":147751,"Q_Id":1697334,"Users Score":5,"Answer":"I wrote a simple program that solved the easy ones. It took its input from a file which was just a matrix with spaces and numbers. The datastructure to solve it was just a 9 by 9 matrix of a bit mask. The bit mask would specify which numbers were still possible on a certain position. Filling in the numbers from the file would reduce the numbers in all rows\/columns next to each known location. When that is done you keep iterating over the matrix and reducing possible numbers. If each location has only one option left you're done. But there are some sudokus that need more work. For these ones you can just use brute force: try all remaining possible combinations until you find one that works.","Q_Score":22,"Tags":"python,algorithm,sudoku","A_Id":1697407,"CreationDate":"2009-11-08T17:54:00.000","Title":"Algorithm for solving Sudoku","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to learn programming in python and am also working against a deadline for setting up a neural network which looks like it's going to feature multidirectional associative memory and recurrent connections among other things. While the mathematics for all these things can be accessed from various texts and sources (and is accessible, so to speak), as a newbie to python (and programming as a profession) I am kinda floating in space looking for the firmament as I try to 'implement' things!! \nInformation on any good online tutorials on constructing neural networks ab initio will be greatly appreciated :) \nIn the meantime I am moonlighting as a MatLab user to nurse the wounds caused by Python :)","AnswerCount":3,"Available Count":1,"Score":0.2605204458,"is_accepted":false,"ViewCount":4234,"Q_Id":1698017,"Users Score":4,"Answer":"If you're familiar with Matlab, check out the excellent Python libraries numpy, scipy, and matplotlib. Together, they provide the most commonly used subset of Matlab functions.","Q_Score":3,"Tags":"python,scipy,neural-network","A_Id":1698110,"CreationDate":"2009-11-08T21:44:00.000","Title":"Neural Networks in Python without using any readymade libraries...i.e., from first principles..help!","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working with complex networks. I want to find group of nodes which forms a cycle of 3 nodes (or triangles) in a given graph. As my graph contains about million edges, using a simple iterative solution (multiple \"for\" loop) is not very efficient.\nI am using python for my programming, if these is some inbuilt modules for handling these problems, please let me know.\nIf someone knows any algorithm which can be used for finding triangles in graphs, kindly reply back.","AnswerCount":11,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":18355,"Q_Id":1705824,"Users Score":0,"Answer":"Do you need to find 'all' of the 'triangles', or just 'some'\/'any'?\nOr perhaps you just need to test whether a particular node is part of a triangle?\nThe test is simple - given a node A, are there any two connected nodes B & C that are also directly connected.\nIf you need to find all of the triangles - specifically, all groups of 3 nodes in which each node is joined to the other two - then you need to check every possible group in a very long running 'for each' loop.\nThe only optimisation is ensuring that you don't check the same 'group' twice, e.g. if you have already tested that B & C aren't in a group with A, then don't check whether A & C are in a group with B.","Q_Score":10,"Tags":"python,graph,geometry,cycle","A_Id":1705913,"CreationDate":"2009-11-10T05:33:00.000","Title":"Finding cycle of 3 nodes ( or triangles) in a graph","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working with complex networks. I want to find group of nodes which forms a cycle of 3 nodes (or triangles) in a given graph. As my graph contains about million edges, using a simple iterative solution (multiple \"for\" loop) is not very efficient.\nI am using python for my programming, if these is some inbuilt modules for handling these problems, please let me know.\nIf someone knows any algorithm which can be used for finding triangles in graphs, kindly reply back.","AnswerCount":11,"Available Count":2,"Score":0.0181798149,"is_accepted":false,"ViewCount":18355,"Q_Id":1705824,"Users Score":1,"Answer":"Even though it isn't efficient, you may want to implement a solution, so use the loops.  Write a test so you can get an idea as to how long it takes.\nThen, as you try new approaches you can do two things:\n1) Make certain that the answer remains the same.\n2) See what the improvement is.\nHaving a faster algorithm that misses something is probably going to be worse than having a slower one.\nOnce you have the slow test, you can see if you can do this in parallel and see what the performance increase is.\nThen, you can see if you can mark all nodes that have less than 3 vertices.\nIdeally, you may want to shrink it down to just 100 or so first, so you can draw it, and see what is happening graphically.\nSometimes your brain will see a pattern that isn't as obvious when looking at algorithms.","Q_Score":10,"Tags":"python,graph,geometry,cycle","A_Id":1705866,"CreationDate":"2009-11-10T05:33:00.000","Title":"Finding cycle of 3 nodes ( or triangles) in a graph","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to call a Python function from MATLAB. how can I do this?","AnswerCount":13,"Available Count":1,"Score":0.0461211021,"is_accepted":false,"ViewCount":132733,"Q_Id":1707780,"Users Score":3,"Answer":"Like Daniel said you can run python commands directly from Matlab using the py. command.  To run any of the libraries you just have to make sure Malab is running the python environment where you installed the libraries:\nOn a Mac:\n\nOpen a new terminal window;\ntype: which python (to find out where the default version of python is installed);\nRestart Matlab;\ntype: pyversion('\/anaconda2\/bin\/python'), in the command line (obviously replace with your path).\nYou can now run all the libraries in your default python installation. \n\nFor example:\n\n\npy.sys.version;\npy.sklearn.cluster.dbscan","Q_Score":76,"Tags":"python,matlab,language-interoperability","A_Id":50057490,"CreationDate":"2009-11-10T12:57:00.000","Title":"Call Python function from MATLAB","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"i 'm just finish labs meeting with my advisor, previous code is written in matlab and it run offline mode not realtime mode, so i decide to convert to python+numpy (in offline version) but after labs meeting, my advisor raise issue about speed of realtime recognition, so i have doubt about speed of python+numpy to do this project. or better in c? my project is about using electronic glove (2x sensors) to get realtime data and do data processing, recognition process","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":914,"Q_Id":1727950,"Users Score":1,"Answer":"I think the answer depends on three things: how well you code in Matlab, how well you code in Python\/Numpy, and your algorithm.  Both Matlab and Python can be fast for number crunching if you're diligent about vectorizing everything and using library calls.\nIf your Matlab code is already very good I would be surprised if you saw much performance benefit moving to Numpy unless there's some specific idiom you can use to your advantage.  You might not even see a large benefit moving to C.  I this case your effort would likely be better spent tuning your algorithm.\nIf your Matlab code isn't so good you could 1) write better Matlab code, 2) rewrite in good Numpy code, or 3) rewrite in C.","Q_Score":2,"Tags":"python,c,numpy,gesture-recognition","A_Id":1730684,"CreationDate":"2009-11-13T08:43:00.000","Title":"Just Curious about Python+Numpy to Realtime Gesture Recognition","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"i am a engineering student and i have to do a lot of numerical processing, plots, simulations etc. The tool that i use currently is Matlab. I use it in my university computers for most of my assignments. However, i want to know what are the free options available. \ni have done some research and many have said that python is a worthy replacement for matlab in various scenarios. i want to know how to do all this with python. i am using a mac so how do i install the different python packages. what are those packages? is it really a viable alternative? what are the things i can and cannot do using this python setup?","AnswerCount":8,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":15393,"Q_Id":1776290,"Users Score":13,"Answer":"I've been programming with Matlab for about 15 years, and with Python for about 10. It usually breaks down this way:\nIf you can satisfy the following conditions:\n    1. You primarily use matrices and matrix operations\n    2. You have the money for a Matlab license\n    3. You work on a platform that mathworks supports\nThen, by all means, use Matlab. Otherwise, if you have data structures other than matrices, want an open-source option that allows you to deliver solutions without worrying about licenses, and need to build on platforms that mathworks does not support; then, go with Python.\nThe matlab language is clunky, but the user interface is slick. The Python language is very nice -- with iterators, generators, and functional programming tools that matlab lacks; however, you will have to pick and choose to put together a nice slick interface if you don't like (or can't use) SAGE.\nI hope that helps.","Q_Score":18,"Tags":"python,matlab","A_Id":1777708,"CreationDate":"2009-11-21T18:24:00.000","Title":"replacing Matlab with python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I do know there are some libraries that allow to use Support vector Machines from python code, but I am looking specifically for libraries that allow one to teach it online (this is, without having to give it all the data at once).\nAre there any?","AnswerCount":5,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":5533,"Q_Id":1783669,"Users Score":0,"Answer":"Why would you want to train it online? Adding trainings instances would usually require to re-solve the quadratic programming problem associated with the SVM.\nA way to handle this is to train a SVM in batch mode, and when new data is available, check if these data points are in the [-1, +1] margin of the hyperplane. If so, retrain the SVM using all the old support vectors, and the new training data that falls in the margin.\nOf course, the results can be slightly different compared to batch training on all your data, as some points can be discarded that would be support vectors later on. So again, why do you want to perform online training of you SVM?","Q_Score":10,"Tags":"python,artificial-intelligence,machine-learning,svm","A_Id":1816714,"CreationDate":"2009-11-23T15:03:00.000","Title":"Any python Support Vector Machine library around that allows online learning?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"how can I replace the NaN value in an array, zero if an operation is performed such that as a result instead of the NaN value is zero operations as\n0 \/ 0 = NaN can be replaced by 0","AnswerCount":4,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":32496,"Q_Id":1803516,"Users Score":0,"Answer":"import numpy\nalpha = numpy.array([1,2,3,numpy.nan,4])\nn = numpy.nan_to_num(alpha)\nprint(n)\noutput : array([1., 2., 3., 0., 4.])","Q_Score":10,"Tags":"python","A_Id":72299692,"CreationDate":"2009-11-26T12:54:00.000","Title":"replace the NaN value zero after an operation with arrays","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a python code computing a matrix, and I would like to use this matrix (or array, or list) from C code.\nI wanted to pickle the matrix from the python code, and unpickle it from c code, but I could not find documentation or example on how to do this.\nI found something about marshalling data, but nothing about unpickling from C.\nEdit :\nCommenters Peter H asked if I was working with numpy arrays. The answer is yes.","AnswerCount":7,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1690,"Q_Id":1881851,"Users Score":0,"Answer":"take a look at module struct ?","Q_Score":1,"Tags":"python,c","A_Id":1881867,"CreationDate":"2009-12-10T15:41:00.000","Title":"How to unpickle from C code","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a large collection of rectangles, all of the same size. I am generating random points that should not fall in these rectangles, so what I wish to do is test if the generated point lies in one of the rectangles, and if it does, generate a new point.\nUsing R-trees seem to work, but they are really meant for rectangles and not points. I could use a modified version of a R-tree algorithm which works with points too, but I'd rather not reinvent the wheel, if there is already some better solution. I'm not very familiar with data-structures, so maybe there already exists some structure that works for my problem?\nIn summary, basically what I'm asking is if anyone knows of a good algorithm, that works in Python, that can be used to check if a point lies in any rectangle in a given set of rectangles.\nedit: This is in 2D and the rectangles are not rotated.","AnswerCount":5,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":13398,"Q_Id":1897779,"Users Score":0,"Answer":"Your R-tree approach is the best approach I know of (that's the approach I would choose over quadtrees, B+ trees, or BSP trees, as R-trees seem convenient to build in your case).  Caveat: I'm no expert, even though I remember a few things from my senior year university class of algorithmic!","Q_Score":11,"Tags":"python,algorithm,point","A_Id":1897910,"CreationDate":"2009-12-13T21:17:00.000","Title":"Test if point is in some rectangle","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a large collection of rectangles, all of the same size. I am generating random points that should not fall in these rectangles, so what I wish to do is test if the generated point lies in one of the rectangles, and if it does, generate a new point.\nUsing R-trees seem to work, but they are really meant for rectangles and not points. I could use a modified version of a R-tree algorithm which works with points too, but I'd rather not reinvent the wheel, if there is already some better solution. I'm not very familiar with data-structures, so maybe there already exists some structure that works for my problem?\nIn summary, basically what I'm asking is if anyone knows of a good algorithm, that works in Python, that can be used to check if a point lies in any rectangle in a given set of rectangles.\nedit: This is in 2D and the rectangles are not rotated.","AnswerCount":5,"Available Count":2,"Score":0.1194272985,"is_accepted":false,"ViewCount":13398,"Q_Id":1897779,"Users Score":3,"Answer":"For rectangles that are aligned with the axes, you only need two points (four numbers) to identify the rectangle - conventionally, bottom-left and top-right corners.  To establish whether a given point (Xtest, Ytest) overlaps with a rectangle (XBL, YBL, XTR, YTR) by testing both:\n\nXtest >= XBL && Xtest <= XTR\nYtest >= YBL && Ytest <= YTR\n\nClearly, for a large enough set of points to test, this could be fairly time consuming.  The question, then, is how to optimize the testing.\nClearly, one optimization is to establish the minimum and maximum X and Y values for the box surrounding all the rectangles (the bounding box): a swift test on this shows whether there is any need to look further.\n\nXtest >= Xmin && Xtest <= Xmax\nYtest >= Ymin && Ytest <= Ymax\n\nDepending on how much of the total surface area is covered with rectangles, you might be able to find non-overlapping sub-areas that contain rectangles, and you could then avoid searching those sub-areas that cannot contain a rectangle overlapping the point, again saving comparisons during the search at the cost of pre-computation of suitable data structures.  If the set of rectangles is sparse enough, there may be no overlapping, in which case this degenerates into the brute-force search.  Equally, if the set of rectangles is so dense that there are no sub-ranges in the bounding box that can be split up without breaking rectangles.\nHowever, you could also arbitrarily break up the bounding area into, say, quarters (half in each direction).  You would then use a list of boxes which would include more boxes than in the original set (two or four boxes for each box that overlapped one of the arbitrary boundaries).  The advantage of this is that you could then eliminate three of the four quarters from the search, reducing the amount of searching to be done in total - at the expense of auxilliary storage.\nSo, there are space-time trade-offs, as ever.  And pre-computation versus search trade-offs.  If you are unlucky, the pre-computation achieves nothing (for example, there are two boxes only, and they don't overlap on either axis).  On the other hand, it could achieve considerable search-time benefit.","Q_Score":11,"Tags":"python,algorithm,point","A_Id":1897962,"CreationDate":"2009-12-13T21:17:00.000","Title":"Test if point is in some rectangle","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Hello I have a 1000 data series with 1500 points in each. \nThey form a (1000x1500) size Numpy array created using np.zeros((1500, 1000)) and then filled with the data. \nNow what if I want the array to grow to say 1600 x 1100? Do I have to add arrays using hstack and vstack or is there a better way?\nI would want the data already in the 1000x1500 piece of the array not to be changed, only blank data (zeros) added to the bottom and right, basically.\nThanks.","AnswerCount":4,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":17603,"Q_Id":1909994,"Users Score":2,"Answer":"No matter what, you'll be stuck reallocating a chunk of memory, so it doesn't really matter if you use arr.resize(), np.concatenate, hstack\/vstack, etc. Note that if you're accumulating a lot of data sequentially, Python lists are usually more efficient.","Q_Score":9,"Tags":"python,arrays,numpy,reshape","A_Id":1916520,"CreationDate":"2009-12-15T20:02:00.000","Title":"How do I add rows and columns to a NUMPY array?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Hello I have a 1000 data series with 1500 points in each. \nThey form a (1000x1500) size Numpy array created using np.zeros((1500, 1000)) and then filled with the data. \nNow what if I want the array to grow to say 1600 x 1100? Do I have to add arrays using hstack and vstack or is there a better way?\nI would want the data already in the 1000x1500 piece of the array not to be changed, only blank data (zeros) added to the bottom and right, basically.\nThanks.","AnswerCount":4,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":17603,"Q_Id":1909994,"Users Score":3,"Answer":"If you want zeroes in the added elements, my_array.resize((1600, 1000)) should work.  Note that this differs from numpy.resize(my_array, (1600, 1000)), in which previous lines are duplicated, which is probably not what you want.\nOtherwise (for instance if you want to avoid initializing elements to zero, which could be unnecessary), you can indeed use  hstack and vstack to add an array containing the new elements; numpy.concatenate() (see pydoc numpy.concatenate) should work too (it is just more general, as far as I understand).\nIn either case, I would guess that a new memory block has to be allocated in order to extend the array, and that all these methods take about the same time.","Q_Score":9,"Tags":"python,arrays,numpy,reshape","A_Id":1910401,"CreationDate":"2009-12-15T20:02:00.000","Title":"How do I add rows and columns to a NUMPY array?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I know python offers random module to do some simple lottery. Let say random.shuffle() is a good one.\nHowever, I want to build my own simple one. What should I look into? Is there any specific mathematical philosophies behind lottery?\nLet say, the simplest situation. 100 names and generate 20 names randomly.\nI don't want to use shuffle, since I want to learn to build one myself.\nI need some advise to start. Thanks.","AnswerCount":5,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1639,"Q_Id":1950539,"Users Score":0,"Answer":"The main shortcoming of software-based methods of generating lottery numbers is the fact that all random numbers generated by software are pseudo-random.\nThis may not be a problem for your simple application, but you did ask about a 'specific mathematical philosophy'. You will have noticed that all commercial lottery systems use physical methods: balls with numbers.\nAnd behind the scenes, the numbers generated by physical lottery systems will be carefully scrutunised for indications of non-randomness and steps taken to eliminate it.\nAs I say, this may not be a consideration for your simple application, but the overriding requirement of a true lottery (the 'specific mathematical philosophy') should be mathematically demonstrable randomness","Q_Score":0,"Tags":"python","A_Id":1950575,"CreationDate":"2009-12-23T03:30:00.000","Title":"python lottery suggestion","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a 3D space (x, y, z) with an additional parameter at each point (energy), giving 4 dimensions of data in total.\nI would like to find a set of x, y, z points which correspond to an iso-energy surface found by interpolating between the known points.\nThe spacial mesh has constant spacing and surrounds the iso-energy surface entirely, however, it does not occupy a cubic space (the mesh occupies a roughly cylindrical space)\nSpeed is not crucial, I can leave this number crunching for a while. Although I'm coding in Python and NumPy, I can write portions of the code in FORTRAN. I can also wrap existing C\/C++\/FORTRAN libraries for use in the scripts, if such libraries exist.\nAll examples and algorithms that I have so far found online (and in Numerical Recipes) stop short of 4D data.","AnswerCount":3,"Available Count":2,"Score":0.0665680765,"is_accepted":false,"ViewCount":5443,"Q_Id":1972172,"Users Score":1,"Answer":"Why not try quadlinear interpolation?\nextend Trilinear interpolation by another dimension.  As long as a linear interpolation model fits your data, it should work.","Q_Score":8,"Tags":"python,algorithm,interpolation","A_Id":1972198,"CreationDate":"2009-12-28T23:46:00.000","Title":"Interpolating a scalar field in a 3D space","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a 3D space (x, y, z) with an additional parameter at each point (energy), giving 4 dimensions of data in total.\nI would like to find a set of x, y, z points which correspond to an iso-energy surface found by interpolating between the known points.\nThe spacial mesh has constant spacing and surrounds the iso-energy surface entirely, however, it does not occupy a cubic space (the mesh occupies a roughly cylindrical space)\nSpeed is not crucial, I can leave this number crunching for a while. Although I'm coding in Python and NumPy, I can write portions of the code in FORTRAN. I can also wrap existing C\/C++\/FORTRAN libraries for use in the scripts, if such libraries exist.\nAll examples and algorithms that I have so far found online (and in Numerical Recipes) stop short of 4D data.","AnswerCount":3,"Available Count":2,"Score":0.1325487884,"is_accepted":false,"ViewCount":5443,"Q_Id":1972172,"Users Score":2,"Answer":"Since you have a spatial mesh with constant spacing, you can identify all neighbors on opposite sides of the isosurface.  Choose some form of interpolation (q.v. Reed Copsey's answer) and do root-finding along the line between each such neighbor.","Q_Score":8,"Tags":"python,algorithm,interpolation","A_Id":1973347,"CreationDate":"2009-12-28T23:46:00.000","Title":"Interpolating a scalar field in a 3D space","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have installed Matplotlib, and I have created two lists, x and y.\nI want the x-axis to have values from 0 to 100 in steps of 10 and the y-axis to have values from 0 to 1 in steps of 0.1. How do I plot this graph?","AnswerCount":4,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":5478,"Q_Id":2048041,"Users Score":2,"Answer":"There is a very good book:\nSandro Tosi, Matplotlib for Python Developers, Packt Pub., 2009.","Q_Score":6,"Tags":"python,matplotlib","A_Id":10303500,"CreationDate":"2010-01-12T10:02:00.000","Title":"How do I plot a graph in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Background\nI have many (thousands!) of data files with a standard field based format (think tab-delimited, same fields in every line, in every file).  I'm debating various ways of making this data available \/ searchable.  (Some options include RDBMS, NoSQL stuff, using the grep\/awk and friends, etc.).  \nProposal\nIn particular, one idea that appeals to me is \"indexing\" the files in some way. Since these files are read-only (and static), I was imagining some persistent files containing binary trees (one for each indexed field, just like in other data stores).  I'm open to ideas about how to this, or to hearing that this is simply insane.  Mostly, my favorite search engine hasn't yielded me any pre-rolled solutions for this.  \nI realize this is a little ill-formed, and solutions are welcome.\nAdditional Details\n\nfiles long, not wide\n\n\nmillions of lines per hour, spread over 100 files per hour\ntab seperated, not many columns (~10) \nfields are short (say < 50 chars per field)\n\nqueries are on fields, combinations of fields, and can be historical\n\nDrawbacks to various solutions:\n(All of these are based on my observations and tests, but I'm open to correction)\nBDB\n\nhas problems with scaling to large file sizes (in my experience, once they're 2GB or so, performance can be terrible)\nsingle writer (if it's possible to get around this, I want to see code!)\nhard to do multiple indexing, that is, indexing on different fields at once (sure you can do this by copying the data over and over). \nsince it only stores strings, there is a serialize \/ deserialize step\n\nRDBMSes\nWins:\n\nflat table model is excellent for querying, indexing\n\nLosses:\n\nIn my experience, the problem comes with indexing.  From what I've seen (and please correct me if I am wrong), the issue with rdbmses I know (sqlite, postgres) supporting either batch load (then indexing is slow at the end), or row by row loading (which is low).  Maybe I need more performance tuning.","AnswerCount":5,"Available Count":3,"Score":0.0399786803,"is_accepted":false,"ViewCount":2801,"Q_Id":2110843,"Users Score":1,"Answer":"If the data is already organized in fields, it doesn't sound like a text searching\/indexing problem. It sounds like tabular data that would be well-served by a database.\nScript the file data into a database, index as you see fit, and query the data in any complex way the database supports.\nThat is unless you're looking for a cool learning project. Then, by all means, come up with an interesting file indexing scheme.","Q_Score":4,"Tags":"python,algorithm,indexing,binary-tree","A_Id":2111067,"CreationDate":"2010-01-21T16:22:00.000","Title":"File indexing (using Binary trees?) in Python","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Background\nI have many (thousands!) of data files with a standard field based format (think tab-delimited, same fields in every line, in every file).  I'm debating various ways of making this data available \/ searchable.  (Some options include RDBMS, NoSQL stuff, using the grep\/awk and friends, etc.).  \nProposal\nIn particular, one idea that appeals to me is \"indexing\" the files in some way. Since these files are read-only (and static), I was imagining some persistent files containing binary trees (one for each indexed field, just like in other data stores).  I'm open to ideas about how to this, or to hearing that this is simply insane.  Mostly, my favorite search engine hasn't yielded me any pre-rolled solutions for this.  \nI realize this is a little ill-formed, and solutions are welcome.\nAdditional Details\n\nfiles long, not wide\n\n\nmillions of lines per hour, spread over 100 files per hour\ntab seperated, not many columns (~10) \nfields are short (say < 50 chars per field)\n\nqueries are on fields, combinations of fields, and can be historical\n\nDrawbacks to various solutions:\n(All of these are based on my observations and tests, but I'm open to correction)\nBDB\n\nhas problems with scaling to large file sizes (in my experience, once they're 2GB or so, performance can be terrible)\nsingle writer (if it's possible to get around this, I want to see code!)\nhard to do multiple indexing, that is, indexing on different fields at once (sure you can do this by copying the data over and over). \nsince it only stores strings, there is a serialize \/ deserialize step\n\nRDBMSes\nWins:\n\nflat table model is excellent for querying, indexing\n\nLosses:\n\nIn my experience, the problem comes with indexing.  From what I've seen (and please correct me if I am wrong), the issue with rdbmses I know (sqlite, postgres) supporting either batch load (then indexing is slow at the end), or row by row loading (which is low).  Maybe I need more performance tuning.","AnswerCount":5,"Available Count":3,"Score":0.0399786803,"is_accepted":false,"ViewCount":2801,"Q_Id":2110843,"Users Score":1,"Answer":"The physical storage access time will tend to dominate anything you do.  When you profile, you'll find that the read() is where you spend most of your time.\nTo reduce the time spent waiting for I\/O, your best bet is compression.\nCreate a huge ZIP archive of all of your files.  One open, fewer reads.  You'll spend more CPU time.  I\/O time, however, will dominate your processing, so reduce I\/O time by zipping everything.","Q_Score":4,"Tags":"python,algorithm,indexing,binary-tree","A_Id":2110912,"CreationDate":"2010-01-21T16:22:00.000","Title":"File indexing (using Binary trees?) in Python","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Background\nI have many (thousands!) of data files with a standard field based format (think tab-delimited, same fields in every line, in every file).  I'm debating various ways of making this data available \/ searchable.  (Some options include RDBMS, NoSQL stuff, using the grep\/awk and friends, etc.).  \nProposal\nIn particular, one idea that appeals to me is \"indexing\" the files in some way. Since these files are read-only (and static), I was imagining some persistent files containing binary trees (one for each indexed field, just like in other data stores).  I'm open to ideas about how to this, or to hearing that this is simply insane.  Mostly, my favorite search engine hasn't yielded me any pre-rolled solutions for this.  \nI realize this is a little ill-formed, and solutions are welcome.\nAdditional Details\n\nfiles long, not wide\n\n\nmillions of lines per hour, spread over 100 files per hour\ntab seperated, not many columns (~10) \nfields are short (say < 50 chars per field)\n\nqueries are on fields, combinations of fields, and can be historical\n\nDrawbacks to various solutions:\n(All of these are based on my observations and tests, but I'm open to correction)\nBDB\n\nhas problems with scaling to large file sizes (in my experience, once they're 2GB or so, performance can be terrible)\nsingle writer (if it's possible to get around this, I want to see code!)\nhard to do multiple indexing, that is, indexing on different fields at once (sure you can do this by copying the data over and over). \nsince it only stores strings, there is a serialize \/ deserialize step\n\nRDBMSes\nWins:\n\nflat table model is excellent for querying, indexing\n\nLosses:\n\nIn my experience, the problem comes with indexing.  From what I've seen (and please correct me if I am wrong), the issue with rdbmses I know (sqlite, postgres) supporting either batch load (then indexing is slow at the end), or row by row loading (which is low).  Maybe I need more performance tuning.","AnswerCount":5,"Available Count":3,"Score":0.0399786803,"is_accepted":false,"ViewCount":2801,"Q_Id":2110843,"Users Score":1,"Answer":"sqlite3 is fast, small, part of python (so nothing to install) and provides indexing of columns. It writes to files, so you wouldn't need to install a database system.","Q_Score":4,"Tags":"python,algorithm,indexing,binary-tree","A_Id":12805622,"CreationDate":"2010-01-21T16:22:00.000","Title":"File indexing (using Binary trees?) in Python","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"i have an array of 27 elements,and i don't want to generate all permutations of array (27!)\ni need 5000 randomly choosed permutations,any tip will be useful...","AnswerCount":6,"Available Count":1,"Score":0.0333209931,"is_accepted":false,"ViewCount":38205,"Q_Id":2124347,"Users Score":1,"Answer":"You may want the itertools.permutations() function. Gotta love that itertools module!\nNOTE: New in 2.6","Q_Score":30,"Tags":"python,permutation","A_Id":2124356,"CreationDate":"2010-01-23T19:12:00.000","Title":"how to generate permutations of array in python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I had to build a concept analyzer for computer science field and I used for this machine learning, the orange library for Python. I have the examples of concepts, where the features are lemma and part of speech, like algorithm|NN|concept. The problem is that any other word, that in fact is not a concept, is classified as a concept, due to the lack of negative examples. It is not feasable to put all the other words in learning file, classified as simple words not concepts(this will work, but is not quite a solution). Any idea?\nThanks.","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":296,"Q_Id":2126383,"Users Score":2,"Answer":"The question is very unclear, but assuming what you mean is that your machine learning algorithm is not working without negative examples and you can't give it every possible negative example, then it's perfectly alright to give it some negative examples.\nThe point of data mining (a.k.a. machine learning) is to try coming up with general rules based on a relatively small samples of data and then applying them to larger data. In real life problems you will never have all the data. If you had all possible inputs, you could easily create a simple sequence of if-then rules which would always be correct. If it was that simple, robots would be doing all our thinking for us by now.","Q_Score":2,"Tags":"python,artificial-intelligence,machine-learning,data-mining","A_Id":2126656,"CreationDate":"2010-01-24T08:07:00.000","Title":"Machine learning issue for negative instances","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a camera that will be stationary, pointed at an indoors area. People will walk past the camera, within about 5 meters of it. Using OpenCV, I want to detect individuals walking past - my ideal return is an array of detected individuals, with bounding rectangles.\nI've looked at several of the built-in samples:\n\nNone of the Python samples really apply\nThe C blob tracking sample looks promising, but doesn't accept live video, which makes testing difficult. It's also the most complicated of the samples, making extracting the relevant knowledge and converting it to the Python API problematic.\nThe C 'motempl' sample also looks promising, in that it calculates a silhouette from subsequent video frames. Presumably I could then use that to find strongly connected components and extract individual blobs and their bounding boxes - but I'm still left trying to figure out a way to identify blobs found in subsequent frames as the same blob.\n\nIs anyone able to provide guidance or samples for doing this - preferably in Python?","AnswerCount":4,"Available Count":2,"Score":0.2449186624,"is_accepted":false,"ViewCount":45934,"Q_Id":2188646,"Users Score":5,"Answer":"Nick,\nWhat you are looking for is not people detection, but motion detection. If you tell us a lot more about what you are trying to solve\/do, we can answer better. \nAnyway, there are many ways to do motion detection depending on what you are going to do with the results. Simplest one would be differencing followed by thresholding while a complex one could be proper background modeling -> foreground subtraction -> morphological ops -> connected component analysis, followed by blob analysis if required. Download the opencv code and look in samples directory. You might see what you are looking for. Also, there is an Oreilly book on OCV.\nHope this helps,\nNand","Q_Score":37,"Tags":"python,opencv,computer-vision,motion-detection","A_Id":2352019,"CreationDate":"2010-02-02T23:50:00.000","Title":"How can I detect and track people using OpenCV?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a camera that will be stationary, pointed at an indoors area. People will walk past the camera, within about 5 meters of it. Using OpenCV, I want to detect individuals walking past - my ideal return is an array of detected individuals, with bounding rectangles.\nI've looked at several of the built-in samples:\n\nNone of the Python samples really apply\nThe C blob tracking sample looks promising, but doesn't accept live video, which makes testing difficult. It's also the most complicated of the samples, making extracting the relevant knowledge and converting it to the Python API problematic.\nThe C 'motempl' sample also looks promising, in that it calculates a silhouette from subsequent video frames. Presumably I could then use that to find strongly connected components and extract individual blobs and their bounding boxes - but I'm still left trying to figure out a way to identify blobs found in subsequent frames as the same blob.\n\nIs anyone able to provide guidance or samples for doing this - preferably in Python?","AnswerCount":4,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":45934,"Q_Id":2188646,"Users Score":2,"Answer":"This is similar to a project we did as part of a Computer Vision course, and I can tell you right now that it is a hard problem to get right.\nYou could use foreground\/background segmentation, find all blobs and then decide that they are a person. The problem is that it will not work very well since people tend to go together, go past each other and so on, so a blob might very well consist of two persons and then you will see that blob splitting and merging as they walk along.\nYou will need some method of discriminating between multiple persons in one blob. This is not a problem I expect anyone being able to answer in a single SO-post.\nMy advice is to dive into the available research and see if you can find anything there. The problem is not unsolvavble considering that there exists products which do this: Autoliv has a product to detect pedestrians using an IR-camera on a car, and I have seen other products which deal with counting customers entering and exiting stores.","Q_Score":37,"Tags":"python,opencv,computer-vision,motion-detection","A_Id":2190799,"CreationDate":"2010-02-02T23:50:00.000","Title":"How can I detect and track people using OpenCV?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I cannot get the example Python programs to run. When executing the Python command \"from opencv import cv\" I get the message \"ImportError: No module named _cv\".  There is a stale _cv.pyd in the site-packages directory, but no _cv.py anywhere.  See step 5 below.\nMS Windows XP, VC++ 2008, Python 2.6, OpenCV 2.0\nHere's what I have done.\n\nDownloaded and ran the MS Windows installer for OpenCV2.0.\nDownloaded and installed CMake\nDownloaded and installed SWIG\nRan CMake. After unchecking \"ENABLE_OPENMP\" in the CMake GUI, I was able to build OpenCV using INSTALL.vcproj and BUILD_ALL.vcproj.  I do not know what the difference is, so I built everything under both of those project files.  The C example programs run fine.\nCopied contents of OpenCV2.0\/Python2.6\/lib\/site-packages to my installed Python2.6\/lib\/site-packages directory.  I notice that it contains an old _cv.pyd and an old libcv.dll.a.","AnswerCount":4,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":16728,"Q_Id":2195441,"Users Score":3,"Answer":"After Step 1 (Installer) just copy the content of C:\\OpenCV2.0\\Python2.6\\Lib\\site-packages to C:\\Python26\\Lib\\site-packages (standard installation path assumed).\nThat's all.\nIf you have a webcam installed you can try the camshift.demo in C:\\OpenCV2.0\\samples\\python\nThe deprecated stuff (C:\\OpenCV2.0\\samples\\swig_python) does not work at the moment as somebody wrote above. The OpenCV People are working on it. Here is the full picture:\n\n31\/03\/10 (hopefully) Next OpenCV Official Release: 2.1.0 is due March\n31st, 2010.\nlink:\/\/opencv.willowgarage.com\/wiki\/Welcome\/Introduction#Announcements\n04\/03\/10 [james]rewriting samples for new Python    5:36 PM Mar 4th\nvia API    link:\/\/twitter.com\/opencvlibrary\n12\/31\/09 We've gotten more serious about OpenCV's software\nengineering. We now have a full C++ and Python interface.\nlink:\/\/opencv.willowgarage.com\/wiki\/OpenCV%20Monthly\n9\/30\/09 Several (actually, most) SWIG-based Python samples do not work\ncorrectly now. The reason is this problem is being investigated and\nthe intermediate update of the OpenCV Python package will be released\nas soon as the problem is sorted out.\nlink:\/\/opencv.willowgarage.com\/wiki\/OpenCV%20Monthly","Q_Score":6,"Tags":"python,opencv","A_Id":2405587,"CreationDate":"2010-02-03T21:07:00.000","Title":"OpenCV 2.0 and Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am running some calculations in an external machine and at the end I get X, Y pairs. I want to apply linear regression and obtain A, B, and R2. In this machine I can not install anything (it runs Linux) and has basic stuff installed on it, python, bash (of course), etc.\nI wonder what would be the best approach to use a script (python, bash, etc) or program (I can compile C and C++) that gives me the linear regression coefficients without the need to add external libraries (numpy, etc)","AnswerCount":3,"Available Count":2,"Score":0.0665680765,"is_accepted":false,"ViewCount":2428,"Q_Id":2204087,"Users Score":1,"Answer":"How about extracting the coeffs into a file, import to another machine and then use Excel\/Matlab\/whatever other program that does this for you?","Q_Score":2,"Tags":"c++,python,bash,linear-algebra","A_Id":2204122,"CreationDate":"2010-02-04T23:45:00.000","Title":"Minimal linear regression program","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I am running some calculations in an external machine and at the end I get X, Y pairs. I want to apply linear regression and obtain A, B, and R2. In this machine I can not install anything (it runs Linux) and has basic stuff installed on it, python, bash (of course), etc.\nI wonder what would be the best approach to use a script (python, bash, etc) or program (I can compile C and C++) that gives me the linear regression coefficients without the need to add external libraries (numpy, etc)","AnswerCount":3,"Available Count":2,"Score":0.1973753202,"is_accepted":false,"ViewCount":2428,"Q_Id":2204087,"Users Score":3,"Answer":"For a single, simple, known function (as in your case: a line) it is not hard to simply code a basic least square routine from scratch (but does require some attention to detail). It is a very common assignment in introductory numeric analysis classes.\nSo, look up least squares on wikipedia or mathworld or in a text book and go to town.","Q_Score":2,"Tags":"c++,python,bash,linear-algebra","A_Id":2204124,"CreationDate":"2010-02-04T23:45:00.000","Title":"Minimal linear regression program","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"Is there any way to turn off antialias for all text in a plot, especially the ticklabels?","AnswerCount":4,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2659,"Q_Id":2219503,"Users Score":1,"Answer":"It seems this is not possible. Some classes such as Line2D have a \"set_antialiased\" method, but Text lacks this. I suggest you file a feature request on the Sourceforge tracker, and send an email to the matplotlib mailing list mentioning the request.","Q_Score":5,"Tags":"python,matplotlib,antialiasing","A_Id":2223165,"CreationDate":"2010-02-08T04:01:00.000","Title":"Matplotlib turn off antialias for text in plot?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm doing an ongoing survey, every quarter. We get people to sign up (where they give extensive demographic info). \nThen we get them to answer six short questions with 5 possible values much worse, worse, same, better, much better. \nOf course over time we will not get the same participants,, some will drop out and some new ones will sign up,, so I'm trying to decide how to best build a db and code (hope to use Python, Numpy?) to best allow for ongoing collection and analysis by the various categories defined by the initial demographic data..As of now we have 700 or so participants, so the dataset is not too big.\nI.E.;\ndemographic, UID,  North, south, residential. commercial Then answer for 6 questions for Q1\nSame for Q2 and so on,, then need  able to slice dice and average the values for the quarterly answers by the various demographics to see trends over time. \nThe averaging, grouping and so forth is modestly complicated by having differing participants each quarter\nAny pointers to design patterns for this sort of DB? and analysis? Is this a sparse matrix?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1157,"Q_Id":2223576,"Users Score":0,"Answer":"On the analysis, if your six questions have been posed in a way that would lead you to believe the answers will be correlated, consider conducting a factor analysis on the raw scores first.   Often comparing the factors across regions or customer type has more statistical power than comparing across questions alone.   Also, the factor scores are more likely to be normally distributed (they are the weighted sum of 6 observations) while the six questions alone would not.   This allows you to apply t-tests based on the normal distibution when comparing factor scores. \nOne watchout, though.  If you assign numeric values to answers - 1 = much worse, 2 = worse, etc. you are implying that the distance between much worse and worse is the same as the distance between worse and same.  This is generally not true - you might really have to screw up to get a vote of \"much worse\" while just being a passive screw up might get you a \"worse\" score.  So the assignment of cardinal (numerics) to ordinal (ordering) has a bias of its own. \nThe unequal number of participants per quarter isn't a problem - there are statistical t-tests that deal with unequal sample sizes.","Q_Score":1,"Tags":"python,design-patterns,statistics,matrix,survey","A_Id":2224074,"CreationDate":"2010-02-08T17:38:00.000","Title":"Design pattern for ongoing survey anayisis","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm working on a project where I need to store a matrix of numbers indexed by two string keys.  The matrix is not jagged, i.e. if a column key exists for any row then it should exist for all rows.  Similarly, if a row key exists for any column then it should exist for all columns.\nThe obvious way to express this is with an associative array of associative arrays, but this is both awkward and inefficient, and it doesn't enforce the non-jaggedness property.  Do any popular programming languages provide an associative matrix either built into the language or as part of their standard libraries?  If so, how do they work, both at the API and implementation level?  I'm using Python and D for this project, but examples in other languages would still be useful because I would be able to look at the API and figure out the best way to implement something similar in Python or D.","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":566,"Q_Id":2247197,"Users Score":2,"Answer":"Why not just use a standard matrix, but then have two dictionaries - one that converts the row keys to row indices and one that converts the columns keys to columns indices. You could make your own structure that would work this way fairly easily I think. You just make a class that contains the matrix and the two dictionaries and go from there.","Q_Score":6,"Tags":"python,data-structures,matrix,d,associative-array","A_Id":2247284,"CreationDate":"2010-02-11T19:39:00.000","Title":"Associative Matrices?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm reading a 6 million entry .csv file with Python, and I want to be able to search through this file for a particular entry.\nAre there any tricks to search the entire file? Should you read the whole thing into a dictionary or should you perform a search every time? I tried loading it into a dictionary but that took ages so I'm currently searching through the whole file every time which seems wasteful.\nCould I possibly utilize that the list is alphabetically ordered? (e.g. if the search word starts with \"b\" I only search from the line that includes the first word beginning with \"b\" to the line that includes the last word beginning with \"b\")\nI'm using import csv.\n(a side question: it is possible to make csv go to a specific line in the file? I want to make the program start at a random line)\nEdit: I already have a copy of the list as an .sql file as well, how could I implement that into Python?","AnswerCount":6,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":20439,"Q_Id":2299454,"Users Score":0,"Answer":"my idea is to use python zodb module to store dictionaty type data and then create new csv file using that data structure. do all your operation at that time.","Q_Score":4,"Tags":"python,dictionary,csv,large-files","A_Id":3279041,"CreationDate":"2010-02-19T20:51:00.000","Title":"How do quickly search through a .csv file in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm reading a 6 million entry .csv file with Python, and I want to be able to search through this file for a particular entry.\nAre there any tricks to search the entire file? Should you read the whole thing into a dictionary or should you perform a search every time? I tried loading it into a dictionary but that took ages so I'm currently searching through the whole file every time which seems wasteful.\nCould I possibly utilize that the list is alphabetically ordered? (e.g. if the search word starts with \"b\" I only search from the line that includes the first word beginning with \"b\" to the line that includes the last word beginning with \"b\")\nI'm using import csv.\n(a side question: it is possible to make csv go to a specific line in the file? I want to make the program start at a random line)\nEdit: I already have a copy of the list as an .sql file as well, how could I implement that into Python?","AnswerCount":6,"Available Count":2,"Score":0.0333209931,"is_accepted":false,"ViewCount":20439,"Q_Id":2299454,"Users Score":1,"Answer":"You can't go directly to a specific line in the file because lines are variable-length, so the only way to know when line #n starts is to search for the first n newlines.  And it's not enough to just look for '\\n' characters because CSV allows newlines in table cells, so you really do have to parse the file anyway.","Q_Score":4,"Tags":"python,dictionary,csv,large-files","A_Id":2443606,"CreationDate":"2010-02-19T20:51:00.000","Title":"How do quickly search through a .csv file in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a range of data that I have approximated using a polynomial of degree 2 in Python. I want to calculate the area underneath this polynomial between 0 and 1.\nIs there a calculus, or similar package from numpy that I can use, or should I just make a simple function to integrate these functions?\nI'm a little unclear what the best approach for defining mathematical functions is.\nThanks.","AnswerCount":5,"Available Count":1,"Score":0.1194272985,"is_accepted":false,"ViewCount":4045,"Q_Id":2352499,"Users Score":3,"Answer":"It might be overkill to resort to general-purpose numeric integration algorithms for your special case...if you work out the algebra, there's a simple expression that gives you the area.\nYou have a polynomial of degree 2: f(x) = ax2 + bx + c\nYou want to find the area under the curve for x in the range [0,1].\nThe antiderivative F(x) = ax3\/3 + bx2\/2 + cx + C\nThe area under the curve from 0 to 1 is: F(1) - F(0) = a\/3 + b\/2 + c \nSo if you're only calculating the area for the interval [0,1], you might consider\nusing this simple expression rather than resorting to the general-purpose methods.","Q_Score":4,"Tags":"python,polynomial-math,numerical-integration","A_Id":2352875,"CreationDate":"2010-02-28T20:18:00.000","Title":"Calculating the area underneath a mathematical function","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to solve a Poisson equation on a rectangular domain which ends up being a linear problem like\n    Ax=b\nbut since I know the boundary conditions, there are nodes where I have the solution values. I guess my question is...\n  How can I solve the sparse system Ax=b if I know what some of the coordinates of x are and the undetermined values depend on these as well? It's the same as a normal solve except I know some of the solution to begin with.\nThanks!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":533,"Q_Id":2361176,"Users Score":1,"Answer":"If I understand correctly, some elements of x are known, and some are not, and you want to solve Ax = b for the unknown values of x, correct?\nLet Ax = [A1 A2][x1; x2] = b, where the vector x = [x1; x2], the vector x1 has the unknown values of x, and vector x2 have the known values of x. Then, A1x1 = b - A2x2. Therefore, solve for x1 using scipy.linalg.solve or any other desired solver.","Q_Score":1,"Tags":"python,numpy,sparse-matrix,poisson","A_Id":2361204,"CreationDate":"2010-03-02T05:57:00.000","Title":"Solving Sparse Linear Problem With Some Known Boundary Values","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a set of X,Y data points (about 10k) that are easy to plot as a scatter plot but that I would like to represent as a heatmap.\nI looked through the examples in MatPlotLib and they all seem to already start with heatmap cell values to generate the image.\nIs there a method that converts a bunch of x,y, all different, to a heatmap (where zones with higher frequency of x,y would be \"warmer\")?","AnswerCount":12,"Available Count":1,"Score":0.0333209931,"is_accepted":false,"ViewCount":284427,"Q_Id":2369492,"Users Score":2,"Answer":"Make a 2-dimensional array that corresponds to the cells in your final image, called say heatmap_cells and instantiate it as all zeroes.\nChoose two scaling factors that define the difference between each array element in real units, for each dimension, say x_scale and y_scale. Choose these such that all your datapoints will fall within the bounds of the heatmap array.\nFor each raw datapoint with x_value and y_value:\nheatmap_cells[floor(x_value\/x_scale),floor(y_value\/y_scale)]+=1","Q_Score":222,"Tags":"python,matplotlib,heatmap,histogram2d","A_Id":2371227,"CreationDate":"2010-03-03T07:42:00.000","Title":"Generate a heatmap in MatPlotLib using a scatter data set","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I process a lot of text\/data that I exchange between Python, R, and sometimes Matlab.\nMy go-to is the flat text file, but also use SQLite occasionally to store the data and access from each program (not Matlab yet though). I don't use GROUPBY, AVG, etc. in SQL as much as I do these operations in R, so I don't necessarily require the database operations.\nFor such applications that requires exchanging data among programs to make use of available libraries in each language, is there a good rule of thumb on which data exchange format\/method to use (even XML or NetCDF or HDF5)?\nI know between Python -> R there is rpy or rpy2 but I was wondering about this question in a more general sense - I use many computers which all don't have rpy2 and also use a few other pieces of scientific analysis software that require access to the data at various times (the stages of processing and analysis are also separated).","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":3563,"Q_Id":2392017,"Users Score":15,"Answer":"If all the languages support SQLite - use it. The power of SQL might not be useful to you right now, but it probably will be at some point, and it saves you having to rewrite things later when you decide you want to be able to query your data in more complicated ways.\nSQLite will also probably be substantially faster if you only want to access certain bits of data in your datastore - since doing that with a flat-text file is challenging without reading the whole file in (though it's not impossible).","Q_Score":8,"Tags":"python,sql,database,r,file-format","A_Id":2392026,"CreationDate":"2010-03-06T09:30:00.000","Title":"SQLite or flat text file?","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm doing some prototyping with OpenCV for a hobby project involving processing of real time camera data. I wonder if it is worth the effort to reimplement this in C or C++ when I have it all figured out or if no significant performance boost can be expected. The program basically chains OpenCV functions, so the main part of the work should be done in native code anyway.","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":1352,"Q_Id":2432792,"Users Score":5,"Answer":"You've answered your own question pretty well. Most of the expensive computations should be within the OpenCV library, and thus independent of the language you use. \nIf you're really concerned about efficiency, you could profile your code and confirm that this is indeed the case. If need be, your custom processing functions, if any, could be coded in C\/C++ and exposed in python through the method of your choice (eg: boost-python), to follow the same approach.\nBut in my experience, python works just fine as a \"composition\" tool for such a use.","Q_Score":1,"Tags":"c++,python,c,performance,opencv","A_Id":2433626,"CreationDate":"2010-03-12T12:54:00.000","Title":"OpenCV performance in different languages","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm doing some prototyping with OpenCV for a hobby project involving processing of real time camera data. I wonder if it is worth the effort to reimplement this in C or C++ when I have it all figured out or if no significant performance boost can be expected. The program basically chains OpenCV functions, so the main part of the work should be done in native code anyway.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1352,"Q_Id":2432792,"Users Score":0,"Answer":"OpenCV used to utilize IPP, which is very fast. However, OpenCV 2.0 does not. You might customize your OpenCV using IPP, for example color conversion routines.","Q_Score":1,"Tags":"c++,python,c,performance,opencv","A_Id":2470491,"CreationDate":"2010-03-12T12:54:00.000","Title":"OpenCV performance in different languages","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am interested to perform kmeans clustering on a list of words with the distance measure being Leveshtein. \n1) I know there are a lot of frameworks out there, including scipy and orange that has a kmeans implementation. However they all require some sort of vector as the data which doesn't really fit me.\n2) I need a good clustering implementation. I looked at python-clustering and realize that it doesn't a) return the sum of all the distance to each centroid, and b) it doesn't have any sort of iteration limit or cut off which ensures the quality of the clustering. python-clustering and the clustering algorithm on daniweb doesn't really work for me.\nCan someone find me a good lib? Google hasn't been my friend","AnswerCount":4,"Available Count":1,"Score":0.049958375,"is_accepted":false,"ViewCount":1947,"Q_Id":2459739,"Users Score":1,"Answer":"Yeah I think there isn't a good implementation to what I need. \nI have some crazy requirements, like distance caching etc.\nSo i think i will just write my own lib and release it as GPLv3 soon.","Q_Score":9,"Tags":"python,cluster-analysis","A_Id":2460285,"CreationDate":"2010-03-17T03:29:00.000","Title":"Python KMeans clustering words","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there any good open-source implementation of Mersenne Twister and other good random number generators in Python available? I would like to use in for teaching math and comp sci majors? I am also looking for the corresponding theoretical support. \nEdit: Source code of Mersenne Twister is readily available in various languages such as C (random.py) or pseudocode (Wikipedia) but I could not find one in Python.","AnswerCount":2,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":5857,"Q_Id":2469031,"Users Score":7,"Answer":"Mersenne Twister is an implementation that is used by standard python library. You can see it in random.py file in your python distribution.\nOn my system (Ubuntu 9.10) it is in \/usr\/lib\/python2.6, on Windows it should be in C:\\Python26\\Lib","Q_Score":4,"Tags":"python,random,open-source,mersenne-twister","A_Id":2469142,"CreationDate":"2010-03-18T10:31:00.000","Title":"Open-source implementation of Mersenne Twister in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"My question is rather complicated for me to explain, as i'm not really good at maths, but i'll try to be as clear as possible.\nI'm trying to code a cluster in python, which will generate words given a charset (i.e. with lowercase: aaaa, aaab, aaac, ..., zzzz) and make various operations on them. \nI'm searching how to calculate, given the charset and the number of nodes, what range each node should work on (i.e.: node1: aaaa-azzz, node2: baaa-czzz, node3: daaa-ezzz, ...). Is it possible to make an algorithm that could compute this, and if it is, how could i implement this in python?\nI really don't know how to do that, so any help would be much appreciated","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":260,"Q_Id":2488670,"Users Score":1,"Answer":"You should be able to treat your words as numerals in a strange base.  For example, let's say you have a..z as your charset (26 characters), 4 character strings, and you want to distribute among equally 10 machines.  Then there are a total of 26^4 strings, so each machine gets 26^4\/10 strings.  The first machine will get strings 0 through 26^4\/10, the next 26^4\/10 through 26^4\/5, etc.\nTo convert the numbers to strings, just write the number in base 26 using your charset as the numbers.  So 0 is 'aaaa' and 26^4\/10 = 2*26^3 + 15*26^2 + 15*26 +15 is 'cppp'.","Q_Score":1,"Tags":"python,algorithm,cluster-analysis,character-encoding","A_Id":2489898,"CreationDate":"2010-03-21T20:55:00.000","Title":"python parallel computing: split keyspace to give each node a range to work on","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using matplotlib for a graphing application. I am trying to create a graph which has strings as the X values. However, the using plot function expects a numeric value for X.\nHow can I use string X values?","AnswerCount":5,"Available Count":1,"Score":0.1586485043,"is_accepted":false,"ViewCount":48768,"Q_Id":2497449,"Users Score":4,"Answer":"Why not just make the x value some auto-incrementing number and then change the label?\n--jed","Q_Score":11,"Tags":"python,matplotlib","A_Id":2497667,"CreationDate":"2010-03-23T03:47:00.000","Title":"Plot string values in matplotlib","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Guys, I here have 200 separate csv files named from SH (1) to SH (200). I want to merge them into a single csv file. How can I do it?","AnswerCount":22,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":189456,"Q_Id":2512386,"Users Score":78,"Answer":"Why can't you just sed 1d sh*.csv > merged.csv?\nSometimes you don't even have to use python!","Q_Score":96,"Tags":"python,csv,merge,concatenation","A_Id":5876058,"CreationDate":"2010-03-25T00:24:00.000","Title":"how to merge 200 csv files in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have FigureCanvasWxAgg instance with a figure displayed on a frame. If user clicks on the canvas another frame with a new FigureCanvasWxAgg containing the same figure will be shown. By now closing the new frame can result in destroying the C++ part of the figure so that it won't be available for the first frame. \nHow can I save the figure? Python deepcopy from copy module does't work in this case.\nThanks in advance.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1759,"Q_Id":2513786,"Users Score":1,"Answer":"I'm not familiar with the inner workings, but could easily imagine how disposing of a frame damages the figure data. Is it expensive to draw? Otherwise I'd take the somewhat chickenish approach of simply redrawing it ;)","Q_Score":8,"Tags":"python,wxpython,copy,matplotlib","A_Id":3120753,"CreationDate":"2010-03-25T07:45:00.000","Title":"How to copy matplotlib figure?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":1,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to create a list containing the 3-D coords of a grid of regularly spaced points, each as a 3-element tuple. I'm looking for advice on the most efficient way to do this. \nIn C++ for instance, I simply loop over three nested loops, one for each coordinate. In Matlab, I would probably use the meshgrid function (which would do it in one command). I've read about meshgrid and mgrid in Python, and I've also read that using numpy's broadcasting rules is more efficient. It seems to me that using the zip function in combination with the numpy broadcast rules might be the most efficient way, but zip doesn't seem to be overloaded in numpy.","AnswerCount":5,"Available Count":1,"Score":0.0798297691,"is_accepted":false,"ViewCount":1877,"Q_Id":2518730,"Users Score":2,"Answer":"I would say go with meshgrid or mgrid, in particular if you need non-integer coordinates.  I'm surprised that Numpy's broadcasting rules would be more efficient, as meshgrid was designed especially for the problem that you want to solve.","Q_Score":4,"Tags":"python,numpy","A_Id":2521903,"CreationDate":"2010-03-25T19:29:00.000","Title":"A 3-D grid of regularly spaced points","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Since Cassandra doesn't have MapReduce built in yet (I think it's coming in 0.7), is it dumb to try and MapReduce with my Python client or should I just use CouchDB or Mongo or something?\nThe application is stats collection, so I need to be able to sum values with grouping to increment counters. I'm not, but pretend I'm making Google analytics so I want to keep track of which browsers appear, which pages they went to, and visits vs. pageviews.\nI would just atomically update my counters on write, but Cassandra isn't very good at counters either.\nMay Cassandra just isn't the right choice for this?\nThanks!","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1625,"Q_Id":2527173,"Users Score":6,"Answer":"Cassandra supports map reduce since version 0.6. (Current stable release is 0.5.1, but go ahead and try the new map reduce functionality in 0.6.0-beta3) To get started I recommend to take a look at the word count map reduce example in 'contrib\/word_count'.","Q_Score":4,"Tags":"python,mongodb,cassandra,couchdb,nosql","A_Id":2528683,"CreationDate":"2010-03-26T22:28:00.000","Title":"Is Using Python to MapReduce for Cassandra Dumb?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I have a application that does a certain experiment 1000 times (multi-threaded, so that multiple experiments are done at the same time). Every experiment needs appr. 50.000 random.random() calls.\nWhat is the best approach to get this really random. I could copy a random object to every experiment and do than a jumpahead of 50.000 * expid. The documentation suggests that jumpahead(1) already scrambles the state, but is that really true?\nOr is there another way to do this in 'the best way'?\n(No, the random numbers are not used for security, but for a metropolis hasting algorithm. The only requirement is that the experiments are independent, not whether the random sequence is somehow predictable or so)","AnswerCount":4,"Available Count":1,"Score":0.1488850336,"is_accepted":false,"ViewCount":2828,"Q_Id":2546039,"Users Score":3,"Answer":"jumpahead(1) is indeed sufficient (and identical to jumpahead(50000) or any other such call, in the current implementation of random -- I believe that came in at the same time as the Mersenne Twister based implementation).  So use whatever argument fits in well with your programs' logic.  (Do use a separate random.Random instance per thread for thread-safety purposes of course, as your question already hints).\n(random module generated numbers are not meant to be cryptographically strong, so it's a good thing that you're not using for security purposes;-).","Q_Score":4,"Tags":"python,random","A_Id":2546266,"CreationDate":"2010-03-30T14:35:00.000","Title":"How should I use random.jumpahead in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there a data viewer in Python\/IPython like the variable editor in MATLAB?","AnswerCount":4,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":2415,"Q_Id":2562697,"Users Score":0,"Answer":"in ipython, ipipe.igrid() can be used to view tabular data.","Q_Score":4,"Tags":"python,numpy,ipython","A_Id":2569226,"CreationDate":"2010-04-01T18:31:00.000","Title":"MATLAB-like variable editor in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there a data viewer in Python\/IPython like the variable editor in MATLAB?","AnswerCount":4,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":2415,"Q_Id":2562697,"Users Score":0,"Answer":"Even Pycharm will be a good option if you are looking for MATLAB like editor.","Q_Score":4,"Tags":"python,numpy,ipython","A_Id":28463696,"CreationDate":"2010-04-01T18:31:00.000","Title":"MATLAB-like variable editor in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm porting an C++ scientific application to python, and as I'm new to python, some problems come to my mind:\n1) I'm defining a class that will contain the coordinates (x,y). These values will be accessed several times, but they only will be read after the class instantiation. Is it better to use an tuple or an numpy array, both in memory and access time wise?\n2) In some cases, these coordinates will be used to build a complex number, evaluated on a complex function, and the real part of this function will be used. Assuming that there is no way to separate real and complex parts of this function, and the real part will have to be used on the end, maybe is better to use directly complex numbers to store (x,y)? How bad is the overhead with the transformation from complex to real in python? The code in c++ does a lot of these transformations, and this is a big slowdown in that code.\n3) Also some coordinates transformations will have to be performed, and for the coordinates the x and y values will be accessed in separate, the transformation be done, and the result returned. The coordinate transformations are defined in the complex plane, so is still faster to use the components x and y directly than relying on the complex variables?\nThank you","AnswerCount":2,"Available Count":2,"Score":0.2913126125,"is_accepted":false,"ViewCount":6196,"Q_Id":2563773,"Users Score":3,"Answer":"A numpy array with an extra dimension is tighter in memory use, and at least as fast!, as a numpy array of tuples; complex numbers are at least as good or even better, including for your third question.  BTW, you may have noticed that -- while questions asked later than yours were getting answers aplenty -- your was laying fallow: part of the reason is no doubt that asking three questions within a question turns responders off.  Why not just ask one question per question?  It's not as if you get charged for questions or anything, you know...!-)","Q_Score":10,"Tags":"python,arrays,numpy,tuples,complex-numbers","A_Id":2564787,"CreationDate":"2010-04-01T21:17:00.000","Title":"Better use a tuple or numpy array for storing coordinates","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm porting an C++ scientific application to python, and as I'm new to python, some problems come to my mind:\n1) I'm defining a class that will contain the coordinates (x,y). These values will be accessed several times, but they only will be read after the class instantiation. Is it better to use an tuple or an numpy array, both in memory and access time wise?\n2) In some cases, these coordinates will be used to build a complex number, evaluated on a complex function, and the real part of this function will be used. Assuming that there is no way to separate real and complex parts of this function, and the real part will have to be used on the end, maybe is better to use directly complex numbers to store (x,y)? How bad is the overhead with the transformation from complex to real in python? The code in c++ does a lot of these transformations, and this is a big slowdown in that code.\n3) Also some coordinates transformations will have to be performed, and for the coordinates the x and y values will be accessed in separate, the transformation be done, and the result returned. The coordinate transformations are defined in the complex plane, so is still faster to use the components x and y directly than relying on the complex variables?\nThank you","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":6196,"Q_Id":2563773,"Users Score":7,"Answer":"In terms of memory consumption, numpy arrays are more compact than Python tuples.\nA numpy array uses a single contiguous block of memory. All elements of the numpy array must be of a declared type (e.g. 32-bit or 64-bit float.) A Python tuple does not necessarily use a contiguous block of memory, and the elements of the tuple can be arbitrary Python objects, which generally consume more memory than numpy numeric types.\nSo this issue is a hands-down win for numpy, (assuming the elements of the array can be stored as a numpy numeric type).\nOn the issue of speed, I think the choice boils down to the question, \"Can you vectorize your code?\"\nThat is, can you express your calculations as operations done on entire arrays element-wise. \nIf the code can be vectorized, then numpy will most likely be faster than Python tuples. (The only case I could imagine where it might not be, is if you had many very small tuples. In this case the overhead of forming the numpy arrays and one-time cost of importing numpy might drown-out the benefit of vectorization.)\nAn example of code that could not be vectorized would be if your calculation involved looking at, say, the first complex number in an array z, doing a calculation which produces an integer index idx, then retrieving z[idx], doing a calculation on that number, which produces the next index idx2, then retrieving z[idx2], etc. This type of calculation might not be vectorizable. In this case, you might as well use Python tuples, since you won't be able to leverage numpy's strength.\nI wouldn't worry about the speed of accessing the real\/imaginary parts of a complex number. My guess is the issue of vectorization will most likely determine which method is faster. (Though, by the way, numpy can transform an array of complex numbers to their real parts simply by striding over the complex array, skipping every other float, and viewing the result as floats. Moreover, the syntax is dead simple: If z is a complex numpy array, then z.real is the real parts as a float numpy array. This should be far faster than the pure Python approach of using a list comprehension of attribute lookups: [z.real for z in zlist].)\nJust out of curiosity, what is your reason for porting the C++ code to Python?","Q_Score":10,"Tags":"python,arrays,numpy,tuples,complex-numbers","A_Id":2564868,"CreationDate":"2010-04-01T21:17:00.000","Title":"Better use a tuple or numpy array for storing coordinates","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am porting code from Matlab to Python and am having trouble finding a replacement for the firls( ) routine.  It is used for, least-squares linear-phase Finite Impulse Response (FIR) filter design.\nI looked at scipy.signal and nothing there looked like it would do the trick.  Of course I was able to replace my remez and freqz algorithsm, so that's good.\nOn one blog I found an algorithm that implemented this filter without weighting, but I need one with weights.\nThanks, David","AnswerCount":7,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":4383,"Q_Id":2568707,"Users Score":0,"Answer":"It seems unlikely that you'll find exactly what you seek already written in Python, but perhaps the Matlab function's help page gives or references a description of the algorithm?","Q_Score":3,"Tags":"python,algorithm,math,matlab,digital-filter","A_Id":2569232,"CreationDate":"2010-04-02T19:23:00.000","Title":"Does Python\/Scipy have a firls( ) replacement (i.e. a weighted, least squares, FIR filter design)?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using Python 3.1.1 on Mac OS X 10.6.2 and need an interface to R. When browsing the internet I found out about RPy. Is this the right choice? \nCurrently, a program in Python computes a distance matrix and, stores it in a file. I invoke R separately in an interactive way and read in the matrix for cluster analysis. In order to\nsimplify computation one could prepare a script file for R then call it from Python and read back the results. Since I am new to Python, I would not like to go back to 2.6.","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":4111,"Q_Id":2573132,"Users Score":20,"Answer":"edit: Rewrite to summarize the edits that accumulated over time.\nThe current rpy2 release (2.3.x series) has full support for Python 3.3, while\nno claim is made about Python 3.0, 3.1, or 3.2.\nAt the time of writing the next rpy2 release (under development, 2.4.x series) is only supporting Python 3.3.\nHistory of Python 3 support:\n\nrpy2-2.1.0-dev \/ Python 3 branch in the repository - experimental support and application for a Google Summer of Code project consisting in porting rpy2 to Python 3 (under the Python umbrella)\napplication was accepted and thanks to Google's funding support for Python 3 slowly got into the main codebase (there was a fair bit of work still to be done after the GSoC - it made it for branch version_2.2.x).","Q_Score":12,"Tags":"python,r,interface,python-3.x","A_Id":2661971,"CreationDate":"2010-04-04T00:25:00.000","Title":"What is the best interface from Python 3.1.1 to R?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I wish to make a Histogram in Matplotlib from an input file containing the raw data (.txt). I am facing issues in referring to the input file. I guess it should be a rather small program. Any Matplotlib gurus, any help ? \nI am not asking for the code, some inputs should put me on the right way !","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":14040,"Q_Id":2590328,"Users Score":0,"Answer":"You can't directly tell matplotlib to make a histogram from an input file - you'll need to open the file yourself and get the data from it. How you'd do that depends on the format of the file - if it's just a file with a number on each line, you can just go through each line, strip() spaces and newlines, and use float() to convert it to a number.","Q_Score":5,"Tags":"python,matplotlib,histogram","A_Id":2590381,"CreationDate":"2010-04-07T06:05:00.000","Title":"Histogram in Matplotlib with input file","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Most of us know that the command random.randint(1,n) in Python (2.X.X) would generate a number in random (pseudo-random) between 1 and n. I am interested in knowing what is the upper limit for n ?","AnswerCount":2,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":19740,"Q_Id":2597444,"Users Score":4,"Answer":"No doubt you have a bounded amount of memory, and address space, on your machine; for example, for a good 64-bit machine, 64 GB of RAM [[about 2**36 bytes]] and a couple of TB of disk (usable as swap space for virtual memory) [[about 2**41 bytes]].  So, the \"upper bound\" of a Python long integer will be the largest one representable in the available memory -- a bit less than 256**(2**40) if you are in absolutely no hurry and can swap like crazy, a bit more than 256**(2*36) (with just a little swapping but not too much) in practical terms.\nUnfortunately it would take quite a bit of time and space to represent these ridiculously humongous numbers in decimal, so, instead of showing them, let me check back with you -- why would you even care about such a ridiculous succession of digits as to constitute the \"upper bound\" you're inquiring about?  I think it's more practical to put it this way: especially on a 64-bit machine with decent amounts of RAM and disk, upper bounds of long integers are way bigger than anything you'll ever compute.  Technically, a mathematician would insist, they're not infinity, of course... but practically, they might as well be!-)","Q_Score":9,"Tags":"python,random","A_Id":2597749,"CreationDate":"2010-04-08T03:41:00.000","Title":"random.randint(1,n) in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"In python the function random() generates a random float uniformly in the semi-open range [0.0, 1.0). In principle can it ever generate 0.0 (i.e. zero) and 1.0 (i.e. unity)? What is the scenario in practicality?","AnswerCount":2,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":1409,"Q_Id":2621055,"Users Score":11,"Answer":"The [ indicates that 0.0 is included in the range of valid outputs. The ) indicates 1.0 is not in the range of valid outputs.","Q_Score":4,"Tags":"python,random","A_Id":2621082,"CreationDate":"2010-04-12T09:44:00.000","Title":"random() in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"In python the function random() generates a random float uniformly in the semi-open range [0.0, 1.0). In principle can it ever generate 0.0 (i.e. zero) and 1.0 (i.e. unity)? What is the scenario in practicality?","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":1409,"Q_Id":2621055,"Users Score":13,"Answer":"0.0 can be generated; 1.0 cannot (since it isn't within the range, hence the ) as opposed to [).\nThe probability of generating 0.0 is equal to the probability of generating any other number within that range, namely, 1\/X where X is the number of different possible results. For a standard unsigned double-precision floating point, this usually means 53 bits of fractional component, for 2^53 possible combinations, leading to a 1\/(2^53) chance of generating exactly 0.0.\nSo while it's possible for it to return exactly 0.0, it's unlikely that you'll see it any time soon - but it's just as unlikely that you'd see exactly any other particular value you might choose in advance.","Q_Score":4,"Tags":"python,random","A_Id":2621096,"CreationDate":"2010-04-12T09:44:00.000","Title":"random() in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am curious if there is an algorithm\/method exists to generate keywords\/tags from a given text, by using some weight calculations, occurrence ratio or other tools.\nAdditionally, I will be grateful if you point any Python based solution \/ library for this.\nThanks","AnswerCount":5,"Available Count":1,"Score":0.0798297691,"is_accepted":false,"ViewCount":28812,"Q_Id":2661778,"Users Score":2,"Answer":"A very simple solution to the problem would be:\n\ncount the occurences of each word in the text\nconsider the most frequent terms as the key phrases\nhave a black-list of 'stop words' to remove common words like the, and, it, is etc\n\nI'm sure there are cleverer, stats based solutions though.\nIf you need a solution to use in a larger project rather than for interests sake, Yahoo BOSS has a key term extraction method.","Q_Score":50,"Tags":"python,tags,machine-learning,nlp,nltk","A_Id":2661789,"CreationDate":"2010-04-18T09:39:00.000","Title":"tag generation from a text content","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a non-computer related data logger, that collects data from the field. This data is stored as text files, and I manually lump the files together and organize them. The current format is through a csv file per year per logger. Each file is around 4,000,000 lines x 7 loggers x 5 years = a lot of data. some of the data is organized as bins item_type, item_class, item_dimension_class, and other data is more unique, such as item_weight, item_color, date_collected, and so on ...\nCurrently, I do statistical analysis on the data using a python\/numpy\/matplotlib program I wrote. It works fine, but the problem is, I'm the only one who can use it, since it and the data live on my computer.\nI'd like to publish the data on the web using a postgres db; however, I need to find or implement a statistical tool that'll take a large postgres table, and return statistical results within an adequate time frame. I'm not familiar with python for the web; however, I'm proficient with PHP on the web side, and python on the offline side.\nusers should be allowed to create their own histograms, data analysis. For example, a user can search for all items that are blue shipped between week x and week y, while another user can search for sort the weight distribution of all items by hour for all year long. \nI was thinking of creating and indexing my own statistical tools, or automate the process somehow to emulate most queries. This seemed inefficient.\nI'm looking forward to hearing your ideas\nThanks","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1770,"Q_Id":2667537,"Users Score":1,"Answer":"I think you can utilize your current combination(python\/numpy\/matplotlib) fully if the number of users are not too big. I do some similar works, and my data size a little more than 10g. Data are stored in a few sqlite files, and i use numpy to analyze data, PIL\/matplotlib to generate chart files(png, gif), cherrypy as a webserver, mako as a template language. \nIf you need more server\/client database, then you can migrate to postgresql, but you can still fully use your current programs if you go with a python web framework, like cherrypy.","Q_Score":4,"Tags":"php,python,postgresql,statistics","A_Id":2669456,"CreationDate":"2010-04-19T12:58:00.000","Title":"Statistical analysis on large data set to be published on the web","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"The question is, basically: what would be more preferable, both performance-wise and design-wise - to have a list of objects of a Python class or to have several lists of numerical properties?\nI am writing some sort of a scientific simulation which involves a rather large system of interacting particles. For simplicity, let's say we have a set of balls bouncing inside a box so each ball has a number of numerical properties, like x-y-z-coordinates, diameter, mass, velocity vector and so on. How to store the system better? Two major options I can think of are:\nto make a class \"Ball\" with those properties and some methods, then store a list of objects of the class, e. g. [b1, b2, b3, ...bn, ...], where for each bn we can access bn.x, bn.y, bn.mass and so on;\nto make an array of numbers for each property, then for each i-th \"ball\" we can access it's 'x' coordinate as xs[i], 'y' coordinate as ys[i], 'mass' as masses[i] and so on;\nTo me it seems that the first option represents a better design. The second option looks somewhat uglier, but might be better in terms of performance, and it could be easier to use it with numpy and scipy, which I try to use as much as I can.\nI am still not sure if Python will be fast enough, so it may be necessary to rewrite it in C++ or something, after initial prototyping in Python. Would the choice of data representation be different for C\/C++? What about a hybrid approach, e.g. Python with C++ extension?\nUpdate: I never expected any performance gain from parallel arrays per se, but in a mixed environment like Python + Numpy (or whatever SlowScriptingLanguage + FastNativeLibrary) using them may (or may not?) let you move more work out of you slow scripting code and into the fast native library.","AnswerCount":4,"Available Count":2,"Score":0.049958375,"is_accepted":false,"ViewCount":1102,"Q_Id":2723790,"Users Score":1,"Answer":"I think it depends on what you're going to be doing with them, and how often you're going to be working with (all attributes of one particle) vs (one attribute of all particles). The former is better suited to the object approach; the latter is better suited to the array approach.\nI was facing a similar problem (although in a different domain) a couple of years ago. The project got deprioritized before I actually implemented this phase, but I was leaning towards a hybrid approach, where in addition to the Ball class I would have an Ensemble class. The Ensemble would not be a list or other simple container of Balls, but would have its own attributes (which would be arrays) and its own methods. Whether the Ensemble is created from the Balls, or the Balls from the Ensemble, depends on how you're going to construct them. \nOne of my coworkers was arguing for a solution where the fundamental object was an Ensemble which might contain only one Ball, so that no calling code would ever have to know whether you were operating on just one Ball (do you ever do that for your application?) or on many.","Q_Score":3,"Tags":"python,performance,data-structures,numpy","A_Id":2726598,"CreationDate":"2010-04-27T18:13:00.000","Title":"List of objects or parallel arrays of properties?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"The question is, basically: what would be more preferable, both performance-wise and design-wise - to have a list of objects of a Python class or to have several lists of numerical properties?\nI am writing some sort of a scientific simulation which involves a rather large system of interacting particles. For simplicity, let's say we have a set of balls bouncing inside a box so each ball has a number of numerical properties, like x-y-z-coordinates, diameter, mass, velocity vector and so on. How to store the system better? Two major options I can think of are:\nto make a class \"Ball\" with those properties and some methods, then store a list of objects of the class, e. g. [b1, b2, b3, ...bn, ...], where for each bn we can access bn.x, bn.y, bn.mass and so on;\nto make an array of numbers for each property, then for each i-th \"ball\" we can access it's 'x' coordinate as xs[i], 'y' coordinate as ys[i], 'mass' as masses[i] and so on;\nTo me it seems that the first option represents a better design. The second option looks somewhat uglier, but might be better in terms of performance, and it could be easier to use it with numpy and scipy, which I try to use as much as I can.\nI am still not sure if Python will be fast enough, so it may be necessary to rewrite it in C++ or something, after initial prototyping in Python. Would the choice of data representation be different for C\/C++? What about a hybrid approach, e.g. Python with C++ extension?\nUpdate: I never expected any performance gain from parallel arrays per se, but in a mixed environment like Python + Numpy (or whatever SlowScriptingLanguage + FastNativeLibrary) using them may (or may not?) let you move more work out of you slow scripting code and into the fast native library.","AnswerCount":4,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":1102,"Q_Id":2723790,"Users Score":2,"Answer":"Having an object for each ball in this example is certainly better design. Parallel arrays are really a workaround for languages that do not support proper objects. I wouldn't use them in a language with OO capabilities unless it's a tiny case that fits within a function (and maybe not even then) or if I've run out of every other optimization option and the profiler shows that property access is the culprit. This applies twice as much to Python as to C++, as the former places a large emphasis on readability and elegance.","Q_Score":3,"Tags":"python,performance,data-structures,numpy","A_Id":2723845,"CreationDate":"2010-04-27T18:13:00.000","Title":"List of objects or parallel arrays of properties?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"my problem is the GIL of course. While I'm analysing data it would be nice to present some plots in between (so it's not too boring waiting for results)\nBut the GIL prevents this (and this is bringing me to the point of asking myself if Python was such a good idea in the first place).\nI can only display the plot, wait till the user closes it and commence calculations after that. A waste of time obviously.\nI already tried the subprocess and multiprocessing modules but can't seem to get them to work. \nAny thoughts on this one?\nThanks\nEdit: Ok so it's not the GIL but show().","AnswerCount":4,"Available Count":3,"Score":0.1488850336,"is_accepted":false,"ViewCount":737,"Q_Id":2744530,"Users Score":3,"Answer":"I think you'll need to put the graph into a proper Windowing system, rather than relying on the built-in show code.\nMaybe sticking the .show() in another thread would be sufficient?\nThe GIL is irrelevant - you've got a blocking show() call, so you need to handle that first.","Q_Score":2,"Tags":"python,matplotlib,parallel-processing,gil","A_Id":2744657,"CreationDate":"2010-04-30T12:41:00.000","Title":"Python: Plot some data (matplotlib) without GIL","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"my problem is the GIL of course. While I'm analysing data it would be nice to present some plots in between (so it's not too boring waiting for results)\nBut the GIL prevents this (and this is bringing me to the point of asking myself if Python was such a good idea in the first place).\nI can only display the plot, wait till the user closes it and commence calculations after that. A waste of time obviously.\nI already tried the subprocess and multiprocessing modules but can't seem to get them to work. \nAny thoughts on this one?\nThanks\nEdit: Ok so it's not the GIL but show().","AnswerCount":4,"Available Count":3,"Score":0.1488850336,"is_accepted":false,"ViewCount":737,"Q_Id":2744530,"Users Score":3,"Answer":"This has nothing to do with the GIL, just modify your analysis code to make it update the graph from time to time (for example every N iterations).\nOnly then if you see that drawing the graph slows the analysis code too much, put the graph update code in a subprocess with multiprocessing.","Q_Score":2,"Tags":"python,matplotlib,parallel-processing,gil","A_Id":2744604,"CreationDate":"2010-04-30T12:41:00.000","Title":"Python: Plot some data (matplotlib) without GIL","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"my problem is the GIL of course. While I'm analysing data it would be nice to present some plots in between (so it's not too boring waiting for results)\nBut the GIL prevents this (and this is bringing me to the point of asking myself if Python was such a good idea in the first place).\nI can only display the plot, wait till the user closes it and commence calculations after that. A waste of time obviously.\nI already tried the subprocess and multiprocessing modules but can't seem to get them to work. \nAny thoughts on this one?\nThanks\nEdit: Ok so it's not the GIL but show().","AnswerCount":4,"Available Count":3,"Score":0.0996679946,"is_accepted":false,"ViewCount":737,"Q_Id":2744530,"Users Score":2,"Answer":"It seems like the draw() method can circumvent the need for show(). \nThe only reason left for .show() in the script is to let it do the blocking part so that the images don't disapear when the script reaches its end.","Q_Score":2,"Tags":"python,matplotlib,parallel-processing,gil","A_Id":2744906,"CreationDate":"2010-04-30T12:41:00.000","Title":"Python: Plot some data (matplotlib) without GIL","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have 4 reasonably complex r scripts that are used to manipulate csv and xml files.  These were created by another department where they work exclusively in r.  \nMy understanding is that while r is very fast when dealing with data, it's not really optimised for file manipulation.  Can I expect to get significant speed increases by converting these scripts to python?  Or is this something of a waste of time?","AnswerCount":6,"Available Count":5,"Score":0.0333209931,"is_accepted":false,"ViewCount":2365,"Q_Id":2770030,"Users Score":1,"Answer":"what do you mean by \"file manipulation?\" are you talking about moving files around, deleting, copying, etc., in which case i would use a shell, e.g., bash, etc. if you're talking about reading in the data, performing calculations, perhaps writing out a new file, etc., then you could probably use Python or R. unless maintenance is an issue, i would just leave it as R and find other fish to fry as you're not going to see enough of a speedup to justify your time and effort in porting that code.","Q_Score":3,"Tags":"python,file,r,performance","A_Id":2770393,"CreationDate":"2010-05-05T01:24:00.000","Title":"R or Python for file manipulation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have 4 reasonably complex r scripts that are used to manipulate csv and xml files.  These were created by another department where they work exclusively in r.  \nMy understanding is that while r is very fast when dealing with data, it's not really optimised for file manipulation.  Can I expect to get significant speed increases by converting these scripts to python?  Or is this something of a waste of time?","AnswerCount":6,"Available Count":5,"Score":0.0333209931,"is_accepted":false,"ViewCount":2365,"Q_Id":2770030,"Users Score":1,"Answer":"Know where the time is being spent. If your R scripts are bottlenecked on disk IO (and that is very possible in this case), then you could rewrite them in hand-optimized assembly and be no faster. As always with optimization, if you don't measure first, you're just pissing into the wind. If they're not bottlenecked on disk IO, you would likely see more benefit from improving the algorithm than changing the language.","Q_Score":3,"Tags":"python,file,r,performance","A_Id":2770138,"CreationDate":"2010-05-05T01:24:00.000","Title":"R or Python for file manipulation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have 4 reasonably complex r scripts that are used to manipulate csv and xml files.  These were created by another department where they work exclusively in r.  \nMy understanding is that while r is very fast when dealing with data, it's not really optimised for file manipulation.  Can I expect to get significant speed increases by converting these scripts to python?  Or is this something of a waste of time?","AnswerCount":6,"Available Count":5,"Score":0.0,"is_accepted":false,"ViewCount":2365,"Q_Id":2770030,"Users Score":0,"Answer":"My guess is that you probably won't see much of a speed-up in time.  When comparing high-level languages, overhead in the language is typically not to blame for performance problems.  Typically, the problem is your algorithm.\nI'm not very familiar with R, but you may find speed-ups by reading larger chunks of data into memory at once vs smaller chunks (less system calls).  If R doesn't have the ability to change something like this, you will probably find that python can be much faster simply because of this ability.","Q_Score":3,"Tags":"python,file,r,performance","A_Id":2770071,"CreationDate":"2010-05-05T01:24:00.000","Title":"R or Python for file manipulation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have 4 reasonably complex r scripts that are used to manipulate csv and xml files.  These were created by another department where they work exclusively in r.  \nMy understanding is that while r is very fast when dealing with data, it's not really optimised for file manipulation.  Can I expect to get significant speed increases by converting these scripts to python?  Or is this something of a waste of time?","AnswerCount":6,"Available Count":5,"Score":0.0,"is_accepted":false,"ViewCount":2365,"Q_Id":2770030,"Users Score":0,"Answer":"R data manipulation has rules for it to be fast.  The basics are: \n\nvectorize\nuse data.frames as little as possible (for example, in the end)\n\nSearch for R time optimization and profiling and you will find many resources to help you.","Q_Score":3,"Tags":"python,file,r,performance","A_Id":2771903,"CreationDate":"2010-05-05T01:24:00.000","Title":"R or Python for file manipulation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have 4 reasonably complex r scripts that are used to manipulate csv and xml files.  These were created by another department where they work exclusively in r.  \nMy understanding is that while r is very fast when dealing with data, it's not really optimised for file manipulation.  Can I expect to get significant speed increases by converting these scripts to python?  Or is this something of a waste of time?","AnswerCount":6,"Available Count":5,"Score":1.2,"is_accepted":true,"ViewCount":2365,"Q_Id":2770030,"Users Score":10,"Answer":"I write in both R and Python regularly.  I find Python modules for writing, reading and parsing information easier to use, maintain and update.  Little niceties like the way python lets you deal with lists of items over R's indexing make things much easier to read.\nI highly doubt you will gain any significant speed-up by switching the language.  If you are becoming the new \"maintainer\" of these scripts and you find Python easier to understand and extend, then I'd say go for it.\nComputer time is cheap ... programmer time is expensive.  If you have other things to do then I'd just limp along with what you've got until you have a free day to putz with them.\nHope that helps.","Q_Score":3,"Tags":"python,file,r,performance","A_Id":2770354,"CreationDate":"2010-05-05T01:24:00.000","Title":"R or Python for file manipulation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a raster file (basically 2D array) with close to a million points. I am trying to extract a circle from the raster (and all the points that lie within the circle). Using ArcGIS is exceedingly slow for this. Can anyone suggest any image processing library that is both easy to learn and powerful and quick enough for something like this?\nThanks!","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":3334,"Q_Id":2770356,"Users Score":1,"Answer":"You need a library that can read your raster. I am not sure how to do that in Python but you could look at geotools (especially with some of the new raster library integration) if you want to program in Java. If you are good with C I would reccomend using something like GDAL.\nIf you want to look at a desktop tool you could look at extending QGIS with python to do the operation above.\nIf I remember correctly, the Raster extension to PostGIS may support clipping rasters based upon vectors. This means you would need to create your circles to features in the DB and then import your raster but then you might be able to use SQL to extract your values.\nIf you are really just a text file with numbers in a grid then I would go with the suggestions above.","Q_Score":4,"Tags":"python,arcgis,raster","A_Id":2785460,"CreationDate":"2010-05-05T03:02:00.000","Title":"Extract points within a shape from a raster","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm developing an application in which I need a structure to represent a huge graph (between 1000000 and 6000000 nodes and 100 or 600 edges per node) in memory. The edges representation will contain some attributes of the relation. \nI have tried a memory map representation, arrays, dictionaries and strings to represent that structure in memory, but these always crash because of the memory limit.\nI would like to get an advice of how I can represent this, or something similar.\nBy the way, I'm using python.","AnswerCount":8,"Available Count":3,"Score":1.0,"is_accepted":false,"ViewCount":6997,"Q_Id":2806806,"Users Score":14,"Answer":"If that is 100-600 edges\/node, then you are talking about 3.6 billion edges.\nWhy does this have to be all in memory?  \nCan you show us the structures you are currently using?\nHow much memory are we allowed (what is the memory limit you are hitting?)\n\nIf the only reason you need this in memory is because you need to be able to read and write it fast, then use a database.  Databases read and write extremely fast, often they can read without going to disk at all.","Q_Score":9,"Tags":"python,memory,data-structures,graph","A_Id":2806868,"CreationDate":"2010-05-10T22:10:00.000","Title":"Huge Graph Structure","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm developing an application in which I need a structure to represent a huge graph (between 1000000 and 6000000 nodes and 100 or 600 edges per node) in memory. The edges representation will contain some attributes of the relation. \nI have tried a memory map representation, arrays, dictionaries and strings to represent that structure in memory, but these always crash because of the memory limit.\nI would like to get an advice of how I can represent this, or something similar.\nBy the way, I'm using python.","AnswerCount":8,"Available Count":3,"Score":0.0996679946,"is_accepted":false,"ViewCount":6997,"Q_Id":2806806,"Users Score":4,"Answer":"I doubt you'll be able to use a memory structure unless you have a LOT of memory at your disposal:\nAssume you are talking about 600 directed edges from each node, with a node being 4-bytes (integer key) and a directed edge being JUST the destination node keys (4 bytes each).\nThen the raw data about each node is 4 + 600 * 4 = 2404 bytes x 6,000,000 = over 14.4GB\nThat's without any other overheads or any additional data in the nodes (or edges).","Q_Score":9,"Tags":"python,memory,data-structures,graph","A_Id":2806891,"CreationDate":"2010-05-10T22:10:00.000","Title":"Huge Graph Structure","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm developing an application in which I need a structure to represent a huge graph (between 1000000 and 6000000 nodes and 100 or 600 edges per node) in memory. The edges representation will contain some attributes of the relation. \nI have tried a memory map representation, arrays, dictionaries and strings to represent that structure in memory, but these always crash because of the memory limit.\nI would like to get an advice of how I can represent this, or something similar.\nBy the way, I'm using python.","AnswerCount":8,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":6997,"Q_Id":2806806,"Users Score":0,"Answer":"Sounds like you need a database and an iterator over the results. Then you wouldn't have to keep it all in memory at the same time but you could always have access to it.","Q_Score":9,"Tags":"python,memory,data-structures,graph","A_Id":2806909,"CreationDate":"2010-05-10T22:10:00.000","Title":"Huge Graph Structure","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a regression model in which the dependent variable is continuous but ninety percent of the independent variables are categorical(both ordered and unordered) and around thirty percent of the records have missing values(to make matters worse they are missing randomly without any pattern, that is, more that forty five percent of the data hava at least one missing value). There is no a priori theory to choose the specification of the model so one of the key tasks is dimension reduction before running the regression. While I am aware of several methods for dimension reduction for continuous variables I am not aware of a similar statical literature for categorical data (except, perhaps, as a part of correspondence analysis which is basically a variation of principal component analysis on frequency table). Let me also add that the dataset is of moderate size 500000 observations with 200 variables. I have two questions.\n\nIs there a good statistical reference out there for dimension reduction for categorical data along with robust imputation (I think the first issue is imputation and then dimension reduction)?\nThis is linked to implementation of above problem. I have used R extensively earlier and tend to use transcan and impute function heavily for continuous variables and use a variation of tree method to impute categorical values. I have a working knowledge of Python so if something is nice out there for this purpose then I will use it. Any implementation pointers in python or R will be of great help.\nThank you.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":16303,"Q_Id":2837850,"Users Score":0,"Answer":"45% of the data have at least one missing value, you say. This is impressive. I would first look if there is no pattern. You say they are missing at random. Have you tested for MAR ? Have you tested for MAR for sub-groups ? \nNot knowing your data I would first look if there are not cases with many missing values and see if there are theoretical or practical reasons to exclude them. Practical reasons are the production of the data. They might be that they were not well observed, the machine producing the data did not turn all the time, the survey did not cover all countries all the time, etc. For instance, you have survey data on current occupation, but part of the respondents are retired. So they have to be (system-)missing. You can not replace these data with some computed value.\nMaybe you can cut slices out of the cases with full and look for the conditions of data production.","Q_Score":24,"Tags":"python,r,statistics","A_Id":53035704,"CreationDate":"2010-05-14T21:50:00.000","Title":"Dimension Reduction in Categorical Data with missing values","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I bumped into a case where I need a big (=huge) python dictionary, which turned to be quite memory-consuming.\nHowever, since all of the values are of a single type (long) - as well as the keys,  I figured I can use python (or numpy, doesn't really matter) array for the values ; and wrap the needed interface (in: x ; out: d[x]) with an object which actually uses these arrays for the keys and values storage.\nI can use a index-conversion object (input --> index, of 1..n, where n is the different-values counter), and return array[index]. I can elaborate on some techniques of how to implement such an indexing-methods with reasonable memory requirement, it works and even pretty good. \nHowever, I wonder if there is such a data-structure-object already exists (in python, or wrapped to python from C\/++), in any package (I checked collections, and some Google searches).\nAny comment will be welcome, thanks.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1303,"Q_Id":2942375,"Users Score":0,"Answer":"You might try using std::map. Boost.Python provides a Python wrapping for std::map out-of-the-box.","Q_Score":3,"Tags":"python,arrays,data-structures,dictionary","A_Id":2943067,"CreationDate":"2010-05-31T08:47:00.000","Title":"python dictionary with constant value-type","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have the following 4 arrays ( grouped in 2 groups ) that I would like to merge in ascending order by the keys array. \nI can use also dictionaries as structure if it is easier.\nHas python any command or something to make this quickly possible?\nRegards\nMN\n\n# group 1\n[7, 2, 3,  5]  #keys\n[10,11,12,26]  #values \n\n[0,   4]   #keys\n[20,  33]  #values \n\n# I would like to have\n[ 0, 2, 3, 4, 5, 7 ] # ordered keys\n[20, 11,12,33,26,33] # associated values","AnswerCount":4,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":125,"Q_Id":2960855,"Users Score":0,"Answer":"Add all keys and associated values from both sets of data to a single dictionary. \nGet the items of the dictionary ans sort them.\nprint out the answer.\nk1=[7, 2, 3,  5]\nv1=[10,11,12,26]\nk2=[0,   4]\nv2=[20,  33] \nd=dict(zip(k1,v1))\nd.update(zip(k2,v2))\nanswer=d.items()\nanswer.sort()\nkeys=[k for (k,v) in answer]\nvalues=[v for (k,v) in answer]\nprint keys\nprint values\n\nEdit: This is for Python 2.6 or below which do not have any ordered dictionary.","Q_Score":1,"Tags":"python","A_Id":2960944,"CreationDate":"2010-06-02T19:16:00.000","Title":"merging in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I two programs running, one in Python and one in C++, and I need to share a two-dimensional array (just of decimal numbers) between them. I am currently looking into serialization, but pickle is python-specific, unfortunately. What is the best way to do this?\nThanks\nEdit: It is likely that the array will only have 50 elements or so, but the transfer of data will need to occur very frequently: 60x per second or more.","AnswerCount":7,"Available Count":2,"Score":0.0285636566,"is_accepted":false,"ViewCount":2433,"Q_Id":2968172,"Users Score":1,"Answer":"I would propose simply to use c arrays(via ctypes on the python side) and simply pull\/push the raw data through an socket","Q_Score":3,"Tags":"c++,python,serialization","A_Id":2968306,"CreationDate":"2010-06-03T17:06:00.000","Title":"How to share an array in Python with a C++ Program?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I two programs running, one in Python and one in C++, and I need to share a two-dimensional array (just of decimal numbers) between them. I am currently looking into serialization, but pickle is python-specific, unfortunately. What is the best way to do this?\nThanks\nEdit: It is likely that the array will only have 50 elements or so, but the transfer of data will need to occur very frequently: 60x per second or more.","AnswerCount":7,"Available Count":2,"Score":0.0285636566,"is_accepted":false,"ViewCount":2433,"Q_Id":2968172,"Users Score":1,"Answer":"Serialization is one problem while IPC is another.  Do you have the IPC portion figured out? (pipes, sockets, mmap, etc?)\nOn to serialization - if you're concerned about performance more than robustness (being able to plug more modules into this architecture) and security, then you should take a look at the struct module.  This will let you pack data into C structures using format strings to define the structure (takes care of padding, alignment, and byte ordering for you!) In the C++ program, cast a pointer to the buffer to the corresponding structure type. \nThis works well with a tightly-coupled Python script and C++ program that is only run internally.","Q_Score":3,"Tags":"c++,python,serialization","A_Id":2968375,"CreationDate":"2010-06-03T17:06:00.000","Title":"How to share an array in Python with a C++ Program?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to generate random numbers from a gaussian distribution. Python has the very useful random.gauss() method, but this is only a one-dimensional random variable. How could I programmatically generate random numbers from this distribution in n-dimensions?\nFor example, in two dimensions, the return value of this method is essentially distance from the mean, so I would still need (x,y) coordinates to determine an actual data point. I suppose I could generate two more random numbers, but I'm not sure how to set up the constraints.\nI appreciate any insights. Thanks!","AnswerCount":4,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":5620,"Q_Id":2969593,"Users Score":5,"Answer":"Numpy has multidimensional equivalents to the functions in the random module \nThe function you're looking for is numpy.random.normal","Q_Score":2,"Tags":"python,random,n-dimensional","A_Id":2969618,"CreationDate":"2010-06-03T20:40:00.000","Title":"Generate n-dimensional random numbers in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to generate random numbers from a gaussian distribution. Python has the very useful random.gauss() method, but this is only a one-dimensional random variable. How could I programmatically generate random numbers from this distribution in n-dimensions?\nFor example, in two dimensions, the return value of this method is essentially distance from the mean, so I would still need (x,y) coordinates to determine an actual data point. I suppose I could generate two more random numbers, but I'm not sure how to set up the constraints.\nI appreciate any insights. Thanks!","AnswerCount":4,"Available Count":2,"Score":0.049958375,"is_accepted":false,"ViewCount":5620,"Q_Id":2969593,"Users Score":1,"Answer":"You need to properly decompose your multi-dimensional distribution into a composition of one-dimensional distributions.  For example, if you want a point at a Gaussian-distributed distance from a given center and a uniformly-distributed angle around it, you'll get the polar coordinates for the delta with a Gaussian rho and a uniform theta (between 0 and 2 pi), then, if you want cartesian coordinates, you of course do a coordinate transformation.","Q_Score":2,"Tags":"python,random,n-dimensional","A_Id":2969634,"CreationDate":"2010-06-03T20:40:00.000","Title":"Generate n-dimensional random numbers in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've increased the font of my ticklabels successfully, but now they're too close to the axis.  I'd like to add a little breathing room between the ticklabels and the axis.","AnswerCount":5,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":89545,"Q_Id":2969867,"Users Score":117,"Answer":"If you don't want to change the spacing globally (by editing your rcParams), and want a cleaner approach, try this:\nax.tick_params(axis='both', which='major', pad=15)\nor for just x axis\nax.tick_params(axis='x', which='major', pad=15)\nor the y axis\nax.tick_params(axis='y', which='major', pad=15)","Q_Score":86,"Tags":"python,matplotlib","A_Id":29524883,"CreationDate":"2010-06-03T21:19:00.000","Title":"How do I add space between the ticklabels and the axes in matplotlib","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am writing an app to do a file conversion and part of that is replacing old account numbers with a new account numbers.\nRight now I have a CSV file mapping the old and new account numbers with around 30K records. I read this in and store it as dict and when writing the new file grab the new account from the dict by key.\nMy question is what is the best way to do this if the CSV file increases to 100K+ records?\nWould it be more efficient to convert the account mappings from a CSV to a sqlite database rather than storing them as a dict in memory?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":109,"Q_Id":2980257,"Users Score":1,"Answer":"As long as they will all fit in memory, a dict will be the most efficient solution.  It's also a lot easier to code.  100k records should be no problem on a modern computer.\nYou are right that switching to an SQLite database is a good choice when the number of records gets very large.","Q_Score":3,"Tags":"python,database,sqlite,dictionary,csv","A_Id":2980269,"CreationDate":"2010-06-05T12:08:00.000","Title":"Efficient way to access a mapping of identifiers in Python","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm doing a project with reasonalby big DataBase. It's not a probper DB file, but a class with format as follows:\nDataBase.Nodes.Data=[[] for i in range(1,1000)] f.e. this DataBase is all together something like few thousands rows. Fisrt question - is the way I'm doing efficient, or is it better to use SQL, or any other \"proper\" DB, which I've never used actually. \nAnd the main question - I'd like to save my DataBase class with all record, and then re-open it with Python in another session. Is that possible, what tool should I use? cPickle - it seems to be only for strings, any other?\nIn matlab there's very useful functionality named save workspace - it saves all Your variables to a file that You can open at another session - this would be vary useful in python!","AnswerCount":3,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":306,"Q_Id":2990995,"Users Score":3,"Answer":"Pickle (cPickle) can handle any (picklable) Python object. So as long, as you're not trying to pickle thread or filehandle or something like that, you're ok.","Q_Score":3,"Tags":"python,serialization,pickle,object-persistence","A_Id":2991030,"CreationDate":"2010-06-07T15:52:00.000","Title":"How to save big \"database-like\" class in python","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"What are some fast and somewhat reliable ways to extract information about images?  I've been tinkering with OpenCV and this seems so far to be the best route plus it has Python bindings.\nSo to be more specific I'd like to determine what I can about what's in an image.  So for example the haar face detection and full body detection classifiers are great - now I can tell that most likely there are faces and \/ or people in the image as well as about how many. \nokay - what else - how about whether there are any buildings and if so what do they seem to be - huts, office buildings etc?  Is there sky visible, grass, trees and so forth.  \nFrom what I've read about training classifiers to detect objects, it seems like a rather laborious process 10,000 or so wrong images and 5,000 or so correct samples to train a classifier.  \nI'm hoping that there are some decent ones around already instead of having to do this all myself for a bunch of different objects - or is there some other way to go about this sort of thing?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1546,"Q_Id":2994398,"Users Score":2,"Answer":"Your question is difficult to answer without more clarification about the types of images you are analyzing and your purpose.\nThe tone of the post seems that you are interested in tinkering -- that's fine. If you want to tinker, one example application might be iris identification using wavelet analysis. You can also try motion tracking; I've done that in OpenCV using the sample projects, and it is kind of interesting. You can try image segmentation for the purpose of scene analysis; take an outdoor photo and segment the image according to texture and\/or color.\nThere is no hard number for how large your training set must be. It is highly application dependent. A few hundred images may suffice.","Q_Score":1,"Tags":"python,image,opencv,identification","A_Id":2994438,"CreationDate":"2010-06-08T02:17:00.000","Title":"Extracting Information from Images","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I just took my first baby step today into real scientific computing today when I was shown a data set where the smallest file is 48000 fields by 1600 rows (haplotypes for several people, for chromosome 22). And this is considered tiny.\nI write Python, so I've spent the last few hours reading about HDF5, and Numpy, and PyTable, but I still feel like I'm not really grokking what a terabyte-sized data set actually means for me as a programmer. \nFor example, someone pointed out that with larger data sets, it becomes impossible to read the whole thing into memory, not because the machine has insufficient RAM, but because the architecture has insufficient address space! It blew my mind.\nWhat other assumptions have I been relying in the classroom that just don't work with input this big? What kinds of things do I need to start doing or thinking about differently? (This doesn't have to be Python specific.)","AnswerCount":4,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":1855,"Q_Id":3012157,"Users Score":0,"Answer":"The main assumptions are about the amount of cpu\/cache\/ram\/storage\/bandwidth you can have in a single machine at an acceptable price. There are lots of answers here at stackoverflow still based on the old assumptions of a 32 bit machine with 4G ram and about a terabyte of storage and 1Gb network. With 16GB DDR-3 ram modules at 220 Eur, 512 GB ram, 48 core machines can be build at reasonable prices. The switch from hard disks to SSD is another important change.","Q_Score":21,"Tags":"python,large-data-volumes,scientific-computing","A_Id":9964718,"CreationDate":"2010-06-10T06:34:00.000","Title":"what changes when your input is giga\/terabyte sized?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I just took my first baby step today into real scientific computing today when I was shown a data set where the smallest file is 48000 fields by 1600 rows (haplotypes for several people, for chromosome 22). And this is considered tiny.\nI write Python, so I've spent the last few hours reading about HDF5, and Numpy, and PyTable, but I still feel like I'm not really grokking what a terabyte-sized data set actually means for me as a programmer. \nFor example, someone pointed out that with larger data sets, it becomes impossible to read the whole thing into memory, not because the machine has insufficient RAM, but because the architecture has insufficient address space! It blew my mind.\nWhat other assumptions have I been relying in the classroom that just don't work with input this big? What kinds of things do I need to start doing or thinking about differently? (This doesn't have to be Python specific.)","AnswerCount":4,"Available Count":3,"Score":0.049958375,"is_accepted":false,"ViewCount":1855,"Q_Id":3012157,"Users Score":1,"Answer":"While some languages have naturally lower memory overhead in their types than others, that really doesn't matter for data this size - you're not holding your entire data set in memory regardless of the language you're using, so the \"expense\" of Python is irrelevant here.  As you pointed out, there simply isn't enough address space to even reference all this data, let alone hold onto it.\nWhat this normally means is either a) storing your data in a database, or b) adding resources in the form of additional computers, thus adding to your available address space and memory.  Realistically you're going to end up doing both of these things.  One key thing to keep in mind when using a database is that a database isn't just a place to put your data while you're not using it - you can do WORK in the database, and you should try to do so.  The database technology you use has a large impact on the kind of work you can do, but an SQL database, for example, is well suited to do a lot of set math and do it efficiently (of course, this means that schema design becomes a very important part of your overall architecture).  Don't just suck data out and manipulate it only in memory - try to leverage the computational query capabilities of your database to do as much work as possible before you ever put the data in memory in your process.","Q_Score":21,"Tags":"python,large-data-volumes,scientific-computing","A_Id":3012350,"CreationDate":"2010-06-10T06:34:00.000","Title":"what changes when your input is giga\/terabyte sized?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I just took my first baby step today into real scientific computing today when I was shown a data set where the smallest file is 48000 fields by 1600 rows (haplotypes for several people, for chromosome 22). And this is considered tiny.\nI write Python, so I've spent the last few hours reading about HDF5, and Numpy, and PyTable, but I still feel like I'm not really grokking what a terabyte-sized data set actually means for me as a programmer. \nFor example, someone pointed out that with larger data sets, it becomes impossible to read the whole thing into memory, not because the machine has insufficient RAM, but because the architecture has insufficient address space! It blew my mind.\nWhat other assumptions have I been relying in the classroom that just don't work with input this big? What kinds of things do I need to start doing or thinking about differently? (This doesn't have to be Python specific.)","AnswerCount":4,"Available Count":3,"Score":1.2,"is_accepted":true,"ViewCount":1855,"Q_Id":3012157,"Users Score":18,"Answer":"I'm currently engaged in high-performance computing in a small corner of the oil industry and regularly work with datasets of the orders of magnitude you are concerned about.  Here are some points to consider:\n\nDatabases don't have a lot of traction in this domain.  Almost all our data is kept in files, some of those files are based on tape file formats designed in the 70s.  I think that part of the reason for the non-use of databases is historic; 10, even 5, years ago I think that Oracle and its kin just weren't up to the task of managing single datasets of O(TB) let alone a database of 1000s of such datasets.\nAnother reason is a conceptual mismatch between the normalisation rules for effective database analysis and design and the nature of scientific data sets.\nI think (though I'm not sure) that the performance reason(s) are much less persuasive today.  And the concept-mismatch reason is probably also less pressing now that most of the major databases available can cope with spatial data sets which are generally a much closer conceptual fit to other scientific datasets.  I have seen an increasing use of databases for storing meta-data, with some sort of reference, then, to the file(s) containing the sensor data.\nHowever, I'd still be looking at, in fact am looking at, HDF5.  It has a couple of attractions for me (a) it's just another file format so I don't have to install a DBMS and wrestle with its complexities, and (b) with the right hardware I can read\/write an HDF5 file in parallel.  (Yes, I know that I can read and write databases in parallel too).\nWhich takes me to the second point: when dealing with very large datasets you really need to be thinking of using parallel computation.  I work mostly in Fortran, one of its strengths is its array syntax which fits very well onto a lot of scientific computing; another is the good support for parallelisation available.  I believe that Python has all sorts of parallelisation support too so it's probably not a bad choice for you.\nSure you can add parallelism on to sequential systems, but it's much better to start out designing for parallelism.  To take just one example: the best sequential algorithm for a problem is very often not the best candidate for parallelisation.  You might be better off using a different algorithm, one which scales better on multiple processors.  Which leads neatly to the next point.\nI think also that you may have to come to terms with surrendering any attachments you have (if you have them) to lots of clever algorithms and data structures which work well when all your data is resident in memory.  Very often trying to adapt them to the situation where you can't get the data into memory all at once, is much harder (and less performant) than brute-force and regarding the entire file as one large array.\nPerformance starts to matter in a serious way, both the execution performance of programs, and developer performance.  It's not that a 1TB dataset requires 10 times as much code as a 1GB dataset so you have to work faster, it's that some of the ideas that you will need to implement will be crazily complex, and probably have to be written by domain specialists, ie the scientists you are working with.  Here the domain specialists write in Matlab.\n\nBut this is going on too long, I'd better get back to work","Q_Score":21,"Tags":"python,large-data-volumes,scientific-computing","A_Id":3012599,"CreationDate":"2010-06-10T06:34:00.000","Title":"what changes when your input is giga\/terabyte sized?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I cannot understand it. Very simple, and obvious functionality:\nYou have a code in any programming language, You run it. In this code You generate variables, than You save them (the values, names, namely everything) to a file, with one command. When it's saved You may open such a file in Your code also with simple command. \nIt works perfect in matlab (save Workspace , load Workspace ) - in python there's some weird \"pickle\" protocol, which produces errors all the time, while all I want to do is save variable, and load it again in another session (?????) \nf.e. You cannot save class with variables (in Matlab there's no problem)\nYou cannot load arrays in cPickle (but YOu can save them (?????) )\nWhy don't make it easier?\nIs there a way to save the current variables with values, and then load them?","AnswerCount":5,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":8265,"Q_Id":3016116,"Users Score":0,"Answer":"I take issue with the statement that the saving of variables in Matlab is an environment function. the \"save\" statement in matlab is a function and part of the matlab language not just a command.  It is a very useful function as you don't have to worry about the trivial minutia of file i\/o and it handles all sorts of variables from scalar, matrix, objects, structures.","Q_Score":3,"Tags":"python,serialization","A_Id":27096538,"CreationDate":"2010-06-10T15:58:00.000","Title":"Save Workspace - save all variables to a file. Python doesn't have it)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I cannot understand it. Very simple, and obvious functionality:\nYou have a code in any programming language, You run it. In this code You generate variables, than You save them (the values, names, namely everything) to a file, with one command. When it's saved You may open such a file in Your code also with simple command. \nIt works perfect in matlab (save Workspace , load Workspace ) - in python there's some weird \"pickle\" protocol, which produces errors all the time, while all I want to do is save variable, and load it again in another session (?????) \nf.e. You cannot save class with variables (in Matlab there's no problem)\nYou cannot load arrays in cPickle (but YOu can save them (?????) )\nWhy don't make it easier?\nIs there a way to save the current variables with values, and then load them?","AnswerCount":5,"Available Count":2,"Score":0.0798297691,"is_accepted":false,"ViewCount":8265,"Q_Id":3016116,"Users Score":2,"Answer":"What you are describing is Matlab environment feature not a programming language. \nWhat you need is a way to store serialized state of some object which could be easily done in almost any programming language. In python world pickle is the easiest way to achieve it and if you could provide more details about the errors it produces for you people would probably be able to give you more details on that.\nIn general for object oriented languages (including python) it is always a good approach to incapsulate a your state into single object that could be serialized and de-serialized and then store\/load an instance of such class. Pickling and unpickling of such objects works perfectly for many developers so this must be something specific to your implementation.","Q_Score":3,"Tags":"python,serialization","A_Id":3016188,"CreationDate":"2010-06-10T15:58:00.000","Title":"Save Workspace - save all variables to a file. Python doesn't have it)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I installed matplotlib using the Mac disk image installer for MacOS 10.5 and Python 2.5. I installed numpy then tried to import matplotlib but got this error: ImportError: numpy 1.1 or later is required; you have 2.0.0.dev8462. It seems to that version 2.0.0.dev8462 would be later than version 1.1 but I am guessing that matplotlib got confused with the \".dev8462\" in the version. Is there any workaround to this?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1283,"Q_Id":3035028,"Users Score":0,"Answer":"Following Justin's comment ... here is the equivalent file for Linux:\n\/usr\/lib\/pymodules\/python2.6\/matplotlib\/__init__.py\nsudo edit that to fix the troublesome line to:\nif not ((int(nn[0]) >= 1 and int(nn[1]) >= 1) or int(nn[0]) >= 2):\nThanks Justin Peel!","Q_Score":1,"Tags":"python,installation,numpy,matplotlib","A_Id":5926995,"CreationDate":"2010-06-14T04:59:00.000","Title":"Can't import matplotlib","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm currently parsing CSV tables and need to discover the \"data types\" of the columns.  I don't know the exact format of the values.  Obviously, everything that the CSV parser outputs is a string.  The data types I am currently interested in are:\n\ninteger\nfloating point\ndate\nboolean\nstring \n\nMy current thoughts are to test a sample of rows (maybe several hundred?) in order to determine the types of data present through pattern matching.  \nI am particularly concerned about the date data type - is their a python module for parsing common date idioms (obviously I will not be able to detect them all)?  \nWhat about integers and floats?","AnswerCount":5,"Available Count":1,"Score":0.1586485043,"is_accepted":false,"ViewCount":4235,"Q_Id":3098337,"Users Score":4,"Answer":"ast.literal_eval() can get the easy ones.","Q_Score":2,"Tags":"python,parsing,csv,input,types","A_Id":3098439,"CreationDate":"2010-06-23T01:31:00.000","Title":"Method for guessing type of data represented currently represented as strings","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've read a number of questions on finding the colour palette of an image, but my problem is slightly different. I'm looking for images made up of pure colours: pictures of the open sky, colourful photo backgrounds, red brick walls etc.\nSo far I've used the App Engine Image.histogram() function to produce a histogram, filter out values below a certain occurrence threshold, and average the remaining ones down. That still seems to leave in a lot of extraneous photographs where there are blobs of pure colour in a mixed bag of other photos.\nAny ideas much appreciated!","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":381,"Q_Id":3106788,"Users Score":1,"Answer":"How about doing this?\n\nBlur the image using some fast blurring algorithm. (Search for stack blur or box blur)\nCompute standard deviation of the pixels in RGB domain, once for each color.\nDiscard the image if the standard deviation is beyond a certain threshold.","Q_Score":1,"Tags":"python,image,image-processing,colors","A_Id":3106854,"CreationDate":"2010-06-24T02:02:00.000","Title":"Finding images with pure colours","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I have a MacBook Pro with Snow Leopard, and the Python 2.6 distribution that comes standard.  Numpy does not work properly on it.  Loadtxt gives errors of the filename being too long, and getfromtxt does not work at all (no object in module error).  So then I tried downloading the py26-numpy port on MacPorts.  Of course when I use python, it defaults the mac distribution.  How can I switch it to use the latest and greatest from MacPorts.  This seems so much simpler than building all the tools I need from source...\nThanks!","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":645,"Q_Id":3134332,"Users Score":1,"Answer":"You need to update your PATH so that the stuff from MacPorts is in front of the standard system directories, e.g., export PATH=\/opt\/local\/bin:\/opt\/local\/sbin:\/opt\/local\/Library\/Frameworks\/Python.framework\/Versions\/Current\/bin\/:$PATH.\nUPDATE: Pay special attention to the fact that \/opt\/local\/Library\/Frameworks\/Python.framework\/Versions\/Current\/bin is in front of your old PATH value.","Q_Score":1,"Tags":"python,osx-snow-leopard,numpy,macports","A_Id":3134369,"CreationDate":"2010-06-28T16:43:00.000","Title":"Switch python distributions","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I am looking for references (tutorials, books, academic literature) concerning structuring unstructured text in a manner similar to the google calendar quick add button.\nI understand this may come under the NLP category, but I am interested only in the process of going from something like \"Levi jeans size 32 A0b293\"\nto: Brand: Levi, Size: 32, Category: Jeans, code: A0b293\nI imagine it would be some combination of lexical parsing and machine learning techniques.\nI am rather language agnostic but if pushed would prefer python, Matlab or C++ references\nThanks","AnswerCount":4,"Available Count":2,"Score":0.049958375,"is_accepted":false,"ViewCount":7405,"Q_Id":3162450,"Users Score":1,"Answer":"Possibly look at \"Collective Intelligence\" by Toby Segaran. I seem to remember that addressing the basics of this in one chapter.","Q_Score":8,"Tags":"python,nlp,structured-data","A_Id":3166594,"CreationDate":"2010-07-01T23:48:00.000","Title":"Unstructured Text to Structured Data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am looking for references (tutorials, books, academic literature) concerning structuring unstructured text in a manner similar to the google calendar quick add button.\nI understand this may come under the NLP category, but I am interested only in the process of going from something like \"Levi jeans size 32 A0b293\"\nto: Brand: Levi, Size: 32, Category: Jeans, code: A0b293\nI imagine it would be some combination of lexical parsing and machine learning techniques.\nI am rather language agnostic but if pushed would prefer python, Matlab or C++ references\nThanks","AnswerCount":4,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":7405,"Q_Id":3162450,"Users Score":0,"Answer":"If you are only working for cases like the example you cited, you are better off using some manual rule-based that is 100% predictable and covers 90% of the cases it might encounter production..  \nYou could enumerable lists of all possible brands and categories and detect which is which in an input string cos there's usually very little intersection in these two lists..  \nThe other two could easily be detected and extracted using regular expressions. (1-3 digit numbers are always sizes, etc) \nYour problem domain doesn't seem big enough to warrant a more heavy duty approach such as statistical learning.","Q_Score":8,"Tags":"python,nlp,structured-data","A_Id":3177235,"CreationDate":"2010-07-01T23:48:00.000","Title":"Unstructured Text to Structured Data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"The simulation tool I have developed over the past couple of years, is written in C++ and currently has a tcl interpreted front-end.  It was written such that it can be run either in an interactive shell, or by passing an input file.  Either way, the input file is written in tcl (with many additional simulation-specific commands I have added).  This allows for quite powerful input files (e.g.- when running monte-carlo sims, random distributions can be programmed as tcl procedures directly in the input file).\nUnfortunately, I am finding that the tcl interpreter is becoming somewhat limited compared to what more modern interpreted languages have to offer, and its syntax seems a bit arcane.  Since the computational engine was written as a library with a c-compatible API, it should be straightforward to write alternative front-ends, and I am thinking of moving to a new interpreter, however I am having a bit of a time choosing (mostly because I don't have significant experience with many interpreted languages).  The options I have begun to explore are as follows:\nRemaining with tcl:\nPros:\n - No need to change the existing code.\n - Existing input files stay the same.  (though I'd probably keep the tcl front end as an option)\n - Mature language with lots of community support.\nCons:\n - Feeling limited by the language syntax.\n - Getting complaints from users as to the difficulty of learning tcl.\nPython:\nPros:\n - Modern interpreter, known to be quite efficient.\n - Large, active community.\n - Well known scientific and mathematical modules, such as scipy.\n - Commonly used in the academic Scientific\/engineering community (typical users of my code)\nCons:\n - I've never used it and thus would take time to learn the language (this is also a pro, as I've been meaning to learn python for quite some time)\n - Strict formatting of the input files (indentation, etc..)\nMatlab:\nPros:\n - Very power and widely used mathematical tool\n - Powerful built-in visualization\/plotting.\n - Extensible, through community submitted code, as well as commercial toolboxes.\n - Many in science\/engineering academia is familiar with and comfortable with matlab.\nCons:\n - Can not distribute as an executable- would need to be an add-on\/toolbox.\n - Would require (?) the matlab compiler (which is pricy).\n - Requires Matlab, which is also pricy.\nThese pros and cons are what I've been able to come up with, though I have very little experience with interpreted languages in general.  I'd love to hear any thoughts on both the interpreters I've proposed here, if these pros\/cons listed are legitimate, and any other interpreters I haven't thought of (e.g.- would php be appropriate for something like this?  lua?).  First hand experience with embedding an interpreter in your code is definitely a plus!","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":453,"Q_Id":3167661,"Users Score":0,"Answer":"Well, unless there are any other suggestions, the final answer I have arrived at is to go with Python.\nI seriously considered matlab\/octave, but when reading the octave API and matlab API, they are different enough that I'd need to build separate interfaces for each (or get very creative with macros).  With python I end up with a single, easier to maintain codebase for the front end, and it is used by just about everyone we know.  Thanks for the tips\/feedback everyone!","Q_Score":6,"Tags":"c++,python,matlab,tcl,interpreter","A_Id":3188680,"CreationDate":"2010-07-02T16:58:00.000","Title":"Picking a front-end\/interpreter for a scientific code","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"The simulation tool I have developed over the past couple of years, is written in C++ and currently has a tcl interpreted front-end.  It was written such that it can be run either in an interactive shell, or by passing an input file.  Either way, the input file is written in tcl (with many additional simulation-specific commands I have added).  This allows for quite powerful input files (e.g.- when running monte-carlo sims, random distributions can be programmed as tcl procedures directly in the input file).\nUnfortunately, I am finding that the tcl interpreter is becoming somewhat limited compared to what more modern interpreted languages have to offer, and its syntax seems a bit arcane.  Since the computational engine was written as a library with a c-compatible API, it should be straightforward to write alternative front-ends, and I am thinking of moving to a new interpreter, however I am having a bit of a time choosing (mostly because I don't have significant experience with many interpreted languages).  The options I have begun to explore are as follows:\nRemaining with tcl:\nPros:\n - No need to change the existing code.\n - Existing input files stay the same.  (though I'd probably keep the tcl front end as an option)\n - Mature language with lots of community support.\nCons:\n - Feeling limited by the language syntax.\n - Getting complaints from users as to the difficulty of learning tcl.\nPython:\nPros:\n - Modern interpreter, known to be quite efficient.\n - Large, active community.\n - Well known scientific and mathematical modules, such as scipy.\n - Commonly used in the academic Scientific\/engineering community (typical users of my code)\nCons:\n - I've never used it and thus would take time to learn the language (this is also a pro, as I've been meaning to learn python for quite some time)\n - Strict formatting of the input files (indentation, etc..)\nMatlab:\nPros:\n - Very power and widely used mathematical tool\n - Powerful built-in visualization\/plotting.\n - Extensible, through community submitted code, as well as commercial toolboxes.\n - Many in science\/engineering academia is familiar with and comfortable with matlab.\nCons:\n - Can not distribute as an executable- would need to be an add-on\/toolbox.\n - Would require (?) the matlab compiler (which is pricy).\n - Requires Matlab, which is also pricy.\nThese pros and cons are what I've been able to come up with, though I have very little experience with interpreted languages in general.  I'd love to hear any thoughts on both the interpreters I've proposed here, if these pros\/cons listed are legitimate, and any other interpreters I haven't thought of (e.g.- would php be appropriate for something like this?  lua?).  First hand experience with embedding an interpreter in your code is definitely a plus!","AnswerCount":3,"Available Count":2,"Score":0.1973753202,"is_accepted":false,"ViewCount":453,"Q_Id":3167661,"Users Score":3,"Answer":"Have you considered using Octave?  From what I gather, it is nearly a drop-in replacement for much of matlab.  This might allow you to support matlab for those who have  it, and a free alternative for those who don't.  Since the \"meat\" of your program appears to be written in another language, the performance considerations seem to be not as important as providing an environment that has: plotting and visualization capabilities, is cross-platform, has a big user base, and in a language that nearly everyone in academia and\/or involved with modelling fluid flow probably already knows.  Matlab\/Octave can potentially have all of those.","Q_Score":6,"Tags":"c++,python,matlab,tcl,interpreter","A_Id":3168060,"CreationDate":"2010-07-02T16:58:00.000","Title":"Picking a front-end\/interpreter for a scientific code","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I find learning new topics comes best with an easy implementation to code to get the idea.  This is how I learned genetic algorithms and genetic programming.  What would be some good introductory programs to write to get started with machine learning?\nPreferably, let any referenced resources be accessible online so the community can benefit","AnswerCount":5,"Available Count":3,"Score":0.0399786803,"is_accepted":false,"ViewCount":5717,"Q_Id":3176967,"Users Score":1,"Answer":"Decision tree. It is frequently used in classification tasks and has a lot of variants. Tom Mitchell's book is a good reference to implement it.","Q_Score":14,"Tags":"python,computer-science,artificial-intelligence,machine-learning","A_Id":3177037,"CreationDate":"2010-07-05T02:25:00.000","Title":"What is a good first-implementation for learning machine learning?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I find learning new topics comes best with an easy implementation to code to get the idea.  This is how I learned genetic algorithms and genetic programming.  What would be some good introductory programs to write to get started with machine learning?\nPreferably, let any referenced resources be accessible online so the community can benefit","AnswerCount":5,"Available Count":3,"Score":0.0399786803,"is_accepted":false,"ViewCount":5717,"Q_Id":3176967,"Users Score":1,"Answer":"Neural nets may be the easiest thing to implement first, and they're fairly thoroughly covered throughout literature.","Q_Score":14,"Tags":"python,computer-science,artificial-intelligence,machine-learning","A_Id":3182779,"CreationDate":"2010-07-05T02:25:00.000","Title":"What is a good first-implementation for learning machine learning?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I find learning new topics comes best with an easy implementation to code to get the idea.  This is how I learned genetic algorithms and genetic programming.  What would be some good introductory programs to write to get started with machine learning?\nPreferably, let any referenced resources be accessible online so the community can benefit","AnswerCount":5,"Available Count":3,"Score":-1.0,"is_accepted":false,"ViewCount":5717,"Q_Id":3176967,"Users Score":-8,"Answer":"There is something called books; are you familiar with those? When I was exploring AI two decades ago, there were many books. I guess now that the internet exists, books are archaic, but you can probably find some in an ancient library.","Q_Score":14,"Tags":"python,computer-science,artificial-intelligence,machine-learning","A_Id":3177077,"CreationDate":"2010-07-05T02:25:00.000","Title":"What is a good first-implementation for learning machine learning?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there anyone that has some ideas on how to implement the AdaBoost (Boostexter) algorithm in python?\nCheers!","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":4289,"Q_Id":3193756,"Users Score":2,"Answer":"Thanks a million Steve! In fact, your suggestion had some compatibility issues with MacOSX (a particular library was incompatible with the system) BUT it helped me find out a more interesting package : icsi.boost.macosx. I am just denoting that in case any Mac-eter finds it interesting! \nThank you again!\nTim","Q_Score":5,"Tags":"python,machine-learning,adaboost","A_Id":3207845,"CreationDate":"2010-07-07T10:07:00.000","Title":"AdaBoost ML algorithm python implementation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"We have a web-cam in our office kitchenette focused at our coffee maker. The coffee pot is clearly visible. Both the location of the coffee pot and the camera are static. Is it possible to calculate the height of coffee in the pot using image recognition? I've seen image recognition used for quite complex stuff like face-recognition. As compared to those projects, this seems to be a trivial task of measuring the height. \n(That's my best guess and I have no idea of the underlying complexities.)\nHow would I go about this? Would this be considered a very complex job to partake? FYI, I've never done any kind of imaging-related work.","AnswerCount":5,"Available Count":2,"Score":0.0798297691,"is_accepted":false,"ViewCount":1297,"Q_Id":3227843,"Users Score":2,"Answer":"First do thresholding, then segmentation.  Then you can more easily detect edges.","Q_Score":25,"Tags":"python,image-processing","A_Id":3227905,"CreationDate":"2010-07-12T10:57:00.000","Title":"Determine height of Coffee in the pot using Python imaging","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"We have a web-cam in our office kitchenette focused at our coffee maker. The coffee pot is clearly visible. Both the location of the coffee pot and the camera are static. Is it possible to calculate the height of coffee in the pot using image recognition? I've seen image recognition used for quite complex stuff like face-recognition. As compared to those projects, this seems to be a trivial task of measuring the height. \n(That's my best guess and I have no idea of the underlying complexities.)\nHow would I go about this? Would this be considered a very complex job to partake? FYI, I've never done any kind of imaging-related work.","AnswerCount":5,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1297,"Q_Id":3227843,"Users Score":0,"Answer":"make pictures of the pot with different levels of coffe in it.\ndownsample the image to maybe 4*10 pixels.\nmake the same in a loop for each new live picture.\ncalculate the difference of each pixels value compared to the reference images.\ntake the reference image with the least difference sum and you get the state of your coffe machine.\n\nyou might experiment if a grayscale version or only red or green might give better results.\nif it gives problems with different light settings this aproach is useless. just buy a spotlight for the coffe machine, or lighten up, or darken each picture till the sum of all pixels reaches a reference value.","Q_Score":25,"Tags":"python,image-processing","A_Id":3231205,"CreationDate":"2010-07-12T10:57:00.000","Title":"Determine height of Coffee in the pot using Python imaging","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"So, I have three numpy arrays which store latitude, longitude, and some property value on a grid -- that is, I have LAT(y,x), LON(y,x), and, say temperature T(y,x), for some limits of x and y.  The grid isn't necessarily regular -- in fact, it's tripolar.\nI then want to interpolate these property (temperature) values onto a bunch of different lat\/lon points (stored as lat1(t), lon1(t), for about 10,000 t...) which do not fall on the actual grid points.  I've tried matplotlib.mlab.griddata, but that takes far too long (it's not really designed for what I'm doing, after all).  I've also tried scipy.interpolate.interp2d, but I get a MemoryError (my grids are about 400x400).\nIs there any sort of slick, preferably fast way of doing this?  I can't help but think the answer is something obvious...  Thanks!!","AnswerCount":6,"Available Count":1,"Score":0.0333209931,"is_accepted":false,"ViewCount":28537,"Q_Id":3242382,"Users Score":1,"Answer":"There's a bunch of options here, which one is best will depend on your data...\nHowever I don't know of an out-of-the-box solution for you\nYou say your input data is from tripolar data. There are three main cases for how this data could be structured.\n\nSampled from a 3d grid in tripolar space, projected back to 2d LAT, LON data.\nSampled from a 2d grid in tripolar space, projected into 2d LAT LON data.\nUnstructured data in tripolar space projected into 2d LAT LON data\n\nThe easiest of these is 2. Instead of interpolating in LAT LON space, \"just\" transform your point back into the source space and interpolate there.\nAnother option that works for 1 and 2 is to search for the cells that maps from tripolar space to cover your sample point. (You can use a BSP or grid type structure to speed up this search) Pick one of the cells, and interpolate inside it.\nFinally there's a heap of unstructured interpolation options .. but they tend to be slow. \nA personal favourite of mine is to use a linear interpolation of the nearest N points, finding those N points can again be done with gridding or a BSP. Another good option is to Delauney triangulate the unstructured points and interpolate on the resulting triangular mesh.\nPersonally if my mesh was case 1, I'd use an unstructured strategy as I'd be worried about having to handle searching through cells with overlapping projections. Choosing the \"right\" cell would be difficult.","Q_Score":27,"Tags":"python,numpy,scipy,interpolation","A_Id":3242538,"CreationDate":"2010-07-13T23:37:00.000","Title":"Interpolation over an irregular grid","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using numpy. I have a matrix with 1 column and N rows and I want to get an array from with N elements.\nFor example, if i have M = matrix([[1], [2], [3], [4]]), I want to get A = array([1,2,3,4]).\nTo achieve it, I use A = np.array(M.T)[0]. Does anyone know a more elegant way to get the same result?\nThanks!","AnswerCount":10,"Available Count":1,"Score":0.0399786803,"is_accepted":false,"ViewCount":319127,"Q_Id":3337301,"Users Score":2,"Answer":"First, Mv = numpy.asarray(M.T), which gives you a 4x1 but 2D array.\nThen, perform A = Mv[0,:], which gives you what you want. You could put them together, as numpy.asarray(M.T)[0,:].","Q_Score":184,"Tags":"python,arrays,matrix,numpy","A_Id":32098823,"CreationDate":"2010-07-26T17:25:00.000","Title":"Numpy matrix to array","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on a Python application that involves running regression analysis on live data, and charting both. That is, the application gets fed with live data, and the regression models re-calculates as the data updates. Please note that I want to plot both the input (the data) and output (the regression analysis) in the same one chart.\nI have previously done some work with Matplotlib. Is that the best framework for this? It seems to be fairly static, I can't find any good examples similar to mine above. It also seems pretty bloated to me. Performance is key, so if there is any fast python charting framework out there with a small footprint, I'm all ears...","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":5782,"Q_Id":3351963,"Users Score":1,"Answer":"I havent worked with Matplotlib but I've always found gnuplot to be adequate for all my charting needs.\nYou have the option of calling gnuplot from python or using gnuplot.py \n(gnuplot-py.sourceforge.net) to interface to gnuplot.","Q_Score":6,"Tags":"python,live,charts","A_Id":3352172,"CreationDate":"2010-07-28T10:28:00.000","Title":"Good framework for live charting in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a C# application that needs to be run several thousand times.  Currently it precomputes a large table of constant values at the start of the run for reference.  As these values will be the same from run to run I would like to compute them independently in a simple python script and then just have the C# app import the file at the start of each run.\nThe table consists of a sorted 2D array (500-3000+ rows\/columns) of simple (int x, double y) tuples.  I am looking for recommendations concerning the best\/simplest way to store and then import this data.  For example, I could store the data in a text file like this \"(x1,y1)|(x2,y2)|(x3,y3)|...|(xn,yn)\"  This seems like a very ugly solution to a problem that seems to lend itself to a specific data structure or library I am currently unaware of.  Any suggestions would be welcome.","AnswerCount":6,"Available Count":2,"Score":0.0665680765,"is_accepted":false,"ViewCount":1046,"Q_Id":3355832,"Users Score":2,"Answer":"You may consider running IronPython - then you can pass values back and forth across C#\/Python","Q_Score":4,"Tags":"c#,python,file","A_Id":3355927,"CreationDate":"2010-07-28T17:52:00.000","Title":"Suggestions for passing large table between Python and C#","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a C# application that needs to be run several thousand times.  Currently it precomputes a large table of constant values at the start of the run for reference.  As these values will be the same from run to run I would like to compute them independently in a simple python script and then just have the C# app import the file at the start of each run.\nThe table consists of a sorted 2D array (500-3000+ rows\/columns) of simple (int x, double y) tuples.  I am looking for recommendations concerning the best\/simplest way to store and then import this data.  For example, I could store the data in a text file like this \"(x1,y1)|(x2,y2)|(x3,y3)|...|(xn,yn)\"  This seems like a very ugly solution to a problem that seems to lend itself to a specific data structure or library I am currently unaware of.  Any suggestions would be welcome.","AnswerCount":6,"Available Count":2,"Score":0.0333209931,"is_accepted":false,"ViewCount":1046,"Q_Id":3355832,"Users Score":1,"Answer":"CSV is fine suggestion, but may be clumsy with values being int and double. Generally tab or semicomma are best separators.","Q_Score":4,"Tags":"c#,python,file","A_Id":3356036,"CreationDate":"2010-07-28T17:52:00.000","Title":"Suggestions for passing large table between Python and C#","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to load a CCITT T.3 compressed tiff into python, and get the pixel matrix from it. It should just be a logical matrix. \nI have tried using pylibtiff and PIL, but when I load it with them, the matrix it returns is empty. I have read in a lot of places that these two tools support loading CCITT but not accessing the pixels. \nI am open to converting the image, as long as I can get the logical matrix from it and do it in python code. The crazy thing is is that if I open one of my images in paint, save it without altering it, then try to load it with pylibtiff, it works. Paint re-compresses it to the LZW compression. \nSo I guess my real question is: Is there a way to either natively load CCITT images to matricies or convert the images to LZW using python?? \nThanks,\ntylerthemiler","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1297,"Q_Id":3355962,"Users Score":0,"Answer":"How about running tiffcp with subprocess to convert to LZW (-c lzw switch), then process normally with pylibtiff? There are Windows builds of tiffcp lying around on the web. Not exactly Python-native solution, but still...","Q_Score":2,"Tags":"python,compression,tiff,imaging,image-formats","A_Id":3357139,"CreationDate":"2010-07-28T18:06:00.000","Title":"What is the best way to load a CCITT T.3 compressed tiff using python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm writing a simple application in Python which displays images.I need to implement Zoom In and Zoom Out by scaling the image.\nI think the Image.transform method will be able to do this, but I'm not sure how to use it, since it's asking for an affine matrix or something like that :P\nHere's the quote from the docs:\n\nim.transform(size, AFFINE, data, filter) => image\nApplies an affine transform to the image, and places the result in a new image with the given size.\nData is a 6-tuple (a, b, c, d, e, f) which contain the first two rows from an affine transform matrix. For each pixel (x, y) in the output image, the new value is taken from a position (a x + b y + c, d x + e y + f) in the input image, rounded to nearest pixel.\nThis function can be used to scale, translate, rotate, and shear the original image.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":7941,"Q_Id":3368740,"Users Score":6,"Answer":"You would be much better off using the EXTENT rather than the AFFINE method. You only need to calculate two things: what part of the input you want to see, and how large it should be. For example, if you want to see the whole image scaled down to half size (i.e. zooming out by 2), you'd pass the data (0, 0, im.size[0], im.size[1]) and the size (im.size[0]\/2, im.size[1]\/2).","Q_Score":7,"Tags":"python,image-processing,python-imaging-library","A_Id":3368847,"CreationDate":"2010-07-30T04:43:00.000","Title":"Zooming With Python Image Library","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a program that contains a large number of objects, many of them Numpy arrays. My program is swapping miserably, and I'm trying to reduce the memory usage, because it actually can't finis on my system with the current memory requirements.\nI am looking for a nice profiler that would allow me to check the amount of memory consumed by various objects (I'm envisioning a memory counterpart to cProfile) so that I know where to optimize.\nI've heard decent things about Heapy, but Heapy unfortunately does not support Numpy arrays, and most of my program involves Numpy arrays.","AnswerCount":5,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":4821,"Q_Id":3372444,"Users Score":0,"Answer":"Can you just save\/pickle some of the arrays to disk in tmp files when not using them? That's what I've had to do in the past with large arrays. Of course this will slow the program down, but at least it'll finish. Unless you need them all at once?","Q_Score":27,"Tags":"python,numpy,memory-management,profile","A_Id":3374079,"CreationDate":"2010-07-30T14:30:00.000","Title":"Profile Memory Allocation in Python (with support for Numpy arrays)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to use R in Python, as provided by the module Rpy2. I notice that R has very convenient [] operations by which you can extract the specific columns or lines. How could I achieve such a function by Python scripts?\nMy idea is to create an R vector and add those wanted elements into this vector so that the final vector is the same as that in R. I created a seq(), but it seems that it has an initial digit 1, so the final result would always start with the digit 1, which is not what I want. So, is there a better way to do this?","AnswerCount":7,"Available Count":1,"Score":0.1137907297,"is_accepted":false,"ViewCount":291651,"Q_Id":3413879,"Users Score":4,"Answer":"As pointed out by Brani, vector() is a solution, e.g.\nnewVector <- vector(mode = \"numeric\", length = 50)\nwill return a vector named \"newVector\" with 50 \"0\"'s as initial values. It is also fairly common to just add the new scalar to an existing vector to arrive at an expanded vector, e.g.\naVector <- c(aVector, newScalar)","Q_Score":104,"Tags":"python,r,vector,rpy2","A_Id":20171517,"CreationDate":"2010-08-05T10:39:00.000","Title":"How to create an empty R vector to add new items","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I saved some plots from matplotlib into a pdf format because it seems to offer a better quality. How do I include the PDF image into a PDF document using ReportLab? The convenience method Image(filepath) does not work for this format.\nThank you.","AnswerCount":4,"Available Count":1,"Score":-1.0,"is_accepted":false,"ViewCount":7751,"Q_Id":3448365,"Users Score":-5,"Answer":"Use from reportlab.graphics import renderPDF","Q_Score":12,"Tags":"python,image,pdf,reportlab","A_Id":4092003,"CreationDate":"2010-08-10T11:23:00.000","Title":"PDF image in PDF document using ReportLab (Python)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to do some image processing using Python.\nIs there a simple way to import .png image as a matrix of greyscale\/RGB values (possibly using PIL)?","AnswerCount":6,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":77563,"Q_Id":3493092,"Users Score":33,"Answer":"scipy.misc.imread() will return a Numpy array, which is handy for lots of things.","Q_Score":31,"Tags":"python,image-processing,numpy,python-imaging-library","A_Id":3494982,"CreationDate":"2010-08-16T12:29:00.000","Title":"Convert image to a matrix in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to do some image processing using Python.\nIs there a simple way to import .png image as a matrix of greyscale\/RGB values (possibly using PIL)?","AnswerCount":6,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":77563,"Q_Id":3493092,"Users Score":7,"Answer":"scipy.misc.imread() is deprecated now. We can use imageio.imread instead of that to read it as a Numpy array","Q_Score":31,"Tags":"python,image-processing,numpy,python-imaging-library","A_Id":50263426,"CreationDate":"2010-08-16T12:29:00.000","Title":"Convert image to a matrix in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"(Couldn't upload the picture showing the integral as I'm a new user.)","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":571,"Q_Id":3520672,"Users Score":1,"Answer":"Yes.  Those integrals (I'll assume they're area integrals over a region in 2D space) can be calculated using an appropriate quadrature rule.\nYou can also use Green's theorem to convert them into contour integrals and use Gaussian quadrature to integrate along the path.","Q_Score":1,"Tags":"python,scipy","A_Id":3520715,"CreationDate":"2010-08-19T10:07:00.000","Title":"Can scipy calculate (double) integrals with complex-valued integrands (real and imaginary parts in integrand)?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"(Couldn't upload the picture showing the integral as I'm a new user.)","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":571,"Q_Id":3520672,"Users Score":0,"Answer":"Thanks duffymo!\nI am calculating Huygens-Fresnel diffraction integrals: plane and other wave diffraction through circular (2D) apertures in polar coordinates.\nAs far as the programming goes: Currently a lot of my code is in Mathematica. I am considering changing to one of: scipy, java + flanagan math library, java + apache commons math library, gnu scientific library, or octave.\nMy first candidate for evaluation is scipy, but if it cannot handle complex-valued integrands, then I have to change my plans for the weekend...","Q_Score":1,"Tags":"python,scipy","A_Id":3526740,"CreationDate":"2010-08-19T10:07:00.000","Title":"Can scipy calculate (double) integrals with complex-valued integrands (real and imaginary parts in integrand)?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've never seen this error before, and none of the hits on Google seem to apply. I've got a very large NumPy array that holds Boolean values. When I try writing the array using numpy.dump(), I get the following error:\nSystemError: NULL result without error in PyObject_Call\nThe array is initialized with all False values, and the only time I ever access it is to set some of the values to True, so I have no idea why any of the values would be null.\nWhen I try running the same program with a smaller array, I get no error. However, since the error occurs at the writing step, I don't think that it's a memory issue. Has anybody else seen this error before?","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":3309,"Q_Id":3576430,"Users Score":1,"Answer":"It appears that this may have been an error from using the 32-bit version of NumPy and not the 64 bit. For whatever reason, though the program has no problem keeping the array in memory, it trips up when writing the array to a file if the number of elements in the array is greater than 2^32.","Q_Score":0,"Tags":"python,numpy","A_Id":3605012,"CreationDate":"2010-08-26T15:00:00.000","Title":"Python\/Numpy error: NULL result without error in PyObject_Call","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've never seen this error before, and none of the hits on Google seem to apply. I've got a very large NumPy array that holds Boolean values. When I try writing the array using numpy.dump(), I get the following error:\nSystemError: NULL result without error in PyObject_Call\nThe array is initialized with all False values, and the only time I ever access it is to set some of the values to True, so I have no idea why any of the values would be null.\nWhen I try running the same program with a smaller array, I get no error. However, since the error occurs at the writing step, I don't think that it's a memory issue. Has anybody else seen this error before?","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":3309,"Q_Id":3576430,"Users Score":1,"Answer":"That message comes directly from the CPython interpreter (see abstract.c method PyObject_Call). You may get a better response on a Python or NumPy mailing list regarding that error message because it looks like a problem in C code. \nWrite a simple example to demonstrating the problem and you should be able to narrow the issue down to a module then a method.","Q_Score":0,"Tags":"python,numpy","A_Id":3576712,"CreationDate":"2010-08-26T15:00:00.000","Title":"Python\/Numpy error: NULL result without error in PyObject_Call","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there any way to import SPSS dataset into Python, preferably NumPy recarray format?\nI have looked around but could not find any answer.\nJoon","AnswerCount":7,"Available Count":3,"Score":0.0855049882,"is_accepted":false,"ViewCount":9605,"Q_Id":3639639,"Users Score":3,"Answer":"Option 1\nAs rkbarney pointed out, there is the Python savReaderWriter available via pypi.  I've run into two issues:\n\nIt relies on a lot of extra libraries beyond the seemingly pure-python implementation.  SPSS files are read and written in nearly every case by the IBM provided SPSS I\/O modules.  These modules differ by platform and in my experience \"pip install savReaderWriter\" doesn't get them running out of the box (on OS X).\nDevelopment on savReaderWriter is, while not dead, less up-to-date than one might hope.  This complicates the first issue.  It relies on some deprecated packages to increase speed and gives some warnings any time you import savReaderWriter if they're not available.  Not a huge issue today but it could be trouble in the future as IBM continues to update the SPSS I\/O modules to deal new SPSS formats (they're on version 21 or 22 already if memory serves).\n\nOption 2\nI've chosen to use R as a middle-man.  Using rpy2, I set up a simple function to read the file into an R data frame and output it again as a CSV file which I subsequently import into python.  It's a bit rube-goldberg but it works.  Of course, this requires R which may also be a hassle to install in your environment (and has different binaries for different platforms).","Q_Score":6,"Tags":"python,import,dataset,spss","A_Id":21390123,"CreationDate":"2010-09-03T21:18:00.000","Title":"Importing SPSS dataset into Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there any way to import SPSS dataset into Python, preferably NumPy recarray format?\nI have looked around but could not find any answer.\nJoon","AnswerCount":7,"Available Count":3,"Score":0.0285636566,"is_accepted":false,"ViewCount":9605,"Q_Id":3639639,"Users Score":1,"Answer":"To be clear, the SPSS ODBC driver does not require an SPSS installation.","Q_Score":6,"Tags":"python,import,dataset,spss","A_Id":3691267,"CreationDate":"2010-09-03T21:18:00.000","Title":"Importing SPSS dataset into Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there any way to import SPSS dataset into Python, preferably NumPy recarray format?\nI have looked around but could not find any answer.\nJoon","AnswerCount":7,"Available Count":3,"Score":0.0855049882,"is_accepted":false,"ViewCount":9605,"Q_Id":3639639,"Users Score":3,"Answer":"SPSS has an extensive integration with Python, but that is meant to be used with SPSS (now known as IBM SPSS Statistics).  There is an SPSS ODBC driver that could be used with Python ODBC support to read a sav file.","Q_Score":6,"Tags":"python,import,dataset,spss","A_Id":3640019,"CreationDate":"2010-09-03T21:18:00.000","Title":"Importing SPSS dataset into Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a function defined by a combination of basic math functions (abs, cosh, sinh, exp, ...).\nI was wondering if it makes a difference (in speed) to use, for example,\nnumpy.abs() instead of abs()?","AnswerCount":3,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":39385,"Q_Id":3650194,"Users Score":25,"Answer":"You should use numpy function to deal with numpy's types and use regular python function to deal with regular python types.\nWorst performance usually occurs when mixing python builtins with numpy, because of types conversion. Those type conversion have been optimized lately, but it's still often better to not use them. Of course, your mileage may vary, so use profiling tools to figure out.\nAlso consider the use of programs like cython or making a C module if you want to optimize further your program. Or consider not to use python when performances matters.\nbut, when your data has been put into a numpy array, then numpy can be really fast at computing bunch of data.","Q_Score":67,"Tags":"python,performance,numpy","A_Id":3650761,"CreationDate":"2010-09-06T09:04:00.000","Title":"Are NumPy's math functions faster than Python's?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm working on an app that takes in webcam data, applies various transformations, blurs and then does a background subtraction and threshold filter. It's a type of optical touch screen retrofitting system (the design is so different that tbeta\/touchlib can't be used).\nThe camera's white balance is screwing up the threshold filter by brightening everything whenever a user's hand is seen and darkening when it leaves, causing one of those to exhibit immense quantities of static.\nIs there a good way to counteract it? Is taking a corner, assuming it's constant and adjusting the rest of the image's brightness so that it stays constant a good idea?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":4888,"Q_Id":3680829,"Users Score":1,"Answer":"You could try interfacing your camera through DirectShow and turn off Auto White Balance through your code or you could try first with the camera software deployed with it. It often gives you ability to do certain modifications as white balance and similar stuff.","Q_Score":4,"Tags":"python,opencv,webcam,touchscreen,background-subtraction","A_Id":3686359,"CreationDate":"2010-09-09T21:49:00.000","Title":"Compensate for Auto White Balance with OpenCV","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on a project on Info Retrieval.\nI have made a Full Inverted Index using Hadoop\/Python. \nHadoop outputs the index as (word,documentlist) pairs which are written on the file.\nFor a quick access, I have created a dictionary(hashtable) using the above file.\nMy question is, how do I store such an index on disk that also has quick access time.\nAt present I am storing the dictionary using python pickle module and loading from it\nbut it brings the whole of index into memory at once (or does it?). \nPlease suggest an efficient way of storing and searching through the index.\nMy dictionary structure is as follows (using nested dictionaries)\n{word : {doc1:[locations], doc2:[locations], ....}}\nso that I can get the documents containing a word by\ndictionary[word].keys() ... and so on.","AnswerCount":6,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":3666,"Q_Id":3687715,"Users Score":0,"Answer":"You could store the repr() of the dictionary and use that to re-create it.","Q_Score":5,"Tags":"python,information-retrieval,inverted-index","A_Id":3688556,"CreationDate":"2010-09-10T19:29:00.000","Title":"Storing an inverted index","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on a project on Info Retrieval.\nI have made a Full Inverted Index using Hadoop\/Python. \nHadoop outputs the index as (word,documentlist) pairs which are written on the file.\nFor a quick access, I have created a dictionary(hashtable) using the above file.\nMy question is, how do I store such an index on disk that also has quick access time.\nAt present I am storing the dictionary using python pickle module and loading from it\nbut it brings the whole of index into memory at once (or does it?). \nPlease suggest an efficient way of storing and searching through the index.\nMy dictionary structure is as follows (using nested dictionaries)\n{word : {doc1:[locations], doc2:[locations], ....}}\nso that I can get the documents containing a word by\ndictionary[word].keys() ... and so on.","AnswerCount":6,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":3666,"Q_Id":3687715,"Users Score":0,"Answer":"I am using anydmb for that purpose. Anydbm provides the same dictionary-like interface, except it allow only strings as keys and values. But this is not a constraint since you can use cPickle's loads\/dumps to store more complex structures in the index.","Q_Score":5,"Tags":"python,information-retrieval,inverted-index","A_Id":5341353,"CreationDate":"2010-09-10T19:29:00.000","Title":"Storing an inverted index","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Hi I have a file that consists of too many columns to open in excel. Each column has 10 rows of numerical values 0-2 and has a row saying the title of the column. I would like the output to be the name of the column and the average value of the 10 rows. The file is too large to open in excel 2000 so I have to try using python. Any tips on an easy way to do this.\nHere is a sample of the first 3 columns:\nTrial1 Trial2 Trial3\n1 0 1\n0 0 0\n0 2 0\n2 2 2\n1 1 1\n1 0 1\n0 0 0\n0 2 0\n2 2 2\n1 1 1\nI want python to output as a test file\nTrial 1 Trial 2 Trial 3\n1 2 1 (whatever the averages are)","AnswerCount":4,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":5272,"Q_Id":3692996,"Users Score":0,"Answer":"Less of an answer than it is an alternative understanding of the problem:\nYou could think of each line being a vector.  In this way, the average done column-by-column is just the average of each of these vectors.  All you need in order to do this is\n\nA way to read a line into a vector object,\nA vector addition operation,\nScalar multiplication (or division) of vectors.\n\nPython comes (I think) with most of this already installed, but this should lead to some easily readable code.","Q_Score":2,"Tags":"python","A_Id":19403571,"CreationDate":"2010-09-11T22:55:00.000","Title":"How to find the average of multiple columns in a file using python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a Bayesian Classifier programmed in Python, the problem is that when I multiply the features probabilities I get VERY small float values like 2.5e-320 or something like that, and suddenly it turns into 0.0. The 0.0 is obviously of no use to me since I must find the \"best\" class based on which class returns the MAX value (greater value).\nWhat would be the best way to deal with this? I thought about finding the exponential portion of the number (-320) and, if it goes too low, multiplying the value by 1e20 or some value like that. But maybe there is a better way?","AnswerCount":4,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":17743,"Q_Id":3704570,"Users Score":20,"Answer":"Would it be possible to do your work in a logarithmic space?  (For example, instead of storing 1e-320, just store -320, and use addition instead of multiplication)","Q_Score":24,"Tags":"python,floating-point,numerical-stability","A_Id":3704637,"CreationDate":"2010-09-13T21:29:00.000","Title":"In Python small floats tending to zero","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm writing an application where quite a bit of the computational time will be devoted to performing basic linear algebra operations (add, multiply, multiply by vector, multiply by scalar, etc.) on sparse matrices and vectors. Up to this point, we've built a prototype using C++ and the Boost matrix library. \nI'm considering switching to Python, to ease of coding the application itself, since it seems the Boost library (the easy C++ linear algebra library) isn't particularly fast anyway. This is a research\/proof of concept application, so some reduction of run time speed is acceptable (as I assume C++ will almost always outperform Python) so long as coding time is also significantly decreased.\nBasically, I'm looking for general advice from people who have used these libraries before. But specifically:\n1) I've found scipy.sparse and and pySparse. Are these (or other libraries) recommended?\n2) What libraries beyond Boost are recommended for C++? I've seen a variety of libraries with C interfaces, but again I'm looking to do something with low complexity, if I can get relatively good performance.\n3) Ultimately, will Python be somewhat comparable to C++ in terms of run time speed for the linear algebra operations? I will need to do many, many linear algebra operations and if the slowdown is significant then I probably shouldn't even try to make this switch.\nThank you in advance for any help and previous experience you can relate.","AnswerCount":5,"Available Count":2,"Score":0.0798297691,"is_accepted":false,"ViewCount":3064,"Q_Id":3761994,"Users Score":2,"Answer":"I don't have directly applicable experience, but the scipy\/numpy operations are almost all implemented in C.  As long as most of what you need to do is expressed in terms of scipy\/numpy functions, then your code shouldn't be much slower than equivalent C\/C++.","Q_Score":4,"Tags":"c++,python,linear-algebra","A_Id":3762217,"CreationDate":"2010-09-21T15:45:00.000","Title":"Python vs. C++ for an application that does sparse linear algebra","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm writing an application where quite a bit of the computational time will be devoted to performing basic linear algebra operations (add, multiply, multiply by vector, multiply by scalar, etc.) on sparse matrices and vectors. Up to this point, we've built a prototype using C++ and the Boost matrix library. \nI'm considering switching to Python, to ease of coding the application itself, since it seems the Boost library (the easy C++ linear algebra library) isn't particularly fast anyway. This is a research\/proof of concept application, so some reduction of run time speed is acceptable (as I assume C++ will almost always outperform Python) so long as coding time is also significantly decreased.\nBasically, I'm looking for general advice from people who have used these libraries before. But specifically:\n1) I've found scipy.sparse and and pySparse. Are these (or other libraries) recommended?\n2) What libraries beyond Boost are recommended for C++? I've seen a variety of libraries with C interfaces, but again I'm looking to do something with low complexity, if I can get relatively good performance.\n3) Ultimately, will Python be somewhat comparable to C++ in terms of run time speed for the linear algebra operations? I will need to do many, many linear algebra operations and if the slowdown is significant then I probably shouldn't even try to make this switch.\nThank you in advance for any help and previous experience you can relate.","AnswerCount":5,"Available Count":2,"Score":0.0399786803,"is_accepted":false,"ViewCount":3064,"Q_Id":3761994,"Users Score":1,"Answer":"Speed nowdays its no longer an issue for python  since ctypes and cython emerged. Whats brilliant about cython is that your write python code and it generates c code without requiring from you to know a single line of c and then compiles to a library or you could even create a stanalone. Ctypes also is similar though abit slower. From the tests I have conducted cython code is as fast as c code and that make sense since cython code is translated to c code. Ctypes is abit slower.\nSo in the end its a question of profiling , see what is slow in python and move it to cython, or you could wrap your existing c libraries for python with cython. Its quite easy to achieve c speeds this way. \nSo I will recommend not to waste the effort you invested creating these c libraries , wrap them with cython and do the rest with python. Or you could do all of it with cython if you wish as cython is python bar some limitations. And even allows you to mix c code as well. So you could do part of it in c and part of it python\/cython. Depending what makes you feel more comfortable.\nNumpy ans SciPy could be used as well for saving more time and providing ready to use solutions to your problems \/ needs.You should certainly check them out. Numpy has even has weaver a tool that let you inline c code inside your python code, just like you can inline assembly code inside your c code. But i think you would prefer to use cython . Remember because cython is both c and python at the same time it allows you to use directly c and python libraries.","Q_Score":4,"Tags":"c++,python,linear-algebra","A_Id":3762759,"CreationDate":"2010-09-21T15:45:00.000","Title":"Python vs. C++ for an application that does sparse linear algebra","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Which one among the two languages is good for statistical analysis? What are the pros and cons, other than accessibility, for each?","AnswerCount":5,"Available Count":2,"Score":0.0798297691,"is_accepted":false,"ViewCount":1096,"Q_Id":3792465,"Users Score":2,"Answer":"SciPy, NumPy and Matplotlib.","Q_Score":8,"Tags":"python,matlab,statistics,analysis","A_Id":3792494,"CreationDate":"2010-09-25T04:12:00.000","Title":"Among MATLAB and Python, which one is good for statistical analysis?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Which one among the two languages is good for statistical analysis? What are the pros and cons, other than accessibility, for each?","AnswerCount":5,"Available Count":2,"Score":0.1194272985,"is_accepted":false,"ViewCount":1096,"Q_Id":3792465,"Users Score":3,"Answer":"I would pick Python because it can be a powerful as Matlab but is free. Also, you can distribute your applications for free and no licensing chains. \nMatlab is awesome and expensive (it had a great statistical package) and it will glow smoother than Python in the beginning, but not so in the long run. \nNow, if you really want the best solution then check out R, the statistical package which is de facto in the community. They even have a Python port for it. R is also free software.","Q_Score":8,"Tags":"python,matlab,statistics,analysis","A_Id":3792582,"CreationDate":"2010-09-25T04:12:00.000","Title":"Among MATLAB and Python, which one is good for statistical analysis?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been researching the similarities\/differences between Ruby and Python generators (known as Enumerators in Ruby), and so far as i can tell they're pretty much equivalent. \nHowever one difference i've noticed is that Python Generators support a close() method whereas Ruby Generators do not. From the Python docs the close() method is said to do the following:\n\nRaises a GeneratorExit at the point where the generator function was paused. If the generator function then raises StopIteration (by exiting normally, or due to already being closed) or GeneratorExit (by not catching the exception), close returns to its caller.\"\n\nIs there a good reason why Ruby Enumerators don't support the close() method? Or is it an accidental \nomission?\nI also discovered that Ruby Enumerators support a rewind() method yet Python generators do not...is there a reason for this too?\nThanks","AnswerCount":3,"Available Count":1,"Score":0.1325487884,"is_accepted":false,"ViewCount":4732,"Q_Id":3794762,"Users Score":2,"Answer":"Generators are stack based, Ruby's Enumerators are often specialised (at the interpreter level) and not stack based.","Q_Score":20,"Tags":"python,ruby,generator,enumerator","A_Id":3796237,"CreationDate":"2010-09-25T17:25:00.000","Title":"Ruby generators vs Python generators","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"In using the numpy.darray, I met a memory overflow problem due to the size of data\uff0cfor example:\nSuppose I have a 100000000 * 100000000 * 100000000 float64 array data source, when I want to read data and process it in memory with np. It will raise a Memoray Error because it works out all memory for storing such a big array in memory.\nThen maybe using a disk file \/ database as a buffer to store the array is a solution, when I want to use data, it will get the necessary data from the file \/ database, otherwise, it is just a python object take few memory.\nIs it possible write such a adapter?\nThanks.\nRgs,\nKC","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":337,"Q_Id":3818881,"Users Score":0,"Answer":"If You have matrices with lots of zeros use scipy.sparse.csc_matrix. \nIt's possible to write everything, for example You can override numarray array class.","Q_Score":1,"Tags":"python,memory-management,numpy","A_Id":3819063,"CreationDate":"2010-09-29T05:10:00.000","Title":"using file\/db as the buffer for very big numpy array to yield data prevent overflow?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have input values of x, y, z coordinates in the following format:\n[-11.235865 5.866001 -4.604924]\n[-11.262565 5.414276 -4.842384]\n[-11.291885 5.418229 -4.849229]\n[-11.235865 5.866001 -4.604924]\nI want to draw polygons and succeeded with making a list of wx.point objects. But I need to plot floating point coordinates so I had to change it to point2D objects but DrawPolygon doesn't seem to understand floating points, which returns error message: TypeError: Expected a sequence of length-2 sequences or wxPoints.\nI can't find anywhere in the API that can draw shapes based on point2D coordinates, could anyone tell me a function name will do the job? \nThanks","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1320,"Q_Id":3852146,"Users Score":0,"Answer":"DC's only use integers. Try using Cairo or wx.GraphicsContext.","Q_Score":1,"Tags":"python,wxpython","A_Id":3855400,"CreationDate":"2010-10-04T00:29:00.000","Title":"How to draw polygons with Point2D in wxPython?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":1,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"What is the fastest way to sort an array of whole integers bigger than 0 and less than 100000 in Python? But not using the built in functions like sort.\nIm looking at the possibility to combine 2 sport functions depending on input size.","AnswerCount":10,"Available Count":1,"Score":0.0798297691,"is_accepted":false,"ViewCount":57933,"Q_Id":3855537,"Users Score":4,"Answer":"Radix sort theoretically runs in linear time (sort time grows roughly in direct proportion to array size ), but in practice Quicksort is probably more suited, unless you're sorting absolutely massive arrays.\nIf you want to make quicksort a bit faster, you can use insertion sort] when the array size becomes small.\nIt would probably be helpful to understand the concepts of algorithmic complexity and Big-O notation too.","Q_Score":20,"Tags":"python,arrays,performance,sorting","A_Id":3859736,"CreationDate":"2010-10-04T13:12:00.000","Title":"Fastest way to sort in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've got a numpy array in Python and I'd like to display it on-screen as a raster image. What is the simplest way to do this? It doesn't need to be particularly fancy or have a nice interface, all I need to do is to display the contents of the array as a greyscale raster image.\nI'm trying to transition some of my IDL code to Python with NumPy and am basically looking for a replacement for the tv and tvscl commands in IDL.","AnswerCount":3,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":41992,"Q_Id":3886281,"Users Score":3,"Answer":"Quick addition: for displaying with matplotlib, if you want the image to appear \"raster\", i.e., pixelized without smoothing, then you should include the option interpolation='nearest' in the call to imshow.","Q_Score":23,"Tags":"python,image,image-processing,numpy","A_Id":25062330,"CreationDate":"2010-10-07T22:19:00.000","Title":"Display array as raster image in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm writing some modelling routines in NumPy that need to select cells randomly from a NumPy array and do some processing on them. All cells must be selected without replacement (as in, once a cell has been selected it can't be selected again, but all cells must be selected by the end).\nI'm transitioning from IDL where I can find a nice way to do this, but I assume that NumPy has a nice way to do this too. What would you suggest?\nUpdate: I should have stated that I'm trying to do this on 2D arrays, and therefore get a set of 2D indices back.","AnswerCount":6,"Available Count":1,"Score":0.0333209931,"is_accepted":false,"ViewCount":15426,"Q_Id":3891180,"Users Score":1,"Answer":"people using numpy version 1.7 or later there can also use the builtin function numpy.random.choice","Q_Score":13,"Tags":"python,random,numpy,shuffle,sampling","A_Id":18177268,"CreationDate":"2010-10-08T13:48:00.000","Title":"Select cells randomly from NumPy array - without replacement","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been tasked to find N clusters containing the most points for a certain data set given that the clusters are bounded by a certain size. Currently, I am attempting to do this by plugging in my data into a kd-tree, iterating over the data and finding its nearest neighbor, and then merging the points if the cluster they make does not exceed a limit. I'm not sure this approach will give me a global solution so I'm looking for ways to tweak it. If you can tell me what type of problem this would go under, that'd be great too.","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1423,"Q_Id":3891645,"Users Score":0,"Answer":"If your number of clusters is fixed and you only want to maximize the number of points that are in these clusters then I think a greedy solution would be good : \n\nfind the rectangle that can contains the maximum number of points,\nremove these points,\nfind the next rectangle\n...\n\nSo how to find the rectangle of maximum area A (in fact each rectangle will have this area) that contains the maximum number of points ?\nA rectangle is not really common for euclidean distance, before trying to solve this, could you precise if you really need rectangle or just some king of limit on the cluster size ? Would a circle\/ellipse work ?\nEDIT : \ngreedy will not work (see comment below) and it really need to be rectangles...","Q_Score":4,"Tags":"python,algorithm,cluster-analysis,classification,nearest-neighbor","A_Id":3893461,"CreationDate":"2010-10-08T14:40:00.000","Title":"Clustering problem","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How do I build numpy 1.5 on ubuntu 10.10?\nThe instructions I found seems outdated or not clear.\nThanks","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1563,"Q_Id":3933923,"Users Score":2,"Answer":"One way to try, which isn't guaranteed to work, but worth a shot is to see if uupdate can sucessfully update the package.  Get a tarball of numpy 1.5.  run \"apt-get source numpy\" which should fetch and unpack the current source from ubuntu.  cd into this source directory and run \"uupdate ..\/numpytarballname\".  This should update the old source package using the newer tarball.  then you can try building with \"apt-get build-dep numpy\" and \"dpkg-buildpackage -rfakeroot\".  This will require you have the build-essential and fakeroot packages installed.","Q_Score":0,"Tags":"python,ubuntu,numpy","A_Id":3934387,"CreationDate":"2010-10-14T13:56:00.000","Title":"build recent numpy on recent ubuntu","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Some coworkers who have been struggling with Stata 11 are asking for my help to try to automate their laborious work. They mainly use 3 commands in Stata:\n\ntsset  (sets a time series analysis)\n\nas in: tsset year_column, yearly\n\nvarsoc (Obtain lag-order selection statistics for VARs)\n\nas in: varsoc column_a column_b\n\nvec (vector error-correction model)\n\nas in: vec column_a  column_b, trend(con) lags(1) noetable\n\nDoes anyone know any scientific library that I can use through python for this same functionality?","AnswerCount":5,"Available Count":2,"Score":0.0798297691,"is_accepted":false,"ViewCount":1936,"Q_Id":3946219,"Users Score":2,"Answer":"Use Rpy2 and call the R var package.","Q_Score":9,"Tags":"python,statistics,numpy,stata","A_Id":4572785,"CreationDate":"2010-10-15T21:16:00.000","Title":"Migrating from Stata to Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Some coworkers who have been struggling with Stata 11 are asking for my help to try to automate their laborious work. They mainly use 3 commands in Stata:\n\ntsset  (sets a time series analysis)\n\nas in: tsset year_column, yearly\n\nvarsoc (Obtain lag-order selection statistics for VARs)\n\nas in: varsoc column_a column_b\n\nvec (vector error-correction model)\n\nas in: vec column_a  column_b, trend(con) lags(1) noetable\n\nDoes anyone know any scientific library that I can use through python for this same functionality?","AnswerCount":5,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1936,"Q_Id":3946219,"Users Score":0,"Answer":"I have absolutely no clue what any of those do, but NumPy and SciPy. Maybe Sage or SymPy.","Q_Score":9,"Tags":"python,statistics,numpy,stata","A_Id":3946648,"CreationDate":"2010-10-15T21:16:00.000","Title":"Migrating from Stata to Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to know how I would go about performing image analysis in R. My goal is to convert images into matrices (pixel-wise information), extract\/quantify color, estimate the presence of shapes and compare images based on such metrics\/patterns.\nI am aware of relevant packages available in Python (suggestions relevant to Python are also welcome), but I am looking to accomplish these tasks in R.\nThank you for your feedback.\n-Harsh","AnswerCount":5,"Available Count":1,"Score":0.0399786803,"is_accepted":false,"ViewCount":4953,"Q_Id":3955077,"Users Score":1,"Answer":"Try the rgdal package. You will be able to read (import) and write (export) GeoTiff image files from\/to R.\nMarcio Pupin Mello","Q_Score":15,"Tags":"python,image,r,analysis","A_Id":3964945,"CreationDate":"2010-10-17T20:12:00.000","Title":"Image analysis in R","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"And what is it called? I don't know how to search for it; I tried calling it ellipsis with the Google.  I don't mean in interactive output when dots are used to indicate that the full array is not being shown, but as in the code I'm looking at, \nxTensor0[...] = xVTensor[..., 0]\nFrom my experimentation, it appears to function the similarly to : in indexing, but stands in for multiple :'s, making x[:,:,1] equivalent to x[...,1].","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1449,"Q_Id":3993125,"Users Score":7,"Answer":"Yes, you're right.  It fills in as many : as required.  The only difference occurs when you use multiple ellipses.  In that case, the first ellipsis acts in the same way, but each remaining one is converted to a single :.","Q_Score":7,"Tags":"python,numpy","A_Id":3993156,"CreationDate":"2010-10-22T00:50:00.000","Title":"What does ... mean in numpy code?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How can I generate random integers between 0 and 9 (inclusive) in Python?\nFor example, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9","AnswerCount":22,"Available Count":1,"Score":0.0272659675,"is_accepted":false,"ViewCount":2497091,"Q_Id":3996904,"Users Score":3,"Answer":"This is more of a mathematical approach but it works 100% of the time:\nLet's say you want to use random.random() function to generate a number between a and b. To achieve this, just do the following:\nnum = (b-a)*random.random() + a;\nOf course, you can generate more numbers.","Q_Score":1721,"Tags":"python,random,integer","A_Id":53562948,"CreationDate":"2010-10-22T12:48:00.000","Title":"Generate random integers between 0 and 9","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to build an internal search engine (I have a very large collection of thousands of XML files) that is able to map queries to concepts.  For example, if I search for \"big cats\", I would want highly ranked results to return documents with \"large cats\" as well.  But I may also be interested in having it return \"huge animals\", albeit at a much lower relevancy score.  \nI'm currently reading through the Natural Language Processing in Python book, and it seems WordNet has some word mappings that might prove useful, though I'm not sure how to integrate that into a search engine.  Could I use Lucene to do this? How?\nFrom further research, it seems \"latent semantic analysis\" is relevant to what I'm looking for but I'm not sure how to implement it.  \nAny advice on how to get this done?","AnswerCount":4,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":1997,"Q_Id":4003840,"Users Score":0,"Answer":"First , write a piece of python code that will return you pineapple , orange , papaya when you input apple. By focusing on \"is\" relation of semantic network. Then continue with has a relationship and so on.\nI think at the end , you might get a fairly sufficient piece of code for a school project.","Q_Score":5,"Tags":"python,search,lucene,nlp,lsa","A_Id":4004384,"CreationDate":"2010-10-23T11:58:00.000","Title":"How to build a conceptual search engine?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to build an internal search engine (I have a very large collection of thousands of XML files) that is able to map queries to concepts.  For example, if I search for \"big cats\", I would want highly ranked results to return documents with \"large cats\" as well.  But I may also be interested in having it return \"huge animals\", albeit at a much lower relevancy score.  \nI'm currently reading through the Natural Language Processing in Python book, and it seems WordNet has some word mappings that might prove useful, though I'm not sure how to integrate that into a search engine.  Could I use Lucene to do this? How?\nFrom further research, it seems \"latent semantic analysis\" is relevant to what I'm looking for but I'm not sure how to implement it.  \nAny advice on how to get this done?","AnswerCount":4,"Available Count":3,"Score":0.049958375,"is_accepted":false,"ViewCount":1997,"Q_Id":4003840,"Users Score":1,"Answer":"This is an incredibly hard problem and it can't be solved in a way that would always produce adequate results. I'd suggest to stick to some very simple principles instead so that the results are at least predictable. I think you need 2 things: some basic morphology engine plus a dictionary of synonyms.\nWhenever a search query arrives, for each word you\n\nLook for a literal match\n\"Normalize\/canonicalze\" the word using the morphology engine, i.e. make it singular, first form, etc and look for matches\nLook for synonyms of the word\n\nThen repeat for all combinations of the input words, i.e. \"big cats\", \"big cat\", \"huge cats\" huge cat\" etc.\nIn fact, you need to store your index data in canonical form, too (singluar, first form etc) along with the literal form.\nAs for concepts, such as cats are also animals - this is where it gets tricky. It never really worked, because otherwise Google would have been returning conceptual matches already, but it's not doing that.","Q_Score":5,"Tags":"python,search,lucene,nlp,lsa","A_Id":4004024,"CreationDate":"2010-10-23T11:58:00.000","Title":"How to build a conceptual search engine?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to build an internal search engine (I have a very large collection of thousands of XML files) that is able to map queries to concepts.  For example, if I search for \"big cats\", I would want highly ranked results to return documents with \"large cats\" as well.  But I may also be interested in having it return \"huge animals\", albeit at a much lower relevancy score.  \nI'm currently reading through the Natural Language Processing in Python book, and it seems WordNet has some word mappings that might prove useful, though I'm not sure how to integrate that into a search engine.  Could I use Lucene to do this? How?\nFrom further research, it seems \"latent semantic analysis\" is relevant to what I'm looking for but I'm not sure how to implement it.  \nAny advice on how to get this done?","AnswerCount":4,"Available Count":3,"Score":1.2,"is_accepted":true,"ViewCount":1997,"Q_Id":4003840,"Users Score":9,"Answer":"I'm not sure how to integrate that into a search engine. Could I use Lucene to do this? How?\n\nStep 1.  Stop.\nStep 2.  Get something to work.\nStep 3.  By then, you'll understand more about Python and Lucene and other tools and ways you might integrate them.\nDon't start by trying to solve integration problems.  Software can always be integrated.  That's what an Operating System does.  It integrates software.  Sometimes you want \"tighter\" integration, but that's never the first problem to solve.\nThe first problem to solve is to get your search or concept thing or whatever it is to work as a dumb-old command-line application.  Or pair of applications knit together by passing files around or knit together with OS pipes or something.\nLater, you can try and figure out how to make the user experience seamless.\nBut don't start with integration and don't stall because of integration questions.  Set integration aside and get something to work.","Q_Score":5,"Tags":"python,search,lucene,nlp,lsa","A_Id":4004314,"CreationDate":"2010-10-23T11:58:00.000","Title":"How to build a conceptual search engine?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I recently came across Pytables and find it to be very cool. It is clear that they are superior to a csv format for very large data sets. I am running some simulations using python. The output is not so large, say 200 columns and 2000 rows. \nIf someone has experience with both, can you suggest which format would be more convenient in the long run for such data sets that are not very large. Pytables has data manipulation capabilities and browsing of the data with Vitables, but the browser does not have as much functionality as, say Excel, which can be used for CSV. Similarly, do you find one better than the other for importing and exporting data, if working mainly in python? Is one more convenient in terms of file organization? Any comments on issues such as these would be helpful.\nThanks.","AnswerCount":6,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":3418,"Q_Id":4022887,"Users Score":0,"Answer":"These are not \"exclusive\" choices.\nYou need both.\nCSV is just a data exchange format.  If you use pytables, you still need to import and export in CSV format.","Q_Score":8,"Tags":"python,csv,pytables","A_Id":4023046,"CreationDate":"2010-10-26T10:42:00.000","Title":"Pytables vs. CSV for files that are not very large","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I recently came across Pytables and find it to be very cool. It is clear that they are superior to a csv format for very large data sets. I am running some simulations using python. The output is not so large, say 200 columns and 2000 rows. \nIf someone has experience with both, can you suggest which format would be more convenient in the long run for such data sets that are not very large. Pytables has data manipulation capabilities and browsing of the data with Vitables, but the browser does not have as much functionality as, say Excel, which can be used for CSV. Similarly, do you find one better than the other for importing and exporting data, if working mainly in python? Is one more convenient in terms of file organization? Any comments on issues such as these would be helpful.\nThanks.","AnswerCount":6,"Available Count":3,"Score":0.0665680765,"is_accepted":false,"ViewCount":3418,"Q_Id":4022887,"Users Score":2,"Answer":"One big plus for PyTables is the storage of metadata, like variables etc.\nIf you run the simulations more often with different parameters you the store the results as an array entry in the h5 file.\nWe use it to store measurement data + experiment scripts to get the data so it is all self contained.\nBTW: If you need to look quickly into a hdf5 file you can use HDFView. It's a Java app for free from the HDFGroup. It's easy to install.","Q_Score":8,"Tags":"python,csv,pytables","A_Id":7753331,"CreationDate":"2010-10-26T10:42:00.000","Title":"Pytables vs. CSV for files that are not very large","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I recently came across Pytables and find it to be very cool. It is clear that they are superior to a csv format for very large data sets. I am running some simulations using python. The output is not so large, say 200 columns and 2000 rows. \nIf someone has experience with both, can you suggest which format would be more convenient in the long run for such data sets that are not very large. Pytables has data manipulation capabilities and browsing of the data with Vitables, but the browser does not have as much functionality as, say Excel, which can be used for CSV. Similarly, do you find one better than the other for importing and exporting data, if working mainly in python? Is one more convenient in terms of file organization? Any comments on issues such as these would be helpful.\nThanks.","AnswerCount":6,"Available Count":3,"Score":0.0333209931,"is_accepted":false,"ViewCount":3418,"Q_Id":4022887,"Users Score":1,"Answer":"i think its very hard to comapre pytables and csv.. pyTable is a datastructure ehile CSV is an exchange format for data.","Q_Score":8,"Tags":"python,csv,pytables","A_Id":4024016,"CreationDate":"2010-10-26T10:42:00.000","Title":"Pytables vs. CSV for files that are not very large","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"What would be a smart data structure to use to represent a Sudoku puzzle? I.e. a 9X9 square where each \"cell\" contains either a number or a blank.\nSpecial considerations include:\n\nAbility to compare across row, column, and in 3X3 \"group\nEase of implementation (specifically in Python)\nEfficiency (not paramount)\n\nI suppose in a pinch, a 2D array might work but that seems to be a less than elegant solution. I just would like to know if there's a better data structure.","AnswerCount":4,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":7767,"Q_Id":4066075,"Users Score":2,"Answer":"Others have reasonably suggested simply using a 2D array.\nI note that a 2D array in most language implementations (anything in which that is implemented as \"array of array of X\" suffers from additional access time overhead (one access to the top level array, a second to the subarray).\nI suggest you implement the data structure abstractly as a 2D array (perhaps even continuing to use 2 indexes), but implement the array as single block of 81 cells, indexed classically by i*9+j. This gives you conceptual clarity, and somewhat more efficient implementation, by avoiding that second memory access.\nYou should be able to hide the 1D array access behind setters and getters that take 2D indexes.  If your language has the capability (dunno if this is true for Python), such small methods can be inlined for additional speed.","Q_Score":6,"Tags":"python,data-structures,graph,sudoku","A_Id":4066155,"CreationDate":"2010-11-01T01:16:00.000","Title":"Proper data structure to represent a Sudoku puzzle?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Given some data of shape 20x45, where each row is a separate data set, say 20 different sine curves with 45 data points each, how would I go about getting the same data, but with shape 20x100?\nIn other words, I have some data A of shape 20x45, and some data B of length 20x100, and I would like to have A be of shape 20x100 so I can compare them better.\nThis is for Python and Numpy\/Scipy.\nI assume it can be done with splines, so I am looking for a simple example, maybe just 2x10 to 2x20 or something, where each row is just a line, to demonstrate the solution.\nThanks!","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":5913,"Q_Id":4072844,"Users Score":0,"Answer":"If your application is not sensitive to precision or you just want a quick overview, you could just fill the unknown data points with averages from neighbouring known data points (in other words, do naive linear interpolation).","Q_Score":7,"Tags":"python,numpy,scipy","A_Id":4072921,"CreationDate":"2010-11-01T20:37:00.000","Title":"Add more sample points to data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am about to start collecting large amounts of numeric data in real-time (for those interested, the bid\/ask\/last or 'tape' for various stocks and futures).  The data will later be retrieved for analysis and simulation.  That's not hard at all, but I would like to do it efficiently and that brings up a lot of questions.  I don't need the best solution (and there are probably many 'bests' depending on the metric, anyway).  I would just like a solution that a computer scientist would approve of.  (Or not laugh at?)\n(1) Optimize for disk space, I\/O speed, or memory?\nFor simulation, the overall speed is important.  We want the I\/O (really, I) speed of the data just faster than the computational engine, so we are not I\/O limited.  \n(2) Store text, or something else (binary numeric)?\n(3) Given a set of choices from (1)-(2), are there any standout language\/library combinations to do the job-- Java, Python, C++, or something else?\nI would classify this code as \"write and forget\", so more points for efficiency over clarity\/compactness of code.  I would very, very much like to stick with Python for the simulation code (because the sims do change a lot and need to be clear).  So bonus points for good Pythonic solutions.\nEdit: this is for a Linux system (Ubuntu)\nThanks","AnswerCount":6,"Available Count":4,"Score":0.0333209931,"is_accepted":false,"ViewCount":2032,"Q_Id":4098509,"Users Score":1,"Answer":"Using D-Bus format to send the information may be to your advantage. The format is standard, binary, and D-Bus is implemented in multiple languages, and can be used to send both over the network and inter-process on the same machine.","Q_Score":2,"Tags":"java,c++,python,storage,simulation","A_Id":4098941,"CreationDate":"2010-11-04T15:51:00.000","Title":"Collecting, storing, and retrieving large amounts of numeric data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am about to start collecting large amounts of numeric data in real-time (for those interested, the bid\/ask\/last or 'tape' for various stocks and futures).  The data will later be retrieved for analysis and simulation.  That's not hard at all, but I would like to do it efficiently and that brings up a lot of questions.  I don't need the best solution (and there are probably many 'bests' depending on the metric, anyway).  I would just like a solution that a computer scientist would approve of.  (Or not laugh at?)\n(1) Optimize for disk space, I\/O speed, or memory?\nFor simulation, the overall speed is important.  We want the I\/O (really, I) speed of the data just faster than the computational engine, so we are not I\/O limited.  \n(2) Store text, or something else (binary numeric)?\n(3) Given a set of choices from (1)-(2), are there any standout language\/library combinations to do the job-- Java, Python, C++, or something else?\nI would classify this code as \"write and forget\", so more points for efficiency over clarity\/compactness of code.  I would very, very much like to stick with Python for the simulation code (because the sims do change a lot and need to be clear).  So bonus points for good Pythonic solutions.\nEdit: this is for a Linux system (Ubuntu)\nThanks","AnswerCount":6,"Available Count":4,"Score":0.0,"is_accepted":false,"ViewCount":2032,"Q_Id":4098509,"Users Score":0,"Answer":"If you are just storing, then use system tools. Don't write your own. If you need to do some real-time processing of the data before it is stored, then that's something completely different.","Q_Score":2,"Tags":"java,c++,python,storage,simulation","A_Id":4098550,"CreationDate":"2010-11-04T15:51:00.000","Title":"Collecting, storing, and retrieving large amounts of numeric data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am about to start collecting large amounts of numeric data in real-time (for those interested, the bid\/ask\/last or 'tape' for various stocks and futures).  The data will later be retrieved for analysis and simulation.  That's not hard at all, but I would like to do it efficiently and that brings up a lot of questions.  I don't need the best solution (and there are probably many 'bests' depending on the metric, anyway).  I would just like a solution that a computer scientist would approve of.  (Or not laugh at?)\n(1) Optimize for disk space, I\/O speed, or memory?\nFor simulation, the overall speed is important.  We want the I\/O (really, I) speed of the data just faster than the computational engine, so we are not I\/O limited.  \n(2) Store text, or something else (binary numeric)?\n(3) Given a set of choices from (1)-(2), are there any standout language\/library combinations to do the job-- Java, Python, C++, or something else?\nI would classify this code as \"write and forget\", so more points for efficiency over clarity\/compactness of code.  I would very, very much like to stick with Python for the simulation code (because the sims do change a lot and need to be clear).  So bonus points for good Pythonic solutions.\nEdit: this is for a Linux system (Ubuntu)\nThanks","AnswerCount":6,"Available Count":4,"Score":0.0333209931,"is_accepted":false,"ViewCount":2032,"Q_Id":4098509,"Users Score":1,"Answer":"Actually, this is quite similar to what I'm doing, which is monitoring changes players make to the world in a game. I'm currently using an sqlite database with python.\nAt the start of the program, I load the disk database into memory, for fast writing procedures. Each change is put in to two lists. These lists are for both the memory database and the disk database. Every x or so updates, the memory database is updated, and a counter is pushed up one. This is repeated, and when the counter equals 5, it's reset and the list with changes for the disk is flushed to the disk database and the list is cleared.I have found this works well if I also set the writing more to WOL(Write Ahead Logging). This method can stand about 100-300 updates a second if I update memory every 100 updates and the disk counter is set to update every 5 memory updates. You should probobly choose binary, sense, unless you have faults in your data sources, would be most logical","Q_Score":2,"Tags":"java,c++,python,storage,simulation","A_Id":4098613,"CreationDate":"2010-11-04T15:51:00.000","Title":"Collecting, storing, and retrieving large amounts of numeric data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am about to start collecting large amounts of numeric data in real-time (for those interested, the bid\/ask\/last or 'tape' for various stocks and futures).  The data will later be retrieved for analysis and simulation.  That's not hard at all, but I would like to do it efficiently and that brings up a lot of questions.  I don't need the best solution (and there are probably many 'bests' depending on the metric, anyway).  I would just like a solution that a computer scientist would approve of.  (Or not laugh at?)\n(1) Optimize for disk space, I\/O speed, or memory?\nFor simulation, the overall speed is important.  We want the I\/O (really, I) speed of the data just faster than the computational engine, so we are not I\/O limited.  \n(2) Store text, or something else (binary numeric)?\n(3) Given a set of choices from (1)-(2), are there any standout language\/library combinations to do the job-- Java, Python, C++, or something else?\nI would classify this code as \"write and forget\", so more points for efficiency over clarity\/compactness of code.  I would very, very much like to stick with Python for the simulation code (because the sims do change a lot and need to be clear).  So bonus points for good Pythonic solutions.\nEdit: this is for a Linux system (Ubuntu)\nThanks","AnswerCount":6,"Available Count":4,"Score":0.0996679946,"is_accepted":false,"ViewCount":2032,"Q_Id":4098509,"Users Score":3,"Answer":"Optimizing for disk space and IO speed is the same thing - these days, CPUs are so fast compared to IO that it's often overall faster to compress data before storing it (you may actually want to do that). I don't really see memory playing a big role (though you should probably use a reasonably-sized buffer to ensure you're doing sequential writes).\nBinary is more compact (and thus faster). Given the amount of data, I doubt whether being human-readable has any value. The only advantage of a text format would be that it's easier to figure out and correct if it gets corrupted or you lose the parsing code.","Q_Score":2,"Tags":"java,c++,python,storage,simulation","A_Id":4098582,"CreationDate":"2010-11-04T15:51:00.000","Title":"Collecting, storing, and retrieving large amounts of numeric data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am writing and distributed image processing application using hadoop streaming, python, matlab, and elastic map reduce. I have compiled a binary executable of my matlab code using the matlab compiler. I am wondering how I can incorporate this into my workflow so the binary is part of the processing on Amazon's elastic map reduce?\nIt looks like I have to use the Hadoop Distributed Cache?\nThe code is very complicated (and not written by me) so porting it to another language is not possible right now.\nTHanks","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1138,"Q_Id":4101815,"Users Score":0,"Answer":"The following is not exactly an answer to your Hadoop question, but I couldn't resist not asking why you don't execute your processing jobs on the Grid resources? There are proven solutions for executing compute intensive workflows on the Grid. And as far as I know matlab runtime environment is usually available on these resources. You may also consider using the Grid especially if you are in academia.\nGood luck","Q_Score":1,"Tags":"python,matlab,amazon-web-services,hadoop,mapreduce","A_Id":4101917,"CreationDate":"2010-11-04T22:01:00.000","Title":"Hadoop\/Elastic Map Reduce with binary executable?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I have a csv file which contains rows from a sqlite3 database. I wrote the rows to the csv file using python.\nWhen I open the csv file with Ms Excel, a blank row appears below every row, but the file on notepad is fine(without any blanks).\nDoes anyone know why this is happenning and how I can fix it?\nEdit: I used the strip() function for all the attributes before writing a row.\nThanks.","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":7209,"Q_Id":4122794,"Users Score":34,"Answer":"You're using open('file.csv', 'w')--try open('file.csv', 'wb'). \nThe Python csv module requires output files be opened in binary mode.","Q_Score":15,"Tags":"python,excel,csv","A_Id":4122980,"CreationDate":"2010-11-08T10:00:00.000","Title":"Csv blank rows problem with Excel","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a csv file which contains rows from a sqlite3 database. I wrote the rows to the csv file using python.\nWhen I open the csv file with Ms Excel, a blank row appears below every row, but the file on notepad is fine(without any blanks).\nDoes anyone know why this is happenning and how I can fix it?\nEdit: I used the strip() function for all the attributes before writing a row.\nThanks.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":7209,"Q_Id":4122794,"Users Score":0,"Answer":"the first that comes into my mind (just an idea) is that you might have used \"\\r\\n\" as row delimiter (which is shown as one linebrak in notepad) but excel expects to get only \"\\n\" or only \"\\r\" and so it interprets this as two line-breaks.","Q_Score":15,"Tags":"python,excel,csv","A_Id":4122816,"CreationDate":"2010-11-08T10:00:00.000","Title":"Csv blank rows problem with Excel","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am getting this error:\n\n\/sw\/lib\/python2.7\/site-packages\/matplotlib\/backends\/backend_macosx.py:235:\n  UserWarning: Python is not installed as a framework. The MacOSX\n  backend may not work correctly if Python is not installed as a\n  framework. Please see the Python documentation for more information on\n  installing Python as a framework on Mac OS X\n\nI installed python27 using fink and it's using the default matplotlib is using macosx framework.","AnswerCount":11,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":25060,"Q_Id":4130355,"Users Score":0,"Answer":"Simply aliasing a new command to launch python in ~\/.bash_profile will do the trick.\nalias vpython3=\/Library\/Frameworks\/Python.framework\/Versions\/3.6(replace with your own python version)\/bin\/python3\nthen 'source ~\/.bash_profile' and use vpython3 to launch python3.\nExplanation: Python is actually by default installed as framework on mac, but using virtualenv would link your python3 command under the created virtual environment, instead of the above framework directory ('which python3' in terminal and you'll see that). Perhaps Matplotlib has to find the bin\/ include\/ lib\/,etc in the python framework.","Q_Score":62,"Tags":"python,macos,matplotlib,fink","A_Id":53662588,"CreationDate":"2010-11-09T03:49:00.000","Title":"python matplotlib framework under macosx?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am getting this error:\n\n\/sw\/lib\/python2.7\/site-packages\/matplotlib\/backends\/backend_macosx.py:235:\n  UserWarning: Python is not installed as a framework. The MacOSX\n  backend may not work correctly if Python is not installed as a\n  framework. Please see the Python documentation for more information on\n  installing Python as a framework on Mac OS X\n\nI installed python27 using fink and it's using the default matplotlib is using macosx framework.","AnswerCount":11,"Available Count":3,"Score":1.2,"is_accepted":true,"ViewCount":25060,"Q_Id":4130355,"Users Score":18,"Answer":"There are two ways Python can be built and installed on Mac OS X.  One is as a traditional flat Unix-y shared library.  The other is known as a framework install, a file layout similar to other frameworks on OS X where all of the component directories (include, lib, bin) for the product are installed as subdirectories under the main framework directory.  The Fink project installs Pythons using the Unix shared library method.  Most other distributors, including the Apple-supplied Pythons in OS X, the python.org installers, and the MacPorts project, install framework versions of Python.  One of the advantages of a framework installation is that it will work properly with various OS X API calls that require a window manager connection (generally GUI-related interfaces) because the Python interpreter is packaged as an app bundle within the framework.\nIf you do need the functions in matplotlib that require the GUI functions, the simplest approach may be to switch to MacPorts which also packages matplotlib (port py27-matplotlib) and its dependencies.  If so, be careful not to mix packages between Fink and MacPorts.  It's best to stick with one or the other unless you are really careful. Adjust your shell path accordingly; it would be safest to remove all of the Fink packages and install MacPorts versions.","Q_Score":62,"Tags":"python,macos,matplotlib,fink","A_Id":4131726,"CreationDate":"2010-11-09T03:49:00.000","Title":"python matplotlib framework under macosx?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am getting this error:\n\n\/sw\/lib\/python2.7\/site-packages\/matplotlib\/backends\/backend_macosx.py:235:\n  UserWarning: Python is not installed as a framework. The MacOSX\n  backend may not work correctly if Python is not installed as a\n  framework. Please see the Python documentation for more information on\n  installing Python as a framework on Mac OS X\n\nI installed python27 using fink and it's using the default matplotlib is using macosx framework.","AnswerCount":11,"Available Count":3,"Score":1.0,"is_accepted":false,"ViewCount":25060,"Q_Id":4130355,"Users Score":31,"Answer":"Optionally you could use the Agg backend which requires no extra installation of anything. Just put backend : Agg into ~\/.matplotlib\/matplotlibrc","Q_Score":62,"Tags":"python,macos,matplotlib,fink","A_Id":33873802,"CreationDate":"2010-11-09T03:49:00.000","Title":"python matplotlib framework under macosx?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"when I used NumPy I stored it's data in the native format *.npy. It's very fast and gave me some benefits, like this one\n\nI could read *.npy from C code as\nsimple binary data(I mean *.npy are\nbinary-compatibly with C structures)\n\nNow I'm dealing with HDF5 (PyTables at this moment). As I read in the tutorial, they are using NumPy serializer to store NumPy data, so I can read these data from C as from simple *.npy files?\nDoes HDF5's numpy are binary-compatibly with C structures too?\nUPD :\nI have matlab client reading from hdf5, but don't want to read hdf5 from C++ because reading binary data from *.npy is times faster, so I really have a need in reading hdf5 from C++ (binary-compatibility)\nSo I'm already using two ways for transferring data - *.npy (read from C++ as bytes,from Python natively) and hdf5 (access from Matlab)\nAnd if it's possible,want to use the only one way - hdf5, but to do this I have to find a way to make hdf5 binary-compatibly with C++ structures, pls help, If there is some way to turn-off compression in hdf5 or something else to make hdf5 binary-compatibly with C++ structures - tell me where i can read about it...","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":6260,"Q_Id":4133327,"Users Score":0,"Answer":"HDF5 takes care of binary compatibility of structures for you.  You simply have to tell it what your structs consist of (dtype) and you'll have no problems saving\/reading record arrays - this is because the type system is basically 1:1 between numpy and HDF5.  If you use H5py I'm confident to say the IO should be fast enough provided you use all native types and large batched reads\/writes - the entire dataset of allowable.  After that it depends on chunking and what filters (shuffle, compression for example) - it's also worth noting sometimes those can speed up by greatly reducing file size so always look at benchmarks.  Note that the the type and filter choices are made on the end creating the HDF5 document.\nIf you're trying to parse HDF5 yourself, you're doing it wrong.  Use the C++ and C apis if you're working in C++\/C.  There are examples of so called \"compound types\" on the HDF5 groups website.","Q_Score":2,"Tags":"python,c,numpy,hdf5,pytables","A_Id":30009455,"CreationDate":"2010-11-09T11:51:00.000","Title":"HDF5 : storing NumPy data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there any package in python nltk that can produce all different parts of speech words for a given word. For example if i give add(verb) then it must produce addition(noun),additive(adj) and so on. Can anyone let me know?","AnswerCount":3,"Available Count":2,"Score":0.1325487884,"is_accepted":false,"ViewCount":430,"Q_Id":4150443,"Users Score":2,"Answer":"There are two options i can think of off the top of my head:\nOption one is to iterate over the sample POS-tagged corpora and simply build this mapping yourself. This gives you the POS tags that are associated with a particular word in the corpora.\nOption two is to build a hidden markov model POS tagger on the corpora, then inspect the values of the model. This gives you the POS tags that are associated with a particular word in the corpora plus their a priori probabilities, as well as some other statistical data. \nDepending on what your use-case is, one may be better than the other. I would start with option one, since it's fast and easy.","Q_Score":0,"Tags":"python,nltk","A_Id":4156445,"CreationDate":"2010-11-11T00:32:00.000","Title":"Extract different POS words for a given word in python nltk","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there any package in python nltk that can produce all different parts of speech words for a given word. For example if i give add(verb) then it must produce addition(noun),additive(adj) and so on. Can anyone let me know?","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":430,"Q_Id":4150443,"Users Score":0,"Answer":"NLTK has a lot of clever things hiding away, so there might be a direct way of doing it. However, I think you may have to write your own code to work with the WordNet database.","Q_Score":0,"Tags":"python,nltk","A_Id":4155066,"CreationDate":"2010-11-11T00:32:00.000","Title":"Extract different POS words for a given word in python nltk","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm working with model output at the moment, and I can't seem to come up with a nice way of combining two arrays of data.  Arrays A and B store different data, and the entries in each correspond to some spatial (x,y) point -- A holds some parameter, and B holds model output.  The problem is that B is a spatial subsection of A -- that is, if the model were for the entire world, A would store the parameter at each point on the earth, and B would store the model output only for those points in Africa.\nSo I need to find how much B is offset from A -- put another way, I need to find the indexes at which they start to overlap.  So if A.shape=(1000,1500), is B the (750:850, 200:300) part of that, or the (783:835, 427:440) subsection?  I have arrays associated with both A and B which store the (x,y) positions of the gridpoints for each.\nThis would seem to be a simple problem -- find where the two arrays overlap.  And I can solve it with scipy.spatial's KDTree simply enough, but it's very slow.  Anyone have any better ideas?","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1367,"Q_Id":4150909,"Users Score":0,"Answer":"Can you say more? What model are you using? What are you modelling? How is it computed?\nCan you make the dimensions match to avoid the fit? (i.e. if B doesn't depend on all of A, only plug in the part of A that B models, or compute boring values for the parts of B that wouldn't overlap A and drop those values later)","Q_Score":6,"Tags":"python,multidimensional-array,numpy,subdomain,overlap","A_Id":4151409,"CreationDate":"2010-11-11T02:26:00.000","Title":"Where do two 2-D arrays begin to overlap each other?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm working with model output at the moment, and I can't seem to come up with a nice way of combining two arrays of data.  Arrays A and B store different data, and the entries in each correspond to some spatial (x,y) point -- A holds some parameter, and B holds model output.  The problem is that B is a spatial subsection of A -- that is, if the model were for the entire world, A would store the parameter at each point on the earth, and B would store the model output only for those points in Africa.\nSo I need to find how much B is offset from A -- put another way, I need to find the indexes at which they start to overlap.  So if A.shape=(1000,1500), is B the (750:850, 200:300) part of that, or the (783:835, 427:440) subsection?  I have arrays associated with both A and B which store the (x,y) positions of the gridpoints for each.\nThis would seem to be a simple problem -- find where the two arrays overlap.  And I can solve it with scipy.spatial's KDTree simply enough, but it's very slow.  Anyone have any better ideas?","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1367,"Q_Id":4150909,"Users Score":0,"Answer":"I need to find the indexes at which they start to overlap\n\nSo are you looking for indexes from A or from B?  And is B strictly rectangular?\nFinding the bounding box or convex hull of B is really cheap.","Q_Score":6,"Tags":"python,multidimensional-array,numpy,subdomain,overlap","A_Id":4191918,"CreationDate":"2010-11-11T02:26:00.000","Title":"Where do two 2-D arrays begin to overlap each other?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I use python heavily for manipulating data and then packaging it for statistical modeling (R through RPy2). \nFeeling a little restless, I would like to branch out into other languages where \n\nFaster than python\nIt's free\nThere's good books, documentations and tutorials\nVery suitable for data manipulation\nLots of libraries for statistical modeling\n\nAny recommendations?","AnswerCount":4,"Available Count":2,"Score":0.049958375,"is_accepted":false,"ViewCount":210,"Q_Id":4155955,"Users Score":1,"Answer":"If you just want to learn a new language you could take a look at scala. The language is influenced by languages like ruby, python and erlang, but is staticaly typed and runs on the JVM. The speed is comparable to Java. And you can use all the java libraries, plus reuse a lot of your python code through jython.","Q_Score":1,"Tags":"python,programming-languages","A_Id":4156169,"CreationDate":"2010-11-11T15:19:00.000","Title":"moving on from python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I use python heavily for manipulating data and then packaging it for statistical modeling (R through RPy2). \nFeeling a little restless, I would like to branch out into other languages where \n\nFaster than python\nIt's free\nThere's good books, documentations and tutorials\nVery suitable for data manipulation\nLots of libraries for statistical modeling\n\nAny recommendations?","AnswerCount":4,"Available Count":2,"Score":0.049958375,"is_accepted":false,"ViewCount":210,"Q_Id":4155955,"Users Score":1,"Answer":"I didn't see you mention SciPy on your list... I tend like R syntax better, but they cover much of the same ground.  SciPy has faster matrix and array structures than the general purpose Python ones.  Mostly places where I have wanted to use Cython, SciPy has been just as easy \/ fast.  \nGNU\/Octave is an open\/free version of Matlab which might also interest you.","Q_Score":1,"Tags":"python,programming-languages","A_Id":4157423,"CreationDate":"2010-11-11T15:19:00.000","Title":"moving on from python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it possible to get more than 9 subplots in matplotlib?\nI am on the subplots command pylab.subplot(449); how can I get a 4410 to work?\nThank you very much.","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":28836,"Q_Id":4158367,"Users Score":72,"Answer":"It was easier than I expected, I just did: pylab.subplot(4,4,10) and it worked.","Q_Score":51,"Tags":"python,charts,matplotlib","A_Id":4158455,"CreationDate":"2010-11-11T19:19:00.000","Title":"more than 9 subplots in matplotlib","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am in dire need of a classification task example using LibSVM in python. I don't know how the Input should look like and which function is responsible for training and which one for testing\nThanks","AnswerCount":8,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":50030,"Q_Id":4214868,"Users Score":13,"Answer":"LIBSVM reads the data from a tuple containing two lists. The first list contains the classes and the second list contains the input data. create simple dataset with two possible classes\nyou also need to specify which kernel you want to use by creating svm_parameter.\n\n\n>> from libsvm import *\n>> prob = svm_problem([1,-1],[[1,0,1],[-1,0,-1]])\n>> param = svm_parameter(kernel_type = LINEAR, C = 10)\n  ## training  the model\n>> m = svm_model(prob, param)\n#testing the model\n>> m.predict([1, 1, 1])","Q_Score":25,"Tags":"python,machine-learning,svm,libsvm","A_Id":4215056,"CreationDate":"2010-11-18T12:44:00.000","Title":"An example using python bindings for SVM library, LIBSVM","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am in dire need of a classification task example using LibSVM in python. I don't know how the Input should look like and which function is responsible for training and which one for testing\nThanks","AnswerCount":8,"Available Count":2,"Score":0.0748596907,"is_accepted":false,"ViewCount":50030,"Q_Id":4214868,"Users Score":3,"Answer":"Adding to @shinNoNoir :\nparam.kernel_type represents the type of kernel function you want to use,\n0: Linear\n1: polynomial\n2: RBF\n3: Sigmoid\nAlso have in mind that, svm_problem(y,x) : here y is the class labels and x is the class instances and x and y can only be lists,tuples and dictionaries.(no numpy array)","Q_Score":25,"Tags":"python,machine-learning,svm,libsvm","A_Id":8302624,"CreationDate":"2010-11-18T12:44:00.000","Title":"An example using python bindings for SVM library, LIBSVM","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Does anyone have any experience using r\/python with data stored in Solid State Drives. If you are doing mostly reads, in theory this should significantly improve the load times of large datasets. I want to find out if this is true and if it is worth investing in SSDs for improving the IO rates in data intensive applications.","AnswerCount":5,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":4485,"Q_Id":4262984,"Users Score":0,"Answer":"The read and write times for SSDs are significantly higher than standard 7200 RPM disks (it's still worth it with a 10k RPM disk, not sure how much of an improvement it is over a 15k). So, yes, you'd get much faster times on data access.\nThe performance improvement is undeniable. Then, it's a question of economics. 2TB 7200 RPM disks are $170 a piece, and 100GB SSDS cost $210. So if you have a lot of data, you may run into a problem.\nIf you read\/write a lot of data, get an SSD. If the application is CPU intensive, however, you'd benefit much more from getting a better processor.","Q_Score":12,"Tags":"python,r,data-analysis,solid-state-drive","A_Id":4263022,"CreationDate":"2010-11-24T02:31:00.000","Title":"Data analysis using R\/python and SSDs","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Does anyone have any experience using r\/python with data stored in Solid State Drives. If you are doing mostly reads, in theory this should significantly improve the load times of large datasets. I want to find out if this is true and if it is worth investing in SSDs for improving the IO rates in data intensive applications.","AnswerCount":5,"Available Count":2,"Score":0.0798297691,"is_accepted":false,"ViewCount":4485,"Q_Id":4262984,"Users Score":2,"Answer":"I have to second John's suggestion to profile your application. My experience is that it isn't the actual data reads that are the slow part, it's the overhead of creating the programming objects to contain the data, casting from strings, memory allocation, etc. \nI would strongly suggest you profile your code first, and consider using alternative libraries (like numpy) to see what improvements you can get before you invest in hardware.","Q_Score":12,"Tags":"python,r,data-analysis,solid-state-drive","A_Id":4264161,"CreationDate":"2010-11-24T02:31:00.000","Title":"Data analysis using R\/python and SSDs","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am planning to use SlopeOne algorithm to predict if a gamer can complete a given level in a Game or not?\nHere is the scenario:\n\nLots of Gamers play and try to complete 100 levels in the game.\nEach gamer can play a level as many times as they want until they cross the level.\nThe system keeps track of the level and the number of ReTries for each level.\nEach Game Level falls into one of the 3 categories (Easy, Medium, Hard)\nApproximate distribution of the levels is 33% across each category meaning 33% of the levels are Easy, 33% of the levels are Hard etc.\n\nUsing this information:\nWhen a new gamer starts playing the game, after a few levels, I want to be able to predict \nwhich level can the Gamer Cross easily and which levels can he\/she not cross easily.\nwith this predictive ability I would like to present the game levels that the user would be able to cross with 50% probability.\nCan I use SlopeOne algorithm for this?\nReasoning is I see a lot of similarities between what I want to with say a movie rating system.\nn users, m items and N ratings to predict user rating for a given item.\nSimilarly, in my case, I have\nn users, m levels and N Retries ...\nThe only difference being in a movie rating system the rating is fixed on a 1-5 scale and in my case the retries can range from 1-x (x could be as high as 30)\nwhile theoretically someone could retry more 30 times, for now I could start with fixing the upper limit at 30 and adjust after I have more data.\nThanks.","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":431,"Q_Id":4273169,"Users Score":2,"Answer":"I think it might work, but I would apply log to the number of tries (you can't do log(0) so retries won't work) first. If someone found a level easy they would try it once or twice, whereas people who found it hard would generally have to do it over and over again. The difference between did it in 1 go vs 2 goes is much greater than 20 goes vs 21 goes. This would remove the need to place an arbitrary limit on the number of goes value.","Q_Score":8,"Tags":"python,algorithm,filtering,prediction,collaborative","A_Id":5586430,"CreationDate":"2010-11-25T02:03:00.000","Title":"Using SlopeOne algorithm to predict if a gamer can complete a level in a Game?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need a reversible hash function (obviously the input will be much smaller in size than the output) that maps the input to the output in a random-looking way. Basically, I want a way to transform a number like \"123\" to a larger number like \"9874362483910978\", but not in a way that will preserve comparisons, so it must not be always true that, if x1 > x2, f(x1) > f(x2) (but neither must it be always false).\nThe use case for this is that I need to find a way to transform small numbers into larger, random-looking ones. They don't actually need to be random (in fact, they need to be deterministic, so the same input always maps to the same output), but they do need to look random (at least when base64encoded into strings, so shifting by Z bits won't work as similar numbers will have similar MSBs).\nAlso, easy (fast) calculation and reversal is a plus, but not required.\nI don't know if I'm being clear, or if such an algorithm exists, but I'd appreciate any and all help!","AnswerCount":5,"Available Count":2,"Score":0.1586485043,"is_accepted":false,"ViewCount":47252,"Q_Id":4273466,"Users Score":4,"Answer":"Why not just XOR with a nice long number?\nEasy.  Fast.  Reversible.\nOr, if this doesn't need to be terribly secure, you could convert from base 10 to some smaller base (like base 8 or base 4, depending on how long you want the numbers to be).","Q_Score":50,"Tags":"python,hash","A_Id":4273543,"CreationDate":"2010-11-25T03:18:00.000","Title":"Reversible hash function?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need a reversible hash function (obviously the input will be much smaller in size than the output) that maps the input to the output in a random-looking way. Basically, I want a way to transform a number like \"123\" to a larger number like \"9874362483910978\", but not in a way that will preserve comparisons, so it must not be always true that, if x1 > x2, f(x1) > f(x2) (but neither must it be always false).\nThe use case for this is that I need to find a way to transform small numbers into larger, random-looking ones. They don't actually need to be random (in fact, they need to be deterministic, so the same input always maps to the same output), but they do need to look random (at least when base64encoded into strings, so shifting by Z bits won't work as similar numbers will have similar MSBs).\nAlso, easy (fast) calculation and reversal is a plus, but not required.\nI don't know if I'm being clear, or if such an algorithm exists, but I'd appreciate any and all help!","AnswerCount":5,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":47252,"Q_Id":4273466,"Users Score":19,"Answer":"What you are asking for is encryption.  A block cipher in its basic mode of operation, ECB, reversibly maps a input block onto an output block of the same size.  The input and output blocks can be interpreted as numbers.\nFor example, AES is a 128 bit block cipher, so it maps an input 128 bit number onto an output 128 bit number.  If 128 bits is good enough for your purposes, then you can simply pad your input number out to 128 bits, transform that single block with AES, then format the output as a 128 bit number.\nIf 128 bits is too large, you could use a 64 bit block cipher, like 3DES, IDEA or Blowfish.\nECB mode is considered weak, but its weakness is the constraint that you have postulated as a requirement (namely, that the mapping be \"deterministic\").  This is a weakness, because once an attacker has observed that 123 maps to 9874362483910978, from then on whenever she sees the latter number, she knows the plaintext was 123.  An attacker can perform frequency analysis and\/or build up a dictionary of known plaintext\/ciphertext pairs.","Q_Score":50,"Tags":"python,hash","A_Id":4274259,"CreationDate":"2010-11-25T03:18:00.000","Title":"Reversible hash function?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been a long time user of R and have recently started working with Python. Using conventional RDBMS systems for data warehousing, and R\/Python for number-crunching, I feel the need now to get my hands dirty with Big Data Analysis.\nI'd like to know how to get started with Big Data crunching.\n- How to start simple with Map\/Reduce and the use of Hadoop\n\nHow can I leverage my skills in R and Python to get started with Big Data analysis. Using the Python Disco project for example.\nUsing the RHIPE package and finding toy datasets and problem areas.\nFinding the right information to allow me to decide if I need to move to NoSQL from RDBMS type databases\n\nAll in all, I'd like to know how to start small and gradually build up my skills and know-how in Big Data Analysis.\nThank you for your suggestions and recommendations.\nI apologize for the generic nature of this query, but I'm looking to gain more perspective regarding this topic.\n\nHarsh","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":18227,"Q_Id":4322559,"Users Score":29,"Answer":"Using the Python Disco project for example.\n\nGood.  Play with that.\n\nUsing the RHIPE package and finding toy datasets and problem areas.\n\nFine.  Play with that, too.\nDon't sweat finding \"big\" datasets.  Even small datasets present very interesting problems.  Indeed, any dataset is a starting-off point.\nI once built a small star-schema to analyze the $60M budget of an organization.  The source data was in spreadsheets, and essentially incomprehensible.  So I unloaded it into a star schema and wrote several analytical programs in Python to create simplified reports of the relevant numbers.\n\nFinding the right information to allow me to decide if I need to move to NoSQL from RDBMS type databases\n\nThis is easy.\nFirst, get a book on data warehousing (Ralph Kimball's The Data Warehouse Toolkit) for example.\nSecond, study the \"Star Schema\" carefully -- particularly all the variants and special cases that Kimball explains (in depth)\nThird, realize the following:  SQL is for Updates and Transactions.  \nWhen doing \"analytical\" processing (big or small) there's almost no update of any kind.  SQL (and related normalization) don't really matter much any more.\nKimball's point (and others, too) is that most of your data warehouse is not in SQL, it's in simple Flat Files.  A data mart (for ad-hoc, slice-and-dice analysis) may be in a relational database to permit easy, flexible processing with SQL.\nSo the \"decision\" is trivial.  If it's transactional (\"OLTP\") it must be in a Relational or OO DB.  If it's analytical (\"OLAP\") it doesn't require SQL except for slice-and-dice analytics; and even then the DB is loaded from the official files as needed.","Q_Score":41,"Tags":"python,r,hadoop,bigdata","A_Id":4323638,"CreationDate":"2010-12-01T08:45:00.000","Title":"How to get started with Big Data Analysis","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to generate a random graph that has small-world properties (exhibits a power law distribution). I just started using the networkx package and discovered that it offers a variety of random graph generation. Can someone tell me if it possible to generate a graph where a given node's degree follows a gamma distribution (either in R or using python's networkx package)?","AnswerCount":4,"Available Count":2,"Score":0.049958375,"is_accepted":false,"ViewCount":9242,"Q_Id":4328837,"Users Score":1,"Answer":"I know this is very late, but you can do the same thing, albeit a little more straightforward, with mathematica. \nRandomGraph[DegreeGraphDistribution[{3, 3, 3, 3, 3, 3, 3, 3}], 4]\nThis will generate 4 random graphs, with each node having a prescribed degree.","Q_Score":7,"Tags":"python,algorithm,r,graph,networkx","A_Id":19205464,"CreationDate":"2010-12-01T20:33:00.000","Title":"Generating a graph with certain degree distribution?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to generate a random graph that has small-world properties (exhibits a power law distribution). I just started using the networkx package and discovered that it offers a variety of random graph generation. Can someone tell me if it possible to generate a graph where a given node's degree follows a gamma distribution (either in R or using python's networkx package)?","AnswerCount":4,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":9242,"Q_Id":4328837,"Users Score":2,"Answer":"I did this a while ago in base Python... IIRC, I used the following method. From memory, so this may not be entirely accurate, but hopefully it's worth something:\n\nChose the number of nodes, N, in your graph, and the density (existing edges over possible edges), D. This implies the number of edges, E.\nFor each node, assign its degree by first choosing a random positive number x and finding P(x), where P is your pdf. The node's degree is (P(x)*E\/2) -1.\nChose a node at random, and connect it to another random node. If either node has realized its assigned degree, eliminate it from further selection. Repeat E times. \n\nN.B. that this doesn't create a connected graph in general.","Q_Score":7,"Tags":"python,algorithm,r,graph,networkx","A_Id":4329072,"CreationDate":"2010-12-01T20:33:00.000","Title":"Generating a graph with certain degree distribution?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to quantize a 24bit image to 16bit color depth using Python Imaging.\nPIL used to provide a method im.quantize(colors, **options) however this has been deprecated for out = im.convert(\"P\", palette=Image.ADAPTIVE, colors=256)\nUnfortunately 256 is the MAXIMUM number of colors that im.convert() will quantize to (8 bit only).\nHow can I quantize a 24bit image down to 16bit using PIL (or similar)?\nthanks","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":4447,"Q_Id":4345337,"Users Score":3,"Answer":"You might want to look into converting your image to a numpy array, performing your quantisation, then converting back to PIL.\nThere are modules in numpy to convert to\/from PIL images.","Q_Score":4,"Tags":"python,python-imaging-library,imaging","A_Id":4345485,"CreationDate":"2010-12-03T12:04:00.000","Title":"Python Imaging, how to quantize an image to 16bit depth?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there a way to save a Matplotlib figure such that it can be re-opened and have typical interaction restored? (Like the .fig format in MATLAB?)\nI find myself running the same scripts many times to generate these interactive figures. Or I'm sending my colleagues multiple static PNG files to show different aspects of a plot. I'd rather send the figure object and have them interact with it themselves.","AnswerCount":7,"Available Count":1,"Score":0.057080742,"is_accepted":false,"ViewCount":98342,"Q_Id":4348733,"Users Score":2,"Answer":"Good question. Here is the doc text from pylab.save:\n\npylab no longer provides a save function, though the old pylab\n      function is still available as matplotlib.mlab.save (you can still\n      refer to it in pylab as \"mlab.save\").  However, for plain text\n      files, we recommend numpy.savetxt.  For saving numpy arrays,\n      we recommend numpy.save, and its analog numpy.load, which are\n      available in pylab as np.save and np.load.","Q_Score":144,"Tags":"python,matplotlib","A_Id":4348902,"CreationDate":"2010-12-03T18:41:00.000","Title":"Saving interactive Matplotlib figures","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an algorithm in python which creates measures for pairs of values, where m(v1, v2) == m(v2, v1) (i.e. it is symmetric). I had the idea to write a dictionary of dictionaries where these values are stored in a memory-efficient way, so that they can easily be retrieved with keys in any order. I like to inherit from things, and ideally, I'd love to write a symmetric_dict where s_d[v1][v2] always equals s_d[v2][v1], probably by checking which of the v's is larger according to some kind of ordering relation and then switching them around so that the smaller element one is always mentioned first. i.e. when calling s_d[5][2] = 4, the dict of dicts will turn them around so that they are in fact stored as s_d[2][5] = 4, and the same for retrieval of the data.\nI'm also very open for a better data structure, but I'd prefer an implementation with \"is-a\" relationship to something which just uses a dict and preprocesses some function arguments.","AnswerCount":7,"Available Count":1,"Score":0.0285636566,"is_accepted":false,"ViewCount":1589,"Q_Id":4368423,"Users Score":1,"Answer":"An obvious alternative is to use a (v1,v2) tuple as the key into a single standard dict, and insert both (v1,v2) and (v2,v1) into the dictionary, making them refer to the same object on the right-hand side.","Q_Score":4,"Tags":"python,inheritance,dictionary","A_Id":4368488,"CreationDate":"2010-12-06T16:11:00.000","Title":"Symmetric dictionary where d[a][b] == d[b][a]","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Does PyPy work with NLTK, and if so, is there an appreciable performance improvement, say for the bayesian classifier? \nWhile we're at it, do any of the other python environments (shedskin, etc) offer better nlkt performance than cpython?","AnswerCount":3,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":2318,"Q_Id":4390129,"Users Score":4,"Answer":"I got a response via email (Seo, please feel free to respond here) that said:\nThe main issues are:\nPyPy implements Python 2.5. This means adding \"from future import with_statement\" here and there, rewriting usages of property.setter, and fixing up new in 2.6 library calls like os.walk.\nNLTK needs PyYAML. Simply symlinking (or copying) stuffs to pypy-1.4\/site-packages work.\nAnd:\nDo you have NLTK running with PyPy, and if so are you seeing performance improvements?\nYes, and yes.\nSo apparently NLTK does run with PyPy and there are performance improvements.","Q_Score":15,"Tags":"python,nltk,pypy","A_Id":4854162,"CreationDate":"2010-12-08T17:01:00.000","Title":"Does PyPy work with NLTK?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Does PyPy work with NLTK, and if so, is there an appreciable performance improvement, say for the bayesian classifier? \nWhile we're at it, do any of the other python environments (shedskin, etc) offer better nlkt performance than cpython?","AnswerCount":3,"Available Count":2,"Score":0.3215127375,"is_accepted":false,"ViewCount":2318,"Q_Id":4390129,"Users Score":5,"Answer":"At least some of NLTK does work with PyPy and there is some performance gain, according to someone on #pypy on freenode. Have you run any tests? Just download PyPy from pypy.org\/download.html and instead of \"time python yourscript.py data.txt\" type \"time pypy yourscript.py data.txt\".","Q_Score":15,"Tags":"python,nltk,pypy","A_Id":4549093,"CreationDate":"2010-12-08T17:01:00.000","Title":"Does PyPy work with NLTK?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"What is the easiest way to save and load data in python, preferably in a human-readable output format?\nThe data I am saving\/loading consists of two vectors of floats. Ideally, these vectors would be named in the file (e.g. X and Y).\nMy current save() and load() functions use file.readline(), file.write() and string-to-float conversion. There must be something better.","AnswerCount":7,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":62035,"Q_Id":4450144,"Users Score":8,"Answer":"If it should be human-readable, I'd\nalso go with JSON. Unless you need to\nexchange it with enterprise-type\npeople, they like XML better. :-)\nIf it should be human editable and\nisn't too complex, I'd probably go\nwith some sort of INI-like format,\nlike for example configparser.\nIf it is complex, and doesn't need to\nbe exchanged, I'd go with just\npickling the data, unless it's very\ncomplex, in which case I'd use ZODB.\nIf it's a LOT of data, and needs to\nbe exchanged, I'd use SQL.\n\nThat pretty much covers it, I think.","Q_Score":14,"Tags":"python,io","A_Id":4450277,"CreationDate":"2010-12-15T13:02:00.000","Title":"easy save\/load of data in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"How can I extract the first paragraph from a Wikipedia article, using Python?\nFor example, for Albert Einstein, that would be:\n\nAlbert Einstein (pronounced \/\u02c8\u00e6lb\u0259rt\n  \u02c8a\u026ansta\u026an\/; German: [\u02c8alb\u0250t \u02c8a\u026an\u0283ta\u026an]\n  ( listen); 14 March 1879 \u2013 18 April\n  1955) was a theoretical physicist,\n  philosopher and author who is widely\n  regarded as one of the most\n  influential and iconic scientists and\n  intellectuals of all time. A\n  German-Swiss Nobel laureate, Einstein\n  is often regarded as the father of\n  modern physics.[2] He received the\n  1921 Nobel Prize in Physics \"for his\n  services to theoretical physics, and\n  especially for his discovery of the\n  law of the photoelectric effect\".[3]","AnswerCount":10,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":49932,"Q_Id":4460921,"Users Score":0,"Answer":"Try a combination of urllib to fetch the site and BeautifulSoup or lxml to parse the data.","Q_Score":42,"Tags":"python,wikipedia","A_Id":4460959,"CreationDate":"2010-12-16T12:49:00.000","Title":"Extract the first paragraph from a Wikipedia article (Python)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been reading a lot of stackoverflow questions about how to use the breadth-first search, dfs, A*, etc, the question is what is the optimal usage and how to implement it in reality verse simulated graphs. E.g.\nConsider you have a social graph of Twitter\/Facebook\/Some social networking site, to me it seems a search algorithm would work as follows:\nIf user A had 10 friends, then one of those had 2 friends and another 3. The search would first figure out who user A's friends were, then it would have to look up who the friends where to each of the ten users. To me this seems like bfs?\nHowever, I'm not sure if that's the way to go about implementing the algorithm.\nThanks,","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1271,"Q_Id":4488783,"Users Score":0,"Answer":"I have around 300 friends in facebook and some of my friends also have 300 friends on an average. If you gonna build a graph out of it , it's gonna be huge . Correct me , if I am wrong ? . A BFS will be quit lot demanding in this scenario ?\nThanks\nJ","Q_Score":2,"Tags":"python,algorithm,social-networking,traversal,breadth-first-search","A_Id":6335027,"CreationDate":"2010-12-20T10:32:00.000","Title":"Python usage of breadth-first search on social graph","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have installed the official python bindings for OpenCv and I am implementing some standard textbook functions just to get used to the python syntax.  I have run into the problem, however, that CvSize does not actually exist, even though it is documented on the site...  \nThe simple function: blah = cv.CvSize(inp.width\/2, inp.height\/2) yields the error 'module' object has no attribute 'CvSize'.  I have imported with 'import cv'.\nIs there an equivalent structure?  Do I need something more?  Thanks.","AnswerCount":4,"Available Count":3,"Score":-0.049958375,"is_accepted":false,"ViewCount":7873,"Q_Id":4516007,"Users Score":-1,"Answer":"Perhaps the documentation is wrong and you have to use cv.cvSize instead of cv.CvSize ?\nAlso, do a dir(cv) to find out the methods available to you.","Q_Score":7,"Tags":"python,opencv,computer-vision","A_Id":4516073,"CreationDate":"2010-12-23T05:06:00.000","Title":"CvSize does not exist?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have installed the official python bindings for OpenCv and I am implementing some standard textbook functions just to get used to the python syntax.  I have run into the problem, however, that CvSize does not actually exist, even though it is documented on the site...  \nThe simple function: blah = cv.CvSize(inp.width\/2, inp.height\/2) yields the error 'module' object has no attribute 'CvSize'.  I have imported with 'import cv'.\nIs there an equivalent structure?  Do I need something more?  Thanks.","AnswerCount":4,"Available Count":3,"Score":1.0,"is_accepted":false,"ViewCount":7873,"Q_Id":4516007,"Users Score":8,"Answer":"It seems that they opted to eventually avoid this structure altogether. Instead, it just uses a python tuple (width, height).","Q_Score":7,"Tags":"python,opencv,computer-vision","A_Id":6534684,"CreationDate":"2010-12-23T05:06:00.000","Title":"CvSize does not exist?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have installed the official python bindings for OpenCv and I am implementing some standard textbook functions just to get used to the python syntax.  I have run into the problem, however, that CvSize does not actually exist, even though it is documented on the site...  \nThe simple function: blah = cv.CvSize(inp.width\/2, inp.height\/2) yields the error 'module' object has no attribute 'CvSize'.  I have imported with 'import cv'.\nIs there an equivalent structure?  Do I need something more?  Thanks.","AnswerCount":4,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":7873,"Q_Id":4516007,"Users Score":0,"Answer":"The right call is cv.cvSize(inp.width\/2, inp.height\/2). \nAll functions in the python opencv bindings start with a lowercased c even in the highgui module.","Q_Score":7,"Tags":"python,opencv,computer-vision","A_Id":5974122,"CreationDate":"2010-12-23T05:06:00.000","Title":"CvSize does not exist?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on a project built on NumPy, and I would like to take advantage of some of NumPy's optional architecture-specific optimizations.  If I install NumPy on a paravirtualized Xen client OS (Ubuntu, in this case - a Linode), can I take advantage of those optimizations?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":99,"Q_Id":4523267,"Users Score":1,"Answer":"Yes. The optimizations run in userland and so shouldn't cause any PV traps.","Q_Score":1,"Tags":"python,ubuntu,numpy,virtualization,xen","A_Id":4523953,"CreationDate":"2010-12-23T23:38:00.000","Title":"NumPy Under Xen Client System","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"What is the equivalent to someArray(:,1,1) in python from Matlab? \nIn python someArray[:][0][0] produces a different value","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":793,"Q_Id":4535359,"Users Score":5,"Answer":"someArray[:,0,0] is the Python NumPy equivalent of MATLAB's someArray(:,1,1).  I've never figured out how to do it in pure Python, the colon slice operation is a total mystery to me with lists-of-lists.","Q_Score":4,"Tags":"python,arrays,matlab,syntax","A_Id":4535370,"CreationDate":"2010-12-26T20:47:00.000","Title":"Colon difference in Matlab and Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've written a simple k-means clustering code for Hadoop (two separate programs - mapper and reducer). The code is working over a small dataset of 2d points on my local box. It's written in Python and I plan to use Streaming API.\nI would like suggestions on how best to run this program on Hadoop.\nAfter each run of mapper and reducer, new centres are generated. These centres are input for the next iteration.\nFrom what I can see, each mapreduce iteration will have to be a separate mapreduce job. And it looks like I'll have to write another script (python\/bash) to extract the new centres from HDFS after each reduce phase, and feed it back to mapper. \nAny other easier, less messier way? If the cluster happens to use a fair scheduler, It will be very long before this computation completes?","AnswerCount":4,"Available Count":1,"Score":0.049958375,"is_accepted":false,"ViewCount":3152,"Q_Id":4537422,"Users Score":1,"Answer":"You needn't write another job. You can put the same job in a loop ( a while loop) and just keep changing the parameters of the job, so that when the mapper and reducer complete their processing, the control starts with creating a new configuration, and then you just automatically have an input file that is the output of the previous phase.","Q_Score":3,"Tags":"python,streaming,hadoop,mapreduce,iteration","A_Id":11489099,"CreationDate":"2010-12-27T08:18:00.000","Title":"Iterative MapReduce","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I've been working on this for a long time, and I feel very worn out; I'm hoping for an [obvious?] insight from SO community that might get my pet project back on the move, so I can stop kicking myself. I'm using Cloudera CDH3, HBase .89 and Hadoop .20.\nI have a Python\/Django app that writes data to a single HBase table using the Thrift interface, and that works great. Now I want to Map\/Reduce it into some more HBase tables. \nThe obvious answer here is either Dumbo or Apache PIG, but with Pig, the HBaseStorage adapter support isn't available for my version yet (Pig is able to load the classes and definitions, but freezes at the \"Map\" step, complaining about \"Input Splits\"; Pig mailing lists suggest this is fixed in Pig 0.8, which is incompatible with CDH3 Hadoop, so I'd have to use edge versions of everything [i think]). I can't find any information on how to make Dumbo use HBaseStorage as a data sink.\nI don't care if it's Python, Ruby, Scala, Clojure, Jython, JRuby or even PHP, I just really don't want to write Java (for lots of reasons, most of them involving the sinking feeling I get every time I have to convert an Int() to IntWritable() etc).\nI've tried literally every last solution and example I can find (for the last 4 weeks) for writing HBase Map\/Reduce jobs in alternative languages, but everything seems to be either outdated or incomplete. Please, Stack Overflow, save me from my own devices!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1322,"Q_Id":4557045,"Users Score":0,"Answer":"It's not precisely an answer, but it's the closest I got --\nI asked in #hbase on irc.freenode.net yesterday, and one of the Cloudera employees responded.\nThe \"Input Splits\" problem I'm having with Pig is specific to Pig 0.7, and Pig 0.8 will be bundled with Cloudera CDH3 Beta 4 (no ETA on that). Therefore, what I want to do (easily write M\/R jobs using HBase tables as both sink and source) will be possible in their next release. It also seems that the HBaseStorage class will be generally improved to help with read\/write operations from ANY JVM language, as well, making Jython, JRuby, Scala and Clojure all much more feasible as well.\nSo the answer to the question, at this time, is \"Wait for CDH3 Beta 4\", or if you're impatient, \"Download the latest version of Pig and pray that it's compatible with your HBase\"","Q_Score":1,"Tags":"python,hadoop,mapreduce,hbase","A_Id":4567653,"CreationDate":"2010-12-29T19:12:00.000","Title":"Easiest non-Java way to write HBase MapReduce on CDH3?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":1},{"Question":"Here's a somewhat simplified example of what I am trying to do. \nSuppose I have a formula that computes credit points, but the formula has no constraints (for example, the score might be 1 to 5000). And a score is assigned to 100 people. \nNow, I want to assign a \"normalized\" score between 200 and 800 to each person, based on a bell curve. So for example, if one guy has 5000 points, he might get an 800 on the new scale.  The people with the middle of my point range will get a score near 500.  In other words, 500 is the median? \nA similar example might be the old scenario of \"grading on the curve\", where a the bulk of the students perhaps get a C or C+.  \nI'm not asking for the code, either a library, an algorithm book or a website to refer to.... I'll probably be writing this in Python (but C# is of some interest as well). There is NO need to graph the bell curve. My data will probably be in a database and I may have even a million people to which to assign this score, so scalability is an issue. \nThanks.","AnswerCount":2,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":4989,"Q_Id":4560554,"Users Score":4,"Answer":"The important property of the bell curve is that it describes normal distribution, which is a simple model for many natural phenomena. I am not sure what kind of \"normalization\" you intend to do, but it seems to me that current score already complies with normal distribution, you just need to determine its properties (mean and variance) and scale each result accordingly.","Q_Score":2,"Tags":"c#,python,algorithm","A_Id":4561924,"CreationDate":"2010-12-30T06:36:00.000","Title":"Bell Curve Gaussian Algorithm (Python and\/or C#)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Can you suggest a module function from numpy\/scipy that can find local maxima\/minima in a 1D numpy array? Obviously the simplest approach ever is to have a look at the nearest neighbours, but I would like to have an accepted solution that is part of the numpy distro.","AnswerCount":12,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":290777,"Q_Id":4624970,"Users Score":27,"Answer":"Another approach (more words, less code) that may help:\nThe locations of local maxima and minima are also the locations of the zero crossings of the first derivative.  It is generally much easier to find zero crossings than it is to directly find local maxima and minima.\nUnfortunately, the first derivative tends to \"amplify\" noise, so when significant noise is present in the original data, the first derivative is best used only after the original data has had some degree of smoothing applied.\nSince smoothing is, in the simplest sense, a low pass filter, the smoothing is often best (well, most easily) done by using a convolution kernel, and \"shaping\" that kernel can provide a surprising amount of feature-preserving\/enhancing capability.  The process of finding an optimal kernel can be automated using a variety of means, but the best may be simple brute force (plenty fast for finding small kernels).  A good kernel will (as intended) massively distort the original data, but it will NOT affect the location of the peaks\/valleys of interest.\nFortunately, quite often a suitable kernel can be created via a simple SWAG (\"educated guess\"). The width of the smoothing kernel should be a little wider than the widest expected \"interesting\" peak in the original data, and its shape will resemble that peak (a single-scaled wavelet).  For mean-preserving kernels (what any good smoothing filter should be) the sum of the kernel elements should be precisely equal to 1.00, and the kernel should be symmetric about its center (meaning it will have an odd number of elements.\nGiven an optimal smoothing kernel (or a small number of kernels optimized for different data content), the degree of smoothing becomes a scaling factor for (the \"gain\" of) the convolution kernel.\nDetermining the \"correct\" (optimal) degree of smoothing (convolution kernel gain) can even be automated: Compare the standard deviation of the first derivative data with the standard deviation of the smoothed data.  How the ratio of the two standard deviations changes with changes in the degree of smoothing cam be used to predict effective smoothing values.  A few manual data runs (that are truly representative) should be all that's needed.\nAll the prior solutions posted above compute the first derivative, but they don't treat it as a statistical measure, nor do the above solutions attempt to performing feature preserving\/enhancing smoothing (to help subtle peaks \"leap above\" the noise).\nFinally, the bad news: Finding \"real\" peaks becomes a royal pain when the noise also has features that look like real peaks (overlapping bandwidth).  The next more-complex solution is generally to use a longer convolution kernel (a \"wider kernel aperture\") that takes into account the relationship between adjacent \"real\" peaks (such as minimum or maximum rates for peak occurrence), or to use multiple convolution passes using kernels having different widths (but only if it is faster: it is a fundamental mathematical truth that linear convolutions performed in sequence can always be convolved together into a single convolution).  But it is often far easier to first find a sequence of useful kernels (of varying widths) and convolve them together than it is to directly find the final kernel in a single step.\nHopefully this provides enough info to let Google (and perhaps a good stats text) fill in the gaps.  I really wish I had the time to provide a worked example, or a link to one.  If anyone comes across one online, please post it here!","Q_Score":152,"Tags":"python,numpy","A_Id":19825314,"CreationDate":"2011-01-07T11:22:00.000","Title":"Finding local maxima\/minima with Numpy in a 1D numpy array","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Here's the problem: I try to randomize n times a choice between two elements (let's say [0,1] -> 0 or 1), and my final list will have n\/2 [0] + n\/2 [1]. I tend to have this kind of result: [0 1 0 0 0 1 0 1 1 1 1 1 1 0 0, until n]: the problem is that I don't want to have serially 4 or 5 times the same number so often. I know that I could use a quasi randomisation procedure, but I don't know how to do so (I'm using Python).","AnswerCount":5,"Available Count":2,"Score":0.1586485043,"is_accepted":false,"ViewCount":588,"Q_Id":4630723,"Users Score":4,"Answer":"To guarantee that there will be the same number of zeros and ones you can generate a list containing n\/2 zeros and n\/2 ones and shuffle it with random.shuffle.\nFor small n, if you aren't happy that the result passes your acceptance criteria (e.g. not too many consecutive equal numbers), shuffle again. Be aware that doing this reduces the randomness of the result, not increases it.\nFor larger n it will take too long to find a result that passes your criteria using this method (because most results will fail). Instead you could generate elements one at a time with these rules:\n\nIf you already generated 4 ones in a row the next number must be zero and vice versa.\nOtherwise, if you need to generate x more ones and y more zeros, the chance of the next number being one is x\/(x+y).","Q_Score":1,"Tags":"python,random","A_Id":4630743,"CreationDate":"2011-01-07T22:07:00.000","Title":"Using Python for quasi randomization","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Here's the problem: I try to randomize n times a choice between two elements (let's say [0,1] -> 0 or 1), and my final list will have n\/2 [0] + n\/2 [1]. I tend to have this kind of result: [0 1 0 0 0 1 0 1 1 1 1 1 1 0 0, until n]: the problem is that I don't want to have serially 4 or 5 times the same number so often. I know that I could use a quasi randomisation procedure, but I don't know how to do so (I'm using Python).","AnswerCount":5,"Available Count":2,"Score":0.0399786803,"is_accepted":false,"ViewCount":588,"Q_Id":4630723,"Users Score":1,"Answer":"Having 6 1's in a row isn't particularly improbable -- are you sure you're not getting what you want?\nThere's a simple Python interface for a uniformly distributed random number, is that what you're looking for?","Q_Score":1,"Tags":"python,random","A_Id":4630745,"CreationDate":"2011-01-07T22:07:00.000","Title":"Using Python for quasi randomization","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"i want to implement 1024x1024 monochromatic grid , i need read data from any cell and insert rectangles with various dimensions, i have tried to make list in list ( and use it like 2d array ), what i have found is that list of booleans is slower than list of integers.... i have tried 1d list, and it was slower than 2d one, numpy is slower about 10 times that standard python list, fastest way that i have found is PIL and monochromatic bitmap used with \"load\" method, but i want it to run a lot faster, so i have tried to compile it with shedskin, but unfortunately there is no pil support there, do you know any way of implementing such grid faster without rewriting it to c or c++ ?","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":651,"Q_Id":4637190,"Users Score":0,"Answer":"One thing I might suggest is using Python's built-in array class (http:\/\/docs.python.org\/library\/array.html), with a type of 'B'. Coding will be simplest if you use one byte per pixel, but if you want to save memory, you can pack 8 to a byte, and access using your own bit manipulation.","Q_Score":3,"Tags":"python,bitmap,performance","A_Id":4637207,"CreationDate":"2011-01-09T01:31:00.000","Title":"Python Fast monochromatic bitmap","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":1,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"i want to implement 1024x1024 monochromatic grid , i need read data from any cell and insert rectangles with various dimensions, i have tried to make list in list ( and use it like 2d array ), what i have found is that list of booleans is slower than list of integers.... i have tried 1d list, and it was slower than 2d one, numpy is slower about 10 times that standard python list, fastest way that i have found is PIL and monochromatic bitmap used with \"load\" method, but i want it to run a lot faster, so i have tried to compile it with shedskin, but unfortunately there is no pil support there, do you know any way of implementing such grid faster without rewriting it to c or c++ ?","AnswerCount":3,"Available Count":2,"Score":0.1325487884,"is_accepted":false,"ViewCount":651,"Q_Id":4637190,"Users Score":2,"Answer":"Raph's suggestin of using array is good, but it won't help on CPython, in fact I'd expect it to be 10-15% slower, however if you use it on PyPy (http:\/\/pypy.org\/) I'd expect excellent results.","Q_Score":3,"Tags":"python,bitmap,performance","A_Id":4637284,"CreationDate":"2011-01-09T01:31:00.000","Title":"Python Fast monochromatic bitmap","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":1,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to embed matplotlib charts into PDFs generated by ReportLab directly - i.e. not saving as a PNG first and then embedding the PNG into the PDF (i think I'll get better quality output).\nDoes anyone know if there's a matplotlib flowable for ReportLab?\nThanks","AnswerCount":4,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":10096,"Q_Id":4690585,"Users Score":4,"Answer":"There is not one, but what I do in my own use of MatPlotLib with ReportLab is to generate PNGs and then embed the PNGs so that I don't need to also use PIL.  However, if you do use PIL, I believe you should be able to generate and embed EPS using MatPlotLib and ReportLab.","Q_Score":16,"Tags":"python,matplotlib,reportlab","A_Id":4880177,"CreationDate":"2011-01-14T11:32:00.000","Title":"Is there a matplotlib flowable for ReportLab?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Suppose I have a NxN matrix M (lil_matrix or csr_matrix) from scipy.sparse, and I want to make it (N+1)xN where M_modified[i,j] = M[i,j] for 0 <= i < N (and all j) and M[N,j] = 0 for all j. Basically, I want to add a row of zeros to the bottom of M and preserve the remainder of the matrix. Is there a way to do this without copying the data?","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":27075,"Q_Id":4695337,"Users Score":7,"Answer":"I don't think that there is any way to really escape from doing the copying. Both of those types of sparse matrices store their data as Numpy arrays (in the data and indices attributes for csr and in the data and rows attributes for lil) internally and Numpy arrays can't be extended.\nUpdate with more information:\nLIL does stand for LInked List, but the current implementation doesn't quite live up to the name. The Numpy arrays used for data and rows are both of type object. Each of the objects in these arrays are actually Python lists (an empty list when all values are zero in a row). Python lists aren't exactly linked lists, but they are kind of close and quite frankly a better choice due to O(1) look-up. Personally, I don't immediately see the point of using a Numpy array of objects here rather than just a Python list. You could fairly easily change the current lil implementation to use Python lists instead which would allow you to add a row without copying the whole matrix.","Q_Score":31,"Tags":"python,scipy,sparse-matrix","A_Id":4695834,"CreationDate":"2011-01-14T19:58:00.000","Title":"expanding (adding a row or column) a scipy.sparse matrix","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a series of 20 plots (not subplots) to be made in a single figure.  I want the legend to be outside of the box.  At the same time, I do not want to change the axes, as the size of the figure gets reduced.  Kindly help me for the following queries:\n\nI want to keep the legend box outside the plot area. (I want the legend to be outside at the right side of the plot area).\nIs there anyway that I reduce the font size of the text inside the legend box, so that the size of the legend box will be small.","AnswerCount":17,"Available Count":1,"Score":0.0235250705,"is_accepted":false,"ViewCount":1360273,"Q_Id":4700614,"Users Score":2,"Answer":"You can also try figlegend.  It is possible to create a legend independent of any Axes object.  However, you may need to create some \"dummy\" Paths to make sure the formatting for the objects gets passed on correctly.","Q_Score":1378,"Tags":"python,matplotlib,legend","A_Id":4710783,"CreationDate":"2011-01-15T16:10:00.000","Title":"How to put the legend outside the plot in Matplotlib","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am about to embark on some signal processing work using NumPy\/SciPy. However, I have never used Python before and don't know where to start.\nI see there are currently two branches of Python in this world: Version 2.x and 3.x. \nBeing a neophile, I instinctively tend to go for the newer one, but there seems to be a lot of talk about incompatibilities between the two. Numpy seems to be compatible with Python 3. I can't find any documents on SciPy.\nWould you recommend to go with Python 3 or 2?\n(could you point me to some resources to get started? I know C\/C++, Ruby, Matlab and some other stuff and basically want to use NumPy instead of Matlab.)","AnswerCount":6,"Available Count":3,"Score":0.0665680765,"is_accepted":false,"ViewCount":5690,"Q_Id":4758693,"Users Score":2,"Answer":"I am quite conservative in this respect, and so I use Python 2.6. That's what comes pre-installed on my Linux box, and it is also the target version for the latest binary releases of SciPy.\nPython 3 is without a doubt a huge step forward, but if you do mainly numerical stuff with NumPy and SciPy, I'd still go for Python 2.","Q_Score":8,"Tags":"python,numpy,python-3.x","A_Id":4758849,"CreationDate":"2011-01-21T12:17:00.000","Title":"I want to use NumPy\/SciPy. Should I use Python 2 or 3?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am about to embark on some signal processing work using NumPy\/SciPy. However, I have never used Python before and don't know where to start.\nI see there are currently two branches of Python in this world: Version 2.x and 3.x. \nBeing a neophile, I instinctively tend to go for the newer one, but there seems to be a lot of talk about incompatibilities between the two. Numpy seems to be compatible with Python 3. I can't find any documents on SciPy.\nWould you recommend to go with Python 3 or 2?\n(could you point me to some resources to get started? I know C\/C++, Ruby, Matlab and some other stuff and basically want to use NumPy instead of Matlab.)","AnswerCount":6,"Available Count":3,"Score":0.0665680765,"is_accepted":false,"ViewCount":5690,"Q_Id":4758693,"Users Score":2,"Answer":"I can recommend Using py3k over py2.6 if possible. Especially if you're a new user, since some of the syntax changes in py3k and it'll be harder to get used the new syntax if you're starting out learning the old.\nThe modules you mention all have support for py3k but as SilentGhost noted you might want to check for compatibility with plotting libraries too.","Q_Score":8,"Tags":"python,numpy,python-3.x","A_Id":4758906,"CreationDate":"2011-01-21T12:17:00.000","Title":"I want to use NumPy\/SciPy. Should I use Python 2 or 3?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am about to embark on some signal processing work using NumPy\/SciPy. However, I have never used Python before and don't know where to start.\nI see there are currently two branches of Python in this world: Version 2.x and 3.x. \nBeing a neophile, I instinctively tend to go for the newer one, but there seems to be a lot of talk about incompatibilities between the two. Numpy seems to be compatible with Python 3. I can't find any documents on SciPy.\nWould you recommend to go with Python 3 or 2?\n(could you point me to some resources to get started? I know C\/C++, Ruby, Matlab and some other stuff and basically want to use NumPy instead of Matlab.)","AnswerCount":6,"Available Count":3,"Score":1.2,"is_accepted":true,"ViewCount":5690,"Q_Id":4758693,"Users Score":3,"Answer":"Both scipy and numpy are compatible with py3k. However, if you'll need to plot stuff: matplotlib is not yet officially compatible with py3k. So, it'll depend on whether your signalling processing involves plotting.\nSyntactic differences are not that great between the two version.","Q_Score":8,"Tags":"python,numpy,python-3.x","A_Id":4758785,"CreationDate":"2011-01-21T12:17:00.000","Title":"I want to use NumPy\/SciPy. Should I use Python 2 or 3?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to write a pretty heavy duty math-based project, which will parse through about 100MB+ data several times a day, so, I need a fast language that's pretty easy to use. I would have gone with C, but, getting a large project done in C is very difficult, especially with the low level programming getting in your way. So, I was about python or java. Both are well equiped with OO features, so I don't mind that. Now, here are my pros for choosing python:\n\nVery easy to use language\nHas a pretty large library of useful stuff\nHas an easy to use plotting library\n\nHere are the cons:\n\nNot exactly blazing\nThere isn't a native python neural network library that is active\nI can't close source my code without going through quite a bit of trouble\nDeploying python code on clients computers is hard to deal with, especially when clients are idiots.\n\nHere are the pros for choosing Java:\n\nHuge library\nWell supported\nEasy to deploy\nPretty fast, possibly even comparable to C++\nThe Encog Neural Network Library is really active and pretty awesome\nNetworking support is really good\nStrong typing\n\nHere are the cons for Java:\n\nI can't find a good graphing library like matplotlib for python\nNo built in support for big integers, that means another dependency (I mean REALLY big integers, not just math.BigInteger size)\nFile IO is kind of awkward compared to Python\nNot a ton of array manipulating or \"make programming easy\" type of features that python has.\n\nSo, I was hoping you guys can tell me what to use. I'm equally familiar with both languages. Also, suggestions for other languages is great too.\nEDIT: WOW! you guys are fast! 30 mins at 10 responses!","AnswerCount":8,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":12225,"Q_Id":4759485,"Users Score":0,"Answer":"What is more important for you?\nIf it's rapid application development, I found Python significantly easier to code for than Java - and I was just learning Python, while I had been coding on Java for years.\nIf it's application speed and the ability to reuse existing code, then you should probably stick with Java. It's reasonably fast and many research efforts at the moment use Java as their language of choice.","Q_Score":21,"Tags":"java,python,math,stocks","A_Id":4759549,"CreationDate":"2011-01-21T13:47:00.000","Title":"Java or Python for math?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to write a pretty heavy duty math-based project, which will parse through about 100MB+ data several times a day, so, I need a fast language that's pretty easy to use. I would have gone with C, but, getting a large project done in C is very difficult, especially with the low level programming getting in your way. So, I was about python or java. Both are well equiped with OO features, so I don't mind that. Now, here are my pros for choosing python:\n\nVery easy to use language\nHas a pretty large library of useful stuff\nHas an easy to use plotting library\n\nHere are the cons:\n\nNot exactly blazing\nThere isn't a native python neural network library that is active\nI can't close source my code without going through quite a bit of trouble\nDeploying python code on clients computers is hard to deal with, especially when clients are idiots.\n\nHere are the pros for choosing Java:\n\nHuge library\nWell supported\nEasy to deploy\nPretty fast, possibly even comparable to C++\nThe Encog Neural Network Library is really active and pretty awesome\nNetworking support is really good\nStrong typing\n\nHere are the cons for Java:\n\nI can't find a good graphing library like matplotlib for python\nNo built in support for big integers, that means another dependency (I mean REALLY big integers, not just math.BigInteger size)\nFile IO is kind of awkward compared to Python\nNot a ton of array manipulating or \"make programming easy\" type of features that python has.\n\nSo, I was hoping you guys can tell me what to use. I'm equally familiar with both languages. Also, suggestions for other languages is great too.\nEDIT: WOW! you guys are fast! 30 mins at 10 responses!","AnswerCount":8,"Available Count":3,"Score":0.024994793,"is_accepted":false,"ViewCount":12225,"Q_Id":4759485,"Users Score":1,"Answer":"If those are the choices, then Java should be the faster for math intensive work. It is compiled (although yes it is still running byte code).\nExelian mentions NumPy. There's also the SciPy package. Both are worth looking at but only really seem to give speed improvements for work with lots of arrays and vector processing.\nWhen I tried using these with NLTK for a math-intensive routine, I found there wasn't that much of a speedup.\nFor math intensive work these days, I'd be using C\/C++ or C# (personally I prefer C# over Java although that shouldn't affect your decision). My first employer out of univ. paid me to use Fortran for stuff that is almost certainly more math intensive than anything you're thinking of. Don't laugh - the Fortran compilers are some of the best for math processing on heavy iron.","Q_Score":21,"Tags":"java,python,math,stocks","A_Id":4759596,"CreationDate":"2011-01-21T13:47:00.000","Title":"Java or Python for math?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to write a pretty heavy duty math-based project, which will parse through about 100MB+ data several times a day, so, I need a fast language that's pretty easy to use. I would have gone with C, but, getting a large project done in C is very difficult, especially with the low level programming getting in your way. So, I was about python or java. Both are well equiped with OO features, so I don't mind that. Now, here are my pros for choosing python:\n\nVery easy to use language\nHas a pretty large library of useful stuff\nHas an easy to use plotting library\n\nHere are the cons:\n\nNot exactly blazing\nThere isn't a native python neural network library that is active\nI can't close source my code without going through quite a bit of trouble\nDeploying python code on clients computers is hard to deal with, especially when clients are idiots.\n\nHere are the pros for choosing Java:\n\nHuge library\nWell supported\nEasy to deploy\nPretty fast, possibly even comparable to C++\nThe Encog Neural Network Library is really active and pretty awesome\nNetworking support is really good\nStrong typing\n\nHere are the cons for Java:\n\nI can't find a good graphing library like matplotlib for python\nNo built in support for big integers, that means another dependency (I mean REALLY big integers, not just math.BigInteger size)\nFile IO is kind of awkward compared to Python\nNot a ton of array manipulating or \"make programming easy\" type of features that python has.\n\nSo, I was hoping you guys can tell me what to use. I'm equally familiar with both languages. Also, suggestions for other languages is great too.\nEDIT: WOW! you guys are fast! 30 mins at 10 responses!","AnswerCount":8,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":12225,"Q_Id":4759485,"Users Score":0,"Answer":"The Apache Commons Math picked up where JAMA left off.  They are quite capable for scientific computing.  \nSo is Python - NumPy and SciPy are excellent.  I also like the fact that Python is a hybrid of object-orientation and functional programming.  Functional programming is awfully handy for numerical methods.\nI'd recommend using the one that you know best, but if the choice is a toss up I might lean towards Python.","Q_Score":21,"Tags":"java,python,math,stocks","A_Id":4765700,"CreationDate":"2011-01-21T13:47:00.000","Title":"Java or Python for math?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using Ubuntu 10.04 and have successfully configured PyDev to work with Python and have written a few simple example projects. Now I am trying to incorporate numpy and matplotlib. I have gotten numpy installed and within PyDev I did not need to alter any paths, etc., and after the installation of numpy I was automatically able to import numpy with no problem. However, following the same procedure with matplotlib hasn't worked. If I run Python from the command line, then import matplotlib works just fine. But within PyDev, I just get the standard error where it can't locate matplotlib when I try import matplotlib.\nSince numpy didn't require any alteration of the PYTHONPATH, I feel that neither should matplotlib, so can anyone help me figure out why matplotlib isn't accessible from within my existing project while numpy is? Thanks for any help.","AnswerCount":3,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":3642,"Q_Id":4788748,"Users Score":0,"Answer":"Right click your project, then go to properties, then click PyDev - Interpreter\/Grammar, click \"Click here to configure an interpreter not listed\". Then select the interpreter you are using, click Install\/Uninstall with pip, then enter matplotlib for . Then restart Eclipse and it should work.","Q_Score":0,"Tags":"python,module,import,matplotlib,pydev","A_Id":52617040,"CreationDate":"2011-01-25T00:22:00.000","Title":"Unable to import matplotlib in PyDev","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using Ubuntu 10.04 and have successfully configured PyDev to work with Python and have written a few simple example projects. Now I am trying to incorporate numpy and matplotlib. I have gotten numpy installed and within PyDev I did not need to alter any paths, etc., and after the installation of numpy I was automatically able to import numpy with no problem. However, following the same procedure with matplotlib hasn't worked. If I run Python from the command line, then import matplotlib works just fine. But within PyDev, I just get the standard error where it can't locate matplotlib when I try import matplotlib.\nSince numpy didn't require any alteration of the PYTHONPATH, I feel that neither should matplotlib, so can anyone help me figure out why matplotlib isn't accessible from within my existing project while numpy is? Thanks for any help.","AnswerCount":3,"Available Count":3,"Score":0.0665680765,"is_accepted":false,"ViewCount":3642,"Q_Id":4788748,"Users Score":1,"Answer":"I added numpy to the Forced Builtins and worked like charm.","Q_Score":0,"Tags":"python,module,import,matplotlib,pydev","A_Id":6766298,"CreationDate":"2011-01-25T00:22:00.000","Title":"Unable to import matplotlib in PyDev","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using Ubuntu 10.04 and have successfully configured PyDev to work with Python and have written a few simple example projects. Now I am trying to incorporate numpy and matplotlib. I have gotten numpy installed and within PyDev I did not need to alter any paths, etc., and after the installation of numpy I was automatically able to import numpy with no problem. However, following the same procedure with matplotlib hasn't worked. If I run Python from the command line, then import matplotlib works just fine. But within PyDev, I just get the standard error where it can't locate matplotlib when I try import matplotlib.\nSince numpy didn't require any alteration of the PYTHONPATH, I feel that neither should matplotlib, so can anyone help me figure out why matplotlib isn't accessible from within my existing project while numpy is? Thanks for any help.","AnswerCount":3,"Available Count":3,"Score":1.2,"is_accepted":true,"ViewCount":3642,"Q_Id":4788748,"Users Score":2,"Answer":"Sounds like the interpreter you setup for Pydev is not pointing to the appropriate version of python (that you've install mpl and np).  In the terminal, it's likely the effect of typing python is tantamount to env python; pydev might not be using this interpreter.  \nBut, if the pydev interpreter is pointed to the right location, you might simply have to rehash the interpreter (basically, set it up again) to have mpl show up.\nYou could try this in the terminal and see if the results are different:\npython -c 'import platform; print platform.python_version()'\n${PYTHONPATH}\/python -c 'import platform; print platform.python_version()'","Q_Score":0,"Tags":"python,module,import,matplotlib,pydev","A_Id":4999217,"CreationDate":"2011-01-25T00:22:00.000","Title":"Unable to import matplotlib in PyDev","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"We need a content classification module. Bayesian classifier seems to be what I am looking for. Should we go for Orange or NLTK ?","AnswerCount":3,"Available Count":2,"Score":0.0665680765,"is_accepted":false,"ViewCount":3710,"Q_Id":4789318,"Users Score":1,"Answer":"NLTK is a toolkit that supports a four state model of natural language processing:\n\nTokenizing: grouping characters as words. This ranges from trivial regex stuff to dealing with contractions like \"can't\"\nTagging. This is applying part-of-speech tags to the tokens (eg \"NN\" for noun, \"VBG\" for verb gerund). This is typically done by training a model (eg Hidden Markov) on a training corpus (i.e. large list of by by hand tagged sentences).\nChunking\/Parsing. This is taking each tagged sentence and extracting features into a tree (eg noun phrases). This can be according to a hand-written grammar or a one trained on a corpus.\nInformation extraction. This is traversing the tree and extracting the data. This is where your specific orange=fruit would be done.\n\nNLTK supports WordNet, a huge semantic dictionary that classifies words. So there are 5 noun definitions for orange (fruit, tree, pigment, color, river in South Africa). Each of these has one or more 'hypernym paths' that are hierarchies of classifications. E.g. the first sense of 'orange' has a two paths:\n\norange\/citrus\/edible_fruit\/fruit\/reproductive_structure\/plant_organ\/plant_part\/natural_object\/whole\/object\/physical_entity\/entity\n\nand\n\norange\/citrus\/edible_fruit\/produce\/food\/solid\/matter\/physical_entity\/entity\n\nDepending on your application domain you can identify orange as a fruit, or a food, or a plant thing. Then you can use the chunked tree structure to determine more (who did what to the fruit, etc.)","Q_Score":11,"Tags":"python,machine-learning,nltk,naivebayes,orange","A_Id":10869425,"CreationDate":"2011-01-25T02:07:00.000","Title":"Orange vs NLTK for Content Classification in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"We need a content classification module. Bayesian classifier seems to be what I am looking for. Should we go for Orange or NLTK ?","AnswerCount":3,"Available Count":2,"Score":0.1973753202,"is_accepted":false,"ViewCount":3710,"Q_Id":4789318,"Users Score":3,"Answer":"I don't know Orange, but +1 for NLTK:\nI've successively used the classification tools in NLTK to classify text and related meta data. Bayesian is the default but there are other alternatives such as Maximum Entropy. Also being a toolkit, you can customize as you see fit - eg. creating your own features (which is what I did for the meta data).\nNLTK also has a couple of good books - one of which is available under Creative Commons (as well as O'Reilly).","Q_Score":11,"Tags":"python,machine-learning,nltk,naivebayes,orange","A_Id":4789820,"CreationDate":"2011-01-25T02:07:00.000","Title":"Orange vs NLTK for Content Classification in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there a way to parse CSV data in Python when the data is not in a file? I'm storing CSV data in my database and I'd like to parse it. I'm looking for something analogous to Ruby's CSV.parse. I know Python has a CSV class but everything I've seen in the docs seems to deal with files as opposed to in-memory CSV data.\n(And it's not an option to parse the data before it goes into the database.)\n(And please don't tell me not to store the CSV data in the database. I know what I'm doing as far as the database goes.)","AnswerCount":4,"Available Count":1,"Score":0.049958375,"is_accepted":false,"ViewCount":6225,"Q_Id":4855523,"Users Score":1,"Answer":"Use the stringio module, which allows you to dress strings as file-like objects. That way you can pass a stringio \"file\" to the CSV module for parsing (or any other parser you may be using).","Q_Score":7,"Tags":"python,csv","A_Id":4855557,"CreationDate":"2011-01-31T20:07:00.000","Title":"Parsing CSV data from memory in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to parse a text and categorize the sentences according to their grammatical structure, but I have a very small understanding of NLP so I don't even know where to start.\nAs far as I have read, I need to parse the text and find out (or tag?) the part-of-speech of every word. Then I search for the verb clause or whatever other defining characteristic I want to use to categorize the sentences.\nWhat I don't know is if there is already some method to do this more easily or if I need to define the grammar rules separately or what.\nAny resources on NLP that discuss this would be great. Program examples are welcome as well. I have used NLTK before, but not extensively. Other parsers or languages are OK too!","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1212,"Q_Id":4895661,"Users Score":0,"Answer":"May be you need simply define patterns like \"noun verb noun\" etc for each type of grammatical structure and search matches in part-of-speach tagger output sequence.","Q_Score":2,"Tags":"python,nlp,grammar,nltk","A_Id":4895933,"CreationDate":"2011-02-04T07:45:00.000","Title":"How would I go about categorizing sentences according to tense (present, past, future, etc.)?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on a project to classify snippets of text using the python nltk module and the naivebayes classifier.  I am able to train on corpus data and classify another set of data but would like to feed additional training information into the classifier after initial training.\nIf I'm not mistaken, there doesn't appear to be a way to do this, in that the NaiveBayesClassifier.train method takes a complete set of training data.  Is there a way to add to the the training data without feeding in the original featureset?\nI'm open to suggestions including other classifiers that can accept new training data over time.","AnswerCount":3,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":3608,"Q_Id":4905368,"Users Score":8,"Answer":"There's 2 options that I know of:\n1) Periodically retrain the classifier on the new data. You'd accumulate new training data in a corpus (that already contains the original training data), then every few hours, retrain & reload the classifier. This is probably the simplest solution.\n2) Externalize the internal model, then update it manually. The NaiveBayesClassifier can be created directly by giving it a label_prodist and a feature_probdist. You could create these separately, pass them in to a NaiveBayesClassifier, then update them whenever new data comes in. The classifier would use this new data immediately. You'd have to look at the train method for details on how to update the probability distributions.","Q_Score":17,"Tags":"python,nltk","A_Id":4908925,"CreationDate":"2011-02-05T05:50:00.000","Title":"How to incrementally train an nltk classifier","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it possible to create a multi-dimensional array of different types in Python? The way I usually solve it is [([None] * n) for i in xrange(m)], but I don't want to use list. I want something which is really a continuous array of pointers in memory, not a list. (Each list itself is continuous, but when you make a list of lists, the different lists may be spread in different places in RAM.)\nAlso, writing [([None] * n) for i in xrange(m)] is quite a convoluted way of initializing an empty array, in contrast to something like empty_array(m, n). Is there a better alternative?","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":5249,"Q_Id":4909748,"Users Score":2,"Answer":"In many cases such arrays are not required as there are more elegant solutions to these problems. Explain what you want to do so someone can give some hints.\nAnyway, if you really, really need such data structure, use array.array.","Q_Score":5,"Tags":"python,arrays","A_Id":4909786,"CreationDate":"2011-02-05T21:23:00.000","Title":"Python: Multi-dimensional array of different types","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've got 2 questions on analyzing a GPS dataset.\n1) Extracting trajectories I have a huge database of recorded GPS coordinates of the form (latitude, longitude, date-time). According to date-time values of consecutive records, I'm trying to extract all trajectories\/paths followed by the person. For instance; say from time M, the (x,y) pairs are continuously changing up until time N. After N, the change in (x,y) pairs decrease, at which point I conclude that the path taken from time M to N can be called a trajectory. Is that a decent approach to follow when extracting trajectories? Are there any well-known approaches\/methods\/algorithms you can suggest? Are there any data structures or formats you would like to suggest me to maintain those points in an efficient manner? Perhaps, for each trajectory, figuring out the velocity and acceleration would be useful?\n2) Mining the trajectories Once I have all the trajectories followed\/paths taken, how can I compare\/cluster them? I would like to know if the start or end points are similar, then how do the intermediate paths compare? \nHow do I compare the 2 paths\/routes and conclude if they are similar or not. Furthermore; how do I cluster similar paths together?\nI would highly appreciate it if you can point me to a research or something similar on this matter.\nThe development will be in Python, but all kinds of library suggestions are welcome.\nThanks in advance.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":6451,"Q_Id":4910510,"Users Score":1,"Answer":"1) Extracting trajectories \nI think you are in right direction. There are probably will be some noise in gps data, and random walking, you should do some smooth like splines to overcome it. \n\n2) Mining the trajectories\nIs there are any business sense in similar trajectories? (This will help build distance metric and then you can use some of mahoot clustering algorithms)\n 1. I think point where some person stoped are more interesting so you can generate statistics for popularity of places.\n 2. If you need route similarity to find different paths to same start-end you need to cluster first start end location and then similare curves by (maximum distance beetween, integral distance - some of well known functional metrics)","Q_Score":10,"Tags":"python,algorithm,gps,gis,data-mining","A_Id":4919289,"CreationDate":"2011-02-05T23:48:00.000","Title":"Comparing\/Clustering Trajectories (GPS data of (x,y) points) and Mining the data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to extract the text information contained in a postscript image file (the captions to my axis labels).\nThese images were generated with pgplot. I have tried ps2ascii and ps2txt on Ubuntu but they didn't produce any useful results. Does anyone know of another method?\nThanks","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1410,"Q_Id":4934669,"Users Score":6,"Answer":"It's likely that pgplot drew the fonts in  the text directly with lines rather than using text. Especially since pgplot is designed to output to a huge range of devices including plotters where you would have to do this.\nEdit:\nIf you have enough plots to be worth\n    the effort than it's a very simple\n    image processing task. Convert each\n    page to something like tiff, in mono\n    chrome Threshold the image to binary, \n    the text will be max pixel value. \nUse a template matching technique.\n    If you have a limited set of\n    possible labels then just match the\n    entire label, you can even start\n    with a template of the correct size\n    and rotation. Then just flag each\n    plot as containing label[1-n], no\n    need to read the actual text. \nIf you\n    don't know the label then you can\n    still do OCR fairly easily, just\n    extract the region around the axis,\n    rotate it for the vertical - and use\n    Google's free OCR lib\nIf you have pgplot you can even\n    build the training set for OCR or\n    the template images directly rather\n    than having to harvest them from the\n    image list","Q_Score":3,"Tags":"python,image,text,postscript","A_Id":4934824,"CreationDate":"2011-02-08T15:03:00.000","Title":"Is there a way to extract text information from a postscript file? (.ps .eps)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm writing a program in Python.  The first thing that happens is a window is displayed (I'm using wxPython) that has some buttons and text.  When the user performs some actions, a plot is displayed in its own window.  This plot is made with R, using rpy2.  The problem is that the plot usually pops up on top of the main window, so the user has to move the plot to see the main window again.  This is a big problem for the user, because he's lazy and good-for-nothing.  He wants the plot to simply appear somewhere else, so he can see the main window and the plot at the same time, without having to lift a finger.\nTwo potential solutions to my problem are:\n(1) display the plot within a wxPython frame (which I think I could control the location of), or\n(2) be able to specify where on the screen the plot window appears.\nI can't figure out how to do either.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":757,"Q_Id":4938556,"Users Score":5,"Answer":"Plot to a graphics file using jpeg(), png() or another device, then display that file on your wxWidget.","Q_Score":3,"Tags":"python,r,plot,rpy2","A_Id":4938599,"CreationDate":"2011-02-08T21:17:00.000","Title":"How do I control where an R plot is displayed, using python and rpy2?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":1,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I can draw matplotlib graph in command line(shell) environment, but I find that I could not draw the same graph inside the eclipse IDE. such as plot([1,2,3]) not show in eclipse, I writed show() in the end but still not show anything \nmy matplotlib use GTKAgg as backend, I use Pydev as plugin of eclipse to develop python.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":3911,"Q_Id":4941162,"Users Score":1,"Answer":"import matplotlib.pyplot as mp\nmp.ion()\nmp.plot(x,y)\nmp.show()","Q_Score":1,"Tags":"python,eclipse,matplotlib","A_Id":5640511,"CreationDate":"2011-02-09T03:56:00.000","Title":"How to draw matplotlib graph in eclipse?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to implement Dijkstra's Algorithm in Python. However, I have to use a 2D array to hold three pieces of information - predecessor, length and unvisited\/visited.\nI know in C a Struct can be used, though I am stuck on how I can do a similar thing in Python, I am told it's possible but I have no idea to be honest","AnswerCount":5,"Available Count":2,"Score":0.0399786803,"is_accepted":false,"ViewCount":5571,"Q_Id":4962202,"Users Score":1,"Answer":"Encapsulate that information in a Python object and you should be fine.","Q_Score":0,"Tags":"python,dijkstra","A_Id":4962223,"CreationDate":"2011-02-10T20:21:00.000","Title":"Python - Dijkstra's Algorithm","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to implement Dijkstra's Algorithm in Python. However, I have to use a 2D array to hold three pieces of information - predecessor, length and unvisited\/visited.\nI know in C a Struct can be used, though I am stuck on how I can do a similar thing in Python, I am told it's possible but I have no idea to be honest","AnswerCount":5,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":5571,"Q_Id":4962202,"Users Score":0,"Answer":"Python is object oriented language. So think of it like moving from Structs in C to Classes of C++. You can use the same class structure in Python as well.","Q_Score":0,"Tags":"python,dijkstra","A_Id":4962291,"CreationDate":"2011-02-10T20:21:00.000","Title":"Python - Dijkstra's Algorithm","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am installing Python 2.7 in addition to 2.7. When installing PyTables again for 2.7, I get this error -\n\nFound numpy 1.5.1 package installed.\n.. ERROR:: Could not find a local HDF5 installation.\nYou may need to explicitly state where your local HDF5 headers and\nlibrary can be found by setting the HDF5_DIR environment\nvariable or by using the --hdf5 command-line option.\n\nI am not clear on the HDF installation. I downloaded again - and copied it into a \/usr\/local\/hdf5 directory. And tried to set the environement vars as suggested in the PyTable install. Has anyone else had this problem that could help?","AnswerCount":5,"Available Count":4,"Score":0.0798297691,"is_accepted":false,"ViewCount":6903,"Q_Id":4972079,"Users Score":2,"Answer":"Do the following steps:\n\nbrew tap homebrew\/science\nbrew install hdf5\nsee where hdf5 is installed, it shows at the end of second step\nexport HDF5_DIR=\/usr\/local\/Cellar\/hdf5\/1.8.16_1\/  (Depending on the location that is installed on your computer)\nThis one worked for me on MAC :-)","Q_Score":7,"Tags":"python,hdf5,pytables","A_Id":38577661,"CreationDate":"2011-02-11T17:23:00.000","Title":"Unable to reinstall PyTables for Python 2.7","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am installing Python 2.7 in addition to 2.7. When installing PyTables again for 2.7, I get this error -\n\nFound numpy 1.5.1 package installed.\n.. ERROR:: Could not find a local HDF5 installation.\nYou may need to explicitly state where your local HDF5 headers and\nlibrary can be found by setting the HDF5_DIR environment\nvariable or by using the --hdf5 command-line option.\n\nI am not clear on the HDF installation. I downloaded again - and copied it into a \/usr\/local\/hdf5 directory. And tried to set the environement vars as suggested in the PyTable install. Has anyone else had this problem that could help?","AnswerCount":5,"Available Count":4,"Score":0.0798297691,"is_accepted":false,"ViewCount":6903,"Q_Id":4972079,"Users Score":2,"Answer":"I had to install libhdf5-8 and libhdf5-serial-dev first.\nThen, for me, the command on Ubuntu was:\nexport HDF5_DIR=\/usr\/lib\/x86_64-linux-gnu\/hdf5\/serial\/","Q_Score":7,"Tags":"python,hdf5,pytables","A_Id":29871380,"CreationDate":"2011-02-11T17:23:00.000","Title":"Unable to reinstall PyTables for Python 2.7","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am installing Python 2.7 in addition to 2.7. When installing PyTables again for 2.7, I get this error -\n\nFound numpy 1.5.1 package installed.\n.. ERROR:: Could not find a local HDF5 installation.\nYou may need to explicitly state where your local HDF5 headers and\nlibrary can be found by setting the HDF5_DIR environment\nvariable or by using the --hdf5 command-line option.\n\nI am not clear on the HDF installation. I downloaded again - and copied it into a \/usr\/local\/hdf5 directory. And tried to set the environement vars as suggested in the PyTable install. Has anyone else had this problem that could help?","AnswerCount":5,"Available Count":4,"Score":0.1586485043,"is_accepted":false,"ViewCount":6903,"Q_Id":4972079,"Users Score":4,"Answer":"My HDF5 was installed with homebrew, so setting the environment variable as follows worked for me:  HDF5_DIR=\/usr\/local\/Cellar\/hdf5\/1.8.9","Q_Score":7,"Tags":"python,hdf5,pytables","A_Id":13868984,"CreationDate":"2011-02-11T17:23:00.000","Title":"Unable to reinstall PyTables for Python 2.7","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am installing Python 2.7 in addition to 2.7. When installing PyTables again for 2.7, I get this error -\n\nFound numpy 1.5.1 package installed.\n.. ERROR:: Could not find a local HDF5 installation.\nYou may need to explicitly state where your local HDF5 headers and\nlibrary can be found by setting the HDF5_DIR environment\nvariable or by using the --hdf5 command-line option.\n\nI am not clear on the HDF installation. I downloaded again - and copied it into a \/usr\/local\/hdf5 directory. And tried to set the environement vars as suggested in the PyTable install. Has anyone else had this problem that could help?","AnswerCount":5,"Available Count":4,"Score":1.2,"is_accepted":true,"ViewCount":6903,"Q_Id":4972079,"Users Score":4,"Answer":"The hdf5 command line option was not stated correctly  ( --hdf5='\/usr\/local\/hdf5' ). Sprinkling print statements in the setup.py made it easier to pin down the problem.","Q_Score":7,"Tags":"python,hdf5,pytables","A_Id":4992253,"CreationDate":"2011-02-11T17:23:00.000","Title":"Unable to reinstall PyTables for Python 2.7","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"which one is faster:\nUsing Lattice Multiplication with threads(big numbers) OR\nUsing common  Multiplication with threads(big numbers)\nDo you know any source code, to test them?\n-----------------EDIT------------------\nThe theads should be implemented in C, or Java for testing","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":519,"Q_Id":4988830,"Users Score":1,"Answer":"If I understand you correctly, \"lattice multiplication\" is different way of doing base-10 multiplication by hand that is supposed to be easier for kids to understand than the classic way.  I assume \"common multiplication\" is the classic way.\nSo really, I think that the best answer is:\n\nNeither \"lattice multiplication\" or \"common multiplication\" are good (efficient) ways of doing multiplication on a computer.  For small numbers (up to 2**64), built-in hardware multiplication is better.  For large numbers, you are best of breaking the numbers into 8 or 32 bit chunks ...\nMulti-threading is unlikely to speed up multiplication unless you have very large numbers.  The inherent cost of creating (or recycling) a thread is likely to swamp any theoretical speedup for smaller numbers.  And for larger numbers (and larger numbers of threads) you need to worry about the bandwidth of copying the data around.\n\nNote there is a bit of material around on parallel multiplication (Google), but it is mostly in the academic literature ... which maybe says something about how practical it really is for the kind of hardware used today for low and high end computing.","Q_Score":0,"Tags":"java,python,c,multiplication","A_Id":4989194,"CreationDate":"2011-02-14T04:31:00.000","Title":"Lattice Multiplication with threads, is it more efficient?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am looking for a \"method\" to get a formula, formula which comes from fitting a set of data (3000 point). I was using Legendre polynomial, but for > 20 points it gives not exact values. I can write chi2 test, but algorithm needs a loot of time to calculate N parameters, and at the beginning I don't know how the function looks like, so it takes time. I was thinking about splines... Maybe ...\nSo the input is: 3000 pints\nOutput         : f(x) = ... something\nI want to have a formula from fit. What is a best way to do this in python?\nLet the force would be with us!\nNykon","AnswerCount":4,"Available Count":2,"Score":0.1488850336,"is_accepted":false,"ViewCount":2988,"Q_Id":5021921,"Users Score":3,"Answer":"Except, a spline does not give you a \"formula\", at least not unless you have the wherewithal to deal with all of the piecewise segments. Even then, it will not be easily written down, or give you anything that is at all pretty to look at.\nA simple spline gives you an interpolant. Worse, for 3000 points, an interpolating spline will give you roughly that many cubic segments! You did say interpolation before. OF course, an interpolating polynomial of that high an order will be complete crapola anyway, so don't think you can just go back there.\nIf all that you need is a tool that can provide an exact interpolation at any point, and you really don't need to have an explicit formula, then an interpolating spline is a good choice.\nOr do you really want an approximant? A function that will APPROXIMATELY fit your data, smoothing out any noise? The fact is, a lot of the time when people who have no idea what they are doing say \"interpolation\" they really do mean approximation, smoothing. This is possible of course, but there are entire books written on the subject of curve fitting, the modeling of empirical data. You first goal is then to choose an intelligent model, that will represent this data. Best of course is if you have some intelligent choice of model from physical understanding of the relationship under study, then you can estimate the parameters of that model using a nonlinear regression scheme, of which there are many to be found.\nIf you have no model, and are unwilling to choose one that roughly has the proper shape, then you are left with generic models in the form of splines, which can be fit in a regression sense, or with high order polynomial models, for which I have little respect.\nMy point in all of this is YOU need to make some choices and do some research on a choice of model.","Q_Score":1,"Tags":"python,numpy,scipy,data-fitting","A_Id":5022089,"CreationDate":"2011-02-16T20:48:00.000","Title":"large set of data, interpolation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am looking for a \"method\" to get a formula, formula which comes from fitting a set of data (3000 point). I was using Legendre polynomial, but for > 20 points it gives not exact values. I can write chi2 test, but algorithm needs a loot of time to calculate N parameters, and at the beginning I don't know how the function looks like, so it takes time. I was thinking about splines... Maybe ...\nSo the input is: 3000 pints\nOutput         : f(x) = ... something\nI want to have a formula from fit. What is a best way to do this in python?\nLet the force would be with us!\nNykon","AnswerCount":4,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":2988,"Q_Id":5021921,"Users Score":0,"Answer":"The only formula would be a polynomial of order 3000.\nHow good does the fit need to be? What type of formula do you expect?","Q_Score":1,"Tags":"python,numpy,scipy,data-fitting","A_Id":5022008,"CreationDate":"2011-02-16T20:48:00.000","Title":"large set of data, interpolation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Has anybody ever encountered problems with fmin_slsqp (or anything else in scipy.optimize) only when using very large or very small numbers?\nI am working on some python code to take a grayscale image and a mask, generate a histogram, then fit multiple gaussians to the histogram.  To develop the code I used a small sample image, and after some work the code was working brilliantly.  However, when I normalize the histogram first, generating bin values <<1, or when I histogram huge images, generating bin values in the hundreds of thousands, fmin_slsqp() starts failing sporadically.  It quits after only ~5 iterations, usually just returning a slightly modified version of the initial guess I gave it, and returns exit mode 8, which means \"Positive directional derivative for linesearch.\"  If I check the size of the bin counts at the beginning and scale them into the neighborhood of ~100-1000, fmin_slsqp() works as usual. I just un-scale things before returning the results. I guess I could leave it like that, but it feels like a hack.\nI have looked around and found folks talking about the epsilon value, which is basically the dx used for approximating derivatives, but tweaking that has not helped.  Other than that I haven't found anything useful yet.  Any ideas would be greatly appreciated.  Thanks in advance.\njames","AnswerCount":3,"Available Count":3,"Score":0.0665680765,"is_accepted":false,"ViewCount":5001,"Q_Id":5023846,"Users Score":1,"Answer":"I got in trouble with this issue too, but I solved it in my project. I'm not sure if this is a general solution.\nThe reason was that the scipy.optimize.fmin_slsqp calculated the gradient by an approximate approach when the argument jac is set by False or default. The gradient produced from the approximate approach was not normalized (with large scale). When calculating the step length, large value of gradient would influence the performance and precision of line search. This might be the reason why we got Positive directional derivative for linesearch.\nYou can try to implement the closed form of the Jacobian matrix to the object function and pass it to the jac argument. More importantly, you should rescale the value of Jacobian matrix (like normalization) to avoid affecting line search.\nBest.","Q_Score":5,"Tags":"python,scipy,histogram,gaussian,least-squares","A_Id":34021509,"CreationDate":"2011-02-17T00:27:00.000","Title":"Problem with scipy.optimize.fmin_slsqp when using very large or very small numbers","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Has anybody ever encountered problems with fmin_slsqp (or anything else in scipy.optimize) only when using very large or very small numbers?\nI am working on some python code to take a grayscale image and a mask, generate a histogram, then fit multiple gaussians to the histogram.  To develop the code I used a small sample image, and after some work the code was working brilliantly.  However, when I normalize the histogram first, generating bin values <<1, or when I histogram huge images, generating bin values in the hundreds of thousands, fmin_slsqp() starts failing sporadically.  It quits after only ~5 iterations, usually just returning a slightly modified version of the initial guess I gave it, and returns exit mode 8, which means \"Positive directional derivative for linesearch.\"  If I check the size of the bin counts at the beginning and scale them into the neighborhood of ~100-1000, fmin_slsqp() works as usual. I just un-scale things before returning the results. I guess I could leave it like that, but it feels like a hack.\nI have looked around and found folks talking about the epsilon value, which is basically the dx used for approximating derivatives, but tweaking that has not helped.  Other than that I haven't found anything useful yet.  Any ideas would be greatly appreciated.  Thanks in advance.\njames","AnswerCount":3,"Available Count":3,"Score":0.2605204458,"is_accepted":false,"ViewCount":5001,"Q_Id":5023846,"Users Score":4,"Answer":"Are you updating your initial guess (\"x0\") when your underlying data changes scale dramatically?  for any iterative linear optimization problem, these problems will occur if your initial guess is far from the data you're trying to fit.  It's more of a optimization problem than a scipy problem.","Q_Score":5,"Tags":"python,scipy,histogram,gaussian,least-squares","A_Id":5690969,"CreationDate":"2011-02-17T00:27:00.000","Title":"Problem with scipy.optimize.fmin_slsqp when using very large or very small numbers","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Has anybody ever encountered problems with fmin_slsqp (or anything else in scipy.optimize) only when using very large or very small numbers?\nI am working on some python code to take a grayscale image and a mask, generate a histogram, then fit multiple gaussians to the histogram.  To develop the code I used a small sample image, and after some work the code was working brilliantly.  However, when I normalize the histogram first, generating bin values <<1, or when I histogram huge images, generating bin values in the hundreds of thousands, fmin_slsqp() starts failing sporadically.  It quits after only ~5 iterations, usually just returning a slightly modified version of the initial guess I gave it, and returns exit mode 8, which means \"Positive directional derivative for linesearch.\"  If I check the size of the bin counts at the beginning and scale them into the neighborhood of ~100-1000, fmin_slsqp() works as usual. I just un-scale things before returning the results. I guess I could leave it like that, but it feels like a hack.\nI have looked around and found folks talking about the epsilon value, which is basically the dx used for approximating derivatives, but tweaking that has not helped.  Other than that I haven't found anything useful yet.  Any ideas would be greatly appreciated.  Thanks in advance.\njames","AnswerCount":3,"Available Count":3,"Score":0.3215127375,"is_accepted":false,"ViewCount":5001,"Q_Id":5023846,"Users Score":5,"Answer":"I've had similar problems optimize.leastsq.  The data I need to deal with often are very small, like 1e-18 and such, and I noticed that leastsq doesn't converge to best fit parameters in those cases.  Only when I scale the data to something more common (like in hundreds, thousands, etc., something you can maintain resolution and dynamic range with integers), I can let leastsq converge to something very reasonable.\nI've been trying around with those optional tolerance parameters so that I don't have to scale data before optimizing, but haven't had much luck with it...\nDoes anyone know a good general approach to avoid this problem with the functions in the scipy.optimize package?  I'd appreciate you could share...  I think the root is the same problem with the OP's.","Q_Score":5,"Tags":"python,scipy,histogram,gaussian,least-squares","A_Id":8394696,"CreationDate":"2011-02-17T00:27:00.000","Title":"Problem with scipy.optimize.fmin_slsqp when using very large or very small numbers","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"So, i have a set of points (x,y), and i want to be able to draw the largest polygon with these points as vertices. I can use patches.Polygon() in matplotlib, but this simply draws lines between the points in the order i give them. This doesn't automatically do what i want. As an example, if a want to draw a square, and sort the points by increasing x, and then by increasing y, i won't get a square, but two connecting triangles. (the line \"crosses over\")\nSo the problem now is to find a way to sort the list of points such that i \"go around the outside\" of the polygon when iterating over this list.\nOr is there maybe some other functionality in Matplotlib which can do this for me?","AnswerCount":8,"Available Count":1,"Score":0.049958375,"is_accepted":false,"ViewCount":5735,"Q_Id":5040412,"Users Score":2,"Answer":"From your comments to other answers, you seem to already get the set of points defining the convex hull, but they're not ordered. The easiest way to order them would be to take a point inside the convex hull as the origin of a new coordinate frame. You then transform the (most probably) Cartesian coordinates of your points into polar coordinates, with respect to this new frame. If you order your points with respect to their polar angle coordinate, you can draw your convex hull. This is only valid if the set of your points defined a convex (non-concave) hull.","Q_Score":9,"Tags":"python,matplotlib","A_Id":5040977,"CreationDate":"2011-02-18T10:54:00.000","Title":"How to draw the largest polygon from a set of points","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am quite new in Python.\nI am trying to use the nltk.cluster package to apply a simple kMeans to a word-document matrix. While it works when the matrix is a list of numpy array-like objects, I wasn't able to make it work for a sparse matrix representation (such as csc_matrix, csr_matrix or lil_matrix). \nAll the information that I found was:\n\nNote that the vectors must use numpy array-like objects. nltk_contrib.unimelb.tacohn.SparseArrays may be used for efficiency when required\n\nI do not understand what this means. Can anyone help me in this matter?\nThanks in advance!","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":824,"Q_Id":5044359,"Users Score":1,"Answer":"It means that when you pass in the input vector, you can either pass in a numpy.array() or a nltk_contrib.unimelb.tacohn.SparseArrays.\nI suggest you look at the package nltk_contrib.unimelb.tacohn to find the SparseArrays class. Then try to create your data with this class before passing it into nltk.cluster","Q_Score":0,"Tags":"python,nltk","A_Id":5085486,"CreationDate":"2011-02-18T17:15:00.000","Title":"nltk.cluster using a sparse representation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I try to do named entity recognition in python using NLTK.\nI want to extract personal list of skills.\nI have the list of skills and would like to search them in requisition and tag the skills.\nI noticed that NLTK has NER tag for predefine tags like Person, Location etc.\nIs there a external gazetter tagger in Python I can use?\nany idea how to do it more sophisticated than search of terms ( sometimes multi words term )?\nThanks,\nAssaf","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":2226,"Q_Id":5084578,"Users Score":1,"Answer":"I haven't used NLTK enough recently, but if you have words that you know are skills, you don't need to do NER- just a text search.\nMaybe use Lucene or some other search library to find the text, and then annotate it? That's a lot of work but if you are working with a lot of data that might be ok. Alternatively, you could hack together a regex search which will be slower but probably work ok for smaller amounts of data and will be much easier to implement.","Q_Score":4,"Tags":"python,nlp,nltk,named-entity-recognition","A_Id":6637984,"CreationDate":"2011-02-22T22:07:00.000","Title":"Named Entity Recognition from personal Gazetter using Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"well i've seen some code to convert RGB to HSL; but how to do it fast in python.\nIts strange to me, that for example photoshop does this within a second on a image, while in python this often takes forever. Well at least the code i use; so think i'm using wrong code to do it\nIn my case my image is a simple but big raw array [r,g,b,r,g,b,r,g,b ....]\nI would like this to be [h,s,l,h,s,l,h,s,l .......]\nAlso i would like to be able to do hsl to rgb\nthe image is actually 640x 480 pixels; \nWould it require some library or wrapper around c code (i never created a wrapper) to get it done fast ?","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":6609,"Q_Id":5112248,"Users Score":1,"Answer":"One option is to use OpenCV. Their Python bindings are pretty good (although not amazing). The upside is that it is a very powerful library, so this would just be the tip of the iceberg.\nYou could probably also do this very efficiently using numpy.","Q_Score":3,"Tags":"python,c,rgb","A_Id":5112349,"CreationDate":"2011-02-25T00:26:00.000","Title":"Python RGB array to HSL and back","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"What i need is a way to get \"fancy indexing\" (y = x[[0, 5, 21]]) to return a view instead of a copy. \nI have an array, but i want to be able to work with a subset of this array (specified by a list of indices) in such a way that the changes in this subset is also put into the right places in the large array. If i just want to do something with the first 10 elements, i can just use regular slicing y = x[0:10]. That works great, because regular slicing returns a view. The problem is if i don't want 0:10, but an arbitrary set of indices.\nIs there a way to do this?","AnswerCount":4,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":3684,"Q_Id":5127991,"Users Score":0,"Answer":"You could theoretically create an object that performs the role of a 'fancy view' into another array, and I can think of plenty of use cases for it. The problem is, that such an object would not be compatible with the standard numpy machinery. All compiled numpy C code relies on data being accessible as an inner product of strides and indices. Generalizing this code to fundamentally different data layout formats would be a gargantuan undertaking. For a project that is trying to take on a challenge along these lines, check out continuum's Blaze.","Q_Score":19,"Tags":"python,numpy","A_Id":21030654,"CreationDate":"2011-02-26T16:05:00.000","Title":"Can I get a view of a numpy array at specified indexes? (a view from \"fancy indexing\")","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I was wondering if someone can clarify this line for me.\n\nCreate a function die(x) which rolls a\n  die x times keeping track of how many\n  times each face comes up and returns a\n  1X6 array containing these numbers.\n\nI am not sure what this means when it says 1X6 array ? I am using the randint function from numpy so the output is already an array (or list) im not sure.\nThanks","AnswerCount":5,"Available Count":2,"Score":0.1194272985,"is_accepted":false,"ViewCount":688,"Q_Id":5136533,"Users Score":3,"Answer":"Since a dice has 6 possible outcomes, if you get a 2 three times, this would be :\n0 3 0 0 0 0","Q_Score":1,"Tags":"python","A_Id":5136547,"CreationDate":"2011-02-27T22:35:00.000","Title":"python random number","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I was wondering if someone can clarify this line for me.\n\nCreate a function die(x) which rolls a\n  die x times keeping track of how many\n  times each face comes up and returns a\n  1X6 array containing these numbers.\n\nI am not sure what this means when it says 1X6 array ? I am using the randint function from numpy so the output is already an array (or list) im not sure.\nThanks","AnswerCount":5,"Available Count":2,"Score":0.0399786803,"is_accepted":false,"ViewCount":688,"Q_Id":5136533,"Users Score":1,"Answer":"if you have the results of the die rolls in a list lst, you can determine the number of times a 4 appeared by doing len([_ for _ in lst if _ == 4]).  you should be able to figure the rest out from there.","Q_Score":1,"Tags":"python","A_Id":5136717,"CreationDate":"2011-02-27T22:35:00.000","Title":"python random number","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to use a easy-to-use text miner with my python code. I will be mainly using the classification algorithms - Naive Bayes, KNN and such. \nPlease let me know what's the best option here - Weka? NLTK? SVM? or something else?\nThanks!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":142,"Q_Id":5150053,"Users Score":2,"Answer":"I would go with NLTK since it's written in Python","Q_Score":0,"Tags":"python","A_Id":5150092,"CreationDate":"2011-03-01T03:03:00.000","Title":"Best text miner to use with python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it possible to use PyArray_NewFromDescr to create numpy array object from a set of contiguous 2d arrays, without copying the data?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":444,"Q_Id":5155597,"Users Score":1,"Answer":"Short answer, no.\nNumpy expects all the data to be laid it in a simple, strided pattern.  When iterating over the array, to advance in a dimension, it adds a constant, the stride size for that dimension, to the position in memory.  So unless your 2-d slices are laid out regularly (e.g. every other row of a larger 3-d array), numpy will need to copy the data.\nIf you do have that order, you can do what you'll want.  You'll need to make a PyArray struct where the data points to the first item, the strides are correct for layout, and the descr is correct as well.  Most importantly, you'll want to set the base member to another python object to keep your big chunk of memory alive while this view exists.","Q_Score":2,"Tags":"c++,python,numpy,python-c-api","A_Id":5159762,"CreationDate":"2011-03-01T14:09:00.000","Title":"creating a 3d numpy array from a non-contigous set of contigous 2d slices","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm considering making the switch from MATLAB to Python. The application is quantitative trading and cost is not really an issue. There are a few things I love about MATLAB and am wondering how Python stacks up (could not find any answers in the reviews I've read).\n\nIs there an IDE for Python that is as good as MATLAB's (variable editor, debugger, profiler)?  I've read good things about Spyder, but does it have a profiler?\nWhen you change a function on the path in MATLAB, it is automatically reloaded.  Do you have to manually re-import libraries when you change them, or can this been done automatically?  This is a minor thing, but actually greatly improves my productivity.","AnswerCount":11,"Available Count":5,"Score":1.0,"is_accepted":false,"ViewCount":47020,"Q_Id":5214369,"Users Score":12,"Answer":"I've been getting on very well with the Spyder IDE in the Python(x,y) distribution. I'm a long term user of Matlab and have known of the existence of Python for 10 years or so but it's only since I installed Python(x,y) that I've started using Python regularly.","Q_Score":26,"Tags":"python,matlab,ide","A_Id":6088759,"CreationDate":"2011-03-06T23:52:00.000","Title":"Python vs Matlab","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm considering making the switch from MATLAB to Python. The application is quantitative trading and cost is not really an issue. There are a few things I love about MATLAB and am wondering how Python stacks up (could not find any answers in the reviews I've read).\n\nIs there an IDE for Python that is as good as MATLAB's (variable editor, debugger, profiler)?  I've read good things about Spyder, but does it have a profiler?\nWhen you change a function on the path in MATLAB, it is automatically reloaded.  Do you have to manually re-import libraries when you change them, or can this been done automatically?  This is a minor thing, but actually greatly improves my productivity.","AnswerCount":11,"Available Count":5,"Score":0.0363476168,"is_accepted":false,"ViewCount":47020,"Q_Id":5214369,"Users Score":2,"Answer":"after long long tryouts with many editors,\ni have settled for aptana ide + ipython (including notebook in internet browser)\ngreat for editing, getting help easy, try fast new things\naptana is the same as eclipse (because of pydev) but aptana has themes and different little things eclipse lacks\nabout python a little,\ndon't forget pandas, as it's (i believe) extremely powerful tool for data analysis\nit will be a beast in the future, my opinion\ni'm researching matlab, and i see some neat things there, especially gui interfaces and some other nice things\nbut python gives you flexibility and ease,\nanyway, you still have to learn the basics of python, matplotlib, numpy (and eventually pandas)\nbut from what i see, numpy and matplotlib are similar to matplotlib concepts (probably they were created with matlab in mind, right?)","Q_Score":26,"Tags":"python,matlab,ide","A_Id":10388279,"CreationDate":"2011-03-06T23:52:00.000","Title":"Python vs Matlab","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm considering making the switch from MATLAB to Python. The application is quantitative trading and cost is not really an issue. There are a few things I love about MATLAB and am wondering how Python stacks up (could not find any answers in the reviews I've read).\n\nIs there an IDE for Python that is as good as MATLAB's (variable editor, debugger, profiler)?  I've read good things about Spyder, but does it have a profiler?\nWhen you change a function on the path in MATLAB, it is automatically reloaded.  Do you have to manually re-import libraries when you change them, or can this been done automatically?  This is a minor thing, but actually greatly improves my productivity.","AnswerCount":11,"Available Count":5,"Score":1.0,"is_accepted":false,"ViewCount":47020,"Q_Id":5214369,"Users Score":6,"Answer":"I've been in the engineering field for a while now and I've always used MATLAB for high-complexity math calculations. I never really had an major problems with it, but I wasn't super enthusiastic about it either. A few months ago I found out I was going to be a TA for a numerical methods class and that it would be taught using Python, so I would have to learn the language. \n   What I at first thought would be extra work turned out to be an awesome hobby. I can't even begin to describe how bad MATLAB is compared to Python! What used to to take me all day to code in Matlab takes me only a few hours to write in Python. My code looks infinitely more appealing as well. Python's performance and flexibility really surprised me. With Python I can literally do anything I used to do in MATLAB and I can do it a lot better. \nIf anyone else is thinking about switching, I suggest you do it. It made my life a lot easier. I'll quote \"Python Scripting for Computational Science\" because they describe the pros of Python over MATLAB better than I do:\n\n\nthe python programming language is more powerful\nthe python environment is completely open and made for integration\n       with external tools,\na complete toolbox\/module with lots of functions and classes can be    contained in a single file (in contrast to a bunch of M-files),\ntransferring functions as arguments to functions is simpler,\nnested, heterogeneous data structures are simple to construct and     use,\nobject-oriented programming is more convenient,\ninterfacing C, C++, and fortran code is better supported and     therefore simpler,\nscalar functions work with array arguments to a larger extent (without modifications of arithmetic operators),\nthe source is free and runs on more platforms.","Q_Score":26,"Tags":"python,matlab,ide","A_Id":41910220,"CreationDate":"2011-03-06T23:52:00.000","Title":"Python vs Matlab","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm considering making the switch from MATLAB to Python. The application is quantitative trading and cost is not really an issue. There are a few things I love about MATLAB and am wondering how Python stacks up (could not find any answers in the reviews I've read).\n\nIs there an IDE for Python that is as good as MATLAB's (variable editor, debugger, profiler)?  I've read good things about Spyder, but does it have a profiler?\nWhen you change a function on the path in MATLAB, it is automatically reloaded.  Do you have to manually re-import libraries when you change them, or can this been done automatically?  This is a minor thing, but actually greatly improves my productivity.","AnswerCount":11,"Available Count":5,"Score":0.0363476168,"is_accepted":false,"ViewCount":47020,"Q_Id":5214369,"Users Score":2,"Answer":"almost everything is covered by others .. i hope you don't need any toolboxes like optimizarion toolbox , neural network etc.. [ I didn't find these for python  may be there are some .. i seriously doubt they might be better than Matlab ones..]\nif u don't need symbolic manipulation capability and are using windows python(x,y) is the way to go[they don't have much activity on their linux port (older versions are available)]\n(or need some minor symbolic manipulations use sympy , i think it comes with EPD and python(x,y) supersedes\/integrates  EPD)\nif you need symbolic capabilities sage is the way to go, IMHO sage stands up good with Matlab as well as Mathematica ..\ni'm also trying to make a switch  ...(need for my engg projs)\ni hope it helps ..","Q_Score":26,"Tags":"python,matlab,ide","A_Id":5214892,"CreationDate":"2011-03-06T23:52:00.000","Title":"Python vs Matlab","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm considering making the switch from MATLAB to Python. The application is quantitative trading and cost is not really an issue. There are a few things I love about MATLAB and am wondering how Python stacks up (could not find any answers in the reviews I've read).\n\nIs there an IDE for Python that is as good as MATLAB's (variable editor, debugger, profiler)?  I've read good things about Spyder, but does it have a profiler?\nWhen you change a function on the path in MATLAB, it is automatically reloaded.  Do you have to manually re-import libraries when you change them, or can this been done automatically?  This is a minor thing, but actually greatly improves my productivity.","AnswerCount":11,"Available Count":5,"Score":0.0181798149,"is_accepted":false,"ViewCount":47020,"Q_Id":5214369,"Users Score":1,"Answer":"I have recently switched from MATLAB to Python (I am about 2 months into the transition), and am getting on fairly well using Sublime Text 2, using the SublimeRope and SublimeLinter plugins to provide some IDE-like capabilities, as well as pudb to provide some graphical interactive debugging capabilities.\nI have not yet explored profilers or variable editors. (I never really used the MATLAB variable editor, anyway).","Q_Score":26,"Tags":"python,matlab,ide","A_Id":9317942,"CreationDate":"2011-03-06T23:52:00.000","Title":"Python vs Matlab","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"What are the differences between python's numpy.ndarray and list datatypes? I have vague ideas, but would like to get a definitive answer about:\n\nSize in memory\nSpeed \/ order of access\nSpeed \/ order of modification in place but preserving length\nEffects of changing length \n\nThanks!","AnswerCount":2,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":5003,"Q_Id":5224420,"Users Score":6,"Answer":"There are several differences:\n\nYou can append elements to a list, but you can't change the size of a\n\u00b4numpy.ndarray\u00b4 without making a full copy.\nLists can containt about everything, in numpy arrays all the\nelements must have the same type.\nIn practice, numpy arrays are faster for vectorial functions than\nmapping functions to lists.\nI think than modification times is not an issue, but iteration over\nthe elements is.\nNumpy arrays have many array related methods (\u00b4argmin\u00b4, \u00b4min\u00b4, \u00b4sort\u00b4,\netc).\n\nI prefer to use numpy arrays when I need to do some mathematical operations (sum, average, array multiplication, etc) and list when I need to iterate in 'items' (strings, files, etc).","Q_Score":4,"Tags":"python,arrays,list,performance,numpy","A_Id":7501276,"CreationDate":"2011-03-07T19:42:00.000","Title":"Differences between python's numpy.ndarray and list datatypes","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a large corpus of n-grams and several external n-grams. I want to calculate the PMI score of each external n-gram based on this corpus (the counts). \nAre there any tools to do this or can someone provide me with a piece of code in Python that can do this?\nThe problem is that my n-grams are 2-grams, 3-grams, 4-grams, and 5-grams. So calculating probabilities for 3-grams and more are really time-consuming.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":4572,"Q_Id":5231627,"Users Score":5,"Answer":"If I'm understanding your problem correctly, you want to compute things like log { P(\"x1 x2 x3 x4 x5\") \/ P(\"x1\") P(\"x2\") ... P(\"x5\") } where P measures the probability that any given 5-gram or 1-gram is a given thing (and is basically a ratio of counts, perhaps with Laplace-style offsets). So, make a single pass through your corpus and store counts of (1) each 1-gram, (2) each n-gram (use a dict for the latter), and then for each external n-gram you do a few dict lookups, a bit of arithmetic, and you're done. One pass through the corpus at the start, then a fixed amount of work per external n-gram.\n(Note: Actually I'm not sure how one defines PMI for more than two random variables; perhaps it's something like log P(a)P(b)P(c)P(abc) \/ P(ab)P(bc)P(a_c). But if it's anything at all along those lines, you can do it the same way: iterate through your corpus counting lots of things, and then all the probabilities you need are simply ratios of the counts, perhaps with Laplace-ish corrections.)\nIf your corpus is so big that you can't fit the n-gram dict in memory, then divide it into kinda-memory-sized chunks, compute n-gram dicts for each chunk and store them on disc in a form that lets you get at any given n-gram's entry reasonably efficiently; then, for each extern n-gram, go through the chunks and add up the counts.\nWhat form? Up to you. One simple option: in lexicographic order of the n-gram (note: if you're working with words rather than letters, you may want to begin by turning words into numbers; you'll want a single preliminary pass over your corpus to do this); then finding the n-gram you want is a binary search or something of the kind, which with chunks 1GB in size would mean somewhere on the order of 15-20 seeks per chunk; you could add some extra indexing to reduce this. Or: use a hash table on disc, with Berkeley DB or something; in that case you can forgo the chunking. Or, if the alphabet is small (e.g., these are letter n-grams rather than word n-grams and you're processing plain English text), just store them in a big array, with direct lookup -- but in that case, you can probably fit the whole thing in memory anyway.","Q_Score":4,"Tags":"python,n-gram","A_Id":5231844,"CreationDate":"2011-03-08T11:09:00.000","Title":"Calculating point-wise mutual information (PMI) score for n-grams in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it possible to pipe numpy data (from one python script ) into the other?\nsuppose that script1.py looks like this:\nx = np.zeros(3, dtype={'names':['col1', 'col2'], 'formats':['i4','f4']})\nprint x\nSuppose that from the linux command, I run the following:\npython script1.py | script2.py\nWill script2.py get the piped numpy data as an input (stdin)? will the data still be in the same format of numpy? (so that I can, for example, perform numpy operations on it from within script2.py)?","AnswerCount":3,"Available Count":2,"Score":0.1973753202,"is_accepted":false,"ViewCount":751,"Q_Id":5268391,"Users Score":3,"Answer":"Check out the save and load functions. I don't think they would object to being passed a pipe instead of a file.","Q_Score":1,"Tags":"python,linux,ubuntu,numpy,pipe","A_Id":5268722,"CreationDate":"2011-03-11T02:39:00.000","Title":"Pipe numpy data in Linux?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it possible to pipe numpy data (from one python script ) into the other?\nsuppose that script1.py looks like this:\nx = np.zeros(3, dtype={'names':['col1', 'col2'], 'formats':['i4','f4']})\nprint x\nSuppose that from the linux command, I run the following:\npython script1.py | script2.py\nWill script2.py get the piped numpy data as an input (stdin)? will the data still be in the same format of numpy? (so that I can, for example, perform numpy operations on it from within script2.py)?","AnswerCount":3,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":751,"Q_Id":5268391,"Users Score":2,"Answer":"No, data is passed through a pipe as text. You'll need to serialize the data in script1.py before writing, and deserialize it in script2.py after reading.","Q_Score":1,"Tags":"python,linux,ubuntu,numpy,pipe","A_Id":5268401,"CreationDate":"2011-03-11T02:39:00.000","Title":"Pipe numpy data in Linux?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to make a program in python that chooses cars from an array, that is filled with the mass of 10 cars. The idea is that it fills a barge, that can hold ~8 tonnes most effectively and minimum space is left unfilled. My idea is, that it makes variations of the masses and chooses one, that is closest to the max weight. But since I'm new to algorithms, I don't have a clue how to do it","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":230,"Q_Id":5299280,"Users Score":1,"Answer":"I'd solve this exercise with dynamic programming. You should be able to get the optimal solution in O(m*n) operations (n beeing the number of cars, m beeing the total mass).\nThat will only work however if the masses are all integers.\nIn general you have a binary linear programming problem. Those are very hard in general (NP-complete).\nHowever, both ways lead to algorithms which I wouldn't consider to be beginners material. You might be better of with trial and error (as you suggested) or simply try every possible combination.","Q_Score":2,"Tags":"python,algorithm,variations","A_Id":5299434,"CreationDate":"2011-03-14T13:35:00.000","Title":"Program, that chooses the best out of 10","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"BACKGROUND\nThe issue I'm working with is as follows:\n\nWithin the context of an experiment I am designing for my research, I produce a large number of large (length 4M) arrays which are somewhat sparse, and thereby could be stored as scipy.sparse.lil_matrix instances, or simply as scipy.array instances (the space gain\/loss isn't the issue here).\nEach of these arrays must be paired with a string (namely a word) for the data to make sense, as they are semantic vectors representing the meaning of that string. I need to preserve this pairing.\nThe vectors for each word in a list are built one-by-one, and stored to disk before moving on to the next word.\nThey must be stored to disk in a manner which could be then retrieved with dictionary-like syntax. For example if all the words are stored in a DB-like file, I need to be able to open this file and do things like vector = wordDB[word].\n\nCURRENT APPROACH\nWhat I'm currently doing:\n\nUsing shelve to open a shelf named wordDB\nEach time the vector (currently using lil_matrix from scipy.sparse) for a word is built, storing the vector in the shelf: wordDB[word] = vector\nWhen I need to use the vectors during the evaluation, I'll do the reverse: open the shelf, and then recall vectors by doing vector = wordDB[word] for each word, as they are needed, so that not all the vectors need be held in RAM (which would be impossible).\n\nThe above 'solution' fits my needs in terms of solving the problem as specified. The issue is simply that when I wish to use this method to build and store vectors for a large amount of words, I simply run out of disk space.\nThis is, as far as I can tell, because shelve pickles the data being stored, which is not an efficient way of storing large arrays, thus rendering this storage problem intractable with shelve for the number of words I need to deal with.\nPROBLEM\nThe question is thus: is there a way of serializing my set of arrays which will:\n\nSave the arrays themselves in compressed binary format akin to the .npy files generated by scipy.save?\nMeet my requirement that the data be readable from disk as a dictionary, maintaining the association between words and arrays?","AnswerCount":4,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":3381,"Q_Id":5330010,"Users Score":2,"Answer":"I would suggest to use scipy.save and have an dictionnary between the word and the name of the files.","Q_Score":7,"Tags":"python,serialization,numpy,scipy","A_Id":5330143,"CreationDate":"2011-03-16T18:30:00.000","Title":"Dictionary-like efficient storing of scipy\/numpy arrays","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm working on a problem and one solution would require an input of every 14x10 matrix that is possible to be made up of 1's and 0's... how can I generate these so that I can input every possible 14x10 matrix into another function? Thank you! \nAdded March 21: It looks like I didn't word my post appropriately. Sorry. What I'm trying to do is optimize the output of 10 different production units (given different speeds and amounts of downtime) for several scenarios. My goal is to place blocks of downtime to minimized the differences in production on a day-to-day basis. The amount of downtime and frequency each unit is allowed is given. I am currently trying to evaluate a three week cycle, meaning every three weeks each production unit is taken down for a given amount of hours. I was asking the computer to determine the order the units would be taken down based on the constraint that the lines come down only once every 3 weeks and the difference in daily production is the smallest possible. My first approach was to use Excel (as I tried to describe above) and it didn't work (no suprise there)... where 1- running, 0- off and when these are summed to calculate production. The calculated production is subtracted from a set max daily production. Then, these differences were compared going from Mon-Tues, Tues-Wed, etc for a three week time frame and minimized using solver. My next approach was to write a Matlab code where the input was a tolerance (set allowed variation day-to-day). Is there a program that already does this or an approach to do this easiest? It seems simple enough, but I'm still thinking through the different ways to go about this. Any insight would be much appreciated.","AnswerCount":11,"Available Count":3,"Score":1.0,"is_accepted":false,"ViewCount":3156,"Q_Id":5357171,"Users Score":6,"Answer":"Generating Every possible matrix of 1's and 0's for 14*10 would generate 2**140 matrixes. I don't believe you would have enough lifetime for this. I don't know, if the sun would still shine before you finish that. This is why it is impossible to generate all those matrices. You must look for some other solution, this looks like a brute force.","Q_Score":1,"Tags":"python,arrays,matlab,matrix","A_Id":5357196,"CreationDate":"2011-03-18T19:55:00.000","Title":"how to generate all possible combinations of a 14x10 matrix containing only 1's and 0's","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm working on a problem and one solution would require an input of every 14x10 matrix that is possible to be made up of 1's and 0's... how can I generate these so that I can input every possible 14x10 matrix into another function? Thank you! \nAdded March 21: It looks like I didn't word my post appropriately. Sorry. What I'm trying to do is optimize the output of 10 different production units (given different speeds and amounts of downtime) for several scenarios. My goal is to place blocks of downtime to minimized the differences in production on a day-to-day basis. The amount of downtime and frequency each unit is allowed is given. I am currently trying to evaluate a three week cycle, meaning every three weeks each production unit is taken down for a given amount of hours. I was asking the computer to determine the order the units would be taken down based on the constraint that the lines come down only once every 3 weeks and the difference in daily production is the smallest possible. My first approach was to use Excel (as I tried to describe above) and it didn't work (no suprise there)... where 1- running, 0- off and when these are summed to calculate production. The calculated production is subtracted from a set max daily production. Then, these differences were compared going from Mon-Tues, Tues-Wed, etc for a three week time frame and minimized using solver. My next approach was to write a Matlab code where the input was a tolerance (set allowed variation day-to-day). Is there a program that already does this or an approach to do this easiest? It seems simple enough, but I'm still thinking through the different ways to go about this. Any insight would be much appreciated.","AnswerCount":11,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":3156,"Q_Id":5357171,"Users Score":0,"Answer":"Instead of just suggesting the this is unfeasible, I would suggest considering a scheme that samples the important subset of all possible combinations instead of applying a brute force approach. As one of your replies suggested, you are doing minimization. There are numerical techniques to do this such as simulated annealing, monte carlo sampling as well as traditional minimization algorithms. You might want to look into whether one is appropriate in your case.","Q_Score":1,"Tags":"python,arrays,matlab,matrix","A_Id":5357340,"CreationDate":"2011-03-18T19:55:00.000","Title":"how to generate all possible combinations of a 14x10 matrix containing only 1's and 0's","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm working on a problem and one solution would require an input of every 14x10 matrix that is possible to be made up of 1's and 0's... how can I generate these so that I can input every possible 14x10 matrix into another function? Thank you! \nAdded March 21: It looks like I didn't word my post appropriately. Sorry. What I'm trying to do is optimize the output of 10 different production units (given different speeds and amounts of downtime) for several scenarios. My goal is to place blocks of downtime to minimized the differences in production on a day-to-day basis. The amount of downtime and frequency each unit is allowed is given. I am currently trying to evaluate a three week cycle, meaning every three weeks each production unit is taken down for a given amount of hours. I was asking the computer to determine the order the units would be taken down based on the constraint that the lines come down only once every 3 weeks and the difference in daily production is the smallest possible. My first approach was to use Excel (as I tried to describe above) and it didn't work (no suprise there)... where 1- running, 0- off and when these are summed to calculate production. The calculated production is subtracted from a set max daily production. Then, these differences were compared going from Mon-Tues, Tues-Wed, etc for a three week time frame and minimized using solver. My next approach was to write a Matlab code where the input was a tolerance (set allowed variation day-to-day). Is there a program that already does this or an approach to do this easiest? It seems simple enough, but I'm still thinking through the different ways to go about this. Any insight would be much appreciated.","AnswerCount":11,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":3156,"Q_Id":5357171,"Users Score":0,"Answer":"Are you saying that you have a table with 140 cells and each value can be 1 or 0 and you'd like to generate every possible output? If so, you would have 2^140 possible combinations...which is quite a large number.","Q_Score":1,"Tags":"python,arrays,matlab,matrix","A_Id":5357209,"CreationDate":"2011-03-18T19:55:00.000","Title":"how to generate all possible combinations of a 14x10 matrix containing only 1's and 0's","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Currently I am working on a python project that contains sub modules and uses numpy\/scipy. Ipython is used as interactive console. Unfortunately I am not very happy with workflow that I am using right now, I would appreciate some advice.\nIn IPython, the framework is loaded by a simple import command. However, it is often necessary to change code in one of the submodules of the framework. At this point a model is already loaded and I use IPython to interact with it. \nNow, the framework contains many modules that depend on each other, i.e. when the framework is initially loaded the main module is importing and configuring the submodules.  The changes to the code are only executed if the module is reloaded using reload(main_mod.sub_mod). This is cumbersome as I need to reload all changed modules individually using the full path. It would be very convenient if reload(main_module) would also reload all sub modules, but without reloading numpy\/scipy..","AnswerCount":14,"Available Count":2,"Score":0.0142847425,"is_accepted":false,"ViewCount":217455,"Q_Id":5364050,"Users Score":1,"Answer":"Note that the above mentioned autoreload only works in IntelliJ if you manually save the changed file (e.g. using ctrl+s or cmd+s). It doesn't seem to work with auto-saving.","Q_Score":475,"Tags":"python,ipython","A_Id":58834043,"CreationDate":"2011-03-19T18:39:00.000","Title":"Reloading submodules in IPython","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Currently I am working on a python project that contains sub modules and uses numpy\/scipy. Ipython is used as interactive console. Unfortunately I am not very happy with workflow that I am using right now, I would appreciate some advice.\nIn IPython, the framework is loaded by a simple import command. However, it is often necessary to change code in one of the submodules of the framework. At this point a model is already loaded and I use IPython to interact with it. \nNow, the framework contains many modules that depend on each other, i.e. when the framework is initially loaded the main module is importing and configuring the submodules.  The changes to the code are only executed if the module is reloaded using reload(main_mod.sub_mod). This is cumbersome as I need to reload all changed modules individually using the full path. It would be very convenient if reload(main_module) would also reload all sub modules, but without reloading numpy\/scipy..","AnswerCount":14,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":217455,"Q_Id":5364050,"Users Score":0,"Answer":"Any subobjects will not be reloaded by this, I believe you have to use IPython's deepreload for that.","Q_Score":475,"Tags":"python,ipython","A_Id":57911718,"CreationDate":"2011-03-19T18:39:00.000","Title":"Reloading submodules in IPython","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have read the topic: How do I calculate a trendline for a graph? \nWhat I am looking for though is how to find the line that touches the outer extreme points of a graph. The intended use is calculation of support, resistance lines for stock charts. So it is not a merely simple regression but it should also limit the number of touch points and there should be a way to find the relevant interval.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2927,"Q_Id":5377347,"Users Score":0,"Answer":"You could think about using a method that calculates the concave hull of your data. There are probably existing python implementations you could find. This will give you the boundary that encloses your timeseries. If there are outliers in your dataset that you wish to exclude, you could apply some sort of filter or smoothing to your data before you calculate the concave hull. I'm not 100% sure what you mean by \"limit the number of touch points\" and \"find the relevant interval\", but hopefully this will get you started.","Q_Score":0,"Tags":"python,stocks","A_Id":5377789,"CreationDate":"2011-03-21T12:07:00.000","Title":"how to calculate trendline for stock charts","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"My primary language is Python. Often when I need to do some cpu heavy task on a numpy array I use scipy.weave.inline to hook up c++ with great results.\nI suspect many of the algorithms (machine learning stuff) can however be written simpler in a functional language (scheme, haskell...).\nI was thinking. Is it possible to access numpy array data (read and write) from a functional language instead of having to use c++?","AnswerCount":4,"Available Count":1,"Score":-0.0996679946,"is_accepted":false,"ViewCount":887,"Q_Id":5392551,"Users Score":-2,"Answer":"I can't imagine trying to use numpy through haskell or scheme will be easier than just writing functional python.  Try using itertools and functools if you want a more functional flavored python.","Q_Score":5,"Tags":"python,haskell,functional-programming,numpy,scipy","A_Id":5392702,"CreationDate":"2011-03-22T14:08:00.000","Title":"access numpy array from a functional language","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to create about 2 million vectors w\/ 1000 slots in each (each slot merely contains an integer).\nWhat would be the best data structure for working with this amount of data? It could be that I'm over-estimating the amount of processing\/memory involved.\nI need to iterate over a collection of files (about 34.5GB in total) and update the vectors each time one of the the 2-million items (each corresponding to a vector) is encountered on a line.\nI could easily write code for this, but I know it wouldn't be optimal enough to handle the volume of the data, which is why I'm asking you experts. :)\nBest,\nGeorgina","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":1730,"Q_Id":5397809,"Users Score":1,"Answer":"Use a sparse matrix assuming most entries are 0.","Q_Score":3,"Tags":"python,data-structures,vector,matrix,large-data-volumes","A_Id":5397912,"CreationDate":"2011-03-22T21:04:00.000","Title":"Python - Best data structure for incredibly large matrix","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to plot a series of rectangles and lines based on a tab delimited text file in matplotlib. The coordinates are quite large in the data and shown be drawn to scale -- except scaled down by some factor X -- in matplotlib.  \nWhat's the easiest way to do this in matplotlib? I know that there are transformations, but I am not sure how to define my own transformation (i.e. where the origin is and what the scale factor is) in matplotlib and have it easily convert between \"data space\" and \"plot space\".  Can someone please show a quick example or point me to the right place?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1909,"Q_Id":5463924,"Users Score":0,"Answer":"If you simply use matplotlib's plot function, the plot will fit into one online window, so you don't really need to 'rescale' explicitly. Linearly rescaling is pretty easy, if you include some code sample to show your formatting of the data, somebody can help you in translating the origin and scaling the coordinates.","Q_Score":2,"Tags":"python,numpy,matplotlib,scipy","A_Id":5627073,"CreationDate":"2011-03-28T19:19:00.000","Title":"transforming coordinates in matplotlib","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"So I am in a bit of a pickle. I am trying to write plotting and fitting extensions to a Fortran77 (why this program was rewritten in F77 is a mystery too me, btw) code that requires command line input, i.e. it prompts the user for input. Currently the program uses GNUplot to plot, but the GNUplot fitting routine is less than ideal in my eyes, and calling GNUplot from Fortran is a pain in the ass to say the least.\nI have mostly been working with Numpy, Scipy and Matplotlib to satisfy my fitting and plotting needs. I was wondering if there is a way to call the F77 program in Python and then have it run like I would any other F77 program until the portion where I need it to fit and spit out some nice plots (none of this GNUplot stuff).\nI know about F2PY, but I have heard mixed things about it. I have also contemplated using pyexpect and go from there, but I have have bad experience with the way it handles changing expected prompts on the screen (or I am just using it incorrectly).\nThanks for any info on this.","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":3047,"Q_Id":5465982,"Users Score":2,"Answer":"Can't you just dump the data generated by the Fortran program to a file and then read it from python ?\nNumpy can read a binary file and treat it as a array.\nGoing from here to matplotlib then should be a breeeze.","Q_Score":2,"Tags":"python,numpy,scipy,fortran77","A_Id":5466008,"CreationDate":"2011-03-28T22:54:00.000","Title":"Run Fortran command line program within Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"We have pretty large files, the order of 1-1.5 GB combined (mostly log files) with raw data that is easily parseable to a csv, which is subsequently supposed to be graphed to generate a set of graph images.\nCurrently, we are using bash scripts to turn the raw data into a csv file, with just the numbers that need to be graphed, and then feeding it into a gnuplot script. But this process is extremely slow. I tried to speed up the bash scripts by replacing some piped cuts, trs etc. with a single awk command, although this improved the speed, the whole thing is still very slow.\nSo, I am starting to believe there are better tools for this process. I am currently looking to rewrite this process in python+numpy or R. A friend of mine suggested using the JVM, and if I am to do that, I will use clojure, but am not sure how the JVM will perform.\nI don't have much experience in dealing with these kind of problems, so any advice on how to proceed would be great. Thanks.\nEdit: Also, I will want to store (to disk) the generated intermediate data, i.e., the csv, so I don't have to re-generate it, should I choose I want a different looking graph.\nEdit 2: The raw data files have one record per one line, whose fields are separated by a delimiter (|). Not all fields are numbers. Each field I need in the output csv is obtained by applying a certain formula on the input records, which may use multiple fields from the input data. The output csv will have 3-4 fields per line, and I need graphs that plot 1-2, 1-3, 1-4 fields in a (may be) bar chart. I hope that gives a better picture.\nEdit 3: I have modified @adirau's script a little and it seems to be working pretty well. I have come far enough that I am reading data, sending to a pool of processor threads (pseudo processing, append thread name to data), and aggregating it into an output file, through another collector thread.\nPS: I am not sure about the tagging of this question, feel free to correct it.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":2960,"Q_Id":5468921,"Users Score":1,"Answer":"I think python+Numpy would be the most efficient way, regarding speed and ease of implementation.\nNumpy is highly optimized so the performance is decent, and python would ease up the algorithm implementation part.\nThis combo should work well for your case, providing you optimize the loading of the file on memory, try to find the middle point between processing a data block that isn't too large but large enough to minimize the read and write cycles, because this is what will slow down the program\nIf you feel that this needs more speeding up (which i sincerely doubt), you could use Cython to speed up the sluggish parts.","Q_Score":5,"Tags":"python,r,numpy,large-files,graphing","A_Id":5469268,"CreationDate":"2011-03-29T06:53:00.000","Title":"Reading and graphing data read from huge files","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there a way to capture WordNet selectional restrictions (such as +animate, +human, etc.) from synsets through NLTK?\nOr is there any other way of providing semantic information about synset? The closest I could get to it were hypernym relations.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":820,"Q_Id":5493565,"Users Score":0,"Answer":"You could try using some of the similarity functions with handpicked synsets, and use that to filter. But it's essentially the same as following the hypernym tree - afaik all the wordnet similarity functions use hypernym distance in their calculations. Also, there's a lot of optional attributes of a synset that might be worth exploring, but their presence can be very inconsistent.","Q_Score":7,"Tags":"python,nlp,nltk,wordnet","A_Id":5494360,"CreationDate":"2011-03-30T23:10:00.000","Title":"Wordnet selectional restrictions in NLTK","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've got a long-running script, and the operations that it performs can be broken down into independent, but order-sensitive function calls. In addition, the pattern here is a bread-first walk over a graph, and restarting a depth after a crash is not a happy option. \nSo then, my idea is as follows: as the script runs, maintain a list of function-argument pairs. This list corresponds to the open list of a depth-first search of the graph. The data is readily accessible, so I don't need to be concerned about losing it. \nIn the (probably unavoidable) event of a crash due to network conditions or an error, the list of function argument pairs is enough to resume the operation. \nSo then, can I store the functions? My intuition says no, but I'd like some advice before I judge either way.","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":852,"Q_Id":5526981,"Users Score":2,"Answer":"You can pickle plain functions. Python stores the name and module and uses that to reload it. If you want to do the same with methods and such that's a little bit trickier. It can be done by breaking such objects down into component parts and pickling those.","Q_Score":1,"Tags":"python,pickle","A_Id":5527011,"CreationDate":"2011-04-03T01:29:00.000","Title":"Can you pickle methods python method objects using any pickle module?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've got a long-running script, and the operations that it performs can be broken down into independent, but order-sensitive function calls. In addition, the pattern here is a bread-first walk over a graph, and restarting a depth after a crash is not a happy option. \nSo then, my idea is as follows: as the script runs, maintain a list of function-argument pairs. This list corresponds to the open list of a depth-first search of the graph. The data is readily accessible, so I don't need to be concerned about losing it. \nIn the (probably unavoidable) event of a crash due to network conditions or an error, the list of function argument pairs is enough to resume the operation. \nSo then, can I store the functions? My intuition says no, but I'd like some advice before I judge either way.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":852,"Q_Id":5526981,"Users Score":0,"Answer":"If the arguments are all that matters, why not store the arguments? Just store it as a straight tuple.","Q_Score":1,"Tags":"python,pickle","A_Id":5526985,"CreationDate":"2011-04-03T01:29:00.000","Title":"Can you pickle methods python method objects using any pickle module?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it possible to specify your own distance function using scikit-learn K-Means Clustering?","AnswerCount":8,"Available Count":3,"Score":1.0,"is_accepted":false,"ViewCount":105962,"Q_Id":5529625,"Users Score":17,"Answer":"Yes you can use a difference metric function; however, by definition, the k-means clustering algorithm relies on the eucldiean distance from the mean of each cluster. \nYou could use a different metric, so even though you are still calculating the mean you could use something like the mahalnobis distance.","Q_Score":213,"Tags":"python,machine-learning,cluster-analysis,k-means,scikit-learn","A_Id":9875395,"CreationDate":"2011-04-03T12:39:00.000","Title":"Is it possible to specify your own distance function using scikit-learn K-Means Clustering?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it possible to specify your own distance function using scikit-learn K-Means Clustering?","AnswerCount":8,"Available Count":3,"Score":1.0,"is_accepted":false,"ViewCount":105962,"Q_Id":5529625,"Users Score":57,"Answer":"Unfortunately no: scikit-learn current implementation of k-means only uses Euclidean distances.\nIt is not trivial to extend k-means to other distances and denis' answer above is not the correct way to implement k-means for other metrics.","Q_Score":213,"Tags":"python,machine-learning,cluster-analysis,k-means,scikit-learn","A_Id":5531148,"CreationDate":"2011-04-03T12:39:00.000","Title":"Is it possible to specify your own distance function using scikit-learn K-Means Clustering?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it possible to specify your own distance function using scikit-learn K-Means Clustering?","AnswerCount":8,"Available Count":3,"Score":0.0996679946,"is_accepted":false,"ViewCount":105962,"Q_Id":5529625,"Users Score":4,"Answer":"Sklearn Kmeans uses the Euclidean distance. It has no metric parameter. This said, if you're clustering time series, you can use the tslearn python package, when you can specify a metric (dtw, softdtw, euclidean).","Q_Score":213,"Tags":"python,machine-learning,cluster-analysis,k-means,scikit-learn","A_Id":56324541,"CreationDate":"2011-04-03T12:39:00.000","Title":"Is it possible to specify your own distance function using scikit-learn K-Means Clustering?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a very large matrix(10x55678) in \"numpy\" matrix format. the rows of this matrix correspond to some \"topics\" and the columns correspond to words(unique words from a text corpus). Each entry i,j in this matrix is a probability, meaning that word j belongs to topic i with probability x. since I am using ids rather than the real words and since the dimension of my matrix is really large I need to visualized it in a way.Which visualization do you suggest? a simple plot? or a more sophisticated and informative one?(i am asking these cause I am ignorant about the useful types of visualization). If possible can you give me an example  that using a numpy matrix? thanks\nthe reason I asked this question is that I want to have a general view of the word-topic distributions in my corpus. any other methods are welcome","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":30834,"Q_Id":5552641,"Users Score":2,"Answer":"The key thing to consider is whether you have important structure along both dimensions in the matrix.  If you do then it's worth trying a colored matrix plot (e.g., imshow), but if your ten topics are basically independent, you're probably better off doing ten individual line or histogram plots.  Both plots have advantages and disadvantages.  \nIn particular, in full matrix plots, the z-axis color values are not very precise or quantitative, so its difficult to see, for example, small ripples on a trend, or quantitative assessments of rates of change, etc, so there's a significant cost to these.  And they are also more difficult to pan and zoom since one can get lost and therefore not examine the entire plot, whereas panning along a 1D plot is trivial.\nAlso, of course, as others have mentioned, 50K points is too many to actually visualize, so you'll need to sort them, or something, to reduce the number of values that you'll actually need to visually assess.\nIn practice though, finding a good visualizing technique for a given data set is not always trivial, and for large and complex data sets, people try everything that has a chance of being helpful, and then choose what actually helps.","Q_Score":8,"Tags":"python,numpy,matplotlib,plot,visualization","A_Id":5555313,"CreationDate":"2011-04-05T13:30:00.000","Title":"plotting a 2D matrix in python, code and most useful visualization","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have to create a python image processing program which reads in two images, one containing a single object and the other containing several objects. However, the first images object is present in the second image but is surrounded by other objects (some similar). \nThe images are both the same size but I am having problems in finding a method of comparing the images, picking out the matching object and then also placing a cross, or pointer of some sort on top of the object which is present in both images. \nThe Program should therefore open up both images originally needing to be compared, then after the comparison has taken place the image containing many objects should be displayed but with a pointer on the object most similar (matching) the object in the first image.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":711,"Q_Id":5555975,"Users Score":1,"Answer":"I guess the most straightforward way to achieve this is to compute the correlation map of the two images. Just convolve the two images using a scientific library such as scipy, apply a low pass filter and find the maximum value of the result.\nYou should check out the following packages:\n\nnumpy\nscipy\nmatplotlib\nPIL if your images are not in png format","Q_Score":1,"Tags":"python,algorithm,image-processing","A_Id":5556151,"CreationDate":"2011-04-05T17:31:00.000","Title":"Finding an object within an image containing many objects (Python)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to check if a matrix is positive definite or positive semidefinite using Python.\nHow can I do that? Is there a dedicated function in SciPy for that or in other modules?","AnswerCount":5,"Available Count":3,"Score":1.2,"is_accepted":true,"ViewCount":19269,"Q_Id":5563743,"Users Score":0,"Answer":"One good solution is to calculate all the minors of determinants and check they are all non negatives.","Q_Score":13,"Tags":"python,math,matrix,scipy,linear-algebra","A_Id":5715781,"CreationDate":"2011-04-06T08:51:00.000","Title":"Check for positive definiteness or positive semidefiniteness","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to check if a matrix is positive definite or positive semidefinite using Python.\nHow can I do that? Is there a dedicated function in SciPy for that or in other modules?","AnswerCount":5,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":19269,"Q_Id":5563743,"Users Score":0,"Answer":"an easier method is to calculate the determinants of the minors for this matrx.","Q_Score":13,"Tags":"python,math,matrix,scipy,linear-algebra","A_Id":5565951,"CreationDate":"2011-04-06T08:51:00.000","Title":"Check for positive definiteness or positive semidefiniteness","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to check if a matrix is positive definite or positive semidefinite using Python.\nHow can I do that? Is there a dedicated function in SciPy for that or in other modules?","AnswerCount":5,"Available Count":3,"Score":1.0,"is_accepted":false,"ViewCount":19269,"Q_Id":5563743,"Users Score":18,"Answer":"I assume you already know your matrix is symmetric. \nA good test for positive definiteness (actually the standard one !) is to try to compute its Cholesky factorization. It succeeds iff your matrix is positive definite.\nThis is the most direct way, since it needs O(n^3) operations (with a small constant), and you would need at least n matrix-vector multiplications to test \"directly\".","Q_Score":13,"Tags":"python,math,matrix,scipy,linear-algebra","A_Id":5563883,"CreationDate":"2011-04-06T08:51:00.000","Title":"Check for positive definiteness or positive semidefiniteness","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have been trying to generate data in Excel.\nI generated .CSV file.\nSo up to that point it's easy.\nBut generating graph is quite hard in Excel...\nI am wondering, is python able to generate data AND graph in excel?\nIf there are examples or code snippets, feel free to post it :)\nOr a workaround can be use python to generate graph in graphical format like .jpg, etc or .pdf file is also ok..as long as workaround doesn't need dependency such as the need to install boost library.","AnswerCount":5,"Available Count":1,"Score":0.0798297691,"is_accepted":false,"ViewCount":48351,"Q_Id":5568319,"Users Score":2,"Answer":"I suggest you to try gnuplot while drawing graph from data files.","Q_Score":6,"Tags":"python,excel,charts,export-to-excel","A_Id":5568485,"CreationDate":"2011-04-06T14:47:00.000","Title":"use python to generate graph in excel","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm confused about what the different between axes and axis is in matplotlib. Could someone please explain in an easy-to-understand way?","AnswerCount":3,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":16337,"Q_Id":5575451,"Users Score":15,"Answer":"in the context of matplotlib,\naxes is not the plural form of axis, it actually denotes the plotting area, including all axis.","Q_Score":75,"Tags":"python,matplotlib","A_Id":55520433,"CreationDate":"2011-04-07T03:00:00.000","Title":"Difference between \"axes\" and \"axis\" in matplotlib?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm confused about what the different between axes and axis is in matplotlib. Could someone please explain in an easy-to-understand way?","AnswerCount":3,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":16337,"Q_Id":5575451,"Users Score":68,"Answer":"Axis is the axis of the plot, the thing that gets ticks and tick labels. The axes is the area your plot appears in.","Q_Score":75,"Tags":"python,matplotlib","A_Id":5575468,"CreationDate":"2011-04-07T03:00:00.000","Title":"Difference between \"axes\" and \"axis\" in matplotlib?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am looking for a scheme-less database to store roughly 10[TB] of data on disk, ideally, using a python client. The suggested solution should be free for commercial use, and have good performance for reads and writes.\nThe main goal here is to store time-series data, including more than billion records, accessed by time stamp.\nData would be stored in the following scheme: \nKEY --> \"FIELD_NAME.YYYYMMDD.HHMMSS\" \nVALUE --> [v1, v2, v3, v4, v5, v6]                (v1..v6 are just floats)\nFor instance, suppose that:\nFIELD_NAME = \"TOMATO\"\nTIME_STAMP = \"20060316.184356\"\nVALUES = [72.34, -22.83, -0.938, 0.265, -2047.23]\nI need to be able to retrieve VALUE (the entire array) given the combination of FIELD_NAME & TIME_STAMP.\nThe query VALUES[\"TOMATO.20060316.184356\"] would return the vector [72.34, -22.83, -0.938, 0.265, -2047.23]. Reads of arrays should be as fast as possible.\nYet, I also need a way to store (in-place) a scalar value within an array . Suppose that I want to assign the 1st element of TOMATO on timestamp 2006\/03\/16.18:43:56 to be 500.867. In such a case, I need to have a fast mechanism to do so -- something like:\nVALUES[\"TOMATO.20060316.184356\"][0] = 500.867  (this would update on disk)\nCan something like MangoDB work? I will be using just one machine (no need for replication etc), running Linux.\nCLARIFICATION: only one machine will be used to store the database. Yet, I need a solution that will allow multiple machines to connect to the same database and update\/insert\/read\/write data to\/from it.","AnswerCount":4,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":401,"Q_Id":5590148,"Users Score":4,"Answer":"MongoDB is likely a good choice related to performance, flexibility and usability (easy approachable). However large databases require careful planning - especially when it comes to aspects of backup and high-availability. Without further insight about project requirements there is little to say if one machine is enough or not (look at replica sets and sharding if you need options scale).\nUpdate: based on your new information - should be doable with MongoDB (test and evaluate it). Easiliy spoken: MongoDB can be the \"MySQL\" of the NoSQL databases....if you know about SQL databases then you should be able to work with MongoDB easily since it borrows a lot of ideas and concept from the SQL world. Looking at your data model...it's trivial and data can be easily retrieved and stored (not going into details)..I suggest download MongoDB and walking through the tutorial.","Q_Score":1,"Tags":"python,linux,mongodb,database,nosql","A_Id":5590275,"CreationDate":"2011-04-08T03:38:00.000","Title":"Scheme-less database solution to work on one machine only?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How to randomly pick all the results, one by one (no repeats) from itertools.permutations(k)? Or this: how to build a generator of randomized permutations? Something like shuffle(permutations(k)).  I\u2019m using Python 2.6.\nYeah, shuffle(r) could be used if r = list(permutations(k)), but such a list will take up too much time and memory when len(k) raises above 10.\nThanks.","AnswerCount":6,"Available Count":2,"Score":0.0665680765,"is_accepted":false,"ViewCount":4221,"Q_Id":5602488,"Users Score":2,"Answer":"I don't know how python implements its shuffle algorithm, but the following scales in linear time, so I don't see why a length of 10 is such a big deal (unless I misunderstand your question?):\n\nstart with the list of items;\ngo through each index in the list in turn, swapping the item at that index it for an item at a random index (including the item itself) in the remainder of the list.\n\nFor a different permutation, just run the same algorithm again.","Q_Score":3,"Tags":"python,permutation,random-sample","A_Id":5602512,"CreationDate":"2011-04-09T02:22:00.000","Title":"Random picks from permutation generator?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"How to randomly pick all the results, one by one (no repeats) from itertools.permutations(k)? Or this: how to build a generator of randomized permutations? Something like shuffle(permutations(k)).  I\u2019m using Python 2.6.\nYeah, shuffle(r) could be used if r = list(permutations(k)), but such a list will take up too much time and memory when len(k) raises above 10.\nThanks.","AnswerCount":6,"Available Count":2,"Score":0.0665680765,"is_accepted":false,"ViewCount":4221,"Q_Id":5602488,"Users Score":2,"Answer":"There's no way of doing what you have asked for without writing your own version of permutations.\nConsider this:\n\nWe have a generator object containing the result of permutations.\nWe have written our own function to tell us the length of the generator.\nWe then pick an entries at random between the beginning of the list and the end.\n\nSince I have a generator if the random function picks an entry near the end of the list the only way to get to it will be to go through all the prior entries and either throw them away, which is bad, or store them in a list, which you have pointed out is problematic when you have a lot of options.\nAre you going to loop through every permutation or use just a few?  If it's the latter it would make more sense to generate each new permutation at random and store then ones you've seen before in a set.  If you don't use that many the overhead of having to create a new permutation each time you have a collision will be pretty low.","Q_Score":3,"Tags":"python,permutation,random-sample","A_Id":5603207,"CreationDate":"2011-04-09T02:22:00.000","Title":"Random picks from permutation generator?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have some complex graphs made using matplotlib. Saving them to a pdf using the savefig command uses a vector format, and the pdf takes ages to open. Is there any way to save the figure to pdf as a raster image to get around this problem?","AnswerCount":2,"Available Count":1,"Score":0.2913126125,"is_accepted":false,"ViewCount":5667,"Q_Id":5609969,"Users Score":3,"Answer":"Not that I know, but you can use the 'convert' program (ImageMagick') to convert a jpg to a pdf: `convert file.jpg file.pdf'.","Q_Score":14,"Tags":"python,pdf,matplotlib,raster","A_Id":5610731,"CreationDate":"2011-04-10T05:27:00.000","Title":"How to save figures to pdf as raster images in matplotlib","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm looking to visualize the data, hopefully make it interactive.  Right now I'm using NetworkX and Matplotlib, which maxes out my 8gb when I attempt to 'draw' the graph.  I don't know what options and techniques exist for handling such a large cluster** of data.  If someone could point me in the right direction, that'd be great.  I also have a CUDA enabled GFX card if that could be of use.  \nRight now I'm thinking of drawing only the most connected nodes, say top 5% of vertices with the most edges, then filling in less connected nodes as the user zooms or clicks.","AnswerCount":6,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":3571,"Q_Id":5648097,"Users Score":2,"Answer":"You should ask on the official wxPython mailing list. There are people there that can probably help you. I am surprised that matplotlib isn't able to do this though. It may just require you to restructure your code in some way. Right now, the main ways to draw in wxPython are via the various DCs, one of the FloatCanvas widgets or for graphing, wx.Plot or matplotlib.","Q_Score":12,"Tags":"python,graph,wxpython,visualization","A_Id":5651029,"CreationDate":"2011-04-13T11:02:00.000","Title":"Python tools to visualize 100k Vertices and 1M Edges?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to find a tool that will manage a bunch of jobs on 100 machines in a cluster (submit the jobs to the machines; make sure that jobs are run etc).\nWhich tool would be more simple to install \/ manage:\n(1) Hadoop?\n(2) Disco?\n(3) Condor?\nIdeally, I am searching for a solution that would be as simple as possible, yet be robust.\nPython integration is also a plus.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1606,"Q_Id":5682175,"Users Score":3,"Answer":"I'm unfamiliar with Disco and Condor, but I can answer regarding Hadoop:\nHadoop pros: \n\nRobust and proven - probably more than anything else out there. Used by many organizations (including the one I work for) to run clusters of 100s of nodes and more.\nLarge ecosystem = support + many subprojects to make life easier (e.g. Pig, Hive)\nPython support should be possible through the streaming MR feature, or maybe Jython?\n\nHadoop cons:\n\nNeither simple nor elegant (imho). You'll have to spend time learning.","Q_Score":4,"Tags":"python,distributed-computing","A_Id":5682297,"CreationDate":"2011-04-15T20:43:00.000","Title":"Hadoop Vs. Disco Vs. Condor?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I want to generate compound charts (e.g: Bar+line) from my database using python.\nHow can i do this ?\nThanks in Advance","AnswerCount":4,"Available Count":1,"Score":0.049958375,"is_accepted":false,"ViewCount":277,"Q_Id":5693151,"Users Score":1,"Answer":"Pretty easy to do with pygooglechart - \nYou can basically follow the bar chart examples that ship with the software and then use the add_data_line method to make the lines on top of the bar chart","Q_Score":0,"Tags":"python,charts","A_Id":6272840,"CreationDate":"2011-04-17T11:09:00.000","Title":"Compoud charts with python","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a trained and pickled NLTK tagger (Brill's transformational rule-based tagger).\nI want to use it on GAE. What the best way to do it?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":312,"Q_Id":5696995,"Users Score":3,"Answer":"If your NLTK tagger code and data is of limited size, then carry it along with your GAE code. \nIf you have to act upon it to retrain the set, then storing the content of the file as a BLOB in the datastore would be an option, so that you get, analyze, retrain and put.But that will limit size of dataitem to be less than 1 MB because of GAE hardlimit.","Q_Score":0,"Tags":"python,google-app-engine,pickle,nltk","A_Id":5697057,"CreationDate":"2011-04-17T22:49:00.000","Title":"Python: How to load and use trained and pickled NLTK tagger to GAE?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there any clean way of setting numpy to use float32 values instead of float64 globally?","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":13987,"Q_Id":5721831,"Users Score":13,"Answer":"Not that I am aware of. You either need to specify the dtype explicitly when you call the constructor for any array, or cast an array to float32 (use the ndarray.astype method) before passing it to your GPU code (I take it this is what the question pertains to?). If it is the GPU case you are really worried about, I favor the latter - it can become very annoying to try and keep everything in single precision without an extremely thorough understanding of the numpy broadcasting rules and very carefully designed code. \nAnother alternative might be to create your own methods which overload the standard numpy constructors (so numpy.zeros, numpy.ones, numpy.empty). That should go pretty close to keeping everything in float32.","Q_Score":28,"Tags":"python,numpy,numbers","A_Id":5729858,"CreationDate":"2011-04-19T19:49:00.000","Title":"Python: Making numpy default to float32","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm working with large, sparse matrices (document-feature matrices generated from text) in python. It's taking quite a bit of processing time and memory to chew through these, and I imagine that sparse matrices could offer some improvements.  But I'm worried that using a sparse matrix library is going to make it harder to plug into other python (and R, through rpy2) modules.\nCan people who've crossed this bridge already offer some advice?  What are the pros and cons of using sparse matrices in python\/R, in terms of performance, scalability, and compatibility?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":872,"Q_Id":5749479,"Users Score":1,"Answer":"There are several ways to represent sparse matrices (documentation for the R SparseM package reports 20 different ways to store sparse matrix data), so complete compatibility with all solutions is probably out of question. The number options also suggests that there is no best-in-all-situations solution.\nPick either the numpy sparse matrices or R's SparseM (through rpy2) according to where your heavy number crunching routines on those matrices are found (numpy or R).","Q_Score":8,"Tags":"python,r,sparse-matrix","A_Id":5887256,"CreationDate":"2011-04-21T20:32:00.000","Title":"Pros and cons to using sparse matrices in python\/R?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"i am getting error:\n\nrbuild build packages numpy --no-recurse \n[Tue Apr 26 13:16:53 2011] Creating rMake build job for 1 items\n\nAdded Job 902\n  numpy:source=rmake-repository.bericots.com@local:taf32-sandbox-1-devel\/1-0.2\n[13:17:23] Watching job 902            \n[13:17:24] [902] Loading 1 out of 1: numpy:source\n[13:17:26] [902] - State: Loaded         \n[13:17:26] [902] - job troves set        \n[13:17:27] [902] - State: Building              \n[13:17:27] [902] - Building troves              \nd all buildreqs: [TroveSpec('python2.4:runtime'), TroveSpec('python2.4:devel')]\n[13:17:27] [902] - numpy:source{x86} - State: Queued\n[13:17:27] [902] - numpy:source{x86} - Ready for dep resolution\n[13:17:28] [902] - numpy:source{x86} - State: Resolving\n[13:17:28] [902] - numpy:source{x86} - Resolving build requirements\n[13:17:28] [902] - State: Failed                   \n[13:17:28] [902] - Failed while building: Build job had failures:\n   * numpy:source: Could not satisfy build requirements: python2.4:runtime=[], python2.4:devel=[]\n\n902 numpy{x86} - Resolving - (Job Failed) ([h]elp)>\nerror: Job 902 has failures, not committing\nerror: Package build failed\n\n\nAny ideas ?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":170,"Q_Id":5794093,"Users Score":0,"Answer":"the command $rbuild build packages numpy --no-recurse , should be executed at the level where the package is, not in Development\/QA or Release Directories.","Q_Score":0,"Tags":"python,numpy,centos","A_Id":5796773,"CreationDate":"2011-04-26T17:26:00.000","Title":"How to build numpy with conary recipe for x-86 CentOs","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How to calculate confidence interval for the least square fit (scipy.optimize.leastsq) in python?","AnswerCount":3,"Available Count":1,"Score":0.2605204458,"is_accepted":false,"ViewCount":6800,"Q_Id":5811043,"Users Score":4,"Answer":"I am not sure what you mean by confidence interval.\nIn general, leastsq doesn't know much about the function that you are trying to minimize, so it can't really give a confidence interval. However, it does return an estimate of the Hessian, in other word the generalization of 2nd derivatives to multidimensional problems.\nAs hinted in the docstring of the function, you could use that information along with the residuals (the difference between your fitted solution and the actual data) to computed the covariance of parameter estimates, which is a local guess of the confidence interval. \nNote that it is only a local information, and I suspect that you can strictly speaking come to a conclusion only if your objective function is strictly convex. I don't have any proofs or references on that statement :).","Q_Score":6,"Tags":"python,scipy,least-squares,confidence-interval","A_Id":5816755,"CreationDate":"2011-04-27T21:50:00.000","Title":"confidence interval with leastsq fit in scipy python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a histogram I'm drawing in matplotlib with some 260,000 values or so.\nThe problem is that the frequency axis (y axis) on the histogram reaches high numbers such as 100,000... What I'd really like is to have the y labels as thousands, so instead of, for instance:\n\n100000\n75000\n50000\n25000\n0\n\nTo have this:\n\n100\n75\n50\n25\n0\n\nAnd then I can simply change the y axis to \"Frequency (000s)\" -- it makes it much easier to read that way. Anyone with any ideas how that can be achieved?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":3743,"Q_Id":5821895,"Users Score":0,"Answer":"Just convert the values yourself before they are entered. In numpy, you can do just array\/1000 instead of array.","Q_Score":3,"Tags":"python,graph,matplotlib,histogram","A_Id":5821933,"CreationDate":"2011-04-28T16:27:00.000","Title":"Matplotlib histogram, frequency as thousands","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Anyone following CUDA will probably have seen a few of my queries regarding a project I'm involved in, but for those who haven't I'll summarize. (Sorry for the long question in advance)\nThree Kernels, One Generates a data set based on some input variables (deals with bit-combinations so can grow exponentially), another solves these generated linear systems, and another reduction kernel to get the final result out. These three kernels are ran over and over again as part of an optimisation algorithm for a particular system.\nOn my dev machine (Geforce 9800GT, running under CUDA 4.0) this works  perfectly, all the time, no matter what I throw at it (up to a computational limit based on the stated exponential nature), but on a test machine (4xTesla S1070's, only one used, under CUDA 3.1) the exact same code (Python base, PyCUDA interface to CUDA kernels), produces the exact results for 'small' cases, but in mid-range cases, the solving stage fails on random iterations. \nPrevious problems I've had with this code have been to do with the numeric instability of the problem, and have been deterministic in nature (i.e fails at exactly the same stage every time), but this one is frankly pissing me off, as it will fail whenever it wants to.\nAs such, I don't have a reliable way to breaking the CUDA code out from the Python framework and doing proper debugging, and PyCUDA's debugger support is questionable to say the least.\nI've checked the usual things like pre-kernel-invocation checking of free memory on the device, and occupation calculations say that the grid and block allocations are fine. I'm not doing any crazy 4.0 specific stuff, I'm freeing everything I allocate on the device at each iteration and I've fixed all the data types as being floats.\nTL;DR, Has anyone come across any gotchas regarding CUDA 3.1 that I haven't seen in the release notes, or any issues with PyCUDA's autoinit memory management environment that would cause intermittent launch failures on repeated invocations?","AnswerCount":2,"Available Count":1,"Score":-0.0996679946,"is_accepted":false,"ViewCount":913,"Q_Id":5827219,"Users Score":-1,"Answer":"You can use nVidia CUDA Profiler and see what gets executed before the failure.","Q_Score":4,"Tags":"python,cuda,gpgpu,pycuda","A_Id":6029955,"CreationDate":"2011-04-29T02:27:00.000","Title":"PyCUDA\/CUDA: Causes of non-deterministic launch failures?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"As the title said, I'm trying to implement an algorithm that finds out the distances between all pairs of nodes in given graph. But there is more: (Things that might help you)\n\nThe graph is unweighted. Meaning that all the edges can be considered as having weight of 1.\n|E| <= 4*|V|\nThe graph is pretty big (at most ~144 depth)\nThe graph is directed\nThere might be cycles \nI'm writing my code in python (please if you reference algorithms, code would be nice too :))\n\nI know about Johnson's algorithm, Floyd-Warshal, and Dijkstra for all pairs. But these algorithms are good when the graph has weights.\nI was wondering if there is a better algorithm for my case, because those algorithms are intended for weighted graphs.\nThanks!","AnswerCount":10,"Available Count":3,"Score":0.0199973338,"is_accepted":false,"ViewCount":6778,"Q_Id":5851154,"Users Score":1,"Answer":"I would refer you to the following paper: \"Sub-cubic Cost Algorithms for the All Pairs Shortest Path Problem\" by Tadao Takaoka. There a sequential algorithm with sub-cubic complexity for graphs with unit weight (actually max edge weight = O(n ^ 0.624)) is available.","Q_Score":14,"Tags":"python,algorithm,dijkstra,shortest-path,graph-algorithm","A_Id":5852526,"CreationDate":"2011-05-01T20:23:00.000","Title":"best algorithm for finding distance for all pairs where edges' weight is 1","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"As the title said, I'm trying to implement an algorithm that finds out the distances between all pairs of nodes in given graph. But there is more: (Things that might help you)\n\nThe graph is unweighted. Meaning that all the edges can be considered as having weight of 1.\n|E| <= 4*|V|\nThe graph is pretty big (at most ~144 depth)\nThe graph is directed\nThere might be cycles \nI'm writing my code in python (please if you reference algorithms, code would be nice too :))\n\nI know about Johnson's algorithm, Floyd-Warshal, and Dijkstra for all pairs. But these algorithms are good when the graph has weights.\nI was wondering if there is a better algorithm for my case, because those algorithms are intended for weighted graphs.\nThanks!","AnswerCount":10,"Available Count":3,"Score":0.0199973338,"is_accepted":false,"ViewCount":6778,"Q_Id":5851154,"Users Score":1,"Answer":"I'm assuming the graph is dynamic; otherwise, there's no reason not to use Floyd-Warshall to precompute all-pairs distances on such a small graph ;)\nSuppose you have a grid of points (x, y) with 0 <= x <= n, 0 <= y <= n. Upon removing an edge E: (i, j) <-> (i+1, j), you partition row j into sets A = { (0, j), ..., (i, j) }, B = { (i+1, j), ..., (n, j) } such that points a in A, b in B are forced to route around E - so you need only recompute distance for all pairs (a, b) in (A, B).\nMaybe you can precompute Floyd-Warshall, then, and use something like this to cut recomputation down to O(n^2) (or so) per graph modification...","Q_Score":14,"Tags":"python,algorithm,dijkstra,shortest-path,graph-algorithm","A_Id":6589501,"CreationDate":"2011-05-01T20:23:00.000","Title":"best algorithm for finding distance for all pairs where edges' weight is 1","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"As the title said, I'm trying to implement an algorithm that finds out the distances between all pairs of nodes in given graph. But there is more: (Things that might help you)\n\nThe graph is unweighted. Meaning that all the edges can be considered as having weight of 1.\n|E| <= 4*|V|\nThe graph is pretty big (at most ~144 depth)\nThe graph is directed\nThere might be cycles \nI'm writing my code in python (please if you reference algorithms, code would be nice too :))\n\nI know about Johnson's algorithm, Floyd-Warshal, and Dijkstra for all pairs. But these algorithms are good when the graph has weights.\nI was wondering if there is a better algorithm for my case, because those algorithms are intended for weighted graphs.\nThanks!","AnswerCount":10,"Available Count":3,"Score":1.0,"is_accepted":false,"ViewCount":6778,"Q_Id":5851154,"Users Score":9,"Answer":"Run a breadth-first search from each node. Total time: O(|V| |E|) = O(|V|2), which is optimal.","Q_Score":14,"Tags":"python,algorithm,dijkstra,shortest-path,graph-algorithm","A_Id":5851436,"CreationDate":"2011-05-01T20:23:00.000","Title":"best algorithm for finding distance for all pairs where edges' weight is 1","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am running Python2.7.1 and OpenCV 2.2 without problems in my WinXP laptop and wrote a tracking program that is working without a glitch. But for some strange reason I cannot get the same program to run in any other computer where I tried to install OpenCV and Python (using the same binaries or appropriate 64 bit binaries). In those computers OpenCV seems to be correctly installed (although I have only tested and CaptureFromCamera() in the webcam of the laptop), but CaptureFromFile() return 'None' and give \"error: Array should be CvMat or IplImage\" after a QueryFrame, for example.\nThis simple code:\nimport cv   \/\nvideofile = cv.CaptureFromFile('a.avi')   \/\nframe = cv.QueryFrame(videofile)   \/\nprint type(videofile)   \/\nprint type(frame)\nreturns:\ntype 'cv.Capture'  \/\ntype 'NoneType'\n\nOpenCV and Python are in the windows PATH...\nI have moved the OpenCV site-packages content back and forth to the Pyhton27 Lib\\Site-packages folder.\nI tried different avi files (just in case it was some CODEC problem). This AVI uses MJPEG encoding (and GSpot reports that ffdshow Video Decoder is used for reading).\nImages work fine (I think): the simple convert code:\nim = cv.LoadImageM(\"c:\\tests\\colormap3.tif\")\ncv.SaveImage(\"c:\\tests\\colormap3-out.png\", im)\nopens, converts and saves the new image...\nI have tested with AVI files in different folders, using \"c:\\\", \"c:\/\", \"c:\\\" and \"c:\/\/\".\n\nI am lost here... Anyone has any idea of what stupid and noob mistake may be the cause of this? Thanks","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1705,"Q_Id":5858446,"Users Score":0,"Answer":"This must be an issue with the default codecs. OpenCV uses brute force methods to open video files or capture from camera. It goes by trial and error through all sources\/codecs\/apis it can find in some reasonable order. (at least 1.1 did so).\nThat means that on n different systems (or days) you may get n different ways of accessing the same video. The order of multiple webcams for instance, is also non-deterministic and may depend on plugging order or butterflies.\nFind out what your laptop uses, (re)install that on all the systems and retry.\nAlso, in the c version, you can look at the capture's properties\nlook for cvGetCaptureProperty and cvSetCaptureProperty where you might be able to hint to the format.\n[EDIT]\nJust looked i tup in the docs, these functions are also available in Python. Take a look, it should help.","Q_Score":1,"Tags":"opencv,python-2.7,iplimage","A_Id":5859924,"CreationDate":"2011-05-02T14:34:00.000","Title":"IplImage 'None' error on CaptureFromFile() - Python 2.7.1 and OpenCV 2.2 WinXP","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":1,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm looking for a way to learn to be comfortable with large data sets.  I'm a university student, so everything I do is of \"nice\" size and complexity.  Working on a research project with a professor this semester, and I've had to visualize relationships between a somewhat large (in my experience) data set.  It was a 15 MB CSV file.\nI wrote most of my data wrangling in Python, visualized using GNUPlot.\nAre there any accessible books or websites on the subject out there?  Bonus points for using Python, more bonus points for a more \"basic\" visualization system than relying on gnuplot.  Cairo or something, I suppose.\nLooking for something that takes me from data mining, to processing, to visualization.\nEDIT:  I'm more looking for something that will teach me the \"big ideas\".  I can write the code myself, but looking for techniques people use to deal with large data sets.  I mean, my 15 MB is small enough where I can put everything I would ever need into memory and just start crunching.  What do people do to visualize 5 GB data sets?","AnswerCount":4,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":2645,"Q_Id":5890935,"Users Score":2,"Answer":"If you are looking for visualization rather than data mining and analysis, The Visual Display of Quantitative Information by Edward Tufte is considered one of the best books in the field.","Q_Score":11,"Tags":"python,dataset,visualization,data-visualization","A_Id":5891605,"CreationDate":"2011-05-04T23:13:00.000","Title":"Acquiring basic skills working with visualizing\/analyzing large data sets","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm looking for a way to learn to be comfortable with large data sets.  I'm a university student, so everything I do is of \"nice\" size and complexity.  Working on a research project with a professor this semester, and I've had to visualize relationships between a somewhat large (in my experience) data set.  It was a 15 MB CSV file.\nI wrote most of my data wrangling in Python, visualized using GNUPlot.\nAre there any accessible books or websites on the subject out there?  Bonus points for using Python, more bonus points for a more \"basic\" visualization system than relying on gnuplot.  Cairo or something, I suppose.\nLooking for something that takes me from data mining, to processing, to visualization.\nEDIT:  I'm more looking for something that will teach me the \"big ideas\".  I can write the code myself, but looking for techniques people use to deal with large data sets.  I mean, my 15 MB is small enough where I can put everything I would ever need into memory and just start crunching.  What do people do to visualize 5 GB data sets?","AnswerCount":4,"Available Count":2,"Score":0.049958375,"is_accepted":false,"ViewCount":2645,"Q_Id":5890935,"Users Score":1,"Answer":"I like the book Data Analysis with Open Source Tools by Janert. It is a pretty broad survey of data analysis methods, focusing on how to understand the system that produced the data, rather than on sophisticated statistical methods. One caveat: while the mathematics used isn't especially advanced, I do think you will need to be comfortable with mathematical arguments to gain much from the book.","Q_Score":11,"Tags":"python,dataset,visualization,data-visualization","A_Id":5908938,"CreationDate":"2011-05-04T23:13:00.000","Title":"Acquiring basic skills working with visualizing\/analyzing large data sets","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a rank-1 numpy.array of which I want to make a boxplot. However, I want to exclude all values equal to zero in the array. Currently, I solved this by looping the array and copy the value to a new array if not equal to zero. However, as the array consists of 86 000 000 values and I have to do this multiple times, this takes a lot of patience.\nIs there a more intelligent way to do this?","AnswerCount":7,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":149688,"Q_Id":5927180,"Users Score":0,"Answer":"[i for i in Array if i != 0.0] if the numbers are float\nor [i for i in SICER if i != 0] if the numbers are int.","Q_Score":51,"Tags":"python,arrays,numpy,filtering","A_Id":67419895,"CreationDate":"2011-05-08T11:36:00.000","Title":"How do I remove all zero elements from a NumPy array?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"sorry for my English in advance. \nI am a beginner with Cassandra and his data model. I am trying to insert one million rows in a cassandra database in local on one node. Each row has 10 columns and I insert those only in one column family.\nWith one thread, that operation took around 3 min. But I would like do the same operation with 2 millions rows, and keeping a good time. Then I tried with 2 threads to insert 2 millions rows, expecting a similar result around 3-4min. bUT i gor a result like 7min...twice the first result. As I check on differents forums, multithreading is recommended to improve performance.\nThat is why I am asking that question : is it useful to use multithreading to insert data in local node (client and server are in the same computer), in only one column family?\nSome informations :\n - I use pycassa\n - I have separated commitlog repertory and data repertory on differents disks\n - I use batch insert for each thread\n - Consistency Level : ONE\n - Replicator factor : 1","AnswerCount":4,"Available Count":4,"Score":0.0,"is_accepted":false,"ViewCount":1686,"Q_Id":5950427,"Users Score":0,"Answer":"It's possible you're hitting the python GIL but more likely you're doing something wrong.\nFor instance, putting 2M rows in a single batch would be Doing It Wrong.","Q_Score":0,"Tags":"python,multithreading,insert,cassandra","A_Id":5950881,"CreationDate":"2011-05-10T13:02:00.000","Title":"Insert performance with Cassandra","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"sorry for my English in advance. \nI am a beginner with Cassandra and his data model. I am trying to insert one million rows in a cassandra database in local on one node. Each row has 10 columns and I insert those only in one column family.\nWith one thread, that operation took around 3 min. But I would like do the same operation with 2 millions rows, and keeping a good time. Then I tried with 2 threads to insert 2 millions rows, expecting a similar result around 3-4min. bUT i gor a result like 7min...twice the first result. As I check on differents forums, multithreading is recommended to improve performance.\nThat is why I am asking that question : is it useful to use multithreading to insert data in local node (client and server are in the same computer), in only one column family?\nSome informations :\n - I use pycassa\n - I have separated commitlog repertory and data repertory on differents disks\n - I use batch insert for each thread\n - Consistency Level : ONE\n - Replicator factor : 1","AnswerCount":4,"Available Count":4,"Score":0.0,"is_accepted":false,"ViewCount":1686,"Q_Id":5950427,"Users Score":0,"Answer":"Try running multiple clients in multiple processes, NOT threads.\nThen experiment with different insert sizes. \n1M inserts in 3 mins is about 5500 inserts\/sec, which is pretty good for a single local client. On a multi-core machine you should be able to get several times this amount provided that you use multiple clients, probably inserting small batches of rows, or individual rows.","Q_Score":0,"Tags":"python,multithreading,insert,cassandra","A_Id":5956519,"CreationDate":"2011-05-10T13:02:00.000","Title":"Insert performance with Cassandra","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"sorry for my English in advance. \nI am a beginner with Cassandra and his data model. I am trying to insert one million rows in a cassandra database in local on one node. Each row has 10 columns and I insert those only in one column family.\nWith one thread, that operation took around 3 min. But I would like do the same operation with 2 millions rows, and keeping a good time. Then I tried with 2 threads to insert 2 millions rows, expecting a similar result around 3-4min. bUT i gor a result like 7min...twice the first result. As I check on differents forums, multithreading is recommended to improve performance.\nThat is why I am asking that question : is it useful to use multithreading to insert data in local node (client and server are in the same computer), in only one column family?\nSome informations :\n - I use pycassa\n - I have separated commitlog repertory and data repertory on differents disks\n - I use batch insert for each thread\n - Consistency Level : ONE\n - Replicator factor : 1","AnswerCount":4,"Available Count":4,"Score":0.0,"is_accepted":false,"ViewCount":1686,"Q_Id":5950427,"Users Score":0,"Answer":"You might consider Redis.  Its single-node throughput is supposed to be faster.  It's different from Cassandra though, so whether or not it's an appropriate option would depend on your use case.","Q_Score":0,"Tags":"python,multithreading,insert,cassandra","A_Id":6078703,"CreationDate":"2011-05-10T13:02:00.000","Title":"Insert performance with Cassandra","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"sorry for my English in advance. \nI am a beginner with Cassandra and his data model. I am trying to insert one million rows in a cassandra database in local on one node. Each row has 10 columns and I insert those only in one column family.\nWith one thread, that operation took around 3 min. But I would like do the same operation with 2 millions rows, and keeping a good time. Then I tried with 2 threads to insert 2 millions rows, expecting a similar result around 3-4min. bUT i gor a result like 7min...twice the first result. As I check on differents forums, multithreading is recommended to improve performance.\nThat is why I am asking that question : is it useful to use multithreading to insert data in local node (client and server are in the same computer), in only one column family?\nSome informations :\n - I use pycassa\n - I have separated commitlog repertory and data repertory on differents disks\n - I use batch insert for each thread\n - Consistency Level : ONE\n - Replicator factor : 1","AnswerCount":4,"Available Count":4,"Score":0.0,"is_accepted":false,"ViewCount":1686,"Q_Id":5950427,"Users Score":0,"Answer":"The time taken doubled because you inserted twice as much data. Is it possible that you are I\/O bound?","Q_Score":0,"Tags":"python,multithreading,insert,cassandra","A_Id":8491215,"CreationDate":"2011-05-10T13:02:00.000","Title":"Insert performance with Cassandra","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm wondering if C has anything similar to the python pickle module that can dump some structured data on disk and then load it back later. \nI know that I can write my structure byte by byte to a file on disk and then read it back later, but with this approach there's still quite some work to do. For example, if I have a single link list structure, I can traverse the list from head to tail and write each node's data on disk. When I read the list back from the on-disk file, I have to reconstruct all links\nbetween each pair of nodes.\nPlease advise if there's an easier way.\nThanks heaps!","AnswerCount":3,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":207,"Q_Id":5987185,"Users Score":4,"Answer":"An emphatic NO on that one, I'm afraid. C has basic file I\/O. Any structuring of data is up to you. Make up a format, dump it out, read it in.\nThere may be libraries which can do this, but by itself no C doesn't do this.","Q_Score":2,"Tags":"python,c","A_Id":5987204,"CreationDate":"2011-05-13T04:19:00.000","Title":"does C has anything like python pickle for object serialisation?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I'm wondering if C has anything similar to the python pickle module that can dump some structured data on disk and then load it back later. \nI know that I can write my structure byte by byte to a file on disk and then read it back later, but with this approach there's still quite some work to do. For example, if I have a single link list structure, I can traverse the list from head to tail and write each node's data on disk. When I read the list back from the on-disk file, I have to reconstruct all links\nbetween each pair of nodes.\nPlease advise if there's an easier way.\nThanks heaps!","AnswerCount":3,"Available Count":2,"Score":0.1325487884,"is_accepted":false,"ViewCount":207,"Q_Id":5987185,"Users Score":2,"Answer":"The C library functions fread(3) and fwrite(3) will read and write 'elements of data', but that's pretty fanciful way of saying \"the C library will do some multiplication and pread(2) or pwrite(2) calls behind the scenes to fill your array\".\nYou can use them on structs, but it is probably not a good idea:\n\nholes in the structs get written and read\nyou're baking in the endianness of your integers\n\nWhile you can make your own format for writing objects, you might want to see if your application could use SQLite3 for on-disk storage of objects. It's well-debugged, and if your application fits its abilities well, it might be just the ticket. (And a lot easier than writing all your own formatting code.)","Q_Score":2,"Tags":"python,c","A_Id":5987230,"CreationDate":"2011-05-13T04:19:00.000","Title":"does C has anything like python pickle for object serialisation?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"Given a numpy.memmap object created with mode='r' (i.e. read-only), is there a way to force it to purge all loaded pages out of physical RAM, without deleting the object itself?\nIn other words, I'd like the reference to the memmap instance to remain valid, but all physical memory that's being used to cache the on-disk data to be uncommitted. Any views onto to the memmap array must also remain valid.\nI am hoping to use this as a diagnostic tool, to help separate \"real\" memory requirements of a script from \"transient\" requirements induced by the use of memmap.\nI'm using Python 2.7 on RedHat.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":487,"Q_Id":6021550,"Users Score":2,"Answer":"If you run \"pmap SCRIPT-PID\", the \"real\" memory shows as \"[ anon ]\" blocks, and all memory-mapped files show up with the file name in the last column.\nPurging the pages is possible at C level, if you manage to get ahold of the pointer to the beginning of the mapping and call madvise(ptr, length, MADV_DONTNEED) on it, but it's going to be cludgy.","Q_Score":0,"Tags":"python,numpy,mmap,memory-mapped-files,large-data","A_Id":6022144,"CreationDate":"2011-05-16T18:13:00.000","Title":"Purging numpy.memmap","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I was wondering whether someone might know the answer to the following.\nI'm using Python to build a character-based suffix tree.  There are over 11 million nodes in the tree which fits in to approximately 3GB of memory.  This was down from 7GB by using the slot class method rather than the Dict method.\nWhen I serialise the tree (using the highest protocol) the resulting file is more than a hundred times smaller.\nWhen I load the pickled file back in, it again consumes 3GB of memory.  Where does this extra overhead come from, is it something to do with Pythons handling of memory references to class instances?\nUpdate\nThank you larsmans and Gurgeh for your very helpful explanations and advice.  I'm using the tree as part of an information retrieval interface over a corpus of texts.\nI originally stored the children (max of 30) as a Numpy array, then tried the hardware version (ctypes.py_object*30), the Python array (ArrayType), as well as the dictionary and Set types. \nLists seemed to do better (using guppy to profile the memory, and __slots__['variable',...]), but I'm still trying to squash it down a bit more if I can. The only problem I had with arrays is having to specify their size in advance, which causes a bit of redundancy in terms of nodes with only one child, and I have quite a lot of them. ;-)\nAfter the tree is constructed I intend to convert it to a probabilistic tree with a second pass, but may be I can do this as the tree is constructed.  As construction time is not too important in my case, the array.array() sounds like something that would be useful to try, thanks for the tip, really appreciated.\nI'll let you know how it goes.","AnswerCount":2,"Available Count":1,"Score":0.2913126125,"is_accepted":false,"ViewCount":415,"Q_Id":6041395,"Users Score":3,"Answer":"Do you construct your tree once and then use it without modifying it further? In that case you might want to consider using separate structures for the dynamic construction and the static usage.\nDicts and objects are very good for dynamic modification, but they are not very space efficient in a read-only scenario. I don't know exactly what you are using your suffix tree for, but you could let each node be represented by a 2-tuple of a sorted array.array('c') and an equally long tuple of subnodes (a tuple instead of a vector to avoid overallocation). You traverse the tree using the bisect-module for lookup in the array. The index of a character in the array will correspond to a subnode in the subnode-tuple. This way you avoid dicts, objects and vector.\nYou could do something similar during the construction process, perhaps using a subnode-vector instead of subnode-tuple. But this will of course make construction slower, since inserting new nodes in a sorted vector is O(N).","Q_Score":10,"Tags":"python,class,serialization,memory-management,pickle","A_Id":6046352,"CreationDate":"2011-05-18T07:42:00.000","Title":"Python memory serialisation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"In numpy\/scipy, what's the canonical way to compute the inverse of an upper triangular matrix?\nThe matrix is stored as 2D numpy array with zero sub-diagonal elements, and the result should also be stored as a 2D array.\nedit The best I've found so far is scipy.linalg.solve_triangular(A, np.identity(n)). Is that it?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":6594,"Q_Id":6042308,"Users Score":7,"Answer":"There really isn't an inversion routine, per se. scipy.linalg.solve is the canonical way of solving a matrix-vector or matrix-matrix equation, and it can be given explicit information about the structure of the matrix which it will use to choose the correct routine (probably the equivalent of BLAS3 dtrsm in this case).\nLAPACK does include doptri for this purpose, and scipy.linalg does expose a raw C lapack interface. If the inverse matrix is really what you want, then you could try using that.","Q_Score":13,"Tags":"python,matrix,numpy,scipy,matrix-inverse","A_Id":6042505,"CreationDate":"2011-05-18T09:09:00.000","Title":"numpy: inverting an upper triangular matrix","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there a way to dump a NumPy array into a CSV file? I have a 2D NumPy array and need to dump it in human-readable format.","AnswerCount":12,"Available Count":1,"Score":0.0333209931,"is_accepted":false,"ViewCount":1040617,"Q_Id":6081008,"Users Score":2,"Answer":"If you want to save your numpy array (e.g. your_array = np.array([[1,2],[3,4]])) to one cell, you could convert it first with your_array.tolist().\nThen save it the normal way to one cell, with delimiter=';'\nand the cell in the csv-file will look like this [[1, 2], [2, 4]]\nThen you could restore your array like this:\nyour_array = np.array(ast.literal_eval(cell_string))","Q_Score":711,"Tags":"python,arrays,csv,numpy","A_Id":40091714,"CreationDate":"2011-05-21T10:01:00.000","Title":"Dump a NumPy array into a csv file","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"i looking for a example of character recognizing (just one - for example X or A)\nusing MLP, Backward propagation.\nI want a simple example, and not the entire library. Language does not matter, preferably one of those Java, Python, PHP","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":162,"Q_Id":6086560,"Users Score":1,"Answer":"Support Vector Machines tend to work much better for character recognition with error rates around 2% generally reported. I suggest that as an alternative if you're just using the character recognition as a module in a larger project.","Q_Score":0,"Tags":"java,php,python,neural-network","A_Id":6086737,"CreationDate":"2011-05-22T07:07:00.000","Title":"Backward propagation - Character Recognizing - Seeking an example","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an 2d array, A that is 6x6.  I would like to take the first 2 values (index 0,0 and 0,1) and take the average of the two and insert the average into a new array that is half the column size of A (6x3) at index 0,0.  Then i would get the next two indexes at A, take average and put into the new array at 0,1.\nThe only way I know how to do this is using a double for loop, but for performance purposes (I will be using arrays as big as 3000x3000) I know there is a better solution out there! Thanks!","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":1976,"Q_Id":6090288,"Users Score":1,"Answer":"I don't think there is a better solution, unless you have some extra information about what's in those arrays.  If they're just random numbers, you have to do (n^2)\/2 calculations, and your algorithm is reflecting that, running in O((n^2)\/2).","Q_Score":1,"Tags":"python,arrays,indexing,numpy,slice","A_Id":6090407,"CreationDate":"2011-05-22T19:43:00.000","Title":"python numpy array slicing","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am doing some simple programs with opencv in python.  I want to write a few algorithms myself, so need to get at the 'raw' image data inside an image.  I can't just do image[i,j] for example, how can I get at the numbers?\nThanks","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":7276,"Q_Id":6127314,"Users Score":0,"Answer":"I do not know opencv python bindings, but in C or C++ you have to get the buffer pointer stored in IplImage. This buffer is coded according to the image format (also stored in IplImage). For RGB you have a byte for R, a byte for G, a byte for B, and so on.\nLook at the API of python bindings,you will find how to access the buffer and then you can get to pixel info.\nmy2c","Q_Score":4,"Tags":"python,opencv,iplimage","A_Id":6127643,"CreationDate":"2011-05-25T15:53:00.000","Title":"Opencv... getting at the data in an IPLImage or CvMat","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a moderately large piece (a few thousand lines) of Python\/Numpy\/Scipy code that is throwing up NaNs with certain inputs. I've looked for, and found, some of the usual suspects (log(0) and the like), but none of the obvious ones seem to be the culprits in this case. \nIs there a relatively painless way (i.e., apart from putting exception handling code around each potential culprit), to find out where these NaNs are coming from?","AnswerCount":2,"Available Count":1,"Score":0.2913126125,"is_accepted":false,"ViewCount":1232,"Q_Id":6213869,"Users Score":3,"Answer":"You can use numpy.seterr to set floating point error handling behaviour globally for all numpy routines. That should let you pinpoint where in the code they are arising from (or a least where numpy see them for the first time).","Q_Score":9,"Tags":"python,debugging,numpy,scipy,nan","A_Id":6213966,"CreationDate":"2011-06-02T11:24:00.000","Title":"Finding the calculation that generates a NaN","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am now trying some stuff with PCA but it's very important for me to know which are the features responsible for each eigenvalue. \nnumpy.linalg.eig gives us the diagonal matrix already sorted but I wanted this matrix with them at the original positions. Does anybody know how I can make it?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":1121,"Q_Id":6227589,"Users Score":1,"Answer":"What Sven mentioned in his comments is correct. There is no \"default\" ordering of the eigenvalues. Each eigenvalue is associated with an eigenvector, and it is important is that the eigenvalue-eigenvector pair is matched correctly. You'll find that all languages and packages will do so. \nSo if R gives you eigenvalues [e1,e2,e3 and eigenvectors [v1,v2,v3], python probably will give you (say) [e3,e2,e1] and [v3,v2,v1].\nRecall that an eigenvalue tells you how much of the variance in your data is explained by the eigenvector associated with it. So, a natural sorting of the eigenvalues (that is intuitive to us) that is useful in PCA, is by size (either ascending or descending). That way, you can easily look at the eigenvalues and identify which ones to keep (large, as they explain most of the data) and which ones to throw (small, which could be high frequency features or just noise)","Q_Score":1,"Tags":"python,pca","A_Id":6229101,"CreationDate":"2011-06-03T13:18:00.000","Title":"Non sorted eigenvalues for finding features in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I just started to learn to program in Python and I am trying to construct a sparse matrix using Scipy package.  I found that there are different types of sparse matrices, but all of them require to store using three vectors like  row, col, data; or if you want to each new entry separately, like S(i,j) = s_ij you need to initiate the matrix with a given size.\nMy question is if there is a way to store the matrix entrywise without needing the initial size, like a dictionary.","AnswerCount":3,"Available Count":1,"Score":0.1325487884,"is_accepted":false,"ViewCount":4150,"Q_Id":6310087,"Users Score":2,"Answer":"No. Any matrix in Scipy, sparse or not, must be instantiated with a size.","Q_Score":1,"Tags":"python,scipy,sparse-matrix","A_Id":6312608,"CreationDate":"2011-06-10T17:38:00.000","Title":"sparse matrix from dictionaries","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to load an object (of a custom class Area) from a file using pickler.  I'm using python 3.1.\nThe file was made with pickle.dump(area, f)\nI get the following error, and I would like help trying to understand and fix it.\n  File \"editIO.py\", line 12, in load\n    area = pickle.load(f)\n  File \"C:\\Python31\\lib\\pickle.py\", line 1356, in load\n    encoding=encoding, errors=errors).load()\nUnicodeDecodeError: 'gbk' codec can't decode bytes in position 0-1: illegal multibyte sequence","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":4835,"Q_Id":6320415,"Users Score":2,"Answer":"It's hard to say without you showing your code, but it looks like you opened the file in text mode with a \"gbk\" encoding. It should probably be opened in binary mode. If that doesn't happen, make a small code example that fails, and paste it in here.","Q_Score":1,"Tags":"python,unicode,python-3.x,decode,pickle","A_Id":6320431,"CreationDate":"2011-06-12T05:49:00.000","Title":"UnicodeDecodeError: 'gbk' codec can't decode bytes","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a python function that generates a list with random values.\nAfter I call this function, I call another function that plots the random values using matplotlib.\nI want to be able to click some key on the keyboard \/ mouse, and have the following happen:\n(1)  a new list of random values will be re-generated\n(2)  the values from (1) will be plotted (replacing the current matplotlib chart)\nMeaning, I want to be able to view new charts with a click of a button. How do I go about doing this in python?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1009,"Q_Id":6333345,"Users Score":0,"Answer":"from matplotlib.widgets import Button                                                                                      \nreal_points = plt.axes().scatter(x=xpts, y=ypts, alpha=.4, s=size, c='green', label='real data')                        \n#Reset Button\n#rect = [left, bottom, width, height]\nreset_axis = plt.axes([0.4, 0.15, 0.1, 0.04])\nbutton = Button(ax=reset_axis, label='Reset', color='lightblue' , hovercolor='0.975')                                      \ndef reset(event):\n    real_points.remove()                                                                                                   \nbutton.on_clicked(reset)                                                                                                   \nplt.show()","Q_Score":1,"Tags":"python,matplotlib","A_Id":26627020,"CreationDate":"2011-06-13T16:32:00.000","Title":"python matplotlib -- regenerate graph?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"What is the fastest FFT implementation in Python?\nIt seems numpy.fft and scipy.fftpack both are based on fftpack, and not FFTW. Is fftpack as fast as FFTW? What about using multithreaded FFT, or using distributed (MPI) FFT?","AnswerCount":6,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":27277,"Q_Id":6365623,"Users Score":2,"Answer":"Where I work some researchers have compiled this Fortran library which setups and calls the FFTW for a particular problem. This Fortran library (module with some subroutines) expect some input data (2D lists) from my Python program.\nWhat I did was to create a little C-extension for Python wrapping the Fortran library, where I basically calls \"init\" to setup a FFTW planner, and another function to feed my 2D lists (arrays), and a \"compute\" function.\nCreating a C-extensions is a small task, and there a lot of good tutorials out there for that particular task.\nTo good thing about this approach is that we get speed .. a lot of speed. The only drawback is in the C-extension where we must iterate over the Python list, and extract all the Python data into a memory buffer.","Q_Score":31,"Tags":"python,numpy,scipy,fft,fftw","A_Id":6368360,"CreationDate":"2011-06-15T23:28:00.000","Title":"Improving FFT performance in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In a matplotlib figure, how can I make the font size for the tick labels using ax1.set_xticklabels() smaller?\nFurther, how can one rotate it from horizontal to vertical?","AnswerCount":10,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":870128,"Q_Id":6390393,"Users Score":23,"Answer":"In current versions of Matplotlib, you can do axis.set_xticklabels(labels, fontsize='small').","Q_Score":421,"Tags":"python,matplotlib","A_Id":34919615,"CreationDate":"2011-06-17T18:49:00.000","Title":"Matplotlib make tick labels font size smaller","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In a matplotlib figure, how can I make the font size for the tick labels using ax1.set_xticklabels() smaller?\nFurther, how can one rotate it from horizontal to vertical?","AnswerCount":10,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":870128,"Q_Id":6390393,"Users Score":16,"Answer":"For smaller font, I use \nax1.set_xticklabels(xticklabels, fontsize=7)\nand it works!","Q_Score":421,"Tags":"python,matplotlib","A_Id":37869225,"CreationDate":"2011-06-17T18:49:00.000","Title":"Matplotlib make tick labels font size smaller","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I can't find any documentation on how numpy handles unmapping of previously memory mapped regions: munmap for numpy.memmap() and numpy.load(mmap_mode).\nMy guess is it's done only at garbage collection time, is that correct?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2924,"Q_Id":6397495,"Users Score":15,"Answer":"Yes, it's only closed when the object is garbage-collected; memmap.close method does nothing.\nYou can call x._mmap.close(), but keep in mind that any further access to the x object will crash python.","Q_Score":15,"Tags":"python,numpy,mmap","A_Id":6398543,"CreationDate":"2011-06-18T16:54:00.000","Title":"Unmap of NumPy memmap","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am not sure if this question is correct, but I am asking to resolve the doubts I have.  \n\nFor Machine Learning\/Data Mining, we need to learn about data, which means you need to learn Hadoop, which has implementation in Java for MapReduce(correct me if I am wrong).  \nHadoop also provides streaming api to support other languages(like python)  \nMost grad students\/researchers I know solve ML problems in python  \nwe see job posts for hadoop and Java combination very often  \n\nI observed that Java and Python(in my observation) are most widely used languages for this domain.  \n\nMy question is what is most popular language for working on this domain.  \nwhat factors involve in deciding which language\/framework one should choose\nI know both Java and python but confused always :\n\nwhether I start programming in Java(because of hadoop implementation)\nwhether I start programming in Python(because its easier and quicker to write)\n\n\nThis is a very open ended question, I am sure the advices might help me and people who have same doubt.\nThanks a lot in advance","AnswerCount":5,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1400,"Q_Id":6429772,"Users Score":0,"Answer":"Python is gaining in popularity, has a lot of libraries, and is very useful for prototyping. I find that due to the many versions of python and its dependencies on C libs to be difficult to deploy though.\nR is also very popular, has a lot of libraries, and was designed for data science. However, the underlying language design tends to make things overcomplicated.\nPersonally, I prefer Clojure because it has great data manipulation support and can interop with Java ecosystem. The downside of it currently is that there aren't too many data science libraries yet!","Q_Score":2,"Tags":"java,python,hadoop,machine-learning,bigdata","A_Id":44827155,"CreationDate":"2011-06-21T17:54:00.000","Title":"ML\/Data Mining\/Big Data : Popular language for programming and community support","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am not sure if this question is correct, but I am asking to resolve the doubts I have.  \n\nFor Machine Learning\/Data Mining, we need to learn about data, which means you need to learn Hadoop, which has implementation in Java for MapReduce(correct me if I am wrong).  \nHadoop also provides streaming api to support other languages(like python)  \nMost grad students\/researchers I know solve ML problems in python  \nwe see job posts for hadoop and Java combination very often  \n\nI observed that Java and Python(in my observation) are most widely used languages for this domain.  \n\nMy question is what is most popular language for working on this domain.  \nwhat factors involve in deciding which language\/framework one should choose\nI know both Java and python but confused always :\n\nwhether I start programming in Java(because of hadoop implementation)\nwhether I start programming in Python(because its easier and quicker to write)\n\n\nThis is a very open ended question, I am sure the advices might help me and people who have same doubt.\nThanks a lot in advance","AnswerCount":5,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1400,"Q_Id":6429772,"Users Score":0,"Answer":"I think in this field most popular combination is Java\/Hadoop. When vacancies requires also python\/perl\/ruby it usually means that they are migrating from those script languages(usually main languages till that time) to java due to moving from startup code base to enterprise. \nAlso in real world data mining application python is frequently used for prototyping, small sized data processing tasks.","Q_Score":2,"Tags":"java,python,hadoop,machine-learning,bigdata","A_Id":6436938,"CreationDate":"2011-06-21T17:54:00.000","Title":"ML\/Data Mining\/Big Data : Popular language for programming and community support","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Given a list of tuples where each tuple consists of a probability and an item I'd like to sample an item according to its probability. For example, give the list [ (.3, 'a'), (.4, 'b'), (.3, 'c')] I'd like to sample 'b' 40% of the time. \nWhat's the canonical way of doing this in python?\nI've looked at the random module which doesn't seem to have an appropriate function and at numpy.random which although it has a multinomial function doesn't seem to return the results in a nice form for this problem. I'm basically looking for something like mnrnd in matlab.\nMany thanks.\nThanks for all the answers so quickly. To clarify, I'm not looking for explanations of how to write a sampling scheme, but rather to be pointed to an easy way to sample from a multinomial distribution given a set of objects and weights, or to be told that no such function exists in a standard library and so one should write one's own.","AnswerCount":9,"Available Count":2,"Score":0.022218565,"is_accepted":false,"ViewCount":12259,"Q_Id":6432499,"Users Score":1,"Answer":"Howabout creating 3 \"a\", 4 \"b\" and 3 \"c\" in a list an then just randomly select one. With enough iterations you will get the desired probability.","Q_Score":29,"Tags":"python,statistics,numpy,probability,random-sample","A_Id":6432586,"CreationDate":"2011-06-21T21:56:00.000","Title":"How to do weighted random sample of categories in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Given a list of tuples where each tuple consists of a probability and an item I'd like to sample an item according to its probability. For example, give the list [ (.3, 'a'), (.4, 'b'), (.3, 'c')] I'd like to sample 'b' 40% of the time. \nWhat's the canonical way of doing this in python?\nI've looked at the random module which doesn't seem to have an appropriate function and at numpy.random which although it has a multinomial function doesn't seem to return the results in a nice form for this problem. I'm basically looking for something like mnrnd in matlab.\nMany thanks.\nThanks for all the answers so quickly. To clarify, I'm not looking for explanations of how to write a sampling scheme, but rather to be pointed to an easy way to sample from a multinomial distribution given a set of objects and weights, or to be told that no such function exists in a standard library and so one should write one's own.","AnswerCount":9,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":12259,"Q_Id":6432499,"Users Score":0,"Answer":"I'm not sure if this is the pythonic way of doing what you ask, but you could use\n    random.sample(['a','a','a','b','b','b','b','c','c','c'],k)\nwhere k is the number of samples you want. \nFor a more robust method, bisect the unit interval into sections based on the cumulative probability and draw from the uniform distribution (0,1) using random.random().  In this case the subintervals would be (0,.3)(.3,.7)(.7,1).  You choose the element based on which subinterval it falls into.","Q_Score":29,"Tags":"python,statistics,numpy,probability,random-sample","A_Id":6432588,"CreationDate":"2011-06-21T21:56:00.000","Title":"How to do weighted random sample of categories in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it possible to do clustering in gensim for a given set of inputs using LDA? How can I go about it?","AnswerCount":4,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":13867,"Q_Id":6486738,"Users Score":0,"Answer":"The basic thing to understand here is that clustering requires your data to be present in a format and is not concerned with how did you arrive at your data. So, whether you apply clustering on the term-document matrix or on the reduced-dimension (LDA output matrix), clustering will work irrespective of that.\nJust do the other things right though, small mistakes in data formats can cost you a lot of time of research.","Q_Score":10,"Tags":"python,algorithm,cluster-analysis,latent-semantic-indexing","A_Id":37608130,"CreationDate":"2011-06-26T21:03:00.000","Title":"Clustering using Latent Dirichlet Allocation algo in gensim","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it possible to do clustering in gensim for a given set of inputs using LDA? How can I go about it?","AnswerCount":4,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":13867,"Q_Id":6486738,"Users Score":10,"Answer":"LDA produces a lower dimensional representation of the documents in a corpus. To this low-d representation you could apply a clustering algorithm, e.g. k-means. Since each axis corresponds to a topic, a simpler approach would be assigning each document to the topic onto which its projection is largest.","Q_Score":10,"Tags":"python,algorithm,cluster-analysis,latent-semantic-indexing","A_Id":6525268,"CreationDate":"2011-06-26T21:03:00.000","Title":"Clustering using Latent Dirichlet Allocation algo in gensim","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Suppose that you have a machine that gets fed with real-time stock prices from the exchange. These prices need to be transferred to 50 other machines in your network in the fastest possible way, so that each of them can run its own processing on the data.\nWhat would be the best \/ fastest way to send the data over to the other 50 machines?\nI am looking for a solution that would work on linux, using python as the programming language. Some ideas that I had are:\n(1) Send it to the other machines using python's ZeroMQ module\n(2) Save the data to a shared folder and have the 50 machines read it using NFS\nAny other ideas?","AnswerCount":3,"Available Count":3,"Score":0.0665680765,"is_accepted":false,"ViewCount":1280,"Q_Id":6549488,"Users Score":1,"Answer":"I'm pretty sure sending with ZeroMQ will be substantially quicker than saving and loading files.\nThere are other ways to send information over the network, such as raw sockets (lower level), AMQP implementations like RabbitMQ (more structured\/complicated), HTTP requests\/replies, and so on. ZeroMQ is a pretty good option, but it probably depends on your situation.\nYou could also look at frameworks for distributed computing, such as that in IPython.","Q_Score":1,"Tags":"python,linux,zeromq","A_Id":6550992,"CreationDate":"2011-07-01T14:44:00.000","Title":"Distributing Real-Time Market Data Using ZeroMQ \/ NFS?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"Suppose that you have a machine that gets fed with real-time stock prices from the exchange. These prices need to be transferred to 50 other machines in your network in the fastest possible way, so that each of them can run its own processing on the data.\nWhat would be the best \/ fastest way to send the data over to the other 50 machines?\nI am looking for a solution that would work on linux, using python as the programming language. Some ideas that I had are:\n(1) Send it to the other machines using python's ZeroMQ module\n(2) Save the data to a shared folder and have the 50 machines read it using NFS\nAny other ideas?","AnswerCount":3,"Available Count":3,"Score":1.2,"is_accepted":true,"ViewCount":1280,"Q_Id":6549488,"Users Score":1,"Answer":"I would go with zeromq with pub\/sub sockets..\nin your 2 option, your \"clients\" will have to refresh in order to get your file modifications.. like polling.. if you have some write error, you will have to handle this by hand, which won't be easy as well..\nzeromq is simple, reliable and powerful.. i think that perfectly fit your case..","Q_Score":1,"Tags":"python,linux,zeromq","A_Id":6552072,"CreationDate":"2011-07-01T14:44:00.000","Title":"Distributing Real-Time Market Data Using ZeroMQ \/ NFS?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"Suppose that you have a machine that gets fed with real-time stock prices from the exchange. These prices need to be transferred to 50 other machines in your network in the fastest possible way, so that each of them can run its own processing on the data.\nWhat would be the best \/ fastest way to send the data over to the other 50 machines?\nI am looking for a solution that would work on linux, using python as the programming language. Some ideas that I had are:\n(1) Send it to the other machines using python's ZeroMQ module\n(2) Save the data to a shared folder and have the 50 machines read it using NFS\nAny other ideas?","AnswerCount":3,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":1280,"Q_Id":6549488,"Users Score":0,"Answer":"Definatly do NOT use the file system. ZeroMQ is a great solution wiht bindings in Py. I have some examples here: www.coastrd.com. Contact me if you need more help.","Q_Score":1,"Tags":"python,linux,zeromq","A_Id":6643883,"CreationDate":"2011-07-01T14:44:00.000","Title":"Distributing Real-Time Market Data Using ZeroMQ \/ NFS?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I'm still confused whether to use list or numpy array.\nI started with the latter, but since I have to do a lot of append \nI ended up with many vstacks slowing my code down.\nUsing list would solve this problem, but I also need to delete elements\nwhich again works well with delete on numpy array.\nAs it looks now I'll have to write my own data type (in a compiled language, and wrap).\nI'm just curious if there isn't a way to get the job done using a python type.\nTo summarize this are the criterions my data type would have to fulfil:\n\n2d n (variable) rows, each row k (fixed) elements\nin memory in one piece (would be nice for efficient operating)\nappend row (with an in average constant time, like C++ vector just always k elements)\ndelete a set of elements (best: inplace, keep free space at the end for later append)\naccess element given the row and column index ( O(1) like data[row*k+ column]\n\nIt appears generally useful to me to have a data type like this and not impossible to implement in C\/Fortran.\nWhat would be the closest I could get with python?\n(Or maybe, Do you think it would work to write a python class for the datatype? what performance should I expect in this case?)","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":762,"Q_Id":6577657,"Users Score":1,"Answer":"As I see it, if you were doing this in C or Fortran, you'd have to have an idea of the size of the array so that you can allocate the correct amount of memory (ignoring realloc!). So assuming you do know this, why do you need to append to the array?\nIn any case, numpy arrays have the resize method, which you can use to extend the size of the array.","Q_Score":1,"Tags":"python,arrays,performance,numpy","A_Id":6581184,"CreationDate":"2011-07-05T03:04:00.000","Title":"Efficient Datatype Python (list or numpy array?)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How can I use pyplot.clabel to attach the file names to the lines being plotted?\nplt.clabel(data) line gives the error","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1086,"Q_Id":6577807,"Users Score":0,"Answer":"You may use plt.annotate or plt.text. \nAnd, as an aside, 1) you probably want to use different variables for the file names and numpy arrays you're loading your data into (what is data in data=plb.loadtxt(data)), \n2) you probably want to move the label positioning into the loop (in your code, what is data in the plt.clabel(data)).","Q_Score":0,"Tags":"python,matplotlib","A_Id":6580497,"CreationDate":"2011-07-05T03:49:00.000","Title":"matplotlib.pyplot how to add labels with .clabel?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am currently working with numpy on a 32bit system (Ubuntu 10.04 LTS).\nCan I expect my code to work fluently, in the same manner, on a 64bit (Ubuntu) system?\nDoes numpy have an compatibility issues with 64bit python?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2531,"Q_Id":6585176,"Users Score":4,"Answer":"NumPy has been used on 64-bit systems of all types for years now. I doubt you will find anything new that doesn't show up elsewhere as well.","Q_Score":1,"Tags":"python,numpy","A_Id":6585193,"CreationDate":"2011-07-05T15:29:00.000","Title":"Python: 64bit Numpy?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to get reproducible results with the genetic programming code in chapter 11 of \"Programming Collective Intelligence\" by Toby Segaran. However, simply setting seed \"random.seed(55)\" does not appear to work, changing the original code \"from random import ....\" to \"import random\" doesn't help, nor does changing Random().  These all seem to do approximately the same thing, the trees start out building the same, then diverge.\nIn reading various entries about the behavior of random, I can find no reason, given his GP code, why this divergence should happen.  There doesn't appear to be anything in the code except calls to random, that has any variability that would account for this behavior.  My understanding is that calling random.seed() should set all the calls correctly and since the code isn't threaded at all, I'm not sure how or why the divergence is happening.\nHas anyone modified this code to behave reproducibly? Is there some form of calling random.seed() that may work better?\nI apologize for not posting an example, but the code is obviously not mine (I'm adding only the call to seed and changing how random is called in the code) and this doesn't appear to be a simple issue with random (I've read all the entries on Python random here and many on the web in general).\nThanks.\nMark L.","AnswerCount":3,"Available Count":1,"Score":0.3215127375,"is_accepted":false,"ViewCount":2656,"Q_Id":6614447,"Users Score":5,"Answer":"I had the same problem just now with some completely unrelated code. I believe my solution was similar to that in eryksun's answer, though I didn't have any trees. What I did have were some sets, and I was doing random.choice(list(set)) to pick values from them. Sometimes my results (the items picked) were diverging even with the same seed each time and I was close to pulling my hair out. After seeing eryksun's answer here I tried random.choice(sorted(set)) instead, and the problem appears to have disappeared. I don't know enough about the inner workings of Python to explain it.","Q_Score":7,"Tags":"python","A_Id":9271325,"CreationDate":"2011-07-07T17:12:00.000","Title":"Python random seed not working with Genetic Programming example code","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am attempting to apply k-means on a set of high-dimensional data points (about 50 dimensions) and was wondering if there are any implementations that find the optimal number of clusters. \nI remember reading somewhere that the way an algorithm generally does this is such that the inter-cluster distance is maximized and intra-cluster distance is minimized but I don't remember where I saw that. It would be great if someone can point me to any resources that discuss this. I am using SciPy for k-means currently but any related library would be fine as well.\nIf there are alternate ways of achieving the same or a better algorithm, please let me know.","AnswerCount":7,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":26563,"Q_Id":6615665,"Users Score":0,"Answer":"If the cluster number is unknow, why not use Hierarchical Clustering instead?\nAt the begining, every isolated one is a cluster, then every two cluster will be merged if their distance is lower than a threshold, the algorithm will end when no more merger goes.\nThe Hierarchical clustering algorithm can carry out a suitable \"K\" for your data.","Q_Score":41,"Tags":"python,machine-learning,data-mining,k-means","A_Id":19444825,"CreationDate":"2011-07-07T18:58:00.000","Title":"Kmeans without knowing the number of clusters?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am attempting to apply k-means on a set of high-dimensional data points (about 50 dimensions) and was wondering if there are any implementations that find the optimal number of clusters. \nI remember reading somewhere that the way an algorithm generally does this is such that the inter-cluster distance is maximized and intra-cluster distance is minimized but I don't remember where I saw that. It would be great if someone can point me to any resources that discuss this. I am using SciPy for k-means currently but any related library would be fine as well.\nIf there are alternate ways of achieving the same or a better algorithm, please let me know.","AnswerCount":7,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":26563,"Q_Id":6615665,"Users Score":0,"Answer":"One way to do it is to run k-means with large k (much larger than what you think is the correct number), say 1000. then, running mean-shift algorithm on the these 1000 point (mean shift uses the whole data but you will only \"move\" these 1000 points). mean shift will find the amount of clusters then.\nRunning mean shift without the k-means before is a possibility but it is just too slow usually O(N^2*#steps), so running k-means before will speed things up: O(NK#steps)","Q_Score":41,"Tags":"python,machine-learning,data-mining,k-means","A_Id":33374054,"CreationDate":"2011-07-07T18:58:00.000","Title":"Kmeans without knowing the number of clusters?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm processing some data for a research project, and I'm writing all my scripts in python. I've been using matplotlib to create graphs to present to my supervisor. However, he is a die-hard MATLAB user and he wants me to send him MATLAB .fig files rather than SVG images.\nI've looked all over but can't find anything to do the job. Is there any way to either export .fig files from matplotlib, convert .svg files to .fig, or import .svg files into MATLAB?","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":2637,"Q_Id":6618132,"Users Score":2,"Answer":"Without access to (or experience with matlab) this is going to be a bit tricky. As Amro stated, .fig files store the underlying data, and not just an image, and you're going to have a hard time saving .fig files from python. There are however a couple of things which might work in your favour, these are: \n\nnumpy\/scipy can read and write matlab .mat files\nthe matplotlib plotting commands are very similar to\/ based on the matlab ones, so the code to generate plots from the data is going to be nearly identical (modulo round\/square brackets and 0\/1 based indexing).\n\nMy approach would be to write your data out as .mat files, and then just put your plotting commands in a script and give that to your supervisor - with any luck it shouldn't be too hard for him to recreate the plots based on that information.\nIf you had access to Matlab to test\/debug, I'm sure it would be possible to create some code which automagically created .mat files and a matlab .m file which would recreate the figures.\nThere's a neat list of matlab\/scipy equivalent commands on the scipy web site.\ngood luck!","Q_Score":4,"Tags":"python,matlab,matplotlib","A_Id":6631872,"CreationDate":"2011-07-07T22:59:00.000","Title":"Any way to get a figure from Python's matplotlib into Matlab?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"INTRODUCTION: I have a list of more than 30,000 integer values ranging from 0 to 47, inclusive, e.g.[0,0,0,0,..,1,1,1,1,...,2,2,2,2,...,47,47,47,...] sampled from some continuous distribution. The values in the list are not necessarily in order, but order doesn't matter for this problem.\nPROBLEM: Based on my distribution I would like to calculate p-value (the probability of seeing greater values) for any given value. For example, as you can see p-value for 0 would be approaching 1 and p-value for higher numbers would be tending to 0.\nI don't know if I am right, but to determine probabilities I think I need to fit my data to a theoretical distribution that is the most suitable to describe my data. I assume that some kind of goodness of fit test is needed to determine the best model.\nIs there a way to implement such an analysis in Python (Scipy or Numpy)?\nCould you present any examples?","AnswerCount":13,"Available Count":1,"Score":0.0461211021,"is_accepted":false,"ViewCount":181959,"Q_Id":6620471,"Users Score":3,"Answer":"What about storing your data in a dictionary where keys would be the numbers between 0 and 47 and values the number of occurrences of their related keys in your original list?\nThus your likelihood p(x) will be the sum of all the values for keys greater than x divided by 30000.","Q_Score":181,"Tags":"python,numpy,statistics,scipy,distribution","A_Id":6620533,"CreationDate":"2011-07-08T06:00:00.000","Title":"Fitting empirical distribution to theoretical ones with Scipy (Python)?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a gz file that with a huge size, is it possible to replace the tail without touching the rest of the file? I tried gzip.open( filePath, mode = 'r+' ) but the write method was blocked .... saying it is a read-only object ... any idea? \nwhat I am doing now is... gzip.open as r and once I get the offset of the start of the tail, I close it and re-open it with gzip.open as a and seek (offset)... which is not likely the best idea \nthanks \nJohn","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":350,"Q_Id":6626629,"Users Score":2,"Answer":"Not possible - you can not replace parts of a compressed file without decompressing it first. At least not with the common compression algorithms.","Q_Score":0,"Tags":"python,gzip,tail","A_Id":6626730,"CreationDate":"2011-07-08T15:20:00.000","Title":"replace\/modify tail of a gz file with gzip.open","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a set() with terms like 'A' 'B' 'C'. I want a 2-d associative array so that i can perform an operation like d['A']['B'] += 1 . What is the pythonic way of doing this, I was thinking a dicts of dicts. Is there a better way.","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":10309,"Q_Id":6696279,"Users Score":3,"Answer":"Is there any reason not to use a dict of dicts?  It does what you want (though note that there's no such thing as ++ in Python), after all.\nThere's nothing stylistically poor or non-Pythonic about using a dict of dicts.","Q_Score":5,"Tags":"python,associative-array","A_Id":6696330,"CreationDate":"2011-07-14T16:02:00.000","Title":"Two dimensional associative array in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I used matplotlib to create some plot, which depends on 8 variables. I would like to study how the plot changes when I change some of them. I created some script that calls the matplotlib one and generates different snapshots that later I convert into a movie, it is not bad, but a bit clumsy. \n\nI wonder if somehow I could interact with the plot regeneration using keyboard keys to increase \/ decrease values of some of the variables and see instantly how the plot changes. \nWhat is the best approach for this? \nAlso if you can point me to interesting links or a link with a plot example with just two sliders?","AnswerCount":6,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":98213,"Q_Id":6697259,"Users Score":2,"Answer":"Use waitforbuttonpress(timeout=0.001) then plot will see your mouse ticks.","Q_Score":70,"Tags":"python,keyboard,matplotlib,interactive","A_Id":11928786,"CreationDate":"2011-07-14T17:10:00.000","Title":"Interactive matplotlib plot with two sliders","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'd like to write a python script (call it parent) that does the following:\n(1) defines a multi-dimensional numpy array\n(2) forks 10 different python scripts (call them children). Each of them must be able to read the contents of the numpy array from (1) at any single point in time (as long as they are alive).\n(3) each of the child scripts will do it's own work (children DO NOT share any info with each other)\n(4) at any point in time, the parent script must be able to accept messages from all of its children. These messages will be parsed by the parent and cause the numpy array from (1) to change.\n\nHow do I go about this, when working in python in a Linux environment? I thought of using zeroMQ and have the parent be a single subscriber while the children will all be publishers; does it make sense or is there a better way for this?\nAlso, how do I allow all the children to continuously read the contents of the numpy array that was defined by the parent ?","AnswerCount":3,"Available Count":1,"Score":-0.1325487884,"is_accepted":false,"ViewCount":14920,"Q_Id":6700149,"Users Score":-2,"Answer":"In ZeroMQ there can only be one publisher per port. The only (ugly) workaround is to start each child PUB socket on a different port and have the parent listen on all those ports. \nbut the pipeline pattern describe on 0MQ, user guide is a much better way to do this.","Q_Score":9,"Tags":"python,zeromq","A_Id":6831300,"CreationDate":"2011-07-14T21:29:00.000","Title":"Python zeromq -- Multiple Publishers To a Single Subscriber?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I know there is a lot of questions about Python and OpenCV but I didn't find help on this special topic.\nI want to extract SIFT keypoints from an image in python OpenCV.\nI have recently installed OpenCV 2.3 and can access to SURF and MSER but not SIFT.\nI can't see anything related to SIFT in python modules (cv and cv2) (well I'm lying a bit: there are 2 constants: cv2.SIFT_COMMON_PARAMS_AVERAGE_ANGLE and cv2.SIFT_COMMON_PARAMS_FIRST_ANGLE).\nThis puzzles me since a while.\nIs that related to the fact that some parts of OpenCV are in C and other in C++?\nAny idea?\nP.S.: I have also tried pyopencv (another python binding for OpenCV <= 2.1) without success.","AnswerCount":5,"Available Count":1,"Score":0.0399786803,"is_accepted":false,"ViewCount":12701,"Q_Id":6722736,"Users Score":1,"Answer":"Are you sure OpenCV is allowed to support SIFT? SIFT is a proprietary feature type, patented within the U.S. by the University of British Columbia and by David Lowe, the inventor of the algorithm. In my own research, I have had to re-write this algorithm many times. In fact, some vision researchers try to avoid SIFT and use other scale-invariant models because SIFT is proprietary.","Q_Score":18,"Tags":"python,opencv,sift","A_Id":7152292,"CreationDate":"2011-07-17T08:40:00.000","Title":"OpenCV Python and SIFT features","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm looking for the fastest way to check for the occurrence of NaN (np.nan) in a NumPy array X. np.isnan(X) is out of the question, since it builds a boolean array of shape X.shape, which is potentially gigantic.\nI tried np.nan in X, but that seems not to work because np.nan != np.nan. Is there a fast and memory-efficient way to do this at all?\n(To those who would ask \"how gigantic\": I can't tell. This is input validation for library code.)","AnswerCount":8,"Available Count":2,"Score":0.1243530018,"is_accepted":false,"ViewCount":183568,"Q_Id":6736590,"Users Score":5,"Answer":"use .any()\nif numpy.isnan(myarray).any()\n\nnumpy.isfinite maybe better than isnan for checking\nif not np.isfinite(prop).all()","Q_Score":146,"Tags":"python,performance,numpy,nan","A_Id":46067557,"CreationDate":"2011-07-18T17:10:00.000","Title":"Fast check for NaN in NumPy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm looking for the fastest way to check for the occurrence of NaN (np.nan) in a NumPy array X. np.isnan(X) is out of the question, since it builds a boolean array of shape X.shape, which is potentially gigantic.\nI tried np.nan in X, but that seems not to work because np.nan != np.nan. Is there a fast and memory-efficient way to do this at all?\n(To those who would ask \"how gigantic\": I can't tell. This is input validation for library code.)","AnswerCount":8,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":183568,"Q_Id":6736590,"Users Score":34,"Answer":"I think np.isnan(np.min(X)) should do what you want.","Q_Score":146,"Tags":"python,performance,numpy,nan","A_Id":6736673,"CreationDate":"2011-07-18T17:10:00.000","Title":"Fast check for NaN in NumPy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working with a program that writes output to a csv file based on the order that files are read in from a directory.  However with a large number of files with the endings 1,2,3,4,5,6,7,8,9,10,11,12.  My program actually reads the files by I guess alphabetical ordering: 1,10,11,12....,2,20,21.....99.  The problem is that another program assumes that the ordering is in numerical ordering, and skews the graph results.\nThe actually file looks like: String.ext.ext2.1.txt, String.ext.ext2.2.txt, and so on...\nHow can I do this with a python script?","AnswerCount":4,"Available Count":1,"Score":0.2449186624,"is_accepted":false,"ViewCount":34380,"Q_Id":6743407,"Users Score":5,"Answer":"Sort your list of files in the program. Don't rely on operating system calls to give the files in the right order, it depends on the actual file system being used.","Q_Score":9,"Tags":"python,sorting,file-io","A_Id":6743440,"CreationDate":"2011-07-19T07:05:00.000","Title":"How to sort files in a directory before reading?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In Matlab, there is something called struct, which allow the user to have a dynamic set of matrices.\nI'm basically looking for a function that allows me to index over dynamic matrices that have different sizes.\nExample: (with 3 matrices)\n\nMatrix 1: 3x2 \nMatrix 2: 2x2 \nMatrix 3: 2x1\n\nBasically I want to store the 3 matrices on the same variable. To called them by their index number afterward (i.e. Matrix[1], Matrx[2]). Conventional python arrays do not allow for arrays with different dimensions to be stacked.\nI was looking into creating classes, but maybe someone her has a better alternative to this.\nThanks","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":4483,"Q_Id":6760380,"Users Score":7,"Answer":"Just use a tuple or list. \nA tuple matrices = tuple(matrix1, matrix2, matrix3) will be slightly more efficient;\nA list matrices = [matrix1, matrix2, matrix3] is more flexible as you can matrix.append(matrix4).\nEither way you can access them as matrices[0] or for matrix in matricies: pass # do stuff.","Q_Score":2,"Tags":"python,matrix","A_Id":6760471,"CreationDate":"2011-07-20T10:22:00.000","Title":"N-Dimensional Matrix Array in Python (with different sizes)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In Matlab, there is something called struct, which allow the user to have a dynamic set of matrices.\nI'm basically looking for a function that allows me to index over dynamic matrices that have different sizes.\nExample: (with 3 matrices)\n\nMatrix 1: 3x2 \nMatrix 2: 2x2 \nMatrix 3: 2x1\n\nBasically I want to store the 3 matrices on the same variable. To called them by their index number afterward (i.e. Matrix[1], Matrx[2]). Conventional python arrays do not allow for arrays with different dimensions to be stacked.\nI was looking into creating classes, but maybe someone her has a better alternative to this.\nThanks","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":4483,"Q_Id":6760380,"Users Score":0,"Answer":"Put those arrays into a list.","Q_Score":2,"Tags":"python,matrix","A_Id":6760481,"CreationDate":"2011-07-20T10:22:00.000","Title":"N-Dimensional Matrix Array in Python (with different sizes)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have ported python3 csv module to C# what license could I use for my module? \nShould I distribute my module? \nShould I put PSF copyright in every header of my module?\nthanks","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":122,"Q_Id":6761201,"Users Score":0,"Answer":"You need to pay a copyright lawyer to tell you that. But my guess is that you need to use the PSF license. Note that PSF does not have the copyright to Python source code. They coders do how that copyright translates into you making a C# port is something only a copyright expert can say. Also note that it is likely to vary from country to country.\nCopyright sucks.","Q_Score":1,"Tags":"python,module,licensing","A_Id":6761407,"CreationDate":"2011-07-20T11:33:00.000","Title":"Ported python3 csv module to C# what license should I use for my module?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've finished gathering my data I plan to use for my corpus, but I'm a bit confused about whether I should normalize the text. I plan to tag & chunk the corpus in the future. Some of NLTK's corpora are all lower case and others aren't.\nCan anyone shed some light on this subject, please?","AnswerCount":1,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":2668,"Q_Id":6767770,"Users Score":9,"Answer":"By \"normalize\" do you just mean making everything lowercase?  \nThe decision about whether to lowercase everything is really dependent of what you plan to do.  For some purposes, lowercasing everything is better because it lowers the sparsity of the data (uppercase words are rarer and might confuse the system unless you have a massive corpus such that the statistics on capitalized words are decent).  In other tasks, case information might be valuable.\nAdditionally, there are other considerations you'll have to make that are similar.  For example, should \"can't\" be treated as [\"can't\"], [\"can\", \"'t\"], or [\"ca\", \"n't\"] (I've seen all three in different corpora).  What about 7-year-old?  Is it one long word?  Or three words that should be separated?\nThat said, there's no reason to reformat the corpus.  You can just have your code make these changes on the fly.  That way the original information is still around later if you ever need it.","Q_Score":6,"Tags":"python,nlp,nltk","A_Id":6767866,"CreationDate":"2011-07-20T20:01:00.000","Title":"NLTK - when to normalize the text?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"The goal of this application is produce a system that can generate quizzes automatically. The user should be able to supply any word or phrase they like (e.g. \"Sachin Tendulkar\"); the system will then look for suitable topics online, identify a range of interesting facts, and rephrase them as quiz questions.\nIf I have the sentence \"Sachin was born in year 1973\", how can I rephrase it to \"Which Year was sachin born?\"","AnswerCount":1,"Available Count":1,"Score":0.6640367703,"is_accepted":false,"ViewCount":1334,"Q_Id":6787345,"Users Score":4,"Answer":"In the general case, this is a very hard open research question.  However, you might be able to get away with a simple solution a long as your \"facts\" follow a pretty simple grammar.  \nYou could write a fairly simple solution by creating a set of transformation rules that act on parse trees.  So if you saw a structure that matched the grammar for \"X was Y in Z\" you could transform it to \"Was X Y in Z ?\", etc.  Then all you would have to do is parse the fact, transform, and read off the question that is produced.","Q_Score":0,"Tags":"python,nlp,nltk","A_Id":6787446,"CreationDate":"2011-07-22T08:20:00.000","Title":"Quiz Generator using NLTK\/Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In Numpy (and Python in general, I suppose), how does one store a slice-index, such as (...,0,:), in order to pass it around and apply it to various arrays? It would be nice to, say, be able to pass a slice-index to and from functions.","AnswerCount":5,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":389,"Q_Id":6795657,"Users Score":0,"Answer":"I think you want to just do myslice = slice(1,2) to for example define a slice that will return the 2nd element (i.e. myarray[myslice] == myarray[1:2])","Q_Score":5,"Tags":"python,indexing,numpy,slice","A_Id":6795732,"CreationDate":"2011-07-22T20:20:00.000","Title":"Numpy: arr[...,0,:] works. But how do I store the data contained in the slice command (..., 0, :)?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to create a numpy array of N elements, but I want to access the\narray with an offset Noff, i.e. the first element should be at Noff and\nnot at 0. In C this is simple to do with some simple pointer arithmetic, i.e.\nI malloc the array and then define a pointer and shift it appropriately.\nFurthermore, I do not want to allocate N+Noff elements, but only N elements.\nNow for numpy there are many methods that come to my mind:\n(1) define a wrapper function to access the array\n(2) overwrite the [] operator\n(3) etc\nBut what is the fastest method to realize this?\nThanks a lot!\nMark","AnswerCount":3,"Available Count":2,"Score":0.1325487884,"is_accepted":false,"ViewCount":2276,"Q_Id":6800534,"Users Score":2,"Answer":"Use A[n-offset]. this turns offset to offset+len(A) into 0 to len(A).","Q_Score":1,"Tags":"python,numpy","A_Id":6801439,"CreationDate":"2011-07-23T13:03:00.000","Title":"numpy array access","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to create a numpy array of N elements, but I want to access the\narray with an offset Noff, i.e. the first element should be at Noff and\nnot at 0. In C this is simple to do with some simple pointer arithmetic, i.e.\nI malloc the array and then define a pointer and shift it appropriately.\nFurthermore, I do not want to allocate N+Noff elements, but only N elements.\nNow for numpy there are many methods that come to my mind:\n(1) define a wrapper function to access the array\n(2) overwrite the [] operator\n(3) etc\nBut what is the fastest method to realize this?\nThanks a lot!\nMark","AnswerCount":3,"Available Count":2,"Score":0.1325487884,"is_accepted":false,"ViewCount":2276,"Q_Id":6800534,"Users Score":2,"Answer":"I would be very cautious about over-riding the [] operator through the __getitem__() method. Although it will be fine with your own code, I can easily imagine that when the array gets passed to an arbitrary library function, you could get problems. \nFor example, if the function explicitly tried to get all values in the array as A[0:-1], it would maps to A[offset:offset-1], which will be an empty array for any positive or negative value of offset. This may be a little contrived, but it illustrates the general problem.\nTherefore, I would suggest that you create a wrapper function for your own use (as a member function may be most convenient), but don't muck around with __getitem__().","Q_Score":1,"Tags":"python,numpy","A_Id":6812332,"CreationDate":"2011-07-23T13:03:00.000","Title":"numpy array access","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to plot some (x,y) points on the same graph and I don't need any special features at all short of support for polar coordinates which would be nice but not necessary. It's mostly for visualizing my data. Is there a simple way to do this? Matplotlib seems like way more than I need right now. Are there any more basic modules available? What do You recommend?","AnswerCount":8,"Available Count":1,"Score":0.024994793,"is_accepted":false,"ViewCount":69333,"Q_Id":6819653,"Users Score":1,"Answer":"You could always write a plotting function that uses the turtle module from the standard library.","Q_Score":10,"Tags":"python,plot","A_Id":6819725,"CreationDate":"2011-07-25T16:55:00.000","Title":"Plotting points in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using a proprietary Matlab MEX file to import some simulation results in Matlab (no source code available of course!). The interface with Matlab is actually really simple, as there is a single function, returning a Matlab struct. I would like to know if there is any way to call this function in the MEX file directly from Python, without having to use Matlab?\nWhat I have in mind is for example using something like SWIG to import the C function into Python by providing a custom Matlab-wrapper around it...\nBy the way, I know that with scipy.io.loadmat it is already possible to read Matlab binary *.mat data files, but I don't know if the data representation in a mat file is the same as the internal representation in Matlab (in which case it might be useful for the MEX wrapper).\nThe idea would be of course to be able to use the function provided in the MEX with no Matlab installation present on the system.\nThanks.","AnswerCount":4,"Available Count":1,"Score":0.049958375,"is_accepted":false,"ViewCount":7858,"Q_Id":6848790,"Users Score":1,"Answer":"A mex function is an api that allows Matlab (i.e. a matlab program) to call a function written in c\/c++.  This function, in turn, can call Matlab own internal functions. As such, the mex function will be linked against Matlab libraries. Thus, to call a mex function directly from a Python program w\/o Matlab libraries doesn't look possible (and doesn't makes sense for that matter). \nOf consideration is why was the mex function created in the first place? Was it to make some non-matlab c libraries (or c code) available to matlab users, or was it to hide some proprietery matlab-code while still making it available to matlab users?  If its the first case, then you could request the owners of the mex function to provide it in a non-mex dynamic lib form that you can include in another c or python program. This should be easy if the mex function doesnt depend on Matlab internal functions.  \nOthers above have mentioned the matlab compiler... yes, you can include a mex function in a stand alone binary callable from unix (thus from python but as a unix call) if you use the Matlab Compiler to produce such binary. This would require the binary to be deployed along with Matlab's runtime environment. This is not quite the same as calling a function directly from python-- there are no return values for example.","Q_Score":7,"Tags":"python,matlab,mex","A_Id":11173545,"CreationDate":"2011-07-27T17:42:00.000","Title":"Embed a function from a Matlab MEX file directly in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In my python environment, the Rpy and Scipy packages are already installed. \nThe problem I want to tackle is such:\n1) A huge set of financial data are stored in a text file. Loading into Excel is not possible\n2) I need to sum a certain fields and get the totals.\n3) I need to show the top 10 rows based on the totals.\nWhich package (Scipy or Rpy) is best suited for this task? \nIf so, could you provide me some pointers (e.g. documentation or online example) that can help me to implement a solution?\nSpeed is a concern. Ideally scipy and Rpy can handle the large files when even when the files are so large that they cannot be fitted into memory","AnswerCount":6,"Available Count":3,"Score":0.0333209931,"is_accepted":false,"ViewCount":3050,"Q_Id":6853923,"Users Score":1,"Answer":"I don't know anything about Rpy.  I do know that SciPy is used to do serious number-crunching with truly large data sets, so it should work for your problem.\nAs zephyr noted, you may not need either one; if you just need to keep some running sums, you can probably do it in Python.  If it is a CSV file or other common file format, check and see if there is a Python module that will parse it for you, and then write a loop that sums the appropriate values. \nI'm not sure how to get the top ten rows.  Can you gather them on the fly as you go, or do you need to compute the sums and then choose the rows?  To gather them you might want to use a dictionary to keep track of the current 10 best rows, and use the keys to store the metric you used to rank them (to make it easy to find and toss out a row if another row supersedes it).  If you need to find the rows after the computation is done, slurp all the data into a numpy.array, or else just take a second pass through the file to pull out the ten rows.","Q_Score":7,"Tags":"python,r,numpy,scipy,memory-mapped-files","A_Id":6854030,"CreationDate":"2011-07-28T03:53:00.000","Title":"Python: handling a large set of data. Scipy or Rpy? And how?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In my python environment, the Rpy and Scipy packages are already installed. \nThe problem I want to tackle is such:\n1) A huge set of financial data are stored in a text file. Loading into Excel is not possible\n2) I need to sum a certain fields and get the totals.\n3) I need to show the top 10 rows based on the totals.\nWhich package (Scipy or Rpy) is best suited for this task? \nIf so, could you provide me some pointers (e.g. documentation or online example) that can help me to implement a solution?\nSpeed is a concern. Ideally scipy and Rpy can handle the large files when even when the files are so large that they cannot be fitted into memory","AnswerCount":6,"Available Count":3,"Score":0.1651404129,"is_accepted":false,"ViewCount":3050,"Q_Id":6853923,"Users Score":5,"Answer":"Neither Rpy or Scipy is necessary, although numpy may make it a bit easier.\nThis problem seems ideally suited to a line-by-line parser.\nSimply open the file, read a row into a string, scan the row into an array (see numpy.fromstring), update your running sums and move to the next line.","Q_Score":7,"Tags":"python,r,numpy,scipy,memory-mapped-files","A_Id":6853981,"CreationDate":"2011-07-28T03:53:00.000","Title":"Python: handling a large set of data. Scipy or Rpy? And how?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In my python environment, the Rpy and Scipy packages are already installed. \nThe problem I want to tackle is such:\n1) A huge set of financial data are stored in a text file. Loading into Excel is not possible\n2) I need to sum a certain fields and get the totals.\n3) I need to show the top 10 rows based on the totals.\nWhich package (Scipy or Rpy) is best suited for this task? \nIf so, could you provide me some pointers (e.g. documentation or online example) that can help me to implement a solution?\nSpeed is a concern. Ideally scipy and Rpy can handle the large files when even when the files are so large that they cannot be fitted into memory","AnswerCount":6,"Available Count":3,"Score":1.2,"is_accepted":true,"ViewCount":3050,"Q_Id":6853923,"Users Score":2,"Answer":"As @gsk3 noted, bigmemory is a great package for this, along with the packages biganalytics and bigtabulate (there are more, but these are worth checking out).  There's also ff, though that isn't as easy to use.\nCommon to both R and Python is support for HDF5 (see the ncdf4 or NetCDF4 packages in R), which makes it very speedy and easy to access massive data sets on disk.  Personally, I primarily use bigmemory, though that's R specific.  As HDF5 is available in Python and is very, very fast, it's probably going to be your best bet in Python.","Q_Score":7,"Tags":"python,r,numpy,scipy,memory-mapped-files","A_Id":7559475,"CreationDate":"2011-07-28T03:53:00.000","Title":"Python: handling a large set of data. Scipy or Rpy? And how?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to effeciently remove duplicate rows from relatively large (several hundred MB) CSV files that are not ordered in any meaningful way.  Although I have a technique to do this, it is very brute force and I am certain there is a moe elegant and more effecient way.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1090,"Q_Id":6863756,"Users Score":2,"Answer":"In order to remove duplicates you will have to have some sort of memory that tells you if you have seen a line before. Either by remembering the lines or perhaps a checksum of them (which is almost safe...)\nAny solution like that will probably have a \"brute force\" feel to it. \nIf you could have the lines sorted before processing them, then the task is fairly easy as duplicates would be next to each other.","Q_Score":1,"Tags":"python,csv,performance","A_Id":6863816,"CreationDate":"2011-07-28T18:21:00.000","Title":"Effeciently Removing Duplicates from a CSV in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"What is the difference between these two algorithms?","AnswerCount":3,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":11234,"Q_Id":6931209,"Users Score":16,"Answer":"In a use case (5D nearest neighbor look ups in a KDTree with approximately 100K points) cKDTree is around 12x faster than KDTree.","Q_Score":42,"Tags":"python,scipy,kdtree","A_Id":15331547,"CreationDate":"2011-08-03T18:16:00.000","Title":"Difference between scipy.spatial.KDTree and scipy.spatial.cKDTree","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have some numpy\/scipy issue. I have a 3D array that represent an ellipsoid in a binary way [ 0 out of the ellipsoid].\nThe thing is I would like to rotate my shape of a certain degree. Do you think it's possible ?\nOr is there an efficient way to write directly the ellipsoid equation with the rotation ?","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":2379,"Q_Id":6938377,"Users Score":1,"Answer":"Take a look at the command numpy.shape\nI used it once to transpose an array, but I don't know if it might fit your needs. \nCheers!","Q_Score":1,"Tags":"python,arrays,numpy,scipy,rotation","A_Id":6938587,"CreationDate":"2011-08-04T08:32:00.000","Title":"How to rotate a numpy array?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have some numpy\/scipy issue. I have a 3D array that represent an ellipsoid in a binary way [ 0 out of the ellipsoid].\nThe thing is I would like to rotate my shape of a certain degree. Do you think it's possible ?\nOr is there an efficient way to write directly the ellipsoid equation with the rotation ?","AnswerCount":2,"Available Count":2,"Score":0.1973753202,"is_accepted":false,"ViewCount":2379,"Q_Id":6938377,"Users Score":2,"Answer":"Just a short answer. If you need more informations or you don't know how to do it, then I will edit this post and add a small example. \nThe right way to rotate your matrix of data points is to do a matrix multiplication. Your rotation matrix would be probably an n*n-matrix and you have to multiply it with every point. If you have your 3d-matrix you have some thing like i*j*k-points for plotting. This means for your case you have to do it i*j*k-times to find the new points. Maybe you should consider an other matrix for plotting which is just a 2D matrix and just store the plotting points and no zero values. \nThere are some algorithm to calculate faster the results for low valued matrix, but just google for this. \nDid you understood me or do you still have some questions? Sorry for this rough overview.\nBest regards","Q_Score":1,"Tags":"python,arrays,numpy,scipy,rotation","A_Id":6941191,"CreationDate":"2011-08-04T08:32:00.000","Title":"How to rotate a numpy array?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to build a heavy duty molecular dynamics simulator. I am wondering if python+numpy is a good choice. This will be used in production, so I wanted to start with a good language. I am wondering if I should rather start with a functional language like eg.scala. Do we have enough library support for scientific computation in scala? Or any other language\/paradigm combination you think is good - and why. If you had actually built something in the past and are talking from experience, please mention it as it will help me with collecting data points.\nthanks much!","AnswerCount":4,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":2217,"Q_Id":6948483,"Users Score":6,"Answer":"I believe that most highly performant MD codes are written in native languages like Fortran, C or C++. Modern GPU programming techniques are also finding favour more recently.\nA language like Python would allow for much more rapid development that native code. The flip side of that is that the performance is typically worse than for compiled native code.\nA question for you. Why are you writing your own MD code? There are many many libraries out there. Can't you find one to suit your needs?","Q_Score":4,"Tags":"python,scala,numpy,simulation,scientific-computing","A_Id":6948576,"CreationDate":"2011-08-04T21:03:00.000","Title":"Best language for Molecular Dynamics Simulator, to be run in production. (Python+Numpy?)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using matplotlib to make a histogram.\nIs there any way to manually set the size of the bins as opposed to the number of bins?","AnswerCount":9,"Available Count":1,"Score":0.1106561105,"is_accepted":false,"ViewCount":355218,"Q_Id":6986986,"Users Score":5,"Answer":"I guess the easy way would be to calculate the minimum and maximum of the data you have,  then   calculate L = max - min.  Then you divide L by the desired bin width (I'm assuming this is what you mean by bin size) and use the ceiling of this value as the number of bins.","Q_Score":185,"Tags":"python,matplotlib,histogram","A_Id":6987109,"CreationDate":"2011-08-08T18:46:00.000","Title":"Bin size in Matplotlib (Histogram)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I managed to plot my data and would like to add a background image (map) to it.\nData is plotted by the long\/lat values and I have the long\/lat values for the image's three corners (top left, top right and bottom left) too.\nI am trying to figure out how to use 'extent' option with imshow. However, the examples I found don't explain how to assign x and y for each corner ( in my case I have the information for three corners).\nHow can I assign the location of three corners for the image when adding it to the plot?\nThanks","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":80144,"Q_Id":6999621,"Users Score":51,"Answer":"Specify, in the coordinates of your current axis, the corners of the rectangle that you want the image to be pasted over\nExtent defines the left and right limits, and the bottom and top limits. It takes four values like so: extent=[horizontal_min,horizontal_max,vertical_min,vertical_max]. \nAssuming you have longitude along the horizontal axis, then use extent=[longitude_top_left,longitude_top_right,latitude_bottom_left,latitude_top_left]. longitude_top_left and longitude_bottom_left should be the same, latitude_top_left and latitude_top_right should be the same, and the values within these pairs are interchangeable. \nIf your first element of your image should be plotted in the lower left, then use the origin='lower' imshow option as well, otherwise the 'upper' default is what you want.","Q_Score":37,"Tags":"python,plot,matplotlib","A_Id":7000381,"CreationDate":"2011-08-09T16:34:00.000","Title":"how to use 'extent' in matplotlib.pyplot.imshow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"does there exist an equivalent to matplotlib's imshow()-function for 3D-drawing of datas stored in a 3D numpy array?","AnswerCount":4,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":11267,"Q_Id":7011428,"Users Score":0,"Answer":"What you want is a kind of 3D image (a block). Maybe you could plot it by slices (using imshow() or whatever the tool you want). \nMaybe you could tell us what kind of plot you want?","Q_Score":8,"Tags":"python,numpy,matplotlib","A_Id":7011948,"CreationDate":"2011-08-10T13:20:00.000","Title":"imshow for 3D? (Python \/ Matplotlib)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am reading in census data using the matplotlib cvs2rec function - works fine gives me a nice ndarray. \nBut there are several columns where all the values are '\"none\"\" with dtype |04. This is cuasing problems when I lode into Atpy \"TypeError: object of NoneType has no len()\". Something like '9999' or other missing would work for me. Mask is not going to work in this case because I am passing the real array to ATPY and it will not convert MASK. The Put function in numpy will not work with none values wich is the best way to change values(I think). I think some sort of boolean array is the way to go but I can't get it to work.  \nSo what is a good\/fast way to change none values and\/or uninitialized numpy array to something like '9999'or other recode. No Masking. \nThanks,\nMatthew","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2140,"Q_Id":7011591,"Users Score":0,"Answer":"you can use mask array when you do calculation. and when pass the array to ATPY, you can call filled(9999) method of the mask array to convert the mask array to normal array with invalid values replaced by 9999.","Q_Score":2,"Tags":"python,arrays,numpy,missing-data","A_Id":7046562,"CreationDate":"2011-08-10T13:30:00.000","Title":"Recode missing data Numpy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a big script in Python. I inspired myself in other people's code so I ended up using the numpy.random module for some things (for example for creating an array of random numbers taken from a binomial distribution) and in other places I use the module random.random.\nCan someone please tell me the major differences between the two?\nLooking at the doc webpage for each of the two it seems to me that numpy.random just has more methods, but I am unclear about how the generation of the random numbers is different.\nThe reason why I am asking is because I need to seed my main program for debugging purposes. But it doesn't work unless I use the same random number generator in all the modules that I am importing, is this correct?\nAlso, I read here, in another post, a discussion about NOT using numpy.random.seed(), but I didn't really understand why this was such a bad idea. I would really appreciate if someone explain me why this is the case.","AnswerCount":4,"Available Count":1,"Score":0.1488850336,"is_accepted":false,"ViewCount":53810,"Q_Id":7029993,"Users Score":3,"Answer":"The source of the seed and the distribution profile used are going to affect the outputs - if you are looking for cryptgraphic randomness, seeding from os.urandom() will get nearly real random bytes from device chatter (ie ethernet or disk) (ie \/dev\/random on BSD)\nthis will avoid you giving a seed and so generating determinisitic random numbers.  However the random calls then allow you to fit the numbers to a distribution (what I call scientific random ness - eventually all you want is a bell curve distribution of random numbers, numpy is best at delviering this.\nSO yes, stick with one generator, but decide what random you want - random, but defitniely from a distrubtuion curve, or as random as you can get without a quantum device.","Q_Score":117,"Tags":"python,random,random-seed","A_Id":7030943,"CreationDate":"2011-08-11T17:05:00.000","Title":"Differences between numpy.random and random.random in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"there are huge number of data, there are various groups. i want to check whether the new data fits in any group and if it does i want to put that data into that group. If datum doesn't fit to any of the group, i want to create new group. So, i want to use linked list for the purpose or is there any other way to doing so??\nP.S. i have way to check the similarity between data and group representative(lets not go that in deatil for now) but i dont know how to add the data to group (each group may be list) or create new one if required. i guess what i needis linked list implementation in python, isn't it?","AnswerCount":2,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":468,"Q_Id":7067726,"Users Score":7,"Answer":"This sounds like a perfect use for a dictionary.","Q_Score":0,"Tags":"python,linked-list","A_Id":7067801,"CreationDate":"2011-08-15T16:30:00.000","Title":"linked list in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm building a web application in Django and I'm looking to generate dynamic graphs based on the data.\nPreviously I was using the Google Image Charts, but I ran into significant limitations with the api, including the URL length constraint.\nI've switched to using matplotlib to create my charts.  I'm wondering if this will be a bad decision for future scaling - do any production sites use matplotlib?  It takes about 100ms to generate a single graph (much longer than querying the database for the data), is this simply out of the question in terms of scaling\/handling multiple requests?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":228,"Q_Id":7078010,"Users Score":0,"Answer":"If performance is such an issue and you don't need fancy graphs, you may be able to get by with not creating images at all. Render explicitly sized and colored divs for a simple bar chart in html. Apply box-shadow and\/or a gradient background for eye candy.\nI did this in some report web pages, displaying a small 5-bar (quintiles) chart in each row of a large table, with huge speed and almost no server load. The users love it for the early and succinct feedback.\nUsing canvas and javascript you could improve on this scheme for other chart types. I don't know if you could use Google's charting code for this without going through their API, but a few lines or circle segments should be easy to paint yourselves if not.","Q_Score":0,"Tags":"graphics,python","A_Id":7078262,"CreationDate":"2011-08-15T02:07:00.000","Title":"Generating dynamic graphs","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I spent the past couple of days trying to get opencv to work with my Python 2.7 install. I kept getting an error saying that opencv module was not found whenever I try \"import cv\".\nI then decided to try installing opencv using Macports, but that didn't work.\nNext, I tried Homebrew, but that didn't work either.\nEventually, I discovered I should modify the PYTHONPATH as such:\nexport PYTHONPATH=\"\/usr\/local\/lib\/python2.6\/site-packages\/:$PYTHONPATH\"\nMy problem is that I didn't find \/usr\/local\/lib\/python2.*...etc\nThe folder simply doesn't exist\nSo my question is this:\nHow do I properly install Python on OS X Snow Leopard for it to work with opencv? \nThanks a lot,","AnswerCount":4,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":7935,"Q_Id":7128761,"Users Score":2,"Answer":"You need to install the module using your python2.7 installation. Pointing your PYTHONPATH at stuff installed under 2.6 to run under 2.7 is a Bad Idea. \nDepending on how you want to install it, do something like python2.7 setup.py or easy_install-2.7 opencv to install.\nfwiw, on OS X the modules are usually installed under \/System\/Library\/Frameworks\/Python.framework\/ but you should almost never need to know where anything installed in your site packages is physically located; if Python can't find them without help you've installed them wrong.","Q_Score":4,"Tags":"python,macos,opencv,homebrew","A_Id":7129002,"CreationDate":"2011-08-20T00:39:00.000","Title":"How to properly install Python on OSX for use with OpenCV?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I have many sets of medical image sequences of the artery on the heart. Each set of sequenced medical images shows the position of the artery as the heart pumps. Each set is taken from different views and has different amount of images taken.\nI want to do a temporal interpolation based on time (i was told that the time could be represented by the frame number. however i have no idea what the frame number is or what does it refer to. Could you enlighten me please?) I have two options: by interpolating the whole image frame or interpolating artery junction positions (coordinates). How do i go about doing both options?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":163,"Q_Id":7162351,"Users Score":0,"Answer":"If you imagine each of the images as a still photo the frame number would be a sequence number that shows what order the images should be displayed in to produce a movie from the stills.  If the images are stored in an array it would be the array index of the individul frame in question.","Q_Score":0,"Tags":"python,interpolation","A_Id":7162436,"CreationDate":"2011-08-23T14:09:00.000","Title":"temporal interpolation of artery angiogram images sequences","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I work in an psudo-operational environment where we make new imagery on receipt of data.  Sometimes when new data comes in, we need to re-open an image and update that image in order to create composites, add overlays, etc.  In addition to adding to the image, this requires modification of titles, legends, etc.  \nIs there something built into matplotlib that would let me store and reload my matplotlib.pyplot object for later use?  It would need to maintain access to all associated objects including figures, lines, legends, etc.  Maybe pickle is what I'm looking for, but I doubt it.","AnswerCount":5,"Available Count":2,"Score":0.0399786803,"is_accepted":false,"ViewCount":43605,"Q_Id":7290370,"Users Score":1,"Answer":"I produced figures for a number of papers using matplotlib. Rather than thinking of saving the figure (as in MATLAB), I would write a script that plotted the data then formatted and saved the figure. In cases where I wanted to keep a local copy of the data (especially if I wanted to be able to play with it again) I found numpy.savez() and numpy.load() to be very useful.\nAt first I missed the shrink-wrapped feel of saving a figure in MATLAB, but after a while I have come to prefer this approach because it includes the data in a format that is available for further analysis.","Q_Score":50,"Tags":"python,matplotlib","A_Id":8822734,"CreationDate":"2011-09-03T00:27:00.000","Title":"Store and reload matplotlib.pyplot object","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I work in an psudo-operational environment where we make new imagery on receipt of data.  Sometimes when new data comes in, we need to re-open an image and update that image in order to create composites, add overlays, etc.  In addition to adding to the image, this requires modification of titles, legends, etc.  \nIs there something built into matplotlib that would let me store and reload my matplotlib.pyplot object for later use?  It would need to maintain access to all associated objects including figures, lines, legends, etc.  Maybe pickle is what I'm looking for, but I doubt it.","AnswerCount":5,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":43605,"Q_Id":7290370,"Users Score":0,"Answer":"Did you try the pickle module?  It serialises an object, dumps it to a file, and can reload it from the file later.","Q_Score":50,"Tags":"python,matplotlib","A_Id":7843630,"CreationDate":"2011-09-03T00:27:00.000","Title":"Store and reload matplotlib.pyplot object","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am writing a scientific program in Python and C with some complex physical simulation algorithms. After implementing algorithm, I found that there are a lot of possible optimizations to improve performance. Common ones are precalculating values, getting calculations out of cycle, replacing simple matrix algorithms with more complex and other. But there arises a problem. Unoptimized algorithm is much slower, but its logic and connection with theory look much clearer and readable. Also, it's harder to extend and modify optimized algorithm. \nSo, the question is - what techniques should I use to keep readability while improving performance? Now I am trying to keep both fast and clear branches and develop them in parallel, but maybe there are better methods?","AnswerCount":4,"Available Count":1,"Score":0.1488850336,"is_accepted":false,"ViewCount":434,"Q_Id":7300903,"Users Score":3,"Answer":"Yours is a very good question that arises in almost every piece of code, however simple or complex, that's written by any programmer who wants to call himself a pro. \nI try to remember and keep in mind that a reader newly come to my code has pretty much the same crude view of the problem and the same straightforward (maybe brute force) approach that I originally had. Then, as I get a deeper understanding of the problem and paths to the solution become clearer, I try to write comments that reflect that better understanding. I sometimes succeed and those comments help readers and, especially, they help me when I come back to the code six weeks later. My style is to write plenty of comments anyway and, when I don't (because: a sudden insight gets me excited; I want to see it run; my brain is fried), I almost always greatly regret it later.\nIt would be great if I could maintain two parallel code streams: the na\u00efve way and the more sophisticated optimized way. But I have never succeeded in that.\nTo me, the bottom line is that if I can write clear, complete, succinct, accurate and up-to-date comments, that's about the best I can do. \nJust one more thing that you know already: optimization usually doesn't mean shoehorning a ton of code onto one source line, perhaps by calling a function whose argument is another function whose argument is another function whose argument is yet another function. I know that some do this to avoid storing a function's value temporarily. But it does very little (usually nothing) to speed up the code and it's a bitch to follow. No news to you, I know.","Q_Score":18,"Tags":"python,performance,algorithm,optimization,code-readability","A_Id":7301095,"CreationDate":"2011-09-04T17:25:00.000","Title":"Preserve code readability while optimising","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Anyone know if OpenCV is capable of loading a multi-frame TIFF stack? \nI'm using OpenCV 2.2.0 with python 2.6.","AnswerCount":6,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":12017,"Q_Id":7335308,"Users Score":8,"Answer":"Unfortunately OpenCV does not support TIFF directories and is able to read only the first frame from multi-frame TIFF files.","Q_Score":10,"Tags":"python,image,opencv","A_Id":7337353,"CreationDate":"2011-09-07T14:10:00.000","Title":"Can I load a multi-frame TIFF through OpenCV?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have about 100 CSV files I have to operate on once a month and I was trying to wrap my head around this but I'm running into a wall.  I'm starting to understand some things about Python, but combining several things is still giving me issues, so I can't figure this out.\nHere's my problem:\nI have many CSV files, and here's what I need done:\nadd a \"column\" to the front of each row (or the back, doesn't matter really, but front is ideal).  In addition, each line has 5 rows (not counting the filename that will be added), and here's the format:\n6-digit ID number,YYYY-MM-DD(1),YYYY-MM-DD(2),YYYY-MM-DD(3),1-2-digit number\nI need to subtract YYYY-MM-DD(3) from YYYY-MM-DD(2) for every line in the file (there is no header row), for every CSV in a given directory.\nI need the filename inside the row because I will combine the files (which, if is included in the script would be awesome, but I think I can figure that part out), and I need to know what file the records came from.  Format of filename is always '4-5-digit-number.csv'\nI hope this makes sense, if it does not, please let me know.  I'm kind of stumped as to where to even begin, so I don't have any sample code that even really began to work for me.  Really frustrated, so I appreciate any help you guys may provide, this site rocks!\nMylan","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2947,"Q_Id":7350851,"Users Score":0,"Answer":"The basic outline of the program is going to be like this:\n\nUse the os module to get the filenames out of the directory\/directories of interest\nRead in each file one at a time\nFor each line in the file, split it into columns with columns = line.split(\",\")\nUse datetime.date to convert strings like \"2011-05-03\" to datetime.dates.\nSubtract the third date from the second, which yields a datetime.timedelta.\nPut all your information in the format you want (hint: str(foo) yields a string representation of foo, for just about any type) and remember it for later\nClose your file, reopen it for writing, and write your new stuff in","Q_Score":2,"Tags":"python,csv,datestamp","A_Id":7351024,"CreationDate":"2011-09-08T15:46:00.000","Title":"Need to do a math operation on every line in several CSV files in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"i have two sq matrix (a, b) of size in order of 100000 X 100000. I have to take difference of these two matrix (c = a-b). Resultant matrix 'c' is a sparse matrix. I want to find the indices of all non-zero elements. I have to do this operation many times (>100).\nSimplest way is to use two for loops. But that's computationally intensive. Can you tell me any algorithm or package\/library preferably in R\/python\/c to do this as quickly as possible?","AnswerCount":6,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":6600,"Q_Id":7361447,"Users Score":4,"Answer":"Since you have two dense matrices then the double for loop is the only option you have. You don't need a sparse matrix class at all since you only want to know the list of indices (i,j) for which a[i,j] != b[i,j].\nIn languages like R and Python the double for loop will perform poorly. I'd probably write this in native code for a double for loop and add the indices to a list object.  But no doubt the wizards of interpreted code (i.e. R, Python etc.) know efficient ways to do it without resorting to native coding.","Q_Score":6,"Tags":"python,algorithm,r,sparse-matrix,indices","A_Id":7362256,"CreationDate":"2011-09-09T12:12:00.000","Title":"How to find indices of non zero elements in large sparse matrix?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to graph alarm counts in Python to give some sort of display to give an idea of the peak amount of network elements down between two timespans. The way that our alarms report handles it is in CSV like this:\nName,Alarm Start,Alarm Clear\nNE1,15:42 08\/09\/11,15:56 08\/09\/11\nNE2,15:42 08\/09\/11,15:57 08\/09\/11\nNE3,15:42 08\/09\/11,16:31 08\/09\/11\nNE4,15:42 08\/09\/11,15:59 08\/09\/11\n\nI am trying to graph the start and end between those two points and how many NE's were down during that time, including the maximum number and when it went under or over a certain count. An example is below:\n15:42 08\/09\/11 - 4 Down\n15:56 08\/09\/11 - 3 Down\netc.\nAny advice where to start on this would be great. Thanks in advance, you guys and gals have been a big help in the past.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":52,"Q_Id":7363997,"Users Score":1,"Answer":"I'd start by parsing your indata to a map indexed by dates with counts as values. Just increase the count for each row with the same date you encounter.\nAfter that, use some plotting module, for instance matplotlib to plot the keys of the map versus the values. That should cover it! \nDo you need any more detailed ideas?","Q_Score":1,"Tags":"python,graph","A_Id":7364602,"CreationDate":"2011-09-09T15:28:00.000","Title":"Graphing the number of elements down based on timestamps start\/end","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Background\nI am working on a fairly computationally intensive project for a computational linguistics project, but the problem I have is quite general and hence I expect that a solution would be interesting to others as well.\nRequirements\nThe key aspect of this particular program I must write is that it must: \n\nRead through a large corpus (between 5G and 30G, and potentially larger stuff down the line)\nProcess the data on each line.\nFrom this processed data, construct a large number of vectors (dimensionality of some of these vectors is > 4,000,000). Typically it is building hundreds of thousands of such vectors.\nThese vectors must all be saved to disk in some format or other.\n\nSteps 1 and 2 are not hard to do efficiently: just use generators and have a data-analysis pipeline. The big problem is operation 3 (and by connection 4)\nParenthesis: Technical Details\nIn case the actual procedure for building vectors affects the solution:\nFor each line in the corpus, one or more vectors must have its basis weights updated. \nIf you think of them in terms of python lists, each line, when processed, updates one or more lists (creating them if needed) by incrementing the values of these lists at one or more indices by a value (which may differ based on the index). \nVectors do not depend on each other, nor does it matter which order the corpus lines are read in.\nAttempted Solutions\nThere are three extrema when it comes to how to do this:\n\nI could build all the vectors in memory. Then write them to disk.\nI could build all the vectors directly on the disk, using shelf of pickle or some such library.\nI could build the vectors in memory one at a time and writing it to disk, passing through the corpus once per vector.\n\nAll these options are fairly intractable. 1 just uses up all the system memory, and it panics and slows to a crawl. 2 is way too slow as IO operations aren't fast. 3 is possibly even slower than 2 for the same reasons.\nGoals\nA good solution would involve:\n\nBuilding as much as possible in memory.\nOnce memory is full, dump everything to disk.\nIf bits are needed from disk again, recover them back into memory to add stuff to those vectors. \nGo back to 1 until all vectors are built.\n\nThe problem is that I'm not really sure how to go about this. It seems somewhat unpythonic to worry about system attributes such as RAM, but I don't see how this sort of problem can be optimally solved without taking this into account. As a result, I don't really know how to get started on this sort of thing.\nQuestion\nDoes anyone know how to go about solving this sort of problem? I python simply not the right language for this sort of thing? Or is there a simple solution to maximise how much is done from memory (within reason) while minimising how many times data must be read from the disk, or written to it?\nMany thanks for your attention. I look forward to seeing what the bright minds of stackoverflow can throw my way.\nAdditional Details\nThe sort of machine this problem is run on usually has 20+ cores and ~70G of RAM. The problem can be parallelised (\u00e0 la MapReduce) in that separate vectors for one entity can be built from segments of the corpus and then added to obtain the vector that would have been built from the whole corpus.\nPart of the question involves determining a limit on how much can be built in memory before disk-writes need to occur. Does python offer any mechanism to determine how much RAM is available?","AnswerCount":11,"Available Count":4,"Score":0.0363476168,"is_accepted":false,"ViewCount":3167,"Q_Id":7381258,"Users Score":2,"Answer":"Two ideas:\n\nUse numpy arrays to represent vectors. They are much more memory-efficient, at the cost that they will force elements of the vector to be of the same type (all ints or all doubles...).\nDo multiple passes, each with a different set of vectors. That is, choose first 1M vectors and do only the calculations involving them (you said they are independent, so I assume this is viable). Then another pass over all the data with second 1M vectors.\n\nIt seems you're on the edge of what you can do with your hardware. It would help if you could describe what hardware (mostly, RAM) is available to you for this task. If there are 100k vectors, each of them with 1M ints, this gives ~370GB. If multiple passes method is viable and you've got a machine with 16GB RAM, then it is about ~25 passes -- should be easy to parallelize if you've got a cluster.","Q_Score":17,"Tags":"python,memory,io","A_Id":7381424,"CreationDate":"2011-09-11T21:08:00.000","Title":"Minimising reading from and writing to disk in Python for a memory-heavy operation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Background\nI am working on a fairly computationally intensive project for a computational linguistics project, but the problem I have is quite general and hence I expect that a solution would be interesting to others as well.\nRequirements\nThe key aspect of this particular program I must write is that it must: \n\nRead through a large corpus (between 5G and 30G, and potentially larger stuff down the line)\nProcess the data on each line.\nFrom this processed data, construct a large number of vectors (dimensionality of some of these vectors is > 4,000,000). Typically it is building hundreds of thousands of such vectors.\nThese vectors must all be saved to disk in some format or other.\n\nSteps 1 and 2 are not hard to do efficiently: just use generators and have a data-analysis pipeline. The big problem is operation 3 (and by connection 4)\nParenthesis: Technical Details\nIn case the actual procedure for building vectors affects the solution:\nFor each line in the corpus, one or more vectors must have its basis weights updated. \nIf you think of them in terms of python lists, each line, when processed, updates one or more lists (creating them if needed) by incrementing the values of these lists at one or more indices by a value (which may differ based on the index). \nVectors do not depend on each other, nor does it matter which order the corpus lines are read in.\nAttempted Solutions\nThere are three extrema when it comes to how to do this:\n\nI could build all the vectors in memory. Then write them to disk.\nI could build all the vectors directly on the disk, using shelf of pickle or some such library.\nI could build the vectors in memory one at a time and writing it to disk, passing through the corpus once per vector.\n\nAll these options are fairly intractable. 1 just uses up all the system memory, and it panics and slows to a crawl. 2 is way too slow as IO operations aren't fast. 3 is possibly even slower than 2 for the same reasons.\nGoals\nA good solution would involve:\n\nBuilding as much as possible in memory.\nOnce memory is full, dump everything to disk.\nIf bits are needed from disk again, recover them back into memory to add stuff to those vectors. \nGo back to 1 until all vectors are built.\n\nThe problem is that I'm not really sure how to go about this. It seems somewhat unpythonic to worry about system attributes such as RAM, but I don't see how this sort of problem can be optimally solved without taking this into account. As a result, I don't really know how to get started on this sort of thing.\nQuestion\nDoes anyone know how to go about solving this sort of problem? I python simply not the right language for this sort of thing? Or is there a simple solution to maximise how much is done from memory (within reason) while minimising how many times data must be read from the disk, or written to it?\nMany thanks for your attention. I look forward to seeing what the bright minds of stackoverflow can throw my way.\nAdditional Details\nThe sort of machine this problem is run on usually has 20+ cores and ~70G of RAM. The problem can be parallelised (\u00e0 la MapReduce) in that separate vectors for one entity can be built from segments of the corpus and then added to obtain the vector that would have been built from the whole corpus.\nPart of the question involves determining a limit on how much can be built in memory before disk-writes need to occur. Does python offer any mechanism to determine how much RAM is available?","AnswerCount":11,"Available Count":4,"Score":0.0181798149,"is_accepted":false,"ViewCount":3167,"Q_Id":7381258,"Users Score":1,"Answer":"Use a database. That problem seems large enough that language choice (Python, Perl, Java, etc) won't make a difference. If each dimension of the vector is a column in the table, adding some indexes is probably a good idea. In any case this is a lot of data and won't process terribly quickly.","Q_Score":17,"Tags":"python,memory,io","A_Id":7381462,"CreationDate":"2011-09-11T21:08:00.000","Title":"Minimising reading from and writing to disk in Python for a memory-heavy operation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Background\nI am working on a fairly computationally intensive project for a computational linguistics project, but the problem I have is quite general and hence I expect that a solution would be interesting to others as well.\nRequirements\nThe key aspect of this particular program I must write is that it must: \n\nRead through a large corpus (between 5G and 30G, and potentially larger stuff down the line)\nProcess the data on each line.\nFrom this processed data, construct a large number of vectors (dimensionality of some of these vectors is > 4,000,000). Typically it is building hundreds of thousands of such vectors.\nThese vectors must all be saved to disk in some format or other.\n\nSteps 1 and 2 are not hard to do efficiently: just use generators and have a data-analysis pipeline. The big problem is operation 3 (and by connection 4)\nParenthesis: Technical Details\nIn case the actual procedure for building vectors affects the solution:\nFor each line in the corpus, one or more vectors must have its basis weights updated. \nIf you think of them in terms of python lists, each line, when processed, updates one or more lists (creating them if needed) by incrementing the values of these lists at one or more indices by a value (which may differ based on the index). \nVectors do not depend on each other, nor does it matter which order the corpus lines are read in.\nAttempted Solutions\nThere are three extrema when it comes to how to do this:\n\nI could build all the vectors in memory. Then write them to disk.\nI could build all the vectors directly on the disk, using shelf of pickle or some such library.\nI could build the vectors in memory one at a time and writing it to disk, passing through the corpus once per vector.\n\nAll these options are fairly intractable. 1 just uses up all the system memory, and it panics and slows to a crawl. 2 is way too slow as IO operations aren't fast. 3 is possibly even slower than 2 for the same reasons.\nGoals\nA good solution would involve:\n\nBuilding as much as possible in memory.\nOnce memory is full, dump everything to disk.\nIf bits are needed from disk again, recover them back into memory to add stuff to those vectors. \nGo back to 1 until all vectors are built.\n\nThe problem is that I'm not really sure how to go about this. It seems somewhat unpythonic to worry about system attributes such as RAM, but I don't see how this sort of problem can be optimally solved without taking this into account. As a result, I don't really know how to get started on this sort of thing.\nQuestion\nDoes anyone know how to go about solving this sort of problem? I python simply not the right language for this sort of thing? Or is there a simple solution to maximise how much is done from memory (within reason) while minimising how many times data must be read from the disk, or written to it?\nMany thanks for your attention. I look forward to seeing what the bright minds of stackoverflow can throw my way.\nAdditional Details\nThe sort of machine this problem is run on usually has 20+ cores and ~70G of RAM. The problem can be parallelised (\u00e0 la MapReduce) in that separate vectors for one entity can be built from segments of the corpus and then added to obtain the vector that would have been built from the whole corpus.\nPart of the question involves determining a limit on how much can be built in memory before disk-writes need to occur. Does python offer any mechanism to determine how much RAM is available?","AnswerCount":11,"Available Count":4,"Score":0.0,"is_accepted":false,"ViewCount":3167,"Q_Id":7381258,"Users Score":0,"Answer":"Split the corpus evenly in size between parallel jobs (one per core) - process in parallel, ignoring any incomplete line (or if you cannot tell if it is incomplete, ignore the first and last line of that each job processes).\nThat's the map part.\nUse one job to merge the 20+ sets of vectors from each of the earlier jobs - That's the reduce step.\nYou stand to loose information from 2*N lines where N is the number of parallel processes, but you gain by not adding complicated logic to try and capture these lines for processing.","Q_Score":17,"Tags":"python,memory,io","A_Id":7433853,"CreationDate":"2011-09-11T21:08:00.000","Title":"Minimising reading from and writing to disk in Python for a memory-heavy operation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Background\nI am working on a fairly computationally intensive project for a computational linguistics project, but the problem I have is quite general and hence I expect that a solution would be interesting to others as well.\nRequirements\nThe key aspect of this particular program I must write is that it must: \n\nRead through a large corpus (between 5G and 30G, and potentially larger stuff down the line)\nProcess the data on each line.\nFrom this processed data, construct a large number of vectors (dimensionality of some of these vectors is > 4,000,000). Typically it is building hundreds of thousands of such vectors.\nThese vectors must all be saved to disk in some format or other.\n\nSteps 1 and 2 are not hard to do efficiently: just use generators and have a data-analysis pipeline. The big problem is operation 3 (and by connection 4)\nParenthesis: Technical Details\nIn case the actual procedure for building vectors affects the solution:\nFor each line in the corpus, one or more vectors must have its basis weights updated. \nIf you think of them in terms of python lists, each line, when processed, updates one or more lists (creating them if needed) by incrementing the values of these lists at one or more indices by a value (which may differ based on the index). \nVectors do not depend on each other, nor does it matter which order the corpus lines are read in.\nAttempted Solutions\nThere are three extrema when it comes to how to do this:\n\nI could build all the vectors in memory. Then write them to disk.\nI could build all the vectors directly on the disk, using shelf of pickle or some such library.\nI could build the vectors in memory one at a time and writing it to disk, passing through the corpus once per vector.\n\nAll these options are fairly intractable. 1 just uses up all the system memory, and it panics and slows to a crawl. 2 is way too slow as IO operations aren't fast. 3 is possibly even slower than 2 for the same reasons.\nGoals\nA good solution would involve:\n\nBuilding as much as possible in memory.\nOnce memory is full, dump everything to disk.\nIf bits are needed from disk again, recover them back into memory to add stuff to those vectors. \nGo back to 1 until all vectors are built.\n\nThe problem is that I'm not really sure how to go about this. It seems somewhat unpythonic to worry about system attributes such as RAM, but I don't see how this sort of problem can be optimally solved without taking this into account. As a result, I don't really know how to get started on this sort of thing.\nQuestion\nDoes anyone know how to go about solving this sort of problem? I python simply not the right language for this sort of thing? Or is there a simple solution to maximise how much is done from memory (within reason) while minimising how many times data must be read from the disk, or written to it?\nMany thanks for your attention. I look forward to seeing what the bright minds of stackoverflow can throw my way.\nAdditional Details\nThe sort of machine this problem is run on usually has 20+ cores and ~70G of RAM. The problem can be parallelised (\u00e0 la MapReduce) in that separate vectors for one entity can be built from segments of the corpus and then added to obtain the vector that would have been built from the whole corpus.\nPart of the question involves determining a limit on how much can be built in memory before disk-writes need to occur. Does python offer any mechanism to determine how much RAM is available?","AnswerCount":11,"Available Count":4,"Score":0.0,"is_accepted":false,"ViewCount":3167,"Q_Id":7381258,"Users Score":0,"Answer":"From another comment I infer that your corpus fits into the memory, and you have some cores to throw at the problem, so I would try this:\n\nFind a method to have your corpus in memory. This might be a sort of ram disk with file system, or a database. No idea, which one is best for you. \nHave a smallish shell script monitor ram usage, and spawn every second another process of the following, as long as there is x memory left (or, if you want to make things a bit more complex, y I\/O bandwith to disk):\n\niterate through the corpus and build and write some vectors\n\nin the end you can collect and combine all vectors, if needed (this would be the reduce part)","Q_Score":17,"Tags":"python,memory,io","A_Id":7381527,"CreationDate":"2011-09-11T21:08:00.000","Title":"Minimising reading from and writing to disk in Python for a memory-heavy operation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to train multiple one class SVMs in different threads.\nDoes anybody know if scikit's SVM releases the GIL?\nI did not find any answers online.\nThanks","AnswerCount":2,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":1679,"Q_Id":7391427,"Users Score":4,"Answer":"Some sklearn Cython classes do release the GIL internally on performance critical sections, for instance the decision trees (used in random forests for instance) as of 0.15 (to be released early 2014) and the libsvm wrappers do.\nThis is not the general rule though. If you identify performance critical cython code in sklearn that could be changed to release the GIL please feel free to send a pull request.","Q_Score":5,"Tags":"python,multithreading,parallel-processing,machine-learning,scikit-learn","A_Id":20999120,"CreationDate":"2011-09-12T17:10:00.000","Title":"Does Scikit-learn release the python GIL?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am going to implement Naive Bayes classifier with Python and classify e-mails as Spam or Not spam. I have a very sparse and long dataset with many entries. Each entry is like the following:\n1 9:3 94:1 109:1 163:1 405:1 406:1 415:2 416:1 435:3 436:3 437:4 ...\nWhere 1 is label (spam, not spam), and each pair corresponds to a word and its frequency. E.g. 9:3 corresponds to the word 9 and it occurs 3 times in this e-mail sample.\nI need to read this dataset and store it in a structure. Since it's a very big and sparse dataset, I'm looking for a neat data structure to store the following variables:\n\nthe index of each e-mail\nlabel of it (1 or -1)\nword and it's frequency per each e-mail\nI also need to create a corpus of all words and their frequency with the label information\n\nAny suggestions for such a data structure?","AnswerCount":4,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":381,"Q_Id":7452917,"Users Score":0,"Answer":"If you assume you didn't care about multiple occurrences of each word in an email, then all you really need to know is (that is, your features are booleans):\nFor each feature, what is the count of positive associations and negative associations?\nYou can do this online very easily in one pass, keeping track of just those two numbers for each feature.\nThe non-boolean features means you'll have to discretize the features some how, but you aren't really asking about how to do that.","Q_Score":1,"Tags":"python,data-structures,machine-learning,spam-prevention","A_Id":7453107,"CreationDate":"2011-09-17T06:22:00.000","Title":"I need a neat data structure suggestion to store a very large dataset (to train Naive Bayes in Python)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm looking for a good implementation for logistic regression (not regularized) in Python. I'm looking for a package that can also get weights for each vector. Can anyone suggest a good implementation \/ package?\nThanks!","AnswerCount":5,"Available Count":1,"Score":-1.0,"is_accepted":false,"ViewCount":23971,"Q_Id":7513067,"Users Score":-5,"Answer":"Do you know Numpy? If no, take a look also to Scipy and matplotlib.","Q_Score":16,"Tags":"python,regression","A_Id":7513167,"CreationDate":"2011-09-22T10:08:00.000","Title":"Weighted logistic regression in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"For the purpose of conducting a psychological experiment I have to divide a set of pictures (240) described by 4 features (real numbers) into 3 subsets with equal number of elements in each subset (240\/3 = 80) in such a way that all subsets are approximately balanced with respect to these features (in terms of mean and standard deviation).\nCan anybody suggest an algorithm to automate that? Are there any packages\/modules in Python or R that I could use to do that? Where should I start?","AnswerCount":5,"Available Count":2,"Score":0.0798297691,"is_accepted":false,"ViewCount":9742,"Q_Id":7539186,"Users Score":2,"Answer":"I would tackle this as follows:\n\nDivide into 3 equal subsets.\nFigure out the mean and variance of each subset.  From them construct an \"unevenness\" measure.\nCompare each pair of elements, if swapping would reduce the \"unevenness\", swap them.  Continue until there are either no more pairs to compare, or the total unevenness is below some arbitrary \"good enough\" threshold.","Q_Score":5,"Tags":"python,algorithm,r","A_Id":7539484,"CreationDate":"2011-09-24T13:02:00.000","Title":"Divide set into subsets with equal number of elements","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"For the purpose of conducting a psychological experiment I have to divide a set of pictures (240) described by 4 features (real numbers) into 3 subsets with equal number of elements in each subset (240\/3 = 80) in such a way that all subsets are approximately balanced with respect to these features (in terms of mean and standard deviation).\nCan anybody suggest an algorithm to automate that? Are there any packages\/modules in Python or R that I could use to do that? Where should I start?","AnswerCount":5,"Available Count":2,"Score":0.0399786803,"is_accepted":false,"ViewCount":9742,"Q_Id":7539186,"Users Score":1,"Answer":"In case you are still interested in the exhaustive search question. You have 240 choose 80 possibilities to choose the first set and then another 160 choose 80 for the second set, at which point the third set is fixed. In total, this gives you:\n120554865392512357302183080835497490140793598233424724482217950647 * 92045125813734238026462263037378063990076729140\nClearly, this is not an option :)","Q_Score":5,"Tags":"python,algorithm,r","A_Id":7548438,"CreationDate":"2011-09-24T13:02:00.000","Title":"Divide set into subsets with equal number of elements","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using matplotlib.pyplot (with Eclipse on Windows). Every time I run my code it opens several pyplot figure windows. \nThe problem is that if I don't close those windows manually they accumulate. I would like to use pyplot to find those windows (opened by another process of python.exe) and re-use them. In other words, I do not want to have multiple windows for the same figure, even across interpreter processes.\nIs there a simple way to do it?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":302,"Q_Id":7655323,"Users Score":1,"Answer":"There is no simple way to reuse plot windows if you must use eclipse to run it.  When I am working interactively with matplotlib, I use either spyder or ipython.  Edit class, reload class, and run code again. If you just want to get rid of all the open plot windows, hit the stacked stop icons to kill all your runing python instances.","Q_Score":3,"Tags":"python,matplotlib","A_Id":7673206,"CreationDate":"2011-10-04T23:56:00.000","Title":"Pyplot\/Matplotlib: How to access figures opened by another interpreter?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to code a Maximum Likelihood Estimator to estimate the mean and variance of some toy data.  I have a vector with 100 samples, created with numpy.random.randn(100). The data should have zero mean and unit variance Gaussian distribution.\nI checked Wikipedia and some extra sources, but I am a little bit confused since I don't have a statistics background.\nIs there any pseudo code for  a maximum likelihood estimator? I get the intuition of MLE but I cannot figure out where to start coding.\nWiki says taking argmax of log-likelihood. What I understand is: I need to calculate log-likelihood by using different parameters and then I'll take the parameters which gave the maximum probability. What I don't get is: where will I find the parameters in the first place? If I randomly try different mean & variance to get a high probability, when should I stop trying?","AnswerCount":4,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":47657,"Q_Id":7718034,"Users Score":4,"Answer":"You need a numerical optimisation procedure. Not sure if anything is implemented in Python, but if it is then it'll be in numpy or scipy and friends.\nLook for things like 'the Nelder-Mead algorithm', or 'BFGS'. If all else fails, use Rpy and call the R function 'optim()'.\nThese functions work by searching the function space and trying to work out where the maximum is. Imagine trying to find the top of a hill in fog. You might just try always heading up the steepest way. Or you could send some friends off with radios and GPS units and do a bit of surveying. Either method could lead you to a false summit, so you often need to do this a few times, starting from different points. Otherwise you may think the south summit is the highest when there's a massive north summit overshadowing it.","Q_Score":28,"Tags":"python,statistics,machine-learning,pseudocode","A_Id":7725290,"CreationDate":"2011-10-10T20:05:00.000","Title":"Maximum Likelihood Estimate pseudocode","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How can I generate a uniformly distributed [-1,1]^d data in Python? E.g. d is a dimension like 10.\nI know how to generate uniformly distributed data like  np.random.randn(N) but dimension thing is confused me a lot.","AnswerCount":4,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":13886,"Q_Id":7733969,"Users Score":2,"Answer":"You can import the random module and call random.random to get a random sample from [0, 1). You can double that and subtract 1 to get a sample from [-1, 1). \nDraw d values this way and the tuple will be a uniform draw from the cube [-1, 1)^d.","Q_Score":7,"Tags":"python,numpy,machine-learning,scipy","A_Id":7734072,"CreationDate":"2011-10-12T00:15:00.000","Title":"Uniformly distributed data in d dimensions","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a complex Python program. I'm trying to use the multiprocess Pool to parallelize it. I get the error message\nPicklingError: Can't pickle <type 'function'>: attribute lookup __builtin__.function failed.\nThe traceback shows the statemen return send(obj)\nMy hypothesis is that its the \"obj\" that is causing the problem and that I need to make it pickle-able. \nHow can I determine which object is the cause of the problem? The program is complex and simply guessing might take a long time.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":82,"Q_Id":7746484,"Users Score":0,"Answer":"The error you're seeing could be caused by passing the wrong kind of function to the multiprocessing.Pool methods. The passed function must be directly importable from its parent module. It cannot be a method of a class, for instance.","Q_Score":0,"Tags":"python,multiprocessing","A_Id":7746882,"CreationDate":"2011-10-12T20:57:00.000","Title":"in Python using the multiprocessing module, how can I determine which object caused a PicklingError?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"How can I implement graph colouring in python using adjacency matrix? Is it possible? I implemented it using list. But it has some problems. I want to implement it using matrix.  Can anybody give me the answer or suggestions to this?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1943,"Q_Id":7758913,"Users Score":0,"Answer":"Implementing using adjacency is somewhat easier than using lists, as lists take a longer time and space. igraph has a quick method neighbors which can be used. However, with adjacency matrix alone, we can come up with our own graph coloring version which may not result in using minimum chromatic number. A quick strategy may be as follows: \nInitalize: Put one distinct color for nodes on each row (where a 1 appears) \nStart: With highest degree node (HDN) row as a reference, compare each row (meaning each node) with the HDN and see if it is also its neighbor by detecting a 1. If yes, then change that nodes color. Proceed like this to fine-tune. O(N^2) approach! Hope this helps.","Q_Score":2,"Tags":"python,graph-algorithm","A_Id":37692584,"CreationDate":"2011-10-13T18:44:00.000","Title":"Graph colouring in python using adjacency matrix","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Suppose I have a 16 core machine, and an embarrassingly parallel program.  I use lots of numpy dot products and addition of numpy arrays, and if I did not use multiprocessing it would be a no-brainer:  Make sure numpy is built against a version of blas that uses multithreading.  However, I am using multiprocessing, and all cores are working hard at all times.  In this case, is there any benefit to be had from using a multithreading blas?\nMost of the operations are (blas) type 1, some are type 2.","AnswerCount":2,"Available Count":2,"Score":0.1973753202,"is_accepted":false,"ViewCount":2321,"Q_Id":7761859,"Users Score":2,"Answer":"If you are already using multiprocessing, and all cores are at max load, then there will be very little, if any, benefit to adding threads that will be waiting around for a processor.\nDepending on your algorithm and what you're doing, it may be more beneficial to use one type over the other, but that's very dependent.","Q_Score":5,"Tags":"python,multithreading,numpy,multiprocessing,blas","A_Id":7761877,"CreationDate":"2011-10-14T00:13:00.000","Title":"Is it worth using a multithreaded blas implementation along with multiprocessing in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Suppose I have a 16 core machine, and an embarrassingly parallel program.  I use lots of numpy dot products and addition of numpy arrays, and if I did not use multiprocessing it would be a no-brainer:  Make sure numpy is built against a version of blas that uses multithreading.  However, I am using multiprocessing, and all cores are working hard at all times.  In this case, is there any benefit to be had from using a multithreading blas?\nMost of the operations are (blas) type 1, some are type 2.","AnswerCount":2,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":2321,"Q_Id":7761859,"Users Score":6,"Answer":"You might need to be a little careful about the assumption that your code is actually used multithreaded BLAS calls. Relatively few numpy operators actually use the underlying BLAS, and relatively few BLAS calls are actually multithreaded. numpy.dot uses either BLAS dot, gemv or gemm, depending on the operation, but of those, only gemm is usually multithreaded, because there is rarely any performance benefit for the O(N) and O(N^2) BLAS calls in doing so. If you are limiting yourself to Level 1 and Level 2 BLAS operations, I doubt you are actually using any multithreaded BLAS calls, even if you are using a numpy implementation built with a mulithreaded BLAS, like Atlas or MKL.","Q_Score":5,"Tags":"python,multithreading,numpy,multiprocessing,blas","A_Id":7765829,"CreationDate":"2011-10-14T00:13:00.000","Title":"Is it worth using a multithreaded blas implementation along with multiprocessing in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to Numpy and trying to search for a function to list out the variables along with their sizes (both the matrix dimensions as well as memory usage). \nI am essentially looking for an equivalent of the \"whos\" command in MATLAB and Octave. Does there exist any such command in NumPy?","AnswerCount":5,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":21664,"Q_Id":7774964,"Users Score":8,"Answer":"Python has a builtin function dir() which returns the list of names in the current local scope.","Q_Score":22,"Tags":"python,matlab,numpy,octave","A_Id":7779373,"CreationDate":"2011-10-15T00:42:00.000","Title":"Equivalent of \"whos\" command in NumPy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to Numpy and trying to search for a function to list out the variables along with their sizes (both the matrix dimensions as well as memory usage). \nI am essentially looking for an equivalent of the \"whos\" command in MATLAB and Octave. Does there exist any such command in NumPy?","AnswerCount":5,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":21664,"Q_Id":7774964,"Users Score":0,"Answer":"try using: type(VAR_NAME) \nthis will output the class type for that particular variable, VAR_NAME","Q_Score":22,"Tags":"python,matlab,numpy,octave","A_Id":60568982,"CreationDate":"2011-10-15T00:42:00.000","Title":"Equivalent of \"whos\" command in NumPy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In Enthought Python Distribution, I saw it includes pyhdf and numpy. Since it includes pyhdf, does it also include HDF 4?\nI am using pylab to code at this moment.\nBecause I want to use a module of the pyhdf package called pyhdf.SD. And it Prerequisites HDF 4 library. So do I still need to install HDF 4 if I want to use pyhdf.SD?\nThanks","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":258,"Q_Id":7786232,"Users Score":1,"Answer":"For the versions of EPD that include pyhdf, you don't need to install HDF 4 separately.  However, note that pyhdf is not included in all versions of EPD---in particular, it's not included in the 64-bit Windows EPD or the 64-bit OS X EPD, though it is in the 32-bit versions.","Q_Score":1,"Tags":"python,image-processing","A_Id":7786451,"CreationDate":"2011-10-16T18:07:00.000","Title":"does Enthought Python distribution includes pyhdf and HDF 4","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have some integer matrices of moderate size (a few hundred rows). I need to solve equations of the form Ax = b where b is a standard basis vector and A is one of my matrices. I have been using numpy.linalg.lstsq for this purpose, but the rounding errors end up being too significant. \nHow can I carry out an exact symbolic computation?\n(PS I don't really need the code to be efficient; I'm more concerned about ease of coding.)","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":403,"Q_Id":7787732,"Users Score":1,"Answer":"Note that if you're serious about your comment that you require your solution vector to be integer, then you're looking for something called the \"integer least squares problem\". Which is believed to be NP-hard. There are some heuristic solvers, but it all gets very complicated.","Q_Score":1,"Tags":"python,numpy","A_Id":7787997,"CreationDate":"2011-10-16T22:18:00.000","Title":"How do I do matrix computations in python without rounding?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have to store\/retrieve a large number of images to use in my program.\nEach image is small: an icon 50x50, and each one has associated a string which is the path the icon is related to.\nSince they are so small I was thinking if there is some library which allows to store all of them in a single file. \nI would need to store both the image and the path string.\nI don't know if pickle is a possible choice - I also heard about much more complicated libraries such as HDF5...\nthanks for your help!\nalessandro","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":165,"Q_Id":7846413,"Users Score":0,"Answer":"You could pickle a dict that associates filenames to byte strings of RGBA data.\nAssuming you have loaded the image with PIL, make sure they have all the same size and color format. Build a dict with images[filename] = im.tostring() and dump() it with pickle. Use Image.fromstring with the right size and mode parameters to get it back.","Q_Score":2,"Tags":"python,database","A_Id":7847894,"CreationDate":"2011-10-21T07:41:00.000","Title":"python: container to memorize a large number of images","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":1,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"When I call random.sample(arr,length)\nan error returns random_sample() takes at most 1 positional argument (2 given).  After some Googling I found out I'm calling Numpy's random sample function when I want to call the sample function of the random module.  I've tried importing numpy under a different name, which doesn't fix the problem.  I need Numpy for the rest of the program, though.\nAny thoughts?  Thanks","AnswerCount":4,"Available Count":1,"Score":0.2449186624,"is_accepted":false,"ViewCount":4375,"Q_Id":7855845,"Users Score":5,"Answer":"This shouldn't happen. Check your code for bad imports like from numpy import *.","Q_Score":5,"Tags":"python,numpy,random-sample","A_Id":7855863,"CreationDate":"2011-10-21T22:09:00.000","Title":"Python's random module made inaccessible by Numpy's random module","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I recently asked about accessing data from SPSS and got some absolutely wonderful help here. I now have an almost identical need to read data from a Confirmit data file. Not finding a ton of confirmit data file format on the web. It appears that Confirmit can export to SPSS *.sav files. This might be one avenue for me. Here's the exact needs:\nI need to be able to extract two different but related types of info from a market research study done using ConfirmIt:\n\nI need to be able to discover the data \"schema\", as in what questions are being asked (the text of the questions) and what the type of the answer is (multiple choice, yes\/no, text) and what text labels are associated with each answer.\nI need to be able to read respondents answers and populate my data model. So for each of the questions discovered as part of step 1 above, I need to build a table of respondent answers.\n\nWith SPSS this was easy thanks to a data access module available freely available by IBM and a nice Python wrapper by Albert-Jan Roskam. Googling I'm not finding much info. Any insight into this is helpful. Something like a Python or Java class to read the confirmit data would be perfect!\nAssuming my best option ends up being to export to SPSS *.sav file, does anyone know if it will meet both of my use cases above (contain the questions, answers schema and also contain each participant's results)?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":768,"Q_Id":7856725,"Users Score":0,"Answer":"I was recently given a data set from confirmit. There are almost 4000 columns in the excel file. I want to enter it into a mysql db. There is not way they are just doing that output from one table. Do you know how the table schema works for confirmit?","Q_Score":2,"Tags":"python,spss","A_Id":11026071,"CreationDate":"2011-10-22T00:57:00.000","Title":"Anyone familiar with data format of Comfirmit?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I recently asked about accessing data from SPSS and got some absolutely wonderful help here. I now have an almost identical need to read data from a Confirmit data file. Not finding a ton of confirmit data file format on the web. It appears that Confirmit can export to SPSS *.sav files. This might be one avenue for me. Here's the exact needs:\nI need to be able to extract two different but related types of info from a market research study done using ConfirmIt:\n\nI need to be able to discover the data \"schema\", as in what questions are being asked (the text of the questions) and what the type of the answer is (multiple choice, yes\/no, text) and what text labels are associated with each answer.\nI need to be able to read respondents answers and populate my data model. So for each of the questions discovered as part of step 1 above, I need to build a table of respondent answers.\n\nWith SPSS this was easy thanks to a data access module available freely available by IBM and a nice Python wrapper by Albert-Jan Roskam. Googling I'm not finding much info. Any insight into this is helpful. Something like a Python or Java class to read the confirmit data would be perfect!\nAssuming my best option ends up being to export to SPSS *.sav file, does anyone know if it will meet both of my use cases above (contain the questions, answers schema and also contain each participant's results)?","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":768,"Q_Id":7856725,"Users Score":0,"Answer":"You can get the data schema from Excel definition export from Confirmit\nYou can export from Confirmit txt file with the same template","Q_Score":2,"Tags":"python,spss","A_Id":10631838,"CreationDate":"2011-10-22T00:57:00.000","Title":"Anyone familiar with data format of Comfirmit?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I using matplotlib to plot some data in python and the plots require a standard colour bar. The data consists of a series of NxM matrices containing frequency information so that a simple imshow() plot gives a 2D histogram with colour describing frequency. Each matrix contains data in different, but overlapping ranges. Imshow normalizes the data in each matrix to the range 0-1 which means that, for example, the plot of matrix A, will appear identical to the plot of the matrix 2*A (though the colour bar will show double the values). What I would like is for the colour red, for example, to correspond to the same frequency in all of the plots. In other words, a single colour bar would suffice for all the plots. Any suggestions would be greatly appreciated.","AnswerCount":4,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":46914,"Q_Id":7875688,"Users Score":9,"Answer":"Easiest solution is to call clim(lower_limit, upper_limit) with the same arguments for each plot.","Q_Score":38,"Tags":"python,matplotlib,colorbar","A_Id":7878155,"CreationDate":"2011-10-24T12:34:00.000","Title":"How can I create a standard colorbar for a series of plots in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"There seems to be many choices for Python to interface with SQLite (sqlite3, atpy) and HDF5 (h5py, pyTables) -- I wonder if anyone has experience using these together with numpy arrays or data tables (structured\/record arrays), and which of these most seamlessly integrate with \"scientific\" modules (numpy, scipy) for each data format (SQLite and HDF5).","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":3647,"Q_Id":7883646,"Users Score":23,"Answer":"Most of it depends on your use case. \nI have a lot more experience dealing with the various HDF5-based methods than traditional relational databases, so I can't comment too much on SQLite libraries for python...\nAt least as far as h5py vs pyTables, they both offer very seamless access via numpy arrays, but they're oriented towards very different use cases.\nIf you have n-dimensional data that you want to quickly access an arbitrary index-based slice of, then it's much more simple to use h5py.  If you have data that's more table-like, and you want to query it, then pyTables is a much better option.\nh5py is a relatively \"vanilla\" wrapper around the HDF5 libraries compared to pyTables.  This is a very good thing if you're going to be regularly accessing your HDF file from another language (pyTables adds some extra metadata).  h5py can do a lot, but for some use cases (e.g. what pyTables does) you're going to need to spend more time tweaking things.  \npyTables has some really nice features.  However, if your data doesn't look much like a table, then it's probably not the best option.\nTo give a more concrete example, I work a lot with fairly large (tens of GB) 3 and 4 dimensional arrays of data.  They're homogenous arrays of floats, ints, uint8s, etc.  I usually want to access a small subset of the entire dataset.  h5py makes this very simple, and does a fairly good job of auto-guessing a reasonable chunk size.  Grabbing an arbitrary chunk or slice from disk is much, much faster than for a simple memmapped file. (Emphasis on arbitrary... Obviously, if you want to grab an entire \"X\" slice, then a C-ordered memmapped array is impossible to beat, as all the data in an \"X\" slice are adjacent on disk.)  \nAs a counter example, my wife collects data from a wide array of sensors that sample at minute to second intervals over several years. She needs to store and run arbitrary querys (and relatively simple calculations) on her data.  pyTables makes this use case very easy and fast, and still has some advantages over traditional relational databases. (Particularly in terms of disk usage and speed at which a large (index-based) chunk of data can be read into memory)","Q_Score":12,"Tags":"python,sqlite,numpy,scipy,hdf5","A_Id":7891137,"CreationDate":"2011-10-25T01:06:00.000","Title":"exporting from\/importing to numpy, scipy in SQLite and HDF5 formats","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In a java application I need to use a specific image processing algorithm that is currently implemented in python.\nWhat would be the best approach, knowing that this script uses the Numpy library ?\nI alreayd tried to compile the script to java using the jythonc compiler but it seems that it doesn't support dependencies on native libraries like Numpy.\nI also tried to use Jepp but I get an ImportError when importing Numpy, too.\nAny suggestion ?","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2255,"Q_Id":7891586,"Users Score":3,"Answer":"If you're using Numpy you probably have to just use C Python, as it's a compiled extension. I'd recommend saving the image to disk, perhaps as a temporary file, and then calling the Python as a subprocess. If you're dealing with binary data you could even try memory mapping the data in Java and passing in in the path to the subprocess.\nAlternatively, depending on your circumstances, you could set up a simple data processing server in Python which accepts requests and returns processed data.","Q_Score":1,"Tags":"java,python,numpy,jython","A_Id":7892106,"CreationDate":"2011-10-25T15:19:00.000","Title":"Run python script (with numpy dependency) from java","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"The most common SQLite interface I've seen in Python is sqlite3, but is there anything that works well with NumPy arrays or recarrays? By that I mean one that recognizes data types and  does not require inserting row by row, and extracts into a NumPy (rec)array...? Kind of like R's SQL functions in the RDB or sqldf libraries, if anyone is familiar with those (they import\/export\/append whole tables or subsets of tables to or from R data tables).","AnswerCount":4,"Available Count":1,"Score":0.049958375,"is_accepted":false,"ViewCount":7905,"Q_Id":7901853,"Users Score":1,"Answer":"This looks a bit older but is there any reason you cannot just do a fetchall() instead of iterating and then just initializing numpy on declaration?","Q_Score":6,"Tags":"python,arrays,sqlite,numpy,scipy","A_Id":12100118,"CreationDate":"2011-10-26T11:15:00.000","Title":"NumPy arrays with SQLite","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to use openCV to detect when a person raises or lowers a hand or both hands. I have looked through the tutorials provided by python opencv and none of them seem to do the job. There is a camera that sits in front of the 2 persons, about 50cm away from them(so you see them from the waist up). The person is able to raise or lower each arm, or both of the arms and I have to detect when they do that.(the camera is mounted on the bars of the rollercoaster; this implies that the background is always changing)\nHow can I detect this in the fastest time possible? It does not have to be real time detection but it does not have to be more than 0.5seconds. The whole image is 640x480. Now, since the hands can appear only in the top of the image, this would reduce the search area by half => 640x240. This would reduce to the problem of searching a certain object(the hands) in a constantly changing background.\nThank you,\n Stefan F.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":3058,"Q_Id":7904055,"Users Score":1,"Answer":"You can try the very basic but so effective and fast solution:\non the upper half of the image:\n\ncanny edge detection\nmorphologyEx with adequate Structuring element(also simple combination of erode\/dilate may be enough)\nconvert to BW using adaptive threshold\nXor the result with a mask representing the expected covered area.\nThe number of ones returned by xor in each area of the mask is the index that you should use.\n\nThis is extremely fast, you can make more than one iteration within the 0.5 sec and use the average. also you may detect faces and use them to adapt the position of your mask, but this will be more expensive :)\nhope that helps","Q_Score":1,"Tags":"python,opencv,computer-vision,motion-detection","A_Id":9572786,"CreationDate":"2011-10-26T14:21:00.000","Title":"Detect Hand using OpenCV","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am faced with the following programming problem. I need to generate n (a, b) tuples for which the sum of all a's is a given A and sum of all b's is a given B and for each tuple the ratio of a \/ b is in the range (c_min, c_max). A \/ B is within the same range, too. I am also trying to make sure there is no bias in the result other than what is introduced by the constraints and the a \/ b values are more-or-less uniformly distributed in the given range.\nSome clarifications and meta-constraints:\n\nA, B, c_min, and c_max are given. \nThe ratio A \/ B is in the (c_min, c_max) range. This has to be so if the problem is to have a solution given the other constraints.\na and b are >0 and non-integer.\n\nI am trying to implement this in Python but ideas in any language (English included) are much appreciated.","AnswerCount":6,"Available Count":3,"Score":0.0333209931,"is_accepted":false,"ViewCount":2895,"Q_Id":7908800,"Users Score":1,"Answer":"Blocked Gibbs sampling is pretty simple and converges to the right distribution (this is along the lines of what Alexandre is proposing).\n\nFor all i, initialize ai = A \/ n and bi = B \/ n.\nSelect i \u2260 j uniformly at random. With probability 1\/2, update ai and aj with uniform random values satisfying the constraints. The rest of the time, do the same for bi and bj.\nRepeat Step 2 as many times as seems to be necessary for your application. I have no idea what the convergence rate is.","Q_Score":12,"Tags":"python,algorithm,random","A_Id":7917373,"CreationDate":"2011-10-26T20:52:00.000","Title":"Generating random numbers under very specific constraints","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am faced with the following programming problem. I need to generate n (a, b) tuples for which the sum of all a's is a given A and sum of all b's is a given B and for each tuple the ratio of a \/ b is in the range (c_min, c_max). A \/ B is within the same range, too. I am also trying to make sure there is no bias in the result other than what is introduced by the constraints and the a \/ b values are more-or-less uniformly distributed in the given range.\nSome clarifications and meta-constraints:\n\nA, B, c_min, and c_max are given. \nThe ratio A \/ B is in the (c_min, c_max) range. This has to be so if the problem is to have a solution given the other constraints.\na and b are >0 and non-integer.\n\nI am trying to implement this in Python but ideas in any language (English included) are much appreciated.","AnswerCount":6,"Available Count":3,"Score":1.2,"is_accepted":true,"ViewCount":2895,"Q_Id":7908800,"Users Score":2,"Answer":"Start by generating as many identical tuples, n, as you need:\n(A\/n, B\/n)\nNow pick two tuples at random.  Make a random change to the a value of one, and a compensating change to the a value of the other, keeping everything within the given constraints.  Put the two tuples back.\nNow pick another random pair.  This times twiddle with the b values.\nLather, rinse repeat.","Q_Score":12,"Tags":"python,algorithm,random","A_Id":7908987,"CreationDate":"2011-10-26T20:52:00.000","Title":"Generating random numbers under very specific constraints","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am faced with the following programming problem. I need to generate n (a, b) tuples for which the sum of all a's is a given A and sum of all b's is a given B and for each tuple the ratio of a \/ b is in the range (c_min, c_max). A \/ B is within the same range, too. I am also trying to make sure there is no bias in the result other than what is introduced by the constraints and the a \/ b values are more-or-less uniformly distributed in the given range.\nSome clarifications and meta-constraints:\n\nA, B, c_min, and c_max are given. \nThe ratio A \/ B is in the (c_min, c_max) range. This has to be so if the problem is to have a solution given the other constraints.\na and b are >0 and non-integer.\n\nI am trying to implement this in Python but ideas in any language (English included) are much appreciated.","AnswerCount":6,"Available Count":3,"Score":0.0665680765,"is_accepted":false,"ViewCount":2895,"Q_Id":7908800,"Users Score":2,"Answer":"I think the simplest thing is to\n\nUse your favorite method to throw n-1 values such that \\sum_i=0,n-1 a_i < A, and set a_n to get the right total. There are several SO question about doing that, though I've never seen a answer I'm really happy with yet. Maybe I'll write a paper or something.\nGet the n-1 b's by throwing the c_i uniformly on the allowed range, and set final b to get the right total and check on the final c (I think it must be OK, but I haven't proven it yet).\n\nNote that since we have 2 hard constrains we should expect to throw 2n-2 random numbers, and this method does exactly that (on the assumption that you can do step 1 with n-1 throws.","Q_Score":12,"Tags":"python,algorithm,random","A_Id":7908989,"CreationDate":"2011-10-26T20:52:00.000","Title":"Generating random numbers under very specific constraints","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an .x3d code which references a python script. I am trying to implement certain functions which make use of the numpy module. However, I am only able to import the builtin modules from Python.\nI am looking for a way to import the numpy module into the script without having to call the interpreter (i.e. \"test.py\", instead of \"python test.py\").\nCurrently I get \"ImportError: No module named numpy\".\nMy question is: Is there a way to import the numpy module without having to call from the interpreter? Is there a way to include numpy as one of the built-in modules of Python?","AnswerCount":3,"Available Count":3,"Score":1.2,"is_accepted":true,"ViewCount":1209,"Q_Id":7909761,"Users Score":4,"Answer":"find where numpy is installed on your system. For me, it's here:\n\/usr\/lib\/pymodules\/python2.7\nimport it explicitly before importing numpy\n\nimport sys\nsys.path.append('\/usr\/lib\/pymodules\/python2.7')\n... if you need help finding the correct path, check the contents of sys.path while using your python interpreter\nimport sys\nprint sys.path","Q_Score":3,"Tags":"python,numpy","A_Id":7909874,"CreationDate":"2011-10-26T22:36:00.000","Title":"Python - Run numpy without the python interpreter","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an .x3d code which references a python script. I am trying to implement certain functions which make use of the numpy module. However, I am only able to import the builtin modules from Python.\nI am looking for a way to import the numpy module into the script without having to call the interpreter (i.e. \"test.py\", instead of \"python test.py\").\nCurrently I get \"ImportError: No module named numpy\".\nMy question is: Is there a way to import the numpy module without having to call from the interpreter? Is there a way to include numpy as one of the built-in modules of Python?","AnswerCount":3,"Available Count":3,"Score":0.1973753202,"is_accepted":false,"ViewCount":1209,"Q_Id":7909761,"Users Score":3,"Answer":"I'm going to guess that your #! line is pointing to a different python interpreter then the one you use normally. Make sure they point to the same one.","Q_Score":3,"Tags":"python,numpy","A_Id":7909895,"CreationDate":"2011-10-26T22:36:00.000","Title":"Python - Run numpy without the python interpreter","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an .x3d code which references a python script. I am trying to implement certain functions which make use of the numpy module. However, I am only able to import the builtin modules from Python.\nI am looking for a way to import the numpy module into the script without having to call the interpreter (i.e. \"test.py\", instead of \"python test.py\").\nCurrently I get \"ImportError: No module named numpy\".\nMy question is: Is there a way to import the numpy module without having to call from the interpreter? Is there a way to include numpy as one of the built-in modules of Python?","AnswerCount":3,"Available Count":3,"Score":0.0665680765,"is_accepted":false,"ViewCount":1209,"Q_Id":7909761,"Users Score":1,"Answer":"Add the num.py libraries to sys.path before you call import","Q_Score":3,"Tags":"python,numpy","A_Id":7909774,"CreationDate":"2011-10-26T22:36:00.000","Title":"Python - Run numpy without the python interpreter","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I couldn't find the right function to add a footnote in my plot.\nThe footnote I want to have is something like an explanation of one item in the legend, but it is too long to put in the legend box. So, I'd like to add a ref number, e.g. [1], to the legend item, and add the footnote in the bottom of the plot, under the x-axis.\nWhich function should I use? Thanks!","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":42816,"Q_Id":7917107,"Users Score":17,"Answer":"One way would be just use plt.text(x,y,'text')","Q_Score":26,"Tags":"python,matplotlib","A_Id":7918549,"CreationDate":"2011-10-27T14:07:00.000","Title":"Add footnote under the x-axis using matplotlib","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"For SciPy sparse matrix, one can use todense() or toarray() to transform to NumPy matrix or array. What are the functions to do the inverse?\nI searched, but got no idea what keywords should be the right hit.","AnswerCount":4,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":141998,"Q_Id":7922487,"Users Score":0,"Answer":"As for the inverse, the function is inv(A), but I won't recommend using it, since for huge matrices it is very computationally costly and unstable. Instead, you should use an approximation to the inverse, or if you want to solve Ax = b you don't really need A-1.","Q_Score":102,"Tags":"python,numpy,scipy,sparse-matrix","A_Id":33510117,"CreationDate":"2011-10-27T21:14:00.000","Title":"How to transform numpy.matrix or array to scipy sparse matrix","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I recently downloaded OpenCV 2.3.1, compiled with the CMake flags withQt and withQtOpenGL turned on. My Qt version is 4.7.4 and is configured with OpenGL enabled. Supposedly I only need to copy cv2.pyd to Python's site-package path:\n\nC:\\Python27\\Lib\\site-packages\n\nAnd in the mean time make sure the OpenCV dlls are somewhere in my PATH. However, when I try to call\n\nimport cv2\n\nin ipython, it returned an error:\n\nImportError: DLL load failed: The specified procedure could not be found.\n\nI also tried OpenCV 2.3, resulting the same error. If OpenCV is compiled without Qt, the import works just fine. Has anyone run into similar problem before? Or is there anyway to get more information, such as which procedure is missing from what DLL?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":488,"Q_Id":7940848,"Users Score":0,"Answer":"Probably need the qt dll's in the same place as the opencv dlls - and they have to be the version built with the same compiler as opencv (and possibly python)","Q_Score":2,"Tags":"python,qt,opencv,import","A_Id":7940923,"CreationDate":"2011-10-29T18:33:00.000","Title":"Error when importing OpenCV python module (when built with Qt and QtOpenGL)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":1,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm in the process of working on programming project that involves some pretty extensive Monte Carlo simulation in Python, and as such the generation of a tremendous number of random numbers. Very nearly all of them, if not all of them, will be able to be generated by Python's built in random module.\nI'm something of a coding newbie, and unfamiliar with efficient and inefficient ways to do things. Is it faster to generate say, all the random numbers as a list, and then iterate through that list, or generate a new random number each time a function is called, which will be in a very large loop?\nOr some other, undoubtedly more clever method?","AnswerCount":5,"Available Count":1,"Score":0.0798297691,"is_accepted":false,"ViewCount":12900,"Q_Id":7988494,"Users Score":2,"Answer":"Code to generate 10M random numbers efficiently and faster:\n\nimport random\nl=10000000\nlistrandom=[]\nfor i in range (l):\n    value=random.randint(0,l)\n    listrandom.append(value)\nprint listrandom\n\nTime taken included the I\/O time lagged in printing on screen:\n\nreal    0m27.116s\nuser    0m24.391s\nsys 0m0.819s","Q_Score":14,"Tags":"python,random","A_Id":36474703,"CreationDate":"2011-11-02T23:14:00.000","Title":"Efficient way to generate and use millions of random numbers in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there a difference between HDF5 files and files created by PyTables? \nPyTables has two functions .isHDFfile() and .isPyTablesFile() suggesting that there\nis a difference between the two formats.\nI've done some looking around on Google and have gathered that PyTables is built on top of HDF, but I wasn't able to find much beyond that.  \nI am specifically interested in interoperability, speed and overhead.\nThanks.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2253,"Q_Id":8002569,"Users Score":17,"Answer":"PyTables files are HDF5 files.\nHowever, as I understand it, PyTables adds some extra metadata to the attributes of each entry in the HDF file.\nIf you're looking for a more \"vanilla\" hdf5 solution for python\/numpy, have a look a h5py.\nIt's less database-like (i.e. less \"table-like\") than PyTables, and doesn't have as many nifty querying features, but it's much more straight-forward, in my opinion.  If you're going to be accessing an hdf5 file from multiple different languages, h5py is probably a better route to take.","Q_Score":14,"Tags":"python,numpy,hdf5,pytables","A_Id":8002777,"CreationDate":"2011-11-03T22:13:00.000","Title":"Difference between HDF5 file and PyTables file","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am looking for an algorithm that is implemented in C, C++, Python or Java that calculates the set of winning coalitions for n agents where each agent has a different amount of votes. I would appreciate any hints. Thanks!","AnswerCount":3,"Available Count":2,"Score":0.1973753202,"is_accepted":false,"ViewCount":373,"Q_Id":8019172,"Users Score":3,"Answer":"In other words, you have an array X[1..n], and want to have all the subsets of it for which sum(subset) >= 1\/2 * sum(X), right?\nThat probably means the whole set qualifies. \nAfter that, you can drop any element k having X[k] < 1\/2 * sum(X), and every such a coalition will be fine as an answer, too.\nAfter that, you can proceed dropping elements one by one, stopping when you've reached half of the sum. \nThis is obviously not the most effective solution: you don't want to drop k1=1,k2=2 if you've already tried k1=2,k2=1\u2014but I believe you can handle this.","Q_Score":1,"Tags":"java,c++,python,c,algorithm","A_Id":8019217,"CreationDate":"2011-11-05T08:59:00.000","Title":"Coalition Search Algorithm","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am looking for an algorithm that is implemented in C, C++, Python or Java that calculates the set of winning coalitions for n agents where each agent has a different amount of votes. I would appreciate any hints. Thanks!","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":373,"Q_Id":8019172,"Users Score":0,"Answer":"Arrange the number of votes for each of the agents into an array, and compute the partial sums from the right, so that you can find out SUM_i = k to n Votes[i] just by looking up the partial sum.\nThen do a backtrack search over all possible subsets of {1, 2, ...n}. At any point in the backtrack you have accepted some subset of agents 0..i - 1, and you know from the partial sum the maximum possible number of votes available from other agents. So you can look to see if the current subset could be extended with agents number >= i to form a winning coalition, and discard it if not.\nThis gives you a backtrack search where you consider a subset only if it is already a winning coalition, or you will extend it to become a winning coalition. So I think the cost of the backtrack search is the sum of the sizes of the winning coalitions you discover, which seems close to optimal. I would be tempted to rearrange the agents before running this so that you deal with the agents with most votes first, but at the moment I don't see an argument that says you gain much from that.\nActually - taking a tip from Alf's answer - life is a lot easier if you start from the full set of agents, and then use backtrack search to decide which agents to discard. Then you don't need an array of partial sums, and you only generate subsets you want anyway. And yes, there is no need to order agents in advance.","Q_Score":1,"Tags":"java,c++,python,c,algorithm","A_Id":8019235,"CreationDate":"2011-11-05T08:59:00.000","Title":"Coalition Search Algorithm","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a fairly complex function f(x) that I want to optimize and I am using the fmin_bfgs function from the scipy.optimize module from Scipy. It forces  me to give the function to minimize and the function of the gradient f'(x) separately, which is a pity because some of the computations for the gradient can be done when evaluating the function f(x). \nIs there a way of combining both functions? I was considering saving the intermediate values required for both functions, but I don't know if the fmin_bfgs function guarantees that f(x) is evaluated before than f'(x).\nThank you","AnswerCount":2,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":557,"Q_Id":8045576,"Users Score":4,"Answer":"The scipy.optimize.minimize methods has a parameter called \"jac\". If set to True, minimize will expect the callable f(x) to both return the function value and it's derivatives.","Q_Score":3,"Tags":"python,optimization,scipy","A_Id":15979342,"CreationDate":"2011-11-08T03:24:00.000","Title":"How to force the functions of the optimize module of scipy to take a function and its gradient simultaneously","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am looking for a function to calculate exponential moving sum in numpy or scipy. I want to avoid using python loops because they are really slow. \nto be specific, I have two series A[] and T[]. T[i] is the timestamp of value A[i]. I define a half-decay period tau. For a given time t, the exponential moving sum is the sum of all the values A[i] that happens before t, with weight exp(-(t-T[i])\/tau) for each A[i]. \nThanks a lot!","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2259,"Q_Id":8052582,"Users Score":0,"Answer":"You can try to improve python loops by doing good \"practices\" (like avoiding dots).\nMaybe you can code you function in C (into a \"numpy library\") and call it from python.","Q_Score":5,"Tags":"python,numpy,scipy,vectorization","A_Id":8052660,"CreationDate":"2011-11-08T15:10:00.000","Title":"exponential moving sum in numpy \/ scipy?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to use OpenCV and Python to stitch together several hundred puzzle piece images into one large, complete image.  All of the images are digitized and are in a PNG format.  The pieces were originally from a scan and extracted into individual pieces, so they have transparent backgrounds and are each a single piece.  What is the process of comparing them and finding their matches using OpenCV?\nThe plan is that the images and puzzle pieces will always be different and this python program will take a scan of all the pieces laid out, crop out the pieces (which it does now), and build the puzzle back.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1769,"Q_Id":8083263,"Users Score":0,"Answer":"If this is a small fun project that you are trying to do, you can compare image histograms or use SIFT\/SURF. I don't think there is implementation of SIFT, SURF in Python API. If you can find compatible equivalent, you can do it.\nComparing images are very much dependent on the data-set that you have. Some techniques work more better than the other.","Q_Score":2,"Tags":"python,image-processing,opencv,image-stitching","A_Id":11141963,"CreationDate":"2011-11-10T16:54:00.000","Title":"Using OpenCV and Python to stitch puzzle images together","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"is there a good algorithm for checking whether there are 5 same elements in a row or a column or diagonally given a square matrix, say 6x6?\nthere is ofcourse the naive algorithm of iterating through every spot and then for each point in the matrix, iterate through that row, col and then the diagonal. I am wondering if there is a better way of doing it.","AnswerCount":7,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":3808,"Q_Id":8091248,"Users Score":0,"Answer":"I don't think you can avoid iteration, but you can at least do an XOR of all elements and if the result of that is 0 => they are all equal, then you don't need to do any comparisons.","Q_Score":6,"Tags":"python,algorithm,matrix","A_Id":8091830,"CreationDate":"2011-11-11T08:08:00.000","Title":"better algorithm for checking 5 in a row\/col in a matrix","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"is there a good algorithm for checking whether there are 5 same elements in a row or a column or diagonally given a square matrix, say 6x6?\nthere is ofcourse the naive algorithm of iterating through every spot and then for each point in the matrix, iterate through that row, col and then the diagonal. I am wondering if there is a better way of doing it.","AnswerCount":7,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":3808,"Q_Id":8091248,"Users Score":0,"Answer":"You can try improve your method with some heuristics: use the knowledge of the matrix size to exclude element sequences that do not fit and suspend unnecessary calculation. In case the given vector size is 6, you want to find 5 equal elements, and the first 3 elements are different, further calculation do not have any sense.\nThis approach can give you a significant advantage, if 5 equal elements in a row happen rarely enough.","Q_Score":6,"Tags":"python,algorithm,matrix","A_Id":8098697,"CreationDate":"2011-11-11T08:08:00.000","Title":"better algorithm for checking 5 in a row\/col in a matrix","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"is there a good algorithm for checking whether there are 5 same elements in a row or a column or diagonally given a square matrix, say 6x6?\nthere is ofcourse the naive algorithm of iterating through every spot and then for each point in the matrix, iterate through that row, col and then the diagonal. I am wondering if there is a better way of doing it.","AnswerCount":7,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":3808,"Q_Id":8091248,"Users Score":0,"Answer":"Your best approach may depend on whether you control the placement of elements.\nFor example, if you were building a game and just placed the most recent element on the grid, you could capture into four strings the vertical, horizontal, and diagonal strips that intersected that point, and use the same algorithm on each strip, tallying each element and evaluating the totals.  The algorithm may be slightly different depending on whether you're counting five contiguous elements out of the six, or allow gaps as long as the total is five.","Q_Score":6,"Tags":"python,algorithm,matrix","A_Id":8091403,"CreationDate":"2011-11-11T08:08:00.000","Title":"better algorithm for checking 5 in a row\/col in a matrix","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been working with HDF5 files with C and Matlab, both using the same way for reading from and writing to datasets:\n\nopen file with h5f\nopen dataset with h5d\nselect space with h5s\n\nand so on...\nBut now I'm working with Python, and with its h5py library I see that it has two ways to manage HDF5: high-level and low-level interfaces. And with the former it takes less lines of code to get the information from a single variable of the file.\nIs there any noticeable loss of performance when using the high-level interface?\nFor example when dealing with a file with many variables inside, and we must read just one of them.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":932,"Q_Id":8096668,"Users Score":2,"Answer":"High-level interfaces are generally going with a performance loss of some sort. After that, whether it is noticeable (worth being investigated) will depend on what you are doing exactly with your code.\nJust start with the high-level interface. If the code is overall too slow, start profiling and move the bottlenecks down to the lower-level interface and see if it helps.","Q_Score":7,"Tags":"python,performance,hdf5,h5py","A_Id":8288377,"CreationDate":"2011-11-11T16:02:00.000","Title":"HDF5 for Python: high level vs low level interfaces. h5py","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would have a quite simple question, but can't find any suitable automated solution for now. \nI have developed an algorithm that performs a lot of stuff (image processing in fact) in Python. \nWhat I want to do now is to optimize it. And for that, I would love to create a graph of my algorithm.\nKind of an UML chart or sequencial chart in fact, in which functions would be displayed with inputs and ouptuts. \nMy algorithm does not imply complex stuff, and is mainly based on a = f(b) operations (no databases, hardware stuff, server, . . . )\nWould you have any hint? \nThanks by advance !","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":435,"Q_Id":8119900,"Users Score":1,"Answer":"UML generation is provided by pyreverse - it's part of pylint package\nIt generates UML in dot format - or png, etc.\nIt creates UML diagram, so you can easily see basic structure of your code\nI'm not sure if it satisfy all your needs, but it might be helpful","Q_Score":5,"Tags":"python,coding-style","A_Id":8121141,"CreationDate":"2011-11-14T10:01:00.000","Title":"Get the complete structure of a program?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm fed up with manually creating graphs in excel and consequently, I'm trying to automate the process using Python to massage the .csv data into a workable form and matplotlib to plot the result.\nUsing matplotlib and generating them is no problem but I can't work out is how to set the aspect ration \/ resolution of the output.\nSpecifically, I'm trying to generate scatter plots and stacked area graphs. Everything I've tried seems to result in one or more of the following:\n\nCramped graph areas (small plot area covered with the legend, axes etc.).\nThe wrong aspect ratio.\nLarge spaces on the sides of the chart area (I want a very wide \/ not very tall image).\n\nIf anyone has some working examples showing how to achieve this result I'd be very grateful!","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1578,"Q_Id":8143439,"Users Score":0,"Answer":"For resolution, you can use the dpi (dots per inch) argument when creating a figure, or in the savefig() function. For high quality prints of graphics dpi=600 or more is recommended.","Q_Score":4,"Tags":"python,matplotlib","A_Id":8147354,"CreationDate":"2011-11-15T21:34:00.000","Title":"How do get matplotlib pyplot to generate a chart for viewing \/ output to .png at a specific resolution?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to simulate\/model a closed-loop, linear, time-invariant system (specifically a locked PLL approximation) with python. \nEach sub-block within the model has a known transfer function which is given in terms of complex frequency H(s) = K \/ ( s * tau + 1 ).  Using the model, I would like to see how the system response as well as the noise response is affected as parameters (e.g. the VCO gain) are changed.  This would involve using Bode plots and root-locus plots.  \nWhat Python modules should I seek out to get the job done?","AnswerCount":5,"Available Count":1,"Score":0.1586485043,"is_accepted":false,"ViewCount":14035,"Q_Id":8144910,"Users Score":4,"Answer":"As @Matt said, I know this is old. But this came up as my first google hit, so I wanted to edit it.\nYou can use scipy.signal.lti to model linear, time invariant systems. That gives you lti.bode.\nFor an impulse response in the form of H(s) = (As^2 + Bs + C)\/(Ds^2 + Es + F), you would enter h = scipy.signal.lti([A,B,C],[D,E,F]). To get the bode plot, you would do plot(*h.bode()[:2]).","Q_Score":6,"Tags":"simulation,python,modeling","A_Id":16157331,"CreationDate":"2011-11-15T22:17:00.000","Title":"Modeling a linear system with Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm looking for a way to track two different colors at the same time using a single camera with OpenCV 2.3 (python bindings). \nI've read through a number of papers regarding OpenCV but can't find any mention as to whether or not it's capable of analyzing multiple histograms at once. \nIs this is even technically possible or do I need a separate camera for each color?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":4911,"Q_Id":8152504,"Users Score":0,"Answer":"I don't really understand your concern. \nWith the camera, you would get an image object. \nWith this image object, you can calculate as much different histograms as you want. \nEach histogram would be a different output object :). \nBasicaly, you could track hundreds of colors at the same time!","Q_Score":0,"Tags":"python,opencv","A_Id":9634189,"CreationDate":"2011-11-16T13:34:00.000","Title":"Tracking two different colors using OpenCV 2.3 and Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm messing around with machine learning, and I've written a K Means algorithm implementation in Python. It takes a two dimensional data and organises them into clusters. Each data point also has a class value of either a 0 or a 1.\nWhat confuses me about the algorithm is how I can then use it to predict some values for another set of two dimensional data that doesn't have a 0 or a 1, but instead is unknown. For each cluster, should I average the points within it to either a 0 or a 1, and if an unknown point is closest to that cluster, then that unknown point takes on the averaged value? Or is there a smarter method?\nCheers!","AnswerCount":4,"Available Count":1,"Score":0.049958375,"is_accepted":false,"ViewCount":14656,"Q_Id":8193563,"Users Score":1,"Answer":"If you are considering assigning a value based on the average value within the nearest cluster, you are talking about some form of \"soft decoder\", which estimates not only the correct value of the coordinate but your level of confidence in the estimate.  The alternative would be a \"hard decoder\" where only values of 0 and 1 are legal (occur in the training data set), and the new coordinate would get the median of the values within the nearest cluster.  My guess is that you should always assign only a known-valid class value (0 or 1) to each coordinate, and averaging class values is not a valid approach.","Q_Score":9,"Tags":"python,machine-learning,data-mining,k-means,prediction","A_Id":8194069,"CreationDate":"2011-11-19T10:58:00.000","Title":"Predicting Values with k-Means Clustering Algorithm","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using matplotlib with interactive mode on and am performing a computation, say an optimization with many steps where I plot the intermediate results at each step for debugging purposes. These plots often fill the screen and overlap to a large extent.\nMy problem is that during the calculation, figures that are partially or fully occluded don't refresh when I click on them. They are just a blank grey. \nI would like to force a redraw if necessary when I click on a figure, otherwise it is not useful to display it. Currently, I insert pdb.set_trace()'s in the code so I can stop and click on all the figures to see what is going on\nIs there a way to force matplotlib to redraw a figure whenever it gains mouse focus or is resized, even while it is busy doing something else?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2049,"Q_Id":8225460,"Users Score":0,"Answer":"Have you tried to call plt.figure(fig.number) before plotting on figure fig and plt.show() after plotting a figure? It should update all the figures.","Q_Score":6,"Tags":"python,matplotlib","A_Id":8232587,"CreationDate":"2011-11-22T10:41:00.000","Title":"Getting matplotlib plots to refresh on mouse focus","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Given: lst = [['John',3],['Blake',4],['Ted',3]]\nResult: lst = [['John',3],['Ted',3],['Blake',4]]\nI'm looking for a way to sort lists in lists first numerically then alphabetically without the use of the \"itemgetter\" syntax.","AnswerCount":5,"Available Count":1,"Score":0.0399786803,"is_accepted":false,"ViewCount":1274,"Q_Id":8236823,"Users Score":1,"Answer":"What's wrong with itemgetter?\nlst.sort(key=lambda l: list(reversed(l)) should do the trick","Q_Score":0,"Tags":"python,list","A_Id":8236857,"CreationDate":"2011-11-23T03:02:00.000","Title":"Python Sorting Lists in Lists","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there a good (small and light) alternative to numpy for python, to do linear algebra?\nI only need matrices (multiplication, addition), inverses, transposes and such.\nWhy?\n\nI am tired of trying to install numpy\/scipy - it is such a pita to get\n  it to work - it never seems to install correctly (esp. since I have\n  two machines, one linux and one windows): no matter what I do: compile\n  it or install from pre-built binaries. How hard is it to make a\n  \"normal\" installer that just works?","AnswerCount":7,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":10052,"Q_Id":8289157,"Users Score":0,"Answer":"I sometimes have this problem..not sure if this works but I often install it using my own account then try to run it in an IDE(komodo in my case) and it doesn't work. Like your issue it says it cannot find it. The way I solve this is to use sudo -i to get into root and then install it from there.\nIf that does not work can you update your answer to provide a bit more info about the type of system your using(linux, mac, windows), version of python\/numpy and how your accessing it so it'll be easier to help.","Q_Score":11,"Tags":"python","A_Id":8289322,"CreationDate":"2011-11-27T21:21:00.000","Title":"Alternative to scipy and numpy for linear algebra?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Anybody able to supply links, advice, or other forms of help to the following? \nObjective - use python to classify 10-second audio samples so that I afterwards can speak into a microphone and have python pick out and play snippets (faded together) of closest matches from db.\nMy objective is not to have the closest match and I don't care what the source of the audio samples is. So the result is probably of no use other than speaking in noise (fun).\nI would like the python app to be able to find a specific match of FFT for example within the 10 second samples in the db. I guess the real-time sampling of the microphone will have a 100 millisecond buffersample. \nAny ideas? FFT? What db? Other?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1085,"Q_Id":8312672,"Users Score":0,"Answer":"Try searching for algorithms on \"music fingerprinting\".","Q_Score":1,"Tags":"python,audio,classification,fft,pyaudioanalysis","A_Id":8314002,"CreationDate":"2011-11-29T14:43:00.000","Title":"python - audio classification of equal length samples \/ 'vocoder' thingy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm analyzing the AST generated by python code for \"fun and profit\", and I would like to have something more graphical than \"ast.dump\" to actually see the AST generated.\nIn theory is already a tree, so it shouldn't be too hard to create a graph, but I don't understand how I could do it.\nast.walk seems to walk with a BFS strategy, and the visitX methods I can't really see the parent or I don't seem to find a way to create a graph...\nIt seems like the only way is to write my own DFS walk function, is does it make sense?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":3299,"Q_Id":8340567,"Users Score":6,"Answer":"If you look at ast.NodeVisitor, it's a fairly trivial class. You can either subclass it or just reimplement its walking strategy to whatever you need. For instance, keeping references to the parent when nodes are visited is very simple to implement this way, just add a visit method that also accepts the parent as an argument, and pass that from your own generic_visit.\nP.S. By the way, it appears that NodeVisitor.generic_visit implements DFS, so all you have to do is add the parent node passing.","Q_Score":11,"Tags":"python,grammar,abstract-syntax-tree","A_Id":8341353,"CreationDate":"2011-12-01T11:23:00.000","Title":"Python ast to dot graph","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I just changed a program I am writing to hold my data as numpy arrays as I was having performance issues, and the difference was incredible. It originally took 30 minutes to run and now takes 2.5 seconds!\nI was wondering how it does it. I assume it is that the because it removes the need for for loops but beyond that I am stumped.","AnswerCount":6,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":47312,"Q_Id":8385602,"Users Score":0,"Answer":"Numpy arrays are extremily similar to 'normal' arrays such as those in c. Notice that every element has to be of the same type. The speedup is great because you can take advantage of prefetching and you can instantly access any element in array by it's index.","Q_Score":84,"Tags":"python,arrays,numpy","A_Id":8385718,"CreationDate":"2011-12-05T12:53:00.000","Title":"Why are NumPy arrays so fast?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using QItemSelectionModel with QTableView to allow the users to select rows. The problem is that when the user then clicks on a column header to sort the rows, the selection disappears and all the sorted data is displayed. How can I keep the selection, and just sort that, rather than having all the rows appear?\nThanks!\n--Erin","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":623,"Q_Id":8388659,"Users Score":0,"Answer":"Here is the way I ended up solving this problem:\n\nWhen row selections are made, put the unique IDs of each hidden row into a list, then hide all hidden rows\nUse self.connect(self.myHorizontalHeader, SIGNAL(\"sectionClicked(int)\"), self.keepSelectionValues) to catch the\nevent when a user clicks on a column header to sort the rows\nIn self.keepSelectionValue, go through each row and if the unique ID is in the hidden row list, hide the row\n\nThis effectively sorts and displays the non-hidden rows without displaying all the rows of the entire table.","Q_Score":0,"Tags":"python,pyqt","A_Id":8539266,"CreationDate":"2011-12-05T16:41:00.000","Title":"How can I keep row selections in QItemSelectionModel when columns are sorted?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":1,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I wanted the imshow() function in matplotlib.pyplot to display images the opposite way, i.e upside down. Is there a simple way to do this?","AnswerCount":4,"Available Count":3,"Score":1.2,"is_accepted":true,"ViewCount":82762,"Q_Id":8396101,"Users Score":221,"Answer":"Specify the keyword argument origin='lower' or origin='upper' in your call to imshow.","Q_Score":115,"Tags":"python,image,matplotlib","A_Id":8396124,"CreationDate":"2011-12-06T06:20:00.000","Title":"Invert image displayed by imshow in matplotlib","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I wanted the imshow() function in matplotlib.pyplot to display images the opposite way, i.e upside down. Is there a simple way to do this?","AnswerCount":4,"Available Count":3,"Score":0.049958375,"is_accepted":false,"ViewCount":82762,"Q_Id":8396101,"Users Score":1,"Answer":"You can use the extent argument. For example, if X values range from -10 and 10 and Y values range from -5 to 5, you should pass extent=(-10,10,-5,5) to imshow().","Q_Score":115,"Tags":"python,image,matplotlib","A_Id":67577958,"CreationDate":"2011-12-06T06:20:00.000","Title":"Invert image displayed by imshow in matplotlib","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I wanted the imshow() function in matplotlib.pyplot to display images the opposite way, i.e upside down. Is there a simple way to do this?","AnswerCount":4,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":82762,"Q_Id":8396101,"Users Score":0,"Answer":"Use ax.invert_yaxis() to invert the y-axis, or ax.invert_xaxis() to invert the x-axis.","Q_Score":115,"Tags":"python,image,matplotlib","A_Id":68682366,"CreationDate":"2011-12-06T06:20:00.000","Title":"Invert image displayed by imshow in matplotlib","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using python to work on my project in image processing. Suppose I got a very large image ( 100000 x 100000), and I need to randomly select a 200 x 200 square from this large image. Are there any easy way to do this job? Please share some light with me. Thank you\n----------------------------- EDIT ------------------------------------\nSorry I don't think it is 100000 x 100000, but the resolution of images are in 1 km and 2km. I am having trouble with selecting the area of 200 x 200.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":465,"Q_Id":8410260,"Users Score":4,"Answer":"If you convert to binary PPM format, then there should be an easy way to seek to the appropriate offsets - it's not compressed, so there should be simple relationships.\nSo pick two random numbers between 0 and 100000-200-1, and go to town.\n(I'm assuming you don't have a system with 10's of gigabytes of RAM)","Q_Score":2,"Tags":"python,image-processing,matplotlib,python-imaging-library","A_Id":8410307,"CreationDate":"2011-12-07T04:06:00.000","Title":"randomly select a 200x200 square inside an image in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using csv.DictReader to read some large files into memory to then do some analysis, so all objects from multiple CSV files need to be kept in memory.  I need to read them as Dictionary to make analysis easier, and because the CSV files may be altered by adding new columns.\nYes SQL can be used, but I'd rather avoid it if it's not needed.\nI'm wondering if there is a better and easier way of doing this.  My concern is that I will have many dictionary objects with same keys and waste memory?  The use of __slots__ was an option, but I will only know the attributes of an object after reading the CSV.  \n[Edit:] Due to being on legacy system and \"restrictions\", use of third party libraries is not possible.","AnswerCount":5,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":490,"Q_Id":8411476,"Users Score":0,"Answer":"Possibilities:\n(1) Benchmark the csv.DictReader approach and see if it causes a problem. Note that the dicts contain POINTERS to the keys and values; the actual key strings are not copied into each dict.\n(2) For each file, use csv.Reader, after the first row, build a class dynamically, instantiate it once per remaining row. Perhaps this is what you had in mind. \n(3) Have one fixed class, instantiated once per file, which gives you a list of tuples for the actual data, a tuple that maps column indices to column names, and a dict that maps column names to column indices. Tuples occupy less memory than lists because there is no extra append-space allocated. You can then get and set your data via (row_index, column_index) and (row_index, column_name).\nIn any case, to get better advice, how about some simple facts and stats: What version of Python? How many files? rows per file? columns per file? total unique keys\/column names?","Q_Score":1,"Tags":"python,memory,dictionary","A_Id":8414694,"CreationDate":"2011-12-07T06:51:00.000","Title":"Python: large number of dict like objects memory use","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using csv.DictReader to read some large files into memory to then do some analysis, so all objects from multiple CSV files need to be kept in memory.  I need to read them as Dictionary to make analysis easier, and because the CSV files may be altered by adding new columns.\nYes SQL can be used, but I'd rather avoid it if it's not needed.\nI'm wondering if there is a better and easier way of doing this.  My concern is that I will have many dictionary objects with same keys and waste memory?  The use of __slots__ was an option, but I will only know the attributes of an object after reading the CSV.  \n[Edit:] Due to being on legacy system and \"restrictions\", use of third party libraries is not possible.","AnswerCount":5,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":490,"Q_Id":8411476,"Users Score":0,"Answer":"If all the data in one column are the same type, you can use NumPy. NumPy's loadtxt and genfromtxt function can be used to read csv file. And because it returns an array, the memory usage is smaller then dict.","Q_Score":1,"Tags":"python,memory,dictionary","A_Id":8411784,"CreationDate":"2011-12-07T06:51:00.000","Title":"Python: large number of dict like objects memory use","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a large matrix (approx. 80,000 X 60,000), and I basically want to scramble all the entries (that is, randomly permute both rows and columns independently). \nI believe it'll work if I loop over the columns, and use randperm to randomly permute each column. (Or, I could equally well do rows.) Since this involves a loop with 60K iterations, I'm wondering if anyone can suggest a more efficient option? \nI've also been working with numpy\/scipy, so if you know of a good option in python, that would be great as well. \nThanks!\nSusan\nThanks for all the thoughtful answers! Some more info: the rows of the matrix represent documents, and the data in each row is a vector of tf-idf weights for that document. Each column corresponds to one term in the vocabulary. I'm using pdist to calculate cosine similarities between all pairs of papers. And I want to generate a random set of papers to compare to. \nI think that just permuting the columns will work, then, because each paper gets assigned a random set of term frequencies. (Permuting the rows just means reordering the papers.) As Jonathan pointed out, this has the advantage of not making a new copy of the whole matrix, and it sounds like the other options all will.","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2124,"Q_Id":8449501,"Users Score":0,"Answer":"Both solutions above are great, and will work, but I believe both will involve making a completely new copy of the entire matrix in memory while doing the work. Since this is a huge matrix, that's pretty painful. In the case of the MATLAB solution, I think you'll be possibly creating two extra temporary copies, depending on how reshape works internally. I think you were on the right track by operating on columns, but the problem is that it will only scramble along columns. However, I believe if you do randperm along rows after that, you'll end up with a fully permuted matrix. This way you'll only be creating temporary variables that are, at worst, 80,000 by 1. Yes, that's two loops with 60,000 and 80,000 iterations each, but internally that's going to have to happen regardless. The algorithm is going to have to visit each memory location at least twice. You could probably do a more efficient algorithm by writing a C MEX function that operates completely in place, but I assume you'd rather not do that.","Q_Score":0,"Tags":"python,matlab,random,permutation","A_Id":8450596,"CreationDate":"2011-12-09T17:39:00.000","Title":"matlab: randomly permuting rows and columns of a 2-D array","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Given any n x n matrix of real coefficients A, we can define a bilinear form bA : Rn x Rn \u2192 R by\n\nbA(x, y) = xTAy ,\n\nand a quadratic form qA : Rn \u2192 R by\n\nqA(x) = bA(x, x) = xTAx .\n\n(For most common applications of quadratic forms qA, the matrix A is symmetric, or even symmetric positive definite, so feel free to assume that either one of these is the case, if it matters for your answer.)\n(Also, FWIW, bI and qI (where I is the n x n identity matrix) are, respectively, the standard inner product, and squared L2-norm on Rn, i.e. xTy and xTx.)\nNow suppose I have two n x m matrices, X and Y, and an n x n matrix A.  I would like to optimize the computation of both bA(x,i, y,i) and qA(x,i) (where x,i and y,i denote the i-th column of X and Y, respectively), and I surmise that, at least in some environments like numpy, R, or Matlab, this will involve some form of vectorization.\nThe only solution I can think of requires generating diagonal block matrices [X], [Y] and [A], with dimensions mn x m, mn x m, and mn x mn, respectively, and with (block) diagonal elements x,i, y,i, and A, respectively.  Then the desired computations would be the matrix multiplications [X]T[A][Y] and [X]T[A][X].  This strategy is most definitely uninspired, but if there is a way to do it that is efficient in terms of both time and space, I'd like to see it.  (It goes without saying that any implementation of it that does not exploit the sparsity of these block matrices would be doomed.)\nIs there a better approach?\nMy preference of system for doing this is numpy, but answers in terms of some other system that supports efficient matrix computations, such as R or Matlab, may be OK too (assuming that I can figure out how to port them to numpy).\nThanks!\n\nOf course, computing the products XTAY and XTAX would compute the desired bA(x,i, y,i) and qA(x,i) (as the diagonal elements of the resulting m x m matrices), along with the O(m2) irrelevant bA(x,i, y,j) and bA(x,i, x,j), (for i \u2260 j), so this is a non-starter.","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":3364,"Q_Id":8457110,"Users Score":1,"Answer":"It's not entirely clear what you're trying to achieve, but in R, you use crossprod to form cross-products: given matrices X and Y with compatible dimensions, crossprod(X, Y) returns XTY. Similarly, matrix multiplication is achieved with the %*% operator: X %*% Y returns the product XY. So you can get XTAY as crossprod(X, A %*% Y) without having to worry about the mechanics of matrix multiplication, loops, or whatever.\nIf your matrices have a particular structure that allows optimising the computations (symmetric, triangular, sparse, banded, ...), you could look at the Matrix package, which has some support for this.\nI haven't used Matlab, but I'm sure it would have similar functions for these operations.","Q_Score":6,"Tags":"python,r,matlab,matrix,numpy","A_Id":8458779,"CreationDate":"2011-12-10T14:15:00.000","Title":"How to vectorize the evaluation of bilinear & quadratic forms?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Say I have a list in the form [[x,y,z], [x,y,z] etc...] etc where each grouping represents a random point.\nI want to order my points by the z coordinate, then within each grouping of z's, sort them by x coordinate. Is this possible?","AnswerCount":4,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":3538,"Q_Id":8522800,"Users Score":0,"Answer":"If you go further down the page Mr. White linked to, you'll see how you can specify an arbitrary function to compute your sort key (using the handy cmp_to_key function provided).","Q_Score":3,"Tags":"python,list,sorting","A_Id":8523192,"CreationDate":"2011-12-15T15:51:00.000","Title":"Sorting a Python list by third element, then by first element, etc?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a pybrain NN up and running, and it seems to be working rather well. Ideally, I would like to train the network and obtain a prediction after each data point (the previous weeks figures, in this case) has been added to the dataset.\nAt the moment I'm doing this by rebuilding the network each time, but it takes an increasingly long time to train the network as each example is added (+2 minutes for each example, in a dataset of 1000s of examples).\nIs there a way to speed up the process by adding the new example to an already trained NN and updating it, or am I overcomplicating the matter, and would be better served by training on a single set of examples (say last years data) and then testing on all of the new examples (this year)?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":985,"Q_Id":8582498,"Users Score":1,"Answer":"It dependes of what is your objective. If you need an updated NN-model you can perform an online training, i.e. performing a single step of back-propagation with the sample acquired at time $t$ starting from the network you had at time $t-1$. Or maybe you can discard the older samples in order to have a fixed amount of training samples or you can reduce the size of the training set performing a sort of clustering (i.e. merging similar samples into a single one). \nIf you explain better your application it'd be simpler suggesting solutions.","Q_Score":3,"Tags":"python,neural-network,pybrain","A_Id":8590831,"CreationDate":"2011-12-20T21:57:00.000","Title":"Retrain a pybrain neural network after adding to the dataset","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I read somewhere that the python library function random.expovariate produces intervals equivalent to Poisson Process events.\nIs that really the case or should I impose some other function on the results?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":12528,"Q_Id":8592048,"Users Score":8,"Answer":"On a strict reading of your question, yes, that is what random.expovariate does.  \nexpovariate gives you random floating point numbers, exponentially distributed.  In a Poisson process the size of the interval between consecutive events is exponential.  \nHowever, there are two other ways I could imagine modelling poisson processes\n\nJust generate random numbers, uniformly distributed and sort them.\nGenerate integers which have a Poisson distribution (i.e. they are distributed like the number of events within a fixed interval in a Poisson process).  Use numpy.random.poisson to do this.\n\nOf course all three things are quite different.  The right choice depends on your application.","Q_Score":5,"Tags":"python,math,statistics,poisson","A_Id":8592302,"CreationDate":"2011-12-21T15:15:00.000","Title":"Is random.expovariate equivalent to a Poisson Process","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am currently working on a system for robust hand detection.\nThe first step is to take a photo of the hand (in HSV color space) with the hand placed in a small rectangle to determine the skin color. I then apply a thresholding filter to set all non-skin pixels to black and all skin pixels white. \nSo far it works quite well, but I wanted to ask if there is a better way to solve this? For example, I found a few papers mentioning concrete color spaces for caucasian people, but none with a comparison for asian\/african\/caucasian color-tones.\nBy the way, I'm working with OpenCV via Python bindings.","AnswerCount":6,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":11923,"Q_Id":8593091,"Users Score":0,"Answer":"Well my experience with the skin modeling are bad, because:\n1) lightning can vary - skin segmentation is not robust\n2) it will mark your face also (as other skin-like objects)\nI would use machine learning techniques like Haar training, which, in my opinion, if far more better approach than modeling and fixing some constraints (like skin detection + thresholding...)","Q_Score":22,"Tags":"python,image-processing,opencv,computer-vision,skin","A_Id":25213056,"CreationDate":"2011-12-21T16:30:00.000","Title":"Robust Hand Detection via Computer Vision","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am doing research for my new project, Following is the details of my project, research and questions:\nProject:\n\nSave the Logs (ex. format is TimeStamp,LOG Entry,Location,Remarks etc ) from different sources. Here Different sources is like, gettting the LOG data from the different systems world wide (Just an Overview)\n(After saving the LOG Entries in Hadoop as specified in 1) Generate Reports of the LOGs saved in Hadoop on demand like drill down, drill up etc\n\nNOTE: For every minute approx. thier will be 50 to 60 MB of LOG Entries from the systems (I checked it).\nResearch and Questions:\n\nFor saving log entries in the Hadoop from different sources, we used Apache Flume.\nWe are creating our own MR programs and servlets.\n\nIs thier any good options other than flume?\nIs thier any Hadoop Data Analysis (Open Source) tool to genarte reports etc?\nI am doing my research, if any of us add some comments to me it will be helpfull.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":321,"Q_Id":8633112,"Users Score":0,"Answer":"I think you can use HIVE. Even I am new to Hadoop but read some where that HIVE is for hadoop analytics. Not sure whether it has GUI or not, but for sure it has SQL capability to query unstructed data.","Q_Score":0,"Tags":"java,python,hadoop","A_Id":8633193,"CreationDate":"2011-12-26T05:17:00.000","Title":"Hadoop - Saving Log Data and Developing GUI","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I am doing research for my new project, Following is the details of my project, research and questions:\nProject:\n\nSave the Logs (ex. format is TimeStamp,LOG Entry,Location,Remarks etc ) from different sources. Here Different sources is like, gettting the LOG data from the different systems world wide (Just an Overview)\n(After saving the LOG Entries in Hadoop as specified in 1) Generate Reports of the LOGs saved in Hadoop on demand like drill down, drill up etc\n\nNOTE: For every minute approx. thier will be 50 to 60 MB of LOG Entries from the systems (I checked it).\nResearch and Questions:\n\nFor saving log entries in the Hadoop from different sources, we used Apache Flume.\nWe are creating our own MR programs and servlets.\n\nIs thier any good options other than flume?\nIs thier any Hadoop Data Analysis (Open Source) tool to genarte reports etc?\nI am doing my research, if any of us add some comments to me it will be helpfull.","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":321,"Q_Id":8633112,"Users Score":1,"Answer":"Have you looked at Datameer ? It provides a GUI to import all these types of files, and create reports as well as dashboards.","Q_Score":0,"Tags":"java,python,hadoop","A_Id":8633276,"CreationDate":"2011-12-26T05:17:00.000","Title":"Hadoop - Saving Log Data and Developing GUI","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I would like to generate a random text using letter frequencies from a book in a .txt file, so that each new character (string.lowercase + ' ') depends on the previous one.\nHow do I use Markov chains to do so? Or is it simpler to use 27 arrays with conditional frequencies for each letter?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":1880,"Q_Id":8660015,"Users Score":1,"Answer":"If each character only depends on the previous character, you could just compute the probabilities for all 27^2 pairs of characters.","Q_Score":4,"Tags":"python,markov-chains","A_Id":8660103,"CreationDate":"2011-12-28T18:53:00.000","Title":"Markov chain on letter scale and random text","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have written a canny edge detection algorithm for a project. I want to know is there any method to link the broken segments of an edge, since i am getting a single edge as a conglomeration of a few segments. I am getting around 100 segments, which i am sure can be decreased with some intelligence. Please help.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":433,"Q_Id":8699665,"Users Score":0,"Answer":"You can use a method named dynamic programming. A very good intro on this can be found on chapter 6 of Sonka's digital image processing book","Q_Score":1,"Tags":"python,image-processing,edge-detection","A_Id":8724586,"CreationDate":"2012-01-02T10:19:00.000","Title":"Linking segments of edges","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm creating a script to convert a whole lot of data into CSV format. It runs on Google AppEngine using the mapreduce API, which is only relevant in that it means each row of data is formatted and output separately, in a callback function.\nI want to take advantage of the logic that already exists in the csv module to convert my data into the correct format, but because the CSV writer expects a file-like object, I'm having to instantiate a StringIO for each row, write the row to the object, then return the content of the object, each time.\nThis seems silly, and I'm wondering if there is any way to access the internal CSV formatting logic of the csv module without the writing part.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":464,"Q_Id":8711147,"Users Score":3,"Answer":"The csv module wraps the _csv module, which is written in C.  You could grab the source for it and modify it to not require the file-like object, but poking around in the module, I don't see any clear way to do it without recompiling.","Q_Score":0,"Tags":"python,csv","A_Id":8711375,"CreationDate":"2012-01-03T10:52:00.000","Title":"Formatting a single row as CSV","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I'm trying to a parallelize an application using multiprocessing which takes in\na very large csv file (64MB to 500MB), does some work line by line, and then outputs a small, fixed size\nfile. \nCurrently I do a list(file_obj), which unfortunately is loaded entirely\ninto memory (I think) and I then I break that list up into n parts, n being the\nnumber of processes I want to run. I then do a pool.map() on the broken up\nlists.  \nThis seems to have a really, really bad runtime in comparison to a single\nthreaded, just-open-the-file-and-iterate-over-it methodology. Can someone\nsuggest a better solution?\nAdditionally, I need to process the rows of the file in groups which preserve\nthe value of a certain column. These groups of rows can themselves be split up,\nbut no group should contain more than one value for this column.","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":14016,"Q_Id":8717179,"Users Score":2,"Answer":"I would keep it simple. Have a single program open the file and read it line by line. You can choose how many files to split it into, open that many output files, and every line write to the next file. This will split the file into n equal parts. You can then run a Python program against each of the files in parallel.","Q_Score":18,"Tags":"python,parallel-processing","A_Id":8717213,"CreationDate":"2012-01-03T18:52:00.000","Title":"Chunking data from a large file for multiprocessing?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a two dimensional table (Matrix)\nI need to process each line in this matrix independently from the others.\nThe process of each line is time consuming.\nI'd like to use parallel computing resources in our university (Canadian Grid something)\nCan I have some advise on how to start ? I never used parallel computing before.\nThanks :)","AnswerCount":4,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1155,"Q_Id":8736396,"Users Score":0,"Answer":"Like the commentators have said, find someone to talk to in your university.  The answer to your question will be specific to what software is installed on the grid.  If you have access to a grid, it's highly likely you also have access to a person whose job it is to answer your questions (and they will be pleased to help) - find this person!","Q_Score":2,"Tags":"python","A_Id":8741894,"CreationDate":"2012-01-05T01:13:00.000","Title":"Parallel computing","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"What's the (best) way to solve a pair of non linear equations using Python. (Numpy, Scipy or Sympy)\neg:\n\n\nx+y^2 = 4\ne^x+ xy = 3\n\n\nA code snippet which solves the above pair will be great","AnswerCount":9,"Available Count":1,"Score":0.0444152037,"is_accepted":false,"ViewCount":135871,"Q_Id":8739227,"Users Score":2,"Answer":"You can use openopt package and its NLP method. It has many dynamic programming algorithms to solve nonlinear algebraic equations consisting:  \ngoldenSection, scipy_fminbound, scipy_bfgs, scipy_cg, scipy_ncg, amsg2p, scipy_lbfgsb, scipy_tnc, bobyqa, ralg, ipopt, scipy_slsqp, scipy_cobyla, lincher, algencan, which you can choose from.  \nSome of the latter algorithms can solve constrained nonlinear programming problem.\nSo, you can introduce your system of equations to openopt.NLP() with a function like this:  \nlambda x: x[0] + x[1]**2 - 4, np.exp(x[0]) + x[0]*x[1]","Q_Score":82,"Tags":"python,numpy,scipy,sympy","A_Id":21651546,"CreationDate":"2012-01-05T07:49:00.000","Title":"How to solve a pair of nonlinear equations using Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"For example in CQL, \nSELECT * from abc_dimension ORDER BY key ASC;\nseems to be not working.\nAny help?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":3328,"Q_Id":8751293,"Users Score":0,"Answer":"Latest versions of Cassandra support aggregations within single partition only.","Q_Score":2,"Tags":"python,cassandra,cql","A_Id":43361173,"CreationDate":"2012-01-05T23:13:00.000","Title":"does cassandra cql support aggregation functions, like group by and order by","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've currently got a project running on PiCloud that involves multiple iterations of an ODE Solver. Each iteration produces a NumPy array of about 30 rows and 1500 columns, with each iterations being appended to the bottom of the array of the previous results.\nNormally, I'd just let these fairly big arrays be returned by the function, hold them in memory and deal with them all at one. Except PiCloud has a fairly restrictive cap on the size of the data that can be out and out returned by a function, to keep down on transmission costs. Which is fine, except that means I'd have to launch thousands of jobs, each running on iteration, with considerable overhead.\nIt appears the best solution to this is to write the output to a file, and then collect the file using another function they have that doesn't have a transfer limit.\nIs my best bet to do this just dumping it into a CSV file? Should I add to the CSV file each iteration, or hold it all in an array until the end and then just write once? Is there something terribly clever I'm missing?","AnswerCount":3,"Available Count":1,"Score":0.1325487884,"is_accepted":false,"ViewCount":8395,"Q_Id":8775786,"Users Score":2,"Answer":"I would recommend looking at the pickle module. The pickle module allows you to serialize python objects as streams of bytes (e.g., strings). This allows you to write them to a file or send them over a network, and then reinstantiate the objects later.","Q_Score":6,"Tags":"python,numpy,scientific-computing","A_Id":8775931,"CreationDate":"2012-01-08T06:12:00.000","Title":"Efficient ways to write a large NumPy array to a file","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How do people usually extract the shape of the lips once the mouth region is found (in my case using haar cascade)? I tried color segmentation and edge\/corner detection but they're very inaccurate for me. I need to find the two corners and the very upper and lower lip at the center. I've heard things about active appearance models but I'm having trouble understanding how to use this with python and I don't have enough context to figure out if this is even the conventional method for detecting different parts of the lips. Is that my best choice or do I have other options? If I should use it, how would I get started with it using python and simplecv?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":4301,"Q_Id":8840127,"Users Score":0,"Answer":"The color segementation involves \"gradient of the difference between the pseudo-hue and luminance (obtaining hybrid contours)\". Try googling for qouted string and you will find multiple research papers on this topic.","Q_Score":5,"Tags":"python,opencv,face-detection,simplecv","A_Id":33917789,"CreationDate":"2012-01-12T18:15:00.000","Title":"OpenCV Lip Segmentation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a list of products that I am trying to classify into categories.  They will be described with incomplete sentences like:\n\"Solid State Drive Housing\"\n\"Hard Drive Cable\"\n\"1TB Hard Drive\"\n\"500GB Hard Drive, Refurbished from Manufacturer\"\nHow can I use python and NLP to get an output like \"Housing, Cable, Drive, Drive\", or a tree that describes which word is modifying which?\nThank you in advance","AnswerCount":4,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2952,"Q_Id":8841569,"Users Score":2,"Answer":"NLP techniques are relatively ill equipped to deal with this kind of text.\nPhrased differently: it is quite possible to build a solution which includes NLP processes to implement the desired classifier but the added complexity doesn't necessarily pays off in term of speed of development nor classifier precision improvements.\nIf one really insists on using NLP techniques, POS-tagging and its ability to identify nouns is the most obvious idea, but Chunking and access to WordNet or other lexical sources are other plausible uses of NLTK.\nInstead, an ad-hoc solution based on simple regular expressions and a few heuristics such as these suggested by NoBugs is probably an appropriate approach to the problem.  Certainly, such solutions bear two main risks:\n\nover-fitting to the portion of the text reviewed\/considered in building the rules\npossible messiness\/complexity of the solution if too many rules and sub-rules are introduced.\n\nRunning some plain statical analysis on the complete (or very big sample) of the texts to be considered should help guide the selection of a few heuristics and also avoid the over-fitting concerns.  I'm quite sure that a relatively small number of rules, associated with a custom dictionary should be sufficient to produce a classifier with appropriate precision as well as speed\/resources performance.\nA few ideas:\n\ncount all the words (and possibly all the bi-grams and tri-grams) in a sizable portion of the corpus a hand.  This info can drive the design of the classifier by allowing to allocate the most effort and the most rigid rules to the most common patterns. \nmanually introduce a short dictionary which associates the most popular words with:\n\ntheir POS function (mostly a binary matter here: i.e. nouns vs. modifiers and other non-nouns.\ntheir synonym root [if applicable]\ntheir class [if applicable]\n\nIf the pattern holds for most of the input text,  consider using the last word before the end of text or before the first comma as the main key to class selection.\nIf the pattern doesn't hold,  just give more weight to the first and to the last word.\nconsider a first pass where the text is re-written with the most common bi-grams replaced by a single word (even an artificial code word) which would be in the dictionary\nconsider also replacing the most common typos or synonyms with their  corresponding synonym root.  Adding regularity in the input helps improve precision and also help making a few rules \/ a few entries in the dictionary have a big return on precision.\nfor words not found in dictionary, assume that words which are mixed with numbers and\/or preceded by numbers are modifiers, not nouns.   Assume that the \nconsider a two-tiers classification whereby inputs which cannot be plausibly assigned a class are put in the \"manual pile\" to prompt additional review which results in additional of rules and\/or dictionary entries.  After a few iterations the classifier should require less and less improvements and tweaks.\nlook for non-obvious features.  For example some corpora are made from a mix of sources but some of the sources, may include particular regularities which help identify the source and\/or be applicable as classification hints.  For example some sources may only contains say uppercase text (or text typically longer than 50 characters,  or truncated words at the end etc.)\n\nI'm afraid this answer falls short of providing Python\/NLTK snippets as a primer towards a solution, but frankly such simple NLTK-based approaches are likely to be disappointing at best.  Also, we should have a much bigger sample set of the input text to guide the selection of plausible approaches, include ones that are based on NLTK or NLP techniques at large.","Q_Score":5,"Tags":"python,nlp,nltk","A_Id":8860314,"CreationDate":"2012-01-12T20:08:00.000","Title":"Find subject in incomplete sentence with NLTK","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a text file containing tabular data.  What I need to do is automate the task of writing to a new text file that is comma delimited instead of space delimited, extract a few columns from existing data, reorder the columns.  \nThis is a snippet of the first 4 lines of the original data:\n\nNumber of rows: 8542\n Algorithm  |Date   |Time   |Longitude  |Latitude   |Country    \n 1  2000-01-03  215926.688  -0.262  35.813  Algeria \n 1  2000-01-03  215926.828  -0.284  35.817  Algeria\n\nHere is what I want in the end:\n\nLongitude,Latitude,Country,Date,Time\n-0.262,35.813,Algeria,2000-01-03,215926.688\n\nAny tips on how to approach this?","AnswerCount":4,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":18146,"Q_Id":8858946,"Users Score":0,"Answer":"str.split() without any arguments will split by any length of whitespace. operator.itemgetter() takes multiple arguments, and will return a tuple.","Q_Score":5,"Tags":"python","A_Id":8858983,"CreationDate":"2012-01-14T00:11:00.000","Title":"converting a space delimited file to a CSV","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a netcdf file that I would like to convert to an image (joed, png, gif) using a command line tool.\nIs someone could please help me with the library name and possibly a link to how it is done.\nRegards\nDavid","AnswerCount":7,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":8951,"Q_Id":8864599,"Users Score":0,"Answer":"IDV is a good visualization tool for NetCDF, but, as far as I know, there is no command line interface.\nI would recommend Matlab. It has read and write functions for NetCDF as well as an extensive plotting library...probably one of the best. You can then compile the matlab code and run it from the command line.","Q_Score":6,"Tags":"python,netcdf","A_Id":9473690,"CreationDate":"2012-01-14T19:03:00.000","Title":"Convert netcdf to image","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Whenever I run the solver 'interalg' (in the SNLE function call from OpenOpt) in a loop my memory usage accumulates until the code stops running.\nIt happen both in my Mac Os X 10.6.8 and in Slackware Linux.\nI would really appreciate some advice, considering that I am not extremely literate in python.\nThank you!\nDaniel","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":143,"Q_Id":8918773,"Users Score":2,"Answer":"Yes, there is clearly a memory leak here. I ran the nlsp demo, that uses SNLE with interalg, using valgrind and found that 295k has been leaked from running the solver once. This should be reported to them.","Q_Score":2,"Tags":"python,memory-leaks,numpy,scipy","A_Id":8959115,"CreationDate":"2012-01-18T22:59:00.000","Title":"'Memory leak' when calling openopt SNLE in a loop","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a wxPython program which reads from different datasets, performs various types of simple on-the-fly analysis on the data and plots various combinations of the datasets to matplotlib canvas. I would like to have the opportunity to dump currently plotted data to file for more sophisticated analysis later on. \nThe question is: are there any methods in matplotlib that allow access to the data currently plotted in matplotlib.Figure?","AnswerCount":5,"Available Count":1,"Score":0.0798297691,"is_accepted":false,"ViewCount":31312,"Q_Id":8938449,"Users Score":2,"Answer":"Its Python, so you can modify the source script directly so the data is dumped before it is plotted","Q_Score":25,"Tags":"python,matplotlib","A_Id":8938840,"CreationDate":"2012-01-20T08:15:00.000","Title":"How to extract data from matplotlib plot","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I know how to implement least-squares with elementary matrix decomposition and other operations, but how can I do it in Python? (I've never tried to use matrices in Python)\n\n(clarification edit to satisfy the shoot-first-and-ask-questions-later -1'er)\nI was looking for help to find out how to use numerical programming in Python. Looks like numpy and scipy are the way to go. I was looking for how to use them, but I found a tutorial.","AnswerCount":3,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":819,"Q_Id":8953991,"Users Score":2,"Answer":"scipy and numpy is the obvious way to go here.\nNote that numpy uses the famous (and well-optimized) BLAS libraries, so it is also very fast. Much faster than any \"pure python\" will ever be.","Q_Score":0,"Tags":"python,matrix,numerical-methods","A_Id":8954018,"CreationDate":"2012-01-21T15:03:00.000","Title":"python: least-squares estimation?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I know how to implement least-squares with elementary matrix decomposition and other operations, but how can I do it in Python? (I've never tried to use matrices in Python)\n\n(clarification edit to satisfy the shoot-first-and-ask-questions-later -1'er)\nI was looking for help to find out how to use numerical programming in Python. Looks like numpy and scipy are the way to go. I was looking for how to use them, but I found a tutorial.","AnswerCount":3,"Available Count":2,"Score":0.0665680765,"is_accepted":false,"ViewCount":819,"Q_Id":8953991,"Users Score":1,"Answer":"Have a look at SciPy.  It's got matrix operations.","Q_Score":0,"Tags":"python,matrix,numerical-methods","A_Id":8954011,"CreationDate":"2012-01-21T15:03:00.000","Title":"python: least-squares estimation?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"i apologize in advance for not being very precise, as a i dont know the mathematical expression for what i want. \ni am using matplotlib to analyze a large dataset. What i have now is a distribution of x,y points. I want to find out the cases in which the x values of my function are the same, but y differs the greatest. So if i plot it, one part of the cases is at the top of my graph, the other is the botton of the graph. \nSo how do i get the points(x,y), (x,y') where f(x)=y and f(x)=y' and y-y'=max ?\ncheers","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":960,"Q_Id":9019949,"Users Score":1,"Answer":"I think what you want is a variance plot. Create a dictionary for distinct x values. Put each distinct value of y in a list associated with each x. Find the stdev (np.std) of the list associated with each x say \"s\". Plot the s vs. x.","Q_Score":2,"Tags":"python,statistics,matplotlib","A_Id":9020306,"CreationDate":"2012-01-26T14:52:00.000","Title":"Finding 'edge cases' in a dataset","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a csv file in which the first column contains an identifier and the second column associated data.  The identifier is replicated an arbitrary number of times so the file looks like this.\ndata1,123\ndata1,345\ndata1,432\ndata2,654\ndata2,431\ndata3,947\ndata3,673  \nI would like to merge the records to generate a single record for each identifier and get.\ndata1,123,345,432\ndata2,654,431\ndata3,947,673  \nIs there an efficient way to do this in python or numpy? Dictionaries appear to be out due to duplicate keys.  At the moment I have the lines in a list of lists then looping through and testing for identity with the previous value at index 0 in the list but this is very clumsy. Thanks for any help.","AnswerCount":3,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":267,"Q_Id":9027355,"Users Score":3,"Answer":"You can use a dictionary if the values are lists. defaultdict in the collections module is very useful for this.","Q_Score":3,"Tags":"python,merge,numpy","A_Id":9027774,"CreationDate":"2012-01-27T00:07:00.000","Title":"merging records in python or numpy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Please help me in creating columns and inserting rows under them in a csv file using python scrapy. I need to write scraped data into 3 columns. So first of all three columns are to be created and then data is to be entered in each row.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":506,"Q_Id":9030953,"Users Score":0,"Answer":"1)you can use custom Csv feed exporter each item key will be treated as a column heading and value as column value.\n2) you can write a pipeline that can write data in csv file using python csv lib.","Q_Score":0,"Tags":"python,scrapy","A_Id":9144538,"CreationDate":"2012-01-27T09:01:00.000","Title":"How to create columns in a csv file and insert row under them in python scrapy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"Please help me in creating columns and inserting rows under them in a csv file using python scrapy. I need to write scraped data into 3 columns. So first of all three columns are to be created and then data is to be entered in each row.","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":506,"Q_Id":9030953,"Users Score":1,"Answer":"CSV is a Comma Saparated Values format. That basically means that it is a text file with some strings separated by commas and line-downs.\nEach line down creates a row and each comma creates a column in that row.\nI guess the simplest way to create a CSV file would be to create a Pythonic dict where each key is a column and the value for each column is a list of rows where None stands for the obvious lack of value.\nYou can then fill in your dict by appending values to the requested column (thus adding a row) and then easily transform the dict into a CSV file by iterating over list indexes and for each column either add a VALUE, entry in the file or a , entry for index-out-of-bound or a None value for the corresponding list.\nFor each row add a line down.","Q_Score":0,"Tags":"python,scrapy","A_Id":9032800,"CreationDate":"2012-01-27T09:01:00.000","Title":"How to create columns in a csv file and insert row under them in python scrapy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"Say I have a huge numpy matrix A taking up tens of gigabytes.  It takes a non-negligible amount of time to allocate this memory.\nLet's say I also have a collection of scipy sparse matrices with the same dimensions as the numpy matrix.  Sometimes I want to convert one of these sparse matrices into a dense matrix to perform some vectorized operations.\nCan I load one of these sparse matrices into A rather than re-allocate space each time I want to convert a sparse matrix into a dense matrix?  The .toarray() method which is available on scipy sparse matrices does not seem to take an optional dense array argument, but maybe there is some other way to do this.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":995,"Q_Id":9041236,"Users Score":1,"Answer":"It does seem like there should be a better way to do this (and I haven't scoured the documentation), but you could always loop over the elements of the sparse array and assign to the dense array (probably zeroing out the dense array first). If this ends up too slow, that seems like an easy C extension to write....","Q_Score":1,"Tags":"python,numpy,scipy,numerical-computing","A_Id":9042475,"CreationDate":"2012-01-27T23:03:00.000","Title":"Load sparse scipy matrix into existing numpy dense matrix","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm preparing a set of reports using open source ReportLab.  The reports contain a number of charts.  Everything works well so far.\nI've been asked to take a (working) bar chart that shows two series of data and overlay a fitted curve for each series.\nI can see how I could overlay a segmented line on the bar graph by creating both a line chart and bar chart in the same ReportLab drawing.  I can't find any reference for fitted curves in ReportLab, however.\nDoes anyone have any insight into plotting a fitted curve to a series of data in ReportLab or, failing that, a suggestion about how to accomplish this task (I'm thinking that chart would need to be produced in matplotlib instead)?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":447,"Q_Id":9045888,"Users Score":1,"Answer":"I would recommend using MatPlotLib.  This is exactly the sort of thing it's designed to handle and it will be much easier than trying to piece together something in ReportLab alone, especially since you'll have to do all the calculation of the line on your own and figure out the details of how to draw it in just the right place.  MatPlotLib integrates easily with ReportLab; I've used the combination several times with great results.","Q_Score":0,"Tags":"python,charts,reportlab,curve-fitting","A_Id":9046880,"CreationDate":"2012-01-28T14:16:00.000","Title":"Fitted curve on chart using ReportLab","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"There is any method\/function in the python wrapper of Opencv that finds black areas in a binary image? (like regionprops in Matlab)\nUp to now I load my source image, transform it into a binary image via threshold and then invert it to highlight the black areas (that now are white).\nI can't use third party libraries such as cvblobslob or cvblob","AnswerCount":5,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":26428,"Q_Id":9056646,"Users Score":0,"Answer":"I know this is an old question, but for completeness I wanted to point out that  cv2.moments() will not always work for small contours. In this case, you can use cv2.minEnclosingCircle() which will always return the center coordinates (and radius), even if you have only a single point. Slightly more resource-hungry though, I think...","Q_Score":14,"Tags":"python,opencv,colors,detection,threshold","A_Id":66779646,"CreationDate":"2012-01-29T20:51:00.000","Title":"Python OpenCV - Find black areas in a binary image","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"There is any method\/function in the python wrapper of Opencv that finds black areas in a binary image? (like regionprops in Matlab)\nUp to now I load my source image, transform it into a binary image via threshold and then invert it to highlight the black areas (that now are white).\nI can't use third party libraries such as cvblobslob or cvblob","AnswerCount":5,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":26428,"Q_Id":9056646,"Users Score":0,"Answer":"Transform it to binary image using threshold with the CV_THRESH_BINARY_INV flag, you get threshold + inversion in one step.","Q_Score":14,"Tags":"python,opencv,colors,detection,threshold","A_Id":15779624,"CreationDate":"2012-01-29T20:51:00.000","Title":"Python OpenCV - Find black areas in a binary image","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"There is any method\/function in the python wrapper of Opencv that finds black areas in a binary image? (like regionprops in Matlab)\nUp to now I load my source image, transform it into a binary image via threshold and then invert it to highlight the black areas (that now are white).\nI can't use third party libraries such as cvblobslob or cvblob","AnswerCount":5,"Available Count":3,"Score":0.0798297691,"is_accepted":false,"ViewCount":26428,"Q_Id":9056646,"Users Score":2,"Answer":"After inverting binary image to turn black to white areas, apply cv.FindContours function. It will give you boundaries of the region you need.\nLater you can use cv.BoundingRect to get minimum bounding rectangle around region. Once you got the rectangle vertices, you can find its center etc.\nOr to find centroid of region, use cv.Moment function after finding contours.  Then use cv.GetSpatialMoments in x and y direction. It is explained in opencv manual. \nTo find area, use cv.ContourArea function.","Q_Score":14,"Tags":"python,opencv,colors,detection,threshold","A_Id":9058880,"CreationDate":"2012-01-29T20:51:00.000","Title":"Python OpenCV - Find black areas in a binary image","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a scientific application that reads a potentially huge data file from disk and transforms it into various Python data structures such as a map of maps, list of lists etc. NumPy is called in for numerical analysis. The problem is, the memory usage can grow rapidly. As swap space is called in, the system slows down significantly. The general strategy I have seen:\n\nlazy initialization: this doesn't seem to help in the sense that many operations require in memory data anyway. \nshelving: this Python standard library seems support writing data object into a datafile (backed by some db) . My understanding is that it dumps data to a file, but if you need it, you still have to load all of them into memory, so it doesn't exactly help. Please correct me if this is a misunderstanding.\nThe third option is to leverage a database, and offload as much data processing to it\n\nAs an example: a scientific experiment runs several days and have generated a huge (tera bytes of data) sequence of:\n\nco-ordinate(x,y) observed event E at time t.\n\nAnd we need to compute a histogram over t for each (x,y) and output a 3-dimensional array.\nAny other suggestions? I guess my ideal case would be the in-memory data structure can be phased to disk based on a soft memory limit and this process should be as transparent as possible. Can any of these caching frameworks help? \nEdit:\nI appreciate all the suggested points and directions. Among those, I found user488551's comments to be most relevant. As much as I like Map\/Reduce, to many scientific apps, the setup and effort for parallelization of code is even a bigger problem to tackle than my original question, IMHO. It is difficult to pick an answer as my question itself is so open ... but Bill's answer is more close to what we can do in real world, hence the choice. Thank you all.","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":294,"Q_Id":9071031,"Users Score":1,"Answer":"Well, if you need the whole dataset in RAM, there's not much to do but get more RAM. Sounds like you aren't sure if you really need to, but keeping all the data resident requires the smallest amount of thinking :) \nIf your data comes in a stream over a long period of time, and all you are doing is creating a histogram, you don't need to keep it all resident.  Just create your histogram as you go along, write the raw data out to a file if you want to have it available later, and let Python garbage collect the data as soon as you have bumped your histogram counters.  All you have to keep resident is the histogram itself, which should be relatively small.","Q_Score":3,"Tags":"python","A_Id":9071479,"CreationDate":"2012-01-30T21:29:00.000","Title":"How to handle large memory footprint in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a scientific application that reads a potentially huge data file from disk and transforms it into various Python data structures such as a map of maps, list of lists etc. NumPy is called in for numerical analysis. The problem is, the memory usage can grow rapidly. As swap space is called in, the system slows down significantly. The general strategy I have seen:\n\nlazy initialization: this doesn't seem to help in the sense that many operations require in memory data anyway. \nshelving: this Python standard library seems support writing data object into a datafile (backed by some db) . My understanding is that it dumps data to a file, but if you need it, you still have to load all of them into memory, so it doesn't exactly help. Please correct me if this is a misunderstanding.\nThe third option is to leverage a database, and offload as much data processing to it\n\nAs an example: a scientific experiment runs several days and have generated a huge (tera bytes of data) sequence of:\n\nco-ordinate(x,y) observed event E at time t.\n\nAnd we need to compute a histogram over t for each (x,y) and output a 3-dimensional array.\nAny other suggestions? I guess my ideal case would be the in-memory data structure can be phased to disk based on a soft memory limit and this process should be as transparent as possible. Can any of these caching frameworks help? \nEdit:\nI appreciate all the suggested points and directions. Among those, I found user488551's comments to be most relevant. As much as I like Map\/Reduce, to many scientific apps, the setup and effort for parallelization of code is even a bigger problem to tackle than my original question, IMHO. It is difficult to pick an answer as my question itself is so open ... but Bill's answer is more close to what we can do in real world, hence the choice. Thank you all.","AnswerCount":2,"Available Count":2,"Score":0.2913126125,"is_accepted":false,"ViewCount":294,"Q_Id":9071031,"Users Score":3,"Answer":"Have you considered divide and conquer? Maybe your problem lends itself to that. One framework you could use for that is Map\/Reduce.\nDoes your problem have multiple phases such that Phase I requires some data as input and generates an output which can be fed to phase II? In that case you can have 1 process do phase I and generate data for phase II. Maybe this will reduce the amount of data you simultaneously need in memory?\nCan you divide your problem into many small problems and recombine the solutions? In this case you can spawn multiple processes that each handle a small sub-problem and have one or more processes to combine these results in the end?\nIf Map-Reduce works for you look at the Hadoop framework.","Q_Score":3,"Tags":"python","A_Id":9071108,"CreationDate":"2012-01-30T21:29:00.000","Title":"How to handle large memory footprint in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"May seem stupid, but after using Matlab for a while (a couple of years), I've tried Python, and despite some Matlab's features that are really handy, I really like Python.\nNow, for work, I'm using Matlab again, and sometimes I miss a structure like Python's 'for' loop. Instead of using the standard 'for' that Matlab provides, there is a structure more similar to process batches of similar data?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":273,"Q_Id":9077225,"Users Score":1,"Answer":"In addition to the given answer, be aware that MATLAB's forloop is very slow.\nMaybe programming in a functional style using arrayfun, cellfun() and structfun() might be a handier solution, and quite close to Python's map().","Q_Score":1,"Tags":"python,matlab,for-loop","A_Id":9079959,"CreationDate":"2012-01-31T09:31:00.000","Title":"For cycle in Python's way in Matlab","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Python has a lot of convenient data structures (lists, tuples, dicts, sets, etc) which can be used to make other 'conventional' data structures (Eg, I can use a Python list to create a stack and a collections.dequeue to make a queue, dicts to make trees and graphs, etc).\nThere are even third-party data structures that can be used for specific tasks (for instance the structures in Pandas, pytables, etc).\nSo, if I know how to use lists, dicts, sets, etc, should I be able to implement any arbitrary data structure if I know what it is supposed to accomplish?\nIn other words, what kind of data structures can the Python data structures not be used for?\nThanks","AnswerCount":4,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2410,"Q_Id":9091252,"Users Score":0,"Answer":"Given that all data structures exist in memory, and memory is effectively just a list (array)... there is no data structure that couldn't be expressed in terms of the basic Python data structures (with appropriate code to interact with them).","Q_Score":2,"Tags":"python,data-structures","A_Id":9091268,"CreationDate":"2012-02-01T05:31:00.000","Title":"Data structures with Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Our company has developed Python libraries to open and display data from files using our proprietary file format. The library only depends on numpy which has been ported to IronPython.\nThe setup.py for our internal distribution imports from setuptools but apparently this is not yet supported in IronPython. Searching the wirenet produces many references to a blog by Jeff Hardy that was written three years ago.\nCan someone explain the relationship between setuptools, ez_install, and distutils?\nIs there a way to distribute our library that is compatible with both CPython and IronPython.\nMany thanks,\nKenny","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":919,"Q_Id":9134717,"Users Score":1,"Answer":"Distribute is a fork of setuptools that supports Python 3, among other things. ez_install is used to install setuptools\/easy_install, and then easy_install can be used to install packages (although pip is better).\nThree years ago IronPython was missing a lot of the pieces needed, like zlib (2.7.0) and zipimport (upcoming 2.7.2). I haven't checked in a while to see it works, though, but any changes now should be minor.","Q_Score":0,"Tags":"ironpython,setuptools","A_Id":9136461,"CreationDate":"2012-02-03T19:59:00.000","Title":"IronPython and setuptools\/ez_install","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to use graphviz in order to draw for a given graph all the maximal cliques that it has.\nTherefore I would like that nodes in the same maximal clique will be visually encapsulated together (meaning that I would like that a big circle will surround them). I know that the cluster option exists - but in all the examples that I saw so far - each node is in one cluster only. In the maximal clique situation, a node can be in multiple cliques.\nIs there an option to visualize this with graphviz?\nIf not, are there any other tools for this task (preferably with a python api).\nThank you.","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":3488,"Q_Id":9213797,"Users Score":0,"Answer":"I don't think you can do this. Clusters are done via subgraphs, which are expected to be separate graphs, not overlapping with other subgraphs.\nYou could change the visualisation though; if you imagine that the members of a clique are members of some set S, then you could simply add a node S and add directed or dashed edges linking each member to the S node. If the S nodes are given a different shape, then it should be clear which nodes are in which cliques.\nIf you really want, you can give the edges connecting members to their clique node high weights, which should bring them close together on the graph.\nNote that there would never be edges between the clique nodes; that would indicate that two cliques are maximally connected, which just implies they are in fact one large clique, not two separate ones.","Q_Score":8,"Tags":"python,graphviz","A_Id":9215067,"CreationDate":"2012-02-09T15:33:00.000","Title":"Graphviz - Drawing maximal cliques","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am having to do a lot of vision related work in Python lately, and I am facing a lot of difficulties switching between formats. When I read an image using Mahotas, I cannot seem to get it to cv2, though they are both using numpy.ndarray. SimpleCV can take OpenCV images easily, but getting SimpleCV image out for legacy cv or mahotas seems to be quite a task.\nSome format conversion syntaxes would be really appreciated. For example, if I open a greyscale image using mahotas, it is treated to be in floating point colour space by default, as I gather. Even when I assign the type as numpy.uint8, cv2 cannot seem to recognise it as an array. I do not know how to solve this problem. I am not having much luck with colour images either. I am using Python 2.7 32bit on Ubuntu Oneiric Ocelot.\nThanks in advance!","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":5140,"Q_Id":9220720,"Users Score":2,"Answer":"I have never used mahotas. But I'm currently working on SimpleCV. I have just sent a pull request for making SimpleCV numpy array compatible with cv2.\nSo, basically,\n\nImage.getNumpy() -> numpy.ndarray for cv2\nImage.getBitmap() ->  cv2.cv.iplimage\nImage.getMatrix() -> cv2.cv.cvmat\n\nTo convert cv2 numpy array to SimpleCV Image object,\n\nImage(cv2_image) -> SimpleCV.ImageClass.Image","Q_Score":5,"Tags":"python,opencv,python-2.7,simplecv,mahotas","A_Id":11412849,"CreationDate":"2012-02-09T23:43:00.000","Title":"Image Conversion between cv2, cv, mahotas, and SimpleCV","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have many 100x100 grids, is there an efficient way using numpy to calculate the median for every grid point and return just one 100x100 grid with the median values? Presently, I'm using a for loop to run through each grid point, calculating the median and then combining them into one grid at the end. I'm sure there's a better way to do this using numpy. Any help would be appreciated! Thanks!","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1689,"Q_Id":9245466,"Users Score":0,"Answer":"How many grids are there? \nOne option would be to create a 3D array that is 100x100xnumGrids and compute the median across the 3rd dimension.","Q_Score":1,"Tags":"python,numpy,statistics","A_Id":9245481,"CreationDate":"2012-02-12T00:51:00.000","Title":"Efficient two dimensional numpy array statistics","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have two sparse matrix A (affinity matrix) and D (Diagonal matrix) with dimension 100000*100000. I have to compute the Laplacian matrix L = D^(-1\/2)*A*D^(-1\/2). I am using scipy CSR format for sparse matrix.\nI didnt find any method to find inverse of sparse matrix. How to find L and inverse of sparse matrix? Also suggest that is it efficient to do so by using python or shall i call matlab function for calculating L?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1503,"Q_Id":9248821,"Users Score":1,"Answer":"In general the inverse of a sparse matrix is not sparse which is why you won't find sparse matrix inverters in linear algebra libraries. Since D is diagonal, D^(-1\/2) is trivial and the Laplacian matrix calculation is thus trivial to write down. L has the same sparsity pattern as A but each value A_{ij} is multiplied by (D_i*D_j)^{-1\/2}.\nRegarding the issue of the inverse, the standard approach is always to avoid calculating the inverse itself. Instead of calculating L^-1, repeatedly solve Lx=b for the unknown x. All good matrix solvers will allow you to decompose L which is expensive and then back-substitute (which is cheap) repeatedly for each value of b.","Q_Score":3,"Tags":"python,linear-algebra,sparse-matrix,matrix-inverse","A_Id":9248907,"CreationDate":"2012-02-12T12:37:00.000","Title":"Python Sparse matrix inverse and laplacian calculation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to build something in python that can analyze an uploaded mp3 and generate the necessary data to build a waveform graphic. Everything I've found is much more complex than I need. Ultimately, I'm trying to build something like you'd see on SoundCloud.\nI've been looking into numpy and fft's, but it all seem more complicated than I need. What's the best approach to this? I'll build the actual graphic using canvas, so don't worry about that part of it, I just need the data to plot.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":1275,"Q_Id":9254671,"Users Score":1,"Answer":"An MP3 file is an encoded version of a waveform. Before you can work with the waveform, you must first decode the MP3 data into a PCM waveform. Once you have PCM data, each sample represents the waveform's amplitude at the point in time. If we assume an MP3 decoder outputs signed, 16-bit values, your amplitudes will range from -16384 to +16383. If you normalize the samples by dividing each by 16384, the waveform samples will then range between +\/- 1.0.\nThe issue really is one of MP3 decoding to PCM. As far as I know, there is no native python decoder. You can, however, use LAME, called from python as a subprocess or, with a bit more work, interface the LAME library directly to Python with something like SWIG. Not a trivial task.\nPlotting this data then becomes an exercise for the reader.","Q_Score":2,"Tags":"python,waveform","A_Id":9254885,"CreationDate":"2012-02-13T01:53:00.000","Title":"Generate volume curve from mp3","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a simple python script which plots some graphs in the same figure. All graphs are created  by the draw() and in the end I call the show() function to block.\nThe script used to work with Python 2.6.6, Matplotlib 0.99.3, and Ubuntu 11.04. Tried to run it under Python 2.7.2, Matplotlib 1.0.1, and Ubuntu 11.10 but the show() function returns immediately without waiting to kill the figure.\nIs this a bug? Or a new feature and we'll have to change our scripts? Any ideas?\nEDIT: It does keep the plot open under interactive mode, i.e., python -i ..., but it used to work without that, and tried to have plt.ion() in the script and run it in normal mode but no luck.","AnswerCount":5,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":84640,"Q_Id":9280171,"Users Score":52,"Answer":"I think that using show(block=True) should fix your problem.","Q_Score":55,"Tags":"python,matplotlib","A_Id":9280538,"CreationDate":"2012-02-14T16:06:00.000","Title":"Matplotlib python show() returns immediately","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a scientific model which I am running in Python which produces a lookup table as output. That is, it produces a many-dimensional 'table' where each dimension is a parameter in the model and the value in each cell is the output of the model.\nMy question is how best to store this lookup table in Python. I am running the model in a loop over every possible parameter combination (using the fantastic itertools.product function), but I can't work out how best to store the outputs.\nIt would seem sensible to simply store the output as a ndarray, but I'd really like to be able to access the outputs based on the parameter values not just indices. For example, rather than accessing the values as table[16][5][17][14] I'd prefer to access them somehow using variable names\/values, for example:\ntable[solar_z=45, solar_a=170, type=17, reflectance=0.37]\nor something similar to that. It'd be brilliant if I were able to iterate over the values and get their parameter values back - that is, being able to find out that table[16]... corresponds to the outputs for solar_z = 45.\nIs there a sensible way to do this in Python?","AnswerCount":4,"Available Count":1,"Score":0.049958375,"is_accepted":false,"ViewCount":1600,"Q_Id":9280488,"Users Score":1,"Answer":"If you want to access the results by name, then you could use a python nested dictionary instead of ndarray, and serialize it in a .JSON text file using json module.","Q_Score":5,"Tags":"python,numpy","A_Id":9280574,"CreationDate":"2012-02-14T16:27:00.000","Title":"How to store numerical lookup table in Python (with labels)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In NLTK, if I write a NaiveBayes classifier for say movie reviews (determining if positive or negative), how can I determine the classifier \"certainty\" when classify a particular review? That is, I know how to run an 'accuracy' test on a given test set to see the general accuracy of the classifier. But is there anyway to have NLTk output its certainess? (perhaps on the basis on the most informative features...)\nThanks\nA","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":524,"Q_Id":9288221,"Users Score":1,"Answer":"I am not sure about the NLTK implementation of Naive Bayes, but the Naive Bayes algorithm outputs probabilities of class membership.  However, they are horribly calibrated.\nIf you want good measures of certainty, you should use a different classification algorithm.  Logistic regression will do a decent job at producing calibrated estimates.","Q_Score":1,"Tags":"python,classification,nltk,probability","A_Id":9300400,"CreationDate":"2012-02-15T05:16:00.000","Title":"NLTK certainty measure?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In NLTK, if I write a NaiveBayes classifier for say movie reviews (determining if positive or negative), how can I determine the classifier \"certainty\" when classify a particular review? That is, I know how to run an 'accuracy' test on a given test set to see the general accuracy of the classifier. But is there anyway to have NLTk output its certainess? (perhaps on the basis on the most informative features...)\nThanks\nA","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":524,"Q_Id":9288221,"Users Score":1,"Answer":"nltk.classify.util.log_likelihood. For this problem, you can also try measuring the results by precision, recall, F-score at the token level, that is, scores for positive and negative respectively.","Q_Score":1,"Tags":"python,classification,nltk,probability","A_Id":9300932,"CreationDate":"2012-02-15T05:16:00.000","Title":"NLTK certainty measure?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm writing a simple 2d brownian motion simulator in Python. It's obviously easy to draw values for x displacement and y displacement from a distribution, but I have to set it up so that the 2d displacement (ie hypotenuse) is drawn from a distribution, and then translate this to new x and y coordinates. This is probably trivial and I'm just too far removed from trigonometry to remember how to do it correctly. Am I going to need to generate a value for the hypotenuse and then translate it into x and y displacements with sin and cos? (How do you do this correctly?)","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":996,"Q_Id":9297679,"Users Score":0,"Answer":"If you have a hypotenuse in the form of a line segment, then you have two points. From two points in the form P0 = (x0, y0) P1 = (x1, y1) you can get the x and y displacements by subtracting x0 from x1 and y0 from y1.\nIf your hypotenuse is actually a vector in a polar coordinate plane, then yes, you'll have to take the sin of the angle and multiply it by the magnitude of the vector to get the y displacement and likewise with cos for the x displacement.","Q_Score":1,"Tags":"python,random,trigonometry,hypotenuse","A_Id":9297835,"CreationDate":"2012-02-15T16:57:00.000","Title":"2d random walk in python - drawing hypotenuse from distribution","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm writing a simple 2d brownian motion simulator in Python. It's obviously easy to draw values for x displacement and y displacement from a distribution, but I have to set it up so that the 2d displacement (ie hypotenuse) is drawn from a distribution, and then translate this to new x and y coordinates. This is probably trivial and I'm just too far removed from trigonometry to remember how to do it correctly. Am I going to need to generate a value for the hypotenuse and then translate it into x and y displacements with sin and cos? (How do you do this correctly?)","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":996,"Q_Id":9297679,"Users Score":1,"Answer":"This is best done by using polar coordinates (r, theta) for your distributions (where r is your \"hypotenuse\")), and then converting the result to (x, y), using x = r cos(theta) and y = r sin(theta).  That is, select r from whatever distribution you like, and then select a theta, usually from a flat, 0 to 360 deg, distribution, and then convert these values to x and y.\nGoing the other way around (i.e., constructing correlated (x, y) distributions that gave a direction independent hypotenuse) would be very difficult.","Q_Score":1,"Tags":"python,random,trigonometry,hypotenuse","A_Id":9298238,"CreationDate":"2012-02-15T16:57:00.000","Title":"2d random walk in python - drawing hypotenuse from distribution","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am currently trying to create a Neural Network with pybrain for stock price forecasting. Up to now I have only used Networks with a binary output. For those Networks sigmoid inner layers were sufficient but I don't think this would be the right approach for Forecasting a price.\nThe problem is, that when I create such a completely linear network I always get an error like\n\nRuntimeWarning: overflow encountered in square while backprop training.\n\nI already scaled down the inputs. Could it be due to the size of my training sets (50000 entries per training set)?\nHas anyone done something like this before?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1055,"Q_Id":9340677,"Users Score":1,"Answer":"Try applying log() to the price-attribute - then scale all inputs and outputs to [-1..1] - of course, when you want to get the price from the network-output you'll have to reverse log() with exp()","Q_Score":1,"Tags":"python,neural-network,backpropagation,forecasting,pybrain","A_Id":9349317,"CreationDate":"2012-02-18T11:15:00.000","Title":"Pybrain: Completely linear network","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"What are the best algorithms for Word-Sense-Disambiguation\nI read a lot of posts, and each one proves in a research document that a specific algorithm is the best, this is very confusing.\nI just come up with 2 realizations 1-Lesk Algorithm is deprecated, 2-Adapted Lesk is good but not the best\nPlease if anybody based on his (Experience) know any other good algorithm that give accuracy up to say 70% or more please mention it . and if there's a link to any Pseudo Code for the algorithm it'll be great, I'll try to implement it in Python or Java .","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":1301,"Q_Id":9355460,"Users Score":1,"Answer":"Well, WSD is an open problem (since it's language... and AI...), so currently each of those claims are in some sense valid.  If you are engaged in a domain-specific project, I think you'd be best served by a statistical method (Support Vector Machines) if you can find a proper corpus.  Personally, if you're using python, unless you're attempting to do some significant original research, I think you should just use the NLTK module to accomplish whatever you're trying to do.","Q_Score":0,"Tags":"python,nlp,nltk,text-processing","A_Id":9355945,"CreationDate":"2012-02-20T02:25:00.000","Title":"What are the best algorithms for Word-Sense-Disambiguation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it possible to have missing values in scikit-learn ? How should they be represented? I couldn't find any documentation about that.","AnswerCount":7,"Available Count":3,"Score":1.0,"is_accepted":false,"ViewCount":39190,"Q_Id":9365982,"Users Score":20,"Answer":"I wish I could provide a simple example, but I have found that RandomForestRegressor does not handle NaN's gracefully. Performance gets steadily worse when adding features with increasing percentages of NaN's. Features that have \"too many\" NaN's are completely ignored, even when the nan's indicate very useful information.\nThis is because the algorithm will never create a split on the decision \"isnan\" or \"ismissing\". The algorithm will ignore a feature at a particular level of the tree if that feature has a single NaN in that subset of samples. But, at lower levels of the tree, when sample sizes are smaller, it becomes more likely that a subset of samples won't have a NaN in a particular feature's values, and a split can occur on that feature.\nI have tried various imputation techniques to deal with the problem (replace with mean\/median, predict missing values using a different model, etc.), but the results were mixed.\nInstead, this is my solution: replace NaN's with a single, obviously out-of-range value (like -1.0). This enables the tree to split on the criteria \"unknown-value vs known-value\". However, there is a strange side-effect of using such out-of-range values: known values near the out-of-range value could get lumped together with the out-of-range value when the algorithm tries to find a good place to split. For example, known 0's could get lumped with the -1's used to replace the NaN's. So your model could change depending on if your out-of-range value is less than the minimum or if it's greater than the maximum (it could get lumped in with the minimum value or maximum value, respectively). This may or may not help the generalization of the technique, the outcome will depend on how similar in behavior minimum- or maximum-value samples are to NaN-value samples.","Q_Score":34,"Tags":"python,machine-learning,scikit-learn,missing-data,scikits","A_Id":17582671,"CreationDate":"2012-02-20T17:56:00.000","Title":"Missing values in scikits machine learning","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it possible to have missing values in scikit-learn ? How should they be represented? I couldn't find any documentation about that.","AnswerCount":7,"Available Count":3,"Score":0.0285636566,"is_accepted":false,"ViewCount":39190,"Q_Id":9365982,"Users Score":1,"Answer":"When you run into missing values on input features, the first order of business is not how to impute the missing. The most important question is WHY SHOULD you. Unless you have clear and definitive mind what the 'true' reality behind the data is, you may want to curtail urge to impute. This is not about technique or package in the first place. \nHistorically we resorted to tree methods like decision trees mainly because some of us at least felt that imputing missing to estimate regression like linear regression, logistic regression, or even NN is distortive enough that we should have methods that do not require imputing missing 'among the columns'. The so-called missing informativeness. Which should be familiar concept to those familiar with, say, Bayesian. \nIf you are really modeling on big data, besides talking about it, the chance is you face large number of columns. In common practice of feature extraction like text analytics, you may very well say missing means count=0. That is fine because you know the root cause. The reality, especially when facing structured data sources, is you don't know or simply don't have time to know the root cause. But your engine forces to plug in a value, be it NAN or other place holders that the engine can tolerate, I may very well argue your model is as good as you impute, which does not make sense. \nOne intriguing question is : if we leave missingness to be judged by its close context inside the splitting process, first or second degree surrogate, does foresting actually make the contextual judgement a moot because the context per se is random selection? This, however, is a 'better' problem. At least it does not hurt that much. It certainly should make preserving missingness unnecessary.\nAs a practical matter, if you have large number of input features, you probably cannot have a 'good' strategy to impute after all. From the sheer imputation perspective, the best practice is anything but univariate. Which is in the contest of RF pretty much means to use the RF to impute before modeling with it. \nTherefore, unless somebody tells me (or us), \"we are not able to do that\", I think we should enable carrying forward missing 'cells', entirely bypassing the subject of how 'best' to impute.","Q_Score":34,"Tags":"python,machine-learning,scikit-learn,missing-data,scikits","A_Id":48199308,"CreationDate":"2012-02-20T17:56:00.000","Title":"Missing values in scikits machine learning","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it possible to have missing values in scikit-learn ? How should they be represented? I couldn't find any documentation about that.","AnswerCount":7,"Available Count":3,"Score":1.0,"is_accepted":false,"ViewCount":39190,"Q_Id":9365982,"Users Score":11,"Answer":"I have come across very similar issue, when running the RandomForestRegressor on data. The presence of NA values were throwing out \"nan\" for predictions. From scrolling around several discussions, the Documentation by Breiman recommends two solutions for continuous and categorical data respectively.\n\nCalculate the Median of the data from the column(Feature) and use\nthis (Continuous Data) \nDetermine the most frequently occurring Category and use this\n(Categorical Data)\n\nAccording to Breiman the random nature of the algorithm and the number of trees will allow for the correction without too much effect on the accuracy of the prediction. This I feel would be the case if the presence of NA values is sparse, a feature containing many NA values I think will most likely have an affect.","Q_Score":34,"Tags":"python,machine-learning,scikit-learn,missing-data,scikits","A_Id":18020591,"CreationDate":"2012-02-20T17:56:00.000","Title":"Missing values in scikits machine learning","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Does anybody know a python function (proven to work and having its description in internet) which able to make minimum search for a provided user function when argument is an array of integers?\nSomething like\nscipy.optimize.fmin_l_bfgs_b\nscipy.optimize.leastsq\nbut for integers","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":569,"Q_Id":9367630,"Users Score":0,"Answer":"There is no general solution for this problem. If you know the properties of the function it should be possible to deduce some bounds for the variables and then test all combinations. But that is not very efficient.\nYou could approximate a solution with scipy.optimize.leastsq and then round the results to integers. The quality of the result of course depends on the structure of the function.","Q_Score":0,"Tags":"python,numpy,scipy","A_Id":9367777,"CreationDate":"2012-02-20T20:01:00.000","Title":"Optimizer\/minimizer for integer argument","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been working with numpy and needed the random.choice() function. Sadly, in version 2.0 it's not in the random or the random.mtrand.RandomState modules. Has it been excluded for a particular reason? There's nothing in the discussion or documentation about it!\nFor info, I'm running Numpy 2.0 on python 2.7 on mac os. All installed from the standard installers provided on the sites.\nThanks!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":6270,"Q_Id":9374885,"Users Score":8,"Answer":"random.choice is as far as I can tell part of python itself, not of numpy. Did you import random?\nUpdate: numpy 1.7 added a new function, numpy.random.choice. Obviously, you need numpy 1.7 for it.\nUpdate2: it seems that in unreleased numpy 2.0, this was temporarily called numpy.random.sample. It has been renamed back. Which is why when using unreleased versions, you really should have a look at the API (pydoc numpy.random) and changelogs.","Q_Score":8,"Tags":"python,numpy,scipy","A_Id":9375030,"CreationDate":"2012-02-21T09:17:00.000","Title":"Why has the numpy random.choice() function been discontinued?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Does anybody know how much memory is used by a numpy ndarray? (with let's say 10,000,000 float elements).","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":14086,"Q_Id":9395758,"Users Score":0,"Answer":"I gauss, easily, we can compute by print(a.size \/\/ 1024 \/\/ 1024, a.dtype)\nit is similar to how much MB is uesd, however with the param dtype, float=8B, int8=1B ...","Q_Score":23,"Tags":"python,arrays,memory,numpy,floating-point","A_Id":62791902,"CreationDate":"2012-02-22T13:29:00.000","Title":"How much memory is used by a numpy ndarray?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using a server where I don't have administrative rights and I need to use the latest version of numpy. The system administrator insists that he cannot update the global numpy to the latest version, so I have to install it locally. \nI can do that without trouble, but how do I make sure that \"import numpy\" results in the newer local install to be imported, as opposed to the older global version? I can adjust my PYTHONPATH, but I will want to use some of the global imports as well so I can't exclude all the global packages.\nI'm on CentOS 6, by the way.\nThanks!","AnswerCount":4,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1857,"Q_Id":9419327,"Users Score":1,"Answer":"Python searches the path in order, so simply put the directory where you installed your NumPy first in the path.\nYou can check numpy.version.version to make sure you're getting the version you want.","Q_Score":1,"Tags":"python,numpy,centos","A_Id":9419462,"CreationDate":"2012-02-23T18:53:00.000","Title":"have local numpy override global","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"So I'm designing a matrix for a computer vision project and believe I have one of my calculations wrong. Unfortunately, I'm not sure where it's wrong. \nI was considering creating a matrix that was 100,000,000 x 100,000,000 with each 'cell' containing a single integer (1 or 0).  If my calculations are correct, it would take  9.53674316 \u00d7 10^9 MB. Is that correct?!?\nMy next question is, if it IS correct, are there ways to reduce memory requirements to a more realistic level while still keeping the matrix the same size?  Of course, there is a real possibility I won't actually need a matrix that size but this is absolute worse case scenario (as put forth by a friend).  The size seems ridiculous to me since we'd be covering such a small distance at a time.\nThanks1\nAnthony","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":793,"Q_Id":9455651,"Users Score":4,"Answer":"In theory, an element of {0, 1} should consume at most 1 bit per cell. That means 8 cells per byte or 1192092895 megabytes or about one petabyte, which is too much, unless you are google :) Not to mention, even processing (or saving) such matrix would take too much time (about a year I'd say).\nYou said that in many cases you won't even need matrix so large. So, you can create smaller matrix at start (10,000 x 10,000) and then double the size every time enlargment is needed, copying old contents.\nIf your matrix is sparse (has much much more 1's than 0's or vice-versa), then it is much more efficient to store just coordinates where ones are in some efficient data structure, depending what operations (search, data access) you need.\nSide note: In many languages, you have to take proper care for that to be true, for example in C, even if you specify variable as boolean, it still takes one byte, 8 times as much as needed.","Q_Score":2,"Tags":"python,matrix","A_Id":9455730,"CreationDate":"2012-02-26T18:05:00.000","Title":"RAM requirements for matrix processing","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have photo images of galaxies. There are some unwanted data on these images (like stars or aeroplane streaks) that are masked out. I don't just want to fill the masked areas with some mean value, but to interpolate them according to surrounding data. How do i do that in python? \nWe've tried various functions in SciPy.interpolate package: RectBivariateSpline, interp2d, splrep\/splev, map_coordinates, but all of them seem to work in finding new pixels between existing pixels, we were unable to make them fill arbitrary \"hole\" in data.","AnswerCount":3,"Available Count":1,"Score":0.1325487884,"is_accepted":false,"ViewCount":5262,"Q_Id":9478347,"Users Score":2,"Answer":"What you want is not interpolation at all. Interpolation depends on the assumption that data between known points is roughly contiguous. In any non-trivial image, this will not be the case.\nYou actually want something like the content-aware fill that is in Photoshop CS5. There is a free alternative available in The GIMP through the GIMP-resynthesize plugin. These filters are extremely advanced and to try to re-implement them is insane. A better choice would be to figure out how to use GIMP-resynthesize in your program instead.","Q_Score":7,"Tags":"python,image-processing,interpolation,mask,astronomy","A_Id":9478656,"CreationDate":"2012-02-28T08:05:00.000","Title":"How do i fill \"holes\" in an image?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I just installed Scipy and Numpy on my machine and added them to the System Library option in eclipse.\nNow the program runs fine, but eclipse editor keeps giving this red mark on the side says \"Unresolved import\".\nI guess I didn't configure correctly.\nAny one know how to fix this ?\nThanks.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":611,"Q_Id":9500524,"Users Score":1,"Answer":"Try to recreate your project in PyDev and add these new libraries.","Q_Score":1,"Tags":"python,eclipse,scipy","A_Id":9500809,"CreationDate":"2012-02-29T14:02:00.000","Title":"Eclipse editor doesn't recognize Scipy content","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I heard that computation results can be very sensitive to choice of random number generator.\n1   I wonder whether it is relevant to program own Mersenne-Twister or other pseudo-random routines to get a good number generator. Also, I don't see why I should not trust native or library generators as random.uniform() in numpy, rand() in C++. I understand that I can build generators on my own for distributions other than uniform (inverse repartition function methor, polar method). But is it evil to use one built-in generator for uniform sampling?\n2   What is wrong with the default 'time' seed? Should one re-seed and how frequently in a code sample (and why)?\n3   Maybe you have some good links on these topics!\n--edit More precisely, I need random numbers for multistart optimization routines, and for uniform space sample to initialize some other optimization routine parameters. I also need random numbers for Monte Carlo methods (sensibility analysis). I hope the precisions help figure out the scope of question.","AnswerCount":4,"Available Count":1,"Score":0.049958375,"is_accepted":false,"ViewCount":3275,"Q_Id":9523570,"Users Score":1,"Answer":"At least in C++, rand is sometimes rather poor quality, so code should rarely use it for anything except things like rolling dice or shuffling cards in children's games. In C++ 11, however, a set of random number generator classes of good quality have been added, so you should generally use them by preference.\nSeeding based on time can work fine under some circumstances, but not if you want to make it difficult for somebody else to duplicate the same series of numbers (e.g., if you're generating nonces for encryption). Normally, you want to seed only once at the beginning of the program, at least in a single-threaded program. With multithreading, you frequently want a separate seed for each thread, in which case you need each one to start out unique to prevent generating the same sequences in all threads.","Q_Score":2,"Tags":"c++,python,random,simulation,probability","A_Id":9523685,"CreationDate":"2012-03-01T20:31:00.000","Title":"Random number generation with C++ or Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there a possibility to retrieve random rows from Cassandra (using it with Python\/Pycassa)?\nUpdate: With random rows I mean randomly selected rows!","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2264,"Q_Id":9566060,"Users Score":1,"Answer":"You might be able to do this by making a get_range request with a random start key (just a random string), and a row_count of 1. \nFrom memory, I think the finish key would need to be the same as start, so that the query 'wraps around' the keyspace; this would normally return all rows, but the row_count will limit that.\nHaven't tried it but this should ensure you get a single result without having to know exact row keys.","Q_Score":3,"Tags":"python,cassandra,uuid,pycassa","A_Id":9567221,"CreationDate":"2012-03-05T11:45:00.000","Title":"Cassandra\/Pycassa: Getting random rows","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm currently opening CSV files in Excel with multiple columns, where values will only appear if a number changes. For example, the data may ACTUALLY be: 90,90,90,90,91. But it will only appear as 90,,,,91. I'd really like the values in between to be filled with 90s. Is there anyway python could help with this? I really appreciate your help!","AnswerCount":4,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":2496,"Q_Id":9570157,"Users Score":0,"Answer":"Can you see the missing values when you open the CSV with wordpad? If so, then Python or any other scripting language should see them too.","Q_Score":0,"Tags":"python,excel,csv","A_Id":9570191,"CreationDate":"2012-03-05T16:23:00.000","Title":"Blank Values in Excel File From CSV (not just rows)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm currently opening CSV files in Excel with multiple columns, where values will only appear if a number changes. For example, the data may ACTUALLY be: 90,90,90,90,91. But it will only appear as 90,,,,91. I'd really like the values in between to be filled with 90s. Is there anyway python could help with this? I really appreciate your help!","AnswerCount":4,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":2496,"Q_Id":9570157,"Users Score":0,"Answer":"You can also do this entirely in excel:\nSelect column (or whatever range you're working with), then go to Edit>Go To  (Ctrl+G) and click Special. \nCheck Blanks & click OK.\nThis will select only the empty cells within the list. \nNow type the = key, then up arrow and ctrl-enter.\nThis will put a formula in every blank cell to equal the cell above it.  You could then copy ^ paste values only to get rid of the formulas.","Q_Score":0,"Tags":"python,excel,csv","A_Id":9570477,"CreationDate":"2012-03-05T16:23:00.000","Title":"Blank Values in Excel File From CSV (not just rows)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a document d1 consisting of lines of form user_id tag_id.\nThere is another document d2 consisting of tag_id tag_name\nI need to generate clusters of users with similar tagging behaviour.\nI want to try this with k-means algorithm in python.\nI am completely new to this and cant figure out how to start on this.\nCan anyone give any pointers?\nDo I need to first create different documents for each user using d1 with his tag vocabulary?\nAnd then apply k-means algorithm on these documents?\nThere are like 1 million users in d1. I am not sure I am thinking in right direction, creating 1 million files ?","AnswerCount":4,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":2252,"Q_Id":9595494,"Users Score":4,"Answer":"Since the data you have is binary and sparse (in particular, not all users have tagged all documents, right)? So I'm not at all convinced that k-means is the proper way to do this.\nAnyway, if you want to give k-means a try, have a look at the variants such as k-medians (which won't allow \"half-tagging\") and convex\/spherical k-means (which supposedly works better with distance functions such as cosine distance, which seems a lot more appropriate here).","Q_Score":3,"Tags":"python,tags,cluster-analysis,data-mining,k-means","A_Id":9597117,"CreationDate":"2012-03-07T03:43:00.000","Title":"Clustering using k-means in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have to solve linear equations system using Jython, so I can't use Num(Sci)Py for this purpose. What are the good alternatives?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2868,"Q_Id":9611746,"Users Score":1,"Answer":"As suggested by @talonmies' comment, the real answer to this is 'find an equivalent Java package.'","Q_Score":1,"Tags":"python,numpy,scipy,jython,linear-algebra","A_Id":9646517,"CreationDate":"2012-03-08T01:31:00.000","Title":"Solve linear system in Python without NumPy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to get my code (running in eclipse) to import pandas.\nI get the following error: \"ImportError: numpy.core.multiarray failed to import\"when I try to import pandas. I'm using python2.7, pandas 0.7.1, and numpy 1.5.1","AnswerCount":4,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":5833,"Q_Id":9641916,"Users Score":0,"Answer":"@user248237:\nI second Keith's suggestion that its probably a 32\/64 bit compatibility issue. I ran into the same problem just this week while trying to install a different module.  Check the versions of each of your modules and make everything matches.  In general, I would stick to the 32 bit versions -- not all modules have official 64 bit support.  I uninstalled my 64 bit version of python and replaced it with a 32 bit one, reinstalled the modules, and haven't had any problems since.","Q_Score":7,"Tags":"python,numpy,pandas","A_Id":11955915,"CreationDate":"2012-03-09T22:36:00.000","Title":"Python Pandas: can't find numpy.core.multiarray when importing pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to get my code (running in eclipse) to import pandas.\nI get the following error: \"ImportError: numpy.core.multiarray failed to import\"when I try to import pandas. I'm using python2.7, pandas 0.7.1, and numpy 1.5.1","AnswerCount":4,"Available Count":3,"Score":0.049958375,"is_accepted":false,"ViewCount":5833,"Q_Id":9641916,"Users Score":1,"Answer":"Just to make sure:\n\nDid you install pandas from the sources ? Make sure it's using the version of NumPy you want.\nDid you upgrade NumPy after installing pandas?  Make sure to recompile pandas, as there can be some changes in the ABI (but w\/ that version of NumPy, I doubt it's the case)\nAre you calling pandas and\/or Numpy from their source directory ? Bad idea, NumPy tends to choke on that.","Q_Score":7,"Tags":"python,numpy,pandas","A_Id":12003130,"CreationDate":"2012-03-09T22:36:00.000","Title":"Python Pandas: can't find numpy.core.multiarray when importing pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to get my code (running in eclipse) to import pandas.\nI get the following error: \"ImportError: numpy.core.multiarray failed to import\"when I try to import pandas. I'm using python2.7, pandas 0.7.1, and numpy 1.5.1","AnswerCount":4,"Available Count":3,"Score":0.049958375,"is_accepted":false,"ViewCount":5833,"Q_Id":9641916,"Users Score":1,"Answer":"Try to update to numpy version 1.6.1. Helped for me!","Q_Score":7,"Tags":"python,numpy,pandas","A_Id":12007981,"CreationDate":"2012-03-09T22:36:00.000","Title":"Python Pandas: can't find numpy.core.multiarray when importing pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a numpy masked matrix. And wanted to do interpolation in the masked regions.\nI tried the RectBivariateSpline but it didn't recognize the masked regions as masked and used those points also to interpolate. I also tried the bisplrep after creating the X,Y,Z 1d vectors. They were each of length 45900. It took a lot of time to calculate the Bsplines. And finally gave a Segmentation fault while running bisplev .\nThe 2d matrix is of the size 270x170. \nIs there any way to make RectBivariateSpline not to include the masked regions in interpolation? Or is there any other method?\nbisplrep was too slow.\nThanking you,\nindiajoe\nUPDATE : \nWhen the grid is small the scipy.interpolate.Rbf with 'linear' function is doing reasonable job. But it gives error when the array is large.\nIs there any other function which will allow me to interpolate and smooth my matrix?\nI have also concluded the following. Do correct me if I am wrong.\n1) RectBivariateSpline requires perfect filled matrix and hence masked matrices cannot be used.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1646,"Q_Id":9644735,"Users Score":1,"Answer":"Very late, but...\nI have a problem similar to yours, and am getting the segmentation fault with bisplines, and also memory error with rbf (in which the \"thin_plate\" function works great for me.\nSince my data is unstructured but is created in a structured manner, I use downsampling to half or one third of the density of data points, so that I can use Rbf. What I advise you to do is (very inefficient, but still better than not doing at all) to subdivide the matrix in many overlapping regions, then create rbf interpolators for each region, then when you interpolate one point you choose the appropriate interpolator.\nAlso, if you have a masked array, you could still perform interpolation in the unmasked array, then apply the mask on the result. (well actually no, see the comments)\nHope this helps somebody","Q_Score":3,"Tags":"python,matrix,numpy,scipy,interpolation","A_Id":11876131,"CreationDate":"2012-03-10T07:29:00.000","Title":"Interpolation of large 2d masked array","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have millions of images containing every day photos. I'm trying to find a way to pick out those in which some certain colours are present, say, red and orange, disregarding the shape or object. The size may matter - e.g., at least 50x50 px.\nIs there an efficient and lightweight library for achieving this? I know there is OpenCV and it seems quite powerful, but would it be too bloated for this task? It's a relatively simple task, right?\nThanks","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":2147,"Q_Id":9650670,"Users Score":1,"Answer":"I do not know if there is a library but you could segment these areas using a simple thresholding segmentation algorithm. Say, you want to find red spots. Extract the red channel from the image, select a threshold, and eliminate pixels that are below the threshold. The resulting pixels are your spots. To find a suitable threshold you can build the image's red channel histogram and find the valley there. The lowest point in the valley is the threshold that you could use. If there are more than one valley, smooth the histogram until there is one valley and two peaks. You can use a Gaussian function to smooth the histogram. To find the spots from the remaining pixels you can use the labeling algorithm and then find the connected components in the graph that the labeling algorithm produced. Yes, it is simple. :)","Q_Score":1,"Tags":"python,image-processing,computer-vision","A_Id":9651522,"CreationDate":"2012-03-10T22:18:00.000","Title":"Image spot detection in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"pymat doesnt seem to work with current versions of matlab, so I was wondering if there is another equivalent out there (I havent been able to find one). The gist of what would be desirable is running an m-file from python (2.6). (and alternatives such as scipy dont fit since I dont think they can run everything from the m-file). \nThanks in advance!","AnswerCount":4,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":5746,"Q_Id":9675386,"Users Score":2,"Answer":"You can always start matlab as separate subprocess and collect results via std.out\/files. (see subprocess package).","Q_Score":2,"Tags":"python,matlab","A_Id":9675452,"CreationDate":"2012-03-12T21:56:00.000","Title":"Running m-files from Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a csv file where one of the columns is a date\/time string.  How do I parse it correctly with pandas?  I don't want to make that column the index.  Thanks!\nUri","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1378,"Q_Id":9723000,"Users Score":7,"Answer":"Pass dateutil.parser.parse (or another datetime conversion function) in the converters argument to read_csv","Q_Score":6,"Tags":"python,parsing,datetime,pandas","A_Id":9739828,"CreationDate":"2012-03-15T15:33:00.000","Title":"How do I tell pandas to parse a particular column as a datetime object, but not make it an index?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"For a class project I am writing a simple matrix multiplier in Python. My professor has asked for it to be threaded. The way I handle this right now is to create a thread for every row and throw the result in another matrix.\nWhat I wanted to know if it would be faster that instead of creating a thread for each row it creates some amount threads that each handles various rows. \nFor example: given Matrix1 100x100 * Matrix2 100x100 (matrix sizes can vary widely):\n\n4 threads each handling 25 rows\n10 threads each handling 10 rows\n\nMaybe this is a problem of fine tuning or maybe the thread creation process overhead is still faster than the above distribution mechanism.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":251,"Q_Id":9731496,"Users Score":2,"Answer":"You will probably get the best performance if you use one thread for each CPU core available to the machine running your application.  You won't get any performance benefit by running more threads than you have processors. \nIf you are planning to spawn new threads each time you perform a matrix multiplication then there is very little hope of your multi-threaded app ever outperforming the single-threaded version unless you are multiplying really huge matrices.  The overhead involved in thread creation is just too high relative to the time required to multiply matrices.  However, you could get a significant performance boost if you spawn all the worker threads once when your process starts and then reuse them over and over again to perform many matrix multiplications.  \nFor each pair of matrices you want to multiply you will want to load the multiplicand and multiplier matrices into memory once and then allow all of your worker threads to access the memory simultaneously.  This should be safe because those matrices will not be changing during the multiplication.\nYou should also be able to allow all the worker threads to write their output simultaneously into the same output matrix because (due to the nature of matrix multiplication) each thread will end up writing its output to different elements of the matrix and there will not be any contention.\nI think you should distribute the rows between threads by maintaining an integer NextRowToProcess that is shared by all of the threads.  Whenever a thread is ready to process another row it calls InterlockedIncrement (or whatever atomic increment operation you have available on your platform) to safely get the next row to process.","Q_Score":2,"Tags":"python,multithreading,matrix,distributed","A_Id":9731658,"CreationDate":"2012-03-16T03:36:00.000","Title":"Creating a thread for each operation or a some threads for various operations?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am training a svm classifier with cross validation (stratifiedKfold) using the scikits interfaces. For each test set (of k), I get a classification result. I want to have a confusion matrix with all the results.\nScikits has a confusion matrix interface:\n    sklearn.metrics.confusion_matrix(y_true, y_pred)\nMy question is how should I accumulate the y_true and y_pred values. They are arrays  (numpy). Should I define the size of the arrays based on my k-fold parameter? And for each result I should add the y_true and y-pred to the array ????","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":4075,"Q_Id":9734403,"Users Score":2,"Answer":"You can either use an aggregate confusion matrix or compute one for each CV partition and compute the mean and the standard deviation (or standard error) for each component in the matrix as a measure of the variability.\nFor the classification report, the code would need to be modified to accept 2 dimensional inputs so as to pass the predictions for each CV partitions and then compute the mean scores and std deviation for each class.","Q_Score":6,"Tags":"python,machine-learning,scikits,scikit-learn","A_Id":9760852,"CreationDate":"2012-03-16T09:05:00.000","Title":"scikits confusion matrix with cross validation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am writing a user-app that takes input from the user as the current open wikipedia page. I have written a piece of code that takes this as input to my module and generates a list of keywords related to that particular article using webscraping and natural language processing.\nI want to expand the functionality of the app by providing in addition to the keywords that i have identified, a set of related topics that may be of interest to the user. Is there any API that wikipedia provides that will do the trick. If there isn't, Can anybody Point me to what i should be looking into (incase i have to write code from scratch). Also i will appreciate any pointers in identifying any algorithm that will train the machine to identify topic maps. I am not seeking any paper but rather a practical implementation of something basic\nso to summarize, \n\nI need a way to find topics related to current article in wikipedia (categories will also do)\nI will also appreciate a sample algorithm for training a machine to identify topics that usually are related and clustered.\n\nps. please be specific because i have researched through a number of obvious possibilities\n appreciate it thank you","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1060,"Q_Id":9760636,"Users Score":0,"Answer":"You can scrape the categories if you want. If you're working with python, you can read the wikitext directly from their API, and use mwlib to parse the article and find the links.\nA more interesting but harder to implement approach would be to create clusters of related terms, and given the list of terms extracted from an article, find the closest terms to them.","Q_Score":1,"Tags":"python,keyword,wikipedia,topic-maps","A_Id":9760985,"CreationDate":"2012-03-18T17:46:00.000","Title":"How to get related topics from a present wikipedia article?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I use a 3rd party library which returns after a lot of computation a ctypes object containing pointers.\nHow can I save the ctypes object and what the pointers are pointing to for later use?\nI tried \n\nscipy.io.savemat => TypeError: Could not convert object to array\ncPickle  => ctypes objects containing pointers cannot be pickled","AnswerCount":3,"Available Count":3,"Score":1.2,"is_accepted":true,"ViewCount":14249,"Q_Id":9768218,"Users Score":11,"Answer":"Python has no way of doing that automatically for you:\nYou will have to build code to pick all the desired Data yourself, putting them in a suitable Python data structure (or just adding the data in a unique bytes-string where you will know where each element is by its offset) - and then save that object to disk.\nThis is not a \"Python\" problem - it is exactly a problem Python solves for you when you use Python objects and data. When coding in C or lower level, you are responsible to know not only where your data is, but also, the length of each chunk of data (and allocate memory for each chunk, and free it when done, and etc). And this is what you have to do in this case.\nYour data structure should give you not only the pointers, but also the length of the data in each pointed location (in a way or the other - if the pointer is to another structure, \"size_of\"  will work for you)","Q_Score":8,"Tags":"python,ctypes,pickle","A_Id":9771616,"CreationDate":"2012-03-19T10:04:00.000","Title":"How to save ctypes objects containing pointers","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I use a 3rd party library which returns after a lot of computation a ctypes object containing pointers.\nHow can I save the ctypes object and what the pointers are pointing to for later use?\nI tried \n\nscipy.io.savemat => TypeError: Could not convert object to array\ncPickle  => ctypes objects containing pointers cannot be pickled","AnswerCount":3,"Available Count":3,"Score":0.0665680765,"is_accepted":false,"ViewCount":14249,"Q_Id":9768218,"Users Score":1,"Answer":"To pickle a ctypes object that has pointers, you would have to define your own __getstate__\/__reduce__ methods for pickling and __setstate__ for unpickling. More information in the docs for pickle module.","Q_Score":8,"Tags":"python,ctypes,pickle","A_Id":41899145,"CreationDate":"2012-03-19T10:04:00.000","Title":"How to save ctypes objects containing pointers","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I use a 3rd party library which returns after a lot of computation a ctypes object containing pointers.\nHow can I save the ctypes object and what the pointers are pointing to for later use?\nI tried \n\nscipy.io.savemat => TypeError: Could not convert object to array\ncPickle  => ctypes objects containing pointers cannot be pickled","AnswerCount":3,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":14249,"Q_Id":9768218,"Users Score":0,"Answer":"You could copy the data into a Python data structure and dereference the pointers as you go (using the contents attribute of a pointer).","Q_Score":8,"Tags":"python,ctypes,pickle","A_Id":9768597,"CreationDate":"2012-03-19T10:04:00.000","Title":"How to save ctypes objects containing pointers","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm implementing a Matrix Product State class, which is some kind of special tensor decomposition scheme in python\/numpy for fast algorithm prototyping.\nI don't think that there already is such a thing out there, and I want to do it myself to get a proper understanding of the scheme.\nWhat I want to have is that, if I store a given tensor T in this format as T_mps, I can access the reconstructed elements by T_mps[ [i0, i1, ..., iL] ]. This is achieved by the getitem(self, key) method and works fine.\nNow I want to use numpy.allclose(T, mps_T) to see if my decomposition is correct.\nBut when I do this I get a type error for my own type:\n\nTypeError: function not supported for these types, and can't coerce safely to supported types\n\nI looked at the documentation of allclose and there it is said, that the function works for \"array like\" objects. Now, what is this \"array like\" concept and where can I find its specification ?\nMaybe I'm better off, implementing my own allclose method ? But that would somewhat be reinventing the wheel, wouldn't it ?\nAppreciate any help\nThanks in advance","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":416,"Q_Id":9801235,"Users Score":3,"Answer":"The term \"arraylike\" is used in the numpy documentation to mean \"anything that can be passed to numpy.asarray() such that it returns an appropriate numpy.ndarray.\" Most sequences with proper __len__() and __getitem__() methods work okay. Note that the __getitem__(i) must be able to accept a single integer index in range(len(self)), not just a list of indices as you seem to indicate. The result from this __getitem__(i) must either be an atomic value that numpy knows about, like a float or an int, or be another sequence as above. Without more details about your Matrix Product State implementation, that's about all I can help.","Q_Score":1,"Tags":"python,numpy,magic-methods,type-coercion","A_Id":9802282,"CreationDate":"2012-03-21T08:48:00.000","Title":"python\/numpy: Using own data structure with np.allclose() ? Where to look for the requirements \/ what are they?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm on OSX Snow Leopard and I run 2.7 in my scripts and the interpreter seems to be running 2.6\nBefore I was able to import numpy but then I would get an error when trying to import matplotlib so I went looking for a solution and updated my PYTHONPATH variable, but I think I did it incorrectly and have now simply screwed everything up.\nThis is what I get when I try and import numpy in my script:\n\nTraceback (most recent call last):\n   File \".\/hh_main.py\", line 5, in \n     import numpy\n   File \"\/Library\/Frameworks\/Python.framework\/Versions\/2.7\/lib\/python2.7\/site->packages\/numpy\/init.py\", line 137, in \n     import add_newdocs\n   File \"\/Library\/Frameworks\/Python.framework\/Versions\/2.7\/lib\/python2.7\/site->packages\/numpy\/add_newdocs.py\", line 9, in \n     from numpy.lib import add_newdoc\n   File \"\/Library\/Frameworks\/Python.framework\/Versions\/2.7\/lib\/python2.7\/site->packages\/numpy\/lib\/init.py\", line 4, in \n     from type_check import *\n   File \"\/Library\/Frameworks\/Python.framework\/Versions\/2.7\/lib\/python2.7\/site->packages\/numpy\/lib\/type_check.py\", line 8, in \n     import numpy.core.numeric as _nx\n   File \"\/Library\/Frameworks\/Python.framework\/Versions\/2.7\/lib\/python2.7\/site->packages\/numpy\/core\/init.py\", line 5, in \n     import multiarray\n  ImportError: dlopen(\/Library\/Frameworks\/Python.framework\/Versions\/2.7\/lib\/python2.7\/site->packages\/numpy\/core\/multiarray.so, 2): Symbol not found: _PyCapsule_Import\n   Referenced from: \/Library\/Frameworks\/Python.framework\/Versions\/2.7\/lib\/python2.7\/site->packages\/numpy\/core\/multiarray.so\n   Expected in: flat namespace\n  in \/Library\/Frameworks\/Python.framework\/Versions\/2.7\/lib\/python2.7\/site->packages\/numpy\/core\/multiarray.so\n\nFurthermore this is what I get from sys.path in the interpreter: \n\n['', '\/Users\/joshuaschneier\/Documents\/python_files', '\/Library\/Frameworks\/Python.framework\/Versions\/2.7\/lib\/python2.7\/site-packages', '\/Library\/Frameworks\/Python.framework\/Versions\/2.7\/lib\/python27.zip', '\/Library\/Frameworks\/Python.framework\/Versions\/2.7\/lib\/python2.7', '\/Library\/Frameworks\/Python.framework\/Versions\/2.7\/lib\/python2.7\/plat-darwin', '\/Library\/Frameworks\/Python.framework\/Versions\/2.7\/lib\/python2.7\/plat-mac', '\/Library\/Frameworks\/Python.framework\/Versions\/2.7\/lib\/python2.7\/plat-mac\/lib-scriptpackages', '\/Library\/Frameworks\/Python.framework\/Versions\/2.7\/lib\/python2.7\/lib-tk', '\/Library\/Frameworks\/Python.framework\/Versions\/2.7\/lib\/python2.7\/lib-old', '\/Library\/Frameworks\/Python.framework\/Versions\/2.7\/lib\/python2.7\/lib-dynload']\n\nAnd this is my PYTHONPATH which I guess I updated wrong:\n\n:\/Library\/Frameworks\/Python.framework\/Versions\/2.7\/lib\/python2.7\/site-packages\/\n\nThanks for any help.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2566,"Q_Id":9817995,"Users Score":1,"Answer":"You'll generally need to install numpy, matplotlib etc once for every version of python you use, as it will install itself to the specific 'python2.x\/site-packages' directory. \nIs the above output generated from a 2.6 or 2.7 session? If it's a 2.6 session, then yes, pointing your PYTHONPATH at 2.7 won't work - numpy includes compiled C code (e.g. the multiarray.so file) which will have been built against a specific version of python. \nIf you don't fancy maintaining two sets of packages, I'd recommend installing numpy, matplotlib etc all for version 2.7, removing that PYTHONPATH setting, and making sure that both scripts and interpreter sessions use version 2.7. \nIf you want to keep both versions you'll just have to install each packages twice (and you'll probably still wnat to undo your PTYHONPATH change)","Q_Score":1,"Tags":"python,path,numpy,matplotlib","A_Id":9820025,"CreationDate":"2012-03-22T07:22:00.000","Title":"Python import works in interpreter, doesn't work in script Numpy\/Matplotlib","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a vector\/array of n elements. I want to choose m elements.\nThe choices must be fair \/ deterministic -- equally many from each subsection.\nWith m=10, n=20 it is easy: just take every second element.\nBut how to do it in the general case? Do I have to calculate the LCD?","AnswerCount":5,"Available Count":1,"Score":0.1194272985,"is_accepted":false,"ViewCount":11243,"Q_Id":9873626,"Users Score":3,"Answer":"Use a loop (int i=0; i < m; i++)\nThen to get the indexes you want, Ceil(i*m\/n).","Q_Score":12,"Tags":"python,algorithm","A_Id":9873885,"CreationDate":"2012-03-26T14:05:00.000","Title":"Choose m evenly spaced elements from a sequence of length n","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to serialize a large (~10**6 rows, each with ~20 values) list, to be used later by myself (so pickle's lack of safety isn't a concern).\nEach row of the list is a tuple of values, derived from some SQL database. So far, I have seen datetime.datetime, strings, integers, and NoneType, but I might eventually have to support additional data types.\nFor serialization, I've considered pickle (cPickle), json, and plain text - but only pickle saves the type information: json can't serialize datetime.datetime, and plain text has its obvious disadvantages.\nHowever, cPickle is pretty slow for data this large, and I'm looking for a faster alternative.","AnswerCount":8,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":35941,"Q_Id":9897345,"Users Score":14,"Answer":"Pickle is actually quite fast so long as you aren't using the (default) ASCII protocol.  Just make sure to dump using protocol=pickle.HIGHEST_PROTOCOL.","Q_Score":33,"Tags":"python,serialization","A_Id":12095050,"CreationDate":"2012-03-27T20:38:00.000","Title":"Pickle alternatives","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using pandas to read a csv file. The data are numbers but stored in the csv file as text. Some of the values are non-numeric when they are bad or missing. How do I filter out these values and convert the remaining data to integers. \nI assume there is a better\/faster way than looping over all the values and using isdigit() to test for them being numeric. \nDoes pandas or numpy have a way of just recognizing bad values in the reader? If not, what is the easiest way to do it? Do I have to specific the dtypes to make this work?","AnswerCount":3,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":3639,"Q_Id":9927711,"Users Score":3,"Answer":"You can pass a custom list of values to be treated as missing using pandas.read_csv . Alternately you can pass functions to the converters argument.","Q_Score":1,"Tags":"python,numpy,pandas","A_Id":9927957,"CreationDate":"2012-03-29T14:42:00.000","Title":"Reading csv in python pandas and handling bad values","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have installed Numpy using ActivePython and when I try to import numpy module, it is throwing the following error:\n\nImportError:\n  \/opt\/ActivePython-2.7\/lib\/python2.7\/site-packages\/numpy\/core\/multiarray.so:\n  undefined symbol: PyUnicodeUCS2_FromUnicode\n\nI am fairly new to python, and I am not sure what to do. I appreciate if you could point me to the right direction. \n\nShould I remove python and configure its compilation with the\n\"--enable-unicode=ucs2\" or \"--with-wide-unicode\" option?\n\nCheers\n\n\nOS: Fedora 16, 64bit;  \nPython version: Python 2.7.2 (default, Mar 26 2012, 10:29:24);\nThe current compile Unicode version: ucs4","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1398,"Q_Id":9929170,"Users Score":0,"Answer":"I suggest that a quick solution to these sort of complications is that you use the Enthought Python Distribpotion (EPD) on Linux which includes a wide range of extensions. Cheers.","Q_Score":0,"Tags":"linux,unicode,numpy,python-2.7,ucs","A_Id":9993744,"CreationDate":"2012-03-29T16:05:00.000","Title":"Numpy needs the ucs2","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to implement a broker using zeromq PUB\/SUB(python eventlets). zeromq 2.1 does not seem to implement filtering at publisher and all messages are broadcasted to all subscribers which inturn apply filter. Is there some kind of workaround to achieve filtering at publisher. If not how bad is the performance if there are ~25 publishers and 25 subscribers exchanging msgs @ max rate of 200 msgs per second where msg_size ~= 5K through the broker. \nAre there any opensource well-tested zero-mq broker implementations.??","AnswerCount":3,"Available Count":1,"Score":0.1325487884,"is_accepted":false,"ViewCount":4347,"Q_Id":9939238,"Users Score":2,"Answer":"From the \u00d8MQ guide:\n\nFrom ZeroMQ v3.x, filtering happens at the publisher side when using a connected protocol (tcp:\/\/ or ipc:\/\/). Using the epgm:\/\/ protocol, filtering happens at the subscriber side. In ZeroMQ v2.x, all filtering happened at the subscriber side.","Q_Score":2,"Tags":"python,zeromq","A_Id":28609198,"CreationDate":"2012-03-30T08:13:00.000","Title":"ZeroMQ PUB\/SUB filtering and performance","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a binary image with contour lines and need to purify each contour line of all unnecessary pixels, leaving behind a minimally connected line.\nCan somebody give me a source, code example or further information for this kind of problem and where to search for help, please?","AnswerCount":4,"Available Count":1,"Score":0.049958375,"is_accepted":false,"ViewCount":6610,"Q_Id":9980270,"Users Score":1,"Answer":"A combination of erosion and dilation (and vice versa) on a binary image can help to get rid of salt n pepper like noise leaving small lines intact. Keywords are 'rank order filters' and 'morphological filters'.","Q_Score":0,"Tags":"python,image-processing,binary","A_Id":9982670,"CreationDate":"2012-04-02T16:37:00.000","Title":"Thinning contour lines in a binary image","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm making a small matrix operations library as a programming challenge to myself(and for the purpose of learning to code with Python), and I've come upon the task of calculating the determinant of 2x2, 3x3 and 4x4 matrices.\nAs far as my understanding of linear algebra goes, I need to implement the Rule of Sarrus in order to do the first 2, but I don't know how to tackle this Pythonically or for matrices of  larger size. Any hints, tips or guides would be much appreciated.","AnswerCount":2,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":3244,"Q_Id":10003232,"Users Score":6,"Answer":"The rule of Sarrus is only a mnemonic for solving 3x3 determinants, and won't be as helpful moving beyond that size.\nYou should investigate the Leibniz formula for calculating the determinant of an arbitrarily large square matrix. The nice thing about this formula is that the determinant of an n*n matrix is that it can be determined in terms of a combination of the determinants of some of its (n-1)*(n-1) sub-matrices, which lends itself nicely to a recursive function solution.\nIf you can understand the algorithm behind the Leibniz formula, and you have worked with recursive functions before, it will be straightforward to translate this in to code (Python, or otherwise), and then you can find the determinant of 4x4 matrices and beyond!","Q_Score":3,"Tags":"python,matrix,linear-algebra","A_Id":10003296,"CreationDate":"2012-04-04T00:08:00.000","Title":"Python determinant calculation(without the use of external libraries)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have hundreds of files containing text I want to use with NLTK. Here is one such file:\n\n\u09ac\u09c7,\u09ac\u099a\u09be \u0987\u09af\u09bc\u09be\u09a3\u09cd\u09a0\u09be,\u09b0\u09cd\u099a\u09be \u09a2\u09be\u09b0\u09cd\u09ac\u09bf\u09a4 \u09a4\u09cb\u0996\u09be\u099f\u09b9 \u09a8\u09a4\u09c1\u09a8, \u0985 \u09aa\u09cd\u09b0\u09ac\u0983\u09be\u09b6\u09bf\u09a4\u0964\n\u09a4\u09ac\u09c7 ' \u098f \u09ac\u0982 \u09ae\u09c1\u09b6\u09be\u09af\u09bc\u09c7\u09b0\u09be ' \u09aa\u09a4\u09cd\u09b0\u09bf\u09ac\u09cd\u09af\u09be\u09af\u09bc \u09aa\u09cd\u09b0\u0995\u09be\u09b6\u09bf\u09a4 \u09a4\u09bf\u09a8\u099f\u09bf \u09b2\u09c7\u0996\u09be\u0987 \u09ac\u0987\u09af\u09c7\n\u09b8\u0982\u09ac\u09cd\u09af\u099c\u09be\u09a8 \u09ac\u09cd\u09af\u09b0\u09be\u09b0 \u099c\u09a8\u09be \u09ac\u09bf\u09b6\u09c7\u09b7\u09ad\u09be\u09ac\u09c7 \u09aa\u09b0\u09bf\u09ac\u09b0\u09cd\u09a7\u09bf\u09a4\u0964 \u09aa\u09be\u099a \u09a6\u09be\u09aa\u09a8\u09bf\u0995\u09c7\u09ac\n\u09a1:\u09ac\u09a8 \u09a8\u09bf\u09af\u09bc\u09c7 \u098f\u0987 \u09ac\u0987 \u09a4\u09c8\u09b0\u09bf \u09ac\u09be\u09ac\u09be\u09b0 \u09aa\u09b0\u09bf\u09ac\u09cd\u09af\u09b2\u09cd\u09aa\u09a8\u09be\u0993 \u09ae\u09cd\u09ad\u09cd\u09b0\u09be\u09b8\u09c1\u09a8\u09a4\u09a8\n\u09b8\u09be\u09ae\u09a8\u09cd\u09a4\u09c7\u09b0\u0987\u0964 \u09a4\u09be\u09b0 \u0986\u09b0 \u09a4\u09be\u09b0 \u09b8\u09b9\u0995\u09be\u09b0\u09c0\u09a6\u09c7\u09ac \u09a8\u09bf\u09b7\u09cd\u09a0\u09be \u099b\u09be\u09a1\u09be \u0985\u09b2\u09cd\u09aa \u09b8\u09ae\u09af\u09bc\u09c7\n\u098f\u0987 \u09ac\u0987 \u09aa\u09cd\u09b0\u09ac\u09cd\u09af\u09be\u09b6\u09bf\u09a4 \u09b9\u09a4\u09c7 \u09aa\u09be\u09b0\u09a4 \u09a8\u09be\u0964,\u09a4\u09be\u0981\u09a6\u09c7\u09b0 \u09b8\u0995\u09b2\u0995\u09c7 \u0986\u09ae\u09be\u09a7\n\u09a8\u09ae\u09b8\u09cd\u0995\u09be\u09b0 \u099c\u09be\u09a8\u09be\u0987\u0964\n\u09ac\u09a4\u09be\u09ac\u09cd\u09af\u09be\u09a4\u09be \u09b6\u09cd\u09b0\u09be\u09ac\u09a8\u09cd\u09a4\u09be \u099c\u09cd\u099c\u09be\u09a3\u09cd\u09a3\u09bf\u0995\n\u099c\u09be\u09a8\u09c1\u09af\u09bc\u09be\u09b0\u09bf \u09e8 \u09a3\u09cd\u099f \u09a3\u09cd\u099f \u09ee \nTotal characters: 378\n\nNote that each line does not contain a new sentence. Rather, the sentence terminator - the equivalent of the period in English - is the '\u0964' symbol.\nCould someone please help me create my corpus? If imported into a variable MyData, I would need to access MyData.words() and MyData.sents(). Also, the last line should not appear in the corpus (it merely contains a character count).\nPlease note that I will need to run operations on data from all the files at once.\nThanks in advance!","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":272,"Q_Id":10006467,"Users Score":1,"Answer":"You don't need to input the files yourself or to provide words and sents methods. \nRead in your corpus with PlaintextCorpusReader, and it will provide those for you.\nThe corpus reader constructor accepts arguments for the path and filename pattern of the files, and for the input encoding (be sure to specify it). \nThe constructor also has optional arguments for the sentence and word tokenization functions, so you can pass it your own method to break up the text into sentences. If word and sentence detection is really simple, i.e., if the | character has other uses, you can configure a tokenization function from the nltk's RegexpTokenizer family, or you can write your own from scratch. (Before you write your own, study the docs and code or write a stub to find out what kind of input it's called with.)\nIf recognizing sentence boundaries is non-trivial, you can later figure out how to train the nltk's PunktSentenceTokenizer, which uses an unsupervized statistical algorithm to learn which uses of the sentence terminator actually end a sentence.\nIf the configuration of your corpus reader is fairly complex, you may find it useful to create a class that specializes PlaintextCorpusReader. But much of the time that's not necessary. Take a look at the NLTK code to see how the gutenberg corpus is implemented: It's just a PlainTextCorpusReader instance with appropriate arguments for the constructor.","Q_Score":0,"Tags":"python,nlp,nltk","A_Id":10054525,"CreationDate":"2012-04-04T07:11:00.000","Title":"Creating a corpus from data in a custom format","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have the Enthought Python Distribution installed.\nBefore that I installed Python2.7 and installed other modules (e.g. opencv). \nEnthought establishes itself as the default python. \nCalled 7.2, but it is 2.7. \nNow if i want to import cv in the Enthought Python it always gives me the Segmentation fault Error.\nIs there anyway to import cv in the Enthought Python ?\nThat would be awesome.\nAlso installing any new module into Enthought, seems to have the same error. \nAny solution for that would be great. \nThanks!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":4794,"Q_Id":10042429,"Users Score":2,"Answer":"Python only SEGFAULTs if \n\nThere is error in a native extension DLL code loaded\nVirtual machine has bugs (it has not)\n\nRun Python in -vvv mode to see more information about import issues.\nYou probably need to recompile the modules you need against the Python build you are using. Python major versions and architecture (32-bit vs. 64-bit) native extensions are not compatible between versions.\nAlso you can use gdb to extract a C stack trace needed to give the exact data where and why it crashes. \nThere are only tips what you should do; because the problem is specific to your configuration only and not repeatable people can only give you information how to further troubleshoot the issue.  Because it is very likely that methods to troubleshoot issue given here might be too advanced, I just recommend reinstall everything.","Q_Score":2,"Tags":"python,segmentation-fault,enthought","A_Id":10043383,"CreationDate":"2012-04-06T10:46:00.000","Title":"Segmentation fault Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Whenever I execute a plt.show() in an Ipython console in spyderlib, the console freezes until I close the figure window.  This only occurs in spyderlib and the blocking does occur when I run ipython --pylab or run ipython normally and call plt.ion() before plotting.  I've tried using plt.draw(), but nothing happens with that command.\nplt.ion() works for ipython, but when I run the same command in spyder it seems to not plot anything altogether (plt.show() no longer works).\nEnviroment Details:\nPython 2.6.5, Qt 4.6.2, PyQt4 (API v2) 4.7.2 on Linux","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1329,"Q_Id":10069680,"Users Score":0,"Answer":"I was having a similar (I think) problem.  Make sure your interpreter is set to execute in the current interpreter (default, should allow for interactive plotting).  If it's set to execute in a new dedicated python interpreter make sure that interact with the python interpreter after execution is selected.  This solved the problem for me.","Q_Score":3,"Tags":"python,matlab,matplotlib,ipython,spyder","A_Id":10724685,"CreationDate":"2012-04-09T06:20:00.000","Title":"Spyder plotting blocks console commands","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How can we search content(text) within images using plone 4.1. I work on linux Suppose an image say a sample.jpg contains text like 'Happy Birthday', on using search 'Birthday' I should get the contents i.e sample.jpg","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":232,"Q_Id":10071609,"Users Score":0,"Answer":"Best is to use collective.DocumentViewer with various options to select from","Q_Score":0,"Tags":"python,plone","A_Id":20901570,"CreationDate":"2012-04-09T09:57:00.000","Title":"Can we search content(text) within images using plone 4.1?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"The output of spearmanr (Spearman correlation) of X,Y gives me the following:\nCorrelation: 0.54542821980327882\nP-Value:     2.3569040685361066e-65\nwhere len(X)=len(Y)=800.\nMy questions are as follows:\n0) What is the confidence (alpha?) here ? \n1) If correlation coefficient > alpha, the hypothesis of the correlation being a coincidence is rejected, thus there is correlation. Is this true ?\nThanks in advance..","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1006,"Q_Id":10076222,"Users Score":4,"Answer":"It's up to you to choose the level of significance (alpha). To be coherent you shall choose it before running the test. The function will return you the lowest alpha you can choose for which you reject the null hypothesis (H0) [reject H0 when p-value < alpha or equivalently -p-value>-alpha].\nYou therefore know that the lowest value for which you reject the null hypothesis (H0) is p-value (2.3569040685361066e-65). Therefore being p-value incredibly small your null hypothesis is rejected for any relevant level of alpha (usually alpha = 0.05).","Q_Score":2,"Tags":"python,statistics,scipy,correlation","A_Id":10076295,"CreationDate":"2012-04-09T16:20:00.000","Title":"scipy: significance of the return values of spearmanr (correlation)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I am using linalg.eig(A) to get the eigenvalues and eigenvectors of a matrix. Is there an easy way to sort these eigenvalues (and associated vectors) in order?","AnswerCount":2,"Available Count":1,"Score":-0.2913126125,"is_accepted":false,"ViewCount":13966,"Q_Id":10083772,"Users Score":-3,"Answer":"np.linalg.eig will often return complex values. You may want to consider using np.sort_complex(eig_vals).","Q_Score":5,"Tags":"python,numpy","A_Id":39361043,"CreationDate":"2012-04-10T05:54:00.000","Title":"python numpy sort eigenvalues","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to initialize an array that has X two-dimensional elements. For example, if X = 3, I want it to be [[0,0], [0,0], [0,0]]. I know that [0]*3 gives [0, 0, 0], but how do I do this for two-dimensional elements?","AnswerCount":4,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1407,"Q_Id":10099619,"Users Score":0,"Answer":"Using the construct \n[[0,0]]*3 \nworks just fine and returns the following:\n[[0, 0], [0, 0], [0, 0]]","Q_Score":2,"Tags":"python,arrays","A_Id":10101163,"CreationDate":"2012-04-11T04:01:00.000","Title":"How do I initialize a one-dimensional array of two-dimensional elements in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to initialize an array that has X two-dimensional elements. For example, if X = 3, I want it to be [[0,0], [0,0], [0,0]]. I know that [0]*3 gives [0, 0, 0], but how do I do this for two-dimensional elements?","AnswerCount":4,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1407,"Q_Id":10099619,"Users Score":0,"Answer":"I believe that it's [[0,0],]*3","Q_Score":2,"Tags":"python,arrays","A_Id":10099628,"CreationDate":"2012-04-11T04:01:00.000","Title":"How do I initialize a one-dimensional array of two-dimensional elements in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a R data.frame containing longitude, latitude which spans over the entire USA map.  When X number of entries are all within a small geographic region of say a few degrees longitude & a few degrees latitude, I want to be able to detect this and then have my program then return the coordinates for the geographic bounding box.  Is there a Python or R CRAN package that already does this? If not, how would I go about ascertaining this information?","AnswerCount":5,"Available Count":1,"Score":0.0399786803,"is_accepted":false,"ViewCount":5580,"Q_Id":10108368,"Users Score":1,"Answer":"A few ideas:\n\nAd-hoc & approximate: The \"2-D histogram\". Create arbitrary \"rectangular\" bins, of the degree width of your choice, assign each bin an ID.  Placing a point in a bin means \"associate the point with the ID of the bin\".  Upon each add to a bin, ask the bin how many points it has.  Downside: doesn't correctly \"see\" a cluster of points that stradle a bin boundary; and: bins of \"constant longitudinal width\" actually are (spatially) smaller as you move north.\nUse the \"Shapely\" library for Python.  Follow it's stock example for \"buffering points\", and do a cascaded union of the buffers.  Look for globs over a certain area, or that \"contain\" a certain number of original points.  Note that Shapely is not intrinsically \"geo-savy\", so you'll have to add corrections if you need them.\nUse a true DB with spatial processing.  MySQL, Oracle, Postgres (with PostGIS), MSSQL all (I think) have \"Geometry\" and \"Geography\" datatypes, and you can do spatial queries on them (from your Python scripts). \n\nEach of these has different costs in dollars and time (in the learning curve)... and different degrees of geospatial accuracy.  You have to pick what suits your budget and\/or requirements.","Q_Score":20,"Tags":"python,r,geolocation,cran","A_Id":10108983,"CreationDate":"2012-04-11T14:47:00.000","Title":"Detecting geographic clusters","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using Latent Dirichlet Allocation with a corpus of news data from six different sources. I am interested in topic evolution, emergence, and want to compare how the sources are alike and different from each other over time. I know that there are a number of modified LDA algorithms such as the Author-Topic model, Topics Over Time, and so on.\nMy issue is that very few of these alternate model specifications are implemented in any standard format. A few are available in Java, but most exist as conference papers only. What is the best way to go about implementing some of these algorithms on my own? I am fairly proficient in R and jags, and can stumble around in Python when given long enough. I am willing to write the code, but I don't really know where to start and I don't know C or Java. Can I build a model in JAGS or Python just having the formulas from the manuscript? If so, can someone point me at an example of doing this? Thanks.","AnswerCount":2,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":2734,"Q_Id":10112500,"Users Score":4,"Answer":"My friend's response is below, pardon the language please.\n\nFirst I wrote up a Python implementation of the collapsed Gibbs sampler seen here (http:\/\/www.pnas.org\/content\/101\/suppl.1\/5228.full.pdf+html) and fleshed out here (http:\/\/cxwangyi.files.wordpress.com\/2012\/01\/llt.pdf). This was slow as balls.\nThen I used a Python wrapping of a C implementation of this paper (http:\/\/books.nips.cc\/papers\/files\/nips19\/NIPS2006_0511.pdf). Which is fast as f*ck, but the results are not as great as one would see with NMF. \nBut NMF implementations I've seen, with scitkits, and even with the scipy sparse-compatible recently released NIMFA library, they all blow the f*ck up on any sizable corpus. My new white whale is a sliced, distributed implementation of the thing. This'll be non-trivial.","Q_Score":7,"Tags":"python,r,nlp,text-mining,lda","A_Id":10177394,"CreationDate":"2012-04-11T19:20:00.000","Title":"Implementing alternative forms of LDA","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Okay, so I probably shouldn't be worrying about this anyway, but I've got some code that is meant to pass a (possibly very long, possibly very short) list of possibilities through a set of filters and maps and other things, and I want to know if my implementation will perform well.\nAs an example of the type of thing I want to do, consider this chain of operations:\n\nget all numbers from 1 to 100\nkeep only the even ones\nsquare each number\ngenerate all pairs [i, j] with i in the list above and j in [1, 2, 3, 4,5]\nkeep only the pairs where i + j > 40\n\nNow, after doing all this nonsense, I want to look through this set of pairs [i, j] for a pair which satisfies a certain condition. Usually, the solution is one of the first entries, in which case I don't even look at any of the others. Sometimes, however, I have to consume the entire list, and I don't find the answer and have to throw an error.\nI want to implement my \"chain of operations\" as a sequence of generators, i.e., each operation iterates through the items generated by the previous generator and \"yields\" its own output item by item (a la SICP streams). That way, if I never look at the last 300 entries of the output, they don't even get processed. I known that itertools provides things like imap and ifilter for doing many of the types of operations I would want to perform. \nMy question is: will a series of nested generators be a major performance hit in the cases where I do have to iterate through all possibilities?","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":1253,"Q_Id":10143637,"Users Score":1,"Answer":"\"Nested\" iterators amount to the composition of the functions that the iterators implement, so in general they pose no particularly novel performance considerations. \nNote that because generators are lazy, they also tend to cut down on memory allocation as compared with repeatedly allocating one sequence to transform into another.","Q_Score":1,"Tags":"python,generator","A_Id":10144447,"CreationDate":"2012-04-13T15:19:00.000","Title":"How fast are nested python generators?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm just starting out with Hadoop and writing some Map Reduce jobs.  I was looking for help on writing a MR job in python that allows me to take some emails and put them into HDFS so I can search on the text or attachments of the email?\nThank you!","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":251,"Q_Id":10144325,"Users Score":1,"Answer":"Yea, you need to use hadoop streaming if you want to use write Python code for running MapReduce Jobs","Q_Score":2,"Tags":"python,map,hadoop,mapreduce,reduce","A_Id":10144991,"CreationDate":"2012-04-13T16:03:00.000","Title":"Emails and Map Reduce Job","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have written code in python to implement DBSCAN clustering algorithm.\nMy dataset consists of 14k users with each user represented by 10 features.\nI am unable to decide what exactly to keep as the value of Min_samples and epsilon as input\nHow should I decide that?\nSimilarity measure is euclidean distance.(Hence it becomes even more tough to decide.) Any pointers?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2333,"Q_Id":10155542,"Users Score":0,"Answer":"DBSCAN is pretty often hard to estimate its parameters.\nDid you think about the OPTICS algorithm? You only need in this case Min_samples which would correspond to the minimal cluster size.\nOtherwise for DBSCAN I've done it in the past by trial and error : try some values and see what happens. A general rule to follow is that if your dataset is noisy, you should have a larger value, and it is also correlated with the number of dimensions (10 in this case).","Q_Score":0,"Tags":"python,cluster-analysis,dbscan","A_Id":10155633,"CreationDate":"2012-04-14T17:04:00.000","Title":"Deciding input values to DBSCAN algorithm","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I had 0.71 pandas before today. I tried to update and I simply ran the .exe file supplied by the website.\nnow I tried \" import pandas\" but then it gives me an error\nImportError: C extensions not built: if you installed already verify that you are not importing from the source directory.\nI am new to python and pandas in general. Anything will help. \nthanks,","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":2351,"Q_Id":10158613,"Users Score":0,"Answer":"I had the same error. I did not build pandas myself so i thought i should not get this error as mentioned on the pandas site. So i was confused on how to resolved this error.\nThe pandas site says that matplotlib is an optional depenedency so i didn't install it initially. But interestingly, after installing matplotlib the error disappeared. I am not sure what effect it had. \nit found something!","Q_Score":4,"Tags":"python,pandas","A_Id":12068757,"CreationDate":"2012-04-15T00:37:00.000","Title":"Importing Confusion Pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I had 0.71 pandas before today. I tried to update and I simply ran the .exe file supplied by the website.\nnow I tried \" import pandas\" but then it gives me an error\nImportError: C extensions not built: if you installed already verify that you are not importing from the source directory.\nI am new to python and pandas in general. Anything will help. \nthanks,","AnswerCount":3,"Available Count":2,"Score":0.0665680765,"is_accepted":false,"ViewCount":2351,"Q_Id":10158613,"Users Score":1,"Answer":"Had the same issue. Resolved by checking dependencies - make sure you have numpy > 1.6.1 and python-dateutil > 1.5 installed.","Q_Score":4,"Tags":"python,pandas","A_Id":11630790,"CreationDate":"2012-04-15T00:37:00.000","Title":"Importing Confusion Pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to know if somebody knows a way to customize the csv output in htsql, and especially the delimiter and the encoding ?\nI would like to avoid iterating over each result and find a way through configuration and\/or extensions.\nThank in advance.\nAnthony","AnswerCount":1,"Available Count":1,"Score":0.537049567,"is_accepted":false,"ViewCount":170,"Q_Id":10205990,"Users Score":3,"Answer":"If you want TAB as a delimiter, use tsv format (e.g. \/query\/:tsv instead of \/query\/:csv).\nThere is no way to specify the encoding other than UTF-8.  You can reencode the output manually on the client.","Q_Score":1,"Tags":"python,sql,htsql","A_Id":10210348,"CreationDate":"2012-04-18T08:52:00.000","Title":"Customizing csv output in htsql","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it efficient to calculate many results in parallel with multiprocessing.Pool.map() in a situation where each input value is large (say 500\u00a0MB), but where input values general contain the same large object?  I am afraid that the way multiprocessing works is by sending a pickled version of each input value to each worker process in the pool.  If no optimization is performed, this would mean sending a lot of data for each input value in map().  Is this the case?  I quickly had a look at the multiprocessing code but did not find anything obvious.\nMore generally, what simple parallelization strategy would you recommend so as to do a map() on say 10,000 values, each of them being a tuple (vector, very_large_matrix), where the vectors are always different, but where there are say only 5 different very large matrices?\nPS: the big input matrices actually appear \"progressively\": 2,000 vectors are first sent along with the first matrix, then 2,000 vectors are sent with the second matrix, etc.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":2008,"Q_Id":10242525,"Users Score":1,"Answer":"I hit a similar issue: parallelizing calculations on a big dataset. As you mentioned multiprocessing.Pool.map pickles the arguments. What I did was to implement my own fork() wrapper that only pickles the return values back to the parent process, hence avoiding pickling the arguments. And a parallel map() on top of the wrapper.","Q_Score":2,"Tags":"python,dictionary,parallel-processing,multiprocessing,large-data","A_Id":10248052,"CreationDate":"2012-04-20T08:10:00.000","Title":"When to and when not to use map() with multiprocessing.Pool, in Python? case of big input values","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm getting a memory error trying to do KernelPCA on a data set of 30.000 texts. RandomizedPCA works alright. I think what's happening is that RandomizedPCA works with sparse arrays and KernelPCA don't. \nDoes anyone have a list of learning methods that are currently implemented with sparse array support in scikits-learn?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":691,"Q_Id":10304280,"Users Score":1,"Answer":"We don't have that yet. You have to read the docstrings of the individual classes for now.\nAnyway, non linear models do not tend to work better than linear model for high dim sparse data such as text documents (and they can overfit more easily).","Q_Score":2,"Tags":"python,machine-learning,scikits,scikit-learn","A_Id":10308680,"CreationDate":"2012-04-24T18:56:00.000","Title":"python, scikits-learn: which learning methods support sparse feature vectors?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"do you know a quick\/elegant Python\/Scipy\/Numpy solution for the following problem:\nYou have a set of x, y coordinates with associated values w (all 1D arrays). Now bin x and y onto a 2D grid (size BINSxBINS) and calculate quantiles (like the median) of the w values for each bin, which should at the end result in a BINSxBINS 2D array with the required quantiles.\nThis is easy to do with some nested loop,but I am sure there is a more elegant solution.\nThanks,\nMark","AnswerCount":4,"Available Count":1,"Score":0.1488850336,"is_accepted":false,"ViewCount":5849,"Q_Id":10305964,"Users Score":3,"Answer":"I'm just trying to do this myself and it sound like you want the command \"scipy.stats.binned_statistic_2d\" from you can find the mean, median, standard devation or any defined function for the third parameter given the bins.\nI realise this question has already been answered but I believe this is a good built in solution.","Q_Score":7,"Tags":"python,numpy,statistics,scipy","A_Id":27016762,"CreationDate":"2012-04-24T21:04:00.000","Title":"Quantile\/Median\/2D binning in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have implemented Tensor Factorization Algorithm in Matlab. But, actually, I need to use it in Web Application. \nSo I implemented web site on Django framework, now I need to merge it with my Tensor Factorization algorithm. \nFor those who are not familiar with tensor factorization, you can think there are bunch of multiplication, addition and division on large matrices of size, for example 10 000 x 8 000. In tensor factorization case we do not have matrices, instead we have 3-dimensional(for my purpose) arrays.\nBy the way, I m using MySQL as my database. \nI am considering to implement this algorithm in Python or in C++. But I can't be sure which one is better. \nDo you have any idea about efficiency of Python and C++ when processing on huge data set? Which one is better? Why?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":291,"Q_Id":10307173,"Users Score":0,"Answer":"Python is just fine. I am a Python person. I do not know C++ personally. However, during my research of python the creator of mathematica stated himself that python is equally as powerful as mathematica. Python is used in many highly accurate calculations. (i. e. engineering software, architecture work, etc. . .)","Q_Score":1,"Tags":"python,c++,mysql,django,large-data","A_Id":10327841,"CreationDate":"2012-04-24T22:58:00.000","Title":"Most efficient language to implement tensor factorization for Web Application","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"As there are multitude of ways to write binary modules for python, i was hopping those of you with experience could advice on the best approach if i wish to improve the performance of some segments of the code as much as possible.\nAs i understand, one can either write an extension using the python\/numpy C-api, or wrap some already written pure C\/C++\/Fortran function to be called from the python code.\nNaturally, tools like Cython are the easiest way to go, but i assume that writing the code by hand gives better control and provide better performance.\nThe question, and it may be to general, is which approach to use. Write a C or C++ extension? wrap external C\/C++ functions or use callback to python functions?\nI write this question after reading chapter 10 in Langtangen's \"Python scripting for computational science\" where there is a comparison of several methods to interface between python and C.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1000,"Q_Id":10351450,"Users Score":3,"Answer":"I would say it depends on your skills\/experience and your project.\nIf this is very ponctual and you are profficient in C\/C++ and you have already written python wrapper, then write your own extension and interface it.\nIf you are going to work with Numpy on other project, then go for the Numpy C-API, it's extensive and rather well documented but it is also quite a lot of documentation to process.\nAt least I had a lot of difficulty processing it, but then again I suck at C.\nIf you're not really sure go Cython, far less time consuming and the performance are in most cases very good. (my choice)\nFrom my point of view you need to be a good C coder to do better than Cython with the 2 previous implementation, and it will be much more complexe and time consuming.\nSo are you a great C coder ?\nAlso it might be worth your while to look into pycuda or some other GPGPU stuff if you're looking for performance, depending on your hardware of course.","Q_Score":0,"Tags":"python,c,numpy","A_Id":10352335,"CreationDate":"2012-04-27T13:23:00.000","Title":"best way to extend python \/ numpy performancewise","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have used import numpy as np in my program and when I try to execute np.zeroes to create a numpy array then it does not recognize the module zeroes in the program.\nThis happens when I execute in the subdirectory where the python program is.\nIf I copy it root folder and execute, then it shows the results.\nCan someone guide me as to why is this happening and what can I do to get the program executed in the subdirectory it self?\nThanks","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":73,"Q_Id":10395691,"Users Score":1,"Answer":"then it does not recognize the module zeroes in the program\n\nMake sure you don't have a file called numpy.py in your subdirectory. If you do, it would shadow the \"real\" numpy module and cause the symptoms you describe.","Q_Score":1,"Tags":"python,numpy","A_Id":10395730,"CreationDate":"2012-05-01T09:12:00.000","Title":"Numpy cannot be accessed in sub directories","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've got a 640x480 binary image (0s and 255s). There is a single white blob in the image (nearly circular) and I want to find the centroid of the blob (it's always convex). Essentially, what we're dealing with is a 2D boolean matrix. I'd like the runtime to be linear or better if possible - is this possible? \nTwo lines of thought so far:\n\nMake use of the numpy.where() function \nSum the values in each column and row, then find where the max value is based on those numbers... but is there a quick and efficient way to do this? This might just be a case of me being relatively new to python.","AnswerCount":4,"Available Count":2,"Score":0.049958375,"is_accepted":false,"ViewCount":1130,"Q_Id":10409674,"Users Score":1,"Answer":"Depending on the size of your blob, I would say that dramatically reducing the resolution of your image may achieve what you want. \nReduce it to a 1\/10 resolution, find the one white pixel, and then you have a precise idea of where to search for the centroid.","Q_Score":4,"Tags":"python,image-processing,numpy,boolean,python-imaging-library","A_Id":10409722,"CreationDate":"2012-05-02T07:46:00.000","Title":"Finding a specific index in a binary image in linear time?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've got a 640x480 binary image (0s and 255s). There is a single white blob in the image (nearly circular) and I want to find the centroid of the blob (it's always convex). Essentially, what we're dealing with is a 2D boolean matrix. I'd like the runtime to be linear or better if possible - is this possible? \nTwo lines of thought so far:\n\nMake use of the numpy.where() function \nSum the values in each column and row, then find where the max value is based on those numbers... but is there a quick and efficient way to do this? This might just be a case of me being relatively new to python.","AnswerCount":4,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":1130,"Q_Id":10409674,"Users Score":2,"Answer":"The centroid's coordinates are arithmetic means of coordinates of the points.\nIf you want the linear solution, just go pixel by pixel, and count means of each coordinates, where the pixels are white, and that's the centroid.\nThere is probably no way you can make it better than linear in general case, however, if your circular object is much smaller than the image, you can speed it up, by searching for it first (sampling a number of random pixels, or a grid of pixels, if you know the blob is big enough) and then using BFS or DFS to find all the white points.","Q_Score":4,"Tags":"python,image-processing,numpy,boolean,python-imaging-library","A_Id":10409877,"CreationDate":"2012-05-02T07:46:00.000","Title":"Finding a specific index in a binary image in linear time?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm working on a research project involving a microscope (with a camera connected to the view port; the video feed is streamed to an application we're developing) and a manipulator arm. The microscope and manipulator arm are both controlled by a Luigs & Neumann control box (very obsolete - the computer interfaces with it with a serial cable and its response time is slowwww.) The microscope can be moved in 3 dimensions; X, Y, and Z, whose axes are at right angles to one another. When the box is queried, it will return decimal values for the position of each axis of each device. Each device can be sent a command to move to a specific position, with sub-micrometer precision.\nThe manipulator arm, however, is adjustable in all 3 dimensions, and thus there is no guarantee that any of its axes are aligned at right angles. We need to be able to look at the video stream from the camera, and then click on a point on the screen where we want the tip of the manipulator arm to move to. Thus, the two coordinate systems have to be calibrated.\nRight now, we have achieved calibration by moving the microscope\/camera's position to the tip of the manipulator arm, setting that as the synchronization point between the two coordinate systems, and moving the manipulator arm +250um in the X direction, moving the microscope to the tip of the manipulator arm at this new position, and then using the differences between these values to define a 3d vector that corresponds to the distance and direction moved by the manipulator, per unit in the microscope coordinate system. This is repeated for each axis of the manipulator arm. \nOnce this data is obtained, in order to move the manipulator arm to a specific location in the microscope coordinate system, a system of equations can be solved by the program which determines how much it needs to move the manipulator in each axis to move it to the center point of the screen. This works pretty reliably so far.\nThe issue we're running into here is that due to the slow response time of the equipment, it can take 5-10 minutes to complete the calibration process, which is complicated by the fact that the tip of the manipulator arm must be changed occasionally during an experiment, requiring the calibration process to be repeated. Our research is rather time sensitive and this creates a major bottleneck in the process.\nMy linear algebra is a little patchy, but it seems like if we measure the units traveled by the tip of the manipulator arm per unit in the microscope coordinate system and have this just hard coded into the program (for now), it might be possible to move all 3 axes of the manipulator a specific amount at once, and then to derive the vectors for each axis from this information. I'm not really sure how to go about doing this (or if it's even possible to do this), and any advice would be greatly appreciated. If there's any additional information you need, or if you need clarification on anything please let me know.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":303,"Q_Id":10420966,"Users Score":0,"Answer":"You really need four data points to characterize three independent axes of movement.\nCan you can add some other constraints, ie are the manipulator axes orthogonal to each other, even if not fixed relative to the stage's axes? Do you know the manipulator's alignment roughly, even if not exactly?\nWhat takes the most time - moving the stage to re-center? Can you move the manipulator and stage at the same time? How wide is the microscope's field of view? How much distance-distortion is there near the edges of the view - does it actually have to be re-centered each time to be accurate? Maybe we could come up with a reverse-screen-distortion mapping instead?","Q_Score":0,"Tags":"python,linear-algebra,robotics,calibration","A_Id":10421975,"CreationDate":"2012-05-02T20:14:00.000","Title":"Manipulator\/camera calibration issue (linear algebra oriented)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a problem when I run a script with python. I haven't done any parallelization in python and don't call any mpi for running the script. I just execute \"python myscript.py\" and it should only use 1 cpu.\nHowever, when I look at the results of the command \"top\", I see that python is using almost 390% of my cpus. I have a quad core, so 8 threads. I don't think that this is helping my script to run faster. So, I would like to understand why python is using more than one cpu, and stop it from doing so.\nInteresting thing is when I run a second script, that one also takes up 390%. If I run a 3rd script, the cpu usage for each of them drops to 250%. I had a similar problem with matlab a while ago, and the way I solved it was to launch matlab with -singlecompthread, but I don't know what to do with python.\nIf it helps, I'm solving the Poisson equation (which is not parallelized at all) in my script.\n\nUPDATE:\nMy friend ran the code on his own computer and it only takes 100% cpu. I don't use any BLAS, MKL or any other thing. I still don't know what the cause for 400% cpu usage is.\nThere's a piece of fortran algorithm from the library SLATEC, which solves the Ax=b system. That part I think is using a lot of cpu.","AnswerCount":3,"Available Count":3,"Score":0.1325487884,"is_accepted":false,"ViewCount":1390,"Q_Id":10427900,"Users Score":2,"Answer":"Your code might be calling some functions that uses C\/C++\/etc. underneath. In that case, it is possible for multiple thread usage. \nAre you calling any libraries that are only python bindings to some more efficiently implemented functions?","Q_Score":1,"Tags":"python,multithreading,parallel-processing,cpu-usage","A_Id":10428163,"CreationDate":"2012-05-03T08:42:00.000","Title":"Stop Python from using more than one cpu","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a problem when I run a script with python. I haven't done any parallelization in python and don't call any mpi for running the script. I just execute \"python myscript.py\" and it should only use 1 cpu.\nHowever, when I look at the results of the command \"top\", I see that python is using almost 390% of my cpus. I have a quad core, so 8 threads. I don't think that this is helping my script to run faster. So, I would like to understand why python is using more than one cpu, and stop it from doing so.\nInteresting thing is when I run a second script, that one also takes up 390%. If I run a 3rd script, the cpu usage for each of them drops to 250%. I had a similar problem with matlab a while ago, and the way I solved it was to launch matlab with -singlecompthread, but I don't know what to do with python.\nIf it helps, I'm solving the Poisson equation (which is not parallelized at all) in my script.\n\nUPDATE:\nMy friend ran the code on his own computer and it only takes 100% cpu. I don't use any BLAS, MKL or any other thing. I still don't know what the cause for 400% cpu usage is.\nThere's a piece of fortran algorithm from the library SLATEC, which solves the Ax=b system. That part I think is using a lot of cpu.","AnswerCount":3,"Available Count":3,"Score":0.0665680765,"is_accepted":false,"ViewCount":1390,"Q_Id":10427900,"Users Score":1,"Answer":"You can always set your process affinity so it run on only one cpu. Use \"taskset\" command on linux, or process explorer on windows. \nThis way, you should be able to know if your script has same performance using one cpu or more.","Q_Score":1,"Tags":"python,multithreading,parallel-processing,cpu-usage","A_Id":10429302,"CreationDate":"2012-05-03T08:42:00.000","Title":"Stop Python from using more than one cpu","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a problem when I run a script with python. I haven't done any parallelization in python and don't call any mpi for running the script. I just execute \"python myscript.py\" and it should only use 1 cpu.\nHowever, when I look at the results of the command \"top\", I see that python is using almost 390% of my cpus. I have a quad core, so 8 threads. I don't think that this is helping my script to run faster. So, I would like to understand why python is using more than one cpu, and stop it from doing so.\nInteresting thing is when I run a second script, that one also takes up 390%. If I run a 3rd script, the cpu usage for each of them drops to 250%. I had a similar problem with matlab a while ago, and the way I solved it was to launch matlab with -singlecompthread, but I don't know what to do with python.\nIf it helps, I'm solving the Poisson equation (which is not parallelized at all) in my script.\n\nUPDATE:\nMy friend ran the code on his own computer and it only takes 100% cpu. I don't use any BLAS, MKL or any other thing. I still don't know what the cause for 400% cpu usage is.\nThere's a piece of fortran algorithm from the library SLATEC, which solves the Ax=b system. That part I think is using a lot of cpu.","AnswerCount":3,"Available Count":3,"Score":0.0665680765,"is_accepted":false,"ViewCount":1390,"Q_Id":10427900,"Users Score":1,"Answer":"Could it be that your code uses SciPy or other numeric library for Python that is linked against Intel MKL or another vendor provided library that uses OpenMP? If the underlying C\/C++ code is parallelised using OpenMP, you can limit it to a single thread by setting the environment variable OMP_NUM_THREADS to 1:\nOMP_NUM_THREADS=1 python myscript.py\nIntel MKL for sure is parallel in many places (LAPACK, BLAS and FFT functions) if linked with the corresponding parallel driver (the default link behaviour) and by default starts as many compute threads as is the number of available CPU cores.","Q_Score":1,"Tags":"python,multithreading,parallel-processing,cpu-usage","A_Id":10445816,"CreationDate":"2012-05-03T08:42:00.000","Title":"Stop Python from using more than one cpu","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"My program plots a large number of lines (~200k) with matplotlib which is pretty greedy for memory. I usually have about 1.5G of free memory before plotting. When I show the figures, the system starts swapping heavily when there's still about 600-800M of free RAM. This behavior is not observed when, say, creating a huge numpy array, it just takes all the available memory instantaneously. It would be nice to figure out whether this is a matplotlib or system problem.\nI'm using 64-bit Arch Linux. \nUPD: The swapiness level is set to 10. Tried setting it to 0, as DoctororDrive suggested, but same thing. However, other programs seem to be ok with filling almost all the memory before the swap is used.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":199,"Q_Id":10481008,"Users Score":1,"Answer":"One thing to take into account for the huge numpy array is that you are not touching it. Memory is allocated lazily by default by the kernel. Try writing some values in that huge array and then check for swapping behaviour.","Q_Score":2,"Tags":"python,linux,matplotlib,archlinux","A_Id":10481152,"CreationDate":"2012-05-07T11:08:00.000","Title":"system swaps before the memory is full","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"What are good libraries for creating a python program for (visually appealing) 3D physics simulations\/visualizations?\nI've looked at Vpython but the simulations I have seen look ugly, I want them to be visually appealing. It also looks like an old library. For 3D programming I've seen suggestions of using Panda3D and python-ogre but I'm not sure if it is really suited for exact simulations. Also, I would prefer a library that combines well with other libraries (E.g. pygame does not combine so well with other libraries).","AnswerCount":4,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":5810,"Q_Id":10489377,"Users Score":4,"Answer":"3D support for python is fairly weak compared to other languages, but with the way that most of them are built, the appearance of the program is far more mutable than you might think. For instance, you talked about Vpython, while many of their examples are not visually appealing, most of them are also from previous releases, the most recent release includes both extrusions, materials, and skins, which allow you to customize your appearance much moreso than before.\nIt is probably worth noting also, that it is simply not possible to make render-quality images in real time (cycles is a huge step in that direction, but it's still not quite there). I believe that most of your issue here is you are looking for something that technology is simply not capable of now, however if you are willing to take on the burden for making your simulation look visually appealing, Vpython (which is a gussied up version of PyOpenGL) is probably your best bet. Below is a run down of different technologies though, in case you are looking for anything more general:\nBlender: The most powerful python graphics program available, however it is made for graphic design and special effects, though it has very complex physics running underneath it, Blender is not made for physics simulations. Self contained.\nPanda3D: A program very often compared to Blender, however mostly useful for games. The game engine is nicer to work with than Blender's, but the render quality is far lower, as is the feature-richness. Self contained\nOgre: A library that was very popular for game development back in the day, with a lot of powerful functionality, especially for creating game environments. Event handling is also very well implemented. Can be made to integrate with other libraries, but with difficulty.\nVPython: A library intended for physics simulations that removes a lot of the texture mapping and rendering power compared to the other methods, however this capability is still there, as VPython is largely built from OpenGL, which is one of the most versatile graphics libraries around. As such, VPython also is very easy to integrate with other libraries.\nPyOpenGL: OpenGL for Python. OpenGL is one of the most widely use graphics libraries, and is without a doubt capable of producing some of the nicest visuals on this list (Except for Blender, which is a class of its own), however it will not be easy to do so. PyOpenGL is very bare bones, and while the functionality is there, it will be harder to implement than anything else. Plays very will with other libraries, but only if you know what you're doing.","Q_Score":6,"Tags":"python,3d,visualization,physics,simulation","A_Id":10859872,"CreationDate":"2012-05-07T21:26:00.000","Title":"What are good libraries for creating a python program for (visually appealing) 3D physics simulations\/visualizations?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm doing a project on document classification using naive bayes classifier in python. I have used the nltk python module for the same. The docs are from reuters dataset. I performed preprocessing steps such as stemming and stopword elimination and proceeded to compute tf-idf of the index terms. i used these values to train the classifier but the accuracy is very poor(53%). What should I do to improve the accuracy?","AnswerCount":4,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":2839,"Q_Id":10515907,"Users Score":0,"Answer":"There could be many reasons for the classifier not working, and there are many ways to tweak it.\n\ndid you train it with enough positive and negative examples?\nhow did you train the classifier? did you give it every word as a feature, or did you also add more features for it to train on(like length of the text for example)?\nwhat exactly are you trying to classify? does the specified classification have specific words that are related to it?\n\nSo the question is rather broad. Maybe If you give more details You could get more relevant suggestions.","Q_Score":2,"Tags":"python,nltk,document-classification","A_Id":10516123,"CreationDate":"2012-05-09T12:17:00.000","Title":"document classification using naive bayes in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm doing a project on document classification using naive bayes classifier in python. I have used the nltk python module for the same. The docs are from reuters dataset. I performed preprocessing steps such as stemming and stopword elimination and proceeded to compute tf-idf of the index terms. i used these values to train the classifier but the accuracy is very poor(53%). What should I do to improve the accuracy?","AnswerCount":4,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":2839,"Q_Id":10515907,"Users Score":1,"Answer":"A few points that might help:\n\nDon't use a stoplist, it lowers accuracy (but do remove punctuation)\nLook at word features, and take only the top 1000 for example. Reducing dimensionality will improve your accuracy a lot;\nUse bigrams as well as unigrams - this will up the accuracy a bit.\n\nYou may also find alternative weighting techniques such as log(1 + TF) * log(IDF) will improve accuracy. Good luck!","Q_Score":2,"Tags":"python,nltk,document-classification","A_Id":21133966,"CreationDate":"2012-05-09T12:17:00.000","Title":"document classification using naive bayes in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to make a line graph with multiple sets of data on the same graph. But they all scale differently so will need individual y axis scales. \nWhat code will put each variable on a separate axis?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":150,"Q_Id":10534950,"Users Score":0,"Answer":"The generic answer is to write a method that allows you to scale the y value for each data set to lay on the graph the way you want it.  Then all your data points will have a y value on the same scale, and you can label the Y axis based upon how you define your translation for each data set.","Q_Score":0,"Tags":"python,graph","A_Id":10535067,"CreationDate":"2012-05-10T13:24:00.000","Title":"How to have multiple y axis on a line graph in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to create a pandas DataFrame and it works fine for a single file. If I need to build it for multiple files which have the same data structure. So instead of single file name I have a list of file names from which I would like to create the DataFrame.\nNot sure what's the way to append to current DataFrame in pandas or is there a way for pandas to suck a list of files into a DataFrame.","AnswerCount":6,"Available Count":2,"Score":0.0333209931,"is_accepted":false,"ViewCount":24945,"Q_Id":10545957,"Users Score":1,"Answer":"I might try to concatenate the files before feeding them to pandas. If you're in Linux or Mac you could use cat, otherwise a very simple Python function could do the job for you.","Q_Score":18,"Tags":"python,pandas","A_Id":10546350,"CreationDate":"2012-05-11T05:36:00.000","Title":"creating pandas data frame from multiple files","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to create a pandas DataFrame and it works fine for a single file. If I need to build it for multiple files which have the same data structure. So instead of single file name I have a list of file names from which I would like to create the DataFrame.\nNot sure what's the way to append to current DataFrame in pandas or is there a way for pandas to suck a list of files into a DataFrame.","AnswerCount":6,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":24945,"Q_Id":10545957,"Users Score":3,"Answer":"Potentially horribly inefficient but...\nWhy not use read_csv, to build two (or more) dataframes, then use join to put them together?\nThat said, it would be easier to answer your question if you provide some data or some of the code you've used thus far.","Q_Score":18,"Tags":"python,pandas","A_Id":10563786,"CreationDate":"2012-05-11T05:36:00.000","Title":"creating pandas data frame from multiple files","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to make a sorting system with card ranks and their values are obtained from a separate dictionary. In a simple deck of 52 cards, we have 2 to Ace ranks, in this case I want a ranking system where 0 is 10, J is 11, Q is 12, K is 13, A is 14 and 2 is 15 where 2 is the largest valued rank. The thing is, if there is a list where I want to sort rank cards in ASCENDING order according to the numbering system, how do I do so?\nFor example, here is a list, [3,5,9,7,J,K,2,0], I want to sort the list into [3,5,7,9,0,J,K,2]. I also made a dictionary for the numbering system as {'A': 14, 'K': 13, 'J': 11, 'Q': 12, '0': 10, '2': 15}.\nTHANKS","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":198,"Q_Id":10569853,"Users Score":0,"Answer":"have you already tried\n\nsorted(list_for_sorting, key=dictionary_you_wrote.__getitem__)\n\n?","Q_Score":3,"Tags":"python,list,sorting,dictionary","A_Id":10569942,"CreationDate":"2012-05-13T06:47:00.000","Title":"Sorting a list with elements containing dictionary values","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"How to use Products.csvreplicata 1.1.7 with Products.PressRoom 3.18 to export PressContacts to csv in Plone 4.1? Or is there any other product to import\/export all the PressRoom contacts into csv.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":153,"Q_Id":10577866,"Users Score":1,"Answer":"Go to Site setup \/ CSV Replicata tool, and select PressRoom content(s) as exportable (and then select the schemata you want to be considered during import\/export).","Q_Score":1,"Tags":"python,plone","A_Id":10596347,"CreationDate":"2012-05-14T05:26:00.000","Title":"How to use Products.csvreplicata 1.1.7 with Products.PressRoom to export PressContacts in Plone 4.1","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I am starting to learn python, I tried to generate random values by passing in a negative and positive number. Let say  -1, 1. \nHow should I do this in python?","AnswerCount":10,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":43543,"Q_Id":10579518,"Users Score":0,"Answer":"If you want to generate 2 random integers between 2 negative values than print(f\"{-random.randint(1, 5)}\") can also do the work.","Q_Score":15,"Tags":"python,random","A_Id":71463872,"CreationDate":"2012-05-14T08:04:00.000","Title":"How to generate negative random value in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I apologise if this question has already been asked.\nI'm really new to Python programming, and what I need to do is this:\nI have a .csv file in which each line represent a person and each column represents a variable.\nThis .csv file comes from an agent-based C++ simulation I have done.\nNow, I need to read each line of this file and for each line generate a new instance of the class Person(), passing as arguments every variable line by line.\nMy problem is this: what is the most pythonic way of generating these agents while keeping their unique ID (which is one of the attributes I want to read from the file)? Do you suggest creating a class dictionary for accessing every instance? But I still need to provide a name to every single instance, right? How can I do that dynamically? Probably the best thing would be to use the unique ID, read from the file, as the instance name, but I think that numbers can't be used as instance names, can they? I miss pointers! :(\nI am sure there is a pythonic solution I cannot see, as I still have to rewire my mind a bit to think in pythonic ways...\nThank you very much, any help would be greatly appreciated!\nAnd please remember that this is my first project in python, so go easy on me! ;)\nEDIT:\nThank you very much for your answers, but I still haven't got an answer on the main point: how to create an instance of my class Person() for every line in my csv file. I would like to do that automatically! Is it possible?\nWhy do I need this? Because I need to create networks of these people with networkx and I would like to have \"agents\" linked in a network structure, not just dictionary items.","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":725,"Q_Id":10583195,"Users Score":1,"Answer":"I have a .csv file\n\nYou're in luck; CSV support is built right in, via the csv module.\n\nDo you suggest creating a class dictionary for accessing every instance?\n\nI don't know what you think you mean by \"class dictionary\". There are classes, and there are dictionaries.\n\nBut I still need to provide a name to every single instance, right? How can I do that dynamically? Probably the best thing would be to use the unique ID, read from the file, as the instance name, but I think that numbers can't be used as instance names, can they?\n\nNumbers can't be instance names, but they certainly can be dictionary keys.\nYou don't want to create \"instance names\" dynamically anyway (assuming you're thinking of having each in a separate variable or something gross like that). You want a dictionary. So just let the IDs be keys.\n\nI miss pointers! :(\n\nI really, honestly, can't imagine how you expect pointers to help here, and I have many years of experience with C++.","Q_Score":0,"Tags":"python,oop","A_Id":10583784,"CreationDate":"2012-05-14T12:19:00.000","Title":"How can I dynamically generate class instances with single attributes read from flat file in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to perform serialisation of some object graph in a modular way. That is I don't want to serialize the whole graph. The reason is that this graph is big. I can keep timestamped version of some part of the graph, and i can do some lazy access to postpone loading of the parts i don't need right now.\nI thought i could manage this with metaprogramming in Python. But it seems that metaprogramming is not strong enough in Python.\nHere's what i do for now. My graph is composed of several different objects. Some of them are instances of a special class. This class describes the root object to be pickled. This is where the modularity come in. Each time i pickle something it starts from one of those instances and i never pickle two of them at the same time. Whenever there is a reference to another instance, accessible by the root object, I replace this reference by a persistant_id, thus ensuring that i won't have two of them in the same pickling stream. The problem comes when unpickling the stream. I can found a persistant_id of an instance which is not loaded yet. When this is the case, i have to wait for the target instance to be loaded before allowing access to it. And i don't see anyway to do that :\n1\/ I tried to build an accessor which get methods return the target of the reference. Unfortunately, accessors must be placed in the class declaration, I can't assign them to the unpickled object.\n2\/ I could store somewhere the places where references have to be resolved. I don't think this is possible in Python : one can't keep reference to a place (a field, or a variable), it is only possible to keep a reference to a value.\nMy problem may not be clear. I'm still looking for a clear formulation. I tried other things like using explicit references which would be instances of some \"Reference\" class. It isn't very convenient though. \nDo you have any idea how to implement modular serialisation with pickle ? Would i have to change internal behaviour of Unpickler to be able to remember places where i need to load the remaining of the object graph ? Is there another library more suitable to achieve similar results ?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":289,"Q_Id":10607350,"Users Score":0,"Answer":"Metaprogramming is strong in Python; Python classes are extremely malleable. You can alter them after declaration all the way you want, though it's best done in a metaclass (decorator). More than that, instances are malleable, independently of their classes.\nA 'reference to a place' is often simply a string. E.g. a reference to object's field is its name. Assume you have multiple node references inside your node object. You could have something like {persistent_id: (object, field_name),..} as your unresolved references table, easy to look up. Similarly, in lists of nodes 'references to places' are indices.\nBTW, could you use a key-value database for graph storage? You'd be able to pull nodes by IDs without waiting.","Q_Score":1,"Tags":"python,pickle","A_Id":10608972,"CreationDate":"2012-05-15T19:16:00.000","Title":"Modular serialization with pickle (Python)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to perform serialisation of some object graph in a modular way. That is I don't want to serialize the whole graph. The reason is that this graph is big. I can keep timestamped version of some part of the graph, and i can do some lazy access to postpone loading of the parts i don't need right now.\nI thought i could manage this with metaprogramming in Python. But it seems that metaprogramming is not strong enough in Python.\nHere's what i do for now. My graph is composed of several different objects. Some of them are instances of a special class. This class describes the root object to be pickled. This is where the modularity come in. Each time i pickle something it starts from one of those instances and i never pickle two of them at the same time. Whenever there is a reference to another instance, accessible by the root object, I replace this reference by a persistant_id, thus ensuring that i won't have two of them in the same pickling stream. The problem comes when unpickling the stream. I can found a persistant_id of an instance which is not loaded yet. When this is the case, i have to wait for the target instance to be loaded before allowing access to it. And i don't see anyway to do that :\n1\/ I tried to build an accessor which get methods return the target of the reference. Unfortunately, accessors must be placed in the class declaration, I can't assign them to the unpickled object.\n2\/ I could store somewhere the places where references have to be resolved. I don't think this is possible in Python : one can't keep reference to a place (a field, or a variable), it is only possible to keep a reference to a value.\nMy problem may not be clear. I'm still looking for a clear formulation. I tried other things like using explicit references which would be instances of some \"Reference\" class. It isn't very convenient though. \nDo you have any idea how to implement modular serialisation with pickle ? Would i have to change internal behaviour of Unpickler to be able to remember places where i need to load the remaining of the object graph ? Is there another library more suitable to achieve similar results ?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":289,"Q_Id":10607350,"Users Score":0,"Answer":"Here's how I think I would go about this.\n\nHave a module level dictionary mapping persistent_id to SpecialClass objects. Every time you initialise or unpickle a SpecialClass instance, make sure that it is added to the dictionary.\nOverride SpecialClass's __getattr__ and __setattr__ method, so that specialobj.foo = anotherspecialobj merely stores a persistent_id in a dictionary on specialobj (let's call it specialobj.specialrefs). When you retrieve specialobj.foo, it finds the name in specialrefs, then finds the reference in the module-level dictionary.\nHave a module level check_graph function which would go through the known SpecialClass instances and check that all of their specialrefs were available.","Q_Score":1,"Tags":"python,pickle","A_Id":10608783,"CreationDate":"2012-05-15T19:16:00.000","Title":"Modular serialization with pickle (Python)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using the Pandas package and it creates a DataFrame object, which is basically a labeled matrix. Often I have columns that have long string fields, or dataframes with many columns, so the simple print command doesn't work well. I've written some text output functions, but they aren't great.\nWhat I'd really love is a simple GUI that lets me interact with a dataframe \/ matrix \/ table. Just like you would find in a SQL tool. Basically a window that has a read-only spreadsheet like view into the data. I can expand columns, page up and down through long tables, etc.\nI would suspect something like this exists, but I must be Googling with the wrong terms. It would be great if it is pandas specific, but I would guess I could use any matrix-accepting tool. (BTW - I'm on Windows.)\nAny pointers?\nOr, conversely, if someone knows this space well and knows this probably doesn't exist, any suggestions on if there is a simple GUI framework \/ widget I could use to roll my own? (But since my needs are limited, I'm reluctant to have to learn a big GUI framework and do a bunch of coding for this one piece.)","AnswerCount":20,"Available Count":1,"Score":0.0099996667,"is_accepted":false,"ViewCount":100991,"Q_Id":10636024,"Users Score":1,"Answer":"I've also been searching very simple gui. I was surprised that no one mentioned gtabview.\nIt is easy to install (just pip3 install gtabview ), and it loads data blazingly fast.\nI recommend using gtabview if you are not using spyder or Pycharm.","Q_Score":70,"Tags":"python,user-interface,pandas,dataframe","A_Id":54660955,"CreationDate":"2012-05-17T12:48:00.000","Title":"Python \/ Pandas - GUI for viewing a DataFrame or Matrix","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have been using the svgwrite library to generate a sequence of svg images. I would like to turn this sequence of images into an animated svg. The support for animated svg in svgwrite seems to only work in the form of algorithmically moving objects in the drawing. Is it possible to use the time slices I have to generate an animated svg or am I stuck rasterizing them and creating a video from the images. Thanks!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1810,"Q_Id":10661381,"Users Score":4,"Answer":"The support for animated svg in svgwrite seems to only work in the form of algorithmically moving objects in the drawing.\n\nWell, yes. That's how SVG animation works; it takes the current objects in the image and applies transformations to them. If you want a \"movie\" then you will need to make a video from the images.","Q_Score":0,"Tags":"python,svg,slice,animated","A_Id":10661419,"CreationDate":"2012-05-19T00:38:00.000","Title":"Generating animated SVG with python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Im trying to produce a usual matrix multiplication between two huge matrices (10*25,000,000).\nMy memory runs out when I do so. How could I use numpy's memmap to be able to handle this?\nIs this even a good idea? I'm not so worried about the speed of the operation, I just want the result even if it means waiting some time. Thank you in advanced!\n8 gbs ram, I7-2617M 1.5 1.5 ghz, Windows7 64 bits. Im using the 64 bit version of everything: python(2.7), numpy, scipy.\nEdit1:\nMaybe h5py is a better option?","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1031,"Q_Id":10669270,"Users Score":2,"Answer":"you might try to use np.memmap, and compute the 10x10 output matrix one element at a time.\nso you just load the first row of the first matrix and the first column of the second, and then np.sum(row1 * col1).","Q_Score":3,"Tags":"python,numpy,memory-management,matrix-multiplication,large-data","A_Id":12110615,"CreationDate":"2012-05-19T22:24:00.000","Title":"Python numpy memmap matrix multiplication","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have successfully install NumPy on Ubuntu; however when inside a virtualenv, NumPy is not available. I must be missing something obvious, but I do not understand why I can not import NumPy when using python from a virtualenv. Can anyone help? I am using Python 2.7.3 as my system-wide python and inside my virtualenv. Thanks in advance for the help.","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":394,"Q_Id":10688601,"Users Score":2,"Answer":"You have to install it inside of your virtual environment. The easiest way to do this is:\n\nsource [virtualenv]\/bin\/activate\npip install numpy","Q_Score":0,"Tags":"python,ubuntu,numpy,virtualenv","A_Id":10688691,"CreationDate":"2012-05-21T16:01:00.000","Title":"Installed NumPy successfully, but not accessible with virtualenv","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I was wondering if there is a way to use my GPU to speed up the training of a network in PyBrain.","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":2082,"Q_Id":10727140,"Users Score":2,"Answer":"Unless PyBrain is designed for that, you probably can't.\nYou might want to try running your trainer under PyPy if you aren't already -- it's significantly faster than CPython for some workloads. Perhaps this is one of those workloads. :)","Q_Score":1,"Tags":"python,neural-network,gpu,pybrain","A_Id":10727166,"CreationDate":"2012-05-23T20:12:00.000","Title":"How can I speed up the training of a network using my GPU?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a monotonically growing sequence of integers. For example \nseq=[(0, 0), (1, 5), (10, 20), (15, 24)]. \nAnd a integer value greater than the largest argument in the sequence (a > seq[-1][0]). I want to estimate value corresponding to the given value. The sequence grows nearly linearly, and earlier values are less important than later. Nevertheless I can't simply take 2 last points and calculate new value, because mistakes are very likely and the curve may change the angle.\nCan anyone suggest a simple solution for this kind of task in Python?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":254,"Q_Id":10768817,"Users Score":0,"Answer":"If the sequence does not have a lot of noise, just use the latest point, and the point for 1\/3 of the current, then estimate your line from that.  Otherwise do something more complicated like a least squares fit for the latter half of the sequence.\nIf you search on Google, there are a number of code samples for doing the latter, and some modules that may help.  (I'm not a Python programmer so I can't give a meaningful recommend for the best one.)","Q_Score":1,"Tags":"python,math,numpy,scipy","A_Id":10768872,"CreationDate":"2012-05-26T18:36:00.000","Title":"How to calculate estimation for monotonically growing sequence in python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Good day,\nI'm attempting to write a sentimental analysis application in python (Using naive-bayes classifier) with the aim to categorize phrases from news as being positive or negative.\nAnd I'm having a bit of trouble finding an appropriate corpus for that.\nI tried using \"General Inquirer\" (http:\/\/www.wjh.harvard.edu\/~inquirer\/homecat.htm) which works OK but I have one big problem there.\nSince it is a word list, not a phrase list I observe the following problem when trying to label the following sentence:\n\nHe is not expected to win.\n\nThis sentence is categorized as being positive, which is wrong. The reason for that is that \"win\" is positive, but \"not\" does not carry any meaning since \"not win\" is a phrase.\nCan anyone suggest either a corpus or a work around for that issue?\nYour help and insight is greatly appriciated.","AnswerCount":2,"Available Count":1,"Score":0.2913126125,"is_accepted":false,"ViewCount":1434,"Q_Id":10789834,"Users Score":3,"Answer":"In this case, the work not modifies the meaning of the phrase expecteed to win, reversing it. To identify this, you would need to POS tag the sentence and apply the negative adverb not to the (I think) verb phrase as a negation. I don't know if there is a corpus that would tell you that not would be this type of modifier or not, however.","Q_Score":5,"Tags":"python,nlp,nltk","A_Id":10790083,"CreationDate":"2012-05-28T19:56:00.000","Title":"Phrase corpus for sentimental analysis","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm currently rewriting some python code to make it more efficient and I have a question about saving python arrays so that they can be re-used \/ manipulated later.\nI have a large number of data, saved in CSV files. Each file contains time-stamped values of the data that I am interested in and I have reached the point where I have to deal with tens of millions of data points. The data has got so large now that the processing time is excessive and inefficient---the way the current code is written the entire data set has to be reprocessed every time some new data is added. \nWhat I want to do is this:\n\nRead in all of the existing data to python arrays\nSave the variable arrays to some kind of database\/file\nThen, the next time more data is added I load my database, append the new data, and resave it. This way only a small number of data need to be processed at any one time.\nI would like the saved data to be accessible to further python scripts but also to be fairly \"human readable\" so that it can be handled in programs like OriginPro or perhaps even Excel.  \n\nMy question is: whats the best format to save the data in? HDF5 seems like it might have all the features I need---but would something like SQLite make more sense? \nEDIT: My data is single dimensional. I essentially have 30 arrays which are (millions, 1) in size. If it wasn't for the fact that there are so many points then CSV would be an ideal format! I am unlikely to want to do lookups of single entries---more likely is that I might want to plot small subsets of data (eg the last 100 hours, or the last 1000 hours, etc).","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1123,"Q_Id":10800039,"Users Score":0,"Answer":"I would use a single file with fixed record length for this usecase.  No specialised DB solution (seems overkill to me in that case), just plain old struct (see the documentation for struct.py) and read()\/write() on a file.  If you have just millions of entries, everything should be working nicely in a single file of some dozens or hundreds of MB size (which is hardly too large for any file system).  You also have random access to subsets in case you will need that later.","Q_Score":3,"Tags":"python,database,arrays,save,hdf5","A_Id":10802164,"CreationDate":"2012-05-29T13:20:00.000","Title":"Saving large Python arrays to disk for re-use later --- hdf5? Some other method?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm currently rewriting some python code to make it more efficient and I have a question about saving python arrays so that they can be re-used \/ manipulated later.\nI have a large number of data, saved in CSV files. Each file contains time-stamped values of the data that I am interested in and I have reached the point where I have to deal with tens of millions of data points. The data has got so large now that the processing time is excessive and inefficient---the way the current code is written the entire data set has to be reprocessed every time some new data is added. \nWhat I want to do is this:\n\nRead in all of the existing data to python arrays\nSave the variable arrays to some kind of database\/file\nThen, the next time more data is added I load my database, append the new data, and resave it. This way only a small number of data need to be processed at any one time.\nI would like the saved data to be accessible to further python scripts but also to be fairly \"human readable\" so that it can be handled in programs like OriginPro or perhaps even Excel.  \n\nMy question is: whats the best format to save the data in? HDF5 seems like it might have all the features I need---but would something like SQLite make more sense? \nEDIT: My data is single dimensional. I essentially have 30 arrays which are (millions, 1) in size. If it wasn't for the fact that there are so many points then CSV would be an ideal format! I am unlikely to want to do lookups of single entries---more likely is that I might want to plot small subsets of data (eg the last 100 hours, or the last 1000 hours, etc).","AnswerCount":3,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":1123,"Q_Id":10800039,"Users Score":2,"Answer":"HDF5 is an excellent choice! It has a nice interface, is widely used (in the scientific community at least), many programs have support for it (matlab for example), there are libraries for C,C++,fortran,python,... It has a complete toolset to display the contents of a HDF5 file. If you later want to do complex MPI calculation on your data, HDF5 has support for concurrently read\/writes. It's very well suited to handle very large datasets.","Q_Score":3,"Tags":"python,database,arrays,save,hdf5","A_Id":10817026,"CreationDate":"2012-05-29T13:20:00.000","Title":"Saving large Python arrays to disk for re-use later --- hdf5? Some other method?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on a telescope project and we are testing the CCD.  Whenever we take pictures things are slightly pink-tinted and we need true color to correctly image galactic objects.  I am planning on writing a small program in python or java to change the color weights but how can I access the weight of the color in a raw data file (it is rgb.bin)?\nWe are using a bayer matrix algorithm to convert monochromatic files to color files and I would imagine the problem is coming from there but I would like to fix it with a small color correcting program.\nThanks!","AnswerCount":4,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1116,"Q_Id":10805356,"Users Score":3,"Answer":"Typical white-balance issues are caused by differing proportions of red, green, and blue in the makeup of the light illuminating a scene, or differences in the sensitivities of the sensors to those colors. These errors are generally linear, so you correct for them by multiplying by the inverse of the error.\nSuppose you measure a point you expect to be perfectly white, and its RGB values are (248,237,236) i.e. pink. If you multiply each pixel in the image by (248\/248,248\/237,248\/236) you will end up with the correct balance.\nYou should definitely ensure that your Bayer filter is producing the proper results first, or the base assumption of linear errors will be incorrect.","Q_Score":1,"Tags":"java,python,image-processing,rgb","A_Id":10807433,"CreationDate":"2012-05-29T19:22:00.000","Title":"Change color weight of raw image file","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to port from labview to python. \nIn labview there is a function \"Integral x(t) VI\" that takes a set of samples as input, performs a discrete integration of the samples and returns a list of values (the areas under the curve) according to Simpsons rule.\nI tried to find an equivalent function in scipy, e.g. scipy.integrate.simps, but those functions return the summed integral across the set of samples, as a float.\nHow do I get the list of integrated values as opposed to the sum of the integrated values? \nAm I just looking at the problem the wrong way around?","AnswerCount":2,"Available Count":1,"Score":0.2913126125,"is_accepted":false,"ViewCount":26770,"Q_Id":10814353,"Users Score":3,"Answer":"There is only one method in SciPy that does cumulative integration which is scipy.integrate.cumtrapz() which does what you want as long as you don't specifically need to use the Simpson rule or another method. For that, you can as suggested always write the loop on your own.","Q_Score":7,"Tags":"python,integration,scipy,integral","A_Id":39332002,"CreationDate":"2012-05-30T10:19:00.000","Title":"Using scipy to perform discrete integration of the sample","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have written an algorithm that takes geospatial data and performs a number of steps. The input data are a shapefile of polygons and covariate rasters for a large raster study area (~150 million pixels). The steps are as follows:\n\nSample points from within polygons of the shapefile\nFor each sampling point, extract values from the covariate rasters\nBuild a predictive model on the sampling points\nExtract covariates for target grid points\nApply predictive model to target grid\nWrite predictions to a set of output grids\n\nThe whole process needs to be iterated a number of times (say 100) but each iteration currently takes more than an hour when processed in series. For each iteration, the most time-consuming parts are step 4 and 5. Because the target grid is so large, I've been processing it a block (say 1000 rows) at a time.\nI have a 6-core CPU with 32 Gb RAM, so within each iteration, I had a go at using Python's multiprocessing module with a Pool object to process a number of blocks simultaneously (steps 4 and 5) and then write the output (the predictions) to the common set of output grids using a callback function that calls a global output-writing function. This seems to work, but is no faster (actually, it's probably slower) than processing each block in series.\nSo my question is, is there a more efficient way to do it? I'm interested in the multiprocessing module's Queue class, but I'm not really sure how it works. For example, I'm wondering if it's more efficient to have a queue that carries out steps 4 and 5 then passes the results to another queue that carries out step 6. Or is this even what Queue is for?\nAny pointers would be appreciated.","AnswerCount":4,"Available Count":1,"Score":0.049958375,"is_accepted":false,"ViewCount":2043,"Q_Id":10843240,"Users Score":1,"Answer":"As python is not really meant to do intensive number-cunching, I typically start converting time-critical parts of a python program to C\/C++ and speed things up a lot.\nAlso, the python multithreading is not very good. Python keeps using a global semaphore for all kinds of things. So even when you use the Threads that python offers, things won't get faster. The threads are useful for applications, where threads will typically wait for things like IO.\nWhen making a C module, you can manually release the global semaphore when processing your data (then, of course, do not access the python values anymore).\nIt takes some practise using the C API, but's its clearly structured and much easier to use than, for example, the Java native API.\nSee 'extending and embedding' in the python documentation.\nThis way you can make the time critical parts in C\/C++, and the slower parts with faster programming work in python...","Q_Score":18,"Tags":"python,iteration,multiprocessing,gdal","A_Id":11115655,"CreationDate":"2012-06-01T01:12:00.000","Title":"Python multiprocessing design","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to write a python program for appending live stock quotes from a csv file to an excel file (which is already open) using xlrd and xlwt.\nThe task is summarised below.\nFrom my stock-broker's application, a csv file is continually being updated on my hard disk.\nI wish to write a program which, when run, would append the new data from csv file to an excel file, which is kept open (I wonder whether it is possible to read & write an open file).\nI wish to keep the file open because I will be having stock-charts in it.\nIs it possible? If yes, how?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1299,"Q_Id":10851726,"Users Score":1,"Answer":"Not directly.  xlutils can use xlrd and xlwt to copy a spreadsheet, and appending to a \"to be written\" worksheet is straightforward.  I don't think reading the open spreadsheet is a problem -- but xlwt will not write to the open book\/sheet.\nYou might write an Excel VBA macro to draw the graphs.  In principle, I think a macro from a command workbook could close your stock workbook, invoke your python code to copy and update, open the new spreadsheet, and maybe run the macro to re-draw the graphs.\nAnother approach is to use matplotlib for the graphs.  I'd think a sleep loop could wake up every n seconds, grab the new csv data, append it to your \"big\" csv data, and re-draw the graph.  Taking this approach keeps you in python and should make things a lot easier, imho.  Disclosure: my Python is better than my VBA.","Q_Score":1,"Tags":"python,xlrd,xlwt","A_Id":10857757,"CreationDate":"2012-06-01T14:01:00.000","Title":"xlrd - append data to already opened workbook","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm attempting to create my own corpus in NLTK. I've been reading some of the documentation on this and it seems rather complicated... all I wanted to do is \"clone\" the movie reviews corpus but with my own text. Now, I know I can just change files in the move reviews corpus to my own... but that limits me to working with just one such corpus at a time (ie. I'd have to continually be swapping files). is there any way i could just clone the movie reviews corpus?\nthanks\nAlex","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":301,"Q_Id":10874994,"Users Score":0,"Answer":"Why don't you a define a new corpus by copying the definition of movie_reviews in nltk.corpus? You can do this all you want with new directories, and then copy the directory structure and replace the files.","Q_Score":0,"Tags":"python,nlp,nltk,corpus","A_Id":10875787,"CreationDate":"2012-06-04T00:09:00.000","Title":"\"Cloning\" a corpus in NLTK?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an image that is created by using a bayer filter and the colors are slightly off.  I need to multiply RG and B of each pixel by a certain factor ( a different factor for R, G and B each) to get the correct color.  I am using the python imaging library and of course writing in python.  is there any way to do this efficiently?\nThanks!","AnswerCount":6,"Available Count":2,"Score":0.0665680765,"is_accepted":false,"ViewCount":12396,"Q_Id":10885984,"Users Score":2,"Answer":"As a basic optimization, it may save a little time if you create 3 lookup tables, one each for R, G, and B, to map the input value (0-255) to the output value (0-255). Looking up an array entry is probably faster than multiplying by a decimal value and rounding the result to an integer. Not sure how much faster.\nOf course, this assumes that the values should always map the same.","Q_Score":2,"Tags":"python,image-processing,python-imaging-library,rgb,pixel","A_Id":10886261,"CreationDate":"2012-06-04T18:07:00.000","Title":"Multiply each pixel in an image by a factor","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an image that is created by using a bayer filter and the colors are slightly off.  I need to multiply RG and B of each pixel by a certain factor ( a different factor for R, G and B each) to get the correct color.  I am using the python imaging library and of course writing in python.  is there any way to do this efficiently?\nThanks!","AnswerCount":6,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":12396,"Q_Id":10885984,"Users Score":0,"Answer":"If the type is numpy.ndarray just img = np.uint8(img*factor)","Q_Score":2,"Tags":"python,image-processing,python-imaging-library,rgb,pixel","A_Id":64900468,"CreationDate":"2012-06-04T18:07:00.000","Title":"Multiply each pixel in an image by a factor","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have build a simple webcam recorder on linux which works quite well.\nI get ~25fps video and good audio.\nI am porting the recorder on windows (win7) and while it works, it is unusable.\nThe QueryFrame function takes something more than 350ms, i.e 2.5fps. \nThe code is in python but the problem really seems to be the lib call.\nI tested on the same machine with the same webcam (a logitech E2500). \nOn windows, I installed openCV v2.2. I cannot check right now but the version might be a bit higher on Ubuntu. \nAny idea what could be the problem ? \nedit : I've just installed openCV2.4 and I have the same slow speed.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":565,"Q_Id":10887836,"Users Score":1,"Answer":"I had same issue and I found out that this is caused by prolonged exposure. It may be the case that Windows drivers increased exposure to increase brightness of picture. Try to point your camera to light source or manually set decreased exposure","Q_Score":1,"Tags":"python,windows,performance,opencv","A_Id":12700150,"CreationDate":"2012-06-04T20:23:00.000","Title":"QueryFrame very slow on Windows","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I'm using sequential seeds (1,2,3,4,...) for generation of random numbers in a simulation. Does the fact that the seeds are near each other make the generated pseudo-random numbers similar as well?\nI think it doesn't change anything, but I'm using python\nEdit: I have done some tests and the numbers don't look similar. But I'm afraid that the similarity cannot be noticed just by looking at the numbers. Is there any theoretical feature of random number generation that guarantees that different seeds give completely independent pseudo-random numbers?","AnswerCount":6,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":2871,"Q_Id":10900852,"Users Score":0,"Answer":"First: define similarity. Next: code a similarity test. Then: check for similarity.\nWith only a vague description of similarity it is hard to check for it.","Q_Score":12,"Tags":"python,random,seed","A_Id":10901418,"CreationDate":"2012-06-05T16:09:00.000","Title":"May near seeds in random number generation give similar random numbers?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using sequential seeds (1,2,3,4,...) for generation of random numbers in a simulation. Does the fact that the seeds are near each other make the generated pseudo-random numbers similar as well?\nI think it doesn't change anything, but I'm using python\nEdit: I have done some tests and the numbers don't look similar. But I'm afraid that the similarity cannot be noticed just by looking at the numbers. Is there any theoretical feature of random number generation that guarantees that different seeds give completely independent pseudo-random numbers?","AnswerCount":6,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":2871,"Q_Id":10900852,"Users Score":0,"Answer":"What kind of simulation are you doing? \nFor simulation purposes your argument is valid (depending on the type of simulation) but if you implement it in an environment other than simulation, then it could be easily hacked if it requires that there are security concerns of the environment based on the generated random numbers.\nIf you are simulating the outcome of a machine whether it is harmful to society or not then the outcome of your results will not be acceptable. It requires maximum randomness in every way possible and I would never trust your reasoning.","Q_Score":12,"Tags":"python,random,seed","A_Id":10904377,"CreationDate":"2012-06-05T16:09:00.000","Title":"May near seeds in random number generation give similar random numbers?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using sequential seeds (1,2,3,4,...) for generation of random numbers in a simulation. Does the fact that the seeds are near each other make the generated pseudo-random numbers similar as well?\nI think it doesn't change anything, but I'm using python\nEdit: I have done some tests and the numbers don't look similar. But I'm afraid that the similarity cannot be noticed just by looking at the numbers. Is there any theoretical feature of random number generation that guarantees that different seeds give completely independent pseudo-random numbers?","AnswerCount":6,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":2871,"Q_Id":10900852,"Users Score":0,"Answer":"To quote the documentation from the random module:\n\nGeneral notes on the underlying Mersenne Twister core generator:\n\nThe period is 2**19937-1.\nIt is one of the most extensively tested generators in existence.\n\n\nI'd be more worried about my code being broken than my RNG not being random enough.  In general, your gut feelings about randomness are going to be wrong - the Human mind is really good at finding patterns, even if they don't exist.\nAs long as you know your results aren't going to be 'secure' due to your lack of random seeding, you should be fine.","Q_Score":12,"Tags":"python,random,seed","A_Id":10905149,"CreationDate":"2012-06-05T16:09:00.000","Title":"May near seeds in random number generation give similar random numbers?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm looking to implement an item-based news recommendation system. There are several ways I want to track a user's interest in a news item; they include: rating (1-5), favorite, click-through, and time spent on news item.\nMy question: what are some good methods to use these different metrics for the recommendation system? Maybe merge and normalize them in some way?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":614,"Q_Id":10920199,"Users Score":0,"Answer":"Recommender systems in the land of research generally work on a scale of 1 - 5. It's quite nice to get such an explicit signal from a user. However I'd imagine the reality is that most users of your system would never actually give a rating, in which case you have nothing to work with. \nTherefore I'd track page views but also try and incorporate some explicit feedback mechanism (1-5, thumbs up or down etc.)\nYour algorithm will have to take this into consideration.","Q_Score":0,"Tags":"python,metrics,recommendation-engine,personalization,cosine-similarity","A_Id":10955138,"CreationDate":"2012-06-06T18:43:00.000","Title":"Recommendation system - using different metrics","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm looking to implement an item-based news recommendation system. There are several ways I want to track a user's interest in a news item; they include: rating (1-5), favorite, click-through, and time spent on news item.\nMy question: what are some good methods to use these different metrics for the recommendation system? Maybe merge and normalize them in some way?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":614,"Q_Id":10920199,"Users Score":0,"Answer":"For recommendation system, there are two problems:\n\nhow to quantify the user's interest in a certain item based on the numbers you collected\nhow to use the quantified interest data to recommend new items to the user\n\nI guess you are more interested in the first problem.\nTo solve the first problem, you need either linear combination or some other fancy functions to combine all the numbers. There is really no a single universal function for all systems. It heavily depends on the type of your users and your items. If you want a high quality recommandation system, you need to have some data to do machine learning to train your functions.\nFor the second problem, it's somehow the same thing, plus you need to analyze all the items to abstract some relationships between each other. You can google \"Netflix prize\" for some interesting info.","Q_Score":0,"Tags":"python,metrics,recommendation-engine,personalization,cosine-similarity","A_Id":10956591,"CreationDate":"2012-06-06T18:43:00.000","Title":"Recommendation system - using different metrics","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to install epdfree on two virtually identical machines: Linux 2.6.18-308.1.1.el5, CentOS release 5.8., 64-bit machines.  (BTW, I'm a bit new to python.)\nAfter the install on one machine, I run python and try to import scipy.  Everything goes fine.\nOn the other machine, I follow all the same steps as far as I can tell, but when I try to import scipy, I am told \u201cImportError: No module named scipy\u201d.\nAs far as I can tell, I am doing everything the same on the two machines.  I installed from the same script, I run the python in the epdfree installation directory, everything I can think of.\nDoes anyone have any idea what would keep \u201cimport scipy\u201d from working on one machine while it works fine on the other?  Thanks.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":242,"Q_Id":10921655,"Users Score":0,"Answer":"The problem is that you don't have  the library scipy installed, which is a totally different library of epdfree.\nyou can install it from apt-get in linux I guess, or going to their website\nwww.scipy.org","Q_Score":0,"Tags":"python,enthought","A_Id":10921702,"CreationDate":"2012-06-06T20:22:00.000","Title":"trouble with installing epdfree","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to install epdfree on two virtually identical machines: Linux 2.6.18-308.1.1.el5, CentOS release 5.8., 64-bit machines.  (BTW, I'm a bit new to python.)\nAfter the install on one machine, I run python and try to import scipy.  Everything goes fine.\nOn the other machine, I follow all the same steps as far as I can tell, but when I try to import scipy, I am told \u201cImportError: No module named scipy\u201d.\nAs far as I can tell, I am doing everything the same on the two machines.  I installed from the same script, I run the python in the epdfree installation directory, everything I can think of.\nDoes anyone have any idea what would keep \u201cimport scipy\u201d from working on one machine while it works fine on the other?  Thanks.","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":242,"Q_Id":10921655,"Users Score":1,"Answer":"Well, turns out there was one difference.  File permissions were being set differently on the two machines.  I installed epdfree as su on both machines.  On the second machine, everything was locked out when I tried to run it without going under \"su\".  Now my next task is to find out why the permissions were set differently.  I guess it's a difference in umask settings?  Well, this I won't bother anyone with.  But feel free to offer an answer if you want to!  Thanks.","Q_Score":0,"Tags":"python,enthought","A_Id":10922030,"CreationDate":"2012-06-06T20:22:00.000","Title":"trouble with installing epdfree","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am looking for a way to pass NumPy arrays to Matlab. \nI've managed to do this by storing the array into an image using scipy.misc.imsave and then loading it using imread, but this of course causes the matrix to contain values between 0 and 256 instead of the 'real' values. \nTaking the product of this matrix divided by 256, and the maximum value in the original NumPy array gives me the correct matrix, but I feel that this is a bit tedious.  \nis there a simpler way?","AnswerCount":9,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":75113,"Q_Id":10997254,"Users Score":0,"Answer":"In latest R2021a, you can pass a python numpy ndarray to double() and it will convert to a native matlab matrix, even when calling in console the numpy array it will suggest at the bottom \"Use double function to convert to a MATLAB array\"","Q_Score":44,"Tags":"python,matlab,numpy","A_Id":69231502,"CreationDate":"2012-06-12T13:02:00.000","Title":"\"Converting\" Numpy arrays to Matlab and vice versa","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"This is a particular kind of lossy compression that's quite easy to implement in numpy.\nI could in principle directly compare original (float64) to reconstructed (float64(float32(original)) and know things like the maximum error.\nOther than looking at the maximum error for my actual data, does anybody have a good idea what type of distortions this creates, e.g. as a function of the magnitude of the original value? \nWould I be better off mapping all values (in 64-bits) onto say [-1,1] first (as a fraction of extreme values, which could be preserved in 64-bits) to take advantage of greater density of floats near zero?\nI'm adding a specific case I have in mind.  Let's say I have 500k to 1e6 values ranging from -20 to 20, that are approximately IID ~ Normal(mu=0,sigma=4) so they're already pretty concentrated near zero and the \"20\" is ~5-sigma rare.  Let's say they are scientific measurements where the true precision is a whole lot less than the 64-bit floats, but hard to really know exactly.  I have tons of separate instances (potentially TB's worth) so compressing has a lot of practical value, and float32 is a quick way to get 50% (and if anything, works better with an additional round of lossless compression like gzip).  So the \"-20 to 20\" eliminates a lot of concerns about really large values.","AnswerCount":3,"Available Count":3,"Score":1.2,"is_accepted":true,"ViewCount":1086,"Q_Id":11007169,"Users Score":6,"Answer":"It is unlikely that a simple transformation will reduce error significantly, since your distribution is centered around zero.\nScaling can have effect in only two ways: One, it moves values away from the denormal interval of single-precision values, (-2-126, 2-126). (E.g., if you multiply by, say, 2123 values that were in [2-249, 2-126) are mapped to [2-126, 2-3), which is outside the denormal interval.) Two, it changes where values lie in each \u201cbinade\u201d (interval from one power of two to the next). E.g., your maximum value is 20, where the relative error may be 1\/2 ULP \/ 20, where the ULP for that binade is 16*2-23 = 2-19, so the relative error may be 1\/2 * 2-19 \/ 20, about 4.77e-8. Suppose you scale by 32\/20, so values just under 20 become values just under 32. Then, when you convert to float, the relative error is at most 1\/2 * 2-19 \/ 32 (or just under 32), about 2.98e-8. So you may reduce the error slightly.\nWith regard to the former, if your values are nearly normally distributed, very few are in (-2-126, 2-126), simply because that interval is so small. (A trillion samples of your normal distribution almost certainly have no values in that interval.) You say these are scientific measurements, so perhaps they are produced with some instrument. It may be that the machine does not measure or calculate finely enough to return values that range from 2-126 to 20, so it would not surprise me if you have no values in the denormal interval at all. If you have no values in the single-precision denormal range, then scaling to avoid that range is of no use.\nWith regard to the latter, we see a small improvement is available at the end of your range. However, elsewhere in your range, some values are also moved to the high end of a binade, but some are moved across a binade boundary to the small end of a new binade, resulting in increased relative error for them. It is unlikely there is a significant net improvement.\nOn the other hand, we do not know what is significant for your application. How much error can your application tolerate? Will the change in the ultimate result be unnoticeable if random noise of 1% is added to each number? Or will the result be completely unacceptable if a few numbers change by as little as 2-200?\nWhat do you know about the machinery producing these numbers? Is it truly producing numbers more precise than single-precision floats? Perhaps, although it produces 64-bit floating-point values, the actual values are limited to a population that is representable in 32-bit floating-point. Have you performed a conversion from double to float and measured the error?\nThere is still insufficient information to rule out these or other possibilities, but my best guess is that there is little to gain by any transformation. Converting to float will either introduce too much error or it will not, and transforming the numbers first is unlikely to alter that.","Q_Score":4,"Tags":"python,numpy,floating-point,compression","A_Id":11027196,"CreationDate":"2012-06-13T01:42:00.000","Title":"What should I worry about if I compress float64 array to float32 in numpy?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"This is a particular kind of lossy compression that's quite easy to implement in numpy.\nI could in principle directly compare original (float64) to reconstructed (float64(float32(original)) and know things like the maximum error.\nOther than looking at the maximum error for my actual data, does anybody have a good idea what type of distortions this creates, e.g. as a function of the magnitude of the original value? \nWould I be better off mapping all values (in 64-bits) onto say [-1,1] first (as a fraction of extreme values, which could be preserved in 64-bits) to take advantage of greater density of floats near zero?\nI'm adding a specific case I have in mind.  Let's say I have 500k to 1e6 values ranging from -20 to 20, that are approximately IID ~ Normal(mu=0,sigma=4) so they're already pretty concentrated near zero and the \"20\" is ~5-sigma rare.  Let's say they are scientific measurements where the true precision is a whole lot less than the 64-bit floats, but hard to really know exactly.  I have tons of separate instances (potentially TB's worth) so compressing has a lot of practical value, and float32 is a quick way to get 50% (and if anything, works better with an additional round of lossless compression like gzip).  So the \"-20 to 20\" eliminates a lot of concerns about really large values.","AnswerCount":3,"Available Count":3,"Score":0.1325487884,"is_accepted":false,"ViewCount":1086,"Q_Id":11007169,"Users Score":2,"Answer":"The exponent for float32 is quite a lot smaller (or bigger in the case of negative exponents), but assuming all you numbers are less than that you only need to worry about the loss of precision. float32 is only good to about 7 or 8 significant decimal digits","Q_Score":4,"Tags":"python,numpy,floating-point,compression","A_Id":11007250,"CreationDate":"2012-06-13T01:42:00.000","Title":"What should I worry about if I compress float64 array to float32 in numpy?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"This is a particular kind of lossy compression that's quite easy to implement in numpy.\nI could in principle directly compare original (float64) to reconstructed (float64(float32(original)) and know things like the maximum error.\nOther than looking at the maximum error for my actual data, does anybody have a good idea what type of distortions this creates, e.g. as a function of the magnitude of the original value? \nWould I be better off mapping all values (in 64-bits) onto say [-1,1] first (as a fraction of extreme values, which could be preserved in 64-bits) to take advantage of greater density of floats near zero?\nI'm adding a specific case I have in mind.  Let's say I have 500k to 1e6 values ranging from -20 to 20, that are approximately IID ~ Normal(mu=0,sigma=4) so they're already pretty concentrated near zero and the \"20\" is ~5-sigma rare.  Let's say they are scientific measurements where the true precision is a whole lot less than the 64-bit floats, but hard to really know exactly.  I have tons of separate instances (potentially TB's worth) so compressing has a lot of practical value, and float32 is a quick way to get 50% (and if anything, works better with an additional round of lossless compression like gzip).  So the \"-20 to 20\" eliminates a lot of concerns about really large values.","AnswerCount":3,"Available Count":3,"Score":1.0,"is_accepted":false,"ViewCount":1086,"Q_Id":11007169,"Users Score":7,"Answer":"The following assumes you are using standard IEEE-754 floating-point operations, which are common (with some exceptions), in the usual round-to-nearest mode.\nIf a double value is within the normal range of float values, then the only change that occurs when the double is rounded to a float is that the significand (fraction portion of the value) is rounded from 53 bits to 24 bits. This will cause an error of at most 1\/2 ULP (unit of least precision). The ULP of a float is 2-23 times the greatest power of two not greater than the float. E.g., if a float is 7.25, the greatest power of two not greater than it is 4, so its ULP is 4*2-23 = 2-21, about 4.77e-7. So the error when double in the interval [4, 8) is converted to float is at most 2-22, about 2.38e-7. For another example, if a float is about .03, the greatest power of two not greater than it is 2-6, so the ULP is 2-29, and the maximum error when converting to double is 2-30.\nThose are absolute errors. The relative error is less than 2-24, which is 1\/2 ULP divided by the smallest the value could be (the smallest value in the interval for a particular ULP, so the power of two that bounds it). E.g., for each number x in [4, 8), we know the number is at least 4 and error is at most 2-22, so the relative error is at most 2-22\/4 = 2-24. (The error cannot be exactly 2-24 because there is no error when converting an exact power of two from float to double, so there is an error only if x is greater than four, so the relative error is less than, not equal to, 2-24.) When you know more about the value being converted, e.g., it is nearer 8 than 4, you can bound the error more tightly.\nIf the number is outside the normal range of a float, errors can be larger. The maximum finite floating-point value is 2128-2104, about 3.40e38. When you convert a double that is 1\/2 ULP (of a float; doubles have finer ULP) more than that or greater to float, infinity is returned, which is, of course, an infinite absolute error and an infinite relative error. (A double that is greater than the maximum finite float but is greater by less than 1\/2 ULP is converted to the maximum finite float and has the same errors discussed in the previous paragraph.)\nThe minimum positive normal float is 2-126, about 1.18e-38. Numbers within 1\/2 ULP of this (inclusive) are converted to it, but numbers less than that are converted to a special denormalized format, where the ULP is fixed at 2-149. The absolute error will be at most 1\/2 ULP, 2-150. The relative error will depend significantly on the value being converted.\nThe above discusses positive numbers. The errors for negative numbers are symmetric.\nIf the value of a double can be represented exactly as a float, there is no error in conversion.\nMapping the input numbers to a new interval can reduce errors in specific situations. As a contrived example, suppose all your numbers are integers in the interval [248, 248+224). Then converting them to float would lose all information that distinguishes the values; they would all be converted to 248. But mapping them to [0, 224) would preserve all information; each different input would be converted to a different result.\nWhich map would best suit your purposes depends on your specific situation.","Q_Score":4,"Tags":"python,numpy,floating-point,compression","A_Id":11019850,"CreationDate":"2012-06-13T01:42:00.000","Title":"What should I worry about if I compress float64 array to float32 in numpy?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In rtree, how can I specify the threshold for float equality testing?\nWhen checking nearest neighbours, rtree can return more than the specified number of results, as if two points are equidistant, it returns both them. To check this equidistance, it must have some threshold since the distances are floats. I want to be able to control this threshold.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":236,"Q_Id":11025297,"Users Score":0,"Answer":"Actually it does not need to have a threshold to handle ties. They just happen.\nAssuming you have the data points (1.,0.) and (0.,1.) and query point (0.,0.), any implementation I've seen of Euclidean distance will return the exact same distance for both, without any threshold.","Q_Score":2,"Tags":"python,indexing,spatial-index,spatial-query,r-tree","A_Id":11028685,"CreationDate":"2012-06-14T00:37:00.000","Title":"In rtree, how can I specify the threshold for float equality testing?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"They both seem exceedingly similar and I'm curious as to which package would be more beneficial for financial data analysis.","AnswerCount":3,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":135225,"Q_Id":11077023,"Users Score":61,"Answer":"Numpy is required by pandas (and by virtually all numerical tools for Python).  Scipy is not strictly required for pandas but is listed as an \"optional dependency\".  I wouldn't say that pandas is an alternative to Numpy and\/or Scipy.  Rather, it's an extra tool that provides a more streamlined way of working with numerical and tabular data in Python.  You can use pandas data structures but freely draw on Numpy and Scipy functions to manipulate them.","Q_Score":202,"Tags":"python,numpy,scipy,pandas","A_Id":11077060,"CreationDate":"2012-06-18T04:45:00.000","Title":"What are the differences between Pandas and NumPy+SciPy in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"They both seem exceedingly similar and I'm curious as to which package would be more beneficial for financial data analysis.","AnswerCount":3,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":135225,"Q_Id":11077023,"Users Score":327,"Answer":"pandas provides high level data manipulation tools built on top of NumPy. NumPy by itself is a fairly low-level tool, similar to MATLAB. pandas on the other hand provides rich time series functionality, data alignment, NA-friendly statistics, groupby, merge and join methods, and lots of other conveniences. It has become very popular in recent years in financial applications. I will have a chapter dedicated to financial data analysis using pandas in my upcoming book.","Q_Score":202,"Tags":"python,numpy,scipy,pandas","A_Id":11077215,"CreationDate":"2012-06-18T04:45:00.000","Title":"What are the differences between Pandas and NumPy+SciPy in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to ask if anyone has an idea or example of how to do support vector regression in python with high dimensional output( more than one) using a python binding of libsvm? I checked the examples and they are all assuming the output to be one dimensional.","AnswerCount":2,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":3847,"Q_Id":11083921,"Users Score":4,"Answer":"libsvm might not be the best tool for this task.\nThe problem you describe is called multivariate regression, and usually for regression problems, SVM's are not necessarily the best choice.\nYou could try something like group lasso (http:\/\/www.di.ens.fr\/~fbach\/grouplasso\/index.htm - matlab) or sparse group lasso (http:\/\/spams-devel.gforge.inria.fr\/ - seems to have a python interface), which solve the multivariate regression problem with different types of regularization.","Q_Score":3,"Tags":"python,machine-learning,svm,regression,libsvm","A_Id":11172695,"CreationDate":"2012-06-18T13:27:00.000","Title":"Support Vector Regression with High Dimensional Output using python's libsvm","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to read in a dictionary file to filter content specified in the hdfs_input, and I have uploaded it to the cluster using the put command, but I don't know how to access it in my program.\nI tried to access it using path on the cluster like normal files, but it gives the error information: IOError: [Errno 2] No such file or directory\nBesides, is there any way to maintain only one copy of the dictionary for all the machines that runs the job ?\nSo what's the correct way of access files other than the specified input in hadoop jobs?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":91,"Q_Id":11095220,"Users Score":0,"Answer":"Problem solved by adding the file needed with the -file option or file= option in conf file.","Q_Score":0,"Tags":"python,hadoop","A_Id":11098023,"CreationDate":"2012-06-19T06:00:00.000","Title":"How to read other files in hadoop jobs?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"The following saves floating values of a matrix into textfiles\nnumpy.savetxt('bool',mat,fmt='%f',delimiter=',')\nHow to save a boolean matrix ? what is the fmt for saving boolean matrix ?","AnswerCount":1,"Available Count":1,"Score":0.537049567,"is_accepted":false,"ViewCount":3549,"Q_Id":11100066,"Users Score":3,"Answer":"Thats correct, bools are integers, so you can always go between the two. \n\n\nimport numpy as np\narr = np.array([True, True, False, False])\nnp.savetxt(\"test.txt\", arr, fmt=\"%5i\")\n\n\nThat gives a file with 1 1 0 0","Q_Score":2,"Tags":"python,numpy,save,boolean","A_Id":11101558,"CreationDate":"2012-06-19T11:33:00.000","Title":"how to save a boolean numpy array to textfile in python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have two dataframes, both indexed by timeseries.  I need to add the elements together to form a new dataframe, but only if the index and column are the same.  If the item does not exist in one of the dataframes then it should be treated as a zero.\nI've tried using .add but this sums regardless of index and column.  Also tried a simple combined_data = dataframe1 + dataframe2 but this give a NaN if both dataframes don't have the element.\nAny suggestions?","AnswerCount":4,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":102462,"Q_Id":11106823,"Users Score":2,"Answer":"Both the above answers - fillna(0) and a direct addition would give you Nan values if either of them have different structures.\nIts Better to use fill_value\ndf.add(other_df, fill_value=0)","Q_Score":55,"Tags":"python,pandas","A_Id":42273797,"CreationDate":"2012-06-19T18:11:00.000","Title":"Adding two pandas dataframes","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Using Python's csv module, is it possible to read an entire, large, csv file into a lazy list of lists?\nI am asking this, because in Clojure there are csv parsing modules that will parse a large file and return a lazy sequence (a sequence of sequences). I'm just wondering if that's possible in Python.","AnswerCount":4,"Available Count":3,"Score":0.049958375,"is_accepted":false,"ViewCount":2302,"Q_Id":11109524,"Users Score":1,"Answer":"The csv module does load the data lazily, one row at a time.","Q_Score":5,"Tags":"python,csv,clojure,lazy-evaluation","A_Id":11109571,"CreationDate":"2012-06-19T21:13:00.000","Title":"Can csv data be made lazy?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Using Python's csv module, is it possible to read an entire, large, csv file into a lazy list of lists?\nI am asking this, because in Clojure there are csv parsing modules that will parse a large file and return a lazy sequence (a sequence of sequences). I'm just wondering if that's possible in Python.","AnswerCount":4,"Available Count":3,"Score":0.0996679946,"is_accepted":false,"ViewCount":2302,"Q_Id":11109524,"Users Score":2,"Answer":"Python's reader or DictReader are generators. A row is produced only when the object's next() method is called.","Q_Score":5,"Tags":"python,csv,clojure,lazy-evaluation","A_Id":11109589,"CreationDate":"2012-06-19T21:13:00.000","Title":"Can csv data be made lazy?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Using Python's csv module, is it possible to read an entire, large, csv file into a lazy list of lists?\nI am asking this, because in Clojure there are csv parsing modules that will parse a large file and return a lazy sequence (a sequence of sequences). I'm just wondering if that's possible in Python.","AnswerCount":4,"Available Count":3,"Score":1.0,"is_accepted":false,"ViewCount":2302,"Q_Id":11109524,"Users Score":6,"Answer":"The csv module's reader is lazy by default.\nIt will read a line in at a time from the file, parse it to a list, and return that list.","Q_Score":5,"Tags":"python,csv,clojure,lazy-evaluation","A_Id":11109568,"CreationDate":"2012-06-19T21:13:00.000","Title":"Can csv data be made lazy?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm currently using FileStorage class for storing matrices XML\/YAML using OpenCV C++ API.\nHowever, I have to write a Python Script that reads those XML\/YAML files.\nI'm looking for existing OpenCV Python API that can read the XML\/YAML files generated by OpenCV C++ API","AnswerCount":6,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":16840,"Q_Id":11141336,"Users Score":0,"Answer":"pip install opencv-contrib-python for video support to install specific version use pip install opencv-contrib-python","Q_Score":21,"Tags":"c++,python,image-processing,opencv","A_Id":60879363,"CreationDate":"2012-06-21T15:18:00.000","Title":"FileStorage for OpenCV Python API","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have to implement the solution to a 0\/1 Knapsack problem with constraints.\nMy problem will have in most cases few variables (~ 10-20, at most 50).\nI recall from university that there are a number of algorithms that in many cases perform better than brute force (I'm thinking, for example, to a branch and bound algorithm).\nSince my problem is relative small, I'm wondering if there is an appreciable advantange in terms of efficiency when using a sophisticate solution as opposed to brute force.\nIf it helps, I'm programming in Python.","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":1291,"Q_Id":11154101,"Users Score":1,"Answer":"You can either use pseudopolynomial algorithm, which uses dynamic programming, if the sum of weights is small enough. You just calculate, whether you can get weight X with first Y items for each X and Y.\nThis runs in time O(NS), where N is number of items and S is sum of weights.\nAnother possibility is to use meet-in-the middle approach. \nPartition items into two halves and:\nFor the first half take every possible combination of items (there are 2^(N\/2) possible combinations in each half) and store its weight in some set.\nFor the second half take every possible combination of items and check whether there is a combination in first half with suitable weight.\nThis should run in O(2^(N\/2)) time.","Q_Score":4,"Tags":"python,algorithm,knapsack-problem","A_Id":11155580,"CreationDate":"2012-06-22T10:02:00.000","Title":"0\/1 Knapsack with few variables: which algorithm?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Are there any C++ (or C) libs that have NumPy-like arrays with support for slicing, vectorized operations, adding and subtracting contents element-by-element, etc.?","AnswerCount":13,"Available Count":1,"Score":0.0614608973,"is_accepted":false,"ViewCount":81916,"Q_Id":11169418,"Users Score":4,"Answer":"Use LibTorch (PyTorch frontend for C++) and be happy.","Q_Score":104,"Tags":"c++,arrays,python-3.x,numpy,dynamic-arrays","A_Id":57324664,"CreationDate":"2012-06-23T12:15:00.000","Title":"NumPy style arrays for C++?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Which is the best to way to export openerp data to csv\/xls file using python so that i can schedule it in openerp( i cant use the client side exporting)? \n\nusing csv python package\nusing xlwt python package\nor any other package?\n\nAnd also how can I dynamically provide the path and name to save this newly created csv file","AnswerCount":2,"Available Count":1,"Score":0.2913126125,"is_accepted":false,"ViewCount":4385,"Q_Id":11187086,"Users Score":3,"Answer":"Why not to use Open ERP client it self.\nyou can go for xlwt if you really require to write a python program to generate it.","Q_Score":0,"Tags":"python,export-to-excel,openerp,export-to-csv","A_Id":11187374,"CreationDate":"2012-06-25T09:57:00.000","Title":"The best way to export openerp data to csv file using python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"sorry if this all seem nooby and unclear, but I'm currently learning Netlogo to model agent-based collective behavior and would love to hear some advice on alternative software choices. My main thing is that I'd very much like to take advantage of PyCuda since, from what I understand, it enables parallel computation. However, does that mean I still have to write the numerical script in some other environment and implement the visuals in yet another one???\nIf so, my questions are:\n\nWhat numerical package should I use? PyEvolve, DEAP, or something else? It appears that PyEvolve is no longer being developed and DEAP is just a wrapper on the outdated(?) EAP.\nGraphic-wise, I find mayavi2 and vtk promising. The problem is, none of the numerical package seems to bind to these readily. Is there no better alternative than to save the numerical output to datafile and feed them into, say, mayavi2?\nAnother option is to generate the data via Netlogo and feed them into a graphing package from (2). Is there any disadvantage to doing this?\n\nThank you so much for shedding light on this confusion.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1399,"Q_Id":11198288,"Users Score":1,"Answer":"You almost certainly do not want to use CUDA unless you are running into a significant performance problem. In general CUDA is best used for solving floating point linear algebra problems. If you are looking for a framework built around parallel computations, I'd look towards OpenCL which can take advantage of GPUs if needed.. \nIn terms of visualization, I'd strongly suggest targeting a a specific data interchange format and then letting some other program do that rendering for you. The only reason I'd use something like VTK is if for some reason you need more control over the visualization process or you are looking for a real time solution.","Q_Score":2,"Tags":"python,netlogo,pycuda,agent-based-modeling,mayavi","A_Id":11198804,"CreationDate":"2012-06-25T22:29:00.000","Title":"ABM under python with advanced visualization","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"It is pretty easy to split a rectangle\/square into smaller regions and enforce a maximum area of each sub-region.  You can just divide the region into regions with sides length sqrt(max_area) and treat the leftovers with some care.\nWith a quadrilateral however I am stumped.  Let's assume I don't know the angle of any of the corners.  Let's also assume that all four points are on the same plane.  Also, I don't need for the the small regions to be all the same size.  The only requirement I have is that the area of each individual region is less than the max area.\nIs there a particular data structure I could use to make this easier?\nIs there an algorithm I'm just not finding?\nCould I use quadtrees to do this?  I'm not incredibly versed in trees but I do know how to implement the structure.\nI have GIS work in mind when I'm doing this, but I am fairly confident that that will have no impact on the algorithm to split the quad.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":1141,"Q_Id":11217855,"Users Score":1,"Answer":"You could recursively split the quad in half on the long sides until the resulting area is small enough.","Q_Score":1,"Tags":"python,math,geometry,gis","A_Id":11217921,"CreationDate":"2012-06-27T00:39:00.000","Title":"Split quadrilateral into sub-regions of a maximum area","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am looking to track topic popularity on a very large number of documents. Furthermore, I would like to give recommendations to users based on topics, rather than the usual bag of words model.\nTo extract the topics I use natural language processing techniques that are beyond the point of this post.\nMy question is how should I persist this data so that:\nI) I can quickly fetch trending data for each topic (in principle, every time a user opens a document, the topics in that document should go up in popularity)\nII) I can quickly compare documents to provide recommendations (here I am thinking of using clustering techniques)\nMore specifically, my questions are:\n1) Should I go with the usual way of storing text mining data? meaning storing a topic occurrence vector for each document, so that I can later measure the euclidean distance between different documents.\n2) Some other way?\nI am looking for specific python ways to do this. I have looked into SQL and NoSQL databases, and also into pytables and h5py, but I am not sure how I would go about implementing a system like this. One of my concerns is how can I deal with an ever growing vocabulary of topics?\nThank you very much","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":612,"Q_Id":11267143,"Users Score":1,"Answer":"Why not have simple SQL tables\nTables:\n\ndocuments with a primary key of id or file name or something\nobservations with foreign key into documents and the term (indexed on both fields probably unique)\n\nThe array approach you mentioned seems like a slow way to get at terms.\nWith sql you can easily allow new terms be added to the observations table.\nEasy to aggregate and even do trending stuff by aggregating by date if the documents table includes a timestamp.","Q_Score":3,"Tags":"python,database,data-mining,text-mining","A_Id":11267361,"CreationDate":"2012-06-29T18:32:00.000","Title":"Storing text mining data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm looking for an algorithm to compare two images (I work with python).\nI find the PIL library, numpy\/scipy and opencv. I know how to transform in greyscale, binary, make an histogram, .... that's ok but I don't know what I have to do with the two images to say \"yes they're similar \/\/ they're probably similar \/\/ they don't match\".\nDo you know the right way to go about it ?","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":1355,"Q_Id":11289652,"Users Score":1,"Answer":"If you want to check if they are binary equal you can count a checksum on them and compare it. If you want to check if they are similar in some other way , it will be more complicated and definitely would not fit into simple answer posted on Stack Overflow. It just depends on how you define similarity but anyway it would require good programming skills and a lot of code written.","Q_Score":0,"Tags":"python,image,compare","A_Id":11289709,"CreationDate":"2012-07-02T07:50:00.000","Title":"How to compare image with python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have recently installed numpy due to ease using the exe installer for Python 2.7. However, when I attempt to install IPython, Pandas or Matplotlib using the exe file, I consistently get a variant of the following error right after the installation commeces (pandas in the following case):\npandas-0.8.0.win32-py2.7[1].exe has stopped working\nThe problem caused the program to stop working correctly: windows close the program and notify whether a solution is available.\nNumPy just worked fine when I installed it. This is extremely frustrating and I would appreciate any insight. \nThanks","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1016,"Q_Id":11289670,"Users Score":0,"Answer":"This happened to me too. It works if you right click and 'Run As Administrator'","Q_Score":0,"Tags":"python,numpy,matplotlib,ipython,pandas","A_Id":11616216,"CreationDate":"2012-07-02T07:52:00.000","Title":".EXE installer crashes when installing Python modules: IPython, Pandas and Matplotlib","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using pandas in python.  I have several Series indexed by dates that I would like to concat into a single DataFrame, but the Series are of different lengths because of missing dates etc.  I would like the dates that do match up to match up, but where there is missing data for it to be interpolated or just use the previous date or something like that.  What is the easiest way to do this?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1369,"Q_Id":11298097,"Users Score":1,"Answer":"If the Series are in a dict data, you need only do:\nframe = DataFrame(data)\nThat puts things into a DataFrame and unions all the dates. If you want to fill values forward, you can call frame = frame.fillna(method='ffill').","Q_Score":2,"Tags":"python,dataframe,concat,pandas","A_Id":11312776,"CreationDate":"2012-07-02T17:06:00.000","Title":"concatenating TimeSeries of different lengths using Pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'd like to change my seterr defaults to be either all 'warn' or all 'ignore'.  This can be done interactively by doing np.seterr(all='ignore').  Is there a way to make it a system default?  There is no .numpyrc as far as I can tell; is there some other configuration file where these defaults can be changed?\n(I'm using numpy 1.6.1)\nEDIT: The problem was not that numpy's default settings had changed, as I had incorrectly suspected, but that another code, pymc, was changing things that are normally ignore or warn to raise, causing all sorts of undesired and unexpected crashes.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":880,"Q_Id":11351264,"Users Score":1,"Answer":"There is no configuration file for this. You will have to call np.seterr() yourself.","Q_Score":1,"Tags":"python,numpy","A_Id":11359378,"CreationDate":"2012-07-05T19:31:00.000","Title":"Change numpy.seterr defaults?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm tokenizing text with nltk, just sentences fed to wordpunct_tokenizer. This splits contractions (e.g. 'don't' to 'don' +\" ' \"+'t') but I want to keep them as one word. I'm refining my methods for a more measured  and precise tokenization of text, so I need to delve deeper into the nltk tokenization module beyond simple tokenization. \nI'm guessing this is common and I'd like feedback from others who've maybe had to deal with the particular issue before.\nedit: \nYeah this a general, splattershot question I know\nAlso, as a novice to nlp, do I need to worry about contractions at all?\nEDIT: \nThe SExprTokenizer or TreeBankWordTokenizer seems to do what I'm looking for for now.","AnswerCount":3,"Available Count":1,"Score":0.1325487884,"is_accepted":false,"ViewCount":12956,"Q_Id":11351290,"Users Score":2,"Answer":"Because the number of contractions are very minimal, one way to do it is to search and replace all contractions to it full equivalent (Eg: \"don't\" to \"do not\") and then feed the updated sentences into the wordpunct_tokenizer.","Q_Score":19,"Tags":"python,nlp,nltk","A_Id":11355186,"CreationDate":"2012-07-05T19:32:00.000","Title":"nltk tokenization and contractions","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it possible to customize Serie (in a simple way, and DataFrame by the way :p) from pandas to append extras informations on the display and in the plots? A great thing will be to have the possibility to append informations like \"unit\", \"origin\" or anything relevant for the user that will not be lost during computations, like the \"name\" parameter.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":148,"Q_Id":11362376,"Users Score":1,"Answer":"Right now there is not an easy way to maintain metadata on pandas objects across computations.\nMaintaining metadata has been an open discussion on github for some time now but we haven't had to time code it up.\nWe'd welcome any additional feedback you have (see pandas on github) and would love to accept a pull-request if you're interested in rolling your own.","Q_Score":1,"Tags":"python,pandas","A_Id":11401559,"CreationDate":"2012-07-06T12:39:00.000","Title":"Append extras informations to Series in Pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"While plotting using Matplotlib, I have found how to change the font size of the labels.\nBut, how can I change the size of the numbers in the scale?\nFor clarity, suppose you plot x^2 from (x0,y0) = 0,0 to (x1,y1) = (20,20).\nThe scale in the x-axis below maybe something like \n\n0   1   2  ...   20.\n\nI want to change the font size of such scale of the x-axis.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":44462,"Q_Id":11379910,"Users Score":0,"Answer":"simply put, you can use the following command to set the range of the ticks and change the size of the ticks\nimport matplotlib.pyplot as plt\nset the range of ticks for x-axis and y-axis\nplt.set_yticks(range(0,24,2))\nplt.set_xticks(range(0,24,2))\nchange the size of ticks for x-axis and y-axis\nplt.yticks(fontsize=12,)\nplt.xticks(fontsize=12,)","Q_Score":14,"Tags":"python,matplotlib","A_Id":68635240,"CreationDate":"2012-07-08T01:03:00.000","Title":"How do I change the font size of the scale in matplotlib plots?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"In NLTK, using a naive bayes classifier, I know from examples its very simply to use a \"bag of words\" approach and look for unigrams or bigrams or both. Could you do the same using two completely different sets of features?\nFor instance, could I use unigrams and length of the training set (I know this has been mentioned once on here)? But of more interest to me would be something like bigrams and \"bigrams\" or combinations of the POS that appear in the document?\nIs this beyond the power of the basic NLTK classifier?\nThanks\nAlex","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1502,"Q_Id":11460115,"Users Score":5,"Answer":"NLTK classifiers can work with any key-value dictionary. I use {\"word\": True} for text classification, but you could also use {\"contains(word)\": 1} to achieve the same effect. You can also combine many features together, so you could have {\"word\": True, \"something something\": 1, \"something else\": \"a\"}. What matters most is that your features are consistent, so you always have the same kind of keys and a fixed set of possible values. Numeric values can be used, but the classifier isn't smart about them - it will treat numbers as discrete values, so that 99 and 100 are just as different as 1 and 100. If you want numbers to be handled in a smarter way, then I recommend using scikit-learn classifiers.","Q_Score":2,"Tags":"python,nlp,nltk","A_Id":11462417,"CreationDate":"2012-07-12T20:28:00.000","Title":"NLTK multiple feature sets in one classifier?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I will need to create array of integer arrays like [[0,1,2],[4,4,5,7]...[4,5]]. The size of internal arrays changeable. Max number of internal arrays is 2^26. So what do you recommend for the fastest way for updating this array. \nWhen I use list=[[]] * 2^26 initialization is very fast but update is very slow. Instead I use \nlist=[] , for i in range(2**26): list.append.([]) . \nNow initialization is slow, update is fast. For example, for 16777216 internal array and 0.213827311993 avarage number of elements on each array for 2^26-element array it takes 1.67728900909 sec. It is good but I will work much bigger datas, hence I need the best way. Initialization time is not important.\nThank you.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":497,"Q_Id":11517143,"Users Score":0,"Answer":"What you ask is quite of a problem. Different data structures have different properties. In general, if you need quick access, do not use lists! They have linear access time, which means, the more you put in them, the longer it will take in average to access an element. \nYou could perhaps use numpy? That library has matrices that can be accessed quite fast, and can be reshaped on the fly. However, if you want to add or delete rows, it will might be a bit slow because it generally reallocates (thus copies) the entire data. So it is a trade off. \nIf you are gonna have so many internal arrays of different sizes, perhaps you could have a dictionary that contains the internal arrays. I think if it is indexed by integers it will be much faster than a list. Then, the internal arrays could be created with numpy.","Q_Score":1,"Tags":"python,performance","A_Id":11517362,"CreationDate":"2012-07-17T06:33:00.000","Title":"Fast access and update integer matrix or array in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to get the suggestion on using No-SQL datastore for my particular requirements.\nLet me explain:\n         I have to process the five csv files. Each csv contains 5 million rows and also The common id field is presented in each csv.So, I need to merge all csv by iterating 5 million rows.So, I go with python dictionary to merge all files based on the common id field.But here the bottleneck is you can't store the 5 million keys in memory(< 1gig) with python-dictionary.\nSo, I decided to use No-Sql.I think It might be helpful to process the 5 million key value storage.Still I didn't have clear thoughts on this.\nAnyway we can't reduce the iteration since we have the five csvs each has to be iterated for updating the values.\nIs it there an simple steps to go with that?\n  If this is the way Could you give me the No-Sql datastore to process the key-value pair?\nNote: We have the values as list type also.","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":347,"Q_Id":11522232,"Users Score":0,"Answer":"If this is just a one-time process, you might want to just setup an EC2 node with more than 1G of memory and run the python scripts there. 5 million items isn't that much, and a Python dictionary should be fairly capable of handling it. I don't think you need Hadoop in this case.\nYou could also try to optimize your scripts by reordering the items in several runs, than running over the 5 files synchronized using iterators so that you don't have to keep everything in memory at the same time.","Q_Score":1,"Tags":"python,nosql","A_Id":11522576,"CreationDate":"2012-07-17T12:15:00.000","Title":"Process 5 million key-value data in python.Will NoSql solve?","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an image that I load using cv2.imread().  This returns an NumPy array.  However, I need to pass this into a 3rd party API that requires the data in IplImage format.\nI've scoured everything I could and I've found instances of converting from IplImage to CvMat,and I've found some references to converting in C++, but not from NumPy to IplImage in Python.  Is there a function that is provided that can do this conversion?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":10288,"Q_Id":11528009,"Users Score":0,"Answer":"2-way to apply:\n\nimg = cv2.imread(img_path)\nimg_buf = cv2.imencode('.jpg', img)[1].tostring()\njust read the image file:\nimg_buf = open(img_path, 'rb').read()","Q_Score":8,"Tags":"python,opencv","A_Id":51053926,"CreationDate":"2012-07-17T17:48:00.000","Title":"OpenCV: Converting from NumPy to IplImage in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an array that looks something like:\n[0  x1  0   0   y1   0   z1 \n 0  0   x2  0   y2  0   z2  \n 0  0   x3  0   0   y3  z3  \n 0  0   x4  0   0   y4  z4 \n 0  x5  0   0   0   y5  z5  \n 0  0   0   0   y6   0   0] \nI need to determine set of connected line (i.e. line that connects to the points [x1,x2,x3..], [y1,y2,y3...], [z1,z2,z3..]) from the array and then need to find maximum value in each line i.e. max{x1,x2,x3,...}, max{y1,y2,y3..} etc.  i was trying to do nearest neighbor search using kdtree but it return the same array. I have array of the size (200 x 8000). is there any easier way to do this? Thx.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":526,"Q_Id":11543991,"Users Score":1,"Answer":"I don't know of anything which provides the functionality you desire out of the box. If you have already written the logic, and it is just slow, have you considered Cython-ing your code. For simple typed looping operations you could get a significant speedup.","Q_Score":1,"Tags":"python,numpy,nearest-neighbor","A_Id":11564801,"CreationDate":"2012-07-18T14:41:00.000","Title":"How to determine set of connected line from an array in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have to read a very large (1.7 million records) csv file to a numpy record array. Two of the columns are strings that need to be converted to datetime objects. Additionally, one column needs to be the calculated difference between those datetimes.\nAt the moment I made a custom iterator class that builds a list of lists. I then use np.rec.fromrecords to convert it to the array. \nHowever, I noticed that calling datetime.strptime() so many times really slows things down. I was wondering if there was a more efficient way to do these conversions. The times are accurate to the second within the span of a date. So, assuming that the times are uniformly distributed (they're not), it seems like I'm doing 20x more conversions that necessary (1.7 million \/ (60 X 60 X 24). \nWould it be faster to store converted values in a dictionary {string dates: datetime obj} and first check the dictionary, before doing unnecessary conversions? \nOr should I be using numpy functions (I am still new to the numpy library)?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":420,"Q_Id":11584856,"Users Score":0,"Answer":"I could be wrong, but it seems to me like your issue is having repeated occurrences, thus doing the same conversion more times than necessary.  IF that interpretation is correct, the most efficient method would depend on how many repeats there are.  If you have 100,000 repeats out of 1.7 million, then writing 1.6 million to a dictionary and checking it 1.7 million times might not be more efficient, since it does 1.6+1.7million read\/writes. However, if you have 1 million repeats, then returning an answer (O(1)) for those rather than doing the conversion an extra million times would be much faster.  \nAll-in-all, though, python is very slow and you might not be able to speed this up much at all, given that you are using 1.7 million inputs.  As for numpy functions, I'm not that well versed in it either, but I believe there's some good documentation for it online.","Q_Score":1,"Tags":"python,numpy","A_Id":11585354,"CreationDate":"2012-07-20T18:18:00.000","Title":"How to efficiently convert dates in numpy record array?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm extracting mass data from a legacy backend system using C\/C++ and move it to Python using distutils. After obtaining the data in Python, I put it into a pandas DataFrame object for data analysis. Now I want to go faster and would like to avoid the second step. \nIs there a C\/C++ API for pandas to create a DataFrame in C\/C++, add my C\/C++ data and pass it to Python? I'm thinking of something that is similar to numpy C API.\nI already thougth of creating numpy array objects in C as a workaround but i'm heavily using timeseries data and would love to have the TimeSeries and date_range objects as well.","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":28186,"Q_Id":11607387,"Users Score":9,"Answer":"All the pandas classes (TimeSeries, DataFrame, DatetimeIndex etc.) have pure-Python definitions so there isn't a C API.  You might be best off passing numpy ndarrays from C to your Python code and letting your Python code construct pandas objects from them.\nIf necessary you could use PyObject_CallFunction etc. to call the pandas constructors, but you'd have to take care of accessing the names from module imports and checking for errors.","Q_Score":20,"Tags":"python,c,api,pandas","A_Id":11610785,"CreationDate":"2012-07-23T06:24:00.000","Title":"Is there a C\/C++ API for python pandas?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am exploring switching to python and pandas as a long-time SAS user.  \nHowever, when running some tests today, I was surprised that python ran out of memory when trying to pandas.read_csv() a 128mb csv file.  It had about 200,000 rows and 200 columns of mostly numeric data.\nWith SAS, I can import a csv file into a SAS dataset and it can be as large as my hard drive. \nIs there something analogous in pandas? \nI regularly work with large files and do not have access to a distributed computing network.","AnswerCount":6,"Available Count":1,"Score":0.0333209931,"is_accepted":false,"ViewCount":76790,"Q_Id":11622652,"Users Score":1,"Answer":"You can use Pytable rather than pandas df.\nIt is designed for large data sets and the file format is in hdf5.\nSo the processing time is relatively fast.","Q_Score":96,"Tags":"python,pandas,sas","A_Id":42165467,"CreationDate":"2012-07-24T00:50:00.000","Title":"Large, persistent DataFrame in pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am reading a bunch of strings from mysql database using python, and after some processing, writing them to a CSV file. However I see some totally junk characters appearing in the csv file. For example when I open the csv using gvim, I see characters like <92>,<89>, <94> etc. \nAny thoughts? I tried doing string.encode('utf-8') before writing to csv but that gave an error that UnicodeDecodeError: 'ascii' codec can't decode byte 0x93 in position 905: ordinal not in range(128)","AnswerCount":4,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1903,"Q_Id":11705114,"Users Score":0,"Answer":"Are all these \"junk\" characters in the range <80> to <9F>? If so, it's highly likely that they're Microsoft \"Smart Quotes\" (Windows-125x encodings). Someone wrote up the text in Word or Outlook, and copy\/pasted it into a Web application. Both Latin-1 and UTF-8 regard these characters as control characters, and the usual effect is that the text display gets cut off (Latin-1) or you see a ?-in-black-diamond-invalid-character (UTF-8).\nNote that Word and Outlook, and some other MS products, provide a UTF-8 version of the text for clipboard use. Instead of <80> to <9F> codes, Smart Quotes characters will be proper multibyte UTF-8 sequences. If your Web page is in UTF-8, you should normally get a proper UTF-8 character instead of the Smart Quote in Windows-125x encoding. Also note that this is not guaranteed behavior, but \"seems to work pretty consistently\". It all depends on a UTF-8 version of the text being available, and properly handled (i.e., you didn't paste into, say, gvim on the PC, and then copy\/paste into a Web text form). This may well also work for various PC applications, so long as they are looking for UTF-8-encoded text.","Q_Score":1,"Tags":"python,mysql,vim,encoding,smart-quotes","A_Id":18619898,"CreationDate":"2012-07-28T22:20:00.000","Title":"Junk characters (smart quotes, etc.) in output file","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've never done any image processing and I was wondering if someone can nudge me in the right direction. \nHere's my issue: I have a bunch of images of black and white images of places around a city. Due to some problems with the camera system, some images contain nothing but a black image with a white vignette around the edge. This vignette is noisy and non-uniform (sometimes it can be on both sides, other times only one).\nWhat are some good ways I can go about detecting these frames? I would just need to be able to  write a bit.\nMy image set is huge, so I would need this to be an automated process and in the end it should use Python since it needs to integrate into my existing code.\nI was thinking some sort of machine learning algorithm but I'm not sure what to do beyond that.","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1550,"Q_Id":11733106,"Users Score":2,"Answer":"If I understand you correctly then you have complete black images with white borders?\nIn this case I think the easiest approach is to compute a histogram of the intensity values of the pixels, i.e. how \u201edark\/bright\u201d is the overall image. I guess that the junk images are significantly darker than the non-junk images. You can then filter the images based on their histogram. For that you have to choose a threshold: Every image darker than this threshold is considered as junk.\nIf this approach is to fuzzy you can easily improve it. For example: just compute the histogram of the inner image without the edges, because this makes the histogram much more darker in comparison to non-junk images.","Q_Score":2,"Tags":"python,image-processing,machine-learning,computer-vision","A_Id":11736635,"CreationDate":"2012-07-31T04:28:00.000","Title":"Detecting Halo in Images","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using rpy2 and I have this issue that's bugging me: I know how to convert a Python array or list to a FloatVector that R (thanks to rpy2) can handle within Python, but I don't know if the opposite can be done, say, I have a FloatVector or Matrix that R can handle and convert it back to a Python array or list...can this be done?\nThanks in advance!","AnswerCount":4,"Available Count":1,"Score":0.1488850336,"is_accepted":false,"ViewCount":5269,"Q_Id":11769471,"Users Score":3,"Answer":"In the latest version of rpy2, you can simply do this in a direct way:\n\nimport numpy as np\narray=np.array(vector_R)","Q_Score":9,"Tags":"python,rpy2","A_Id":52399670,"CreationDate":"2012-08-02T00:30:00.000","Title":"rpy2: Convert FloatVector or Matrix back to a Python array or list?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I do not know the right way to import modules.\nI have a main file which initializes the code, does some preliminary calculations etc.\nI also have 5 functions f1, f2, ... f5. The main code and all functions need Numpy. \nIf I define all functions in the main file, the code runs fine.\n(Importing with : import numpy as np)\nIf I put the functions in a separate file, I get an error:\nError : Global name 'linalg' is not defined. \nWhat is the right way to import modules such that the functions f1 - f5 can access the Numpy functionality?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":9291,"Q_Id":11788950,"Users Score":1,"Answer":"You have to import modules in every file in which you use them.  Does that answer your question?","Q_Score":4,"Tags":"python,numpy,python-import","A_Id":11788967,"CreationDate":"2012-08-03T03:40:00.000","Title":"Importing Numpy into functions","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a python script that can output a plot using matplotlib and command line inputs.\nWhat i'm doing right now is making the script print the location\/filename of the generated plot image and then when PHP sees it, it outputs an img tag to display it.\nThe python script deletes images that are older than 20 minutes when it runs. It seems like too much of a workaround, and i'm wondering if there's a better solution.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2549,"Q_Id":11789917,"Users Score":2,"Answer":"You could modify your python script so it outputs an image (image\/jpeg) instead of saving it to a file. Then use the  tag as normal, but pointing directly to the python script. Your php wouldn't call the python script at all, It would just include it as the src of the image.","Q_Score":2,"Tags":"php,python,matplotlib","A_Id":11790050,"CreationDate":"2012-08-03T05:42:00.000","Title":"What's a good way to output matplotlib graphs on a PHP website?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to generate a heat map image of a floor. I have the following things:\n\nA black & white .png image of the floor\nA three column array stored in Matlab.\n-- The first two columns indicate the X & Y coordinates of the floorpan image\n-- The third coordinate denotes the \"temperature\" of that particular coordinate\n\nI want to generate a heat map of the floor that will show the \"temperature\" strength in those coordinates. However, I want to display the heat map on top of the floor plan so that the viewers can see which rooms lead to which \"temperatures\".\nIs there any software that does this job? Can I use Matlab or Python to do this?\nThanks,\nNazmul","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":4036,"Q_Id":11805983,"Users Score":2,"Answer":"One way to do this would be:\n1) Load in the floor plan image with Matlab or NumPy\/matplotlib.\n2) Use some built-in edge detection to locate the edge pixels in the floor plan.\n3) Form a big list of (x,y) locations where an edge is found in the floor plan.\n4) Plot your heat map\n5) Scatterplot the points of the floor plan as an overlay.\nIt sounds like you know how to do each of these steps individually, so all you'll need to do is look up some stuff on how to overlay plots onto the same axis, which is pretty easy in both Matlab and matplotlib.\nIf you're unfamiliar, the right commands look at are things like meshgrid and surf, possibly contour and their Python equivalents. I think Matlab has a built-in for Canny edge detection. I believe this was more difficult in Python, but if you use the PIL library, the Mahotas library, the scikits.image library, and a few others tailored for image manipulation, it's not too bad. SciPy may actually have an edge filter by now though, so check there first.\nThe only sticking point will be if your (x,y) data for the temperature are not going to line up with the (x,y) pixel locations in the image. In that case, you'll have to play around with some x-scale factor and y-scale factor to transform your heat map's coordinates into pixel coordinates first, and then plot the heat map, and then the overlay should work.\nThis is a fairly low-tech way to do it; I assume you just need a quick and dirty plot to illustrate how something's working. This method does have the advantage that you can change the style of the floorplan points easily, making them larger, thicker, thinner, different colors, or transparent, depending on how you want it to interact with the heat map. However, to do this for real, use GIMP, Inkscape, or Photoshop and overlay the heatmap onto the image after the fact.","Q_Score":3,"Tags":"matlab,python-3.x,heatmap,color-mapping","A_Id":11809642,"CreationDate":"2012-08-04T04:49:00.000","Title":"Heat map generator of a floor plan image","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I downloaded opencv 2.4 source code from svn, and then I used the command 'cmake -D BUILD_TESTS=OFF' to generate makefile and make and install. I found that the python module was successfully made. But when I import cv2 in python, no module cv2 exists. Is there anything else I should configure? Thanks for your help.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":329,"Q_Id":11824697,"Users Score":0,"Answer":"Have you run 'make install' or 'sudo make install'? While not absolutely necessary, it copies the generated binaries to your system paths.","Q_Score":0,"Tags":"python,opencv","A_Id":11826057,"CreationDate":"2012-08-06T08:15:00.000","Title":"Python For OpenCV2.4","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I downloaded opencv 2.4 source code from svn, and then I used the command 'cmake -D BUILD_TESTS=OFF' to generate makefile and make and install. I found that the python module was successfully made. But when I import cv2 in python, no module cv2 exists. Is there anything else I should configure? Thanks for your help.","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":329,"Q_Id":11824697,"Users Score":2,"Answer":"You should either copy the cv2 library to a location in your PYTHONPATH or add your current directory to the PYTHONPATH.","Q_Score":0,"Tags":"python,opencv","A_Id":11824855,"CreationDate":"2012-08-06T08:15:00.000","Title":"Python For OpenCV2.4","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm puzzling over an embedded Python 2.7.2 interpreter issue.  I've embedded the interpreter in a Visual C++ 2010 application and it essentially just calls user-written scripts.\nMy end-users want to use matplotlib - I've already resolved a number of issues relating to its dependence on numpy - but when they call savefig(), the application crashes with:\n**Fatal Python Error: PyEval_RestoreThread: NULL tstate\nThis isn't an issue running the same script using the standard Python 2.7.2 interpreter, even using the same site-packages, so it seems to definitely be something wrong with my embedding.  I call Py_Initialize() - do I need to do something with setting up Python threads?\nI can't quite get the solution from other questions here to work, but I'm more concerned that this is symptomatic of a wider problem in how I'm setting up the Python interpreter.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2495,"Q_Id":11844628,"Users Score":3,"Answer":"Finally resolved this - so going to explain what occurred for the sake of Googlers!\nThis only happened when using third-party libraries like numpy or matplotlib, but actually related to an error elsewhere in my code.  As part of the software I wrote, I was extending the Python interpreter following the same basic pattern as shown in the Python C API documentation.\nAt the end of this code, I called the Py_DECREF function on some of the Python objects I had created along the way.  My mistake was that I was calling this function on borrowed references, which should not be done.\nThis caused the software to crash with the error above when it reached the Py_Finalize command that I used to clean up.  Removing the DECREF on the borrowed references fixed this error.","Q_Score":1,"Tags":"python,c,matplotlib","A_Id":13551794,"CreationDate":"2012-08-07T11:08:00.000","Title":"Matplotlib with TkAgg error: PyEval_RestoreThread: null tstate on save_fig() - do I need threads enabled?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working with survey data loaded from an h5-file as hdf = pandas.HDFStore('Survey.h5') through the pandas package. Within this DataFrame, all rows are the results of a single survey, whereas the columns are the answers for all questions within a single survey. \nI am aiming to reduce this dataset to a smaller DataFrame including only the rows with a certain depicted answer on a certain question, i.e. with all the same value in this column. I am able to determine the index values of all rows with this condition, but I can't find how to delete this rows or make a new df with these rows only.","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":92185,"Q_Id":11881165,"Users Score":0,"Answer":"If you just need to get the top rows; you can use df.head(10)","Q_Score":46,"Tags":"python,pandas,slice","A_Id":71602253,"CreationDate":"2012-08-09T10:15:00.000","Title":"Slice Pandas DataFrame by Row","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there an equivalent MATLAB function for the range() function in Python?\nI'd really like to be able to type something like range(-10, 11, 5) and get back [-10, -5, 0, 5, 10] instead of having to write out the entire range by hand.","AnswerCount":3,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":21375,"Q_Id":11890437,"Users Score":15,"Answer":"Yes, there is the : operator. The command -10:5:11 would produce the vector [-10, -5, 0, 5, 10];","Q_Score":10,"Tags":"python,matlab","A_Id":11890470,"CreationDate":"2012-08-09T19:17:00.000","Title":"Is there an equivalent of the Python range function in MATLAB?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have two large 2-d arrays and I'd like to find their set difference taking their rows as elements. In Matlab, the code for this would be setdiff(A,B,'rows'). The arrays are large enough that the obvious looping methods I could think of take too long.","AnswerCount":3,"Available Count":1,"Score":-0.0665680765,"is_accepted":false,"ViewCount":13283,"Q_Id":11903083,"Users Score":-1,"Answer":"I'm not sure what you are going for, but this will get you a boolean array of where 2 arrays are not equal, and will be numpy fast:\n\nimport numpy as np\na = np.random.randn(5, 5)\nb = np.random.randn(5, 5)\na[0,0] = 10.0\nb[0,0] = 10.0 \na[1,1] = 5.0\nb[1,1] = 5.0\nc = ~(a-b==0)\nprint c\n[[False  True  True  True  True]\n [ True False  True  True  True]\n [ True  True  True  True  True]\n [ True  True  True  True  True]\n [ True  True  True  True  True]]","Q_Score":18,"Tags":"python,numpy,set-difference","A_Id":11903766,"CreationDate":"2012-08-10T13:50:00.000","Title":"Find the set difference between two large arrays (matrices) in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I was bummed out to see that scikit-learn does not support Python 3...Is there a comparable package anyone can recommend for Python 3?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":3322,"Q_Id":11910481,"Users Score":0,"Answer":"Old question, Scikit-Learn is supported by Python3 now.","Q_Score":19,"Tags":"python,python-3.x,machine-learning,scikit-learn","A_Id":58766819,"CreationDate":"2012-08-10T23:24:00.000","Title":"Best Machine Learning package for Python 3x?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am looking for some methods for detecting movement. I've tried two of them. One method is to have background frame that has been set on program start and other frames are compared (threshold) to that background frame. Other method is to compare current frame (let's call that frame A) with frame-1 (frame before A). None of these methods are great. I want to know other methods that work better.","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":637,"Q_Id":11925782,"Users Score":1,"Answer":"Please go through the book Learning OpenCV: Computer Vision with the OpenCV Library\nIt has theory as well as example codes.","Q_Score":0,"Tags":"python,opencv","A_Id":11926117,"CreationDate":"2012-08-12T20:48:00.000","Title":"What are some good methods for detecting movement using a camera? (opencv)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"So I have a video with 3 green spots on it. These spots have a bunch of \"good features to track\" around their perimeter.\nThe spots are very far away from each other so using KMeans I am easily able to identify them as separate clusters.\nThe problem comes in that the ordering of the clusters changes from frame to frame. In one frame a particular cluster is the first in the output list. In the next cluster it is the second in the output list.\nIt is making for a difficult time measuring angles.\nHas anyone come across this or can think of a fix other than writing extra code to compare each list to the list of the previous frame?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":232,"Q_Id":11944796,"Users Score":1,"Answer":"Since k-means is a randomized approach, you will probably encounter this problem even when analyzing the same frame multiple times.\nTry to use the previous frames cluster centers as initial centers for k-means. This may make the ordering stable enough for you, and it may even significantly speed up k-means (assuming that the green spots don't move too fast).\nAlternatively, just try reordering the means so that they are closest to the previous images means.","Q_Score":2,"Tags":"python,opencv,cluster-analysis,k-means","A_Id":12489308,"CreationDate":"2012-08-14T02:02:00.000","Title":"cv.KMeans2 clustering indices inconsistent","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to understand the effect of resize() function on numpy array vs. an h5py dataset. In my application, I am reading a text file line by line and then after parsing the data, write into an hdf5 file. What would be a good approach to implement this. Should I add each new row into a numpy array and keep resizing (increasing the axis) for numpy array (eventually writing the complete numpy array into h5py dataset) or should I just add each new row data into h5py dataset directly and thus resizing the h5py dataset in memory. How does resize() function affects the performance if we keep resizing after each row? Or should I resize after every 100 or 1000 rows? \nThere can be around 200,000 lines in each dataset. \nAny help is appreciated.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":1987,"Q_Id":11979316,"Users Score":1,"Answer":"NumPy arrays are not designed to be resized. It's doable, but wasteful in terms of memory (because you need to create a second array larger than your first one, then fill it with your data... That's two arrays you have to keep) and of course in terms of time (creating the temporary array).\nYou'd be better off starting with lists (or regular arrays, as suggested by @HYRY), then convert to ndarrays when you have a chunk big enough. \nThe question is, when do you need to do the conversion ?","Q_Score":3,"Tags":"python,numpy,h5py","A_Id":11998662,"CreationDate":"2012-08-16T00:57:00.000","Title":"efficient way to resize numpy or dataset?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to use sklearn to predict a variable that represents rotation. Because of the unfortunate jump from -pi to pi at the extremes of rotation, I think a much better method would be to use a complex number as the target. That way an error from 1+0.01j to 1-0.01j is not as devastating.\nI cannot find any documentation that describes whether sklearn supports complex numbers as targets to classifiers. In theory the distance metric should work just fine, so it should work for at least some regression algorithms.\nCan anyone suggest how I can get a regression algorithm to operate with complex numbers as targets?","AnswerCount":3,"Available Count":3,"Score":1.2,"is_accepted":true,"ViewCount":2406,"Q_Id":11999147,"Users Score":2,"Answer":"So far I discovered that most classifiers, like linear regressors, will automatically convert complex numbers to just the real part.\nkNN and RadiusNN regressors, however, work well - since they do a weighted average of the neighbor labels and so handle complex numbers gracefully.\nUsing a multi-target classifier is another option, however I do not want to decouple the x and y directions since that may lead to unstable solutions as Colonel Panic mentions, when both results come out close to 0.\nI will try other classifiers with complex targets and update the results here.","Q_Score":7,"Tags":"python,numpy,scipy,scikit-learn","A_Id":12011024,"CreationDate":"2012-08-17T02:48:00.000","Title":"Is it possible to use complex numbers as target labels in scikit learn?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to use sklearn to predict a variable that represents rotation. Because of the unfortunate jump from -pi to pi at the extremes of rotation, I think a much better method would be to use a complex number as the target. That way an error from 1+0.01j to 1-0.01j is not as devastating.\nI cannot find any documentation that describes whether sklearn supports complex numbers as targets to classifiers. In theory the distance metric should work just fine, so it should work for at least some regression algorithms.\nCan anyone suggest how I can get a regression algorithm to operate with complex numbers as targets?","AnswerCount":3,"Available Count":3,"Score":0.0665680765,"is_accepted":false,"ViewCount":2406,"Q_Id":11999147,"Users Score":1,"Answer":"Good question. How about transforming angles into a pair of labels, viz. x and y co-ordinates. These are continuous functions of angle (cos and sin). You can combine the results from separate x and y classifiers for an angle? $\\theta = \\sign(x) \\arctan(y\/x)$. However that result will be unstable if both classifiers return numbers near zero.","Q_Score":7,"Tags":"python,numpy,scipy,scikit-learn","A_Id":12003586,"CreationDate":"2012-08-17T02:48:00.000","Title":"Is it possible to use complex numbers as target labels in scikit learn?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to use sklearn to predict a variable that represents rotation. Because of the unfortunate jump from -pi to pi at the extremes of rotation, I think a much better method would be to use a complex number as the target. That way an error from 1+0.01j to 1-0.01j is not as devastating.\nI cannot find any documentation that describes whether sklearn supports complex numbers as targets to classifiers. In theory the distance metric should work just fine, so it should work for at least some regression algorithms.\nCan anyone suggest how I can get a regression algorithm to operate with complex numbers as targets?","AnswerCount":3,"Available Count":3,"Score":0.2605204458,"is_accepted":false,"ViewCount":2406,"Q_Id":11999147,"Users Score":4,"Answer":"Several regressors support multidimensional regression targets. Just view the complex numbers as 2d points.","Q_Score":7,"Tags":"python,numpy,scipy,scikit-learn","A_Id":12004759,"CreationDate":"2012-08-17T02:48:00.000","Title":"Is it possible to use complex numbers as target labels in scikit learn?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Suppose I have two classes, say Manager and Graph, where each Graph has a reference to its manager, and each Manager has references to a collection of graphs that it owns. I want to be able to do two things\n1) Copy a graph, which performs a deepcopy except that the new graph references the same manager as the old one.\n2) Copy a manager, which creates a new manager and also copies all the graphs it owns.\nWhat is the best way to do this? I don't want to have to roll my own deepcopy implementation, but the standard copy.deepcopy doesn't appear to provide this level of flexibility.","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1009,"Q_Id":12014042,"Users Score":1,"Answer":"If there are no other objects referenced in graph (just simple fields), then copy.copy(graph) should make a copy, while copy.deepcopy(manager) should copy the manager and its graphs, assuming there is a list such as manager.graphs.\nBut in general you are right, the copy module does not have this flexibility, and for slightly fancy situations you'd probably need to roll your own.","Q_Score":2,"Tags":"python,deep-copy","A_Id":12014071,"CreationDate":"2012-08-17T22:34:00.000","Title":"Python muliple deepcopy behaviors","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am currently working on a project which involves performing a lot of statistical calculations on many relatively small datasets. Some of these calculations are as simple as computing a moving average, while others involve slightly more work, like Spearman's Rho or Kendell's Tau calculations. \nThe datasets are essentially a series of arrays packed into a dictionary, whose keys relate to a document id in MongoDb that provides further information about the subset. Each array in the dictionary has no more than 100 values. The dictionaries, however, may be infinitely large. In all reality however, around 150 values are added each year to the dictionary.\nI can use mapreduce to perform all of the necessary calculations. Alternately, I can use Celery and RabbitMQ on a distributed system, and perform the same calculations in python.\nMy question is this: which avenue is most recommended or best-practice?\nHere is some additional information:\n\nI have not benchmarked anything yet, as I am just starting the process of building the scripts to compute the metrics for each dataset. \nUsing a celery\/rabbitmq distributed queue will likely increase the number of queries made against the Mongo database. \nI do not envision the memory usage of either method being a concern, unless the number of simultaneous tasks is very large. The majority of the tasks themselves are merely taking an item within a dataset, loading it, doing a calculation, and then releasing it. So even if the amount of data in a dataset is very large, not all of it will be loaded into memory at one time. Thus, the limiting factor, in my mind, comes down to the speed at which mapreduce or a queued system can perform the calculations. Additionally, it is dependent upon the number of concurrent tasks.\n\nThanks for your help!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":944,"Q_Id":12045278,"Users Score":0,"Answer":"It's impossible to say without benchmarking for certain, but my intuition leans toward doing more calculations in Python rather than mapreduce. My main concern is that mapreduce is single-threaded: One MongoDB process can only run one Javascript function at a time. It can, however, serve thousands of queries simultaneously, so you can take advantage of that concurrency by querying MongoDB from multiple Python processes.","Q_Score":1,"Tags":"python,mongodb,mapreduce,celery,distributed-computing","A_Id":12079563,"CreationDate":"2012-08-20T21:13:00.000","Title":"Data analysis using MapReduce in MongoDb vs a Distributed Queue using Celery & RabbitMq","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"The Numpy 'modulus' function is used in a code to check if a certain time is an integral multiple of the time-step.\nBut some weird behavior is seeen. \n\nnumpy.mod(121e-12,1e-12) returns 1e-12\nnumpy.mod(60e-12,1e-12) returns 'a very small value' (compared to 1e-12).\n\nIf you play around numpy.mode('122-126'e-12,1e-12) it gives randomly 0 and 1e-12.\nCan someone please explain why?\nThanks much","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":4240,"Q_Id":12150513,"Users Score":0,"Answer":"According to the doc, np.mod(x1,x2)=x1-floor(x1\/x2)*x2. The problem here is that you are working with very small values, a dark domain where floating point errors (truncation...) happen quite often and results are often unpredictable...\nI don't think you should spend a lot of time worrying about that.","Q_Score":0,"Tags":"python,numpy,scipy","A_Id":12150625,"CreationDate":"2012-08-27T22:31:00.000","Title":"Numpy\/Scipy modulus function","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a text file with five columns. First column has year(2011 to 2040), 2nd has Tmax, 3rd has Tmin, 4th has Precip, and fifth has Solar for 30 years. I would like to write a python code which shuffles the first column (year) 10 times with remaining columns having the corresponding original values in them, that is: I want to shuffle year columns only for 10 times so that year 1 will have the corresponding values.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1120,"Q_Id":12150908,"Users Score":0,"Answer":"Are you familiar with NumPy ? Once you have your data in a numpy ndarray, it's a breeze to shuffle the rows while keeping the column orders, without the hurdle of creating many temporaries.\nYou could use a function like np.genfromtxt to read your data file and create a ndarray with different named fields. You could then use the np.random.shuffle function to reorganize the rows.","Q_Score":2,"Tags":"python-2.7","A_Id":12151167,"CreationDate":"2012-08-27T23:27:00.000","Title":"How do I shuffle in Python for a column (years) with keeping the corrosponding column values?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm looking for the fastest algorithm\/package i could use to compute the null space of an extremely large (millions of elements, and not necessarily square) matrix. Any language would be alright, preferably something in Python\/C\/C++\/Java. Your help would be greatly appreciated!","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":766,"Q_Id":12161182,"Users Score":2,"Answer":"The manner to avoid trashing CPU caches greatly depends on how the matrix is stored\/loaded\/transmitted, a point that you did not address.\nThere are a few generic recommendations:\n\ndivide the problem into worker threads addressing contiguous rows per threads\nincrement pointers (in C) to traverse rows and keep the count on a per-thread basis\nconsolidate the per-thread results at the end of all worker threads.\n\nIf your matrix cells are made of bits (instead of bytes, ints, or arrays) then you can read words (either 4-byte or 8-byte on 32-bit\/64-bit platforms) to speedup the count.\nThere are too many questions left unanswered in the problem description to give you any further guidance.","Q_Score":0,"Tags":"c++,python,c,algorithm,matrix","A_Id":12161433,"CreationDate":"2012-08-28T14:12:00.000","Title":"Computing the null space of a large matrix","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm looking for the fastest algorithm\/package i could use to compute the null space of an extremely large (millions of elements, and not necessarily square) matrix. Any language would be alright, preferably something in Python\/C\/C++\/Java. Your help would be greatly appreciated!","AnswerCount":2,"Available Count":2,"Score":-0.0996679946,"is_accepted":false,"ViewCount":766,"Q_Id":12161182,"Users Score":-1,"Answer":"In what kind of data structure is your matrix represented? \nIf you use an element list to represent the matrix, i.e. \"column, row, value\" tuple for one matrix element, then the solution would be just count the number of the tuples (subtracted by the matrix size)","Q_Score":0,"Tags":"c++,python,c,algorithm,matrix","A_Id":12161500,"CreationDate":"2012-08-28T14:12:00.000","Title":"Computing the null space of a large matrix","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have two pandas arrays, A and B, that result from groupby operations. A has a 2-level multi-index consisting of both quantile and date. B just has an index for date.\nBetween the two of them, the date indices match up (within each quantile index for A).\nIs there a standard Pandas function or idiom to \"broadcast\" B such that it will have an extra level to its multi-index that matches the first multi-index level of A?","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":1335,"Q_Id":12167324,"Users Score":2,"Answer":"If you just want to do simple arithmetic operations, I think something like A.div(B, level='date') should work.\nAlternatively, you can do something like B.reindex(A.index, level='date') to manually match the indices.","Q_Score":6,"Tags":"python,arrays,pandas,multi-index","A_Id":12170479,"CreationDate":"2012-08-28T20:49:00.000","Title":"How to broadcast to a multiindex","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to use matplotlib from my Sublime Text 2 directly via the build command.\nDoes anybody know how I accomplish that? I'm really confused about the whole multiple python installations\/environments. Google didn't help.\nMy python is installed via homebrew and in my terminal (which uses brew python), I have no problem importing matplotlib from there. But Sublime Text shows me an import Error (No module named matplotlib.pyplot).\nI have installed Matplotlib via EPD free. The main matplotlib .dmg installer refused to install it on my disk, because no system version 2.7 was found. I have given up to understand the whole thing. I just want it to work.\nAnd, I have to say, for every bit of joy python brings with it, the whole thing with installations and versions and path, environments is a real hassle. \nBeneath a help for this specific problem I would appreciate any helpful link to understand and this environment mess.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":3245,"Q_Id":12173541,"Users Score":5,"Answer":"I had the same problem and the following fix worked for me:\n1 - Open Sublime Text 2 -> Preferences -> Browse Packages\n2 - Go to the Python folder, select file Python.sublime-build\n3 - Replace the existing cmd line for this one:\n\n\"cmd\": [\"\/Library\/Frameworks\/Python.framework\/Versions\/Current\/bin\/python\", \"$file\"],\n\nThen click CMD+B and your script with matplotlib stuff will work.","Q_Score":3,"Tags":"python,matplotlib,sublimetext2,sublimetext","A_Id":12610165,"CreationDate":"2012-08-29T08:16:00.000","Title":"MatPlotLib with Sublime Text 2 on OSX","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using Gaussian Mixture Model from python scikit-learn package to train my dataset , however , I fount that when I code\n-- G=mixture.GMM(...)\n-- G.fit(...)\n-- G.score(sum feature)\nthe resulting log probability is positive real number... why is that?\nisn't log probability guaranteed to be negative?\nI get it. what Gaussian Mixture Model returns to us i the log probability \"density\" instead of probability \"mass\" so positive value is totally reasonable.\nIf the covariance matrix is near to singular, then the GMM will not perfomr well, and generally it means the data is not good for such generative task","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":5301,"Q_Id":12175404,"Users Score":13,"Answer":"Positive log probabilities are okay.\nRemember that the GMM computed probability is a probability density function (PDF), so can be greater than one at any individual point.\nThe restriction is that the PDF must integrate to one over the data domain.\nIf the log probability grows very large, then the inference algorithm may have reached a degenerate solution (common with maximum likelihood estimation if you have a small dataset).\nTo check that the GMM algorithm has not reached a degenerate solution, you should look at the variances for each component. If any of the variances is close to zero, then this is bad. As an alternative, you should use a Bayesian model rather than maximum likelihood estimation (if you aren't doing so already).","Q_Score":7,"Tags":"python,machine-learning,scikit-learn,mixture-model","A_Id":12199026,"CreationDate":"2012-08-29T10:01:00.000","Title":"scikit-learn GMM produce positive log probability","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to write UDFs in Apache Pig. I'll be using Python UDFs. \nMy issue is I have tons of data to analyse and need packages like NumPy and SciPy. Buy this they dont have Jython support I cant use them along with Pig. \nDo we have a substitue ?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":524,"Q_Id":12183759,"Users Score":0,"Answer":"You can stream through a (C)Python script that imports scipy.\nI am for instance using this to cluster data inside bags, using import scipy.cluster.hierarchy","Q_Score":1,"Tags":"python,numpy,scipy,apache-pig","A_Id":12618627,"CreationDate":"2012-08-29T17:55:00.000","Title":"Using Numpy and SciPy on Apache Pig","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I am trying to optimize (memorywise the multiplication of X and its transpose X'\nDoes anyone know if numpys matrix multiplication takes into consideration that X' is just the transpose of X. What I mean is that if it detects this and therfore does not create the object X' but just works on the cols\/rows of X to produce the product? Thank you for any help on this!\nJ.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":505,"Q_Id":12185117,"Users Score":3,"Answer":"In numpy convention, the transpose of X is represented byX.T and you're in luck, X.T is just a view of the original array X, meaning that no copy is done.","Q_Score":1,"Tags":"python,matrix,numpy,python-2.7,matrix-multiplication","A_Id":12185246,"CreationDate":"2012-08-29T19:26:00.000","Title":"Python - Numpy matrix multiplication","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I can't find any reference on funcionality to perform Johansen cointegration test in any Python module dealing with statistics and time series analysis (pandas and statsmodel). Does anybody know if there's some code around that can perform such a test for cointegration among time series?","AnswerCount":4,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":18067,"Q_Id":12186994,"Users Score":9,"Answer":"statsmodels doesn't have a Johansen cointegration test. And, I have never seen it in any other python package either.\nstatsmodels has VAR and structural VAR, but no VECM (vector error correction models) yet.\nupdate: \nAs Wes mentioned, there is now a pull request for Johansen's cointegration test for statsmodels. I have translated the matlab version in LeSage's spatial econometrics toolbox and wrote a set of tests to verify that we get the same results. \nIt should be available in the next release of statsmodels.\nupdate 2:\nThe test for cointegration coint_johansen was included in statsmodels 0.9.0 together with the vector error correction models VECM.\n(see also 3rd answer)","Q_Score":13,"Tags":"python,statistics,pandas,statsmodels","A_Id":12187770,"CreationDate":"2012-08-29T21:47:00.000","Title":"Johansen cointegration test in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've seen several other topics on whether to use 2.x or 3.x. However, most of these are at least two years old and do not distinguish between 2.6 and 2.7. \nI am rebooting a scientific project that I ultimately may want to release by 2013. I make use of numpy, scipy, and pylab, among standard 2.6+ modules like itertools. Which version, 2.6 or 2.7, would be better for this?\nThis would also clear up whether or not to use optparse when making my scripts. \nEdit: I am working at a university and the workstation I picked up had Python 2.4. Picking between 2.6 and 2.7 determines which distro to upgrade to. Thanks for the advice!","AnswerCount":5,"Available Count":3,"Score":0.0399786803,"is_accepted":false,"ViewCount":169,"Q_Id":12187115,"Users Score":1,"Answer":"I personally use Debian stable for my own projects so naturally I gravitate toward what the distribution uses as the default Python installation.  For Squeeze (current stable), it's 2.6.6 but Wheezy will use 2.7.\nWhy is this relevant?  Well, as a programmer there are a number of times I wish I had access to new features from more recent versions of Python, but Debian in general is so conservative that I find it's a good metric of covering wider audience who may be running an older OS.\nSince Wheezy probably will become stable by the end of the year (or earlier next year), I'll be moving to 2.7 as well.","Q_Score":2,"Tags":"python,numpy,version,scipy,optparse","A_Id":12205078,"CreationDate":"2012-08-29T21:59:00.000","Title":"Open Source Scientific Project - Use Python 2.6 or 2.7?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've seen several other topics on whether to use 2.x or 3.x. However, most of these are at least two years old and do not distinguish between 2.6 and 2.7. \nI am rebooting a scientific project that I ultimately may want to release by 2013. I make use of numpy, scipy, and pylab, among standard 2.6+ modules like itertools. Which version, 2.6 or 2.7, would be better for this?\nThis would also clear up whether or not to use optparse when making my scripts. \nEdit: I am working at a university and the workstation I picked up had Python 2.4. Picking between 2.6 and 2.7 determines which distro to upgrade to. Thanks for the advice!","AnswerCount":5,"Available Count":3,"Score":0.0798297691,"is_accepted":false,"ViewCount":169,"Q_Id":12187115,"Users Score":2,"Answer":"If you intend to distribute this code, your answer depends on your target audience, actually. A recent stint in some private sector research lab showed me that Python 2.5 is still often use. \nAnother example: EnSight, a commercial package for 3D visualization\/manipulation, ships with Python 2.5 (and NumPy 1.3 or 1.4, if I'm not mistaken).\nFor a personal project, I'd shoot for 2.7. For a larger audience, I'd err towards 2.6.","Q_Score":2,"Tags":"python,numpy,version,scipy,optparse","A_Id":12187327,"CreationDate":"2012-08-29T21:59:00.000","Title":"Open Source Scientific Project - Use Python 2.6 or 2.7?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've seen several other topics on whether to use 2.x or 3.x. However, most of these are at least two years old and do not distinguish between 2.6 and 2.7. \nI am rebooting a scientific project that I ultimately may want to release by 2013. I make use of numpy, scipy, and pylab, among standard 2.6+ modules like itertools. Which version, 2.6 or 2.7, would be better for this?\nThis would also clear up whether or not to use optparse when making my scripts. \nEdit: I am working at a university and the workstation I picked up had Python 2.4. Picking between 2.6 and 2.7 determines which distro to upgrade to. Thanks for the advice!","AnswerCount":5,"Available Count":3,"Score":1.2,"is_accepted":true,"ViewCount":169,"Q_Id":12187115,"Users Score":9,"Answer":"If everything you need would work with 2.7 I would use it, no point staying with 2.6. Also, .format() works a bit nicer (no need to specify positions in the {} for the arguments to the formatting directives). \nFWIW, I usually use 2.7 or 3.2 and every once in a while I end up porting some code to my Linux box which still runs 2.6.5 and the format() thing is annoying enough :)\n2.7 has been around enough to be supported well - and 3.x is hopefully getting there too.","Q_Score":2,"Tags":"python,numpy,version,scipy,optparse","A_Id":12187140,"CreationDate":"2012-08-29T21:59:00.000","Title":"Open Source Scientific Project - Use Python 2.6 or 2.7?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a device that is connected to my Mac via bluetooth. I would like to use R (or maybe Python, but R is preferred) to read the data real-time and process it. Does anyone know how I can do the data streaming using R on a Mac?\nCheers","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":377,"Q_Id":12187795,"Users Score":0,"Answer":"there is a strong probability that you can enumerate the bluetooth as serial port for the bluetooth and use pyserial module to communicate pretty easily...\nbut if this device does not enumerate serially you will have a very large headache trying to do this... \nsee if there are any com ports that are available if there are its almost definitely enumerating as a serial connection","Q_Score":0,"Tags":"python,macos,r,bluetooth","A_Id":12187989,"CreationDate":"2012-08-29T23:16:00.000","Title":"How can I stream data, on my Mac, from a bluetooth source using R?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"When get_range_slice returns, in what order are the columns returned? Is it random or the order in which the columns were created? Is it best practice to iterate through all resulting columns for each row and compare the column name prior to using the value or can one just index into the returning array?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":161,"Q_Id":12212321,"Users Score":4,"Answer":"The columns for each row will be returned in sorted order, sorted by the column key, depending on you comparator_type.  The row ordering will depend on your partitioner, and if you use the random partitioner, the rows will come back in a 'random' order.\nIn Cassandra, it is possible for each row to have a different set of columns, so you should really read the column key before using the value.  This will depend on the data you have inserted into you cluster.","Q_Score":2,"Tags":"c#,java,c++,python,cassandra","A_Id":12212637,"CreationDate":"2012-08-31T09:14:00.000","Title":"Cassandra get_range_slice","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have been experimenting on making a random map for a top down RPG I am making in Python. (and Pyglet) So far I have been making island by starting at 0,0 and going in a random direction 500 times (x+=32 or y -=32 sort of thing) However this doesn't look like a real image very much so I had a look at the Perlin Noise approach. How would I get a randomly generated map out of this :\/ (preferably an island) and is it better than the random direction method?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":5966,"Q_Id":12232901,"Users Score":0,"Answer":"You could also use 1d perlin noise to calculate the radius from each point to the \"center\" of the island. It should be really easy to implement, but it will make more circular islands, and won't give each point different heights.","Q_Score":1,"Tags":"python,pyglet,terrain,perlin-noise","A_Id":14346374,"CreationDate":"2012-09-02T02:01:00.000","Title":"How to make a 2d map with perlin noise python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am die hard fan of Artificial intelligence and machine learning. I don't know much about them but i am ready to learn. I am currently a web programmer in PHP , and  I am learning python\/django for a website.\nNow as this AI field is very wide and there are countless algorithms I don't know where to start.\nBut eventually my main target is to use whichever algorithms; like Genetic Algorithms , Neural networks , optimization which can be programmed in web application to show some stuff.\nFor Example : Recommendation of items in amazon.com\nNow what I want is that in my personal site I have the demo of each algorithm where if I click run and I can show someone what this algorithm can do.\nSo can anyone please guide which algorithms should I study for web based applications.\nI see lot of example in sci-kit python library but they are very calculation and graph based.\nI don't think I can use them from web point of view.\nAny ideas how should I go?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1093,"Q_Id":12242054,"Users Score":1,"Answer":"I assume you are mostly concerned with a general approach to implementing AI in a web context, and not in the details of the AI algorithms themselves. Any computable algorithm can be implemented in any turing complete language (i.e.all modern programming languages). There's no special limitations for what you can do on the web, it's just a matter of representation, and keeping track of session-specific data, and shared data. Also, there is no need to shy away from \"calculation\" and \"graph based\" algorithms; most AI-algorithms will be either one or the other  (or indeed both) - and that's part of the fun.\nFor example, as an overall approach for a neural net, you could:\n\nImplement a standard neural network using python classes\nPossibly train the set with historical data\nLoad the state of the net on each request (i.e. from a pickle)\nFeed a part of the request string (i.e. a product-ID) to the net, and output the result (i.e. a weighted set of other products, like \"users who clicked this, also clicked this\")\nAlso, store the relevant part of the request (i.e. the product-ID) in a session variable (i.e. \"previousProduct\"). When a new request (i.e. for another product) comes in from the same user, strengthen\/create the connection between the first product and the next.\nSave the state of the net between each request (i.e. back to pickle)\n\nThat's just one, very general example. But keep in mind - there is nothing special about web-programming in this context, except keeping track of session-specific data, and shared data.","Q_Score":0,"Tags":"python,web,machine-learning,artificial-intelligence","A_Id":12243670,"CreationDate":"2012-09-03T04:37:00.000","Title":"What algorithms i can use from machine learning or Artificial intelligence which i can show via web site","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I would like to integrate a function in python and provide the probability density (measure) used to sample values. If it's not obvious, integrating f(x)dx in [a,b] implicitly use the uniform probability density over [a,b], and I would like to use my own probability density (e.g. exponential).\nI can do it myself, using np.random.* but then \n\nI miss the optimizations available in scipy.integrate.quad. Or maybe all those optimizations assume the uniform density?\nI need to do the error estimation myself, which is not trivial. Or maybe it is? Maybe the error is just the variance of sum(f(x))\/n?\n\nAny ideas?","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":545,"Q_Id":12245859,"Users Score":0,"Answer":"Another possibilty would be to integrate x -> f( H(x)) where H is the inverse of the cumulative distribution of your probability distribtion. \n[This is because of change of variable: replacing y=CDF(x) and noting that p(x)=CDF'(x) yields the change dy=p(x)dx and thus int{f(x)p(x)dx}==int{f(x)dy}==int{f(H(y))dy with H the inverse of CDF.]","Q_Score":1,"Tags":"python,numpy,scipy,probability,numerical-methods","A_Id":12283724,"CreationDate":"2012-09-03T10:14:00.000","Title":"Integrating a function using non-uniform measure (python\/scipy)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to integrate a function in python and provide the probability density (measure) used to sample values. If it's not obvious, integrating f(x)dx in [a,b] implicitly use the uniform probability density over [a,b], and I would like to use my own probability density (e.g. exponential).\nI can do it myself, using np.random.* but then \n\nI miss the optimizations available in scipy.integrate.quad. Or maybe all those optimizations assume the uniform density?\nI need to do the error estimation myself, which is not trivial. Or maybe it is? Maybe the error is just the variance of sum(f(x))\/n?\n\nAny ideas?","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":545,"Q_Id":12245859,"Users Score":0,"Answer":"Just for the sake of brevity, 3 ways were suggested for calculating the expected value of f(x) under the probability p(x):\n\nAssuming p is given in closed-form, use scipy.integrate.quad to evaluate f(x)p(x)\nAssuming p can be sampled from, sample N values x=P(N), then evaluate the expected value by np.mean(f(X)) and the error by np.std(f(X))\/np.sqrt(N)\nAssuming p is available at stats.norm, use stats.norm.expect(f)\nAssuming we have the CDF(x) of the distribution rather than p(x), calculate H=Inverse[CDF] and then integrate f(H(x)) using scipy.integrate.quad","Q_Score":1,"Tags":"python,numpy,scipy,probability,numerical-methods","A_Id":12268227,"CreationDate":"2012-09-03T10:14:00.000","Title":"Integrating a function using non-uniform measure (python\/scipy)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"how can I clear a complete csv file with python. Most forum entries that cover the issue of deleting row\/columns basically say, write the stuff you want to keep into a new file. I need to completely clear a file - how can I do that?","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":41353,"Q_Id":12277864,"Users Score":1,"Answer":"The Python csv module is only for reading and writing whole CSV files but not for manipulating them. If you need to filter data from file then you have to read it, create a new csv file and write the filtered rows back to new file.","Q_Score":8,"Tags":"python,csv","A_Id":12277912,"CreationDate":"2012-09-05T09:02:00.000","Title":"python clear csv file","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using matplotlib to draw a bar chart with many different colors. I also draw a number of markers on the plot with scatter.   \nSince I am already using many different colors for the bars, I do not want to use a separate contrasting color for the marks, as that would add a big limit to the color space I can choose my bars from.\nTherefore, the question is whether it is possible to have scatter draw marks, not with a given color, but with a color that is the inverse of the color that happens to be behind any given mark, wherever it is placed.   \nAlso, note that the marks may fully overlap bars, partly overlap bars, or not overlap a bar at all.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":385,"Q_Id":12350693,"Users Score":0,"Answer":"I post here a schematic approach how to solve your problem with out any real python code, it might help though.\nWhen  actually plotting you need to store all in some kind of two lists, which will enable you to access them later. \n\nFor each element, bar and marker you can get the color.\nFor each marker you can find if it overlapping or inside a bar, for example you could usenxutils.pntpoly() [point in polygon test] from matplotlib itself.\nNow you can decide on the best color. If you know the color of the bar in RGB format you can\ncalculate the completing color of the marker using some simple rules you can define.\nWhen you got the color use the method set_color() or the appropriate method the object has.","Q_Score":1,"Tags":"python,matplotlib","A_Id":12363743,"CreationDate":"2012-09-10T11:25:00.000","Title":"Inverted color marks in matplotlib","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have data that needs to stay in the exact sequence it is entered in (genome sequencing) and I want to search approximately one billion nodes of around 18 members each to locate patterns.\nObviously speed is an issue with this large of a data set, and I actually don't have any data that I can currently use as a discrete key, since the basis of the search is to locate and isolate (but not remove) duplicates.\nI'm looking for an algorithm that can go through the data in a relatively short amount of time to locate these patterns and similarities, and I can work out the regex expressions for comparison, but I'm not sure how to get a faster search than O(n).\nAny help would be appreciated.\nThanks","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":386,"Q_Id":12470094,"Users Score":0,"Answer":"probably what you want is called \"de novo assembly\"\nan approach would be to calculate N-mers, and use these in an index\nnmers will become more important if you need partial matches \/ mismatches\nif billion := 1E9, python might be too weak\nalso note that 18 bases* 2 bits := 36 bits of information to enumerate them. That is tentavely close to 32 bits and could fit into 64 bits. hashing \/ bitfiddling might be an option","Q_Score":1,"Tags":"python,sql,dna-sequence,genome","A_Id":12474645,"CreationDate":"2012-09-18T03:43:00.000","Title":"Fast algorithm comparing unsorted data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I use imshow function with interpolation='nearest' on a grayscale image and get a nice color picture as a result, looks like it does some sort of color segmentation for me, what exactly is going on there? \nI would also like to get something like this for image processing, is there some function on numpy arrays like interpolate('nearest') out there?\nEDIT: Please correct me if I'm wrong, it looks like it does simple pixel clustering (clusters are colors of the corresponding colormap) and the word 'nearest' says that it takes the nearest colormap color (probably in the RGB space) to decide to which cluster the pixel belongs.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":22970,"Q_Id":12473511,"Users Score":25,"Answer":"interpolation='nearest' simply displays an image without trying to interpolate between pixels if the display resolution is not the same as the image resolution (which is most often the case). It will result an image in which pixels are displayed as a square of multiple pixels.\nThere is no relation between interpolation='nearest' and the grayscale image being displayed in color. By default imshow uses the jet colormap to display an image. If you want it to be displayed in greyscale, call the gray() method to select the gray colormap.","Q_Score":17,"Tags":"python,image-processing,numpy,matplotlib","A_Id":12473913,"CreationDate":"2012-09-18T08:52:00.000","Title":"What does matplotlib `imshow(interpolation='nearest')` do?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'd like to perform some array calculations using NumPy for a view callable in Pyramid.  The array I'm using is quite large (3500x3500), so I'm wondering where the best place to load it is for repeated use.\nRight now my application is a single page and I am using a single view callable.\nThe array will be loaded from disk and will not change.","AnswerCount":2,"Available Count":2,"Score":0.2913126125,"is_accepted":false,"ViewCount":409,"Q_Id":12497545,"Users Score":3,"Answer":"If the array is something that can be shared between threads then you can store it in the registry at application startup (config.registry['my_big_array'] = ??). If it cannot be shared then I'd suggest using a queuing system with workers that can always have the data loaded, probably in another process. You can hack this by making the value in the registry be a threadlocal and then storing a new array in the variable if one is not there already, but then you will have a copy of the array per thread and that's really not a great idea for something that large.","Q_Score":1,"Tags":"python,numpy,pyramid","A_Id":12497790,"CreationDate":"2012-09-19T15:09:00.000","Title":"Using NumPy in Pyramid","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'd like to perform some array calculations using NumPy for a view callable in Pyramid.  The array I'm using is quite large (3500x3500), so I'm wondering where the best place to load it is for repeated use.\nRight now my application is a single page and I am using a single view callable.\nThe array will be loaded from disk and will not change.","AnswerCount":2,"Available Count":2,"Score":0.1973753202,"is_accepted":false,"ViewCount":409,"Q_Id":12497545,"Users Score":2,"Answer":"I would just load it in the obvious place in the code, where you need to use it (in your view, I guess?) and see if you have performance problems. It's better to work with actual numbers than try to guess what's going to be a problem. You'll usually be surprised by the reality.\nIf you do see performance problems, assuming you don't need a copy for each of multiple threads, try just loading it in the global scope after your imports. If that doesn't work, try moving it into its own module and importing that. If that still doesn't help... I don't know what then.","Q_Score":1,"Tags":"python,numpy,pyramid","A_Id":12497850,"CreationDate":"2012-09-19T15:09:00.000","Title":"Using NumPy in Pyramid","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"It would be useful to save the session variables which could be loaded easily into memory at a later stage.","AnswerCount":5,"Available Count":1,"Score":0.0399786803,"is_accepted":false,"ViewCount":10066,"Q_Id":12504951,"Users Score":1,"Answer":"There is also a magic command, history, that can be used to write all the commands\/statements given by user.\nSyntax : %history -f file_name.\nAlso %save file_name start_line-end_line, where star_line is the starting line number and end_line is ending line number. Useful in case of selective save.\n%run can be used to execute the commands in the saved file","Q_Score":22,"Tags":"python,ipython,pandas","A_Id":42903054,"CreationDate":"2012-09-20T01:20:00.000","Title":"Save session in IPython like in MATLAB?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have data points in x,y,z format. They form a point cloud of a closed manifold. How can I interpolate them using R-Project or Python? (Like polynomial splines)","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1960,"Q_Id":12534813,"Users Score":1,"Answer":"By \"compact manifold\" do you mean a lower dimensional function like a trajectory or a surface that is embedded in 3d? You have several alternatives for the surface-problem in R depending on how \"parametric\" or \"non-parametric\" you want to be. Regression splines of various sorts could be applied within the framework of estimating mean f(x,y) and if these values were \"tightly\" spaced you may get a relatively accurate and simple summary estimate.  There are several non-parametric methods such as found in packages 'locfit', 'akima' and 'mgcv'. (I'm not really sure how I would go about statistically estimating a 1-d manifold in 3-space.)   \nEdit: But if I did want to see a 3D distribution and get an idea of whether is was a parametric curve or trajectory, I would reach for package:rgl and just plot it in a rotatable 3D frame.\nIf you are instead trying to form the convex hull (for which the word interpolate is probably the wrong choice), then I know there are 2-d solutions and suspect that searching would find 3-d solutions as well. Constructing the right search strategy will depend on specifics whose absence the 2 comments so far reflects. I'm speculating that attempting to model lower and higher order statistics like the 1st and 99th percentile as a function of (x,y) could be attempted if you wanted to use a regression effort to create boundaries. There is a quantile regression package, 'rq' by Roger Koenker that is well supported.","Q_Score":2,"Tags":"python,r,3d,interpolation,splines","A_Id":12536067,"CreationDate":"2012-09-21T16:54:00.000","Title":"How interpolate 3D coordinates","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've installed new instance of python-2.7.2 with brew. Installed numpy from pip, then from sources. I keep getting \nnumpy.distutils.npy_pkg_config.PkgNotFound: Could not find file(s) ['\/usr\/local\/Cellar\/python\/2.7.2\/Frameworks\/Python.framework\/Versions\/2.7\/lib\/python2.7\/site-packages\/numpy\/core\/lib\/npy-pkg-config\/npymath.ini']\nwhen I try to install scipy, either from sources or by pip, and it drives me mad.\nScipy's binary installer tells me, that python 2.7 is required and that I don't have it (I have 2 versions installed).","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":253,"Q_Id":12565351,"Users Score":0,"Answer":"EPD distribution saved the day.","Q_Score":0,"Tags":"numpy,python-2.7,scipy","A_Id":12616286,"CreationDate":"2012-09-24T12:49:00.000","Title":"Trouble installing scipy on Mac OSX Lion","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm making live video GUI using Python and Glade-3, but I'm finding it hard to convert the Numpy array that I have into something that can be displayed in Glade. The images are in black and white with just a single value giving the brightness of each pixel. I would like to be able to draw over the images in the GUI so I don't know whether there is a specific format I should use (bitmap\/pixmap etc) ?\nAny help would be much appreciated!","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":760,"Q_Id":12580198,"Users Score":2,"Answer":"In the end i decided to create a buffer for the pixels using: \nself.pixbuf = gtk.gdk.Pixbuf(gtk.gdk.COLORSPACE_RGB,0,8,1280,1024)\nI then set the image from the pixel buffer:\nself.liveImage.set_from_pixbuf(self.pixbuf)","Q_Score":2,"Tags":"python,arrays,numpy,gtk,glade","A_Id":12638921,"CreationDate":"2012-09-25T09:37:00.000","Title":"How do you display a 2D numpy array in glade-3 ?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":1,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using Arrayfire on Python and I can't use the af.sum() function since my input array has NaNs in it and it would return NAN as sum.\nUsing numpy.nansum\/numpy.nan_to_num is not an option due to speed problems.\nI just need a way to convert those NaNs to floating point zeros in arrayfire.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":299,"Q_Id":12582140,"Users Score":1,"Answer":"bottleneck is worth looking into.  They have performed several optimizations over the numpy.nanxxx functions which, in my experience, makes it around 5x faster than numpy.","Q_Score":3,"Tags":"python,arrayfire","A_Id":12584253,"CreationDate":"2012-09-25T11:36:00.000","Title":"Check Arrayfire Array against NaNs","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to reconcile two separate dataframes.  Each row within the two dataframes has a unique id that I am using to match the two dataframes.  Without using a loop, how can I reconcile one dataframe against another and vice-versa?  \nI tried merging the two dataframes on an index (unique id) but the problem I run into when I do this is when there are duplicate rows of data.  Is there a way to identify duplicate rows of data and put that data into an array or export it to a CSV?\nYour help is much appreciated.  Thanks.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":745,"Q_Id":12593759,"Users Score":0,"Answer":"Try DataFrame.duplicated and DataFrame.drop_duplicates","Q_Score":1,"Tags":"python,pandas","A_Id":12594030,"CreationDate":"2012-09-26T02:30:00.000","Title":"Pandas Data Reconcilation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm writing some code in fortran (f2py) in order to gain some speed because of a large amount of calculations that would be quite bothering to do in pure Python.\nI was wondering if setting NumPy arrays in Python as order=Fortran will kind of slow down\nthe main python code with respect to the classical C-style order.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":502,"Q_Id":12606027,"Users Score":1,"Answer":"There shouldn't be any slow-down. Since NumPy 1.6, most ufuncs (ie, the basic 'universal' functions) take an optional argument allowing a user to specify the memory layout of her array: by default, it's K, meaning that the 'the element ordering of the inputs (is matched) as closely as possible`. \nSo, everything should be taken care of below the hood.\nAt worst, you could always switch from one order to another with the order parameter of np.array (but that will copy your data and is probably not worth it).","Q_Score":1,"Tags":"python,performance,numpy,f2py","A_Id":12606715,"CreationDate":"2012-09-26T16:12:00.000","Title":"f2py speed with array ordering","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to write an algorithm that would benefit from the GPU's superior hashing capability over the CPU.\nIs PyOpenGL the answer? I don't want to use drawing tools, but simply run a \"vanilla\" python script ported to the GPU.\nI have an ATI\/AMD GPU if that means anything.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":389,"Q_Id":12699376,"Users Score":2,"Answer":"Is PyOpenGL the answer?\n\nNo. At least not in the way you expect it. If your GPU does support OpenGL-4.3 you could use Compute Shaders in OpenGL, but those are not written in Python\n\nbut simply run a \"vanilla\" python script ported to the GPU.\n\nThat's not how GPU computing works. You have to write the shaders of computation kernels in a special language. Either OpenCL or OpenGL Compute Shaders or, specific to NVIDIA, in CUDA.\nPython would then just deliver the framework for getting the GPU computation running.","Q_Score":1,"Tags":"python,opengl","A_Id":12699435,"CreationDate":"2012-10-02T22:30:00.000","Title":"Can normal algos run on PyOpenGL?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Currently I have a list of 110,000 donors in Excel.  One of the pieces of information they give to us is their occupation.  I would like to condense this list down to say 10 or 20 categories that I define.\nNormally I would just chug through this, going line by line, but since I have to do this for a years worth of data, I don't really have the time to do a line by line of 1,000,000+ rows.\nIs there anyway to define my 10 or 20 categories and then have python sort it out from there?  \nUpdate:\nThe data is poorly formatted. People self populate a field either online or on a slip of paper and then mail it into a data processing company. There is a great deal of variance. CEO, Chief Executive, Executive Office, the list goes on. \nI used a SORT UNIQ comand and found that my list has ~13,000 different professions.","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":1266,"Q_Id":12711743,"Users Score":1,"Answer":"I assume that the data are noisy, in the sense that it could just be anything at all, written in. The main difficulty here is going to be how to define the mapping between your input data, and categories, and that is going to involve, in the first place, looking through the data.\nI suggest that you look at what you have, and draw up a list of mappings from input occupations to categories. You can then use pretty much any tool (and if you're using excel, stick with excel) to apply that mapping to each row. Some rows will not fall into any category. You should look at them, and figure out if that is because your mapping is inadequate (e.g. you didn't think of how to deal with veterinarians), or if it is because the data are noisy. If it's noise, you can either deal with the remainder by hand, or try to use some other technique to categorise the data, e.g. regular expressions or some kind of natural language processing library.\nOnce you have figured out what your problem cases are, come back and ask us about them, with sample data, and the code you have been using.\nIf you can't even take the first step in figuring out how to run the mapping, do some research, try to write something, then come back with a specific question about that.","Q_Score":3,"Tags":"python,list","A_Id":12711895,"CreationDate":"2012-10-03T15:27:00.000","Title":"categorizing items in a list with python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am looking to compute similarities between users and text documents using their topic representations. I.e. each document and user is represented by a vector of topics (e.g. Neuroscience, Technology, etc) and how relevant that topic is to the user\/document.\nMy goal is then to compute the similarity between these vectors, so that I can find similar users, articles and recommended articles.\nI have tried to use Pearson Correlation but it ends up taking too much memory and time once it reaches ~40k articles and the vectors' length is around 10k.\nI am using numpy.\nCan you imagine a better way to do this? or is it inevitable (on a single machine)?\nThank you","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1318,"Q_Id":12713797,"Users Score":0,"Answer":"My tricks are using a search engine such as ElasticSearch, and it works very well, and in this way we unified the api of all our recommend systems. Detail is listed as below:\n\nTraining the topic model by your corpus, each topic is an array of words and each of the word is with a probability, and we take the first 6 most probable words as a representation of a topic.\nFor each document in your corpus, we can inference a topic distribution for it, the distribution is an array of probabilities for each topic.\nFor each document, we generate a fake document with the topic distribution and the representation of the topics, for example the size of the fake document is about 1024 words.\nFor each document, we generate a query with the topic distribution and the representation of the topics, for example the size of the query is about 128 words.\n\nAll preparation is finished as above. When you want to get a list of similar articles or others, you can just perform a search:\n\nGet the query for your document, and then perform a search by the query on your fake documents.\n\nWe found this way is very convenient.","Q_Score":3,"Tags":"python,numpy,recommendation-engine,topic-modeling,gensim","A_Id":15066821,"CreationDate":"2012-10-03T17:32:00.000","Title":"Topic-based text and user similarity","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a classifier that I trained using Python's scikit-learn. How can I use the classifier from a Java program? Can I use Jython? Is there some way to save the classifier in Python and load it in Java? Is there some other way to use it?","AnswerCount":6,"Available Count":1,"Score":0.0333209931,"is_accepted":false,"ViewCount":42287,"Q_Id":12738827,"Users Score":1,"Answer":"I found myself in a similar situation.\nI'll recommend carving out a classifier microservice. You could have a classifier microservice which runs in python and then expose calls to that service over some RESTFul API yielding JSON\/XML data-interchange format. I think this is a cleaner approach.","Q_Score":35,"Tags":"java,python,jython,scikit-learn","A_Id":50292755,"CreationDate":"2012-10-05T02:50:00.000","Title":"How can I call scikit-learn classifiers from Java?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to come up with a topic-based recommender system to suggest relevant text documents to users.\nI trained a latent semantic indexing model, using gensim, on the wikipedia corpus. This lets me easily transform documents into the LSI topic distributions. My idea now is to represent users the same way. However, of course, users have a history of viewed articles, as well as ratings of articles.\nSo my question is: how to represent the users?\nAn idea I had is the following: represent a user as the aggregation of all the documents viewed. But how to take into account the rating?\nAny ideas?\nThanks","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":602,"Q_Id":12763608,"Users Score":0,"Answer":"\"represent a user as the aggregation of all the documents viewed\" : that might work indeed, given that you are in linear spaces. You can easily add all the documents vectors in one big vector.\nIf you want to add the ratings, you could simply put a coefficient in the sum.\nSay you group all documents rated 2 in a vector D2, rated 3 in D3 etc... you then simply define a user vector as U=c2*D2+c3*D3+...\nYou can play with various forms for c2, c3, but the easiest approach would be to simply multiply by the rating, and divide by the max rating for normalisation reasons. \nIf your max rating is 5, you could define for instance c2=2\/5, c3=3\/5 ...","Q_Score":2,"Tags":"python,machine-learning,recommendation-engine,latent-semantic-indexing,topic-modeling","A_Id":14583682,"CreationDate":"2012-10-06T20:31:00.000","Title":"User profiling for topic-based recommender system","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to come up with a topic-based recommender system to suggest relevant text documents to users.\nI trained a latent semantic indexing model, using gensim, on the wikipedia corpus. This lets me easily transform documents into the LSI topic distributions. My idea now is to represent users the same way. However, of course, users have a history of viewed articles, as well as ratings of articles.\nSo my question is: how to represent the users?\nAn idea I had is the following: represent a user as the aggregation of all the documents viewed. But how to take into account the rating?\nAny ideas?\nThanks","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":602,"Q_Id":12763608,"Users Score":1,"Answer":"I don't think that's working with lsa. \nBut you maybe could do some sort of k-NN classification, where each user's coordinates are the documents viewed. Each object (=user) sends out radiation (intensity is inversely proportional to the square of the distance). The intensity is calculated from the ratings on the single documents.\nThen you can place a object (user) in in this hyperdimensional space, and see what other users give the most 'light'.\nBut: Can't Apache Lucene do that whole stuff for you?","Q_Score":2,"Tags":"python,machine-learning,recommendation-engine,latent-semantic-indexing,topic-modeling","A_Id":12764041,"CreationDate":"2012-10-06T20:31:00.000","Title":"User profiling for topic-based recommender system","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I read that Apriori algorithm is used to fetch association rules from the dataset like a set of tuples. It helps us to find the most frequent 1-itemsets, 2-itemsets and so-on. My problem is bit different. I have a dataset, which is a set of tuples, each of varying size - as follows :\n(1, 234, 56, 32)\n(25, 4575, 575, 464, 234, 32)\n. . . different size tuples\nThe domain for entries is huge, which means that I cannot have a binary vector for each tuple, that tells me if item 'x' is present in tuple. Hence, I do not see Apriori algorithm fitting here.\nMy target is to answer questions like :\n\nGive me the ranked list of 5 numbers, that occur with 234 most of the time\nGive me the top 5 subsets of size 'k' that occur most frequently together\n\nRequirements : Exact representation of numbers in output (not approximate), Domain of numbers can be thought of as 1 to 1 billion. \nI have planned to use the simple counting methods, if no standard algorithm fits here. But, if you guys know about some algorithm that can help me, please let me know","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":631,"Q_Id":12803495,"Users Score":1,"Answer":"For Apriori, you do not need to have tuples or vectors. It can be implemented with very different data types. The common data type is a sorted item list, which could as well look like 1 13 712 1928 123945 191823476 stored as 6 integers. This is essentially equivalent to a sparse binary vector and often very memory efficient. Plus, APRIORI is actually designed to run on data sets too large for your main memory!\nScalability of APRIORI is a mixture of the number of transactions and the number of items. Depending of how they are, you might prefer different data structures and algorithms.","Q_Score":1,"Tags":"python,data-mining,graph-algorithm,recommendation-engine,apriori","A_Id":12807402,"CreationDate":"2012-10-09T15:31:00.000","Title":"Algorithms for Mining Tuples of Data on huge sample space","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am having a hard time understanding what scipy.cluster.vq really does!!  \nOn Wikipedia it says Clustering can be used to divide a digital image into distinct regions for border detection or object recognition.\non other sites and books it says we can use clustering methods for clustering images for finding groups of similar images.\nAS i am interested in image processing ,I really need to fully understand what clustering is .\nSo\nCan anyone show me simple examples about using scipy.cluster.vq with images??","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1663,"Q_Id":12808050,"Users Score":0,"Answer":"The second is what clustering is: group objects that are somewhat similar (and that could be images). Clustering is not a pure imaging technique.\nWhen processing a single image, it can for example be applied to colors. This is a quite good approach for reducing the number of colors in an image. If you cluster by colors and pixel coordinates, you can also use it for image segmentation, as it will group pixels that have a similar color and are close to each other. But this is an application domain of clustering, not pure clustering.","Q_Score":1,"Tags":"python,scipy,cluster-analysis","A_Id":12810026,"CreationDate":"2012-10-09T20:39:00.000","Title":"Can anyone provide me with some clustering examples?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have numpy two dimension array P such that P[i, j] >= 0 and all P[i, j] sums to one. How to choose pair of indexes (i, j) with probability P[i, j] ?\nEDIT: I am interested in numpy build function. Is there something for this problem? May be for one dimensional array?","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":1166,"Q_Id":12810499,"Users Score":1,"Answer":"Here's a simple algorithm in python that does what you are expecting.\nLet's take for example a single dimension array P equal to [0.1,0.3,0.4,0.2]. The logic can be extended to any number of dimensions.\nNow we set each element to the sum of all the elements that precede it:\nP => [0, 0.1, 0.4, 0.8, 1]\nUsing a random generator, we generate numbers that are between 0 and 1. Let's say x = 0.2.\nUsing a simple binary search, we can determine that x is between the first element and the second element. We just pick the first element for this value of x.\nIf you look closely, the chance that 0 =< X  < 0.1 is 0.1. The chance that 0.1 =< x < 0.4 is 0.3 and so on.\nFor the 2D array, it is better to convert it to a 1D array, even though, you should be able to implement a 2D array binary search algorithm.","Q_Score":2,"Tags":"python,numpy,statistics","A_Id":12810655,"CreationDate":"2012-10-10T00:56:00.000","Title":"randomly choose pair (i, j) with probability P[i, j] given stochastic matrix P","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've read a paper that uses ngram counts as feature for a classifier, and I was wondering what this exactly means.\nExample text: \"Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam\"\nI can create unigrams, bigrams, trigrams, etc. out of this text, where I have to define on which \"level\" to create these unigrams. The \"level\" can be character, syllable, word, ...\nSo creating unigrams out of the sentence above would simply create a list of all words? \nCreating bigrams would result in word pairs bringing together words that follow each other? \nSo if the paper talks about ngram counts, it simply creates unigrams, bigrams, trigrams, etc. out of the text, and counts how often which ngram occurs? \nIs there an existing method in python's nltk package? Or do I have to implement a version of my own?","AnswerCount":4,"Available Count":1,"Score":-0.049958375,"is_accepted":false,"ViewCount":21407,"Q_Id":12821201,"Users Score":-1,"Answer":"I don't think there is a specific method in nltk to help with this. This isn't tough though. If you have a sentence of n words (assuming you're using word level), get all ngrams of length 1-n, iterate through each of those ngrams and make them keys in an associative array, with the value being the count. Shouldn't be more than 30 lines of code, you could build your own package for this and import it where needed.","Q_Score":14,"Tags":"python,nlp,nltk","A_Id":12821336,"CreationDate":"2012-10-10T14:01:00.000","Title":"What are ngram counts and how to implement using nltk?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have Toronto Stock Exchange stock data in a maxtor hard drive. The data is a TBF file with .dat and .pos components. The .dat file contains all the Stamp format transmission information in binary format.\nI can read .pos file using R. It has 3 column with numbers, which make no sense to me. The data is information on stock and I think it is the result of Streambase.\nI need to get 2007 price, value, and etc. information on some stocks that I am interested in.\nCould you please suggest any way to read the data? Should I use some particular software to make sense of this data?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":188,"Q_Id":12830437,"Users Score":0,"Answer":"Have you looked in using something like FileViewerPro? Its free download tool in order to open files. Also, what windows programs have you tried so far, like notepad, excel?","Q_Score":3,"Tags":"c#,python,sql,database,sml","A_Id":19391151,"CreationDate":"2012-10-11T00:25:00.000","Title":"How to read tbf file with STAMP encryption","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a Python app built with Python, OpenCv and py2exe. \nWhen I distribute this app and try to run it on a windows XP machine, I have an error on startup due to error loading cv2.pyd (opencv python wrapper)\nI looked at cv2.pyd with dependency walker and noticed that some dlls are missing : ieshims.dll and wer.dll. Unfortunately copying these libs doesn't solve the issues some other dlls are missing or not up-to-date.\nAny idea?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":941,"Q_Id":12899513,"Users Score":5,"Answer":"The problem comes from 4 dlls which are copyied by py2exe: msvfw32.dll msacm32.dll, avicap32.dll and avifil32.dll\nAs I am building on Vista, I think that it forces the use of Vista dlls on Windows XP causing some mismatch when trying to load it.\nI removed these 4 dlls and everything seems to work ok (in this case it use the regular system dlls.)","Q_Score":1,"Tags":"python,opencv,windows-xp,py2exe","A_Id":12930212,"CreationDate":"2012-10-15T16:02:00.000","Title":"Python: OpenCV can not be loaded on windows xp","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How can I use numpy unique without sorting the result but just in the order they appear in the sequence? Something like this?\na = [4,2,1,3,1,2,3,4]\nnp.unique(a) = [4,2,1,3]\nrather than\nnp.unique(a) = [1,2,3,4]\nUse naive solution should be fine to write a simple function. But as I need to do this multiple times, are there any fast and neat way to do this?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":32836,"Q_Id":12926898,"Users Score":62,"Answer":"You can do this with the return_index parameter:\n\n>>> import numpy as np\n>>> a = [4,2,1,3,1,2,3,4]\n>>> np.unique(a)\narray([1, 2, 3, 4])\n>>> indexes = np.unique(a, return_index=True)[1]\n>>> [a[index] for index in sorted(indexes)]\n[4, 2, 1, 3]","Q_Score":36,"Tags":"python,numpy","A_Id":12926989,"CreationDate":"2012-10-17T03:58:00.000","Title":"numpy unique without sort","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a lengthy plot, composed o several horizontal subplots organized into a column.\nWhen I call fig.savefig('what.pdf'), the resulting output file shows all the plots crammed onto a single page.\nQuestion: is there a way to tell savefig to save on any number (possibly automatically determined) of pdf pages?\nI'd rather avoid multiple files and then os.system('merge ...'), if possible.","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":16939,"Q_Id":12938568,"Users Score":1,"Answer":"I suspect that there is a more elegant way to do this, but one option is to use tempfiles or StringIO to avoid making traditional files on the system and then you can piece those together.","Q_Score":12,"Tags":"python,pdf,pagination,matplotlib","A_Id":12938704,"CreationDate":"2012-10-17T16:07:00.000","Title":"Matplotlib savefig into different pages of a PDF","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to apply a 3x3 or larger image filter (gaussian or median) on a 2-d array.\nThough there are several ways for doing that such as scipy.ndimage.gaussian_filter or applying a loop, I want to know if there is a way to apply a 3x3 or larger filter on each pixel of a mxn array simultaneously, because it would save a lot of time bypassing loops. Can functional programming be used for the purpose?? \nThere is a module called scipy.ndimage.filters.convolve, please tell whether it is able to perform simultaneous operations.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":854,"Q_Id":12968446,"Users Score":1,"Answer":"Even if python did provide functionality to apply an operation to an NxM array without looping over it, the operation would still not be executed simultaneously in the background since the amount of instructions a CPU can handle per cycle is limited and thus no time could be saved. For your use case this might even be counterproductive since the fields in your arrays proably have dependencies and if you don't know in what order they are accessed this will most likely end up in a mess.\nHugues provided some useful links about parallel processing in Python, but be careful when accessing the same data structure such as an array with multiple threads at the same time. If you don't synchronize the threads they might access the same part of the array at the same time and mess things up.\nAnd be aware, the amount of threads that can effectively be run in parallel is limited by the number of processor cores.","Q_Score":0,"Tags":"python,image,filter,numpy,scipy","A_Id":13009064,"CreationDate":"2012-10-19T06:20:00.000","Title":"Python: Perform an operation on each pixel of a 2-d array simultaneously","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to use Panda3D for my personal project, but after reading the documentation and some example sourcecodes, I still have a few questions: \n\nHow can I render just one frame and save it in a file? \nIn fact I would need to render 2 different images: a single object, and a scene of multiple objects including the previous single object, but just one frame for each and they both need to be saved as image files. \nThe application will be coded in Python, and needs to be very scalable (be used by thousand of users). Would Panda3D fit the bill here? (about my program in Python, it's almost a constant complexity so no problem here, and 3D models will be low-poly and about 5 to 20 per scene). \nI need to calculate the perspective projection of every object to the camera. Is it possible to directly access the vertexes and faces (position, parameters, etc..)? \nCan I recolor my 3D objects? I need to set a simple color for the whole object, but a different color per object. Is it possible? \n\nPlease also note that I'm quite a newbie in the field of graphical and game development, but I know some bits of 3D modelling and 3D theory, as well as computer imaging theory. \nThank you for reading me. \nPS: My main alternative currently is to use Soya3D or PySoy, but they don't seem to be very actively developped nor optimized, so although they would both have a smaller memory footprints, I don't know if they would really perform faster than Panda3D since they're not very optimized...","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":2225,"Q_Id":12981607,"Users Score":2,"Answer":"You can use a buffer with setOneShot enabled to make it render only a single frame.  You can start Panda3D without a window by setting the \"window-type\" PRC variable to \"none\", and then opening an offscreen buffer yourself.  (Note: offscreen buffers without a host window may not be supported universally.)\nIf you set \"window-type\" to \"offscreen\", base.win will actually be a buffer (which may be a bit easier than having to set up your own), after which you can call base.graphicsEngine.render_frame() to render a single frame while avoiding the overhead of the task manager.  You have to call it twice because of double-buffering.\nYes.  Panda3D is used by Disney for some of their MMORPGs.  I do have to add that Panda's high-level networking interfaces are poorly documented.\nYou can calculate the transformation from an object to the camera using nodepath.get_transform(base.cam), which is a TransformState object that you may optionally convert into a matrix using ts.get_mat().  This is surprisingly fast, since Panda maintains a composition cache for transformations so that this doesn't have to happen multiple times.  You can get the projection matrix (from view space to clip space) using lens.get_projection_mat() or the inverse using lens.get_projection_mat_inv().\nYou may also access the individual vertex data using the Geom interfaces, this is described to detail in the Panda3D manual.\nYou can use set_color to change the base colour of the object (replacing any vertex colours), or you can use set_color_scale to tint the objects, ie. applying a colour that is multiplied with the existing colour.  You can also apply a Material object if you use lights and want to use different colours for the diffuse, specular and ambient components.","Q_Score":1,"Tags":"python,3d,rendering,panda3d","A_Id":15449900,"CreationDate":"2012-10-19T19:58:00.000","Title":"Panda3D and Python, render only one frame and other questions","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have four one dimensional lists: X1, Y1, X2, Y2. \n\nX1 and Y1 each have 203 data points. \nX2 and Y2 each have 1532 data points. \nX1 and X2 are at different intervals, but both measure time. \n\nI want to graph Y1 vs Y2.\nI can plot just fine once I get the interpolated data, but can't think of how to interpolate data. I've thought and researched this a couple hours, and just can't figure it out. I don't mind a linear interpolation, but just can't figure out a way.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2501,"Q_Id":12990315,"Users Score":0,"Answer":"If you use matplotlib, you can just call plot(X1, Y1, 'bo', X2, Y2, 'r+'). Change the formatting as you'd like, but it can cope with different lengths just fine. You can provide more than two without any issue.","Q_Score":3,"Tags":"python,graph,plot,interpolation","A_Id":12990987,"CreationDate":"2012-10-20T16:17:00.000","Title":"Data interpolation in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"This is probably very easy, but after looking through documentation and possible examples online for the past several hours I cannot figure it out.\nI have a large dataset (a spreadsheet) that gets heavily cleaned by a DO file. In the DO file I then want to save certain variables of the cleaned data as a temp .csv run some Python scripts, that produce a new CSV and then append that output to my cleaned data.\nIf that was unclear here is an example.\nAfter cleaning my data set (XYZ) goes from variables A to Z with 100 observations. I want to take variables A and D through F and save it as test.csv. I then want to run a python script that takes this data and creates new variables AA to GG. I want to then take that information and append it to the XYZ dataset (making the dataset now go from A to GG with 100 observations) and then be able to run a second part of my DO file for analysis.\nI have been doing this manually and it is fine but the file is going to start changing quickly and it would save me a lot of time.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2897,"Q_Id":13014789,"Users Score":0,"Answer":"Type \"help shell\" in Stata.  What you want to do is shell out from Stata, call Python, and then have Stata resume whatever you want it to do after the Python script has completed.","Q_Score":1,"Tags":"python,merge,python-3.x,stata","A_Id":13016728,"CreationDate":"2012-10-22T15:35:00.000","Title":"Calling Python from Stata","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In the light of a project I've been playing with Python NLTK and Document Classification and the Naive Bayes classifier.  As I understand from the documentation, this works very well if your different documents are tagged with either pos or neg as a label (or more than 2 labels)\nThe documents I'm working with that are already classified don't have labels, but they have a score, a floating point between 0 and 5. \nWhat I would like to do is build a classifier, like the movies example in the documentation, but that would predict the score of a piece of text, rather than the label. I believe this is mentioned in the docs but never further explored as 'probabilities of numeric features'\nI am not a language expert nor a statistician so if someone has an example of this lying around I would be most grateful if you would share this with me. Thanks!","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1239,"Q_Id":13015593,"Users Score":0,"Answer":"This is a very late answer, but perhaps it will help someone.\nWhat you're asking about is regression. Regarding Jacob's answer, linear regression is only one way to do it. However, I agree with his recommendation of scikit-learn.","Q_Score":8,"Tags":"python,nltk","A_Id":15627502,"CreationDate":"2012-10-22T16:22:00.000","Title":"NLTK: Document Classification with numeric score instead of labels","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want an algorithm to detect if an image is of high professional quality or is done with poor contrast, low lighting etc. How do I go about designing such an algorithm. \nI feel that it is feasible, since if I press a button in picassa it tries to fix the lighting, contrast and color. Now I have seen that in good pictures if I press the auto-fix buttons the change is not that high as in the bad images. Could this be used as a lead? \nPlease throw any ideas at me. Also if this has already been done before, and I am doing the wheel invention thing, kindly stop me and point me to previous work. \nthanks much,","AnswerCount":2,"Available Count":1,"Score":0.2913126125,"is_accepted":false,"ViewCount":3360,"Q_Id":13018968,"Users Score":3,"Answer":"You are making this way too hard. I handled this in production code by generating a histogram of the image, throwing away outliers (1 black pixel doesn't mean that the whole image has lots of black; 1 white pixel doesn't imply a bright image), then seeing if the resulting distribution covered a sufficient range of brightnesses.\nIn stats terms, you could also see if the histogram approximates a Gaussian distribution with a satisfactorily large standard deviation. If the whole image is medium gray with a tiny stddev, then you have a low contrast image - by definition. If the mean is approximately medium-gray but the stddev covers brightness levels from say 20% to 80%, then you have a decent contrast.\nBut note that neither of these approaches require anything remotely resembling machine learning.","Q_Score":1,"Tags":"python,image-processing","A_Id":13019636,"CreationDate":"2012-10-22T20:05:00.000","Title":"How to automatically detect if image is of high quality?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to be use GridSearchCV to determine the parameters of a classifier, and using pipelines seems like a good option.\nThe application will be for image classification using Bag-of-Word features, but the issue is that there is a different logical pipeline depending on whether training or test examples are used. \nFor each training set, KMeans must run to produce a vocabulary that will be used for testing, but for test data no KMeans process is run. \nI cannot see how it is possible to specify this difference in behavior for a pipeline.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1601,"Q_Id":13057113,"Users Score":3,"Answer":"You probably need to derive from the KMeans class and override the following methods to use your vocabulary logic:\n\nfit_transform will only be called on the train data\ntransform will be called on the test data\n\nMaybe class derivation is not alway the best option. You can also write your own transformer class that wraps calls to an embedded KMeans model and provides the fit \/ fit_transform \/ transform API that is expected by the Pipeline class for the first stages.","Q_Score":2,"Tags":"python,machine-learning,scikit-learn","A_Id":13057566,"CreationDate":"2012-10-24T20:24:00.000","Title":"Using custom Pipeline for Cross Validation scikit-learn","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Does anyone know a fast algorithm to detect main colors in an image?\nI'm currently using k-means to find the colors together with Python's PIL but it's very slow. One 200x200 image takes 10 seconds to process. I've several hundred thousand images.","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":4441,"Q_Id":13060069,"Users Score":0,"Answer":"K-means is a good choice for this task because you know number of main colors beforehand. You need to optimize K-means. I think you can reduce your image size, just scale it down to 100x100 pixels or so. Find the size on witch your algorithm works with acceptable speed. Another option is to use dimensionality reduction before k-means clustering.\nAnd try to find fast k-means implementation. Writing such things in python is a misuse of python. It's not supposed to be used like this.","Q_Score":8,"Tags":"python,algorithm,colors,python-imaging-library","A_Id":13062863,"CreationDate":"2012-10-25T00:50:00.000","Title":"Fast algorithm to detect main colors in an image?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have come across a problem and am not sure which would be the best suitable technology to implement it. Would be obliged if you guys can suggest me some based on your experience.\nI want to load data from 10-15 CSV files each of them being fairly large 5-10 GBs. By load data I mean convert the CSV file to XML and then populate around 6-7 stagings tables in Oracle using this XML. \nThe data needs to be populated such that the elements of the XML and eventually the rows of the table come from multiple CSV files. So for e.g. an element A would have sub-elements coming data from CSV file 1, file 2 and file 3 etc.\nI have a framework built on Top of Apache Camel, Jboss on Linux. Oracle 10G is the database server.\nOptions I am considering,\n\nSmooks - However the problem is that Smooks serializes one CSV at a time and I cant afford to hold on to the half baked java beans til the other CSV files are read since I run the risk of running out of memory given the sheer number of beans I would need to create and hold on to before they are fully populated written to disk as XML.\nSQLLoader - I could skip the XML creation all together and load the CSV directly to the staging tables using SQLLoader. But I am not sure if I can a. load multiple CSV files in SQL Loader to the same tables updating the records after the first file. b. Apply some translation rules while loading the staging tables.\nPython script to convert the CSV to XML.\nSQLLoader to load a different set of staging tables corresponding to the CSV data and then writing stored procedure to load the actual staging tables from this new set of staging tables (a path which I want to avoid given the amount of changes to my existing framework it would need).\n\nThanks in advance. If someone can point me in the right direction or give me some insights from his\/her personal experience it will help me make an informed decision.\nregards,\n-v-\nPS: The CSV files are fairly simple with around 40 columns each. The depth of objects or relationship between the files would be around 2 to 3.","AnswerCount":3,"Available Count":2,"Score":0.0665680765,"is_accepted":false,"ViewCount":2011,"Q_Id":13061800,"Users Score":1,"Answer":"Create a process \/ script that will call a procedure to load csv files to external Oracle table and another script to load it to the destination table.\nYou can also add cron jobs to call these scripts that will keep track of incoming csv files into the directory, process it and move the csv file to an output\/processed folder.\nExceptions also can be handled accordingly by logging it or sending out an email. Good Luck.","Q_Score":3,"Tags":"python,csv,etl,sql-loader,smooks","A_Id":14449025,"CreationDate":"2012-10-25T04:54:00.000","Title":"Choice of technology for loading large CSV files to Oracle tables","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have come across a problem and am not sure which would be the best suitable technology to implement it. Would be obliged if you guys can suggest me some based on your experience.\nI want to load data from 10-15 CSV files each of them being fairly large 5-10 GBs. By load data I mean convert the CSV file to XML and then populate around 6-7 stagings tables in Oracle using this XML. \nThe data needs to be populated such that the elements of the XML and eventually the rows of the table come from multiple CSV files. So for e.g. an element A would have sub-elements coming data from CSV file 1, file 2 and file 3 etc.\nI have a framework built on Top of Apache Camel, Jboss on Linux. Oracle 10G is the database server.\nOptions I am considering,\n\nSmooks - However the problem is that Smooks serializes one CSV at a time and I cant afford to hold on to the half baked java beans til the other CSV files are read since I run the risk of running out of memory given the sheer number of beans I would need to create and hold on to before they are fully populated written to disk as XML.\nSQLLoader - I could skip the XML creation all together and load the CSV directly to the staging tables using SQLLoader. But I am not sure if I can a. load multiple CSV files in SQL Loader to the same tables updating the records after the first file. b. Apply some translation rules while loading the staging tables.\nPython script to convert the CSV to XML.\nSQLLoader to load a different set of staging tables corresponding to the CSV data and then writing stored procedure to load the actual staging tables from this new set of staging tables (a path which I want to avoid given the amount of changes to my existing framework it would need).\n\nThanks in advance. If someone can point me in the right direction or give me some insights from his\/her personal experience it will help me make an informed decision.\nregards,\n-v-\nPS: The CSV files are fairly simple with around 40 columns each. The depth of objects or relationship between the files would be around 2 to 3.","AnswerCount":3,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":2011,"Q_Id":13061800,"Users Score":2,"Answer":"Unless you can use some full-blown ETL tool (e.g. Informatica PowerCenter, Pentaho Data Integration), I suggest the 4th solution - it is straightforward and the performance should be good, since Oracle will handle the most complicated part of the task.","Q_Score":3,"Tags":"python,csv,etl,sql-loader,smooks","A_Id":13062737,"CreationDate":"2012-10-25T04:54:00.000","Title":"Choice of technology for loading large CSV files to Oracle tables","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I got linearsvc working against training set and test set using load_file method i am trying to get It working on Multiprocessor enviorment.\nHow can i get multiprocessing work on LinearSVC().fit() LinearSVC().predict()? I am not really familiar  with datatypes of scikit-learn yet.\nI am also thinking about splitting samples into multiple arrays but i am not familiar with numpy arrays and scikit-learn data structures. \nDoing this it will be easier to put into multiprocessing.pool() , with that , split samples into chunks , train them and combine trained set  back later , would it work ? \nEDIT:\nHere is my scenario:\nlets say , we have 1 million files in training sample set , when we want to distribute processing of Tfidfvectorizer on several processors we have to split those samples (for my case it will only have two categories , so lets say 500000 each samples to train) . My server have 24 cores with 48 GB , so i want to split each topics into number of chunks 1000000 \/ 24 and process Tfidfvectorizer on them. Like that i would do to Testing sample set , as well as SVC.fit() and decide(). Does it make sense? \nThanks. \nPS: Please do not close this .","AnswerCount":2,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":11435,"Q_Id":13068257,"Users Score":11,"Answer":"For linear models (LinearSVC, SGDClassifier, Perceptron...) you can chunk your data, train independent models on each chunk and build an aggregate linear model (e.g. SGDClasifier) by sticking in it the average values of coef_ and intercept_ as attributes. The predict method of LinearSVC, SGDClassifier, Perceptron compute the same function (linear prediction using a dot product with an intercept_ threshold and One vs All multiclass support) so the specific model class you use for holding the average coefficient is not important.\nHowever as previously said the tricky point is parallelizing the feature extraction and current scikit-learn (version 0.12) does not provide any way to do this easily.\nEdit: scikit-learn 0.13+ now has a hashing vectorizer that is stateless.","Q_Score":10,"Tags":"python,multithreading,numpy,machine-learning,scikit-learn","A_Id":13084224,"CreationDate":"2012-10-25T12:10:00.000","Title":"Multiprocessing scikit-learn","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have three vectors in 3D a,b,c. Now I want to calculate a rotation r that when applied to a yields a result parallel to b. Then the rotation r needs to be applied to c. \nHow do I do this in python? Is it possible to do this with numpy\/scipy?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1659,"Q_Id":13136828,"Users Score":0,"Answer":"I'll assume the \"geometry library for python\" already answered in the comments on the question. So once you have a transformation that takes 'a' parallel to 'b', you'll just apply it to 'c'\nThe vectors 'a' and 'b' uniquely define a plane. Each vector has a canonical representation as a point difference from the origin, so you have three points: the head of 'a', the head of 'b', and the origin. First compute this plane. It will have an equation in the form Ax + By + Cz = 0. \nA normal vector to this plane defines both the axis of rotation and the sign convention for the direction of rotation. All you need is one normal vector to the plane, since they're all collinear. You can solve for such a vector by picking at two non-collinear vectors in the plane and taking the dot product with the normal vector. This gives a pair of linear equations in two variables that you can solve with standard methods such as Cramer's rule. In all of these manipulations, if any of A, B, or C are zero, you have a special case to handle.\nThe angle of the rotation is given by the cosine relation for the dot product of 'a' and 'b' and their lengths. The sign of the angle is determined by the triple product of 'a', 'b', and the normal vector. Now you've got all the data to construct a rotation matrix in one of the many canonical forms you can look up.","Q_Score":1,"Tags":"python,3d,geometry","A_Id":13242515,"CreationDate":"2012-10-30T10:15:00.000","Title":"Rotations in 3D","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"My objective is to start with an \"empty\" matrix and repeatedly add columns to it until I have a large matrix.","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":255,"Q_Id":13150020,"Users Score":2,"Answer":"Add columns to ndarray(or matrix) need full copy of the content, so you should use other method such as list or the array module, or create a large matrix first, and fill data in it.","Q_Score":1,"Tags":"python,numpy","A_Id":13150059,"CreationDate":"2012-10-31T01:28:00.000","Title":"Is it possible to create a numpy matrix with 10 rows and 0 columns?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a set of butterfly images for training my system to segment a butterfly from a given input image. For this purpose, I want to extract the features such as edges, corners, region boundaries, local maximum\/minimum intensity etc.\nI found many feature extraction methods like Harris corner detection, SIFT but they didn't work well when the image background had the same color as that of the butterfly's body\/boundary color. \nCould anyone please tell whether there is any good feature extraction method which works well for butterfly segmentation? I'm using the Python implementation of OpenCV.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2622,"Q_Id":13151428,"Users Score":2,"Answer":"Are you willing to write your own image processing logic? \nYour best option will likely be to optimize the segmentation\/feature extraction for your problem, instead of using previous implementations like opencv meant for more general use-cases.\nAn option that I've found to work well in noisy\/low-contrast environments is to use a sliding window (i.e. 10x10 pixels) and build a gradient orientation histogram. From this histogram you can recognize the presence of more dominant edges (they accumulate in the histogram) and their orientations (allowing for detection for things like corners) and see the local maximum\/minimums. (I can give more details if needed)\nIf your interested in segmentation as a whole AND user interaction is possible, I would recommend graph cut or grab cut. In graph cut users would be able to fine tune the segmentation. Grab cut is already in opencv, but may result in the same problems as it takes a single input from the user, then automatically segments the image.","Q_Score":5,"Tags":"python,image-processing,opencv,image-segmentation","A_Id":13162109,"CreationDate":"2012-10-31T04:47:00.000","Title":"Feature extraction for butterfly images","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am looking for a GUI python module that is best suited for the following job:\nI am trying to plot a graph with many columns (perhaps hundreds), each column representing an individual. The user should be able to drag the columns around and drop them onto different columns to switch the two. Also, there are going to be additional dots drawn on the columns and by hovering over those dots, the user should see the values corresponding to those dots. What is the best way to approach this?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":244,"Q_Id":13151907,"Users Score":0,"Answer":"You can do what you want with Tkinter, though there's no specific widget that does what you ask. There is a general purpose canvas widget that allows you to draw objects (rectangles, circles, images, buttons, etc), and it's pretty easy to add the ability to drag those items around.","Q_Score":1,"Tags":"python,graph,matplotlib,tkinter,wxwidgets","A_Id":13156356,"CreationDate":"2012-10-31T05:45:00.000","Title":"Looking for a specific python gui module to perform the following task","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I had developed scatter and lasso selection plots with Chaco. Now, I need to embed a BaseMap [with few markers on a map] onto the plot area side by side.\nI created a BaseMap and tried to add to the traits_view; but it is failing with errors.\nPlease give me some pointers to achieve the same.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":156,"Q_Id":13190187,"Users Score":0,"Answer":"Chaco and matplotlib are completely different tools. Basemap has been built on top of matplotlib so it is not possible to add a Basemap map on a Chaco plot.\nI'm afraid I couldn't find any mapping layer to go with Chaco. Is there a reason you cannot use matplotlib for you plot?","Q_Score":1,"Tags":"python,matplotlib-basemap,chaco","A_Id":16198408,"CreationDate":"2012-11-02T06:08:00.000","Title":"How to use BaseMap with chaco plots","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I found forecast package from R the best solution for time series analysis and forecasting. \nI want to use it in Python. Could I use rpy and after take the forecast package in Python?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1673,"Q_Id":13197097,"Users Score":5,"Answer":"Yes, you could use the [no longer developed or extended, but maintained] package RPy, or you could use the newer package RPy2 which is actively developed.\nThere are other options too, as eg headless network connections to Rserve.","Q_Score":1,"Tags":"python,time-series,forecasting","A_Id":13198574,"CreationDate":"2012-11-02T14:22:00.000","Title":"Forecast Package from R in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Say I have some Python list, my_list which contains N elements. Single elements may be indexed by using my_list[i_1], where i_1 is the index of the desired element. However, Python lists may also be indexed my_list[i_1:i_2] where a \"slice\" of the list from i_1 to i_2 is desired. What is the Big-O (worst-case) notation to slice a list of size N?\nPersonally, if I were coding the \"slicer\" I would iterate from i_1 to i_2, generate a new list and return it, implying O(N), is this how Python does it?\nThank you,","AnswerCount":3,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":47961,"Q_Id":13203601,"Users Score":10,"Answer":"For a list of size N, and a slice of size M, the iteration is actually only O(M), not O(N). Since M is often << N, this makes a big difference.\nIn fact, if you think about your explanation, you can see why. You're only iterating from i_1 to i_2, not from 0 to i_1, then I_1 to i_2.","Q_Score":61,"Tags":"python,list,big-o","A_Id":13203622,"CreationDate":"2012-11-02T21:59:00.000","Title":"Big-O of list slicing","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it possible to integrate any Ordinary Differential Equation backward in time\nusing scipy.integrate.odeint ?\nIf it is possible, could someone tell me what should be the arguement 'time' in 'odeint.","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":4017,"Q_Id":13227115,"Users Score":0,"Answer":"You can make a change of variables s = t_0 - t, and integrate the differential equation with respect to s. odeint doesn't do this for you.","Q_Score":2,"Tags":"python-2.7,scipy","A_Id":13229534,"CreationDate":"2012-11-05T06:38:00.000","Title":"Backward integration in time using scipy odeint","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Suppose I have a csv file with 400 columns.  I cannot load the entire file into a DataFrame (won't fit in memory).  However, I only really want 50 columns, and this will fit in memory.  I don't see any built in Pandas way to do this.  What do you suggest?  I'm open to using the PyTables interface, or pandas.io.sql.  \nThe best-case scenario would be a function like:  pandas.read_csv(...., columns=['name', 'age',...,'income']).  I.e. we pass a list of column names (or numbers) that will be loaded.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":7641,"Q_Id":13236098,"Users Score":2,"Answer":"There's no default way to do this right now. I would suggest chunking the file and iterating over it and discarding the columns you don't want.\nSo something like pd.concat([x.ix[:, cols_to_keep] for x in pd.read_csv(..., chunksize=200)])","Q_Score":9,"Tags":"python,pandas,csv","A_Id":13236277,"CreationDate":"2012-11-05T16:20:00.000","Title":"How to load only specific columns from csv file into a DataFrame","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am importing study data into a Pandas data frame using read_csv. \nMy subject codes are 6 numbers coding, among others, the day of birth. For some of my subjects this results in a code with a leading zero (e.g. \"010816\").\nWhen I import into Pandas, the leading zero is stripped of and the column is formatted as int64.\nIs there a way to import this column unchanged maybe as a string? \nI tried using a custom converter for the column, but it does not work - it seems as if the custom conversion takes place before Pandas converts to int.","AnswerCount":6,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":62070,"Q_Id":13250046,"Users Score":3,"Answer":"You Can do This , Works On all Versions of Pandas\npd.read_csv('filename.csv', dtype={'zero_column_name': object})","Q_Score":76,"Tags":"python,pandas,csv,types","A_Id":58968554,"CreationDate":"2012-11-06T11:27:00.000","Title":"How to keep leading zeros in a column when reading CSV with Pandas?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to sort a list of unknown values, either ints or floats or both, in ascending order. i.e, [2,-1,1.0] would become [-1,1.0,2]. Unfortunately, the sorted() function doesn't seem to work as it seems to sort in descending order by absolute value. Any ideas?","AnswerCount":2,"Available Count":2,"Score":0.1973753202,"is_accepted":false,"ViewCount":5933,"Q_Id":13318611,"Users Score":2,"Answer":"I had the same problem. The answer: Python will sort numbers by the absolute value if you have them as strings. So as your key, make sure to include an int() or float() argument. My working syntax was\ndata = sorted(data, key = lambda x: float(x[0]))\n...the lambda x part just gives a function which outputs the thing you want to sort by. So it takes in a row in my list, finds the float 0th element, and sorts by that.","Q_Score":2,"Tags":"sorting,python-2.7,absolute-value","A_Id":29600848,"CreationDate":"2012-11-10T02:17:00.000","Title":"Sort a list of ints and floats with negative and positive values?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to sort a list of unknown values, either ints or floats or both, in ascending order. i.e, [2,-1,1.0] would become [-1,1.0,2]. Unfortunately, the sorted() function doesn't seem to work as it seems to sort in descending order by absolute value. Any ideas?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":5933,"Q_Id":13318611,"Users Score":0,"Answer":"In addition to doublefelix,below code gives the absolute order to me from string.\nsiparis=sorted(siparis, key=lambda sublist:abs(float(sublist[1])))","Q_Score":2,"Tags":"sorting,python-2.7,absolute-value","A_Id":65985248,"CreationDate":"2012-11-10T02:17:00.000","Title":"Sort a list of ints and floats with negative and positive values?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a gray image in which I want to map every pixel to N other matrices of size LxM.How do I initialize such a matrix?I tried\n result=numpy.zeros(shape=(i_size[0],i_size[1],N,L,M)) for which I get the Value Error 'array is too big'.Can anyone suggest an alternate method?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":3179,"Q_Id":13321042,"Users Score":0,"Answer":"If I understand correctly, every pixel in the gray image is mapped to a single pixel in N other images. In that case, the map array is numpy.zeros((i.shape[0], i.shape[1], N, 2), dtype=numpy.int32) since you need to store 1 x and 1 y coordinate into each other N arrays, not the full Nth array every time.  Using integer indices will further reduce memory use.\nThen result[y,x,N,0] and result[y,x,N,1] are the y and x mappings into the Nth image.","Q_Score":1,"Tags":"python,numpy","A_Id":13345287,"CreationDate":"2012-11-10T10:01:00.000","Title":"Creating a 5D array in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using Naive Bayes classifier in python for text classification. Is there any smoothing methods to avoid zero probability for unseen words in python NLTK? Thanks in advance!","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":1430,"Q_Id":13356348,"Users Score":2,"Answer":"I'd suggest to replace all the words with low (specially 1) frequency to <unseen>, then train the classifier in this data. \nFor classifying you should query the model for <unseen> in the case of a word that is not in the training data.","Q_Score":4,"Tags":"python,nltk,smoothing","A_Id":13397869,"CreationDate":"2012-11-13T06:20:00.000","Title":"Smoothing in python NLTK","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it possible to use in Python the svm_model, generated in matlab? (I use libsvm)","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":77,"Q_Id":13383684,"Users Score":0,"Answer":"Normally you would just call a method in libsvm to save your model to a file. You then can just use it in Python using their svm.py. So yes, you can - it's all saved in libsvm format.","Q_Score":0,"Tags":"python,matlab,libsvm","A_Id":13445709,"CreationDate":"2012-11-14T17:09:00.000","Title":"Is it possible to use in Python the svm_model, generated in matlab?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Let me explain what I'm trying to achieve. In the past while working on Java platform, I used to write Java codes(say, to push or pull data from MySQL database etc.) then create a war file which essentially bundles all the class files, supporting files etc and put it under a servlet container like Tomcat and this becomes a web service and can be invoked from any platform. \nIn my current scenario, I've majority of work being done in Java, however the Natural Language Processing(NLP)\/Machine Learning(ML) part is being done in Python using the NLTK, Scipy, Numpy etc libraries. I'm trying to use the services of this Python engine in existing Java code. Integrating the Python code to Java through something like Jython is not that straight-forward(as Jython does not support calling any python module which has C based extensions, as far as I know), So I thought the next option would be to make it a web service, similar to what I had done with Java web services in the past. Now comes the actual crux of the question, how do I run the ML engine as a web service and call the same from any platform, in my current scenario this happens to be Java. I tried looking in the web, for various options to achieve this and found things like CherryPy, Werkzeug etc but not able to find the right approach or any sample code or anything that shows how to invoke a NLTK-Python script and serve the result through web, and eventually replicating the functionality Java web service provides. In the Python-NLTK code, the ML engine does a data-training on a large corpus(this takes 3-4 minutes) and we don't want the Python code to go through this step every time a method is invoked. If I make it a web service, the data-training will happen only once, when the service starts and then the service is ready to be invoked and use the already trained engine. \nNow coming back to the problem, I'm pretty new to this web service things in Python and would appreciate any pointers on how to achieve this .Also, any pointers on achieving the goal of calling NLTK based python scripts from Java, without using web services approach and which can deployed on production servers to give good performance would also be helpful and appreciable. Thanks in advance.\nJust for a note, I'm currently running all my code on a Linux machine with Python 2.6, JDK 1.6 installed on it.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1090,"Q_Id":13394969,"Users Score":0,"Answer":"NLTK based system tends to be slow at response per request, but good throughput can be achieved given enough RAM.","Q_Score":4,"Tags":"python,machine-learning,cherrypy","A_Id":13399425,"CreationDate":"2012-11-15T09:51:00.000","Title":"How to expose an NLTK based ML(machine learning) Python Script as a Web Service?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"What is the preferred way of doing the conversion using PIL\/Numpy\/SciPy today?","AnswerCount":5,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":55003,"Q_Id":13405956,"Users Score":0,"Answer":"At the moment I haven't found a good package to do that. You have to bear in mind that RGB is a device-dependent colour space so you can't convert accurately to XYZ or CIE Lab if you don't have a profile. \nSo be aware that many solutions where you see converting from RGB to CIE Lab without specifying the colour space or importing a colour profile must be carefully evaluated. Take a look at the code under the hood most of the time they assume that you are dealing with sRGB colour space.","Q_Score":49,"Tags":"python,numpy,scipy,python-imaging-library,color-space","A_Id":60718937,"CreationDate":"2012-11-15T20:49:00.000","Title":"Convert an image RGB->Lab with python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I noticed a bug in my program and the reason it is happening is because it seems that pandas is copying by reference a pandas dataframe instead of by value. I know immutable objects will always be passed by reference but pandas dataframe is not immutable so I do not see why it is passing by reference. Can anyone provide some information? \nThanks!\nAndrew","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":17836,"Q_Id":13419822,"Users Score":41,"Answer":"All functions in Python are \"pass by reference\", there is no \"pass by value\". If you want to make an explicit copy of a pandas object, try new_frame = frame.copy().","Q_Score":19,"Tags":"python,pandas","A_Id":13420016,"CreationDate":"2012-11-16T15:43:00.000","Title":"pandas dataframe, copy by value","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I aim to start opencv little by little but first I need to decide which API of OpenCV is more useful. I predict that Python implementation is shorter but running time will be more dense and slow compared to the native C++ implementations. Is there any know can comment about performance and coding differences between these two perspectives?","AnswerCount":5,"Available Count":2,"Score":0.1194272985,"is_accepted":false,"ViewCount":76398,"Q_Id":13432800,"Users Score":3,"Answer":"Why choose?\nIf you know both Python and C++, use Python for research using Jupyter Notebooks and then use C++ for implementation.\nThe Python stack of Jupyter, OpenCV (cv2) and Numpy provide for fast prototyping.\nPorting the code to C++ is usually quite straight-forward.","Q_Score":92,"Tags":"c++,python,performance,opencv","A_Id":66955473,"CreationDate":"2012-11-17T17:14:00.000","Title":"Does performance differ between Python or C++ coding of OpenCV?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I aim to start opencv little by little but first I need to decide which API of OpenCV is more useful. I predict that Python implementation is shorter but running time will be more dense and slow compared to the native C++ implementations. Is there any know can comment about performance and coding differences between these two perspectives?","AnswerCount":5,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":76398,"Q_Id":13432800,"Users Score":6,"Answer":"You're right, Python is almost always significantly slower than C++ as it requires an interpreter, which C++ does not. However, that does require C++ to be strongly-typed, which leaves a much smaller margin for error. Some people prefer to be made to code strictly, whereas others enjoy Python's inherent leniency.\nIf you want a full discourse on Python coding styles vs. C++ coding styles, this is not the best place, try finding an article.\nEDIT:\nBecause Python is an interpreted language, while C++ is compiled down to machine code, generally speaking, you can obtain performance advantages using C++.  However, with regard to using OpenCV, the core OpenCV libraries are already compiled down to machine code, so the Python wrapper around the OpenCV library is executing compiled code.  In other words, when it comes to executing computationally expensive OpenCV algorithms from Python, you're not going to see much of a performance hit since they've already been compiled for the specific architecture you're working with.","Q_Score":92,"Tags":"c++,python,performance,opencv","A_Id":13432830,"CreationDate":"2012-11-17T17:14:00.000","Title":"Does performance differ between Python or C++ coding of OpenCV?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"My score was to get the most frequent color in a image, so I implemented a k-means algorithm.\nThe algorithm works good, but the result is not the one I was waiting for.\nSo now I'm trying to do some improvements, the first I thought was to implement k-means++, so I get a beter position for the inicial clusters centers.\nFirst I select a random point, but how can I select the others. I mean how I define the minimal distance between them.\nAny help for this? Thanks","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1239,"Q_Id":13436032,"Users Score":0,"Answer":"You can use a vector quantisation.  You can make a list of each pixel and each adjacent pixel in x+1 and y+1 direction and pick the difference and plot it along a diagonale. Then you can calculate a voronoi diagram and get the mean color and compute a feature vector. It's a bit more effectice then to use a simple grid based mean color.","Q_Score":0,"Tags":"python,colors,cluster-computing,k-means","A_Id":13436279,"CreationDate":"2012-11-17T23:55:00.000","Title":"K-Means plus plus implementation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on a visualization that models the trajectory of an object over a planar surface.  Currently, the algorithm I have been provided with uses a simple trajectory function (where velocity and gravity are provided) and Runge-Kutta integration to check n points along the curve for a point where velocity becomes 0.  We are discounting any atmospheric interaction.\nWhat I would like to do it introduce a non-planar surface, say from a digital terrain model (raster).  My thought is to calculate a Reimann sum at each pixel and determine if the offset from the planar surface is equal to or less than the offset of the underlying topography from the planar surface.\nIs it possible, using numpy or scipy, to calculate the height of a Reimann rectangle?  Conversely, the area of the rectangle (midpoint is fine) would work, as I know the width nd can calculate the height.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":329,"Q_Id":13460428,"Users Score":0,"Answer":"For computing Reimann sums you could look into numpy.cumsum().  I am not sure if you can do a surface or only an array with this method.  However, you could always loop through all the rows of your terrain and store each row in a two dimensional array as you go.  Leaving you with an array of all the terrain heights.","Q_Score":0,"Tags":"python,numpy,scipy","A_Id":13463491,"CreationDate":"2012-11-19T19:08:00.000","Title":"Scipy \/ Numpy Reimann Sum Height","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Assuming performance is not an issue, is there a way to deploy numpy in a environment where a compiler is unavailable, and no pre-built binaries can be installed? \nAlternatively, is there a pure-python numpy implementation?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":435,"Q_Id":13466939,"Users Score":3,"Answer":"a compiler is unavailable, and no pre-built binaries can be installed\n\nThis... makes numpy impossible.  If you cannot install numpy binaries, and you cannot compile numpy source code, then you are left with no options.","Q_Score":2,"Tags":"python,numpy","A_Id":13467084,"CreationDate":"2012-11-20T05:08:00.000","Title":"Installing (and using) numpy without access to a compiler or binaries","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm extracting a large CSV file (200Mb) that was generated using R with Python (I'm the one using python).\nI do some tinkling with the file (normalization, scaling, removing junk columns, etc) and then save it again using numpy's savetxt with data delimiter as ',' to kee the csv property.\nThing is, the new file is almost twice as large than the original (almost 400Mb). The original data as well as the new one are only arrays of floats.\nIf it helps, it looks as if the new file has really small values, that need exponential values, which the original did not have.\nAny idea on why is this happening?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":280,"Q_Id":13491731,"Users Score":2,"Answer":"Have you looked at the way floats are represented in text before and after?  You might have a line \"1.,2.,3.\" become \"1.000000e+0, 2.000000e+0,3.000000e+0\" or something like that, the two are both valid and both represent the same numbers.\nMore likely, however, is that if the original file contained floats as values with relatively few significant digits (for example \"1.1, 2.2, 3.3\"), after you do normalization and scaling, you \"create\" more digits which are needed to represent the results of your math but do not correspond to real increase in precision (for example, normalizing the sum of values to 1.0 in the last example gives \"0.1666666, 0.3333333, 0.5\").\nI guess the short answer is that there is no guarantee (and no requirement) for floats represented as text to occupy any particular amount of storage space, or less than the maximum possible per float; it can vary a lot even if the data remains the same, and will certainly vary if the data changes.","Q_Score":0,"Tags":"python,numpy","A_Id":13491927,"CreationDate":"2012-11-21T10:55:00.000","Title":"Numpy save file is larger than the original","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In time series we can find peak (min and max values). There are algorithms to find peaks. My question is:\nIn python are there libraries for peak detection in time series data?\nor \nsomething in R using RPy?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1640,"Q_Id":13520319,"Users Score":1,"Answer":"Calculate the derivation of your sample points, for example for every 5 points (THRESHOLD!) calculate the slope of the five points with Least squares methods (search on wiki if you dont know what it is. Any lineair regression function uses it). And when this slope is almost (THRESHOLD!) zero there is a peak.","Q_Score":3,"Tags":"python,r,time-series","A_Id":13520565,"CreationDate":"2012-11-22T21:37:00.000","Title":"Peak detection in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to track a multicolored object(4 colors). Currently, I am parsing the image into HSV and applying multiple color filter ranges on the camera feed and finally adding up filtered images. Then I filter the contours based on area. \nThis method is quite stable most of the time but when the external light varies a bit,  the object is not recognized as the hue values are getting messed up and it is getting difficult to track the object. \nAlso since I am filtering the contours based on area I often have false positives and the object is not being tracked properly sometimes.\nDo you have any suggestion for getting rid of these problems. Could I use some other method to track it instead of filtering individually on colors and then adding up the images and searching for contours?","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":475,"Q_Id":13526654,"Users Score":0,"Answer":"For a full proof track you need to combine more than one method...following are some of the hints...\n\nif you have prior knowledge of the object then you can use template matching...but template matching is little process intensive...if you are using GPU then you might have some benefit\nfrom your write up i presume external light varies to a lesser extent...so on that ground you can use goodfeaturestotrack function of opencv and use optical flow to track only those ponits found by goodfeaturestotrack in the next frames of the video\nif the background is stable except some brightness variation and the object is moving comparatively more than background then you can subtract the previous frame from the present frame to get the position of the moving object...this is kind of fast and easy change detection technique...\nFiltering contours based on area is good idea but try to add some more features to the filtering criteria...i.e. you can try filtering based on ellpticity,aspect ratio of the bounding box etc...\nlastly...if you have any prior knowledge about the motion path of the object you can use kalman filter...\nif the background is all most not variant or to some extent variant then you can try gaussian mixture model to model the background...while the changing ball is your fore ground...","Q_Score":2,"Tags":"python,opencv,tracking","A_Id":13530687,"CreationDate":"2012-11-23T09:57:00.000","Title":"Tracking a multicolor object","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to track a multicolored object(4 colors). Currently, I am parsing the image into HSV and applying multiple color filter ranges on the camera feed and finally adding up filtered images. Then I filter the contours based on area. \nThis method is quite stable most of the time but when the external light varies a bit,  the object is not recognized as the hue values are getting messed up and it is getting difficult to track the object. \nAlso since I am filtering the contours based on area I often have false positives and the object is not being tracked properly sometimes.\nDo you have any suggestion for getting rid of these problems. Could I use some other method to track it instead of filtering individually on colors and then adding up the images and searching for contours?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":475,"Q_Id":13526654,"Users Score":0,"Answer":"You might try having multiple or an infinite number of models of the object depending upon the light sources available, and then classifying your object as either the object with one of the light sources or not the object.  Note: this is a machine learning-type approach to the problem.\nFiltering with a Kalman, extended Kalman filter, or particle filter (depending on your application) would be a good idea, so that you can have a \"memory\" of the recently tracked features and have expectations for the next tracked color\/feature in the near term (i.e. if you just saw the object, there is a high likelihood that it hasn't disappeared in the next frame).\nIn general, this is a difficult problem that I have run into a few times doing robotics research.  The only robust solution is to learn models and to confirm or deny them with what your system actually sees.  Any number of machine learning approaches should work, but the easiest would probably be support vector machines.  The most robust would probably be something like Gaussian Processes (if you want to do an infinite number of models).  Good luck and don't get too frustrated; this is not an easy problem!","Q_Score":2,"Tags":"python,opencv,tracking","A_Id":13534342,"CreationDate":"2012-11-23T09:57:00.000","Title":"Tracking a multicolor object","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to do a execution time analysis of the bellman ford algorithm on a large number of graphs and in order to do that I need to generate a large number of random DAGS with the possibility of having negative edge weights.\nI am using networkx in python. There are a lot of random graph generators in the networkx library but what will be the one that will return the directed graph with edge weights and the source vertex.\nI am using networkx.generators.directed.gnc_graph() but that does not quite guarantee to return only a single source vertex.\nIs there a way to do this with or even without networkx?","AnswerCount":4,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":4642,"Q_Id":13543069,"Users Score":1,"Answer":"I noticed that the generated graphs have always exactly one sink vertex which is the first vertex. You can reverse direction of all edges to get a graph with single source vertex.","Q_Score":5,"Tags":"python,random,graph,networkx,bellman-ford","A_Id":13544567,"CreationDate":"2012-11-24T16:25:00.000","Title":"how to create random single source random acyclic directed graphs with negative edge weights in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"PyOpenGL docs say:\n\nBecause of the way OpenGL and ctypes handle, for instance, pointers, to array data, it is often necessary to ensure that a Python data-structure is retained (i.e. not garbage collected).  This is done by storing the data in an array of data-values that are indexed by a context-specific key.  The functions to provide this functionality are provided by the OpenGL.contextdata module.\n\nWhen exactly is it the case?\nOne situation I've got in my mind is client-side vertex arrays back from OpenGL 1, but they have been replaced by buffer objects for years. A client side array isn't required any more after a buffer object is filled (= right after glBufferData returns, I pressume).\nAre there any scenarios I'm missing?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":176,"Q_Id":13584900,"Users Score":1,"Answer":"Are there any scenarios I'm missing?\n\nBuffer mappings obtained through glMapBuffer","Q_Score":1,"Tags":"python,opengl,ctypes,pyopengl","A_Id":13585375,"CreationDate":"2012-11-27T13:04:00.000","Title":"What is PyOpenGL's \"context specific data\"?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":1,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using multiple threads to access and delete data in my pandas dataframe. Because of this, I am wondering is pandas dataframe threadsafe?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":17987,"Q_Id":13592618,"Users Score":18,"Answer":"The data in the underlying ndarrays can be accessed in a threadsafe manner, and modified at your own risk. Deleting data would be difficult as changing the size of a DataFrame usually requires creating a new object. I'd like to change this at some point in the future.","Q_Score":22,"Tags":"python,thread-safety,pandas","A_Id":13593942,"CreationDate":"2012-11-27T20:38:00.000","Title":"python pandas dataframe thread safe?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have read several related posts about installing numpy for python version 2.7 on a 64 bit windows7 OS.  Before I try these, does anybody know if the 32bit version will work on a 64bit system?","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":380,"Q_Id":13594953,"Users Score":0,"Answer":"It should work if you're using 32-bit Python. If you're using 64-bit Python you'll need 64-bit Numpy.","Q_Score":0,"Tags":"windows,numpy,python-2.7,64-bit","A_Id":13595084,"CreationDate":"2012-11-27T23:15:00.000","Title":"numpy for 64 bit windows","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have read several related posts about installing numpy for python version 2.7 on a 64 bit windows7 OS.  Before I try these, does anybody know if the 32bit version will work on a 64bit system?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":380,"Q_Id":13594953,"Users Score":0,"Answer":"If you are getting it from pip and you want a 64 bit version of NumPy, you need MSVS 2008.  pip needs to compile NumPy module with the same compiler that Python binary was compiled with.\nThe last I checked (this Summer), python's build.py on Windows only supported up to that version of MSVS.  Probably because build.py isn't updated for compilers which are not clearly available for free as compile-only versions.  There is an \"Express\" version of MSVS 2010, 2012 and 2013 (which would satisfy that requirement).  But I am not sure if there is a dedicated repository for them and if they have a redistribution license.  If there is, then the only problem is that no one got around to upgrading build.py to support the newer vertsions of MSVS.","Q_Score":0,"Tags":"windows,numpy,python-2.7,64-bit","A_Id":33553807,"CreationDate":"2012-11-27T23:15:00.000","Title":"numpy for 64 bit windows","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am currently working on a project, a simple sentiment analyzer such that there will be 2 and 3 classes in separate cases. I am using a corpus that is pretty rich in the means of unique words (around 200.000). I used bag-of-words method for feature selection and to reduce the number of unique features, an elimination is done due to a threshold value of frequency of occurrence. The final set of features includes around 20.000 features, which is actually a 90% decrease, but not enough for intended accuracy of test-prediction. I am using LibSVM and SVM-light in turn for training and prediction (both linear and RBF kernel) and also Python and Bash in general.\nThe highest accuracy observed so far is around 75% and I need at least 90%. This is the case for binary classification. For multi-class training, the accuracy falls to ~60%. I need at least 90% at both cases and can not figure how to increase it: via optimizing training parameters or via optimizing feature selection?\nI have read articles about feature selection in text classification and what I found is that three different methods are used, which have actually a clear correlation among each other. These methods are as follows:\n\nFrequency approach of bag-of-words (BOW)\nInformation Gain (IG)\nX^2 Statistic (CHI)\n\nThe first method is already the one I use, but I use it very simply and need guidance for a better use of it in order to obtain high enough accuracy. I am also lacking knowledge about practical implementations of IG and CHI and looking for any help to guide me in that way.\nThanks a lot, and if you need any additional info for help, just let me know.\n\n\n@larsmans: Frequency Threshold: I am looking for the occurrences of unique words in examples, such that if a word is occurring in different examples frequently enough, it is included in the feature set as a unique feature.   \n@TheManWithNoName: First of all thanks for your effort in explaining the general concerns of document classification. I examined and experimented all the methods you bring forward and others. I found Proportional Difference (PD) method the best for feature selection, where features are uni-grams and Term Presence (TP) for the weighting (I didn't understand why you tagged Term-Frequency-Inverse-Document-Frequency (TF-IDF) as an indexing method, I rather consider it as a feature weighting approach).  Pre-processing is also an important aspect for this task as you mentioned. I used certain types of string elimination for refining the data as well as morphological parsing and stemming. Also note that I am working on Turkish, which has different characteristics compared to English. Finally, I managed to reach ~88% accuracy (f-measure) for binary classification and ~84% for multi-class. These values are solid proofs of the success of the model I used. This is what I have done so far. Now working on clustering and reduction models, have tried LDA and LSI and moving on to moVMF and maybe spherical models (LDA + moVMF), which seems to work better on corpus those have objective nature, like news corpus. If you have any information and guidance on these issues, I will appreciate. I need info especially to setup an interface (python oriented, open-source) between feature space dimension reduction methods (LDA, LSI, moVMF etc.) and clustering methods (k-means, hierarchical etc.).","AnswerCount":5,"Available Count":1,"Score":0.0399786803,"is_accepted":false,"ViewCount":30760,"Q_Id":13603882,"Users Score":1,"Answer":"Linear svm is recommended for high dimensional features. Based on my experience the ultimate limitation of SVM accuracy depends on the positive and negative \"features\". You can do a grid search (or in the case of linear svm you can just search for the best cost value) to find the optimal parameters for maximum accuracy, but in the end you are limited by the separability of your feature-sets. The fact that you are not getting 90% means that you still have some work to do finding better features to describe your members of the classes.","Q_Score":53,"Tags":"python,nlp,svm,sentiment-analysis,feature-extraction","A_Id":13615685,"CreationDate":"2012-11-28T11:21:00.000","Title":"Feature Selection and Reduction for Text Classification","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am having a little bit of trouble creating a haar classifier. I need to build up a classifier to detect cars. At the moment I made a program in python that reads in an image, I draw a rectangle around the area the object is in, Once the rectangle is drawn, it outputs the image name, the top left and bottom right coordinates of the rectangle. I am unsure of where to go from here and how to actually build up the classifier. Can anyone offer me any help?\nEDIT*\nI am looking for help on how to use the opencv_traincascade. I have looked at the documentation but I can't quite figure out how to use it to create the xml file to be used in the detection program.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1867,"Q_Id":13611126,"Users Score":1,"Answer":"This looks like you need to determine what features you would like to train your classifier on first, as using the haar classifier it benefits from those extra features. From there you will need to train the classifier, this requires you to get a lot of images that have cars and those that do not have cars in them and then run it over this and having it tweak the average it is shooting for in order to classify to the best it can with your selected features.\nTo get a better classifier you will have to figure out the order of your features and the optimal order you put them together to further dive into the object and determine if it is in fact what you are looking for. Again this will require a lot of examples for your particular features and your problem as a whole.","Q_Score":1,"Tags":"python,opencv,machine-learning,computer-vision,object-detection","A_Id":13612350,"CreationDate":"2012-11-28T17:38:00.000","Title":"Creating a haar classifier using opencv_traincascade","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Any ideas on how EdgeNgram treats numbers?\nI'm running haystack with an ElasticSearch backend.  I created an indexed field of type EdgeNgram.  This field will contain a string that may contain words as well as numbers.\nWhen I run a search against this field using a partial word, it works how it's supposed to.  But if I put in a partial number, I'm not getting the result that I want.\nExample:\nI search for the indexed field \"EdgeNgram 12323\" by typing \"edgen\" and I'll get the index returned to me.  If I search for that same index by typing \"123\" I get nothing.\nThoughts?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2671,"Q_Id":13636419,"Users Score":3,"Answer":"if you're using the edgeNGram tokenizer, then it will treat \"EdgeNGram 12323\" as a single token and then apply the edgeNGram'ing process on it. For example, if min_grams=1 max_grams=4, you'll get the following tokens indexed: [\"E\", \"Ed\", \"Edg\", \"Edge\"]. So I guess this is not what you're really looking for - consider using the edgeNGram token filter instead:\nIf you're using the edgeNGram token filter, make sure you're using a tokenizer that actually tokenizes the text \"EdgeNGram 12323\" to produce two tokens out of it: [\"EdgeNGram\", \"12323\"] (standard or whitespace tokenizer will do the trick). Then apply the edgeNGram filter next to it.\nIn general, edgeNGram will take \"12323\" and produce tokens such as \"1\", \"12\", \"123\", etc...","Q_Score":6,"Tags":"python,elasticsearch,django-haystack","A_Id":13637244,"CreationDate":"2012-11-29T23:05:00.000","Title":"ElasticSearch: EdgeNgrams and Numbers","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have data and a time 'value' associated with it (Tx and X).\nHow can I perform a fast Fourier transform on my data.\nTx is an array I have and X is another array I have. The length of both arrays are of course the same and they are associated by Tx[i] with X[i] , where i goes from 0 to len(X).\nHow can I perform a fft on such data to ultimately achieve a Power Spectral Density plot frequency against |fft|^2.","AnswerCount":1,"Available Count":1,"Score":0.537049567,"is_accepted":false,"ViewCount":1901,"Q_Id":13636758,"Users Score":3,"Answer":"If the data is not uniformly sampled (i.e. Tx[i]-Tx[i-1] is constant), then you cannot do an FFT on it.\nHere's an idea:\nIf you have a pretty good idea of the bandwidth of the signal, then you could create a resampled version of the DFT basis vectors R.  I.e. the complex sinusoids evaluated at the Tx times.  Then solve the linear system x = A*z: where x is your observation, z is the unknown frequency content of the signal, and A is the resamapled DFT basis. Note that A may not actually be a basis depending on the severity of the non-uniformity.  It will almost certainly not be an orthogonal basis like the DFT.","Q_Score":2,"Tags":"python,numpy,scipy,fft","A_Id":13645588,"CreationDate":"2012-11-29T23:33:00.000","Title":"Fast Fourier Transform (fft) with Time Associated Data Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"How can I filter which lines of a CSV to be loaded into memory using pandas?  This seems like an option that one should find in read_csv.  Am I missing something?\nExample: we've a CSV with a timestamp column and we'd like to load just the lines that with a timestamp greater than a given constant.","AnswerCount":7,"Available Count":2,"Score":-1.0,"is_accepted":false,"ViewCount":95461,"Q_Id":13651117,"Users Score":-4,"Answer":"You can specify nrows parameter.\n\nimport pandas as pd\ndf = pd.read_csv('file.csv', nrows=100)\n\nThis code works well in version 0.20.3.","Q_Score":123,"Tags":"python,pandas","A_Id":53256590,"CreationDate":"2012-11-30T18:38:00.000","Title":"How can I filter lines on load in Pandas read_csv function?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How can I filter which lines of a CSV to be loaded into memory using pandas?  This seems like an option that one should find in read_csv.  Am I missing something?\nExample: we've a CSV with a timestamp column and we'd like to load just the lines that with a timestamp greater than a given constant.","AnswerCount":7,"Available Count":2,"Score":0.1137907297,"is_accepted":false,"ViewCount":95461,"Q_Id":13651117,"Users Score":4,"Answer":"If the filtered range is contiguous (as it usually is with time(stamp) filters), then the fastest solution is to hard-code the range of rows. Simply combine skiprows=range(1, start_row) with nrows=end_row parameters. Then the import takes seconds where the accepted solution would take minutes. A few experiments with the initial start_row are not a huge cost given the savings on import times. Notice we kept header row by using range(1,..).","Q_Score":123,"Tags":"python,pandas","A_Id":60026814,"CreationDate":"2012-11-30T18:38:00.000","Title":"How can I filter lines on load in Pandas read_csv function?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I realize Dataframe takes a map of {'series_name':Series(data, index)}.  However, it automatically sorts that map even if the map is an OrderedDict().\nIs there a simple way to pass a list of Series(data, index, name=name) such that the order is preserved and the column names are the series.name?  Is there an easy way if all the indices are the same for all the series? \nI normally do this by just passing a numpy column_stack of series.values and specifying the column names.  However, this is ugly and in this particular case the data is strings not floats.","AnswerCount":7,"Available Count":1,"Score":0.1137907297,"is_accepted":false,"ViewCount":51167,"Q_Id":13653030,"Users Score":4,"Answer":"Check out DataFrame.from_items too","Q_Score":26,"Tags":"python,pandas","A_Id":13852311,"CreationDate":"2012-11-30T20:54:00.000","Title":"How do I Pass a List of Series to a Pandas DataFrame?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to implement a hadoop reducer for word counting.\nIn my reducer I use a hash table to count the words.But if my file is extremely large the hash table will use extreme amount of memory.How I can address this issue ?\n(E.g A file with 10 million lines each reducer receives 100million words how can he count the words  a hash table requires 100million keys)\nMy current implementation is in python.\nIs there a smart way to reduce the amount of memory?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":442,"Q_Id":13663294,"Users Score":0,"Answer":"The most efficient way to do this is to maintain a hash map of word frequency in your mappers, and flush them to the output context when they reach a certain size (say 100,000 entries). Then clear out the map and continue (remember to flush the map in the cleanup method too).\nIf you still truely have 100 of millions of words, then you'll either need to wait a long time for the reducers to finish, or increase your cluster size and use more reducers.","Q_Score":0,"Tags":"python,hadoop,hadoop-streaming","A_Id":13663566,"CreationDate":"2012-12-01T20:12:00.000","Title":"Efficient Hadoop Word counting for large file","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"When I have a pandas.DataFrame df with columns [\"A\", \"B\", \"C\", \"D\"], I can filter it using constructions like df[df[\"B\"] == 2].\nHow do I do the equivalent of df[df[\"B\"] == 2], if B is the name of a level in a MultiIndex instead? (For example, obtained by df.groupby([\"A\", \"B\"]).mean() or df.setindex([\"A\", \"B\"]))","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":148,"Q_Id":13701035,"Users Score":0,"Answer":"I see two ways of getting this, both of which look like a detour \u2013 which makes me think there must be a better way which I'm overlooking.\n\nConverting the MultiIndex into columns: df[df.reset_index()[\"B\"] == 2]\nSwapping the name I want to use to the start of the MultiIndex and then use lookup by index: df.swaplevel(0, \"B\").ix[2]","Q_Score":1,"Tags":"python,pandas","A_Id":13701036,"CreationDate":"2012-12-04T10:39:00.000","Title":"boolean indexing on index (instead of dataframe)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When I have a pandas.DataFrame df with columns [\"A\", \"B\", \"C\", \"D\"], I can filter it using constructions like df[df[\"B\"] == 2].\nHow do I do the equivalent of df[df[\"B\"] == 2], if B is the name of a level in a MultiIndex instead? (For example, obtained by df.groupby([\"A\", \"B\"]).mean() or df.setindex([\"A\", \"B\"]))","AnswerCount":3,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":148,"Q_Id":13701035,"Users Score":1,"Answer":"I would suggest either:\ndf.xs(2, level='B')\nor\ndf[df.index.get_level_values('B') == val]\nI'd like to make the syntax for the latter operation a little nicer.","Q_Score":1,"Tags":"python,pandas","A_Id":13755051,"CreationDate":"2012-12-04T10:39:00.000","Title":"boolean indexing on index (instead of dataframe)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"So I am trying to create a realtime plot of data that is being recorded to a SQL server.  The format is as follows:\nDatabase: testDB\nTable: sensors\nFirst record contains 3 records.  The first column is an auto incremented ID starting at 1.  The second column is the time in epoch format.  The third column is my sensor data.  It is in the following format:\n23432.32 112343.3 53454.322 34563.32 76653.44 000.000 333.2123\nI am completely lost on how to complete this project.  I have read many pages showing examples dont really understand them.  They provide source code, but I am not sure where that code goes.  I installed httpd on my server and that is where I stand.  Does anyone know of a good how-to from beginning to end that I could follow?  Or could someone post a good step by step for me to follow?\nThanks for your help","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":428,"Q_Id":13772857,"Users Score":0,"Answer":"Install a httpd server\nInstall php\nWrite a php script to fetch the data from the database and render it\nas a webpage.\n\nThis is fairly elaborate request, with relatively little details given.  More information will allow us to give better answers.","Q_Score":0,"Tags":"python,mysql,flot","A_Id":13774224,"CreationDate":"2012-12-07T23:52:00.000","Title":"Plotting data using Flot and MySQL","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"So I have this list called sumErrors that's 16000 rows and 1 column, and this list is already presorted into 5 different clusters. And what I'm doing is slicing the list for each cluster and finding the index of the minimum value in each slice.\nHowever, I can only find the first minimum index using argmin(). I don't think I can just delete the value, because otherwise it would shift the slices over and the indices is what I have to recover the original ID. Does anyone know how to get argmin() to spit out indices for the lowest three?\nOr perhaps a more optimal method? Maybe I should just assign ID numbers, but I feel like there maybe a more elegant method.","AnswerCount":2,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":11034,"Q_Id":13783071,"Users Score":4,"Answer":"numpy.argpartition(cluster, 3) would be much more effective.","Q_Score":14,"Tags":"python,list,numpy,min","A_Id":37094880,"CreationDate":"2012-12-08T23:33:00.000","Title":"Finding the indices of the top three values via argmin() or min() in python\/numpy without mutation of list?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"What does the error Numpy error: Matrix is singular mean specifically (when using the linalg.solve function)? I have looked on Google but couldn't find anything that made it clear when this error occurs.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":60944,"Q_Id":13795682,"Users Score":25,"Answer":"A singular matrix is one that is not invertible. This means that the system of equations you are trying to solve does not have a unique solution; linalg.solve can't handle this. \nYou may find that linalg.lstsq provides a usable solution.","Q_Score":13,"Tags":"python,numpy","A_Id":13795874,"CreationDate":"2012-12-10T05:47:00.000","Title":"Numpy error: Singular matrix","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"My question is about the algorithm which is used in Numpy's FFT function.\nThe documentation of Numpy says that it uses the Cooley-Tukey algorithm. However, as you may know, this algorithm works only if the number N of points is a power of 2. \nDoes numpy pad my input vector x[n] in order to calculate its FFT X[k]? (I don't think so, since the number of points I have in the output is also N). How could I actually \"see\" the code which is used by numpy for its FFT function?\nCheers!","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":6896,"Q_Id":13841296,"Users Score":2,"Answer":"In my experience the algorithms don't do automatic padding, or at least some of them don't.  For example, running the scipy.signal.hilbert method on a signal that wasn't of length == a power of two took about 45 seconds.  When I padded the signal myself with zeros to such a length, it took 100ms.\nYMMV but it's something to double check basically any time you run a signal processing algorithm.","Q_Score":4,"Tags":"python,numpy,fft","A_Id":19329962,"CreationDate":"2012-12-12T13:52:00.000","Title":"FFT in Numpy (Python) when N is not a power of 2","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a python script I hope to do roughly this:\n\ncalls some particle positions into an array\nruns algorithm over all 512^3 positions to distribute them to an NxNxN matrix\nfeed that matrix back to python\nuse plotting in python to visualise matrix (i.e. mayavi)\n\nFirst I have to write it in serial but ideally I want to parrallelize step 2 to speed up computation. What tools\/strategy might get me started. I know Python and Fortran well but not much about how to connect the two for my particular problem. At the moment I am doing everything in Fortran then loading my python program - I want to do it all at once.I've heard of py2f but I want to get experienced people's opinions before I go down one particular rabbit hole. Thanks\nEdit: The thing I want to make parallel is 'embarrassingly parallel' in that is is just a loop of N particles and I want to get through that loop as quickly as possible.","AnswerCount":3,"Available Count":1,"Score":0.1325487884,"is_accepted":false,"ViewCount":929,"Q_Id":13852646,"Users Score":2,"Answer":"An alternative approach to VladimirF's suggestion, could be to set up the two parts as a client server construct, where your Python part could talk to the Fortran part using sockets. Though this comes with the burden to implement some protocol for the interaction, it has the advantage, that you get a clean separation and can even go on running them on different machines with an interaction over the network.\nIn fact, with this approach you even could do the embarrassing parallel part, by spawning as many instances of the Fortran application as needed and feed them all with different data.","Q_Score":5,"Tags":"python,arrays,parallel-processing,fortran,f2py","A_Id":13858423,"CreationDate":"2012-12-13T03:51:00.000","Title":"I want Python as front end, Fortran as back end. I also want to make fortran part parallel - best strategy?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to do K-Means Clustering using Kruskal's Minimum Spanning Tree Algorithm. My original design was to run the full-length Kruskal algorithm of the input and produce an MST, after which delete the last k-1 edges (or equivalently k-1 most expensive edges).\nOf course this is the same as running Kruskal algorithm and stopping it just before it adds its last k-1 edges.\nI want to use the second strategy i.e instead of running the full length Kruskal algorithm, stop it just after the number of clusters so far equals K. I'm using Union-Find data structure and using a list object in this Union-Find data structure.\nEach vertex on this graph is represented by its current cluster on this list e.g [1,2,3...] means vertices 1,2,3 are in their distinct independent clusters. If two vertices are joined  their corresponding indices on the list data structure are updated to reflect this.\ne.g merging vertices 2 and 3 leaves the list data object as [1,2,2,4,5.....]\nMy strategy is then every time two nodes are merged, count the number of DISTINCT elements in the list and if it equals the number of desired clusters, stop. My worry is that this may not be the most efficient option. Is there a way I could count the number of distinct objects in a list efficiently?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":296,"Q_Id":13875584,"Users Score":1,"Answer":"One way is to sort your list and then run over the elements by comparing each one to the previous one. If they are not equal sum 1 to your \"distinct counter\". This operation is O(n), and for sorting you can use the sorting algorithm you prefer, such as quick sort or merge sort, but I guess there is an available sorting algorithm in the lib you use.\nAnother option is to create a hash table and add all the elements. The number of insertions will be the distinct elements, since repeated elements will not be inserted. I think this is O(1) in the best case so maybe this is the better solution. Good luck!\nHope this helps,\nD\u00eddac P\u00e9rez","Q_Score":0,"Tags":"python,python-3.x,k-means","A_Id":13875710,"CreationDate":"2012-12-14T09:09:00.000","Title":"Efficient way to find number of distinct elements in a list","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"New to Python.\nIn R, you can get the dimension of a matrix using dim(...).   What is the corresponding function in Python Pandas for their data frame?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":147970,"Q_Id":13921647,"Users Score":165,"Answer":"df.shape, where df is your DataFrame.","Q_Score":104,"Tags":"python,pandas","A_Id":13921674,"CreationDate":"2012-12-17T20:27:00.000","Title":"Python - Dimension of Data Frame","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"sklearn.svm.SVC doesn't give the index of support vectors for sparse dataset. Is there any hack\/way to get the index of SVs?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":265,"Q_Id":13982983,"Users Score":0,"Answer":"Not without going to the cython code I am afraid. This has been on the todo list for way to long. Any help with it would be much appreciated. It shouldn't be too hard, I think.","Q_Score":2,"Tags":"python,machine-learning,libsvm,scikit-learn,scikits","A_Id":13986712,"CreationDate":"2012-12-21T01:14:00.000","Title":"sklearn.svm.SVC doesn't give the index of support vectors for sparse dataset?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using Pylot 1.26 with Python 2.7 on Windows 7 64bit having installed Numpy 1.6.2 and Matplotlib 1.1.0. The test case executes and produces a report but the response time graph is empty (no data) and the throughput graph is just one straight line. \nI've tried the 32 bit and 64 bit installers but the result is the same.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":682,"Q_Id":13989166,"Users Score":0,"Answer":"I had the same identical problem. I spent sometime on it today debugging few things, I realized the problem with me was that the data collected to plot charts wasn't correct and i needed to adjust. What I did was just changing the time from absolute to relative and dynamically adjusting the range of the axis. I'm not that good in python and so my code doesn't look that good.","Q_Score":0,"Tags":"python,numpy,matplotlib,pylot","A_Id":15980514,"CreationDate":"2012-12-21T11:18:00.000","Title":"Why are my Pylot graphs blank?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"After plotting streamlines using 'matplotlib.streamplot' I need to change the U V data and update the plot. For imshow and quiver there are the functions 'set_data' and 'set_UVC', respectively. There does not seem to be any similar function for streamlines. Is there any way to still updateget similar functionality?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1159,"Q_Id":14020155,"Users Score":0,"Answer":"I suspect the answer is no, because if you change the vectors, it would need to re-compute the stream lines. The objects returned by streamline are a line and patch collections, which know nothing about the vectors. To get this functionality would require writing a new class to wrap everything up and finding a sensible way to re-use the existing objects.\nThe best bet is to use cla() (as suggested by dmcdougall) to clear your axes and just re-plot them. A slightly less drastic approach would be to just remove the artists added by streamplot.","Q_Score":4,"Tags":"python,matplotlib,scipy","A_Id":15859052,"CreationDate":"2012-12-24T10:35:00.000","Title":"update U V data for matplotlib streamplot","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"quite new to OpenCV so please bear with me:\nI need to open up a temporary window for user input, but I need to be certain it won't overwrite a previously opened window.\nIs there a way to open up either an anonymous window, or somehow create a guaranteed unique window name?\nObviously a long random string would be pretty safe, but that seems like a hack.\nP.S. I'm using the python bindings at the moment, but If you want to write a response in c\/c++ that's fine, I'm familiar with them.","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":343,"Q_Id":14035161,"Users Score":2,"Answer":"In modules\/highgui\/src\/window_w32.cpp(or in some other file if you are not using windows - look at void cv::namedWindow( const string& winname, int flags ) in ...src\/window.cpp) there is a function static CvWindow* icvFindWindowByName( const char* name ) which probably is what you need, but it's internal so authors of OpenCV for some reason didn't want other to use it(or doesn't know someone may need it).     \nI think that the best option is to use system api to find whether a window with specific name exists.\nEventually use something that is almost impossible to be a window name, for example current time in ms + user name + random number + random string(yeah i know that window name  \"234564312cyriel123234123dgbdfbddfgb#$%grw$\" is not beautiful).","Q_Score":2,"Tags":"python,opencv","A_Id":14048691,"CreationDate":"2012-12-26T01:23:00.000","Title":"OpenCV anonymous\/guaranteed unique window","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a list of X and Y coordinates from geodata of a specific part of the world. I want to assign each coordinate, a weight, based upon where it lies in the graph.\nFor Example: If a point lies in a place where there are a lot of other nodes around it, it lies in a high density area, and therefore has a higher weight. \nThe most immediate method I can think of is drawing circles of unit radius around each point and then calculating if the other points lie within in and then using a function, assign a weight to that point. But this seems primitive.\nI've looked at pySAL and NetworkX but it looks like they work with graphs. I don't have any edges in the graph, just nodes.","AnswerCount":4,"Available Count":1,"Score":0.049958375,"is_accepted":false,"ViewCount":12400,"Q_Id":14070565,"Users Score":1,"Answer":"Yes, you do have edges, and they are the distances between the nodes. In your case, you have a complete graph with weighted edges.\nSimply derive the distance from each node to each other node -- which gives you O(N^2) in time complexity --, and use both nodes and edges as input to one of these approaches you found.\nHappens though your problem seems rather an analysis problem other than anything else; you should try to run some clustering algorithm on your data, like K-means, that clusters nodes based on a distance function, in which you can simply use the euclidean distance.\nThe result of this algorithm is exactly what you'll need, as you'll have clusters of close elements, you'll know what and how many elements are assigned to each group, and you'll be able to, according to these values, generate the coefficient you want to assign to each node.\nThe only concern worth pointing out here is that you'll have to determine how many clusters -- k-means, k-clusters -- you want to create.","Q_Score":7,"Tags":"python","A_Id":14070812,"CreationDate":"2012-12-28T13:53:00.000","Title":"Calculating Point Density using Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there a standard practice for representing vectors as 1d or 2d ndarrays in NumPy?  I'm moving from MATLAB which represents vectors as 2d arrays.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":1655,"Q_Id":14126201,"Users Score":1,"Answer":"In matlab (for historical reason I would argue) the basic type is an M-by-N array (matrix) so that scalars are 1-by-1 arrays and vectors either N-by-1 or 1-by-N arrays. (Memory layout is always Fortran style).\nThis \"limitation\" is not present in numpy: you have true scalars and ndarray's can have as many dimensions you like. (Memory layout can be C or Fortran-contigous.) For this reason there is no preferred (standard) practice. It is up to you, according to your application, to choose the one which better suits your needs.","Q_Score":5,"Tags":"python,matlab,numpy,linear-algebra","A_Id":14126790,"CreationDate":"2013-01-02T17:14:00.000","Title":"Performance\/standard using 1d vs 2d vectors in numpy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have two 2-D arrays with the same shape (105,234) named A & B essentially comprised of mean values from other arrays.  I am familiar with Python's scipy package, but I can't seem to find a way to test whether or not the two arrays are statistically significantly different at each individual array index.  I'm thinking this is just a large 2D paired T-test, but am having difficulty. Any ideas or other packages to use?","AnswerCount":4,"Available Count":1,"Score":-0.0996679946,"is_accepted":false,"ViewCount":7302,"Q_Id":14176280,"Users Score":-2,"Answer":"Go to MS Excel.  If you don't have it your work does, there are alternatives \nEnter the array of numbers in Excel worksheet.  Run the formula in the entry field, =TTEST (array1,array2,tail).  One tail is one, Two tail is two...easy peasy.  It's a simple Student's T and I believe you may still need a t-table to interpret the statistic (internet).  Yet it's quick for on the fly comparison of samples.","Q_Score":0,"Tags":"python,arrays,numpy,statistics,scipy","A_Id":26791595,"CreationDate":"2013-01-05T20:49:00.000","Title":"Test for statistically significant difference between two arrays","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to analyze microarray data using hierarchical clustering of the microarray columns (results from the individual microarray replicates) and PCA. \nI'm new to python. I have python 2.7.3, biopyhton, numpy, matplotlib, and networkx.\nAre there functions in python or biopython (similar to MATLAB's clustergram and mapcaplot) that I can use to do this?","AnswerCount":4,"Available Count":1,"Score":0.049958375,"is_accepted":false,"ViewCount":1029,"Q_Id":14191487,"Users Score":1,"Answer":"I recommend to use R Bioconductor and free software like Expander and MeV. Good flexible choice is a Cluster software with TreeViews. You can also run R and STATA or JMP from your Python codes and completely automate your data management.","Q_Score":3,"Tags":"python,bioinformatics,pca,biopython,hierarchical-clustering","A_Id":28408552,"CreationDate":"2013-01-07T07:14:00.000","Title":"Microarray hierarchical clustering and PCA with python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using python to prototype the algorithms of a computer vision system I'm creating. I would like to be able to easily log heterogeneous data, for example: images, numpy arrays, matplotlib plots, etc, from within the algorithms, and do that using two keys, one for the current frame number and another to describe the logged object. Then I would like to be able to browse all the data from a web browser. Finally, I would like to be able to easily process the logs to generate summaries, for example retrieve the key \"points\" for all the frame numbers and calculate some statistics on them. My intention is to use this logging subsystem to facilitate debugging the behaviour of the algorithms and produce summaries for benchmarking. \nI'm set to create this subsystem myself but I thought to ask first if someone has already done something similar. Does anybody know of any python package that I can use to do what I ask?\notherwise, does anybody have any advice on which tools to use to create this myself?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":281,"Q_Id":14201284,"Users Score":1,"Answer":"Another option for storage could be using hdf5 or pytables. Depending on how you structure the data, with pytables you can query the data at key \"points\". As noted in comments, I dont think an off the shelf solution exists.","Q_Score":1,"Tags":"python,logging,numpy,matplotlib","A_Id":14223556,"CreationDate":"2013-01-07T17:50:00.000","Title":"heterogeneous data logging and analysis","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am looking for a library, example or similar that allows me to loads a set of 2D projections of an object and then converts it into a 3D volume.\nFor example, I could have 6 pictures of a small toy and the program should allow me to view it as a 3D volume and eventually save it.\nThe object I need to convert is very similar to a cylinder (so the program doesn't have to 'understand' what type of object it is).","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":1288,"Q_Id":14232451,"Users Score":2,"Answer":"There are several things you can mean, I think none of which currently exists in free software (but I may be wrong about that), and they differ in how hard they are to implement:\nFirst of all, \"a 3D volume\" is not a clear definition of what you want. There is not one way to store this information. A usual way (for computer games and animations) is to store it as a mesh with textures. Getting the textures is easy: you have the photographs. Creating the mesh can be really hard, depending on what exactly you want.\nYou say your object looks like a cylinder. If you want to just stitch your images together and paste them as a texture over a cylindrical mesh, that should be possible. If you know the angles at which the images are taken, the stitching will be even easier.\nHowever, the really cool thing that most people would want is to create any mesh, not just a cylinder, based on the stitching \"errors\" (which originate from the parallax effect, and therefore contain information about the depth of the pictures). I know Autodesk (the makers of AutoCAD) have a web-based tool for this (named 123-something), but they don't let you put it into your own program; you have to use their interface. So it's fine for getting a result, but not as a basis for a program of your own.\nOnce you have the mesh, you'll need a viewer (not view first, save later; it's the other way around). You should be able to use any 3D drawing program, for example Blender can view (and edit) many file types.","Q_Score":0,"Tags":"python,image-processing,3d,2d","A_Id":14233016,"CreationDate":"2013-01-09T09:53:00.000","Title":"2D image projections to 3D Volume","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":1,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a relatively simple function with three unknown input parameters for which I only know the upper and lower bounds. I also know what the output Y should be for all of my data. \nSo far I have done a simple grid search in python, looping through all of the possible parameter combinations and returning those results where the error between Y predicted and Y observed is within a set limit.  \nI then look at the results to see which set of parameters performs best for each group of samples, look at the trade-off between parameters, see how outliers effect the data etc.. \nSo really my questions is - whilst the grid search method I'm using is a bit cumbersome, what advantages would there be in using Monte Carlo methods such as metropolis hastings instead?\nI am currently researching into MCMC methods, but don\u2019t have any practical experience in using them and, in this instance, can\u2019t quite see what might be gained.\nI\u2019d greatly appreciate any comments or suggestions\nMany Thanks","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":983,"Q_Id":14236371,"Users Score":2,"Answer":"When the search space becomes larger, it can become infeasible to do an exhaustive search. So we turn to Monte Carlo methods out of necessity.","Q_Score":1,"Tags":"python,montecarlo","A_Id":14236501,"CreationDate":"2013-01-09T13:31:00.000","Title":"Advantage of metropolis hastings or MonteCarlo methods over a simple grid search?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am totally new to Python, and I have to use some modules in my code, like numpy and scipy, but I have no permission on my hosting to install new modules using easy-install or pip ( and of course I don't know how to install new modules in a directory where I have permission [ I have SSH access ] ).\nI have downloaded numpy and used from numpy import * but it doesn't work. I also tried the same thing with scipy : from scipy import *, but it also don't work.\nHow to load \/ use new modules in Python without installing them [ numpy, scipy .. ] ?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1476,"Q_Id":14242764,"Users Score":0,"Answer":"Use the --user option to easy_install or setup.py to indicate where the installation is to take place. It should point to a directory where you have write access.\nOnce the module has been built and installed, you then need to set the environmental variable PYTHONPATH to point to that location. When you next run the python command, you should be able to import the module.","Q_Score":1,"Tags":"python,numpy,scipy,python-module","A_Id":14242912,"CreationDate":"2013-01-09T17:18:00.000","Title":"use \/ load new python module without installation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using scikit-learn in Python to develop a classification algorithm to predict the gender of certain customers. Amongst others, I want to use the Naive Bayes classifier but my problem is that I have a mix of categorical data (ex: \"Registered online\", \"Accepts email notifications\" etc) and continuous data (ex: \"Age\", \"Length of membership\" etc). I haven't used scikit much before but I suppose that that Gaussian Naive Bayes is suitable for continuous data and that Bernoulli Naive Bayes can be used for categorical data. However, since I want to have both categorical and continuous data in my model, I don't really know how to handle this. Any ideas would be much appreciated!","AnswerCount":6,"Available Count":3,"Score":1.0,"is_accepted":false,"ViewCount":29596,"Q_Id":14254203,"Users Score":15,"Answer":"The simple answer: multiply result!! it's the same.\nNaive Bayes based on applying Bayes\u2019 theorem with the \u201cnaive\u201d assumption of independence between every pair of features - meaning you calculate the Bayes probability dependent on a specific feature without holding the others - which means that the algorithm multiply each probability from one feature with the probability from the second feature (and we totally ignore the denominator - since it is just a normalizer).\nso the right answer is:\n\ncalculate the probability from the categorical variables.\ncalculate the probability from the continuous variables.\nmultiply 1. and 2.","Q_Score":76,"Tags":"python,machine-learning,data-mining,classification,scikit-learn","A_Id":34036255,"CreationDate":"2013-01-10T09:08:00.000","Title":"Mixing categorial and continuous data in Naive Bayes classifier using scikit-learn","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using scikit-learn in Python to develop a classification algorithm to predict the gender of certain customers. Amongst others, I want to use the Naive Bayes classifier but my problem is that I have a mix of categorical data (ex: \"Registered online\", \"Accepts email notifications\" etc) and continuous data (ex: \"Age\", \"Length of membership\" etc). I haven't used scikit much before but I suppose that that Gaussian Naive Bayes is suitable for continuous data and that Bernoulli Naive Bayes can be used for categorical data. However, since I want to have both categorical and continuous data in my model, I don't really know how to handle this. Any ideas would be much appreciated!","AnswerCount":6,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":29596,"Q_Id":14254203,"Users Score":0,"Answer":"You will need the following steps:\n\nCalculate the probability from the categorical variables (using predict_proba method from BernoulliNB)\nCalculate the probability from the continuous variables (using predict_proba method from GaussianNB)\nMultiply 1. and 2. AND\nDivide by the prior (either from BernoulliNB or from GaussianNB since they are the same) AND THEN\nDivide 4. by the sum (over the classes) of 4. This is the normalisation step.\n\nIt should be easy enough to see how you can add your own prior instead of using those learned from the data.","Q_Score":76,"Tags":"python,machine-learning,data-mining,classification,scikit-learn","A_Id":69929209,"CreationDate":"2013-01-10T09:08:00.000","Title":"Mixing categorial and continuous data in Naive Bayes classifier using scikit-learn","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using scikit-learn in Python to develop a classification algorithm to predict the gender of certain customers. Amongst others, I want to use the Naive Bayes classifier but my problem is that I have a mix of categorical data (ex: \"Registered online\", \"Accepts email notifications\" etc) and continuous data (ex: \"Age\", \"Length of membership\" etc). I haven't used scikit much before but I suppose that that Gaussian Naive Bayes is suitable for continuous data and that Bernoulli Naive Bayes can be used for categorical data. However, since I want to have both categorical and continuous data in my model, I don't really know how to handle this. Any ideas would be much appreciated!","AnswerCount":6,"Available Count":3,"Score":1.2,"is_accepted":true,"ViewCount":29596,"Q_Id":14254203,"Users Score":74,"Answer":"You have at least two options:\n\nTransform all your data into a categorical representation by computing percentiles for each continuous variables and then binning the continuous variables using the percentiles as bin boundaries. For instance for the height of a person create the following bins: \"very small\", \"small\", \"regular\", \"big\", \"very big\" ensuring that each bin contains approximately 20% of the population of your training set. We don't have any utility to perform this automatically in scikit-learn but it should not be too complicated to do it yourself. Then fit a unique multinomial NB on those categorical representation of your data.\nIndependently fit a gaussian NB model on the continuous part of the data and a multinomial NB model on the categorical part. Then transform all the dataset by taking the class assignment probabilities (with predict_proba method) as new features: np.hstack((multinomial_probas, gaussian_probas)) and then refit a new model (e.g. a new gaussian NB) on the new features.","Q_Score":76,"Tags":"python,machine-learning,data-mining,classification,scikit-learn","A_Id":14255284,"CreationDate":"2013-01-10T09:08:00.000","Title":"Mixing categorial and continuous data in Naive Bayes classifier using scikit-learn","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm wondering if there's a way to generate decreasing numbers within a certain range?\nI want to program to keep outputting until it reaches 0, and the highest number in the range must be positive.\nFor example, if the range is (0, 100), this could be a possible output:\n96\n57\n43\n23\n9\n0\nSorry for the confusion from my original post","AnswerCount":8,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":5706,"Q_Id":14260923,"Users Score":18,"Answer":"I would generate a list of n random numbers then sort them highest to lowest.","Q_Score":4,"Tags":"python,random,python-2.7,numbers","A_Id":14260955,"CreationDate":"2013-01-10T15:06:00.000","Title":"How to randomly generate decreasing numbers in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have tried to puzzle out an answer to this question for many months while learning pandas.  I use SAS for my day-to-day work and it is great for it's out-of-core support.  However, SAS is horrible as a piece of software for numerous other reasons.\nOne day I hope to replace my use of SAS with python and pandas, but I currently lack an out-of-core workflow for large datasets.  I'm not talking about \"big data\" that requires a distributed network, but rather files too large to fit in memory but small enough to fit on a hard-drive.\nMy first thought is to use HDFStore to hold large datasets on disk and pull only the pieces I need into dataframes for analysis.  Others have mentioned MongoDB as an easier to use alternative.  My question is this:\nWhat are some best-practice workflows for accomplishing the following:\n\nLoading flat files into a permanent, on-disk database structure\nQuerying that database to retrieve data to feed into a pandas data structure\nUpdating the database after manipulating pieces in pandas\n\nReal-world examples would be much appreciated, especially from anyone who uses pandas on \"large data\".\nEdit -- an example of how I would like this to work:\n\nIteratively import a large flat-file and store it in a permanent, on-disk database structure.  These files are typically too large to fit in memory.\nIn order to use Pandas, I would like to read subsets of this data (usually just a few columns at a time) that can fit in memory.\nI would create new columns by performing various operations on the selected columns.\nI would then have to append these new columns into the database structure.\n\nI am trying to find a best-practice way of performing these steps. Reading links about pandas and pytables it seems that appending a new column could be a problem.\nEdit -- Responding to Jeff's questions specifically:\n\nI am building consumer credit risk models. The kinds of data include phone, SSN and address characteristics; property values; derogatory information like criminal records, bankruptcies, etc... The datasets I use every day have nearly 1,000 to 2,000 fields on average of mixed data types: continuous, nominal and ordinal variables of both numeric and character data.  I rarely append rows, but I do perform many operations that create new columns.\nTypical operations involve combining several columns using conditional logic into a new, compound column. For example, if var1 > 2 then newvar = 'A' elif var2 = 4 then newvar = 'B'.  The result of these operations is a new column for every record in my dataset.\nFinally, I would like to append these new columns into the on-disk data structure.  I would repeat step 2, exploring the data with crosstabs and descriptive statistics trying to find interesting, intuitive relationships to model.\nA typical project file is usually about 1GB.  Files are organized into such a manner where a row consists of a record of consumer data.  Each row has the same number of columns for every record.  This will always be the case.\nIt's pretty rare that I would subset by rows when creating a new column.  However, it's pretty common for me to subset on rows when creating reports or generating descriptive statistics.  For example, I might want to create a simple frequency for a specific line of business, say Retail credit cards.  To do this, I would select only those records where the line of business = retail in addition to whichever columns I want to report on.  When creating new columns, however, I would pull all rows of data and only the columns I need for the operations.\nThe modeling process requires that I analyze every column, look for interesting relationships with some outcome variable, and create new compound columns that describe those relationships.  The columns that I explore are usually done in small sets.  For example, I will focus on a set of say 20 columns just dealing with property values and observe how they relate to defaulting on a loan.  Once those are explored and new columns are created, I then move on to another group of columns, say college education, and repeat the process.  What I'm doing is creating candidate variables that explain the relationship between my data and some outcome.  At the very end of this process, I apply some learning techniques that create an equation out of those compound columns.\n\nIt is rare that I would ever add rows to the dataset.  I will nearly always be creating new columns (variables or features in statistics\/machine learning parlance).","AnswerCount":16,"Available Count":4,"Score":-0.024994793,"is_accepted":false,"ViewCount":341120,"Q_Id":14262433,"Users Score":-2,"Answer":"At the moment I am working \"like\" you, just on a lower scale, which is why I don't have a PoC for my suggestion.\nHowever, I seem to find success in using pickle as caching system and outsourcing execution of various functions into files - executing these files from my commando \/ main file; For example i use a prepare_use.py to convert object types, split a data set into test, validating and prediction data set.\nHow does your caching with pickle work?\nI use strings in order to access pickle-files that are dynamically created, depending on which parameters and data sets were passed (with that i try to capture and determine if the program was already run, using .shape for data set, dict for passed parameters). \nRespecting these measures, i get a String to try to find and read a .pickle-file and can, if found, skip processing time in order to jump to the execution i am working on right now.\nUsing databases I encountered similar problems, which is why i found joy in using this solution, however - there are many constraints for sure - for example storing huge pickle sets due to redundancy.\nUpdating a table from before to after a transformation can be done with proper indexing - validating information opens up a whole other book (I tried consolidating crawled rent data and stopped using a database after 2 hours basically - as I would have liked to jump back after every transformation process)\nI hope my 2 cents help you in some way.\nGreetings.","Q_Score":1156,"Tags":"python,mongodb,pandas,hdf5,large-data","A_Id":59647574,"CreationDate":"2013-01-10T16:20:00.000","Title":"\"Large data\" workflows using pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have tried to puzzle out an answer to this question for many months while learning pandas.  I use SAS for my day-to-day work and it is great for it's out-of-core support.  However, SAS is horrible as a piece of software for numerous other reasons.\nOne day I hope to replace my use of SAS with python and pandas, but I currently lack an out-of-core workflow for large datasets.  I'm not talking about \"big data\" that requires a distributed network, but rather files too large to fit in memory but small enough to fit on a hard-drive.\nMy first thought is to use HDFStore to hold large datasets on disk and pull only the pieces I need into dataframes for analysis.  Others have mentioned MongoDB as an easier to use alternative.  My question is this:\nWhat are some best-practice workflows for accomplishing the following:\n\nLoading flat files into a permanent, on-disk database structure\nQuerying that database to retrieve data to feed into a pandas data structure\nUpdating the database after manipulating pieces in pandas\n\nReal-world examples would be much appreciated, especially from anyone who uses pandas on \"large data\".\nEdit -- an example of how I would like this to work:\n\nIteratively import a large flat-file and store it in a permanent, on-disk database structure.  These files are typically too large to fit in memory.\nIn order to use Pandas, I would like to read subsets of this data (usually just a few columns at a time) that can fit in memory.\nI would create new columns by performing various operations on the selected columns.\nI would then have to append these new columns into the database structure.\n\nI am trying to find a best-practice way of performing these steps. Reading links about pandas and pytables it seems that appending a new column could be a problem.\nEdit -- Responding to Jeff's questions specifically:\n\nI am building consumer credit risk models. The kinds of data include phone, SSN and address characteristics; property values; derogatory information like criminal records, bankruptcies, etc... The datasets I use every day have nearly 1,000 to 2,000 fields on average of mixed data types: continuous, nominal and ordinal variables of both numeric and character data.  I rarely append rows, but I do perform many operations that create new columns.\nTypical operations involve combining several columns using conditional logic into a new, compound column. For example, if var1 > 2 then newvar = 'A' elif var2 = 4 then newvar = 'B'.  The result of these operations is a new column for every record in my dataset.\nFinally, I would like to append these new columns into the on-disk data structure.  I would repeat step 2, exploring the data with crosstabs and descriptive statistics trying to find interesting, intuitive relationships to model.\nA typical project file is usually about 1GB.  Files are organized into such a manner where a row consists of a record of consumer data.  Each row has the same number of columns for every record.  This will always be the case.\nIt's pretty rare that I would subset by rows when creating a new column.  However, it's pretty common for me to subset on rows when creating reports or generating descriptive statistics.  For example, I might want to create a simple frequency for a specific line of business, say Retail credit cards.  To do this, I would select only those records where the line of business = retail in addition to whichever columns I want to report on.  When creating new columns, however, I would pull all rows of data and only the columns I need for the operations.\nThe modeling process requires that I analyze every column, look for interesting relationships with some outcome variable, and create new compound columns that describe those relationships.  The columns that I explore are usually done in small sets.  For example, I will focus on a set of say 20 columns just dealing with property values and observe how they relate to defaulting on a loan.  Once those are explored and new columns are created, I then move on to another group of columns, say college education, and repeat the process.  What I'm doing is creating candidate variables that explain the relationship between my data and some outcome.  At the very end of this process, I apply some learning techniques that create an equation out of those compound columns.\n\nIt is rare that I would ever add rows to the dataset.  I will nearly always be creating new columns (variables or features in statistics\/machine learning parlance).","AnswerCount":16,"Available Count":4,"Score":1.0,"is_accepted":false,"ViewCount":341120,"Q_Id":14262433,"Users Score":21,"Answer":"One more variation\nMany of the operations done in pandas can also be done as a db query (sql, mongo)\nUsing a RDBMS or mongodb allows you to perform some of the aggregations in the DB Query (which is optimized for large data, and uses cache and indexes efficiently)\nLater, you can perform post processing using pandas.\nThe advantage of this method is that you gain the DB optimizations for working with large data, while still defining the logic in a high level declarative syntax - and not having to deal with the details of deciding what to do in memory and what to do out of core.\nAnd although the query language and pandas are different, it's usually not complicated to translate part of the logic from one to another.","Q_Score":1156,"Tags":"python,mongodb,pandas,hdf5,large-data","A_Id":29910919,"CreationDate":"2013-01-10T16:20:00.000","Title":"\"Large data\" workflows using pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have tried to puzzle out an answer to this question for many months while learning pandas.  I use SAS for my day-to-day work and it is great for it's out-of-core support.  However, SAS is horrible as a piece of software for numerous other reasons.\nOne day I hope to replace my use of SAS with python and pandas, but I currently lack an out-of-core workflow for large datasets.  I'm not talking about \"big data\" that requires a distributed network, but rather files too large to fit in memory but small enough to fit on a hard-drive.\nMy first thought is to use HDFStore to hold large datasets on disk and pull only the pieces I need into dataframes for analysis.  Others have mentioned MongoDB as an easier to use alternative.  My question is this:\nWhat are some best-practice workflows for accomplishing the following:\n\nLoading flat files into a permanent, on-disk database structure\nQuerying that database to retrieve data to feed into a pandas data structure\nUpdating the database after manipulating pieces in pandas\n\nReal-world examples would be much appreciated, especially from anyone who uses pandas on \"large data\".\nEdit -- an example of how I would like this to work:\n\nIteratively import a large flat-file and store it in a permanent, on-disk database structure.  These files are typically too large to fit in memory.\nIn order to use Pandas, I would like to read subsets of this data (usually just a few columns at a time) that can fit in memory.\nI would create new columns by performing various operations on the selected columns.\nI would then have to append these new columns into the database structure.\n\nI am trying to find a best-practice way of performing these steps. Reading links about pandas and pytables it seems that appending a new column could be a problem.\nEdit -- Responding to Jeff's questions specifically:\n\nI am building consumer credit risk models. The kinds of data include phone, SSN and address characteristics; property values; derogatory information like criminal records, bankruptcies, etc... The datasets I use every day have nearly 1,000 to 2,000 fields on average of mixed data types: continuous, nominal and ordinal variables of both numeric and character data.  I rarely append rows, but I do perform many operations that create new columns.\nTypical operations involve combining several columns using conditional logic into a new, compound column. For example, if var1 > 2 then newvar = 'A' elif var2 = 4 then newvar = 'B'.  The result of these operations is a new column for every record in my dataset.\nFinally, I would like to append these new columns into the on-disk data structure.  I would repeat step 2, exploring the data with crosstabs and descriptive statistics trying to find interesting, intuitive relationships to model.\nA typical project file is usually about 1GB.  Files are organized into such a manner where a row consists of a record of consumer data.  Each row has the same number of columns for every record.  This will always be the case.\nIt's pretty rare that I would subset by rows when creating a new column.  However, it's pretty common for me to subset on rows when creating reports or generating descriptive statistics.  For example, I might want to create a simple frequency for a specific line of business, say Retail credit cards.  To do this, I would select only those records where the line of business = retail in addition to whichever columns I want to report on.  When creating new columns, however, I would pull all rows of data and only the columns I need for the operations.\nThe modeling process requires that I analyze every column, look for interesting relationships with some outcome variable, and create new compound columns that describe those relationships.  The columns that I explore are usually done in small sets.  For example, I will focus on a set of say 20 columns just dealing with property values and observe how they relate to defaulting on a loan.  Once those are explored and new columns are created, I then move on to another group of columns, say college education, and repeat the process.  What I'm doing is creating candidate variables that explain the relationship between my data and some outcome.  At the very end of this process, I apply some learning techniques that create an equation out of those compound columns.\n\nIt is rare that I would ever add rows to the dataset.  I will nearly always be creating new columns (variables or features in statistics\/machine learning parlance).","AnswerCount":16,"Available Count":4,"Score":1.0,"is_accepted":false,"ViewCount":341120,"Q_Id":14262433,"Users Score":167,"Answer":"I think the answers above are missing a simple approach that I've found very useful. \nWhen I have a file that is too large to load in memory, I break up the file into multiple smaller files (either by row or cols)\nExample: In case of 30 days worth of trading data of ~30GB size, I break it into a file per day of ~1GB size. I subsequently process each file separately and aggregate results at the end\nOne of the biggest advantages is that it allows parallel processing of the files (either multiple threads or processes)\nThe other advantage is that file manipulation (like adding\/removing dates in the example) can be accomplished by regular shell commands, which is not be possible in more advanced\/complicated file formats\nThis approach doesn't cover all scenarios, but is very useful in a lot of them","Q_Score":1156,"Tags":"python,mongodb,pandas,hdf5,large-data","A_Id":20690383,"CreationDate":"2013-01-10T16:20:00.000","Title":"\"Large data\" workflows using pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have tried to puzzle out an answer to this question for many months while learning pandas.  I use SAS for my day-to-day work and it is great for it's out-of-core support.  However, SAS is horrible as a piece of software for numerous other reasons.\nOne day I hope to replace my use of SAS with python and pandas, but I currently lack an out-of-core workflow for large datasets.  I'm not talking about \"big data\" that requires a distributed network, but rather files too large to fit in memory but small enough to fit on a hard-drive.\nMy first thought is to use HDFStore to hold large datasets on disk and pull only the pieces I need into dataframes for analysis.  Others have mentioned MongoDB as an easier to use alternative.  My question is this:\nWhat are some best-practice workflows for accomplishing the following:\n\nLoading flat files into a permanent, on-disk database structure\nQuerying that database to retrieve data to feed into a pandas data structure\nUpdating the database after manipulating pieces in pandas\n\nReal-world examples would be much appreciated, especially from anyone who uses pandas on \"large data\".\nEdit -- an example of how I would like this to work:\n\nIteratively import a large flat-file and store it in a permanent, on-disk database structure.  These files are typically too large to fit in memory.\nIn order to use Pandas, I would like to read subsets of this data (usually just a few columns at a time) that can fit in memory.\nI would create new columns by performing various operations on the selected columns.\nI would then have to append these new columns into the database structure.\n\nI am trying to find a best-practice way of performing these steps. Reading links about pandas and pytables it seems that appending a new column could be a problem.\nEdit -- Responding to Jeff's questions specifically:\n\nI am building consumer credit risk models. The kinds of data include phone, SSN and address characteristics; property values; derogatory information like criminal records, bankruptcies, etc... The datasets I use every day have nearly 1,000 to 2,000 fields on average of mixed data types: continuous, nominal and ordinal variables of both numeric and character data.  I rarely append rows, but I do perform many operations that create new columns.\nTypical operations involve combining several columns using conditional logic into a new, compound column. For example, if var1 > 2 then newvar = 'A' elif var2 = 4 then newvar = 'B'.  The result of these operations is a new column for every record in my dataset.\nFinally, I would like to append these new columns into the on-disk data structure.  I would repeat step 2, exploring the data with crosstabs and descriptive statistics trying to find interesting, intuitive relationships to model.\nA typical project file is usually about 1GB.  Files are organized into such a manner where a row consists of a record of consumer data.  Each row has the same number of columns for every record.  This will always be the case.\nIt's pretty rare that I would subset by rows when creating a new column.  However, it's pretty common for me to subset on rows when creating reports or generating descriptive statistics.  For example, I might want to create a simple frequency for a specific line of business, say Retail credit cards.  To do this, I would select only those records where the line of business = retail in addition to whichever columns I want to report on.  When creating new columns, however, I would pull all rows of data and only the columns I need for the operations.\nThe modeling process requires that I analyze every column, look for interesting relationships with some outcome variable, and create new compound columns that describe those relationships.  The columns that I explore are usually done in small sets.  For example, I will focus on a set of say 20 columns just dealing with property values and observe how they relate to defaulting on a loan.  Once those are explored and new columns are created, I then move on to another group of columns, say college education, and repeat the process.  What I'm doing is creating candidate variables that explain the relationship between my data and some outcome.  At the very end of this process, I apply some learning techniques that create an equation out of those compound columns.\n\nIt is rare that I would ever add rows to the dataset.  I will nearly always be creating new columns (variables or features in statistics\/machine learning parlance).","AnswerCount":16,"Available Count":4,"Score":1.0,"is_accepted":false,"ViewCount":341120,"Q_Id":14262433,"Users Score":72,"Answer":"If your datasets are between 1 and 20GB, you should get a workstation with 48GB of RAM. Then Pandas can hold the entire dataset in RAM. I know its not the answer you're looking for here, but doing scientific computing on a notebook with 4GB of RAM isn't reasonable.","Q_Score":1156,"Tags":"python,mongodb,pandas,hdf5,large-data","A_Id":19739768,"CreationDate":"2013-01-10T16:20:00.000","Title":"\"Large data\" workflows using pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on a large project that does SPC analysis and have 1000's of different unrelated dataframe objects. Does anyone know of a module for storing objects in memory?  I could use a python dictionary but would like it more elaborate and functional mechanisms like locking, thread safe, who has it and a waiting list etc? I was thinking of creating something that behaves like my local public library system.  The way it checks in and out books to one owner ...etc.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":1701,"Q_Id":14270163,"Users Score":1,"Answer":"Redis with redis-py is one solution. Redis is really fast and there are nice Python bindings. Pytables, as mentioned above, is a good choice as well. PyTables is HDF5, and is really really fast.","Q_Score":1,"Tags":"python,object,pandas,dataframe,storage","A_Id":14271696,"CreationDate":"2013-01-11T01:18:00.000","Title":"Pandas storing 1000's of dataframe objects","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using the Scikit-learn Extremely Randomized Trees algorithm to get info about the relative feature importances and I have a question about how \"redundant features\" are ranked.\nIf I have two features that are identical (redundant) and important to the classification, the extremely randomized trees cannot detect the redundancy of the features. That is, both features get a high ranking.  Is there any other way to detect that two features are actualy redundant?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":203,"Q_Id":14304420,"Users Score":0,"Answer":"Maybe you could extract the top n important features and then compute pairwise Spearman's or Pearson's correlations for those in order to detect redundancy only for the top informative features as it might not be feasible to compute all pairwise feature correlations (quadratic with the number of features).\nThere might be more clever ways to do the same by exploiting the statistics of the relative occurrences of the features as nodes in the decision trees though.","Q_Score":1,"Tags":"python-2.7,scikit-learn","A_Id":14309807,"CreationDate":"2013-01-13T14:27:00.000","Title":"Feature importance based on extremely randomize trees and feature redundancy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I read in an large python array from a csv file (20332 *17009) using window7 64 bit OS machine with 12 G ram. The array has values in the half of places, like the example below. I only need the array where has values for analysis, rather than the whole array. \n[0 0 0 0 0 0\n0 0 0 3 8 0\n0 4 2 7 0 0\n0 0 5 2 0 0\n0 0 1 0 0 0]\nI am wondering: is it possible to ignore 0 value for analysis and save more memory?\nThanks in advance!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":733,"Q_Id":14308889,"Users Score":2,"Answer":"Given your description, a sparse representation may not be very useful to you. There are many other options, though: \n\nMake sure your values are represented using the smallest data type possible. The example you show above is best represented as single-byte integers. Reading into a numpy array or python array will give you good control over data type.\nYou can trade memory for performance by only reading a part of the data at a time. If you re-write the entire dataset as binary instead of CSV, then you can use mmap to access the file as if it were already in memory (this would also make it faster to read and write).\nIf you really need the entire dataset in memory (and it really doesn't fit), then some sort of compression may be necessary. Sparse matrices are an option (as larsmans mentioned in the comments, both scipy and pandas have sparse matrix implementations), but these will only help if the fraction of zero-value entries is large. Better compression options will depend on the nature of your data. Consider breaking up the array into chunks and compressing those with a fast compression algorithm like RLE, SZIP, etc.","Q_Score":0,"Tags":"python,arrays","A_Id":14309992,"CreationDate":"2013-01-13T22:23:00.000","Title":"How to save memory for a large python array?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In short ... I have a Python Pandas data frame that is read in from an Excel file using 'read_table'.  I would like to keep a handful of the series from the data, and purge the rest.  I know that I can just delete what I don't want one-by-one using 'del data['SeriesName']', but what I'd rather do is specify what to keep instead of specifying what to delete.\nIf the simplest answer is to copy the existing data frame into a new data frame that only contains the series I want, and then delete the existing frame in its entirety, I would satisfied with that solution ... but if that is indeed the best way, can someone walk me through it?\nTIA ... I'm a newb to Pandas.  :)","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":30755,"Q_Id":14363640,"Users Score":1,"Answer":"You can also specify a list of columns to keep with the usecols option in pandas.read_table. This speeds up the loading process as well.","Q_Score":18,"Tags":"python,pandas","A_Id":44592825,"CreationDate":"2013-01-16T16:57:00.000","Title":"Python Pandas - Deleting multiple series from a data frame in one command","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I do data mining research and often have Python scripts that load large datasets from SQLite databases, CSV files, pickle files, etc. In the development process, my scripts often need to be changed and I find myself waiting 20 to 30 seconds waiting for data to load. \nLoading data streams (e.g. from a SQLite database) sometimes works, but not in all situations -- if I need to go back into a dataset often, I'd rather pay the upfront time cost of loading the data.\nMy best solution so far is subsampling the data until I'm happy with my final script. Does anyone have a better solution\/design practice?\nMy \"ideal\" solution would involve using the Python debugger (pdb) cleverly so that the data remains loaded in memory, I can edit my script, and then resume from a given point.","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":806,"Q_Id":14369696,"Users Score":0,"Answer":"Write a script that does the selects, the object-relational conversions, then pickles the data to a local file.\nYour development script will start by unpickling the data and proceeding.\nIf the data is significantly smaller than physical RAM, you can memory map a file shared between two processes, and write the pickled data to memory.","Q_Score":3,"Tags":"python,performance,data-mining,pdb,large-data","A_Id":14369860,"CreationDate":"2013-01-16T23:21:00.000","Title":"How do I make large datasets load quickly in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I do data mining research and often have Python scripts that load large datasets from SQLite databases, CSV files, pickle files, etc. In the development process, my scripts often need to be changed and I find myself waiting 20 to 30 seconds waiting for data to load. \nLoading data streams (e.g. from a SQLite database) sometimes works, but not in all situations -- if I need to go back into a dataset often, I'd rather pay the upfront time cost of loading the data.\nMy best solution so far is subsampling the data until I'm happy with my final script. Does anyone have a better solution\/design practice?\nMy \"ideal\" solution would involve using the Python debugger (pdb) cleverly so that the data remains loaded in memory, I can edit my script, and then resume from a given point.","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":806,"Q_Id":14369696,"Users Score":0,"Answer":"Jupyter notebook allows you to load a large data set into a memory resident data structure, such as a Pandas dataframe in one cell. Then you can operate on that data structure in subsequent cells without having to reload the data.","Q_Score":3,"Tags":"python,performance,data-mining,pdb,large-data","A_Id":63300344,"CreationDate":"2013-01-16T23:21:00.000","Title":"How do I make large datasets load quickly in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am attempting to fill holes in a binary image.  The image is rather large so I have broken it into chunks for processing.\nWhen I use the scipy.ndimage.morphology.binary_fill_holes functions, it fills larger holes that belong in the image.  So I tried using scipy.ndimage.morphology.binary_closing, which gave the desired results of filling small holes in the image.  However, when I put the chunks back together, to create the entire image, I end up with seamlines because the binary_closing function removes any values from the border pixels of each chunk.\nIs there any way to avoid this effect?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2670,"Q_Id":14385921,"Users Score":1,"Answer":"Operations that involve information from neighboring pixels, such as closing will always have trouble at the edges.  In your case, this is very easy to get around: just process subimages that are slightly larger than your tiling, and keep the good parts when stitching together.","Q_Score":6,"Tags":"python,image,image-processing,numpy,scipy","A_Id":14386145,"CreationDate":"2013-01-17T18:42:00.000","Title":"Scipy Binary Closing - Edge Pixels lose value","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to compress a huge python object ~15G, and save it on the disk. Due to requrement constraints I need to compress this file as much as possible. I am presently using zlib.compress(9). My main concern is the memory taken exceeds what I have available on the system 32g during compression, and going forward the size of the object is expected to increase. Is there a more efficient\/better way to achieve this.\nThanks.\nUpdate: Also to note the object that I want to save is a sparse numpy matrix, and that I am serializing the data before compressing, which also increases the memory consumption. Since I do not need the python object after it is serialized, would gc.collect() help?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1086,"Q_Id":14389279,"Users Score":5,"Answer":"Incremental (de)compression should be done with zlib.{de,}compressobj() so that memory consumption can be minimized. Additionally, higher compression ratios can be attained for most data by using bz2 instead.","Q_Score":1,"Tags":"python,memory,numpy,compression","A_Id":14389347,"CreationDate":"2013-01-17T22:26:00.000","Title":"Compress large python objects","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using PyML's SVM to classify reads, but would like to set the discriminant to a higher value than the default (which I assume is 0). How do I do it?\nPs. I'm using a linear kernel with the liblinear-optimizer if that matters.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":80,"Q_Id":14396632,"Users Score":1,"Answer":"Roundabout way of doing it below:\nUse the result.getDecisionFunction() method and choose according to your own preference. \nReturns a list of values like:\n[-1.0000000000000213, -1.0000000000000053, -0.9999999999999893]\nBetter answers still appreciated.","Q_Score":0,"Tags":"python,svm,pyml","A_Id":14396884,"CreationDate":"2013-01-18T10:13:00.000","Title":"Setting the SVM discriminant value in PyML","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to set up a slightly customised version of Spyder.  When Spyder starts, it automatically imports a long list of modules, including things from matplotlib, numpy, scipy etc.  Is there a way to add my own modules to that list?\nIn case it makes a difference, I'm using the Spyder configuration provided by the Python(X,Y) Windows installer.","AnswerCount":3,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":15729,"Q_Id":14400993,"Users Score":2,"Answer":"The startup script for Spyder is in site-packages\/spyderlib\/scientific_startup.py.\nCarlos' answer would also work, but this is what I was looking for.","Q_Score":8,"Tags":"python,import,module,spyder","A_Id":15023264,"CreationDate":"2013-01-18T14:27:00.000","Title":"Spyder default module import list","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to set up a slightly customised version of Spyder.  When Spyder starts, it automatically imports a long list of modules, including things from matplotlib, numpy, scipy etc.  Is there a way to add my own modules to that list?\nIn case it makes a difference, I'm using the Spyder configuration provided by the Python(X,Y) Windows installer.","AnswerCount":3,"Available Count":2,"Score":-0.1325487884,"is_accepted":false,"ViewCount":15729,"Q_Id":14400993,"Users Score":-2,"Answer":"If Spyder is executed as a python script by python binary, then you should be able to simply edit Spyder python sources and include the modules you need. You should take a look into how is it actually executed upon start.","Q_Score":8,"Tags":"python,import,module,spyder","A_Id":14401134,"CreationDate":"2013-01-18T14:27:00.000","Title":"Spyder default module import list","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Can we load a pandas DataFrame in .NET space using iron python? If not I am thinking of converting pandas df into a csv file and then reading in .net space.","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":8863,"Q_Id":14432059,"Users Score":10,"Answer":"No, Pandas is pretty well tied to CPython. Like you said, your best bet is to do the analysis in CPython with Pandas and export the result to CSV.","Q_Score":10,"Tags":"python,.net,pandas,ironpython,python.net","A_Id":14443106,"CreationDate":"2013-01-21T03:21:00.000","Title":"Can we load pandas DataFrame in .NET ironpython?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using Gensim python toolkit to build tf-idf model for documents. So I need to create a dictionary for all documents first. However, I found Gensim does not use stemming before creating the dictionary and corpus. Am I right ?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":940,"Q_Id":14468078,"Users Score":0,"Answer":"I was also struggling with the same case. To overcome i first stammed documents using NLTK and later processed it with gensim. Probably it can be a easier and handy way to perform your task.","Q_Score":2,"Tags":"python,nlp,gensim","A_Id":35618939,"CreationDate":"2013-01-22T21:11:00.000","Title":"Is stemming used when gensim creates a dictionary for tf-idf model?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to create a matrix containing 2 708 000 000 elements. When I try to create a numpy array of this size it gives me a value error. Is there any way I can increase the maximum array size?\na=np.arange(2708000000)\nValueError                                Traceback (most recent call last)\nValueError: Maximum allowed size exceeded","AnswerCount":4,"Available Count":2,"Score":-1.0,"is_accepted":false,"ViewCount":82332,"Q_Id":14525344,"Users Score":-9,"Answer":"It is indeed related to the system maximum address length, to say it simply, 32-bit system or 64-bit system. Here is an explanation for these questions, originally from Mark Dickinson\nShort answer: the Python object overhead is killing you. In Python 2.x on a 64-bit machine, a list of strings consumes 48 bytes per list entry even before accounting for the content of the strings. That's over 8.7 Gb of overhead for the size of array you describe. On a 32-bit machine it'll be a bit better: only 28 bytes per list entry.\nLonger explanation: you should be aware that Python objects themselves can be quite large: even simple objects like ints, floats and strings. In your code you're ending up with a list of lists of strings. On my (64-bit) machine, even an empty string object takes up 40 bytes, and to that you need to add 8 bytes for the list pointer that's pointing to this string object in memory. So that's already 48 bytes per entry, or around 8.7 Gb. Given that Python allocates memory in multiples of 8 bytes at a time, and that your strings are almost certainly non-empty, you're actually looking at 56 or 64 bytes (I don't know how long your strings are) per entry.\nPossible solutions:\n(1) You might do (a little) better by converting your entries from strings to ints or floats as appropriate.\n(2) You'd do much better by either using Python's array type (not the same as list!) or by using numpy: then your ints or floats would only take 4 or 8 bytes each.\nSince Python 2.6, you can get basic information about object sizes with the sys.getsizeof function. Note that if you apply it to a list (or other container) then the returned size doesn't include the size of the contained list objects; only of the structure used to hold those objects. Here are some values on my machine.","Q_Score":30,"Tags":"arrays,numpy,python-2.7,size,max","A_Id":23838980,"CreationDate":"2013-01-25T15:55:00.000","Title":"What's the maximum size of a numpy array?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to create a matrix containing 2 708 000 000 elements. When I try to create a numpy array of this size it gives me a value error. Is there any way I can increase the maximum array size?\na=np.arange(2708000000)\nValueError                                Traceback (most recent call last)\nValueError: Maximum allowed size exceeded","AnswerCount":4,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":82332,"Q_Id":14525344,"Users Score":19,"Answer":"You're trying to create an array with 2.7 billion entries. If you're running 64-bit numpy, at 8 bytes per entry, that would be 20 GB in all.\nSo almost certainly you just ran out of memory on your machine. There is no general maximum array size in numpy.","Q_Score":30,"Tags":"arrays,numpy,python-2.7,size,max","A_Id":14525604,"CreationDate":"2013-01-25T15:55:00.000","Title":"What's the maximum size of a numpy array?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"So far I have been using python to generate permutations of matrices for finding magic squares.  So what I have been doing so far (for 3x3 matrices) is that I find all the possible permutations of the set {1,2,3,4,5,6,7,8,9} using itertools.permutations, store them as a list and do my calculations and print my results.\nNow I want to find out magic squares of order 4.  Since finding all permutations means 16! possibilities, I want to increase efficiency by placing likely elements in the corner, for example 1, 16 on diagonal one corners and 4, 13 on diagonal two corners.\nSo how would I find permutations of set {1,2....16} where some elements are not moved is my question","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":2502,"Q_Id":14535650,"Users Score":1,"Answer":"Just pull the placed numbers out of the permutation set. Then insert them into their proper position in the generated permutations.\nFor your example you'd take out 1, 16, 4, 13. Permute on (2, 3, 5, 6, 7,  8, 9, 10, 11, 12, 14, 15), for each permutation, insert 1, 16, 4, 13 where you have pre-selected to place them.","Q_Score":0,"Tags":"python,math,matrix,permutation,itertools","A_Id":14535721,"CreationDate":"2013-01-26T09:36:00.000","Title":"How would I go about finding all possible permutations of a 4x4 matrix with static corner elements?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to go through every row in a DataFrame index and remove all rows that are not between a certain time.\nI have been looking for solutions but none of them separate the Date from the Time, and all I want to do is drop the rows that are outside of a Time range.","AnswerCount":3,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":19605,"Q_Id":14539992,"Users Score":3,"Answer":"You can also do:\n\n\ufeffrng = pd.date_range('1\/1\/2000', periods=24, freq='H')\nts = pd.Series(pd.np.random.randn(len(rng)), index=rng)\nts.ix[datetime.time(10):datetime.time(14)]\nOut[4]: \n2000-01-01 10:00:00   -0.363420\n2000-01-01 11:00:00   -0.979251\n2000-01-01 12:00:00   -0.896648\n2000-01-01 13:00:00   -0.051159\n2000-01-01 14:00:00   -0.449192\nFreq: H, dtype: float64\n\nDataFrame works same way.","Q_Score":17,"Tags":"python,pandas","A_Id":17913278,"CreationDate":"2013-01-26T18:15:00.000","Title":"Pandas Drop Rows Outside of Time Range","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I know how I can solve for a root in python using scipy.optimize.fsolve. \nI have a function defined \nf = lambda : -1*numpy.exp(-x**2) and I want to solve for x setting the function to a certain nonzero. For instance, I want to solve for x using f(x) = 5. \nIs there a way to do this with fsolve or would I need to use another tool in scipy? In other words, I'm looking for something analogous to Maple's fsolve.","AnswerCount":1,"Available Count":1,"Score":0.6640367703,"is_accepted":false,"ViewCount":380,"Q_Id":14544838,"Users Score":4,"Answer":"This is easy if you change your definition of f(x).  e.g. if you want f(x) = 5, define your function: g(x) = f(x) - 5 = 0","Q_Score":0,"Tags":"python,scipy,root,solver","A_Id":14544871,"CreationDate":"2013-01-27T05:55:00.000","Title":"find a value other than a root with fsolve in python's scipy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"For a lot of functions, it is possible to use either native Python or numpy to proceed. \nThis is the case for math functions, that are available with Python native import math, but also with numpy methods. This is also the case when it comes to arrays, with narray from numpy and pythons list comprehensions, or tuples. \nI have two questions relative to these features that are in Python and also in numpy\n\nin general, if method is available in native Python AND numpy, which of both solutions would you prefer ? In terms of efficiency ? Is it different and how Python and numpy would differ in their proceeding?\nMore particularly, regarding arrays, and basic functions that are dealing with arrays, like sort, concatenate..., which solution is more efficient ? What makes the efficiency of the most efficient solution?\n\nThis is very open and general question. I guess that will not impact my code greatly, but I am just wondering.","AnswerCount":3,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":1039,"Q_Id":14545602,"Users Score":4,"Answer":"In general, it probably matters most (efficiency-wise) to avoid conversions between the two. If you're mostly using non-numpy functions on data, then they'll be internally operating using standard Python data types, and thus using numpy arrays would be inefficient due to the need to convert back and forth.\nSimilarly, if you're using a lot of numpy functions to manipulate data, transforming it all back to basic Python types in between would also be inefficient.\n\nAs far as choosing functions goes, use whichever one was designed to operate on the form your data is already in - e.g. if you already have a numpy array, use the numpy functions on it; similarly, if you have a basic Python data type, use the Python functions on it. numpy's functions are going to be optimized for working with numpy's data types.","Q_Score":3,"Tags":"python,performance,numpy","A_Id":14545631,"CreationDate":"2013-01-27T08:11:00.000","Title":"numpy vs native Python - most efficient way","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"For a lot of functions, it is possible to use either native Python or numpy to proceed. \nThis is the case for math functions, that are available with Python native import math, but also with numpy methods. This is also the case when it comes to arrays, with narray from numpy and pythons list comprehensions, or tuples. \nI have two questions relative to these features that are in Python and also in numpy\n\nin general, if method is available in native Python AND numpy, which of both solutions would you prefer ? In terms of efficiency ? Is it different and how Python and numpy would differ in their proceeding?\nMore particularly, regarding arrays, and basic functions that are dealing with arrays, like sort, concatenate..., which solution is more efficient ? What makes the efficiency of the most efficient solution?\n\nThis is very open and general question. I guess that will not impact my code greatly, but I am just wondering.","AnswerCount":3,"Available Count":2,"Score":0.0665680765,"is_accepted":false,"ViewCount":1039,"Q_Id":14545602,"Users Score":1,"Answer":"When there's a choice between working with NumPy array and numeric lists, the former are typically faster.\nI don't quite understand the second question, so won't try to address it.","Q_Score":3,"Tags":"python,performance,numpy","A_Id":14545646,"CreationDate":"2013-01-27T08:11:00.000","Title":"numpy vs native Python - most efficient way","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to make computations in a python program, and I would prefer to make some of them in R. Is it possible to embed R code in python ?","AnswerCount":3,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":13823,"Q_Id":14551472,"Users Score":3,"Answer":"When I need to do R calculations, I usually write R scripts, and run them from Python using the subprocess module. The reason I chose to do this was because the version of R I had installed (2.16 I think) wasn't compatible with RPy at the time (which wanted 2.14).\nSo if you already have your R installation \"just the way you want it\", this may be a better option.","Q_Score":13,"Tags":"python,r","A_Id":14552819,"CreationDate":"2013-01-27T19:48:00.000","Title":"Embed R code in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I learned, that neural networks can replicate any function. \nNormally the neural network is fed with a set of descriptors to its input neurons and then gives out a certain score at its output neuron. I want my neural network to recognize certain behaviours from a screen. Objects on the screen are already preprocessed and clearly visible, so recognition should not be a problem. \nIs it possible to use the neural network to recognize a pixelated picture of the screen and make decisions on that basis? The amount of training data would be huge of course. Is there way to teach the ANN by online supervised learning?\nEdit:\nBecause a commenter said the programming problem would be too general: \nI would like to implement this in python first, to see if it works. If anyone could point me to a resource where i could do this online-learning thing with python, i would be grateful.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":888,"Q_Id":14559547,"Users Score":0,"Answer":"This is not entirely correct.\nA 3-layer feedforward MLP can theoretically replicate any CONTINUOUS function.\nIf there are discontinuities, then you need a 4th layer.  \nSince you are dealing with pixelated screens and such, you probably would need to consider a fourth layer. \nFinally, if you are looking at circular shapes, etc., than a radial basis function (RBF) network may be more suitable.","Q_Score":0,"Tags":"python,neural-network,image-recognition,online-algorithm","A_Id":14756113,"CreationDate":"2013-01-28T10:00:00.000","Title":"Can a neural network recognize a screen and replicate a finite set of actions?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need some advice on selecting statistics package for Python, I've done quite some search, but not sure if I get everything right, specifically on the differences between statsmodels and scipy.stats.\nOne thing that I know is those with scikits namespace are specific \"branches\" of scipy, and what used to be scikits.statsmodels is now called statsmodels. On the other hand there is also scipy.stats. What are the differences between the two, and which one is the statistics package for Python?\nThanks.\n--EDIT--\nI changed the title because some answers are not really related to the question, and I suppose that's because the title is not clear enough.","AnswerCount":3,"Available Count":3,"Score":1.2,"is_accepted":true,"ViewCount":16292,"Q_Id":14573728,"Users Score":38,"Answer":"Statsmodels has scipy.stats as a dependency. Scipy.stats has all of the probability distributions and some statistical tests. It's more like library code in the vein of numpy and scipy. Statsmodels on the other hand provides statistical models with a formula framework similar to R and it works with pandas DataFrames. There are also statistical tests, plotting, and plenty of helper functions in statsmodels. Really it depends on what you need, but you definitely don't have to choose one. They have different aims and strengths.","Q_Score":26,"Tags":"python,scipy,scikits,statsmodels","A_Id":14575243,"CreationDate":"2013-01-29T00:28:00.000","Title":"Python statistics package: difference between statsmodel and scipy.stats","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need some advice on selecting statistics package for Python, I've done quite some search, but not sure if I get everything right, specifically on the differences between statsmodels and scipy.stats.\nOne thing that I know is those with scikits namespace are specific \"branches\" of scipy, and what used to be scikits.statsmodels is now called statsmodels. On the other hand there is also scipy.stats. What are the differences between the two, and which one is the statistics package for Python?\nThanks.\n--EDIT--\nI changed the title because some answers are not really related to the question, and I suppose that's because the title is not clear enough.","AnswerCount":3,"Available Count":3,"Score":-0.0665680765,"is_accepted":false,"ViewCount":16292,"Q_Id":14573728,"Users Score":-1,"Answer":"I think THE statistics package is numpy\/scipy. It works also great if you want to plot your data using matplotlib. \nHowever, as far as I know, matplotlib doesn't work with Python 3.x yet.","Q_Score":26,"Tags":"python,scipy,scikits,statsmodels","A_Id":14574087,"CreationDate":"2013-01-29T00:28:00.000","Title":"Python statistics package: difference between statsmodel and scipy.stats","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need some advice on selecting statistics package for Python, I've done quite some search, but not sure if I get everything right, specifically on the differences between statsmodels and scipy.stats.\nOne thing that I know is those with scikits namespace are specific \"branches\" of scipy, and what used to be scikits.statsmodels is now called statsmodels. On the other hand there is also scipy.stats. What are the differences between the two, and which one is the statistics package for Python?\nThanks.\n--EDIT--\nI changed the title because some answers are not really related to the question, and I suppose that's because the title is not clear enough.","AnswerCount":3,"Available Count":3,"Score":0.3215127375,"is_accepted":false,"ViewCount":16292,"Q_Id":14573728,"Users Score":5,"Answer":"I try to use pandas\/statsmodels\/scipy for my work on a day-to-day basis, but sometimes those packages come up a bit short (LOESS, anybody?). The problem with the RPy module is (last I checked, at least) that it wants a specific version of R that isn't current---my R installation is 2.16 (I think) and RPy wanted 2.14. So either you have to have two parallel installations of R, or you have to downgrade. (If you don't have R installed, then you can just install the correct version of R and use RPy.)\nSo when I need something that isn't in pandas\/statsmodels\/scipy I write R scripts, and run them with the subprocess module. This lets me interact with R as little as possible (which I really don't like programming in), but I can still leverage all the stuff that R has that the Python packages don't.\nThe lesson is that there isn't ever one solution to any problem---you have to assemble a whole bunch of parts that are all useful to you (and maybe write some of your own), in a way that you understand, to solve problems. (R aficionados will disagree, of course!)","Q_Score":26,"Tags":"python,scipy,scikits,statsmodels","A_Id":14575672,"CreationDate":"2013-01-29T00:28:00.000","Title":"Python statistics package: difference between statsmodel and scipy.stats","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an IPython Notebook that is using Pandas to back-test a rule-based trading system.\nI have a function that accepts various scalars and functions as parameters and outputs a stats pack as some tables and a couple of plots.\nFor automation, I want to be able to format this nicely into a \"page\" and then call the function in a loop while varying the inputs and have it output a number of pages for comparison, all from a single notebook cell.\nThe approach I am taking is to create IpyTables and then call _repr_html_(), building up the HTML output along the way so that I can eventually return it from the function that runs the loop.\nHow can I capture the output of the plots this way - matplotlib subplot objects don't seem to implement _repr_html_()?\nFeel free to suggest another approach entirely that you think might equally solve the problem.\nTIA","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2790,"Q_Id":14580684,"Users Score":1,"Answer":"Ok, if you go that route, this answer stackoverflow.com\/a\/5314808\/243434 on how to capture >matplotlib figures as inline PNGs may help \u2013 @crewbum\nTo prevent duplication of plots, try running with pylab disabled (double-check your config >files and the command line). \u2013 @crewbum\n\n--> this last requires a restart of the notebook: ipython notebook --pylab (NB no inline)","Q_Score":4,"Tags":"python,pandas,matplotlib,jupyter-notebook","A_Id":14600682,"CreationDate":"2013-01-29T10:25:00.000","Title":"How to grab matplotlib plot as html in ipython notebook?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"SciPy\/Numpy seems to support many filters, but not the root-raised cosine filter.  Is there a trick to easily create one rather than calculating the transfer function?  An approximation would be fine as well.","AnswerCount":6,"Available Count":1,"Score":-0.0665680765,"is_accepted":false,"ViewCount":13131,"Q_Id":14614966,"Users Score":-2,"Answer":"SciPy will support any filter.  Just calculate the impulse response and use any of the appropriate scipy.signal filter\/convolve functions.","Q_Score":7,"Tags":"python,numpy,scipy,signal-processing","A_Id":15783554,"CreationDate":"2013-01-30T22:20:00.000","Title":"Easy way to implement a Root Raised Cosine (RRC) filter using Python & Numpy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm about to write my very own scaling, rotation, normalization functions in python. Is there a convenient way to avoid this? I found NumPy, but it kind-a seems like an overkill for my little 2D-needs.\nAre there basic vector operations available in the std python libs?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1480,"Q_Id":14635549,"Users Score":3,"Answer":"No, the standard in numpy. I wouldn't think of it as overkill, think of it as a very well written and tested library, even if you do just need a small portion of it. All the basic vector & matrix operations are implemented efficiently (falling back to C and Fortan) which makes it fast and memory efficient. Don't make your own, use numpy.","Q_Score":0,"Tags":"python,math,vector-graphics","A_Id":14635675,"CreationDate":"2013-01-31T21:33:00.000","Title":"Python vector transformation (normalize, scale, rotate etc.)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a data set which I do multiple mappings on. \nAssuming that I have 3 key-values pair for the reduce function, how do I modify the output such that I have 3 blobfiles - one for each of the key value pair?\nDo let me know if I can clarify further.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":139,"Q_Id":14635693,"Users Score":2,"Answer":"I don't think such functionality exists (yet?) in the  GAE Mapreduce library.\nDepending on the size of your dataset, and the type of output required, you can small-time-investment hack your way around it by co-opting the reducer as another output writer. For example, if one of the reducer outputs should go straight back to the datastore, and another output should go to a file, you could open a file yourself and write the outputs to it. Alternatively, you could serialize and explicitly store the intermediate map results to a temporary datastore using operation.db.Put, and perform separate Map or Reduce jobs on that datastore. Of course, that will end up being more expensive than the first workaround.\nIn your specific key-value example, I'd suggest writing to a Google Cloud Storage File, and postprocessing it to split it into three files as required. That'll also give you more control over final file names.","Q_Score":0,"Tags":"python,google-app-engine,mapreduce","A_Id":20688782,"CreationDate":"2013-01-31T21:42:00.000","Title":"GAE MapReduce, How to write Multiple Outputs","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"i tried searching stackoverflow for the tags [a-star] [and] [python] and [a-star] [and] [numpy], but nothing. i also googled it but whether due to the tokenizing or its existence, i got nothing.\nit's not much harder than your coding-interview tree traversals to implement. but, it would be nice to have a correct efficient implementation for everyone.\ndoes numpy have A*?","AnswerCount":4,"Available Count":1,"Score":0.049958375,"is_accepted":false,"ViewCount":15316,"Q_Id":14636918,"Users Score":1,"Answer":"No, there is no A* search in Numpy.","Q_Score":11,"Tags":"python,numpy,a-star","A_Id":42075989,"CreationDate":"2013-01-31T23:16:00.000","Title":"A-star search in numpy or python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to write a Monte Carlo simulation. In my simulation I need to generate many random variates from a discrete probability distribution. \nI do have a closed-form solution for the distribution and it has finite support; however, it is not a standard distribution. I am aware that I could draw a uniform[0,1) random variate and compare it to the CDF get a random variate from my distribution, but the parameters in the distributions are always changing. Using this method is too slow. \nSo I guess my question has two parts:\n\nIs there a method\/algorithm to quickly generate finite, discrete random variates without using the CDF?\nIs there a Python module and\/or a C++ library which already has this functionality?","AnswerCount":3,"Available Count":3,"Score":1.2,"is_accepted":true,"ViewCount":374,"Q_Id":14655681,"Users Score":0,"Answer":"Acceptance\\Rejection:\nFind a function that is always higher than the pdf. Generate 2 Random variates. The first one you scale to calculate the value, the second you use to decide whether to accept or reject the choice. Rinse and repeat until you accept a value.\nSorry I can't be more specific, but I haven't done it for a while..\nIts a standard algorithm, but I'd personally implement it from scratch, so I'm not aware of any implementations.","Q_Score":0,"Tags":"c++,python,algorithm,random,montecarlo","A_Id":14655846,"CreationDate":"2013-02-01T21:55:00.000","Title":"Is there a way to generate a random variate from a non-standard distribution without computing CDF?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to write a Monte Carlo simulation. In my simulation I need to generate many random variates from a discrete probability distribution. \nI do have a closed-form solution for the distribution and it has finite support; however, it is not a standard distribution. I am aware that I could draw a uniform[0,1) random variate and compare it to the CDF get a random variate from my distribution, but the parameters in the distributions are always changing. Using this method is too slow. \nSo I guess my question has two parts:\n\nIs there a method\/algorithm to quickly generate finite, discrete random variates without using the CDF?\nIs there a Python module and\/or a C++ library which already has this functionality?","AnswerCount":3,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":374,"Q_Id":14655681,"Users Score":0,"Answer":"Indeed acceptance\/rejection is the way to go if you know analytically your pdf. Let's call it f(x). Find a pdf g(x) such that there exist a constant c, such that c.g(x) > f(x), and such that you know how to simulate a variable with pdf g(x) - For example, as you work with a distribution with a finite support, a uniform will do: g(x) = 1\/(size of your domain) over the domain.\nThen draw a couple (G, U) such that G is simulated with pdf g(x), and U is uniform on [0, c.g(G)]. Then, if U < f(G), accept U as your variable. Otherwise draw again. The U you will finally accept will have f as a pdf.\nNote that the constant c determines the efficiency of the method. The smaller c, the most efficient you will be - basically you will need on average c drawings to get the right variable. Better get a function g simple enough (don't forget you need to draw variables using g as a pdf) but will the smallest possible c.","Q_Score":0,"Tags":"c++,python,algorithm,random,montecarlo","A_Id":14657373,"CreationDate":"2013-02-01T21:55:00.000","Title":"Is there a way to generate a random variate from a non-standard distribution without computing CDF?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to write a Monte Carlo simulation. In my simulation I need to generate many random variates from a discrete probability distribution. \nI do have a closed-form solution for the distribution and it has finite support; however, it is not a standard distribution. I am aware that I could draw a uniform[0,1) random variate and compare it to the CDF get a random variate from my distribution, but the parameters in the distributions are always changing. Using this method is too slow. \nSo I guess my question has two parts:\n\nIs there a method\/algorithm to quickly generate finite, discrete random variates without using the CDF?\nIs there a Python module and\/or a C++ library which already has this functionality?","AnswerCount":3,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":374,"Q_Id":14655681,"Users Score":0,"Answer":"If acceptance rejection is also too inefficient you could also try some Markov Chain MC method, they generate a sequence of samples each one dependent on the previous one, so by skipping blocks of them one can subsample obtaining a more or less independent set. They only need the PDF, or even just a multiple of it. Usually they work with fixed distributions, but can also be adapted to slowly changing ones.","Q_Score":0,"Tags":"c++,python,algorithm,random,montecarlo","A_Id":18890513,"CreationDate":"2013-02-01T21:55:00.000","Title":"Is there a way to generate a random variate from a non-standard distribution without computing CDF?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need some help taking data from a .txt file and putting it into an array. I have a very rudimentary understanding of Python, and I have read through the documentation sited in threads relevant to my problem, but after hours of attempting to do this I still have not been able to get anywhere. The data in my file looks like this:\n\n\n0.000000000000000000e+00 7.335686114232199684e-02\n1.999999999999999909e-07 7.571960558042964973e-01\n3.999999999999999819e-07 9.909475704320810374e-01\n5.999999999999999728e-07 3.412754086075696081e-01\n\n\nI used numpy.genfromtxt, but got the following output: array(nan)\nCould you tell me what the proper way to do this is?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":132,"Q_Id":14665379,"Users Score":0,"Answer":"numpy.loadtxt() is the function you are looking for. This returns a two-dimenstional array.","Q_Score":0,"Tags":"python,numpy","A_Id":14665480,"CreationDate":"2013-02-02T19:03:00.000","Title":"Taking data from a text file and putting it into a numpy array","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"ive got this big\/easy problem that i need to solve but i cant..\nWhat im trying to do is to count cars on a highway, and i actually can detect the moving cars and put bounding boxes on them... but when i try to count them, i simply cant. I tried making a variable  (nCars) and increment everytime the program creates a bounding box, but that seems to increment to many times.. \nThe question is: Whats the best way to count moving cars\/objects?\nPS: I Dont know if this is a silly question but im going nutts.... Thanks for everything (:\nAnd im new here but i know this website for some time (: Its great!","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":1748,"Q_Id":14677763,"Users Score":1,"Answer":"I guess you are detecting the cars in each frame and creating a new bounding box each time a car is detected. This would explain the many increments of your variable.\nYou have to find a way to figure out if the car detected in one frame is the same car from the frame before (if you had a car detected in the previous frame). You might be able to achieve this by simply comparing the bounding box distances between two frames; if the distance is less than a threshold value, you can say that it's the same car from the previous frame. This way you can track the cars.\nYou could increment the counter variable when the detected car leaves the camera's field of view (exits the frame).\nThe tracking procedure I proposed here is very simple, try searching for \"object tracking\" to see what else you can use (maybe have a look at OpenCV's KLT tracking).","Q_Score":0,"Tags":"python,video,image-processing,opencv,computer-vision","A_Id":14727117,"CreationDate":"2013-02-03T21:55:00.000","Title":"Counting Cars in OpenCV + Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"ive got this big\/easy problem that i need to solve but i cant..\nWhat im trying to do is to count cars on a highway, and i actually can detect the moving cars and put bounding boxes on them... but when i try to count them, i simply cant. I tried making a variable  (nCars) and increment everytime the program creates a bounding box, but that seems to increment to many times.. \nThe question is: Whats the best way to count moving cars\/objects?\nPS: I Dont know if this is a silly question but im going nutts.... Thanks for everything (:\nAnd im new here but i know this website for some time (: Its great!","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1748,"Q_Id":14677763,"Users Score":0,"Answer":"You should use an sqlite database for store cars' informations.","Q_Score":0,"Tags":"python,video,image-processing,opencv,computer-vision","A_Id":14678095,"CreationDate":"2013-02-03T21:55:00.000","Title":"Counting Cars in OpenCV + Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it possible to add some meta-information\/metadata to a pandas DataFrame?\nFor example, the instrument's name used to measure the data, the instrument responsible, etc.\nOne workaround would be to create a column with that information, but it seems wasteful to store a single piece of information in every row!","AnswerCount":13,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":59446,"Q_Id":14688306,"Users Score":14,"Answer":"Just ran into this issue myself. As of pandas 0.13, DataFrames have a _metadata attribute on them that does persist through functions that return new DataFrames. Also seems to survive serialization just fine (I've only tried json, but I imagine hdf is covered as well).","Q_Score":127,"Tags":"python,pandas","A_Id":25715719,"CreationDate":"2013-02-04T13:59:00.000","Title":"Adding meta-information\/metadata to pandas DataFrame","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm currently working on a NLP project that is trying to differentiate between synonyms (received from Python's NLTK with WordNet) in a context.  I've looked into a good deal of NLP concepts trying to find exactly what I want, and the closest thing I've found is n-grams, but its not quite a perfect fit.\nSuppose I am trying to find the proper definition of the verb \"box\".  \"Box\" could mean \"fight\" or \"package\"; however, somewhere else in the text, the word \"ring\" or \"fighter\" appears.  As I understand it, an n-gram would be \"box fighter\" or \"box ring\", which is rather ridiculous as a phrase, and not likely to appear.  But on a concept map, the \"box\" action might be linked with a \"ring\", since they are conceptually related.\nIs n-gram what I want? Is there another name for this? Any help on where to look for retrieving such relational data?\nAll help is appreciated.","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":1365,"Q_Id":14718543,"Users Score":2,"Answer":"You might want to look into word sense disambiguation (WSD), it is the problem of determining which \"sense\" (meaning) of a word is activated by the use of the word in a particular context, a process which appears to be largely unconscious in people.","Q_Score":3,"Tags":"python,nlp,nltk,n-gram","A_Id":14718635,"CreationDate":"2013-02-05T22:55:00.000","Title":"Natural Language Processing - Similar to ngram","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm a beginner in python and easily get stucked and confused...\nWhen I read a file which contains a table with numbers with digits, it reads it as an numpy.ndarray\nPython is changing the display of the numbers. \nFor example: \nIn the input file i have this number: 56143.0254154\nand in the output file the number is written as: 5.61430254e+04\nbut i want to keep the first format in the output file.\ni tried to use the string.format or locale.format functions but it doesn't work\nCan anybody help me to do this?\nThanks!\nRuxy","AnswerCount":3,"Available Count":1,"Score":0.1325487884,"is_accepted":false,"ViewCount":1536,"Q_Id":14733471,"Users Score":2,"Answer":"Try numpy.set_printoptions() -- there you can e.g. specify the number of digits that are printed and suppress the scientific notation. For example, numpy.set_printoptions(precision=8,suppress=True) will print 8 digits and no \"...e+xx\".","Q_Score":2,"Tags":"python,numpy,string-formatting,multidimensional-array","A_Id":14734299,"CreationDate":"2013-02-06T16:06:00.000","Title":"python changes format numbers ndarray many digits","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"This is probably a very easy question, but all the sources I have found on interpolation in Matlab are trying to correlate two values, all I wanted to benefit from is if I have data which is collected over an 8 hour period, but the time between data points is varying, how do I adjust it such that the time periods are equal and the data remains consistent?\nOr to rephrase from the approach I have been trying: I have GPS lat,lon and Unix time for these points; what I want to do is take the lat and lon at time 1 and time 3 and for the case where I don't know time 2, simply fill it with data from time 1 - is there a functional way to do this? (I know in something like Python Pandas you can use fill) but I'm unsure of how to do this in Matlab.","AnswerCount":5,"Available Count":1,"Score":0.0798297691,"is_accepted":false,"ViewCount":705,"Q_Id":14742893,"Users Score":2,"Answer":"What you can do is use interp1 function. This function will fit in deifferent way the numbers for a new X series.\nFor example if you have \nx=[1 3 5 6 10 12] \ny=[15 20 17 33 56 89]\nThis means if you want to fill in for x1=[1 2 3 4 5 6 7 ... 12], you will type \ny1=interp1(x,y,x1)","Q_Score":2,"Tags":"python,matlab,pandas,gps","A_Id":14754539,"CreationDate":"2013-02-07T03:13:00.000","Title":"Interpolation Function","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a list of user:friends (50,000) and a list of event attendees (25,000 events and list of attendees for each event). I want to find top k friends with whom the user goes to the event. This needs to be done for each user. \nI tried traversing lists but is computationally very expensive. I am also trying to do it by creating weighted graph.(Python)\nLet me know if there is any other approach.","AnswerCount":4,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":177,"Q_Id":14826245,"Users Score":0,"Answer":"I'd give you a code sample if I better understood what your current data structures look like, but this sounds like a job for a pandas dataframe groupby (in case you don't feel like actually using a database as others have suggested).","Q_Score":2,"Tags":"python,data-structures","A_Id":14827656,"CreationDate":"2013-02-12T05:48:00.000","Title":"Search in Large data set","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":1,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a list of user:friends (50,000) and a list of event attendees (25,000 events and list of attendees for each event). I want to find top k friends with whom the user goes to the event. This needs to be done for each user. \nI tried traversing lists but is computationally very expensive. I am also trying to do it by creating weighted graph.(Python)\nLet me know if there is any other approach.","AnswerCount":4,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":177,"Q_Id":14826245,"Users Score":0,"Answer":"Can you do something like this.\nIm assuming friends of a user is relatively less, and the events attended by a particular user is also much lesser than total number of events.\nSo have a boolean vector of attended events for each friend of the user.\nDoing a dot product and those that have max will be the friend who most likely resembles the user.\nAgain,.before you do this..you will have to filter some events to keep the size of your vectors manageable.","Q_Score":2,"Tags":"python,data-structures","A_Id":14826472,"CreationDate":"2013-02-12T05:48:00.000","Title":"Search in Large data set","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":1,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm solving a classification problem with sklearn's logistic regression in python.\nMy problem is a general\/generic one.  I have a dataset with two classes\/result (positive\/negative or 1\/0), but the set is highly unbalanced.  There are ~5% positives and ~95% negatives.\nI know there are a number of ways to deal with an unbalanced problem like this, but have not found a good explanation of how to implement properly using the sklearn package.\nWhat I've done thus far is to build a balanced training set by selecting entries with a positive outcome and an equal number of randomly selected negative entries.  I can then train the model to this set, but I'm stuck with how to modify the model to then work on the original unbalanced population\/set.\nWhat are the specific steps to do this?  I've poured over the sklearn documentation and examples and haven't found a good explanation.","AnswerCount":2,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":18285,"Q_Id":14863125,"Users Score":22,"Answer":"Have you tried to pass to your class_weight=\"auto\" classifier? Not all classifiers in sklearn support this, but some do. Check the docstrings.\nAlso you can rebalance your dataset by randomly dropping negative examples and \/ or over-sampling positive examples (+ potentially adding some slight gaussian feature noise).","Q_Score":22,"Tags":"python,scikit-learn,classification","A_Id":14864547,"CreationDate":"2013-02-13T21:06:00.000","Title":"sklearn logistic regression with unbalanced classes","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"i want to change the default icon images of a matplotplib.\neven when i replaced the image with the same name and size from the image location \ni.e. C:\\Python27\\Lib\\site-packages\\matplotlib\\mpl-data\\images\\home.png \nits still plotting the the graphs with the same default images.\nIf I need to change the code of the image location in any file kindly direct me to the code and the code segment.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":888,"Q_Id":14869145,"Users Score":1,"Answer":"I suspect that exactly what you will have to do will depend on you gui toolkit.  The code that you want to look at is in matplotlib\/lib\/matplotlib\/backends and you want to find the class that sub-classes NavigationToolbar2 in which ever backend you are using.","Q_Score":1,"Tags":"python,matplotlib","A_Id":14877368,"CreationDate":"2013-02-14T06:40:00.000","Title":"customize the default toolbar icon images of a matplotlib graph","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I was thinking of doing a little project that involves recognizing simple two-dimensional objects using some kind of machine learning. I think it's better that I have each network devoted to recognizing only one type of object. So here are my two questions:\n\nWhat kind of network should I use? The two I can think of that could work are simple feed-forward networks and Hopfield networks. Since I also want to know how much the input looks like the target, Hopfield nets are probably not suitable.\nIf I use something that requires supervised learning and I only want one output unit that indicates how much the input looks like the target, what counter-examples should I show it during the training process? Just giving it positive examples I'm pretty sure won't work (the network will just learn to always say 'yes').\n\nThe images are going to be low resolution and black and white.","AnswerCount":2,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":2529,"Q_Id":14875450,"Users Score":4,"Answer":"First, a note regarding the classification method to use. \nIf you intend to use the image pixels themselves as features, neural network might be a fitting classification method. In that case, I think it might be a better idea to train the same network to distinguish between the various objects, rather than using a separate network for each, because it would allow the network to focus on the most discriminative features. \nHowever, if you intend to extract synthetic features from the image and base the classification on them, I would suggest considering other classification methods, e.g. SVM.\nThe reason is that neural networks generally have many parameters to set (e.g. network size and architecture), making the process of building a classifier longer and more complicated.\nSpecifically regarding your NN-related questions, I would suggest using a feedforward network, which is relatively easy to build and train, with a softmax output layer, which allows assigning probabilities to the various classes. \nIn case you're using a single network for classification, the question regarding negative examples is irrelevant; for each class, other classes would be its negative examples. If you decide to use different networks, you can use the same counter-examples (i.e. other classes), but as a rule of thumb, I'd suggest showing no more than 2-10 negative examples per positive example.\nEDIT: \nbased on the comments below, it seems the problem is to decide how fitting is a given image (drawing) to a given concept, e.g. how similar to a tree is the the user-supplied tree drawing. \nIn this case, I'd suggest a radically different approach: extract visual features from each drawing, and perform knn classification, based on all past user-supplied drawings and their classifications (possibly, plus a predefined set generated by you). You can score the similarity either by the nominal distance to same-class examples, or by the class distribution of the closest matches. \nI know that this is not neccessarily what you're asking, but this seems to me an easier and more direct approach, especially given the fact that the number of examples and classes is expected to constantly grow.","Q_Score":0,"Tags":"python,language-agnostic,machine-learning,object-recognition,pybrain","A_Id":14877671,"CreationDate":"2013-02-14T13:01:00.000","Title":"How to Train Single-Object Recognition?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a text file with a bunch of number that contains newlines every 32 entries. I want to read this file a a column vector using Numpy. How can I use numpy.loadtxt and ignore the newlines such that the generated array is of size 1024x1 and not 32x32?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":174,"Q_Id":14884214,"Users Score":1,"Answer":"Just use loadtxt and reshape (or ravel) the resulting array.","Q_Score":0,"Tags":"python,numpy","A_Id":14884277,"CreationDate":"2013-02-14T21:18:00.000","Title":"Read Vector from Text File","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a matrix of the form, say e^(Ax) where A is a square matrix. How can I integrate it from a given value a to another value bso that the output is a corresponding array?","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":1283,"Q_Id":14899139,"Users Score":2,"Answer":"Provided A has the right properties, you could transform it to the diagonal form A0 by calculating its eigenvectors and eigenvalues. In the diagonal form, the solution is sol = [exp(A0*b) - exp(A0*a)] * inv(A0), where A0 is the diagonal matrix with the eigenvalues and inv(A0) just contains the inverse of the eigenvalues in its diagonal. Finally, you transform back the solution by multiplying it with the transpose of the eigenvalues from the left and the eigenvalues from the right: transpose(eigvecs) * sol * eigvecs.","Q_Score":1,"Tags":"python,matrix,numpy,integration,exponential","A_Id":14900251,"CreationDate":"2013-02-15T16:35:00.000","Title":"how to find the integral of a matrix exponential in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"According to the Enthought website, the EPD Python distribution uses MKL for numpy and scipy.  Does EPD Free also use MKL?  If not does it use another library for BLAS\/LAPACK?  I am using EPD Free 7.3-2\nAlso, what library does the windows binary installer for numpy that can be found on scipy.org use?","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":538,"Q_Id":14946512,"Users Score":2,"Answer":"The EPD Free 7.3 installers do not include MKL. The BLAS\/LAPACK libraries which they use are ATLAS on Linux & Windows and Accelerate on OSX.","Q_Score":1,"Tags":"lapack,blas,enthought,intel-mkl,epd-python","A_Id":14966265,"CreationDate":"2013-02-18T22:31:00.000","Title":"Does the EPD Free distribution use MKL?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to fit a straight line to my data to find out if there is a gradient.\nI am currently doing this with scipy.stats.linregress.\nI'm a little confused though, because one of the outputs of linregress is the \"standard error\", but I'm not sure how linregress calculated this, as the uncertainty of your data points is not given as an input.\nSurely the uncertainty on the data points influence how uncertain the given gradient is?\nThank you!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":340,"Q_Id":14998497,"Users Score":1,"Answer":"The standard error of a linear regression is the standard deviation of the serie obtained by substracting the fitted model to your data points. It indicates how well your data points can be fitted by a linear model.","Q_Score":0,"Tags":"python,scipy","A_Id":14999516,"CreationDate":"2013-02-21T09:20:00.000","Title":"error in Python gradient measurement","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to build a simple evolution simulation of agents controlled by neural network. In the current version each agent has feed-forward neural net with one hidden layer. The environment contains fixed amount of food represented as a red dot. When an agent moves, he loses energy, and when he is near the food, he gains energy. Agent with 0 energy dies. the input of the neural net is the current angle of the agent and a vector to the closest food. Every time step, the angle of movement of each agent is changed by the output of its neural net. The aim  of course is to see food-seeking behavior evolves after some time. However, nothing happens. \nI don't know if the problem is the structure the neural net (too simple?) or the reproduction mechanism: to prevent population explosion, the initial population is about 20 agents, and as the population becomes close to 50, the reproduction chance approaches zero. When reproduction does occur, the parent is chosen by going over the list of agents from beginning to end, and checking for each agent whether or not a random number between 0 to 1 is less than the ratio between this agent's energy and the sum of the energy of all agents. If so, the searching is over and this agent becomes a parent, as we add to the environment a copy of this agent with some probability of mutations in one or more of the weights in his neural network.\nThanks in advance!","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2480,"Q_Id":15008875,"Users Score":6,"Answer":"If the environment is benign enough (e.g it's easy enough to find food) then just moving randomly may be a perfectly viable strategy and reproductive success may be far more influenced by luck than anything else.  Also consider unintended consequences: e.g if offspring is co-sited with its parent then both are immediately in competition with each other in the local area and this might be sufficiently disadvantageous to lead to the death of both in the longer term.\nTo test your system, introduce an individual with a \"premade\" neural network set up to steer the individual directly towards the nearest food (your model is such that such a thing exists and is reasobably easy to write down, right?  If not, it's unreasonable to expect it to evolve!).  Introduce that individual into your simulation amongst the dumb masses.  If the individual doesn't quickly dominate, it suggests your simulation isn't set up to reinforce such behaviour.  But if the individual enjoys reproductive success and it and its descendants take over, then your simulation is doing something right and you need to look elsewhere for the reason such behaviour isn't evolving.\nUpdate in response to comment:\nSeems to me this mixing of angles and vectors is dubious.  Whether individuals can evolve towards the \"move straight towards nearest food\" behaviour must rather depend on how well an atan function can be approximated by your network (I'm sceptical).  Again, this suggests more testing: \n\nset aside all the ecological simulation and just test perturbing a population\nof your style of random networks to see if they can evolve towards the expected function.\n(simpler, better) Have the network output a vector (instead of an angle): the direction the individual should move in (of course this means having 2 output nodes instead of one).  Obviously the \"move straight towards food\" strategy is then just a straight pass-through of the \"direction towards food\" vector components, and the interesting thing is then to see whether your random networks evolve towards this simple \"identity function\" (also should allow introduction of a readymade optimised individual as described above).\n\nI'm dubious about the \"fixed amount of food\" too.  (I assume you mean as soon as a red dot is consumed, another one is introduced).  A more \"realistic\" model might be to introduce food at a constant rate, and not impose any artificial population limits: population limits are determined by the limitations of food supply.  e.g If you introduce 100 units of food a minute and individuals need 1 unit of food per minute to survive, then your simulation should find it tends towards a long term average population of 100 individuals without any need for a clamp to avoid a \"population explosion\" (although boom-and-bust, feast-or-famine dynamics may actually emerge depending on the details).","Q_Score":8,"Tags":"python,artificial-intelligence,neural-network,artificial-life","A_Id":15011126,"CreationDate":"2013-02-21T17:44:00.000","Title":"Artificial life with neural networks","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there anyway to attach a descriptive version to an Index Column?\nFor Example, I use ISO3 CountryCode's to merge from different data sources\n'AUS' -> Australia etc. This is very convenient for merging different data sources, but when I want to print the data I would like the description version (i.e. Australia). I am imagining a dictionary attached to the Index Column of 'CountryCode' (where CountryCode is Key and CountryName is Value) and a flag that will print the Value instead of the Key which is used for data manipulation. \nIs the best solution to generate my own Dictionary() and then when it comes time to print or graph to then merge the country names in? This is ok, except it would be nice for ALL of the dataset information to be carried within the dataframe object.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":102,"Q_Id":15016187,"Users Score":1,"Answer":"I think the simplest solution split this into two columns in your DataFrame, one for country_code and country_name (you could name them something else).\nWhen you print or graph you can select which column is used.","Q_Score":1,"Tags":"python,pandas","A_Id":15016437,"CreationDate":"2013-02-22T03:09:00.000","Title":"Pandas: Attaching Descriptive Dict() to Hierarchical Index (i.e. CountryCode and CountryName)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I know generally speaking FFT and multiplication is usually faster than direct convolve operation, when the array is relatively large. However, I'm convolving a very long signal (say 10 million points) with a very short response (say 1 thousand points). In this case the fftconvolve doesn't seem to make much sense, since it forces a FFT of the second array to the same size of the first array. Is it faster to just do direct convolve in this case?","AnswerCount":3,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":10751,"Q_Id":15018526,"Users Score":3,"Answer":"FFT fast convolution via the overlap-add or overlap save algorithms can be done in limited memory by using an FFT that is only a small multiple (such as 2X) larger than the impulse response.  It breaks the long FFT up into properly overlapped shorter but zero-padded FFTs.\nEven with the overlap overhead, O(NlogN) will beat M*N in efficiency for large enough N and M.","Q_Score":11,"Tags":"python,scipy,fft,convolution","A_Id":15020070,"CreationDate":"2013-02-22T06:54:00.000","Title":"Python SciPy convolve vs fftconvolve","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to use the car evaluation dataset from the UCI repository and I wonder whether there is a convenient way to binarize categorical variables in sklearn. One approach would be to use the DictVectorizer of LabelBinarizer but here I'm getting k different features whereas you should have just k-1 in order to avoid collinearization. \n    I guess I could write my own function and drop one column but this bookkeeping is tedious, is there an easy way to perform such transformations and get as a result a sparse matrix?","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":22439,"Q_Id":15021521,"Users Score":15,"Answer":"DictVectorizer is the recommended way to generate a one-hot encoding of categorical variables; you can use the sparse argument to create a sparse CSR matrix instead of a dense numpy array. I usually don't care about multicollinearity and I haven't noticed a problem with the approaches that I tend to use (i.e. LinearSVC, SGDClassifier, Tree-based methods).\nIt shouldn't be a problem to patch the DictVectorizer to drop one column per categorical feature - you simple need to remove one term from DictVectorizer.vocabulary at the end of the fit method. (Pull requests are always welcome!)","Q_Score":12,"Tags":"python,machine-learning,scikit-learn","A_Id":15038477,"CreationDate":"2013-02-22T10:05:00.000","Title":"How to encode a categorical variable in sklearn?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have created a 10 by 10 game board. It is a 2D list, with another list of 2 inside. I used \nboard = [[['O', 'O']] * 10 for x in range(1, 11)]. So it will produce something like \n['O', 'O'] ['O', 'O']...\n['O', 'O'] ['O', 'O']...\nLater on I want to set a single cell to have 'C' I use board.gameBoard[animal.y][animal.x][0] = 'C'\nboard being the class the gameBoard is in, and animal is a game piece, x & y are just ints. Some times it will work and the specified cell will become ['C', 'O'], other times it will fill the entire row with ['C', 'O']['C', 'O']['C', 'O']['C', 'O']\nDoes anyone know why that might be happening?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":168,"Q_Id":15036694,"Users Score":0,"Answer":"Your board is getting multiple references to the same array. \n  You need to replace the * 10 with another list comprehension.","Q_Score":1,"Tags":"python,list","A_Id":15036718,"CreationDate":"2013-02-23T03:25:00.000","Title":"2D Python list will have random results","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":1,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to compute the cosine similarity between strings in a list. For example, I have a list of over 10 million strings, each string has to determine similarity between itself and every other string in the list. What is the best algorithm I can use to efficiently and quickly do such task? Is the divide and conquer algorithm applicable? \nEDIT\nI want to determine which strings are most similar to a given string and be able to have a measure\/score associated with the similarity. I think what I want to do falls in line with clustering where the number of clusters are not initially known.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2043,"Q_Id":15041647,"Users Score":0,"Answer":"Work with the transposed matrix. That is what Mahout does on Hadoop to do this kind of task fast (or just use Mahout).\nEssentially, computing cosine similarity the naive way is bad. Because you end up computing a lot of 0 * something. Instead, you better work in columns, and leave away all 0s there.","Q_Score":8,"Tags":"java,python,algorithm,divide-and-conquer,cosine-similarity","A_Id":15042390,"CreationDate":"2013-02-23T14:34:00.000","Title":"How to efficiently compute the cosine similarity between millions of strings","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to represent a number as the product of its factors.The number of factors that are used to represent the number should be from 2 to number of prime factors of the same number(this i s the maximum possible number of factors for a number).\nfor example taking the number 24:\nrepresentation of the number as two factors multiplication are 2*12, 8*3, 6*4 and so on...,\nrepresentation of the number as three factors multiplication are 2*2*6, 2*3*4 and so on...,\nrepresentation of the number as four factors multiplication(prime factors alone) are 2*2*2*3.\nplease help me get some simple and generic algorithm for this","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1491,"Q_Id":15068698,"Users Score":0,"Answer":"I know one...\nIf you're using python, you can use dictionary's to simplify the storage...\nYou'll have to check for every prime less than square root of the number.\nNow, suppose p^k divides your number n, your task, I suppose is to find k.\nHere's the method:\n\nint c = 0; int temp = n; while(temp!=0) { temp \/= p; c+= temp; }\n\nThe above is a C++ code but you'll get the idea...\nAt the end of this loop you'll have c = k\nAnd yeah, the link given by will is a perfect python implementation of the same algorithm","Q_Score":1,"Tags":"python","A_Id":15068825,"CreationDate":"2013-02-25T13:59:00.000","Title":"representation of a number as multiplication of its factors","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I know about these column slice methods:\ndf2 = df[[\"col1\", \"col2\", \"col3\"]] and df2 = df.ix[:,0:2]\nbut I'm wondering if there is a way to slice columns from the front\/middle\/end of a dataframe in the same slice without specifically listing each one.\nFor example, a dataframe df with columns: col1, col2, col3, col4, col5 and col6.\nIs there a way to do something like this?\ndf2 = df.ix[:, [0:2, \"col5\"]]\nI'm in the situation where I have hundreds of columns and routinely need to slice specific ones for different requests.  I've checked through the documentation and haven't seen something like this.  Have I overlooked something?","AnswerCount":3,"Available Count":1,"Score":0.3215127375,"is_accepted":false,"ViewCount":28966,"Q_Id":15072005,"Users Score":5,"Answer":"If your column names have information that you can filter for, you could use df.filter(regex='name*').\nI am using this to filter between my 189 data channels from a1_01 to b3_21 and it works fine.","Q_Score":17,"Tags":"python,pandas","A_Id":15107442,"CreationDate":"2013-02-25T16:48:00.000","Title":"keep\/slice specific columns in pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I was asked in an interview to come up with a solution with linear time for cartesian product. I did the iterative manner O(mn) and a recursive solution also which is also O(mn). But I could not reduce the complexity further. Does anyone have ideas on how this complexity can be improved? Also can anyone suggest an efficient recursive approach?","AnswerCount":2,"Available Count":2,"Score":0.3799489623,"is_accepted":false,"ViewCount":1149,"Q_Id":15079069,"Users Score":4,"Answer":"There are mn results; the minimum work you have to do is write each result to the output. So you cannot do better than O(mn).","Q_Score":2,"Tags":"python,algorithm,cartesian-product","A_Id":15079557,"CreationDate":"2013-02-26T00:15:00.000","Title":"Linear time algorithm to compute cartesian product","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I was asked in an interview to come up with a solution with linear time for cartesian product. I did the iterative manner O(mn) and a recursive solution also which is also O(mn). But I could not reduce the complexity further. Does anyone have ideas on how this complexity can be improved? Also can anyone suggest an efficient recursive approach?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1149,"Q_Id":15079069,"Users Score":0,"Answer":"The question that comes to my mind reading this is, \"Linear with respect to what?\"  Remember that in mathematics, all variables must be defined to have meaning.  Big-O notation is no exception.  Simply saying an algorithm is O(n) is meaningless if n is not defined.\nAssuming the question was meaningful, and not a mistake, my guess is that they wanted you to ask for clarification.  Another possibility is that they wanted to see how you would respond when presented with an impossible situation.","Q_Score":2,"Tags":"python,algorithm,cartesian-product","A_Id":20166514,"CreationDate":"2013-02-26T00:15:00.000","Title":"Linear time algorithm to compute cartesian product","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am plotting a contourmap. When first plotting I noticed I had my axes wrong. So I switched the axes and noticed that the structure of both plots is different. On the first plot the axes and assignments are correct, but the structure is messy. On the second plot it is the other way around.\nSince it's a square matrix I don't see why there should be a sampling issue.\nTransposing the matrix with z-values or the meshgrid of x and y does not help either. Whatever way I plot x and y correctly it keeps looking messy.\nDoes anybody here know any more ideas which I can try or what might solve it?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":50,"Q_Id":15087303,"Users Score":0,"Answer":"The problem was the sampling. Although the arrays have the same size, the stepsize in the plot is not equal for x and y axis.","Q_Score":1,"Tags":"python,matplotlib,axes","A_Id":15090586,"CreationDate":"2013-02-26T10:56:00.000","Title":"contourf result differs when switching axes","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I build a class with some iteration over coming data. The data are in an array form without use of numpy objects. On my code I often use .append to create another array. At some point I changed one of the big array 1000x2000 to numpy.array. Now I have an error after error. I started to convert all of the arrays into ndarray but comments like .append does not work any more. I start to have a problems with pointing to rows, columns or cells. and have to rebuild all code.\nI try to google an answer to the question: \"what is and advantage of using ndarray over normal array\" I can't find a sensible answer. Can you write when should I start to use ndarrays and if in your practice do you use both of them or stick to one only.\nSorry if the question is a novice level, but I am new to python, just try to move from Matlab and want to understand what are pros and cons. Thanks","AnswerCount":4,"Available Count":3,"Score":1.0,"is_accepted":false,"ViewCount":1532,"Q_Id":15111230,"Users Score":7,"Answer":"There are at least two main reasons for using NumPy arrays:\n\nNumPy arrays require less space than Python lists. So you can deal with more data in a NumPy array (in-memory) than you can with Python lists.\nNumPy arrays have a vast library of functions and methods unavailable\nto Python lists or Python arrays.\n\nYes, you can not simply convert lists to NumPy arrays and expect your code to continue to work. The methods are different, the bool semantics are different. For the best performance, even the algorithm may need to change.\nHowever, if you are looking for a Python replacement for Matlab, you will definitely find uses for NumPy. It is worth learning.","Q_Score":5,"Tags":"python,numpy,multidimensional-array","A_Id":15111407,"CreationDate":"2013-02-27T11:42:00.000","Title":"what is a reason to use ndarray instead of python array","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I build a class with some iteration over coming data. The data are in an array form without use of numpy objects. On my code I often use .append to create another array. At some point I changed one of the big array 1000x2000 to numpy.array. Now I have an error after error. I started to convert all of the arrays into ndarray but comments like .append does not work any more. I start to have a problems with pointing to rows, columns or cells. and have to rebuild all code.\nI try to google an answer to the question: \"what is and advantage of using ndarray over normal array\" I can't find a sensible answer. Can you write when should I start to use ndarrays and if in your practice do you use both of them or stick to one only.\nSorry if the question is a novice level, but I am new to python, just try to move from Matlab and want to understand what are pros and cons. Thanks","AnswerCount":4,"Available Count":3,"Score":1.2,"is_accepted":true,"ViewCount":1532,"Q_Id":15111230,"Users Score":8,"Answer":"NumPy and Python arrays share the property of being efficiently stored in memory.\nNumPy arrays can be added together, multiplied by a number, you can calculate, say, the sine of all their values in one function call, etc. As HYRY pointed out, they can also have more than one dimension. You cannot do this with Python arrays.\nOn the other hand, Python arrays can indeed be appended to. Note that NumPy arrays can however be concatenated together (hstack(), vstack(),\u2026). That said, NumPy arrays are mostly meant to have a fixed number of elements.\nIt is common to first build a list (or a Python array) of values iteratively and then convert it to a NumPy array (with numpy.array(), or, more efficiently, with numpy.frombuffer(), as HYRY mentioned): this allows mathematical operations on arrays (or matrices) to be performed very conveniently (simple syntax for complex operations). Alternatively, numpy.fromiter() might be used to construct the array from an iterator. Or loadtxt() to construct it from a text file.","Q_Score":5,"Tags":"python,numpy,multidimensional-array","A_Id":15111278,"CreationDate":"2013-02-27T11:42:00.000","Title":"what is a reason to use ndarray instead of python array","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I build a class with some iteration over coming data. The data are in an array form without use of numpy objects. On my code I often use .append to create another array. At some point I changed one of the big array 1000x2000 to numpy.array. Now I have an error after error. I started to convert all of the arrays into ndarray but comments like .append does not work any more. I start to have a problems with pointing to rows, columns or cells. and have to rebuild all code.\nI try to google an answer to the question: \"what is and advantage of using ndarray over normal array\" I can't find a sensible answer. Can you write when should I start to use ndarrays and if in your practice do you use both of them or stick to one only.\nSorry if the question is a novice level, but I am new to python, just try to move from Matlab and want to understand what are pros and cons. Thanks","AnswerCount":4,"Available Count":3,"Score":0.049958375,"is_accepted":false,"ViewCount":1532,"Q_Id":15111230,"Users Score":1,"Answer":"Another great advantage of using NumPy arrays over built-in lists is the fact that NumPy has a C API that allows native C and C++ code to access NumPy arrays directly. Hence, many Python libraries written in low-level languages like C are expecting you to work with NumPy arrays instead of Python lists.\nReference: Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython","Q_Score":5,"Tags":"python,numpy,multidimensional-array","A_Id":53073528,"CreationDate":"2013-02-27T11:42:00.000","Title":"what is a reason to use ndarray instead of python array","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there any documentation on the interdependencies and relationship between packages in the the scipy, numpy, pandas, scikit ecosystem?","AnswerCount":2,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":198,"Q_Id":15143253,"Users Score":6,"Answer":"AFAIK, here is the dependency tree (numpy is a dependency of everything):\n\nnumpy\n\nscipy\n\nscikit-learn\n\npandas","Q_Score":3,"Tags":"python,numpy,scipy,pandas,scikit-learn","A_Id":15143804,"CreationDate":"2013-02-28T18:45:00.000","Title":"Is there any documentation on the interdependencies between packages in the scipy, numpy, pandas, scikit ecosystem? Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to train svm.sparse.SVC in scikit-learn. Right now the dimension of the feature vectors is around 0.7 million and the number of feature vectors being used for training is 20k. I am providing input using csr sparse matrices as only around 500 dimensions are non-zero in each feature vector. The code is running since the past 5 hours. Is there any estimate on how much time it will take? Is there any way to do the training faster? Kernel is linear.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1802,"Q_Id":15154690,"Users Score":3,"Answer":"Try using sklearn.svm.LinearSVC. This also has a linear kernel, but the underlying implementation is liblinear, which is known to be faster. With that in mind, your data set isn't very small, so even this classifier might take a while.\n\nEdit after first comment:\nIn that I think you have several options, neither of which is perfect: \n\nThe non-solution option: call it a day and hope that training of svm.sparse.SVC has finished tomorrow morning. If you can, buy a better computer.\nThe cheat option: give up on probabilities. You haven't told us what your problem is, so they may not be essential.\nThe back-against-the-wall option: if you absolutely need probabilities and things must run faster, use a different classifier. Options include sklearn.naive_bayes.*, sklearn.linear_model.LogisticRegression. etc. These will be much faster to train, but the price you pay is somewhat reduced accuracy.","Q_Score":2,"Tags":"python,svm,scikit-learn","A_Id":15155284,"CreationDate":"2013-03-01T09:44:00.000","Title":"svm.sparse.SVC taking a lot of time to get trained","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm working with OpenCV and python and would like to obtain the real world translation between two cameras. I'm using a single calibrated camera which is moving. I've already worked through feature matching, calculation of F via RANSAC, and calculation of E. To get the translation between cameras, I think I can use: w, u, vt = cv2.SVDecomp and then my t vector could be: t = u[:,2] An example output is:\n[[ -1.16399893   9.78967574   1.40910252]\n [ -7.79802049  -0.26646268 -13.85252956]\n [ -2.67690676  13.89538682   0.19209676]]\nt vector: [ 0.81586158  0.0750399  -0.57335756]\nI think I understand how the translation is not in real world scale so I need to provide that scale somehow if I want a real world translation. If I do know the distance between the cameras, can I just apply it directly to my t vector by multiplication? I think I'm missing something here...","AnswerCount":2,"Available Count":1,"Score":-0.0996679946,"is_accepted":false,"ViewCount":726,"Q_Id":15157756,"Users Score":-1,"Answer":"I have the same problem.I think the monocular camera may need a object known the 3D coordinate.That may help .","Q_Score":1,"Tags":"python,opencv,image-processing","A_Id":29629579,"CreationDate":"2013-03-01T12:25:00.000","Title":"Where do I add a scale factor to the Essential Matrix to produce a real world translation value","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have to make comparison between 155 image feature vectors. Every feature vector has got  5 features.\nMy image are divided in 10 classes.\nUnfortunately i need at least 100 images for class for using support vector machine , There is any alternative?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1430,"Q_Id":15177490,"Users Score":0,"Answer":"If your images that belong to the same class are results of a transformations to some starting image you can increase your training size by making transofrmations to your labeled examples. \nFor example if you are doing character recognition, afine or elastic transforamtions can be used. P.Simard in  Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis describes it in more detail. In the paper he uses Neural Networks but the same applies for SVM.","Q_Score":1,"Tags":"python,opencv,machine-learning,scikit-learn,classification","A_Id":15181340,"CreationDate":"2013-03-02T17:45:00.000","Title":"Alternative to support vector machine classifier in python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there any library available to have inverse of a function? To be more specific, given a function y=f(x) and domain, is there any library which can output x=f(y)? Sadly I cannot use matlab\/mathematics in my application, looking for C\/Python library..","AnswerCount":2,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":36620,"Q_Id":15200560,"Users Score":8,"Answer":"As has already been mentioned, not all functions are invertible. In some cases imposing additional constraints helps: think about the inverse of sin(x).\nOnce you are sure your function has a unique inverse, solve the equation f(x) = y. The solution gives you the inverse, y(x).\nIn python, look for nonlinear solvers from scipy.optimize.","Q_Score":11,"Tags":"python,c,math","A_Id":15200968,"CreationDate":"2013-03-04T11:32:00.000","Title":"Calculate inverse of a function--Library","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm doing the sentiment analysis for the Arabic language , I want to creat my own corpus , to do that , I collect 300 status from facebook and I classify them into positive and negative , now I want to do the tokenization of these status , in order to obain a list of words , and hen generate unigrams and bigrams, trigrams and use the cross fold validation , I'm using for the moment the nltk python, is this software able to do this task fr the arabic language or the rapis Minner will be better to work with , what do you think and I'm wondering how to generate the bigrams, trigrams and use the cross fold validation , is there any idea ??","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1057,"Q_Id":15282336,"Users Score":0,"Answer":"Well, I think that rapidminer is very interesting and can handle this task. It contains several operators dealing with text mining. Also, it allows the creation of new operators with high fluency.","Q_Score":2,"Tags":"python,nlp,nltk,sentiment-analysis,rapidminer","A_Id":15319789,"CreationDate":"2013-03-07T21:42:00.000","Title":"creating arabic corpus","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to create a function that takes in two lists, the lists are not guaranteed to be of equal length, and returns all the interleavings between the two lists.\nInput: Two lists that do not have to be equal in size.\nOutput: All possible interleavings between the two lists that preserve the original list's order.\nExample: AllInter([1,2],[3,4]) -> [[1,2,3,4], [1,3,2,4], [1,3,4,2], [3,1,2,4], [3,1,4,2], [3,4,1,2]]\nI do not want a solution. I want a hint.","AnswerCount":4,"Available Count":2,"Score":0.049958375,"is_accepted":false,"ViewCount":1387,"Q_Id":15306231,"Users Score":1,"Answer":"As suggested by @airza, the itertools module is your friend.\nIf you want to avoid using encapsulated magical goodness, my hint is to use recursion.\nStart playing the process of generating the lists in your mind, and when you notice you're doing the same thing again, try to find the pattern. For example:\n\nTake the first element from the first list\nEither take the 2nd, or the first from the other list\nEither take the 3rd, or the 2nd if you didn't, or another one from the other list\n...\n\nOkay, that is starting to look like there's some greater logic we're not using. I'm just incrementing the numbers. Surely I can find a base case that works while changing the \"first element, instead of naming higher elements?\nPlay with it. :)","Q_Score":5,"Tags":"python,algorithm","A_Id":15306292,"CreationDate":"2013-03-09T01:45:00.000","Title":"How to calculate all interleavings of two lists?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to create a function that takes in two lists, the lists are not guaranteed to be of equal length, and returns all the interleavings between the two lists.\nInput: Two lists that do not have to be equal in size.\nOutput: All possible interleavings between the two lists that preserve the original list's order.\nExample: AllInter([1,2],[3,4]) -> [[1,2,3,4], [1,3,2,4], [1,3,4,2], [3,1,2,4], [3,1,4,2], [3,4,1,2]]\nI do not want a solution. I want a hint.","AnswerCount":4,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1387,"Q_Id":15306231,"Users Score":0,"Answer":"You can try something a little closer to the metal and more elegant(in my opinion) iterating through different possible slices. Basically step through and iterate through all three arguments to the standard slice operation, removing anything added to the final list. Can post code snippet if you're interested.","Q_Score":5,"Tags":"python,algorithm","A_Id":15307850,"CreationDate":"2013-03-09T01:45:00.000","Title":"How to calculate all interleavings of two lists?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"So I have algorithms (easily searchable on the net) for prime factorization and divisor acquisition but I don't know how to scale it to finding those divisors within a range. For example all divisors of 100 between 23 and 49 (arbitrary). But also something efficient so I can scale this to big numbers in larger ranges. At first I was thinking of using an array that's the size of the range and then use all the primes <= the upper bound to sieve all the elements in that array to return an eventual list of divisors, but for large ranges this would be too memory intensive.\nIs there a simple way to just directly generate the divisors?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1052,"Q_Id":15329256,"Users Score":0,"Answer":"As Malvolio was (indirectly) going about, I personal wouldn't find a use for prime factorization if you want to find factors in a range, I would start at int t = (int)(sqrt(n)) and then decremnt until1. t is a factor2. Complete util t or t\/n range has been REACHED(a flag) and then (both) has left the range\nOr if your range is relatively small, check versus those values themselves.","Q_Score":1,"Tags":"c++,python,algorithm,primes,prime-factoring","A_Id":15329656,"CreationDate":"2013-03-11T00:01:00.000","Title":"How can I efficiently get all divisors of X within a range if I have X's prime factorization?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a NumPy array that is of size (3, 3). When I print shape of the array within __main__ module I get (3, 3). However I am passing this array to a function and when I print its size in the function I get (3, ).\nWhy does this happen?\nAlso, what does it mean for a tuple to have its last element unspecified? That is, shouldn't (3, ) be an invalid tuple in the first place?","AnswerCount":3,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":1032,"Q_Id":15330521,"Users Score":2,"Answer":"To answer your second question:\nTuples in Python are n-dimensional. That is you can have a 1-2-3-...-n tuple. Due to syntax, the way you represent a 1-dimensional tuple is ('element',) where the trailing comma is mandatory. If you have ('element') then this is just simply the expression inside the parenthesis. So (3) + 4 == 7, but (3,) + 4 == TypeError. Likewise ('element') == 'element'.\nTo answer your first question:\nYou're more than likely doing something wrong with passing the array around. There is no reason for the NumPy array to misrepresent itself without some type of mutation to the array.","Q_Score":1,"Tags":"python,numpy,scipy","A_Id":15330625,"CreationDate":"2013-03-11T03:01:00.000","Title":"NumPy array size issue","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a NumPy array that is of size (3, 3). When I print shape of the array within __main__ module I get (3, 3). However I am passing this array to a function and when I print its size in the function I get (3, ).\nWhy does this happen?\nAlso, what does it mean for a tuple to have its last element unspecified? That is, shouldn't (3, ) be an invalid tuple in the first place?","AnswerCount":3,"Available Count":2,"Score":0.1325487884,"is_accepted":false,"ViewCount":1032,"Q_Id":15330521,"Users Score":2,"Answer":"A tuple like this: (3, ) means that it's a tuple with a single element (a single dimension, in this case). That's the correct syntax - with a trailing , because if it looked like this: (3) then Python would interpret it as a number surrounded by parenthesis, not a tuple.\nIt'd be useful to see the actual code, but I'm guessing that you're not passing the entire array, only a row (or a column) of it.","Q_Score":1,"Tags":"python,numpy,scipy","A_Id":15330600,"CreationDate":"2013-03-11T03:01:00.000","Title":"NumPy array size issue","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have to search about 100 words in blocks of data (20000 blocks approximately) and each block consists of about 20 words. The blocks should be returned in the decreasing order of the number of matches. The brute force technique is very cumbersome because you have to search for all the 100 words one by one and then combine the number of related searches in a complicated manner. Is there any other algorithm which allows to search multiple words at the same time and store the number of matching words?\nThank you","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":77,"Q_Id":15330568,"Users Score":0,"Answer":"Why no consider using multithreading to store the result? Make an array with size equal to number of blocks, then each thread count for the result in one block, then the thread writes the result to the corresponding entry in the array. Later on you sort the array by decreasing order then you get the result.","Q_Score":0,"Tags":"c++,python","A_Id":15330640,"CreationDate":"2013-03-11T03:08:00.000","Title":"Searching multiple words in many blocks of data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Right now I have a python program building a fairly large 2D numpy array and saving it as a tab delimited text file using numpy.savetxt.  The numpy array contains only floats.  I then read the file in one row at a time in a separate C++ program.\nWhat I would like to do is find a way to accomplish this same task, changing my code as little as possible such that I can decrease the size of the file I am passing between the two programs.  \nI found that I can use numpy.savetxt to save to a compressed .gz file instead of a text file.  This lowers the file size from ~2MB to ~100kB.  \nIs there a better way to do this?  Could I, perhaps, write the numpy array in binary to the file to save space?  If so, how would I do this so that I can still read it into the C++ program?\nThank you for the help.  I appreciate any guidance I can get.\nEDIT:\nThere are a lot of zeros (probably 70% of the values in the numpy array are 0.0000) I am not sure of how I can somehow exploit this though and generate a tiny file that my c++ program can read in","AnswerCount":5,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":7265,"Q_Id":15369985,"Users Score":0,"Answer":"If you don't mind installing additional packages (for both python and c++), you can use [BSON][1] (Binary JSON).","Q_Score":6,"Tags":"python,numpy,scipy","A_Id":15370151,"CreationDate":"2013-03-12T19:07:00.000","Title":"python - saving numpy array to a file (smallest size possible)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Right now I have a python program building a fairly large 2D numpy array and saving it as a tab delimited text file using numpy.savetxt.  The numpy array contains only floats.  I then read the file in one row at a time in a separate C++ program.\nWhat I would like to do is find a way to accomplish this same task, changing my code as little as possible such that I can decrease the size of the file I am passing between the two programs.  \nI found that I can use numpy.savetxt to save to a compressed .gz file instead of a text file.  This lowers the file size from ~2MB to ~100kB.  \nIs there a better way to do this?  Could I, perhaps, write the numpy array in binary to the file to save space?  If so, how would I do this so that I can still read it into the C++ program?\nThank you for the help.  I appreciate any guidance I can get.\nEDIT:\nThere are a lot of zeros (probably 70% of the values in the numpy array are 0.0000) I am not sure of how I can somehow exploit this though and generate a tiny file that my c++ program can read in","AnswerCount":5,"Available Count":3,"Score":0.0399786803,"is_accepted":false,"ViewCount":7265,"Q_Id":15369985,"Users Score":1,"Answer":"numpy.ndarray.tofile and numpy.fromfile are useful for direct binary output\/input from python.  std::ostream::write std::istream::read are useful for binary output\/input in c++.\nYou should be careful about endianess if the data are transferred from one machine to another.","Q_Score":6,"Tags":"python,numpy,scipy","A_Id":15370191,"CreationDate":"2013-03-12T19:07:00.000","Title":"python - saving numpy array to a file (smallest size possible)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Right now I have a python program building a fairly large 2D numpy array and saving it as a tab delimited text file using numpy.savetxt.  The numpy array contains only floats.  I then read the file in one row at a time in a separate C++ program.\nWhat I would like to do is find a way to accomplish this same task, changing my code as little as possible such that I can decrease the size of the file I am passing between the two programs.  \nI found that I can use numpy.savetxt to save to a compressed .gz file instead of a text file.  This lowers the file size from ~2MB to ~100kB.  \nIs there a better way to do this?  Could I, perhaps, write the numpy array in binary to the file to save space?  If so, how would I do this so that I can still read it into the C++ program?\nThank you for the help.  I appreciate any guidance I can get.\nEDIT:\nThere are a lot of zeros (probably 70% of the values in the numpy array are 0.0000) I am not sure of how I can somehow exploit this though and generate a tiny file that my c++ program can read in","AnswerCount":5,"Available Count":3,"Score":0.0399786803,"is_accepted":false,"ViewCount":7265,"Q_Id":15369985,"Users Score":1,"Answer":"Use the an hdf5 file, they are really simple to use through h5py and you can use set compression a flag. Note that hdf5 has also a c++ interface.","Q_Score":6,"Tags":"python,numpy,scipy","A_Id":19226920,"CreationDate":"2013-03-12T19:07:00.000","Title":"python - saving numpy array to a file (smallest size possible)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using a rather large dataset of ~37 million data points that are hierarchically indexed into three categories country, productcode, year. The country variable (which is the countryname) is rather messy data consisting of items such as: 'Austral' which represents 'Australia'. I have built a simple guess_country() that matches letters to words, and returns a best guess and confidence interval from a known list of country_names. Given the length of the data and the nature of hierarchy it is very inefficient to use .map() to the Series: country. [The guess_country function takes ~2ms \/ request]\nMy question is: Is there a more efficient .map() which takes the Series and performs map on only unique values? (Given there are a LOT of repeated countrynames)","AnswerCount":3,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":2544,"Q_Id":15425492,"Users Score":3,"Answer":"There isn't, but if you want to only apply to unique values, just do that yourself.  Get mySeries.unique(), then use your function to pre-calculate the mapped alternatives for those unique values and create a dictionary with the resulting mappings.  Then use pandas map with the dictionary.  This should be about as fast as you can expect.","Q_Score":0,"Tags":"python,pandas","A_Id":15425560,"CreationDate":"2013-03-15T05:37:00.000","Title":"Pandas: More Efficient .map() function or method?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to choose the best parameters for the hysteresis phase in the canny function of OpenCV. I found some similar questions in stackoverflow but they didn't solve my problem. So far I've found that there are two main approaches:\n\nCompute mean and standard deviation and set the thresholds as: lowT = mean - std, highT = mean+std\nCompute the median and set the thresholds as: 0.6*median, 1.33*median\n\nHowever, any of these thresholds is the best fit for my data. Manually, I've found that lowT=100, highT=150 are the best values. The data (gray-scale image) has the following properties:\nmedian=202.0, mean=206.6283375, standard deviation = 35.7482520742\nDoes anyvbody know where is the problem? or knows where can I found more information about this?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":2035,"Q_Id":15463191,"Users Score":1,"Answer":"Such image statistics as mean, std etc. are not sufficient to answer the question, and canny may not be the best approach; it all depends on characteristics of the image. To learn about those characteristics and approaches, you may google for a survey of image segmentation \/ edge detection methods. And this kind of problems often involve some pre-processing and post-processing steps.","Q_Score":2,"Tags":"python,opencv,image-processing,computer-vision","A_Id":18670288,"CreationDate":"2013-03-17T16:29:00.000","Title":"Choosing the threshold values for hysteresis","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there any way to combine different classifiers into one in sklearn? I find sklearn.ensamble package. It contains different models, like AdaBoost and RandofForest, but they use decision trees under the hood and I want to use different methods, like SVM and Logistic regression. Is it possible with sklearn?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":694,"Q_Id":15471372,"Users Score":2,"Answer":"Do you just want to do majority voting? This is not implemented afaik. But as I said, you can just average the predict_proba scores. Or you can use LabelBinarizer of the predictions and average those. That would implement a voting scheme.\nEven if you are not interested in the probabilities, averaging the predicted probabilities might be more robust than doing a simple voting. This is hard to tell without trying out, though.","Q_Score":1,"Tags":"python,machine-learning,scikit-learn","A_Id":15743100,"CreationDate":"2013-03-18T07:06:00.000","Title":"Ensamble methods with scikit-learn","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a large dataset. It's currently in the form of uncompressed numpy array files that were created with numpy.array.tofile(). Each file is approximately 100000 rows of 363 floats each. There are 192 files totalling 52 Gb.\nI'd like to separate a random fifth of this data into a test set, and a random fifth of that test set into a validation set. \nIn addition, I can only train on 1 Gb at a time (limitation of GPU's onboard memory) So  I need to randomize the order of all the data so that I don't introduce a bias by training on the data in the order it was collected.\nMy main memory is 8 Gb in size. Can any recommend a method of randomizing and partitioning this huge dataset?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":756,"Q_Id":15512276,"Users Score":0,"Answer":"You could assign a unique sequential number to each row, then choose a random sample of those numbers, then serially extract each relevant row to a new file.","Q_Score":0,"Tags":"python,numpy,dataset","A_Id":15513160,"CreationDate":"2013-03-19T23:12:00.000","Title":"How should I divide a large (~50Gb) dataset into training, test, and validation sets?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In an python application I'm developing I have an array of 3D points (of size between 2 and 100000) and I have to find the points that are within a certain distance from each other (say between two values, like 0.1 and 0.2). I need this for a graphic application and this search should be very fast (~1\/10 of a second for a sample of 10000 points)\nAs a first experiment I tried to use the scipy.spatial.KDTree.query_pairs implementation, and with a sample of 5000 point it takes 5 second to return the indices. Do you know any approach that may work for this specific case?\nA bit more about the application:\nThe points represents atom coordinates and the distance search is useful to determine the bonds between atoms. Bonds are not necessarily fixed but may change at each step, such as in the case of hydrogen bonds.","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":772,"Q_Id":15514641,"Users Score":1,"Answer":"The first thing that comes to my mind is:\nIf we calculate the distance between each two atoms in the set it will be O(N^2) operations. It is very slow.\nWhat about to introduce the statical orthogonal grid with some cells size (for example close to the distance you are interested) and then determine the atoms belonging to the each cell of the grid (it takes O(N) operations) After this procedure you can reduce the time for searching of the neighbors.","Q_Score":0,"Tags":"python,performance,algorithm,numpy,kdtree","A_Id":15514922,"CreationDate":"2013-03-20T03:15:00.000","Title":"Finding points in space closer than a certain value","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In an python application I'm developing I have an array of 3D points (of size between 2 and 100000) and I have to find the points that are within a certain distance from each other (say between two values, like 0.1 and 0.2). I need this for a graphic application and this search should be very fast (~1\/10 of a second for a sample of 10000 points)\nAs a first experiment I tried to use the scipy.spatial.KDTree.query_pairs implementation, and with a sample of 5000 point it takes 5 second to return the indices. Do you know any approach that may work for this specific case?\nA bit more about the application:\nThe points represents atom coordinates and the distance search is useful to determine the bonds between atoms. Bonds are not necessarily fixed but may change at each step, such as in the case of hydrogen bonds.","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":772,"Q_Id":15514641,"Users Score":5,"Answer":"Great question! Here is my suggestion:\nDivide each coordinate by your \"epsilon\" value of 0.1\/0.2\/whatever and round the result to an integer. This creates a \"quotient space\" of points where distance no longer needs to be determined using the distance formula, but simply by comparing the integer coordinates of each point. If all coordinates are the same, then the original points were within approximately the square root of three times epsilon from each other (for example). This process is O(n) and should take 0.001 seconds or less.\n(Note: you would want to augment the original point with the three additional integers that result from this division and rounding, so that you don't lose the exact coordinates.)\nSort the points in numeric order using dictionary-style rules and considering the three integers in the coordinates as letters in words. This process is O(n * log(n)) and should take certainly less than your 1\/10th of a second requirement.\nNow you simply proceed through this sorted list and compare each point's integer coordinates with the previous and following points. If all coordinates match, then both of the matching points can be moved into your \"keep\" list of points, and all the others can be marked as \"throw away.\" This is an O(n) process which should take very little time.\nThe result will be a subset of all the original points, which contains only those points that could be possibly involved in any bond, with a bond being defined as approximately epsilon or less apart from some other point in your original set.\nThis process is not mathematically exact, but I think it is definitely fast and suited for your purpose.","Q_Score":0,"Tags":"python,performance,algorithm,numpy,kdtree","A_Id":15514859,"CreationDate":"2013-03-20T03:15:00.000","Title":"Finding points in space closer than a certain value","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have received several recommendations to use virtualenv to clean up my python modules.  I am concerned because it seems too good to be true. Has anyone found downside related to performance or memory issues in working with multicore settings, starcluster, numpy, scikit-learn, pandas, or iPython notebook.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1022,"Q_Id":15540640,"Users Score":0,"Answer":"There's no performance overhead to using virtualenv.  All it's doing is using different locations in the filesystem.\nThe only \"overhead\" is the time it takes to set it up. You'd need to install each package in your virtualenv (numpy, pandas, etc.)","Q_Score":5,"Tags":"python-2.7,virtualenv,scientific-computing","A_Id":15540786,"CreationDate":"2013-03-21T06:10:00.000","Title":"Are there any downsides to using virtualenv for scientific python and machine learning?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have received several recommendations to use virtualenv to clean up my python modules.  I am concerned because it seems too good to be true. Has anyone found downside related to performance or memory issues in working with multicore settings, starcluster, numpy, scikit-learn, pandas, or iPython notebook.","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":1022,"Q_Id":15540640,"Users Score":3,"Answer":"Virtualenv is the best and easiest way to keep some sort of order when it comes to dependencies. Python is really behind Ruby (bundler!) when it comes to dealing with installing and keeping track of modules. The best tool you have is virtualenv.\nSo I suggest you create a virtualenv directory for each of your applications, put together a file where you list all the 'pip install' commands you need to build the environment and ensure that you have a clean repeatable process for creating this environment.\nI think that the nature of the application makes little difference. There should not be any performance issue since all that virtualenv does is to load libraries from a specific path rather than load them from the directory where they are saved by default.\nIn any case (this may be completely irrelevant), but if performance is an issue, then perhaps you ought to be looking at a compiled language. Most likely though, any performance bottlenecks could be improved with better coding.","Q_Score":5,"Tags":"python-2.7,virtualenv,scientific-computing","A_Id":15540795,"CreationDate":"2013-03-21T06:10:00.000","Title":"Are there any downsides to using virtualenv for scientific python and machine learning?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using a python program to produce some data, plotting the data using matplotlib.pyplot and then displaying the figure in a latex file. \nI am currently saving the figure as a .png file but the image quality isn't great. I've tried changing the DPI in matplotlib.pyplot.figure(dpi=200) etc but this seems to make little difference. I've also tried using differnet image formats but they all look a little faded and not very sharp.\nHas anyone else had this problem? \nAny help would be much appreciated","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":18876,"Q_Id":15575466,"Users Score":24,"Answer":"You can save the images in a vector format so that they will be scalable without quality loss. Such formats are PDF and EPS. Just change the extension to .pdf or .eps and matplotlib will write the correct image format. Remember LaTeX likes EPS and PDFLaTeX likes PDF images. Although most modern LaTeX executables are PDFLaTeX in disguise and convert EPS files on the fly (same effect as if you included the epstopdf package in your preamble, which may not perform as well as you'd like).\nAlternatively, increase the DPI, a lot. These are the numbers you should keep in mind:\n\n300dpi: plain paper prints\n600dpi: professional paper prints. Most commercial office printers reach this in their output.\n1200dpi: professional poster\/brochure grade quality.\n\nI use these to adapt the quality of PNG figures in conjunction with figure's figsize option, which allows for correctly scaled text and graphics as you improve the quality through dpi.","Q_Score":18,"Tags":"python,graph,matplotlib","A_Id":15578952,"CreationDate":"2013-03-22T16:35:00.000","Title":"How do you improve matplotlib image quality?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a huge csv file which contains millions of records and I want to load it into Netezza DB using python script I have tried simple insert query but it is very very slow. \nCan point me some example python script or some idea how can I do the same?\nThank you","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":4583,"Q_Id":15592980,"Users Score":0,"Answer":"You need to get the nzcli installed on the machine that you want to run nzload from - your sysadmin should be able to put it on your unix\/linux application server. There's a detailed process to setting it all up, caching the passwords, etc - the sysadmin should be able to do that to.\nOnce it is set up, you can create NZ control files to point to your data files and execute a load. The Netezza Data Loading guide has detailed instructions on how to do all of this (it can be obtained through IBM).\nYou can do it through aginity as well if you have the CREATE EXTERNAL TABLE privledge - you can do a INSERT INTO FROM EXTERNAL ... REMOTESOURCE ODBC to load the file from an ODBC connection.","Q_Score":2,"Tags":"python,netezza","A_Id":15643468,"CreationDate":"2013-03-23T22:45:00.000","Title":"How to use NZ Loader (Netezza Loader) through Python Script?","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a huge csv file which contains millions of records and I want to load it into Netezza DB using python script I have tried simple insert query but it is very very slow. \nCan point me some example python script or some idea how can I do the same?\nThank you","AnswerCount":3,"Available Count":2,"Score":0.0665680765,"is_accepted":false,"ViewCount":4583,"Q_Id":15592980,"Users Score":1,"Answer":"you can use nz_load4  to load the data,This is the support utility \/nz\/support\/contrib\/bin\nthe syntax is same like nzload,by default nz_load4 will load the data using 4 thread and you can go upto 32 thread by using -tread option\nfor more details use nz_load4 -h \nThis will create the log files based on the number of thread,like if","Q_Score":2,"Tags":"python,netezza","A_Id":17522337,"CreationDate":"2013-03-23T22:45:00.000","Title":"How to use NZ Loader (Netezza Loader) through Python Script?","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying desperately to make a fullscreen plot in matplotlib on Ubuntu 12.10. I have tried everything I can find on the web. I need my plot to go completely fullscreen, not just maximized. Has anyone ever gotten this to work? If so, could you please share how?\nThanks.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1451,"Q_Id":15619825,"Users Score":1,"Answer":"SOLVED - My problem was that I was not up to the latest version of Matplotlib. I did the following steps to get fullscreen working in Matplotlib with Ubuntu 12.10.\n\nUninstalled matplotlib with sudo apt-get remove python-matplotlib\nInstalled build dependencies for matplotlib sudo apt-get build-dep python-matplotlib\nInstalled matplotlib 1.2 with pip sudo pip install matplotlib\nSet matplotlib to use the GTK backend with matplotlib.rcParams['backend'] = 'GTK'\nUsed keyboard shortcut 'f' when the plot was onscreen and it worked!","Q_Score":1,"Tags":"python,numpy,matplotlib,scipy","A_Id":15623721,"CreationDate":"2013-03-25T16:24:00.000","Title":"Matplotlib fullscreen not working","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have two vectors x and y, and I want to compute a rolling regression for those, e.g a on (x(1:4),y(1:4)), (x(2:5),y(2:5)), ...\nIs there already a function for that? The best algorithm I have in mind for this is O(n), but applying separate linear regressions on every subarrays would be O(n^2).\nI'm working with Matlab and Python (numpy).","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":4368,"Q_Id":15636796,"Users Score":2,"Answer":"No, there is NO function that will do a rolling regression, returning all the statistics you wish, doing it efficiently.\nThat does not mean you can't write such a function. To do so would mean multiple calls to a tool like conv or filter. This is how a Savitsky-Golay tool would work, which DOES do most of what you want. Make one call for each regression coefficient.\nUse of up-dating and down-dating tools to use\/modify the previous regression estimates will not be as efficient as the calls to conv, since you only need factorize a linear system ONCE when you then do the work with conv. Anyway, there is no need to do an update, as long as the points are uniformly spaced in the series. This is why Savitsky-Golay works.","Q_Score":2,"Tags":"python,matlab,numpy,linear-regression,rolling-computation","A_Id":15638779,"CreationDate":"2013-03-26T12:11:00.000","Title":"Efficient way to do a rolling linear regression","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a lot of data stored at disk in large arrays. I cant load everything in memory altogether.\nHow one could calculate the mean and the standard deviation?","AnswerCount":2,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":6151,"Q_Id":15638612,"Users Score":6,"Answer":"Sounds like a math question. For the mean, you know that you can take the mean of a chunk of data, and then take the mean of the means. If the chunks aren't the same size, you'll have to take a weighted average. \nFor the standard deviation, you'll have to calculate the variance first. I'd suggest doing this alongside the calculation of the mean. For variance, you have\nVar(X) = Avg(X^2) - Avg(X)^2\nSo compute the average of your data, and the average of your (data^2). Aggregate them as above, and the take the difference.\nThen the standard deviation is just the square root of the variance.\nNote that you could do the whole thing with iterators, which is probably the most efficient.","Q_Score":4,"Tags":"python,statistics","A_Id":15638712,"CreationDate":"2013-03-26T13:44:00.000","Title":"calculating mean and standard deviation of the data which does not fit in memory using python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I did a code which generates random numbers below and i save them in a csv which look like below, I am trying to play around and learn the group by function. I would like for instance do the sum or average of those group by timestamp. I am new in Python, i cannot find anywhere to start though. Ulitmately i would like to do the same but for 1min or 5min (every 5min starting from 00:00:00, not enough data in my example below but that would do something like 13:35:00 to 13:40:00 and the next one 13:40:00 included to 13:45:00 excluded, etc), i think i could figure out the 1min in extracting the minute part from the timestamp but the 5min seems complex. Not asking for a copy paste of a code, but i have no idea where to start to be honest.\n\nLevel   Timestamp\n99  03\/04\/2013 13:37:20\n98  03\/04\/2013 13:37:20\n98  03\/04\/2013 13:37:20\n99  03\/04\/2013 13:37:20\n105 03\/04\/2013 13:37:20\n104 03\/04\/2013 13:37:20\n102 03\/04\/2013 13:37:21\n102 03\/04\/2013 13:37:21\n103 03\/04\/2013 13:37:22\n82  03\/04\/2013 13:37:23\n83  03\/04\/2013 13:37:23\n82  03\/04\/2013 13:37:23\n83  03\/04\/2013 13:37:23\n54  03\/04\/2013 13:37:24\n55  03\/04\/2013 13:37:24\n54  03\/04\/2013 13:37:24\n55  03\/04\/2013 13:37:24\n56  03\/04\/2013 13:37:25\n57  03\/04\/2013 13:37:25","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":584,"Q_Id":15790467,"Users Score":0,"Answer":"There are several ways to approach this, but you're effectively \"binning\" on the times. I would approach it in a few steps:\nYou don't want to parse the time yourself with string manipulation, it will blow up in your face; trust me! Parse out the timestamp into a datetime object (google should give you a pretty good answer).  Once you have that, you can do lots of fun stuff like compare two times.\nNow that you have the datetime objects, you can start to \"bin\" them. I'm going to assume the records are in order. Start with the first record's time of \"03\/04\/2013 13:37:20\" and create a new datetime object at \"03\/04\/2013 13:37:00\" [hint: set seconds=0 on the datetime object you read in]. This is the start of your first \"bin\". Now add one minute to your start datetime [hint: endDT = startDT + timedelta(seconds=60)], that's the end of your first bin.\nNow start going through your records checking if the record is less than your endDT, if it is, add it to a list for that bin. If the record is greater than your endDT, you're in the next bin. To start the new bin, add one minute to your endDT and create a new list to hold those items and keep chugging along in your loop.\nOnce you go through the loop, you can run max\/min\/avg on the lists. Ideally, you'll store the lists in a dictionary that looks like {datetimeObject : [34, 23, 45, 23]}. It'll make printing and sorting easy.\nThis isn't the most efficient\/flexible\/cool way to do it, but I think it's probably the most helpful to start with.","Q_Score":2,"Tags":"python,group-by,timestamp,time-series","A_Id":15791843,"CreationDate":"2013-04-03T14:41:00.000","Title":"Grouping data by frequency","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I manage to install mapnik for python and am able to render maps using a provided shp file. Is there a possibility to retrieve dynamically the shape file for the map I want to render (given coordinates) from python? or do I need to download the whole OSM files, and import them into my own database? \nthanks","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":286,"Q_Id":15792073,"Users Score":0,"Answer":"Dynamic retrieval of data from shapefiles is not suggested for large applications. The best practice is to dump the shapefile in databases like postgres (shp2pgsql) & generated the map using mapnik & tile them using tilecache.","Q_Score":0,"Tags":"python,openstreetmap,shapefile,mapnik","A_Id":16271420,"CreationDate":"2013-04-03T15:52:00.000","Title":"openstreet maps: dynamically retrieve shp files in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Sorry if this has been answered somewhere already, I couldn't find the answer.\nI have installed python 2.7.3 onto a windows 7 computer. I then downloaded the pandas-0.10.1.win-amd64-py2.7.exe and tried to install it. I have gotten past the first window, but then it states \"Python 2.7 is required, which was not found in the registry\".\nI then get the option to put the path in to find python, but I cannot get it to work.\nHow would I fix this? Sorry for the silly question.\nThanks.\n~Kututo","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":3609,"Q_Id":15832445,"Users Score":0,"Answer":"After you have installed python check to see if the  appropriate path variables are set by typing the following at the command line:\necho %PATH%\nif you do not see something like:\nC:\\Python27;C:\\Python27\\Scripts \non the output (probably with lots of other paths) then type this:\nset PATH=%PATH%;C:\\\\Python27\\\\;C:\\\\Python27\\Scripts\nThen try installing the 32-bit pandas executable.","Q_Score":4,"Tags":"python,windows,installation,pandas","A_Id":15847067,"CreationDate":"2013-04-05T11:12:00.000","Title":"Cannot seem to install pandas for python 2.7 on windows","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Sorry if this has been answered somewhere already, I couldn't find the answer.\nI have installed python 2.7.3 onto a windows 7 computer. I then downloaded the pandas-0.10.1.win-amd64-py2.7.exe and tried to install it. I have gotten past the first window, but then it states \"Python 2.7 is required, which was not found in the registry\".\nI then get the option to put the path in to find python, but I cannot get it to work.\nHow would I fix this? Sorry for the silly question.\nThanks.\n~Kututo","AnswerCount":3,"Available Count":2,"Score":0.1325487884,"is_accepted":false,"ViewCount":3609,"Q_Id":15832445,"Users Score":2,"Answer":"I faced the same issue. Here is what worked\n\nChanged to PATH to include C:\\Python27;C:\\Python27\\Lib\\site-packages\\;C:\\Python27\\Scripts\\;\nuninstall 64bit numpy and pandas\ninstall 32win 2.7 numpy and pandas \nI had to also install dateutil and pytz\npandas and numpy work import work fine","Q_Score":4,"Tags":"python,windows,installation,pandas","A_Id":25210272,"CreationDate":"2013-04-05T11:12:00.000","Title":"Cannot seem to install pandas for python 2.7 on windows","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"pyplot.hist() documentation specifies that when setting a range for a histogram \"lower and upper outliers are ignored\".\nIs it possible to make the first and last bins of a histogram include all outliers without changing the width of the bin?\nFor example, let's say I want to look at the range 0-3 with 3 bins: 0-1, 1-2, 2-3 (let's ignore cases of exact equality for simplicity). I would like the first bin to include all values from minus infinity to 1, and the last bin to include all values from 2 to infinity. However, if I explicitly set these bins to span that range, they will be very wide. I would like them to have the same width. The behavior I am looking for is like the behavior of hist() in Matlab.\nObviously I can numpy.clip() the data and plot that, which will give me what I want. But I am interested if there is a builtin solution for this.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":7794,"Q_Id":15837810,"Users Score":8,"Answer":"No. Looking at matplotlib.axes.Axes.hist and the direct use of numpy.histogram I'm fairly confident in saying that there is no smarter solution than using clip (other than extending the bins that you histogram with).\nI'd encourage you to look at the source of matplotlib.axes.Axes.hist (it's just Python code, though admittedly hist is slightly more complex than most of the Axes methods) - it is the best way to verify this kind of question.","Q_Score":19,"Tags":"python,numpy,matplotlib","A_Id":16186805,"CreationDate":"2013-04-05T15:29:00.000","Title":"Making pyplot.hist() first and last bins include outliers","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Hi I have big dataset which has both strings and numerical values\nex.\nUser name (str) ,  handset(str), number of requests(int), number of downloads(int) ,.......\nI have around 200 such columns. \nIs there a way\/algorithm which can handle both strings and integers during feature selection ?\nOr how should I approach this issue.\nthanks","AnswerCount":3,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":1683,"Q_Id":15868108,"Users Score":0,"Answer":"Feature selection algorithms assigns weights to different features based on their impact in the classification. In my best knowledge the features types does not make difference when computing different weights. I suggest to convert string features to numerical based on their ASCII codes or any other techniques. Then you can use the existing feature selection algorithm in rapid miner.","Q_Score":3,"Tags":"python,machine-learning,weka,rapidminer,feature-selection","A_Id":15887123,"CreationDate":"2013-04-07T21:34:00.000","Title":"Feature Selection in dataset containing both string and numerical values?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Hi I have big dataset which has both strings and numerical values\nex.\nUser name (str) ,  handset(str), number of requests(int), number of downloads(int) ,.......\nI have around 200 such columns. \nIs there a way\/algorithm which can handle both strings and integers during feature selection ?\nOr how should I approach this issue.\nthanks","AnswerCount":3,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":1683,"Q_Id":15868108,"Users Score":0,"Answer":"I've used Weka Feature Selection and although the attribute evaluator methods I've tried can't handle string attributes you can temporary remove them in the Preprocess > Filter > Unsupervised > Attribute > RemoveType, then perform the feature selection and, later, include strings again to do the classification.","Q_Score":3,"Tags":"python,machine-learning,weka,rapidminer,feature-selection","A_Id":17920216,"CreationDate":"2013-04-07T21:34:00.000","Title":"Feature Selection in dataset containing both string and numerical values?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Hi I have big dataset which has both strings and numerical values\nex.\nUser name (str) ,  handset(str), number of requests(int), number of downloads(int) ,.......\nI have around 200 such columns. \nIs there a way\/algorithm which can handle both strings and integers during feature selection ?\nOr how should I approach this issue.\nthanks","AnswerCount":3,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":1683,"Q_Id":15868108,"Users Score":0,"Answer":"There are a set of operators you could use in the Attribute Weighting group within RapidMiner. For example, Weight By Correlation or Weight By Information Gain.\nThese will assess how much weight to give an attribute based on its relevance to the label (in this case the download flag). The resulting weights can then be used with the Select by Weights operator to eliminate those that are not needed. This approach considers attributes by themselves.\nYou could also build a classification model and use the forward selection operators to add more and more attributes and monitor performance. This approach will consider the relationships between attributes.","Q_Score":3,"Tags":"python,machine-learning,weka,rapidminer,feature-selection","A_Id":16003658,"CreationDate":"2013-04-07T21:34:00.000","Title":"Feature Selection in dataset containing both string and numerical values?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've trained a Random Forest (regressor in this case) model using scikit learn (python), and I'would like to plot the error rate on a validation set based on the numeber of estimators used. In other words, there's a way to predict using only a portion of the estimators in your RandomForestRegressor?\nUsing predict(X) will give you the predictions based on the mean of every single tree results. There is a way to limit the usage of the trees? Or eventually, get each single output for each single tree in the forest?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":1415,"Q_Id":15869919,"Users Score":1,"Answer":"Once trained, you can access these via the \"estimators_\" attribute of the random forest object.","Q_Score":0,"Tags":"python,limit,scikit-learn,prediction,random-forest","A_Id":15892422,"CreationDate":"2013-04-08T01:28:00.000","Title":"Random Forest - Predict using less estimators","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"What is the difference between ndarray and array in Numpy? And where can I find the implementations in the numpy source code?","AnswerCount":5,"Available Count":2,"Score":-0.0798297691,"is_accepted":false,"ViewCount":148479,"Q_Id":15879315,"Users Score":-2,"Answer":"I think with np.array() you can only create C like though you mention the order, when you check using np.isfortran() it says false. but with np.ndarrray() when you specify the order it creates based on the order provided.","Q_Score":340,"Tags":"python,arrays,numpy,multidimensional-array,numpy-ndarray","A_Id":52104659,"CreationDate":"2013-04-08T12:41:00.000","Title":"What is the difference between ndarray and array in numpy?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"What is the difference between ndarray and array in Numpy? And where can I find the implementations in the numpy source code?","AnswerCount":5,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":148479,"Q_Id":15879315,"Users Score":66,"Answer":"numpy.array is a function that returns a numpy.ndarray.  There is no object type numpy.array.","Q_Score":340,"Tags":"python,arrays,numpy,multidimensional-array,numpy-ndarray","A_Id":15879428,"CreationDate":"2013-04-08T12:41:00.000","Title":"What is the difference between ndarray and array in numpy?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using pyplot.bar but I'm plotting so many points that the color of the bars is always black.  This is because the borders of the bars are black and there are so many of them that they are all squished together so that all you see is the borders (black).  Is there a way to remove the bar borders so that I can see the intended color?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":72444,"Q_Id":15904042,"Users Score":135,"Answer":"Set the edgecolor to \"none\": bar(..., edgecolor = \"none\")","Q_Score":75,"Tags":"python,graph,matplotlib,border","A_Id":15904277,"CreationDate":"2013-04-09T14:02:00.000","Title":"matplotlib bar graph black - how do I remove bar borders","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"how does ImageFilter in PIL normalize the pixel values(not the kernel) between 0 and 255 after filtering with Kernel or mask?(Specially zero-summing kernel like:( -1,-1,-1,0,0,0,1,1,1 ))\n\nmy code was like:\nimport Image\nimport ImageFilter\nHoriz = ImageFilter.Kernel((3, 3), (-1,-2,-1,0,0,0,1,2,1), scale=None, offset=0) #sobel mask\nim_fltd = myimage.filter(Horiz)","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":1691,"Q_Id":15906368,"Users Score":1,"Answer":"The above answer of Mark states his theory regarding what happens when a Zero-summing kernel is used with scale argument 0 or None or not passed\/mentioned. Now talking about how PIL handles calculated pixel values after applying kernel,scale and offset, which are not in [0,255] range. My theory about how it normalizes calculated pixel value is that it simply do: Any resulting value <= 0 becomes 0 and anything > 255 becomes 255.","Q_Score":3,"Tags":"image,image-processing,python-2.7,python-imaging-library","A_Id":15957090,"CreationDate":"2013-04-09T15:40:00.000","Title":"how does ImageFilter in PIL normalize the pixel values between 0 and 255 after filtering with Kernel or mask","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am currently working with hundreds of files, all of which I want to read in and view as a numpy array. Right now I am using os.walk to pull all the files from a directory. I have a for loop that goes through the directory and will then create the array, but it is not stored anywhere. Is there a way to create arrays \"on the go\" or to somehow allocate a certain amount of memory for empty arrays?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":57,"Q_Id":15915255,"Users Score":0,"Answer":"Python's lists are dynamic, you can change their length on-the-fly, so just store them in a list. Or if you wanted to reference them by name instead of number, use a dictionary, whose size can also change on the fly.","Q_Score":0,"Tags":"python,arrays,os.walk","A_Id":15915785,"CreationDate":"2013-04-10T01:08:00.000","Title":"Creating many arrays at once","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"There are many functions in OpenCV 2.4 not available using Python. \nPlease advice me how to convert the C++ functions so that I can use in Python 2.7.\nThanks in advance.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":61,"Q_Id":15924060,"Users Score":0,"Answer":"You should have a look at Python Boost. This might help you to bind C++ functions you need to python.","Q_Score":1,"Tags":"python,opencv","A_Id":15925696,"CreationDate":"2013-04-10T11:03:00.000","Title":"using python for opencv","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on a text classification problem using scikit-learn classifiers and text feature extractor, particularly TfidfVectorizer class.\nThe problem is that I have two kinds of features, the first are captured by the n-grams obtained from TfidfVectorizer and the other are domain specific features that I extract from each document. I need to combine both features in a single feature vector for each document; to do this I need to update the scipy sparse matrix returned by TfidfVectorizer by adding a new dimension in each row holding the domain feature for this document. However, I can't find a neat way to do this, by neat I mean not converting the sparse matrix into a dense one since simply it won't fit in memory.\nProbably I am missing a feature in scikit-learn or something, since I am new to both scipy and scikit-learn.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":692,"Q_Id":15938025,"Users Score":5,"Answer":"I think the easiest would be to create a new sparse matrix with your custom features and then use scipy.sparse.hstack to stack the features.\nYou might also find the \"FeatureUnion\" from the pipeline module helpful.","Q_Score":5,"Tags":"python-2.7,scipy,sparse-matrix,scikit-learn","A_Id":15949294,"CreationDate":"2013-04-10T23:02:00.000","Title":"How to Extend Scipy Sparse Matrix returned by sklearn TfIdfVectorizer to hold more features","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How do I get the number of rows of a pandas dataframe df?","AnswerCount":15,"Available Count":1,"Score":0.0532828229,"is_accepted":false,"ViewCount":3270072,"Q_Id":15943769,"Users Score":4,"Answer":"Either of this can do it (df is the name of the DataFrame):\nMethod 1: Using the len function:\nlen(df) will give the number of rows in a DataFrame named df.\nMethod 2: using count function:\ndf[col].count() will count the number of rows in a given column col.\ndf.count() will give the number of rows for all the columns.","Q_Score":1569,"Tags":"python,pandas,dataframe","A_Id":61413025,"CreationDate":"2013-04-11T08:14:00.000","Title":"How do I get the row count of a Pandas DataFrame?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am running Python 2.7.2 on my machine. I am trying to install numpy with easy_install and pip, but none of them are able to do so. So, when I try:\nsudo easy_install-2.7 numpy\nI get this error:\n\"The package setup script has attempted to modify files on your system\nthat are not within the EasyInstall build area, and has been aborted.\nThis package cannot be safely installed by EasyInstall, and may not\nsupport alternate installation locations even if you run its setup\nscript by hand.  Please inform the package's author and the EasyInstall\nmaintainers to find out if a fix or workaround is available.\"\nMoreover, when I try with pip:\nsudo pip-2.7 install numpy\nI get this error:\nRuntimeError: Broken toolchain: cannot link a simple C program\nIs there any fix available for this?","AnswerCount":2,"Available Count":1,"Score":0.2913126125,"is_accepted":false,"ViewCount":5561,"Q_Id":15957071,"Users Score":3,"Answer":"I was facing the same error while installing the requirements for my django project. This worked for me.\nUpgrade your setuptools version via pip install --upgrade setuptools and run the command for installing the packages again.","Q_Score":3,"Tags":"python,numpy,pip,easy-install","A_Id":59262156,"CreationDate":"2013-04-11T19:21:00.000","Title":"easy_install and pip giving errors when trying to install numpy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on a relatively large text-based web classification problem and I am planning on using the multinomial Naive Bayes classifier in sklearn in python and the scrapy framework for the crawling. However, I am a little concerned that sklearn\/python might be too slow for a problem that could involve classifications of millions of websites. I have already trained the classifier on several thousand websites from DMOZ. \nThe research framework is as follows:\n1) The crawler lands on a domain name and scrapes the text from 20 links on the site (of depth no larger than one). (The number of tokenized words here seems to vary between a few thousand to up to 150K for a sample run of the crawler)\n2) Run the sklearn multionmial NB classifier with around 50,000 features and record the domain name depending on the result\nMy question is whether a Python-based classifier would be up to the task for such a large scale application or should I try re-writing the classifier (and maybe the scraper and word tokenizer as well) in a faster environment? If yes what might that environment be?\nOr perhaps Python is enough if accompanied with some parallelization of the code?\nThanks","AnswerCount":2,"Available Count":1,"Score":0.4621171573,"is_accepted":false,"ViewCount":940,"Q_Id":15989610,"Users Score":5,"Answer":"Use the HashingVectorizer and one of the linear classification modules that supports the partial_fit API for instance SGDClassifier, Perceptron or PassiveAggresiveClassifier to incrementally learn the model without having to vectorize and load all the data in memory upfront and you should not have any issue in learning a classifier on hundreds of millions of documents with hundreds of thousands (hashed) features.\nYou should however load a small subsample that fits in memory (e.g. 100k documents) and grid search good parameters for the vectorizer using a Pipeline object and the RandomizedSearchCV class of the master branch. You can also fine tune the value of the regularization parameter (e.g. C for PassiveAggressiveClassifier or alpha for SGDClassifier) using the same RandomizedSearchCVor a larger, pre-vectorized dataset that fits in memory (e.g. a couple of millions of documents).\nAlso linear models can be averaged (average the coef_ and intercept_ of 2 linear models) so that you can partition the dataset, learn linear models independently and then average the models to get the final model.","Q_Score":5,"Tags":"python,scrapy,classification,scikit-learn","A_Id":15998577,"CreationDate":"2013-04-13T15:44:00.000","Title":"Using sklearn and Python for a large application classification\/scraping exercise","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"In Python, are there any advantages \/ disadvantages of working with a list of lists versus working with a dictionary, more specifically when doing numerical operations with them? I'm writing a class of functions to solve simple matrix operations for my linear algebra class. I was using dictionaries, but then I saw that numpy uses list of lists instead, so I guess there must be some advantages in it.\nExample: [[1,2,3],[4,5,6],[7,8,9]] as opposed to {0:[1,2,3],1:[4,5,6],2:[7,8,9]}","AnswerCount":2,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":20631,"Q_Id":15990456,"Users Score":12,"Answer":"When the keys of the dictionary are 0, 1, ..., n, a list will be faster, since no hashing is involved. As soon as the keys are not such a sequence, you need to use a dict.","Q_Score":14,"Tags":"python,arrays,list,math,dictionary","A_Id":15990493,"CreationDate":"2013-04-13T17:00:00.000","Title":"List of lists vs dictionary","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to generate a radial basis function where the input variables are defined at runtime. The SciPy.interpolate.Rbf function seems to request discrete lists for each input and output variable, eg: rbf(x,y,z). This restricts you to defining fixed variables before hand. \nI have tried unsuccessfully to pass a list or array of variables to the Rbf function (eg. rbf(list(x,...)) with no success. Has anyone else found a solution to this problem using this Rbf library? I would like to avoid switching to a different library or rewriting one if possible. Is there a way to generate discrete variables at runtime to feed into a function?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":512,"Q_Id":16063698,"Users Score":0,"Answer":"After looking through the source for the SciPy function, I will just subclass it and override init where the individual inputs are combined into an array anyway.","Q_Score":0,"Tags":"python,scipy","A_Id":16064603,"CreationDate":"2013-04-17T15:07:00.000","Title":"Alternative inputs to SciPy Radial Basis Functions","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Suppose I have a pandas DataFrame with two columns named 'A' and 'B'.\nNow suppose I also have a dictionary with keys 'A' and 'B', and the dictionary points to a scalar. That is, dict['A'] = 1.2 and similarly for 'B'.\nIs there a simple way to multiply each column of the DataFrame by these scalars?\nCheers!","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":9702,"Q_Id":16112209,"Users Score":5,"Answer":"As Wouter said, the recommended method is to convert the dict to a pandas.Series and multiple the two objects together:\nresult = df * pd.Series(myDict)","Q_Score":4,"Tags":"python,pandas","A_Id":16225932,"CreationDate":"2013-04-19T19:32:00.000","Title":"Multiplying Columns by Scalars in Pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am plotting some data using pylab and everything works perfect as I expect. I have 6 different graphs to plot and I can individually plot them in separate figures. But when I try to subplot() these graphs, the last one (subplot(3,2,6)) doesn't show anything.\nWhat confuses me is that this 6th graph is drawn perfectly when put in a separate figure but not in the subplot - with identical configurations.\nAny ideas what may be causing the problem ?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":375,"Q_Id":16133206,"Users Score":1,"Answer":"I found out that subplot() should be called before the plot(), issue resolved.","Q_Score":1,"Tags":"python,matplotlib,plot","A_Id":16133372,"CreationDate":"2013-04-21T16:07:00.000","Title":"Python Pylab subplot bug?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to save multiple numpy arrays along with the user input that was used to compute the data these arrays contain in a single file.  I'm having a hard time finding a good procedure to use to achieve this or even what file type to use. The only thing i can think of is too put the computed arrays along with the user input into one single array and then save it using numpy.save.  Does anybody know any better alternatives or good file types for my use?","AnswerCount":4,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":106,"Q_Id":16149187,"Users Score":0,"Answer":"I had this problem long ago so i dont have the code near to show you, but i used a binary write in a tmp file to get that done.\nEDIT: Thats is, pickle is what i used. Thanks SpankMe and RoboInventor","Q_Score":0,"Tags":"python,file,numpy","A_Id":16149290,"CreationDate":"2013-04-22T14:06:00.000","Title":"Saving python data for an application","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to save multiple numpy arrays along with the user input that was used to compute the data these arrays contain in a single file.  I'm having a hard time finding a good procedure to use to achieve this or even what file type to use. The only thing i can think of is too put the computed arrays along with the user input into one single array and then save it using numpy.save.  Does anybody know any better alternatives or good file types for my use?","AnswerCount":4,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":106,"Q_Id":16149187,"Users Score":2,"Answer":"How about using pickle and then storing pickled array objects in a storage of your choice, like database or files?","Q_Score":0,"Tags":"python,file,numpy","A_Id":16149283,"CreationDate":"2013-04-22T14:06:00.000","Title":"Saving python data for an application","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a numerical matrix of 2500*2500. To calculate the MIC (maximal information coefficient) for each pair of vectors, I am using minepy.MINE,  but this is taking forever, can I make it faster?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":425,"Q_Id":16171519,"Users Score":1,"Answer":"first, use the latest version of minepy. Second, you can use a smaller value of \"alpha\" parameter, say 0.5 or 0.45. In this way, you will reduce the computational time in despite of characteristic matrix accuracy.\nDavide","Q_Score":0,"Tags":"python,python-2.7","A_Id":16401807,"CreationDate":"2013-04-23T14:07:00.000","Title":"how to make minepy.MINE run faster?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am drawing a histogram using matplotlib in python, and would like to draw a line representing the average of the dataset, overlaid on the histogram as a dotted line (or maybe some other color would do too). Any ideas on how to draw a line overlaid on the histogram?\nI am using the plot() command, but not sure how to draw a vertical line (i.e. what value should I give for the y-axis?\nthanks!","AnswerCount":3,"Available Count":1,"Score":0.1325487884,"is_accepted":false,"ViewCount":127120,"Q_Id":16180946,"Users Score":2,"Answer":"I would look at the largest value in your data set (i.e. the histogram bin values) multiply that value by a number greater than 1 (say 1.5) and use that to define the y axis value. This way it will appear above your histogram regardless of the values within the histogram.","Q_Score":90,"Tags":"python,matplotlib,axis","A_Id":16180974,"CreationDate":"2013-04-23T23:35:00.000","Title":"Drawing average line in histogram (matplotlib)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have just come across an interesting interview style type of question which I couldn't get my head around. \nBasically, given a number to alphabet mapping such that [1:A, 2:B, 3:C ...], print out all possible combinations.\nFor instance \"123\" will generate [ABC, LC, AW] since it can be separated into 12,3 and 1,23. \nI'm thinking it has to be some type of recursive function where it checks with windows of size 1 and 2 and appending to a previous result if it's a valid letter mapping. \nIf anyone can formulate some pseudo\/python code that'd be much appreciated.","AnswerCount":6,"Available Count":1,"Score":0.0333209931,"is_accepted":false,"ViewCount":1582,"Q_Id":16183941,"Users Score":1,"Answer":"As simple as a tree \nLet suppose you have give \"1261\"\nConstruct a tree with it a Root .\nBy defining the node(left , right ) , where left is always direct map and right is combo \nversion suppose for the if you take given Number as  1261\n1261 -> \n(1(261) ,12(61)) ->  1 is left-node(direct map -> a) 12 is right node(combo-map1,2->L)\n(A(261) , L(61)) ->\n(A(2(61),26(1))) ,L(6(1)) ->\n(A(B(6(1)),Z(1)) ,L(F(1))) ->\n(A(B(F(1)),Z(A)) ,L(F(A))) ->\n(A(B(F(A)),Z(A)) ,L(F(A)))\nso now you have got all the leaf node..\njust print all paths from root to leaf node , this gives you all possible combinations .\nlike in this case\nABFA , AZA , LFA\nSo once you are done with the construction of tree just print all paths from root to node \nwhich is your requirement .","Q_Score":1,"Tags":"python,algorithm,alphabetical","A_Id":16186125,"CreationDate":"2013-04-24T05:21:00.000","Title":"How to generate a list of all possible alphabetical combinations based on an input of numbers","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"We have a very simple program (single-threaded) where we we do a bunch of random sample generation. For this we are using several calls of the numpy random functions (like normal or random_sample). Sometimes the result of one random call determines the number of times another random function is called.\nNow I want to set a seed in the beginning s.th. multiple runs of my program should yield the same result. For this I'm using an instance of the numpy class RandomState. While this is the case in the beginning, at some time the results become different and this is why I'm wondering.\nWhen I am doing everything correctly, having no concurrency and thereby a linear call of the functions AND no other random number generator involded, why does it not work?","AnswerCount":2,"Available Count":2,"Score":-0.0996679946,"is_accepted":false,"ViewCount":1368,"Q_Id":16220585,"Users Score":-1,"Answer":"If reproducibility is very important to you, I'm not sure I'd fully trust any PRNG to always produce the same output given the same seed.  You might consider capturing the random numbers in one phase, saving them for reuse; then in a second phase, replay the random numbers you've captured.  That's the only way to eliminate the possibility of non-reproducibility -- and it solves your current problem too.","Q_Score":3,"Tags":"python,random,numpy,prng","A_Id":16223497,"CreationDate":"2013-04-25T16:57:00.000","Title":"Python numpy - Reproducibility of random numbers","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"We have a very simple program (single-threaded) where we we do a bunch of random sample generation. For this we are using several calls of the numpy random functions (like normal or random_sample). Sometimes the result of one random call determines the number of times another random function is called.\nNow I want to set a seed in the beginning s.th. multiple runs of my program should yield the same result. For this I'm using an instance of the numpy class RandomState. While this is the case in the beginning, at some time the results become different and this is why I'm wondering.\nWhen I am doing everything correctly, having no concurrency and thereby a linear call of the functions AND no other random number generator involded, why does it not work?","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":1368,"Q_Id":16220585,"Users Score":5,"Answer":"Okay, David was right. The PRNGs in numpy work correctly. Throughout every minimal example I created, they worked as they are supposed to.\nMy problem was a different one, but finally I solved it. Do never loop over a dictionary within a deterministic algorithm. It seems that Python orders the items arbitrarily when calling the .item() function for getting in iterator.\nSo I am not that disappointed that this was this kind of error, because it is a useful reminder of what to think about when trying to do reproducible simulations.","Q_Score":3,"Tags":"python,random,numpy,prng","A_Id":16296438,"CreationDate":"2013-04-25T16:57:00.000","Title":"Python numpy - Reproducibility of random numbers","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am just starting out with nltk, and I am following the book. Chapter six is about text classification, and i am a bit confused about something. In the examples (the names, and movie reviews) the classifier is trained to select between two well-defined labels (male-female, and pos-neg). But how to train if you have only one label. \nSay I have a bunch of movie plot outlines, and I am only interested in fishing out movies from the sci-fi genre. Can I train a classifier to only recognize sci-fi plots, en say f.i. if classification confidence is > 80%, then put it in the sci-fi group, otherwise, just ignore it.\nHope somebody can clarify, thank you,","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":441,"Q_Id":16230984,"Users Score":0,"Answer":"I see two questions \n\nHow to train the system?\nCan the system consist of \"sci-fi\" and \"others\"?\n\nThe answer to 2 is yes. Having a 80% confidence threshold idea also makes sense, as long as you see with your data, features and algorithm that 80% is a good threshold. (If not, you may want to consider lowering it if not all sci-fi movies are being classified as sci-fi, or lowering it, if too many non-sci-fi movies are being categorized as sci-fi.)\nThe answer to 1 depends on the data you have, the features you can extract, etc. Jared's approach seems reasonable. Like Jared, I'd also to emphasize the importance of enough and representative data.","Q_Score":0,"Tags":"python,machine-learning,nlp,classification,nltk","A_Id":16231323,"CreationDate":"2013-04-26T07:29:00.000","Title":"train nltk classifier for just one label","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am just starting out with nltk, and I am following the book. Chapter six is about text classification, and i am a bit confused about something. In the examples (the names, and movie reviews) the classifier is trained to select between two well-defined labels (male-female, and pos-neg). But how to train if you have only one label. \nSay I have a bunch of movie plot outlines, and I am only interested in fishing out movies from the sci-fi genre. Can I train a classifier to only recognize sci-fi plots, en say f.i. if classification confidence is > 80%, then put it in the sci-fi group, otherwise, just ignore it.\nHope somebody can clarify, thank you,","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":441,"Q_Id":16230984,"Users Score":0,"Answer":"You can simply train a binary classifier to distinguish between sci-fi and not sci-fi\nSo train on the movie plots that are labeled as sci-fi and also on a selection of all other genres. It might be a good idea to have a representative sample of the same size for the other genres such that not all are of the romantic comedy genre, for instance.","Q_Score":0,"Tags":"python,machine-learning,nlp,classification,nltk","A_Id":16231216,"CreationDate":"2013-04-26T07:29:00.000","Title":"train nltk classifier for just one label","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to use function  np.random.curve_fit(x,a,b,c,...,z) with a big but fixed number of fitting parameters. Is it possible to use tuples or lists here for shortness, like np.random.curve_fit(x,P), where P=(a,b,c,...,z)?","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":77,"Q_Id":16236652,"Users Score":4,"Answer":"Well, to convert your example, you would use  np.random.normal(x, *P). However, np.random.normal(x,a,b,c,...,z) wouldn't actually work. Maybe you meant another function?","Q_Score":0,"Tags":"python","A_Id":16236700,"CreationDate":"2013-04-26T12:37:00.000","Title":"list instead of separated arguments in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"My objective is to cluster words based on how similar they are with respect to a corpus of text documents. I have computed Jaccard Similarity between every pair of words. In other words, I have a sparse distance matrix available with me. Can anyone point me to any clustering algorithm (and possibly its library in Python) which takes distance matrix as input ? I also do not know the number of clusters beforehand. I only want to cluster these words and obtain which words are clustered together.","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":28598,"Q_Id":16246066,"Users Score":0,"Answer":"Recommend to take a look at agglomerative clustering.","Q_Score":24,"Tags":"python,cluster-computing,scikit-learn,hierarchical-clustering","A_Id":54499731,"CreationDate":"2013-04-26T22:19:00.000","Title":"Clustering words based on Distance Matrix","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"What would be the most efficient algorithm to solve a linear equation in one variable given as a string input to a function? For example, for input string: \n\n\"x + 9 \u2013 2 - 4 + x = \u2013 x + 5 \u2013 1 + 3 \u2013 x\" \n\nThe output should be 1.\nI am considering using a stack and pushing each string token onto it as I encounter spaces in the string. If the input was in polish notation then it would have been easier to pop numbers off the stack to get to a result, but I am not sure what approach to take here.\nIt is an interview question.","AnswerCount":4,"Available Count":1,"Score":0.049958375,"is_accepted":false,"ViewCount":3057,"Q_Id":16273351,"Users Score":1,"Answer":"The first thing is to parse the string, to identify the various tokens (numbers, variables and operators), so that an expression tree can be formed by giving operator proper precedences.\nRegular expressions can help, but that's not the only method (grammar parsers like boost::spirit are good too, and you can even run your own: its all a \"find and recourse\").\nThe tree can then be manipulated reducing the nodes executing those operation that deals with constants and by grouping variables related operations, executing them accordingly.\nThis goes on recursively until you remain with a variable related node and a constant node.\nAt the point the solution is calculated trivially.\nThey are basically the same principles that leads to the production of an interpreter or a compiler.","Q_Score":1,"Tags":"c++,python,algorithm,linear-algebra","A_Id":16273799,"CreationDate":"2013-04-29T07:30:00.000","Title":"Solving a linear equation in one variable","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to generate a random number between 0.1 and 1.0.\nWe can't use rand.randint because it returns integers.\nWe have also tried random.uniform(0.1,1.0), but it returns a value >= 0.1 and < 1.0, we can't use this, because our search includes also 1.0.\nDoes somebody else have an idea for this problem?","AnswerCount":10,"Available Count":2,"Score":0.0199973338,"is_accepted":false,"ViewCount":37037,"Q_Id":16288749,"Users Score":1,"Answer":"Try \n    random.randint(1, 10)\/100.0","Q_Score":26,"Tags":"python,random,floating-point","A_Id":31707656,"CreationDate":"2013-04-29T21:43:00.000","Title":"Generate random number between 0.1 and 1.0. Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to generate a random number between 0.1 and 1.0.\nWe can't use rand.randint because it returns integers.\nWe have also tried random.uniform(0.1,1.0), but it returns a value >= 0.1 and < 1.0, we can't use this, because our search includes also 1.0.\nDoes somebody else have an idea for this problem?","AnswerCount":10,"Available Count":2,"Score":0.0199973338,"is_accepted":false,"ViewCount":37037,"Q_Id":16288749,"Users Score":1,"Answer":"The standard way would be random.random() * 0.9 + 0.1 (random.uniform() internally does just this).  This will return numbers between 0.1 and 1.0 without the upper border.\nBut wait!  0.1 (aka \u00b9\/\u2081\u2080) has no clear binary representation (as \u2153 in decimal)!  So You won't get a true 0.1 anyway, simply because the computer cannot represent it internally.  Sorry ;-)","Q_Score":26,"Tags":"python,random,floating-point","A_Id":16289924,"CreationDate":"2013-04-29T21:43:00.000","Title":"Generate random number between 0.1 and 1.0. Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"What is a good way to sample integers in the range {0,...,n-1} according to (a discrete version of) the exponential distribution?  random.expovariate(lambd) returns a real number  from 0 to positive infinity.\nUpdate. Changed title to make it more accurate.","AnswerCount":3,"Available Count":1,"Score":-0.0665680765,"is_accepted":false,"ViewCount":2007,"Q_Id":16317420,"Users Score":-1,"Answer":"The simple answer is: pick a random number from geometric distribution and return mod n.\nEg: random.geometric(p)%n\nP(x) = p(1-p)^x+ p(1-p)^(x+n) + p(1-p)^(x+2n) ....\n= p(1-p)^x *(1+(1-p)^n +(1-p)^(2n) ... )\nNote that second part is a constant for a given p and n. The first part is geometric.","Q_Score":2,"Tags":"python,math","A_Id":16319018,"CreationDate":"2013-05-01T11:37:00.000","Title":"Sample integers from truncated geometric distribution","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've come up with 2 methods to generate relatively short random strings- one is much faster and simpler and the other much slower but I think more random.  Is there a not-super-complicated method or way to measure how random the data from each method might be?\nI've tried compressing the output strings (via zlib) figuring the more truly random the data, the less it will compress but that hasn't proved much.","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":740,"Q_Id":16320412,"Users Score":0,"Answer":"You can use some mapping to convert strings to numeric and then apply standard tests like Diehard and TestU01. Note that long sequences of samples are needed (typically few MB files will do)","Q_Score":10,"Tags":"python,random","A_Id":16320580,"CreationDate":"2013-05-01T14:49:00.000","Title":"Quantifying randomness","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've come up with 2 methods to generate relatively short random strings- one is much faster and simpler and the other much slower but I think more random.  Is there a not-super-complicated method or way to measure how random the data from each method might be?\nI've tried compressing the output strings (via zlib) figuring the more truly random the data, the less it will compress but that hasn't proved much.","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":740,"Q_Id":16320412,"Users Score":0,"Answer":"An outcome is considered random if it can't be predicted ahead of time with certainty.  If it can be predicted with certainty it is considered deterministic.  This is a binary categorization, outcomes either are deterministic or random, there aren't degrees of randomness. There are, however, degrees of predictability.  One measure of predictability is entropy, as mentioned by EMS.\nConsider two games.  You don't know on any given play whether you're going to win or lose.  In  game 1, the probability of winning is 1\/2, i.e., you win about half the time in the long run.  In game 2, the odds of winning are 1\/100.  Both games are considered random, because the outcome isn't a dead certainty.  Game 1 has greater entropy than game 2, because the outcome is less predictable - while there's a chance of winning, you're pretty sure you're going to lose on any given trial.\nThe amount of compression that can be achieved (by a good compression algorithm) for a sequence of values is related to the entropy of the sequence.  English has pretty low entropy (lots of redundant info both in the relative frequency of letters and the sequences of words that occur as groups), and hence tends to compress pretty well.","Q_Score":10,"Tags":"python,random","A_Id":16328721,"CreationDate":"2013-05-01T14:49:00.000","Title":"Quantifying randomness","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a csv file of [66k, 56k] size (rows, columns). Its a sparse matrix. I know that numpy can handle that size a matrix. I would like to know based on everyone's experience, how many features scikit-learn algorithms can handle comfortably?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":2840,"Q_Id":16326699,"Users Score":1,"Answer":"Some linear model (Regression, SGD, Bayes) will probably be your best bet if you need to train your model frequently. \nAlthough before you go running any models you could try the following \n1) Feature reduction. Are there features in your data that could easily be removed? For example if your data is text or ratings based there are lots known options available. \n2) Learning curve analysis. Maybe you only need a small subset of your data to train a model, and after that you are only fitting to your data or gaining tiny increases in accuracy. \nBoth approaches could allow you to greatly reduce the training data required.","Q_Score":6,"Tags":"python,numpy,machine-learning,scipy,scikit-learn","A_Id":16332805,"CreationDate":"2013-05-01T21:18:00.000","Title":"How many features can scikit-learn handle?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using mayavi.mlab to display 3D data extracted from images. The data is as follows:\n\n3D camera parameters as 3 lines in the x, y, x direction around the camera center, usually for about 20 cameras using mlab.plot3d().\n3D coloured points in space for about 4000 points using mlab.points3d().\n\nFor (1) I have a function to draw each line for each camera seperately. If I am correct, all these lines are added to the mayavi pipeline for the current scene. Upon mlab.show() the scene takes about 10 seconds to render all these lines.\nFor (2) I couldn't find a way to plot all the points at once with each point a different color, so at the moment I iterate with mlab.points3d(x,y,z, color = color). I have newer waited for this routine to finish as it takes to long. If I plot all the points at once with the same color, it takes about 2 seconds.\nI already tried to start my script with fig.scene.disable_render = True and resetting fig.scene.disable_render = False before displaying the scene with mlab.show().\nHow can I display my data with mayavi within a reasonable waiting time?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1606,"Q_Id":16364311,"Users Score":2,"Answer":"The general principle is that vtk objects have a lot of overhead, and so you for rendering performance you want to pack as many things into one object as possible.  When you call mlab convenience functions like points3d it creates a new vtk object to handle that data.  Thus iterating and creating thousands of single points as vtk objects is a very bad idea.\nThe trick of temporarily disabling the rendering as in that other question -- the \"right\" way to do it is to have one VTK object that holds all of the different points.\nTo set the different points as different colors, give scalar values to the vtk object.\nx,y,z=np.random.random((3,100))\n   some_data=mlab.points3d(x,y,z,colormap='cool')\n   some_data.mlab_source.dataset.point_data.scalars=np.random.random((100,))\nThis only works if you can adequately represent the color values you need in a colormap.  This is easy if you need a small finite number of colors or a small finite number of simple colormaps, but very difficult if you need completely arbitrary colors.","Q_Score":7,"Tags":"python,mayavi","A_Id":17346232,"CreationDate":"2013-05-03T17:13:00.000","Title":"Render a mayavi scene with a large pipeline faster","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a function f(x_1, x_2, ..., x_n) where n >= 1 that I would like to integrate. What algorithm should I use to provide a decently stable \/ accurate solution?\nI would like to program it in Python so any open source examples are more than welcome!\n(I realize that I should use a library but this is just a learning exercise.)","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":270,"Q_Id":16382019,"Users Score":2,"Answer":"It depends on your context and the performance criteria. I assume that you are looking for a numerical approximation (as opposed to a algebraic integration)\nA Riemann Sum is the standard 'educational' way of numerically calculating integrals but several computationally more efficient algorithms exist.","Q_Score":0,"Tags":"python,algorithm,math,integral","A_Id":16382307,"CreationDate":"2013-05-05T06:30:00.000","Title":"How do I integrate a multivariable function?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to delete the first three rows of a dataframe in pandas.\nI know df.ix[:-1] would remove the last row, but I can't figure out how to remove first n rows.","AnswerCount":8,"Available Count":2,"Score":0.1243530018,"is_accepted":false,"ViewCount":412922,"Q_Id":16396903,"Users Score":5,"Answer":"inp0= pd.read_csv(\"bank_marketing_updated_v1.csv\",skiprows=2)\nor if you want to do in existing dataframe\nsimply do following command","Q_Score":249,"Tags":"python,pandas","A_Id":61941548,"CreationDate":"2013-05-06T10:35:00.000","Title":"Delete the first three rows of a dataframe in pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to delete the first three rows of a dataframe in pandas.\nI know df.ix[:-1] would remove the last row, but I can't figure out how to remove first n rows.","AnswerCount":8,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":412922,"Q_Id":16396903,"Users Score":9,"Answer":"A simple way is to use tail(-n) to remove the first n rows\ndf=df.tail(-3)","Q_Score":249,"Tags":"python,pandas","A_Id":52984033,"CreationDate":"2013-05-06T10:35:00.000","Title":"Delete the first three rows of a dataframe in pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a bunch of code that deals with document clustering.  One step involves calculating the similarity (for some unimportant definition of \"similar\") of every document to every other document in a given corpus, and storing the similarities for later use.  The similarities are bucketed, and I don't care what the specific similarity is for purposes of my analysis, just what bucket it's in.  For example, if documents 15378 and 3278 are 52% similar, the ordered pair (3278, 15378) gets stored in the [0.5,0.6) bucket.  Documents sometimes get either added or removed from the corpus after initial analysis, so corresponding pairs get added to or removed from the buckets as needed.\nI'm looking at strategies for storing these lists of ID pairs.  We found a SQL database (where most of our other data for this project lives) to be too slow and too large disk-space-wise for our purposes, so at the moment we store each bucket as a compressed list of integers on disk (originally zlib-compressed, but now using lz4 instead for speed).  Things I like about this:\n\nReading and writing are both quite fast\nAfter-the-fact additions to the corpus are fairly straightforward to add (a bit less so for lz4 than for zlib because lz4 doesn't have a framing mechanism built in, but doable)\nAt both write and read time, data can be streamed so it doesn't need to be held in memory all at once, which would be prohibitive given the size of our corpora\n\nThings that kind of suck:\n\nDeletes are a huge pain, and basically involve streaming through all the buckets and writing out new ones that omit any pairs that contain the ID of a document that's been deleted\nI suspect I could still do better both in terms of speed and compactness with a more special-purpose data structure and\/or compression strategy\n\nSo: what kinds of data structures should I be looking at?  I suspect that the right answer is some kind of exotic succinct data structure, but this isn't a space I know very well.  Also, if it matters: all of the document IDs are unsigned 32-bit ints, and the current code that handles this data is written in C, as Python extensions, so that's probably the general technology family we'll stick with if possible.","AnswerCount":3,"Available Count":2,"Score":0.0665680765,"is_accepted":false,"ViewCount":704,"Q_Id":16426469,"Users Score":1,"Answer":"How about using one hash table or B-tree per bucket?\nOn-disk hashtables are standard. Maybe the BerkeleyDB libraries (availabe in stock python) will work for you; but be advised that they since they come with transactions they can be slow, and may require some tuning. There are a number of choices: gdbm, tdb that you should all give a try. Just make sure you check out the API and initialize them with appropriate size. Some will not resize automatically, and if you feed them too much data their performance just drops a lot.\nAnyway, you may want to use something even more low-level, without transactions, if you have a lot of changes.\nA pair of ints is a long - and most databases should accept a long as a key; in fact many will accept arbitrary byte sequences as keys.","Q_Score":6,"Tags":"python,c,data-structures,integer","A_Id":16444230,"CreationDate":"2013-05-07T18:54:00.000","Title":"Data structure options for efficiently storing sets of integer pairs on disk?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a bunch of code that deals with document clustering.  One step involves calculating the similarity (for some unimportant definition of \"similar\") of every document to every other document in a given corpus, and storing the similarities for later use.  The similarities are bucketed, and I don't care what the specific similarity is for purposes of my analysis, just what bucket it's in.  For example, if documents 15378 and 3278 are 52% similar, the ordered pair (3278, 15378) gets stored in the [0.5,0.6) bucket.  Documents sometimes get either added or removed from the corpus after initial analysis, so corresponding pairs get added to or removed from the buckets as needed.\nI'm looking at strategies for storing these lists of ID pairs.  We found a SQL database (where most of our other data for this project lives) to be too slow and too large disk-space-wise for our purposes, so at the moment we store each bucket as a compressed list of integers on disk (originally zlib-compressed, but now using lz4 instead for speed).  Things I like about this:\n\nReading and writing are both quite fast\nAfter-the-fact additions to the corpus are fairly straightforward to add (a bit less so for lz4 than for zlib because lz4 doesn't have a framing mechanism built in, but doable)\nAt both write and read time, data can be streamed so it doesn't need to be held in memory all at once, which would be prohibitive given the size of our corpora\n\nThings that kind of suck:\n\nDeletes are a huge pain, and basically involve streaming through all the buckets and writing out new ones that omit any pairs that contain the ID of a document that's been deleted\nI suspect I could still do better both in terms of speed and compactness with a more special-purpose data structure and\/or compression strategy\n\nSo: what kinds of data structures should I be looking at?  I suspect that the right answer is some kind of exotic succinct data structure, but this isn't a space I know very well.  Also, if it matters: all of the document IDs are unsigned 32-bit ints, and the current code that handles this data is written in C, as Python extensions, so that's probably the general technology family we'll stick with if possible.","AnswerCount":3,"Available Count":2,"Score":0.0665680765,"is_accepted":false,"ViewCount":704,"Q_Id":16426469,"Users Score":1,"Answer":"Why not just store a table containing stuff that was deleted since the last re-write?\nThis table could be the same structure as your main bucket, maybe with a Bloom filter for quick membership checks.\nYou can re-write the main bucket data without the deleted items either when you were going to re-write it anyway for some other modification, or when the ratio of deleted items:bucket size exceeds some threshold.\n\nThis scheme could work either by storing each deleted pair alongside each bucket, or by storing a single table for all deleted documents: I'm not sure which is a better fit for your requirements.\nKeeping a single table, it's hard to know when you can remove an item unless you know how many buckets it affects, without just re-writing all buckets whenever the deletion table gets too large. This could work, but it's a bit stop-the-world.\nYou also have to do two checks for each pair you stream in (ie, for (3278, 15378), you'd check whether either 3278 or 15378 has been deleted, instead of just checking whether pair (3278, 15378) has been deleted.\nConversely, the per-bucket table of each deleted pair would take longer to build, but be slightly faster to check, and easier to collapse when re-writing the bucket.","Q_Score":6,"Tags":"python,c,data-structures,integer","A_Id":16444440,"CreationDate":"2013-05-07T18:54:00.000","Title":"Data structure options for efficiently storing sets of integer pairs on disk?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"i am trying to install opencv on my MacbookPro OSX 10.6.8 (snow leopard)\nand Xcode version is 3.2.6\nand result of \"which python\" is\n\nHong-Jun-Choiui-MacBook-Pro:~ teemo$ which python\n  \/Library\/Frameworks\/Python.framework\/Versions\/2.7\/bin\/python\n\nand i am suffering from this below..\n\nLinking CXX shared library ..\/..\/lib\/libopencv_contrib.dylib\n[ 57%] Built target opencv_contrib\nmake: * [all] Error 2\n\nFull log is here link by \"brew install -v opencv\"\n        54 248 246 33:7700\/log.txt\nany advice for me?\ni just need opencv lib for python.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":447,"Q_Id":16436260,"Users Score":0,"Answer":"Try using macports it builds opencv including python bindings without any issue. \nI have used this for osx 10.8.","Q_Score":0,"Tags":"python,macos,opencv","A_Id":16495361,"CreationDate":"2013-05-08T08:45:00.000","Title":"openCV install Error using brew","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"My Question is as follows:\nI know a little bit about ML in Python (using NLTK), and it works ok so far. I can get predictions given certain features. But I want to know, is there a way, to display the best features to achieve a label? I mean the direct opposite of what I've been doing so far (put in all circumstances, and get a label for that)\nI try to make my question clear via an example:\nLet's say I have a database with Soccer games.\nThe Labels are e.g.  'Win', 'Loss', 'Draw'.\nThe Features are e.g. 'Windspeed', 'Rain or not', 'Daytime', 'Fouls committed' etc. \nNow I want to know: Under which circumstances will a Team achieve a Win, Loss or Draw? Basically I want to get back something like this:\nBest conditions for Win: Windspeed=0, No Rain, Afternoon, Fouls=0 etc\nBest conditions for Loss: ...\nIs there a way to achieve this?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":879,"Q_Id":16442055,"Users Score":0,"Answer":"You could compute the representativeness of each feature to separate the classes via feature weighting. The most common method for feature selection (and therefore feature weighting) in Text Classification is chi^2. This measure will tell you which features are better. Based on this information you can analyse the specific values that are best for every case. I hope this helps.\nRegards,","Q_Score":3,"Tags":"python,machine-learning,nltk","A_Id":16446014,"CreationDate":"2013-05-08T13:30:00.000","Title":"Machine Learning in Python - Get the best possible feature-combination for a label","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there any built-in way to get scikit-learn to perform shuffled stratified k-fold cross-validation? This is one of the most common CV methods, and I am surprised I couldn't find a built-in method to do this.\nI saw that cross_validation.KFold() has a shuffling flag, but it is not stratified. Unfortunately cross_validation.StratifiedKFold() does not have such an option, and cross_validation.StratifiedShuffleSplit() does not produce disjoint folds.\nAm I missing something? Is this planned?\n(obviously I can implement this by myself)","AnswerCount":4,"Available Count":1,"Score":-0.1488850336,"is_accepted":false,"ViewCount":9847,"Q_Id":16448988,"Users Score":-3,"Answer":"As far as I know, this is actually implemented in scikit-learn. \n\n\"\"\"\nStratified ShuffleSplit cross validation iterator\nProvides train\/test indices to split data in train test sets.\nThis cross-validation object is a merge of StratifiedKFold and\nShuffleSplit, which returns stratified randomized folds. The folds\nare made by preserving the percentage of samples for each class.\nNote: like the ShuffleSplit strategy, stratified random splits\ndo not guarantee that all folds will be different, although this is\nstill very likely for sizeable datasets.\n\"\"\"","Q_Score":6,"Tags":"python,machine-learning,scikit-learn,cross-validation","A_Id":16950633,"CreationDate":"2013-05-08T19:46:00.000","Title":"Randomized stratified k-fold cross-validation in scikit-learn?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to use the gaussian function in python to generate some numbers between a specific range giving the mean and variance \nso lets say I have a range between 0 and 10 \nand I want my mean to be 3 and variance to be 4\nmean = 3, variance = 4\nhow can I do that ?","AnswerCount":6,"Available Count":1,"Score":0.0333209931,"is_accepted":false,"ViewCount":36597,"Q_Id":16471763,"Users Score":1,"Answer":"If you have a small range of integers, you can create a list with a gaussian distribution of the numbers within that range and then make a random choice from it.","Q_Score":10,"Tags":"python,gaussian","A_Id":16471982,"CreationDate":"2013-05-09T21:51:00.000","Title":"Generating numbers with Gaussian function in a range using python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"When using RandomForestRegressor from Sklearn, how do you get the residuals of the regression? I would like to plot out these residuals to check the linearity.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2682,"Q_Id":16502445,"Users Score":5,"Answer":"There is no function for that, as we like to keep the interface very simple.\nYou can just do\ny - rf.predict(X)","Q_Score":4,"Tags":"python,python-2.7,numpy,scipy,scikit-learn","A_Id":16524872,"CreationDate":"2013-05-11T22:51:00.000","Title":"Residuals of Random Forest Regression (Python)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am fitting data points using a logistic model. As I sometimes have data with a ydata error, I first used curve_fit and its sigma argument to include my individual standard deviations in the fit. \nNow I switched to leastsq, because I needed also some Goodness of Fit estimation that curve_fit could not provide. Everything works well, but now I miss the possibility to weigh the least sqares as \"sigma\" does with curve_fit.\nHas someone some code example as to how I could weight the least squares also in leastsq?\nThanks, Woodpicker","AnswerCount":3,"Available Count":1,"Score":0.2605204458,"is_accepted":false,"ViewCount":4123,"Q_Id":16510227,"Users Score":4,"Answer":"Assuming your data are in arrays x, y with yerr, and the model is f(p, x), just define the error function to be minimized as (y-f(p,x))\/yerr.","Q_Score":6,"Tags":"python,scipy,curve-fitting,least-squares","A_Id":16521114,"CreationDate":"2013-05-12T17:36:00.000","Title":"Python \/ Scipy - implementing optimize.curve_fit 's sigma into optimize.leastsq","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using the Scikit module for Python to implement Stochastic Gradient Boosting. My data set has 2700 instances and 1700 features (x) and contains binary data. My output vector is 'y', and contains 0 or 1 (binary classification). My code is,\n\ngb = GradientBoostingClassifier(n_estimators=1000,learn_rate=1,subsample=0.5)\ngb.fit(x,y)\nprint gb.score(x,y)\n\nOnce I ran it, and got an accuracy of 1.0 (100%), and sometimes I get an accuracy of around 0.46 (46%). Any idea why there is such a huge gap in its performance?","AnswerCount":2,"Available Count":1,"Score":0.4621171573,"is_accepted":false,"ViewCount":1888,"Q_Id":16579775,"Users Score":5,"Answer":"First, a couple of remarks:\n\nthe name of the algorithm is Gradient Boosting (Regression Trees or Machines) and is not directly related to Stochastic Gradient Descent\nyou should never evaluate the accuracy of a machine learning algorithm on you training data, otherwise you won't be able to detect the over-fitting of the model. Use: sklearn.cross_validation.train_test_split to split X and y into a X_train, y_train for fitting and X_test, y_test for scoring instead.\n\nNow to answer your question, GBRT models are indeed non deterministic models. To get deterministic \/ reproducible runs, you can pass random_state=0 to seed the pseudo random number generator (or alternatively pass max_features=None but this is not recommended). \nThe fact that you observe such big variations in your training error is weird though. Maybe your output signal if very correlated with a very small number of informative features and most other features are just noise?\nYou could try to fit a RandomForestClassifier model to your data and use the computed feature_importance_ array to discard noisy features and help stabilize your GBRT models.","Q_Score":0,"Tags":"python,machine-learning,scikit-learn,scikits","A_Id":16583343,"CreationDate":"2013-05-16T05:44:00.000","Title":"Stochastic Gradient Boosting giving unpredictable results","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I just picked up Pandas to do with some data analysis work in my biology research. Turns out one of the proteins I'm analyzing is called 'NA'.\nI have a matrix with pairwise 'HA, M1, M2, NA, NP...' on the column headers, and the same as \"row headers\" (for the biologists who might read this, I'm working with influenza). \nWhen I import the data into Pandas directly from a CSV file, it reads the \"row headers\" as 'HA, M1, M2...' and then NA gets read as NaN. Is there any way to stop this? The column headers are fine - 'HA, M1, M2, NA, NP etc...'","AnswerCount":2,"Available Count":1,"Score":0.4621171573,"is_accepted":false,"ViewCount":11645,"Q_Id":16596188,"Users Score":5,"Answer":"Just ran into this issue--I specified a str converter for the column instead, so I could keep na elsewhere: \npd.read_csv(... , converters={ \"file name\": str, \"company name\": str})","Q_Score":15,"Tags":"python,pandas,bioinformatics","A_Id":42148893,"CreationDate":"2013-05-16T19:48:00.000","Title":"Pandas Convert 'NA' to NaN","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"So, I'm trying to install pandas for Python 3.3 and have been having a really hard time- between Python 2.7 and Python 3.3 and other factors.\nSome pertinent information:  I am running Mac OSX Lion 10.7.5.  I have both Python 2.7 and Python 3.3 installed, but for my programming purposes only use 3.3.\nThis is where I'm at:  I explicitly installed pip-3.3 and can now run that command to install things.  I have XCode installed, and have also installed the command line tools (from 'Preferences').  I have looked through a number of pages through Google as well as through this site and haven't had any luck getting pandas to download\/download and install.\nI have tried downloading the tarball, 'cd' into the downloaded file and running setup.py install, but to no avail.\nI have downloaded and installed EPD Free, and then added 'Library\/Framework\/Python.framework\/Versions\/Current\/bin:${PATH} to .bash_profile - still doesn't work.\nI'm not sure where to go frome here...when I do pip-3.3 install pandas terminal relates that There was a problem confirming the ssl certificate: <urlopen error [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed (_ssl.c:547)> and so nothing ends up getting downloaded or installed, for either pandas, or I also tried to the same for numpy as I thought that could be a problem, but the same error was returned.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2260,"Q_Id":16599357,"Users Score":0,"Answer":"Thanks, I just had the same issue with Angstrom Linux on the BeagleBone Black board and the easy_install downgrade solution solved it.  One thing I did need to do, is after installing easy_install using \nopkg install python-setuptools\nI then had to go into the easy_install file (located in \/usr\/bin\/easy_install)\nand change the top line from #!\/usr\/bin\/python-native\/python to #!\/usr\/bin\/python\nthis fixed easy_install so it would detect python on the BeagleBone and then I could run your solution.","Q_Score":3,"Tags":"python,pandas,pip","A_Id":21592812,"CreationDate":"2013-05-16T23:58:00.000","Title":"Python 3.3 pandas, pip-3.3","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In other words, each element of the outer array will be a row vector from the original 2D array.","AnswerCount":4,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":5758,"Q_Id":16601049,"Users Score":0,"Answer":"I think it makes little sense to use numpy arrays to do that, just think you're missing out on all the advantages of numpy.","Q_Score":0,"Tags":"python,arrays,numpy,nested,vectorization","A_Id":16607943,"CreationDate":"2013-05-17T03:42:00.000","Title":"How do I convert a 2D numpy array into a 1D numpy array of 1D numpy arrays?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have some sampled (univariate) data - but the clock driving the sampling process is inaccurate - resulting in a random slip of (less than) 1 sample every 30.  A more accurate clock at approximately 1\/30 of the frequency provides reliable samples for the same data ... allowing me to establish a good estimate of the clock drift.\nI am looking to interpolate the sampled data to correct for this so that I 'fit' the high frequency data to the low-frequency.  I need to do this 'real time' - with no more than the latency of a few low-frequency samples.\nI recognise that there is a wide range of interpolation algorithms - and, among those I've considered, a spline based approach looks most promising for this data.\nI'm working in Python - and have found the scipy.interpolate package - though I could see no obvious way to use it to 'stretch' n samples to correct a small timing error.  Am I overlooking something?\nI am interested in pointers to either a suitable published algorithm, or - ideally - a Python library function to achieve this sort of transform.  Is this supported by SciPy (or anything else)?\nUPDATE...\nI'm beginning to realise that what, at first, seemed a trivial problem isn't as straightforward as I first thought.  I am no-longer convinced that naive use of splines will suffice.  I've also realised that my problem can be better described without reference to 'clock drift'... like this:\nA single random variable is sampled at two different frequencies - one low and one high, with no common divisor - e.g. 5hz and 144hz.  If we assume sample 0 is identical at both sample rates, sample 1 @5hz falls between samples 28 amd 29.  I want to construct a new series - at 720hz, say - that fits all the known data points \"as smoothly as possible\".\nI had hoped to find an 'out of the box' solution.","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":426,"Q_Id":16625298,"Users Score":1,"Answer":"Before you can ask the programming question, it seems to me you need to investigate a more fundamental scientific one.\nBefore you can start picking out particular equations to fit badfastclock to goodslowclock, you should investigate the nature of the drift. Let both clocks run a while, and look at their points together. Is badfastclock bad because it drifts linearly away from real time? If so, a simple quadratic equation should fit badfastclock to goodslowclock, just as a quadratic equation describes the linear acceleration of a object in gravity; i.e., if badfastclock is accelerating linearly away from real time, you can deterministically shift badfastclock toward real time. However, if you find that badfastclock is bad because it is jumping around, then smooth curves -- even complex smooth curves like splines -- won't fit. You must understand the data before trying to manipulate it.","Q_Score":2,"Tags":"python,scipy,numeric,numerical-methods","A_Id":16652325,"CreationDate":"2013-05-18T14:20:00.000","Title":"Interpolaton algorithm to correct a slight clock drift","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have some sampled (univariate) data - but the clock driving the sampling process is inaccurate - resulting in a random slip of (less than) 1 sample every 30.  A more accurate clock at approximately 1\/30 of the frequency provides reliable samples for the same data ... allowing me to establish a good estimate of the clock drift.\nI am looking to interpolate the sampled data to correct for this so that I 'fit' the high frequency data to the low-frequency.  I need to do this 'real time' - with no more than the latency of a few low-frequency samples.\nI recognise that there is a wide range of interpolation algorithms - and, among those I've considered, a spline based approach looks most promising for this data.\nI'm working in Python - and have found the scipy.interpolate package - though I could see no obvious way to use it to 'stretch' n samples to correct a small timing error.  Am I overlooking something?\nI am interested in pointers to either a suitable published algorithm, or - ideally - a Python library function to achieve this sort of transform.  Is this supported by SciPy (or anything else)?\nUPDATE...\nI'm beginning to realise that what, at first, seemed a trivial problem isn't as straightforward as I first thought.  I am no-longer convinced that naive use of splines will suffice.  I've also realised that my problem can be better described without reference to 'clock drift'... like this:\nA single random variable is sampled at two different frequencies - one low and one high, with no common divisor - e.g. 5hz and 144hz.  If we assume sample 0 is identical at both sample rates, sample 1 @5hz falls between samples 28 amd 29.  I want to construct a new series - at 720hz, say - that fits all the known data points \"as smoothly as possible\".\nI had hoped to find an 'out of the box' solution.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":426,"Q_Id":16625298,"Users Score":0,"Answer":"Bsed on your updated question, if the data is smooth with time, just place all the samples in a time trace, and interpolate on the sparse grid (time).","Q_Score":2,"Tags":"python,scipy,numeric,numerical-methods","A_Id":16708058,"CreationDate":"2013-05-18T14:20:00.000","Title":"Interpolaton algorithm to correct a slight clock drift","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I reconized numpy can link with blas, and I thought of why not using gpu accelerated blas library.\nDid anyone use to do so?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2411,"Q_Id":16629529,"Users Score":0,"Answer":"If memory servers, pyCuda at least, probably also pyOpenCL can work with numPy","Q_Score":2,"Tags":"python,numpy,opencl,gpgpu","A_Id":18478963,"CreationDate":"2013-05-18T22:19:00.000","Title":"Can I link numpy with AMD's gpu accelerated blas library","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Which python data type should I use to create a huge 2d array (7Mx7M) with fast random access? I want to write each element once and read many times.\nThanks","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":390,"Q_Id":16656850,"Users Score":1,"Answer":"It would help to know more about your data, and what kind of access you need to provide. How fast is \"fast enough\" for you? Just to be clear, \"7M\" means 7,000,000 right?\nAs a quick answer without any of that information, I have had positive experiences working with redis and tokyo tyrant for fast read access to large amounts of data, either hundreds of megabytes or gigabytes.","Q_Score":0,"Tags":"python,arrays,2d,large-data","A_Id":16657039,"CreationDate":"2013-05-20T19:25:00.000","Title":"Which python data type should I use to create a huge 2d array (7Mx7M) with fast random access?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"In matplotlib.pyplot, what is the difference between plt.clf() and plt.close()? Will they function the same way?\nI am running a loop where at the end of each iteration I am producing a figure and saving the plot. On first couple tries the plot was retaining the old figures in every subsequent plot. I'm looking for, individual plots for each iteration without the old figures, does it matter which one I use? The calculation I'm running takes a very long time and it would be very time consuming to test it out.","AnswerCount":4,"Available Count":3,"Score":1.0,"is_accepted":false,"ViewCount":49996,"Q_Id":16661790,"Users Score":9,"Answer":"I think it is worth mentioning that plt.close() releases the memory, thus is preferred when generating and saving many figures in one run.\nUsing plt.clf() in such case will produce a warning after 20 plots (even if they are not going to be shown by plt.show()):\n\nMore than 20 figures have been opened. Figures created through the\n  pyplot interface (matplotlib.pyplot.figure) are retained until\n  explicitly closed and may consume too much memory.","Q_Score":32,"Tags":"python,matplotlib","A_Id":46957388,"CreationDate":"2013-05-21T03:47:00.000","Title":"Difference between plt.close() and plt.clf()","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"In matplotlib.pyplot, what is the difference between plt.clf() and plt.close()? Will they function the same way?\nI am running a loop where at the end of each iteration I am producing a figure and saving the plot. On first couple tries the plot was retaining the old figures in every subsequent plot. I'm looking for, individual plots for each iteration without the old figures, does it matter which one I use? The calculation I'm running takes a very long time and it would be very time consuming to test it out.","AnswerCount":4,"Available Count":3,"Score":0.0996679946,"is_accepted":false,"ViewCount":49996,"Q_Id":16661790,"Users Score":2,"Answer":"plt.clf() clears the entire current figure with all its axes, but leaves the window opened, such that it may be reused for other plots.\nplt.close() closes a window, which will be the current window, if not specified otherwise.","Q_Score":32,"Tags":"python,matplotlib","A_Id":44976331,"CreationDate":"2013-05-21T03:47:00.000","Title":"Difference between plt.close() and plt.clf()","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"In matplotlib.pyplot, what is the difference between plt.clf() and plt.close()? Will they function the same way?\nI am running a loop where at the end of each iteration I am producing a figure and saving the plot. On first couple tries the plot was retaining the old figures in every subsequent plot. I'm looking for, individual plots for each iteration without the old figures, does it matter which one I use? The calculation I'm running takes a very long time and it would be very time consuming to test it out.","AnswerCount":4,"Available Count":3,"Score":1.2,"is_accepted":true,"ViewCount":49996,"Q_Id":16661790,"Users Score":41,"Answer":"plt.close() will close the figure window entirely, where plt.clf() will just clear the figure - you can still paint another plot onto it.\nIt sounds like, for your needs, you should be preferring plt.clf(), or better yet keep a handle on the line objects themselves (they are returned in lists by plot calls) and use .set_data on those in subsequent iterations.","Q_Score":32,"Tags":"python,matplotlib","A_Id":16661815,"CreationDate":"2013-05-21T03:47:00.000","Title":"Difference between plt.close() and plt.clf()","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a Matlab cell array, each of whose cells contains an N x M matrix. The value of M varies across cells.\nWhat would be an efficient way to represent this type of a structure in Python using numpy or any standard Python data-structure?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1299,"Q_Id":16689681,"Users Score":5,"Answer":"Have you considered a list of numpy.arrays?","Q_Score":1,"Tags":"python,matlab,data-structures,cell","A_Id":16689864,"CreationDate":"2013-05-22T10:41:00.000","Title":"Alternative to Matlab cell data-structure in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to decide about a Python computer vision library. I had used OpenCV in C++, and like it very much. However this time I need to develop my algorithm in Python. My short list has three libraries:\n1- OpenCV (Python wrapper)\n2- PIL (Python Image Processing Library)\n3- scikit-image\nWould you please help me to compare these libraries?\nI use numpy, scipy, scikit-learn in the rest of my code. The performance and ease of use is an important factor, also, portability is an important factor for me.\nThanks for your help","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2421,"Q_Id":16697391,"Users Score":7,"Answer":"I have worked mainly with OpenCV and also with scikit-image. I would say that while OpenCV is more focus on computer vision (classification, feature detection and extraction,...). However lately scikit-image is improving rapidly. \nI faced that some algorithms perform faster under OpenCV, however in most cases I find much more easier working with scikit-image, OpenCV documentations is quite cryptic.\nAs long as OpenCV 2.x bindings works with numpy as well as scikit-image I would take into account using both libraries, trying to take the better of each of them. At least is what I have done in my last project.","Q_Score":8,"Tags":"python,opencv,image-processing,computer-vision,scikit-learn","A_Id":16698292,"CreationDate":"2013-05-22T16:49:00.000","Title":"Comparing computer vision libraries in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am implementing a huge directed graph consisting of 100,000+ nodes. I am just beginning python so I only know of these two search algorithms. Which one would be more efficient if I wanted to find the shortest distance between any two nodes? Are there any other methods I'm not aware of that would be even better?\nThank you for your time","AnswerCount":4,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1736,"Q_Id":16710374,"Users Score":0,"Answer":"When you want to find the shortest path you should use BFS and not DFS because BFS explores the closest nodes first so when you reach your goal you know for sure that you used the shortest path and you can stop searching. Whereas DFS explores one branch at a time so when you reach your goal you can't be sure that there is not another path via another branch that is shorter.\nSo you should use BFS.\nIf your graph have different weights on its edges, then you should use Dijkstra's algorithm which is an adaptation of BFS for weighted graphs, but don't use it if you don't have weights.\nSome people may recommend you to use Floyd-Warshall algorithm but it is a very bad idea for a graph this large.","Q_Score":0,"Tags":"python,algorithm,graph","A_Id":16710763,"CreationDate":"2013-05-23T09:35:00.000","Title":"Breadth First Search or Depth First Search?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am implementing a huge directed graph consisting of 100,000+ nodes. I am just beginning python so I only know of these two search algorithms. Which one would be more efficient if I wanted to find the shortest distance between any two nodes? Are there any other methods I'm not aware of that would be even better?\nThank you for your time","AnswerCount":4,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1736,"Q_Id":16710374,"Users Score":0,"Answer":"If there are no weights for the edges on the graph, a simple Breadth-first search where you access nodes in the graph iteratively and check if any of the new nodes equals the destination-node can be done. If the edges have weights, DJikstra's algorithm and Bellman-Ford algoriths are things which you should be looking at, depending on your expected time and space complexities that you are looking at.","Q_Score":0,"Tags":"python,algorithm,graph","A_Id":16710738,"CreationDate":"2013-05-23T09:35:00.000","Title":"Breadth First Search or Depth First Search?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm currently doing this by using a sort of a greedy algorithm by iterating over the sets from largest to smallest set. What would be a good algorithm to choose if i'm more concerned about finding the best solution rather than efficiency?\nDetails:\n1) Each set has a predefined range\n2) My goal is to end up with a lot of densely packed sets rather than reducing the total number of sets.\nExample: Suppose the range is 8.\nThe sets might be: [1,5,7], [2,6], [3,4,5], [1,2] , [4], [1]\nA good result would be [1,5,7,2,6,4], [3,4,5,1,2], [1]","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":124,"Q_Id":16731960,"Users Score":0,"Answer":"I am not sure whether it will give an optimal solution, but would simply repeatedly merging the two largest non-overlapping sets not work?","Q_Score":0,"Tags":"python,algorithm","A_Id":16735119,"CreationDate":"2013-05-24T09:41:00.000","Title":"What is a good way to merge non intersecting sets in a list to end up with denseley packed sets?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Has anyone seen this error in celery (a distribute task worker in Python) before?\nTraceback (most recent call last):\n  File \"\/home\/mcapp\/.virtualenv\/lister\/local\/lib\/python2.7\/site-packages\/celery\/task\/trace.py\", line 228, in trace_task\n    R = retval = fun(*args, **kwargs)\n  File \"\/home\/mcapp\/.virtualenv\/lister\/local\/lib\/python2.7\/site-packages\/celery\/task\/trace.py\", line 415, in protected_call\n    return self.run(*args, **kwargs)\n  File \"\/home\/mcapp\/lister\/lister\/tasks\/init.py\", line 69, in update_playlist_db\n    video_update(videos)\n  File \"\/home\/mcapp\/lister\/lister\/tasks\/init.py\", line 55, in video_update\n    chord(tasks)(update_complete.s(update_id=update_id, update_type='db', complete=True))\n  File \"\/home\/mcapp\/.virtualenv\/lister\/local\/lib\/python2.7\/site-packages\/celery\/canvas.py\", line 464, in call\n    _chord = self.type\n  File \"\/home\/mcapp\/.virtualenv\/lister\/local\/lib\/python2.7\/site-packages\/celery\/canvas.py\", line 461, in type\n    return self._type or self.tasks[0].type.app.tasks['celery.chord']\nIndexError: list index out of range\nThis particular version of celery is 3.0.19, and happens when the celery chord feature is used.  We don't think there is any error in our application, as 99% of the time our code works correctly, but under heavier loads this error would happen.  We are trying to find out if this is an actual bug in our application or a celery bug, any help would be greatly appreciated.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":859,"Q_Id":16745487,"Users Score":1,"Answer":"This is an error that occurs when a chord header has no tasks in it. Celery tries to access the tasks in the header using self.tasks[0] which results in an index error since there are no tasks in the list.","Q_Score":2,"Tags":"python,runtime-error,celery","A_Id":18938559,"CreationDate":"2013-05-25T01:12:00.000","Title":"celery.chord gives IndexError: list index out of range error in celery version 3.0.19","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"Suppose I have a graph of N nodes, and each pair of nodes have a weight associated with them. I would like to split this graph into n smaller graphs to reduce the overall weight.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":161,"Q_Id":16751995,"Users Score":0,"Answer":"Remove the k-1 edges with the highest weights.","Q_Score":2,"Tags":"python,algorithm","A_Id":16752052,"CreationDate":"2013-05-25T17:13:00.000","Title":"Split a weighted graph into n graphs to minimize the sum of weights in each graph","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Suppose I have a graph of N nodes, and each pair of nodes have a weight associated with them. I would like to split this graph into n smaller graphs to reduce the overall weight.","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":161,"Q_Id":16751995,"Users Score":1,"Answer":"What you are searching for is called weighted max-cut.","Q_Score":2,"Tags":"python,algorithm","A_Id":16752049,"CreationDate":"2013-05-25T17:13:00.000","Title":"Split a weighted graph into n graphs to minimize the sum of weights in each graph","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Hi I am trying to read a csv file into a double list which is not the problem atm.\nWhat I am trying to do is just print all the sL values between two lines. I.e i want to print sL [200] to sl [300] but i dont want to manually have to type print sL for all values between these two numbers is there a code that can be written to print all values between these two lines that would be the same as typing sL out individually all the way from 200 to 300","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":56,"Q_Id":16766587,"Users Score":0,"Answer":"sed -n 200,300p, perhaps, for 200 to 300 inclusive; adjust the numbers by \u00b11 if exclusive or whatever?","Q_Score":0,"Tags":"python,list,printing,lines","A_Id":16766609,"CreationDate":"2013-05-27T05:05:00.000","Title":"Reading certain lines of a string","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Hi I am trying to read a csv file into a double list which is not the problem atm.\nWhat I am trying to do is just print all the sL values between two lines. I.e i want to print sL [200] to sl [300] but i dont want to manually have to type print sL for all values between these two numbers is there a code that can be written to print all values between these two lines that would be the same as typing sL out individually all the way from 200 to 300","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":56,"Q_Id":16766587,"Users Score":0,"Answer":"If it is a specific column ranging between 200 and 300, use filter() function.\nnew_array = filter(lambda x: x['column'] >= 200 or z['column'] <= 300, sl)","Q_Score":0,"Tags":"python,list,printing,lines","A_Id":16766726,"CreationDate":"2013-05-27T05:05:00.000","Title":"Reading certain lines of a string","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Today I upgraded to Xubuntu 13.04 which comes with Python 3.3. Before that, I was working with Pyton 3.2, which was working perfectly fine.\nWhen running my script under Python 3.3, I get an\n\nImportError: No module named 'pylab'\n\nin import pylab.\nRunning in Python 3.2, which I reinstalled, throws\n\nImportError: cannot import name multiarray\n\nin import numpy.\nScipy, numpy and matplotlib are, recording to apt, on the newest version.\nI don't have much knowledge about this stuff. Do you have any recommendations on how to get my script to work again, preferably on Python 3.2?\nThanks in advance,\nKatrin\nEdit:\nWe solved the problem: Apparently, there where a lot of fragments \/ pieces of the packages in different paths, as I installed from apt, pip as well as manually. After deleting all packages and installing them only via pip, everything works fine. Thank you very much for the help!","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":312,"Q_Id":16820903,"Users Score":1,"Answer":"I suspect you need to install python3-matplotlib, python3-numpy, etc.  python-matlab is the python2 version.","Q_Score":1,"Tags":"python,matplotlib,python-3.3","A_Id":16823062,"CreationDate":"2013-05-29T18:04:00.000","Title":"Pylab after upgrading","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a Pandas data frame, one of the column contains date strings in the format YYYY-MM-DD\nFor e.g. '2013-10-28'\nAt the moment the dtype of the column is object.\nHow do I convert the column values to Pandas date format?","AnswerCount":10,"Available Count":2,"Score":-0.0199973338,"is_accepted":false,"ViewCount":297830,"Q_Id":16852911,"Users Score":-1,"Answer":"Try to convert one of the rows into timestamp using the pd.to_datetime function and then use .map to map the formular to the entire column","Q_Score":133,"Tags":"python,date,pandas","A_Id":60213042,"CreationDate":"2013-05-31T08:23:00.000","Title":"How do I convert strings in a Pandas data frame to a 'date' data type?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a Pandas data frame, one of the column contains date strings in the format YYYY-MM-DD\nFor e.g. '2013-10-28'\nAt the moment the dtype of the column is object.\nHow do I convert the column values to Pandas date format?","AnswerCount":10,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":297830,"Q_Id":16852911,"Users Score":25,"Answer":"Now you can do df['column'].dt.date\nNote that for datetime objects, if you don't see the hour when they're all 00:00:00, that's not pandas. That's iPython notebook trying to make things look pretty.","Q_Score":133,"Tags":"python,date,pandas","A_Id":33577649,"CreationDate":"2013-05-31T08:23:00.000","Title":"How do I convert strings in a Pandas data frame to a 'date' data type?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Trying to use gridsearch CV with multiple jobs via the n_jobs argument and I can see using htop that they're all launched and running but they all get stuck\/assigned on the same core using 25% each (I'm using a 4 core machine). I'm on Ubuntu 12.10 and I'm running the latest master pulled from github. Anyone know how to fix this?\nThanks!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":391,"Q_Id":16877448,"Users Score":2,"Answer":"Found it, there's a note on the svm page that if you enable verbose settings multiprocessing may break. Disabling verbose fixed it","Q_Score":1,"Tags":"python,scikit-learn","A_Id":16877799,"CreationDate":"2013-06-01T21:29:00.000","Title":"Scikit-Learn n_jobs Multiprocessing Locked To One Core","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to save some arrays as TIFF with matplotlib, but I'm getting 24 bit RGB files instead with plt.imsave().\nCan I change that without resorting to the PIL? It's quite important for me to keep everything in pure matplotlib.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":792,"Q_Id":16893102,"Users Score":2,"Answer":"Using matplotlib to export to TIFF will use PIL anyway. As far as I know, matplotlib has native support only for PNG, and uses PIL to convert to other file formats. So when you are using matplotlib to export to TIFF, you can use PIL immediately.","Q_Score":0,"Tags":"python,matplotlib,tiff","A_Id":16893364,"CreationDate":"2013-06-03T08:54:00.000","Title":"How to save 32\/64 bit grayscale floats to TIFF with matplotlib?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it possible to delete a group (by group name) from a groupby object in pandas? That is, after performing a groupby, delete a resulting group based on its name.","AnswerCount":4,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":27093,"Q_Id":16910114,"Users Score":2,"Answer":"it is so simple, you need to use the filter function and lambda exp:\ndf_filterd=df.groupby('name').filter(lambda x:(x.name == 'cond1' or...(other condtions )))\nyou need to take care that if you want to use more than condtion to put it in brackets()..\nand you will get back a DataFrame not GroupObject.","Q_Score":22,"Tags":"python,pandas","A_Id":69644920,"CreationDate":"2013-06-04T05:00:00.000","Title":"Delete a group after pandas groupby","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it possible to delete a group (by group name) from a groupby object in pandas? That is, after performing a groupby, delete a resulting group based on its name.","AnswerCount":4,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":27093,"Q_Id":16910114,"Users Score":0,"Answer":"Should be easy: \ndf.drop(index='group_name',inplace=True)","Q_Score":22,"Tags":"python,pandas","A_Id":58868271,"CreationDate":"2013-06-04T05:00:00.000","Title":"Delete a group after pandas groupby","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a large dataset with 500 million rows and 58 variables. I need to sort the dataset using one of the 59th variable which is calculated using the other 58 variables. The variable happens to be a floating point number with four places after decimal.\nThere are two possible approaches:\n\nThe normal merge sort\nWhile calculating the 59th variables, i start sending variables in particular ranges to to particular nodes. Sort the ranges in those nodes and then combine them in the reducer once i have perfectly sorted data and now I also know where to merge what set of data; It basically becomes appending.\n\nWhich is a better approach and why?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":889,"Q_Id":16925802,"Users Score":0,"Answer":"I'll assume that you are looking for a total sort order without a secondary sort for all your rows. I should also mention that 'better' is never a good question since there is typically a trade-off between time and space and in Hadoop we tend to think in terms of space rather than time unless you use products that are optimized for time (TeraData has the capability of putting Databases in memory for Hadoop use)\nOut of the two possible approaches you mention, I think only one would work within the Hadoop infrastructure. Num 2, Since Hadoop leverages many nodes to do one job, sorting becomes a little trickier to implement and we typically want the 'shuffle and sort' phase of MR to take care of the sorting since distributed sorting is at the heart of the programming model.\nAt the point when the 59th variable is generated, you would want to sample the distribution of that variable so that you can send it through the framework then merge like you mentioned. Consider the case when the variable distribution of x contain 80% of your values. What this might do is send 80% of your data to one reducer who would do most of the work. This assumes of course that some keys will be grouped in the sort and shuffle phase which would be the case unless you programmed them unique. It's up to the programmer to set up partitioners to evenly distribute the load by sampling the key distribution.\nIf on the other hand we were to sort in memory then we could accomplish the same thing during reduce but there are inherent scalability issues since the sort is only as good as the amount of memory available in the node currently running the sort and dies off quickly when it starts to use HDFS to look for the rest of the data that did not fit into memory. And if you ignored the sampling issue you will likely run out of memory unless all your key values pairs are evenly distributed and you understand the memory capacity within your data.","Q_Score":0,"Tags":"python,sorting,hadoop,bigdata,hadoop-streaming","A_Id":16927877,"CreationDate":"2013-06-04T19:19:00.000","Title":"Sorting using Map-Reduce - Possible approach","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Entering arbitrary sized matrices to manipulate them using different operations.","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":51,"Q_Id":16969190,"Users Score":1,"Answer":"Read the input till Ctrl+d, split by newline symbols  first and then split the results by spaces.","Q_Score":0,"Tags":"python,matrix","A_Id":16969259,"CreationDate":"2013-06-06T18:12:00.000","Title":"I am working on a project where you have to input a matrix that has an arbitrary size. I am using python. Suggestions?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Entering arbitrary sized matrices to manipulate them using different operations.","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":51,"Q_Id":16969190,"Users Score":1,"Answer":"Think about who is using this programme, and how, then develop an interface which meets those needs.","Q_Score":0,"Tags":"python,matrix","A_Id":16971642,"CreationDate":"2013-06-06T18:12:00.000","Title":"I am working on a project where you have to input a matrix that has an arbitrary size. I am using python. Suggestions?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have installed Enthought Canopy 32 - bit which comes with python 2.7 32 bit . And I downloaded windows installer scikit-learn-0.13.1.win32-py2.7 .. My machine is 64 bit. I could'nt find 64 bit scikit learn installer for intel processor, only AMD is available.\nPython 2.7 required which was not found in the registry is the error message I get when I try to run the installer. How do I solve this?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":494,"Q_Id":16981708,"Users Score":2,"Answer":"Enthought Canopy 1.0.1 does not register the user's Python installation as the main one for the system. This has been fixed and will work in the upcoming release.","Q_Score":0,"Tags":"python,python-2.7,scikit-learn,enthought,epd-python","A_Id":16982178,"CreationDate":"2013-06-07T10:15:00.000","Title":"Scikit-Learn windows Installation error : python 2.7 required which was not found in the registry","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"is there any best practice for adding a \"random\" sort criteria to the old style collection in Plone?\nMy versions:\n\nPlone 4.3 (4305)\nCMF 2.2.7\nZope 2.13.19","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":109,"Q_Id":17001402,"Users Score":0,"Answer":"There is no random sort criteria. Any randomness will need to be done in custom application code.","Q_Score":0,"Tags":"python,collections,plone,zope","A_Id":17032461,"CreationDate":"2013-06-08T16:10:00.000","Title":"New sort criteria \"random\" for Plone 4 old style collections","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In Scikit-learn , K-Means have n_jobs but MiniBatch K-Means is lacking it.\nMBK is faster than KMeans but at large sample sets we would like it distribute the processing across multiprocessing (or other parallel processing libraries).\nIs MKB's Partial-fit the answer?","AnswerCount":1,"Available Count":1,"Score":0.537049567,"is_accepted":false,"ViewCount":1981,"Q_Id":17053548,"Users Score":3,"Answer":"I don't think this is possible. You could implement something with OpenMP inside the minibatch processing. I'm not aware of any parallel minibatch k-means procedures. Parallizing stochastic gradient descent procedures is somewhat hairy.\nBtw, the n_jobs parameter in KMeans only distributes the different random initializations afaik.","Q_Score":6,"Tags":"python,machine-learning,multiprocessing,scikit-learn","A_Id":17070022,"CreationDate":"2013-06-11T20:48:00.000","Title":"How can i distribute processing of minibatch kmeans (scikit-learn)?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have to run jobs on a regular basis on compute servers that I share with others in the department and when I start 10 jobs, I really would like it to just take 10 cores and not more; I don't care if it takes a bit longer with a single core per run: I just don't want it to encroach on the others' territory, which would require me to renice the jobs and so on. I just want to have 10 solid cores and that's all.\nI am using Enthought 7.3-1 on Redhat, which is based on Python 2.7.3 and numpy 1.6.1, but the question is more general.","AnswerCount":5,"Available Count":3,"Score":1.2,"is_accepted":true,"ViewCount":25964,"Q_Id":17053671,"Users Score":40,"Answer":"Set the MKL_NUM_THREADS environment variable to 1. As you might have guessed, this environment variable controls the behavior of the Math Kernel Library which is included as part of Enthought's numpy build.\nI just do this in my startup file, .bash_profile, with export MKL_NUM_THREADS=1. You should also be able to do it from inside your script to have it be process specific.","Q_Score":63,"Tags":"python,multithreading,numpy","A_Id":17054932,"CreationDate":"2013-06-11T20:56:00.000","Title":"How do you stop numpy from multithreading?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have to run jobs on a regular basis on compute servers that I share with others in the department and when I start 10 jobs, I really would like it to just take 10 cores and not more; I don't care if it takes a bit longer with a single core per run: I just don't want it to encroach on the others' territory, which would require me to renice the jobs and so on. I just want to have 10 solid cores and that's all.\nI am using Enthought 7.3-1 on Redhat, which is based on Python 2.7.3 and numpy 1.6.1, but the question is more general.","AnswerCount":5,"Available Count":3,"Score":1.0,"is_accepted":false,"ViewCount":25964,"Q_Id":17053671,"Users Score":12,"Answer":"In more recent versions of numpy I have found it necessary to also set NUMEXPR_NUM_THREADS=1.\nIn my hands, this is sufficient without setting MKL_NUM_THREADS=1, but under some circumstances you may need to set both.","Q_Score":63,"Tags":"python,multithreading,numpy","A_Id":21673595,"CreationDate":"2013-06-11T20:56:00.000","Title":"How do you stop numpy from multithreading?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have to run jobs on a regular basis on compute servers that I share with others in the department and when I start 10 jobs, I really would like it to just take 10 cores and not more; I don't care if it takes a bit longer with a single core per run: I just don't want it to encroach on the others' territory, which would require me to renice the jobs and so on. I just want to have 10 solid cores and that's all.\nI am using Enthought 7.3-1 on Redhat, which is based on Python 2.7.3 and numpy 1.6.1, but the question is more general.","AnswerCount":5,"Available Count":3,"Score":1.0,"is_accepted":false,"ViewCount":25964,"Q_Id":17053671,"Users Score":52,"Answer":"Only hopefully this fixes all scenarios and system you may be on. \n\nUse numpy.__config__.show() to see if you are using OpenBLAS or MKL\n\nFrom this point on there are a few ways you can do this.\n2.1. The terminal route export OPENBLAS_NUM_THREADS=1 or export MKL_NUM_THREADS=1\n2.2 (This is my preferred way) In your python script import os and add the line os.environ['OPENBLAS_NUM_THREADS'] = '1' or os.environ['MKL_NUM_THREADS'] = '1'.\nNOTE when setting os.environ[VAR] the number of threads must be a string! Also, you may need to set this environment variable before importing numpy\/scipy.\nThere are probably other options besides openBLAS or MKL but step 1 will help you figure that out.","Q_Score":63,"Tags":"python,multithreading,numpy","A_Id":48665619,"CreationDate":"2013-06-11T20:56:00.000","Title":"How do you stop numpy from multithreading?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Eprime outputs a .txt file like this: \n*** Header Start ***\nVersionPersist: 1\nLevelName: Session\nSubject: 7\nSession: 1\nRandomSeed: -1983293234\nGroup: 1\nDisplay.RefreshRate: 59.654\n*** Header End ***\n    Level: 2\n    *** LogFrame Start ***\n    MeansEffectBias: 7\n    Procedure: trialProc\n    itemID: 7\n    bias1Answer: 1\n    *** LogFrame End ***\n    Level: 2\n    *** LogFrame Start ***\n    MeansEffectBias: 2\n    Procedure: trialProc\n    itemID: 2\n    bias1Answer: 0\n\n\nI want to parse this and write it to a .csv file but with a number of lines deleted.\nI tried to create a dictionary that took the text appearing before the colon as the key and\nthe text after as the value: \n {subject: [7, 7], bias1Answer : [1, 0], itemID: [7, 2]} \n\ndef load_data(filename):\n    data = {}\n    eprime = open(filename, 'r')\n    for line in eprime:\n        rows = re.sub('\\s+', ' ', line).strip().split(':')\n        try:\n            data[rows[0]] += rows[1]\n        except KeyError:\n            data[rows[0]] = rows[1]\n    eprime.close()\n    return data\n\n\nfor line in open(fileName, 'r'):\n    if ':' in line:\n        row = line.strip().split(':')\n        fullDict[row[0]] = row[1]\nprint fullDict\n\nboth of the scripts below produce garbage:\n\n{'\\x00\\t\\x00M\\x00e\\x00a\\x00n\\x00s\\x00E\\x00f\\x00f\\x00e\\x00c\\x00t\\x00B\\x00i\\x00a\\x00s\\x00': '\\x00 \\x005\\x00\\r\\x00', '\\x00\\t\\x00B\\x00i\\x00a\\x00s\\x002\\x00Q\\x00.\\x00D\\x00u\\x00r\\x00a\\x00t\\x00i\\x00o\\x00n\\x00E\\x00r\\x00r\\x00o\\x00r\\x00': '\\x00 \\x00-\\x009\\x009\\x009\\x009\\x009\\x009\\x00\\r\\x00'\n\nIf I could set up the dictionary, I can write it to a csv file that would look like this!!:\n Subject  itemID ... bias1Answer \n  7       7             1\n  7       2             0","AnswerCount":4,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1142,"Q_Id":17056818,"Users Score":0,"Answer":"I know this is an older question so maybe you have long since solved it but I think you are approaching this in a more complex way than is needed. I figure I'll respond in case someone else has the same problem and finds this. \nIf you are doing things this way because you do not have a software key, it might help to know that the E-Merge and E-DataAid programs for eprime don't require a key. You only need the key for editing build files. Whoever provided you with the .txt files should probably have an install disk for these programs. If not, it is available on the PST website (I believe you need a serial code to create an account, but not certain)\nEprime generally creates a .edat file that matches the content of the text file you have posted an example of. Sometimes though if eprime crashes you don't get the edat file and only have the .txt. Luckily you can generate the edat file from the .txt file. \nHere's how I would approach this issue:\n\nIf you do not have the edat files available first use E-DataAid to recover the files.\nThen presuming you have multiple participants you can use E-Merge to merge all of the edat files together for all participants in who completed this task.\nOpen the merged file. It might look a little chaotic depending on how much you have in the file. You can got to Go to tools->Arrange columns. This will show a list of all your variables. \nAdjust so that only the desired variables are in the right hand box. Hit ok. \nThen you should have something resembling your end goal which can be exported as a csv.\n\nIf you have many procedures in the program you might at this point have lines that just have startup info and NULL in the locations where your variables or interest are. You can fix this by going to tools->filter and creating a filter to eliminate those lines.","Q_Score":7,"Tags":"python,csv,file-io","A_Id":21414260,"CreationDate":"2013-06-12T02:33:00.000","Title":"Parsing a txt file into a dictionary to write to csv file","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I was looking at the option of embedding python into fortran90 to add python functionality to my existing fortran90 code. I know that it can be done the other way around by extending python with fortran90 using the f2py from numpy. But, i want to keep my super optimized main loop in fortran and add python to do some additional tasks \/ evaluate further developments before I can do it in fortran, and also to ease up code maintenance. I am looking for answers for the following questions:\n1) Is there a library that already exists from which I can embed python into fortran? (I am aware of f2py and it does it the other way around)\n2) How do we take care of data transfer from fortran to python and back?\n3) How can we have a call back functionality implemented? (Let me describe the scenario a bit....I have my main_fortran program in Fortran, that call Func1_Python module in python. Now, from this Func1_Python, I want to call another function...say Func2_Fortran in fortran)\n4) What would be the impact of embedding the interpreter of python inside fortran in terms of performance....like loading time, running time, sending data (a large array in double precision) across etc.\nThanks a lot in advance for your help!!\nEdit1: I want to set the direction of the discussion right by adding some more information about the work I am doing. I am into scientific computing stuff. So, I would be working a lot on huge arrays \/ matrices in double precision and doing floating point operations. So, there are very few options other than fortran really to do the work for me. The reason i want to include python into my code is that I can use NumPy for doing some basic computations if necessary and extend the capabilities of the code with minimal effort. For example, I can use several libraries available to link between python and some other package (say OpenFoam using PyFoam library).","AnswerCount":7,"Available Count":1,"Score":0.1137907297,"is_accepted":false,"ViewCount":9062,"Q_Id":17075418,"Users Score":4,"Answer":"There is a very easy way to do this using f2py. Write your python method and add it as an input to your Fortran subroutine. Declare it in both the cf2py hook and the type declaration as EXTERNAL and also as its return value type, e.g. REAL*8. Your Fortran code will then have a pointer to the address where the python method is stored. It will be SLOW AS MOLASSES, but for testing out algorithms it can be useful. I do this often (I port a lot of ancient spaghetti Fortran to python modules...) It's  also a great way to use things like optimised Scipy calls in legacy fortran","Q_Score":8,"Tags":"python,fortran,embed","A_Id":23725918,"CreationDate":"2013-06-12T21:09:00.000","Title":"Embed python into fortran 90","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Right now I'm importing a fairly large CSV as a dataframe every time I run the script. Is there a good solution for keeping that dataframe constantly available in between runs so I don't have to spend all that time waiting for the script to run?","AnswerCount":13,"Available Count":1,"Score":0.0307595242,"is_accepted":false,"ViewCount":432843,"Q_Id":17098654,"Users Score":2,"Answer":"Another quite fresh test with to_pickle().\nI have 25 .csv files in total to process and the final dataframe consists of roughly 2M items.\n(Note: Besides loading the .csv files, I also manipulate some data and extend the data frame by new columns.)\nGoing through all 25 .csv files and create the dataframe takes around 14 sec.\nLoading the whole dataframe from a pkl file takes less than 1 sec","Q_Score":415,"Tags":"python,pandas,dataframe","A_Id":63390537,"CreationDate":"2013-06-13T23:05:00.000","Title":"How to reversibly store and load a Pandas dataframe to\/from disk","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"My project currently uses NumPy, only for memory-efficient arrays (of bool_, uint8, uint16, uint32).\nI'd like to get it running on PyPy which doesn't support NumPy. (failed to install it, at any rate)\nSo I'm wondering: Is there any other memory-efficient way to store arrays of numbers in Python? Anything that is supported by PyPy? Does PyPy have anything of it's own?\nNote: array.array is not a viable solution, as it uses a lot more memory than NumPy in my testing.","AnswerCount":1,"Available Count":1,"Score":0.537049567,"is_accepted":false,"ViewCount":919,"Q_Id":17099850,"Users Score":3,"Answer":"array.array is a memory efficient array. It packs bytes\/words etc together, so there is only a few bytes of extra overhead for the entire array.\nThe one place where numpy can use less memory is when you have a sparse array (and are using one of the sparse array implementations)\nIf you are not using sparse arrays, you simply measured it wrong.\narray.array also doesn't have a packed bool type, so you can implement that as wrapper around an array.array('I') or a bytearray() or even just use bit masks with a Python long","Q_Score":3,"Tags":"python,arrays,numpy,pypy","A_Id":17101084,"CreationDate":"2013-06-14T01:39:00.000","Title":"PyPy and efficient arrays","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have been researching RBMs for a couple months, using Python along the way, and have read all your papers. I am having a problem, and I thought, what the hey? Why not go to the source? I thought I would at least take the chance you may have time to reply.\nMy question is regarding the Log-Likelihood in a Restricted Boltzmann Machine. I have read that finding the exact log-likelihood in all but very small models is intractable, hence the introduction of contrastive divergence, PCD, pseudo log-likelihood etc. My question is, how do you find the exact log-likelihood in even a small model? \nI have come across several definitions of this formula, and all seem to be different. In Tielemen\u2019s 2008 paper \u201cTraining Restricted Boltzmann Machines using Approximations To the Likelihood Gradient\u201d, he performs a log-likelihood version of the test to compare to the other types of approximations, but does not say the formula he used. The closest thing I can find is the probabilities using the energy function over the partition function, but I have not been able to code this, as I don\u2019t completely understand the syntax.\nIn Bengio et al \u201cRepresentation Learning: A Review and New Perspectives\u201d, the equation for the log-likelihood is:\n sum_t=1 to T (log P(X^T, theta)) \nwhich is equal to sum_t=1 to T(log * sum_h in {0,1}^d_h(P(x^(t), h; theta))\n where T is training examples. This is (14) on page 11. \nThe only problem is that none of the other variables are defined. I assume x is the training data instance, but what is the superscript (t)? I also assume theta are the latent variables h, W, v\u2026 But how do you translate this into code?\nI guess what I\u2019m asking is can you give me a code (Python, pseudo-code, or any language) algorithm for finding the log-likelihood of a given model so I can understand what the variables stand for? That way, in simple cases, I can find the exact log-likelihood and then compare them to my approximations to see how well my approximations really are.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1642,"Q_Id":17113613,"Users Score":0,"Answer":"Assume you have v visible units, and h hidden units, and v < h.  The key idea is that once you've fixed all the values for each visible unit, the hidden units are independent. \nSo you loop through all 2^v subsets of visible unit activations.  Then computing the likelihood for the RBM with this particular activated visible subset is tractable, because the hidden units are independent[1].  So then loop through each hidden unit, and add up the probability of it being on and off conditioned on your subset of visible units.  Then multiply out all of those summed on\/off hidden probabilities to get the probability that particular subset of visible units.  Add up all subsets and you are done.\nThe problem is that this is exponential in v.  If v > h, just \"transpose\" your RBM, pretending the hidden are visible and vice versa.\n[1] The hidden units can't influence each other, because you influence would have to go through the visible units (no h to h connections), but you've fixed the visible units.","Q_Score":0,"Tags":"python,machine-learning,artificial-intelligence,neural-network","A_Id":17117416,"CreationDate":"2013-06-14T16:55:00.000","Title":"Finding log-likelihood in a restricted boltzmann machine","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a text data labelled into 3 classes and class 1 has 1% data, class 2 - 69% and class 3 - 30%. Total data size is 10000. I am using 10-fold cross validation. For classification, SVM of scikit learn python library is used with class_weight=auto. But the code for 1 step of 10-fold CV has been running for 2 hrs and has not finished. This implies that for code will take at least 20 hours for completion. Without adding the class_weight=auto, it finishes in 10-15min. But then, no data is labelled of class 1 in the output. Is there some way to achieve solve this issue ?","AnswerCount":1,"Available Count":1,"Score":0.6640367703,"is_accepted":false,"ViewCount":2914,"Q_Id":17125247,"Users Score":4,"Answer":"First, for text data you don't need a non linear kernel, so you should use an efficient linear SVM solver such as LinearSVC or PassiveAggressiveClassifier instead.\nThe SMO algorithm of SVC \/ libsvm is not scalable: the complexity is more than quadratic which is practice often makes it useless for dataset larger than 5000 samples.\nAlso to deal with the class imbalance you might want to try to subsample the class 2 and class 3 to have a number of samples maximum twice the number of samples of class 1.","Q_Score":2,"Tags":"python,python-2.7,machine-learning,svm,scikit-learn","A_Id":17133162,"CreationDate":"2013-06-15T15:35:00.000","Title":"SVM Multiclass Classification using Scikit Learn - Code not completing","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I think I have a relatively simply question but am not able to locate an appropriate answer to solve the coding problem.\nI have a pandas column of string:\ndf1['tweet'].head(1)\n0                                        besides food,\nName: tweet\nI need to extract the text and push it into a Python str object, of this format:\ntest_messages = [\"line1\",\n    \"line2\",\n    \"etc\"]\nThe goal is to classify a test set of tweets and therefore believe the input to: X_test = tfidf.transform(test_messages) is a str object.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":8156,"Q_Id":17125248,"Users Score":1,"Answer":"Get the Series head(), then access the first value:\ndf1['tweet'].head(1).item()\nor: Use the Series tolist() method, then slice the 0'th element:\ndf.height.tolist()\n[94, 170]\ndf.height.tolist()[0]\n94\n\n(Note that Python indexing is 0-based, but head() is 1-based)","Q_Score":2,"Tags":"python,string,pandas,series","A_Id":52143806,"CreationDate":"2013-06-15T15:35:00.000","Title":"Get first element of Pandas Series of string","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to export dataframe to .xls file using to_excel() method. But while execution it was throwing an error: \"UnicodeDecodeError: 'ascii' codec can't decode byte 0xe2 in position 892: ordinal not in range(128)\". Just few moments back it was working fine. \nThe code I used is: \n             :csv2.to_excel(\"C:\\\\Users\\\\shruthi.sundaresan\\\\Desktop\\\\csat1.xls\",sheet_name='SAC_STORE_DATA',index=False).\ncsv2 is the dataframe. Why does this kind of error happens and how to avoid this is in the future?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":474,"Q_Id":17140080,"Users Score":0,"Answer":"The problem you are facing is that your excel has a character that cannot be decoded to unicode. It was probably working before but maybe you edited this xls file somehow in Excel\/Libre. You just need to find this character and either get rid of it or replace it with the one that is acceptable.","Q_Score":0,"Tags":"python-2.7,pandas,xls","A_Id":17141432,"CreationDate":"2013-06-17T03:36:00.000","Title":"Unable to export pandas dataframe into excel file","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a pyplot polar scatter plot with signed values.  Pyplot does the \"right\" thing and creates only a positive axis, then reflects negative values to look as if they are a positive value 180 degrees away.\nBut, by default, pyplot plots all points using the same color.  So positive and negative values are indistinguishable.\nI'd like to easily tell positive values at angle x from negative values at angle (x +\/- 180), with positive values red and negative values blue.\nI've made no progress creating what should be a very simple color map for this situation.\nHelp?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":785,"Q_Id":17154006,"Users Score":2,"Answer":"I'm not sure if this is the \"proper\" way to do this, but you could programmatically split your data into two subsets: one containing the positive values and the second containing the negative values.  Then you can call the plot function twice, specifying the color you want for each subset.\nIt's not an elegant solution, but a solution nonetheless.","Q_Score":1,"Tags":"python,colors,matplotlib,scatter","A_Id":17154439,"CreationDate":"2013-06-17T18:11:00.000","Title":"Pyplot polar scatter plot color for sign","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"If scipy.weave.inline is called inside a massive parallel MPI-enabled application that is run on a cluster with a home-directory that is common to all nodes, every instance accesses the same catalog for compiled code: $HOME\/.pythonxx_compiled. This is bad for obvious reasons and leads to many error messages. How can this problem be circumvented?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":282,"Q_Id":17154381,"Users Score":0,"Answer":"One quick workaround is to use a local directory on each node (e.g. \/tmp as Wesley said), but use one MPI task per node, if you have the capacity.","Q_Score":3,"Tags":"python,scipy,cluster-computing,mpi","A_Id":20356186,"CreationDate":"2013-06-17T18:34:00.000","Title":"How can scipy.weave.inline be used in a MPI-enabled application on a cluster?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a sequence of actions taking place on a video like, say \"zooming in and zooming out\" a webpage.\nI want to catch the frames that had a visual change from a some previous frame and so on.\nBasically, want to catch the visual difference happening in the video.\nI have tried using feature detection using SURF. It just detects random frames and does not detect most of the times.\nI have also tried, histograms and it does not help.\nAny directions and pointers?\nThanks in advance,","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":371,"Q_Id":17180409,"Users Score":2,"Answer":"For effects like zooming in and out, optical flow seems the best choice. Search for research papers on \"Shot Detection\" for other possible approaches.\nAs for the techniques you mention, did you apply some form of noise reduction before using them?","Q_Score":2,"Tags":"python,opencv,image-processing,frame,python-imaging-library","A_Id":17181542,"CreationDate":"2013-06-18T23:01:00.000","Title":"change detection on video frames","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"i need to find out contact angle between 2 edges in an image using open cv and python so can anybody suggest me how to find it? if not code please let me know algorithm for the same.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":531,"Q_Id":17209762,"Users Score":0,"Answer":"Simplify edge A and B into a line equation (using only the few last pixels)\nGet the line equations of the two lines (form y = mx + b)\nGet the angle orientations of the two lines \u03b8=atan|1\/m|\nSubtract the two angles from each other\n\nMake sure to do the special case of infinite slope, and also do some simple math to get Final_Angle = (0;pi)","Q_Score":1,"Tags":"opencv,python-2.7","A_Id":17212232,"CreationDate":"2013-06-20T09:13:00.000","Title":"to measure Contact Angle between 2 edges in an image","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I hope this hasn't already been answered but I haven't found this anywhere.\nMy problem is quite simple : I'm trying to compute an oblic profile of an image using scipy.\nOne great way to do it is to : \n\nlocate the segment along which I want my profile by giving the beginning and the end of my segment, \nextract the minimal image containing the segment, \ncompute the angle from which my image should be rotated to get the desired profile along a nice raw, \nextract said raw.\n\nThat's the theory.\nI'm stuck right now on (4), because my profile should fall in raw number array.shape[0]\/2, but rotate  seems to add sometimes lines of zeros below the data and columns on the left. The correct raw number can thus be shifted...\nHas anyone any idea how to get the right raw number, for example using the correspondence matrix used by scipy ?\nThanks.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":131,"Q_Id":17218051,"Users Score":0,"Answer":"After some time of debugging, I realized that depending on the angle - typically under and over n*45 degrees - scipy adds a row and a column to the output image. \na simple test of the angle adding one to the indices solved my problem.\nI hope this can help the future reader of this topic.","Q_Score":1,"Tags":"python,indexing,scipy,rotatetransform,correspondence","A_Id":18653827,"CreationDate":"2013-06-20T15:44:00.000","Title":"finding corresponding pixels before and after scipy.ndimage.interpolate.rotate","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Hi I have a MATLAB function that graphs the trajectory of different divers (the Olympic sport diving) depending on the position of a slider at the bottom of the window. The file takes multiple .mat files (with trajectory information in 3 dimensions) as input. I am trying to put this MATLAB app on to the internet. What would be the easiest\/most efficient way of doing this? I have experience programming in Python and little experience programming in Java.\nHere are the options that I have considered:\n1. MATLAB Builder JA (too expensive)\n2. Rewrite entire MATLAB function into Java (not experienced enough in Java)\n3. Implement MATLAB file using mlabwrapper and using Django to deploy into web app. (having a lot of trouble installing mlabwrapper onto OSX)\n4. Rewrite MATLAB function into Python using SciPy, NumPy, and matlibplot and then using Django.\nI do not have any experience with Django but I am willing to learn it. Can someone point me in the right direction?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":934,"Q_Id":17219344,"Users Score":0,"Answer":"You could always just host the MATLAB code and sample .mat on a website for people to download and play with on their own machines if they have a MATLAB license.   If you are looking at having some sort of embedded app on your website you are going to need to rewrite your code in another language.  The project sounds doable in python using the packages you mentioned however hosting it online will not be as simple as running a program from your command line.  Django would help you build a website but I do not think that it will allow you to just run a python script in the browser.","Q_Score":0,"Tags":"python,django,matlab,web-applications,octave","A_Id":17220530,"CreationDate":"2013-06-20T16:51:00.000","Title":"MATLAB to web app","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"Hi I have a MATLAB function that graphs the trajectory of different divers (the Olympic sport diving) depending on the position of a slider at the bottom of the window. The file takes multiple .mat files (with trajectory information in 3 dimensions) as input. I am trying to put this MATLAB app on to the internet. What would be the easiest\/most efficient way of doing this? I have experience programming in Python and little experience programming in Java.\nHere are the options that I have considered:\n1. MATLAB Builder JA (too expensive)\n2. Rewrite entire MATLAB function into Java (not experienced enough in Java)\n3. Implement MATLAB file using mlabwrapper and using Django to deploy into web app. (having a lot of trouble installing mlabwrapper onto OSX)\n4. Rewrite MATLAB function into Python using SciPy, NumPy, and matlibplot and then using Django.\nI do not have any experience with Django but I am willing to learn it. Can someone point me in the right direction?","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":934,"Q_Id":17219344,"Users Score":1,"Answer":"A cheap and somewhat easy way (with limited functionality) would be:\nInstall MATLAB on your server, or use the MATLAB Compiler to create a stand alone executable (not sure if that comes with your version of MATLAB or not). If you don't have the compiler and can't install MATLAB on your server, you could always go to a freelancing site such as elance.com, and pay someone $20 to compile your code for you into a windows exe file. \nEither way, the end goal is to make your MATLAB function callable from the command line (the server will be doing the calling) You could make your input arguments into the slider value, and the .mat files you want to open, and the compiled version of MATLAB will know how to handle this. Once you do that, have the code create a plot and save an image of it. (using getframe or other figure export tools, check out FEX). Have your server output this image to the client.\nTah-dah, you have a crappy low cost work around! \nI hope this helps , if not, I apologize!","Q_Score":0,"Tags":"python,django,matlab,web-applications,octave","A_Id":17224492,"CreationDate":"2013-06-20T16:51:00.000","Title":"MATLAB to web app","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"Do you know how to get the index or column of a DataFrame as a NumPy array or python list?","AnswerCount":8,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":558431,"Q_Id":17241004,"Users Score":75,"Answer":"You can use df.index to access the index object and then get the values in a list using df.index.tolist(). Similarly, you can use df['col'].tolist() for Series.","Q_Score":289,"Tags":"python,pandas","A_Id":17241104,"CreationDate":"2013-06-21T17:25:00.000","Title":"How do I convert a pandas Series or index to a Numpy array?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"While trying to compute inverse of a matrix in python using numpy.linalg.inv(matrix), I get singular matrix error. Why does it happen? Has it anything to do with the smallness of the values in the matrix. The numbers in my matrix are probabilities and add up to 1.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":7873,"Q_Id":17257056,"Users Score":2,"Answer":"It may very well have to do with the smallness of the values in the matrix.\nSome matrices that are not, in fact, mathematically singular (with a zero determinant) are totally singular from a practical point of view, in that the math library one is using cannot process them properly.\nNumerical analysis is tricky, as you know, and how well it deals with such situations is a measure of the quality of a matrix library.","Q_Score":1,"Tags":"python,numpy,matrix-inverse","A_Id":17257084,"CreationDate":"2013-06-23T01:56:00.000","Title":"Inverse of a Matrix in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"PEP8 has naming conventions for e.g. functions (lowercase), classes (CamelCase) and constants (uppercase).\nIt seems to me that distinguishing between numpy arrays and built-ins such as lists is probably more important as the same operators such as \"+\" actually mean something totally different.\nDoes anyone have any naming conventions to help with this?","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":415,"Q_Id":17270293,"Users Score":1,"Answer":"numpy arrays and lists should occupy similar syntactic roles in your code and as such I wouldn't try to distinguish between them by naming conventions. Since everything in python is an object the usual naming conventions are there not to help distinguish type so much as usage. Data, whether represented in a list or a numpy.ndarray has the same usage.\nI agree that it's awkward that eg. + means different things for lists and arrays. I implicitly deal with this by never putting anything like numerical data in a list but rather always in an array. That way I know if I want to concatenate blocks of data I should be using numpy.hstack. That said, there are definitely cases where I want to build up a list through concatenation and turn it into a numpy array when I'm done. In those cases the code block is usually short enough that it's clear what's going on. Some comments in the code never hurt.","Q_Score":2,"Tags":"python,numpy,naming-conventions","A_Id":17270654,"CreationDate":"2013-06-24T07:39:00.000","Title":"how do you distinguish numpy arrays from Python's built-in objects","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"PEP8 has naming conventions for e.g. functions (lowercase), classes (CamelCase) and constants (uppercase).\nIt seems to me that distinguishing between numpy arrays and built-ins such as lists is probably more important as the same operators such as \"+\" actually mean something totally different.\nDoes anyone have any naming conventions to help with this?","AnswerCount":2,"Available Count":2,"Score":0.1973753202,"is_accepted":false,"ViewCount":415,"Q_Id":17270293,"Users Score":2,"Answer":"You may use a prefix np_ for numpy arrays, thus distinguishing them from other variables.","Q_Score":2,"Tags":"python,numpy,naming-conventions","A_Id":17270547,"CreationDate":"2013-06-24T07:39:00.000","Title":"how do you distinguish numpy arrays from Python's built-in objects","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am thinking about a problem I haven't encountered before and I'm trying to determine the most efficient algorithm to use.\nI am iterating over two lists, using each pair of elements to calculate a value that I wish to sort on. My end goal is to obtain the top twenty results. I could store the results in a third list, sort that list by absolute value, and simply slice the top twenty, but that is not ideal. \nSince these lists have the potential to become extremely large, I'd ideally like to only store the top twenty absolute values, evicting old values as a new top value is calculated. \nWhat would be the most efficient way to implement this in python?","AnswerCount":4,"Available Count":1,"Score":0.049958375,"is_accepted":false,"ViewCount":911,"Q_Id":17300419,"Users Score":1,"Answer":"Have a list of size 20  tupples initialised with less than the minimum result of the calculation and two indices of -1.  On calculating a result append it to the results list, with the indices of the pair that resulted, sort on the value only and trim the list to length 20.  Should be reasonably efficient as you only ever sort a list of length 21.","Q_Score":7,"Tags":"python,algorithm,sorting","A_Id":17300620,"CreationDate":"2013-06-25T14:43:00.000","Title":"Python Sort On The Fly","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a vanilla pandas dataframe with an index. I need to check if the index is sorted. Preferably without sorting it again.\ne.g. I can test an index to see if it is unique by index.is_unique() is there a similar way for testing sorted?","AnswerCount":4,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":16454,"Q_Id":17315881,"Users Score":84,"Answer":"How about:\ndf.index.is_monotonic","Q_Score":44,"Tags":"python,pandas","A_Id":17347945,"CreationDate":"2013-06-26T09:07:00.000","Title":"How can I check if a Pandas dataframe's index is sorted","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am wondering whether there is any computational or storage disadvantage to using Panels instead of multi-indexed DataFrames in pandas.\nOr are they the same behind the curtain?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":169,"Q_Id":17353773,"Users Score":0,"Answer":"they have a similiar storage mechanism, and only really differ in the indexing scheme. Performance wise they should be similar. There is more support (code-wise) for multi-level df's as they are more often used. In addition Panels have different silicing semantics, so dtype guarantees are different.","Q_Score":2,"Tags":"python,pandas,data-analysis","A_Id":17370686,"CreationDate":"2013-06-27T21:46:00.000","Title":"Are pandas Panels as efficient as multi-indexed DataFrames?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"From what I've read about Numpy arrays, they're more memory efficient that standard Python lists. What confuses me is that when you create a numpy array, you have to pass in a python list. I assume this python list gets deconstructed, but to me, it seems like it defeats the purpose of having a memory efficient data structure if you have to create a larger inefficient structure to create the efficient one. \nDoes numpy.zeros get around this?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":4676,"Q_Id":17371059,"Users Score":1,"Answer":"Numpy in general is more efficient if you pre-allocate the size.  If you know you're going to be populating an MxN matrix...create it first then populate as opposed to using appends for example.\nWhile the list does have to be created, a lot of the improvement in efficiency comes from acting on that structure.  Reading\/writing\/computations\/etc.","Q_Score":4,"Tags":"python,numpy","A_Id":17371090,"CreationDate":"2013-06-28T18:08:00.000","Title":"numpy array memory allocation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm basically trying to plot some images based on a given set of parameters of a .fits file. However, this made me curious: what IS a .fits array? When I type in img[2400,3456] or some random values in the array, I get some output. \nI guess my question is more conceptual than code-based, but, it boils down to this: what IS a .fits file, and what do the arrays and the outputs represent?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":119,"Q_Id":17376904,"Users Score":1,"Answer":"A FITS file consists of header-data units. A header-data unit contains an ASCII-type header with\nkeyword-value-comment triples plus either binary FITS tables or (hyperdimensional) image cubes.\nEach entry in a table of a binary FITS table may itself contain hyperdimensional image cubes. An array\nis some slice through some dimensions of any of these cubes.\nNow as a shortcut to images stored in the first (a.k.a primary) header-data unit, many viewers\nallow to indicate in square brackets some indices of windows into these images (which in most\ncommon cases is based on the equivalent support by the cfitsio library).","Q_Score":0,"Tags":"python,arrays,astronomy,fits","A_Id":19917484,"CreationDate":"2013-06-29T05:07:00.000","Title":"What IS a .fits file, as in, what is a .fits array?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"First of all, I am aware that matrix and array are two different data types in NumPy. But I put both in the title to make it a general question. If you are editing this question, please feel free to remove one. Ok, here is my question,\nHere is an edit to the original question. Consider a Markov Chain with a 2 dimensional state vector x_t=(y_t,z_t) where y_t and z_t are both scalars. What is the best way of representing\/storing\/manipulating transition matrix of this Markov Chain?\nNow, what I explained is a simplified version of my problem. My Markov Chain state vector is a 5*1 vector. \nHope this clarifies","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":846,"Q_Id":17416448,"Users Score":2,"Answer":"Let's say you're trying to use a Markov chain to model english sentence syntax.  Your transition matrix will give you the probability of going from one part of speech to another part of speech.  Now let's suppose that we're using a 3rd-order Markov model.  This would give use the probability of going from state 123 to 23X, where X is a valid state. \nThe Markov transition matrix would be N3 x N, which is still a 2-dimensional matrix regardless of the dimensionality of the states, themselves.  If you're generating the probability distributions based on empirical evidence, then, in this case, there's going to be states with probability 0.  \nIf you're worried about sparsity, perhaps arrays are not the best choice.  Instead of using an array of arrays, perhaps you should use a dictionary of dictionaries.  Or if you have many transition matrices, an array of dictionaries of dictionaries.\nEDIT (based off comment):\nYou're right, that is more complicated.  Nonetheless, for any state, (i,j), there exists a probability distribution for going to the next state, (m,n).  Hence, we have our \"outer\" dictionary, whose keys are all the possible states.  Each key (state) points to a value that is a dictionary, which holds the probability distribution for that state.","Q_Score":2,"Tags":"python,numpy,linear-algebra,multidimensional-array","A_Id":17416531,"CreationDate":"2013-07-02T02:21:00.000","Title":"Multiplication of Multidimensional matrices (arrays) in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on a traffic study and I have the following problem:\nI have a CSV file that contains time-stamps and license plate numbers of cars for a location and another CSV file that contains the same thing. I am trying to find matching license plates between the two files and then find the time difference between the two. I know how to match strings but is there a way I can find matches that are close maybe to detect user input error of the license plate number?\nEssentially the data looks like the following:\n\nA = [['09:02:56','ASD456'],...]\nB = [...,['09:03:45','ASD456'],...]\n\nAnd I want to find the time difference between the two sightings but say if the data was entered slightly incorrect and the license plate for B says 'ASF456' that it will catch that","AnswerCount":4,"Available Count":1,"Score":0.049958375,"is_accepted":false,"ViewCount":1137,"Q_Id":17456233,"Users Score":1,"Answer":"What you're asking is about a fuzzy search, from what it sounds like.  Instead of checking string equality, you can check if the two string being compared have a levenshtein distance of 1 or less.  Levenshtein distance is basically a fancy way of saying how many insertions, deletions or changes will it take to get from word A to B.  This should account for small typos.\nHope this is what you were looking for.","Q_Score":0,"Tags":"python,comparison","A_Id":17456347,"CreationDate":"2013-07-03T19:09:00.000","Title":"Python Matching License Plates","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a pandas dataFrame created through a mysql call which returns the data as object type.\nThe data is mostly numeric, with some 'na' values.\nHow can I cast the type of the dataFrame so the numeric values are appropriately typed (floats) and the 'na' values are represented as numpy NaN values?","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":4908,"Q_Id":17457418,"Users Score":1,"Answer":"df = df.convert_objects(convert_numeric=True) will work in most cases.\nI should note that this copies the data.  It would be preferable to get it to a numeric type on the initial read.  If you post your code and a small example, someone might be able to help you with that.","Q_Score":4,"Tags":"python,numpy,pandas","A_Id":17457967,"CreationDate":"2013-07-03T20:21:00.000","Title":"Converting Pandas Dataframe types","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using Libsvm in a 5x2 cross validation to classify a very huge amount of data, that is, I have 47k samples for training and 47k samples for testing in 10 different configurations.\nI usually use the Libsvm's script easy.py to classify the data, but it's taking so long, I've been waiting for results for more than 3 hours and nothing, and I still have to repeat this procedure more 9 times!\ndoes anybody know how to use the libsvm faster with a very huge amount of data? does the C++ Libsvm functions work faster than the python functions?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":3432,"Q_Id":17457460,"Users Score":0,"Answer":"easy.py is a script for training and evaluating a classifier. it does a metatraining for the SVM parameters with grid.py. in grid.py is a parameter \"nr_local_worker\" which is defining the mumber of threads. you might wish to increase it (check processor load).","Q_Score":3,"Tags":"python,c++,svm,libsvm","A_Id":18509671,"CreationDate":"2013-07-03T20:24:00.000","Title":"Large training and testing data in libsvm","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an empirical network with 585 nodes and 5,441 edges. This is a scale-free network with max node degree of 179 and min node degree of 1. I am trying to create an equivalent random graph (using random_degree_sequence_graph from networkx), but my python just keeps running. I did similar exercise fro the network with 100 nodes - it took just a second to create a random graph. But with 585 nodes it takes forever. The result of is_valid_degree_sequence command is True. Is it possible that python goes into some infinite loop with my degree sequence or does it actually take a very long time (more than half hour) to create a graph of such size? Please let me know if anyone has had any experience with this. I am using Python 2.7.4.\nThanks in advance!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":532,"Q_Id":17464274,"Users Score":1,"Answer":"That algorithm's run time could get very long for some degree sequences.  And it is not guaranteed to produce a graph.  Depending on your end use you might consider using the configuration_model(). Though it doesn't sample graphs uniformly at random and might produce parallel edges and self loops it will always finish.","Q_Score":0,"Tags":"python,networkx","A_Id":17471244,"CreationDate":"2013-07-04T07:28:00.000","Title":"An issue with generating a random graph with given degree sequence: time consuming or some error?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I accidentally installed python 2.7 again on my mac (mountain lion), when trying to install scipy using macports:\n\nsudo port install py27-scipy\n--->  Computing dependencies for py27-scipy\n--->  Dependencies to be installed: SuiteSparse gcc47 cctools cctools-headers llvm-3.3 libffi llvm_select cloog gmp isl gcc_select\n  ld64 libiconv libmpc mpfr libstdcxx ppl glpk zlib py27-nose\n  nosetests_select py27-setuptools python27 bzip2 db46 db_select\n  gettext expat ncurses libedit openssl python_select sqlite3 py27-numpy\n  fftw-3 swig-python swig pcre\n\nI am still using my original install of python (and matplotlib and numpy etc), without scipy. How do I remove this new version? It is taking up ~2Gb space.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":609,"Q_Id":17486322,"Users Score":0,"Answer":"How about sudo port uninstall python27?","Q_Score":0,"Tags":"python,scipy,reinstall","A_Id":17488424,"CreationDate":"2013-07-05T10:10:00.000","Title":"Installing python (same version) on accident twice","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"Can't get the titles right in matplotlib: \n'technologie\u00ebn in \u00b0C' gives: technologie\u00c3n in \u00c3C\nPossible solutions already tried:\n\nu'technologie\u00ebn in \u00b0C' doesn't work\nneither does: # -*- coding: utf-8 -*- at the beginning of the code-file.\n\nAny solutions?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":6904,"Q_Id":17525882,"Users Score":0,"Answer":"In Python3, there is no need to worry about all that troublesome UTF-8 problems.\nOne note that you will need to set a Unicode font before plotting.\nmatplotlib.rc('font', family='Arial')","Q_Score":5,"Tags":"python,matplotlib,unicode,python-2.x","A_Id":42853468,"CreationDate":"2013-07-08T11:50:00.000","Title":"How to pass Unicode title to matplotlib?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"TFIDFVectorizer takes so much memory ,vectorizing 470 MB of 100k documents takes over 6 GB , if we go 21 million documents it will not fit 60 GB of RAM we have.\nSo we go for HashingVectorizer but still need  to know how to distribute the hashing vectorizer.Fit and partial fit does nothing so how to work with Huge Corpus?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":4523,"Q_Id":17536394,"Users Score":1,"Answer":"One way to overcome the inability of HashingVectorizer to account for IDF is to index your data into elasticsearch or lucene and retrieve termvectors from there using which you can calculate Tf-IDF.","Q_Score":3,"Tags":"python,numpy,machine-learning,scipy,scikit-learn","A_Id":28424354,"CreationDate":"2013-07-08T21:36:00.000","Title":"How can i reduce memory usage of Scikit-Learn Vectorizers?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm proficient in Python but a noob at Scala. I'm about to write some dirty experiment code in Scala, and came across the thought that it would be really handy if Scala had a function like help() in Python. For example, if I wanted to see the built-in methods for a Scala Array I might want to type something like help(Array), just like I would type help(list) in Python. Does such a thing exist for Scala?","AnswerCount":5,"Available Count":2,"Score":0.0399786803,"is_accepted":false,"ViewCount":2972,"Q_Id":17536758,"Users Score":1,"Answer":"Similarly, IDEA has its \"Quick Documentation Look-up\" command, which works for Scala as well as Java (-Doc) JARs and source-code documentation comments.","Q_Score":11,"Tags":"python,scala,equivalent","A_Id":17538468,"CreationDate":"2013-07-08T22:03:00.000","Title":"Scala equivalent of Python help()","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm proficient in Python but a noob at Scala. I'm about to write some dirty experiment code in Scala, and came across the thought that it would be really handy if Scala had a function like help() in Python. For example, if I wanted to see the built-in methods for a Scala Array I might want to type something like help(Array), just like I would type help(list) in Python. Does such a thing exist for Scala?","AnswerCount":5,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":2972,"Q_Id":17536758,"Users Score":0,"Answer":"In scala , you can try using the below ..( similar to the one we have in python )..\nhelp(RDD1) in python will give you the rdd1 description with full details.\nScala > RDD1.[tab] \nOn hitting tab you will find the list of options available to the specified RDD1,  similar option you find in eclipse .","Q_Score":11,"Tags":"python,scala,equivalent","A_Id":55942086,"CreationDate":"2013-07-08T22:03:00.000","Title":"Scala equivalent of Python help()","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a working conjungate gradient method implementation in pycuda, that I want to optimize. It uses a self written matrix-vector-multiplication and the pycuda-native gpuarray.dot and gpuarray.mul_add functions\nProfiling the program with kernprof.py\/line_profiler returned most time (>60%) till convergence spend in one gpuarray.dot() call. (About .2 seconds)\nAll following calls of gpuarray.dot() take about 7 microseconds. All calls have the same type of input vectors (size: 400 doubles)\nIs there any reason why? I mean in the end it's just a constant, but it is making the profiling difficult.\nI wanted to ask the question at the pycuda mailing list. However I wasn't able to subscribe with an @gmail.com adress. If anyone has either an explanation for the strange .dot() behavior or my inability to subscribe to that mailing list please give me a hint ;)","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":748,"Q_Id":17574547,"Users Score":2,"Answer":"One reason would be that Pycuda is compiling the kernel before uploading it. As far as I remember thought that should happen only the very first time it executes it.\nOne solution could be to \"warm up\" the kernel by executing it once and then start the profiling procedure.","Q_Score":1,"Tags":"python,cuda,pycuda,mailing-list","A_Id":17581063,"CreationDate":"2013-07-10T15:19:00.000","Title":"pycuda.gpuarray.dot() very slow at first call","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need some help from python programmers to solve the issue I'm facing in processing data:-\n\nI have .csv files placed in a directory structure like this:-\n-MainDirectory\n\nSub directory 1\n\nsub directory 1A\n\nfil.csv\n\n\nSub directory 2\n\nsub directory 2A\n\nfile.csv\n\n\nsub directory 3\n\nsub directory 3A\n\nfile.csv\n\n\n\nInstead of going into each directory and accessing the .csv files, I want to run a script that can combine the data of the all the sub directories. \n\nEach file has the same type of header. And I need to maintain 1 big .csv file with one header only and all the .csv file data can be appended one after the other. \nI have the python script that can combine all the files in a single file but only when those files are placed in one folder. \nCan you help to provide a script that can handle the above directory structure?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1781,"Q_Id":17586573,"Users Score":0,"Answer":"you can use os.listdir() to get list of files in directory","Q_Score":3,"Tags":"python,csv,python-2.7","A_Id":17587872,"CreationDate":"2013-07-11T06:34:00.000","Title":"Python - Combing data from different .csv files. into one","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"The case is, I have a 2D array and can convert it to a plot. How can I read the y value of a point with given x?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":37,"Q_Id":17658836,"Users Score":0,"Answer":"Just access the input data that you used to generate the plot. Either this is a mathematical function which you can just evaluate for a given x or this is a two-dimensional data set which you can search for any given x. In the latter case, if x is not contained in the data set, you might want to interpolate or throw an error.","Q_Score":0,"Tags":"python,matplotlib","A_Id":17659174,"CreationDate":"2013-07-15T16:12:00.000","Title":"How to read out point position of a given plot in matplotlib?(without using mouse)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Have a  2-dimensional array, like -\n\na[0] = [ 0 , 4 , 9 ]\na[1] = [ 2 , 6 , 11 ]\na[2] = [ 3 , 8 , 13 ]\na[3] = [ 7 , 12 ]\nNeed to select one element from each of the sub-array in a way that the resultant set of numbers are closest, that is the difference between the highest number and lowest number in the set is minimum.\nThe answer to the above will be = [ 9 , 6 , 8 , 7 ].\nHave made an algorithm, but don't feel its a good one.\nWhat would be a efficient algorithm to do this in terms of time and space complexity?\nEDIT - My Algorithm (in python)-\n\nINPUT - Dictionary : table{}\nOUTPUT - Dictionary : low_table{}\n#\nN = len(table)\nfor word_key in table:\n    for init in table[word_key]:\n        temp_table = copy.copy(table)\n        del temp_table[word_key]\n        per_init = copy.copy(init)\n        low_table[init]=[]\n        for ite in range(N-1):\n            min_val = 9999\n            for i in temp_table:\n                for nums in temp_table[i]:\n                    if min_val > abs(init-nums):\n                        min_val = abs(init-nums)\n                        del_num = i\n                        next_num = nums\n            low_table[per_init].append(next_num)\n            init = (init+next_num)\/2\n            del temp_table[del_num]\nlowest_val = 99\nlowest_set = []\nfor x in low_table:\n    low_table[x].append(x)\n    low_table[x].sort()\n    mini = low_table[x][-1]-low_table[x][0]\n    if mini < lowest_val:\n        lowest_val = mini\n        lowest_set = low_table[x]\nprint lowest_set","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":667,"Q_Id":17667022,"Users Score":11,"Answer":"collect all the values to create a single ordered sequence, with each element tagged with the array it came from:\n0(0), 2(1), 3(2), 4(0), 6(1), ... 12(3), 13(2)\nthen create a window across them, starting with the first (0(0)) and ending it at the first position that makes the window span all the arrays (0(0) -> 7(3))\nthen roll this window by incrementing the start of the window by one, and increment the end of the window until you again have a window that covers all elements.\nthen roll it again: (2(1), 3(2), 4(0), ... 7(3)), and so forth.\nat each step keep track of the the difference between the largest and the smallest. Eventually you find the one with the smallest window. I have the feeling that in the worst case this is O(n^2) but that's just a guess.","Q_Score":14,"Tags":"python,algorithm","A_Id":17667109,"CreationDate":"2013-07-16T02:26:00.000","Title":"Finding N closest numbers","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm having a little bug in my program where you give the computer a random number to try to guess, and a range to guess between and the amount of guesses it has. After the computer generates a random number, it asks you if it is your number, if not, it asks you if it is higher or lower than it. My problem is, if your number is 50, and it generates 53, you would say \"l\" or \"lower\" or something that starts with \"l\". Then it would generate 12 or something, you would say \"higher\" or something, and it might give you 72. How could I make it so that it remembers to be lower than 53?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":53,"Q_Id":17716737,"Users Score":3,"Answer":"Create 2 variables that contain the lowest and highest possible values. Whenever you get a response, store it in the appropriate variable. Make the RNG pick a value between the two.","Q_Score":1,"Tags":"python,random","A_Id":17716804,"CreationDate":"2013-07-18T07:04:00.000","Title":"Random number indexing past inputs Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have the following problem:\nThere are 12 samples around 20000 elements each from unknown distributions (sometimes the distributions are not uni-modal so it's hard to automatically estimate an analytical family of the distributions).\nBased on these distributions I compute different quantities. How can I explore the distribution of the target quantity in the most efficient (and simplest) way?\nTo be absolutely clear, here's a simple example: quantity A is equal to B*C\/D\nB,C,D are distributed according to unknown laws but I have samples from their distributions and based on these samples I want to compute the distribution of A.\nSo in fact what I want is a tool to explore the distribution of the target quantity based on samples of the variables.\nI know that there are MCMC algorithms to do that. But does anybody know a good implementation of an MCMC sampler in Python or C? Or are there any other ways to solve the problem?\nMaxim","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":838,"Q_Id":17740281,"Users Score":0,"Answer":"The simplest way to explore the distribution of A is to generate samples based on the samples of B, C, and D, using your rule. That is, for each iteration, draw one value of B, C, and D from their respective sample sets, independently, with repetition, and calculate A = B*C\/D.\nIf the sample sets for B, C, and D have the same size, I recommend generating a sample for A of the same size. Much fewer samples would result in loss of information, much more samples would not gain much. And yes, even though many samples will not be drawn, I still recommend drawing with repetition.","Q_Score":1,"Tags":"python,c,statistics,mcmc","A_Id":19030208,"CreationDate":"2013-07-19T07:21:00.000","Title":"MCMC implementation in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a program for learning Artificial Neural Network and it takes a 2-d numpy array as training data. The size of the data array I want to use is around 300,000 x 400 floats. I can't use chunking here because the library I am using (DeepLearningTutorials) takes a single numpy array as training data.\nThe code shows MemoryError when the RAM usage is around 1.6Gb by this process(I checked it in system monitor) but I have a total RAM of 8GB. Also, the system is Ubuntu-12.04 32-bit.\nI checked for the answers ofor other similar questions but somewhere it says that there is nothing like allocating memory to your python program and somewhere the answer is not clear as to how to increase the process memory.\nOne interesting thing is I am running the same code on a different machine and it can take a numpy array of almost 1,500,000 x 400 floats without any problem. The basic configurations are similar except that the other machine is 64-bit and this one is 32-bit.\nCould someone please give some theoretical answer as to why there is so much difference in this or is this the only reason for my problem?","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":452,"Q_Id":17756791,"Users Score":2,"Answer":"A 32-bit OS can only address up to aroung 4gb of ram, while a 64-bit OS can take advantage of a lot more ram (theoretically 16.8 million terabytes).  Since your OS is 32-bit, your OS can only take  advantage of 4gb, so your other 4gb isn't used.\nThe other 64-bit machine doesn't have the 4gb ram limit, so it can take advantage of all of its installed ram.\nThese limits come from the fact that a 32-bit machine can only store memory address (pointers) of 32-bytes, so there are 2^32 different possible memory locations that the computer can identify.  Similarly, a 64-bit machine can identify 2^64 different possible memory locations, so it can address 2^64 different bytes.","Q_Score":2,"Tags":"python,numpy,ubuntu-12.04,32-bit","A_Id":17756813,"CreationDate":"2013-07-19T23:01:00.000","Title":"Python Process using only 1.6 GB RAM Ubuntu 32 bit in Numpy Array","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"suppose I have a dataframe with index as monthy timestep, I know I can use dataframe.groupby(lambda x:x.year) to group monthly data into yearly and apply other operations. Is there some way I could quick group them, let's say by decade? \nthanks for any hints.","AnswerCount":4,"Available Count":1,"Score":0.2449186624,"is_accepted":false,"ViewCount":31949,"Q_Id":17764619,"Users Score":5,"Answer":"if your Data Frame has Headers say : DataFrame ['Population','Salary','vehicle count']\nMake your index as Year: DataFrame=DataFrame.set_index('Year')\nuse below code to resample data in decade of 10 years and also gives you some of all other columns within that dacade \ndatafame=dataframe.resample('10AS').sum()","Q_Score":15,"Tags":"python,pandas","A_Id":54003707,"CreationDate":"2013-07-20T17:12:00.000","Title":"pandas dataframe group year index by decade","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Are there implementations available for any co-clustering algorithms in python? The scikit-learn package has k-means and hierarchical clustering but seems to be missing this class of clustering.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":720,"Q_Id":17767807,"Users Score":0,"Answer":"The fastest clustering algorithm I know of does this:\nRepeat O(log N) times:\nC = M x X\nWhere X is N x dim and M is clus x N...\nIf your clusters are not \"flat\"...\nPerform f(X) = ... This just projects X onto some \"flat\" space...","Q_Score":3,"Tags":"python,machine-learning,scipy,scikit-learn,unsupervised-learning","A_Id":17768482,"CreationDate":"2013-07-20T23:55:00.000","Title":"Co-clustering algorithm in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have data which is of the gaussian form when plotted as histogram. I want to plot a gaussian curve on top of the histogram to see how good the data is. I am using pyplot from matplotlib. Also I do NOT want to normalize the histogram. I can do the normed fit, but I am looking for an Un-normalized fit. Does anyone here know how to do it?\nThanks!\nAbhinav Kumar","AnswerCount":3,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":12313,"Q_Id":17779316,"Users Score":3,"Answer":"Another way of doing this is to find the normalized fit and multiply the normal distribution with (bin_width*total length of data)\nthis will un-normalize your normal distribution","Q_Score":6,"Tags":"python,matplotlib,histogram,gaussian","A_Id":20057520,"CreationDate":"2013-07-22T03:04:00.000","Title":"Un-normalized Gaussian curve on histogram","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using the ODRPACK library in Python to fit some 1d data. It works quite well, but I have one question: is there any possibility to make constraints on the fitting parameters? For example if I have a model y = a * x + b and for physical reasons parameter a can by only in range (-1, 1). I've found that such constraints can be done in original Fortran implementation of the ODRPACK95 library, but I can't find how to do that in Python.\nOf course, I can implement my functions such that they will return very big values, if the fitting parameters are out of bounds and chi squared will be big too, but I wonder if there is a right way to do that.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":780,"Q_Id":17783481,"Users Score":3,"Answer":"I'm afraid that the older FORTRAN-77 version of ODRPACK wrapped by scipy.odr does not incorporate constraints. ODRPACK95 is a later extension of the original ODRPACK library that predates the scipy.odr wrappers, and it is unclear that we could legally include it in scipy. There is no explicit licensing information for ODRPACK95, only the general ACM TOMS non-commercial license.","Q_Score":3,"Tags":"python,scipy,curve-fitting","A_Id":17786438,"CreationDate":"2013-07-22T09:01:00.000","Title":"Constraints on fitting parameters with Python and ODRPACK","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Sorry about the title, SO wasn't allowing the word \"problem\" in it. I have the following problem:\nI have packages of things I want to sell, and each package has a price. When someone requests things X, Y and Z, I want to look through all the packages, some of which contain more than one item, and give the user the combination of packages that will cover their things and have the minimal price.\nFor example, I might suggest [(X, Y), (Z, Q)] for $10, since [(X), (Y), (Z)] costs $11. Since these are prices, I can't use the greedy weighted set cover algorithm, because two people getting the same thing for different prices would be bad.\nHowever, I haven't been able to find a paper (or anything) detailing an optimal algorithm for the weighted set cover problem. Can someone help with an implementation (I'm using Python), a paper, or even a high-level description of how it works?\nThe packages I have are in the hundreds, and the things are 5-6, so running time isn't really an issue.\nThanks!","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":1472,"Q_Id":17813029,"Users Score":2,"Answer":"If you want an exponential algorithm, just try every subset of the set of packages and take the cheapest one that contains all the things you need.","Q_Score":0,"Tags":"python,algorithm,set,cover","A_Id":17813855,"CreationDate":"2013-07-23T14:25:00.000","Title":"An optimal algorithm for the weighted set cover issue?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a set of points for which I want to construct KD Tree. After some time I want to add few more points to this KDTree periodically. Is there any way to do this in scipy implementation","AnswerCount":1,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":6831,"Q_Id":17817889,"Users Score":20,"Answer":"The problem with k-d-trees is that they are not designed for updates.\nWhile you can somewhat easily insert objects (if you use a pointer based representation, which needs substantially more memory than an array-based tree), and do deletions with tricks such as tombstone messages, doing such changes will degrate the performance of the tree.\nI am not aware of a good method for incrementally rebalancing a k-d-tree. For 1-dimensional trees you have red-black-trees, B-trees, B*-trees, B+-trees and such things. These don't obviously work with k-d-trees because of the rotating axes and thus different sorting. So in the end, with a k-d-tree, it may be best to just collect changes, and from time to time do a full tree rebuild. Then at least this part of the tree will be quite good.\nHowever, there exists a similar structure (that in my experiments often outperforms the k-d-tree!): the R*-tree. Instead of performing binary splits, it uses rectangular bounding boxes to collect objects, and a lot of thought was put into making the tree a dynamic data structure. This is also where the R*-tree performs much better than the R-tree: it has a much more clever split for kNN search, and it performs incremental rebalancing to improve its structure.","Q_Score":22,"Tags":"python,scipy,kdtree","A_Id":17822258,"CreationDate":"2013-07-23T18:13:00.000","Title":"Is there any way to add points to KD tree implementation in Scipy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am loading a text file into pandas, and have a field that contains year.  I want to make sure that this field is a string when pulled into the dataframe.  \nI can only seem to get this to work if I specify the exact length of the string using the code below:\ndf = pd.read_table('myfile.tsv', dtype={'year':'S4'})\nIs there a way to do this without specifying length?  I will need to perform this action on different columns that vary in length.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":4022,"Q_Id":17822595,"Users Score":1,"Answer":"I believe we enabled in 0.12\nyou can pass str,np.str_,object in place of an S4\nwhich all convert to object dtype in any event\nor after you read it in\ndf['year'].astype(object)","Q_Score":0,"Tags":"python,pandas","A_Id":17822815,"CreationDate":"2013-07-23T23:17:00.000","Title":"Specify DataType using read_table() in Pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In python and igraph I have many nodes with high degree. I always need to consider the edges from a node in order of their weight. It is slow to sort the edges each time I visit the same node. Is there some way to persuade igraph to always give the edges from a node in weight sorted order, perhaps by some preprocessing?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":71,"Q_Id":17844688,"Users Score":0,"Answer":"As far as I understand, you wont have access to the C backend from Python. What about storing the sorted edge in an attribute of the vertices eg in g.vs[\"sortedOutEdges\"] ?","Q_Score":0,"Tags":"python,igraph","A_Id":17845330,"CreationDate":"2013-07-24T20:58:00.000","Title":"Fast processing","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to compare two histograms by having the Y axis show the percentage of each column from the overall dataset size instead of an absolute value. Is that possible? I am using Pandas and matplotlib.\nThanks","AnswerCount":6,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":86215,"Q_Id":17874063,"Users Score":19,"Answer":"I know this answer is 6 years later but to anyone using density=True (the substitute for the normed=True), this is not doing what you might want to. It will normalize the whole distribution so that the area of the bins is 1. So if you have more bins with a width < 1 you can expect the height to be > 1 (y-axis). If you want to bound your histogram to [0;1] you will have to calculate it yourself.","Q_Score":78,"Tags":"python,pandas,matplotlib","A_Id":58946534,"CreationDate":"2013-07-26T06:04:00.000","Title":"Is there a parameter in matplotlib\/pandas to have the Y axis of a histogram as percentage?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a non-technical client who has some hierarchical product data that I'll be loading into a tree structure with Python.  The tree has a variable number of levels, and a variable number nodes and leaf nodes at each level.\nThe client already knows the hierarchy of products and would like to put everything into an Excel spreadsheet for me to parse.\nWhat format can we use that allows the client to easily input and maintain data, and that I can easily parse into a tree with Python's CSV?  Going with a column for each level isn't without its hiccups (especially if we introduce multiple node types)","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":13410,"Q_Id":17900112,"Users Score":0,"Answer":"If spreadsheet is a must in this solution, hierarchy can be represented by indents on the Excel side (empty cells at the beginnings of rows), one row per node\/leaf. On the Python side, one can parse them to tree structure (of course, one needs to filter out empty rows and some other exceptions). Node type can be specified on it's own column. For example, it could even be the first non-empty cell.\nI guess, hierarchy level is limited (say, max 8 levels), otherwise Excel is not good idea at all.\nAlso, there is a library called openpyxl, which can help reading Excel files directly, without user needing to convert them to CSV (it adds usability to the overall approach).\nAnother approach is to put a level number in the first cell. The number should never be incremented by 2 or more.\nYet another approach is to use some IDs for each node and each node leaf would need to specify parent's id. But this is not very user-friendly.","Q_Score":14,"Tags":"python,excel,csv,tree,hierarchy","A_Id":17900531,"CreationDate":"2013-07-27T16:38:00.000","Title":"Represent a tree hierarchy using an Excel spreadsheet to be easily parsed by Python CSV reader?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I tried installing pandas using easy_install and it claimed that it successfully installed the pandas package in my Python Directory.\nI switch to IDLE and try import pandas and it throws me the following error - \n\nTraceback (most recent call last):\n  File \"<pyshell#0>\", line 1, in <module>\n    import pandas\n  File \"C:\\Python27\\lib\\site-packages\\pandas-0.12.0-py2.7-win32.egg\\pandas\\__init__.py\", line 6, in <module>\n    from . import hashtable, tslib, lib\n  File \"numpy.pxd\", line 157, in init pandas.hashtable (pandas\\hashtable.c:20282)\nValueError: numpy.dtype has the wrong size, try recompiling\n\nPlease help me diagnose the error.\nFYI: I have already installed the numpy package","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":26604,"Q_Id":17904600,"Users Score":0,"Answer":"Panda does not work with python 2.7 , do you will need python 3.6 or higer","Q_Score":3,"Tags":"python-2.7,pandas,easy-install","A_Id":61605458,"CreationDate":"2013-07-28T03:06:00.000","Title":"Pandas import error","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How do I divide a list into smaller not evenly sized intervals, give the ideal initial and final values of each interval?\nI have a list of 16383 items. I also have a separate list of the values at which each interval should end and the following should enter.\nI would need to use the given intervals to assign each element to the partition it belongs to, depending on its value.\nI have tried reading stuff, but I encountered only the case when given the original list, people split it into evenly sized partitions...\nThanks\nBlaise","AnswerCount":5,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":141,"Q_Id":17925460,"Users Score":0,"Answer":"For each range in your limits list create an empty list plus one for the overflow values as a tupple with the max value in the list and the min value for that list, the last one will have a max on None\nFor each value in the values list run through your tupples until you find the one that your value is > min and < max or the max is None.  \nWhen you find the right list append the value to it and go on to the next.","Q_Score":0,"Tags":"python,arrays,slice","A_Id":17938780,"CreationDate":"2013-07-29T13:32:00.000","Title":"Dividing an array into partitions NOT evenly sized, given the points where each partition should start or end, in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I currently have a function PushLogUtility(p,w,f) that I am looking to optimise w.r.t f (2xk) list for fixed p (9xk list) and w (2xk) list.\nI am using the scipy.optimize.fmin function but am getting errors I believe because f is 2-dimensional. I had written a previous function LogUtility(p,q,f) passing a 1-dimensional input and it worked.\nOne option it seems is to write the p, w and f into 1-dimensional lists but this would be time-consuming and less readable. Is there any way to make fmin optimise a function with a 2D input?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":380,"Q_Id":17950492,"Users Score":0,"Answer":"It seems it is in fact impossible to pass a 2D list to numpy.optimize.fmin. However flattening the input f was not that much of a problem and while it makes the code slightly uglier, the optimisation now works.\nInterestingly I also coded the optimisation in Matlab which does take 2D inputs to its fminsearch function. Both programs give the same output (y).","Q_Score":0,"Tags":"python,optimization,scipy","A_Id":17951581,"CreationDate":"2013-07-30T14:57:00.000","Title":"Passing 2D argument into numpy.optimize.fmin error","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have Python 3.3 and 2.7 installed on my computer\nFor Python 3.3, I installed many libraries like numpy, scipy, etc\nSince I also want to use opencv, which only supports python 2.7 so far, I installed opencv under Python 2.7.\nHey, here comes the problem, what if I want to import numpy as well as cv in the same script?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1449,"Q_Id":17961391,"Users Score":1,"Answer":"You'll have to install all the libraries you want to use together with OpenCV for Python 2.7. This is not much of a problem, you can do it with pip in one line, or choose one of the many pre-built scientific Python packages.","Q_Score":0,"Tags":"python,opencv","A_Id":17971361,"CreationDate":"2013-07-31T04:04:00.000","Title":"OpenCV Python 3.3","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a huge list of company names and a huge list of zipcodes associated with those names. (>100,000). \nI have to output similar names (for example, AJAX INC and AJAX are the same company, I have chosen a threshold of 4 characters for edit distance), but only if their corresponding zipcodes match too. \nThe trouble is that I can put all these company names in a dictionary, and associate a list of zipcode and other characteristics with that dictionary key. However, then I have to match each pair, and with O(n^2), it takes forever. Is there a faster way to do it?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":164,"Q_Id":18023356,"Users Score":1,"Answer":"Create a dictionary keyed by zipcode, with lists of company names as the values. Now you only have to match company names per zipcode, a much smaller search space.","Q_Score":0,"Tags":"python,levenshtein-distance","A_Id":18023402,"CreationDate":"2013-08-02T18:02:00.000","Title":"Disambiguation of Names using Edit Distance","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have been wondering... If I am reading, say, a 400MB csv file into a pandas dataframe (using read_csv or read_table), is there any way to guesstimate how much memory this will need? Just trying to get a better feel of data frames and  memory...","AnswerCount":7,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":124007,"Q_Id":18089667,"Users Score":116,"Answer":"Here's a comparison of the different methods - sys.getsizeof(df) is simplest.\nFor this example, df is a dataframe with 814 rows, 11 columns (2 ints, 9 objects) - read from a 427kb shapefile\nsys.getsizeof(df)\n\n>>> import sys\n>>> sys.getsizeof(df)\n(gives results in bytes)\n462456\n\ndf.memory_usage()\n\n>>> df.memory_usage()\n...\n(lists each column at 8 bytes\/row)\n\n>>> df.memory_usage().sum()\n71712\n(roughly rows * cols * 8 bytes)\n\n>>> df.memory_usage(deep=True)\n(lists each column's full memory usage)\n\n>>> df.memory_usage(deep=True).sum()\n(gives results in bytes)\n462432\n\n\ndf.info()\nPrints dataframe info to stdout. Technically these are kibibytes (KiB), not kilobytes - as the docstring says, \"Memory usage is shown in human-readable units (base-2 representation).\" So to get bytes would multiply by 1024, e.g. 451.6 KiB = 462,438 bytes.\n\n>>> df.info()\n...\nmemory usage: 70.0+ KB\n\n>>> df.info(memory_usage='deep')\n...\nmemory usage: 451.6 KB","Q_Score":170,"Tags":"python,pandas","A_Id":47751572,"CreationDate":"2013-08-06T20:18:00.000","Title":"How to estimate how much memory a Pandas' DataFrame will need?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have been wondering... If I am reading, say, a 400MB csv file into a pandas dataframe (using read_csv or read_table), is there any way to guesstimate how much memory this will need? Just trying to get a better feel of data frames and  memory...","AnswerCount":7,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":124007,"Q_Id":18089667,"Users Score":10,"Answer":"Yes there is. Pandas will store your data in 2 dimensional numpy ndarray structures grouping them by dtypes. ndarray is basically a raw C array of data with a small header. So you can estimate it's size just by multiplying the size of the dtype it contains with the dimensions of the array.\nFor example: if you have 1000 rows with 2 np.int32 and 5 np.float64 columns, your DataFrame will have one 2x1000 np.int32 array and one 5x1000 np.float64 array which is:\n4bytes*2*1000 + 8bytes*5*1000 = 48000 bytes","Q_Score":170,"Tags":"python,pandas","A_Id":18089887,"CreationDate":"2013-08-06T20:18:00.000","Title":"How to estimate how much memory a Pandas' DataFrame will need?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a slight variant on the \"find k nearest neighbours\" algorithm which involves rejecting those that don't satisfy a certain condition and I can't think of how to do it efficiently. \nWhat I'm after is to find the k nearest neighbours that are in the current line of sight. Unfortunately scipy.spatial.cKDTree doesn't provide an option for searching with a filter to conditionally reject points.\nThe best algorithm I can come up with is to query for n nearest neighbours and if there aren't k that are in the line of sight then query it again for 2n nearest neighbours and repeat. Unfortunately this would mean recomputing the n nearest neighbours repeatedly in the worst cases. The performance hit gets worse the more times I have to repeat this query. On the other hand setting n too high is potentially wasteful if most of the points returned aren't needed.\nThe line of sight changes frequently so I can't recompute the cKDTree each time either. Any suggestions?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":631,"Q_Id":18144810,"Users Score":0,"Answer":"If you are looking for the neighbours in a line of sight, couldn't use an method like \n\ncKDTree.query_ball_point(self, x, r, p, eps)\n\nwhich allows you to query the KDTree for neighbours that are inside a radius of size r around the x array points.\nUnless I misunderstood your question, it seems that the line of sight is known and is equivalent to this r value.","Q_Score":4,"Tags":"python,constraints,nearest-neighbor,kdtree","A_Id":18339341,"CreationDate":"2013-08-09T10:36:00.000","Title":"nearest k neighbours that satisfy conditions (python)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a numpy fft for a large number of samples. How do I reduce the resolution bandwidth, so that it will show me fewer frequency bins, with averaged power output?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":921,"Q_Id":18150150,"Users Score":1,"Answer":"The bandwidth of each FFT result bin is inversely proportional to the length of the FFT window.  For a wider bandwidth per bin, use a shorter FFT.  If you have more data, then Welch's method can be used with sequential STFT windows to get an average estimate.","Q_Score":0,"Tags":"python,numpy,fft","A_Id":18153590,"CreationDate":"2013-08-09T15:19:00.000","Title":"FFT resolution bandwidth","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a large corpus of data (text) that I have converted to a sparse term-document matrix (I am using scipy.sparse.csr.csr_matrix to store sparse matrix). I want to find, for every document, top n nearest neighbour matches. I was hoping that NearestNeighbor routine in Python scikit-learn library (sklearn.neighbors.NearestNeighbor to be precise) would solve my problem, but efficient algorithms that use space partitioning data structures such as KD trees or Ball trees do not work with sparse matrices. Only brute-force algorithm works with sparse matrices (which is infeasible in my case as I am dealing with large corpus).\nIs there any efficient implementation of nearest neighbour search for sparse matrices (in Python or in any other language)?\nThanks.","AnswerCount":2,"Available Count":1,"Score":0.2913126125,"is_accepted":false,"ViewCount":3890,"Q_Id":18164348,"Users Score":3,"Answer":"You can try to transform your high-dimensional sparse data to low-dimensional dense data using TruncatedSVD then do a ball-tree.","Q_Score":9,"Tags":"python,scipy,scikit-learn,nearest-neighbor","A_Id":18201497,"CreationDate":"2013-08-10T17:07:00.000","Title":"Efficient nearest neighbour search for sparse matrices","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a 200k lines list of number ranges like start_position,stop position.\nThe list includes all kinds of overlaps in addition to nonoverlapping ones.\nthe list looks like this\n\n[3,5] \n[10,30]\n[15,25]\n[5,15]\n[25,35]\n...\n\nI need to find the ranges that a given number fall in. And will repeat it for 100k numbers.\nFor example if 18 is the given number with the list above then the function should return\n[10,30]\n[15,25]\nI am doing it in a overly complicated way using bisect, can anybody give a clue on how to do it in a faster way.\nThanks","AnswerCount":6,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":3636,"Q_Id":18179680,"Users Score":0,"Answer":"How about,\n\nsort by first column O(n log n)\nbinary search to find indices that are out of range O(log n)\nthrow out values out of range\nsort by second column O(n log n)\nbinary search to find indices that are out of range O(log n)\nthrow out values out of range\nyou are left with the values in range\n\nThis should be O(n log n)\nYou can sort rows and cols with np.sort and a binary search should only be a few lines of code.\nIf you have lots of queries, you can save the first sorted copy for subsequent calls but not the second.  Depending on the number of queries, it may turn out to be better to do a linear search than to sort then search.","Q_Score":6,"Tags":"python,algorithm","A_Id":18180322,"CreationDate":"2013-08-12T04:47:00.000","Title":"finding a set of ranges that a number fall in","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I just installed ArcGIS v10.2 64bit background processing which installs Python 2.7.3 64bit and NumPy 1.6.1.  I installed SciPy 0.12.0 64bit to the same Python installation.\nWhen I opened my Python interpreter I was able to successfully import arcpy, numpy, and scipy.  However, when I tried to import scipy.ndimage I got an error that said  numpy.core.multiarray failed to import.  Everything I have found online related to this error references issues between scipy and numpy and suggest upgrading to numpy 1.6.1.  I'm already at numpy 1.6.1.\nAny ideas how to deal with this?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":6801,"Q_Id":18282568,"Users Score":3,"Answer":"So it seems that the cause of the error was incompatibility between scipy 0.12.0 and the much older numpy 1.6.1.\nThere are two ways to fix this - either to upgrade numpy (to ~1.7.1) or to downgrade scipy (to ~0.10.1).\nIf ArcGIS 10.2 specifically requires Numpy 1.6.1, the easiest option is to downgrade scipy.","Q_Score":7,"Tags":"python,numpy,scipy","A_Id":18321537,"CreationDate":"2013-08-16T21:44:00.000","Title":"SciPy 0.12.0 and Numpy 1.6.1 - numpy.core.multiarray failed to import","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to open a csv file, select 1000 random rows and save those rows to a new file. I'm stuck and can't see how to do it. Can anyone help?","AnswerCount":2,"Available Count":1,"Score":-0.0996679946,"is_accepted":false,"ViewCount":11279,"Q_Id":18314913,"Users Score":-1,"Answer":"The basic procedure is this:\n1. Open the input file\nThis can be accomplished with the basic builtin open function.\n2.  Open the output file\nYou'll probably use the same method that you chose in step #1, but you'll need to open the file in write mode.\n3. Read the input file to a variable\nIt's often preferable to read the file one line at a time, and operate on that one line before reading the next, but if memory is not a concern, you can also read the entire thing into a variable all at once.\n4.  Choose selected lines\nThere will be any number of ways to do this, depending on how you did step #3, and your requirements.  You could use filter, or a list comprehension, or a for loop with an if statement, etc.  The best way depends on the particular constraints of your goal.\n5.  Write the selected lines\nTake the selected lines you've chosen in step #4 and write them to the file.\n6.  Close the files\nIt's generally good practice to close the files you've opened to prevent resource leaks.","Q_Score":0,"Tags":"python,random-sample","A_Id":18315125,"CreationDate":"2013-08-19T13:23:00.000","Title":"Selecting random rows with python and writing to a new file","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Using HoughLinesP raises  \"<'unknown'> is not a numpy array\", but my array is really a numpy array.\nIt works on one of my computer, but not on my robot...","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":158,"Q_Id":18352493,"Users Score":0,"Answer":"For me, it wasn't working when the environment was of ROS Fuerte but it worked when the environment was of ROS Groovy. \nAs Alexandre had mentioned above, it must be the problem with the opencv2 versions. Fuerte had 2.4.2 while Groovy had 2.4.6","Q_Score":0,"Tags":"python,opencv","A_Id":21596301,"CreationDate":"2013-08-21T08:30:00.000","Title":"OpenCv2: Using HoughLinesP raises \" is not a numpy array\"","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Using HoughLinesP raises  \"<'unknown'> is not a numpy array\", but my array is really a numpy array.\nIt works on one of my computer, but not on my robot...","AnswerCount":2,"Available Count":2,"Score":0.1973753202,"is_accepted":false,"ViewCount":158,"Q_Id":18352493,"Users Score":2,"Answer":"Found it: \nI don't have the same opencv version on my robot and on my computer !\nFor the records calling HoughLinesP:\n\nworks fine on 2.4.5 and 2.4.6\nleads to \"<unknown> is not a numpy array\" with version $Rev: 4557 $","Q_Score":0,"Tags":"python,opencv","A_Id":18353075,"CreationDate":"2013-08-21T08:30:00.000","Title":"OpenCv2: Using HoughLinesP raises \" is not a numpy array\"","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a directed graph in which I want to efficiently find a list of all K-th order neighbors of a node. K-th order neighbors are defined as all nodes which can be reached from the node in question in exactly K hops.\nI looked at networkx and the only function relevant was neighbors. However, this just returns the order 1 neighbors. For higher order, we need to iterate to determine the full set. I believe there should be a more efficient way of accessing K-th order neighbors in networkx.\nIs there a function which efficiently returns the K-th order neighbors, without incrementally building the set?\nEDIT: In case there exist other graph libraries in Python which might be useful here, please do mention those.","AnswerCount":6,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":10646,"Q_Id":18393842,"Users Score":0,"Answer":"Yes,you can get a k-order ego_graph of a node\nsubgraph = nx.ego_graph(G,node,radius=k)\nthen neighbors are nodes of the subgraph\nneighbors= list(subgraph.nodes())","Q_Score":11,"Tags":"python,networkx,adjacency-list","A_Id":71715493,"CreationDate":"2013-08-23T02:45:00.000","Title":"K-th order neighbors in graph - Python networkx","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a directed graph in which I want to efficiently find a list of all K-th order neighbors of a node. K-th order neighbors are defined as all nodes which can be reached from the node in question in exactly K hops.\nI looked at networkx and the only function relevant was neighbors. However, this just returns the order 1 neighbors. For higher order, we need to iterate to determine the full set. I believe there should be a more efficient way of accessing K-th order neighbors in networkx.\nIs there a function which efficiently returns the K-th order neighbors, without incrementally building the set?\nEDIT: In case there exist other graph libraries in Python which might be useful here, please do mention those.","AnswerCount":6,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":10646,"Q_Id":18393842,"Users Score":27,"Answer":"You can use:\nnx.single_source_shortest_path_length(G, node, cutoff=K)\nwhere G is your graph object.","Q_Score":11,"Tags":"python,networkx,adjacency-list","A_Id":21031826,"CreationDate":"2013-08-23T02:45:00.000","Title":"K-th order neighbors in graph - Python networkx","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a text file which is 10k lines long and I need to build a function to extract 10 random lines each time from this file. I already found how to generate random numbers in Python with numpy and also how to open a file but I don't know how to mix it all together. Please help.","AnswerCount":6,"Available Count":1,"Score":0.0333209931,"is_accepted":false,"ViewCount":3957,"Q_Id":18455589,"Users Score":1,"Answer":"It is possible to do the job with one pass and without loading the entire file into memory as well. Though the code itself is going to be much more complicated and mostly unneeded unless the file is HUGE. \nThe trick is the following:\nSuppose we only need one random line, then first save first line into a variable, then for ith line, replace the currently with probability 1\/i. Return the saved line when reaching end of file. \nFor 10 random lines, then have an list of 10 element and do the process 10 times for each line in the file.","Q_Score":4,"Tags":"python,numpy","A_Id":18456597,"CreationDate":"2013-08-27T01:35:00.000","Title":"Retrieve 10 random lines from a file","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am creating a GUI program using wxPython.  I am also using matplotlib to graph some data.  This data needs to be animated. To animate the data I am using the FuncAnimate function, which is part of the matplotlib package.\nWhen I first started to write my code I was using a PC, running windows 7.  I did my initial testing on this computer and everything was working fine.  However my program needs to be cross platform.  So I began to run some test using a Mac.  This is where I began to encounter an error.  As I explained before, in my code I have to animate some data.  I programmed it such that the user has the ability to play and pause the animation.  Now when the user pauses the animation I get the following error: AttributeError: 'FigureCanvasWxAgg' object has no attribute '_idletimer'.  Now I find this to be very strange because like I said I ran this same code on a PC and never got this error.  \nI was wondering if anyone could explain to me what is meant by this _idletimer error and what are possible causes for this.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":287,"Q_Id":18472394,"Users Score":1,"Answer":"_idletimer is likely to be a private, possibly implementation specific member of one of the classes - since you do not include the code or context I can not tell you which.\nIn general anything that starts with an _ is private and if it is not your own, and specific to the local class, should not be used by your code as it may change or even disappear when you rely on it.","Q_Score":1,"Tags":"python,macos,matplotlib,wxpython","A_Id":18474882,"CreationDate":"2013-08-27T17:55:00.000","Title":"AttributeError: 'FigureCanvasWxAgg' object has no attribute '_idletimer'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":1,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Im wanting to use imshow() to create an image of a 2D histogram. However on several of the examples ive seen the 'extent' is defined. What does 'extent' actually do and how do you choose what values are appropriate?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":128,"Q_Id":18511206,"Users Score":2,"Answer":"Extent defines the images max and min of the horizontal and vertical values. It takes four values like so: extent=[horizontal_min,horizontal_max,vertical_min,vertical_max].","Q_Score":1,"Tags":"python,matplotlib,histogram2d","A_Id":18511409,"CreationDate":"2013-08-29T12:36:00.000","Title":"What does extent do within imshow()?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been running sci-kit learn's DBSCAN implementation to cluster a set of geotagged photos by lat\/long. For the most part, it works pretty well, but I came across a few instances that were puzzling. For instance, there were two sets of photos for which the user-entered text field specified that the photo was taken at Central Park, but the lat\/longs for those photos were not clustered together. The photos themselves confirmed that they both sets of observations were from Central Park, but the lat\/longs were in fact further apart than epsilon.\nAfter a little investigation, I discovered that the reason for this was because the lat\/long geotags (which were generated from the phone's GPS) are pretty imprecise. When I looked at the location accuracy of each photo, I discovered that they ranged widely (I've seen a margin of error of up to 600 meters) and that when you take the location accuracy into account, these two sets of photos are within a nearby distance in terms of lat\/long.\nIs there any way to account for margin of error in lat\/long when you're doing DBSCAN? \n(Note: I'm not sure if this question is as articulate as it should be, so if there's anything I can do to make it more clear, please let me know.)","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":723,"Q_Id":18519356,"Users Score":1,"Answer":"Note that DBSCAN doesn't actually need the distances.\nLook up Generalized DBSCAN: all it really uses is a \"is a neighbor of\" relationship.\nIf you really need to incorporate uncertainty, look up the various DBSCAN variations and extensions that handle imprecise data explicitely. However, you may get pretty much the same results just by choosing a threshold for epsilon that is somewhat reasonable. There is room for choosing a larger epsilon that the one you deem adequate: if you want to use epsilon = 1km, and you assume your data is imprecise on the range of 100m, then use 1100m as epsilon instead.","Q_Score":0,"Tags":"python,algorithm,cluster-analysis,data-mining,dbscan","A_Id":18537377,"CreationDate":"2013-08-29T19:19:00.000","Title":"DBSCAN with potentially imprecise lat\/long coordinates","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"OpenCV2 for python have 2 function\n\n[Function 1]\n\nPython: cv2.ellipse(img, center, axes, angle, startAngle, endAngle, color[, thickness[, lineType[, shift]]]) \u2192 None\n\n[Function 2]\n\nPython: cv2.ellipse(img, box, color[, thickness[, lineType]]) \u2192 None\n\n\nI want to use [Function 1]\nBut when I use this Code\n\ncv2.ellipse(ResultImage, Circle, Size, Angle, 0, 360, Color, 2, cv2.CV_AA, 0)\n\nIt raise\n\nTypeError: ellipse() takes at most 5 arguments (10 given)\n\n\nCould you help me?","AnswerCount":5,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":13427,"Q_Id":18595099,"Users Score":0,"Answer":"these parameters should be integer, or it will raise TypeError","Q_Score":14,"Tags":"python,opencv","A_Id":20780282,"CreationDate":"2013-09-03T14:39:00.000","Title":"How I can use cv2.ellipse?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"OpenCV2 for python have 2 function\n\n[Function 1]\n\nPython: cv2.ellipse(img, center, axes, angle, startAngle, endAngle, color[, thickness[, lineType[, shift]]]) \u2192 None\n\n[Function 2]\n\nPython: cv2.ellipse(img, box, color[, thickness[, lineType]]) \u2192 None\n\n\nI want to use [Function 1]\nBut when I use this Code\n\ncv2.ellipse(ResultImage, Circle, Size, Angle, 0, 360, Color, 2, cv2.CV_AA, 0)\n\nIt raise\n\nTypeError: ellipse() takes at most 5 arguments (10 given)\n\n\nCould you help me?","AnswerCount":5,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":13427,"Q_Id":18595099,"Users Score":7,"Answer":"Make sure all the ellipse parameters are int otherwise it raises \"TypeError: ellipse() takes at most 5 arguments (10 given)\". Had the same problem and casting the parameters to int, fixed it.\nPlease note that in Python, you should round the number first and then use int(), since int function will cut the number:\nx = 2.7 , int(x)  will be 2 not 3","Q_Score":14,"Tags":"python,opencv","A_Id":28592694,"CreationDate":"2013-09-03T14:39:00.000","Title":"How I can use cv2.ellipse?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working a lot with pytables and HDF5 data and I have a question regarding the attributes of nodes (the attributes you access via pytables 'node._v_attrs' property).\nAssume that I set such an attribute of an hdf5 node. I do that over and over again, setting a particular attribute\n(1) always to the same value (so overall the value stored in the hdf5file does not change qualitatively)\n(2) always with a different value\nHow are these operations in terms of speed and memory? What I mean is the following, does setting the attribute really imply deletion of the attribute in the hdf5 file and adding a novel attribute with the same name as before? If so, does that mean every time I reset an existing attribute the size of the hdf5 file is slightly increased and keeps slowly growing until my hard disk is full?\nIf this is true, would it be more beneficial to check before I reset whether I have case (1) [and I should not store at all but compare data to the attribute written on disk] and only reassign if I face case (2) [i.e. the attribute value in the hdf5file is not the one I want to write to the hdf5 file].\nThanks a lot and best regards,\nRobert","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":986,"Q_Id":18638461,"Users Score":3,"Answer":"HDF5 attribute access is notoriously slow.  HDF5 is really built for and around the array data structure.  Things like groups and attributes are great helpers but they are not optimized. \nThat said while attribute reading is slow, attribute writing is even slower.  Therefore, it is always worth the extra effort to do what you suggest.  Check if the attribute exists and if it has the desired value before writing it.  This should give you a speed boost as compared to just writing it out every time.  \nLuckily, the effect on memory of attributes -- both on disk and in memory -- is minimal.  This is because ALL attributes on a node fit into 64 kb of special metadata space.  If you try to write more than 64 kb worth of attributes, HDF5 and PyTables will fail.\nI hope this helps.","Q_Score":0,"Tags":"python,hdf5,pytables","A_Id":18641432,"CreationDate":"2013-09-05T14:02:00.000","Title":"Pytables, HDF5 Attribute setting and deletion,","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using Enthought canopy for data analysis. I didn't find any option to create a .py file to write my code and save it for later use. I tried File> New >IPython Notebook, wrote my code and saved it. But the next time I opened it within Canopy editor, it wasn't editable. I need something like a Python shell where you just open a 'New Window', write all your code and press F5 to execute it. It could be saved for later use as well. Although pandas and numpy work in canopy editor, they are not recognized by Python shell (whenever I write import pandas as pd, it says no module named pandas). Please help.","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":1009,"Q_Id":18646039,"Users Score":1,"Answer":"Umair, ctrl + n or File > Python File will do what you want.\nBest, \nJonathan","Q_Score":0,"Tags":"python,enthought","A_Id":18647689,"CreationDate":"2013-09-05T21:12:00.000","Title":"How to create a .py file within canopy?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using Enthought canopy for data analysis. I didn't find any option to create a .py file to write my code and save it for later use. I tried File> New >IPython Notebook, wrote my code and saved it. But the next time I opened it within Canopy editor, it wasn't editable. I need something like a Python shell where you just open a 'New Window', write all your code and press F5 to execute it. It could be saved for later use as well. Although pandas and numpy work in canopy editor, they are not recognized by Python shell (whenever I write import pandas as pd, it says no module named pandas). Please help.","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":1009,"Q_Id":18646039,"Users Score":1,"Answer":"Let me add that if you need to open the file, even if it's a text file but you want to be able to run it as a Python file (or whatever language format) just look at the bottom of the Canopy window and select the language you want to use. In some cases it may default to just text. Click it and select the language you want. Once you've done that, you'll see that the run button will be active and the command appear in their respective color.","Q_Score":0,"Tags":"python,enthought","A_Id":19578868,"CreationDate":"2013-09-05T21:12:00.000","Title":"How to create a .py file within canopy?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have to generate a various points and its xyz coordinates and then calculate the distance.\n     Lets say I want create random point coordinates from point a away from 2.5 cm in all directions. so that i can calculate the mutual distance and angles form a to all generated point (red)\nI want to remove the redundant point and all those points which do not satisfy my criteria and also have same position.\n![enter image description here][1]\nfor example, I know the coordinates for the two points a (-10, 12, 2) and b (-9, 11, 5).      The distance between a and b is 5 cm.\nThe question is: How can I generate the red points' coordinates.     I knew how to calculate the distance and angle.    So far, I have tried the following calculation:\nI am not able to define the points randomly.\nI found a few solutions that don't work.\nAny help would be appreciated.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":5337,"Q_Id":18670974,"Users Score":1,"Answer":"Generically you can  populate your points in two ways:\n1) use random to create the coordinates for your points within the outer bounds of the solution. If a given random point falls outside the max or inside the inner limit.\n2) You can do it using polar coordinates: generate a random distance between the inner and outer bound and a yaw rotation. In 3d, you'd have to use two rotations,  one for yaw and another for pitch. This avoids the need for rejecting points.\nYou can simplify the code for both by generating all the points in a circle (or sphere) around origin (0,0,0) instead of in place.  Them move the whole set of points to the correct blue circle location by adding it's position to the position of each point.","Q_Score":3,"Tags":"python","A_Id":18687144,"CreationDate":"2013-09-07T07:35:00.000","Title":"How to get a series of random points in a specific range of distances from a reference point and generate xyz coordinates","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to perform clustering in Python using Random Forests. In the R implementation of Random Forests, there is a flag you can set to get the proximity matrix. I can't seem to find anything similar in the python scikit version of Random Forest. Does anyone know if there is an equivalent calculation for the python version?","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":6625,"Q_Id":18703136,"Users Score":19,"Answer":"We don't implement proximity matrix in Scikit-Learn (yet). \nHowever, this could be done by relying on the apply function provided in our implementation of decision trees. That is, for all pairs of samples in your dataset, iterate over the decision trees in the forest (through forest.estimators_) and count the number of times they fall in the same leaf, i.e., the number of times apply give the same node id for both samples in the pair. \nHope this helps.","Q_Score":14,"Tags":"python,scikit-learn,random-forest","A_Id":18719287,"CreationDate":"2013-09-09T16:49:00.000","Title":"Proximity Matrix in sklearn.ensemble.RandomForestClassifier","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm testing some things in image retrival and i was thinking about how to sort out bad pictures of a dataset. For e.g there are only pictures  of houses and in between there is a  picture of people and some of cars. So at the end i want to get only the houses.\nAt the Moment my approach looks like:\n\ncomputing descriptors (Sift) of all pictures\nclustering all descriptors with k-means\ncreating histograms of the pictures by computing the euclidean distance between the cluster centers and the descriptors of a picture\nclustering the histograms again.\n\nat this moment i have got a first sort (which isn't really good). Now my Idea is to take all pictures which are clustered to a center with len(center) > 1 and cluster them again and again. So the Result is that the pictures which are particular in a center will be sorted out. Maybe its enough to fit the result again to the same k-means without clustering again?!\nthe result isn't satisfying so maybe someone has got a good idea.\nFor Clustering etc. I'm using k-means of scikit learn.","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":550,"Q_Id":18721204,"Users Score":1,"Answer":"K-means is not very robust to noise; and your \"bad pictures\" probably can be considered as such. Furthermore, k-means doesn't work too well for sparse data; as the means will not be sparse.\nYou may want to try other, more modern, clustering algorithms that can handle this situation much better.","Q_Score":0,"Tags":"python,computer-vision,cluster-analysis,scikit-learn,k-means","A_Id":18735714,"CreationDate":"2013-09-10T14:09:00.000","Title":"Sort out bad pictures of a dataset (k-means, clustering, sklearn)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm testing some things in image retrival and i was thinking about how to sort out bad pictures of a dataset. For e.g there are only pictures  of houses and in between there is a  picture of people and some of cars. So at the end i want to get only the houses.\nAt the Moment my approach looks like:\n\ncomputing descriptors (Sift) of all pictures\nclustering all descriptors with k-means\ncreating histograms of the pictures by computing the euclidean distance between the cluster centers and the descriptors of a picture\nclustering the histograms again.\n\nat this moment i have got a first sort (which isn't really good). Now my Idea is to take all pictures which are clustered to a center with len(center) > 1 and cluster them again and again. So the Result is that the pictures which are particular in a center will be sorted out. Maybe its enough to fit the result again to the same k-means without clustering again?!\nthe result isn't satisfying so maybe someone has got a good idea.\nFor Clustering etc. I'm using k-means of scikit learn.","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":550,"Q_Id":18721204,"Users Score":1,"Answer":"I don't have the solution to your problem but here is a sanity check to perform prior to the final clustering, to check that the kind of features you extracted is suitable for your problem:\n\nextract the histogram features for all the pictures in your dataset\ncompute the pairwise distances of all the pictures in your dataset using the histogram features (you can use sklearn.metrics.pairwise_distance)\n\nnp.argsort the raveled distances matrix to find the indices of the 20 top closest pairs of distinct pictures according to your features (you have to filter out the zero-valued diagonal elements of the distance matrix) and do the same to extract the top 20 most farest pairs of pictures based on your histogram features.\nVisualize (for instance with plt.imshow) the pictures of top closest pairs and check that they are all pairs that you would expect to be very similar.\nVisualize the pictures of the top farest pairs and check that they are all very dissimilar.\nIf one of those 2 checks fails, then it means that histogram of bag of SIFT words is not suitable to your task. Maybe you need to extract other kinds of features (e.g. HoG features) or reorganized the way your extract the cluster of SIFT descriptors, maybe using a pyramidal pooling structure to extract info on the global layout of the pictures at various scales.","Q_Score":0,"Tags":"python,computer-vision,cluster-analysis,scikit-learn,k-means","A_Id":18735840,"CreationDate":"2013-09-10T14:09:00.000","Title":"Sort out bad pictures of a dataset (k-means, clustering, sklearn)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am doing some animating plots with ion()function. I want to draw and delete some lines. I found out axvspan() function, I can plot the lines and shapes with it as I want. But as long as I am doing an animation I also want to delete that lines and shapes. I couldn't find a way to delete them.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":159,"Q_Id":18743895,"Users Score":0,"Answer":"Ok I have found the necessary functions. I used dir() function to find methods. axvspan() returns a matplotlib.patches.Polygon result. This type of data has set_visible method, using it as x.set_visible(0) I removed the lines and shapes.","Q_Score":0,"Tags":"python,matplotlib,interactive-mode","A_Id":18815103,"CreationDate":"2013-09-11T14:26:00.000","Title":"Deleting Lines of a Plot Which has Plotted with axvspan()","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Recently, I have been studying OpenCV to detect and recognize faces using C++. In order to execute source code demonstration from the OpenCV website I need to run Python to crop image first. Unfortunately, the message error is 'ImportError: No module named Image' when I run the Python script (this script is provided by OpenCV website). I installed  \"python-2.7.amd64\" and downloaded \"PIL-1.1.7.win32-py2.7\" to install Image library.  However, the message error is 'Python version 2.7 required, which was not found in the registry'. And then, I downloaded the script written by Joakim L\u00f6w for Secret Labs AB \/ PythonWare to register registry in my computer. But the message error is \"Unable to register. You probably have the another Python installation\".\nI spent one month to search this issue on the internet but I cannot find the answer.  Please support me to resolve my issue.   \nThanks,\nTran Dang Bao","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":22097,"Q_Id":18776988,"Users Score":1,"Answer":"Try to put the python(2.7) at your Windows path.\nDo the following steps:\n\nOpen System Properties (Win+Pause) or My Computer and right-click then Properties\nSwitch to the Advanced tab\nClick Environment Variables\nSelect PATH in the System variables section\nClick Edit\nAdd python's path to the end of the list (the paths are separated by semicolons).\nexample C:\\Windows;C:\\Windows\\System32;C:\\Python27","Q_Score":3,"Tags":"python,windows,opencv,installation","A_Id":18777073,"CreationDate":"2013-09-13T01:48:00.000","Title":"Python 2.7 - ImportError: No module named Image","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to make a very simple image that will illustrate a cash flow diagram based on user input. Basically, I just need to make an axis and some arrows facing up and down and proportional to the value of the cash flow. I would like to know how to do this with matplot.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":875,"Q_Id":18778266,"Users Score":0,"Answer":"If you simply need arrows pointing up and down, use Unicode arrows like \"\u2191\" and \"\u2193\". This would be really simple if rendering in a browser.","Q_Score":0,"Tags":"python,image,matplotlib","A_Id":18778542,"CreationDate":"2013-09-13T04:21:00.000","Title":"Cash flow diagram in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to plot a heatmap of a big microarray dataset (45K rows per 446 columns).\nUsing pcolor from matplotlib I am unable to do it because my pc goes easily out of memory (more than 8G)..\nI'd prefer to use python\/matplotlib instead of R for personal opinion..\nAny way to plot heatmaps in an efficient way?\nThanks","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1446,"Q_Id":18791469,"Users Score":0,"Answer":"I solved by downsampling the matrix to a smaller matrix.\nI decided to try two methodologies:\n\nsupposing I want to down-sample a matrix of 45k rows to a matrix of 1k rows, I took a row value every 45 rows\nanother methodology is, to down-sample 45k rows to 1k rows, to group the 45k rows into 1k groups (composed by 45 adjacent rows) and to take the average for each group as representative row\n\nHope it helps.","Q_Score":1,"Tags":"python,matplotlib,bigdata,heatmap","A_Id":36248111,"CreationDate":"2013-09-13T16:55:00.000","Title":"How to plot a heatmap of a big matrix with matplotlib (45K * 446)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have some very large IPython (1.0) notebooks, which I find very unhandy to work with. I want to split the large notebook into several smaller ones, each covering a specific part of my analysis. However, the notebooks need to share data and (unpickleable) objects.\nNow, I want these notebooks to connect to the same kernel. How do I do this? How can I change the kernel to which a notebook is connected? (And any ideas how to automate this step?)\nI don't want to use the parallel computing mechanism (which would be a trivial solution), because it would add much code overhead in my case.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1689,"Q_Id":18882510,"Users Score":1,"Answer":"When I have a long noetbook, I create functions from my code, and hide it into python modules, which I then import in the notebook.\nSo that I can have huge chunk of code hidden on the background, and my notebook smaller for handier manipulation.","Q_Score":6,"Tags":"ipython,ipython-notebook","A_Id":35315482,"CreationDate":"2013-09-18T21:17:00.000","Title":"How to share Ipython notebook kernels?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"In wakari, how do I download a CSV file and create a new CSV file with each of the rows in the original file repeated N number of times in the new CSV file.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":61,"Q_Id":18886383,"Users Score":0,"Answer":"cat dataset.csv dataset.csv dataset.csv dataset.csv > bigdata.csv","Q_Score":0,"Tags":"python","A_Id":18897337,"CreationDate":"2013-09-19T04:49:00.000","Title":"Repeat rows in files in wakari","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"After only briefly looking at numpy arrays, I don't understand how they are different than normal Python lists. Can someone explain the difference, and why I would use a numpy array as opposed to a list?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2487,"Q_Id":18907998,"Users Score":0,"Answer":"Numpy is an extension, and demands that all the objects on it are of the same type , defined on creation. It also provides a set of linear algebra operations. Its more like a mathematical framework for python to deal with Numeric Calculations (matrix, n stuffs).","Q_Score":1,"Tags":"python,arrays,list,numpy,multidimensional-array","A_Id":18908045,"CreationDate":"2013-09-20T02:37:00.000","Title":"Difference between a numpy array and a multidimensional list in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"to provide some context: Issues in an application are logged in an excel sheet and one of the columns in that sheet contains the email communication between the user (who had raised the issue) and the resolve team member. There are bunch of other columns containing other useful information. My job is to find useful insights from this data for Business.\n\nFind out what type of issue was that? e.g. was that a training issue for the user or access issue etc. This would mean that I analyze the mail text and figure out by some means the type of issue.\nHow many email conversations have happened for one issue?\nIs it a repeat issue?\nThere are other simple statistical problems e.g. How many issues per week etc...\n\nI read that NLP with Python can be solution to my problems. I also looked at Rapidminer for the same.\nNow my Question is \na. \"Am I on the right track?, Is NLP(Natural Language Processing) the solution to these problems?\"\nb. If yes, then how to start.. I have started reading book on NLP with Python, but that is huge, any specific areas that I should concentrate on and can start my analysis?\nc. How is Rapidminer tool? Can it answer all of these questions? The data volume is not too huge (may be 100000 rows)... looks like it is quite easy to build a process in rapidminer, hence started on it...\nAppreciate any suggestions!!!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":637,"Q_Id":18910200,"Users Score":0,"Answer":"Try xlrd Python Module to read and process excel sheets.\nI think an appropriate implementation using this module is an easy way to solve your problem.","Q_Score":0,"Tags":"python,nlp","A_Id":18910584,"CreationDate":"2013-09-20T06:26:00.000","Title":"Analyze Text to find patterns and useful information","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"While I am trying to install scikit-learn for my portable python, its saying \" Python 2.7 is not found in the registry\". In the next window, it does ask for an installation path but neither am I able to copy-paste the path nor write it manually. Otherwise please suggest some other alternative for portable python which has numpy, scipy and scikit-learn by default. Please note that I don't have administrative rights of the system so a portable version is preferred.","AnswerCount":4,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":1485,"Q_Id":18973863,"Users Score":2,"Answer":"you can easily download SciKit executable, extract it with python, copy SciKit folder and content to c:\\Portable Python 2.7.5.1\\App\\Lib\\site-packages\\ and you'll have SciKit in your portable python.\nI just had this problem and solved this way.","Q_Score":2,"Tags":"python-2.7,scikit-learn,portable-python","A_Id":20853862,"CreationDate":"2013-09-24T05:49:00.000","Title":"How to install scikit-learn for Portable Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm running python 2.7.5 with scikit_learn-0.14 on my Mac OSX Mountain Lion.\nEverything I run a svmlight command however, I get the following warning:\n\nDeprecationWarning: using a non-integer number instead of an integer will result in an error >in the future","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":34473,"Q_Id":18994787,"Users Score":0,"Answer":"I also met this problem when I assigned numbers to a matrix.\nlike this:\nQmatrix[list2[0], list2[j]] = 1\nthe component may be a non-integer number, so I changed to this:\nQmatrix[int(list2[0]), int(list2[j])] = 1\nand the warning removed","Q_Score":12,"Tags":"python-2.7,scipy,svmlight","A_Id":44480375,"CreationDate":"2013-09-25T01:33:00.000","Title":"Python Svmlight Error: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm completely new to Python and want to use it for data analysis. I just installed Python 2.7 on my mac running OSX 10.8. I need the NumPy, SciPy, matplotlib and csv packages. I read that I could simply install the Anaconda package and get all in one. So I went ahead and downloaded\/installed Anaconda 1.7.\nHowever, when I type in:\n    import numpy as np\nI get an error telling me that there is no such module. I assume this has to do with the location of the installation, but I can't figure out how to:\nA. Check that everything is actually installed properly\nB. Check the location of the installation.\nAny pointers would be greatly appreciated!\nThanks","AnswerCount":4,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":69121,"Q_Id":19029333,"Users Score":0,"Answer":"I don't think the existing answer answers your specific question (about installing packages within Anaconda). When I install a new package via conda install <PACKAGE>, I then run conda list to ensure the package is now within my list of Anaconda packages.","Q_Score":21,"Tags":"python,macos,numpy,installation,anaconda","A_Id":38722056,"CreationDate":"2013-09-26T13:14:00.000","Title":"How to check that the anaconda package was properly installed","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm completely new to Python and want to use it for data analysis. I just installed Python 2.7 on my mac running OSX 10.8. I need the NumPy, SciPy, matplotlib and csv packages. I read that I could simply install the Anaconda package and get all in one. So I went ahead and downloaded\/installed Anaconda 1.7.\nHowever, when I type in:\n    import numpy as np\nI get an error telling me that there is no such module. I assume this has to do with the location of the installation, but I can't figure out how to:\nA. Check that everything is actually installed properly\nB. Check the location of the installation.\nAny pointers would be greatly appreciated!\nThanks","AnswerCount":4,"Available Count":2,"Score":0.049958375,"is_accepted":false,"ViewCount":69121,"Q_Id":19029333,"Users Score":1,"Answer":"Though the question is not relevant to Windows environment, FYI for windows. In order to use anaconda modules outside spyder or in cmd prompt, try to update the PYTHONPATH & PATH with C:\\Users\\username\\Anaconda3\\lib\\site-packages.\nFinally, restart the command prompt.\nAdditionally, sublime has a plugin 'anaconda' which can be used for sublime to work with anaconda modules.","Q_Score":21,"Tags":"python,macos,numpy,installation,anaconda","A_Id":41600022,"CreationDate":"2013-09-26T13:14:00.000","Title":"How to check that the anaconda package was properly installed","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to optimize functions with GPU calculation in Python, so I prefer to store all my data as ndarrays with dtype=float32. \nWhen I am using scipy.optimize.fmin_l_bfgs_b, I notice that the optimizer always passes a float64 (on my 64bit machine) parameter to my objective and gradient functions, even when I pass a float32 ndarray as the initial search point x0. This is different when I use the cg optimizer scipy.optimize.fmin_cg, where when I pass in a float32 array as x0, the optimizer will use float32 in all consequent objective\/gradient function invocations. \nSo my question is: can I enforce scipy.optimize.fmin_l_bfgs_b to optimize on float32 parameters like in scipy.optimize.fmin_cg?\nThanks!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1678,"Q_Id":19041486,"Users Score":1,"Answer":"I am not sure you can ever do it. fmin_l_bfgd_b is provided not by pure python code, but by a extension (a wrap of FORTRAN code). In Win32\/64 platform it can be found at \\scipy\\optimize\\_lbfgsb.pyd. What you want may only be possible if you can compile the extension differently or modify the FORTRAN code. If you check that FORTRAN code, it has double precision all over the place, which is basically float64. I am not sure just changing them all to single precision will do the job.\nAmong the other optimization methods, cobyla is also provided by FORTRAN. Powell's methods too.","Q_Score":1,"Tags":"python,optimization,scipy,gpu,multidimensional-array","A_Id":19042578,"CreationDate":"2013-09-27T01:53:00.000","Title":"How to enforce scipy.optimize.fmin_l_bfgs_b to use 'dtype=float32'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In matplotlib, how do I specify the line width and color of a legend frame?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":13797,"Q_Id":19058485,"Users Score":20,"Answer":"For the width: legend.get_frame().set_linewidth(w)\nFor the color: legend.get_frame().set_edgecolor(\"red\")","Q_Score":19,"Tags":"python,matplotlib","A_Id":28295797,"CreationDate":"2013-09-27T19:18:00.000","Title":"Specifying the line width of the legend frame, in matplotlib","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a pandas dataframe with the following column names:\nResult1, Test1, Result2, Test2, Result3, Test3, etc...\nI want to drop all the columns whose name contains the word \"Test\". The numbers of such columns is not static but depends on a previous function.\nHow can I do that?","AnswerCount":11,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":184940,"Q_Id":19071199,"Users Score":6,"Answer":"This method does everything in place. Many of the other answers create copies and are not as efficient:\ndf.drop(df.columns[df.columns.str.contains('Test')], axis=1, inplace=True)","Q_Score":184,"Tags":"python,pandas,dataframe","A_Id":61194900,"CreationDate":"2013-09-28T20:10:00.000","Title":"Drop columns whose name contains a specific string from pandas DataFrame","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using  scipy.cluster.hierarchy as sch to draw a dendogram after makeing an hierarchical clustering. The problem is that the clustering happens on the top of the dendogram in between 0.8 and 1.0 which is the similarity degree in the y axis. How can i \"cut\" all the graph from 0 to 0.6 where nothing \"interesting\" graphically is happening?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":362,"Q_Id":19088527,"Users Score":0,"Answer":"If your're really only interested in distance proportions between the fusions, you could \n\nadapt your input linkage (cut off an offset in the third column of the linkage matrix). This will screw the absolute cophenetic distances of course. \ndo some normalization of your input data, before clustering it\n\nOr you\n\nmanipulate the dendrogram axes \/ adapt limits (I didn't try that)","Q_Score":2,"Tags":"python,scipy,hierarchical-clustering,dendrogram","A_Id":21080034,"CreationDate":"2013-09-30T07:19:00.000","Title":"How to resize y axis of a dendogram","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to figure out the fastest method to find the determinant of sparse symmetric and real matrices in python. using scipy sparse module but really surprised that there is no determinant function. I am aware I could use LU factorization to compute determinant but don't see a easy way to do it because the return of scipy.sparse.linalg.splu is an object and instantiating a dense L and U matrix is not worth it - I may as well do sp.linalg.det(A.todense()) where A is my scipy sparse matrix. \nI am also a bit surprised why others have not faced the problem of efficient determinant computation within scipy. How would one use splu to compute determinant? \nI looked into pySparse and scikits.sparse.chlmod. The latter is not practical right now for me - needs package installations and also not sure sure how fast the code is before I go into all the trouble. \nAny solutions? Thanks in advance.","AnswerCount":4,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":6098,"Q_Id":19107617,"Users Score":6,"Answer":"The \"standard\" way to solve this problem is with a cholesky decomposition, but if you're not up to using any new compiled code, then you're out of luck. The best sparse cholesky implementation is Tim Davis's CHOLMOD, which is licensed under the LGPL and thus not available in scipy proper (scipy is BSD).","Q_Score":26,"Tags":"python,numpy,scipy,linear-algebra,sparse-matrix","A_Id":19616987,"CreationDate":"2013-10-01T03:48:00.000","Title":"How to compute scipy sparse matrix determinant without turning it to dense?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have raw-rgb video coming from PAL 50i camera. How can I detect the start of frame, just like I would detect the keyframe of h264 video, in gstreamer? I would like to do that for indexing\/cutting purposes.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":234,"Q_Id":19130365,"Users Score":1,"Answer":"If this really is raw rgb video, there is no (realistic) way to detect the start of the frame. I would assume your video would come as whole frames, so one buffer == one frame, and hence no need for such detection.","Q_Score":0,"Tags":"python,video,rgb,gstreamer","A_Id":19220952,"CreationDate":"2013-10-02T05:12:00.000","Title":"How to detect start of raw-rgb video frame?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have to compute massive similarity computations between vectors in a sparse matrix. What is currently the best tool, scipy-sparse or pandas, for this task?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":839,"Q_Id":19171822,"Users Score":1,"Answer":"After some research I found that both pandas and Scipy have structures to represent sparse matrix efficiently in memory. But none of them have out of box support for compute similarity between vectors like cosine, adjusted cosine, euclidean etc. Scipy support this on dense matrix only. For sparse, Scipy support dot products and others linear algebra basic operations.","Q_Score":2,"Tags":"python,numpy,matrix,pandas","A_Id":19389797,"CreationDate":"2013-10-04T01:40:00.000","Title":"Scipy or pandas for sparse matrix computations?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a continuous random variable given by its density distribution function or by cumulative probability distribution function.\nThe distribution functions are not analytical. They are given numerically (for example as a list of (x,y) values).\nOne of the things that I would like to do with these distributions is to find a convolution of two of them (to have a distribution of a sum of two random properties).\nI do not want to write my own function for that if there is already something standard and tested. Does anybody know if it is the case?","AnswerCount":1,"Available Count":1,"Score":0.6640367703,"is_accepted":false,"ViewCount":287,"Q_Id":19184975,"Users Score":4,"Answer":"How about numpy.convolve?  It takes two arrays, rather than two functions, which seems ideal for your use.  I'll also mention the ECDF function in the statsmodels package in case you really want to turn your observations into (step) functions.","Q_Score":3,"Tags":"python,random,numpy,scipy,distribution","A_Id":19185124,"CreationDate":"2013-10-04T15:22:00.000","Title":"Is there a standard way to work with numerical probability density functions in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a JPG image, and I would like to find a way to:\n\nDecompose the image into red, green and blue intensity layers (8 bit per channel).\nColorise each of these now 'grayscale' images with its appropriate color\nProduce 3 output images in appropriate color, of each channel.\n\nFor example if I have an image:\ndog.jpg\nI want to produce:\ndog_blue.jpg dog_red.jpg and dog_green.jpg\nI do not want grayscale images for each channel. I want each image to be represented by its correct color.\nI have managed to use the decompose function in gimp to get the layers, but each one is grayscale and I can't seem to add color to it.\nI am currently using OpenCV and Python bindings for other projects so any suitable code that side may be useful if it is not easy to do with gimp","AnswerCount":4,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":2596,"Q_Id":19213407,"Users Score":0,"Answer":"As the blue,green,red images each has 1 channel only.So, this is basically a gray-scale image.\nIf you want to add colors in the dog_blue.jpg for example then you create a 3-channel image and copy the contents in all the channels or do cvCvtColor(src,dst,CV_GRAY2BGR). Now you will be able to add colors to it as it has become 3-channel image.","Q_Score":1,"Tags":"python,opencv,rgb,gimp","A_Id":19217476,"CreationDate":"2013-10-06T20:09:00.000","Title":"How do I use Gimp \/ OpenCV Color to separate images into coloured RGB layers?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a JPG image, and I would like to find a way to:\n\nDecompose the image into red, green and blue intensity layers (8 bit per channel).\nColorise each of these now 'grayscale' images with its appropriate color\nProduce 3 output images in appropriate color, of each channel.\n\nFor example if I have an image:\ndog.jpg\nI want to produce:\ndog_blue.jpg dog_red.jpg and dog_green.jpg\nI do not want grayscale images for each channel. I want each image to be represented by its correct color.\nI have managed to use the decompose function in gimp to get the layers, but each one is grayscale and I can't seem to add color to it.\nI am currently using OpenCV and Python bindings for other projects so any suitable code that side may be useful if it is not easy to do with gimp","AnswerCount":4,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":2596,"Q_Id":19213407,"Users Score":0,"Answer":"In the BGR image, you have three channel. When you split the channel using the split() function, like B,G,R=cv2.split(img), then B,G,R becomes a single or monochannel image. So you need to add two extra channel with zeros to make it 3 channel image but activated for a specific color channel.","Q_Score":1,"Tags":"python,opencv,rgb,gimp","A_Id":55448010,"CreationDate":"2013-10-06T20:09:00.000","Title":"How do I use Gimp \/ OpenCV Color to separate images into coloured RGB layers?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Have anyone known the total columns in pandas, python? \nI have just created a dataframe for pandas included more than 20,000 columns but I got memory error.\nThanks a lot","AnswerCount":2,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":2587,"Q_Id":19221694,"Users Score":6,"Answer":"You get an out of memory error because you run out of memory, not because there is a limit on the number of columns.","Q_Score":1,"Tags":"python,pandas","A_Id":19221774,"CreationDate":"2013-10-07T09:52:00.000","Title":"How many columns in pandas, python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a mac server and I have both FINK and macport installation of python\/numpy\/scipy\nI was wondering if having both will affect the other? In terms of memory leaks\/unusual results? \nIn case you are wondering why both ? Well I like FINK but macports allows me to have python2.4 which FINK does not provide (yes I needed an old version for a piece of code I have)\nI wonder this since I tried to use homebrew once and it complained about the machine having port and FINK (I did not realize that port provided python2.4 so was looking at homebrew but when I realized port did give 2.4 I abandoned it)","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":421,"Q_Id":19228380,"Users Score":0,"Answer":"In terms of how your Python interpreter works, no: there is no negative effect on having Fink Python as well as MacPorts Python installed on the same machine, just as there is no effect from having multiple installations of Python by anything.","Q_Score":0,"Tags":"python,macos,port,fink","A_Id":19228974,"CreationDate":"2013-10-07T15:10:00.000","Title":"macport and FINK","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I know in a linked list there are a head node and a tail node. Well, for my data structures assignment, we are suppose to create a linked matrix with references to a north, south, east, and west node. I am at a loss of how to implement this. A persistent problem that bothers me is the head node and tail node. The user inputs the number of rows and the number of columns. Should I have multiple head nodes then at the beginning of each row and multiple tail nodes at the end of each row? If so, should I store the multiple head\/tail nodes in a list? \nThank you.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1333,"Q_Id":19234950,"Users Score":0,"Answer":"There's more than one way to interpret this, but one option is:\nHave a single \"head\" node at the top-left corner and a \"tail\" node at the bottom-right. There will then be row-head, row-tail, column-head, and column-tail nodes, but these are all accessible from the overall head and tail, so you don't need to keep track of them, and they're already part of the linked matrix, so they don't need to be part of a separate linked list.\n(Of course a function that builds up an RxC matrix of zeroes will probably have local variables representing the current row's head\/tail, but that's not a problem.)","Q_Score":0,"Tags":"python,list,matrix,linked-list","A_Id":19235077,"CreationDate":"2013-10-07T21:17:00.000","Title":"Linked Matrix Implementation in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I know in a linked list there are a head node and a tail node. Well, for my data structures assignment, we are suppose to create a linked matrix with references to a north, south, east, and west node. I am at a loss of how to implement this. A persistent problem that bothers me is the head node and tail node. The user inputs the number of rows and the number of columns. Should I have multiple head nodes then at the beginning of each row and multiple tail nodes at the end of each row? If so, should I store the multiple head\/tail nodes in a list? \nThank you.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1333,"Q_Id":19234950,"Users Score":0,"Answer":"It really depends on what options you want\/need to efficiently support.\nFor instance, a singly linked list with only a head pointer can be a stack (insert and remove at the head).  If you add a tail pointer you can insert at either end, but only remove at the head (stack or queue).  A doubly linked list can support insertion or deletion at either end (deque).  If you try to implement an operation that your data structure is not designed for you incur an O(N) penalty.\nSo I would start with a single pointer to the (0,0) element and then start working on the operations your instructor asks for.  You may find you need additional pointers, you may not.  My guess would be that you will be fine with a single head pointer.","Q_Score":0,"Tags":"python,list,matrix,linked-list","A_Id":19237061,"CreationDate":"2013-10-07T21:17:00.000","Title":"Linked Matrix Implementation in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataset of time-series examples.  I want to calculate the similarity between various time-series examples, however I do not want to take into account differences due to scaling (i.e. I want to look at similarities in the shape of the time-series, not their absolute value).  So, to this end, I need a way of normalizing the data.  That is, making all of the time-series examples fall between a certain region e.g [0,100].  Can anyone tell me how this can be done in python","AnswerCount":5,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":20885,"Q_Id":19256930,"Users Score":11,"Answer":"The solutions given are good for a series that aren\u2019t incremental nor decremental(stationary). In financial time series( or any other series with a a bias) the formula given is not right. It should, first be detrended or perform a scaling based in the latest 100-200 samples.\nAnd if the time series doesn't come from a normal distribution ( as is the case in finance) there is advisable to apply a non linear function ( a standard CDF funtion for example) to compress the outliers.\nAronson and Masters book (Statistically sound Machine Learning for algorithmic trading) uses the following formula ( on 200 day chunks ):  \nV = 100 * N ( 0.5( X -F50)\/(F75-F25)) -50   \nWhere:\n  X : data point\nF50 : mean of the latest 200 points\nF75 : percentile 75\nF25 : Percentile 25\n  N : normal CDF","Q_Score":9,"Tags":"python,time-series","A_Id":43874003,"CreationDate":"2013-10-08T19:46:00.000","Title":"Python - how to normalize time-series data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataset of time-series examples.  I want to calculate the similarity between various time-series examples, however I do not want to take into account differences due to scaling (i.e. I want to look at similarities in the shape of the time-series, not their absolute value).  So, to this end, I need a way of normalizing the data.  That is, making all of the time-series examples fall between a certain region e.g [0,100].  Can anyone tell me how this can be done in python","AnswerCount":5,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":20885,"Q_Id":19256930,"Users Score":0,"Answer":"I'm not going to give the Python code, but the definition of normalizing, is that for every value (datapoint) you calculate \"(value-mean)\/stdev\". Your values will not fall between 0 and 1 (or 0 and 100) but I don't think that's what you want. You want to compare the variation. Which is what you are left with if you do this.","Q_Score":9,"Tags":"python,time-series","A_Id":21486466,"CreationDate":"2013-10-08T19:46:00.000","Title":"Python - how to normalize time-series data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I usually have to rerun (most parts of) a notebook when reopen it, in order to get access to previously defined variables and go on working. \nHowever, sometimes I'd like to skip some of the cells, which have no influence to subsequent cells (e.g., they might comprise a branch of analysis that is finished) and could take very long time to run. These cells can be scattered throughout the notebook, so that something like \"Run All Below\" won't help much.\nIs there a way to achieve this?\nIdeally, those cells could be tagged with some special flags, so that they could be \"Run\" manually, but would be skipped when \"Run All\".\nEDIT\n%%cache (ipycache extension) as suggested by @Jakob solves the problem to some extent.\nActually, I don't even need to load any variables (which can be large but unnecessary for following cells) when re-run, only the stored output matters as analyzing results.\nAs a work-around, put %%cache folder\/unique_identifier to the beginning of the cell. The code will be executed only once and no variables will be loaded when re-run unless you delete the unique_identifier file.\nUnfortunately, all the output results are lost when re-run with %%cache...\nEDIT II (Oct 14, 2013)\nThe master version of ipython+ipycache now pickles (and re-displays) the codecell output as well.\nFor rich display outputs including Latex, HTML(pandas DataFrame output), remember to use IPython's display() method, e.g., display(Latex(r'$\\alpha_1$'))","AnswerCount":7,"Available Count":1,"Score":0.057080742,"is_accepted":false,"ViewCount":40298,"Q_Id":19309287,"Users Score":2,"Answer":"The simplest way to skip python code in jupyter notebook cell from running, I temporarily convert those cells to markdown.","Q_Score":61,"Tags":"python,ipython,ipython-notebook,ipython-magic","A_Id":64969412,"CreationDate":"2013-10-11T02:25:00.000","Title":"How to (intermittently) skip certain cells when running IPython notebook?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to visualize \"Words\/grams\" used in columns of TfidfVectorizer outut in python-scikit library . Is there a way ?\nI tried to to convert csr to array , but cannot see header composed of grams.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":31,"Q_Id":19316788,"Users Score":1,"Answer":"Use get_feature_names method as specified in comments by larsmans","Q_Score":1,"Tags":"python,scikit-learn,tf-idf","A_Id":19317456,"CreationDate":"2013-10-11T11:15:00.000","Title":"Is there a way to see column 'grams' of the TfidfVectoririzer output?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a 2 column array, 1st column weights and the 2 nd column values which I am plotting using python. I would like to draw 20 samples from this weighted array, proportionate to their weights. Is there a python\/numpy command which does that?","AnswerCount":2,"Available Count":1,"Score":-0.0996679946,"is_accepted":false,"ViewCount":1324,"Q_Id":19352225,"Users Score":-1,"Answer":"You need to refine your problem statement better. For example, if your array has only  1 row, what do you expect. If your array has 20,000 rows what do you expect? ...","Q_Score":2,"Tags":"python,numpy,histogram,sample","A_Id":19352360,"CreationDate":"2013-10-14T01:23:00.000","Title":"Sample from weighted histogram","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm looking for a data structure that preserves the order of its elements (which may change over the life of the data structure, as the client may move elements around).\nIt should allow fast search, insertion before\/after a given element, removal of a given element, lookup of the first and last elements, and bidirectional iteration starting at a given element.\nWhat would be a good implementation? \nHere's my first attempt:\nA class deriving from both collections.abc.Iterable and collections.abc.MutableSet that contains a linked list and a dictionary. The dictionary's keys are elements, values are nodes in the linked list. The dictionary would handle search for a node given an element. Once an element is found, the linked list would handle insertion before\/after, deletion, and iteration. The dictionary would be updated by adding or deleting the relevant key\/value pair. Clearly, with this approach the elements must be hashable and unique (or else, we'll need another layer of indirection where each element is represented by an auto-assigned numeric identifier, and only those identifiers are stored as keys).\nIt seems to me that this would be strictly better in asymptotic complexity than either list or collections.deque, but I may be wrong. [EDIT: Wrong, as pointed out by @roliu. Unlike list or deque, I would not be able to find an element by its numeric index in O(1). As of now, it is O(N) but I am sure there's some way to make it O(log N) if necessary.]","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":708,"Q_Id":19355986,"Users Score":1,"Answer":"Using doubly-linked lists in Python is a bit uncommon.  However, your own proposed solution of a doubly-linked list and a dictionary has the correct complexity: all the operations you ask for are O(1).\nI don't think there is in the standard library a more direct implementation.  Trees might be nice theoretically, but also come with drawbacks, like O(log n) or (precisely) their general absence from the standard library.","Q_Score":1,"Tags":"python,data-structures,python-3.x,deque","A_Id":19356586,"CreationDate":"2013-10-14T08:15:00.000","Title":"How can I implement a data structure that preserves order and has fast insertion\/removal?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Most languages have a NaN constant you can use to assign a variable the value NaN.  Can python do this without using numpy?","AnswerCount":6,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":180714,"Q_Id":19374254,"Users Score":7,"Answer":"You can do float('nan') to get NaN.","Q_Score":116,"Tags":"python,constants,nan","A_Id":19374300,"CreationDate":"2013-10-15T06:02:00.000","Title":"Assigning a variable NaN in python without numpy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am looking to efficiently merge two (fairly arbitrary) data structures: one representing a set of defaults values and one representing overrides. Example data below. (Naively iterating over the structures works, but is very slow.) Thoughts on the best approach for handling this case?\n\n\n_DEFAULT   = { 'A': 1122, 'B': 1133, 'C': [ 9988, {            'E': [ { 'F': 6666,            },       ], }, ], }\n\n_OVERRIDE1 = {            'B': 1234, 'C': [ 9876, { 'D': 2345, 'E': [ { 'F': 6789, 'G': 9876, }, 1357, ], }, ], }\n_ANSWER1   = { 'A': 1122, 'B': 1234, 'C': [ 9876, { 'D': 2345, 'E': [ { 'F': 6789, 'G': 9876, }, 1357, ], }, ], }\n\n_OVERRIDE2 = {                       'C': [ 6543, {            'E': [ {            'G': 9876, },       ], }, ], }\n_ANSWER2   = { 'A': 1122, 'B': 1133, 'C': [ 6543, {            'E': [ { 'F': 6666, 'G': 9876, },       ], }, ], }\n\n_OVERRIDE3 = {            'B': 3456, 'C': [ 1357, { 'D': 4567, 'E': [ { 'F': 6677, 'G': 9876, }, 2468, ], }, ], }\n_ANSWER3   = { 'A': 1122, 'B': 3456, 'C': [ 1357, { 'D': 4567, 'E': [ { 'F': 6677, 'G': 9876, }, 2468, ], }, ], }\n\n\nThis is an example of how to run the tests:\n(The dictionary update doesn't work, just an stub function.)\n\n\n    import itertools\n\n    def mergeStuff( default, override ):\n        # This doesn't work\n        result = dict( default )\n        result.update( override )\n        return result\n\n    def main():\n        for override, answer in itertools.izip( _OVERRIDES, _ANSWERS ):\n           result = mergeStuff(_DEFAULT, override)\n           print('ANSWER:   %s'   % (answer) )\n           print('RESULT:   %s\\n' % (result) )","AnswerCount":4,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1800,"Q_Id":19378143,"Users Score":0,"Answer":"If you know one structure is always a subset of the other, then just iterate the superset and in O(n) time you can check element by element whether it exists in the subset and if it doesn't, put it there. As far as I know there's no magical way of doing this other than checking it manually element by element. Which, as I said, is not bad as it can be done in with O(n) complexity.","Q_Score":3,"Tags":"python","A_Id":19378238,"CreationDate":"2013-10-15T09:52:00.000","Title":"Python: Merging two arbitrary data structures","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to draw the curve a generic cubic function using matplotlib. I want to draw curves that are defined by functions such as: x^3 + y^3 + y^2 + 2xy^2 = 0. Is this possible to do?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2800,"Q_Id":19422749,"Users Score":0,"Answer":"my 2 cents:\nx^3+y^3+y^2+2xy^2=0\ny^2=-x^3-y^3-2xy^2\ny^2>0 => -x^3-y^3-2xy^2>0 => x^3+y^3+2xy^2<0 =>\nx(x^2+2y^2)+y^3<0 => x(x^2+2y^2)<-y^3 => (x^2+2y^2)<-y^3\/x\n0<(x^2+2y^2) => 0<-y^3\/x => 0>y^3\/x =>\n(x>0 && y<0) || (x<0 && y>0)\nyour graph will span across the 2nd and 4th quadrants","Q_Score":2,"Tags":"python,matplotlib","A_Id":19423078,"CreationDate":"2013-10-17T09:22:00.000","Title":"how to draw a nonlinear function using matplotlib?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I used the Python Pandas library as a wrap-around instead of using SQL.  Everything worked perfectly, except when I open the output excel file, the cells appear blank, but when I click on the cell, I can see the value in the cell above.  Additionally, Python and Stata recognize the value in the cell, even though the eye cannot see it.  Furthermore, if I do \"text to columns\", then the values in the cell become visible to the eye.\nClearly it's a pain to go through every column and click \"text to columns\", and I'm wondering the following:\n(1) Why is the value not visible to the eye when it exists in the cell?\n(2) What's the easiest way to make all the values visible to the eye aside from the cumbersome \"text to columns\" for all columns approach?\n(3) I did a large number of tests to make sure the non-visible values in the cells in fact worked in analysis.  Is my assumption that the non-visible values in the cells will always be accurate, true?  \nThanks in advance for any help you can provide!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":311,"Q_Id":19436220,"Users Score":0,"Answer":"It sounds to me like your python code is inserting a carriage return either before or after the value.\nI've replicated this behavior in Excel 2016 and can confirm that the cell appears blank, but does contain a value.\nFurthermore, I've verified that using the text to columns will parse the carriage return out.","Q_Score":2,"Tags":"python,excel,pandas","A_Id":41857586,"CreationDate":"2013-10-17T20:06:00.000","Title":"Python Pandas Excel Display","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there a reset_index equivalent for the column headings? In other words, if the column names are an MultiIndex, how would I drop one of the levels?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":4607,"Q_Id":19474693,"Users Score":0,"Answer":"Transpose df, reset index, and transopse again.\ndf.T.reset_index().T","Q_Score":6,"Tags":"python,pandas","A_Id":58662181,"CreationDate":"2013-10-20T06:48:00.000","Title":"Pandas DataFrame.reset_index for columns","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have two numpy arrays x and y, which have length 10,000.\nI would like to plot a random subset of 1,000 entries of both x and y.\nIs there an easy way to use the lovely, compact random.sample(population, k) on both x and y to select the same corresponding indices? (The y and x vectors are linked by a function y(x) say.)\nThanks.","AnswerCount":4,"Available Count":1,"Score":0.049958375,"is_accepted":false,"ViewCount":15029,"Q_Id":19485641,"Users Score":1,"Answer":"Using the numpy.random.randint function, you generate a list of random numbers, meaning that you can select certain datapoints twice.","Q_Score":29,"Tags":"python,random,numpy","A_Id":69479472,"CreationDate":"2013-10-21T03:04:00.000","Title":"Python random sample of two arrays, but matching indices","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I will have this random number generated e.g 12.75 or 1.999999999 or 2.65\nI want to always round this number down to the nearest integer whole number so 2.65 would be rounded to 2.\nSorry for asking but I couldn't find the answer after numerous searches, thanks :)","AnswerCount":6,"Available Count":1,"Score":0.0333209931,"is_accepted":false,"ViewCount":42133,"Q_Id":19501279,"Users Score":1,"Answer":"I'm not sure whether you want math.floor, math.trunc, or int, but... it's almost certainly one of those functions, and you can probably read the docs and decide more easily than you can explain enough for usb to decide for you.","Q_Score":10,"Tags":"python,integer,rounding","A_Id":19501335,"CreationDate":"2013-10-21T17:46:00.000","Title":"How do I ONLY round a number\/float down in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Basically I want to reshape tensors represented by numpy.ndarray.\nFor example, I want to do something like this (latex notation)\nA_{i,j,k,l,m,n,p} -> A_{i,jk,lm,np}\nor\nA_{i,j,k,l,m,n,p} -> A_{ij,k,l,m,np}\nwhere A is an ndarray. i,j,k,... denotes the original axes.\nso the new axis 2 becomes the \"flattened\" version of axis 2 and 3, etc. If I simply use numpy.reshape, I don't think it knows what axes I want to merge, so it seems ambiguous and error prone.\nIs there any neat way of doing this rather than creating another ndarray manually?","AnswerCount":1,"Available Count":1,"Score":0.6640367703,"is_accepted":false,"ViewCount":802,"Q_Id":19509314,"Users Score":4,"Answer":"Using reshape is never ambiguous. It doesn't change the memory-layout of the data.\nIndexing is always done using the strides determined by the shape.\nThe right-most axis has stride 1, while the axes to the left have strides given by the product of the sizes to their right.\nThat means for you: as long as you collect neighboring axes, it will do the \"right\" thing.","Q_Score":4,"Tags":"python,numpy,reshape","A_Id":19510028,"CreationDate":"2013-10-22T05:00:00.000","Title":"How to merge specific axes without ambuigity with numpy.ndarray","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Hi I'm using Opencv and I want to find the n most common colors of an image using x sensitivity. How could I do this? Are there any opencv functions to do this? \nCheers!\n*Note: this isn't homework, i'm just using opencv for fun!","AnswerCount":2,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":4153,"Q_Id":19524905,"Users Score":4,"Answer":"I would transform the images to the HSV color space and then compute a histogram of the H values. Then, take the bins with the largest values.","Q_Score":3,"Tags":"python,opencv","A_Id":19540052,"CreationDate":"2013-10-22T17:50:00.000","Title":"Get most common colours in an image using OpenCV","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"baseline - I have CSV data with 10,000 entries. I save this as 1 csv file and load it all at once. \nalternative - I have CSV data with 10,000 entries. I save this as 10,000 CSV files and load it individually. \nApproximately how much more inefficient is this computationally. I'm not hugely interested in memory concerns. The purpose of the alternative method is because I frequently need to access subsets of the data and don't want to have to read the entire array. \nI'm using python. \nEdit: I can other file formats if needed. \nEdit1: SQLite wins. Amazingly easy and efficient compared to what I was doing before.","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":1356,"Q_Id":19532159,"Users Score":1,"Answer":"I would write all the lines to one file. For 10,000 lines it's probably not worthwhile, but you can pad all the lines to the same length - say 1000 bytes.\nThen it's easy to seek to the nth line, just multiply n by the line length","Q_Score":0,"Tags":"python,sqlite,csv","A_Id":19532207,"CreationDate":"2013-10-23T03:18:00.000","Title":"Most efficient way to store data on drive","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to calculate eigenvalues and eigenvectors in python. numpy and scipy do not work. They both write Illegal instruction (core dumped). I found out that to resolve the problem I need to check my blas\/lapack. So, I thought that may be an easier way is to write\/find a small function to solve the eigenvalue problem. Does anybody know if such solutions exist?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":12872,"Q_Id":19582197,"Users Score":0,"Answer":"Writing a program to solve an eigenvalue problem is about 100 times as much work as fixing the library mismatch problem.","Q_Score":3,"Tags":"python,linear-algebra","A_Id":19650488,"CreationDate":"2013-10-25T06:02:00.000","Title":"How to find eigenvectors and eigenvalues without numpy and scipy?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to use scikits.bvp_solver in python.\nI currently use Canopy as my standard Python interface, where this package isn't available. Is there another available package for solving boundary value problems? I have also tried downloading using macports but the procedure sticks when it tries building gcc48 dependency.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":611,"Q_Id":19591907,"Users Score":0,"Answer":"You can try to download the package tar.gz and use easy_install . Or you can unpack the package and use the standard way of python setup.py install. I believe both ways require a fortran compiler.","Q_Score":0,"Tags":"python,scipy,enthought,scikits,canopy","A_Id":19674497,"CreationDate":"2013-10-25T13:59:00.000","Title":"Installing scikits.bvp_solver","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to write a script in python which given coordinates of 2 points in 3d space finds a collinear point in distane 1 unit from one the given points. This third point must lay between those two given.\nI think I will manage with scripting but I am not really sure how to calculate it from mathematical point of view. I found some stuff on google, but they do not answer my question.\nThanks for any advice.","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":1351,"Q_Id":19611177,"Users Score":2,"Answer":"Given 2 points, (x1,y1,z1) and (x2,y2,z2), you can take the difference between the two, so you end up with (x2-x1,y2-y1,z2-z1).  Take the norm of this (i.e. take the distance between the original 2 points), and divide (x2-x1,y2-y1,z2-z1) by that value. You now have a vector with the same slope as the line between the first 2 points, but it has magnitude one, since you normalized it (by dividing by its magnitude).  Then add\/subtract that vector to one of the original points to get your final answer.","Q_Score":2,"Tags":"python,algorithm,3d,point","A_Id":19611533,"CreationDate":"2013-10-26T19:52:00.000","Title":"Find a point in 3d collinear with 2 other points","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to install scikit-learn but this library needs scipy and numpy too.\nI tried to add them on the setup.py but I had an error with numpy. I handle to install scikit-learn and numpy from virtenv, but I cannot install scipy.\nI tried pip install scipy. The procedure finished without any problem but there isn't any scipy folder on site-packages.\nAlso, I tried to add only scipy on setup.py. The same as above. The procedure finished without an error but scipy isn't there.\nAny help?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":651,"Q_Id":19619253,"Users Score":0,"Answer":"You will probably find more info sshing into your app ad typing tail_all.","Q_Score":1,"Tags":"python,scipy,openshift","A_Id":19621115,"CreationDate":"2013-10-27T14:37:00.000","Title":"cannot install scipy on openshift","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an iPython notebook that contains an inline plot (i.e. it contains the command plot(x,y)). When I issue the command ipython nbconvert --to latex --post PDF --SphinxTransformer.author='Myself' MyNotebook.ipynb the resulting .PDF file contains the figure, but it has been exported to .PNG, so it doesn't look very good (pixelated). How can I tell nbconvert to export all plots\/figures to .EPS instead?\nThank you","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":5738,"Q_Id":19659864,"Users Score":0,"Answer":"NBconvert does not run your code. So if you haven't plotted with SVG matplotlib backend it is not possible.\nIf you did so, then you need to write a nbconvert preprocessor that does svg-> eps and extend the relevant template to know how to embed EPS.","Q_Score":10,"Tags":"ipython","A_Id":19662546,"CreationDate":"2013-10-29T13:40:00.000","Title":"iPython nbconvert and latex: use .eps instead of .png for plots","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to run CCA for a multi label\/text classification problem but keep getting following warning and an error which I think are related \n\nwarnings.warn('Maximum number of iterations reached')\n  \/Library\/Python\/2.7\/site-packages\/sklearn\/cross_decomposition\/pls_.py:290:\n  UserWarning: X scores are null at iteration 0   warnings.warn('X\n  scores are null at iteration %s' % k)\nwarnings.warn('Maximum number of iterations reached')\n  \/Library\/Python\/2.7\/site-packages\/sklearn\/cross_decomposition\/pls_.py:290:\n  UserWarning: X scores are null at iteration 1\n\nwarnings.warn('X scores are null at iteration %s' % k)\n...\nfor all the 400 iterations and then following error at the end which I think is a side effect of above warning:\n\nTraceback (most recent call last):   File \"scikit_fb3.py\", line 477,\n  in \n      getCCA(shorttestfilepathPreProcessed)   File \"scikit_fb3.py\", line 318, in getCCA\n      X_CCA = cca.fit(x_array, Y_indicator).transform(X)   File \"\/Library\/Python\/2.7\/site-packages\/sklearn\/cross_decomposition\/pls_.py\",\n  line 368, in transform\n      Xc = (np.asarray(X) - self.x_mean_) \/ self.x_std_   File \"\/usr\/local\/bin\/src\/scipy\/scipy\/sparse\/compressed.py\", line 389, in\n  sub\n      raise NotImplementedError('adding a nonzero scalar to a ' NotImplementedError: adding a nonzero scalar to a sparse matrix is not\n  supported\n\nWhat could possibly be wrong?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":877,"Q_Id":19673279,"Users Score":0,"Answer":"CCA doesn't support sparse matrices. By default, you should assume scikit-learn estimators do not grok sparse matrices and check their docstrings to find out if by chance you found one that does.\n(I admit the warning could have been friendlier.)","Q_Score":0,"Tags":"python,classification,scikit-learn","A_Id":19696243,"CreationDate":"2013-10-30T03:27:00.000","Title":"UserWarning: X scores are null at iteration","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"For example, if we have a numpy array A, and we want a numpy array B with the same elements.\nWhat is the difference between the following (see below) methods? When is additional memory allocated, and when is it not?\n\nB = A\nB[:] = A (same as B[:]=A[:]?)\nnumpy.copy(B, A)","AnswerCount":3,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":76805,"Q_Id":19676538,"Users Score":155,"Answer":"All three versions do different things:\n\nB = A\nThis binds a new name B to the existing object already named A. Afterwards they refer to the same object, so if you modify one in place, you'll see the change through the other one too.\nB[:] = A (same as B[:]=A[:]?)\nThis copies the values from A into an existing array B. The two arrays must have the same shape for this to work. B[:] = A[:] does the same thing (but B = A[:] would do something more like 1).\nnumpy.copy(B, A)\nThis is not legal syntax. You probably meant B = numpy.copy(A). This is almost the same as 2, but it creates a new array, rather than reusing the B array. If there were no other references to the previous B value, the end result would be the same as 2, but it will use more memory temporarily during the copy.\nOr maybe you meant numpy.copyto(B, A), which is legal, and is equivalent to 2?","Q_Score":127,"Tags":"python,arrays,numpy","A_Id":19676762,"CreationDate":"2013-10-30T07:44:00.000","Title":"Numpy array assignment with copy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"For example, if we have a numpy array A, and we want a numpy array B with the same elements.\nWhat is the difference between the following (see below) methods? When is additional memory allocated, and when is it not?\n\nB = A\nB[:] = A (same as B[:]=A[:]?)\nnumpy.copy(B, A)","AnswerCount":3,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":76805,"Q_Id":19676538,"Users Score":33,"Answer":"B=A creates a reference\nB[:]=A makes a copy\nnumpy.copy(B,A) makes a copy\n\nthe last two need additional memory.\nTo make a deep copy you need to use B = copy.deepcopy(A)","Q_Score":127,"Tags":"python,arrays,numpy","A_Id":19676652,"CreationDate":"2013-10-30T07:44:00.000","Title":"Numpy array assignment with copy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to use scikit-learn to do some machine learning on natural language data. I've got my corpus transformed into bag-of-words vectors (which take the form of a sparse CSR matrix) and I'm wondering if there's a supervised dimensionality reduction algorithm in sklearn capable of taking high-dimensional, supervised data and projecting it into a lower dimensional space which preserves the variance between these classes. \nThe high-level problem description is that I have a collection of documents, each of which can have multiple labels on it, and I want to predict which of those labels will get slapped on a new document based on the content of the document.\nAt it's core, this is a supervised, multi-label, multi-class problem using a sparse representation of BoW vectors. Is there a dimensionality reduction technique in sklearn that can handle that sort of data? Are there other sorts of techniques people have used in working with supervised, BoW data in scikit-learn?\nThanks!","AnswerCount":5,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":4068,"Q_Id":19714108,"Users Score":0,"Answer":"Use a multi-layer neural net for classification. If you want to see what the representation of the input is in the reduced dimension, look at the activations of the hidden layer. The role of the hidden layer is by definition optimised to distinguish between the classes, since that's what's directly optimised when the weights are set.\nYou should remember to use a softmax activation on the output layer, and something non-linear on the hidden layer (tanh or sigmoid).","Q_Score":12,"Tags":"python,machine-learning,scikit-learn,dimensionality-reduction","A_Id":19724359,"CreationDate":"2013-10-31T18:33:00.000","Title":"Supervised Dimensionality Reduction for Text Data in scikit-learn","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to use scikit-learn to do some machine learning on natural language data. I've got my corpus transformed into bag-of-words vectors (which take the form of a sparse CSR matrix) and I'm wondering if there's a supervised dimensionality reduction algorithm in sklearn capable of taking high-dimensional, supervised data and projecting it into a lower dimensional space which preserves the variance between these classes. \nThe high-level problem description is that I have a collection of documents, each of which can have multiple labels on it, and I want to predict which of those labels will get slapped on a new document based on the content of the document.\nAt it's core, this is a supervised, multi-label, multi-class problem using a sparse representation of BoW vectors. Is there a dimensionality reduction technique in sklearn that can handle that sort of data? Are there other sorts of techniques people have used in working with supervised, BoW data in scikit-learn?\nThanks!","AnswerCount":5,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":4068,"Q_Id":19714108,"Users Score":0,"Answer":"Try ISOMAP. There's a super simple built-in function for it in scikits.learn. Even if it doesn't have some of the preservation properties you're looking for, it's worth a try.","Q_Score":12,"Tags":"python,machine-learning,scikit-learn,dimensionality-reduction","A_Id":19714792,"CreationDate":"2013-10-31T18:33:00.000","Title":"Supervised Dimensionality Reduction for Text Data in scikit-learn","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Given a large 2d numpy array, I would like to remove a range of rows, say rows 10000:10010 efficiently. I have to do this multiple times with different ranges, so I would like to also make it parallelizable.\nUsing something like numpy.delete() is not efficient, since it needs to copy the array, taking too much time and memory. Ideally I would want to do something like create a view, but I am not sure how I could do this in this case. A masked array is also not an option since the downstream operations are not supported on masked arrays.\nAny ideas?","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2948,"Q_Id":19719746,"Users Score":3,"Answer":"Because of the strided data structure that defines a numpy array, what you want will not be possible without using a masked array.  Your best option might be to use a masked array (or perhaps your own boolean array) to mask the deleted the rows, and then do a single real delete operation of all the rows to be deleted before passing it downstream.","Q_Score":8,"Tags":"python,numpy","A_Id":19719936,"CreationDate":"2013-11-01T02:07:00.000","Title":"How can one efficiently remove a range of rows from a large numpy array?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How can I convert 2D cvMat to 1D? I have tried converting 2D cvMat to Numpy array then used ravel() (I want that kind of resultant matrix).When I tried converting it back to\n cvMat using cv.fromarray() it gives an error that the matrix must be 2D or 3D.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":606,"Q_Id":19732097,"Users Score":0,"Answer":"Use matrix.reshape((-1, 1)) to turn the n-element 1D matrix into an n-by-1 2D one before converting it.","Q_Score":0,"Tags":"python,opencv,numpy","A_Id":19732333,"CreationDate":"2013-11-01T17:34:00.000","Title":"Conversion of 2D cvMat to 1D","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have 1 million 3d points I am passing to numpy.linalg.svd but it runs out of memory very quickly. Is there a way to break down this operation into smaller chunks?\nI don't know what it's doing but am I only supposed to pass arrays that represent a 3x3, 4x4 matrix? Because I have seen uses of it online where they were passing arrays with arbitrary number of elements.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":4417,"Q_Id":19743525,"Users Score":0,"Answer":"Try to use scipy.linalg.svd instead of numpy's func.","Q_Score":5,"Tags":"python,numpy,matrix,linear-algebra,svd","A_Id":38743037,"CreationDate":"2013-11-02T15:32:00.000","Title":"Is there a way to prevent numpy.linalg.svd running out of memory?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Can you tell me when to use these vectorization methods with basic examples? \nI see that map is a Series method whereas the rest are DataFrame methods. I got confused about apply and applymap methods though. Why do we have two methods for applying a function to a DataFrame? Again, simple examples which illustrate the usage would be great!","AnswerCount":11,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":436006,"Q_Id":19798153,"Users Score":12,"Answer":"Probably simplest explanation the difference between apply and applymap:\napply takes the whole column as a parameter and then assign the result to this column\napplymap takes the separate cell value as a parameter and assign the result back to this cell.\nNB If apply returns the single value you will have this value instead of the column after assigning and eventually will have just a row instead of matrix.","Q_Score":624,"Tags":"python,pandas,dataframe,vectorization","A_Id":37336872,"CreationDate":"2013-11-05T20:20:00.000","Title":"Difference between map, applymap and apply methods in Pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using pandas to get hourly data from a dataset with fifteen minute sampling intervals.  My problem using the resample('H', how='ohlc') method is that it provides values within that hour and I want the value closest to the hour.  For instance, I would like to take a value sampled at 2:55 instead of one from 3:10, but can't figure out how to find the value that is closest if it occurs prior to the timestamp being evaluated against.\nAny help would be greatly appreciated.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":630,"Q_Id":19799893,"Users Score":0,"Answer":"I suppose you could create another column that is the Hour and subtract the time in question and get the absolute (unsigned) value that you could then do the min function on.. It is not code but I think the logic is right (or at least close.. after you find the mins,, then you can select them and then do your resample..","Q_Score":1,"Tags":"python,pandas","A_Id":19800482,"CreationDate":"2013-11-05T21:59:00.000","Title":"Using Pandas to get the closest value to a timestamp","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a numpy array, filtered__rows, comprised of LAS data [x, y, z, intensity, classification].  I have created a cKDTree of points and have found nearest neighbors, query_ball_point, which is a list of indices for the point and its neighbors.\nIs there a way to filter filtered__rows to create an array of only points whose index is in the list returned by query_ball_point?","AnswerCount":5,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":89890,"Q_Id":19821425,"Users Score":0,"Answer":"The fastest way to do this is X[tuple(index.T)], where X is the ndarray with the elements and index is the ndarray of indices wished to be retrieved.","Q_Score":48,"Tags":"python,numpy,scipy,nearest-neighbor","A_Id":70856990,"CreationDate":"2013-11-06T19:50:00.000","Title":"How to filter numpy array by list of indices?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to use the cv2.getAffineTransform(src,dst) function in openCV, but it crashes because my inputs are arrays containing 125 pairs of x,y coordinates and getAffineTransform wants its input to have three columns. Can I just concat a row full of zeros onto my array or is there a special transformation I should do?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":935,"Q_Id":19865347,"Users Score":3,"Answer":"No I think there is something else that is the problem.  Docs say: cv2.getAffineTransform Calculates an affine transform from three pairs of the corresponding points.\nThe problem is you are giving it 125 pairs of points.  It only wants 3 pairs of point correspondences.  This is of course the number of correspondences needed to solve the linear system of equations.  If you are looking to estimate an affine transformation from noisy correspondences then you will need to use something like weighted least squares or RANSAC.  To estimate affine transform from noisy data with a prepackaged algorithm it looks like cv2.estimateRigidTransform might work setting fullAffine = True","Q_Score":1,"Tags":"python,opencv","A_Id":19867945,"CreationDate":"2013-11-08T17:44:00.000","Title":"Converting x,y coordinates into a format for getAffineTransform?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there a better way to determine whether a variable in Pandas and\/or NumPy is numeric or not ? \nI have a self defined dictionary with dtypes as keys and numeric \/ not as values.","AnswerCount":10,"Available Count":1,"Score":0.0399786803,"is_accepted":false,"ViewCount":132614,"Q_Id":19900202,"Users Score":2,"Answer":"Just to add to all other answers, one can also use df.info() to get whats the data type of each column.","Q_Score":129,"Tags":"python,pandas,numpy","A_Id":50679155,"CreationDate":"2013-11-11T06:32:00.000","Title":"How to determine whether a column\/variable is numeric or not in Pandas\/NumPy?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there an easy way to use PyMC's MCMC algorithms to efficiently sample a parameter space for a frequentists analysis? I'm not interested in the point density (for Bayesian analysis), but rather want a fast and efficient way to sample a multidimensional parameter space, so I would like to trace all tested points (i.e. in particular also the rejected points), while recurring points need to be saved only once in the trace.\nI would be grateful for any helpful comments. \nBtw, thanks for developing PyMC, it is a great package!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":155,"Q_Id":19913421,"Users Score":0,"Answer":"You can create custom StepMethods to perform any kind of sampling you like. See the docs for how to create your own.","Q_Score":3,"Tags":"python,pymc","A_Id":19960199,"CreationDate":"2013-11-11T18:38:00.000","Title":"Using MCMC from PyMC as an efficient sampler for frequentist analysis?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Scikit-learn CountVectorizer for bag-of-words approach currently gives two sub-options: (a) use a custom vocabulary (b) if custom vocabulary is unavailable, then it makes a vocabulary based on all the words present in the corpus. \nMy question: Can we specify a custom vocabulary to begin with, but ensure that it gets updated when new words are seen while processing the corpus. I am assuming this is doable since the matrix is stored via a sparse representation. \nUsefulness: It will help in cases when one has to add additional documents to the training data, and one should not have to start from the beginning.","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":2027,"Q_Id":19945334,"Users Score":2,"Answer":"No, this is not possible at present. It's also not \"doable\", and here's why.\nCountVectorizer and TfidfVectorizer are designed to turn text documents into vectors. These vectors need to all have an equal number of elements, which in turn is equal to the size of the vocabulary, because that conventions is ingrained in all scikit-learn code. If the vocabulary is allowed to grow, then the vectors produced at various times have different lengths. This affects e.g. the number of parameters in a linear (or other parametric) classifiers trained on such vectors, which then also needs to be able to grow. It affects k-means and dimensionality reduction classes. It even affects something as simple as matrix multiplications, which can no longer be handled with a simple call to NumPy's dot routine, requiring custom code instead. In other words, allowing this flexibility in the vectorizers makes little sense unless you adapt all of scikit-learn to handle the result.\nWhile this would be possible, I (as a core scikit-learn developer) would strongly oppose the change because it makes the code very complicated, probably slower, and even if it would work, it would make it impossible to distinguish between a \"growing vocabulary\" and the much more common situation of a user passing data in the wrong way, so that the number of dimensions comes out wrong.\nIf you want to feed data in in batches, then either using a HashingVectorizer (no vocabulary) or do two passes over the data to collect the vocabulary up front.","Q_Score":5,"Tags":"python,numpy,scipy,scikit-learn,scikits","A_Id":24098425,"CreationDate":"2013-11-13T04:30:00.000","Title":"Adding new words to text vectorizer in scikit-learn","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to find a transformation matrix that relates 2 3D point clouds. According to the documentation, cv2.estimateAffine3D(src, dst) --> retval, out, inliers. \nsrc is the first 3D point set, dst is the second 3D point set.\nI'm assuming that retval is a boolean.\nOut is the 3x4 affine transformation matrix, and inliers is a vector.\nMy question is, what are the shapes of the input point sets? Do the points have to be homogeneous, i.e. 4xN?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":515,"Q_Id":20008825,"Users Score":1,"Answer":"As I can understand from source code, they have to be Point3D, i.e. non-homogenous.","Q_Score":0,"Tags":"python,opencv","A_Id":20009112,"CreationDate":"2013-11-15T19:14:00.000","Title":"OpenCV estimateAffine3D in Python: shapes of input matrix?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have data coming from a csv which has a few thousand columns and ten thousand (or so) rows. Within each column the data is of the same type, but different columns have data of different type*. Previously I have been pickling the data from numpy and storing on disk, but it's quite slow, especially because usually I want to load some subset of the columns rather than all of them.\nI want to put the data into hdf5 using pytables, and my first approach was to put the data in a single table, with one hdf5 column per csv column. Unfortunately this didn't work, I assume because of the 512 (soft) column limit.\nWhat is a sensible way to store this data?\n* I mean, the type of the data after it has been converted from text.","AnswerCount":5,"Available Count":3,"Score":1.2,"is_accepted":true,"ViewCount":2271,"Q_Id":20045535,"Users Score":3,"Answer":"This might not, in fact, be possible to do in a naive way.  HDF5 allocates 64 kb of space for meta-data for every data set.  This meta data includes the types of the columns.  So while the number of columns is a soft limit, somewhere in the 2-3 thousand range you typically run out of space to store the meta data (depending on the length of the column names, etc).  \nFurthermore, doesn't numpy limit the number of columns to 32?  How are you representing the data with numpy now?  Anything that you can get into a numpy array should correspond to a pytables Array class.","Q_Score":11,"Tags":"python,numpy,hdf5,pytables","A_Id":20099740,"CreationDate":"2013-11-18T10:30:00.000","Title":"How to store wide tables in pytables \/ hdf5","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have data coming from a csv which has a few thousand columns and ten thousand (or so) rows. Within each column the data is of the same type, but different columns have data of different type*. Previously I have been pickling the data from numpy and storing on disk, but it's quite slow, especially because usually I want to load some subset of the columns rather than all of them.\nI want to put the data into hdf5 using pytables, and my first approach was to put the data in a single table, with one hdf5 column per csv column. Unfortunately this didn't work, I assume because of the 512 (soft) column limit.\nWhat is a sensible way to store this data?\n* I mean, the type of the data after it has been converted from text.","AnswerCount":5,"Available Count":3,"Score":0.0399786803,"is_accepted":false,"ViewCount":2271,"Q_Id":20045535,"Users Score":1,"Answer":"you should be able to use pandas dataframe\nit can be saved to disk without converting to csv","Q_Score":11,"Tags":"python,numpy,hdf5,pytables","A_Id":20155746,"CreationDate":"2013-11-18T10:30:00.000","Title":"How to store wide tables in pytables \/ hdf5","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have data coming from a csv which has a few thousand columns and ten thousand (or so) rows. Within each column the data is of the same type, but different columns have data of different type*. Previously I have been pickling the data from numpy and storing on disk, but it's quite slow, especially because usually I want to load some subset of the columns rather than all of them.\nI want to put the data into hdf5 using pytables, and my first approach was to put the data in a single table, with one hdf5 column per csv column. Unfortunately this didn't work, I assume because of the 512 (soft) column limit.\nWhat is a sensible way to store this data?\n* I mean, the type of the data after it has been converted from text.","AnswerCount":5,"Available Count":3,"Score":0.0399786803,"is_accepted":false,"ViewCount":2271,"Q_Id":20045535,"Users Score":1,"Answer":"IMHO it depends on what do you want to do with the data afterwards and how much of it do you need at one time. I had to build a program for statistical validation a while ago and we had two approaches:\n\nSplit the columns in separate tables (e.g. using a FK). The overhead of loading them is not too high\nTranspose the table, resulting in something like a key-value store, where the key is a tuple of (column, row)\n\nFor both we used postgres.","Q_Score":11,"Tags":"python,numpy,hdf5,pytables","A_Id":20240079,"CreationDate":"2013-11-18T10:30:00.000","Title":"How to store wide tables in pytables \/ hdf5","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"The title may not be as explicit as I wish it would be but here is what I am trying to achieve:\nUsing Boost.Python, I expose a set of class\/functions to Python in the typical BOOST_PYTHON_MODULE(MyPythonModule) macro from C++ that produces MyPythonModule.pyd after compilation. I can now invoke a python script from C++ and play around with MyPythonModule without any issue (eg. create objects, call methods and use my registered converters). FYI: the converter I'm refering to is a numpy.ndarray to cv::Mat converter.\nThis works fine, but when I try to write a standalone Python script that uses MyPythonModule, my converters are not available. I tried to expose the C++ method that performs the converter registration to Python without any luck.\nIf my explanation isn't clear enough, don't hesitate to ask questions in the comments.\nThanks a lot for your help \/ suggestions.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":84,"Q_Id":20055758,"Users Score":0,"Answer":"I found the problem... The prototype of my C++ function was taking cv::Mat& as argument and the converter was registered for cv::Mat without reference.\nThat was silly.","Q_Score":0,"Tags":"python,boost,converters","A_Id":20058784,"CreationDate":"2013-11-18T19:05:00.000","Title":"Boost.Python: Converters unavailable from standalone python script","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there a way of a procedure similar to plt.gca() to get a handle to the current axes. I first do a=mlab.surf(x, y, u2,warp_scale='auto')\nand then\nb=mlab.plot3d(yy, yy, (yy-40)**2 ,tube_radius=20.0)\nbut the origin of a and b are different and the plot looks incorrect. So I want to put b into the axes of a\nIn short, what would be the best way in mayavi to draw a surface and a line on same axes?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":501,"Q_Id":20062512,"Users Score":1,"Answer":"What you are expecting to be able to do from your matplotlib experience is not how mayavi axes work.  In matplotlib the visualization is a child of the axes and the axes determines its coordinates.  In mayavi or vtk, visualization sources consist of points in space.  Axes are objects that surround a source and provide tick markings of the coordinate extent of those objects, that are not necessary for the visualizations, and where they exist they are children of sources.","Q_Score":0,"Tags":"python,mayavi","A_Id":20076370,"CreationDate":"2013-11-19T03:21:00.000","Title":"mayavi mlab get current axes","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using Open CV 2.4.6 with C++ (with Python sometimes too but it is irrelevant). I would like to know if there is a simple way to get all the available frame sizes from a capture device?\nFor example, my webcam can provide 640x480, 320x240 and 160x120. Suppose that I don't know about these frame sizes a priori... Is it possible to get a vector or an iterator, or something like this that could give me these values?\nIn other words, I don't want to get the current frame size (which is easy to obtain) but the sizes I could set the device to.\nThanks!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1874,"Q_Id":20081818,"Users Score":1,"Answer":"When you retrieve a frame from a camera, it is the maximum size that that camera can give. If you want a smaller image, you have to specify it when you get the image, and opencv will resize it for you.\nA normal camera has one sensor of one size, and it sends one kind of image to the computer. What opencv does with it thereafter is up to you to specify.","Q_Score":1,"Tags":"c++,python,opencv,video-capture,image-capture","A_Id":20085854,"CreationDate":"2013-11-19T20:55:00.000","Title":"Getting all available frame size from capture device with OpenCV","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a Pandas dataframe and I want to find all the unique values in that dataframe...irrespective of row\/columns. If I have a 10 x 10 dataframe, and suppose they have 84 unique values, I need to find them - Not the count.\nI can create a set and add the values of each rows by iterating over the rows of the dataframe. But, I feel that it may be inefficient (cannot justify that). Is there an efficient way to find it? Is there a predefined function?","AnswerCount":2,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":90919,"Q_Id":20084382,"Users Score":7,"Answer":"Or you can use:\ndf.stack().unique()\nThen you don't need to worry if you have NaN values, as they are excluded when doing the stacking.","Q_Score":51,"Tags":"python,pandas,dataframe","A_Id":42714576,"CreationDate":"2013-11-19T23:26:00.000","Title":"Find unique values in a Pandas dataframe, irrespective of row or column location","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am faced with this problem:\nI have to build an FFNN that has to approximate an unknown function f:R^2 -> R^2. The data in my possession to check the net is a one-dimensional R vector. I know the function g:R^2->R that will map the output of the net into the space of my data. So I would use the neural network as a filter against bias in the data. But I am faced with two problems:\nFirstly, how can I train my network in this way?\nSecondly, I am thinking about adding an extra hidden layer that maps R^2->R and lets the net train itself to find the correct maps and then remove the extra layer. Would this algorithm be correct? Namely, would the output be the same that I was looking for?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":203,"Q_Id":20179255,"Users Score":1,"Answer":"Your idea with additional layer is good, although the problem is, that your weights in this layer have to be fixed. So in practise, you have to compute the partial derivatives of your R^2->R mapping, which can be used as the error to propagate through your network during training. Unfortunately, this may lead to the well known \"vanishing gradient problem\" which stopped the development of NN for many years.\nIn short - you can either manually compute the partial derivatives, and given expected output in R, simply feed the computed \"backpropagated\" errors to the network looking for R^2->R^2 mapping or as you said - create additional layer, and train it normally, but you will have to make the upper weights constant (which will require some changes in the implementation).","Q_Score":0,"Tags":"python,algorithm,neural-network,pybrain","A_Id":20200810,"CreationDate":"2013-11-24T18:35:00.000","Title":"Train a feed forward neural network indirectly","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a data set comprising a vector of features, and a target - either 1.0 or 0.0 (representing two classes). If I fit a  RandomForestRegressor and call its predict function, is it equivalent to using RandomForestClassifier.predict_proba()?\nIn other words if the target is 1.0 or 0.0 does RandomForestRegressor output probabilities?\nI think so, and the results I a m getting suggest so, but I would like to get a second opinion...\nThanks\nWeasel","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":4377,"Q_Id":20179267,"Users Score":4,"Answer":"There is a major conceptual diffrence between those, based on different tasks being addressed:\nRegression: continuous (real-valued) target variable. \nClassification: discrete target variable (classes).\nFor a general classification method, term probability of observation being class X may be not defined, as some classification methods, knn for example, do not deal with probabilities.\nHowever for Random Forest (and some other classification methods), classification is reduced to regression of classes probabilities destibution. Predicted class is taked then as argmax of computed \"probabilities\". In your case, you feed the same input, you get the same result. And yes, it is ok to treat values returned by RandomForestRegressor as probabilities.","Q_Score":4,"Tags":"python,scikit-learn","A_Id":20180148,"CreationDate":"2013-11-24T18:36:00.000","Title":"using RandomForestClassifier.predict_proba vs RandomForestRegressor.predict","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using nbconvert to produce something as close as possible to a polished journal article. \nI have successfully hidden input code using a custom nbconvert template. The doc is now looking very nice. \nBut I don't know how to suppress the bright red 'out[x]' statement in the top left corner of the output cells. Anyone know of any settings or hacks that are able to remove this also ?\nThanks, \nJohn","AnswerCount":3,"Available Count":1,"Score":0.3215127375,"is_accepted":false,"ViewCount":5150,"Q_Id":20184994,"Users Score":5,"Answer":"%%HTML\n<style>\ndiv.prompt {display:none}\n<\/style>\nThis will hide both In and Out prompts\nNote that this is only in your browser, the notebook itself isn't modified of course, and nbconvert will work just the same as before.\nIn case you want this in the nbconverted code as well, just put the <style>div.prompt {display:none}<\/style> in a Raw NBConvert cell.","Q_Score":8,"Tags":"ipython,ipython-notebook","A_Id":30234937,"CreationDate":"2013-11-25T04:53:00.000","Title":"ipython notebook nbconvert - how to remove red 'out[N]' text in top left hand corner of cell output?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a huge database of 180 columns and 200,000 rows. To illustrate in a better way, I have a matrix of 180 x 200000. Each matrix is a single digit number. I need to find their co-occurrence count.\nFor example I have a data of 5 columns having values 1,2,3,4,5. I need to find the number of times (1,2),(1,3),(1,4),(1,5),(2,3),(2,4),(2,5),(3,4),(3,5),(4,5) have occurred in the database. Can you please suggest me an approach to this problem?\nI have an exposure to R and python. So any suggestion using those will really help. \nCan this also be done using AWS map reducer? Any help or pointers on those lines would also be helpful.","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":188,"Q_Id":20202179,"Users Score":2,"Answer":"Counts of pairs are just products of counts of singletons.\nThis takes 5 seconds on my year old MacBook Pro using R:\nGenerate a matrix of 200000 rows and 180 columns whose elements are digits:\n\nmat <- matrix(sample(0:9,180*200000,repl=T),nc=180)\n\nNow table digits in each row:\n\ntab <- sapply( 0:9, function(x) rowSums( mat==x ))\n\nNow find the pair counts in each row:\n\ncp <- combn( 0:9, 2, function(x) tab[,1+x[1] ] * tab[,1+x[2] ])\n\nSum the rows:\n\ncolSums(cp)\n\nVerify the result for the first row:\n\ntab2 <- table( matrix(mat[1,], nr=180, nc=180), matrix(mat[1,], nr=180, nc=180, byrow=TRUE))\nall( tab2[ lower.tri(tab2)] == cp[1,] )","Q_Score":1,"Tags":"python,r","A_Id":20208600,"CreationDate":"2013-11-25T20:04:00.000","Title":"Can co-occurance of word be calculated using R\/ python\/ Map reducer?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there a way to make the scipy optimization modules use a smaller step size?\nI am optimizing a problem with a large set of variables (approximately 40) that I believe are near the optimal value, however when I run the scipy minimization modules (so far I have tried L-BFGS and CG) they do not converge because the initial step size is too large.","AnswerCount":2,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":5636,"Q_Id":20222657,"Users Score":4,"Answer":"I believe cobyla is the only technique that supports this in scipy.optimize.minimize. You can essentially control how big it's steps are with the rhobeg parameter. (It's not really the step size since it's a sequential linear method, but it has the same effect).","Q_Score":6,"Tags":"python,optimization,scipy","A_Id":20255287,"CreationDate":"2013-11-26T16:32:00.000","Title":"Method to set scipy optimization minimization step size","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"From what I can tell, there's no way to access OpenCV's OpenCL (OCL) module from the python cv2 bindings.  Does anyone know of a straightforward way to do this?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1033,"Q_Id":20232889,"Users Score":2,"Answer":"unfortunately, - no way.\nopencv uses special Mat types for this, ocl::Mat or cuda::Mat , \nand those are not exposed to the wrappers (so, same problem for java and matlab)","Q_Score":1,"Tags":"python,opencv,opencl","A_Id":20238109,"CreationDate":"2013-11-27T02:38:00.000","Title":"Is it possible to access OpenCV OCL (OpenCL) methods from python (cv2)?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Perhaps this is too long-winded. Simple question about sklearn's random forest: \nFor a true\/false classification problem, is there a way in sklearn's random forest to specify the sample size used to train each tree, along with the ratio of true to false observations?\nMore details are below:\n\nIn the R implementation of random forest, called randomForest, there's an option sampsize(). This allows you to balance the sample used to train each tree based on the outcome. \nFor example, if you're trying to predict whether an outcome is true or false and 90% of the outcomes in the training set are false, you can set sampsize(500, 500). This means that each tree will be trained on a random sample (with replacement) from the training set with 500 true and 500 false observations. In these situations, I've found models perform much better predicting true outcomes when using a 50% cut-off, yielding much higher kappas. \nIt doesn't seem like there is an option for this in the sklearn implementation. \n\nIs there any way to mimic this functionality in sklearn? \nWould simply optimizing the cut-off based on the Kappa statistic achieve a similar result or is something lost in this approach?","AnswerCount":4,"Available Count":4,"Score":1.2,"is_accepted":true,"ViewCount":2604,"Q_Id":20252484,"Users Score":2,"Answer":"After reading over the documentation, I think that the answer is definitely no. Kudos to anyone who adds the functionality though. As mentioned above the R package randomForest contains this functionality.","Q_Score":6,"Tags":"python,r,scikit-learn,classification,random-forest","A_Id":20557736,"CreationDate":"2013-11-27T20:27:00.000","Title":"Can sklearn Random Forest classifier adjust sample size by tree, to handle class imbalance?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Perhaps this is too long-winded. Simple question about sklearn's random forest: \nFor a true\/false classification problem, is there a way in sklearn's random forest to specify the sample size used to train each tree, along with the ratio of true to false observations?\nMore details are below:\n\nIn the R implementation of random forest, called randomForest, there's an option sampsize(). This allows you to balance the sample used to train each tree based on the outcome. \nFor example, if you're trying to predict whether an outcome is true or false and 90% of the outcomes in the training set are false, you can set sampsize(500, 500). This means that each tree will be trained on a random sample (with replacement) from the training set with 500 true and 500 false observations. In these situations, I've found models perform much better predicting true outcomes when using a 50% cut-off, yielding much higher kappas. \nIt doesn't seem like there is an option for this in the sklearn implementation. \n\nIs there any way to mimic this functionality in sklearn? \nWould simply optimizing the cut-off based on the Kappa statistic achieve a similar result or is something lost in this approach?","AnswerCount":4,"Available Count":4,"Score":0.0,"is_accepted":false,"ViewCount":2604,"Q_Id":20252484,"Users Score":0,"Answer":"As far as I am aware, the scikit-learn forest employ bootstrapping i.e. the sample set sizes each tree is trained with are always of the same size and drawn from the original training set by random sampling with replacement.\nAssuming you have a large enough set of training samples, why not balancing this itself out to hold 50\/50 positive\/negative samples and you will achieve the desired effect. scikit-learn provides functionality for this.","Q_Score":6,"Tags":"python,r,scikit-learn,classification,random-forest","A_Id":28440842,"CreationDate":"2013-11-27T20:27:00.000","Title":"Can sklearn Random Forest classifier adjust sample size by tree, to handle class imbalance?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Perhaps this is too long-winded. Simple question about sklearn's random forest: \nFor a true\/false classification problem, is there a way in sklearn's random forest to specify the sample size used to train each tree, along with the ratio of true to false observations?\nMore details are below:\n\nIn the R implementation of random forest, called randomForest, there's an option sampsize(). This allows you to balance the sample used to train each tree based on the outcome. \nFor example, if you're trying to predict whether an outcome is true or false and 90% of the outcomes in the training set are false, you can set sampsize(500, 500). This means that each tree will be trained on a random sample (with replacement) from the training set with 500 true and 500 false observations. In these situations, I've found models perform much better predicting true outcomes when using a 50% cut-off, yielding much higher kappas. \nIt doesn't seem like there is an option for this in the sklearn implementation. \n\nIs there any way to mimic this functionality in sklearn? \nWould simply optimizing the cut-off based on the Kappa statistic achieve a similar result or is something lost in this approach?","AnswerCount":4,"Available Count":4,"Score":0.1488850336,"is_accepted":false,"ViewCount":2604,"Q_Id":20252484,"Users Score":3,"Answer":"In version 0.16-dev, you can now use class_weight=\"auto\" to have something close to what you want to do. This will still use all samples, but it will reweight them so that classes become balanced.","Q_Score":6,"Tags":"python,r,scikit-learn,classification,random-forest","A_Id":28648499,"CreationDate":"2013-11-27T20:27:00.000","Title":"Can sklearn Random Forest classifier adjust sample size by tree, to handle class imbalance?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Perhaps this is too long-winded. Simple question about sklearn's random forest: \nFor a true\/false classification problem, is there a way in sklearn's random forest to specify the sample size used to train each tree, along with the ratio of true to false observations?\nMore details are below:\n\nIn the R implementation of random forest, called randomForest, there's an option sampsize(). This allows you to balance the sample used to train each tree based on the outcome. \nFor example, if you're trying to predict whether an outcome is true or false and 90% of the outcomes in the training set are false, you can set sampsize(500, 500). This means that each tree will be trained on a random sample (with replacement) from the training set with 500 true and 500 false observations. In these situations, I've found models perform much better predicting true outcomes when using a 50% cut-off, yielding much higher kappas. \nIt doesn't seem like there is an option for this in the sklearn implementation. \n\nIs there any way to mimic this functionality in sklearn? \nWould simply optimizing the cut-off based on the Kappa statistic achieve a similar result or is something lost in this approach?","AnswerCount":4,"Available Count":4,"Score":0.0,"is_accepted":false,"ViewCount":2604,"Q_Id":20252484,"Users Score":0,"Answer":"Workaround in R only, for classification one can simply use all cores of the machine with 100% CPU utilization.\nThis matches the time and speed of Sklearn RandomForest classifier.\nAlso for regression there is a package RandomforestParallel on GitHub, which is much faster than Python Sklearn Regressor.\nClassification: I have tested and works well.","Q_Score":6,"Tags":"python,r,scikit-learn,classification,random-forest","A_Id":30005356,"CreationDate":"2013-11-27T20:27:00.000","Title":"Can sklearn Random Forest classifier adjust sample size by tree, to handle class imbalance?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have two questions relating to repeated Gaussian blur. \nWhat happens when we repeatedly apply gaussian blur to an image keeping the sigma and the radius same ?\nAnd is it possible that after n iterations of repeatedly applying gaussian blur (sigma = s1) the image becomes the same as it would be on applying gaussian blur ( of sigma = s2; s1 < s2 )  1 time on the original image. And if so what is the general formula for deriving that n number of times we have to apply gaussian blur with s1, given s1 and s2 (s1 < s2).","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2451,"Q_Id":20294916,"Users Score":13,"Answer":"Successively applying multiple gaussian blurs to an image has the same effect as applying a single, larger gaussian blur, whose radius is the square root of the sum of the squares of the blur radii that were actually applied. In your case, s2 = sqrt(n*s1^2), and the blur radii is approximated as 3*si where i = 1, 2, which means pixels at a distance of more than 3si are small enough to be considered effectively zero during the blurring process.","Q_Score":10,"Tags":"python,image,matlab,image-processing,gaussian","A_Id":20295628,"CreationDate":"2013-11-29T23:58:00.000","Title":"Repeated Gaussian Blur in Image Processing","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Let's say I am given an array of n points(pair of coordinates). I want to generate a 2D array of points, where ith row has all elements sorted according to their distance from the ith  point. There may be better and more efficient algorithms to get the final result, but for some reasons I want to do it by naive algorithm, i.e., brute-force. Also I don't want to write my own sorting function.\nIn C language, one can use the qsort function, but it's compare function only takes two parameters whereas I will be needing to pass three parameters: the reference point and two other points to be compared.\nIn Python too, one can use sorted function, but again, it's key function only takes one parameter, whereas in this case, I will need to pass two parameters.\nSo how do I do it?","AnswerCount":6,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":202,"Q_Id":20295446,"Users Score":0,"Answer":"Maybe use a nested for loop with the outside one looking at the ith point.  then inside loop and calculate all the distances.  After calculation use a native python sort for that row and then add it to the main 2d array.","Q_Score":0,"Tags":"python,c,arrays,algorithm,sorting","A_Id":20295483,"CreationDate":"2013-11-30T01:18:00.000","Title":"Custom sorting in for loop","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"There are n choose k subsets of {1,2,...,n} of size k. These can be naturally ordered by sorting the elements and using the lexigraphical order. \nIs there a fast way to determine the index of a given subset, i.e. its index in the sorted list of all subsets of size k? One method would be to create a dictionary from subsets to indices by enumerating all subsets of size k, but this requires n choose k space and time. For my application n and k are infeasibly large, but I only need to determine the indices of comparatively few subsets.\nI'm coding in Python, but I'm more interested in a general algorithm than any specific implementation. Of course, if there's an especially fast way to do this in Python that would be great.\nMotivation: The subsets of {1,2,...,n} of size k correspond bijectively to basis vectors of the kth exterior power of a vector space with dimension n. I'm performing computations in the exterior algebra of a vector space and trying to convert the resulting lists of vectors into sparse matrices to do linear algebra, and to do that I need to index the vectors by integers rather than by lists.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":397,"Q_Id":20295961,"Users Score":1,"Answer":"I think you could do this by recursively narrowing down ranges, right? You know that all subsets beginning with a given integer will be adjacent, and that for a given first element d there will be (n - d) choose (k-1) of them. You can skip ahead as far as necessary in the virtual list of subsets until you're in the range of subsets beginning with the first element of the target sorted subset, then recurse to narrow it down precisely.\nEG, suppose n=20, k=6. If your target subset is {5, 8, 12, 14, 19}, none of the subsets beginning with 1-4 are valid choices. You know that the index first subset beginning with 5 will be ((19 choose 5) + (18 choose 5) + (17 choose 5) + (16 choose 5)). Call that index i0. Now you have (15 choose 5) subsets that all begin with 5 to index into, and none of the ones beginning with 5, 1-7... are interesting. (14 choose 4) of them start with 1, (13 choose 4) start with 2, etc. So the index of the first set beginning with 5, 8 will be i0 + (14 choose 4) + (13 choose 4) + (12 choose 4) + (11 choose 4) + (10 choose 4) + (9 choose 4) + (8 choose 4). Etc.\nWriting the algorithm out is kind of painful, but I think it should work nicely with a computer keeping track of the fiddly details.","Q_Score":2,"Tags":"python,algorithm,indexing,subset","A_Id":20295997,"CreationDate":"2013-11-30T02:52:00.000","Title":"Indexing subsets of size k","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I put goog_appengine inside android located at \/mnt\/sdcard\nI also put wsgiref folder  at same location.\nfrom Qpython I manage to \"send control key\" + \"d\"\nI got sh $\nI put command like the ff:\n\"$python \/mnt\/sdcard\/google_appengine\/appcfg.py\"\nBut Igot ImportError: no module _csv\nI feel putting these is not same architecture \" \/usr\/lib\/python2.7\/lib-dynload\/_csv.x86_64-linux-gnu.so\"\nThat come from ubuntu 13.04.  \nWhat to do next, Where I can find _csv module for Qpython+android_version.\nIs it possible to upload my code through android?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":840,"Q_Id":20308674,"Users Score":0,"Answer":"To install _csv or any other module, follow these steps.  Here's what I did to install websocket (and all of its dependencies):\nTo install websocket on the phone:\n\nStart QPython   \nClick the big button\nSelect \u201crun local script\u201d\nSelect \u201cpip_console.py\u201d\nType \u201cpip install websocket\u201d","Q_Score":0,"Tags":"importerror,qpython","A_Id":31173939,"CreationDate":"2013-12-01T04:17:00.000","Title":"ImportError: No module named _csv . Qpython for android logs","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":1,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I put goog_appengine inside android located at \/mnt\/sdcard\nI also put wsgiref folder  at same location.\nfrom Qpython I manage to \"send control key\" + \"d\"\nI got sh $\nI put command like the ff:\n\"$python \/mnt\/sdcard\/google_appengine\/appcfg.py\"\nBut Igot ImportError: no module _csv\nI feel putting these is not same architecture \" \/usr\/lib\/python2.7\/lib-dynload\/_csv.x86_64-linux-gnu.so\"\nThat come from ubuntu 13.04.  \nWhat to do next, Where I can find _csv module for Qpython+android_version.\nIs it possible to upload my code through android?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":840,"Q_Id":20308674,"Users Score":0,"Answer":"You can install _csv from QPython's system. ( You can find system icon in qpython's new version 0.9.6.2 )","Q_Score":0,"Tags":"importerror,qpython","A_Id":21482814,"CreationDate":"2013-12-01T04:17:00.000","Title":"ImportError: No module named _csv . Qpython for android logs","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":1,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"In Python 2.7,I need to record high precision floats (such as np.float64 from numpy or Decimal from decimal module) to a binary file and later read it back. How could I do it? I would like to store only bit image of a high precision float, without any overhead. \nThanks in advance!","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":239,"Q_Id":20350989,"Users Score":0,"Answer":"The struct module can handle 64 bit floats. Decimals are another matter - the binary representation is a string of digits. Probably not what you want. You could covert it to BCD to halve the amount of storage.","Q_Score":0,"Tags":"python,python-2.7,floating-point,floating-point-precision","A_Id":20351305,"CreationDate":"2013-12-03T12:17:00.000","Title":"How to store high precision floats in a binary file, Python 2.7?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a n x n matrix in numpy which has 0 and non-0 values. Is there a way to easily convert it to a boolean matrix?\nThanks.","AnswerCount":4,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":34445,"Q_Id":20373039,"Users Score":16,"Answer":"You should use array.astype(bool) (or array.astype(dtype=bool)). Works with matrices too.","Q_Score":18,"Tags":"python,numpy","A_Id":20375614,"CreationDate":"2013-12-04T10:38:00.000","Title":"How do I convert a numpy matrix into a boolean matrix?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a n x n matrix in numpy which has 0 and non-0 values. Is there a way to easily convert it to a boolean matrix?\nThanks.","AnswerCount":4,"Available Count":2,"Score":0.1973753202,"is_accepted":false,"ViewCount":34445,"Q_Id":20373039,"Users Score":4,"Answer":"Simply use equality check:\nSuppose a is your numpy matrix, use b = (a == 0) or b = (a != 0) to get the boolean value matrix.\nIn some case, since the value maybe sufficiently small but non-zero, you may use abs(a) < TH, where TH is the numerical threshold you set.","Q_Score":18,"Tags":"python,numpy","A_Id":20373327,"CreationDate":"2013-12-04T10:38:00.000","Title":"How do I convert a numpy matrix into a boolean matrix?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using Python for kernel density estimations and gaussian mixture models to rank likelihood of samples of multidimensional data.\nEvery piece of data is an angle, and I'm not sure how to handle the periodicity of angular data for machine learning.\nFirst I removed all negative angles by adding 360 to them, so all angles that were negative became positive, -179 becoming 181.  I believe this elegantly handles the case of -179 an similar being not significantly different than 179 and similar, but it does not handle instances like 359 being not dissimilar from 1.  \nOne way I've thought of approaching the issue is keeping both negative and negative+360 values and using the minimum of the two, but this would require modification of the machine learning algorithms.\nIs there a good preprocessing-only solution to this problem?  Anything built into scipy or scikit?\nThanks!","AnswerCount":4,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":3296,"Q_Id":20382484,"Users Score":0,"Answer":"Another simpler way could be to use time as angle measurements than degree measurements (not DMS though). Since many analytics software features time as a datatype, you can use its periodicity to do your job.\nBut remember, you need to scale 360 degrees to 24 hours.","Q_Score":8,"Tags":"python,machine-learning,scipy,scikit-learn,angle","A_Id":49957628,"CreationDate":"2013-12-04T17:56:00.000","Title":"Periodic Data with Machine Learning (Like Degree Angles -> 179 is 2 different from -179)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a large dataset that I perform experiments on. It takes 30 mins to load the dataset from file into memory using a python program. Then I perform variations of an algorithm on the dataset. Each time I have to vary the algorithm, I have to load the dataset into memory again, which eats up 30 minutes.\nIs there any way to load the dataset into memory once and for always. And then each time to run a variation of an algorithm, just use that pre loaded dataset?\nI know the question is a bit abstract, suggestions to improve the framing of the question are welcome. Thanks.\nEDITS:\nIts a text file, contains graph data, around 6 GB. If I only load a portion of the dataset, it doesn't make for a very good graph. I do not do computation while loading the dataset.","AnswerCount":3,"Available Count":2,"Score":0.0665680765,"is_accepted":false,"ViewCount":233,"Q_Id":20389291,"Users Score":1,"Answer":"You could write a very quick CLI which would load the data, and then ask for a python filename, which it would then eval() on the data...","Q_Score":0,"Tags":"python","A_Id":20389326,"CreationDate":"2013-12-05T00:51:00.000","Title":"load dataset into memory for future computation in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a large dataset that I perform experiments on. It takes 30 mins to load the dataset from file into memory using a python program. Then I perform variations of an algorithm on the dataset. Each time I have to vary the algorithm, I have to load the dataset into memory again, which eats up 30 minutes.\nIs there any way to load the dataset into memory once and for always. And then each time to run a variation of an algorithm, just use that pre loaded dataset?\nI know the question is a bit abstract, suggestions to improve the framing of the question are welcome. Thanks.\nEDITS:\nIts a text file, contains graph data, around 6 GB. If I only load a portion of the dataset, it doesn't make for a very good graph. I do not do computation while loading the dataset.","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":233,"Q_Id":20389291,"Users Score":0,"Answer":"One possible solution is to use Jupyter to load it once and keep the Jupyter session running. Then you modify your algorithm in a cell and always rerun that cell alone. You can operate on the loaded dataset in RAM as much as you want until you terminate the Jupyter session.","Q_Score":0,"Tags":"python","A_Id":68645793,"CreationDate":"2013-12-05T00:51:00.000","Title":"load dataset into memory for future computation in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a giant (100Gb) csv file with several columns and a smaller (4Gb) csv also with several columns. The first column in both datasets have the same category. I want to create a third csv with the records of the big file which happen to have a matching first column in the small  csv. In database terms it would be a simple join on the first column. \nI am trying to find the best approach to go about this in terms of efficiency. As the smaller dataset fits in memory, I was thinking of loading it in a sort of set structure and then read the big file line to line and querying the in memory set, and write to file on positive.\nJust to frame the question in SO terms, is there an optimal way to achieve this?\nEDIT: This is a one time operation.\nNote: the language is not relevant, open to suggestions on column, row oriented databases, python, etc...","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":154,"Q_Id":20389982,"Users Score":0,"Answer":"If you are only doing this once, your approach should be sufficient. The only improvement I would make is to read the big file in chunks instead of line by line. That way you don't have to hit the file system as much. You'd want to make the chunks as big as possible while still fitting in memory.\nIf you will need to do this more than once, consider pushing the data into some database. You could insert all the data from the big file and then \"update\" that data using the second, smaller file to get a complete database with one large table with all the data. If you use a NoSQL database like Cassandra this should be fairly efficient since Cassandra is pretty good and handling writes efficiently.","Q_Score":0,"Tags":"c#,python,database,bigdata","A_Id":20390085,"CreationDate":"2013-12-05T02:00:00.000","Title":"Intersecting 2 big datasets","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How can I find the index of the 3 smallest and 3 largest values in a column in my pandas dataframe? I saw ways to find max and min, but none to get the 3.","AnswerCount":5,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":7247,"Q_Id":20415414,"Users Score":5,"Answer":"What have you tried? You could sort with s.sort() and then call s.head(3).index and s.tail(3).index.","Q_Score":4,"Tags":"python,pandas,dataframe","A_Id":20415698,"CreationDate":"2013-12-06T03:29:00.000","Title":"python pandas 3 smallest & 3 largest values","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using windows 7 home basic 64 bit. I wanted to work with FITS file in python 3.3 so downloaded pyfits and numpy for 64 bit. When I import pyfits I get the following error:\n\nTraceback (most recent call last):   File \"\", line 1, in\n       import pyfits as py   File\n  \"C:\\Python33\\lib\\site-packages\\pyfits__init__.py\", line 26, in\n       import pyfits.core   File\n  \"C:\\Python33\\lib\\site-packages\\pyfits\\core.py\", line 38, in \n  import pyfits.py3compat   File\n  \"C:\\Python33\\lib\\site-packages\\pyfits\\py3compat.py\", line 12, in\n       import pyfits.util   File\n  \"C:\\Python33\\lib\\site-packages\\pyfits\\util.py\", line 29, in \n  import numpy as np   File\n  \"C:\\Python33\\lib\\site-packages\\numpy__init__.py\", line 168, in\n       from . import add_newdocs   File\n  \"C:\\Python33\\lib\\site-packages\\numpy\\add_newdocs.py\", line 13, in\n       from numpy.lib import add_newdoc   File\n  \"C:\\Python33\\lib\\site-packages\\numpy\\lib__init__.py\", line 8, in\n       from .type_check import *   File\n  \"C:\\Python33\\lib\\site-packages\\numpy\\lib\\type_check.py\", line 11, in\n       import numpy.core.numeric as _nx   File\n  \"C:\\Python33\\lib\\site-packages\\numpy\\core__init__.py\", line 6, in\n       from . import multiarray ImportError: DLL load failed: %1\n  is not a valid Win32 application.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":718,"Q_Id":20426690,"Users Score":0,"Answer":"This is a problem importing numpy, not pyfits. You can tell because the traceback ended upon trying to import the numpy multiarray module. \nThis error suggests that the numpy you have installed was not built for the same architecture as your Python installation.","Q_Score":0,"Tags":"python,numpy,windows64,pyfits","A_Id":20436137,"CreationDate":"2013-12-06T14:43:00.000","Title":"pyfits not working for windows 64 bit","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to automate the analyses of many similar images which are basic lots of small blackish blobs on a somewhat homogeneous brown  background.\nI have tried the find_blobs method from simpleCV but it is not accurate enough. However with gimps contiguous selection tool, also known as Magic wand, I was able to achieve much better results, in separating the background from my blobs.\nMy problem is that I need to automate this process, so I can't have a person clicking on each image. Any suggestion of a Python friendly library in which I can find this functionality? Is using Gimp in batch mode the only way?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1258,"Q_Id":20471228,"Users Score":2,"Answer":"pdb.gimp_image_select_contiguous_color is the programatic way - in a Python plug-in - of doing the magic wand. The drawback is that you have to issue suitable starting coordinates for it to work well.\nMaye repeating the process in 3 distant points of the image, and if the selection does not diverge by much in two of those, assume that to be the one you want.\nThe procedure does not return the selection drawable, so you have to get it by issuing\npdb.gimp_image_get_selection afterwards. You will also need to set the threshold by calling pdb.gimp_context_set_sample_threshold before calling it.\n(My suggestion: copy it to another, new image, resize that to an 8x8pixel image, from which you can get the pixel values and compare directly against other selections made);","Q_Score":1,"Tags":"python,image-processing,gimp","A_Id":20483571,"CreationDate":"2013-12-09T12:56:00.000","Title":"Programmatic equivalent of Gimp's Contiguous selection tool","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a function compare_images(k, a, b) that compares two 2d-arrays a and b\nInside the funcion, I apply a gaussian_filter with sigma=k to a My idea is to estimate how much I must to smooth image a in order for it to be similar to image b\nThe problem is my function compare_images will only return different values if k variation is over 0.5, and if I do fmin(compare_images, init_guess, (a, b) it usually get stuck to the init_guess value.\nI believe the problem is fmin (and minimize) tends to start with very small steps, which in my case will reproduce the exact same return value for compare_images, and so the method thinks it already found a minimum. It will only try a couple times.\nIs there a way to force fmin or any other minimizing function from scipy to take larger steps? Or is there any method better suited for my need?\nEDIT:\nI found a temporary solution.\nFirst, as recommended, I used xtol=0.5 and higher as an argument to fmin.\nEven then, I still had some problems, and a few times fmin would return init_guess.\nI then created a simple loop so that if fmin == init_guess, I would generate another, random init_guess and try it again.\nIt's pretty slow, of course, but now I got it to run. It will take 20h or so to run it for all my data, but I won't need to do it again.\nAnyway, to better explain the problem for those still interested in finding a better solution:\n\nI have 2 images, A and B, containing some scientific data.\nA looks like a few dots with variable value (it's a matrix of in which each valued point represents where a event occurred and it's intensity)\nB looks like a smoothed heatmap (it is the observed density of occurrences)\nB looks just like if you applied a gaussian filter to A with a bit of semi-random noise.\nWe are approximating B by applying a gaussian filter with constant sigma to A. This sigma was chosen visually, but only works for a certain class of images. \nI'm trying to obtain an optimal sigma for each image, so later I could find some relations of sigma and the class of event showed in each image.\n\nAnyway, thanks for the help!","AnswerCount":4,"Available Count":1,"Score":0.1488850336,"is_accepted":false,"ViewCount":4444,"Q_Id":20478949,"Users Score":3,"Answer":"I realize this is an old question but I haven't been able to find many discussion of similar topics. I am facing a similar issue with scipy.optimize.least_squares. I found that xtol did not do me much good. It did not seem to change the step size at all. What made a big difference was diff_step. This sets the step size taken when numerically estimating the Jacobian according to the formula step_size = x_i*diff_step, where x_i is each independent variable. You are using fmin so you aren't calculating Jacobians, but if you used another scipy function like minimize for the same problem, this might help you.","Q_Score":13,"Tags":"python,optimization,numpy,scipy,gaussian","A_Id":61375377,"CreationDate":"2013-12-09T19:25:00.000","Title":"How to force larger steps on scipy.optimize functions?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to model a data with gaussian with parameters (mu=1, sig=2, height=1) and pass initial parameters x0 = (0.8, 0.8, 0.9). \nI am wondering  how does the optimizer knows the order of parameters. I could have taken the parameters as (mu,height,sig) or in any other order.\nEdit:\nGaussian model (mu=1,sig=1.5,height=0.8)\nInitial parameters passed x0=(0.8,0.8,0.8)\nHow can I be sure that the optimizer understands it as (mu,sig,height) and not as (sig,mu,height)?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":160,"Q_Id":20498752,"Users Score":0,"Answer":"The order in which function parameters are passed is only ever not known when the function is defined with **kwargs (unnamed keyword arguments -- the function then has a dict called kwargs that contains them, obviously unordered).\nWhen the function is defined with named parameters, though, the f(a=b, c=d) syntax does not create a dict -- it simply assigns the values to the corresponding named parameters within the function.","Q_Score":2,"Tags":"python,optimization,parameters,scipy","A_Id":20499162,"CreationDate":"2013-12-10T15:42:00.000","Title":"How scipy.optimize.leastsq knows the order of parameters passed to it?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to write a MPI program where the master thread continuously submits new job to the workers (i.e. not just at the start, like in the MapReduce pattern).\nInitially, lets say, I submit 100 jobs onto 100 workers.\nThen, I would like to be notified when a worker is finished with a job. I would send the next job off, whose parameters depend on all the results received so far. The order of results does not have to be preserved, I would just need them as they finish.\nI can work with C\/C++\/Python.\nFrom the documentation, it seems like I can broadcast N jobs, and gather the results. But this is not what I need, as I don't have all of them available, and gather would block. I am looking for a asynchronous, any-worker recv call, essentially.","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":182,"Q_Id":20500675,"Users Score":2,"Answer":"You can use MPI_ANY_SOURCE and MPI_ANY_TAG for receiving from anywhere. After receiving you can read the Information (source and tag) out of the MPI_Status structure that has to passed to the MPI_Recv call.\nIf you use this you do not neccessary need any asynchronous communication, since the master 'listens' to everybody asking for new jobs and returning results; and each slave does his task and then sends the result to the master asks for new work and waits for the answer from the master.\nYou should not have to work with scatter\/gather at all since those are ment for use on an array of data and your problem seems to have more or less independant tasks.","Q_Score":1,"Tags":"c++,python,c,mpi,distributed-computing","A_Id":20500867,"CreationDate":"2013-12-10T17:03:00.000","Title":"MPI distributed, unordered work","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I really appreciate any responses. I am thoroughly confused and never knew this experiment design\/builder software was so complicated!\nI am a fast learner, but still a newbie so please be patient.\nYes I have googled the answers for my question but no answers to similar questions seem to work.\nI have an experiment, there are 8 conditions,\neach condition needs to show 1 image, chosen at random from a folder of similar images.\nEach trial needs to be different (so each participant sits a differently ordered set of conditions) AND each condition's selected image will be different.\nSo;\nCondition- Image\nA        - 1 to 140\nB        - 1 to 80\netc..\nRecording data is not a problem as this can be done by hand, but I just need the images to be randomly selected from a pre-defined group.\nI have tried using code to randomise and shuffle the order, but have got nowhere.\nPlease help,\nTom","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":65,"Q_Id":20509103,"Users Score":1,"Answer":"To generate retrieve the path of a random image in a folder img_folder, it's not too difficult. You can use img_path_array = os.listdir('...\/img_folder'). Randomly generate an integer between 0 and len(img_path_array) using random_index = randrange(len(img_path_array)) (import random to use this function), and gain access to the random file url by calling img_path_array[random_index].","Q_Score":1,"Tags":"python,random","A_Id":20509223,"CreationDate":"2013-12-11T01:54:00.000","Title":"how to randomise the image shown in a sketchpad","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Numpy's logical_or function takes no more than two arrays to compare.  How can I find the union of more than two arrays?  (The same question could be asked with regard to Numpy's logical_and and obtaining the intersection of more than two arrays.)","AnswerCount":9,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":64891,"Q_Id":20528328,"Users Score":9,"Answer":"Building on abarnert's answer for n-dimensional case:\nTL;DR: np.logical_or.reduce(np.array(list))","Q_Score":129,"Tags":"python,arrays,numpy","A_Id":41922008,"CreationDate":"2013-12-11T19:33:00.000","Title":"Numpy `logical_or` for more than two arguments","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Using VTK version 5.1, I'm having some issues with some models not displaying correctly in OpenGL.\nThe process for getting the models into VTK is a bit roundabout, but gets there and is fairly simple. Each model is a manifold mesh comprised of only quads and tris.\nBlender models->custom export format containing points, point normals, and polygons\nCustom export format->Custom C++ parser->vtkPolyData\nvtkPolydata->vtkTriangleFilter->vtkStripper->vtkPolyDataNormals->final product\nAs our final product was showing irregular and missing normals when it was rendered, I had VTK write the object to a plaintext file, which I then parsed back into Blender using python.\nInitial results were that the mesh was correct and matched the original model, however, when I used the Blender \"select non-manifold\" option, about 15% of the model showed to be nonmanifold. A bit of reading around online suggested the \"remove doubles\" as a solution, which did in fact solve the issue of making the mesh closed, but the normals were still irregular.\nSo, I guess I'm hoping there are some additional options\/functions\/filters I can use to ensure the models are properly read and\/or processed through the filters.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":175,"Q_Id":20528456,"Users Score":0,"Answer":"This was solved by requesting that Blender perform a triangulization of the mesh prior to  the export operation. \nThe mangling was due to Blender performing implicit triangulization of quads, resulting in faces which were stored as 4 non-coplanar points. By forcing explicit triangulation in advance, I was able to successfully perform the export and maintain model integrity\/manifold-ness. The holes that were being experienced were due to the implicit triangulation not being copied by the exporter and thus causing loss of data.","Q_Score":0,"Tags":"c++,python,opengl,blender,vtk","A_Id":25369835,"CreationDate":"2013-12-11T19:41:00.000","Title":"vtk Filters causing point doubling and mangling normal values","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":1,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am dealing with a very silly error, and wondering if any of you have the same problem. When I try to import pandas using import pandas as pd I get an error in copy.py. I debugged into the pamdas imports, and I found that the copy error is thrown when pandas tries to import this:  from pandas.io.html import read_html\n\nThe exception that is throwns is: \nun(shallow)copyable object of type <type 'Element'>\nI do not get this error if I try to straight up run the code and not use the PVTS debugger. I am using the python 2.7 interpreter, pandas version 0.12 which came with the python xy 2.7.5.1 distro and MS Visual Studio 2012. \nAny help would be appreciated. Thanks!","AnswerCount":4,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":3689,"Q_Id":20532621,"Users Score":0,"Answer":"I faced the same issue, but just hitting 'Continue' will cause it to be ignored and the code execution will proceed in the usual way.\nOr you could uncheck the \"Break when this exception type is user-handled\" option that comes up in the dialog box displaying the error.","Q_Score":5,"Tags":"python,python-2.7,visual-studio-2012,pandas,ptvs","A_Id":45561466,"CreationDate":"2013-12-12T00:14:00.000","Title":"Pandas import error when debugging using PVTS","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am dealing with a very silly error, and wondering if any of you have the same problem. When I try to import pandas using import pandas as pd I get an error in copy.py. I debugged into the pamdas imports, and I found that the copy error is thrown when pandas tries to import this:  from pandas.io.html import read_html\n\nThe exception that is throwns is: \nun(shallow)copyable object of type <type 'Element'>\nI do not get this error if I try to straight up run the code and not use the PVTS debugger. I am using the python 2.7 interpreter, pandas version 0.12 which came with the python xy 2.7.5.1 distro and MS Visual Studio 2012. \nAny help would be appreciated. Thanks!","AnswerCount":4,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":3689,"Q_Id":20532621,"Users Score":0,"Answer":"I had a system crash while developing a PTVS app and then ran into this problem, re-running the Intellisense 'refresh DB' cleared it.","Q_Score":5,"Tags":"python,python-2.7,visual-studio-2012,pandas,ptvs","A_Id":37602619,"CreationDate":"2013-12-12T00:14:00.000","Title":"Pandas import error when debugging using PVTS","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am dealing with a very silly error, and wondering if any of you have the same problem. When I try to import pandas using import pandas as pd I get an error in copy.py. I debugged into the pamdas imports, and I found that the copy error is thrown when pandas tries to import this:  from pandas.io.html import read_html\n\nThe exception that is throwns is: \nun(shallow)copyable object of type <type 'Element'>\nI do not get this error if I try to straight up run the code and not use the PVTS debugger. I am using the python 2.7 interpreter, pandas version 0.12 which came with the python xy 2.7.5.1 distro and MS Visual Studio 2012. \nAny help would be appreciated. Thanks!","AnswerCount":4,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":3689,"Q_Id":20532621,"Users Score":0,"Answer":"I had the same problem for a while,  disabling \"Debug standard library\" didn't help, then I downloaded the latest version of Python (3.4), pip installed the libs (for example NLTK), and it worked!","Q_Score":5,"Tags":"python,python-2.7,visual-studio-2012,pandas,ptvs","A_Id":36753326,"CreationDate":"2013-12-12T00:14:00.000","Title":"Pandas import error when debugging using PVTS","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"wls_prediction_std returns standard deviation and confidence interval of my fitted model data. I would need to know the way the confidence intervals are calculated from the covariance matrix. (I already tried to figure it out by looking at the source code but wasn't able to) I was hoping some of you guys could help me out by writing out the mathematical expression behind wls_prediction_std.","AnswerCount":1,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":2096,"Q_Id":20572706,"Users Score":7,"Answer":"There should be a variation on this in any textbook, without the weights.\nFor OLS, Greene (5th edition, which I used) has\nse = s^2 (1 + x (X'X)^{-1} x')\nwhere s^2 is the estimate of the residual variance, x is vector or explanatory variables for which we want to predict and X are the explanatory variables used in the estimation.\nThis is the standard error for an observation, the second part alone is the standard error for the predicted mean y_predicted = x beta_estimated.\nwls_prediction_std uses the variance of the parameter estimate directly.\nAssuming x is fixed, then y_predicted is just a linear transformation of the random variable beta_estimated, so the variance of y_predicted is just\nx Cov(beta_estimated) x'\nTo this we still need to add the estimate of the error variance.\nAs far as I remember, there are estimates that have better small sample properties.\nI added the weights, but never managed to verify them, so the function has remained in the sandbox for years. (Stata doesn't return prediction errors with weights.)\nAside:\nUsing the covariance of the parameter estimate should also be correct if we use a sandwich robust covariance estimator, while Greene's formula above is only correct if we don't have any misspecified heteroscedasticity.\nWhat wls_prediction_std doesn't take into account is that, if we have a model for the heteroscedasticity, then the error variance could also depend on the explanatory variables, i.e. on x.","Q_Score":5,"Tags":"python,statsmodels","A_Id":20573996,"CreationDate":"2013-12-13T17:41:00.000","Title":"Mathematical background of statsmodels wls_prediction_std","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am having problem to understand normalization concept in artificial neural networks. If you could explain how it works. For example if I want input basketball score 58-72 or if I want input word \u201ccat\u201d (as a natural language word). How it works if the range is [-1,1]. Be aware that I am very new with ANN and normalization concept.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":756,"Q_Id":20579179,"Users Score":0,"Answer":"One way to do this is to bit-encode your input; have one neuron per bit of the maximal length input string; and feed 0 as -1, and 1 as 1. If you desire a bit-string as output, then interpret positive outputs as 1 and negative outputs as 0.","Q_Score":3,"Tags":"python,neural-network,normalization","A_Id":20579228,"CreationDate":"2013-12-14T02:55:00.000","Title":"Normalization in the artificial neural network","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am currently using numpy.random.random_sample to compute a large set of random numbers. If I delete, say, every 10th of these numbers, is the result still going to be as random as before? Or would I introduce some sort of skew by doing this?\nEDIT: As pointed out this boils down to how good my RNG is. How can I find out if I can trust a RNG, or how would I spot a potential skew?","AnswerCount":1,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":101,"Q_Id":20617587,"Users Score":8,"Answer":"If they were indistinguishable from to true random begin with, they will be indistinguishable from true random afterwards.\nThe reason is that any correlation or bias that exists among the remaining numbers would also constitute a correlation or bias among the complete set. Therefore if the complete set is good then the subset is good.\nOf course, this would not necessarily be the case if you deleted the numbers selectively based on their value, rather than based solely on their position in the sequence.\nAlso, if the numbers are not good to begin with then they might conceivably be worse afterwards than before. For an extreme example, consider a sequence that consists of 9 zeros followed by the result of a coin toss, 9 zeros and another coin toss, etc. This data source has some entropy (1 bit per 10 values), but if you remove every 10th element then it has none (the remaining output is known in advance).","Q_Score":1,"Tags":"python,random,numpy","A_Id":20617702,"CreationDate":"2013-12-16T17:58:00.000","Title":"Will random numbers still be random if I systematically delete every nth one?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working with an Oracle database with millions of rows and 100+ columns. I am attempting to store this data in an HDF5 file using pytables with certain columns indexed. I will be reading subsets of these data in a pandas DataFrame and performing computations.\nI have attempted the following:\nDownload the the table, using a utility into a csv file, read the csv file chunk by chunk using pandas and append to HDF5 table using pandas.HDFStore. I created a dtype definition and provided the maximum string sizes.\nHowever, now when I am trying to download data directly from Oracle DB and post it to HDF5 file via pandas.HDFStore, I run into some problems.\npandas.io.sql.read_frame does not support chunked reading. I don't have enough RAM to be able to download the entire data to memory first.\nIf I try to use cursor.fecthmany() with a fixed number of records, the read operation takes ages at the DB table is not indexed and I have to read records falling under a date range. I am using DataFrame(cursor.fetchmany(), columns = ['a','b','c'], dtype=my_dtype) \nhowever, the created DataFrame always infers the dtype rather than enforce the dtype I have provided (unlike read_csv which adheres to the dtype I provide). Hence, when I append this DataFrame to an already existing HDFDatastore, there is a type mismatch for e.g. a float64 will maybe interpreted as int64 in one chunk.\nAppreciate if you guys could offer your thoughts and point me in the right direction.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":5171,"Q_Id":20618523,"Users Score":0,"Answer":"Okay, so I don't have much experience with oracle databases, but here's some thoughts:\nYour access time for any particular records from oracle are slow, because of a lack of indexing, and the fact you want data in timestamp order. \nFirstly, you can't enable indexing for the database?\nIf you can't manipulate the database, you can presumably request a found set that only includes the ordered unique ids for each row?\nYou could potentially store this data as a single array of unique ids, and you should be able to fit into memory. If you allow 4k for every unique key (conservative estimate, includes overhead etc), and you don't keep the timestamps, so it's just an array of integers, it might use up about 1.1GB of RAM for 3 million records. That's not a whole heap, and presumably you only want a small window of active data, or perhaps you are processing row by row?\nMake a generator function to do all of this. That way, once you complete iteration it should free up the memory, without having to del anything, and it also makes your code easier to follow and avoids bloating the actual important logic of your calculation loop.\nIf you can't store it all in memory, or for some other reason this doesn't work, then the best thing you can do, is work out how much you can store in memory. You can potentially split the job into multiple requests, and use multithreading to send a request once the last one has finished, while you process the data into your new file. It shouldn't use up memory, until you ask for the data to be returned. Try and work out if the delay is the request being fulfilled, or the data being downloaded.\nFrom the sounds of it, you might be abstracting the database, and letting pandas make the requests. It might be worth looking at how it's limiting the results. You should be able to make the request for all the data, but only load the results one row at a time from the database server.","Q_Score":12,"Tags":"python,pandas,hdf5,pytables","A_Id":29225626,"CreationDate":"2013-12-16T18:50:00.000","Title":"Reading a large table with millions of rows from Oracle and writing to HDF5","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a 1D array. each element holds a unique value IE [2013 12 16 1 10] so array[0,0] would be [2013] array[0,1] would be [12]. array[0,0:2] would be [2013 12].\nWhen I try array.index(array[0,0:5]). It creates error and says that list indicies must be integers, not tuple. find the index of a specific element if the element is [2013 12 16 1 10] a tuple...?","AnswerCount":4,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":979,"Q_Id":20623847,"Users Score":0,"Answer":"If you have a 1 dimensional array, that is simply an array of values like you mentioned where the array could equal [2013, 12, 16, 1, 10]. You access individual items in the array by using array[index]. However, there are actually 3 parameters used for getting array values:\narray[start:end:step]\narray[0, 1] is invalid, as the syntax is using colons not commas. 0,1 evaluates to a tuple of 2 values, (0, 1). If you want to get the value of 12, you need to say array[1]","Q_Score":2,"Tags":"python,arrays,multidimensional-array,indexing,tuples","A_Id":20623886,"CreationDate":"2013-12-17T00:43:00.000","Title":"Python, find the index of 1D array that is filled with arrays of tuple","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to import a .csv file using pandas.read_csv(), however, I don't want to import the 2nd row of the data file (the row with index = 1 for 0-indexing).\nI can't see how not to import it because the arguments used with the command seem ambiguous:\nFrom the pandas website:\n\nskiprows : list-like or integer\nRow numbers to skip (0-indexed) or number of rows to skip (int) at the\nstart of the file.\"\n\nIf I put skiprows=1 in the arguments, how does it know whether to skip the first row or skip the row with index 1?","AnswerCount":6,"Available Count":1,"Score":-0.0665680765,"is_accepted":false,"ViewCount":269629,"Q_Id":20637439,"Users Score":-2,"Answer":"skip[1] will skip second line, not the first one.","Q_Score":121,"Tags":"python,pandas,csv,readfile","A_Id":55944660,"CreationDate":"2013-12-17T14:59:00.000","Title":"Skip rows during csv import pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to machine learning and wondering the difference between kmeans and kmeans2 in scipy. According to the doc both of them are using the 'k-means' algorithm, but how to choose them?","AnswerCount":1,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":3357,"Q_Id":20661142,"Users Score":7,"Answer":"Based on the documentation, it seems kmeans2 is the standard k-means algorithm and runs until converging to a local optimum - and allows you to change the seed initialization. \nThe kmeans function will terminate early based on a lack of change, so it may not even reach a local optimum. Further, the goal of it is to generate a codebook to map feature vectors to. The codebook itself is not necessarily generated from the stoping point, but will use the iteration that had the lowest \"distortion\" to generate the codebook. This method will also run kmeans multiple times. The documentation goes into more specifics. \nIf you just want to run k-means as an algorithm, pick kmeans2. If you just want a codebook, pick kmeans.","Q_Score":10,"Tags":"python,machine-learning,scipy,k-means","A_Id":20661301,"CreationDate":"2013-12-18T14:44:00.000","Title":"What's the difference between kmeans and kmeans2 in scipy?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I develop a library that uses Cython at a low level to solve flow problems across 2D arrays.  If these arrays are numpy arrays I can statically type them thus avoiding the Python interpreter overhead of random access into those arrays.  To handle arrays of sizes so big they don't fit in memory, I plan to use hd5file Arrays from pytables in place of numpy, but I can't figure out if it's possible to statically type a CArray.\nIs it possible to statically type hd5file CArrays in Cython to avoid Python interpreter overhead when randomly accessing those arrays?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":100,"Q_Id":20711941,"Users Score":1,"Answer":"If you use the h5py package, you can use numpy.asarray() on the datasets it gives you, then you have a more familiar NumPy array that you already know how to deal with.\nPlease note that h5py had a bug related to this until a couple years ago which caused disastrously slow performance when doing asarray() but this was solved so please don't use a very old version if you're going to try this.","Q_Score":3,"Tags":"python,cython,hdf5,pytables","A_Id":20714204,"CreationDate":"2013-12-20T21:06:00.000","Title":"Can I statically type a h5file array in Cython?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am producing an imshow plot in Python.\nIn the final plot, I have strips\/columns of data, between which there is no data at all - kind of like a barcode.\nAt the moment, where I have no data, I have just set all values to zero. The color of these regions of no data is therefore whatever colour represents zero in my colorbar - green in my case.\nWhat I really want is for these columns\/strips just to be white, and to make to really clear that these are regions of NO data.\nI realise that I could change the colorbar so that the zero is white, but I really want to distinguish the regions of no data from any zeros that might be in the data.\nThank you.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":622,"Q_Id":20728942,"Users Score":1,"Answer":"try setting those values to np.nan ( or float('nan')); you may also want to pass interpolation='nearest' to imshow as an argument.","Q_Score":1,"Tags":"python,imshow","A_Id":20729020,"CreationDate":"2013-12-22T11:34:00.000","Title":"highlight regions of no data in a Python imshow plot","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"So, I compiled NumPy from source, linking to MKL. Now I want to compare NumPy's performance with and without MKL. Is there any way I can \"tell\" NumPy not to use MKL, so I can produce the benchmarks? For instance, with numExpr we can do numexpr.use_vml = False. Is there anything similar for NumPy? I really don't wanna have to compile NumPy without MKL just for this.\n(Ubuntu 12.04, Python 2.7.3, NumPy 1.8, Intel Composer XE 2013 SP1)","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1214,"Q_Id":20778806,"Users Score":0,"Answer":"As far as I know the only way is to compile NumPy again without MKL, preferably in a virtualenv....","Q_Score":4,"Tags":"python,numpy,ubuntu-12.04,scientific-computing,intel-mkl","A_Id":20780564,"CreationDate":"2013-12-26T03:38:00.000","Title":"compare NumPy vs. NumPy+MKL performance","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How do I plot a line of transparent colour or no colour ?\nFor example :\nplot(x,y,color='transparent')\nor\nplot(x,y,color='None')\nBTW , color='white' is not what I'm looking for .\nThanks in advance invisible computer people .","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":18046,"Q_Id":20795990,"Users Score":1,"Answer":"Try color = '#FF000000' its the Hex code.","Q_Score":0,"Tags":"python","A_Id":70174450,"CreationDate":"2013-12-27T06:31:00.000","Title":"Python . Select transparent colour or no colour for lines \/ points etc","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am a beginner in learning image processing and I am a bit confused with the concept of spatial and temporal characterization.  So, for Spatial characterization, is it like a 2D map which contains some statistical information about the map?  And in terms of the temporal characterization, is the value with respect to time?  What does it mean and why do we care?  Thanks!","AnswerCount":3,"Available Count":2,"Score":0.1325487884,"is_accepted":false,"ViewCount":72641,"Q_Id":20818230,"Users Score":2,"Answer":"Spatial = Space\nExample, Image consist of pixel values which needs memory space to store. Spatial information = Pixel values stored into Memory\nTemporal = Time\nExample, Video consists of image frame sequence. With respect to time the frames are changed in video. This is called Temporal Information.","Q_Score":13,"Tags":"java,python,image,matlab,image-processing","A_Id":33393680,"CreationDate":"2013-12-28T19:02:00.000","Title":"what's the difference between spatial and temporal characterization in terms of image processing?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am a beginner in learning image processing and I am a bit confused with the concept of spatial and temporal characterization.  So, for Spatial characterization, is it like a 2D map which contains some statistical information about the map?  And in terms of the temporal characterization, is the value with respect to time?  What does it mean and why do we care?  Thanks!","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":72641,"Q_Id":20818230,"Users Score":0,"Answer":"Example of Spatial analysis application: Inspecting an image e.g. pose estimation on an image.\nExample of Temporal analysis application: Inspecting a video e.g. vehicle monitoring or pedestrian path prediction","Q_Score":13,"Tags":"java,python,image,matlab,image-processing","A_Id":70253700,"CreationDate":"2013-12-28T19:02:00.000","Title":"what's the difference between spatial and temporal characterization in terms of image processing?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Here goes a difficult one for the expert Numpyer!\nDoes someone know or can come up with a way to use pure Numpy arrays and functions to draw as well as fill colored polygons on top of a numpy array grid? \nThis I think would take two steps:\n\nThe ability to fill the grid array with color values so that the\npolygon filling could be written in one color, and the outline in\nanother one. What would be ideal and fastest for such a system, eg\ntuples of rgb values, color name strings, etc?\nThe ability to draw and fill the inside of a polygon based on an\narray of its pixel coordinates. Drawing the outline of it I think\nshould be pretty easy by just using the coordinates as indexes to\nthe grid array and setting them to the outline color values. More\ndifficult would be to fill the polygon. One way would be to iterate\nthrough all pixel cell coordinates (or better yet only for the\nneighboring cells of each polygon coordinate point) and test if its\ncoordinates are within the polygon pixel coordinates. I know there\nare some simple inside-outside\/biggerthan-smallerthan algorithms to\ntest for point in poly using regular Python, but for my purpose this\nwould be too slow and I am wondering if someone has the numpy skills\nto set up such an advanced linked code using only the speed of the\nnumpy builtin functions to return True if a numpy pixel coordinate\nis inside a polygon.\nLastly, if someone would know how to draw a line between two\npixel\/grid\/array coordinates by filling in all the cells in between\ntwo pixels with a True or color value?\n\nI know this is a tall order, but I was just curious to see if anyone would know. If so, then this could make for a pretty powerful and fast Numpy-based drawing library. In the end I would like to save the grid array as an image which is easy by passing it to PIL. \nI am aware that PIL and Aggdraw can do the polygon drawings and that this has been suggested in many similar posts, but they are not very effective when receiving a numpy array of xy polygon\/line coordinates. The assumption here is that my polygon coordinates are already in a numpy array format and I want to avoid the overhead of having to copy them into lists for every drawing (when we're talking about thousands of polygons frequently). So the difference in this post is about how to fill a polygon using pure Numpy.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2144,"Q_Id":20897140,"Users Score":1,"Answer":"In this case the point to achieve speed is more the used algorithms than the language of choice. Drawing and filling poligons rasterized over a grid of pixel falls into the domain of image processing algorithms and for sure AggDraw is using algorithms from that field.\nThe idea is that if you evaluate for each points a function that considers the vectorial nature of the polygon you need to do a number of operations that is at least O(2*p*A) where:\n\nA = image area\np = average number of points in the perimeter of the polygons.\n\nConversely if you use image processing algorithms for each point you can consider to have a fixed and low number of operations. For example if you consider the FloodFill algorithm it is O(A) and I can say it is less than 30*A (about 30 operations per pixel).\nSo basically since the GADM polygons has many vertex is better to eliminate the vectorial nature of the problem as soon as possible and go with something like this:\n\nconstruct the pixel map of the boundary\nfind one internal pixel\nuse the Floodfill algorithm that will work without any need to know about polygons as vectorial entities\n\nThe same algorithms can for sure be implemented in Numpy but before going for a Numpy graphical lib I would suggest to do the following:\n\nmeasure the time spent in your code for the various steps:\n\nNumpy array to AggDraw lists\/sequences conversion\ntime taken by AggDraw\n\ntry to decimate the vertex of the polygons removing the ones that stay in the same pixel based on the current Zoom level and see if an how the times will be reduced","Q_Score":3,"Tags":"python,image,numpy,fill,polygons","A_Id":20914427,"CreationDate":"2014-01-03T05:28:00.000","Title":"How to draw and fill a polygon on a grid array using pure Numpy functions?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need a hint were to look algo ( maybe even in python )\nSo I have huge amount of graphs some, and I need to find common shortest and longest path for this graphs. or common parts ( shortest or longest )\nUpd for more clear describing:\nBefore analysis graphs already have connections between nodes ? so they are already like a path.\nAnd as result it's needed to have common possible path for all graphs depending on connections between nodes","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":798,"Q_Id":20901030,"Users Score":0,"Answer":"If you have graphs with the same set of nodes (V1,...,VN) (if some nodes are unique that it does not matter, you simply ignore them as they cannot be the part of any common path) and want to find a shortest\/longest path you can proceed as follows:\n\nGenerate the intersection of all the graphs, that is: a graph, that has  nodes (V1,...,VN) and node Vi is connected to Vj iff in all your graphs Vi is connected to Vj. If you have adjacency matrices of each graph, it is just an element-wise multiplication of this matrices\nFind the shortest\/longest path in the resulting graph, it has a guarantee to be the shortest (between some two vertices I suppose?) \/longest (common) among all of them.","Q_Score":0,"Tags":"python,graph","A_Id":20901191,"CreationDate":"2014-01-03T10:03:00.000","Title":"Common longest and shortest path in graphs","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to choose a random seed for numpy.random and save it to a variable.  I can set the seed using numpy.random.seed(seed=None) but how do you get numpy to choose a random seed and tell you what it is?\nNumber seems to use \/dev\/urandom on linux by default.","AnswerCount":5,"Available Count":1,"Score":0.0399786803,"is_accepted":false,"ViewCount":6170,"Q_Id":20911147,"Users Score":1,"Answer":"When people need a random seed that can be recorded, people usually use the system time as a random seed. This means your program will act differently each time it is run, but can be saved and captured. Why don't you try that out?\nIf you don't want to do that for some reason, use the null version, numpy.random.seed(seed=None), then get a random number from it, then set the seed to that new random number.","Q_Score":11,"Tags":"python,numpy","A_Id":20911192,"CreationDate":"2014-01-03T19:20:00.000","Title":"Choose random seed and save it","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"For text that contains company names I want to train a model that automatically tags contractors (company executing the task) and principals (company hiring the contractor).\nAn example sentence would be:\n\nBlossom Inc. hires the consultants of Big Think to develop an outsourcing strategy.\n\nwith Blossom Inc as the principal and Big Think as the contractor.\nMy first question: Is it enough to tag only the principals and contractors in my training set or is it better to additionally use POS-tagging?\nIn other words, either\n\nBlossom\/PRINCIPAL Inc.\/PRINCIPAL hires\/NN the\/NN consultants\/NN of\/NN Big\/CONTRACTOR Think\/CONTRACTOR to\/NN develop\/NN an\/NN outsourcing\/NN strategy\/NN .\/.\n\nor\n\nBlossom\/PRINCIPAL Inc.\/PRINCIPAL hires\/VBZ the\/DT consultants\/NNS of\/IN Big\/CONTRACTOR Think\/CONTRACTOR to\/TO develop\/VB an\/DT outsourcing\/NN strategy\/NN .\/.\n\nSecond question: Once I have my training set, which algorithm(s) of the nltk-package is\/are most promising? N-Gram Tagger, Brill Tagger, TnT Tagger, Maxent Classifier, Naive Bayes, ...? Or am I completely on the wrong track here?\nI am new to NLP and I just wanted to ask for advice before I invest a lot of time in tagging my training set. And my text is in German, which might add some difficulties... Thanks for any advice!","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2772,"Q_Id":20971073,"Users Score":0,"Answer":"Named Entity Recognition(Stanford) is enough for your problem.\nUsing POS tagging will not help your problem.\nA sufficient amount of training data for generating the NER model would give you good results.\nIf you use the Stanford NER then it uses the CRF classifier and algorithm.","Q_Score":2,"Tags":"python,nlp,nltk,named-entity-recognition,pos-tagger","A_Id":33822675,"CreationDate":"2014-01-07T11:59:00.000","Title":"Named-entity recognition: How to tag the training set and chose the algorithm?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a (large) directory CSV with columns [0:3] = Phone Number, Name, City, State.\nI created a random sample of 20,000 entries, but it was, of course, weighted drastically to more populated states and cities.\nHow would I write a python code (using CSV or Pandas - I don't have linecache available) that would equally prioritize\/weight each unique city and each state (individually, not as a pair), and also limit each unique city to 3 picks?\n\nTRICKIER idea: How would I write a python code such that for each random line that gets picked, it checks whether that city has been picked before.  If that city has been picked before, it ignores it and picks a random line again, reducing the number of considered previous picks for that city by one.  So, say that it randomly picks San Antonio, which has been picked twice before.  The script ignores this pick, places it back into the list, reduces the number of currently considered previous San Antonio picks, then randomly chooses a line again.  IF it picks a line from San Antonio again, then it repeats the previous process, now reducing considered San Antonio picks to 0.  So it would have to pick San Antonio three times in a row to add another line from San Antonio.  For future picks, it would have to pick San Antonio four times in a row, plus one for each additional pick.\nI don't know how well the second option would work to \"scatter\" my random picks - it's just an idea, and it looks like a fun way to learn more pythonese.  Any other ideas along the same line of thought would be greatly appreciated.  Insights into statistical sampling and sample scattering would also be welcome.","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":114,"Q_Id":20984266,"Users Score":2,"Answer":"There are many ways to implement this, but the abstract algorithm should be something like this.\nFirst, to create a new CSV that meets your second critera about each state being drawn with equal probability, draw each row as follows. \n1) From the set of states, draw a state (each state is drawn with probability 1 \/ # of states). Let that state be s.\n2) From the large CSV, draw a row from the set of rows where STATE = s. \nAs you draw rows, keep a record of the number of rows drawn from a given state\/city pair. You could do this with a dictionary. Then, each time you draw a successive row, if there are any state\/city pairs equal to the cap set by the user, exclude those state\/city pairs from your conditional draw in step 2 above. This will satisfy your first requirement. \nDoes that make sense? If you get started with a bit of code that attempts to implement this, I'll happily tidy it up for you if it has any problems. \nIf you wanted to do the \"somewhat trickier\" algorithm in which the probability of selecting a city decreases with each selection, you could do that without much trouble. Basically, condition on the cities within state s after you draw s, then weight according to the number of times each city in that state has been drawn (you have this information because you've been storing it to implement the first requirement). You'll have to come up with the form of the weighting function, as it isn't implied by your description. \nAgain, if you try to code this up, I'm happy to take a look at any code you post and make suggestions.","Q_Score":0,"Tags":"python,python-2.7,csv,random,pandas","A_Id":20984538,"CreationDate":"2014-01-07T23:39:00.000","Title":"Dispersing Random Sampling in CSV through Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have started a grid search for SVM parameters in a rather wide range. \nThe most of the search space have been calculated and now I got one last process, which goes already for 100 hours.\nI'd like to see the results, that already have been calculated.\nIs there any way to do it?\nThanks in advance!","AnswerCount":2,"Available Count":1,"Score":0.2913126125,"is_accepted":false,"ViewCount":849,"Q_Id":20984918,"Users Score":3,"Answer":"You can set the verbose option to a value >0. That will at least give you the results on stdout.","Q_Score":5,"Tags":"python,scikit-learn,scikits","A_Id":21009215,"CreationDate":"2014-01-08T00:39:00.000","Title":"How to obtain GridSearchCV partly finished results?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In numpy, is it recommended to create column arrays (2d arrays) rather than 1d arrays? For example, whereas Z = np.zeros((12,)) defines a 1-dimensional list, it might also be preferable to form Z = np.zeros((12,1)).","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":133,"Q_Id":21025774,"Users Score":1,"Answer":"Depends on the use case. Both possibilities exist for a reason: if Z can be a matrix but just happens to have one column, make it a column vector. If Z is always a single vector, make it 1-d unless some operation (or library) requires the other format; 1-d is usually a bit easier to work with.","Q_Score":1,"Tags":"python,numpy","A_Id":21025867,"CreationDate":"2014-01-09T16:27:00.000","Title":"Python numpy: column arrays (2d) or lists (1d)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am messing around in PsychoPy now, trying to modify the Sternberg demo for a class project. I want the stimulus text\u2014the number set\u2014to display in a variety of colors: say, one digit is red, the next blue, the next brown, etc. A variety within the same stimulus.\nI can only find how to change the color of the entire set. I was wondering if I could add another variable to the spreadsheet accompanying the experiment and have the values in the cells be comma separated (red,blue,brown\u2026). Is this possible?","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":1012,"Q_Id":21026487,"Users Score":5,"Answer":"No, that isn't possible right now. There's an experimental new stimulus class called TextBox that will allow it, but you'd have to write code to use that (not available yet from the Builder interface). Or just create some tif images of your stimuli and use those?","Q_Score":3,"Tags":"python,text,colors,psychopy","A_Id":21063050,"CreationDate":"2014-01-09T16:57:00.000","Title":"Text with multiple colors in PsychoPy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am messing around in PsychoPy now, trying to modify the Sternberg demo for a class project. I want the stimulus text\u2014the number set\u2014to display in a variety of colors: say, one digit is red, the next blue, the next brown, etc. A variety within the same stimulus.\nI can only find how to change the color of the entire set. I was wondering if I could add another variable to the spreadsheet accompanying the experiment and have the values in the cells be comma separated (red,blue,brown\u2026). Is this possible?","AnswerCount":2,"Available Count":2,"Score":0.1973753202,"is_accepted":false,"ViewCount":1012,"Q_Id":21026487,"Users Score":2,"Answer":"The current way to implement this is to have a separate text stimulus for each digit, each with the desired colour.\nIf the text representation of the number is contained in a variable called, say, stimulusText then in the Text field for the first text component put \"$stimulusText[0]\" so that it contains just the first digit. In the next text component , use \"$stimulusText[1]\", and so on.\nThe colour of each text component can be either fixed or vary according to separate column variables specified in a conditions file.","Q_Score":3,"Tags":"python,text,colors,psychopy","A_Id":23425589,"CreationDate":"2014-01-09T16:57:00.000","Title":"Text with multiple colors in PsychoPy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to bootstrap a yield curve in Python using QuantLib library.\nI know that when doing bootstrapping using C++, there is a function for bootstrapping called PiecewiseYieldCurve in QuantLiab, but when I am using Python, there is no such function in Python QuantLib.\nI wonder that if in Python QuantLib there is an alias of PiecewiseYieldCurve, so I have to call the alias function name in order to use PiecewiseYieldCurve function\nShould I creating my own function to bootstrap the yield curve?\nThanks!","AnswerCount":1,"Available Count":1,"Score":0.761594156,"is_accepted":false,"ViewCount":1387,"Q_Id":21046422,"Users Score":5,"Answer":"PiecewiseYieldCurve is a class template, so it can't be exported to Python as such. By default, we're exporting to Python a particular instantiation of it; it's exported as PiecewiseFlatForward and it correspond to PiecewiseYieldCurve<ForwardRate,BackwardFlat>.\nIf you need another instantiation, you can edit QuantLib-SWIG\/SWIG\/piecewiseyieldcurve.i, add it (it you look at the end of the file, you'll find a few examples of how to do it) and regenerate and recompile the Python wrappers.\nFinally, an example of bootstrap is available in QuantLib-SWIG\/Python\/examples\/swap.py.","Q_Score":3,"Tags":"python,c++,bootstrapping,quantlib","A_Id":21050187,"CreationDate":"2014-01-10T14:10:00.000","Title":"Bootstrapping using Quantlib Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am pretty new to Python, having just taken a course and now trying to apply what I learned to convert matlab code to python.  I have to plot some things, so I tried to import matplotlib.pyplot but keep getting \n\nIncompatible library version: _png.so requires version 42.0.0 or later, but libpng12.0.dylib provides version 41.0.0\n\nI don't really understand how to either update my libpng12.0.dylib (since I am not really a programmer, just someone who wants to learn python, so please be easy on me if this is a super easy question!), or tell my _png.so to look somewhere else, if that is appropriate.  I have done a lot of digging in to this, and I know that there are a number of issues with installing matplotlib on osX, but I haven't seen anything about how to resolve this one.\nI am running Enthought Canopy, using python 2.7, and I am running OS X 10.8\nI really appreciate any help","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":295,"Q_Id":21056015,"Users Score":1,"Answer":"Check your DYLD_LIBRARY_PATH and LD_LIBRARY_PATH. Make sure that you have your library paths in the right order. I changed mine recently due to a matlab install and it took ages before I made the connection that it was my LD_LIBRARY_PATH that was stuffed. Programs go searching for the libraries in the order specified by those paths. If you have another libpng (as I did) in a library path before the canopy one, then it will use that. Fine if the version is recent, otherwise you get these errors.\nFirst unset them both and then run python and your plot. Hopefully that works. Then go about fixing your DYLD_LIBRARY_PATH and LD_LIBRARY_PATH.\nI put these at the front of both \/opt\/local\/lib:\/Users\/xxxxx\/Library\/Enthought\/Canopy_64bit\/User\/lib\nMy error was ... \n\/Library\/Enthought\/Canopy_64bit\/User\/lib\/python2.7\/site-packages\/matplotlib\/_png.so\n  Reason: Incompatible library version: _png.so requires version 41.0.0 or later, but libpng12.dylib provides version 40.0.0","Q_Score":2,"Tags":"macos,python-2.7,matplotlib,enthought,canopy","A_Id":24621287,"CreationDate":"2014-01-10T22:57:00.000","Title":"Issue importing matplotlib.pyplot","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to use scipy.spatial.distance.cosine in my code. I can import the spatial submodule if I do something like import scipy.spatial or from scipy import spatial, but if I simply import scipy calling scipy.spatial.distance.cosine(...) results in the following error: AttributeError: 'module' object has no attribute 'spatial'.\nWhat is wrong with the second approach?","AnswerCount":3,"Available Count":1,"Score":-0.0665680765,"is_accepted":false,"ViewCount":26860,"Q_Id":21071715,"Users Score":-1,"Answer":"Use scipy version 1.2.1 to solve this issue......","Q_Score":26,"Tags":"python,scipy","A_Id":60296334,"CreationDate":"2014-01-12T05:40:00.000","Title":"Why does from scipy import spatial work, while scipy.spatial doesn't work after import scipy?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"What is the proper way to diagnose what is happening when parallel jobs get stuck in Scikit-Learn?\nSpecifically, I have had several jobs that appear to finish (htop shows no CPU activity), but python stops responding.  Pressing Ctrl+c doesn't exit (though it does register a KeyboardInterrupt, it doesn't kill the python process), and the process must be killed from shell.  Total memory usage approaches the capacity of the machine, but I get no explicit errors that there was a MemoryError.\nThis has occurred with RandomForestRegressor, and also with cross_validation.cross_val_score, under both 0.14 and master on Ubuntu\/Debian.\nI suspect this is a memory issue, since the jobs seem to complete without a problem on machines with more memory.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":935,"Q_Id":21086215,"Users Score":0,"Answer":"I was unable to definitively find the cause of this problem, but it stopped happening when I increased the amount of memory available.  So it seems reasonable to conclude that one of the children processes encountered a MemoryError and just died.","Q_Score":1,"Tags":"python,parallel-processing,scikit-learn","A_Id":21118718,"CreationDate":"2014-01-13T07:48:00.000","Title":"Scikit Learn - Diagnosing when parallel jobs get stuck","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using PyTables to store a data array, which works fine; along with it I need to store a moderately large (50K-100K) Unicode string containing JSON data, and I'd like to compress it.\nHow can I do this in PyTables? It's been a long time since I've worked with HDF5, and I can't remember the right way to store character arrays so they can be compressed. (And I can't seem to find a similar example of doing this on the PyTables website.)","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":3616,"Q_Id":21126295,"Users Score":3,"Answer":"PyTables does not natively support unicode - yet.  To store unicode. First convert the string to bytes and then store a VLArray of length-1 strings or uint8.  To get compression simply instantiate your array with a Filters instance that has a non-zero complevel.\nAll of the examples I know of storing JSON data like this do so using the HDF5 C-API.","Q_Score":6,"Tags":"python,string,unicode,compression,pytables","A_Id":21128497,"CreationDate":"2014-01-14T23:33:00.000","Title":"How do you create a compressed dataset in pytables that can store a Unicode string?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I often find myself needing to create heatmap-style visualizations in Python with matplotlib.  Matplotlib provides several functions which apparently do the same thing.  pcolormesh is recommended instead of pcolor but what is the difference (from a practical point of view as a data plotter) between imshow and pcolormesh?  What are the pros\/cons of using one over the other?  In what scenarios would one or the other be a clear winner?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":24635,"Q_Id":21166679,"Users Score":56,"Answer":"Fundamentally, imshow assumes that all data elements in your array are to be rendered at the same size, whereas pcolormesh\/pcolor associates elements of the data array with rectangular elements whose size may vary over the rectangular grid.\nIf your mesh elements are uniform, then imshow with interpolation set to \"nearest\" will look very similar to the default pcolormesh display (without the optional X and Y args). The obvious differences are that the imshow y-axis will be inverted (w.r.t. pcolormesh) and the aspect ratio is maintained, although those characteristics can be altered to look like the pcolormesh output as well.\nFrom a practical point of view, pcolormesh is more convenient if you want to visualize the data array as cells, particularly when the rectangular mesh is non-uniform or when you want to plot the boundaries\/edges of the cells. Otherwise, imshow is more convenient if you have a fixed cell size, want to maintain aspect ratio, want control over pixel interpolation, or want to specify RGB values directly.","Q_Score":51,"Tags":"python,matplotlib","A_Id":21169703,"CreationDate":"2014-01-16T15:58:00.000","Title":"When to use imshow over pcolormesh?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"There is no information on how the direc argument of fmin-powell is supposed to be entered. All the scipy documentation for fmin_powell says is\n\ndirec : ndarray, optional\nInitial direction set.\n\nI thought that by giving direc=(0.1,0.1,1), I was telling it to start with step sizes of 0.1 for the first two fitting parameters and 1 for the third, which are needed in my case since the 3rd parameter is not sensitive to step sizes of 0.1. However, with this code it starts with 0.1 for all of the fitting parameters. If I try direc=(1,0.1,1), it uses an initial step of 1 for all parameters which destroys the fit, as the second parameter has a range of (0,1) and results in a division by zero if it ever goes negative. How are you supposed to set this argument?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":385,"Q_Id":21171095,"Users Score":0,"Answer":"Apparently with wants vector sets, so direc=([1,0,0],[0,0.1,0],[0,0,1]) will do the job. However, still unclear on how this is arranged and functions, so not sure what would happen if some of those zeros were changed.","Q_Score":2,"Tags":"python,numpy,scipy,data-fitting","A_Id":21172252,"CreationDate":"2014-01-16T19:30:00.000","Title":"Scipy.opimize.fmin_powell direc argument syntax","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"There is no information on how the direc argument of fmin-powell is supposed to be entered. All the scipy documentation for fmin_powell says is\n\ndirec : ndarray, optional\nInitial direction set.\n\nI thought that by giving direc=(0.1,0.1,1), I was telling it to start with step sizes of 0.1 for the first two fitting parameters and 1 for the third, which are needed in my case since the 3rd parameter is not sensitive to step sizes of 0.1. However, with this code it starts with 0.1 for all of the fitting parameters. If I try direc=(1,0.1,1), it uses an initial step of 1 for all parameters which destroys the fit, as the second parameter has a range of (0,1) and results in a division by zero if it ever goes negative. How are you supposed to set this argument?","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":385,"Q_Id":21171095,"Users Score":1,"Answer":"For Powell minimization, the initial set of direction vectors don't need to be aligned with the axes (although normally they are).  As the algorithm runs, it updates the direction vectors to be whatever direction is best in order to step downhill quickly.\nBut, imagine a case where the surface defined by your function is almost all flat near the starting point.  Except, in one particular direction (not aligned with any axis) there is a narrow gulley that descends downward rapidly to the function minimum.  In this case, using the direction of the gulley as one of the initial direction vectors might be helpful.  Otherwise, conceivably, it might take a while for the algorithm to find a good direction to start moving in.","Q_Score":2,"Tags":"python,numpy,scipy,data-fitting","A_Id":21224406,"CreationDate":"2014-01-16T19:30:00.000","Title":"Scipy.opimize.fmin_powell direc argument syntax","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to process data from csv file in such a way that output should print three columns e.g.\nc1,c2 and c3 where c1 and c2 must use group by clause like in mysql and c3 is sum of two other  columns.\nI am new to python, Ideas will really help me.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":128,"Q_Id":21197311,"Users Score":0,"Answer":"I've done a little bit of this in C#. First you open up the file and start reading lines of text. The first line in a .csv should be the header column, so handle that separately. The next lines should be your data.\nNow once you have your line of text insert it into a string and then split using commas. That will give you a string array. Then make an int array by converting the strings to text. This should not be a problem as long as all data in the column are integers. If not, test for non-integer values and convert them to strings that are valid intergers. E.G. if  array[0] == \"no data\" array[0] = \"0\", or array[0] = null. Then create column 3 by adding the integer values for the first and second columns together.","Q_Score":0,"Tags":"csv,python-3.x,io,pandas,pandastream","A_Id":21197452,"CreationDate":"2014-01-17T22:41:00.000","Title":"Aggregation of data from CSV file using Pandas python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have two dataframes, both of which contain an irregularly spaced, millisecond resolution timestamp column. My goal here is to match up the rows so that for each matched row, 1) the first time stamp is always smaller or equal to the second timestamp, and 2) the matched timestamps are the closest for all pairs of timestamps satisfying 1). \nIs there any way to do this with pandas.merge?","AnswerCount":4,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":16509,"Q_Id":21201618,"Users Score":11,"Answer":"Pandas now has the function merge_asof, doing exactly what was described in the accepted answer.","Q_Score":22,"Tags":"python,pandas","A_Id":49123783,"CreationDate":"2014-01-18T08:00:00.000","Title":"pandas.merge: match the nearest time stamp >= the series of timestamps","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Background:\nMy program currently assembles arrays in Python. These arrays are connected to a front-end UI and as such have interactive elements (i.e. user specified values in array elements). These arrays are then saved to .txt files (depending on their later use). The user must then leave the Python program and run a separate Fortran script which simulates a system based on the Python output files. While this only takes a couple of minutes at most, I would ideally like to automate the process without having to leave my Python UI. \n\nAssemble Arrays (Python) -> Edit Arrays (Python) -> Export to File (Python)\n-> Import File (Fortran) -> Run Simulation (Fortran) -> Export Results to File (Fortran)\n-> Import File to UI, Display Graph (Python)\n\n\nQuestion:\nIs this possible? What are my options for automating this process? Can I completely remove the repeated export\/import of files altogether?\nEdit:\nI should also mention that the fortran script uses Lapack, I don't know if that makes a difference.","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":2760,"Q_Id":21205596,"Users Score":2,"Answer":"I love the Python+Fortran stack. :)\nWhen needing close communication between your Python front-end and Fortran engine, a good option is to use the subprocess module in Python. Instead of saving the arrays to a text file, you'll keep them as arrays. Then you'll execute the Fortran engine as a subprocess within the Python script. You'll pipe the Python arrays into the Fortran engine and then pipe the results out to display.\nThis solution will require changing the file I\/O in both the Python and Fortran codes to writing and reading to\/from a pipe (on the Python side) and from\/to standard input and output (on the Fortran side), but in practice this isn't too much work.\nGood luck!","Q_Score":1,"Tags":"python,arrays,fortran,f2py","A_Id":21223365,"CreationDate":"2014-01-18T14:45:00.000","Title":"Passing Arrays from Python to Fortran (and back)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"on ubuntu 12.04 x32 I have installed python 2.7.3, numpy 1.6.1 via sudo apt-get install python-numpy. I run the test() from numpy via numpy.test() and I get:\n\nFAIL: test_pareto (test_random.TestRandomDist)\nTraceback (most recent call last):\n    File \"\/usr\/lib\/python2.7\/dist-packages\/numpy\/random\/tests\/test_random.py\", line 313, in test_pareto\n      np.testing.assert_array_almost_equal(actual, desired, decimal=15)\n    File \"\/usr\/lib\/python2.7\/dist-packages\/numpy\/testing\/utils.py\", line 800, in assert_array_almost_equal\n      header=('Arrays are not almost equal to %d decimals' % decimal))\n    File \"\/usr\/lib\/python2.7\/dist-packages\/numpy\/testing\/utils.py\", line 636, in assert_array_compare\n      raise AssertionError(msg)\n  AssertionError: \n  Arrays are not almost equal to 15 decimals\n(mismatch 16.6666666667%)\n   x: array([[  2.46852460e+03,   1.41286881e+03],\n         [  5.28287797e+07,   6.57720981e+07],\n         [  1.40840323e+02,   1.98390255e+05]])\n   y: array([[  2.46852460e+03,   1.41286881e+03],\n         [  5.28287797e+07,   6.57720981e+07],\n         [  1.40840323e+02,   1.98390255e+05]])\n\nRan 3169 tests in 17.483s\nFAILED (KNOWNFAIL=3, SKIP=4, failures=1)\n  \n\nWhat should I do? did I miss a dependency or so?\nThanks.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2909,"Q_Id":21220842,"Users Score":5,"Answer":"Notes for the future me, when trying to redo the stuff:\n\nthere are some prerequisites for working with numpy\/scipy: g++ gfortran blas atlas lapack.\nit seems to be better -- and time consuming -- to compile the numpy\/scipy sources. pip install does this.\n\nThe commands were:\n\nsudo apt-get install g++ gfortran liblapack-dev libopenblas-dev python-dev python-pip\nsudo pip install nose\nsudo pip install numpy\npython -c \"import numpy; numpy.test()\"\n\nFor the scipy library the following worked:\n\nsudo pip install scipy\npython -c \"import scipy; scipy.test()\"","Q_Score":0,"Tags":"python-2.7,ubuntu,numpy","A_Id":21222072,"CreationDate":"2014-01-19T18:31:00.000","Title":"Numpy test() finished with errors","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on a reccommender algorithm for songs. I have a matrix of values that I get the cosine similiarity of in python ( numPy). The problem is that every time i run the program i need to recompute the similarity of every vector to every other vector. I want to store the results of computations locally so i don't have to compute it every time. \nThe first thing that comes to my mind is storing them in a text file, or in the database itself. Surely theres a better way though?","AnswerCount":3,"Available Count":1,"Score":-0.0665680765,"is_accepted":false,"ViewCount":846,"Q_Id":21234887,"Users Score":-1,"Answer":"It is hard to answer you question. Because i don't know about ur data volume and type.\nBut i can tell you now.\nIf u are thinking about file for that, it may have scale out issue, if u scale out python server to # of box. So u may need a shared storage. In that case u have to think about shared storage file system like glusterFS or Hadoop. (glusterFS is more eaisier). But the access time will be very poor.\nThe other option is u can think about Redis. It is memory based key & value store. It also supports file persistance. (because of that it's characteristics is little different from memcahed.)\nFinal option is u can think about NoSQL which can support scalability and performance. But it is always depends on your requirement.","Q_Score":2,"Tags":"python,numpy,storage,similarity","A_Id":21235208,"CreationDate":"2014-01-20T13:02:00.000","Title":"Store data locally long term","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a set of points extracted from an image. I need to join these points to from a smooth curve. After drawing the curve on the image, I need to find the tangent to the curve and represent it on the image. I looked at cv2.approxPolyDP but it already requires a curve??","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2929,"Q_Id":21274959,"Users Score":0,"Answer":"If your question is related to the points being extracted in random order, the tool you need is probably the so called 2D alpha-shape. It is a generalization of the convex hull and will let you trace the \"outline\" of your set of points, and from there perform interpolation.","Q_Score":0,"Tags":"python,opencv,math,geometry","A_Id":21278176,"CreationDate":"2014-01-22T05:31:00.000","Title":"Draw a curve joining a set of points in opencv python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm new to python and was curious as to how, given a large set of data consisting of census information, I could plot a histogram or graph of some sort.  My main question is how to access the file, not exactly how the graph should be coded.  Do I import the file directly? How do I extract the data from the file? How is this done?\nThanks","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":498,"Q_Id":21287667,"Users Score":0,"Answer":"what format is your data in?\nPython offers modules to read data from a variety of data formats (CSV, JSON, XML, ...)\nCSV is a very common one that suffices for many cases (the csv module is part of the standard library)\nTypically you write a small routine that casts the different fields as expected (string to floating point numbers, or dates, integers,...) and cast your data in a numpy matrix (np.array) where each row corresponds to a sample and each column to an observation\nfor the plots check matplotlib. It is really easy to generate graphs, especially if you have some previous experience with Matlab","Q_Score":1,"Tags":"python,histogram,data-analysis","A_Id":21287882,"CreationDate":"2014-01-22T15:53:00.000","Title":"Plotting in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using opencv2. I am able to capture frames from my web cam using cap.read() but not with cap.retrieve(). I like retrieve() because it does not block so my frame speed is faster. My retrieve() used to work but it stopped and now returns a black screen. both functions return true in the return status. I must have installed something like a different usb driver. I am using Ubuntu linux on a pcduino2.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":117,"Q_Id":21323878,"Users Score":0,"Answer":"After reading the docs and performing many tests, I realized the I had not grabbed a frame before calling retrieve(). My thought was that the USB driver would automatically tick in the web cameras image into a buffer. Then, at any time, I could read that video frame's memory with having to wait.","Q_Score":0,"Tags":"python,opencv,python-2.7,ubuntu,arduino","A_Id":21363163,"CreationDate":"2014-01-24T03:29:00.000","Title":"opencv2 capture using retrieve() does not work but read() works","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have two DataFrames with TimesSeriesIndex, df1, df2. \ndf2's index is a subset of df1.index.\nHow can I extract the index dates from df1 which also contained by df2, so I can run analysis on these dates.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1685,"Q_Id":21333102,"Users Score":1,"Answer":"Take the intersection of their indices.\n\nIn [1]: import pandas as pd\n\nIn [2]: index1 = pd.DatetimeIndex(start='2000-1-1', freq='1T', periods=1000000)\n\nIn [3]: index2 = pd.DatetimeIndex(start='2000-1-1', freq='1D', periods=1000)\n\nIn [4]: index1\nOut[4]: \n\n[2000-01-01 00:00:00, ..., 2001-11-25 10:39:00]\nLength: 1000000, Freq: T, Timezone: None\n\nIn [5]: index2\nOut[5]: \n\n[2000-01-01 00:00:00, ..., 2002-09-26 00:00:00]\nLength: 1000, Freq: D, Timezone: None\n\nIn [6]: index1 & index2\nOut[6]: \n\n[2000-01-01 00:00:00, ..., 2001-11-25 00:00:00]\nLength: 695, Freq: D, Timezone: None\n\nIn your case, do the following:\n\nindex1 = df1.index\nindex2 = df2.index\n\nThen take the intersection of these as defined before.\nLater you may wish to do something like the following to get the df at intersection index.\n\ndf1_intersection =df1.ix[index1 & index2]","Q_Score":1,"Tags":"python,pandas","A_Id":21333741,"CreationDate":"2014-01-24T12:45:00.000","Title":"pandas: how to extract a set of dates from a DataFrame with datetime index?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there a way to remove the labels of the x axis, but not the grid lines?\nBoth solutions to remove the labels also removed my grid lines.\nI tried: axes.get_xaxis().set_visible(False) and axes.get_xaxis().set_ticks([])","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":90,"Q_Id":21483236,"Users Score":2,"Answer":"Try this: axes.xaxis.set_tick_params(label1On=False)","Q_Score":1,"Tags":"python,matplotlib,label,axes","A_Id":21483417,"CreationDate":"2014-01-31T15:06:00.000","Title":"Python Matplotlib remove xlabels but not grid","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How to label axes in Matplotlib using LaTeX expression $\\langle B_{\\mathrm{e}} \\rangle$?\nI need to label my axis with nice looking \"<\" and \">\" LaTeX brackets.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":7377,"Q_Id":21485769,"Users Score":11,"Answer":"Try ax.set_ylabel(r'$\\langle B_{\\mathrm{e}} \\rangle$') for labeling Y-Axis or ax.set_title(r'$\\langle B_{\\mathrm{e}} \\rangle$') for the title of the axes.","Q_Score":6,"Tags":"python,matplotlib,latex","A_Id":21486017,"CreationDate":"2014-01-31T17:00:00.000","Title":"How to label axes in Matplotlib using LaTeX brackets?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need help plotting a dictionary, below is the data sample data set. I want to create a graph where x:y are (x,y) coordinates and title'x' would be the title of the graph.. I want to create individual graphs for each data set so one for title1':{x:y, x:y}, another one for title2:{x:y, x:y}....and so on. \nAny help would be greatly appreciated. Thank you.\ndata = {'title1':{x:y, x:y},title2:{x:y,x:y,x:y},'title3':{x:y,x:y}....}","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":6297,"Q_Id":21507956,"Users Score":3,"Answer":"Creating sample data\n\nIn [3]: data = {'title1': {10:20, 4:10}, 'title2':{8:10, 9:20, 10:30}}\n\nIn [4]: data\nOut[4]: {'title1': {4: 10, 10: 20}, 'title2': {8: 10, 9: 20, 10: 30}}\n\nIterating over data; creating x and y for each title and plotting it in new figure\n\nIn [5]: for title, data_dict in data.iteritems():\n   ...:     x = data_dict.keys()\n   ...:     y = data_dict.values()\n   ...:     plt.figure()\n   ...:     plt.plot(x,y)\n   ...:     plt.title(title)\n\nIf you are not using IPython\n\nplt.show()","Q_Score":1,"Tags":"python,matplotlib","A_Id":21508062,"CreationDate":"2014-02-02T06:58:00.000","Title":"Plotting dictionaries within a dictionary in Myplotlib python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using scikit-learn. I want to cluster a 6gb dataset of documents and find clusters of documents.\nI only have about 4Gb ram though. Is there a way to get k-means to handle large datasets in scikit-learn?\nThank you, Please let me know if you have any questions.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":2177,"Q_Id":21532724,"Users Score":1,"Answer":"Clustering is not in itself that well-defined a problem (a 'good' clustering result depends on your application) and k-means algorithm only gives locally optimal solutions based on random initialization criteria. Therefore I doubt that the results you would get from clustering a random 2GB subsample of the dataset would be qualitatively different from the results you would get clustering over the entire 6GB. I would certainly try clustering on the reduced dataset as a first port of call. Next options are to subsample more intelligently, or do multiple training runs with different subsets and do some kind of selection\/ averaging across multiple runs.","Q_Score":1,"Tags":"python,scikit-learn","A_Id":21532842,"CreationDate":"2014-02-03T16:39:00.000","Title":"Handling K-means with large dataset 6gb with scikit-learn?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a lot of 10 by 10 (0,1)-matrices and I would like to determine which have determinant exactly 0 (that is which are singular). Using scipy.linalg.det I get a floating point number which I have to test to see if it is close to zero. Is it possible to do the calculation exactly so I can be sure I am not finding false positives?\nOn the other hand, maybe there is some guarantee about the smallest eigenvalue which can be used to make sure the floating point method never makes a false positive?","AnswerCount":5,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":2357,"Q_Id":21612677,"Users Score":0,"Answer":"As the entries in the matrices are either 1 or 0 the smallest non-zero absolute value of a determinant is 1.  So there is no need to fear a true non-zero value that is very close to 0.\nAlternatively one can apparently use sympy to get an exact answer.","Q_Score":4,"Tags":"python,math,numpy,linear-algebra","A_Id":21613541,"CreationDate":"2014-02-06T19:54:00.000","Title":"Determine if determinant is exactly zero","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a lot of 10 by 10 (0,1)-matrices and I would like to determine which have determinant exactly 0 (that is which are singular). Using scipy.linalg.det I get a floating point number which I have to test to see if it is close to zero. Is it possible to do the calculation exactly so I can be sure I am not finding false positives?\nOn the other hand, maybe there is some guarantee about the smallest eigenvalue which can be used to make sure the floating point method never makes a false positive?","AnswerCount":5,"Available Count":2,"Score":0.1194272985,"is_accepted":false,"ViewCount":2357,"Q_Id":21612677,"Users Score":3,"Answer":"You can use Gaussian elimination to bring the matrix to a triangular form. \nSince your elements are all 0 or 1, the calculation even using floating point arithmetic will be exact (you are only multiplying\/dividing\/adding\/subtracting by -1, 0 and 1, which is exact).\nThe determinant is then 0 if one element of the diagonal is zero and nonzero otherwise.\nSo for this specific algorithm (Gaussian elimination), calculation of the determinant will be exact even in floating point arithmetic. \nThis algorithm also should be pretty efficient. It can even be implemented using integers, which is faster and shows even in a more obvious way that the problem is exactly solvable.\nEDIT: the point is, that an algorithm which operates on the 0,1 matrix can be exact. It depends on the algorithm. I would check how det() is implemented and maybe, there is no issue with numerical noise, and, in fact, you could just test for det(M) == 0.0 and get neither false negatives nor false positives.","Q_Score":4,"Tags":"python,math,numpy,linear-algebra","A_Id":21613054,"CreationDate":"2014-02-06T19:54:00.000","Title":"Determine if determinant is exactly zero","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using the AdaBoostClassifier in Scikit-learn and always get an average probability of 0.5 regardless of how unbalanced the training sets are. The class predictions (predict_) seems to give correct estimates, but these aren't reflected in the predict_probas method which always average to 0.5.\nIf my \"real\" probability is 0.02, how do I transform the standardized probability to reflect that proportion?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":213,"Q_Id":21633136,"Users Score":0,"Answer":"Do you mean you get probabilities per sample that are 1\/n_classes on average? That's necessarily the case; the probabilities reported by predict_proba are the conditional class probability distribution P(y|X) over all values for y. To produce different probabilities, perform any necessary computations according to your probability model.","Q_Score":0,"Tags":"python-2.7,probability,scikit-learn,prediction,adaboost","A_Id":21645757,"CreationDate":"2014-02-07T16:38:00.000","Title":"The predict method shows standardized probability?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a large dataset stored in a numpy array (A) I am trying to sum by block's using:\nB=numpy.add.reduceat(numpy.add.reduceat(A, numpy.arange(0, A.shape[0], n),axis=0), numpy.arange(0, A.shape[1], n), axis=1)\nit work's fine when i try it on a test array but with my data's I get the following message:  \nTypeError: Cannot cast array data from dtype('float64') to dtype('int32') according to the rule 'safe'\nDoes someone now how to handle this?\nThanks for the help.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":4600,"Q_Id":21640028,"Users Score":1,"Answer":"In case anyone else has a similar problem but the chosen answer doesn't solve it, one possibility could be that in Python3, some index or integer quantity fed into a np function is an expression using '\/' for example n\/2, which ought to be '\/\/'.","Q_Score":3,"Tags":"python,arrays,numpy","A_Id":24217870,"CreationDate":"2014-02-08T00:08:00.000","Title":"Error when trying to sum an array by block's","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am getting problems to access Stanford parser through python NLTK (they developed an interface for NLTK)\nimport nltk.tag.stanford\nTraceback (most recent call last):\nFile \"\", line 1, in \nImportError: No module named stanford","AnswerCount":2,"Available Count":1,"Score":-0.1973753202,"is_accepted":false,"ViewCount":5543,"Q_Id":21652251,"Users Score":-2,"Answer":"There is no module named stanford in NLTK.You can store output of stanford parser and make use of it through python program.","Q_Score":2,"Tags":"python,nlp,nltk,stanford-nlp","A_Id":21656184,"CreationDate":"2014-02-08T21:53:00.000","Title":"nltk interface to stanford parser","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have multiple large (>10GB) SAS datasets that I want to convert for use in pandas, preferably in HDF5. There are many different data types (dates, numerical, text) and some numerical fields also have different error codes for missing values (i.e. values can be ., .E, .C, etc.) I'm hoping to keep the column names and label metadata as well. Has anyone found an efficient way to do this?\nI tried using MySQL as a bridge between the two, but I got some Out of range errors when transferring, plus it was incredibly slow. I also tried export from SAS in Stata .dta format, but SAS (9.3) exports in an old Stata format that is not compatible with read_stat() in pandas. I also tried the sas7bdat package, but from the description it has not been widely tested so I'd like to load the datasets another way and compare the results to make sure everything is working properly.\nExtra details: the datasets I'm looking to convert are those from CRSP, Compustat, IBES and TFN from WRDS.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2301,"Q_Id":21667547,"Users Score":1,"Answer":"I haven't had much luck with this in the past.  We (where I work) just use Tab separated files for transport between SAS and Python -- and we do it a lot.  \nThat said, if you are on Windows, you can attempt to setup an ODBC connection and write the file that way.","Q_Score":10,"Tags":"python,pandas,sas,hdf5","A_Id":21668162,"CreationDate":"2014-02-10T01:17:00.000","Title":"Converting large SAS dataset to hdf5","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have to apply Nearest Neighbors in Python, and I am looking ad the scikit-learn and the scipy libraries, which both require the data as input, then will compute the distances and apply the algorithm.\nIn my case I had to compute a non-conventional distance, therefore I would like to know if there is a way to directly feed the distance matrix.","AnswerCount":4,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":5868,"Q_Id":21675570,"Users Score":0,"Answer":"Want to add to ford's answer that you have to do like this\nmetric = DistanceMetric.get_metric('pyfunc',func=\/your function name\/)\nYou cannot just put your own function as the second argument, you must name the argument as \"func\"","Q_Score":7,"Tags":"python,machine-learning,scipy,scikit-learn","A_Id":23423563,"CreationDate":"2014-02-10T11:11:00.000","Title":"Nearest Neighbors in Python given the distance matrix","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using the built in python version in OSX, I also installed pip by sudo easy_install pip and secondly I installed numpy by sudo pip install numpy.\nHowever, when I run any python file which uses numpy I get an error message like:\n\n\nImport error: No module named numpy\n\n\nLike numpy isn't installed in system. When I called locate numpy I found out most of outputs tell numpy is installed at: \/System\/Library\/Frameworks\/Python.framework\/Versions\/2.7\/Extras\/lib\/Python\/numpy\nHow can I get it to work?","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":273,"Q_Id":21685980,"Users Score":2,"Answer":"Using the built-in python for OS X is not recommended and will likely cause more headaches in the future (assuming it's not behind your current problems).\nAssuming your python is fine, there's still the issue of getting numpy working. In my experience, installing numpy with pip will often run into problems.\nIn addition to CT Zhu's advice, if you just want numpy and python, the Enthought distribution is quite good and free for students. \nAlso getting Homebrew working is a good idea and, because it's quite well supported, is not hard. With homebrew, installing numpy is as easy as brew install numpy -- and it makes installing other packages that also often don't install right with pip (sklearn, scipy, etc) easy too.","Q_Score":1,"Tags":"python,macos,numpy","A_Id":21687176,"CreationDate":"2014-02-10T19:18:00.000","Title":"How to install numpy in OSX properly?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to start with an empty data frame and then add to it one row each time. \nI can even start with a 0 data frame data=pd.DataFrame(np.zeros(shape=(10,2)),column=[\"a\",\"b\"]) and then replace one line each time. \nHow can I do that?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":63571,"Q_Id":21723830,"Users Score":0,"Answer":"If you are replacing the entire row then you can just use an index and not need row,column slices.\n...\ndata.loc[2]=5,6","Q_Score":14,"Tags":"python,pandas,dataframe","A_Id":59060580,"CreationDate":"2014-02-12T09:34:00.000","Title":"replace rows in a pandas data frame","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Title says it all. It seems like it ought be possible (somehow) to implement python-side pickling for PyObjC objects whose Objective-C classes implement NSCoding without re-implementing everything from scratch. That said, while value-semantic members would probably be straightforward, by-reference object graphs and conditional coding might be tricky. How might you get the two sides to \"collaborate\" on the object graph parts?","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":261,"Q_Id":21730339,"Users Score":1,"Answer":"PyObjC does support writing Python objects to a (keyed) archive (that is, any object that can be pickled implements NSCoding). \nThat\u2019s probably the easiest way to serialize arbitrary graphs of Python and Objective-C objects. \nAs I wrote in the comments for another answer I ran into problems when trying to find a way to implement pickle support for any object that implements NSCoding due to incompatibilities in how NSArchiver and pickle traverse the object graph (IIRC primarily when restoring the archive).","Q_Score":2,"Tags":"python,pickle,nscoding,pyobjc","A_Id":21734013,"CreationDate":"2014-02-12T14:12:00.000","Title":"PyObjC: How can one use NSCoding to implement python pickling?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Title says it all. It seems like it ought be possible (somehow) to implement python-side pickling for PyObjC objects whose Objective-C classes implement NSCoding without re-implementing everything from scratch. That said, while value-semantic members would probably be straightforward, by-reference object graphs and conditional coding might be tricky. How might you get the two sides to \"collaborate\" on the object graph parts?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":261,"Q_Id":21730339,"Users Score":0,"Answer":"Shouldn't it be pretty straightforward?\nOn pickling, call encodeWithCoder on the object using an NSArchiver or something. Have pickle store that string.\nOn unpickling, use NSUnarchiver to create an NSObject from the pickled string.","Q_Score":2,"Tags":"python,pickle,nscoding,pyobjc","A_Id":21733669,"CreationDate":"2014-02-12T14:12:00.000","Title":"PyObjC: How can one use NSCoding to implement python pickling?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using a Java program to extract some data points, and am planning on using scipy to determine the correlation coefficients. I plan on extracting the data into a csv-style file. How should I format each corresponding dataset, so that I can easily read it into scipy?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":73,"Q_Id":21740498,"Users Score":1,"Answer":"Each dataset is a column and all the datasets combined to make a CSV. It get read as a 2D array by numpy.genfromtxt() and then call numpy.corrcoef() to get correlation coefficients.\nNote: you should also consider the same data layout, but using pandas. Read CSV into a dataframe by pandas.read_csv() and get the correlation coefficients by .corr()","Q_Score":1,"Tags":"python,csv,scipy,correlation","A_Id":21740743,"CreationDate":"2014-02-12T21:50:00.000","Title":"Best format to pack data for correlation determination?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I need to read a CSV with a couple million rows.  The file grows throughout the day.  After each time I process the file (and zip each row into a dict), I start the process over again, except creating the dict only for the new lines.  \nIn order to get to the new lines though, I have to iterate over each line with CSV reader and compare the line number to my 'last line read' number (as far as I know).\nIs there a way to just 'skip' to that line number?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1904,"Q_Id":21762173,"Users Score":0,"Answer":"If I were doing this I think I would add a marker line after each read - before the file is saved again , then I would read the file in as a string , split on the marker, convert back to a list and feed the list to the process.","Q_Score":4,"Tags":"python,csv","A_Id":21762285,"CreationDate":"2014-02-13T18:09:00.000","Title":"Python CSV reader start at line_num","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to compute the difference in pixel values of two images, but I'm running into memory problems because the images I have are quite large. Is there way in python that I can read an image lets say in 10x10 chunks at a time rather than try to read in the whole image? I was hoping to solve the memory problem by reading an image in small chunks, assigning those chunks to numpy arrays and then saving those numpy arrays using pytables for further processing. Any advice would be greatly appreciated.\nRegards,\nBerk","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":3371,"Q_Id":21765647,"Users Score":4,"Answer":"You can use numpy.memmap and let the operating system decide which parts of the image file to page in or out of RAM. If you use 64-bit Python the virtual memory space is astronomic compared to the available RAM.","Q_Score":1,"Tags":"python,image,image-processing,numpy,pytables","A_Id":21765862,"CreationDate":"2014-02-13T21:16:00.000","Title":"How to read a large image in chunks in python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataframe where some columns (not row) are like [\"\",\"\",\"\",\"\"].\nThose columns with that characteristic I would like to delete.\nIs there an efficient way of doing that?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":72,"Q_Id":21773514,"Users Score":0,"Answer":"In pandas it would be del df['columnname'].","Q_Score":1,"Tags":"python,pandas","A_Id":21773776,"CreationDate":"2014-02-14T07:45:00.000","Title":"Detecting certain columns and deleting these","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"tl;dr - I want to write a Python unittest function that deletes a file, runs a test, and the restores the file. This causes race conditions because unittest runs multiple tests in parallel, and deleting and creating the file for one test messes up other tests that happen at the same time.\nLong Specific Example:\nI have a Python module named converter.py and it has associated tests in test_converter.py. If there is a file named config_custom.csv in the same directory as converter.py, then the custom configuration will be used. If there is no custom CSV config file, then there is a default configuration built into converter.py.\nI wrote a unit test using unittest from the Python 2.7 standard library to validate this behavior. The unit test in setUp() would rename config_custom.csv to wrong_name.csv, then it would run the tests (hopefully using the default config), then in tearDown() it would rename the file back the way it should be.\nProblem: Python unit tests run in parallel, and I got terrible race conditions. The file config_custom.csv would get renamed in the middle of other unit tests in a non-deterministic way. It would cause at least one error or failure about 90% of the time that I ran the entire test suite.\nThe ideal solution would be to tell unittest: Do NOT run this test in parallel with other tests, this test is special and needs complete isolation.\nMy work-around is to add an optional argument to the function that searches for config files. The argument is only passed by the test suite. It ignores the config file without deleting it. Actually deleting the test file is more graceful, that is what I actually want to test.","AnswerCount":5,"Available Count":2,"Score":-0.0399786803,"is_accepted":false,"ViewCount":2425,"Q_Id":21773821,"Users Score":-1,"Answer":"The problem is that the name of config_custom.csv should itself be a configurable parameter. Then each test can simply look for config_custom_<nonce>.csv, and any number of tests may be run in parallel.\nCleanup of the overall suite can just clear out config_custom_*.csv, since we won't be needing any of them at that point.","Q_Score":7,"Tags":"python,unit-testing,python-unittest","A_Id":21775708,"CreationDate":"2014-02-14T08:03:00.000","Title":"Making a Python unit test that never runs in parallel","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"tl;dr - I want to write a Python unittest function that deletes a file, runs a test, and the restores the file. This causes race conditions because unittest runs multiple tests in parallel, and deleting and creating the file for one test messes up other tests that happen at the same time.\nLong Specific Example:\nI have a Python module named converter.py and it has associated tests in test_converter.py. If there is a file named config_custom.csv in the same directory as converter.py, then the custom configuration will be used. If there is no custom CSV config file, then there is a default configuration built into converter.py.\nI wrote a unit test using unittest from the Python 2.7 standard library to validate this behavior. The unit test in setUp() would rename config_custom.csv to wrong_name.csv, then it would run the tests (hopefully using the default config), then in tearDown() it would rename the file back the way it should be.\nProblem: Python unit tests run in parallel, and I got terrible race conditions. The file config_custom.csv would get renamed in the middle of other unit tests in a non-deterministic way. It would cause at least one error or failure about 90% of the time that I ran the entire test suite.\nThe ideal solution would be to tell unittest: Do NOT run this test in parallel with other tests, this test is special and needs complete isolation.\nMy work-around is to add an optional argument to the function that searches for config files. The argument is only passed by the test suite. It ignores the config file without deleting it. Actually deleting the test file is more graceful, that is what I actually want to test.","AnswerCount":5,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":2425,"Q_Id":21773821,"Users Score":0,"Answer":"The best testing strategy would be to make sure your testing on disjoint data sets. This will bypass any race conditions and make the code simpler. I would also mock out open or __enter__ \/ __exit__ if your using the context manager. This will allow you to fake the event that a file doesn't exist.","Q_Score":7,"Tags":"python,unit-testing,python-unittest","A_Id":34140669,"CreationDate":"2014-02-14T08:03:00.000","Title":"Making a Python unit test that never runs in parallel","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I got a pandas dataframe, containing timestamps 'expiration' and 'date'.\nI want to filter for rows with a certain maximum delta between expiration and date.\nWhen doing fr.expiration - fr.date I obtain timedelta values, but don't know how \nto get a filter criteria such as fr[timedelta(fr.expiration-fr.date)<=60days]","AnswerCount":2,"Available Count":1,"Score":0.2913126125,"is_accepted":false,"ViewCount":2238,"Q_Id":21790816,"Users Score":3,"Answer":"for the 60 days you're looking to compare to, create a timedelta object of that value timedelta(days=60) and use that for the filter. and if you're already getting timedelta objects from the subtraction, recasting it to a timedelta seems unnecessary.\nand finally, make sure you check the signs of the timedeltas you're comparing.","Q_Score":3,"Tags":"python-2.7,pandas","A_Id":21791001,"CreationDate":"2014-02-14T22:43:00.000","Title":"filter pandas dataframe for timedeltas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataset of users and their music plays, with every play having location data. For every user i want to cluster their plays to see if they play music in given locations.\nI plan on using the sci-kit learn k-means package, but how do I get this to work with location data, as opposed to its default, euclidean distance? \nAn example of it working would really help me!","AnswerCount":2,"Available Count":2,"Score":0.1973753202,"is_accepted":false,"ViewCount":976,"Q_Id":21802946,"Users Score":2,"Answer":"Is the data already in vector space e.g. gps coordinates? If so you can cluster on it directly, lat and lon are close enough to x and y that it shouldn't matter much. If not, preprocessing will have to be applied to convert it to a vector space format (table lookup of locations to coords for instance). Euclidean distance is a good choice to work with vector space data.\nTo answer the question of whether they played music in a given location, you first fit your kmeans model on their location data, then find the \"locations\" of their clusters using the cluster_centers_ attribute. Then you check whether any of those cluster centers are close enough to the locations you are checking for. This can be done using thresholding on the distance functions in scipy.spatial.distance.\nIt's a little difficult to provide a full example since I don't have the dataset, but I can provide an example given arbitrary x and y coords instead if that's what you want.\nAlso note KMeans is probably not ideal as you have to manually set the number of clusters \"k\" which could vary between people, or have some more wrapper code around KMeans to determine the \"k\". There are other clustering models which can determine the number of clusters automatically, such as meanshift, which may be more ideal in this case and also can tell you cluster centers.","Q_Score":2,"Tags":"python,scikit-learn,cluster-analysis,data-mining,k-means","A_Id":21824056,"CreationDate":"2014-02-15T20:07:00.000","Title":"Computing K-means clustering on Location data in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataset of users and their music plays, with every play having location data. For every user i want to cluster their plays to see if they play music in given locations.\nI plan on using the sci-kit learn k-means package, but how do I get this to work with location data, as opposed to its default, euclidean distance? \nAn example of it working would really help me!","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":976,"Q_Id":21802946,"Users Score":5,"Answer":"Don't use k-means with anything other than Euclidean distance.\nK-means is not designed to work with other distance metrics (see k-medians for Manhattan distance, k-medoids aka. PAM for arbitrary other distance functions).\nThe concept of k-means is variance minimization. And variance is essentially the same as squared Euclidean distances, but it is not the same as other distances.\nHave you considered DBSCAN? sklearn should have DBSCAN, and it should by now have index support to make it fast.","Q_Score":2,"Tags":"python,scikit-learn,cluster-analysis,data-mining,k-means","A_Id":21825022,"CreationDate":"2014-02-15T20:07:00.000","Title":"Computing K-means clustering on Location data in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I know it is possible to use python language over Hadoop.\nBut is it possible to use scikit-learn's machine learning algorithms on Hadoop ?\nIf the answer is no, is there some machine learning library for python and Hadoop ?\nThanks for your Help.","AnswerCount":2,"Available Count":1,"Score":0.2913126125,"is_accepted":false,"ViewCount":5638,"Q_Id":21826863,"Users Score":3,"Answer":"Look out for jpype module. By using jpype you can run Mahout Algorithms and you will be writing code in Python. However I feel this won't be the best of solution. If you really want massive scalability than go with Mahout directly. I practice, do POC's, solve toy problems using scikit-learn, however when I need to do massive big data clustering and so on than I go Mahout.","Q_Score":7,"Tags":"python,hadoop,machine-learning,bigdata,scikit-learn","A_Id":21828293,"CreationDate":"2014-02-17T10:42:00.000","Title":"Is it possible to run Python's scikit-learn algorithms over Hadoop?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In igraph, what's the least cpu-expensive way to find:\n\nthe two most remote vertices (in term of shortest distances form one another) of a graph. Unlike the farthest.points() function, which chooses the first found pair of vertices with the longest shortest distance if more than one pair exists, I'd like to randomly select this pair.\nsame thing with the closest vertices of a graph.\n\nThanks!","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":99,"Q_Id":21836929,"Users Score":2,"Answer":"For the first question, you can find all shortest paths, and then choose between the pairs making up the longest distances.\nI don't really understand the second question. If you are searching for unweighted paths, then every pair of vertices at both ends of an edge have the minimum distance (1). That is, if you don't consider paths to the vertices themselves, these have length zero, by definition.","Q_Score":0,"Tags":"python,igraph,shortest-path","A_Id":21840597,"CreationDate":"2014-02-17T18:47:00.000","Title":"least cpu-expensive way to find the two most (and least) remote vertices of a graph [igraph]","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":1,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am unable to import numpy and matplotlib package in python33. I am getting this error. I have tried to install this two packages but unable to import. I am getting the following error:\n\n\n\nimport numpy\n      Traceback (most recent call last):\n        File \"\", line 1, in \n          import numpy\n      ImportError: No module named 'numpy'\n      import matplotlib\n      Traceback (most recent call last):\n        File \"\", line 1, in \n          import matplotlib\n      ImportError: No module named 'matplotlib'","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":275,"Q_Id":21846661,"Users Score":1,"Answer":"I would suggest that first uninstall numpy and matplotlib using pip uninstall command then install again using pip install from python command line terminal and restart your system.","Q_Score":1,"Tags":"python,numpy,matplotlib","A_Id":59014783,"CreationDate":"2014-02-18T07:13:00.000","Title":"i have python 33 but unable to import numpy and matplotlib package","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In a script where I create many figures with fix, ax = plt.subplots(...), I get the warning RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (matplotlib.pyplot.figure) are retained until explicitly closed and may consume too much memory. \nHowever, I don't understand why I get this warning, because after saving the figure with fig.savefig(...), I delete it with fig.clear(); del fig. At no point in my code, I have more than one figure open at a time. Still, I get the warning about too many open figures.  What does that mean \/ how can I avoid getting the warning?","AnswerCount":7,"Available Count":1,"Score":0.0285636566,"is_accepted":false,"ViewCount":164804,"Q_Id":21884271,"Users Score":1,"Answer":"matplotlib by default keeps a reference of all the figures created through pyplot. If a single variable used for storing matplotlib figure (e.g \"fig\") is modified and rewritten without clearing the figure, all the plots are retained in RAM memory. Its important to use plt.cla() and plt.clf() instead of modifying and reusing the fig variable.  If you are plotting thousands of different plots and saving them without clearing the figure, then eventually your RAM will get exhausted and program will get terminated. Clearing the axes and figures have a significant impact on memory usage if you are plotting many figures. You can monitor your RAM consumption in task manager (Windows) or in system monitor (Linux). First your RAM will get exhausted, then the OS starts consuming SWAP memory. Once both are exhausted, the program will get automatically terminated. Its better to clear figures, axes and close them if they are not required.","Q_Score":233,"Tags":"python,python-3.x,matplotlib","A_Id":71455335,"CreationDate":"2014-02-19T15:00:00.000","Title":"warning about too many open figures","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm looking for a solution to the following graph problem in order to perform graph analysis in Python. \nBasically, I have a directed graph of N nodes where I know the following:\n\nThe sum of the weights of the out-edges for each node\nThe sum of the weights of the in-edges for each node\nFollowing from the above, the sum of the sum across all nodes of the in-edges equals the sum of the sum of out-edges\nNo nodes have edges with themselves\nAll weights are positive (or zero)\nHowever, I know nothing about to which nodes a given node might have an edge to, or what the weights of any edges are\n\nRepresented as a weighted adjacency matrix, I know the column sums and row sums but not the value of the edges themselves. I've realized that there is not a unique solution to this problem (Does anyone how to prove that, given the above, there is an assured solution?). However, I'm hoping that I can at least arrive at a solution to this problem that minimizes the sum of the edge weights or maximizes the number of 0 edge weights or something along those lines (Basically, out of infinite choices, I'd like the most 'simple' graph).\nI've thought about representing it as:\nMin Sum(All Edge Weights) s.t. for each node, the sum of its out-edge weights equals the known sum of these, and the sum of its in-edge weights equals the known sum of these. Additionally, constrained such that all weights are >= 0\nI'm primarily using this for data analysis in Scipy and Numpy. However, using their constrained minimization techniques, I'll end up with approximately 2N^2-2N constraints from the edge-weight sum portion, and N constraints from the positive portion. I'm worried this will be unfeasible for large data sets. I could have up to 500 nodes. Is this a feasible solution using SciPy's fmin_cobyla? Is there another way to layout this problem \/ another solver in Python that would be more efficient?\nThanks so much! First post on StackOverflow.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":200,"Q_Id":21893973,"Users Score":0,"Answer":"The prohibition against self-flows makes some instances of this problem infeasible (e.g., one node that has in- and out-flows of 1). Otherwise, a reasonably sparse solution with at most one self-flow always can be found as follows. Initialize two queues, one for the nodes with positive out-flow from lowest ID to highest and one for the nodes with positive in-flow from highest ID to lowest. Add a flow from the front node of the first queue to the front node of the second, with quantity equal to the minimum of the out-flow of the former and the in-flow of the latter. Update the out- and in-flows to their residual values and remove the exhausted node(s) from their queues. Since the ID of the front of the first queue increases, and the ID of the front of the second queue decreases, the only node that self-flows is the one where the ID numbers cross.\nMinimizing the total flow is trivial; it's constant. Finding the sparsest solution is NP-hard; there's a reduction from subset sum where each of the elements being summed has a source node with that amount of out-flow, and two more sink nodes have in-flows, one of which is equal to the target sum. The subset sum instance is solvable if and only if no source flows to both sinks. The algorithm above is a 2-approximation.\nTo get rid of the self-flow on that one bad node sparsely: repeatedly grab a flow not involving the bad node and split it into two, via the bad node. Stop when we exhaust the self-flow. This fails only if there are no flows left that don't use the bad node and there is still a self-flow, in which case the bad node has in- and out-flows that sum to more than the total flow, a necessary condition for the existence of a solution. This algorithm is a 4-approximation in sparsity.","Q_Score":0,"Tags":"python,algorithm,graph,scipy,mathematical-optimization","A_Id":21894459,"CreationDate":"2014-02-19T22:28:00.000","Title":"SciPy - Constrained Minimization derived from a Directed Graph","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a class that returns large NumPy arrays. These arrays are cached within the class. I would like the returned arrays to be copy-on-write arrays. If the caller ends up just reading from the array, no copy is ever made. This will case no extra memory will be used. However, the array is \"modifiable\", but does not modify the internal cached arrays.\nMy solution at the moment is to make any cached arrays readonly (a.flags.writeable = False). This means that if the caller of the function may have to make their own copy of the array if they want to modify it. Of course, if the source was not from cache and the array was already writable, then they would duplicate the data unnecessarily.\nSo, optimally I would love something like a.view(flag=copy_on_write). There seems to be a flag for the reverse of this UPDATEIFCOPY which causes a copy to update the original once deallocated.\nThanks!","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2415,"Q_Id":21896030,"Users Score":5,"Answer":"Copy-on-write is a nice concept, but explicit copying seems to be \"the NumPy philosophy\". So personally I would keep the \"readonly\" solution if it isn't too clumsy.\nBut I admit having written my own copy-on-write wrapper class. I don't try to detect write access to the array. Instead the class has a method \"get_array(readonly)\" returning its (otherwise private) numpy array. The first time you call it with \"readonly=False\" it makes a copy. This is very explicit, easy to read and quickly understood.\nIf your copy-on-write numpy array looks like a classical numpy array, the reader of your code (possibly you in 2 years) may have a hard time.","Q_Score":4,"Tags":"python,numpy,copy-on-write","A_Id":21900644,"CreationDate":"2014-02-20T01:06:00.000","Title":"NumPy Array Copy-On-Write","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to plot weather data over the span of several days every half hour, but I only want to label the days at the start as a string in the format 'mm\/dd\/yy'. I want to leave the rest unmarked.\nI would also want to control where such markings are placed along the x axis, and control the range of the axis.\nI also want to plot multiple sets of measurements taken over different intervals on the same figure. Therefore being able to set the axis and plot the measurements for a given day would be best.\nAny suggestions on how to approach this with matplotlib?","AnswerCount":3,"Available Count":2,"Score":0.0665680765,"is_accepted":false,"ViewCount":2934,"Q_Id":21918718,"Users Score":1,"Answer":"Matplotlib xticks are your friend. Will allow you to set where the ticks appear. \nAs for date formatting, make sure you're using dateutil objects, and you'll be able to handle the formatting.","Q_Score":0,"Tags":"python,matplotlib,plot,weather","A_Id":21919317,"CreationDate":"2014-02-20T20:23:00.000","Title":"How to label certain x values","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to plot weather data over the span of several days every half hour, but I only want to label the days at the start as a string in the format 'mm\/dd\/yy'. I want to leave the rest unmarked.\nI would also want to control where such markings are placed along the x axis, and control the range of the axis.\nI also want to plot multiple sets of measurements taken over different intervals on the same figure. Therefore being able to set the axis and plot the measurements for a given day would be best.\nAny suggestions on how to approach this with matplotlib?","AnswerCount":3,"Available Count":2,"Score":0.1325487884,"is_accepted":false,"ViewCount":2934,"Q_Id":21918718,"Users Score":2,"Answer":"You can use a DayLocator as in: plt.gca().xaxis.set_major_locator(dt.DayLocator())\nAnd DateFormatter as in: plt.gca().xaxis.set_major_formatter(dt.DateFormatter(\"%d\/%m\/%Y\"))\nNote: import matplotlib.dates as dt","Q_Score":0,"Tags":"python,matplotlib,plot,weather","A_Id":21919748,"CreationDate":"2014-02-20T20:23:00.000","Title":"How to label certain x values","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an NFL dataset with a 'description' column with details about the play. Each successful pass and run play has a string that's structured like:\n\"(12:25) (No Huddle Shotgun) P.Manning pass short left to W.Welker pushed ob at DEN 34 for 10 yards (C.Graham).\"\nHow do I locate\/extract the number after \"for\" in the string, and place it in a new column?","AnswerCount":2,"Available Count":1,"Score":-0.0996679946,"is_accepted":false,"ViewCount":4497,"Q_Id":21947487,"Users Score":-1,"Answer":"This will grab the number 10 and put it in a variable called yards.\n\nx = \"(12:25) (No Huddle Shotgun) P.Manning pass short left to W.Welker pushed ob at DEN 34 for 10 yards (C.Graham).\"\nyards = (x.split(\"for \")[-1]).split(\" yards\")[0]","Q_Score":4,"Tags":"python,string,pandas,extract","A_Id":21947575,"CreationDate":"2014-02-21T23:49:00.000","Title":"Extract a certain part of a string after a key phrase using pandas?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to detect arcs inside an image. The information that I have for certain with me is the radius of the arc. I can try and maybe get the centre of the circle whose arc I want to identify.\nIs there any algorithm in Open CV which can tell us that the detected contour ( or edge from canny edge is an arc or an approximation of an arc)\nAny help on how this would be possible in OpenCV with Python or even a general approach would be very helpful\nThanks","AnswerCount":3,"Available Count":2,"Score":0.1325487884,"is_accepted":false,"ViewCount":5077,"Q_Id":21986356,"Users Score":2,"Answer":"If you think that there will not be any change in the shape (i mean arc won't become line or something like this) then you can have a look a Generalized Hough Transform (GHT) which can detect any shape you want.\nCons:\n\nThere is no directly function in openCV library for GHT but you can get several source code at internet.\nIt is sometimes slow but can become fast if you set the parameters properly.\nIt won't be able to detect if the shape changes. for exmaple, i tried to detect squares using GHT and i got good results but when square were not perfect squares (i.e. rectangle or something like that), it didn't detect.","Q_Score":1,"Tags":"python,opencv,image-processing","A_Id":21987220,"CreationDate":"2014-02-24T11:24:00.000","Title":"Detect an arc from an image contour or edge","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to detect arcs inside an image. The information that I have for certain with me is the radius of the arc. I can try and maybe get the centre of the circle whose arc I want to identify.\nIs there any algorithm in Open CV which can tell us that the detected contour ( or edge from canny edge is an arc or an approximation of an arc)\nAny help on how this would be possible in OpenCV with Python or even a general approach would be very helpful\nThanks","AnswerCount":3,"Available Count":2,"Score":0.0665680765,"is_accepted":false,"ViewCount":5077,"Q_Id":21986356,"Users Score":1,"Answer":"You can do it this way:\n\nConvert the image to edges using canny filter.\nMake the image binary using threshold function there is an option for regular threshold, otsu or adaptive.\nFind contours with sufficient length (findContours function)\nIterate all the contours and try to fit ellipse (fitEllipse function)\nValidate fitted ellipses by radius.\nCheck if detected ellipse is good fit - checking how much of the contour pixels are on the detected ellipse.\nSelect the best one.\n\nYou can try to increase the speed using RANSAC each time selecting 6 points from binarized image and trying to fit.","Q_Score":1,"Tags":"python,opencv,image-processing","A_Id":22008350,"CreationDate":"2014-02-24T11:24:00.000","Title":"Detect an arc from an image contour or edge","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I understand that the ends of whiskers in matplotlib's box plot function extend to max value below 75% + 1.5 IQR and minimum value above 25% - 1.5 IQR.  I would like to change it to represent max and minimum values of the data or the 5th and 95th quartile of the data. Is is possible to do this?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":8736,"Q_Id":21997897,"Users Score":18,"Answer":"To get the whiskers to appear at the min and max of the data, set the whis parameter to an arbitrarily large number. In other words: boxplots = ax.boxplot(myData, whis=np.inf).\nThe whis kwarg is a scaling factor of the interquartile range. Whiskers are drawn to the outermost data points within whis * IQR away from the quartiles. \nNow that v1.4 is out:\nIn matplotlib v1.4, you can say: boxplots = ax.boxplot(myData, whis=[5, 95]) to set the whiskers at the 5th and 95th percentiles. Similarly, you'll be able to say boxplots = ax.boxplot(myData, whis='range') to set the whiskers at the min and max.\nNote: you could probably modify the artists contained in the boxplots dictionary returned by the ax.boxplot method, but that seems like a huge hassle","Q_Score":11,"Tags":"python,matplotlib","A_Id":21998600,"CreationDate":"2014-02-24T20:07:00.000","Title":"Changing what the ends of whiskers represent in matplotlib's boxplot function","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to read a csv with pandas using the read_csv command.  However, one of my columns is a 15 digit number which is read in as a float and then truncated to exponential notation.  So the entries in this column become 2.09228E+14 instead of the 15 digit number I want.  I've tried reading it as a string, but I get '2.09228E+14' instead of the number.  Any suggestions?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1866,"Q_Id":22001176,"Users Score":1,"Answer":"Just do\nstr(int(float('2.09228E+14'))) which should give you '209228000000000'","Q_Score":0,"Tags":"python,csv,pandas","A_Id":22001369,"CreationDate":"2014-02-24T23:12:00.000","Title":"Pandas read csv data type","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"So I am trying to run various large images which gets put into an array using numpy so that I can then do some calculations. The calculations get done per image and the opening and closing of each image is done in a loop. I a have reached a frustration point because I have no errors in the code (well none to my knowledge nor any that python is complaining about), and as a matter of fact my code runs for one loop, and then it simply does not run for the second, third, or other loops. \nI get no errors! No memory error, no syntax error, no nothing. I have used Spyder and even IDLE, and it simply runs all the calculations sometimes only for one image, sometimes for two, then it just quits the loop (again WITH NO ERROR) as if it had completed running for all images (when it has only ran for one\/two images).\nI am assuming its a  memory error? - I mean it runs one loop , sometimes two, but never the rest? -- so ...\nI have attempted to clear the tracebacks using this: \nsys.exc_clear() \nsys.exc_traceback = sys.last_traceback = None \nI have also even tried to delete each variable when I am done with it \nie. del variable\nHowever, nothing seems to fix it -- \nAny ideas of what could be wrong would be appreciated!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":107,"Q_Id":22024577,"Users Score":0,"Answer":"The exit code of the python process should reveal the reason for the process exiting. In the event of an adverse condition, the exit code will be something other than 0. If you are running in a Bash shell or similar, you can run \"echo $?\" in your shell after running Python to see its exit status.\nIf the exit status is indeed 0, try putting some print statements in your code to trace the execution of your program. In any case, you would do well to post your code for better feedback.\nGood luck!","Q_Score":1,"Tags":"python,numpy","A_Id":22024672,"CreationDate":"2014-02-25T19:45:00.000","Title":"Large dataset - no error - but it wont run - python memory issue?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a speed\/efficiency related question about python:\nI need to write a large number of very large R dataframe-ish files, about 0.5-2 GB sizes. This is basically a large tab-separated table, where each line can contain floats, integers and strings.\nNormally, I would just put all my data in numpy dataframe and use np.savetxt to save it, but since there are different data types it can't really be put into one array.\nTherefore I have resorted to simply assembling the lines as strings manually, but this is a tad slow. So far I'm doing:\n1) Assemble each line as a string\n2) Concatenate all lines as single huge string\n3) Write string to file\nI have several problems with this:\n1) The large number of string-concatenations ends up taking a lot of time\n2) I run of of RAM to keep strings in memory\n3) ...which in turn leads to more separate file.write commands, which are very slow as well.\nSo my question is: What is a good routine for this kind of problem? One that balances out speed vs memory-consumption for most efficient string-concatenation and writing to disk.\n... or maybe this strategy is simply just bad and I should do something completely different?\nThanks in advance!","AnswerCount":3,"Available Count":1,"Score":0.3215127375,"is_accepted":false,"ViewCount":15877,"Q_Id":22026393,"Users Score":5,"Answer":"Unless you are running into a performance issue, you can probably write to the file line by line. Python internally uses buffering and will likely give you a nice compromise between performance and memory efficiency. \nPython buffering is different from OS buffering and you can specify how you want things buffered by setting the buffering argument to open.","Q_Score":5,"Tags":"python,python-2.7,file-io,dataframe,string-concatenation","A_Id":22026711,"CreationDate":"2014-02-25T21:17:00.000","Title":"Python: Fast and efficient way of writing large text file","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"So I already know how to use scipy.optimize.curve_fit for normal fitting needs, but what do I do if both my x data and my y data both have error bars?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":104,"Q_Id":22029142,"Users Score":2,"Answer":"Try scipy.odr. It allows to specify weights\/errors in both input and response variable.","Q_Score":0,"Tags":"python,scipy","A_Id":22044916,"CreationDate":"2014-02-26T00:21:00.000","Title":"Scipy: Fitting Data with Two Dimensional Error","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In numpy, some of the operations return in shape (R, 1) but some return (R,). This will make matrix multiplication more tedious since explicit reshape is required. For example, given a matrix M, if we want to do numpy.dot(M[:,0], numpy.ones((1, R))) where R is the number of rows (of course, the same issue also occurs column-wise). We will get matrices are not aligned error since M[:,0] is in shape (R,) but numpy.ones((1, R)) is in shape (1, R).\nSo my questions are:\n\nWhat's the difference between shape (R, 1) and (R,). I know literally it's list of numbers and list of lists where all list contains only a number. Just wondering why not design numpy so that it favors shape (R, 1) instead of (R,) for easier matrix multiplication.\nAre there better ways for the above example? Without explicitly reshape like this: numpy.dot(M[:,0].reshape(R, 1), numpy.ones((1, R)))","AnswerCount":8,"Available Count":1,"Score":0.049958375,"is_accepted":false,"ViewCount":193239,"Q_Id":22053050,"Users Score":2,"Answer":"The data structure of shape (n,) is called a rank 1 array. It doesn't behave consistently as a row vector or a column vector which makes some of its operations and effects non intuitive. If you take the transpose of this (n,) data structure, it'll look exactly same and the dot product will give you a number and not a matrix.\nThe vectors of shape (n,1) or (1,n) row or column vectors are much more intuitive and consistent.","Q_Score":394,"Tags":"python,numpy,matrix,multidimensional-array","A_Id":65142140,"CreationDate":"2014-02-26T20:55:00.000","Title":"Difference between numpy.array shape (R, 1) and (R,)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am not sure if what I am thinking would be possible, I would need the help from someone experienced working with HDF5\/PyTables.\nThe escenario would be like this:\nLet's say that we have a process, or a machine or a connexion etc, acquiring data, and storing in a HDF5\/PyTable format. I will call it store software.\nWould it be possible to have another software, I will call it analysis software, running on time?.\nIf it helps, the store software and the analysis software would be totally independent, even wrote on different languages.\nMy doubt is that, if the store program is writing the PyTable, mode='w', then, at the same time, can the analysis program access in mode='r', and read some data to perform some basic analysis, averages, etc, etc??. \nThe basic idea of this is to be able to analyze data stored in a PyTable on real time.\nOf course any other proposed solution would be appreciated.","AnswerCount":2,"Available Count":1,"Score":-0.1973753202,"is_accepted":false,"ViewCount":996,"Q_Id":22062837,"Users Score":-2,"Answer":"This is definitely possible.  It is especially easy if you only have one process in 'w' and multiple processes in 'r' mode. Just make sure in your 'w' process to flush() the file and\/or the datasets occasionally.  If you do this, the 'r' process will be able to see the data.","Q_Score":2,"Tags":"python,real-time,pytables","A_Id":22161505,"CreationDate":"2014-02-27T08:23:00.000","Title":"write and read on real time pytables","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How do you transpose a matrix without using numpy or zip or other imports?\nI thought this was the answer but it does not work if the matrix has only 1 row...\n[[row[i] for row in data] for i in range(len(data[1]))]","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":4612,"Q_Id":22079882,"Users Score":6,"Answer":"[[row[i] for row in data] for i in range(len(data[0]))]","Q_Score":1,"Tags":"python,matrix,transpose","A_Id":22080238,"CreationDate":"2014-02-27T20:42:00.000","Title":"How to transpose a matrix without using numpy or zip (or other imports)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"How I can create a graph with \n-predefined number of connections for each node, say 3\n-given distribution of connections (say Poisson distribution with given mean) \nThanks","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":1817,"Q_Id":22084435,"Users Score":1,"Answer":"It seems to me you should\n\ndecide how many nodes you will have\ngenerate the number of links per node in your desired distribution - make sure the sum is even\nstart randomly connecting pairs of nodes until all link requirements are satisfied\n\nThere are a few more constraints - no pair of nodes should be connected more than once, no node should have more than (number of nodes - 1) links, maybe you want to ensure the graph is fully connected - but basically that's it.","Q_Score":3,"Tags":"python,networkx","A_Id":22084546,"CreationDate":"2014-02-28T01:33:00.000","Title":"Python: Create a graph with defined number of edges per node","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to compute some portfolio statistics using Python Pandas, and I am looking for a way to query stock data with DataReader using the ISIN (International Securities Identification Number). \nHowever, as far as I can see, DataReader is not compatible with such ids, although both YahooFinance and GoogleFinance can handle such queries.\nHow can I use DataReader with stock ISINs?","AnswerCount":2,"Available Count":1,"Score":-0.0996679946,"is_accepted":false,"ViewCount":3894,"Q_Id":22091306,"Users Score":-1,"Answer":"Forget about Python. There is absolutely no way to convert an ISIN to a Ticker Symbol. You have completely misunderstood the wikipedia page.","Q_Score":3,"Tags":"python,pandas,stock","A_Id":60347465,"CreationDate":"2014-02-28T09:37:00.000","Title":"Pandas: DataReader in combination with ISIN identification","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Figures rendered with the PDF backend have a 'butt' capstyle in my reader. (If I zoom at the corner of a figure in a pdf, I do not see a square corner, but the overlap of shortened lines.) I would like either a 'round' or 'projecting' (what matplotlib calls the 'square' capstyle) cap. Thus the Spine objects are in question, and a Spine is a Patch is an Artist, none of which seem to have anything like the set_solid_capstyle() of Line2D, so I'm not sure how or where to force a particular capstyle, or if it's even possible.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":176,"Q_Id":22108095,"Users Score":2,"Answer":"I don't think it's possible. I did a little bit of the backend's work in my main script, setting up a RendererPdf (defined in backend_pdf.py) and conatining a GraphicsContextPdf which is a GraphicsContextBase which keeps a capstyle, intialized as butt. As confirmed by grep, this is the only place where butt is hardcoded as a capstyle. After some ipython debugging, I've found that a new GraphicsContextPdf or 'gc' is generated each time a patch is drawn (c.f. patches.py:392, called by way of a necessary fig.draw() in the main script), and the settings for the new gc (again initialized as butt) are incorporated into the original RendererPdf's gc. So everything gets a butt capstyle. Line2D objects are not patches, so they can maintain a particular capstyle.","Q_Score":4,"Tags":"python,matplotlib","A_Id":22109817,"CreationDate":"2014-02-28T23:58:00.000","Title":"set capstyle of spines for pdf backend","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Recently I was contemplating the choice of using either R or Python to train support vector machines.\nAside from the particular strengths and weaknesses intrinsic to both programming languages, I'm wondering if there is any heuristic guidelines for making a decision on which way to go, based on the packages themselves.\nI'm thinking in terms of speed of training a model, scalability, availability of different kernels, and other such performance-related aspects.\nGiven some data sets of different sizes, how could one decide which path to take?  \nI apologize in advance for such a possibly vague question.","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":381,"Q_Id":22122506,"Users Score":4,"Answer":"I do not have experiece with e1070, however from googling it it seems that it either uses or is based on LIBSVM (I don't know enough R to determine which from the cran entry). Scilearnkit also uses LIBSVM. \nIn both cases the model is going to be trained by LIBSVM. Speed, scalability, variety of options available is going to be exactly the same, and in using SVMs with these libraries the main limitations you will face are the limitations of LIBSVM.\nI think that giving further advice is going to be difficult unless you clarify a couple of things in your question: what is your objective? Do you already know LIBSVM? Is this a learning project? Who is paying for your time? Do you feel more comfortable in Python or in R?","Q_Score":1,"Tags":"python,r,machine-learning,scikit-learn,svm","A_Id":22123913,"CreationDate":"2014-03-02T00:52:00.000","Title":"What's the difference between using libSVM in sci-kit learn, or e1070 in R, for training and using support vector machines?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Recently I was contemplating the choice of using either R or Python to train support vector machines.\nAside from the particular strengths and weaknesses intrinsic to both programming languages, I'm wondering if there is any heuristic guidelines for making a decision on which way to go, based on the packages themselves.\nI'm thinking in terms of speed of training a model, scalability, availability of different kernels, and other such performance-related aspects.\nGiven some data sets of different sizes, how could one decide which path to take?  \nI apologize in advance for such a possibly vague question.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":381,"Q_Id":22122506,"Users Score":0,"Answer":"Sometime back I had the same question. Yes, both e1070 and scikit-learn use LIBSVM. I have experience with e1070 only.\nBut there are some areas where R is better. I have read in the past that Python does not handle categorical features properly (at least not right out of the box). This could be a big deal for some.\nI also prefer R's formula interface. And some of the nice data manipulation packages.\nPython is definitely better for general purpose programming and scikit-learn aids in using a single programming language for all tasks.","Q_Score":1,"Tags":"python,r,machine-learning,scikit-learn,svm","A_Id":22189863,"CreationDate":"2014-03-02T00:52:00.000","Title":"What's the difference between using libSVM in sci-kit learn, or e1070 in R, for training and using support vector machines?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I know the equivalent functions of  conv2 and corr2 of MATLAB are scipy.signal.correlate and scipy.signal.convolve. But the function imfilter has the property of dealing with the outside the bounds of the array. Like as symmetric, replicate and circular. Can Python do that things","AnswerCount":4,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":12599,"Q_Id":22142369,"Users Score":2,"Answer":"Using the functions  scipy.ndimage.filters.correlate and scipy.ndimage.filters.convolve","Q_Score":11,"Tags":"python,matlab,scipy","A_Id":22165531,"CreationDate":"2014-03-03T08:58:00.000","Title":"The equivalent function of Matlab imfilter in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I was surprised when I started learning numpy that there are N dimensional arrays. I'm a programmer and all I thought that nobody ever use more than 2D array. Actually I can't even think beyond a 2D array. I don't know how think about 3D, 4D, 5D arrays or more. I don't know where to use them.\nCan you please give me examples of where 3D, 4D, 5D ... etc arrays are used? And if one used  numpy.sum(array, axis=5) for a 5D array would what happen?","AnswerCount":5,"Available Count":5,"Score":0.0,"is_accepted":false,"ViewCount":141,"Q_Id":22143644,"Users Score":0,"Answer":"They are very applicable in scientific computing. Right now, for instance, I am running simulations which output data in a 4D array: specifically \n| Time | x-position | y-position | z-position |.\nAlmost every modern spatial simulation will use multidimensional arrays,  along with programming for computer games.","Q_Score":1,"Tags":"python,arrays,numpy","A_Id":37731398,"CreationDate":"2014-03-03T10:02:00.000","Title":"Examples on N-D arrays usage","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I was surprised when I started learning numpy that there are N dimensional arrays. I'm a programmer and all I thought that nobody ever use more than 2D array. Actually I can't even think beyond a 2D array. I don't know how think about 3D, 4D, 5D arrays or more. I don't know where to use them.\nCan you please give me examples of where 3D, 4D, 5D ... etc arrays are used? And if one used  numpy.sum(array, axis=5) for a 5D array would what happen?","AnswerCount":5,"Available Count":5,"Score":0.0,"is_accepted":false,"ViewCount":141,"Q_Id":22143644,"Users Score":0,"Answer":"There are so many examples... The way you are trying to represent it is probably wrong, let's take a simple example:\n\nYou have boxes and a box stores N items in it. You can store up to 100 items in each box.\nYou've organized the boxes in shelves. A shelf allows you to store M boxes. You can identify each box by a index.\nAll the shelves are in a warehouse with 3 floors. So you can identify any shelf using 3 numbers: the row, the column and the floor.\n\nA box is then identified by: row, column, floor and the index in the shelf.\nAn item is identified by: row, column, floor, index in shelf, index in box.\nBasically, one way (not the best one...) to model this problem would be to use a 5D array.","Q_Score":1,"Tags":"python,arrays,numpy","A_Id":22146242,"CreationDate":"2014-03-03T10:02:00.000","Title":"Examples on N-D arrays usage","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I was surprised when I started learning numpy that there are N dimensional arrays. I'm a programmer and all I thought that nobody ever use more than 2D array. Actually I can't even think beyond a 2D array. I don't know how think about 3D, 4D, 5D arrays or more. I don't know where to use them.\nCan you please give me examples of where 3D, 4D, 5D ... etc arrays are used? And if one used  numpy.sum(array, axis=5) for a 5D array would what happen?","AnswerCount":5,"Available Count":5,"Score":0.0399786803,"is_accepted":false,"ViewCount":141,"Q_Id":22143644,"Users Score":1,"Answer":"A few simple examples are:\n\nA n x m 2D array of p-vectors represented as an n x m x p 3D matrix, as might result from computing the gradient of an image\nA 3D grid of values, such as a volumetric texture\nThese can even be combined in the case of a gradient of a volume in which case you get a 4D matrix\nStaying with the graphics paradigm, adding time adds an extra dimension, so a time-variant 3D gradient texture would be 5D\n\nnumpy.sum(array, axis=5) is not valid for a 5D-array (as axes are numbered starting at 0)","Q_Score":1,"Tags":"python,arrays,numpy","A_Id":22144505,"CreationDate":"2014-03-03T10:02:00.000","Title":"Examples on N-D arrays usage","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I was surprised when I started learning numpy that there are N dimensional arrays. I'm a programmer and all I thought that nobody ever use more than 2D array. Actually I can't even think beyond a 2D array. I don't know how think about 3D, 4D, 5D arrays or more. I don't know where to use them.\nCan you please give me examples of where 3D, 4D, 5D ... etc arrays are used? And if one used  numpy.sum(array, axis=5) for a 5D array would what happen?","AnswerCount":5,"Available Count":5,"Score":0.0,"is_accepted":false,"ViewCount":141,"Q_Id":22143644,"Users Score":0,"Answer":"For example, a 3D array could be used to represent a movie, that is a 2D image that changes with time.\nFor a given time, the first two axes would give the coordinate of a pixel in the image, and the corresponding value would give the color of this pixel, or a grey scale level. The third axis would then represent time. For each time slot, you have a complete image. \nIn this example, numpy.sum(array, axis=2) would integrate the exposure in a given pixel. If you think about a film taken in low light conditions, you could think of doing something like that to be able to see anything.","Q_Score":1,"Tags":"python,arrays,numpy","A_Id":22144263,"CreationDate":"2014-03-03T10:02:00.000","Title":"Examples on N-D arrays usage","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I was surprised when I started learning numpy that there are N dimensional arrays. I'm a programmer and all I thought that nobody ever use more than 2D array. Actually I can't even think beyond a 2D array. I don't know how think about 3D, 4D, 5D arrays or more. I don't know where to use them.\nCan you please give me examples of where 3D, 4D, 5D ... etc arrays are used? And if one used  numpy.sum(array, axis=5) for a 5D array would what happen?","AnswerCount":5,"Available Count":5,"Score":0.0,"is_accepted":false,"ViewCount":141,"Q_Id":22143644,"Users Score":0,"Answer":"Practical applications are hard to come up with but I can give you a simple example for 3D.\nImagine taking a 3D world (a game or simulation for example) and splitting it into equally sized cubes. Each cube could contain a specific value of some kind (a good example is temperature for climate modelling). The matrix can then be used for further operations (simple ones like calculating its Transpose, its Determinant etc...).\nI recently had an assignment which involved modelling fluid dynamics in a 2D space. I could have easily extended it to work in 3D and this would have required me to use a 3D matrix instead.\nYou may wish to also further extend matrices to cater for time, which would make them 4D. In the end, it really boils down to the specific problem you are dealing with.\nAs an end note however, 2D matrices are still used for 3D graphics (You use a 4x4 augmented matrix).","Q_Score":1,"Tags":"python,arrays,numpy","A_Id":22144157,"CreationDate":"2014-03-03T10:02:00.000","Title":"Examples on N-D arrays usage","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Using Pandas, what are the reasons to use a Panel versus a MultiIndex DataFrame?\nI have personally found significant difference between the two in the ease of accessing different dimensions\/levels, but that may just be my being more familiar with the interface for one versus the other. I assume there are more substantive differences, however.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2444,"Q_Id":22156258,"Users Score":10,"Answer":"In my practice, the strongest, easiest-to-see difference is that a Panel needs to be homogeneous in every dimension. If you look at a Panel as a stack of Dataframes, you cannot create it by stacking Dataframes of different sizes or with different indexes\/columns. You can  indeed handle more non-homogeneous type of data with multiindex.\nSo the first choice has to be made based on how your data is to be organized.","Q_Score":14,"Tags":"python,pandas","A_Id":25162895,"CreationDate":"2014-03-03T20:04:00.000","Title":"Pandas MultiIndex versus Panel","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"So in the examples of matplotlib.animation there are two main functions that are used to make animations: AritstAnimation and FuncAnimation. \nAccording to the documentation the use of each of them is:\n.ArtistAnimation:\nBefore calling this function, all plotting should have taken place and the relevant artists saved.\nFuncAnimation\nMakes an animation by repeatedly calling a function func, passing in (optional) arguments in fargs.\nSo it appears to me that ArtistAnimation is useful when you have already the whole array, list or set of whatever object you want to make an animation from. FuncAnimation in the other hand seems to be more useful whenever you have a function that is able to give it your next result. \nIs my intuition above correct about this? My question in general is when is more convenient to use one or the other.\nThanks in advance","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":2205,"Q_Id":22158395,"Users Score":2,"Answer":"I think you are right, although it is simple to go from a list to a function (just iterate over it) or back (store function values in an array).\nSo it really doesn't matter too much, but you can pick the one that best suits your code, as you described.\n(Personally I find ArtistAnimation to be the most convenient)\nIf your result is very large, it might be good to use FuncAnimation so you don't need to store your data. MPL still stores it's own copy for plotting, but this factor two might make a difference.","Q_Score":2,"Tags":"python,animation,matplotlib","A_Id":22512378,"CreationDate":"2014-03-03T22:00:00.000","Title":"ArtistAnimation vs FuncAnimation matplotlib animation matplotlib.animation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Over time, I have seen IPython (and equivalently matplotlib) using two locations for config files:\n~\/.ipython\/profile_default\/\n~\/.config\/ipython\/profile_default\nwhich is the right one? Do these packages check both?\nIn case it matters, I am using Anaconda on OS X and on Linux","AnswerCount":3,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":555,"Q_Id":22159215,"Users Score":5,"Answer":"We (IPython) have kind of gone back and forth on the best location for config on Linux. We used to always use ~\/.ipython, but then we switched to ~\/.config\/ipython, which is the XDG-specified location (more correct, for a given value of correct), while still checking both. In IPython 2, we're switching back to ~\/.ipython by default, to make it more consistent across the different platforms we support.\nHowever, I don't think it should have been using ~\/.config on a Mac - it should always have been ~\/.ipython there.","Q_Score":1,"Tags":"python,matplotlib,ipython","A_Id":22161688,"CreationDate":"2014-03-03T22:48:00.000","Title":"IPython & matplotlib config profiles and files","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Over time, I have seen IPython (and equivalently matplotlib) using two locations for config files:\n~\/.ipython\/profile_default\/\n~\/.config\/ipython\/profile_default\nwhich is the right one? Do these packages check both?\nIn case it matters, I am using Anaconda on OS X and on Linux","AnswerCount":3,"Available Count":2,"Score":0.1325487884,"is_accepted":false,"ViewCount":555,"Q_Id":22159215,"Users Score":2,"Answer":"As far as matplotlib is concerned, on OS X the config file (matplotlibrc) will be looked for first in the current directory, then in ~\/.matplotlib, and finally in INSTALL\/matplotlib\/mpl-data\/matplotlibrc, where INSTALL is the Python site-packages directory. With a standard install of Python from python.org, this is \/Library\/Frameworks\/Python.framework\/Versions\/X.Y\/lib\/pythonX.Y\/site-packages, where X.Y is the version you're using, like 2.7 or 3.3.","Q_Score":1,"Tags":"python,matplotlib,ipython","A_Id":22161946,"CreationDate":"2014-03-03T22:48:00.000","Title":"IPython & matplotlib config profiles and files","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"So I'm making a python class which calculates the tfidf weight of each word in a document. Now in my dataset I have 50 documents. In these documents many words intersect, thus having multiple same word features but with different tfidf weight. So the question is how do I sum up all the weights into one singular weight?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":722,"Q_Id":22159351,"Users Score":2,"Answer":"First, let's get some terminology clear.  A term is a word-like unit in a corpus.  A token is a term at a particular location in a particular document.  There can be multiple tokens that use the same term.  For example, in my answer, there are many tokens that use the term \"the\".  But there is only one term for \"the\".\nI think you are a little bit confused.  TF-IDF style weighting functions specify how to make a per term score out of the term's token frequency in a document and the background token document frequency in the corpus for each term in a document.  TF-IDF converts a document into a mapping of terms to weights.  So more tokens sharing the same term in a document will increase the corresponding weight for the term, but there will only be one weight per term.  There is no separate score for tokens sharing a term inside the doc.","Q_Score":0,"Tags":"python,text,machine-learning,classification,tf-idf","A_Id":22159481,"CreationDate":"2014-03-03T22:58:00.000","Title":"(Text Classification) Handling same words but from different documents [TFIDF ]","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a large sparse matrix, implemented as a lil sparse matrix from sci-py. I just want a statistic for how sparse the matrix is once populated. Is there a method to find out this?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":157,"Q_Id":22185277,"Users Score":4,"Answer":"m.nnz is the number of nonzero elements in the matrix m, you can use m.size to get the total number of elements.","Q_Score":2,"Tags":"python,scipy,sparse-matrix","A_Id":22185324,"CreationDate":"2014-03-04T23:17:00.000","Title":"Determine sparsity of sparse matrix ( Lil matrix )","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"By generating and checking p random pairs. \nSomewhat confused on how to go about doing this. I know I could make an algorithm that determines whether or not two integers are relatively prime. I am also having difficulty understanding what generating and checking p random pairs means.","AnswerCount":3,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":519,"Q_Id":22188097,"Users Score":3,"Answer":"Without a complete enumeration of the relative primeness of all numbers between 0 and k (a huge task and one that grows as the square of k) you can make an estimate by selecting a relatively large number of random pairs (p of them) and determine whether they are relatively prime.\nThe assumption is that as the sample size increases the proportion of relative primes tends towards the required probability value (i.e. if you take 10,000 sampled pairs and you find that 7,500 of them are relatively prime then you'd estimate the probability of relative primeness at 0.75).\nin Python random.randint(0, k) selects a (pseudo-)random integer between 0 and k.","Q_Score":0,"Tags":"python,primes","A_Id":22188201,"CreationDate":"2014-03-05T03:46:00.000","Title":"Estimate probability that two random integers between 0 and k are relatively prime","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Given an array length 1 or more of ints, return the smallest value in the array.\nmy_min([10, 3, 5, 6]) -> 3\nThe program starts with def my_min(nums):","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":58,"Q_Id":22214166,"Users Score":0,"Answer":"Is this ok? using C\nint def my_min(nums)\n{\nint i,min;\nint min[N];\nfor(i=0;i<nums;i++)\n{\nscanf(\"%d\",&min[i]);\nif (i==0)\n{\nmin=min[0];\n}\nelse\n{\nif(min>min[i])\n{\nmin=min[i];\n}\n}\n}\nreturn min;\n}","Q_Score":0,"Tags":"python-2.7","A_Id":22214465,"CreationDate":"2014-03-06T03:20:00.000","Title":"Given an array length 1 or more of ints, return the smallest value in the array","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to do two subplots with imshow using the same colormap by which I mean: if points in both plots have the same color, they correspond to the same value. But how can I get imshow to use only 9\/10 or so of the colormap for the first plot, because it's maximal value is only 9\/10 of the maximal value in the second plot?\nThanks,\nAlice","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":105,"Q_Id":22274186,"Users Score":1,"Answer":"imgshow takes two arguments vmin and vmax for the color scale. You could do what you want by putting the same vmin and vmax for both your subplots. \nTo find vmin you can take the minimum between the minimum of all the values in your data (and same reasoning for vmax).","Q_Score":0,"Tags":"python,matplotlib,color-mapping","A_Id":22274333,"CreationDate":"2014-03-08T19:45:00.000","Title":"one colormap for multiple subplots with different maximum values","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Hi is there anyway to group this list such that it would return a string(first element) and a list within a tuple for each equivalent first element?\nie.,\n\n[('106', '1', '1', '43009'), ('106', '1', '2', '43179'), ('106', '1', '4', '43619'), ('171', '1', '3', '59111'), ('171', '1', '4', '57089'), ('171', '1', '5', '57079'), ('184', '1', '18', '42149'), ('184', '1', '19', '12109'), ('184', '1', '20', '12099')]\n\nbecomes :\n\n[('106',[('106', '1', '1', '43009'), ('106', '1', '2', '43179'), ('106', '1', '4', '43619')]),\n  ('171',[('171', '1', '3', '59111'), ('171', '1', '4', '57089'), ('171', '1', '5', '57079')]),\n  ('184'[(('184', '1', '18', '42149'), ('184', '1', '19', '12109'), ('184', '1', '20', '12099')])]","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":53,"Q_Id":22279611,"Users Score":0,"Answer":"I would use a dictionary with the first element as key. \nAlso look into ordered dictionaries.","Q_Score":0,"Tags":"python,sorting","A_Id":22281914,"CreationDate":"2014-03-09T07:37:00.000","Title":"Grouping a list in python specifically","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"In a folder, I have a number of .csv files (count varies) each of which has 5 rows and 1200 columns of numerical data(float). \nNow I want to average the data in these files (i.e. R1C1 of files gives one averaged value in a resulting file, and so on for every position (R2C2 of all files gives one value in the same position of resulting file etc.).\nHow do I sequentially input all files in that folder into a couple of arrays; what functions in numpy can be used to just find the mean among the files (now arrays) that have been read into these arrays. Is there a better way to this? New to computing, appreciate any help.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":59,"Q_Id":22283494,"Users Score":0,"Answer":"I think you're certainly on the right track (because python together with numpy is a great combination for this task), but in order to do what you want to do, you do need some basic programming skills. I'll assume you at least know a little about working in an interactive python shell and how to import modules etc. :-)\nThen probably the easiest approach is to only have a single numpy array: one that contains the sum of the data in your files. After that it's just a matter of dividing by the number of files that you have.\nSo for example you could follow the following approach:\n\nloop over all files in a folder with a for-loop and the os.listdir method\ncheck if the file belongs to the data collection, for example by using something like str.endswith('.csv')\nconvert the filename to a full path by using os.path.join\nread the data to a numpy array with numpy.loadtxt\nadd this data to the array containing the sums, which is initialized with np.zeros before the loop\nkeep a count of how many files you processed\nafter the loop, calculate the means by dividing the sums by the number of files processed","Q_Score":0,"Tags":"python,arrays,numpy","A_Id":22284670,"CreationDate":"2014-03-09T14:35:00.000","Title":"Python averaging with ndarrays, csvfile data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When using pandas.read_csv setting sep = None for automatic delimiter detection, the message Using Python parser to sniff delimiter is printed to STDOUT. My code calls this function often so this greatly annoys me, how can I prevent this from happening short of going into the source and deleting the print statement.\nThis is with pandas 0.13.1, Python 2.7.5","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":113,"Q_Id":22308688,"Users Score":1,"Answer":"This is a 'bug' in that I think this is a debugging message.\nTo work-around, pass engine='python' to disable the message.","Q_Score":1,"Tags":"python,python-2.7,pandas","A_Id":22323918,"CreationDate":"2014-03-10T18:46:00.000","Title":"Using Python parser to sniff delimiter Spammed to STDOUT","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm having problems to compare the output of two code because of random number state.\nI'm comparing the MATLAB randperm function with the output of the equivalent numpy.random.permutation function but, even if I've set the seed to the same value with a MATLAB rand('twister',0) and a python numpy.random.seed(0) I'm obtaining different permutations.\nI've to say that the result of MATLAB's rand and numpy numpy.random.rand are the same if the seed are set like above.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1337,"Q_Id":22346684,"Users Score":1,"Answer":"This is a common issue. While the random number generator is identical, the function which converts your random number stream into a random permutation is different. There is no specified standard algorithm which describes the expected result.\nTo solve this issue, you have to use the same library in both tools.","Q_Score":2,"Tags":"python,matlab,random,numpy,permutation","A_Id":22346834,"CreationDate":"2014-03-12T09:10:00.000","Title":"Compare Numpy and Matlab code that uses random permutation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am learning some model based on examples ${((x_{i1},x_{i2},....,x_{ip}),y_i)}_{i=1...N}$ using a neural network of Feed Forward Multilayer Perceptron (newff) (using python library neurolab). I expect the output of the NN to be positive for any further simulation of the NN. \nHow can I make sure that the results of simulation of my learned NN are always positive? \n(how I do it in neurolab?)","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":948,"Q_Id":22360412,"Users Score":0,"Answer":"Simply use a standard sigmoid\/logistic activation function on the output neuron. sigmoid(x) > 0 forall real-valued x so that should do what you want.\nBy default, many neural network libraries will use either linear or symmetric sigmoid outputs (which can go negative).\nJust note that it takes longer to train networks with a standard sigmoid output function. It's usually better in practice to let the values go negative and instead transform the outputs from the network into the range [0,1] after the fact (shift up by the minimum, divide by the range (aka max-min)).","Q_Score":1,"Tags":"python,neural-network","A_Id":22360726,"CreationDate":"2014-03-12T18:17:00.000","Title":"Python Neurolab - fixing output range","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am running code on two separate machines, it works on one machine and not on the other. I have a Pandas panel object x and I am using x.truncate('2002-01-01'). It works on one machine and not the other. \nThe error thrown is DateParseError: 'tuple' object has no attribute 'year'.\nI have some inkling there is something wrong with the dateUtil package upgrade but didn't know if there's a better fix than backwardating the install.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":179,"Q_Id":22370760,"Users Score":1,"Answer":"In dateutil 2.2 there was an internal API change. Pandas 0.12 shows this bug as it relies on this API.\nPandas >= 0.13 works around, or you can downgrade to dateutil 2.1","Q_Score":0,"Tags":"python,pandas","A_Id":22375190,"CreationDate":"2014-03-13T06:28:00.000","Title":"Panel truncate error: tuple object has no attribute 'year'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Say I have an array of shape (32,).\nEach element can have one of four int values:0 to 3\nIf I wanted to create an array for each possible combination I would have 432 ( approximately 1.84 x 1019) arrays - this is overly burdensome.\nIs there a straightforward way to pick fewer arrays, say 1 x 106, by picking the 'most dissimilar' combinations?\nBy 'most dissimilar' I mean avoiding arrays that are different by one (or few) values and picking arrays that have many dissimilar values. \nAlso, if there is an area of mathematics that I should be looking at to improve my description please let me know.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":120,"Q_Id":22383642,"Users Score":0,"Answer":"Your algorithm could look like this:\n\nKeep the last X (say 10) of the combinations that have been used in a list of some sort.\nPick Y (say 10) combinations randomly.\nAnalyze each of the Y combinations against the last X combinations to find the most dissimilar combination.  This would involve writing a method that would generate a score as to how dissimilar the combination is with each of the last X combinations.  Average the score and pick the one that is most dissimilar.","Q_Score":0,"Tags":"python,arrays,math,numpy,combinations","A_Id":22389392,"CreationDate":"2014-03-13T15:39:00.000","Title":"How can I create a subset of the 'most dissimilar' arrays from a set of possible combinations?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In scikit-learn's RandomForestClassifier, there is no setting to specify how many samples each tree should be built from. That is, how big the subsets should be that are randomly pulled from the data to build each tree.\nI'm having trouble finding how many samples scikit-learn pulls by default. Does anyone know?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":280,"Q_Id":22418958,"Users Score":1,"Answer":"I believe RandomForestClassier will use the entire training set to build each tree. Typically building each tree involves selecting the features which have the most predictive power(the ones which create the largest 'split'), and having more data makes computing that more accurate.","Q_Score":3,"Tags":"python,scikit-learn,random-forest","A_Id":22419620,"CreationDate":"2014-03-15T02:54:00.000","Title":"Scikit-learn, random forests - How many samples does each tree contain?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have two sets of data in separated .h5 files (Hierarchical Data Format 5, HDF5), obtained with python scripts, and I would like to perform statistical analysis to find correlations between them. My experience here is limited; I don't know any R.\nI would like to load the data into SPSS, but SPSS doesn't seem to support .h5. What would be the best way to go here? I can write everything to a .csv file, but I would loose the names of the variables. Is there a way to convert the data without loosing any information? And why doesn't SPSS support h5 anyway?\nI am aware of the existence of the Rpy module. Do you think it is worthwhile to learn programming in R? Would this give me the same arsenal of methods as I have in SPSS?\nThank you for your input!","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":398,"Q_Id":22436515,"Users Score":1,"Answer":"Is there a way to convert the data without losing any information?\n\nIf the HDF5 data is regular enough, you can just load it in Python or R and save it out again as CSV (or even SPSS .sav format if you're a bit more adventurous and\/or care about performance).\n\nWhy doesn't SPSS support h5 anyway?\n\nWho knows.  It probably should.  Oh well.\n\nDo you think it is worthwhile to learn programming in R?\n\nIf you find SPSS useful, you may also find R useful.  Since you mentioned Python, you may find that useful too, but it's more of a general-purpose language: more flexible, but less focused on math and stats.\n\nWould R give me the same arsenal of methods as I have in SPSS?\n\nProbably, depending on exactly what you're doing.  R has most stuff for math and stats, including some fairly esoteric and\/or new algorithms in installable packages.  It has a few things Python doesn't have (yet), but Python also covers most of the bases for many users.","Q_Score":0,"Tags":"python,r,hdf5,statistics,h5py","A_Id":22436696,"CreationDate":"2014-03-16T11:55:00.000","Title":"Statistical analysis of .h5 files (SPSS?)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have list of nested Json objects which I want to save into cassandra (1.2.15).\nHowever the constraint I have is that I do not know the column family's column data types before hand i.e each Json object has got a different structure with fields of different datatypes.\nSo I am planning to use dynamic composite type for creating a column family.\nSo I would like to know if there is an API or suggest some ideas on how to save such Json object list into cassandra.\nThanks","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":1459,"Q_Id":22437058,"Users Score":2,"Answer":"If you don't need to be able to query individual items from the json structure, just store the whole serialized string into one column.\nIf you do need to be able to query individual items, I suggest using one of the collection types: list, set, or map.  As far as typing goes, I would leave the value as text or blob and rely on json to handle the typing.  In other words, json encode the values before inserting and then json decode the values when reading.","Q_Score":1,"Tags":"java,python,json,cassandra,cassandra-cli","A_Id":22519952,"CreationDate":"2014-03-16T12:51:00.000","Title":"Import nested Json into cassandra","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using python.\nI have what might be an easy question, though I can't see it.\nIf I have an array x = array([1.0,0.0,1.5,0.0,6.0]) and y = array([1,2,3,4,5])\nI'm looking for an efficient way to pick randomly between 1.0,1.5,6.0 ignoring all zeros while retaining the index for comparison with another array such as y. So if I was to randomly pick 6.0 I could still relate it to y[4].\nThe reason for the efficient bit is that eventually I want to pick between possibly 10 values from an array of 1000+ with the rest zero. Depending on how intensive the other calculations become depends on the size of the array though it could easily become much larger than 1000.\nThanks","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":1746,"Q_Id":22440923,"Users Score":1,"Answer":"If you're willing to accept probabilistic times and you have fewer than 50% ignored values, you can just retry until you have an acceptable value.\nIf you can't, you're going to have to go over the entire array at least once to know which values to ignore, but that takes n memory.","Q_Score":2,"Tags":"python,arrays,numpy","A_Id":22440992,"CreationDate":"2014-03-16T18:23:00.000","Title":"How to pick a random element in an np array only if it isn't a certain value","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"My final goal is to use Python scripts with SciPy, NumPy, Theano and openCV libraries to write code for a machine learning application. Everything worked so far apart from the openCV.\nI am trying to install openCV 2.4.8 to use in Python projects in my Eclipse Kepler installation on my MBA running Mac OSX 10.9.2. I have the PyDef plugin v2.7 and a installation of Anaconda v1.9.1.\nHere is what I did to install opencv:\n\nsudo port selfupdate\nsudo port upgrade outdated\nsudo port install opencv\n\nThen I realized that I can't use it that way in Python and did another:\n\nsudo port install opencv +python27\n\nOk, then I had another Python installation and I added it to my PYTHONPATH in Eclipse>Preferences>PyDev>Interpreter-Python>Libraries.\nBefore the installation I got an error in the line import cv2, and everything else looked promising. Now this error disappeared but I get other errors when using any functions or variables of cv2. For example  I get two errors in this line: cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\nAlso Python crashes and has to be restarted when I run a simple test program which worked fine before.\nWith this PYTHONPATH everything works but I have no openCV:\n\n\/System\/Library\/Frameworks\/Python.framework\/Versions\/2.7\/lib\/python2.7\n\/System\/Library\/Frameworks\/Python.framework\/Versions\/2.7\/lib\/python2.7\/plat-darwin\n\/System\/Library\/Frameworks\/Python.framework\/Versions\/2.7\/lib\/python2.7\/plat-mac\n\/System\/Library\/Frameworks\/Python.framework\/Versions\/2.7\/lib\/python2.7\/plat-mac\/lib-scriptpackages\n\/System\/Library\/Frameworks\/Python.framework\/Versions\/2.7\/Extras\/lib\/python\n\/System\/Library\/Frameworks\/Python.framework\/Versions\/2.7\/lib\/python2.7\/lib-tk\n\/System\/Library\/Frameworks\/Python.framework\/Versions\/2.7\/lib\/python2.7\/lib-dynload\n\/System\/Library\/Frameworks\/Python.framework\/Versions\/2.7\/Extras\/lib\/python\/pyObjC\n\/Library\/Python\/2.7\/site-packages\/\n\/Users\/xxx\/anaconda\/lib\/python2.7\/site-packages\n\nWhen I add this new folder to the PYTHONPATH...\n\n\/opt\/local\/Library\/Frameworks\/Python.framework\/Versions\/2.7\/lib\/python2.7\/site-packages\n\n... openCV seems to work but I have the crashes and the other issue described above.\nSo, can anyone tell me what the problem is and what I can do to make this work?\nThanks for reading this so far and any help\/hint you can provide! Please don't be too harsh, I am, as you can probably easily see just a beginner.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":611,"Q_Id":22460645,"Users Score":0,"Answer":"Ok, it's working now. Here is what I did:\n\nInstall Python and every package I need for it with Macports\nSet the Macports version as standard\nAdjust PATH and PYTHONPATH\nReboot (not sure if needed)\nRemove old interpreter and libs in Eclipse\nChoose the new Python installation as Interpreter in Eclipse\nConfirm the new libs in Eclipse\nRestart Eclipse\nDone","Q_Score":0,"Tags":"eclipse,macos,opencv,python-2.7,pydev","A_Id":22673856,"CreationDate":"2014-03-17T17:03:00.000","Title":"Python with openCV on MAC crashes","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In scikit-learn, which models do I need to break categorical variables into dummy binary fields?\nFor example, if the column is political-party, and the values are democrat, republican and green, for many algorithms, you have to break this into three columns where each row can only hold one 1, and all the rest must be 0. \nThis avoids enforcing an ordinality that doesn't exist when discretizing [democrat, republican and green] => [0, 1, 2], since democrat and green aren't actually \"farther\" away then another pair. \nFor which algorithms in scikit-learn is this transformation into dummy variables necessary? And for those algorithms that aren't, it can't hurt, right?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":3572,"Q_Id":22460948,"Users Score":4,"Answer":"For which algorithms in scikit-learn is this transformation into dummy variables necessary? And for those algorithms that aren't, it can't hurt, right?\n\nAll algorithms in sklearn with the notable exception of tree-based methods require one-hot encoding (also known as dummy variables) for nominal categorical variables.\nUsing dummy variables for categorical features with very large cardinalities might hurt tree-based methods, especially randomized tree methods by introducing a bias in the feature split sampler. Tree-based method tend to work reasonably well with a basic integer encoding of categorical features.","Q_Score":2,"Tags":"python,machine-learning,scikit-learn","A_Id":22463625,"CreationDate":"2014-03-17T17:15:00.000","Title":"scikit learn creation of dummy variables","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am implementing a bandpass filter in Python using scipy.signal (using the firwin function). My original signal consists of two frequencies (w_1=600Hz, w_2=800Hz). There might be a lot more frequencies that's why I need a bandpass filter. \nIn this case I want to filter the frequency band around 600 Hz, so I took 600 +\/- 20Hz as cutoff frequencies. When I implemented the filter and reproduced the signal in the time domain using lfilter the frequency is fine. \nThe amplitude is reproduced in the right magnitude as well. But the problem is the signal is shifted in the y-direction. For example: s(t)=s_1(t)+s_2(t) with s_1(t)=sin(w_1 t)+3 and  s_2(t)=sin(w_2 t) returns a filtered signal which varies around 0 but not [2,4]`.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":794,"Q_Id":22488460,"Users Score":0,"Answer":"So finally I adapted one filter to get the zerofrequency and another bandpassfilter to get the 600 Hz frequency. Passzero has to be true just for the zerofrequency then it works.\nI'm not yet happy with the phase delay but I'm working on it.\n1)bandpass 600 Hz:\ntaps_bp = bandpass_fir(ntaps, lowcut, highcut, fs)\nFunction for the bandpassfilter\ndef bp_fir(ntaps, lowcut, highcut, fs, window = 'hamming')\n    taps = scipy.signal.firwin(ntaps,[lowcut, highcut],nyq, pass_zero=False)\n    return taps\n2)zerofrequency filter\ntaps_zerofrequency = zero_fir(ntaps, zerofreq=1, fs)\nFunction for the zerofrequency filter\ndef zero_fir(ntaps, zerofreq, fs, window = 'hamming')\n    taps = scipy.signal.firwin(ntaps,[zerofreq],nyq, pass_zero=True)\n    return taps","Q_Score":0,"Tags":"python,scipy,filtering","A_Id":22663453,"CreationDate":"2014-03-18T18:51:00.000","Title":"Time signal shifted in amplitude, FIR filter with scipy.signal","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm currently working with some lifetime data that corresponds to the Installation date and Failure date of units. The data is field data, so I do have a major number of suspensions (units that haven't presented a failure yet). I would like to make some Weibull analysis with this data using Scipy stats library (fitting the data to a weibull curve and obtaining the parameters of the distribution for instance). I'm quite new to Python and Scipy so I can't find a way to include the suspended data in any avaiable Weibull distribution (dweibull, exponweibull, minweibull, maxweibull). Is there a easy way to work with suspensions? I would not like to recriate the wheel, but I'm having difficulties in estimating the parameters of the Weibull from my data. Can anyone help me?\nThanks a lot!","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":1430,"Q_Id":22506268,"Users Score":1,"Answer":"If I understand correctly, then this requires estimation with censored data.\nNone of the scipy.stats.distribution will directly estimate this case. You need to combine the likelihood function of the non-censored and the likelihood function of the censored observations.\nYou can use the pdf and the cdf, or better sf, of the scipy.stats.distributions for the two parts. Then, you could just use scipy optimize to minimize the negative log-likelihood, or try the GenericLikelihoodModel in statsmodels if you are also interested in the uncertainty of the parameter estimates.","Q_Score":2,"Tags":"python,scipy,weibull","A_Id":22522819,"CreationDate":"2014-03-19T12:45:00.000","Title":"Weibull Censored Data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"At some point in my python script, I require to make the calculation: 1*(-inf + 6.28318530718j). I understand why this will return -inf + nan*j since the imaginary component of 1 is obviously 0, but I would like the multiplication to have the return value of -inf + 6.28318530718j as would be expected. I also want whatever solution to be robust to any of these kinds of multiplications. Any ideas?\nEdit:\nA Complex multiplication like x*y where x = (a+ib) and y = (c+id) I assume is handled like (x.real*y.real-x.imag*y.imag)+1j*(x.real*y.imag+x.imag*y.real) in python as this is what the multiplication comes down to mathematically. Now if say x=1.0 and y=-inf+1.0j then the result will contain nan's as inf*0 will be undefined. I want a way for python to interpret * so that the return value to this example will be -inf+1.0j. It seems unnecessary to have to define my own multiplication operator (via say a function cmultiply(x,y)) such that I get the desired result.","AnswerCount":2,"Available Count":1,"Score":0.2913126125,"is_accepted":false,"ViewCount":584,"Q_Id":22536589,"Users Score":3,"Answer":"The short answer is that the C99 standard (Annex G) on complex number arithmetic recognizes only a single complex infinity (think: Riemann sphere). (inf, nan) is one representation for it, and (-inf, 6j)  is another, equivalent representation.","Q_Score":3,"Tags":"python,numpy,complex-numbers","A_Id":22562740,"CreationDate":"2014-03-20T14:48:00.000","Title":"How to deal with indeterminate form in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Can we run scikit-learn models on Pandas DataFrames or do we need to convert DataFrames into NumPy arrays?","AnswerCount":2,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":9121,"Q_Id":22562540,"Users Score":6,"Answer":"Pandas DataFrames are very good at acting like Numpy arrays when they need to. If in doubt, you can always use the values attribute to get a Numpy representation (df.values will give you a Numpy array of the values in DataFrame df.","Q_Score":11,"Tags":"python,pandas,scikit-learn","A_Id":22584181,"CreationDate":"2014-03-21T14:31:00.000","Title":"Pandas dataset into an array for modelling in Scikit-Learn","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've recently started using openCV in python. I've come across various posts comparing cv and cv2 and with an overview saying how cv2 is based on numpy and makes use of an array (cvMat) as opposed  to cv makes use of old openCV bindings that was using Iplimage * (correct me if i'm wrong).\nHowever I would really like know how basic techniques (Iplimage* and cvMat) differ and why later is faster and better and how that being used in cv and cv2 respectively makes difference in terms of performance.\nThanks.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":230,"Q_Id":22590811,"Users Score":1,"Answer":"there is no question at all, - use cv2\nthe old cv api, that wraps IplImage and CvMat is being phased out, and will be no more available in the next release of opencv\nthe newer cv2 api uses numpy arrays for almost anything, so you can easily combine it with scipy, matplotlib, etc.","Q_Score":1,"Tags":"python,c++,opencv,numpy","A_Id":22591329,"CreationDate":"2014-03-23T12:18:00.000","Title":"Can anyone in detail explain how cv and cv2 are different and what makes cv2 better and faster than cv?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there a minimalistic python module out there that I can use to plot real time data that comes in  every 2-3 seconds?\nI've tried matplotlib but I'm having a couple errors trying to get it to run so I'm not looking for something as robust and with many features.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":133,"Q_Id":22593328,"Users Score":1,"Answer":"Tkinter, which is part of python, comes with a canvas widget that can be used for some simple plotting. It can draw lines and curves, and one datapoint every couple of seconds is very easy for it to handle.","Q_Score":0,"Tags":"python,user-interface,matplotlib,tkinter,wxpython","A_Id":22595047,"CreationDate":"2014-03-23T16:03:00.000","Title":"Python widget for real time plotting","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using scikit-learn to train classifiers. I want also to do cross validation, but after cross-validation I want to train on the entire dataset. I found that cross_validation.cross_val_score() just returns the scores.\nEdit: I would like to train the classifier that had the best cross-validation score with all of my data.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1088,"Q_Id":22597239,"Users Score":0,"Answer":"My recommendation is to not use the cross-validation split that had the best performance. That could potential give you problems with high bias. Afterall, the performance just happened to be good because there was a fold used for testing that just happened to match the data used for training. When you generalize it to the real world, that probably won't happen.\nA strategy I got from Andrew Ng is to have a train, dev, and test sets. I would first split your dataset into a test and train set. Then use cross fold validation on your training set, where effectively the training set will be split into training and dev sets. Do cross fold validation to validate your model and store the precision and recall and other metrics to build a ROC curve. Average the values and report those. You can also tune the hyperparameters using your dev set as well.\nNext, train the model with the entire training set, then validate the model with your hold out test set.","Q_Score":2,"Tags":"python,scikit-learn","A_Id":48487656,"CreationDate":"2014-03-23T21:22:00.000","Title":"Making scikit-learn train on all training data after cross-validation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In python which library is able to extract SIFT visual descriptors? I know opencv has an implementation but it is not free to use and skimage does not include SIFT particularly.","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":2214,"Q_Id":22608905,"Users Score":1,"Answer":"OpenCV is free to use. \nBut SIFT itself as algorithm is patented, so if you would make your own implementation of SIFT, not based on Lowe`s code, you still could not use it in commercial application. So, unless you have got a license for SIFT, no library with it, is free.\nBut you can consult with patent guys - some countries like Russia does not allow to patent algorithms - so you can you SIFT inside such country.","Q_Score":2,"Tags":"python-2.7,computer-vision,python-module","A_Id":22609701,"CreationDate":"2014-03-24T12:03:00.000","Title":"what library is able to extract SIFT features in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In python which library is able to extract SIFT visual descriptors? I know opencv has an implementation but it is not free to use and skimage does not include SIFT particularly.","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":2214,"Q_Id":22608905,"Users Score":2,"Answer":"I would like to suggest VLFeat, another open source vision library. It also has a python wrapper. The implementation of SIFT in VLFeat is modified from the original algorithm, but I think the performance is good.","Q_Score":2,"Tags":"python-2.7,computer-vision,python-module","A_Id":23098414,"CreationDate":"2014-03-24T12:03:00.000","Title":"what library is able to extract SIFT features in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How can i see in if a binary image is almost all black or all white in numpy or scikit-image modules ?\nI thought about numpy.all function or numpy.any but i do not know how neither for a total black image nor for a almost black image.","AnswerCount":2,"Available Count":2,"Score":0.1973753202,"is_accepted":false,"ViewCount":1554,"Q_Id":22619506,"Users Score":2,"Answer":"Here is a list of ideas I can think of:\n\nget the np.sum() and if it is lower than a threshold, then consider it almost black\ncalculate np.mean() and np.std() of the image, an almost black image is an image that has low mean and low variance","Q_Score":0,"Tags":"python,opencv,image-processing,numpy,scikit-image","A_Id":22619589,"CreationDate":"2014-03-24T20:04:00.000","Title":"How can i check in numpy if a binary image is almost all black?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How can i see in if a binary image is almost all black or all white in numpy or scikit-image modules ?\nI thought about numpy.all function or numpy.any but i do not know how neither for a total black image nor for a almost black image.","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":1554,"Q_Id":22619506,"Users Score":2,"Answer":"Assuming that all the pixels really are ones or zeros, something like this might work (not at all tested):\n\ndef is_sorta_black(arr, threshold=0.8):\n    tot = np.float(np.sum(arr))\n    if tot\/arr.size  > (1-threshold):\n       print \"is not black\" \n       return False\n    else:\n       print \"is kinda black\"\n       return True","Q_Score":0,"Tags":"python,opencv,image-processing,numpy,scikit-image","A_Id":22619838,"CreationDate":"2014-03-24T20:04:00.000","Title":"How can i check in numpy if a binary image is almost all black?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to convert all my codes to Python. I want to sort an array which has two columns so that the sorting must be based on the 2th column in the ascending order. Then I need to sum the first column data (from first line to, for example, 100th line). I used \"Data.sort(axis=1)\", but it doesn't work. Does anyone have any idea to solve this problem?","AnswerCount":2,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":75440,"Q_Id":22698687,"Users Score":4,"Answer":"sorted(Data, key=lambda row: row[1]) should do it.","Q_Score":25,"Tags":"python,arrays,sorting,numpy","A_Id":22698775,"CreationDate":"2014-03-27T20:37:00.000","Title":"How to sort 2D array (numpy.ndarray) based to the second column in python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a list of dates I'd like to cluster into 3 clusters.  Now, I can see hints that I should be looking at k-means, but all the examples I've found so far are related to coordinates, in other words, pairs of list items.\nI want to take this list of dates and append them to three separate lists indicating whether they were before, during or after a certain event.  I don't have the time for this event, but that's why I'm guessing it by breaking the date\/times into three groups.\nCan anyone please help with a simple example on how to use something like numpy or scipy to do this?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":7440,"Q_Id":22702428,"Users Score":1,"Answer":"k-means is exclusively for coordinates. And more precisely: for continuous and linear values.\nThe reason is the mean functions. Many people overlook the role of the mean for k-means (despite it being in the name...)\nOn non-numerical data, how do you compute the mean?\nThere exist some variants for binary or categorial data. IIRC there is k-modes, for example, and there is k-medoids (PAM, partitioning around medoids).\nIt's unclear to me what you want to achieve overall... your data seems to be 1-dimensional, so you may want to look at the many questions here about 1-dimensional data (as the data can be sorted, it can be processed much more efficiently than multidimensional data).\nIn general, even if you projected your data into unix time (seconds since 1.1.1970), k-means will likely only return mediocre results for you. The reason is that it will try to make the three intervals have the same length.\nDo you have any reason to suspect that \"before\", \"during\" and \"after\" have the same duration? If not, don't use k-means.\nYou may however want to have a look at KDE; and plot the estimated density. Once you have understood the role of density for your task, you can start looking at appropriate algorithms (e.g. take the derivative of your density estimation, and look for the largest increase \/ decrease, or estimate an \"average\" level, and look for the longest above-average interval).","Q_Score":2,"Tags":"python-2.7,numpy,scipy,cluster-analysis","A_Id":22731897,"CreationDate":"2014-03-28T01:16:00.000","Title":"Clustering a list of dates","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using the community module to extract communities from a networkx graph. For the community module, the order in which the nodes are processed makes a difference. I tried to set the seed of random to get consistent results but that is not working. Any idea on how to do this?\nthanks","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":165,"Q_Id":22719863,"Users Score":0,"Answer":"I had to change the seed inside every class I used.","Q_Score":1,"Tags":"python,networkx","A_Id":24051792,"CreationDate":"2014-03-28T17:48:00.000","Title":"Fix the seed for the community module in Python that uses networkx module","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":1,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"With pandas I can do grouping using df.groupby('product_name').size(). But if I'm only interested rows whose \"product_name\" is unique, i.e. those records with groupby.size equal to one, how can I filter the df to see only such rows? In other words, can I perform filtering on a database using pandas, based on the number of times an attribute occurs in the database? (I could do that with SQL alright.)","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2961,"Q_Id":22720349,"Users Score":1,"Answer":"I've made it work with records.groupby('product_name').filter(lambda x: len(x['url']) == 1). Note that simply using len(x) doesn't work. With a dataframe with more than two columns (which is probably most of the real-life dataframes), one has to specify a column for x: any column, except the one to group by with. Also, this code initially didn't work for me because my index on the dataframe was not unique. I'm not sure why this should interfere with the function of filtering, but it did. After reindexing the dataframe, I finally got it to work.","Q_Score":0,"Tags":"python,pandas","A_Id":22724963,"CreationDate":"2014-03-28T18:17:00.000","Title":"Can I select rows based on group size with pandas? Or do I have to use SQL?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using a sigmoid function for my artificial neural network. The value that I'm passing to the function ranges from 10,000 to 300,000. I need a high-precision answer because that would serve as the weights of the connection between the nodes in my artificial neural network. I've tried looking in numpy but no luck. Is there a way to compute the e^(-x)","AnswerCount":3,"Available Count":1,"Score":0.2605204458,"is_accepted":false,"ViewCount":3244,"Q_Id":22729223,"Users Score":4,"Answer":"@Paul already gave you the answer for computational question\nHowever - from neural network point of view your problem is indication that you are doing something wrong. There is no reasonable use of neural networks, where you have to compute such number. You seem to forget about at least one of:\n\nInput data scaling\/normalization\/standarization\nsmall weights bounds initizlization\nregularization term which keeps weights small when the size of network grows\n\nall these elements are basic and crucial parts of working with neural networks. I recommend to have a look at Neural Networks and Learning Machines by Haykin.","Q_Score":5,"Tags":"python,math,numpy,artificial-intelligence","A_Id":22730167,"CreationDate":"2014-03-29T09:21:00.000","Title":"How to calculate exp(x) for really big integers in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When I check the shape of an array using numpy.shape(), I sometimes get (length,1) and sometimes (length,).  It looks like the difference is a column vs. row vector... but It doesn't seem like that changes anything about the array itself [except some functions complain when I pass an array with shape (length,1)].\nWhat is the difference between these two?\nWhy isn't the shape just, (length)?","AnswerCount":4,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":18706,"Q_Id":22737000,"Users Score":6,"Answer":"In Python, (length,) is a tuple, with one 1 item.  (length) is just parenthesis around a number.\nIn numpy, an array can have any number of dimensions, 0, 1, 2, etc.  You are asking about the difference between 1 and 2 dimensional objects.  (length,1) is a 2 item tuple, giving you the dimensions of a 2d array.\nIf you are used to working with MATLAB, you might be confused by the fact that there, all arrays are 2 dimensional or larger.","Q_Score":15,"Tags":"python,arrays,math,numpy","A_Id":22737241,"CreationDate":"2014-03-29T21:15:00.000","Title":"one-dimensional array shapes (length,) vs. (length,1) vs. (length)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When I check the shape of an array using numpy.shape(), I sometimes get (length,1) and sometimes (length,).  It looks like the difference is a column vs. row vector... but It doesn't seem like that changes anything about the array itself [except some functions complain when I pass an array with shape (length,1)].\nWhat is the difference between these two?\nWhy isn't the shape just, (length)?","AnswerCount":4,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":18706,"Q_Id":22737000,"Users Score":0,"Answer":"A vector in Python is actually a two-dimensional array. It's just a coincidence that the number of rows is 1 (for row vectors), or the number of columns is 1 (for column vectors).\nBy contrast, a one-dimensional array is not a vector (neither a row vector nor a column vector). To understand this, think a concept in geometry, scalar. A scalar only has one attribute, which is numerical. By contrast, a vector has two attributes, number and direction. Fortunately, in linear algebra, vectors also have \"directions\", although only two possible directions - either horizontal or vertical (unlike infinite possible directions in geometry). A one-dimensional array only has numerical meaning - it doesn't show which direction this array is pointing to. This is why we need two-dimensional arrays to describe vectors.","Q_Score":15,"Tags":"python,arrays,math,numpy","A_Id":61132626,"CreationDate":"2014-03-29T21:15:00.000","Title":"one-dimensional array shapes (length,) vs. (length,1) vs. (length)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Can anyone give inputs\/clue\/direction on installation of compatible version of numpy and scipy in abaqus python 2.6.2?\nI tried installing numpy-1.6.2, numpy-1.7.1  and numpy-1.8.1. But all gives an error of unable to find vcvarsall.bat. because it doesn't have a module named msvccomplier. based on the some of the answers, I verified the visual studio version and it is 2008. \nCould anyone please give direction on this?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2294,"Q_Id":22764021,"Users Score":0,"Answer":"What you should do is: install python 2.6.2 separately onto your system (it looks like you are using windows, right?), and then install scipy corresponding to python 2.6.2, and then copy the site-packages to the abaqus folder.\nNote that 1) you can't use matplotlib due to the tkinter problem; 2) the numpy is already coming with abaqus so you don't need to install it by hand.","Q_Score":1,"Tags":"python,numpy,scipy","A_Id":22766449,"CreationDate":"2014-03-31T14:17:00.000","Title":"Installation of Compatible version of Numpy and Scipy on Abaqus 6.13-2 with python 2.6.2","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I implemented face recognition algorithm in raspberry pi(python 2.7 is was used), i have many sets of faces, if the captured face is one in database then the face is detected(i am using eigen faces algo). My question is can i know whose face(persons name) is detected? (can we have sort of tags to image and display name corresponding to it when the face is detected) Note: OpenCV used","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":599,"Q_Id":22775681,"Users Score":1,"Answer":"You can use the filename of the image for that purpose. All you need to do is keep the filenames stored somewhere in your application, alongside the Mat objects.","Q_Score":0,"Tags":"python-2.7,opencv","A_Id":22776862,"CreationDate":"2014-04-01T02:24:00.000","Title":"How to know name of the person in the image?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In Pandas, I can use df.dropna() to drop any NaN entries. Is there anything similar in Pandas to drop non-finite (e.g. Inf) entries?","AnswerCount":3,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":18662,"Q_Id":22799208,"Users Score":3,"Answer":"You can use .dropna() after a DF[DF==np.inf]=np.nan, (unless you still want to keep the NANs and only drop the infs)","Q_Score":20,"Tags":"python,pandas","A_Id":22799245,"CreationDate":"2014-04-02T00:05:00.000","Title":"Keep finite entries only in Pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Trying to uninstall numpy. I tried pip uninstall numpy. It tells me that it isn't installed. However, numpy is still installed at \/Library\/Frameworks\/Python.framework\/Versions\/2.7\/lib\/python2.7\/site-packages\/numpy.\nHow can I make sure pip finds the numpy package?","AnswerCount":2,"Available Count":1,"Score":0.2913126125,"is_accepted":false,"ViewCount":7578,"Q_Id":22817533,"Users Score":3,"Answer":"Maybe run deactivate if you are running virtualenv?","Q_Score":4,"Tags":"python,python-2.7,numpy,pip,python-packaging","A_Id":22817669,"CreationDate":"2014-04-02T16:27:00.000","Title":"Pip doesn\u2019t know where numpy is installed","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I know basic python programming and thus want to stay on the python datasci path.\nProblem is, there are many R packages that appeal to me as a social science person.\nCan Rpy2 allow full use of any general arbitrary r package, or is there a catch. How well does it work in practice?\nIf Rpy2 is too limited, I'd unfortunately have to branch over to r, but I would rather not, because of the extra overhead.\nThanks\nTai","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":488,"Q_Id":22839403,"Users Score":3,"Answer":"Bring the presumed limitations on.\nRpy2 is, at its lower level (the rpy2.rinterface level), exposing a very large part of the R C-API. Technically, one can do more with rpy2 than one can from R itself (writing C extension for R would possibly be the only way to catch up). As an amusing fact,doing \"R stuff\" from rpy2 can be faster than doing the same from R itself (see the rpy2 documentation benchmarking the access of elements in an R vector).\nThe higher level in rpy2 (rpy2.robject level) is adding a layer that makes \"doing R stuff\" more \"pythonic\" (although by surrendering the performance claim mentioned above). R packages look like Python modules, has classes such as Formula, Factor, etc... to have all R objects as Python classes, has a conversion system to let one complex R structures can be mapped to Python objects automagically (see example with lme4 in the rpy2 documentation0, translates on the fly invalid R variable names ('.' is a valid character for variable names in R), create on the fly Python docstrings from R documentation.","Q_Score":2,"Tags":"python,rpy2","A_Id":22848834,"CreationDate":"2014-04-03T13:34:00.000","Title":"What are the operational limits of Rpy2?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to install scipy library through pip on python 3.3.5. By the end of the script, i'm getting this error:\n\nCommand \/usr\/local\/opt\/python3\/bin\/python3.3 -c \"import setuptools, tokenize;file='\/private\/tmp\/pip_build_root\/scipy\/setup.py';exec(compile(getattr(tokenize, 'open', open)(file).read().replace('\\r\\n', '\\n'), file, 'exec'))\" install --record \/tmp\/pip-9r7808-record\/install-record.txt --single-version-externally-managed --compile failed with error code 1 in \/private\/tmp\/pip_build_root\/scipy\n  Storing debug log for failure in \/Users\/dan\/.pip\/pip.log","AnswerCount":4,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":11676,"Q_Id":22878109,"Users Score":18,"Answer":"I was getting the same thing when using pip, I went to the install and it pointed to the following dependencies.\nsudo apt-get install python python-dev libatlas-base-dev gcc gfortran g++","Q_Score":7,"Tags":"python,python-3.x,scipy,pip","A_Id":23772908,"CreationDate":"2014-04-05T07:47:00.000","Title":"Error installing scipy library through pip on python 3: \"compile failed with error code 1\"","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have y - a 100 row by 5 column Pandas DataFrame\nI have x - a 100 row by 5 column Pandas DataFrame\nFor i=0,...,4 I want to regress y[:,i] against x[:,i].\nI know how to do it using a loop.\nBut is there a way to vectorise the linear regression, so that I don't have the loop in there?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":472,"Q_Id":22897243,"Users Score":0,"Answer":"as far as I know, there is no way to put this all at once in the optimized Fortran library, LAPACK, since each regression is it's own independent optimization problem.\nnote that the loop over 4 items is not taking any time relative to the regression itself, that you need to fully compute because each regression is an isolated linear algebra problem... so I don't think there is much time to save here...","Q_Score":0,"Tags":"python,pandas,linear-regression","A_Id":22897471,"CreationDate":"2014-04-06T17:12:00.000","Title":"Perform n linear regressions, simultaneously","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a Pandas DataFrame with a 'date' column. Now I need to filter out all rows in the DataFrame that have dates outside of the next two months. Essentially, I only need to retain the rows that are within the next two months. \nWhat is the best way to achieve this?","AnswerCount":16,"Available Count":2,"Score":0.049958375,"is_accepted":false,"ViewCount":624303,"Q_Id":22898824,"Users Score":4,"Answer":"You could just select the time range by doing: df.loc['start_date':'end_date']","Q_Score":275,"Tags":"python,datetime,pandas,filtering,dataframe","A_Id":62341726,"CreationDate":"2014-04-06T19:24:00.000","Title":"Filtering Pandas DataFrames on dates","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a Pandas DataFrame with a 'date' column. Now I need to filter out all rows in the DataFrame that have dates outside of the next two months. Essentially, I only need to retain the rows that are within the next two months. \nWhat is the best way to achieve this?","AnswerCount":16,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":624303,"Q_Id":22898824,"Users Score":43,"Answer":"If you have already converted the string to a date format using pd.to_datetime you can just use:\ndf = df[(df['Date'] > \"2018-01-01\") & (df['Date'] < \"2019-07-01\")]","Q_Score":275,"Tags":"python,datetime,pandas,filtering,dataframe","A_Id":63021426,"CreationDate":"2014-04-06T19:24:00.000","Title":"Filtering Pandas DataFrames on dates","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've stuck with such a problem.\nI have a set of observation of passenger traffic. Data is stored in .xlsx file with the following structure: date_of_observation, time, station_name, boarding, alighting.\nI wonder if it's possible to create Dataframe with DatetimeIndex from such data if I need only 'time' component of datetime. (No dublicates of time is presented in dataset).\nThe reason for this requirement is that I use specific logic based on circular time (for example, 23.00 < 0.00, but 0.01 < 0.02 when compared), so I don't want to convert them to datetime.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":938,"Q_Id":22911865,"Users Score":0,"Answer":"No, it is not possible, only with datetime or with float index.\nHowever, variant offered by unutbu is very useful.","Q_Score":1,"Tags":"python,pandas","A_Id":23057921,"CreationDate":"2014-04-07T12:02:00.000","Title":"DatetimeIndex with time part only: is it possible","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been working for a while with very large DataFrames and I've been using the csv format to store input data and results. I've noticed that a lot of time goes into reading and writing these files which, for example, dramatically slows down batch processing of data. I was wondering if the file format itself is of relevance. Is there a\npreferred file format for faster reading\/writing Pandas DataFrames and\/or Numpy arrays?","AnswerCount":5,"Available Count":1,"Score":0.1586485043,"is_accepted":false,"ViewCount":31690,"Q_Id":22941147,"Users Score":4,"Answer":"If the priority is speed I would recommend:\n\nfeather - the fastest\nparquet - a bit slower, but saves lots of disk space","Q_Score":31,"Tags":"python,numpy,pandas","A_Id":62222727,"CreationDate":"2014-04-08T15:13:00.000","Title":"Fastest file format for read\/write operations with Pandas and\/or Numpy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm working with numpy in python to calculate a vector multiplication.\nI have a vector x of dimensions n x 1 and I want to calculate x*x_transpose. \nThis gives me problems because x.T or x.transpose() doesn't affect a 1 dimensional vector (numpy represents vertical and horizontal vectors the same way). \nBut how do I calculate a (n x 1) x (1 x n) vector multiplication in numpy?\nnumpy.dot(x,x.T) gives a scalar, not a 2D matrix as I want.","AnswerCount":5,"Available Count":1,"Score":0.0399786803,"is_accepted":false,"ViewCount":11187,"Q_Id":22949966,"Users Score":1,"Answer":"If you want an inner product then use numpy.dot(x,x) for outer product use numpy.outer(x,x)","Q_Score":13,"Tags":"python,numpy","A_Id":22949986,"CreationDate":"2014-04-08T23:11:00.000","Title":"dot product of two 1D vectors in numpy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an input array, which is a masked array.\nWhen I check the mean, I get a nonsensical number: less than the reported minimum value!\nSo, raw array: numpy.mean(A) < numpy.min(A). Note A.dtype returns float32.\nFIX: A3=A.astype(float). A3 is still a masked array, but now the mean lies between the minimum and the maximum, so I have some faith it's correct! Now for some reason A3.dtype is float64. Why?? Why did that change it, and why is it correct at 64 bit and wildly incorrect at 32 bit?\nCan anyone shed any light on why I needed to recast the array to accurately calculate the mean? (with or without numpy, it turns out).\nEDIT: I'm using a 64-bit system, so yes, that's why recasting changed it to 64bit. It turns out I didn't have this problem if I subsetted the data (extracting from netCDF input using netCDF4 Dataset), smaller arrays did not produce this problem - therefore it's caused by overflow, so switching to 64-bit prevented the problem.\nSo I'm still not clear on why it would have initially loaded as float32, but I guess it aims to conserve space even if it is a 64-bit system. The array itself is 1872x128x256, with non-masked values around 300, which it turns out is enough to cause overflow :)","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":226,"Q_Id":22980487,"Users Score":0,"Answer":"If you're working with large arrays, be aware of potential overflow problems!!\nChanging from 32-bit to 64-bit floats in this instance avoids an (unflagged as far as I can tell) overflow that lead to the anomalous mean calculation.","Q_Score":2,"Tags":"python,arrays,numpy,floating-accuracy,floating-point-conversion","A_Id":23002130,"CreationDate":"2014-04-10T06:46:00.000","Title":"Why is the mean smaller than the minimum and why does this change with 64bit floats?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have two raster files which I have converted into NumPy arrays (arcpy.RasterToNumpyArray) to work with the values in the raster cells with Python.\nOne of the raster has two values True and False. The other raster has different values in the range between 0 to 1000. Both rasters have exactly the same extent, so both NumPy arrays are build up identically (columns and rows), except the values.\nMy aim is to identify all positions in NumPy array A which have the value True. These positions shall be used for getting the value at these positions from NumPy array B.\nDo you have any idea how I can implement this?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":143,"Q_Id":22996507,"Users Score":4,"Answer":"If I understand your description right, you should just be able to do B[A].","Q_Score":2,"Tags":"python,arrays,numpy,arcgis,arcpy","A_Id":22996581,"CreationDate":"2014-04-10T18:48:00.000","Title":"How to search in one NumPy array for positions for getting at these position the value from a second NumPy array?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Assuming that in each row of the array, all 1's come before the 0's, how would I be able to come up with an (O)nlogn algorithm to count the 1's in the array. I think first I would have to make a counter, search each row for 1's (n), and add that to the counter. Where does the \"log n part\" come into play? I read that a recursive algorithm to do this has nlogn complexity, but Im not too sure how I would do this. I know how to do this in O(n^2) with for loops. Pseudo code or hints would be helpful! Thank you","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":100,"Q_Id":23001932,"Users Score":4,"Answer":"Since all 1's come before the 0's, you can find the index of the first 0 using Binary search algorithm (which is log N) and you just have to do this for all the N rows. So the total complexity is NlogN.","Q_Score":1,"Tags":"python,algorithm,count","A_Id":23001960,"CreationDate":"2014-04-11T01:26:00.000","Title":"Counting 1's in a n x n array of 0's and 1's","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Does scikit's GradientBoostingRegressor make any assumptions on the feature's type? Or does it treat all features as continuous? I'm asking because I have several features that are truly categorical that I have encoded using LabelEncoder().","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":138,"Q_Id":23019076,"Users Score":4,"Answer":"All features are continuous for gradient boosting (and practically all other estimators).\nTree-based models should be able to learn splits in categorical features that are encoded as \"levels\" (1, 2, 3) rather than dummy variables ([1, 0, 0], [0, 1, 0], [0, 0, 1]), but this requires deep trees instead of stumps and the exact ordering may still affect the outcome of learning.","Q_Score":1,"Tags":"python,scikit-learn","A_Id":23028931,"CreationDate":"2014-04-11T17:33:00.000","Title":"Scikit's GBM assumptions on feature type","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to estimate a statistical model using MLE in python using the fmin_BFGS function in Scipy.Optimize and a numerically computed Hessian. \nIt is currently giving me the following warning: Desired error not necessarily achieved due to precision loss. \nWhen I print the results of each evaluation, I see that while the starting guess yields a reasonable log-likelihood. However, after a few guesses, the cost function jumps from ~230,000 to 9.5e+179. \nThen it gives a runtime warning: RuntimeWarning: overflow encountered in double_scalars when trying to compute radical = B * B - 3 * A * C in the linesearch part of the routine.\nI suspect that the algo is trying to estimate the cost function at a point that approaches an overflow. Is there a way to reduce the rate at which the algorithm changes parameter values to keep the function in a well-behaved region? (I would use the constrained BFGS routine but I don't have good priors over what the parameter values should be)","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":236,"Q_Id":23023851,"Users Score":3,"Answer":"I recently ran into the same problem with fmin_bfgs.\nAs far as I could see, the answer is negative. I didn't see a way to limit the stepsize.\nMy workaround was to first run Nelder-Mead fmin for some iterations, and then switch to fmin_bfgs. Once I was close enough to the optimum, the curvature of my function was much nicer and fmin_bfgs didn't have problems anymore.\nIn my case the problem was that the gradient of my function was very large at points further away from the optimum.\nfmin_l_bfgs_b works also without constraints, and several users have reported reliable performance.\naside: If you are able to convert your case to a relatively simple test case, then you could post it to the scipy issue tracker so that a developer or contributor can look into it.","Q_Score":0,"Tags":"python,scipy,mathematical-optimization","A_Id":23089696,"CreationDate":"2014-04-11T22:51:00.000","Title":"Restricting magnitude of change in guess in fmin_bfgs","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to build a content-based recommender system in python\/pandas\/numpy\/sklearn.\nHere are the matrix involved and their size:\nX: n_customers * n_features (contains the features of each customer)\nY: n_customers *n_products (contains the scores given by each customer to each product)\nTheta: n_features * n_products\nThe aim is to learn Theta in order to be able to predict the score given by a customer to all products (X*Theta). Indeed, Y is a sparse matrix, a customer score only a very small % of the whole quantity of products. This is why Y contains a lot of NaN values.\nHere is my problem:\nThis is a regression problem with many targets (here target=product). But I want to do the regression only on not null values. because the number of NaN differ from one product to another, how can I vectorize that ? \nAssume there are 1000 products and 100 000 customers, each one having 20 features.\nFor each product I need to the regression on the not null values. So without vectorization, I would need 1000 different regressor learning each one a Theta vector of length 20.\nIf possible I would like to solve this problem with sklearn. The ridge regression for example takes into account multiple targets (Y as a matrix)\nI hope it's clear enough.\nThank you for your help.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":3553,"Q_Id":23030284,"Users Score":1,"Answer":"I believe You can use centered cosine similarity \/pearson corelation to make this work and make use of collaborative filtering technique to achieve this \nBefore you use pearson co -relation you need to fill the Null ( the fields which dont have any entries) with zero ,now pearson co relation centers the similarity matrix around zero ,which gives optimum recommendation .","Q_Score":3,"Tags":"python,numpy,machine-learning,scikit-learn,recommendation-engine","A_Id":27126021,"CreationDate":"2014-04-12T12:19:00.000","Title":"Content based recommender system with sklearn or numpy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to programming.\nis creating a sorting algorithm a common task for a professional developer?\nOn what task do developer need to create a sorting algorithm?\nAnd Finally, what are the advantages of an in-place sorting algorithm?\nany help is appreciated!!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":330,"Q_Id":23038760,"Users Score":3,"Answer":"is creating a sorting algorithm a common task for a professional developer?\n\nNo. It's good to be able to do it, but most of the time, you'll just use sorts other people already wrote.\n\nOn what task do developer need to create a sorting algorithm?\n\nIf you're providing a sorting routine for other people to use, you may need to implement it yourself. For example, Python's list.sort. Alternatively, if the standard sorts don't provide some property or capability you need, you may need to write your own.\n\nwhat are the advantages of an in-place sorting algorithm?\n\nLow extra memory usage. Sometimes we care about that; usually we don't.","Q_Score":0,"Tags":"python,sorting","A_Id":23038786,"CreationDate":"2014-04-13T03:25:00.000","Title":"Is In place sorting algorithm always faster? What are the adavantage of in place sorting? Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to convert an RGB image to HSI,I found lot of inbuilt functions like rgb_to_hsv, rgb_to_hls, etc. Is there any function for conversion from RGB to HSI color model in python??","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":3209,"Q_Id":23045695,"Users Score":0,"Answer":"To convert the available RGB image to HSI format( Hue,Saturation, Intensity), you can make use of the CV_RGB2HSI function available in the openCV docs.","Q_Score":5,"Tags":"python,python-imaging-library","A_Id":50678634,"CreationDate":"2014-04-13T17:09:00.000","Title":"RGB to HSI function in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a curve of >1000 points that I would like to fit to a differential equation in the form of x'' = (a*x'' + b x' + c x + d), where a,b,c,d are constants. How would I proceed in doing this using Python 2.7?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":756,"Q_Id":23064886,"Users Score":0,"Answer":"Certainly you intend to have the third derivative on the right. \nGroup your data in relatively small bins, possibly overlapping. For each bin, compute a cubic approximation of the data. From that compute the derivatives in the center point of the group. With the derivatives of all groups you now have a classical linear regression problem.\nIf the samples are equally spaced, you might try to move the problem into frequency space via FFT. A sensible truncation of the data might be a problem here. In the frequency space, the task reduces to a polynomial linear regression.","Q_Score":2,"Tags":"python-2.7,physics,curve-fitting,differential-equations","A_Id":23065974,"CreationDate":"2014-04-14T16:06:00.000","Title":"Curve fitting differential equations in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm talking about time taken to dynamically write values to a csv file vs appending those same values to an array. Eventually, I will append that array to the csv file, but that's out of the scope of this question.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":658,"Q_Id":23086241,"Users Score":0,"Answer":"I am assuming you mean to ask about separate calls to writer.writerow() vs. building a list, then writing that list with writer.writerows().\nMemory wise, the former is more efficient. That said, don't worry too much about speed here; writing to disk is your bottleneck, not how you build the data.","Q_Score":0,"Tags":"python,csv,dynamic","A_Id":23086286,"CreationDate":"2014-04-15T14:11:00.000","Title":"Is dynamically writing to a csv file slower than appending to an array in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using OpenSUSE 13.1 64-bit on an Lenovo ThinkPad Edge E145.\nI tryed to play a bit around with Python(2.7) and Python-OpenCV(2.4). Both is installed by using YAST.\nWhen i start the Python-Interactive-Mode (by typing \"python\") and try to \"import cv\" there are 2 things that happen:\ncase 1: \"import cv\" --> End's up with:\nTraceback (most recent call last):\n  File \"<stdin>\", line 1, in <module>\n  File \"\/usr\/lib64\/python2.7\/site-packages\/cv.py\", line 1, in <module>\n    from cv2.cv import *\nImportError: \/usr\/lib64\/python2.7\/site-packages\/cv2.so: undefined symbol: _ZN2cv23adaptiveBilateralFilterERKNS_11_InputArrayERKNS_12_OutputArrayENS_5Size_IiEEddNS_6Point_IiEEi\ncase 2: \"import cv2\" --> End's up with:\n\nMemoryAccessError\n\nand the interactive mode shutdown and i'm back at the normal commandline.\nHave anyone any idea how can i solve this problem?\nGreetings","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":892,"Q_Id":23117242,"Users Score":0,"Answer":"Not exactly a prompt answer (nor a direct one). I had the same issue and (re)installing various dependencies didn't help either.\nUltimately, I cloned (from git) and compiled opencv (which includes the cv2.so library) from scratch, replaced the old cv2.so library and got it to work.\nHere is the git repo: https:\/\/github.com\/Itseez\/opencv.git","Q_Score":0,"Tags":"python-2.7,opencv,opensuse,undefined-symbol","A_Id":25503548,"CreationDate":"2014-04-16T18:21:00.000","Title":"Python OpenCV \"ImportError: undefined Symbol\" or Memory Access Error","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a list of dataframes but when I call the content of the list it returns the content of the called dataframe.\n\n\n\nList = [df1, df2, df3, ..., dfn]\nList[1]\n\n\n\nwill give,\nclass 'pandas.core.frame.DataFrame'>\nDatetimeIndex: 4753 entries, etc\nbut I want it to give \n\n\n\nstr(List[1])???\n\n\n\n'df2'\nThanks for the help","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":70,"Q_Id":23163508,"Users Score":1,"Answer":"The DataFrame doesn't know the name of the variable you've assigned to it.\nDepending on how you're printing the object, either the __str__ or __repr__ method will get called to get a description of the object.  If you want to get back 'df2', you could put them into a dictionary to map the name back to the object. \nIf you want to be very sneaky, you could patch the object's __str__ or __repr__ methods to return what you want.  This is probably a very bad idea, though.","Q_Score":0,"Tags":"python,string,list,pandas","A_Id":23163657,"CreationDate":"2014-04-18T22:43:00.000","Title":"Call the name of a data frame rather than its content (Pandas)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to use one of the random graph-generators of NetworkX (version 1.8.1):\nrandom_powerlaw_tree(n, gamma=3, seed=None, tries=100)\nHowever, I always get this error\nFile \"\/Library\/Python\/2.7\/site-packages\/networkx\/generators\/random_graphs.py\", line 840, in random_powerlaw_tree\n\"Exceeded max (%d) attempts for a valid tree sequence.\"%tries)\nnetworkx.exception.NetworkXError: Exceeded max (100) attempts for a valid tree sequence.\nfor any n > 10, that is starting with\nG = nx.random_powerlaw_tree(11)\nI would like to generate trees with hundreds of nodes. Does anyone know how to correctly set these parameters in order to make it run correctly?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":451,"Q_Id":23173427,"Users Score":1,"Answer":"To generate trees with more nodes it is only needed to increase the \"number of tries\" (parameter of random_powerlaw_tree). 100 tries is not enough even to have a tree with 11 nodes (it gives an error). For example, with 1000 tries I manage to generate trees with 100 nodes, using networkX 1.8.1 and python 3.4.0","Q_Score":1,"Tags":"python,networkx","A_Id":23183710,"CreationDate":"2014-04-19T17:49:00.000","Title":"Parameters to let random_powerlaw_tree() generate trees with more than 10 nodes","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to classify text and i am using Text blob python module to achieve it.I can use either Naive Bayes classifier\/Decision tree. I am concern about the below mentioned points.\n1) I Need to classify sentences as argument\/ Not an argument. I am using two classifiers and training the model using apt data sets. My question is all about do i need to train the model with only keywords ? or i can train the data set with all possible argument and non argument sample sentences? Which would be the best approach in terms of text classification accuracy and time to retrieve?\n2) Since the classification would be either argument\/not an argument, which classifier would fetch exact results? It is Naive Bayes \/Decision tree\/Positive Naive bayes?\nThanks in advance.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1170,"Q_Id":23178275,"Users Score":1,"Answer":"Ideally, it is said that the more you train your data, the 'better' your results are but it really depends after you've tested it and compared it to the real results you've prepared. \nSo to answer your question, training the model with keywords may give you too broad results that may not be arguments. But really, you have to compare it to something, so I suggest you might want to also train your model with some sentence structure that arguments seem to follow (a pattern of some sort), it might eliminate the ones that are not arguments. Again, do this and then test it to see if you get higher accuracy than the previous model. \nTo answer your next question: Which would be the best approach in terms of text classification accuracy and time to retrieve? It really depends on the data your using, I can't really answer this question because you have to perform cross-validation to see if your model achieves high accuracy. Obviously, the more features you are looking, the poorer your learning algorithm's performance. And if you are dealing with gigabytes of text to analyze, I suggest using Mapreduce to perform this job. \nYou might want to check out SVMs as your learning model, test it out with the learning models (naive bayes, positive naive bayes and decision trees) and see which one performs better. \nHope this helps.","Q_Score":6,"Tags":"python,python-3.x,machine-learning,classification,bayesian","A_Id":29718358,"CreationDate":"2014-04-20T04:01:00.000","Title":"Text classification in python - (NLTK Sentence based)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"There are many questions over here which checks if two images are \"nearly\" similar or not. \nMy task is simple. With OpenCV, I want to find out if two images are 100% identical or not. \nThey will be of same size but can be saved with different filenames.","AnswerCount":6,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":15991,"Q_Id":23195522,"Users Score":0,"Answer":"I have done this task.\nCompare file sizes.\nCompare exif data.\nCompare first 'n' byte, where 'n' is 128 to 1024 or so.\nCompare last 'n' bytes.\nCompare middle 'n' bytes.\nCompare checksum","Q_Score":11,"Tags":"python,c++,opencv","A_Id":71003756,"CreationDate":"2014-04-21T10:28:00.000","Title":"OpenCV - Fastest method to check if two images are 100% same or not","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a pandas data frame with few columns.\nNow I know that certain rows are outliers based on a certain column value.\nFor instance\n\ncolumn 'Vol' has all values around 12xx and one value is 4000 (outlier).\n\nNow I would like to exclude those rows that have Vol column like this.\nSo, essentially I need to put a filter on the data frame such that we select all rows where the values of a certain column are within, say, 3 standard deviations from mean.\nWhat is an elegant way to achieve this?","AnswerCount":18,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":440407,"Q_Id":23199796,"Users Score":13,"Answer":"scipy.stats has methods trim1() and trimboth() to cut the outliers out in a single row, according to the ranking and an introduced percentage of removed values.","Q_Score":319,"Tags":"python,pandas,filtering,dataframe,outliers","A_Id":39591989,"CreationDate":"2014-04-21T14:51:00.000","Title":"Detect and exclude outliers in a pandas DataFrame","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to build a Deep Believe Network with scikit-learn. As I know one should train many Restricted Boltzmann Machines (RBM) individually. Then one should create a Multilayer Perceptron (MLP) that has the same number of layers as the number of (RBMs), and the weights of the MLP should be initialized with the weights of the RBMs. However I'm unable to find a way to get the weights of the RBMs from scikit-learn's BernoulliRBM. Also it doesn't seem to be a way also to initialize the weights of a MLP in scikit-learn.\nIs there a way to do what I described?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":674,"Q_Id":23217264,"Users Score":1,"Answer":"scikit-learn does not currently have an MLP implemented which you can initialize via an RBM, but you can still access the weights which are stored in the components_ attribute and the bias which is stored in the intercept_hidden_ attribute.\nIf you're interested in using modern MLPs, torch7, pylearn2, and deepnet are all modern libraries and most of them contain pretraining routines like you describe.","Q_Score":0,"Tags":"python,scikit-learn","A_Id":23232968,"CreationDate":"2014-04-22T10:44:00.000","Title":"Initializing the weights of a MLP with the RBM weights","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm making a Animal guessing game and i finish the program but i want to add pickle so it save  questions to disk, so they won't go away when\nthe program exits. Anyone can help?","AnswerCount":1,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":3645,"Q_Id":23234103,"Users Score":14,"Answer":"Save an object containing the game state before the program exits:\npickle.dump(game_state, open('gamestate.pickle', 'wb'))\nLoad the object when the program is started:\ngame_state = pickle.load(open('gamestate.pickle', 'rb'))\nIn your case, game_state may be a list of questions.","Q_Score":4,"Tags":"python,pickle","A_Id":23234151,"CreationDate":"2014-04-23T03:13:00.000","Title":"How to use pickle to save data to disk?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have opened a FITS file in pyfits. The HEADER file reads XTENSION='BINTABLE' with DIMENSION= 52989R x 36C with 36 column tags like, 'ZBEST', 'ZQUALITY', 'M_B', 'UB', 'PGAL' etc.\nNow, I have to choose objects from the data with 'ZQUALITY' greater than 2 & 'PGAL' equals to 3. Then I have to make a histogram for the 'ZBEST' of the corresponding objects obeying the above conditions. Also I have to plot 'M_B' vs 'UB' for those objects. \nAt last I want to slice the 'ZBEST' into three slices (zbest < 0.5), (0.5 < zbest < 1.0), (zbest > 1.0) and want to plot histogram and 'M_B' vs 'UB' diagram of them separately. \nI am stuck at choosing the data obeying the two conditions. Can anyone please tell me how can I choose the objects from the data satisfying both the conditions ('ZQUALITY' > 2 & 'PGAL' == 3 )? I am using like: data.field[('zquality' > 2) & ('pgal'==3)] but it's not working.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":229,"Q_Id":23246013,"Users Score":1,"Answer":"The expression data.field[('zquality' > 2) & ('pgal'==3)] is asking for fields where the string 'zquality' is greater than 2 (always true) and where the string 'pgal' is equal to 3 (also always false).\nActually chances are you're getting an exception because data.field is a method on the Numpy recarray objects that PyFITS returns tables in.\nYou want something like data[(data['zquality'] > 2) & (data['pgal'] == 3)].\nThis expression means \"give me the rows of the 'zquality' column of data containing values greater than 2.  Then give me the rows of the 'pgal' column of data with values equal to three.  Now give me the full rows of data selected from the logical 'and' of the two row masks.","Q_Score":0,"Tags":"python,numpy,fits,pyfits","A_Id":23254015,"CreationDate":"2014-04-23T13:34:00.000","Title":"Condtionally selecting values from a Numpy array returned from PyFITS","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm implementing decision tree based on CART algorithm and I have a question. Now I can classify data, but my task is not only classify data. I want have a probability of right classification in end nodes. \nFor example. I have dataset that contains data of classes A and B. When I put an instance of some class to my tree I want see with what probability the instance belongs to class A and class B.\nHow can I do that? How can I improve CART to have probability distribution in the end nodes?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2896,"Q_Id":23264037,"Users Score":0,"Answer":"When you train your tree using the training data set, every time you do a split on your data, the left and right node will end up with a certain proportion of instances from class A and class B. The percentage of instances of class A (or class B) can be interpreted as probability. \nFor example, assume your training data set includes 50 items from class A and 50 items from class B. You build a tree of one level, by splitting the data once. Assume after the split, your left node ends up having 40 instances of class A and 10 instances of class B and the right node has 10 instances of class A and 40 instances of class B. Now the probabilities in the nodes will be 40\/(10+40) = 80% for class A in left node, and 10\/(10+40) = 20% for class A in left node (and vice versa for class B).\nExactly the same applies for deeper trees: you count the instances of classes and compute the proportion.","Q_Score":0,"Tags":"python,machine-learning,decision-tree,cart-analysis","A_Id":23264484,"CreationDate":"2014-04-24T08:45:00.000","Title":"Get probability of classification from decision tree","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there a way to use pandas to_excel function to write to the desktop, no matter which user is running the script? I've found answers for VBA but nothing for python or pandas.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":781,"Q_Id":23279546,"Users Score":1,"Answer":"This depends on your operating system.\nYou're saying you'd like to save the file on the desktop of the user who is running the script right? \nOn linux (not sure if this is true of every distribution) you could pass in \"~\/desktop\/my_file.xls\" as the path where you're saving the file","Q_Score":2,"Tags":"python,pandas","A_Id":23279735,"CreationDate":"2014-04-24T20:51:00.000","Title":"to_excel on desktop regardless of the user","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"The operation that I want to do is similar to merger. For example, with the inner merger we get a data frame that contains rows that are present in the first AND second data frame. With the outer merger we get a data frame that are present EITHER in the first OR in the second data frame.\nWhat I need is a data frame that contains rows that are present in the first data frame AND NOT present in the second one? Is there a fast and elegant way to do it?","AnswerCount":4,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":34025,"Q_Id":23284409,"Users Score":28,"Answer":"Consider Following:\n\ndf_one is first DataFrame\ndf_two is second DataFrame\n\nPresent in First DataFrame and Not in Second DataFrame\nSolution: by Index\n\ndf = df_one[~df_one.index.isin(df_two.index)]\n\nindex can be replaced by required column upon which you wish to do exclusion.\nIn above example, I've used index as a reference between both Data Frames\nAdditionally, you can also use a more complex query using boolean pandas.Series to solve for above.","Q_Score":18,"Tags":"python,merge,pandas","A_Id":38764796,"CreationDate":"2014-04-25T04:49:00.000","Title":"How to subtract rows of one pandas data frame from another?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it possible to open PDFs and read it in using python pandas or do I have to use the pandas clipboard for this function?","AnswerCount":7,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":82529,"Q_Id":23284759,"Users Score":6,"Answer":"this is not possible. PDF is a data format for printing. The table structure is therefor lost. with some luck you can extract the text with pypdf and guess the former table columns.","Q_Score":34,"Tags":"python,pdf,pandas","A_Id":23285666,"CreationDate":"2014-04-25T05:24:00.000","Title":"Opening a pdf and reading in tables with python pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"Is it possible to open PDFs and read it in using python pandas or do I have to use the pandas clipboard for this function?","AnswerCount":7,"Available Count":2,"Score":0.0855049882,"is_accepted":false,"ViewCount":82529,"Q_Id":23284759,"Users Score":3,"Answer":"Copy the table data from a PDF and paste into an Excel file (which usually gets pasted as a single rather than multiple columns). Then use FlashFill (available in  Excel 2016, not sure about earlier Excel versions) to separate  the data into the columns originally viewed in the PDF. The process  is fast and easy. Then use Pandas to wrangle the Excel data.","Q_Score":34,"Tags":"python,pdf,pandas","A_Id":41133523,"CreationDate":"2014-04-25T05:24:00.000","Title":"Opening a pdf and reading in tables with python pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I have problem with classification using SVM. Let's say that I have 10 classes, digts from 0 to 9. I can train SVM to recognize theese classes, but sometimes I get image which is not digt, but SVM still tries to categorize this image. Is there a way to set threshold for SVM on the output maybe (as I can set it for Neural Networks) to reject bad images? May I ask for code sample (in C++ or Python with opencv)?\nThanks in advance.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":645,"Q_Id":23299694,"Users Score":1,"Answer":"As a simple approach, you can train an additional classifier to determine if your feature is a digit or not. Use non-digit images as positive examples and the other classes' positives (i.e. images of digits 0-9) as the negative samples of this classifier. You'll need a huge amount of non-digit images to make it work, and also it's recommendable to use strategies as the selection of hard negatives: negative samples classified as \"false positives\" after the first training stage, which are used to re-train the classifier.\nHope that it helps!","Q_Score":0,"Tags":"python,opencv,svm","A_Id":23300115,"CreationDate":"2014-04-25T17:44:00.000","Title":"Classification using SVM from opencv","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am currently using python pandas and want to know if there is a way to output the data from pandas into julia Dataframes and vice versa. (I think you can call python from Julia with Pycall but I am not sure if it works with dataframes) Is there a way to call Julia from python and have it take in pandas dataframes? (without saving to another file format like csv) \nWhen would it be advantageous to use Julia Dataframes than Pandas other than extremely large datasets and running things with many loops(like neural networks)?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":9570,"Q_Id":23322025,"Users Score":1,"Answer":"I'm a novice at this sort of thing but have definitely been using both as of late. Truth be told, they seem very quite comparable but there is far more documentation, Stack Overflow questions, etc pertaining to Pandas so I would give it a slight edge. Do not let that fact discourage you however because Julia has some amazing functionality that I'm only beginning to understand. With large datasets, say over a couple gigs, both packages are pretty slow but again Pandas seems to have a slight edge (by no means would I consider my benchmarking to be definitive). Without a more nuanced understanding of what you are trying to achieve, it's difficult for me to envision a circumstance where you would even want to call a Pandas function while working with a Julia DataFrame or vice versa. Unless you are doing something pretty cerebral or working with really large datasets, I can't see going too wrong with either. When you say \"output the data\" what do you mean? Couldn't you write the Pandas data object to a file and then open\/manipulate that file in a Julia DataFrame (as you mention)? Again, unless you have a really good machine reading gigs of data into either pandas or a Julia DataFrame is tedious and can be prohibitively slow.","Q_Score":16,"Tags":"python,pandas,dataframe,julia","A_Id":23440098,"CreationDate":"2014-04-27T10:09:00.000","Title":"Julia Dataframes vs Python pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a set of {(v_i, c_i), i=1,..., n}, where v_i in R and c_i in {-1, 0, 1} are the discrimination value  and  label of the i-th training example. \nI would like to learn a threshold t so that the training error is the minimum when I declare the i-th example has label -1 if v_i < t, 0 if v_i=t, and 1 if v_i>t.\nHow can I learn the threshold t from {(v_i, c_i), i=1,..., n}, and what is an efficient algorithm for that? \nI am implementing that in Python, although I also hope to know how to implement that in  R efficiently.\nThanks!\nBtw, why SO doesn't support LaTeX for math expressions? (I changed them to be code instead).","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":72,"Q_Id":23326430,"Users Score":1,"Answer":"Sort the points, group them by value, and try all <=2n+1 thresholds that classify differently (<=n+1 gaps between distinct data values including the sentinels +-infinity and <=n distinct data values). The latter step is linear-time if you try thresholds lesser to greater and keep track of how many points are misclassified in each way.","Q_Score":0,"Tags":"python,r,algorithm","A_Id":23326609,"CreationDate":"2014-04-27T17:13:00.000","Title":"learn a threshold from labels and discrimination values?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"My problem is as follows:\nI am given a number of chi-squared values for the same collection of data sets, fitted with different models. (so, for example, for 5 collections of points, fitted with either a single binomial distribution, or both binomial and normal distributions, I would have 10 chi-squared values).\nI would like to use machine learning categorization to categorize the data sets into \"models\":\ne.g. data sets (1,2,5 and 7) are best fitted using only binomial distributions, whereas sets (3,4,6,8,9,10) - using normal distribution as well. \nNotably, the number of degrees of freedom is likely to be different for both chi-squared distributions and is always known, as is the number of models.\nMy (probably) naive guess for a solution would be as follows:\n\nRandomly distribute the points (10 chi-squared values in this case) into the number of categories (2).\nFit each of the categories using the particular chi-squared distributions (in this case with different numbers of degrees of freedom) \nMove outlying points from one distribution to the next.\nRepeat steps 2 and 3 until happy with result.\n\nHowever I don't know how I would select the outlying points, or, for that matter, if there already is an algorithm that does it.\nI am extremely new to machine learning and fairly new to statistics, so any relevant keywords would be appreciated too.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":52,"Q_Id":23393456,"Users Score":0,"Answer":"The principled way to do this is to assign probabilities to different model types and to different parameters within a model type. Look for \"Bayesian model estimation\".","Q_Score":0,"Tags":"python,machine-learning,statistics,categorization","A_Id":23421883,"CreationDate":"2014-04-30T16:31:00.000","Title":"Categorizing points using known distributions","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using NLTK to extract named entities and I'm wondering how it would be possible to determine the sentiment between entities in the same sentence. So for example for \"Jon loves Paris.\" i would get two entities Jon and Paris. How would I be able to determine the sentiment between these two entities? In this case should be something like Jon -> Paris = positive","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":301,"Q_Id":23396807,"Users Score":1,"Answer":"In short \"you cannot\". This task is far beyond simple text processing which is provided with NLTK. Such objects relations sentiment analysis could be the topic of the research paper, not something solvable with a simple approach. One possible method would be to perform a grammar analysis, extraction of the conceptual relation between objects and then independent sentiment analysis of words included, but as I said before - it is rather a reasearch topic.","Q_Score":0,"Tags":"python,nlp,nltk","A_Id":23396854,"CreationDate":"2014-04-30T19:47:00.000","Title":"How to determine the \"sentiment\" between two named entities with Python\/NLTK?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using scikit-learn to train classifiers. I'm particularly using linear_model.LogisticRegression. But my question is: what's the stopping criteria for the training?! because I don't see any parameter that indicates the number of epochs!\nAlso the same for random forests?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1201,"Q_Id":23458792,"Users Score":5,"Answer":"There's no hard limit to the number of iterations for LogisticRegression; instead it tries to detect convergence with a specified tolerance, tol: the smaller tol, the longer the algorithm will run.\nFrom the source code, I gather that the algorithms stops when the norm of the objective's gradient is less than tol times its initial value, before training started. This is worth documenting.\nAs for random forests, training stops when n_estimators trees have been fit of maximum depth max_depth, constrained by the parameters min_samples_split, min_samples_leaf and max_leaf_nodes. Tree learning is completely different from iterative linear model learning.","Q_Score":4,"Tags":"python,scikit-learn","A_Id":23472135,"CreationDate":"2014-05-04T16:42:00.000","Title":"When does fit() stop running in scikit?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I can test the rank of a matrix using np.linalg.matrix_rank(A) . But how can I test if all the rows of A are orthogonal efficiently?\nI could take all pairs of rows and compute the inner product between them but is there a better way?\nMy matrix has fewer rows than columns and the rows are not unit vectors.","AnswerCount":4,"Available Count":1,"Score":0.1488850336,"is_accepted":false,"ViewCount":7018,"Q_Id":23503667,"Users Score":3,"Answer":"Approach #3: Compute the QR decomposition of AT\nIn general, to find an orthogonal basis of the range space of some matrix X, one can compute the QR decomposition of this matrix (using Givens rotations or Householder reflectors). Q is an orthogonal matrix and R upper triangular. The columns of Q corresponding to non-zero diagonal entries of R form an orthonormal basis of the range space.\nIf the columns of X=AT, i.e., the rows of A, already are orthogonal, then the QR decomposition will necessarily have the R factor diagonal, where the diagonal entries are plus or minus the lengths of the columns of X resp. the rows of A.\nCommon folklore has it that this approach is numerically better behaved than the computation of the product A*AT=RT*R. This may only matter for larger matrices. The computation is not as straightforward as the matrix product, however, the amount of operations is of the same size.","Q_Score":7,"Tags":"python,math,numpy,scipy","A_Id":23552362,"CreationDate":"2014-05-06T19:49:00.000","Title":"How to detect if all the rows of a non-square matrix are orthogonal in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"The Dark Eye is a popular fantasy role-playing game, the German equivalent of Dungeons and Dragons. In this system a character has a number of attributes and talents. Each talent has several attributes associated with it, and to make a talent check the player rolls a d20 (a 20-sided die) for each associated attribute. Each time a roll result exceeds the attribute score, the difference between roll and attribute is added up. If this difference total (the excess ) is greater than the talent value, the check fails.\nYour first task for this tutorial is to write a function that takes as input a list of attribute scores associated with a talent as well as the talent ranks, and returns the probability that the test succeeds. Your algorithm must work efficiently for lists of arbitrary length.\nHint: First write a function that computes the probability distribution for the excess. This can be done efficiently using dynamic programming.\n\nWhat does this mean? I solved the knapsack problem without any major issues (both 0\/1 and unbounded), but I have no idea what to do with this? \nThe smallest problem to solve first would be rank 1 with a single attribute of say 12 (using the example above) - the probability of passing would be 12\/20 right? then rank 2 would be 13\/20 then rank 3 would be 14\/20?\nAm I on the right track? I feel like I might be misunderstanding the actual game rules.","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1791,"Q_Id":23603762,"Users Score":0,"Answer":"So the problem is: how many ways can you roll with attributes 12, 13 and 12 and a talent of 7. Lets assume you know the outcome of the first dice, lets say its 11. Then the problem is reduced to how many ways can you roll with attributes 13 and 12 and with a talent of 7. Now try it with a different first roll, lets say you rolled 14 for the first time. You are over by 2, so the problem now is how many ways can you roll with attributes 13 and 12 and with a talent of 5. Now try with a first roll of 20. The question now is how many ways can you roll with attributes 13 and 12 and with a talent of -1 (in the last case its obviously 0).","Q_Score":1,"Tags":"python,algorithm","A_Id":23613409,"CreationDate":"2014-05-12T07:57:00.000","Title":"Dynamic Programming for Dice probability in a Role Playing Pen & Paper game","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a high intensity model that is written in python, with array calculations involving over 200,000 cells for over 4000 time steps. There are two arrays, one a fine grid array, one a coarser grid mesh, Information from the fine grid array is used to inform the characteristics of the coarse grid mesh. When the program is run, it only uses 1% of the cpu but maxes out the ram (8GB). It takes days to run. What would be the best way to start to solve this problem? Would GPU processing be a good idea or do I need to find a way to offload some of the completed calculations to the HDD?\nI am just trying to find avenues of thought to move towards a solution. Is my model just pulling too much data into the ram, resulting in slow calculations?","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":151,"Q_Id":23621423,"Users Score":1,"Answer":"If the grid is sparsely populated, you might be better off tracking just the populated parts, using a different data structure, rather than a giant python list (array).","Q_Score":3,"Tags":"python,arrays,memory-management,anaconda,cellular-automata","A_Id":23622236,"CreationDate":"2014-05-13T01:23:00.000","Title":"All of Ram being used in a Cellular Automata Python Script","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a high intensity model that is written in python, with array calculations involving over 200,000 cells for over 4000 time steps. There are two arrays, one a fine grid array, one a coarser grid mesh, Information from the fine grid array is used to inform the characteristics of the coarse grid mesh. When the program is run, it only uses 1% of the cpu but maxes out the ram (8GB). It takes days to run. What would be the best way to start to solve this problem? Would GPU processing be a good idea or do I need to find a way to offload some of the completed calculations to the HDD?\nI am just trying to find avenues of thought to move towards a solution. Is my model just pulling too much data into the ram, resulting in slow calculations?","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":151,"Q_Id":23621423,"Users Score":1,"Answer":"Sounds like your problem is memory management. You're likely writing to your swap file, which would drastically slow down your processing. GPU wouldn't help you with this, as you said you're maxing out your RAM, not your processing (CPU). You probably need to rewrite your algorithm or use different datatypes, but you haven't shared your code, so it's hard to diagnose just based on what you've written. I hope this is enough information to get you heading in the right direction.","Q_Score":3,"Tags":"python,arrays,memory-management,anaconda,cellular-automata","A_Id":23622159,"CreationDate":"2014-05-13T01:23:00.000","Title":"All of Ram being used in a Cellular Automata Python Script","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Lately I have observed that xdot utility which is implemented in python to view dot graphs is giving me following error when I am trying to open any dot file.\n\nFile \"\/usr\/bin\/xdot\", line 4, in xdot.main()\n  File \"\/usr\/lib\/python2.7\/dist-packages\/xdot.py\", line 1947, in main win.open_file(args[0])\n  File \"\/usr\/lib\/python2.7\/dist-packages\/xdot.py\", line 1881, in open_file self.set_dotcode(fp.read(), filename)\n  File \"\/usr\/lib\/python2.7\/dist-packages\/xdot.py\", line 1863, in set_dotcode if self.widget.set_dotcode(dotcode, filename):\n  File \"\/usr\/lib\/python2.7\/dist-packages\/xdot.py\", line 1477, in set_dotcode self.set_xdotcode(xdotcode)\n  File \"\/usr\/lib\/python2.7\/dist-packages\/xdot.py\", line 1497, in set_xdotcode self.graph = parser.parse()\n  File \"\/usr\/lib\/python2.7\/dist-packages\/xdot.py\", line 1167, in parse DotParser.parse(self)\n  File \"\/usr\/lib\/python2.7\/dist-packages\/xdot.py\", line 977, in parse self.parse_graph()\n  File \"\/usr\/lib\/python2.7\/dist-packages\/xdot.py\", line 986, in parse_graph self.parse_stmt()\n  File \"\/usr\/lib\/python2.7\/dist-packages\/xdot.py\", line 1032, in parse_stmt   self.handle_node(id, attrs)\n  File \"\/usr\/lib\/python2.7\/dist-packages\/xdot.py\", line 1142, in handle_node shapes.extend(parser.parse()) \n  File \"\/usr\/lib\/python2.7\/dist-packages\/xdot.py\", line 612, in parse w = s.read_number()\n  File \"\/usr\/lib\/python2.7\/dist-packages\/xdot.py\", line 494, in read_number return   int(self.read_code())\n  ValueError: invalid literal for int() with base 10: '206.05'\n\nI have observed few things;\n\nThe same utility works fine for me on previous ubuntu versions(12.04, 13.04). The problem is when this is run on ubuntu 14.04. I am not sure if it is an ubuntu problem.\nAs per the trace log above the int() function has encounterd some float value which is causing the exception at the end of log.But the contents of my dot files does not contain any float value, so how come the trace shows ValueError: invalid literal for int() with base 10: '206.05'?\n\nAny clue will be helpful.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1384,"Q_Id":23623717,"Users Score":0,"Answer":"This is a bug in latest ubuntu xdot package, please use xdot in pip repository:\nsudo apt-get remove xdot\nsudo pip install xdot","Q_Score":0,"Tags":"python,python-2.7,ubuntu,graphviz","A_Id":34070637,"CreationDate":"2014-05-13T05:54:00.000","Title":"Graphviz xdot utility fails to parse graphs","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to NLTK. I would like to create the negative of a sentence (which will usually be in the present tense). For example, is there a function to allow me to convert:\n 'I run' to 'I do not run'\nor\n'She runs' to 'She does not run'.\nI suppose I could use POS to detect the verb and its preceding pronoun but I just wondered if there was a simpler built in function","AnswerCount":3,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":2143,"Q_Id":23634759,"Users Score":8,"Answer":"No there is not. What is more important it is quite a complex problem, which can be a topic of research, and not something that \"simple built in function\" could solve. Such operation requires semantic analysis of the sentence, think about for example \"I think that I could run faster\" which of the 3 verbs should be negated? We know that \"think\", but for the algorithm they are just the same. Even the case of detection whether you should use \"do\" or \"does\" is not so easy. Consider \"Mary and Jane walked down the road\" and \"Jane walked down the road\", without parse tree you won't be able to distinguish the singular\/plural problem. To sum up, there is no, and cannot be any simple solution. You can design any kind of heuristic you want (one of such is proposed POS-based negation) and if it fails, start a research in this area.","Q_Score":5,"Tags":"python,nlp,nltk","A_Id":23634933,"CreationDate":"2014-05-13T14:56:00.000","Title":"How to create the negative of a sentence in nltk","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using python with Hadoop streaming to do a project, and I need the similar functionality provided by the TotalOrderPartitioner and  InputSampler in Hadoop, that is, I need to sample the data first and create a partition file, then use the partition file to decide which K-V pair will go to which reducer in the mapper. I need to do it in Hadoop 1.0.4.\nI could only find some Hadoop streaming examples with KeyFieldBasedPartitioner and customized partitioners, which use the -partitioner option in the command to tell Hadoop to use these partitioners. The examples I found using TotalOrderPartitioner and  InputSampler are all in Java, and they need to use the writePartitionFile() of InputSampler and the DistributedCache class to do the job. So I am wondering if it is possible to use TotalOrderPartitioner with hadoop streaming? If it is possible, how can I organize my code to use it? If it is not, is it practical to implement the total partitioner in python first and then use it?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":909,"Q_Id":23644545,"Users Score":0,"Answer":"Did not try, but taking the example with KeyFieldBasedPartitioner and simply replacing:\n-partitioner org.apache.hadoop.mapred.lib.KeyFieldBasedPartitioner\nwith \n-partitioner org.apache.hadoop.mapreduce.lib.partition.TotalOrderPartitioner\nShould work.","Q_Score":0,"Tags":"python,hadoop","A_Id":23644971,"CreationDate":"2014-05-14T02:11:00.000","Title":"Using TotalOrderPartitioner in Hadoop streaming","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I'm using scipy.optimize.minimize for unrestricted optimization of an objective function which receives a couple of parameters and runs a complex numerical simulation based on these parameters. This simulation does not always converge in which case I make the objective function return inf, in some cases, in others NaN.\nI thought that this hack would prevent the minimization from converging anywhere near a set of parameters that makes the simulation diverge. Instead, I encountered a case where the simulation won't even converge for the starting set of parameters but instead of failing, the optimization terminates \"successfully\" with 0 iterations. It doesn't seem to care about the objective function returning inf.\nIs there a way to tell scipy.optimize.minimize to fail, e.g. by raising some sort of exception. While in this case it's obvious that the optimization didn't terminate successfully - because of 0 iterations and the fact that I know the optimal result - at some point I want to run problems that I don't know the solution for and I need to rely on minimize to tell me if shit hit the fan. If returning lots of nans and infs doesn't \"break\" the algorithm I guess I'll have to do it by brute force.\n\nHere is an example of what the almost-iteration looks like.\nThe function - a function of two variables - is called 4 times over all:\n1) at the starting point -> simulation diverges, f(x) = inf\n2) at a point 1e-5 to the right (gradient approximation) -> simulation diverges, f(x) = inf\n3) at a point 1e-5 higher (grad. appr.) -> simulation converges, f(x) = some finite value\n4) once more at the starting point -> simulation diverges, f(x) = inf","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2291,"Q_Id":23659698,"Users Score":1,"Answer":"You have 2 options I can think of:\n\nopt for constrained optimization\nmodify your objective function to diverge whenever your numerical simulation does not converge. Basically this means returning a large value, large compared to a 'normal' value, which depends on your problem at hand. minimize will then try to optimize going in another direction\n\nI am however a bit surprised that minimize does not understand inf as a large value, and does not try to look for a solution in another direction. Could it be that it returns with 0 iterations only when your objective function returns nan? You could try debugging the issue by printing the value just before the return statement in your objective function.","Q_Score":3,"Tags":"python,scipy,mathematical-optimization,minimize","A_Id":23659957,"CreationDate":"2014-05-14T16:01:00.000","Title":"Tell scipy.optimize.minimize to fail","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How to split one big rectangle on N smaller rectangles to look random ?\nI need to generate couple divisions for different value of n.\nIs there library for this in boost for c++ or some for python ?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1261,"Q_Id":23667700,"Users Score":3,"Answer":"One rectangle can be divided into two rectangles by drawing either a horizontal or a vertical line. Divide one of those rectangles and the result is three rectangles. Continue until you have N rectangles.  Some limitations to observe to improve the results\n\nDon't divide a rectangle with a horizontal line if the height is\nbelow some threshold\nDon't divide a rectangle with a vertical line if the width is below\nsome threshold\nDivide by quarters, thirds, or halves, i.e. avoid 90\/10 splits\nKeep a list of rectangles sorted by area, and always divide the\nrectangle with the largest area","Q_Score":0,"Tags":"python,c++,algorithm,boost","A_Id":23668100,"CreationDate":"2014-05-15T01:15:00.000","Title":"How to split one big rectangle on N smaller rectangles to look random?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Recently, I have used sklearn(a python meachine learning library) to do a short-text classification task. I found that SelectKBest class can choose K best of features. However, the first argument of SelectKBest is a score function, which \"taking two arrays X and y, and returning a pair of arrays (scores, pvalues)\". I know that scores, but what is the meaning of pvalues?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":6714,"Q_Id":23677734,"Users Score":8,"Answer":"In general the p-value indicates how probable a given outcome or a more extreme outcome is under the null hypothesis. In your case of feature selection, the null hypothesis is something like this feature contains no information about the prediction target, where no information is to be interpreted in the sense of the scoring method: If your scoring method tests e.g. univariate linear interaction (f_classif, f_regression in sklearn.feature_selection are options for your scoring function), then the null hypothesis says that this linear interaction is not present.\nTL;DR The p-value of a feature selection score indicates the probability that this score or a higher score  would be obtained if this variable showed no interaction with the target.\nAnother general statement: scores are better if greater, p-values are better if smaller (and losses are better if smaller)","Q_Score":5,"Tags":"python,classification,scikit-learn,feature-selection","A_Id":23682058,"CreationDate":"2014-05-15T12:06:00.000","Title":"What's the meaning of p-values which produced by feature selection (i.e. chi2 method)?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have two dataframes, each with a series of dates as the index. The dates to not overlap (in other words one date range from, say, 2013-01-01 through 2016-06-15 by month and the second DataFrame will start on 2016-06-15 and run quarterly through 2035-06-15.  \nMost of the column names overlap (i.e. are the same) and the join just does fine. However, there is one columns in each DataFrame that I would like to preserve as 'belonging' to the original DataFrame so that I have them both available for future use.  I gave each a different name. For example, DF1 has a column entitled opselapsed_time and DF2 has a column entitled constructionelapsed_time.\nWhen I try to combine DF1 and DF2 together using the command DF1.combine_first(DF2) or vice versa I get this error:  ValueError: Cannot convert NA to integer.\nCould someone please give me advice on how best to resolve?\nDo I need to just stick with using a merge\/join type solution instead of combine_first?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":373,"Q_Id":23679951,"Users Score":0,"Answer":"Found the best solution:\npd.tools.merge.concat([test.construction,test.ops],join='outer')\nJoins along the date index and keeps the different columns.  To the extent the column names are the same, it will join 'inner' or 'outer' as specified.","Q_Score":0,"Tags":"python,pandas","A_Id":23698952,"CreationDate":"2014-05-15T13:38:00.000","Title":"'combine first' in pandas produces NA error","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"In this period I am working with matplotlib. I studied many examples, and was able to modify it to suit several of my needs. But I would like to better understand the general structure of the library. For this reason, apart reading a lot of tutorials on the web, I also purchased the ebook \"Matplotlib for Python Developers\", by Tosi. While it is full of good examples, I still don't fully grasp the relationships between all the different levels.\nThe book clearly explains that matplotlib has 3 main \"modes\":\n1) pylab, to work similarly to Matlab\n2) pyplot, to work in a procedural way\n3) the full OO system\nConcerning the objects of the OO system, the book lists 3 levels:\n1) FigureCanvas, the container class for the Figure instance\n2) Figure, the container for Axes instances\n3) Axes, the areas that hold the basic elements (lines, points, text...)\nThe problem is that, by reading the official documentation, I also encountered the notions of Backends and Artists. While I understand the basic logic of them, I am quite confused about their role respect to the previous classifications. In particular, are the Artists in an intermediate level between the FigureCanvas and the Figure, or is that hierarchy not suitable in this case?\nI would be grateful to receive some clarifications, eventually also referring to other documentation I could have missed.\nThanks.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1693,"Q_Id":23688227,"Users Score":9,"Answer":"I do not like those layers\n\npylab : massive namespace dump that pulls in everything from pyplot and numpy.  It is not a 'layer' so much as a very cluttered name space.\npyplot : state-machine based layer (it knows what your 'current axes' and 'current figure' are and applies the functions to that axes\/figure.  You should use this only when you are playing around in an interactive terminal (other than plt.subplots or plt.figure for setting up the figure\/axes objects).  Making a distiction between this layer and pylab is dumb. This is the layer that makes it 'like MATLAB'\nthe OO layer : which is what you should use in all of your scripts\n\nFigures are the top level container objects.  They can contain Axes objects and Artist objects (technically Axes are Artists, but it is useful for pedagogical reasons to distinguish between the Axes objects in a figure and the other artists (such as text objects) that are in the Figure, but not associated with an Axes) and know about the Canvas object.  Each Axes can contain more Artists objects.  The Artists are the useful things you want to put on your graph (lines, text, images, etc).  Artists know how to draw themselves on a Canvas.  When you call fig.savefig (or render the figure to the screen) the Figure object loops over all of it's children and tell them to draw them selves on to it's Canvas.  \nThe different Backends provide implementations of the Canvas objects, hence the same figure can be rendered to a raster or vector graphic by just changing the Canvas object that is being used.\nUnless you want to write a new backend, many of these details are not important and the fact that matplotlib hides them from you is why it is useful.\nIf the book couldn't get this correct, I would take everything it says with grain of salt.","Q_Score":4,"Tags":"python,matplotlib","A_Id":23688578,"CreationDate":"2014-05-15T20:41:00.000","Title":"Confusion about Artist place in matplotlib hierarchy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been working on this for hours but couldn't figure it out. \nDefine a permutation's degree to be the minimum number of transpositions that need to be composed to create it. So a the degree of (0, 1, 2, 3) is 0, the degree of (0, 1, 3, 2) is 1, the degree of (1, 0, 3, 2) is 2, etc.\nLook at the space Snd as the space of all permutations of a sequence of length n that have degree d.\nI want two algorithms. One that takes a permutation in that space and assigns it an index number, and another that takes an index number of an item in Snd and retrieves its permutation. The index numbers should obviously be successive (i.e. in the range 0 to len(Snd)-1, with each permutation having a distinct index number.)\nI'd like this implemented in O(sane); which means that if you're asking for permutation number 17, the algorithm shouldn't go over all the permutations between 0 and 16 to retrieve your permutation.\nAny idea how to solve this? \n(If you're going to include code, I prefer Python, thank you.) \nUpdate:\nI want a solution in which\n\nThe permutations are ordered according to their lexicographic order (and not by manually ordering them, but by an efficient algorithm that gives them with lexicographic order to begin with) and \nI want the algorithm to accept a sequence of different degrees as well, so I could say \"I want permutation number 78 out of all permutations of degrees 1, 3 or 4 out of the permutation space of range(5)\". (Basically the function would take a tuple of degrees.) This'll also affect the reverse function that calculates index from permutation; based on the set of degrees, the index would be different.\n\nI've tried solving this for the last two days and I was not successful. If you could provide Python code, that'd be best.","AnswerCount":7,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1866,"Q_Id":23699378,"Users Score":0,"Answer":"The first part is straight forward if you work wholly in the lexiographic side of things. Given my answer on the other thread, you can go from a permutation to the factorial representation instantly. Basically, you imagine a list {0,1,2,3} and the number that I need to go along is the factorial representation, so for 1,2,3,4, i keep taking the zeroth element and get 000 (0*3+0*!2+0*!1!).\n0,1,2,3, => 000\n1032 = 3!+1! = 8th permuation (as 000 is the first permutation) => 101\nAnd you can work out the degree trivially, as each transposition which swaps a pair of numbers (a,b) a\n\nSo 0123 -> 1023 is 000 -> 100.\nif a>b you swap the numbers and then subtract one from the right hand number. \nGiven two permuations\/lexiographic numbers, I just permute the digits from right to left like a bubble sort, counting the degree that I need, and building the new lexiographic number as I go. So to go from 0123 to the 1032 i first move the 1 to the left, then the zero is in the right position, and then I move the 2 into position, and both of those had pairs with the rh number greater than the left hand number, so both add a 1, so 101.\nThis deals with your first problem. The second is much more difficult, as the numbers of degree two are not evenly distributed. I don't see anything better than getting the global lexiographic number (global meaning here the number without any exclusions) of the permutation you want, e.g. 78 in your example, and then go through all the lexiographic numbers and each time that you get to one which is degree 2, then add one to your global lexiographic number, e.g. 78 -> 79 when you find the first number of degree 2. Obvioulsy, this will not be fast. Alternatively you could try generating all the numbers of degree to. Given a set of n elements, there are (n-1)(n-2) numbers of degree 2, but its not clear that this holds going forward, at least to me, which might easily be a lot less work than computing all the numbers up to your target. and you could just see which ones have lexiographic number less than your target number, and again add one to its global lexiographic number. \nIll see if i can come up with something better.","Q_Score":13,"Tags":"python,algorithm,permutation,time-complexity,combinatorics","A_Id":23734414,"CreationDate":"2014-05-16T15:40:00.000","Title":"Get permutation with specified degree by index number","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to resample a 1-D signal using an FFT method (basically, the one from scipy.signal). However, the code is taking forever to run, even though my input signal is a power of two in length. After looking at profiling, I found the root of the problem.\nBasically, this method takes an FFT, then removes part of the fourier spectrum, then takes an IFFT to bring it back to the time domain at a lower sampling rate.\nThe problem is that that the IFFT is taking far longer to run than the FFT:\nncalls  tottime  percall  cumtime  percall filename:lineno(function)\n     1 6263.996 6263.996 6263.996 6263.996 basic.py:272(ifft)\n     1    1.076    1.076    1.076    1.076 basic.py:169(fft)\n\nI assume that this has something to do with the amount of fourier points remaining after the cutoff. That said, this is an incredible slowdown so I want to make sure that:\nA. This behavior is semi-reasonable and isn't definitely a bug.\nB. What could I do to avoid this problem and still downsample effectively.\nRight now I can pad my input signal to a power of two in order to make the FFT run really quickly, but not sure how to do the same kind of thing for the reverse operation. I didn't even realize that this was an issue for IFFTs :P","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":285,"Q_Id":23716904,"Users Score":1,"Answer":"If your IFFT's length is different from that of the FFT, and the length of the IFFT isn't composed of only very small prime factors (2,3,etc.), then the efficiency can drop off significantly.\nThus, this method of resampling is only efficient if the two sample rates are different by ratios with small prime factors, such as 2, 3 and 7 (hint).","Q_Score":0,"Tags":"python,numpy,scipy,signal-processing,fft","A_Id":23717381,"CreationDate":"2014-05-17T22:58:00.000","Title":"IFFT taking orders of magnitude more than FFT","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am a grad student in Engineering and I am beginning to realise how important a skill programming is in my profession. In undergrad studies we were introduced to MATLAB (Actually Octave) and I used to think that that was the way to go, but I have been doing some research and it seems that the scientific community is starting to move over to Python, through it's numpy and scipy packages, at an alarming rate.\nI feel that I am at the beginning of my 'programming journey' and have been told a few times that I should choose a language and stick to it.\nI have basically made up my mind that I want to move over to Python, but would like some informed opinions on my decision?\nI am not looking to start a MATLAB vs Python style thread as this will only lead to people giving their opinions as facts and I know this is not the style of this forum. I am simply looking for validation that a move from MATLAB to Python is a good idea for a person in my position. \nP.S. I know Python is free and MATLAB is expensive, but that it simply not a good enough reason for me to make this decision.","AnswerCount":4,"Available Count":3,"Score":0.1488850336,"is_accepted":false,"ViewCount":375,"Q_Id":23721725,"Users Score":3,"Answer":"From my experience, using Python is more rewarding, especially for a beginner in enginnering. In comparison to Matlab, Python is a general purpose language, and knowing it makes many more tasks than, say, signal analysis easy to accomplish. In my opinion it's easier to interface with external hardware or to do other tasks where you need a \"glue\" language. \nAnd with respect to signal processing, numpy, scipy, and matplotlib are a very good choice! I never felt I would miss out on anything! It was rather the other way around that with Matlab I was missing all the general purpose stuff and the \"batteries included\" nature of Python. The number of freely available libraries for Python is just overwhelming.\nOn top, basing your work on an open source project pays back. As a student, you can simply install Python on all the machines that matter to you (no additional costs), you can benefit from reading the source of others (great learning experience), and once you are doing some \"production\" stuff later on, you have the power to fix stuff yourself. With Matlab and other closed-source packages, you always depend on somebody else.\nGood luck!","Q_Score":3,"Tags":"python,matlab,numpy,scipy","A_Id":23722134,"CreationDate":"2014-05-18T12:09:00.000","Title":"Allthough not very experienced, I have a great interest in scientific programming. Is Python a good choice compared to MATLAB?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am a grad student in Engineering and I am beginning to realise how important a skill programming is in my profession. In undergrad studies we were introduced to MATLAB (Actually Octave) and I used to think that that was the way to go, but I have been doing some research and it seems that the scientific community is starting to move over to Python, through it's numpy and scipy packages, at an alarming rate.\nI feel that I am at the beginning of my 'programming journey' and have been told a few times that I should choose a language and stick to it.\nI have basically made up my mind that I want to move over to Python, but would like some informed opinions on my decision?\nI am not looking to start a MATLAB vs Python style thread as this will only lead to people giving their opinions as facts and I know this is not the style of this forum. I am simply looking for validation that a move from MATLAB to Python is a good idea for a person in my position. \nP.S. I know Python is free and MATLAB is expensive, but that it simply not a good enough reason for me to make this decision.","AnswerCount":4,"Available Count":3,"Score":1.0,"is_accepted":false,"ViewCount":375,"Q_Id":23721725,"Users Score":6,"Answer":"You should consider what particular capabilities you need, and see if Numpy and Scipy can meet them.  Matlab's real value isn't in the base package, which is more-or-less matched by a combination of numpy, scipy and matplotlib, but in the various toolboxes one can purchase.  For instance, I'm not aware of a Robust Control toolbox equivalent for Python.\nAnother feature of Matlab that doesn't have an easy-to-use Python equivalent is Simulink, especially the mature real-time hardware-in-the-loop simulation and embedded code-generation.  There are open-source projects with similar goals: JModelica is worth looking at, as is Scilab's Scicos.\nA final consideration is what is used in the industry you plan to work in.\nHaving said all that, if you can use Python, you should; it's more fun, and it's (probably) a fundamentally better language.  If you do become proficient in Python, switching to Matlab if you have to won't be very difficult.\nMy experience is that using Python made me a better Matlab programmer; Python's basic facilities (list comprehensions, dictionaries, modules, etc.) made me look for similar capabilities in Matlab, and made me organize my Matlab code better.","Q_Score":3,"Tags":"python,matlab,numpy,scipy","A_Id":23722826,"CreationDate":"2014-05-18T12:09:00.000","Title":"Allthough not very experienced, I have a great interest in scientific programming. Is Python a good choice compared to MATLAB?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am a grad student in Engineering and I am beginning to realise how important a skill programming is in my profession. In undergrad studies we were introduced to MATLAB (Actually Octave) and I used to think that that was the way to go, but I have been doing some research and it seems that the scientific community is starting to move over to Python, through it's numpy and scipy packages, at an alarming rate.\nI feel that I am at the beginning of my 'programming journey' and have been told a few times that I should choose a language and stick to it.\nI have basically made up my mind that I want to move over to Python, but would like some informed opinions on my decision?\nI am not looking to start a MATLAB vs Python style thread as this will only lead to people giving their opinions as facts and I know this is not the style of this forum. I am simply looking for validation that a move from MATLAB to Python is a good idea for a person in my position. \nP.S. I know Python is free and MATLAB is expensive, but that it simply not a good enough reason for me to make this decision.","AnswerCount":4,"Available Count":3,"Score":0.1488850336,"is_accepted":false,"ViewCount":375,"Q_Id":23721725,"Users Score":3,"Answer":"I personally feel that working with Python is a lot better, As @bdoering mentioned working on Opensource projects is far better than working on closed source. \nMatlab is quite industry specific, and is still not wide spread in the industry. If you work with these softwares, sooner or later you will be stuck between different kinds of them too (ex, Matlab vs Mathematica). However, Syntax will be easy to write and modules will run quickly and simulate. But in the end there will always be a limitation with Matlab. My observation says that using using a software like Matlab may provide you quick simulations of graphs and models, but will limit your learning curve.\nGo for Python!","Q_Score":3,"Tags":"python,matlab,numpy,scipy","A_Id":23722879,"CreationDate":"2014-05-18T12:09:00.000","Title":"Allthough not very experienced, I have a great interest in scientific programming. Is Python a good choice compared to MATLAB?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to summarize text documents that belong to legal domain. \nI am referring to the site deeplearning.net on how to implement the deep learning architectures. I have read quite a few research papers on document summarization (both single document and multidocument) but I am unable to figure to how exactly the summary is generated for each document. \nOnce the training is done, the network stabilizes during testing phase. So even if I know the set of features (which I have figured out) that are learnt during the training phase, it would be difficult to find out the importance of each feature (because the weight vector of the network is stabilized) during the testing phase where I will be trying to generate summary for each document. \nI tried to figure this out for a long time but it's in vain. \nIf anybody has worked on it or have any idea regarding the same, please give me some pointers. I really appreciate your help. Thank you.","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":2167,"Q_Id":23729919,"Users Score":2,"Answer":"I think you need to be a little more specific. When you say \"I am unable to figure to how exactly the summary is generated for each document\", do you mean that you don't know how to interpret the learned features, or don't you understand the algorithm? Also, \"deep learning techniques\" covers a very broad range of models - which one are you actually trying to use?\nIn the general case, deep learning models do not learn features that are humanly intepretable (albeit, you can of course try to look for correlations between the given inputs and the corresponding activations in the model). So, if that's what you're asking, there really is no good answer. If you're having difficulties understanding the model you're using, I can probably help you :-) Let me know.","Q_Score":3,"Tags":"python,theano,summarization,deep-learning","A_Id":23765727,"CreationDate":"2014-05-19T04:53:00.000","Title":"Text summarization using deep learning techniques","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have huge pandas DataFrames I work with. 20mm rows, 30 columns. The rows have a lot of data, and each row has a \"type\" that uses certain columns. Because of this, I've currently designed the DataFrame to have some columns that are mixed dtypes for whichever 'type' the row is.\nMy question is, performance wise, should I split out mixed dtype columns into two separate columns or keep them as one? I'm running into problems getting some of these DataFrames to even save(to_pickle) and trying to be as efficient as possible.\nThe columns could be mixes of float\/str, float\/int, float\/int\/str as currently constructed.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":581,"Q_Id":23784578,"Users Score":0,"Answer":"Seems to me that it may depend on what your subsequent use case is. But IMHO I would make each column unique type otherwise functions such as group by with totals and other common Pandas functions simply won't work.","Q_Score":0,"Tags":"python,pandas","A_Id":23784889,"CreationDate":"2014-05-21T13:25:00.000","Title":"Pandas performance: Multiple dtypes in one column or split into different dtypes?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Pandas has a number of dependencies, e.g matplotlib, statsmodels, numexpr etc.\nSay I have Pandas installed, and I update many of its dependencies, if I don't update Pandas, could I run into any problems?","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":184,"Q_Id":23786694,"Users Score":1,"Answer":"Yes, you will, Pandas sources those dependencies.","Q_Score":0,"Tags":"python,pandas","A_Id":38926745,"CreationDate":"2014-05-21T14:48:00.000","Title":"Updating Pandas dependencies after installing pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Pandas has a number of dependencies, e.g matplotlib, statsmodels, numexpr etc.\nSay I have Pandas installed, and I update many of its dependencies, if I don't update Pandas, could I run into any problems?","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":184,"Q_Id":23786694,"Users Score":3,"Answer":"If your version of pandas is old (i.e., not 0.13.1), you should definitely update it to take advantage of any new features\/optimizations of the dependencies, and any new features\/bug fixes of pandas itself. It is a very actively-maintained project, and there are issues with older versions being fixed all the time.\nOf course, if you have legacy code that depends on an older version, you should test it in a virtualenv with the newer versions of pandas and the dependencies before updating your production libraries, but at least in my experience the newer versions are pretty backwards-compatible, as long as you're not relying on buggy behavior.","Q_Score":0,"Tags":"python,pandas","A_Id":23787041,"CreationDate":"2014-05-21T14:48:00.000","Title":"Updating Pandas dependencies after installing pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"First: Any recs on how to modify the title?\nI am using my own named entity recognition algorithm to parse data from plain text.  Specifically, I am trying to extract lawyer practice areas.  A common sentence structure that I see is:\n1) Neil focuses his practice on employment, tax, and copyright litigation.\nor\n2) Neil focuses his practice on general corporate matters including securities, business organizations, contract preparation, and intellectual property protection.\nMy entity extraction is doing a good job of finding the key words, for example, my output from sentence one might look like this:\nNeil focuses his practice on (employment), (tax), and (copyright litigation).\nHowever, that doesn't really help me.  What would be more helpful is if i got an output that looked more like this:\nNeil focuses his practice on (employment - litigation), (tax - litigation), and (copyright litigation).\nIs there a way to accomplish this goal using an existing python framework such as nltk (after my algo extracts the practice areas) can I use ntlk to extract the other words that my \"practice areas\" modify in order to get a more complete picture?","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":1842,"Q_Id":23793628,"Users Score":1,"Answer":"I do not think your \"algo\" is even doing entity recognition... however, stretching the problem you presented quite a bit, what you want to do looks like coreference resolution in coordinated structures containing ellipsis. Not easy at all: start by googling for some relevant literature in linguistics and computational linguistics. I use the standard terminology from the field below.\nIn practical terms, you could start by assigning the nearest antecedent (the most frequently used approach in English). Using your examples:\n\nfirst extract all the \"entities\" in a sentence\nfrom the entity list, identify antecedent candidates (\"litigation\", etc.). This is a very difficult task, involving many different problems... you might avoid it if you know in advance the \"entities\" that will be interesting for you.\nfinally, you assign (resolve) each anaphora\/cataphora to the nearest antecedent.","Q_Score":0,"Tags":"python,nlp,nltk","A_Id":23816393,"CreationDate":"2014-05-21T20:56:00.000","Title":"How to extract meaning from sentences after running named entity recognition?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a 2D data and it contains five peaks. Could I fit five 2D Gaussians function to obtain the peaks?  In my problem, the peaks do not refer to the clustering problem.  Which I think EM would be an appropriate answer for it. \nIn my case I measure a variable in x-y space and it shows maximum in more than one position. Is still fitting Fourier series or using Expectation-Maximization method an applicable solution to my problem?\nIn order to make my likelihood, do I need to just add up the five 2D Gaussians distributions with x and y and the height of each peak as variables?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":190,"Q_Id":23808446,"Users Score":1,"Answer":"If I understand what you're asking, check out Gaussian Mixture Models and Expectation Maximization. I don't know of any pre-implemented versions of these in Python, although I haven't looked too hard.","Q_Score":1,"Tags":"python,numpy,statistics,scipy,probability","A_Id":23809181,"CreationDate":"2014-05-22T13:34:00.000","Title":"Define a 2D Gaussian probability with five peaks","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Current conditions:\n\nC code being rewritten to do almost the same type of simulation every time (learning behavior in mice)\nMatlab code being written for every simulation to plot results (2D, potentially 3D graphs)\n\nHere are my goals:\n\nDesign GUI (wxPython) that allows me to build a dynamic simulator\nGUI also displays results of simulation via OpenGL (or perhaps Matplotlib)\nUse a C wrapper (CFFI) to run the simulation and send the results (averages) to OpenGL or Matplotlib\n\nQuestion:\n\nIn order to have this software run as efficiently as possible, it makes sense to me that CFFI should be used to run the simulation...what I'm not sure about is if it would be better to have that FFI instance (or a separate one?) use an OpenGL C binding to do all the graphics stuff and pass the resulting graphs up to the Python layer to display in the GUI, or have CFFI send the averages of the simulations (the data that gets plotted) to variables in the Python level and use PyOpenGL or Matplotlib to plot graphs.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":461,"Q_Id":23819504,"Users Score":1,"Answer":"It would help to know what the turnaround time for the simulation runs is and how fast you want to display and update graphs. More or less realtime, tens of milliseconds for each? Seconds? Minutes?\nIf you want to draw graphs, I'd recommend Matplotlib rather than OpenGL. Even hacking the Matplotlib code yourself to make it do exactly what you want will probably still be easier than doing stuff in OpenGL. And Matplotlib also has \"XKCD\" style graphs :-)\nPyOpenGL works fine with wxPython. Most of the grunt work in modern 3D is done by the GPU so it probably won't be worth doing 3D graphics in C rather than Python if you decide to go that route.\nHope this helps.","Q_Score":1,"Tags":"python,c,opengl,interop,python-cffi","A_Id":23861390,"CreationDate":"2014-05-23T01:04:00.000","Title":"Interoperability advice - Python, C, Matplotlib\/OpenGL run-time efficency","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":1,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm doing some classification with Python and scikit-learn.  I have a question which doesn't seem to be covered in the documentation:  if I'm doing, for example, classification with SVM, does the order of the input examples matter?  If I have binary labels, will the results be less accurate if I put all the examples with label 0 next to each other and all the examples with label 1 next to each to other, or would it be better to mix them up?  What about the other algorithms scikit provides?","AnswerCount":1,"Available Count":1,"Score":0.537049567,"is_accepted":false,"ViewCount":138,"Q_Id":23838453,"Users Score":3,"Answer":"No, the ordering of the patterns in the training set do not matter. While the ordering of samples can affect stochastic gradient descent learning algorithms (like for example the one for the NN) they are in most cases coded in a way that ensures internal randomness. SVM on the other hand is globally convergant and it will result in the exact same solution regardless of the ordering.","Q_Score":0,"Tags":"python,numpy,machine-learning,scipy,scikit-learn","A_Id":23841290,"CreationDate":"2014-05-23T21:17:00.000","Title":"Example order in machine learning algorithms (Scikit Learn)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need, for a simulation, to find the argument (parameters) that maximizes a multivariable function with constraints.\nI've seen that scipy.optimize.minimize gives the minimum of a function (and, the maximum of the minus function) of a given function and I can use constraints and bounds. But, reading the doc, I've find out that it returns the minimum value but not the parameter that minimizes it (am I right?)\nscipy.optiminize.fmin does give the parameter that minimize the function, but this doesn't accept bounds or contraints.\nLooking in numpy, there is a function called argmin but it takes a vector as argument and return the \"parameter\" that minimizes it.\nIs there such a function that, like minimize, accept constraint and, like fmin, return the parameter that minimize the function?\nThanks in advance.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":4417,"Q_Id":23845235,"Users Score":0,"Answer":"The returned value of scipy.optimize.minimize is of type Result:\nResult contains, among other things, the inputs (x) which minimize f.","Q_Score":2,"Tags":"python,numpy,scipy","A_Id":23876265,"CreationDate":"2014-05-24T12:49:00.000","Title":"Constrained optimization in SciPy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to initialize an empty dataframe with 5 column values. Say column1, column2, column3, column4, column5.\nNow I want  to read data from database and want to insert specific column values from the database to this dataframe. Since there are 5 columns its easier to do it individually. But i have to extend the number of columns of the dataframe to 70. For that I am using for loop.\nTo update the coulmn value I was using \ndataframe['column \"+count+\"'] = .... where count is an incremental variable ranging upto 70.\nBut the above code adds a new column to the dataframe. How can I use the count variable to access these column names?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":193,"Q_Id":23895408,"Users Score":1,"Answer":"i would recommend just using pandas.io.sql to download your database data. it returns your data in a DataFrame. \nbut if, for some reason, you want to access the columns, you already have your answer:\nassignment: df['column%d' % count] = data\n\nretrieval: df['column%d' % count]","Q_Score":1,"Tags":"python,pandas","A_Id":23895466,"CreationDate":"2014-05-27T17:22:00.000","Title":"Change the column name of dataframe at runtime","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a complex function that performs math operations that cannot be vectorized. I have found that using NUMBA jit compiler actually slows performance. It it probably because I use within this function calls to python math.sqrt.\nHow can I force NUMBA to replace calls to python math.sqrt to faster C calls to sqrt?\n--\nregards\nKes","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":1612,"Q_Id":23908547,"Users Score":1,"Answer":"Numba is mapping math.sqrt calls to sqrt\/sqrtf in libc already.  The slowdown probably comes from the overhead of Numba.  This overhead comes from (un)boxing PyObjects and detecting if errors occurred in the compiled code.  It affects calling small functions from Python but less when calling from another Numba compiled function because there is no (un)boxing\nIf you set the environment variable NUMBA_OPT=3, aggressive optimization will turn on, eliminating some of the overhead but increases the code generation time.","Q_Score":1,"Tags":"python,performance,jit,numba","A_Id":23944220,"CreationDate":"2014-05-28T10:02:00.000","Title":"how improve speed of math.sqrt() with numba jit compiler in python 2.7","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a complex function that performs math operations that cannot be vectorized. I have found that using NUMBA jit compiler actually slows performance. It it probably because I use within this function calls to python math.sqrt.\nHow can I force NUMBA to replace calls to python math.sqrt to faster C calls to sqrt?\n--\nregards\nKes","AnswerCount":2,"Available Count":2,"Score":0.3799489623,"is_accepted":false,"ViewCount":1612,"Q_Id":23908547,"Users Score":4,"Answer":"Numba already does replace calls to math.sqrt to calls to a machine-code library for sqrt.  So, if you are getting slower performance it might be something else.  \nCan you post the code you are trying to speed up.   Also, which version of Numba are you using.  In the latest version of Numba, you can call the inspect_types method of the decorated function to print a listing of what is being interpreted as python objects (and therefore still being slow).","Q_Score":1,"Tags":"python,performance,jit,numba","A_Id":23943709,"CreationDate":"2014-05-28T10:02:00.000","Title":"how improve speed of math.sqrt() with numba jit compiler in python 2.7","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a pipeline where I transform some data and fit a curve to it. Is there a preferred\/standard way for masking the outliers in the data?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":906,"Q_Id":23924714,"Users Score":1,"Answer":"There's no support for masking in scikit-learn; outlier detection is done ad hoc by some estimators (e.g. DBSCAN, or RANSAC, which will appear in the next release).\nIf you want to remove outliers yourself, just use NumPy indexing.","Q_Score":2,"Tags":"python,scikit-learn,outliers","A_Id":23930543,"CreationDate":"2014-05-29T02:14:00.000","Title":"Is it possible to mask outliers within a scikit learn pipeline?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I understand that GMPY2 supports the GMP library and numpy has fast numerical libraries. I want to know how the speed compares to actually writing C (or C++) code with GMP. Since Python is a scripting language, I don't think it will ever be as fast as a compiled language, however I have been wrong about these generalizations before. \nI can't get GMP to work on my computer, so I can't run any tests. If I could, just general math like addition and maybe some trig functions. I'll figure out GMP later.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2014,"Q_Id":23944242,"Users Score":7,"Answer":"numpy and GMPY2 have different purposes.\nnumpy has fast numerical libraries but to achieve high performance, numpy is effectively restricted to working with vectors or arrays of low-level types - 16, 32, or 64 bit integers, or 32 or 64 bit floating point values. For example, numpy access highly optimized routines written in C (or Fortran) for performing matrix multiplication.\nGMPY2 uses the GMP, MPFR, and MPC libraries for multiple-precision calculations. It isn't targeted towards vector or matrix operations.\nThe Python interpreter adds overhead to each call to an external library. Whether or not the slowdown is significant depends on the how much time is spend by the external library. If the running time of the external library is very short, say 10e-8 seconds, then Python's overhead is significant. If the running time of the external library is relatively long, several seconds or longer, then Python's overhead is probably insignificant.\nSince you haven't said what you are trying to accomplish, I can't give a better answer.\nDisclaimer: I maintain GMPY2.","Q_Score":2,"Tags":"python,c,numpy,gmp,gmpy","A_Id":23946348,"CreationDate":"2014-05-29T22:46:00.000","Title":"How do numpy and GMPY2 compare with GMP in terms of speed?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I initialized ds = SupervisedDataSet(12288,1)\nand add data ds.appendLinked(im3.flatten(),10) where im3 is an openCV picture.\nand this is my trainer -> trainer = BackpropTrainer(red, ds)\nWhen the running process reach BackpropTrainer, i get an AssertionError on backprop.py line 35 self.setData(dataset).\nIt's a pybrain error on Windows, I developed it on Linux and it run without problem. I don't know what else to do, I tried to reinstall all but it still gets the same error. Can anyone help me?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":424,"Q_Id":24000654,"Users Score":0,"Answer":"The assert statement checks if a condition is true. In this case, if the inner dimension (indim) of your network is the same as your dataset, ds. Check if im3.flatten() is igual 12288\n\n\n\nassert ds.indim == network.indim   # 12288 != im3.flatten(), error!","Q_Score":2,"Tags":"python,neural-network,backpropagation,pybrain","A_Id":27366408,"CreationDate":"2014-06-02T18:06:00.000","Title":"I get a PyBrain BackpropTrainer AssertionError on Windows 7, which requirement is missin?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to use mmh3 for hashing. However, when I run \n\"python MultiwayJoin.py R.csv S.csv T.csv -r emr > output.txt\" in terminal, it returned an error said that:\nFile \"MultiwayJoin.py\", line 5, in \nimport mmh3\nImportError: No module named mmh3","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1466,"Q_Id":24001364,"Users Score":1,"Answer":"You can use pip install mmh3 to install it.","Q_Score":0,"Tags":"python,amazon-web-services,elastic-map-reduce","A_Id":29903784,"CreationDate":"2014-06-02T18:55:00.000","Title":"mmh3 not installed on Elastic MapReduce in AWS","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to use part of speech (POS) returned from nltk.pos_tag for sklearn classifier, How can I convert them to vector and use it?\ne.g.\nsent = \"This is POS example\"\ntok=nltk.tokenize.word_tokenize(sent)\npos=nltk.pos_tag(tok)\nprint (pos)\nThis returns following\n[('This', 'DT'), ('is', 'VBZ'), ('POS', 'NNP'), ('example', 'NN')]\nNow I am unable to apply any of the vectorizer (DictVectorizer, or FeatureHasher, CountVectorizer from scikitlearn to use in classifier\nPls suggest","AnswerCount":4,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":11721,"Q_Id":24002485,"Users Score":0,"Answer":"I think a better method would be to :\n\nStep-1: Create word\/sentence embeddings for each text\/sentence.\nStep-2: Calculate the POS-tags. Feed the POS-tags to a embedder as Step-1.\nStep-3: Elementwise multiply the two vectors. (This is to ensure that the word-embeddings in each sentence is weighted by the POS-tags associated with it.\n\nThanks","Q_Score":13,"Tags":"python,machine-learning,scikit-learn,nltk","A_Id":64190596,"CreationDate":"2014-06-02T20:13:00.000","Title":"python: How to use POS (part of speech) features in scikit learn classfiers (SVM) etc","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a python a script that generates an animation using matplotlib's animation.FuncAnimation and animation.FFMpegWriter. It works well, but there's an issue when running the code in IPython: each frame of the animation is displayed on screen while being generated, which slows down the movie generation process.\nI've tried issuing plt.ioff() before running the animation code, but the figure is still displayed on screen. Is there a way to disable this behavior in IPython?\nOn a related note, if a run the script from a shell (i.e. python myMovieGenScript.py), only one frame is shown, blocking execution. I can close it and the rest of the frames are rendered off screen (which is what I want). Is there a way to prevent that single frame to be displayed, so no user interaction is required?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":285,"Q_Id":24009656,"Users Score":0,"Answer":"As @tcaswell pointed out, the problem was caused by the callback that was indirectly calling plt.show().","Q_Score":0,"Tags":"python,animation,matplotlib,ipython","A_Id":24048037,"CreationDate":"2014-06-03T07:42:00.000","Title":"Matplotlib animation + IPython: temporary disabling interactive mode?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to generate 100 age values between 23 and 72 and the mean value must be 42 years. \nDo you think such a function already exists in standard python? If not, I think I know python just enough and should be able to code the algorithm but I am hoping something is already there for use. Any hints?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":110,"Q_Id":24024736,"Users Score":0,"Answer":"Yes, there is. It is random.triangular(min, max, av). \nYour mean value will be close, but not equal to av.\nEdit: see comments below, this has drawbacks.","Q_Score":0,"Tags":"python,random","A_Id":24024883,"CreationDate":"2014-06-03T20:54:00.000","Title":"How to generate random int around specific mean?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it possible to output individual figures from Bokeh as pdf or svg images? I feel like I'm missing something obvious, but I've checked the online help pages and gone through the bokeh.objects api and haven't found anything...","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":17265,"Q_Id":24026618,"Users Score":0,"Answer":"It seems that since bokeh uses html5 canvas as a backend, it will be writing things to static html pages.  You could always export the html to pdf later.","Q_Score":29,"Tags":"python,bokeh","A_Id":24030090,"CreationDate":"2014-06-03T23:25:00.000","Title":"Exporting figures from Bokeh as svg or pdf?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I am currently trying to compute out an integral with scipy.integrate.quad, and for certain values I get the following error:\n\/Library\/Frameworks\/Python.framework\/Versions\/7.3\/lib\/python2.7\/site-packages\/scipy\/integrate\/quadrature.py:616: AccuracyWarning: divmax (20) exceeded. Latest difference = 2.005732e-02\n  AccuracyWarning)\nLooking at the documentation, it isn't entirely clear to me why this warning is raised, what it means, and why it only occurs for certain values.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2346,"Q_Id":24091411,"Users Score":1,"Answer":"in scipy.integrate.quad there's a lower-level call to a difference function that is iterated over, and it's iterated over divmax times.  In your case, the default for divmax=20.  There are some functions that you can override this default -- for example scipy.integrate.quadrature.romberg allows you to set divmax (default here is 10) as a keyword.  The warning is thrown when the tolerances aren't met for the difference function.  Setting divmax to a higher value will run longer, and hopefully meet the tolerance requirements.","Q_Score":3,"Tags":"python-2.7,scipy,integrate","A_Id":24771340,"CreationDate":"2014-06-06T22:15:00.000","Title":"SciPy Quad Integration: Accuracy Warning","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to plot values of z binned in (x,y). This would look like a hist2d in matplotlib but with the bin content being defined by another array instead of representing the number of counts. Is there any way to set the bin content in hist2d?","AnswerCount":2,"Available Count":1,"Score":-0.2913126125,"is_accepted":false,"ViewCount":1859,"Q_Id":24121883,"Users Score":-3,"Answer":"Do you want to set the number of elements in each bin? I think that's a bar plot rather than a histogram. So simply use ax.bar instead.\nEDIT Good point by tcaswell, for multidimensional images the equivalent is imshow rather than bar.","Q_Score":1,"Tags":"python,matplotlib,histogram","A_Id":24121929,"CreationDate":"2014-06-09T13:59:00.000","Title":"How to set bin content in a hist2d (matplotlib)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I took a new clean install of OSX 10.9.3 and installed pip, and then did\n\npip install pandas\npip install numpy\n\nBoth installs seemed to be perfectly happy, and ran without any errors (though there were a zillion warnings).  When I tried to run a python script with import pandas, I got the following error:\n\n\n    numpy.dtype has the wrong size, try recompiling Traceback (most recent call last): \n    File \".\/moen.py\", line 7, in  import pandas File \"\/Library\/Python\/2.7\/site-packages\/pandas\/__init__.py\", line 6, in  from . import hashtable, tslib, lib \n    File \"numpy.pxd\", line 157, in init pandas.hashtable (pandas\/hashtable.c:22331) \n    ValueError: numpy.dtype has the wrong size, try recompiling\n\n\nHow do I fix this error and get pandas to load properly?","AnswerCount":6,"Available Count":1,"Score":0.0333209931,"is_accepted":false,"ViewCount":27508,"Q_Id":24122850,"Users Score":1,"Answer":"pip uninstall numpy uninstalls the old version of numpy\npip install numpy finds and installs the latest version of numpy","Q_Score":16,"Tags":"python,numpy,pandas","A_Id":27997033,"CreationDate":"2014-06-09T14:50:00.000","Title":"pandas ValueError: numpy.dtype has the wrong size, try recompiling","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Pandas read_csv causes a timeout on my production server with python 2.7, django 1.6.5, apache and nginx. This happens only when using a string buffer like StringIO.StringIO or io.BytesIO. When supplying a filename as argument to read_csv everything works fine.\nDebugging does not help because on my development server this problem does not occur.\nAny ideas?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1792,"Q_Id":24185302,"Users Score":0,"Answer":"This is over a year old, but this is the only SO thread I found on this issue so thought I'd comment on what we did to fix it. It turns out there are issues with pd.read_csv(FileObject, engine=\"C\") on an embedded wsgi process.  We ended up solving this issue by upgrading to pandas 0.17.0.  Another working solution was to run mod_wsgi in daemon mode as this issue seems to relate to some conflict in how MPM is running read_csv with the C-engine while in embedded mode. We still aren't quite sure what the exact issue is however...","Q_Score":0,"Tags":"django,python-2.7,pandas","A_Id":35639956,"CreationDate":"2014-06-12T13:11:00.000","Title":"timeout pandas read_csv stringio timeout","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Year    Month   MeanTemp Max Temp Min Temp Total Rain(mm) Total Snow(cm)\n2003    12      -0.1     9        -10.8    45             19.2\n1974    1       -5.9     8.9      -20      34.3           35.6\n2007    8       22.4     34.8     9.7      20.8           0\n1993    7       21.7     32.5     11       87.7           0\n1982    6       15.2     25.4     4        112.5          0\n1940    10      7.4      22.8     -6.1     45.5           0\n\nMy data list is a tab-separated file, resembles this and goes on from 1938-2012. My issue is finding the yearly mean temperatures when all the months and years are out of order.\nAny sort of beginning steps would be helpful, thanks","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":554,"Q_Id":24209812,"Users Score":0,"Answer":"Use a dictionary with the year as the key and the temp and a counter as the value (as a list). If the year isn't found add an entry with the mean temp and the counter at 1. If the year is already there add the temperature to the existing temp and increment the counter.\nThe rest should be easy. Note this gives you the average mean temperature for the years. If you want the true mean it will be a bit more involved.","Q_Score":0,"Tags":"python,mean","A_Id":24209906,"CreationDate":"2014-06-13T16:20:00.000","Title":"Finding the yearly mean temperatures from scrambled months python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"The new anaconda spyder has an integrated iPython console.  I really dislike the way dataframes are displayed in this console.  There are some border graphics around the dataframe and resizing that occurs with window resizing that make it difficult to examine the contents.  In addition, often for large dataframes if one just types the variable name, we can only see a XX rows x YY columns display, not a display of the top corner of the dataframe.\nHow can I reset the dataframe display so that it displays the way a standard iPython (or iPython QT) console would display a dataframe, i.e. with no graphics?\nThanks.","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":1871,"Q_Id":24213788,"Users Score":2,"Answer":"The development version of Spyder has a Pandas Dataframe editor (and an numpy array editor, up to 3d). You can run this from source or wait for the next release, 2.3.1.\nThis is probably more adequate to edit or visualize dataframe than using the embedded qtconsole.","Q_Score":2,"Tags":"ipython,spyder","A_Id":25560508,"CreationDate":"2014-06-13T21:04:00.000","Title":"Anaconda Spyder integrated IPython display for dataframes","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"When I run 'pip freeze' it shows that numpy==1.8.1; however, when I start Python and import numpy and then check the version number via numpy.version.version I get 1.6.\nThis is confusing to me and it's also creating a problem for scipy and matplotlib. For example, when I attempt to do the following import 'from matplotlib import pyplot' I get an error saying 'RuntimeError: module compiled against API version 9 but this version of numpy is 6'. This I'm guessing has something to do with the numpy versions being wrong.\nAny suggestions?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":469,"Q_Id":24214364,"Users Score":0,"Answer":"From our conversation in chat and adding path to .bashrc not working:\nPutting:\n\/usr\/local\/bin first in \/etc\/paths will resolve the issue","Q_Score":1,"Tags":"python,macos,numpy","A_Id":24214946,"CreationDate":"2014-06-13T22:00:00.000","Title":"Mac OS Mavericks numpy Version Issue","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a data frame in which column A is the start time of an activity and column B is the finish time of that activity, and each row represents an activity (rows are arranged chronologically).  I want to compute the difference in time between the end of one activity and the start of the next activity, i.e. df[i+1][A] - df[i][B].\nIs there a Pandas function to do this (the only thing I can find is diff(), but that only appears to work on a single column).","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":5331,"Q_Id":24232701,"Users Score":6,"Answer":"You can shift A column first:\ndf['A'].shift(-1) - df['B']","Q_Score":5,"Tags":"python,python-2.7,pandas,offset","A_Id":24232729,"CreationDate":"2014-06-15T18:23:00.000","Title":"Pandas diff() functionality on two columns in a dataframe","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I knew that there is no builtin sph_hankel1 in scipy then i want to know that how to implement it in the right way?\nAdditional: Just show me one correct implementation of sph_hankel1 either using of Scipy or Sympy.","AnswerCount":2,"Available Count":1,"Score":0.2913126125,"is_accepted":false,"ViewCount":997,"Q_Id":24284390,"Users Score":3,"Answer":"Although it would be nice if there were an existing routine for calculating the spherical Hankel functions (like there is for the ordinary Hankel functions), they are just a (complex) linear combination of the spherical Bessel functions of the first and second kind so can be easily calculated from existing routines.  Since the Hankel functions are complex and depending on your application of them, it can be advantageous to rewrite your expression in terms of the Bessel functions of the first and second kind, ie entirely real quantities, particularly if your final result is real.","Q_Score":3,"Tags":"python,numpy,scipy,sympy","A_Id":24289392,"CreationDate":"2014-06-18T11:30:00.000","Title":"How can i implement spherical hankel function of the first kind by scipy\/numpy or sympy?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've completed writing a multiclass classification algorithm that uses boosted classifiers. One of the main calculations consists of weighted least squares regression. \nThe main libraries I've used include:\n\nstatsmodels (for regression) \nnumpy (pretty much everywhere) \nscikit-image (for extracting HoG features of images)\n\nI've developed the algorithm in Python, using Anaconda's Spyder. \nI now need to use the algorithm to start training classification models. So I'll be passing approximately 7000-10000 images to this algorithm, each about 50x100, all in gray scale. \nNow I've been told that a powerful machine is available in order to speed up the training process. And they asked me \"am I using GPU?\" And a few other questions.\nTo be honest I have no experience in CUDA\/GPU, etc. I've only ever heard of them. I didn't develop my code with any such thing in mind. In fact I had the (ignorant) impression that a good machine will automatically run my code faster than a mediocre one, without my having to do anything about it. (Apart from obviously writing regular code efficiently in terms of loops, O(n), etc). \nIs it still possible for my code to get speeded up simply by virtue of being on a high performance computer? Or do I need to modify it to make use of a parallel-processing machine?","AnswerCount":2,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":6194,"Q_Id":24306285,"Users Score":7,"Answer":"The comments and Moj's answer give a lot of good advice. I have some experience on signal\/image processing with python, and have banged my head against the performance wall repeatedly, and I just want to share a few thoughts about making things faster in general. Maybe these help figuring out possible solutions with slow algorithms.\nWhere is the time spent?\nLet us assume that you have a great algorithm which is just too slow. The first step is to profile it to see where the time is spent. Sometimes the time is spent doing trivial things in a stupid way. It may be in your own code, or it may even be in the library code. For example, if you want to run a 2D Gaussian filter with a largish kernel, direct convolution is very slow, and even FFT may be slow. Approximating the filter with computationally cheap successive sliding averages may speed things up by a factor of 10 or 100 in some cases and give results which are close enough.\nIf a lot of time is spent in some module\/library code, you should check if the algorithm is just a slow algorithm, or if there is something slow with the library. Python is a great programming language, but for pure number crunching operations it is not good, which means most great libraries have some binary libraries doing the heavy lifting. On the other hand, if you can find suitable libraries, the penalty for using python in signal\/image processing is often negligible. Thus, rewriting the whole program in C does not usually help much.\nWriting a good algorithm even in C is not always trivial, and sometimes the performance may vary a lot depending on things like CPU cache. If the data is in the CPU cache, it can be fetched very fast, if it is not, then the algorithm is much slower. This may introduce non-linear steps into the processing time depending on the data size. (Most people know this from the virtual memory swapping, where it is more visible.) Due to this it may be faster to solve 100 problems with 100 000 points than 1 problem with 10 000 000 points.\nOne thing to check is the precision used in the calculation. In some cases float32 is as good as float64 but much faster. In many cases there is no difference.\nMulti-threading\nPython - did I mention? - is a great programming language, but one of its shortcomings is that in its basic form it runs a single thread. So, no matter how many cores you have in your system, the wall clock time is always the same. The result is that one of the cores is at 100 %, and the others spend their time idling. Making things parallel and having multiple threads may improve your performance by a factor of, e.g., 3 in a 4-core machine.\nIt is usually a very good idea if you can split your problem into small independent parts. It helps with many performance bottlenecks.\nAnd do not expect technology to come to rescue. If the code is not written to be parallel, it is very difficult for a machine to make it parallel.\nGPUs\nYour machine may have a great GPU with maybe 1536 number-hungry cores ready to crunch everything you toss at them. The bad news is that making GPU code is a bit different from writing CPU code. There are some slightly generic APIs around (CUDA, OpenCL), but if you are not accustomed to writing parallel code for GPUs, prepare for a steepish learning curve. On the other hand, it is likely someone has already written the library you need, and then you only need to hook to that.\nWith GPUs the sheer number-crunching power is impressive, almost frightening. We may talk about 3 TFLOPS (3 x 10^12 single-precision floating-point ops per second). The problem there is how to get the data to the GPU cores, because the memory bandwidth will become the limiting factor. This means that even though using GPUs is a good idea in many cases, there are a lot of cases where there is no gain.\nTypically, if you are performing a lot of local operations on the image, the operations are easy to make parallel, and they fit well a GPU. If you are doing global operations, the situation is a bit more complicated. A FFT requires information from all over the image, and thus the standard algorithm does not work well with GPUs. (There are GPU-based algorithms for FFTs, and they sometimes make things much faster.)\nAlso, beware that making your algorithms run on a GPU bind you to that GPU. The portability of your code across OSes or machines suffers.\nBuy some performance\nAlso, one important thing to consider is if you need to run your algorithm once, once in a while, or in real time. Sometimes the solution is as easy as buying time from a larger computer. For a dollar or two an hour you can buy time from quite fast machines with a lot of resources. It is simpler and often cheaper than you would think. Also GPU capacity can be bought easily for a similar price.\nOne possibly slightly under-advertised property of some cloud services is that in some cases the IO speed of the virtual machines is extremely good compared to physical machines. The difference comes from the fact that there are no spinning platters with the average penalty of half-revolution per data seek. This may be important with data-intensive applications, especially if you work with a large number of files and access them in a non-linear way.","Q_Score":2,"Tags":"python,performance,numpy,cuda,gpu","A_Id":24317131,"CreationDate":"2014-06-19T12:10:00.000","Title":"How to speed up Python code for running on a powerful machine?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've completed writing a multiclass classification algorithm that uses boosted classifiers. One of the main calculations consists of weighted least squares regression. \nThe main libraries I've used include:\n\nstatsmodels (for regression) \nnumpy (pretty much everywhere) \nscikit-image (for extracting HoG features of images)\n\nI've developed the algorithm in Python, using Anaconda's Spyder. \nI now need to use the algorithm to start training classification models. So I'll be passing approximately 7000-10000 images to this algorithm, each about 50x100, all in gray scale. \nNow I've been told that a powerful machine is available in order to speed up the training process. And they asked me \"am I using GPU?\" And a few other questions.\nTo be honest I have no experience in CUDA\/GPU, etc. I've only ever heard of them. I didn't develop my code with any such thing in mind. In fact I had the (ignorant) impression that a good machine will automatically run my code faster than a mediocre one, without my having to do anything about it. (Apart from obviously writing regular code efficiently in terms of loops, O(n), etc). \nIs it still possible for my code to get speeded up simply by virtue of being on a high performance computer? Or do I need to modify it to make use of a parallel-processing machine?","AnswerCount":2,"Available Count":2,"Score":0.3799489623,"is_accepted":false,"ViewCount":6194,"Q_Id":24306285,"Users Score":4,"Answer":"I am afraid you can not speed up your program by just running it on a powerful computer. I had this issue while back. I first used python (very slow), then moved to C(slow) and then had to use other tricks and techniques. for example it is sometimes possible to apply some dimensionality reduction to speed up things while having reasonable accurate result, or as you mentioned using multi processing techniques.\nSince you are dealing with image processing problem, you do a lot of matrix operations and GPU for sure would be a great help. there are some nice and active cuda wrappers in python that you can easily use, by not knowing too much CUDA. I tried Theano, pycuda and scikit-cuda (there should be more than that since then).","Q_Score":2,"Tags":"python,performance,numpy,cuda,gpu","A_Id":24306811,"CreationDate":"2014-06-19T12:10:00.000","Title":"How to speed up Python code for running on a powerful machine?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there a way generate a chart on google spreadsheet automatically using Python? I checked gspread. There seems no api for making charts.\nThanks~","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1378,"Q_Id":24312068,"Users Score":1,"Answer":"AFAIK, no. There is no way to do this with python. \nGoogle-apps-script can do this, but the spreadsheet-api (Gdata) can't.  \nYou can make a call from Python to Google-apps-script and pass parameters.","Q_Score":1,"Tags":"python-2.7,charts,google-sheets,google-spreadsheet-api","A_Id":24347728,"CreationDate":"2014-06-19T16:47:00.000","Title":"Is there a way generate a chart on google spreadsheet automatically using Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using scikit-learn Multinomial Naive Bayes classifier for binary text classification (classifier tells me whether the document belongs to the category X or not). I use a balanced dataset to train my model and a balanced test set to test it and the results are very promising.\nThis classifer needs to run in real time and constantly analyze documents thrown at it randomly.\nHowever, when I run my classifier in production, the number of false positives is very high and therefore I end up with a very low precision. The reason is simple: there are many more negative samples that the classifer encounters in the real-time scenario (around 90 % of the time) and this does not correspond to the ideal balanced dataset I used for testing and training.\nIs there a way I can simulate this real-time case during training or are there any tricks that I can use (including pre-processing on the documents to see if they are suitable for the classifer)? \nI was planning to train my classifier using an imbalanced dataset with the same proportions as I have in real-time case but I am afraid that might bias Naive Bayes towards the negative class and lose the recall I have on the positive class.\nAny advice is appreciated.","AnswerCount":3,"Available Count":2,"Score":0.1325487884,"is_accepted":false,"ViewCount":9405,"Q_Id":24367141,"Users Score":2,"Answer":"I think gustavodidomenico makes a good point.  You can think of Naive Bayes as learning a probability distribution, in this case of words belonging to topics.  So the balance of the training data matters.  If you use decision trees, say a random forest model, you learn rules for making the assignment (yes there are probability distributions involved and I apologise for the hand waving explanation but sometimes intuition helps). In many cases trees are more robust than Naive Bayes, arguably for this reason.","Q_Score":15,"Tags":"python,machine-learning,classification,scikit-learn,text-classification","A_Id":24524206,"CreationDate":"2014-06-23T13:25:00.000","Title":"Naive Bayes: Imbalanced Test Dataset","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using scikit-learn Multinomial Naive Bayes classifier for binary text classification (classifier tells me whether the document belongs to the category X or not). I use a balanced dataset to train my model and a balanced test set to test it and the results are very promising.\nThis classifer needs to run in real time and constantly analyze documents thrown at it randomly.\nHowever, when I run my classifier in production, the number of false positives is very high and therefore I end up with a very low precision. The reason is simple: there are many more negative samples that the classifer encounters in the real-time scenario (around 90 % of the time) and this does not correspond to the ideal balanced dataset I used for testing and training.\nIs there a way I can simulate this real-time case during training or are there any tricks that I can use (including pre-processing on the documents to see if they are suitable for the classifer)? \nI was planning to train my classifier using an imbalanced dataset with the same proportions as I have in real-time case but I am afraid that might bias Naive Bayes towards the negative class and lose the recall I have on the positive class.\nAny advice is appreciated.","AnswerCount":3,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":9405,"Q_Id":24367141,"Users Score":11,"Answer":"You have encountered one of the problems with classification with a highly imbalanced class distribution. I have to disagree with those that state the problem is with the Naive Bayes method, and I'll provide an explanation which should hopefully illustrate what the problem is.\nImagine your false positive rate is 0.01, and your true positive rate is 0.9. This means your false negative rate is 0.1 and your true negative rate is 0.99.\nImagine an idealised test scenario where you have 100 test cases from each class. You'll get (in expectation) 1 false positive and 90 true positives. Great! Precision is 90 \/ (90+1) on your positive class!\nNow imagine there are 1000 times more negative examples than positive. Same 100 positive examples at test, but now there are 1000000 negative examples. You now get the same 90 true positives, but (0.01 * 1000000) = 10000 false positives. Disaster! Your precision is now almost zero (90 \/ (90+10000)).\nThe point here is that the performance of the classifier hasn't changed; false positive and true positive rates remained constant, but the balance changed and your precision figures dived as a result.\nWhat to do about it is harder. If your scores are separable but the threshold is wrong, you should look at the ROC curve for thresholds based on the posterior probability and look to see if there's somewhere where you get the kind of performance you want. If your scores are not separable, try a bunch of different classifiers and see if you can get one where they are (logistic regression is pretty much a drop-in replacement for Naive Bayes; you might want to experiment with some non-linear classifiers, however, like a neural net or non-linear SVM, as you can often end up with non-linear boundaries delineating the space of a very small class).\nTo simulate this effect from a balanced test set, you can simply multiply instance counts by an appropriate multiplier in the contingency table (for instance, if your negative class is 10x the size of the positive, make every negative instance in testing add 10 counts to the contingency table instead of 1).\nI hope that's of some help at least understanding the problem you're facing.","Q_Score":15,"Tags":"python,machine-learning,classification,scikit-learn,text-classification","A_Id":24528969,"CreationDate":"2014-06-23T13:25:00.000","Title":"Naive Bayes: Imbalanced Test Dataset","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using scikit to perform text classification and I'm trying to understand where the points lie with respect to my hyperplane to decide how to proceed. But I can't seem to plot the data that comes from the CountVectorizer() function. I used the following function: pl.scatter(X[:, 0], X[:, 1]) and it gives me the error: ValueError: setting an array element with a sequence.\nAny idea how to fix this?`","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":99,"Q_Id":24400012,"Users Score":0,"Answer":"If X is a sparse matrix, you probably need X = X.todense() in order to get access to the data in the correct format. You probably want to check X.shape before doing this though, as if X is very large (but very sparse) it may consume a lot of memory when \"densified\".","Q_Score":0,"Tags":"python,numpy,matplotlib,scikit-learn","A_Id":24424870,"CreationDate":"2014-06-25T04:05:00.000","Title":"How to plot text documents in a scatter map?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I was trying to Google above, but knowing absolutely nothing about SPSS I wasn't sure what search phrase I should be using.\nFrom my initial search (tried using words: \"Dictionary\" and \"Scripting Dictionary\") it seems there is something called Data Dictionary in SPSS, but description suggest it is not the same as Python Dictionaries.\nWould someone be kind enough just to confirm that SPSS has similar functionality and if yes, can you please suggest key words to be used in Google?\nMany thanks\ndce","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":176,"Q_Id":24450211,"Users Score":1,"Answer":"A Python dictionary is an in-memory hash table where lookup of individual elements requires fixed time, and there is no deterministic order.  SPSS data files are disk-based and sequential and are designed for fast, in-order access for arbitrarily large amounts of data.\nSo these are intended for quite different purposes, but there is nothing stopping you from using a Python dictionary within Statistics using the apis in the Python Essentials to complement what Statistics does with the casewise data.","Q_Score":1,"Tags":"python,dictionary,spss","A_Id":24466862,"CreationDate":"2014-06-27T11:01:00.000","Title":"SPSS equivalent of Python Dictionary","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to solve systems of linear inequalities in 3 or more variables. That is, to find all possible solutions.\nI originally found GLPK and tried the python binding, but the last few updates to GLPK changed the APIs and broke the bindings. I haven't been able to find a way to making work. \nI would like to have the symbolic answer, but numeric approximations will be fine too. \nI would also be happy to use a library that solves maximization problem. I can always re-write the problems to be solve in that way.","AnswerCount":1,"Available Count":1,"Score":-0.1973753202,"is_accepted":false,"ViewCount":1233,"Q_Id":24493849,"Users Score":-1,"Answer":"make a matrix object and use Crammer's method","Q_Score":2,"Tags":"python,python-2.7","A_Id":24494507,"CreationDate":"2014-06-30T15:36:00.000","Title":"Solving system of linear inequalities in 3 or more variables - Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm creating a histogram(which is NOT normalized) using matplotlib.\nI want to get the exact size of each bin. That is, not the width but the length. \nIn other words, number of data contained in each bin.\nAny tips???","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":148,"Q_Id":24500522,"Users Score":0,"Answer":"Use the numpy function histogram, which returns arrays with the bin locations and sizes.","Q_Score":0,"Tags":"python,matplotlib","A_Id":24500552,"CreationDate":"2014-06-30T23:43:00.000","Title":"Histogram bin size(matplotlib)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm performing a stepwise model selection, progressively dropping variables with a variance inflation factor over a certain threshold.\nIn order to do this, I'm running OLS many, many times on datasets ranging from a few hundred MB to 10 gigs.\nWhat is the quickest implementation of OLS would be for larger datasets? The Statsmodel OLS implementation seems to be using numpy to invert matrices. Would a gradient descent based method be quicker? Does scikit-learn have an especially quick implementation?\nOr maybe an mcmc based approach using pymc is quickest...\nUpdate 1: Seems that the scikit learn implementation of LinearRegression is a wrapper for the scipy implementation.\nUpdate 2: Scipy OLS via scikit learn LinearRegression is twice as fast as statsmodels OLS in my very limited tests...","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":3098,"Q_Id":24503344,"Users Score":3,"Answer":"The scikit-learn SGDRegressor class is (iirc) the fastest, but would probably be more difficult to tune than a simple LinearRegression.\nI would give each of those a try, and see if they meet your needs. I also recommend subsampling your data - if you have many gigs but they are all samples from the same distibution, you can train\/tune your model on a few thousand samples (dependent on the number of features). This should lead to faster exploration of your model space, without wasting a bunch of time on \"repeat\/uninteresting\" data.\nOnce you find a few candidate models, then you can try those on the whole dataset.","Q_Score":4,"Tags":"python,scipy,scikit-learn,statsmodels,pymc","A_Id":24505486,"CreationDate":"2014-07-01T05:58:00.000","Title":"Quickest linear regression implementation in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a scikits-learn linear svm.SVC classifier designed to classify text into 2 classes (-1,1). The classifier uses 250 features from the training set to make its predictions, and it works fairly well. \nHowever, I can't figure out how plot the hyperplane or the support vectors in matplotlib. All the examples online use only 2 features to derive the decision boundary and the support vector points. I can't seem to find any that plot hyperplanes or support vectors that have more than 2 features or lack fixed features. I know that there is a fundamental mathematical step that I am missing here, and any help would be appreciated.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1876,"Q_Id":24515783,"Users Score":2,"Answer":"If your linear SVM classifier works quite well, then that suggests there is a hyperplane which separates your data.  So there will be a nice 2D geometric representation of the decision boundary.  \nTo understand the \"how\" you need to look at the support vectors themselves, see which ones contribute to which side of the hyperplane, e.g., by feeding individual support vectors into the trained classifier.  In general, visualising text algos is not straightforward.","Q_Score":3,"Tags":"python,matplotlib,plot","A_Id":24524359,"CreationDate":"2014-07-01T17:06:00.000","Title":"How do you plot the hyperplane of an sklearn svm with more than 2 features in matplotlib?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a 2D numpy array consisting of ca. 15'000'000 datapoints. Each datapoint has a timestamp and an integer value (between 40 and 200). I must create histograms of the datapoint distribution (16 bins: 40-49, 50-59, etc.), sorted by year, by month within the current year, by week within the current year, and by day within the current month.\nNow, I wonder what might be the most efficient way to accomplish this. Given the size of the array, performance is a conspicuous consideration. I am considering nested \"for\" loops, breaking down the arrays by year, by month, etc. But I was reading that numpy arrays are highly memory-efficient and have all kinds of tricks up their sleeve for fast processing. So I was wondering if there is a faster way to do that. As you may have realized, I am an amateur programmer (a molecular biologist in \"real life\") and my questions are probably rather na\u00efve.","AnswerCount":3,"Available Count":1,"Score":0.1325487884,"is_accepted":false,"ViewCount":4163,"Q_Id":24516396,"Users Score":2,"Answer":"First, fill in your 16 bins without considering date at all.\nThen, sort the elements within each bin by date.\nNow, you can use binary search to efficiently locate a given year\/month\/week within each bin.","Q_Score":3,"Tags":"python,arrays,performance,numpy,histogram","A_Id":24516539,"CreationDate":"2014-07-01T17:47:00.000","Title":"efficient, fast numpy histograms","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using NLTK stemmer to remove grammatical variations of a stem word.\nHowever, the Port or Snowball stemmers remove the trailing \"e\" of the original form of a noun or verb, e.g., Profile becomes Profil.\nHow can I prevent this from happening? I know I can use a conditional to guard against this. But obviously it will fail on different cases.\nIs there an option or another API for what I want?","AnswerCount":3,"Available Count":2,"Score":0.2605204458,"is_accepted":false,"ViewCount":3819,"Q_Id":24517722,"Users Score":4,"Answer":"The goal of a stemmer is to remove as much of the word as possible to allow it to cover as many cases as possible, yet retain the core of the word. One reason profile might go to profil is to cover the case of profiling. You would need a conditional or another stemmer in order to guard against this, although I would imagine the majority of them will remove the trailing 'e'. (Especially giving the number of 'e' -> 'ing' cases)","Q_Score":7,"Tags":"python,nlp,nltk","A_Id":24518289,"CreationDate":"2014-07-01T19:18:00.000","Title":"How to stop NLTK stemmer from removing the trailing \"e\"?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using NLTK stemmer to remove grammatical variations of a stem word.\nHowever, the Port or Snowball stemmers remove the trailing \"e\" of the original form of a noun or verb, e.g., Profile becomes Profil.\nHow can I prevent this from happening? I know I can use a conditional to guard against this. But obviously it will fail on different cases.\nIs there an option or another API for what I want?","AnswerCount":3,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":3819,"Q_Id":24517722,"Users Score":8,"Answer":"I agree with Philip that the goal of stemmer is to retain only the stem. For this particular case you can try a lemmatizer instead of stemmer which will supposedly retain more of a word and is meant to remove exactly different forms of a word like 'profiles' --> 'profile'. There is a class in NLTK for this - try WordNetLemmatizer() from nltk.stem.\nBeware that it's still not perfect (like nothing when working with text) because I used to get 'physic' from 'physics'.","Q_Score":7,"Tags":"python,nlp,nltk","A_Id":24521458,"CreationDate":"2014-07-01T19:18:00.000","Title":"How to stop NLTK stemmer from removing the trailing \"e\"?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm looking to use scikit-learn's HashingVectorizer because it's a great fit for online learning problems (new tokens in text are guaranteed to map to a \"bucket\").  Unfortunately the implementation included in scikit-learn doesn't seem to include support for tf-idf features.  Is passing the vectorizer output through a TfidfTransformer the only way to make online updates work with tf-idf features, or is there a more elegant solution out there?","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":2712,"Q_Id":24517793,"Users Score":3,"Answer":"Intrinsically you can not use TF IDF in an online fashion, as the IDF of all past features will change with every new document - which would mean re-visiting and re-training on all the previous documents, which would no-longer be online. \nThere may be some approximations, but you would have to implement them yourself.","Q_Score":2,"Tags":"python,machine-learning,nlp,scikit-learn,vectorization","A_Id":24519951,"CreationDate":"2014-07-01T19:22:00.000","Title":"Online version of scikit-learn's TfidfVectorizer","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm looking to use scikit-learn's HashingVectorizer because it's a great fit for online learning problems (new tokens in text are guaranteed to map to a \"bucket\").  Unfortunately the implementation included in scikit-learn doesn't seem to include support for tf-idf features.  Is passing the vectorizer output through a TfidfTransformer the only way to make online updates work with tf-idf features, or is there a more elegant solution out there?","AnswerCount":2,"Available Count":2,"Score":0.3799489623,"is_accepted":false,"ViewCount":2712,"Q_Id":24517793,"Users Score":4,"Answer":"You can do \"online\" TF-IDF, contrary to what was said in the accepted answer.\nIn fact, every search engine (e.g. Lucene) does.\nWhat does not work if assuming you have TF-IDF vectors in memory.\nSearch engines such as lucene naturally avoid keeping all data in memory. Instead they load one column at a time (which due to sparsity is not a lot). IDF arises trivially from the length of the inverted list.\nThe point is, you don't transform your data into TF-IDF, and then do standard cosine similarity.\nInstead, you use the current IDF weights when computing similarities, using a weighted cosine similarity (often modified with additional weighting, boosting terms, penalizing terms, etc.)\nThis approach will work essentially with any algorithm that allows attribute weighting at evaluation time. Many algorithms will do, but very few implementations are flexible enough, unfortunately. Most expect you to multiply the weights into your data matrix before training, unfortunately.","Q_Score":2,"Tags":"python,machine-learning,nlp,scikit-learn,vectorization","A_Id":24841469,"CreationDate":"2014-07-01T19:22:00.000","Title":"Online version of scikit-learn's TfidfVectorizer","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a problem with Matplotlib. I usually make big plots with many data points and then, after zooming or setting limits, I save in pdf only a specific subset of the original plot. The problem comes when I open this file: matplotlib saves all the data into the pdf making not visible the one outside of the range. This makes almost impossible to open afterwards those plots or to import them into latex. \nAny idea of how I could solve this problem is really welcome.\nThanks a lot","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":247,"Q_Id":24519113,"Users Score":0,"Answer":"If you don't have a requirement to use PDF figures, you can save the matplotlib figures as .png; this format just contains the data on the screen, e.g. I tried saving a large scatter plot as PDF, its size was 198M; as png it came out as 270K; plus I've never had any problems using png inside latex.","Q_Score":1,"Tags":"python,pdf,matplotlib,plot","A_Id":24519425,"CreationDate":"2014-07-01T20:54:00.000","Title":"Matplotlib saves pdf with data outside set","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using matplotlib to draw a graph using some data and I have saved it in Pdf format.Now I want to add a logo to this file.How can I do this.\nThanks in advance","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":141,"Q_Id":24529823,"Users Score":1,"Answer":"If you can do it the other way round, it is easier:\n\nplot the image\nload the logo from file with, e.g. Image module (PIL)\nadd the logo with plt.imshow, use the extent keyword to place it correctly\nsave the image into PDF\n\n(You may even want to plot the logo first, so that it stays in the background.)\nUnfortunately, this does not work with vector graphics, but as logos usually are not that large, you may use a .png or even a .jpg.\nIf you already have the PDF's then this is not a matplotlib or python question. You need some PDF editing tools or libraries to add the logo. Possible, but an entirely different thing.","Q_Score":0,"Tags":"image,python-2.7,matplotlib,pdf-generation","A_Id":24531535,"CreationDate":"2014-07-02T11:22:00.000","Title":"I have generated a pdf file using matplotlib and I want to add a logo to this pdf file. How can I do it","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to do an image processing in python. i want to use wavelet transform as the filterbank. Can anyone suggest me which one library should i use? \nI had pywavelet installed, but i don't know how to combine it with opencv. If i use wavedec2 command, it raise ValueError(\"Expected 2D input data.\")\nCan anyone help me?","AnswerCount":2,"Available Count":1,"Score":0.4621171573,"is_accepted":false,"ViewCount":11865,"Q_Id":24536552,"Users Score":5,"Answer":"Answer of Navaneeth is correct but with two correction:\n1- Opencv read and save the images as BGR not RGB so you should do cv2.COLOR_BGR2GRAY to be exact.\n2- Maximum level of _multilevel.py is 7 not 10, so you should do : w2d(\"test1.png\",'db1',7)","Q_Score":7,"Tags":"python,opencv,image-processing,dwt","A_Id":45240779,"CreationDate":"2014-07-02T16:36:00.000","Title":"How to Combine pyWavelet and openCV for image processing?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"What function can I use in Python if I want to sample a truncated integer power law?\nThat is, given two parameters a and m, generate a random integer x in the range [1,m) that follows a distribution proportional to 1\/x^a.\nI've been searching around numpy.random, but I haven't found this distribution.","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2482,"Q_Id":24579269,"Users Score":0,"Answer":"Use numpy.random.zipf and just reject any samples greater than or equal to m","Q_Score":6,"Tags":"python,numpy,random,distribution","A_Id":61270549,"CreationDate":"2014-07-04T18:21:00.000","Title":"Sample a truncated integer power law in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Im using openCV on Ubuntu 14.04, but some of the functions that I require particularly in cv2 library (cv2.drawMatches, cv2.drawMatchesKnn) does not work in 2.4.9. How do I uninstall 2.4.9 and install 3.0.0 from the their git? I know the procedure for installing 3.0.0 but how do I make sure that 2.4.9 get completely removed from disk?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":18476,"Q_Id":24598160,"Users Score":3,"Answer":"The procedure depends on whether or not you built OpenCV from source with CMake, or snatched it from a repository.\nFrom repository\nsudo apt-get purge libopencv* will cleanly remove all traces. Substitute libopencv* as appropriate in case you were using an unofficial ppa.\nFrom source\nIf you still have the files generated by CMake (the directory from where you executed sudo make install), cd there and sudo make uninstall. Otherwise, you can either rebuild them with the exact same configuration and use the above command, or recall your CMAKE_INSTALL_PREFIX (\/usr\/local by default), and remove everything with opencv in its name within that directory tree.","Q_Score":5,"Tags":"python,opencv,ubuntu,uninstallation","A_Id":24598296,"CreationDate":"2014-07-06T16:54:00.000","Title":"Unistall opencv 2.4.9 and install 3.0.0","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm  a data analysis student and I'm starting to explore Genetic Algorithms at the moment. I'm trying to solve a problem with GA but I'm not sure about the formulation of the problem. \nBasically I have a state of a variable being 0 or 1 (0 it's in the normal range of values, 1 is in a critical state). When the state is 1 I can apply 3 solutions (let's consider Solution A, B and C) and for each solution I know the time that the solution was applied and the time where the state of the variable goes to 0. \nSo I have for the problem a set of data that have a critical event at 1, the solution applied and the time interval (in minutes) from the critical event to the application of the solution, and the time interval (in minutes) from the application of the solution until the event goes to 0. \nI want with a genetic algorithm to know which is the best solution for a critical event and the fastest one. And if it is possible to rank the solutions acquired so if in the future on solution can't be applied I can always apply the second best for example.\nI'm thinking of developing the solution in Python since I'm new to GA.\nEdit: Specifying the problem (responding to AMack)\nYes is more a less that but with some nuances. For example the function A can be more suitable to make the variable go to F but because exist other problems with the variable are applied more than one solution. So on the data that i receive for an event of V, sometimes can be applied 3 ou 4 functions but only 1 or 2 of them are specialized for the problem that i want to analyze. My objetive is to make a decision support on the solution to use when determined problem appear. But the optimal solution can be more that one because for some event function A acts very fast but in other case of the same event function A don't produce a fast response and function C is better in that case. So in the end i pretend a solution where is indicated what are the best solutions to the problem but not only the fastest because the fastest in the majority of the cases sometimes is not the fastest in the same issue but with a different background.","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":335,"Q_Id":24615687,"Users Score":0,"Answer":"To start, let's make sure I understand your problem.\nYou have a set of sample data, each element containing a time series of a binary variable (we'll call it V). When V is set to True, a function (A, B, or C) is applied which returns V to it's False state. You would like to apply a genetic algorithm to determine which function (or solution) will return V to False in the least amount of time.\nIf this is the case, I would stay away from GAs. GAs are typically used for some kind of function optimization \/ tuning. In general, the underlying assumption is that what you permute is under your control during the algorithm's application (i.e., you are modifying parameters used by the algorithm that are independent of the input data). In your case, my impression is that you just want to find out which of your (I assume) static functions perform best in a wide variety of cases. If you don't feel your current dataset provides a decent approximation of your true input distribution, you can always sample from it and permute the values to see what happens; however, this would not be a GA.\nHaving said all of this, I could be wrong. If anyone has used GAs in verification like this, please let me know. I'd certainly be interested in learning about it.","Q_Score":0,"Tags":"python,artificial-intelligence,genetic-algorithm","A_Id":24616160,"CreationDate":"2014-07-07T16:46:00.000","Title":"Genetic Algorithm in Optimization of Events","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm  a data analysis student and I'm starting to explore Genetic Algorithms at the moment. I'm trying to solve a problem with GA but I'm not sure about the formulation of the problem. \nBasically I have a state of a variable being 0 or 1 (0 it's in the normal range of values, 1 is in a critical state). When the state is 1 I can apply 3 solutions (let's consider Solution A, B and C) and for each solution I know the time that the solution was applied and the time where the state of the variable goes to 0. \nSo I have for the problem a set of data that have a critical event at 1, the solution applied and the time interval (in minutes) from the critical event to the application of the solution, and the time interval (in minutes) from the application of the solution until the event goes to 0. \nI want with a genetic algorithm to know which is the best solution for a critical event and the fastest one. And if it is possible to rank the solutions acquired so if in the future on solution can't be applied I can always apply the second best for example.\nI'm thinking of developing the solution in Python since I'm new to GA.\nEdit: Specifying the problem (responding to AMack)\nYes is more a less that but with some nuances. For example the function A can be more suitable to make the variable go to F but because exist other problems with the variable are applied more than one solution. So on the data that i receive for an event of V, sometimes can be applied 3 ou 4 functions but only 1 or 2 of them are specialized for the problem that i want to analyze. My objetive is to make a decision support on the solution to use when determined problem appear. But the optimal solution can be more that one because for some event function A acts very fast but in other case of the same event function A don't produce a fast response and function C is better in that case. So in the end i pretend a solution where is indicated what are the best solutions to the problem but not only the fastest because the fastest in the majority of the cases sometimes is not the fastest in the same issue but with a different background.","AnswerCount":3,"Available Count":2,"Score":0.0665680765,"is_accepted":false,"ViewCount":335,"Q_Id":24615687,"Users Score":1,"Answer":"I'm unsure of what your question is, but here are the elements you need for any GA:\nA population of initial \"genomes\"\nA ranking function\nSome form of mutation, crossing over within the genome\nand reproduction.\nIf a critical event is always the same, your GA should work very well.  That being said, if you have a different critical event but the same genome you will run into trouble.  GA's evolve functions towards the best possible solution for A Set of conditions.  If you constantly run the GA so that it may adapt to each unique situation you will find a greater degree of adaptability, but have a speed issue.\nYou have a distinct advantage using python because string manipulation (what you'll probably use for the genome) is easy, however...\npython is slow.\nIf the genome is short, the initial population is small, and there are very few generations this shouldn't be a problem. You lose possibly better solutions that way but it will be significantly faster.\nhave fun...","Q_Score":0,"Tags":"python,artificial-intelligence,genetic-algorithm","A_Id":24616007,"CreationDate":"2014-07-07T16:46:00.000","Title":"Genetic Algorithm in Optimization of Events","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there a way to allow embedded Matplotlib charts in the IPython console that is activated within PyCharm? I'm looking for similar behavior to what can be done with the QT console version of IPython, i.e. ipython qtconsole --matplotlib inline","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2490,"Q_Id":24638043,"Users Score":2,"Answer":"It doesn't look like you can do it: PyCharm does not use the 'qtconsole' of ipython, but either a plain text console (when you open the \"Python console\" tab in PyCharm) or ipython notebook (when you open a *.ipynb file). Moreover, PyCharm is done in Java, while to have an interactive plot Matplotlib needs to have a direct connection\/knowledge\/understanding of the underlying graphic toolkit used... Matplotlib doesn't support any Java based backend, so i guess Pycharm would need to \"bridge\" the native underlying toolkit...","Q_Score":8,"Tags":"python,matplotlib,pycharm","A_Id":28782077,"CreationDate":"2014-07-08T17:26:00.000","Title":"Embedded charts in PyCharm IPython console","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I tried all the nltk methods for stemming but it gives me weird results with some words. \nExamples\nIt often cut end of words when it shouldn't do it :\n\npoodle => poodl\narticle articl\n\nor doesn't stem very good : \n\neasily and easy are not stemmed in the same word\nleaves, grows, fairly are not stemmed\n\nDo you know other stemming libs in python, or a good dictionary?\nThank you","AnswerCount":7,"Available Count":1,"Score":0.1418931938,"is_accepted":false,"ViewCount":74285,"Q_Id":24647400,"Users Score":5,"Answer":"Stemming is all about removing suffixes(usually only suffixes, as far as I have tried none of the nltk stemmers could remove a prefix, forget about infixes).\nSo we can clearly call stemming as a dumb\/ not so intelligent program. It doesn't check if a word has a meaning before or after stemming.\nFor eg. If u try to stem \"xqaing\", although not a word, it will remove \"-ing\" and give u \"xqa\".\nSo, in order to use a smarter system, one can use lemmatizers.\nLemmatizers uses well-formed lemmas (words) in form of wordnet and dictionaries.\nSo it always returns and takes a proper word. However, it is slow because it goes through all words in order to find the relevant one.","Q_Score":43,"Tags":"python,nltk,stemming","A_Id":54384472,"CreationDate":"2014-07-09T07:12:00.000","Title":"What is the best stemming method in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on a project which involves using a thermal video camera to detect objects of a certain temperature.  The output I am receiving from the camera is an image where the pixels of interest (within the specified temperature range) are colored yellow-orange depending on intensity, and all other pixels are grayscale.  I have tried using cv2.inRange() to filter for the colored pixels, but results have been spotty, as the pixel colors I have been provided with in the color lookup tables do not match those actually output by the camera.  \nI figured then that it would be easiest for me to just filter out all grayscale pixels, as then I will be left with only colored pixels (pixels of interest).  I have tried looping through each pixel of each frame of the video and checking to see if each channel has the same intensity, but this takes far too long to do.  Is there a better way to filter out all grayscale pixels than this?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1335,"Q_Id":24663825,"Users Score":1,"Answer":"filter out greyscale or filter in the allowed colors\nIdk if the range of colors or range of greyscale is larger but maybe whitelisting instead of blacklisting is helpful here","Q_Score":1,"Tags":"python,opencv,image-processing","A_Id":24663901,"CreationDate":"2014-07-09T21:07:00.000","Title":"How to filter out all grayscale pixels in an image?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using an ipython notebook that is running on a remote server. I want to save data from the notebook (e.g. a pandas dataframe) locally. \nCurrently I'm saving the data as a .csv file on the remote server and then move it over to my local machine via scp. Is there a more elegant way directly from the notebook?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":671,"Q_Id":24681509,"Users Score":0,"Answer":"Maybe some combination of cPickle and bash magic for scp?","Q_Score":3,"Tags":"python,ipython-notebook","A_Id":30565768,"CreationDate":"2014-07-10T16:14:00.000","Title":"Locally save data from remote iPython notebook","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Sorry if the topic was already approached, I didn't find it.\nI am trying to read with Python a bench of large csv files (>300 MB) that are not located in a local drive. \nI am not an expert in programming but I know that if you copy it into a local drive first it should take less time that reading it (or am I wrong?).\nThe thing is that I tested both methods and the computation times are similar.\nAm I missing something? Can someone explain \/ give me a good method to read those file as fast as possible?\nFor copying to local drive I am using: shutil.copy2\nFor reading the file: for each line in MyFile\nThanks a lot for your help,\nChristophe","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":214,"Q_Id":24687248,"Users Score":0,"Answer":"Copying a file is sequentially reading it and saving in another place.\nThe performance of application might vary depending on the data access patterns, computation to I\/O time, network latency and network bandwidth.\nIf you execute your script once, and read through it sequentially it's the same as copying the file, except you perform computations on it instead of saving. Even if you process small chunks of data it probably gets buffered. In fact in case of a single execution, if you copy 1st you actually read the same data twice, once for copy, and once the copy for computation.\nIf you execute your script multiple times, then you need to check what is your data troughput.  For example, if you have a gigabit ethernet then 1GBit\/s is 125MByte\/s, if you process data slower, then you are not limited by the bandwidth.\nNetwork latency comes into play when you send multiple requests for small chunks of data. Upon a read request you send a network request and get data back in a finite time, this is the latency. If you make a request for one big chunk of data you \"pay\" the latency limit once, if you ask 1000 times for 1\/1000 of the big chunk you will need to \"pay\" it 1000 times. However, this is probably abstracted from you by the network file system and in case of a sequential read it will get buffered. It would manifest itself in randon jumping over file and reading small chunks of it.\nYou can check what you are limited by checking how much bytes you process per second and compare it to limits of the hardware. If it's close to HDD speed (which in your case i bet is not) you are bound by I\/O - HDD. If it's lower, close to network bandwidth, you are limited by I\/O - network. If it's even lower, it's either you are bound by processing speed of data or I\/O network latency. However, if you were bound by I\/O you should see difference between the two approaches, so if you are seeing the same results it's computation.","Q_Score":0,"Tags":"python,memory,local-storage,large-files","A_Id":24687460,"CreationDate":"2014-07-10T22:19:00.000","Title":"Reading Large File from non local disk in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need  the quantile of some distributions in python. In r it is possible to compute these values using the qf, qnorm and qchi2 functions.\nIs there any python equivalent of these R functions?\nI have been looking on scipy but I did non find anything.","AnswerCount":2,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":32670,"Q_Id":24695174,"Users Score":48,"Answer":"The equivalent of the R pnorm() function is: scipy.stats.norm.cdf() with python\nThe equivalent of the R qnorm() function is: scipy.stats.norm.ppf() with python","Q_Score":31,"Tags":"python,r,scipy","A_Id":42065440,"CreationDate":"2014-07-11T10:03:00.000","Title":"python equivalent of qnorm, qf and qchi2 of R","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there any automated way to evaluate convergence of the SGDClassifier?\nI'm trying to run an elastic net logit in python and am using scikit learn's SGDClassifier with log loss and elastic net penalty. When I fit the model in python, I get all zeros for my coefficients. When I run glmnet in R, I get significant non-zero coefficients.\nAfter some twiddling I found that the scikit learn coefficients approach the R coefficients after around 1000 iterations. \nIs there any method that I'm missing in scikit learn to iterate until the change in coefficients is relatively small (or a max amount of iterations has been performed), or do I need to do this myself via cross-validation.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":822,"Q_Id":24707836,"Users Score":3,"Answer":"This a known limitation of the current implementation of scikit-learn's SGD classifier, there is currently no automated convergence check on that model. You can set verbose=1 to get some feedback when running though.","Q_Score":1,"Tags":"python,scikit-learn","A_Id":24708214,"CreationDate":"2014-07-11T22:58:00.000","Title":"Evaluating convergence of SGD classifier in scikit learn","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Say I have a categorical feature, color, which takes the values\n['red', 'blue', 'green', 'orange'],\nand I want to use it to predict something in a random forest.  If I one-hot encode it (i.e. I change it to four dummy variables), how do I tell sklearn that the four dummy variables are really one variable?  Specifically, when sklearn is randomly selecting features to use at different nodes, it should either include the red, blue, green and orange dummies together, or it shouldn't include any of them.\nI've heard that there's no way to do this, but I'd imagine there must be a way to deal with categorical variables without arbitrarily coding them as numbers or something like that.","AnswerCount":5,"Available Count":2,"Score":0.0399786803,"is_accepted":false,"ViewCount":72780,"Q_Id":24715230,"Users Score":1,"Answer":"Maybe you can use 1~4 to replace these four color, that is, it is the number rather than the color name in that column. And then the column with number can be used in the models","Q_Score":71,"Tags":"python,scikit-learn,random-forest,one-hot-encoding","A_Id":66810359,"CreationDate":"2014-07-12T16:54:00.000","Title":"Can sklearn random forest directly handle categorical features?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Say I have a categorical feature, color, which takes the values\n['red', 'blue', 'green', 'orange'],\nand I want to use it to predict something in a random forest.  If I one-hot encode it (i.e. I change it to four dummy variables), how do I tell sklearn that the four dummy variables are really one variable?  Specifically, when sklearn is randomly selecting features to use at different nodes, it should either include the red, blue, green and orange dummies together, or it shouldn't include any of them.\nI've heard that there's no way to do this, but I'd imagine there must be a way to deal with categorical variables without arbitrarily coding them as numbers or something like that.","AnswerCount":5,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":72780,"Q_Id":24715230,"Users Score":16,"Answer":"You have to make the categorical variable into a series of dummy variables. Yes I know its annoying and seems unnecessary but that is how sklearn works. \nif you are using pandas. use pd.get_dummies, it works really well.","Q_Score":71,"Tags":"python,scikit-learn,random-forest,one-hot-encoding","A_Id":35471754,"CreationDate":"2014-07-12T16:54:00.000","Title":"Can sklearn random forest directly handle categorical features?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"how do you drop rows from an RDD in PySpark? Particularly the first row, since that tends to contain column names in my datasets. From perusing the API, I can't seem to find an easy way to do this. Of course I could do this via Bash \/ HDFS, but I just want to know if this can be done from within PySpark.","AnswerCount":6,"Available Count":1,"Score":0.0333209931,"is_accepted":false,"ViewCount":49327,"Q_Id":24718697,"Users Score":1,"Answer":"Personally I think just using a filter to get rid of this stuff is the easiest way. But per your comment I have another approach. Glom the RDD so each partition is an array (I'm assuming you have 1 file per partition, and each file has the offending row on top) and then just skip the first element (this is with the scala api).\ndata.glom().map(x => for (elem <- x.drop(1){\/*do stuff*\/}) \/\/x is an array so just skip the 0th index\nKeep in mind one of the big features of RDD's is that they are immutable, so naturally removing a row is a tricky thing to do\nUPDATE:\nBetter solution.\nrdd.mapPartions(x => for (elem <- x.drop(1){\/*do stuff*\/} )\nSame as the glom but doesn't have the overhead of putting everything into an array, since x is an iterator in this case","Q_Score":28,"Tags":"python,apache-spark,pyspark","A_Id":24736966,"CreationDate":"2014-07-13T01:08:00.000","Title":"PySpark Drop Rows","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I recently started using Python and I've been working on an Open CV based project for over a month now.\nI am using Simple Thresholding to detect a coloured blob and I have thresholded the HSV values to detect the blob. All works well, but when the blob goes out of the FOV of the camera, the program gets stuck. I was wondering if there could be a while\/if condition that I can add at the top of the loop in order to skip the whole loop in case the blob goes outside FOV of the camera and then enter the loop when the blob returns.\nWould really appreciate your help on this one! Cheers.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":111,"Q_Id":24732112,"Users Score":0,"Answer":"You can use a while loop and check if the blob region is not null and then find contours!\nit would be helpful if you posted your code. We can explain the answer in a better way then.","Q_Score":0,"Tags":"python,opencv,camera,detection,hsv","A_Id":24732678,"CreationDate":"2014-07-14T08:09:00.000","Title":"Program gets stuck at finding the Contour while using Open CV","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Before I updated, I would run my script and output the html file. There would be my one plot in the window. I would make changes to my script, run it, output the html file, look at the new plot. Then I installed the library again to update it using conda. I made some changes to my script, ran it again, and the output file included both the plot before I made some changes AND a plot including the changes. I ran the script again out of curiosity. Three plots in the one file! Ran it again. Four! Deleted the html file (instead of overwriting). Five! Changed the name of the output html file. Six! I even tried changing the name of the script. The plots just keep piling up.\nWhat's going on? Why is it plotting every version of the graph I've ever made?","AnswerCount":2,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":2333,"Q_Id":24739390,"Users Score":10,"Answer":"as of 0.5.1 there is now bokeh.plotting.reset_output that will clear all output_modes and state. This is especially useful in situations where a new interpreter is not started in between executions (e.g., Spyder and the notebook)","Q_Score":9,"Tags":"python,plot,bokeh","A_Id":24967653,"CreationDate":"2014-07-14T14:52:00.000","Title":"Updated Bokeh to 0.5.0, now plots all previous versions of graph in one window","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm building a NaiveBayes classifier using scikit-learn, and so far things are going well if I have a set body of data to train. However, for the particular project I'm working on, there will be new data coming in every day that ideally would be part of the training set.\nI'm aware that you can pickle the classifier to store it for later use, but is there any way to \"update\" the classifier with new data? \nRe-training the classifier from scratch every day is obviously an option, but that would require drawing a lot of historical data each time, for a growing time period.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":346,"Q_Id":24744409,"Users Score":2,"Answer":"Use the partial_fit method on the naive Bayes estimator.","Q_Score":0,"Tags":"python,scikit-learn","A_Id":24757540,"CreationDate":"2014-07-14T19:33:00.000","Title":"Updating a NaiveBayes Classifier (in scikit-learn) over time","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to convert a pylearn2 GPU model to a CPU compatible version for prediction on a remote server -- how can I convert CudaNdarraySharedVariable's to TensorVariable's to avoid an error calling cuda code on a GPU-less machine?  The experimental theano flag unpickle_gpu_to_cpu seems to have left a few CudaNdarraySharedVariable's hanging around (specifically model.layers[n].transformer._W).","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":505,"Q_Id":24744701,"Users Score":1,"Answer":"For a plain CudaNdarray variable, something like this should work:\n'''x = CudaNdarray...    x_new=theano.tensor.TensorVariable(CudaNdarrayType([False] * tensor_dim))\nf = theano.function([x_new], x_new)\nconverted_x = f(x)\n'''","Q_Score":1,"Tags":"python,machine-learning,neural-network,gpu,theano","A_Id":24765554,"CreationDate":"2014-07-14T19:49:00.000","Title":"Convert CudaNdarraySharedVariable to TensorVariable","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a large number of images of different categories, e.g. \"Cat\", \"Dog\", \"Bird\". The images have some hierarchical structure, like a dict. So for example the key is the animal name and the value is a list of animal images, e.g. animalPictures[animal][index].\nI want to manipulate each image (e.g. compute histogram) and then save the manipulated data in an identical corresponding structure, e.g. animalPictures['bird'][0] has its histogram stored in animalHistograms['bird'][0].\nThe only issue is I do not have enough memory to load all the images, perform all the manipulations, and create an additional structure of the transformed images.\nIs it possible to load an image from disk, manipulate the image, and stream the data to a dict on disk? This way I could work on a per-image basis and not worry about loading everything into memory at once.\nThanks!","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":258,"Q_Id":24761787,"Users Score":0,"Answer":"It is possible, if you us NumPy and especially numpy.memmap to store the image data. That way the image data looks as if it were in memory but is on the disk using the mmap mechanism. The nice thing is that the numpy.memmap arrays are not more difficult to handle than ordinary arrays.\nThere is some performance overhead as all memmap arrays are saved to disk. The arrays could be described as \"disk-backed arrays\", i.e. the data is also kept in RAM as long as possible. This means that if you access some data array very often, it is most likely in memory, and there is no disk read overhead.\nSo, keep your metadata in a dict in memory, but memmap your bigger arrays.\nThis is probably the easiest way. However, make sure you have a 64-bit Python in use, as the 32-bit one runs out of addresses ad 2 GiB.\nOf course, there are a lot of ways to compress the image data. If your data may be compressed, then you might consider using compression to save memory.","Q_Score":0,"Tags":"python,image","A_Id":24762074,"CreationDate":"2014-07-15T15:09:00.000","Title":"Manipulating Large Amounts of Image Data in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a folder with a large number of Excel workbooks.  Is there a way to convert every file in this folder into a CSV file using Python's xlrd, xlutiles, and xlsxWriter?\nI would like the newly converted CSV files to have the extension '_convert.csv'.\nOTHERWISE...\nIs there a way to merge all the Excel workbooks in the folder to create one large file?\nI've been searching for ways to do both, but nothing has worked...","AnswerCount":5,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2934,"Q_Id":24785824,"Users Score":0,"Answer":"Look at openoffice's python library. Although, I suspect openoffice would support MS document files. \nPython has no native support for Excel file.","Q_Score":1,"Tags":"python,csv,xlrd,xlsxwriter","A_Id":24785891,"CreationDate":"2014-07-16T16:20:00.000","Title":"Converting a folder of Excel files into CSV files\/Merge Excel Workbooks","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm working on a problem that involves creating a graph which shows the areas of intersection of three or more circles (each circle is the same size). I have many sets of circles, each set containing at least three circles. I need to graph the area common to the interior of each and every circle in the set, if it even exists. If there is no area where all the circles within the set intersect, I have nothing to graph. So the final product is a graph with little \"slivers\" of intersecting circles all over.\nI already have a solution for this written in Python with matplotlib, but it doesn't perform very well. This wasn't an issue before, but now I need to apply it to a larger data set so I need a better solution. My current approach is basically a test-and-check brute force method: I check individual points within an area to see if they are in that common intersection (by checking distance from the point to the center of each circle). If the point meets that criteria, I graph it and move on. Otherwise, I just don't graph it and move on. So it works, but it takes forever.\nJust to clarify, I don't scan through every point in the entire plane for each set of circles. First, I narrow my \"search\" area to a rectangle tightly bounded around the first two (arbitrarily chosen) circles in the set, and then test-and-check each point in there.\nI was thinking it would be nice if there were a way for me to graph each circle in a set (say there 5 circles in the set), each with an alpha value of 0.1. Then, I could go back through and only keep the areas with an alpha value of 0.5, because that's the area where all 5 circles intersect, which is all I want. I can't figure out how to implement this using matplotlib, or using anything else, for that matter, without resorting to the same brute force test-and-check strategy.\nI'm also familiar with Java and C++, if anyone has a good idea involving those languages. Thank you!","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1241,"Q_Id":24793636,"Users Score":1,"Answer":"Maybe you should try something more analytical? It should not be very difficult:\n\nFind the circle pairs whose distance is less than the sum of their radii; they intersect.\nCalculate the intersection angles by simple trigonometry.\nDraw a polygon (path) by using a suitably small delta angle in both cases (half of the polygon comes from one circle, the other half from the other circle.\nCollect the paths to a PathCollection\n\nNone of the steps should be very long or difficult.","Q_Score":0,"Tags":"python,graph,matplotlib,geometry,intersection","A_Id":24797554,"CreationDate":"2014-07-17T01:51:00.000","Title":"How to find and graph the intersection of 3+ circles with Matplotlib","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a model(Neural Network) in python which I want to convert into a PMML file . I have tried the following:\n1.)py2pmml  -> Not able to find the source code for this\n2.)in R     -> PMML in R works fine but my model is in Python.(Cant run the data in R to generate the same model in R) . Does not work for my dataset.\n3.)  Now I am trying to use augustus to make the PMML file. But augustus has examples of using a already built PMML file but not how to make one\nI am not able to find proper examples on how to use augustus in Python to customize the model. \nAny suggestion will be good.\nThanks in advance.\nGGR","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":382,"Q_Id":24875008,"Users Score":0,"Answer":"Finally I found my own solution. I wrote my own PMML Parser and scorer . PMML is very much same as XML so its easy to build and retrieve fields accordingly. If anyone needs more information please comment below. \nThanks ,\nRaghu.","Q_Score":1,"Tags":"python,neural-network,pmml","A_Id":26639275,"CreationDate":"2014-07-21T21:21:00.000","Title":"Produce a PMML file for the Nnet model in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a neural network with one input, three hidden neurons and one output. I have 720 input and corresponding target values, 540 for training, 180 for testing.\nWhen I train my network using Logistic Sigmoid or Tan Sigmoid function, I get the same outputs while testing, i.e. I get same number for all 180 output values. When I use Linear activation function, I get NaN, because apparently, the value gets too high.\nIs there any activation function to use in such a case? Or any improvements to be done? I can update the question with details and code if required.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":815,"Q_Id":24894231,"Users Score":1,"Answer":"Neural nets are not stable when fed input data on arbitrary scales (such as between approximately 0 and 1000 in your case). If your output units are tanh they can't even predict values outside the range -1 to 1 or 0 to 1 for logistic units!\nYou should try recentering\/scaling the data (making it have mean zero and unit variance - this is called standard scaling in the datascience community). Since it is a lossless transformation you can revert back to your original scale once you've trained the net and predicted on the data. \nAdditionally, a linear output unit is probably the best as it makes no assumptions about the output space and I've found tanh units to do much better on recurrent neural networks in low dimensional input\/hidden\/output nets.","Q_Score":0,"Tags":"python,machine-learning,neural-network,pybrain","A_Id":24902112,"CreationDate":"2014-07-22T17:44:00.000","Title":"What activation function to use or modifications to make when neural network gives same output on regression with PyBrain?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When I do data analysis on IPython Notebook, I often feel the need to move up or down several adjacent input cells, for better flow of the analysis story. \nI'd expected that once I'd create a heading, all cells under that heading would move together if I move the heading. But this is not the case.\nAny way I can do this?\nEdit: To clarify, I can of course move cells individually, and the keyboard shortcuts are handy; but what I'm looking for is a way to group cells so that I can move (or even delete) them all together.","AnswerCount":4,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":6408,"Q_Id":24895714,"Users Score":2,"Answer":"Latest version of Ipython\/Jupyter notebook allows selection of multiple cells using shift key which can be useful for batch operations such as copy, paste, delete, etc.","Q_Score":14,"Tags":"python,ipython,ipython-notebook","A_Id":36859613,"CreationDate":"2014-07-22T19:07:00.000","Title":"Is it possible to create grouping of input cells in IPython Notebook?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to implement my own Imputer. Under certain conditions, I would like to filter some of the train samples (that I deem low quality). \nHowever, since the transform method returns only X and not y, and y itself is a numpy array (which I can't filter in place to the best of my knowledge), and moreover - when I use GridSearchCV- the y my transform method receives is None, I can't seem to find a way to do it. \nJust to clarify: I'm perfectly clear on how to filter arrays. I can't find a way to fit sample filtering on the y vector into the current API.\nI really want to do that from a BaseEstimator implementation so that I could use it with GridSearchCV (it has a few parameters). Am I missing a different way to achieve sample filtration (not through BaseEstimator, but GridSearchCV compliant)? is there some way around the current API?","AnswerCount":2,"Available Count":1,"Score":0.4621171573,"is_accepted":false,"ViewCount":2829,"Q_Id":24896178,"Users Score":5,"Answer":"The scikit-learn transformer API is made for changing the features of the data (in nature and possibly in number\/dimension), but not for changing the number of samples. Any transformer that drops or adds samples is, as of the existing versions of scikit-learn, not compliant with the API (possibly a future addition if deemed important).\nSo in view of this it looks like you will have to work your way around standard scikit-learn API.","Q_Score":12,"Tags":"python,scikit-learn","A_Id":24897058,"CreationDate":"2014-07-22T19:31:00.000","Title":"sklearn: Have an estimator that filters samples","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In my neural network, I have inputs varying from 0 to 719, and targets varying from 0 to 1340. So, I standardize the inputs and targets by standard scaling such that the mean is 0 and variance is 1. Now, I calculate the outputs using back-propagation. All my outputs lie between -2 and 2. How do I convert these outputs to the original scale, i.e. lying in the range (0,1340)?\nEDIT: I have 1 input, 5 hidden neurons and 1 output. I have used logistic sigmoid activation function. I did the standard scaling by taking mean and then dividing by standard deviation. In particular, my output lies between -1.28 and 1.64.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1506,"Q_Id":24912521,"Users Score":0,"Answer":"Assuming the mean and standard deviation of the targets are mu and sigma, the normalized value of a target y should be (y-mu)\/sigma. In that case if you get an output y', you can move it back to original scale by converting y' -> mu + y' * sigma.","Q_Score":0,"Tags":"python,machine-learning,neural-network","A_Id":24912790,"CreationDate":"2014-07-23T13:59:00.000","Title":"How to convert standardized outputs of a neural network back to original scale?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to run an external library of python called NLopt within Abaqus through python. The issue is that the NLopt I found is compiled against the latest release of Numpy, i.e. 1.9, whereas Abaqus 6.13-2 is compiled against Numpy 1.4. I tried to replace the Numpy folder under the site-packages under the Abaqus installation folder with the respective one of version 1.9 that I created externally through an installation of Numpy 1.9 over Python 2.6 (version that Abaqus uses).\nAbaqus couldn't even start so I guess that such approach is incorrect.\nAre there any suggestions on how to overcome such issue?\nThanks guys","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":318,"Q_Id":24935230,"Users Score":0,"Answer":"I have similar problems. As an (annoying) work around I usually write out important data in text files using the regular python. Afterwards, using a bash script, I start a second python (different version) to further analyse the data (matplotlib etc).","Q_Score":2,"Tags":"python,numpy,nlopt,abaqus","A_Id":25251918,"CreationDate":"2014-07-24T13:33:00.000","Title":"How to overcome version incompatibility with Abaqus and Numpy (Python's library)?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a large dataset which I plan to do logistic regression on. It has lots of categorical variables, each having thousands of features which I am planning to use one hot encoding on. I will need to deal with the data in small batches. My question is how to make sure that one hot encoding sees all the features of each categorical variable during the first run?","AnswerCount":2,"Available Count":1,"Score":0.2913126125,"is_accepted":false,"ViewCount":3004,"Q_Id":24966984,"Users Score":3,"Answer":"There is no way around finding out which possible values your categorical features can take, which probably implies that you have to go through your data fully once in order to obtain a list of unique values of your categorical variables.\nAfter that it is a matter of transforming your categorical variables to integer values and setting the n_values= kwarg in OneHotEncoder to an array corresponding to the number of different values each variable can take.","Q_Score":2,"Tags":"python,scikit-learn","A_Id":25012484,"CreationDate":"2014-07-26T02:41:00.000","Title":"One-hot encoding of large dataset with scikit-learn","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm a newbee of python. And recently I heard some people say that numpy is a good module for dealing with huge data.\nI'm curious what can numpy do for us in the daily work.\nAs I know, most of us were not scientists and researchers, at what circumstances numpy can bring us benefit? \nCan you share a good practice with me?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":669,"Q_Id":24971400,"Users Score":1,"Answer":"Long answer short, when you need do huge mathematical operations, like vector multiplications and so on which requires writing lots of loops and what not, yet your codes gets unreadable yet not efficient you should use Numpy.\nFew key benefits:\n\nNumPy arrays have a fixed size at creation, unlike Python lists (which can grow dynamically). Changing the size of an ndarray will create a new array and delete the original. So it is more memory efficient than the other.\nThe elements in a NumPy array are all required to be of the same data type, and thus will be the same size in memory. The exception: one can have arrays of (Python, including NumPy) objects, thereby allowing for arrays of different sized elements.\nNumPy arrays facilitate advanced mathematical and other types of operations on large numbers of data. Typically, such operations are executed more efficiently and with less code than is possible using Python\u2019s built-in sequences.\nA growing plethora of scientific and mathematical Python-based packages are using NumPy arrays; though these typically support Python-sequence input, they convert such input to NumPy arrays prior to processing, and they often output NumPy arrays. In other words, in order to efficiently use much (perhaps even most) of today\u2019s scientific\/mathematical Python-based software, just knowing how to use Python\u2019s built-in sequence types is insufficient - one also needs to know how to use NumPy arrays.\n-Vector operations comes handy in Numpy. You don't need to go through writing loops but yet pythonic.\n-Object oriented approach","Q_Score":1,"Tags":"python,numpy","A_Id":24971512,"CreationDate":"2014-07-26T13:17:00.000","Title":"When should I use numpy?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Given a list of X,Y coordinate points on a 2D grid, what is the most efficient algorithm to create a list of groups of adjacent coordinate points?\nFor example, given a list of points making up two non-adjacent squares (3x3) on a grid (15x15), the result of this algorithm would be two groups of points corresponding to the two squares. \nI suppose you could do a flood fill algorithm, but this seems overkill and not very efficient for a large 2D array of say 1024 size.","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":7135,"Q_Id":24985127,"Users Score":3,"Answer":"You can hash all coordinate points (e.g. using dictionary structure in python) and then for each coordinate point, hash the adjacent neighbors of the point to find pairs of points that are adjacent and \"merge\" them. Also, for each point you can maintain a pointer to the connected component that that point belongs to (using the dictionary structure), and for each connected component you maintain a list of points that belong to the component. \nThen, when you hash a neighbor of a point and find a match, you merge the two connected component sets that the points belong to and update the group pointers for all new points in the union set. You can show that you only need to hash all the neighbors of all points just once and this will find all connected components, and furthermore, if you update the pointers for the smaller of the two connected components sets when two connected component sets are merged, then the run-time will be linear in the number of points.","Q_Score":8,"Tags":"python,algorithm,grid","A_Id":24985330,"CreationDate":"2014-07-27T20:05:00.000","Title":"Efficiently grouping a list of coordinates points by location in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is anyone aware of an implemented version (perhaps using scipy\/numpy) of parallel exact matrix diagonalization (equivalently, finding the eigensystem)? If it helps, my matrices are symmetric and sparse. I would hate to spend a day reinventing the wheel.\nEDIT:\nMy matrices are at least 10,000x10,000 (but, preferably, at least 20 times larger). For now, I only have access to a 4-core Intel machine (with hyperthreading, so 2 processes per core), ~3.0Ghz each with 12GB of RAM. I may later have access to a 128-core node ~3.6Ghz\/core with 256GB of RAM, so single machine\/multiple cores should do it (for my other parallel tasks, I have been using multiprocessing). I would prefer for the algorithms to scale well. \nI do need exact diagonalization, so scipy.sparse routines are not be good for me (tried, didn't work well). I have been using numpy.linalg.eigh (I see only single core doing all the computations).\nAlternatively (to the original question): is there an online resource where I can find out more about compiling SciPy so as to insure parallel execution?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":3153,"Q_Id":25004564,"Users Score":1,"Answer":"For symmetric sparse matrix eigenvalue\/eigenvector finding, you may use scipy.sparse.linalg.eigsh. It uses ARPACK behind the scenes, and there are parallel ARPACK implementations. AFAIK, SciPy can be compiled with one if your scipy installation uses the serial version.\nHowever, this is not a good answer, if you need all eigenvalues and eigenvectors for the matrix, as the sparse version uses the Lanczos algorithm.\nIf your matrix is not overwhelmingly large, then just use numpy.linalg.eigh. It uses LAPACK or BLAS and may use parallel code internally.\nIf you end up rolling your own, please note that SciPy\/NumPy does all the heavy lifting with different highly optimized linear algebra packages, not in pure Python. Due to this the performance and degree of parallelism depends heavily on the libraries your SciPy\/NumPy installation is compiled with.\n(Your question does not reveal if you just want to have parallel code running on several processors, or on several computers. Also, the size of your matrix has a big impact on the best method. So, this answer may be completely off-the-mark.)","Q_Score":1,"Tags":"python,numpy,scipy,linear-algebra,numerical-methods","A_Id":25005318,"CreationDate":"2014-07-28T21:21:00.000","Title":"Parallel exact matrix diagonalization with Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"My question is i want to run feature selection on the data with several categorical variables. I have used get_dummies in pandas to generate all the sparse matrix for these categorical variables. My question is how sklearn knows that one specific sparse matrix actually belongs to one feature and select\/drop them all? For example, I have a variable called city. There are New York, Chicago and Boston three levels for that variable, so the sparse matrix looks like:\n[1,0,0]\n [0,1,0]\n [0,0,1]\n\n How can I inform the sklearn that in these three \"columns\" actually belong to one feature, which is city and won't end up with choosing New York, and delete Chicago and Boston?\nThank you so much!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":3078,"Q_Id":25020482,"Users Score":6,"Answer":"You can't. The feature selection routines in scikit-learn will consider the dummy variables independently of each other. This means they can \"trim\" the domains of categorical variables down to the values that matter for prediction.","Q_Score":6,"Tags":"python,scikit-learn,feature-selection","A_Id":25036779,"CreationDate":"2014-07-29T16:35:00.000","Title":"How can sklearn select categorical features based on feature selection","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using python to write a report which is put into an excel spreadshet.\nThere are four columns, namely:\nProduct Name | Previous Value | Current Value | Difference \nWhen I am done putting in all the values I then want to sort them based on Current Value. Is there a way I can do this in xlwt? I've only seen examples of sorting a single column.","AnswerCount":1,"Available Count":1,"Score":-0.1973753202,"is_accepted":false,"ViewCount":1410,"Q_Id":25024437,"Users Score":-1,"Answer":"You will get data from queries, right? Then you will write them to an excel by xlwt. Just before writing, you can sort them. If you can show us your code, then maybe I can optimize them. Otherwise, you have to follow wnnmaw's advice, do it in a more complicate way.","Q_Score":1,"Tags":"python,excel,xlwt","A_Id":25032965,"CreationDate":"2014-07-29T20:32:00.000","Title":"Sorting multiple columns in excel via xlwt for python","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have to perform linear regressions on a rolling window on Y and a time lagged version of X, ie finding Y(t) = aX(t-1) + b. The window size is fixed at 30 samples. I want to return a numpy array of all the beta coefficients. Is there a quick way of doing this? I read about the Savitsky-Golay filter, but it regresses only X with the time lagged version of itself. Thanks!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":149,"Q_Id":25057063,"Users Score":0,"Answer":"There are some rather quick ways.\nI assume you are only interested in the slope and average of the signal Y. In order to calculate these, you need to have:\n\nsum(Y)\nsum(X)\nsum(X.X)\nsum(X.Y)\n\nAll sums are over the samples in the window. When you have these, the average is:\nsum(Y) \/ n\nand the slope:\n(sum(X.Y) - sum(X) sum(Y) \/ n) \/ (sum(X.X) - sum(X)^2 \/ n)\nTo make a quick algorithm it is worth noting that all of these except for sum(X.Y) can be calculated in a trivial way from either X or Y. The rolling sums are very fast to calculate as they are cumulative sums of differences of two samples (\"incoming to window\" minus \"outgoing from the window\").\nOnly sum(X.Y) needs to be calculated separately for each time step.\nAll these operations can be vectorized, even though the time lag is probably easier to write as a loop without any notable performance hit. This way you will be able to calculate tens of millions of regressions per second. Is that fast enough?","Q_Score":0,"Tags":"python,signals,filtering,time-series","A_Id":25069634,"CreationDate":"2014-07-31T10:52:00.000","Title":"Performing a rolling vector auto regression with two variables X and Y, with time lag on X in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm running a simulation on a 2D space with periodic boundary conditions.  A continuous function is represented by its values on a grid.  I need to be able to evaluate the function and its gradient at any point in the space.  Fundamentally, this isn't a hard problem -- or to be precise, it's an almost already solved problem.  The function can be interpolated using a cubic spline with scipy.interpolate.RectBivariateSpline.  The reason it's almost solved is that RectBivariateSpline cannot handle periodic boundary conditions, nor can anything else in scipy.interpolate, as far as I can figure out from the documentation.  \nIs there a python package that can do this?  If not, can I adapt scipy.interpolate to handle periodic boundary conditions?  For instance, would it be enough to put a border of, say, four grid elements around the entire space and explicitly represent the periodic condition on it?\n[ADDENDUM] A little more detail, in case it matters: I am simulating the motion of animals in a chemical gradient.  The continuous function I mentioned above is the concentration of a chemical that they are attracted to.  It changes with time and space according to a straightforward reaction\/diffusion equation.  Each animal has an x,y position (which cannot be assumed to be at a grid point). They move up the gradient of attractant.  I'm using periodic boundary conditions as a simple way of imitating an unbounded space.","AnswerCount":4,"Available Count":1,"Score":0.049958375,"is_accepted":false,"ViewCount":3228,"Q_Id":25087111,"Users Score":1,"Answer":"Another function that could work is scipy.ndimage.interpolation.map_coordinates.\nIt does spline interpolation with periodic boundary conditions.\nIt does not not directly provide derivatives, but you could calculate them numerically.","Q_Score":7,"Tags":"python,interpolation","A_Id":32809719,"CreationDate":"2014-08-01T19:11:00.000","Title":"2D Interpolation with periodic boundary conditions","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to write a function to return several data frames (different dims) and put them into a larger \"container\" and then select each from the \"container\" using indexing. I think I want to find some data structure like list in R, which can have different kinds of objects.\nWhat can I use to do this?","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":104,"Q_Id":25096357,"Users Score":1,"Answer":"I haven't done much with Panels, but what exactly is the functionality that you need? Is there a reason a simple python list wouldn't work? Or, if you want to refer by name and not just by list position, a dictionary?","Q_Score":0,"Tags":"python,pandas","A_Id":25110368,"CreationDate":"2014-08-02T15:00:00.000","Title":"What is the data structure in python that can contain multiple pandas data frames?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using Spyder from the Anaconda scientific package set (3.x) and consistently work with very large arrays. I want to be able to see these arrays in my console window so I use these two commands: \nset_printoptions(linewidth=1000)\nto set the maximum characters displayed on a single line to 1000 and:\nset_printoptions(threshold='nan') \nto prevent truncation of large arrays. Putting these two lines of code into the startup option as such \nset_printoptions(linewidth=1000),set_printoptions(threshold='nan') \ncauses Spyder to hang and crash upon a new session of ipython in the console. Is there a way to run these lines of code without having me type them all the time. Also, the console window only allows me to scroll up to a certain point then stops. This can be a problem when I want to view large arrays. Is there any way to increase the scroll buffer? (Note, I'm very new to Python having just switched over from MATLAB).","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":604,"Q_Id":25101081,"Users Score":1,"Answer":"You may find Spyder's array editor better suited for large arrays than the qt console.","Q_Score":1,"Tags":"python,numpy,scipy,ipython,spyder","A_Id":25123660,"CreationDate":"2014-08-03T02:31:00.000","Title":"Spyder, Python IDE startup code crashing GUI","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm currently in the process of trying to redesign the general workflow of my lab, and am coming up against a conceptual roadblock that is largely due to my general lack of knowledge in this subject. \nOur data currently is organized in a typical file system structure along the lines of: \nDate\\Cell #\\Sweep #\nwhere for a specific date there are generally multiple Cell folders, and within those Cell folders there are multiple Sweep files (these are relatively simple .csv files where the recording parameters are saved separated in .xml files). So within any Date folder there may be a few tens to several hundred files for recordings that day organized within multiple Cell subdirectory folders. \nOur workflow typically involves opening multiple sweep files within a Cell folder, averaging them, and then averaging those with data points from other Cell folders, often across multiple days. \nThis is relatively straightforward to do with the Pandas and Numpy, although there is a certain \u201cmanual\u201d feel to it when remotely accessing folders saved to the lab server. We also, on occasion, run into issues because we often have to pull in data from many of these files at once. While this isn\u2019t usually an issue, the files can range between a few MBs to 1000s of MBs in size. In the latter case we have to take steps to not load the entire file into memory (or not load multiple files at once at the very least) to avoid memory issues. \nAs part of this redesign I have been reading about Pytables for data organization and for accessing data sets that may be too large to store within memory. So I guess my 2 main questions are \n\nIf the out-of-memory issues aren\u2019t significant (i.e. that utility wouldn\u2019t be utilized often), are there any significant advantages to using something like Pytables for data organization over simply maintaining a file system on a server (or locally)? \nIs there any reason NOT to go the Pytables database route? We are redesigning our data collection as well as our storage, and one option is to collect the data directly into Pandas dataframes and save the files in the HDF5 file type. I\u2019m currently weighing the cost\/benefit of doing this over the current system of the data being stored into csv files and then loaded into Pandas for analysis later on. \n\nMy thinking is that by creating a database vs. the filesystem we current have we may 1. be able to reduce (somewhat anyway) file size on disk through the compression that hdf5 offers and 2. accessing data may overall become easier because of the ability to query based on different parameters. But my concern for 2 is that ultimately, since we\u2019re usually just opening an entire file, that we won\u2019t be utilizing that to functionality all that much \u2013 that we\u2019d basically be performing the same steps that we would need to perform to open a file (or a series of files) within a file system. Which makes me wonder whether the upfront effort that this would require is worth it in terms of our overall workflow.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":332,"Q_Id":25110089,"Users Score":0,"Answer":"First of all, I am a big fan of Pytables, because it helped me manage huge data files (20GB or more per file), which I think is where Pytables plays out its strong points (fast access, built-in querying etc.). If the system is also used for archiving, the compression capabilities of HDF5 will reduce space requirements and reduce network load for transfer. I do not think that 'reproducing' your file system inside an HDF5 file has advantages (happy to be told I'm wrong on this). I would suggest a hybrid approach: keep the normal filesystem structure and put the experimental data in hdf5 containers with all the meta-data. This way you keep the flexibility of your normal filesystem (access rights, copying, etc.) and can still harness the power of pytables if you have bigger files where memory is an issue. Pulling the data from HDF5 into normal pandas or numpy is very cheap, so your 'normal' work flow shouldn't suffer.","Q_Score":0,"Tags":"python,csv,organization,pytables","A_Id":25122607,"CreationDate":"2014-08-03T23:40:00.000","Title":"Benefits of Pytables \/ databases over file system for data organization?","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have previously saved a dictionary which maps image_name -> list of feature vectors, with the file being ~32 Gb. I have been using cPickle to load the dictionary in, but since I only have 8 GB of RAM, this process takes forever. Someone suggested using a database to store all the info, and reading from that, but would that be a faster\/better solution than reading a file from disk? Why?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":811,"Q_Id":25122947,"Users Score":-1,"Answer":"Use a database because it allows you to query faster. I've done this before. I would suggest against using cPickle. What specific implementation are you using?","Q_Score":1,"Tags":"python,database,computer-vision,pickle","A_Id":25288486,"CreationDate":"2014-08-04T16:07:00.000","Title":"Using Pickle vs database for loading large amount of data?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I used micro CT (it generates a kind of 3D image object) to evaluate my samples which were shaped like a cone. However the main surface which should be flat can not always be placed parallel to the surface of image stacks. To perform the transform, first of all, I have to find a way to identify the flat surface. Therefore I learnt python to read the image data into numpy array. \nThen I realized that I totally have no clue how to achieve the idea in a mathematical way. \nIf you have any idea or any suggestion or even packages would be so appreciated.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":435,"Q_Id":25138508,"Users Score":0,"Answer":"Firstly, all lines in 3d correspond to an equation; secondly, all lines in 3d that lie on a particular plane for part of their length correspond to equations that belong to a set of linear equations that share certain features, which you would need to determine. The first thing you should do is identify the four corners of the supposed plane - they will have x, y or z values more extreme than the other points. Then check that the lines between the corners have equations in the set - three points in 3d always define a plane, four points may not. Then you should 'plot' the points of two parallel sides using the appropriate linear equations. All the other points in the supposed plane will be 'on' lines (whose equations are also in the set) that are perpendicular between the two parallel sides. The two end points of a perpendicular line on the sides will define each equation. The crucial thing to remember when determining whether a point is 'on' a line is that it may not be, even if the supposed plane was inputted as a plane. This is because x, y and z values as generated by an equation will be rounded so as correspond to 'real' points as defined by the resolution that the graphics program allows. Therefore you must allow for a (very small) discrepancy between where a point 'should be' and where it actually is - this may be just one pixel (or whatever unit of resolution is being used). To look at it another way - a point may be on a perpendicular between two sides but not on a perpendicular between the other two solely because of a rounding error with one of the two equations. If you want to test for a 'bumpy' plane, for whatever reason, just increase the discrepancy allowed. If you post a carefully worded question about the set of equations for the lines in a plane on math.stackexchange.com someone may know more about it.","Q_Score":0,"Tags":"python,image,3d,transform","A_Id":25142706,"CreationDate":"2014-08-05T12:11:00.000","Title":"How to check a random 3d object surface if is flat in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a corpus which has around 8 million news articles, I need to get the TFIDF representation of them as a sparse matrix. I have been able to do that using scikit-learn for relatively lower number of samples, but I believe it can't be used for such a huge dataset as it loads the input matrix into memory first and that's an expensive process.\nDoes anyone know, what would be the best way to extract out the TFIDF vectors for large datasets?","AnswerCount":4,"Available Count":1,"Score":-0.0996679946,"is_accepted":false,"ViewCount":28481,"Q_Id":25145552,"Users Score":-2,"Answer":"The lengths of the documents The number of terms in common Whether the terms are common or unusual How many times each term appears","Q_Score":42,"Tags":"python,lucene,nlp,scikit-learn,tf-idf","A_Id":67782295,"CreationDate":"2014-08-05T18:09:00.000","Title":"TFIDF for Large Dataset","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"My installed version of the python(2.7) module pandas (0.14.0) will not import. The message I receive is this:\nUserWarning: Installed openpyxl is not supported at this time. Use >=1.6.1 and <2.0.0.\nHere's the problem - I already have openpyxl version 1.8.6 installed so I can't figure out what the problem might be! Does anybody know where the issue may lie? Do I need a different combination of versions?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":114,"Q_Id":25168058,"Users Score":0,"Answer":"The best thing would be to remove the version of openpyxl you installed and let Pandas take care.","Q_Score":0,"Tags":"python,pandas,openpyxl,versions","A_Id":25178533,"CreationDate":"2014-08-06T18:55:00.000","Title":"Python pandas module openpxyl version issue","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I installed pandas using pip and get the following message \"pip install pandas\nRequirement already satisfied (use --upgrade to upgrade): pandas in \/Library\/Frameworks\/Python.framework\/Versions\/2.7\/lib\/python2.7\/site-packages\nCleaning up...\"\nWhen I load up python and try to import pandas, it says module not found. Please help","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":1176,"Q_Id":25169506,"Users Score":1,"Answer":"Try to locate your pandas lib in \/python*\/lib\/site-packages, add dir to your sys.path file.","Q_Score":0,"Tags":"python,pandas","A_Id":25170275,"CreationDate":"2014-08-06T20:22:00.000","Title":"error in import pandas after installing it using pip","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"How to test the default NLTK NER chunker's accuracy on own corpus?\nI've tagged a percentage of my own corpus. I'm curious if it's possible to use the default NLTK tagger to see accuracy rate on this corpus? \nI already know about the ne_chunker.evaluate() function, but it's not immediately clear to me how to input in my own corpus for evaluation (rather than the gold standard corpus)","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":269,"Q_Id":25192029,"Users Score":0,"Answer":"Read in the chunked portion of your corpus and convert it into the format that the NLTK expects, i.e. as a list of shallow Trees. Once you have it in this form, you can pass it to the evaluate() method just like you would pass the \"gold standard\" examples. \nThe evaluate method will strip off the chunks, run your text through the chunker, and compare the result with the chunks you supplied to calculate accuracy.","Q_Score":1,"Tags":"python,nltk","A_Id":25414604,"CreationDate":"2014-08-07T20:56:00.000","Title":"How to test the default NLTK NER chunker's accuracy on own corpus?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm working with a large 3 dimensional array of data that is binary, each value is one of two possible values. I currently have this data stored in the numpy array as int32 objects that are either 1 or 0. \nIt works fine for small arrays but eventually i will need to make the array 5000x5000x20, which I can't even get close to without getting \"Memory Error\". \nDoes anyone have any suggestions for a better way to do this? I am really hoping that I can keep it all together in one data structure because I will need to access slices of it along all three axes.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":119,"Q_Id":25233539,"Users Score":1,"Answer":"Another possibility is to represent the last axis of 20 bits as a single 32 bit integer. This way a 5000x5000 array would suffice.","Q_Score":0,"Tags":"python","A_Id":25233675,"CreationDate":"2014-08-10T22:16:00.000","Title":"Large Array of binary data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Recently I was visualizing my datasets using python modules scikit and scipy hierarchical clustering and dendrogram. Dendrogram method drawing me a graph and now I need to export this tree as a graph in my code. I am wondering is there any way to get this data. Any help would be really appreciated. Thanks.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":682,"Q_Id":25238028,"Users Score":0,"Answer":"The solution was that scipy had it's own built in function to turn linkage matrix to binary tree. The function name is scipy.to_tree(matrix)","Q_Score":0,"Tags":"python,scipy,cluster-analysis,hierarchical-clustering","A_Id":25242915,"CreationDate":"2014-08-11T07:36:00.000","Title":"Python hierarchical clustering visualization dump [scipy]","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Background: I got a lot of text that has some technical expressions, which are not always standard.\nI know how to find the bigrams and filter them. \nNow, I want to use them when tokenizing the sentences. So words that should stay together (according to the calculated bigrams) are kept together. \nI would like to know if there is a correct way to doing this within NLTK. If not, I can think of various non efficient ways of rejoining all the broken words by checking dictionaries.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":317,"Q_Id":25288032,"Users Score":1,"Answer":"The way how topic modelers usually pre-process text with n-grams is they connect them by underscore (say, topic_modeling or white_house). You can do that when identifying big rams themselves. And don't forget to make sure that your tokenizer does not split by underscore (Mallet does if not setting token-regex explicitly).\nP.S. NLTK native bigrams collocation finder is super slow - if you want something more efficient look around if you haven't yet or create your own based on, say, Dunning (1993).","Q_Score":0,"Tags":"python,python-2.7,nlp,nltk","A_Id":25298846,"CreationDate":"2014-08-13T13:52:00.000","Title":"Python NLTK tokenizing text using already found bigrams","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I converted a Pandas dataframe to an HTML output using the DataFrame.to_html function. When I save this to a separate HTML file, the file shows truncated output.\nFor example, in my TEXT column,\ndf.head(1) will show\nThe film was an excellent effort...\ninstead of\nThe film was an excellent effort in deconstructing the complex social sentiments that prevailed during this period.\nThis rendition is fine in the case of a screen-friendly format of a massive Pandas dataframe, but I need an HTML file that will show complete tabular data contained in the dataframe, that is, something that will show the latter text element rather than the former text snippet.\nHow would I be able to show the complete, non-truncated text data for each element in my TEXT column in the HTML version of the information? I would imagine that the HTML table would have to display long cells to show the complete data, but as far as I understand, only column-width parameters can be passed into the DataFrame.to_html function.","AnswerCount":9,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":438196,"Q_Id":25351968,"Users Score":0,"Answer":"For those who like to reduce typing (i.e., everyone!): pd.set_option('max_colwidth', None) does the same thing","Q_Score":374,"Tags":"python,html,pandas","A_Id":63317500,"CreationDate":"2014-08-17T17:52:00.000","Title":"How can I display full (non-truncated) dataframe information in HTML when converting from Pandas dataframe to HTML?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I have an existing PyQt5\/Python3.4 application that works great, and would now like to add \"real-time\" data graphing to it.  Since matplotlib installation specifically looks for Python 3.2, and NumPhy \/ ipython each have there own Python version requirements, I thought I'd use a python distribution to avoid confusion.   \nBut out of all the distros (pythonxy, winpython, canopy epd) Anaconda is the only one that supports Python 3.4, however it only has PyQt 4.10.4.   Is there a way I can install Anaconda, and use matplotlib from within my existing PyQt5 gui app?\nWould I be better off just using another charting package (pyqtgraph, pyqwt, guiqwt, chaco, etc) that might work out of the box with PyQt5\/Python3.4?","AnswerCount":4,"Available Count":1,"Score":0.049958375,"is_accepted":false,"ViewCount":15580,"Q_Id":25468397,"Users Score":1,"Answer":"I use Anaconda and with Python v2.7.X and qt5 doesn't work. The work-around I found was \nTools -> Preferences -> Python console -> External modules -> Library: PySlide","Q_Score":8,"Tags":"matplotlib,anaconda,python-3.4,pyqt5","A_Id":44932618,"CreationDate":"2014-08-24T03:57:00.000","Title":"Using Anaconda Python 3.4 with PyQt5","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":1,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a 16-bit grayscale image and I want to convert it to an 8-bit grayscale image in OpenCV for Python to use it with various functions (like findContours etc.). How can I do this in Python?","AnswerCount":7,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":66029,"Q_Id":25485886,"Users Score":0,"Answer":"This is the simplest way I found: img8 = cv2.normalize(img, None, 0, 255, cv2.NORM_MINMAX, dtype=cv2.CV_8U)","Q_Score":27,"Tags":"python,numpy,opencv,image-processing","A_Id":71228716,"CreationDate":"2014-08-25T12:23:00.000","Title":"How to convert a 16-bit to an 8-bit image in OpenCV?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a 16-bit grayscale image and I want to convert it to an 8-bit grayscale image in OpenCV for Python to use it with various functions (like findContours etc.). How can I do this in Python?","AnswerCount":7,"Available Count":2,"Score":0.0285636566,"is_accepted":false,"ViewCount":66029,"Q_Id":25485886,"Users Score":1,"Answer":"Yes you can in Python. To get the expected result, choose a method based on what you want the values mapped from say uint16 to uint8 be.\nFor instance,\nif you do    img8 = (img16\/256).astype('uint8') values below 256 are mapped to 0\nif you do    img8 = img16.astype('uint8') values above 255 are mapped to 0\nIn the LUT method as described and corrected above, you have to define the mapping.","Q_Score":27,"Tags":"python,numpy,opencv,image-processing","A_Id":63122556,"CreationDate":"2014-08-25T12:23:00.000","Title":"How to convert a 16-bit to an 8-bit image in OpenCV?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using pandas to analyse large CSV data files. They are around 100 megs in size.\nEach load from csv takes a few seconds, and then more time to convert the dates.\nI have tried loading the files, converting the dates from strings to datetimes, and then re-saving them as pickle files. But loading those takes a few seconds as well.\nWhat fast methods could I use to load\/save the data from disk?","AnswerCount":4,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":36848,"Q_Id":25508510,"Users Score":4,"Answer":"One thing to check is the actual performance of the disk system itself. Especially if you use spinning disks (not SSD), your practical disk read speed may be one of the explaining factors for the performance. So, before doing too much optimization, check if reading the same data into memory (by, e.g., mydata = open('myfile.txt').read()) takes an equivalent amount of time. (Just make sure you do not get bitten by disk caches; if you load the same data twice, the second time it will be much faster because the data is already in RAM cache.)\nSee the update below before believing what I write underneath\nIf your problem is really parsing of the files, then I am not sure if any pure Python solution will help you. As you know the actual structure of the files, you do not need to use a generic CSV parser.\nThere are three things to try, though:\n\nPython csv package and csv.reader\nNumPy genfromtext\nNumpy loadtxt\n\nThe third one is probably fastest if you can use it with your data. At the same time it has the most limited set of features. (Which actually may make it fast.)\nAlso, the suggestions given you in the comments by crclayton, BKay, and EdChum are good ones. \nTry the different alternatives! If they do not work, then you will have to do write something in a compiled language (either compiled Python or, e.g. C).\nUpdate: I do believe what chrisb says below, i.e. the pandas parser is fast.\nThen the only way to make the parsing faster is to write an application-specific parser in C (or other compiled language). Generic parsing of CSV files is not straightforward, but if the exact structure of the file is known there may be shortcuts. In any case parsing text files is slow, so if you ever can translate it into something more palatable (HDF5, NumPy array), loading will be only limited by the I\/O performance.","Q_Score":32,"Tags":"python,pandas","A_Id":25508739,"CreationDate":"2014-08-26T14:34:00.000","Title":"Fastest way to parse large CSV files in Pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"By 'graph' I mean 'function' in the mathematical sense, where you always find one unchanging y value per x value.\nPython's random.Random class's seed behaves as the x-coordinate of a random graph and each new call to random.random() gives a new random graph with all new x-y mappings.\nIs there a way to directly refer to random.Random's nth graph, or in other words, the nth value in a certain seed's series without calling random.random() n times?\nI am making a set of classes that I call Transformers that take any (x,y) coordinates as input and output another pair of (x,y) coordinates. Each transformer has two methods: transform and untransform. One of the transformers that I want adds a random value to the input y coordinate depending on the the input x coordinate. Say that I then want this transformer to untransform(x, y), now I need to subtract the same value I added from y if x is the same. This can be done by setting the seed to the same value it had when I added to y, so acting like the x value. Now say that I want two different instances of the transformer that adds random values to y. My question is about my options for making this new random transformer give different values than the first one.","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":172,"Q_Id":25532502,"Users Score":0,"Answer":"Well you're probably going to need to come up with some more detailed requirements but yes, there are ways: \n\npre-populate a dictionary with however many terms in the series you require for a given seed and then at run-time simply look the nth term up. \nif you're not fussed about the seed values and\/or do not require some n terms for any given seed, then find a O(1) way of generating different seeds and only use the first term in each series.  \n\nOtherwise, you may want to stop using the built-in python functionality & devise your own (more predictable) algo. \nEDIT wrt the new infos:\nOk. so i also looked at your profile & so you are doing something (musical?) other than any new crypto thing. if that's the case, then it's unfortunately mixed blessings, because while you don't require security, you also still won't want (audible) patterns appearing. so you unfortunately probably do still need a strong prng.\n\nOne of the transformers that I want adds a random value to the input y\n  coordinate depending on the the input x coordinate\n\nIt's not yet clear to me if there is actually any real requirement for y to depend upon x...\n\nNow say that I want two different instances of the transformer that\n  adds random values to y. My question is about my options for making\n  this new random transformer give different values than the first one.\n\n..because here, i'm getting the impression that all you really require is for two different instances to be different in some random way.\nBut, assuming you have some object containing tuple (x,y) and you really do want a transform function to randomly vary y for the same x; and you want an untransform function to quickly undo any transform operations, then why not just keep a stack of the state changes throughout the lifetime of any single instance of an object; and then in the untransform implementation, you just pop the last transformation off the stack ?","Q_Score":2,"Tags":"python,algorithm,random,python-3.4","A_Id":25533547,"CreationDate":"2014-08-27T16:32:00.000","Title":"Many independent pseudorandom graphs each with same arbitrary y for any input x","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Let a be a numpy array of length n.\nDoes the statement \na == max(a)\ncalculate the expression max(a) n-times or just one?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":70,"Q_Id":25553781,"Users Score":1,"Answer":"It computes max(a) once, then it compares the (scalar) result against each (scalar) element in a, and creates a bool-array for the result.","Q_Score":0,"Tags":"python,optimization,numpy","A_Id":25554002,"CreationDate":"2014-08-28T16:35:00.000","Title":"Numpy array element-by-element comparison optimization","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"In pandas, is there any function that returns the negative of the values in a column?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2315,"Q_Id":25562570,"Users Score":6,"Answer":"Just use the negative sign on the column directly.  For instance, if your DataFrame has a column \"A\", then -df[\"A\"] gives the negatives of those values.","Q_Score":1,"Tags":"python,pandas","A_Id":25562736,"CreationDate":"2014-08-29T06:09:00.000","Title":"How to return 'negative' of a value in pandas dataframe?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Given an integer k, I am looking for a pythonic way to generate a nxm matrix (or nested list) which has every integer from 0..k-1 but no integer appears more than once in each row.\nCurrently I'm doing something like this\nrandom.sample(list(combinations(xrange(k), m)), n)\nbut this does not guarantee every number from 0..k-1 is included, only that no integer appears more than once in each row. Also this has combinatorial complexity which is obviously undesirable.\nThanks.","AnswerCount":4,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":407,"Q_Id":25593876,"Users Score":1,"Answer":"You want to generate a random n*m matrix of integers 1..k with every integer used, and no integer used twice in any row.  And you want to do it efficiently.\nIf you just want to generate a reasonable answer, reasonably quickly, you can generate the rows by taking a random selection of elements, and putting them into a random order.  numpy.random.random_sample and numpy.random.shuffle can do that.  You will avoid the duplicate element issue.  If you fail to use all of your elements, then what you can do is randomly \"evolve\" this towards a correct solution,  At every step identify all elements that are repeated more than once in the matrix, randomly select one, and convert it to an as yet unused integer from 1..k.  This will not cause duplications within rows, and will in at most k steps give you a matrix of the desired form.\nOdds are that this is a good enough answer for what you want, and is what you should do.  But it is imperfect - matrices of this form do not all happen with exactly equal probability.  (In particular ones with lots of elements only appearing once show up slightly more than they should.)  If you need a perfectly even distribution, then you're going to have to do a lot more work.\nTo get there you need a bit of theory.  If you have that theory, then you can understand the answer as, \"Do a dynamic programming solution forwards to find a count of all possible solutions, then run that backwards making random decisions to identify a random solution.\"  Odds are that you don't have that theory.\nI'm not going to give a detailed explanation of that theory.  I'll just outline what you do.\nYou start with the trivial statement, \"There are n!\/(n-m)! ways in which I could have a matrix with 1 row satisfying my condition using m of the k integers, and none which use more.\"\nFor i from 1..n, for j from m to k, you figure out the count of ways in which you could build i rows using j of the k integers.  You ALSO keep track of how many of those ways came from which previous values for j for the previous row.  (You'll need that later.)  This step can be done in a double loop.\nNote that the value in the table you just generated for j=k and i=n is the number of matrices that satisfy all of your conditions.  We'll build a random one from the bottom up.\nFirst you generate a random row for the last row of your matrix - all are equally likely.\nFor each row until you get to the top, you use the table you built to randomly decide how many of the elements that you used in the last row you generated will never be used again.  Randomly decide which those elements will be.  Generate a random row from the integers that you are still using.\nWhen you get to the top you'll have chosen a random matrix meeting your description, with no biases in how it was generated.","Q_Score":2,"Tags":"python,algorithm","A_Id":25594611,"CreationDate":"2014-08-31T16:14:00.000","Title":"Generate random matrix with every number 0..k","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am converting 100 csv files into dataframes and storing them in an HDFStore.\nWhat are the pros and cons of\na - storing the csv file as 100 different HDFStore files?\nb - storing all the csv files as separate items in a single HDFStore?\nOther than performance issues, I am asking the question as I am having stability issues and my HDFStore files often get corrupted. So, for me, there is a risk associated with a single HDFStore. However, I am wondering if there are benefits to having a single store.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":202,"Q_Id":25602155,"Users Score":1,"Answer":"These are the differences:\nmultiple files \n\nwhen using multiple files you can only corrupt a single file when writing (eg you have a power failure when writing)\nyou can parallelize writing with multiple files (note - never, ever try to parallelize with a single file a this will corrupt it!!!)\n\nsingle file\n\ngrouping if logical sets\n\nIMHO the advantages of multiple files outweigh using a single file as you can easily replicate the grouping properties by using sub directories","Q_Score":0,"Tags":"python,pandas,hdfstore","A_Id":25607452,"CreationDate":"2014-09-01T08:54:00.000","Title":"Multiple files or single files into HDFStore","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to use matplotlib on Ubuntu 12.04. So I built a wheel with pip:\npython .local\/bin\/pip wheel --wheel-dir=wheel\/ --build=build\/ matplotlib\nThen successfully installed it:\npython .local\/bin\/pip install --user --no-index --find-links=wheel\/ --build=build\/ matplotlib\nBut when I'm trying to import it in ipython ImportError occures:\n\nIn [1]: import matplotlib\nIn [2]: matplotlib.get_backend()\n  Out[2]: u'agg'\nIn [3]: import matplotlib.pyplot                                      \nImportError\n  Traceback (most recent call\n  last) \/place\/home\/yefremat\/ in\n  ()\n  ----> 1 import matplotlib.pyplot\n\/home\/yefremat\/.local\/lib\/python2.7\/site-packages\/matplotlib\/pyplot.py\n  in ()\n       32 from matplotlib import docstring\n       33 from matplotlib.backend_bases import FigureCanvasBase\n  ---> 34 from matplotlib.figure import Figure, figaspect\n       35 from matplotlib.gridspec import GridSpec\n       36 from matplotlib.image import imread as _imread\n\/home\/yefremat\/.local\/lib\/python2.7\/site-packages\/matplotlib\/figure.py\n  in ()\n       38 import matplotlib.colorbar as cbar\n       39 \n  ---> 40 from matplotlib.axes import Axes, SubplotBase, subplot_class_factory\n       41 from matplotlib.blocking_input import BlockingMouseInput, BlockingKeyMouseInput\n       42 from matplotlib.legend import Legend\n\/home\/yefremat\/.local\/lib\/python2.7\/site-packages\/matplotlib\/axes\/init.py\n  in ()\n        2                         unicode_literals)\n        3 \n  ----> 4 from ._subplots import *\n        5 from ._axes import *\n\/home\/yefremat\/.local\/lib\/python2.7\/site-packages\/matplotlib\/axes\/_subplots.py\n  in ()\n        8 from matplotlib import docstring\n        9 import matplotlib.artist as martist\n  ---> 10 from matplotlib.axes._axes import Axes\n       11 \n       12 import warnings\n\/home\/yefremat\/.local\/lib\/python2.7\/site-packages\/matplotlib\/axes\/_axes.py\n  in ()\n       36 import matplotlib.ticker as mticker\n       37 import matplotlib.transforms as mtransforms\n  ---> 38 import matplotlib.tri as mtri\n       39 import matplotlib.transforms as mtrans\n       40 from matplotlib.container import BarContainer, ErrorbarContainer, StemContainer\n\/home\/yefremat\/.local\/lib\/python2.7\/site-packages\/matplotlib\/tri\/init.py\n  in ()\n        7 import six\n        8 \n  ----> 9 from .triangulation import *\n       10 from .tricontour import *\n       11 from .tritools import *\n\/home\/yefremat\/.local\/lib\/python2.7\/site-packages\/matplotlib\/tri\/triangulation.py\n  in ()\n        4 import six\n        5 \n  ----> 6 import matplotlib._tri as _tri\n        7 import matplotlib._qhull as _qhull\n        8 import numpy as np\nImportError:\n  \/home\/yefremat\/.local\/lib\/python2.7\/site-packages\/matplotlib\/_tri.so:\n  undefined symbol: _ZNSt8__detail15_List_node_base9_M_unhookEv\n\nMay be I'm doing somethig wrong? Or may be there is a way to turn off gui support of matplotlib?\nThanks in advance.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":520,"Q_Id":25627100,"Users Score":0,"Answer":"Okay, the problem was in gcc version. During building and creating wheel of package pip uses system gcc (which version is 4.7.2). I'm using python from virtualenv, which was built with gcc 4.4.3. So version of libstdc++ library is different in IPython and one that pip used. \nAs always there are two solutions (or even more): pass LD_PRELOAD environment variable with correct libstdc++ before entering IPython or to use same version of gcc during creating wheel and building virtualenv. I prefred the last one.\nThank you all.","Q_Score":0,"Tags":"python,matplotlib,ubuntu-12.04","A_Id":25691635,"CreationDate":"2014-09-02T15:30:00.000","Title":"Installing matplotlib via pip on Ubuntu 12.04","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to create a column in a pandas data frame that is an integer representation of the number of days in a timedelta column.  Is it possible to use 'datetime.days' or do I need to do something more manual?\ntimedelta column \n\n7 days, 23:29:00\n\nday integer column\n\n7","AnswerCount":6,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":343961,"Q_Id":25646200,"Users Score":35,"Answer":"Timedelta objects have read-only instance attributes .days, .seconds, and .microseconds.","Q_Score":164,"Tags":"python,pandas,timedelta","A_Id":48203281,"CreationDate":"2014-09-03T13:53:00.000","Title":"Python: Convert timedelta to int in a dataframe","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am running some K-Means clustering from the sklearn package.\nAlthough I am setting the parameter n_jobs = 1 as indicated in the sklearn documentation, and  although a single process is running, that process will apparently consume all the CPUs on my machine. That is, in top, I can see the python job is using, say 400% on a 4 core machine.\nTo be clear, if I set n_jobs = 2, say, then I get two python instances running, but each one uses 200% CPU, again consuming all 4 of my machine's cores.\nI believe the issue may be parallelization at the level of NumPy\/SciPy.\nIs there a way to verify my assumption? Is there a way to turn off any parallelization in NumPy\/SciPy, for example?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":525,"Q_Id":25693870,"Users Score":1,"Answer":"Indeed BLAS, or in my case OpenBLAS, was performing the parallelization.\nThe solution was to set the environment variable OMP_NUM_THREADS to 1.\nThen all is right with the world.","Q_Score":4,"Tags":"python,numpy,parallel-processing,scipy,scikit-learn","A_Id":25750072,"CreationDate":"2014-09-05T20:56:00.000","Title":"Inspecting or turning off Numpy\/SciPy Parallelization","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"This is a bit hard to explain. I have a list of integers. So, for example, [1, 2, 4, 5, 8, 7, 6, 4, 1] - which, when plotted against element number, would resemble a convex graph. How do I somehow extract this 'shape' characteristic from the list? It doesn't have to particularly accurate - just the general shape, convex w\/ one hump, concave w\/ two, straight line, etc - would be fine.\nI could use conditionals for every possible shape: for example, if the slope is positive upto a certain index, and negative after, it's a slope, with the skewness depending on index\/list_size.\nIs there some cleverer, generalised way? I suppose this could be a classification problem - but is it possible without ML?\nCheers.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2040,"Q_Id":25703792,"Users Score":0,"Answer":"How about if you difference the data (I.e., x[i+1] - x[i]) repeatedly until all the results are the same sign? For example, if you difference it twice and all the results are nonnegative, you know it's convex. Otherwise difference again and check the signs. You could set a limit, say 10 or so, beyond which you figure the sequence is too complex to characterize. Otherwise, your shape is characterized by the number of times you difference, and the ultimate sign.","Q_Score":3,"Tags":"python,statistics,classification,computer-science,differentiation","A_Id":25735046,"CreationDate":"2014-09-06T19:09:00.000","Title":"Find the 'shape' of a list of numbers (straight-line\/concave\/convex, how many humps)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a very long list of list and I am converting it to a numpy array using numpy.asarray(), is it safe to delete the original list after getting this matrix or does the newly created numpy array will also be affected by this action?","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":1721,"Q_Id":25714046,"Users Score":1,"Answer":"Yes, it is safe to delete it if your input data consists of a list. From the documentation No copy is performed (ONLY) if the input is already an ndarray.","Q_Score":3,"Tags":"python,numpy","A_Id":25714220,"CreationDate":"2014-09-07T19:40:00.000","Title":"does numpy asarray() refer to original list","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a poem and I want the Python code to just print those words which are rhyming with each other. \nSo far I am able to:\n\nBreak the poem sentences using wordpunct_tokenize()\nClean the words by removing the punctuation marks\nStore the last word of each sentence of the poem in a list\nGenerate another list using cmudict.entries() with elements as those last words and their pronunciation. \n\nI am stuck with the next step. How should I try to match those pronunciations? In all, my major task is to find out if two given words rhyme or not. If rhyme, then return True, else False.","AnswerCount":4,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":14269,"Q_Id":25714531,"Users Score":2,"Answer":"Use soundex or double metaphone to find out if they rhyme. NLTK doesn't seem to implement these but a quick Google search showed some implementations.","Q_Score":21,"Tags":"python,nltk","A_Id":25714754,"CreationDate":"2014-09-07T20:36:00.000","Title":"Find rhyme using NLTK in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am currently searching for the best solution + environment for a problem I have. I'm simplifying the problem a bit, but basically: \n\nI have a huge number of small files uploaded to Amazon S3.\nI have a rule system that matches any input across all file content (including file names) and then outputs a verdict classifying each file. NOTE: I cannot combine the input files because I need an output for each input file.\n\nI've reached the conclusion that Amazon EMR with MapReduce is not a good solution for this. I'm looking for a big data solution that is good at processing a large number of input files and performing a rule matching operation on the files, outputting a verdict per file. Probably will have to use ec2.\nEDIT: clarified 2 above","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":148,"Q_Id":25818198,"Users Score":1,"Answer":"Problem with Hadoop is when you get a very large number of files that you do not combine with CombineFileInput format, it makes the job less efficient.\nSpark doesnt seem to have a problem with this though, Ive had jobs run without problems with 10s of 1000s of files and output 10s of 1000s of files. Not tried to really push the limits, not sure if there even is one!","Q_Score":0,"Tags":"python,amazon-ec2,bigdata,amazon-sqs","A_Id":25824846,"CreationDate":"2014-09-12T23:14:00.000","Title":"What big data solution can I use to process a huge number of input files?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":1,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"Just as the title says, I want to raise an exception when I send in an input A that should be an array containing floats. That is, if A contains at least one item that is not a float it should return an TypeError.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":828,"Q_Id":25824415,"Users Score":2,"Answer":"All numbers in a numpy array have the same dtype. So you can quickly check what dtype the array has by looking at array.dtype. If this is float or float64 then every single item in the array will be of type float.\nNumpy can also create arrays with mixed dtypes similar to normal python lists but then array.dtype=np.object, in this case anything can be in the array elements. But in my experience there are only a few cases where you actually need np.object\nTo check if the dtype is either of float16,float32, float64 use\nif not issubclass(array.dtype.type, numpy.float):\n    raise TypeError('float type expected')","Q_Score":0,"Tags":"python,numpy","A_Id":25824686,"CreationDate":"2014-09-13T15:00:00.000","Title":"How do raise exception if all elements of numpy array are not floats?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"my csv file has multiple tables in a single file for example\n\nname age gender\nn1    10  f\nn2    20  m\nn3    30  m\ncity  population \ncity1   10\ncity2   20\ncity3   30\n\nHow can I print from city row to city3 row.using python csv module","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":764,"Q_Id":25830569,"Users Score":2,"Answer":"You need to come up with a way to detect the start and end of the relevant section of the file; the csv module does not contain any built-in mechanism for doing this by itself, because there is no general and unambiguous delimiter for the beginning and end of a particular section.\nI have to question the wisdom of jamming multiple CSV files together like this. Is there a reason that you can't separate the sections into  individual files?","Q_Score":0,"Tags":"python,csv","A_Id":25830628,"CreationDate":"2014-09-14T06:08:00.000","Title":"Print from specific row of csv file","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm working on exercise 13.7 from Think Python: How to Think Like a Computer Scientist. The goal of the exercise is to come up with a relatively efficient algorithm that returns a random word from a file of words (let's say a novel), where the probability of the word being returned is correlated to its frequency in the file.\nThe author suggests the following steps (there may be a better solution, but this is assumably the best solution for what we've covered so far in the book). \n\nCreate a histogram showing {word: frequency}.\nUse the keys method to get a list of words in the book.\nBuild a list that contains the cumulative sum of the word frequencies, so that the last item in this list is the total number of words in the book, n. \nChoose a random number from 1 to n.\nUse a bisection search to find the index where the random number would be inserted in the cumulative sum.\nUse the index to find the corresponding word in the word list.\n\nMy question is this: What's wrong with the following solution?\n\nTurn the novel into a list t of words, exactly as they as they appear in the novel, without eliminating repeat instances or shuffling.\nGenerate a random integer from 0 to n, where n = len(t) \u2013 1.\nUse that random integer as an index to retrieve a random word from t.\n\nThanks.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":242,"Q_Id":25836133,"Users Score":1,"Answer":"Your approach is (also) correct, but it uses space proportional to the input text size.  The approach suggested by the book uses space proportional only to the number of distinct words in the input text, which is usually much smaller.  (Think about how often words like \"the\" appear in English text.)","Q_Score":0,"Tags":"python,algorithm,random,cumulative-sum,cumulative-frequency","A_Id":25836165,"CreationDate":"2014-09-14T17:43:00.000","Title":"Why is a list of cumulative frequency sums required for implementing a random word generator?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Does anyone know if there is a way to configure anaconda such that @vectorize does not take all the processors available in the machine?  For example, if I have an eight core machine, I only want @vectorize to use four cores.","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":430,"Q_Id":25847411,"Users Score":2,"Answer":"You can limit the number of threads that target=parallel will use by setting the NUMBA_NUM_THREADS envvar.  Note that you can't change this after Numba is imported, it gets set when you first start it up.  You can check whether it works by examining the value of numba.config.NUMBA_DEFAULT_NUM_THREADS","Q_Score":1,"Tags":"python,vectorization,numba","A_Id":40950835,"CreationDate":"2014-09-15T11:49:00.000","Title":"Numba vectorize maxing out all processors","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm am trying to identify phonemes in voices using a training database of known ones. \nI'm wondering if there is a way of identifying common features within my training sample and using that to classify a new one. \nIt seems like there are two paths: \n\nGive the process raw\/normalised data and it will return similar ones \nExtract certain metrics such as pitch, formants etc and compare to training set\n\nMy interest is the first!\nAny recommendations on machine learning or regression methods\/algorithms?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":401,"Q_Id":25899286,"Users Score":0,"Answer":"Your points 1 and 2 are not very different: 1) is the end results of a classification problem 2) is the feature that you give for classification. What you need is a good classifier (SVM, decision trees, hierarchical classifiers etc.) and a good set of features (pitch, formants etc. that you mentioned).","Q_Score":0,"Tags":"python,audio,machine-learning,signal-processing","A_Id":25902242,"CreationDate":"2014-09-17T19:58:00.000","Title":"Signal feature identification","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"The problem requires me to regularize weights of selected features while training a linear classifier. I am using python SKlearn.\nHaving googled a lot about incorporating asymmetric regularization for classifiers in SKlearn, I could not find any solution. The core library function that performs this task is provided as a DLL for windows hence modifying the existing library is not possible.\nIs there any machine learning library for python with this kind of flexibility? Any kind of help will be appreciated.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":206,"Q_Id":25949733,"Users Score":0,"Answer":"Depending on the amount of data you have and the classifier you would like to use, it might be easier to implement the loss and then use a standard solver like lbfgs or newton, or do stochastic gradient descent, if you have a lot of data.\nUsing a simple custom solver will most likely be much slower than using scikit-learn code, but it will also be much easier to write. In particular if you are after logistic regression, for example, you would need to dig into LibLinear C code.\nOn the other hand, I'm pretty certain that you can implement it in ~10 lines of python using lbfgs in an unoptimized way.","Q_Score":0,"Tags":"python,machine-learning,scikit-learn,asymmetric,regularized","A_Id":25961702,"CreationDate":"2014-09-20T14:23:00.000","Title":"asymmetric regularization in machine learning libraries (e.g. scikit ) in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an application that creates text files with one line each and dumps it to hdfs.\nThis location is in turn being used as the input directory for a hadoop streaming job.\nThe expectation is that the number of mappers will be equal to the \"input file split\" which is equal to the number of files in my case. Some how all the mappers are not getting triggered and I see a weird issue in the streaming output dump:\nCaused by: java.io.IOException: Cannot run program \"\/mnt\/var\/lib\/hadoop\/tmp\/nm-local-dir\/usercache\/hadoop\/appcache\/application_1411140750872_0001\/container_1411140750872_0001_01_000336\/.\/CODE\/python_mapper_unix.py\": error=26, Text file busy\n\"python_mapper.py\" is my mapper file.\nEnvironment Details:\nA 40 node aws r3.xlarge AWS EMR cluster [No other job runs on this cluster]\nWhen this streaming jar is running, no other job is running on the cluster, hence none of the external processes should be trying to open the \"python_mapper.py\" file\nHere is the streaming jar command:\nssh -o StrictHostKeyChecking=no -i  hadoop@ hadoop jar \/home\/hadoop\/contrib\/streaming\/hadoop-streaming.jar -files CODE -file  CODE\/congfiguration.conf -mapper CODE\/python_mapper.py -input \/user\/hadoop\/launchidlworker\/input\/1 -output \/user\/hadoop\/launchidlworker\/output\/out1 -numReduceTasks 0","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":317,"Q_Id":25963463,"Users Score":0,"Answer":"Can you please try stopping all the daemons using 'stop-all' first and then rerun your MR job after restarting the daemons (using 'start-all')? \nLets see if it helps!","Q_Score":0,"Tags":"python,hadoop,mapreduce,streaming","A_Id":26011654,"CreationDate":"2014-09-21T20:21:00.000","Title":"\"Text file busy\" error for the mapper in a Hadoop streaming job execution","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"Can you use Reinforcement Learning from Pybrain on dynamic changing output. For example weather: lets say you have 2 attributes Humidity and Wind and the output will be either Rain or NO_Rain ( and all attributes are either going to have a 1 for true or 0 for false in the text file i am using). can you use Reinforcement Learning on this type of problem? the reason I ask is sometime even if we have humidity it does not guaranty that its going to rain.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":241,"Q_Id":25965953,"Users Score":0,"Answer":"It is certainly possible to train a neural network (based on pybrain or otherwise) to make predictions of this sort that are better than a coin toss.\nHowever, weather prediction is a very complex art, even for people who do it as their full-time profession and have been for decades.  Those weather forecasters have much bigger neural networks inside their head than pybrain can simulate.  If it were possible to make accurate predictions in the way you describe, then it would have been done long ago.  For this reason, I  wouldn't expect to do better than (or even as well as) the local weather forecaster.  So, if your goal is to learn pybrain, I would pick a less complex system to model, and if your goal is to predict the weather, I would suggest www.wunderground.com.","Q_Score":1,"Tags":"python,pybrain,reinforcement-learning","A_Id":25996185,"CreationDate":"2014-09-22T02:34:00.000","Title":"Pybrain Reinforcement Learning dynamic output","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm developing a distributed application using IPython parallel. There are several tasks which are carried out one after another on the IPython cluster engines.\nOne of these tasks inevitably makes use of closures. Hence, I have to tell IPython to use Dill instead of Pickle by calling dv.use_dill(). Though this should be temporarily.\nIs there any way to activate Pickle again once Dill is enabled? I couldn't find any function (something of the form dv.use_pickle()) which would make such an option explicit.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":186,"Q_Id":26011787,"Users Score":1,"Answer":"I'm the dill author.  I don't know if IPython does anything unusual, but you can revert to pickle if you like through dill directly with dill.extend(False)\u2026 although this is a relatively new feature (not yet in a stable release).\nIf IPython doesn't have a dv.use_pickle() (it doesn't at the moment), it should\u2026 and could just use the above to do it.","Q_Score":0,"Tags":"ipython,pickle,ipython-parallel,dill","A_Id":26202109,"CreationDate":"2014-09-24T08:11:00.000","Title":"Tell IPython Parallel to use Pickle again after Dill has been activated","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I installed numpy, scipy, matplotlib, etc through Anaconda. I set my PYTHONPATH environment variable to include C:\/\/Anaconda; C:\/\/Anaconda\/\/Scripts; C:\/\/Anaconda\/\/pkgs;.\nimport sys sys.path shows that IDLE is searching in these Anaconda directories. conda list in the command prompt shows that all the desired packages are installed on Anaconda. But import numpy in IDLE gives me the error No module named numpy.\nSuggestions? How do I tell IDLE where to look for modules\/packages installed via Anaconda? I feel like I'm missing something obvious but I can't find an answer on any previous Overflow questions.","AnswerCount":3,"Available Count":2,"Score":-0.0665680765,"is_accepted":false,"ViewCount":7062,"Q_Id":26047185,"Users Score":-1,"Answer":"You should try starting IDLE with the anaconda interpreter instead. AFAIK it's too primitive an IDE to be configurable which interpreter to use. So if anaconda doesn't ship one, use a different IDE instead, such as PyCharm, PyDev, Eric, Sublime2, Vim, Emacs.","Q_Score":6,"Tags":"python,anaconda","A_Id":26047701,"CreationDate":"2014-09-25T20:07:00.000","Title":"import anaconda packages to IDLE?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I installed numpy, scipy, matplotlib, etc through Anaconda. I set my PYTHONPATH environment variable to include C:\/\/Anaconda; C:\/\/Anaconda\/\/Scripts; C:\/\/Anaconda\/\/pkgs;.\nimport sys sys.path shows that IDLE is searching in these Anaconda directories. conda list in the command prompt shows that all the desired packages are installed on Anaconda. But import numpy in IDLE gives me the error No module named numpy.\nSuggestions? How do I tell IDLE where to look for modules\/packages installed via Anaconda? I feel like I'm missing something obvious but I can't find an answer on any previous Overflow questions.","AnswerCount":3,"Available Count":2,"Score":0.1325487884,"is_accepted":false,"ViewCount":7062,"Q_Id":26047185,"Users Score":2,"Answer":"You need to add those directories to PATH, not PYTHONPATH, and it should not include the pkgs directory.","Q_Score":6,"Tags":"python,anaconda","A_Id":26067690,"CreationDate":"2014-09-25T20:07:00.000","Title":"import anaconda packages to IDLE?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have stack overflow data dump file in .xml format,nearly 27GB and I want to convert them in .csv file. Please somebody tell me, tools to convert xml to csv file or python program","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":704,"Q_Id":26081880,"Users Score":0,"Answer":"Use one of the python xml modules to parse the .xml file.  Unless you have much more that 27GB ram, you will need to do this incrementally, so limit your choices accordingly.  Use the csv module to write the .csv file.\nYour real problem is this. Csv files are lines of fields. They represent a rectangular table.  Xml files, in general, can represent more complex structures: hierarchical databases, and\/or multiple tables.  So your real problem to to understand the data dump format well enough to extract records to write to the .csv file.","Q_Score":2,"Tags":"python-3.x,data-dump","A_Id":26089798,"CreationDate":"2014-09-28T05:16:00.000","Title":"How to convert xml file of stack overflow dump to csv file","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":1,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"While using scipy 0.13.0, ode(f).set_integrator('dopri5'), I get the error message -\nlarger nmax is needed\nI looked for nmax in the ode.py but I can't see the variable. I guess that the number call for integration exceeds the allowed default value.\nHow can I increase the nmax value?","AnswerCount":1,"Available Count":1,"Score":0.537049567,"is_accepted":false,"ViewCount":2600,"Q_Id":26096209,"Users Score":3,"Answer":"nmax refers to the maximum number of internal steps that the solver will take. The default is 500.  You can change it with the nsteps argument of the set_integrator method.  E.g.\node(f).set_integrator('dopri5', nsteps=1000)\n(The Fortran code calls this NMAX, and apparently the Fortran name was copied to the error message in the python code for the \"dopri5\" solver.  In the ode class API, all the solvers (\"dopri5\", \"vode\", \"lsoda\", etc) consistently call this solver parameter nsteps, so scipy should change the error message used in the python code to say nsteps.)","Q_Score":3,"Tags":"python,scipy","A_Id":26099751,"CreationDate":"2014-09-29T09:06:00.000","Title":"python scipy ode dopri5 'larger nmax needed'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"My csv file has no column name for the first column, and I want to rename it. Usually, I would do data.rename(columns={'oldname':'newname'}, inplace=True), but there is no name in the csv file, just ''.","AnswerCount":9,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":116651,"Q_Id":26098710,"Users Score":12,"Answer":"The solution can be improved as data.rename( columns={0 :'new column name'}, inplace=True ). There is no need to use 'Unnamed: 0', simply use the column number, which is 0 in this case and then supply the 'new column name'.","Q_Score":53,"Tags":"python,pandas,csv","A_Id":54617685,"CreationDate":"2014-09-29T11:21:00.000","Title":"Rename unnamed column pandas dataframe","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"My csv file has no column name for the first column, and I want to rename it. Usually, I would do data.rename(columns={'oldname':'newname'}, inplace=True), but there is no name in the csv file, just ''.","AnswerCount":9,"Available Count":2,"Score":0.1106561105,"is_accepted":false,"ViewCount":116651,"Q_Id":26098710,"Users Score":5,"Answer":"Try the below code,\ndf.columns = [\u2018A\u2019, \u2018B\u2019, \u2018C\u2019, \u2018D\u2019]","Q_Score":53,"Tags":"python,pandas,csv","A_Id":58729636,"CreationDate":"2014-09-29T11:21:00.000","Title":"Rename unnamed column pandas dataframe","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to build and implement a regression tree algorithm on some raster data in python, and can't seem to find the best way to do so. I will attempt to explain what I'm trying to do:\nMy desired output is a raster image, whose values represent lake depth, call it depth.tif. I have a series of raster images, each representing the reflectance values in different Landsat bands, say [B1.tif, B2.tif, ..., B7.tif] that I want to use as my independent variables to predict lake depth.\nFor my training data, I have a shapefile of ~6000 points of known lake depth. To create a tree, I extracted the corresponding reflectance values for each of those points, then exported that to a table. I then used that table in weka, a machine-learning software, to create a 600-branch regression tree that would predict depth values based on the set of reflectance values. But because the tree is so large, I can't write it in python manually. I ran into the python-weka-wrapper module so I can use weka in python, but have gotten stuck with the whole raster part. Since my data has an extra dimension (if converted to array, each independent variable is actually a set of ncolumns x nrows values instead of just a row of values, like in all of the examples), I don't know if it can do what I want. In all the examples for the weka-python-wrapper, I can't find one that deals with spatial data, and I think this is what's throwing me off.\nTo clarify, I want to use the training data (which is a point shapefile\/table right now but can- if necessary- be converted into a raster of the same size as the reflectance rasters, with no data in all cells except for the few points I have known depth data at), to build a regression tree that will use the reflectance rasters to predict depth. Then I want to apply that tree to the same set of reflectance rasters, in order to obtain a raster of predicted depth values everywhere.\nI realize this is confusing and I may not be doing the best job at explaining. I am open to other options besides just trying to implement weka in python, such as sklearn, as long as they are open source. My question is, can what I described be done? I'm pretty sure it can, as it's very similar to image classification, with the exception that the target values (depth) are continuous and not discrete classes but so far I have failed. If so, what is the best\/most straight-forward method and\/or are there any examples that might help?\nThanks","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":709,"Q_Id":26104434,"Users Score":0,"Answer":"I have had some experience using LandSat Data for the prediction of environmental properties of soil, which seems to be somewhat related to the problem that you have described above.  Although I developed my own models at the time, I could describe the general process that I went through in order to map the predicted data.\nFor the training data, I was able to extract the LandSat values (in addition to other properties) for the spatial points where known soil samples were taken.  This way, I could use the LandSat data as inputs for predicting the environmental data.  A part of this data would also be reserved for testing to confirm that the trained models were not overfitting to training data and that it predicted the outputs well.\nOnce this process was completed, it would be possible to map the desired area by getting the spatial information at each point of the desired area (matching the resolution of the desired image).  From there, you should be able to input these LandSat factors into the model for prediction and the output used to map the predicted depth.  You could likely just use Weka in this case to predict all of the cases, then use another tool to build the map from your estimates.\nI believe I whipped up some code long ago to extract each of my required factors in ArcGIS, but it's been a while since I did this.  There should be some good tutorials out there that could help you in that direction.\nI hope this helps in your particular situation.","Q_Score":0,"Tags":"python,regression,weka,raster,landsat","A_Id":26111537,"CreationDate":"2014-09-29T16:21:00.000","Title":"method for implementing regression tree on raster data - python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to build and implement a regression tree algorithm on some raster data in python, and can't seem to find the best way to do so. I will attempt to explain what I'm trying to do:\nMy desired output is a raster image, whose values represent lake depth, call it depth.tif. I have a series of raster images, each representing the reflectance values in different Landsat bands, say [B1.tif, B2.tif, ..., B7.tif] that I want to use as my independent variables to predict lake depth.\nFor my training data, I have a shapefile of ~6000 points of known lake depth. To create a tree, I extracted the corresponding reflectance values for each of those points, then exported that to a table. I then used that table in weka, a machine-learning software, to create a 600-branch regression tree that would predict depth values based on the set of reflectance values. But because the tree is so large, I can't write it in python manually. I ran into the python-weka-wrapper module so I can use weka in python, but have gotten stuck with the whole raster part. Since my data has an extra dimension (if converted to array, each independent variable is actually a set of ncolumns x nrows values instead of just a row of values, like in all of the examples), I don't know if it can do what I want. In all the examples for the weka-python-wrapper, I can't find one that deals with spatial data, and I think this is what's throwing me off.\nTo clarify, I want to use the training data (which is a point shapefile\/table right now but can- if necessary- be converted into a raster of the same size as the reflectance rasters, with no data in all cells except for the few points I have known depth data at), to build a regression tree that will use the reflectance rasters to predict depth. Then I want to apply that tree to the same set of reflectance rasters, in order to obtain a raster of predicted depth values everywhere.\nI realize this is confusing and I may not be doing the best job at explaining. I am open to other options besides just trying to implement weka in python, such as sklearn, as long as they are open source. My question is, can what I described be done? I'm pretty sure it can, as it's very similar to image classification, with the exception that the target values (depth) are continuous and not discrete classes but so far I have failed. If so, what is the best\/most straight-forward method and\/or are there any examples that might help?\nThanks","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":709,"Q_Id":26104434,"Users Score":0,"Answer":"It sounds like you are not using any spatial information to build your tree\n(such as information on neighboring pixels), just reflectance.  So, you can\napply your decision tree to the pixels as if the pixels were all in a\none-dimensional list or array.\nA 600-branch tree for a 6000 point training data file seems like it may be\noverfit.  Consider putting in an option that requires the tree to stop splitting\nwhen there are fewer than N points at a node or something similar.  There may\nbe a pruning factor that can be set as well.  You can test different settings\ntill you find the one that gives you the best statistics from cross-validation or\na held-out test set.","Q_Score":0,"Tags":"python,regression,weka,raster,landsat","A_Id":26123179,"CreationDate":"2014-09-29T16:21:00.000","Title":"method for implementing regression tree on raster data - python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using an SGDClassifier in combination with the partial fit method to train with lots of data. I'd like to monitor when I've achieved an acceptable level of convergence, which means I'd like to know the loss every n iterations on some data (possibly training, possibly held-out, maybe both).\nI know this information is available if I pass verbose=1 in the constructor of the classifier, but I'd like to query it programmatically rather than visually. I also know I can use the score method to get accuracy, but I'd like actual loss as measured by my chosen loss function.\nDoes anyone know how to do this?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1528,"Q_Id":26178035,"Users Score":0,"Answer":"The above answer was too short, outdated and might result in misleading. \nUsing score method could only give accuracy (it's in the BaseEstimator). If you want the loss function, you could either call private function _get_loss_function (defined in the BaseSGDClassifier). Or accessing BaseSGDClassifier.loss_functions class attribute which will give you a dict and whose entry is the callable for loss function (with default setting)\nAlso using sklearn.metrics might not get exact loss used for minimization (due to regularization and what to minimize, but you can hand compute anyway). The exact code for Loss function is defined in cython code (sgd_fast.pyx, you could look up the code in scikit-learn github repo)\nI'm looking for a good way to plot the minimization progress. Probably will redirect stdout and parse the output. \nBTW, I'm using 0.17.1. So a update for the answer.","Q_Score":0,"Tags":"python,machine-learning,scikit-learn","A_Id":38604808,"CreationDate":"2014-10-03T11:19:00.000","Title":"Way to compute the value of the loss function on data for an SGDClassifier?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Suppose you have this type of array (Sonny Rollins|Who Cares?|Sonny Rollins And Friends|Jazz|\nVarious|, Westminster Philharmonic Orchestra conducted by Kenneth Alwyn|Curse of the Werewolf: Prelude|Horror!|Soundtrack|1996). Is there any possible way to only take out Sonny Rollins and Who cares from the array?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":27,"Q_Id":26199088,"Users Score":0,"Answer":"Assuming this array is a string array of x strings, you can create a substring from the index of the second instance of '|' to the end of the string.","Q_Score":0,"Tags":"python,python-2.7","A_Id":26199110,"CreationDate":"2014-10-05T02:30:00.000","Title":"Is there a ways to take out the artist and song from the array?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"So I have some 1 million lines of twitter comments data in csv format. I need to classify them in certain categories like if somebody is talking about  : \"product longevity\", \"cheap\/costly\", \"on sale\/discount\" etc. \nAs you can see I have multiple classes to classify these tweets data into.\nThe thing is that how do I even generate\/create a training data for such a huge data.Silly question but I was wondering whether\/not there are already preclassified\/tagged comments data to train our model with? If not then what is the best approach to create a training data for multi-class classification of text\/comments ?\nWhile I have tried and tested NaiveBayes for sentiment classification for a smaller dataset, could you please suggest  which classifier shall I use for this problem (multiple categories to classify the comments into).\nThanks!!!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":149,"Q_Id":26211308,"Users Score":0,"Answer":"The thing is that how do I even generate\/create a training data for\n  such a huge data\n\nI would suggest finding a training data set that could help you with the categories you are interested in. So let's say price related articles, you might want to find a training data set that is all about price related articles and then perhaps expand it by using synonyms for key-words like cheap or so. And perhaps look into sentence structure to find out whether if the structure of the sentence helps with your classifier algorithm.  \n\nIf not then what is the best approach to create a training data for\n  multi-class classification of text\/comments? key-words, pulling articles that are all about related categories and then go from there. \n\nLastly, I suggest being very familiar with NLTK's corpus library, this might also help you with retrieving training data as well. \nAs for your last question, I'm kinda confused to what you mean by 'multiple categories to classify the comments into', do you mean having multiple classifiers for a particular comment to belong in? So a comment can belong to 1 to more classifiers?","Q_Score":1,"Tags":"python,twitter,machine-learning,classification,nltk","A_Id":29718799,"CreationDate":"2014-10-06T07:04:00.000","Title":"preclassified trained twitter comments for categorization","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have been searching around for an implementation of DBSCAN for 3 dimensional points without much luck. Does anyone know I library that handles this or has any experience with doing this? I am assuming that the DBSCAN algorithm can handle 3 dimensions, by having the e value be a radius metric and the distance between points measured by euclidean separation. If anyone has tried implementing this and would like to share that would also be greatly appreciated, thanks.","AnswerCount":4,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":15341,"Q_Id":26246015,"Users Score":0,"Answer":"Why not just flatten the data to 2 dimensions with PCA and use DBSCAN with only 2 dimensions? Seems easier than trying to custom build something else.","Q_Score":11,"Tags":"python,cluster-analysis,dbscan","A_Id":68602469,"CreationDate":"2014-10-07T21:59:00.000","Title":"Python: DBSCAN in 3 dimensional space","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am solving a large non-linear system of equations and I need a high degree of numerical precision. I am currently using sympy.lambdify to convert symbolic expressions for the system of equations and its Jacobian into vectorized functions that take ndarrays as inputs and return an ndarray as outputs.\nBy default, lambdify returns an array with dtype of numpy.float64. Is it possible to have it return an array with dtype numpy.float128? Perhaps this requires the inputs to have dtype of numpy.float128?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":667,"Q_Id":26258420,"Users Score":1,"Answer":"If you need a lot of precision, you can try using SymPy floats, or mpmath directly (which is part of SymPy), which provides arbitrary precision. For example, sympy.Float('2.0', 100) creates a float of 2.0 with 100 digits of precision. You can use something like sympy.sin(2).evalf(100) to get 100 digits of sin(2) for instance. This will be a lot slower than numpy because it is arbitrary precision, meaning it doesn't use machine floats, and it is implemented in pure Python (whereas numpy is written in Fortran and C).","Q_Score":2,"Tags":"python,numpy,sympy","A_Id":26331726,"CreationDate":"2014-10-08T13:41:00.000","Title":"Can lambdify return an array with dytpe np.float128?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"The data that i have is stored in a 2D list where one column represents a frequency and the other column is its corresponding dB. I would like to programmatically identify the frequency of the 3db points on either end of the passband. I have two ideas on how to do this but they both have drawbacks.\n\nFind maximum point then the average of points in the passband then find points about 3dB lower\nUse the sympy library to perform numerical differentiation and identify the critical points\/inflection points\nuse a histogram\/bin function to find the amplitude of the passband. \n\ndrawbacks\n\nsensitive to spikes, not quite sure how to do this\ni don't under stand the math involved and the data is noisy which could lead to a lot of false positives\ncorrelating the amplitude values with list index values could be tricky\n\nCan you think of better ideas and\/or ways to implement what I have described?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1113,"Q_Id":26280838,"Users Score":0,"Answer":"Assuming that you've loaded multiple readings of the PSD from the signal analyzer, try averaging them before attempting to find the bandedges. If the signal isn't changing too dramatically, the averaging process might smooth away any peaks and valleys and noise within the passband, making it easier to find the edges. This is what many spectrum analyzers can do to make for a smoother PSD.\nIn case that wasn't clear, assume that each reading gives you 128 tuples of the frequency and power and that you capture 100 of these buffers of data. Now average the 100 samples from bin 0, then samples from 1, 2, ..., 128. Now try and locate the bandpass on this data. It should be easier than on any single buffer. Note I used 100 as an example. If your data is very noisy, it may require more. If there isn't much noise, fewer.\nBe careful when doing the averaging. Your data is in dB. To add the samples together in order to find an average, you must first convert the dB data back to decimal, do the adds, do the divide to find the average, and then convert the averaged power back into dB.","Q_Score":0,"Tags":"python,math,signal-processing","A_Id":26286979,"CreationDate":"2014-10-09T14:19:00.000","Title":"How can I find the break frequencies\/3dB points from a bandpass filter frequency sweep data in python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have 1,000 objects, each object has 4 attribute lists: a list of words, images, audio files and video files.\nI want to compare each object against:\n\na single object, Ox, from the 1,000.\nevery other object.\n\nA comparison will be something like:\nsum(words in common+ images in common+...).\nI want an algorithm that will help me find the closest 5, say, objects to Ox and (a different?) algorithm to find the closest 5 pairs of objects \nI've looked into cluster analysis and maximal matching and they don't seem to exactly fit this scenario. I don't want to use these method if something more apt exists, so does this look like a particular type of algorithm to anyone, or can anyone point me in the right direction to applying the algorithms I mentioned to this?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":543,"Q_Id":26299978,"Users Score":1,"Answer":"If your comparison works with \"create a sum of all features and find those which the closest sum\", there is a simple trick to get close objects:\n\nPut all objects into an array\nCalculate all the sums\nSort the array by sum.\n\nIf you take any index, the objects close to it will now have a close index as well. So to find the 5 closest objects, you just need to look at index+5 to index-5 in the sorted array.","Q_Score":0,"Tags":"python,algorithm,pattern-matching,cluster-analysis,data-mining","A_Id":26303687,"CreationDate":"2014-10-10T12:55:00.000","Title":"Algorithm for matching objects","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Spotfire 5.5\nHi, I am looking for a way to color code or group columns together in a Spotfire cross-table. I have three categories (nearest, any, all) and three columns associated with each category. Is there a way I can visually group these columns with their corresponding category.\n\nIs there a way to change column heading color?\nIs there a way to put a border around the three column groups?\nCan I display their category above the three corresponding columns?\n\nThanks","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":3241,"Q_Id":26303493,"Users Score":1,"Answer":"One can make a color according to category by using Properties->Color->Add Rule , where you can see many conditions to apply your visualization.","Q_Score":0,"Tags":"python,colors,crosstab,tibco,spotfire","A_Id":26714084,"CreationDate":"2014-10-10T16:00:00.000","Title":"Spotfire column title colors","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on a fluid dynamics simulation tool in Python.  Traditionally (at least in my field), integer indices refer to the center of a cell.  Some quantities are stored on the faces between cells, and in the literature are denoted by half-integer indices.  In codes, however, these are shifted to integers to fit into arrays.  The problem is, the shift is not always consistent: do you add a half or subtract?  If you switch between codes enough, you quickly lose track of the conventions for any particular code.  And honestly, there are enough quirky conventions in each code that eliminating a few would be a big help... but not if the remedy is worse than the original problem.\nI have thought about using even indices for cell centers and odd for faces, but that is counterintuitive.  Also, it's rare for a quantity to exist on both faces and in cell centers, so you never use half of your indices.  You could also implement functions plus_half(i) and minus_half(i), but that gets verbose and inelegant (at least in my opinion).  And of course floating point comparisons are always problematic in case someone gets cute in how they calculate 1\/2.\nDoes anyone have a suggestion for an elegant way to implement half-integer indices in Python?  I'm sure I'm not the first person to wish for this, but I've never seen it done in a simple way that is obvious to a user (without requiring the user to memorize the shift convention you've chosen).\nAnd just to clarify: I assume there is likely to be a remap step hidden from the user to get to integer indices (I intend to wrap NumPy arrays for my data grids).  I'm thinking of the interface exposed to the user, rather than how the data is stored.","AnswerCount":1,"Available Count":1,"Score":-0.1973753202,"is_accepted":false,"ViewCount":149,"Q_Id":26310822,"Users Score":-1,"Answer":"You can make  __getitem__ which takes arbitrary objects as indices (and floating point numbers in particular).","Q_Score":0,"Tags":"python,indexing","A_Id":26310892,"CreationDate":"2014-10-11T03:41:00.000","Title":"Half-integer indices","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a large dataset with a date column (which is not the index) with the following format %Y-%m-%d %H:%M:%S.\nI would like to create quarterly subsets of this data frame i.e. the data frame dfQ1 would contain all rows where the date was between month [1 and 4], dfQ2 would contain all rows where the date was between month [5 and 8], etc... The header of the subsets is the same as that of the main data frame.\nHow can I do this?\nThanks!","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1077,"Q_Id":26339828,"Users Score":0,"Answer":"Assuming you're using Pandas.  \ndfQ1 = df[(df.date > Qstartdate) & (df.date < Qenddate)]","Q_Score":0,"Tags":"python,date,subset","A_Id":26340459,"CreationDate":"2014-10-13T12:17:00.000","Title":"How can I subset a data frame based on dates, when my dates column is not the index in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to load a matrix from a file using numpy. When I use any dtype other than float I get this error:\nOverflowError: Python int too large to convert to C long \nThe code: \nX = np.loadtxt(feats_file_path, delimiter=' ', dtype=np.int64 )\nThe problem is that my matrix has only integers and I can't use a float because the first column in the matrix are integer \"keys\" that refer to node ids. When I use a float, numpy \"rounds\" the integer id into something like 32423e^10, and I don't want this behavior.\nSo my questions:\n\nHow to solve the OverflowError?\nIf it's not possible to solve, then how could I prevent numpy from doing that to the ids?","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":2553,"Q_Id":26362010,"Users Score":0,"Answer":"You need to use a compound dtype, with a separate type per column.  Or you can use np.genfromtxt without specifying any dtype, and it will be determined automatically, per each column, which may give you what you need with less effort (but perhaps slightly less performance and less error checking too).","Q_Score":2,"Tags":"python,numpy","A_Id":26362076,"CreationDate":"2014-10-14T13:24:00.000","Title":"Numpy's loadtxt(): OverflowError: Python int too large to convert to C long","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to load a matrix from a file using numpy. When I use any dtype other than float I get this error:\nOverflowError: Python int too large to convert to C long \nThe code: \nX = np.loadtxt(feats_file_path, delimiter=' ', dtype=np.int64 )\nThe problem is that my matrix has only integers and I can't use a float because the first column in the matrix are integer \"keys\" that refer to node ids. When I use a float, numpy \"rounds\" the integer id into something like 32423e^10, and I don't want this behavior.\nSo my questions:\n\nHow to solve the OverflowError?\nIf it's not possible to solve, then how could I prevent numpy from doing that to the ids?","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":2553,"Q_Id":26362010,"Users Score":0,"Answer":"Your number looks like it would fit in the uint64_t type, which is available if you have C99.","Q_Score":2,"Tags":"python,numpy","A_Id":26362482,"CreationDate":"2014-10-14T13:24:00.000","Title":"Numpy's loadtxt(): OverflowError: Python int too large to convert to C long","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Testing against NumPy\/SciPy includes testing against several versions of them, since there is the need to support all versions since Numpy 1.6 and Scipy 0.11.\nTesting all combinations would explode the build matrix in continuous integration (like travis-ci). I've searched the SciPy homepage for notes about version compatibility or sane combinations, but have not found something useful.\nSo my question is how to safely reduce the amount of combinations, while maintaining maximum testing compliance.\nIs it possible to find all combinations in the wild? Or are there certain dependencies between Scipy and Numpy?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":69,"Q_Id":26363853,"Users Score":1,"Answer":"This doesn't completely answer your question, but I think the policy of scipy release management since 0.11 or earlier has been to support all of the numpy versions from 1.5.1 up to the numpy version in development at the time of the scipy release.","Q_Score":1,"Tags":"python,testing,numpy,scipy,integration-testing","A_Id":26368952,"CreationDate":"2014-10-14T14:51:00.000","Title":"Testing against NumPy\/SciPy sane version pairs","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I was looking into \"scipy.stats.multivariate_normal\" function, there they mentioned that they are using the pseudo inverse, and pseudo determinant.\n\nThe covariance matrix cov must be a (symmetric) positive semi-definite matrix. The determinant and inverse of cov are computed as the pseudo-determinant and pseudo-inverse, respectively, so that cov does not need to have full rank.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":239,"Q_Id":26393254,"Users Score":0,"Answer":"Short answer\nA positive semi-definite matrix does not have to have full rank, thus might not be invertible using the normal inverse.\nLong answer\nIf cov does not have full rank, it does have some eigenvalues equal to zero and its inverse is not defined (because some of its eigenvalues would be infinitely large). Thus, in order to be able to invert a positive semi-definite covariance matrix (\"semi\": not all eigenvalues are larger than zero), they use the pseudo-inverse. The latter inverts the non-zero eigenvalues and preserves the zero eigenvalues rather than inverting them to infinity.\nThe determinant of a matrix without full rank is zero. The pseudo-determinant only considers non-zero eigenvalues, yielding a non-zero result.\nIf, however, cov does have full rank, the results should be the same as with the usual inverse and determinant.","Q_Score":0,"Tags":"python,matrix,scipy","A_Id":26393748,"CreationDate":"2014-10-15T22:33:00.000","Title":"Why we calculate pseudo inverse over inverse","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to sample only some elements of a vector from a sum of gaussians that is given by their means and covariance matrices.\nSpecifically:\nI'm imputing data using gaussian mixture model (GMM). I'm using the following procedure and sklearn:\n\nimpute with mean\nget means and covariances with GMM (for example 5 components)\ntake one of the samples and sample only the missing values. the other values stay the same.\nrepeat a few times\n\nThere are two problems that I see with this. (A) how do I sample from the sum of gaussians, (B) how do I sample only part of the vector. I assume both can be solved at the same time. For (A), I can use rejection sampling or inverse transform sampling but I feel that there is a better way utilizing multivariate normal distribution generators in numpy. Or, some other efficient method. For (B), I just need to multiply the sampled variable by a gaussian that has known values from the sample as an argument. Right?\nI would prefer a solution in python but an algorithm or pseudocode would be sufficient.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":1040,"Q_Id":26442403,"Users Score":1,"Answer":"Since for sampling only relative proportion of the distribution matters, scaling preface or can be thrown away. For diagonal covariance matrix, one can just use the covariance submarine and mean subvector that has dimensions of missing data. For covariance with off-diagonal elements, the mean and std dev of a sampling gaussian will need to be changed.","Q_Score":1,"Tags":"python,numpy,random-sample,normal-distribution,mixture-model","A_Id":26565108,"CreationDate":"2014-10-18T17:10:00.000","Title":"Sampling parts of a vector from gaussian mixture model","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a numpy array for an image and am trying to dump it into the libsvm format of LABEL I0:V0 I1:V1 I2:V2..IN:VN. I see that scikit-learn has a dump_svmlight_file and would like to use that if possible since it's optimized and stable. \nIt takes parameters of X, y, and file output name. The values I'm thinking about would be:\nX - numpy array\ny - ????\nfile output name - self-explanatory \nWould this be a correct assumption for X? I'm very confused about what I should do for y though. \nIt appears it needs to be a feature set of some kind. I don't know how I would go about obtaining that however. Thanks in advance for the help!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2922,"Q_Id":26509319,"Users Score":2,"Answer":"The svmlight format is tailored to classification\/regression problems. Therefore, the array X is a matrix with as many rows as data points in your set, and as many columns as features. y is the vector of instance labels.\nFor example, suppose you have 1000 objects (images of bicycles and bananas, for example), featurized in 400 dimensions. X would be 1000x400, and y would be a 1000-vector with a 1 entry where there should be a bicycle, and a -1 entry where there should be a banana.","Q_Score":1,"Tags":"python,arrays,numpy,svm,libsvm","A_Id":26509674,"CreationDate":"2014-10-22T14:02:00.000","Title":"How to convert numpy array into libsvm format","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"So I'm trying to do a prediction using python's statsmodels.api to do logistic regression on a binary outcome. I'm using Logit as per the tutorials.\nWhen I try to do a prediction on a test dataset, the output is in decimals between 0 and 1 for each of the records.\nShouldn't it be giving me zero and one? or do I have to convert these using a round function or something. \nExcuse the noobiness of this question. I am staring my journey.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":10827,"Q_Id":26528019,"Users Score":0,"Answer":"If the response is on the unit interval interpreted as a probability, in addition to loss considerations, the other perspective which may help is looking at it as a Binomial outcome, as a count instead of a Bernoulli.  In particular, in addition to the probabilistic response in your problem, is there any counterpart to numbers of trials in each case?  If there were, then the logistic regression could be reexpressed as a Binomial (count) response, where the (integer) count would be the rounded expected value, obtained by product of the probability and the number of trials.","Q_Score":3,"Tags":"python,statistics,statsmodels,logistic-regression","A_Id":29172738,"CreationDate":"2014-10-23T12:24:00.000","Title":"Python statsmodel.api logistic regression (Logit)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I wish to compare around 1000 lists of varying size. Each list might have thousands of items. I want to compare each pair of lists, so potentially around 500000 comparisons. Each comparison consists of counting how many of the smaller list exists in the larger list (if same size, pick either list).Ultimately I want to cluster the lists using these counts. I want to be able to do this for two types of data: \n\nany textual data\nstrings of binary digits of the same length.\n\nIs there an efficient way of doing this in python? I've looked at LShash and other clustering related algorithms, but they seem to require same length lists. TIA.\nAn example to try to clarify what I am aiming to do:\nList A: car, dig, dog, the.\nList B: fish, the, dog.\n(No repeats in any list. Not sorted although I suppose they could be fairly easily. Size of lists varies.)\nResult:2, since 'dog' and 'the' are in both lists. \nIn reality the length of each list can be thousands and there are around 1000 such lists, each having to be compared with every other.\nContinuing the example:\nList C: dog, the, a, fish, fry.\nResults:\nAB: 2\nAC: 2\nBC: 3","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":159,"Q_Id":26550430,"Users Score":0,"Answer":"Nothing is going to be superfast, and there's a lot of data there (half a million results, to start with), but the following should fit in your time and space budget on modern hardware.\nIf possible, start by sorting the lists by length, from longest to shortest. (I don't mean sort each lists; the order of elements in the list is irrelevant. I mean, sort the collection of lists so that you can process the longest list first.) The only point of doing this is to allow the similarity metrics to be stored in a half-diagonal matrix instead of full matrix, which saves half the matrix space. So if you don't know the length of the lists before you start, it's not a crisis; it just means that you'll need a bit more space.\nNote 1: The important thing is that the metric you propose is completely symmetric as long as no list has repeated elements.Without repeated elements, the metric is simply |A\u22c2B|, regardless of whether A or B is longer, so when you compute the size of the intersection of A and B you could fill in the similarity matrix for both (A,B) and (B,A).)\nNote 2: The description of the algorithm seemed confusing to me when I reread it so I changed the word \"list\" to \"list\" when it refers to one of the thousand input lists, leaving \"list\" to mean an ordinary Python list. Because lists can't be keys in Python dictionaries, working on the assumption that lists are implemented as lists, it's necessary to somehow identify each list with an identifier which can be used as a key. I hope that's clear.\nThe Algorithm:\nWe need two auxiliary structures: one is the (half-diagonal) result matrix, keyed by pairs of list identifiers, which we initialize to all 0s. The other one is a dictionary keyed by unique data element, mapping onto a list of list identifiers.\nThen, taking each list in turn, for each element in that list we do the following:\n\nIf the element is not yet present in the dictionary, add it, mapping to a single element list consisting of the current list's identifier.\nIf the element is present in the dictionary but the last element in the corresponding list of ids is the current id, then we've found a repeated element. Since we don't expect repeated elements, either ignore it or issue an error message.\nOtherwise, we've seen the element before and we have a list of identifiers of lists in which the element appears. For each such identifier, increment the similarity count between the current identifier and the identifier in the list. (Note that if we scan lists in reverse order by length, all the identifiers in the list correspond to lists which are at least as long as the current list, which is why we sorted the lists in the first place.) Finally, append the current identifier to the end of the list, so that the next time that data element is found, the current list will be present.\n\nThat's it. The space requirement is O(N2 + M) where N is the number of lists and M is the total size of all the lists. The time requirement is essentially O(M2) in the worst case -- that being the case where every list has exactly one element and they are all the same element. (More precisely, it's the sum of the squares of the frequencies of each unique element.)","Q_Score":2,"Tags":"python,algorithm,memory-efficient","A_Id":26559152,"CreationDate":"2014-10-24T14:50:00.000","Title":"efficient different sized list comparisons","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Currently I am storing duration in a pandas column using strings.\nFor example '12:05' stands for 12 minutes and 5 seconds.\nI would like to convert this pandas column from string to a format that allows arithmetic, while retaining the MM:SS format.\nI would like to avoid storing day, hour, dates, etc.","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":173,"Q_Id":26591805,"Users Score":1,"Answer":"If you don't want to convert to date time but still want to do math with them you'd most like be best off converting them to seconds in a different column while retaining the string format of them or creating a function that converts to string and applying that after any computations.","Q_Score":0,"Tags":"python,pandas","A_Id":26591932,"CreationDate":"2014-10-27T16:06:00.000","Title":"how to store duration in a pandas column in minutes:second format that allows arithemtic?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Although I've been doing things with python by myself for a while now, I'm completely new to using python with external libraries. As a result, I seem to be having trouble getting numpy to work with PyDev.\nRight now I'm using PyDev in Eclipse, so I first tried to go to My Project > Properties > PyDev - PYTHONPATH > External  Libraries > Add zip\/jar\/egg, similar to how I would add libraries in Eclipse. I then selected the numpy-1.9.0.zip file that I had downloaded. I tried importing numpy and using it, but I got the following error message in Eclipse:\n\nUndefined variable from import: array.\n\nI looked this up, and tried a few different things. I tried going into Window > Preferences > PyDev > Interpreters > Python Interpreters. I selected Python 3.4.0, then went to Forced Builtins > New, and entered \"numpy\". This had no effect, so I tried going back to Window > Preferences > PyDev > Interpreters > Python Interpreters, selecting Python 3.4.0, and then, under Libraries, choosing New Egg\/Zip(s), then adding the numpy-1.9.0.zip file. This had no effect. I also tried the String Substitution Variables tab under Window > Preferences > PyDev > Interpreters > Python Interpreters (Python 3.4.0). This did nothing.\nFinally, I tried simply adding # @UndefinedVariable to the broken lines. When I ran it, it gave me the following error:\n\nImportError: No module named 'numpy'\n\nWhat can I try to get this to work?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2141,"Q_Id":26595519,"Users Score":0,"Answer":"I recommend you to either use the setup.py from the downloaded archive or to download the \"superpack\"-executable for windows, if you work on windows anyway.\nIn PyDev, i overcame problems with new libraries by using the autoconfig button. If that doesn't work, another solution could be deleting and reconfiguring the python interpreter.","Q_Score":1,"Tags":"python,eclipse,numpy,pydev","A_Id":26625834,"CreationDate":"2014-10-27T19:39:00.000","Title":"Using numpy with PyDev","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have hundreds of large CSV files that I would like to merge into one. However, not all CSV files contain all columns. Therefore, I need to merge files based on column name, not column position.\nJust to be clear: in the merged CSV, values should be empty for a cell coming from a line which did not have the column of that cell.\nI cannot use the pandas module, because it makes me run out of memory.\nIs there a module that can do that, or some easy code?","AnswerCount":5,"Available Count":1,"Score":0.0798297691,"is_accepted":false,"ViewCount":16477,"Q_Id":26599137,"Users Score":2,"Answer":"For those of us using 2.7, this adds an extra linefeed between records in \"out.csv\". To resolve this, just change the file mode from \"w\" to \"wb\".","Q_Score":14,"Tags":"python,csv,merge","A_Id":41861621,"CreationDate":"2014-10-28T00:40:00.000","Title":"Merge CSVs in Python with different columns","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'd like to have 2 separate networks running in Theano at the same time, where the first network trains on the results of the second. I could embed both networks in the same structure but that would be a real mess in the entire forward pass (and probably won't even work because of the shared variables etc.)\nThe problem is that when I define a theano function I don't specify the model it's applied on, meaning if I'm having a predict and a train function they'll both work on the first model I define.\nIs there a way to overcome that issue?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":111,"Q_Id":26615835,"Users Score":-2,"Answer":"In a rather simplified way I've managed to find a nice solution. The trick was to create one model, define its function and then create the other model and define the second function. Works like a charm","Q_Score":0,"Tags":"python,theano","A_Id":26640860,"CreationDate":"2014-10-28T18:29:00.000","Title":"Multiple networks in Theano","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I found out that it's impossible to install NumPy\/SciPy via installers on Windows 64-bit, that's only possible on 32-bit. Because I need more memory than a 32-bit installation gives me, I need the 64-bit version of everything.\nI tried to install everything via Pip and most things worked. But when I came to SciPy, it complained about missing a Fortran compiler. So I installed Fortran via MinGW\/MSYS. But you can't install SciPy right away after that, you need to reinstall NumPy. So I tried that, but now it doesn't work anymore via Pip nor via easy_install. Both give these errors:\n\nThere are a lot of errors about LNK2019 and LNK1120,.\nI get a lot of errors in the range of C: C2065,C2054,C2085,C2143`, etc. They belong together I believe.\nThere is no Fortran linker found, but I have no idea how to install that, can't find anything on it.\nAnd many more errors which are already out of the visible part of my cmd-windows...\nThe fatal error is about LNK1120:\n\nbuild\\lib.win-amd64-2.7\\numpy\\linalg\\lapack_lite.pyd : fatal error LNK1120: 7 unresolved externals\n  error: Setup script exited with error: Command \"C:\\Users\\me\\AppData\\Local\\Programs\\Common\\Microsoft\\Visual C++ for Python\\9.0\\VC\\Bin\\amd64\\link.exe \/DLL \/nologo \/INCREMENTAL:NO \/LIBPATH:C:\\BLAS \/LIBPATH:C:\\Python27\\libs \/LIBPATH:C:\\Python27\\PCbuild\\amd64 \/LIBPATH:build\\temp.win-amd64-2.7 lapack.lib blas.lib \/EXPORT:initlapack_lite build\\temp.win-amd64-2.7\\Release\\numpy\\linalg\\lapack_litemodule.obj \/OUT:build\\lib.win-amd64-2.7\\numpy\\linalg\\lapack_lite.pyd \/IMPLIB:build\\temp.win-amd64-2.7\\Release\\numpy\\linalg\\lapack_lite.lib \/MANIFESTFILE:build\\temp.win-amd64-2.7\\Release\\numpy\\linalg\\lapack_lite.pyd.manifest\" failed with exit status 1120\n\n\nWhat is the correct way to install the 64-bit versions NumPy and SciPy on a 64-bit Windows machine? Did I miss anything? Do I need to specify something somewhere? There is no information for Windows on these problems that I can find, only for Linux or Mac OS X, but they don't help me as I can't use their commands.","AnswerCount":14,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":131918,"Q_Id":26657334,"Users Score":0,"Answer":"Follow these steps:\n\nOpen CMD as administrator\nEnter this command : cd..\ncd..\ncd Program Files\\Python38\\Scripts\nDownload the package you want and put it in Python38\\Scripts folder.\npip install packagename.whl\nDone\n\nYou can write your python version instead of \"38\"","Q_Score":31,"Tags":"python,numpy,scipy,windows64","A_Id":62499396,"CreationDate":"2014-10-30T15:39:00.000","Title":"Installing NumPy and SciPy on 64-bit Windows (with Pip)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I found out that it's impossible to install NumPy\/SciPy via installers on Windows 64-bit, that's only possible on 32-bit. Because I need more memory than a 32-bit installation gives me, I need the 64-bit version of everything.\nI tried to install everything via Pip and most things worked. But when I came to SciPy, it complained about missing a Fortran compiler. So I installed Fortran via MinGW\/MSYS. But you can't install SciPy right away after that, you need to reinstall NumPy. So I tried that, but now it doesn't work anymore via Pip nor via easy_install. Both give these errors:\n\nThere are a lot of errors about LNK2019 and LNK1120,.\nI get a lot of errors in the range of C: C2065,C2054,C2085,C2143`, etc. They belong together I believe.\nThere is no Fortran linker found, but I have no idea how to install that, can't find anything on it.\nAnd many more errors which are already out of the visible part of my cmd-windows...\nThe fatal error is about LNK1120:\n\nbuild\\lib.win-amd64-2.7\\numpy\\linalg\\lapack_lite.pyd : fatal error LNK1120: 7 unresolved externals\n  error: Setup script exited with error: Command \"C:\\Users\\me\\AppData\\Local\\Programs\\Common\\Microsoft\\Visual C++ for Python\\9.0\\VC\\Bin\\amd64\\link.exe \/DLL \/nologo \/INCREMENTAL:NO \/LIBPATH:C:\\BLAS \/LIBPATH:C:\\Python27\\libs \/LIBPATH:C:\\Python27\\PCbuild\\amd64 \/LIBPATH:build\\temp.win-amd64-2.7 lapack.lib blas.lib \/EXPORT:initlapack_lite build\\temp.win-amd64-2.7\\Release\\numpy\\linalg\\lapack_litemodule.obj \/OUT:build\\lib.win-amd64-2.7\\numpy\\linalg\\lapack_lite.pyd \/IMPLIB:build\\temp.win-amd64-2.7\\Release\\numpy\\linalg\\lapack_lite.lib \/MANIFESTFILE:build\\temp.win-amd64-2.7\\Release\\numpy\\linalg\\lapack_lite.pyd.manifest\" failed with exit status 1120\n\n\nWhat is the correct way to install the 64-bit versions NumPy and SciPy on a 64-bit Windows machine? Did I miss anything? Do I need to specify something somewhere? There is no information for Windows on these problems that I can find, only for Linux or Mac OS X, but they don't help me as I can't use their commands.","AnswerCount":14,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":131918,"Q_Id":26657334,"Users Score":0,"Answer":"for python 3.6, the following worked for me\nlaunch cmd.exe as administrator\npip install numpy-1.13.0+mkl-cp36-cp36m-win32\npip install scipy-0.19.1-cp36-cp36m-win32","Q_Score":31,"Tags":"python,numpy,scipy,windows64","A_Id":44685941,"CreationDate":"2014-10-30T15:39:00.000","Title":"Installing NumPy and SciPy on 64-bit Windows (with Pip)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"My assignment is Tic-Tac_Toe using pygame and numpy. I Have almost all of the program done. I just need help understanding how to find if a winner is found. I winner is found if the summation of ANY row, column, or diagonal is equal to 3. \nI have two 3x3 matrices filled with 0's. Let's call them xPlayer and oPlayer. The matrices get filled with 1 every time player x or player o chooses their choice at a certain location. So if, player x selects [0,0], the matrix location at [0,0] gets a 1 value. This should continue until the summation of any row, column, or diagonal is 3. If All the places in both the matrices are 1, then there is no winner. \nI need help finding the winner. I'm really new to python so I don't know much about indexing though a matrix. Any help would be greatly appreciated! \nEDIT: Basically, how do you find out the summation of every row, column, and diagonal to check if ANY of them are equal to 3.","AnswerCount":2,"Available Count":1,"Score":-0.1973753202,"is_accepted":false,"ViewCount":1887,"Q_Id":26726950,"Users Score":-2,"Answer":"set a bool to checks every turn if someone has won. if it returns true, then whosever turn it is has won\nso, for instance, it is x turn, he plays the winning move, bool checks if someone has won,returns true, print out (player whose turn it is) has won! and end game.","Q_Score":1,"Tags":"python,numpy,matrix,indexing,pygame","A_Id":26727002,"CreationDate":"2014-11-04T02:49:00.000","Title":"Summation of every row, column and diagonal in a 3x3 matrix numpy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm developing a little tool which is able to classify musical genres. To do this, I would like to use a K-nn algorithm (or another one, but this one seems to be good enough) and I'm using python-yaafe for the feature extraction.\nMy problem is that, when I extract a feature from my song (example: mfcc), as my songs are 44100Hz-sampled, I retrieve a lot (number of sample windows) of 12-values-array, and I really don't know how to deal with that. Is there an approach to get just one representative value per feature and per song?","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":449,"Q_Id":26733418,"Users Score":2,"Answer":"One approach would be to take the least RMS energy value of the signal as a parameter for classification.\nYou should use a music segment, rather than using the whole music file for classification.Theoretically, the part of the music of 30 sec, starting after the first 30 secs of the music, is best representative for genre classification.\nSo instead of taking the whole array, what you can do is to consider the part which corresponds to this time window, 30sec-59sec. Calculate the RMS energy of the signal separately for every music file, averaged over the whole time. You may also take other features into account, eg. , MFCC. \nIn order to use MFCC, you may go for the averaged value of all signal windows for a particular music file. Make a feature vector out of it. \nYou may use the difference between the features as the distance between the data points for classification.","Q_Score":5,"Tags":"python,algorithm,classification,extraction","A_Id":29713740,"CreationDate":"2014-11-04T10:57:00.000","Title":"Processing musical genres using K-nn algorithm, how to deal with extracted feature?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to shuffle a relatively long array (length ~400). While I am not a cryptography expert, I understand that using a random number generator with a period of less than 400! will limit the space of the possible permutations that can be generated.\nI am trying to use Python's random.SystemRandom number generator class, which, in Windows, uses CryptGenRandom as its RNG.\nDoes anyone smarter than me know what the period of this number generator is? Will it be possible for this implementation to reach the entire space of possible permutations?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":431,"Q_Id":26763448,"Users Score":0,"Answer":"You are almost correct: you need a generator not with a period of 400!, but with an internal state of more than log2(400!) bits (which will also have a period larger than 400!, but the latter condition is not sufficient). So you need at least 361 bytes of internal state. CryptGenRandom doesn't qualify, but it ought to be sufficient to generate 361 or more bytes with which to seed a better generator.\nI think Marsaglia has versions of MWC with 1024 and 4096 bytes of state.","Q_Score":0,"Tags":"python,random","A_Id":26763840,"CreationDate":"2014-11-05T17:30:00.000","Title":"Can CryptGenRandom generate all possible permutations?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to analyze results from several thermal building simulations. Each simulation produces hourly data for several variables and for each room of the analyzed building. Simulations can be repeated for different scenarios and each one of these scenarios will produce a different hourly set of data for each room and each variable. \nAt the moment I create a separate dataframe for each scenario (Multiindex with variables and rooms). My goal is to be able to compare different scenarios along different dimensions: same room, rooms average, time average, etc.. \nFrom what I have understood so far there are two options:\n\ncreate a dictionary of dataframes where the keys represents the scenarios\nadd an additional level (3rd) to the multiindex in the same dataframe representing the scenario\n\nWhich of the above will give me the best results in terms of performance and flexibility.\nTypical questions could be:\n\nin which scenario the average room temperature is below a threshold for more hours\nin which scenario the maximum room temperature is below a threshold\nwhat's the average temperature in July for each room\n\nAs you can see I need to perform operations at different hierarchical levels: within a scenario and also comparison between different scenarios.\nIs it better to keep everything in the same dataframe or distribute the data?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":173,"Q_Id":26766803,"Users Score":0,"Answer":"In general in my experience is more difficult to compare different Data Frames, so I would suggest to use one. \nWith some practical example I can try to give better advice. \nHowever, personally I prefer to use an extra column instead of many Multiindex levels, but it's just my personal opinion.","Q_Score":1,"Tags":"python,pandas,hierarchy,multi-index","A_Id":26769689,"CreationDate":"2014-11-05T20:48:00.000","Title":"Multiindex or dictionaries","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I use the k-means algorithm to clustering set of documents.  \n(parameters are - number of clusters=8, number of runs for different centroids =10)\nThe number of documents are 5800\nSurprisingly the result for the clustering is \n90% of documents belong to cluster - 7 (final cluster)\n9% of documents belong to cluster - 0 (first cluster)\nand the rest 6 clusters have only a single sample. What might be the reason for this?","AnswerCount":3,"Available Count":3,"Score":0.0665680765,"is_accepted":false,"ViewCount":272,"Q_Id":26793585,"Users Score":1,"Answer":"K-means is indeed sensitive to noise BUT investigate your data! \nHave you pre-processed your \"real-data\" before applying the distance measure on it? \nAre you sure your distance metric represents proximity as you'll expected?  \nThere are a lot of possible \"bugs\" that may cause this scenario.. not necessary k-means fault","Q_Score":0,"Tags":"python,cluster-analysis,k-means","A_Id":26826242,"CreationDate":"2014-11-07T03:30:00.000","Title":"What can be the reasons for 90% of samples belong to one cluster when there is 8 clusters?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I use the k-means algorithm to clustering set of documents.  \n(parameters are - number of clusters=8, number of runs for different centroids =10)\nThe number of documents are 5800\nSurprisingly the result for the clustering is \n90% of documents belong to cluster - 7 (final cluster)\n9% of documents belong to cluster - 0 (first cluster)\nand the rest 6 clusters have only a single sample. What might be the reason for this?","AnswerCount":3,"Available Count":3,"Score":0.0665680765,"is_accepted":false,"ViewCount":272,"Q_Id":26793585,"Users Score":1,"Answer":"K-means is highly sensitive to noise!\nNoise, which is farther away from the data, becomes even more influential when your square its deviations. This makes k-means really sensitive to this.\nProduce a data set, with 50 points distributed N(0;0.1), 50 points distributed N(1;0.1) and 1 point at 100. Run k-means with k=2, and you are bound to get that one point a cluster, and the two real clusters merged.\nIt's just how k-means is supposed to work: find a least-squared quantization of the data; it does not care about \"clumps\" in your data set or not.\nNow it may often be beneficial (with respect to the least-squares objective) to make one-element clusters if there are outliers (here, you apparently have at least 6 such outliers). In such cases, you may need to increase k by the number of such one-element clusters you get. Or use outlier detection methods, or a clustering algorithm such as DBSCAN which is tolerant wrt. noise.","Q_Score":0,"Tags":"python,cluster-analysis,k-means","A_Id":26817383,"CreationDate":"2014-11-07T03:30:00.000","Title":"What can be the reasons for 90% of samples belong to one cluster when there is 8 clusters?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I use the k-means algorithm to clustering set of documents.  \n(parameters are - number of clusters=8, number of runs for different centroids =10)\nThe number of documents are 5800\nSurprisingly the result for the clustering is \n90% of documents belong to cluster - 7 (final cluster)\n9% of documents belong to cluster - 0 (first cluster)\nand the rest 6 clusters have only a single sample. What might be the reason for this?","AnswerCount":3,"Available Count":3,"Score":1.2,"is_accepted":true,"ViewCount":272,"Q_Id":26793585,"Users Score":1,"Answer":"K-means clustering attempts to minimize sum of distances between each point and a centroid of a cluster each point belongs to. Therefore, if 90% of your points are close together the sum of distances between those points and the cluster centroid is fairly small, Therefore, the k-means solving algorithm puts a centroid there. Single points are put in their own cluster because they are really far from other points, and a cluster of those points with other points would not be optimal.","Q_Score":0,"Tags":"python,cluster-analysis,k-means","A_Id":26793992,"CreationDate":"2014-11-07T03:30:00.000","Title":"What can be the reasons for 90% of samples belong to one cluster when there is 8 clusters?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have fitted a k-means algorithm on 5000+ samples using the python scikit-learn library. I want to have the 50 samples closest to a cluster center as an output. How do I perform this task?","AnswerCount":3,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":9095,"Q_Id":26795535,"Users Score":11,"Answer":"One correction to the @snarly's answer.\nafter performing d = km.transform(X)[:, j],\nd has elements of distances to centroid(j), not similarities.\nso in order to give closest top 50 indices, you should remove '-1', i.e.,\n ind = np.argsort(d)[::][:50] \n(normally, d has sorted score of distance in ascending order.)\nAlso, perhaps the shorter way of doing \nind = np.argsort(d)[::-1][:50] \ncould be\nind = np.argsort(d)[:-51:-1].","Q_Score":10,"Tags":"python,scikit-learn,k-means","A_Id":36050176,"CreationDate":"2014-11-07T06:39:00.000","Title":"Output 50 samples closest to each cluster center using scikit-learn.k-means library","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm involved in a raspberry pi project and I use python language. I installed scipy, numpy, matplotlib and other libraries correctly. But when I type \nfrom scipy.io import wavfile \nit gives error as \"ImportError: No module named scipy.io\"\nI tried to re-install them, but when i type the sudo cord, it says already the new version of scipy is installed. I'm stucked in this point and please help me... Thank you","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1955,"Q_Id":26883835,"Users Score":1,"Answer":"I would take a guess and say your Python doesnt know where you isntalled scipy.io. add the scipy path to PYTHONPATH.","Q_Score":1,"Tags":"python,scipy","A_Id":26883907,"CreationDate":"2014-11-12T09:49:00.000","Title":"Import error when using scipy.io module","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm looking for an implementation of n-grams count vectorization that is more efficient than scikit-learn's CountVectorizer.  I've identified the CountVectorizer.transform() call as a huge bottleneck in a bit of software, and can dramatically increase model throughput if we're able to make this part of the pipeline more efficient.  Fit time is not important, we're only concerned with transform time.  The end output must be a scipy.sparse vector.  If anyone has any leads for potential alternatives it would be much appreciated.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1295,"Q_Id":26898410,"Users Score":4,"Answer":"Have you tried HashingVectorizer? It's slightly faster (up to 2X if I remember correctly). Next step is to profile the code, strip the features of CountVectorizer or HashingVectorizer that you don't use and rewrite the remaining part in optimized Cython code (after profiling again).\nVowpal Wabbit's bare-bone feature processing that uses the hashing trick by default might give you a hint of what is achievable.","Q_Score":3,"Tags":"python,machine-learning,nlp,scikit-learn,vectorization","A_Id":26904535,"CreationDate":"2014-11-12T23:08:00.000","Title":"Fastest Count Vectorizer Implementation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a 3Gb csv file. I would like to write all of the data to GAE datastore. I have tried reading the file row by row and then posting the data to my app, but I can only create around 1000 new entities before I exceed the free tier and start to incur pretty hefty costs. What is the most efficient \/ cost effective way to upload this data to datastore?","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":102,"Q_Id":26917114,"Users Score":2,"Answer":"If you need to store each row as a separate entity, it does not matter how you create these entities - you can improve the performance by batching your requests, but it won't affect the costs.\nThe costs depend on how many indexed properties you have in each entity. Make sure that you only index the properties that you need to be indexed.","Q_Score":0,"Tags":"python-2.7,google-app-engine,google-cloud-datastore","A_Id":26917183,"CreationDate":"2014-11-13T19:45:00.000","Title":"What is the most efficient way to write 3GB of data to datastore?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":1},{"Question":"I'm trying to get data from a zipped csv file. Is there a way to do this without unzipping the whole files? If not, how can I unzip the files and read them efficiently?","AnswerCount":10,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":70401,"Q_Id":26942476,"Users Score":5,"Answer":"Yes.  You want the module 'zipfile'\nYou open the zip file itself with zipfile.ZipInfo([filename[, date_time]])\nYou can then use ZipFile.infolist() to enumerate each file within the zip, and extract it with ZipFile.open(name[, mode[, pwd]])","Q_Score":52,"Tags":"python-2.7,csv,zip","A_Id":26942545,"CreationDate":"2014-11-15T04:16:00.000","Title":"Reading csv zipped files in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to create a class from a pandas dataframe that is created from csv. Is the best way to do it, by using a @staticmethod? so that I do not have to read in dataframe separately for each object","AnswerCount":2,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":37394,"Q_Id":26949755,"Users Score":4,"Answer":"I would think you could create the dataframe in the first instance with\na = MyClass(my_dataframe)\nand then just make a copy\nb = a.copy()\nThen b is independent of a","Q_Score":11,"Tags":"python,class,pandas","A_Id":55484853,"CreationDate":"2014-11-15T19:23:00.000","Title":"Pass pandas dataframe into class","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a set of data distributed on a sphere and I am trying to understand what metrics must be given to the function DBSCAN distributed by scikit-learn. It cannot be the Euclidean metrics, because the metric the points are distributed with is not Euclidean. Is there, in the sklearn packet, a metric implemented for such cases or is dividing the data in small subsets the easiest (if long and tedious) way to proceed?\nP.S. I am a noob at python\nP.P.S. In case I \"precompute\" the metric, in what form do I have to submit my precomputed data?\nLike this? \n0 - event1 - event2 - ...\nevent1 - 0 - distance(event1,event2) - ...\nevent2 - distance(event1,event2) - 0\nPlease, help?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":816,"Q_Id":26955646,"Users Score":0,"Answer":"Have you tried metric=\"precomputed\"?\nThen pass the distance matrix to the DBSCAN.fit function instead of the data.\nFrom the documentation:\n\nX array [n_samples, n_samples] or [n_samples, n_features] :\nArray of distances between samples, or a feature array. The array is treated as a feature array unless the metric is given as \u2018precomputed\u2019.","Q_Score":1,"Tags":"python,scikit-learn,dbscan,metric","A_Id":26963180,"CreationDate":"2014-11-16T09:45:00.000","Title":"How to use sklearn's DBSCAN with a spherical metric?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm writing an interface to matplotlib, which requires that lists of floats are treated as corresponding to a colour map, but other types of input are treated as specifying a particular colour.\nTo do this, I planned to use matplotlib.colors.colorConverter, which is an instance of a class that converts the other types of input to matplotlib RGBA colour tuples. However, it will also convert floats to a grayscale colour map. This conflicts with the existing functionality of the package I'm working on and I think that would be undesirable.\nMy question is: is it appropriate\/Pythonic to use an isinstance() check prior to using colorConverter to make sure that I don't incorrectly handle lists of floats? Is there a better way that I haven't thought of?\nI've read that I should generally code to an interface, but in this case, the interface has functionality that differs from what is required.","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":888,"Q_Id":26958759,"Users Score":2,"Answer":"Why not write two separate functions, one that treats its input as a color map, and another that treats its input as a color?  This would be the simplest way to deal with the problem, and would both avoid surprises, and leave you room to expand functionality in the future.","Q_Score":1,"Tags":"python,matplotlib,duck-typing,isinstance","A_Id":26958901,"CreationDate":"2014-11-16T15:45:00.000","Title":"Using isinstance() versus duck typing","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I know that you can adjust a graphs overall opacity in the plot function (opacity = (0 to 1)), but I cannot find anything in the manual or online searches that speak of altering the edge opacity (or transparency)?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1146,"Q_Id":26966487,"Users Score":5,"Answer":"Edge opacity can be altered with the color attribute of the edge or with the edge_color keyword argument of plot(). The colors that you specify there are passed through the color_name_to_rgba function so you can use anything that color_name_to_rgba understands there; the easiest is probably an (R, G, B, A) tuple or the standard HTML #rrggbbaa syntax, where A is the opacity. Unfortunately this is not documented well but I'll fix it in the next release.","Q_Score":3,"Tags":"python,igraph,opacity","A_Id":26967730,"CreationDate":"2014-11-17T06:03:00.000","Title":"Is there a way to alter the edge opacity in Python igraph?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have three arrays a, b, c.\nThe are the shapes (1496,) (1496,) (1496, 1852). I want to join them into a single array or dataframe.\nThe first two arrays are single column vector, where the other has several columns. All three have 1496 rows.\nMy logic is to join into a single array by df=np.concontenate((a,b,c))\nBut the error says dimensions must be the same size. \nI also tried np.hstack()\nThanks\nMPG","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":47,"Q_Id":27003660,"Users Score":0,"Answer":"try np.hstack((a.reshape(1496, 1), b.reshape(1496, 1), c)). To be more general, it is np.hstack((a.reshape(a.size, 1), b.reshape(b.size, 1), c))","Q_Score":0,"Tags":"python,numpy","A_Id":27003691,"CreationDate":"2014-11-18T20:44:00.000","Title":"numpy arrays will not concontenate","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there any reason why I should use numpy to represent pixels in an image manipulation program as opposed to just storing the values in my own array of numbers? Currently I am doing the latter but I see lots of people talking about using numpy for representing pixels as multidimensional arrays. Other than that are there any reasons why I should be using numpy as opposed to my own implementation?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":291,"Q_Id":27011456,"Users Score":2,"Answer":"Well I think you could do that, but maybe less convenient. The reasons could be:\n\nnumpy supports all the matrix manipulations and since it is optimized, could be much faster (You can also switch to OpenBLAS to make it amazingly faster). For image-processing problems, in some cases where images become larger, it could be much demanding for the speed.\nnumpy has lots of useful function handles, such as numpy.fft for Fourier Tranformation, or numpy.conv to do the convolution. This could be critical for image processing.\nAll the modules, or packages are nearly all based on numpy, such as scipy, graphlab and matplotlib. For example, you should use 'import matplotlib.pyplot as plt; plt.imshow()' to show images; well some other arrays could be hardly accepted as the arguments.","Q_Score":2,"Tags":"python,arrays,image-processing,numpy","A_Id":27011549,"CreationDate":"2014-11-19T07:39:00.000","Title":"Shoud I use numpy for a image manipulation program? why","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working with a dataset that has users as Strings (ie. B000GKXY4S). I would like to convert each of these users to int, so I can use  Rating(user: Int, product: Int, rating: Double) class in Apache Spark ALS. What is the most efficient way to do this? Preferably using Spark Scala functions or python native functions.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":735,"Q_Id":27019270,"Users Score":1,"Answer":"If you just want any matchable String to an int - String.hashCode(). However you will have to deal with possible hash collisions. Alternatively you'd have to convert each character to its int value and append (not add) all of these together.","Q_Score":0,"Tags":"java,python,scala,apache-spark","A_Id":27019410,"CreationDate":"2014-11-19T14:24:00.000","Title":"Convert String containing letters to Int efficiently - Apache Spark","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am attempting to import a file into QGIS using a python script.  I'm having a problem getting it to accept the CRS.  Code so far\nfrom PyQt4.QtGui import *\nfrom PyQt4.QtCore import *\nfrom qgis.core import *\nfrom qgis.utils import iface\n----1 Set file name here\nInFlnm='Input.CSV'\n---2 Set pathname here\nInDrPth='G:\/test'\n---3 Build the file name and path for uri\nInFlPth=\"file:\/\/\/\"+InDrPth+InFlnm\n---4 Set import Sting here note only need to set x and y others come for free!\nuri = InFlPth+\"?delimiter=%s&xField=%s&yField=%s\" % (\",\",\"x\",\"y\")\n---5 Load the points into a layer\nbh = QgsVectorLayer(uri, InFlnm, \"delimitedtext\")\n---6 Set the CRS (Not sure if this is working seems to?)\nbh.setCrs(QgsCoordinateReferenceSystem(32365, QgsCoordinateReferenceSystem.EpsgCrsId)\n---7 Display the layer in QGIS (Here I get a syntax error?)\nQgsMapLayerRegistry.instance().addMapLayer(bh)\nNow all the above works OK and QGIC prompts me for a CRS before executing the last line of the script to display the layer - as long as I comment-out step 6\nHowever, if a attempt to set the CRS removing ### from step 6 I get a syntax error reporting on the last line that displays the points (Step 7).  Note sure what the trick is here - I'm pretty new to Python but know my way around some other programming lagnuages","AnswerCount":3,"Available Count":1,"Score":0.1325487884,"is_accepted":false,"ViewCount":8411,"Q_Id":27033261,"Users Score":2,"Answer":"There is a parenthesis missing from the end of  your --6 line of code.","Q_Score":4,"Tags":"python,csv,qgis","A_Id":27033373,"CreationDate":"2014-11-20T06:34:00.000","Title":"Import csv into QGIS using Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to get the beta and the error term from a linear regression(OLS) in python. I am stuck at the statement X=sm.add_constant(X, prepend=True), which is returning an \n\nerror:\"AttributeError: 'module' object has no attribute 'add_constant'\"\n\nI already installed the statsmodels module.","AnswerCount":3,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":8510,"Q_Id":27035257,"Users Score":1,"Answer":"If sm is a defined object in statsmodels, you need to invoke it by statsmodels.sm, or using from statsmodel import sm, then you can invoke sm directly.","Q_Score":0,"Tags":"python,regression,linear-regression","A_Id":27035506,"CreationDate":"2014-11-20T08:50:00.000","Title":"X=sm.add_constant(X, prepend=True) is not working","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to get the beta and the error term from a linear regression(OLS) in python. I am stuck at the statement X=sm.add_constant(X, prepend=True), which is returning an \n\nerror:\"AttributeError: 'module' object has no attribute 'add_constant'\"\n\nI already installed the statsmodels module.","AnswerCount":3,"Available Count":2,"Score":0.3215127375,"is_accepted":false,"ViewCount":8510,"Q_Id":27035257,"Users Score":5,"Answer":"Try importing statsmodel.api\nimport statsmodels.api as sm","Q_Score":0,"Tags":"python,regression,linear-regression","A_Id":61634875,"CreationDate":"2014-11-20T08:50:00.000","Title":"X=sm.add_constant(X, prepend=True) is not working","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to calculate the primal variables w with a polynomial kernel svm, but to do this i need to compute clf.coef_ * clf.support_vectors_. Access is restricted to .coef_ on all kernel types except for linear - is there a reason for this, and is there another way to derive w in that case?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1416,"Q_Id":27050055,"Users Score":6,"Answer":"They simply don't exist for kernels that are not linear: The kernel SVM is solved in the dual space, so in general you only have access to the dual coefficients.\nIn the linear case this can be translated to primal feature space coefficients. In the general case these coefficients would have to live in the feature space spanned by the chosen kernel, which can be infinite dimensional.","Q_Score":1,"Tags":"python,machine-learning,scikit-learn,svm","A_Id":27050808,"CreationDate":"2014-11-20T21:38:00.000","Title":"Is there a reason that scikit-learn only allows access to clf.coef_ with linear svms?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"This is more of a general question about how feasible is it to store data sets under source control.\nI have 20 000 csv files with number data that I update every day. The overall size of the directory is 100Mbytes or so, that are stored on a local disk on ext4 partition.\nEach day changes should be diffs of about 1kbyte.\nI may have to issue corrections on the data, so I am considering versioning the whole directory= 1 toplevel contains 10 level1 dirs, each contain 10 level2 dirs, each containing 200 csv files.\nThe data is written to files by python processes( pandas frames ).\nThe question is about performance of writes where the deltas are small like this compared to the entire data.\nsvn and git come to mind, and they would have python modules to use them.\nWhat works best?\nOther solutions are I am sure possible but I would stick to keeping data is files as is...","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":50,"Q_Id":27069898,"Users Score":0,"Answer":"If you're asking whether it would be efficient to put your datasets under version control, based on your description of the data, I believe the answer is yes.  Both Mercurial and Git are very good at handling thousands of text files. Mercurial might be a better choice for you, since it is written in python and is easier to learn than Git. (As far as I know, there is no good reason to adopt Subversion for a new project now that better tools are available.)\nIf you're asking whether there's a way to speed up your application's writes by borrowing code from a version control system, I think it would be a lot easier to make your application modify existing files in place. (Maybe that's what you're doing already?  It's not clear from what you wrote.)","Q_Score":0,"Tags":"python,git,svn,csv","A_Id":27070113,"CreationDate":"2014-11-21T20:36:00.000","Title":"medium datasets under source control","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have 80,000 documents that are about a very vast number of topics. What I want to do is for every article, provide links to recommend other articles (something like top 5 related articles) that are similar to the one that a user is currently reading. If I don't have to, I'm not really interested in classifying the documents, just similarity or relatedness, and ideally I would like to output a 80,000 x 80,000 matrix of all the documents with the corresponding distance (or perhaps correlation? similarity?) to other documents in the set.\nI'm currently using NLTK to process the contents of the document and get ngrams, but from there I'm not sure what approach I should take to calculate the similarity between documents.\nI read about using tf-idf and cosine similarity, however because of the vast number of topics I'm expecting a very high number of unique tokens, so multiplying two very long vectors might be a bad way to go about it. Also 80,000 documents might call for a lot of multiplication between vectors. (Admittedly, it would only have to be done once though, so it's still an option).\nIs there a better way to get the distance between documents without creating a huge vector of ngrams? Spearman Correlation? or would a more low-tech approach like taking the top ngrams and finding other documents with the same ngrams in the top k-ngrams be more appropriate? I just feel like surely I must be going about the problem in the most brute force way possible if I need to multiply possibly 10,000 element vectors together 320 million times (sum of the arithmetic series 79,999 + 79,998... to 1).\nAny advice for approaches or what to read up on would be greatly appreciated.","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":2909,"Q_Id":27086753,"Users Score":2,"Answer":"You should learn about hashing mechanisms that can be used to calculate similarity between documents. \nTypical hash functions are designed to minimize collision mapping near duplicates to very different hash keys. In cryptographic hash functions, if the data is changed with one bit, the hash key will be changed to a completely different one. \nThe goal of similarity hashing is to create a similarity hash function. Hash based techniques for near duplicate detection are designed for the opposite intent of cryptographic hash algorithms. Very similar documents map to very similar hash keys, or even to the same key. The difference between bitwise hamming distance of keys is a measure of similarity. \nAfter calculating the hash keys, keys can be sorted to increase the speed of near duplicate detection from O(n2) to O(nlog(n)). A threshold can be defined and tuned by analysing accuracy of training data. \nSimhash, Minhash and Local sensitive hashing are three implementations of hash based methods. You can google and get more information about these. There are a lot of research papers related to this topic...","Q_Score":3,"Tags":"python,scikit-learn,nltk","A_Id":27140986,"CreationDate":"2014-11-23T07:50:00.000","Title":"Finding the most similar documents (nearest neighbours) from a set of documents","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on a UCI data set about wine quality. I have applied multiple classifiers and k-nearest neighbor is one of them. I was wondering if there is a way to find the exact value of k for nearest neighbor using 5-fold cross validation. And if yes, how do I apply that?  And how can I get the depth of a decision tree using 5-fold CV?\nThanks!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":185,"Q_Id":27091319,"Users Score":0,"Answer":"I assume here that you mean the value of k that returns the lowest error in your wine quality model.\nI find that a good k can depend on your data.  Sparse data might prefer a lower k whereas larger datasets might work well with a larger k.  In most of my work, a k between 5 and 10 have been quite good for problems with a large number of cases.\nTrial and Error can at times be the best tool here, but it shouldn't take too long to see a trend in the modelling error.\nHope this Helps!","Q_Score":2,"Tags":"ipython,classification,decision-tree,nearest-neighbor,cross-validation","A_Id":27095449,"CreationDate":"2014-11-23T16:30:00.000","Title":"Using cross-validation to find the right value of k for the k-nearest-neighbor classifier","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been trying to write a program that locates clock's face on picture and then proceeds to read time from it. Locating works fairly well, reading time - not so much.\nThe cv2.HoughLines function returns angles at which lines lay (measuring from the top of the image) and their distance from upper-left corner of the image. After a bit of tweaking I've managed to convince my code to find a single line for each of clock's hands, but as for now I remain unable to actually read time from it.\nUsing appropriate formulas I could find intersection of those lines (middle of the clock) and then iterate along the hands in both directions at once. This could tell me the length of each hand (allowing me to tell them apart) as well as at which direction are they pointing. I'm fairly hesitant about implementing this solution though - not only does it seem somehow ugly but also creates certain risks. For example: problems with rounding could cause the program to check the wrong pixel and find the end of line prematurely.\nSo, would you kindly suggest an alternative solution?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2056,"Q_Id":27091836,"Users Score":3,"Answer":"I've managed to solve my problem.\nI've been trying to use Hough Line Transform where I was supposed to use Hough Probabilistic Transform. The moment I got it, I grouped lines drawn along similar functions, sorted them by length, and used arcsine as well as locations of their ends to find precise degrees at wchich hands stood.","Q_Score":4,"Tags":"python,opencv,hough-transform","A_Id":27152696,"CreationDate":"2014-11-23T17:15:00.000","Title":"Reading time from analog clock using Hough Line Transform in Python (OpenCV)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to build my own pmml exporter for Naive Bayes model that I have built in scikit learn.  In reading the PMML documentation it seems that for each feature vector you can either output the model in terms of count data if it is discrete or as a Gaussian\/Poisson distribution if it is continous.  But the coefficients of my scikit learn model are in terms of Empirical log probability of features i.e p(y|x_i).  Is it possible to specify the Bayes input parameters in terms of these probability rather than counts?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":170,"Q_Id":27138752,"Users Score":1,"Answer":"Since the PMML representation of the Naive Bayes model implements representing joint probabilities via the \"PairCounts\" element, one can simply replace that ratio with the probabilities output (not the log probability). Since the final probabilities are normalized, the difference doesn't matter. If the requirements involve a large number of proabilities which are mostly 0, the \"threshold\" attribute of the model can be used to set the default values for such probabilities.","Q_Score":0,"Tags":"python,machine-learning,scikit-learn,pmml","A_Id":27368753,"CreationDate":"2014-11-25T23:31:00.000","Title":"naive bayes feature vectors in pmml","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How to find biggest sum of items not exceeding some value? For example I have 45 values like this: 1.0986122886681098, 1.6094379124341003, 3.970291913552122, 3.1354942159291497, 2.5649493574615367. I need to find biggest possible combination not exceeding 30.7623.\nI can't use bruteforce to find all combinations as amount of combination will be huge. So I need to use some greedy algorithm.","AnswerCount":4,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1217,"Q_Id":27145789,"Users Score":0,"Answer":"This problem can be phrased as a zero-one assignment problem, and solved with a linear programming package, such as GLPK, which can handle integer programming problems. The problem is to find binary variables x[i] such that the sum of x[i]*w[i] is as large as possible, and less than the prescribed limit, where w[i] are the values which are added up. \nMy advice is to use an existing package; combinatorial optimization algorithms are generally very complex. There is probably a Python interface for some package you can use; I don't know if GLPK has such an interface, but probably some package does.","Q_Score":0,"Tags":"python,algorithm,mathematical-optimization,knapsack-problem,greedy","A_Id":27156673,"CreationDate":"2014-11-26T09:43:00.000","Title":"How to find biggest sum of items not exceeding some value?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Looking up at different feature matching tutorials I've noticed that it's tipical to illustrate how the matching works by plotting side by side the same image in two different version (one normal and the other one rotated or distorted). I want to work on feature matching by using two distinct images (same scene shot from slightly different angles). How do I plot them together side by side? \nI'm willing to use skimage on python 2.7","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":722,"Q_Id":27152624,"Users Score":1,"Answer":"See skimage.feature.plot_matches, pass empty list of keypoints and matches if you only want to plot the images without points.","Q_Score":3,"Tags":"python-2.7,image-processing,plot,scikit-image","A_Id":27156595,"CreationDate":"2014-11-26T15:14:00.000","Title":"Plot two images side by side with skimage","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In one run my python script calculates and returns the results for the variables A, B, C. \nI would like to append the results run by run, row by row to a human-readable file. \nAfter the runs i, I want to read the data back as numpy.arrays of the columns.\ni   | A B C \n1 | 3 4 6 \n2 | 4 6 7 \nAnd maybe even access the row where e.g. A equals 3 specifically.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":105,"Q_Id":27157087,"Users Score":0,"Answer":"Thanks for your thoughts. These two options came to my mind too but I need the mixture of both: My specific use case requires the file to be human readable - as far as I know pickling does not provide that and saving to a dictionary destroys the order. I need the data to be dropped as they need to be manipulated in other scripts before the next data is produced. \nThe not very elegant way I am doing it now: numpy.savetxt() to files labeled by the run, and bash \"cat\" applied in the end.","Q_Score":0,"Tags":"python,numpy,save,append","A_Id":27238940,"CreationDate":"2014-11-26T19:10:00.000","Title":"Append data to end of human-readable file Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"For unit testing a method, I want to compare a CSV file generated by that method (the actual result) against a manually created CSV (the expected result). \nThe files are considered equal, if the fields of the first row are exactly the same (i.e. the headers), and if the remaining row contain the same information.\nThe following things must not matter: order of the columns, order of the rows (except for the header row), empty lines, end-of-line encoding, encoding of boolean values.","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":1472,"Q_Id":27162717,"Users Score":1,"Answer":"store in a dictionary with corresponding head of csv file as key and first row as values\n  read the second file and check against with dictionary.","Q_Score":1,"Tags":"python,unit-testing,csv","A_Id":27162750,"CreationDate":"2014-11-27T03:42:00.000","Title":"Python: How to check that two CSV files with header rows contain same information disregarding row and column order?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have uploaded some data into Elastic server as \" job id , job place , job req , job desc \". My index is my_index and doctype = job_list.\nI need to write a query to find a particular term say \" Data Analyst \" and it should give me back matching results with a specified field like \" job place \" .\nie, Data Analyst term matching in the documents , and I need to have all \"job place\" information only.\nAny help. I tried curd . but not working. if it is in python good.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":5426,"Q_Id":27166357,"Users Score":1,"Answer":"the above search example looks correct.Try lowercasing the Data \"Analyst\" as \"data analyst\".\nif doesn't help post your mappings,query you firing and response you are getting.","Q_Score":0,"Tags":"python,search,curl,elasticsearch","A_Id":27177167,"CreationDate":"2014-11-27T08:43:00.000","Title":"Elastic Search query filtering","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a tf-idf matrix already, with rows for terms and columns for documents. Now I want to train a LDA model with the given terms-documents matrix. The first step seems to be using gensim.matutils.Dense2Corpus to convert the matrix into the corpus format. But how to construct the id2word parameter? I have the list of the terms (#terms==#rows) but I don't know the format of the dictionary so I cannot construct the dictionary from functions like gensim.corpora.Dictionary.load_from_text. Any suggestions? Thank you.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":505,"Q_Id":27177721,"Users Score":1,"Answer":"id2word must map each id (integer) to term (string).\nIn other words, it must support id2word[123] == 'koala'.\nA plain Python dict is the easiest option.","Q_Score":3,"Tags":"python-3.x,tf-idf,lda,topic-modeling,gensim","A_Id":27378733,"CreationDate":"2014-11-27T19:37:00.000","Title":"Training a LDA model with gensim from some external tf-idf matrix and term list","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to generate a uniformly random coordinate that is inside a convex bounding box defined by its (at least) 4 vertices (for the case of a tetrahedron).\nCan someone suggest an algorithm that I can use?\nThanks!\n\nIf a point is generated in a bounding box, how do you detect whether or not it is outside the geometry but inside the box?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1463,"Q_Id":27192467,"Users Score":3,"Answer":"There's a lot that's unspecified in your question, such as what distribution you want to use. For the sake of this answer, I'll assume a uniform distribution.\nThe straightforward way to handle an arbitrary volume uniform distribution is to choose three uniformly random numbers as coordinates in the range of the bounding rectilinear solid enclosing your volume, then check to see if the chosen coordinate lies within the volume. If the coordinate is not within the volume, discard it and generate a new one.\nIf this is not sufficient, due to its non-constant performance or whatever other reason, you'll need to constrain your problem (say, to only tetrahedra) and do a bunch of calculus to compute the necessary random distributions and model the dependencies between the axes.\nFor example, you could start with the x axis and integrate the area of the intersecting shapes between the volume and the plane where x = t. This will give you a function p(x) which, when normalized, is the probability density function along the X axis. (If you want nonuniform distribution, you need to put that in the integrated function, too.)\nThen you need to do another set of integrals to determine p(y|x0), the probability distribution function on the Y axis given the chosen x coordinate. Finally, you'll need to determine p(z|x0,y0), the probability distribution function on the z axis.\nOnce you have all this, you need to use whatever random number algorithm you have to choose random numbers in these distributions: first choose x0 from p(x), then use that to choose y0 from p(y|x0), then use those to choose z0 from p(z|x0,y0), and you'll have your result (x0, y0, z0).\n\nThere are various algorithms to determine if a point is outside a volume, but a simple one could be:\n\nFor each polygon face:\n\nCompute its characteristic planes.\n\nUse cross product to compute plane normals.\nOne vertex of the face and the plane normal are sufficient to define the plane.\nRemember the right-hand rule and choose the points so that the plane normal consistently points into or out of the polyhedron.\n\nCheck that the random point lies on the \"inside\" half-space of that plane.\n\nA half-space is the set of all points on one side of the plane.\nCompute the vector from the plane vertex to the random point.\nCompute the dot product between the plane normal and this vector.\n\n\nIf you defined the plane normals to point out of the polyhedron, then all dot products must be negative.\nIf you defined the plane normals to point into the polyhedron, then all dot products must be positive.\n\nNote that you only have to recompute characteristic planes when the volume moves, not for each random point.\nThere are probably much better algorithms out there, and their discussion is outside the scope of this question and answer. This algorithm is what I could come up with with no research, and is probably as good as a bubble sort.","Q_Score":0,"Tags":"python,random,spatial,coordinate","A_Id":27192613,"CreationDate":"2014-11-28T16:11:00.000","Title":"Generate a random point in space (x, y, z) with a boundary","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":1,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am having an algorithm written in python (not hadoop compatible i.e. not mapper.py and reducer.py) and it is running perfectly in local system (not hadoop).  My objective is to run this in hadoop.\nOption 1: Hadoop streaming.  But, I need to convert this python script into mapper and reducer.  Any other way?\nOption 2: To run this python script through Storm.  But, I am using cloudera which doesn't have Storm.  either I need to install storm in cloudera or need to use Spark.  If I install storm in cloudera.  Is it better option?\nOption 3: To run this python script through Spark (Cloudera).  Is it possible.  \nThis algorithm is not for real time processing.  But, we want to process it in hadoop technology.\nPlease help with other suitable solution.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1217,"Q_Id":27192852,"Users Score":1,"Answer":"First and foremost what are you trying to achieve? What does running on Hadoop technology mean to you? If the goal is to work with a lot of data, this is one thing, if it's to parallelize the algorithm, it's another. My guess is you want both.\nFirst thing is: is the algorithm parallelizable? Can it run on multiple pieces of data at the same time and gather them all in the end to make the final answer? Some algorithms are not, especially if they are recursive and require previously computed data to process the next.\nIn any case, running on Hadoop means running using Hadoop tools, whether it is Spark, Storm or other services that can run on Python, taking advantage of Hadoop means writing your algorithm for it.\nif your algorithm is parallelizable, then likely you can easily take the piece that processes one piece of data and adapt it to run with Spark or Storm on huge datasets.","Q_Score":0,"Tags":"python,hadoop,apache-spark","A_Id":27195171,"CreationDate":"2014-11-28T16:35:00.000","Title":"To run python script in apache spark\/Storm","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I've been installing few Library\/Toolkit for Python like NLTK, SciPy and NumPy on my Ubuntu. I would like to try to use Anaconda distribution though. Should I remove my existing libraries before installing Anaconda?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":3390,"Q_Id":27215170,"Users Score":4,"Answer":"There is no need to remove your system Python. Anaconda sits alongside it. When it installs, it adds a line to your .bashrc that adds the Anaconda directory first in your PATH. This means that whenever you type python or ipython in the terminal, it will use the Anaconda Python (and the Anaconda Python will automatically use all the Anaconda Python libraries like numpy and scipy rather than the system ones). You should leave the system Python alone, as some system tools use it. The important points are:\n\nWhichever Python is first on your PATH is what gets used when you use Python in the terminal. If you create a conda environment with conda and use source activate it will put that environment first on the PATH. \nEach Python (Anaconda or the system) will use its own libraries and not look at the others (this is not true if you set the PYTHONPATH environment variable, but I recommend that you don't).","Q_Score":5,"Tags":"python,anaconda","A_Id":27239565,"CreationDate":"2014-11-30T16:04:00.000","Title":"Integrating exisiting Python Library to Anaconda","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"So I have a particular task I need help with, but I was not sure how to do it. I have a model for the formation of ties between a fixed set of network nodes. So I want to set up a window or visualization that shows the set of all nodes on some sort of 2-dimensional or 3-dimensional grid. Then for each timestep, I want to update the visualization window with the latest set of ties between nodes. So I would start with a set of nodes positioned in space, and then with each timestep the visualization will gradually add the new edges. \nThe challenge here is that I know in something like networkx, redrawing the network at each timestep won't work. Many of the common network display algorithms randomly place nodes so that as to maximize the distance between thenm and better show the edges. So if I were to redraw the network at each timestep, the nodes would end up in different locations each time, and it would be hard to identify the pattern of network growth. That is why I want a set of static nodes, so I can see how the edges get added at each timestep.\nI am looking to visualize about 100 nodes at a time. So I will start with a small number of nodes like 20 or so, and gradually build up to 100 nodes. After the model is validated, then I would build up to 1000 or 2000 nodes. Of course it is hard to visualize 1000 or 2000 node network, that is why I just want to make sure I can visualize the network when I just have 100 nodes in the simulation.\nI was not sure if I could do this in webgl or something, or if there is a good way to do this in python. I can use Vispy for communication between python and webgl if needed.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":308,"Q_Id":27217051,"Users Score":0,"Answer":"This looks like a good use-case for Vispy indeed. You'd need to use a PointVisual for the nodes, and a LineVisual for the edges. Then you can update the edges in real time as the simulation is executed.\nThe animation would also work in the IPython notebook with WebGL.\nNote that other graphics toolkits might also work for you (although you'd not necessarily have GPU acceleration through OpenGL) if you specify static positions for the nodes. I think you can fix the nodes positions with d3js or networkx instead of relying on an automatic layout algorithm.","Q_Score":0,"Tags":"python,opengl,webgl,ipython,vispy","A_Id":27259240,"CreationDate":"2014-11-30T19:08:00.000","Title":"network animation with static nodes in python or even webgl","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have been trying to build a prediction model using a user\u2019s data. Model\u2019s input is documents\u2019 metadata (date published, title etc) and document label is that user\u2019s preference (like\/dislike). I would like to ask some questions that I have come across hoping for some answers:\n\nThere are way more liked documents than disliked. I read somewhere that if somebody train\u2019s a model using way more inputs of one label than the other this affects the performance in a bad way (model tends to classify everything to the label\/outcome that has the majority of inputs\nIs there possible to have input to a ML algorithm e.g logistic regression be hybrid in terms of numbers and words and how that could be done, sth like:\ninput = [18,23,1,0,\u2019cryptography\u2019] with  label = [\u2018Like\u2019]\nAlso can we use a vector ( that represents a word, using tfidf etc) as an input feature (e.g. 50-dimensions vector) ?\nIn order to construct a prediction model using textual data the only way to do so is by deriving a dictionary out of every word mentioned in our documents and then construct a binary input that will dictate if a term is mentioned or not? Using such a version though we lose the weight of the term in the collection right?\nCan we use something as a word2vec vector as a single input in a supervised learning model?\n\nThank you for your time.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":232,"Q_Id":27255560,"Users Score":1,"Answer":"You either need to under-sample the bigger class (take a small random sample to match the size of the smaller class), over-sample the smaller class (bootstrap sample), or use an algorithm that supports unbalanced data - and for that you'll need to read the documentation.\nYou need to turn your words into a word vector. Columns are all the unique words in your corpus. Rows are the documents. Cell values are one of: whether the word appears in the document, the number of times it appears, the relative frequency of its appearance, or its TFIDF score. You can then have these columns along with your other non-word columns.\n\nNow you probably have more columns than rows, meaning you'll get a singularity with matrix-based algorithms, in which case you need something like SVM or Naive Bayes.","Q_Score":2,"Tags":"python,machine-learning,language-features,feature-selection","A_Id":27256151,"CreationDate":"2014-12-02T17:36:00.000","Title":"Training a Machine Learning predictor","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Can you shed some light on the interaction between the Python interpreter distributed with OS X and the one that can be installed through MacPorts?\nWhile installing networkx and matplotlib I am having difficulties with the interaction of \/usr\/bin\/python and \/opt\/local\/bin\/python2.7. (The latter is itself a soft pointer to \/opt\/local\/Library\/Frameworks\/Python.framework\/Versions\/2.7\/bin\/python2.7)\nHow can I be certain which Python, pip, and Python libraries I am using at any one time?\nMore importantly, it appears that installing matplotlib is not possible on Lion. It fails with Requires numpy 1.6 or later to build.  (Found 1.5.1). If I upgrade by running sudo pip install --upgrade numpy, it does not help. Subsequently attempting to install matplotlib (sudo \/usr\/local\/bin\/pip install matplotlib) still fails with the same (Requires numpy 1.6...) message. How can I install matplotlib?","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":10161,"Q_Id":27308234,"Users Score":8,"Answer":"Points to keep in mind about Python\n\nIf a script foobar.py starts with #!\/usr\/bin\/env python, then you will always get the OS X Python. That's the case even though MacPorts puts \/opt\/local\/bin ahead of \/usr\/bin in your path. The reason is that MacPorts uses the name python2.7. If you want to use env and yet use MacPorts Python, you have to write #!\/usr\/bin\/env python2.7.\nIf a script foobar.py starts explicitly with #!\/usr\/bin\/python or with #!\/opt\/local\/bin\/python2.7, then the corresponding Python interpreter will be used.\n\nWhat to keep in mind about pip\n\nTo install pip for \/usr\/bin\/python, you need to run sudo \/usr\/bin\/easy_install pip. You then call pip (which will not be installed by easy_install in \/usr\/bin\/pip, but rather in \/usr\/local\/bin\/pip)\nTo install pip for \/opt\/local\/bin\/python2.7, you need to run sudo port install py27-pip. You would then call pip-2.7. You will get the pip in \/opt\/local\/bin. Be careful, because if you type pip2.7 you will get \/usr\/local\/bin\/pip2.7 (the OS X pip).\n\nInstalling networkx and matplotlib\n\nTo install networkx for the OS X Python you would run sudo \/usr\/local\/bin\/pip install networkx. I don't know how to install matplotlib on OS X Lion. It may be that OS X has to stick to numpy 1.5.1 because it uses it internally.\nTo install networkx and matplotlib for MacPorts-Python, call sudo pip-2.7 install networkx and sudo pip-2.7 install matplotlib. matplotlib installs with a lot of warnings, but it passes.","Q_Score":6,"Tags":"python,macos,python-2.7,numpy,matplotlib","A_Id":27308244,"CreationDate":"2014-12-05T03:25:00.000","Title":"\/usr\/bin\/python vs \/opt\/local\/bin\/python2.7 on OS X","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":1,"Web Development":0},{"Question":"Can you shed some light on the interaction between the Python interpreter distributed with OS X and the one that can be installed through MacPorts?\nWhile installing networkx and matplotlib I am having difficulties with the interaction of \/usr\/bin\/python and \/opt\/local\/bin\/python2.7. (The latter is itself a soft pointer to \/opt\/local\/Library\/Frameworks\/Python.framework\/Versions\/2.7\/bin\/python2.7)\nHow can I be certain which Python, pip, and Python libraries I am using at any one time?\nMore importantly, it appears that installing matplotlib is not possible on Lion. It fails with Requires numpy 1.6 or later to build.  (Found 1.5.1). If I upgrade by running sudo pip install --upgrade numpy, it does not help. Subsequently attempting to install matplotlib (sudo \/usr\/local\/bin\/pip install matplotlib) still fails with the same (Requires numpy 1.6...) message. How can I install matplotlib?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":10161,"Q_Id":27308234,"Users Score":0,"Answer":"May I also suggest using Continuum Analytics \"anaconda\" distribution. One benefit in doing so would be that you won't then need to modify he standard OS X python environment.","Q_Score":6,"Tags":"python,macos,python-2.7,numpy,matplotlib","A_Id":27400616,"CreationDate":"2014-12-05T03:25:00.000","Title":"\/usr\/bin\/python vs \/opt\/local\/bin\/python2.7 on OS X","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":1,"Web Development":0},{"Question":"I am using the function numpy.random.normal(0,0.1,20) to generate some numbers. Given below is the output I get from the function. The problem is I want these numbers to be in an array format. \n[ 0.13500488  0.11023982  0.09908623 -0.01437589  0.00619559 -0.17200946\n -0.00501746  0.07422642  0.1226481  -0.01422786 -0.02986386 -0.02507335\n -0.12959589 -0.09346143 -0.01287027  0.02656667 -0.07538371 -0.10534301\n -0.02208811 -0.14634084]\nCan anyone help me?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":330,"Q_Id":27311941,"Users Score":1,"Answer":"Just putting a list(...) call around your call to normal will turn it into a regular Python list.","Q_Score":0,"Tags":"python,arrays,numpy","A_Id":27312169,"CreationDate":"2014-12-05T08:45:00.000","Title":"How to convert numpy distribution to an array?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Trying to update NumPY by running pip install -U numpy, which yields \"Requirement already up-to-date: numpy in \/Library\/Python\/2.7\/site-packages\". Then checking the version with import numpy and numpy.version.version yields '1.6.2' (old version). Python is importing numpy via the path '\/System\/Library\/Frameworks\/Python.framework\/Versions\/2.7\/Extras\/lib\/python\/numpy'. Please help me out here.","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":708,"Q_Id":27327104,"Users Score":0,"Answer":"The new NumPY version would install (via pip) into the System path, where it wasn't being recognized by Python. To solve this I ran pip install --user numpy==1.7.1 to specify I want NumPY version 1.7.1 on my Python (user) path.\n:)","Q_Score":1,"Tags":"python,macos,numpy","A_Id":27370090,"CreationDate":"2014-12-06T01:02:00.000","Title":"OS X not using most recent NumPY version","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Trying to update NumPY by running pip install -U numpy, which yields \"Requirement already up-to-date: numpy in \/Library\/Python\/2.7\/site-packages\". Then checking the version with import numpy and numpy.version.version yields '1.6.2' (old version). Python is importing numpy via the path '\/System\/Library\/Frameworks\/Python.framework\/Versions\/2.7\/Extras\/lib\/python\/numpy'. Please help me out here.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":708,"Q_Id":27327104,"Users Score":0,"Answer":"You can remove the old version of numpy from \n\n\/System\/Library\/Frameworks\/Python.framework\/Versions\/2.7\/Extras\/lib\/python\/numpy\n\n.\nJust delete the numpy package from there and then try to import numpy from the python shell.","Q_Score":1,"Tags":"python,macos,numpy","A_Id":27328371,"CreationDate":"2014-12-06T01:02:00.000","Title":"OS X not using most recent NumPY version","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've got a database with property owners; I would like to count the number of properties owned by each person, but am running into standard mismatch problems:\nREDEVELOPMENT AUTHORITY vs. REDEVELOPMENT AUTHORITY O vs. PHILADELPHIA REDEVELOPMEN vs. PHILA. REDEVELOPMENT AUTH\nCOMMONWEALTH OF PENNA vs. COMMONWEALTH OF PENNSYLVA vs. COMMONWEALTH OF PA\nTRS UNIV OF PENN vs. TRUSTEES OF THE UNIVERSIT\nFrom what I've seen, this is a pretty common problem, but my problem differs from those with solutions I've seen for two reasons:\n1) I've got a large number of strings (~570,000), so computing the 570000 x 570000 matrix of edit distances (or other pairwise match metrics) seems like a daunting use of resources\n2) I'm not focused on one-off comparisons--e.g., as is most common for what I've seen from big data fuzzy matching questions, matching user input to a database on file. I have one fixed data set that I want to condense once and for all.\nAre there any well-established routines for such an exercise? I'm most familiar with Python and R, so an approach in either of those would be ideal, but since I only need to do this once, I'm open to branching out to other, less familiar languages (perhaps something in SQL?) for this particular task.","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":963,"Q_Id":27383896,"Users Score":1,"Answer":"That is exactly what I am facing at my new job daily (but lines counts are few million). My approach is to: \n1) find a set of unique strings by using p = unique(a) \n2) remove punctuation, split strings in p by whitespaces, make a table of words' frequencies, create a set of rules and use gsub to \"recover\" abbreviations, mistyped words, etc. E.g. in your case \"AUTH\" should be recovered back to \"AUTHORITY\", \"UNIV\" -> \"UNIVERSITY\" (or vice versa)\n3) recover typos if I spot them by eye\n4) advanced: reorder words in strings (to often an improper English) to see if the two or more strings are identical albeit word order (e.g. \"10pack 10oz\" and \"10oz 10pack\").","Q_Score":0,"Tags":"python,sql,r,fuzzy-search,fuzzy-logic","A_Id":27384466,"CreationDate":"2014-12-09T16:26:00.000","Title":"fuzzy matching lots of strings","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've got a database with property owners; I would like to count the number of properties owned by each person, but am running into standard mismatch problems:\nREDEVELOPMENT AUTHORITY vs. REDEVELOPMENT AUTHORITY O vs. PHILADELPHIA REDEVELOPMEN vs. PHILA. REDEVELOPMENT AUTH\nCOMMONWEALTH OF PENNA vs. COMMONWEALTH OF PENNSYLVA vs. COMMONWEALTH OF PA\nTRS UNIV OF PENN vs. TRUSTEES OF THE UNIVERSIT\nFrom what I've seen, this is a pretty common problem, but my problem differs from those with solutions I've seen for two reasons:\n1) I've got a large number of strings (~570,000), so computing the 570000 x 570000 matrix of edit distances (or other pairwise match metrics) seems like a daunting use of resources\n2) I'm not focused on one-off comparisons--e.g., as is most common for what I've seen from big data fuzzy matching questions, matching user input to a database on file. I have one fixed data set that I want to condense once and for all.\nAre there any well-established routines for such an exercise? I'm most familiar with Python and R, so an approach in either of those would be ideal, but since I only need to do this once, I'm open to branching out to other, less familiar languages (perhaps something in SQL?) for this particular task.","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":963,"Q_Id":27383896,"Users Score":1,"Answer":"You can also use agrep() in R for fuzzy name matching, by giving a percentage of allowed mismatches.  If you pass it a fixed dataset, then you can grep for matches out of your database.","Q_Score":0,"Tags":"python,sql,r,fuzzy-search,fuzzy-logic","A_Id":27385088,"CreationDate":"2014-12-09T16:26:00.000","Title":"fuzzy matching lots of strings","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've installed on my raspberry opencv python module and everything was working fine. Today I've compiled a C++ version of OpenCV and now when I want to run my python script i get this error:\n\nTraceback (most recent call last):\n    File \"wiz.py\", line 2, in \n      import cv2.cv as cv\n  ImportError: No module named cv","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1523,"Q_Id":27384395,"Users Score":3,"Answer":"Check the API docs for 3.0. Some python functions return more parameters or in a different order.\nexample: cv2.cv.CV_HAAR_SCALE_IMAGE was replaced with cv2.CASCADE_SCALE_IMAGE\nor\n(cnts, _) = cv2.findContours(...) now returning the modified image as well\n(modImage, cnts, _) = cv2.findContours(...)","Q_Score":0,"Tags":"python,opencv,raspberry-pi","A_Id":27387097,"CreationDate":"2014-12-09T16:53:00.000","Title":"OpenCV python on raspberry","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I know this is a very vague question but I'm trying to figure out the best way to do document classification. I have two sets training and testing. The training set is a set of documents each labeled 1 or 0. The documents are labeled 1 if is it a informative summary and a 0 if it is not. I'm trying to create a supervised classifier. I can't decide which nlp toolkit to use. I'm thinking nltk. Do you have any suggestions? I have to write the classifier in python. Also any specific types of classifiers. I've been doing research but can't seem to get a good answer.","AnswerCount":3,"Available Count":1,"Score":0.1325487884,"is_accepted":false,"ViewCount":211,"Q_Id":27393613,"Users Score":2,"Answer":"I generally recommend using Scikit as Slater suggested. Its more scalable than NLTK. For this task using Naive Bayes Classifier or Support Vector Machine is your best bet. You are dealing with binary classification so you don't have multi classes. \nAs for the features that you should extract, try unigrams, bigrams, trigrams, and TFIDF features. Also, LDA might turn out useful, but start with the easier ones such as unigrams. \nThis also depends on the type and length of texts you are dealing with. Document classification has been around for more than a decade and there are so many good papers that you could find useful.\nLet me know if you have any further questions.","Q_Score":1,"Tags":"python,machine-learning,nlp,nltk","A_Id":27412604,"CreationDate":"2014-12-10T04:58:00.000","Title":"binary document classifcation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I installed numpy, scipy and scikit-learn using pip on mac os. However in PyCharm, all imports work except when i try importing sklearn. I tried doing it in the Python shell and it worked fine. Any ideas as to what is causing this? \nAlso, not sure if it is relevant, but i installed scikit-learn last.\nThe error I receive is unresolved reference","AnswerCount":3,"Available Count":2,"Score":0.3215127375,"is_accepted":false,"ViewCount":10664,"Q_Id":27406345,"Users Score":5,"Answer":"This worked for me:\nIn my PyCharm Community Edition 5.0.4, Preference -> Project Interpreter ->  check whether sklearn package is installed for the current project interpreter, if not, install it.","Q_Score":10,"Tags":"python,scikit-learn,pycharm","A_Id":38023538,"CreationDate":"2014-12-10T16:43:00.000","Title":"import sklearn not working in PyCharm","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I installed numpy, scipy and scikit-learn using pip on mac os. However in PyCharm, all imports work except when i try importing sklearn. I tried doing it in the Python shell and it worked fine. Any ideas as to what is causing this? \nAlso, not sure if it is relevant, but i installed scikit-learn last.\nThe error I receive is unresolved reference","AnswerCount":3,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":10664,"Q_Id":27406345,"Users Score":6,"Answer":"I managed to figure it out, i had to go to the project interpreter and change the python distribution as it had defaulted the OS installed Python rather than my own installed distribution.","Q_Score":10,"Tags":"python,scikit-learn,pycharm","A_Id":27422973,"CreationDate":"2014-12-10T16:43:00.000","Title":"import sklearn not working in PyCharm","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"When I start Spyder, it automatically imports pandas and numpy.  Is it possible to have Spyder ignore these modules?\nI see these are imported in multiple Spyderlib files.  For example, pandas gets imported in spyderlib\/widgets\/importwizard.py, spyderlib\/baseconfig.py, etc.\n(I'm trying to debug something in pandas and I'd like to import it for the first time in a debugging session in Spyder)","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":651,"Q_Id":27414466,"Users Score":1,"Answer":"(Spyder dev here) This is not possible. If Pandas is installed on the same Python installation where Spyder is, then Spyder will import Pandas to: a) report to its users the minimal version needed to view DataFrames in the Variable Explorer and b) import csv files as DataFrames.\nThe only solution I can suggest you is this:\n\nCreate a new virtualenv or conda environment\nInstall there Spyder and its dependencies, but not Pandas. Spyder dependencies can be checked under the menu Help > Optional dependencies\nStart your virtualenv\/conda env Spyder\nGo to\nTools > Preferences > Console > Advanced Settings > Python executable\nselect the option Use the following Python interpreter and write (or select) there the path to the interpreter where you have Pandas installed (e.g. \/usr\/bin\/python)\nStart a new Python\/IPython console and import pandas there.","Q_Score":0,"Tags":"python,spyder","A_Id":27453175,"CreationDate":"2014-12-11T02:35:00.000","Title":"Stop Spyder from importing modules like `numpy`, `pandas`, etc","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I recently came across the doc2vec addition to Gensim. How can I use pre-trained word vectors (e.g. found in word2vec original website) with doc2vec?\nOr is doc2vec getting the word vectors from the same sentences it uses for paragraph-vector training?\nThanks.","AnswerCount":4,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":40470,"Q_Id":27470670,"Users Score":25,"Answer":"Note that the \"DBOW\" (dm=0) training mode doesn't require or even create word-vectors as part of the training. It merely learns document vectors that are good at predicting each word in turn (much like the word2vec skip-gram training mode). \n(Before gensim 0.12.0, there was the parameter train_words mentioned in another comment, which some documentation suggested will co-train words. However, I don't believe this ever actually worked. Starting in gensim 0.12.0, there is the parameter dbow_words, which works to skip-gram train words simultaneous with DBOW doc-vectors. Note that this makes training take longer \u2013 by a factor related to window. So if you don't need word-vectors, you may still leave this off.)\nIn the \"DM\" training method (dm=1), word-vectors are inherently trained during the process along with doc-vectors, and are likely to also affect the quality of the doc-vectors. It's theoretically possible to pre-initialize the word-vectors from prior data. But I don't know any strong theoretical or experimental reason to be confident this would improve the doc-vectors. \nOne fragmentary experiment I ran along these lines suggested the doc-vector training got off to a faster start \u2013 better predictive qualities after the first few passes \u2013 but this advantage faded with more passes. Whether you hold the word vectors constant or let them continue to adjust with the new training is also likely an important consideration... but which choice is better may depend on your goals, data set, and the quality\/relevance of the pre-existing word-vectors. \n(You could repeat my experiment with the intersect_word2vec_format() method available in gensim 0.12.0, and try different levels of making pre-loaded vectors resistant-to-new-training via the syn0_lockf values. But remember this is experimental territory: the basic doc2vec results don't rely on, or even necessarily improve with, reused word vectors.)","Q_Score":44,"Tags":"python,nlp,gensim,word2vec,doc2vec","A_Id":30337118,"CreationDate":"2014-12-14T15:13:00.000","Title":"How to use Gensim doc2vec with pre-trained word vectors?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"It seesms like we can have n dimensional array by numpy.array\nalso numpy.matrix is exact matrix I want.\nwhich one is generally used?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":83,"Q_Id":27521836,"Users Score":1,"Answer":"Objects of type numpy.array are n-dimensional, meaning they can represent 2-dimensional matrices, as well as 3D, 4D, 5D, etc.\nThe numpy.matrix, however, is designed specifically for the purpose of 2-dimensional matrices. As part of this specialisation, some of the operators are modified, for example * refers to matrix multiplication.\nUse whichever is most sensible for your work, but make sure you remain consistent. If you'll occasionally have to deal with higher-dimensional data then it makes sense to use numpy.array all the time (you can still do matrix multiplication with 2D numpy.array, but you have to use a method as opposed to the * operator).","Q_Score":0,"Tags":"python,numpy","A_Id":27522080,"CreationDate":"2014-12-17T09:09:00.000","Title":"Which object in Numpy Python is good for matrix manipulation? numpy.array or numpy.matrix?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using Python for my research. I have both version of Python on my system: 3.3.2 and 2.7.6. However due to the compatibility with the required packages (openCV, Numpy, Scipy, etc.) and the legacy code, I work most of the time with Python 2.7.6.\nIt took me quite a lot of effort at the beginning to set up the environment ready for my works. E.g. I didn't follow the \"easy\" way of installing all-in-one Anaconda or Enthought Canopy software, instead I installed packages one by one (using pip..), some packages,(scipy, sympy, pandas, lxml) could not be installed by pip and I had to installed using a MSI file.\nNow all of them are working fine. I see that Python released the newer version: 2.7.9. My questions are:\n(1) is it worth upgrading from 2.7.6 to 2.7.9, any benefit in performance, security, stability,...?\n(2) will it break\/overwrite the current setup of my 2.7.6 environment? I notice there are 2 folders on my C:\\, which are Python27 and Python33. As the logic, Python 2.7.9 will also be in the same folder Python27 (as 2.7.6). Do I need to re-install\/reconfigure the packages again?\n(If there will be a lot of hassles, then perhaps I'll follow the mantra: \"if it is not broken, don't fix it\"...)\n\nEDIT:\nThanks for the comments, but as my understanding, this site is about Q&A: got question & get answered.\nIt's a simple and direct question, let me make it clearer: e.g. Python has Lib\/site-packages folder with my packages inside. Will the new installation overwrite that folder, etc. \nPeople may know or not know about this fact, based on their knowledge or experience. I don't want to experiment myself so I asked, just for my curiosity.\nI know there's a trend to reform SO to get better question and answer quality, but I don't know since when the people can be so ridiculously sensitive :) If this one is asked in \"Stack Exchange Programming\" site, then I can understand that it's not well-suited for that site. Now I understand another effect of the trolls: they make a community become over-sensitive and drive the new\/naive newbie away.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":11082,"Q_Id":27568886,"Users Score":1,"Answer":"Upgrading to new version can give you more stable and featured version. Usually this is the case - version 2.7 is mature and stable. I think you do not need to re-install\/reconfigure the packages again because of this stability (2.7.6 and 2.7.9 are 2.7 anyway). Problems are hardly possible, although they may be in very small number of cases. And folder with the subversion X.X will be overwrited, because there are no any folders for minor versions X.X.X\nUnfortunately, I can not give more precise answer.","Q_Score":4,"Tags":"python,python-2.7,opencv,numpy,upgrade","A_Id":27580194,"CreationDate":"2014-12-19T15:18:00.000","Title":"Install Python 2.7.9 over 2.7.6","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a pandas series series.  If I want to get the element-wise floor or ceiling, is there a built in method or do I have to write the function and use apply?  I ask because the data is big so I appreciate efficiency.  Also this question has not been asked with respect to the Pandas package.","AnswerCount":5,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":103550,"Q_Id":27592456,"Users Score":119,"Answer":"You can use NumPy's built in methods to do this: np.ceil(series) or np.floor(series).\nBoth return a Series object (not an array) so the index information is preserved.","Q_Score":92,"Tags":"python,pandas,series,floor,ceil","A_Id":27592508,"CreationDate":"2014-12-21T18:32:00.000","Title":"Floor or ceiling of a pandas series in python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have some values that I need to print in scientific notation (values of the order of 10^-8, -9)\nBut I would like to don't print a long number, only two digits after the .\nsomething as:\n9.84e-08\nand not\n9.84389879870496809597e-08\nHow can I do it? I tried to use \n     \"%.2f\" % a\nwhere 'a' is the number containing the value but these numbers appear as 0.00","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":93,"Q_Id":27604441,"Users Score":0,"Answer":"%f stands for Fixed Point and will force the number to show relative to the number 1 (1e-3 is shown as 0.001). %e stands for Exponential Notation and will give you what you want (1e-3 is shown as 1e-3).","Q_Score":0,"Tags":"python","A_Id":27604701,"CreationDate":"2014-12-22T14:16:00.000","Title":"how to print finite number of digits USING the scientific notation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have some values that I need to print in scientific notation (values of the order of 10^-8, -9)\nBut I would like to don't print a long number, only two digits after the .\nsomething as:\n9.84e-08\nand not\n9.84389879870496809597e-08\nHow can I do it? I tried to use \n     \"%.2f\" % a\nwhere 'a' is the number containing the value but these numbers appear as 0.00","AnswerCount":3,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":93,"Q_Id":27604441,"Users Score":2,"Answer":"try with this :\nprint \"%.2e\"%9.84389879870496809597e-08 #'9.84e-08'","Q_Score":0,"Tags":"python","A_Id":27604491,"CreationDate":"2014-12-22T14:16:00.000","Title":"how to print finite number of digits USING the scientific notation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I read an image (of unknown format, most frequent are PNGs or JPGs) from a buffer.\nI can decode it with cv2.imdecode, I can even check if it is valid (imdecode returns non-None).\nBut how can I reveal the image type (PNG, JPG, something else) of the buffer I've just read?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2355,"Q_Id":27608053,"Users Score":1,"Answer":"There is a standard Python functionimghdr.what. It rulez!\n^__^","Q_Score":1,"Tags":"python,opencv,image-processing","A_Id":27689079,"CreationDate":"2014-12-22T18:02:00.000","Title":"OpenCV: how to get image format if reading from buffer?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm doing a Monte Carlo simulation in Python in which I obtain a set of intensities at certain 2D coordinates and then fit a 2D Gaussian to them. I'm using the scipy.optimize.leastsq function and it all seems to work well except for the following error:\nIntel MKL ERROR: Parameter 6 was incorrect on entry to DGELSD. \nThe problem occurs multiple times in a simulation. I have looked around and understand it is something to do with a bug in Intel's MKL library. I can't seem to find a solution to the problem and so I was looking at an alternative fitting function In could use. If someone does know how to get rid of the problem that would be good also.","AnswerCount":1,"Available Count":1,"Score":-0.1973753202,"is_accepted":false,"ViewCount":860,"Q_Id":27629227,"Users Score":-1,"Answer":"You could try Intel's python distribution. It includes a pre-built scipy optimized with MKL.","Q_Score":6,"Tags":"python,scipy,least-squares,intel-mkl","A_Id":45621086,"CreationDate":"2014-12-23T22:57:00.000","Title":"Intel MKL Error with Gaussian Fitting in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm only aware of the describe() function. Are there any other functions similar to str(), summary(), and head()?","AnswerCount":8,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":53740,"Q_Id":27637281,"Users Score":27,"Answer":"summary() ~ describe()\nhead() ~  head()\n\nI'm not sure about the str() equivalent.","Q_Score":69,"Tags":"python,r,pandas","A_Id":27637837,"CreationDate":"2014-12-24T12:53:00.000","Title":"What are Python pandas equivalents for R functions like str(), summary(), and head()?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've accumulated a set of 500 or so files, each of which has an array and header that stores metadata.  Something like:\n\n2,.25,.9,26  #<-- header, which is actually cryptic metadata\n1.7331,0\n1.7163,0\n1.7042,0\n1.6951,0\n1.6881,0\n1.6825,0\n1.678,0\n1.6743,0\n1.6713,0\n\nI'd like to read these arrays into memory selectively.  We've built a GUI that lets users select one or multiple files from disk, then each are read in to the program.  If users want to read in all 500 files, the program is slow opening and closing each file.  Therefore, my question is: will it speed up my program to store all of these in a single structure?  Something like hdf5?  Ideally, this would have faster access than the individual files.  What is the best way to go about this?  I haven't ever dealt with these types of considerations.  What's the best way to speed up this bottleneck in Python?  The total data is only a few MegaBytes, I'd even be amenable to storing it in the program somewhere, not just on disk (but don't know how to do this)","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":66,"Q_Id":27641616,"Users Score":1,"Answer":"Reading 500 files in python should not take much time, as the overall file size is around few MB. Your data-structure is plain and simple in your file chunks, it ll not even take much time to parse I guess. \nIs the actual slowness is bcoz of opening and closing file, then there may be OS related issue (it may have very poor I\/O.)\nDid you timed it like how much time it is taking to read all the files.?\nYou can also try using small database structures like sqllite. Where you can store your file data and access the required data in a fly.","Q_Score":0,"Tags":"python,database,numpy,dataset,storage","A_Id":27641772,"CreationDate":"2014-12-24T19:51:00.000","Title":"Better way to store a set of files with arrays?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am generating load test data in a Python script for Cassandra.\nIs it better to insert directly into Cassandra from the script, or to write a CSV file and then load that via Cassandra?\nThis is for a couple million rows.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":364,"Q_Id":27678990,"Users Score":0,"Answer":"For a few million, I'd say just use CSV (assuming rows aren't huge); and see if it works. If not, inserts it is :)\nFor more heavy duty stuff, you might want to create sstables and use sstable loader.","Q_Score":0,"Tags":"python,cassandra,load-testing","A_Id":27688141,"CreationDate":"2014-12-28T17:47:00.000","Title":"Python\/Cassandra: insert vs. CSV import","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an image and want to find the sum of a part of it and then compared to a threshold.\nI have a rectangle drawn on the image and this is the area I need to apply the sum.\nI know the cv2.integral function,  but this gives me a matrix as a result. Do you have any suggestion?","AnswerCount":2,"Available Count":2,"Score":0.2913126125,"is_accepted":false,"ViewCount":17785,"Q_Id":27714535,"Users Score":3,"Answer":"sumElems function in OpenCV will help you to find out the sum of the pixels of the whole of the image in python. If you want to find only the sum of a particular portion of an image, you will have to select the ROI of the image on the sum is to be calculated.\nAs a side note, if you had found out the integral image, the very last pixel represents the sum of all the pixels of the image.","Q_Score":2,"Tags":"python,opencv,pixel,integral","A_Id":27717883,"CreationDate":"2014-12-31T00:22:00.000","Title":"how to do the sum of pixels with Python and OpenCV","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an image and want to find the sum of a part of it and then compared to a threshold.\nI have a rectangle drawn on the image and this is the area I need to apply the sum.\nI know the cv2.integral function,  but this gives me a matrix as a result. Do you have any suggestion?","AnswerCount":2,"Available Count":2,"Score":0.4621171573,"is_accepted":false,"ViewCount":17785,"Q_Id":27714535,"Users Score":5,"Answer":"np.sum(img[y1:y2, x1:x2, c1:c2]) Where c1 and c2 are the channels.","Q_Score":2,"Tags":"python,opencv,pixel,integral","A_Id":27738842,"CreationDate":"2014-12-31T00:22:00.000","Title":"how to do the sum of pixels with Python and OpenCV","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have been successful at training a classifier before but today I started getting errors. \nProblem:\nWhen I try to train a classifier using opencv_traincascade.exe I get the following message: \n\"Training parameters are loaded from the parameter file in data folder!\nPlease empty the data folder if you want to use your own set of parameters.\"\nThe trainer then stops midway in stage 0 with the following message:\n===== TRAINING 0-stage =====\nBEGIN\nPOS count : consumed   2 : 2\nTrain dataset for temp stage can not be filled. Branch training terminated.\nCascade classifier can't be trained. Check the used training parameters.\nHere is how I got to the problem:\nI had a parameters file inside the classifier folder where my trainer would usually train classifiers to. I forgot to delete this parameters file before running the traincascade.exe file. Even though I erased the parameter file I still got the same error. \nThanks for helping.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":359,"Q_Id":27788609,"Users Score":0,"Answer":"I solved it!\nI downloaded opencv and all other required programs on another computer and tried running train classifier on another set of pictures. After I verified that it worked in the other computer I copied all files back to my computer and used them.","Q_Score":0,"Tags":"python,opencv,classification,cascade","A_Id":27810170,"CreationDate":"2015-01-05T21:58:00.000","Title":"opencv_traincascade.exe error, \"Please empty the data folder\"?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have to work on large 3D cubes of data. I want to store them in HDF5 files (using h5py or maybe pytables). I often want to perform analysis on just a section of these cubes. This section is too large to hold in memory. I would like to have a numpy style view to my slice of interest, without copying the data to memory (similar to what you could do with a numpy memmap). Is this possible? As far as I know, performing a slice using h5py, you get a numpy array in memory.\nIt has been asked why I would want to do this, since the data has to enter memory at some point anyway. My code, out of necessity, already run piecemeal over data from these cubes, pulling small bits into memory at a time. These functions are simplest if they simply iterate over the entirety of the datasets passed to them. If I could have a view to the data on disk, I simply could pass this view to these functions unchanged. If I cannot have a view, I need to write all my functions to only iterate over the slice of interest. This will add complexity to the code, and make it more likely for human error during analysis. \nIs there any way to get a view to the data on disk, without copying to memory?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":568,"Q_Id":27803331,"Users Score":0,"Answer":"It is unavoidable to not copy that section of the dataset to memory.\nReason for that is simply because you are requesting the entire section, not just a small part of it.\nTherefore, it must be copied completely.\nSo, as h5py already allows you to use HDF5 datasets in the same way as NumPy arrays, you will have to change your code to only request the values in the dataset that you currently need.","Q_Score":10,"Tags":"python,hdf5,pytables,h5py","A_Id":55962515,"CreationDate":"2015-01-06T16:50:00.000","Title":"Is there a way to get a numpy-style view to a slice of an array stored in a hdf5 file?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there any way to plot a histogram in matplot lib with log scale that include 0?\nplt.ylim( ymin = 0 ) doesn't work because log(0) is NaN and matplot lib removes is... :(","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":45,"Q_Id":27819021,"Users Score":2,"Answer":"So I assume that you want to have a logscale on the y axis from what you have written.\nObviously, what you want to achieve won't be possible. log(0) ist NaN because log(0) is not defined mathematically. You could, in theory, set ylim to a very small number close to 0, but that wouldn't help you either. Your y axis would become larger and larger as you approach 0, so you couldn't display whatever you want to show in a way that would make any sense.","Q_Score":0,"Tags":"python,matplotlib,scale,histogram,logarithm","A_Id":27820207,"CreationDate":"2015-01-07T11:58:00.000","Title":"Distinct 0 and 1 on histogram with logscale","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm working on a tool that converts raster layers to arrays for processing with NumPy, and ideally I would like to be able to work with rasters that come packaged in a .gdb without exporting them all (especially if this requires engaging ArcGIS or ArcPy).  \nIs this possible with the OpenFileGDB driver?  From what I can tell this driver seems to treat raster layers the same as vector layers, which gives you access to some data about the layer but doesn't give you the ReadAsArray functionality.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2796,"Q_Id":27821571,"Users Score":5,"Answer":"Currently both FileGDB and OpenFileGDB drivers handle only vector datasets. Raster support is not part of Esri's FGDB API.\nYou will need to use Esri tools to export the rasters to another format, such as GeoTIFF.","Q_Score":0,"Tags":"python,gdal","A_Id":27829029,"CreationDate":"2015-01-07T14:24:00.000","Title":"Working with rasters in file geodatabase (.gdb) with GDAL","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Question\nDo scipy.sparse functions, like csr._mul_matvec release the GIL?\nContext\nPython functions that wrap foreign code (like C) often release the GIL during execution, enabling parallelism with multi-threading.  This is common in the numpy codebase.  Is it also common in scipy.sparse?  If so which operations release the GIL?  If they don't release the GIL then is there a fundamental issue here why not or is it just lack of man-power?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":209,"Q_Id":27883769,"Users Score":2,"Answer":"They do, for Scipy versions >= 0.14.0","Q_Score":4,"Tags":"python,numpy,scipy,sparse-matrix,gil","A_Id":27961586,"CreationDate":"2015-01-11T03:49:00.000","Title":"Do scipy.sparse functions release the GIL?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to collect some data from a .txt file into my python script. The problem is that when the data was collected, it could not collect data in one of the columns, which has given me more commas than normally. It looks like this:\n0,0,,-2235\n1,100,,-2209\n2,200,,-2209\nAll I want is to load the data and remove the commas but when I try with numpy.loadtxt it gives me a value error. What do I do?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":192,"Q_Id":27961552,"Users Score":0,"Answer":"I don't know if this is an option but you could pre-process it using tr -s ',' file.txt. This is a shell command so you'd have to do it either before calling python or using system. The latter might not be the best way since dragon2fly solved the issue using a python function.","Q_Score":0,"Tags":"python,numpy,comma,data-files","A_Id":27961800,"CreationDate":"2015-01-15T10:39:00.000","Title":"Loading data file with too many commas in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm running Paraview 4.2 on Linux. Here's what's happening:\n\nI load my XDMF\/hdf5 data into PV, which contains vector data.\nI apply a glyph filter to the loaded data, and hit apply (thereby using the default mode of Uniform Spatial Distribution).\nNo glyphs appear on screen, and the information tab shows that the filter has no data (0 points, etc.).\nIf I switch to All Points, or Every Nth Point, it works fine and displays the glyphs oriented correctly.\n\nAnnoyingly, if I then make a cone source, and change the input of the glyph to the cone, Uniform Spatial Distribution works fine for the cone.\nNo errors come up anywhere when I do this, whether in the PV gui or through pvpython.\nAny ideas?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":653,"Q_Id":27977626,"Users Score":0,"Answer":"Uniform distribution works by picking a set of random locations in space and finding data points closet to those locations to glyph. Try playing with the Seed to see if that helps pick different random locations that yield better results.\nIf you could share the data, that'd make it easier to figure out what could be going on here, as well.","Q_Score":1,"Tags":"python,paraview","A_Id":27987246,"CreationDate":"2015-01-16T05:07:00.000","Title":"Paraview glyph uniform distribution does not work on my dataset","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":1,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"please how can I randomly select 3 numbers whose sum is 356 and each of these 3 is more than 30?\nSo output should be for example [100, 34, 222]\n(but not [1,5,350])\nI would like to use random module to do this. thank you!","AnswerCount":4,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":131,"Q_Id":28009390,"Users Score":6,"Answer":"This question is rather subjective to the definition of random, and the distribution you wish to replicate.\nThe simplest solution:\n\nChoose a one random number, rand1 : [30,296]\nChoose a second random number, rand2 : [30, (326-Rand1)]\nThen the third cannot be random due to the constraint so calc via 356-(rand1+rand2)","Q_Score":0,"Tags":"python,python-2.7","A_Id":28009442,"CreationDate":"2015-01-18T11:56:00.000","Title":"randomly select 3 numbers whose sum is 356 and each of these 3 is more than 30","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to train a linear model on a very large dataset. \nThe feature space is small but there are too many samples to hold in memory. \nI'm calculating the Gram matrix on-the-fly and trying to pass it as an argument to sklearn Lasso (or other algorithms) but, when I call fit, it needs the actual X and y matrices. \nAny idea how to use the 'precompute' feature without storing the original matrices?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1183,"Q_Id":28024191,"Users Score":0,"Answer":"(My answer is based on the usage of svm.SVC, Lasso may be different.)\nI think that you are supposed pass the Gram matrix instead of X to the fit method.\nAlso, the Gram matrix has shape (n_samples, n_samples) so it should also be too large for memory in your case, right?","Q_Score":4,"Tags":"python,machine-learning,scikit-learn","A_Id":28024414,"CreationDate":"2015-01-19T12:00:00.000","Title":"Using precomputed Gram matrix in sklearn linear models (Lasso, Lars, etc)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dictionary with many entries and a huge vector as values. These vectors can be 60.000 dimensions large and I have about 60.000 entries in the dictionary. To save time, I want to store this after computation. However, using a pickle led to a huge file. I have tried storing to JSON, but the file remains extremely large (like 10.5 MB on a sample of 50 entries with less dimensions). I have also read about sparse matrices. As most entries will be 0, this is a possibility. Will this reduce the filesize? Is there any other way to store this information? Or am I just unlucky?\nUpdate:\nThank you all for the replies. I want to store this data as these are word counts. For example, when given sentences, I store the amount of times word 0 (at location 0 in the array) appears in the sentence. There are obviously more words in all sentences than appear in one sentence, hence the many zeros. Then, I want to use this array tot train at least three, maybe six classifiers. It seemed easier to create the arrays with word counts and then run the classifiers over night to train and test. I use sklearn for this. This format was chosen to be consistent with other feature vector formats, which is why I am approaching the problem this way. If this is not the way to go, in this case, please let me know. I am very much aware that I have much to learn in coding efficiently! \nI also started implementing sparse matrices. The file is even bigger now (testing with a sample set of 300 sentences).\nUpdate 2:\nThank you all for the tips. John Mee was right by not needing to store the data. Both he and Mike McKerns told me to use sparse matrices, which sped up calculation significantly! So thank you for your input. Now I have a new tool in my arsenal!","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":6975,"Q_Id":28057407,"Users Score":0,"Answer":"With 60,000 dimensions do you mean 60,000 elements? if this is the case and the numbers are 1..10 then a reasonably compact but still efficient approach is to use a dictionary of Python array.array objects with 1 byte per element (type 'B').\nThe size in memory should be about 60,000 entries x 60,000 bytes, totaling 3.35Gb of data.\nThat data structure is pickled to about the same size to disk too.","Q_Score":5,"Tags":"python,dictionary,storage,store,pickle","A_Id":28057921,"CreationDate":"2015-01-20T23:59:00.000","Title":"Store large dictionary to file in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to implement a machine learning algorithm in scikit learn, but I don't understand what this parameter random_state does? Why should I use it? \nI also could not understand what is a Pseudo-random number.","AnswerCount":7,"Available Count":2,"Score":0.057080742,"is_accepted":false,"ViewCount":236863,"Q_Id":28064634,"Users Score":2,"Answer":"If there is no randomstate provided the system will use a randomstate that is generated internally. So, when you run the program multiple times you might see different train\/test data points and the behavior will be unpredictable. In case, you have an issue with your model you will not be able to recreate it as you do not know the random number that was generated when you ran the program.\nIf you see the Tree Classifiers - either DT or RF, they try to build a try using an optimal plan. Though most of the times this plan might be the same there could be instances where the tree might be different and so the predictions. When you try to debug your model you may not be able to recreate the same instance for which a Tree was built. So, to avoid all this hassle we use a random_state while building a DecisionTreeClassifier or RandomForestClassifier.\nPS: You can go a bit in depth on how the Tree is built in DecisionTree to understand this better.\nrandomstate is basically used for reproducing your problem the same every time it is run. If you do not use a randomstate in traintestsplit, every time you make the split you might get a different set of train and test data points and will not help you in debugging in case you get an issue.\nFrom Doc:\nIf int, randomstate is the seed used by the random number generator; If RandomState instance, randomstate is the random number generator; If None, the random number generator is the RandomState instance used by np.random.","Q_Score":189,"Tags":"python,random,scikit-learn","A_Id":57910696,"CreationDate":"2015-01-21T10:17:00.000","Title":"Random state (Pseudo-random number) in Scikit learn","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to implement a machine learning algorithm in scikit learn, but I don't understand what this parameter random_state does? Why should I use it? \nI also could not understand what is a Pseudo-random number.","AnswerCount":7,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":236863,"Q_Id":28064634,"Users Score":23,"Answer":"If you don't specify the random_state in your code, then every time you run(execute) your code a new random value is generated and the train and test datasets would have different values each time.\nHowever, if a fixed value is assigned like random_state = 42 then no matter how many times you execute your code the result would be the same .i.e, same values in train and test datasets.","Q_Score":189,"Tags":"python,random,scikit-learn","A_Id":50672222,"CreationDate":"2015-01-21T10:17:00.000","Title":"Random state (Pseudo-random number) in Scikit learn","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been looking into this for a while and was wondering the feasibility of using something like feature detection in OpenCV to do the following:\nI'm working on a project that requires identifying items within a grocery store that do not have barcodes (i.e. produce). I want to build a local database of the various items to be identified and, using images of the items, compare them to the database and tell what the item is.\nThis doesn't need to be perfectly accurate (it doesn't need to tell between different types of apples, for example), but I want it to be able to tell between something like a peach and an orange, but still be able to tell a banana is a banana even though its color is slightly different.\nMy question is, is what I'm trying to do possible using OpenCV? From what I've been reading, identical objects can be tracked with relative ease, but I'm having trouble stumbling upon anything more like what I'm attempting to do.\nAny nudges in the right direction would be immensely appreciated.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":243,"Q_Id":28078555,"Users Score":0,"Answer":"Very open question, but OpenCV is where to be looking. Your best bet would probably be building Haar cascade classifiers. Plenty of reading material on the topic, somewhat overwhelming at first but that is what I would be looking into.","Q_Score":0,"Tags":"python,opencv,image-processing","A_Id":28078626,"CreationDate":"2015-01-21T23:07:00.000","Title":"Detecting similar objects using OpenCV","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I just started using python and definitely need the matplotlib. I'm confused by the fact that there is even not a clear explanation for the basic ideas behind install a lib\/package in python generally. Anyway, I'm using windows and have installed Python 3.4.2 downloaded from the offical website, how should I install the matplotlib?\nThanks in advance!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":986,"Q_Id":28083203,"Users Score":0,"Answer":"Yes the matplotlib site above will do the job!\nThe same procedure you will have to follow to install numpy, which I guess you will also need.","Q_Score":0,"Tags":"python,matplotlib","A_Id":28083896,"CreationDate":"2015-01-22T07:19:00.000","Title":"How to install matplotlib on windows","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working with a matplotlib-based routine that returns a figure and, as separate objects, the axes that it contains.  Is there any way, that I can display these things and edit them (annotate, change some font sizes, things like that)?  \"fig.show()\" doesn't work, just returns an error.  Thanks.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1408,"Q_Id":28092518,"Users Score":0,"Answer":"Figures need a canvas to draw on.\nTry fig.draw()","Q_Score":2,"Tags":"python,matplotlib,figures","A_Id":28105601,"CreationDate":"2015-01-22T15:27:00.000","Title":"How to display a matplotlib figure object","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to install numpy for python 2.7, I've downloaded the zip, unzipped it and was expecting a Windows download file (.exe), but there isn't one. \nWhich of these files do I use to install it? \nI tried running the setup.py file but don't seem to be getting anywhere. \nThanks!!!","AnswerCount":4,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":21104,"Q_Id":28109268,"Users Score":0,"Answer":"Wasted a lot of time trying to install on Windows from various binaries and installers, which all seemed to install a broken version, until I found that this worked: navigate to the python install directory and do python .\\site-packages\\pip install numpy","Q_Score":5,"Tags":"python,windows,numpy","A_Id":47223192,"CreationDate":"2015-01-23T11:49:00.000","Title":"How do I install Numpy for Python 2.7 on Windows?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a function I want to minimize with scipy.optimize.fmin. Note that I force a print when my function is evaluated.\nMy problem is, when I start the minimization, the value printed decreases untill it reaches a certain point (the value 46700222.800). There it continues to decrease by very small bites, e.g., 46700222.797,46700222.765,46700222.745,46700222.699,46700222.688,46700222.678\nSo intuitively, I feel I have reached the minimum, since the length of each step are minus then 1. But the algorithm keeps running untill I get a \"Maximum number of function evaluations has been exceeded\" error.\nMy question is: how can I force my algorithm to accept the value of the parameter when the function evaluation reaches a value from where it does not really evolve anymore (let say, I don't gain more than 1 after an iteration). I read that the options ftol could be used but it has absolutely no effect on my code. In fact, I don't even know what value to put for ftol. I tried everything from 0.00001 to 10000 and there is still no convergence.","AnswerCount":4,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":13104,"Q_Id":28167648,"Users Score":10,"Answer":"There is actually no need to see your code to explain what is happening. I will answer point by point quoting you.\n\nMy problem is, when I start the minimization, the value printed decreases\n  untill it reaches a certain point (the value 46700222.800). There it\n  continues to decrease by very small bites, e.g.,\n  46700222.797,46700222.765,46700222.745,46700222.699,46700222.688,46700222.678 \n\nNotice that the difference between the last 2 values is -0.009999997913837433, i.e. about 1e-2. In the convention of minimization algorithm, what you call values is usually labelled x. The algorithm stops if these 2 conditions are respected AT THE SAME TIME at the n-th iteration:\n\nconvergence on x: the absolute value of the difference between x[n] and the next iteration x[n+1] is smaller than xtol\nconvergence on f(x): the absolute value of the difference between f[n] and f[n+1] is smaller than ftol.\n\nMoreover, the algorithm stops also if the maximum number of iterations is reached.\nNow notice that xtol defaults to a value of 1e-4, about 100 times smaller than the value 1e-2 that appears for your case. The algorithm then does not stop, because the first condition on xtol is not respected, until it reaches the maximum number of iterations.\n\nI read that the options ftol could be used but it has absolutely no\n  effect on my code. In fact, I don't even know what value to put for\n  ftol. I tried everything from 0.00001 to 10000 and there is still no\n  convergence.\n\nThis helped you respecting the second condition on ftol, but again the first condition was never reached.\nTo reach your aim, increase also xtol.\nThe following methods will also help you more in general when debugging the convergence of an optimization routine.\n\ninside the function you want to minimize, print the value of x and the value of f(x) before returning it. Then run the optimization routine. From these prints you can decide sensible values for xtol and ftol.\nconsider nondimensionalizing the problem. There is a reason if ftol and xtol default both to 1e-4. They expect you to formulate the problem so that x and f(x) are of order O(1) or O(10), say numbers between -100 and +100. If you carry out the nondimensionalization you handle a simpler problem, in the way that you often know what values to expect and what tolerances you are after.\nif you are interested just in a rough calculation and can't estimate typical values for xtol and ftol, and you know (or you hope) that your problem is well behaved, i.e. that it will converge, you can run fmin in a try block, pass to fmin only maxiter=20 (say), and catch the error regarding the Maximum number of function evaluations has been exceeded.","Q_Score":6,"Tags":"python,optimization,scipy","A_Id":28249829,"CreationDate":"2015-01-27T10:00:00.000","Title":"seeking convergence with optimize.fmin on scipy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a function I want to minimize with scipy.optimize.fmin. Note that I force a print when my function is evaluated.\nMy problem is, when I start the minimization, the value printed decreases untill it reaches a certain point (the value 46700222.800). There it continues to decrease by very small bites, e.g., 46700222.797,46700222.765,46700222.745,46700222.699,46700222.688,46700222.678\nSo intuitively, I feel I have reached the minimum, since the length of each step are minus then 1. But the algorithm keeps running untill I get a \"Maximum number of function evaluations has been exceeded\" error.\nMy question is: how can I force my algorithm to accept the value of the parameter when the function evaluation reaches a value from where it does not really evolve anymore (let say, I don't gain more than 1 after an iteration). I read that the options ftol could be used but it has absolutely no effect on my code. In fact, I don't even know what value to put for ftol. I tried everything from 0.00001 to 10000 and there is still no convergence.","AnswerCount":4,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":13104,"Q_Id":28167648,"Users Score":0,"Answer":"Your question is a bit ambiguous. Are you printing the value of your function, or the point where it is evaluated?\nMy understanding of xtol and ftol is as follows. The iteration stops\n\nwhen the change in the value of the function between iterations is less than ftol\n\nAND\n\nwhen the change in x between successive iterations is less than xtol\n\nWhen you say \"...accept the value of the parameter...\", this suggests you should change xtol.","Q_Score":6,"Tags":"python,optimization,scipy","A_Id":28219470,"CreationDate":"2015-01-27T10:00:00.000","Title":"seeking convergence with optimize.fmin on scipy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a set of experimentally determined (x, y, z) points which correspond to a parabola. Unfortunately, the data is not aligned along any particular axis, and hence corresponds to a rotated parabola. \nI have the following general surface:\nAx^2 + By^2 + Cz^2 + Dxy + Gyz + Hzx + Ix + Jy + Kz + L = 0\nI need to produce a model that can represent the parabola accurately using (I'm assuming) least squares fitting. I cannot seem to figure out how this works. I have though of rotating the parabola until its central axis lines up with z-axis but I do not know what this axis is. Matlab's cftool only seems to fit equations of the form z = f(x, y) and I am not aware of anything in python that can solve this.\nI also tried solving for the parameters numerically. When I tried making this into a matrix equation and solving by least squares, the matrix turned out to be invertible and hence my parameters were just all zero. I also am stuck on this and any help would be appreciated. I don't really mind the method as I am familiar with matlab, python and linear algebra if need be. \nThanks","AnswerCount":2,"Available Count":2,"Score":0.1973753202,"is_accepted":false,"ViewCount":1508,"Q_Id":28187233,"Users Score":2,"Answer":"Dont use any toolboxes, GUIs or special functions for this problem. Your problem is very common and the equation you provided may be solved in a very straight-forward manner. The solution to the linear least squares problem can be outlined as:\n\nThe basis of the vector space is x^2, y^2, z^2, xy, yz, zx, x, y, z, 1. Therefore your vector has 10 dimensions.\nYour problem may be expressed as Ap=b, where p = [A B C D E F G H I J K L]^T is the vector containing your parameters. The right hand side b should be all zeros, but will contain some residual due to model errors, uncertainty in the data or for numerical reasons. This residual has to be minimized.\nThe matrix A has a dimension of N by 10, where N denotes the number of known points on surface of the parabola.\n\nA = [x(1)^2 y(1)^2 ... y(1) z(1) 1\n...\nx(N)^2 y(N)^2 ... y(N) z(N) 1] \n\nSolve the overdetermined system of linear equations by computing p = A\\b.","Q_Score":0,"Tags":"python,matlab,curve-fitting,least-squares,surface","A_Id":28189659,"CreationDate":"2015-01-28T07:56:00.000","Title":"Rotated Paraboloid Surface Fitting","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a set of experimentally determined (x, y, z) points which correspond to a parabola. Unfortunately, the data is not aligned along any particular axis, and hence corresponds to a rotated parabola. \nI have the following general surface:\nAx^2 + By^2 + Cz^2 + Dxy + Gyz + Hzx + Ix + Jy + Kz + L = 0\nI need to produce a model that can represent the parabola accurately using (I'm assuming) least squares fitting. I cannot seem to figure out how this works. I have though of rotating the parabola until its central axis lines up with z-axis but I do not know what this axis is. Matlab's cftool only seems to fit equations of the form z = f(x, y) and I am not aware of anything in python that can solve this.\nI also tried solving for the parameters numerically. When I tried making this into a matrix equation and solving by least squares, the matrix turned out to be invertible and hence my parameters were just all zero. I also am stuck on this and any help would be appreciated. I don't really mind the method as I am familiar with matlab, python and linear algebra if need be. \nThanks","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1508,"Q_Id":28187233,"Users Score":0,"Answer":"Do you have enough data points to fit all 10 parameters - you will need at least 10?\nI also suspect that 10 parameters are to many to describe a general paraboloid, meaning that some of the parameters are dependent. My fealing is that a translated and rotated paraboloid needs 7 parameters (although I'm not really sure)","Q_Score":0,"Tags":"python,matlab,curve-fitting,least-squares,surface","A_Id":28188683,"CreationDate":"2015-01-28T07:56:00.000","Title":"Rotated Paraboloid Surface Fitting","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Suppose my labeled data has two classes 1 and 0. When I run predict_proba on the test set it returns an array with two columns. Which column corresponds to which class ?","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":655,"Q_Id":28197444,"Users Score":3,"Answer":"Column 0 corresponds to the class 0, column 1 corresponds to the class 1.","Q_Score":1,"Tags":"python,machine-learning,scikit-learn","A_Id":28198700,"CreationDate":"2015-01-28T16:31:00.000","Title":"Python predict_proba class identification","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Suppose my labeled data has two classes 1 and 0. When I run predict_proba on the test set it returns an array with two columns. Which column corresponds to which class ?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":655,"Q_Id":28197444,"Users Score":0,"Answer":"You can check that by printing the classes with print(estimator.classes_). The array will have the same order like the output.","Q_Score":1,"Tags":"python,machine-learning,scikit-learn","A_Id":56207791,"CreationDate":"2015-01-28T16:31:00.000","Title":"Python predict_proba class identification","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using the statsmodels.formulas.api.quantreg() for quantile regression in Python. I see that when fitting the quantile regression model, there is an option to specify the significance level for confidence intervals of the regression coefficients, and the confidence interval result appears in the summary of the fit.\nWhat statistical method is being used to generate confidence intervals about the regression coefficients? It does not appear to be documented and I've dug through the source code for quantile_regression.py and summary.py to find this with no luck. Can anyone shed some light on this?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":843,"Q_Id":28197813,"Users Score":2,"Answer":"Inference for parameters is the same across models and is mostly inherited from the base classes.\nQuantile regression has a model specific covariance matrix of the parameters.\ntvalues, pvalues, confidence intervals, t_test and wald_test are all based on the assumption of an asymptotic normal distribution of the estimated parameters with the given covariance, and are \"generic\".\nLinear models like OLS and WLS, and optionally some other models can use the t and F distribution instead of normal and chisquare distribution for the Wald test based inference.\nspecifically conf_int is defined in statsmodels.base.models.LikelihoodModelResults\npartial correction:\nQuantReg uses t and F distributions for inference, since it is currently treated as a linear regression model, and not normal and chisquare distributions as the related M-estimators, RLM, in statsmodels.robust.\nMost models have now a use_t option to choose the inference distributions, but it hasn't been added to QuantReg.","Q_Score":2,"Tags":"python,statsmodels","A_Id":28202075,"CreationDate":"2015-01-28T16:50:00.000","Title":"statsmodels: Method used to generate condifence intervals for quantile regression coefficients?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an array of graysale image read in from a color one. If I use matplotlib to imshow the grayscale image, it looks just fine. But when I io.imsave it, it's ruined (by an outrageous amount of noise). However, if I numpy.around it first before io.imsave-ing, then it's significantly better, but black and white are still all swapped (dark regions appear white, and bright regions appear dark)\nscipy.misc.imsave, on the other hand, works perfectly.\nThank you.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":998,"Q_Id":28225600,"Users Score":3,"Answer":"I think I've figured out why. By convention, floats in skimage are supposed to be in the range [0, 1].","Q_Score":1,"Tags":"python,image,image-processing,matplotlib,scipy","A_Id":28225707,"CreationDate":"2015-01-29T22:10:00.000","Title":"skimage.io.imsave \"destroys\" grayscale image?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How is using a BaggingClassifier with baseestimator=RandomForestClassifier differ from a RandomForestClassifier in sklearn?","AnswerCount":1,"Available Count":1,"Score":0.761594156,"is_accepted":false,"ViewCount":668,"Q_Id":28232551,"Users Score":5,"Answer":"The RandomForestClassifier introduces randomness externally (relative to the individual tree fitting) via bagging as BaggingClassifier does.\nHowever it injects randomness also deep inside the tree construction procedure by sub-sampling the list of features that are candidate for splitting: a new random set of features is considered at each new split. This randomness is controlled via the max_features parameter of RandomForestClassifier that has no equivalent in BaggingClassifier(base_estimator=DecisionTreeClassifier()).","Q_Score":3,"Tags":"python-3.x,scikit-learn,random-forest","A_Id":28232764,"CreationDate":"2015-01-30T09:13:00.000","Title":"RandomForestClassifier differ from BaggingClassifier","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've written a python\/webdriver script that scrapes a table online, dumps it into a list and then exports it to a CSV.  It does this daily.\nWhen I open the CSV in Excel, it is unformatted, and there are fifteen (comma-delimited) columns of data in each row of column A.\nOf course, I then run 'Text to Columns' and get everything in order.  It looks and works great.\nBut tomorrow, when I run the script and open the CSV,  I've got to reformat it.\nHere is my question:   \"How can I open this CSV file with the data already spread across the columns in Excel?\"","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":24,"Q_Id":28238830,"Users Score":0,"Answer":"Try importing it as a csv file, instead of opening it directly on excel.","Q_Score":1,"Tags":"python,excel,csv","A_Id":28238935,"CreationDate":"2015-01-30T15:11:00.000","Title":"Retain Excel Settings When Adding New CSV","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been trying to write on a fast (ish) image matching program which doesn't match rotated or scale deformed image, in Python.\nThe goal is to be able to find small sections of an image that are similar to other images in color features, but dissimilar if rotated or warped.\nI found out about perceptual image hashing, and I've had a look at the ImageHash module for Python and SSIM, however most of the things I've looked at do not have in color as a major factor, ie they average the color and only work in one channel, and phash in particular doesn't care if images are rotated.\nI would like to be able to have an algorithm which would match images which at a distance would appear the same (but which would not necessarily need to be the same image).\nCan anyone suggest how I would structure and write such an algorithm in python? or suggest a function which would be able to compare images in this manner?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":852,"Q_Id":28258468,"Users Score":1,"Answer":"I found a couple of ways to do this.\nI ended up using a Mean Squared Error function that I wrote myself:\ndef mse(reference, query):\n    return (((reference).astype(\"double\")-(query).astype(\"double\"))**2).mean()\n\nUntil, upon later tinkering I found a function that seemed to do something similar (compare image similarity, bit by bit), but a good amount faster:\ndef linalg_norm(reference, query):\n    return np.linalg.norm(reference-query)\n\nI have no theoretical knowledge of what the second function does, however practically it doesn't matter. I am not averse to learning how it works.","Q_Score":2,"Tags":"python,image-processing,hash","A_Id":30613008,"CreationDate":"2015-02-01T01:23:00.000","Title":"Color Perceptual Image Hashing","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm displaying an image and want to specify the x and y axis numbering rather than having row and column numbers show up there.  Any ideas?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":31,"Q_Id":28270435,"Users Score":1,"Answer":"Aha, one needs to use the \"extent\" argument, as in:\nplt.imshow(H, cmap=plt.gray(), extent=[-5, 3, 6, 9])","Q_Score":1,"Tags":"python,matplotlib","A_Id":28270527,"CreationDate":"2015-02-02T02:42:00.000","Title":"pyplot - Is there a way to explicitly specify the x and y axis numbering?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Just getting started with this library... having some issues (i've read the docs but didn't get clarity) with RandomForestClassifiers\nMy question is pretty simple, say i have a train data set like \nA     B     C\n1     2     3\nWhere A is the independent variable (y) and B-C are the dependent variables (x).  Let's say the test set looks the same, however the order is \nB     A     C\n1     2     3\nWhen I call forest.fit(train_data[0:,1:],train_data[0:,0])\ndo I then need to reorder the test set to match this order before running? (Ignoring the fact that I need to remove the already predicted y value (a), so lets just say B and C are out of order... )","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2722,"Q_Id":28270967,"Users Score":6,"Answer":"Yes, you need to reorder them. Imagine a simpler case, Linear Regression. The algorithm will calculate the weights for each of the features, so for example if feature 1 is unimportant, it will get assigned a close to 0 weight. \nIf at prediction time the order is different, an important feature will be multiplied by this almost null weight, and the prediction will be totally off.","Q_Score":4,"Tags":"python,scikit-learn","A_Id":28287768,"CreationDate":"2015-02-02T04:06:00.000","Title":"Scikitlearn - order of fit and predict inputs, does it matter?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataset that I split in two for training and testing a random forest classifier with scikit learn. \nI have 87 classes and 344 samples. The output of predict_proba is, most of the times, a 3-dimensional array (87, 344, 2) (it's actually a list of 87 numpy.ndarrays of (344, 2) elements). \nSometimes, when I pick a different subset of samples for training and testing, I only get a 2-dimensional array (87, 344) (though I can't work out in which cases). \nMy two questions are:\n\nwhat do these dimensions represent? I worked out that to get a ROC AUC score, I have to take one half of the output (that is (87, 344, 2)[:,:,1], transpose it, and then compare it with my ground truth (roc_auc_score(ground_truth, output_of_predict_proba[:,:,1].T) essentially) . But I don't understand what it really means. \nwhy does the output change with different subsets of the data? I can't understand in which cases it returns a 3D array and in which cases a 2D one.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2825,"Q_Id":28282706,"Users Score":0,"Answer":"classifier.predict_proba() returns the class probabilities. The n dimension of the array will vary depending on how many classes there are in the subset you train on","Q_Score":3,"Tags":"python,scikit-learn,random-forest","A_Id":28315175,"CreationDate":"2015-02-02T16:54:00.000","Title":"Scikit-learn RandomForestClassifier output of predict_proba","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have some traffic camera images, and I want to extract only the pixels on the road.  I have used remote sensing software before where one could specify an operation like\nimg1 * img2 = img3\nwhere img1 is the original image and img2 is a straight black-and-white mask.  Essentially, the white parts of the image would evaluate to\nimg1 * 1 = img3\nand the black parts would evaluate to\nimg1 * 0 = img3\nAnd so one could take a slice of the image and let all of the non-important areas go to black.\nIs there a way to do this using PIL? I can't find anything similar to image algebra like I'm used to seeing.  I have experimented with the blend function but that just fades them together.  I've read up a bit on numpy and it seems like it might be capable of it but I'd like to know for sure that there is no straightforward way of doing it in PIL before I go diving in.\nThank you.","AnswerCount":2,"Available Count":1,"Score":0.2913126125,"is_accepted":false,"ViewCount":10471,"Q_Id":28358379,"Users Score":3,"Answer":"You can use the PIL library to mask the images. Add in the alpha parameter to img2, As you can't just paste this image over img1. Otherwise, you won't see what is underneath, you need to add an alpha value.\nimg2.putalpha(128) #if you put 0 it will be completly transparent, keep image opaque\nThen you can mask both the images\nimg1.paste(im=img2, box=(0, 0), mask=img2)","Q_Score":2,"Tags":"python,image,image-processing,python-imaging-library,mask","A_Id":63671324,"CreationDate":"2015-02-06T04:03:00.000","Title":"Is it possible to mask an image in Python Imaging Library (PIL)?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm having trouble figuring out how to create a 10x1 numpy array with the number 5 in the first 3 elements and the other 7 elements with the number 0. Any thoughts on how to do this efficiently?","AnswerCount":6,"Available Count":1,"Score":0.0333209931,"is_accepted":false,"ViewCount":5701,"Q_Id":28384481,"Users Score":1,"Answer":"Just do the following.\nimport numpy as np\narr = np.zeros(10)\narr[:3] = 5","Q_Score":1,"Tags":"python,arrays,numpy","A_Id":53429718,"CreationDate":"2015-02-07T16:26:00.000","Title":"Create a numpy array (10x1) with zeros and fives","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"i Launch cluster spark cassandra with datastax dse in aws cloud. So my dataset storage in S3. But i don't know how transfer data from S3 to my cluster cassandra. Please help me","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1657,"Q_Id":28417806,"Users Score":1,"Answer":"The details depend on your file format and C* data model but it might look something like this:\n\nRead the file from s3 into an RDD\nval rdd = sc.textFile(\"s3n:\/\/mybucket\/path\/filename.txt.gz\")\nManipulate the rdd\nWrite the rdd to a cassandra table:\nrdd.saveToCassandra(\"test\", \"kv\", SomeColumns(\"key\", \"value\"))","Q_Score":0,"Tags":"python,cassandra,datastax-enterprise","A_Id":28419293,"CreationDate":"2015-02-09T19:34:00.000","Title":"How import dataset from S3 to cassandra?","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm implementing a DNN with Theano. At the last layer of DNN, I'm using a softmax as a nonlinear function from theano.tensor.nnet.softmax\nAs a lost function i'm using cross entropy from T.nnet.binary_crossentropy\nBut I get a strange error:\n\"The following error happened while compiling the node', GpuDnnSoftmaxGrad{tensor_format='bc01' ...\"\nI'm a newbie with theano and can't figure out what's wrong with this model. Your help is appreciated\nPS: my guess is it is somehow related to the fact that softmax takes a 2D tensor and returns a 2D tensor.\nPS2:I'm using the bleeding edge Theano (just cloned) my CUDA version is old it is 4.2 BUT I'm almost sure that that's not the problem since I'm working without error with other DNN tools written based on Theano.\nI'm using pylearn2 to accelerate and that's not the problem either since I already used it successfully with the current Theano and CUDA in another DNN.\nThe error happens at this line: train= theano.function([idx], train_loss, givens=givens, updates=updates)\nThe full error message is:\n\ncmodule.py\", line 293, in dlimport\n    rval = __import__(module_name, {}, {}, [module_name])\nRuntimeError: ('The following error happened while compiling the node', GpuDnnSoftmaxGrad{tensor_format='bc01', mode='channel', algo='accurate'}(GpuContiguous.0, GpuContiguous.0), '\\n', 'could not create cuDNN handle: The handle was not initialized(Is your driver recent enought?).', \"[GpuDnnSoftmaxGrad{tensor_format='bc01', mode='channel', algo='accurate'}(<CudaNdarrayType(float32, (False, False, True, True))>, <CudaNdarrayType(float32, (False, False, True, True))>)]\")\nThe Cross entropy funcion I'm using is defined as:\nerror = T.mean(T.nnet.binary_crossentropy(input, target_y)\nwhere input is the output of the softmax layer and target_y is the labels.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1358,"Q_Id":28418823,"Users Score":0,"Answer":"solved. I had to use T.nnet.categorical_crossentropy since my target variable is an integer vector.","Q_Score":1,"Tags":"python,theano,softmax","A_Id":34094065,"CreationDate":"2015-02-09T20:34:00.000","Title":"getting error with softmax and cross entropy in theano","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there a way to test whether a dataframe is sorted by a given column that's not an index (i.e. is there an equivalent to is_monotonic() for non-index columns) without calling a sort all over again, and without converting a column into an index?","AnswerCount":3,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":17253,"Q_Id":28419877,"Users Score":52,"Answer":"Meanwhile, since 0.19.0, there is pandas.Series.is_monotonic_increasing, pandas.Series.is_monotonic_decreasing, and pandas.Series.is_monotonic.","Q_Score":33,"Tags":"python,pandas","A_Id":57704035,"CreationDate":"2015-02-09T21:37:00.000","Title":"Check whether non-index column sorted in Pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a csv file where a line of data might look like this:\n10,\"Apple, Banana\",20,...\nWhen I load the data in Python, the extra comma inside the quotes shifts all my column indices around, so my data is no longer a consistent structure. While I could probably write a complex algorithm that iterates through each row and fixes the issue, I was hoping there was an elegant way to just pass an extra parameter to loadtxt (or some other function) that will properly ignore commas inside quotes and treat the entire quote as one value.\nNote, when I load the CSV file into Excel, Excel correctly recognizes the string as one value.","AnswerCount":5,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":5651,"Q_Id":28444272,"Users Score":0,"Answer":"While there is not such a parameter in numpy.loadtxt to ignore quoted or otherwise escaped commas, one alternative that has not been suggested yet would be the following...\nPerform a find and replace using some text editor to replace commas with tabs\nOR\nsave the file in Excel as tab delimited.\nWhen you use numpy.loadtxt, you will simply specify delimiter='\\t' instead of comma delimited.\nSimple solution that could save you some code...","Q_Score":7,"Tags":"python,csv,numpy","A_Id":64579432,"CreationDate":"2015-02-11T00:08:00.000","Title":"numpy.loadtxt: how to ignore comma delimiters that appear inside quotes?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"If the same edge is added twice to the networkx edge data structure, will it then have two edges between the nodes or still just one? For example, would a spring layout show the nodes converge more with edges [(a,b),(a,b),(a,b),(a,b)] than [(a,b),(a,b)]? If I want to weight the edge, how would I go about it?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":13392,"Q_Id":28488559,"Users Score":12,"Answer":"You can test it pretty quickly, but it only adds them once. Edges and nodes are represented as a dictionaries inside the graph structure, and they are only added if they don't actually exist.\nFor already existing edges, adding them again has no effect.","Q_Score":8,"Tags":"python,networkx","A_Id":28488596,"CreationDate":"2015-02-12T21:54:00.000","Title":"Networkx duplicate edges","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using Gensim for some topic modelling and I have gotten to the point where I am doing similarity queries using the LSI and tf-idf models. I get back the set of IDs and similarities, eg. (299501, 0.64505910873413086). \nHow do I get the text document that is related to the ID, in this case 299501?\nI have looked at the docs for corpus, dictionary, index, and the model and cannot seem to find it.","AnswerCount":2,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":2031,"Q_Id":28488714,"Users Score":4,"Answer":"Sadly, as far as I can tell, you have to start from the very beginning of the analysis knowing that you'll want to retrieve documents by the ids.  This means you need to create your own mapping between ids and the original documents and make sure the ids gensim uses are preserved throughout the process.  As is, I don't think gensim keeps such a mapping handy.\nI could definitely be wrong, and in fact I'd love it if someone tells me there is an easier way, but I spent many hours trying to avoid re-running a gigantic LSI model on a wikipedia corpus to no avail.  Eventually I had to carry along a list of ids and the associated documents so I could use gensim's output.","Q_Score":10,"Tags":"python,gensim","A_Id":40033364,"CreationDate":"2015-02-12T22:04:00.000","Title":"Retrieve string version of document by ID in Gensim","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm generating time series data in ocaml which are basically long lists of floats, from a few kB to hundreds of MB. I would like to read, analyze and plot them using the python numpy and pandas libraries. Right now, i'm thinking of writing them to csv files. \nA binary format would probably be more efficient? I'd use HDF5 in a heartbeat but Ocaml does not have a binding. Is there a good binary exchange format that is usable easily from both sides? Is writing a file the best option, or is there a better protocol for data exchange? Potentially even something that can be updated on-line?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":245,"Q_Id":28510059,"Users Score":1,"Answer":"First of all I would like to mention, that there're actually bindings for HDF-5 for OCaml. But, when I was faced with the same problem I didn't find one that suits my purposes and is mature enough. So I wouldn't suggest you to use it, but who knows, maybe today there is something more descent. \nSo, to my experience the best way to store numeric data in OCaml is Bigarrays. They are actually wrappers around the C-pointer, that can be allocated outside of OCaml runtime. They also can be a memory mapped regions. So, for me this is the most efficient way to share data between different processes (potentially written in different languages). You can share data using memory mapping with OCaml, Python, Matlab or whatever with very little pain, especially if you're not trying to modify it from different processes simultaneously. \nOther approaches, is to use MPI, ZMQ or bare sockets. I would prefer the latter for the only reason that the former doesn't support bigarrays. Also, I would suggest you to look for capn'proto, it is also very efficient, and have bindings for OCaml and Python, and for your particular use case, can work very fine.","Q_Score":0,"Tags":"python,numpy,ocaml,export-to-csv,hdf5","A_Id":28511785,"CreationDate":"2015-02-13T22:48:00.000","Title":"data exchange format ocaml to python numpy or pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"So I just learned about sorting algorithm s bubble, merge, insertion, sort etc. they all seem to be very similar in their methods of sorting with what seems to me minimal changes in their approach. So why do they produce such different sorting times ie O(n^2) vs O(nlogn) as an example","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":297,"Q_Id":28523247,"Users Score":8,"Answer":"The \"similarity\" (?!) that you see is completely illusory.\nThe elementary, O(N squared), approaches, repeat their workings over and over, without taking any advantage, for the \"next step\", of any work done on the \"previous step\".  So the first step takes time proportional to N, the second one to N-1, and so on -- and the resulting sum of integers from 1 to N is proportional to N squared.\nFor example, in selection sort, you are looking each time for the smallest element in the I:N section, where I is at first 0, then 1, etc.  This is (and must be) done by inspecting all those elements, because no care was previously taken to afford any lesser amount of work on subsequent passes by taking any advantage of previous ones.  Once you've found that smallest element, you swap it with the I-th element, increment I, and continue.  O(N squared) of course.\nThe advanced, O(N log N), approaches, are cleverly structured to take advantage in following steps of work done in previous steps.  That difference, compared to the elementary approaches, is so pervasive and deep, that, if one cannot perceive it, that speaks chiefly about the acuity of one's perception, not about the approaches themselves:-).\nFor example, in merge sort, you logically split the array into two sections, 0 to half-length and half-length to length.  Once each half is sorted (recursively by the same means, until the length gets short enough), the two halves are merged, which itself is a linear sub-step.\nSince you're halving every time, you clearly need a number of steps proportional to log N, and, as each step is O(N), obviously you get the very desirable O(N log N) as a result.\nPython's \"timsort\" is a \"natural mergesort\", i.e, a variant of mergesort tuned to take advantage of already-sorted (or reverse-sorted) parts of the array, which it recognizes rapidly and avoids spending any further work on.  This doesn't change big-O because that's about worst-case time -- but expected time crashes much further down because in so many real-life cases some partial sortedness is present.\n(Note that, going by the rigid definition of big-O, quicksort isn't quick at all -- it's worst-case proportional to N squared, when you just happen to pick a terrible pivot each and every time... expected-time wise it's fine, though nowhere as good as timsort, because in real life the situations where you repeatedly pick a disaster pivot are exceedingly rare... but, worst-case, they might happen!-).\ntimsort is so good as to blow away even very experienced programmers.  I don't count because I'm a friend of the inventor, Tim Peters, and a Python fanatic, so my bias is obvious.  But, consider...\n...I remember a \"tech talk\" at Google where timsort was being presented.  Sitting next to me in the front row was Josh Bloch, then also a Googler, and Java expert extraordinaire.  Less than mid-way through the talk he couldn't resist any more - he opened his laptop and started hacking to see if it could possibly be as good as the excellent, sharp technical presentation seemed to show it would be.\nAs a result, timsort is now also the sorting algorithm in recent releases of the Java Virtual Machine (JVM), though only for user-defined objects (arrays of primitives are still sorted the old way, quickersort [*] I believe -- I don't know which Java peculiarities determined this \"split\" design choice, my Java-fu being rather weak:-).\n[*] that's essentially quicksort plus some hacks for pivot choice to try and avoid the poison cases -- and it's also what Python used to use before Tim Peters gave this one immortal contribution out of the many important ones he's made over the decades.\nThe results are sometimes surprising to people with CS background (like Tim, I have the luck of having a far-ago academic background, not in CS, but in EE, which helps a lot:-).  Say, for example, that you must maintain an ever-growing array that is always sorted at any point in time, as new incoming data points must get added to the array.\nThe classic approach would use bisection, O(log N), to find the proper insertion point for each new incoming data point -- but then, to put the new data in the right place, you need to shift what comes after it by one slot, that's O(N).\nWith timsort, you just append the new data point to the array, then sort the array -- that's O(N) for timsort in this case (as it's so awesome in exploiting the already-sorted nature of the first N-1 items!-).\nYou can think of timsort as pushing the \"take advantage of work previously done\" to a new extreme -- where not only work previously done by the algorithm itself, but also other influences by other aspects of real-life data processing (causing segments to be sorted in advance), are all exploited to the hilt.\nThen we could move into bucket sort and radix sort, which change the plane of discourse -- which in traditional sorting limits one to being able to compare two items -- by exploiting the items' internal structure.\nOr a similar example -- presented by Bentley in his immortal book \"Programming Pearls\" -- of needing to sort an array of several million unique positive integers, each constrained to be 24 bits long.\nHe solved it with an auxiliary array of 16M bits -- just 2M bytes after all -- initially all zeroes: one pass through the input array to set the corresponding bits in the auxiliary array, then one pass through the auxiliary array to form the required integers again where 1s are found -- and bang, O(N) [and very speedy:-)] sorting for this special but important case!-)","Q_Score":1,"Tags":"python,algorithm,sorting,time-complexity,computation-theory","A_Id":28523302,"CreationDate":"2015-02-15T05:19:00.000","Title":"Sorting algorithm times using sorting methods","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a series of integers. What I would like to do is split them into 5 discrete categories. I tried z-scores with bounds (-oo, -2), [-2, -1), [-1, +1], (+1, +2], (+2, +oo) but it doesn't seem to work probably because of the right-skewed data. So, I though that it might work with some sort of clustering. Any ideas?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":80,"Q_Id":28545060,"Users Score":0,"Answer":"On skewed data, it can help a lot to go into logspace.\nYou may first want to understand the distribution better, then split them.\nHave you tried visualizing them, to identify appropriate splitting values?\nOne-dimensional data can be well visualized, and the results of a manual approach are often better than those of some blackbox clustering.","Q_Score":0,"Tags":"python,cluster-analysis","A_Id":28546908,"CreationDate":"2015-02-16T15:41:00.000","Title":"How to cluster a series of right-skewed integers","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In numpy operation, I have two vectors, let's say vector A is 4X1, vector B is 1X5, if I do AXB, it should result a matrix of size 4X5.\nBut I tried lot of times, doing many kinds of reshape and transpose, they all either raise error saying not aligned or return a single value.\nHow should I get the output product of matrix I want?","AnswerCount":3,"Available Count":1,"Score":0.1325487884,"is_accepted":false,"ViewCount":49959,"Q_Id":28578302,"Users Score":2,"Answer":"If you are using numpy.\nFirst, make sure you have two vectors. For example, vec1.shape = (10, ) and vec2.shape = (26, ); in numpy, row vector and column vector are the same thing.\nSecond, you do res_matrix = vec1.reshape(10, 1) @ vec2.reshape(1, 26) ;.\nFinally, you should have: res_matrix.shape = (10, 26).\nnumpy documentation says it will deprecate np.matrix(), so better not use it.","Q_Score":43,"Tags":"python,numpy,matrix,vector,matrix-multiplication","A_Id":55585713,"CreationDate":"2015-02-18T07:33:00.000","Title":"How to multiply two vector and get a matrix?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How to get the content of a row of a Numpy array ? For example I have a Numpy array with 3 rows color=np.array([[255,0,0],[255,255,0],[0,255,0]]) and I want to retrieve the content of the first row [255,0,0].","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":41,"Q_Id":28608320,"Users Score":1,"Answer":"Use array indexing as below:\ncolor[0]","Q_Score":1,"Tags":"arrays,python-3.x,numpy","A_Id":28608797,"CreationDate":"2015-02-19T14:02:00.000","Title":"How to get the content of a row of a Numpy array?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have python 2.7.9 (which comes with pip already installed), I have numpy 1.8.2 and scipy 0.15.1 installed as well.\nWhen I try to install scikit-learn, I get the following error \n\n\n\npip install -U scikit-learn\nSyntaxError: invalid syntax\n\n\n\nWhat am I doing wrong? \nOr is there another way to install scikit- learn on windows, if I can't use pip ?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":771,"Q_Id":28614874,"Users Score":0,"Answer":"Changing the directory worked in my case.\nSuppose your python 2.7.9 is in C drive so you set you directory as follows and write your command like this : \nC:\\python27\\scripts> pip install -U scikit-learn","Q_Score":0,"Tags":"python-2.7,pip,scikit-learn","A_Id":35262231,"CreationDate":"2015-02-19T19:11:00.000","Title":"unable to install scikit-earn on python 2.7.9 in Windows?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a csv file in S3 and I'm trying to read the header line to get the size (these files are created by our users so they could be almost any size). Is there a way to do this using boto? I thought maybe I could us a python BufferedReader, but I can't figure out how to open a stream from an S3 key. Any suggestions would be great. Thanks!","AnswerCount":10,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":83468,"Q_Id":28618468,"Users Score":20,"Answer":"I know it's a very old question.\nBut as for now, we can just use s3_conn.get_object(Bucket=bucket, Key=key)['Body'].iter_lines()","Q_Score":35,"Tags":"python,amazon-web-services,amazon-s3,boto","A_Id":58636713,"CreationDate":"2015-02-19T22:42:00.000","Title":"Read a file line by line from S3 using boto?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":1,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"The np.fft.fft() returns a complex array .... what is the meaning of the complex number ?\nI suppose the real part is the amplitude !\nThe imaginary part is phase-shift ? phase-angle ? Or something else !\nI figured out the position in the array represent the frequency.","AnswerCount":3,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":29182,"Q_Id":28618591,"Users Score":3,"Answer":"The magnitude, r, at a given frequency represents the amount of that frequency in the original signal. The complex argument represents the phase angle, theta.\nx + i*y = r * exp(i*theta)\nWhere x and y are the numbers that that the numpy FFT returns.","Q_Score":12,"Tags":"python,numpy,fft","A_Id":28618872,"CreationDate":"2015-02-19T22:54:00.000","Title":"numpy.fft() what is the return value amplitude + phase shift OR angle?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"What is python's equivalent of R's NA?\nTo be more specific:\nR has NaN, NA, NULL, Inf and -Inf. NA is generally used when there is missing data. What is python's equivalent?\nHow libraries such as numpy and pandas handle missing values?\nHow does scikit-learn handle missing values?\nIs it different for python 2.7 and python 3?","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":54508,"Q_Id":28654325,"Users Score":9,"Answer":"Scikit-learn doesn't handle missing values currently.\nFor most machine learning algorithms, it is unclear how to handle missing values, and so we rely on the user of handling them prior to giving them to the algorithm.\nNumpy doesn't have a \"missing\" value. Pandas uses NaN, but inside numeric algorithms that might lead to confusion. It is possible to use masked arrays, but we don't do that in scikit-learn (yet).","Q_Score":33,"Tags":"python,numpy,pandas,scikit-learn,data-scrubbing","A_Id":28665731,"CreationDate":"2015-02-22T03:53:00.000","Title":"What is python's equivalent of R's NA?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In Python, I have an ndarray y\nthat is printed as array([0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1])\nI'm trying to count how many 0s and how many 1s are there in this array. \nBut when I type y.count(0) or y.count(1), it says \n\nnumpy.ndarray object has no attribute count\n\nWhat should I do?","AnswerCount":31,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":883665,"Q_Id":28663856,"Users Score":7,"Answer":"y.tolist().count(val)\nwith val 0 or 1\nSince a python list has a native function count, converting to list before using that function is a simple solution.","Q_Score":558,"Tags":"python,numpy,multidimensional-array,count","A_Id":37332201,"CreationDate":"2015-02-22T22:05:00.000","Title":"How to count the occurrence of certain item in an ndarray?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"In Python, I have an ndarray y\nthat is printed as array([0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1])\nI'm trying to count how many 0s and how many 1s are there in this array. \nBut when I type y.count(0) or y.count(1), it says \n\nnumpy.ndarray object has no attribute count\n\nWhat should I do?","AnswerCount":31,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":883665,"Q_Id":28663856,"Users Score":0,"Answer":"here I have something, through which you can count the number of occurrence of a particular number:\naccording to your code\ncount_of_zero=list(y[y==0]).count(0) \nprint(count_of_zero)\n\/\/ according to the match there will be boolean values and according to True value the number 0 will be return","Q_Score":558,"Tags":"python,numpy,multidimensional-array,count","A_Id":59595030,"CreationDate":"2015-02-22T22:05:00.000","Title":"How to count the occurrence of certain item in an ndarray?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to calculate PDFs of mixture of Dirichlet distribution in python. But for each mixture component there is the normalizing constant, which is the inverse beta function which has gamma function of sum of the hyper-parameters as the numerator. So even for a sum of hyper-parameters of size '60' it goes unbounded. Please suggest me a work around for this problem. What happens when I ignore the normalizing constant?\nFirst its not the calculation of NC itself that is the problem. For a single dirichlet I have no problem . But what I have here is a mixture of product of dirichlets, so each mixture component is a product of many dirichlets each with its own NCs. So the product of these goes unbounded. Regarding my objective, I have a joint distribution of p(s,T,O), where 's' is discrete, 'T' and 'O' are the dirichlet variables i.e. a set of vectors of parameters which sum to '1'. Now since 's' is discrete and finite I have |S| set of mixture of product of dirichlet components for each 's'. Now my objective here is to find p(s|T,O). So I directly substitute a particular (T,O) and calculate the value of each p('s'|T,O). For this I need to calc the NCs. If there is only one mixture component then I can ignore the norm constant, calc. and renormalise finally, but since I have several mixture components each components will have different scaling and so I can't renormalise. This is my conundrum.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":492,"Q_Id":28689687,"Users Score":0,"Answer":"Some ideas. (1) To calculate the normalizing factor exactly, maybe you can rewrite the gamma function via gamma(a_i + 1) = a_i gamma(a_i) (a_i need not be an integer, let the base case be a_i < 1) and then you'll have sum(a_i, i, 1, n) terms in the numerator and denominator and you can reorder them so that you divide the largest term by the largest term and multiply those single ratios together instead of computing an enormous numerator and an enormous denominator and dividing those. (2) If you don't need to be exact, maybe you can apply Stirling's approximation. (3) Maybe you don't need the pdf at all. For some purposes, you just need a function which is proportional to the pdf. I believe Markov chain Monte Carlo is like that. So, what is the larger goal you are trying to achieve here?","Q_Score":0,"Tags":"python,machine-learning,statistics,probability,dirichlet","A_Id":28703110,"CreationDate":"2015-02-24T06:45:00.000","Title":"Normalizing constant of mixture of dirichlet distribution goes unbounded","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am currently need to run FFT on 1024 sample points signal.  So far I have implementing my own DFT algorithm in python, but it is very slow. If I use the NUMPY fftpack, or even move to C++ and use FFTW, do you guys think it would be better?","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":4655,"Q_Id":28752126,"Users Score":7,"Answer":"If you are implementing the DFFT entirely within Python, your code will run orders of magnitude slower than either package you mentioned. Not just because those libraries are written in much lower-level languages, but also (FFTW in particular) they are written so heavily optimized, taking advantage of cache locality, vector units, and basically every trick in the book, that it would not surprise me if they ran at 10,000x the speed of a naive Python implementation. Even if you are using numpy in your implementation, it will still pale in comparison.\nSo yes; use numpy's fftpack. If that is not fast enough, you can try the python bindings for FFTW (PyFFTW), but the speedup from fftpack to fftw will not be nearly as dramatic.  I really doubt there's a need to drop into C++ just for FFTs - they're sort of the ideal case for Python bindings.","Q_Score":0,"Tags":"python,numpy,fft,fftw","A_Id":28753451,"CreationDate":"2015-02-26T20:28:00.000","Title":"Numpy fft.pack vs FFTW vs Implement DFT on your own","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"With GradientBoostingClassifier suppose I set n_estimators to 2000 and use GridSearchCV to search across learning_rate in [0.01, 0.05, 0.10] - how do I know the number of boosting iterations that produced the optimal result - is the model always going to fit 2000 trees for each value of learning_rate or is it going to use the optimal number of boosting iterations for each of these values? It wouldn't make sense to also include n_estimators in the grid search and search for all values in [1,2000].","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1119,"Q_Id":28790032,"Users Score":0,"Answer":"Currently there is no way to directly get the optimum number of estimators from GradientBoostingClassifier. If you also pass n_estimators in the parameter grid to GridSearchCV it will only try the exact values you give it, and return one of these.\nWe are looking to improve this, by searching over the number of estimators automatically.","Q_Score":0,"Tags":"python,scikit-learn","A_Id":28871022,"CreationDate":"2015-03-01T04:17:00.000","Title":"Obtain optimal number of boosting iterations in GradientBoostingClassifier using grid search","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need other splitting criteria for a Desicion tree than the provided 'gini' and 'entropy. I want to use the wonderful sklearn package as base though. Is there a way to go around the C-implementation of the tree building process? As in implementing the criterion in Python and let the TreeBuilder work with it?","AnswerCount":1,"Available Count":1,"Score":-0.1973753202,"is_accepted":false,"ViewCount":601,"Q_Id":28829805,"Users Score":-1,"Answer":"As Andreas said above, splitting criteria are coded in cython.","Q_Score":0,"Tags":"python,scikit-learn","A_Id":28948827,"CreationDate":"2015-03-03T10:46:00.000","Title":"Create splitting criterion for sklearn trees","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it possible to write nice-formatted excel files with dataframe.to_excel-xlsxwriter combo?\nI am aware that it is possible to format cells when writing with pure xlsxwriter. But dataframe.to_excel takes so much less space.\nI would like to adjust cell width and add some colors to column names.\nWhat other alternatives would you suggest?","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":100,"Q_Id":28839976,"Users Score":2,"Answer":"I found xlwings. It's intuitive and does all the things I want to do. Also, it does well with all pandas data types.","Q_Score":1,"Tags":"python,pandas,xlsxwriter","A_Id":28862593,"CreationDate":"2015-03-03T19:10:00.000","Title":"How to do formmating with combination of pandas dataframe.to_excel and xlsxwriter?","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"maybe some of you can point me in the right direction.\nI've been playing around with OpenCV FaceRecognition for some time with Eigenfaces to let it learn to recognize faces. Now I would like to let it run during windows logon. \nPrecisely, I want to make Snapshots of Faces when I log into a user so after the software has learned the faces after x logins and to which user it belongs, it will log me in automatically when it recognises me during typing. \nAs a prototype it would be enough to somehow get a textfield and get textinput working to \"simulate\" login in. Im using python.\nI hope you understand what i want to achieve and can help me out.\nEdit: Another Question\/Idea: If i want to build an Application in Visual Studio, can I reuse my python Code or do I have to use c++? I could make a Windows Store app or something like that.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":211,"Q_Id":28850205,"Users Score":0,"Answer":"You can store the snapshots in an array, run your recognition on each image and see if the user is recognized as one of the users you have trained your model on.\nIf not then prompt the user for their name, if the name matches one of the users you trained your model on, add these snapshots to their training set and re-train, so the model is more up to date, if it does not match any names, then you assume this is a new user and create a new label and folder for them, and retrain your recognizer.\nBeware though that each time you add more snapshots the training time will increase, so perhaps limit each snapshot capture to 1 FPS or something.","Q_Score":0,"Tags":"python,windows,visual-studio,opencv,login","A_Id":28854271,"CreationDate":"2015-03-04T08:57:00.000","Title":"opencv FaceRecognition during login","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am pretty new to Python and pandas library, i just learned how to read a csv file using pandas. \nmy data is actually raw packets i captured from sensor networks, to analyze corrupt packets.\nwhat i have now is, thousands of rows and hundreds of columns, literally, and the values are all in Hex. i need to convert all the values to binary with trailing zeros.\ni am at lost on how to accomplish that once i have read the CSV file successfully using pandas.\nI'd appreciate every kind of help, even a simple direction.\nhere is my sample data:\n\n00 FF FF 00 00 29 00 89 29 41 88 44 22 00 FF FF 01 00 3F 06 55 55 55\n  55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55\n  55 55 0A \n00 FF FF 00 00 29 00 89 29 41 88 45 22 00 FF FF 01 00 3F 06 55 55 55\n  55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55\n  55 55 0A \n00 FF FF 00 00 29 00 89 29 41 88 46 22 00 FF FF 01 00 3F 06 55 55 55\n  55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55\n  55 55 0A \n00 FF FF 00 00 29 00 89 29 41 88 47 22 00 FF FF 01 00 3F 06 55 55 55\n  55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55 55\n  55 55 0A","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":778,"Q_Id":28854821,"Users Score":0,"Answer":"If I understood, in column 1 you have 00, column 2 : 55, ...\nIf I am right, you first need to concat three columns in a string value = str(col1)+str(col2)+str(col3) and then use the method to convert it in binary.","Q_Score":1,"Tags":"python,pandas,binary,hex","A_Id":28858326,"CreationDate":"2015-03-04T12:42:00.000","Title":"converting dataframe from Hex to binary in using python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am a beginner in igraph.\nI have a graph data of 60000 nodes and 900K edges. I could successfully create the graph using python-igraph and write to disk. My machine has 3G memory. \nWhen I wrote the graph to disk in graphml format, the memory usage was around 19%; with write_pickle, the usage went up to 50% and took significantly more time. \nWhat is the reason behind this behavior of igraph? When should and when should I not use the pickle format? \nPlease shed light into this.","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":650,"Q_Id":28885814,"Users Score":1,"Answer":"Pickle is a serializer from the standard library in Python. These guesses seem quite likely to me:\nWhen igraph was started they did not want to create an own file format so they used pickle. Now the default behavior for saving graphs is not pickle but the own format.\nWhen saving objects with igraph in graphml, the library knows what is important and what is not and will use minimal memory. Pickle, however, can serialize many Python objects to strings and will save every object in a list or dictionary in case it is reused or has cyclic references.","Q_Score":1,"Tags":"python-2.7,igraph","A_Id":28891088,"CreationDate":"2015-03-05T19:13:00.000","Title":"python-igraph pickling efficiency","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am a beginner in igraph.\nI have a graph data of 60000 nodes and 900K edges. I could successfully create the graph using python-igraph and write to disk. My machine has 3G memory. \nWhen I wrote the graph to disk in graphml format, the memory usage was around 19%; with write_pickle, the usage went up to 50% and took significantly more time. \nWhat is the reason behind this behavior of igraph? When should and when should I not use the pickle format? \nPlease shed light into this.","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":650,"Q_Id":28885814,"Users Score":1,"Answer":"Pickling is a generic format to store arbitrary objects, which may reference other objects, which may in turn also reference other objects. Therefore, when Python is pickling an object, it must keep track of all the objects that it has \"seen\" and serialized previously to avoid getting stuck in an infinite loop. That's probably the reason why pickling is slower (and uses more memory).\nThe advantage of using pickling is that the pickled representation will preserve the exact Python type of every single graph, vertex or edge attribute (provided that you use types that support pickling). GraphML won't keep the exact types because there is no unambiguous mapping from Python types to GraphML types; for instance, all numeric attributes would be converted to doubles in the GraphML representation, irrespectively of whether the original attributes were Python ints, longs, or floating-point numbers.","Q_Score":1,"Tags":"python-2.7,igraph","A_Id":28895245,"CreationDate":"2015-03-05T19:13:00.000","Title":"python-igraph pickling efficiency","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am setting up a classification system using scikit-learn.  After training a classifier I would like to save it for reuse along with the necessary transforms such as the DictVectorizer.  \nI am looking for a way to filter the incoming stream of unclassified data that will feed into the feature transforms and classifier.  Ideally, I would like to remove and flag vectors that contain new values for categorical attributes and\/or altogether new attributes.  \nI have used the DictVectorizer.restrict() method to filter input data but this only results in the vectorizer filtering new attributes and zeroing new values, where I would also like to put aside inconsistent data.  Is there an easy way to pull out rows with values and attribute that were not in the initial data set?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":34,"Q_Id":28908468,"Users Score":1,"Answer":"Not with anything built into scikit-learn, as removing rows is something that is not easily done in the current API.\nIt should be quite easy to write a custom function \/ class that does that based on the output of DictVectorizer.","Q_Score":0,"Tags":"python,scikit-learn","A_Id":28910572,"CreationDate":"2015-03-06T22:04:00.000","Title":"Identify Data Vectors with New Attributes and\/or Values","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"If I have a list of prices, say [4,2,5,6,9,3,1,2,5] and I have a transaction cost of $2 and I am able to buy and short sell then the optimal strategy is buy at 2 switch positions at 9 and and switch again at 1. So the optimal buy indices are [1,6] and the optimal sell indices are [4]. How can this be solved programmatically? Specifically I'm looking to be pointed in the right direction (i.e. This is a perfect case for A* ... or whatever) rather than a solution.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":717,"Q_Id":28933388,"Users Score":0,"Answer":"It seems that optimal selling indices are those i such that\nprice[i-1] < price[i] and price[i+1] <= price[i] and for some j > i, price[i] - price[j] > 2. I don't know about names for an algorithm like that, but list comprehensions and the function any should be enough.","Q_Score":1,"Tags":"python,algorithm","A_Id":28933536,"CreationDate":"2015-03-09T00:01:00.000","Title":"How can I find the optimal buy and sell points for a stock if I have a transaction cost?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am wondering seriously about the effects of cv2.imwrite() function of OpenCV.\nI noticed that when I read pictures with cv2.imread() and save them again with cv2.imwrite() function, their quality is not the same any more for the human eyes.\nI ask you how can I keep the quality of the image the same as the original after saving it using cv2.imwrite() function.\nI ask this question because I have really a serious issue in a larger program and when I checked the quality of the pictures saved by this function, I guesses that my problem comes certainly from this function.\nFor example, I draw using the mouse movements small  red (Red=255) squares on picture . When I save the picture and count the number of pixels that have Red color equal to 255 I get very few of them only even if I draw a lot of them in pure red color. But when I check the image by my eyes, I notice the red pixels I drawed are not correctly saved in the correct red color I chosed (255).\nAny one does know how to resolve this problem ? I mean to save the pictures using OpenCV without degrading its quality.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2752,"Q_Id":28940711,"Users Score":5,"Answer":"JPEG is a lossy format, you need to save your images as PNG as it is a lossless format.","Q_Score":1,"Tags":"python,opencv,colors,python-2.x","A_Id":28940905,"CreationDate":"2015-03-09T11:18:00.000","Title":"Not losing the quality of pictures saved with cv2.imwrite()","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to color a pixel in an image. I use opencv and python.\nI tried img[x,y]=[255 255 255] to color a pixel(x,y) but it wont work :(   \nIs there is any mistake in this?\nCan you suggest any method?\nThanks in advance.","AnswerCount":3,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":24622,"Q_Id":28985490,"Users Score":6,"Answer":"img[x,y]=[255, 255, 255] is wrong  because opencv img[a,b] is a matrics then you need to change x,y then you must use img[y,x] \nactualy mistake in the order of x,y \n if you want to change color of point x,y use this >>  img[y,x] = color","Q_Score":4,"Tags":"python,opencv,image-processing","A_Id":49022627,"CreationDate":"2015-03-11T11:30:00.000","Title":"Color a pixel in python opencv","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I recently started working on Document clustering using SciKit module in python. However I am having a hard time understanding the basics of document clustering. \nWhat I know ?\n\nDocument clustering is typically done using TF\/IDF. Which essentially\nconverts the words in the documents to vector space model which is\nthen input to the algorithm.\nThere are many algorithms like k-means, neural networks, hierarchical\nclustering to accomplish this.\n\nMy Data :\n\nI am experimenting with linkedin data, each document would be the\nlinkedin profile summary, I would like to see if similar job\ndocuments get clustered together.\n\nCurrent Challenges: \n\nMy data has huge summary descriptions, which end up becoming 10000's\nof words when I apply TF\/IDF. Is there any proper way to handle this\nhigh dimensional data.\nK - means and other algorithms requires I specify the no. of clusters\n( centroids ), in my case I do not know the number of clusters\nupfront. This I believe is a completely unsupervised learning. Are\nthere algorithms which can determine the no. of clusters themselves?\nI've never worked with document clustering before, if you are aware\nof tutorials , textbooks or articles which address this issue, please\nfeel free to suggest.\n\nI went through the code on SciKit webpage, it consists of too many technical words which I donot understand, if you guys have any code with good explanation or comments please share. Thanks in advance.","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":6083,"Q_Id":28994857,"Users Score":0,"Answer":"For the large matrix after TF\/IDF transformation, consider using sparse matrix.\nYou could try different k values. I am not an expert in unsupervised clustering algorithms, but I bet with such algorithms and different parameters, you could also end up with a varied number of clusters.","Q_Score":4,"Tags":"python,machine-learning,scikit-learn,cluster-analysis,unsupervised-learning","A_Id":28997147,"CreationDate":"2015-03-11T18:44:00.000","Title":"Document Clustering in python using SciKit","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a very large dictionary of the following format {str: [0, 0, 1, 2.5, 0, 0, 0, ...], str: [0, 0, 0, 1.1, 0, 0, ...], ...}. The number of elements for each str key can be very big so I need an effective way to store and make calculations over this data. \nFor example right now my dict of str keys has 100 keys. Each key has one value which is a list of 7000 float elements. The length of str keys and values is constant. So, let's say str key is of length 5 and its value (which is a list) is 7000. \nAfter some reading I found that scipy.sparse module has a nice collection of various matrices to store sparse data but scipy documentation is so sparse that I can barely understand what's going on. \nCan you provide an example of how to convert the dictionary above to correct matrix type?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1646,"Q_Id":29018843,"Users Score":1,"Answer":"With standard dict methods you can get a list of the keys, and another list of the values.  Pass the 2nd to numpy.array and you should get a 100 x 7000 array.  The keys list could also be made into array, but it might not be any more useful than the list.  The values array could be turned into a sparse matrix.  But its size isn't exceptional, and arrays have more methods.\nTomorrow I can add sample code if needed.","Q_Score":1,"Tags":"python,numpy,matrix,scipy","A_Id":29026455,"CreationDate":"2015-03-12T19:40:00.000","Title":"How to convert a sparse dict to scipy.sparse matrix in python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I realize this is a fairly basic question, but I couldn't find what I'm looking for through searching (partly because I'm not sure how to summarize what I want). In any case:\nI have a dataframe that has the following columns:\n* ID (each one represents a specific college course)\n* Year\n* Term (0 = fall semester, 1 = spring semester)\n* Rating (from 0 to 5)  \nMy goal is to create another column for Previous Rating. This column would be equal to the course's rating the last time the course was held, and would be NaN for the first offering of the course. The goal is to use the course's rating from the last time the course was offered in order to predict the current semester's enrollment. I am struggling to figure out how to find the last offering of each course for a given row. \nI'd appreciate any help in performing this operation! I am working in Pandas but could move my data to R if that'd make it easier. Please let me know if I need to clarify my question.","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":1352,"Q_Id":29049985,"Users Score":1,"Answer":"Use this function to create the new column...\nDataFrame.shift(periods=1, freq=None, axis=0, **kwds)\nShift index by desired number of periods with an optional time freq","Q_Score":1,"Tags":"python,pandas,time-series","A_Id":29050296,"CreationDate":"2015-03-14T14:23:00.000","Title":"Pandas Time-Series: Find previous value for each ID based on year and semester","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an arff file as input. I read the arff file and put the element values in a numpy ndarray.Now my arff file contains some '?' as some of the elements. Basically these are property values of matrices calculated by anamod. Whichever values anamod cannot calculate it plugs in a '?' character for those. I want to do a Naive baiyes, Random Forest etc prediction for my data.  So to handle the '?' I want to use an imputer which is like : \nImputer(missing_values='NaN', strategy='mean', axis=0)\nThe  missing_values above is of type string of course. My question is that how to change the type of a few numpy ndarray elements to string from float. I used my_numpy_ndarray.astype('str') == 'NaN' to check for NaN values and I could do it successfully but I am not sure how to change the type of numpyndarray float element to string.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":117,"Q_Id":29056302,"Users Score":0,"Answer":"You can't change the type of parts of an ordinary ndarray.  An ndarray requires all elements in the array to have the same numpy type (the dtype), so that mathematical operations can be done efficiently.  The only way to do this is to change the dtype to object, which allows you to store arbitrary types in each element.  However, this will drastically reduce the speed of most operations, and make some operations impossible or unreliable (such as adding two arrays).","Q_Score":1,"Tags":"python,numpy,casting","A_Id":29058672,"CreationDate":"2015-03-15T01:35:00.000","Title":"change the type of numpyndarray float element to string","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using numpy for reading an arff file and I'm getting the following error:\n\nValueError: Input contains NaN, infinity or a value too large for dtype('float64').\n\nI used np.isnan(X2.any()) and np.isfinite(X2.all())to check if it's a nan or infinite case. But it's none of these. This means it's the third case, which is infinity or a value too large for dtype('float64').\nI would appreciate if someone could tell me how to take care of this error.\nThanks.","AnswerCount":1,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":26758,"Q_Id":29060962,"Users Score":8,"Answer":"Ok I got it. After i used Imputer(missing_values='NaN', strategy='median', axis=1) imp.fit(X2). I also had to write : \nX2 = imp.fit_transform(X2). The reason being  sklearn.preprocessing.Imputer.fit_transform returns a new array, it doesn't alter the argument array","Q_Score":9,"Tags":"python,numpy,scikit-learn","A_Id":29061597,"CreationDate":"2015-03-15T13:11:00.000","Title":"a value too large for dtype('float64')","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to optimize a pyOpenCL program. For this reason I was wondering if there is a way to profile the program and see where most of the time is needed for.\nDo you have any idea how to approach this problem?\nThanks in advance\nAndi\nEDIT: For example nvidias nvprof for CUDA would do the trick for pyCuda, however, not for pyOpenCL.","AnswerCount":5,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2168,"Q_Id":29068229,"Users Score":0,"Answer":"CodeXL from AMD works very well.","Q_Score":5,"Tags":"python,opencl,pyopencl","A_Id":42962569,"CreationDate":"2015-03-16T01:12:00.000","Title":"Is there a way to profile an OpenCL or a pyOpenCL program?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a Matlab application that writes in to a .csv file and a Python script that reads from it. These operations happen concurrently and at their own respective periods (not necessarily the same). All of this runs on Windows 7.\nI wish to know :\n\nWould the OS inherently provide some sort of locking mechanism so that only one of the two applications - Matlab or Python - have access to the shared file?\nIn the Python application, how do I check if the file is already \"open\"ed by Matlab application? What's the loop structure for this so that the Python application is blocked until it gets access to read the file?","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":212,"Q_Id":29085298,"Users Score":0,"Answer":"I am not sure about window's API for locking files\nHeres a possible solution:\n\nWhile matlab has the file open, you create an empty file called \"data.lock\" or something to that effect.\nWhen python tries to read the file, it will check for the lock file, and if it is there, then it will sleep for a given interval.\nWhen matlab is done with the file, it can delete the \"data.lock\" file.\n\nIts a programmatic solution, but it is simpler than digging through the windows api and finding the right calls in matlab and python.","Q_Score":2,"Tags":"python,windows,matlab,file,shared","A_Id":29085388,"CreationDate":"2015-03-16T19:24:00.000","Title":"Shared file access between Python and Matlab","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am currently making an application with OpenCV and a web server that finds certain car brands as part of an ongoing game in my family.\nHowever, I don't know where to start. I googled it but all I found was a post on finding a yellow ball. I want to find a car logo from a picture (which could be angled or glaring) so I identify the car brand and add points to the score.\nI know it seems like a tall order but could anybody help?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1164,"Q_Id":29088095,"Users Score":0,"Answer":"You could probably use Haar cascades in openCv to do this. You will need to train haar detectors with both positive and negative samples of the logo but there is already utilities in openCv to help you with this. Just read up about haar in openCv documentation","Q_Score":2,"Tags":"python,opencv","A_Id":29088394,"CreationDate":"2015-03-16T22:27:00.000","Title":"Logo recognition in OpenCV","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a large data-set (I can't fit entire data on memory). I want to fit a GMM on this data set. \nCan I use GMM.fit() (sklearn.mixture.GMM) repeatedly on mini batch of data ??","AnswerCount":4,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":3292,"Q_Id":29095769,"Users Score":0,"Answer":"As Andreas Mueller mentioned, GMM doesn't have partial_fit yet which will allow you to train the model in an iterative fashion. But you can make use of warm_start by setting it's value to True when you create the GMM object. This allows you to iterate over batches of data and continue training the model from where you left it in the last iteration.\nHope this helps!","Q_Score":3,"Tags":"python,scikit-learn,bigdata,mixture-model","A_Id":60111168,"CreationDate":"2015-03-17T09:42:00.000","Title":"Sklearn-GMM on large datasets","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a large data-set (I can't fit entire data on memory). I want to fit a GMM on this data set. \nCan I use GMM.fit() (sklearn.mixture.GMM) repeatedly on mini batch of data ??","AnswerCount":4,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":3292,"Q_Id":29095769,"Users Score":0,"Answer":"I think you can set the init_para to empty string '' when you create the GMM object, then you might be able to train the whole data set.","Q_Score":3,"Tags":"python,scikit-learn,bigdata,mixture-model","A_Id":36488496,"CreationDate":"2015-03-17T09:42:00.000","Title":"Sklearn-GMM on large datasets","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a large data-set (I can't fit entire data on memory). I want to fit a GMM on this data set. \nCan I use GMM.fit() (sklearn.mixture.GMM) repeatedly on mini batch of data ??","AnswerCount":4,"Available Count":3,"Score":0.0996679946,"is_accepted":false,"ViewCount":3292,"Q_Id":29095769,"Users Score":2,"Answer":"fit will always forget previous data in scikit-learn. For incremental fitting, there is the partial_fit function. Unfortunately, GMM doesn't have a partial_fit (yet), so you can't do that.","Q_Score":3,"Tags":"python,scikit-learn,bigdata,mixture-model","A_Id":29109730,"CreationDate":"2015-03-17T09:42:00.000","Title":"Sklearn-GMM on large datasets","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to generate the inverse matrix using numpy package in python. Unfortunately , I'm not getting the answers I expected.\nOriginal matrix:\n([17 17 5]\n [21 18 21]\n [2 2 19])\nInverting the original matrix by Cramer's rule gives:\n([4 9 15]\n [15 17 6]\n [24 0 17])\nApparently using numpy.linalg.inv()  gives\n-3.19488818e-01,3.80191693e-01,-6.38977636e-03,\n3.33333333e-01, -3.33333333e-01, 2.26123699e-18, \n-2.84345048e-01, 2.68370607e-01, 5.43130990e-02n\nI expected that multiplying the original matrix and the inverse would have given an identity matrix but as you can see I give a matrix filled with floating points.\nWhere can be the issue?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2359,"Q_Id":29119880,"Users Score":0,"Answer":"linalg is right and you are wrong.\nThe matrix it gave you is indeed the inverse. However, if you are using np.array instead of np.matrix then the multiplication operator doesn't work as expected, since it calculates the component-wise product.\nIn that case you have to do mat.dot(inv(mat)).\nIn any case, what you will get will not be a perfect identity matrix due to rounding errors (when I tried it the off-diagonal matrix entries were in the order of 10 ** (-16)).","Q_Score":1,"Tags":"python,numpy,matrix-inverse","A_Id":29120093,"CreationDate":"2015-03-18T10:43:00.000","Title":"How to calculate inverse using cramer's rule in python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working with the Mean Shift clustering algorithm, which is based on the kernel density estimate of a dataset. I would like to generate a large, high dimensional dataset and I thought the Scikit-Learn function make_blobs would be suitable. But when I try to generate a 1 million point, 8 dimensional dataset, I end up with almost every point being treated as a separate cluster. \nI am generating the blobs with standard deviation 1, and then setting the bandwidth for the Mean Shift to the same value (I think this makes sense, right?). For two dimensional datasets this produced fine results, but for higher dimensions I think I'm running into the curse of dimensionality in that the distance between points becomes too big for meaningful clustering. \nDoes anyone have any tips\/tricks on how to get a good high-dimensional dataset that is suitable for (something like) Mean Shift clustering? (or am I doing something wrong? (which is of course a good possibility))","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":681,"Q_Id":29148746,"Users Score":1,"Answer":"The standard deviation of the clusters isn't 1.\nYou have 8 dimensions, each of which has a stddev of 1, so you have a total standard deviation of sqrt(8) or something like that.\nKernel density estimation does not work well in high-dimensional data because of bandwidth problems.","Q_Score":1,"Tags":"python,scikit-learn,cluster-analysis,mean-shift","A_Id":29149537,"CreationDate":"2015-03-19T15:32:00.000","Title":"Generating high dimensional datasets with Scikit-Learn","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I use the MPI_Sendrecv MPI function to communicate arrays of data between processes. I do this in Python using mpi4py, but I'm pretty sure my question is independent of the language used.\nWhat I really want is to add an array residing on another process to an existing local array. This should be done for all processes, so I use the MPI_Sendrecv function to send and receive the arrays in one go. I can then add the received array in the recvbuf to the local array and I'm done.\nIt would be nice however if I could save the step of having a separate recvbuf array, and simply receiving the data directly into the local array without overwriting the existing data, but rather updating it using some operation (addition in my case).\nI guess what I'm looking for is a combined MPI_Sendrecv\/MPI_Reduce function. Do some function like this exist in MPI?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":127,"Q_Id":29157039,"Users Score":1,"Answer":"MPI_Recvreduce is what you're looking for. Unfortunately, it doesn't exist yet. It's something that the MPI Forum has been looking at adding to a future version of the standard, but hasn't yet been adopted and won't be in the upcoming MPI 3.1.","Q_Score":2,"Tags":"python,c,mpi,mpi4py","A_Id":29256366,"CreationDate":"2015-03-19T23:50:00.000","Title":"MPI_Sendrecv with operation on recvbuf?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I have some data containing usernames and their respective genders.\nFor example, an entry in my data list may look like:\n{User: 'abc123', Gender: 'M'}\nFor each username, I am also given a bag of text, images, and locations attached to each of them, although it's not necessary that a user has at least one text, one image and one location attached to them.\nFor each data source, I can translate them into a vector of features, which I then feed into a classifier. I can then confirm if the classifier is effective via 10-fold cross-validation.\nI want to combine some output from all the classifiers such that I can feed them into a meta-classifier to hopefully improve accuracy.\nThe problem is that since the data is incomplete, I can't simply combine all the vectors generated from each data source and feed them into one classifier. Some users may not have image data, or others may not have location data.\nMy current idea is to use each classifier to obtain some category probability set for each user, something like [Male: 0.75, Female: 0.25] from each data source's classifier, multiply all the categories' values, and use the highest value as the program's predicted category.\nSo if I have 1 classifier for each data source (text, image, location), then I have a total of 3 classifiers. Even if one or two of the data sources are missing for some users, I can still obtain a category probability set for those users.\nDoes scikit-learn have any algorithm that can output a probability weight that the user is of some gender instead of just classifying them?\nOr is there some other algorithm that satisfies my needs?\nThanks for going through my wall of text!","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":74,"Q_Id":29183178,"Users Score":1,"Answer":"I think you can make use of a \"naive Bayes\" classifier here. In that case, the class (M or F) probability is a product of terms, one term for each available feature set, and you just ignore (exclude from the product) any feature set that is missing.\nHere is the justification. Let's say the feature sets are X1, X2, X3. Each of these is a vector of features. The naive Bayes assumption is that feature sets are independent given the class, i.e., P(X1, X2, X3 | C) = P(X1 | C) P(X2 | C) P(X3 | C). (Remember that this is just a simplifying assumption -- it might or might not be true!) When all feature sets are present, the posterior class probability is just P(C | X1, X2, X3) = P(X1, X2, X3 | C) P(C) \/ Z = P(X1 | C) P(X2 | C) P(X3 | C) P(C) \/ Z, where Z is the normalizing constant that makes the probabilities of the 2 classes add up to 1. So to make use of this formulation, you need a density model for each of the feature sets; if this approach makes sense to you, we can talk about those density models.\nNow what if a feature set (let's say X3) is missing? That means we need to calculate P(C | X1, X2) = P(X1, X2 | C) P(C) \/ Z. But note that P(X1, X2 | C) = integral P(X1, X2, X3 | C) dX3 = integral P(X1 | C) P(X2 | C) P(X3 | C) dX3 = P(X1 | C) P(X2 | C) integral P(X3 | C) dX3 by the naive Bayes assumption. Note that integral P(X3 | C) dX3 = 1, so P(X1, X2 | C) = P(X1 | C) P(X2 | C), i.e., the naive Bayes assumption still holds for just the observed feature sets, so you can go ahead and calculate P(C | X1, X2) = P(X1 | C) P(X2 | C) P(C) \/ Z, i.e., when some feature set is missing in a naive Bayes model, just ignore it.","Q_Score":1,"Tags":"python,statistics,scipy,scikit-learn,scikits","A_Id":29191059,"CreationDate":"2015-03-21T13:18:00.000","Title":"Classifying users by demographic using incomplete data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using bokeh (0.8.1) in combination with the ipython notebook to generate GridPlots. I would like to automatically store the generated GridPlots to a single png (hopefully pdf one day) on disk, but when using \"Preview\/Save\" it cycles through all of the figures asking me to store them separately. Is there a more efficient way?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1852,"Q_Id":29205574,"Users Score":0,"Answer":"First you save your grid in an object, \nLet's say \"a\" the you confirms that \"a\" is a grid. Example\ngrid = bly.gridplot(graficas,ncols=3) # Here you save your grid\nbpl.show(grid). # Here you show your grid\nfrom bokeh.io import export_png # You need this library to export\nExportar grid\nexport_png(grid, filename=\"your path\/name.png\")","Q_Score":4,"Tags":"python,bokeh","A_Id":61935681,"CreationDate":"2015-03-23T08:07:00.000","Title":"How to save a bokeh gridplot as single file","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I don't know much about Python and I'm trying to use it to do some simple polynomial interpolation, but there's something I'm not understanding about one of the built-in functions. \nI'm trying to use polyval(p,x) to evaluate a polynomial p at x. \nI made an example polynomial p(x) = 1 + 2x + x^3, I created an array p = [1,2,0,1] to define it and I want to know the values at x = 0,1,2 so I created another array x = [0,1,2]. \nDoing polyval(p,x) gave me an output of [1, 4, 17]. p(0) and p(1) are correct, but p(2) should be 13, not 17. Why is it giving me 17?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":5765,"Q_Id":29212385,"Users Score":1,"Answer":"Your p array is in the wrong order. You should start with the coefficient of the highest exponent.\nTry with p=[1,0,2,1].","Q_Score":1,"Tags":"python,numpy","A_Id":29212482,"CreationDate":"2015-03-23T14:08:00.000","Title":"Polyval(p,x) to evaluate polynomials","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"This will be my new post if I go wrong please don't judge me hard :)\nI'm developing an OpenCV project with Python and also I'm developing its mobile interface in Android. My purpose is to compare plant pictures and decide their species.Researchers who use Android application will take plant photos and upload them (such as 'tree leaf photo'). I intent to execute comparison algorithms in the cloud. I use Parse as mobile database so I thought that I could use Cloud Code in Parse to run my Python codes. Can I do this ?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":89,"Q_Id":29240840,"Users Score":0,"Answer":"No. JavaScript is the only language currently supported for writing CloudCode.","Q_Score":0,"Tags":"android,python,opencv,parse-platform","A_Id":29240968,"CreationDate":"2015-03-24T19:05:00.000","Title":"Using OpenCV in Parse Cloud Code","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"This will be my new post if I go wrong please don't judge me hard :)\nI'm developing an OpenCV project with Python and also I'm developing its mobile interface in Android. My purpose is to compare plant pictures and decide their species.Researchers who use Android application will take plant photos and upload them (such as 'tree leaf photo'). I intent to execute comparison algorithms in the cloud. I use Parse as mobile database so I thought that I could use Cloud Code in Parse to run my Python codes. Can I do this ?","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":89,"Q_Id":29240840,"Users Score":0,"Answer":"You can have Parse Cloud Code call out to your Python code using HTTP if you want. Just as you can do the same from the Android app. This code can tell the web hook what images to download and process based on some condition (such as a researcher has uploaded a photo to be processed). Purely up to you how you trigger this. Your Cloud Code is JavaScript only though, same as your Android app is Java only (usually, unless you use Xamarin with C# or something else of course).","Q_Score":0,"Tags":"android,python,opencv,parse-platform","A_Id":29246105,"CreationDate":"2015-03-24T19:05:00.000","Title":"Using OpenCV in Parse Cloud Code","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How can I read in a .csv file (with no headers) and when I only want a subset of the columns (say 4th and 7th out of a total of 20 columns), using pandas? I cannot seem to be able to do usecols","AnswerCount":4,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":539397,"Q_Id":29287224,"Users Score":14,"Answer":"Make sure you specify pass header=None and add usecols=[3,6] for the 4th and 7th columns.","Q_Score":347,"Tags":"python,pandas","A_Id":29287544,"CreationDate":"2015-03-26T19:27:00.000","Title":"Pandas read in table without headers","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Basically I am running some optimisation algorithms that I have created using Numpy and I want to plot the log of the error against the number of iterations. Having done this with linear regression and having had no issues, it is very strange that I seem to get issues when doing the exact same thing with logistic regression. I get the following \"warning\":  \n\n\/usr\/lib64\/python2.6\/site-packages\/matplotlib\/axis.py:1004:\n  UserWarning: Unable to find pixel distance along axis for interval\n  padding; assuming no interval padding needed.   warnings.warn(\"Unable\n  to find pixel distance along axis for interval padding; assuming no\n  interval padding needed.\")\n\nHowever, when I don't use a log axis for the y axis, I don't get the error either. All the elements of the array that I am using are also positive, so it shouldn't have anything to do with taking the log of a non-positive number.\nHas anyone ever encountered this before? Does anyone know what it may be referring to?\nThanks","AnswerCount":1,"Available Count":1,"Score":-0.1973753202,"is_accepted":false,"ViewCount":1303,"Q_Id":29300510,"Users Score":-1,"Answer":"not sure if this issue is still open, but I had a similar problem and updating seaborn fixed the issue for me. For you it might be matplotlib, depending which package you used for creating the graph.","Q_Score":1,"Tags":"python,matplotlib","A_Id":60968639,"CreationDate":"2015-03-27T12:19:00.000","Title":"matplotlib UserWarning When log axis is used in some cases","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"The smooth.spline function in R allows a tradeoff between roughness (as defined by the integrated square of the second derivative) and fitting the points (as defined by summing the squares of the residuals).  This tradeoff is accomplished by the spar or df parameter.  At one extreme you get the least squares line, and the other you get a very wiggly curve which intersects all of the data points (or the mean if you have duplicated x values with different y values)\nI have looked at scipy.interpolate.UnivariateSpline and other spline variants in Python, however, they seem to only tradeoff by increasing the number of knots, and setting a threshold (called s) for the allowed SS residuals.  By contrast, the smooth.spline in R allows having knots at all the x values, without necessarily having a wiggly curve that hits all the points -- the penalty comes from the second derivative.\nDoes Python have a spline fitting mechanism that behaves in this way?  Allowing all knots but penalizing the second derivative?","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":4069,"Q_Id":29312005,"Users Score":1,"Answer":"From research on google, I concluded that\nBy contrast, the smooth.spline in R allows having knots at all the x values, without necessarily having a wiggly curve that hits all the points -- the penalty comes from the second derivative.","Q_Score":18,"Tags":"python,r,smoothing,splines","A_Id":56953319,"CreationDate":"2015-03-27T23:56:00.000","Title":"Is there a Python equivalent to the smooth.spline function in R","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to be able to use geom_smooth in ggplot. However, when I typed conda install ggplot, I get the error no packages found in current win-32 channels matching ggplot. Anyone know what is going on?","AnswerCount":6,"Available Count":3,"Score":0.0333209931,"is_accepted":false,"ViewCount":19615,"Q_Id":29312985,"Users Score":1,"Answer":"I ran across the same issue when installing ggplot. None of the methods worked, eventually I reinstalled anaconda. Then everything works smoothly.","Q_Score":5,"Tags":"python,windows,anaconda,python-ggplot","A_Id":45366940,"CreationDate":"2015-03-28T02:26:00.000","Title":"Cannot install ggplot with anaconda","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to be able to use geom_smooth in ggplot. However, when I typed conda install ggplot, I get the error no packages found in current win-32 channels matching ggplot. Anyone know what is going on?","AnswerCount":6,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":19615,"Q_Id":29312985,"Users Score":0,"Answer":"As of Jan 2016, ggplot now comes installed by default if you are using the Anaconda distribution so you can just use install ggplot.  New to Python so this is still tripping me up.","Q_Score":5,"Tags":"python,windows,anaconda,python-ggplot","A_Id":41456234,"CreationDate":"2015-03-28T02:26:00.000","Title":"Cannot install ggplot with anaconda","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to be able to use geom_smooth in ggplot. However, when I typed conda install ggplot, I get the error no packages found in current win-32 channels matching ggplot. Anyone know what is going on?","AnswerCount":6,"Available Count":3,"Score":0.1325487884,"is_accepted":false,"ViewCount":19615,"Q_Id":29312985,"Users Score":4,"Answer":"I think ggplot is simply not packaged for Anaconda as conda search ggplot doesn't find anything.  How it can be easily installed via pip --  pip install ggplot.","Q_Score":5,"Tags":"python,windows,anaconda,python-ggplot","A_Id":29313575,"CreationDate":"2015-03-28T02:26:00.000","Title":"Cannot install ggplot with anaconda","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"when I chunk text, I get lots of codes in the output like\nNN, VBD, IN, DT, NNS, RB.\nIs there a list documented somewhere which tells me the meaning of these? \nI have tried googling nltk chunk code nltk chunk grammar nltk chunk tokens.\nBut I am not able to find any documentation which explains what these codes mean.","AnswerCount":3,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":25538,"Q_Id":29332851,"Users Score":22,"Answer":"Even though the above links have all kinds. But hope this is still helpful for someone, added a few that are missed on other links.\nCC: Coordinating conjunction\nCD: Cardinal number\nDT: Determiner\nEX: Existential there\nFW: Foreign word\nIN: Preposition or subordinating conjunction\nJJ: Adjective\nVP: Verb Phrase\nJJR: Adjective, comparative\nJJS: Adjective, superlative\nLS: List item marker\nMD: Modal\nNN: Noun, singular or mass\nNNS: Noun, plural\nPP: Preposition Phrase\nNNP: Proper noun, singular Phrase\nNNPS: Proper noun, plural\nPDT: Pre determiner\nPOS: Possessive ending\nPRP: Personal pronoun Phrase\nPRP: Possessive pronoun Phrase\nRB: Adverb\nRBR: Adverb, comparative\nRBS: Adverb, superlative\nRP: Particle\nS: Simple declarative clause\nSBAR: Clause introduced by a (possibly empty) subordinating conjunction\nSBARQ: Direct question introduced by a wh-word or a wh-phrase. \nSINV: Inverted declarative sentence, i.e. one in which the subject follows the tensed verb or modal.\nSQ: Inverted yes\/no question, or main clause of a wh-question, following the wh-phrase in SBARQ.\nSYM: Symbol\nVBD: Verb, past tense\nVBG: Verb, gerund or present participle\nVBN: Verb, past participle\nVBP: Verb, non-3rd person singular present\nVBZ: Verb, 3rd person singular present\nWDT: Wh-determiner\nWP: Wh-pronoun\nWP: Possessive wh-pronoun\nWRB: Wh-adverb","Q_Score":29,"Tags":"python,nlp,nltk,text-parsing,pos-tagger","A_Id":54869627,"CreationDate":"2015-03-29T18:08:00.000","Title":"What does NN VBD IN DT NNS RB means in NLTK?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"When I use Matplotlib to plot some graphs, it is usually fine for the default inline drawing. However, when I draw some 3D graphs, I'd like to have them in a separate window so that interactions like rotation can be enabled. Can I configure in Python code which figure to display inline and which one to display in a new window? \nI know that in Spyder, click Tools, Preferences, Ipython Console, Graphics and under Graphics Backend select \u201cautomatic\u201d instead of \u201cinline\u201d. However, this make all the figures to be in new windows. It can be messy when I have a lot of plots. So I want only those 3D plot to be in new windows, but all the other 2D plots remain inline. Is it possible at all?\nThanks!","AnswerCount":5,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":218892,"Q_Id":29356269,"Users Score":50,"Answer":"Go to\nTools >> Preferences >> IPython console >> Graphics >> Backend:Inline, change \"Inline\" to \"Automatic\", click \"OK\"\nReset the kernel at the console, and the plot will appear in a separate window","Q_Score":67,"Tags":"python,matplotlib,spyder","A_Id":46616236,"CreationDate":"2015-03-30T21:05:00.000","Title":"Plot inline or a separate window using Matplotlib in Spyder IDE","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"We need to get the count of each key (the keys are not known before executing), and do some computation dynamically in each Mapper. The key count could be global or only in each Mapper. What is the best way to implement that? In Hadoop this is similar to an aggregator function.\nThe accumulator in Spark needs to be defined before the Mapper jobs run. But we do not know what and how many keys are there.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":110,"Q_Id":29358494,"Users Score":0,"Answer":"You can use pairRDD.countByKey() function for counting the rows according their keys.","Q_Score":0,"Tags":"java,python,hadoop,mapreduce,apache-spark","A_Id":29516550,"CreationDate":"2015-03-31T00:14:00.000","Title":"Get the count of each key in each Mapper or globally in Spark MapReduce model","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new in coding. Now I have a question. I have an object who keep moving in an rectangle area. And I also have a lot of circle in this area too. I want to get all the intersection point between the trajectory and the all the circle. As the object is moving step by step, so was thinking that I can calculate the distance between the position of object and all the centre of each circle and compare the distance with radius of the circle. But I think that this will do a lot of computation as you need to calculate the distance at each step. Do you have any good idea or reference. By the way, I am woking on python. Thank you. As I do not have enough reputation , I can not add a picture about the problem","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1435,"Q_Id":29384494,"Users Score":0,"Answer":"In general I would recommend to first make your algorithm work and then make it faster if you need to. You would be amazed by how fast Python in combination with a set of carefully selected libraries can be.\nSo for your problem, I would do the following:\n1.) Install a set of libraries that makes your life easier:\n   - Matplotlib for 2D plotting of the rectangle, the circle and \n     the trajectory\n2.) Numpy for general purpose array manipulation\n3.) Optionally Scipy for its KDTree support (nearest neighbor search)\n4.) Start implementing your problem\n  a.) Create a rectangle and visualize it using Matplotlib\nb.) Create a set of circles and plot them within the rectangular area of 4a\nc.) Create a trajectory and plot them within the rectangular area\nNow the more difficult part starts. The way forward depends a little on how your trajectory is defined. For example, if your trajectory consists of line segments, you could calculate the intersection point between a circle and a line segment analytically. Three possible solutions exist, no intersection, 1 intersection (line touches circle) and 2 intersections. If your trajectory is more complex, you could discretize it by generating many points along it and than calculate if this point is on the edge of one of the circles. You have to be a little clever though about how the 3 possible solutions can be identified, because the points along the trajectory are finite. \nAnother option would be to also discretize the points on the edges of the circles. This would mean that the problem reduces for a large part to nearest neighbor search for which you can use the Scipy KDTree class.","Q_Score":0,"Tags":"python,geometry,intersection","A_Id":29385747,"CreationDate":"2015-04-01T06:46:00.000","Title":"The intersection between a trajectory and the circles in the same area","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new in coding. Now I have a question. I have an object who keep moving in an rectangle area. And I also have a lot of circle in this area too. I want to get all the intersection point between the trajectory and the all the circle. As the object is moving step by step, so was thinking that I can calculate the distance between the position of object and all the centre of each circle and compare the distance with radius of the circle. But I think that this will do a lot of computation as you need to calculate the distance at each step. Do you have any good idea or reference. By the way, I am woking on python. Thank you. As I do not have enough reputation , I can not add a picture about the problem","AnswerCount":3,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":1435,"Q_Id":29384494,"Users Score":1,"Answer":"Let a be a number somewhere between the radius and diameter of the larger circles (if they have different radii). \nGenerate a grid of square tiles of side length a, so that grid(i,k) is the square from (i*a,k*a) to ((i+1)*a, (k+1)*a).\nEach tile of the grid contains a list with pointers to circles or indices into the circle array.\nFor each circle, register it with each tile that it intersects with. Should be less than 4.\n\nNow to test the point (x,y) of the trajectory for circle intersections resp. containment inside the corresponding disk, you only need to test it against the list of circles in tile ((int)(x\/a), (int)(y\/a).","Q_Score":0,"Tags":"python,geometry,intersection","A_Id":29388615,"CreationDate":"2015-04-01T06:46:00.000","Title":"The intersection between a trajectory and the circles in the same area","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to apply Machine Learning to a Kaggle.com dataset. The dimension of my dataset is 244768 x 34756. Now at this size none of the scikit algorithms work.\nI thought i would apply PCA , but even that doesnt scale up to this dataset. \nIs there anyway i can reduce redundant data from my training dataset? I can reduce dimension by applying PCA , but if i could apply PCA.\nSince i am doing document classification , i resampled my dataset to 244768*5672 , by reducing the word vector size. PCA cant be applied to even this dataset.\nCan i apply PCA by doing this method. Suppose my matrix is A - \nX = A.T * A\npca(X) (X becomes 5672 x 5672 matrix)\nWill this give me wrong answers?\nAlso when i apply Logistic regression , can i train the model incrementally , .ie\nIf A = 10000 x 500\nCan i take 1000 x 500 , to logistic.fit(A) and then do the same for other rows? Is this kind of training wrong?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":110,"Q_Id":29422093,"Users Score":0,"Answer":"you can segment your data on few models which output will be input to the next model which will give you result.\nBasically its RNN architecture.\nPut such massive data in one network just not possible due to memory limitation.","Q_Score":1,"Tags":"python,machine-learning,scikit-learn,pca,logistic-regression","A_Id":45776219,"CreationDate":"2015-04-02T20:42:00.000","Title":"Machine Learning -Issues with big dataset","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to store a table on disk, and be able to retrieve a subset of that table into a numpy.ndarray very fast. What's the best way to do that? I don't mind spending the time to preprocess this dataset before storing it on disk, since it won't be changed once it's created.\nI'd prefer not to write any C code, and instead rely on existing python libraries. I am considering HDF5 (with either pytables or h5py), sqlite, numpy's memmap, or a custom binary file format.\nFor a custom file, I would sort the rows by the identifier, and add to the file a table of contents which, for every identifier, would specify the beginning and ending file offsets that encompass the data related to this identifier. This would probably be very fast in terms of I\/O, but at a cost of using python rather than C code (since I don't think there's a library that does precisely that).\nDetails:\n~100 million rows, ~5 columns of float and str data. One of the columns contains 100,000 different identifiers (so there are about 1000 rows per identifier). The subset to be retrieved is always specified by a set of identifiers (usually I need to retrieve ~2000 identifiers, so ~2% of the entire dataset).\nPython 3.4, Linux, SSD drive (so random access is as fast as sequential).","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":179,"Q_Id":29430979,"Users Score":0,"Answer":"You could create a region reference dataset where each element relates to one of the ~2000 identifiers.\nThen the Python code to reference a particular identifier would look like this: \n  reg_ref - reg_ref_dset[identifier]\n  mysub = data_dset[reg_ref]","Q_Score":2,"Tags":"python,sqlite,python-3.x,numpy,hdf5","A_Id":29477966,"CreationDate":"2015-04-03T11:04:00.000","Title":"Storing a large table on disk, with fast retrieval of a specified subset to np.ndarray","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have 3 sets of comparison data(y axes) which needs to be plotted against a target source values. I'm comparing exports, gdp, standard of living values of different countries against a target countries values for different years. But values of each category are haphazard i.e exports in millions of dollars, gdp in percentage and standard of living scale of 1 to 10. Moreover I have years value for comparison as well.\nWhat I want to see is over the years how different parameters for each country over different years vary against the target country parameters. All of this plotted in one graph in plotly.\nI can plot multiple y axes in plotly, but the scale doesn't match.\nHas anyone any suggestions how to fit all the comparison in one layout. Maybe this is more of a graphing suggestion needed rather than help in plotly? Any ideas how to squeeze all in one graph?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":925,"Q_Id":29445943,"Users Score":1,"Answer":"Full disclosure, I work for Plotly.\nHere's my shot at summarizing your problem in general, you've got 4 dimensions for each country (year, exports, gdp, standard of living).\nYou might be able to use either or both of these solutions:\n\nvisualize this in two dimensions using x-value, y-value, marker-size, and marker-line-size (a bubble chart in 2d)\nvisualize this in three dimensions using x-value, y-value, z-value, and marker-size\n\nI'll leave a link to a notebook in the comments, but since it's not a very permanent link, I won't include it in the answer here.","Q_Score":0,"Tags":"python,graph,plotly","A_Id":29460131,"CreationDate":"2015-04-04T11:38:00.000","Title":"graph of multiple y axes in plotly","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to apply a simple Monte Carlo simulation on a variable that has three distinct values. Should I use random.random and assign the float to a variable value, or use random.choice([\"a\", \"b\", \"c\"])?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":143,"Q_Id":29469458,"Users Score":0,"Answer":"Do you want a uniform distribution of these 3 values? If so, random.choice will give you exactly that.","Q_Score":0,"Tags":"python,montecarlo","A_Id":29469527,"CreationDate":"2015-04-06T10:28:00.000","Title":"random.random or random.choice for Monte Carlo simulation?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have used the python's DecisionTreeRegressor() to segment data based on a Predictor that is continuous, and it works well.\nIn the present project I have been asked to use Categorical data as Predictor.\nPredictor - Industry Domain, Response - Revenue.\nOn using DecisionTreeRegressor() it threw error \n\n\"Cannot change string to float : Industry Domain\".\n\nCan you suggest if there is any way to resolve this problem?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":385,"Q_Id":29481698,"Users Score":0,"Answer":"For this kind of decision tree you need to use DecisionTreeClassifier().  It appears that DecisionTreeRegressor only works with numerical predictor data.  DecisionTreeClassifier() only works with class predictor data.  I really wanted one that does both, but it doesn't appear possible.","Q_Score":0,"Tags":"python,decision-tree","A_Id":35924390,"CreationDate":"2015-04-07T00:15:00.000","Title":"How to use DecisionTreeRegressor() for Categorical Segmentation?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there any inbuilt or online Implementation of GNFS factoring in Python? I need a version that can easily be used to factor integers in other programs so I would need to import and preferably is comparable with or only needs minimal change to work with Python 3. I need this to factor (multiple) numbers of over 90 digits in length and elliptic curve factorization is too slow for the purpose.\nI have checked online and could only find Perl and C++ implementations.\nIf not is there any online resource that could guide me step by step to my own implementation of this algorithm?","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2960,"Q_Id":29539678,"Users Score":0,"Answer":"the pearl wrapper for GGNFS (c implementation) was rewritten into python by Brian Gladman.\nLook for factmsieve.py","Q_Score":5,"Tags":"python","A_Id":29547417,"CreationDate":"2015-04-09T13:20:00.000","Title":"Is there a pre-existing implementation of the General Number Field Sieve (GNFS) in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have the below requirements and confused about which one to choose for high performance. I am not java developer. I am comfort with Hive, Pig and Python. \nI am using HDP2.1 with tez engine. Data sources are text files(80 GB) and Oracle table(15GB). Both are structured data. I heard Hive will suite for structure data and Python map reduce streaming concept too will have high performance than hive & Pig. Please clarify. \nI am using Hive and the reasons are:\n\nneed to join those two sources based on one column.  \nusing ORC format table to store the join results since the data size is huge \ntext file name will be used to generate one output column and that has been performed with virtual column concept input__file__name field.\nAfter join need to do some arithmetic operations on each row and doing that via python UDF\n\nNow the complete execution time from  data copy into HDFS to final result taken 2.30 hrs with 4 node cluster using Hive and Python UDF. \nMy questions are:\n1) I heard Java Mapreduce always faster. Will that be true with Python Map reduce streaming concept too?\n2) Can I achieve all the above functions in Python like join, retrieval of  text file name, compressed data flow like ORC since the volume is high?\n3) Will Pig join would be better than Hive? If yes can we get input text file name in Pig to generate output column?\nThanks in advance.","AnswerCount":1,"Available Count":1,"Score":0.537049567,"is_accepted":false,"ViewCount":2382,"Q_Id":29552853,"Users Score":3,"Answer":"Python Map Reduce or anything using Hadoop Streaming interface will most likely be slower. That is due to the overhead of passing data through stdin and stdout and the implementation of the streaming API consumer (in your case python). Python UDF's in Hive and Pig do the same thing.\nYou might not want to compress data flow into ORC on the Python side. You'll be subjected to using Python's ORC libraries, which I am not sure if they are available. It would be easier if you let Python return your serialized object and the Hadoop reduce steps to compress and store as ORC (Python as a UDF for computation)\nYes. Pig and Python have some what of a nice programmatic interface where in you can write python scripts to dynamically generate Pig Logic and submit it in parallel. look up Embedding Pig Latin in Python. It's robust enough to define Python UDFS and let Pig do the overall abstraction and job optimization. Pig does a lazy evaluation so in cases of multiple joins or multiple transformations it can demonstrate pretty good performance in the optimizing the complete pipe line. \n\nYou say HDP 2.1. Have you had a look at Spark ? If performance is important to you and looking at the datasets size which dont look huge you ll expect many time faster overall pipeline execution than Hadoop s native MR engine","Q_Score":2,"Tags":"python,hadoop,mapreduce,hive,apache-pig","A_Id":29991069,"CreationDate":"2015-04-10T03:30:00.000","Title":"Which will give the best performance Hive or Pig or Python Mapreduce with text file and oracle table as source?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I know about the Python wrappers for Stanford CoreNLP package but this package does not seem to contain neural net based dependency parser model. Rather it is present in Stanford-parser-full-****-- package for which I can't find any Python wrapper. My Question: Is there a Python wrapper that would parse using Stanford Neural Net based dependency parser? Any suggestions or directions would be helpful. Thanks!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":207,"Q_Id":29575034,"Users Score":1,"Answer":"I don't know of any such wrapper at the moment, and there are no plans at Stanford to build one. (Maybe the NLTK developers would be up for the challenge?)","Q_Score":1,"Tags":"python,parsing,nlp,neural-network,stanford-nlp","A_Id":29593497,"CreationDate":"2015-04-11T06:52:00.000","Title":"Is there a Python wrapper for Stanford Neural Net based dependency parser?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to perform a basic moving average convolution of an array where each pixel is replaced by the average of its surrounding pixels. \nBut my problem scenario goes like this : \n\nI have two arrays valueArray and radiiArray. Both the arrays have the same shape. \nI need to apply the moving average to the valueArray but instead of having a constant radius to get my neighboring pixels, I want each pixel's average be calculated with a different radii - values of which are stored in the radiiArray \n\nI could implement it using for loops but that would be time consuming since the arrays are too large. Is this possible using the numpy library?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":267,"Q_Id":29598769,"Users Score":0,"Answer":"Since you are attempting a rather customized moving window average convolution, it is unlikely that you will find it in an existing library. \nInstead you can implement this in a straightforward way with loops. Then use Cython, f2py or numba, etc to speed this up to a level comparable with a native C\/Fortran implementation, while still using numpy to access and store data in memory.","Q_Score":1,"Tags":"python,arrays,numpy","A_Id":29637968,"CreationDate":"2015-04-13T06:05:00.000","Title":"Moving Window Average Convolution with different Radii - Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Here i have one RGB image where i need want extract plane of intensity.\nI have tried HSL, in this i took L Luminosity but its not similar with Intensity, and tried RGB2GRAY but this also little bit similar but not actual.\nso is there any special code to get intensity of the image? or is there any calculation of Intensity?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":954,"Q_Id":29598848,"Users Score":1,"Answer":"Try to use BGR2GRAY(and so on - BGR2HSL etc) instead of RGB2GRAY - OpenCV usually use BGR channel order, not RGB.","Q_Score":0,"Tags":"python,opencv","A_Id":29605232,"CreationDate":"2015-04-13T06:12:00.000","Title":"How to convert RGB to Intensity in Python 2.7 using Opencv","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a PostgreSQL db. Pandas has a 'to_sql' function to write the records of a dataframe into a database. But I haven't found any documentation on how to update an existing database row using pandas when im finished with the dataframe.\nCurrently I am able to read a database table into a dataframe using pandas read_sql_table. I then work with the data as necessary. However I haven't been able to figure out how to write that dataframe back into the database to update the original rows.\nI dont want to have to overwrite the whole table. I just need to update the rows that were originally selected.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":11070,"Q_Id":29607222,"Users Score":0,"Answer":"For sql alchemy case of read table as df, change df, then update table values based on df, I found the df.to_sql to work with name=<table_name> index=False if_exists='replace'\nThis should replace the old values in the table with the ones you changed in the df","Q_Score":25,"Tags":"python,postgresql,pandas","A_Id":68004057,"CreationDate":"2015-04-13T14:01:00.000","Title":"Update existing row in database from pandas df","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on Python multi-threading application.\nThe scenario is:\nThe source data(thousands of small files per hour) contains data about many subjects(range 1-100). Each row starts with \"subject1|col1|col2|...|coln|\". \nRight now users are interested in only 10(example) subjects. But in future they can add(or remove) more subjects as they prefer.\nFor this I wrote a program, which created a Queue and adds all source files in the directory to it. Configurable # of threads start and get a file from the queue and start parsing the data. I want to generate one large file per subject. so since user currently wants 10 subject parsed, I will generate 10 output files.\nSince there can be 16-32 threads parsing the source data and writing to 10 output files, I don't want to create 10 locks, one for each output files (hard code the subject_lock per output file) since I want to provide flexibility for the program to dynamically start parsing new subject.\nI am not able to think of any way to dynamically control the thread locks to the target output files (other than old school way of creating a empty file touch file(with subject_timestamp for debugging) which is generated based on the subject list, which is read from configuration file every few seconds). \nAny recommendations or suggestions are appreciated.\nThanks,\nVenkat","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":184,"Q_Id":29663838,"Users Score":0,"Answer":"It sounds like you will have potentially more than one thread writing to a single output file, so you want to make the writes thread-safe, while allowing another output file to be created and written to if a subject is added.\nI would recommend having each thread simply lock the output file during the write. This would not prevent another output file from being created; you would simply ensure that that file gets locked during a write as well. Then, whether you have 16 or 32 threads or something in between, it doesn't matter how many output files you have -- simply ensure they are locked during the write, so any other thread that tries to write there will need to wait.","Q_Score":0,"Tags":"python,multithreading,locking,queue","A_Id":29663998,"CreationDate":"2015-04-16T01:30:00.000","Title":"Python (instantiate) locks to multiple outputs","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"My data is in this format - \"1.424304064E9\"\nI have tried pandas.to_datetime(df['ts']) but no success. \nWhat am I missing?","AnswerCount":2,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":298,"Q_Id":29692575,"Users Score":4,"Answer":"use datetime.datetime.fromtimestamp(float(\"1.424304064E9\"))","Q_Score":0,"Tags":"python,pandas","A_Id":29692821,"CreationDate":"2015-04-17T07:11:00.000","Title":"How to convert string like 1.424304064E9 to datetime in pandas dataframe?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"How can I use Dijkstra's algorithm on an adjacency matrix with no costs for edges in Python? It has 1 if there is an edge between 2 vertices and 0 otherwise. The explanations that I've found on the internet are all for graphs with costs.","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2624,"Q_Id":29698896,"Users Score":0,"Answer":"When I had to implement Dijkstra's algorithm in php to find the shorter way between 2 tables of a database, I constructed the matrix with 3 values : 0 if the 2 points are the same, 1 if they are linked by an edge, -1 otherwise.\nAfter that the algorithm just worked as intended.","Q_Score":0,"Tags":"python,algorithm","A_Id":29699268,"CreationDate":"2015-04-17T12:13:00.000","Title":"Dijkstra's algorithm on adjacency matrix in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've installed Anaconda Python distribution with scikit-learn.\nWhile importing RandomForestClassifier:\nfrom sklearn.ensemble import RandomForestClassifier\nI have the following error:\nFile \"C:\\Anaconda\\lib\\site-packages\\sklearn\\tree\\tree.py\", line 36, in <module>\n    from . import _tree\nImportError: cannot import name _tree\nWhat the problem can be there?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":10431,"Q_Id":29735766,"Users Score":0,"Answer":"The problem was that I had the 64bit version of Anaconda and the 32bit sklearn.","Q_Score":2,"Tags":"python,scikit-learn,random-forest","A_Id":30233155,"CreationDate":"2015-04-19T20:57:00.000","Title":"RandomForestClassifier import","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using NLTK and FreqDist().plot() . But for curiosity, it's there a way to transform the line graph into an histogram? and how I can put labels in both cases?\nI've searched in the documentation, but sadly it isn't detailed for it.\nThanks in advance","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1774,"Q_Id":29760119,"Users Score":1,"Answer":"It seems NTLK has a tabulate() method, which gives you the numeric data. From there on you could use pylab to generate the hist() function (or bar() for a bar plot).","Q_Score":1,"Tags":"python,python-2.7,nltk","A_Id":29762672,"CreationDate":"2015-04-20T23:18:00.000","Title":"FreqDist().plot() as an histogram","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to use Spark SQL and MLib together to create a recommendation program (extending movie recommendation program) in python. It was working fine with 1.2.0.\nHowever, in 1.3.1, by default spark create Dataframe objects instead of SchemaRDD objects as output of a SQL. hence, mlib.ALS.train method is failing with an assertion error:\nassert(ratings,RDD) \n(of course ratings is not RDD anymore :) )\nAnyone facing this issue? Any workaround (I am thinking to use a map just to convert DF to RDD, but thats stupid :) )","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":208,"Q_Id":29764424,"Users Score":0,"Answer":"I think community is going to patch this. But for now, we can use Dataframe.rdd in ALS.train (or any other place where we see only RDDs are allowed)","Q_Score":1,"Tags":"python,apache-spark,apache-spark-sql","A_Id":29786975,"CreationDate":"2015-04-21T06:23:00.000","Title":"spark 1.3.1: Dataframe breaking MLib API","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a list of unique dates in chronological order.\nI have a dataframe with dates in it. I want to use the list of dates in the dataframe to get the NEXT date in the list (find the date in dataframe in the list, return the date to the right of it ( next chronological date).\nAny ideas?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":366,"Q_Id":29785134,"Users Score":0,"Answer":"It appears that printing the list wouldn't work, and you haven't provided us with any code to work with, or an example print of what your date time looks like. My best suggestion is to use the sort function.\ndataframe.sort()\nIf I wanted a specific date to print, I would have to say to print it by index number once you have it sorted. Without knowing what your computers ability is to handle print statements of this size, I suggest copying this sorted file to a out txt file to ensure that you are getting the proper response.","Q_Score":0,"Tags":"python,list,pandas","A_Id":29785304,"CreationDate":"2015-04-21T23:43:00.000","Title":"get next value in list Pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on a project that requires OpenCV and I am doing it in PyCharm on a Mac. I have managed to successfully install OpenCV using Homebrew, and I am able to import cv2 when I run Python (version 2.7.6) in Terminal and I get no errors. The issue arises when I try importing it in PyCharm. I get a red underline with:\n\nno module named cv2\n\nI assume that PyCharm is unable to locate my cv2.so file but I have the latest PyCharm version (4.0.6) and none of the forums I've looked at are helpful for this version. How do I get PyCharm to recognise my cv2 file? I went in Project Interpreter but there is no option for importing OpenCV from my own machine. Furthermore in Edit Configurations I defined an environment variable \n\nPYTHONPATH \n\nand set it to \n\n\/usr\/local\/lib\/python2.7\/site-packages:$PYTHONPATH \n\nbut this didn't help either.\nAny ideas?\nEDIT: I set up a\u00a0virtualenv to no avail and figured out how to add a path to the current framework on the new PyCharm version and it turns out the path to cv2.so has already been given yet it is still complaining.","AnswerCount":4,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":8253,"Q_Id":29797893,"Users Score":0,"Answer":"Do the following steps:\n\nDownload and install the OpenCV executable.\nAdd OpenCV in the system path(%OPENCV_DIR% = \/path\/of\/opencv\/directory)\nGo to C:\\opencv\\build\\python\\2.7\\x86 folder and copy cv2.pyd file.\nGo to C:\\Python27\\DLLs directory and paste the cv2.pyd file.\nGo to C:\\Python27\\Lib\\site-packages directory and paste the cv2.pyd file.\nGo to PyCharm IDE and go to DefaultSettings > PythonInterpreter.\nSelect the Python which you have installed.\nInstall the packages numpy, matplotlib and pip in pycharm.\nRestart your PyCharm.","Q_Score":7,"Tags":"python,opencv,pycharm","A_Id":56717998,"CreationDate":"2015-04-22T12:40:00.000","Title":"Cannot import cv2 in PyCharm","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on a project that requires OpenCV and I am doing it in PyCharm on a Mac. I have managed to successfully install OpenCV using Homebrew, and I am able to import cv2 when I run Python (version 2.7.6) in Terminal and I get no errors. The issue arises when I try importing it in PyCharm. I get a red underline with:\n\nno module named cv2\n\nI assume that PyCharm is unable to locate my cv2.so file but I have the latest PyCharm version (4.0.6) and none of the forums I've looked at are helpful for this version. How do I get PyCharm to recognise my cv2 file? I went in Project Interpreter but there is no option for importing OpenCV from my own machine. Furthermore in Edit Configurations I defined an environment variable \n\nPYTHONPATH \n\nand set it to \n\n\/usr\/local\/lib\/python2.7\/site-packages:$PYTHONPATH \n\nbut this didn't help either.\nAny ideas?\nEDIT: I set up a\u00a0virtualenv to no avail and figured out how to add a path to the current framework on the new PyCharm version and it turns out the path to cv2.so has already been given yet it is still complaining.","AnswerCount":4,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":8253,"Q_Id":29797893,"Users Score":0,"Answer":"Have you selected the right version of python ? \nor rather, when you have installed opencv with brew, this last probably has installed a new version of python that you can find in Cellar's Directory. You can see this immediately; from the main window of PyCharm select:\n\n\nConfigure -> Preferences -> Project Interpreter\n\n\nclick on Project Interpreter Combobox and be careful if there is a instance of python in Cellar's Directory, if yes, select it and you can see the cv2 in the list below.","Q_Score":7,"Tags":"python,opencv,pycharm","A_Id":44804084,"CreationDate":"2015-04-22T12:40:00.000","Title":"Cannot import cv2 in PyCharm","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on a project that requires OpenCV and I am doing it in PyCharm on a Mac. I have managed to successfully install OpenCV using Homebrew, and I am able to import cv2 when I run Python (version 2.7.6) in Terminal and I get no errors. The issue arises when I try importing it in PyCharm. I get a red underline with:\n\nno module named cv2\n\nI assume that PyCharm is unable to locate my cv2.so file but I have the latest PyCharm version (4.0.6) and none of the forums I've looked at are helpful for this version. How do I get PyCharm to recognise my cv2 file? I went in Project Interpreter but there is no option for importing OpenCV from my own machine. Furthermore in Edit Configurations I defined an environment variable \n\nPYTHONPATH \n\nand set it to \n\n\/usr\/local\/lib\/python2.7\/site-packages:$PYTHONPATH \n\nbut this didn't help either.\nAny ideas?\nEDIT: I set up a\u00a0virtualenv to no avail and figured out how to add a path to the current framework on the new PyCharm version and it turns out the path to cv2.so has already been given yet it is still complaining.","AnswerCount":4,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":8253,"Q_Id":29797893,"Users Score":0,"Answer":"I got the same situation under win7x64 with pycharm version 2016.1.1, after a quick glimpse into the stack frame, I think it is a bug!\nPycharm ipython patches import action for loading QT, matplotlib, ..., and finally sys.path lost its way!\nanyway, there is a workaround, copy Lib\/site-packages\/cv2.pyd or cv2.so to $PYTHONROOT, problem solved!","Q_Score":7,"Tags":"python,opencv,pycharm","A_Id":39482840,"CreationDate":"2015-04-22T12:40:00.000","Title":"Cannot import cv2 in PyCharm","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a python program that uses OpenCV to get frames from a video file for processing. I then create a standalone executable using py2exe (also tried pyinstaller and got same error). My computer and the target computer are both Windows 7, but the target computer does not have python installed. I use OpenCV to read the frame rate and individual images from a video file. \nProblem: When I run the executable on the target computer the frame rate is returned as 0.0 and I cannot read frames.\nIf python is installed on the target machine then the executable runs as expected, otherwise it produces this error. So it seems that something is missing in the executable, but I get no errors when creating the executable to indicate what might be missing.\nOthers who have reported similar issues usually have not included the numpy dependency (and get an error indicating this), but I have included numpy. I have also tried including the entire PyQt4 module since this is listed as a dependency on the python xy site for OpenCV (I already have parts of PyQt4 for other parts of the code) and this does not solve the problem either.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":6352,"Q_Id":29811423,"Users Score":0,"Answer":"I guess I will go ahead and post an answer for this, but solution was provided by @otterb in the comments to the question. I am pasting the text here:\n\"py2exe is not perfect so will often miss some libraries or dll, pyd etc needed. Most likely you are missing opencv_highgui249.dl\u200c\u200bl and opencv_ffmpeg249.dll etc. I would use py2exe with no single executable option enabled. And, start manually copying files that might be needed for your app. After identifying them, modify setup.py for py2exe to include them automatically.\"\nI will note however that I use pyinstaller rather than py2exe, since I get fewer problems while building. I still have to manually copy the opencv dll files though.On Windows 7 they are located here: \"C:\\Python27\\DLLs\" and they need to be copied into the distribution folder so that they are on the same path as the other dll files that go with the distribution.","Q_Score":0,"Tags":"python,opencv,py2exe,pyinstaller","A_Id":38987705,"CreationDate":"2015-04-23T00:28:00.000","Title":"OpenCV with standalone python executable (py2exe\/pyinstaller)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to implement a Dynamic Programming algorithm to solve the Traveling Salesman problem in time that beats Brute Force Search for computing distances between points. For this I need to index subproblems by size and the value of each subproblem will be a float (the length of the tour). However holding the array in memory will take about 6GB RAM if I use python floats (which actually have double precision) and so to try and halve that amount (I only have 4GB RAM) I will need to use single precision floats. However I do not know how I can get single precision floats in Python (I am using Python 3). Could someone please tell me where I can find them (I was not able to find much on this on the internet). Thanks.\nEDIT: I notice that numpy also has a float16 type which will allow for even more memory savings. The distances between points are around 10000 and there are 25 unique points and my answer needs to be to the nearest integer. Will float16 provide enought accuracy or do I need to use float32?","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":4158,"Q_Id":29826523,"Users Score":2,"Answer":"You could try the c_float type from the ctypes standard library.  Alternatively, if you are capable of installing additional packages you might try the numpy package.  It includes the float32 type.","Q_Score":6,"Tags":"python,floating-point,double,precision","A_Id":29826612,"CreationDate":"2015-04-23T14:30:00.000","Title":"Python float precision float","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am looking for Ripley's k function implementation in Python. But so far haven't been able to find any spatial modules implementing this in scipy or elsewhere. \nI have created Voronoi tessellation of a fibre composite and need to perform analysis using Ripley's K and pair distribution functions compared to a Poisson distribution.\nCannot upload images-not enough rep.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2325,"Q_Id":29828922,"Users Score":4,"Answer":"Solved my problem, this is for others looking to do same analysis.\nDefinitely recommend using R for spatial analysis. A transfer from python is simple because all you need is coordinates of your point pattern.\n\nWrite a csv of x,y and z coordinates of your points using python\nR has good functionality of reading csv using command\nread.csv(\"filename\"). Make sure the directory is set properly using setwd command.\nConvert the csv you just read to a ppp(point\npattern which R understands) using as.ppp command.\nContinue to use\n    Kest, Gest etc for required spatial analysis.\n\nCheers.","Q_Score":7,"Tags":"python,spatial","A_Id":32484856,"CreationDate":"2015-04-23T16:08:00.000","Title":"Ripley's K Function (Second order intensity function) Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a lot of time series data. Almost 3 GB of csv files. The dimensions are 50k columns with 6000 rows. Now I need to process them row by row. They are time ordered and its important that for each row, I look at each column. \nWould importing this in to pandas as a pivot table and iterating them over row by row efficient? Any suggestions?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":662,"Q_Id":29837153,"Users Score":0,"Answer":"Row by row. Pandas is not the ideal tool for this.\nI would suggest you look into Map\/Reduce. It is designed for exactly this. Streaming is the key to row by row processing.","Q_Score":1,"Tags":"python,pandas","A_Id":29892429,"CreationDate":"2015-04-24T01:27:00.000","Title":"Python Pandas Large Row Processing","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"This is a broad question. I am running a very long simulation (in Python) that generates a sizeable amount of data (about 10,000 729*729 matrices). I only need the data to plot a couple of graphs and then I'm done with it. At the moment I save the data in (numpy) arrays. When the simulation is complete I plot the data. \nOne alternative would be to write the data to a file, and then access the file after simulation to plot graphs etc. \nIn general is there consensus on the best (i.e. quickest) way to manage large temporary data sets. Is either of these \"best practice\"?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":626,"Q_Id":29840006,"Users Score":0,"Answer":"Try to make the data obsolete as fast as possible by further processing\/accumulating e.g. plotting immediately.\nYou did not give details about the memory\/storage needed. for sparse matrices there are efficient representations. if your matrices are not sparse there are roughly 500k entries per matrix and therefore 5G entries altogether. without knowing your data type this could be typically 40GB of memory.\nI strongly suggest to review your algorithms for achieving a smaller memory footprint.","Q_Score":0,"Tags":"python,performance,numpy,save","A_Id":29841705,"CreationDate":"2015-04-24T06:15:00.000","Title":"Is it better to store temp data in arrays or save it to file for access later?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"So I have a program where a ball subject to gravity bounces off of lines created by a user with mouse clicks. These lines are normally sloped. My collision bounces work perfectly EXCEPT in the case where ball does approximately this:\n ->O ------\nMy code works by finding the normal vector of the line such that the scalar product of the incident velocity vec of the ball  and the normal of the line is negative (this means the vector have opposite directions). \nThen I decompose the velocity into terms parallel and perpendicular to the normal,\nand the reverse the direction of the parallel component.\nDuring the edge case described above ball moves basically along the line. How can I account for this? Any advice?>","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":157,"Q_Id":29870031,"Users Score":0,"Answer":"Possible Solutions:\n\nInstead of using a single 1D 'line', you could construct a 2D rectangle (that is as this as you want\/need it to be) --- composed of 4 separate 'lines'.  I.e. you can have collisions with any of the 4 faces of the rectangle object.  Would that work?\nDo some sort of corner collision -- if the ball is 'hits' the start or end of a line, have it bounce off appropriately.  I think the way this would be done is as follows:\ni. Collision occurs if the corner falls within the radius of the ball.\nii. Define a line between the corner and the center of the ball.\niii. Reverse the component of the ball's velocity along this line.","Q_Score":1,"Tags":"python,math,vector,line,collision","A_Id":29870156,"CreationDate":"2015-04-25T19:56:00.000","Title":"Ball-Line Segment Collision on End-Point of Line","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on a project where I need to deal with 3 dimensional large array. I was using numpy 3d array but most of my entries are going to be zero, so it's lots of wastage of memory. Scipy sparse seems to allow only 2D matrix. Is there any other way I can store 3D sparse array?","AnswerCount":3,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":9257,"Q_Id":29871669,"Users Score":6,"Answer":"scipy.sparse has a number of formats, though only a couple have an efficient set of numeric operations.  Unfortunately, those are the harder ones to extend.\ndok uses a tuple of the indices as dictionary keys.  So that would be easy to generalize from 2d to 3d or more.  coo has row, col, data attribute arrays.  Conceptually then, adding a third depth(?) is easy.  lil probably would require lists within lists, which could get messy.\nBut csr and csc store the array in indices, indptr and data arrays.  This format was worked out years ago by mathematicians working with linear algebra problems, along with efficient math operations (esp matrix multiplication).  (The relevant paper is cited in the source code).\nSo representing 3d sparse arrays is not a problem, but implementing efficient vector operations could require some fundamental mathematical research.\nDo you really need the 3d layout to do the vector operations?  Could you, for example, reshape 2 of the dimensions into 1, at least temporarily?\nElement by element operations (*,+,-) work just as well with the data of a flattened array as with the 2 or 3d version.  np.tensordot handles nD matrix multiplication by reshaping the inputs into 2D arrays, and applying np.dot.  Even when np.einsum is used on 3d arrays, the product summation is normally over just one pair of dimensions (e.g. 'ijk,jl->ikl')\n3D representation can be conceptually convenient, but I can't think of a mathematical operation that requires it (instead of 2 or 1d).\nOverall I think you'll get more speed from reshaping your arrays than from trying to find\/implement genuine 3d sparse operations.","Q_Score":14,"Tags":"python,scipy","A_Id":29883739,"CreationDate":"2015-04-25T22:57:00.000","Title":"Python multi dimensional sparse array","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to plot arrows pointing at a point on a curve in python using matplotlib.\nOn this line i need to point vertical arrows at specific points.\nThis is for indicating forces acting on a beam, so their direction is very important. Where the curve is the beam and the arrow is the force.\nI know the coordinate of said point, exactly, but it is of cause changing with the input.\nThis input should also dictate whether the arrow points upwards or downwards from the line. (negative and positive forces applied).\nI have tried endlessly with plt.arrow, but because the scale changes drastically and so does the quadrant in which the arrow has to be. So it might have to start at y < 0 and end in a point where y > 0.\nThe problem is that the arrowhead length then points the wrong way like this --<. instead of -->. \nSo before I go bald because of this, I would like to know if there is an easy way to apply a vertical arrow (could be infinite in the opposite direction for all i care) pointing to a point on a curve, of which I can control whether it point upwards to the curve, or downwards to the curve.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1773,"Q_Id":29881872,"Users Score":0,"Answer":"The inverted arrowhead is due to a negative sign of the head_length variable. Probably you are scaling it using a negative value. Using head_length= abs(value)*somethingelse should take care of your problem.","Q_Score":3,"Tags":"python,matplotlib,plot","A_Id":41026037,"CreationDate":"2015-04-26T18:40:00.000","Title":"Arrow pointing to a point on a curve","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm currently having trouble installing scipy via PyCharm's package manager. I have installed numpy successfully and do have the Microsoft Visual Studio C\/C++ compiler in the System Variables.\nHowever, when it's time to install scipy in PyCharm, the following error occurs:\nExecuted Command: pip install scipy\nError occured: numpy.distutils.system_info.NotFoundError: no lapack\/blas resources found\nI have seen other resources on installing blas \/ lapack on windows, but I'm unsure if it will work with PyCharm's installations.\nIf anybody has the solution \/ resources to redirect me to, please let me know.","AnswerCount":5,"Available Count":1,"Score":0.0399786803,"is_accepted":false,"ViewCount":16017,"Q_Id":29883690,"Users Score":1,"Answer":"I had the same issue, and downloading Anaconda, and switching the project interpreter in PyCharm to \\Anaconda3\\python.exe helped solve this.\nGood luck!","Q_Score":11,"Tags":"python,pycharm,lapack,blas","A_Id":39548461,"CreationDate":"2015-04-26T21:20:00.000","Title":"Trouble installing scipy via pyCharm windows 8 - no lapack \/ blas resources found","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to draw a dynamic picture for a neural network to watch the weights changed and the activation of neurons during learning. How could I simulate the process in Python? \nMore precisely, if the network shape is: [1000, 300, 50], \nthen I wish to draw a three layer NN which contains 1000, 300 and 50 neurons respectively. \nFurther, I hope the picture could reflect the saturation of neurons on each layer during each epoch.\nI've no idea about how to do it. Can someone shed some light on me?","AnswerCount":9,"Available Count":1,"Score":0.022218565,"is_accepted":false,"ViewCount":35856,"Q_Id":29888233,"Users Score":1,"Answer":"Draw the network with nodes as circles connected with lines. The line widths must be proportional to the weights. Very small weights can be displayed even without a line.","Q_Score":29,"Tags":"python,image,neural-network","A_Id":29889993,"CreationDate":"2015-04-27T05:58:00.000","Title":"How to visualize a neural network","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a pyQT4 application where the user is asked for a savefile (QFileDialog and all that...)\nOne annoyance is it does not remember the last directory so multiple call always defaults to the working directory of the application (or whatever I set the 3rd argument to)\nIf I set the option to not use the native file browser it remembers but \"it is not native to windows\" (note this doesn't bug me as I am a linux user, but others are not...) \nOne option I was considering was saving the last working directory and populating the 3rd argument with that for every call but this seems quite brutal, especially as it seems matplotlib appears to be remembering the last directory (so it is possible) \nAny ideas?\nfilename = QtGui.QFileDialog.getSaveFileName(self, \"Save Plot to CSV\", '', \"CSV Data (*.csv)\")","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":305,"Q_Id":29914909,"Users Score":0,"Answer":"The QFileDialog.saveState() and QFileDialog.restoreState() methods can save and restore the current directory of the dialog box.","Q_Score":1,"Tags":"windows,python-3.x,pyqt4","A_Id":29999330,"CreationDate":"2015-04-28T09:02:00.000","Title":"pyQT4 native file dialog remembering last directory","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":1,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a CSR sparse matrix in scipy of size 444075 x 444075. I wish to multiply it by its transpose. However, when I do m * m.T it causes a segmentation fault 11 error. Is this a memory issue, and if so, is there a way to allocate more memory to the program? Is there a clever workaround\/hack using subroutines other routines from scipy to multiply a different way?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":850,"Q_Id":29924590,"Users Score":0,"Answer":"Resolved the issue, turns out this is a memory problem. I ran the operation on another machine and received a MemoryIssue (whereas my machine gives a segfault), and when given more memory it turns into a \"negative dimensions not allowed error\" a long way into it, which I presume is an integer overflow in the calculation.","Q_Score":0,"Tags":"python,numpy,segmentation-fault,scipy,sparse-matrix","A_Id":29927508,"CreationDate":"2015-04-28T16:12:00.000","Title":"Scipy - Multiplying large sparse matrix causes segmentation fault?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a set of data that is below some metadata. I'm looking to put the headers into a numpy array to be used later. However the first header needs to be ignored as that is the x data header, then the other columns are the y headers. How do i read this?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":118,"Q_Id":29926772,"Users Score":0,"Answer":"Assuming I have understood what you mean by headers (it would be easier to tell with a few complete lines, even if you had to scale it down from your actual file)...\nI would first read the irregular lines with normal python then, on the regular lines, use genfromtxt with skip_header and usecols (make a tuple like (i for i in range(2,102))","Q_Score":0,"Tags":"python,csv,numpy","A_Id":29929834,"CreationDate":"2015-04-28T18:05:00.000","Title":"putting headers into an array, python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to decomposing signals in components (matrix factorization) in a large sparse matrix in Python using the sklearn library. \nI made use of scipy's scipy.sparse.csc_matrix to construct my matrix of data. However I'm unable to perform any analysis such as factor analysis or independent component analysis. The only thing I'm able to do is use truncatedSVD or scipy's scipy.sparse.linalg.svds and perform PCA.\nDoes anyone know any work-arounds to doing ICA or FA on a sparse matrix in python? Any help would be much appreciated! Thanks.","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1097,"Q_Id":29930160,"Users Score":0,"Answer":"Even the input matrix is sparse the output will not be a sparse matrix.  If the system does not support a dense matrix neither the results will not be supported","Q_Score":2,"Tags":"python,scipy,scikit-learn,sparse-matrix,pca","A_Id":29930257,"CreationDate":"2015-04-28T21:21:00.000","Title":"Performing Decomposition on Sparse Matrices in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a binary multidimensional image. And I want to get some implementation of flood fill that will give me the next:\n\nList of connected regions (with adjacent pixels with value True).\nFor each region I want to get its bounding box and list of pixel coordinates of all pixels from the interconnected region.\n\nIs something like that implemented?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":897,"Q_Id":29942739,"Users Score":0,"Answer":"Hell yeah! scipy.ndimage.measurements module helps!","Q_Score":1,"Tags":"python,image-processing,flood-fill","A_Id":30708149,"CreationDate":"2015-04-29T11:44:00.000","Title":"Python: flood filling of multidimensional image","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In python for the random module, what is the difference between random.uniform() and random.random()? They both generate pseudo random numbers, random.uniform() generates numbers from a uniform distribution and random.random() generates the next random number. What is the difference?","AnswerCount":5,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":131400,"Q_Id":30030659,"Users Score":9,"Answer":"In random.random()  the output lies between 0 & 1 , and it takes no input parameters\nWhereas random.uniform() takes parameters , wherein you can submit the range of the random number.\ne.g.\nimport random as ra\n print ra.random()\n print ra.uniform(5,10)\nOUTPUT:-\n0.672485369423\n 7.9237539416","Q_Score":63,"Tags":"python,random,uniform","A_Id":40606389,"CreationDate":"2015-05-04T12:51:00.000","Title":"In python, what is the difference between random.uniform() and random.random()?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataset of a tennis game. This dataset contains the ball positions in each rally and the current score. I already 3d-visualized the game and ball positions in mayavi.\nNow I want to plot 2d line diagrams in mayavi that visualizes the score developement after specific events (such as after: a break, a set-win, set-loss,...). \nI came up with some ideas, but none of them are satisfying:\n\nI could use imshow and \"draw\" the diagram\nI could use points3d to plot the diagram\nMaybe I can somehow use pyplot to plot the diagram, then make a screenshot und then plot this screenshot in mayavi...  Any idea if this is possible?\n\nDo you have any other idea how I could plot a 2d line diagram in mayavi?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":783,"Q_Id":30035123,"Users Score":0,"Answer":"Mayavi is not really good at plotting 2d-diagramms, you can cheat a little by setting your camera position parallel to an 2d image. If you want to plot 2d-diagramms try using matplotlib.","Q_Score":0,"Tags":"python,matplotlib,mayavi","A_Id":30125166,"CreationDate":"2015-05-04T16:31:00.000","Title":"Plot 2d line diagram in mayavi","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to build a TFIDF model with TfidfVectorizer. The feature name list namely the number of column of sparse matrix is shorter than the length of word set of documents even though I set min_df as 1. What happened?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":525,"Q_Id":30047341,"Users Score":1,"Answer":"Did you check the stop_words and max_features? If you provide values in either of these two, it will exclude some words.","Q_Score":0,"Tags":"python,nlp,tf-idf","A_Id":30048240,"CreationDate":"2015-05-05T08:03:00.000","Title":"TfidfVectorizer does not use the whole set of words in all documents?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm facing something strange, the function sort and the attribut argsort don't give me the same results.\nI have a Data array (CFD results) with the following structure:\ndtype([('nodenumber', '<f8'), ('    x-coordinate', '<f8'), ('    y-coordinate', '<f8'), ('    z-coordinate', '<f8'), ('        pressure', '<f8'), ('  total-pressure', '<f8'), ('      x-velocity', '<f8'), ('      y-velocity', '<f8'), ('      z-velocity', '<f8'), ('     temperature', '<f8'), ('total-temperature\\n', '<f8')])\n\nSo, each column contains one measured parameters at one point. I would like to sort the array by increasing 'z-coordinate' AND of course move line by line during the sorting (1line <=> 1 point and coresponding values).\nI tried this function:\nData_sorted = np.sort(Data,axis=0,kind='mergesort',order='    z-coordinate')\nIt returns me a sorted array but the lines are completely messed up. For example, the previous point 1 has now a completely different z-coordinate. I don't want that.\nThen I used this function (The 3rd column is the z-coordinate) :\norder = Data[:, 3].argsort(kind='mergesort')\nData_sorted = np.take(Data, order, axis=0)\nAnd... it works ! The array has been sorted by increasing z-coordinate and the points are still coherent (it seems, at least).\nDo you have an idea why these both similar function provide different results ?\nBecause in a 2nd step, I will need to do something like that:\nData_sorted = np.sort(Data,axis=0,kind='mergesort',order=('    z-coordinate','    y-coordinate'))\\=","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":906,"Q_Id":30049051,"Users Score":0,"Answer":"I figure out why it didn't work with np.sort().\nI misused the structured array function.\nWith the following dtype, I have created my array with the following line:\nData = np.zeros((78000,11),dtype=dtype2)\nI though that I had to create 1 row for each structured data. WRONG ! The right line is: Data = np.zeros((78000,1),dtype=dtype2)","Q_Score":0,"Tags":"python,arrays,sorting","A_Id":30059056,"CreationDate":"2015-05-05T09:25:00.000","Title":"Python: np.sort VS array.argsort()","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using python and openCV to create face recognition with Eigenfaces. I stumbled on a problem, since I don't know how to create training set.\nDo I need multiple faces of people I want to recognize(myself for example), or do I need a lot of different faces to train my model?\nFirst I tried training my model with 10 pictures of my face and 10 pictures of ScarJo face, but my prediction was not working well.\nNow I'm trying to train my model with 20 different faces (mine is one of them). \nAm I doing it wrong and if so what am I doing wrong?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":247,"Q_Id":30049490,"Users Score":0,"Answer":"I later found the answer and would like to share it if someone will be facing the same challenges.\nYou need pictures only for the different people you are trying to recognise. I created my training set with 30 images of every person (6 persons) and figured out that histogram equalisation can play an important role when creating the training set and later when recognising faces. Using the histogram equalisation model accuracy was greatly increased. Another thing to consider is eye axis alignment so that all pictures have their eye axis aligned before they enter face recognition.","Q_Score":0,"Tags":"python,opencv,training-data","A_Id":32845800,"CreationDate":"2015-05-05T09:45:00.000","Title":"What does eigenfaces training set have to look like?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need a list of all scikit-learn classifiers that support the predict_proba() method. Since the documentation provides no easy way of getting that information, how can get this programatically?","AnswerCount":4,"Available Count":2,"Score":0.1488850336,"is_accepted":false,"ViewCount":8716,"Q_Id":30056331,"Users Score":3,"Answer":"AdaBoostClassifier\nBaggingClassifier\nBayesianGaussianMixture\nBernoulliNB\nCalibratedClassifierCV\nComplementNB\nDecisionTreeClassifier\nExtraTreeClassifier\nExtraTreesClassifier\nGaussianMixture\nGaussianNB\nGaussianProcessClassifier\nGradientBoostingClassifier\nKNeighborsClassifier\nLabelPropagation\nLabelSpreading\nLinearDiscriminantAnalysis\nLogisticRegression\nLogisticRegressionCV\nMLPClassifier\nMultinomialNB\nNuSVC\nQuadraticDiscriminantAnalysis\nRandomForestClassifier\nSGDClassifier\nSVC\n_BinaryGaussianProcessClassifierLaplace\n_ConstantPredictor","Q_Score":22,"Tags":"python,scikit-learn","A_Id":56863216,"CreationDate":"2015-05-05T14:50:00.000","Title":"How to list all scikit-learn classifiers that support predict_proba()","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need a list of all scikit-learn classifiers that support the predict_proba() method. Since the documentation provides no easy way of getting that information, how can get this programatically?","AnswerCount":4,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":8716,"Q_Id":30056331,"Users Score":0,"Answer":"If you are interested in a spesific type of estimator(say classifier), you could go with:\nimport sklearn\nestimators = sklearn.utils.all_estimators(type_filter=\"classifier\")\nfor name, class_ in estimators:\nif not hasattr(class_, 'predict_proba'):\nprint(name)","Q_Score":22,"Tags":"python,scikit-learn","A_Id":72497753,"CreationDate":"2015-05-05T14:50:00.000","Title":"How to list all scikit-learn classifiers that support predict_proba()","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to make a table with the TPR and FPR values, as well as precision and recall. I am using the roc_curve and precision_recall_curve functions from sklearn.metrics package in python. My problem is that every function give me a different vector for the thresholds, and I need only one, to merge the values as columns in a single table. Could anyone help me?\nThanks in advance","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1042,"Q_Id":30056603,"Users Score":0,"Answer":"The threshold values have two major differences.\n\nThe orders are different. roc_curve has thresholds in decreasing order, while precision_recall_curve has thresholds in increasing order.\nThe numbers are different. In roc_curve, n_thresholds = len(np.unique(probas_pred)), while in precision_recall_curve the number n_thresholds = len(np.unique(probas_pred)) - 1. In the latter, the smallest threshold value from roc_curve is not included. In the same time, the last precision and recall values are 1. and 0. respectively with no corresponding threshold. Therefore, the numbers of items for tpr, fpr, precision and recall are the same.\n\nSo, back to your question, how to make a table to include tpr, fpr, precision and recall with corresponding thresholds? Here are the steps:\n\nDiscard the last precision and recall values\nReverse the precision and recall values\nCompute the precision and recall values corresponding to the lowest threshold value from the thresholds of roc_curve\nPut all the values into the same table","Q_Score":0,"Tags":"python,scikit-learn,roc,precision-recall","A_Id":30084402,"CreationDate":"2015-05-05T15:02:00.000","Title":"How to get the same thresholds values for both functions precision_recall_curve and roc_curve in sklearn.metrics","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Suppose I have got a black an white image, how do I convert the colour intensity at each point into a numerical value that represents its relativity intensity? \nI checked somewhere on the web and found the following:\n    Intensity = np.asarray(PIL.Image.open('test.jpg'))\nWhat's the difference between asarray and array?\nBesides, the shape of the array Intensity is '181L, 187L, 3L'. The size of the image test.jpg is 181x187, so what does the extra '3' represent?\nAnd are there any other better ways of extracting the colour intensity of an image?\nthank you.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":473,"Q_Id":30063430,"Users Score":1,"Answer":"The image is being opened as a color image, not as a black and white one. The shape is 181x187x3 because of that: the 3 is there because each pixel is an RGB value. Quite often images in black and white are actually stored in an RGB format. For an image image, if np.all(image[:,:,0]==image[:,:,1]) and so on, then you can just choose to use any of them (eg, image[:,:,0]). Alternatively, you could take the mean with np.mean(image,axis=2).\nNote too that the range of values will depend on the format, and so depending upon what you mean by color intensity, you may need to normalize them. In the case of a jpeg, they are probably uint8s, so you may want image[:,:,0].astype('float')\/255 or something similar.","Q_Score":0,"Tags":"python,arrays,image,image-processing,colors","A_Id":30063676,"CreationDate":"2015-05-05T21:21:00.000","Title":"how to extract the relative colour intensity in a black and white image in python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"While multiplying large matrices (say A and B, A.dot(B)), does numpy use spatial locality by computing the transpose of the B and using row wise multiplication, or does it access the elements of B in column-wise fashion which would lead to many cache misses. I have observed that memory bandwidth is becoming a bottleneck when I run multiple instances of the same program. For example, if I run 4 independent instances of a program which does matrix multiplication (for large matrices) on a 20 core machine, I only see a 2.3 times speedup.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":312,"Q_Id":30065905,"Users Score":1,"Answer":"Numpy dot is implemented in multiarraymodule.c as PyArray_MatrixProduct2. The implementation it actually uses is dependent upon a number of factors.\nIf you have numpy linked to a BLAS implementation, your dtypes are all double, cdouble, float, or cfloat, and your arrays have 2 or fewer dimensions each, then numpy hands off the array to the BLAS implementation. What is does is dependent upon the package you're using.\nOtherwise, no, it doesn't do this. However, at least on my machine, doing this (or just a dot product in general) with a transpose and einsum is ten times slower than just using dot, because dot pushes to BLAS.","Q_Score":1,"Tags":"python,numpy,matrix,matrix-multiplication","A_Id":30066780,"CreationDate":"2015-05-06T01:22:00.000","Title":"Does numpy use spatial locality in memory while doing matrix multiplication?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have been using numpy\/scipy for data analysis. I recently started to learn Pandas. \nI have gone through a few tutorials and I am trying to understand what are the major improvement of Pandas over Numpy\/Scipy. \nIt seems to me that the key idea of Pandas is to wrap up different numpy arrays in a Data Frame, with some utility functions around it. \nIs there something revolutionary about Pandas that I just stupidly missed?","AnswerCount":3,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":3241,"Q_Id":30067051,"Users Score":14,"Answer":"Pandas is not particularly revolutionary and does use the NumPy and SciPy ecosystem to accomplish it's goals along with some key Cython code.   It can be seen as a simpler API to the functionality with the addition of key utilities like joins and simpler group-by capability that are particularly useful for people with Table-like data or time-series.   But, while not revolutionary, Pandas does have key benefits. \nFor a while I had also perceived Pandas as just utilities on top of NumPy for those who liked the DataFrame interface.  However, I now see Pandas as providing these key features (this is not comprehensive):\n\nArray of Structures (independent-storage of disparate types instead of the contiguous storage of structured arrays in NumPy) --- this will allow faster processing in many cases.\nSimpler interfaces to common operations (file-loading, plotting, selection, and joining \/ aligning data) make it easy to do a lot of work in little code.\nIndex arrays which mean that operations are always aligned instead of having to keep track of alignment yourself. \nSplit-Apply-Combine is a powerful way of thinking about and implementing data-processing\n\nHowever, there are downsides to Pandas: \n\nPandas is basically a user-interface library and not particularly suited for writing library code.   The \"automatic\" features can lull you into repeatedly using them even when you don't need to and slowing down code that gets called over and over again.   \nPandas typically takes up more memory as it is generous with the creation of object arrays to solve otherwise sticky problems of things like string handling. \nIf your use-case is outside the realm of what Pandas was designed to do, it gets clunky quickly.   But, within the realms of what it was designed to do, Pandas is powerful and easy to use for quick data analysis.","Q_Score":7,"Tags":"python,numpy,pandas,scipy,data-analysis","A_Id":30067640,"CreationDate":"2015-05-06T03:39:00.000","Title":"Python - What are the major improvement of Pandas over Numpy\/Scipy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have been using numpy\/scipy for data analysis. I recently started to learn Pandas. \nI have gone through a few tutorials and I am trying to understand what are the major improvement of Pandas over Numpy\/Scipy. \nIt seems to me that the key idea of Pandas is to wrap up different numpy arrays in a Data Frame, with some utility functions around it. \nIs there something revolutionary about Pandas that I just stupidly missed?","AnswerCount":3,"Available Count":2,"Score":0.0665680765,"is_accepted":false,"ViewCount":3241,"Q_Id":30067051,"Users Score":1,"Answer":"A main point is that it introduces new data structures like dataframes, panels etc. and has good interfaces to other structure and libs. So in generally its more an great extension to the python ecosystem than an improvement over other libs. For me its a great tool among others like numpy, bcolz. Often i use it to reshape my data, get an overview before starting to do data mining etc.","Q_Score":7,"Tags":"python,numpy,pandas,scipy,data-analysis","A_Id":30096156,"CreationDate":"2015-05-06T03:39:00.000","Title":"Python - What are the major improvement of Pandas over Numpy\/Scipy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using fmin_l_bfgs_b for a bounded minimization on 4 parameters. \nI would like to inspect the gradient at the minimum of the cost function and for this I call the d['grad'] parameter as described in the documentation of fmin_l_bfgs_b. My problem is that d['grad'] is an array of size 4 looking like:\n'grad': array([  8.38440428e-05,  -5.72697445e-04,   3.21875859e-03,\n        -2.21115926e+00])\nI would expect it to be a single value close to zero. Does this have something to do with the number of the parameters I am using for the minimization (4)..? Not what I would expect but  any help would be appreciated.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":255,"Q_Id":30113538,"Users Score":0,"Answer":"What you are getting is the gradient of the cost function with respect to each parameter, in turn.\nTo picture it, suppose there were only two parameters, x and y. The cost function is a surface z as a function of x and y.\nThe optimization is finding a minimum point on that surface.\nThat's where the gradients with respect to both x and y are zero (or close to it).\nIf either gradient is not zero, you are not at a minimum, and you would descend further.\nAs a further point, you could well be interested in the curvature, or second derivative, because high curvature means a narrow (precise) minimum, while low curvature means a nearly flat minimum, with very uncertain estimates.\nThe second derivative in the x,y case would not be a 2-vector, but a 2x2-matrix (called a \"Hessian\", just to snow your friends).\nYou might want to think about why it's a 2x2-matrix.","Q_Score":0,"Tags":"python,optimization,scipy,gradient","A_Id":30114151,"CreationDate":"2015-05-07T23:50:00.000","Title":"gradient at the minimum in fmin_l_bfgs_b","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to store a massive numpy vector to disk. Right now the vector that I am trying to store is ~2.4 billion elements long and the data is float64. This takes about 18GB of space when serialized out to disk.\nIf I use struct.pack() and use float32 (4 bytes) I can reduce it to ~9GB. I don't need anywhere near this amount of precision disk space is going to quickly becomes an issue as I expect the number of values I need to store could grow by an order of magnitude or two.\nI was thinking that if I could access the first 4 significant digits I could store those values in an int and only use 1 or 2 bytes of space. However, I have no idea how to do this efficiently. Does anyone have any idea or suggestions?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":934,"Q_Id":30130277,"Users Score":0,"Answer":"Use struct.pack() with the f type code to get them into 4-byte packets.","Q_Score":7,"Tags":"python,numpy,scipy","A_Id":30130970,"CreationDate":"2015-05-08T18:12:00.000","Title":"Binary storage of floating point values (between 0 and 1) using less than 4 bytes?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to sample from my posterior distribution using the pymc package. \nI am wondering if there is a limit on the number of dimensions such algorithm can handle. My log likelihood is the sum of 3 Gaussians and 1 mixture of Gaussians. I have approx 750 parameters in my model. Can pymc handle such a big number of parameters?","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":267,"Q_Id":30142780,"Users Score":2,"Answer":"I recently ran (successfully) a model with 2,958 parameters. It was on a 8 Gb Windows machine. You should be fine with 750.","Q_Score":1,"Tags":"python,bayesian,pymc,mcmc","A_Id":31457037,"CreationDate":"2015-05-09T17:08:00.000","Title":"Number of parameters in MCMC","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to find the distribution of data, which is from a retail chain network( demand of product across all stores). I tried to fit distribution using EasyFit (which has 82 distribution to check the best distributions) but no distribution fits the data. What can be done? Is there any way to find if the data distribution is a sum or convolution of multiple distribution? I have removed the spikes or seasonality or promotional data from the dataset but still no distribution fits.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":714,"Q_Id":30150071,"Users Score":0,"Answer":"Have you tried transforming the data? Simulate multiple transformations and take the best approximation to a distribution amenable for statistical inference.","Q_Score":0,"Tags":"python,r,distribution,frequency-distribution,goodness-of-fit","A_Id":30153269,"CreationDate":"2015-05-10T10:03:00.000","Title":"I need to find the distribution of data, which is from a retail chain network. No distribution fits the data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How does one blur a circular portion of an image in the python bindings of Open CV. Can one apply blurring on images without making new images?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":566,"Q_Id":30177229,"Users Score":1,"Answer":"It doesn't look like the OpenCV's blurring and filtering functions allow masking the input. I suggest applying the filter on a Rect around the circular portion, then assign the elements of the blurred sub matrix to the original image while masking the elements that do not correspond to the circle.","Q_Score":0,"Tags":"python,opencv","A_Id":30209202,"CreationDate":"2015-05-11T20:39:00.000","Title":"Opencv Blur just within Circle","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have 3 1D-arrays (lat, lon and temperature) and would like to plot the data using Basemap in python. However, Basemap seems to need 2D-arrays to be able to plot the data according to the latitudes and longitudes I have.\nHow would I do that?\nThanks for you help!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":451,"Q_Id":30195646,"Users Score":0,"Answer":"Maybe try zip?\nCalling zip(a,b) where a and b are some iterable things will return a new array of tuples along the lines of [ (a[0], b[0]) , (a[1],b[1]) , ... , (a[n], b[n]) ] where n is the number of things in the lists.\nYou could match up the lat\/lon into pairs first and then pair them with the temperature.","Q_Score":0,"Tags":"python,multidimensional-array,matplotlib-basemap","A_Id":30196520,"CreationDate":"2015-05-12T15:43:00.000","Title":"Create a 2D grid of hexagonal cells using lat\/lon coordinates in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In sciki learn, RandomForestClassifier() and train_test_split() both have random_state parameter. \nStatistically, does it matter if I set them to be the same seed? Will that be wrong? Thanks.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":105,"Q_Id":30203229,"Users Score":1,"Answer":"Nothing will be wrong to set the same seed.","Q_Score":0,"Tags":"python,scikit-learn","A_Id":30209508,"CreationDate":"2015-05-12T23:38:00.000","Title":"Does it matter if I set random_state same for RandomForestClassifier() and train_test_split()?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I can find sum of all (non-zero) elements in scipy sparse matrix by mat.sum(), but how can I find their product? There's no mat.prod() method.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":44,"Q_Id":30203785,"Users Score":0,"Answer":"If you are using any plugin that is named as \"infinite posts scroll\" or \"jetpack\" or any thing similar delete it.","Q_Score":0,"Tags":"python,scipy,sparse-matrix","A_Id":33983181,"CreationDate":"2015-05-13T00:42:00.000","Title":"Product of elements of scipy sparse matrix","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I try to make classification multiclass with SVM on OpenCV (I use openCV for python). Let's say if I have 5 class and training it well. I have been test it, and got good result.\nThe problem appear when object from 6th class come to this classification. Althought I haven't train this class before, why I got result this object (that come from 6th class) recognize as object from one of the class that I have been train before (It classified as member of 1st,or 2nd, ect class). While machine should say didn't know it from what class. SVM at OpenCV didn't return the probability, it just return the label of the class.\nI have an idea to make it 2 times classification. First with biner classification, with all of the sample as the training set. Second I classify it to multiclass.\nBut the problem, how should I find the negative sample for the first classification while I didn't know the other object (let's say that come from 6th or 7th class). Anybody can help me, what should I do? Which samples that should I use as Negative Sample? It is the great idea or stupid idea? Is there other way to solve this problem?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":87,"Q_Id":30204877,"Users Score":1,"Answer":"The classic SVM does part the n-dimensional feature space with planes. That means every point in space is in one of the partitions and therefore belongs to one of the trained classes. there is no outlier detection.\nHowever there is also the concept of a one-class SVM that tries to encapsulate the \"known\" space and classifies into \"known\" and \"unknown\". The libSVM package also has probabilities, you could try to analyse if that helps. You could also try other classification concepts to  detect outliers like nearest neighbour","Q_Score":0,"Tags":"python,opencv,svm","A_Id":30208490,"CreationDate":"2015-05-13T02:58:00.000","Title":"SVM find member of outside training set","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a tensor A such that A.shape = (32, 19, 2) and a binary matrix B such that B.shape = (32, 19). Is there a one-line operation I can perform to get a matrix C, where C.shape = (32, 19) and C(i,j) = A[i, j, B[i,j]]?\nEssentially, I want to use B as an indexing matrix, where if B[i,j] = 1 I take A[i,j,1] to form C(i,j).","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":338,"Q_Id":30225539,"Users Score":0,"Answer":"C = A[:,:,0]*(B==0) + A[:,:,1]*(B==1) should work. You can generalize this as np.sum([A[:,:,k]*(B==k) for k in np.arange(A.shape[-1])], axis=0) if you need to index more planes.","Q_Score":3,"Tags":"python,numpy,matrix","A_Id":30227232,"CreationDate":"2015-05-13T21:33:00.000","Title":"Indexing tensor with binary matrix in numpy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am reading a 5 G file on my 8G memory MacBook : pd.read_csv(filepath). \nI see the memory usage going to 12 G (orange, then red),  and then suddenly the memory usage drops back to 6G, and then slowly goes back up.... And my script doesn't deliver anything, not even an exit error....\nWhat can happen ? Seems like python is completely blocked (ventilator are very silencious...)","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":54,"Q_Id":30242603,"Users Score":0,"Answer":"I believe the file you are loading is far to large reference what your computer can handle. Unless you need to load all the data at once, I would try to load the data based on what you need at that time e.g., load data based on a specific criteria then run your program over those specifics. This should help with the two things. First, you will be able to load your data and run your program. Second, your program will run faster as it is only working on subsets of data at a time.","Q_Score":0,"Tags":"python,pandas","A_Id":30242748,"CreationDate":"2015-05-14T16:36:00.000","Title":"Pandas is blocking python when it reads big file","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am reading a 800 Mb CSV file with pandas.read_csv, and then use the original Python pickle.dump(datfarame) to save it. The result is a 4 Gb pkl file, so the CSV size is multiplied by 5. \nI expected pickle to compress data rather than extend it. Also because I can do a gzip on the CSV file which compress it to 200 Mb, dividing it by 4.\nI am willing to accelerate the loading time of my program, and thought that pickling would help, but considering disk access is the main bottleneck I am understanding that I would rather have to compress the files and then use the compression option from pandas.read_csv to speed up the loading time.\nIs that correct?\nIs it normal that pickling pandas dataframe extend the data size?\nHow do you speed up loading time usually? \nWhat are the data-size limit would you load with pandas?","AnswerCount":4,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":7611,"Q_Id":30253976,"Users Score":0,"Answer":"Dont load 800MB file to memory. It will increase your loading time. Pickle objects too takes more time to load. Instead store the csv file as a sqlite3 (which comes along with python) table. And then query the table every time depending upon your need.","Q_Score":5,"Tags":"python,csv,pandas,pickle","A_Id":30254159,"CreationDate":"2015-05-15T07:32:00.000","Title":"Pickling pandas dataframe multiplies by 5 the file size","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a Python script that writes and reads multiple CSV files. On OS X, in order to perform any calculations on any values in the CSV file, I have to float the value float(d['var_returns']). This works perfectly fine on OS X and the entire script works as performed.\nHowever, using the exact same code on Windows, I get:\nValueError: could not convert string to float: var_returns\nI was wondering why this is happening, and how I can fix it? The typical value in var_returns would be 0.050244079","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":32,"Q_Id":30278630,"Users Score":0,"Answer":"It turns out there was just a problem in the csv file. What happened was that I was running the same file, which caused another header row to be entered. When there is more then one header row, the script tries to convert something like \"ind_sharpe\" into a float which obviously will not work, and breaks the program.\nThe solution, overwrite header rows, don't stack them.","Q_Score":0,"Tags":"python,csv","A_Id":30278960,"CreationDate":"2015-05-16T17:22:00.000","Title":"Python reads CSV on OS X differently then Windows?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I built Spark 1.4 from the GH development master, and the build went through fine. But when I do a bin\/pyspark I get the Python 2.7.9 version. How can I change this?","AnswerCount":5,"Available Count":3,"Score":0.1586485043,"is_accepted":false,"ViewCount":93816,"Q_Id":30279783,"Users Score":4,"Answer":"Have a look into the file. The shebang line is probably pointed to the 'env' binary which searches the path for the first compatible executable.\nYou can change python to python3. Change the env to directly use hardcoded the python3 binary. Or execute the binary directly with python3 and omit the shebang line.","Q_Score":99,"Tags":"python,python-3.x,apache-spark","A_Id":30279862,"CreationDate":"2015-05-16T19:19:00.000","Title":"Apache Spark: How to use pyspark with Python 3","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I built Spark 1.4 from the GH development master, and the build went through fine. But when I do a bin\/pyspark I get the Python 2.7.9 version. How can I change this?","AnswerCount":5,"Available Count":3,"Score":1.0,"is_accepted":false,"ViewCount":93816,"Q_Id":30279783,"Users Score":155,"Answer":"Just set the environment variable:\nexport PYSPARK_PYTHON=python3\nin case you want this to be a permanent change add this line to pyspark script.","Q_Score":99,"Tags":"python,python-3.x,apache-spark","A_Id":32094874,"CreationDate":"2015-05-16T19:19:00.000","Title":"Apache Spark: How to use pyspark with Python 3","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I built Spark 1.4 from the GH development master, and the build went through fine. But when I do a bin\/pyspark I get the Python 2.7.9 version. How can I change this?","AnswerCount":5,"Available Count":3,"Score":1.0,"is_accepted":false,"ViewCount":93816,"Q_Id":30279783,"Users Score":9,"Answer":"1,edit profile :vim ~\/.profile\n2,add the code into the file: export PYSPARK_PYTHON=python3\n3, execute command :  source ~\/.profile\n4,  .\/bin\/pyspark","Q_Score":99,"Tags":"python,python-3.x,apache-spark","A_Id":38938668,"CreationDate":"2015-05-16T19:19:00.000","Title":"Apache Spark: How to use pyspark with Python 3","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am running the following: output.to_csv(\"hi.csv\")  where output is a pandas dataframe.\nMy variables all have values but when I run this in iPython, no file is created.  What should I do?","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":3754,"Q_Id":30295848,"Users Score":3,"Answer":"Better give the complete path for your output csv file. May be that you are checking in a wrong folder.","Q_Score":1,"Tags":"python,file,pandas,permissions","A_Id":30296559,"CreationDate":"2015-05-18T05:43:00.000","Title":"How to write .csv file in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using a colorbar in matplotlib and because I want my graph to be more sensitive I take the square root of each value. This gives me square rooted values on my colorbar, but now I want to scale the ticks labels back to the real values.  I am thusly having a hard time doing. I see that colorbar has a set_ticks function but I need to be able to get my ticks in the first place to do this generally.  Is there an easy way to do this that I am not seeing, or some other way around this?","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":984,"Q_Id":30331208,"Users Score":4,"Answer":"Calling the locator for the colorbar instance should return the tick locations. \ncolorbar.locator().","Q_Score":5,"Tags":"python,matplotlib","A_Id":30336990,"CreationDate":"2015-05-19T16:28:00.000","Title":"Get Colorbar Ticks","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using a colorbar in matplotlib and because I want my graph to be more sensitive I take the square root of each value. This gives me square rooted values on my colorbar, but now I want to scale the ticks labels back to the real values.  I am thusly having a hard time doing. I see that colorbar has a set_ticks function but I need to be able to get my ticks in the first place to do this generally.  Is there an easy way to do this that I am not seeing, or some other way around this?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":984,"Q_Id":30331208,"Users Score":0,"Answer":"In matplotlib 2.1 you may use method colorbar.get_ticks().","Q_Score":5,"Tags":"python,matplotlib","A_Id":49789479,"CreationDate":"2015-05-19T16:28:00.000","Title":"Get Colorbar Ticks","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How do I write a variable that includes multiple values and commas to a CSV file?\nfor example the variable might hold jimmy,5,250,james and I want to write this to a CSV file then start a new line and write the next batch of variables in the loop again in the same fashion.\n\noutFile = open('outputfile.csv','w')\n   csvFile_out = csv.writer(outFile, delimiter=',')   csvFile_out.writerows(valuesvariable)","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1136,"Q_Id":30369641,"Users Score":0,"Answer":"For a variable that holds multiple values I would use an array and loop through that with a foreach loop or something similar. That way every value stays properly seperated regardless of punctuation.","Q_Score":2,"Tags":"python,csv","A_Id":30369767,"CreationDate":"2015-05-21T09:23:00.000","Title":"How do I write a variable that includes multiple values and commas to a csv file?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I actually know a problem in Spyder. When I Work with matplotlib.pyplot, it automatically shows the figure without any kind of plt.show(). So when I make many different figures, it always shows them all on the same.\nI've recently made a program, which is saving one figure by iteration in an for loop, it also opens a figure and at the end, the loop crashes before ending because of too many figures showed.\nI would like to know if someone also knows such problems, because I've encountered the in many computers running spyder","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":441,"Q_Id":30377127,"Users Score":1,"Answer":"You likely are running in iPython with Spyder. iPython automatically runs plt.show(). Press f6 (default), and see if \"execute in current python or iPython console\" is selected. If so, check the console, the 'tab' should show a blue icon with \"iP\" and have 'kernal' (or similar) following it.\nSimple fix: switch to \"execute in dedicated python console\" or open a new python console (tab header \"python 1\")\nSource: had same problem.","Q_Score":0,"Tags":"python,matplotlib,spyder","A_Id":30377236,"CreationDate":"2015-05-21T14:45:00.000","Title":".Spyder opens a figure instead of absence of plt.show()","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a LDA model with the 10 most common topics in 10K documents. Now it's just an overview of the words with corresponding probability distribution for each topic. \nI was wondering if there is something available for python to visualize these topics?","AnswerCount":5,"Available Count":1,"Score":0.0798297691,"is_accepted":false,"ViewCount":12860,"Q_Id":30397550,"Users Score":2,"Answer":"Word clouds are popular ways of visualizing topic distributions. To generate a word cloud in python consider cloning the wordcloud library.","Q_Score":11,"Tags":"python,data-visualization,lda,topic-modeling","A_Id":36701858,"CreationDate":"2015-05-22T13:07:00.000","Title":"Visualizing an LDA model, using Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a large A.csv file (~5 Gb) with several columns. One of the columns is Model.\nThere is another large B.csv file (~15 Gb) with Vendor, Name and Model columns.\nTwo questions:\n1) How can I create result file that combines all columns from A.csv and corresponding Vendor and Name from B.csv (join on Model). The trick is - how to do it when my RAM is 4 Gb only, and I'm using python.\n2) How can I create a sample (say, 1 Gb) result file that combines random subsample from A.csv (all columns) joined with Vendor and Name from B.csv. The trick is, again, in 4 Gb of RAM.\nI know how to do it in pandas, but 4 Gb is limiting factor I can't overcome (","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":862,"Q_Id":30441107,"Users Score":0,"Answer":"As @Marc B said, reading one row at a time is the solution.\nAbout the join I would do the following (pseudocode: I don't know python).\n\n\"Select distinct Model from A\" on first file A.csv\n\nRead all rows, search for Model field and collect distinct values in a list\/array\/map\n\n\"Select distinct Model from B\" on second file B.csv\n\nSame operation as 1, but using another list\/array\/map\n\nFind matching models\n\nCompare the two lists\/arrays\/maps finding only matching models (they will be part of the join)\n\nDo the join\n\nReading rows of file A which match model, read all the rows of file B which match same model and write a file C with join result. To this for all models.\nNote: it's not particularly optimized.\nFor point 2 just choose a subset of matching models and\/or read a part of rows of file A and\/or B with maching models.","Q_Score":0,"Tags":"python,file,memory,merge","A_Id":30441330,"CreationDate":"2015-05-25T14:57:00.000","Title":"Concatenate large files in sql-like way with limited RAM","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"While working in Pandas in Python...\nI'm working with a dataset that contains some missing values, and I'd like to return a dataframe which contains only those rows which have missing data.  Is there a nice way to do this?\n(My current method to do this is an inefficient \"look to see what index isn't in the dataframe without the missing values, then make a df out of those indices.\")","AnswerCount":6,"Available Count":1,"Score":-0.0665680765,"is_accepted":false,"ViewCount":100635,"Q_Id":30447083,"Users Score":-2,"Answer":"If you are looking for a quicker way to find the total number of missing rows in the dataframe, you can use this:\nsum(df.isnull().values.any(axis=1))","Q_Score":57,"Tags":"python,pandas,missing-data","A_Id":61126661,"CreationDate":"2015-05-25T23:03:00.000","Title":"Python, Pandas : Return only those rows which have missing values","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on a project where I need to extract the Mel-Cepstral Frequency Coefficients (MFCC) from audio signals. The first step for this process is to read the audio file into Python.\nThe audio files I have are stored in a .sph format. I am unable to find a method to read these files directly into Python. I would like to have the sampling rate, and a NumPy array with the data, similar to how wav read works.\nSince the audio files I will be dealing with are large in size, I would prefer not to convert to .wav format for reading. Could you please suggest a possible method to do so?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":3923,"Q_Id":30449860,"Users Score":0,"Answer":"You can read sph files via audioreadwith ffmpeg codecs.","Q_Score":6,"Tags":"python,audio","A_Id":50763111,"CreationDate":"2015-05-26T05:05:00.000","Title":"Read .sph files in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a few large sets of data which I have used to create non-standard probability distributions (using numpy.histogram to bin the data, and scipy.interpolate's interp1d function to interpolate the resulting curves). I have also created a function which can sample from these custom PDFs using the scipy.stats package.\nMy goal is to see how varying the size of my samples changes the goodness of fit to both the distributions they came from, and the other PDFs as well, and determine how large a sample is necessary to completely determine whether it came from one or other of my custom PDFs.\nTo do this I've gathered that I need to use some sort of nonparametric statistical analysis, i.e. seeing whether a set of data has been drawn from a provided probability distribution. Doing a bit of research, it seems like the Anderson-Darling test is ideal for this, however its implementation in python (scipy.stats.anderson) seems to only be usable for preset probability distributions such as normal, exponential, etc.\nSo my question is: given my many nonstandard PDFs (or CDFs if necessary, or the data I used to create them) what is the best way to work out how well a set of sample data fits each model in Python? If it is the Anderson-Darling test, is there some way of defining a custom PDF to test against?\nThanks. Any help is much appreciated.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":452,"Q_Id":30459398,"Users Score":2,"Answer":"(1) \"Is it from distribution X\" is generally a question which can be answered a priori, if at all; a statistical test for it will only tell you \"I have a large sample \/ not a large sample\", which may be true but not too useful. If you are trying to classify new data into one distribution or another, my advice is to look at it as a classification problem and use your constructed pdf's to compute p(class | data) = p(data | class) p(class) \/ p(data) where the key part p(data | class) is your histogram. Maybe you can say more about your problem domain. \n(2) You could apply the Kolmogorov-Smirnov test, but it's really pointless, as mentioned above.","Q_Score":4,"Tags":"python,scipy,statistics,probability,statistical-test","A_Id":30470049,"CreationDate":"2015-05-26T13:03:00.000","Title":"Comparing datasets to nonstandard probability distributions in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm working on calculating the real world coordinates of an object in a scene by using a pair of stereo images. The images are simulations of perfect pinhole cameras so there is no distortion to correct and there is no rotation. I know OpenCV has a bunch of functions to calibrate stereo cameras and create disparity maps, but if all I want to calculate is the coordinates of one point, is there a simple way to do that?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1838,"Q_Id":30490625,"Users Score":5,"Answer":"1) Case of no rotation, only translation parallel to the horizontal axis of the image plane, cameras with equal focal lengths. \nDenote with \"f\" the common focal length. Denote with \"b\" the baseline of the stereo pair, namely the distance between the cameras' optical centers. Given a 3D point P, visible in both cameras at horizontal image coordinates x_left and x_right, denote with \"d\" their disparity, namely the difference d = x_left - x_right. \nBy elementary geometry it then follows that the depth z_left of P in the left camera coordinates is: \nz_left = b * f \/ d.\n2) Any other case (unequal focal lengths, differences in other intrinsic parameters, non-linear lens distortion, inter-camera rotation, translation not parallel to the x axis, etc.):\nDon't bother, use OpenCV,","Q_Score":4,"Tags":"python,opencv,computer-vision,coordinates,stereo-3d","A_Id":30506462,"CreationDate":"2015-05-27T18:42:00.000","Title":"Calculating real world co-ordinates using stereo images in Python and OpenCV","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a large but sparse train data. I would like to use it with ExtraTreeClassifier. I am not sure considering computational time whether I need to use sparse csr_matrix or the raw data. Which version of the data runs faster with that classifier and can we generalize its answer to all sparse capable models?","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":982,"Q_Id":30494067,"Users Score":1,"Answer":"Depends on your data\nMemory consumption.\nIf your data is dense, a dense representation needs d*sizeof(double) bytes for your data (i.e. usually d * 8 bytes). A sparse representation usually needs sparsity*d*(sizeof(int)+sizeof(double)). Depending on your programming language and code quality, it can also be much more due to memory management overhead. A typical Java implementation adds 8 bytes of overhead, and will round to 8 bytes size; so sparse vectors may easily use 16 + sparsity * d * 24 bytes. then.\nIf your sparsity is 1, this means a sparse representation needs 50% more memory. I guess the memory tradeoff in practise will be somewhere around 50% sparsity; and if your implementation isn't carefull optimized, maybe even 30% - so 1 out of 3 values should be a zero.\nMemory consumption is usually a key problem. The more memory you use, the more pagefaults and cache misses your CPU will have, which can have a big impact on performance (which is why e.g. BLAS perform large matrix multiplications in block sizes optimized for your CPU caches).\nOptimizations and SIMD.\nDense vector code (e.g. BLAS) is usually much better optimized than sparse operations. In particular, SIMD (single instruction, multiple data) CPU instructions usually only work with dense data.\nRandom access.\nMany algorithms may need random access to vectors. If your data is represented as a double[] array, random access is O(1). If your data is a sparse vector, random access usually is O(sparsity*d), i.e. you will have to scan the vector to check if there is a value present. It may thus be beneficial to transpose the matrix for some operations, and work with sparse columns instead of sparse rows.\nOn the other hand, some algorithms may exactly benefit from this. But many implementations have such optimizations built in, and will take care of this automatically. Sometimes you also have different choices available. For example APRIORI works on rows, and thus will work well with row-sparse data. Eclat on the other hand is an algorithm to solve the same problem, but it first transforms all data into a row-sparse form, then even computes column differences to further optimize.\nCode complexity.\nCode to process sparse data usually is much more complex. In particular, it cannot make use of SSE and similar fast CPU instructions easily. It is one of the reasons why sparse matrix multiplications are much slower than dense operations - optimizing these operations without knowing certain characteristics of your data is surprisingly hard. :-(","Q_Score":2,"Tags":"python,machine-learning,scikit-learn","A_Id":30640900,"CreationDate":"2015-05-27T22:18:00.000","Title":"Does using sparse matrices make an algorithm slower or faster in Sklearn?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a large but sparse train data. I would like to use it with ExtraTreeClassifier. I am not sure considering computational time whether I need to use sparse csr_matrix or the raw data. Which version of the data runs faster with that classifier and can we generalize its answer to all sparse capable models?","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":982,"Q_Id":30494067,"Users Score":1,"Answer":"If your data are sparse, the extra tree classifier will be faster with a csc_matrix. In doubt, I would suggest you to benchmark with both version. \nAll algorithms should benefit from using the appropriate sparse format if your data are sufficiently sparse. For instance, algorithms based on dot product will be a lot faster with sparse data.","Q_Score":2,"Tags":"python,machine-learning,scikit-learn","A_Id":30640578,"CreationDate":"2015-05-27T22:18:00.000","Title":"Does using sparse matrices make an algorithm slower or faster in Sklearn?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to optimize a function of a small number of variables (somewhere from 2 to 10). What I am trying to do is calculate the minimum of the function on a bounded hypercube \n[0,1] x [0,1] x ... x [0,1]\nThe calculation of the function, its gradient and its hessian is al relatively simple, quick and accurate.\nNow, my problem is this:\nUsing scipy, I can use either scipy.optimize.minimize(..., method='Newton-CG') or scipy.optimize.minimize(..., method='TNC') to calculate the minimum of the function, however:\n\nThe first method uses the Hessian matrix, but I cannot set the bounds for the variables I am optimizing\nThe second method allows me to set bounds on the variables, but the method does not use the Hessian.\n\nIs there any method that will use both?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":923,"Q_Id":30500311,"Users Score":1,"Answer":"l-bfgs-b does a bounded optimisation. Like any quasi-Newton method it approximates the Hessian. But this is often better than using the real Hessian.","Q_Score":0,"Tags":"python,numpy,optimization,scipy","A_Id":30553182,"CreationDate":"2015-05-28T07:47:00.000","Title":"Bounded optimization using the Hessian matrix (scipy)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using spark 1.4.0-rc2 so I can use python 3 with spark.  If I add export PYSPARK_PYTHON=python3 to my .bashrc file, I can run spark interactively with python 3.  However, if I want to run a standalone program in local mode, I get an error:\n\nException: Python in worker has different version 3.4 than that in\ndriver 2.7, PySpark cannot run with different minor versions\n\nHow can I specify the version of python for the driver?  Setting export PYSPARK_DRIVER_PYTHON=python3 didn't work.","AnswerCount":19,"Available Count":4,"Score":0.010525927,"is_accepted":false,"ViewCount":165951,"Q_Id":30518362,"Users Score":1,"Answer":"I had the same problem, just forgot to activate my virtual environment.","Q_Score":95,"Tags":"python,apache-spark,pyspark","A_Id":63025280,"CreationDate":"2015-05-28T22:52:00.000","Title":"How do I set the driver's python version in spark?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using spark 1.4.0-rc2 so I can use python 3 with spark.  If I add export PYSPARK_PYTHON=python3 to my .bashrc file, I can run spark interactively with python 3.  However, if I want to run a standalone program in local mode, I get an error:\n\nException: Python in worker has different version 3.4 than that in\ndriver 2.7, PySpark cannot run with different minor versions\n\nHow can I specify the version of python for the driver?  Setting export PYSPARK_DRIVER_PYTHON=python3 didn't work.","AnswerCount":19,"Available Count":4,"Score":1.0,"is_accepted":false,"ViewCount":165951,"Q_Id":30518362,"Users Score":6,"Answer":"I came across the same error message and I have tried three ways mentioned above. I listed the results as a complementary reference to others.\n\nChange the PYTHON_SPARK and PYTHON_DRIVER_SPARK value in spark-env.sh does not work for me.\nChange the value inside python script using os.environ[\"PYSPARK_PYTHON\"]=\"\/usr\/bin\/python3.5\"\nos.environ[\"PYSPARK_DRIVER_PYTHON\"]=\"\/usr\/bin\/python3.5\" does not work for me.\nChange the value in ~\/.bashrc works like a charm~","Q_Score":95,"Tags":"python,apache-spark,pyspark","A_Id":46797446,"CreationDate":"2015-05-28T22:52:00.000","Title":"How do I set the driver's python version in spark?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using spark 1.4.0-rc2 so I can use python 3 with spark.  If I add export PYSPARK_PYTHON=python3 to my .bashrc file, I can run spark interactively with python 3.  However, if I want to run a standalone program in local mode, I get an error:\n\nException: Python in worker has different version 3.4 than that in\ndriver 2.7, PySpark cannot run with different minor versions\n\nHow can I specify the version of python for the driver?  Setting export PYSPARK_DRIVER_PYTHON=python3 didn't work.","AnswerCount":19,"Available Count":4,"Score":1.0,"is_accepted":false,"ViewCount":165951,"Q_Id":30518362,"Users Score":9,"Answer":"I just faced the same issue and these are the steps that I follow in order to provide Python version. I wanted to run my PySpark jobs with Python 2.7 instead of 2.6. \n\nGo to the folder where $SPARK_HOME is pointing to (in my case is \/home\/cloudera\/spark-2.1.0-bin-hadoop2.7\/)\nUnder folder conf, there is a file called spark-env.sh. In case you have a file called spark-env.sh.template you will need to copy the file to a new file called spark-env.sh.\nEdit the file and write the next three lines\n\nexport PYSPARK_PYTHON=\/usr\/local\/bin\/python2.7\nexport PYSPARK_DRIVER_PYTHON=\/usr\/local\/bin\/python2.7\nexport SPARK_YARN_USER_ENV=\"PYSPARK_PYTHON=\/usr\/local\/bin\/python2.7\"\n\nSave it and launch your application again :) \n\nIn that way, if you download a new Spark standalone version, you can set the Python version which you want to run PySpark to.","Q_Score":95,"Tags":"python,apache-spark,pyspark","A_Id":50399085,"CreationDate":"2015-05-28T22:52:00.000","Title":"How do I set the driver's python version in spark?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using spark 1.4.0-rc2 so I can use python 3 with spark.  If I add export PYSPARK_PYTHON=python3 to my .bashrc file, I can run spark interactively with python 3.  However, if I want to run a standalone program in local mode, I get an error:\n\nException: Python in worker has different version 3.4 than that in\ndriver 2.7, PySpark cannot run with different minor versions\n\nHow can I specify the version of python for the driver?  Setting export PYSPARK_DRIVER_PYTHON=python3 didn't work.","AnswerCount":19,"Available Count":4,"Score":1.2,"is_accepted":true,"ViewCount":165951,"Q_Id":30518362,"Users Score":34,"Answer":"You need to make sure the standalone project you're launching is launched with Python 3. If you are submitting your standalone program through spark-submit then it should work fine, but if you are launching it with python make sure you use python3 to start your app.\nAlso, make sure you have set your env variables in .\/conf\/spark-env.sh (if it doesn't exist you can use spark-env.sh.template as a base.)","Q_Score":95,"Tags":"python,apache-spark,pyspark","A_Id":30518974,"CreationDate":"2015-05-28T22:52:00.000","Title":"How do I set the driver's python version in spark?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In numpy there are two ways to mark missing values: I can either use a NaN or a masked array. I understand that using NaNs is (potentially) faster while masked array offers more functionality (which?).\nI guess my question is if\/ when should I use one over the other?\nWhat is the use case of np.NaN in a regular array vs. a masked array? \nI am sure the answer must be out there but I could not find it...","AnswerCount":3,"Available Count":2,"Score":0.1325487884,"is_accepted":false,"ViewCount":2235,"Q_Id":30528852,"Users Score":2,"Answer":"From what I understand NaN represents anything that is not a number, while a masked array marks missing values OR values that are numbers but are not valid for your data set.\nI hope that helps.","Q_Score":13,"Tags":"python,numpy,nan","A_Id":30529135,"CreationDate":"2015-05-29T11:49:00.000","Title":"numpy: difference between NaN and masked array","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In numpy there are two ways to mark missing values: I can either use a NaN or a masked array. I understand that using NaNs is (potentially) faster while masked array offers more functionality (which?).\nI guess my question is if\/ when should I use one over the other?\nWhat is the use case of np.NaN in a regular array vs. a masked array? \nI am sure the answer must be out there but I could not find it...","AnswerCount":3,"Available Count":2,"Score":0.0665680765,"is_accepted":false,"ViewCount":2235,"Q_Id":30528852,"Users Score":1,"Answer":"Keep in mind that strange np.nan behaviours, mentioned by jrmyp, include unexpected results for example when using functions of the statsmodels (e.g. ttest) or numpy module (e.g. average). From experience, most those functions have workarounds for NaNs, but it has the potential of driving you mad for a while. This seems like a reason to mask arrays whenever possible.","Q_Score":13,"Tags":"python,numpy,nan","A_Id":52755040,"CreationDate":"2015-05-29T11:49:00.000","Title":"numpy: difference between NaN and masked array","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Here is a Python 3.4 user, in VS 2013 and PTVS...\nI'd written a program to plot something by Matplotlib... The output had been generating and everything was ok...\nSo, I closed VS and now I've opened it again after an hour, running the very script, but this time as soon as I press F5, a window appears and says Python has stopped working...\nThere is a short log in the output window, which asserts that:\nThe program '[9952] python.exe' has exited with code -1073741819 (0xc0000005) 'Access violation'.\nWho could decrypt this error, please?!...\nKind Regards\n.........................................\nEdit:\nI just tested again with no change... The error has been changed to:\nThe program '[11284] python.exe' has exited with code -1073741819 (0xc0000005) 'Access violation'.\nDebug shows that when the program points to the drawing command of the matloptlib, i.e. plt.show(), this crash will happen...","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":659,"Q_Id":30535810,"Users Score":0,"Answer":"All of the attempts sounds futile... \nEven removing the VS is a challenging stuff... and as people consider, changing the OS is the most stable way to get rid of VS in the presence of such anomalies regarding its libraries...\nSo... I changed the OS... and installed the VS and PTVS again...","Q_Score":1,"Tags":"python,visual-studio,python-3.x,matplotlib,ptvs","A_Id":30576274,"CreationDate":"2015-05-29T17:38:00.000","Title":"Python access violation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Here is a Python 3.4 user, in VS 2013 and PTVS...\nI'd written a program to plot something by Matplotlib... The output had been generating and everything was ok...\nSo, I closed VS and now I've opened it again after an hour, running the very script, but this time as soon as I press F5, a window appears and says Python has stopped working...\nThere is a short log in the output window, which asserts that:\nThe program '[9952] python.exe' has exited with code -1073741819 (0xc0000005) 'Access violation'.\nWho could decrypt this error, please?!...\nKind Regards\n.........................................\nEdit:\nI just tested again with no change... The error has been changed to:\nThe program '[11284] python.exe' has exited with code -1073741819 (0xc0000005) 'Access violation'.\nDebug shows that when the program points to the drawing command of the matloptlib, i.e. plt.show(), this crash will happen...","AnswerCount":2,"Available Count":2,"Score":-0.0996679946,"is_accepted":false,"ViewCount":659,"Q_Id":30535810,"Users Score":-1,"Answer":"It seems to be a problem with your python and PTVS. Try to remove every .pyc file and have another go at it","Q_Score":1,"Tags":"python,visual-studio,python-3.x,matplotlib,ptvs","A_Id":30536536,"CreationDate":"2015-05-29T17:38:00.000","Title":"Python access violation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to plot a ratio between a function introduced thorough a discrete data set, imported from a text file, for example:\nx,y,z=np.loadtxt('example.txt',usecols=(0,1,2),unpack=True),\nand a continuous function defined using the np.arange command, for example:\nw=np.arange(0,0.5,0.01)\nexfunct=w**4.\nClearly, solutions as\nplt.plot(w,1-(x\/w),'k--',color='blue',lw=2) as well\nplt.plot(y,1-(x\/w),'k--',color='blue',lw=2)\ndo not work. Despite having looked for the answer in the site (and outside it), I can not find any solution to my problem. Should I fit the discrete data set, to obtain a continuous function, and then define it in the same interval as the \"exfunct\"? Any suggestion? Thank you a lot.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1808,"Q_Id":30538062,"Users Score":0,"Answer":"At the end the solution has been easier than I thought. I had simply to define the continuous variable through the discrete data, as, for example:\nw=x\/y,\nthen define the function as already said:\nexfunct=w**4\nand finally plot the \"continuous-discrete\" function:\nplt.plot(x,x\/exfunct),'k-',color='red',lw=2)\nI hope this can be useful.","Q_Score":0,"Tags":"python,function,matplotlib,dataset","A_Id":30674814,"CreationDate":"2015-05-29T19:56:00.000","Title":"matplotlib discrete data versus continuous function","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am wondering how i could generate random numbers that appear in a circular distribution.  \nI am able to generate random points in a rectangular distribution such that the points are generated within the square of (0 <= x < 1000, 0 <= y < 1000):  \nHow would i go upon to generate the points within a circle such that:\n(x\u2212500)^2 + (y\u2212500)^2 < 250000 ?","AnswerCount":7,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":31584,"Q_Id":30564015,"Users Score":8,"Answer":"FIRST ANSWER:\nAn easy solution would be to do a check to see if the result satisfies your equation before proceeding.\nGenerate x, y (there are ways to randomize into a select range)\nCheck if ((x\u2212500)^2 + (y\u2212500)^2 < 250000) is true\nif not, regenerate.\nThe only downside would be inefficiency.\nSECOND ANSWER:\nOR, you could do something similar to riemann sums like for approximating integrals. Approximate your circle by dividing it up into many rectangles. (the more rectangles, the more accurate), and use your rectangle algorithm for each rectangle within your circle.","Q_Score":14,"Tags":"python,distribution,point","A_Id":30564059,"CreationDate":"2015-06-01T00:10:00.000","Title":"How to generate random points in a circular distribution","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'd like to create a random permutation of the numbers [1,2,...,N] where N is a big number. So I don't want to store all elements of the permutation in memory, but rather iterate over the elements of my particular permutation without holding former values in memory.\nAny idea how to do that in Python?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1933,"Q_Id":30615536,"Users Score":0,"Answer":"This is a generic issue and rather not a Python-specific. In most languages, even when iterators are used for using structures, the whole structure is kept in memory. So, iterators are mainly used as \"functional\" tools and not as \"memory-optimization\" tools. \nIn python, a lot of people end up using a lot of memory due to having really big structures (dictionaries etc.). However, all the variables-objects of the program will be stored in memory in any way. The only solution is the serialization of the data (save in filesystem, Database etc.).\nSo, in your case, you could create a customized function that would create the list of the permutations. But, instead of adding each element of the permutation to a list, it would save the element either in a file (or in a database with the corresponding structure). Then, you would be able to retrieve one-by-one each permutation from the file (or the database), without bringing the whole list in memory.\nHowever, as mentioned before, you will have to always know in which permutation you currently are. In order to avoid retrieving all the created permutations from Database (which would create the same bottleneck), you could have an index for each place holding the symbol used in the previously generated permutation (and create the permutations adding the symbols and a predefined sequence).","Q_Score":6,"Tags":"python,algorithm,iterator,permutation","A_Id":30615998,"CreationDate":"2015-06-03T09:06:00.000","Title":"Generate random permutation of huge list (in Python)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Tips are there for dropping column and rows depending on some condition.\nBut I want to drop the whole dataframe created in pandas.\nlike in R : rm(dataframe) or in SQL: drop table \nThis will help to release the ram utilization.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":9398,"Q_Id":30642356,"Users Score":5,"Answer":"Generally creating a new object and binding it to a variable will allow the deletion of any object the variable previously referred to. del, mentioned in @EdChum's comment, removes both the variable and any object it referred to.\nThis is an over-simplification, but it will serve.","Q_Score":2,"Tags":"python,pandas,dataframe","A_Id":30648685,"CreationDate":"2015-06-04T11:12:00.000","Title":"how to drop dataframe in pandas?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to randomly access the elements of a large array (>7GB) that I load into Python as a either an HDF5 dataset (h5py.Dataset), or a memory-mapped array (numpy.memmap).\nIf this file lives on an spinning-platter HD, these random accesses take forever, for obvious reasons.\nIs there a way to check (assert) that the file in question lives on an SSD, before attempting these random accesses?\nI am running python in Linux (Ubuntu 14.04). I don't mind non-cross-platform solutions.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":889,"Q_Id":30647758,"Users Score":1,"Answer":"cat \/sys\/block\/sda\/queue\/rotational is a good way of finding out if your hard drive is a SSD or a hard disk. You can also slightly change this command in order to get other useful information like cat \/sys\/block\/sdb\/queue\/rotational.","Q_Score":0,"Tags":"python,numpy,h5py,solid-state-drive,memory-mapping","A_Id":30650664,"CreationDate":"2015-06-04T15:15:00.000","Title":"In python, can I see if a file lives on an HD or an SSD?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"This question has potentially two parts but maybe only one if the first part can be encapsulated by the second. I am using python with numpy and netCDF4\nFirst:\nI have four lists of different variable values (hereafter referred to elevation values) each of which has a length of 28. These four lists are one set of 5 different latitude values of which are one set of the 24 different time values. \nSo 24 times...each time with 5 latitudes...each latitude with four lists...each list with 28 values.\nI want to create an array with the following dimensions (elevation, latitude, time, variable)\nIn words, I want to be able to specify which of the four lists I access,which index in the list, and specify a specific time and latitude. So an index into this array would look like this:\narray(0,1,2,3) where 0 specifies the first index of the the 4th list specified by the 3. 1 specifies the 2nd latitude, and 2 specifies the 3rd time and the output is the value at that point.\nI won't include my code for this part since literally the only things of mention are the lists\nlist1=[...]\nlist2=[...]\nlist3=[...]\nlist4=[...]\nHow can I do this, is there an easier structure of the array, or is there anything else I a missing?\nSecond:\nI have created a netCDF file with variables with these four dimensions. I need to set those variables to the array structure made above. I have no idea how to do this and the netCDF4 documentation does a 1-d array in a fairly cryptic way. If the arrays can be made directly into the netCDF file bypassing the need to use numpy first, by all means show me how. \nThanks!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1886,"Q_Id":30655378,"Users Score":0,"Answer":"After talking to a few people where I work we came up with this solution:\nFirst we made an array of zeroes using the following argument:\narray1=np.zeros((28,5,24,4))\nThen appended this array by specifying where in the array we wanted to change:\narray1[:,0,0,0]=list1\nThis inserted the values of the list into the first entry in the array.\nNext to write the array to a netCDF file, I created a netCDF in the same program I made the array, made a single variable and gave it values like this:\nnetcdfvariable[:]=array1\nHope that helps anyone who finds this.","Q_Score":0,"Tags":"python,arrays,numpy,multidimensional-array,netcdf","A_Id":30713394,"CreationDate":"2015-06-04T22:35:00.000","Title":"Creating and Storing Multi-Dimensional Array in a netCDF File","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to transfer python codes in CPU to GPU, but I failed to find the numpy function hstack in gnumpy. Who can give me some hints to implement adding some extra rows to a existing matrix(garray) like hstack in numpy. Thank you.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":75,"Q_Id":30662065,"Users Score":3,"Answer":"You can use gnumpy.concatenate. For 1D arrays you need to reshape to 2D first.","Q_Score":1,"Tags":"python,numpy","A_Id":30662556,"CreationDate":"2015-06-05T08:42:00.000","Title":"Is there any implementation of hstack in gnumpy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've compared the built-in csv reader with Pandas's read_csv. The former is significantly slower. However, I have a need to stream csv files due to memory limitation. What streaming csv reader that is as fast or almost as fast as Pandas?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1231,"Q_Id":30665447,"Users Score":1,"Answer":"In pandas.read_csv you can use the \"chunksize\" option, if you do, the object returned by pandas will be an iterator (of type TextFileReader) which when iterated over will return a DataFrame reading over number_of_rows<=chunksize (I hadn't realized the option existed until I read the source code...).","Q_Score":1,"Tags":"python,csv,pandas","A_Id":30667520,"CreationDate":"2015-06-05T11:31:00.000","Title":"What is the fastest way to stream a large csv file?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm working on a project where I have to store about 17 million 128-dimensional integer arrays e.g [1, 2, 1, 0, ..., 2, 6, 4] and I'm trying to figure out what's the best way to do it. \nThe perfect solution would be one that makes it fast to both store and retrieve the arrays, since I need to access ALL of them to make calculations. With such a vast amount of data, I obviously can't store them all in memory in order to make calculations, so accessing batches of arrays should be as fast as possible.\nI'm working in Python.\nWhat do you recommend ? Using a DB (SQL vs NOSQL ?), storing it in a text file, using python's Pickle?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1207,"Q_Id":30682311,"Users Score":0,"Answer":"It seems not so big with numpy arrays, if your integers are 8 bits.   a=numpy.ones((17e6,128),uint8) is created in less than  a second on my computer.  but ones((17e6,128),uint16) is difficult, and  ones((17e6,128),uint64) crashed.","Q_Score":2,"Tags":"python,sql,arrays,database,nosql","A_Id":30684782,"CreationDate":"2015-06-06T11:32:00.000","Title":"Fastest way to store and retrieve arrays","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to Python and am having trouble loading numpy in Wing IDE. I can load the module and use it fine in the command line but not in Wing IDE. Below is what I am seeing:\ncode:\nimport numpy as np\na=np.arange(15)\nresult:\n[evaluate numpy.py]\nTraceback (most recent call last):\n  File \"C:\\Users[my name]\\Documents\\Python\\practice\\numpy.py\", line 2, in 0\nbuiltins.NameError: name 'arange' is not defined\nI have also tried to use the help() command:\ncode:\nhelp(np)\nresult:\nHelp on module numpy:\nNAME\n    numpy\nFILE\n    c:\\users[my name]\\documents\\python\\practice\\numpy.py","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1987,"Q_Id":30688828,"Users Score":1,"Answer":"It is probably due to your code being in a file named numpy.py   If you do this then 'import numpy' may import your module and not numpy.  This depends on what's on the Python Path and possibly current directory, which probably explains why it works outside of Wing.","Q_Score":0,"Tags":"python,wing-ide","A_Id":30711755,"CreationDate":"2015-06-07T00:21:00.000","Title":"Load Modules in Wing IDE","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to Python. I am running Ubuntu 14.04, and I have both Python 2.7 and 3.4 on it.\nI want to use the newer 3.x version, with the NumPy, SciPy, and NLTK libraries. I set the Python REPL path to Python 3.x in the ~\/.bash_aliases file like so:\n\nalias python=python3\n\nAfter this I installed several libs, including python-numpy, python-scipy, and python-matplotlib.\n\n$ sudo apt-get install python-numpy python-scipy python-matplotlib ipython ipython-notebook python-pandas python-sympy python-nose\n\nUnfortunately, I am facing issues since I am guessing that the libraries got installed for the older 2.7 version of Python; I am unable to access the libraries using the 3.4 REPL.\n\nimport numpy\n\nImportError: No module named 'numpy'\n\n\nHowever, I am able to access the libraries using the older version:\n\n$ \/usr\/bin\/python2.7\n\nHow do I get this this work?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":154,"Q_Id":30704214,"Users Score":1,"Answer":"When you install Python packages using apt-get, you're relying on the distribution package manager.  The Ubuntu convention is to prefix Python 2 packages with python-, and Python 3 packages with python3-.\nThis distinction is necessary because Python 3 introduced some incompatible changes from Python 2.  It's thus not possible to simply recompile (most) packages for Python 3, meaning both need to be made available.\nAlternatively, you can use the Python package manager, pip (or pip3).  The catch with this is that some packages (like scipy) require certain compiler toolchains which you might not have installed.\nIt's generally a good idea to stick to either apt-get or pip for a particular machine.  You probably won't have issues if you mix them, but it's better to be consistent.","Q_Score":0,"Tags":"python,python-2.7,python-3.x,compatibility","A_Id":30704359,"CreationDate":"2015-06-08T08:18:00.000","Title":"Python 2.7 and 3.4: Libraries inaccessible across versions","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"My use case is simple, i have performed some kind of operation on image and the resulting feature vector is a numpy object of shape rowX1000(what i mean to say is that the row number can be variable but column number is always 1000)\nI want to store this numpy array in mysql. No kind of operation is to be performed on this array. The query will be simple given a image name return the whole feature vector. so is there any way in which the array can be stored (something like a magic container which encapsulates the array and then put it on the table and on retrieval it retrieves the magic container and pops out the array)\nI want to do this in python. If possible support with a short code snippit of how to put the data in the mysql database.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":7366,"Q_Id":30713062,"Users Score":8,"Answer":"You could use ndarray.dumps() to pickle it to a string then write it to a BLOB field? Recover it using numpy.loads()","Q_Score":9,"Tags":"python,mysql,arrays,numpy","A_Id":30713767,"CreationDate":"2015-06-08T15:20:00.000","Title":"store numpy array in mysql","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a time series in a log file having the following form (timestamp, value) :\n1433787443, -60\n1433787450, -65\n1433787470, -57\n1433787483, -70\nIs there any available python code\/library that takes as input the log file and a window size, apply a median filter to the time series to remove noise and outliers, and outputs the filtered signal to a new file ?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":4246,"Q_Id":30716541,"Users Score":1,"Answer":"Load the data using any method you prefer. I see that your file can be treated as csv format, therefore you could use numpy.genfromtxt('file.csv', delimiter=',') function.\nUse the scipy function for median filtering: scipy.signal.medfilt(data, window_len). Keep in mind that window length must be odd number.\nSave the results to a file. You can do it for example by using the numpy.savetxt('out.csv', data, delimiter=',') function.","Q_Score":1,"Tags":"python,filtering,time-series,signal-processing,noise-reduction","A_Id":30721305,"CreationDate":"2015-06-08T18:32:00.000","Title":"Simple Python Median Filter for time series","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"i have tried import file csv using bulk insert but it is failed, is there another way in query to import csv file without using bulk insert ?\nso far this is my query but it use bulk insert :\nbulk insert [dbo].[TEMP] from \n\n'C:\\Inetpub\\vhosts\\topimerah.org\\httpdocs\\SNASPV0374280960.txt' with\n  (firstrow=2,fieldterminator = '~', rowterminator = ' ');","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":777,"Q_Id":30724143,"Users Score":0,"Answer":"My answer is to work with bulk-insert.\n1. Make sure you have bulk-admin permission in server.\n2. Use SQL authentication login (For me most of the time window authentication login haven't worked.) for bulk-insert operation.","Q_Score":0,"Tags":"python,mysql,sql,sql-server,csv","A_Id":30724975,"CreationDate":"2015-06-09T06:05:00.000","Title":"how to import file csv without using bulk insert query?","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"We are developing a scientific application which has the interface in python 2.7 and the computation routines written in Intel Visual Fortran. Reading the source files is done using python, then only the required data for computations has to be passed to standalone Fortran algorithms. Once the computations done, the data has to be read by python once again. \nUsing formatted text files seems to be taking too long and not efficient. Further, we would like to have a standard intermediate format. There can be about 20 arrays and those are huge (if written to formatted text, the file is about 500 MB).\nQ1. In a similar situation where Python and Fortran data exchange is necessary. What would be recommended way of interaction? (e.g.: writing an intermediate data to be read by the other or calling Fortran from within Python or using numpy to create compatible arrays or etc.)\nQ2. If writing intermediate structures is recommended, What format is good for data exchange? (We came across CDF, NETCdf, binary streaming, but didn't try any so far.)","AnswerCount":2,"Available Count":1,"Score":0.2913126125,"is_accepted":false,"ViewCount":325,"Q_Id":30736698,"Users Score":3,"Answer":"I would interface directly between Python and Fortran. It is relatively straightforward to allocate memory using Numpy and pass a pointer through to Fortran. You use iso_c_binding to write C-compatible wrapper routines for your Fortran routines and ctypes to load the Fortran .dll and call the wrappers. If you're interested I can throw together a simple example (but I am busy right this moment).","Q_Score":2,"Tags":"python-2.7,fortran","A_Id":30748701,"CreationDate":"2015-06-09T15:38:00.000","Title":"Data exchange - Python and Fortran","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I just installed a intel MKL optimized version of scipy and when running my benchmarks, I got remarkable speedup with it. I then looked closer and found out it was running on 20 cores ... how do I restrict it to single threaded mode? Is there a way I could have installed it to single threaded mode by default, while leaving the option open to run on a specified number of cores?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":225,"Q_Id":30736765,"Users Score":2,"Answer":"Upon further investigation it looks you are able to set the environment variable MKL_NUM_THREADS to achieve this.","Q_Score":2,"Tags":"python,multithreading,numpy,intel,intel-mkl","A_Id":30737140,"CreationDate":"2015-06-09T15:41:00.000","Title":"Restrict MKL optimized scipy to single thread","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"scipy.sparse.coo_matrix.max returns the maximum value of each row or column, given an axis. I would like to know not the value, but the index of the maximum value of each row or column. I haven't found a way to make this in an efficient manner yet, so I'll gladly accept any help.","AnswerCount":6,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":4988,"Q_Id":30742572,"Users Score":2,"Answer":"From scipy version 0.19, both csr_matrix and csc_matrix support argmax() and argmin() methods.","Q_Score":13,"Tags":"python,scipy,sparse-matrix","A_Id":43092480,"CreationDate":"2015-06-09T20:50:00.000","Title":"Argmax of each row or column in scipy sparse matrix","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I wish to know the default number of iterations in gensim's LDA (Latent Dirichlet Allocation) algorithm. I don't think the documentation talks about this. (Number of iterations is denoted by the parameter iterations while initializing the LdaModel ). Thanks !","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":6280,"Q_Id":30745184,"Users Score":0,"Answer":"The default number of iterations = 50","Q_Score":3,"Tags":"python,topic-modeling,gensim","A_Id":47763498,"CreationDate":"2015-06-10T00:37:00.000","Title":"Gensim LDA - Default number of iterations","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Hi FolksI have been working on a python module which will convert a binary string into a CSV record. A 3rd Party application does this usually, however I'm trying to build this logic into my code. The records before and after conversion are as follows:\nCSV Record After Conversion\n0029.6,000.87,002.06,0029.2,0010.6,0010.0,0002.1,0002.3,00120,00168,00054,00111,00130,00000,00034,00000,00000,00039,00000,0313.1,11:09:01,06-06-2015,00000169\nI'm trying to figure out the conversion logic that has been used by the 3rd party tool, if anyone can help me with some clues regarding this, it would be great! One thing I have analysed is that each CSV value corresponds to an unsigned short in the byte stream. TIA, cheers!","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":3548,"Q_Id":30750849,"Users Score":0,"Answer":"From the binary into meaningful strings, we must know that the binary code protocol We can't resolve the binary out of thin air.","Q_Score":0,"Tags":"python,csv,binary,data-conversion,hexdump","A_Id":30751250,"CreationDate":"2015-06-10T08:19:00.000","Title":"Binary to CSV record Converstion","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Hi FolksI have been working on a python module which will convert a binary string into a CSV record. A 3rd Party application does this usually, however I'm trying to build this logic into my code. The records before and after conversion are as follows:\nCSV Record After Conversion\n0029.6,000.87,002.06,0029.2,0010.6,0010.0,0002.1,0002.3,00120,00168,00054,00111,00130,00000,00034,00000,00000,00039,00000,0313.1,11:09:01,06-06-2015,00000169\nI'm trying to figure out the conversion logic that has been used by the 3rd party tool, if anyone can help me with some clues regarding this, it would be great! One thing I have analysed is that each CSV value corresponds to an unsigned short in the byte stream. TIA, cheers!","AnswerCount":3,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":3548,"Q_Id":30750849,"Users Score":1,"Answer":"As already mentioned, without knowing the binary protocol it will be difficult to guess the exact encoding that is being used. There may be special case logic that is not apparent in the given data.\nIt would be useful to know the name of the 3rd party application or a least what field this relates to. Anything to give an idea as to what the encoding could be.\nThe following are clues as you requested on how to proceed:\n\nThe end of the CSV shows a date, this can be seen at the start\n\n\n31 08 11 06 06 15 20 AA A8 00 00 00 28 01 57 00 CE 00 24 01 6A 00\n  64 00 15 00 17 00 78 00 A8 00 36 00 6F 00 82 00 00 00 22 00 00 00 00\n  00 27 00 00 00\n\n\nThe end value 169 (hex A9) is suspiciously in between the next two hex values\n\n\n31 08 11 06 06 15 20 AA A8 00 00 00 28 01 57 00 CE 00 24 01 6A 00\n  64 00 15 00 17 00 78 00 A8 00 36 00 6F 00 82 00 00 00 22 00 00 00 00\n  00 27 00 00 00\n\n\n\"00039,\" could refer to the last 4 digits\n\n\n31 08 11 06 06 15 20 AA A8 00 00 00 28 01 57 00 CE 00 24 01 6A 00 64\n  00 15 00 17 00 78 00 A8 00 36 00 6F 00 82 00 00 00 22 00 00 00 00 00\n  27 00 00 00\n\nor:\n\n31 08 11 06 06 15 20 AA A8 00 00 00 28 01 57 00 CE 00 24 01 6A 00 64\n  00 15 00 17 00 78 00 A8 00 36 00 6F 00 82 00 00 00 22 00 00 00 00 00\n  27 00 00 00 ....or 27 00 00 00\n\n...you guess two bytes are used so perhaps the others are separate 0 value fields.\n\n\"00034,\" could refer to:\n\n\n31 08 11 06 06 15 20 AA A8 00 00 00 28 01 57 00 CE 00 24 01 6A 00 64\n  00 15 00 17 00 78 00 A8 00 36 00 6F 00 82 00 00 00 22 00 00 00 00\n  00 27 00 00 00\n\nand so on... simply convert some of the decimal numbers into hex and search for possible locations in the data. Consider that fields might be big or little endian or a combination thereof.\nYou should take a look at the struct python library which can be useful in dealing with such conversions once you know the formatting that is being used.\nWith more data examples the above theories could then be tested.","Q_Score":0,"Tags":"python,csv,binary,data-conversion,hexdump","A_Id":30752427,"CreationDate":"2015-06-10T08:19:00.000","Title":"Binary to CSV record Converstion","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"This problem may seem a little strange, but a while (about 1-2 weeks) ago I wrote a little Python script which I tested and everything worked just fine. Today when I was taking lines from this latter script, the lines would run without any error in the Spyder IDE Python console, but when I try to put those same line in a new .py file, Spyder gives me errors!\nSo I tried to compile the old script again, then I got errors!  \nA few examples to maybe clear things up:  \n\nI load and image using PIL Image:  im = Image.open(\"test.jpg\")\nthen in the Spyder console I can do: im.layers which gives me the number of color channels. Even though this attribute doesn't exist in PIL Image docs!\nBut using this same attribute in a python file would give an error!  \nUsing: a = array( [ [ 1, 2, 3], [4, 5, 6], [...] ] ) I can create a 2D array (or matrix). This is possible through Spyder, but not regular Python interpreter (which leads to NameError: global name 'array' is not defined\n)!\n\nAnd a few more examples like these.\nCould anyone help me understand what is going on, knowing that I'm sort of a Python noob?\nPython version: 2.7.6 | GCC 4.8.2 | Spyder 2.2.5","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1345,"Q_Id":30754748,"Users Score":2,"Answer":"Spyder 2.2.5 is an older version (the last version is 2.3.4).  When it is started automatically imports numpy and matplotlib.  The regular Python interpreter  needs an explicit import numpy as np in order to define an array, A=np.array([[1,2,3], [4,5,6]]).","Q_Score":0,"Tags":"python,spyder","A_Id":30756777,"CreationDate":"2015-06-10T11:10:00.000","Title":"Compilation difference in Spyder IDE and Python intrepeter","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have implemented the baum-welch algorithm in python but I am now encountering a problem when attempting to train HMM (hidden markov model) parameters A,B, and pi. The problem is that I have many observation sequences Y = (Y_1=y_1, Y_2=y_2,...,Y_t=y_t). And each observation variable Y_t can take on K possible values, K=4096 in my case. Luckily I only have two states N=2, but my emission matrix B is N by K so 2 rows by 4096 columns.\nNow when you initialize B, each row must sum to 1. Since there are 4096 values in each of the two rows, the numbers are very small. So small that when I go to compute alpha and beta their rows eventually approach 0 as t increases. This is a problem because you cannot compute gamma as it tries to compute x\/0 or 0\/0. How can I run the algorithm without it crashing and without permanently altering my values?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":869,"Q_Id":30768182,"Users Score":0,"Answer":"This sounds like the standard HMM scaling problem.  Have a look at \"A Tutorial on Hidden Markov Models ...\" (Rabiner, 1989), section V.A \"Scaling\".\nBriefly, you can rescale alpha at each time to sum to 1, and rescale beta using the same factor as the corresponding alpha, and everything should work.","Q_Score":0,"Tags":"python,hidden-markov-models","A_Id":30769647,"CreationDate":"2015-06-10T22:10:00.000","Title":"Baum-Welch many possible observations","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Using Python, how to create two(or more) color contour plots, each with their own color map, but overlaid into a single image with the same x and y axes?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":67,"Q_Id":30814133,"Users Score":0,"Answer":"You can save each layer into a PNG file with a transparent background and overlay them in Photoshop, Gimp or ImageMagick.","Q_Score":0,"Tags":"python,contour","A_Id":30814337,"CreationDate":"2015-06-13T01:03:00.000","Title":"How to make overlaid contour plots with python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am calling a function like this:\nclassifier = NaiveBayesClassifier.train(training_set)\nand I would like to debug the code inside the train() function. The problem is that if I add print statements or pdb calls nothing changes. \nI am importing this:\nfrom nltk.classify.naivebayes import NaiveBayesClassifier\nbut even if I change something in nltk\/classify\/naivebayes.py nothing happens. I can also delete all the content of this file and I still have a working output. So I suppose that the function I am calling is somewhere else, but I cannot find it.\nIs there a way to check where my function call is actually going? I am quite confused.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":50,"Q_Id":30820896,"Users Score":1,"Answer":"Step in the function using pdb.\nuse pdb.set_trace() some where before you are calling train method.\nSomething like this\nimport pdb; pdb.set_trace()\nclassifier = NaiveBayesClassifier.train(training_set)\nWhen you debug. Stop at the line where you are calling train method.\npress s to step in the function. This will take you inside the train function. From there you can debug normally.","Q_Score":0,"Tags":"python","A_Id":30821067,"CreationDate":"2015-06-13T16:23:00.000","Title":"How to find out what function I am calling","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm doing a data science course on udemy using python 2.7, running Anaconda. My OS is Ubuntu 14.04.\nI'm getting the following error running with the pandas module:\nTraceback (most recent call last):\n  File \"\/home\/flyveren\/PycharmProjects\/Udemy\/15_DataFrames.py\", line 13, in <module>\n    nfl_frame = pd.read_clipboard()\n  File \"\/home\/flyveren\/anaconda\/lib\/python2.7\/site-packages\/pandas\/io\/clipboard.py\", line 51, in read_clipboard\n    return read_table(StringIO(text), **kwargs)\n  File \"\/home\/flyveren\/anaconda\/lib\/python2.7\/site-packages\/pandas\/io\/parsers.py\", line 474, in parser_f\n    return _read(filepath_or_buffer, kwds)\n  File \"\/home\/flyveren\/anaconda\/lib\/python2.7\/site-packages\/pandas\/io\/parsers.py\", line 260, in _read\n    return parser.read()\n  File \"\/home\/flyveren\/anaconda\/lib\/python2.7\/site-packages\/pandas\/io\/parsers.py\", line 721, in read\n    ret = self._engine.read(nrows)\n  File \"\/home\/flyveren\/anaconda\/lib\/python2.7\/site-packages\/pandas\/io\/parsers.py\", line 1170, in read\n    data = self._reader.read(nrows)\n  File \"pandas\/parser.pyx\", line 769, in pandas.parser.TextReader.read (pandas\/parser.c:7544)\n  File \"pandas\/parser.pyx\", line 791, in pandas.parser.TextReader._read_low_memory (pandas\/parser.c:7784)\n  File \"pandas\/parser.pyx\", line 844, in pandas.parser.TextReader._read_rows (pandas\/parser.c:8401)\n  File \"pandas\/parser.pyx\", line 831, in pandas.parser.TextReader._tokenize_rows (pandas\/parser.c:8275)\n  File \"pandas\/parser.pyx\", line 1742, in pandas.parser.raise_parser_error (pandas\/parser.c:20691)\npandas.parser.CParserError: Error tokenizing data. C error: Expected 11 fields in line 5, saw 12\nI've tried conda uninstall pandas and subsequently conda install pandas again to see, however with the same result. The package is there, it tells me an error if I uninstall and try to run the code again with missing package, but it gives this error when it's properly installed.\nAnyone knows what's up?","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":332,"Q_Id":30861956,"Users Score":1,"Answer":"So, the solution was essentially to create a virtual environment and install the needed packages independently. Some issues with dependencies on my system, I believe.","Q_Score":0,"Tags":"python,python-2.7,ubuntu,pandas","A_Id":30865729,"CreationDate":"2015-06-16T08:03:00.000","Title":"Python 2.7 Anaconda Pandas error(Ubuntu 14.04)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm doing a data science course on udemy using python 2.7, running Anaconda. My OS is Ubuntu 14.04.\nI'm getting the following error running with the pandas module:\nTraceback (most recent call last):\n  File \"\/home\/flyveren\/PycharmProjects\/Udemy\/15_DataFrames.py\", line 13, in <module>\n    nfl_frame = pd.read_clipboard()\n  File \"\/home\/flyveren\/anaconda\/lib\/python2.7\/site-packages\/pandas\/io\/clipboard.py\", line 51, in read_clipboard\n    return read_table(StringIO(text), **kwargs)\n  File \"\/home\/flyveren\/anaconda\/lib\/python2.7\/site-packages\/pandas\/io\/parsers.py\", line 474, in parser_f\n    return _read(filepath_or_buffer, kwds)\n  File \"\/home\/flyveren\/anaconda\/lib\/python2.7\/site-packages\/pandas\/io\/parsers.py\", line 260, in _read\n    return parser.read()\n  File \"\/home\/flyveren\/anaconda\/lib\/python2.7\/site-packages\/pandas\/io\/parsers.py\", line 721, in read\n    ret = self._engine.read(nrows)\n  File \"\/home\/flyveren\/anaconda\/lib\/python2.7\/site-packages\/pandas\/io\/parsers.py\", line 1170, in read\n    data = self._reader.read(nrows)\n  File \"pandas\/parser.pyx\", line 769, in pandas.parser.TextReader.read (pandas\/parser.c:7544)\n  File \"pandas\/parser.pyx\", line 791, in pandas.parser.TextReader._read_low_memory (pandas\/parser.c:7784)\n  File \"pandas\/parser.pyx\", line 844, in pandas.parser.TextReader._read_rows (pandas\/parser.c:8401)\n  File \"pandas\/parser.pyx\", line 831, in pandas.parser.TextReader._tokenize_rows (pandas\/parser.c:8275)\n  File \"pandas\/parser.pyx\", line 1742, in pandas.parser.raise_parser_error (pandas\/parser.c:20691)\npandas.parser.CParserError: Error tokenizing data. C error: Expected 11 fields in line 5, saw 12\nI've tried conda uninstall pandas and subsequently conda install pandas again to see, however with the same result. The package is there, it tells me an error if I uninstall and try to run the code again with missing package, but it gives this error when it's properly installed.\nAnyone knows what's up?","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":332,"Q_Id":30861956,"Users Score":1,"Answer":"I watch same lecture at udemy and face same problem.\nI change my browser from internet explorer to chrome. (I'm using windows7 & VS2013 with PTVS)\nThen, error does not occur.\nHowever, delimeter has some problem.\nSpace should not be used as delimeter according to lecture, however, it does.\nSo, result is not perfect.","Q_Score":0,"Tags":"python,python-2.7,ubuntu,pandas","A_Id":40701927,"CreationDate":"2015-06-16T08:03:00.000","Title":"Python 2.7 Anaconda Pandas error(Ubuntu 14.04)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am writing a program that should spit out a random sentence of a complexity of my choosing. As a concrete example, I would like to aid my language learning by spitting out valid sentences of a grammar structure and using words that I have already learned. I would like to use python and nltk to do this, although I am open to other ideas.\nIt seems like there are a couple of approaches:\n\nDefine a grammar file that uses the grammar and lexicon I know about, and then generate all valid sentences from this list, then selecting a random answer.\nLoad in corpora to train ngrams, which then can be used to construct a sentence. \n\nAm I thinking about this correctly? Is one approach preferred over the other? Any tips are appreciated. Thanks!","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1204,"Q_Id":30868980,"Users Score":2,"Answer":"If I'm getting it right and if the purpose is to test yourself on the vocabulary you already have learned, then another approach could be taken:\nInstead of going through the difficult labor of NLG (Natural Language Generation), you could create a search program that goes online, reads news feeds or even simply Wikipedia,  and finds sentences with only the words you have defined.\nIn any case, for what you want, you will have to create lists of words that you have learned. You could then create search algorithms for sentences that contain only \/ nearly only these words.\nThat would have the major advantage of testing yourself on real sentences, as opposed to artificially-constructed ones (which are likely to sound not quite right in a number of cases).\nAn app like this would actually be a great help for learning a foreign language. If you did it nicely I'm sure a lot of people would benefit from using it.","Q_Score":3,"Tags":"python,nltk,n-gram,sentence","A_Id":30869829,"CreationDate":"2015-06-16T13:27:00.000","Title":"Generate Random Sentence From Grammar or Ngrams?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I was working with Apache Log File. And I created RDD with tuple (day,host) from each log line. Next step was to Group up host and then display the result.\nI used distinct() with mapping of first RDD into (day,host) tuple. When I don't use distinct I get different result as when I do. So how does a result change when using distinct() in spark??","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":172,"Q_Id":30886340,"Users Score":0,"Answer":"I think when you only use map action on FIRST_RDD(logs) you will get SECOND_RDD count of new this SECOND_RDD will be equal to count of FIRST_RDD.But if you use distinct on SECOND_RDD, count will decrease to number of distinct tuples present in SECOND_RDD.","Q_Score":0,"Tags":"python,apache-spark","A_Id":30887058,"CreationDate":"2015-06-17T08:35:00.000","Title":"How does result changes by using .distinct() in spark?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a CSV file with over 4,000 lines formatted like...\nname, price, cost, quantity\nHow do I trim my CSV file so only the 20 names I want remain? I am able to parse\/trim the CSV file, I am coming up blank on how to search column 1.","AnswerCount":4,"Available Count":1,"Score":0.049958375,"is_accepted":false,"ViewCount":145,"Q_Id":30902045,"Users Score":1,"Answer":"You can loop though the csv.reader(). It will return you rows. Rows consist of lists. Compare the first element of the list ie row[0]. If it is what you want, add the row to an output list.","Q_Score":3,"Tags":"python","A_Id":30902169,"CreationDate":"2015-06-17T20:44:00.000","Title":"How to parse CSV file and search by item in first column","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to implement Naive Bayes classifier in Python. My attributes are of different data types : Strings, Int, float, Boolean, Ordinal \nI could use Gaussian Naive Bayes classifier (Sklearn.naivebayes : Python package) , But I do not know how the different data types are to be handled. The classifier throws an error, stating cannot handle data types other than Int or float\nOne way I could possibly think of is encoding the strings to numerical values. But I also doubt , how good the classifier would perform if I do this.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":3764,"Q_Id":30937667,"Users Score":3,"Answer":"Yes, you will need to convert the strings to numerical values\nThe naive Bayes classifier can not handle strings as there is not a way an string can enter in a mathematical equation.  \nIf your strings have some \"scalar value\" for example \"large, medium, small\" you might want to classify them as \"3,2,1\",\nHowever, if your strings are things without order such as colours or names, you can do this or assign binary variables with every variable referring to a colour or name, if they are not many.\nFor example if you are classifying cars an they can be red blue and green you can define the variables 'Red' 'Blue' 'Green' that take the values 0\/1, depending on the colour of your car.","Q_Score":1,"Tags":"python,scikit-learn,gaussian,naivebayes","A_Id":30938653,"CreationDate":"2015-06-19T12:01:00.000","Title":"NaiveBayes classifier handling different data types in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In Pandas, why does a TimedeltaProperties object have no attribute 'years'?\nAfter all, the datetime object has this property.  \nIt seems like a very natural thing for an object that is concerned with time to have. Especially if it already has an hours, seconds, etc attribute.\nIs there a workaround so that my column, which is full of values like\n10060 days,\ncan be converted to years?  Or better yet, just converted to an integer representation for years?","AnswerCount":3,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":19665,"Q_Id":30950198,"Users Score":6,"Answer":"This workaround gets you closer.\nround((df[\"Accident Date\"] - df[\"Iw Date Of Birth\"]).dt.days \/ 365, 1)","Q_Score":14,"Tags":"python,datetime,pandas","A_Id":54027432,"CreationDate":"2015-06-20T04:16:00.000","Title":"AttributeError: 'TimedeltaProperties' object has no attribute 'years' in Pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am interested to implement LZ algorithms for the compression of ECG signal and want to optimized the code with relevant to Micro controller. \nSo that it would Entropy efficient and take less time to compress and decompress the ECG signal. I am totally stuck how I go through to achieve this. I am open to any programming language. \nI have searched the internet for the source codes and I found a very long code which is difficult to understand in a short period of time.\nAny suggestion...?","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":339,"Q_Id":30960617,"Users Score":2,"Answer":"Reconsider your choice of the LZ 77\/78 algorithms. ECG waves look similar but they are not binary identical so the dictionary-based compression algorithms don't provide ideal results.\nComplicated algorithms can hardly be expressed in few lines of code.","Q_Score":1,"Tags":"python,c,matlab,lzw,lz77","A_Id":30961062,"CreationDate":"2015-06-21T02:07:00.000","Title":"LZ 77, 78 algorithm for ECG Compression","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have code that runs basic TF-IDF vectorizer on a collection of documents, returning a sparse matrix of D X F where D is the number of documents and F is the number of terms. No problem.\nBut how do I find the TF-IDF score of a specific term in the document? i.e. is there some sort of dictionary between terms (in their textual representation) and their position in the resulting sparse matrix?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":11861,"Q_Id":30976120,"Users Score":0,"Answer":"@kinkajou, No, TF and IDF are not same but they belong to the same algorithm- TF-IDF, i.e Term frequency Inverse document Frequency","Q_Score":7,"Tags":"python,scikit-learn,tf-idf","A_Id":56899773,"CreationDate":"2015-06-22T09:13:00.000","Title":"Find the tf-idf score of specific words in documents using sklearn","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I wonder if it is implemented in networkx.drawing to hold one dimension during the layout optimization with a predefined position array.\nLets say you want to optimize the layout of a graph and have the x dimension of the positions already given, so you only want to optimize the y directions of the vertices. \nSo far I've only noticed, that one can hold positions of certain vertices, but then of course non of those are being moved.\nIn the Python package grandalf, they have DicgoLayout, so I'd expect something similar in networkx.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":380,"Q_Id":30987425,"Users Score":0,"Answer":"I ended up with forking the spring layout with a hold_dim parameter, so during updating the positions only x or y is being changed.","Q_Score":0,"Tags":"python,networkx","A_Id":31006818,"CreationDate":"2015-06-22T18:40:00.000","Title":"Drawing layout with constraints in networkx","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In a standard 3D python plot, each data point is, by default, represented as a sphere in 3D.  For the data I'm plotting, the z-axis is very sensitive, while the x and y axes are very general, so is there a way to make each point on the scatter plot spread out over the x and y direction as it normally would with, for example, s=500, but not spread at all along the z-axis?  Ideally this would look like a set of stacked discs, rather than overlapping spheres.  \nAny ideas?  I'm relatively new to python and I don't know if there's a way to make custom data points like this with a scatter plot.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":612,"Q_Id":30990501,"Users Score":1,"Answer":"I actually was able to do this using the matplotlib.patches library, creating a patch for every data point, and then making it whatever shape I wanted with the help of mpl_toolkits.mplot3d.art3d.","Q_Score":0,"Tags":"python,matplotlib,3d,data-visualization","A_Id":31486223,"CreationDate":"2015-06-22T21:54:00.000","Title":"How to make data points in a 3D python scatter plot look like \"discs\" instead of \"spheres\"","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have 10 CSV files with million records. I want to read the 10 files in parallel, but with a specific rate (10 records per 5 sec). What is the efficient way to do so ?. I am using Windows in case someone will suggest to use OS scheduler","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":90,"Q_Id":30992964,"Users Score":0,"Answer":"Open 10 files.\nRead 1 record from each file (or 10, the question is not clear).\nUse these records.\nWait until the current 5 second interval elapses.\nGo to 2.","Q_Score":0,"Tags":"python,file,python-2.7","A_Id":30993345,"CreationDate":"2015-06-23T02:38:00.000","Title":"Read many files in parallel with a specific sample rate","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am writing a program with a while loop, which would write giant amount of data into a csv file. There maybe more than 1 million rows.\nConsidering running time, memory usage, debugging and so on, what is the better option between the two:\n\nopen a CSV file, keep it open and write line by line, until the 1 million all written\nOpen a file, write about 100 lines, close(), open again, write about 100 lines, ......\n\nI guess I just want to know would it take more memories if we're to keep the file open all the time? And which one will take longer? \nI can't run the code to compare because I'm using a VPN for the code, and testing through testing would cost too much $$ for me. So just some rules of thumb would be enough for this thing.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2830,"Q_Id":31009284,"Users Score":2,"Answer":"I believe the write will immediately write to the disk, so there isn't any benefit that I can see from closing and reopening the file. The file isn't stored in memory when it's opened, you just get essentially a pointer to the file, and then load or write a portion of it at a time.  \nEdit\nTo be more explicit, no, opening a large file will not use a large amount of memory. Similarly writing a large amount of data will not use a large amount of memory as long as you don't hold the data in memory after it has been written to the file.","Q_Score":3,"Tags":"python,csv","A_Id":31009436,"CreationDate":"2015-06-23T17:16:00.000","Title":"When writing large data into .csv file, is it better to open and close file often?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to save a dataframe to a .csv file with the name '123\/123', but it will split it in to two strings if I just type like df.to_csv('123\/123.csv'). \nAnyone knows how to keep the slash in the name of the file?","AnswerCount":4,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":373,"Q_Id":31022379,"Users Score":0,"Answer":"You can not use any of these chars in the file name ;\n\/:*?\\\"|","Q_Score":0,"Tags":"python,pandas","A_Id":31022952,"CreationDate":"2015-06-24T09:09:00.000","Title":"How to save a dataframe as a csv file with '\/' in the file name","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to simulate a particle bouncing off the sides of a cylinder (inside) or any closed curved surface in 3 dimensions. \nAt the moment of interaction with the surface, I have the position vector, the velocity vector, and the plane tangent to the surface at the intersection, and I am looking to derive the new velocity vector. \nCurrently coding in Python, but pseudocode\/general algorithm would be immensely helpful as well.","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":222,"Q_Id":31058446,"Users Score":0,"Answer":"Get the normal vector of the plane. (you can make a cross product between two non parallel vectors in the plane for that).\nTake the velocity vector components relative to the normal vector and:\n\nMake parallel component negative\nKeep perpendicular components the same. \n\nFollowing an approach similar to @jacdeh:\n\nNormalize the normal vector     \nMake the inner product of velocity with unit normal  (that is the scalar speed against the surface)      \nMultiply the inner product with unit normal (that is the parallel component I mentioned).      \nSubtract 2 times that component from velocity and this is the result.","Q_Score":2,"Tags":"python,3d,geometry,computational-geometry","A_Id":31058586,"CreationDate":"2015-06-25T18:49:00.000","Title":"How to model particle bouncing off of a curved surface in 3D?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to simulate a particle bouncing off the sides of a cylinder (inside) or any closed curved surface in 3 dimensions. \nAt the moment of interaction with the surface, I have the position vector, the velocity vector, and the plane tangent to the surface at the intersection, and I am looking to derive the new velocity vector. \nCurrently coding in Python, but pseudocode\/general algorithm would be immensely helpful as well.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":222,"Q_Id":31058446,"Users Score":0,"Answer":"Compute normal vector as cross product of two tangent vectors to your surface\nNormalize it to get unit normal\nCompute inner product (dot product) of minus velocity and normal vector\nMultiply inner product with unit normal to get projection on normal\nSubtract projection on normal from minus velocity\nAdd that to projection to get new speed vector","Q_Score":2,"Tags":"python,3d,geometry,computational-geometry","A_Id":31058674,"CreationDate":"2015-06-25T18:49:00.000","Title":"How to model particle bouncing off of a curved surface in 3D?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there any way to determine where a class is coming from in python (especially sklearn)?  I want to determine if a class is from sklearn.linear_models or sklearn.ensemble.  \nAs an example, I would like to be able to determine if Ridge() is a member of sklearn.linear_model.\nThe fit function is a bit different depending on the model so formulas fed to each via patsy need to be different.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":62,"Q_Id":31062620,"Users Score":6,"Answer":"Use the __module__ attribute, i.e.: Ridge.__module__\nIf you want to know it from an instance of the class: inst.__class__.__module__\nIf you need the module object (not just the name as string): sys.modules[Ridge.__module__]","Q_Score":4,"Tags":"python,scikit-learn","A_Id":31062641,"CreationDate":"2015-06-25T23:43:00.000","Title":"Determine where a class is imported from in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm writing a parsing script in python and am curious as to how I could create a graph with two axis's under different data ranges.\nIs this possible in openpyxl?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":196,"Q_Id":31079888,"Users Score":0,"Answer":"This will be possible in version 2.3.","Q_Score":0,"Tags":"python,excel,parsing,openpyxl","A_Id":31088239,"CreationDate":"2015-06-26T18:30:00.000","Title":"Multi-Axis Graph using the OpenPyxl library in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I installed matplotlib using (pip install --user matplotlib) and the installation was successful. when I try to import it using (import matplotlib) in python shell I get this error: \nTraceback (most recent call last):\n  File \"\", line 1, in \n    import matplotlib\nImportError: No module named matplotlib\nI couldn't find another question similar to mine because I'm not using anaconda.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":3341,"Q_Id":31092232,"Users Score":0,"Answer":"Try pip uninstall matplotlib then python -mpip install matplotlib see if it helps. Otherwise, i'd recommend using a virtual environment which you can install using sudo easy_install virtualenv or pip install virtualenv This way you could just install and use matplotlib by following these steps:\n\nCreate a new virtual environment : user@yourmac$ virtualenv .env (.env can be any name or directory)\nActivate it by doing : user@yourmac$ source .env\/bin\/activate\nThen once it's activated just do a pip install matplotlib and use it to your satisfaction.\n\nHopefully this should solve your problem.","Q_Score":1,"Tags":"python,matplotlib,import,module,importerror","A_Id":52132286,"CreationDate":"2015-06-27T18:41:00.000","Title":"Cant import matplotlib on python 2.7.5","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Numpy is a library for efficient numerical arrays.\nmpmath, when backed by gmpy, is a library for efficient multiprecision numbers.\nHow do I put them together efficiently? Or is it already efficient to just use a Numpy array with mpmath numbers?\nIt doesn't make sense to ask for \"as efficient as native floats\", but you can ask for it to be close to the efficiency of equivalent C code (or, failing that, Java\/C# code). In particular, an efficient array of multi-precision numbers would mean that you can do vectorized operations and not have to look up, say, __add__ a million times in the Global Interpreter.\nEdit: To the close voter: My question is about an efficient way of putting them together. The answer in the possible duplicate specifically points out that the naive approach is not efficient.\n\nHaving a numpy array of dtype=object can be a liitle misleading, because the powerful numpy machinery that makes operations with the standard dtypes super fast, is now taken care of by the default object's python operators, which means that the speed will not be there anymore","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":807,"Q_Id":31093986,"Users Score":0,"Answer":"As far as I'm aware, there is no existing Python library that supports vectorized array operations on multiple precision values. There's unfortunately no particularly efficient way to use multiple precision values within a numpy ndarray, and it's extremely unlikely that there ever will be, since multiple precision values are incompatible with numpy's basic array model.\nEach element in a floating point numpy ndarray takes up the same number of bytes, so the array can be represented in terms of the memory address of the first element, the dimensions, and a regular byte offset (or stride) between consecutive array elements.\nThis scheme has significant performance benefits - adjacent array elements are located at adjacent memory addresses, so sequential reads\/writes to an array benefit from better locality of reference. Striding is also very important for usability, since it allows you to do things like operating on views of the same array without creating new copies in memory. When you do x[::2], you are really just doubling the stride over the first axis of the array, such that you address every other element.\nBy contrast, an array containing multiple precision values would have to contain elements of unequal size, since higher precision values would take up more bytes than low-precision values. A multiple precision array therefore cannot be regularly strided, and loses out on the benefits mentioned above.\nIn addition to the problems with constructing arrays, even plain arithmetic on multiple precision scalars is likely to be much slower than for floating point scalars. Almost all modern processors have specialized floating point units, whereas multiple precision arithmetic must be implemented in software rather than hardware.\nI suspect that these performance issues might be a big part of the reason why there isn't already a Python library that provides the functionality you're looking for.","Q_Score":1,"Tags":"python,numpy,mpmath,gmpy","A_Id":31280601,"CreationDate":"2015-06-27T22:05:00.000","Title":"Efficient multiple precision numerical arrays","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"As matlab2015a has been able to support python. But it seems it can only call standard library in matlab. If I want to import other library such as numpy, scipy or sklearn, what should I do? And can I execute the python script directly. Unfortunately, the offical document of matlab has not given enough explanations. If anyone can explain, I will be really appreciated!","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":88,"Q_Id":31096849,"Users Score":2,"Answer":"Matlab comes with it's own python Installation which is located in your Matlab installation directory. There these 3rd party libraries are probably not installed. \nGo to the python folder inside the Matlab installation directory, search for pip and use it to install the libraries you need.","Q_Score":2,"Tags":"python,matlab","A_Id":31097467,"CreationDate":"2015-06-28T06:33:00.000","Title":"call python from matlab2015a","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to solve some system in the form of matrices using linalg, but the resulting solutions should sum up to 1. For example, suppose there are 3 unknowns, x, y, z. After solving the system their values should sum up to 1, like .3, .5, .2. Can anyone please tell me how I can do that? \nCurrently, I am using something like result = linalg.solve(A, B), where A and B are matrices. But this doesn't return solutions in the range [0, 1].","AnswerCount":3,"Available Count":1,"Score":0.1325487884,"is_accepted":false,"ViewCount":9737,"Q_Id":31098228,"Users Score":2,"Answer":"You could add a row consisting of ones to A and add one to B. After that use \nresult = linalg.lstsq(A, B)[0]\nOr you can replace one of A's rows to row consisting of ones, also replace value in B to one in the same row. Then use \nresult = linalg.solve(A, B)","Q_Score":7,"Tags":"python,numpy,matrix,equation-solving","A_Id":48433206,"CreationDate":"2015-06-28T09:40:00.000","Title":"Solving system using linalg with constraints","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a CSV file with approximately 700 columns and 10,000 rows.  Each of these columns contains attribute information for the object in column 1 of each row.  I would like to search through this \"database\" for specific records that match a set of requirements based on their attribute information.\nFor instance, one column contains state information in the 2 letter abbreviation form.  Another column might contain an acronym referring to a certain geographic characteristic.  Suppose I'm looking for all rows where the state is NY, and the acronym in GRG.  \nWhat packages should I use to handle this work\/data anlaysis in R?\nIf there are no good packages in R, for handling such a large dataset, what should I look to using?\nI am familiar with R, Python, Office and some SQL commands.\nEdit:  I am not going to modify the dataset, but record (print out or create a subset from) the results of the querying.  I'll have a total of 10-12 queries at first to determine if this dataset truly serves my need. But I may possibly have hundreds of queries later - at which point I'd like to switch from manual querying of the dataset to an automated querying (if possible).","AnswerCount":3,"Available Count":1,"Score":0.1325487884,"is_accepted":false,"ViewCount":104,"Q_Id":31130348,"Users Score":2,"Answer":"Depending how much data is in each column and if you're planning to do statistical analysis, I would definitely go with R. If no analysis then python with pandas is a good solution. Do not use office for those files, it'll give you a headache.\nIf you're brave and your data is going to increase, implement MongoDB with either R or python depending on previous need.","Q_Score":1,"Tags":"python,r,database,csv","A_Id":31130479,"CreationDate":"2015-06-30T05:37:00.000","Title":"Does it make sense to use R to read in and search through extremely large CSV file?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have python scripts (no Spark here) producing some data files, that I want to be readable easily as Dataframes in a scala\/spark application.\nWhat's the best choice ?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":57,"Q_Id":31131855,"Users Score":1,"Answer":"If your data doesn't have newlines in then a simple text-based format such as TSV is probably best.\nIf you need to include binary data then a separated format like protobuf makes sense - anything for which a hadoop InputFormat exists should be fine.","Q_Score":0,"Tags":"python,scala,apache-spark","A_Id":31134116,"CreationDate":"2015-06-30T07:13:00.000","Title":"A file format writable by python, readable as a Dataframe in Spark","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How can I change the sort order of files in the notebook list? I would like it to be alphabetical for all file types and not case sensitive. Directories can appear before files or within the files. \nCurrently my list is sorted as follows:\n- Directories (starting with upper case)\n- directories (starting with lower case)\n- IPython notebooks (starting with upper case)\n- ipython noteooks (starting with lower case)\n- Other files (starting with upper case)\n- other files (staring with lower case)\nI'm using Jupyter 3.0.0-f75fda4","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":2153,"Q_Id":31142810,"Users Score":1,"Answer":"If you are handy with jQuery, you can edit IPython's static files and customize the \"tree\" page you are referring to.\nOn my Ubuntu machine I could manipulate the page by editing the file \/usr\/local\/lib\/python2.7\/dist-packages\/IPython\/html\/static\/tree\/js\/notebooklist.js","Q_Score":7,"Tags":"ipython-notebook,jupyter","A_Id":31546622,"CreationDate":"2015-06-30T15:46:00.000","Title":"Sort (sorting) order of files in notebook list","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"The context is : I already have clusters of words (phrases actually) resulting from kmeans applied to internet search queries and using common urls in the results of the search engine as a distance (co-occurrence of urls rather than words if I simplify a lot).\nI would like to automatically label the clusters using semantics, in other words I'd like to extract the main concept surrounding a group of phrases considered together. \nFor example - sorry for the subject of my example - if I have the following bunch of queries : ['my husband attacked me','he was arrested by the police','the trial is still going on','my husband can go to jail for harrassing me ?','free lawyer']\nMy study deals with domestic violence, but clearly this cluster is focused on the legal aspect of the problem so the label could be \"legal\" for example.\nI am new to NPL but I have to precise that I don't want to extract words using POS tagging (or at least this is not the expected final outcome but maybe a necessary preliminary step).\nI read about Wordnet for sense desambiguation and I think that might be a good track, but I don't want to calculate similarity between two queries (since the clusters are the input) nor obtain the definition of one selected word thanks to the context provided by the whole bunch of words (which word to select in this case ?). I want to use the whole bunch of words to provide a context (maybe using synsets or categorization with the xml structure of the wordnet) and then summarize the context in one or few words.\nAny ideas ? I can use R or python, I read a little about nltk but I don't find a way to use it in my context.","AnswerCount":5,"Available Count":1,"Score":0.1586485043,"is_accepted":false,"ViewCount":3398,"Q_Id":31148582,"Users Score":4,"Answer":"Your best bet is probably is to label the clusters manually, especially if there are few of them. This a difficult problem even for humans to solve, because you might need a domain expert. Anyone claiming they could do that automatically and reliably (except in some very limited domains) is probably running a startup and trying to get your business.\nAlso, going through the clusters yourself will have benefits. 1) you may discover you had the wrong number of clusters (k parameter) or that there was too much junk in the input to begin with. 2) you will gain qualitative insight into what is being talked about and what topic there are in the data (which you probably can't know before looking at the data). Therefore, label manually if qualitative insight is what you are after. If you need quantitative result too, you could then train a classifier on the manually labelled topics to 1) predict topics for the rest of the clusters, or 2) for future use, if you repeat the clustering, get new data, ...","Q_Score":9,"Tags":"python,r,nlp,nltk,wordnet","A_Id":31157260,"CreationDate":"2015-06-30T21:11:00.000","Title":"How to automatically label a cluster of words using semantics?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to change the way that random forest algorithm using in subsetting features for every node. The original algorithm as it is implemented in Scikit-learn way is randomly subsetting. I want to define which subset for every new node from several choices of several subsets. Is there direct way in scikit-learn to control such method? If not, is there any way to update the same code of Scikit-learn? If yes, which function in the source code is what you think should be updated?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":525,"Q_Id":31188415,"Users Score":1,"Answer":"Short version: This is all you.\nI assume by \"subsetting features for every node\" you are referring to the random selection of a subset of samples and possibly features used to train individual trees in the forest. If that's what you mean, then you aren't building a random forest; you want to make a nonrandom forest of particular trees.\nOne way to do that is to build each DecisionTreeClassifier individually using your carefully specified subset of features, then use the VotingClassifier to combine the trees into a forest. (That feature is only available in 0.17\/dev, so you may have to build your own, but it is super simple to build a voting classifier estimator class.)","Q_Score":2,"Tags":"python-3.x,scikit-learn,random-forest","A_Id":31191159,"CreationDate":"2015-07-02T15:12:00.000","Title":"How to control feature subsetting in random forest in scikit-learn?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on a Python program that processes map data from Openstreetmap, and I need to be able to identify pairs of streets (ways) that are close to each other and parallel. Right now, the basic algorithm I'm using is quite inefficient:\n\nPut all of the streets (Street objects) into a large list\nFind every possible pair of two streets in the list using nested for loops; for each pair, draw a rectangle around the two streets and calculate the angle at which each street is oriented.\nIf the rectangles overlap, the overlapping area is big enough, and the angles are similar, the two streets in the pair are considered parallel and close to each other.\n\nThis works well for small maps but with large maps, the biggest problem obviously is that there would be a huge number of pairs to iterate through since there could be thousands of streets in a city. I want to be able to run the program on a large area (like a city) without having to split the area into smaller pieces.\nOne idea I'm thinking of is sorting the list of streets by latitude or longitude, and only comparing pairs of streets that are within, say, 50 positions away from each other in the list. It would probably be more efficient but it still doesn't seem very elegant; is there any better way?\nEach Street is composed of Node objects, and I can easily retrieve both the Node objects and the lat\/long position of each Node. I can also easily retrieve the angle at which a street is oriented.","AnswerCount":2,"Available Count":1,"Score":0.2913126125,"is_accepted":false,"ViewCount":750,"Q_Id":31192752,"Users Score":3,"Answer":"Your idea of processing the segments in bins is not bad. You do need to think through what happens to road segments that traverse bin boundaries.\nAnother idea is to Hough transform all the road segments. The infinite line that each segment lies on corresponds to a point in 2d Hough space: the polar angle of the line is one axis and the distance to the origin of the line's nearest point is the other.  The transformation from two points on a line to a Hough point is simple algebra. \nNow you can detect nearly co-linear road segments by using a closest point pair algorithm. Happily this can be done in O(n log n) expected time. E.g. using a k-d tree. Insert all the points in the tree. Use the standard k-d tree algorithm to find each point's nearest neighbor. Sort the pair distances and take a prefix of the result as pairs to consider, stopping where the pairs are too far apart to meet your criterion of \"nearby and parallel\".  There are O(n) of such nearest neighbor pairs.\nAll that's left is to filter out segment pairs that - though nearly co-linear - don't overlap. These segments lie on or near different parts of the same infinite line, but they're not of interest. This is just a little more algebra.\nThere are reasonably good Wikipedia articles on all of the algorithms mentioned here. Look them up if they're not familiar.","Q_Score":4,"Tags":"python,algorithm,openstreetmap","A_Id":31197249,"CreationDate":"2015-07-02T19:10:00.000","Title":"Efficient algorithm for finding pairs of nearby, parallel streets in a map","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have to deal with a large data-set. I need to store term frequency of each sentence; which I can do either using a dictionary list or using NumPy array. \nBut, I will have to sort and append (in case the word already exists)- Which will be better in this case?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":1345,"Q_Id":31202124,"Users Score":1,"Answer":"As you mention in the comments you don't know the size of the words\/tweets matrix that you will eventually obtain, so that makes using an array a cumbersome solution.\nIt feels more natural to use a dictionary here, for the reasons you noted.\nThe keys of the dictionary will be the words in the tweets, and the values can be lists with (tweet_id, term_frequency) elements.\nEventually you might want to do something else (e.g. classification) with your term frequencies. I suspect this is why you want to use a numpy array from the start.\nIt should not be too hard to convert the dictionary to a numpy array afterwards though, if that is what you wish to do. \nHowever note that this array is likely to be both very big (1M * number of words) and very sparse, which means it will contain mostly zeros. \nBecause this numpy array will take a lot of memory to store a lot of zeros, you might want to look at a data structure that is more memory efficient to store sparse matrix (see scipy.sparse).\nHope this helps.","Q_Score":6,"Tags":"python,sorting,numpy,dictionary,scikit-learn","A_Id":31213214,"CreationDate":"2015-07-03T08:29:00.000","Title":"NumPy or Dictionary?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am looking for any existing code for CreditGrades model.\nAny python code is OKAY.\nAlso, I will need numeric differentiation for hedge ratio. \nAny suggestion on numeric differentiation in python.\nThanks a lot.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":140,"Q_Id":31219085,"Users Score":0,"Answer":"I guess I can try Symbolic differentiation. Also I can use SymPy with NumPy.","Q_Score":0,"Tags":"python-3.x","A_Id":31219138,"CreationDate":"2015-07-04T09:30:00.000","Title":"Is there any python code for CreditGrades model?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am making an application for multilabel text classification .\nI've tried different machine learning algorithm.\nNo doubt the SVM with linear kernel gets the best results.\nI have also tried to sort through the algorithm Radom Forest and the results I have obtained have been very bad, both the recall and precision are very low.\nThe fact that the linear kernel to respond better result gives me an idea of the different categories are linearly separable.\nIs there any reason the Random Forest results are so low?","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":2289,"Q_Id":31225935,"Users Score":1,"Answer":"It is very hard to answer this question without looking at the data in question.\nSVM does have a history of working better with text classification - but machine learning by definition is context dependent.\nConsider the parameters by which you are running the random forest algorithm. What are your number and depth of trees, are you pruning branches? Are you searching a larger parameter space for SVMs therefore are more likely to find a better optimum.","Q_Score":0,"Tags":"python,machine-learning,svm,random-forest,text-classification","A_Id":31226011,"CreationDate":"2015-07-04T23:30:00.000","Title":"Random Forest for multi-label classification","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am making an application for multilabel text classification .\nI've tried different machine learning algorithm.\nNo doubt the SVM with linear kernel gets the best results.\nI have also tried to sort through the algorithm Radom Forest and the results I have obtained have been very bad, both the recall and precision are very low.\nThe fact that the linear kernel to respond better result gives me an idea of the different categories are linearly separable.\nIs there any reason the Random Forest results are so low?","AnswerCount":2,"Available Count":2,"Score":0.1973753202,"is_accepted":false,"ViewCount":2289,"Q_Id":31225935,"Users Score":2,"Answer":"The ensemble of the random forest performs well across many domains and types of data.   They are excellent at reducing error from variance and don't over fit if trees are kept simple enough. \nI would expect a forest to perform comparably to a SVM with a linear kernel. \nThe SVM will tend to overfit more because it does not benefit from being an ensemble.   \nIf you are not using cross validation of some kind.   At minimum measuring performance on unseen data using a test\/training regimen than i could see you obtaining this type of result. \nGo back and make sure performance is measured on unseen data and likelier you'll see the RF performing more comparably.\nGood luck.","Q_Score":0,"Tags":"python,machine-learning,svm,random-forest,text-classification","A_Id":31234627,"CreationDate":"2015-07-04T23:30:00.000","Title":"Random Forest for multi-label classification","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an issue with matplotlib and multiprocessing.\nI launch a first process, where I display an image and select an area, and close the figure. Then I launch another process, where I call a graph function that is regularly updated. Up this point, eveything works fine.\nThen when I try to launch another process with the SAME graph function, it freeze my whole computer, BUT the background processes stil work...\nI only have one of these errors (it's not always the same):\nerror 1 :\n\nXIO:  fatal IO error 25 (Inappropriate ioctl for device) on X server\n  \":0.0\"\n        after 4438 requests (4438 known processed) with 30 events remaining. XIO:  fatal IO error 11 (Resource temporarily unavailable)\n  on X server \":0.0\"\n        after 4443 requests (4443 known processed) with 31 events remaining. [xcb] Unknown sequence number while processing queue [xcb]\n  Most likely this is a multi-threaded client and XInitThreads has not\n  been called [xcb] Aborting, sorry about that. python:\n  ..\/..\/src\/xcb_io.c:274: poll_for_event: Assertion\n  `!xcb_xlib_threads_sequence_lost' failed.\n\nerror 2 :\n\nX Error of failed request:  BadIDChoice (invalid resource ID chosen\n  for this connection)   Major opcode of failed request:  53\n  (X_CreatePixmap)   Resource id in failed request:  0x5600299   Serial\n  number of failed request:  4793   Current serial number in output\n  stream:  4795 XIO:  fatal IO error 25 (Inappropriate ioctl for device)\n  on X server \":0.0\"\n        after 4788 requests (4788 known processed) with 31 events remaining. XIO:  fatal IO error 25 (Inappropriate ioctl for device) on\n  X server \":0.0\"\n        after 4793 requests (4793 known processed) with 32 events remaining.\n\nThe weird part is that I can totaly launch several process calling the graph function without any issue, it's the coupling with the first plot that make it unstable.\nWhen trying to debug, I found out that a simple fig=plt.figure() is enough to crash everything : in fact, any call to plt ...\nI read here and there that you can force matplotlib to use the agg backend and it helps with the multiprocess, but some widgets doesn't work with it so I would like to avoid this.\nI don't really understand why using matplotlib in differents processes could cause problems, so if anyone could explain the reasons and\/or help me with a workaround, it would be very nice.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":1823,"Q_Id":31341127,"Users Score":1,"Answer":"I just had a very similar issue in which I have a class which produces plots in parallel. The first time I create a new instance of that class and run the plotting function, everything works perfectly. But if I create a new instance and plot, everything freezes.\nI fixed it by writing a bash script which will in turn run a python script with the code for a single class instantiation + plot call. In other words, closing python between one plot call and the next one makes a clean slate of your working environment the computer does not freeze anymore. This is not an optimal solution, but it's working :)","Q_Score":6,"Tags":"python,python-2.7,matplotlib,multiprocessing","A_Id":38479294,"CreationDate":"2015-07-10T12:44:00.000","Title":"Matplotlib with multiprocessing freeze computer","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I tend to import .csv files into pandas, but sometimes I may get data in other formats to make DataFrame objects.\nToday, I just found out about read_table as a \"generic\" importer for other formats, and wondered if there were significant performance differences between the various methods in pandas for reading .csv files, e.g. read_table, from_csv, read_excel.\n\nDo these other methods have better performance than read_csv? \nIs read_csv much different than from_csv for creating a DataFrame?","AnswerCount":2,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":20524,"Q_Id":31362573,"Users Score":10,"Answer":"I've found that CSV and tab-delimited text (.txt) are equivalent in read and write speed, both are much faster than reading and writing MS Excel files. However, Excel format compresses the file size a lot.\n\nFor the same 320 MB CSV file (16 MB .xlsx) \n(i7-7700k, SSD, running Anaconda Python 3.5.3, Pandas 0.19.2)\nUsing the standard convention import pandas as pd\n2 seconds to read .csv df = pd.read_csv('foo.csv') (same for pd.read_table)\n15.3 seconds to read .xlsx df = pd.read_excel('foo.xlsx')\n10.5 seconds to write .csv df.to_csv('bar.csv', index=False)\n(same for .txt)\n34.5 seconds to write .xlsx df.to_excel('bar.xlsx', sheet_name='Sheet1', index=False)\n\nTo write your dataframes to tab-delimited text files you can use:\ndf.to_csv('bar.txt', sep='\\t', index=False)","Q_Score":15,"Tags":"python,performance,csv,pandas,dataframe","A_Id":43903727,"CreationDate":"2015-07-11T22:43:00.000","Title":"Performance difference in pandas read_table vs. read_csv vs. from_csv vs. read_excel?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I tend to import .csv files into pandas, but sometimes I may get data in other formats to make DataFrame objects.\nToday, I just found out about read_table as a \"generic\" importer for other formats, and wondered if there were significant performance differences between the various methods in pandas for reading .csv files, e.g. read_table, from_csv, read_excel.\n\nDo these other methods have better performance than read_csv? \nIs read_csv much different than from_csv for creating a DataFrame?","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":20524,"Q_Id":31362573,"Users Score":28,"Answer":"read_table is read_csv with sep=',' replaced by sep='\\t', they are two thin wrappers around the same function so the performance will be identical.  read_excel uses the xlrd package to read xls and xlsx files into a DataFrame, it doesn't handle csv files.\nfrom_csv calls read_table, so no.","Q_Score":15,"Tags":"python,performance,csv,pandas,dataframe","A_Id":31362987,"CreationDate":"2015-07-11T22:43:00.000","Title":"Performance difference in pandas read_table vs. read_csv vs. from_csv vs. read_excel?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using Spark 1.3.1 (PySpark) and I have generated a table using a SQL query. I now have an object that is a DataFrame. I want to export this DataFrame object (I have called it \"table\") to a csv file so I can manipulate it and plot the columns. How do I export the DataFrame \"table\" to a csv file?\nThanks!","AnswerCount":9,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":340481,"Q_Id":31385363,"Users Score":0,"Answer":"try display(df) and use the download option in the results. Please note: only 1 million rows can be downloaded with this option but its really quick.","Q_Score":106,"Tags":"python,apache-spark,dataframe,apache-spark-sql,export-to-csv","A_Id":69462087,"CreationDate":"2015-07-13T13:56:00.000","Title":"How to export a table dataframe in PySpark to csv?","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to build a classifier for forum posts that will automatically categorize these\nposts into some defined categories(so multiclass classification not only binary\nclassification) by using semantic word representations. For this task I want to make use\nof word2vec and doc2vec and check the feasability of using these models to support a fast\nselection of training data for the classifier. At this moment I have tried both models and\nthey work like charm. However, as I do not want to manually label each sentence to predict\nwhat is it describing, I want to leave this task for the word2vec or doc2vec models. So,\nmy question is : what algorithm can I use in Python for the classifier? ( I was thinking\nto apply some clustering over word2vec or doc2vec - manually label each cluster (this\nwould require some time and is not the best solution). Previously, I made use of\n\"LinearSVC\"(from SVM) and OneVsRestClassifier, however, I labeled each sentence (by\nmanually training a vector \"y_train\" ) in order to predict to which class a new test\nsentence would belong to. What would be a good alghorithm and method in python to use for\nthis type of classifier(making use of semantic word representations to train data)?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1109,"Q_Id":31390838,"Users Score":0,"Answer":"For a multi-class classification problem of sentences, doc2vec can work just fine, since context rarely changes a lot within the sentence.\nIf you want to use only python, I will recommend, doc2vec (for building features) followed by xgboost (for training classifier), which has worked for me in similar problems.","Q_Score":1,"Tags":"python,classification,word2vec","A_Id":31619724,"CreationDate":"2015-07-13T18:32:00.000","Title":"Using semantic word representation (e.g. word2vec) to build a classifier","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to build a classifier for forum posts that will automatically categorize these\nposts into some defined categories(so multiclass classification not only binary\nclassification) by using semantic word representations. For this task I want to make use\nof word2vec and doc2vec and check the feasability of using these models to support a fast\nselection of training data for the classifier. At this moment I have tried both models and\nthey work like charm. However, as I do not want to manually label each sentence to predict\nwhat is it describing, I want to leave this task for the word2vec or doc2vec models. So,\nmy question is : what algorithm can I use in Python for the classifier? ( I was thinking\nto apply some clustering over word2vec or doc2vec - manually label each cluster (this\nwould require some time and is not the best solution). Previously, I made use of\n\"LinearSVC\"(from SVM) and OneVsRestClassifier, however, I labeled each sentence (by\nmanually training a vector \"y_train\" ) in order to predict to which class a new test\nsentence would belong to. What would be a good alghorithm and method in python to use for\nthis type of classifier(making use of semantic word representations to train data)?","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":1109,"Q_Id":31390838,"Users Score":2,"Answer":"The issue with things like word2vec\/doc2vec and so on - actually any usupervised classifier - is that it just uses context. So, for example if I have a sentence like \"Today is a hot day\" and another like \"Today is a cold day\" it thinks hot and cold are very very similar and should be in the same cluster.\nThis makes it pretty bad for tagging. Either way, there is a good implementation of Doc2Vec and Word2Vec in gensim module for python - you can quickly use the google-news dataset's prebuilt binary and test whether you get meaningful clusters.\nThe other way you could try is implement a simple lucene\/solr system on your computer and begin tagging a few sentences randomly. Over time lucene\/solr will suggest tags clearfor your document, and they do come out to be pretty decent tags if your data is not really bad.\nThe issue here is the problem youre trying to solve isnt particularly easy nor is completely solvable - If you have very good\/clear data, then you may be able to auto classify about 80-90% of your data ... but if it is bad, you wont be able to auto classify it much.","Q_Score":1,"Tags":"python,classification,word2vec","A_Id":31393600,"CreationDate":"2015-07-13T18:32:00.000","Title":"Using semantic word representation (e.g. word2vec) to build a classifier","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Currently I am trying to import numpy using the python command but I am getting the error which is:\nTraceback (most recent call last):\n   File \"<stdin>\", line 1, in <module>\n ImportError: No module named numpy\nI have already installed the latest version of numpy and my python version is 2.7.10.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":103,"Q_Id":31397675,"Users Score":0,"Answer":"try pip freeze to find whether the module numpy is installed, If its not then try pip install numpy, also check weather in the environmental variable that the \/Scripts is added because that's where all the packages reside.","Q_Score":0,"Tags":"python,linux,ubuntu","A_Id":31399878,"CreationDate":"2015-07-14T04:30:00.000","Title":"Cannot import python-numpy using Ubuntu","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a very large list that I want to write to file. My list is 2 dimensional, and each element of the list is a 1 dimensional list. Different elements of the 2 dimensional list has 1 dimensional lists of varying size. \nWhen my 2D list was small, pickle dump worked great. But now it just gives me memory error. \nAny suggestions on how to store and reload such arrays to disk?\nThanks!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1136,"Q_Id":31417091,"Users Score":0,"Answer":"If you really want to keep it simple and use something like pickle, the best thing is to use cPickle. This library is written in C and can handle bigger files and is faster than pickle.","Q_Score":0,"Tags":"python,list,pickle","A_Id":31429440,"CreationDate":"2015-07-14T20:51:00.000","Title":"Python pickle dump memory error","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using LogisticRegression from the sklearn package, and have a quick question about classification. I built a ROC curve for my classifier, and it turns out that the optimal threshold for my training data is around 0.25. I'm assuming that the default threshold when creating predictions is 0.5. How can I change this default setting to find out what the accuracy is in my model when doing a 10-fold cross-validation? Basically, I want my model to predict a '1' for anyone greater than 0.25, not 0.5. I've been looking through all the documentation, and I can't seem to get anywhere.","AnswerCount":7,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":39588,"Q_Id":31417487,"Users Score":16,"Answer":"That is not a built-in feature. You can \"add\" it by wrapping the LogisticRegression class in your own class, and adding a threshold attribute which you use inside a custom predict() method.\nHowever, some cautions:\n\nThe default threshold is actually 0. LogisticRegression.decision_function() returns a signed distance to the selected separation hyperplane. If you are looking at predict_proba(), then you are looking at logit() of the hyperplane distance with a threshold of 0.5. But that's more expensive to compute.\nBy selecting the \"optimal\" threshold like this, you are utilizing information post-learning, which spoils your test set (i.e., your test or validation set no longer provides an unbiased estimate of out-of-sample error). You may therefore be inducing additional over-fitting unless you choose the threshold inside a cross-validation loop on your training set only, then use it and the trained classifier with your test set.\nConsider using class_weight if you have an unbalanced problem rather than manually setting the threshold. This should force the classifier to choose a hyperplane farther away from the class of serious interest.","Q_Score":29,"Tags":"python,scikit-learn,classification,regression","A_Id":31417912,"CreationDate":"2015-07-14T21:12:00.000","Title":"sklearn LogisticRegression and changing the default threshold for classification","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am learning the book Python for Data Analysis, after running the code from the book I got a pandas dataframe diversity like this:\n\nsex  F  M\nyear \n1880 [38] [14]\n1881 [38] [14]\n\nWhen I want to use diversity.plot() to draw some pictures, there is TypeError: \nEmpty 'DataFrame': no numeric data to plot \nSo, my question is how to deal with this dataframe to make it as numeric?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":352,"Q_Id":31420095,"Users Score":1,"Answer":"It seems that you have a list of int in your data frame. To convert it to you need to select the value inside and form data frame.\nI suggest you this code to convert\n\nfor col in df:\n     df[col] = df[col].apply(lambda x: x[0])","Q_Score":0,"Tags":"python,numpy,pandas","A_Id":31422570,"CreationDate":"2015-07-15T01:39:00.000","Title":"How to make dataframe in pandas as numeric?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Has anyone succeeded in speeding up scikit-learn models using numba and jit compilaition. The specific models I am looking at are regression models such as Logistic Regressions. \nI am able to use numba to optimize the functions I write using sklearn models, but the model functions themselves are not affected by this and are not optimized, thus not providing a notable increase in speed. Is there are way to optimize the sklearn functions?\nAny info about this would be much appreciated.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":6532,"Q_Id":31435024,"Users Score":7,"Answer":"Scikit-learn makes heavy use of numpy, most of which is written in C and already compiled (hence not eligible for JIT optimization). \nFurther, the LogisticRegression model is essentially LinearSVC with the appropriate loss function. I could be slightly wrong about that, but in any case, it uses LIBLINEAR to do the solving, which is again a compiled C library.\nThe makers of scikit-learn also make heavy use of one of the python-to-compiled systems, Pyrex I think, which again results in optimized machine compiled code ineligible for JIT compilation.","Q_Score":9,"Tags":"python,scikit-learn,numba","A_Id":31441162,"CreationDate":"2015-07-15T15:42:00.000","Title":"Using Numba with scikit-learn","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm running a relatively large job, which involves doing a randomized grid search on a dataset, which (with a small n_iter_search) already takes a long time. \nI'm running it on a 64 core machine, and for about 2 hours it kept 2000 threads active working on the first folds. It then stopped reporting completely into the stdout. It's last report was: \n[Parallel(n_jobs=-1)]: Done   4 out of  60 | elapsed: 84.7min remaining: 1185.8min \nI've noticed on htop that almost all cores are at 0%, which would not happen when training random forests. No feedback or errors from the program, if it weren't for htop I would assume it is still training. This has happened before, so it is a recurring problem. The machine is perfectly responsive and the process seems alive. \nI already have verbose = 10. Any thoughts on how I can diagnose what it going on inside the RandomizedSearchCV? \nThe grid search I'm doing:\n\nrfc = RandomForestClassifier(n_jobs=-1) \nparam_grid = {\n      'n_estimators': sp_randint(100, 5000),\n      'max_features' : ['auto', None],\n      'min_samples_split' : sp_randint(2, 6)\n  }  \nn_iter_search = 20\nCV_rfc = RandomizedSearchCV(estimator=rfc, param_distributions=param_grid, n_iter = n_iter_search, verbose = 10,n_jobs = -1)","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":6252,"Q_Id":31491583,"Users Score":3,"Answer":"As a first step, adding the verbose parameter to the RandomForestClassifier as well could let you see if the search is really stuck. It will display progress in fitting the trees (building tree 88 out of 100 ...). \nI don't really know why your search got stuck, but thinking about it removing the search on n_estimators should enable you to grid search the entire space of parameters you specified here in just 8 iterations.","Q_Score":2,"Tags":"python,scikit-learn,random-forest,cross-validation,grid-search","A_Id":31493214,"CreationDate":"2015-07-18T13:20:00.000","Title":"How can I get randomized grid search to be more verbose? (seems stopped, but can't diagnose)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to use a function in numpy package, say numpy.random.choice (another Python lib function random.choice samples the list uniformly while I want it to do that from some discrete distributions). \nMy program will be distributed to a lot of people to develop and test. So that means they should also install numpy before they are able to run the code. I'm now trying to find a way to get rid of installing the whole numpy library. \nDefinitely rewriting the function myself is a solution (for example using alias method). But I'm wondering that is there a way that I can only install the part of numpy related to numpy.random.choice?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1071,"Q_Id":31493649,"Users Score":4,"Answer":"This is probably not worth the hassle but it's up to you to make that trade-off. numpy.random.choice is not implemented in Python but in a .pyx file which needs to be compiled to C using Cython.\nYou could refactor it and construct a new package which implements only that functionality (possibly with a few related data structures). But with recent improvements with Python wheels files installation of numpy should be much easier than in the past. So I reckon it's easier to install numpy as it is and accept that you have it as a dependency.","Q_Score":6,"Tags":"python,numpy,pip,python-wheel","A_Id":31493846,"CreationDate":"2015-07-18T17:24:00.000","Title":"How to 'partially' install a Python package","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to run fast-rcnn on a cluster, where cv2.so is not installed for public use. So I directly move the cv2.so into a PATH, but it turns as:\n\/lib64\/libc.so.6: version `GLIBC_2.14' not found \nSo I have to install the opencv on my local path again, this time it says:\nImportError: \/home\/username\/.local\/lib\/python2.7\/site-packages\/cv2.so: undefined symbol: _ZN2cv11arrowedLineERNS_3MatENS_6Point_IiEES3_RKNS_7Scalar_IdEEiiid\nThis really confused me, could anyone give me a hand?","AnswerCount":4,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":12670,"Q_Id":31496020,"Users Score":2,"Answer":"The problem has been solved by some tryings.\nSince I installed under my \/.~local path, it should be noticed that [include],[bin] and [lib] should all point to the local version by modifying the bashrc.\nI just change the lib path while the other 2 paths remained unchanged, which point to the cluster's opencv version 2.4.9.(Mine is 2.4.11)","Q_Score":2,"Tags":"python,opencv,caffe","A_Id":31497506,"CreationDate":"2015-07-18T22:03:00.000","Title":"ImportError on cv2.so","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to run fast-rcnn on a cluster, where cv2.so is not installed for public use. So I directly move the cv2.so into a PATH, but it turns as:\n\/lib64\/libc.so.6: version `GLIBC_2.14' not found \nSo I have to install the opencv on my local path again, this time it says:\nImportError: \/home\/username\/.local\/lib\/python2.7\/site-packages\/cv2.so: undefined symbol: _ZN2cv11arrowedLineERNS_3MatENS_6Point_IiEES3_RKNS_7Scalar_IdEEiiid\nThis really confused me, could anyone give me a hand?","AnswerCount":4,"Available Count":2,"Score":0.1488850336,"is_accepted":false,"ViewCount":12670,"Q_Id":31496020,"Users Score":3,"Answer":"I know this is a little late, but I just got this same error with python 2.7 and opencv 3.1.0 on Ubuntu.  Turns out I had to reinstall opencv-python.  Running sudo pip install opencv-python did the trick.","Q_Score":2,"Tags":"python,opencv,caffe","A_Id":42237346,"CreationDate":"2015-07-18T22:03:00.000","Title":"ImportError on cv2.so","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In iterative algorithms, it is common to use large numpy arrays many times. Frequently the arrays need to be manually \"reset\" on each iteration. Is there a performance difference between filling an existing array (with nans or 0s) and creating a new array? If so, why?","AnswerCount":1,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":1368,"Q_Id":31498784,"Users Score":6,"Answer":"The answer depends on the size of your arrays. While allocating a new memory region takes nearly a fixed amount of time, the time to fill this memory region grows linear with size.\nBut, filling a new allocated memory with numpy.zeros is nearly twice as fast, as filling an existing array with numpy.fill, and three times faster than item setting x[:] = 0.\nSo on my machine, filling vectors with less than 800 elements is faster than creating new vectors, with more than 800 elements creating new vectors gets faster.","Q_Score":6,"Tags":"python,numpy","A_Id":31499284,"CreationDate":"2015-07-19T07:02:00.000","Title":"Performance difference between filling existing numpy array and creating a new one","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to build a hashmap using text files and map values using that hashmap.\nThe files are already in HDFS.\nI want to map data using this hashmap.\nThe text files are fairly small (I have around 10 files each few MB that I need to use for building the hashmap). \nIf the files are already on HDFS is there anything else that I can do to optimize the processing, so that building the hashmap and the lookup will happen in a distributed fashion?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":502,"Q_Id":31503613,"Users Score":0,"Answer":"Although the effect depends on your data set and operations, here are options I come up with\n\nOptimize text format (Avro, Kryo etc) so that hashmap can build quickly\nCombine the files into one HDFS file and increase block replication factor of the file, so that many Spark executors can get read the files locally\nUse Spark broadcast variables for the hashmap so that executors don't need to deserialize it","Q_Score":1,"Tags":"python,apache-spark,pyspark","A_Id":31504403,"CreationDate":"2015-07-19T16:55:00.000","Title":"Distributed mapping and lookup with Spark","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a WAV file which I would like to visualize in the frequency domain. Next, I would like to write a simple script that takes in a WAV file and outputs whether the energy at a certain frequency \"F\" exceeds a threshold \"Z\" (whether a certain tone has a strong presence in the WAV file). There are a bunch of code snippets online that show how to plot an FFT spectrum in Python, but I don't understand a lot of the steps.\n\nI know that wavfile.read(myfile) returns the sampling rate (fs) and the data array (data), but when I run an FFT on it (y = numpy.fft.fft(data)), what units is y in? \nTo get the array of frequencies for the x-axis, some posters do this where n = len(data): \nX = numpy.linspace(0.0, 1.0\/(2.0*T), n\/2)\nand others do this:\nX = numpy.fft.fftfreq(n) * fs)[range(n\/2)] \nIs there a difference between these two methods and is there a good online explanation for what these operations do conceptually?\nSome of the online tutorials about FFTs mention windowing, but not a lot of posters  use windowing in their code snippets. I see that numpy has a numpy.hamming(N), but what should I use as the input to that method and how do I \"apply\" the output window to my FFT arrays?\nFor my threshold computation, is it correct to find the frequency in X that's closest to my desired tone\/frequency and check if the corresponding element (same index) in Y has an amplitude greater than the threshold?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1122,"Q_Id":31527206,"Users Score":1,"Answer":"FFT data is in units of normalized frequency where the first point is 0 Hz and one past the last point is fs Hz. You can create the frequency axis yourself with linspace(0.0, (1.0 - 1.0\/n)*fs, n). You can also use fftfreq but the components will be negative.\nThese are the same if n is even. You can also use rfftfreq I think. Note that this is only the \"positive half\" of your frequencies, which is probably what you want for audio (which is real-valued). Note that you can use rfft to just produce the positive half of the spectrum, and then get the frequencies with rfftfreq(n,1.0\/fs).\nWindowing will decrease sidelobe levels, at the cost of widening the mainlobe of any frequencies that are there. N is the length of your signal and you multiply your signal by the window. However, if you are looking in a long signal you might want to \"chop\" it up into pieces, window them, and then add the absolute values of their spectra.\n\"is it correct\" is hard to answer. The simple approach is as you said, find the bin closest to your frequency and check its amplitude.","Q_Score":3,"Tags":"python,audio,signal-processing,fft","A_Id":31528542,"CreationDate":"2015-07-20T22:10:00.000","Title":"FFT in Python with Explanations","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an excel file with 234 rows and 5 columns. I want to create an array for each column so that when I can read each column separately in xlrd. Does anyone can help please?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":898,"Q_Id":31540437,"Users Score":0,"Answer":"I might, but as a former user of the excellent xlrd package, I really would recommend switching to pyopenxl. Quite apart from other benefits, each worksheet has a columns attribute that is a list of columns, each column being a list of cells. (There is also a rows) attribute.\nConverting your code would be relatively painless as long as there isn't too much and it's reasonably well-written. I believe I've never had do do anything other than pip install pyopenxl to add it to a virtual environment.\nI observe that there's no code in your question, and it's harder (and more time-consuming) to write examples than point out required changes in code, so since you are an xlrd user I'm going to assume that you can take it from here. If you need help, edit the question and add your problem code. If you get through to what you want, submit it as an answer and mark it correct.\nSuffice to say I recently wrote some code to extract Pandas datasets from UK government health statistics, and pyopenxl was amazingly helpful in my investigations and easy to use.\nSince it appears from the comments this is not a new question I'll leave it at that.","Q_Score":1,"Tags":"python","A_Id":31540754,"CreationDate":"2015-07-21T13:27:00.000","Title":"Creating arrays in Python by using excel data sheet","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I couldn't find any resources on this topic. There are a few questions with good answers describing solutions to problems which call for data stored on disk (pickle, shelve, databases in general), but I want to learn how to implement my own. \n1) If I were to create a disk based graph structure in Python, I'd have to implement the necessary methods by writing to disk. But how do I do that? \n2) One of the benefits on disk based structures is having the efficiency of the structure while working with data that might not all fit on memory. If the data does not fit in memory, only some parts of it are accessed at once. How does one access only part of the structure at once?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1607,"Q_Id":31580478,"Users Score":1,"Answer":"There's quite a number of problems you have to solve, some are quite straight forward and some are a little bit more elaborate, but since you want to do it yourself I don't think you minding about filling out details yourself (so I'll skip some parts).\nFirst simple step is to serialize and deserialize nodes (in order to be able to store on disk at all). That could be done in an ad hoc manner by having your nodes having an serialize\/deserialize method - in addition you might want to have the serialized data to have an type indicator so you can know which class' deserialize you should use to deserialize data. Note that on disk representation of a node must reference other nodes by file offset (either directly or indirectly).\nThe actual reading or writing of the data is done by ordinary (binary) file operations, but you have to seek to the right position in the file first.\nSecond step is to have the possibility to allocate space in the file. If you only want to have a write-once-behaviour it's quiete forward to just grow the file, but if you want to modify the data in the file (adding and removing nodes or even replacing them) you will have to cope with situation where regions in the file that are no longer in use and either reuse these or even pack the layout of the file.\nFurther steps could involve making the update atomic in some sense. One solution is to have a region where you write enough information so that the update can be completed (or abandoned) if it were terminated prematurely in it's most simple form it might just be a list of indempotent operations (operation that yields the same result if you repeat them, fx writing particular data to a particular place in the file).\nNote that while (some of) the builtin solutions does indeed handle writing and reading the entire graph to\/from disk they do not really handle the situation where you want to read only part of the graph or modifying the graph very efficient (you have to read mostly the whole graph and writing the complete graph in one go). Databases are the exception where you may read\/write smaller parts of your data in a random manner.","Q_Score":1,"Tags":"python,file,data-structures,disk","A_Id":31581102,"CreationDate":"2015-07-23T07:10:00.000","Title":"Creating a disk-based data structure","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an excel file composed of several sheets. I need to load them as separate dataframes individually. What would be a similar function as pd.read_csv(\"\") for this kind of task?\nP.S. due to the size I cannot copy and paste individual sheets in excel","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":15976,"Q_Id":31582821,"Users Score":0,"Answer":"exFile = ExcelFile(f) #load file f\ndata = ExcelFile.parse(exFile) #this creates a dataframe out of the first sheet in file","Q_Score":4,"Tags":"python,excel,import","A_Id":31582822,"CreationDate":"2015-07-23T09:04:00.000","Title":"How to open an excel file with multiple sheets in pandas?","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In MATLAB, there is a very convenient option to copy the current figure to the clipboard. Although Python\/numpy\/scipy\/matplotlib is a great alternative to MATLAB, such an option is unfortunately missing.\nCan this option easily be added to Matplotlib figures? Preferably, all MPL figures should automatically benefit from this functionality.\nI'm using MPL's Qt4Agg backend, with PySide.","AnswerCount":4,"Available Count":1,"Score":0.049958375,"is_accepted":false,"ViewCount":12554,"Q_Id":31607458,"Users Score":1,"Answer":"The last comment is very useful.\n\nInstall the package with\npip install addcopyfighandler\n\nImport the module after importing matplotlib, for instance:\nimport matplotlib.pyplot as plt \nimport matplotlib.font_manager as fm \nfrom matplotlib.cm import get_cmap\nimport addcopyfighandler\n\nUse ctr + C to copy the Figure to the clipboard\n\n\nAnd enjoy.","Q_Score":13,"Tags":"python,matplotlib,plot,scipy,clipboard","A_Id":63382231,"CreationDate":"2015-07-24T09:53:00.000","Title":"How to add clipboard support to Matplotlib figures?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have sklearn random forest regressor. It's very heavy, 1.6 GBytes, and works very long time when predicting values.\nI want to prune it to make lighter. As I know pruning is not implemented for decision trees and forests. I can't implement it by myself since tree code is written on C and I don't know it.\nDoes anyone know the solution?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":5467,"Q_Id":31611075,"Users Score":0,"Answer":"You could try ensemble pruning. This boils down to removing from your random forest a number of the decision trees that make it up.\nIf you remove trees at random, the expected outcome is that the performance of the ensemble will gradually deteriorate with the number of removed trees. However, you can do something more clever like removing those trees whose predictions are highly correlated with the predictions of the rest of the ensemble, and thus do to significantly modify the outcome of the whole ensemble.\nAlternatively, you can train a linear classifier that uses as inputs the outputs of the individual ensembles, and include some kind of l1 penalty in the training to enforce sparse weights on the classifier. The weights with 0 or very small value will hint which trees could be removed from the ensemble with a small impact on accuracy.","Q_Score":4,"Tags":"python,machine-learning,scikit-learn,random-forest,pruning","A_Id":66269162,"CreationDate":"2015-07-24T12:54:00.000","Title":"Random Forest pruning","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have sklearn random forest regressor. It's very heavy, 1.6 GBytes, and works very long time when predicting values.\nI want to prune it to make lighter. As I know pruning is not implemented for decision trees and forests. I can't implement it by myself since tree code is written on C and I don't know it.\nDoes anyone know the solution?","AnswerCount":2,"Available Count":2,"Score":0.2913126125,"is_accepted":false,"ViewCount":5467,"Q_Id":31611075,"Users Score":3,"Answer":"The size of the trees can be a solution for you. Try to limit the size of the trees in the forest (max leaf noders, max depth, min samples split...).","Q_Score":4,"Tags":"python,machine-learning,scikit-learn,random-forest,pruning","A_Id":31611419,"CreationDate":"2015-07-24T12:54:00.000","Title":"Random Forest pruning","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been studying KD Trees and KNN searching in 2D & 3D space. The thing I cannot seem to find a good explanation of is how to identify which objects are being referenced by each node of the tree. \nExample would be an image comparison database. If you generated descriptors for all the images, would you push all the descriptor data on to one tree? If so, how do you know which nodes are related to which original images? If not, would you generate a tree for each image, and then do some type of KD-Tree Random Forest nearest neighbor queries to determine which trees are closest to each other in 3-D space?\nThe image example might not be a good use case for KD-Trees since it's highly dimensional space, but I'm more using it to help explain the question I'm asking.\nAny guidance on practical applications of KD-Tree KNN queries for comparing objects is greatly appreciated. \nThanks!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":245,"Q_Id":31611089,"Users Score":0,"Answer":"A typical KD tree node contains a reference to the data point.\nA KD tree that only keeps the coordinates is much less useful.\nThis way, you can easily identify them.","Q_Score":1,"Tags":"python,tree,kdtree","A_Id":31646627,"CreationDate":"2015-07-24T12:54:00.000","Title":"How to identify objects related to KD Tree data?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am looking for some suggestions about how to do continue training in theano. For example, I have the following:\n\nclassifier = my_classifier()\n\ncost = ()\nupdates = []\ntrain_model = theano.function(...)\neval_model = theano.function(...)\n\nbest_accuracy = 0\nwhile (epoch < n_epochs):\n\n    train_model()\n\n    current_accuracy = eval_model()\n    if current_accuracy > best_accuracy:\n        save classifier or save theano functions?\n        best_accuracy = current_accuracy\n    else:\n        load saved classifier or save theano functions?\n        if we saved classifier previously, do we need to redefine train_model and eval_model functions?\n\n    epoch+=1\n\n#training is finished\nsave classifier\n\nI want to save the current trained model if it has higher accuracy than previously trained models, and load the saved model later if the current trained model accuracy is lower than the best accuracy. \nMy questions are:\nWhen saving, should I save the classifier, or theano functions? \nIf the classifier needs to be saved, do I need to redefine theano functions when loading it, since classifier is changed. \nThanks,","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":246,"Q_Id":31612074,"Users Score":0,"Answer":"When pickling models, it is always better to save the parameters and when loading re-create the shared variable and rebuild the graph out of this. This allow to swap the device between CPU and GPU.\nBut you can pickle Theano functions. If you do that, pickle all associated function at the same time. Otherwise, they will have each of them a different copy of the shared variable. Each call to load() will create new shared variable if they where pickled. This is a limitation of pickle.","Q_Score":0,"Tags":"python,theano,deep-learning","A_Id":31778280,"CreationDate":"2015-07-24T13:39:00.000","Title":"Theano continue training","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to find out if you strings are almost similar. For example, string like 'Mohan Mehta' should match 'Mohan Mehte' and vice versa. Another example, string like 'Umesh Gupta' should match 'Umash Gupte'. \nBasically one string is correct and other one is a mis-spelling of it. All my strings are names of people.\nAny suggestions on how to achieve this.\nSolution does not have to be 100 percent effective.","AnswerCount":6,"Available Count":1,"Score":0.0333209931,"is_accepted":false,"ViewCount":17280,"Q_Id":31642940,"Users Score":1,"Answer":"You could split the string and check to see if it contains at least one first\/last name that is correct.","Q_Score":22,"Tags":"python,regex,string","A_Id":53998316,"CreationDate":"2015-07-26T23:19:00.000","Title":"Finding if two strings are almost similar","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Please allow me to start the question with a simplest task\uff1aIf I have four points which are vertices of a rectangle, stored in a 4x2 matrix, how can I turn this into a rectangular window? (Please do not use any special command specific to drawing rectangles as the rectangle is raised just to represent a general class of regular geometrical object)\nTo make things more complicated, suppose I have a nx2 matrix, how can I connect all of the n points so that it becomes a polygon? Note the object is not necessarily convex. I think the main difficulty is that, how can R know which point should be connected with which?\nThe reason I am asking is that I was doing some image processing on a fish, and I managed to get the body line of the fish by finding the contour with opencv in python, and output it as a nx2 csv file. When I read the csv file into R and tried to use the SpatialPolygnos in the sp package to turn this into a polygon, some very unexpected behavior happened; there seems to be a break somewhere in the middle that the polygon got cut in half, i.e. the boundary of the polygon was not connected. Is there anyway I can fix this problem?\nThank you.\nEdit: Someone kindly pointed out that this is possibly a duplicate of another question: drawing polygons in R. However the solution to that question relies on the shape being drawn is convex and hence it makes sense to order by angels; However here the shape is not necessarily convex and it will not work.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":422,"Q_Id":31643100,"Users Score":1,"Answer":"Do you want it to be a spatstat study region (of class owin) since you have the spatstat tag on there? In that case you can just use owin(poly=x) where x is your nx2 matrix (after loading the spatstat library of course). The rows in this matrix should contain the vertices of the polygon in the order that you want them connected (that's how R knows which point to connect with which). See help(owin) for more details.","Q_Score":1,"Tags":"python,r,opencv,computational-geometry,spatstat","A_Id":31643427,"CreationDate":"2015-07-26T23:41:00.000","Title":"Spatstat: Given a list of 2-d points, how to connect them into a polygon, and further make it the study region?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have generated the spectrogram with GNU Radio and want to save the output graph but have no idea how to do it.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":791,"Q_Id":31660214,"Users Score":1,"Answer":"The \"QT GUI Frequency Sink\" block will display the frequency domain representation of a signal.  You can save a static image of the spectrum by accessing the control panel using center-click and choosing \"Save\".","Q_Score":1,"Tags":"python,gnuradio,gnuradio-companion","A_Id":31667553,"CreationDate":"2015-07-27T18:07:00.000","Title":"How to save a graph that is generated by GNU Radio?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How can I specify the *nix read\/write permissions for an output file (e.g. PDF) produced by the matplotlib savefig() command from within a Python script? i.e. without having to use chmod after the file has been produced.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1542,"Q_Id":31669940,"Users Score":1,"Answer":"matplotlib.pyplot.savefig() does not have capability to change file permissions. It has to be done afterwards with os.chmod(path, mode) for example os.chmod(fname, 0o400).","Q_Score":1,"Tags":"python,matplotlib,permissions","A_Id":31671206,"CreationDate":"2015-07-28T07:27:00.000","Title":"How to set output file permissions using matplotlib savefig()?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have some parallel code I have written using numpy and mpi4py modules. Till now I was running it on my laptop but now I want to attack bigger problem sizes by using the computing clusters at my university. The trouble is that they don't have mpi4py installed. Is there anyway to use the  module by copying the necessary files to my home directory in the cluster?\n\nI tried some ways to install it with out root access but that didn't workout. So I am looking for a way to use the module by just copying it to the remote machine\nI access the cluster using ssh from terminal","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":283,"Q_Id":31675214,"Users Score":1,"Answer":"Did you try pip install --user mpi4py?\nHowever, I think the best solution would be to just talk to the people in charge of the cluster and see if they will install it.  It seems pretty useless to have a cluster without mpi4py installed.","Q_Score":2,"Tags":"python,python-2.7,numpy,mpi4py","A_Id":31676562,"CreationDate":"2015-07-28T11:38:00.000","Title":"Using mpi4py (or any python module) without installing","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I've got BOW vectors and I'm wondering if there's a supervised dimensionality reduction algorithm in sklearn or gensim capable of taking high-dimensional, supervised data and projecting it into a lower dimensional space which preserves the variance between these classes.\nActually I'm trying to find a proper metric for the classification\/regression, and I believe using dimensionality can help me. I know there's unsupervised methods, but I want to keep the label information along the way.","AnswerCount":2,"Available Count":1,"Score":-0.0996679946,"is_accepted":false,"ViewCount":468,"Q_Id":31687263,"Users Score":-1,"Answer":"you can only perform dimensionality reduction in an unsupervised manner OR supervised but with different labels than your target labels.\nFor example you could train a logistic regression classifier with a dataset containing 100 topics. the output of this classifier (100 values) using your training data could be your dimensionality reduced feature set.","Q_Score":0,"Tags":"python,machine-learning,gensim,dimensionality-reduction","A_Id":31699669,"CreationDate":"2015-07-28T21:17:00.000","Title":"supervised dimensionality redunction\/topic model using sklearn or gensim","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am looking for a solution to avoid deepcopy() in my task using Python.\nI am implementing a statistical dependency parser using chu-liu-edmonds algorithm. I have a graph represented as a dictionary with every head node stored as a key with each having a list containing one or more objects of the class arc as the value.\nIn the cle-algorithm, I need to modify the graph (contract a cycle). That means, that I need to delete arc objects and heads, and add others, while I later need the original graph to expand those contracted cycles. Right now, I achieve this by deepcopying the original graph and pass it to the contract function.\nNow I ran my programm with cProfile and found out that everything that has to do with deepcopy is by far the part of the algorithm that takes the most time.\nSo my question is: Is there any way to avoid\/reduce this in my situation?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1493,"Q_Id":31693319,"Users Score":0,"Answer":"As @dmargol1 suggested in the comments, I was able to avoid deepcopy() and copy() by instead building the graph from scratch, rather than copying and modifying it, which was actually a lot faster.\nIf that is possible: do it!\nIf copying is neccessary, there are two ways. If you don't need to alter the values, copy() is the way to go, since it is a lot faster than deepcopy() (see the comment of @george-solymosi). If alteration of the values is needed, deepcopy is the only way (see the comment of @gall).","Q_Score":0,"Tags":"python,parsing,runtime","A_Id":32441545,"CreationDate":"2015-07-29T06:57:00.000","Title":"Avoid deepcopy() in Python when changing dictionary","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Using the randomForest package in R, I was able to train a random forest that minimized overall error rate. However, what I want to do is train two random forests, one that first minimizes false positive rate (~ 0) and then overall error rate, and one that first maximizes sensitivity (~1), and then overall error. Another construction of the problem would be: given a false positive rate and sensitivity rate, train two different random forests that satisfy one of the rates respectively, and then minimize overall error rate. Does anyone know if theres an r package or python package, or any other software out there that does this and or how to do this? Thanks for the help.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1366,"Q_Id":31711555,"Users Score":0,"Answer":"You can do a grid serarch over the 'regularazation' parameters to best match your target behavior.\nParameters of interest:\n\nmax depth\nnumber of features","Q_Score":1,"Tags":"python,r,machine-learning,random-forest","A_Id":31742947,"CreationDate":"2015-07-29T21:34:00.000","Title":"random forest with specified false positive and sensitivity","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Currently I am using windows 8.1 64 bit machine and anaconda as IDE. I am getting the error as shown below. please help me how to update module. Import cv2 is working fine but not with sift features.\nFile \"C:\\Users\\conquistador\\Anaconda\\lib\\site-packages\\spyderlib\\widgets\\externalshell\\sitecustomize.py\", line 71, in execfile exec(compile(scripttext, filename, 'exec'), glob, loc)\nFile \"C:\/Users\/conquistador\/Documents\/opencv\/test8.py\", line 15, in sift = cv2.xfeatures2d.SIFT()\nAttributeError: 'module' object has no attribute 'xfeatures2d'","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":366,"Q_Id":31740332,"Users Score":0,"Answer":"This is happening because SIFT (which is considered patent-encumbered or non-free) has been moved from the opencv package to the opencv \"contrib\" repo. You need a version of cv2 that has been compiled specifically with the contrib included.\nAlternatively in cv2, use ORB instead of SIFT.","Q_Score":0,"Tags":"python,opencv","A_Id":37935771,"CreationDate":"2015-07-31T07:20:00.000","Title":"Regarding opencv 3.0.0 and updating sift feature module in anaconda IDE","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to generate random number in a function which is paralleled using Joblib. However, the random number generated from the cores are exactly the same. \nCurrently I solved the problem by assigning random seeds for different cores. Is there any simple way to solve this problem?","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":591,"Q_Id":31740561,"Users Score":2,"Answer":"This is expected, although unfortunate.\nThe reason is that joblib (based on the standard multiprocessing Python tool) relies on forking under Unix. Forking creates the exact same processes and thus the same pseudo-random number generation.\nThe right way to solve this problem is to pass to the function that you are calling in parallel a seed for each call, eg a randomly-generated integer. That seed is then used inside the function to seed the local random number generation.","Q_Score":2,"Tags":"python,random,parallel-processing,joblib","A_Id":33232261,"CreationDate":"2015-07-31T07:32:00.000","Title":"Random number generator using Joblib","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm fairly sure this is a duplicate, but suppose I have a pandas DataFrame and I've sorted the rows based on the values of some column.  Originally the indices were the integers 0, 1, \u2026, n-1 but now they're out of order.  How do I reassign these indices to be in the proper order for the new sorted DataFrame?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":3763,"Q_Id":31763800,"Users Score":5,"Answer":"df.reset_index(drop=True, inplace=True)","Q_Score":3,"Tags":"python,pandas,dataframe","A_Id":31763839,"CreationDate":"2015-08-01T16:33:00.000","Title":"Reassigning index in pandas DataFrame","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to use scipy.generic_filter to process an image. However, I need to further subset the window within the function I am applying. In another words I need to know the process (function) used to convert the 2D window to 1D array within the generic filter, so I can recreate the 2D array within the applied function in the right way. Does anybody know what function doe the scipy filter use to reshape the 2D to 1D?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":64,"Q_Id":31786076,"Users Score":0,"Answer":"This is really late, but the data that gets passed to your filter function is a numpy array. You should just be able to reshape the data like normal\narr = arr.reshape((y, x))","Q_Score":2,"Tags":"python,scipy,ndimage","A_Id":66423995,"CreationDate":"2015-08-03T11:29:00.000","Title":"Scipy.generic_filter - window translation to 1D","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am currently working on a project where I have to bin up to 10-dimensional data. This works totally fine with numpy.histogramdd, however with one have a serious obstacle:\nMy parameter space is pretty large, but only a fraction is actually inhabited by data (say, maybe a few % or so...). In these regions, the data is quite rich, so I would like to use relatively small bin widths. The problem here, however, is that the RAM usage totally explodes. I see usage of 20GB+ for only 5 dimensions which is already absolutely not practical. I tried defining the grid myself, but the problem persists...\nMy idea would be to manually specify the bin edges, where I just use very large bin widths for empty regions in the data space. Only in regions where I actually have data, I would need to go to a finer scale.\nI was wondering if anyone here knows of such an implementation already which works in arbitrary numbers of dimensions.\nthanks","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":740,"Q_Id":31794158,"Users Score":2,"Answer":"I think you should first remap your data, then create the histogram, and then interpret the histogram knowing the values have been transformed. One possibility would be to tweak the histogram tick labels so that they display mapped values.\nOne possible way of doing it, for example, would be:\n\nSort one dimension of data as an unidimensional array;\nIntegrate this array, so you have a cumulative distribution;\nFind the steepest part of this distribution, and choose a horizontal interval corresponding to a \"good\" bin size for the peak of your histogram - that is, a size that gives you good resolution;\nFind the size of this same interval along the vertical axis. That will give you a bin size to apply along the vertical axis;\nCreate the bins using the vertical span of that bin - that is, \"draw\" horizontal, equidistant lines to create your bins, instead of the most common way of drawing vertical ones;\n\nThat way, you'll have lots of bins where data is more dense, and lesser bins where data is more sparse.\nTwo things to consider:\n\nThe mapping function is the cumulative distribution of the sorted values along that dimension. This can be quite arbitrary. If the distribution resembles some well known algebraic function, you could define it mathematically and use it to perform a two-way transform between actual value data and \"adaptive\" histogram data;\nThis applies to only one dimension. Care must be taken as how this would work if the histograms from multiple dimensions are to be combined.","Q_Score":1,"Tags":"python,numpy","A_Id":31794556,"CreationDate":"2015-08-03T18:22:00.000","Title":"Python adaptive histogram widths","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm looking for software that can fit a signed distance function to a vector image or output from either AutoCAD or FreeCAD.  Preferable CAD data because that output is 3D.  I'm looking at coding something in either C or Python but I thought I'd check to see if there was anything out there because I couldn't find anything using google. \nThanks for the help","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":122,"Q_Id":31798097,"Users Score":0,"Answer":"You might look into the DXF file type. (drawing exchange format)\nAutoCAD and most freeCAD will read\/write DXF files.","Q_Score":1,"Tags":"python,autocad,euclidean-distance,freecad","A_Id":31815098,"CreationDate":"2015-08-03T23:02:00.000","Title":"Fit a level set field to CAD data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm curious in Python why x[0] retrieves the first element of x while x[-1] retrieves the first element when reading in the reverse order.  The syntax seems inconsistent to me since in the one case we're counting distance from the first element, whereas we don't count distance from the last element when reading backwards.  Wouldn't something like x[-0] make more sense?  One thought I have is that intervals in Python are generally thought of as inclusive with respect to the lower bound but exclusive for the upper bound, and so the index could maybe be interpreted as distance from a lower or upper bound element.  Any ideas on why this notation was chosen? (I'm also just curious why zero indexing is preferred at all.)","AnswerCount":4,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":180,"Q_Id":31822523,"Users Score":0,"Answer":"In python: 0 == -0, so x[0] == x[-0].\nWhy is sequence indexing zero based instead of one based? It is a choice the language designer should do. Most languages I know of use 0 based indexing. Xpath uses 1 based for selection.\nUsing negative indexing is also a convention for the language. Not sure why it was chosen, but it allows for circling or looping the sequence by simple addition (subtraction) on the index.","Q_Score":4,"Tags":"python,indexing","A_Id":31822646,"CreationDate":"2015-08-05T02:39:00.000","Title":"Logic behind Python indexing","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm relatively new to Python and have already written a code to randomly select from two tables based on user input but the next function I need to create is more complex and I'm having trouble wrapping my head around.\nI'm going to have some code that's going to take user input and generate an amount of money I'm going to add to a variable, lets say, wallet.\nI then want to write some code that takes random objects from an array based on price.\nNow here's the caveat(s).  Lets say array A is chosen.  In Array A there will be 3-4 other sub arrays.  Within those arrays are 4 objects first, second, third, and fourth.  With the first being the cheapest and the fourth being the most expensive.  I want this code to NOT be able to buy object second without having bought object first.  I don't want an object purchasable unless the prerequisite is also purchased.\nI'm just having a hard time thinking it through (a weakness in general in programming I need to overcome) but any advice or links to a concept similar to what I'm aiming to do would be greatly appreciated.  Thanks!","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":45,"Q_Id":31880759,"Users Score":0,"Answer":"From your description, it might be enough to have a counter associated with each sub-array as to how many of the items in that sub-array have already been bought.  Give that you haven't shown any details as to your representation of these things, I can't give more details as to how to implement this.","Q_Score":0,"Tags":"python,arrays","A_Id":31881089,"CreationDate":"2015-08-07T14:53:00.000","Title":"Python - Pick from Array based on price with a caveat","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm relatively new to Python and have already written a code to randomly select from two tables based on user input but the next function I need to create is more complex and I'm having trouble wrapping my head around.\nI'm going to have some code that's going to take user input and generate an amount of money I'm going to add to a variable, lets say, wallet.\nI then want to write some code that takes random objects from an array based on price.\nNow here's the caveat(s).  Lets say array A is chosen.  In Array A there will be 3-4 other sub arrays.  Within those arrays are 4 objects first, second, third, and fourth.  With the first being the cheapest and the fourth being the most expensive.  I want this code to NOT be able to buy object second without having bought object first.  I don't want an object purchasable unless the prerequisite is also purchased.\nI'm just having a hard time thinking it through (a weakness in general in programming I need to overcome) but any advice or links to a concept similar to what I'm aiming to do would be greatly appreciated.  Thanks!","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":45,"Q_Id":31880759,"Users Score":0,"Answer":"It's difficult to understand what you're getting at, because you're not expressing your ideas very well. You're finding this general programming difficult as programming can be considered as the precise expression of ideas.\nSo, in very general terms you are trying to simulate some sort of curated shopping experience. You need to:\n\nTrack a value of currency.\nManage a product catalogue.\nAllow a selection of products based on value and constraints based on prior selections.\n\nIf I were doing this, I might write a class that I'd use to manage the basket. I might instantiate a basket with a budget figure and a product catalogue to select from. I might express the constraints in the catalogue, but enforce them in the basket.\nI would probably use the basket (budget, tally and selections) to filter the product catalogue to highlight eligible products.\nIf multiple transactions are allowed, the basket would need to have knowledge of previous purchases and therefore which prerequisites have already been fulfilled.","Q_Score":0,"Tags":"python,arrays","A_Id":31881413,"CreationDate":"2015-08-07T14:53:00.000","Title":"Python - Pick from Array based on price with a caveat","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a 10 x 10 grid of cells (as a numpy array).  I also have a list of 3 points on that grid.  For each cell on the grid, I need to find the closest of the three points.  I can do this in series of nested loops in python (2.7) which works but is slow (especially if I upscale to larger grids) but I suspect there is a faster way.  Does anyone have any suggestions?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2279,"Q_Id":31888624,"Users Score":0,"Answer":"The simplest way I know of to calculate the distance between two points on a plane is using the Pythagorean theorem.\nThat is, picture a right angle triangle where the hypotenuse goes between the two points and the base of the triangle is parallel to the x axis and the height is parallel to the y axis. We then know that the distance (represented by the length of the hypotenuse) h adheres to the following: h^2 = a^2 + b^2, where a and b are the lengths of the two remaining sides of the triangle.\nIt's hard to give any other help without seeing your code. Have you tried something similar yet? You need to specify your question more if you want more specific answers.","Q_Score":2,"Tags":"python,python-2.7,numpy,grid,distance","A_Id":31888691,"CreationDate":"2015-08-08T01:08:00.000","Title":"Calculating distances on grid","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I run an iPython Notebook server, and would like users to be able to download a pandas dataframe as a csv file so that they can use it in their own environment. There's no personal data, so if the solution involves writing the file at the server (which I can do) and then downloading that file, I'd be happy with that.","AnswerCount":7,"Available Count":1,"Score":-0.0285636566,"is_accepted":false,"ViewCount":50655,"Q_Id":31893930,"Users Score":-1,"Answer":"My simple approach to download all the files from the jupyter notebook would be by simply using this wonderful command\n!tar cvfz my_compressed_file_name.tar.gz *\nThis will download all the files of the server including the notebooks.\nIn case if your server has multiple folders, you might be willing to use the following command. write ..\/ before the * for every step up the directory.\ntar cvfz zipname.tar.gz ..\/..\/*\nHope it helps..","Q_Score":33,"Tags":"csv,pandas,ipython-notebook","A_Id":53851540,"CreationDate":"2015-08-08T14:08:00.000","Title":"Download CSV from an iPython Notebook","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Say I have a large array of value 0~255. I wanted every element in this array that is higher than 100 got multiplied by 1.2, otherwise, got multiplied by 0.8.\nIt sounded simple but I could not find anyway other than iterate through all the variable and multiply it one by one.","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":6219,"Q_Id":31908956,"Users Score":1,"Answer":"np.where is the answer. I spend time messing with np.place without knowing its existence.","Q_Score":4,"Tags":"python,arrays,numpy","A_Id":31909251,"CreationDate":"2015-08-09T21:21:00.000","Title":"Numpy conditional multiply data in array (if true multiply A, false multiply B)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I just new in python. How to save variable data to file like save command in MATLAB.\nThank you","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1761,"Q_Id":31915120,"Users Score":2,"Answer":"Use pickle.dump in Python 3.x, or cPickle.dump in Python 2.x.","Q_Score":2,"Tags":"python-2.7","A_Id":31915492,"CreationDate":"2015-08-10T08:39:00.000","Title":"Python save variable to file like save() in MATLAB","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have used ckanext-qa but its seems its not as per my requirement I am looking for extension by which Logged in user can be able to rate form 1 to 5 for each dataset over ckan.\nAnybody have an idea how to do like that","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":261,"Q_Id":31942911,"Users Score":1,"Answer":"I'm not aware of any extensions that do this.\nYou could write one to add this info in a dataset extra field. You may wish to store it as JSON and record the ratings given by each user.\nAlternatively you could try the rating_create API function - this is old functionality which has no UI, but it may just do what you want.","Q_Score":0,"Tags":"python,ckan","A_Id":32591030,"CreationDate":"2015-08-11T13:18:00.000","Title":"How to show star rating on ckan for datasets","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using Scikit-learn SVM classifier to make predictions and i'm using a rbf kernel. I have set the class_weight = 'auto'. Am I right in thinking that classes that appear more often will get lower weights? Say I had two classes, A and B. If A appeared a lot more than B does that mean that later on when making the predictions, there will be fewer A predictions than if I hadn't set the class_weight= 'auto'?\nI'm pretty new to this so I'm just trying to get my head around what is happening and why.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":847,"Q_Id":31944799,"Users Score":3,"Answer":"Using class weighting has nothing to do w linear\/non-linear kernel. It simply controls the cost of missclassifing particular sample during training. Per-class weight simply put constant weight on each sample in a given class. When you use auto, classes samples gets weightes invertibly proportional to the class size. So if you have class A twice as big as B, then samples from A are twice \"cheapier\" to missclassify. This will lead to highly balanced model structure, in particular, such SVM tries to maximize Balanced Accuracy (BAC), not \"classical\" accuracy.","Q_Score":2,"Tags":"python,machine-learning,scikit-learn,svm","A_Id":31945716,"CreationDate":"2015-08-11T14:37:00.000","Title":"How does one interpret class weight when working with non linear SVMs?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In python, is it necessary to use threading when plotting in real time? In my specific case, I wish to continuously plot from a .csv that is having lines of data added to it indefinitely. Can't I just create a function that re-reads the data source with every iteration of the plot update?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":82,"Q_Id":31950928,"Users Score":0,"Answer":"Short answer: it depends on your use case. \nIf you simply want a static graph that updates in real time, then your approach is totally fine.\nOn the other hand, threading is useful when the user needs to somehow interact with the plot (like zoom in\/out, etc) because otherwise I\/O operations would block the main routine that handles user interaction, so the user might experience \"pauses\" periodically.","Q_Score":0,"Tags":"python,multithreading,csv,plot,real-time","A_Id":31951440,"CreationDate":"2015-08-11T20:02:00.000","Title":"Plotting in real time","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm very confused as to what np.exp() actually does. In the documentation it says that it: \"Calculates the exponential of all elements in the input array.\" I'm confused as to what exactly this means. Could someone give me more information to what it actually does?","AnswerCount":3,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":221837,"Q_Id":31951980,"Users Score":127,"Answer":"The exponential function is e^x where e is a mathematical constant called Euler's number, approximately 2.718281. This value has a close mathematical relationship with pi and the slope of the curve e^x is equal to its value at every point. np.exp() calculates e^x for each value of x in your input array.","Q_Score":120,"Tags":"python,numpy,statistics,exp","A_Id":31952102,"CreationDate":"2015-08-11T21:08:00.000","Title":"What exactly does numpy.exp() do?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm very confused as to what np.exp() actually does. In the documentation it says that it: \"Calculates the exponential of all elements in the input array.\" I'm confused as to what exactly this means. Could someone give me more information to what it actually does?","AnswerCount":3,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":221837,"Q_Id":31951980,"Users Score":62,"Answer":"It calculates ex for each x in your list where e is Euler's number (approximately 2.718). In other words, np.exp(range(5)) is similar to [math.e**x for x in range(5)].","Q_Score":120,"Tags":"python,numpy,statistics,exp","A_Id":31952092,"CreationDate":"2015-08-11T21:08:00.000","Title":"What exactly does numpy.exp() do?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Let's say I have a pandas series with numerical values. What's the shortest way to plot the sorted series against an increasing integer index?\nThe plot should show:\nx-axis: 0,1,2,3,4,...\ny-axis: the sorted values of the series.\n(please notice that I cannot plot it against the series' index, because the index is not necessarily an increasing index. In my case it's some id that I use for different reasons)\nThanks","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":21421,"Q_Id":31974942,"Users Score":16,"Answer":"This will first sort the series and then plot, ignoring the index of the series:\nts = pd.Series(np.random.randn(100), index=pd.date_range('1\/1\/2000', periods=100))\nts.sort_values().plot(use_index=False)","Q_Score":8,"Tags":"python,pandas","A_Id":36915460,"CreationDate":"2015-08-12T20:38:00.000","Title":"pandas - plot sorted column to increasing integer index","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using the LDA algorithm from the gensim package to find topics in a given text.\nI've been asked that the resulting topics will include different words for each topic, E.G If topic A has the word 'monkey' in it then no other topic should include the word 'monkey' in its list.\nMy thoughts so far: run it multiple times and each time add the previous words to the stop words list.\nSince:\nA) I'm not even sure of algorithmically\/logically it's the right thing to do.\nB) I hope there's a built in way to do it that i'm not aware of.\nC) This is a large database, and it takes about 20 minutes to run the LDA\neach time (using the multi-core version).\nQuestion: Is there a better way to do it?\nHope to get some help,\nThanks.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":925,"Q_Id":31975754,"Users Score":0,"Answer":"LDA provides for each topic and each word a probability that the topic generates that word. You can try assigning words to topics by just taking the max over all topics of the probability to generate the word. In other words if topic A generates \"monkey\" with probability 0.01 and topic B generates the word monkey with probability 0.02 then you can assign the word monkey to topic B.","Q_Score":1,"Tags":"python,algorithm,api,lda,gensim","A_Id":31977464,"CreationDate":"2015-08-12T21:30:00.000","Title":"gensim LDA: How can i generate topics with different words for each topic?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a 512x512x512 numpy array. Is there any efficient way to perform a mean filter where every array value is substituted by all 3x3x3 local values?\nWe are seeking somethin similar to scipy.ndimage.filters.median_filter but insted of median with mean.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":9128,"Q_Id":31989903,"Users Score":6,"Answer":"scipy.ndimage.filters.convolve() with a weight: np.full((3, 3, 3), 1.0\/27).","Q_Score":4,"Tags":"python-3.x,numpy,scipy","A_Id":31990326,"CreationDate":"2015-08-13T13:39:00.000","Title":"Local mean filter in numpy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"There is one distance function I can pass to pdist use to create the distance matrix that is given to linkage. There is a second distance function that I can pass to linkage as the metric. \nWhy are there two possible distance functions? \nIf they are different, how are they used? For instance, does linkage use the distances in the distance matrix for its initial iterations, i.e. to see if any two original observations should be combined into a cluster, and then use the metric function for further combinations, i.e. of two clusters or of a cluster with an original observation?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":206,"Q_Id":32059711,"Users Score":0,"Answer":"The parameter 'method' is used to measure the similarities between clusters through the hierarchical clustering process. The parameter 'metric' is used to measure the distance between two objects in the dataset.\nThe 'metric' is closely related to the nature of the data (e.g., you could want to use 'euclidean' distance for objects with the same number of features, or Dynamic Time Warping for time series with different durations).\nThe thing is that there are two ways of using the linkage function. The first parameter is y, and it can be either the data itself or a distance matrix produced previously by a given measure.\nIf you choose to feed 'linkage' with a distance matrix, then you won't need the 'metric' parameter, because you have already calculated all the distance between all objects.","Q_Score":3,"Tags":"python,scipy,hierarchical-clustering","A_Id":70254029,"CreationDate":"2015-08-17T21:06:00.000","Title":"In scipy, what's the point of the two different distance functions used in hierarchical clustering?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have successfully saved my graphs using the plt.savefig() function in the matplotlib library. \nWhen I try to open my graph using vi, the file is there but there are a lot of strange characters. I guess I'm viewing the code and other info rather than the visualization of the graph.  How do I see the graph in its pictoral form?","AnswerCount":1,"Available Count":1,"Score":0.537049567,"is_accepted":false,"ViewCount":204,"Q_Id":32061658,"Users Score":3,"Answer":"Vi is a text editor, and can't view images as images. The Windows Paint program should be able to view them, however, or on a Mac, Preview should work.","Q_Score":0,"Tags":"python,matplotlib,graph","A_Id":32062644,"CreationDate":"2015-08-18T00:14:00.000","Title":"How to view a saved matplotlib plot","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"When I pass my training data to the scikit neural network it is a NumPy array of all my 24*24 image matrices. I check and this is the shape of the data: (3237, 24, 24) - 3237 24*24 images.\nHowever, once I feed it into the neural network, I get this warning:\n\nWARNING:sknn: - Reshaping input array from (3237, 24, 24) to (2589, 24, 24, 1).\n\nThe program still runs, I just don't understand why it is reshaping my array.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":218,"Q_Id":32076040,"Users Score":0,"Answer":"Looking at the source, scikit will reshape input arrays if the X (input samples) you pass in when initializing is a different size from creating\/splitting the dataset in the MLP backend. This is to reduce overfitting by training and validating on the same samples.","Q_Score":0,"Tags":"python,arrays,numpy,scikit-learn,neural-network","A_Id":32094372,"CreationDate":"2015-08-18T15:03:00.000","Title":"Why does scikit neural network reshape my input array automatically?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I created an Excel spreadsheet using Pandas and xlsxwriter, which has all the data in the right rows and columns. However, the formatting in xlsxwriter is pretty basic, so I want to solve this problem by writing my Pandas spreadsheet on top of a template spreadsheet with Pyxl.\nFirst, however, I need to get Pyxl to only import data up to the first blank row, and to get rid of the column headings. This way I could write my Excel data from the xlsxwriter output to the template.\nI have no clue how to go about this and can't find it here or in the docs. Any ideas? \nHow about if I want to read data from the first column after the first blank column? (I can think of a workaround for this, but it would help if I knew how)","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":256,"Q_Id":32077627,"Users Score":1,"Answer":"To be honest I'd be tempted to suggest you use openpyxl all the way if there is something that xlsxwriter doesn't do, though I think that it's formatting options are pretty extensive. The most recent version of openpyxl is as fast as xlsxwriter if lxml is installed.\nHowever, it's worth noting that Pandas has tended to ship with an older version of openpyxl because we changed the style API.\nOtherwise you can use max_row to get the highest row but this won't check for an empty row.","Q_Score":1,"Tags":"python,excel,openpyxl","A_Id":32119557,"CreationDate":"2015-08-18T16:17:00.000","Title":"Use Pyxl to read Excel spreadsheet data up to a certain row","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to make a simple neural network which uses the ReLU function. Can someone give me a clue of how can I implement the function using numpy.","AnswerCount":9,"Available Count":1,"Score":0.022218565,"is_accepted":false,"ViewCount":165706,"Q_Id":32109319,"Users Score":1,"Answer":"ReLU(x) also is equal to (x+abs(x))\/2","Q_Score":91,"Tags":"python,numpy,machine-learning,neural-network","A_Id":62125080,"CreationDate":"2015-08-20T03:58:00.000","Title":"How to implement the ReLU function in Numpy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to calculate correlation amongst three columns in a dataset. The dataset is relatively large (4 GB in size). When I calculate correlation among the columns of interest, I get small values like 0.0024, -0.0067 etc. I am not sure this result makes any sense or not. Should I sample the data and then try calculating correlation?\nAny thoughts\/experience on this topic would be appreciated.","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":116,"Q_Id":32126189,"Users Score":1,"Answer":"Firstly, make sure you're applying the right formula for correlation.  Remember, given vectors x and y, correlation is ((x-mean(x)) * (y - mean(y)))\/(length(x)*length(y)), where * represents the dot-product and length(x) is the square root of the sum of the squares of the terms in x.  (I know that's silly, but noticing a mis-typed formula is a lot easier than redoing a program.)\nDo you have a strong hunch that there should be some correlation among these columns?  If you don't, then those small values are reasonable.  On the other hand, if you're pretty sure that there ought to be a strong correlation, then try sampling a random 100 pairs and either finding the correlation there, or plotting them for visual inspection, which can also show you if there is correlation present.","Q_Score":0,"Tags":"python,statistics,correlation,pyspark","A_Id":32126371,"CreationDate":"2015-08-20T18:56:00.000","Title":"How to calculate correlation on large number of records?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to calculate correlation amongst three columns in a dataset. The dataset is relatively large (4 GB in size). When I calculate correlation among the columns of interest, I get small values like 0.0024, -0.0067 etc. I am not sure this result makes any sense or not. Should I sample the data and then try calculating correlation?\nAny thoughts\/experience on this topic would be appreciated.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":116,"Q_Id":32126189,"Users Score":0,"Answer":"There is nothing special about correlation of large data sets. All you need to do is some simple aggregation.\nIf you want to improve your numerical precision (remember that floating point math is lossy) you can use Kahan summation and similar techniques, in particular for values close to 0.\nBut maybe your data justt doesn't have strong correlation?\nTry visualizing a sample!","Q_Score":0,"Tags":"python,statistics,correlation,pyspark","A_Id":32127507,"CreationDate":"2015-08-20T18:56:00.000","Title":"How to calculate correlation on large number of records?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I was going through the documentation of spark. I got a bit confused with rdd.repartition() function and the number of partitions we pass during context initialization in sc.parallelize().\nI have 4 cores on my machine, if I sc.parallelize(data, 4) everything works fine, but when I rdd.repartition(4) and apply rdd.mappartitions(fun) sometimes the partitions has no data and my function fails in such cases.\nSo, just wanted to understand what is the difference between these two ways of partitioning.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2919,"Q_Id":32130000,"Users Score":3,"Answer":"By calling repartition(N) spark will do a shuffle to change the number of partitions (and will by default result in a HashPartitioner with that number of partitions). When you call sc.parallelize with a desired number of partitions it splits your data (more or less) equally up amongst the slices (effectively similar to a range partitioner), you can see this in ParallelCollectionRDD inside of the slice function.\nThat being said, it is possible that both of these sc.parallelize(data, N) and rdd.reparitition(N) (and really almost any form of reading in data) can result in RDDs with empty partitions (its a pretty common source of errors with mapPartitions code so I biased the RDD generator in spark-testing-base to create RDDs with empty partitions). A really simple fix for most functions is just checking if you've been passed in an empty iterator and just returning an empty iterator in that case.","Q_Score":5,"Tags":"python,apache-spark,rdd","A_Id":32147241,"CreationDate":"2015-08-20T23:19:00.000","Title":"what is the difference between rdd.repartition() and partition size in sc.parallelize(data, partitions)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to interpolate a linear function and I can't use numpy or scipy. \nI'm given this data points ((0,10), (1,4), (2,3), (3,5), (4,12)) and a point on `x = 2.8. the data points is polyline (linear between 2 coordinates)\nof course I need to use the closest x-points from the data for 2.8. which is from (2,3) and (3,5) because 2.8 lies between the 2 and 3.\nHow do I make a function to find this closest points?","AnswerCount":4,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":3022,"Q_Id":32138651,"Users Score":0,"Answer":"You can compute the distance of chosen points. \n1) Search for the minimun distance X value (left and right). \n2) Search each points corresponding with X_MIN_LEFT and X_MIN_RIGHT. At the same time you can check the distance with Y and find the minimum Y distance. \nThat's it.","Q_Score":2,"Tags":"python,algorithm,function","A_Id":32138958,"CreationDate":"2015-08-21T11:00:00.000","Title":"Finding the points nearest to a given x?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Can somebody tell me the difference between truncated SVD as implemented in sklearn and partial SVD as implemented in, say, fbpca?\nI couldn't find a definitive answer as I haven't seen anybody use truncated SVD for principal component pursuit (PCP).","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":1242,"Q_Id":32148219,"Users Score":2,"Answer":"Truncated or partial means that you only calculate a certain number of components\/singular vector-value pairs (the strongest ones).\nIn scikit-learn parlance, \"partial\" usually refers to the fact that a method is on line, meaning that it can be fed with partial data. The more data you give it the better it will converge to the expected optimum.\nBoth can be combined, and have been, also in sklearn: sklearn.decomposition.IncrementalPCA does this.","Q_Score":1,"Tags":"python-2.7,scikit-learn,pca","A_Id":32236196,"CreationDate":"2015-08-21T19:46:00.000","Title":"Truncated SVD vs Partial SVD","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to 'follow-tcp-stream' in Hadoop sequence file that structured as follows:\ni. Time stamp as key \nii. Raw Ethernet frame as value \nThe file contains a single TCP session, and because the record is very long, sequence-id of TCP frame overflows (which means that seq-id not necessarily unique and data cannot be sorted by seq-id because then it will get scrambled).\nI use Apache Spark\/Python\/Scapy.\n\nTo create the TCP-stream I intended to: \n1.) Filter out any non TCP-with-data frames\n2.) Sort the RDD by TCP-sequence-ID (within each overflow cycle)\n3.) Remove any duplicates of sequence-ID (within each overflow cycle)\n4.) Map each element to TCP data\n5.) Store the resulting RDD as testFile within HDFS\n\nIllustration of operation on RDD:\ninput: [(time:100, seq:1), (time:101, seq:21), (time:102, seq:11), (time:103, seq:21), ... , (time:1234, seq=1000), (time:1235, seq:2),  (time:1236, seq:30), (time:1237, seq:18)]\noutput:[(seq:1, time:100), (seq:11, time:102), (seq:21, time:101), ... ,(seq=1000, time:1234), (seq:2, time:1235), (seq:18, time:1237), (seq:30, time:1236)]\n\n\n\nSteps 1 and 4 or obvious. The ways I came up for solving 2 and 3 required comparison between adjacent elements within the RDD, with the option to return any number of new elements (not necessarily 2, Without making any action of course - so the code will run in parallel). Is there any way to do this? I went over RDD class methods few times nothing came up.\n\nAnother issue the storage of the RDD (step 5). Is it done in parallel? Each node stores his part of the RDD to different Hadoop block? Or the data first forwarded to Spark client app and then it stores it?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":237,"Q_Id":32184638,"Users Score":1,"Answer":"It seems like what your looking for might be best done with something like reduceByKey where you can remove the duplicates as you go for each sequence (assuming that the resulting amount of data for each sequence isn't too large, in your example it seems pretty small). Sorting the results can be done with the standard sortBy operator.\nSaving the data out to HDFS is indeed done in parallel on the workers, forwarding the data to the Spark client app would create a bottleneck and sort of defeat the purpose (although if you do want to bring the data back locally you can use collect provided that the data is pretty small).","Q_Score":0,"Tags":"python,tcp,apache-spark,pyspark,rdd","A_Id":32192487,"CreationDate":"2015-08-24T14:19:00.000","Title":"Apache Spark RDD transformations with 2 elements as input","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I have a very large sparse matrix which represents a transition martix in a Markov Chain, i.e. the sum of each row of the matrix equals one and I'm interested in finding the first eigenvalue and its corresponding vector which is smaller than one. I know that the eigenvalues are bounded in the section [-1, 1] and they are all real (non-complex).\nI am trying to calculate the values using python's scipy.sparse.eigs function, however, one of the parameters of the functions is the number of eigenvalues\/vectors to estimate and every time I've increased the number of parameters to estimate, the numbers of eigenvalues which are exactly one grew as well.\nNeedless to say, I am using the which parameter with the value 'LR' in order to get the k largest eigenvalues, with k being the number of values to estimate.\nDoes anyone have an idea how to solve this problem (finding the first eigenvalue smaller than one and its corresponding vector)?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1334,"Q_Id":32184915,"Users Score":1,"Answer":"I agree with @pv. If your matrix S was symmetric, you could see it as a laplacian matrix of the matrix I - S. The number of connected components of I - S is the number of zero-eigenvalues of this matrix (i.e, the dimension of the space associated to eigenvalue 1 of S). You could check the number of connected components of the graph whose similarity matrix is I - S*S' for a start, e.g. with scipy.sparse.csgraph.connected_components.","Q_Score":20,"Tags":"python,scipy,sparse-matrix,eigenvector,eigenvalue","A_Id":34873978,"CreationDate":"2015-08-24T14:32:00.000","Title":"Calculating eigen values of very large sparse matrices in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a large data set of restaurant dishes (for example, \"Pulled Pork\", \"Beef Brisket\"...)\nI am trying to \"normalize\" (wrong word) the dishes. I want \"Pulled Pork\" and \"Pulled Pork Sandwich\" and \"Jumbo Pork Slider\" all to map to a single dish, \"Pulled Pork\". \nSo far I have gotten started with NLTK using Python and had some fun playing around with frequency distributions and such.\nDoes anyone have a high-level strategy to approach this problem? Perhaps some keywords I could google?\nThanks","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":183,"Q_Id":32235272,"Users Score":4,"Answer":"You might want look for TFIDF and cosine similarity.\nThere are challenging cases, however. Let's say you have the following three dishes:\n\nPulled pork\nPulled egg\nEgg sandwich\n\nWhich of the two you are going to combine? \n\nPulled pork and pulled egg  \nPulled egg and egg sandwich\n\nUsing TFIDF, you can find the most representative  words. For example the word sandwich may happen to be in many dishes, hence not very representative. (Tuna sandwich, egg sandwich, cheese sandwich, etc.) Merging tuna sandwich and cheese sandwich may not be a good idea. \nAfter you have the TFIDF vectors, you can use cosine similarity (using the TFIDF vectors) and maybe a static threshold, you can decide whether to merge them or not. \nThere is also another issue arises: When you match, what are you going to name them? (Pulled egg or egg sandwich?)\nUpdate:\n@alvas suggests to use clustering after having the similarity\/dissimilarity values. I think that would be good idea. You can first create your nxn distance\/similarity matrix using the cosine similarity with TFIDF vectors. And after you have the distance matrix, you can cluster them using a clustering algorithm.","Q_Score":5,"Tags":"python,machine-learning,nlp,nltk","A_Id":32235511,"CreationDate":"2015-08-26T19:37:00.000","Title":"Normalizing a list of restaurant dishes","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Currently, I am trying to implement a basic logistic regression algorithm in Python to differentiate between A vs. B.\nFor my training and test data, I have ~50,000 samples of A vs. 1000 samples of B. Is this a problem if I use half the data of each to train the algorithm and the other half as testing data (25000 train A, 500 train B and so on for testing accuracy).\nIf so, how can I overcome this problem. Should I consider resampling, doing some other \"fancy stuff\".","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":69,"Q_Id":32281130,"Users Score":2,"Answer":"How much of a problem it is depends on the nature of your data. The bigger issue will be that you simply have a huge class imbalance (50 As for every B). If you end up getting good classification accuracy anyway, then fine - nothing to do. What to do next depends on your data and the nature of the problem and what is acceptable in a solution. There really isn't a dead set \"do this\" answer for this question.","Q_Score":1,"Tags":"python-2.7,machine-learning,statistics,logistic-regression","A_Id":32281179,"CreationDate":"2015-08-28T23:23:00.000","Title":"Computational Logistic Regression With Python, Different Sample Sizes","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to choose between numpy.interp vs scipy.interpolate.interp1d (with kind='linear' of course). I realize they have different interfaces but that doesn't matter much to me (I can code around either interface). I'm wondering whether there are other differences I should be aware of. Thanks.","AnswerCount":2,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":7258,"Q_Id":32290233,"Users Score":10,"Answer":"While numpy returns an array with discrete datapoints, 'interp1d' returns a function. You can use the generated function later in your code as often as you want. Furthermore, you can choose other methods than linear interpoationn","Q_Score":22,"Tags":"python,numpy,scipy","A_Id":32290589,"CreationDate":"2015-08-29T20:09:00.000","Title":"Choosing between numpy.interp vs scipy.interpolate.interp1d (with kind='linear')","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I try to understand the capabilities of Spark, but I fail to see if the following is possible in Python.\nI have some objects that are non Pickable (wrapped from C++ with SWIG).\nI have a list of those objects obj_list = [obj1, obj2, ...]\nAll those objects have a member function called .dostuff\nI'd like to parallelized the following loop in Spark (in order to run it on AWS since I don't have a big architecture internally. We could probably use multiprocessing, but I don't think I can easily send the objects over the network):\n[x.dostuff() for x in obj_list]\nAny pointers would be appreciated.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":335,"Q_Id":32300616,"Users Score":0,"Answer":"If your object's aren't picklable your options are pretty limited. If you can create them on the executor side though (frequently a useful option for things like database connections), you can parallelize a  regular list (e.g. maybe a list of the constructor parameters) and then use map if your dostuff function returns (picklable) values you want to use or foreach if your dostuff function is called for its side effects (like updating a database or similar).","Q_Score":0,"Tags":"python,apache-spark,cloud","A_Id":32304062,"CreationDate":"2015-08-30T20:04:00.000","Title":"Passing objects to Spark","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have been searching and found many libraries (scipy, numpy, matplotlib) for Python that lets a user easily shift from MATLAB to Python. However, I am unable to find any library that is related to the Simulink in MATLAB. I would like to know if such a library exists or something else that resembles Simulink in it's GUI and computation features.","AnswerCount":3,"Available Count":1,"Score":0.2605204458,"is_accepted":false,"ViewCount":28386,"Q_Id":32345638,"Users Score":4,"Answer":"Until now there is no library like Simulink in Python. The closest match is the Modelica language with OpenModelica and a python implementation JModelica.","Q_Score":14,"Tags":"python,matlab,simulink","A_Id":32347770,"CreationDate":"2015-09-02T06:00:00.000","Title":"Simulink for Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm working on detecting license plates with openCV, python and a raspberry pi. Most of it is already covered. What I want is to detect the ROI (Region of interest) of the plate, track it on a few frames and add those together to get a more clear and crisp image of the plate.\nI want to get a better image of the plate by taking the information from several frames. I detect the plate, and have a collection of plates from several frames, as many as I wish and as many as the car is moving by the camera. How can I take all those and get a better version?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":330,"Q_Id":32363678,"Users Score":0,"Answer":"You need to ensure that your frame rate is fast enough to get a decent still of the moving car. When filming, each frame will most likely be blurry, and our brain pieces together the number plate on playback. Of course a blurry frame is no good for letter recognition, so is something you'll need to deal with on the hardware side, rather than software side.\nRemember the old saying: Garbage in; Garbage out.","Q_Score":1,"Tags":"python,opencv,raspberry-pi","A_Id":32363759,"CreationDate":"2015-09-02T21:47:00.000","Title":"openCV track object in video and obtain a better image from multiple frames","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"During one of technical interview got this question.\nI know way to solve this problem using (in java) HashSet.\nBut i could not understand when interviewer forced on the word \"a very large array let's say 10 million elements in the given array\".\nDo i need to change the approach? If not, what should be the efficient to achieve this?\nPS: Algo or implementation is language agnostic. \nThank you.","AnswerCount":5,"Available Count":3,"Score":0.1586485043,"is_accepted":false,"ViewCount":3410,"Q_Id":32409274,"Users Score":4,"Answer":"You can do it in O(nlog(n)):\n\nSort the array\nfind the duplicates (they will be next to each other) in one pass.\n\nI think that is what the interviewer wanted to hear.\nif you did a merge sort or a quick sort, finding the duplicates could be done at merging in hidden time.\nThese can be implemented \"in-place\", or \"by-part\" if the array is too large to fit in memory.","Q_Score":3,"Tags":"javascript,java,python,c,algorithm","A_Id":32409317,"CreationDate":"2015-09-05T03:38:00.000","Title":"Algo to find duplicates in a very large array","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"During one of technical interview got this question.\nI know way to solve this problem using (in java) HashSet.\nBut i could not understand when interviewer forced on the word \"a very large array let's say 10 million elements in the given array\".\nDo i need to change the approach? If not, what should be the efficient to achieve this?\nPS: Algo or implementation is language agnostic. \nThank you.","AnswerCount":5,"Available Count":3,"Score":0.1586485043,"is_accepted":false,"ViewCount":3410,"Q_Id":32409274,"Users Score":4,"Answer":"There were some key things, which interviewer expected you to ask back like: if you can not load the array in memory, then how much I can load. These are the steps to solve the problem:\n\nyou need to divide the array in how much memory is available to you.\nLet's say you can load 1M number at a time. You have split the data in k parts. You load the first 1M and build Min Heap of it. Then remove the top and apply the Heapify on Min Heap.\nRepeat the same for other parts of the data.\nNow you will have K sorted splits.\nNow fetch a first number from each K split and again build a Min Heap.\nNow remove the top from the Min Heap and store the value in temporary variable as well for comparing with the next coming number for finding the duplicates.\nNow fetch the next number from the same split (part) whose number got removed last time. Put that number on top of Min Heap and apply Heapify.\nNow the top of the Min Heap is your next sorted number and compare it with the temporary variable for finding the duplicates. Update thetemporary variable` if number is not duplicate.","Q_Score":3,"Tags":"javascript,java,python,c,algorithm","A_Id":32409400,"CreationDate":"2015-09-05T03:38:00.000","Title":"Algo to find duplicates in a very large array","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"During one of technical interview got this question.\nI know way to solve this problem using (in java) HashSet.\nBut i could not understand when interviewer forced on the word \"a very large array let's say 10 million elements in the given array\".\nDo i need to change the approach? If not, what should be the efficient to achieve this?\nPS: Algo or implementation is language agnostic. \nThank you.","AnswerCount":5,"Available Count":3,"Score":1.2,"is_accepted":true,"ViewCount":3410,"Q_Id":32409274,"Users Score":3,"Answer":"One thing to keep in mind is that O-notation doesn't necessarily tell you what algorithm is fastest.  If one algorithm is O(n log n) and another algorithm is O(n2), then there is some value M such that the first algorithm is faster for all n > M.  But M could be much larger than the amount of data you'll ever have to deal with.\nThe reason I'm bringing this up is that I think a HashSet is probably still the best answer, although I'd have to profile it to find out for sure.  Assuming that you aren't allowed to set up a hash table with 10 million buckets, you may still be able to set up a reasonable-sized table.  Say you can create a HashSet with table size 100,000.  The buckets will then be sets of objects.  If n is the size of the array, the average bucket size will be n \/ 100000.  So to see if an element is already in the HashSet, and add it if not, will take a fixed amount of time to compute the hash value, and O(n) to search the elements in the bucket if they're stored in a linear list(*).  Technically, this means that the algorithm to find all duplicates is O(n2).  But since one of the n's in n2 is for a linear list that is so much smaller than the array size (by a factor of 100000), it seems likely to me that it will still take much less time than a O(n log n) sort, for 10 million items.  The value of M, the point at which the O(n log n) sort becomes faster, is likely to be much, much larger than that.  (I'm just guessing, though; to find out for certain would require some profiling.)\nI'd tend to lean against using a sort anyway, because if all you need to do is find duplicates, a sort is doing more work than you need.  You shouldn't need to put the elements in order, just to find duplicates.  That to me suggests that a sort is not likely to be the best answer.\n(*) Note that in Java 8, the elements in each bucket will be in some kind of search tree, probably a red-black tree, instead of in a linear list.  So the algorithm will still be O(n log n), and still probably lots faster than a sort.","Q_Score":3,"Tags":"javascript,java,python,c,algorithm","A_Id":32409590,"CreationDate":"2015-09-05T03:38:00.000","Title":"Algo to find duplicates in a very large array","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using PTVS to debug some code I've written. I'd like to get it to break into the debugger whenever a numpy exception is raised. Currently, it only breaks into the debugger when a standard Python exception is raised; whenever a numpy exception is raised, all I get is a traceback print out.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":92,"Q_Id":32428313,"Users Score":0,"Answer":"You can add your own exception types in the Exceptions dialog (in Debug -> Exceptions), and then check them to have it break on them.","Q_Score":1,"Tags":"python,numpy,ptvs","A_Id":32428486,"CreationDate":"2015-09-06T21:06:00.000","Title":"Break into debugger on numpy exception in PTVS","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to use word_tokenize, pos_tag, FreqDist. I don't want to download all nltk as default. I want to use nltk.download(info_or_id=''). What options I should put in info_or_id to get the POS tagging and its frequency. POS tagging - Penn Treebank POS.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":283,"Q_Id":32434810,"Users Score":0,"Answer":"Your question confuses the nltk itself with nltk_data. You can't really download just part of the nltk (though you could manually trim it down, carefully, if you need to save space). But I think you're trying to avoid downloading all of the nltk data. As @barny wrote, you can see the IDs of different resources when you open the interactive nltk.download() window. \n\nTo use the treebank pos tagger, you need its pickled training tables (not the treebank corpus); you'll find them in the \"Models\" tab under the ID maxent_treebank_pos_tagger. (Hence: nltk.download(\"maxent_treebank_pos_tagger\").\nThe FreqDist class doesn't have or need any trained model. \nNeither does word_tokenize, which takes a sentence as a single string and breaks it up into words. However, you'll probably need the model for sent_tokenize, which breaks up a longer text into sentences. That's handled by the \"Punkt\" sentence tokenizer, and you can download its model with nltk.download(\"punkt\").\n\nPS. For general-purpose use, I recommend downloading everything in the \"book\" collection, i.e. nltk.download(\"book\"). It's only a fraction of the total, and it lets you do most things without scrambling every so often to figure out what's missing.","Q_Score":0,"Tags":"python,nltk","A_Id":32554802,"CreationDate":"2015-09-07T09:02:00.000","Title":"POS tagging - NLTK- Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Suppose I have a process where I push a button, and after a certain amount of time (from 1 to 30 minutes), an event occurs.  I then run a very large number of trials, and record how long it takes the event to occur for each trial.  This raw data is then reduced to a set of 30 data points where the x value is the number of minutes it took for the event to occur, and the y value is the percentage of trials which fell into that bucket.  I do not have access to the original data.\nHow can I use this set of 30 points to identify an appropriate probability distribution which I can then use to generate representative random samples?\nI feel like scipy.stats has all the tools I need built in, but for the life of me I can't figure out how to go about it. Any tips?","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":2122,"Q_Id":32488808,"Users Score":1,"Answer":"You can also use the histogram, piecewise uniform distribution directly, then you get exactly the corresponding random numbers instead of an approximation. \nThe inverse cdf, ppf, is piecewise linear and linear interpolation can be used to transform uniform random numbers appropriately.","Q_Score":3,"Tags":"python,statistics,scipy","A_Id":32510602,"CreationDate":"2015-09-09T20:41:00.000","Title":"Use Histogram data to generate random samples in scipy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a CSV file where the date is formatted as yy\/mm\/dd, but Excel is reading it wrongly as dd\/mm\/yyyy (e.g. 8th September 2015 is read as 15th of September 2008).  \nI know how to change the format that Excel outputs, but how can I change the format it uses to interpret the CSV data?  \nI'd like to keep it to Excel if possible, but I could work with a Python program.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1588,"Q_Id":32490561,"Users Score":4,"Answer":"Option 3. Import it properly\nUse DATA, Get External Data, From Text and when the wizard prompts you choose the appropriate DMY combination (Step 3 of 3, Under Column data format, and Date).","Q_Score":0,"Tags":"python,excel,date,csv,import-from-csv","A_Id":32490655,"CreationDate":"2015-09-09T23:12:00.000","Title":"Change date format when importing from CSV","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have class imbalance problem and want to solve this using cost sensitive learning. \n\nunder sample and over sample \ngive weights to class to use a modified loss function \n\nQuestion \nScikit learn has 2 options called class weights and sample weights. Is sample weight actually doing option 2) and class weight options 1). Is option 2) the the recommended way of handling class imbalance.","AnswerCount":2,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":12531,"Q_Id":32492550,"Users Score":8,"Answer":"sample_weight and class_weight have a similar function, that is to make your estimator pay more attention to some samples. \nActual sample weights will be sample_weight * weights from class_weight.\nThis serves the same purpose as under\/oversampling but the behavior is likely to be different: say you have an algorithm that randomly picks samples (like in random forests), it matters whether you oversampled or not.\nTo sum it up:\nclass_weight and sample_weight both do 2), option 2) is one way to handle class imbalance. I don't know of an universally recommended way, I would try 1), 2) and 1) + 2) on your specific problem to see what works best.","Q_Score":22,"Tags":"python,machine-learning,scikit-learn,classification","A_Id":32502985,"CreationDate":"2015-09-10T03:17:00.000","Title":"What is the difference between sample weight and class weight options in scikit learn?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to classify the data using BayesNet in Python. I have used scikit learn for other classifiers like Random Forests, SVM etc. I know it has Naive Bayes but I am looking for Bayesian Network alone. If anyone could help me with it it would be very helpful Also, if there is an implementation of it for reference that would be even more helpful.\nThanks","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1288,"Q_Id":32511232,"Users Score":1,"Answer":"You can use Weka for classify the data using BayesNet in Python.You can train your data using Weka and save your model as XML then you can write prediction API's in python for that saved model.","Q_Score":6,"Tags":"python,scikit-learn,bayesian-networks","A_Id":46094876,"CreationDate":"2015-09-10T20:40:00.000","Title":"Does scikit-learn have Bayes Net ? If yes is there an implementation for reference","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Installed cygwin64, including Python 2.7, on my new computer running Windows10. \nPython runs fine, adding modules like matplotlib or bitstream goes fine, but when trying to add scipy the build eventually, after about an hour, having successfully compiled lots of fortran and C\/C++ files,  fails with:\nerror: Setup script exited with error: Command \"g++ -fno-strict-aliasing -ggdb -O2 -pipe -Wimplicit-function-declaration -fdebug-prefix-map=\/usr\/src\/ports\/python\/python-2.7.10-1.x86_64\/build=\/usr\/src\/debug\/python-2.7.10-1 -fdebug-prefix-map=\/usr\/src\/ports\/python\/python-2.7.10-1.x86_64\/src\/Python-2.7.10=\/usr\/src\/debug\/python-2.7.10-1 -DNDEBUG -g -fwrapv -O3 -Wall -I\/usr\/include\/python2.7 -I\/usr\/lib\/python2.7\/site-packages\/numpy\/core\/include -Iscipy\/spatial\/ckdtree\/src -I\/usr\/lib\/python2.7\/site-packages\/numpy\/core\/include -I\/usr\/include\/python2.7 -c scipy\/spatial\/ckdtree\/src\/ckdtree_query.cxx -o build\/temp.cygwin-2.2.1-x86_64-2.7\/scipy\/spatial\/ckdtree\/src\/ckdtree_query.o\" failed with exit status 1\nI've tried both pip install and easy_install, both result in the same error. \nGreatful for any hints on what to try next.","AnswerCount":2,"Available Count":1,"Score":0.2913126125,"is_accepted":false,"ViewCount":620,"Q_Id":32519166,"Users Score":3,"Answer":"I suffered for days with the same issue. My final solution was to install scipy0.15.1: pip install scipy==0.15.1. Hope it works for you too.","Q_Score":3,"Tags":"python-2.7,scipy,cygwin,windows-10","A_Id":32912189,"CreationDate":"2015-09-11T08:40:00.000","Title":"Scipy installation cygwin64 Windows10 fails at late stage","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Beginner here, please be gentle! I\u2019m receiving an error that reads \n\nImportError: No module named sklearn when using pycharm.  \n\nI\u2019m trying to import matplotlib, numpy, and sklearn. I\u2019ve downloaded scikit_learn. I\u2019ve also downloaded anaconda. \nI have \u201ctwo\u201d pythons. Looks like this\u2026  \n\nMacHD\/Library\/Frameworks\/python.framework\/versions\/3.4\/site-packages\/sklearn\nMacHD\/Library\/Python\/2.7\/ ... in here is pip and scikit_learn\n\nThe strange thing is that matplotlib and numpy work but not sklearn. How can I figure out what's wrong?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":809,"Q_Id":32544925,"Users Score":1,"Answer":"The MacHD\/Library\/Frameworks\/python.framework\/versions\/3.4\/site-packages\/sklearn is for Python 3.4 (note the 3.4 in the path) and the MacHD\/Library\/Python\/2.7\/ is for Python 2.7. The packages for each are independent of each other.","Q_Score":0,"Tags":"python,numpy,matplotlib,scikit-learn,anaconda","A_Id":32574361,"CreationDate":"2015-09-12T23:49:00.000","Title":"Cannot import scikit-learn","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have data with missing values and I would like to build a classifier for it. I know that scikit-learn will help you impute values for the missing data. However, in my case it is not clear this is the right thing to do or even easy. The problem is that the features in the data are correlated so it's not obvious now to do this imputation in a sensible way.\nI  know that in R some of the classifiers (decision trees, random forests) can directly handle missing values without your having to do any imputation.\nCan any of the classifiers in scikit learn 0.16.1 do likewise and if so, how should I represent the missing values to help it?  \nI have read discussions on the scikit learn github about this topic but I can''t work out what has actually been implemented and what hasn't.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2290,"Q_Id":32546992,"Users Score":4,"Answer":"RandomForests in scikit-learn don't handle missing values at the moment [as of 0.16 and upcoming 0.17], and you do need to impute the values before.","Q_Score":2,"Tags":"python,scikit-learn","A_Id":32551334,"CreationDate":"2015-09-13T06:35:00.000","Title":"Which classifiers handle missing values in scikit learn 0.16.1","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a collection X of documents, all of which are of class A (the only class in which I'm interested or know anything about). I also have a much larger collection Y of documents that I know nothing about. The documents in X and Y come from the same source and have similar formats and somewhat similar subject matters. I'd like to use the TF-IDF feature vectors of the documents in X to find the documents in Y that are most likely to be of class A.\nIn the past, I've used TF-IDF feature vectors to build naive Bayes classifiers, but in these situations, my training set X consisted of documents of many classes, and my objective was to classify each document in Y as one of the classes seen in X.\nThis seems like a different situation. Here, my entire training set has the same class (I have no documents that I know are not of class A), and I'm only interested in determining if documents in Y are or are not of that class.\nA classifier seems like the wrong route, but I'm not sure what the best next step is. Is there a different algorithm that can use that TF-IDF matrix to determine the likelihood that a document is of the same class?\nFYI, I'm using scikit-learn in Python 2.7, which obviously made computing the TF-IDF matrix of X (and Y) simple.","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":264,"Q_Id":32553806,"Users Score":2,"Answer":"What I think you have is an unsupervised learning application.  Clustering.  Using the combined X & Y dataset, generate clusters.  Then overlay the X boundary; the boundary that contains all X samples.  All items from Y in the X boundary can be considered X.  And the X-ness of a given sample from Y is the distance from the X cluster centroid.  Something like that.","Q_Score":1,"Tags":"python,machine-learning,tf-idf,text-classification","A_Id":32555599,"CreationDate":"2015-09-13T19:39:00.000","Title":"If my entire training set of documents is class A, how can I use TF-IDF to find other documents of class A?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a collection X of documents, all of which are of class A (the only class in which I'm interested or know anything about). I also have a much larger collection Y of documents that I know nothing about. The documents in X and Y come from the same source and have similar formats and somewhat similar subject matters. I'd like to use the TF-IDF feature vectors of the documents in X to find the documents in Y that are most likely to be of class A.\nIn the past, I've used TF-IDF feature vectors to build naive Bayes classifiers, but in these situations, my training set X consisted of documents of many classes, and my objective was to classify each document in Y as one of the classes seen in X.\nThis seems like a different situation. Here, my entire training set has the same class (I have no documents that I know are not of class A), and I'm only interested in determining if documents in Y are or are not of that class.\nA classifier seems like the wrong route, but I'm not sure what the best next step is. Is there a different algorithm that can use that TF-IDF matrix to determine the likelihood that a document is of the same class?\nFYI, I'm using scikit-learn in Python 2.7, which obviously made computing the TF-IDF matrix of X (and Y) simple.","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":264,"Q_Id":32553806,"Users Score":1,"Answer":"The easiest thing to do is what was already proposed - clustering. More specifically, you extract a single feature vector from set X and then apply K-means clustering to the whole X & Y set. \nps: Be careful not to confuse k-means with kNN (k-nearest neighbors). You are able to apply only unsupervised learning methods.","Q_Score":1,"Tags":"python,machine-learning,tf-idf,text-classification","A_Id":32604626,"CreationDate":"2015-09-13T19:39:00.000","Title":"If my entire training set of documents is class A, how can I use TF-IDF to find other documents of class A?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm currently playing around with the Apache Spark Service in IBM Bluemix. There is a quick start composite application (Boilerplate) consisting of the Spark Service itself, an OpenStack Swift service for the data and an IPython\/Jupyter Notebook. \nI want to add some 3rd party libraries to the system and I'm wondering how this could be achieved. Using an python import statement doesn't really help since the libraries are then expected to be located on the SparkWorker nodes.\nIs there a ways of loading python libraries in Spark from an external source during job runtime (e.g. a Swift or ftp source)?\nthanks a lot!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":157,"Q_Id":32573995,"Users Score":1,"Answer":"You cannot add 3rd party libraries at this point in the beta. This will most certainly be coming later in the beta as it's a popular requirement ;-)","Q_Score":2,"Tags":"python,apache-spark,ipython,ibm-cloud,jupyter","A_Id":32574697,"CreationDate":"2015-09-14T21:09:00.000","Title":"How can I reference libraries for ApacheSpark using IPython Notebook only?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"Basically I have a class which subclasses ndarray and has additional information. When I call np.asarray() on my object, it returns just the numpy array and destroys my additional information.\nMy question is then this: Is there a way in Python to change how np.asarray() acts on my subclass of ndarray from within my subclass? I don't want to change numpy of course, and I do not want to go through every instance where np.asarray() is called to take care of this.\nThanks in advance!\nChris","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":54,"Q_Id":32640830,"Users Score":0,"Answer":"No.  Numpy's asarray() is coded to instantiate a regular numpy array, and you can't change that without editing asarray() or changing the caller's code to call your special method instead of asarray()","Q_Score":0,"Tags":"python,numpy,multidimensional-array,scipy,subclass","A_Id":32641163,"CreationDate":"2015-09-17T22:16:00.000","Title":"In Python, can you change how a method from class 1 acts on class 2 from within class 2?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to install and use OpenCV library for python development. I want to use it for PyCharm IDE. I am trying to do it without the package manager.\nThe environment is a windows 64 bit architecture. For Python I am using Python 2.7.10. \nI have already included the OpenCV directory in the system path.\nI am using python 2.7.10 interpreter for PyCharm and have installed the pip and numpy packages. \nopencv version is 3.0.0 \nHow do I enable OpenCV and make it working in PyCharm?","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":66287,"Q_Id":32660114,"Users Score":28,"Answer":"I finally figured out on how to solve this issue:\nSteps to follow:\n\nInstall Python 2.7.10\nInstall Pycharm(If you have not done it already)\nDownload and install the OpenCV executable.\nAdd OpenCV in the system path(%OPENCV_DIR% = \/path\/of\/opencv\/directory)\nGoto C:\\opencv\\build\\python\\2.7\\x86 folder and copy cv2.pyd file.\nGoto C:\\Python27\\DLLs directory and paste the cv2.pyd file.\nGoto C:\\Python27\\Lib\\site-packages directory and paste the cv2.pyd file.\nGoto PyCharm IDE and goto DefaultSettings>PythonInterpreter.\nSelect the Python which you have installed on Step1.\nInstall the packages numpy,matplotlib and pip in pycharm.\nRestart your PyCharm.\nPyCharm now has OpenCV library installed and working.","Q_Score":8,"Tags":"python,windows,opencv,installation,pycharm","A_Id":32664411,"CreationDate":"2015-09-18T20:00:00.000","Title":"How to install OpenCV on Windows and enable it for PyCharm without using the package manager","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Beginner here.\nI\u2019m trying to use sklearn in pycharm. When importing sklearn I get an error that reads \u201cImport error: No module named sklearn\u201d\nThe project interpreter in pycharm is set to 2.7.10 (\/anaconda\/bin\/python.app), which should be the right one. \nUnder default preferenes, project interpreter, I see all of anacondas packages. I've double clicked and installed the packages scikit learn and sklearn. I still receive the \u201cImport error: No module named sklearn\u201d\nDoes anyone know how to solve this problem?","AnswerCount":8,"Available Count":4,"Score":0.024994793,"is_accepted":false,"ViewCount":67839,"Q_Id":32675024,"Users Score":1,"Answer":"SOLVED:\n\nreinstalled Python 3.7.9 (not the latet)\ninstalled numpy 1.17.5 (not the latest)\ninstalled scikit-learn (latest)\n\nsklearn works now!","Q_Score":24,"Tags":"python,scikit-learn,python-import,anaconda","A_Id":65357723,"CreationDate":"2015-09-20T02:07:00.000","Title":"Getting PyCharm to import sklearn","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Beginner here.\nI\u2019m trying to use sklearn in pycharm. When importing sklearn I get an error that reads \u201cImport error: No module named sklearn\u201d\nThe project interpreter in pycharm is set to 2.7.10 (\/anaconda\/bin\/python.app), which should be the right one. \nUnder default preferenes, project interpreter, I see all of anacondas packages. I've double clicked and installed the packages scikit learn and sklearn. I still receive the \u201cImport error: No module named sklearn\u201d\nDoes anyone know how to solve this problem?","AnswerCount":8,"Available Count":4,"Score":0.0,"is_accepted":false,"ViewCount":67839,"Q_Id":32675024,"Users Score":0,"Answer":"For Mac OS:\nPyCharm --> Preferences --> Project Interpreter --> Double Click on pip (a new window will open with search option) --> mention 'Scikit-learn' on the search bar --> Install Packages --> Once installed, close that new window --> OK on the existing window \nand you are done.","Q_Score":24,"Tags":"python,scikit-learn,python-import,anaconda","A_Id":53357368,"CreationDate":"2015-09-20T02:07:00.000","Title":"Getting PyCharm to import sklearn","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Beginner here.\nI\u2019m trying to use sklearn in pycharm. When importing sklearn I get an error that reads \u201cImport error: No module named sklearn\u201d\nThe project interpreter in pycharm is set to 2.7.10 (\/anaconda\/bin\/python.app), which should be the right one. \nUnder default preferenes, project interpreter, I see all of anacondas packages. I've double clicked and installed the packages scikit learn and sklearn. I still receive the \u201cImport error: No module named sklearn\u201d\nDoes anyone know how to solve this problem?","AnswerCount":8,"Available Count":4,"Score":1.0,"is_accepted":false,"ViewCount":67839,"Q_Id":32675024,"Users Score":8,"Answer":"please notice that, in the packages search 'Scikit-learn', instead 'sklearn'","Q_Score":24,"Tags":"python,scikit-learn,python-import,anaconda","A_Id":54702828,"CreationDate":"2015-09-20T02:07:00.000","Title":"Getting PyCharm to import sklearn","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Beginner here.\nI\u2019m trying to use sklearn in pycharm. When importing sklearn I get an error that reads \u201cImport error: No module named sklearn\u201d\nThe project interpreter in pycharm is set to 2.7.10 (\/anaconda\/bin\/python.app), which should be the right one. \nUnder default preferenes, project interpreter, I see all of anacondas packages. I've double clicked and installed the packages scikit learn and sklearn. I still receive the \u201cImport error: No module named sklearn\u201d\nDoes anyone know how to solve this problem?","AnswerCount":8,"Available Count":4,"Score":0.024994793,"is_accepted":false,"ViewCount":67839,"Q_Id":32675024,"Users Score":1,"Answer":"Same error occurs to me i have fixed by selecting File Menu-> Default Settings-> Project Interpreter -> Press + button and type 'sklearn' Press install button. Installation will be done in 10 to 20 seconds.\nIf issue not resolved please check you PyCharm Interpreter path. Sometimes your machine have Python 2.7 and Python 3.6 both installed and there may be some conflict by choosing one.","Q_Score":24,"Tags":"python,scikit-learn,python-import,anaconda","A_Id":50808255,"CreationDate":"2015-09-20T02:07:00.000","Title":"Getting PyCharm to import sklearn","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on with the Face detection using cascade Classifier in opencv python. Its working fine. But i want to develop my code to detect only one face and also the largest face only to detect.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":216,"Q_Id":32697663,"Users Score":0,"Answer":"Sort the detected faces by size and keep the biggest one only?","Q_Score":0,"Tags":"python,opencv,face-detection","A_Id":32709770,"CreationDate":"2015-09-21T14:22:00.000","Title":"Face detection for two faces-Cascade opencv python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I installed OpenCV 3.0.0 but I'm having troubles any C++ or Python code using OpenCV. For testing, I went into the directory opencv-3.0.0\/samples and ran cmake to build the samples. I got the following error:\nCMake Error at \/usr\/share\/cmake-2.8\/Modules\/FindPackageHandleStandardArgs.cmake:108 (message):\nCould NOT find CUDA: Found unsuitable version \"5.5\", but required is exact version \"7.0\" (found \/usr)\nHowever, I'm pretty sure that I have the CUDA 7.0 installed and I verified by getting the following output from nvcc --version on the command line:\nnvcc: NVIDIA (R) Cuda compiler driver\nCopyright (c) 2005-2015 NVIDIA Corporation\nBuilt on Mon_Feb_16_22:59:02_CST_2015\nCuda compilation tools, release 7.0, V7.0.27\nWhy might OpenCV think that I got the wrong version installed? Is there a workaround for this?","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":1214,"Q_Id":32756140,"Users Score":2,"Answer":"Upgrading from CMake 2.8 to CMake 3.2.2 seems to have solved this particular issue.\n[This answer has been added from information gleaned from comments in order to get the question off the unanswered list]","Q_Score":0,"Tags":"python,c++,opencv,cuda,nvidia","A_Id":32797781,"CreationDate":"2015-09-24T07:54:00.000","Title":"OpenCV can't find the right version of CUDA","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"This is not a real issue, but I'd like to understand:\n\nrunning sklearn from Anaconda distrib on a Win7 4 cores 8 GB system\nfitting a KMeans model on a 200.000 samples*200 values table.\nrunning with n-jobs = -1: (after adding the if __name__ == '__main__': line to my script) I see the script starting 4 processes with\n10 threads each. Each process uses about 25% of the CPU (total:\n100%). Seems to work as expected\nrunning with n-jobs = 1: stays on a single process (not a surprise), with 20 threads, and also uses 100% of the CPU.\n\nMy question: what is the point of using n-jobs (and joblib) if the the library uses all cores anyway? Am I missing something? Is it a Windows-specific behaviour?","AnswerCount":3,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":46321,"Q_Id":32761556,"Users Score":8,"Answer":"what is the point of using n-jobs (and joblib) if the the library uses all cores anyway?\n\nIt does not, if you specify n_jobs to -1, it will use all cores. If it is set to 1 or 2, it will use one or two cores only (test done scikit-learn 0.20.3 under Linux).","Q_Score":19,"Tags":"python,parallel-processing,scikit-learn,joblib","A_Id":55287554,"CreationDate":"2015-09-24T12:38:00.000","Title":"Python scikit learn n_jobs","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"This is not a real issue, but I'd like to understand:\n\nrunning sklearn from Anaconda distrib on a Win7 4 cores 8 GB system\nfitting a KMeans model on a 200.000 samples*200 values table.\nrunning with n-jobs = -1: (after adding the if __name__ == '__main__': line to my script) I see the script starting 4 processes with\n10 threads each. Each process uses about 25% of the CPU (total:\n100%). Seems to work as expected\nrunning with n-jobs = 1: stays on a single process (not a surprise), with 20 threads, and also uses 100% of the CPU.\n\nMy question: what is the point of using n-jobs (and joblib) if the the library uses all cores anyway? Am I missing something? Is it a Windows-specific behaviour?","AnswerCount":3,"Available Count":2,"Score":-0.0665680765,"is_accepted":false,"ViewCount":46321,"Q_Id":32761556,"Users Score":-1,"Answer":"You should either use n_jobs or joblib, don't use both simultaneously.","Q_Score":19,"Tags":"python,parallel-processing,scikit-learn,joblib","A_Id":59874972,"CreationDate":"2015-09-24T12:38:00.000","Title":"Python scikit learn n_jobs","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using python 2.7.9 on win8. When I tried to plot using matplotlib, the following error showed up:\n\nfrom pylab import *\n  plot([1,2,3,4])\n[matplotlib.lines.Line2D object at 0x0392A9D0]\n\nI tried the test code \"python simple_plot.py --verbose-helpful\", and the following warning showed up:\n\n$HOME=C:\\Users\\XX\n  matplotlib data path C:\\Python27\\lib\\site-packages\\matplotlib\\mpl-data\n\nYou have the following UNSUPPORTED LaTeX preamble customizations:\nPlease do not ask for support with these customizations active.\n\nloaded rc file C:\\Python27\\lib\\site-packages\\matplotlib\\mpl-data\\matplotlibrc\n  matplotlib version 1.4.3\n  verbose.level helpful\n  interactive is False\n  platform is win32\n  CACHEDIR=C:\\Users\\XX.matplotlib\n  Using fontManager instance from C:\\Users\\XX.matplotlib\\fontList.cache\n  backend TkAgg version 8.5\n  findfont: Matching :family=sans-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=medium to Bitstream\n  Vera Sans (u'C:\\Python27\\lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\Vera.ttf') with score of 0.000000\n\nWhat does this mean? How could I get matplotlib working?","AnswerCount":5,"Available Count":2,"Score":-0.0399786803,"is_accepted":false,"ViewCount":61800,"Q_Id":32774520,"Users Score":-1,"Answer":"Had this problem. You just have to use show() function to show it in a window. Use pyplot.show()","Q_Score":26,"Tags":"python,matplotlib","A_Id":59199255,"CreationDate":"2015-09-25T03:54:00.000","Title":"why matplotlib give the error []?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using python 2.7.9 on win8. When I tried to plot using matplotlib, the following error showed up:\n\nfrom pylab import *\n  plot([1,2,3,4])\n[matplotlib.lines.Line2D object at 0x0392A9D0]\n\nI tried the test code \"python simple_plot.py --verbose-helpful\", and the following warning showed up:\n\n$HOME=C:\\Users\\XX\n  matplotlib data path C:\\Python27\\lib\\site-packages\\matplotlib\\mpl-data\n\nYou have the following UNSUPPORTED LaTeX preamble customizations:\nPlease do not ask for support with these customizations active.\n\nloaded rc file C:\\Python27\\lib\\site-packages\\matplotlib\\mpl-data\\matplotlibrc\n  matplotlib version 1.4.3\n  verbose.level helpful\n  interactive is False\n  platform is win32\n  CACHEDIR=C:\\Users\\XX.matplotlib\n  Using fontManager instance from C:\\Users\\XX.matplotlib\\fontList.cache\n  backend TkAgg version 8.5\n  findfont: Matching :family=sans-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=medium to Bitstream\n  Vera Sans (u'C:\\Python27\\lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\Vera.ttf') with score of 0.000000\n\nWhat does this mean? How could I get matplotlib working?","AnswerCount":5,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":61800,"Q_Id":32774520,"Users Score":0,"Answer":"When you run plt.plot() on Spider, you will now receive the following notification:\n\nFigures now render in the Plots pane by default. To make them also appear inline in the Console, uncheck \"Mute Inline Plotting\" under the Plots pane options menu.\n\nI followed this instruction, and it works.","Q_Score":26,"Tags":"python,matplotlib","A_Id":62611119,"CreationDate":"2015-09-25T03:54:00.000","Title":"why matplotlib give the error []?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to bigdata, I want to parse the whole data, so I cant split it when i try to use numpy array for processing 1 GB data it takes 4GB memory (In real time I am dealing with huge data). Is there any optimized way to use these array for this much data or any special function to handle huge data.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1066,"Q_Id":32776134,"Users Score":5,"Answer":"Memory consumption depends very much on the way data is stored. For example 1 as string takes only one byte, as an int it takes two bytes and eight bytes as double. Then there is the overhead of creating it as in Object of DaataFrame and Series. All this is done for efficient processing.\nAs a general rule of thumb data respresentation in memory will have larger size than in storage.\nBigData means data which is too large to fit in the memory (or process in a single machine). So it makes no sense to parse the whole data and load it in memory.  \nAll BigData processing engines depend splitting the data into chunks and processing the chunks individually (and parallely), then combining these intermediate results into one.","Q_Score":0,"Tags":"python,numpy,pandas,bigdata","A_Id":32776321,"CreationDate":"2015-09-25T06:31:00.000","Title":"Why Numpy and Pandas arrays consuming more memory than source data?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm designing a program which:\n\nIncludes randomness\nCan stop executing and save its state at certain points (in XML)\nCan start executing starting from a saved state\nIs deterministic (so the program can run from the same state twice and produces the same result)\n\nThe problem here is saving the randomness.  I can initialize it at start, but from state to state I may generate anywhere from 0 to 1000 random numbers.\nTherefore, I have 3 options I can see:\n\nStore the seed, and number of times a number has been randomly generated, then when loading the state, run the random number generator that many times.\nOn state save, increment the seed by N\nOn state save, randomly generate the next seed\n\nThe problem with option 1 is the run time, and is pretty infeasible.\nHowever, I'm unsure whether 2 or 3 will produce good random results.  If I run two random generators, one seeded with X, the other seeded with X+1, how different will their results be?  What if the first is seeded with X, and the second is seeded with X.random()?\nIn case it makes a difference, I'm using Python 3.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2249,"Q_Id":32808686,"Users Score":6,"Answer":"You can save the state of the PRNG using random.getstate() (then, e.g., use pickle to save it to disk. Later, a random.setstate(state) will return your PRNG to exactly the state it was in.","Q_Score":4,"Tags":"python,random","A_Id":32809283,"CreationDate":"2015-09-27T13:58:00.000","Title":"Storing a Random state","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have text classification data with predictions depending on categories, 'descriptions' and 'components'.  I could do the classification using bag of words in python with scikit on 'descriptions'. But  I want to get predictions using both categories in bag of words with weights to individual feature sets\nx = descriptions + 2* components\nHow should  I proceed?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":713,"Q_Id":32859460,"Users Score":0,"Answer":"You can train individual classifiers for descriptions and merchants, and obtain a final score using score = w1 * predictions + w2 * components.\nThe values of w1 and w2 should be obtained using cross validation.\nAlternatively, you can train a single multiclass classifier by combining the training dataset. \nYou will now have 4 classes: \n\nNeither 'predictions' nor 'components'\n'predictions' but not 'components'\nnot 'predictions' but 'components'\n'predictions' and 'components'\n\nAnd you can go ahead and train as usual.","Q_Score":0,"Tags":"python-2.7,machine-learning,scikit-learn,text-mining,text-classification","A_Id":32859613,"CreationDate":"2015-09-30T06:41:00.000","Title":"How to combine multiple feature sets in bag of words","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to generate an orphan mesh on a part with python.\nI have already defined the nodes by using a code giving by Tim in another post.\nHowever, the with the following command:\nListElem.append(myTrabPart.Element(nodes=tup,elemShape=HEX8)\nI ended up by the message \"there is no mesh to edit\". It seems that the ListElem is empty in my case. The list lengths are correct.\nDo you have any advice which could help me?\nThanks,\nRomain","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":424,"Q_Id":32869355,"Users Score":0,"Answer":"In Abaqus you can only edit Native Meshes. This time, as you said, you have an Orphan Mesh. The only way to edit this kind of mesh is doing it by yourself with an external script.","Q_Score":0,"Tags":"python,mesh,abaqus,orphan","A_Id":33103479,"CreationDate":"2015-09-30T14:56:00.000","Title":"Generate orphan mesh in abaqus python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to aggregate session data without a true session \"key\" in PySpark. I have data where an individual is detected in an area at a specific time, and I want to aggregate that into a duration spent in each area during a specific visit (see below).\nThe tricky part here is that I want to infer the time someone exits each area as the time they are detected in the next area. This means that I will need to use the start time of the next area ID as the end time for any given area ID. Area IDs can also show up more than once for the same individual.\nI had an implementation of this in MapReduce where I iterate over all rows and aggregate the time until a new AreaID or Individual is detected, then output the record. Is there a way to do something similar in Spark? Is there a better way to approach the problem?\nAlso of note, I do not want to output a record unless the individual has been detected in another area (e.g. IndividualY, AreaT below)\nI have a dataset in the following format:\n\nIndividual  AreaID  Datetime of Detection\nIndividualX AreaQ   1\/7\/2015 0:00\nIndividualX AreaQ   1\/7\/2015 1:00\nIndividualX AreaW   1\/7\/2015 3:00\nIndividualX AreaQ   1\/7\/2015 4:00\nIndividualY AreaZ   2\/7\/2015 4:00\nIndividualY AreaZ   2\/7\/2015 5:00\nIndividualY AreaW   2\/7\/2015 6:00\nIndividualY AreaT   2\/7\/2015 7:00\n\nI would like the desired output of:\n\nIndividual  AreaID  Start_Time      End_Time        Duration (minutes)\nIndividualX AreaQ   1\/7\/2015 0:00   1\/7\/2015 3:00   180\nIndividualX AreaW   1\/7\/2015 3:00   1\/7\/2015 4:00   60\nIndividualY AreaZ   2\/7\/2015 4:00   2\/7\/2015 6:00   120\nIndividualY AreaW   2\/7\/2015 6:00   2\/7\/2015 7:00   60","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":445,"Q_Id":32880370,"Users Score":0,"Answer":"Zero323's solution works great but wanted to post an rdd implementation as well. I think this will be helpful for people trying to translate streaming MapReduce to pyspark. My implementation basically maps keys (individuals in this case) to a list of list for the streaming values that would associate with that key (areas and times) and then iterates over the list to satisfy the iterative component - and the rest is just normal reducing by keys and mapping.\n\n\n    from pyspark import SparkContext, SparkFiles, SparkConf\n    from datetime import datetime\n\n    conf = SparkConf()\n    sc = SparkContext(conf=conf)\n\n    rdd = sc.parallelize([\"IndividualX|AreaQ|1\/7\/2015 0:00\",\n                          \"IndividualX|AreaQ|1\/7\/2015 1:00\",\n                          \"IndividualX|AreaW|1\/7\/2015 3:00\",\n                          \"IndividualX|AreaQ|1\/7\/2015 4:00\",\n                          \"IndividualY|AreaZ|2\/7\/2015 4:00\",\n                          \"IndividualY|AreaZ|2\/7\/2015 5:00\",\n                          \"IndividualY|AreaW|2\/7\/2015 6:00\",\n                          \"IndividualY|AreaT|2\/7\/2015 7:00\"])\n\n    def splitReduce(x):\n        y = x.split('|')\n        return (str(y[0]),[[str(y[2]),str(y[1])]])\n\n    def resultSet(x):\n\n        processlist = sorted(x[1], key=lambda x: x[0])\n\n        result = []\n        start_area = processlist[0][1]\n        start_date = datetime.strptime(processlist[0][0], '%d\/%m\/%Y %H:%M')\n        dur = 0\n\n        if len(processlist) > 1:\n\n            for datearea in processlist[1::]:\n\n                end_date = datetime.strptime(datearea[0],'%d\/%m\/%Y %H:%M')\n                end_area = datearea[1]\n\n                dur = (end_date-start_date).total_seconds()\/60\n\n                if start_area != end_area:\n                    result.append([start_area,start_date,end_date,dur])\n                    start_date = datetime.strptime(datearea[0], '%d\/%m\/%Y %H:%M')\n                    start_area = datearea[1]\n                    dur = 0\n\n        return (x[0],result)\n\n    def finalOut(x):\n        return str(x[0]) + '|' + str(x[1][0]) + '|' + str(x[1][1]) + '|' + str(x[1][2]) + '|' + str(x[1][3])\n\n    footfall = rdd\\\n    .map(lambda x: splitReduce(x))\\\n    .reduceByKey(lambda a, b: a + b)\\\n    .map(lambda x: resultSet(x))\\\n    .flatMapValues(lambda x: x)\\\n    .map(lambda x: finalOut(x))\\\n    .collect()\n\n    print footfall\n\n\nProvides output of:\n\n['IndividualX|AreaQ|2015-07-01 00:00:00|2015-07-01 03:00:00|180.0',\n'IndividualX|AreaW|2015-07-01 03:00:00|2015-07-01 04:00:00|60.0',\n'IndividualY|AreaZ|2015-07-02 04:00:00|2015-07-02 06:00:00|120.0',\n'IndividualY|AreaW|2015-07-02 06:00:00|2015-07-02 07:00:00|60.0']","Q_Score":2,"Tags":"python,apache-spark,pyspark,mapreduce,apache-spark-sql","A_Id":32900703,"CreationDate":"2015-10-01T05:58:00.000","Title":"PySpark - Combining Session Data without Explicit Session Key \/ Iterating over All Rows","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been using caffe for a while, with some success, but I have noticed in examples given that there is only ever a two way split on the data set with TRAIN and TEST phases, where the TEST set seems to act as a validation set.\nIdeally I would like to have three sets, so that once the model is trained, I can save it and test it on a completely new test set - stored in a completed separate lmdb folder. Does anyone have any experience of this? Thanks.","AnswerCount":1,"Available Count":1,"Score":0.761594156,"is_accepted":false,"ViewCount":4078,"Q_Id":32908025,"Users Score":5,"Answer":"Differentiating between validation and testing is made to imply that hyperparameters may be tuned to the validation set while nothing is fitted to the test set in any way.\ncaffe doesn't optimize anything but the weights, and since the test is only there for evaluation, it does exactly as expected.\nAssuming you're tuning hyper parameters between solver optimization runs. The lmdb passed to caffe for testing is really the validation set. If you're done with tuning your hyperparameters and do one more solver optimization with an lmdb for testing that holds data never used in previous runs. That last lmdb is your test set.\nSince caffe doesn't optimize hyperparameters, its test set is what it is, a test set. It's possible to come up with a some python code around the solver optimization calls that iterates through hyperparameter values. After it's done it can swap in a new lmdb with unseen data to tell you about how well the network generalizes with it.\nI don't recommend modifying caffe for an explicit val\/test distinction. You don't even have to do anything elaborate with setting up the prototxt file for the solver and network definition. You can do the val\/test swap at the end by simply moving the val lmdb somewhere else and moving the test lmdb in its place using shutil.copy(src, dst)","Q_Score":2,"Tags":"python,machine-learning,neural-network,caffe,conv-neural-network","A_Id":32909946,"CreationDate":"2015-10-02T13:14:00.000","Title":"Caffe: train, validation and test split","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataset of 15M+ training instances in form of svmlight dataset. I read these data using sklearn.datasets.load_svmlight_file(). The data itself is not sparse, so I don't mind converting it to any other dense representation (I will prefer that).\nAt some point in my program I need to add millions of new data records (instances) to my training data (in random positions). I used vstack and also tried converting to dense matrices but was either inefficient or failed to give correct results (details below). Is there any way to do this task efficiently?\nI'm implementing DAgger algorithm and in the first iteration it is trying to add about 7M new training instances. I want to add these new instances in random positions. I tried vstack (given my data was in csr format I was expecting it not to be too inefficient at least). However after 24hours it's not done yet. \nI tried converting my data to numpy.matrix format just after loading them in svmlight format. A sampling showed it can help me speed things up but interestingly the results I get from training on the converted dataset and the original dataset seem not to match with each other. It appears sklearn does not work with numpy matrix in the way I thought. I couldn't find anything in the sklearn documentation.\nAnother approach I thought was to define a larger dataset from the beginning so that it will \"reserve\" enough space in memory, but when I'm using sklearn train or test features I'll index my dataset to the last \"true\" record. In this way, I presume, vstack will not require opening up a new large space in memory which can make the whole operation take longer. Any thoughts on this?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":162,"Q_Id":32912567,"Users Score":0,"Answer":"FIRST: I'm guessing the reason sparse data is giving a different answer than the same data converted to dense, is that my representation of sparse was starting feature indices from one rather than zero (because oll library that I used previously required so). So my first column was all zero, when converted to dense it was not preserved and that's the reason for slightly better results when using dense representation.\nSECOND: adding new rows to the sparse matrix in that scale is not efficient. Not even if you reserve a large matrix at the beginning (with padded zeros) to replace later. This can be because of the structure sparse matrix is stored in (It uses three arrays, in case of crs one for row number, one for non-zero column indices in rows and one for values themselves; check the documentation).\nSOLUTION: best way I found is to use dense representations from the beginning (if that's an option of course). Collect all the instances that need to be added to the training set. Instantiate a new matrix to the size of aggregated data and then start adding instances \"randomly\" from both last training set and also from new instances that you want to add. To make it random I generate a sorted list of random positions that tell me when I should add data from new instances otherwise copy from the older ones.","Q_Score":1,"Tags":"python-2.7,numpy,scipy,scikit-learn","A_Id":33026758,"CreationDate":"2015-10-02T17:23:00.000","Title":"inserting training instances to scikit-learn dataset","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When running theano, I get an error: not enough memory. See below.\nWhat are some possible actions that can be taken to free up memory?\nI know I can close applications etc, but I just want see if anyone has other ideas. For example, is it possible to reserve memory?\n\nTHEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python \n  conv_exp.py Using gpu device 0: GeForce GT 650M Trying to run under a\n  GPU.  If this is not desired, then modify network3.py to set the GPU\n  flag to False. Error allocating 156800000 bytes of device memory (out\n  of memory). Driver report 64192512 bytes free and 1073414144 bytes\n  total  Traceback (most recent call last):   File \"conv_exp.py\", line\n  25, in \n      training_data, validation_data, test_data = network3.load_data_shared()   File\n  \"\/Users\/xr\/courses\/deep_learning\/con_nn\/neural-networks-and-deep-learning\/src\/network3.py\",\n  line 78, in load_data_shared\n      return [shared(training_data), shared(validation_data), shared(test_data)]   File\n  \"\/Users\/xr\/courses\/deep_learning\/con_nn\/neural-networks-and-deep-learning\/src\/network3.py\",\n  line 74, in shared\n      np.asarray(data[0], dtype=theano.config.floatX), borrow=True)   File\n  \"\/Library\/Frameworks\/Python.framework\/Versions\/2.7\/lib\/python2.7\/site-packages\/theano\/compile\/sharedvalue.py\",\n  line 208, in shared\n      allow_downcast=allow_downcast, **kwargs)   File \"\/Library\/Frameworks\/Python.framework\/Versions\/2.7\/lib\/python2.7\/site-packages\/theano\/sandbox\/cuda\/var.py\",\n  line 203, in float32_shared_constructor\n      deviceval = type_support_filter(value, type.broadcastable, False, None) MemoryError: ('Error allocating 156800000 bytes of device memory\n  (out of memory).', \"you might consider using 'theano.shared(...,\n  borrow=True)'\")","AnswerCount":1,"Available Count":1,"Score":0.537049567,"is_accepted":false,"ViewCount":3994,"Q_Id":32936166,"Users Score":3,"Answer":"If borrow is set to true garbage collection is on (default true: config.allow_gc=True) and the video card is not currently being used as a display device (doubtful, since you're using a mobile gpu), the only other options are to reduce the parameters of the network or possibly the batch size of the model.  The latter will be especially effective if the model uses dropout or noise-based masks (these will be equal to the number of examples in the batch x the number of parameters dropped out or noised).  \nOtherwise maybe you could boot to the command prompt to save a few mbs?\n:\/","Q_Score":3,"Tags":"python-2.7,gpu,gpgpu,theano","A_Id":33177976,"CreationDate":"2015-10-04T17:18:00.000","Title":"How do you free up gpu memory?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have objects and a distance function, and want to cluster these using DBSCAN method in scikit-learn. My objects don't have a representation in Euclidean space. I know, that it is possible to useprecomputed metric, but in my case it's very impractical, due to large size of distance matrix. Is there any way to overcome this in scikit-learn?   Maybe, are there another python implementations of DBSCAN that can do so?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":6418,"Q_Id":32938494,"Users Score":0,"Answer":"I wrote my own distance code ref the top answer, just as it says, it was extremely slow, the built-in distance code was much better. I'm wondering how to speed up.","Q_Score":4,"Tags":"python,scikit-learn,cluster-analysis,data-mining,dbscan","A_Id":71779306,"CreationDate":"2015-10-04T21:15:00.000","Title":"DBSCAN (with metric only) in scikit-learn","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a pandas dataframe with a large number of columns and I need to find which columns are binary (with values 0 or 1 only) without looking at the data. Which function should be used?","AnswerCount":7,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":9639,"Q_Id":32982034,"Users Score":0,"Answer":"You can just use the unique() function from pandas on each column in your dataset.\nex: df[\"colname\"].unique()\nThis will return a list of all unique values in the specified column.\nYou can also use for loop to traverse through all the columns in the dataset.\nex: [df[cols].unique() for cols in df]","Q_Score":6,"Tags":"python,numpy,pandas","A_Id":64074702,"CreationDate":"2015-10-07T00:50:00.000","Title":"Which columns are binary in a Pandas DataFrame?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Given a sentence, Using python NLTK how can I know which Verb is talking about which Noun.  \nEg: Cat sat on the mat.\nHere \"sat(verb)\" is talking about \"Cat(noun)\".  \nConsider a complex sentence which has more nouns & verbs\nThank You.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1077,"Q_Id":32988413,"Users Score":1,"Answer":"That's a good suggestion, I will try it with anaphora too. \nFor now, my problem is solved by the concept of noun phrase & verb phrase.   \n\nI extracted clause(s) from the sentence\nidentified verbs & nouns in each, and\nrelated them through an iterative technique.  \n\nThank you for the help.","Q_Score":0,"Tags":"python,nlp,nltk","A_Id":32994584,"CreationDate":"2015-10-07T09:18:00.000","Title":"NLP - Find which Verb is talking about the Noun in a sentence","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Why doesn't Pandas build DataFrames directly from lists? Why was such a thing as a series created in the first place?\nOr: If the data in a DataFrame is actually stored in memory as a collection of Series, why not just use a collection of lists?\nYet another way to ask the same question: what's the purpose of Series over lists?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":221,"Q_Id":33027086,"Users Score":2,"Answer":"This isn't going to be a very complete answer, but hopefully is an intuitive \"general\" answer.\nPandas doesn't use a list as the \"core\" unit that makes up a DataFrame because Series objects make assumptions that lists do not. A list in python makes very little assumptions about what is inside, it could be pretty much anything, which makes it great as a core component of python.\nHowever, if you want to build a more specialized package that gives you extra functionality liked Pandas, then you want to create your own \"core\" data object and start building extra functionality on top of that. Compared with lists, you can do a lot more with a custom Series object (as witnessed by pulling a single column from a DataFrame and seeing what methods are available to the output).","Q_Score":0,"Tags":"python,pandas","A_Id":33027650,"CreationDate":"2015-10-08T22:37:00.000","Title":"What's the purpose of Series instead of lists in Pandas and Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using the function MiniBatchKMeans() from scikitlearn. Well, \nin its documentation there are:\n\nbatch_size : int, optional, default: 100\n  Size of the mini batches.\ninit_size : int, optional, default: 3 * batch_size\n  Number of samples to randomly sample for speeding up the initialization (sometimes at the expense of accuracy): the only algorithm is initialized by running a batch KMeans on a random subset of the data. This needs to be larger than n_clusters.\n\nI didn't understand it very well, because it seems that the final dimension of the mini batch is 3*batch_size and not the one specified by batch_size argument.\nAm I misunderstanding something. If so, some one can explain those two arguments. It I am right, why there are these two arguments since they seems to be redundant.\nThanks!!!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":879,"Q_Id":33039884,"Users Score":2,"Answer":"The batch size is defined by batch_size, period. Furthermore you can define init_size which is the size of samples taken to initiallize the process, and by default it is 3*batch_size. You can simply set bath_size=100 and init_size=10 and then 10 samples are used to perform initialization (kmeans is not globaly convergent, there are many techniques to deal with it onthe initialization stage) and later on batch of 100 will be used during the algorithm execution.","Q_Score":1,"Tags":"python,machine-learning,scikit-learn,cluster-computing","A_Id":33040012,"CreationDate":"2015-10-09T13:43:00.000","Title":"MiniBatchKMeans Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have downloaded a subset of million song data set which is about 2GB. However, the data is broken down into folders and sub folders. In the sub-folder they are all in several 'H5 file' format. I understand it can be read using Python. But I do not know how to extract and load then into HDFS so I can run some data analysis in Pig. \nDo I extract them as CSV and load to Hbase or Hive ? It would help if someone can point me to right resource.","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":726,"Q_Id":33043704,"Users Score":1,"Answer":"If it's already in the CSV or any format on the linux file system, that PIG can understand, just do a hadoop fs -copyFromLocal  to \nIf you want to read\/process the raw H5 File format using Python on HDFS, look at hadoop-streaming (map\/reduce)\nPython can handle 2GB on a decent linux system- not sure if you need hadoop for it.","Q_Score":0,"Tags":"python,hadoop,hive,hbase,bigdata","A_Id":33043867,"CreationDate":"2015-10-09T17:06:00.000","Title":"How to load big datasets like million song dataset into BigData HDFS or Hbase or Hive?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have downloaded a subset of million song data set which is about 2GB. However, the data is broken down into folders and sub folders. In the sub-folder they are all in several 'H5 file' format. I understand it can be read using Python. But I do not know how to extract and load then into HDFS so I can run some data analysis in Pig. \nDo I extract them as CSV and load to Hbase or Hive ? It would help if someone can point me to right resource.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":726,"Q_Id":33043704,"Users Score":0,"Answer":"Don't load such amount of small files into HDFS. Hadoop doesn't handle well lots of small files. Each small file will incur in overhead because the block size (usually 64MB) is much bigger.\nI want to do it myself, so I'm thinking of solutions. The million song dataset files don't have more than 1MB. My approach will be to aggregate data somehow before importing into HDFS. \nThe blog post \"The Small Files Problem\" from Cloudera may shed some light.","Q_Score":0,"Tags":"python,hadoop,hive,hbase,bigdata","A_Id":50411499,"CreationDate":"2015-10-09T17:06:00.000","Title":"How to load big datasets like million song dataset into BigData HDFS or Hbase or Hive?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"The following program displays 'foreground' completely black and not 'frame'. I also checked that all the values in 'frame' is equal to the values in 'foreground'.\nThey have same channels,data type etc.\nI am using python 2.7.6 and OpenCV version 2.4.8\n\n    import cv2\n    import numpy as np\n\n    def subtractBackground(frame,background):\n        foreground = np.absolute(frame - background)\n        foreground = foreground >= 0\n        foreground = foreground.astype(int)\n        foreground = foreground * frame\n        cv2.imshow(\"foreground\",foreground)\n        return foreground\n\n    def main():\n        cap = cv2.VideoCapture(0)\n        dump,background = cap.read()\n\n        while cap.isOpened():\n            dump,frame = cap.read()\n            frameCopy = subtractBackground(frame,background)\n            cv2.imshow('Live',frame)\n            k = cv2.waitKey(10)\n            if k == 32:\n                break\n\n    if __name__ == '__main__':\n        main()","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":3991,"Q_Id":33054711,"Users Score":0,"Answer":"replace  foreground = np.absolute(frame - background)\nwith foreground = cv2.absdiff(frame, background)","Q_Score":1,"Tags":"python-2.7,opencv","A_Id":67288557,"CreationDate":"2015-10-10T13:51:00.000","Title":"Subtracting Background From Image using Opencv in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to run a Python module using a Jupyter Notebook on Azure HDInsight, but I continue to get the following error message: A connection to the notebook server could not be established. The notebook will continue trying to reconnect, but until it does, you will NOT be able to run code. Check your network connection or notebook server configuration.\nI have an Azure subscription, created a cluster, created a storage blob, and have created a Jupyter Notebook. I am successfully logged into the cluster, so I am not sure why I cannot connect to the notebook. Any insight into this problem would be hugely appreciated.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2411,"Q_Id":33055691,"Users Score":0,"Answer":"Just saw this question way too late, but I will venture that you are using an unsupported browser.\nPlease use Chrome to connect to Jupyter.","Q_Score":0,"Tags":"python,azure,apache-spark,azure-hdinsight,jupyter","A_Id":38753037,"CreationDate":"2015-10-10T15:35:00.000","Title":"Cannot connect to Jupyter Notebook server in Azure HDInsight","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to import CSV files in a python script. Column and row numbers are not fixed , first row contains name of the variables and next rows are values of those variables. \nI am new to Python, any help is appreciated. thanks.","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":4532,"Q_Id":33080794,"Users Score":0,"Answer":"First of all, at the top of the code do import csv\nAfter that you need to set a variable name; this is so you can open the CSV file. For example, data=open('CSV name', 'rt') You will need to fill in where it says CSV name. That's how you open it. \nTo read a CSV file, you set another variable. For example, data2=csv.reader(data)","Q_Score":0,"Tags":"python,csv,python-3.x","A_Id":40980117,"CreationDate":"2015-10-12T12:10:00.000","Title":"importing csv file in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"The quality of video recording that is required for our project is not met by the webcams. Is it possible to use high megapixel digital cameras (Sony, Canon, Olympus) with OpenCV ?\nHow to talk to the digital cameras using OpenCV (and specifically using Python)","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1805,"Q_Id":33082220,"Users Score":0,"Answer":"Install Drivers for required camera, connect it, and use cv2.VideoCapture(int). Here, instead of 0, use a different integer according to the camera. By default, 0 is for the inbuilt webcam.\ne.g.: cv2.VideoCapture(1)","Q_Score":0,"Tags":"python,opencv","A_Id":33101046,"CreationDate":"2015-10-12T13:19:00.000","Title":"OpenCV - using digital cameras.","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to print a Dataframe to a text file,\nSay I have a table with 4 lines, and 12 columns.\nIt looks quite nice when I just use print df, with all the values of a column aligned to the right, however, when there are too many columns (8 in my case) it breaks the table down so that the last 4 columns are printed after 4 lines of 8 values. Probably as Pandas tries to make the table fit in a 80 chars line.\nI tried df.to_csv().replace(',','\\t'), but then entries longer than a tab cause a jump in the line, and the lines are no longer aligned.\nHow can I get the nice, orderly, aligned to right, fashion, but not enforcing 80 characters per line?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":259,"Q_Id":33086758,"Users Score":3,"Answer":"Try changing pandas.options.display.width. (It's 80 by default)","Q_Score":3,"Tags":"python,pandas","A_Id":33087239,"CreationDate":"2015-10-12T17:16:00.000","Title":"print a pandas dataframe to text with lines longer than 80 chars","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am currently reading up on t-SNE visualization technique and it was mentioned that one of the drawback of using PCA for visualizing high dimension data is that it only preserves large  pairwise distances between the points. Meaning points which are far apart in high dimension would also appear far apart in low dimensions but other than that all other points distances get screwed up. \nCould someone help me understand why is that and what does it mean graphically?. \nThanks a lot!","AnswerCount":2,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":1245,"Q_Id":33092493,"Users Score":6,"Answer":"Don't confuse PCA with dimensionality reduction.\nPCA is a rotation transformation that aligns the data with the axes in such a way that the first dimension has maximum variance, the second maximum variance among the remainder, etc. Rotations preserve pairwise distances.\nWhen you use PCA for dimensionality reduction, you discard dimensions of your rotated data that have the least variance. High variance is achieved when points are spread far from the mean. Low-variance dimensions are those, in which the values are mostly the same, so their absence is presumed to have the least effect on pairwise distances.","Q_Score":3,"Tags":"python,matplotlib,machine-learning,visualization,pca","A_Id":33094494,"CreationDate":"2015-10-13T00:55:00.000","Title":"What is meant by PCA preserving only large pairwise distances?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am currently reading up on t-SNE visualization technique and it was mentioned that one of the drawback of using PCA for visualizing high dimension data is that it only preserves large  pairwise distances between the points. Meaning points which are far apart in high dimension would also appear far apart in low dimensions but other than that all other points distances get screwed up. \nCould someone help me understand why is that and what does it mean graphically?. \nThanks a lot!","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1245,"Q_Id":33092493,"Users Score":0,"Answer":"If I can re-phrase @Don Reba's comment:\nThe PCA transformation itself does not alter distances.\nThe 2-dimensional plot often used to visualise the PCA results takes into account only two dimensions, disregards all the other dimensions, and as such this visualisation provides a distorted representation of distances.","Q_Score":3,"Tags":"python,matplotlib,machine-learning,visualization,pca","A_Id":66142281,"CreationDate":"2015-10-13T00:55:00.000","Title":"What is meant by PCA preserving only large pairwise distances?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am wondering if Orange can read in a text format rule file and use it to score another dataset. For example, a rule.txt file was previously created in Orange through rule_to_string function and contains rules in this IF...THEN  format:\n\"IF sex=['female'] AND status=['first'] THEN survived=yes\". Can Orange read in the rule.txt file and use it to score a test.csv dataset? Thank you very much for helping!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":86,"Q_Id":33112874,"Users Score":0,"Answer":"No, there is no function to do this. Apparently nobody ever needed it.\nYou can do it yourself, but if you know some Python it should be easier to test a list of rules without using Orange.","Q_Score":0,"Tags":"python,orange","A_Id":33124530,"CreationDate":"2015-10-13T21:23:00.000","Title":"Can Orange read in IF...THEN text format rule file and use it to score another dataset?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Which method of caching pandas DataFrame objcts will provide the highest performance? By storing it to a flat file on disk using pickle, or by storing it in a key-value store like Redis?","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":1886,"Q_Id":33150684,"Users Score":2,"Answer":"I have a DF of ~ 1 GB of plain text data. Assuming the dumping to disk is always slower than reading I compared HDF5 write performance with pickle.\nHDF5 took 35 sec while pickle did 190 sec. So, you could consider using HDF5 instead of pickle","Q_Score":0,"Tags":"python,caching,pandas,redis","A_Id":33152516,"CreationDate":"2015-10-15T14:05:00.000","Title":"Caching Pandas Dataframe by Serialization or In-memory KV Store","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Apparently, importing seaborn sets the marker edges in a matplotlib.pyplot.plot to zero or deletes them. \ne.g. plt.plot(x,y,maker='s',markerfacecolor='none')\nresults in a plot without markers. \nIs there a way to get the edges back?\nmarkeredgecolor='k' has no effect.","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":3009,"Q_Id":33161270,"Users Score":1,"Answer":"A solution to this is after importing seaborn do the following:\nmatplotlib.rcParams['lines.markeredgewidth'] = 1","Q_Score":4,"Tags":"python,matplotlib,seaborn","A_Id":44307542,"CreationDate":"2015-10-16T01:47:00.000","Title":"Seaborn Restore marker edges","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Apparently, importing seaborn sets the marker edges in a matplotlib.pyplot.plot to zero or deletes them. \ne.g. plt.plot(x,y,maker='s',markerfacecolor='none')\nresults in a plot without markers. \nIs there a way to get the edges back?\nmarkeredgecolor='k' has no effect.","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":3009,"Q_Id":33161270,"Users Score":1,"Answer":"Give edgecolor='k' a try. This worked for me in a similar scatter plot.","Q_Score":4,"Tags":"python,matplotlib,seaborn","A_Id":43644522,"CreationDate":"2015-10-16T01:47:00.000","Title":"Seaborn Restore marker edges","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using python pandas to read csv file. The csv file has a datetime column that has second precisions \"9\/1\/2015  9:25:00 AM\", but if I open in excel, it has only minute precisions \"9\/1\/15 9:25\". Moreover, when I use the pd.read_csv() function, it only shows up to minute precision. Is there any way that I could solve the problem using python? Thanks much in advance.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1815,"Q_Id":33162320,"Users Score":0,"Answer":"This is a time formatting problem\/philosophy by Excel.  For some reason, Microsoft prefers to hide seconds and sub-seconds on user displays:   even MSDOS's dir command omitted seconds.\nIf I were you, I'd use Excel's format operation and set it to display seconds, then save the spreadsheet as CSV and see if it put anything in it to record the improved formatting.\nIf that doesn't work, you might explore creating a macro which does the formatting, or use one of the IPC to Excel to command it to do your bidding.","Q_Score":2,"Tags":"python,excel,csv,pandas","A_Id":33162435,"CreationDate":"2015-10-16T03:56:00.000","Title":"Use Python to Change csv Data Column Format","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I wonder if anyone can give me a pointer to really fast\/efficient Kalman filter implementation, possibly in Python (or Cython, but C\/C++ could also work if it is much faster). I have a problem with many learning epochs (possibly hundreds of millions), and many input (cues; say, between tens to hundred thousands). Thus, updating a covariance matrix will be a big issue. I read a bit about Ensemble KF, but, for now, I would really like to stick with the standard KF. [ I started reading and testing it, and I would like to give it a try with my real data. ]","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":1111,"Q_Id":33165668,"Users Score":2,"Answer":"The size of the covariance matrix is driven by the size of your state. Another question relates to the assumptions on your model and if this can bring up significant optimizations (obviously, optimizing implies reworking the \"standard KF\"). \nFrom my POV, your situation roughly depends on the value (number_of_states\u00b2 * number_of_iterations)\/(processing_power).","Q_Score":1,"Tags":"python,cython,kalman-filter","A_Id":33167366,"CreationDate":"2015-10-16T08:21:00.000","Title":"Fast Kalman Filter","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I wonder if anyone can give me a pointer to really fast\/efficient Kalman filter implementation, possibly in Python (or Cython, but C\/C++ could also work if it is much faster). I have a problem with many learning epochs (possibly hundreds of millions), and many input (cues; say, between tens to hundred thousands). Thus, updating a covariance matrix will be a big issue. I read a bit about Ensemble KF, but, for now, I would really like to stick with the standard KF. [ I started reading and testing it, and I would like to give it a try with my real data. ]","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":1111,"Q_Id":33165668,"Users Score":1,"Answer":"If you have many measurements per update, you should look at the information form of the Kalman filter.  Each additional measurement is just addition.  The tradeoff is a more complex predict step, and the cost of inverting the information matrix whenever you want to get your state out.","Q_Score":1,"Tags":"python,cython,kalman-filter","A_Id":33264437,"CreationDate":"2015-10-16T08:21:00.000","Title":"Fast Kalman Filter","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a classification problem with time series data.\nEach example has 10 variables which are measured at irregular intervals and in the end the object is classified into 1 of the 2 possible classes (binary classification).\nI have only the final class of the example to learn from during training. But when given a new example, I would like to make a prediction at each timestamp (in an online manner). So, if the new example had 25 measurements, I would like to make 25 predictions of its class; one at each timestamp.\nThe way I am implementing this currently is by using the min, mean and max of the measurements of its 10 variables till that point as features for classification. Is this optimal ? What would be a better way.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":317,"Q_Id":33168836,"Users Score":1,"Answer":"If you have to make predictions at each time stamp, then this doesn't become a a time series problem (unless you plan to use the sequence of previous observations to make your next prediction, in which case you will need to train a sequence based model). Assuming you can only train a model based on the final data you observe, there can be many approaches, but I'd recommend you use Random Forest with large number of trees and 3 or 4 variables in each tree. That way even if some variables don't give you the desired input other trees can still make predictions to a fair amount of accuracy. Besides this there can be many ensemble approaches.\nThe way you're currently doing may be a very loose approximation and practical but doesn't make much statistical sense.","Q_Score":1,"Tags":"python,r,machine-learning,scikit-learn","A_Id":33170242,"CreationDate":"2015-10-16T10:59:00.000","Title":"Implementing online learning with time series","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a code that makes Minimum Spanning Trees of many sets of points (about 25000 data sets containing 40-10000 points in each set) and this is obviously taking a while. I am using the MST algorithm from scipy.sparse.csgraph.\nI have been told that the MST is a subset of the Delaunay Triangulation, so it was suggested I speed up my code by finding the DT first and finding the MST from that.\nDoes anyone know how much difference this would make? Also, if this makes it quicker, why is it not part of the algorithm in the first place? If it is quicker to calculate the DT and then the MST, then why would scipy.sparse.csgraph.minimum_spanning_tree do something else instead?\nPlease note: I am not a computer whizz, some people may say I should be using a different language but Python is the only one I know well enough to do this sort of thing, and please use simple language in your answers, no jargon please!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":659,"Q_Id":33172090,"Users Score":1,"Answer":"NB: this assumes we're working in 2-d\nI suspect that what you are doing now is feeding all point to point distances to the MST library. There are on the order of N^2 of these distances and the asymptotic runtime of Kruskal's algorithm on such an input is N^2 * log N.\nMost algorithms for Delaunay triangulation take N log N time. Once the triangulation has been computed only the edges in the triangulation need to be considered (since an MST is always a subset of the triangulation). There are O(N) such edges so the runtime of Kruskal's algorithm in scipy.sparse.csgraph should be N log N. So this brings you to an asymptotic time complexity of N log N.  \nThe reason that scipy.sparse.csgraph doesn't incorporate Delaunay triangulation is that the algorithm works on arbitrary input, not only Euclidean inputs.\nI'm not quite sure how much this will help you in practice but that's what it looks like asymptotically.","Q_Score":0,"Tags":"python,algorithm,performance,minimum-spanning-tree,delaunay","A_Id":33175701,"CreationDate":"2015-10-16T13:45:00.000","Title":"Speed up Python MST calculation using Delaunay Triangulation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've a nx2 ndarray which represent a height profile of the form h(x), with x being a non-negative real number and h(x) the height value in x. The x-values are irregular distributed, meaning:\nx[i] - x[i - 1] != x[i + 1] - x[i]\nI would like to take my array and create a new one with evenly spaced x-values with the corresponding heights. The distance between the x-values can be any positive number. Is there an efficient way to do something like this using numpy?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":142,"Q_Id":33194779,"Users Score":0,"Answer":"Following Warren Weckesser's comment, the answer is using scipy.interpolate.interp1d","Q_Score":1,"Tags":"python,arrays,numpy,resampling","A_Id":33197300,"CreationDate":"2015-10-18T05:51:00.000","Title":"Resampling an irregular distributed 1-D signal in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to calculate the contrast of an color image, so the steps that was given to me are,\n\ncomputed the histogram for RGB channel separately and combined it together as Histogram = histOfRedC + histOfBlueC + histOfgreenC.\nnormalize it to unit length, as each image is of different size.\nThe contrast quality,  is equal to the width of the middle 98% mass of the histogram.\n\nI have done the first 2 steps but unable to understand what to compute in 3rd step. Can somebody please explain me what it means?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":140,"Q_Id":33196427,"Users Score":0,"Answer":"Let the total mass of the histogram be M.\nAccumulate the mass in the bins, starting from index zero, until you pass 0.01 M. You get an index Q01.\nDecumulate the mass in the bins, starting from the maximum index, until you pass 0.99 M. You get an index Q99.\nThese indexes are the so-called first and last percentiles. The contrast is estimated as Q99-Q01.","Q_Score":0,"Tags":"python-2.7,image-processing,histogram,contrast","A_Id":33197079,"CreationDate":"2015-10-18T09:36:00.000","Title":"How to Calculate width of the middle 98% mass of the gray level histogram of a image","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How to extract data from plot data filter in paraview through python script?? I want to get data through python script by which paraview is drawing the graph.\nIf anyone know this answer please help\nThank you","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":628,"Q_Id":33216350,"Users Score":1,"Answer":"Usually plots are made by plotting one data array versus another. You can often obtain that data directly from the filter\/source that produced it and save it to a CSV file. To do this, select \nYou can save data from a filter as a CSV file by selecting the filter\/source in the Pipeline Browser and choosing File -> Save Data. Choose the CSV File (*.csv) file type. Arrays in the filter\/source output are written to different columns in the CSV file.","Q_Score":0,"Tags":"python,paraview","A_Id":33282334,"CreationDate":"2015-10-19T13:55:00.000","Title":"extracting the data through python script in paraview","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataframe with two indexes. (Both timestamps but thats probably not relevant).\nI need to get out a numpy matrix with shape (len(first_index), maxlen, num_columns).\nmaxlen is some number (likely the max of all of the len(second_index)) or just something simple like 1000. \nI can do this with arr = df.as_matrix(...) and then arr.resize((len(first_index), maxlen, num_columns)). Elements in new rows should be 0 so .resize(...) works well.\nIs there a simpler and more efficient way to do this within the dataframe? Numpy works just fine but I need maximum efficiency because I have millions of rows.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1511,"Q_Id":33227369,"Users Score":0,"Answer":"If you want to stay within 'Pandas', I would suggest one of the following:\ndf.unstack() which would result in shape (len(index2), maxlen * num_columns) following your notation; here columns will be stored as a MultiIndex.\nAlternatively, you can use df.to_panel(); Panel is a natural Pandas data structure used for 3 dimensions, as in your case. I believe that the shape should be (num_columns, len(index1), maxlen).\nYou can then fill any nans with .fillna(0).","Q_Score":0,"Tags":"python,arrays,numpy,pandas,resize","A_Id":33228345,"CreationDate":"2015-10-20T03:15:00.000","Title":"Pandas' version of numpy.resize for efficient matrix resizing","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using OpenCV 3.0.0 on Python 2.7.9. I'm trying to track an object in a video with a still background, and estimate some of its properties. Since there can be multiple moving objects in an image, I want to be able to differentiate between them and track them individually throughout the remaining frames of the video.\nOne way I thought I could do that was by converting the image to binary, getting the contours of the blobs (tracked object, in this case) and get the coordinates of the object boundary. Then I can go to these boundary coordinates in the grayscale image, get the pixel intensities surrounded by that boundary, and track this color gradient\/pixel intensities in the other frames. This way, I could keep two objects separate from each other, so they won't be considered as new objects in the next frame.\nI have the contour boundary coordinates, but I don't know how to retrieve the pixel intensities within that boundary. Could someone please help me with that?\nThanks!","AnswerCount":3,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":32084,"Q_Id":33234363,"Users Score":3,"Answer":"Answer from @rayryeng is excellent!\nOne small thing from my implementation is:\nThe np.where() returns a tuple, which contains an array of row indices and an array of column indices. So, pts[0] includes a list of row indices, which correspond to height of the image, pts[1] includes a list of column indices, which correspond to the width of the image. The img.shape returns (rows, cols, channels). So I think it should be img[pts[0], pts[1]] to slice the ndarray behind the img.","Q_Score":15,"Tags":"python,image,opencv,image-processing,opencv-contour","A_Id":42767296,"CreationDate":"2015-10-20T10:35:00.000","Title":"Access pixel values within a contour boundary using OpenCV in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to plot the learning curves for my SVC classifier with sklearn.learning_curve. From the plot, I find that both of my training scores and test scores increases simultaneously. But the gap between the training curve and cross-validation curve becomes larger with the increasing number of the samples. As I know, the training scores should decrease when more samples supplied. Do you guys have any sense about this problem?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":959,"Q_Id":33249904,"Users Score":0,"Answer":"If the gap between the training and cross-validation accuracy is increasing then this is an indication that your model is overfitting on the training data.\nWith every iteration (supplying additional training data) your model is better able to capture the training data, however it is no longer able to better generalise (and thus the cross-validation accuracy converges).","Q_Score":0,"Tags":"python,machine-learning,scikit-learn","A_Id":33290142,"CreationDate":"2015-10-21T02:23:00.000","Title":"About learning curves","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a question per below - I need to transform multiple rows of ID into one row, and let the different \"output\"-values become columns with binary 1\/0, like example.\nHere is my table!\n\nID    Output      Timestamp\n1     out1        1501\n1     out2        1501\n1     out5        1501\n1     out9        1501\n2     out3        1603\n2     out4        1603\n2     out9        1603\n\nTo be transformed into the following:\n\nID     out1     out2     out3     out4     out5     out9      timestamp\n1      1        1        0        0        1        1         1501\n2      0        0        1        1        0        1         1603\n\nCan someone help me do this in a flexible way in Python, preferably Pandas? I'm quite new to this, have been using SAS for a good many years so any \"transition tips\" are greatly appreciated.\nBr,","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1859,"Q_Id":33261261,"Users Score":0,"Answer":"You need to make a dictionary, where the key is the id.  Each value of that is going to be another dictionary of outN to value.\nRead a line. You get an id, outN, and a value. Check you have an dict for that id first, and if not, create one. Then shove the value for that outN into the dict for that id.\nSecond step: You need to collect a list of all the outNs. Make a new set. For each value in your dict, add each of its outN keys to your set. At the end, get a list from the set, and sort it.\nThird step: Go through each id in your dict keys, and then each outn in your new sorted list of outns, and print the value of that, with a fallback to zero.   outnval_by_ids[id].get(outn, \"0\")\nThere's a weird case here, in that you have a lot of timestamps that you are assuming are duplicate by id. Be careful that is really the case. Assumptions like that cause bugs.","Q_Score":2,"Tags":"python,pandas,group-by","A_Id":33261884,"CreationDate":"2015-10-21T13:54:00.000","Title":"Python multiple rows to one row","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Let's assume I've got a 4 nodes Hadoop cluster (Cloudera distro in my case) with a user named 'hadoop' on each node ('\/home\/hadoop'). Also, I've got a fifth server with installed on it, Jupyter and Anaconda with a user named 'ipython', but without hadoop installation.\nLet's say I want to start Jupyter remotely from that fifth server in 'yarn_client' mode by keeping the 'ipython' user, my problem is that I've got an issue from logs which says that the user 'ipython' isn't allowed (or something like that).\nFor info I copied-paste a dummy directory (to set the HADOOP_CONF_DIR environment variable) from the Hadoop cluster to that fifth server. Everything works well with the 'local[*]' setting in my 'kernel.json' file (fortunately), but the issue appears back when I change the master value into 'yarn_client' (unfortunately)...\nIs there a trick to solve that issue ? Or maybe several different tricks ?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1327,"Q_Id":33292063,"Users Score":0,"Answer":"I have a working deployment of CDH5.5 + jupyter with pyspark and scala native spark. In my case I am using a dedicated user to start a jupyter server and then connecting to it from a client browser.\nBefore sharing some thoughts about your problem I would like to point out that if your fifth server is not close connected to your cluster you should avoid launching pyspark in yarn-client mode, as the communication latency would surely slow your jobs. As far as I know yarn-cluster mode cannot be invoked remotely without pyspark-submit \nIf you still want your driver node to be executing in that 5th server, make sure that your user \"ipython\" has the correct permission to access hdfs and other hadoop conf directories, you might need to create that user in your other hadoop nodes.\nAlso make sure that your yarn-conf.xml is correctly configured to reflect the address of your yarn ResourceManager.","Q_Score":1,"Tags":"hadoop,apache-spark,ipython,pyspark,jupyter","A_Id":34088151,"CreationDate":"2015-10-22T22:50:00.000","Title":"Spark: How to start remotely Jupyter in 'yarn_client' mode from a different user","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Usually I do plotting inside of IPython Notebook with pylab mode.\nWhenever I use Bokeh, I like to enable output_notebook() to show my plot inside of the IPython notebook.\nMost annoying part is that Bokeh enable wheel_zoom by default which cause unintended zoom in IPython notebook.\nI know I can avoid this by passing comma separated tools string what I want to include into bokeh.plotting.figure. but with this solution, I should list up the other tools but wheel_zoom.\nIs there any way to exclude wheel_zoom only? or Can I disable wheel_zoom in global setting or something like that?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":513,"Q_Id":33324475,"Users Score":2,"Answer":"There is an open PR to improve this, it will be in the the 0.11 release.","Q_Score":3,"Tags":"python,bokeh","A_Id":33339510,"CreationDate":"2015-10-24T23:31:00.000","Title":"How to disable wheel_zoom in Bokeh?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"That is the difference between groupby(\"x\").count and groupby(\"x\").size in pandas ?\nDoes size just exclude nil ?","AnswerCount":5,"Available Count":1,"Score":0.0399786803,"is_accepted":false,"ViewCount":60325,"Q_Id":33346591,"Users Score":1,"Answer":"When we are dealing with normal dataframes then only difference will be an inclusion of NAN values, means count does not include NAN values while counting rows.\nBut if we are using these functions with the groupby then, to get the correct results by count() we have to associate any numeric field with the groupby to get the exact number of groups where for size() there is no need for this type of association.","Q_Score":131,"Tags":"python,pandas,numpy,nan,difference","A_Id":42668700,"CreationDate":"2015-10-26T13:08:00.000","Title":"What is the difference between size and count in pandas?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there a way to prevent PyTables from printing out \nClosing remaining open files:path\/to\/store.h5...done?\nI want to get rid of it just because it is clogging up the terminal.\nI'm using pandas.HDFStore if that matters.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1063,"Q_Id":33350153,"Users Score":0,"Answer":"You really have to close the open store manually.  There is no other way.\nWhy?   PyTables uses a file registry to track open files.  A destructor for this file registry is registered with Python's atexit module, which is called when the Python interpreter exits.  If this destructor method is called, it will print out the names of every open file.  This feature is not configurable.","Q_Score":4,"Tags":"python,pandas,pytables","A_Id":52785994,"CreationDate":"2015-10-26T15:55:00.000","Title":"Preventing PyTables (in Pandas) from printing \"Closing remaining open files...\"","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want a random number between 0 and 1, like 0.3452. I used random.randrange(0, 1) but it is always 0 for me. What should I do?","AnswerCount":7,"Available Count":1,"Score":0.1137907297,"is_accepted":false,"ViewCount":517528,"Q_Id":33359740,"Users Score":4,"Answer":"random.randrange(0,2) this works!","Q_Score":207,"Tags":"python,random","A_Id":37483626,"CreationDate":"2015-10-27T03:59:00.000","Title":"Random number between 0 and 1?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to use \"tkinter\", \"opencv\" (cv2) and \"numpy\" in windows(8 - 64 bit and x64) with python2.7 - the same as I have running perfectly well in Linux (Elementary and DistroAstro) on other machines. I've downloaded the up to date Visual Studio and C++ compiler and installed these, as well as the latest version of PIP following error messages with the first attempts with PIP and numpy\n\nfirst I tried winpython, which already has numpy present but this comes without tkinter, although openCV would install. I don't want to use qt.\nso I tried vanilla Python, which installs to Python27. Numpy won't install with PIP or EasyInstall (unless it takes over an hour -same for SciPy), and the -.exe installation route for Numpy bombs becausee its looking for Python2.7 (not Python27). openCV won't install with PIP (\"no suitable version\")\nextensive searches haven't turned up an answer as to how to get a windows Python 2.7.x environment with all three of numpy, tkinter and cv2 working.\n\nAny help would be appreciated!","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":170,"Q_Id":33418678,"Users Score":0,"Answer":"Finally did it with .whl files. Download them, copy to C:\\python27\\Scripts and then open \"cmd\" and navigate to that folder with \"cd\\\" etc. Once there run:\npip install numpy-1.10.1+mkl-cp27-none-win_amd64.whl\nfor example.\nIn IDLE I then get:\nimport numpy\nnumpy.version\n'1.10.1'","Q_Score":0,"Tags":"python,windows,opencv,numpy,tkinter","A_Id":33434056,"CreationDate":"2015-10-29T15:41:00.000","Title":"tkinter opencv and numpy in windows with python2.7","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":1,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to use \"tkinter\", \"opencv\" (cv2) and \"numpy\" in windows(8 - 64 bit and x64) with python2.7 - the same as I have running perfectly well in Linux (Elementary and DistroAstro) on other machines. I've downloaded the up to date Visual Studio and C++ compiler and installed these, as well as the latest version of PIP following error messages with the first attempts with PIP and numpy\n\nfirst I tried winpython, which already has numpy present but this comes without tkinter, although openCV would install. I don't want to use qt.\nso I tried vanilla Python, which installs to Python27. Numpy won't install with PIP or EasyInstall (unless it takes over an hour -same for SciPy), and the -.exe installation route for Numpy bombs becausee its looking for Python2.7 (not Python27). openCV won't install with PIP (\"no suitable version\")\nextensive searches haven't turned up an answer as to how to get a windows Python 2.7.x environment with all three of numpy, tkinter and cv2 working.\n\nAny help would be appreciated!","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":170,"Q_Id":33418678,"Users Score":0,"Answer":"small remark: WinPython has tkinter, as it's included by Python Interpreter itself","Q_Score":0,"Tags":"python,windows,opencv,numpy,tkinter","A_Id":33441221,"CreationDate":"2015-10-29T15:41:00.000","Title":"tkinter opencv and numpy in windows with python2.7","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":1,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a pivot table which has an index of dates ranging from 01-01-2014 to 12-31-2015. I would like the index to range from 01-01-2013 to 12-31-2016 and do not know how without modifying the underlying dataset by inserting a row in my pandas dataframe with those dates in the column I want to use as my index for the pivot table.\nIs there a way to accomplish this wihtout modifying the underlying dataset?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":214,"Q_Id":33420633,"Users Score":0,"Answer":"I'm going to be general here, since there was no sample code or data provided. Let's say your original dataframe is called df and has columns Date and Sales.\nI would try creating a list that has all dates from 01-01-2014 to 12-31-2015. Let's call this list dates. I would also create an empty list called sales (i.e. sales = []). At the end of this workflow, sales should include data from dt['Sales'] AND placeholders for dates that are not within the data frame. In your case, these placeholders will be 0. In my answer, the names of the columns in the dataframe are capitalized; names of lists start with a lower case. \nNext, I would iterate through dates and check to see if each date is in dt['Date']. Each iteration through the list dates will be called date (i.e. date = dates[i]).\n\nIf date is in dt['Date'], I would append the Sales data for that date into sales. You can find the date in the dataframe through this command: df['Date']==date.  So, to append the corresponding Sales data into the list, I would use this command sales.append(df[df['Date']==date]['Sales'].\nIf date is NOT in dt['Date'], I would append a placeholder into sales (i.e. sales.append(0).\n\nOnce you iterate through all the dates in the list, I would create the final dataframe with dates and sales. The final dataframe should have both your original data and placeholders for dates that were not in the original data.","Q_Score":1,"Tags":"python,python-2.7,pandas,dataframe,pivot-table","A_Id":33421040,"CreationDate":"2015-10-29T17:14:00.000","Title":"Padding python pivot tables with 0","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been tasked with solving a sentiment classification problem using scikit-learn, python, and mapreduce. I need to use mapreduce to parallelize the project, thus creating multiple SVM classifiers. I am then supposed to \"average\" the classifiers together, but I am not sure how that works or if it is even possible. The result of the classification should be one classifier, the trained, averaged classifier. \nI have written the code using scikit-learn SVM Linear kernel, and it works, but now I need to bring it into a map-reduce, parallelized context, and I don't even know how to begin.\nAny advice?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":453,"Q_Id":33450285,"Users Score":0,"Answer":"Make sure that all of the required libraries (scikit-learn, NumPy, pandas) are installed on every node in your cluster.\nYour mapper will process each line of input, i.e., your training row and emit a key that basically represents the fold for which you will be training your classifier.\nYour reducer will collect the lines for each fold and then run the sklearn classifier on all lines for that fold.\nYou can then average the results from each fold.","Q_Score":1,"Tags":"python,mapreduce,scikit-learn,svm","A_Id":35586970,"CreationDate":"2015-10-31T09:59:00.000","Title":"Combining SVM Classifiers in MapReduce","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a huge dataframe, and I index it like so:\ndf.ix[<integer>]\nDepending on the index, sometimes this will have only one row of values.  Pandas automatically converts this to a Series, which, quite frankly, is annoying because I can't operate on it the same way I can a df.  \nHow do I either:\n1)  Stop pandas from converting and keep it as a dataframe ?\nOR\n2)  easily convert the resulting series back to a dataframe ?\npd.DataFrame(df.ix[<integer>]) does not work because it doesn't keep the original columns.  It treats the <integer> as the column, and the columns as indices.  Much appreciated.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1239,"Q_Id":33458865,"Users Score":8,"Answer":"You can do df.ix[[n]] to get a one-row dataframe of row n.","Q_Score":5,"Tags":"python,pandas,dataframe,series","A_Id":33458868,"CreationDate":"2015-11-01T03:15:00.000","Title":"how to make 1 by n dataframe from series in pandas?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"This question is on Augmented Dickey\u2013Fuller test implementation in statsmodels.tsa.stattools python library - adfuller().\nIn principle, AIC and BIC are supposed to compute information criterion for a set of available models and pick up the best (the one with the lowest information loss).\nBut how do they operate in the context of Augmented Dickey\u2013Fuller?\nThe thing which I don't get: I've set maxlag=30, BIC chose lags=5 with some informational criterion. I've set maxlag=40 - BIC still chooses lags=5 but the information criterion have changed! Why in the world would information criterion for the same number of lags differ with maxlag changed?\nSometimes this leads to change of the choice of the model, when BIC switches from lags=5 to lags=4 when maxlag is changed from 20 to 30, which makes no sense as lag=4 was previously available.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1081,"Q_Id":33464294,"Users Score":2,"Answer":"When we request automatic lag selection in adfulller, then the function needs to compare all models up to the given maxlag lags. For this comparison we need to use the same observations for all models. Because lagged observations enter the regressor matrix we loose observations as initial conditions corresponding to the largest lag included.\nAs a consequence autolag uses nobs - maxlags observations for all models. For calculating the test statistic for adfuller itself, we don't need model comparison anymore and we can use all observations available for the chosen lag, i.e. nobs - best_lag.\nMore general, how to treat initial conditions and different number of initial conditions is not always clear cut, autocorrelation and partial autocorrelation are largely based on using all available observations, full MLE for AR and ARMA models uses the stationary model to include the initial conditions, while conditional MLE or least squares drops them as necessary.","Q_Score":3,"Tags":"python,statsmodels","A_Id":33479441,"CreationDate":"2015-11-01T16:16:00.000","Title":"How exactly BIC in Augmented Dickey\u2013Fuller test work in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need several very similar plotting functions in python that share many arguments, but differ in some and of course also differ slightly in what they do. This is what I came up with so far:\n\nObviously just defining them one after the other and copying the code they share is a possibility, though not a very good one, I reckon.\nOne could also transfer the \"shared\" part of the code to helper functions and call these from inside the different plotting functions. This would make it tedious though, to later add features that all functions should have.\nAnd finally I've also thought of implementing one \"big\" function, making possibly not needed arguments optional and then deciding on what to do in the function body based on additional arguments. This, I believe, would make it difficult though, to find out what really happens in a specific case as one would face a forest of arguments.\n\nI can rule out the first option, but I'm hard pressed to decide between the second and third. So I started wondering: is there another, maybe object-oriented, way? And if not, how does one decide between option two and three?\nI hope this question is not too general and I guess it is not really python-specific, but since I am rather new to programming (I've never done OOP) and first thought about this now, I guess I will add the python tag.\nEDIT:\nAs pointed out by many, this question is quite general and it was intended to be so, but I understand that this makes answering it rather difficult. So here's some info on the problem that caused me to ask:\nI need to plot simulation data, so all the plotting problems have simulation parameters in common (location of files, physical parameters,...). I also want the figure design to be the same. But depending on the quantity, some plots will be 1D, some 2D, some should contain more than one figure, sometimes I need to normalize the data or take a logarithm before plotting it. The output format might also vary.\nI hope this helps a bit.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":444,"Q_Id":33465685,"Users Score":0,"Answer":"More information needs to be given to fully understand the context. But, in a general sense, I'd do a mix of all of them. Use helper functions for \"shared\" parts, and use conditional statements too. Honestly, a lot of it comes down to just what is easier for you to do?","Q_Score":1,"Tags":"python,oop","A_Id":33465756,"CreationDate":"2015-11-01T18:32:00.000","Title":"What is a good way to implement several very similar functions?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a simple scikit-learn Pipeline of two steps: a TfIdfVectorizer followed by a LinearSVC.\nI have fit the pipeline using my data. All good.\nNow I want to transform (not predict!) an item, using my fitted pipeline.\nI tried pipeline.transform([item]), but it gives a different result compared to pipeline.named_steps['tfidf'].transform([item]). Even the shape and type of the result is different: the first is a 1x3000 CSR matrix, the second a 1x15000 CSC matrix. Which one is correct? Why do they differ?\nHow do I transform items, i.e. get an item's vector representation before the final estimator, when using scikit-learn's Pipeline?","AnswerCount":2,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":7410,"Q_Id":33469633,"Users Score":7,"Answer":"The reason why the results are different (and why calling transform even workds) is that LinearSVC also has a transform (now deprecated) that does feature selection\nIf you want to transform using just the first step, pipeline.named_steps['tfidf'].transform([item]) is the right thing to do.\nIf you would like to transform using all but the last step, olologin's answer provides the code.\nBy default, all steps of the pipeline are executed, so also the transform on the last step, which is the feature selection performed by the LinearSVC.","Q_Score":10,"Tags":"python,machine-learning,scikit-learn","A_Id":33504368,"CreationDate":"2015-11-02T02:01:00.000","Title":"How to transform items using sklearn Pipeline?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to read data from text file (which is output given by Tesseract OCR) and save the same in excel file. The problem i am facing here is the text files are in space separated format, and there are multiple files. Now i need to read all the files and save the same in excel sheet.\nI am using MATLAB to import and export data. I even thought of using python to convert the files into CSV format so that i can easily import the same in MATLAB and simply excelwrite the same. But no good solution.\nAny guidance would be of great help.\nthank you","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":212,"Q_Id":33479646,"Users Score":0,"Answer":"To read a text file in Matlab you can use fscanf or textscan then to export to excel you can use xlswrite that write directly to the excel file.","Q_Score":0,"Tags":"matlab,python-2.7,csv,export-to-csv","A_Id":33481202,"CreationDate":"2015-11-02T14:14:00.000","Title":"Importing data from text file and saving the same in excel","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've created a flask application that I'm trying to deploy on an apache server. I've installed a conda distribution of python where I've downloaded associated modules, including flask, matplotlib and others. I'm using wsgi to launch the application.\nThe problem I'm having is when the server runs wsgi script it fails saying that when trying to import matplotlib it can't find the correct version libz\n\nImportError: \/lib64\/libz.so.1: version `ZLIB_1.2.3.4' not found (required by \/mypath\/miniconda\/lib\/python2.7\/site-packages\/matplotlib\/..\/..\/..\/.\/libpng16.so.16)\n\nHowever the correct version of libz is found at \/mypath\/miniconda\/lib\/libz.*\nThe wsgi module was built with this version of python. In addition the apache init script sets the PATH environment variable this location of python (and there are no other python 2.7 on the system). When I print the ldd path of libpng via the wsgi script it points to the python version of libz as the one it should be loading.\n\nlinux-vdso.so.1 =>  (0x00007fff9fe00000)\nlibz.so.1 => \/mypath\/miniconda\/lib\/python2.7\/site-packages\/matplotlib\/..\/..\/..\/.\/.\/libz.so.1 (0x00007fb2e4388000)\nlibm.so.6 => \/lib64\/libm.so.6 (0x00007fb2e40e8000)\nlibc.so.6 => \/lib64\/libc.so.6 (0x00007fb2e3d50000)\n\/lib64\/ld-linux-x86-64.so.2 (0x00000035a9e00000)\n\nso why is it trying to load from \/lib64 ?? When I try load the module via the same python from a terminal, it loads fine. I understand my environment is not going to be the same as the apache environment but offhand I couldn't see any major differences.\nI haven't tried explicitly setting the LD_LIBRARY_PATH or WSGIPythonHome, neither which seem like they should be necessary. But that's the next avenue I'll try. Even if that works (but especially if it doesn't), I'd be curious if anyone has any ideas as to what's going on.\nThanks in advance.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":725,"Q_Id":33497639,"Users Score":1,"Answer":"Copy all the files libz.so* to any path in your LD_LIBRARY_PATH\nShort story long, I have miniconda and stuck at the same issue. I realised that conda prefer to search for library in LD_LIBRARY_PATH than its own libs.\nHence, you need to make missing library available in LD_LIBRARY_PATH, adding the whole conda lib directory to LD_LIBRARY_PATH never a good idea (i.e. it just breaks your whole system). \nAs a result, copy appropriate lib from conda library to any folder in your LD_LIBRARY_PATH is best solution.\nNote the path must show up before \/lib64 in your LD_LIBRARY_PATH (i.e. export LD_LIBRARY_PATH=\/your\/path:$LD_LIBRARY_PATH)","Q_Score":4,"Tags":"python-2.7,mod-wsgi","A_Id":34476701,"CreationDate":"2015-11-03T11:09:00.000","Title":"Why is wsgi looking for a library in \/lib64 when the correct version is in the python distribution","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have trained the imdb_lstm.py on my PC.\nNow I want to test the trained network by inputting some text of my own. How do I do it?\nThank you!","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2818,"Q_Id":33536182,"Users Score":7,"Answer":"So what you basically need to do is as follows:\n\nTokenize sequnces: convert the string into words (features): For example: \"hello my name is georgio\" to [\"hello\", \"my\", \"name\", \"is\", \"georgio\"].\nNext, you want to remove stop words (check Google for what stop words are).\nThis stage is optional, it may lead to faulty results but I think it worth a try. Stem your words (features), that way you'll reduce the number of features which will lead to a faster run. Again, that's optional and might lead to some failures, for example: if you stem the word 'parking' you get 'park' which has a different meaning.\nNext thing is to create a dictionary (check Google for that). Each word gets a unique number and from this point we will use this number only.\nComputers understand numbers only so we need to talk in their language. We'll take the dictionary from stage 4 and replace each word in our corpus with its matching number.\nNow we need to split our data set to two groups: training and testing sets. One (training) will train our NN model and the second (testing) will help us to figure out how good is our NN. You can use Keras' cross validation function.\nNext thing is defining whats the max number of features our NN can get as an input. Keras call this parameter - 'maxlen'. But you don't really have to do this manually, Keras can do that automatically just by searching for the longest sentence you have in your corpus. \nNext, let's say that Keras found out that the longest sentence in your corpus has 20 words (features) and one of your sentences is the example in the first stage, which its length is 5 (if we'll remove stop words it'll be shorter), in such case we'll need to add zeros, 15 zeros actually. This is called pad sequence, we do that so every input sequence will be in the same length.","Q_Score":2,"Tags":"python,sentiment-analysis,lstm,keras","A_Id":34154972,"CreationDate":"2015-11-05T03:30:00.000","Title":"Testing the Keras sentiment classification with model.predict","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Eigenface method is a powerful method in face recognition. It uses the training images to find the eigenfaces and then use these eigenfaces to represent a new test image. Do the images in training dataset need to be labeled, or it is unsupervised training?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":116,"Q_Id":33552557,"Users Score":0,"Answer":"Eigenfaces require supervised learning.  You generally supply several of each subject, classifying them by identifying the subject.  The eigenface model then classifies later images (often real-time snapshots) as to identity.","Q_Score":0,"Tags":"python,face-recognition","A_Id":33553902,"CreationDate":"2015-11-05T18:55:00.000","Title":"Does Eigenface method use unsupervised trainning","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have been trying to find a fast algorithm of calculating all the angle between n vectors that are of length x. For example if x=3 and n=4, my data would look something like this:\nA: [1,2,3]\nB: [2,3,4]\nC: [...]\nD: [...]\nI was wondering is it acceptable to find the the angle between all of be vectors (A,B,C,D) with respect to some fix vector (i.e. X:[100,100,100,100]) and then the subtract the angles of (A,B,C,D) found with respect to that fixed value, to find the angle between all of them. I want to do this because I would only have to compute the angle once and then I can subtract angles all of my vectors to find the different between them. In short, I want to know is it safe to make this assumption?\nangle_between(A,B) == angle_between(A,X) - angle_between(B,X)\nand the  angle_between function is the Cosine similarity.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":390,"Q_Id":33560269,"Users Score":0,"Answer":"That approach will only work for 2-D vectors.  For higher dimensions any two vectors will define a hyperplane, and only if the third (reference) vector also lies within this hyperplane will your approach work.  Unfortunately instead of only calculating n angles and subtracting, in order to determine the angles between each pair of vectors you would have to calculate all n choose 2 of them.","Q_Score":0,"Tags":"python,cosine-similarity,trigonometry","A_Id":33560748,"CreationDate":"2015-11-06T05:45:00.000","Title":"Calculating the Angle Between Vectors by using a vector as a reference point:","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to implement a blob detector based on LOG, the steps are:\n\ncreating an array of n levels of LOG filters\nuse each of the filters on the input image to create a 3d array of h*w*n where h = height, w = width and n = number of levels.\nfind a local maxima and circle the blob in the original image.\n\nI already created the filters and the 3d array (which is an array of 2d images).\nI used padding to make sure I don't have any problems around the borders (which includes creating a constant border for each image and create 2 extra empty images).\nNow I'm trying to figure out how to find the local maxima in the array.\nI need to compare each pixel to its 26 neighbours (8 in the same picture and the 9 pixels in each of the two adjacent scales)\nThe brute force way of checking the pixel value directly seems ugly and not very efficient.\nWhats the best way to find a local maxima point in python using openCV?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1030,"Q_Id":33587761,"Users Score":0,"Answer":"I'd take advantage of the fact that dilations are efficiently implemented in OpenCV. If a point is a local maximum in 3d, then it is also in any 2d slice, therefore:\n\nDilate each image in the array with a 3x3 kernel, keep as candidate maxima the points whose intensity is unchanged.\nBrute-force test the candidates against their upper and lower slices.","Q_Score":0,"Tags":"python,opencv,image-processing,computer-vision","A_Id":33596513,"CreationDate":"2015-11-07T21:04:00.000","Title":"finding a local maximum in a 3d array (array of images) in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Here's what I'm trying to do:\nI have an image.\nI want to take a circular region in the image, and have it appear as normal. \nThe rest of the image should appear darker. \nThis way, it will be as if the circular region is \"highlighted\".\nI would much appreciate feedback on how to do it in Python. \nManually, in Gimp, I would create a new layer with a color of gray (less than middle gray). I would then create a circualr region on that layer, and make it middle gray. Then I would change the blending mode to soft light. Essentially, anything that is middle gray on the top layer will show up without modification, and anything darker than middle gray would show up darker.\n(Ideally, I'd also blur out the top layer so that the transition isn't abrupt). \nHow can I do this algorithmically in Python? I've considered using the Pillow library, but it doesn't have these kinds of blend modes. I also considered using the Blit library, but I couldn't import (not sure it's maintained any more). Am open to scikit-image as well. I just need pointers on the library and some relevant functions. \nIf there's no suitable library, I'm open to calling command line tools (e.g. imagemagick) from within the Python code.\nThanks!","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":687,"Q_Id":33603304,"Users Score":1,"Answer":"I finally did it with ImageMagick, using Python to calculate the various coordinates, etc.\nThis command will create the desired circle (radius 400, centered at (600, 600):\nconvert -size 1024x1024 xc:none -stroke black -fill steelblue -strokewidth 1 -draw \"translate 600,600 circle 0,0 400,0\" drawn.png \nThis command will then convert it to B\/W to get a rudimentary mask:\nconvert drawn.png -alpha extract mask.png\nThis command will blur the mask (radius 180, sigma 16):\nconvert -channel RGBA  -blur 100x16 mask.png mask2.png\nThe above three commands gives me the mask I need.\nThis command will darken the whole image (without the mask):\nconvert image.jpg -level 0%,130%,0.7 dark.jpg\nAnd this command will put all 3 images together (original image, darkened image, and mask):\ncomposite image.jpg dark.jpg mask2.png out.jpg","Q_Score":1,"Tags":"python,image-processing","A_Id":33729058,"CreationDate":"2015-11-09T06:03:00.000","Title":"Create a \"spotlight\" in an image using Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'd like to run a logistic regression on a dataset with 0.5% positive class by re-balancing the dataset through class or sample weights.  I can do this in scikit learn, but it doesn't provide any of the inferential stats for the model (confidence intervals, p-values, residual analysis).\nIs this possible to do in statsmodels?  I don't see a sample_weights or class_weights argument in statsmodels.discrete.discrete_model.Logit.fit\nThank you!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":3765,"Q_Id":33605979,"Users Score":2,"Answer":"programmer's answer:\nstatsmodels Logit and other discrete models don't have weights yet. (*)\nGLM Binomial has implicitly defined case weights through the number of successful and unsuccessful trials per observation. It would also allow manipulating the weights through the GLM variance function, but that is not officially supported and tested yet.\nupdate statsmodels Logit still does not have weights, but GLM has obtained var_weights and freq_weights several statsmodels releases ago. GLM Binomial can be used to estimate a Logit or a Probit model.\nstatistician's\/econometrician's answer:\nInference, standard errors, confidence intervals, tests and so on, are based on having a random sample. If weights are manipulated, then this should affect the inferential statistics.\nHowever, I never looked at the problem for rebalancing the data based on the observed response. In general, this creates a selection bias. A quick internet search shows several answers, from rebalancing doesn't have a positive effect in Logit to penalized estimation as alternative.\nOne possibility is to also try different link function, cloglog or other link functions have asymmetric or heavier tails that are more appropriate for data with small risk in one class or category.\n(*) One problem with implementing weights is to decide what their interpretation is for inference. Stata, for example, allows for 3 kinds of weights.","Q_Score":3,"Tags":"python,statistics,statsmodels","A_Id":33611826,"CreationDate":"2015-11-09T09:27:00.000","Title":"Statsmodels Logistic Regression class imbalance","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"how can I run a mean shift clustering on a 1D array?\nHere there is my dataframe:\n    >>>df\n        INFO         FREQ\n     R2   31    0.2468213\n     R5   27  0.003670532\n     UR   25   0.00337465\nI need to apply the clustering on the \"INFO\" column.\nWhit the kmeans I solved this problem using the reshape(-1,1) command:\nkmeans.fit(df[\"INFO\"].values.reshape(-1,1)) , but with the mean shift clustering I get this error:\nmeanshift.fit(df[\"INFO\"].values.reshape(-1,1)) output: ValueError: Invalid shape in axis 1: 0.","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":1483,"Q_Id":33608541,"Users Score":2,"Answer":"It does not make sense to run mean-shift on one-dimensional data.\nDo regular kernel density estimation instead. Locate the minima, and split the data set there.\nMean shift is for data that is too complex for proper KDE.\nOne dimensional data never is.","Q_Score":1,"Tags":"python,scikit-learn,cluster-analysis","A_Id":33617441,"CreationDate":"2015-11-09T11:55:00.000","Title":"scikit learn mean shift clustering in one-dimensional array","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have setup a Spark on YARN cluster on my laptop, and have problem running multiple concurrent jobs in Spark, using python multiprocessing. I am running on yarn-client mode. I tried two ways to achieve this:\n\nSetup a single SparkContext and create multiple processes to submit jobs. This method does not work, and the program crashes. I guess a single SparkContext does not support python multiple processes\nFor each process, setup a SparkContext and submit the job. In this case, the job is submitted successfully to YARN, but the jobs are run serially, only one job is run at a time while the rest are in queue. Is it possible to start multiple jobs concurrently?\nUpdate on the settings\nYARN: \nyarn.nodemanager.resource.cpu-vcores 8 \nyarn.nodemanager.resource.memory-mb 11264\nyarn.scheduler.maximum-allocation-vcores 1\nSpark:\nSPARK_EXECUTOR_CORES=1\nSPARK_EXECUTOR_INSTANCES=2\nSPARK_DRIVER_MEMORY=1G\nspark.scheduler.mode = FAIR\nspark.dynamicAllocation.enabled = true\nspark.shuffle.service.enabled = true\n\nyarn will only run one job at a time, using 3 containers, 3 vcores, 3GB ram. So there are ample vcores and rams available for the other jobs, but they are not running","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":5740,"Q_Id":33614453,"Users Score":1,"Answer":"How many CPUs do you have and how many are required per job? YARN will schedule the jobs and assign what it can on your cluster: if you require 8CPUs for your job and your system has only 8CPUs, then other jobs will be queued and ran serially.\nIf you requested 4 per job then you would see 2 jobs run in parallel at any one time.","Q_Score":2,"Tags":"python-2.7,apache-spark,hadoop-yarn,pyspark","A_Id":33621420,"CreationDate":"2015-11-09T17:12:00.000","Title":"How to run multiple concurrent jobs in Spark using python multiprocessing","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"sns.lmplot(x=\"size\", y=\"tip\", data=tips)\ngives a scatter plot. By default the markers have no edges. \nHow can I add markeredges? Sometimes I prefer to use edges transparent facecolor. Especially with dense data. However, \nNeither markeredgewidth nor mew nor linewidths are accepted as keywords.\nDoes anyone know how to add edges to the markers?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1371,"Q_Id":33614947,"Users Score":1,"Answer":"As per the comment from @S\u00f6ren, you can add the markeredges with the keyword scatter_kws. For example scatter_kws={'linewidths':1,'edgecolor':'k'}","Q_Score":1,"Tags":"python,seaborn,marker","A_Id":46858249,"CreationDate":"2015-11-09T17:42:00.000","Title":"Add markeredges in seaborn lmplot?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Google released TensorFlow today.\nI have been poking around in the code, and I don't see anything in the code or API about training across a cluster of GPU servers.\nDoes it have distributed training functionality yet?","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":4046,"Q_Id":33616593,"Users Score":1,"Answer":"Update\nAs you may have noticed. Tensorflow has already supported distributed DNN training for quite some time. Please refer to its offcial website for details.\n=========================================================================\nPrevious\nNo, it doesn't support distribute training yet, which is a little disappointing. But I don't think it is difficult to extend from single machine to multi-machine. Compared to other open source libraries, like Caffe, TF's data graph structure is more suitable for cross-machine tasks.","Q_Score":14,"Tags":"python,parallel-processing,deep-learning,tensorflow","A_Id":33622414,"CreationDate":"2015-11-09T19:23:00.000","Title":"How do I use distributed DNN training in TensorFlow?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to morphologically close a volume with a ball structuring element created by the function SE3 = skimage.morphology.ball(8).\nWhen using closing = cv2.morphologyEx(volume_start, cv2.MORPH_CLOSE, SE) it returns TypeError: src data type = 0 is not supported\nDo you know how to solve this issue? \nThank you","AnswerCount":2,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":16369,"Q_Id":33628679,"Users Score":15,"Answer":"Make sure volume_start is dtype=uint8. You can convert it with volume_start = np.array(volume_start, dtype=np.uint8).\nOr nicer:\nvolume_start = volume_start.astype(np.uint8)","Q_Score":9,"Tags":"python,opencv,mathematical-morphology","A_Id":44216923,"CreationDate":"2015-11-10T11:13:00.000","Title":"Python Opencv morphological closing gives src data type = 0 is not supported","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to listen to changes in the transfer function in how the color and opacity (ctf\/otf) of my data is represented.\nListening to sensible-sounding traits such as mayavi.modules.volume.Volume._ctf does not trigger my callback.\nI would expect this to be changed by the user either through the \"standard\" mayavi pipeline display (as part of EngineRichView) or through including the Volume object's view directly. No such luck either way.\nIt is maybe telling that when you press the big red \"record\" button, the recorder also does not seem to notice user changes to the ctf.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":96,"Q_Id":33642997,"Users Score":0,"Answer":"You are going to wade into dangerous territory. As you noted the recorder has idosyncratic behavior -- what that really means is that it uses features to programatically \"disable\" the trait notifications while it is doing things.\nYou can probably figure out a way to do it that way, but most likely you'll have to dig deeply into the code that assigns the vtk modules.\nWhat would probably make the most sense, is for you to write a GUI that does exactly what you want. That is, instead of listening to something like Volume._ctf, and then opening up the menu and changing the color, you can make a GUI and add a button that says \"Change volume color\" that when clicked brings the user to a color wheel. Then it's just a matter of listening to the GUI elements that you explicitly code for.","Q_Score":1,"Tags":"python-2.7,enthought,mayavi,traitsui","A_Id":33675492,"CreationDate":"2015-11-11T01:53:00.000","Title":"listen for ctf otf changes with traits in mayavi volume rendering","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a clustering of data performed by a human based solely on their knowledge of the system. I also have a feature vector for each element. I have no knowledge about the meaning of the features, nor do I know what the reasoning behind the human clustering was.\nI have complete information about which elements belong to which cluster. I can assume that the human was not stupid and there is a way to derive the clustering from the features.\nIs there an intelligent way to reverse-engineer the clustering? That is, how can I select the features and the clustering algorithm that will yield the same clustering most of the time (on this data set)?\nSo far I have tried the naive approach - going through the clustering algorithms provided by the sklearn library in python and comparing the obtained clusters to the source one. This approach does not yield good results.\nMy next approach would be to use some linear combinations of the features, or subsets of features. Here, again, my question is if there is a more intelligent way to do this than to go through as many combinations as possible.\nI can't shake the feeling that this is a standard problem and I'm just missing the right term to find the solution on Google.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":497,"Q_Id":33677932,"Users Score":0,"Answer":"Are you sure it was done automatically?\nIt sounds to me as if you should be treating this as a classification problem: construct a classifier that does the same as the human did.","Q_Score":0,"Tags":"python,scikit-learn,cluster-analysis,feature-selection","A_Id":33683680,"CreationDate":"2015-11-12T17:46:00.000","Title":"Reverse-engineering a clustering algorithm from the clusters","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have two python compilers on my Ubuntu 14.04 VM. I have installed matplotlib as\npip install matplotlib\nBut the matplotlib cannot be used from python3.It can be used from python2.7\nIf I use import matplotlib.pyplot as plt inside my script test.py and run it as \npython3 test.py\nI get the error\nImportError: No module named 'matplotlib'\nHow can this be fixed.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":1128,"Q_Id":33681281,"Users Score":1,"Answer":"You can install the package from your distro with:\nsudo apt-get install python3-matplotlib\nIt will probably throw an error when you import matplotlib, but it is solved by installing the package tkinter with:\nsudo apt-get install python3-tk","Q_Score":1,"Tags":"python,matplotlib,pip","A_Id":42054866,"CreationDate":"2015-11-12T21:01:00.000","Title":"matplotlib can't be used from python3","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've written a script that gives me the result of dividing two variables (\"A\" and \"B\") -- and the output of each variable is a numpy array with 26 elements. Usually, with any two elements from \"A\" and \"B,\" the result of the operation is a float, and the the element in the output array that corresponds to that operation shows up as a float. But strangely, even if the output is supposed to be an integer (almost always 0 or 1), the integer will show up as \"0.\" or \"1.\" in the output array. Is there any way to turn these specific elements of the array back into integers, rather than keep them as floats?\nI'd like to write a simple if statement that will convert any output elements that are supposed to be integers back into integers (i.e., make \"0.\" into \"0\"). But I'm having some trouble with that. Any ideas?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":82,"Q_Id":33713472,"Users Score":1,"Answer":"The strength of Numpy arrays is that many low-level operations can be quickly performed on the data because most (not all) types used by these arrays have a fixed-size in memory. For instance, the floats you are using probably require 8 bytes each. The most important thing in that case is that all datas share the same type and fit in the same amount of memory. You can play a little around that if you really want (and need) to, but I would not suggest you to start by such special cases. Try to learn the strength of these arrays when used with this requirement (but this involves accepting the fact that you can't mix integers and floats in the same array).","Q_Score":2,"Tags":"python,arrays,numpy","A_Id":33713612,"CreationDate":"2015-11-14T21:17:00.000","Title":"How to convert specific elements within a numpy array to integers?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"In a distributed computing project, we are using Pyro to pass objects over the wire between nodes; Pyro internally serializes and deserializes objects using pickle.\nSome classes in the project have two implementations: one pure-Python (for ease of installation, especially for Windows users), one in c++\/boost::python (much faster, but requires boost + knowledge of how to compile the extension module). Both python and c++ classes support pickling (in c++, that is done via boost::python).\nThese classes have different fully-qualified name (mupif.Octree.Octant vs. mupif.fastOctant.Octant), but the latter is aliased to the former and overwrites the pure-Python definition (mupif.Octree.Octant=mupif.fastOctant.Octant), so it is transparent to the user and the fast variant is always used if available on the node.\nHowever, pickle uses __module__ and __class__ to identify the instance, thus when the c++-based object is passed over the wire to another node which does not support it, unpickling will fail.\nWhat is a solution to this? Is it acceptable to change the classe's __module__, i.e. foo.fastOctant.Octant.__class__.__module__='mupif.Octree'? Can it have some side-effects I don't see yet?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":109,"Q_Id":33753224,"Users Score":1,"Answer":"Does it help to alias in another way (fast = normal) if there is no fast implementation available? Maybe this could be done only for the time of unpickling and then reversed, to avoid confusing checks in other code?","Q_Score":0,"Tags":"python,c++,alias,pickle","A_Id":33754768,"CreationDate":"2015-11-17T09:24:00.000","Title":"Pickling\/unpickling alternative (API-compatible) class implementations","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I already have a function that converts an image to a matrix, and back. But I was wondering how to manipulate the matrix so that the picture becomes blurry, or pixified?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":161,"Q_Id":33756970,"Users Score":0,"Answer":"To make it blurry filter it using any low-pass filter (mean filter, gaussian filter etc.).","Q_Score":0,"Tags":"python,image-processing","A_Id":33757187,"CreationDate":"2015-11-17T12:29:00.000","Title":"How can I blur or pixify images in python by using matrixes?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"After you train a model in Tensorflow: \n\nHow do you save the trained model?\nHow do you later restore this saved model?","AnswerCount":28,"Available Count":2,"Score":0.0214252921,"is_accepted":false,"ViewCount":468965,"Q_Id":33759623,"Users Score":3,"Answer":"Use tf.train.Saver to save a model. Remember, you need to specify the var_list if you want to reduce the model size. The val_list can be:\n\ntf.trainable_variables or\ntf.global_variables.","Q_Score":640,"Tags":"python,tensorflow","A_Id":53183223,"CreationDate":"2015-11-17T14:37:00.000","Title":"How to save\/restore a model after training?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"After you train a model in Tensorflow: \n\nHow do you save the trained model?\nHow do you later restore this saved model?","AnswerCount":28,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":468965,"Q_Id":33759623,"Users Score":55,"Answer":"There are two parts to the model, the model definition, saved by Supervisor as graph.pbtxt in the model directory and the numerical values of tensors, saved into checkpoint files like model.ckpt-1003418.\nThe model definition can be restored using tf.import_graph_def, and the weights are restored using Saver.\nHowever, Saver uses special collection holding list of variables that's attached to the model Graph, and this collection is not initialized using import_graph_def, so you can't use the two together at the moment (it's on our roadmap to fix). For now, you have to use approach of Ryan Sepassi -- manually construct a graph with identical node names, and use Saver to load the weights into it.\n(Alternatively you could hack it by using by using import_graph_def, creating variables manually, and using tf.add_to_collection(tf.GraphKeys.VARIABLES, variable) for each variable, then using Saver)","Q_Score":640,"Tags":"python,tensorflow","A_Id":33763208,"CreationDate":"2015-11-17T14:37:00.000","Title":"How to save\/restore a model after training?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When working with the default global graph, is it possible to remove nodes after they've been added, or alternatively to reset the default graph to empty? When working with TF interactively in IPython, I find myself having to restart the kernel repeatedly. I would like to be able to experiment with graphs more easily if possible.","AnswerCount":5,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":75804,"Q_Id":33765336,"Users Score":5,"Answer":"Tensorflow 2.0 Compatible Answer: In Tensorflow Version >= 2.0, the Command to Reset Entire Default Graph, when run in Graph Mode is tf.compat.v1.reset_default_graph.\nNOTE: The default graph is a property of the current thread. This function applies only to the current thread. Calling this function while a tf.compat.v1.Session or tf.compat.v1.InteractiveSession is active will result in undefined behavior. Using any previously created tf.Operation or tf.Tensor objects after calling this function will result in undefined behavior. \nRaises: AssertionError: If this function is called within a nested graph.","Q_Score":69,"Tags":"python,tensorflow","A_Id":60106544,"CreationDate":"2015-11-17T19:24:00.000","Title":"Remove nodes from graph or reset entire default graph","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using sklearn's EllipticEnvelope to find outliers in dataset. But I am not sure about how to model my problem? Should I just use all the data (without dividing into training and test sets) and apply fit? Also how would I obtain the outlyingness of each datapoint? Should I use predict on the same dataset?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2901,"Q_Id":33778802,"Users Score":2,"Answer":"Right way to do this is:\n\nDivide data into normal and outliers.\nTake large sample from normal data as normal_train for fitting the novelty detection model.\nCreate a test set with a sample from normal that is not used in training (say normal_test) and a sample from outlier (say outlier_test) in a way such that the distribution of the test data (normal_test + outlier_test) retains population distribution.\nPredict on this test data to get usual metrics (accuracy, sensitivity, positive-predictive-value, etc.)\n\nWow. I have come a long way!","Q_Score":2,"Tags":"python,scikit-learn,outliers","A_Id":42991702,"CreationDate":"2015-11-18T11:24:00.000","Title":"How to apply sklearn's EllipticEnvelope to find out top outliers in the given dataset?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"After training the cnn model, I want to visualize the weight or print out the weights, what can I do?\nI cannot even print out the variables after training.\nThank you!","AnswerCount":4,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":53725,"Q_Id":33783672,"Users Score":0,"Answer":"Using the tensorflow 2 API, There are several options:\nWeights extracted using the  get_weights() function.\nweights_n = model.layers[n].get_weights()[0]\nBias extracted using the numpy() convert function.\nbias_n = model.layers[n].bias.numpy()","Q_Score":46,"Tags":"python,tensorflow","A_Id":68254814,"CreationDate":"2015-11-18T15:13:00.000","Title":"How can I visualize the weights(variables) in cnn in Tensorflow?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to implement an algorithm in python, but I am not sure when I should use fftshift(fft(fftshift(x))) and when only fft(x) (from numpy). Is there a rule of thumb based on the shape of input data?\n I am using fftshift instead of ifftshift due to the even number of values in the vector x.","AnswerCount":2,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":13324,"Q_Id":33846123,"Users Score":6,"Answer":"fft(fftshift(x)) rotates the input vector so the the phase of the complex FFT result is relative to the center of the original data window.  If the input waveform is not exactly integer periodic in the FFT width, phase relative to the center of the original window of data may make more sense than the phase relative to some averaging between the discontinuous beginning and end.  fft(fftshift(x)) also has the property that the imaginary component of a result will always be positive for a positive zero crossing at the center of the window of any antisymmetric waveform component.\nfftshift(fft(y)) rotates the FFT results so that the DC bin is in the center of the result, halfway between -Fs\/2 and Fs\/2, which is a common spectrum display format.","Q_Score":7,"Tags":"python,fft","A_Id":33846706,"CreationDate":"2015-11-21T17:03:00.000","Title":"When should I use fftshift(fft(fftshift(x))) and when fft(x)?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been trying to install numpy and pandas for python 3.5 but it keeps telling me that I have an issue. \nCould it be because numpy can't run on python 3.5 yet?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":4445,"Q_Id":33851716,"Users Score":1,"Answer":"I was corresponding with some ppl at python.org and they told me to use\npy -3.5 -m pip install SomePackage\nThis works.","Q_Score":0,"Tags":"python,numpy","A_Id":33871559,"CreationDate":"2015-11-22T04:50:00.000","Title":"Installing numpy and pandas for python 3.5","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"So I have some python code that plots a few graphs using pyplot. Every time I run the script new plot windows are created that I have to close manually. How do I close all open pyplot windows at the start of the script? Ie. closing windows that were opened during previous executions of the script?\nIn MatLab this can be done simply by using closeall.","AnswerCount":7,"Available Count":2,"Score":-1.0,"is_accepted":false,"ViewCount":61115,"Q_Id":33853801,"Users Score":-5,"Answer":"On *nix you can use killall command.  \nkillall app \ncloses every instance of window with app for the window name.\nYou can also use the same command from inside your python script.\nYou can use os.system(\"bashcommand\") to run the bashcommand.","Q_Score":30,"Tags":"python,matplotlib,pycharm","A_Id":33853861,"CreationDate":"2015-11-22T10:31:00.000","Title":"How do I close all pyplot windows (including ones from previous script executions)?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"So I have some python code that plots a few graphs using pyplot. Every time I run the script new plot windows are created that I have to close manually. How do I close all open pyplot windows at the start of the script? Ie. closing windows that were opened during previous executions of the script?\nIn MatLab this can be done simply by using closeall.","AnswerCount":7,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":61115,"Q_Id":33853801,"Users Score":0,"Answer":"As there seems no absolutely trivial solution to do this automatically from the script itself: the possibly simplest way to close all existing figures in pycharm is killing the corresponding processes (as jakevdp suggested in his comment):\nMenu Run\\Stop... (Ctrl-F2). You'll find the windows closed with a delay of few seconds.","Q_Score":30,"Tags":"python,matplotlib,pycharm","A_Id":52167731,"CreationDate":"2015-11-22T10:31:00.000","Title":"How do I close all pyplot windows (including ones from previous script executions)?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there a way to have a variable (resulting from one script) accessible to other scripts while Excel is running? \nI have tried from script1 import df but it runs script1 again to produce df. I have a script that runs when I first open the workbook and it reads a dataframe and I need that dataframe for other scripts (or for other button clicks). Is there a way to store it in memory or should I read it every time I need it?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":90,"Q_Id":33910358,"Users Score":1,"Answer":"There's no way to share the dataframe (each DataNitro script runs in its own process). You can read the frame each time, or if reading is slow, you can have the first script store it somewhere the other scripts can access it (e.g. as a csv or by pickling it).","Q_Score":1,"Tags":"python,datanitro","A_Id":33919992,"CreationDate":"2015-11-25T07:03:00.000","Title":"datanitro - pass variables from one script to others","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I get a float by dividing two numbers. I know that the numbers are divisible, so I always have an integer, only it's of type float. However, I need an actual int type. I know that int() strips the decimals (i.e., floor rounding). I am concerned that since floats are not exact, if I do e.g. int(12.\/3) or int(round(12.\/3)) it may end up as 3 instead of 4 because the floating point representation of 4 could be 3.9999999593519561 (it's not, just an example).\nWill this ever happen and can I make sure it doesn't?\n(I am asking because while reshaping a numpy array, I got a warning saying that the shape must be integers, not floats.)","AnswerCount":3,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":3905,"Q_Id":33918043,"Users Score":5,"Answer":"Casting a float to an integer truncates the value, so if you have 3.999998, and you cast it to an integer, you get 3.\nThe way to prevent this is to round the result. int(round(3.99998)) = 4, since the round function always return a precisely integral value.","Q_Score":10,"Tags":"python","A_Id":33918217,"CreationDate":"2015-11-25T13:42:00.000","Title":"Will casting an \"integer\" float to int always return the closest integer?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I get a float by dividing two numbers. I know that the numbers are divisible, so I always have an integer, only it's of type float. However, I need an actual int type. I know that int() strips the decimals (i.e., floor rounding). I am concerned that since floats are not exact, if I do e.g. int(12.\/3) or int(round(12.\/3)) it may end up as 3 instead of 4 because the floating point representation of 4 could be 3.9999999593519561 (it's not, just an example).\nWill this ever happen and can I make sure it doesn't?\n(I am asking because while reshaping a numpy array, I got a warning saying that the shape must be integers, not floats.)","AnswerCount":3,"Available Count":2,"Score":0.0665680765,"is_accepted":false,"ViewCount":3905,"Q_Id":33918043,"Users Score":1,"Answer":"I ended up using integer division (a\/\/b) since I divided integers. Wouldn't have worked if I divided e.g. 3.5\/0.5=7 though.","Q_Score":10,"Tags":"python","A_Id":33918503,"CreationDate":"2015-11-25T13:42:00.000","Title":"Will casting an \"integer\" float to int always return the closest integer?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a set of 3D coordinates points: [lat,long,elevation] ([X,Y,Z]), derived from LIDAR data.\nThe points are not sorted and the steps size between the points is more or less random.\nMy goal is to build a function that converts this set of points to a 2D numpy matrix of a constant number of pixels where each (X,Y) cell hold the Z value, then plot it as elevations heatmap.\n\nscales must remain realistic, X and Y should have same step size.\nthe matrix doesn't have to catch the exact elevations picture, It will obviously need some kind of resolution reduction in order to have a constant number of pixels.\n\nThe solution I was thinking of is to build a bucket for each pixel, iterate over the points and put each in a bucket according to it's (X,Y) values. At last create a matrix where each sell holds the mean of the Z values in the corresponding bucket.\n\nSince I don't have lots of experience in this field I would love to hear some tips and specially if there are better ways to address this task.\n\nIs there a numpy function for converting my set of points to the desired matrix? (maybe meshgrid with steps of a constant value?)\n\nIf I build very sparse matrix, where the step size is\nmin[min{Xi,Xj} , min{Yk,Yl}] for all i,j,k,l\nis there a way to \"reduce\" the resolution and convert it to a matrix with the required size?\n\n\nThanks!","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2868,"Q_Id":33926704,"Users Score":0,"Answer":"I am aware that I am not answering half of your questions but this is how I would do it: \n\nCreate a 2D array of the desired resolution,\nThe \"leftmost\" values correspond to the smallest values of x and so forth\nFill the array with the elevation value of the closest match in terms of x and y values\nSmoothen the result.","Q_Score":3,"Tags":"python,numpy,matrix,matplotlib,lidar","A_Id":33926859,"CreationDate":"2015-11-25T21:28:00.000","Title":"Converting coordinates vector to numpy 2D matrix","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to use OpenCV in my Django application. As OpenCV is a library, I thought we can use it like any other library.\nWhen I try to import it using import cv2 in the views of Django, it works fine but when I try to make library function calls in Django view like cap = cv2.VideoCapture(0) and try to run the app on my browser, nothing happens: the template does not load and no traceback in the terminal and the application remains loading forever.\nDon't know why but the cv2 function call is not executing as expected. Since there is no traceback, I am not able to understand what is the problem. If anyone can suggest what is wrong ? Is it the right way to use OpenCV with Django ?","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":4890,"Q_Id":33954438,"Users Score":1,"Answer":"Am I right that you dream about Django application able to capture video from your camera? This will not work (at least not in a way you expect).\nDid you check any stack traces left by your web server (the one hosts Django app or the one started as Django built-in)?\nI suggest you start playing with OpenCV a bit just from Python command line. If you're on Windows use IDLE. Observe behaviour of your calls from there.\nDjango application is running inside WSGI application server where there are several constraints what a module of particular type can and cannot do. I didn't try to repeat what you've done (I don't have camera I can access).\nProper way of handling camera in web application requires browser side handling in JavaScript.\nSmall disclaimer at the end: I'm not saying you cannot use OpenCV at all in Django application, but attempt to access the camera is not a way to go.","Q_Score":3,"Tags":"python,django,opencv","A_Id":33954610,"CreationDate":"2015-11-27T09:41:00.000","Title":"Using OpenCV with Django","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I want to use OpenCV in my Django application. As OpenCV is a library, I thought we can use it like any other library.\nWhen I try to import it using import cv2 in the views of Django, it works fine but when I try to make library function calls in Django view like cap = cv2.VideoCapture(0) and try to run the app on my browser, nothing happens: the template does not load and no traceback in the terminal and the application remains loading forever.\nDon't know why but the cv2 function call is not executing as expected. Since there is no traceback, I am not able to understand what is the problem. If anyone can suggest what is wrong ? Is it the right way to use OpenCV with Django ?","AnswerCount":2,"Available Count":2,"Score":0.1973753202,"is_accepted":false,"ViewCount":4890,"Q_Id":33954438,"Users Score":2,"Answer":"Use a separate thread for the cv2 function call and the app should work like a charm. From what I figure..infinite loading is probably because the video never ceases recording and hence the code further up ahead is never taken into account, ergo an infinite loading page. Threads should probably do it. \n:) :)","Q_Score":3,"Tags":"python,django,opencv","A_Id":35443792,"CreationDate":"2015-11-27T09:41:00.000","Title":"Using OpenCV with Django","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I spent couple days trying to solve this problem, but no luck so I turn to you. I have file for a photometry of a star with time and amplitude data. I'm supposed to use this data to find period changes. I used Lomb-Scargle from pysca library, but I have to use Fourier analysis. I tried fft (dft) from scipy and numpy but I couldn't get anything that would resemble frequency spectrum or Fourier coefficients. I even tried to use nfft from pynfft library because my data are not evenly sampled, but I did not get anywhere with this. So if any of you know how to get from Fourier analysis main frequency in periodical data, please let me know.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":243,"Q_Id":33975835,"Users Score":0,"Answer":"Before doing the FFT, you will need to resample or interpolate the data until you get a set of amplitude values equally spaced in time.","Q_Score":0,"Tags":"python,fft,dft,period","A_Id":33980259,"CreationDate":"2015-11-28T20:01:00.000","Title":"Fourier series of time domain data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an array which is a 1X3 matrix, where:\n    column 1 = x coordinate\n    column 2 = y coordinate\n    column 3 = direction of vector.\nI am tracking a series of points along a path.\nAt each point i want to store the x,y and direction back into the array, as a row.\nSo in the end, my array has grown vertically, with more and more rows that represents points along the path.\nIm struggling to build this function inside a class.\nHelp plz?\nXx","AnswerCount":4,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":645,"Q_Id":33997336,"Users Score":0,"Answer":"Use .append('item-goes-here') to append.","Q_Score":0,"Tags":"python,arrays,list,matrix,append","A_Id":33997375,"CreationDate":"2015-11-30T11:16:00.000","Title":"How do I append rows to an array in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"While something like matplotlib.pyplot.xlabel(r'Wavelenghth [$\\mu$m]') works in  python2 I get error when I use it in python 3\n\nTypeError: startswith first arg must be str or a tuple of str, not\n  bytes\n\nDoes anyone know what it the problem? Is it from my latex installation?!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":623,"Q_Id":34005438,"Users Score":0,"Answer":"I could  solve the problem using rc('text', usetex=False) which apparently make the matplotlib to use the internal mathtext instead of my default  latex installation.\nStill I can not figure out the reason why my OS latex installation fails.","Q_Score":0,"Tags":"python-3.x,matplotlib,pdflatex","A_Id":34017212,"CreationDate":"2015-11-30T18:27:00.000","Title":"Can't render latex in matplotlib.pyplot in python3","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Reading the scikit-learn documentation and looking for similar topics I couldn't figure out an answer. \nCan I apply GridSearchCV having scoring=\"roc_auc\" on Random Forest or Decision Trees without any drawback? \nThank you in advance for any clarification.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":456,"Q_Id":34025404,"Users Score":0,"Answer":"The answer is: It is possible. \n                                                                     However, the feature is only available to binary cases under the stated question. As explained by @AndreasMueller.","Q_Score":0,"Tags":"python,scikit-learn","A_Id":34321420,"CreationDate":"2015-12-01T16:38:00.000","Title":"scoring=\"roc_auc\" on GridSearchCV for RF and DT","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"So I have three matplotlib subplots. I can use a pick event to pull off and re-plot the data in any one of the subplot. Is it possible to read the pick event and to find out what subplot number was selected?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":531,"Q_Id":34031206,"Users Score":1,"Answer":"Place the axes in a list or dictionary when creating. Then when a pick event has occurred, match the pick event axis to the dictionary.\nThank you all.","Q_Score":0,"Tags":"python,matplotlib,interactive","A_Id":34044627,"CreationDate":"2015-12-01T22:13:00.000","Title":"Find Subplot Number from Matplotlib Pick Event","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Seemingly straightforward problem: I want to create an array that gives the count since the last occurence of a given condition.  In this condition, let the condition be that a > 0:\nin:  [0, 0, 5, 0, 0, 2, 1, 0, 0]\nout: [0, 0, 0, 1, 2, 0, 0, 1, 2]\nI assume step one would be something like np.cumsum(a > 0), but not sure where to go from there.\nEdit: Should clarify that I want to do this without iteration.","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":79,"Q_Id":34097020,"Users Score":0,"Answer":"Split the array based on the condition and use the lengths of the remaining pieces and the condition state of the first and last element in the array.","Q_Score":1,"Tags":"python,numpy","A_Id":34097320,"CreationDate":"2015-12-04T20:39:00.000","Title":"Count since last occurence in NumPy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How to convert a tensor into a numpy array when using Tensorflow with Python bindings?","AnswerCount":12,"Available Count":3,"Score":1.0,"is_accepted":false,"ViewCount":668313,"Q_Id":34097281,"Users Score":84,"Answer":"To convert back from tensor to numpy array you can simply run .eval() on the transformed tensor.","Q_Score":274,"Tags":"python,numpy,tensorflow","A_Id":34097344,"CreationDate":"2015-12-04T20:55:00.000","Title":"Convert a tensor to numpy array in Tensorflow?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How to convert a tensor into a numpy array when using Tensorflow with Python bindings?","AnswerCount":12,"Available Count":3,"Score":0.0665680765,"is_accepted":false,"ViewCount":668313,"Q_Id":34097281,"Users Score":4,"Answer":"You can convert a tensor in tensorflow to numpy array in the following ways.\nFirst:\nUse np.array(your_tensor)\nSecond:\nUse your_tensor.numpy","Q_Score":274,"Tags":"python,numpy,tensorflow","A_Id":65860219,"CreationDate":"2015-12-04T20:55:00.000","Title":"Convert a tensor to numpy array in Tensorflow?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How to convert a tensor into a numpy array when using Tensorflow with Python bindings?","AnswerCount":12,"Available Count":3,"Score":0.0333209931,"is_accepted":false,"ViewCount":668313,"Q_Id":34097281,"Users Score":2,"Answer":"If you see there is a method _numpy(),\ne.g for an EagerTensor simply call the above method and you will get an ndarray.","Q_Score":274,"Tags":"python,numpy,tensorflow","A_Id":63803837,"CreationDate":"2015-12-04T20:55:00.000","Title":"Convert a tensor to numpy array in Tensorflow?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a pretty complex computation code written in Octave and a python script which receives user input, and needs to run the Octave code based on the user inputs. As I see it, I have these options:\n\nPort the Octave code to python.\nUse external libraries (i.e. oct2py) which enable you to run the Octave\/Matlab engine from python.\nCommunicate between a python process and an octave process. One such possibility would be to use subprocess from the python code and wait for the answer.\n\nSince I'm pretty reluctant to port my code to python and I don't want to rely on maintenance of external libraries such as oct2py, I am in favor of option 3. However, since the system should scale well,  I do not want to spawn a new octave process for every request, and a tasks queue system seems more reasonable. Is there any (recommended) tasks queue system to enqueue tasks in python and have an octave worker on the other end process it?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1133,"Q_Id":34115098,"Users Score":0,"Answer":"All three options are reasonable depending on your particular case.\n\nI don't want to rely on maintenance of external libraries such as oct2py,  I am in favor of option 3\n\noct2py is implemented using option 3. You can reinvent what it already does or use it directly. oct2py is pure Python and it has permissive license: if its development were to stop tomorrow; you could include its code alongside yours.","Q_Score":2,"Tags":"python,subprocess,octave,message-queue,oct2py","A_Id":34116773,"CreationDate":"2015-12-06T07:26:00.000","Title":"Running Octave tasks from Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"My code\nb=[((1,1)),((1,2)),((2,1)),((2,2)),((1,3))] \nfor i in range(len(b)): \n    print b[i]\n\nObtained output:\n(1, 1)\n(1, 2)\n(2, 1)\n(2, 2)\n(1, 3)\nhow do i sort this list by the first element or\/and second element in each index value to get the output as:\n(1, 1)\n(1, 2)\n(1, 3)\n(2, 1)\n(2, 2)  \nOR\n(1, 1)\n(2, 1)\n(1, 2)\n(2, 2)\n(1, 3)\nIt would be nice if both columns are sorted as shown in the desired output, how ever if either of the output columns is sorted it will suffice.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":48,"Q_Id":34119746,"Users Score":1,"Answer":"Try this: b = sorted(b, key = lambda i: (i[0], i[1]))","Q_Score":1,"Tags":"python-2.7,sorting","A_Id":34119821,"CreationDate":"2015-12-06T16:25:00.000","Title":"how to sort list in python which has two numbers per index value?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Ive come across an issue using Naive Bayes on Document classification into various classes problem.\nActually I was wondering that P(C) or the prior probability of classes that we have at our hands initially will keep on changing over the course of time.\nFor instance for classes - [music, sports, news] initial probabilities are [.25, .25, .50] \nNow suppose that over the time during a certain month if we had a deluge of \nsports related documents (eg 80% sports ) then our NaiveBayes will fail as it will be based on a prior probability factor which says only 25% are sports. \nHow do we deal with such a situation ?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":211,"Q_Id":34122417,"Users Score":1,"Answer":"If you know that priors change, you should refit them periodically (through gathering new training set representable for a new priors). In general - every ML method will fail in terms of accuracy if the priors change and you will not give this information to your classifier. You need at least some kind of feedback for the classifier. Then, if you for example have a closed loop where you get info if the classification is right or not, and you assume that only priors change - you can simply learn changing priors online (through any optimization, as it is rather easy to fit new priors).\nIn general you should look at concept drift phenomen.","Q_Score":1,"Tags":"python,machine-learning,nltk,naivebayes","A_Id":34122819,"CreationDate":"2015-12-06T20:36:00.000","Title":"Multiclass NaiveBayes classification on a text dataset with changing prior probabilities","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm doing some development in Python, mostly using a simple text editor (Sublime Text). I'm mostly dealing in databases that I fit in Pandas DataFrames. My issue is, I often lose track of the column names, and occasionally the column types as well. Is there some IDE \/ plug-in \/ debug tool that would allow me to look into each DataFrame and see how it's defined, a little bit like Eclipse can do for Java classes?\nThank you,","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":308,"Q_Id":34132184,"Users Score":1,"Answer":"I don't believe that something like that exists, but you can always use df.info().","Q_Score":0,"Tags":"python,debugging,pandas,ide","A_Id":34132511,"CreationDate":"2015-12-07T11:12:00.000","Title":"Python Pandas IDE that would \"know\" columns and types","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to do sentiment analysis on a review dataset. Since I care more about identifying (extracting) negative sentiments in reviews (unlabeled now but I try to manually label a few hundreds or use Alchemy API), if the review is overall neutral or positive but a part has negative sentiment, I'd like my model to consider it more toward as a negative review. Could someone give me advices on how to do this? I'm thinking about using bag of words\/word2vect with supervised (random forest, SVM) \/unsupervised learning models (Kmeans).","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":268,"Q_Id":34146996,"Users Score":0,"Answer":"When you annotate the sentiment, don't annotate 'Positive', 'Negative', and 'Neutral'. Instead, annotate them as either \"has negative\" or \"doesn't have negative\". Then your sentiment classification will only be concerned with how strongly the features indicate negative sentiment, which appears to be what you want.","Q_Score":0,"Tags":"python,machine-learning,nlp,sentiment-analysis","A_Id":34163258,"CreationDate":"2015-12-08T02:33:00.000","Title":"Review data sentiment analysis, focusing on extracting negative sentiment?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to study customers behavior. Basically, I have information on customer's loyalty points activities data (e.g. how many points they have earned, how many points they have used, how recent they have used\/earn points etc). I'm using R to conduct this analysis\nI'm just wondering how should I go about segmenting customers based on the above information? I'm trying to apply the RFM concept then use K-means to segment my customers(although I have a few more variables than just R,F,M , as i have recency,frequency and monetary on both points earn and use, as well as other ratios and metrics) . Is this a good way to do this?\nEssentially I have two objectives:\n1. To segment customers\n2. Via segmenting customers, identify customers behavior(e.g.customers who spend all of their points before churning), provided that segmentation is the right method for such task?\nClustering <- kmeans(RFM_Values4,centers = 10)\nPlease enlighten me, need some guidance on the best methods to tackle such problems.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":436,"Q_Id":34153844,"Users Score":0,"Answer":"Your attempts is always less then 5 because there is no variable increment. So your loop is infinite","Q_Score":0,"Tags":"python,loops,selenium","A_Id":34154600,"CreationDate":"2015-12-08T10:46:00.000","Title":"Python Selenium infinite loop","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Python ggplot is great, but missing many customization options.\nThe commit history on github for the past year does not look very promising...\nDoes anyone know if it is still being developed?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":154,"Q_Id":34173840,"Users Score":1,"Answer":"Yes. They are currently doing a major rewrite.","Q_Score":2,"Tags":"python,plot,graphing,python-ggplot","A_Id":34457756,"CreationDate":"2015-12-09T08:17:00.000","Title":"Is python ggplot still being developed?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"What is the best way to quickly see the updated graph in the most recent event file in an open TensorBoard session? Re-running my Python app results in a new log file being created with potentially new events\/graph. However, TensorBoard does not seem to notice those differences, unless restarted.","AnswerCount":3,"Available Count":2,"Score":0.1973753202,"is_accepted":false,"ViewCount":21777,"Q_Id":34190298,"Users Score":3,"Answer":"I advise to always start tensorboard with --reload_multifile True to force reloading all event files.","Q_Score":19,"Tags":"python,tensorflow,tensorboard","A_Id":61137400,"CreationDate":"2015-12-09T22:33:00.000","Title":"What's the best way to refresh TensorBoard after new events\/logs were added?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"What is the best way to quickly see the updated graph in the most recent event file in an open TensorBoard session? Re-running my Python app results in a new log file being created with potentially new events\/graph. However, TensorBoard does not seem to notice those differences, unless restarted.","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":21777,"Q_Id":34190298,"Users Score":0,"Answer":"My issue is different. Each time I refresh 0.0.0.0:6006, it seems the new graph keep appending to the old one, which is quite annoying.\nAfter trying kill process and delete old log several times, I realized the issue comes from writer.add_graph(sess.graph),  because I didn't reset the graph in jupyter notebook.  After resetting, the tensorboard could show the newest gragh.","Q_Score":19,"Tags":"python,tensorflow,tensorboard","A_Id":44359908,"CreationDate":"2015-12-09T22:33:00.000","Title":"What's the best way to refresh TensorBoard after new events\/logs were added?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I work in an environment in which computational resources are shared, i.e., we have a few server machines equipped with a few Nvidia Titan X GPUs each.\nFor small to moderate size models, the 12 GB of the Titan X is usually enough for 2\u20133 people to run training concurrently on the same GPU. If the models are small enough that a single model does not take full advantage of all the computational units of the GPU, this can actually result in a speedup compared with running one training process after the other. Even in cases where the concurrent access to the GPU does slow down the individual training time, it is still nice to have the flexibility of having multiple users simultaneously train on the GPU.\nThe problem with TensorFlow is that, by default, it allocates the full amount of available GPU memory when it is launched. Even for a small two-layer neural network, I see that all 12 GB of the GPU memory is used up.\nIs there a way to make TensorFlow only allocate, say, 4 GB of GPU memory, if one knows that this is enough for a given model?","AnswerCount":16,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":237456,"Q_Id":34199233,"Users Score":7,"Answer":"Shameless plug: If you install the GPU supported Tensorflow, the session will first allocate all GPUs whether you set it to use only CPU or GPU. I may add my tip that even you set the graph to use CPU only you should set the same configuration(as answered above:) ) to prevent the unwanted GPU occupation.\nAnd in an interactive interface like IPython and Jupyter, you should also set that configure, otherwise, it will allocate all memory and leave almost none for others. This is sometimes hard to notice.","Q_Score":349,"Tags":"python,tensorflow,tensorflow2.0,tensorflow2.x,nvidia-titan","A_Id":44128902,"CreationDate":"2015-12-10T10:19:00.000","Title":"How to prevent tensorflow from allocating the totality of a GPU memory?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I work in an environment in which computational resources are shared, i.e., we have a few server machines equipped with a few Nvidia Titan X GPUs each.\nFor small to moderate size models, the 12 GB of the Titan X is usually enough for 2\u20133 people to run training concurrently on the same GPU. If the models are small enough that a single model does not take full advantage of all the computational units of the GPU, this can actually result in a speedup compared with running one training process after the other. Even in cases where the concurrent access to the GPU does slow down the individual training time, it is still nice to have the flexibility of having multiple users simultaneously train on the GPU.\nThe problem with TensorFlow is that, by default, it allocates the full amount of available GPU memory when it is launched. Even for a small two-layer neural network, I see that all 12 GB of the GPU memory is used up.\nIs there a way to make TensorFlow only allocate, say, 4 GB of GPU memory, if one knows that this is enough for a given model?","AnswerCount":16,"Available Count":2,"Score":0.012499349,"is_accepted":false,"ViewCount":237456,"Q_Id":34199233,"Users Score":1,"Answer":"i tried to train unet on voc data set but because of huge image size, memory finishes. i tried all the above tips, even tried with batch size==1, yet to no improvement. sometimes TensorFlow version also causes the memory issues. try by using \n\npip install tensorflow-gpu==1.8.0","Q_Score":349,"Tags":"python,tensorflow,tensorflow2.0,tensorflow2.x,nvidia-titan","A_Id":52828871,"CreationDate":"2015-12-10T10:19:00.000","Title":"How to prevent tensorflow from allocating the totality of a GPU memory?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using Tensorflow to build up the Neural Network, and I would like to show training results on the Tensorboard. So far everything works fine. But I have a question on \"event file\" for the Tensorboard. I notice that every time when I run my python script, it generates different event files. And when I run my local server using \n$ python \/usr\/local\/lib\/python2.7\/dist-packages\/tensorflow\/tensorboard\/tensorboard.py --logdir=\/home\/project\/tmp\/, it shows up error if there are more than 1 event files. It seems to be annoying since whenever I run my local server, I have to delete all previous event files to make it work. So I'm wondering if there is any solution to prevent this issue. I would really appreciate it.","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":1651,"Q_Id":34233767,"Users Score":2,"Answer":"The best solution from a TensorBoard perspective is to have a root directory for your experiment, e.g. ~\/tensorflow\/mnist_experiment, and then to create a new subdirectory for each run, e.g. ~\/tensorflow\/mnist_experiment\/run1\/...\nThen run TensorBoard against the root directory, and every time you invoke your code, setup the SummaryWriter pointing to a new subdirectory. TensorBoard will then interpret all of the event files correctly, and it will also make it easy to compare between your different runs.","Q_Score":3,"Tags":"python,tensorflow,tensorboard","A_Id":35853184,"CreationDate":"2015-12-11T22:44:00.000","Title":"Event files in Google Tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"TensorFlow graph is usually built gradually from inputs to outputs, and then executed. Looking at the Python code, the inputs lists of operations are immutable which suggests that the inputs should not be modified. Does that mean that there is no way to update\/modify an existing graph?","AnswerCount":4,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":13045,"Q_Id":34235225,"Users Score":12,"Answer":"Yes, tf.Graph are build in an append-only fashion as @mrry puts it.\nBut there's workaround:\nConceptually you can modify an existing graph by cloning it and perform the modifications needed along the way. As of r1.1, Tensorflow provides a module named tf.contrib.graph_editor which implements the above idea as a set of convinient functions.","Q_Score":24,"Tags":"python,tensorflow","A_Id":44353399,"CreationDate":"2015-12-12T01:38:00.000","Title":"Is it possible to modify an existing TensorFlow computation graph?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to know how the contours levels are chosen in pyplot.contour. What I mean by this is, given a function f(x, y), the level curves are usually chosen by evaluating the points where f(x, y) = c, c=0,1,2,... etc. However if f(x, y) is an array A of nxn points, how do the level points get chosen? I don't mean how do the points get connected, just simply the points that correspond to A = c","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":976,"Q_Id":34275096,"Users Score":0,"Answer":"The function is evaluated at every grid node, and compared to the iso-level. When there is a change of sign along a cell edge, a point is computed by linear interpolation between the two nodes. Points are joined in pairs by line segments. This is an acceptable approximation when the grid is dense enough.","Q_Score":0,"Tags":"python,math,matplotlib","A_Id":34277060,"CreationDate":"2015-12-14T19:30:00.000","Title":"How are the points in a level curve chosen in pyplot?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using OSX El Capitan and trying to import matplotlib.pyplot\nwhen I do that I get recursive error and at the end it says \"ValueError: insecure string pickle\"\nHere is the whole log:\n\n--------------------------------------------------------------------------- ValueError                                Traceback (most recent call\n  last)  in ()\n        4 stats = Statistics(\"HumanData.xlsx\")\n        5 \n  ----> 6 get_ipython().magic(u'matplotlib inline')\n        7 \n        8 #matplotlib.pyplot.hist(stats.getActionData(\"Human\", \"Pacman\", \"Left\"))\n\/Users\/AhmedKhalifa\/anaconda\/lib\/python2.7\/site-packages\/IPython\/core\/interactiveshell.pyc\n  in magic(self, arg_s)    2334         magic_name, _, magic_arg_s =\n  arg_s.partition(' ')    2335         magic_name =\n  magic_name.lstrip(prefilter.ESC_MAGIC)\n  -> 2336         return self.run_line_magic(magic_name, magic_arg_s)    2337     2338    \n-------------------------------------------------------------------------\n\/Users\/AhmedKhalifa\/anaconda\/lib\/python2.7\/site-packages\/IPython\/core\/interactiveshell.pyc\n  in run_line_magic(self, magic_name, line)    2255\n  kwargs['local_ns'] = sys._getframe(stack_depth).f_locals    2256\n  with self.builtin_trap:\n  -> 2257                 result = fn(*args,**kwargs)    2258             return result    2259 \n\/Users\/AhmedKhalifa\/anaconda\/lib\/python2.7\/site-packages\/IPython\/core\/magics\/pylab.pyc\n  in matplotlib(self, line)\n\/Users\/AhmedKhalifa\/anaconda\/lib\/python2.7\/site-packages\/IPython\/core\/magic.pyc\n  in (f, *a, **k)\n      191     # but it's overkill for just that one bit of state.\n      192     def magic_deco(arg):\n  --> 193         call = lambda f, *a, **k: f(*a, **k)\n      194 \n      195         if callable(arg):\n\/Users\/AhmedKhalifa\/anaconda\/lib\/python2.7\/site-packages\/IPython\/core\/magics\/pylab.pyc\n  in matplotlib(self, line)\n       98             print(\"Available matplotlib backends: %s\" % backends_list)\n       99         else:\n  --> 100             gui, backend = self.shell.enable_matplotlib(args.gui)\n      101             self._show_matplotlib_backend(args.gui, backend)\n      102 \n\/Users\/AhmedKhalifa\/anaconda\/lib\/python2.7\/site-packages\/IPython\/core\/interactiveshell.pyc\n  in enable_matplotlib(self, gui)    3130                 gui, backend =\n  pt.find_gui_and_backend(self.pylab_gui_select)    3131 \n  -> 3132         pt.activate_matplotlib(backend)    3133         pt.configure_inline_support(self, backend)    3134 \n\/Users\/AhmedKhalifa\/anaconda\/lib\/python2.7\/site-packages\/IPython\/core\/pylabtools.pyc\n  in activate_matplotlib(backend)\n      272     matplotlib.rcParams['backend'] = backend\n      273 \n  --> 274     import matplotlib.pyplot\n      275     matplotlib.pyplot.switch_backend(backend)\n      276 \n\/Users\/AhmedKhalifa\/anaconda\/lib\/python2.7\/site-packages\/matplotlib\/pyplot.py\n  in ()\n       27 from cycler import cycler\n       28 import matplotlib\n  ---> 29 import matplotlib.colorbar\n       30 from matplotlib import style\n       31 from matplotlib import _pylab_helpers, interactive\n\/Users\/AhmedKhalifa\/anaconda\/lib\/python2.7\/site-packages\/matplotlib\/colorbar.py\n  in ()\n       32 import matplotlib.artist as martist\n       33 import matplotlib.cbook as cbook\n  ---> 34 import matplotlib.collections as collections\n       35 import matplotlib.colors as colors\n       36 import matplotlib.contour as contour\n\/Users\/AhmedKhalifa\/anaconda\/lib\/python2.7\/site-packages\/matplotlib\/collections.py\n  in ()\n       25 import matplotlib.artist as artist\n       26 from matplotlib.artist import allow_rasterization\n  ---> 27 import matplotlib.backend_bases as backend_bases\n       28 import matplotlib.path as mpath\n       29 from matplotlib import _path\n\/Users\/AhmedKhalifa\/anaconda\/lib\/python2.7\/site-packages\/matplotlib\/backend_bases.py\n  in ()\n       60 \n       61 import matplotlib.tight_bbox as tight_bbox\n  ---> 62 import matplotlib.textpath as textpath\n       63 from matplotlib.path import Path\n       64 from matplotlib.cbook import mplDeprecation, warn_deprecated\n\/Users\/AhmedKhalifa\/anaconda\/lib\/python2.7\/site-packages\/matplotlib\/textpath.py\n  in ()\n       13 from matplotlib.path import Path\n       14 from matplotlib import rcParams\n  ---> 15 import matplotlib.font_manager as font_manager\n       16 from matplotlib.ft2font import FT2Font, KERNING_DEFAULT, LOAD_NO_HINTING\n       17 from matplotlib.ft2font import LOAD_TARGET_LIGHT\n\/Users\/AhmedKhalifa\/anaconda\/lib\/python2.7\/site-packages\/matplotlib\/font_manager.py\n  in ()    1418                 verbose.report(\"Using\n  fontManager instance from %s\" % _fmcache)    1419         except:\n  -> 1420             _rebuild()    1421     else:    1422         _rebuild()\n\/Users\/AhmedKhalifa\/anaconda\/lib\/python2.7\/site-packages\/matplotlib\/font_manager.py\n  in _rebuild()    1403     def _rebuild():    1404         global\n  fontManager\n  -> 1405         fontManager = FontManager()    1406         if _fmcache:    1407             pickle_dump(fontManager, _fmcache)\n\/Users\/AhmedKhalifa\/anaconda\/lib\/python2.7\/site-packages\/matplotlib\/font_manager.py\n  in init(self, size, weight)    1041         #  Load TrueType fonts\n  and create font dictionary.    1042 \n  -> 1043         self.ttffiles = findSystemFonts(paths) + findSystemFonts()    1044         self.defaultFamily = {    1045\n  'ttf': 'Bitstream Vera Sans',\n\/Users\/AhmedKhalifa\/anaconda\/lib\/python2.7\/site-packages\/matplotlib\/font_manager.py\n  in findSystemFonts(fontpaths, fontext)\n      321                     fontfiles[f] = 1\n      322 \n  --> 323             for f in get_fontconfig_fonts(fontext):\n      324                 fontfiles[f] = 1\n      325 \n\/Users\/AhmedKhalifa\/anaconda\/lib\/python2.7\/site-packages\/matplotlib\/font_manager.py\n  in get_fontconfig_fonts(fontext)\n      273         pipe = subprocess.Popen(['fc-list', '--format=%{file}\\n'],\n      274                                 stdout=subprocess.PIPE,\n  --> 275                                 stderr=subprocess.PIPE)\n      276         output = pipe.communicate()[0]\n      277     except (OSError, IOError):\n\/Users\/AhmedKhalifa\/anaconda\/lib\/python2.7\/subprocess.pyc in\n  init(self, args, bufsize, executable, stdin, stdout, stderr, preexec_fn, close_fds, shell, cwd, env, universal_newlines,\n  startupinfo, creationflags)\n      708                                 p2cread, p2cwrite,\n      709                                 c2pread, c2pwrite,\n  --> 710                                 errread, errwrite)\n      711         except Exception:\n      712             # Preserve original exception in case os.close raises.\n\/Users\/AhmedKhalifa\/anaconda\/lib\/python2.7\/subprocess.pyc in\n  _execute_child(self, args, executable, preexec_fn, close_fds, cwd, env, universal_newlines, startupinfo, creationflags, shell, to_close,\n  p2cread, p2cwrite, c2pread, c2pwrite, errread, errwrite)    1332\n  if e.errno != errno.ECHILD:    1333                         raise\n  -> 1334                 child_exception = pickle.loads(data)    1335                 raise child_exception    1336 \n\/Users\/AhmedKhalifa\/anaconda\/lib\/python2.7\/pickle.pyc in loads(str)\n  1386 def loads(str):    1387     file = StringIO(str)\n  -> 1388     return Unpickler(file).load()    1389     1390 # Doctest\n\/Users\/AhmedKhalifa\/anaconda\/lib\/python2.7\/pickle.pyc in load(self)\n      862             while 1:\n      863                 key = read(1)\n  --> 864                 dispatchkey\n      865         except _Stop, stopinst:\n      866             return stopinst.value\n\/Users\/AhmedKhalifa\/anaconda\/lib\/python2.7\/pickle.pyc in\n  load_string(self)\n      970             if rep.startswith(q):\n      971                 if len(rep) < 2 or not rep.endswith(q):\n  --> 972                     raise ValueError, \"insecure string pickle\"\n      973                 rep = rep[len(q):-len(q)]\n      974                 break\nValueError: insecure string pickle\n\nAny help with that?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":704,"Q_Id":34277148,"Users Score":0,"Answer":"I have the exact same problem. I'm not sure what the issue is but every once in a while, when trying to import matplotlib inside ipython I encounter this error and restarting the computer solves the issue. Maybe that would help in locating the issue?","Q_Score":0,"Tags":"python,python-2.7,matplotlib","A_Id":35905473,"CreationDate":"2015-12-14T21:43:00.000","Title":"Matplotlib error in importing","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've this CSV file which has comments (tweets,  comments). I want to classify them into 4 categories, viz. \n\nPre Sales\nPost Sales\nPurchased\nService query\n\nNow the problems that I'm facing are these :\n\nThere is a huge number of overlapping words between each of the\ncategories, hence using NaiveBayes is failing.\nThe size of tweets being only 160 chars, what is the best way to\nprevent words from one category falling into the another.  \nWhat all ways should I select the features which can take care of both the 160 char tweets and a bit lengthier facebook comments.\nPlease let me know of any reference link\/tutorial link to follow up the same, being a newbee in this field\n\nThanks","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":622,"Q_Id":34284385,"Users Score":1,"Answer":"I wouldn't be so quick to write off Naive Bayes. It does fine in many domains where there are lots of weak clues (as in \"overlapping words\"), but no absolutes. It all depends on the features you pass it. I'm guessing you are blindly passing it the usual \"bag of words\" features, perhaps after filtering for stopwords. Well, if that's not working, try a little harder. \nA good approach is to read a couple of hundred tweets and see how you know which category you are looking at. That'll tell you what kind of things you need to distill into features. But be sure to look at lots of data, and focus on the general patterns.\nAn example (but note that I haven't looked at your corpus): Time expressions may be good clues on whether you are pre- or post-sale, but they take some work to detect. Create some features \"past expression\", \"future expression\", etc. (in addition to bag-of-words features), and see if that helps. Of course you'll need to figure out how to detect them first, but you don't have to be perfect: You're after anything that can help the classifier make a better guess. \"Past tense\" would probably be a good feature to try, too.","Q_Score":3,"Tags":"python,twitter,nltk,document-classification","A_Id":34300675,"CreationDate":"2015-12-15T08:37:00.000","Title":"Classifying sentences with overlapping words","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to read a large and complex CSV file with pandas.read_csv.\nThe exact command is\npd.read_csv(filename, quotechar='\"', low_memory=True, dtype=data_types, usecols= columns, true_values=['T'], false_values=['F'])\nI am pretty sure that the data types are correct. I can read the first 16 million lines (setting nrows=16000000) without problems but somewhere after this I get the following error\n\nValueError: could not convert string to float: '1,123'\n\nAs it seems, for some reason pandas thinks two columns would be one.\nWhat could be the problem? How can I fix it?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":8440,"Q_Id":34319011,"Users Score":0,"Answer":"I found the mistake. The problem was a thousand separator.\nWhen writing the CSV file, most numbers were below thousand and were correctly written to the CSV file. However, this one value was greater than thousand and it was written as \"1,123\" which pandas did not recognize as a number but as a string.","Q_Score":2,"Tags":"python,csv,pandas","A_Id":34323744,"CreationDate":"2015-12-16T18:01:00.000","Title":"Pandas: Read CSV: ValueError: could not convert string to float","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have written a simple PCA code that calculates the covariance matrix and then uses linalg.eig on that covariance matrix to find the principal components. When I use scikit's PCA for three principal components I get almost the equivalent result. My PCA function outputs the third column of transformed data with flipped signs to what scikit's PCA function does. Now I think there is a higher probability that scikit's built-in PCA is correct than to assume that my code is correct. I have noticed that the third principal component\/eigenvector has flipped signs in my case. So if scikit's third eigenvector is (a,-b,-c,-d) then mine is (-a,b,c,d). I might a bit shabby in my linear algebra, but I assume those are different results. The way I arrive at my eigenvectors is by computing the eigenvectors and eigenvalues of the covariance matrix using linalg.eig. I would gladly try to find eigenvectors by hand, but doing that for a 4x4 matrix (I am using iris data set) is not fun.\nIris data set has 4 dimensions, so at most I can run PCA for 4 components. When I run for one component, the results are equivalent. When I run for 2, also equivalent. For three, as I said, my function outputs flipped signs in the third column. When I run for four, again signs are flipped in the third column and all other columns are fine. I am afraid I cannot provide the code for this. This is a project, kind of.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":593,"Q_Id":34323027,"Users Score":1,"Answer":"So if scikit's third eigenvector is (a,-b,-c,-d) then mine is (-a,b,c,d).\n\nThat's completely normal. If v is an eigenvector of a matrix, then -v is an eigenvector with the same eigenvalue.","Q_Score":0,"Tags":"python,python-2.7,numpy,pca","A_Id":34323069,"CreationDate":"2015-12-16T22:08:00.000","Title":"Alternative to numpy's linalg.eig?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have installed tensorflow on Windows using Docker, I want to go to the folder \"tensorflow\/models\/image\/imagenet\" that contains \"classify_image.py\" python file..\nCan someone please how to reach this mentioned path?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1104,"Q_Id":34337788,"Users Score":1,"Answer":"If you're using one of the devel tags (:latest-devel or :latest-devel-gpu), the file should be in \/tensorflow\/tensorflow\/models\/image\/imagenet\/classify_image.py.\nIf you're using the base container (b.gcr.io\/tensorflow\/tensorflow:latest), it's not included -- that image just has the binary installed, not a full source distribution, and classify_image.py isn't included in the binary distribution.","Q_Score":2,"Tags":"python,windows,docker,tensorflow","A_Id":34340617,"CreationDate":"2015-12-17T15:06:00.000","Title":"Location of tensorflow\/models.. in Windows","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I have an array A that has shape (480, 640, 3), and an array B with shape (480, 640).\nHow can I append these two as one array with shape (480, 640, 4)? \nI tried np.append(A,B) but it doesn't keep the dimension, while the axis option causes the ValueError: all the input arrays must have same number of dimensions.","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":65088,"Q_Id":34357617,"Users Score":2,"Answer":"using np.stack should work\nbut the catch is both arrays should be of 2D form.\nnp.stack([A,B])","Q_Score":36,"Tags":"python,arrays,numpy,append","A_Id":70073546,"CreationDate":"2015-12-18T14:15:00.000","Title":"Append 2D array to 3D array, extending third dimension","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a 3D regular grid of data.  I would like to write a routine allowing the user to specify a plane slicing through the data with arbitrary orientation and returning a contour plot of the data in the plane.  Is there a ready-made way in matplotlib to do this?  Could find anything in the docs.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":494,"Q_Id":34360265,"Users Score":0,"Answer":"You can use roll function of numpy to rotate your plane and make it parallel with a base plane. now you can choose your plane and plot. Only problem is that at close to edges the value from one side will be added to opposite side.","Q_Score":0,"Tags":"python,matplotlib,3d","A_Id":34361488,"CreationDate":"2015-12-18T16:46:00.000","Title":"plotting 2D slice of arbitrary orientation through 3D data in matplotlib","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a support vector machine trained on ~300,000 examples, and it takes roughly 1.5-2 hours to train this model, and I pickled(serialized) it. Currently, I want to add\/remove a couple of the parameters of the model. Is there a way to do this without having to retrain the entire model? I am using sklearn in python.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":44,"Q_Id":34377210,"Users Score":1,"Answer":"If you are using SVC from sklearn then the answer is no. There is no way to do it, this implementation is purely batch training based. If you are training linear SVM using SGDClassifier from sklearn then the answer is yes as you can simply start the optimization from the previous solution (when removing feature - simply with removed corresponding weight, and when adding - with added any weight there).","Q_Score":1,"Tags":"python,scikit-learn,svm","A_Id":34377289,"CreationDate":"2015-12-20T01:43:00.000","Title":"Adding and removing SVM parameters without having to totally retrain","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to build and train a machine learning data science algorithm that correctly predicts what presidential won in what county. I have the following information for training data.\nTotal population    Median age  % BachelorsDeg or higher    Unemployment rate   Per capita income   Total households    Average household size  % Owner occupied housing    % Renter occupied housing   % Vacant housing    Median home value   Population growth   House hold growth   Per capita income growth    Winner\nI am new to data science. I do know Naive Bayes is a good classifier for algorithms trying to predict with multiple properties. However, I read the first step for a naive bayes classifier requires a frequency table. My problem is all of the above properties are continuous numerical properties and don't fall into \"Yes\" or \"No\" categories. Do I not use a Naive Bayes classifier then?\nI also considered using a k nearest neighbor algorithm, but that doesn't look like it will be the most accurate and weight the properties correctly for me...I am looking for a supervised algorithm because I have training data. Can anyone give me any recommendations as to what algorithm to use? In addition, being new to the field, how can I figure out what algorithm to use on my own in the future.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":229,"Q_Id":34390336,"Users Score":1,"Answer":"For Naive Bayes you can discretize your continuous numerical properties.\nFor example, for \"% Owner occupied housing\" you split all 100% scale into ten partitions(0-10%, 10-20%, ..., 90-100%) and get the frequency table.\nFor some properties you can move to binary values: Unemployment rate < 30% - yes\/no.\nGood luck in learning Machine Learning :)","Q_Score":1,"Tags":"python,algorithm,machine-learning,naivebayes,data-science","A_Id":34391185,"CreationDate":"2015-12-21T06:56:00.000","Title":"What data science programming algorithm is like Naive Bayes for continuous variables?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"What is the difference between a \"Local\" layer and a \"Dense\" layer in a convolutional neural network?  I am trying to understand the CIFAR-10 code in TensorFlow, and I see it uses \"Local\" layers instead of regular dense layers. Is there any class in TF that supports implementing \"Local\" layers?","AnswerCount":2,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":2001,"Q_Id":34393876,"Users Score":4,"Answer":"I am quoting user2576346's comments under the question: \n\nAs I understand, either it should be densely connected or be a convolutional layer ...\n\nNo this is not true. A more accurate way to phrase that statement would be that layers are either fully connected (dense) or locally connected.\nA convolutional layer is an example of a locally connected layer. In general a locally connected layer is a layer in which each of its units is only connected to a local portion of the input. A convolutional layer is a special type of local layer which exhibits a spatial translation invariance as each convolutional feature detector is strided across the entire image in local receptive windows, e.g. of size 3x3 or 5x5 for example.","Q_Score":6,"Tags":"python,convolution,deep-learning,tensorflow,conv-neural-network","A_Id":34568528,"CreationDate":"2015-12-21T10:47:00.000","Title":"Difference between local and dense layers in CNNs","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am making an experiment, and the participant must get the possibility to correct himself when he has given the wrong answer.\nThe goal is that the experiment goes on to the next trial when the correct answer is given. When the wrong answer is given, you get another chance. \nFor the moment, the experiment crashes after the first trial and it always waits for the second chance answer (even when the right answer was given).","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":152,"Q_Id":34467177,"Users Score":0,"Answer":"You probably placed your Answerrunning = False at the wrong place. And probably you need to put break at the end of each branch. Please explain more what you want to do, I don't understand.\nIf you say you need to count tries, then I guess you should have something like number_of_tries = 0 and number_of_tries += 1 somewhere in your code.","Q_Score":0,"Tags":"python,while-loop,psychopy","A_Id":34479029,"CreationDate":"2015-12-25T23:02:00.000","Title":"Python: How to give participant the possibility to an answer","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I was wondering how to make a 5D tensor in Theano.\nSpecifically, I tried dtensor = T.TensorType('float32', (False,)*5). However, the only issue is that dtensor.shapereturns: AttributeError: 'TensorType' object has no attribute 'shape'\nWhereas if I used a standard tensor type likedtensor = T.tensor3('float32'), I don't get this issue when I call dtensor.shape.\nIs there a way to have this not be an issue with a 5D tensor in Theano?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":506,"Q_Id":34483277,"Users Score":1,"Answer":"Theano variables do not have explicit shape information since they are symbolic variables, not numerical. Even dtensor3 = T.tensor3(T.config.floatX) does not have an explicit shape. When you type dtensor3.shape you'll get an object Shape.0 but when you do dtensor3.shape.eval() to get its value you'll get an error.\nFor both cases however, dtensor.ndim works and prints out 5 and 3 respectively.","Q_Score":1,"Tags":"python,theano,symbolic-computation","A_Id":34484383,"CreationDate":"2015-12-27T18:04:00.000","Title":"5D tensor in Theano","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Any binary one-hot encoding is aware of only values seen in training, so features not encountered during fitting will be silently ignored. For real time, where you have millions of records in a second, and features have very high cardinality, you need to keep your hasher\/mapper updated with the data.\nHow can we do an incremental update to the hasher (rather calculating the entire fit() every time we incounter a new feature-value pair)? What is the suggested approach here the tackle this?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":237,"Q_Id":34493556,"Users Score":1,"Answer":"It depends on the learning algorithm that you are using. If you are using a method that has been designated for sparse data sets (FTRL, FFM, linear SVM) one possible approach is the following (note that it will introduce collisions in the features and a lot of constant columns).\nFirst allocate for each element of your sample a (as large as possible) vector V, of length D.\nFor each categorical variable, evaluate hash(var_name + \"_\" + var_value) % D. This gives you an integer i, and you can store V[i] = 1.\nTherefore, V never grows larger as new features appear. However, as soon as the number of features is large enough, some features will collide (i.e. be written at the same place) and this may result in an increased error rate...\nEdit. You can write your own vectorizer to avoid collisions. First call L the current number of features. Prepare the same vector V of length 2L (this 2 will allow you to avoid collisions as new features arrive - at least for some time, depending of the arrival rate of new features).\nStarting with an emty dictionary<input_type,int>, associate to each feature an integer. If have already seen the feature, return the int corresponding to the feature. If not, create a new entry with an integer corresponding to the new index. I think (but I am not sure) this is what LabelEncoder does for you.","Q_Score":1,"Tags":"machine-learning,categorical-data,python,scikit-learn","A_Id":34493557,"CreationDate":"2015-12-28T07:14:00.000","Title":"Using sklearn DictVectorizer in real-time systems","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Let's say that I have pandas DataFrame with a column called \"fruit\" that represents what fruit my classroom of kindergartners had for a morning snack. I have 20 students in my class. Breakdown would be something like this.\nOranges = 7, Grapes = 3, Blackberries = 4, Bananas = 6\nI used sort to group each of these fruit types, but it is grouping based on alphabetical order. I would like it to group based on the largest quantity of entries for that class of fruit. In this case, I would like Oranges to turn up  first so that I can easily see that Oranges is the most popular fruit.\nI'm thinking that sort is not the best way to go about this. I checked out groupby but could not figure out how to use that appropriately either.\nThanks in advance.","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":38,"Q_Id":34502840,"Users Score":2,"Answer":"To sort by name: df.fruit.value_counts().sort_index()\nTo sort by counts: df.fruit.value_counts().sort_values()","Q_Score":0,"Tags":"python,pandas","A_Id":34502877,"CreationDate":"2015-12-29T00:37:00.000","Title":"Python pandas: determining which \"group\" has the most entries","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"According to Spark documentation \nspark.storage.memoryFraction: Fraction of Java heap to use for Spark's memory cache. This should not be larger than the \"old\" generation of objects in the JVM, which by default is given 0.6 of the heap, but you can increase it if you configure your own old generation size.\nI found several blogs and article where it is suggested to set it to zero in yarn mode. Why is that better than set it to something close to 1? And in general, what is a reasonable value for it ?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":7480,"Q_Id":34509593,"Users Score":11,"Answer":"The Spark executor is set up into 3 regions.\n\nStorage - Memory reserved for caching\nExecution - Memory reserved for object creation\nExecutor overhead.\n\nIn Spark 1.5.2 and earlier:\nspark.storage.memoryFraction sets the ratio of memory set for 1 and 2.  The default value is .6, so 60% of the allocated executor memory is reserved for caching.  In my experience, I've only ever found that the number is reduced.  Typically when a developer is getting a GC issue, the application has a larger \"churn\" in objects, and one of the first places for optimizations is to change the memoryFraction.\nIf your application does not cache any data, then setting it to 0 is something you should do.  Not sure why that would be specific to YARN, can you post the articles?\nIn Spark 1.6.0 and later:\nMemory management is now unified.  Both storage and execution share the heap.  So this doesnt really apply anymore.","Q_Score":6,"Tags":"java,python,apache-spark,hadoop-yarn","A_Id":34515890,"CreationDate":"2015-12-29T10:59:00.000","Title":"spark.storage.memoryFraction setting in Apache Spark","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there a PyMC3 equivalent to the pymc.rnormal function, or has it been dropped in favor of numpy.random.normal?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":93,"Q_Id":34526093,"Users Score":0,"Answer":"Found it... a bit silly of me. pymc3.Normal(mu,sd).random(), which basically just calls scipy.stats.norm","Q_Score":0,"Tags":"python,pymc,pymc3","A_Id":34560260,"CreationDate":"2015-12-30T08:55:00.000","Title":"What is the PyMC3 equivalent of the 'pymc.rnormal' function?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Python scikit-learn SGDClassifier() supports both l1, l2, and elastic, it seems to be important to find optimal value of regularization parameter.\nI got an advice to use SGDClassifier() with GridSearchCV() to do this, but in SGDClassifier serves only regularization parameter alpha.\nIf I use loss functions such as SVM or LogisticRegression, I think there should be C instead of alpha for parameter optimization.\nIs there any way to set optimal parameter in SGDClassifier() when using Logisitic Regression or SVM?\nIn addition, I have one more question about iteration parameter n_iter, but I did not understand what this parameter mean. Does it work like a bagging if used with shuffle option together? So, if I use l1 penalty and large value of n_iter, would it work like RandomizedLasso()?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":4643,"Q_Id":34556476,"Users Score":10,"Answer":"C and alpha both have the same effect. The difference is a choice of terminology. C is proportional to 1\/alpha. You should use GridSearchCV to select either alpha or C the same way, but remember a higher C is more likely to overfit, where a lower alpha is more likely to overfit.\nL2 will produce a model with many small coefficients, where L1 will choose a model with a large number of 0 coefficients and a few large coefficients. Elastic net is a combination of the two.\nSGDClassifier uses stochastic gradient descent in which the data is fed through the learning algorithm sample by sample. The n_iter tells it how many passes it should make over the data. As the number of iterations goes up and the learning rate goes down, SGD becomes more like batch gradient descent, but it becomes slower as well.","Q_Score":3,"Tags":"python,machine-learning,scikit-learn","A_Id":34558742,"CreationDate":"2016-01-01T13:19:00.000","Title":"Regularization parameter and iteration of SGDClassifier in scikit-learn","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I could not find a particular way to phrase this into a google search that returned any solid results so asking StackOverflow. Would appreciate the help all!\nI am using a CSV file, Iris, in Python to do some basic matplot plotting. Within Iris, I am looking to reference a particular column called Petal.Length.\nNormally, for any other column, like Species, I would use Iris.Species to call that particular column. However for Petal.Length, there is already a period inside meaning I cannot use Iris.Petal.Length. What is the proper syntax to call a column that already contains a period? Thanks all.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":79,"Q_Id":34572133,"Users Score":0,"Answer":"You can use the indexing notation: Iris['Petal.Length'].  Using . (as in Iris.Species) only works if the column name is a valid Python identifier, but [] always works, even if the column name contains spaces or other symbols.","Q_Score":1,"Tags":"python,matplotlib,dataframe","A_Id":34572201,"CreationDate":"2016-01-03T00:21:00.000","Title":"How do I quote a column in a dataframe that has a period?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Here is my question:\nI have a 3-d numpy array Data which in the shape of (1000, 100, 100).\nAnd I want to save it as a .txt or .csv file, how to achieve that?\nMy first attempt was to reshape it into a 1-d array which length 1000*100*100, and transfer it into pandas.Dataframe, and then, I save it as .csv file.    \nWhen I wanted to call it next time,I would reshape it back to 3-d array.    \nI think there must be some methods easier.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":53,"Q_Id":34574396,"Users Score":1,"Answer":"If you need to re-read it quickly into numpy you could just use the cPickle module.\nThis is going to be much faster that parsing it back from an ASCII dump (but however only the program will be able to re-read it). As a bonus with just one instruction you could dump more than a single matrix (i.e. any data structure built with core python and numpy arrays).\nNote that parsing a floating point value from an ASCII string is a quite complex and slow operation (if implemented correctly down to ulp).","Q_Score":2,"Tags":"python,numpy","A_Id":34574445,"CreationDate":"2016-01-03T07:43:00.000","Title":"How to save the n-d numpy array data and read it quickly next time?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Given some coordinates in 3D (x-, y- and z-axes), what I would like to do is to get a polynomial (fifth order). I know how to do it in 2D (for example just in x- and y-direction) via numpy. So my question is: Is it possible to do it also with the third (z-) axes?\nSorry if I missed a question somewhere.\nThank you.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":260,"Q_Id":34597732,"Users Score":0,"Answer":"Numpy has functions for multi-variable polynomial evaluation in the polynomial package -- polyval2d, polyval3d -- the problem is getting the coefficients. For fitting, you need the polyvander2d, polyvander3d functions that create the design matrices for the least squares fit. The multi-variable polynomial coefficients thus determined can then be reshaped and used in the corresponding evaluation functions. See the documentation for those functions for more details.","Q_Score":0,"Tags":"python,numpy,curve-fitting,algebra","A_Id":38167204,"CreationDate":"2016-01-04T18:45:00.000","Title":"Create 3D- polynomial via numpy etc. from given coordinates","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to export a file as csv. I need to wrap text for a particular column while writing in csv file. I have a too long string. i need to write it in a csv file using python. While trying to write, it doesn't write in a single cell. some of the lines are written into next rows. I need to write the whole string in a single cell. Please help me to do this.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":4140,"Q_Id":34613958,"Users Score":0,"Answer":"CSV is not formatted. If you want to format the text in the cells, you should consider writing a proper Excel or PDF file.\nAnyway it looks like that newline characters (\\n or \\r\\n) can be used in CSV files if using semicolon as separator, so this may not be portable.\nTo write an Excel file, you can use libraries like openpyxl or xlwt.","Q_Score":2,"Tags":"python,django,export-to-csv","A_Id":34614120,"CreationDate":"2016-01-05T14:24:00.000","Title":"How to wrap text with in a column while writing in csv file using python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"There is absolutely helpful class GridSearchCV in scikit-learn to do grid search and cross validation, but I don't want to do cross validataion. I want to do grid search without cross validation and use whole data to train.\nTo be more specific, I need to evaluate my model made by RandomForestClassifier with \"oob score\" during grid search.\nIs there easy way to do it? or should I make a class by myself?\nThe points are\n\nI'd like to do grid search with easy way.\nI don't want to do cross validation.\nI need to use whole data to train.(don't want to separate to train data and test data)\nI need to use oob score to evaluate during grid search.","AnswerCount":4,"Available Count":1,"Score":0.1488850336,"is_accepted":false,"ViewCount":20780,"Q_Id":34624978,"Users Score":3,"Answer":"Although the question has been solved years ago, I just found a more natural way if you insist on using GridSearchCV() instead of other means (ParameterGrid(), etc.): \n\nCreate a sklearn.model_selection.PredefinedSplit(). It takes a parameter called test_fold, which is a list and has the same size as your input data. In the list, you set all samples belonging to training set as -1 and others as 0.\nCreate a GridSearchCV object with cv=\"the created PredefinedSplit object\".\n\nThen, GridSearchCV will generate only 1 train-validation split, which is defined in test_fold.","Q_Score":30,"Tags":"python,scikit-learn,random-forest,grid-search","A_Id":59507979,"CreationDate":"2016-01-06T03:09:00.000","Title":"Is there easy way to grid search without cross validation in python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In scikit-learn new  version ,there is a new function called apply() in Gradient boosting. I'm really confused about it .\nDoes it like the method:GBDT + LR that facebook has used?\nIf dose, how can we make it work like GBDT + LR?","AnswerCount":2,"Available Count":1,"Score":0.2913126125,"is_accepted":false,"ViewCount":1064,"Q_Id":34649751,"Users Score":3,"Answer":"From the Sci-Kit Documentation\n\napply(X)  Apply trees in the ensemble to X, return leaf indices\n\nThis function will take input data X and each data point (x) in it will be applied to each non-linear classifier tree. After application, data point x will have associated with it the leaf it end up at for each decision tree. This leaf will have its associated classes ( 1 if binary ). \napply(X) returns the above information, which is of the form [n_samples, n_estimators, n_classes].\nThus, the apply(X) function doesn't really have much to do with the Gradient Boosted Decision Tree + Logic Regression (GBDT+LR) classification and feature transform methods. It is a function for the application of data to an existing classification model.\nI'm sorry if I have misunderstood you in any way, though a few grammar\/syntax errors in your question made it harder to decipher.","Q_Score":0,"Tags":"python,machine-learning,scikit-learn","A_Id":34653721,"CreationDate":"2016-01-07T07:41:00.000","Title":"What the function apply() in scikit-learn can do?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to solve a binary classification problem with a class imbalance. I have a dataset of 210,000 records in which 92 %  are 0s and 8% are 1s. I am using sklearn (v 0.16) in python for random forests .\nI see there are two parameters sample_weight and class_weight  while constructing the classifier. I am currently using the parameter class_weight=\"auto\". \nAm I using this correctly? \nWhat does class_weight and sample weight actually do and What should I be using ?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":3318,"Q_Id":34655628,"Users Score":3,"Answer":"Class weights are what you should be using.\nSample weights allow you to specify a multiplier for the impact a particular sample has. Weighting a sample with a weight of 2.0 roughly has the same effect as if the point was present twice in the data (although the exact effect is estimator dependent).\nClass weights have the same effect, but it used for applying a set multiplier to every sample that falls into the specified class. In terms of functionality, you could use either, but class_weights is provided for convenience so you do not have to manually weight each sample. Also it is possible to combined the usage of the two in which the class weights are multiplied by the sample weights. \nOne of the main uses for sample_weights on the fit() method is to allow boosting meta-algorithms like AdaBoostClassifier to operate on existing decision tree classifiers and increase or decrease the weights of individual samples as needed by the algorithm.","Q_Score":5,"Tags":"python,scikit-learn,random-forest,supervised-learning","A_Id":34661470,"CreationDate":"2016-01-07T12:51:00.000","Title":"How to handle class imbalance in sklearn random forests. Should I use sample weights or class weight parameter","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In Spotfire text area, for drop down property, by default sort order for date column is coming with the oldest to latest. We need to display the dates order from newest to oldest. Can you please advise. \nDefault Order:12\/29\/2015 12\/30\/2015  12\/31\/2015    01\/01\/2016\nNeeded 1\/1\/2016   12\/31\/2015  12\/30\/2015  12\/29\/2015\nThanks","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":975,"Q_Id":34671202,"Users Score":0,"Answer":"You can apply custom sorting to STRING columns. \nOne way to achieve your goal is to create calculated columns for the Year and Month, and use these in your date axis. Then you can apply a custom sorting in Column Properties > Sort Order on your data table.","Q_Score":0,"Tags":"python,spotfire","A_Id":37417767,"CreationDate":"2016-01-08T06:59:00.000","Title":"How to change the sorting order of dates in the drop down property of spotfire","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to do bulk indexing in elasticsearch using Python for a big file (~800MB). However, everytime I try \n\n[2016-01-08 15:06:49,354][WARN ][http.netty               ] [Marvel Man] Caught exception while handling client http tra\n  ffic, closing connection [id: 0x2d26baec, \/0:0:0:0:0:0:0:1:58923 => \/0:0:0:0:0:0:0:1:9200]\n  org.jboss.netty.handler.codec.frame.TooLongFrameException: HTTP content length exceeded 104857600 bytes.\n          at org.jboss.netty.handler.codec.http.HttpChunkAggregator.messageReceived(HttpChunkAggregator.java:169)\n          at org.jboss.netty.channel.SimpleChannelUpstreamHandler.handleUpstream(SimpleChannelUpstreamHandler.java:70)\n          at org.jboss.netty.channel.DefaultChannelPipeline.sendUpstream(DefaultChannelPipeline.java:564)\n          at org.jboss.netty.channel.DefaultChannelPipeline$DefaultChannelHandlerContext.sendUpstream(DefaultChannelPipeli\n  ne.java:791)\n          at org.jboss.netty.handler.codec.http.HttpContentDecoder.messageReceived(HttpContentDecoder.java:135)\n          at org.jboss.netty.channel.SimpleChannelUpstreamHandler.handleUpstream(SimpleChannelUpstreamHandler.java:70)\n          at org.jboss.netty.channel.DefaultChannelPipeline.sendUpstream(DefaultChannelPipeline.java:564)\n          at org.jboss.netty.channel.DefaultChannelPipeline$DefaultChannelHandlerContext.sendUpstream(DefaultChannelPipeli\n  ne.java:791)\n          at org.jboss.netty.channel.Channels.fireMessageReceived(Channels.java:296)\n          at org.jboss.netty.handler.codec.frame.FrameDecoder.unfoldAndFireMessageReceived(FrameDecoder.java:459)\n          at org.jboss.netty.handler.codec.replay.ReplayingDecoder.callDecode(ReplayingDecoder.java:536)\n          at org.jboss.netty.handler.codec.replay.ReplayingDecoder.messageReceived(ReplayingDecoder.java:435)\n          at org.jboss.netty.channel.SimpleChannelUpstreamHandler.handleUpstream(SimpleChannelUpstreamHandler.java:70)\n          at org.jboss.netty.channel.DefaultChannelPipeline.sendUpstream(DefaultChannelPipeline.java:564)\n          at org.jboss.netty.channel.DefaultChannelPipeline$DefaultChannelHandlerContext.sendUpstream(DefaultChannelPipeli\n  ne.java:791)\n          at org.elasticsearch.common.netty.OpenChannelsHandler.handleUpstream(OpenChannelsHandler.java:75)\n          at org.jboss.netty.channel.DefaultChannelPipeline.sendUpstream(DefaultChannelPipeline.java:564)\n          at org.jboss.netty.channel.DefaultChannelPipeline.sendUpstream(DefaultChannelPipeline.java:559)\n          at org.jboss.netty.channel.Channels.fireMessageReceived(Channels.java:268)\n          at org.jboss.netty.channel.Channels.fireMessageReceived(Channels.java:255)\n          at org.jboss.netty.channel.socket.nio.NioWorker.read(NioWorker.java:88)\n          at org.jboss.netty.channel.socket.nio.AbstractNioWorker.process(AbstractNioWorker.java:108)\n          at org.jboss.netty.channel.socket.nio.AbstractNioSelector.run(AbstractNioSelector.java:337)\n          at org.jboss.netty.channel.socket.nio.AbstractNioWorker.run(AbstractNioWorker.java:89)\n          at org.jboss.netty.channel.socket.nio.NioWorker.run(NioWorker.java:178)\n          at org.jboss.netty.util.ThreadRenamingRunnable.run(ThreadRenamingRunnable.java:108)\n          at org.jboss.netty.util.internal.DeadLockProofWorker$1.run(DeadLockProofWorker.java:42)\n          at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)\n          at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)\n          at java.lang.Thread.run(Thread.java:745)\n\nCan anyone please help me understand what is happening here, and how I can solve this issue?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2316,"Q_Id":34686119,"Users Score":2,"Answer":"You have to increase the content uploading length which is by default 100mb.\nGo to elasticsearch.yml in config folder\nadd\/update - \n\nhttp.max_content_length: 300M","Q_Score":3,"Tags":"java,python,elasticsearch","A_Id":36937080,"CreationDate":"2016-01-08T21:11:00.000","Title":"Unable to do bulk indexing for large file in elasticsearch","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I just started learning Pandas and was wondering if there is any difference between groupby() and pivot_table() functions. Can anyone help me understand the difference between them.\nHelp would be appreciated.","AnswerCount":2,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":55623,"Q_Id":34702815,"Users Score":12,"Answer":"It's more appropriate to use .pivot_table() instead of .groupby() when you need to show aggregates with both rows and column labels.\n.pivot_table() makes it easy to create row and column labels at the same time and is preferable, even though you can get similar results using .groupby() with few extra steps.","Q_Score":92,"Tags":"python,pandas","A_Id":56676656,"CreationDate":"2016-01-10T06:38:00.000","Title":"Pandas: group by and Pivot table difference","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm working on a machine learning application in Python (using the sklearn module), and am currently trying to decide on a model for performing inference. A brief description of the problem:\nGiven many instances of user data, I'm trying to classify them into various categories based on relative keyword containment. It is supervised, so I have many, many instances of pre-classified data that are already categorized. (Each piece of data is between 2 and 12 or so words.)\nI am currently trying to decide between two potential models:\n\nCountVectorizer + Multinomial Naive Bayes. Use sklearn's CountVectorizer to obtain keyword counts across the training data. Then, use Naive Bayes to classify data using sklearn's MultinomialNB model.\nUse tf-idf term weighting on keyword counts + standard Naive Bayes. Obtain a keyword count matrix for the training data using CountVectorizer, transform that data to be tf-idf weighted using sklearn's TfidfTransformer, and then dump that into a standard Naive Bayes model.\n\nI've read through the documentation for the classes use in both methods, and both seem to address my problem very well.\nAre there any obvious reasons for why tf-idf weighting with a standard Naive Bayes model might outperform a multinomial Naive Bayes for this type of problem? Are there any glaring issues with either approach?","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":2014,"Q_Id":34735016,"Users Score":2,"Answer":"Naive Bayes and MultinomialNB are the same algorithms. The difference that you get is from the tfidf transformation which penalises the words that occur in lots of documents in your corpus.\nMy advice:\nUse tfidf and tune the sublinear_tf, binary parameters and normalization parameters of TfidfVectorization for features.\nAlso try all kind of different classifiers available in scikit-learn which i suspect will give you better results if you properly tune the value of regularization type (penalty eighther l1 or l2) and the regularization parameter (alpha).\nIf you tune them properly I suspect you can get much better results using SGDClassifier with 'log' loss (Logistic Regression) or 'hinge' loss (SVM).\nThe way people usually tune the parameters is through GridSearchCV class in scikit-learn.","Q_Score":2,"Tags":"python,machine-learning,scikit-learn,feature-selection","A_Id":34736355,"CreationDate":"2016-01-12T03:17:00.000","Title":"Choosing an sklearn pipeline for classifying user text data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"what is difference between numpy.array([[1., 2], [3, 4], [5, 6]]) and numpy.array([[1, 2], [3, 4], [5, 6]]). I came across one code using two different type of declaration but could not find its meaning.","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":38,"Q_Id":34787213,"Users Score":2,"Answer":"It has nothing to do with the array. 1. means 1.0. 1. is a float, 1 is an int.","Q_Score":1,"Tags":"python,numpy","A_Id":34787266,"CreationDate":"2016-01-14T10:30:00.000","Title":"meaning of \".\" after first element in numpy array","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am currently working on large data sets in csv format. In some cases, it is faster to use excel functions to get the work done. However, I want to write python scripts to read\/write csv and carry out the required function. In what cases would python scripts be better than using excel functions for data manipulation tasks? What would be the long term advantages?","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":236,"Q_Id":34787957,"Users Score":1,"Answer":"Using python is recommended for below scenarios:\n\nRepeated action: Perform similar set of action over a similar dataset repeatedly. For ex, say you get a monthly forecast data and you have to perform various slicing & dicing and plotting. Here the structure of data and the steps of analysis is more or less the same, but the data differs for every month. Using Python and Pandas will save you a bulk of time and also reduces manual error.\nExploratory analysis: Once you establish a certain familiarity with Pandas, Numpy and Matplotlib, analysis using these python libraries are faster and much efficient than Excel Analysis. One simple usecase to justify this statement is backtracking. With Pandas, you can quickly trace back and regain the dataset to its original form or an earlier analysed form. With Excel, you could get lost after a maze of analysis, and might be in a lose of backtrack to an earlier form outside of cntrl + z\nTeaching Tool: In my opinion, this is the most underutilized feature. IPython notebook could be an excellent teaching tool and reference document for data analysis. Using this, you can efficiently transfer knowledge between colleagues rather than sharing a complicated excel file.","Q_Score":1,"Tags":"python,excel,csv","A_Id":34788487,"CreationDate":"2016-01-14T11:02:00.000","Title":"CSV format data manipulation: why use python scripts instead of MS excel functions?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am currently working on large data sets in csv format. In some cases, it is faster to use excel functions to get the work done. However, I want to write python scripts to read\/write csv and carry out the required function. In what cases would python scripts be better than using excel functions for data manipulation tasks? What would be the long term advantages?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":236,"Q_Id":34787957,"Users Score":0,"Answer":"After learning python, you are more flexible. The operations you can do with\non the user interface of MS excel are limited, whereas there are no limits \nif you use python.\nThe benefit is also, that you automate the modifications, e.g. you can re-use\nit or re-apply it to a different dataset. The speed depends heavily on the\nalgorithm and library you use and on the operation.\nYou can also use VB script \/ macros in excel to automate things, but\nusually python is less cumbersome and more flexible.","Q_Score":1,"Tags":"python,excel,csv","A_Id":34788093,"CreationDate":"2016-01-14T11:02:00.000","Title":"CSV format data manipulation: why use python scripts instead of MS excel functions?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to get the largest\/smallest number returned out of two or more numpy.array of equal length. Since max()\/min() function doesn't work on multiple arrays, this is some of the best(worst) I've come up with:\nmax(max(a1), max(a2), max(a3), ...) \/ min(min(a1), min(a2), min(a3), ...)\nAlternately one can use numpy's maximum, but those only work for two arrays at time.\nThanks in advance","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":143,"Q_Id":34796147,"Users Score":0,"Answer":"Combine your arrays into one, then take the min\/max along the new axis. \nA = np.array([a1,a2, ... , an])\nA.min(axis=0), A.max(axis=0)","Q_Score":0,"Tags":"python,arrays,numpy","A_Id":34796644,"CreationDate":"2016-01-14T17:37:00.000","Title":"How do you find the largest\/smallest number amongst several array's?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In tensorflow, I would like to rotate an image from a random angle, for data augmentation. But I don't find this transformation in the tf.image module.","AnswerCount":8,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":28208,"Q_Id":34801342,"Users Score":0,"Answer":"For rotating an image or a batch of images counter-clockwise by multiples of 90 degrees, you can use tf.image.rot90(image,k=1,name=None).\nk denotes the number of 90 degrees rotations you want to make.\nIn case of a single image, image is a 3-D Tensor of shape [height, width, channels] and in case of a batch of images, image is a 4-D Tensor of shape [batch, height, width, channels]","Q_Score":15,"Tags":"python,tensorflow","A_Id":53392066,"CreationDate":"2016-01-14T22:56:00.000","Title":"tensorflow: how to rotate an image for data augmentation?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am doing machine learning with python (scikit-learn) using the same data but with different classifiers. When I use 500k of data, LR and SVM (linear kernel) take about the same time, SVM (with polynomial kernel) takes forever. But using 5 million data, it seems LR is faster than SVM (linear) by a lot, I wonder if this is what people normally find?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":4058,"Q_Id":34814891,"Users Score":5,"Answer":"Faster is a bit of a weird question, in part because it is hard to compare apples to apples on this, and it depends on context. LR and SVM are very similar in the linear case. The TLDR for the linear case is that Logistic Regression and SVMs are both very fast and the speed difference shouldn't normally be too large, and both could be faster\/slower in certain cases. \nFrom a mathematical perspective, Logistic regression is strictly convex [its loss is also smoother] where SVMs are only convex, so that helps LR be \"faster\" from an optimization perspective, but that doesn't always translate to faster in terms of how long you wait.\nPart of this is because, computationally, SVMs are simpler. Logistic Regression requires computing the exp function, which is a good bit more expensive than just the max function used in SVMs, but computing these doesn't make the majority of the work in most cases. SVMs also have hard zeros in the dual space, so a common optimization is to perform \"shrinkage\", where you assume (often correctly) that a data point's contribution to the solution won't change in the near future and stop visiting it \/ checking its optimality. The hard zero of the SVM loss and the C regularization term in the soft margin form allow for this, where LR has no hard zeros to exploit like that.  \nHowever, when you want something to be fast, you usually don't use an exact solver. In this case, the issues above mostly disappear, and both tend to learn just as quick as the other in this scenario. \nIn my own experience, I've found Dual Coordinate Descent based solvers to be the fastest for getting exact solutions to both, with Logistic Regression usually being faster in wall clock time than SVMs, but not always (and never by more than a 2x factor). However, if you try and compare different solver methods for LRs and SVMs you may get very different numbers on which is \"faster\", and those comparisons won't necessarily be fair. For example, the SMO solver for SVMs can be used in the linear case, but will be orders of magnitude slower because it is not exploiting the fact that you only care are Linear solutions.","Q_Score":4,"Tags":"python-2.7,machine-learning,svm,logistic-regression","A_Id":34815680,"CreationDate":"2016-01-15T15:51:00.000","Title":"Which one is faster? Logistic regression or SVM with linear kernel?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"New at Python, so please...\nJust came across comprehensions and I understand that they are soon going to possibly ramify into perhaps dot products or matrix multiplications (although the fact that the result is a set makes them more interesting), but I at this point I want to ask whether there is any formula to determine the length of a comprehension such as:\n{x * y for x in {3, 4, 5} for y in {4, 5, 6}}.\nEvidently I don't mean for this particular one: \nlen({x * y for x in {3, 4, 5} for y in {4, 5, 6}}) = 8, but of any general operation of this type with an element-wise multiplication of two sets, and taking as the result the set of the resultant integers (no repetitions), for any given length of x and y, consecutive integers, and known x[1] and y[1].\nI understand that this question is at the crossroads  of coding and math, but I am asking it here on the off chance that it happened to be a somewhat common, or well-known computational issue, since I have read that comprehensions are very widely used. It is only in this sense that I am interested in the question. Base on the comments so far, my sense is that this is not the case.\nEDIT:\nFor instance, here is a pattern: If x = {1, 2, 3} the len(x * y) comprehensions is equal to 9 provided y[1] = or > 3. For example, len({x * y for x in {1, 2, 3} for y in {1111, 1112, 1113}}) = 9. So tentatively, length = length(x) * length(y), provided there is no overlap in the elements of x and y. Does it work with 4-element sets? Sure: len({x * y for x in {1, 2, 3, 4} for y in {1111, 1112, 1113, 1114}}) = 16. In fact, the integers don't need to be consecutive, just not overlap: len({x*y for x in {11,2,39} for y in {3,4,5}}) = 9. \nAnd, yes, it doesn't work... Check this out:\n{x * y for x in {0, 1, 3} for y in {36, 12, 4}} = {0, 4, 12, 36, 108}","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":95,"Q_Id":34821065,"Users Score":2,"Answer":"No, this is impossible with just the length of the inputs. You can use math to determine the length by computing common prime factors, but the work involved would not improve upon just computing the results and taking the len of that, and it requires knowledge of the set contents, not just their length.\nAfter all, with just the length, {2, 3} multiplied with {2, 3} (producing {4, 6, 9}) couldn't be distinguished from {2, 3} multiplied with {10, 11}, which would produce entirely unique outputs (four total). Makes for a simple proof by contradiction; knowing the input lengths alone is insufficient to determine the length of the output, no single operation on (2, 2) can possibly produce both 3 and 4 without additional inputs.","Q_Score":1,"Tags":"python,list-comprehension","A_Id":34821190,"CreationDate":"2016-01-15T22:39:00.000","Title":"Length of comprehensions in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm currently creating graphics usind the pgf backend for matplotlib. It works very well for integrating graphs generated in python in latex. However, transparency does not seem to be supported, even though I believe this should be possible in pgf. I am currently using version 1.5.1 of matplotlib.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1196,"Q_Id":34828545,"Users Score":0,"Answer":"Yes. The .pgf backend does support transparency. If the *.png and *.pdf files come out as transparent, but the *.pgf does not than it may be a problem with your viewer, or tex packages. \nFor me it was the package \"transparent\" which enables transparent text on pictures, but I wasn't actually using, which clashed with pgf.","Q_Score":1,"Tags":"python,matplotlib,latex,pgf","A_Id":34841121,"CreationDate":"2016-01-16T15:13:00.000","Title":"Does the Matplotlib pgf backend support transparency?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am receiving data from Kafka into a Spark Streaming application. It comes in the format of Transformed DStreams. I then keep only the features i want.\nfeatures=data.map(featurize)\nwhich gives me the \"name\",\"age\",\"whatever\".\nI then want to keep only the name of all the data\nfeatures=data.map(featurize).map(lambda Names: Names[\"name\"]\nNow, when i print this command, i get all the names coming from the streaming application, but i want to work on each one separately.\nMore specifically, I want to check each name and if I have already came across it in the past i want to apply a function on it. Otherwise i will just continue with my application. So I want each name to be a string so that I can insert it into my function that checks if one string has been seen in the past.\nI know that foreach will give me each RDD , but still I want to work on each name of the RDDs separately.\nIs there any way in pyspark to do so?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":184,"Q_Id":34857074,"Users Score":0,"Answer":"So what i did was to define a function that checks if I have seen that name in the past and then use the .filter(myfunc) to only work with the names i want...\nThe problem now is that in each new streaming window the function is being applied from the beggining , so if i have seen the name John in the first window 7 times , i will keep it only once , but then if i have seen the name John in the second window 5 times i will keep it again once...\nI want to keep the name John once for all the streaming application...\nAny thoughts on that ?","Q_Score":1,"Tags":"python,pyspark,spark-streaming,rdd","A_Id":34874093,"CreationDate":"2016-01-18T14:25:00.000","Title":"work on distinct elements of RDD-pyspark","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am running the cifar10 multi-GPU example from the tensorflow repository. I am able to utilize more than one GPUs. My ubuntu PC has two Titan X's, I see memory are fully occupied by the process on both GPUs. However, only one GPU is actually computing. I obtain no speedup. I have tried tensorflow 0.5.0 and 0.6.0 pip binaries. I have also tried compiled from source.\nEDIT:\nThe problem disappeared after I installed an older version of nvidia driver.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":749,"Q_Id":34860281,"Users Score":3,"Answer":"The problem disappeared after I installed an older version (352.55) of nvidia driver.","Q_Score":3,"Tags":"python,gpu,tensorflow","A_Id":34868531,"CreationDate":"2016-01-18T17:07:00.000","Title":"unable to run tensorflow on multiple GPUs","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When I put my python code in \"~\/Downloads\/\" folder, it works.\nHowever, I failed and gave me the message \"ImportError: numpy.core.multiarray failed to import\" when I put the test.py file in a deep location like \"\/Git\/Pyehon\/.....\" Why?\nI run this on Mac","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":858,"Q_Id":34869018,"Users Score":0,"Answer":"I've found the reaseon, there is a file named time.py in the same foder. I'm sure that's the reason I failed to import numpy. \nPlus, if i put the file time.py in the same folder and run python test.py, then I got the message \"TypeError: 'module' object is not callable\"\nNext, without closing the console, and delete the time.py, then \"import numpy\", I got the message \"ImportError: cannot import name multiarray\"\nclose and open the console again, it works.\nBut this time, I did't see \"ImportError: numpy.core.multiarray failed to import\" ,But it does work!","Q_Score":0,"Tags":"python,macos,opencv","A_Id":34869483,"CreationDate":"2016-01-19T05:21:00.000","Title":"Python\/OpenCV\/Mac ImportError: numpy.core.multiarray failed to import","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a Pandas DataFrame with a multiIndex. The index consists of a date and a text string. Some of the values are NaN and when I use dropna(), the row disappears as expected. However, when I look at the index using df.index, the dropped dates are still there. This is problematic as when I use the to_panel function, the dropped dates reappear.\nAm I using dropna incorrectly or how can I resolve this?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":1798,"Q_Id":34871128,"Users Score":1,"Answer":"For me this actually worked : \ndf1=df1[pd.notnull(df1['Cloumn Name'])]","Q_Score":3,"Tags":"python,pandas","A_Id":39321804,"CreationDate":"2016-01-19T07:45:00.000","Title":"Pandas dropna does not work as expected on a MultiIndex","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have very big multi dimension array for example 2d inside for loop. \nI would like to return one element from this array at each iteration and this element should not returned before. I mean return an element once in the iteration.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":38,"Q_Id":34882862,"Users Score":1,"Answer":"Without seeing any code, this is what I would try.\n\nMake an identically sized 2D array with just Booleans all set to True (available) by default\nWhen your code randomly generates an X,Y location in your 2D array, check the Availability array first: \n\n\nIf the value at that location is True (available), return that value in the other Array (whatever values are stored there) and then set that available value to False.\nIf the value at that location is False (not available), keep trying the next value in the array until you find one available. (Do this instead of hitting the random number generator again. The less elements available, the more you'd have to \"re-roll\" which would eventually become painfully slow.)\n\n\nMake sense?\nEDIT: I can think of at least 2 other ways of doing this that might be faster or more efficient, but this is the simple verson.","Q_Score":0,"Tags":"python,python-2.7,numpy","A_Id":34883536,"CreationDate":"2016-01-19T17:09:00.000","Title":"How do I return a random element from 2 numpy array without repeats?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm doing some NLP where I'm finding out when patients were diagnosed with multiple sclerosis.\nI'd like to use nltk to tell me that the noun of a sentence was multiple sclerosis. Problem is, doctors frequently refer to multiple sclerosis as MS which nltk picks up as a proper noun.\nFor example, this sentence, \"His MS was diagnosed in 1999.\" Is tagged as: [('His', 'PRP$'), ('MS', 'NNP'), ('was', 'VBD'), ('diagnosed', 'VBN'), ('in', 'IN'), ('1999', 'CD'), ('.', '.')]\nMS should be a noun here. Any suggestions?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":436,"Q_Id":34911264,"Users Score":0,"Answer":"Summing it up, you have the following options:\n\nCorrecting the tag in the post-processing - a bit ugly but quick and easy.\nEmploy an external Name Entity Recognizer (Stanford NER as @Bob Dylan has thoughtfully suggested) - this one is more involved, particularly because Stanford NER is in java and is not particularly fast.\nRetrain a POS Tagger on domain-specific data (do you have a large enough annotated dataset to use it for that?)\nUse WSD (Word Sense Disambiguation) approach - for a start you need to have a good domain dictionary to use.","Q_Score":5,"Tags":"python,nlp,nltk","A_Id":46003443,"CreationDate":"2016-01-20T21:48:00.000","Title":"Is there a way to tell NLTK that a certain word isn't a proper noun but a noun?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"seaborn has a conveninent keyword named size=, that aims to make the plots a certain size. However, the plots significantly differ in size depending on the xy-ticks and the axis labels. What is the best way to generate plots with exactly the same dimensions regardless of ticks and axis labels?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":219,"Q_Id":34930986,"Users Score":0,"Answer":"Seaborn sizing options vary by the plot type, which can be a bit confusing, so this is a useful universal approach.\nFirst run this: import matplotlib as plt\nThen add the line plt.figure(figsize=(9, 9)) in the notebook cells for each of the plots. You can adjust the integer values as you see fit.","Q_Score":1,"Tags":"python,matplotlib,size,seaborn","A_Id":34931076,"CreationDate":"2016-01-21T17:59:00.000","Title":"seaborn plots the same size?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Can pickle\/dill\/cpickle be used to pickle an imported module to improve import speed? The Shapely module for example takes 5 seconds on my system to find and load all of the required dependencies, which I'd really like to avoid. \nCan I pickle my imports once, then reuse that pickle instead of having to do slow imports every time?","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":627,"Q_Id":34939388,"Users Score":1,"Answer":"The import latency is most likely due to loading the dependent shared objects of the GEOS-library.\nOptimising this could maybe done, but it would be very hard. One way would be to build a statically compiled custom python interpreter with all DLLs and extension modules built in. But maintaining that would be a major PITA (trust me - I do it for work).\nAnother option is to turn your application into a service, thus only incurring the runtime-cost of starting the interpreter up once.\nIt depends on your actual problem if this is suitable.","Q_Score":1,"Tags":"python,import,pickle,dill","A_Id":37116604,"CreationDate":"2016-01-22T05:06:00.000","Title":"Can Python's pickle\/cpickle\/dill speed up imports?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a df that looks something like:\na b c d e  0 1 2 3 5 1 4 0 5 2 5 8 9 6 0  4 5 0 0 0\nI would like to output the number of numbers in column c that are not zero.","AnswerCount":2,"Available Count":1,"Score":0.2913126125,"is_accepted":false,"ViewCount":21246,"Q_Id":34968223,"Users Score":3,"Answer":"Numpy's count_nonzero function is efficient for this.\nnp.count_nonzero(df[\"c\"])","Q_Score":6,"Tags":"python,numpy,pandas","A_Id":57711857,"CreationDate":"2016-01-23T20:03:00.000","Title":"counting the number of non-zero numbers in a column of a df in pandas\/python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When it comes to normal ANNs, or any of the standard machine learning techniques, I understand what the training, testing, and validation sets should be (both conceptually, and the rule-of-thumb ratios). However, for a bidirectional LSTM (BLSTM) net, how to split the data is confusing me.\nI am trying to improve prediction on individual subject data that consists of monitored health values. In the simplest case, for each subject, there is one long time series of values (>20k values), and contiguous parts of that time series are labeled from a set of categories, depending on the current health of the subject. For a BLSTM, the net is trained on all of the data going forwards and backwards simultaneously. The problem then is, how does one split a time series for one subject? \n\nI can't just take the last 2,000 values (for example), because they might all fall into a single category.\nAnd I can't chop the time series up randomly, because then both the learning and testing phases would be made of disjointed chunks.\n\nFinally, each of the subjects (as far as I can tell) has slightly different (but similar) characteristics. So, maybe, since I have thousands of subjects, do I train on some, test on some, and validate on others? However, since there are inter-subject differences, how would I set up the tests if I was only considering one subject to start?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1028,"Q_Id":34970818,"Users Score":0,"Answer":"I think this has more to do with your particular dataset than Bi-LSTMs in general.\nYou're confusing splitting a dataset for training\/testing vs. splitting a sequence in a particular sample. It seems like you have many different subjects, which constitute a different sample. For a standard training\/testing split, you would split your dataset between subjects, as you suggested in the last paragraph.\nFor any sort of RNN application, you do NOT split along your temporal sequence; you input your entire sequence as a single sample to your Bi-LSTM. So the question really becomes whether such a model is well-suited to your problem, which has multiple labels at specific points in the sequence. You can use a sequence-to-sequence variant of the LSTM model to predict which label each time point in the sequence belongs to, but again you would NOT be splitting the sequence into multiple parts.","Q_Score":1,"Tags":"python,neural-network,time-series,keras,recurrent-neural-network","A_Id":45060104,"CreationDate":"2016-01-24T00:34:00.000","Title":"Training, testing, and validation sets for bidirectional LSTM (BLSTM)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a series of points (long, lat)\n1) Found the haversine distance between all the points\n2) Saved this to a csv file (source, destination, weight)\n3) Read the csv file and generated weighted a graph (where weight is the haversine distance)\n4) Used igraphs community detection algorithm - fastgreedy\nI was expecting clusters with low distance to be highly each other, I was expecting something similar to kmeans (without the distinct partitions in space) but there was no order in my results.\nQuestion:\nWhy does the community detection algorithm not give me results similar kmeans clustering? If im using the same points\/ distances between points then why is there so much overlap between the communities? I'm just looking for some intuition as to why this isnt work as I expected.\nThanks","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":347,"Q_Id":34975419,"Users Score":1,"Answer":"Your approach doesn't work because the fast greedy community detection expects similarities as weights, not distances.\n(Actually, this is probably only one of the reasons. The other is that the community detection algorithms in igraph were designed for sparse graphs. If you have calculated all the distances between all pairs of points, your graph is dense, and these algorithms will not be suitable).","Q_Score":1,"Tags":"python-2.7,cluster-analysis,igraph,k-means","A_Id":34978549,"CreationDate":"2016-01-24T11:53:00.000","Title":"igraph community detection result has too much overlap","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to compile a python program in py2exe. It is returning a bunch of missing modules, and when I run the executable, it says: \"MKL FATAL ERROR: Cannot load mkl_intel_thread.dll\"\nAll my 'non-plotting' scripts work perfectly, just scripts utilizing 'matplotlib', and 'pyqtgraph' don't work.\nI've even found the file in Numpy\/Core\/mkl_intel_thread.dll, and placed it into the folder with the .exe, and it still doesn't work. Does anyone have any idea how this can be solved?\nI'm using Anaconda Python 3.4, and matplotlib 1.5.1","AnswerCount":2,"Available Count":1,"Score":0.4621171573,"is_accepted":false,"ViewCount":2857,"Q_Id":34985134,"Users Score":5,"Answer":"Never mind! I managed to solve it, by copying the required dll from inside numpy\/core, into the dist folder that py2exe creates, not outside of it.","Q_Score":4,"Tags":"python,matplotlib,py2exe","A_Id":34985405,"CreationDate":"2016-01-25T04:30:00.000","Title":"py2exe: MKL FATAL ERROR: Cannot load mkl_intel_thread.dll","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have made an Azure Machine Learning Experiment which takes a small dataset (12x3 array) and some parameters and does some calculations using a few Python modules (a linear regression calculation and some more). This all works fine.\nI have deployed the experiment and now want to throw data at it from the front-end of my application. The API-call goes in and comes back with correct results, but it takes up to 30 seconds to calculate a simple linear regression. Sometimes it is 20 seconds, sometimes only 1 second. I even got it down to 100 ms one time (which is what I'd like), but 90% of the time the request takes more than 20 seconds to complete, which is unacceptable.\nI guess it has something to do with it still being an experiment, or it is still in a development slot, but I can't find the settings to get it to run on a faster machine.\nIs there a way to speed up my execution?\nEdit: To clarify: The varying timings are obtained with the same test data, simply by sending the same request multiple times. This made me conclude it must have something to do with my request being put in a queue, there is some start-up latency or I'm throttled in some other way.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1088,"Q_Id":34990561,"Users Score":8,"Answer":"First, I am assuming you are doing your timing test on the published AML endpoint.\nWhen a call is made to the AML the first call must warm up the container. By default a web service has 20 containers. Each container is cold, and a cold container can cause a large(30 sec) delay. In the string returned by the AML endpoint, only count requests that have the isWarm flag set to true. By smashing the service with MANY requests(relative to how many containers you have running) can get all your containers warmed.\nIf you are sending out dozens of requests a instance, the endpoint might be getting throttled. You can adjust the number of calls your endpoint can accept by going to manage.windowsazure.com\/\n\nmanage.windowsazure.com\/\nAzure ML Section from left bar\nselect your workspace\ngo to web services tab\nSelect your web service from list\nadjust the number of calls with slider\n\nBy enabling debugging onto your endpoint you can get logs about the execution time for each of your modules to complete. You can use this to determine if a module is not running as you intended which may add to the time.\nOverall, there is an overhead when using the Execute python module, but I'd expect this request to complete in under 3 secs.","Q_Score":8,"Tags":"python,azure,azure-machine-learning-studio","A_Id":35020997,"CreationDate":"2016-01-25T10:40:00.000","Title":"Azure Machine Learning Request Response latency","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a set of points in a plane where each point has an associated altitude. I'm thinking of using the scipy.spatial library to compute the Delaunay triangulation of the point set and then use the result to interpolate for the points in between.\nThe library implements a nice function that, given a point, finds the triangle it lies in. This would be particularly useful when calculating the depth map from the mesh. I assume though (please do correct me if I'm wrong) that the search function searches from the same starting point every time it is called. Since the points I will be looking for will tend to lie either on the triangle the previous one lied on or on an adjacent one, I figure that's unneccessary, but can't seem to find a way to optimize the search, other than to implement it myself.\nIs there a way to set the initial triangle for the search, or to optimize the depth map calculation otherwise?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":651,"Q_Id":34991430,"Users Score":1,"Answer":"You can try point in location test, especially Kirkpatrick algorithm\/data structure. Basically you subdivide the mesh in both axis and re-triangulate it. A better and simpler solution is to give each triangle a color and draw a bitmap then check the color of the bitmap with the point.","Q_Score":0,"Tags":"python,search,scipy,triangulation,delaunay","A_Id":34992250,"CreationDate":"2016-01-25T11:25:00.000","Title":"Optimizing scipy.spatial.Delaunay.find_simplex","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there a way to get the borders of a matplotlib.pyplot.hexbin plot?\nSay, i have a pd.DataFrame with spatial latitude and longitude values, which i plot in a hexbin plot. Afterwards i want to assign the corresponding bin of the hexbin grid to each instance of my DataFrame, by checking if the latitude and longitude values of an instance fall in one of the hexbin bins. \nCan i assign names or indices to the different bins?\nI have already looked in the documentation for the hexbin plot, all i can find are line properties, which describe the lines that are drawn in the plot.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":399,"Q_Id":34995645,"Users Score":0,"Answer":"I'm wishing to do something similar for small hexbins, thinking to:\n(1) Get the hexbin centres: \n    hexobj_cen=hexobj.get_offsets()\n    lon_hex=hexobj_cen[:,0]       #hexbin lon centre\n    lat_hex=hexobj_cen[:,1]      #hexbin lat centre\n(2) Run a for loop (for each hexbin centre) to find the Cartesian distance (N.hypot) between that centre from all points put into an array\nThen ask if for each hexbin centre, point-hexbin distance is greater than some maximum distance (half the distance between two opposing vertices.\nGreat if there is a standard way (within pylab.hexbin) to do this, but also couldn't yet find it.","Q_Score":1,"Tags":"python,matplotlib","A_Id":35028696,"CreationDate":"2016-01-25T15:01:00.000","Title":"Matplotlib hexbin - get bin borders","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In Pyspark, I am trying to broadcast a large numpy array of size around 8GB. But it fails with the error \"OverflowError: cannot serialize a string larger than 4GiB\". I have 15g in executor memory and 25g driver memory. I have tried using default and kyro serializer. Both didnot work and show same error. \nCan anyone suggest how to get rid of this error and the most efficient way to tackle large broadcast variables?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":3217,"Q_Id":34998280,"Users Score":1,"Answer":"This is not the problem of PySpark, this is a limit of Spark implement. \nSpark use a scala array to store the broadcast elements, since the max Integer of Scala is 2*10^9, so the total string bytes is 2*2*10^9 = 4GB, you can view the Spark code.","Q_Score":1,"Tags":"python,apache-spark,python-3.4,pyspark","A_Id":35013791,"CreationDate":"2016-01-25T17:10:00.000","Title":"Broadcast large array in pyspark (~ 8GB)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a csv file contain of daily precipitation with (253 rows and 191 column daily) so for one year I have 191 * 365 column. \nI want to extract data for certain row and column that are my area of interest example row 20 and column 40 for the first day and the 2,3,4 ... 365 days has the same distance between the column.\nI'm new in python, is there any way that I can extract the data and store it in a new csv for a certain row and column for one year? \nThanks","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":91,"Q_Id":35001306,"Users Score":0,"Answer":"Apart from extracting the data, the first thing you need to do is rearrange your data.\nAs it is now, 191 columns are added every day. To do that, the whole file needs to be parsed (probably in memory, data growing every day), data gets added to the end of each row, and everything has to be fully written to disk again.\nUsually, to add data to a csv, rows are added at the end of the file. No need to parse and rewrite the whole file each time.\nOn top of that, most software to read csv files starts having problems when the number of columns gets higher.\nSo it would be a lot better to add the daily data as rows at the end of the csv file.\nWhile we're at it: assuming the 253 x 191 is some sort of grid, or at least every cell has te same data type, this would be a great candidate for binary storage (not sure how\/if Python can handle that).\nAll data could be stored in it's binary form resulting in a fixed length field\/cell. To access a field, it's position could simply be calculated and there would be no need to parse and convert all the data each time. Retrieving data would be almost instant.","Q_Score":2,"Tags":"python,csv","A_Id":35015910,"CreationDate":"2016-01-25T20:04:00.000","Title":"Cut selected data from daily precipitation CSV files","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am not quite sure about how file-queue works. I am trying to use a large dataset like imagenet as input. So preloading data is not the case, so I am wondering how to use the file-queue. According to the tutorial, we can convert data to TFRecords file as input. Now we have a single big TFRecords file. So when we specify a FIFO queue for the reader, does it mean the program would fetch a batch of data each time and feed the graph instead of loading the whole file of data?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2670,"Q_Id":35004619,"Users Score":2,"Answer":"The amount of pre-fetching depends on your queue capacity. If you use string_input_producer for your filenames and batch for batching, you will have 2 queues - filename queue, and prefetching queue created by batch. Queue created by batch has default capacity of 32, controlled by batch(...,capacity=) argument, therefore it can prefetch up to 32 images. If you follow outline in TensorFlow official howto's, processing examples (everything after batch) will happen in main Python thread, whereas filling up the queue will happen in threads created\/started by batch\/start_queue_runners, so prefetching new data and running prefetched data through the network will occur concurrently, blocking when the queue gets full or empty.","Q_Score":3,"Tags":"python,deep-learning,tensorflow","A_Id":35004791,"CreationDate":"2016-01-25T23:46:00.000","Title":"reading a large dataset in tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"My training set has 970 samples and validation set has 243 samples.\nHow big should batch size and number of epochs be when fitting a model to optimize the val_acc? Is there any sort of rule of thumb to use based on data input size?","AnswerCount":8,"Available Count":3,"Score":1.0,"is_accepted":false,"ViewCount":132079,"Q_Id":35050753,"Users Score":81,"Answer":"Since you have a pretty small dataset (~ 1000 samples), you would probably be safe using a batch size of 32, which is pretty standard. It won't make a huge difference for your problem unless you're training on hundreds of thousands or millions of observations. \nTo answer your questions on Batch Size and Epochs:\nIn general: Larger batch sizes result in faster progress in training, but don't always converge as fast. Smaller batch sizes train slower, but can converge faster. It's definitely problem dependent. \nIn general, the models improve with more epochs of training, to a point. They'll start to plateau in accuracy as they converge. Try something like 50 and plot number of epochs (x axis) vs. accuracy (y axis). You'll see where it levels out. \nWhat is the type and\/or shape of your data? Are these images, or just tabular data? This is an important detail.","Q_Score":90,"Tags":"python,machine-learning,deep-learning","A_Id":38405970,"CreationDate":"2016-01-28T00:21:00.000","Title":"How big should batch size and number of epochs be when fitting a model?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"My training set has 970 samples and validation set has 243 samples.\nHow big should batch size and number of epochs be when fitting a model to optimize the val_acc? Is there any sort of rule of thumb to use based on data input size?","AnswerCount":8,"Available Count":3,"Score":1.0,"is_accepted":false,"ViewCount":132079,"Q_Id":35050753,"Users Score":11,"Answer":"I use Keras to perform non-linear regression on speech data. Each of my speech files gives me features that are 25000 rows in a text file, with each row containing 257 real valued numbers. I use a batch size of 100, epoch 50 to train  Sequential model in Keras with 1 hidden layer. After 50 epochs of training, it converges quite well to a low val_loss.","Q_Score":90,"Tags":"python,machine-learning,deep-learning","A_Id":38457655,"CreationDate":"2016-01-28T00:21:00.000","Title":"How big should batch size and number of epochs be when fitting a model?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"My training set has 970 samples and validation set has 243 samples.\nHow big should batch size and number of epochs be when fitting a model to optimize the val_acc? Is there any sort of rule of thumb to use based on data input size?","AnswerCount":8,"Available Count":3,"Score":1.0,"is_accepted":false,"ViewCount":132079,"Q_Id":35050753,"Users Score":7,"Answer":"I used Keras to perform non linear regression for market mix modelling. I got best results with a batch size of 32 and epochs = 100 while training a Sequential model in Keras with 3 hidden layers. Generally batch size of 32 or 25 is good, with epochs = 100 unless you have large dataset. in case of large dataset you can go with batch size of 10 with epochs b\/w 50 to 100.\nAgain the above mentioned figures have worked fine for me.","Q_Score":90,"Tags":"python,machine-learning,deep-learning","A_Id":44901953,"CreationDate":"2016-01-28T00:21:00.000","Title":"How big should batch size and number of epochs be when fitting a model?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a .csv file on disk, formatted so that I can read it into a pandas DataFrame easily, to which I periodically write rows. I need this database to have a row index, so every time I write a new row to it I need to know the index of the last row written. \nThere are plenty of ways to do this: \n\nI could read the entire file into a DataFrame, append my row, and then print the entire DataFrame to memory again. This might get a bit slow as the database grows.\nI could read the entire index column into memory, and pick the largest value off, then append my row to the .csv file. This might be a little better, depending on how column-reading is implemented.\n\nI am curious if there is a way to just get that one cell directly, without having to read a whole bunch of extra information into memory. Any suggestions?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":511,"Q_Id":35063946,"Users Score":1,"Answer":"Reading the entire index column will still need to read and parse the whole file.\nIf no fields in the file are multiline, you could scan the file backwards to find the first newline (but with a check if there is a newline past the data). The value following that newline will be your last index.\nStoring the last index in another file would also be a possibility, but you would have to make sure both files stay consistent.\nAnother way would be to reserve some (fixed amount of) bytes at the beginning of the file and write (in place) the last index value there as a comment. But your parser would have to support comments, or be able to skip rows.","Q_Score":2,"Tags":"python-2.7,csv,pandas,pandasql","A_Id":35064268,"CreationDate":"2016-01-28T14:17:00.000","Title":"reading the last index from a csv file using pandas in python2.7","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been looking through the documentation (and stack overflow) and am having trouble figuring out how rearrange a pandas data frame the way described below. I wish to have a row where there is a column name, a row name and the value of that specific row and column:\nInput:\n\n  A B C\nX 1 2 3\nY 4 5 6\n\nOutput:\n\nX A 1\nX B 2\nX C 3\nY A 4\nY B 5\nY C 6\n\nAny help would be much appreciated","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":121,"Q_Id":35069440,"Users Score":1,"Answer":"Look into the DataFrame.pivot method","Q_Score":2,"Tags":"python,pandas","A_Id":35069535,"CreationDate":"2016-01-28T18:40:00.000","Title":"Rearranging Data in Pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been using iPython (aka Jupyter) quite a bit lately for data analysis and some machine learning. But one big headache is copying results from the notebook app (browser) into either Excel or Google Sheets so I can manipulate results or share them with people who don't use iPython.\nI know how to convert results to csv and save. But then I have to dig through my computer, open the results and paste them into Excel or Google Sheets. That takes too much time.\nAnd just highlighting a resulting dataframe and copy\/pasting usually completely messes up the formatting, with columns overflowing. (Not to mention the issue of long resulting dataframes being truncated when printed in iPython.)\nHow can I easily copy\/paste an iPython result into a spreadsheet?","AnswerCount":6,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":17289,"Q_Id":35074209,"Users Score":37,"Answer":"Try using the to_clipboard() method. E.g., for a dataframe, df: df.to_clipboard() will copy said dataframe to your clipboard. You can then paste it into Excel or Google Docs.","Q_Score":19,"Tags":"python,excel,google-sheets,ipython,ipython-notebook","A_Id":35090610,"CreationDate":"2016-01-28T23:33:00.000","Title":"How to copy\/paste a dataframe from iPython into Google Sheets or Excel?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been using iPython (aka Jupyter) quite a bit lately for data analysis and some machine learning. But one big headache is copying results from the notebook app (browser) into either Excel or Google Sheets so I can manipulate results or share them with people who don't use iPython.\nI know how to convert results to csv and save. But then I have to dig through my computer, open the results and paste them into Excel or Google Sheets. That takes too much time.\nAnd just highlighting a resulting dataframe and copy\/pasting usually completely messes up the formatting, with columns overflowing. (Not to mention the issue of long resulting dataframes being truncated when printed in iPython.)\nHow can I easily copy\/paste an iPython result into a spreadsheet?","AnswerCount":6,"Available Count":2,"Score":0.0333209931,"is_accepted":false,"ViewCount":17289,"Q_Id":35074209,"Users Score":1,"Answer":"Paste the output to an IDE like Atom and then paste in Google Sheets\/Excel","Q_Score":19,"Tags":"python,excel,google-sheets,ipython,ipython-notebook","A_Id":66239699,"CreationDate":"2016-01-28T23:33:00.000","Title":"How to copy\/paste a dataframe from iPython into Google Sheets or Excel?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have looked for a proper way to install OpenCV, but all I can find are people fudging around with Python 2.old or virtualenv or other things that are utterly irrelevant. I just want be able to run import cv2 without any import errors.\nHow do I install OpenCV on OS X 10.11 for use with Python 3.5.1?","AnswerCount":3,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":4202,"Q_Id":35085809,"Users Score":3,"Answer":"For me the only working way was using conda:\nconda install --channel https:\/\/conda.anaconda.org\/menpo opencv3\nand then import it using import cv2","Q_Score":2,"Tags":"python,opencv,python-3.5","A_Id":39967831,"CreationDate":"2016-01-29T13:30:00.000","Title":"Installing OpenCV 3.1 on OS X El Capitan using Python 3.5.1","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm working with huge multidimensional NumPy arrays in an IPython notebook with Python3 and things are slow going. \nIs it appreciably quicker to convert the .ipynb file into a .py file and run via the command line?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":155,"Q_Id":35091235,"Users Score":0,"Answer":"I have never noticed any performance penalty (5-6 million x 8 arrays here) with IPython\/Jupyter, but even if there is some small difference it is unlikely to be noticeable. A much greater speed increase with a similarly low effort would come from writing performance sensitive code in cython, adding type annotations in cython would yield even greater increases. In my own work I have observed speedups of orders of magnitude from using cython smartly.","Q_Score":0,"Tags":"arrays,python-3.x,numpy,ipython-notebook","A_Id":35093550,"CreationDate":"2016-01-29T18:11:00.000","Title":"Are there performance benchmarks of NumPy arrays in an IPython Notebook versus a .py script file?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there an easy way to read pickle files (.pkl) from Pandas Dataframe into R? \nOne possibility is to export to CSV and have R read the CSV but that seems really cumbersome for me because my dataframes are rather large. Is there an easier way to do so?\nThanks!","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":36680,"Q_Id":35121192,"Users Score":14,"Answer":"Edit: If you can install and use the {reticulate} package, then this answer is probably outdated.  See the other answers below for an easier path.\nYou could load the pickle in python and then export it to R via the python package rpy2 (or similar).  Once you've done so, your data will exist in an R session linked to python.  I suspect that what you'd want to do next would be to use that session to call R and saveRDS to a file or RAM disk.  Then in RStudio you can read that file back in.  Look at the R packages rJython and rPython for ways in which you could trigger the python commands from R.\nAlternatively, you could write a simple python script to load your data in Python (probably using one of the R packages noted above) and write a formatted data stream to stdout.  Then that entire system call to the script (including the argument that specifies your pickle) can use used as an argument to fread in the R package data.table.  Alternatively, if you wanted to keep to standard functions, you could use combination of system(..., intern=TRUE) and read.table.\nAs usual, there are \/many\/ ways to skin this particular cat.  The basic steps are:\n\nLoad the data in python\nExpress the data to R (e.g., exporting the object via rpy2 or writing formatted text to stdout with R ready to receive it on the other end)\nSerialize the expressed data in R to an internal data representation  (e.g., exporting the object via rpy2 or fread)\n(optional) Make the data in that session of R accessible to another R session (i.e., the step to close the loop with rpy2, or if you've been using fread then you're already done).","Q_Score":39,"Tags":"python,r,pandas,dataframe","A_Id":35121242,"CreationDate":"2016-02-01T00:13:00.000","Title":"Reading a pickle file (PANDAS Python Data Frame) in R","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How to cluster only words in a given set of Data: i have been going through few algorithms online like k-Means algotihm,but it seems they are related to document clustering instead of word clustering.Can anyone suggest me some way to only cluster words in a given set of data???.\nplease am new to python.","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":4147,"Q_Id":35123248,"Users Score":2,"Answer":"Word clustering will be really disappointing because the computer does not understand language.\nYou could use levenshtein distance and then do hierarchical clustering.\nBut:\n\ndog and fog have a distance of 1, i.e. are highly similar.\ndog and cat have 3 out of 3 letters different.\n\nSo unless you can define a good measure of similarity, don't cluster words.","Q_Score":0,"Tags":"python,machine-learning,cluster-analysis,word","A_Id":35149501,"CreationDate":"2016-02-01T04:59:00.000","Title":"Word clustering in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am training a seq2seq model since many days on a custom parallel corpus of about a million sentences with default settings for the seq2seq model.\nFollowing is the output log which has crossed 350k steps as mentioned in the tutorial. I saw that the bucket perplexity have suddenly increased significantly the overall train perplexity is constant at 1.02 since a long time now , also the learning rate was initialized at 0.5 but now it shows about 0.007 , so the learning rate has also significantly decreased, Also the output of the system is not close to satisfactory.\nHow can I know if the epoch point is reached and should I stop and reconfigure settings like parameter tuning and optimizer improvements?\n\nglobal step 372800 learning rate 0.0071 step-time 1.71 perplexity 1.02\n  eval: bucket 0 perplexity 91819.49\n  eval: bucket 1 perplexity 21392511.38\n  eval: bucket 2 perplexity 16595488.15\n  eval: bucket 3 perplexity 7632624.78\nglobal step 373000 learning rate 0.0071 step-time 1.73 perplexity 1.02\n  eval: bucket 0 perplexity 140295.51\n  eval: bucket 1 perplexity 13456390.43\n  eval: bucket 2 perplexity 7234450.24\n  eval: bucket 3 perplexity 3700941.57\nglobal step 373200 learning rate 0.0071 step-time 1.69 perplexity 1.02\n  eval: bucket 0 perplexity 42996.45\n  eval: bucket 1 perplexity 37690535.99\n  eval: bucket 2 perplexity 12128765.09\n  eval: bucket 3 perplexity 5631090.67\nglobal step 373400 learning rate 0.0071 step-time 1.82 perplexity 1.02\n  eval: bucket 0 perplexity 119885.35\n  eval: bucket 1 perplexity 11166383.51\n  eval: bucket 2 perplexity 27781188.86\n  eval: bucket 3 perplexity 3885654.40\nglobal step 373600 learning rate 0.0071 step-time 1.69 perplexity 1.02\n  eval: bucket 0 perplexity 215824.91\n  eval: bucket 1 perplexity 12709769.99\n  eval: bucket 2 perplexity 6865776.55\n  eval: bucket 3 perplexity 5932146.75\nglobal step 373800 learning rate 0.0071 step-time 1.78 perplexity 1.02\n  eval: bucket 0 perplexity 400927.92\n  eval: bucket 1 perplexity 13383517.28\n  eval: bucket 2 perplexity 19885776.58\n  eval: bucket 3 perplexity 7053727.87\nglobal step 374000 learning rate 0.0071 step-time 1.85 perplexity 1.02\n  eval: bucket 0 perplexity 46706.22\n  eval: bucket 1 perplexity 35772455.34\n  eval: bucket 2 perplexity 8198331.56\n  eval: bucket 3 perplexity 7518406.42\nglobal step 374200 learning rate 0.0070 step-time 1.98 perplexity 1.03\n  eval: bucket 0 perplexity 73865.49\n  eval: bucket 1 perplexity 22784461.66\n  eval: bucket 2 perplexity 6340268.76\n  eval: bucket 3 perplexity 4086899.28\nglobal step 374400 learning rate 0.0069 step-time 1.89 perplexity 1.02\n  eval: bucket 0 perplexity 270132.56\n  eval: bucket 1 perplexity 17088126.51\n  eval: bucket 2 perplexity 15129051.30\n  eval: bucket 3 perplexity 4505976.67\nglobal step 374600 learning rate 0.0069 step-time 1.92 perplexity 1.02\n  eval: bucket 0 perplexity 137268.32\n  eval: bucket 1 perplexity 21451921.25\n  eval: bucket 2 perplexity 13817998.56\n  eval: bucket 3 perplexity 4826017.20\n\n\nAnd when will this stop ?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":972,"Q_Id":35126954,"Users Score":1,"Answer":"It looks like there is a difference between your dev and train data:\n\nglobal step 374600 learning rate 0.0069 step-time 1.92 perplexity 1.02\neval: bucket 0 perplexity 137268.32\n\nYour training perplexity is 1.02 -- the model is basically perfect on the data it receives for training. But your dev perplexity is enormous, the model does not work at all for the dev set. How did it look in earlier epochs? I would suspect that there is some mismatch. Maybe the tokenization is different for train and dev? Maybe you loaded the wrong file? Maybe the sizes of the buckets from the original translation model are not appropriate for your dev data? It's hard to say without knowing more details.\nAs to when to stop: the original translation model has an infinite training loop because it has a large data-set and capacity and could continue improving for many weeks of training. But it also lowers the learning rate when it's not improving any more, so if your learning rate is very low (as it seems to be in your case), it's a clear signal you can stop.","Q_Score":1,"Tags":"python,tensorflow,recurrent-neural-network","A_Id":35147923,"CreationDate":"2016-02-01T09:27:00.000","Title":"How can I know if the epoch point is reached in seq2seq model?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"What I'm trying to do is build a regressor based on a value in a feature. \nThat is to say, I have some columns where one of them is more important (let's suppose it is gender)  (of course it is different from the target value Y). \nI want to say:\n- If the gender is Male then use the randomForest regressor\n- Else use another regressor\nDo you have any idea about if this is possible using sklearn or any other library in python?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":47,"Q_Id":35132569,"Users Score":0,"Answer":"I personally am new to Python but I would use the data type of a list. I would then proceed to making a membership check and reference the list you just wrote. Then proceed to say that if member = true then run\/use randomForest regressor. If false use\/run another regressor.","Q_Score":4,"Tags":"python,feature-selection,supervised-learning","A_Id":35132831,"CreationDate":"2016-02-01T14:06:00.000","Title":"Use a different estimator based on value","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"This question is with respect to accessing individual elements in a tensor, say [[1,2,3]]. I need to access the inner element [1,2,3] (This can be performed using .eval() or sess.run()) but it takes longer when the size of the tensor is huge) \nIs there any method to do the same faster?\nThanks in Advance.","AnswerCount":4,"Available Count":2,"Score":0.049958375,"is_accepted":false,"ViewCount":79845,"Q_Id":35146444,"Users Score":1,"Answer":"You simply can't get value of 0th element of [[1,2,3]] without run()-ning or eval()-ing an operation which would be getting it. Because before you 'run' or 'eval', you have only a description how to get this inner element(because TF uses symbolic graphs\/calculations). So even if you would use tf.gather\/tf.slice, you still would have to get values of these operations via eval\/run. See @mrry's answer.","Q_Score":53,"Tags":"python,python-2.7,tensorflow","A_Id":35172568,"CreationDate":"2016-02-02T06:21:00.000","Title":"Tensorflow python : Accessing individual elements in a tensor","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"This question is with respect to accessing individual elements in a tensor, say [[1,2,3]]. I need to access the inner element [1,2,3] (This can be performed using .eval() or sess.run()) but it takes longer when the size of the tensor is huge) \nIs there any method to do the same faster?\nThanks in Advance.","AnswerCount":4,"Available Count":2,"Score":0.049958375,"is_accepted":false,"ViewCount":79845,"Q_Id":35146444,"Users Score":1,"Answer":"I suspect it's the rest of the computation that takes time, rather than accessing one element.\nAlso the result might require a copy from whatever memory is stored in, so if it's on the graphics card it will need to be copied back to RAM first and then you get access to your element. If this is the case you might skip it by adding an tensorflow operation to take the first element, and only return that.","Q_Score":53,"Tags":"python,python-2.7,tensorflow","A_Id":35148137,"CreationDate":"2016-02-02T06:21:00.000","Title":"Tensorflow python : Accessing individual elements in a tensor","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I built a cnn network in Theano. Input are many images, but the size of them are different. The elements of numpy.array have the same size.\nHow can I make them as the input?\nThanks a lot.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":61,"Q_Id":35152052,"Users Score":0,"Answer":"You need to do some data formatting. The input size of a NN is constant, so if the images for your CNN have different sizes you need to resize them to your input size before feeding them in. Its like a person being too close or far away from a painting, your field of view is contant, in order to see everything clearly you need to adjust your distance from the image.","Q_Score":0,"Tags":"python-2.7,theano","A_Id":35153245,"CreationDate":"2016-02-02T11:21:00.000","Title":"Take input of arbitrary size in theano","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I was working on a project where I doing regressions and I wanted to used scipy.optomize.curve_fit which takes a function and tries to find the right parameters for it. The odd part was that it was never given how many parameters the function took. Eventually we guessed that it used foo.__code__.co_nlocals, but in the case we would've used it I needed 33  arguments. \nTo the question: Is there a way use the def foo(*x) notation to let python know it needs a certain range of number of arguments? Like a def foo(*x[:32])? I'm sure I will never use this in any real code, but it would be interesting to know.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":40,"Q_Id":35159748,"Users Score":1,"Answer":"Is there a way use the def foo(*x) notation to let python know it needs a certain range of number of arguments?\n\nNope. Also, scipy.optimize.curve_fit ultimately gets its argument count information from f.__code__.co_argcount, not co_nlocals or n_locals (which doesn't exist).","Q_Score":1,"Tags":"python","A_Id":35159902,"CreationDate":"2016-02-02T17:16:00.000","Title":"Python set number of arguments to capture","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have seen that transpose and reshape together can help but I don't know how to use.\nEg. dimshuffle(0, 'x')\nWhat is its equivalent by using transpose and reshape? or is there a better way?\nThank you.","AnswerCount":4,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":4682,"Q_Id":35163789,"Users Score":0,"Answer":"tf.transpose is probably what you are looking for.   it takes an arbitrary permutation.","Q_Score":7,"Tags":"python,numpy,theano,tensorflow","A_Id":50763868,"CreationDate":"2016-02-02T21:04:00.000","Title":"Theano Dimshuffle equivalent in Google's TensorFlow?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using python to interact with PSS\/E (siemens software) and I'm trying to create *.acc file for pss\/e, from python. I can do this easily using pss\/e itself:\n1 - create *.sub, *.mon, *.con files\n2 - create respective *.dfx file\n3 - and finally create *.acc file\nThe idea is to perform all these 3 tasks automatically, using python. So, using the record tool from pss\/e I get this code:\npsspy.bsys(0,0,[ 230., 230.],1,[1],0,[],0,[],0,[])\npsspy.bsys(0,0,[ 230., 230.],1,[1],0,[],0,[],0,[])\npsspy.dfax([1,1],r\"\"\"PATH\\reports.sub\"\"\",r\"\"\"PATH\\reports.mon\"\"\",r\"\"\"PATH\\reports.con\"\"\",r\"\"\"PATH\\reports.dfx\"\"\")\npsspy.accc_with_dsp_3( 0.5,[0,0,0,1,1,2,0,0,0,0,0],r\"\"\"IEEE\"\"\",r\"\"\"PATH\\reports.dfx\"\"\",r\"\"\"PATH\\reports.acc\"\"\",\"\",\"\",\"\")\npsspy.accc_single_run_report_4([1,1,2,1,1,0,1,0,0,0,0,0],[0,0,0,0,6000],[ 0.5, 5.0, 100.0,0.0,0.0,0.0, 99999.],r\"\"\"PATH\\reports.acc\"\"\")\nIt happens that when I run this code on python, the *.sub, *.mon, *.con and *.dfx files are not created thus the API accc_single_run_report_4() reports an error. Can anyone tell me why these files aren't being created with this code?\nThanks in advance for your time","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1112,"Q_Id":35183538,"Users Score":0,"Answer":"@Magalhaes, the auxiliary files *.sub, *.mon and *.con are input files.  You have to write them; PSSE doesn't generate them.  Your recording shows that you defined a bus subsystem twice, generated a *.dfx from existing auxiliary files, ran an AC contingency solution, then generated an *.acc report.  So when you did this recording, you must have started with already existing auxiliary files.","Q_Score":1,"Tags":"python,python-2.7","A_Id":36043728,"CreationDate":"2016-02-03T17:16:00.000","Title":"Creating Contingency Solution Output File for PSS\/E using Python 2.7","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I am continously getting the error:\n\"(shapes (10, 1), (10,) mismatch)\"\nwhen doing a NumPy operation and I am somewhat confused.\nWouldn't (10,1) and (10,) be identical shapes? And if for whatever reason this is not valid, is there a way to convert (10,1) to (10,)? I cannot seem to find it in the NumPy doucmentation.\nThanks","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1521,"Q_Id":35184815,"Users Score":2,"Answer":"In numpy,  (10, 1), (10,) are not the same at all:\n\n(10, 1) is a two dimensional array, with a single column.\n(10, ) is a one dimensional array\n\nIf you have an array a, and print out len(a.shape), you'll see the difference.","Q_Score":4,"Tags":"python,numpy","A_Id":35185050,"CreationDate":"2016-02-03T18:27:00.000","Title":"Shape Mismatch Numpy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I often work with csv files that are 100s of GB in size.  Is there any way to tell read_csv to only read a fixed number of MB from a csv file?\nUpdate:\nIt looks like chunks and chunksize can be used for this, but the documentation looks a bit slim here. What would be an example of how to do this with a real csv file? (e.g. say a 100GB file, read only rows up to approximately ~10MB)","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":779,"Q_Id":35184894,"Users Score":1,"Answer":"You can pass nrows=number_of_rows_to_read to your read_csv function to limit the lines that are read.","Q_Score":1,"Tags":"python-3.x,pandas","A_Id":35184973,"CreationDate":"2016-02-03T18:31:00.000","Title":"Limiting the number of GB to read in read_csv in Pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have vector a.\nI want to calculate np.inner(a, a)\nBut I wonder whether there is prettier way to calc it.\n[The disadvantage of this way, that if I want to calculate it for a-b or a bit more complex expression, I have to do that with one more line. c = a - b and np.inner(c, c) instead of somewhat(a - b)]","AnswerCount":4,"Available Count":1,"Score":0.2449186624,"is_accepted":false,"ViewCount":38727,"Q_Id":35213592,"Users Score":5,"Answer":"I don't know if the performance is any good, but (a**2).sum() calculates the right value and has the non-repeated argument you want. You can replace a with some complicated expression without binding it to a variable, just remember to use parentheses as necessary, since ** binds more tightly than most other operators: ((a-b)**2).sum()","Q_Score":7,"Tags":"python,numpy,inner-product","A_Id":35213773,"CreationDate":"2016-02-04T23:19:00.000","Title":"NumPy calculate square of norm 2 of vector","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm currently computing the spectrogram with the matplotlib. I specify NFFT=512 but the resulting image has a height of 257. I then tried to just do a STFT (short time fourier transform) which gives me 512 dimensional vectors (as expected). If I plot the result of the STFT I can see that half of the 512 values are just mirrored so really I only get 257 values (like the matplotlib). Can somebody explain to me why that is the case? I always thought of the FT as a basis transform, why would it introduce this redundancy?\nThank you.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1806,"Q_Id":35229136,"Users Score":1,"Answer":"The redundancy is because you input a strictly real signal to your FFT, thus the DFT result is complex conjugate (Hermitian) symmetric.  This redundancy is due to the fact that all the imaginary components of strictly real input are zero.  But the output of this DFT can include non-zero imaginary components to indicate phase.  Thus, the this DFT result has to be conjugate symmetric so that all the imaginary components in the result will cancel out between the two DFT result halves (same magnitudes, but opposite phases), indicating strictly real input.  Also, the lower 257 bins of the basis transform will have 512 degrees of (scaler)freedom, just like the input.  However, a spectrogram throws away all phase information, so it can only display 257 unique values (magnitude-only).\nIf you input a complex (quadrature, for instance) signal to a DFT, then there would likely not be Hermitian redundancy, and you would have 1024 degrees of freedom from a 512 length DFT.  \nIf you want an image height of 512 (given real input), try an FFT size of 1024.","Q_Score":1,"Tags":"python,matplotlib,fft,spectrogram","A_Id":35238504,"CreationDate":"2016-02-05T16:37:00.000","Title":"Matplotlib spectrogram versus STFT","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've installed tensor flow on Mac OS X.\nSuccessfully ran simple command line test.\nNow trying the first tutorial.\nFail on the first python line:\n[python prompt:] \nimport tensorflow.examples.tutorials.mnist.input_data\nTraceback (most recent call last):\n  File \"\", line 1, in \nImportError: No module named examples.tutorials.mnist.input_data\nBut the file seems to be there:\nnew-host-4:~ karlovitz$ ls \/Library\/Python\/2.7\/site-packages\/tensorflow\/examples\/tutorials\/mnist\/\nBUILD           fully_connected_feed.py mnist.py        mnist_with_summaries.py\ninit.py     input_data.py       mnist_softmax.py","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":5529,"Q_Id":35236851,"Users Score":0,"Answer":"@mkarlovitz Looks like \/Library\/Python\/2.7\/site-packages\/ is not in the list of paths python is looking for.   \nTo see what paths are  python uses to find packages, do the below ( you can use command line for this ).\n1. import sys\n2. sys.path  ( This tells the list of Paths )  \nIf the  \/Library\/Python\/2.7\/site-packages\/  is not in the above list.\nAdd it as follows:\nIn the Python file\/script you are executing.\n1. import sys\n2. sys.path.append('\/Library\/Python\/2.7\/site-packages\/')","Q_Score":1,"Tags":"python-2.7,tensorflow","A_Id":35237068,"CreationDate":"2016-02-06T03:00:00.000","Title":"tensorflow no module named example.tutorials.mnist.input_data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to all these methods and am trying to get a simple answer to that or perhaps if someone could direct me to a high level explanation somewhere on the web. My googling only returned kaggle sample codes.\nAre the extratree and randomforrest essentially the same? And xgboost uses boosting when it chooses the features for any particular tree i.e. sampling the features. But then how do the other two algorithms select the features?\nThanks!","AnswerCount":1,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":2360,"Q_Id":35237044,"Users Score":9,"Answer":"Extra-trees(ET) aka. extremely randomized trees is quite similar to random forest (RF). Both methods are bagging methods aggregating some fully grow decision trees. RF will only try to split by e.g. a third of features, but evaluate any possible break point within these features and pick the best. However, ET will only evaluate a random few break points and pick the best of these. ET can bootstrap samples to each tree or use all samples. RF must use bootstrap to work well.\nxgboost is an implementation of gradient boosting and can work with decision trees, typical smaller trees. Each tree is trained to correct the residuals of previous trained trees. Gradient boosting can be more difficult to train, but can achieve a lower model bias than RF. For noisy data bagging is likely to be most promising. For low noise and complex data structures boosting is likely to be most promising.","Q_Score":8,"Tags":"python,random-forest,xgboost,kaggle","A_Id":35248119,"CreationDate":"2016-02-06T03:34:00.000","Title":"What is the difference between xgboost, extratreeclassifier, and randomforrestclasiffier?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on some problem where i have to take 15th power of numbers, when I do it in python console i get the correct output, however when put these numbers in pandas data frame and then try to take the 15th power, i get a negative number. \nExample, \n1456 ** 15 = 280169351358921184433812095498240410552501272576L, however when similar operation is performed in pandas i get negative values. Is there a limit on the size of number which pandas can hold and how can we change this limit.","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":112,"Q_Id":35237874,"Users Score":0,"Answer":"If the operations are done in the pydata stack (numpy\/pandas), you're limited to \nfixed precision numbers, up to 64bit.\nArbitrary precision numbers as string, perhaps?","Q_Score":0,"Tags":"python-2.7,pandas","A_Id":35237949,"CreationDate":"2016-02-06T05:53:00.000","Title":"Python Pandas largest number","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on some problem where i have to take 15th power of numbers, when I do it in python console i get the correct output, however when put these numbers in pandas data frame and then try to take the 15th power, i get a negative number. \nExample, \n1456 ** 15 = 280169351358921184433812095498240410552501272576L, however when similar operation is performed in pandas i get negative values. Is there a limit on the size of number which pandas can hold and how can we change this limit.","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":112,"Q_Id":35237874,"Users Score":0,"Answer":"I was able to overcome by changing the data type from int to float, as doing this gives the answer to 290 ** 15 = 8.629189e+36, which is good enough for my exercise.","Q_Score":0,"Tags":"python-2.7,pandas","A_Id":35237988,"CreationDate":"2016-02-06T05:53:00.000","Title":"Python Pandas largest number","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a graph and want to calculate its indegree and outdegree centralization. I tried to do this by using python networkx, but there I can only find a method to calculate indegree and outdegree centrality for each node. Is there a way to calculate in- and outdegree centralization of a graph in networkx?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2646,"Q_Id":35243795,"Users Score":0,"Answer":"This answer has been taken from a Google Groups on the issue (in the context of using R) that helps clarify the maths taken along with the above answer:\n\nFreeman's approach measures \"the average difference in centrality \n  between the most central actor and all others\". \nThis 'centralization' is exactly captured in the mathematical  formula\nsum(max(x)-x)\/(length(x)-1) \nx refers to any centrality measure! That is, if you want to calculate \n  the degree centralization of a network, x has simply to capture the \n  vector of all degree values in the network. To compare various \n  centralization measures, it is best to use standardized centrality \n  measures, i.e. the centrality values should always be smaller than 1 \n  (best position in any possible network) and greater than 0 (worst \n  position)... if you do so, the centralization will also be in the \n  range of [0,1]. \nFor degree, e.g., the 'best position' is to have an edge to all other \n  nodes (i.e. incident edges = number of nodes minus 1) and the 'worst \n  position' is to have no incident edge at all.","Q_Score":4,"Tags":"python,networkx,graph-theory","A_Id":45540101,"CreationDate":"2016-02-06T17:03:00.000","Title":"calculate indegree centralization of graph with python networkx","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":1,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Getting the following error when working through the ipython notebooks on Google's tensorflow udacity course:    \n\nAttributeError: 'module' object has no attribute 'compat'\n\nTrying to call:\n\ntf.compat.as_str(f.read(name)).split()\n\nRunning on Ubuntu 14.04 and wondering if this a tensorflow early bug issue or just me being stupid. :P","AnswerCount":4,"Available Count":1,"Score":0.1488850336,"is_accepted":false,"ViewCount":4727,"Q_Id":35248476,"Users Score":3,"Answer":"You're most likely using an older version of TensorFlow.  I just noticed that some of our install docs still link to 0.5 -- try upgrading to 0.6 or to head.\nI'll fix the docs soon, but in the meantime, if you installed via pip, you can just change the 0.5 to 0.6 in the path.  If you're building from source, just check out the appropriate release tag (or head).","Q_Score":3,"Tags":"python,tensorflow","A_Id":35256493,"CreationDate":"2016-02-07T00:54:00.000","Title":"Tensorflow compat modules issues?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to train a neural net (backprop + gradient descent) in python with features I am constructing on top of the google books 2-grams (English), it will end up being around a billion rows of data with 20 features each row. This will easily exceed my memory and hence using in-memory arrays such as numpy would not be an option as it requires loading the complete training set.\nI looked into memory mapping in numpy which could solve the problem for input layer (which are readonly), but I will also need to store and manipulate my internal layers in the net which requires extensive data read\/write and considering the size of data, performance is extremely crucial in this process as could save days of processing for me.\nIs there a way to train the model without having to load the complete training set in memory for each iteration of cost (loss) minimization?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":597,"Q_Id":35250611,"Users Score":0,"Answer":"What you are probably looking for is minibatching. In general many methods of training neural nets are gradient based, and as your loss function is a function of trianing set - so is the gradient. As you said - it may exceed your memory. Luckily, for additive loss functions (and most you will ever use - are additive) one can prove that you can substitute full gradient descent with stochastic (or minibatch) gradient descent and still converge to a local minima. Nowadays it is very often practise to use batches of 32, 64 or 128 rows, thus rather easy to fit in your memory. Such networks can actually converge faster to solution than the ones trained with full gradient, as you make N \/ 128 moves per dataset instead of just one. Even if each of them is rather rough - as a combination they work pretty well.","Q_Score":0,"Tags":"python,machine-learning,neural-network,large-data","A_Id":35255569,"CreationDate":"2016-02-07T06:47:00.000","Title":"Processing array larger than memory for training a neural net in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"So I have a data in the form [UID obj1 obj2..] x timestamp and I want to cluster this data in python using kmeans from sklearn. Where should I start?\nEDIT:\nSo basically I'm trying to cluster users based on clickstream data, and classify them based on usage patterns.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2413,"Q_Id":35283654,"Users Score":0,"Answer":"You can add more features based on the raw data, and using methods like RFM Analysis. RFM = recency, frequency, monetary\nFor example:\nHow often the user logged in?\nThe last time the user logged in?","Q_Score":0,"Tags":"python,numpy,pandas,machine-learning,scikit-learn","A_Id":47066621,"CreationDate":"2016-02-09T04:02:00.000","Title":"How to cluster a time series using KMeans in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How do I drop a pandas dataframes after I store them in a database. I can only find a way to drop columns or rows from a dataframe but how can I drop a complete data frame to free my computer memory?","AnswerCount":1,"Available Count":1,"Score":0.537049567,"is_accepted":false,"ViewCount":86,"Q_Id":35295491,"Users Score":3,"Answer":"del dataframe will unpollute your namespace and free your memory, while dataframe = None will only free your memory. Hope that helps!","Q_Score":0,"Tags":"python,pandas","A_Id":35295661,"CreationDate":"2016-02-09T15:11:00.000","Title":"How to drop a pandas dataframe after storing in database","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to find groups in one dimensional array where order\/position matters. I tried to use numpys kmeans2 but it works only when i have numbers in increasing order. \nI have to maximize average difference between neigbour sub-arrays\nFor example: if I have array [1,2,2,8,9,0,0,0,1,1,1] and i want to get 4 groups the result should be something like [1,2,2], [8,9], [0,0,0], [1,1,1]\nIs there a way to do it in better then O(n^k)\nanswer: I ended up with modiied dendrogram, where I merge neigbors only.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":112,"Q_Id":35318602,"Users Score":1,"Answer":"K-means is about minimizing the least squares. Among it's largest drawbacks (there are many) is that you need to know k. Why do you want to inherit this drawback?\nInstead of hacking k-means into not ignoring the order, why don't you instead look at time series segmentation and change detection approaches that are much more appropriate for this problem?\nE.g. split your time series if abs(x[i] - x[-1]) > stddev where stddev is the standard deviation of your data set. Or the standard deviation of the last 10 samples (in above series, the standard deviation is about 3, so it would split as [1,2,2], [8,9], [0,0,0,1,1,1] because the change 0 to 1 is not significant.","Q_Score":1,"Tags":"python,cluster-analysis,data-mining,k-means","A_Id":35321747,"CreationDate":"2016-02-10T15:01:00.000","Title":"Modify kmeans alghoritm for 1d array where order matters","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've got a 3d array of shape (time,latitude,longitude). I'd like to calculate the linear trend at each lon\/lat point. \nI know I can simply loop over all points and use spicy.stats.linregress at each point. However, that gets quite slow for large arrays. \nThe scipy function \"detrend\" can calculate and remove linear trends for n-dimensional arrays, and is really fast. But I can't find any method to just calculate the trends. \nDoes anyone know of a fast way to calculate slope, intercept, r value and p value for each point on a large grid?\nAny help\/suggestion is greatly appreciated!\nCheers\nJoakim","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":1476,"Q_Id":35327272,"Users Score":1,"Answer":"I would look into numpy.polyfit but I'm not sure what performance gain it has over scipy.stats.linregress.\nIt's pretty fast from my experience. You might have to do some math on your own to get r and p values from residuals and covariance matrix.","Q_Score":1,"Tags":"python,arrays,scipy,trend","A_Id":35329441,"CreationDate":"2016-02-10T22:29:00.000","Title":"How do I calculate linear trend for a multi-dimensional array in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am a beginner user of Python. I used to work with matlab intensively. Now I am shifting to python. I have a question about the dimension of an array.\nI import Numpy\nI first create an array X, then I use some embedded function, like, sum, to play with my array. Eventually, when I try to check the dimension of my array X, it becomes: X.shape, outputs (81,).  The number 81 is what I expected, but I also expect the 2nd dimension is 1, rather than just omitted. This makes me feel very uncomfortable even though when I directly type X, it output correctly, i.e., one column and the figures in X are all as expected. \nThen when I use another array Y, which literally has Y.shape, outputs (81,1), then if I type X*Y, which I expected to see one array of dimension (81,1) but instead, I saw an array of dimension (81,81).\nI don't know what is the underlying mechanism to produce this results. \nThe way I solve this problem is very stupid. I first create a new array C = zeros((81,1)), so C literally has dimension (81,1), then I assign my X to C by typing C[:,0]=X, then C.shape = (81,1). Note that if I type C=X, then C.shape=(81,), which goes back to my problem. So I can solve my problem, but I am sure there is better method to solve my problem and I also don't understand why python would produce something like (81,), with the 2nd dimension omitted.","AnswerCount":3,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":2639,"Q_Id":35330282,"Users Score":3,"Answer":"Numpy does not omit the dimension of an array. It is a library built for multidimensional arrays (not just 1d or 2d arrays), and so it makes very clear distinctions between arrays of different dimensions (it cannot assume that any array is just a degenerate form of a higher dimension array, because the number of dimensions is infinite, conceptually).\n\nAn array with dimension (81, 1) is a 2d array with the value of the 2nd dimension equal to 1.\nAn array with dimension (81, ) is just a 1d array.\n\n\nWhen you write C[:,0], you are referring to a column. Therefore\n\nIf you write C[: 0] = X, you are assigning a column to one of the columns of C (which happens to be the only one), and therefore are not changing the dimension of C.\nIf you write C = X, then you are saying that C is now a column as well, and therefore are changing the dimension of C.","Q_Score":5,"Tags":"python,arrays,numpy","A_Id":35330365,"CreationDate":"2016-02-11T03:29:00.000","Title":"Why Numpy sometimes omits the dimension of an array","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"What matrix multiplication library would you recommend for Raspberry Pi 2?\nI think about BLAS or NumPy, What do you think?\nI'm wondering if there is an external hardware module for matrix multiplication available.\nThank you!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":357,"Q_Id":35341566,"Users Score":1,"Answer":"Mathematica is part of the standard Raspbian distribution. It should be able to multiply matrices.","Q_Score":0,"Tags":"python,c,raspberry-pi,matrix-multiplication,raspberry-pi2","A_Id":35343669,"CreationDate":"2016-02-11T14:09:00.000","Title":"Raspberry pi matrix multiplication","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Why does np.arange(5, 60, 0.1)[150] yield 19.999999999999947. But np.arange(5, 60, 0.5)[30] yield 20.0?\nWhy does this happen?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1265,"Q_Id":35362241,"Users Score":1,"Answer":"That's because floats (most of the time) cannot represent the exact value you put in. Try print(\"%.25f\" % np.float64(0.1)) which returns 0.1000000000000000055511151 that's not exactly 0.1.\nNumpy already provides a good workaround for almost-equal (floating point) comparisons: np.testing.assert_almost_equal so you can test by using np.testing.assert_almost_equal(20,np.arange(5, 60, 0.1)[150]).\nThe reason why your second example provides the real valus is because 0.5 can be represented as exact float 2**(-1) = 0.5 and therefore multiplications with this value do not suffer from that floating point problem.","Q_Score":1,"Tags":"python,python-2.7,numpy,floating-point","A_Id":35362451,"CreationDate":"2016-02-12T12:05:00.000","Title":"numpy.arange floating point errors","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been working in Jupyter IPython notebook (using Python 2.7) and haven't had any issues before this importing and installing packages. Most of my packages were installed via Anaconda. Now I'm randomly having problems importing packages that I've always been able to import. I have an example below. Please help. I'm pretty new to Python so I'm completing stuck on what the problem is and how to fix it. \nimport pandas as pd\nImportError                               Traceback (most recent call last)\n in ()\n----> 1 import pandas as pd\nC:\\Users\\IBM_ADMIN\\Anaconda2\\lib\\site-packages\\pandas__init__.py in ()\n     11                       \"pandas from the source directory, you may need to run \"\n     12                       \"'python setup.py build_ext --inplace' to build the C \"\n---> 13                       \"extensions first.\".format(module))\n     14 \n     15 from datetime import datetime\nImportError: C extension: No module named numpy not built. If you want to import pandas from the source directory, you may need to run 'python setup.py build_ext --inplace' to build the C extensions first.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1319,"Q_Id":35363795,"Users Score":0,"Answer":"Running this solved the problem: pip install scipy-0.16.1-cp27-none-win_amd64.whl After doing this, all other packages were able to be re-installed and successfully imported.","Q_Score":0,"Tags":"python,windows,python-2.7","A_Id":35412636,"CreationDate":"2016-02-12T13:25:00.000","Title":"Error Importing Python Packages into Jupyter","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using three dataframes to analyze sequential numeric data - basically numeric data captured in time. There are 8 columns, and 360k entries. I created three identical dataframes - one is the raw data, the second a \"scratch pad\" for analysis and a third dataframe contains the analyzed outcome. This runs really slowly. I'm wondering if there are ways to make this analysis run faster? Would it be faster if instead of three separate 8 column dataframes I had one large one 24 column dataframe?","AnswerCount":3,"Available Count":2,"Score":0.0665680765,"is_accepted":false,"ViewCount":69,"Q_Id":35380802,"Users Score":1,"Answer":"Use cProfile and lineprof to figure out where the time is being spent.\nTo get help from others, post your real code and your real profile results.\nOptimization is an empirical process. The little tips people have are often counterproductive.","Q_Score":0,"Tags":"python,pandas","A_Id":35383780,"CreationDate":"2016-02-13T13:56:00.000","Title":"Making Dataframe Analysis faster","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using three dataframes to analyze sequential numeric data - basically numeric data captured in time. There are 8 columns, and 360k entries. I created three identical dataframes - one is the raw data, the second a \"scratch pad\" for analysis and a third dataframe contains the analyzed outcome. This runs really slowly. I'm wondering if there are ways to make this analysis run faster? Would it be faster if instead of three separate 8 column dataframes I had one large one 24 column dataframe?","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":69,"Q_Id":35380802,"Users Score":0,"Answer":"Most probably it doesn't matter because pandas stores each column separately anyway (DataFrame is a collection of Series). But you might get better data locality (all data next to each other in memory) by using a single frame, so it's worth trying. Check this empirically.","Q_Score":0,"Tags":"python,pandas","A_Id":35383875,"CreationDate":"2016-02-13T13:56:00.000","Title":"Making Dataframe Analysis faster","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Let's say I have a dictionary of about 100k pairs of strings, and a numpy matrix of shape (100k, 500). I would like to save them to the disk in a same file.\nWhat I'm doing right now is using cPickle to dump the dictionary, and scipy.io.savemat to dump the matrix. This way, the dump \/ load is very fast. But the problem is that since I use different methods I obtain 2 files, and I would like to have just one file containing my 2 objects. How can I do this?\nI could cPickle them both in the same file, but cPickle is incredibly slow on big arrays.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":498,"Q_Id":35382336,"Users Score":0,"Answer":"You could use dill.  dill.dump accesses and uses the dump method from numpy to store an array or matrix object, so it's stored the same way it would be if you did it directly from the method on the numpy object.  You'd just dill.dump the dictionary.\ndill also has the ability to store pickles in compressed format, but it's slower.  As mentioned in the comments, there's also joblib, which can also do the same as dill\u2026  but basically, joblib leverages cloudpickle (which is another serializer) or can also use dill, to do the serialization.\nIf you have a huge dictionary, and don't need all of the contents at once\u2026 maybe a better option would be klepto, which can use advanced serialization methods (from dill) to store a dict to several files on disk (or a database), where you have a proxy dict in memory that enables you to only get the entries you need.\nAll of these packages give you a fast unified dump for standard python and also for numpy objects.","Q_Score":0,"Tags":"python,numpy,scipy,pickle","A_Id":35384823,"CreationDate":"2016-02-13T16:29:00.000","Title":"dumping several objects into the same file","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"How can I plot multiple data series with different number of elements and have them fill the graph along the x axis?\nAt the moment if I graph a = [1,2,3,4,5] and b = [1,2,3] the b lines only covers half the graph.  Is this possible or do I need to somehow combine the graphs after plotting\/rendering them?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":110,"Q_Id":35404323,"Users Score":0,"Answer":"Never mind, I got it sorted, I was using the wrong graph type","Q_Score":0,"Tags":"python,pygal","A_Id":35423410,"CreationDate":"2016-02-15T08:12:00.000","Title":"pygal data series of different lengths","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm optimizing a Python program that performs some sort of calculation. It uses NumPy quite extensively. The code is sprinkled with logger.debug calls (logger is the standard Python log object).\nWhen I run cProfile I see that Numpy's function that converts an array to string takes 50% of the execution time. This is surprising, since there is no handler that outputs messages as the DEBUG level, only INFO and above.\nWhy is the logger converting its arguments to string even though nobody is going to use this string? Is there a way to prevent it (other than not performing the logger calls)?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2623,"Q_Id":35411265,"Users Score":0,"Answer":"Use logger.debug('%s', myArray) rather than logger.debug(myArray). The first argument is expected to be a format string (as all the documentation and examples show) and is not assumed to be computationally expensive. However, as @dwanderson points out, the logging will actually only happen if the logger is enabled for the level.\nNote that you're not forced to pass a format string as the first parameter - you can pass a proxy object that will return the string when str() is called on it (this is also documented). In your case, that's what'll probably do the array to string conversion.\nIf you use logging in the manner that you're supposed to (i.e. as documented), there shouldn't be the problem you describe here.","Q_Score":2,"Tags":"python,logging,optimization","A_Id":35420774,"CreationDate":"2016-02-15T13:56:00.000","Title":"Python logger.debug converting arguments to string without logging","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I trained a linear regression model(using sklearn with python3),\nmy train set was with 94 features and the class of them was 0 or 1..\nthan i went to check my linear regression model on the test set and it gave me those results:\n1.[ 0.04988957] its real value is 0 on the test set\n2.[ 0.00740425] its real value is 0 on the test set\n3.[ 0.01907946] its real value is 0 on the test set\n4.[ 0.07518938] its real value is 0 on the test set\n5.[ 0.15202335] its real value is 0 on the test set\n6.[ 0.04531345] its real value is 0 on the test set\n7.[ 0.13394644] its real value is 0 on the test set\n8.[ 0.16460608] its real value is 1 on the test set\n9.[ 0.14846777] its real value is 0 on the test set\n10.[ 0.04979875] its real value is 0 on the test set\nas you can see that at row 8 it gave the highest value but the thing is that i want to use my_model.predict(testData) and it will give only 0 or 1 as results, how can i possibly do it? the model got any threshold or auto cutoff that i can use?","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":2479,"Q_Id":35436599,"Users Score":1,"Answer":"There is a linear classifier sklearn.linear_model.RidgeClassifer(alpha=0.) that you can use for this. Setting the Ridge penalty to 0. makes it do exactly the linear regression you want and set the threshold to divide between classes.","Q_Score":1,"Tags":"python,scikit-learn,linear-regression","A_Id":35438322,"CreationDate":"2016-02-16T15:32:00.000","Title":"can i make linear regression predict like a classification?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to save to csv, but have date values in the series that are below 1900 (ie Mar 1 1899), which is preventing this from happening. I get ValueError: year=1899 is before 1900; the datetime strftime() methods require year >= 1900. It seems a little absurd for a function like this to work only for dates above 1900s, so I think there must be something I am missing. What is the right way of getting a csv when you're working with a dataframe that has a column with dates before the 1900s?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":136,"Q_Id":35441310,"Users Score":1,"Answer":"Upgrading pandas from 0.15.1 to 0.17.1 resolved this issue.","Q_Score":0,"Tags":"python-2.7,csv,pandas","A_Id":35441725,"CreationDate":"2016-02-16T19:22:00.000","Title":"pandas to_csv on dataframe with a column that has dates below 1900","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there a way to create a 3d numpy array by appending 2d numpy arrays? What I currently do is append my 2d numpy array into an initialized list of pre determined 2d numpy array, i.e., List=[np.zeros((600,600))]. After appending all my 2d numpy arrays I use numpy.dstack to create 3d numpy array. I think this is not a very efficient method. Any suggestions.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":1796,"Q_Id":35464138,"Users Score":1,"Answer":"By definition you cannot append anything to an array because when the array is declared in memory it has to reserve as much space as it is going to need.\nWhat you can do is to either declare an array with the known geometry and initial values and then rewrite the new values per row keeping a counter of the rows \"appended\" or you can double the size of the array when you run out of space.","Q_Score":0,"Tags":"python,numpy,memory,arrays","A_Id":35464658,"CreationDate":"2016-02-17T14:21:00.000","Title":"Creating a 3d numpy array matrix using append method","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have searched quite a bit regarding this and I've tried some of these methods myself but I'm unable to work with OpenCV.So can anyone of you help me install OpenCV for python 3.5.1?\nI'm using anaconda along with Pycharm in windows \nOr is this not possible and i have to use python 2.7?\nThanks in advance","AnswerCount":5,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":79914,"Q_Id":35466429,"Users Score":7,"Answer":"For anyone who would like to install OpenCV on Python 3.5.1,use this library called \n\nopencv-contrib-python\n\nThis library works for Python 3.5.1","Q_Score":10,"Tags":"python,python-3.x,opencv","A_Id":44714952,"CreationDate":"2016-02-17T19:53:00.000","Title":"OpenCV for Python 3.5.1","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have searched quite a bit regarding this and I've tried some of these methods myself but I'm unable to work with OpenCV.So can anyone of you help me install OpenCV for python 3.5.1?\nI'm using anaconda along with Pycharm in windows \nOr is this not possible and i have to use python 2.7?\nThanks in advance","AnswerCount":5,"Available Count":2,"Score":0.0399786803,"is_accepted":false,"ViewCount":79914,"Q_Id":35466429,"Users Score":1,"Answer":"For OS:Windows 10 and Python version: 3.5.1 & 3.6, this worked for me\npip install opencv-contrib-python","Q_Score":10,"Tags":"python,python-3.x,opencv","A_Id":63226795,"CreationDate":"2016-02-17T19:53:00.000","Title":"OpenCV for Python 3.5.1","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"We have hourly time series data having 2 columns, one is the timestamp and other is the error rate. We used H2O deep-learning model to learn and predict future error-rate but looks like it requires at least 2 features (except timestamp) for creating the model.\nIs there any way h2o can learn this type of data (time, value) having only one feature and predict the value given future time?","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":2186,"Q_Id":35472785,"Users Score":1,"Answer":"I have tried to use many of the default methods inside H2O with time series data. If you treat the system as a state machine where the state variables are a series of lagged prior states, it's possible, but not entirely effective as the prior states don't maintain their causal order.  One way to alleviate this is to assign weights to each lagged state set based on time past, similar to how an EMA gives precedence to more recent data.\nIf you are looking to see how easy or effective the DL\/ML can be for a non-linear time series model, I would start with an easy problem to validate the DL approach gives any improvement over a simple 1 period ARIMA\/GARCH type process.\nI have used this technique, with varying success.  What I have had success with is taking well known non linear time series models and improving their predictive qualities with additional factors using the the handcrafted non linear model as an input into the DL method.  It seems that certain qualities that I haven't manually worked out about the entire parameter space are able to supplement a decent foundation.\nThe real question at that point is there is now an introduction of immense complexity that isn't entirely understood.  Is that complexity warranted in the compiled landscape when the nonlinear model encapsulates about 95% of the information between the two stages?","Q_Score":1,"Tags":"r,python-2.7,machine-learning,prediction,h2o","A_Id":41201223,"CreationDate":"2016-02-18T04:23:00.000","Title":"Can we predict time-series single-dimensional data using H2O?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using pandas to input a list of names and phone numbers, clean that list, then export it. When I export the list, all of the phone numbers have '.0' tacked on to the end. I tried two solutions:\nA: round()\nB: converting to integer then converting to text (which has worked in the past)\nFor some reason when I tried A, the decimal still comes out when I export to a text file and when I tried B, I got an unexpected negative ten digit number\nAny ideas about what's happening here and\/or how to fix it?\nThanks!","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":79,"Q_Id":35485629,"Users Score":0,"Answer":"figured it out. specified the data type on import with dtype = {\"phone\" : str, \"other_phone\" : str})","Q_Score":0,"Tags":"python,pandas","A_Id":35486318,"CreationDate":"2016-02-18T15:29:00.000","Title":"Removing decimals on export python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have two data set with different size.\n1) Data set 1 is with high dimensions 4500 samples (sketches).\n2) Data set 2 is with low dimension 1000 samples (real data).\nI suppose that \"both data set have the same distribution\"\nI want to train an non linear SVM model using sklearn on the first data set (as a pre-training ), and after that I want to update the model on a part of the second data set (to fit the model).\nHow can I develop a kind of update on sklearn. How can I update a SVM model?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":4307,"Q_Id":35492556,"Users Score":12,"Answer":"In sklearn you can do this only for linear kernel and using SGDClassifier (with appropiate selection of loss\/penalty terms, loss should be hinge, and penalty L2). Incremental learning is supported through partial_fit methods, and this is not implemented for neither SVC nor LinearSVC. \nUnfortunately, in practise fitting SVM in incremental fashion for such small datasets is rather useless. SVM has easy obtainable global solution, thus you do not need pretraining of any form, in fact it should not matter at all, if you are thinking about pretraining in the neural network sense. If correctly implemented, SVM should completely forget previous dataset. Why not learn on the whole data in one pass? This is what SVM is supposed to do. Unless you are working with some non-convex modification of SVM (then pretraining makes sense).\nTo sum up:\n\nFrom theoretical and practical point of view there is no point in pretraining SVM. You can either learn only on the second dataset, or on both in the same time. Pretraining is only reasonable for methods which suffer from local minima (or hard convergence of any kind) thus need to start near actual solution to be able to find reasonable model (like neural networks). SVM is not one of them.\nYou can use incremental fitting (although in sklearn it is very limited) for efficiency reasons, but for such small dataset you will be just fine fitting whole dataset at once.","Q_Score":7,"Tags":"python,numpy,machine-learning,computer-vision,scikit-learn","A_Id":35492991,"CreationDate":"2016-02-18T21:14:00.000","Title":"How to update an SVM model with new data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have been trying to install and use scikit-learn and nltk. However, I get the following error while importing anything: \n\nTraceback (most recent call last):\n    File \"\", line 1, in \n    File \"\/usr\/local\/lib\/python2.7\/site-packages\/sklearn\/init.py\", line 57, in \n      from .base import clone\n    File \"\/usr\/local\/lib\/python2.7\/site-packages\/sklearn\/base.py\", line 11, in \n      from .utils.fixes import signature\n    File \"\/usr\/local\/lib\/python2.7\/site-packages\/sklearn\/utils\/init.py\", line 10, in \n      from .murmurhash import murmurhash3_32\n    File \"numpy.pxd\", line 155, in init sklearn.utils.murmurhash (sklearn\/utils\/murmurhash.c:5029)\n  ValueError: numpy.dtype has the wrong size, try recompiling \n\nI did a pip uninstall numpy followed by a pip install numpy and also a pip uninstall scikit-learn and again reinstalled it. But the error persists.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":474,"Q_Id":35522767,"Users Score":1,"Answer":"I met the same problem today. Now I have solved it. \nBecause I have installed numPy manually, and I use the command \"pip\" to install the else package.\nSolve way:\n\nfind the old version of numPy.\nYou can import numPy and print the path of it.\ndelete the folder.\nuse pip to install again.","Q_Score":0,"Tags":"python,numpy,scikit-learn","A_Id":37745749,"CreationDate":"2016-02-20T11:45:00.000","Title":"Installation of nltk and scikit-learn","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"So I'm generating a summary report from a data set. I used .describe() to do the heavy work but it doesn't generate everything I need i.e. the second most common thing in the data set.\nI noticed that if I use .mode() it returns the most common value, is there an easy way to get the second most common?","AnswerCount":4,"Available Count":1,"Score":0.049958375,"is_accepted":false,"ViewCount":2825,"Q_Id":35531367,"Users Score":1,"Answer":"Try this method:\n\nCreate a duplicate data set.\nUse .mode() to find the most common value.\nPop all items with that value from the set.\nRun .mode() again on the modified data set.","Q_Score":0,"Tags":"python,pandas","A_Id":35531393,"CreationDate":"2016-02-21T01:51:00.000","Title":"In pandas, how to get 2nd mode","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Backgroud\nI am using django-rest-pandas for serving json & xls. \nObservation\nWhen I hit url with format=xls, I get complete data in the downloaded file. But for format=josn, the index field of dataframe is not part of the records. \nQuestion\nHow can I make django-rest-pandas to include dataframe's index field in json response? \nNote that the index field is present as part of serializer (extending serializer.ModelSerializer).","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":363,"Q_Id":35534039,"Users Score":0,"Answer":"One simple way would be to just reset the index in transform_dataframe method.\ndf = df.reset_index()\nThis would just add a new index and set your old index as a column, included in the output.","Q_Score":0,"Tags":"python,django,django-rest-framework","A_Id":38318621,"CreationDate":"2016-02-21T08:39:00.000","Title":"Include django rest pandas dataframe Index field in json response","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"In matlab we have rgb2ntsc() function to get YIQ components of a RGB image. Is there a similar function available in python (numpy ,matplotlib or scipy libraries)?\nAlso to apply discrete cosine transform (compress it) we can use dct2() , in matlab , is there a similar function in python?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1415,"Q_Id":35539077,"Users Score":1,"Answer":"Are you willing to use a library outside of numpy, scipy, and matplotlib?\nIf so you can use skimage.color.rgb2yiq() from the scikit-image library.","Q_Score":1,"Tags":"python,matplotlib,dct","A_Id":41886452,"CreationDate":"2016-02-21T17:03:00.000","Title":"how to convert a RGB image to its YIQ components in python , and then apply dct transform to compress it?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there a way to change the default port (6006) on TensorBoard so we could open multiple TensorBoards? Maybe an option like --port=\"8008\"?","AnswerCount":3,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":72323,"Q_Id":35551326,"Users Score":27,"Answer":"You should provide a port flag (--port=6007). \nBut I am here to explain how you can find it and other flags without any documentation. Almost all command line tools have a flag -h or --help which shows all possible flags this tool allows.\nBy running it you will see information about a port flag and that --logdir allows you to \n\nalso pass a comma separated list of log directories\n\nand you can also inspect separate event-files and tags with --event_file and --tag flags","Q_Score":63,"Tags":"python,tensorflow,tensorboard","A_Id":43568782,"CreationDate":"2016-02-22T10:31:00.000","Title":"Tensorflow Tensorboard default port","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a gridded velocity field that I want to interpolate in Python. Currently I'm using scipy.interpolate's RectBivariateSpline to do this, but I want to be able to define edges of my field by setting certain values in the grid to NaN. However, when I do this it messes up the interpolation of the entire grid, effectively making it NaN everywhere. Apparently this is an error in the scipy fitpack, so what would be the best way to work around this? I want to be able to keep the NaNs in the grid to work with edges and out of bounds later on, but I don't want it to affect the interpolation in the rest of the grid.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":776,"Q_Id":35552667,"Users Score":0,"Answer":"Spline fitting\/interpolation is global, so it's likely that even a single nan is messing up the whole mesh.","Q_Score":0,"Tags":"python,scipy,interpolation,nan","A_Id":35576424,"CreationDate":"2016-02-22T11:39:00.000","Title":"Ignoring NaN when interpolating grid in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a gridded velocity field that I want to interpolate in Python. Currently I'm using scipy.interpolate's RectBivariateSpline to do this, but I want to be able to define edges of my field by setting certain values in the grid to NaN. However, when I do this it messes up the interpolation of the entire grid, effectively making it NaN everywhere. Apparently this is an error in the scipy fitpack, so what would be the best way to work around this? I want to be able to keep the NaNs in the grid to work with edges and out of bounds later on, but I don't want it to affect the interpolation in the rest of the grid.","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":776,"Q_Id":35552667,"Users Score":1,"Answer":"All languages that implement floating point correctly (which includes python) allow you to test for a NaN by comparing a number with itself.\nx is not equal to x if, and only if, x is NaN.\nYou'll be able to use that to filter your data set accordingly.","Q_Score":0,"Tags":"python,scipy,interpolation,nan","A_Id":35552764,"CreationDate":"2016-02-22T11:39:00.000","Title":"Ignoring NaN when interpolating grid in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have installed numpy-1.11.0b3 by, pip install \"numpy-1.11.0b3+mkl-cp35-cp35m-win32.whl\". The installation became successful.\nBut, when I write \"import numpy\" at the Python Shell (3.5.1), I am getting the error as - ImportError: No module named 'numpy'.\nCan anyone suggest me regarding this ?\nRegards, Arpan Ghose","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":429,"Q_Id":35577179,"Users Score":0,"Answer":"It may be possible that you have have installed pip for some lower version of python. To check it first look for your default python version by:\n\n$python\n\nNow check for your linked version of python with pip\n\n$pip --version\n\nNow see if the two python versions match.\nIf they don't match then, you need to upgrade you pip :\n\n$pip install -U pip\n\nNow install numpy for this:\n\nsudo pip install numpy\n\nHope this helps !","Q_Score":0,"Tags":"python,numpy","A_Id":48353933,"CreationDate":"2016-02-23T12:06:00.000","Title":"getting error for importing numpy at Python 3.5.1","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have about a million rows of data with lat and lon attached, and more to come. Even now reading the data from SQLite file (I read it with pandas, then create a point for each row) takes a lot of time. \nNow, I need to make a spatial joint over those points to get a zip code to each one, and I really want to optimise this process. \nSo I wonder: if there is any relatively easy way to parallelize those computations?","AnswerCount":3,"Available Count":2,"Score":0.0665680765,"is_accepted":false,"ViewCount":939,"Q_Id":35581528,"Users Score":1,"Answer":"I am assuming you have already implemented GeoPandas and are still finding difficulties?\nyou can improve this by further hashing your coords data. similar to how google hashes their search data. Some databases already provide support for these types of operations (eg mongodb). Imagine if you took the first (left) digit of your coords, and put each set of cooresponding data into a seperate sqlite file. each digit can be a hash pointing to the correct file to look for. now your lookup time has improved by a factor of 20 (range(-9,10)), assuming your hash lookup takes minimal time in comparison","Q_Score":2,"Tags":"python,multithreading,pandas,geopandas","A_Id":35583196,"CreationDate":"2016-02-23T15:28:00.000","Title":"Fastest approach for geopandas (reading and spatialJoin)","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have about a million rows of data with lat and lon attached, and more to come. Even now reading the data from SQLite file (I read it with pandas, then create a point for each row) takes a lot of time. \nNow, I need to make a spatial joint over those points to get a zip code to each one, and I really want to optimise this process. \nSo I wonder: if there is any relatively easy way to parallelize those computations?","AnswerCount":3,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":939,"Q_Id":35581528,"Users Score":1,"Answer":"As it turned out, the most convenient solution in my case is to use pandas.read_SQL function with specific chunksize parameter. In this case, it returns a generator of data chunks, which can be effectively feed to the mp.Pool().map() along with the job; \nIn this (my) case job consists of 1) reading  geoboundaries, 2) spatial joint of the chunk 3) writing the chunk to the database.","Q_Score":2,"Tags":"python,multithreading,pandas,geopandas","A_Id":35786998,"CreationDate":"2016-02-23T15:28:00.000","Title":"Fastest approach for geopandas (reading and spatialJoin)","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am creating a plot in Bokeh with percentages on the y-axis. The data is represented as a percent (e.g. '99.0') as opposed to a likelihood (e.g. '0.990'). I want to add a '%' sign after each number on the axis, but when using NumeralTickFormatter(format='0 %') my values are multiplied by 100 because it expects a likelihood. I don't want to change the data representation to a likelihood, so is there some other way I can get the '%' sign to appear on the axis ticks?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1425,"Q_Id":35585580,"Users Score":1,"Answer":"To elaborate on what bigreddot proposed: PrintfTickFormatter(format='%0.0f %%') worked. One thing to note is the %% to properly escape the %.","Q_Score":0,"Tags":"python,formatting,bokeh","A_Id":35589047,"CreationDate":"2016-02-23T18:41:00.000","Title":"Bokeh: add a % sign to axis ticks without changing numeric value","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a 2D numpy array that represents the coordinates (x, y) of a curve, and I want to split that curve into parts of the same length, obtaining the coordinates of the division points.\nThe most easy example is a line defined for two points, for example [[0,0],[1,1]], and if I want to split it in two parts the result would be [0.5,0.5], and for three parts [[0.33,0.33],[0.67,0.67]] and so on.\nHow can I do that in a large array where the data is less simple? I'm trying to split the array by its length but the results aren't good.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":279,"Q_Id":35604173,"Users Score":1,"Answer":"take the length of the line on every axes, the split as you want.\nexample:\npoint 1: [0,0]\npoint 2: [1,1]\nthen:\nlength of the line on X axes: 1-0 = 1\nalso in the Y axes.\nnow, if you want to split it in two, just divide these lengths, and create a new array.\n[0,0],[.5,.5],[1,1]","Q_Score":4,"Tags":"python,arrays,numpy,curves,curvesmoothing","A_Id":35604894,"CreationDate":"2016-02-24T13:57:00.000","Title":"Split numpy array into similar array based on its content","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am building graphics using Matplotlib and I sometimes have wrong values in my Csv files, it creates spikes in my graph that I would like to suppress, also sometimes I have lots of zeros ( when the sensor is disconnected ) but I would prefer the graph showing blank spaces than wrong zeros that could be interpreted as real values.","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":21,"Q_Id":35628756,"Users Score":1,"Answer":"Forgive me for I'm not familiar with matplotlib but I'm presuming that you're reading the csv file directly into matplotlib. If so is there an option to read the csv file into your app as a list of ints or as a string and then do the data validation before passing that string to the library?  \nApologies if my idea is not applicable.","Q_Score":0,"Tags":"python-3.x,matplotlib","A_Id":35629019,"CreationDate":"2016-02-25T13:32:00.000","Title":"Hide wrong values of a graph","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am building graphics using Matplotlib and I sometimes have wrong values in my Csv files, it creates spikes in my graph that I would like to suppress, also sometimes I have lots of zeros ( when the sensor is disconnected ) but I would prefer the graph showing blank spaces than wrong zeros that could be interpreted as real values.","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":21,"Q_Id":35628756,"Users Score":0,"Answer":"I found a way that works:\nI used the Xlim to set my max and min x values and then i set all the values that i didnt want to nan !","Q_Score":0,"Tags":"python-3.x,matplotlib","A_Id":35629514,"CreationDate":"2016-02-25T13:32:00.000","Title":"Hide wrong values of a graph","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I hope this doesn't sound as an open question for discussion. I am going to give some details for my specific case.\nI am new to Pandas and I need to store several 2D arrays, where columns represent frequencies and rows represent directions (2D waves spectra, if you are curious). Each array represent a specific time.\nI am storing these arrays as Pandas DataFrames, but for keeping them in a single object I thought of 2 options:\n\nStoring the DataFrames in a dictionary where the key is the time stamp.\nStoring the DataFrames in a Pandas Panel where the item is the time stamp.\n\nThe first option seems simple and has the flexibility to store arrays with different sizes, indexes and column names. The second option seems better for processing the data, since Panels have specific methods, and can also be easily  saved or exported (e.g. to csv or pickle).\nWhich of the two options is better suited in terms of: speed, memory use, flexibility and data analysis?\nRegards","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1554,"Q_Id":35635870,"Users Score":1,"Answer":"I don't think you need a panel. I recommend a nested dataframe approach.","Q_Score":5,"Tags":"python,dictionary,pandas,dataframe,panel","A_Id":35638383,"CreationDate":"2016-02-25T18:53:00.000","Title":"Is it better to store Pandas Data Frames in a dictionary or in a Panel?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm building a fMRI paradigm and I have a stimulus that disappears when a user presses a button (up to 4s), then a jitter (0-12s), then another stimulus presentation. I'm locking the stimuli presentation to the 1s TR of the scanner so I'm curious how I can round up the jitter time to the nearest second. \nSo, the task is initialized as:\nstimulus 1 ( \u22644 s)  -- jitter (e.g. 6 s) -- stimulus 2\nBut if the user responds to stimulus-1 at 1.3 seconds, then the task becomes\nstimulus-1 (1.3 s) -- jitter (6.7 s) -- stimulus-2\nDoes that make sense? Thanks for the help!","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":188,"Q_Id":35637501,"Users Score":0,"Answer":"difference = 1.0 - (RT - int(RT))","Q_Score":0,"Tags":"python,psychopy","A_Id":35664986,"CreationDate":"2016-02-25T20:19:00.000","Title":"Rounding up a jitter in psychopy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"If I have a dataset with events where each event has data with 1000 possible items with only 100 being correct for each event. How do I force my classifier to select only 100 for each event?\nAfter I run it through my training model (with 18 features and always has 100 targets\/event flagged as 1) the classifier selects anywhere between 60-80 items instead of 100. Even if I give each event an event number that doesn't help.\nI'm using python sklearn gradient boosting and random forest method.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":33,"Q_Id":35637968,"Users Score":1,"Answer":"Just do it yourself. Each classifier in scikit-learn gives you access to decision_function or predict_proba, both of which are support for predict operation (predict is just argmax of these). Thus - just select 100 with the highest support.","Q_Score":0,"Tags":"python,machine-learning,classification,random-forest","A_Id":35638661,"CreationDate":"2016-02-25T20:46:00.000","Title":"Force classifer to select a fixed number of targets","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to find a good statistical method to compare a given value with an existing set of values. Currently I am considering mean of the existing numbers and comparing it with the given value. If the value is off by 50% of the mean then I would say it is off the flow. I am using python programming language for all calculations. Is there any other method possible which is more efficient?\nEx: 1,4,7,0,0,0 are the values that exist currently.\nI get the mean of these : 2\nIf the given value is 10, I would say it is off the mark.\nCan there be a more efficient way?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":37,"Q_Id":35645776,"Users Score":1,"Answer":"As I'm understanding it, you want some measure of central tendency. There are three of these: mean, median, and mode. Which one you want to use depends on your goals and priorities. Mean is very popular and understandable to people. It has a lot of useful statistical properties. However, it is subject to outliers. On the other hand, mode and median are not (as) influenced by outliers, but they have fewer statistical usages. Further, in the case of the median and mean, the value you calculate may not actually be in your data set, whereas the mode will.\nWhich of these considerations matter for you?\nBut even after you pick the measure of central tendency you like, how are you going to determine when something is \"too far\" out of the set? In your question you're doing it as just a percentage, but this might not be the best way.\nFor most problems, I would probably use the mean as my measure of central tendency and use standard deviation as the statistic to determine if a figure is \"off the mark.\" But something else might work better for you.","Q_Score":0,"Tags":"python,statistics,mathematical-optimization","A_Id":35646195,"CreationDate":"2016-02-26T07:20:00.000","Title":"Statistic to consider while analysing a value with previous set of values","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Let us assume I have a smooth, nonlinear function f: R^n -> R with the (known) maximum number of roots N. How can I find the roots efficiently? Right now I have calculated the function on the grid on a preselected area, refined the grid where the function is below a predefined threshold and continued that routine, but this does not seem to be very efficient, though, because I have noticed that it is difficult to select the area correctly before and to define the threshold accordingly.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":866,"Q_Id":35648212,"Users Score":0,"Answer":"You can square the function and use global optimization software to locate all the minima inside a domain and pick those with a zero value.\nStochastic multistart based global optimization methods with clustering are quite proper for this task.","Q_Score":0,"Tags":"python,numerical-methods,nonlinear-functions,nonlinear-optimization","A_Id":35835011,"CreationDate":"2016-02-26T09:42:00.000","Title":"Find all roots of a nonlinear function","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"We want to use Python 3.x with packages like NumPy, Pandas,etc. on top of Spark.\nWe know the Python distribution with these packages needs to be present\/distributed on all the datanodes for Spark to use these packages.\nInstead of setting up this Python distro on all the datanodes, will putting it on a NAS mount to which all datanodes are connected work?\nThanks","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":144,"Q_Id":35655693,"Users Score":2,"Answer":"Yes, putting the packages on a NAS mount to which all the datanodes are connected will work up to dozens and perhaps 100 nodes if you have a good NAS.   However,  this solution will break down at scale as all the nodes try to import the files they need.  The Python import mechanism usese a lot of os.stat calls to the file-system and this can cause bottle-necks when all the nodes are trying to load the same code.","Q_Score":0,"Tags":"python,numpy,apache-spark,pyspark","A_Id":35658350,"CreationDate":"2016-02-26T15:32:00.000","Title":"Python packages for Spark on datanodes","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a video consisting of different objects such as square, rectangle , triangle. I somehow need to detect and show only square objects. So in each frame, if there is a square, it is fine but if there is a triangle or rectangle then it should display it. I am using background subtraction and I am able to detect all the three objects and create a bounding box around them. But I am not able to figure out how to display only square object.","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":948,"Q_Id":35661419,"Users Score":1,"Answer":"How are your objects filled or just an outline?\nIn either case the approach I would take is to detect the vertices by finding the maximum gradient or just by the bounding box. The vertices will be on the bounding box. Once you have the vertices, you can say whether the object is a square or a rectangle just by finding the distances between the consecutive vertices.","Q_Score":0,"Tags":"python,opencv,frames","A_Id":35663902,"CreationDate":"2016-02-26T20:46:00.000","Title":"How to detect objects in a video opencv with Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a video consisting of different objects such as square, rectangle , triangle. I somehow need to detect and show only square objects. So in each frame, if there is a square, it is fine but if there is a triangle or rectangle then it should display it. I am using background subtraction and I am able to detect all the three objects and create a bounding box around them. But I am not able to figure out how to display only square object.","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":948,"Q_Id":35661419,"Users Score":3,"Answer":"You can use the following algorithm:\n-Perform Background subtraction, as you're doing currently\n-enclose foreground in contours (using findContours(,,,) then drawContours(,,,) function)\n-enclose obtained contours in bounding boxes (using boundingRect(,,,) function)\n-if area of bounding box is approximately equal to that of enclosed contour, then the shape is a square or rectangle, not a triangle. \n(A large part of the box enclosing a triangle will lie outside the triangle)\n-if boundingBox height is approximately equal to its width, then it is a square. (access height and width by Rect.height and Rect.width)","Q_Score":0,"Tags":"python,opencv,frames","A_Id":35788514,"CreationDate":"2016-02-26T20:46:00.000","Title":"How to detect objects in a video opencv with Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a df in pandas\nimport pandas as pd\nimport pandas as pd\ndf = pd.DataFrame(['AA', 'BB', 'CC'], columns = ['value'])\nI want to iterate over rows in df. For each row i want rows value and next rows value.\nHere is the desired result.\n0 1 AA BB\n1 2 BB CC\nI have tried a pairwise() function with itertools.\nfrom itertools import tee, izip\ndef pairwise(iterable):\n    \"s -> (s0,s1), (s1,s2), (s2, s3), ...\"\n    a, b = tee(iterable)\n    next(b, None)\n    return izip(a, b)\n\nimport pandas as pd\ndf = pd.DataFrame(['AA', 'BB', 'CC'], columns = ['value'])\n\nfor (i1, row1), (i2, row2) in pairwise(df.iterrows()):\n    print i1, i2, row1[\"value\"], row2[\"value\"]\nBut, its too slow. Any idea how to achieve the output with iterrows ? I would like to try pd.apply for a large dataset.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":908,"Q_Id":35670348,"Users Score":0,"Answer":"While it's not the most \"fancy\" way - I would just use a numeric iterator and access lines i and i+1","Q_Score":1,"Tags":"python,pandas,apply,next","A_Id":35670515,"CreationDate":"2016-02-27T13:24:00.000","Title":"python pandas ... alternatives to iterrows in pandas to get next rows value (NEW)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm not really new to matplotlib and I'm deeply ashamed to admit I have always used it as a tool for getting a solution as quick and easy as possible. So I know how to get basic plots, subplots and stuff and have quite a few code which gets reused from time to time...but I have no \"deep(er) knowledge\" of matplotlib.\nRecently I thought I should change this and work myself through some tutorials. However, I am still confused about matplotlibs plt, fig(ure) and ax(arr). What is really the difference?\nIn most cases, for some \"quick'n'dirty' plotting I see people using just pyplot as plt and directly plot with plt.plot. Since I am having multiple stuff to plot quite often, I frequently use f, axarr = plt.subplots()...but most times you see only code putting data into the axarr and ignoring the figure f.\nSo, my question is: what is a clean way to work with matplotlib? When to use plt only, what is or what should a figure be used for? Should subplots just containing data? Or is it valid and good practice to everything like styling, clearing a plot, ..., inside of subplots?\nI hope this is not to wide-ranging. Basically I am asking for some advice for the true purposes of plt <-> fig <-> ax(arr) (and when\/how to use them properly).\nTutorials would also be welcome. The matplotlib documentation is rather confusing to me. When one searches something really specific, like rescaling a legend, different plot markers and colors and so on the official documentation is really precise but rather general information is not that good in my opinion. Too much different examples, no real explanations of the purposes...looks more or less like a big listing of all possible API methods and arguments.","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":8825,"Q_Id":35677767,"Users Score":0,"Answer":"pyplot is matlab like API for those who are familiar with matlab and want to make quick and dirty plots\nfigure is object-oriented API for those who doesn't care about matlab style plotting\n\nSo you can use either one but perhaps not both together.","Q_Score":39,"Tags":"python,matplotlib,plot,figure","A_Id":35677835,"CreationDate":"2016-02-28T01:47:00.000","Title":"Understanding matplotlib: plt, figure, ax(arr)?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Sorry in advance, I know almost nothing about efficiency, so I might need a little extra help. \nI have a function that calls another function and it passes in pretty big arrays. Due to limitations in memory, I've just been saving the arrays to pickle files and unpickling inside the second function. Just for context, I have 64 pickle files that is each 47 megabytes (3 gigs total). I'm calling this function upwards of 100,000 times. I'm assuming that unpickling is far less efficient than unpickling once and passing the arrays, but I was wondering on the order of how much time I'd be losing in   doing the way I'm doing it now and whether there are more efficient ways of doing this","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":45,"Q_Id":35731339,"Users Score":0,"Answer":"It depends on your requirement. If you pass an array to the function, python passes the reference of that array. Whereas if you pass the array in the form of unpacked arguments, objects comprising the array is passed. \nWithin the function if the order of your original array is not getting modified, it is better to pass it as reference i.e as a argument.","Q_Score":0,"Tags":"python,arrays,performance","A_Id":35731697,"CreationDate":"2016-03-01T19:08:00.000","Title":"Efficiency of passing arrays to a function vs unpickling the array inside the function in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Sorry in advance, I know almost nothing about efficiency, so I might need a little extra help. \nI have a function that calls another function and it passes in pretty big arrays. Due to limitations in memory, I've just been saving the arrays to pickle files and unpickling inside the second function. Just for context, I have 64 pickle files that is each 47 megabytes (3 gigs total). I'm calling this function upwards of 100,000 times. I'm assuming that unpickling is far less efficient than unpickling once and passing the arrays, but I was wondering on the order of how much time I'd be losing in   doing the way I'm doing it now and whether there are more efficient ways of doing this","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":45,"Q_Id":35731339,"Users Score":0,"Answer":"If you memory limitations doesn't allow to keep all files in memory, you will read the file each time and pass list to another function (it will be passed by reference).","Q_Score":0,"Tags":"python,arrays,performance","A_Id":35732141,"CreationDate":"2016-03-01T19:08:00.000","Title":"Efficiency of passing arrays to a function vs unpickling the array inside the function in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm plotting about 10,000 items in an array. They are of around 1,000 unique values.\nThe plotting has been running half an hour now. I made sure rest of the code works.\nIs it that slow? This is my first time plotting histograms with pyplot.","AnswerCount":9,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":21439,"Q_Id":35738199,"Users Score":0,"Answer":"If you are working with pandas, make sure the data you passed in plt.hist() is a 1-d series rather than a dataframe. This helped me out.","Q_Score":17,"Tags":"python,matplotlib,histogram","A_Id":60751250,"CreationDate":"2016-03-02T03:54:00.000","Title":"Matplotlib.pyplot.hist() very slow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm plotting about 10,000 items in an array. They are of around 1,000 unique values.\nThe plotting has been running half an hour now. I made sure rest of the code works.\nIs it that slow? This is my first time plotting histograms with pyplot.","AnswerCount":9,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":21439,"Q_Id":35738199,"Users Score":0,"Answer":"For me it took calling figure.canvas.draw() after the call to hist to update immediately, i.e. hist was actually fast (discovered that after timing it), but there was a delay of a few seconds before figure was updated. I was calling hist inside a matplotlib callback in a jupyter lab cell (qt5 backend).","Q_Score":17,"Tags":"python,matplotlib,histogram","A_Id":58707409,"CreationDate":"2016-03-02T03:54:00.000","Title":"Matplotlib.pyplot.hist() very slow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm plotting about 10,000 items in an array. They are of around 1,000 unique values.\nThe plotting has been running half an hour now. I made sure rest of the code works.\nIs it that slow? This is my first time plotting histograms with pyplot.","AnswerCount":9,"Available Count":3,"Score":0.0444152037,"is_accepted":false,"ViewCount":21439,"Q_Id":35738199,"Users Score":2,"Answer":"For me, the problem is that the data type of pd.Series, say S, is 'object' rather than 'float64'. After I use S = np.float64(S), then plt.hist(S) is very quick.","Q_Score":17,"Tags":"python,matplotlib,histogram","A_Id":56879388,"CreationDate":"2016-03-02T03:54:00.000","Title":"Matplotlib.pyplot.hist() very slow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Every time I go to plot a 2D array in matplotlib using, for example, pcolormesh, I have the same question: Is the resultant image showing the array rightside-up or upside-down?  That is, is index (0, 0) at the top left of the plot or the bottom left?\nIt's tedious to write a test every six months to remind myself.  This should be clearly documented in an obvious place, like SO.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":335,"Q_Id":35785178,"Users Score":0,"Answer":"The array is plotted upside-down, meaning the index (0, 0) is at the bottom left.","Q_Score":0,"Tags":"python,image,matplotlib,plot","A_Id":35886350,"CreationDate":"2016-03-03T23:30:00.000","Title":"Does pcolormesh plot the 2D array rightside-up or upside-down?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to deeplearning and face recognition. After searching, I found this python package about deeplearning applied to face recognition called OpenFace. From its documentation, I think it is build on top of Torch for the neural nets computation.\nI want to install the package in a virtual environment, so basically these are steps I made:\n\nbrew install the necessary system requirements: bash,coreutils,curl,findutils,opencv, python and boost-python\nMake a virtual environment and install dlib, numpy, scipy, pandas, scikit-learn, scikit-image\nCloned the openface github repository \nInstall Torch \ncurl -s https:\/\/raw.githubusercontent.com\/torch\/ezinstall\/master\/install-deps | bash\ngit clone https:\/\/github.com\/torch\/distro.git torch --recursive\ncd torch\n.\/install.sh\nsource install\/bin\/torch-activate\nluarocks install csvigo\nluarocks install dpnn\nluarocks install nn\ncd to cloned openface repo and run \npython setup.py install\n\nHowever when I run python:\n>>>import openface\nI get:\nSegmentation Fault: 11\nHow do I fix this? Also, are there any other tutorials for using openface?\nHow to properly install OpenFace?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":4278,"Q_Id":35800893,"Users Score":3,"Answer":"As I posted in the comments, this segfault was caused by compiling dlib with one Python version and running it with another. This was resolved by manually installing dlib rather than using their pip package.","Q_Score":2,"Tags":"python,deep-learning,face-recognition,torch","A_Id":35881615,"CreationDate":"2016-03-04T16:17:00.000","Title":"Trouble Installing OpenFace in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I was trying to train a randomforest classifier in python. However, in my original pandas.dataframe, there are float64, object, datetime64, int64 and bool dtypes(nearly all kinds of dtypes allowed in pandas). \n\nIs it necessary to convert a bool to float or int?\nFor a two-value object column, should I convert it to bool, int, or float? Which one would perform better? Or does it not matter?\n\nThanks!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1673,"Q_Id":35801638,"Users Score":4,"Answer":"Nearly all scikit-learn estimators will convert input data to float before running the algorithm, regardless of the original types in the array. This holds for the random forest implementation.","Q_Score":4,"Tags":"python,pandas,scikit-learn","A_Id":35953956,"CreationDate":"2016-03-04T16:52:00.000","Title":"Which dtype performs better when training a randomforest in python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm working on a project involving Neural Networks (using theano) with a big data set 50,000 images of 3072 pixels. The computational process gets expensive when training the Neural Network as you may expect. \nI was using PyCharm to debug and write the code but since I had some trouble using matplotlib and other libraries I decided to go for iPython Notebook. So far I'm just using it to do dummy plots etc but my main concern is : Is it a good idea to use iPython Notebook to run this kind of expensive computationally projects? Is there any drawbacks when using the notebook instead of just running a python script from the terminal?\nI researched about good IDE's for Data Analysis and Scientific computation for python and I found that iPtyhon Notebook is the best but any other recommendations are very appreciated.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":265,"Q_Id":35823618,"Users Score":1,"Answer":"It doesn't matter. It just runs a Python kernel in the background which is no different from one you would run from the command line.\nThe only thing you should avoid, obviously, is displaying huge amounts of data in your notebook (like plotting your whole image set at once).","Q_Score":3,"Tags":"python,matplotlib,neural-network,ipython,jupyter-notebook","A_Id":35827519,"CreationDate":"2016-03-06T05:47:00.000","Title":"iPython Notebook for big \/ complex analysis. Good idea or not?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Why do we use numpy arrays in place of lists in python? What is the main difference between them?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":17191,"Q_Id":35825802,"Users Score":10,"Answer":"Numpy arrays is a typed array, the array in memory stores a homogenous, densely packed numbers.\nPython list is a heterogeneous list, the list in memory stores references to objects rather than the number themselves. \nThis means that Python list requires dereferencing a pointer every time the code needs to access the number. While numpy array can be processed directly by numpy vector operations, which makes these vector operations much faster than anything you can code with list.\nThe drawback of numpy array is that if you need to access single items in the array, numpy will need to box\/unbox the number into a python numeric object, which can make it slow in certain situations; and that it can't hold heterogeneous data.","Q_Score":13,"Tags":"python,arrays,list,numpy","A_Id":35825863,"CreationDate":"2016-03-06T10:34:00.000","Title":"What is the difference between a NumPy array and a python list?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been developing a tool that automatically preprocesses data in pandas.DataFrame format. During this preprocessing step, I want to treat continuous and categorical data differently. In particular, I want to be able to apply, e.g., a OneHotEncoder to only the categorical data.\nNow, let's assume that we're provided a pandas.DataFrame and have no other information about the data in the DataFrame. What is a good heuristic to use to determine whether a column in the pandas.DataFrame is categorical?\nMy initial thoughts are:\n1) If there are strings in the column (e.g., the column data type is object), then the column very likely contains categorical data\n2) If some percentage of the values in the column is unique (e.g., >=20%), then the column very likely contains continuous data\nI've found 1) to work fine, but 2) hasn't panned out very well. I need better heuristics. How would you solve this problem?\nEdit: Someone requested that I explain why 2) didn't work well. There were some tests cases where we still had continuous values in a column but there weren't many unique values in the column. The heuristic in 2) obviously failed in that case. There were also issues where we had a categorical column that had many, many unique values, e.g., passenger names in the Titanic data set. Same column type misclassification problem there.","AnswerCount":7,"Available Count":4,"Score":0.0285636566,"is_accepted":false,"ViewCount":13722,"Q_Id":35826912,"Users Score":1,"Answer":"IMO the opposite strategy, identifying categoricals is better because it depends on what the data is about. Technically address data can be thought of as unordered categorical data, but usually I wouldn't use it that way.\nFor survey data, an idea would be to look for Likert scales, e.g. 5-8 values, either strings (which might probably need hardcoded (and translated) levels to look for \"good\", \"bad\", \".agree.\", \"very .*\",...) or int values in the 0-8 range + NA.\nCountries and such things might also be identifiable...\nAge groups (\".-.\") might also work.","Q_Score":28,"Tags":"python,pandas,scikit-learn","A_Id":35827413,"CreationDate":"2016-03-06T12:38:00.000","Title":"What is a good heuristic to detect if a column in a pandas.DataFrame is categorical?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been developing a tool that automatically preprocesses data in pandas.DataFrame format. During this preprocessing step, I want to treat continuous and categorical data differently. In particular, I want to be able to apply, e.g., a OneHotEncoder to only the categorical data.\nNow, let's assume that we're provided a pandas.DataFrame and have no other information about the data in the DataFrame. What is a good heuristic to use to determine whether a column in the pandas.DataFrame is categorical?\nMy initial thoughts are:\n1) If there are strings in the column (e.g., the column data type is object), then the column very likely contains categorical data\n2) If some percentage of the values in the column is unique (e.g., >=20%), then the column very likely contains continuous data\nI've found 1) to work fine, but 2) hasn't panned out very well. I need better heuristics. How would you solve this problem?\nEdit: Someone requested that I explain why 2) didn't work well. There were some tests cases where we still had continuous values in a column but there weren't many unique values in the column. The heuristic in 2) obviously failed in that case. There were also issues where we had a categorical column that had many, many unique values, e.g., passenger names in the Titanic data set. Same column type misclassification problem there.","AnswerCount":7,"Available Count":4,"Score":0.1418931938,"is_accepted":false,"ViewCount":13722,"Q_Id":35826912,"Users Score":5,"Answer":"There's are many places where you could \"steal\" the definitions of formats that can be cast as \"number\". ##,#e-# would be one of such format, just to illustrate. Maybe you'll be able to find a library to do so.\nI try to cast everything to numbers first and what is left, well, there's no other way left but to keep them as categorical.","Q_Score":28,"Tags":"python,pandas,scikit-learn","A_Id":35827781,"CreationDate":"2016-03-06T12:38:00.000","Title":"What is a good heuristic to detect if a column in a pandas.DataFrame is categorical?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been developing a tool that automatically preprocesses data in pandas.DataFrame format. During this preprocessing step, I want to treat continuous and categorical data differently. In particular, I want to be able to apply, e.g., a OneHotEncoder to only the categorical data.\nNow, let's assume that we're provided a pandas.DataFrame and have no other information about the data in the DataFrame. What is a good heuristic to use to determine whether a column in the pandas.DataFrame is categorical?\nMy initial thoughts are:\n1) If there are strings in the column (e.g., the column data type is object), then the column very likely contains categorical data\n2) If some percentage of the values in the column is unique (e.g., >=20%), then the column very likely contains continuous data\nI've found 1) to work fine, but 2) hasn't panned out very well. I need better heuristics. How would you solve this problem?\nEdit: Someone requested that I explain why 2) didn't work well. There were some tests cases where we still had continuous values in a column but there weren't many unique values in the column. The heuristic in 2) obviously failed in that case. There were also issues where we had a categorical column that had many, many unique values, e.g., passenger names in the Titanic data set. Same column type misclassification problem there.","AnswerCount":7,"Available Count":4,"Score":0.0285636566,"is_accepted":false,"ViewCount":13722,"Q_Id":35826912,"Users Score":1,"Answer":"I think the real question here is whether you'd like to bother the user once in a while or silently fail once in a while.\nIf you don't mind bothering the user,  maybe detecting ambiguity and raising an error is the way to go.\nIf you don't mind failing silently, then your heuristics are ok. I don't think you'll find anything that's significantly better. I guess you could make this into a learning problem if you really want to. Download a bunch of datasets, assume they are collectively a decent representation of all data sets in the world, and train based on features over each data set \/ column to predict categorical vs. continuous.\nBut of course in the end nothing can be perfect. E.g. is the column [1, 8, 22, 8, 9, 8] referring to hours of the day or to dog breeds?","Q_Score":28,"Tags":"python,pandas,scikit-learn","A_Id":35828098,"CreationDate":"2016-03-06T12:38:00.000","Title":"What is a good heuristic to detect if a column in a pandas.DataFrame is categorical?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been developing a tool that automatically preprocesses data in pandas.DataFrame format. During this preprocessing step, I want to treat continuous and categorical data differently. In particular, I want to be able to apply, e.g., a OneHotEncoder to only the categorical data.\nNow, let's assume that we're provided a pandas.DataFrame and have no other information about the data in the DataFrame. What is a good heuristic to use to determine whether a column in the pandas.DataFrame is categorical?\nMy initial thoughts are:\n1) If there are strings in the column (e.g., the column data type is object), then the column very likely contains categorical data\n2) If some percentage of the values in the column is unique (e.g., >=20%), then the column very likely contains continuous data\nI've found 1) to work fine, but 2) hasn't panned out very well. I need better heuristics. How would you solve this problem?\nEdit: Someone requested that I explain why 2) didn't work well. There were some tests cases where we still had continuous values in a column but there weren't many unique values in the column. The heuristic in 2) obviously failed in that case. There were also issues where we had a categorical column that had many, many unique values, e.g., passenger names in the Titanic data set. Same column type misclassification problem there.","AnswerCount":7,"Available Count":4,"Score":0.0285636566,"is_accepted":false,"ViewCount":13722,"Q_Id":35826912,"Users Score":1,"Answer":"I've been thinking about a similar problem and the more that I consider it, it seems that this itself is a classification problem that could benefit from training a model. \nI bet if you examined a bunch of datasets and extracted these features for each column \/ pandas.Series:\n\n% floats: percentage of values that are float\n% int: percentage of values that are whole numbers\n% string: percentage of values that are strings\n% unique string: number of unique string values \/ total number\n% unique integers: number of unique integer values \/ total number\nmean numerical value (non numerical values considered 0 for this)\nstd deviation of numerical values\n\nand trained a model, it could get pretty good at inferring column types, where the possible output values are: categorical, ordinal, quantitative.\nSide note: as far as a Series with a limited number of numerical values goes, it seems like the interesting problem would be determining categorical vs ordinal; it doesn't hurt to think a variable is ordinal if it turns out to be quantitative right? The preprocessing steps would encode the ordinal values numerically anyways without one-hot encoding.\nA related problem that is interesting: given a group of columns, can you tell if they are already one-hot encoded? E.g in the forest-cover-type-prediction kaggle contest, you would automatically know that soil type is a single categorical variable.","Q_Score":28,"Tags":"python,pandas,scikit-learn","A_Id":38108924,"CreationDate":"2016-03-06T12:38:00.000","Title":"What is a good heuristic to detect if a column in a pandas.DataFrame is categorical?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In the documentation of  SciKit-Learn Random Forest classifier , it is stated that \n\nThe sub-sample size is always the same as the original input sample size but the samples are drawn with replacement if bootstrap=True (default).\n\nWhat I dont understand is that if the sample size is always the same as the input sample size than how can we talk about a random selection. There is no selection here because we use all the (and naturally the same) samples at each training. \nAm I missing something here?","AnswerCount":4,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":3033,"Q_Id":35827446,"Users Score":2,"Answer":"Certainly not all samples are selected for each tree. Be default each sample has a 1-((N-1)\/N)^N~0.63 chance of being sampled for one particular tree and 0.63^2 for being sampled twice, and 0.63^3 for being sampled 3 times... where N is the sample size of the training set.\nEach bootstrap sample selection is in average enough different from other bootstraps, such that decision trees are adequately different, such that the average prediction of trees is robust toward the variance of each tree model. If sample size could be increased to 5 times more than training set size, every observation would probably be present 3-7 times in each tree and the overall ensemble prediction performance would suffer.","Q_Score":6,"Tags":"python,scikit-learn,random-forest,subsampling","A_Id":35847976,"CreationDate":"2016-03-06T13:30:00.000","Title":"How can SciKit-Learn Random Forest sub sample size may be equal to original training data size?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using the LIBSVM for the first time.\nI was able to train a data(for images) and my model is ready \"trainingdata.svm.model\"\nNow, when I run my classification against an unknown test data it is giving me two files:\n1. trainingdata.svm.prediction (This file contains 1's and 0's) against my each of test data.\n2. It is giving me Accuracy = 8 %\nTHE QUESTION:\n1. How do I interpret the 1s and 0s in my \"trainingdata.svm.prediction\". Note: I am classifying genders where 1 could be male and 0 could be female.\n\nHow is Accuracy calculated? How can a program calculate accuracy since the test data is an unknown entity and we do not know the labels yet. \n\nThanks","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":190,"Q_Id":35835787,"Users Score":0,"Answer":"The file \"trainingdata.svm.prediction\" is predicting the labels 1 and 0 for your set (1 means the sample was predicted to be male, 0 is female).\nIt assumes all the labels belong to class index 0, I believe.","Q_Score":0,"Tags":"python,machine-learning,svm,libsvm,prediction","A_Id":35836274,"CreationDate":"2016-03-07T03:39:00.000","Title":"How is \"accuracy\" calculated using Libsvm - SVM_Predict.exe","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"On wikipedia one can read the following criticism about HDF5:\n\nCriticism of HDF5 follows from its monolithic design and lengthy\n  specification. Though a 150-page open standard, there is only a single\n  C implementation of HDF5, meaning all bindings share its bugs and\n  performance issues. Compounded with the lack of journaling, documented\n  bugs in the current stable release are capable of corrupting entire\n  HDF5 databases. Although 1.10-alpha adds journaling, it is\n  backwards-incompatible with previous versions. HDF5 also does not\n  support UTF-8 well, necessitating ASCII in most places. Furthermore\n  even in the latest draft, array data can never be deleted.\n\nI am wondering if this is just applying to the C implementation of HDF5 or if this is a general flaw of HDF5?\nI am doing scientific experiments which sometimes generate Gigabytes of data and in all cases at least several hundred Megabytes of data. Obviously data loss and especially corruption would be a huge disadvantage for me.\nMy scripts always have a Python API, hence I am using h5py (version 2.5.0).\nSo, is this criticism relevant to me and should I be concerned about corrupted data?","AnswerCount":1,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":1980,"Q_Id":35837243,"Users Score":6,"Answer":"Declaration up front: I help maintain h5py, so I probably have a bias etc.\nThe wikipedia page has changed since the question was posted, here's what I see:\n\nCriticism\nCriticism of HDF5 follows from its monolithic design and lengthy specification.\n\nThough a 150-page open standard, the only other C implementation of HDF5 is just a HDF5 reader.\nHDF5 does not enforce the use of UTF-8, so client applications may be expecting ASCII in most places.\nDataset data cannot be freed in a file without generating a file copy using an external tool (h5repack).\n\n\nI'd say that pretty much sums up the problems with HDF5, it's complex (but people need this complexity, see the virtual dataset support), it's got a long history with backwards compatibly as it's focus, and it's not really designed to allow for massive changes in files. It's also not the best on Windows (due to how it deals with filenames).\nI picked HDF5 for my research because of the available options, it had decent metadata support (HDF5 at least allows UTF-8, formats like FITS don't even have that), support for multidimensional arrays (which formats like Protocol Buffers don't really support), and it supports more than just 64 bit floats (which is very rare).\nI can't comment about known bugs, but I have seen corruption (this happened when I was writing to a file and linux OOM'd my script). However, this shouldn't be a concern as long as you have proper data hygiene practices (as mentioned in the hackernews link), which in your case would be to not continuously write to the same file, but for each run create a new file. You should also not modify the file, instead any data reduction should produce new files, and you should always backup the originals.\nFinally, it is worth pointing out there are alternatives to HDF5, depending on what exactly your requirements are: SQL databases may fit you needs better (and sqlite comes with Python by default, so it's easy to experiment with), as could a simple csv file. I would recommend against custom\/non-portable formats (e.g. pickle and similar), as they're neither more robust than HDF5, and more complex than a csv file.","Q_Score":8,"Tags":"python,python-2.7,hdf5,h5py,hdf","A_Id":46668033,"CreationDate":"2016-03-07T06:09:00.000","Title":"HDF5 possible data corruption or loss?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I know this has been asked many times, but when I try this, I always get a list of lists.\nThe data in my input file (col.csv) looks like: \n1,2,\"black\", \"orange\"\nThere are NO hard returns in the data (\\n)(it's a csv file, right?). When I use the csv module in python to import to a list using reader, I end up with list of lists, with the first entry, list [0][0] containing all the data.\nHow do I import the data into a list such that each comma separated value is a single list entry? The typical method I see uses for row in..., but I don't have rows \u2013 there are no returns in the data. Sorry for such a rank amateur question.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":55,"Q_Id":35849754,"Users Score":0,"Answer":"OK, I should close this. My comment resolved the question above. I  reformatted my input csv files to be values, each on a separate \"line\", I was able to read the lines in one by one and append them to a list. This seems really sloppy and wasteful. I was hoping for a method to read a csv file and in one line assign it to a single list \u2013 not a list of lists, a single list.","Q_Score":0,"Tags":"python,list,csv","A_Id":35880616,"CreationDate":"2016-03-07T17:09:00.000","Title":"I am trying to read several csv files in python 2.7 and assign to a list variable","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I try to import vpython into anaconda. It seems to work so far, but if I call \nfrom visual import * it gives me an error.\nHowever, it does work when I type from vpython import * ,which is really weird since in all programs I only see the from visual import * command.\nNow to the real problem: I can't draw graphs. I have to call from visual.graph import * but this does not work (from vpython.graph import * doesn't work either).\nI am receiving the error below:\n\nImportError Traceback (most recent call last) \n   in () ----> 1  from visual\n  import *  ImportError: No module named visual","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":3831,"Q_Id":35866967,"Users Score":0,"Answer":"The graph functions now live in the main vpython library when using Jupyter. So, \nfrom vpython import *\nshould be sufficient. (P.S. I'd recommend not importing * but rather importing the functions you plan to use or just import vpython.) \nNote however that some functions change name in the Jupyter-compatible version of VPython, so display becomes canvas and gdisplay becomes graph, and you have to explicitly use vector(x,y,z) rather than (x,y,z) and have to use obj.pos.x rather than obj.x","Q_Score":4,"Tags":"python,anaconda,vpython","A_Id":39803190,"CreationDate":"2016-03-08T12:07:00.000","Title":"Issues with importing vpython for anaconda","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am a Python beginner and getting confused by these different forms of storing data? When should one use which. Also which of these is suitable to store a matrix (and a vector)?","AnswerCount":3,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":80,"Q_Id":35881832,"Users Score":0,"Answer":"Here's a general overview (partial credit to online documentation and Mark Lutz and Wes McKinney O'Reilly books):\n\nlist: General selection object available in Python's standard\nlibrary. Lists are positionally ordered collections of arbitrarily\ntyped objects, and have no fixed size. They are also mutable (str\nfor example, are not).\nnumpy.ndarray: Stores a collection of items of the same type. Every\nitem takes up the same size block of memory (not necessarily the case\nin a list). How each item in the array is to be interpreted is\nspecified by a separate data-type object (dtype, not to be confused\nwith type). Also, differently from lists, ndarrays can't be have\nitems appended in place (i.e. the .append method returns a new\narray with the appended items, differently from lists).\nA single ndarray is a vector, an ndarray of same-sized ndarrays is a 2-d array (a.k.a matrix) and so on. You can make arbitrary n-dimensional objects by nesting.\npandas.Series: A one-dimensional array-like object containing an\narray of data (of any dtype) and an associated array of data\nlabels, called its index. It's basically a glorified numpy.ndarray,\nwith labels (stored inside a Series as an Index object) for each\nitems and some handy extra functionality. Also, a Series can\ncontain multiple objects of different dtypes (more like a list).\npandas.DataFrame: A collection of multiple Series, forming a\ntable-like object, with a lot of very handy functionality for data\nanalysis.","Q_Score":1,"Tags":"python,arrays,numpy,pandas,dataframe","A_Id":35882190,"CreationDate":"2016-03-09T02:30:00.000","Title":"Can someone consolidate the definition and the differences between a list, an array, a numpy array, a pandas dataframe , series?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am a Python beginner and getting confused by these different forms of storing data? When should one use which. Also which of these is suitable to store a matrix (and a vector)?","AnswerCount":3,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":80,"Q_Id":35881832,"Users Score":0,"Answer":"list - the original Python way of storing multiple values\narray - a little used Python module (let's ignore it)\nnumpy array - the closest thing in Python to the arrays, matrices and vectors used in mathematics and languages like MATLAB\ndataframe, datseries - pandas structures, generally built on numpy, better suited for the kind of data found in tables and databases.\n\nTo be more specific, you need to give us an idea of what kinds of problems you need to solve.  What kind of data are you using, and what do you need to do with it?\nlists can change in size, and can contain a wide mix of elements.\nnumpy.array is fixed in size, and contains a uniform type of elements. It is multidimensional, and has implemented many mathematical functions.","Q_Score":1,"Tags":"python,arrays,numpy,pandas,dataframe","A_Id":35882181,"CreationDate":"2016-03-09T02:30:00.000","Title":"Can someone consolidate the definition and the differences between a list, an array, a numpy array, a pandas dataframe , series?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am a Python beginner and getting confused by these different forms of storing data? When should one use which. Also which of these is suitable to store a matrix (and a vector)?","AnswerCount":3,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":80,"Q_Id":35881832,"Users Score":0,"Answer":"Lists: lists are very flexible and can hold completely heterogeneous, arbitrary data, and they can be appended to very efficiently. \nArray: The array.array type, on the other hand, is just a thin wrapper on C arrays. It can hold only homogeneous data, all of the same type, and so it uses only sizeof(one object) * length bytes of memory.\nNumpy arrays: However, if you want to do math on a homogeneous array of numeric data, then you're much better off using NumPy, which can automatically vectorize operations on complex multi-dimensional arrays.\nPandas: Pandas provides high level data manipulation tools built on top of NumPy. NumPy by itself is a fairly low-level tool.\nPandas provides a bunch of C or Cython optimized routines that can be faster than numpy \"equivalents\" (e.g. reading text). For something like a dot product, pandas DataFrames are generally going to be slower than a numpy array\nFYI: Taken from different web sources","Q_Score":1,"Tags":"python,arrays,numpy,pandas,dataframe","A_Id":35882258,"CreationDate":"2016-03-09T02:30:00.000","Title":"Can someone consolidate the definition and the differences between a list, an array, a numpy array, a pandas dataframe , series?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a question about the 4D tensor on keras about Convolution2D Layers.\nThe Keras doc says:\n4D tensor with shape: (samples, channels, rows, cols) if dim_ordering='th' or 4D tensor with shape: (samples, rows, cols, channels) if dim_ordering='tf'.\nI use 'tf', how about my input? When I use (samples, channels, rows, cols), it is ok, but when I use (samples, rows, cols, channels) as input, it has some problems.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1225,"Q_Id":35881949,"Users Score":0,"Answer":"Keras assumes that if you are using tensorflow, you are going with (samples, channels, rows, cols)","Q_Score":1,"Tags":"python,tensorflow,keras","A_Id":43664585,"CreationDate":"2016-03-09T02:45:00.000","Title":"the input shape of array about Keras on Tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"in text mining\/classification when a vectorizer is used to transform a text into numerical features, in the training TfidfVectorizer(...).fit_transform(text) or TfidfVectorizer(...).fit(text) is used. In testing it supposes to utilize former training info and just transform the data following the training fit. \nIn general case the test run(s) is completely separate from train run. But it needs some info regarding the fit obtained during the training stage otherwise the transformation fails with error sklearn.utils.validation.NotFittedError: idf vector is not fitted . It's not just a dictionary, it's something else. \nWhat should be saved after the training is done, to make the test stage passing smoothly?\nIn other words train and test are separated in time and space, how to make test working, utilizing training results?\nDeeper question would be what 'fit' means in scikit-learn context, but it's probably out of scope","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":172,"Q_Id":35882062,"Users Score":0,"Answer":"In the test phase you should use the same model names as you used in the trainings phase. In this way you will be able to use the model parameters which are derived in the training phase. Here is an example below;\n\nFirst give a name to your vectorizer and to your predictive algoritym (It is NB in this case)\n\n\nvectorizer = TfidVectorizer()\nclassifier = MultinomialNB()\n\n\nThen, use these names to vectorize and predict your data\n\n\ntrainingdata_counts = vectorizer.fit_transform(trainingdata.values)\nclassifier.fit(trainingdata_counts, trainingdatalabels)\ntestdata_counts = vectorizer.transform(testdata.values)\npredictions=classifier.predict(testdata_counts)\n\nBy this way, your code will be able to process training and the test phases continuously.","Q_Score":0,"Tags":"python,scikit-learn","A_Id":35889624,"CreationDate":"2016-03-09T02:58:00.000","Title":"Vectorizer where or how fit information is stored?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have about 3,000 words and I would like to group them into about 20-50 different categories. My words are typical phrases you might find in company names. \"Face\", \"Book\", \"Sales\", \"Force\", for example.\nThe libraries I have been looking at so far are pandas and scikit-learn. I'm wondering if there is a machine-learning or deep-learning algorithm that would be well suited for this?\nThe topics I have been looking are Classification: identifying which category an object belongs to, and Dimensionality Reduction: reducing the random number of variables to consider.\nWhen I search for putting words into categories on Google, it brings up kids puzzles such as \"things you do with a pencil\" - draw. Or \"parts of a house\" - yard, room.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":717,"Q_Id":35887212,"Users Score":1,"Answer":"for deep learning to work on this you would have to develop a large dataset, most likely manually. the largest natural language processing dataset was, in fact, created manually.\nBUT even if you were able to find a dataset which a model could learn off. THEN a model such as gradient boosted trees would be one, amongst others, that would be well suited to multi-class classification like this. A classic library for this is xgboost.","Q_Score":0,"Tags":"python,machine-learning,deep-learning","A_Id":35887508,"CreationDate":"2016-03-09T09:09:00.000","Title":"Sorting words into categories in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I get a gtk-WARNING when trying:\ncv2.imshow(\"WindowName\", image)\nI'm using this to watch a live stream one frame at a time. Are there any alternative libraries I could use? I tried several other options like PIL and Tkinter as well as wand, but could get none of them to work for various different reason.","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":1687,"Q_Id":35901246,"Users Score":2,"Answer":"You need to use a windowing system to display images using imshow.\n(That can be enabled in settings running sudo raspi-config)\nIf you absolutely, positively need to display images without using a windowing system, consider providing an html\/web interface. Two options that come to mind when serving a web interface are:\n\nCreating an HTTP video stream (serve the output image as if it's an IP camera, kind of)\nStream the output matrix as a jpg blob via websockets","Q_Score":2,"Tags":"python,linux,opencv,terminal,raspberry-pi","A_Id":35901608,"CreationDate":"2016-03-09T19:48:00.000","Title":"Show image using OpenCV in Python on a Raspberry Pi terminal","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":1,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to run a lasso or ridge regression, but where the L1 or L2 constraint on the coefficients is on some of the coefficients, not all.\nAnother way to say it: I would like to use my own custom cost function inside the lasso or ridge algorithm.\nI would like to avoid having to rewrite the whole algorithm. Is there a module in python that allows this? I looked into scipy and sckit-learn so far, but could not find that.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":787,"Q_Id":35919948,"Users Score":0,"Answer":"I suspect there is no a readily available module that suits your needs.\nIf I were you I would:\n\npartition the features into 2 groups: one for simple linear regression and another one for regularized regression. Train two models on two different (maybe overlapping?) sets of features. When you cross-validate your models, to prevent information leakage between folds, I'd suggest fixing folds and training both models on the same fixed set of folds.\nOn top, stack and train any other regression model.","Q_Score":0,"Tags":"python,python-2.7,machine-learning,scipy,scikit-learn","A_Id":35923537,"CreationDate":"2016-03-10T14:57:00.000","Title":"Inject custom cost function for linear regression","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using the whitegrid style and it's fine except for the vertical lines in the background.\nI just want to retain the horizontal lines.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":202,"Q_Id":35938891,"Users Score":0,"Answer":"Passing arguments into ax.yaxis.grid() and ax.xaxis.grid() will include or omit grids in the graphs","Q_Score":0,"Tags":"python,matplotlib,seaborn","A_Id":35939091,"CreationDate":"2016-03-11T11:18:00.000","Title":"matplotlib & seaborn: how to get rid of lines?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Can someone explain why does the random_state parameter affects the model so much?\nI have a RandomForestClassifier model and want to set the random_state (for reproducibility pourpouses), but depending on the value I use I get very different values on my overall evaluation metric (F1 score)\nFor example, I tried to fit the same model with 100 different random_state  values and after the training ad testing the smallest F1 was 0.64516129 and the largest 0.808823529). That is a huge difference.\nThis behaviour also seems to make very hard to compare two models.\nThoughts?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":743,"Q_Id":35944725,"Users Score":3,"Answer":"If the random_state affects your results it means that your model has a high variance. In case of Random Forest this simply means that you use too small forest and should increase number of trees (which due to bagging - reduce variance). In scikit-learn this is controlled by n_estimators parameters in the constructor.\nWhy this happens? Each ML method tries to minimize the error, which from matematial perspective can be usually decomposed to bias and variance [+noise] (see bias variance dillema\/tradeoff). Bias is simply how far from true values your model has to end up in the expectation - this part of an error usually comes from some prior assumptions, such as using linear model for nonlinear problem etc. Variance is how much your results differ when you train on different subsets of data (or use different hyperparameters, and in case of randomized methods random seed is a parameter). Hyperparameters are initialized by us and Parameters are learnt by the model itself in the training process.  Finally - noise is not reducible error coming from the problem itself (or data representation). Thus, in your case - you simply encountered model with high variance, decision trees are well known for their extremely high variance (and small bias). Thus to reduce variance, Breiman proposed the specific bagging method, known today as Random Forest. The larger the forest - stronger the effect of variance reduction. In particular - forest with 1 tree has huge variance, forest of 1000 trees is nearly deterministic for moderate size problems.\nTo sum up, what you can do?\n\nIncrease number of trees - this has to work, and is well understood and justified method\ntreat random_seed as a hyperparameter during your evaluation, because this is exactly this - a meta knowledge you need to fix before hand if you do not wish to increase size of the forest.","Q_Score":2,"Tags":"python,random,machine-learning,scikit-learn,evaluation","A_Id":35948671,"CreationDate":"2016-03-11T16:01:00.000","Title":"random_state parameter in classification models","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"when I try to import pandas using the data nitro shell, I get the error that there is no module named pandas. \nI have pandas through the canopy distribution, but somehow the data nitro shell isn't \"finding\" it. \nI suspect this has to do with the directory in which pandas is stored, but I don't know how to \"extract\" pandas from that directory and put it into the appropriate directory for data nitro. \nAny ideas would be super appreciated. Thank you!!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":193,"Q_Id":35963580,"Users Score":0,"Answer":"DataNitro is probably using a different copy of Python on your machine. Go to Settings in the DataNitro ribbon, uncheck \"use default Python\", and select the Canopy python directory manually. Then, restart Excel and see if importing works.","Q_Score":1,"Tags":"python,pandas,datanitro","A_Id":35964006,"CreationDate":"2016-03-12T21:34:00.000","Title":"Can't find pandas in data nitro","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have installed the packages TensorFlow and scikit_learn and all its dependencies. When I try importing them using python 2.7.6 or 2.7.10 (I have tried both) in the terminal, it works fine. However, when I do it using pycharm it gives an error.\nIn the case of scikit_learn with the launcher 2.7.6 says:\n\nImportError: dynamic module does not define init function (init_check_build)\n\nIn the case of scikit_learn with the launcher 2.7.10 says:\n\nValueError: numpy.dtype has the wrong size, try recompiling\n\nIn the case of TensorFlow with the launcher 2.7.6 says:\n\nImportError: dlopen(\/Library\/Python\/2.7\/site-packages\/tensorflow\/python\/_pywrap_tensorflow.so, 2): no suitable image found.  Did find:\n          \/Library\/Python\/2.7\/site-packages\/tensorflow\/python\/_pywrap_tensorflow.so: mach-o, but wrong architecture\n\nIn the case of TensorFlow with the launcher 2.7.10 says:\n\nImportError: No module named copyreg\nError importing tensorflow.  Unless you are using bazel,\n      you should not try to import tensorflow from its source directory;\n      please exit the tensorflow source tree, and relaunch your python interpreter\n      from there.\n\nI have tried to search in the net but the solutions did not work for me. I have tried to uninstall them and install them again with pip, conda and directly the source code and it gives always the same errors. I have even tried reinstalling pycharm with no better luck.\nOther libraries, such as scilab or numpy, work fine in pycharm.\nAny ideas? It is just driving me mental. By the way, I am using a Mac OS 10.10.5.","AnswerCount":5,"Available Count":4,"Score":0.0,"is_accepted":false,"ViewCount":2043,"Q_Id":35964994,"Users Score":0,"Answer":"I had a similar problem. My code was was not working on PyCharm professional. I had PyCharm CE previously installed and it worked from there. I had configured PyCharm CE a while ago and I had forgotten what setup I used but if issues persist, make sure that the packages are installed under Preferences > Project > Project Interpreter","Q_Score":2,"Tags":"python,macos,scikit-learn,pycharm,tensorflow","A_Id":47130715,"CreationDate":"2016-03-13T00:16:00.000","Title":"pycharm error while importing, even though it works in the terminal","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have installed the packages TensorFlow and scikit_learn and all its dependencies. When I try importing them using python 2.7.6 or 2.7.10 (I have tried both) in the terminal, it works fine. However, when I do it using pycharm it gives an error.\nIn the case of scikit_learn with the launcher 2.7.6 says:\n\nImportError: dynamic module does not define init function (init_check_build)\n\nIn the case of scikit_learn with the launcher 2.7.10 says:\n\nValueError: numpy.dtype has the wrong size, try recompiling\n\nIn the case of TensorFlow with the launcher 2.7.6 says:\n\nImportError: dlopen(\/Library\/Python\/2.7\/site-packages\/tensorflow\/python\/_pywrap_tensorflow.so, 2): no suitable image found.  Did find:\n          \/Library\/Python\/2.7\/site-packages\/tensorflow\/python\/_pywrap_tensorflow.so: mach-o, but wrong architecture\n\nIn the case of TensorFlow with the launcher 2.7.10 says:\n\nImportError: No module named copyreg\nError importing tensorflow.  Unless you are using bazel,\n      you should not try to import tensorflow from its source directory;\n      please exit the tensorflow source tree, and relaunch your python interpreter\n      from there.\n\nI have tried to search in the net but the solutions did not work for me. I have tried to uninstall them and install them again with pip, conda and directly the source code and it gives always the same errors. I have even tried reinstalling pycharm with no better luck.\nOther libraries, such as scilab or numpy, work fine in pycharm.\nAny ideas? It is just driving me mental. By the way, I am using a Mac OS 10.10.5.","AnswerCount":5,"Available Count":4,"Score":1.2,"is_accepted":true,"ViewCount":2043,"Q_Id":35964994,"Users Score":0,"Answer":"At the end, I ended up creating a virtual environment, reinstalling everything in there, and calling it through pycharm. I am not entirely sure what was the problem between conda and pycharm, I probably messed up somewhere. \nI am now using a different virtual environment depending on the project and I am happier than ever :).","Q_Score":2,"Tags":"python,macos,scikit-learn,pycharm,tensorflow","A_Id":40260973,"CreationDate":"2016-03-13T00:16:00.000","Title":"pycharm error while importing, even though it works in the terminal","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have installed the packages TensorFlow and scikit_learn and all its dependencies. When I try importing them using python 2.7.6 or 2.7.10 (I have tried both) in the terminal, it works fine. However, when I do it using pycharm it gives an error.\nIn the case of scikit_learn with the launcher 2.7.6 says:\n\nImportError: dynamic module does not define init function (init_check_build)\n\nIn the case of scikit_learn with the launcher 2.7.10 says:\n\nValueError: numpy.dtype has the wrong size, try recompiling\n\nIn the case of TensorFlow with the launcher 2.7.6 says:\n\nImportError: dlopen(\/Library\/Python\/2.7\/site-packages\/tensorflow\/python\/_pywrap_tensorflow.so, 2): no suitable image found.  Did find:\n          \/Library\/Python\/2.7\/site-packages\/tensorflow\/python\/_pywrap_tensorflow.so: mach-o, but wrong architecture\n\nIn the case of TensorFlow with the launcher 2.7.10 says:\n\nImportError: No module named copyreg\nError importing tensorflow.  Unless you are using bazel,\n      you should not try to import tensorflow from its source directory;\n      please exit the tensorflow source tree, and relaunch your python interpreter\n      from there.\n\nI have tried to search in the net but the solutions did not work for me. I have tried to uninstall them and install them again with pip, conda and directly the source code and it gives always the same errors. I have even tried reinstalling pycharm with no better luck.\nOther libraries, such as scilab or numpy, work fine in pycharm.\nAny ideas? It is just driving me mental. By the way, I am using a Mac OS 10.10.5.","AnswerCount":5,"Available Count":4,"Score":0.0,"is_accepted":false,"ViewCount":2043,"Q_Id":35964994,"Users Score":0,"Answer":"Add this 'DYLD_LIBRARY_PATH=\/usr\/local\/cuda\/lib' to Python environment variable. Run-> Edit Configurations -> Environment variables. Hope it works.","Q_Score":2,"Tags":"python,macos,scikit-learn,pycharm,tensorflow","A_Id":40121055,"CreationDate":"2016-03-13T00:16:00.000","Title":"pycharm error while importing, even though it works in the terminal","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have installed the packages TensorFlow and scikit_learn and all its dependencies. When I try importing them using python 2.7.6 or 2.7.10 (I have tried both) in the terminal, it works fine. However, when I do it using pycharm it gives an error.\nIn the case of scikit_learn with the launcher 2.7.6 says:\n\nImportError: dynamic module does not define init function (init_check_build)\n\nIn the case of scikit_learn with the launcher 2.7.10 says:\n\nValueError: numpy.dtype has the wrong size, try recompiling\n\nIn the case of TensorFlow with the launcher 2.7.6 says:\n\nImportError: dlopen(\/Library\/Python\/2.7\/site-packages\/tensorflow\/python\/_pywrap_tensorflow.so, 2): no suitable image found.  Did find:\n          \/Library\/Python\/2.7\/site-packages\/tensorflow\/python\/_pywrap_tensorflow.so: mach-o, but wrong architecture\n\nIn the case of TensorFlow with the launcher 2.7.10 says:\n\nImportError: No module named copyreg\nError importing tensorflow.  Unless you are using bazel,\n      you should not try to import tensorflow from its source directory;\n      please exit the tensorflow source tree, and relaunch your python interpreter\n      from there.\n\nI have tried to search in the net but the solutions did not work for me. I have tried to uninstall them and install them again with pip, conda and directly the source code and it gives always the same errors. I have even tried reinstalling pycharm with no better luck.\nOther libraries, such as scilab or numpy, work fine in pycharm.\nAny ideas? It is just driving me mental. By the way, I am using a Mac OS 10.10.5.","AnswerCount":5,"Available Count":4,"Score":0.0,"is_accepted":false,"ViewCount":2043,"Q_Id":35964994,"Users Score":0,"Answer":"you should start PyCharm from terminal\ncd \/usr\/lib\/pycharm-community\/bin \n.\/pycharm.sh","Q_Score":2,"Tags":"python,macos,scikit-learn,pycharm,tensorflow","A_Id":38749298,"CreationDate":"2016-03-13T00:16:00.000","Title":"pycharm error while importing, even though it works in the terminal","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I understand that partitionBy function partitions my data. If I use rdd.partitionBy(100) it will partition my data by key into 100 parts. i.e. data associated with similar keys will be grouped together\n\nIs my understanding correct?\nIs it advisable to have number of partitions equal to number of\n    available cores? Does that make processing more efficient?\nwhat if my data is not in key,value format. Can i still use this function?\nlets say my data is serial_number_of_student,student_name. In this\n        case can i partition my data by student_name instead of the\n        serial_number?","AnswerCount":2,"Available Count":1,"Score":-0.0996679946,"is_accepted":false,"ViewCount":23512,"Q_Id":35973590,"Users Score":-1,"Answer":"I recently used partitionby. What I did was restructure my data so that all those which I want to put in same partition have the same key, which in turn is a value from the data. my data was a list of dictionary, which I converted into tupples with key from dictionary.Initially the partitionby was not keeping same keys in same partition. But then I realized the keys were strings. I cast them to int. But the problem persisted. The numbers were very large. I then mapped these numbers to small numeric values and it worked. So my take away was that the keys need to be small integers.","Q_Score":11,"Tags":"python,apache-spark,pyspark,partitioning,rdd","A_Id":44932978,"CreationDate":"2016-03-13T17:45:00.000","Title":"pyspark partitioning data using partitionby","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to use Scikit-Learn's Neural Network to classify my dataset using a Backpropagation with Momentum. I need to specify these parameters: Hidden neurons, Hidden layers, Training set, Learning rate and Momentum. \nI found MLPClassifier in Sklearn.neural_network package. The problem is that this package is part of Scikit-learn V0.18 which is a dev version.\nIs there a way I could use Scikit-Learn V0.17 to do this?\nUsing Anaconda, but I can change that if it causes problems.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":903,"Q_Id":35974957,"Users Score":0,"Answer":"If anybody ever needs an answer for this, I actually decided to run everything on a Linux VM. I then followed the instructions to install the dev version and everything(well almost) worked perfectly. Running it on Linux is way easier than Windows because you can just install the package from git and run it without having to download required software to compile it. I still struggled a little bit though.","Q_Score":1,"Tags":"python,scikit-learn,backpropagation,momentum","A_Id":37115845,"CreationDate":"2016-03-13T19:48:00.000","Title":"Backpropagation with Momentum using Scikit-Learn","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"This is probably a rookie mistake that I'm missing somewhere but I can't for the life of me find anything related to my problem on the web.\nI have a vector b1 of size 5 by 1, and i have another vector dsdb1 which is also 5 by 1.\nWhen I write b1 += tau*dsdb1 I get the error \"non-broadcastable output operand with shape (5,1) doesn't match the broadcast shape (5,5)\"\nNow, no one of these is a matrix. I even deleted this line and instead printed both sizes for b1 and dsdb1. For b1 it printed (5,1) and for dsdb1 it printed (5,). tau is just a scalar.\nWhy is it changing dsdb1 to a 5 by 5 matrix when computing?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":23,"Q_Id":35976192,"Users Score":0,"Answer":"I managed to fix it by using a temp variable, setting it to correct size, and iterating over dsdb1. i still don't know what caused the bug.","Q_Score":0,"Tags":"python,python-2.7,matrix,vector,linear-algebra","A_Id":35976436,"CreationDate":"2016-03-13T21:38:00.000","Title":"vector changes to matrix at computation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to deploy a python2.7 application on google app engine. It uses few modules like numpy,flask,pandas and scikit-learn. Though I am able to install and use other modules. Installing scikit-learn in lib folder of project give following error:-\nTraceback (most recent call last): File \"\/base\/data\/home\/runtimes\/python27\/python27_lib\/versions\/1\/google\/appengine\/runtime\/wsgi.py\", line 240, in Handle handler = _config_handle.add_wsgi_middleware(self._LoadHandler()) File \"\/base\/data\/home\/runtimes\/python27\/python27_lib\/versions\/1\/google\/appengine\/runtime\/wsgi.py\", line 299, in _LoadHandler handler, path, err = LoadObject(self._handler) File \"\/base\/data\/home\/runtimes\/python27\/python27_lib\/versions\/1\/google\/appengine\/runtime\/wsgi.py\", line 85, in LoadObject obj = __import__(path[0]) File \"\/base\/data\/home\/apps\/s~category-prediction-1247\/v1.391344087004233892\/deploynew.py\", line 6, in  import sklearn File \"\/base\/data\/home\/apps\/s~category-prediction-1247\/v1.391344087004233892\/lib\/sklearn\/__init__.py\", line 56, in  from . import __check_build File \"\/base\/data\/home\/apps\/s~category-prediction-1247\/v1.391344087004233892\/lib\/sklearn\/__check_build\/__init__.py\", line 46, in  raise_build_error(e) File \"\/base\/data\/home\/apps\/s~category-prediction-1247\/v1.391344087004233892\/lib\/sklearn\/__check_build\/__init__.py\", line 41, in raise_build_error %s\"\"\" % (e, local_dir, ''.join(dir_content).strip(), msg)) ImportError: dynamic module does not define init function (init_check_build) ___________________________________________________________________________ Contents of \/base\/data\/home\/apps\/s~category-prediction-1247\/v1.391344087004233892\/lib\/sklearn\/__check_build: setup.pyc __init__.py _check_build.so setup.py __init__.pyc ___________________________________________________________________________ It seems that scikit-learn has not been built correctly. If you have installed scikit-learn from source, please do not forget to build the package before using it: run python setup.py install or make in the source directory. If you have used an installer, please check that it is suited for your Python version, your operating system and your platform.\nIs their any way of using scikit-learn on google app engine?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1664,"Q_Id":35987785,"Users Score":0,"Answer":"The newly-released 2nd Generation Python 3.7 Standard Environment (experimental) can run all modules. It's still in beta, though.","Q_Score":8,"Tags":"python,python-2.7,google-app-engine,scikit-learn","A_Id":52134247,"CreationDate":"2016-03-14T12:39:00.000","Title":"Using Scikit-learn google app engine","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"How do I add a quadratic constraint using the scip python interface?\nIn one of the examples, I see something like model.addCons(x*x+y*y<=6)\nHowever, since I have a lot of variables(x1..xn and my constraint is of the form x'Qx<=0.2, where x is n*1 and Q is n*n), this method is rather impossible. How can I put the quadratic constraint in a python dictionary of coeffs as I do the linear constraints? (coeffs={x**2:3.0\uff0cy**2:1.0,z**2:5.0} for example if I want 3x^2+y^2+5z^2<=10)","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":103,"Q_Id":36003924,"Users Score":0,"Answer":"This is currently not supported. You need to loop through your quadratic constraints and add them one after the other using the expression method.","Q_Score":0,"Tags":"python,scip","A_Id":36008452,"CreationDate":"2016-03-15T06:15:00.000","Title":"How do I add a quadratic constraint using coeff in the scipsuite python interface","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an rbf SVM that I'm tuning with gridsearchcv. How do I tell if my good results are actually good results or whether they are overfitting?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1516,"Q_Id":36018586,"Users Score":3,"Answer":"Overfitting is generally associated with high variance, meaning that the model parameters that would result from being fitted to some realized data set have a high variance from data set to data set. You collected some data, fit some model, got some parameters ... you do it again and get new data and now your parameters are totally different.\nOne consequence of this is that in the presence of overfitting, usually the training error (the error from re-running the model directly on the data used to train it) will be very low, or at least low in contrast to the test error (running the model on some previously unused test data).\nOne diagnostic that is suggested by Andrew Ng is to separate some of your data into a testing set. Ideally this should have been done from the very beginning, so that happening to see the model fit results inclusive of this data would never have the chance to impact your decision. But you can also do it after the fact as long as you explain so in your model discussion.\nWith the test data, you want to compute the same error or loss score that you compute on the training data. If training error is very low, but testing error is unacceptably high, you probably have overfitting.\nFurther, you can vary the size of your test data and generate a diagnostic graph. Let's say that you randomly sample 5% of your data, then 10%, then 15% ... on up to 30%. This will give you six different data points showing the resulting training error and testing error.\nAs you increase the training set size (decrease testing set size), the shape of the two curves can give some insight.\nThe test error will be decreasing and the training error will be increasing. The two curves should flatten out and converge with some gap between them.\nIf that gap is large, you are likely dealing with overfitting, and it suggests to use a large training set and to try to collect more data if possible.\nIf the gap is small, or if the training error itself is already too large, it suggests model bias is the problem, and you should consider a different model class all together.\nNote that in the above setting, you can also substitute a k-fold cross validation for the test set approach. Then, to generate a similar diagnostic curve, you should vary the number of folds (hence varying the size of the test sets). For a given value of k, then for each subset used for testing, the other (k-1) subsets are used for training error, and averaged over each way of assigning the folds. This gives you both a training error and testing error metric for a given choice of k. As k becomes larger, the training set sizes becomes bigger (for example, if k=10, then training errors are reported on 90% of the data) so again you can see how the scores vary as a function of training set size.\nThe downside is that CV scores are already expensive to compute, and repeated CV for many different values of k makes it even worse.\nOne other cause of overfitting can be too large of a feature space. In that case, you can try to look at importance scores of each of your features. If you prune out some of the least important features and then re-do the above overfitting diagnostic and observe improvement, it's also some evidence that the problem is overfitting and you may want to use a simpler set of features or a different model class.\nOn the other hand, if you still have high bias, it suggests the opposite: your model doesn't have enough feature space to adequately account for the variability of the data, so instead you may want to augment the model with even more features.","Q_Score":3,"Tags":"python,scikit-learn,svm,cross-validation,grid-search","A_Id":36019131,"CreationDate":"2016-03-15T17:51:00.000","Title":"Identifying overfitting in a cross validated SVM when tuning parameters","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"For starters, I am doing a Runge-Kutta on a three-DOF NumPy array. My array looks like this:\nstates = [[X], [Vx], [Y], [Vy], [Z], [Vz]]\nI run my Runge-Kutta, and get my four K values, which I transpose with [newaxis]. So when I try to append the new states to my states array as follows:\nstates = append(states, states[:,i] + (K1.T + 2 * K2.T + 2 * K3.T + K4.T)\/6, 1)\nwhere \"i\" is a counter that starts at 0 and counts up for each iteration.\nHowever, when I run my code my resulting states array is not two columns of six elements. It appears that I am appending a row vector instead of a column vector to my states array. I ran the code with two elements (X, Vx) in the column, and everything appended just fine (or at least my result made sense).\nI have tried forcing the result of my Runge-Kutta to be a column vector, but that messes up my calculation of the K-values. I have tried variations of my append code, and still have the same result.\nThis is a clone of a Matlab code, and I have been unable to find anything on NumPy arrays and indexing that helps me.\nAny help is appreciated.\nThanks.\nUPDATE:\nstates[:,0] = [[0], [2300], [0], [0], [-1600], [500]] - original\nstates[:,1] = [[2300], [2100], [0], [0], [-2100], [450]] - append\nstates = [[0, 2300], [2300, 2100], [0, 0], [0, 0], [-1600, -2100], [500, 450]] - final\nThese are column vectors.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":536,"Q_Id":36024460,"Users Score":0,"Answer":"You should not append arrays, if you can avoid, due to efficiency issues. Appending means changing the allocated memory size, which can run into non-contiguous memory space, hence inefficient allocation or reallocation would be necessary. These can slow down your program a lot, specially for large arrays.\nIf you are implementing a fixed time-step Runge-Kutta you know beforehand how many points your solution is going to have at time T. It's N = (T-t0)\/h+1, where T is the final time, t0 the initial time, and h the time step. You can initialize your array with zeros (using states = np.zeros((N,3))) and fill the values as you go, associating the index i to the time t[i] = t0 +i*h. This would be inside the loop: \nstates[:,i+1] = states[:,i] + RK4_step(states[:,i]),\nwhere RK4_step(states[:,i]) is a function returning an array (column) with your variation of the state values in one step of the Runge-Kutta method.\nEven if your time-step is variable you should still do that, but with nonuniform times t[i] = t0 +i*h. \nOr, you could use numpy.integrate.ode_int(), which returns the solution of an ODE at the required times.","Q_Score":0,"Tags":"python,arrays,numpy","A_Id":43882395,"CreationDate":"2016-03-15T23:57:00.000","Title":"Appending to NumPy (Python) Array","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Since i've installed the last version of matplotlib (1.5.1), I have the following error when i try to plot values with datetime as X.\n\nDeprecationWarning: Using both 'count' and 'until' is inconsistent\n  with RFC 2445 and has been deprecated in dateutil. Future versions\n  will raise an error.\n\nDoes someone met ths error, and knows how to correct it ?","AnswerCount":3,"Available Count":3,"Score":0.0665680765,"is_accepted":false,"ViewCount":842,"Q_Id":36033095,"Users Score":1,"Answer":"First of all it is not an error. It's a warning. \nSecond, most likely the problem is not your problem rather a problem in Matplotlib, which need to fix how they call a function or some method form python-dateutil. Most likely, you can ignore this warning, and it will be fixed in the next Matplotlib version.","Q_Score":1,"Tags":"python,datetime,matplotlib,python-dateutil","A_Id":36033515,"CreationDate":"2016-03-16T10:33:00.000","Title":"DeprecationWarning with matplotlib and dateutil","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Since i've installed the last version of matplotlib (1.5.1), I have the following error when i try to plot values with datetime as X.\n\nDeprecationWarning: Using both 'count' and 'until' is inconsistent\n  with RFC 2445 and has been deprecated in dateutil. Future versions\n  will raise an error.\n\nDoes someone met ths error, and knows how to correct it ?","AnswerCount":3,"Available Count":3,"Score":0.0665680765,"is_accepted":false,"ViewCount":842,"Q_Id":36033095,"Users Score":1,"Answer":"Best solution : \nin the file \/matplotlib\/dates.py, the line 830 :\nself.rule.set(dtstart=start, until=stop, count=self.MAXTICKS + 1)\nshall be commented","Q_Score":1,"Tags":"python,datetime,matplotlib,python-dateutil","A_Id":36626778,"CreationDate":"2016-03-16T10:33:00.000","Title":"DeprecationWarning with matplotlib and dateutil","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Since i've installed the last version of matplotlib (1.5.1), I have the following error when i try to plot values with datetime as X.\n\nDeprecationWarning: Using both 'count' and 'until' is inconsistent\n  with RFC 2445 and has been deprecated in dateutil. Future versions\n  will raise an error.\n\nDoes someone met ths error, and knows how to correct it ?","AnswerCount":3,"Available Count":3,"Score":0.1973753202,"is_accepted":false,"ViewCount":842,"Q_Id":36033095,"Users Score":3,"Answer":"The issue have been fixed on matplotlib, but not released in a finalised version (>= 1.5.2)\nI had to install the current working version with\npip install git+https:\/\/github.com\/matplotlib\/matplotlib","Q_Score":1,"Tags":"python,datetime,matplotlib,python-dateutil","A_Id":36423287,"CreationDate":"2016-03-16T10:33:00.000","Title":"DeprecationWarning with matplotlib and dateutil","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataframe in pandas and my goal is to write each row of the dataframe as a new json file.\nI'm a bit stuck right now. My intuition was to iterate over the rows of the dataframe (using df.iterrows) and use json.dumps to dump the file but to no avail.\nAny thoughts?","AnswerCount":4,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":43885,"Q_Id":36051134,"Users Score":30,"Answer":"Looping over indices is very inefficient.\nA faster technique:\ndf['json'] = df.apply(lambda x: x.to_json(), axis=1)","Q_Score":22,"Tags":"python,json,pandas","A_Id":52089783,"CreationDate":"2016-03-17T03:26:00.000","Title":"Pandas row to json","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm doing a Random Forest implementation (for classification), and I have some questions regarding the tree growing algorithm mentioned in literature.  \nWhen training a decision tree, there are 2 criteria to stop growing a tree:\na. Stop when there are no more features left to split a node on.\nb. Stop when the node has all samples in it belonging to the same class.   \nBased on that,\n1. Consider growing one tree in the forest. When splitting a node of the tree, I randomly select m of the M total features, and then from these m features I find that one feature with maximum information gain. After I've found this one feature, say f, should I remove this feature from the feature list, before proceeding down to the children of the node? If I don't  remove this feature, then this feature might get selected again down the tree.\nIf I implement the algorithm without removing the feature selected at a node, then the only way to stop growing the tree is when the leaves of the tree become \"pure\". When I did this, I got the \"maximum recursion depth\" reached error in Python, because the tree couldn't reach that \"pure\" condition earlier.\nThe RF literature even those written by Breiman say that the tree should be grown to the maximum . What does this mean?  \n2. At a node split, after selecting the best feature to split on (by information gain), what should be the threshold on which to split? One approach is to have no threshold, create one child node for every unique value of the feature; but I've continuous-valued features too, so that means creating one child node per sample!","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":586,"Q_Id":36081370,"Users Score":1,"Answer":"Q1\n\nYou shouldn't remove the features from M. Otherwise it will not be able to detect some types of relationships (ex: linear relationships)\nMaybe you can stop earlier, in your condition it might go up to leaves with only 1 sample, this will have no statistical significance. So it's better to stop at say, when the number of samples at  leaf is <= 3\n\nQ2\n\nFor continuous features maybe you can bin them to groups and use them to figure out a splitting point.","Q_Score":2,"Tags":"python,algorithm,machine-learning,random-forest,decision-tree","A_Id":36081837,"CreationDate":"2016-03-18T10:00:00.000","Title":"Random forest tree growing algorithm","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using the nnls algorithm from scipy and am shocked to find that I cannot control the final tolerance, as can be done in Matlab using Tolx i.e, termination tolerance. \nModifying the Fortran code or even reading it is extremely hard. I am only new to python since a month. \nI need to do $\\| A x - b\\|_2 \\leq \\epsilon\\| b\\|_2$(latex); How can I do this, other than write my own nnls! \\epsilon is the tolerance! \nI found another link that had to add an extra constraint, but that was an equality constriant!","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":129,"Q_Id":36086330,"Users Score":1,"Answer":"You can use lsq_linear which is available in scipy version 0.17.","Q_Score":1,"Tags":"python,scipy,spyder","A_Id":36104821,"CreationDate":"2016-03-18T13:58:00.000","Title":"Scipy nnls algorithm does not have termination tolerance option","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"We are students that are working on a graduation project related to the Data Science, we are developing a Recommender Engine using Spark with python (Pyspark) with Android Application (Interface for the users) and we have a faced a lot of roadblocks, one of them was how to keep the Spark script up and running on a cloud for a fast processing and real-time results.\nAll we knew about EMR that it's newer than EC2 and already has the Hadoop installed on it.\nWe still have hard time taking the decision on which to use and what are the differences between them dealing with Spark.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":3688,"Q_Id":36141570,"Users Score":3,"Answer":"EMR provides a simple to use Hadoop\/spark as service. You just have to select the components you want to be installed (spark, hadoop), their versions, how many machines you want to use and a couple other options and then it installs everything for you. Since you are students I assume you don't have experience in automation tools like Ansible, Puppet or Chef and probably you never had to maintain your own hadoop cluster. If that is the case I would definitively suggest EMR. As an experienced hadoop\/spark user, at the same time I can tell you that it has its own limitations. When I used it 6 months ago I wanted to use the latest version of EMR (4.0 If remember correctly) because it supported the latest version of Spark and I had few headaches to customise it to install Java 8 instead of the provided Java 7. I believe it was their early days of supporting Java 8 and they should have fixed that by now. But this is what you miss with all the \"all included\" solutions, flexibility especially if you are an expert user.","Q_Score":1,"Tags":"python,amazon-web-services,amazon-ec2,apache-spark,amazon-emr","A_Id":36143671,"CreationDate":"2016-03-21T21:04:00.000","Title":"Spark running on EC2 vs EMR","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I'm trying to analyse an asymmetric FIR filter with complex coefficients. I know I can use the numpy function freqz to analyse the frequency response of an FIR or IIR-filter with real coefficients. At the moment I'm just using a regular FFT of the FIR filter and I use fftshift to put the negative frequencies in front of 0 and then I do fftfreqs to calculate the frequency bins and finally, I add the carrier frequency to all the frequencies in the array which is given by fftfreqs. Anyway, I'm pretty sure that that's the wrong way.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":631,"Q_Id":36155061,"Users Score":0,"Answer":"Never mind, it is absolutely possible to pass a set of complex coefficients to freqz, I got confused because I tried to plot the response without specifying I wanted the absolute value of h, which rendered the warning: \nComplexWarning: Casting complex values to real discards the imaginary part. Trap for young players like myself!","Q_Score":0,"Tags":"python,numpy,scipy","A_Id":36157336,"CreationDate":"2016-03-22T12:59:00.000","Title":"How to use freqz to get response of complex FIR filter","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using the sklearn.cluster KMeans package. Once I finish the clustering if I need to know which values were grouped together how can I do it?\nSay I had 100 data points and KMeans gave me 5 cluster. Now I want to know which data points are in cluster 5. How can I do that.\nIs there a function to give the cluster id and it will list out all the data points in that cluster?","AnswerCount":6,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":62619,"Q_Id":36195457,"Users Score":0,"Answer":"You can Simply store the labels in an array. Convert the array to a data frame. Then Merge the data that you used to create K means with the new data frame with clusters. \nDisplay the dataframe. Now you should see the row with corresponding cluster. If you want to list all the data with specific cluster, use something like data.loc[data['cluster_label_name'] == 2], assuming 2 your cluster for now.","Q_Score":40,"Tags":"python,scikit-learn,cluster-analysis,k-means","A_Id":50800687,"CreationDate":"2016-03-24T07:56:00.000","Title":"How to get the samples in each cluster?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am playing around with tensorflow and today I have noticed that google also open-sourced Python SDK for their dataflow.\nCurrently when I need to train and evaluate several networks in parallel I usually use either luigi and run one model training after another or I use spark and I am performing each model training within the map step.\nWhole this data processing is just a part of the pipeline.\nI am wondering if there is or if there is planned something like perform tensorflow model training step inside of the dataflow pipeline? \nIs there currently some best practice around this? \nOr do I have to run each model setting within the map step?\nI went through the documentation and for now it seems to be really vague, so I'm asking here if someone has some experience with this.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":310,"Q_Id":36200052,"Users Score":1,"Answer":"There is nothing planned at this time.\nIf you can run the Tensorflow training on a single machine (it sounds like this is what you were doing with Spark) then it should be possible to do the training within a DoFn of a Dataflow pipeline.","Q_Score":2,"Tags":"python,tensorflow,google-cloud-dataflow","A_Id":36207559,"CreationDate":"2016-03-24T12:18:00.000","Title":"Running tensorflow model training from dataflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to install matplotlib a server that is sequestered and has no internet connection. When I run pip with this command:\npip.exe install matplotlib-1.5.1-cp27-none-win_amd64.whl --no-index\nI get this message:\nCould not find a version that satisfies the requirement pyparsing!=2.0.4,>=1.5.6 (from matplotlib==1.5.1) (from versions: )\nNo matching distribution found for pyparsing!=2.0.4,>=1.5.6 (from matplotlib==1.5.1)\nI'm relatively new with this and have only used Pip once or twice before but always with an internet connection. This computer is sequestered on a DoD compound and is for sandbox development only. I have to go through three layers of RDP in order to get to it. I've tried everything I can think of or find but no luck.\nAny suggestions are greatly appreciated.\nThanks","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":518,"Q_Id":36209363,"Users Score":1,"Answer":"On the computer with the internet, use pip wheel matplotlib to download all the wheel files needed for the installation (including dependencies).\nThen, on the computer without the internet, use pip install matplotlib --find-links=\"<directory of the wheel files>\". This will install matplotlib from the local files.","Q_Score":0,"Tags":"python,matplotlib,installation,pip","A_Id":36209490,"CreationDate":"2016-03-24T20:23:00.000","Title":"No internet access but Pip wants to look for newer version","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am taking input values from a django model admin screen and on pre_save calling h2o to do predictions for other values and save them. \nCurrently I convert my input from pandas (trying to work with sklearn preprocessing easily here) by using:\nmodelH2OFrame = h2o.H2OFrame(python_obj = model_data_frame.to_dict('list')) \nIt parses and loads. Hell it even creates a frame with values when I do it step by step.\nBUT. When I run this inside of the Django pre_save, the H2OFrame comes back completely empty. \nIdeas for why this may be happening? Sometimes I get errors connecting to the h2o cluster or timeouts--maybe that is a related issue? I load the H2O models in the pre_save call and do the predictions, allocate them to model fields, and then shut down the h2o cluster (in one function).","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":276,"Q_Id":36212815,"Users Score":2,"Answer":"It seems the Pandas DataFrame to H2OFrame conversion works fine outside Django, but fails inside Django. The problem might be with Django's pre_save not allowing the writing\/reading of the temporary .csv file that H2O creates when ingesting a python object. A possible workaround is to explicitly write the Pandas DataFrame to a .csv file with model_data_frame.to_csv(<path>, index=False) and then import the file into H2O with h2o.import_file(<path>).","Q_Score":0,"Tags":"python,django,pandas,scikit-learn,h2o","A_Id":36227222,"CreationDate":"2016-03-25T01:44:00.000","Title":"H2OFrame converts dict to all zeros","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I am not able to understand the parameters passed to detectMultiScale. I know that the general syntax is detectMultiScale(image, rejectLevels, levelWeights) \nHowever, what do the parameters rejectLevels and levelWeights mean? And what are the optimal values used for detecting objects?\nI want to use this to detect pupil of the eye","AnswerCount":4,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":78382,"Q_Id":36218385,"Users Score":0,"Answer":"detectMultiScale function is used to detect the faces. This function will return a rectangle with coordinates(x,y,w,h) around the detected face.\nIt takes 3 common arguments \u2014 the input image, scaleFactor, and minNeighbours.\nscaleFactor specifies how much the image size is reduced with each scale. In a group photo, there may be some faces which are near the camera than others. Naturally, such faces would appear more prominent than the ones behind. This factor compensates for that.\nminNeighbours specifies how many neighbours each candidate rectangle should have to retain it. You can read about it in detail here. You may have to tweak these values to get the best results. This parameter specifies the number of neighbours a rectangle should have to be called a face.\nWe obtain these values after trail and test over a specific range.","Q_Score":27,"Tags":"python,opencv,object-detection","A_Id":66138989,"CreationDate":"2016-03-25T10:42:00.000","Title":"Parameters of detectMultiScale in OpenCV using Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am not able to understand the parameters passed to detectMultiScale. I know that the general syntax is detectMultiScale(image, rejectLevels, levelWeights) \nHowever, what do the parameters rejectLevels and levelWeights mean? And what are the optimal values used for detecting objects?\nI want to use this to detect pupil of the eye","AnswerCount":4,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":78382,"Q_Id":36218385,"Users Score":20,"Answer":"Amongst these parameters, you need to pay more attention to four of them:\n\nscaleFactor \u2013 Parameter specifying how much the image size is reduced at each image scale.\n\nBasically, the scale factor is used to create your scale pyramid. More explanation, your model has a fixed size defined during training, which is visible in the XML. This means that this size of the face is detected in the image if present. However, by rescaling the input image, you can resize a larger face to a smaller one, making it detectable by the algorithm.\n1.05 is a good possible value for this, which means you use a small step for resizing, i.e. reduce the size by 5%, you increase the chance of a matching size with the model for detection is found. This also means that the algorithm works slower since it is more thorough. You may increase it to as much as 1.4 for faster detection, with the risk of missing some faces altogether.\n\nminNeighbors \u2013 Parameter specifying how many neighbors each candidate rectangle should have to retain it.\n\nThis parameter will affect the quality of the detected faces. Higher value results in fewer detections but with higher quality. 3~6 is a good value for it.\n\nminSize \u2013 Minimum possible object size. Objects smaller than that are ignored.\n\nThis parameter determines how small size you want to detect. You decide it! Usually, [30, 30] is a good start for face detection.\n\nmaxSize \u2013 Maximum possible object size. Objects bigger than this are ignored.\n\nThis parameter determines how big size you want to detect. Again, you decide it! Usually, you don't need to set it manually, the default value assumes you want to detect without an upper limit on the size of the face.","Q_Score":27,"Tags":"python,opencv,object-detection","A_Id":55628240,"CreationDate":"2016-03-25T10:42:00.000","Title":"Parameters of detectMultiScale in OpenCV using Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In the latest version of tensorflow, when I save the model I find two files are produced: model_xxx and model_xxx.meta.\nDoes model_xxx.meta specify the network? Can I resume training using model_xxx and model_xxx.meta without specify the network in the code? What about training queue structure, are they stored in model_xxx.meta?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":429,"Q_Id":36221588,"Users Score":0,"Answer":"Not sure if this will work for you, but at least for DNNCLassifiers you can specify the model_dir parameter when creating it and that will construct the model from the files and then you can continue the training.\nFor the DNNClassifiers you specify model_dir when first creating the object and the training will store checkpoints and other files on this directory. You can come then after that, and create another DNNClassifier specifying the same model_dir and that will restore your pre-trained model.","Q_Score":0,"Tags":"python,tensorflow","A_Id":38489976,"CreationDate":"2016-03-25T14:13:00.000","Title":"How to resume training from *.meta in tensorflow?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I am confused by something in pandas 0.18.0. In my input csv data, a field is supposed to consist dates as YYYYMMDD strings, but some rows have this missing or misformatted. I want to represent this column as datetime with dates where possible, and missing where not.\nI tried several options, and what got me furthest was not using parse_dates upon reading the table in (with read_table), but then coercing the conversion with pandas.to_datetime(DataFrame['Seriesname'],  errors='coerce',format='%Y%m%d'). This is robust to typos where the number cannot represent a date (think '20100231', column imported as int64 first) or when the string does not represent a number at all (think '2o1oo228', column an object upon import).\nWhat this procedure is not robust to is when the columns contains only numbers but one field is empty. Then read_table imports the entire column as a float64 (not an int64, which has no missing values in numpy), and the conversion above produces all missing, even for rows where the data makes sense.\nIs there a way around this?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":61,"Q_Id":36225950,"Users Score":0,"Answer":"The best way might be to avoid floats altogether. Preempt the conversion to numerics in read_table by specifying dtype, with the column in question being kept an object. to_datetime handles that as intended.\nHT: BrenBarn in the comments.","Q_Score":0,"Tags":"python,datetime,numpy,pandas,types","A_Id":36226280,"CreationDate":"2016-03-25T18:42:00.000","Title":"Why does to_datetime produce only missing values for a float column in pandas?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Given a pandas dataframe containing possible NaN values scattered here and there:\nQuestion: How do I determine which columns contain NaN values? In particular, can I get a list of the column names containing NaNs?","AnswerCount":14,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":303847,"Q_Id":36226083,"Users Score":0,"Answer":"df.isna() return True values for NaN, False for the rest. So, doing:\ndf.isna().any()\nwill return True for any column having a NaN, False for the rest","Q_Score":231,"Tags":"python,pandas,dataframe,nan","A_Id":64681838,"CreationDate":"2016-03-25T18:50:00.000","Title":"How to find which columns contain any NaN value in Pandas dataframe","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Given a pandas dataframe containing possible NaN values scattered here and there:\nQuestion: How do I determine which columns contain NaN values? In particular, can I get a list of the column names containing NaNs?","AnswerCount":14,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":303847,"Q_Id":36226083,"Users Score":0,"Answer":"features_with_na=[features for features in dataframe.columns if dataframe[features].isnull().sum()>0]\nfor feature in features_with_na:\nprint(feature, np.round(dataframe[feature].isnull().mean(), 4),  '% missing values')\nprint(features_with_na)\nit  will give % of missing value for each column in dataframe","Q_Score":231,"Tags":"python,pandas,dataframe,nan","A_Id":68703088,"CreationDate":"2016-03-25T18:50:00.000","Title":"How to find which columns contain any NaN value in Pandas dataframe","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Given a pandas dataframe containing possible NaN values scattered here and there:\nQuestion: How do I determine which columns contain NaN values? In particular, can I get a list of the column names containing NaNs?","AnswerCount":14,"Available Count":3,"Score":1.0,"is_accepted":false,"ViewCount":303847,"Q_Id":36226083,"Users Score":40,"Answer":"You can use df.isnull().sum(). It shows all columns and the total NaNs of each feature.","Q_Score":231,"Tags":"python,pandas,dataframe,nan","A_Id":47418973,"CreationDate":"2016-03-25T18:50:00.000","Title":"How to find which columns contain any NaN value in Pandas dataframe","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on a project that needs to be able to classify modifiers like \"a lot\", \"a few\", \"lots\", \"some\" etc. into minimum percentages\nFor example \"a lot\" -> 80%\nRight now I'm thinking of simply creating a large dictionary that relates these modifiers and numerical values e.g.\na few -> 15%\nsome -> 10%\nlots -> 80%\nHowever this is very laborious and probably won't cover all scenarios. Is there an easier way to do this, or is there a NLP tool that already exists for this purpose - preferably in python (or a database out there already?)","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":76,"Q_Id":36267978,"Users Score":1,"Answer":"Similarity is actually a difficult problem in NLP. I recommend you to use Word2Vec and generate word embeddings of each word. Then you can compare the distance of each word pair and see if could word better than your way. The key to improve the effectiveness of word embedding is to pick a corpus which is large enough and specifies on the area closer to your problem.","Q_Score":1,"Tags":"python,nlp","A_Id":36270781,"CreationDate":"2016-03-28T17:57:00.000","Title":"Easy way to classify words like \"A lot\", \"A few\", \"some\"","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm building a random forest classification model with the response variable split being 98%(False)-2%(True). I'm using Scikit Learn's RandomForest classifier for this. \nWhat is the best way to handle this unbalanced data and avoid oversampling?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1888,"Q_Id":36275005,"Users Score":0,"Answer":"You can use parameter class_weight .\nWeights associated with classes in the form {class_label: weight}\nYou can give more weight to your small class and find best weight using cross-validation.\nFor example class_weight={1: 10, 0:1}. Gives more weight to class labeled 1.","Q_Score":2,"Tags":"python,scikit-learn,classification,random-forest","A_Id":36281252,"CreationDate":"2016-03-29T03:43:00.000","Title":"Stratified sampling for Random forest -Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm building a random forest classification model with the response variable split being 98%(False)-2%(True). I'm using Scikit Learn's RandomForest classifier for this. \nWhat is the best way to handle this unbalanced data and avoid oversampling?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1888,"Q_Id":36275005,"Users Score":0,"Answer":"In newer versions of sklearn's random forest classifier, you can simply set class_weight=\"balanced\".","Q_Score":2,"Tags":"python,scikit-learn,classification,random-forest","A_Id":54748830,"CreationDate":"2016-03-29T03:43:00.000","Title":"Stratified sampling for Random forest -Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have one huge data matrix X, of which subsets of rows correspond to different tasks that are related but also have different idiosyncratic properties.\nThus I want to train a Multi-Task model with some regularization and chose sklearn's linear_model MultiTaskElasticNet function. \nI am confused with the inputs of fitting the model. It says that both the X and the Y matrix are 2-dimensional. The 2nd dimension in Y corresponds to the number of tasks. That makes sense, but in my understanding the X matrix should be 3-dimensional right? In that way I have selected which subsets of my data correspond to different tasks as I know that in advance (obviously).\nDoes someone know how to enter my data correctly for this scikit-learn module?\nThank you!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":511,"Q_Id":36288578,"Users Score":0,"Answer":"I don't see why you'd want X to vary for each task: the point of multitask learning is that the same feature space is used to represent instances for multiple tasks which can be mutually informative. I get that you may not have ground truth y for all instances for all tasks, though this is currently assumed in the scikit-learn implementation.","Q_Score":0,"Tags":"python,machine-learning,scikit-learn","A_Id":36298305,"CreationDate":"2016-03-29T15:25:00.000","Title":"Sklearn multi-task: Input data not 3-dimensional?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to use NLTK's KMeans Clustering Algorithm. \nIt is generally going fine. \nI want to use the Metrics package of NLTK to determine precision,recall and f measure.\nI searched for some examples in web and in other references but may be without a clue. \nIf any one may kindly cite an example or reference. \nThanks in Advance.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2455,"Q_Id":36291392,"Users Score":0,"Answer":"Precision, Recall, and thus the F-measure are inappropriate for cluster analysis. Clustering is not classification, and clusters are not classes!\nCommon measures for clustering (if you are trying to compare with existing labels, which does not make a whole lot of sense - if you already know the classes, then use classification and not clustering) are the Adjusted Rand Index and its variants.","Q_Score":2,"Tags":"python,machine-learning,nltk,cluster-analysis,k-means","A_Id":36304549,"CreationDate":"2016-03-29T17:42:00.000","Title":"How may I calculate Accuracy in NLTK KMeans Clustering","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a sensor attached to a drill. The sensor outputs orientation in heading roll and pitch. From what I can tell these are intrinsic rotations in that order. The Y axis of the sensor is parallel to the longitudinal axis of the drill bit. I want to take a set of outputs from the sensor and find the maximum change in orientation from the final orientation. \nSince the drill bit will be spinning about the pitch axis, I believe it can be neglected.\nMy first thought would be to try to convert heading and roll to unit vectors assuming pitch is 0. Once I have their vectors, call them v and vf, the angle between them would be\n\u0398=arccos(v . vf)\nIt should then be fairly straight forward to have python calculate \u0398 for a given set of orientations and pull the largest out.\nMy question is, is there a simpler way to do this using python, and, if not what is the most efficient way to convert these intrinsic rotations to unit vectors.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":914,"Q_Id":36292230,"Users Score":0,"Answer":"Suppose u(1), u(2), ..., u(m), v are all unit vectors. You want to determine i such that the angle between u(i) and v is maximized.  This is equivalent to finding the i such that np.dot(u(i), v) is minimized. So if you have a matrix U where the rows are the u(i), you can simply do i = np.argmin(np.dot(U, v)) to find the i that has the angle between u(i) and v maximized.","Q_Score":1,"Tags":"python,math,trigonometry,angle,euler-angles","A_Id":36292344,"CreationDate":"2016-03-29T18:25:00.000","Title":"I need to find the angle between two sets of Roll and Yaw angles","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am writing a function which takes a pandas df, column name and a list of values and gives the filtered df. This function uses df.query() internally.\nIn one specific case, I have a dataframe which has a column in which both integers and strings are present. My function should filter this df on a list whose elements are all integers. At the moment, I get an empty df as strings can't be compared to int. Even though in the dataframe and lookup list are same - for eg. '345' & 345.\nWhat is a general way to handle this in pandas? I could coerce the list of integers to strings but I would like to stay away from that. This is because I want my function to be able to handle non-integral values as well. I am not sure if coercing to strings would be safe then: for eg. for floats.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":54,"Q_Id":36351403,"Users Score":0,"Answer":"You have many otions but I think they can be summarized. I couldn't tell which one would make more sense to you without more context.\n\nConvert numeric strings to numbers\n\nIf you are afraid of issues with floats, convert only integers.\nIf you want to keep your data as is, store the converted values in a different column \/ object and use it just for filtering.\nIf you want to keep the data types in the filtered data, filter the converted data and use the filtered index to subset the original data.\n\nConvert numbers to strings (same considerations as above)\nFilter by both the numbers in the lookup list and their string representation.","Q_Score":0,"Tags":"python,pandas","A_Id":36353073,"CreationDate":"2016-04-01T08:19:00.000","Title":"Querying a dataframe if column and values are of different types","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using MultiRNNCell from tensorflow.models.rnn.rnn_cell. This is how declare my MultiRNNCell\nCode:\ne_cell = rnn_cell.GRUCell(self.rnn_size)\ne_cell = rnn_cell.MultiRNNCell([e_cell] * 2)\nLater on I use it from inside seq2seq.embedding_rnn_decoder as follows\nouputs, mem_states = seq2seq.embedding_rnn_decoder(decoder_inputs, enc_state, e_cell, vocab_size, output_projection=(W, b), feed_previous=False)#\nOn doing this I get the following error\nError:\ntensorflow.python.framework.errors.InvalidArgumentError: Expected size[1] in [0, 0], but got 1024\n         [[Node: en\/embedding_rnn_decoder_1\/rnn_decoder\/MultiRNNCell\/Cell1\/Slice = Slice[Index=DT_INT32, T=DT_FLOAT, _device=\"\/job:localhost\/replica:0\/task:0\/gpu:0\"](Sigmoid_2, en\/embedding_rnn_decoder_1\/rnn_decoder\/MultiRNNCell\/Cell1\/Slice\/begin, en\/embedding_rnn_decoder_1\/rnn_decoder\/MultiRNNCell\/Cell1\/Slice\/size)]]\n         [[Node: en\/embedding_rnn_decoder\/rnn_decoder\/loop_function_17\/StopGradient\/_1230 = _Recv[client_terminated=false, recv_device=\"\/job:localhost\/replica:0\/task:0\/cpu:0\", send_device=\"\/job:localhost\/replica:0\/task:0\/gpu:0\", send_device_incarnation=1, tensor_name=\"edge_11541_en\/embedding_rnn_decoder\/rnn_decoder\/loop_function_17\/StopGradient\", tensor_type=DT_INT64, _device=\"\/job:localhost\/replica:0\/task:0\/cpu:0\"]()]]\nHas anyone seen a similar error? Any pointers?","AnswerCount":4,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":1345,"Q_Id":36362190,"Users Score":0,"Answer":"Seem like an invalid argument into embedding_rnn_decoder. \nMaybe try to change enc_state:\nouputs, mem_states = seq2seq.embedding_rnn_decoder(decoder_inputs, enc_state[-1], e_cell, vocab_size, output_projection=(W, b), feed_previous=False)","Q_Score":1,"Tags":"python,tensorflow","A_Id":36371050,"CreationDate":"2016-04-01T17:17:00.000","Title":"Slice error when using MultiRNNCell","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using MultiRNNCell from tensorflow.models.rnn.rnn_cell. This is how declare my MultiRNNCell\nCode:\ne_cell = rnn_cell.GRUCell(self.rnn_size)\ne_cell = rnn_cell.MultiRNNCell([e_cell] * 2)\nLater on I use it from inside seq2seq.embedding_rnn_decoder as follows\nouputs, mem_states = seq2seq.embedding_rnn_decoder(decoder_inputs, enc_state, e_cell, vocab_size, output_projection=(W, b), feed_previous=False)#\nOn doing this I get the following error\nError:\ntensorflow.python.framework.errors.InvalidArgumentError: Expected size[1] in [0, 0], but got 1024\n         [[Node: en\/embedding_rnn_decoder_1\/rnn_decoder\/MultiRNNCell\/Cell1\/Slice = Slice[Index=DT_INT32, T=DT_FLOAT, _device=\"\/job:localhost\/replica:0\/task:0\/gpu:0\"](Sigmoid_2, en\/embedding_rnn_decoder_1\/rnn_decoder\/MultiRNNCell\/Cell1\/Slice\/begin, en\/embedding_rnn_decoder_1\/rnn_decoder\/MultiRNNCell\/Cell1\/Slice\/size)]]\n         [[Node: en\/embedding_rnn_decoder\/rnn_decoder\/loop_function_17\/StopGradient\/_1230 = _Recv[client_terminated=false, recv_device=\"\/job:localhost\/replica:0\/task:0\/cpu:0\", send_device=\"\/job:localhost\/replica:0\/task:0\/gpu:0\", send_device_incarnation=1, tensor_name=\"edge_11541_en\/embedding_rnn_decoder\/rnn_decoder\/loop_function_17\/StopGradient\", tensor_type=DT_INT64, _device=\"\/job:localhost\/replica:0\/task:0\/cpu:0\"]()]]\nHas anyone seen a similar error? Any pointers?","AnswerCount":4,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":1345,"Q_Id":36362190,"Users Score":0,"Answer":"I've got a problem similar to yours.{tensorflow.python.framework.errors.InvalidArgumentError: Expected size[1] in [0, 0], but got 40}\nI also use rnn_cell.GRUCell(self.rnn_size)\nI want to share my experience ,maybe it's helpful.\nHere is how I fixed it.\nI want to use gru cell and basic rnn cell ,so I adapt the programme from others which is coded for lstm cell.\nThe different between lstm and GRU\/BasicRnn is the state_size.\nHere is lstm celldef state_size(self):return 2 * self._num_units\nHere is GRU\/BasicRnn cell def state_size(self):return self._num_units\nTherefore the shape of matrix is different,and the tensor is not suit for the op.I advice you to check your code contains tf.slice","Q_Score":1,"Tags":"python,tensorflow","A_Id":36794050,"CreationDate":"2016-04-01T17:17:00.000","Title":"Slice error when using MultiRNNCell","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using MultiRNNCell from tensorflow.models.rnn.rnn_cell. This is how declare my MultiRNNCell\nCode:\ne_cell = rnn_cell.GRUCell(self.rnn_size)\ne_cell = rnn_cell.MultiRNNCell([e_cell] * 2)\nLater on I use it from inside seq2seq.embedding_rnn_decoder as follows\nouputs, mem_states = seq2seq.embedding_rnn_decoder(decoder_inputs, enc_state, e_cell, vocab_size, output_projection=(W, b), feed_previous=False)#\nOn doing this I get the following error\nError:\ntensorflow.python.framework.errors.InvalidArgumentError: Expected size[1] in [0, 0], but got 1024\n         [[Node: en\/embedding_rnn_decoder_1\/rnn_decoder\/MultiRNNCell\/Cell1\/Slice = Slice[Index=DT_INT32, T=DT_FLOAT, _device=\"\/job:localhost\/replica:0\/task:0\/gpu:0\"](Sigmoid_2, en\/embedding_rnn_decoder_1\/rnn_decoder\/MultiRNNCell\/Cell1\/Slice\/begin, en\/embedding_rnn_decoder_1\/rnn_decoder\/MultiRNNCell\/Cell1\/Slice\/size)]]\n         [[Node: en\/embedding_rnn_decoder\/rnn_decoder\/loop_function_17\/StopGradient\/_1230 = _Recv[client_terminated=false, recv_device=\"\/job:localhost\/replica:0\/task:0\/cpu:0\", send_device=\"\/job:localhost\/replica:0\/task:0\/gpu:0\", send_device_incarnation=1, tensor_name=\"edge_11541_en\/embedding_rnn_decoder\/rnn_decoder\/loop_function_17\/StopGradient\", tensor_type=DT_INT64, _device=\"\/job:localhost\/replica:0\/task:0\/cpu:0\"]()]]\nHas anyone seen a similar error? Any pointers?","AnswerCount":4,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":1345,"Q_Id":36362190,"Users Score":0,"Answer":"This problem is occurring because you have doubled your GRU cell but your initial vector is not doubled. \nIf your initial_vector size is [batch_size,50].\nThen initial_vector = tf.concat(1,[initial_vector,initial_vector])\nNow input this to decoder as initial vector.","Q_Score":1,"Tags":"python,tensorflow","A_Id":38411187,"CreationDate":"2016-04-01T17:17:00.000","Title":"Slice error when using MultiRNNCell","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"total noob question. I have a file that contains a number on each line, there are approximately 5 millions rows, each row has a different number, how do i find the top 5 values in the file using spark and python.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":54,"Q_Id":36363502,"Users Score":1,"Answer":"You distribute the data you read among nodes.\nEvery node finds it's 5 local maximums.\nYou combine all the local maximums and you keep the 5 max of them,\nwhich is the answer.","Q_Score":0,"Tags":"python,apache-spark","A_Id":36363565,"CreationDate":"2016-04-01T18:34:00.000","Title":"spark python product top 5 numbers from a file","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it possible to delete an object from a numpy array without knowing the index of the object but instead knowing the object itself?\nI have seen that it is possible using the index of the object using the np.delete function, but I'm looking for a way to do it having the object but not its index.\nExample:\n\n[a,b,c,d,e,f]\nx = e\n\nI would like to delete x.","AnswerCount":5,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":51262,"Q_Id":36365990,"Users Score":0,"Answer":"arr = np.array(['a','b','c','d','e','f'])\nThen\narr = [x for x in arr if arr != 'e']","Q_Score":32,"Tags":"python,list,numpy","A_Id":53074632,"CreationDate":"2016-04-01T21:18:00.000","Title":"How to delete an object from a numpy array without knowing the index","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Sklearn PCA is pca.components_ the loadings? I am pretty sure it is, but I am trying to follow along a research paper and I am getting different results from their loadings. I can't find it within the sklearn documentation.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":9712,"Q_Id":36380183,"Users Score":0,"Answer":"This previous answer is mostly correct except about the loadings. components_ is in fact the loadings, as the question asker originally stated. The result of the fit_transform function will give you the principal components (the transformed\/reduced matrix).","Q_Score":8,"Tags":"python,scikit-learn,pca","A_Id":61442480,"CreationDate":"2016-04-03T00:16:00.000","Title":"Sklearn PCA is pca.components_ the loadings?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Sklearn PCA is pca.components_ the loadings? I am pretty sure it is, but I am trying to follow along a research paper and I am getting different results from their loadings. I can't find it within the sklearn documentation.","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":9712,"Q_Id":36380183,"Users Score":13,"Answer":"pca.components_ is the orthogonal basis of the space your projecting the data into. It has shape (n_components, n_features). If you want to keep the only the first 3 components (for instance to do a 3D scatter plot) of a datasets with 100 samples and 50 dimensions (also named features), pca.components_ will have shape (3, 50).\nI think what you call the \"loadings\" is the result of the projection for each sample into the vector space spanned by the components. Those can be obtained by calling pca.transform(X_train) after calling pca.fit(X_train). The result will have shape (n_samples, n_components), that is (100, 3) for our previous example.","Q_Score":8,"Tags":"python,scikit-learn,pca","A_Id":36386315,"CreationDate":"2016-04-03T00:16:00.000","Title":"Sklearn PCA is pca.components_ the loadings?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to train my network on a data that has a normal distribution, I've noticed that my neural net has a very high tendency to only predict the most occurring class label in a csv file I exported (comparing its prediction with the actual label). \nWhat are some suggestions (except cleaning the data to produce  an evenly distributed training data), that would help my neural net to not go and only predict the most occurring label?\nUPDATE: Just wanted to mention that, indeed the suggestions made in the comment sections worked. I, however, found out that adding an extra layer to my NN, mitigated the problem.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":82,"Q_Id":36380696,"Users Score":0,"Answer":"Assuming the NN is trained using mini-batches, it is possible to simulate (instead of generate) an evenly distributed training data by making sure each mini-batch is evenly distributed. \nFor example, assuming a 3-class classification problem and a minibatch size=30, construct each mini-batch by randomly selecting 10 samples per class (with repetition, if necessary).","Q_Score":1,"Tags":"python,machine-learning,neural-network","A_Id":36381235,"CreationDate":"2016-04-03T01:36:00.000","Title":"How to cancel the huge negative effect of my training data distribution on subsequent neural network classification function?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"While looking at indexing in pandas, I had some questions which should be simple enough. If df is a sufficiently long DataFrame, then df[1:2] gives the second row, however, df[1] gives an error and df[[1]] gives the second column. Why is that?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":432,"Q_Id":36394194,"Users Score":1,"Answer":"Use df.iloc[1] to select the second row of the dataframe (it uses zero based indexing).  To select the second column, use df.iloc[:, 1] (the : is slice notation to select all rows).","Q_Score":2,"Tags":"python,pandas","A_Id":36395078,"CreationDate":"2016-04-04T03:38:00.000","Title":"Pandas indexing confusion","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm evaluating orange as a potential solution to helping new entrants into data science to get started. I would like to have them save out model objects created from different algorithms as pkl files similar to how it is done in scikit-learn with joblib or pickle.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":61,"Q_Id":36415572,"Users Score":0,"Answer":"I don't understand what \"exported with joblib\" refers to, but you can save trained Orange models by pickling them, or with Save Classifier widget if you are using the GUI.","Q_Score":0,"Tags":"python,orange","A_Id":36516264,"CreationDate":"2016-04-04T19:00:00.000","Title":"Can the model object for a learner be exported with joblib?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an array of over 2 million records, each record has a 10 minutes resolution timestamp in datetime.datetime format, as well as several other values in other columns.\nI only want to retain the records which have timestamps that occur 20 or more times in the array.  What's the fastest way to do this?  I've got plenty of RAM, so I'm looking for processing speed.\nI've tried [].count() in a list comprehension but started to lose the will to live waiting for it to finish.  I've also tried numpy.bincount() but tragically it doesn't like datetime.datetime\nAny suggestions would be much appreciated.\nThanks!","AnswerCount":5,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":964,"Q_Id":36431659,"Users Score":0,"Answer":"Sort your array \nCount contiguous occurrences by going through it once, & filter for frequency >= 20\n\nThe running time is O(nlog(n)) whereas your list comprehension was probably O(n**2)... that makes quite a difference on 2 million entries.\nDepending on how your data is structured, you might be able to sort only the axis and data you need from the numpy array that holds it.","Q_Score":1,"Tags":"python,datetime,numpy,pandas,filtering","A_Id":36431809,"CreationDate":"2016-04-05T16:09:00.000","Title":"filter numpy array of datetimes by frequency of occurance","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an array of over 2 million records, each record has a 10 minutes resolution timestamp in datetime.datetime format, as well as several other values in other columns.\nI only want to retain the records which have timestamps that occur 20 or more times in the array.  What's the fastest way to do this?  I've got plenty of RAM, so I'm looking for processing speed.\nI've tried [].count() in a list comprehension but started to lose the will to live waiting for it to finish.  I've also tried numpy.bincount() but tragically it doesn't like datetime.datetime\nAny suggestions would be much appreciated.\nThanks!","AnswerCount":5,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":964,"Q_Id":36431659,"Users Score":0,"Answer":"Thanks for all of your suggestions.\nI ended up doing something completely different with dictionaries in the end and found it much faster for the processing that I required.\nI created a dictionary with a unique set of timestamps as the keys and empty lists as the values and then looped once through the unordered list (or array) and populated the value lists with the values that I wanted to count.\nThanks again!","Q_Score":1,"Tags":"python,datetime,numpy,pandas,filtering","A_Id":36454679,"CreationDate":"2016-04-05T16:09:00.000","Title":"filter numpy array of datetimes by frequency of occurance","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a numpy array which is (1, 2048, 1, 1). I need to assign the first two dimensions to another numpy array which is (1, 2048), but I am confused on how to index it correctly. Hope you can help!","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":96,"Q_Id":36470440,"Users Score":0,"Answer":"I solved it by using np.squeeze(x) to remove the singleton dimensions.","Q_Score":0,"Tags":"python,arrays,numpy,matrix,indexing","A_Id":36471599,"CreationDate":"2016-04-07T08:17:00.000","Title":"Indexing a Numpy Array (4 dimensions)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working with discrete Kalman Filter on a system.\n\nx(k+1)=A_k x(k)+B_k u(k)\ny(k)=C_k x(k)\n\nI have estimated the state from the available noised y(k), which one is generated from the same system state equations with Reference Trajectory of the state. Then I have tested it with wrong initial state x0 and a big initial co-variance (simulation 1). I have noticed that the KF works very well, after a few steps the gain k quickly converges to a very small value near zero. I think it is may be caused by the process noise Q. I have set it small because the Q stands for the accuracy of the model.\nNow I want to modify it to a steady state Kalman Filter. I used the steady gain from simulation-1 as constant instead of the calculation in every iteration. And then, the five equations can be simplified to one equation:\n\nx(k+1)^=(I-KC)A x(k)^+(I-KC)B u(k)+K y(k+1)\n\nI want to test it with same initial state and co-variance matrix as the one in simulation-1. But the result is very different from reference trajectory and even the result of simulation-1. I have tested it with the co-variance matrix p_infi, which is solved from the Discrete Riccati Equation:\n\nk_infi=p_infiC'\/(Cp_infi*C'+R)\n\nThis neither works.\nI am wondering-\n\nHow should I apply steady state KF and how should I set the initial state for it?\nIs steady state KF used for scalar system?\nShould I use it with LQ-controller or some others?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":764,"Q_Id":36480233,"Users Score":0,"Answer":"Steady state KF requires the initial state matches the steady state covariance. Otherwise, the KF could diverge. You can start using the steady state KF when the filter enters the steady state. \nThe steady state Kalman filter can be used for systems with multiple dimension state.","Q_Score":0,"Tags":"python,math,simulation,kalman-filter","A_Id":36484420,"CreationDate":"2016-04-07T15:08:00.000","Title":"State Estimation of Steady Kalman Filter","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working with discrete Kalman Filter on a system.\n\nx(k+1)=A_k x(k)+B_k u(k)\ny(k)=C_k x(k)\n\nI have estimated the state from the available noised y(k), which one is generated from the same system state equations with Reference Trajectory of the state. Then I have tested it with wrong initial state x0 and a big initial co-variance (simulation 1). I have noticed that the KF works very well, after a few steps the gain k quickly converges to a very small value near zero. I think it is may be caused by the process noise Q. I have set it small because the Q stands for the accuracy of the model.\nNow I want to modify it to a steady state Kalman Filter. I used the steady gain from simulation-1 as constant instead of the calculation in every iteration. And then, the five equations can be simplified to one equation:\n\nx(k+1)^=(I-KC)A x(k)^+(I-KC)B u(k)+K y(k+1)\n\nI want to test it with same initial state and co-variance matrix as the one in simulation-1. But the result is very different from reference trajectory and even the result of simulation-1. I have tested it with the co-variance matrix p_infi, which is solved from the Discrete Riccati Equation:\n\nk_infi=p_infiC'\/(Cp_infi*C'+R)\n\nThis neither works.\nI am wondering-\n\nHow should I apply steady state KF and how should I set the initial state for it?\nIs steady state KF used for scalar system?\nShould I use it with LQ-controller or some others?","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":764,"Q_Id":36480233,"Users Score":1,"Answer":"Let me first simplify the discussion to a filter with a fixed transition matrix, A rather then A_k above.  When the Kalman filter reaches steady-state in this case, one can extract the gains and make a fixed-gain filter that utilizes the steady-state Kalman gains.  That filter is not a Kalman filter, it is a fixed-gain filter.  It's start-up performance will generally be worse than the Kalman filter.  That is the price one pays for replacing the Kalman gain computation with fixed gains.\nA fixed-gain filter has no covariance (P) and no Q or R. \nGiven A, C and Q, the steady-state gains can be computed directly.  Using the discrete Kalman filter model, and setting the a-posteriori covarance matrix equal to the propagated a-posteriori covariance matrix from the prior measurement cycle one has:\nP = (I - KC) (A P A^T + Q)\nSolving that equation for K results in the steady-state Kalman gains for fixed A, Q and C.\nWhere is R?  Well it has no role in the steady-state gain computation because the measurement noise has been averaged down in steady-state.  Steady-state means the state estimate is as good as it can get with the amount of process noise (Q) we have.\nIf A is time-varying, much of this discussion does not hold.  There is no assurance that a Kalman filter will reach steady-state.  There may be no Pinf.\nAnother implicit assumption in using the steady-state gains from a Kalman filter in a fixed gain filter is that all of the measurements will remain available at the same rates.  The Kalman filter is robust to measurement loss, the fixed-gain filter is not.","Q_Score":0,"Tags":"python,math,simulation,kalman-filter","A_Id":36666450,"CreationDate":"2016-04-07T15:08:00.000","Title":"State Estimation of Steady Kalman Filter","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've recently built a python script that interacts with an Arduino and a piece of hardware that uses LIDAR to map out a room. Everything works great, but anytime you click on the plot that is generated with maptotlib, the computer freaks out and crashes the script that is running. This is partly because I was given a $300 computer to run this on, so it's not very powerful. However, I feel like even a $300 computer should be able to handle a mouse click.\nHow can I ignore mouse clicks entirely with matplotlib so that the computer doesn't freak out and crash the script?\nIf that's not the correct solution, what might be a better solution?\nEdit: This is an interactive plotting session (sort of, I just replace the old data with the new data, there is no plot.ion() command called). So, I cannot just save the plot and show it. The Arduino transmits data constantly.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":175,"Q_Id":36482154,"Users Score":1,"Answer":"I feel that this might be more easily resolved by altering the hardware - can you temporarily unplug the mouse, or tape over the track pad to stop people fiddling with it?\nI suggest this because your crashing script will always process mouse-clicks in some way, and if you don't know what's causing the crashes then you may be better off just ensuring that there are no clicks.","Q_Score":0,"Tags":"python,matplotlib,event-handling,mouseevent","A_Id":36489509,"CreationDate":"2016-04-07T16:32:00.000","Title":"Ignore all mouse clicks on a matplotlib plot","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've implemented a phoneme classifier using an autoencoder (Given an audio file array it returns all the recognized phonemes). I want to extend this project so that word recognition is possible. Does there exist an already trained HMM model (in English) that will recognize a word given a list of phonemes?\nThanks everybody.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":354,"Q_Id":36519225,"Users Score":0,"Answer":"I am not aware of any decoder that could help you. Speech recognition software does not work this way.\nUsually such thing requires custom implementation for dynamic beam search. That is not a huge task, maybe 100 lines of code. It also depends on what your phonetic decoder produces. Is it phonetic lattice (ideally) or is it a 1-best result with scores or simply 1-best result without scores.\nIn case you have a proper lattice you might want to try openfst toolkit where you convert LM and dictionary to FST, then compose with lattice FST and then use fstbestpath to find the best path. Still, instead of all those phonetic conversions you can simply write a dynamic search.\nBaidu in their projects also convert speech to letters and then use language model to fix letter sequence. But they say that without langauge model it works equally well.","Q_Score":0,"Tags":"python,speech-recognition,cmusphinx,htk,autoencoder","A_Id":36650716,"CreationDate":"2016-04-09T16:01:00.000","Title":"Already trained HMM model for word recognition","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it possible the value pixel of image is change after image rotate? I rotate an image, ex, I rotate image 13 degree, so I pick a random pixel before the image rotate and say it X, then I brute force in image has been rotate, and I not found pixel value as same as X. so is it possible the value pixel can change after image rotate? I rotate with opencv library in python.\nAny help would be appreciated.","AnswerCount":2,"Available Count":2,"Score":0.1973753202,"is_accepted":false,"ViewCount":1412,"Q_Id":36532089,"Users Score":2,"Answer":"Yes, it is possible for the initial pixel value not to be found in the transformed image.\nTo understand why this would happen, remember that pixels are not infinitely small dots, but they are rectangles with horizontal and vertical sides, with small but non-zero width and height.\nAfter a 13 degrees rotation, these rectangles (which have constant color inside) will not have their sides horizontal and vertical anymore. \nTherefore an approximation needs to be made in order to represent the rotated image using pixels of constant color, with sides horizontal and vertical.","Q_Score":0,"Tags":"python,opencv,rotation","A_Id":36532299,"CreationDate":"2016-04-10T16:05:00.000","Title":"pixel value change after image rotate","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it possible the value pixel of image is change after image rotate? I rotate an image, ex, I rotate image 13 degree, so I pick a random pixel before the image rotate and say it X, then I brute force in image has been rotate, and I not found pixel value as same as X. so is it possible the value pixel can change after image rotate? I rotate with opencv library in python.\nAny help would be appreciated.","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":1412,"Q_Id":36532089,"Users Score":-1,"Answer":"If you just rotate the same image plane the image pixels will remain same. Simple maths","Q_Score":0,"Tags":"python,opencv,rotation","A_Id":36532213,"CreationDate":"2016-04-10T16:05:00.000","Title":"pixel value change after image rotate","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"While I admire, and am somewhat baffled by, the documentation's commitment to mediating everything related to TensorFlow Serving through Bazel, my understanding of it is tenuous at best. I'd like to minimize my interaction with it.\nI'm implementing my own TF Serving server by adapting code from the Inception + TF Serving tutorial. I find the BUILD files intimidating enough as it is, and rather than slogging through a lengthy debugging process, I decided to simply edit BUILD to refer to the .cc file, in lieu of also building the python stuff which (as I understand it?) isn't strictly necessary.\nHowever, my functional installation of TF Serving can't be imported into python. With normal TensorFlow you build a .whl file and install it that way; is there something similar you can do with TF Serving? That way I could keep the construction and exporting of models in the realm of the friendly python interactive shell rather than editing it, crossing all available fingers, building in bazel, and then \/bazel-bin\/path\/running\/whatever. \nSimply adding the directory to my PYTHONPATH has so far been unsuccessful. \nThanks!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":443,"Q_Id":36561231,"Users Score":0,"Answer":"You are close, you need to update the environment as they do in this script\n...\/serving\/bazel-bin\/tensorflow_serving\/example\/mnist_export\nI printed out the environment update, did it manually \nexport PYTHONPATH=...\nthen I was able to import tensorflow_serving","Q_Score":4,"Tags":"python,tensorflow,tensorflow-serving","A_Id":37579943,"CreationDate":"2016-04-11T23:47:00.000","Title":"de-Bazel-ing TensorFlow Serving","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I have a DataFrame in Python and I need to preprocess my data. Which is the best method to preprocess data?, knowing that some variables have huge scale and others doesn't. Data hasn't huge deviance either. I tried with preprocessing.Scale function and it works, but I'm not sure at all if is the best method to proceed to the machine learning algorithms.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1399,"Q_Id":36575776,"Users Score":0,"Answer":"always split your data to train and test split to prevent overfiting.\n\nif some of your features has big scale and some doesnt you should standard the data.make sure to sandard the data only on the train set not to couse overfiting.\n\nyou also have to look for missing datas and replace or remove them.\nif less than 0.5% of the data in a column is missing you can use 'dropna' otherwise you have to replace it with something(you can replace ut with zero,mean,the previous data...)\n\nyou also have to check outliers by using boxplot.\noutliers are point that are significantly different from other data in the same group can also affects your prediction in machine learning.\n\nits the best if we check the multicollinearity.\nif some features have correlation we have multicollinearity can couse wrong prediction for our model.\n\nfor using your data some of the columns might be categorical with sholud be converted to numerical.","Q_Score":0,"Tags":"python,pandas,machine-learning","A_Id":70480217,"CreationDate":"2016-04-12T14:09:00.000","Title":"Data Preprocessing Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a numpy array U with shape (20, 50): 20 spatial points, in a space of 50 dimensions.\nHow can I transform it into a (20, 1, 50) array, i.e. 20 rows, 1 column, and each element is a 50 dimension point? Kind of encapsulating each row as a numpy array.\nContext\nThe point is that I want to expand the array along the columns (actually, replicating the same array along the columns X times) using numpy.concatenate. But if I would do it straight away I would not get the result I want.\nE.g., if I would expand it once along the columns, I would get an array with shape (20, 100). But what I would like is to access each element as a 50-dimensional point, so when I expand it I would expect to have a new U' with shape (20, 2, 50).","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":71,"Q_Id":36582318,"Users Score":1,"Answer":"You can do U[:, None, :] to add a new dimension to the array.","Q_Score":0,"Tags":"python,arrays,numpy","A_Id":36582371,"CreationDate":"2016-04-12T19:26:00.000","Title":"Numpy group scalars into arrays","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Python function max(3,6) works under pyspark shell. But if it is put in an application and submit, it will throw an error:\nTypeError: _() takes exactly 1 argument (2 given)","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":2764,"Q_Id":36604460,"Users Score":1,"Answer":"If you get this error even after verifying that you have NOT used from pyspark.sql.functions import *, then try the following:\nUse import builtins as py_builtin \nAnd then correspondingly call it with the same prefix. \nEg: py_builtin.max()\n*Adding David Arenburg's and user3610141's comments as an answer, as that is what help me fix my problem in databricks where there was a name collision with min() and max() of pyspark with python build ins.","Q_Score":4,"Tags":"python,pyspark","A_Id":59394797,"CreationDate":"2016-04-13T16:32:00.000","Title":"Python function such as max() doesn't work in pyspark application","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"So I am trying to perform a frequency shift on a set of real valued points.  In order to achieve a frequency shift, one has to multiply the data by a complex exponential, making the resulting data complex. If I multiply by just a cosine I get results at both the sum and difference frequencies. I want just the sum or the difference. \nWhat I have done is multiply the data by a complex exponential, use fft.fft() to compute the fft, then used fft.irfft() on only the positive frequencies to obtain a real valued dataset that has only a sum or difference shift in frequency. This seems to work great, but I want to know if there are any cons to doing this, or maybe a more appropriate way of accomplishing the same goal. Thanks in advance for any help you can provide!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":581,"Q_Id":36606390,"Users Score":1,"Answer":"What you are doing is perfectly fine.  You are generating the analytic signal to accommodate the negative frequencies in the same way a discrete Hilbert transform would.  You will have some scaling issues - you need to double all the non-DC and non-Nyquist signals in the real frequency portion of the FFT results.\nSome practical concerns are that this method imparts a delay of the window size, so if you are trying to do this in real-time you should probably examine using a FIR Hilbert transformer and the appropriate sums.  The delay will be the group delay of the Hilbert transformer in that case.\nAnother item of concern is that you need to remember that the DC component of your signal will also shift along with all the other frequencies.  As such I would recommend that you demean the data (save the value) before shifting, zero out the DC bin after you FFT the data (to remove whatever frequency component ended up in the DC bin), then add the mean back to preserve the signal levels at the end.","Q_Score":1,"Tags":"python,numpy,fft,ifft","A_Id":36609298,"CreationDate":"2016-04-13T18:15:00.000","Title":"In python, If I perform an fft on complex data, then irfft only the positive frequencies, how does that affect the data?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When I read in a CSV, I can say pd.read_csv('my.csv', index_col=3) and it sets the third column as index.\nHow can I do the same if I have a pandas dataframe in memory? And how can I say to use the first row also as an index? The first column and row are strings, rest of the matrix is integer.","AnswerCount":4,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":160606,"Q_Id":36606931,"Users Score":0,"Answer":"Maybe try df = pd.read_csv(header = 0)","Q_Score":63,"Tags":"python,python-3.x,pandas","A_Id":69174336,"CreationDate":"2016-04-13T18:43:00.000","Title":"How to set in pandas the first column and row as index?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Blockquote\n\nI am a student and  beginner in Machine Learning. I want to do feature\nselection  of columns.  My dataset is 50000 X 370 and it is a  binary \nclassification problem.\nFirst i removed the columns with std.deviation = 0, then i removed duplicate columns, After that i checked out top 20 features with highest ROC curve area. What should be the next step apart doing PCA? Can anybody give a sequence of steps to be followed for feature selection?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":541,"Q_Id":36615987,"Users Score":0,"Answer":"You are already doing a lot of preprocessing. The only additional step I recommend is to normalize the values after PCA. Then your data should be ready to be fed into your learning algorithm.\nOr do you want to avoid PCA? If the correlation between your features is not too strong, this might be ok. Then skip PCA and just normalize the values.","Q_Score":0,"Tags":"python-3.x,machine-learning,data-analysis,feature-selection,data-science","A_Id":36617627,"CreationDate":"2016-04-14T07:05:00.000","Title":"Suggestions on Feature selection techniques?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to save a numpy matrix as a .png image, but I cannot do so using the matplotlib since I would like to retain its original size (which apperently the matplotlib doesn't do so since it adds the scale and white background etc). Anyone knows how I can go around this problem using numpy or the PIL please? Thanks","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":617,"Q_Id":36627362,"Users Score":1,"Answer":"Solved using scipy library\nimport scipy.misc\n...(code)\nscipy.misc.imsave(name,array,format)\nor\nscipy.misc.imsave('name.ext',array) where ext is the extension and hence determines the format at which the image will be stored.","Q_Score":1,"Tags":"python,image,numpy,matrix,matplotlib","A_Id":36628839,"CreationDate":"2016-04-14T15:17:00.000","Title":"Converting a matrix into an image in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have two python arrays of the same length. They are generated from reading two separate text files. One represents labels; let it be called \"labelArray\". The other is an array of data arrays; let it be called \"dataArray\". I want to turn them into an RDD object of LabeledPoint. How can I do this?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":1437,"Q_Id":36630260,"Users Score":1,"Answer":"Spark have a function takeSample  which can merge two RDD in to an RDD.","Q_Score":0,"Tags":"python,apache-spark,pyspark","A_Id":36644976,"CreationDate":"2016-04-14T17:40:00.000","Title":"RDD from label Array and data Array in python\/spark","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on some neural networks. But my dataset is same 95 features and about 120 datasets. \nSo while importing theano i get warning g++ not detected and it will degrade the performance. \nDo this will effect even a small dataset?\nI will have a 2-3 hidden layers.\nMy shape of neural network will be (95, 200,200, 4)\nI hope to hear.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":112,"Q_Id":36639002,"Users Score":0,"Answer":"On Windows, you need to install mingw to support g++. Usually, it is advisable to use Anaconda distribution to install Python. Theano works with Python3.4 or older versions. You can use conda install command to install mingw.","Q_Score":0,"Tags":"python,machine-learning,g++,theano","A_Id":38653850,"CreationDate":"2016-04-15T05:37:00.000","Title":"G++ not detected","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am currently working on some project related to machine learning.\nI extracted some features from the object.\nSo I train and test that features with NB, SVM and other classification algorithms and got result about 70 to 80 %\nWhen I train the same features with neural networks using nolearn.dbn and then test it I got about 25% correctly classified. I had 2 hidden layers.\nI still don't understand what is wrong with neural networks.\nI hope to have some help. \nThanks","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":473,"Q_Id":36647169,"Users Score":1,"Answer":"Try increasing the number of hidden units and the learning rate. The power of neural networks comes from the hidden layers. Depending on the size of your dataset, the number of hidden layers can go upto a few thousands. Also, please elaborate on the kind, and number of features you're using. If the feature set is small, you're better off using SVMs and RandomForests instead of neural networks.","Q_Score":0,"Tags":"python,machine-learning,neural-network,deep-learning,nolearn","A_Id":36647234,"CreationDate":"2016-04-15T12:27:00.000","Title":"Python: Deep neural networks","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to python and have recently learnt to create a series in python using Pandas. I can define a series eg:  x = pd.Series([1, 2, 3, 4, 5]) but how to define the series for a range, say 1 to 100 rather than typing all elements from 1 to 100?","AnswerCount":5,"Available Count":1,"Score":0.0399786803,"is_accepted":false,"ViewCount":69375,"Q_Id":36667548,"Users Score":1,"Answer":"try pd.Series([0 for i in range(20)]).\nIt will create a pd series with 20 rows","Q_Score":27,"Tags":"python,python-3.x,pandas,range,series","A_Id":68226844,"CreationDate":"2016-04-16T17:45:00.000","Title":"How to create a series of numbers using Pandas in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Based on the documentation, the default GPU is the one with the lowest id:\n\nIf you have more than one GPU in your system, the GPU with the lowest\n  ID will be selected by default.\n\nIs it possible to change this default from command line or one line of code?","AnswerCount":4,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":32954,"Q_Id":36668467,"Users Score":0,"Answer":"If you want to run your code on the second GPU,it assumes that your machine has two GPUs, You can do the following trick.\n\nopen Terminal\nopen tmux by typing tmux (you can install it by  sudo apt-get install tmux)\nrun this line of code in tmux: CUDA_VISIBLE_DEVICES=1 python YourScript.py\n\nNote: By default, tensorflow uses the first GPU, so with above trick, you can run your another code on the second GPU, separately.\nHope it would be helpful!!","Q_Score":17,"Tags":"python,tensorflow","A_Id":60068593,"CreationDate":"2016-04-16T19:06:00.000","Title":"Change default GPU in TensorFlow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a DataFrame in which a column might have three kinds of values, integers (12331), integers as strings ('345') or some other string ('text').\nIs there a way to drop all rows with the last kind of string from the dataframe, and convert the first kind of string into integers? Or at least some way to ignore the rows that cause type errors if I'm summing the column.\nThis dataframe is from reading a pretty big CSV file (25 GB), so I'd like some solution that would work when reading in chunks.","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":17034,"Q_Id":36685347,"Users Score":1,"Answer":"you can use df._get_numeric_data() directly.","Q_Score":9,"Tags":"python,pandas","A_Id":36686758,"CreationDate":"2016-04-18T04:11:00.000","Title":"Ignoring non-numerical string values in pandas dataframe","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am doing some work in document classification with scikit-learn. For this purpose, I represent my documents in a tf-idf matrix and feed a Random Forest classifier with this information, works perfectly well. I was just wondering which similarity measure is used by the classifier (cosine, euclidean, etc.) and how I can change it. Haven't found any parameters or informatin in the documentation.\nThanks in advance!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":350,"Q_Id":36687929,"Users Score":0,"Answer":"As with most supervised learning algorithms, Random Forest Classifiers do not use a similarity measure, they work directly on the feature supplied to them. So decision trees are built based on the terms in your tf-idf vectors.\nIf you want to use similarity then you will have to compute a similarity matrix for your documents and use this as your features.","Q_Score":1,"Tags":"python-2.7,scikit-learn,text-classification","A_Id":36693072,"CreationDate":"2016-04-18T07:35:00.000","Title":"similarity measure scikit-learn document classification","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a question regarding the command for running the training in Linux. I am using GoogleNet model in caffe framework for binary classification of my images. I used the following command to train my dataset\n.\/build\/tools\/caffe train --solver=models\/MyModelGoogLenet\/quick_solver.prototxt\nBut I received this error\nbash: .\/build\/tools\/caffe: No such file or directory\nHow can I resolve this error? Any suggestions would be of great help.","AnswerCount":2,"Available Count":2,"Score":0.3799489623,"is_accepted":false,"ViewCount":7193,"Q_Id":36721348,"Users Score":4,"Answer":"Follow the below instructions and see if it works:\n\nOpen a terminal\ncd to caffe root directory\nMake sure the file caffe exists by listing them using ls .\/build\/tools\nIf the file is not present, type make. Running step 3 will list the file now.\nType .\/build\/tools\/caffe, No such file error shouldn't get triggered this time.","Q_Score":2,"Tags":"bash,python-2.7,machine-learning,neural-network,deep-learning","A_Id":36728171,"CreationDate":"2016-04-19T14:27:00.000","Title":".\/build\/tools\/caffe: No such file or directory","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I have a question regarding the command for running the training in Linux. I am using GoogleNet model in caffe framework for binary classification of my images. I used the following command to train my dataset\n.\/build\/tools\/caffe train --solver=models\/MyModelGoogLenet\/quick_solver.prototxt\nBut I received this error\nbash: .\/build\/tools\/caffe: No such file or directory\nHow can I resolve this error? Any suggestions would be of great help.","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":7193,"Q_Id":36721348,"Users Score":2,"Answer":"You should specify absolute paths to all your files and commands, to be on the safer side. If \/home\/user\/build\/tools\/caffe train still doesn't work, check if you have a build directory in your caffe root. If not, then use \/home\/user\/tools\/caffe train instead.","Q_Score":2,"Tags":"bash,python-2.7,machine-learning,neural-network,deep-learning","A_Id":36724914,"CreationDate":"2016-04-19T14:27:00.000","Title":".\/build\/tools\/caffe: No such file or directory","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I have a large (10-100GB) data file of 16-bit integer data, which represents a time series from a data acquisition device. I would like to write a piece of python code that scans through it, plotting a moving window of a few seconds of this data. Ideally, I would like this to be as continuous as possible.\nThe data is sampled at 4MHz, so to plot a few seconds of data involves plotting ~10 million data points on a graph. Unfortunately I cannot really downsample since the features I want to see are sparse in the file. \nmatplotlib is not really designed to do this. It is technically possible, and I have a semi-working matplotlib solution which allows me to plot any particular time window, but it's far too slow and cumbersome to do a continuous scan of incrementally changing data - redrawing the figure takes several seconds, which is far too long. \nCan anyone suggest a python package or approach do doing this?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":373,"Q_Id":36725361,"Users Score":0,"Answer":"Something that has worked for me in a similar problem (time varying heat-maps) was to run a batch job of producing several thousands such plots over night, saving each as a separate image. At 10s a figure, you can produce 3600 in 10h. You can then simply scan through the images which could provide you with the insight you're looking for.","Q_Score":0,"Tags":"python-2.7,matplotlib,plot","A_Id":44015767,"CreationDate":"2016-04-19T17:25:00.000","Title":"scanning plot through a large data file using python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to know if there are any implementations of machine learning algorithms in python which can work even if there are missing values in the dataset. Please note that I don't want algorithms imputing the missing values first.(I could have done that using the Imputer package ). I would like to know about the implementation of algorithms which work even if there are missing values present in the dataset without imputation.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":76,"Q_Id":36738514,"Users Score":0,"Answer":"AFAIK scikit-learn doesn't have ML algorithms that can work with missing values without preprocessing them first. R does though.","Q_Score":0,"Tags":"algorithm,python-2.7,machine-learning,scikit-learn,missing-data","A_Id":36814562,"CreationDate":"2016-04-20T08:50:00.000","Title":"Implementations of algorithms without imputation of missing values","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm building a chatbot and I'm new to NLP.\n(api.ai & AlchemyAPI are too expensive for my use case. And wit.ai seems to be buggy and constantly changing at the moment.)\nFor the NLP experts, how easily can I replicate their services locally?\nMy vision so far (with node, but open to Python):\n\nentity extraction via StanfordNER\nintent via NodeNatural's LogisticRegressionClassifier\ntraining UI with text and validate\/invalidate buttons (any prebuilt tools for this?)\n\nAre entities and intents all I'll need for a chatbot? How good will NodeNatural\/StanfordNER be compared to NLP-as-a-service? What headaches am I not seeing?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2286,"Q_Id":36746071,"Users Score":0,"Answer":"Two things to think about are: How are you planning on handling the generation side of things? Entity extraction and classification are going to be useful for the Natural language understanding (NLU) side of things, but generation can be tricky in itself.\nAnother thing to think about is that the training and development of the pipeline of these models is often a separate problem form the deployment. The fact that you want to use node suggests that you already know about deploying software, I think. But remember that deploying large machine learning models in a pipeline can be complicated, and I suspect that these API's may offer neatly packaged pipelines for you.","Q_Score":6,"Tags":"python,node.js,nlp,chatbot","A_Id":54928184,"CreationDate":"2016-04-20T13:54:00.000","Title":"Building your own NLP API","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Contrary to np.testing.assert_equal(), np.testing.assert_raises() does not accept an err_msg parameter. Is there a clean way to display an error message when this assert fails?\nMore generally, why do some assert_* methods accept this parameter, while some others don't?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":173,"Q_Id":36746446,"Users Score":0,"Answer":"These functions are implemented in numpy\/testing\/utils.py.  Studying that code may be your best option.\nI see that assert_raises passes the task on to nose.tools.assert_raises(*args,**kwargs).  So it depends on what that does.  And if I recall use of this in other modules correctly, you are usually more interested in the error message raised by the Error, as opposed to displaying your own.  Remember, unittests are more for your own diagnostic purposes, not as a final user-friendly tool.\nassert_equal is a complex function that tests various kinds of objects, and builds the error message accordingly.  It may including information of the objects.\nChoices in this part of the code were determined largely by what has been useful to the developers.  They are written primarily to test the numpy code itself.  So being systematic is not a priority.","Q_Score":0,"Tags":"python,unit-testing,numpy","A_Id":36749915,"CreationDate":"2016-04-20T14:09:00.000","Title":"Use error message with numpy.testing.assert_raises()","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"This question is related to Python Multiprocessing. I am asking for a suitable interprocess communication data-structure for my specific scenario:\nMy scenario\nI have one producer and one consumer.\n\nThe producer produces a single fairly small panda Dataframe every 10-ish secs, then the producer puts it on a python.multiprocess.queue.\nThe consumer is a GUI polling that python.multiprocess.queue every 100ms. It is VERY CRITICAL that the consumer catches every single DataFrame the producer produces.\n\nMy thinking\npython.multiprocess.queue is serving the purpose (I think), and amazingly simple to use! (praise the green slithereen lord!). But I am clearly not utilizing queue's full potential with only one producer one consumer and a max of one item on the queue. That leads me to believe that there is simpler thing than queue. I tried to search for it, I got overwhelmed by options listed in: python 3.5 documentation: 18. Interprocess Communication and Networking. I am also suspecting there may be a way not involving interprocess communication data-structure at all for my need.\nPlease Note\n\nPerformance is not very important\nI will stick with multiprocessing for now, instead of multithreading.\n\nMy Question\nShould I be content with queue? or is there a more recommended way? I am not a professional programmer, so I insist on doing things the tried and tested way.\nI also welcome any suggestions of alternative ways of approaching my problem.\nThanks","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":300,"Q_Id":36748120,"Users Score":3,"Answer":"To me, the most important thing you mentioned is this:\n\nIt is VERY CRITICAL that the consumer catches every single DataFrame the producer produces.\n\nSo, let's suppose you used a variable to store the DataFrame. The producer would set it to the produced value, and the consumer would just read it. That would work very fine, I guess.\nBut what would happen if somehow the consumer got blocked by more than one producing cycle? Then some old value would be overwritten before reading. And that's why I think a (thread-safe) queue is the way to go almost \"by definition\".\nBesides, beware of premature optimization. If it works for your case, excellent. If some day, for some other case, performance comes to be a problem, only then you should spend the extra work, IMO.","Q_Score":4,"Tags":"python,python-3.x,pandas,multiprocessing,interprocess","A_Id":36754207,"CreationDate":"2016-04-20T15:13:00.000","Title":"1 producer, 1 consumer, only 1 piece of data to communicate, is queue an overkill?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I found that some of the stock exchanges is not supported for datareader. Example, Singapore. Any workaround?\nquery = web.DataReader((\"SGX:BLA\"), 'google', start, now) return such error`\nIOError: after 3 tries, Google did not return a 200 for url 'http:\/\/www.google.com\/finance\/historical?q=SGX%3ABLA&startdate=Jan+01%2C+2015&enddate=Apr+20%2C+2016&output=csv\nIt works for IDX indonesia\nquery = web.DataReader((\"IDX:CASS\"), 'google', start, now)","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":850,"Q_Id":36749105,"Users Score":0,"Answer":"That URL is a 404 - pandas isn't at fault, maybe just check the URL? Perhaps they're on different exchanges with different google finance support.","Q_Score":1,"Tags":"python-2.7,pandas,datareader,google-finance,pandas-datareader","A_Id":36783492,"CreationDate":"2016-04-20T15:52:00.000","Title":"Pandas: datareader unable to get historical stock data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":1,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"So, I know that in R you can provide data for a logistic regression in this form:\nmodel <- glm( cbind(count_1, count_0) ~ [features] ..., family = 'binomial' )\nIs there a way to do something like cbind(count_1, count_0) with sklearn.linear_model.LogisticRegression? Or do I actually have to provide all those duplicate rows?  (My features are categorical, so there would be a lot of redundancy.)","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":308,"Q_Id":36757158,"Users Score":0,"Answer":"If they are categorical - you should provide binarized version of it. I don't know how that code in R works, but you should binarize your categorical feature always. Because you have to emphasize that each value of your feature is not related to other one, i.e. for feature \"blood_type\" with possible values 1,2,3,4 your classifier must learn that 2 is not related to 3, and 4 is not related to 1 in any sense. These is achieved by binarization.\nIf you have too many features after binarization - you can reduce dimensionality of binarized dataset by FeatureHasher or more sophisticated methods like PCA.","Q_Score":0,"Tags":"python,scikit-learn,logistic-regression,bernoulli-probability","A_Id":36758704,"CreationDate":"2016-04-20T23:47:00.000","Title":"Can you use counts in sklearn logistic regression input?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I was using Tensorflow sequence to sequence example code. for some reason, I don't want to add softmax to output. instead, I want to get the raw output of decoder without softmax. I was wondering if anyone know how to do it based on sequence to sequence example code? Or I need to create it from scratch or modify the the seq2seq.py (under the \/tensorflow\/tensorflow\/python\/ops\/seq2seq.py)?\nThank you","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":389,"Q_Id":36759037,"Users Score":1,"Answer":"The model_with_buckets() function in seq2seq.py returns 2 tensors: the output and the losses. The outputs variable contains the raw output of the decoder that you're looking for (that would normally be fed to the softmax).","Q_Score":0,"Tags":"python,tensorflow","A_Id":36870610,"CreationDate":"2016-04-21T03:24:00.000","Title":"tensorflow sequence to sequence without softmax","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a ~220 million row, 7 column csv file. I need to remove row 2636759.\nThis file is 7.7GB, more than will fit in memory. I'm most familiar with R, but could also do this in python or bash.\nI can't read or write this file in one operation. What is the best way to build this file incrementally on disk, instead of trying to do this all in memory?\nI've tried to find this on SO but have only been able to find how to do this with files that are small enough to read\/write in memory, or with rows that are at the beginning of the file.","AnswerCount":3,"Available Count":1,"Score":0.1325487884,"is_accepted":false,"ViewCount":1468,"Q_Id":36779522,"Users Score":2,"Answer":"use sed '2636759d' file.csv > fixedfile.csv\nAs a test for a 40,001 line 1.3G csv, removing line 40,000 this way takes 0m35.710s. The guts of the python solution from @en_Knight (just stripping the line and writing to a temp file) is ~ 2 seconds faster for this same file.\nedit OK sed (or some implementations) may not work (based on feedback from questioner)\nYou could, in plain bash, to remove row n from a file of N rows, file.csv, you can do head -[n-1] file.csv > file_fixed.csv and tail -[N-n] file.csv >> file_fixed.csv (in both of these the expression in brackets is replaced by a plain number). \nTo do this, though you need to know N. The python solution is better...","Q_Score":6,"Tags":"python,r,csv","A_Id":36780531,"CreationDate":"2016-04-21T20:12:00.000","Title":"remove known exact row in huge csv","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Imagine I have the following feature vectors:\nTraining vectors:\nClass 1:\n[ 3,  5,  4,  2,  0,  3,  2], \n[ 33,  50,  44,  22,   0,  33,  20]\nClass 2:\n[ 1,  2,  3,  1,  0,  0,  4], \n[ 11,  22,  33,  11,   0,  0,  44]\nTesting vectors:\nClass 1:\n[ 330,  550,  440,  220,    0,  330,  200]\nClass 2:\n[ 110,  220,  333,  111,    0,  0,  444]\nI am using SVM, which learns from the training vectors and then classifies the test samples.\nAs you can see the feature vectors have very different dimensions: the training set features are very low value numbers and the test set vectors are very high value numbers.\nMy question is whether it is confusing for SVM to learn from such feature vectors?\nOf course when I do vector scaling the difference is still there:\nfor example after applying standardScaler() on the feature vectors for Class 1:\nTraining:\n[ 0.19  1.53  0.86  -0.48 -1.82  0.19 -0.48]\n[ 20.39  31.85  27.80  12.99  -1.82  20.39  11.64]\nTest:\n[ 220.45  368.63  294.54  146.35   -1.82  220.45  132.88]\nBasically, this is a real world problem, and I am asking this since I have developed a way to pre-scale those feature vectors for my particular case. \nSo after I would use my pre-scaling method, the feature vectors for Class 1 would become:\nTraining:\n[ 3.  5.  4.  2.  0.  3.  2.]\n[ 2.75        4.16666667  3.66666667  1.83333333  0.          2.75\n  1.66666667]\nTest:\n[ 2.84482759  4.74137931  3.79310345  1.89655172  0.          2.84482759\n  1.72413793]\nwhich makes them very similar in nature.\nThis looks even better when standardScaler() is applied onto the pre-scaled vectors:\nTraining:\n[ 0.6  1.   0.8  0.4  0.   0.6  0.4]\n[ 0.55        0.83333333  0.73333333  0.36666667  0.          0.55\n  0.33333333]\nTest:\n[ 0.56896552  0.94827586  0.75862069  0.37931034  0.          0.56896552\n  0.34482759]\nThe ultimate question is whether my pre-scaling method is going to help the SVM in any way? This is more of a theoretical question, any insight into this is appreciated.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":62,"Q_Id":36817217,"Users Score":3,"Answer":"Yes, it will affect the performance of the SVM. It seems your test vectors are just scaled versions of your training vectors. The SVM has no way of knowing that the scaling is irrelevant in your case (unless you present it alot of differently scaled training vectors)\nA common practice for feature vectors where the scaling is irrelevant is to scale all the test and train vectors to a common length.","Q_Score":0,"Tags":"python,machine-learning,svm,feature-extraction","A_Id":36822368,"CreationDate":"2016-04-23T22:32:00.000","Title":"How does Support Vector Machine deal with confusing feature vectors?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Edited for the confusion in the problem, thanks for the answers!\nMy original problem was that I have a list [1,2,3,4,5,6,7,8], and I want to select every chunk of size x with gap of one. So if I want to select select every other chunk of size 2, the outcome would be [1,2,4,5,7,8]. A chunk size of three would give me [1,2,3,5,6,7].\nI've searched a lot on slicing and I couldn't find a way to select chunks instead of element. Make multiple slice operations then join and sort seems a little too expensive. The input can either be a python list or numpy ndarray. Thanks in advance.","AnswerCount":6,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1225,"Q_Id":36827155,"Users Score":0,"Answer":"A simple list comprehension can do the job:\n[ L[i] for i in range(len(L)) if i%3 != 2 ]\nFor chunks of size n \n[ L[i] for i in range(len(L)) if i%(n+1) != n ]","Q_Score":3,"Tags":"python,arrays,list,numpy,slice","A_Id":36827834,"CreationDate":"2016-04-24T18:24:00.000","Title":"Python\/Numpy fast way to selecting every nth chunk in list","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have noticed that you can put various numbers inside of numpy.random.seed(), for example numpy.random.seed(1), numpy.random.seed(101). What do the different numbers mean? How do you choose the numbers?","AnswerCount":7,"Available Count":2,"Score":0.1418931938,"is_accepted":false,"ViewCount":20730,"Q_Id":36847022,"Users Score":5,"Answer":"What is normally called a random number sequence in reality is a \"pseudo-random\" number sequence because the values are computed using a deterministic algorithm and probability plays no real role.\nThe \"seed\" is a starting point for the sequence and the guarantee is that if you start from the same seed you will get the same sequence of numbers. This is very useful for example for debugging (when you are looking for an error in a program you need to be able to reproduce the problem and study it, a non-deterministic program would be much harder to debug because every run would be different).","Q_Score":18,"Tags":"python,numpy-random","A_Id":44995504,"CreationDate":"2016-04-25T17:17:00.000","Title":"What numbers that I can put in numpy.random.seed()?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have noticed that you can put various numbers inside of numpy.random.seed(), for example numpy.random.seed(1), numpy.random.seed(101). What do the different numbers mean? How do you choose the numbers?","AnswerCount":7,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":20730,"Q_Id":36847022,"Users Score":0,"Answer":"One very specific answer: np.random.seed can take values from 0 and 2**32 - 1, which interestingly differs from random.seed which can take any hashable object.","Q_Score":18,"Tags":"python,numpy-random","A_Id":60006676,"CreationDate":"2016-04-25T17:17:00.000","Title":"What numbers that I can put in numpy.random.seed()?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to OpenCV.\nI am using OpenCV 3.1 and python 2.7.\nI have 5 images of bikes and 5 images of cars.\nI want to find out given any image is it a car or a bike .\nOn the internet I found out that using haar cascade we can train,\nbut most of the examples contain only one trained data means, the user will train only car images and \nwith query image and  they will try to find it it is a car or not,\nbut I want to check if it is a car or a bike or nothing.\nI want to match images based on shape of objects.\nAnother option I was thinking that take query image and compare with stored images and depending upon similarity give the result. But I know this would take longer time which would be not good.\nAre there any better option? There is also template matching but I dont know which would be better options to go for this kind of solution since I dont have knowledge about OpenCV.","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1017,"Q_Id":36856532,"Users Score":1,"Answer":"Regarding your question about the haar cascades. You can use them to classify the images the way you want: \nTrain two haar cascades, one for cars and one for bikes. Both cascades will return a value of how certain they are, that the image contains the object they were trained for. If both are uncertain, the image probably contains nothing. Otherwise you take the class with the higher certainty for the content of the image.","Q_Score":4,"Tags":"python,opencv","A_Id":36857440,"CreationDate":"2016-04-26T06:09:00.000","Title":"OpenCV to recognize image using python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I know that for fft.fft, I can use fft.fftfreq. But there seems to be no such thing as fft.fftfreq2. Can I somehow use fft.fftfreq to calculate the frequencies in 2 dimensions, possibly with meshgrid? Or is there some other way?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":343,"Q_Id":36859840,"Users Score":0,"Answer":"Yes. Apply fftfreq to each spatial vector (x and y) separately. Then create a meshgrid from those frequency vectors. \nNote that you need to use fftshift if you want the typical representation (zero frequencies in center of spatial spectrum) to both the output and your new spatial frequencies (before using meshgrid).","Q_Score":0,"Tags":"python,numpy,fft","A_Id":36976133,"CreationDate":"2016-04-26T08:49:00.000","Title":"How to extract the frequencies associated with fft2 values in numpy?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am just curious on the interpretation of sklearn's feature_importances_ attribute. I know that the features with highests coefficients are the features that would highly predict the outcome. My question is - Are these the features strongly predictive to return a 1 (or yes) or not necessarily? (Supervised Learning - Binary response - yes(1) or no(0)).\nFor example, after building the predictive model, I found out that these words are the top features - insider-trading, theft, embezzlement, investment. The response is 'illegal'(1) or 'legal'(0).\nDoes it mean that when a certain text has those words, there's a huge chance it's illegal or not necessarily? And, it just simply means that the value of these words would lead to a strong prediction (either illegal or legal). Appreciate any answer to such.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":213,"Q_Id":36864116,"Users Score":0,"Answer":"It means those words are \"strongly associated\" with one of the responses, in your case probably illegal(1). Depending on your classifier, the exact technical definition of strongly associated will vary. It could be the joint probability of the word and response, P(X='theft', Y='illegal'), or it could be the conditional probabilityP(X='theft' | Y='illegal').\nIntuitively, whenever these terms appear in a document, the probability of that document belonging to the illegal category is increased.","Q_Score":1,"Tags":"python,scikit-learn","A_Id":36864312,"CreationDate":"2016-04-26T11:49:00.000","Title":"sklearn's feature importances_","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataframe df which is sparse and for memory efficiency I wish to convert it using to_sparse()\nHowever it seems that the new representation ends up with the dtype=float64, even when my df is dtype=int8.\nIs there a way specify the data type\/ prevent auto conversion to dtype=float64 when using to_sparse() ?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":141,"Q_Id":36864863,"Users Score":1,"Answer":"In short. No.\nYou see, dtypes is not a pandas controlled entity. Dtypes is typically a numpy thing.\nDtypes are not controllable in any way, they are automagically asserted by numpy and can only change when you change the data inside the dataframe or numpy array.\nThat being said, the typical reason for ending up with a float instead of an int as a dtype is because of the introduction of NaN values into the series or numpy array. This is a pandas gotcha some say. I personally would argue it is due to the (too) close coupling between pandas and numpy.\nIn general, dtypes should never be trusted for anything, they are incredibly unreliable. I think everyone working with numpy\/pandas would live a better life if they were never exposed to dtypes at all.\nIf you really really hate floats, the only other option for you as far as I know is to use string representations, which of course causes even more problems in most cases.","Q_Score":1,"Tags":"python,pandas","A_Id":36866111,"CreationDate":"2016-04-26T12:23:00.000","Title":"Defining dtype of df.to_sparse() result","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have around 7k samples and 11 features which I concentrated into one. This concentrated value I call ResVal and is a weighted sum of previous features. Then I gathered these ResVals into 1D array.\nNow I want to cluster this results with AgglomerativeClustering but console complains about 1D array.\nHow can I fix it and get cluster results by line number?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":28,"Q_Id":36867924,"Users Score":1,"Answer":"Make the array into a column: use x[:, np.newaxis] instead of x","Q_Score":0,"Tags":"python,arrays,scikit-learn","A_Id":36892239,"CreationDate":"2016-04-26T14:29:00.000","Title":"How to use agglometative clustering with 1 dimensional array valueset?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm interested to approach the confirming point in polygon problem from another direction. I have a dataframe containing series of coordinates, known to be in certain polygon (administrative area). I have other dataframes with coordinates not assigned to any admin area. Would using SciKit offer an alternate means to assign these to the admin area.\nExample:\nI know (x, y) point 1 is in admin area a if (x, y) point 2 is within specified radius of point (1, i) can assign it to the same admin area. Does this approach sound viable?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":152,"Q_Id":36876389,"Users Score":1,"Answer":"Discriminant analisys (a.k.a. supervised  classification) is the way to go. You adjust the model by using the coordinates of the points and the information on the node they belong to. As a result, you obtain a model you can use to predict the node for new points as they are known. Linear discriminant analysis is one of the simplest algorithms.","Q_Score":0,"Tags":"python,scikit-learn","A_Id":36876832,"CreationDate":"2016-04-26T21:48:00.000","Title":"Using cluster analysis as alternative to point in polygon assignment","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a 60000 by 200 numpy array. I want to make it 60000 by 201 by adding a column of 1's to the right. (so every row is [prev, 1])\nConcatenate with axis = 1 doesn't work because it seems like concatenate requires all input arrays to have the same dimension.\nHow should I do this? I can't find any existing useful answer, and most of the answers about this were written a few years ago so things might be different now.","AnswerCount":5,"Available Count":1,"Score":0.0399786803,"is_accepted":false,"ViewCount":64625,"Q_Id":36878089,"Users Score":1,"Answer":"I think the numpy method column_stack is more interesting because you do not need to create a column numpy array to stack it in the matrix of interest. With the column_stack you just need to create a normal numpy array.","Q_Score":24,"Tags":"python,arrays,numpy","A_Id":36878371,"CreationDate":"2016-04-27T00:19:00.000","Title":"Python: Add a column to numpy 2d array","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In machine learning task. We should get a group of random w.r.t normal distribution with bound. We can get a normal distribution number with np.random.normal() but it does't offer any bound parameter. I want to know how to do that?","AnswerCount":4,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":43386,"Q_Id":36894191,"Users Score":0,"Answer":"You can subdivide your targeted range (by convention) to equal partitions and then calculate the integration of each and all area, then call uniform method on each partition according to the surface.\nIt's implemented in python:\nquad_vec(eval('scipy.stats.norm.pdf'), 1, 4,points=[0.5,2.5,3,4],full_output=True)","Q_Score":44,"Tags":"python,numpy,random,machine-learning,normal-distribution","A_Id":60298484,"CreationDate":"2016-04-27T15:24:00.000","Title":"How to get a normal distribution within a range in numpy?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it possible to extract the 'true' color of building fa\u00e7ade from a photo\/ a set of similar photos and removing the distraction of shadow? Currently, I'm using K-means clustering to get the dominant colors, however, it extracts darker colors (if the building is red, then the 1st color would be dark red) as there are lots of shadow areas in real photos.\nAny suggestions are greatly appreciated!\nThanks in advance!","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":383,"Q_Id":36894358,"Users Score":1,"Answer":"If the shadows cover a significant part of the image then this problem is non-trivial.\nIf the shadow is a small fraction of the area you're interested though you could try using k-medoids instead of k-means and as Piglet mentioned using a different color space with separate chromaticity and luminance channels may help.","Q_Score":5,"Tags":"python,opencv,image-processing,machine-learning,computer-vision","A_Id":36908550,"CreationDate":"2016-04-27T15:31:00.000","Title":"What is a good way to extract dominant colors from image without the shadow?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a Pandas Dataframe which has a field txn['time_diff']\n     Send_Agent  Pay_Agent           Send_Time            Pay_Time  score  \\\n0     AKC383903  AXX100000 2014-08-19 18:52:35 2015-05-01 22:08:39      1\n1     AWA280699  AXX100000 2014-08-19 19:32:18 2015-05-01 17:12:32      1\n2     ALI030170  ALI030170 2014-08-26 10:11:40 2015-05-01 22:20:09      1\n3     AKC403474  AXX100000 2014-08-19 20:35:53 2015-05-01 21:27:12      1\n4     AED002616  AED002616 2014-09-28 18:37:32 2015-05-01 14:06:17      1\n5     ALI030170  ALI030170 2014-08-20 05:08:03 2015-05-01 21:29:43      1\n6     ADA414187  ADA414187 2014-09-26 17:46:24 2015-05-01 21:37:51      1\n7     AWA042396  AWA042396 2014-08-27 12:07:11 2015-05-01 17:39:31      1\n8     AED002616  AED002616 2014-08-23 04:53:03 2015-05-01 13:33:12      1\n9     ALA500685  AXX100000 2014-08-27 16:41:26 2015-05-01 19:01:52      1\n10    AWA263407  AXX100000 2014-08-27 18:04:24 2015-05-01 10:39:14      1\n11    ACH928457  ACH928457 2014-08-28 10:26:41 2015-05-01 11:55:59      1\n             time_diff\n0    255 days 03:16:04\n1    254 days 21:40:14\n2    248 days 12:08:29\n3    255 days 00:51:19\n4    214 days 19:28:45\n5    254 days 16:21:40\n6    217 days 03:51:27\n7    247 days 05:32:20\n8    251 days 08:40:09\n9    247 days 02:20:26\n10   246 days 16:34:50\n11   246 days 01:29:18\ntxn['time_diff'].min() works fine. But txn['time_diff'].groupby(txn['Send_Agent']).min() gives me the output in seconds\nSend_Agent\nA03010016       86546000000000\nA03020048       53056000000000\nA10001087      113459000000000\nA11120030      680136000000000\nA11120074      787844000000000\nA11120106     1478045000000000\nA11120117     2505686000000000\nA11120227      923508000000000\nA11120294     1460320000000000\nA11120304      970226000000000\nA11120393     3787969000000000\nA11120414     2499079000000000\nA11120425       65753000000000\nA11140016      782269000000000\nBut I want it in terms of days , hours , mins.\nI did the following \ntxn = txn.astype(str)\nTime_diff_min = txn['time_diff'].groupby(txn['Send_Agent']).min()\nThe output I get is in the right format but is erroneous and is fetching the \"first\" value it finds for that \"groupby\"\nIn [15]: Time_diff_min = txn['time_diff'].groupby(txn['Send_Agent']).min()\nIn [16]: Time_diff_min\nOut[16]:\nSend_Agent\nA03010016      1 days 00:02:26.000000000\nA03020048      0 days 14:44:16.000000000\nA10001087      1 days 07:30:59.000000000\nA11120030     13 days 06:29:35.000000000\nA11120074      9 days 02:50:44.000000000\nA11120106     17 days 02:34:05.000000000\nA11120117     29 days 00:01:26.000000000\nA11120227     10 days 16:31:48.000000000\nA11120294     16 days 21:38:40.000000000\nA11120304     11 days 05:30:26.000000000\nA11120393     43 days 20:12:49.000000000\nA11120414     28 days 22:11:19.000000000\nA11120425      0 days 18:15:53.000000000\nA11140016      9 days 01:17:49.000000000\nA11140104      0 days 15:33:06.000000000\nA11140126      1 days 18:36:07.000000000\nA11140214     23 days 02:30:07.000000000\nAlso \n Time_diff_min = txn['time_diff']..min().groupby(txn['Send_Agent'])\nthrows an error that I cannot groupby on a timedelta","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":110,"Q_Id":36901311,"Users Score":1,"Answer":"Sorry for the garbled question. in order to do a groupby for a timedelta value the best way is to do a pd.numeric on the 'timedelta value' and once the results are obtained we can again do a pd.to_timedelta on it.","Q_Score":0,"Tags":"python-2.7,pandas","A_Id":36946734,"CreationDate":"2016-04-27T21:32:00.000","Title":"obtaining the min value of time_diff for a Pandas Dataframe","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In python, is there a vectorized efficient way to calculate the cosine distance of a sparse array u to a sparse matrix v, resulting in an array of elements [1, 2, ..., n] corresponding to cosine(u,v[0]), cosine(u,v[1]), ..., cosine(u, v[n])?","AnswerCount":5,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":3904,"Q_Id":36920262,"Users Score":0,"Answer":"Below worked for me, have to provide correct signature\n\nfrom scipy.spatial.distance import cosine\n\ndef cosine_distances(embedding_matrix, extracted_embedding):\n  return cosine(embedding_matrix, extracted_embedding)\ncosine_distances = np.vectorize(cosine_distances, signature='(m),(d)->()')\n\ncosine_distances(corpus_embeddings, extracted_embedding)\n\nIn my case \ncorpus_embeddings is a (10000,128) matrix\nextracted_embedding is a 128-dimensional vector","Q_Score":5,"Tags":"python,vectorization,cosine-similarity","A_Id":65979818,"CreationDate":"2016-04-28T16:19:00.000","Title":"Cosine distance of vector to matrix","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Scikit-learn MiniBatchKMeans has an inertia field that can be used to see how tight clusters are. Does the Birch clustering algorithm have an equivalent? There does not seem to be in the documentation.\nIf there is no built in way to check this measurement, does it make sense to find the average euclidian distance for each point's closest neighbor in each cluster., then find the mean of those average distances?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":886,"Q_Id":36926819,"Users Score":1,"Answer":"Nothing is free, and you don't want algorithms to perform unnecessary computations.\nInertia is only sensible for k-means (and even then, do not compare different values of k), and it's simply the variance sum of the data. I.e. compute the mean of every cluster, then the squared deviations from it. Don't compute distances, the equation is simply ((x-mu)**2).sum()","Q_Score":0,"Tags":"python,scikit-learn,cluster-analysis","A_Id":36930521,"CreationDate":"2016-04-28T22:54:00.000","Title":"Can I get \"inertia\" for sklearn Birch clusters?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a numpy array of values and I wanted to scale (zoom) it. With floats I was able to use scipy.ndimage.zoom but now my array contains complex values which are not supported by scipy.ndimage.zoom. My workaround was to separate the array into two parts (real and imaginary) and scale them independently. After that I add them back together. Unfortunately this produces a lot of tiny artifacts in my 'image'. Does somebody know a better way? Maybe there also exists a python library for this? I couldn't find one.\nThank you!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":215,"Q_Id":36927432,"Users Score":0,"Answer":"This is not a good answer but it seems to work quite well. Instead of using the default parameters for the zoom method, I'm using order=0. I then proceed to deal with the real and imaginary part separately, as described in my question. This seems to reduce the artifacts although some smaller artifacts remain. It is by no means perfect and if somebody has a better answer, I would be very interested.","Q_Score":0,"Tags":"python,numpy,scipy,complex-numbers,zooming","A_Id":36928073,"CreationDate":"2016-04-29T00:05:00.000","Title":"Scipy zoom with complex values","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Having some problems with scipy. Installed latest version using pip (0.17.0). Run scipy.test() and I'm getting the following errors. Are they okay to ignore? I'm using python 2.7.6. \nThanks for your help.\n\n======================================================================\nERROR: test_add_function_ordered (test_catalog.TestCatalog)\nTraceback (most recent call last):\n    File \"\/usr\/local\/lib\/python2.7\/dist-packages\/scipy\/weave\/tests\/test_catalog.py\", line 477, in test_add_function_ordered\n      q.add_function('f',string.upper)\n    File \"\/usr\/local\/lib\/python2.7\/dist-packages\/scipy\/weave\/catalog.py\", line 833, in add_function\n      self.add_function_persistent(code,function)\n    File \"\/usr\/local\/lib\/python2.7\/dist-packages\/scipy\/weave\/catalog.py\", line 849, in add_function_persistent\n      cat = get_catalog(cat_dir,mode)\n    File \"\/usr\/local\/lib\/python2.7\/dist-packages\/scipy\/weave\/catalog.py\", line 486, in get_catalog\n      sh = shelve.open(catalog_file,mode)\n    File \"\/usr\/lib\/python2.7\/shelve.py\", line 239, in open\n      return DbfilenameShelf(filename, flag, protocol, writeback)\n    File \"\/usr\/lib\/python2.7\/shelve.py\", line 222, in init\n      import anydbm\n    File \"\/usr\/lib\/python2.7\/anydbm.py\", line 50, in \n      _errors.append(_mod.error)\n  AttributeError: 'module' object has no attribute 'error'\n======================================================================\nERROR: test_add_function_persistent1 (test_catalog.TestCatalog)\nTraceback (most recent call last):\n    File \"\/usr\/local\/lib\/python2.7\/dist-packages\/scipy\/weave\/tests\/test_catalog.py\", line 466, in test_add_function_persistent1\n      q.add_function_persistent('code',i)\n    File \"\/usr\/local\/lib\/python2.7\/dist-packages\/scipy\/weave\/catalog.py\", line 849, in add_function_persistent\n      cat = get_catalog(cat_dir,mode)\n    File \"\/usr\/local\/lib\/python2.7\/dist-packages\/scipy\/weave\/catalog.py\", line 486, in get_catalog\n      sh = shelve.open(catalog_file,mode)\n    File \"\/usr\/lib\/python2.7\/shelve.py\", line 239, in open\n      return DbfilenameShelf(filename, flag, protocol, writeback)\n    File \"\/usr\/lib\/python2.7\/shelve.py\", line 222, in init\n      import anydbm\n    File \"\/usr\/lib\/python2.7\/anydbm.py\", line 50, in \n      _errors.append(_mod.error)\n  AttributeError: 'module' object has no attribute 'error'\n======================================================================\nERROR: test_get_existing_files2 (test_catalog.TestCatalog)\nTraceback (most recent call last):\n    File \"\/usr\/local\/lib\/python2.7\/dist-packages\/scipy\/weave\/tests\/test_catalog.py\", line 394, in test_get_existing_files2\n      q.add_function('code', os.getpid)\n    File \"\/usr\/local\/lib\/python2.7\/dist-packages\/scipy\/weave\/catalog.py\", line 833, in add_function\n      self.add_function_persistent(code,function)\n    File \"\/usr\/local\/lib\/python2.7\/dist-packages\/scipy\/weave\/catalog.py\", line 849, in add_function_persistent\n      cat = get_catalog(cat_dir,mode)\n    File \"\/usr\/local\/lib\/python2.7\/dist-packages\/scipy\/weave\/catalog.py\", line 486, in get_catalog\n      sh = shelve.open(catalog_file,mode)\n    File \"\/usr\/lib\/python2.7\/shelve.py\", line 239, in open\n      return DbfilenameShelf(filename, flag, protocol, writeback)\n    File \"\/usr\/lib\/python2.7\/shelve.py\", line 222, in init\n      import anydbm\n    File \"\/usr\/lib\/python2.7\/anydbm.py\", line 50, in \n      _errors.append(_mod.error)\n  AttributeError: 'module' object has no attribute 'error'\n======================================================================\nERROR: test_create_catalog (test_catalog.TestGetCatalog)\nTraceback (most recent call last):\n    File \"\/usr\/local\/lib\/python2.7\/dist-packages\/scipy\/weave\/tests\/test_catalog.py\", line 286, in test_create_catalog\n      cat = catalog.get_catalog(pardir,'c')\n    File \"\/usr\/local\/lib\/python2.7\/dist-packages\/scipy\/weave\/catalog.py\", line 486, in get_catalog\n      sh = shelve.open(catalog_file,mode)\n    File \"\/usr\/lib\/python2.7\/shelve.py\", line 239, in open\n      return DbfilenameShelf(filename, flag, protocol, writeback)\n    File \"\/usr\/lib\/python2.7\/shelve.py\", line 222, in init\n      import anydbm\n    File \"\/usr\/lib\/python2.7\/anydbm.py\", line 50, in \n      _errors.append(_mod.error)\n  AttributeError: 'module' object has no attribute 'error'\n\nRan 20343 tests in 138.416s\nFAILED (KNOWNFAIL=98, SKIP=1679, errors=4)","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":82,"Q_Id":36943283,"Users Score":1,"Answer":"All these are in weave, which is not used anywhere else in scipy itself. So unless you're using weave directly, you're likely OK. And there is likely no reason to use weave in new code anyway.","Q_Score":0,"Tags":"python-2.7,scipy","A_Id":36951224,"CreationDate":"2016-04-29T16:25:00.000","Title":"scipy.test() results in errors","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a simple problem: multiply a matrix by a vector. However, the implementation of the multiplication is complicated because the matrix is 18 gb (3000^2 by 500). \nSome info:\n\nThe matrix is stored in HDF5 format. It's Matlab output. It's dense so no sparsity savings there.\nI have to do this matrix multiplication roughly 2000 times over the course of my algorithm (MCMC Bayesian Inversion)\nMy program is a combination of Python and C, where the Python code handles most of the MCMC procedure: keeping track of the random walk, generating perturbations, checking MH Criteria, saving accepted proposals, monitoring the burnout, etc. The C code is simply compiled into a separate executable and called when I need to solve the forward (acoustic wave) problem. All communication between the Python and C is done via the file system. All this is to say I don't already have ctype stuff going on. \nThe C program is already parallelized using MPI, but I don't think that's an appropriate solution for this MV multiplication problem. \nOur program is run mainly on linux, but occasionally on OSX and Windows. Cross-platform capabilities without too much headache is a must. \n\nRight now I have a single-thread implementation where the python code reads in the matrix a few thousand lines at a time and performs the multiplication. However, this is a significant bottleneck for my program since it takes so darn long. I'd like to multithread it to speed it up a bit. \nI'm trying to get an idea of whether it would be faster (computation-time-wise, not implementation time) for python to handle the multithreading and to continue to use numpy operations to do the multiplication, or to code an MV multiplication function with multithreading in C and bind it with ctypes.\nI will likely do both and time them since shaving time off of an extremely long running program is important. I was wondering if anyone had encountered this situation before, though, and had any insight (or perhaps other suggestions?)\nAs a side question, I can only find algorithmic improvements for nxn matrices for m-v multiplication. Does anyone know of one that can be used on an mxn matrix?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":342,"Q_Id":36959589,"Users Score":3,"Answer":"Hardware\nAs Sven Marnach wrote in the comments, your problem is most likely I\/O bound since disk access is orders of magnitude slower than RAM access.\nSo the fastest way is probably to have a machine with enough memory to keep the whole matrix multiplication and the result in RAM. It would save lots of time if you read the matrix only once.\nReplacing the harddisk with an SSD would also help, because that can read and write a lot faster.\nSoftware\nBarring that, for speeding up reads from disk, you could use the mmap module. This should help, especially once the OS figures out you're reading pieces of the same file over and over and starts to keep it in the cache.\nSince the calculation can be done by row, you might benefit from using numpy in combination with a multiprocessing.Pool for that calculation. But only really if a single process cannot use all available disk read bandwith.","Q_Score":1,"Tags":"python,c,multithreading,linear-algebra,hdf5","A_Id":36959985,"CreationDate":"2016-04-30T19:59:00.000","Title":"Efficient Matrix-Vector Multiplication: Multithreading directly in Python vs. using ctypes to bind a multithreaded C function","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using Scikit-Learn and Pandas libraries of Python for Data Analysis. \nHow to interface Python with data visualization tools such as Qlikview?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":8577,"Q_Id":37001538,"Users Score":5,"Answer":"There's no straightforward route to calling Python from QlikView. I have used this:\n\nCreate a Python program that outputs CSV (or any file format that QlikView can read)\nInvoke your Python program from the QlikView script: EXEC python3 my_program.py > my_output.csv\nRead the output into QlikView: LOAD * FROM my_output.csv (...)\n\nNote that the EXEC command requires the privilege \"Can Execute External Programs\" on the Settings tab of the script editor.","Q_Score":2,"Tags":"python,scikit-learn,tableau-api,qlikview","A_Id":37810021,"CreationDate":"2016-05-03T10:20:00.000","Title":"How to interface Python with Qlikview for data visualization?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm hitting MemoryError: In RendererAgg: Out of memory when I plot several pandas.scatter_matrix() figures. \nNormally I use:\nplt.close(fig) \nto close matplotlib figures, so that I release the memory used, but pandas.scatter_matrix() does not return a matplotlib figure, rather it returns the axes object. For example:\n\nimport pandas as pd\nimport numpy as np\ndf = pd.DataFrame(np.random.randn(1000, 4), columns=['A','B','C','D'])\nax = pd.scatter_matrix(df, alpha=0.2)\n\nHow do I close this figure?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":521,"Q_Id":37005545,"Users Score":0,"Answer":"After a bit of investigation, I realized that I could just use:\nplt.close()\nwith no argument to close the current figure, or:\nplt.close('all')\nto close all of the opened figures.","Q_Score":0,"Tags":"python,pandas,matplotlib","A_Id":37005546,"CreationDate":"2016-05-03T13:28:00.000","Title":"How to close pandas.scatter_matrix() figure","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm very interested in the field of machine learning and recently I got the idea for a project for the next few weeks. \nBasically I want to create an AI that can beat every human at Tic Tac Toe. The algorithm must be scalable for every n*n board size, and maybe even for other dimensions (for a 3D analogue of the game, for example). \nAlso I don't want the algorithm to know anything of the game in advance: it must learn on its own. So no hardcoded ifs, and no supervisioned learning. \nMy idea is to use an Artificial Neural Network for the main algorithm itself, and to train it through the use of a genetic algorithm. So I have to code only the rules of the game, and then each population, battling with itself, should learn from scratch. \nIt's a big project, and I'm not an expert on this field, but I hope, with such an objective, to learn lots of things.\n\nFirst of all, is that possible? I mean, is it possible to reach a good result within a reasonable amount of time?\nAre there good libraries in Python that I can use for this project? And is Python a suitable language for this kind of project?","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":700,"Q_Id":37056608,"Users Score":2,"Answer":"Yes, this is possible. But you have to tell your AI the rules of the game, beforehand (well, that's debatable, but it's ostensibly better if you do so - it'll define your search space a little better).\nNow, the vanilla tic-tac-toe game is far too simple - a minmax search will more than suffice. Scaling up the dimensionality or the size of the board does make the case for more advanced algorithms, but even so, the search space is quite simple (the algebraic nature of the dimensionality increase leads to a slight transformation of the search space, which should still be tractable by simpler methods).\nIf you really want to throw a heavy machine learning technique at a problem, take a second look at chess (Deep Blue really just brute forced the sucker). Arimaa is interesting for this application as well. You might also consider looking at Go (perhaps start with some of the work done on AlphaGo)\nThat's my two cents' worth","Q_Score":6,"Tags":"python,neural-network,artificial-intelligence,genetic-algorithm,tic-tac-toe","A_Id":37056824,"CreationDate":"2016-05-05T17:23:00.000","Title":"How can I create an AI for tic tac toe in Python using ANN and genetic algorithm?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I installed Jupyter notebooks in Ubuntu 14.04 via Anaconda earlier, and just now I installed TensorFlow. I would like TensorFlow to work regardless of whether I am working in a notebook or simply scripting. In my attempt to achieve this, I ended up installing TensorFlow twice, once using Anaconda, and once using pip. The Anaconda install works, but I need to preface any call to python with \"source activate tensorflow\". And the pip install works nicely, if start python the standard way (in the terminal) then tensorflow loads just fine. \nMy question is: how can I also have it work in the Jupyter notebooks? \nThis leads me to a more general question: it seems that my python kernel in Jupyter\/Anaconda is separate from the python kernel (or environment? not sure about the terminology here) used system wide. It would be nice if these coincided, so that if I install a new python library, it becomes accessible to all the varied ways I have of running python.","AnswerCount":14,"Available Count":2,"Score":0.057080742,"is_accepted":false,"ViewCount":87389,"Q_Id":37061089,"Users Score":4,"Answer":"Here is what I did to enable tensorflow in Anaconda -> Jupyter. \n\nInstall Tensorflow using the instructions provided at \nGo to \/Users\/username\/anaconda\/env and ensure Tensorflow is installed\nOpen the Anaconda navigator and go to \"Environments\" (located in the left navigation)\nSelect \"All\" in teh first drop down and search for Tensorflow\nIf its not enabled, enabled it in the checkbox and confirm the process that follows. \nNow open a new Jupyter notebook and tensorflow should work","Q_Score":34,"Tags":"python,tensorflow,jupyter","A_Id":46785026,"CreationDate":"2016-05-05T22:08:00.000","Title":"Trouble with TensorFlow in Jupyter Notebook","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I installed Jupyter notebooks in Ubuntu 14.04 via Anaconda earlier, and just now I installed TensorFlow. I would like TensorFlow to work regardless of whether I am working in a notebook or simply scripting. In my attempt to achieve this, I ended up installing TensorFlow twice, once using Anaconda, and once using pip. The Anaconda install works, but I need to preface any call to python with \"source activate tensorflow\". And the pip install works nicely, if start python the standard way (in the terminal) then tensorflow loads just fine. \nMy question is: how can I also have it work in the Jupyter notebooks? \nThis leads me to a more general question: it seems that my python kernel in Jupyter\/Anaconda is separate from the python kernel (or environment? not sure about the terminology here) used system wide. It would be nice if these coincided, so that if I install a new python library, it becomes accessible to all the varied ways I have of running python.","AnswerCount":14,"Available Count":2,"Score":-0.0142847425,"is_accepted":false,"ViewCount":87389,"Q_Id":37061089,"Users Score":-1,"Answer":"Open an Anaconda Prompt screen:   (base) C:\\Users\\YOU>conda create -n tf tensorflow\nAfter the environment is created type: conda activate tf\nPrompt moves to (tf) environment, that is: (tf) C:\\Users\\YOU>\nthen install Jupyter Notebook in this (tf) environment:\nconda install -c conda-forge jupyterlab - jupyter notebook\nStill in (tf) environment, that is type\n(tf) C:\\Users\\YOU>jupyter notebook\nThe notebook screen starts!!\nA New notebook then can import tensorflow \nFROM THEN ON\nTo open a session\nclick Anaconda prompt,\ntype conda activate tf\nthe prompt moves to tf environment\n(tf) C:\\Users\\YOU>\nthen type (tf) C:\\Users\\YOU>jupyter notebook","Q_Score":34,"Tags":"python,tensorflow,jupyter","A_Id":67094115,"CreationDate":"2016-05-05T22:08:00.000","Title":"Trouble with TensorFlow in Jupyter Notebook","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm currently playing around with the Keras framework. And have done some simple classification tests, etc. I'd like to find a way to run the network in reverse, using the outputs as inputs and vice versa. Any way to do this?","AnswerCount":4,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":4095,"Q_Id":37074244,"Users Score":3,"Answer":"There is no such thing as \"running a neural net in reverse\", as a generic architecture of neural net does not define any not-forward data processing. There is, however, a subclass of models which do - the generative models, which are not a part of keras right now. The only thing you can do is to create a network which somehow \"simulates\" the generative process you are interested in. But this is paricular model specific method, and has no general solution.","Q_Score":8,"Tags":"python,machine-learning,neural-network,keras","A_Id":37104201,"CreationDate":"2016-05-06T13:55:00.000","Title":"Run model in reverse in Keras","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm currently playing around with the Keras framework. And have done some simple classification tests, etc. I'd like to find a way to run the network in reverse, using the outputs as inputs and vice versa. Any way to do this?","AnswerCount":4,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":4095,"Q_Id":37074244,"Users Score":0,"Answer":"What you are looking for, I think, is the \"Auto-Associative\" neural network. it has an input of n dimensions, several layers, one of which is the \"middle layer\" of m dimensions, and then several more layers leading to an output layer which has the same number of dimensions as the input layer, n.\nThe key here is that m is much smaller than n.\nHow it works is that you train the network to recreate the input, exactly at the output. Then you cut the network into two halves. The front half goes from n to m dimensions (encoding the input into a smaller space). The back half goes from m dimensions to n dimensions (decoding, or \"reverse\" if you will).\nVery useful for encryption, compression, unsupervised learning, etc.","Q_Score":8,"Tags":"python,machine-learning,neural-network,keras","A_Id":51939867,"CreationDate":"2016-05-06T13:55:00.000","Title":"Run model in reverse in Keras","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I extract multiple Feature vectors from different sensors, and I trained these features by using SVM individually. My question is there any method to combine these classifiers in a way to obtain a better result. \nthanks in advance","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":662,"Q_Id":37087996,"Users Score":0,"Answer":"First of all - the idea of training separate models is rather bad. Unless you have very good reasons to do so (some external limitations you cannot ignore) you should not do so. Why? Because you are efficiently loosing information, you are unable to model complex dependencies between signals from two classifiers. Training everything jointly gives statistical method ability to choose which data use when, this way it can model for example - that for some particular types of data it will use one part of the input and for another - the rest. When you build independent classifiers - you bias the whole process, as these classifiers have \"no idea\" that remaining ones exist.\nHaving said that, here is the solution assuming that somehow you cannot learn joint model. In such scenario (where your models are kind of black-boxes which convert your input representation to decision functions) the basic idea is to simply treat them as preprocessors and fit new model on top, thats all. In other words you have your data point x split into feature-vectors x1,x2,...,xk and k different models mi you built previously, thus you use them to construct a preprocessing method f(x) = [m1(x1), m2(x2), ..., mk(xk)] and this is just a single point in R^k space, which can be now fitted into new classifier to learn how to combine these information. The problematic part is that due to your very specific process, you now need new training set to learn combination rule, as using the same data that was used to construct mi can easily lead to overfitting. To fight that people sometimes use heuristic approaches instead - a priori assuming that these models are already good enough and construct ensemble of these, which either vote for classes (for example weighted by their certeinty) or build whole system around it. I would still argue that you should not go this way in the first place, if you have to - go with learning combination rule with the use of new data, and finally if you cannot do any of the above - settle with some heuristic ensemble technique.","Q_Score":0,"Tags":"python,machine-learning,computer-vision,scikit-learn","A_Id":37092686,"CreationDate":"2016-05-07T11:31:00.000","Title":"Method to combine multiple svm classifiers (or \"any ML classifier\" by using scikit-learn. \"decision-feature classifiers\"","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a large dataframe in Pandas with lots of zeros. \nI want to apply additive smoothing but instead of writing it from scratch, I am wondering if there is any better way of producing a \"smoothed\" dataframe in Pandas. Thanks!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1278,"Q_Id":37091587,"Users Score":3,"Answer":"Additive smoothing is just a basic mathematical operation, requiring few additions and division - there is no \"special\" function for that, you simply write a one-liner operating on particular columns of your dataframe.","Q_Score":1,"Tags":"python,pandas,machine-learning,smoothing,naivebayes","A_Id":37092622,"CreationDate":"2016-05-07T17:27:00.000","Title":"Additive Smoothing for Dataframe Pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am going to use nltk.tokenize.word_tokenize on a cluster where my account is very limited by space quota. At home, I downloaded all nltk resources by nltk.download() but, as I found out, it takes ~2.5GB.\nThis seems a bit overkill to me. Could you suggest what are the minimal (or almost minimal) dependencies for nltk.tokenize.word_tokenize? So far, I've seen nltk.download('punkt') but I am not sure whether it is sufficient and what is the size. What exactly should I run in order to make it work?","AnswerCount":4,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":57023,"Q_Id":37101114,"Users Score":39,"Answer":"You are right. You need Punkt Tokenizer Models. It has 13 MB and nltk.download('punkt') should do the trick.","Q_Score":21,"Tags":"python,nltk","A_Id":37101713,"CreationDate":"2016-05-08T14:49:00.000","Title":"What to download in order to make nltk.tokenize.word_tokenize work?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have some data with say, L features. I have standardized them using StandardScaler() by doing a fit_transform on X_train. Now while predicting, i did clf.predict(scaler.transform(X_test)). So far so good... now if I want to pickle the model for later reuse, how would I go about predicting on the new data in future with this saved model ? the new (future) data will not be standardized and I didn't pickle the scaler.\nIs there anything else that I have to do before pickling the model the way I am doing it right now (to be able to predict on non-standardized data)?\nreddit post: https:\/\/redd.it\/4iekc9\nThanks. :)","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":51,"Q_Id":37101361,"Users Score":0,"Answer":"To solve this problem you should use a pipeline. The first stage there is scaling, and the second one is your model. Then you can pickle the whole pipeline and have fun with your new data.","Q_Score":0,"Tags":"python,scikit-learn","A_Id":37102874,"CreationDate":"2016-05-08T15:12:00.000","Title":"predicting new non-standardized data with classifier trained on standardized data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I found in many available neural network code implemented using TensorFlow that regularization terms are often implemented by manually adding an additional term to loss value.\nMy questions are:\n\nIs there a more elegant or recommended way of regularization than doing it manually?\nI also find that get_variable has an argument regularizer. How should it be used? According to my observation, if we pass a regularizer to it (such as tf.contrib.layers.l2_regularizer, a tensor representing regularized term will be computed and added to a graph collection named tf.GraphKeys.REGULARIZATOIN_LOSSES. Will that collection be automatically used by TensorFlow (e.g. used by optimizers when training)? Or is it expected that I should use that collection by myself?","AnswerCount":10,"Available Count":1,"Score":0.0798297691,"is_accepted":false,"ViewCount":80148,"Q_Id":37107223,"Users Score":4,"Answer":"I tested tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES) and tf.losses.get_regularization_loss() with one l2_regularizer in the graph, and found that they return the same value. By observing the value's quantity, I guess reg_constant has already make sense on the value by setting the parameter of tf.contrib.layers.l2_regularizer.","Q_Score":95,"Tags":"python,neural-network,tensorflow,deep-learning","A_Id":51513887,"CreationDate":"2016-05-09T03:04:00.000","Title":"How to add regularizations in TensorFlow?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In my machine learning class, we have learned about appending a 1 to each sample's feature vector when using many machine learning models to account for bias. For example, if we are doing linear regression and a sample has features f_1, f_2, ..., f_d, we need to add a \"fake\" feature value of 1 to allow for the regression function to not have to pass through the origin.\nWhen using sklearn models, do you need to do this yourself, or do their implementations do it for you? Specifically, I'm interested in whether or not this is necessary when using any of their regression models or their SVM models.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1556,"Q_Id":37126576,"Users Score":0,"Answer":"No, you do not add any biases, models define biases in their own way. What you learned during course is generic, although not perfect - solution. It matters for models such as SVM, which should not ever have appended \"1\"s, as then this bias would get regularized, which is simply wrong for SVMs. Thus, while this is nice theoretical trick to show that you can actually create methods completely ignoring bias, in practise - it is often treated in a specific way, and scikit-learn does it for you.","Q_Score":0,"Tags":"python,machine-learning,scikit-learn","A_Id":37126992,"CreationDate":"2016-05-09T22:41:00.000","Title":"Need to append bias term when using `sklearn` models?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I was just wondering what is the best way to save the state of a model while it it optimizing.  I want to do this so I can run it for a while, save it, and come back to it some time later.  I know there is a function to save the weights and another function to save the model as JSON.  During learning I would need to save both the weights and the parameters of the model.  This includes parameters like the momentum and learning rate.  Is there a way to save both the model and weights in the same file.  I read that it is not considered good practice to use pickle.  Also would the momentums for the graident decent be included with the models JSON or in the weights?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":4558,"Q_Id":37128886,"Users Score":1,"Answer":"You could create a tar archive containing the weights and the architecture, as well as a pickle file containing the optimizer state returned by model.optimizer.get_state().","Q_Score":6,"Tags":"python,keras","A_Id":37155400,"CreationDate":"2016-05-10T03:40:00.000","Title":"Keras, best way to save state when optimizing","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I developed a recommender system using Matrix Factorization in Python. The ratings are in the range [1-5]. It works very well. This system is made for client advisors rather than clients themselves. Hence, the system recommends some products to the client advisor and then this one decides which products he's gonna recommend to his client. \nIn my application I want to have 2  additional buttons: relevant, irrelevant. Thus, for each recommendation the client advisor would press the button irrelevant if the recommendation is not good but its rating is high and he would press the button relevant if the recommendation is good but its rating is low. \nThe problem is that I can't figure how to update the ratings when one of the buttons is pressed. Please give me some idea about how to handle that feature. I insist on having only two buttons (relevant and irrelevant), the client advisor can't modify the rating himself.\nThank you very much.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":62,"Q_Id":37138777,"Users Score":0,"Answer":"Based on your comment above, I would manipulate the Number of times they purchased the product field.  You need to basically transform the Number of times they purchased the product field into an implicit rating field.  I would maybe scale the product rating system to 1-5.  If they press the don't like the product button, the rating is a 1, if they press the like the product button, they get a 5.  If they have bought the product frequently, it's a 5, otherwise it starts at a 3 on the first purchase and scales up to a 4 then 5, based on your data.  If they have never bought the product AND have never rated the product, it's a null value, so won't contribute to ratings.","Q_Score":0,"Tags":"python,machine-learning,recommendation-engine,rating,collaborative-filtering","A_Id":37142380,"CreationDate":"2016-05-10T12:36:00.000","Title":"Manually update ratings in recomender system","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I currently have a giant time-series array with times-series data of multiple securities and economic statistics.\nI've already written a function to classify the data, using sci-kit learn, but the function only uses non-lagged time-series data.\nIs there a way, in Python, using sci-kit, to automatically lag all of these time-series to find what time-series (if any) tend to lag other data?\nI'm working on creating a model using historic data to predict future performance.","AnswerCount":4,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":2999,"Q_Id":37152723,"Users Score":2,"Answer":"No. there is not a way, in Python, using sci-kit, to automatically lag all of these time-series to find what time-series (if any) tend to lag other data. You'll have to write some code.","Q_Score":0,"Tags":"python,scikit-learn,time-series,quantitative-finance","A_Id":37176185,"CreationDate":"2016-05-11T03:42:00.000","Title":"How to auto-discover a lagging of time-series data in scikit-learn and classify using time-series data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I know that there is a \"mirror\" parameter in the default data layer, but is there a way to do arbitrary rotations (really, I would just like to do multiples of 90 degrees), preferably in Python?","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":792,"Q_Id":37170740,"Users Score":2,"Answer":"You can make use of Python Layer to do the same. The usage of a Python Layer is demonstrated in caffe_master\/examples\/py_caffe\/. Here you could make use of a python script as the input layer to your network. You could describe the behavior of rotations in this layer.","Q_Score":0,"Tags":"python,deep-learning,caffe","A_Id":37247216,"CreationDate":"2016-05-11T18:32:00.000","Title":"How to rotate images in Caffe on-the-fly for training set augmentation?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I could not get Theano running in my system in Enthought canopy Python. When I give import theano and test run, I get the following error. \nimport blas\n  File \"\/Users\/rajesh\/Library\/Enthought\/Canopy_64bit\/User\/lib\/python2.7\/site-    packages\/theano\/tensor\/blas.py\", line 135, in \n    numpy.dtype('float32'):scipy.linalg.blas.fblas.sgemv,\n    AttributeError: 'module' object has no attribute 'fblas'\nCan you please guide me the direction to resolve this ?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":342,"Q_Id":37174808,"Users Score":1,"Answer":"That's possibly because you installed an old version of Theano package. Try upgrade it or install the newest version by pip install theano.","Q_Score":0,"Tags":"python-2.7,theano,enthought,canopy","A_Id":39967880,"CreationDate":"2016-05-11T23:20:00.000","Title":"module object has no attribute 'fblas' error when running theano.test() in Canopy Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I use numpy and scipy in different environments (MacOS, Ubuntu, RedHat).\nUsually I install numpy by using the package manager that is available (e.g., mac ports, apt, yum).\nHowever, if you don't compile Numpy manually, how can you be sure that it uses a BLAS library? Using mac ports, ATLAS is installed as a dependency. However, I am not sure if it is really used. When I perform a simple benchmark, the numpy.dot() function requires approx. 2 times the time than a dot product that is computed using the Eigen C++ library. I am not sure if this is a reasonable result.","AnswerCount":3,"Available Count":1,"Score":0.2605204458,"is_accepted":false,"ViewCount":28680,"Q_Id":37184618,"Users Score":4,"Answer":"numpy.show_config() just tells that info is not available on my Debian Linux. \nHowever \/usr\/lib\/python3\/dist-packages\/scipy\/lib has a subdirectory for blas which may tell you what you want. There are a couple of test programs for BLAS in subdirectory tests.\nHope this helps.","Q_Score":33,"Tags":"python,c++,macos,numpy,blas","A_Id":37185292,"CreationDate":"2016-05-12T10:44:00.000","Title":"Find out if\/which BLAS library is used by Numpy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm interested in tips for my algorithm that I use to find out the divisors of a very large number, more specifically \"n over k\" or C(n, k). The number itself can range very high, so it really needs to take time complexity into the 'equation' so to say. \nThe formula for n over k is n! \/ (k!(n-k)!) and I understand that I must try to exploit the fact that factorials are kind of 'recursive' somehow - but I havent yet read too much discrete mathematics so the problem is both of a mathematical and a programming nature. \nI guess what I'm really looking for are just some tips heading me in the right direction - I'm really stuck.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":846,"Q_Id":37207589,"Users Score":2,"Answer":"First you could start with the fact that : C(n,k) = (n\/k) C(n-1,k-1).\nYou can prouve that C(n,k) is divisible by n\/gcd(n,k).\nIf n is prime then n divides C(n,k).\nCheck Kummer's theorem: if p is a prime number, n a positive number, and k a positive number with 0< k < n then the greatest exponent r for which p^r divides C(n,k) is the number of carries needed in the subtraction n-k in base p.\nLet us suppose that n>4 :\n\nif p>n then p cannot divide C(n,k) because in base p, n and k are only one digit wide \u2192 no carry in the subtraction\n\nso we have to check for prime divisors in [2;n]. As C(n,k)=C(n,n-k) we can suppose k\u2264n\/2 and n\/2\u2264n-k\u2264n\n\nfor the prime divisors in the range [n\/2;n] we have n\/2 < p\u2264n, or equivalently p\u2264n<2p. We have p\u22652 so p\u2264n < p\u00b2 which implies that n has exactly 2 digits when written in base p and the first digit has to be 1. As k\u2264n\/2 < p, k can only be one digit wide. Either the subtraction as one carry and one only when n-k< p \u21d2 p divides C(n,k); either the subtraction has no carry and p does not divide C(n,k).\nThe first result is :\nevery prime number in [n-k;n] is a prime divisor of C(n,k) with exponent 1.\nno prime number in [n\/2;n-k] is a prime divisor of C(n,k).\n\nin [sqrt(n); n\/2] we have 2p\u2264n< p\u00b2, n is exactly 2 digits wide in base p, k< n implies k has at most 2 digits. Two cases: only one carry on no carry at all. A carry exists only if the last digit of n is greater than the last digit of p iif n modulo p < k modulo p\nThe second result is :\nFor every prime number p in [sqrt(n);n\/2]\np divides C(n;k) with exponent 1 iff n mod p < k mod p\np does not divide C(n;k) iff n mod p \u2265 k mod p\n\nin the range [2; sqrt(n)] we have to check all the prime numbers. It's only in this range that a prime divisor will have an exponent greater than 1","Q_Score":3,"Tags":"algorithm,python-3.x,discrete-mathematics,binomial-coefficients","A_Id":37212468,"CreationDate":"2016-05-13T10:25:00.000","Title":"Smart algorithm for finding the divisors of a binomial coefficient","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am running a Python script on a Windows HPC cluster. A function in the script uses starmap from the multiprocessing package to parallelize a certain computationally intensive process.\nWhen I run the script on a single non-cluster machine, I obtain the expected speed boost. When I log into a node and run the script locally, I obtain the expected speed boost. However, when the job manager runs the script, the speed boost from multiprocessing is either completely mitigated or, sometimes, even 2x slower. We have noticed that memory paging occurs when the starmap function is called. We believe that this has something to do with the nature of Python's multiprocessing, i.e. the fact that a separate Python interpreter is kicked off for each core.\nSince we had success running from the console from a single node, we tried to run the script with HPC_CREATECONSOLE=True, to no avail.\nIs there some kind of setting within the job manager that we should use when running Python scripts that use multiprocessing? Is multiprocessing just not appropriate for an HPC cluster?","AnswerCount":1,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":1975,"Q_Id":37221133,"Users Score":8,"Answer":"Unfortunately I wasn't able to find an answer in the community. However, through experimentation, I was able to better isolate the problem and find a workable solution.\nThe problem arises from the nature of Python's multiprocessing implementation. When a Pool object is created (i.e. the manager class that controls the processing cores for the parallel work), a new Python run-time is started for each core. There are multiple places in my code where the multiprocessing package is used and a Pool object instantiated... every function that requires it creates a Pool object as needed and then joins and terminates before exiting. Therefore, if I call the function 3 times in the code, 8 instances of Python are spun up and then closed 3 times. On a single machine, the overhead of this was not significant at all compared to the computational load of the functions... however on the HPC it was absurdly high.\nI re-architected the code so that a Pool object is created at the very beginning of the calling of process and then passed to each function as needed. It is closed, joined, and terminated at the end of the overall process.\nWe found that the bulk of the time was spent in the creation of the Pool object on each node. This was an improvement though because it was only being created once! We then realized that the underlying problem was that multiple nodes were trying to access Python at the same time in the same place from over the network (it was only installed on the head node). We installed Python and the application on all nodes, and the problem was completely fixed.\nThis solution was the result of trial and error... unfortunately our knowledge of cluster computing is pretty low at this point. I share this answer in the hopes that it will be critiqued so that we can obtain even more insight. Thank you for your time.","Q_Score":4,"Tags":"python-3.x,multiprocessing,distributed-computing,hpc","A_Id":37321401,"CreationDate":"2016-05-14T00:24:00.000","Title":"Using Python multiprocessing on an HPC cluster","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"For a Deep Learning application I am building, I have a dataset of about 50k grayscale images, ranging from about 300*2k to 300*10k pixels. Loading all this data into memory is not possible, so I am looking for a proper way to handle reading in random batches of data. One extra complication with this is, I need to know the width of each image before building my Deep Learning model, to define different size-buckets within the data (for example: [2k-4k, 4k-6k, 6k-8k, 8k-10k].\nCurrently, I am working with a smaller dataset and just load each image from a png file, bucket them by size and start learning. When I want to scale up this is no longer possible. \nTo train the model, each batch of the data should be (ideally) fully random from a random bucket. A naive way of doing this would be saving the sizes of the images beforehand, and just loading each random batch when it is needed. However, this would result in a lot of extra loading of data and not very efficient memory management.\nDoes anyone have a suggestion how to handle this problem efficiently?\nCheers!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":811,"Q_Id":37227938,"Users Score":0,"Answer":"Why not add a preprocessing step, where you would either (a) physically move the images to folders associated with bucket and\/or rename them, or (b) first scan through all images (headers only) to build the in-memory table of image filenames and their sizes\/buckets, and then the random sampling step would be quite simple to implement.","Q_Score":0,"Tags":"python,deep-learning","A_Id":37228094,"CreationDate":"2016-05-14T14:37:00.000","Title":"Proper way of loading large amounts of image data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In tensorflow, tf.train.GradientDescentOptimizer does gradient descent for all variables in default. Can i just do gradient descent for only a few of my variables and 'lock' the others?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":206,"Q_Id":37234114,"Users Score":1,"Answer":"To lock the ones that you don't want to train you can use tf.Variable(..., trainable=False)","Q_Score":0,"Tags":"python-2.7,tensorflow","A_Id":37234653,"CreationDate":"2016-05-15T02:59:00.000","Title":"How to do gradient descent for not all variables in tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am running ubuntu 14.04 with an anaconda2 installation and would like to use tensorflow in combination with CUDA. So far the steps I performed are:\n\nInstalled CUDA 7.5 and cudnn\nInstalled tensorflow (GPU version) through a DEB package. Note that I don't want to use the conda package of tensorflow since that one is not the GPU version.\nAdded Anaconda, CUDA and cudnn to path.\nCreated a conda environment for tensorflow (conda create -n tensorflow python=2.7)\n\nNow if I start python or IDLE from the terminal, I can import tensorflow and it will find all the CUDA dependencies, great!\n...however, if I start ipython or spyder from the same terminal, running \"import tensorflow as tf\" gives me a cold-hearted \"ImportError: No module named tensorflow\".\nMy question: How can I get ipython and spyder to find the tensorflow library just like in an IDLE and a python instance?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1863,"Q_Id":37236677,"Users Score":0,"Answer":"To solve your issue you have 3 options here: \n1 just start spyder from terminal \n2  move PATH variable definition from .bash_profile to session init scripts\n3 Duplicate your PATH in spyder's run configuration","Q_Score":1,"Tags":"python,anaconda,tensorflow,spyder","A_Id":40741488,"CreationDate":"2016-05-15T09:36:00.000","Title":"Use GPU installation of tensorflow\/cuda in spyder under ubuntu 14.04","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a (long) list of x,y tuples that collectively describe a path (ie. mouse-activity sampled at a constant rate despite non-constant velocities).\nMy goal is to animate that path at a constant rate. So I have segments that are curved, and segments that are straight, and the delta-d between any two points isn't guaranteed to be the same. \nGiven data like:\n[(0,0), (0,2), (4,6).... ] where the length of that list is ~1k-2k points, is there any way besides brute-force counting line-segment lengths between each point and then designating every n-length a \"frame\"?","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":159,"Q_Id":37245832,"Users Score":0,"Answer":"I'm sure there's an elegant way to do this with pandas, but until then, here's a simple idea if you can live with some error. You can do this a few different ways but here's the gist of it:\nTreat each tuple as a node in a linked list. Define the desired length, D, between each point. As you move through the list, if the next node is not a distance D from the current node, adjust its x,y coordinates accordingly (or insert\/delete nodes as necessary) so that it is a distance D from the current node along the line segments that connect the nodes. \nLike I said, you'll have to live with some error because your original points will be adjusted\/deleted. If you generate points to create more resolution prior to this, you can probably lessen the error.","Q_Score":4,"Tags":"python,path,geometry","A_Id":37246380,"CreationDate":"2016-05-16T02:22:00.000","Title":"How to calculate even distance along an interpolated path (Python2.7)?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a (long) list of x,y tuples that collectively describe a path (ie. mouse-activity sampled at a constant rate despite non-constant velocities).\nMy goal is to animate that path at a constant rate. So I have segments that are curved, and segments that are straight, and the delta-d between any two points isn't guaranteed to be the same. \nGiven data like:\n[(0,0), (0,2), (4,6).... ] where the length of that list is ~1k-2k points, is there any way besides brute-force counting line-segment lengths between each point and then designating every n-length a \"frame\"?","AnswerCount":3,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":159,"Q_Id":37245832,"Users Score":1,"Answer":"If you use Numpy arrays to represent your data then you can vectorise the computation. That's as efficient as you're going to get.","Q_Score":4,"Tags":"python,path,geometry","A_Id":37246950,"CreationDate":"2016-05-16T02:22:00.000","Title":"How to calculate even distance along an interpolated path (Python2.7)?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am retrieving structured numerical data (float 2-3 decimal spaces) via http requests from a server. The data comes in as sets of numbers which are then converted into an array\/list. I want to then store each set of data locally on my computer so that I can further operate on it. \nSince there are very many of these data sets which need to be collected, simply writing each data set that comes in to a .txt file does not seem very efficient. On the other hand I am aware that there are various solutions such as mongodb, python to sql interfaces...ect but i'm unsure of which one I should use and which would be the most appropriate and efficient for this scenario.\nAlso the database that is created must be able to interface and be queried from different languages such as MATLAB.","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":989,"Q_Id":37246342,"Users Score":1,"Answer":"Have you considered HDF5? It's very efficient for numerical data, and is supported by both Python and Matlab.","Q_Score":1,"Tags":"python,sql,arrays,mongodb,database","A_Id":37246905,"CreationDate":"2016-05-16T03:38:00.000","Title":"Python, computationally efficient data storage methods","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been trying to deploy my Python Flask App that uses Conv nets using Theano on local IIS. When I try to load a pickled Neural Network , I get following Errors\n\nUnable to Create compiledir.\n\nI solved this by changing compiledir path in configdefaults.py and giving read\/write rights to IIS on that directory. Now compiledir gets created.\n\nNow I'm getting MissingGXX error \"g++ not available! We can't compile\n  c code.\". G++ is there in my PythonFolder\\Scripts and I've added this\n  path to my environmental variable PATH.\n\nI just Want to know that what causes this error? Is it because Theano can't find g++ and it's all about path issues or it has something to do with compiledir lock\nPS: I can run the code from my Winpython console and everything works fine. I've seen the contents of %PATH% and %PYTHONPATH% from my Win python console and replicated the same on my deployed IIS web App.\nHere's the header of the stack trace :\n\n(MissingGXX('The following error happened while compiling the node',\n  Shape_i{0}(input.input), '\\n', \"g++ not available! We can't compile c\n  code.\", '[Shape_i{0}(input.input)]'), , (","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1208,"Q_Id":37255003,"Users Score":1,"Answer":"I've solved the problem , I had two g++ executables in my WinPython environment at following paths\n\nWinPythonDir\\python-2.7.10.amd64\\Scripts\\g++.exe\nWinPythonDir\\python-2.7.10.amd64\\share\\mingwpy\\bin\\g++.exe\n\nSpyder used the correct one (2) and IIS seems to use the one mentioned in 1. I explicitly added path to 2 in my PATH env variable on IIS. Spyder didn't have 2 in PATH (that's strange) but it used the one mentioned in 2 (I confirmed that after logging in Theano files).\nAfter this my MissingGxx error was gone but Now Theano was unable to create compilation directory because IIS uses System profile and Theano uses that profile to generate path to compile_dir , It was somewhere in C;|Windows|System32\\config\\SystemsProfile|Theano\\compile_dir and IIS didn't have rights to it (Spyder uses my local USERPROFILE). I changed the default_base_compiledir path in Theano's configdefaults.py and gave IIS rights to access and modify it. I wasn't able to assign rights to previous compiledir in SystemsProfile beacause that location is pretty sensitive and OS restricted me to do so.\nPS : I copied PATH by doing \n\necho %PATH%\n\nfrom my WinPython console and concatenated g++ path mentioned at 2 with it and added to PATH variable on IIS because WinPython PATH variable didn't have 2 in it.","Q_Score":3,"Tags":"python,iis,gcc,g++,theano","A_Id":37435858,"CreationDate":"2016-05-16T13:22:00.000","Title":"Theano : MissingGxx , g++ not available","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have been reading a very old documentation of Numpy and found out a weird notation which eludes my understanding. The documentation says a[i:...] is a shortcut for a[i,:,:,:]. \nThe documentation being old is very vague and I would welcome any comments. \nThanks,\nPrerit","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1494,"Q_Id":37258911,"Users Score":2,"Answer":"arr[:,:,1] is fancy indexing used by numpy that selects the first element of the last column in arr. Fancy indexing can only be used in numpy arrays and not in python's traditional lists.\nAlso, like its pointed out in the comments, a[,:,:,] is a syntax error.\nIt is helpful because you can select columns easily","Q_Score":0,"Tags":"python,numpy,slice,ellipsis","A_Id":37767201,"CreationDate":"2016-05-16T16:38:00.000","Title":"How slicing and ellipsis works in numpy?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"The problem:\nThere is a set of n days that Bob is planning to work, and on each day i there is a mission; each mission lasts exactly one day, must be done on day i in which it is given, and pays bob x_i dollars. Bob cannot work more than 5 consecutive missions at a time. That is, he must take at least 1 rest day every 5 days.\nGiven numbers x_1...x_n, on which days should Bob perform missions, and on which days should he rest, in order to make as much money as possible and not work more than 5 days? Your solution should be O(n)\nMy issue:\nI am having trouble coming up with the recurrence for this problem. I have been thinking about this problem for a long time. My original thought was to let p[i] = max{x_i + x_i-1 + .... + x_i-4}, where p[i] is the max profit earnable from days i-4 to i. However, I realize, one, that this does take in to account that the optimal solution might have two consecutive work days, and two, I am not building off previous solutions. \nMy Question: Can anyone give me insight on understanding the structure of this problem? I feel like I am just not understanding the key properties that would make the solution easy to see.\nThanks in advance!","AnswerCount":4,"Available Count":1,"Score":0.049958375,"is_accepted":false,"ViewCount":766,"Q_Id":37277713,"Users Score":1,"Answer":"Dynamically build a table of dimensions 6 x n. The entry table[w_i][d_j] will denote the maximal reachable value when Bob has worked for the last i days consecutively (including today) and it is day j.\nThe first column is easy to fill in:\ntable[1][0] = x_0 if Bob decides to work on the first day, all other values are 0 (table[0][0] => Bob doesn't work on the first day, table[2..5][0] => Bob can't work for multiple consecutive days on day 1.)\nGo on to complete the table column-by-column according to the following rules:\nThe maximum value on day j with 0 consecutive days of work is the maximum of any value of the previous day and not working today:\ntable[0][d_j] = max{ table[0..5][d_j-1] }\nThe maximum value on day j with 1 consecutive day of work is the maximum of the previous 2 days with no consecutive days of work plus x_j. (It never makes sense to rest more than 2 days, as we could have just worked the day(s) in between.):\ntable[1][d_j] = max{ table[0][d_j-2], table[0][d_j-1] } + x[d_j]\nOtherwise, table[w_i][d_j] = table[w_i-1][d_j-1] + x[d_j].\nThe solution will be the maximum value in the last column.","Q_Score":3,"Tags":"python,algorithm,dynamic-programming","A_Id":37280915,"CreationDate":"2016-05-17T13:33:00.000","Title":"Dynamic Programming: Mission Per Day, Scheduling for Maximum Profit","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"What is best way to store and analyze high-dimensional date in python? I like Pandas DataFrame and Panel where I can easily manipulate the axis. Now I have a hyper-cube (dim >=4) of data. I have been thinking of stuffs like dict of Panels, tuple as panel entries. I wonder if there is a high-dim panel thing in Python. \nupdate 20\/05\/16: \nThanks very much for all the answers. I have tried MultiIndex and xArray, however I am not able to comment on any of them. In my problem I will try to use ndarray instead as I found the label is not essential and I can save it separately.\nupdate 16\/09\/16:\nI came up to use MultiIndex in the end. The ways to manipulate it are pretty tricky at first, but I kind of get used to it now.","AnswerCount":3,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":2444,"Q_Id":37311699,"Users Score":3,"Answer":"If you need labelled arrays and pandas-like smart indexing, you can use xarray package which is essentially an n-dimensional extension of pandas Panel (panels are being deprecated in pandas in future in favour of xarray).\nOtherwise, it may sometimes be reasonable to use plain numpy arrays which can be of any dimensionality; you can also have arbitrarily nested numpy record arrays of any dimension.","Q_Score":6,"Tags":"python,numpy,pandas,machine-learning,multi-index","A_Id":37311742,"CreationDate":"2016-05-18T23:33:00.000","Title":"High-dimensional data structure in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am playing around with some audio processing in python.  Right now I have the audio as a 2x(Large Number) numpy array.  I want to combine the channels since I only want to try some simple stuff.  I am just unsure how I should do this mathematically.  At first I thought this is kind of like converting an RGB image to gray-scale where you would average each of the color channels to create a gray pixel.  Then I thought that maybe I should add them due to the superposition principal of waves (then again average is just adding and dividing by two.)  Does anyone know the best way to do this?","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":3971,"Q_Id":37313320,"Users Score":1,"Answer":"i handle this by using Matlab.python can do the same. (left-channel+right-channel)\/2.0","Q_Score":1,"Tags":"python,audio","A_Id":37313404,"CreationDate":"2016-05-19T03:10:00.000","Title":"How to convert two channel audio into one channel audio","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am playing around with some audio processing in python.  Right now I have the audio as a 2x(Large Number) numpy array.  I want to combine the channels since I only want to try some simple stuff.  I am just unsure how I should do this mathematically.  At first I thought this is kind of like converting an RGB image to gray-scale where you would average each of the color channels to create a gray pixel.  Then I thought that maybe I should add them due to the superposition principal of waves (then again average is just adding and dividing by two.)  Does anyone know the best way to do this?","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":3971,"Q_Id":37313320,"Users Score":1,"Answer":"To convert any stereo audio to mono, what I have always seen is the following:\nFor each pair of left and right samples:\n\nAdd the values of the samples together in a way that will not overflow\nDivide the resulting value by two\nUse this resulting value as the sample in the mono track -  make sure to round it properly if you are converting it to an integer value from a floating point value","Q_Score":1,"Tags":"python,audio","A_Id":37313414,"CreationDate":"2016-05-19T03:10:00.000","Title":"How to convert two channel audio into one channel audio","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it possible to annotate or add markers of any form to Bokeh charts (specifically Bar graphs)? Say I want to add an * (asterisks) on top of a bar; is this possible?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":240,"Q_Id":37331559,"Users Score":0,"Answer":"It's always possible to use .add_glyph directly, but it is a bit of a pain. The feature to add all the \"glyph methods\" e.g. .circle, .rect, etc. is in GitHub master, and will be available in the upcoming 0.12 release.","Q_Score":1,"Tags":"python,plot,bokeh,glyph","A_Id":37333670,"CreationDate":"2016-05-19T18:40:00.000","Title":"Annotating bokeh chart plot","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to make a numpy-like multi-dimensional array of non-numerical objects in python. I believe Numpy arrays only allow numerical values. List-of-lists are much less convenient to index- for example, I'd like to be able to ask for myarray[1,:,2] which requires much more complicated calls from lists of lists. \nIs there a good tool for this?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":42,"Q_Id":37352895,"Users Score":1,"Answer":"NumPy arrays actually do allow non-numerical contents, so you can just use NumPy.","Q_Score":0,"Tags":"python,multidimensional-array","A_Id":37352954,"CreationDate":"2016-05-20T17:49:00.000","Title":"Python arrays for objects","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Introduction\nI'd like to know what other topic modellers consider to be an optimal topic-modelling workflow all the way from pre-processing to maintenance. While this question consists of a number of sub-questions (which I will specify below), I believe this thread would be useful for myself and others who are interested to learn about best practices of end-to-end process.\nProposed Solution Specifications\nI'd like the proposed solution to preferably rely on R for text processing (but Python is fine also) and topic-modelling itself to be done in MALLET (although if you believe other solutions work better, please let us know). I tend to use the topicmodels package in R, however I would like to switch to MALLET as it offers many benefits over topicmodels. It can handle a lot of data, it does not rely on specific text pre-processing tools and it appears to be widely used for this purpose. However some of the issues outline below are also relevant for topicmodels too. I'd like to know how others approach topic modelling and which of the below steps could be improved. Any useful piece of advice is welcome.\nOutline\nHere is how it's going to work: I'm going to go through the workflow which in my opinion works reasonably well, and I'm going to outline problems at each step.\nProposed Workflow\n1. Clean text\nThis involves removing punctuation marks, digits, stop words, stemming words and other text-processing tasks. Many of these can be done either as part of term-document matrix decomposition through functions such as for example TermDocumentMatrix from R's package tm.\nProblem: This however may need to be performed on the text strings directly, using functions such as gsub in order for MALLET to consume these strings. Performing in on the strings directly is not as efficient as it involves repetition (e.g. the same word would have to be stemmed several times)\n2. Construct features\nIn this step we construct a term-document matrix (TDM), followed by the filtering of terms based on frequency, and TF-IDF values. It is preferable to limit your bag of features to about 1000 or so. Next go through the terms and identify what requires to be (1) dropped (some stop words will make it through), (2) renamed or (3) merged with existing entries. While I'm familiar with the concept of stem-completion, I find that it rarely works well. \nProblem: (1) Unfortunately MALLET does not work with TDM constructs and to make use of your TDM, you would need to find the difference between the original TDM -- with no features removed -- and the TDM that you are happy with. This difference would become stop words for MALLET. (2) On that note I'd also like to point out that feature selection does require a substantial amount of manual work and if anyone has ideas on how to minimise it, please share your thoughts.\nSide note: If you decide to stick with R alone, then I can recommend the quanteda package which has a function dfm that accepts a thesaurus as one of the parameters. This thesaurus allows to to capture patterns (usually regex) as opposed to words themselves, so for example you could have a pattern \\\\bsign\\\\w*.?ups? that would match sign-up, signed up and so on. \n3. Find optimal parameters\nThis is a hard one. I tend to break data into test-train sets and run cross-validation fitting a model of k topics and testing the fit using held-out data. Log likelihood is recorded and compared for different resolutions of topics.\nProblem: Log likelihood does help to understand how good is the fit, but (1) it often tends to suggest that I need more topics than it is practically sensible and (2) given how long it generally takes to fit a model, it is virtually impossible to find or test a grid of optimal values such as iterations, alpha, burn-in and so on.\nSide note: When selecting the optimal number of topics, I generally select a range of topics incrementing by 5 or so as incrementing a range by 1 generally takes too long to compute.\n4. Maintenance\nIt is easy to classify new data into a set existing topics. However if you are running it over time, you would naturally expect that some of your topics may cease to be relevant, while new topics may appear. Furthermore, it might be of interest to study the lifecycle of topics. This is difficult to account for as you are dealing with a problem that requires an unsupervised solution and yet for it to be tracked over time, you need to approach it in a supervised way.\nProblem: To overcome the above issue, you would need to (1) fit new data into an old set of topics, (2) construct a new topic model based on new data (3) monitor log likelihood values over time and devise a threshold when to switch from old to new; and (4) merge old and new solutions somehow so that the evolution of topics would be revealed to a lay observer.\nRecap of Problems\n\nString cleaning for MALLET to consume the data is inefficient.\nFeature selection requires manual work.\nOptimal number of topics selection based on LL does not account for what is practically sensible\nComputational complexity does not give the opportunity to find an optimal grid of parameters (other than the number of topics)\nMaintenance of topics over time poses challenging issues as you have to retain history but also reflect what is currently relevant.\n\nIf you've read that far, I'd like to thank you, this is a rather long post. If you are interested in the suggest, feel free to either add more questions in the comments that you think are relevant or offer your thoughts on how to overcome some of these problems.\nCheers","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1478,"Q_Id":37386595,"Users Score":4,"Answer":"Thank you for this thorough summary!\nAs an alternative to topicmodels try the package mallet in R. It runs Mallet in a JVM directly from R and allows you to pull out results as R tables. I expect to release a new version soon, and compatibility with tm constructs is something others have requested.\nTo clarify, it's a good idea for documents to be at most around 1000 tokens long (not vocabulary). Any more and you start to lose useful information. The assumption of the model is that the position of a token within a given document doesn't tell you anything about that token's topic. That's rarely true for longer documents, so it helps to break them up.\nAnother point I would add is that documents that are too short can also be a problem. Tweets, for example, don't seem to provide enough contextual information about word co-occurrence, so the model often devolves into a one-topic-per-doc clustering algorithm. Combining multiple related short documents can make a big difference.\nVocabulary curation is in practice the most challenging part of a topic modeling workflow. Replacing selected multi-word terms with single tokens (for example by swapping spaces for underscores) before tokenizing is a very good idea. Stemming is almost never useful, at least for English. Automated methods can help vocabulary curation, but this step has a profound impact on results (much more than the number of topics) and I am reluctant to encourage people to fully trust any system.\nParameters: I do not believe that there is a right number of topics. I recommend using a number of topics that provides the granularity that suits your application. Likelihood can often detect when you have too few topics, but after a threshold it doesn't provide much useful information. Using hyperparameter optimization makes models much less sensitive to this setting as well, which might reduce the number of parameters that you need to search over.\nTopic drift: This is not a well understood problem. More examples of real-world corpus change would be useful. Looking for changes in vocabulary (e.g. proportion of out-of-vocabulary words) is a quick proxy for how well a model will fit.","Q_Score":4,"Tags":"python,r,text-mining,lda,mallet","A_Id":37416493,"CreationDate":"2016-05-23T08:52:00.000","Title":"What is the optimal topic-modelling workflow with MALLET?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using cross_val_score from scikit-learn (package sklearn.cross_validation) to evaluate my classifiers.\nIf I use f1 for the scoring parameter, the function will return the f1-score for one class. To get the average I can use f1_weighted but I can't find out how to get the f1-score of the other class. (precision and recall analogous)\nThe functions in sklearn.metrics have a labels parameter which does this, but I can't find anything like this in the documentation.\nIs there a way to get the f1-score for all classes at once or at least specify the class which should be considered with cross_val_score?","AnswerCount":4,"Available Count":1,"Score":-0.049958375,"is_accepted":false,"ViewCount":13883,"Q_Id":37413302,"Users Score":-1,"Answer":"For individual scores of each class, use this : \nf1 = f1_score(y_test, y_pred, average= None)\nprint(\"f1 list non intent: \", f1)","Q_Score":5,"Tags":"python,scikit-learn,cross-validation","A_Id":53760310,"CreationDate":"2016-05-24T12:15:00.000","Title":"f1 score of all classes from scikits cross_val_score","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have familiarized myself with the basics of H5 in python. What I would like to do now is two things:\n\nWrite images (numpy arrays) into an H5 file.\nOnce that is done, be able to pick out $M$ randomly. \n\nWhat is meant here is the following: I would like to write a total of $N=100000$ numpy arrays (images), into one H5 file. Once that is done, then I would like to randomly select say, $M=50$ images from the H5 file at random, and read them. Then, I would like to pick another $M=50$ at random, and read them in as well, etc etc, until I have gone through all $N$ images. (Basically, sample without replacement). \nIs there an elegant way to do this? I am currently experimenting with having each image be stored as a separate key-value pair, but I am not sure if this is the most elegant. \nAnother solution is to store the entire volume of $N$ images, and then randomly select from there, but I am not sure that is elegant either, as it requires me to read in the entire block.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":274,"Q_Id":37421035,"Users Score":1,"Answer":"An elegant way for sampling without replacement is computing a random permutation of the numbers 1..N (numpy.random.permutation) and then using chunks of size M from it.\nStoring data in an h5py file is kind of arbitrary. You could use a single higher dimensional data set or a group containing the N two dimensional data sets. It's up to you. I would actually prefer to have the two dimensional data sets separately (gives you more flexibility) and to iterate over it using Group.iteritems.","Q_Score":1,"Tags":"python,file,vectorization,hdf5,h5py","A_Id":37845330,"CreationDate":"2016-05-24T18:17:00.000","Title":"H5 file with images in Python: Want to randomly select without replacement","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Still new to this, sorry if I ask something really stupid. What are the differences between a Python ordered dictionary and a pandas series? \nThe only difference I could think of is that an orderedDict can have nested dictionaries within the data. Is that all? Is that even true?  \nWould there be a performance difference between using one vs the other?\nMy project is a sales forecast, most of the data will be something like: {Week 1 : 400 units, Week 2 : 550 units}... Perhaps an ordered dictionary would be redundant since input order is irrelevant compared to Week#?   \nAgain I apologize if my question is stupid, I am just trying to be thorough as I learn. \nThank you!\n-Stephen","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":1177,"Q_Id":37423208,"Users Score":1,"Answer":"Ordered dict is implemented as part of the python collections lib. These collection are very fast containers for specific use cases. If you would be looking for only dictionary related functionality (like order in this case) i would go for that. While you say you are going to do more deep analysis in an area where pandas is really made for (eg plotting, filling missing values). So i would recommend you going for pandas.Series.","Q_Score":5,"Tags":"python,python-3.x,pandas,series,ordereddictionary","A_Id":37522101,"CreationDate":"2016-05-24T20:25:00.000","Title":"orderedDict vs pandas series","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am am trying to run the Quandl module on a virtualenv which I have uninstalled packages only pandas and then Quandl, \nI am running Python 2.7.10 - I have uninstalled all other python versions, but its still giving me the issue of 'ImportError: No module named Quandl'. Do you know what might be wrong? Thanks","AnswerCount":15,"Available Count":7,"Score":0.0,"is_accepted":false,"ViewCount":49345,"Q_Id":37426196,"Users Score":0,"Answer":"I am following a Youtube tutorial where they use 'Quandl'. It should be quandl. Change it and it won't throw error.","Q_Score":20,"Tags":"python-2.7,importerror,quandl","A_Id":38984971,"CreationDate":"2016-05-25T00:55:00.000","Title":"import error; no module named Quandl","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am am trying to run the Quandl module on a virtualenv which I have uninstalled packages only pandas and then Quandl, \nI am running Python 2.7.10 - I have uninstalled all other python versions, but its still giving me the issue of 'ImportError: No module named Quandl'. Do you know what might be wrong? Thanks","AnswerCount":15,"Available Count":7,"Score":1.0,"is_accepted":false,"ViewCount":49345,"Q_Id":37426196,"Users Score":12,"Answer":"Use below syntax all in lower case \n\nimport quandl","Q_Score":20,"Tags":"python-2.7,importerror,quandl","A_Id":38992511,"CreationDate":"2016-05-25T00:55:00.000","Title":"import error; no module named Quandl","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am am trying to run the Quandl module on a virtualenv which I have uninstalled packages only pandas and then Quandl, \nI am running Python 2.7.10 - I have uninstalled all other python versions, but its still giving me the issue of 'ImportError: No module named Quandl'. Do you know what might be wrong? Thanks","AnswerCount":15,"Available Count":7,"Score":0.0133325433,"is_accepted":false,"ViewCount":49345,"Q_Id":37426196,"Users Score":1,"Answer":"check whether it exists with the installed modules by typing \n\npip list\n\nin the command prompt and if there is no module with the name quandl then type\n\npip install quandl\n\nin  the command prompt . Worked for me in the jupyter","Q_Score":20,"Tags":"python-2.7,importerror,quandl","A_Id":60087731,"CreationDate":"2016-05-25T00:55:00.000","Title":"import error; no module named Quandl","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am am trying to run the Quandl module on a virtualenv which I have uninstalled packages only pandas and then Quandl, \nI am running Python 2.7.10 - I have uninstalled all other python versions, but its still giving me the issue of 'ImportError: No module named Quandl'. Do you know what might be wrong? Thanks","AnswerCount":15,"Available Count":7,"Score":0.0,"is_accepted":false,"ViewCount":49345,"Q_Id":37426196,"Users Score":0,"Answer":"quandl has now changed, you require an api key, go the site and register your email.\nimport quandl\nquandl.ApiConfig.api_key = 'your_api_key_here'\ndf = quandl.get('WIKI\/GOOGL')","Q_Score":20,"Tags":"python-2.7,importerror,quandl","A_Id":59360553,"CreationDate":"2016-05-25T00:55:00.000","Title":"import error; no module named Quandl","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am am trying to run the Quandl module on a virtualenv which I have uninstalled packages only pandas and then Quandl, \nI am running Python 2.7.10 - I have uninstalled all other python versions, but its still giving me the issue of 'ImportError: No module named Quandl'. Do you know what might be wrong? Thanks","AnswerCount":15,"Available Count":7,"Score":0.0,"is_accepted":false,"ViewCount":49345,"Q_Id":37426196,"Users Score":0,"Answer":"With Anaconda\\Jupyter notebook go to the install directory (C:\\Users\\<USER_NAME>\\AppData\\Local\\Continuum\\anaconda3) where <USER_NAME> is your logged in Username. Then execute in command prompt:\n\npython -m pip install Quandl\nimport quandl","Q_Score":20,"Tags":"python-2.7,importerror,quandl","A_Id":52893601,"CreationDate":"2016-05-25T00:55:00.000","Title":"import error; no module named Quandl","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am am trying to run the Quandl module on a virtualenv which I have uninstalled packages only pandas and then Quandl, \nI am running Python 2.7.10 - I have uninstalled all other python versions, but its still giving me the issue of 'ImportError: No module named Quandl'. Do you know what might be wrong? Thanks","AnswerCount":15,"Available Count":7,"Score":0.0,"is_accepted":false,"ViewCount":49345,"Q_Id":37426196,"Users Score":0,"Answer":"install quandl for version 3.1.0\nCheck package path where you installed, make sure it's name is quandl not Quandl (my previous name is Quandl, so when I use import quandl, it always said \"no module named quandl\")\nIf your package's name is Quandl, delete it and reinstall it. (I use anaconda to install my package, it's covenient!)","Q_Score":20,"Tags":"python-2.7,importerror,quandl","A_Id":43598162,"CreationDate":"2016-05-25T00:55:00.000","Title":"import error; no module named Quandl","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am am trying to run the Quandl module on a virtualenv which I have uninstalled packages only pandas and then Quandl, \nI am running Python 2.7.10 - I have uninstalled all other python versions, but its still giving me the issue of 'ImportError: No module named Quandl'. Do you know what might be wrong? Thanks","AnswerCount":15,"Available Count":7,"Score":-0.0133325433,"is_accepted":false,"ViewCount":49345,"Q_Id":37426196,"Users Score":-1,"Answer":"Sometimes the quandl module is present with \"Quandl\" in following location\nC:\\Program Files (x86)\\Anaconda\\lib\\site-packages\\Quandl.\nBut the scripts from Quandl refer to quandl in import statements. \nSo, renaming folder Quandl to quandl worked for me.\nNew path:\n\"C:\\Program Files (x86)\\Anaconda\\lib\\site-packages**quandl**\".","Q_Score":20,"Tags":"python-2.7,importerror,quandl","A_Id":45563219,"CreationDate":"2016-05-25T00:55:00.000","Title":"import error; no module named Quandl","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Suppose that i have multiple .csv files with columns of same kind . If i wanted to access data of a particular column from a specified .csv file , how is it possible?\nAll .csv files have been stored in list.data\nfor ex:\nSuppose that here , list.data[1] gives me the first .csv file.\nHow will i access a column of this file?\nI have tried list.data[1]$nameofthecolumn.\nBut this is giving me null values.\nI am not much familiar with R.\n\nlist.data[1]$name1\n  NULL\nlist.data[1]$name1\n  NULL\nlist.data[1]\n  $\n  NULL","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":51,"Q_Id":37480728,"Users Score":0,"Answer":"you can use list.data[[1]]$name1","Q_Score":0,"Tags":"python,r,csv","A_Id":37480887,"CreationDate":"2016-05-27T10:10:00.000","Title":"how to access a particular column of a particular csv file from many imported csv files","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"import pandas\n\u3000\u3000\u3000\u3000Traceback (most recent call last):\n  \u3000\u3000\u3000\u3000File \"\", line 1, in \n\u3000\u3000\u3000\u3000File \"\/Users\/...\/anaconda\/lib\/python2.7\/site-packages\/pandas\/init.py\", line 37\n, in \n    import pandas.core.config_init\n \u3000\u3000\u3000\u3000 File \"\/Users\/...\/anaconda\/lib\/python2.7\/site-packages\/pandas\/core\/config_init.py\",\n line 18, in \n    \u3000\u3000\u3000\u3000from pandas.formats.format import detect_console_encoding\n  \u3000\u3000\u3000\u3000File \"\/Users\/...\/anaconda\/lib\/python2.7\/site-packages\/pandas\/formats\/format.py\", l\nine 16, in \n    \u3000\u3000\u3000\u3000from pandas.io.common import _get_handle, UnicodeWriter, _expand_user\n  \u3000\u3000\u3000\u3000File \"\/Users\/...\/anaconda\/lib\/python2.7\/site-packages\/pandas\/io\/common.py\", line 5\n, in \n  \u3000\u3000\u3000\u3000  import csv\n \u3000\u3000\u3000\u3000 File \"csv.py\", line 10, in \n   \u3000\u3000\u3000\u3000 field_size_limit, \\\n \u3000\u3000\u3000\u3000 File \"csv_test.py\", line 4, in \n   \u3000\u3000\u3000\u3000 from scipy import stats\n \u3000\u3000\u3000\u3000 File \"scipy\/init.py\", line 103, in \n    \u3000\u3000\u3000\u3000raise ImportError(msg)\n\u3000\u3000\u3000\u3000ImportError: Error importing scipy: you cannot import scipy while\n        \u3000\u3000\u3000\u3000being in scipy source directory; please exit the scipy source\n       \u3000\u3000\u3000\u3000 tree first, and relaunch your python intepreter.  \nI tried to import pandas by invoking python from bash.\nBefore doing so,I used 'which python' command to make sure which python I'm using.\nIt interacted to me saying \"\/Users\/...(Usersname)\/anaconda\/bin\/python\".\nBut when I imported pandas from IPython of anaconda's distribution, successfully imported.\nfor the record,I added csv_test.py module by hand.\nguess this module is doing something wrong?\nit just imports scipy.stats and matplotlib.pyplot.\nSo I hope it is not affecting any other module.\nseems something seriously wrong is going on here,judging from Error sentence..","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":466,"Q_Id":37484932,"Users Score":1,"Answer":"The csv_test.py file you have \"added by hand\" tries to import scipy; as the error message says, don't do that. \nYou can probably place your test code in a private location, without messing with the scipy installation directory.\nI suggest uninstalling and reinstalling at least pandas and scipy, possibly everything, to eliminate intentional and unintentional alterations.","Q_Score":0,"Tags":"python-2.7,pandas,matplotlib,anaconda,importerror","A_Id":37485073,"CreationDate":"2016-05-27T13:36:00.000","Title":"Import error: Error importing scipy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I tried to use pandas to read an excel sheet into a dataframe but for floating point columns, the data is read incorrectly. I use the function read_excel() to do the task\nIn excel, the value is 225789.479905466 while in the dataframe, the value is 225789.47990546614 which creates discrepancy for me to import data from excel to database.  \nDoes anyone face the same issue with pandas.read_exel(). I have no issue reading a csv to dataframe.\nJeremy","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":4533,"Q_Id":37492173,"Users Score":0,"Answer":"Excel might be truncating your values, not pandas. If you export to .csv from Excel and are careful about how you do it, you should then be able to read with pandas.read_csv and maintain all of your data. pandas.read_csv also has an undocumented float_precision kwarg, that might be useful, or not useful.","Q_Score":4,"Tags":"python,excel,pandas,dataframe,precision","A_Id":37596921,"CreationDate":"2016-05-27T20:59:00.000","Title":"loss of precision when using pandas to read excel","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am struggling with the best choice for a classification\/prediction problem. Let me explain the task - I have a database of keywords from abstracts for different research papers, also I have a list of journals with specified impact factors. I want to build a model for article classification based on their keywords, the result is the possible impact factor (taken just as a number without any further journal description) with a given keywords. I removed the unique keyword tags as they do not have much statistical significance so I have only keywords that are repeated 2 and more times in my abstract list (6000 keyword total). I think about dummy coding - for each article I will create a binary feature vector 6000 attributes in length - each attribute refers to presence of the keyword in the abstract and classify the whole set by SVM. I am pretty sure that this solution is not very elegant and probably also not correct, do you have any suggestions for a better deal?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":539,"Q_Id":37497795,"Users Score":0,"Answer":"There is nothing wrong with using this coding strategy for text and support vector machines.\nFor your actual objective:\n\nsupport vector regression (SVR) may be more appropriate\nbeware of the journal impact factor. It is very crude. You need to take temporal aspects into account; and many very good work is not published in journals at all","Q_Score":0,"Tags":"python,r,classification,data-mining,text-classification","A_Id":37499607,"CreationDate":"2016-05-28T10:19:00.000","Title":"Classification of sparse data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm looking for an efficient method to find all the roots of a function f on an interval [a,b]. \nThe problem I have is that all the nice methods from scipy.optimize require either that f(a) and f(b) have different signs, or that I provide an initial guess x0, but I know nothing about my roots before running the code.\nNote: The function f is smooth (at least C1), and doesn't have a pathological behaviour [nothing like sin(1\/x)]. However, it requires building a matrix A(x) and finding its eigenvalues, and is therefore time-consuming. It is expected to have between 0 and 10 roots on [a,b], whose position is completely arbitrary. I can't afford missing any of them (e.g. I can't take 100 initial guesses x0 and just hope that i'll catch all the roots).\nI was thinking about implementing something like this:\n\nFind all the extrema {m_1, m_2.., m_k} of f with scipy.optimize [maybe fmin, but I don't know which method is the most efficient]:\n\n\nSearch for a minimum m_1 starting from point a [initial guess for gradient algorithm]\nSearch for a maximum m_2 starting from point m_1 + dx [forcing the gradient algorithm to go forward]\nSearch for a minimum m_3...\n\nIf two consecutive extrema m_i and m_(i+1) have opposite signs, apply brentq on interval [m_i, m_(i+1)] to find a root.\n\n\n\nIs there a better way of solving this problem?\nIf not, are fmin and brentq the best choices among the scipy.optimize library in order to minimize the number of calls to my function f?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1874,"Q_Id":37504035,"Users Score":0,"Answer":"Depends on your function, but it might be possible to solve symbolically using SymPy. That would give all roots. It can find eigenvalues symbolically if necessary.\nFinding all extrema is the same as finding all roots of the derivative of your function, so it won't be any easier than finding all roots (as WarrenWeckesser mentioned).\nFinding all roots numerically will require using knowledge about the function. As a simple example, say you knew some minimum spacing between roots. You could try to recursively partition the interval and find roots in each. Stop after finding the maximum number of roots. But if the spacing is small, this could require many function evaluations (e.g. in the worst case when there are zero roots). The more constraints you can impose, the more you can cut down on function evaluations.","Q_Score":0,"Tags":"python,optimization,scipy","A_Id":37506002,"CreationDate":"2016-05-28T21:51:00.000","Title":"Finding multiple roots on an interval with Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have got a strange issue.\nI am now using graphlab\/numpy to develop a project via Pycharm 5. OS is Mac OS 10.11.5. I created a p2.7 virtual environment for the project. Programme runs well. But after I install ipython, I can no longer import graphlab and numpy correctly.\nError message:\n\nAttributeError: 'module' object has no attribute 'core'\n\nSystem keeps telling that attribute \u2018core' and 'connect' are missing. But I am pretty sure that they can be found in graphlab\/numpy folders, and no duplicates. \nProject runs all right in terminal. Now I have to uninstall ipython. Then everything is ok again.\nPlease kindly help.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":398,"Q_Id":37505970,"Users Score":0,"Answer":"Please remember that console applications and GUI application do not share the same environment on OS X. \nThis also means that if you install a Python package from the console, this would probably not be visible to PyCharm. \nUsually you need to install packages using PyCharm in order to be able to use them.","Q_Score":0,"Tags":"python,python-2.7,ipython,graphlab","A_Id":37531473,"CreationDate":"2016-05-29T03:49:00.000","Title":"Error importing numpy & graphlab after installing ipython","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm looking for a way to use Intel GPU as a GPGPU with Theano.\nI've already installed Intel OpenCL and libgpuarray, but a test code 'python -c \"import pygpu;pygpu.test()\"' crashed the process. And I found out devname method caused it. It seems there would be a lot more errors.\nIs it easy to fixed them to work well? I understand Intel OpenCL project can use GPGPU but it might not be supported by libgpuarray.\nEnvironment\n\nWindows 7 x64\nIntel(R) HD Graphics 4600\nPython 2.7 x86 or x64","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":920,"Q_Id":37507758,"Users Score":1,"Answer":"libgpuarray have been made to support OpenCL, but we don't have time to finish it. Many thinks work, but we don't have the time to make sure it work everywhere.\nIn any cases, you must find an OpenCL version that support that GPU, install it and reinstall libgpuarray to have it use it.\nAlso, I'm not sure that GPU will give you any speed up. Don't spend too much time on tring to make it work.","Q_Score":1,"Tags":"python,gpu,gpgpu,theano","A_Id":37516498,"CreationDate":"2016-05-29T08:26:00.000","Title":"Is there any way to use libgpuarray with Intel GPU?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In a hypothetical scenario where I have a large amount of data that is received, and is generally in chronological order upon receipt, is there any way to \"play\" the data forward or backward, thereby recreating the flow of new information on demand? I know that in a simplistic sense, I can always have a script (with whatever output being unimportant) that starts with a for loop that takes in whatever number of events or observations and does something, then takes in more observations, updates what was previously output with a new result, and so on. Is there a way of doing that is more scaleable than a simple for loop?\nBasically, any time I look into this subject I quickly find myself navigating to the subject area of High Frequency Trading, specifically algorithm efficacy by way of backtesting against historical data. While my question is about doing this in a more broad sense where our observation do not need to be stock\/option\/future price pips, the same principles must apply. Does anyone have such experience on how such a platform is built on a more scaleable level than just a for loop with logic below it? Another example would be health data \/ claims where one can see backward and forward what happened as more claims come in over time.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":392,"Q_Id":37516730,"Users Score":0,"Answer":"The problem in optimising the loop is that say you have 3 years and you have only 3 events that you are interested. Then you can use an event based backtesting with 3 iteration only. \nThe problem here is that you have to precompute the event which will need to the data anyway and most of the time you will need statistics about you backtrading that will need the data also like max-drawdown. \nSo most backtrading framework will use the loop anyway. Or vectorisation if you are on R\/Matlab Numpy \nIf you really need to optimize it, you probably need to precompute and store all the information and then just do look-ups","Q_Score":0,"Tags":"python,pandas,back-testing","A_Id":43774149,"CreationDate":"2016-05-30T02:03:00.000","Title":"Backtesting with Data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I 'm using svm to predict the label from the title of a book. However I want to give more weight to some features pre defined. For example, if the title of the book contains words like fairy, Alice I want to label them as children's books. I'm using word n-gram svm. Please suggest how to achieve this using sklearn.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":156,"Q_Id":37538068,"Users Score":0,"Answer":"You can create one more feature vector in your training data, if the name of the book contains your predefined words then make it one otherwise zero.","Q_Score":1,"Tags":"python,svm","A_Id":37538260,"CreationDate":"2016-05-31T06:11:00.000","Title":"giving more weight to a feature using sklearn svm","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataframe with 71 columns and 30597 rows. I want to replace all non-nan entries with 1 and the nan values with 0.\nInitially I tried for-loop on each value of the dataframe which was taking too much time.\nThen I used data_new=data.subtract(data) which was meant to subtract all the values of the dataframe to itself so that I can make all the non-null values 0. \nBut an error occurred as the dataframe had multiple string entries.","AnswerCount":10,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":42508,"Q_Id":37543647,"Users Score":0,"Answer":"Use: df.fillna(0)\nto fill NaN with 0.","Q_Score":32,"Tags":"python,pandas,dataframe","A_Id":37543931,"CreationDate":"2016-05-31T10:50:00.000","Title":"How to replace all non-NaN entries of a dataframe with 1 and all NaN with 0","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataframe with 71 columns and 30597 rows. I want to replace all non-nan entries with 1 and the nan values with 0.\nInitially I tried for-loop on each value of the dataframe which was taking too much time.\nThen I used data_new=data.subtract(data) which was meant to subtract all the values of the dataframe to itself so that I can make all the non-null values 0. \nBut an error occurred as the dataframe had multiple string entries.","AnswerCount":10,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":42508,"Q_Id":37543647,"Users Score":0,"Answer":"Generally there are two steps - substitute all not NAN values and then substitute all NAN values.\n\ndataframe.where(~dataframe.notna(), 1) - this line will replace all not nan values to 1.\ndataframe.fillna(0) - this line will replace all NANs to 0\n\nSide note: if you take a look at pandas documentation, .where replaces all values, that are False - this is important thing. That is why we use inversion to create a mask ~dataframe.notna(), by which .where() will replace values","Q_Score":32,"Tags":"python,pandas,dataframe","A_Id":65940835,"CreationDate":"2016-05-31T10:50:00.000","Title":"How to replace all non-NaN entries of a dataframe with 1 and all NaN with 0","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a bspline created with scipy.interpolate.splrep with points (x_0,y_0) to (x_n,y_n). Usual story. But I would like to add a data point (x_n+1,y_n+1) and appropriate knot without recomputing the entire spline. Can anyone think of a way of doing this elegantly? \nI could always take the knot list returned by splrep, and add on the last knot of a smaller spline created with (x_n-2, y_n-2) to (x_n+1,y_n+1) but that seems less efficient than it could be.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":56,"Q_Id":37552035,"Users Score":0,"Answer":"Short answer: No.\nSpline construction is a global process, so if you add a data point, you really need to recompute the whole spline. Which involves solving an N-by-N linear system etc. \nIf you're adding many knots sequentially, you probably can construct a process where you're using a factorization of the colocation matrix on step n to compute things on step n+1. You'll need to carefully check the stability of this process. And splrep and friends do not give you any help here, so you'll need to write this yourself. (If you do, you might find it helpful to check the sources of interpolate.CubicSpline).\nBut before you start on that, consider using a local interpolator instead.\nIf all you want is to add a knot given data, then there's scipy.interpolate.insert.","Q_Score":0,"Tags":"python,numpy,scipy,bspline","A_Id":37553823,"CreationDate":"2016-05-31T17:24:00.000","Title":"B-splines with Scipy : can I add a datapoint without full recompute?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am having some difficulties installing opencv with python 3.5.\nI have linked the cv files, but upon import cv2 I get an error saying ImportError: dlopen(\/Library\/Frameworks\/Python.framework\/Versions\/3.5\/lib\/python3.5\/site-packages\/cv2.so, 2): Symbol not found: _PyCObject_Type or more specifically:\n\n\/Library\/Frameworks\/Python.framework\/Versions\/3.5\/bin\/python3.5 \/Users\/Jamie\/Desktop\/tester\/test.py\n  Traceback (most recent call last):\n    File \"\/Users\/Jamie\/Desktop\/tester\/test.py\", line 2, in \n      import cv\n    File \"\/Library\/Frameworks\/Python.framework\/Versions\/3.5\/lib\/python3.5\/site-packages\/cv.py\", line 1, in \n      from cv2.cv import *\n  ImportError:dlopen(\/Library\/Frameworks\/Python.framework\/Versions\/3.5\/lib\/python3.5\/site-packages\/cv2.so, 2): Symbol not found: _PyCObject_Type\n    Referenced from: \/Library\/Frameworks\/Python.framework\/Versions\/3.5\/lib\/python3.5\/site-packages\/cv2.so\n    Expected in: flat namespace\n   in \/Library\/Frameworks\/Python.framework\/Versions\/3.5\/lib\/python3.5\/site-packages\/cv2.so\n\nI have linked cv.py and cv2.so from location \/usr\/local\/Cellar\/opencv\/2.4.12_2\/lib\/python2.7\/site-packages correctly into \/Library\/Frameworks\/Python.framework\/Versions\/3.5\/bin\nWould anybody be able to help please?\nThanks very much","AnswerCount":3,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":12712,"Q_Id":37555890,"Users Score":6,"Answer":"No need to change the python version, you can just use the pip command\nopen cmd ( admin mode) and type \npip install opencv-python","Q_Score":4,"Tags":"python,opencv","A_Id":43214847,"CreationDate":"2016-05-31T21:24:00.000","Title":"Python OpenCV import error with python 3.5","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm working with the yelp dataset and of course it is millions of entries long so I was wondering if there was any way where you can just download what you need or do you have to pick away at it manually? For example, yelp has reviews on everything from auto repair to beauty salons but I only want the reviews on the restaurants. So do I have to read the entire thing and then drop the rows I don't need?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":412,"Q_Id":37556334,"Users Score":0,"Answer":"Typically yes, load everything, then filter your dataset.\nBut if you really want to pre-filter, and you're on a unix like system, you can prefilter using grep before even starting Python.\nA compromise between them is to write the prefilter using Python and Pandas, this way you download the data, prefilter them (write prefiltered data to another csv) and play with your prefiltered data.\nThe way to go depend on the number of times you need to load the whole dataset, if you want to read once and discard it, there's no need to prefilter, but while working on the code, if you want to test it a lot of time, prefiltering may save you some seconds. But here again, there's another possibility: use ipython notebook, so you'll be able to load your dataset, filter it, then execute the block of code on which you're currently working any number of time on this already-loaded dataset, it's even faster than loading a prefiltered dataset.\nSo no real answer here, it really depends on your usage and personal tastes.","Q_Score":0,"Tags":"python,csv,pandas,dataframe,yelp","A_Id":37556551,"CreationDate":"2016-05-31T22:00:00.000","Title":"Is there a way to prefilter data before using read_csv to download data into pandas dataframe?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I installed Python 3.5.1 from www.python.org. Everything works great. Except that you can't install pandas using pip (it needs visualstudio to compile, which I don't have). So I installed Anaconda (www.continuum.io\/downloads). Now I can see pandas as part of the list of installed modules, but when I run python programs I still get:\nImportError: No module named 'pandas'\nHow do I set up my environment to use the modules from Anaconda?\nNote: I have Anaconda's home directory and Library\/bin on my path, as well as Python's home directory. I do not have PYTHONPATH or PYTHONHOME set, and I know I have the correct privileges to see everything.","AnswerCount":2,"Available Count":1,"Score":-0.0996679946,"is_accepted":false,"ViewCount":1823,"Q_Id":37557174,"Users Score":-1,"Answer":"Anaconda has included one version of Python with it. You have to change your system environment path with Anaconda's instead of the former one to avoid conflict. Also, if you want to make the whole process easy, it is recommended to use PyCharm, and it will ask you to choose the Python interpreter you want.","Q_Score":0,"Tags":"python,python-3.x,pandas,packages","A_Id":37557227,"CreationDate":"2016-05-31T23:24:00.000","Title":"How do I get python to import pandas?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I looked around online but couldn't find anything, but I may well have missed a piece of literature on this.  I am running a basic neural net on a 289 component vector to produce a 285 component vector.  In my input, the last 4 pieces of data are critical to change the rest of the input into the resultant 285 for the output.  That is to say, the input is 285 + 4, such that the 4 morph the rest of the input into the output.\nBut when running a neural network on this, I am not sure how to reflect this.  Would I need to use convolution on the rest of the input? I want my system to emphasize the 4 data points that critically affect the other 285.  I am still new to all of this, so a few pointers would be great!\nAgain, if there is something already written on this, then that would be awesome too.","AnswerCount":4,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1982,"Q_Id":37571165,"Users Score":0,"Answer":"To emphasize on any vector elements in your input vector you will have to give less information of the unimportant vector to your neural network.\nTry to encode the first less important 285 numbers into one number or any vector size you like, with a a multiplayer neural network then use that number with other 4 number as a input to a neural network. \nExample:\nv1=[1,2,3,..........285]\nv2=[286,287,288,289]\nv_out= Neural_network(input_vector=v1,neurons=[100,1]) # 100 hidden unit with one outpt.\nv_final=Neural_network(input_vector=v_out,neurons=[100,1]) # 100 hidden unit with one outpt.","Q_Score":5,"Tags":"python,neural-network,tensorflow,data-science","A_Id":37574806,"CreationDate":"2016-06-01T14:11:00.000","Title":"How can I make my neural network emphasize that some data is more important than the rest?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have data regarding metallicity in stars, I want to compare it with a student's t distribution. To do this I am running a Kolmogorov-Smirnov test using scipy.stats.kstest on python\n    KSstudentst = scipy.stats.kstest(data,\"t\",args=(a,b))\nBut I am unable to find what the arguments are supposed to be. I know the student's t requires a degree of freedom (df) parameter but what is the other parameter. Also which one of the two is the df parameter.\nIn the documentation for scipy.stats.t.cdf the inputs are the position at which value is to be calculated and df, but in the KS test there is no sense in providing the position.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":3626,"Q_Id":37575270,"Users Score":0,"Answer":"The args argument must be a tuple but it can be a single variable. You can do your test using ks_statistic, pvalue = scipy.stats.kstest(x, 't', (10,)) if 10 is the degrees of freedom.","Q_Score":2,"Tags":"python,scipy,kolmogorov-smirnov","A_Id":37575927,"CreationDate":"2016-06-01T17:32:00.000","Title":"What Arguments to use while doing a KS test in python with student's t distribution?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"What's the easiest way to produce a numpy Boolean array representation of an integer?  For example, map 6 to np.array([False, True, True], dtype=np.bool).","AnswerCount":4,"Available Count":1,"Score":0.049958375,"is_accepted":false,"ViewCount":653,"Q_Id":37580272,"Users Score":1,"Answer":"Maybe it is not the easiest, but a compact way is\nfrom numpy import array\narray([i for i in bin(5)[2:]]) == '1'","Q_Score":2,"Tags":"python,numpy","A_Id":37580756,"CreationDate":"2016-06-01T23:14:00.000","Title":"numpy Boolean array representation of an integer","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have trained a classifier model using RapidMiner after a trying a lot of algorithms and evaluate it on my dataset. \nI also export the model from RapidMiner as XML and pkl file, but I can't read it in my python program (scikit-learn). \nIs there any way to import RapidMiner classifier\/model in a python program and use it to predict or classify new data in my end application?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2438,"Q_Id":37592608,"Users Score":1,"Answer":"Practically, I would say no - just train your model in sklearn from the beginning if that's where you want it.\nYour RapidMiner model is some kind of object.  The two formats you are exporting as are just storage methods.  Sklearn models are a different kind of object.  You can't directly save one and load it into the other. A similar example would be to ask if you can take an airplane engine and load it into a train.\nTo do what you're asking, you'll need to take the underlying data that your classifier saved, find the format, and then figure out a way to get it in the same format as a sklearn classifier.  This is dependent on what type of classifier you have.  For example, if you're using a bayesian model, you could somehow capture the prior probabilities and then use those, but this isn't trivial.","Q_Score":1,"Tags":"python,machine-learning,scikit-learn,rapidminer","A_Id":37593003,"CreationDate":"2016-06-02T13:00:00.000","Title":"Can I export RapidMiner model to integrate with python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"pip list inside conda env:\npip list\nmatplotlib (1.4.0)\nnose (1.3.7)\nnumpy (1.9.1)\npandas (0.15.2)\npip (8.1.2)\npyparsing (2.0.1)\npython-dateutil (2.4.1)\npytz (2016.4)\nscikit-learn (0.15.2)\nscipy (0.14.0)\nsetuptools (21.2.1)\nsix (1.10.0)\nwheel (0.29.0)\nwhich python:\n\/Users\/xxx\/anaconda\/envs\/pythonenvname\/bin\/python\n(pythonenvname)pc-xx-xx:oo xxx$ which pip\n\/Users\/xxx\/anaconda\/envs\/pythonenvname\/bin\/pip\npython\nPython 3.4.4 |Anaconda custom (x86_64)| (default, Jan  9 2016, 17:30:09)\n[GCC 4.2.1 (Apple Inc. build 5577)] on darwin\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\n\n\n\nimport pandas as pd error:\nsh: sysctl: command not found","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":204,"Q_Id":37600960,"Users Score":0,"Answer":"Finally, I figured out the answer. It is all about the PATH variable. It was pointing to os python rather than anaconda python. Thanks all for your time.","Q_Score":0,"Tags":"python,python-3.x,anaconda,conda","A_Id":37614696,"CreationDate":"2016-06-02T19:54:00.000","Title":"anaconda env couldn't import any of the packages","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataframe with 250.000 rows but 140 columns and I'm trying to construct a pair plot. of the variables.\nI know the number of subplots is huge, as well as the time it takes to do the plots. (I'm waiting for more than an hour on an i5 with 3,4 GHZ and 32 GB RAM).\nRemebering that scikit learn allows to construct random forests in parallel, I was checking if this was possible also with seaborn.\nHowever, I didn't find anything. The source code seems to call the matplotlib plot function for every single image.\nCouldn't this be parallelised? If yes, what is a good way to start from here?","AnswerCount":3,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":15683,"Q_Id":37612434,"Users Score":34,"Answer":"Rather than parallelizing, you could downsample your DataFrame to say, 1000 rows to get a quick peek, if the speed bottleneck is indeed occurring there. 1000 points is enough to get a general idea of what's going on, usually. \ni.e. sns.pairplot(df.sample(1000)).","Q_Score":25,"Tags":"python,performance,parallel-processing,seaborn","A_Id":48292419,"CreationDate":"2016-06-03T10:50:00.000","Title":"What are ways to speed up seaborns pairplot","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to use pyspark.mllib.stat.Statistics.corr function to compute correlation between two columns of pyspark.sql.dataframe.DataFrame object. corr function expects to take an rdd of Vectors objects. How do I translate a column of df['some_name'] to rdd of Vectors.dense object?","AnswerCount":4,"Available Count":1,"Score":0.1488850336,"is_accepted":false,"ViewCount":31767,"Q_Id":37618977,"Users Score":3,"Answer":"df.stat.corr(\"column1\",\"column2\")","Q_Score":13,"Tags":"python,apache-spark,pyspark,apache-spark-sql,apache-spark-mllib","A_Id":60295986,"CreationDate":"2016-06-03T16:06:00.000","Title":"PySpark computing correlation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"what is the proper way to close tensorboard with jupyter notebook? \nI'm coding tensorflow on my jupyter notebook.                                        To launch, I'm doing:                                                                     1.  \n\n!tensorboard --logdir = logs\/\nopen a new browser tab and type in localhost:6006\n\nto close, I just do:\n\nclose the tensorflow tab on my browser\non jupyter notebook, I click on interrupt kernel \n\nJust wondering if this is the proper way....\nBTW, in my code I set my log file as '.\/log\/log1'. \nwhen starting tensorboard, should I use --logdir = .\/log  or --logdir = .\/log\/log1?   \nthank you very much.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":2652,"Q_Id":37632393,"Users Score":1,"Answer":"The jupyter stuff seems fine. In general, if you don't close TensorBoard properly, you'll find out as soon as you try to turn on TensorBoard again and it fails because port 6006 is taken. If that isn't happening, then your method is fine.\nAs regards the logdir, passing in the top level logdir is generally best because that way you will get support for comparing multiple \"runs\" of the same code in TensorBoard. However, for this to work, it's important that each \"run\" be in its own subdirectory, e.g.:\nlogs\/run1\/..tfevents..\nlogs\/run2\/..tfevents..\ntensorboard --logdir=logs","Q_Score":1,"Tags":"python,tensorflow,jupyter-notebook,tensorboard","A_Id":37682991,"CreationDate":"2016-06-04T16:13:00.000","Title":"how to close tensorboard server with jupyter notebook","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've tried installing tflearn through pip as follows \npip install tflearn\nand now when I open python, the following happens:\n>>> import tflearn\nTraceback (most recent call last):\n  File \"<stdin>\", line 1, in <module>\n  File \"\/\/anaconda\/lib\/python2.7\/site-packages\/tflearn\/__init__.py\", line 22, in <module>\n    from . import activations\n  File \"\/\/anaconda\/lib\/python2.7\/site-packages\/tflearn\/activations.py\", line 7, in <module>\n    from . import initializations\n  File \"\/\/anaconda\/lib\/python2.7\/site-packages\/tflearn\/initializations.py\", line 5, in <module>\n    from tensorflow.contrib.layers.python.layers.initializers import \\\nImportError: cannot import name variance_scaling_initializer\nAny ideas? I'm using an anaconda installation of python.","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":3984,"Q_Id":37686139,"Users Score":0,"Answer":"Last tflearn update had a compatibility issue with old TensorFlow versions (like mrry said, caused by 'variance_scaling_initializer()' that was only compatible with TensorFlow 0.9).\nThat error had already been fix, so you can just update TFLearn and it should works fine with any TensorFlow version over 0.7.","Q_Score":4,"Tags":"python,tensorflow","A_Id":37693149,"CreationDate":"2016-06-07T17:54:00.000","Title":"TFLearn pip installation bug","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Let say I have made a program to detect a green ball in a video. Whenever there is a green ball detected, I want to print out the duration of video at the time the green ball is detected. Is it possible?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":5963,"Q_Id":37695376,"Users Score":1,"Answer":"You can simply measure a certain position in the video in milliseconds using\ntime_milli = cap.get(cv2.CAP_PROP_POS_MSEC)\nand then divide time_milli by 1000 to get the time in seconds.","Q_Score":3,"Tags":"python,opencv,video","A_Id":65464031,"CreationDate":"2016-06-08T07:00:00.000","Title":"Python and OpenCV - getting the duration time of a video at certain points","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"i have a 16 bit image in PhoPhoto RGB color space.For equalizzation I want to convert it in Lab colorspace and then equalize L-channel without losing precision. I have used skimage.color.rgb2lab but this convert image in float64.\nhelp me!!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":455,"Q_Id":37702630,"Users Score":0,"Answer":"skimage version of rgb2lab uses floating point input where the color range is 0-1. You can normalize your 16bit image to this range and use the rgb2lab routine.","Q_Score":1,"Tags":"python,image,16-bit,lab-color-space","A_Id":46964844,"CreationDate":"2016-06-08T12:33:00.000","Title":"Convert a 16-bit image from rgb to Lab without losing precision","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"What is the efficient way of keeping and comparing generated sub-graphs from given input graph G in Python?\nSome details:\n\nInput graph G is a directed, simple graph with number of vertices varying from n=100-10000. Number of edges - it can be assumed that maximum would be 10% of complete graph (usually less) so it gives in that case maximum number of n*(n-1)\/10\nThere is an algorithm that can generate from input graph G sub-graphs in number of hundreds\/thousands. And for each sub-graph are made some (time consuming) computations.\nPair \"subgraph, computation results\" must be stored for later use (dynamic programming approach - if given sub-graph were already processed we want to re-use its results).\nBecause of point (2.) it would be really nice to store sub-graph\/results pairs in kind of dictionary where sub-graph is a key. How it can be done efficiently? Some ideas of efficient calculation of sub-graph hash value maybe?\nLet's assume that memory is not a problem and I can find machine with enough memory to keep a data - so let's focus only on speed.\n\nOf course If there are already nice to use data-structures that might be helpful in this problem (like sparse matrices from scipy) they are very welcome.\nI just would like to know your opinions about it and maybe some hints regarding approach to this problem.\nI know that there are nice graph\/network libraries for Python like NetworkX, igraph, graph-tool which have very efficient algorithms to process provided graph. But seems (or I could not find) efficient way to fulfill points (2. 3.)","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":437,"Q_Id":37720588,"Users Score":0,"Answer":"The key point here is the data format of the graphs already generated by your algorithm. Does it contruct a new graph by adding vertices and edges ? Is it rewritable ? Does it uses a given format (matrix, adjacency list, vertices and nodes sets etc.)\nIf you have the choice however, because your subgraph have a \"low\" cardinality and because space is not an issue, I would store subgraphs as arrays of bitmasks (the bitmask part is optional, but it is hashable and makes a compact set). A subgraph representation would be then \n\nL a list of node references in your global graph G. It can also be a bitmask to be used as a hash\nA an array of bitmask (matrix) where A[i][j] is the truth value of the edge L[i] -> L[j]\n\nThis takes advantage of the infinite size low space requirement of Python integers. The space complexity is O(n*n) but you get efficient traversal and can easily hash your structure.","Q_Score":1,"Tags":"python,performance,data-structures,graph","A_Id":37721273,"CreationDate":"2016-06-09T08:26:00.000","Title":"Efficient representing sub-graphs (data structure) in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am a newbie in big data, I have an assignment in which I was given a CSV file and date field is one of the fields in that file.  The file size is only 10GB, but I need to create a much larger file, 2TB in size, for big data practice purpose, by duplicating the file's content but increasing the date in order making the duplicated records different from the original one.  Then have the new 2TB file accessible via Hive.  Need help on how the best way I suppose to implement this?  Is it best using pig in hadoop or python?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":59,"Q_Id":37727899,"Users Score":0,"Answer":"It really depends on what do you want to achieve and what hardware do you use. \nIf you need to process this file fast and you actually have real Hadoop cluster (bigger than 1 or 2 nodes), then probably the best way would be to write Pig script or even simple Hadoop MapReduce job to process this file. With this approach you would get output file on HDFS so it will be easily accessible via Hive. \nOn the other hand if you have single computer or some \"toy\" Hadoop cluster processing that file using Hadoop will take much longer time than simply executing python script on this file. It's because Hadopp processing has quite big overhead on data serialization and sending data through network. Of course in that case you will have to deal with the fact that input and output file may not fit into your RAM on your own.","Q_Score":1,"Tags":"python,csv,hive,apache-pig","A_Id":37729976,"CreationDate":"2016-06-09T13:51:00.000","Title":"creating new CSV by duplicating and modifying existing records multiple times from the source CSV","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it better to implement my own K-means Algorithm in Python or use the pre-implemented K-mean Algorithm in Python libraries like for example Scikit-Learn?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":614,"Q_Id":37751430,"Users Score":1,"Answer":"Before answering which is better, here is a quick reminder of the algorithm:\n\n\"Choose\" the number of clusters K\nInitiate your first centroids\nFor each point, find the closest centroid\naccording to a distance function D\nWhen all points are attributed to a cluster, calculate the barycenter of the cluster which become its new centroid\nRepeat step 3. and step 4. until convergence\n\nAs stressed previously, the algorithm depends on various parameters:\n\nThe number of clusters\nYour initial centroid positions \nA distance function to calculate distance between any point and centroid\nA function to calculate the barycenter of each new cluster\nA convergence metric\n...\n\nIf none of the above is familiar to you, and you want to understand the role of each parameter, I would recommend to re-implement it on low-dimensional data-sets. Moreover, the implemented Python libraries might not match your specific requirements - even though they provide good tuning possibilities.\nIf your point is to use it quickly with a big-picture understanding, you can use existing implementation - scikit-learn would be a good choice.","Q_Score":0,"Tags":"python,machine-learning,scikit-learn,computer-science,k-means","A_Id":37752325,"CreationDate":"2016-06-10T15:00:00.000","Title":"K-Means Implementation in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have been working on machine learning KNN (K Nearest Neighbors) algorithm with Python and Python's Scikit-learn machine learning API.\nI have created sample code with toy dataset simply using python and Scikit-learn and my KNN is working fine. But As we know Scikit-learn API is build to work on single machine and hence once I will replace my toy data with millions of dataset it will decrease my output performance.\nI have searched for many options, help and code examples, which will distribute my machine learning processing parallel using spark with Scikit-learn API, but I was not found any proper solution and examples.\nCan you please let me know how can I achieve and increase my performance with Apache Spark and Scikit-learn API's K Nearest Neighbors?    \nThanks in advance!!","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":5059,"Q_Id":37767790,"Users Score":1,"Answer":"Well according to discussions https:\/\/issues.apache.org\/jira\/browse\/SPARK-2336 here MLLib (Machine Learning Library for Apache Spark) does not have an implementation of KNN. \nYou could try https:\/\/github.com\/saurfang\/spark-knn.","Q_Score":1,"Tags":"python,scala,apache-spark,machine-learning,scikit-learn","A_Id":37768933,"CreationDate":"2016-06-11T19:33:00.000","Title":"Scikit-learn KNN(K Nearest Neighbors ) parallelize using Apache Spark","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a Wizard of Oz experiment using Choregraphe to make a NAO perform certain tasks running on machine A. The participant interacting with the NAO also interacts with a machine B. When I start the experiment (in Choregraphe on machine A) I want a certain MATLAB script to start on machine B. I.e. Choregraphe will initiate the MATLAB script.\nDo you have any suggestions of how to do this? My programming are limited to that of MATLAB and R, while Choregraphe is well integrated with Python and C++ hence my question here on Stack.\nKind Regards,\nKD","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":107,"Q_Id":37785380,"Users Score":0,"Answer":"Using NAO C++ SDK, it may be possible to make a MEX-FILE in Matlab that \"listens\" to NAO. Then NAO just has to raise an event in its memory (ALMemory) that Matlab would catch to start running the script.","Q_Score":0,"Tags":"python,c++,matlab,nao-robot","A_Id":37796440,"CreationDate":"2016-06-13T08:50:00.000","Title":"Sync Choregraphe and Matlab","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm working on a project with another person. My part of the project involves analytics with Spark's Machine Learning, while my teammate is using Spark Streaming to pipeline data from the source to the program and out to an interface.\nI am planning to use Scala since it has the best support for Spark. However, my teammate does not have any experience with Scala, and would probably prefer to use Python.\nGiven that our parts of the program are doing two different things, would it be a good idea for us to have his Python script call my Scala executable? Or would using different languages raise complications later on?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":452,"Q_Id":37820668,"Users Score":1,"Answer":"An experienced developer will be able to pick up a new language and become productive fairly quickly.\nI would only consider using the two languages together if:\n\nThe deadlines are too tight to allow for the developer to get up to speed,\nThe integration between the modules is quite limited (and you're confident that won't change) and\nThere is a clear deployment strategy.\n\nI would suggest doing a small-scale test first to confirm the deployment and integration plans you have will work.","Q_Score":2,"Tags":"python,scala,apache-spark,pyspark","A_Id":37821215,"CreationDate":"2016-06-14T19:36:00.000","Title":"Using Multiple Languages while developing a Spark application","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm building a neural net using TensorFlow and Python, and using the Kaggle 'First Steps with Julia' dataset to train and test it. The training images are basically a set of images of different numbers and letters picked out of Google street view, from street signs, shop names, etc. The network has 2 fully-connected hidden layers.\nThe problem I have is that the network will very quickly train itself to only give back one answer: the most common training letter (in my case 'A'). The output is in the form of a (62, 1) vector of probabilities, one for each number and letter (upper- and lower-case). This vector is EXACTLY the same for all input images.\nI've then tried to remove all of the 'A's from my input data, at which point the network changed to only give back the next most common input type (an 'E').\nSo, is there some way to stop my network stopping at a local minima (not sure if that's the actual term)? Is this even a generic problem for neural networks, or is it just that my network is broken somehow?\nI'm happy to provide code if it would help.\nEDIT: These are the hyperparameters of my network:\nInput size : 400 (20x20 greyscale images)\nHidden layer 1 size : 100\nHidden layer 2 size : 100\nOutput layer size : 62 (Alphanumeric, lower- and upper-case)\nTraining data size : 4283 images\nValidation data size : 1000 images\nTest data size : 1000 images\nBatch size : 100\nLearning rate : 0.5\nDropout rate : 0.5\nL2 regularisation parameter : 0","AnswerCount":4,"Available Count":3,"Score":0.0996679946,"is_accepted":false,"ViewCount":379,"Q_Id":37829169,"Users Score":2,"Answer":"Trying to squeeze blood from a stone!\nI'm skeptical that with 4283 training examples your net will learn 62 categories...that's a big ask for such a small amount of data.  Especially since your net is not a conv net...and it's forced to reduce its dimensionality to 100 at the first layer.  You may as well pca it and save time.\nTry this:\nStep 1:  download an MNIST example and learn how to train and run it.\nStep 2:  use the same mnist network design and throw your data at it...see how it goes. you may need to pad your images.  Train and then run it on your test data.\nNow step 3: take your fully trained step 1 mnist model and \"finetune\" it by continuing to train with your data(only) and with a lower learning rate for a few epochs(ultimately determine #epochs by validation).  Then run it on your test data again and see how it does.  Look up \"transfer learning\"...and a \"finetuning example\" for your toolkit.(Note that for finetuning you need to mod the output layer of the net)\nI'm not sure how big your original source images are but you can resize them and throw a pre-trained cifar100 net at it(finetuned) or even an imagenet if the source images are big enough. Hmmm cifar\/imagnet are for colour images...but you could replicate your greyscale to each rgb band for fun.\nMark my words...these steps may \"seem simple\"...but if you can work through it and get some results(even if they're not great results) by finetuning with your own data, you can consider yourself a decent NN technician.  \nOne good tutorial for finetuning is on the Caffe website...flickr style(I think)...there's gotta be one for TF too.\nThe last step is to design your own CNN...be careful when changing filter sizes--you need to understand how it affects outputs of each layer and how information is preserved\/lost.\nI suppose another thing to do is to do \"data augmentation\" to get yourself some more of it.  slight rotations\/resizing\/lighting...etc.  Tf has some nice preprocessing for doing some of this...but some will need to be done by yourself.\ngood luck!","Q_Score":0,"Tags":"python,neural-network,tensorflow","A_Id":37846825,"CreationDate":"2016-06-15T07:46:00.000","Title":"Neural network only learns most common training image","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm building a neural net using TensorFlow and Python, and using the Kaggle 'First Steps with Julia' dataset to train and test it. The training images are basically a set of images of different numbers and letters picked out of Google street view, from street signs, shop names, etc. The network has 2 fully-connected hidden layers.\nThe problem I have is that the network will very quickly train itself to only give back one answer: the most common training letter (in my case 'A'). The output is in the form of a (62, 1) vector of probabilities, one for each number and letter (upper- and lower-case). This vector is EXACTLY the same for all input images.\nI've then tried to remove all of the 'A's from my input data, at which point the network changed to only give back the next most common input type (an 'E').\nSo, is there some way to stop my network stopping at a local minima (not sure if that's the actual term)? Is this even a generic problem for neural networks, or is it just that my network is broken somehow?\nI'm happy to provide code if it would help.\nEDIT: These are the hyperparameters of my network:\nInput size : 400 (20x20 greyscale images)\nHidden layer 1 size : 100\nHidden layer 2 size : 100\nOutput layer size : 62 (Alphanumeric, lower- and upper-case)\nTraining data size : 4283 images\nValidation data size : 1000 images\nTest data size : 1000 images\nBatch size : 100\nLearning rate : 0.5\nDropout rate : 0.5\nL2 regularisation parameter : 0","AnswerCount":4,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":379,"Q_Id":37829169,"Users Score":0,"Answer":"Which optimizer are you using? If you've only tried gradient descent, try using one of the adaptive ones (e.g. adagrad\/adadelta\/adam).","Q_Score":0,"Tags":"python,neural-network,tensorflow","A_Id":37846462,"CreationDate":"2016-06-15T07:46:00.000","Title":"Neural network only learns most common training image","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm building a neural net using TensorFlow and Python, and using the Kaggle 'First Steps with Julia' dataset to train and test it. The training images are basically a set of images of different numbers and letters picked out of Google street view, from street signs, shop names, etc. The network has 2 fully-connected hidden layers.\nThe problem I have is that the network will very quickly train itself to only give back one answer: the most common training letter (in my case 'A'). The output is in the form of a (62, 1) vector of probabilities, one for each number and letter (upper- and lower-case). This vector is EXACTLY the same for all input images.\nI've then tried to remove all of the 'A's from my input data, at which point the network changed to only give back the next most common input type (an 'E').\nSo, is there some way to stop my network stopping at a local minima (not sure if that's the actual term)? Is this even a generic problem for neural networks, or is it just that my network is broken somehow?\nI'm happy to provide code if it would help.\nEDIT: These are the hyperparameters of my network:\nInput size : 400 (20x20 greyscale images)\nHidden layer 1 size : 100\nHidden layer 2 size : 100\nOutput layer size : 62 (Alphanumeric, lower- and upper-case)\nTraining data size : 4283 images\nValidation data size : 1000 images\nTest data size : 1000 images\nBatch size : 100\nLearning rate : 0.5\nDropout rate : 0.5\nL2 regularisation parameter : 0","AnswerCount":4,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":379,"Q_Id":37829169,"Users Score":0,"Answer":"Your learning rate is way too high. It should be around 0.01, you can experiment around it but 0.5 is too high.\nWith a high learning rate, the network is likely to get stuck in a configuration and output something fixed, like you observed.\n\nEDIT\nIt seems the real problem is the unbalanced classes in the dataset. You can try:\n\nto change the loss so that less frequent examples get a higher loss\nchange your sampling strategy by using balanced batches of data. When selecting the 64 examples in your batch, select randomly in the dataset but with the same probability for each class.","Q_Score":0,"Tags":"python,neural-network,tensorflow","A_Id":37829933,"CreationDate":"2016-06-15T07:46:00.000","Title":"Neural network only learns most common training image","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using PySpark 1.6.1 for my spark application. I have additional modules which I am loading using the argument --py-files. I also have a  h5 file which I need to access from one of the modules for initializing the ApolloNet.\nIs there any way I could access those files from the modules if I put them in the same archive? I tried this approach but it was throwing an error because the files are not there in every worker. I can think of copying the file to each of the workers but I want to know if there are better ways to do it?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":84,"Q_Id":37832937,"Users Score":0,"Answer":"Found that you can add the additional files to all the workers by using --files argument in spark-submit.","Q_Score":0,"Tags":"python,pyspark,caffe","A_Id":37876437,"CreationDate":"2016-06-15T10:33:00.000","Title":"Adding h5 files in a zip to use with PySpark","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I'm in the process of converting a large (several GBs) bin file to csv format using Python so the resulting data can be plotted. I'm doing this conversion because the bin file is not in a format that a plotting tool\/module could understand, so there needs to be some decoding\/translation. Right now it seems like Gnuplot is the way to go for such large data size.\nI'm wondering if instead of waiting for the whole file to finish converting and then running Gnuplot, is there a way to plot the data as it's being processed? Perhaps I could bypass the csv file altogether.\nEverything I've read so far points to plotting a file with data, but I have not seen any ways of plotting\/appending individual data points.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":67,"Q_Id":37839265,"Users Score":0,"Answer":"You can plot the data as it is being processed, but there's a couple of issues that come along with it in terms of efficiency.\n\nGnuplot needs to do work each time to process your data\nGnuplot needs to wait for your operating system to paint your screen whenever it updates\nYour program needs to wait for Gnuplot to do any of this in order to go to the next step\n\nAll of these will greatly impact the amount of time you spend waiting for your data. You could potentially have it run every x iterations (eg. every 5 iterations), but even this wouldn't give you much of a speed-up.","Q_Score":1,"Tags":"python,plot,gnuplot","A_Id":37839374,"CreationDate":"2016-06-15T15:08:00.000","Title":"How to plot data while it's being processed","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I have a list of points given by their x, y, z coordinates. I would like to plot these points on a computer. I have managed to do this with gnuplot and with the python library matplotlib separately. However for these two solutions, it seems hard to change the 'viewing point', or the point from which the projection of the 3D point cloud to the 2D screen is done.\n1) Is there any easy way to, preferably continuously, move the viewing point in gnuplot (the splot command) or with matplotlib (the plot command)?\n2) What other libraries are there for which this is an easy task?\nEDIT: I want to move the viewing point (like the player in an first-person shooter, say), not change the viewing angles.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1343,"Q_Id":37845256,"Users Score":0,"Answer":"No, gnuplot cannot really move the viewing point, for the good reason that the viewing point is at infinity: all you can do is set an angle and magnification (using set view) and an offset within the viewing window (with set origin).  That means, you can move the viewing point on a sphere at infinity, but not among the points you're plotting.\n(Question 2 is off-topic as a software advice, but you're looking for a rendering software such as paraview)","Q_Score":2,"Tags":"python,matplotlib,gnuplot,visualization","A_Id":37890119,"CreationDate":"2016-06-15T20:33:00.000","Title":"Plotting a point cloud and moving the camera","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"What does the low_memory parameter do in the read_csv function from the pandas library?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":113,"Q_Id":37851796,"Users Score":1,"Answer":"This come from the docs themselves. Have you read them?\n\nlow_memory : boolean, default True\n      Internally process the file in chunks, resulting in lower memory use\n      while parsing, but possibly mixed type inference.  To ensure no mixed\n      types either set False, or specify the type with the dtype parameter.\n      Note that the entire file is read into a single DataFrame regardless,\n      use the chunksize or iterator parameter to return the data in chunks.\n      (Only valid with C parser)","Q_Score":1,"Tags":"python,pandas,ipython,spyder","A_Id":37851865,"CreationDate":"2016-06-16T06:50:00.000","Title":"low_memory parameter in read_csv function","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"These two look like they should be very much equivalent and therefore what works for one should work for the other? So why does accumulate only work for maximum but not argmax?\nEDIT: A natural follow-up question is then how does one go about creating an efficient argmax accumulate in the most pythonic\/numpy-esque way?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1719,"Q_Id":37855059,"Users Score":4,"Answer":"Because max is associative, but argmax is not:\n\nmax(a, max(b, c)) == max(max(a, b), c)\nargmax(a, argmax(b, c)) != argmax(argmax(a, b), c)","Q_Score":3,"Tags":"python,numpy,numpy-ufunc","A_Id":37855371,"CreationDate":"2016-06-16T09:24:00.000","Title":"Why does accumulate work for numpy.maximum but not numpy.argmax","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a question, I have been given x,y,z coordinate values at cell centers of a grid. I would like to create structured grid using these cell center coordinates. \nAny ideas how to do this?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":425,"Q_Id":37879558,"Users Score":1,"Answer":"If you grid is regular:\n\nYou have calculate dx = x[i+1]-x[i], dy = y[i+1]-y[i], dz = z[i+1]-z[i].\nThen calculate new arrays of points:\nx1[i] = x[i]-dx\/2, y1[i] = y[i]-dy\/2, z1[i] = z[i]-dz\/2.\n\nIf mesh is irregular you have to do the same but dx,dy,dz you have to define for every grid cell.","Q_Score":1,"Tags":"python,numpy,scipy","A_Id":37879801,"CreationDate":"2016-06-17T10:42:00.000","Title":"Creating a 3D grid using X,Y,Z coordinates at cell centers","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a Pandas Series, that needs to be log-transformed to be normal distributed. But I can\u00b4t log transform yet, because there are values =0 and values below 1 (0-4000). Therefore I want to normalize the Series first. I heard of StandardScaler(scikit-learn), Z-score standardization and Min-Max scaling(normalization).\nI want to cluster the data later, which would be the best method?\nStandardScaler and Z-score standardization use mean, variance etc. Can I use them on \"not yet normal distibuted\" data?","AnswerCount":2,"Available Count":1,"Score":0.4621171573,"is_accepted":false,"ViewCount":64605,"Q_Id":37890849,"Users Score":5,"Answer":"If your data is in the range (-1;+1) (assuming you lost the minus in your question) then log transform is probably not what you need. At least from a theoretical point of view, it's obviously the wrong thing to do.\nMaybe your data has already been preprocessed (inadequately)? Can you get the raw data? Why do you think log transform will help?\nIf you don't care about what is the meaningful thing to do, you can call log1p, which is the same as log(1+x) and which will thus work on (-1;\u221e).","Q_Score":13,"Tags":"python,pandas,normalization","A_Id":37905017,"CreationDate":"2016-06-17T21:49:00.000","Title":"Pandas Series: Log Normalize","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm starting to learn Keras, which I believe is a layer on top of Tensorflow and Theano.  However, I only have access to AMD GPUs such as the AMD R9 280X.\nHow can I setup my Python environment such that I can make use of my AMD GPUs through Keras\/Tensorflow support for OpenCL?\nI'm running on OSX.","AnswerCount":8,"Available Count":1,"Score":-0.049958375,"is_accepted":false,"ViewCount":121342,"Q_Id":37892784,"Users Score":-2,"Answer":"Technically you can if you use something like OpenCL, but Nvidia's CUDA is much better and OpenCL requires other steps that may or may not work.  I would recommend if you have an AMD gpu, use something like Google Colab where they provide a free Nvidia GPU you can use when coding.","Q_Score":87,"Tags":"python,python-2.7,opencl,tensorflow,keras","A_Id":64930005,"CreationDate":"2016-06-18T02:51:00.000","Title":"Using Keras & Tensorflow with AMD GPU","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there any way by which 3D models can be represented as 3D arrays? Are there any libraries that take .obj or .blend files as input and give an array representation of the same?\nI thought that I would slice object and export the slices to an image. I would then use those images in opencv to build arrays for each slice. In the end I would combine all the arrays of all the slices to finally get a 3D array representation of my .obj file. But I gave up halfway through because it is a painfully long process to get the image slices aligned to each other.\nIs there any other index based representation I could use to represent 3D models in code?\nA 3D array would be very convenient for my purposes.","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":3543,"Q_Id":37896090,"Users Score":1,"Answer":"If I understand correctly, you want to create a voxel representation of 3D models? Something like the visible human displays?\nI would use one of the OBJ file loaders recommended above to import the model into an OpenGL program. Rotate and scale to whatever alignment you want along XYZ.\nThen draw the object with a fragment shader that discards any pixel with Z < 0.001 or Z >= 0.002 (or whatever resolution works - I'm just trying to explain the method). This gives you the first image slice, which you store or save. Clear and draw again this time discarding Z < 0.002 or Z >= 0.003 \u2026 Because it's the same model in the same position, all your slices will be aligned.\nHowever, are you aware that OBJ (and nearly all other 3D formats) are surface descriptions, not solid? They're hollow inside like origami models. So your 3D array representation will be mostly empty.\nHope this helps.","Q_Score":5,"Tags":"python,c++,arrays,opencv,3d","A_Id":37902814,"CreationDate":"2016-06-18T10:46:00.000","Title":"How to represent a 3D .obj object as a 3D array?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using the sknn package to build a neural network. In order to optimize the parameters of the neural net for the dataset I am using I am using an evolutionary algorithm. Since the package allows me to build a neural net where each layer has a different activation function, I was wondering if that is a practical choice, or whether I should just use one activation function per net? Does having multiple activation functions in a neural net harm, does no damage, or benefit the neural network?\nAlso what is the maximum amount of neuron per layer I should have, and the maximum amount of layers per net should I have?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":5281,"Q_Id":37947558,"Users Score":6,"Answer":"A neural network is just a (big) mathematical function. You could even use different activation functions for different neurons in the same layer. Different activation functions allow for different non-linearities which might work better for solving a specific function. Using a sigmoid as opposed to a tanh will only make a marginal difference. What is more important is that the activation has a nice derivative. The reason tanh and sigmoid are usually used is that for values close to 0 they act like a linear function while for big absolute values they act more like the sign function ((-1 or 0) or 1 ) and they have a nice derivative. A relatively new introduced one is the ReLU (max(x,0)), which has a very easy derivative (except for at x=0), is non-linear but importantly is fast to compute so nice for deep networks with high training times.\nWhat it comes down to is that for the global performance the choice in this is not very important, the non-linearity and capped range is important. To squeeze out the last percentage points this choice will matter however but is mostly dependent on your specific data. This choice just like the number of hidden layers and the number of neurons inside these layers will have to be found by crossvalidation, although you could adapt your genetic operators to include these.","Q_Score":5,"Tags":"python,neural-network,scikits,activation-function","A_Id":37947823,"CreationDate":"2016-06-21T14:42:00.000","Title":"Neural Network composed of multiple activation functions","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am on Windows 10, 64bits, use Anaconda 4 and I created an environment with python 2.7 (C:\/Anaconda3\/envs\/python2\/python.exe)\nIn this environment, I successfully installed numpy and when I type \"python\", enter, \"import numpy\", enter, it works perfectly in the anaconda prompt window.\nIn spyder however, when I open a python console and type \"import numpy\", I get \"cannot import name multiarray\". I have obviously changed the path of the python interpreter used by spyder to match the python.exe of the environment I created (C:\/Anaconda3\/envs\/python2\/python.exe). I also updated the PYTHONSTARTUP to C:\/Anaconda3\/envs\/python2\/Lib\/site-packages\/spyderlib\/scientific_startup.py\nIt's supposed to be the exact same python program running but it's two different behavior. How is it possible and how to fix it ?\nPS: I already tried the various solutions to this error like uninstalling numpy and reinstalling it. It shouldn't be a problem with numpy since it works just fine in the python console of the anaconda prompt window.","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":833,"Q_Id":37948852,"Users Score":1,"Answer":"I solved the problem by executing the spyder version of the python2 environment.\nIt is located in Anaconda3\\envs\\python2\\Scripts\\spyder.exe","Q_Score":0,"Tags":"python-2.7,numpy,spyder","A_Id":37971709,"CreationDate":"2016-06-21T15:39:00.000","Title":"spyder, numpy, anaconda : cannot import name multiarray","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am on Windows 10, 64bits, use Anaconda 4 and I created an environment with python 2.7 (C:\/Anaconda3\/envs\/python2\/python.exe)\nIn this environment, I successfully installed numpy and when I type \"python\", enter, \"import numpy\", enter, it works perfectly in the anaconda prompt window.\nIn spyder however, when I open a python console and type \"import numpy\", I get \"cannot import name multiarray\". I have obviously changed the path of the python interpreter used by spyder to match the python.exe of the environment I created (C:\/Anaconda3\/envs\/python2\/python.exe). I also updated the PYTHONSTARTUP to C:\/Anaconda3\/envs\/python2\/Lib\/site-packages\/spyderlib\/scientific_startup.py\nIt's supposed to be the exact same python program running but it's two different behavior. How is it possible and how to fix it ?\nPS: I already tried the various solutions to this error like uninstalling numpy and reinstalling it. It shouldn't be a problem with numpy since it works just fine in the python console of the anaconda prompt window.","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":833,"Q_Id":37948852,"Users Score":1,"Answer":"I have encountered same issue. I have followed every possible solution, which is stated on stack-overflow. But no luck. The cause of error might be the python console. I have installed a 3.5 Anaconda, and the default console is the python 2.7, which I have installed primarily with pydev. I did this and now it is working absolutely fine. Go to tools>preferences and click on reset to defaults. It might solve the issue.  Or another solution is to uninstall the current Anaconda i.e. y.x and installing the correct one according to the default. In my case 2.7 Anaconda instead of 3.5","Q_Score":0,"Tags":"python-2.7,numpy,spyder","A_Id":42165864,"CreationDate":"2016-06-21T15:39:00.000","Title":"spyder, numpy, anaconda : cannot import name multiarray","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a large dataset: 20,000 x 40,000 as a numpy array. I have saved it as a pickle file.\nInstead of reading this huge dataset into memory, I'd like to only read a few (say 100) rows of it at a time, for use as a minibatch.\nHow can I read only a few randomly-chosen (without replacement) lines from a pickle file?","AnswerCount":3,"Available Count":1,"Score":-0.1973753202,"is_accepted":false,"ViewCount":14112,"Q_Id":37954324,"Users Score":-3,"Answer":"Thanks everyone. I ended up finding a workaround (a machine with more RAM so I could actually load the dataset into memory).","Q_Score":10,"Tags":"python,numpy,pickle","A_Id":38003329,"CreationDate":"2016-06-21T20:49:00.000","Title":"How to load one line at a time from a pickle file?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"This is my first question on this site.  I'm attempting to practice neural networks by having my program predict whether the temperature will go up or down on a given day relative to the previous day.  My training data set consists of the previous ten days and whether they went up or down relative to the day before them.  I'm not implying this is an effective way to predict weather, but that makes my problem even more confusing.\nWhen I train the program over 25 days (50 days ago to 25 days ago) then test it on the next 25 days (25 days ago to yesterday) I consistently get 100% accuracy in the test set.  I've added an alpha for gradient descent and have around 60 hidden layers, and if I make the alpha something bigger like 0.7 the accuracy will reduce to ~40%, so I think my testing code is correct.\nAssuming I have a true 100% accuracy, I had the program predict tomorrow's weather, then use that and 9 days of historical to predict the day after tomorrow, and so on until I've predicted 5 days in the future.  I then waited to see what the weather would be and my program was comically bad in its predictions.  I ran this for a week to test, and had an accuracy of predicting the next day of about 60% and after that only around 10%.\nTL;DR\nSorry for rambling the details, but my question is what would cause a neural network to be 100% accuracy in testing and then fail spectacularly in practice?  Thanks for the help, I can post code if needed (and someone explains how to in a comment)","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":68,"Q_Id":37970895,"Users Score":0,"Answer":"What Ashafix says was my first though, you should post your training and test data also the data that you use for 'real world'.\nAnother problem it could be that when you are testing, you are using only previous correct whether data(data you already have), and when you are in practice you are using your predicts and correct whether data. You should be consistent here.\nPD. Sorry for my english, I'm still learning.","Q_Score":0,"Tags":"python,neural-network,prediction","A_Id":37971491,"CreationDate":"2016-06-22T14:22:00.000","Title":"Neural Network converging and accurate in training, but failing in real world","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"So I have a neural network in tensorflow (python2.7) and I need to retrieve its output using Java. I have a simple python function getValue(input) which starts the session and retrieves the value. I am open to any suggestions. I believe Jython wont work cause tensorflow is not in the library. I need the call to be as fast as possible. JNI exists for Java calling C so can I convert with cython and compile then use JNI? Is there a way to pass the information in RAM or some other way I haven't thought of?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1916,"Q_Id":37992129,"Users Score":0,"Answer":"I've had the same problem, Java+Python+TensorFlow. I've ended up setting up a simple http server. If that's too slow for you, you can shave off some overhead by employing sockets directly.","Q_Score":1,"Tags":"java,python-2.7,tensorflow","A_Id":37997580,"CreationDate":"2016-06-23T12:48:00.000","Title":"Java calling python function with tensorflow graph","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I'm using Python's non-parametric tests to check whether two samples are consistent with being drawn from the same underlying parent populations: scipy.stats.ks_2samp (2-sample Kolmogorov-Smirnov), scipy.stats.anderson_ksamp (Anderson-Darling for k samples), and scipy.stats.ranksums (Mann-Whitney-Wilcoxon for 2 samples). My significance threshold to say that two samples are significantly different from each other is p = 0.01.\nIf these three tests return extremely low p-values (sometimes like 10^-30 or lower), then do I need to worry about something having gone wrong with the scipy functions? Are these ridiculously small p-values reliable, and can I just report p << 0.01 (p much less than my threshold)?","AnswerCount":3,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":1931,"Q_Id":37996628,"Users Score":3,"Answer":"You do not need to worry about something going wrong with the scipy functions.  P values that low just mean that it's really unlikely that your samples have the same parent populations.\nThat said, if you were not expecting the distributions to be (that) different, now is a good time to make sure you're measuring what you think you're measuring, i.e. you are feeding in the right data to scipy.","Q_Score":2,"Tags":"python,scipy,statistics,distribution,kolmogorov-smirnov","A_Id":37996674,"CreationDate":"2016-06-23T16:02:00.000","Title":"Extremely low p-values from non-parametric tests","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using Python's non-parametric tests to check whether two samples are consistent with being drawn from the same underlying parent populations: scipy.stats.ks_2samp (2-sample Kolmogorov-Smirnov), scipy.stats.anderson_ksamp (Anderson-Darling for k samples), and scipy.stats.ranksums (Mann-Whitney-Wilcoxon for 2 samples). My significance threshold to say that two samples are significantly different from each other is p = 0.01.\nIf these three tests return extremely low p-values (sometimes like 10^-30 or lower), then do I need to worry about something having gone wrong with the scipy functions? Are these ridiculously small p-values reliable, and can I just report p << 0.01 (p much less than my threshold)?","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1931,"Q_Id":37996628,"Users Score":0,"Answer":"Well, you've bumped into a well-known feature of significance tests, which is that the p-value typically goes to zero as the sample size increases without bound. If the null hypothesis is false (which can often be established a priori), then you can get as small a p-value as you wish, just by increasing the sample size. \nMy advice is to think about what practical difference it makes that the distributions differ. Try to quantify that in terms of cost, either real (dollars) or abstract. Then devise a measurement for that.","Q_Score":2,"Tags":"python,scipy,statistics,distribution,kolmogorov-smirnov","A_Id":38006265,"CreationDate":"2016-06-23T16:02:00.000","Title":"Extremely low p-values from non-parametric tests","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Just wondering if there is a tried and tested method for setting the dpi output of your figure in matplotlib to be governed by some maximum file size, e.g., 15MB.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":80,"Q_Id":38042987,"Users Score":0,"Answer":"You should do a for loop over different dpi values in decreasing order and in every loop save the image, check the file size and delete the image if filesize > 15 MB. After filesize < 15 MB break the loop.","Q_Score":0,"Tags":"python,image,matplotlib,save","A_Id":46231053,"CreationDate":"2016-06-26T20:41:00.000","Title":"matplotlib: How to assign the dpi of your figure to meet some set maximum file size?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Just wondering if there is a tried and tested method for setting the dpi output of your figure in matplotlib to be governed by some maximum file size, e.g., 15MB.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":80,"Q_Id":38042987,"Users Score":0,"Answer":"There can not be such a mechanism, because the file size can only be determined by actually rendering the finished drawing to a file, and that must happen after setting up the figure (where you set the DPI). \nHow, for example, should anyone know, before rendering your curve, how well it's compressible as PNG? Or how big the PDF you might alternatively generate might be, without knowing how many lines you'll plot?\nAlso, matplotlib potentially has a lot of different output formats.\nHence, no, this is impossible.","Q_Score":0,"Tags":"python,image,matplotlib,save","A_Id":38043037,"CreationDate":"2016-06-26T20:41:00.000","Title":"matplotlib: How to assign the dpi of your figure to meet some set maximum file size?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to find frequent ( ordered or unordered) patterns in a column. The column contains numeric IDs. for eg:\ns=[1 2 3 4 1 2 6 7 8 2 1 10 11]\nHere 1 2 or 2 1 taking as a same case is the most frequent set. \nPlease help me to solve this problem, I could think of apriori, FP algorithms but I don't have any transaction, just a sequence.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":735,"Q_Id":38060783,"Users Score":0,"Answer":"There are a couple of business decisions you have to take before you will have a normal algorithm. The first and the most important decision is what size of the set do you want. Clearly if you have {a, b, ... x} is the most frequent set, then every subset (like {a, x} or {c, d, y} will be at least with the same frequency).\nYou need to know which one do you need (may be all or any). Also what would you do in case of these frequencies {a, b} with frequency 100 and {a, c, d, e, f, g} with frequency 20. Clearly the first one is more frequent, but the second is also pretty frequent and really long.\n\nOne way to approach this is to iterate over all 1 element subsequences and find their frequency. Then all 2 element and so on. Then create some weighted score which can be the frequency multiplied by some function based on the length of the sequence. Select the highest score.","Q_Score":0,"Tags":"python,algorithm,pattern-matching,sequence,apriori","A_Id":38061355,"CreationDate":"2016-06-27T18:27:00.000","Title":"Trying to find frequent patterns in a sequence python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm looking to create a master array\/list that takes several two dimensional lists and integrates them into the larger list.\nFor example I have a TableA[] which has dates as one array\/list and prices as another array\/list. I have another as TableB[] which has the same. TableA[0][0] has the first date; TableA[0][1] has the first price; TableA[1][0] has the second date, and so on.\nI would like to create BigTabe[] that has BigTable[0][0][0] = TableA[0][0] and BigTable[1][0][0] = TableB[0][0].\nAny guidance would be much appreciated. Thank you!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1397,"Q_Id":38064885,"Users Score":0,"Answer":"Python's fairly friendly about this sort of thing and will let you have lists as elements of lists . Here's an example of one way to do it.\nTableA = [['01\/01\/2000', '$10'], ['02\/01\/2000', '$11']]\nIf you entered this straight into the python interpreter, you'd define TableA as a list with two elements. Both of these elements are also lists. If you then entered in TableA[0] you'd get ['01\/01\/2000', '$10']. Furthermore, by entering TableA[0][0] you'd get '01\/01\/2000' as that's the first element of the first list in TableA.\nExtending this further, you can have lists of lists of lists (and so on). First, let's define TableA and TableB.\nTableA =  [['01\/01\/2000', '$10'], ['02\/01\/2000', '$11']]\nTableB =  [['03\/01\/2000', '$13'], ['04\/01\/2000', '$14']]\nNow we can simply define BigTable as having TableA and TableB as its elements.\nBigTable = [TableA, TableB]\nNow, BigTable[0] is just TableA so BigTable[0][0][0] will be the same as TableA[0][0]\nIf at some point down the line you realise that you want BigTable to have more lists in it, say a TableC or TableD. Just use the append function.\nBigTable.append(TableC)\nBy the way, you'll probably want to have prices and dates expressed as numbers rather than strings, but it's easier to follow the example this way.","Q_Score":0,"Tags":"python,arrays,list,multidimensional-array","A_Id":38065115,"CreationDate":"2016-06-27T23:42:00.000","Title":"Python: multi-dimensional lists - appending one 2D list into another list","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using Tensorflow 0.8 to train the Deep Neural Networks. Currently, I encounter an issue that I want to define two exact same Neural Networks N1 and N2, and I train N1, during the training loop, I copy updated weights from N1 to N2 every 4 iterations. In fact, I know there is way using tf.train.saver.save() to save all N1 weights into a .ckpt file on Disk, and using tf.train.saver.restore() to load those weights from .ckpt file, which is equivalent to the copy functionality. However, this load\/reload will impact the training speed, and I wonder if there are other more efficient ways to do the copy (For example, do in-memory copy, etc.). Thanks!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":539,"Q_Id":38065448,"Users Score":0,"Answer":"If you could have your code\/ more detail here that would be beneficial. However, you can return the session you're using to train N1 and access it while you want to train N2.","Q_Score":1,"Tags":"python-3.x,neural-network,tensorflow","A_Id":38067029,"CreationDate":"2016-06-28T00:57:00.000","Title":"Tensorflow Copy Weights Issue","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to plot boxplots for each of the dataframes side by side. Below is an example dataset.\ndata 1 :\nid |type |activity | feature1\n1 | A | ACTIVE | 12\n2 | B | INACTIVE| 10\n3 | C | ACTIVE| 9\ndata 2:\nid | type | activity | feature1\n1 | A | ACTIVE | 13\n2 | B | INACTIVE | 14\n3 | C | ACTIVE | 15\nFirst boxplot should be to plot the feature1 grouped by type and the second boxplot should be to plot the feature1 grouped by activity.Both the plots should be placed in the same figure.\nNote : I do not want to do combined grouping.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":865,"Q_Id":38071436,"Users Score":0,"Answer":"Use return_type='axes' to get data1.boxplot to return a matplotlib Axes object. Then pass that axes to the second call to boxplot using ax=ax. This will cause both boxplots to be drawn on the same axes.\nAlternatively if you just want them plotted side to side use matplotlib subplot","Q_Score":0,"Tags":"python,pandas,matplotlib,boxplot","A_Id":38072512,"CreationDate":"2016-06-28T08:52:00.000","Title":"Plot 2 boxplots , each from different pandas dataframe in a figure?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a problem M*y' = f(y) that are going to be solved in Python, where M is the mass matrix, y' the derivative and y is a vector, such that y1, y2 etc. refers to different points in r. \nHave anyone used a mass matrix on a similar problem in Python?   \nThe problem is a 2D-problem in r- and z-direction. The r-direction is discretized  to reduce the problem to a 1D-problem. The mass matrix is a diagonal matrix with ones and zeros on the diagonal.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":680,"Q_Id":38078299,"Users Score":0,"Answer":"Since the mass matrix is singular, this is a \"differential-algebraic equation\".  You can find off-the-shelf solvers for DAEs, such as the IDA solver from the SUNDIALS library.  SUNDIALS has python bindings in the scikit.odes package.","Q_Score":0,"Tags":"python,matrix,ode","A_Id":38833074,"CreationDate":"2016-06-28T13:59:00.000","Title":"Implicit DAE Mass Matrix Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"The problem is that my train data could not be placed into RAM due to train data size. So I need a method which first builds one tree on whole train data set, calculate residuals build another tree and so on (like gradient boosted tree do). Obviously if I call model = xgb.train(param, batch_dtrain, 2) in some loop - it will not help, because in such case it just rebuilds whole model for each batch.","AnswerCount":10,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":48270,"Q_Id":38079853,"Users Score":0,"Answer":"To paulperry's code, If change one line from \"train_split = round(len(train_idx) \/ 2)\" to \"train_split = len(train_idx) - 50\". model 1+update2 will changed from 14.2816257268 to 45.60806270012028. And a lot of \"leaf=0\" result in dump file.\nUpdated model is not good when update sample set is relative small.\nFor binary:logistic, updated model is unusable when update sample set has only one class.","Q_Score":51,"Tags":"python,machine-learning,xgboost","A_Id":54546005,"CreationDate":"2016-06-28T15:05:00.000","Title":"How can I implement incremental training for xgboost?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm running Python 3.5.1 on a Windows 7 machine. I've been using Anaconda without issue for several months now. This morning, I updated my packages (conda update --all) and now I can't import numpy (version 1.11.0) or pandas(version 0.18.1).\nThe error I get from Python is:\nSyntax Error: (unicode error) 'unicodeescape' codec can't decode bytes in position 2-3: truncated \\UXXXXXXXX escape. This error occurs when the import statement is executed.\nI'm able to import other packages, some from anaconda's bundle and some from other sources without issue.\nAny thoughts on how to resolve this?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":604,"Q_Id":38083176,"Users Score":0,"Answer":"I was able to resolve this issue using conda to remove and reinstall the packages that were failing to import. I will leave the question marked unanswered to see if anyone else has a better solution, or guidance on how to prevent this in the future.","Q_Score":2,"Tags":"python,numpy,pandas,anaconda","A_Id":38084884,"CreationDate":"2016-06-28T17:58:00.000","Title":"Python 3.5.1 Unable to import numpy after update","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"So I have a rather comlicated matlab function (it calls a simulation that in turn calls an external optimisation suite (cplex or gurobi)).  And for certain settings and inputs the MATLAB function and the python function called from Matlab give the same result but for others they differ (correct answer is ~4500) python sometimes returns 0.015... or 162381, so widely varying results I can't spot a pattern or correlation for. \nmy guess would be either something with int\/ float \/ double variable conversions, or some form of memory problem. The result comes straight from CPLEX so I'm  little confused as to why it changes. \nOn a side note, if I return a structure that contains a structure of arrays, that kills the python kernel. That makes debugging from python a little harder (I have pymatbridge and metakernel installed)\nHas anyone, had similar issues of unreliable matlab functions in python? Solution ideas other than, executing matlab from the console and reading in a results file?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":62,"Q_Id":38105605,"Users Score":0,"Answer":"So turns out, that on occasion the code will run if some variables aren't specified as double, and in other cases it integer division or what not, results false results. I have no idea how this correlates to the input as it really shouldn't but I just went and specified all variables in the relevant section of code to be doubles and that fixed it. So \ntl;dr: Even if it runs, just enforce all variables are doubles and the problem is solved. \nReally something Mathworks should fix in their python api","Q_Score":0,"Tags":"python,matlab,python-2.7,cplex","A_Id":38106001,"CreationDate":"2016-06-29T16:45:00.000","Title":"Odd results from MATLAB function called in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Even though I believe I have installed correctly OpenCV, I cannot overcome the following problem. When I start a new python project from IDLE (2.7) the cv2 module is imported successfully. If I close IDLE and try to run the .py file, an error message is displayed that says \"ImportError: No module named cv2\". Then if I create a clean project through IDLE it works until I close it. What could be the problem?\nP.S. I am using Python 2.7 and OpenCV 3.1, but tried also with 2.4.13 on Windows 10.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2805,"Q_Id":38109860,"Users Score":0,"Answer":"Try to reinstall it by sudo apt-get install python-opencv,\nBut first you may check out something you might be skipping.\nMake sure the script you are running on the terminal is on the same python version\/ location as on IDLE. \nMaybe your IDLE is running on a different interpreter(different location).\nOpen IDLE and check the path of cv2 module by cv2.__file__.\nOr you can check the executables path by sys.path. \nThen check the executable python path by running the script from the terminal, it must be same or else you need to explicitly set the PYTHONPATH to executable path shown in IDLE.\nEdit: \nAccording to the comments the problem you are facing is with the execution path,  add the idle execution path to the environment variable path - on Windows.  You can do it on the go by \nSET PATH=%PATH%;c:\\python27 on cmd\nChange the path to your context (of the IDLE).","Q_Score":0,"Tags":"python,python-2.7,opencv","A_Id":38110151,"CreationDate":"2016-06-29T20:55:00.000","Title":"OpenCV Python - cv2 Module not found","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"i want to install pythons library CV2 on a spark cluster using databricks community edition and i'm going to:\nworkspace-> create -> library , as the normal procedure and then selecting python in the Language combobox, but in the \"PyPi Package\" textbox , i tried \"cv2\" and \"opencv\" and had no luck. Does anybody has tried this? Do you know if cv2 can be installed on the cluster through this method? and if so, which name should be used in the texbox?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":1543,"Q_Id":38115108,"Users Score":1,"Answer":"Try to download numpy first followed by opencv-python it will work.\nSteps:\n\nNavigate to Install Library-->Select PyPI----->In Package--->numpy\n(after installation completes, proceed to step 2)\n\nNavigate to Install Library-->Select PyPI----->In Package--->opencv-python","Q_Score":0,"Tags":"python,opencv,apache-spark,pyspark,databricks","A_Id":63753350,"CreationDate":"2016-06-30T06:05:00.000","Title":"Install python CV2 on spark cluster(data bricks)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"here is two method of normalize :\n1:this one is using in the data Pre-Processing:     sklearn.preprocessing.normalize(X, norm='l2')\n2:the other method is using in the classify method :  sklearn.svm.LinearSVC(penalty='l2')\ni want to know ,what is the different between them?  and does the two step must be used in a completely model ?  is it right that just use a method is enough?","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":378,"Q_Id":38116078,"Users Score":2,"Answer":"These 2 are different things and you normally need them both in order to make a good SVC model.\n1) The first one means that in order to scale (normalize) the X data matrix you need to divide with the L2 norm of each column, which is just this : sqrt(sum(abs(X[:,j]).^2)) , where j is each column in your data matrix X . This ensures that none of the values of each column become too big, which makes it tough for some algorithms to converge.\n2) Irrespective of how scaled (and small in values) your data is, there still may be outliers or some features (j) that are way too dominant and your algorithm (LinearSVC()) may over trust them while it shouldn't. This is where L2 regularization comes into play , that says apart from the function the algorithm minimizes, a cost will be applied to the coefficients so that they don't become too big . In other words the coefficients of the model become additional cost for the SVR cost function. How much cost ? is decided by the C (L2) value as C*(beta[j])^2 \nTo sum up, first one tells with which value to divide each column of the X matrix. How much weight should a coefficient burden the cost function with is the second.","Q_Score":1,"Tags":"python,scikit-learn","A_Id":38124167,"CreationDate":"2016-06-30T07:04:00.000","Title":"python sklearn: what is the different between \"sklearn.preprocessing.normalize(X, norm='l2')\" and \"sklearn.svm.LinearSVC(penalty='l2')\"","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'd like to know if there's a method or a Python Package that can make me use a large dataset without writing it in RAM.\nI'm also using pandas for statistical function.\nI need to have access on the entire dataset because many statistical functions needs the entire dataset to return credible results.\nI'm using PyDev (with interpreter Python 3.4) on LiClipse with Windows 10.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":5299,"Q_Id":38118942,"Users Score":0,"Answer":"If all you need is a virtualization of the disk as a large RAM memory you might set up a swap file on the system. The kernel will then automatically swap pages in and out as needed, using heuristics to figure out what pages should be swapped and which should stay on disk.","Q_Score":5,"Tags":"python,pandas,memory,pydev","A_Id":38119245,"CreationDate":"2016-06-30T09:24:00.000","Title":"Use hard drive instead of RAM in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"First of all, sorry if it's not the place to post this question, I know it is more related to the software I'm using to program than programming itself, but I figured someone here would probably know the answer.\nI often use PyCharm (currently on version 2016.1.2) and its useful debugger to code in Python. I'm currently translating Matlab code to Python code and I often need to compare outputs of functions. In PyCharm's debugger, I can right click on a variable in the variable space and then press \u00ab View as array \u00bb. This gives me a nice grid view of my array (Excel kind of grid) and I can easily compare with my array in Matlab, which can also be displayed in a grid. However, sometimes, this option won't work in PyCharm and I don't know why! For example, I have a variable of type numpy.ndarray containing 137 by 60 floats and when I press \u00ab view as array \u00bb, it displays the window, but instead of showing the grid, it shows \u00ab Nothing to show \u00bb. Curiously, I tried to copy the first 30 lines in another variable and this time PyCharm was able to show me the grid associated with this new variable. Usually, the number doesn't seem to be a problem. I tried to display a 500 by 500 array containing floats and it did just fine.\nIf someone could tell me why this happens and how I can overcome this problem, I'd be very glad. Also, if anyone has another way to display a matrix in Python in an elegant way, I'd take it too since it could also help me in my task! \nThanks!","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":2669,"Q_Id":38128164,"Users Score":0,"Answer":"You need to ensure that after you \"view as array\" you then enter the correct slice. I.e. if you view a color image which has shape (500, 1000, 3) as an array, the default slicing option will be image[0]. This is the first row of pixels and will appear as a (1000, 3) array. In order to see one of the three color channels you must change the slicing option to image[:, :, color], then you will see one of the three color channels slices appear as a (500, 1000) array.","Q_Score":6,"Tags":"python,matlab,debugging,numpy,pycharm","A_Id":57539584,"CreationDate":"2016-06-30T15:59:00.000","Title":"Pycharm debugger, view as array option","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"First of all, sorry if it's not the place to post this question, I know it is more related to the software I'm using to program than programming itself, but I figured someone here would probably know the answer.\nI often use PyCharm (currently on version 2016.1.2) and its useful debugger to code in Python. I'm currently translating Matlab code to Python code and I often need to compare outputs of functions. In PyCharm's debugger, I can right click on a variable in the variable space and then press \u00ab View as array \u00bb. This gives me a nice grid view of my array (Excel kind of grid) and I can easily compare with my array in Matlab, which can also be displayed in a grid. However, sometimes, this option won't work in PyCharm and I don't know why! For example, I have a variable of type numpy.ndarray containing 137 by 60 floats and when I press \u00ab view as array \u00bb, it displays the window, but instead of showing the grid, it shows \u00ab Nothing to show \u00bb. Curiously, I tried to copy the first 30 lines in another variable and this time PyCharm was able to show me the grid associated with this new variable. Usually, the number doesn't seem to be a problem. I tried to display a 500 by 500 array containing floats and it did just fine.\nIf someone could tell me why this happens and how I can overcome this problem, I'd be very glad. Also, if anyone has another way to display a matrix in Python in an elegant way, I'd take it too since it could also help me in my task! \nThanks!","AnswerCount":2,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":2669,"Q_Id":38128164,"Users Score":6,"Answer":"I encountered the same problem when I tried to view a complex arrays with 'Color' check box checked. Unchecking the check box showed the array. Maybe some inf or nan value present in you array which does not allow to show colored array.","Q_Score":6,"Tags":"python,matlab,debugging,numpy,pycharm","A_Id":41962870,"CreationDate":"2016-06-30T15:59:00.000","Title":"Pycharm debugger, view as array option","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there an equivalent method in numpy or scipy for matlab's perms function? In matlab, perms returns a matrix of all possible permutations of the input in reverse lexicographical order.","AnswerCount":3,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":865,"Q_Id":38130008,"Users Score":3,"Answer":"Python has a built-in function called itertools.permutations. You can call it on any iterable in python and it returns all full length permutations.","Q_Score":0,"Tags":"python,matlab,numpy,scipy","A_Id":38130043,"CreationDate":"2016-06-30T17:44:00.000","Title":"Python equivalent for matlab's perms","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"My question is specific to scikit-learn python module, but I had similar issues with matplotlib as well.\nWhen I want to use sklearn, if I just do 'import sklearn' and then call whatever submodule I need, like ' sklearn.preprocessing.scale()', I get an error \n\"AttributeError: 'module' object has no attribute 'preprocessing'\"\nOn the other hand, when I do 'from sklearn import preprocessing'\nand then use 'preprocessing.scale()' it works normally.\nWhen I use other modules like Numpy, it is sufficient to just 'import numpy' and it works well.\nTherefore, I would like to ask if anyone can tell me why is this happening and if I am doing something wrong?\nThanks.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":1297,"Q_Id":38143991,"Users Score":1,"Answer":"Numpy conveniently imports its submodules in its __init__.py file and adds them to __all__. There's not much you can do about it when using a library - it either does it or not. sklearn apparently doesn't.","Q_Score":2,"Tags":"python,numpy,import,module,scikit-learn","A_Id":38144150,"CreationDate":"2016-07-01T11:13:00.000","Title":"importing whole python module doesn't allow using submodules","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I was wondering if Python is smart enough enough to cache repeatedly accessed files, e.g. when reading the same CSV with pandas or unpickling the same file multiple times.\nIs this even Python's responsibility, or should the operating system take care of it?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1776,"Q_Id":38144825,"Users Score":7,"Answer":"No, Python is just a language and doesn't really do anything on its own. A particular Python library might implement caching, but the standard functions you use to open and read files don't do so. The higher-level file-loading functions in Pandas and the CSV module don't do any caching either.\nThe operating system might do some caching of its own, but you can't control that from within Python.","Q_Score":5,"Tags":"python,pandas","A_Id":38145044,"CreationDate":"2016-07-01T11:56:00.000","Title":"Does Python cache repeatedly accessed files?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to cache a pandas dataframe into tornado requesthandler. So i don't want to repeat the pd.read_csv() for every hit to that particular url.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":906,"Q_Id":38146607,"Users Score":0,"Answer":"Depends on how and where you want to be able to access this cache in the future, and how you want to handle invalidation. If the CSV files don't change then this could be as simple as @functools.lru_cache or a global dict. If you need one cache shared across multiple processes then you could use something like memcached or redis, but then you'll still have some parsing overhead depending on what format you use. In any case, there's not really anything Tornado-specific about this.","Q_Score":0,"Tags":"python,caching,tornado,requesthandler","A_Id":38439059,"CreationDate":"2016-07-01T13:23:00.000","Title":"Where can i cache pandas dataframe in tornado requesthandler","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I am estimating the fundamental matrix and the essential matrix by using the inbuilt functions in opencv.I provide input points to the function by using ORB and brute force matcher.These are the problems that i am facing:\n1.The essential matrix that i compute from in built function does not match with the one i find from mathematical computation using fundamental matrix as E=k.t()FK.\n2.As i vary the number of points used to compute F and E,the values of F and E are constantly changing.The function uses Ransac method.How do i know which value is the correct one??\n3.I am also using an inbuilt function to decompose E and find the correct R and T from the 4 possible solutions.The value of R and T also change with the changing E.More concerning is the fact that the direction vector T changes without a pattern.Say it was in X direction at a value of E,if i change the value of E ,it changes to Y or Z.Y is this happening????.Has anyone else had the same problem.???\nHow do i resolve this problem.My project involves taking measurements of objects from images.\nAny suggestions or help would be welcome!!","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1100,"Q_Id":38146821,"Users Score":0,"Answer":"Yes, Computing Fundamental Matrix gives a different matrix every time as it is defined up to a scale factor.\nIt is a Rank 2 matrix with 7DOF(3 rot, 3 trans, 1 scaling).\nThe fundamental matrix is a 3X3 matrix, F33(3rd col and 3rd row) is scale factor.\nYou make ask why do we append matrix with constant at F33, Because of (X-Left)F(x-Right)=0, This is a homogenous equation with infinite solutions, we are adding a constraint by making F33 constant.","Q_Score":2,"Tags":"python,opencv,matrix,computer-vision","A_Id":57898590,"CreationDate":"2016-07-01T13:33:00.000","Title":"Estimation of fundamental matrix or essential matrix from feature matching","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am estimating the fundamental matrix and the essential matrix by using the inbuilt functions in opencv.I provide input points to the function by using ORB and brute force matcher.These are the problems that i am facing:\n1.The essential matrix that i compute from in built function does not match with the one i find from mathematical computation using fundamental matrix as E=k.t()FK.\n2.As i vary the number of points used to compute F and E,the values of F and E are constantly changing.The function uses Ransac method.How do i know which value is the correct one??\n3.I am also using an inbuilt function to decompose E and find the correct R and T from the 4 possible solutions.The value of R and T also change with the changing E.More concerning is the fact that the direction vector T changes without a pattern.Say it was in X direction at a value of E,if i change the value of E ,it changes to Y or Z.Y is this happening????.Has anyone else had the same problem.???\nHow do i resolve this problem.My project involves taking measurements of objects from images.\nAny suggestions or help would be welcome!!","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":1100,"Q_Id":38146821,"Users Score":1,"Answer":"Both F and E are defined up to a scale factor. It may help to normalize the matrices, e. g. by dividing by the last element.\nRANSAC is a randomized algorithm, so you will get a different result every time. You can test how much it varies by triangulating the points, or by computing the reprojection errors. If the results vary too much, you may want to increase the number of RANSAC trials or decrease the distance threshold, to make sure that RANSAC converges to the correct solution.","Q_Score":2,"Tags":"python,opencv,matrix,computer-vision","A_Id":38615979,"CreationDate":"2016-07-01T13:33:00.000","Title":"Estimation of fundamental matrix or essential matrix from feature matching","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Can you please help understand what are the main differences (if any) between the native int type and the numpy.int32 or numpy.int64 types?","AnswerCount":3,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":41529,"Q_Id":38155039,"Users Score":39,"Answer":"There are several major differences.  The first is that python integers are flexible-sized (at least in python 3.x).  This means they can grow to accommodate any number of any size (within memory constraints, of course).  The numpy integers, on the other hand, are fixed-sized. This means there is a maximum value they can hold.  This is defined by the number of bytes in the integer (int32 vs. int64), with more bytes holding larger numbers, as well as whether the number is signed or unsigned (int32 vs. uint32), with unsigned being able to hold larger numbers but not able to hold negative number.\nSo, you might ask, why use the fixed-sized integers?  The reason is that modern processors have built-in tools for doing math on fixed-size integers, so calculations on those are much, much, much faster.  In fact, python uses fixed-sized integers behind-the-scenes when the number is small enough, only switching to the slower, flexible-sized integers when the number gets too large.\nAnother advantage of fixed-sized values is that they can be placed into consistently-sized adjacent memory blocks of the same type.  This is the format that numpy arrays use to store data.  The libraries that numpy relies on are able to do extremely fast computations on data in this format, in fact modern CPUs have built-in features for accelerating this sort of computation.  With the variable-sized python integers, this sort of computation is impossible because there is no way to say how big the blocks should be and no consistentcy in the data format.  \nThat being said, numpy is actually able to make arrays of python integers.  But rather than arrays containing the values, instead they are arrays containing references to other pieces of memory holding the actual python integers.  This cannot be accelerated in the same way, so even if all the python integers fit within the fixed integer size, it still won't be accelerated.\nNone of this is the case with Python 2.  In Python 2, Python integers are fixed integers and thus can be directly translated into numpy integers.  For variable-length integers, Python 2 had the long type.  But this was confusing and it was decided this confusion wasn't worth the performance gains, especially when people who need performance would be using numpy or something like it anyway.","Q_Score":54,"Tags":"python,numpy","A_Id":38156630,"CreationDate":"2016-07-01T23:34:00.000","Title":"What is the difference between native int type and the numpy.int types?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm a beginner in opencv using python. I have many 16 bit gray scale images and need to detect the same object every time in the different images. Tried template matching in opencv python but needed to take different templates for different images which could be not desirable. Can any one suggest me any algorithm in python to do it efficiently.","AnswerCount":4,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":905,"Q_Id":38156827,"Users Score":0,"Answer":"Your question is way too general. Feature matching is a very vast field.\nThe type of algorithm to be used totally depends on the object you want to detect, its environment etc.\nSo if your object won't change its size or angle in the image then use Template Matching.\nIf the image will change its size and orientation you can use SIFT or SURF.\nIf your object has unique color features that is different from its background, you can use hsv method.\nIf you have to classify a group of images as you object,for example all the cricket bats should be detected then you can train a number of positive images to tell the computer how the object looks like and negative image to tell how it doesn't, it can be done using haar training.","Q_Score":0,"Tags":"python,python-2.7,opencv","A_Id":38158929,"CreationDate":"2016-07-02T05:30:00.000","Title":"opencv-python object detection","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm a beginner in opencv using python. I have many 16 bit gray scale images and need to detect the same object every time in the different images. Tried template matching in opencv python but needed to take different templates for different images which could be not desirable. Can any one suggest me any algorithm in python to do it efficiently.","AnswerCount":4,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":905,"Q_Id":38156827,"Users Score":0,"Answer":"u can try out sliding window method. if ur object is the same in all samples","Q_Score":0,"Tags":"python,python-2.7,opencv","A_Id":38674476,"CreationDate":"2016-07-02T05:30:00.000","Title":"opencv-python object detection","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to know when I use pandas.read_csv('file.csv') function to read csv file, did it load all data of file.csv into DataFrame?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":106,"Q_Id":38176645,"Users Score":0,"Answer":"The way to check is len(df). This will give you the number of lines in DataFrame.  Then, you need to check the csv file for lines. On linux, use wc -l. Or, you may need to use something else to findout lines in csv. Notepad++, Nano, SumlineText.","Q_Score":0,"Tags":"python,pandas","A_Id":38176821,"CreationDate":"2016-07-04T03:42:00.000","Title":"Does pandas.read_csv loads all data at once?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there any way in SAS , Python to find the most influential variables in rank order apart from correlation ? \nI might be missing something and any suggestion would be appreciated how to interpret it.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":64,"Q_Id":38178028,"Users Score":0,"Answer":"In SAS, apart from the correlation (Pearson index) you can use a ranking index like the Spearman coefficient (proc corr).\nIn addition, supposing you have the correct modules (STAT\/MINER) licensed you can use:\n\na linear (logistic) regression on standardized regressors and compare the betas\na tree and compare the variables on one of the available metrics (Gini, Chi2)","Q_Score":0,"Tags":"python,sas,regression","A_Id":38181950,"CreationDate":"2016-07-04T06:22:00.000","Title":"Regression: Variable influence","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on a multilabel text classification problem with 10 labels. \nThe dataset is small, +- 7000 items and +-7500 labels in total. I am using python sci-kit learn and something strange came up in the results. As a baseline I started out with using the countvectorizer and was actually planning on using the tfidf vectorizer which I thought would work better. But it doesn't.. with the countvectorizer I get a performance of a 0,1 higher f1score. (0,76 vs 0,65)\nI cannot wrap my head around why this could be the case? \nThere are 10 categories and one is called miscellaneous. Especially this one gets a much lower performance with tfidf. \nDoes anyone know when tfidf could perform worse than count?","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":1238,"Q_Id":38203983,"Users Score":1,"Answer":"There is no reason why idf would give more information for a classification task. It performs well for search and ranking, but classification needs to gather similarity, not singularities.\nIDF is meant to spot the singularity between one sample vs the rest of the corpus, what you are looking for is the singularity between one sample vs the other clusters. IDF smoothens the intra-cluster TF similarity.","Q_Score":0,"Tags":"python-2.7,scikit-learn,tf-idf","A_Id":38220858,"CreationDate":"2016-07-05T12:52:00.000","Title":"TF-IDF vectorizer doesn't work better than countvectorizer (sci-kit learn","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on a multilabel text classification problem with 10 labels. \nThe dataset is small, +- 7000 items and +-7500 labels in total. I am using python sci-kit learn and something strange came up in the results. As a baseline I started out with using the countvectorizer and was actually planning on using the tfidf vectorizer which I thought would work better. But it doesn't.. with the countvectorizer I get a performance of a 0,1 higher f1score. (0,76 vs 0,65)\nI cannot wrap my head around why this could be the case? \nThere are 10 categories and one is called miscellaneous. Especially this one gets a much lower performance with tfidf. \nDoes anyone know when tfidf could perform worse than count?","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":1238,"Q_Id":38203983,"Users Score":1,"Answer":"The question is, why not ? Both are different solutions.\nWhat is your dataset, how many words, how are they labelled, how do you extract your features ?\ncountvectorizer simply count the words, if it does a good job, so be it.","Q_Score":0,"Tags":"python-2.7,scikit-learn,tf-idf","A_Id":38204179,"CreationDate":"2016-07-05T12:52:00.000","Title":"TF-IDF vectorizer doesn't work better than countvectorizer (sci-kit learn","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to import a sas dataset(.sas7bdat format) using pandas function read_sas(version 0.17) but it is giving me the following error:\n\nUnicodeDecodeError: 'ascii' codec can't decode byte 0xc2 in position 12: ordinal not in range(128)","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":9854,"Q_Id":38203988,"Users Score":1,"Answer":"read_sas from pandas seem to not like encoding = \"utf-8\". I had a similar problem. Using SAS7BDAT('foo.sas7bdata').to_data_frame() solved the decoding issues of sas files for me.","Q_Score":2,"Tags":"python,pandas,encoding,sas","A_Id":44310145,"CreationDate":"2016-07-05T12:52:00.000","Title":"Encoding in .sas7dbat","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a ~1.0gb CSV file, and when trying to load it into Excel just to view, Excel crashes. I don't know the schema of the file, so it's difficult for me to load it into R or Python. The file contains restaurant reviews and has commas in it.\nHow can I open just a portion of the file (say, the first 100 rows, or 1.0mb's worth) in Windows Notepad or Excel?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":1514,"Q_Id":38204346,"Users Score":1,"Answer":"If you want to do somewhat more selective fishing for particular rows, then the python csv module will allow you to read the csv file row by row into Python data structures. Consult the documentation.\nThis may be useful if just grabbing the first hundred lines reveals nothing about many of the columns because they are blank in all those rows. So you could easily write a program in Python to read as many rows as it takes to find and write out a few rows with non-blank data in particular columns. Likewise if you want to analyze a subset of the data matching particular criteria, you can read all the rows in and write only the interesting ones out for further analysis.\nAn alternative to csv is pandas. Bigger learning curve, but it is probably the right tool for analyzing big data. (1Gb is not very big these days).","Q_Score":2,"Tags":"python,excel,csv","A_Id":38206084,"CreationDate":"2016-07-05T13:10:00.000","Title":"Viewing a portion of a very large CSV file?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"It's my first time using R in jupyter. I've installed everything like jupyter, python, R and lRkernel. I can use just typing or calculation in jupyter but whenever I want to use a graph library like plot or ggplot2 it shows\n\nError in loadNamespace(name): there is no package called 'Cairo'\n  Traceback: plot without title\n\nSomeone please guide me about how to handle this issue.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":411,"Q_Id":38207942,"Users Score":1,"Answer":"I had the precise same issue and fixed it by installing Cairo package:\n\ninstall.packages(\"Cairo\")`\nlibrary(Cairo)\n\nThanks to my collegue Bartek for providing this solution","Q_Score":2,"Tags":"python,r,jupyter,cairo","A_Id":38773030,"CreationDate":"2016-07-05T16:03:00.000","Title":"Error in loadNamespace(name) Cairo","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"The output of LDAModel.topicsMatrix() is unclear to me.\nI think I understand the concept of LDA and that each topic is represented by a distribution over terms.\nIn the LDAModel.describeTopics() it is clear (I think):\nThe highest sum of likelihoods of words of a sentence per topic, indicates the evidence of this tweet belonging to a topic.\nWith n topics, the output of describeTopics() is a n times m matrix where m stands for the size of the vocabulary. The values in this matrix are smaller or equal to 1.\nHowever in the LDAModel.topicsMatrix(), I have no idea what I am looking at. The same holds when reading the documentation.\nThe matrix is a m times n matrix, the dimensions have changed and the values in this matrix are larger than zero (and thus can take the value 2, which is not a probability value). What are these values? The occurrence of this word in the topic perhaps?\nHow do I use these values do calculate the distance of a sentence to a topic?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":391,"Q_Id":38210820,"Users Score":0,"Answer":"i think the matrix is m*n m is the words number and n is the topic number","Q_Score":3,"Tags":"python,apache-spark-mllib,bayesian,lda","A_Id":53956744,"CreationDate":"2016-07-05T19:04:00.000","Title":"What is the output of Spark MLLIB LDA topicsmatrix?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using jep for running python script in java, I basically need to run the script that uses scikit package. But it shows me error when I try to run, which I couldn't understand. \nThis is the piece of code in my program,\nJep jep = new Jep();\njep.eval(\"import sklearn\");\nIt shows the below error,but sklearn works perfectly well in python. \nJul 06, 2016 5:31:50 PM JepEx main\nSEVERE: null\njep.JepException: jep.JepException: : \/usr\/local\/lib\/python2.7\/dist-packages\/sklearn\/__check_build\/_check_build.so: undefined symbol: _PyThreadState_Current\n\nContents of \/usr\/local\/lib\/python2.7\/dist-packages\/sklearn\/check_build:\nsetup.py                  __init.pyc              _check_build.so\nbuild                     init.py               setup.pyc\n\nIt seems that scikit-learn has not been built correctly.\nIf you have installed scikit-learn from source, please do not forget\nto build the package before using it: run python setup.py install or\nmake in the source directory.\nIf you have used an installer, please check that it is suited for your\nPython version, your operating system and your platform.\n    at jep.Jep.eval(Jep.java:485)\n    at JepEx.executeCommand(JepEx.java:26)\n    at JepEx.main(JepEx.java:38)\nCaused by: jep.JepException: : \/usr\/local\/lib\/python2.7\/dist-packages\/sklearn\/__check_build\/_check_build.so: undefined symbol: _PyThreadState_Current\n\nContents of \/usr\/local\/lib\/python2.7\/dist-packages\/sklearn\/check_build:\nsetup.py                  __init.pyc              _check_build.so\nbuild                     init.py               setup.pyc\n\nIt seems that scikit-learn has not been built correctly.\nIf you have installed scikit-learn from source, please do not forget\nto build the package before using it: run python setup.py install or\nmake in the source directory.\nIf you have used an installer, please check that it is suited for your\nPython version, your operating system and your platform.\n    at \/usr\/local\/lib\/python2.7\/dist-packages\/sklearn\/check_build\/__init.raise_build_error(init.py:41)\n    at \/usr\/local\/lib\/python2.7\/dist-packages\/sklearn\/check_build\/__init.(init.py:46)\n    at \/usr\/local\/lib\/python2.7\/dist-packages\/sklearn\/init.(init.py:56)","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":860,"Q_Id":38223546,"Users Score":1,"Answer":"The _PyThreadState_Current error implies that it's using the wrong Python.  You should be able to fix it by setting PATH and LD_LIBRARY_PATH to the python\/bin and python\/lib directories you want to use (and built Jep and sklearn against) before launching the process.  That will ensure that Python, Jep, and sklearn are all using the same libraries.\nIf that doesn't work, it's possible that Jep or sklearn were built with different versions of Python than you're running.","Q_Score":0,"Tags":"java,python-2.7,machine-learning,scikit-learn,jepp","A_Id":43283828,"CreationDate":"2016-07-06T12:04:00.000","Title":"jep for using scikit model in java","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to incorporate a preexisting spectrogram from Matlab into my python code, using Matplotlib. However, when I enter the window value, there is an issue: in Matlab, the value is a scalar, but Matplotlib requires a vector. Why is this so?","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":865,"Q_Id":38223687,"Users Score":1,"Answer":"The value in Matlab is a scalar as it represents the size of the window, and Matlab uses a Hamming window by default. The Window argument also accepts a vector, so you can pass in any windowing function you want.","Q_Score":1,"Tags":"python,matlab,matplotlib,spectrogram","A_Id":38223850,"CreationDate":"2016-07-06T12:10:00.000","Title":"Difference between Matlab spectrogram and matplotlib specgram?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to incorporate a preexisting spectrogram from Matlab into my python code, using Matplotlib. However, when I enter the window value, there is an issue: in Matlab, the value is a scalar, but Matplotlib requires a vector. Why is this so?","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":865,"Q_Id":38223687,"Users Score":1,"Answer":"The arguments are just organized differently.\nIn matplotlib, the window size is specified using the NFFT argument. The window argument, on the other hand, is only for specifying the window itself, rather than the size.  So, like MATLAB, the window argument accepts a vector.  However, unlike MATLAB, it also accepts a function that should take an arbitrary-length vector and return another vector of the same size.  This allows you to use functions for windows instead of just vectors.\nSo to put it in MATLAB terms, the MATLAB window argument is split into the window and NFFT arguments in matplotlib, while the MATLAB NFFT argument is equivalent to the matplotlib pad_to argument.\nAs for the reason, specifying the window and window size independently allows you to use a function as the argument for window (which, in fact, is the default).  This is impossible with the MATLAB arguments.  \nIn Python, functions are first-class objects, which isn't the case in MATLAB.  So it tends to be much more common to use functions as arguments to other functions in Python compared to MATLAB.  Python also allows you to specify arguments by name, something MATLAB really doesn't.  So in MATLAB it is much more common to have arguments that do different things depending on the inputs, while similar functions in Python tend to split those into multiple independent arguments.","Q_Score":1,"Tags":"python,matlab,matplotlib,spectrogram","A_Id":38225574,"CreationDate":"2016-07-06T12:10:00.000","Title":"Difference between Matlab spectrogram and matplotlib specgram?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a very strange problem that I get different results on the same code and same data on different machines.\nI have a python code based on numpy\/scipy\/sklearn and I use anaconda as my base python distribution. Even when I copy the entire project directory (which includes all the data and code) from my main machine to another machine and run it, the results I get are different. Specifically, I'm doing a classification task and I get 3 percent difference in accuracy. I am using the same version of python and anaconda on the two machines. My main machine is ubuntu 16.04 and the results on it are lower than several other machines with various OS on which I tried (OSX, ubuntu 14.04 and Centos). So, there should be something wrong with my current system configuration because all other machines show consistent results. Since the version of my anaconda is consistent among all machines, I have no idea what else could be the problem. Any ideas what else I should check or what could be the source of the problem? \nI also removed and reinstalled anaconda from scratch but it didn't help.","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":6808,"Q_Id":38228088,"Users Score":2,"Answer":"If your code uses linear algebra, check it. Generally, roundoff errors are not deterministic, and if you have badly conditioned matrices, it can be it.","Q_Score":10,"Tags":"python,numpy,scipy,scikit-learn,anaconda","A_Id":38233222,"CreationDate":"2016-07-06T15:40:00.000","Title":"Same Python code, same data, different results on different machines","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Hi, I feel like this question might be completely stupid, but I am still going to ask it, as I have been thinking about it.\nWhat are the advantages, of using a plotter like matplotlib, instead of an existing software, or grapher.\nFor now, I have guessed that although it takes a lot more time to use such a library, you have more possibilities?\nPlease, let me know what your opinion is. I am just starting to learn about data science with Python, so I would like to make things clear.","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":146,"Q_Id":38230462,"Users Score":2,"Answer":"Matplotlib gives you a nice level of access:\n\nyou can change all details of the plots, modify ticks, labels, spacing, ...\nit has many sensible defaults, so a oneliner plot(mydata) produces fairly nice plots\nit plays well with numpy and other numerical tools, so you can pass your data science objects directly to the plotting tool without going through some intermediate io","Q_Score":0,"Tags":"python,matplotlib,graph,data-science","A_Id":38230601,"CreationDate":"2016-07-06T17:46:00.000","Title":"Why use matplotlib instead of some existing software\/grapher","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Hi, I feel like this question might be completely stupid, but I am still going to ask it, as I have been thinking about it.\nWhat are the advantages, of using a plotter like matplotlib, instead of an existing software, or grapher.\nFor now, I have guessed that although it takes a lot more time to use such a library, you have more possibilities?\nPlease, let me know what your opinion is. I am just starting to learn about data science with Python, so I would like to make things clear.","AnswerCount":2,"Available Count":2,"Score":0.2913126125,"is_accepted":false,"ViewCount":146,"Q_Id":38230462,"Users Score":3,"Answer":"Adding to Robin's answer, I think reproducibility is key.\nWhen you make your graphs with matplotlib, since you are coding everything rather than using an interface, all of you work is reproducible, you can just run your script again. Using other software, specifically programs with user interfaces, means that each time you want to remake your graphs, you have to start from scratch, and if someone asks you the specifics of your graph (ie what scale an axis used, what units something is in that might not be labeled) it is difficult for you to go back and figure it out, since there isn't code to examine.","Q_Score":0,"Tags":"python,matplotlib,graph,data-science","A_Id":38230638,"CreationDate":"2016-07-06T17:46:00.000","Title":"Why use matplotlib instead of some existing software\/grapher","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to save a sklearn model on a Windows server using sklearn.joblib.dump and then joblib.load the same file on a linux server (centOS71). I get the error below:\nValueError: non-string names in Numpy dtype unpickling\nThis is what I have tried:\n\nTried both python27 and python35\nTried the built in open() with 'wb' and 'rb' arguments\n\nI really don't care how the file is moved, I just need to be able to move and load it in a reasonable amount of time.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":610,"Q_Id":38252931,"Users Score":0,"Answer":"Python pickle should run between windows\/linux. There may be incompatibilities if:\n\npython versions on the two hosts are different (If so, try installing same version of python on both hosts); AND\/OR \nif one machine is 32-bit and another is 64-bit (I dont know any fix so far for this problem)","Q_Score":0,"Tags":"python,linux,windows,scikit-learn,pickle","A_Id":39263619,"CreationDate":"2016-07-07T18:35:00.000","Title":"Dump Python sklearn model in Windows and read it in Linux","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"What's the essential difference(s) between pd.DataFrame.merge() and pd.concat()?\nSo far, this is what I found, please comment on how complete and accurate my understanding is:\n\n.merge() can only use columns (plus row-indices) and it is semantically suitable for database-style operations. .concat() can be used with either axis, using only indices, and gives the option for adding a hierarchical index. \nIncidentally, this allows for the following redundancy: both can combine two dataframes using the rows indices.\npd.DataFrame.join() merely offers a shorthand for a subset of the use cases of .merge()\n\n(Pandas is great at addressing a very wide spectrum of use cases in data analysis. It can be a bit daunting exploring the documentation to figure out what is the best way to perform a particular task. )","AnswerCount":7,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":114669,"Q_Id":38256104,"Users Score":0,"Answer":"Only concat function has axis parameter. Merge is used to combine dataframes side-by-side based on values in shared columns so there is no need for axis parameter.","Q_Score":141,"Tags":"python,pandas,join,merge,concat","A_Id":65132518,"CreationDate":"2016-07-07T22:12:00.000","Title":"Difference(s) between merge() and concat() in pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"What's the essential difference(s) between pd.DataFrame.merge() and pd.concat()?\nSo far, this is what I found, please comment on how complete and accurate my understanding is:\n\n.merge() can only use columns (plus row-indices) and it is semantically suitable for database-style operations. .concat() can be used with either axis, using only indices, and gives the option for adding a hierarchical index. \nIncidentally, this allows for the following redundancy: both can combine two dataframes using the rows indices.\npd.DataFrame.join() merely offers a shorthand for a subset of the use cases of .merge()\n\n(Pandas is great at addressing a very wide spectrum of use cases in data analysis. It can be a bit daunting exploring the documentation to figure out what is the best way to perform a particular task. )","AnswerCount":7,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":114669,"Q_Id":38256104,"Users Score":14,"Answer":"pd.concat takes an Iterable as its argument. Hence,  it cannot take DataFrames directly as its argument. Also Dimensions of the DataFrame should match along axis while concatenating.\npd.merge can take DataFrames as its argument, and is used to combine two DataFrames with same columns or index, which can't be done with pd.concat since it will show the repeated column in the DataFrame.\nWhereas join can be used to join two DataFrames with different indices.","Q_Score":141,"Tags":"python,pandas,join,merge,concat","A_Id":49564930,"CreationDate":"2016-07-07T22:12:00.000","Title":"Difference(s) between merge() and concat() in pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Forgive me if my questions is too general, or if its been asked before. I've been tasked to manipulate (e.g. copy and paste several range of entries, perform calculations on them, and then save them all to a new csv file) several large datasets in Python3. \nWhat are the pros\/cons of using the aforementioned libraries? \nThanks in advance.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":114,"Q_Id":38291701,"Users Score":1,"Answer":"You should always try to use as much as possible the work that other people have already been doing for you (such as programming the pandas library). This saves you a lot of time. Pandas has a lot to offer when you want to process such files so this seems to me to be the the best way to deal with such files. Since the question is very general, I can also only give a general answer... When you use pandas, you will however need to read more in the documentation. But I would not say that this is a downside.","Q_Score":1,"Tags":"python,csv","A_Id":38291737,"CreationDate":"2016-07-10T12:07:00.000","Title":"Using pandas over csv library for manipulating CSV files in Python3","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to find the GLCM of an image using greycomatrix from skimage library. I am having issues with the selection of levels. Since it's an 8-bit image, the obvious selection should be 256; however, if I select values such as 8 (for the purpose of binning and to prevent sparse matrices from forming), I am getting errors. \nQUESTIONS:\n\nDoes anyone know why?\nCan anyone suggest any ideas of binning these values into a 8x8 matrix instead of a 256x256 one?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2092,"Q_Id":38297765,"Users Score":2,"Answer":"The simplest way for binning 8-bits images is to divide each value by 32. Then each pixel value is going to be in [0,8[.\nBtw, more than avoiding sparse matrices (which are not really an issue), binning makes the GLCM more robust to noise.","Q_Score":1,"Tags":"python,image-processing,scikit-image,glcm","A_Id":38297891,"CreationDate":"2016-07-11T00:52:00.000","Title":"Grey Level Co-Occurrence Matrix \/\/ Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I was wondering if it is possible to use dynamic weights in sklearn's VotingClassifier. Overall i have 3 labels 0 = Other, 1 = Spam, 2 = Emotion. By dynamic weights I mean the following:\nI have 2 classifiers. First one is a Random Forest which performs best on Spam detection. Other one is a CNN which is superior for topic detection (good distinction between Other and Emotion). What I would like is a VotingClassifier that gives a higher weight to RF when it assigns the label \"Spam\/1\".\nIs VotingClassifier the right way to go?\nBest regards,\nStefan","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":139,"Q_Id":38304942,"Users Score":0,"Answer":"I thing Voting Classifier only accepts different static weights for each estimator. However you may solve the problem by assigning class weights with the class_weight parameter of the random forest estimator by calculating the class weights on your train set.","Q_Score":1,"Tags":"python,scikit-learn,classification,multilabel-classification,voting","A_Id":53769151,"CreationDate":"2016-07-11T10:42:00.000","Title":"Ensembling with dynamic weights","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"The TensorFlow tutorial for using CNN for the cifar10 data set has the following advice:\nEXERCISE: When experimenting, it is sometimes annoying that the first training step can take so long. Try decreasing the number of images that initially fill up the queue. Search for NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN in cifar10.py.\nIn order to play around with it, I tried decreasing this number by a lot but it doesn't seem to change the training time. Is there anything I can do? I tried even changing it to something as low as 5 and the training session still continued very slowly. \nAny help would be appreciated!","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":174,"Q_Id":38314964,"Users Score":1,"Answer":"Note that this exercise only speeds up the first step time by skipping the prefetching of a larger from of the data. This exercise does not speed up the overall training\nThat said, the tutorial text needs to be updated. It should read \n\nSearch for min_fraction_of_examples_in_queue in cifar10_input.py.\n\nIf you lower this number, the first step should be much quicker because the model will not attempt to prefetch the input.","Q_Score":1,"Tags":"python,tensorflow","A_Id":38317151,"CreationDate":"2016-07-11T19:43:00.000","Title":"Speeding up TensorFlow Cifar10 Example for Experimentation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to apply tanh(dot(x,y));\nx and y are batch data of my RNN.  \nx,y have shape (n_batch, n_length, n_dim) like (2,3,4) ; 2 samples with 3 sequences, each is 4 dimensions.\nI want to do inner or dot production to last dimension.  Then tanh(dot(x,y)) should have shape of (n_batch, n_length) = (2, 3)\nWhich function should I use?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":291,"Q_Id":38321248,"Users Score":1,"Answer":"This expression should do the trick:\n\ntheano.tensor.tanh((x * y).sum(2))\n\nThe dot product is computed 'manually' by doing element-wise multiplication, then summing over the last dimension.","Q_Score":0,"Tags":"python,numpy,theano,deep-learning,keras","A_Id":38353930,"CreationDate":"2016-07-12T06:15:00.000","Title":"how to use dot production on batch data?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Does anybody use \"n_jobs\" of sklearn-classes? I am work with sklearn in Anaconda 3.4 64 bit. Spyder version is 2.3.8. My script can't finish its execution after setting \"n_jobs\" parameter of some sklearn-class to non-zero value.Why is this happening?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1402,"Q_Id":38328159,"Users Score":0,"Answer":"Several scikit-learn tools such as GridSearchCV and cross_val_score rely internally on Python\u2019s multiprocessing module to parallelize execution onto several Python processes by passing n_jobs > 1 as argument.\nTaken from Sklearn documentation: \n\nThe problem is that Python multiprocessing does a fork system call\n  without following it with an exec system call for performance reasons.\n  Many libraries like (some versions of) Accelerate \/ vecLib under OSX,\n  (some versions of) MKL, the OpenMP runtime of GCC, nvidia\u2019s Cuda (and\n  probably many others), manage their own internal thread pool. Upon a\n  call to fork, the thread pool state in the child process is corrupted:\n  the thread pool believes it has many threads while only the main\n  thread state has been forked. It is possible to change the libraries\n  to make them detect when a fork happens and reinitialize the thread\n  pool in that case: we did that for OpenBLAS (merged upstream in master\n  since 0.2.10) and we contributed a patch to GCC\u2019s OpenMP runtime (not\n  yet reviewed).","Q_Score":4,"Tags":"python,scikit-learn","A_Id":43412660,"CreationDate":"2016-07-12T11:52:00.000","Title":"n_jobs don't work in sklearn-classes","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to do SEMANTIC keyword search on list of topics with NLP(Natural Language Processing ). It would be very appreciable if you post any reference links or ideas.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":296,"Q_Id":38344740,"Users Score":0,"Answer":"Your questions is somewhat vague but I will try nonetheless...\nIf I understand you correctly then what you want to do (depending on the effort you want to spend) is the following:\n\nExpand the keyword to a synonym list that you will use to search for in the topics (you can use WordNet for this).\nUse collocations (n-gram model) to extend the keyword to the likely bi-, tri-grams and search for these in the texts.\nDepending on the availability of the data you may also want to create a classifier (e.g. using good old SVM or CRF) that maps list of keywords into topics (where topic is a class).\nAssuming that you have a number of documents per each topic, you may also want to create a list of most frequent words per topic (eliminating stop-words).\n\nMost of the functionality is available via NLTK, Pandas, etc. for Python and OpenNLP, libsvm, LingPipe in Java.","Q_Score":0,"Tags":"java,python,search,nlp,semantics","A_Id":38437943,"CreationDate":"2016-07-13T07:03:00.000","Title":"How to do semantic keyword search with nlp","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've got some clustered classes, and a sample with a prediction. Now, i want to know the \"orientation\" of the sample, which varies from 0 to 1, where 0 - right in the class center, 1 - right on the class border(radius). I guess, it's going to be\norientation=dist_from_center\/class_radius\nSo, I'm struggled to find class radius. The first idea is to take the distance from a center to the most distant sample, but iwould like to use smth more 'academic' and less custom","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":41,"Q_Id":38375062,"Users Score":0,"Answer":"The way you're defining the orientation to us seems like you've got the right idea. If you use the farthest distance from the center as the denominator, then you'll get 0 as your minimum (cluster center) and 1 as your maximum (the farthest distance) and a linear distance in-between.","Q_Score":0,"Tags":"python,scikit-learn,k-means","A_Id":38375229,"CreationDate":"2016-07-14T13:04:00.000","Title":"Sample orientation in the class, clustered by k-means in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Sorry if I messed up the title, I didn't know how to phrase this. Anyways, I have a tensor of a set of values, but I want to make sure that every element in the tensor has a range from 0 - 255, (or 0 - 1 works too). However, I don't want to make all the values add up to 1 or 255 like softmax, I just want to down scale the values.\nIs there any way to do this?\nThanks!","AnswerCount":6,"Available Count":1,"Score":0.1651404129,"is_accepted":false,"ViewCount":25931,"Q_Id":38376478,"Users Score":5,"Answer":"sigmoid(tensor) * 255 should do it.","Q_Score":22,"Tags":"python,tensorflow,conv-neural-network","A_Id":38376532,"CreationDate":"2016-07-14T14:08:00.000","Title":"Changing the scale of a tensor in tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Overview: I have data something like this (each row is a string):\n\n81:0A:D7:19:25:7B, 2016-07-14 14:29:13, 2016-07-14 14:29:15, -69, 22:22:22:22:22:23,null,^M\n3B:3F:B9:0A:83:E6, 2016-07-14 01:28:59, 2016-07-14 01:29:01, -36, 33:33:33:33:33:31,null,^M\nB3:C0:6E:77:E5:31, 2016-07-14 08:26:45, 2016-07-14 08:26:47, -65, 33:33:33:33:33:32,null,^M\n61:01:55:16:B5:52, 2016-07-14 06:25:32, 2016-07-14 06:25:34, -56, 33:33:33:33:33:33,null,^M\n\nAnd I want to sort each row based on the first timestamp that is present in the each String, which for these four records is:\n\n2016-07-14 01:28:59\n2016-07-14 06:25:32\n2016-07-14 08:26:45\n2016-07-14 14:29:13\n\nNow I know the sort() method but I don't understand how can I use here to sort all the rows based on this (timestamp) quantity, and I do need to keep the final sorted data in the same format as some other service is going to use it.\nI also understand I can make the key() but I am not clear how that can be made to sort on the timestamp field.","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":358,"Q_Id":38388799,"Users Score":1,"Answer":"you can use string.split()\uff0cstring.split(',')[1]","Q_Score":5,"Tags":"python,list,python-2.7,sorting","A_Id":38389853,"CreationDate":"2016-07-15T05:57:00.000","Title":"Sort A list of Strings Based on certain field","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataframe in Python. Can I write this data to Redshift as a new table?\nI have successfully created a db connection to Redshift and am able to execute simple sql queries.\nNow I need to write a dataframe to it.","AnswerCount":7,"Available Count":1,"Score":0.1418931938,"is_accepted":false,"ViewCount":57271,"Q_Id":38402995,"Users Score":5,"Answer":"Assuming you have access to S3, this approach should work:\nStep 1: Write the DataFrame as a csv to S3 (I use AWS SDK boto3 for this)\nStep 2: You know the columns, datatypes, and key\/index for your Redshift table from your DataFrame, so you should be able to generate a create table script and push it to Redshift to create an empty table\nStep 3: Send a copy command from your Python environment to Redshift to copy data from S3 into the empty table created in step 2\nWorks like a charm everytime.\nStep 4: Before your cloud storage folks start yelling at you delete the csv from S3\nIf you see yourself doing this several times, wrapping all four steps in a function keeps it tidy.","Q_Score":32,"Tags":"python,pandas,dataframe,amazon-redshift,psycopg2","A_Id":42047026,"CreationDate":"2016-07-15T18:33:00.000","Title":"How to write data to Redshift that is a result of a dataframe created in Python?","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to add more fine grained grid on a plotted graph. The problem is all of the examples require access to the axis object.\nI want to add specific grid to already plotted graph (from inside ipython).\nHow do I gain access to the current figure and axis in ipython ?","AnswerCount":2,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":55702,"Q_Id":38415774,"Users Score":86,"Answer":"plt.gcf() to get current figure\nplt.gca() to get current axis","Q_Score":37,"Tags":"python,matplotlib,ipython,axis,figure","A_Id":38433583,"CreationDate":"2016-07-16T21:25:00.000","Title":"ipython : get access to current figure()","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using Sympy to process randomly generated expressions which may contain the boolean operators 'and', 'or', and 'not'.\n'and' and 'or' work well:\n\na = 0\nb = 1\na and b\n\n0\n\na or b\n\n1\nBut 'not' introduces a 2nd term 'False' in addition to the desired value:\n\na, not b\n\n(0, False)\nWhen processed by Sympy (where 'data' (below) provides realworld values to substitute for the variables a and b):\n\nalgo_raw = 'a, not b'\nalgo_sym = sympy.sympify(algo_raw)\nalgo_sym.subs(data)\n\nIt chokes on 'False'.\nI need to disable the 2nd term 'False' such that I get only the desired output '0'.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":35,"Q_Id":38416381,"Users Score":0,"Answer":"If you use the operators &, |, and ~ for and, or, and not, respectively, you will get a symbolic boolean expression. I also recommend using sympy.true and sympy.false instead of 1 and 0.","Q_Score":1,"Tags":"python,sympy,logical-operators","A_Id":38424476,"CreationDate":"2016-07-16T22:56:00.000","Title":"Need to disable Sympy output of 'False' (0, False) in logical operator 'not'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using Sympy to process randomly generated expressions which may contain the boolean operators 'and', 'or', and 'not'.\n'and' and 'or' work well:\n\na = 0\nb = 1\na and b\n\n0\n\na or b\n\n1\nBut 'not' introduces a 2nd term 'False' in addition to the desired value:\n\na, not b\n\n(0, False)\nWhen processed by Sympy (where 'data' (below) provides realworld values to substitute for the variables a and b):\n\nalgo_raw = 'a, not b'\nalgo_sym = sympy.sympify(algo_raw)\nalgo_sym.subs(data)\n\nIt chokes on 'False'.\nI need to disable the 2nd term 'False' such that I get only the desired output '0'.","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":35,"Q_Id":38416381,"Users Score":1,"Answer":"a, not b doesn't do what you think it does. You are actually asking for, and correctly receiving, a tuple of two items containing:\n\na\nnot b\n\nAs the result shows, a is 0 and not b is False, 1 being truthy and the not of truthy being False.\nThe fact that a happens to be the same value as the result you want doesn't mean it's giving you the result you want as the first item of the tuple and you just need to throw away the second item! That would be equivalent to just writing a.\nWhat you want to do, I assume, is write your condition as a and not b.","Q_Score":1,"Tags":"python,sympy,logical-operators","A_Id":38416665,"CreationDate":"2016-07-16T22:56:00.000","Title":"Need to disable Sympy output of 'False' (0, False) in logical operator 'not'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there a method that I can call to create a random orthonormal matrix in python? Possibly using numpy? Or is there a way to create a orthonormal matrix using multiple numpy methods? Thanks.","AnswerCount":7,"Available Count":1,"Score":0.1137907297,"is_accepted":false,"ViewCount":28284,"Q_Id":38426349,"Users Score":4,"Answer":"if you want a none Square Matrix with orthonormal column vectors you could create a square one with any of the mentioned method and drop some columns.","Q_Score":30,"Tags":"python,numpy,linear-algebra,orthogonal","A_Id":47932683,"CreationDate":"2016-07-17T22:01:00.000","Title":"How to create random orthonormal matrix in python numpy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a curve of some data that I am plotting using matplotlib.  The small value x-range of the data consists entirely of NaN values, so that my curve starts abruptly at some value of x>>0 (which is not necessarily the same value for different data sets I have).  I would like to place a vertical dashed line where the curve begins, extending from the curve, to the x axis.  Can anyone advise how I could do this? Thanks","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":4256,"Q_Id":38433584,"Users Score":3,"Answer":"Assuming you know where the curve begins, you can just use:\nplt.plot((x1, x2), (y1, y2), 'r-') to draw the line from the point (x1, y1) to the point (x2, y2) \nHere in your case, x1 and x2 will be same, only y1 and y2 should change, as it is a straight vertical line that you want.","Q_Score":1,"Tags":"python,matplotlib","A_Id":38433637,"CreationDate":"2016-07-18T09:49:00.000","Title":"Python - Plotting vertical line","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In the official explanation, there is no natural ordering between the topics in LDA.\nAs for the method show_topics(), if it returned num_topics <= self.num_topics subset of all topics is therefore arbitrary and may change between two LDA training runs.\nBut I tends to find the top ten frequent topics of corpus. Is there any other ways to achieve this?\nMany thanks.","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":836,"Q_Id":38442161,"Users Score":2,"Answer":"Like the documentation says, there is no natural ordering between topics in LDA.  If you have your own criterion for ordering the topics, such as frequency of appearance, you can always retrieve the entire list of topics from your model and sort them yourself.\nHowever, even the notion of \"top ten most frequent topics\" is ambiguous, and one could reasonably come up with several different definitions of frequency.  Do you mean the topic that has been assigned to the largest number of word tokens?  Do you mean the topic with the highest average proportions among all documents?  This ambiguity is the reason gensim has no built-in way to sort topics.","Q_Score":0,"Tags":"python,lda,gensim,topic-modeling","A_Id":38469723,"CreationDate":"2016-07-18T16:53:00.000","Title":"How to print top ten topics using Gensim?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Tensorflow's scalar\/histogram\/image_summary functions are very useful for logging data for viewing with tensorboard. But I'd like that information printed to the console as well (e.g. if I'm a crazy person without a desktop environment). \nCurrently, I'm adding the information of interest to the fetch list before calling sess.run, but this seems redundant as I'm already fetching the merged summaries. Fetching the merged summaries returns a protobuf, so I imagine I could scrape it using some generic python protobuf library, but this seems like a common enough use case that there should be an easier way.\nThe main motivation here is encapsulation. Let's stay I have my model and training script in different files. My model has a bunch of calls to tf.scalar_summary for the information that useful to log. Ideally, I'd be able to specify whether or not to additionally print this information to console by changing something in the training script without changing the model file. Currently, I either pass all of the useful information to the training script (so I can fetch them), or I pepper the model file with calls to tf.Print","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":1812,"Q_Id":38446706,"Users Score":2,"Answer":"Overall, there isn't first class support for your use case in TensorFlow, so I would parse the merged summaries back into a tf.Summary() protocol buffer, and then filter \/ print data as you see fit. \nIf you come up with a nice pattern, you could then merge it back into TensorFlow itself. I could imagine making this an optional setting on the tf.train.SummaryWriter, but it is probably best to just have a separate class for console-printing out interesting summaries.\nIf you want to encode into the graph itself which items should be summarized and printed, and which items should only be summarized (or to setup a system of different verbosity levels) you could use the Collections argument to the summary op constructors to organize different summaries into different groups. E.g. the loss summary could be put in collections [GraphKeys.SUMMARIES, 'ALWAYS_PRINT'], but another summary could be in collection [GraphKeys.SUMMARIES, 'PRINT_IF_VERBOSE'], etc. Then you can have different merge_summary ops for the different types of printing, and control which ones are run via command line flags.","Q_Score":2,"Tags":"python,tensorflow,protocol-buffers,tensorboard","A_Id":38447138,"CreationDate":"2016-07-18T22:05:00.000","Title":"Print out summaries in console","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using Glueviz 0.7.2 as part of the Anaconda package, on OSX. Glueviz is a data visualization and exploration tool. \nI am regularly regenerating an updated version of the same data set from an external model, then importing that data set into Glueviz. \nCurrently I can not find a way to have Glueviz refresh or update an existing imported data set. \nI can add a new data set, ie a second more updated version of the data from the model as a new import data set, but this does not replace the original, and does not enable the new data to show in the graphs set up in Glueviz in a simple way. \nIt seems the only solution to plot the updated data, is to start a new session, and needing to take some time to set up all the plots again. Most tedious!\nAs a python running application, Glueviz must  be storing the imported data set somewhere. Hence I thinking a work around would be to replace that existing data with the updated data. With a restart of Glueviz, and a reload of that saved session, I imagine it will not know the difference and simply graph the updated data set within the existing graphs. Problem solved. \nI am not sure how Glueviz as a python package stores the data file, and what python application would be the best to use to update that data file.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":464,"Q_Id":38459234,"Users Score":0,"Answer":"As it turns out, the data is not stored in the Glueviz session file, but rather loaded fresh each time the saved session is opened from the original data source file. \nHence the solution is simple:  Replace the data source file with a new file (of the same type) in with the updated data.  \nThe updated data file must have the exact same name, be in the exact same location, and I assume, must have only values within the source data file changed, not the amount of data or columns titles or other aspects changed from the original file. \nHaving done that, reopen Glueviz, reload that session file, and the graphs in Glueviz should update with the updated data.","Q_Score":1,"Tags":"python","A_Id":38460641,"CreationDate":"2016-07-19T12:55:00.000","Title":"Python Glueviz - is there a way to replace ie update the imported data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am pretty new to the whole neural network scene, and I was just going through a couple of tutorials on LSTM cells, specifically, tensorflow.\nIn the tutorial, they have an object tf.nn.rnn_cell.MultiRNNCell, which from my understanding, is a vertical layering of LSTM cells, similar to layering convolutional networks. However, I couldn't find anything about horizontal LSTM cells, in which the output of one cell is the input of another. \nI understand that because the cells are recurrent, they wouldn't need to do this, but I was just trying to see if this is straight out possible.\nCheers!","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":301,"Q_Id":38490811,"Users Score":1,"Answer":"However, I couldn't find anything about horizontal LSTM cells, in which the output of one cell is the input of another.\n\nThis is the definition of recurrence. All RNNs do this.","Q_Score":1,"Tags":"python,tensorflow,neural-network,recurrent-neural-network,lstm","A_Id":38638969,"CreationDate":"2016-07-20T21:03:00.000","Title":"Horizontally layering LSTM cells","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am pretty new to the whole neural network scene, and I was just going through a couple of tutorials on LSTM cells, specifically, tensorflow.\nIn the tutorial, they have an object tf.nn.rnn_cell.MultiRNNCell, which from my understanding, is a vertical layering of LSTM cells, similar to layering convolutional networks. However, I couldn't find anything about horizontal LSTM cells, in which the output of one cell is the input of another. \nI understand that because the cells are recurrent, they wouldn't need to do this, but I was just trying to see if this is straight out possible.\nCheers!","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":301,"Q_Id":38490811,"Users Score":0,"Answer":"Horizontally stacked is useless in any case I can think of. A common confusion is that there are multiple cells (with different parameters) due to the visualization of the process within an RNN.\nRNNs loop over themselves so for every input they generate new input for the cell itself. So they use the same weights over and over. If you would like to separate these connected RNNs and train them on generated sequences (different time steps), I think the weights will descend towards approximately similar parameters. So it will be similar (or equal) to just using one RNN cell using its output as input.\nYou can use multiple cells kind of 'horizontal' when using it in an encoder decoder model.","Q_Score":1,"Tags":"python,tensorflow,neural-network,recurrent-neural-network,lstm","A_Id":71838557,"CreationDate":"2016-07-20T21:03:00.000","Title":"Horizontally layering LSTM cells","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I recently installed Anaconda on Arch Linux from the Arch repositories. By default, it was set to Python3, whereas I would like to use Python2.7. I followed the Anaconda documentation to create a new Python2 environment. Upon running my Python script which uses Numpy, I got the error No module named NumPy. I found this rather strange, as one of the major points of using Anaconda is easy installation of the NumPy\/SciPy stack...\nNevertheless, I ran conda install numpy and it installed. Now, I still cannot import numpy, but when I run conda install numpy it says it is already installed. What gives?\nOutput of which conda: \/opt\/anaconda\/envs\/python2\/bin\/conda\nOutput of which python: \/opt\/anaconda\/envs\/python2\/bin\/python","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1790,"Q_Id":38493608,"Users Score":0,"Answer":"The anaconda package in the AUR is broken. If anyone encounters this, simply install anaconda from their website. The AUR attempts to do a system-wide install, which gets rather screwy with the path.","Q_Score":0,"Tags":"python,python-2.7,numpy,anaconda","A_Id":38503517,"CreationDate":"2016-07-21T01:54:00.000","Title":"Python Anaconda - no module named numpy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"This code runs perfect when I set master to localhost. The problem occurs when I submit on a cluster with two worker nodes. \nAll the machines have same version of python and packages. I have also set the path to point to the desired python version i.e. 3.5.1. when I submit my spark job on the master ssh session. I get the following error -\n\npy4j.protocol.Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.runJob.\n  : org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 2.0 failed 4 times, most recent failure: Lost task 0.3 in stage 2.0 (TID 5, .c..internal): org.apache.spark.api.python.PythonException: Traceback (most recent call last):\n    File \"\/hadoop\/yarn\/nm-local-dir\/usercache\/\/appcache\/application_1469113139977_0011\/container_1469113139977_0011_01_000004\/pyspark.zip\/pyspark\/worker.py\", line 98, in main\n      command = pickleSer._read_with_length(infile)\n    File \"\/hadoop\/yarn\/nm-local-dir\/usercache\/\/appcache\/application_1469113139977_0011\/container_1469113139977_0011_01_000004\/pyspark.zip\/pyspark\/serializers.py\", line 164, in _read_with_length\n      return self.loads(obj)\n    File \"\/hadoop\/yarn\/nm-local-dir\/usercache\/\/appcache\/application_1469113139977_0011\/container_1469113139977_0011_01_000004\/pyspark.zip\/pyspark\/serializers.py\", line 419, in loads\n      return pickle.loads(obj, encoding=encoding)\n    File \"\/hadoop\/yarn\/nm-local-dir\/usercache\/\/appcache\/application_1469113139977_0011\/container_1469113139977_0011_01_000004\/pyspark.zip\/pyspark\/mllib\/init.py\", line 25, in \n      import numpy\n  ImportError: No module named 'numpy'\n\nI saw other posts where people did not have access to their worker nodes. I do. I get the same message for the other worker node. not sure if I am missing some environment setting. Any help will be much appreciated.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":206,"Q_Id":38515096,"Users Score":0,"Answer":"Not sure if this qualifies as a solution. I submitted the same job using dataproc on google platform and it worked without any problem. I believe the best way to run jobs on google cluster is via the utilities offered on google platform. The dataproc utility seems to iron out any issues related to the environment.","Q_Score":0,"Tags":"python-3.x,numpy,pyspark,google-cloud-platform,gcp","A_Id":38572975,"CreationDate":"2016-07-21T22:03:00.000","Title":"module error in multi-node spark job on google cloud cluster","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I have a program that i load millions of rows into dataframes, and i declare them as global so my functions (>50) can all use them like i use a database in the past.  I read that using globals are a bad, and due to the memory mapping for it, it is slower to use globals. \nI like to ask if globals are bad, how would the good practice be? passing > 10 dataframes around functions and nested functions dont seems to be very clean code as well. \nRecently the program is getting unwieldy as different functions also update different cells, insert, delete data from the dataframes, so  i am thinking of wrapping the dataframes in a class to make it more manageable. Is that a good idea?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":151,"Q_Id":38517334,"Users Score":2,"Answer":"Yes. Instead of using globals, you should wrap your data into an object and pass that object around to your functions instead (see dependency injection).\nWrapping it in an object instead of using a global will :\n\nAllow you to unit test your code. This is absolutely the most important reason. Using globals will make it painfully difficult to test your code, since it is impossible to test any of your code in isolation due to its global nature.\nPerform operations on your code safely without the fear of random mutability bugs\nStop awful concurrency bugs that happen because everything is global.","Q_Score":1,"Tags":"python,pandas,global","A_Id":38517371,"CreationDate":"2016-07-22T02:43:00.000","Title":"Global dataframes - good or bad","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have imported a SAS dataset in python dataframe using Pandas read_sas(path) \nfunction. REPORT_MONTH is a column in sas dataset defined and saved as DATE9. format. This field is imported as float64 datatype in dataframe and having numbers which is basically a sas internal numbers for storing a date in a sas dataset. Now wondering how can I convert this originally a date field into a date field in dataframe?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":398,"Q_Id":38518000,"Users Score":0,"Answer":"I don't know how python stores dates, but SAS stores dates as numbers, counting the number of days from Jan 1, 1960. Using that you should be able to convert it in python to a date variable somehow.\nI'm fairly certain that when data is imported to python the formats aren't honoured so in this case it's easy to work around this, in others it may not be.\nThere's probably some sort of function in python to create a date of Jan 1, 1960 and then increment by the number of days you get from the imported dataset to get the correct date.","Q_Score":0,"Tags":"python,pandas,dataframe,import,sas","A_Id":38518356,"CreationDate":"2016-07-22T04:09:00.000","Title":"Date field in SAS imported in Python pandas Dataframe","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have installed LDA plibrary (using pip)\nI have a very simple test code (the next two rows)\n\nimport lda\nprint lda.datasets.load_reuters()\n\nBut i keep getting the error\n\nAttributeError: 'module' object has no attribute 'datasets'\n\nin fact i get that each time i access any attribute\/function under lda!","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":4833,"Q_Id":38537125,"Users Score":2,"Answer":"Do you have a module named lda.py or lda.pyc in the current directory?\nIf so, then your import statement is finding that module instead of the \"real\" lda module.","Q_Score":1,"Tags":"python,module,dataset,attributeerror,lda","A_Id":38537431,"CreationDate":"2016-07-23T00:58:00.000","Title":"AttributeError: 'module' object has no attribute '__version__'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to represent sparse matrix in Python in a data structure that does not waste space but in the same time preserves constant access time. Is there any easy\/trivial way of doing it? I know that libraries such as scipy have it.","AnswerCount":3,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":331,"Q_Id":38547996,"Users Score":0,"Answer":"Dict with tuples as keys might work.","Q_Score":1,"Tags":"python,data-structures","A_Id":38548024,"CreationDate":"2016-07-24T01:44:00.000","Title":"Represent sparse matrix in Python without library usage","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to represent sparse matrix in Python in a data structure that does not waste space but in the same time preserves constant access time. Is there any easy\/trivial way of doing it? I know that libraries such as scipy have it.","AnswerCount":3,"Available Count":3,"Score":0.0665680765,"is_accepted":false,"ViewCount":331,"Q_Id":38547996,"Users Score":1,"Answer":"The scipy.sparse library uses different formats depending on the purpose.  All implement a 2d matrix\n\ndictionary of keys - the data structure is a dictionary, with a tuple of the coordinates as key.  This is easiest to setup and use.\nlist of lists - has 2 lists of lists. One list has column coordinates, the other column data.  One sublist per row of matrix.\ncoo - a classic design.  3 arrays, row coordinates, column coordinates and data values\ncompressed row (or column) - a more complex version of coo, optimized for mathematical operations; based on linear algebra mathematics decades old\ndiagonal - suitable for matrices were most values are on a few diagonals","Q_Score":1,"Tags":"python,data-structures","A_Id":38548640,"CreationDate":"2016-07-24T01:44:00.000","Title":"Represent sparse matrix in Python without library usage","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to represent sparse matrix in Python in a data structure that does not waste space but in the same time preserves constant access time. Is there any easy\/trivial way of doing it? I know that libraries such as scipy have it.","AnswerCount":3,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":331,"Q_Id":38547996,"Users Score":0,"Answer":"Lots of ways to do it.  For example you could keep a list where each list element is either one of your data objects, or an integer representing N blank items.","Q_Score":1,"Tags":"python,data-structures","A_Id":38548006,"CreationDate":"2016-07-24T01:44:00.000","Title":"Represent sparse matrix in Python without library usage","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on a python project where I will need to work with a matrix whose size is around 10000X10000X10000.\nConsidering that:\n\nThe matrix will be dense, and should be stored in the RAM.\nI will need to perform linear algebra (with numpy, I think) on that matrix, with around O(n^3) where n=10000 operations (that are parallelizable).\n\nAre my requirements realistic? Which will be the hardware requirements I would need to work in such way in a decent time?\nI am also open to switch language (for example, performing the linear algebra operations in C) if this could improve the performances.","AnswerCount":3,"Available Count":2,"Score":0.2605204458,"is_accepted":false,"ViewCount":230,"Q_Id":38555120,"Users Score":4,"Answer":"Well, the first question is, wich type of value will you store in your matrix?\nSuposing it will be of integers (and suposing that every bytes uses the ISO specification for size, 4 bytes), you will have 4*10^12 bytes to store. That's a large amount of information (4 TB), so, in first place, I don't know from where you are taking all that information, and I suggest you to only load parts of it, that you can manage easily.\nOn the other side, as you can paralellize it, I will recommend you using CUDA, if you can afford a NVIDIA card, so you will have much better performance.\nIn summary, it's hard to have all that information only in RAM, and, use paralell languajes.\nPD: You are using wrong the O() stimation about algorith time complexity. You should have said that you have a O(n), being n=size_of_the_matrix or O(nmt), being n, m and t, the dimensions of the matrix.","Q_Score":1,"Tags":"python,numpy,matrix","A_Id":38555266,"CreationDate":"2016-07-24T18:03:00.000","Title":"Hardware requirements to deal with a big matrix - python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on a python project where I will need to work with a matrix whose size is around 10000X10000X10000.\nConsidering that:\n\nThe matrix will be dense, and should be stored in the RAM.\nI will need to perform linear algebra (with numpy, I think) on that matrix, with around O(n^3) where n=10000 operations (that are parallelizable).\n\nAre my requirements realistic? Which will be the hardware requirements I would need to work in such way in a decent time?\nI am also open to switch language (for example, performing the linear algebra operations in C) if this could improve the performances.","AnswerCount":3,"Available Count":2,"Score":0.0665680765,"is_accepted":false,"ViewCount":230,"Q_Id":38555120,"Users Score":1,"Answer":"Actually, the memory would be a big issue here. Depending on the type of the matrix elements. Each float takes 24 bytes for example as it is a boxed object. As your matrix is 10^12 you can do the math. \nSwitching to C would probably make it more memory-efficient, but not faster, as numpy is essentially written in C with lots of optimizations","Q_Score":1,"Tags":"python,numpy,matrix","A_Id":38555206,"CreationDate":"2016-07-24T18:03:00.000","Title":"Hardware requirements to deal with a big matrix - python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"The Tensorflow documentation states that a Variable can be used any place a Tensor can be used, and they seem to be fairly interchangeable. For example, if v is a Variable, then x = 1.0 + v becomes a Tensor.\nWhat is the difference between the two, and when would I use one over the other?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":3747,"Q_Id":38556078,"Users Score":15,"Answer":"It's true that a Variable can be used any place a Tensor can, but the key differences between the two are that a Variable maintains its state across multiple calls to run() and a variable's value can be updated by backpropagation (it can also be saved, restored etc as per the documentation). \nThese differences mean that you should think of a variable as representing your model's trainable parameters (for example, the weights and biases of a neural network), while you can think of a Tensor as representing the data being fed into your model and the intermediate representations of that data as it passes through your model.","Q_Score":11,"Tags":"python,tensorflow","A_Id":38556752,"CreationDate":"2016-07-24T19:47:00.000","Title":"In Tensorflow, what is the difference between a Variable and a Tensor?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have created Mxnet Rec data through Im2rec. I would like to feed this into Tensorflow. Is it possible ? and How would i do that? Any idea ?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":365,"Q_Id":38561304,"Users Score":1,"Answer":"You can probably feed the data. \nYou will need to use MXNet Iterators to get the data out of the records, and then each record you will need to cast to something that Tensorflow understands.","Q_Score":1,"Tags":"python,tensorflow,mxnet","A_Id":45135108,"CreationDate":"2016-07-25T06:48:00.000","Title":"Feed Mxnet Rec to Tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a pandas dataframe with mixed column names:\n1,2,3,4,5, 'Class'\nWhen I save this dataframe to h5file, it says that the performance will be affected due to mixed types. How do I convert the integer to string in pandas?","AnswerCount":3,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":62888,"Q_Id":38577126,"Users Score":16,"Answer":"You can simply use     df.columns = df.columns.map(str)\nDSM's first answer df.columns = df.columns.astype(str) didn't work for my dataframe. (I got TypeError: Setting  dtype to anything other than float64 or object is not supported)","Q_Score":42,"Tags":"python,pandas","A_Id":48574315,"CreationDate":"2016-07-25T20:45:00.000","Title":"Convert Column Name from int to string in pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Recently I've been working a lot with pySpark, so I've been getting used to it's syntax, the different APIs and the HiveContext functions. Many times when I start working on a project I'm not fully aware of what its scope will be, or the size of the input data, so sometimes I end up requiring the full power of distributed computing, while on others I end up with some scripts that will run just fine on my local machine.\nMy question is, is there a disadvantage to coding with pySpark as my main language as compared to regular Python\/Pandas, even for just some exploratory analysis? I ask mainly because of the cognitive work of switching between languages, and the hassle of moving my code from Python to pySpark if I do en up requiring to distribute the work.\nIn term of libraries I know Python would have more capabilities, but on my current projects so far don't use any library not covered by Spark, so I'm mostly concerned about speed, memory and any other possible disadvantage; which would perform better on my local machine?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1310,"Q_Id":38601730,"Users Score":0,"Answer":"I'm in a similar situation. We've done most of our development in Python (primarily Pandas) and now we're moving into Spark as our environment has matured to the point that we can use it. \nThe biggest disadvantage I see to PySpark is when we have to perform operations across an entire DataFrame but PySpark doesn't directly support the library or method. For example, when trying to use the Lifetimes library, this is not supported by PySpark so we either have to convert the PySpark Dataframe to a Pandas Dataframe (which takes a lot of time and loses the advantage of the cluster) or convert the code to something PySpark can consume and parallelize across the PySpark DataFrame.","Q_Score":1,"Tags":"python,apache-spark,pyspark","A_Id":45575576,"CreationDate":"2016-07-27T00:03:00.000","Title":"Programming on PySpark (local) vs. Python on Jupyter Notebook","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am currently working on my thesis, which involves dealing with quite a sizable dataset: ~4mln observations and ~260ths features. It is a dataset of chess games, where most of the features are player dummies (130k for each colour).  \nAs for the hardware and the software, I have around 12GB of RAM on this computer. I am doing all my work in Python 3.5 and use mainly pandas and scikit-learn packages.  \nMy problem is that obviously I can't load this amount of data to my RAM. What I would love to do is to generate the dummy variables, then slice the database into like a thousand or so chunks, apply the Random Forest and aggregate the results again.  \nHowever, to do that I would need to be able to first create the dummy variables, which I am not able to do due to memory error, even if I use sparse matrices. Theoretically, I could just slice up the database first, then create the dummy variables. However, the effect of that will be that I will have different features for different slices, so I'm not sure how to aggregate such results.  \nMy questions:\n1. How would you guys approach this problem? Is there a way to \"merge\" the results of my estimation despite having different features in different \"chunks\" of data?\n2. Perhaps it is possible to avoid this problem altogether by renting a server. Are there any trial versions of such services? I'm not sure exactly how much CPU\/RAM would I need to complete this task.  \nThanks for your help, any kind of tips will be appreciated :)","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":392,"Q_Id":38610955,"Users Score":4,"Answer":"I would suggest you give CloudxLab a try. \nThough it is not free it is quite affordable ($25 for a month). It provides complete environment to experiment with various tools such as HDFS, Map-Reduce, Hive, Pig, Kafka, Spark, Scala, Sqoop, Oozie, Mahout, MLLib, Zookeeper, R, Scala etc. Many of the popular trainers are using CloudxLab.","Q_Score":3,"Tags":"python,pandas,scikit-learn,sparse-matrix,bigdata","A_Id":38612762,"CreationDate":"2016-07-27T10:53:00.000","Title":"Dealing with big data to perform random forest classification","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I download the theano from github, and install it.\nBut when I try to import the theano in ipython, I meet this problem\nIn [1]: import theano\nImportError                               Traceback (most recent call last)\n<ipython-input-1-3397704bd624> in <module>()\n----> 1 import theano\nC:\\Anaconda3\\lib\\site-packages\\theano\\__init__.py in <module>()\n     40 from theano.version import version as version\n     41\n---> 42 from theano.configdefaults import config\n     43\n     44 # This is the api version for ops that generate C code.  External ops\nC:\\Anaconda3\\lib\\site-packages\\theano\\configdefaults.py in <module>()\n     14\n     15 import theano\n---> 16 from theano.configparser import (AddConfigVar, BoolParam, ConfigParam, EnumStr,\n     17                                  FloatParam, IntParam, StrParam,\n     18                                  TheanoConfigParser, THEANO_FLAGS_DICT)\nC:\\Anaconda3\\lib\\site-packages\\theano\\configparser.py in <module>()\n     13\n     14 import theano\n---> 15 from theano.compat import configparser as ConfigParser\n     16 from six import string_types\n     17\nC:\\Anaconda3\\lib\\site-packages\\theano\\compat\\__init__.py in <module>()\n      4 # Python 3.x compatibility\n      5 from six import PY3, b, BytesIO, next\n----> 6 from six.moves import configparser\n      7 from six.moves import reload_module as reload\n      8 import collections\nC:\\Anaconda3\\lib\\site-packages\\six.py in __get__(self, obj, tp)\n     90\n     91     def __get__(self, obj, tp):\n---> 92         result = self._resolve()\n     93         setattr(obj, self.name, result)  # Invokes __set__.\n     94         try:\nC:\\Anaconda3\\lib\\site-packages\\six.py in _resolve(self)\n    113\n    114     def _resolve(self):\n--> 115         return _import_module(self.mod)\n    116\n    117     def __getattr__(self, attr):\nC:\\Anaconda3\\lib\\site-packages\\six.py in _import_module(name)\n     80 def _import_module(name):\n     81     \"\"\"Import module, returning the module after the last dot.\"\"\"\n---> 82     __import__(name)\n     83     return sys.modules[name]\n     84\nC:\\Anaconda3\\Lib\\site-packages\\theano\\configparser.py in <module>()\n     13\n     14 import theano\n---> 15 from theano.compat import configparser as ConfigParser\n     16 from six import string_types\n     17\nWhen I get into the files, I indeed can not find configparser.py in the directory, but the original file do not have it neither.\nImportError: cannot import name 'configparser'","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":437,"Q_Id":38611999,"Users Score":0,"Answer":"Just found the temperary solution , rename configparser.py to config_parser or any other name that are not confilct . \nand change name of each module include it to config_parser .","Q_Score":0,"Tags":"python,anaconda,theano","A_Id":43754593,"CreationDate":"2016-07-27T11:43:00.000","Title":"Import Theano on Anaconda of platform windows10","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to analyze a text which is given by lines, and I wish to vectorize the lines using sckit-learn package's TF-IDF-vectorization in python.\nThe problem is that the vectorization can be done either by words or n-grams but I want them to be done for lines, and I already ruled out a work around that just vectorize each line as a single word (since in that way the words and their meaning wont be considered).\nLooking through the documentation I didnt find how to do that, so is there any such option?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":791,"Q_Id":38615088,"Users Score":1,"Answer":"You seem to be misunderstanding what the TF-IDF vectorization is doing. For each word (or N-gram), it assigns a weight to the word which is a function of both the frequency of the term (TF) and of its inverse frequency of the other terms in the document (IDF). It makes sense to use it for words (e.g. knowing how often  the word \"pizza\" comes up) or for N-grams (e.g. \"Cheese pizza\" for a 2-gram)\nNow, if you do it on lines, what will happen? Unless you happen to have a corpus in which lines are repeated exactly (e.g. \"I need help in Python\"), your TF-IDF transformation will be garbage, as each sentence will appear exactly once in the document. And if your sentences are indeed always similar to the punctuation mark, then for all intents and purposes they are not sentences in your corpus, but words. This is why there is no option to do TF-IDF with sentences: it makes zero practical or theoretical sense.","Q_Score":2,"Tags":"python,scikit-learn,vectorization,tf-idf,text-analysis","A_Id":38615418,"CreationDate":"2016-07-27T13:58:00.000","Title":"Tf-Idf vectorizer analyze vectors from lines instead of words","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How can you convert a tensor into a Numpy ndarray,  without using eval or sess.run()?\nI need to pass a tensor into a feed dictionary and I already have a session running.","AnswerCount":3,"Available Count":1,"Score":-0.1973753202,"is_accepted":false,"ViewCount":13763,"Q_Id":38647353,"Users Score":-3,"Answer":".numpy() will convert tensor to an array.","Q_Score":8,"Tags":"python,numpy,tensorflow","A_Id":54919826,"CreationDate":"2016-07-28T21:52:00.000","Title":"Tensorflow: Convert Tensor to numpy array WITHOUT .eval() or sess.run()","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am doing some data science analysis on jupyter and I wonder how to get all the output of my cell saved into a pdf file ?\nthanks","AnswerCount":2,"Available Count":1,"Score":0.2913126125,"is_accepted":false,"ViewCount":19046,"Q_Id":38657054,"Users Score":3,"Answer":"When I want to save a Jupyter Notebook I right click the mouse, select print, then change Destination to Save as PDF. This does not save the analysis outputs though. So if I want to save a regression output, for example, I highlight the output in Jupyter Notebook, right click, print, Save as PDF. This process creates fairly nice looking documents with code, interpretation and graphics all-in-one. There are programs that allow you to save more directly but I haven't been able to get them to work.","Q_Score":6,"Tags":"python-2.7,pdf,jupyter-notebook","A_Id":41493134,"CreationDate":"2016-07-29T10:55:00.000","Title":"how to save jupyter output into a pdf file","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I have a very large network structure which I am working with in igraph. There are many different file formats which igraph Graph objects can write to and then be loaded from. I ran into memory problems when using g.write_picklez, and Graph.Read_Lgl() takes about 5 minutes to finish. I was wondering if anyone had already profiled the numerous file format choices for write and load speed as well as memory footprint. FYI this network has ~5.7m nodes and ~130m edges.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":484,"Q_Id":38706050,"Users Score":1,"Answer":"If you don't have vertex or edge attributes, your best bet is a simple edge list, i.e. Graph.Read_Edgelist(). The disadvantage is that it assumes that vertex IDs are in the range [0; |V|-1], so you'll need to have an additional file next to it where line i contains the name of the vertex with ID=i.","Q_Score":0,"Tags":"python,profiling,igraph","A_Id":38720955,"CreationDate":"2016-08-01T18:50:00.000","Title":"fastest format to load saved graph structure into python-igraph","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Having trouble with my upgrade to Seaborn 0.7.1. Conda only has 0.7.0 so I removed it and installed 0.7.1 with pip.\nI am now getting this error:\nAttributeError: module 'seaborn' has no attribute 'axlabel'\nfrom this line of code\nsns.axlabel(xlabel=\"SAMPLE GROUP\", ylabel=y_label, fontsize=16)\nI removed and reinstalled 0.7.0 and it fixed the issue. However, in 0.7.1, axlabel appears to still be there and I didn't see anything about changes to it in the release notes. What am I missing?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":853,"Q_Id":38709439,"Users Score":3,"Answer":"Changes were made in 0.7.1 to clean up the top-level namespace a bit. axlabel was not used anywhere in the documentation, so it was moved to make the main functions more discoverable. You can still access it with sns.utils.axlabel. Sorry for the inconvenience.\nNote that it's usually just as easy to do ax.set(xlabel=\"...\", ylabel=\"...\"), though it won't get you exactly what you want here because you can't set the size to something different than the default in that line.","Q_Score":2,"Tags":"python,seaborn","A_Id":38718933,"CreationDate":"2016-08-01T23:11:00.000","Title":"Upgraded Seaborn 0.7.0 to 0.7.1, getting AttribueError for missing axlabel","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In pandas, is it possible to reference the row number for a function.  I am not talking about .iloc.\niloc takes a location i.e. a row number and returns a dataframe value.\nI want to access the location number in the dataframe.  \nFor instance, if the function is in the cell that is 3 rows down and 2 columns across, I want a way to return the integer 3.  Not the entry that is in that location.\nThanks.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":118,"Q_Id":38711966,"Users Score":0,"Answer":"Sorry I couldnt add a code sample but Im on my phone.  piRSquared confirmed my fears when he said the info is lost.  I guess ill have to do a loop everytime or add a column with numbers ( that will get scrambled if i sort them : \/ ).\nThanks everyone.","Q_Score":2,"Tags":"python,pandas,numbers,row","A_Id":38721746,"CreationDate":"2016-08-02T04:48:00.000","Title":"Function that depends on the row number","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have created a python script for predictive analytics using pandas,numpy etc. I want to send my result set to java application . Is their simple way to do it. I found we can use Jython for java python integration but it doesn't use many data analysis libraries. Any help will be great . Thank you .","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":2769,"Q_Id":38724255,"Users Score":0,"Answer":"Have you tried using xml to transfer the data between the two applications ? \nMy next suggestion would be to output the data in JSON format in a txt file and then call the java application which will read the JSON from the text file.","Q_Score":2,"Tags":"java,python,pandas,numpy,jython","A_Id":38724313,"CreationDate":"2016-08-02T15:10:00.000","Title":"Sending pandas dataframe to java application","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I have created a python script for predictive analytics using pandas,numpy etc. I want to send my result set to java application . Is their simple way to do it. I found we can use Jython for java python integration but it doesn't use many data analysis libraries. Any help will be great . Thank you .","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":2769,"Q_Id":38724255,"Users Score":0,"Answer":"Better approach here is to use java pipe input like python pythonApp.py | java read. Output of python application can be used as an input for java application till the format of data is consitent and known. Above soultions of creating a file and then reading also works but is prone to more errors.","Q_Score":2,"Tags":"java,python,pandas,numpy,jython","A_Id":57166461,"CreationDate":"2016-08-02T15:10:00.000","Title":"Sending pandas dataframe to java application","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I got two questions when I was plotting graph in ipython.\n\nonce, i implement %matplotlib inline, I don't know how to switch back to use floating windows. \nwhen I search for the method to switch back, people told me to implement \n%matplotlib osx or %matplotlib, however, I finally get an error, which is \n\nGtk* backend requires pygtk to be installed.\n\n\nCan anyone help me, giving me some idea?\np.s. I am using windows 10 and python 2.7","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":336,"Q_Id":38727035,"Users Score":0,"Answer":"You need to install pyGTK. How to do so depends on what you're using to run Python. You could also not use '%matplotlib inline' and then it'll default to whatever is installed on your system.","Q_Score":0,"Tags":"python,matplotlib,ipython","A_Id":38729045,"CreationDate":"2016-08-02T17:35:00.000","Title":"How to turn off matplotlib inline function and install pygtk?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have some very basic lstm code with tensorflow and python, where my code is \noutput = tf.nn.rnn(tf.nn.rnn_cell.BasicLSTMCell(10), input_flattened, initial_state=tf.placeholder(\"float\", [None, 20]))\nwhere my input flattened is shape [?, 5, 22501]\nI'm getting the error TypeError: inputs must be a sequence on the state parameter of the lstm, and I'm ripping my hair out trying to find out why it is giving me this error. Any help would be greatly appreciated.","AnswerCount":1,"Available Count":1,"Score":0.6640367703,"is_accepted":false,"ViewCount":3118,"Q_Id":38728501,"Users Score":4,"Answer":"I think when you use the tf.nn.rnn function it is expecting a list of tensors and not just a single tensor.  You should unpack input in the time direction so that it is a list of tensors of shape [?, 22501].  You could also use tf.nn.dynamic_rnn which I think can handle this unpack for you.","Q_Score":1,"Tags":"python,neural-network,tensorflow,recurrent-neural-network","A_Id":38740100,"CreationDate":"2016-08-02T19:00:00.000","Title":"Inputs not a sequence wth RNNs and TensorFlow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"On OS X 10.11.6 and python 2.7.10 I need to import from sklearn  manifold. \nI have numpy 1.8 Orc1, scipy .13 Ob1 and scikit-learn 0.17.1 installed.\nI used pip to install sklearn(0.0), but when I try to import from sklearn manifold I get the following: \n\nTraceback (most recent call last):   File \"\", line 1, in\n     File\n  \"\/Library\/Python\/2.7\/site-packages\/sklearn\/init.py\", line 57, in\n       from .base import clone   File\n  \"\/Library\/Python\/2.7\/site-packages\/sklearn\/base.py\", line 11, in\n       from .utils.fixes import signature   File\n  \"\/Library\/Python\/2.7\/site-packages\/sklearn\/utils\/init.py\", line\n  10, in      from .murmurhash import murmurhash3_32   File\n  \"numpy.pxd\", line 155, in init sklearn.utils.murmurhash\n  (sklearn\/utils\/murmurhash.c:5029) ValueError: numpy.dtype has the\n  wrong size, try recompiling.\n\nWhat is the difference between scikit-learn and sklearn?  Also, \nI cant import scikit-learn because of a syntax error","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":81337,"Q_Id":38733220,"Users Score":59,"Answer":"You might need to reinstall numpy. It doesn't seem to have installed correctly.\nsklearn is how you type the scikit-learn name in python.\nAlso, try running the standard tests in scikit-learn and check the output. You will have detailed error information there.\nDo you have nosetests installed? Try: nosetests -v sklearn. You type this in bash, not in the python interpreter.","Q_Score":57,"Tags":"python,python-2.7,scikit-learn","A_Id":38733854,"CreationDate":"2016-08-03T02:07:00.000","Title":"Difference between scikit-learn and sklearn","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using the MiniBatchKMeans model from the sklearn.cluster module in anaconda. I am clustering a data-set that contains approximately 75,000 points. It looks something like this:\ndata = np.array([8,3,1,17,5,21,1,7,1,26,323,16,2334,4,2,67,30,2936,2,16,12,28,1,4,190...])\nI fit the data using the process below.\nfrom sklearn.cluster import MiniBatchKMeans\nkmeans = MiniBatchKMeans(batch_size=100)\nkmeans.fit(data.reshape(-1,1)\n\nThis is all well and okay, and I proceed to find the centroids of the data:\ncentroids = kmeans.cluster_centers_\nprint centroids\nWhich gives me the following output:\narray([[   13.09716569],\n       [ 2908.30379747],\n       [   46.05089228],\n       [  725.83453237],\n       [   95.39868475],\n       [ 1508.38356164],\n       [  175.48099948],\n       [  350.76287263]])\nBut, when I run the process again, using the same data, I get different values for the centroids, such as this:\narray([[   29.63143489],\n       [ 1766.7244898 ],\n       [  171.04417206],\n       [ 2873.70454545],\n       [   70.05295277],\n       [ 1074.50387597],\n       [  501.36134454],\n       [    8.30600975]])\nCan anyone explain why this is?","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":1020,"Q_Id":38751364,"Users Score":1,"Answer":"The behavior you are experiencing probably has to do with the under the hood implementation of k-means clustering that you are using. k-means clustering is an NP-hard problem, so all the implementations out there are heuristic methods. What this means practically is that for a given seed, it will converge toward a local optima that isn't necessarily consistent across multiple seeds.","Q_Score":1,"Tags":"python,statistics,scikit-learn,cluster-analysis,k-means","A_Id":38751473,"CreationDate":"2016-08-03T18:42:00.000","Title":"MiniBatchKMeans gives different centroids after subsequent iterations","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using the MiniBatchKMeans model from the sklearn.cluster module in anaconda. I am clustering a data-set that contains approximately 75,000 points. It looks something like this:\ndata = np.array([8,3,1,17,5,21,1,7,1,26,323,16,2334,4,2,67,30,2936,2,16,12,28,1,4,190...])\nI fit the data using the process below.\nfrom sklearn.cluster import MiniBatchKMeans\nkmeans = MiniBatchKMeans(batch_size=100)\nkmeans.fit(data.reshape(-1,1)\n\nThis is all well and okay, and I proceed to find the centroids of the data:\ncentroids = kmeans.cluster_centers_\nprint centroids\nWhich gives me the following output:\narray([[   13.09716569],\n       [ 2908.30379747],\n       [   46.05089228],\n       [  725.83453237],\n       [   95.39868475],\n       [ 1508.38356164],\n       [  175.48099948],\n       [  350.76287263]])\nBut, when I run the process again, using the same data, I get different values for the centroids, such as this:\narray([[   29.63143489],\n       [ 1766.7244898 ],\n       [  171.04417206],\n       [ 2873.70454545],\n       [   70.05295277],\n       [ 1074.50387597],\n       [  501.36134454],\n       [    8.30600975]])\nCan anyone explain why this is?","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":1020,"Q_Id":38751364,"Users Score":1,"Answer":"Read up on what mini-batch k-means is.\nIt will never even converge. Do one more iteration, the result will change again.\nIt is design for data sets so huge you cannot load them into memory at once. So you load a batch, pretend this were the full data set, do one iterarion. Repeat woth the next batch. If your batches are large enough and random, then the result will be \"close enough\" to be usable. While itjs never optimal.\nThus:\n\nthe minibatch results are even more random than regular k-means results. They change every iteration.\nif you can load your data into memory, don't use minibatch. Instead use a fast k-means implementation. (most are surprisingly slow).\n\nP.S. on one-dimensional data, sort your data set and then use an algorithm that benefits from the sorting instead of k-means.","Q_Score":1,"Tags":"python,statistics,scikit-learn,cluster-analysis,k-means","A_Id":38754035,"CreationDate":"2016-08-03T18:42:00.000","Title":"MiniBatchKMeans gives different centroids after subsequent iterations","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In neural network theory - setting up the size of hidden layers seems to be a really important issue. Is there any criteria how to choose the number of neurons in a hidden layer?","AnswerCount":3,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":638,"Q_Id":38759647,"Users Score":2,"Answer":"Yes - this is a really important issue. Basically there are two ways to do that:\n\nTry different topologies and choose best: due to the fact that number of neurons and layers are a discrete parameters you cannot differentiate your loss function with respect to this parameters in order to use a gradient descent methods. So the easiest way is to simply set up different topologies and compare them using either cross-validation or division of your training set to - training \/ testing \/ validating parts. You can also use a grid \/ random search schemas to do that. Libraries like scikit-learn have appropriate modules for that.\nDropout: the training framework called dropout could also help. In this case you are setting up relatively big number of nodes in your layers and trying to adjust a dropout parameter for each layer. In this scenario - e.g. assuming that you will have a two-layer network with 100 nodes in your hidden layer with dropout_parameter = 0.6 you are learning the mixture of models - where every model is a neural network with size 40 (approximately 60 nodes are turned off). This might be also considered as figuring out the best topology for your task.","Q_Score":0,"Tags":"python-2.7,machine-learning,neural-network","A_Id":38763173,"CreationDate":"2016-08-04T06:08:00.000","Title":"How should we set the number of the neurons in the hidden layer in neural network?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In neural network theory - setting up the size of hidden layers seems to be a really important issue. Is there any criteria how to choose the number of neurons in a hidden layer?","AnswerCount":3,"Available Count":2,"Score":0.0665680765,"is_accepted":false,"ViewCount":638,"Q_Id":38759647,"Users Score":1,"Answer":"You have to set the number of neurons in hidden layer in such a way that it shouldn't be more than # of your training example. There are no thumb rule for number of neurons.\nEx: If you are using MINIST Dataset then you might have ~ 78K training example. So make sure that combination of Neural Network (784-30-10) = 784*30 + 30*10 which are less than training examples. but if you use like (784-100-10) then it exceeds the # of training example and highly probable to over-fit.\nIn short, make sure you are not over-fitting and hence you have good chances to get good result.","Q_Score":0,"Tags":"python-2.7,machine-learning,neural-network","A_Id":38776068,"CreationDate":"2016-08-04T06:08:00.000","Title":"How should we set the number of the neurons in the hidden layer in neural network?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using Scikit-Learn to classify texts (in my case tweets) using LinearSVC. Is there a way to classify texts as unclassified when they are a poor fit with any of the categories defined in the training set? For example if I have categories for sport, politics and cinema and attempt to predict the classification on a tweet about computing it should remain unclassified.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":198,"Q_Id":38767481,"Users Score":1,"Answer":"In the supervised learning approach as it is, you cannot add extra category.\nTherefore I would use some heuristics. Try to predict probability for each category. Then, if all 4 or at least 3 probabilities are approximately equal, you can say that the sample is \"unknown\". \nFor this approach LinearSVC or other type of Support Vector Classifier is bad\nsuited, because it does not naturally gives you probabilities. Another classifier (Logistic Regression, Bayes, Trees, Forests) would be better","Q_Score":3,"Tags":"python,scikit-learn,text-classification","A_Id":38788040,"CreationDate":"2016-08-04T12:34:00.000","Title":"Scikit-Learn- How to add an 'unclassified' category?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"What is the difference between Kivy Camera and opencv ? I am asking this because in Kivy Camera the image gets adjusted according to frame size but in opencv this does not happen. Also I am not able to do motion detection in kivy camera whereas I found a great tutorial for motion detection on opencv. If someone can clarify the difference it would be appreciated ! Thanks :)","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":386,"Q_Id":38774748,"Users Score":0,"Answer":"opencv is a computer vision framework (hence the c-v) which can interact with device cameras. Kivy is a cross-platform development tool which can interact with device cameras.  It makes sense that there are good motion detection tutorials for opencv but not kivy camera, since this isnt really what kivy is for.","Q_Score":0,"Tags":"python,opencv,camera,kivy,motion-detection","A_Id":38774932,"CreationDate":"2016-08-04T18:23:00.000","Title":"Difference between Kivy camera and opencv camera","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":1,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a NumPy array A with shape (m,n) and want to run all the elements through some function f. For a non-constant function such as for example f(x) = x or f(x) = x**2 broadcasting works perfectly fine and returns the expected result. For f(x) = 1, applying the function to my array A however just returns the scalar 1. \nIs there a way to force broadcasting to keep the shape, i.e. in this case to return an array of 1s?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1182,"Q_Id":38794622,"Users Score":0,"Answer":"Use x.fill(1). Make sure to return it properly as fill doesn't return a new variable, it modifies x","Q_Score":0,"Tags":"python,numpy,array-broadcasting","A_Id":38794707,"CreationDate":"2016-08-05T17:14:00.000","Title":"How to get constant function to keep shape in NumPy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am attempting to use MiniBatchKMeans to stream NLP data in and cluster it, but have no way of determining how many clusters I need. What I would like to do is periodically take the silhouette score and if it drops below a certain threshold, increase the number of centroids. But as far as I can tell, n_clusters is set when you initialize the clusterer and can't be changed without restarting. Am I wrong here? Is there another way to approach this problem that would avoid this issue?","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":77,"Q_Id":38795912,"Users Score":2,"Answer":"It is not a good idea to do this during optimization, because it changes the optimization procedure substantially. It will essentially reset the whole optimization. There are strategies such as bisecting k-means that try to learn the value of k during clustering, but they are a bit more tricky than increasing k by one - they decide upon one particular cluster to split, and try to choose good initial centroids for this cluster to keep things somewhat stable.\nFurthermore, increasing k will not necessarily improve Silhouette. It will trivially improve SSQ, so you cannot use SSQ as a heuristic for choosing k, either.\nLast but not least, computing the Silhouette is O(n^2). It is too expensive to run often. If you have large enough amount of data to require MiniBatchKMeans (which really is only for massive data), then you clearly cannot afford to compute Silhouette at all.","Q_Score":0,"Tags":"python,scikit-learn,cluster-analysis,k-means","A_Id":38803263,"CreationDate":"2016-08-05T18:43:00.000","Title":"Is it possible to increase the number of centroids in KMeans during fitting?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm facing problem like this. I used tweepy to collect +10000 tweets, i use nltk naive-bayes classification and filtered the tweets into +5000. \nI want to generate a graph of user friendship from that classified 5000 tweet. The problem is that I am able to check it with tweepy.api.show_frienship(), but it takes so much and much time and sometime ended up with endless ratelimit error. \nis there any way i can check the friendship more eficiently?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":592,"Q_Id":38818981,"Users Score":0,"Answer":"I don't know much about the limits with Tweepy, but you can always write a basic web scraper with urllib and BeautifulSoup to do so.\nYou could take a website such as www.doesfollow.com which accomplishes what you are trying to do. (not sure about request limits with this page, but there are dozens of other websites that do the same thing) This website is interesting because the url is super simple.\nFor example, in order to check if Google and Twitter are \"friends\" on Twitter, the link is simply www.doesfollow.com\/google\/twitter.\nThis would make it very easy for you to run through the users as you can just append the users to the url such as 'www.doesfollow.com\/'+ user1 + '\/' + user2\nThe results page of doesfollow has this tag if the users are friends on Twitter:\n<div class=\"yup\">yup<\/div>, \nand this tag if the users are not friends on Twitter:\n<div class=\"nope\">nope<\/div>\nSo you could parse the page source code and search to find which of those tags exist to determine if the users are friends on Twitter.\nThis might not be the way that you wanted to approach the problem, but it's a possibility. I'm not entirely sure how to approach the graphing part of your question though. I'd have to look into that.","Q_Score":3,"Tags":"python,twitter,tweepy","A_Id":38819049,"CreationDate":"2016-08-07T22:03:00.000","Title":"Most efficient way to check twitter friendship? (over 5000 check)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":1,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using Ubuntu 16.04 . I tried to install Tensorflow using Anaconda 2 . But it installed a Environment inside ubuntu . So i had to create a virtual environment and then use Tensorflow . Now how can i use both Tensorflow and Sci-kit learn together in a single environment .","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2492,"Q_Id":38828829,"Users Score":1,"Answer":"Anaconda defaults doesn't provide tensorflow yet, but conda-forge do, conda install -c conda-forge tensorflow should see you right, though (for others reading!) the installed tensorflow will not work on CentOS < 7 (or other Linux Distros of a similar vintage).","Q_Score":2,"Tags":"python,pycharm,tensorflow,anaconda,ubuntu-16.04","A_Id":39021770,"CreationDate":"2016-08-08T12:08:00.000","Title":"How to use Tensorflow and Sci-Kit Learn together in one environment in PyCharm?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I know it's possible to import Google BigQuery tables to R through bigrquery library. But is it possible to export tables\/data frames created in R to Google BigQuery as new tables? \nBasically, is there an R equivalent of Python's temptable.insert_data(df) or df.to_sql() ? \nthanks for your help,\nKasia","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":635,"Q_Id":38847743,"Users Score":1,"Answer":"It looks like bigrquery package does the job with insert_upload_job(). In the package documentation, it says this function \n> is only suitable for relatively small datasets\nbut it doesn't specify any size limits. For me, it's been working for tens of thousands of rows.","Q_Score":0,"Tags":"python,r,dataframe,google-bigquery","A_Id":39119230,"CreationDate":"2016-08-09T10:03:00.000","Title":"Exporting R data.frame\/tbl to Google BigQuery table","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"As far as I know, I need to fix the number of topics for LDA modeling in Python\/ R. However, say I set topic=10 while the results show that, for a document, nine topics are all about 'health' and the distinct number of topics for this document is 2 indeed. How can I spot it without examining the key words of each topic and manually count the real distinct topics?\nP.S. I googled and learned that there are Vocabulary Word Lists (Word Banks) by Theme, and I could pair each topic with a theme according to the word lists. If several topics fall into the same theme, then I can combine them into one distinct topic. I guess it's an approach worth trying and I'm looking for smarter ideas, thanks.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":618,"Q_Id":38903061,"Users Score":1,"Answer":"First, your question kind of assumes that topics identified by LDA correspond to real semantic topics - I'd be very careful about that assumption and take a look at the documents and words assigned to topics you want to interpret that way, as LDA often have random extra words assigned, can merge two or more actual topics into one (especially with few topics overall) and may not be meaningful at all (\"junk\" topics). \nIn answer to you questions then: the idea of a \"distinct number of topics\" isn't clear at all. Most of the work I've seen uses a simple threshold to decide if a documents topic proportion is \"significant\". \nA more principled way is to look at the proportion of words assigned to that topic that appear in the document - if it's \"significantly\" higher than average, the topic is significant in the document, but again, this is involves a somewhat arbitrary threshold. I don't think anything can beat close reading of some examples to make meaningful choices here.\nI should note that, depending on how you set the document-topic prior (usually beta), you may not have each document focussed on just a few topics (as seems to be your case), but a much more even mix. In this case \"distinct number of topics\" starts to be less meaningful. \nP.S. Using word lists that are meaningful in your application is not a bad way to identify candidate topics of interest. Especially useful if you have many topics in your model (:\nP.P.S.: I hope you have a reasonable number of documents (at least some thousands), as LDA tends to be less meaningful with less, capturing chance word co-occurences rather than meaningful ones.\nP.P.P.S.: I'd go for a larger number of topics with parameter optimisation (as provided by the Mallet LDA implementation) - this effectively chooses a reasonable number of topics for your model, with very few words assigned to the \"extra\" topics.","Q_Score":1,"Tags":"python,r,lda,topic-modeling,text-analysis","A_Id":38916691,"CreationDate":"2016-08-11T17:53:00.000","Title":"Find the Number of Distinct Topics After LDA in Python\/ R","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to write a huge amount number-number pairs into a NumPy array. Since a lot of these pairs have a second value of 0, I thought of making something akin to a dictionary. The problem is that I've read through the NumPy documentation on structured arrays and it seems like dictionaries built like those on the page can only use strings as keys.\nOther than that, I need insertion and searching to have log(N) complexity. I thought of making my own Red-black tree structure using a regular NumPy array as storage, but I'm fairly certain there's an easier way to go about this.\nLanguage is Python 2.7.12.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":4918,"Q_Id":38921975,"Users Score":1,"Answer":"The most basic form of a dictionary is a structure called a HashMap.  Implementing a hashmap relies on turning your key into a value that can be quickly looked up.  A pathological example would be using ints as keys:  The value for key 1 would go in array[1], the value for key 2 would go in array[2], the Hash Function is simply the identity function.  You can easily implement that using a numpy array.\nIf you want to use other types, it's just a case of writing a good hash function to turn those keys into unique indexes into your array.  For example, if you know you've got a (int, int) tuple, and the first value will never be more than 100, you can do 100*key[1] + key[0].\nThe implementation of your hash function is what will make or break your dictionary replacement.","Q_Score":3,"Tags":"python,arrays,numpy,dictionary,red-black-tree","A_Id":38924162,"CreationDate":"2016-08-12T15:51:00.000","Title":"How can I implement a dictionary with a NumPy array?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"An ellipse of width 50, height 100, and angle 0, would be identical to an ellipse of width 100, height 50, and angle 90 - i.e. one is the rotation of the other.\nHow does cv2.fitEllipse handle this? Does it return ellipses in some normalized form (i.e. angle is picked such that width is always < height), or can it provide any output?\nI ask as I'm trying to determine whether two fit ellipses are similar, and am unsure whether I have to account for these things. The documentation doesn't address this at all.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1827,"Q_Id":38945695,"Users Score":0,"Answer":"Empirically, I ran code matching thousands of ellipses, and I never got one return value where the returned width was greater than the returned height. So it seems OpenCV normalizes the ellipse such that height >= width.","Q_Score":1,"Tags":"python,opencv,ellipse,data-fitting","A_Id":38945813,"CreationDate":"2016-08-14T19:11:00.000","Title":"How does cv2.fitEllipse handle width\/height with regards to rotation?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using PyCharm 2016.2.1 . When I try to view a Pandas dataframe through the newly added feature 'View as DataFrame' in the debugger, this works as expected for a small (e.g. 4x4) DataFrame. \nHowever when I try to view a DataFrame (generated by custom script) of ~10,000 rows x ~50 columns, I get the message: \"Nothing to show\". \nWhen I run the same script (that generates the DataFrame) in Spyder, I am able to view it, so I am pretty sure it's not an error in my script. \nDoes anyone know if there is a maximum size to the DataFrames that can be viewed in PyCharm, and if there is a way to change this? \nEDIT:\nIt seems that the maximum size allowed is 1000 x 15 , as in some cases it gets truncated to this size (when the number of rows is too large, but when there are too many columns pycharm just says 'nothing to show').\nStill, I would like to know if there is a way to increase the maximum allowed rows and columns viewable through the DataFrame viewer.","AnswerCount":10,"Available Count":6,"Score":1.0,"is_accepted":false,"ViewCount":27005,"Q_Id":38956660,"Users Score":24,"Answer":"I have faced the same problem with PyCharm 2018.2.2. The reason was having a special character in a column's name as mentioned by Yunzhao . \nIf your having a column name like 'R&D' changing it to 'RnD' will fix the problem. It's really strange JetBrains hasn't solved this problem for over 2 years.","Q_Score":57,"Tags":"python,pandas,pycharm","A_Id":52064081,"CreationDate":"2016-08-15T14:07:00.000","Title":"Dataframe not showing in Pycharm","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using PyCharm 2016.2.1 . When I try to view a Pandas dataframe through the newly added feature 'View as DataFrame' in the debugger, this works as expected for a small (e.g. 4x4) DataFrame. \nHowever when I try to view a DataFrame (generated by custom script) of ~10,000 rows x ~50 columns, I get the message: \"Nothing to show\". \nWhen I run the same script (that generates the DataFrame) in Spyder, I am able to view it, so I am pretty sure it's not an error in my script. \nDoes anyone know if there is a maximum size to the DataFrames that can be viewed in PyCharm, and if there is a way to change this? \nEDIT:\nIt seems that the maximum size allowed is 1000 x 15 , as in some cases it gets truncated to this size (when the number of rows is too large, but when there are too many columns pycharm just says 'nothing to show').\nStill, I would like to know if there is a way to increase the maximum allowed rows and columns viewable through the DataFrame viewer.","AnswerCount":10,"Available Count":6,"Score":1.0,"is_accepted":false,"ViewCount":27005,"Q_Id":38956660,"Users Score":9,"Answer":"I have met the same problems. \nI figured it was because of the special characters in column names (in my case)\nIn my case, I have \"%\" in the column name, then it doesn't show the data in View as DataFrame function. After I remove it, everything was correctly shown.\nPlease double check if you also have some special characters in the column names.","Q_Score":57,"Tags":"python,pandas,pycharm","A_Id":51483568,"CreationDate":"2016-08-15T14:07:00.000","Title":"Dataframe not showing in Pycharm","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using PyCharm 2016.2.1 . When I try to view a Pandas dataframe through the newly added feature 'View as DataFrame' in the debugger, this works as expected for a small (e.g. 4x4) DataFrame. \nHowever when I try to view a DataFrame (generated by custom script) of ~10,000 rows x ~50 columns, I get the message: \"Nothing to show\". \nWhen I run the same script (that generates the DataFrame) in Spyder, I am able to view it, so I am pretty sure it's not an error in my script. \nDoes anyone know if there is a maximum size to the DataFrames that can be viewed in PyCharm, and if there is a way to change this? \nEDIT:\nIt seems that the maximum size allowed is 1000 x 15 , as in some cases it gets truncated to this size (when the number of rows is too large, but when there are too many columns pycharm just says 'nothing to show').\nStill, I would like to know if there is a way to increase the maximum allowed rows and columns viewable through the DataFrame viewer.","AnswerCount":10,"Available Count":6,"Score":0.0399786803,"is_accepted":false,"ViewCount":27005,"Q_Id":38956660,"Users Score":2,"Answer":"In my situation, the problem is caused by two same cloumn name in my dataframe.\nCheck it by:df.columns.shape[0] == len(set(df.columns))","Q_Score":57,"Tags":"python,pandas,pycharm","A_Id":57003355,"CreationDate":"2016-08-15T14:07:00.000","Title":"Dataframe not showing in Pycharm","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using PyCharm 2016.2.1 . When I try to view a Pandas dataframe through the newly added feature 'View as DataFrame' in the debugger, this works as expected for a small (e.g. 4x4) DataFrame. \nHowever when I try to view a DataFrame (generated by custom script) of ~10,000 rows x ~50 columns, I get the message: \"Nothing to show\". \nWhen I run the same script (that generates the DataFrame) in Spyder, I am able to view it, so I am pretty sure it's not an error in my script. \nDoes anyone know if there is a maximum size to the DataFrames that can be viewed in PyCharm, and if there is a way to change this? \nEDIT:\nIt seems that the maximum size allowed is 1000 x 15 , as in some cases it gets truncated to this size (when the number of rows is too large, but when there are too many columns pycharm just says 'nothing to show').\nStill, I would like to know if there is a way to increase the maximum allowed rows and columns viewable through the DataFrame viewer.","AnswerCount":10,"Available Count":6,"Score":0.0399786803,"is_accepted":false,"ViewCount":27005,"Q_Id":38956660,"Users Score":2,"Answer":"I use PyCharm 2019.1.1 (Community Edition) and I run Python 3.7.\nWhen I first click on \"View as DataFrame\" there seems to be the same issue, but if I wait a few second the content pops up. For me it is a matter of loading.","Q_Score":57,"Tags":"python,pandas,pycharm","A_Id":55593342,"CreationDate":"2016-08-15T14:07:00.000","Title":"Dataframe not showing in Pycharm","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using PyCharm 2016.2.1 . When I try to view a Pandas dataframe through the newly added feature 'View as DataFrame' in the debugger, this works as expected for a small (e.g. 4x4) DataFrame. \nHowever when I try to view a DataFrame (generated by custom script) of ~10,000 rows x ~50 columns, I get the message: \"Nothing to show\". \nWhen I run the same script (that generates the DataFrame) in Spyder, I am able to view it, so I am pretty sure it's not an error in my script. \nDoes anyone know if there is a maximum size to the DataFrames that can be viewed in PyCharm, and if there is a way to change this? \nEDIT:\nIt seems that the maximum size allowed is 1000 x 15 , as in some cases it gets truncated to this size (when the number of rows is too large, but when there are too many columns pycharm just says 'nothing to show').\nStill, I would like to know if there is a way to increase the maximum allowed rows and columns viewable through the DataFrame viewer.","AnswerCount":10,"Available Count":6,"Score":0.0399786803,"is_accepted":false,"ViewCount":27005,"Q_Id":38956660,"Users Score":2,"Answer":"For the sake of completeness: I face the same problem, due to the fact that some elements in the index of the dataframe contain a question mark '?'. One should avoid that too, if you still want to use the data viewer. Data viewer still worked, if the index strings contain hashes or less-than\/greather-than signs though.","Q_Score":57,"Tags":"python,pandas,pycharm","A_Id":57313249,"CreationDate":"2016-08-15T14:07:00.000","Title":"Dataframe not showing in Pycharm","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using PyCharm 2016.2.1 . When I try to view a Pandas dataframe through the newly added feature 'View as DataFrame' in the debugger, this works as expected for a small (e.g. 4x4) DataFrame. \nHowever when I try to view a DataFrame (generated by custom script) of ~10,000 rows x ~50 columns, I get the message: \"Nothing to show\". \nWhen I run the same script (that generates the DataFrame) in Spyder, I am able to view it, so I am pretty sure it's not an error in my script. \nDoes anyone know if there is a maximum size to the DataFrames that can be viewed in PyCharm, and if there is a way to change this? \nEDIT:\nIt seems that the maximum size allowed is 1000 x 15 , as in some cases it gets truncated to this size (when the number of rows is too large, but when there are too many columns pycharm just says 'nothing to show').\nStill, I would like to know if there is a way to increase the maximum allowed rows and columns viewable through the DataFrame viewer.","AnswerCount":10,"Available Count":6,"Score":0.0199973338,"is_accepted":false,"ViewCount":27005,"Q_Id":38956660,"Users Score":1,"Answer":"As of 2020-10-02, using PyCharm 2020.1.4, I found that this issue also occurs if the DataFrame contains a column containing a tuple.","Q_Score":57,"Tags":"python,pandas,pycharm","A_Id":64172890,"CreationDate":"2016-08-15T14:07:00.000","Title":"Dataframe not showing in Pycharm","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there a data class or type in Python that matches these criteria?\nI am trying to build an object that looks something like this:\n\nExperimentData\n\nID 1\n\nsample_info_1:  character string\nsample_info_2:  character string\nDataframe_1: pandas data frame\nDataframe_2: pandas data frame\n\nID 2\n\n(etc.)\n\n\n\nRight now, I am using a dict to hold the object ('ExperimentData'), which containsnamedtuple's for each ID. Each of the namedtuple's has a named field for the corresponding data attached to the sample. This allows me to keep all the ID's indexed, and have all of the fields under each ID indexed as well. \nHowever, I need to update and\/or replace the entries under each ID during downstream analysis. Since a tuple is immutable, this does not seem to be possible. \nIs there a better implementation of this?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":68,"Q_Id":38960221,"Users Score":1,"Answer":"You could use a dict of dicts instead of a dict of namedtuples.  Dicts are mutable, so you'll be able to modify the inner dicts.\nGiven what you said in the comments about the structures of each DataFrame-1 and -2 being comparable, you could also group all of each into one big DataFrame, by adding a column to each DataFrame containing the value of sample_info_1 repeated across all rows, and likewise for sample_info_2.  Then you could concat all the DataFrame-1s into a big one, and likewise for the DataFrame-2s, getting all your data into two DataFrames.  (Depending on the structure of those DataFrames, you could even join them into one.)","Q_Score":1,"Tags":"python,pandas,tuples","A_Id":38966174,"CreationDate":"2016-08-15T17:49:00.000","Title":"Mutable indexed heterogeneous data structure?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to train a Keras model on AWS GPU. \nHow would you load images (training data) in S3 for a deep learning model with EC2 instance (GPU)?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":764,"Q_Id":38964041,"Users Score":0,"Answer":"you can do it using Jupyter notebook otherwise use:\nDuck for MAC, Putty for windows.\nI hope it helps","Q_Score":2,"Tags":"python,amazon-web-services,amazon-s3,amazon-ec2,keras","A_Id":45422652,"CreationDate":"2016-08-15T22:35:00.000","Title":"How to load images in S3 for a deep learning model with EC2 instance (GPU)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In a Keras model with the Functional API I need to call fit_generator to train on augmented images data using an ImageDataGenerator.\nThe problem is my model has two outputs: the mask I'm trying to predict and a binary value.\nI obviously only want to augment the input and the mask output and not the binary value.\nHow can I achieve this?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":21904,"Q_Id":38972380,"Users Score":0,"Answer":"The best way to achieve this seems to be to create a new generator class expanding the one provided by Keras that parses the data augmenting only the images and yielding all the outputs.","Q_Score":22,"Tags":"python,deep-learning,keras","A_Id":39922584,"CreationDate":"2016-08-16T10:22:00.000","Title":"Keras: How to use fit_generator with multiple outputs of different type","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Background: I have a sequence of images. In each image, I map a single pixel to a number. Then I want to create a pandas dataframe where each pixel is in its own column and images are rows. The reason I want to do that is so that I can use things like forward fill.\nChallenge: I have transformed each image into a one dimensional array of numbers, each of which is about 2 million entries and I have thousands of images. Simply doing pd.DataFrame(array) is very slow (testing it on a smaller number of images). Is there a faster solution for this? Other ideas how to do this efficiently are also welcome, but using non-core different libraries may be a challenge (corporate environment).","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":310,"Q_Id":38976431,"Users Score":0,"Answer":"Out of curiosity, is there a reason you want to use Pandas for this?  Image analysis is typically handled in matrices making NumPy a clear favorite.  If I'm not mistaken, both sk-learn and PIL\/IMAGE use NumPy arrays to do their analysis and operations.\nAnother option:  avoid the in-memory step!  Do you need to access all 1K+ images at the same time?  If not, and you're operating on each one individually, you can iterate over the files and perform your operations there.  For an even more efficient step, break your files into lists of 200 or so images, then use Python's MultiProcessing capabilities to analyze in parallel.\nJIC, do you have PIL or IMAGE installed, or sk-learn?  Those packages have some nice image analysis algorithms already packaged in which may save you some time in not having to re-invent the wheel.","Q_Score":1,"Tags":"python,pandas,numpy,large-data","A_Id":38976616,"CreationDate":"2016-08-16T13:35:00.000","Title":"Initializing a very large pandas dataframe","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When I built my random forest model using scikit learn in python, I set a condition (where clause in sql query) so that the training data only contain values whose value is greater than 0.\nI am curious to know how random forest handles test data whose value is less than 0, which the random forest model has never seen before in the training data.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":709,"Q_Id":38980544,"Users Score":0,"Answer":"They will be treated in the same manner as the minimal value already encountered in the training set. RF is just a bunch of voting decision trees, and (basic) DTs can only form decisions in form of \"if feature X is > then T go left, otherwise go right\". Consequently, if you fit it to data which, for a given feature, has only values in [0, inf], it will either not use this feature at all or use it in a form given above (as decision of form \"if X is > than T\", where T has to be from (0, inf) to make any sense for the training data). Consequently if you simply take your new data and change negative values to \"0\", the result will be identical.","Q_Score":0,"Tags":"python,machine-learning,scikit-learn,random-forest","A_Id":38980686,"CreationDate":"2016-08-16T16:57:00.000","Title":"What does Random Forest do with unseen data?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am scraping approximately 200,000 websites, looking for certain types of media posted on the websites of small businesses. I have a pickled linearSVC, which I've trained to predict the probability that a link found on a web page contains media of the type that I'm looking for, and it performs rather well (overall accuracy around 95%). However, I would like the scraper to periodically update the classifier with new data as it scrapes.\nSo my question is, if I have loaded a pickled sklearn LinearSVC, is there a way to add in new training data without re-training the whole model? Or do I have to load all of the previous training data, add the new data, and train an entirely new model?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":902,"Q_Id":38984069,"Users Score":3,"Answer":"You cannot add data to SVM and achieve the same result as if you would add it to the original training set. You can either retrain with extended training set starting with the previous solution (should be faster) or train on new data only and completely diverge from the previous solution.\nThere are only few models that can do what you would like to achieve here - like for example Ridge Regression or Linear Discriminant Analysis (and their Kernelized - Kernel Ridge Regression or Kernel Fischer Discriminant, or \"extreme\"-counterparts - ELM or EEM), which have a property of being able to add new training data \"on the fly\".","Q_Score":2,"Tags":"python,machine-learning,scikit-learn","A_Id":38984364,"CreationDate":"2016-08-16T20:39:00.000","Title":"add training data to existing LinearSVC","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have protein 3D creo-EM scan, such that it contains a chain which bends and twists around itself - and has in 3-dimension space 2 chain endings (like continuous rope). I need to detect (x,y,z) location within given cube space of two or possibly multiplier of 2 endings. Cube space of scan is presented by densities in each voxel (in range 0 till 1) provided by scanning EM microscope, such that \"existing matter\" gives values closer to 1, and \"no matter\" gives density values closer to 0. I need a method to detect protein \"rope\" edges (possible \"rope ending\" definition is lack of continuation in certain tangled direction. Intuitively, I think there could be at least 2 methods: 1) Certain method in graph theory (I can't specify precisely - if you know one - please name or describe it. 2) Derivatives from analytic algebra - but again I can't specify specific attitude - so please name or explain one. Please specify computation complexity of suggested method. My project is implemented in Python. Please help. Thanks in advance.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":97,"Q_Id":38987464,"Users Score":1,"Answer":"One approach would be to choose a threshold density, convert all voxels below this threshold to 0 and all above it to 1, and then look for the pair of 1-voxels whose shortest path is longest among all pairs of 1-voxels.  These two voxels should be near the ends of the longest \"rope\", regardless of the exact shape that rope takes.\nYou can define a graph where there is a vertex for each 1-voxel and an edge between each 1-voxel and its 6 (or possibly 14) neighbours.  You can then compute the lengths of the shortest paths between some given vertex u and every other vertex in O(|V|) time and space using breadth first search (we don't need Dijkstra or Floyd-Warshall here since every edge has weight 1).  Repeating this for each possible start vertex u gives an O(|V|^2)-time algorithm.  As you do this, keep track of the furthest pair so far.\nIf your voxel space has w*h*d cells, there could be w*h*d vertices in the graph (if every single voxel is a 1-voxel), so this could take O(w^2*h^2*d^2) time in the worst case, which is probably quite a lot.  Luckily there are many ways to speed this up if you can afford a slightly imprecise answer:\n\nOnly compute shortest paths from start vertices that are at the boundary -- i.e. those vertices that have fewer than 6 (or 14) neighbours.  (I believe this won't sacrifice an optimal solution.)\nAlternatively, first \"skeletonise\" the graph by repeatedly getting rid of all such boundary vertices whose removal will not disconnect the graph.\nA good order for choosing starting vertices is to first choose any vertex, and then always choose a vertex that was found to be at maximum possible distance from the last one (and which has not yet been tried, of course).  This should get you a very good approximation to the longest shortest path after just 3 iterations: the furthest vertex from the start vertex will be near one of the two rope ends, and the furthest vertex from that vertex will be near the other end!\n\nNote: If there is no full-voxel gap between distant points on the rope that are near each other due to bending, then the shortest paths will \"short-circuit\" through these false connections and possibly reduce the accuracy.  You might be able to ameliorate this by increasing the threshold.  OTOH, if the threshold is too high then the rope can become disconnected.  I expect you want to choose the highest threshold that results in only 1 connected component.","Q_Score":2,"Tags":"python,algorithm,graph,analytics,d3dimage","A_Id":38987964,"CreationDate":"2016-08-17T03:05:00.000","Title":"How to detect ending location (x,y,z) of certain sequence in 3D domain","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I know how to install external modules using the pip command but for Scikit-learn I need to install NumPy and Matplotlib as well.\nHow can I install these modules using the pip command?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":2435,"Q_Id":38989896,"Users Score":0,"Answer":"Old post, but right answer is,\n'sudo pip install -U numpy matplotlib --upgrade' for python2 or 'sudo pip3 install -U numpy matplotlib --upgrade' for python3","Q_Score":1,"Tags":"python,windows,scikit-learn","A_Id":46249521,"CreationDate":"2016-08-17T06:49:00.000","Title":"How to install scikit-learn","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I know how to install external modules using the pip command but for Scikit-learn I need to install NumPy and Matplotlib as well.\nHow can I install these modules using the pip command?","AnswerCount":2,"Available Count":2,"Score":-0.0996679946,"is_accepted":false,"ViewCount":2435,"Q_Id":38989896,"Users Score":-1,"Answer":"Using Python 3.4, I run the following from the command line:\nc:\\python34\\python.exe -m pip install package_name\nSo you would substitute \"numpy\" and \"matplotlib\" for 'package_name'","Q_Score":1,"Tags":"python,windows,scikit-learn","A_Id":38990089,"CreationDate":"2016-08-17T06:49:00.000","Title":"How to install scikit-learn","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have trained a logistic regression model in sklearn and saved the model to .pkl files. Is there a method of using this pkl file from within spark?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":374,"Q_Id":38991799,"Users Score":0,"Answer":"The fact that you are using spark shouldn't hold you from using external python libraries.\nYou can import sklearn library in your spark-python code, and use sklearn logistic regression model with the saved pkl file.","Q_Score":0,"Tags":"python,apache-spark,scikit-learn,pyspark","A_Id":38994681,"CreationDate":"2016-08-17T08:36:00.000","Title":"Scikit-learn and pyspark integration","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using Keras with Theano to train a basic logistic regression model.\nSay I've got a training set of 1 million entries, it's too large for my system to use the standard model.fit() without blowing away memory.\n\nI decide to use a python generator function and fit my model using model.fit_generator().  \nMy generator function returns batch sized chunks of the 1M training examples (they come from a DB table, so I only pull enough records at a time to satisfy each batch request, keeping memory usage in check).  \nIt's an endlessly looping generator, once it reaches the end of the 1 million, it loops and continues over the set\n\nThere is a mandatory argument in fit_generator() to specify samples_per_epoch.  The documentation indicates\n\nsamples_per_epoch: integer, number of samples to process before going to the next epoch.\n\nI'm assuming the fit_generator() doesn't reset the generator each time an epoch runs, hence the need for a infinitely running generator.\nI typically set the samples_per_epoch to be the size of the training set the generator is looping over.\nHowever, if samples_per_epoch this is smaller than the size of the training set the generator is working from and the nb_epoch > 1:\n\nWill you get odd\/adverse\/unexpected training resulting as it seems the epochs will have differing sets training examples to fit to?\nIf so, do you 'fastforward' you generator somehow?","AnswerCount":1,"Available Count":1,"Score":0.537049567,"is_accepted":false,"ViewCount":1961,"Q_Id":39001104,"Users Score":3,"Answer":"I'm dealing some something similar right now. I want to make my epochs shorter so I can record more information about the loss or adjust my learning rate more often. \nWithout diving into the code, I think the fact that .fit_generator works with the randomly augmented\/shuffled data produced by the keras builtin ImageDataGenerator supports your suspicion that it doesn't reset the generator per epoch. So I believe you should be fine, as long as the model is exposed to your whole training set it shouldn't matter if some of it is trained in a separate epoch.\nIf you're still worried you could try writing a generator that randomly samples your training set.","Q_Score":4,"Tags":"python,machine-learning,deep-learning,theano,keras","A_Id":39093328,"CreationDate":"2016-08-17T15:48:00.000","Title":"In Keras, If samples_per_epoch is less than the 'end' of the generator when it (loops back on itself) will this negatively affect result?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How can I transform my resulting dask.DataFrame into pandas.DataFrame (let's say I am done with heavy lifting, and just want to apply sklearn to my aggregate result)?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":32382,"Q_Id":39008391,"Users Score":0,"Answer":"MRocklin's answer is correct and this answer gives more details on when it's appropriate to convert from a Dask DataFrame to and Pandas DataFrame (and how to predict when it'll cause problems).\nEach partition in a Dask DataFrame is a Pandas DataFrame.  Running df.compute() will coalesce all the underlying partitions in the Dask DataFrame into a single Pandas DataFrame.  That'll cause problems if the size of the Pandas DataFrame is bigger than the RAM on your machine.\nIf df has 30 GB of data and your computer has 16 GB of RAM, then df.compute() will blow up with a memory error.  If df only has 1 GB of data, then you'll be fine.\nYou can run df.memory_usage(deep=True).sum() to compute the amount of memory that your DataFrame is using.  This'll let you know if your DataFrame is sufficiently small to be coalesced into a single Pandas DataFrame.\nRepartioning changes the number of underlying partitions in a Dask DataFrame.  df.repartition(1).partitions[0] is conceptually similar to df.compute().\nConverting to a Pandas DataFrame is especially possible after performing a big filtering operation.  If you filter a 100 billion row dataset down to 10 thousand rows, then you can probably just switch to the Pandas API.","Q_Score":47,"Tags":"python,pandas,dask","A_Id":69125753,"CreationDate":"2016-08-18T00:56:00.000","Title":"How to transform Dask.DataFrame to pd.DataFrame?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have trained a regression network with caffe. I use \"EuclideanLoss\" layer  in both the train and test phase. I have plotted these and the results look promising. \nNow I want to deploy the model and use it. I know that if SoftmaxLoss is used, the final layer must be Softmax in the deploy file. What should this be in the case of Euclidean loss?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":594,"Q_Id":39017998,"Users Score":2,"Answer":"For deploy you only need to discard the loss layer, in your case the \"EuclideanLoss\" layer. The output of your net is the \"bottom\" you fed the loss layer.\nFor \"SoftmaxWithLoss\" layer (and \"SigmoidCrossEntropy\") you need to replace the loss layer, since the loss layer includes an extra layer inside it (for computational reasons).","Q_Score":2,"Tags":"python,neural-network,deep-learning,caffe,conv-neural-network","A_Id":39018076,"CreationDate":"2016-08-18T12:15:00.000","Title":"Deploy caffe regression model","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a Hadoop Cluster running on Centos 7. I am running a program (sitting on HDFS) to extract tweets and I need to import tweepy for that. I did pip install tweepy as root on all the nodes of the cluster but i still get an import error when I run the program. \nError says: ImportError: No module named tweepy\n\nI am sure Tweepy is installed because, pip freeze | grep \"tweepy\" returns tweepy==3.5.0. \nI created another file x.py with just one line import tweepy in the \/tmp folder and that runs without an error. Error occurs only on HDFS.\nAlso, my default python is Python 2.7.12 which I installed using Anaconda. Can someone help me with this issue? The same code is running without any such errors on another cluster running on Centos 6.6. Is it an OS issue? Or do I have to look into the Cluster?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":192,"Q_Id":39045825,"Users Score":0,"Answer":"It looks like you're using Anaconda's Python to run your script, but you installed tweepy into CentOS's system installation of Python using pip. Either use conda to install tweepy, or use Anaconda's pip executable to install tweepy onto your Hadoop cluster.","Q_Score":0,"Tags":"python-2.7,hadoop,hdfs,tweepy,centos7","A_Id":39046078,"CreationDate":"2016-08-19T18:42:00.000","Title":"Tweepy import Error on HDFS running on Centos 7","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"my program's purpose is to take 2 images and decide how similar they are.\nim not talking here about identical, but similarity. for example, if i take 2 screenshots of 2 different pages of the same website, their theme colors would probably be very similar and therefor i want the program to declare that they are similar.\nmy problem starts when both images have a white background that pretty much takes over the histogram calculation (over then 30% of the image is white and the rest is distributed).\nin that case,  the cv2.compareHist (using correlation method, which works for the other cases) gives very bad results, that is, the grade is very high even though they look very different.\ni have thought about taking the white (255) off the histogram before comparing, but that requires me to calculate the histogram with 256 bins, which is not good when i want to check similarity (i thought that using 32 or 64 bins would be best)\nunfortunately i cant add images im working with due to legal reasons\nif anyone can help with an idea, or code that solves it i would be very grateful\nthank you very much","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":161,"Q_Id":39116877,"Users Score":0,"Answer":"You can remove the white color,  rebin the histogra and then compare:\n\nCompute a histrogram with 256 bins.\nRemove the white bin (or make it zero).\nRegroup the bins to have 64 bins by adding the values of 4 consecutive bins. \nPerform the compareHist().\n\nThis would work for any \"predominant color\". To generalize, you can do the following:\nCompare full histrograms. If they are different, then finish.\nIf they are similar, look for the predominant color (with a 256-bin histogram), and perform the procedure described above, to remove the predominant color from the comparisson.","Q_Score":0,"Tags":"python,python-2.7,opencv,image-processing,histogram","A_Id":39119151,"CreationDate":"2016-08-24T07:22:00.000","Title":"python2.7 histogram comparison - white background anomaly","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using the LSTM model that comes by default in tensorflow. I would like to check or to know how to save or show the values of the forget gate in each step, has anyone done this before or at least something similar to this?\nTill now I have tried with tf.print but many values appear (even more than the ones I was expecting) I would try plotting something with tensorboard but I think those gates are just variables and not extra layers that I can print (also cause they are inside the TF script)\nAny help will be well received","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":1695,"Q_Id":39168025,"Users Score":2,"Answer":"If you are using tf.rnn_cell.BasicLSTMCell , the variable you are looking for will have the following suffix in its name : <parent_variable_scope>\/BasicLSTMCell\/Linear\/Matrix . This is a concatenated matrix for all the four gates. Its first dimension matches the sum of the second dimensions of the input matrix and the state matrix (or output of the cell to be exact). The second dimension is 4 times the number of cell size.\nThe other complementary variable is <parent_variable_scope>\/BasicLSTMCell\/Linear\/Bias that is a vector of the same size as the second dimension of the abovementioned tensor (for obvious reasons).\nYou can retrieve the parameters for the four gates by using tf.split() along dimension 1. The split matrices would be in the order [input], [new input], [forget], [output]. I am referring to the code here form rnn_cell.py.\nKeep in mind that the variable represents the parameters of the Cell and not the output of the respective gates. But with the above info, I am sure you can get that too, if you so desire.\nEdit:\nAdded more specific information about the actual tensors Matrix and Bias","Q_Score":5,"Tags":"python,neural-network,tensorflow,lstm","A_Id":39177157,"CreationDate":"2016-08-26T13:54:00.000","Title":"Tensorflow: show or save forget gate values in LSTM","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a number of data sets, each containing x, y, and y_error values, and I'm simply trying to calculate the average value of y at each x across these data sets. However the data sets are not quite the same length. I thought the best way to get them to an equal length would be to use scipy's interoplate.interp1d for each data set. However, I still need to be able to calculate the error on each of these averaged values, and I'm quite lost on how to accomplish that after doing an interpolation.\nI'm pretty new to Python and coding in general, so I appreciate your help!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":403,"Q_Id":39172559,"Users Score":0,"Answer":"As long as you can assume that your errors represent one-sigma intervals of normal distributions, you can always generate synthetic datasets, resample and interpolate those, and compute the 1-sigma errors of the results.\nOr just interpolate values+err and values-err, if all you need is a quick and dirty rough estimate.","Q_Score":0,"Tags":"python,scipy,interpolation","A_Id":39174418,"CreationDate":"2016-08-26T18:23:00.000","Title":"Python: How to interpolate errors using scipy interpolate.interp1d","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a function in Python that outputs a dict. I run this function into MATLAB and save the output to a parameter (say tmp) which is a dict of nested other dicts itself. Now I want to convert this file into a useful format such as structure.\nTo elaborate: tmp is a dict. data = struct(tmp) is a structure but the fields are other dicts. I tried to go through every field and convert it individually, but this is not very efficient. \nAnother option: I have the output saved in a JSON file and can load it into MATLAB. However, it is still not useable.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1329,"Q_Id":39217618,"Users Score":0,"Answer":"So python -> MATLAB is a bit tricky with dictionaries\/structs because the type of object that MATLAB is expecting is a dictionary object where each key is a single variable you want from python as a simple data type (array,int, etc).  It doesn't like having nested dictionaries.  \nI recommend \n1: Store each dictionary separately instead of as part of a higher level object.\nor 2: even though it is not very nice converting the structs to individual variables.\nMATLAB should be able to handle simple non-nested structures like that.","Q_Score":0,"Tags":"python,matlab,dictionary,struct","A_Id":39229405,"CreationDate":"2016-08-30T01:45:00.000","Title":"Convert Python dict files into MATLAB struct","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using beam to process time series data over overlapping windows. At the end of my pipeline I am writing each element to a file. Each element represents a csv row and one of the fields is a timestamp of the associated window. I would like to write the elements in order of that timestamp. Is there a way to do this using the python beam library?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":998,"Q_Id":39235274,"Users Score":1,"Answer":"While this isn't part of the base distribution, this is something you could implement by processing these elements and sorting them as part of a global window before writing out to a file, with the following caveats:\n\nThe entire contents of the window would need to fit in memory, or you would need to chunk up the file into smaller global windows.\nIf you are doing the second option, you'd need to have a strategy for writing the smaller windows in order to the file.","Q_Score":4,"Tags":"python,google-cloud-dataflow,apache-beam","A_Id":39255667,"CreationDate":"2016-08-30T19:05:00.000","Title":"In python apache beam, is it possible to write elements in a specific order?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there a way to index a 3 dimensional array using some form of time index (datetime etc.) on the 3rd dimension?\nMy problem is that I am doing time series analysis on several thousand radar images and I need to get, for example, monthly averages. However if i simply average over every 31 arrays in the 3rd dimension it becomes inacurate due to shorter months and missing data etc.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":949,"Q_Id":39249639,"Users Score":1,"Answer":"Posting the psuedo solution I used:\nThe problem here is the lack of date-time indexing for 3d array data (i.e. satillite, radar). Whilst there is time series functions in pandas there is not for arrays (as far as i'm aware). \nThis solution was possible because the data files I use have date-time in the name e.g. '200401010000' is 'yyyymmddhhMM'. \n\nconstruct a 3d array with all the data (missing times in places).\nUsing the list of data file (os.listdir), create a list of timestamps (length matches 3d array length)\ncreate dfa using timestamps (2) as dfa index and create a column 'inx' of running integers (range(0, len(array) = integers = index of 3d array)\ncreate a datetime index using data start and end times and known frequency of data (no missing datetimes). Create a new dfb using this as the index. \ndfb in (4) left merge with dfa in (3). Aka dfa now has accurate datetime index and 'inx' column containg the 3d array index postition and with nan's at missing data. \n\nUsing this you can then resmaple the df, to for example 1 day, taking the Min and Max of 'inx'. This gives you the start - end position for your array functions. \nYou can also insert arrays of nans at mising datetimes (i.e. 'inx' min max = nan) so that your 3d array matches the length of actual datetimes. \nComment if you have Q's or if you know of a better solution \/ package to this problem.","Q_Score":1,"Tags":"python,arrays,datetime,numpy,multidimensional-array","A_Id":40301138,"CreationDate":"2016-08-31T12:26:00.000","Title":"python 3D numpy array time index","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When I create a notebook with plotly plots, save and reopen the notebook, the plots fail to render upon reopening - there are just blank blocks where the plots should be. Is this expected behavior? If not, is there a known fix?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1438,"Q_Id":39256913,"Users Score":0,"Answer":"I also meet this annoying issue. I find no way to reveal them on the notebook, but I find a compromise way to display them on an html page that is File -> Print Preview.","Q_Score":8,"Tags":"python,ipython,jupyter-notebook,plotly","A_Id":56981228,"CreationDate":"2016-08-31T18:47:00.000","Title":"Plotly + iPython Notebook - Plots Disappear on Reopen","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I currently have several python pandas scripts that I keep separate because of 1) readability, and 2) sometimes I am interested in the output of these partial individual scripts. \nHowever, generally, the CSV file output of one of these scripts is the CSV input of the next and in each I have to re-read datetimes which is inconvenient.\nWhat best practices do you suggest for this task? Is it better to just combine all the scripts into one for when I'm interested in running the whole program or is there a more Python\/Pandas way to deal with this?\nthank you and I appreciate all your comments,","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":66,"Q_Id":39273012,"Users Score":1,"Answer":"Instead of writing a CSV output which you have to re-parse, you can write and read the pandas.DataFrame in efficient binary format with the methods pandas.DataFrame.to_pickle() and pandas.read_pickle(), respectively.","Q_Score":0,"Tags":"python,pandas","A_Id":39273086,"CreationDate":"2016-09-01T13:34:00.000","Title":"Suggestions to handle multiple python pandas scripts","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to remove spaces, apostrophes, and double quote in each column data using this for loop\nfor c in data.columns:\n    data[c] = data[c].str.strip().replace(',', '').replace('\\'', '').replace('\\\"', '').strip()\nbut I keep getting this error:\nAttributeError: 'Series' object has no attribute 'strip'\ndata is the data frame and was obtained from an excel file\nxl = pd.ExcelFile('test.xlsx');\ndata = xl.parse(sheetname='Sheet1')\nAm I missing something?  I added the str but that didn't help.  Is there a better way to do this.\nI don't want to use the column labels, like so data['column label'], because the text can be different.  I would like to iterate each column and remove the characters mentioned above.\nincoming data:\nid        city      country\n1       Ontario       Canada\n2     Calgary '        Canada'\n3   'Vancouver        Canada\ndesired output:\nid  city      country\n1   Ontario     Canada\n2   Calgary     Canada\n3   Vancouver   Canada","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":5363,"Q_Id":39280278,"Users Score":0,"Answer":"data[c] does not return a value, it returns a series (a whole column of data).  \nYou can apply the strip operation to an entire column df.apply.  You can apply the strip function this way.","Q_Score":0,"Tags":"python,pandas","A_Id":39280341,"CreationDate":"2016-09-01T20:21:00.000","Title":"Pandas - how to remove spaces in each column in a dataframe?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to solve a matrix equation such as A.B = C. The A is the unknown matrix and i must find it. \nI have B(n*1) and C(n*1), so A must be n*n. \nI used the BT* A.T =C.T method (numpy.linalg.solve(B.T, C.T)).\nBut it produces an error:\n\nLinAlgError: Last 2 dimensions of the array must be square.\n\nSo the problem is that B isn't square.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":339,"Q_Id":39281149,"Users Score":0,"Answer":"If you're solving for the matrix, there is an infinite number of solutions (assuming that B is nonzero). Here's one of the possible solutions: \nChoose an nonzero element of B, Bi. Now construct a matrix A such that the ith column is C \/ Bi, and the other columns are zero.\nIt should be easy to verify that multiplying this matrix by B gives C.","Q_Score":0,"Tags":"python,matrix,equation-solving","A_Id":39290812,"CreationDate":"2016-09-01T21:30:00.000","Title":"Solving matrix equation A B = C. with B(n* 1) and C(n *1)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to use scikit-learn's neural network module in iPython... running Python 3.5 on a Win10, 64-bit machine.\nWhen I try to import from sknn.mlp import Classifier, Layer , I get back the following AttributeError: module 'theano' has no attribute 'gof' ...\nThe command line highlighted for the error is class DisconnectedType(theano.gof.type.Type), within theano\\gradient.py\nTheano version is 0.8.2, everything installed via pip.\nAny lights on what may be causing this and how to fix it?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":331,"Q_Id":39332901,"Users Score":0,"Answer":"Apparently it was caused by some issue with Visual Studio. The import worked when I reinstalled VS and restarted the computer.\nThanks @super_cr7 for the prompt reply!","Q_Score":0,"Tags":"python,installation,attributes,scikit-learn,theano","A_Id":39334690,"CreationDate":"2016-09-05T14:36:00.000","Title":"Failure to import sknn.mlp \/ Theano","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm creating a list of Sparsetensors in Tensorflow. I want to access them in later sessions of my program. I've read online that you can store Python lists as json files but how do I save a list of Sparsetensors to a json file and then use that later on?\nThanks in advance","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":70,"Q_Id":39382725,"Users Score":1,"Answer":"A Tensor in TensorFlow is a node in the graph which, when run, will produce a tensor. So you can't save the SparseTensor directly because it's not a value (you can serialize the graph). If you do evaluate the sparsetensor, you get a SparseTensorValue object back which can be serialized as it's just a tuple.","Q_Score":0,"Tags":"python,json,tensorflow","A_Id":39395872,"CreationDate":"2016-09-08T04:42:00.000","Title":"Saving Python list containing Tensorflow Sparsetensors to file for later access?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"With pyspark dataframe, how do you do the equivalent of Pandas df['col'].unique().\nI want to list out all the unique values in a pyspark dataframe column.\nNot the SQL type way (registertemplate then SQL query for distinct values).\nAlso I don't need groupby then countDistinct, instead I want to check distinct VALUES in that column.","AnswerCount":10,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":344799,"Q_Id":39383557,"Users Score":21,"Answer":"You can use df.dropDuplicates(['col1','col2']) to get only distinct rows based on colX in the array.","Q_Score":149,"Tags":"python,apache-spark,pyspark,apache-spark-sql","A_Id":44253561,"CreationDate":"2016-09-08T06:03:00.000","Title":"Show distinct column values in pyspark dataframe","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"With pyspark dataframe, how do you do the equivalent of Pandas df['col'].unique().\nI want to list out all the unique values in a pyspark dataframe column.\nNot the SQL type way (registertemplate then SQL query for distinct values).\nAlso I don't need groupby then countDistinct, instead I want to check distinct VALUES in that column.","AnswerCount":10,"Available Count":2,"Score":0.0199973338,"is_accepted":false,"ViewCount":344799,"Q_Id":39383557,"Users Score":1,"Answer":"If you want to select ALL(columns) data as distinct frrom a DataFrame (df), then\ndf.select('*').distinct().show(10,truncate=False)","Q_Score":149,"Tags":"python,apache-spark,pyspark,apache-spark-sql","A_Id":60578769,"CreationDate":"2016-09-08T06:03:00.000","Title":"Show distinct column values in pyspark dataframe","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I was reading an xlm file using pandas.read_html and works almost perfect, the problem is that the file has commas as decimal separators instead of dots (the default in read_html). \nI could easily replace the commas by dots in one file, but i have almost 200 files with that configuration. \nwith pandas.read_csv you can define the decimal separator, but i don't know why in pandas.read_html you can only define the thousand separator. \nany guidance in this matter?, there is another way to automate the comma\/dot replacement before it is open by pandas? \nthanks in advance!","AnswerCount":4,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":4626,"Q_Id":39412829,"Users Score":20,"Answer":"This did not start working for me until I used both  decimal=',' and thousands='.'\nPandas version: 0.23.4\nSo try to use both decimal and thousands:\ni.e.:\npd.read_html(io=\"http:\/\/example.com\", decimal=',', thousands='.')\nBefore I would only use decimal=',' and the number columns would be saved as type str with the numbers just omitting the comma.(weird behaviour) For example 0,7 would be \"07\" and \"1,9\" would be \"19\"\nIt is still being saved in the dataframe as type str but at least I don't have to manually put in the dots. The numbers are correctly displayed; 0,7 -> \"0.7\"","Q_Score":14,"Tags":"python,pandas,decimal,xlm","A_Id":52673944,"CreationDate":"2016-09-09T13:35:00.000","Title":"pandas.read_html not support decimal comma","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I have arrays t_array and dMdt_array of x and y points. Let's call M = trapz(dMdt_array, t_array). I want to find at what value of t the integral of dM\/dt vs t is equal to a certain value -- say 0.05*M. In python, is there a nice way to do this?\nI was thinking something like F = interp1d(t_array, dMdt_array). Then some kind of root find for where the integral of F is equal to 0.05*M. Can I do this in python?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":113,"Q_Id":39433108,"Users Score":1,"Answer":"One simple way is to use the CubicSpline class instead. Then it's CubicSpline(x, y).antiderivative().solve(0.05*M) or thereabouts.","Q_Score":0,"Tags":"python,scipy","A_Id":39433909,"CreationDate":"2016-09-11T05:16:00.000","Title":"Find when integral of an interpolated function is equal to a specific value (python)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to predict an output (regression) where multiple groups have spacial (x,y) coordinates. I've been using scikit-learn's neural network packages (MLPClassifier and MLPRegressor), which I know can be trained with spacial data by inputting a 1-D array per observation (ex. the MNIST dataset). \nI'm trying to figure out the best way to tell the model that group 1 has this set of spacial coordinates AND group 2 has a different set of spacial coordinates, and that combination yielded a result. Would it make more sense to input a single array where a group 1 location is represented by 1 and group 2 location is represented by -1? Or to create an array for group 1 and group to and append them? Still pretty new to neural nets - hopefully this question makes sense.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":244,"Q_Id":39442327,"Users Score":0,"Answer":"If I got this right, you are basically trying to implement classification variables into your input, and this is basically done by adding an input variable for each possible class (in your case \"group 1\" and \"group 2\") that holds binary values (1 if the sample belongs to the group, 0 if it doesn't). Wheather or not you would want to retain the actual coordinates depends on wheather you would like your network to process actual spatial data, or simply base it's output on the group that the sample belongs to. As I don't have much experience with the particular module you're using, I am unable to provide actual code, but I hope this helps.","Q_Score":0,"Tags":"python,machine-learning,scikit-learn,neural-network,regression","A_Id":39446832,"CreationDate":"2016-09-12T01:34:00.000","Title":"Training a neural network with two groups of spacial coordinates per observation?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there a easy way to implement the segmentation of a binary image in python?\nMy 2d-\"images\" are numpy arrays. The used values are 1.0 and 0.0. I would require a list of all objects with the value 1.0. Every black pixel is a pixel of an object. An object may contain many touching pixels with the value 1.0.\nI can use numpy and also scipy.\nI already tried to iterate over all pixels and create sets of pixels and fill the new pixel in old sets (or create a new set). Unfortunately the implementation was poor, extremely buggy and also very slow.\nHopyfully somthing like this already exists or there is an easy way to do this?\nThank you very much","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1440,"Q_Id":39446262,"Users Score":2,"Answer":"To my mind, this is exactly what can be done using scipy.ndimage.measurements.label  and scipy.ndimage.measurements.find_objects\nYou have to specify what \"touching\" means. If it means edge-sharing, then the default structure of ndimage.measurements.label is the one you need so you just need to pass your array. If touching means also corner sharing, you will find the right structure in the docstring.\nfind_objects can then yield a list of slices for the objects.","Q_Score":1,"Tags":"python-2.7,image-segmentation,binary-image","A_Id":39447667,"CreationDate":"2016-09-12T08:34:00.000","Title":"Python: Binary image segmentation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"So I've heard that the YUV and YPbPr colour system is essentially the same.\nWhen I convert BGR to YUV, presumably to the Color_BGR2YUV opencv command, what are the ranges for the values that return for Y, U and V? Because on Colorizer.org, the values seem to be decimals, but I haven't seen opencv spit out any decimal places before.\nSo basically what I'm asking (in a very general, but hopefully easily answerable way)\nWhat does YUV look like in an array? (ranges and such comparable to the Colorizers.org)","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":220,"Q_Id":39449728,"Users Score":0,"Answer":"You should take care of how YUV is arranged in memory. There are various formats involved. The most common being YUV NV12 and NV21. In general, data is stored as unsigned bytes. While the range of Y is from 0~255, it is -128~127 for U and V. As both U and V approach 0, you have less saturation and approach grayscale. In the case of both NV12 and NV21, it is cols * rows of Y followed by 0.5 * cols * rows of U and V. Both NV12 and NV21 is a semi-planar format, so U and V are interleaved. The former starts with U and the latter with V. In the case of a planar format, there is no interleaving involved.","Q_Score":0,"Tags":"python-2.7,opencv,yuv","A_Id":39451022,"CreationDate":"2016-09-12T11:53:00.000","Title":"Python 2.7 opencv Yuv\/ YPbPr","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to use scikit-learn LassoCV\/RidgeCV while applying a 'StandardScaler' on each fold training set. I do not want to apply the scaler before the cross-validation to avoid leakage but I cannot figure out how I am supposed to do that with LassoCV\/RidgeCV. \nIs there a way to do this ? Or should I create a pipeline with Lasso\/Ridge and 'manually' search for the hyperparameters (using GridSearchCV for instance) ?\nMany thanks.","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":726,"Q_Id":39466671,"Users Score":2,"Answer":"I got the answer through the scikit-learn mailing list so here it is: \n'There is no way to use the \"efficient\" EstimatorCV objects with pipelines.\nThis is an API bug and there's an open issue and maybe even a PR for that.'\nMany thanks to Andreas Mueller for the answer.","Q_Score":1,"Tags":"python,machine-learning,scikit-learn","A_Id":39481787,"CreationDate":"2016-09-13T09:34:00.000","Title":"Use of Scaler with LassoCV, RidgeCV","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am getting an architecture error while importing any package, i understand my Python might not be compatible, can't understand it.\nCurrent Python Version - 2.7.10\n\n`MyMachine:desktop *********$ python pythonmath.py\n  Traceback (most recent call last):\n    File \"pythonmath.py\", line 1, in \n      import math\n  ImportError: dlopen(\/Users\/*********\/anaconda\/lib\/python2.7\/lib-dynload\/math.so, 2): no suitable image found.  Did find:\n      \/Users\/**********\/anaconda\/lib\/python2.7\/lib-dynload\/math.so: mach-o, but wrong architecture\n  MyMachine:desktop ***********$ python pythonmath.py\n  Traceback (most recent call last):\n    File \"pythonmath.py\", line 1, in \n      import math\n  ImportError: dlopen(\/Users\/anaconda\/lib\/python2.7\/lib-dynload\/math.so, 2): no suitable image found.  Did find:\n      \/Users\/***********\/anaconda\/lib\/python2.7\/lib-dynload\/math.so: mach-o, but wrong architecture","AnswerCount":4,"Available Count":2,"Score":0.049958375,"is_accepted":false,"ViewCount":7799,"Q_Id":39477023,"Users Score":1,"Answer":"you are mixing 32bit and 64bit versions of python.\nprobably you installed 64bit python version on a 32bit computer.\ngo on and uninstall python and reinstall it with the right configuration.","Q_Score":1,"Tags":"python,macos,python-2.7","A_Id":39477667,"CreationDate":"2016-09-13T18:47:00.000","Title":"Error \"mach-o, but wrong architecture\" after installing anaconda on mac","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":1,"Web Development":0},{"Question":"I am getting an architecture error while importing any package, i understand my Python might not be compatible, can't understand it.\nCurrent Python Version - 2.7.10\n\n`MyMachine:desktop *********$ python pythonmath.py\n  Traceback (most recent call last):\n    File \"pythonmath.py\", line 1, in \n      import math\n  ImportError: dlopen(\/Users\/*********\/anaconda\/lib\/python2.7\/lib-dynload\/math.so, 2): no suitable image found.  Did find:\n      \/Users\/**********\/anaconda\/lib\/python2.7\/lib-dynload\/math.so: mach-o, but wrong architecture\n  MyMachine:desktop ***********$ python pythonmath.py\n  Traceback (most recent call last):\n    File \"pythonmath.py\", line 1, in \n      import math\n  ImportError: dlopen(\/Users\/anaconda\/lib\/python2.7\/lib-dynload\/math.so, 2): no suitable image found.  Did find:\n      \/Users\/***********\/anaconda\/lib\/python2.7\/lib-dynload\/math.so: mach-o, but wrong architecture","AnswerCount":4,"Available Count":2,"Score":0.1488850336,"is_accepted":false,"ViewCount":7799,"Q_Id":39477023,"Users Score":3,"Answer":"Below steps resolved this problem for me.\n\nQuit the terminal.\nGo to Finder => Apps\nRight Click on Terminal\nGet Info\nCheck the checkbox Open using Rosetta\n\nNow, open the terminal and try again.\nPS: Rosetta allows Mac with M1 architecture to use apps built for Mac with Intel chip. Most of the times the reason behind most of the architecture problems is this chip compatibility reason only. So, 'Open using Rosetta' for terminal allows us to use Rosetta by default for such applications.","Q_Score":1,"Tags":"python,macos,python-2.7","A_Id":70210511,"CreationDate":"2016-09-13T18:47:00.000","Title":"Error \"mach-o, but wrong architecture\" after installing anaconda on mac","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":1,"Web Development":0},{"Question":"I am stuck with python and matplotlib imshow(). Aim is it to show a twodimensonal color map which represents three dimensions. \nMy x-axis is represented by an array'TG'(93 entries). My y-axis is a set of arrays dependend of my 'TG' To be precise we have 93 different arrays with the length of 340. My z-axis is also a set of arrays depended of my 'TG' equally sized then y (93x340).\nBasically what I have is a set of two-dimensonal measurements which I want to plot in color dependend on a third array. Is there a clever way to do that. I was trying to find out on my own first, but all I found is that most common is the problem with just a z-plane(two-dimensonal plot). So I have two matrices of the order of (93x340) and one array(93). Do you know a helpful advise.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":343,"Q_Id":39491258,"Users Score":0,"Answer":"Without more detail on your specific problem, it's hard to guess what is the best way to represent your data. I am going to give an example, hopefully it is relevant.\nSuppose we are collecting height and weight of a group of people. Maybe the index of the person is your first dimension, and the height and weight depends on who it is. Then one way to represent this data is use height and weight as the x and y axes, and plot each person as a dot in that two dimensional space.\nIn this example, the person index doesn't really have much meaning, thus no color is needed.","Q_Score":0,"Tags":"python,matplotlib","A_Id":46030298,"CreationDate":"2016-09-14T13:06:00.000","Title":"Matplotlib imshow()","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I know using == for float is generally not safe. But does it work for the below scenario?\n\nRead from csv file A.csv, save first half of the data to csv file B.csv without doing anything.\nRead from both A.csv and B.csv. Use == to check if data match everywhere in the first half.\n\nThese are all done with Pandas. The columns in A.csv have types datetime, string, and float. Obviously == works for datetime and string, so if == works for float as well in this case, it saves a lot of work.\nIt seems to be working for all my tests, but can I assume it will work all the time?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":717,"Q_Id":39493732,"Users Score":3,"Answer":"The same string representation will become the same float representation when put through the same parse routine. The float inaccuracy issue occurs either when mathematical operations are performed on the values or when high-precision representations are used, but equality on low-precision values is no reason to worry.","Q_Score":0,"Tags":"python,pandas,floating-point","A_Id":39493833,"CreationDate":"2016-09-14T14:59:00.000","Title":"Python how does == work for float\/double?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dictionary, T, with keys in the form k,i with an associated value that is a real number (float). Let's suppose I choose a particular key a,b from the dictionary T with corresponding value V1\u2014what's the most efficient way to find the closest value to V1 for a key that has the form a+1,i, where i is an integer that ranges from 0 to n? (k,a, and b are also integers.) To add one condition on the values of items in T, as i increases in the key, the value associated to T[a+1,i] is strictly increasing (i.e. T[a+1,i+1] > T[a+1,i]).\nI was planning to simply run a while loop that starts from i = 0 and compares the valueT[a+1,i] to V1. To be more clear, the loop would simply stop at the point at which np.abs(T[a+1,i] - V1) < np.abs(T[a+1,i+1] - V1), as I would know the item associated to T[a+1,i] is the closest to T[a,b] = V1. But given the strictly increasing condition I have imposed, is there a more efficient method than running a while loop that iterates over the dictionary elements? i will go from 0 to n where n could be an integer in the millions. Also, this process would be repeated frequently so efficiency is key.","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2461,"Q_Id":39517040,"Users Score":0,"Answer":"Since the values for a given a are strictly increasing with successive i values, you can do a binary search for the value that is closest to your target.\nWhile it's certainly possible to write your own binary search code on your dictionary, I suspect you'd have an easier time with a different data structure. If you used nested lists (with a as the index to the outer list, and i as the index to an inner list), you could use the bisect module to search the inner list efficiently.","Q_Score":2,"Tags":"python,python-2.7,loops,dictionary,iteration","A_Id":39517206,"CreationDate":"2016-09-15T17:29:00.000","Title":"Finding closest value in a dictionary","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am running Python on a low memory system.\nI want to know whether or not importing pandas will increase memory usage significantly.\nAt present I just want to import pandas so that I can use the date_range function.","AnswerCount":4,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":493,"Q_Id":39520532,"Users Score":0,"Answer":"After introducing pandas to my script and loaded dataframe with 0.8MB data, ran the script and surprised to see the memory usage got increased from 13MB to 49MB. I suspected my existing script has some memory leak and I used memory profiler to check what is consuming much memory and finally the culprit is pandas. Just import statement which loads the library into memory is taking around 30MB. Importing only specific item like (from pandas import Dataframe) didn't help much in saving memory.\nJust import pandas takes around 30MB memory\nOnce import is done, memory of dataframe object can be checked by using print(df.memory_usage(deep=True)) which depends on the data loaded to dataframe","Q_Score":0,"Tags":"python,pandas","A_Id":62178663,"CreationDate":"2016-09-15T21:18:00.000","Title":"How to measure the memory footprint of importing pandas?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am running Python on a low memory system.\nI want to know whether or not importing pandas will increase memory usage significantly.\nAt present I just want to import pandas so that I can use the date_range function.","AnswerCount":4,"Available Count":2,"Score":0.1488850336,"is_accepted":false,"ViewCount":493,"Q_Id":39520532,"Users Score":3,"Answer":"You may also want to use a Memory Profiler to get an idea of how much memory is allocated to your Pandas objects. There are several Python Memory Profilers you can use (a simple Google search can give you an idea). PySizer is one that I used a while ago.","Q_Score":0,"Tags":"python,pandas","A_Id":39520649,"CreationDate":"2016-09-15T21:18:00.000","Title":"How to measure the memory footprint of importing pandas?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have to send some applications in python to a Apache Spark cluster. There is given a Clustermanager and some worker nodes with the addresses to send the Application to.\nMy question is, how to setup and to configure Spark on my local computer to send those requests with the data to be worked out to the cluster?\nI am working on Ubuntu 16.xx and already installed java and scala. I have searched the inet but the most find is how to build the cluster or some old advices how to do it, which are out of date.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":35,"Q_Id":39525214,"Users Score":0,"Answer":"your question is unclear. If the data are on your local machine, you should first copy your data to the cluster on HDFS filesystem. Spark can works in 3 modes with YARN (are u using YARN or MESOS ?): cluster, client and standalone. What you are looking for is client-mode or cluster mode. But if you want to start the application from your local machine, use client-mode. If you have an SSH access, you are free to use both.\nThe simplest way is to copy your code directly on the cluster if it is properly configured then start the application with the .\/spark-submit script, providing the class to use as an argument. It works with python script and java\/scala classes (I only use python so I don't really know)","Q_Score":0,"Tags":"java,python,scala,apache-spark,pyspark","A_Id":39530209,"CreationDate":"2016-09-16T06:41:00.000","Title":"Configurate Spark by given Cluster","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I'm using SWIG to wrap a C++ library with its own polynomial type. I'd like to create a typemap to automatically convert that to a numpy polynomial. However, browsing the docs for the numpy C API, I'm not seeing anything that would allow me to do this, only numpy arrays. Is it possible to typemap to a polynomial?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":42,"Q_Id":39530054,"Users Score":0,"Answer":"Numpy's polynomial package is largely a collection of functions that can accept array-like objects as the polynomial. Therefore, it is sufficient to convert to a normal ndarray, where the value at index n is the coefficient for the term with exponent n.","Q_Score":0,"Tags":"python,c++,numpy,swig","A_Id":39576294,"CreationDate":"2016-09-16T11:10:00.000","Title":"Is it possible to create a polynomial through Numpy's C API?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Firstly I am not sure what such an algorithm is called, which is the primary problem - so first part of the question is what is this algorithm called?\nBasically I have a DiGraph() into which I insert the nodes [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] and the edges ([1,3],[2,3],[3,5],[4,5],[5,7],[6,7],[7,8],[7,9],[7,10])\nFrom this I'm wondering if it's possible to get a collection as follows: [[1, 2, 4, 6], [3], [5], [7], [8, 9, 10]]\nEDIT: Let me add some constraints if it helps.\n- There are no cycles, this is guaranteed\n- There is no one start point for the graph\nWhat I'm trying to do is to collect the nodes at the same level such that their processing can be parallelized, but within the outer collection, the processing is serial.\nEDIT2: So clearly I hadn't thought about this enough, so the easiest way to describe \"level\" is interms of deepest predecessor, all nodes that have the same depth of predecessors. So the first entry in the above list are all nodes that have 0 as the deepest predecessor, the second has one, third has two and so on. Within each list, the order of the siblings is irrelevant as they will be parallelly processed.","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":661,"Q_Id":39538363,"Users Score":0,"Answer":"Why would bfs not solve it? A bfs algorithm is breadth traversal algorithm, i.e. it traverses the tree level wise. This also means, all nodes at same level are traversed at once, which is your desired output.\nAs pointed out in comment, this will however, assume a starting point in the graph.","Q_Score":4,"Tags":"python,algorithm,graph,graph-theory,networkx","A_Id":39538477,"CreationDate":"2016-09-16T18:52:00.000","Title":"Given an acyclic directed graph, return a collection of collections of nodes \"at the same level\"?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been trying to implement an OCR program with Python that reads numbers with a specific format, XXX-XXX. I used Google's Cloud Vision API Text Recognition, but the results were unreliable. Out of 30 high-contrast 1280 x 1024 bmp images, only a handful resulted in the correct output, or at least included the correct output in the results. The program tends to omit some numbers, output in non-English languages or sneak in a few special characters.\nThe goal is to at least output the correct numbers consecutively, doesn't matter if the results are sprinkled with other junk. Is there a way to help the program recognize numbers better, for example limit the results to a specific format, or to numbers only?","AnswerCount":2,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":5635,"Q_Id":39540741,"Users Score":10,"Answer":"I am unable to tell you why this works, perhaps it has to do with how the language is read, o vs 0, l vs 1, etc. But whenever I use OCR and I am specifically looking for numbers, I have read to set the detection language to \"Korean\". It works exceptionally well for me and has influenced the accuracy greatly.","Q_Score":17,"Tags":"python,ocr,google-cloud-platform,google-cloud-vision,text-recognition","A_Id":39809660,"CreationDate":"2016-09-16T22:06:00.000","Title":"Google Cloud Vision - Numbers and Numerals OCR","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there a way to configure the default argument for histtype of matplotlib's hist() function? The default behavior is to make bar-chart type histograms, which I basically never want to look at, since it is horrible for comparing multiple distributions that have significant overlap.\nIn case it's somehow relevant, the default behavior I would like to attain is to have histtype='step'.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":602,"Q_Id":39541655,"Users Score":1,"Answer":"Thank you for prompting me to look at this, as I much prefer 'step' style histograms too! I solved this problem by going into the matplotlib source code. I use anaconda, so it was located in anaconda\/lib\/site-packages\/python2.7\/matplotlib.\nTo change the histogram style I edited two of the files. Assuming that the current directory is matplotlib\/, then open up axes\/_axes.py and locate the hist() function there (it's on line 5690 on my machine, matplotlib version 1.5.1). You should see the histtype argument there. Change this to 'step'.\nNow open up pyplot.py and again locate the hist() function and make the same change to the histtype argument (line 2943 in version 1.5.1 and on my machine). There is a comment about not editing this function, but I only found this to be an issue when I didn't also edit axes\/_axes.py as well.\nThis worked for me! Another alternative would be just to write a wrapper around hist() yourself that changes the default argument.","Q_Score":3,"Tags":"python,matplotlib,data-analysis","A_Id":39543370,"CreationDate":"2016-09-17T00:17:00.000","Title":"Setting default histtype in matplotlib?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have several threads that either update the weights of my network or run inference on it. I use the use_locking parameter for the optimizer to prevent concurrent updates of the weights.\nInference should always use a recent, and importantly, consistent, version of the weights. In other words, I want to prevent using a weight matrix for inference for which some of the elements are already updated but others are not.\nIs this guaranteed? If not, how can I ensure this? There doesn't seem to be a tf.Lock or similar.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":67,"Q_Id":39555060,"Users Score":0,"Answer":"Do your inference calls need to be on an up-to-date version of the graph? If you don't mind some delay, you could make a copy of the graph by calling sess.graph.as_graph_def on the training thread, and then create a new session on the inference thread using that graph_def periodically.","Q_Score":0,"Tags":"python,multithreading,thread-safety,locking,tensorflow","A_Id":39577466,"CreationDate":"2016-09-18T07:09:00.000","Title":"Can I safely do inference from another thread while training the network?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm working on a project that involves creating CUDA kernels in Python. Numba works quite well (what these guys have accomplished is quite incredible), and so does PyCUDA.\nMy problem is that I want to call a C device function from my Python generated kernel. I couldn't find a way to accomplish this. Numba can call CFFI modules but only in CPU code. In PyCUDA I can add my C device functions to the SourceModule, but I couldn't figure out how to include functions that already exist in another library.\nIs there a way to accomplish this?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":604,"Q_Id":39555235,"Users Score":2,"Answer":"As far as I am aware, this isn't possible in either language. Neither exposes the necessary toolchain controls for separate compilation or APIs to do runtime linking of device code.","Q_Score":2,"Tags":"python,cuda,cython,numba,pycuda","A_Id":39557406,"CreationDate":"2016-09-18T07:38:00.000","Title":"Calling a C++ CUDA device function from a Python kernel","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":1,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I used joblib.dump in python to save models from 5 fold cross validation modelling using random forest. As a result I have 5 models for each dataset saved as: MDL_1.pkl, MDL_2.pkl, MDL_3.pkl, MDL_4.pkl, MDL_5.pkl. Now I want to use these models for prediction of external dataset using predict_proba when the final prediction for each line in my external dataset is an average of 5 models. What is the best way to proceed?\nThank you for your help","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":43,"Q_Id":39562939,"Users Score":0,"Answer":"First of all, you should not save the result of cross validation. Cross validation is not a training method, it is an evaluation scheme. You should build a single model on your whole dataset and use it to predict.\nIf, for some reason, you can no longer train your model, you can still use this 5 predictions by averaging them (as random forest itself is a simple averagin ensemble of trees), however going back and retraining should give you bettter results.","Q_Score":0,"Tags":"python","A_Id":39563394,"CreationDate":"2016-09-18T21:40:00.000","Title":"predict external dataset with models from random forest","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"New to dask,I have a 1GB CSV file when I read it in dask dataframe it creates around 50 partitions after my changes in the file when I write, it creates as many files as partitions.\nIs there a way to write all partitions to single CSV file and is there a way access partitions?\nThank you.","AnswerCount":2,"Available Count":1,"Score":0.4621171573,"is_accepted":false,"ViewCount":14834,"Q_Id":39566809,"Users Score":5,"Answer":"you can convert your dask dataframe to a pandas dataframe with the compute function and then use the to_csv. something like this:\n\ndf_dask.compute().to_csv('csv_path_file.csv')","Q_Score":34,"Tags":"python,dask","A_Id":57812535,"CreationDate":"2016-09-19T06:39:00.000","Title":"Writing Dask partitions into single file","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have nothing against scikit-learn, but I had to install anaconda to get it, which is a bit obtrusive.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":72,"Q_Id":39574567,"Users Score":0,"Answer":"So, the situation as of today is: there is no distributed Python implementation of KMeans++ other than in scikit-learn. That situation may change if a good implementation finds its way into scipy.","Q_Score":0,"Tags":"python-2.7,scipy,scikit-learn,k-means","A_Id":39581743,"CreationDate":"2016-09-19T13:35:00.000","Title":"Is there any K-means++ implementation outside of scikit-learn for Python 2.7?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"During debuging the Tensorflow code, I would like to output the shape of a tensor, say, print(\"mask's shape is: \",mask.get_shape())  However, the corresponding output is mask's shape is (?,?,?,1)  How to explain this kind of output, is there anyway to know the exactly value of the first three dimensions of this tensor?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":569,"Q_Id":39582974,"Users Score":1,"Answer":"This output means that TensorFlow's shape inference has only been able to infer a partial shape for the mask tensor. It has been able to infer (i) that mask is a 4-D tensor, and (ii) its last dimension is 1; but it does not know statically the shape of the first three dimensions.\nIf you want to get the actual shape of the tensor, the main approaches are:\n\nCompute mask_val = sess.run(mask) and print mask_val.shape.\nCreate a symbolic mask_shape = tf.shape(mask) tensor, compute mask_shape_val = sess.run(mask_shape) and print `mask_shape.\n\nShapes usually have unknown components if the shape depends on the data, or if the tensor is itself a function of some tensor(s) with a partially known shape. If you believe that the shape of the mask should be static, you can trace the source of the uncertainty by (recursively) looking at the inputs of the operation(s) that compute mask and finding out where the shape becomes partially known.","Q_Score":0,"Tags":"python,tensorflow","A_Id":39583123,"CreationDate":"2016-09-19T22:21:00.000","Title":"regarding the tensor shape is (?,?,?,1)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've tried searching StackOverflow, googling, and even using symbolhound to do character searches, but was unable to find an answer. Specifically, I'm confused about Ch. 1 of Nielsen's Neural Networks and Deep Learning, where he says \"It is assumed that the input a is an (n, 1) Numpy ndarray, not a (n,) vector.\"\nAt first I thought (n,) referred to the orientation of the array - so it might refer to a one-column vector as opposed to a vector with only one row. But then I don't see why we need (n,) and (n, 1) both - they seem to say the same thing. I know I'm misunderstanding something but am unsure.\nFor reference a refers to a vector of activations that will be input to a given layer of a neural network, before being transformed by the weights and biases to produce the output vector of activations for the next layer.\nEDIT: This question equivocates between a \"one-column vector\" (there's no such thing) and a \"one-column matrix\" (does actually exist). Same for \"one-row vector\" and \"one-row matrix\". \nA vector is only a list of numbers, or (equivalently) a list of scalar transformations on the basis vectors of a vector space. A vector might look like a matrix when we write it out, if it only has one row (or one column). Confusingly, we will sometimes refer to a \"vector of activations\" but actually mean \"a single-row matrix of activation values transposed so that it is a single-column.\"\nBe aware that in neither case are we discussing a one-dimensional vector, which would be a vector defined by only one number (unless, trivially, n==1, in which case the concept of a \"column\" or \"row\" distinction would be meaningless).","AnswerCount":2,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":2722,"Q_Id":39604918,"Users Score":6,"Answer":"(n,) is a tuple of length 1, whose only element is n. (The syntax isn't (n) because that's just n instead of making a tuple.)\nIf an array has shape (n,), that means it's a 1-dimensional array with a length of n along its only dimension. It's not a row vector or a column vector; it doesn't have rows or columns. It's just a vector.","Q_Score":9,"Tags":"python,numpy,machine-learning,neural-network","A_Id":39605142,"CreationDate":"2016-09-20T22:51:00.000","Title":"What does (n,) mean in the context of numpy and vectors?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Here is the problem:\nGiven the input n = 4 x = 5, we must imagine a chessboard that is 4 squares across (x-axis) and 5 squares tall (y-axis). (This input changes, all the up to n = 200 x = 200)\nThen, we are asked to determine the minimum shortest path from the bottom left square on the board to the top right square on the board for the Knight (the Knight can move 2 spaces on one axis, then 1 space on the other axis).\nMy current ideas:\nUse a 2d array to store all the possible moves, perform breadth-first \nsearch(BFS) on the 2d array to find the shortest path.\nFloyd-Warshall shortest path algorithm.\nCreate an adjacency list and perform BFS on that (but I think this would be inefficient).\nTo be honest though I don't really have a solid grasp on the logic.\nCan anyone help me with psuedocode, python code, or even just a logical walk-through of the problem?","AnswerCount":4,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":3995,"Q_Id":39607721,"Users Score":0,"Answer":"Try something. Draw boards of the following sizes: 1x1, 2x2, 3x3, 4x4, and a few odd ones like 2x4 and 3x4. Starting with the smallest board and working to the largest, start at the bottom left corner and write a 0, then find all moves from zero and write a 1, find all moves from 1 and write a 2, etc. Do this until there are no more possible moves.\nAfter doing this for all 6 boards, you should have noticed a pattern: Some squares couldn't be moved to until you got a larger board, but once a square was \"discovered\" (ie could be reached), the number of minimum moves to that square was constant for all boards not smaller than the board on which it was first discovered. (Smaller means less than n OR less than x, not  less than (n * x) )\nThis tells something powerful, anecdotally. All squares have a number associated with them that must be discovered. This number is a property of the square, NOT the board, and is NOT dependent on size\/shape of the board. It is always true. However, if the square cannot be reached, then obviously the number is not applicable.\nSo you need to find the number of every square on a 200x200 board, and you need a way to see if a board is a subset of another board to determine if a square is reachable.\nRemember, in these programming challenges, some questions that are really hard can be solved in O(1) time by using lookup tables. I'm not saying this one can, but keep that trick in mind. For this one, pre-calculating the 200x200 board numbers and saving them in an array could save a lot of time, whether it is done only once on first run or run before submission and then the results are hard coded in.\nIf the problem needs move sequences rather than number of moves, the idea is the same: save move sequences with the numbers.","Q_Score":0,"Tags":"python,algorithm,chess","A_Id":39607825,"CreationDate":"2016-09-21T04:42:00.000","Title":"Number of shortest paths","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Here is the problem:\nGiven the input n = 4 x = 5, we must imagine a chessboard that is 4 squares across (x-axis) and 5 squares tall (y-axis). (This input changes, all the up to n = 200 x = 200)\nThen, we are asked to determine the minimum shortest path from the bottom left square on the board to the top right square on the board for the Knight (the Knight can move 2 spaces on one axis, then 1 space on the other axis).\nMy current ideas:\nUse a 2d array to store all the possible moves, perform breadth-first \nsearch(BFS) on the 2d array to find the shortest path.\nFloyd-Warshall shortest path algorithm.\nCreate an adjacency list and perform BFS on that (but I think this would be inefficient).\nTo be honest though I don't really have a solid grasp on the logic.\nCan anyone help me with psuedocode, python code, or even just a logical walk-through of the problem?","AnswerCount":4,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":3995,"Q_Id":39607721,"Users Score":0,"Answer":"My approach to this question would be backtracking as the number of squares in the x-axis and y-axis are different. \nNote: Backtracking algorithms can be slow for certain cases and fast for the other \nCreate a 2-d Array for the chess-board. You know the staring index and the final index. To reach to the final index u need to keep close to the diagonal that's joining the two indexes. \nFrom the starting index see all the indexes that the knight can travel to, choose the index which is closest to the diagonal indexes and keep on traversing, if there is no way to travel any further backtrack one step and move to the next location available from there.\nPS : This is a bit similar to a well known problem Knight's Tour, in which choosing any starting point you have to find that path in which the knight whould cover all squares. I have codes this as a java gui application, I can send you the link if you want any help\nHope this helps!!","Q_Score":0,"Tags":"python,algorithm,chess","A_Id":39608395,"CreationDate":"2016-09-21T04:42:00.000","Title":"Number of shortest paths","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have 3 milion abstracts and I would like to extract 4-grams from them. I want to build a language model so I need to find the frequencies of these 4-grams. \nMy problem is that I can't extract all these 4-grams in memory. How can I implement a system that it can estimate all frequencies for these 4-grams?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":83,"Q_Id":39613555,"Users Score":0,"Answer":"Sounds like you need to store the intermediate frequency counts on disk rather than in memory. Luckily most databases can do this, and python can talk to most databases.","Q_Score":1,"Tags":"python,n-gram,language-model","A_Id":39613813,"CreationDate":"2016-09-21T10:09:00.000","Title":"N-grams - not in memory","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a Hidden Markov Model that has been pickled with joblib using the sklearn.hmm module. Apparently, in version 0.17.x this module has been deprecated and moved to hmmlearn. I am unable to load the model and I get the following error:\n\nImportError: No module named 'sklearn.hmm'\n\nI have tried to revert back to version 0.16.x but still cannot load the model. I get the following error:\n\nImportError: libopenblas.so.0: cannot open shared object file: No such file or directory\n\nI do not have access to the source code to recreate the model and re-pickle it\nI am running Python 3.5\nHas anyone else experienced this problem and have you found a solution? Does anyone know if scikit-learn has any way to guarantee persistence since the deprecation?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":576,"Q_Id":39618985,"Users Score":0,"Answer":"After reverting to scikit-learn 0.16.x, I just needed to install OpenBlas for Ubuntu. It appears that the problem was more a feature of the operating system rather than Python.","Q_Score":0,"Tags":"python,scikit-learn,joblib","A_Id":39621200,"CreationDate":"2016-09-21T14:08:00.000","Title":"Deprecated Scikit-learn module prevents joblib from loading it","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to train a huge dataset with sklearn's logistic regression.\nI've set the parameter n_jobs=-1 (also have tried n_jobs = 5, 10, ...), but when I open htop, I can see that it still uses only one core.\nDoes it mean that logistic regression just ignores the n_jobs parameter?\nHow can I fix this? I really need this process to become parallelized...\nP.S. I am using sklearn 0.17.1","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":2100,"Q_Id":39620185,"Users Score":1,"Answer":"the parallel process backend also depends on the solver method. if you want to utilize multi core, the multiprocessing backend is needed. \nbut solver like 'sag' can only use threading backend.\nand also mostly, it can be blocked due to a lot of pre-processing.","Q_Score":2,"Tags":"python,python-2.7,parallel-processing,scikit-learn,logistic-regression","A_Id":39620443,"CreationDate":"2016-09-21T15:03:00.000","Title":"sklearn Logistic Regression with n_jobs=-1 doesn't actually parallelize","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying loop over many arrays and create files stored in different folders.\nIs there a way to have np.savetxt creating the folders I need as well?\nThanks","AnswerCount":2,"Available Count":2,"Score":0.1973753202,"is_accepted":false,"ViewCount":6937,"Q_Id":39627787,"Users Score":2,"Answer":"Actually in order to make all intermediate directories if needed the os.makedirs(path, exist_ok=True) . If not needed the command will not throw an error.","Q_Score":2,"Tags":"python,numpy","A_Id":39640835,"CreationDate":"2016-09-21T23:01:00.000","Title":"Create Folder with Numpy Savetxt","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying loop over many arrays and create files stored in different folders.\nIs there a way to have np.savetxt creating the folders I need as well?\nThanks","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":6937,"Q_Id":39627787,"Users Score":3,"Answer":"savetxt just does a open(filename, 'w').  filename can include a directory as part of the path name, but you'll have to first create the directory with something like os.mkdir.  In other words, use the standard Python directory and file functions.","Q_Score":2,"Tags":"python,numpy","A_Id":39628096,"CreationDate":"2016-09-21T23:01:00.000","Title":"Create Folder with Numpy Savetxt","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it possible to group tensorflow FLAGS by type?\nE.g.\nSome flags are system related (e.g. # of threads) while others are model hyperparams.\nThen, is it possible to use the model hyperparams FLAGS, in order to generate a string? (the string will be used to identify the model filename)\nThanks","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":79,"Q_Id":39643256,"Users Score":0,"Answer":"I'm guessing that you're wanting to automatically store the hyper-parameters as part of the file name in order to organize your experiments better? Unfortunately there isn't a good way to do this with TensorFlow, but you can look at some of the high-level frameworks built on top of it to see if they offer something similar.","Q_Score":0,"Tags":"python,machine-learning,computer-vision,tensorflow","A_Id":39664586,"CreationDate":"2016-09-22T15:34:00.000","Title":"Is it possible to group tensorflow FLAGS by type and generate a string from them?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a large numpy array with positive data, negative data and 0s. I want to convert it to an array with the signs of the current values such that 0 is considered positive. If I use numpy.sign it returns 0 if the current value is 0 but I want something that returns 1 instead. Is there an easy way to do this?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2087,"Q_Id":39650312,"Users Score":3,"Answer":"If x is the array, you could use 2*(x >= 0) - 1.\nx >= 0 will be an array of boolean values (i.e. False and True), but when you do arithmetic with it, it is effectively cast to an array of 0s and 1s.\nYou could also do np.sign(x) + (x == 0).  (Note that np.sign(x) returns floating point values, even when x is an integer array.)","Q_Score":1,"Tags":"python,numpy","A_Id":39650350,"CreationDate":"2016-09-22T23:24:00.000","Title":"Convert a numpy array into an array of signs with 0 as positive","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"We have so may versions of wheel. \nHow could we know which version should be installed into my system?\nI remember there is a certain command which could check my system environment.\nOr is there any other ways?\n---------------------Example Below this line -----------\nscikit_learn-0.17.1-cp27-cp27m-win32.whl\nscikit_learn-0.17.1-cp27-cp27m-win_amd64.whl\nscikit_learn-0.17.1-cp34-cp34m-win32.whl\nscikit_learn-0.17.1-cp34-cp34m-win_amd64.whl\nscikit_learn-0.17.1-cp35-cp35m-win32.whl\nscikit_learn-0.17.1-cp35-cp35m-win_amd64.whl\nscikit_learn-0.18rc2-cp27-cp27m-win32.whl\nscikit_learn-0.18rc2-cp27-cp27m-win_amd64.whl\nscikit_learn-0.18rc2-cp34-cp34m-win32.whl\nscikit_learn-0.18rc2-cp34-cp34m-win_amd64.whl\nscikit_learn-0.18rc2-cp35-cp35m-win32.whl\nscikit_learn-0.18rc2-cp35-cp35m-win_amd64.whl","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1458,"Q_Id":39652553,"Users Score":0,"Answer":"You don't have to know.  Use pip - it will select the most specific wheel available.","Q_Score":0,"Tags":"python,python-wheel,python-install","A_Id":39652742,"CreationDate":"2016-09-23T04:16:00.000","Title":"How to know which .whl module is suitable for my system with so many?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I am calculating an homography between two images img1 and img2 (the images contain mostly one planar object, so the homography works well between them) using standard methods in OpenCV in python. Namely, I compute point matches between the images using sift and then call cv2.findHomography.\nTo make the computation faster I scale down the two images into small1 and small2 and perform the calculations on these smaller copies, so I calculate the homography matrix H, which maps small1 into small2.\nHowever, at the end, I would like to use calculate the homography matrix to project one full-size image img1 onto the other the full-size image img2. \nI thought I could simply transform the homography matrix H in the following way H_full_size = A * H * A_inverse where A is the matrix representing the scaling from img1 to small1 and A_inverse is its inverse. \nHowever, that does not work. If I apply cv2.warpPerspective to the scaled down image small1 with H, everything goes as expected and the result (largely) overlaps with small2. If I apply cv2.warpPerspective to the full size image img1 with H_full_size the result does not map to img2.\nHowever, if I project the point matches (detected on the scaled down images) using A (using something like projected_pts = cv2.perspectiveTransform(pts, A)) and then I calculate H_full_size from these, everything works fine. \nAny idea what I could be doing wrong here?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":2276,"Q_Id":39668174,"Users Score":1,"Answer":"The way I see it, the problem is that homography applies a perspective projection which is a non linear transformation (it is linear only while homogeneous coordinates are being used) that cannot be represented as a normal transformation matrix. Multiplying such perspective projection matrix with some other transformations therefore produces undesirable results.\nYou can try multiplying your original matrix H element wise with:\nS = [1,1,scale ; 1,1,scale ; 1\/scale, 1\/scale, 1]\nH_full_size = S * H\nwhere scale is for example 2, if you decreased the size of original image by 2.","Q_Score":2,"Tags":"python,opencv,coordinate-transformation,homography","A_Id":39668864,"CreationDate":"2016-09-23T19:24:00.000","Title":"homography and image scaling in opencv","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm teaching myself data structures through this python book and I'd appreciate if someone can correct me if I'm wrong since a hash set seems to be extremely similar to a hash map.\nImplementation:\nA Hashset is a list [] or array where each index points to the head of a linkedlist\nSo some hash(some_item) --> key, and then list[key] and then  add to the head of a LinkedList. This occurs in O(1) time\nWhen removing a value from the linkedlist, in python we replace it with a placeholder because hashsets are not allowed to have Null\/None values, correct?\nWhen the list[] gets over a certain % of load\/fullness, we copy it over to another list\nRegarding Time Complexity Confusion:\nSo one question is, why is Average search\/access O(1) if there can be a list of N items at the linkedlist at a given index?\nWouldnt the average case be the searchitem is in the middle of its indexed linkedlist so it should be O(n\/2) -> O(n)?\nAlso, when removing an item, if we are replacing it with a placeholder value, isn't this considered a waste of memory if the placeholder is never used?\nAnd finally, what is the difference between this and a HashMap other than HashMaps can have nulls? And HashMaps are key\/value while Hashsets are just value?","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1427,"Q_Id":39671661,"Users Score":0,"Answer":"The lookup time wouldn't be O(n) because not all items need to be searched, it also depends on the number of buckets. More buckets would decrease the probability of a collision and reduce the chain length.\nThe number of buckets can be kept as a constant factor of the number of entries by resizing the hash table as needed. Along with a hash function that evenly distributes the values, this keeps the expected chain length bounded, giving constant time lookups.\nThe hash tables used by hashmaps and hashsets are the same except they store different values. A hashset will contain references to a single value, and a hashmap will contain references to a key and a value. Hashsets can be implemented by delegating to a hashmap where the keys and values are the same.","Q_Score":0,"Tags":"python,algorithm,data-structures","A_Id":39671924,"CreationDate":"2016-09-24T01:34:00.000","Title":"Is my understanding of Hashsets correct?(Python)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm teaching myself data structures through this python book and I'd appreciate if someone can correct me if I'm wrong since a hash set seems to be extremely similar to a hash map.\nImplementation:\nA Hashset is a list [] or array where each index points to the head of a linkedlist\nSo some hash(some_item) --> key, and then list[key] and then  add to the head of a LinkedList. This occurs in O(1) time\nWhen removing a value from the linkedlist, in python we replace it with a placeholder because hashsets are not allowed to have Null\/None values, correct?\nWhen the list[] gets over a certain % of load\/fullness, we copy it over to another list\nRegarding Time Complexity Confusion:\nSo one question is, why is Average search\/access O(1) if there can be a list of N items at the linkedlist at a given index?\nWouldnt the average case be the searchitem is in the middle of its indexed linkedlist so it should be O(n\/2) -> O(n)?\nAlso, when removing an item, if we are replacing it with a placeholder value, isn't this considered a waste of memory if the placeholder is never used?\nAnd finally, what is the difference between this and a HashMap other than HashMaps can have nulls? And HashMaps are key\/value while Hashsets are just value?","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1427,"Q_Id":39671661,"Users Score":0,"Answer":"For your first question - why is the average time complexity of a lookup O(1)? - this statement is in general only true if you have a good hash function. An ideal hash function is one that causes a nice spread on its elements. In particular, hash functions are usually chosen so that the probability that any two elements collide is low. Under this assumption, it's possible to formally prove that the expected number of elements to check is O(1). If you search online for \"universal family of hash functions,\" you'll probably find some good proofs of this result.\nAs for using placeholders - there are several different ways to implement a hash table. The approach you're using is called \"closed addressing\" or \"hashing with chaining,\" and in that approach there's little reason to use placeholders. However, other hashing strategies exist as well. One common family of approaches is called \"open addressing\" (the most famous of which is linear probing hashing), and in those setups placeholder elements are necessary to avoid false negative lookups. Searching online for more details on this will likely give you a good explanation about why.\nAs for how this differs from HashMap, the HashMap is just one possible implementation of a map abstraction backed by a hash table. Java's HashMap does support nulls, while other approaches don't.","Q_Score":0,"Tags":"python,algorithm,data-structures","A_Id":39671749,"CreationDate":"2016-09-24T01:34:00.000","Title":"Is my understanding of Hashsets correct?(Python)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm planning to develop a genetic algorithm for a series of acceleration records in a search to find optimum match with a target.\nAt this point my data is array-like with a unique ID column, X,Y,Z component info in the second, time in the third etc...\nThat being said each record has several \"attributes\". Do you think it would be beneficial to create a (records) class considering the fact I will want to to do a semi-complicated process with it as a next step?\nThanks","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":17,"Q_Id":39672376,"Users Score":0,"Answer":"I would say yes. Basically I want to:\n\nTake the unique set of data\nFilter it so that just a subset is considered (filter parameters can be time of recording for example)\nUse a genetic algorithm the filtered data to match on average a target.\n\nStep 3 is irrelevant to the post, I just wanted to give the big picture in order to make my question more clear.","Q_Score":1,"Tags":"python,class","A_Id":39672721,"CreationDate":"2016-09-24T04:07:00.000","Title":"Organizing records to classes","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"The original question was in regard to TensorFlow implementations specifically. However, the answers are for implementations in general. This general answer is also the correct answer for TensorFlow.\nWhen using batch normalization and dropout in TensorFlow (specifically using the contrib.layers) do I need to be worried about the ordering?\nIt seems possible that if I use dropout followed immediately by batch normalization there might be trouble. For example, if the shift in the batch normalization trains to the larger scale numbers of the training outputs, but then that same shift is applied to the smaller (due to the compensation for having more outputs) scale numbers without dropout during testing, then that shift may be off. Does the TensorFlow batch normalization layer automatically compensate for this? Or does this not happen for some reason I'm missing?\nAlso, are there other pitfalls to look out for in when using these two together? For example, assuming I'm using them in the correct order in regards to the above (assuming there is a correct order), could there be trouble with using both batch normalization and dropout on multiple successive layers? I don't immediately see a problem with that, but I might be missing something.\nThank you much!\nUPDATE:\nAn experimental test seems to suggest that ordering does matter. I ran the same network twice with only the batch norm and dropout reverse. When the dropout is before the batch norm, validation loss seems to be going up as training loss is going down. They're both going down in the other case. But in my case the movements are slow, so things may change after more training and it's just a single test. A more definitive and informed answer would still be appreciated.","AnswerCount":9,"Available Count":2,"Score":0.022218565,"is_accepted":false,"ViewCount":130653,"Q_Id":39691902,"Users Score":1,"Answer":"The correct order is: Conv > Normalization > Activation > Dropout > Pooling","Q_Score":206,"Tags":"python,neural-network,tensorflow,conv-neural-network","A_Id":54791471,"CreationDate":"2016-09-25T21:12:00.000","Title":"Ordering of batch normalization and dropout?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"The original question was in regard to TensorFlow implementations specifically. However, the answers are for implementations in general. This general answer is also the correct answer for TensorFlow.\nWhen using batch normalization and dropout in TensorFlow (specifically using the contrib.layers) do I need to be worried about the ordering?\nIt seems possible that if I use dropout followed immediately by batch normalization there might be trouble. For example, if the shift in the batch normalization trains to the larger scale numbers of the training outputs, but then that same shift is applied to the smaller (due to the compensation for having more outputs) scale numbers without dropout during testing, then that shift may be off. Does the TensorFlow batch normalization layer automatically compensate for this? Or does this not happen for some reason I'm missing?\nAlso, are there other pitfalls to look out for in when using these two together? For example, assuming I'm using them in the correct order in regards to the above (assuming there is a correct order), could there be trouble with using both batch normalization and dropout on multiple successive layers? I don't immediately see a problem with that, but I might be missing something.\nThank you much!\nUPDATE:\nAn experimental test seems to suggest that ordering does matter. I ran the same network twice with only the batch norm and dropout reverse. When the dropout is before the batch norm, validation loss seems to be going up as training loss is going down. They're both going down in the other case. But in my case the movements are slow, so things may change after more training and it's just a single test. A more definitive and informed answer would still be appreciated.","AnswerCount":9,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":130653,"Q_Id":39691902,"Users Score":0,"Answer":"ConV\/FC - BN - Sigmoid\/tanh - dropout.\nIf activiation func is Relu or otherwise, the order of normalization and dropout depends on your task","Q_Score":206,"Tags":"python,neural-network,tensorflow,conv-neural-network","A_Id":63051525,"CreationDate":"2016-09-25T21:12:00.000","Title":"Ordering of batch normalization and dropout?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Please help me fill any gaps in my knowledge(teaching myself):\nSo far I understand that given a graph of N vertices, and edges we want to form a MST that will have N-1 Edges\n\nWe order the edges by their weight\nWe create a set of subsets where each vertice is given its own subset. So if we have {A,B,C,D} as our initial set of vertices, we now have {{A}, {B}, {C}, {D}}\nWe also create a set A that will hold the answer\nWe go down the list of ordered edges. We look at it's vertices, so V1 and V2. If they are in seperate subsets, we can join the two subsets, and add the edge into the set A that holds our edges. If they are in the same subset, we go to the next option (because its a cycle)\nWe continue this pattern until we reach the end of the Edge's list or we reach the Number of vertices - 1 for the length of our set A.\n\nIf the above assertions are true, my following questions regard the implementation:\nIf we use a list[] to hold the subsets of the set that contains the vertice:\nsubsets = [[1][2][3][4][5][6][7]]\nand each edge is composed of needing to look for two subsets\nso we need to find (6,7)\nthe result would be\nmy_path = [(6,7)] #holds all paths\nsubsets = [[1][2][3][4][5][6,7]]\nwouldn't finding the subset in subsets be taking too long to be O(nlog(n))\nIs there a better approach or am i doing this correctly?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":329,"Q_Id":39713798,"Users Score":0,"Answer":"Actually the running time of the algorithm is O(E log(V)).\nThe key to its performance lies on your point 4, more specifically, the implementation of determining for a light edge e = (a, b) if 'a' and 'b' belongs to the same set and, if not, performing the union of their respective sets.\nFor more clarifications on the topic I recommend you the book: \"Introduction to Algoritms\", from MIT Press, ISBN 0-262-03293-7, pag 561 (for the general topic of MST) and pag 568 (for Kruskal's Algorithm).\nAs it states, and I quote: \n\n\"The running time of Kruskal\u2019s algorithm for a graph G = (V, E) depends\n  on the implementation of the disjoint-set data structure. We shall assume the\n  disjoint-set-forest implementation of Section 21.3 with the union-by-rank and\n  path-compression heuristics, since it is the asymptotically fastest implementation\n  known.\"\n\nFew lines later and with some simple \"Time Complexity Theory\" calculus, it proves its time complexity to be of O(E log(V)).","Q_Score":0,"Tags":"python,graph,kruskals-algorithm","A_Id":39714032,"CreationDate":"2016-09-26T23:18:00.000","Title":"Need some clarification on Kruskals and Union-Find","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am currently working in Python and using OpenCV's videocapture and cv.imshow to show a video. I am trying to put an overlay on this video so I can draw on it using cv.line, cv.rectangle, etc. Each time the frame changes it clears the image that was drawn so I am hoping if I was to put an overlay of some sort on top of this that it would allow me to draw multiple images on the video without clearing. Any advice? Thanks ahead!","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1160,"Q_Id":39715472,"Users Score":0,"Answer":"What you need are 2 Mat objects- one to stream the camera (say Mat_cam), and the other to hold the overlay (Mat_overlay).\nWhen you draw on your main window, save the line and Rect objects on Mat_overlay, and make sure that it is not affected by the streaming video\nWhen the next frame is received, Mat_cam will be updated and it'll have the next video frame, but Mat_overlay will be the same, since it will not be cleared\/refreshed with every 'for' loop iteration. Adding Mat_overlay and Mat_cam using Weighted addition will give you the desired result.","Q_Score":1,"Tags":"python,opencv,video,overlay","A_Id":39716115,"CreationDate":"2016-09-27T03:22:00.000","Title":"How to put an overlay on a video","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am currently working in Python and using OpenCV's videocapture and cv.imshow to show a video. I am trying to put an overlay on this video so I can draw on it using cv.line, cv.rectangle, etc. Each time the frame changes it clears the image that was drawn so I am hoping if I was to put an overlay of some sort on top of this that it would allow me to draw multiple images on the video without clearing. Any advice? Thanks ahead!","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1160,"Q_Id":39715472,"Users Score":0,"Answer":"I am not sure that I have understood your question properly.What I got from your question is that you want the overlay to remain on your frame, streamed from Videocapture,  for that one simple solution is to declare your \"Mat_cam\"(camera streaming variable) outside the loop that is used to capture frames so that \"Mat_cam\" variable will not be freed every time you loop through it.","Q_Score":1,"Tags":"python,opencv,video,overlay","A_Id":39721387,"CreationDate":"2016-09-27T03:22:00.000","Title":"How to put an overlay on a video","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Given a classification problem, sometimes we do not just predict a class, but need to return the probability that it is a class.\ni.e. P(y=0|x), P(y=1|x), P(y=2|x), ..., P(y=C|x)\nWithout building a new classifier to predict y=0, y=1, y=2... y=C respectively. Since training C classifiers (let's say C=100) can be quite slow.\nWhat can be done to do this? What classifiers naturally can give all probabilities easily (one I know is using neural network with 100 out nodes)? But if I use traditional random forests, I can't do that, right? I use the Python Scikit-Learn library.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1945,"Q_Id":39738703,"Users Score":0,"Answer":"Random forests do indeed give P(Y\/x) for multiple classes. In most cases\nP(Y\/x) can be taken as:\nP(Y\/x)= the number of trees which vote for the class\/Total Number of trees.\nHowever you can play around with this, for example in one case if the highest class has 260 votes, 2nd class 230 votes and other 5 classes 10 votes, and in another case class 1 has 260 votes, and other classes have 40 votes each, you migth feel more confident in your prediction in 2nd case as compared to 1st case, so you come up with a confidence metric according to your use case.","Q_Score":1,"Tags":"python,machine-learning,scikit-learn","A_Id":39743987,"CreationDate":"2016-09-28T05:27:00.000","Title":"How do you get a probability of all classes to predict without building a classifier for each single class?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How to change the ARRAY U(Nz,Ny, Nx) to U(Nx,Ny, Nz) by using numpy? thanks","AnswerCount":2,"Available Count":1,"Score":0.4621171573,"is_accepted":false,"ViewCount":79,"Q_Id":39747900,"Users Score":5,"Answer":"Just numpy.transpose(U) or U.T.","Q_Score":0,"Tags":"python,numpy","A_Id":39747938,"CreationDate":"2016-09-28T12:42:00.000","Title":"The efficient way of Array transformation by using numpy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am developing a Python program that involves displaying X-Y-Z Trajectories in 3D space. I'm using the Spyder IDE that naturally comes with Anaconda, and I've been running my scripts in IPython Consoles.\nSo I've been able to generate the 3D plot successfully and use pyplot.show() to display it on the IPython Console. However, when displayed in IPython, only one angle of the graph is shown. And I've read that MPlot3D can be used to create interactive plots. Am I correct in believing that I should be able to rotate and zoom the 3D graph? Or does IPython and\/or the Spyder IDE not support this feature? Should I work on rotating the plot image within the script? How do I interact with this plot?","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":472,"Q_Id":39752700,"Users Score":1,"Answer":"I initially faced same issue\nEverything seems to be alright but couldn't rotate the picture.\nAfter toggling between graphical and automatic in\nTools > preferences > IPython console > Graphics > Graphics backend > Backend: ....\nI could rotate the image","Q_Score":0,"Tags":"python,ipython,spyder,mplot3d","A_Id":47186550,"CreationDate":"2016-09-28T16:13:00.000","Title":"MPlot3D Image Manipulation in IPython","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am developing a Python program that involves displaying X-Y-Z Trajectories in 3D space. I'm using the Spyder IDE that naturally comes with Anaconda, and I've been running my scripts in IPython Consoles.\nSo I've been able to generate the 3D plot successfully and use pyplot.show() to display it on the IPython Console. However, when displayed in IPython, only one angle of the graph is shown. And I've read that MPlot3D can be used to create interactive plots. Am I correct in believing that I should be able to rotate and zoom the 3D graph? Or does IPython and\/or the Spyder IDE not support this feature? Should I work on rotating the plot image within the script? How do I interact with this plot?","AnswerCount":2,"Available Count":2,"Score":0.3799489623,"is_accepted":false,"ViewCount":472,"Q_Id":39752700,"Users Score":4,"Answer":"Yes, you can rotate and interact with Mplot3d plots in Spyder, you just have to change the setting so that plots appear in a separate window, rather than in the IPython console. Just change the inline setting to automatic:\nTools > preferences > IPython console > Graphics > Graphics backend > Backend: Automatic\nThen click Apply, close Spyder, and restart.","Q_Score":0,"Tags":"python,ipython,spyder,mplot3d","A_Id":46331190,"CreationDate":"2016-09-28T16:13:00.000","Title":"MPlot3D Image Manipulation in IPython","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"My system is SLES 11.4 having python 2.6.9.\nI know little about python and have not found where to download rpm's that give me needed python packages.\nI acquired numpy 1.4 and 1.11 and I believe did a successful python setup.py build followed by python setup.py install on numpy.\nGoing from memory I think this installed under \/usr\/local\/lib64\/python2.6\/...\nNext I tried building & installing matplotlib (which requires numpy) and when I do python setup.py build it politely responds with cannot find numpy.  So my questions are\ndo i need to set some kind of python related environment variable, something along the lines of LD_LIBRARY_PATH or PATH ?\nAs I get more involved with using python installing packages that I have to build from source I need to understand where things currently are per the default install of python, where new things should go, and where the core settings for python are to know how and where to recognize new packages.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":44,"Q_Id":39759680,"Users Score":0,"Answer":"think i figured it out.  Apparently SLES 11.4 does not include the development headers in the default install from their SDK for numpy 1.8.\nAnd of course they don't offer matplotlib along with a bunch of common python packages.\nThe python packages per the SLES SDK are the system default are located under\/usr\/lib64\/python2.6\/site-packages\/ and it is under here I see numpy version 1.8.  So using the YAST software manager if you choose various python packages this is where they are located.\nTo this point without having the PYTHONPATH environment variable set I can launch python, type import numpy, and for the most part use it.  But if I try to build matplotlib 0.99.1 it responds that it cannot find the header files for numpy version 1.8, so it knows numpy 1.8 is installed but the development package needs to be installed.\nAssuming by development headers they mean .h files,\nIf I search under \/usr\/lib64\/python2.6\/site-packages I have no .h files for anything!\nI just downloaded the source for numpy-1.8.0.tar.gz  and easily did a python setup.py.build followed by python setup.py install and noticed it installed under \/usr\/local\/lib64\/python2.6\/site-packages\/\nWithout the PYTHONPATH environment variable set, if i try to build matplotlib I still get the error about header files not found.\nbut in my bash shell, as root, after I do export PYTHONPATH=\/usr\/local\/lib64\/python2.6\/site-packages I can successfully do the build and install of matplotlib 0.99.1 which also installs \/usr\/local\/lib64\/python2.6\/site-packages\nNotes:\nI also just did a successful build & install of numpy-1.11 and that got thrown in under \/usr\/local\/lib64\/python2.6\/site-packages however when i try to then build matplotlib 0.99.1 with PYTHONPATH set it reports outright that numpy is not installed that version 1.1 or greater is needed.  So here it seems this older version of matplotlib needs to use a certain version range of numpy, that the latest numpy 1.11 is not compatible.\nAnd the only other environment variable i have which is set by the system is PYTHONSTARTUP which points to the file \/etc\/pythonstart.","Q_Score":0,"Tags":"python","A_Id":39778818,"CreationDate":"2016-09-29T00:44:00.000","Title":"manually building installing python packages in linux so they are recognized","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to write the file to my company's project folder which is unix system and the location is \/department\/projects\/data\/. So I used the following code\ndf.to_csv(\"\/department\/projects\/data\/Test.txt\", sep='\\t', header = 0)\nThe error message shows it cannot find the locations. how to specify the file location in Unix using python?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":43,"Q_Id":39779412,"Users Score":0,"Answer":"I have find the solution. It might because I am using Spyder from anaconda. As long as I use \"\\\" instead of \"\\\", python can recognize the location.","Q_Score":0,"Tags":"python,unix","A_Id":39813792,"CreationDate":"2016-09-29T20:27:00.000","Title":"how to export data to unix system location using python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to use seaborn to perform a sns.barplot where the values are ordered e.g. in ascending order.\nIn case the order parameter of seaborn is set the plot seems to duplicate the labels for all non-NaN labels.\nTrying to pre-sort the values like mydf.sort_values(['myValueField'], ascending=False) does not change the result as seaborn does not seem to interpret it.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":144,"Q_Id":39817545,"Users Score":1,"Answer":"Do you save the changes by pd.sort_values? If not, probably you have to add the inplace keyword:\nmydf.sort_values(['myValueField'], ascending=False, inplace=True)","Q_Score":1,"Tags":"python,sorting,plot,bar-chart,seaborn","A_Id":39817575,"CreationDate":"2016-10-02T13:33:00.000","Title":"Python sorted plot","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How can I convert 1 column and the index of a Pandas dataframe with several columns to a Numpy array with the dates lining up with the correct column value from the dataframe?\nThere are a few issues here with data type and its driving my nuts trying to get both the index and the column out and into the one array!!\nHelp would be much appreciated!","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":4044,"Q_Id":39832735,"Users Score":4,"Answer":"If A is dataframe and col the column:\nimport pandas as pd\noutput = pd.np.column_stack((A.index.values, A.col.values))","Q_Score":2,"Tags":"python,arrays,pandas,numpy,dataframe","A_Id":39833244,"CreationDate":"2016-10-03T13:23:00.000","Title":"Convert Pandas Dataframe Date Index and Column to Numpy Array","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have re-installed Anaconda2. \nAnd I got the following error when 'python -c 'import tensorflow''\n\nImportError: \/home\/jj\/anaconda2\/bin\/..\/lib\/libstdc++.so.6: version `CXXABI_1.3.8' not found (required by \/home\/jj\/anaconda2\/lib\/python2.7\/site-packages\/tensorflow\/python\/_pywrap_tensorflow.so)\n\nenvironment\n\nCUDA8.0\ncuDNN 5.1\ngcc 5.4.1\ntensorflow r0.10 \nAnaconda2 : 4.2\n\nthe following is in bashrc file\n\nexport PATH=\"\/home\/jj\/anaconda2\/bin:$PATH\"\nexport CUDA_HOME=\/usr\/local\/cuda-8.0\nexport PATH=\/usr\/local\/cuda-8.0\/bin${PATH:+:${PATH}}\nexport LD_LIBRARY_PATH=\/usr\/local\/cuda-8.0\/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}","AnswerCount":4,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":24320,"Q_Id":39844772,"Users Score":13,"Answer":"I solved this problem by copying the libstdc++.so.6 file which contains version CXXABI_1.3.8. \nTry run the following search command first:\n$ strings \/usr\/lib\/x86_64-linux-gnu\/libstdc++.so.6 | grep CXXABI_1.3.8\nIf it returns CXXABI_1.3.8. Then you can do the copying.\n$ cp \/usr\/lib\/x86_64-linux-gnu\/libstdc++.so.6 \/home\/jj\/anaconda2\/bin\/..\/lib\/libstdc++.so.6","Q_Score":12,"Tags":"python,tensorflow","A_Id":39856855,"CreationDate":"2016-10-04T05:30:00.000","Title":"\"'CXXABI_1.3.8' not found\" in tensorflow-gpu - install from source","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"In TensorFlow it is pretty straight forward to visualize filters and activation layers given a single input.\nBut I'm more interested in the opposite way: feeding a class (as one-hot vector) to the output layer and see something like the optimal input image for that specific class.\nIs there a way to do so or to run the graph reversed?\nBackground: I'm using Googles Inception V3 with 15 classes and I've trained the network already with a large amount of data up to a good precision. Now I'm interested in understanding why and how the model distinguishes the different classes.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":95,"Q_Id":39856291,"Users Score":1,"Answer":"The \"basic\" version of this is straightforward. You use the same graph as for training the network, but instead of optimizing w.r.t. the parameters of the network, you optimize w.r.t the input (which has to be a variable with the shape of your input image). Your optimization target is the negative (because you want to maximize, but TF optimizers minimize) logit of your target class. You want to run it with a couple of different initial values for the image.\nThere's also a few related techniques, if you search for DeepDream and adversarial examples you should find a lot of literature.","Q_Score":1,"Tags":"python-2.7,tensorflow,deep-learning","A_Id":39858096,"CreationDate":"2016-10-04T15:29:00.000","Title":"How to visualize DNNs dependent of the output class in TensorFlow?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have created a rather large CSV file (63000 rows and around 40 columns) and I want to join it with an ESRI Shapefile.\nI have used ArcPy but the whole process takes 30! minutes. If I make the join with the original (small) CSV file, join it with the Shapefile and then make my calculations with ArcPy and continously add new fields and calculate the stuff it takes 20 minutes. I am looking for a faster solution and found there are other Python modules such as PySHP or DBFPy but I have not found any way for joining tables, hoping that could go faster.\nMy goal is already to get away from ArcPy as much as I can and preferable only use Python, so preferably no PostgreSQL and alikes either.\nDoes anybody have a solution for that? Thanks a lot!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":377,"Q_Id":39868163,"Users Score":0,"Answer":"Not exactly a programmatical solution for my problem but a practical one:\nMy shapefile is always static, only the attributes of the features will change. So I copy my original shapefile (only the basic files with endings .shp, .shx, .prj) to my output folder and rename it to the name I want. \nThen I create my CSV-File with all calculations and convert it to DBF and save it with the name of my new shapefile to the output folder too. ArcGIS will now load the shapefile along with my own DBF file and I don't even need to do any tablejoin at all!\nNow my program runs through in only 50 seconds!\nI am still interested in more solutions for the table join problem, maybe I will encounter that problem again in the future where the shapefile is NOT always static. I did not really understand Nan's solution, I am still at \"advanced beginner\" level in Python :)\nCheers","Q_Score":2,"Tags":"python-2.7,dbf,arcpy,pyshp","A_Id":39892009,"CreationDate":"2016-10-05T07:45:00.000","Title":"DBF Table Join without using Arcpy?","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Suppose I have N images which are a multiresolution representation of a single image (the Nth image being the coarsest one). If my finest scale is a  16x16 image, the next scale is a 8x8 image and so on.\nHow should I store such data to fastly be able to access, at a given scale and for a given pixel, its unique parent in the next coarser scale and its children at each finest scale?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":46,"Q_Id":39882632,"Users Score":2,"Answer":"You could just use a list of numpy arrays. Assuming a scale factor of two, for the i,jth pixel at scale n:\n\nThe indices of its \"parent\" pixel at scale n-1 will be (i\/\/2, j\/\/2)\nIts \"child\" pixels at scale n+1 can be indexed by (slice(2*i, 2*(i+1)), slice(2*j, 2*(j+1)))","Q_Score":0,"Tags":"python,numpy,image-processing","A_Id":39883453,"CreationDate":"2016-10-05T19:56:00.000","Title":"How would you store a pyramidal image representation in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a network edgelist and I want to use the Label Spreading\/Label Propagation algorithm from scikit-learn. I have a set of nodes that are labeled and want to spread the labels on the unlabeled portion of the network. I can generate the adjacency matrix or confusion matrix if needed. \nCan someone point me in the right direction using scikit?  The documentation seems so limited in what I can do with it.\nThank you in advance.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":620,"Q_Id":39908430,"Users Score":1,"Answer":"To use Label Spreading you should follow these steps:\n1. create a vector of labels (y), where all the unlabeled instances are set to -1.\n2. fit the model using your feature data (X) and y.\n3. create predict_entropies vector using stats.distributions.entropy(yourmodelname.label_distributions_.T)\n4. create an uncertainty index by sorting the predict_entropies vector.\n5. send the samples of lowest certainty for label query.\nI hope this framework will help you get started.","Q_Score":0,"Tags":"python,scikit-learn","A_Id":40723826,"CreationDate":"2016-10-07T02:27:00.000","Title":"Is it possible to use label spreading scikit algorithm on edgelist?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am engaged in a competition where we have to build a system using given data set. I am trying to learn the proceedings in linguistics research.\nThe main goal of this task is to identify the sentence level sentiment polarity of the code-mixed dataset of Indian languages pairs. Each of the sentences is annotated with language information as well as polarity at the sentence level. \nAnyone interested to participate with me??\nIf anyone can help me over it. It will be great.\nPlease reach me out soon as possible.","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":165,"Q_Id":39925325,"Users Score":0,"Answer":"I think easy solution is remove navbar-inverse class and place this css.\n.navbar {\n    background-color: blue;\n}","Q_Score":0,"Tags":"python-3.x,machine-learning,nlp,stanford-nlp,opennlp","A_Id":39926150,"CreationDate":"2016-10-07T20:29:00.000","Title":"NLP Code Mixed : Code Switching","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to setup keras deep learning library for Python3.5 on Ubuntu 16.04 LTS and use Tensorflow as a backend. I have Python2.7 and Python3.5 installed. I have installed Anaconda and with help of it  Tensorflow, numpy, scipy, pyyaml. Afterwards I have installed keras with command\n\nsudo python setup.py install\n\nAlthough I can see \/usr\/local\/lib\/python3.5\/dist-packages\/Keras-1.1.0-py3.5.egg directory, I cannot use keras library. When I try to import it in python it says\n\nImportError: No module named 'keras'\n\nI have tried to install keras usingpip3, but got the same result. \nWhat am I doing wrong? Any Ideas?","AnswerCount":4,"Available Count":2,"Score":0.1973753202,"is_accepted":false,"ViewCount":129794,"Q_Id":39930952,"Users Score":4,"Answer":"I had pip referring by default to pip3, which made me download the libs for python3. On the contrary I launched the shell as python (which opened python 2) and the library wasn't installed there obviously. \nOnce I matched the names pip3 -> python3, pip -> python (2) all worked.","Q_Score":29,"Tags":"python,ubuntu,tensorflow,anaconda,keras","A_Id":50913862,"CreationDate":"2016-10-08T09:46:00.000","Title":"Cannot import keras after installation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to setup keras deep learning library for Python3.5 on Ubuntu 16.04 LTS and use Tensorflow as a backend. I have Python2.7 and Python3.5 installed. I have installed Anaconda and with help of it  Tensorflow, numpy, scipy, pyyaml. Afterwards I have installed keras with command\n\nsudo python setup.py install\n\nAlthough I can see \/usr\/local\/lib\/python3.5\/dist-packages\/Keras-1.1.0-py3.5.egg directory, I cannot use keras library. When I try to import it in python it says\n\nImportError: No module named 'keras'\n\nI have tried to install keras usingpip3, but got the same result. \nWhat am I doing wrong? Any Ideas?","AnswerCount":4,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":129794,"Q_Id":39930952,"Users Score":0,"Answer":"Firstly checked the list of installed Python packages by:\npip list | grep -i keras\nIf there is keras shown then install it by:\npip install keras --upgrade --log .\/pip-keras.log\nnow check the log, if there is any pending dependencies are present, it will affect your installation. So remove dependencies and then again install it.","Q_Score":29,"Tags":"python,ubuntu,tensorflow,anaconda,keras","A_Id":55900347,"CreationDate":"2016-10-08T09:46:00.000","Title":"Cannot import keras after installation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to do object recognition in an embedded environment, and for this I'm using Raspberry Pi (Specifically version 2).\nI'm using OpenCV Library and as of now I'm using feature detection algorithms contained in OpenCV.\nSo far I've tried different approaches:\n\nI tried different keypoint extraction and description algorithms: SIFT, SURF, ORB. SIFT and SURF are too heavy and ORB is not so good.\nThen I tried using different algorithms for keypoint extraction and then description. The first approach was to use FAST algorithm to extract key points and then ORB or SURF for description, the results were not good and not rotation invariant, then i tried mixing the others.\n\nI now am to the point where I get the best results time permitting using ORB for keypoint extraction and SURF for description. But it is still really slow.\nSo do you have any suggestions or new ideas to obtain better results? Am I missing something?\nAs additional information, I'm using Python 3.5 with OpenCV 3.1","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":406,"Q_Id":39936967,"Users Score":0,"Answer":"I have done similar project in my Masters Degree. \nI had used Raspberry Pi 3 because it is faster than Pi 2 and has more resources for image processing. \nI had used KNN algorithm in OpenCV for Number Detection. It was fast and had good efficiency. \nThe main advantage of KNN algorithm is it is very light weight.","Q_Score":1,"Tags":"python,algorithm,opencv,raspberry-pi,computer-vision","A_Id":41820410,"CreationDate":"2016-10-08T20:19:00.000","Title":"Feature detection for embedded platform OpenCV","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have several images (their number might increase over time) and their corresponding annotated images - let's call them image masks.\nI want to convert the original images to Grayscale and the annotated masks to Binary images (B&W) and then save the gray scale values in a Pandas DataFrame\/CSV file based on the B&W pixel coordinates. \nSo that means a lot of switching back and forth the original image and the binary images.\nI don't want to read every time the images from file because it might be very time consuming.\nAny suggestion which data structure should be used for storing several types of images in Python?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":783,"Q_Id":39957657,"Users Score":0,"Answer":"I used several lists and list.append() for storing the image.\nFor finding the white regions in the black & white images I used cv2.findNonZero().","Q_Score":0,"Tags":"python,image,algorithm,opencv,image-processing","A_Id":39973094,"CreationDate":"2016-10-10T11:53:00.000","Title":"store multiple images efficiently in Python data structure","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have several images (their number might increase over time) and their corresponding annotated images - let's call them image masks.\nI want to convert the original images to Grayscale and the annotated masks to Binary images (B&W) and then save the gray scale values in a Pandas DataFrame\/CSV file based on the B&W pixel coordinates. \nSo that means a lot of switching back and forth the original image and the binary images.\nI don't want to read every time the images from file because it might be very time consuming.\nAny suggestion which data structure should be used for storing several types of images in Python?","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":783,"Q_Id":39957657,"Users Score":1,"Answer":"PIL and Pillow are only marginally useful for this type of work. \nThe basic algorithm used for \"finding and counting\" objects like you are trying to do goes something like this: 1. Conversion to grayscale 2. Thresholding (either automatically via Otsu method, or similar, or by manually setting the threshold values) 3. Contour detection 4. Masking and object counting based on your contours. \nYou can just use a Mat (of integers, Mat1i) would be Data structure fits in this scenario.","Q_Score":0,"Tags":"python,image,algorithm,opencv,image-processing","A_Id":39973408,"CreationDate":"2016-10-10T11:53:00.000","Title":"store multiple images efficiently in Python data structure","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a sparse matrix X, shape (6000, 300). I'd like something like a scatterplot which has a dot where the X(i, j) != 0, and blank space otherwise. I don't know how many nonzero entries there are in each row of X. X[0] has 15 nonzero entries, X[1] has 3, etc. The maximum number of nonzero entries in a row is 16.\nAttempts:\n\nplt.imshow(X) results in a tall, skinny graph because of the shape of X. Using plt.imshow(X, aspect='auto) will stretch out the graph horizontally, but the dots get stretched out to become ellipses, and the plot becomes hard to read.\nax.spy suffers from the same problem. \nbokeh seems promising, but really taxes my jupyter kernel. \n\nBonus:\n\nThe nonzero entries of X are positive real numbers. If there was some way to reflect their magnitude, that would be great as well (e.g. colour intensity, transparency, or across a colour bar).\nEvery 500 rows of X belong to the same class. That's 12 classes * 500 observations (rows) per class = 6000 rows. E.g. X[:500] are from class A, X[500:1000] are from class B, etc. Would be nice to colour-code the dots by class. For the moment I'll settle for manually including horizontal lines every 500 rows to delineate between classes.","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":2877,"Q_Id":39970515,"Users Score":0,"Answer":"It seems to me heatmap is the best candidate for this type of plot. imshow() will return u a colored matrix with color scale legend. \nI don't get ur stretched ellipses problem, shouldnt it be a colored squred for each data point? \nu can try log color scale if it is sparse. also plot the 12 classes separately to analyze if theres any inter-class differences.","Q_Score":1,"Tags":"python,matplotlib","A_Id":39971118,"CreationDate":"2016-10-11T04:29:00.000","Title":"Python: Plot a sparse matrix","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a sparse matrix X, shape (6000, 300). I'd like something like a scatterplot which has a dot where the X(i, j) != 0, and blank space otherwise. I don't know how many nonzero entries there are in each row of X. X[0] has 15 nonzero entries, X[1] has 3, etc. The maximum number of nonzero entries in a row is 16.\nAttempts:\n\nplt.imshow(X) results in a tall, skinny graph because of the shape of X. Using plt.imshow(X, aspect='auto) will stretch out the graph horizontally, but the dots get stretched out to become ellipses, and the plot becomes hard to read.\nax.spy suffers from the same problem. \nbokeh seems promising, but really taxes my jupyter kernel. \n\nBonus:\n\nThe nonzero entries of X are positive real numbers. If there was some way to reflect their magnitude, that would be great as well (e.g. colour intensity, transparency, or across a colour bar).\nEvery 500 rows of X belong to the same class. That's 12 classes * 500 observations (rows) per class = 6000 rows. E.g. X[:500] are from class A, X[500:1000] are from class B, etc. Would be nice to colour-code the dots by class. For the moment I'll settle for manually including horizontal lines every 500 rows to delineate between classes.","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":2877,"Q_Id":39970515,"Users Score":0,"Answer":"plt.matshow also turned out to be a feasible solution. I could also plot a heatmap with colorbars and all that.","Q_Score":1,"Tags":"python,matplotlib","A_Id":40127976,"CreationDate":"2016-10-11T04:29:00.000","Title":"Python: Plot a sparse matrix","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I recently installed ArcGIS10.4 and now when I run python 2.7 programs using Idle (for purposes unrelated to ArcGIS) it uses the version of python attached to ArcGIS.\nOne of the programs I wrote needs an updated version of the pandas module. When I try to update the pandas module in this verion of python (by opening command prompt as an administrator, moving to C:\\Python27\\ArcGIS10.4\\Scripts and using the command pip install --upgrade pandas) the files download ok but there is an access error message when PIP tries to upgrade. I have tried restarting the computer in case something was open.  The error message is quite long and I can't cut and paste from command prompt but it finishes with \n\" Permission denied:  'C:\\Python27\\ArcGIS10.4\\Lib\\site-packages\\numpy\\core\\multiarray.pyd'  \"\nI've tried the command to reinstall pandas completely which also gave an error message.  I've tried installing miniconda in the hope that I could get a second version of python working and then use that version instead of the version attached to ArcMap. However I don't know how to direct Idle to choose the newly installed version. \nSo overall I don't mind having 2 versions of python if someone could tell me how to choose which one runs or if there's some way to update the ArcMap version that would be even better. I don't really want to uninstall ArcMap at the moment.\nAny help is appreciated! Thanks!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":212,"Q_Id":39972261,"Users Score":0,"Answer":"I reinstalled python again directly from python.org and then installed pandas which seems to work.  \nI guess this might stop the ArcMap version of python working properly but since I'm not using python with ArcMap at the moment it's not a big problem.","Q_Score":0,"Tags":"python,pandas,upgrade,arcmap","A_Id":39972738,"CreationDate":"2016-10-11T07:25:00.000","Title":"How to update pandas when python is installed as part of ArcGIS10.4, or another solution","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"As the title says, I am using fit_transform with the CountVectorizer on the training data .. and then I am using tranform only with the testing data ... will this gives me the same as using fit only on the training and tranform only on the testing data ?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":2610,"Q_Id":40002232,"Users Score":1,"Answer":"If you use fit only on the training and transform on the test data, you won't get the correct result.\nWhen using fit_transform on the training data, it means that the machine is learning from the parameters in the feature space and also transforming (scaling) the training data. On the other hand, you should only use transform on the test data to scale it according to the parameters learned from the training data.","Q_Score":0,"Tags":"python,scikit-learn","A_Id":44608692,"CreationDate":"2016-10-12T15:14:00.000","Title":"fit_transform with the training data and transform with the testing","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using scatter_matrix for correlation visualization and calculating correlation values using corr().  Is it possible to have the scatter_matrix visualization draw the regression line in the scatter plots?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":674,"Q_Id":40004334,"Users Score":0,"Answer":"I think this is a misleading question\/thought process. \nIf you think of data in strictly 2 dimension then a regression line on a scatter plot makes sense. But let's say you have 5 dimensions of data you are plotting in your scatter matrix. In this case the regression for each pair of dimensions is not an accurate representation of the global regression.\nI would be wary presenting that to anyone as I can easily see where it could create confusion.  \nThat being said if you don't care about a regression across all of your dimensions then you could write your own function to do this. A quick walk through of steps may be:\n1. Identify number of dimensions N\n2. Create figure\n3. Double for loop on N, first will walk down rows, second will walk across rows\n4. At each point add subplot, calculate regression (if not kde\/hist position), plot scatter cloud and regression line or kde\/hist","Q_Score":0,"Tags":"python-2.7,pandas","A_Id":40025421,"CreationDate":"2016-10-12T17:01:00.000","Title":"Putting a Regression Line When Using Pandas scatter_matrix","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am starting a new project with a friend of mine, we want to design a system that would alert the driver if the car is diverting from its original path and its dangerous.\nso in a nutshell we have to design a real-time algorithm that would take pictures from the camera and process them. All of this will be done in Python.\nI was wondering if anyone has any advises for us or maybe point out some stuff that we have to consider\nCheers !","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":124,"Q_Id":40007759,"Users Score":0,"Answer":"You can search for this libraries: dlib, PIL (pillow), opencv and scikit learn image. This libraries are image processing libraries for python.\nHope it helps.","Q_Score":0,"Tags":"python,image-processing","A_Id":40007828,"CreationDate":"2016-10-12T20:27:00.000","Title":"Digital Image Processing via Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have recently started to use NLTK toolkit for creating few solutions using Python.\nI hear a lot of community activity regarding using Stanford NLP.\nCan anyone tell me the difference between NLTK and Stanford NLP? Are they two different libraries? I know that NLTK has an interface to Stanford NLP but can anyone throw some light on few basic differences or even more in detail.\nCan Stanford NLP be used using Python?","AnswerCount":7,"Available Count":2,"Score":0.0285636566,"is_accepted":false,"ViewCount":15755,"Q_Id":40011896,"Users Score":1,"Answer":"NLTK can be used for the learning phase to and perform natural language process from scratch and basic level.\nStandford NLP gives you high-level flexibility to done task very fast and easiest way.\nIf you want fast and production use, can go for Standford NLP.","Q_Score":30,"Tags":"python,nlp,nltk,stanford-nlp","A_Id":57003384,"CreationDate":"2016-10-13T03:36:00.000","Title":"NLTK vs Stanford NLP","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have recently started to use NLTK toolkit for creating few solutions using Python.\nI hear a lot of community activity regarding using Stanford NLP.\nCan anyone tell me the difference between NLTK and Stanford NLP? Are they two different libraries? I know that NLTK has an interface to Stanford NLP but can anyone throw some light on few basic differences or even more in detail.\nCan Stanford NLP be used using Python?","AnswerCount":7,"Available Count":2,"Score":0.0285636566,"is_accepted":false,"ViewCount":15755,"Q_Id":40011896,"Users Score":1,"Answer":"I would add to this answer that if you are looking to parse date\/time events StanfordCoreNLP contains SuTime which is the best datetime parser available. The support for arbitrary texts like 'Next Monday afternoon' is not present in any other package.","Q_Score":30,"Tags":"python,nlp,nltk,stanford-nlp","A_Id":50968392,"CreationDate":"2016-10-13T03:36:00.000","Title":"NLTK vs Stanford NLP","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"1) I have Spark on Bluemix platform, how do I add a library there ?\nI can see the preloaded libraries but cant add a library that I want.\nAny command line argument that will install a library?\n\npip install --package is not working there\n\n2)  I have Spark and Mongo DB running, but I am not able to connect both of them.\n\ncon ='mongodb:\/\/admin:ITCW....ssl=true'\n  ssl1 =\"LS0tLS .....\"\n  client = MongoClient(con,ssl=True)\n  db = client.mongo11 \n  collection = db.mongo11 \n  ff=db.sammy.find() \n\n Error I am getting is : \nSSL handshake failed: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed (_ssl.c:590)","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":106,"Q_Id":40020767,"Users Score":2,"Answer":"In a Python notebook:\n!pip install <package>\nand then\nimport <package>","Q_Score":0,"Tags":"python,apache-spark,ibm-cloud,ibm-cloud-plugin","A_Id":40021035,"CreationDate":"2016-10-13T12:17:00.000","Title":"Add a library in Spark in Bluemix & connect MongoDB , Spark together","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to write an algorithm that can work out the 'unexpectedness' or 'information complexity' of a sentence. More specifically I'm trying to sort a set of sentences so the least complex come first. \nMy thought was that I could use a compression library, like zlib?, 'pre train' it on a large corpus of text in the same language (call this the 'Corpus') and then append to that corpus of text the different sentences.\nThat is I could define the complexity measure for a sentence to be how many more bytes it requires to compress the whole corpus with that sentence appended, versus the whole corpus with a different sentence appended. (The fewer extra bytes, the more predictable or 'expected' that sentence is, and therefore the lower the complexity). Does that make sense?\nThe problem is with trying to find the right library that will let me do this, preferably from python.\nI could do this by literally appending sentences to a large corpus and asking a compression library to compress the whole shebang, but if possible, I'd much rather halt the processing of the compression library at the end of the corpus, take a snapshot of the relevant compression 'state', and then, with all that 'state' available try to compress the final sentence. (I would then roll back to the snapshot of the state at the end of the corpus and try a different sentence).\nCan anyone help me with a compression library that might be suitable for this need? (Something that lets me 'freeze' its state after 'pre training'.)\nI'd prefer to use a library I can call from Python, or Scala. Even better if it is pure python (or pure scala)","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":101,"Q_Id":40034334,"Users Score":2,"Answer":"All this is going to do is tell you whether the words in the sentence, and maybe phrases in the sentence, are in the dictionary you supplied. I don't see how that's complexity. More like grade level. And there are better tools for that. Anyway, I'll answer your question.\nYes, you can preset the zlib compressor a dictionary. All it is is up to 32K bytes of text. You don't need to run zlib on the dictionary or \"freeze a state\" -- you simply start compressing the new data, but permit it to look back at the dictionary for matching strings. However 32K isn't very much. That's as far back as zlib's deflate format will look, and you can't load much of the English language in 32K bytes.\nLZMA2 also allows for a preset dictionary, but it can be much larger, up to 4 GB. There is a Python binding for the LZMA2 library, but you may need to extend it to provide the dictionary preset function.","Q_Score":1,"Tags":"python,scala,compression,information-theory","A_Id":40046686,"CreationDate":"2016-10-14T03:12:00.000","Title":"Using compression library to estimate information complexity of an english sentence?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"There seem to be a few options for exporting PMML models out of scikit-learn, such as sklearn2pmml, but a lot less information going in the other direction.  My case is an XGboost model previously built in R, and saved to PMML using r2pmml, that I would like to use in Python.  Scikit normally uses pickle to save\/load models, but is it also possible to import models into scikit-learn using PMML?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2878,"Q_Id":40048987,"Users Score":3,"Answer":"You can't connect different specialized representations (such as R and Scikit-Learn native data structures) over a generalized representation (such as PMML). You may have better luck trying to translate R data structures to Scikit-Learn data structures directly.\nXGBoost is really an exception to the above rule, because its R and Scikit-Learn implementations are just thin wrappers around the native XGBoost library. Inside a trained R XGBoost object there's a blob raw, which is the model in its native XGBoost representation. Save it to a file, and load in Python using the xgb.Booster.load_model(fname) method. \nIf you know that you need to the deploy XGBoost model in Scikit-Learn, then why do you train it in R?","Q_Score":7,"Tags":"python,r,scikit-learn,pmml","A_Id":40049831,"CreationDate":"2016-10-14T17:36:00.000","Title":"Importing PMML models into Python (Scikit-learn)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Tensorflow: Converting a tenor [B, T, S] to list of B tensors shaped [T, S]. Where B, T, S are whole positive numbers ...\nHow can I convert this? I can't do eval because no session is running at the time I want to do this.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":76,"Q_Id":40065396,"Users Score":0,"Answer":"It sounds like you want tf.unpack()","Q_Score":0,"Tags":"python,tensorflow,deep-learning,lstm","A_Id":40065428,"CreationDate":"2016-10-15T23:49:00.000","Title":"Tensorflow: Converting a tenor [B, T, S] to list of B tensors shaped [T, S]","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I pretty new to python, and I'm looking for the most efficient pythonic way to interpolate from a grid to another one.\nThe original grid is a structured grid (the terms regular or rectangular grid are also used), and the spacing is not uniform.\nThe new grid is a regularly spaced grid. Both grids are 2D. For now it's ok using a simple interpolation method like linear interpolation, pheraps in the future I could try something different like bicubic.\nI'm aware that there are methods to interpolate from an irregular grid to a regular one, however given the regular structure of my original grid, more efficient methods should be available.\nAfter searching in the scipy docs, I have found 2 methods that seem to fit my case:  scipy.interpolate.RegularGridInterpolator and scipy.interpolate.RectBivariateSpline.\nI don't understand the difference between the two functions, which one should I use?\nIs the difference purely in the interpolation methods? Also, while the non-uniform spacing of the original grid is explicitly mentioned in RegularGridInterpolator, RectBivariateSpline doc is silent about it.\nThanks,\nAndrea","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":401,"Q_Id":40085367,"Users Score":2,"Answer":"RectBivariateSpline\nImagine your grid as a canyon, where the high values are peaks and the low values are valleys. The bivariate spline is going to try to fit a thin sheet over that canyon to interpolate. This will still work on irregularly spaced input, as long as the x and y array you supply are also irregularly spaced, and everything still lies on a rectangular grid.\nRegularGridInterpolator\nSame canyon, but now we'll linearly interpolate the surrounding gridpoints to interpolate. We'll assume the input data is regularly spaced to save some computation. It sounds like this won't work for you.\nNow What?\nBoth of these map 2D-1D. It sounds like you have an irregular input space with, but rectangularly spaced sample points, and an output space with regularly spaced sample points. You might just try LinearNDInterpolator, since you're in 2D it won't be that much more expensive.\nIf you're trying to interpolate a mapping between two 2D things, you'll want to do two interpolations, one that interpolates (x1, y1) -> x2 and one that interpolates (x1, y1) -> y2.\nVstacking the output of those will give you an array of points in your output space.\nI don't know of a more efficient method in scipy for taking advantage of the expected structure of the interpolation output, given an irregular grid input.","Q_Score":0,"Tags":"python,numpy,scipy,grid","A_Id":40087542,"CreationDate":"2016-10-17T11:36:00.000","Title":"Which scipy function should I use to interpolate from a rectangular grid to regularly spaced rectangular grid in 2D?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to run a neural network with pycaffe on gpu.\nThis works when I call the script for the first time.\nWhen I run the same script for the second time, CUDA throws the error in the title.\nBatch size is 1, image size at this moment is 243x322, the gpu has 8gb RAM.\nI guess I am missing a command that resets the memory?\nThank you very much!\nEDIT: \nMaybe I should clarify a few things: I am running caffe on windows. \nWhen i call the script with python script.py, the process terminates and the gpu memory gets freed, so this works.\nWith ipython, which I use for debugging, the GPU memory indeed does not get freed (after one pass, 6 of the 8 bg are in use, thanks for the nvidia-smi suggestion!)\nSo, what I am looking for is a command I can call from within pyhton, along the lines of:\nrun network\nprocess image output\nfree gpu memory","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":2965,"Q_Id":40090892,"Users Score":2,"Answer":"This happens when you run out of memory in the GPU. Are you sure you stopped the first script properly? Check the running processes on your system (ps -A in ubuntu) and see if the python script is still running. Kill it if it is. You should also check the memory being used in your GPU (nvidia-smi).","Q_Score":3,"Tags":"python,gpu,caffe","A_Id":40091765,"CreationDate":"2016-10-17T16:01:00.000","Title":"Check failed: error == cudaSuccess (2 vs. 0) out of memory","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am reading other's pickle file that may have data type based on xarray. Now I cannot read in the pickle file with the error \"No module named core.dataset\".\nI guess this maybe a xarray issue. My collaborator asked me to change my version to his version and try again.\nMy version is 0.8.2, and his version 0.8.0. So how can I change back to his version?\nThanks!","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":999,"Q_Id":40099001,"Users Score":1,"Answer":"Use \"conda install xarray==0.8.0\" if you're using anaconda, or \"pip install xarray==0.8.0\" otherwise.","Q_Score":1,"Tags":"python-xarray","A_Id":40099554,"CreationDate":"2016-10-18T03:26:00.000","Title":"how to install previous version of xarray","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"So I have OpenCV on my computer all sorted out, I can use it in C\/C++ and the Python 2.7.* that came with my OS.\nMy computer runs on Linux Deepin and whilst I usually use OpenCV on C++, I need to use Python 3.4.3 for some OpenCV tasks.\nProblem is, I've installed python 3.4.3 now but whenever I try to run an OpenCV program on it, it doesn't recognize numpy or cv2, the modules I need for OpenCV. I've already built and installed OpenCV and I'd rather not do it again\nIs there some way I can link my new Python 3.4.3 environment to numpy and the opencv I already built so I can use OpenCV on Python 3.4.3?\nThanks in advance","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1213,"Q_Id":40109379,"Users Score":0,"Answer":"You can try:\n\nDownload the OpenCV module\nCopy the .\/opencv\/build\/python\/3.4\/x64\/cv2.pyd file \nTo the python installation directory path: .\/Python34\/Lib\/site-packages.\n\nI hope this helps","Q_Score":1,"Tags":"python,python-2.7,python-3.x,opencv,numpy","A_Id":40109662,"CreationDate":"2016-10-18T13:16:00.000","Title":"How do I link python 3.4.3 to opencv?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I`m using scipy.optimize.curve_fit for fitting a sigmoidal curve to data. I need to bound one of parameters from [-3, 0.5] and [0.5, 3.0]\nI tried fit curve without bounds, and next if parameter is lower than zero, I fit once more with bounds [-3, 0.5] and in contrary  with[0.5, 3.0]\nIs it possible, to bound function curve_fit with two intervals?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":569,"Q_Id":40110260,"Users Score":0,"Answer":"No, least_squares (hence curve_fit) only supports box constraints.","Q_Score":0,"Tags":"python,scipy,curve-fitting","A_Id":40116907,"CreationDate":"2016-10-18T13:56:00.000","Title":"Python, scipy, curve_fit, bounds: How can I constraint param by two intervals?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to use the XGBClassifier wrapper provided by sklearn for a multiclass problem. My classes are [0, 1, 2], the objective that I use is multi:softmax. When I am trying to fit the classifier I get \n\nxgboost.core.XGBoostError: value 0for Parameter num_class should be greater equal to 1\n\nIf I try to set the num_class parameter the I get the error\n\ngot an unexpected keyword argument 'num_class'\n\nSklearn is setting this parameter automatically so I am not supposed to pass that argument. But why do I get the first error?","AnswerCount":4,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":14175,"Q_Id":40116215,"Users Score":17,"Answer":"In my case, the same error was thrown during a regular fit call. The root of the issue was that the objective was manually set to multi:softmax, but there were only 2 classes. Changing it to binary:logistic solved the problem.","Q_Score":20,"Tags":"python,scikit-learn,xgboost","A_Id":51822131,"CreationDate":"2016-10-18T19:11:00.000","Title":"xgboost sklearn wrapper value 0for Parameter num_class should be greater equal to 1","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have tried a lot of online posts to install opencv but they are not working for Ubuntu 16.04. May anyone please give me the steps to install openCV 2.4.13 on it?","AnswerCount":4,"Available Count":1,"Score":0.049958375,"is_accepted":false,"ViewCount":29525,"Q_Id":40128751,"Users Score":1,"Answer":"sudo apt-get install build-essential cmake git pkg-config\nsudo apt-get install libjpeg8-dev libtiff4-dev libjasper-dev libpng12-dev\nsudo apt-get install libgtk2.0-dev\nsudo apt-get install libavcodec-dev libavformat-dev libswscale-dev libv4l-dev\nsudo apt-get install libatlas-base-dev gfortran\nsudo apt-get install python2.7-dev\nsudo pip install numpy\nsudo apt-get install python-opencv\n\nThen you can have a try:\n\n$ python\nPython 2.7.6 (default, Oct 26 2016, 20:30:19) \n[GCC 4.8.4] on linux2\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\n>>> import cv\n>>> import cv2","Q_Score":10,"Tags":"python-2.7,opencv,ubuntu","A_Id":45497131,"CreationDate":"2016-10-19T10:28:00.000","Title":"How to install openCV 2.4.13 for Python 2.7 on Ubuntu 16.04?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I plotted some points on google maps using gmplot's scatter method (python). I want to add some text to the points so when someone clicks on those points they can see the text.\nI am unable to find any documentation or example that shows how to do this. \nAny pointers are appreciated.","AnswerCount":1,"Available Count":1,"Score":0.761594156,"is_accepted":false,"ViewCount":8147,"Q_Id":40142959,"Users Score":5,"Answer":"Just looking for the answer to this myself. gmplot was updated to June  2016 to include a hovertext functionality for the marker method, but unfortunately this isn't available for the scatter method. The enthusiastic user will find that the scatter method simply calls the marker method over and over, and could modify the scatter method itself to accept a title or range of titles.\nIf like myself you are using an older version, make sure to run \npip install --upgrade gmplot\nand to place a marker with hovertext (mouse hovering over pin without clicking)\n\ngmap=gmplot.GoogleMapPlotter(\"Seattle\")\ngmap.marker(47.61028142523736, -122.34147349538826, title=\"A street corner in Seattle\")\nst=\"testmap.html\"\ngmap.draw(st)","Q_Score":3,"Tags":"python","A_Id":41708567,"CreationDate":"2016-10-19T23:10:00.000","Title":"Add text to scatter point using python gmplot","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a pandas dataframe called \"df_combo\" which contains columns \"worker_id\", \"url_entrance\", \"company_name\". I am trying to produce an output column that would tell me if the URLs in \"url_entrance\" column contains any word in \"company_name\" column. Even a close match like fuzzywuzzy would work. \nFor example, if the URL is \"www.grandhotelseattle.com\" and the \"company_name\" is \"Hotel Prestige Seattle\", then the fuzz ratio might be somewhere 70-80. \nI have tried the following script: \n>>>fuzz.ratio(df_combo['url_entrance'],df_combo['company_name']) \nbut it returns only 1 number which is the overall fuzz ratio for the whole column. I would like to have fuzz ratio for every row and store those ratios in a new column.","AnswerCount":1,"Available Count":1,"Score":0.6640367703,"is_accepted":false,"ViewCount":3169,"Q_Id":40143675,"Users Score":4,"Answer":"Thanks everyone for your inputs. I have solved my problem! The link that \"agg3l\" provided was helpful. The \"TypeError\" I saw was because either the \"url_entrance\" or \"company_name\" has some floating types in certain rows. I converted both columns to string using the following scripts, re-ran the fuzz.ratio script and got it to work!\n\n\n\ndf_combo['url_entrance']=df_combo['url_entrance'].astype(str)\n      df_combo['company_name']=df_combo['company_name'].astype(str)","Q_Score":2,"Tags":"python,python-3.x,pandas,fuzzywuzzy","A_Id":40163636,"CreationDate":"2016-10-20T00:36:00.000","Title":"fuzzy match between 2 columns (Python)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I build and installed tensorflow in my ubuntu 16.04 with gpu. In command line I can easily activate tensorflow environment but while I try to run the code through spyder it show this : \"No module named tensorflow.examples.tutorials.mnist\"\nhow can I run my python code from spyder with tensorflow?","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":771,"Q_Id":40166386,"Users Score":2,"Answer":"Enter the enviornment\nsource activate tensorflow\ninstall spyder\nconda install spyder\nRun spyder\nspyder\n\n`","Q_Score":0,"Tags":"python-2.7,ubuntu,tensorflow,spyder","A_Id":40695844,"CreationDate":"2016-10-21T00:03:00.000","Title":"spyder cant load tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"What is the difference between \nnumpy.zeros(n)\nand \nnumpy.zeros(n,1)?\nThe output for the first statement is\n[0 0 ..... n times]\nwhereas the second one is \n([0]\n [0]\n.... n rows)","AnswerCount":1,"Available Count":1,"Score":0.537049567,"is_accepted":false,"ViewCount":1550,"Q_Id":40188251,"Users Score":3,"Answer":"The first argument indicates the shape of the array. A scalar argument implies a \"flat\" array (vector), whereas a tuple argument is interpreted as the dimensions of a tensor. So if the argument is the tuple (m,n), numpy.zeros will return a matrix with m rows and n columns. In your case, it is returning a matrix with n rows and 1 column.\nAlthough your two cases are equivalent in some sense, linear algebra routines that require a vector as input will likely expect something like the first form.","Q_Score":4,"Tags":"python,numpy","A_Id":40188461,"CreationDate":"2016-10-22T04:16:00.000","Title":"Difference between numpy.zeros(n) and numpy.zeros(n,1)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a CSV file and I want to check if the first row has only strings in it (ie a header). I'm trying to avoid using any extras like pandas etc. I'm thinking I'll use an if statement like if row[0] is a string print this is a CSV but I don't really know how to do that :-S any suggestions?","AnswerCount":8,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":24553,"Q_Id":40193388,"Users Score":0,"Answer":"I think the best way to check this is -> simply reading 1st line from file and then match your string instead of any library.","Q_Score":11,"Tags":"python,python-2.7,csv","A_Id":65651852,"CreationDate":"2016-10-22T14:38:00.000","Title":"How to check if a CSV has a header using Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to machine learning and Python. I am trying to understand when to use the functions in sklearn.linear_model (linearregression and logisticregression) and when to implement my own code for the same. Any suggestions or references will be highly appreciated.\nregards\nSouvik","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":72,"Q_Id":40224973,"Users Score":0,"Answer":"I recommend you use as much as possible the functions given by sklearn or another ML library (I like TensorFlow). That's because it's very difficult to get the performance of any library. They are calculating in a low level of the operating system, meanwhile the common users don't implement the computational actions outside the python environment.\nMoreover, python by itself is not very efficient in relation to the data structures. For example,a simple array is implemented as a LinkedList. The ML libraries use Numpy to their computations to get a better performance.","Q_Score":0,"Tags":"python,machine-learning","A_Id":40236052,"CreationDate":"2016-10-24T18:22:00.000","Title":"Python machine learning sklearn.linear_model vs custom code","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Right now I'm using CountVectorizer to extract features.  However, I need to count words not seen during fitting.\nDuring transforming, the default behavior of CountVectorizer is to ignore words that were not observed during fitting.  But I need to keep a count of how many times this happens!\nHow can I do this?\nThanks!","AnswerCount":1,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":1618,"Q_Id":40230865,"Users Score":6,"Answer":"There is no inbuilt way in scikit-learn to do this, you need to write some additional code to be able to do this. However you could use the vocabulary_ attribute of CountVectorizer to achieve this.\n\nCache the current vocabulary\nCall fit_transform\nCompute the diff with the new vocabulary and the cached vocabulary","Q_Score":7,"Tags":"python,scikit-learn","A_Id":48947334,"CreationDate":"2016-10-25T03:25:00.000","Title":"CountVectorizer and Out-Of-Vocabulary (OOV) tokens?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"There has a nd array A with shape [100,255,255,3], which correspond to 100 255*255 images.  I would like to iterate this multi-dimensional array, and each iteration I get one image. This is what I do, A1 = A[i,:,:,:] The resulting A1 has shape [255,255,3]. However, i would like to enforce it have the shape [1,255,255,3]. How can I do it?","AnswerCount":5,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1297,"Q_Id":40231128,"Users Score":0,"Answer":"Sure, no problem.  Use 'reshape'.  Assuming A1 is a numpy array\nA1 = A1.reshape([1,255,255,3])\nThis will reshape your matrix.\nIf A1 isn't a numpy array then use\nA1 = numpy.array(A1).reshape([1,255,255,3])","Q_Score":1,"Tags":"python,numpy,scipy","A_Id":40231181,"CreationDate":"2016-10-25T03:56:00.000","Title":"add a dummy dimension for a multi-dimensional array","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to blur around specific regions in a 2D image (the data is an array of size m x n).\nThe points are specified by an m x n mask. cv2 and scikit avaiable.\nI tried:\n\nSimply applying blur filters to the masked image. But that isn't not working.\nExtracting the points to blur by np.nan the rest, blurring and reassembling. Also not working, because the blur obviously needs the surrounding points to work correctly.\n\nAny ideas?\nCheers","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1655,"Q_Id":40235643,"Users Score":2,"Answer":"What was the result in the first case? It sounds like a good approach. What did you expect and what you get?\nYou can also try something like that:\n\nEither create a copy of a whole image or just slightly bigger ROI (to include samples that will be used for blurring)\nApply blur on the created image\nApply masks on two images (from original image take everything except ROI and from blurred image take ROI)\nAdd two masked images\n\nIf you want more smooth transition make sure that masks aren't binary. You can smooth them using another blurring (blur one mask and create the second one by calculating: mask2 = 1 - mask1. By doing so you will be sure that weights always add up to one).","Q_Score":0,"Tags":"python,opencv,image-processing,scikit-image","A_Id":40236282,"CreationDate":"2016-10-25T08:58:00.000","Title":"Python: Blur specific region in an image","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"The method available in python scipy sps.rand() generates sparse matrix of random values in the range (0,1). How can we generate discrete random values greater than 1 like 2, 3,etc. ? Any method in scipy, numpy ?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":258,"Q_Id":40264741,"Users Score":1,"Answer":"sparse.rand calls sparse.random.  random adds a optional data_rvs.\nI haven't used data_rvs.  It can probably emulate the dense randint, but definition is more complicated.\nAnother option is to generate the random floats and then convert them with a bit of math to the desired integers.  You have to be a little careful since some operations will produce a Sparse Efficiency warning.  You want operations that will change the nonzero values without touching the zeros.\n(I suspect the data_rvs parameter was added in newer Scipy version, but I don't see an indication in the docs).","Q_Score":0,"Tags":"python-3.x,scipy,sparse-matrix","A_Id":40268763,"CreationDate":"2016-10-26T14:17:00.000","Title":"Generate random sparse matrix filled with values greater than 1 python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to run a Python script with the pyspark library.\nI create a SparkConf() object using the following commands: \nconf = SparkConf().setAppName('test').setMaster(<spark-URL>)\nWhen I run the script, that line runs into an error:\n\nPicked up _JAVA_OPTIONS: -Xmx128m\nPicked up _JAVA_OPTIONS: -Xmx128m\nError occurred during initialization of VM Initial heap size set to a larger value than the maximum heap size.\n\nI tried to fix the problem by setting the configuration property spark.driver.memory to various values, but nothing changed.\nWhat is the problem and how can I fix it?\nThanks","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1509,"Q_Id":40266372,"Users Score":0,"Answer":"This is because you're setting the maximum available heap size (128M) to be larger than the initial heap size error. Check the _JAVA_OPTIONS parameter that you're passing or setting in the configuration file. Also, note that the changes in the spark.driver.memory won't have any effect because the Worker actually lies within the driver JVM process that is started on starting spark-shell and the default memory used for that is 512M.\nThis creates a conflict as spark tries to initialize a heap size equal to 512M, but the maximum allowed limit set by you is only 128M.\nYou can set the minimum heap size through the --driver-java-options command line option or in your default properties file","Q_Score":1,"Tags":"java,python,apache-spark,pyspark","A_Id":44125243,"CreationDate":"2016-10-26T15:30:00.000","Title":"Java heap size error when running Spark from Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using the python apache_beam version of dataflow. I have about 300 files with an array of 4 million entries each. The whole thing is about 5Gb, stored on a gs bucket.\nI can easily produce a PCollection of the arrays {x_1, ... x_n} by reading each file, but the operation I now need to perform is like the python zip function: I want a PCollection ranging from 0 to n-1, where each element i contains the array of all the x_i across the files. I tried yielding (i, element) for each element and then running GroupByKey, but that is much too slow and inefficient (it won't run at all locally because of memory constraints, and it took 24 hours on the cloud, whereas I'm sure I can at least load all of the dataset if I want).\nHow do I restructure the pipeline to do this cleanly?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":283,"Q_Id":40282480,"Users Score":0,"Answer":"As jkff pointed out in the above comment, the code is indeed correct and the procedure is the recommended way of programming a tensorflow algorithm. The DoFn applied to each element was the bottleneck.","Q_Score":1,"Tags":"python,google-cloud-dataflow","A_Id":47528448,"CreationDate":"2016-10-27T10:38:00.000","Title":"what's the equivalent of the python zip function in dataflow?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Imagine I have a numpy array in python that has complex numbers as its elements.\nI would like to know if it is possible to split any matrix of this kind into a hermitian and anti-hermitian part? My intuition says that this is possible, similar to the fact that any function can be split into an even and an uneven part.\nIf this is indeed possible, how would you do this in python? So, I'm looking for a function that takes as input any matrix with complex elements and gives a hermitian and non-hermitian matrix as output such that the sum of the two outputs is the input.\n(I'm working with python 3 in Jupyter Notebook).","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":309,"Q_Id":40284296,"Users Score":4,"Answer":"The Hermitian part is (A + A.T.conj())\/2, the anti-hermitian part is (A - A.T.conj())\/2 (it is quite easy to prove).\nIf A = B + C with B Hermitian and C anti-Hermitian, you can take the conjugate (I'll denote it *) on both sides, uses its linearity and obtain A* = B - C, from which the values of B and C follow easily.","Q_Score":0,"Tags":"python,numpy,matrix","A_Id":40284356,"CreationDate":"2016-10-27T12:12:00.000","Title":"python: split matrix in hermitian and anti-hermitian part","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I think I have a pretty reasonable idea on how to do go about accomplishing this, but I'm not 100% sure on all of the steps. This question is mostly intended as a sanity check to ensure that I'm doing this in the most efficient way, and that my math is actually sound (since my statistics knowledge is not completely perfect).\nAnyways, some explanation about what I'm trying to do:\nI have a lot of time series data that I would like to perform some linear regressions on. In particular, I have roughly 2000 observations on 500 different variables. For each variable, I need to perform a regression using two explanatory variables (two additional vectors of roughly 2000 observations). So for each of 500 different Y's, I would need to find a and b in the following regression Y = aX_1 + bX_2 + e.\nUp until this point, I have been using the OLS function in the statsmodels package to perform my regressions. However, as far as I can tell, if I wanted to use the statsmodels package to accomplish my problem, I would have to call it hundreds of times, which just seems generally inefficient.\nSo instead, I decided to revisit some statistics that I haven't really touched in a long time. If my knowledge is still correct, I can put all of my observations into one large Y matrix that is roughly 2000 x 500. I can then stick my explanatory variables into an X matrix that is roughly 2000 x 2, and get the results of all 500 of my regressions by calculating (X'Y)\/(X'X). If I do this using basic numpy stuff (matrix multiplication using * and inverses using matrix.I), I'm guessing it will be much faster than doing hundreds of statsmodel OLS calls.\nHere are the questions that I have:\n\nIs the numpy stuff that I am doing faster than the earlier method of calling statsmodels many times? If so, is it the fastest\/most efficient way to accomplish what I want? I'm assuming that it is, but if you know of a better way then I would be happy to hear it. (Surely I'm not the first person to need to calculate many regressions in this way.)\nHow do I deal with missing data in my matrices? My time series data is not going to be nice and complete, and will be missing values occasionally. If I just try to do regular matrix multiplication in numpy, the NA values will propagate and I'll end up with a matrix of mostly NAs as my end result. If I do each regression independently, I can just drop the rows containing NAs before I perform my regression, but if I do this on the large 2000 x 500 matrix I will end up dropping actual, non-NA data from some of my other variables, and I obviously don't want that to happen.\nWhat is the most efficient way to ensure that my time series data actually lines up correctly before I put it into the matrices in the first place? The start and end dates for my observations are not necessarily the same, and some series might have days that others do not. If I were to pick a method for doing this, I would put all the observations into a pandas data frame indexed by the date. Then pandas will end up doing all of the work aligning everything for me and I can extract the underlying ndarray after it is done. Is this the best method, or does pandas have some sort of overhead that I can avoid by doing the matrix construction in a different way?","AnswerCount":1,"Available Count":1,"Score":0.6640367703,"is_accepted":false,"ViewCount":3105,"Q_Id":40287113,"Users Score":4,"Answer":"some brief answers\n1) Calling statsmodels repeatedly is not the fastest way. If we just need parameters, prediction and residual and we have identical explanatory variables, then I usually just use params = pinv(x).dot(y) where y is 2 dimensional and calculate the rest from there. The problem is that inference, confidence intervals and similar require work, so unless speed is crucial and only a restricted set of results is required, statsmodels OLS is still more convenient.\nThis only works if all y and x have the same observations indices, no missing values and no gaps.\nAside: The setup is a Multivariate Linear Model which will be supported by statsmodels in, hopefully not very far, future.\n2) and 3) The fast simple linear algebra of case 1) does not work if there are missing cells or no complete overlap of observation (indices). In the analog to panel data, the first case requires \"balanced\" panels, the other cases imply \"unbalanced\" data. The standard way is to stack the data with the explanatory variables in a block-diagonal form. Since this increases the memory by a large amount, using sparse matrices and sparse linear algebra is better. It depends on the specific cases whether building and solving the sparse problem is faster than looping over individual OLS regressions. \nSpecialized code: (Just a thought):\nIn case 2) with not fully overlapping or cellwise missing values, we would still need to calculate all x'x, and x'y matrices for all y, i.e. 500 of those. Given that you only have two regressors 500 x 2 x 2 would still not require a large memory. So it might be possible to calculate params, prediction and residuals by using the non-missing mask as weights in the cross-product calculations.\nnumpy has vectorized linalg.inv, as far as I know. So, I think, this could be done with a few vectorized calculations.","Q_Score":4,"Tags":"python,python-3.x,numpy,linear-regression","A_Id":40293068,"CreationDate":"2016-10-27T14:20:00.000","Title":"Fastest way to calculate many regressions in python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Currently, I have a 3D Python list in jagged array format.\nA = [[[0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0], [0], [0]]]\nIs there any way I could convert this list to a NumPy array, in order to use certain NumPy array operators such as adding a number to each element.\nA + 4 would give [[[4, 4, 4], [4, 4, 4], [4, 4, 4]], [[4], [4], [4]]].\nAssigning B = numpy.array(A) then attempting to B + 4 throws a type error.\nTypeError: can only concatenate list (not \"float\") to list\nIs a conversion from a jagged Python list to a NumPy array possible while retaining the structure (I will need to convert it back later) or is looping through the array and adding the required the better solution in this case?","AnswerCount":5,"Available Count":1,"Score":0.0399786803,"is_accepted":false,"ViewCount":7011,"Q_Id":40289943,"Users Score":1,"Answer":"Looping and adding is likely better, since you want to preserve the structure of the original. Plus, the error you mentioned indicates that you would need to flatten the numpy array and then add to each element. Although numpy operations tend to be faster than list operations, converting, flattening, and reverting is cumbersome and will probably offset any gains.","Q_Score":5,"Tags":"python,list,python-3.x,numpy","A_Id":40290127,"CreationDate":"2016-10-27T16:35:00.000","Title":"Converting a 3D List to a 3D NumPy array","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am preparing a Doc2Vec model using tweets. Each tweet's word array is considered as a separate document and is labeled as \"SENT_1\", SENT_2\" etc.\n\ntaggeddocs = []\nfor index,i in enumerate(cleaned_tweets):\n    if len(i) > 2: # Non empty tweets\n        sentence = TaggedDocument(words=gensim.utils.to_unicode(i).split(), tags=[u'SENT_{:d}'.format(index)])\n        taggeddocs.append(sentence)\n\n# build the model\nmodel = gensim.models.Doc2Vec(taggeddocs, dm=0, alpha=0.025, size=20, min_alpha=0.025, min_count=0)\n\nfor epoch in range(200):\n    if epoch % 20 == 0:\n        print('Now training epoch %s' % epoch)\n    model.train(taggeddocs)\n    model.alpha -= 0.002  # decrease the learning rate\n    model.min_alpha = model.alpha  # fix the learning rate, no decay\n\nI wish to find tweets similar to a given tweet, say \"SENT_2\". How?\nI get labels for similar tweets as:\n\nsims = model.docvecs.most_similar('SENT_2')\nfor label, score in sims:\n    print(label)\n\nIt prints as:\n\nSENT_4372\nSENT_1143\nSENT_4024\nSENT_4759\nSENT_3497\nSENT_5749\nSENT_3189\nSENT_1581\nSENT_5127\nSENT_3798\n\nBut given a label, how do I get original tweet words\/sentence? E.g. what are the tweet words of, say, \"SENT_3497\". Can I query this to Doc2Vec model?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1260,"Q_Id":40296765,"Users Score":1,"Answer":"Gensim's Word2Vec\/Doc2Vec models don't store the corpus data \u2013 they only examine it, in multiple passes, to train up the model. If you need to retrieve the original texts, you should populate your own lookup-by-key data structure, such as a Python dict (if all your examples fit in memory). \nSeparately, in recent versions of gensim, your code will actually be doing 1,005 training passes over your taggeddocs, including many with a nonsensically\/destructively negative alpha value. \n\nBy passing it into the constructor, you're telling the model to train itself, using your parameters and defaults, which include a default number of iter=5 passes. \nYou then do 200 more loops. Each call to train() will do the default 5 passes. And by decrementing alpha from 0.025 by 0.002 199 times, the last loop will use an effective alpha of 0.025-(200*0.002)=-0.375 - a negative value essentially telling the model to make a large correction in the opposite direction of improvement each training-example. \n\nJust use the iter parameter to choose the desired number of passes. Let the class manage the alpha changes itself. If supplying the corpus when instantiating the model, no further steps are necessary. But if you don't supply the corpus at instantiation, you'll need to do model.build_vocab(tagged_docs) once, then model.train(tagged_docs) once.","Q_Score":1,"Tags":"python,nlp,gensim,doc2vec","A_Id":41733461,"CreationDate":"2016-10-28T01:49:00.000","Title":"How to extract words used for Doc2Vec","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to data mining. I have a data set which includes directors' names. What is the right way to convert them to something that Scikit learn estimators can use without problem? \nFrom what I found on the internet I thought that sklearn.preprocessing.LabelEncoder is the right choice.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":861,"Q_Id":40298966,"Users Score":2,"Answer":"Depends what type of model you're using, make_pipeline(LabelEncoder, OneHotEncoder) or pd.get_dummies) is the usual choice, and can work well with classifiers from either linear_model or tree. LabelEncoder on its own would another choice, although this won't work well unless there's a natural ordering on your labels (like level of education or something) or unless you're using very deep trees, which are able to separate individual labels.","Q_Score":0,"Tags":"python,scikit-learn,data-mining,categorical-data","A_Id":40315040,"CreationDate":"2016-10-28T06:04:00.000","Title":"How to handle nominal data in scikit learn, python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a list of numbers and I need to group it. itertools.grouby work perfectly for sequences of same numbers but I need same behavior for numbers with a threshold (2-3%)\nE.X: lst = [1, 500, 19885, 19886, 19895, 90000000]\nand I expect [[1], [500], [19885, 19886, 19895], [90000000]]\nCan you suggest me something?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":415,"Q_Id":40299538,"Users Score":0,"Answer":"Bucket them. You'd need to manually work out the breaks in advance. Can you sort then, in advance? That would make it easier.\nActually, if you use log, then a multiplicative threshold turns into a constant threshold, e.g. 0.98..1.02 in log-land ~= (-0.02, +0.02).\nSo, use the log of all your numbers.\nYou'll still need to bucket them before doing groupby.\nIf you want code, give us a better (random-seeded) reproducible example that has more numbers testing the corner-cases.","Q_Score":0,"Tags":"python,group-by,itertools","A_Id":40299648,"CreationDate":"2016-10-28T06:46:00.000","Title":"Python groupby threshold","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I can find the number of rows in a column in a pandas dataframe that do NOT follow a pattern but not the number of rows that follow the very same pattern!\nThis works:\ndf.report_date.apply(lambda x: (not re.match(r'[0-9]{4}-[0-9]{1,2}-[0-9]{1,2}', x))).sum()\nThis does not: removing 'not' does not tell me how many rows match but raises a TypeError. Any idea why that would be the case?\n\ndf.report_date.apply(lambda x: (re.match(r'[0-9]{4}-[0-9]{1,2}-[0-9]{1,2}', x))).sum()","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":883,"Q_Id":40309777,"Users Score":2,"Answer":"The problem is that the match function does not return True when it matches, it returns a match object. Pandas cannot add this match object because it is not an integer value. The reason you get a sum when you are using 'not' is because it returns a boolean value of True, which pandas can sum the True value and return a number.","Q_Score":3,"Tags":"python,regex,pandas","A_Id":40310470,"CreationDate":"2016-10-28T16:55:00.000","Title":"cannot sum rows that match a regular expression in pandas \/ python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a 3D tensor (sequence_length, batch_size, word_dim), I need to do matmul operation with \"word_dim\" dimension so that I can change tensor into (sequence_length, batch_size, hidden_dim). It seems that matmul operation can only be used in 2D tensor. And I can not change the 3D tensor into 2D because of the \"batch_size\". How can I do?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":118,"Q_Id":40326169,"Users Score":0,"Answer":"I would first reshape your Tensor to be (sequence_length * batch_size, word_dim), do the matmul to get (sequence_length * batch_size, hidden_dim), then reshape again to get (sequence_length, batch_size, hidden_dim). There is no copying involved with reshape(), and this is equivalent to multiplying each of the batch_size matrices individually if you only have one matrix to multiply them with.","Q_Score":0,"Tags":"python,tensorflow,deep-learning","A_Id":40351277,"CreationDate":"2016-10-30T05:05:00.000","Title":"How to do matmul operation in specific dimension in tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to implement a multiscale CNN in python. My aim is to use three different CNNs for three different scales and concatenate the final outputs of the final layers and feed them to a FC layer to take the output predictions.\nBut I don't understand how can I implement this. I know how to implement a single scale CNN. \nCould anyone help me in this?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1527,"Q_Id":40329307,"Users Score":0,"Answer":"I do not understand why to have 3 CNNs because you would mostly have the same results than on a single CNN. Maybe you could train faster.\nPerhaps you could also do pooling and some resnet operation (I guess this could prove similar to what you want).\nNevertheless, for each CNN you need a cost function in order to optimize the \"heuristic\" you use (eg: to improve recognition). Also, you could do something as in the NN Style Transfer in which you compare results between several \"targets\" (the content and the style matrices); or simply train 3 CNNs then cutoff the last layers (or freeze them) and train again with the already trained weights but now with your target FN layer...","Q_Score":4,"Tags":"python,neural-network,concatenation,convolution,keras","A_Id":52020891,"CreationDate":"2016-10-30T12:52:00.000","Title":"Multiscale CNN - Keras Implementation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am plotting a contour on matplotlib using the contourf command.\nI get huge numbers at the start of frequency, but lower peaks after that.\nxlim only works in hiding the higher numbers - but I want the lower peaks to become the maximum on the colorbar (not shown in my images).\nHow to rescale the contour after the xlim has hidden the unrequired contour? Basically, the light blue (cool) portion of should become the red (hot) area after applying xlim(10,100)","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":30,"Q_Id":40339355,"Users Score":1,"Answer":"Without providing any code it's hard to give you a code example of what you should do, but I'm assuming you are providing contourplot some 2d numpy array of values that drives the visualization. What I would suggest is then to set the x-limit in that data-structure rather than providing the limit to matplotlib. If Xis your datastructure, then just do plt.contour(X[:10, :]).","Q_Score":1,"Tags":"python,matplotlib,contour","A_Id":40340683,"CreationDate":"2016-10-31T09:20:00.000","Title":"Plot maximum of whats remaining on plot after xlim","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a list data.keys() which returns \ndict_keys([2016-09-27, 2016-09-28, 2016-09-29, 2016-09-30, 2016-09-26])\nData type for each of the element is quantlib.time.date.Date\nHow do I create an index for pandas with the above so that the index is of type pandas.tslib.Timestamp?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":411,"Q_Id":40349868,"Users Score":0,"Answer":"Looks like something like this might work:\ndataframe.set_index = [ pandas.to_datetime(str(a)) for a in list(data.keys()) ]\nbut I get the following error:\nKeyError: Timestamp('2016-09-27 00:00:00')\nPress any key to continue . . .","Q_Score":0,"Tags":"python,pandas,quantlib","A_Id":40349994,"CreationDate":"2016-10-31T20:12:00.000","Title":"How to convert quantlib.time.date.Date to pandas.tslib.Timestamp","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm currently trying to train the MLPClassifier implemented in sklearn...\nWhen i try to train it with the given values i get this error:\nValueError: setting an array element with a sequence.\nThe format of the feature_vector is \n[ [one_hot_encoded brandname], [different apps scaled to mean 0 and variance 1] ]\nDoes anybody know what I'm doing wrong ?\nThank you!\n\n\n\nfeature_vectors: \n[\narray([ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,\n        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.]),\narray([ 0.82211852, -0.22976818, -0.22976818, -0.22976818, -0.22976818,\n       -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,\n       -0.22976818, -0.22976818,  4.45590895, -0.22976818, -0.22976818,\n       -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,\n       -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,\n       -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,\n       -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,\n       -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,\n       -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,\n       -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,\n       -0.22976818,  0.3439882 , -0.22976818, -0.22976818, -0.22976818,\n        4.93403927, -0.22976818, -0.22976818, -0.22976818,  0.63086639,\n        1.10899671, -0.22976818, -0.22976818, -0.22976818, -0.22976818,\n       -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,\n       -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,\n       -0.22976818, -0.22976818, -0.22976818,  1.58712703, -0.22976818,\n        1.77837916, -0.22976818, -0.22976818, -0.22976818, -0.22976818,\n       -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,\n       -0.22976818, -0.22976818,  2.16088342, -0.22976818,  2.16088342,\n       -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,\n       -0.22976818, -0.22976818, -0.22976818,  9.42846428, -0.22976818,\n       -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,\n        0.91774459, -0.22976818, -0.22976818,  4.16903076, -0.22976818,\n       -0.22976818, -0.22976818, -0.22976818, -0.22976818,  2.44776161,\n       -0.22976818, -0.22976818, -0.22976818,  1.96963129,  1.96963129,\n        1.96963129, -0.22976818, -0.22976818, -0.22976818, -0.22976818,\n       -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,\n       -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,\n       -0.22976818, -0.22976818, -0.22976818, -0.22976818,  7.13343874,\n        5.98592598, -0.22976818, -0.22976818, -0.22976818, -0.22976818,\n       -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,\n       -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,\n        3.02151799,  4.26465682, -0.22976818, -0.22976818, -0.22976818,\n       -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,\n       -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,\n       -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,\n       -0.22976818,  2.25650948, -0.22976818, -0.22976818, -0.22976818,\n       -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,\n       -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,\n       -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,\n       -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,\n       -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,\n       -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,\n       -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,\n        1.30024884, -0.22976818, -0.22976818, -0.22976818, -0.22976818,\n       -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,\n       -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,\n       -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,\n       -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,\n       -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,\n       -0.22976818, -0.22976818, -0.22976818,  4.74278714, -0.22976818,\n       -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,\n       -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,\n       -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,\n       -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,\n       -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,\n       -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,\n       -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,\n       -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,\n       -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,\n       -0.22976818, -0.22976818, -0.22976818, -0.22976818,  0.3439882 ,\n       -0.22976818,  0.3439882 , -0.22976818, -0.22976818, -0.22976818,\n       -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,\n       -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,\n       -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,\n       -0.22976818, -0.22976818,  0.53524033, -0.22976818, -0.22976818,\n       -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,\n       -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,\n       -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,\n       -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818,\n       -0.22976818, -0.22976818, -0.22976818, -0.22976818,  3.49964831,\n       -0.22976818, -0.22976818, -0.22976818, -0.22976818, -0.22976818])\n]\ng_a_group: \n[ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  1.  0.]\n\n\n\nMLP:\nfrom sklearn.neural_network import MLPClassifier\nclf = MLPClassifier(solver='lbfgs', alpha=1e-5,\n                   hidden_layer_sizes=(5, 2), random_state=1)\nclf.fit(feature_vectors, g_a_group)","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":437,"Q_Id":40369042,"Users Score":1,"Answer":"Your data does not make any sense from scikit-learn perspective of what is expected in the .fit call. Feature vectors is supposed to be a matrix of size N x d, where N - number of data points and d number of features, and your second variable should hold labels, thus it should be vector of length N (or N x k where k is number of outputs\/labels per point). Whatever is represented in your variables - their sizes do not match what they should represent.","Q_Score":0,"Tags":"python,machine-learning,scikit-learn,artificial-intelligence","A_Id":40370495,"CreationDate":"2016-11-01T21:35:00.000","Title":"Python MLPClassifier Value Error","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a question about tensorflow tensor.\nIf I have a NeuralNet like y=xw+b as an example.\nthen x is placeholder([7,7] dims), w is Variable([7,1]) and b is Variable([1,1])\nSo, y is tensorflow tensor with [7,1] dims.\nthen, in this case. can I make a new tensor like\nnew_y = [tf.reduce_sum(y[0:3]), tf.reduce_sum(y[3:5]), tf.reduce_sum(y[5:])] \nand use it for training step?\nIf possible, how can I make it?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":54,"Q_Id":40370004,"Users Score":0,"Answer":"You should just make your label (y) in your reduced sum format (i.e. 3 bits), and train to that label.  The neural net should be smart enough to adjust the weights to imitate your reduce_sum logic.","Q_Score":0,"Tags":"python-2.7,tensorflow","A_Id":40370096,"CreationDate":"2016-11-01T23:07:00.000","Title":"Generate new tensorflow tensor according to the element index of original tensor","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a file called train.dat which has three fields - userID, movieID and rating. \nI need to predict the rating in the test.dat file based on this.\nI want to know how I can use scikit-learn's KMeans to group similar users given that I have only feature - rating. \nDoes this even make sense to do? After the clustering step, I could do a regression step to get the ratings for each user-movie pair in test.dat\nEdit: I have some extra files which contain the actors in each movie, the directors and also the genres that the movie falls into. I'm unsure how to use these to start with and I'm asking this question because I was wondering whether it's possible to get a simple model working with just rating and then enhance it with the other data. I read that this is called content based recommendation. I'm sorry, I should've written about the other data files as well.","AnswerCount":2,"Available Count":1,"Score":-0.0996679946,"is_accepted":false,"ViewCount":733,"Q_Id":40389402,"Users Score":-1,"Answer":"Clustering users makes sense. But if your only feature is the rating, I don't think it could produce a useful model for prediction. Below are my assumptions to make this justification:\n\nThe quality of movie should be distributed with a gaussion distribution.\nIf we look at the rating distribution of a common user, it should be something like gaussian.\nI don't exclude the possibility that a few users only give ratings when they see a bad movie (thus all low ratings); and vice versa. But on a large scale of users, this should be unusual behavior. \n\nThus I can imagine that after clustering, you get small groups of users in the two extreme cases; and most users are in the middle (because they share the gaussian-like rating behavior). Using this model, you probably get good results for users in the two small (extreme) groups; however for the majority of users, you cannot expect good predictions.","Q_Score":0,"Tags":"python,machine-learning,scikit-learn,k-means","A_Id":40390555,"CreationDate":"2016-11-02T20:39:00.000","Title":"Clustering before regression - recommender system","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using TfIdfVectorizer of sklearn for document clustering. I have 20 million texts, for which i want to compute clusters. But calculating TfIdf matrix is taking too much time and system is getting stuck. \nIs there any technique to deal with this problem ? is there any alternative method for this in any python module ?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":261,"Q_Id":40399023,"Users Score":0,"Answer":"start small.\nFirst cluster only 100.00 documents. Only once it works (because it probably won't), then think about scaling up.\nIf you don't succeed clustering the subset (and text clusters are usually pretty bad), then you won't fare well on the large set.","Q_Score":0,"Tags":"python,scikit-learn,cluster-analysis,tf-idf","A_Id":40411804,"CreationDate":"2016-11-03T10:21:00.000","Title":"python : facing memory issue in document clustering using sklearn","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using TfIdfVectorizer of sklearn for document clustering. I have 20 million texts, for which i want to compute clusters. But calculating TfIdf matrix is taking too much time and system is getting stuck. \nIs there any technique to deal with this problem ? is there any alternative method for this in any python module ?","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":261,"Q_Id":40399023,"Users Score":1,"Answer":"Well, a corpus of 20 million texts is very large, and without a meticulous and comprehensive preprocessing nor some good computing instances (i.e.  a lot of memory and good CPUs), the TF-IDF calculation may take a lot of time.\nWhat you can do :\n\nLimit your text corpus to some hundred of thousands of samples (let's say 200.000 texts). Having too much texts might not introduce more variance than much smaller (but reasonable) datasets.\nTry to preprocess your texts as much as you can. A basic approach would be : tokenize your texts, use stop words, word stemming, use carefully n_grams. \nOnce you've done all these steps, see how much you've reduced the size of your vocabulary. It should be much more smaller than the original one.\n\nIf not too big (talking about your dataset), these steps might help you compute the TF-IDF much faster .","Q_Score":0,"Tags":"python,scikit-learn,cluster-analysis,tf-idf","A_Id":40399357,"CreationDate":"2016-11-03T10:21:00.000","Title":"python : facing memory issue in document clustering using sklearn","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I know this question has been asked before but those answers seem to revolve around Hadoop.  For Spark you don't really need all the extra Hadoop cruft.  With the spark-ec2 script (available via GitHub for 2.0) your environment is prepared for Spark.  Are there any compelling use cases (other than a far superior boto3 sdk interface) for running with EMR over EC2?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":129,"Q_Id":40410975,"Users Score":3,"Answer":"This question boils down to the value of managed services, IMHO.\nRunning Spark as a standalone in local mode only requires you get the latest Spark, untar it, cd to its bin path and then running spark-submit, etc\nHowever, creating a multi-node cluster that runs in cluster mode requires that you actually do real networking, configuring, tuning, etc. This means you've got to deal with IAM roles, Security groups, and there are subnet considerations within your VPC.\nWhen you use EMR, you get a turnkey cluster in which you can 1-click install many popular applications (spark included), and all of the Security Groups are already configured properly for network communication between nodes, you've got logging already setup and pointing at S3, you've got easy SSH instructions, you've got an already-installed apparatus for tunneling and viewing the various UI's, you've got visual usage metrics at the IO level, node level, and job submission level, you also have the ability to create and run Steps -- which are jobs that can be run in the command line of the drive node or as Spark applications that leverage the whole cluster. Then, on top of that, you can export that whole cluster, steps included, and copy paste the CLI script into a recurring job via DataPipeline and literally create an ETL pipeline in 60 seconds flat.\nYou wouldn't get any of that if you built it yourself in EC2. I know which one I would choose... EMR. But that's just me.","Q_Score":3,"Tags":"python-3.x,apache-spark,amazon-ec2","A_Id":40413120,"CreationDate":"2016-11-03T20:48:00.000","Title":"Does EMR still have any advantages over EC2 for Spark?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":1,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an imbalanced training data and i am using logistic regression in weka to classify.\n\nThere are two classes good and bad. Good has 75000 instances and bad\n  3000.  My test data has 10000 good data.\n\nWhen i train it is more inclined to good data i.e it classifies almost all bad instances good. What should i do ?\nI tried to have 10000 good instances in training data instead of 75000 but still the problem is same.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1258,"Q_Id":40411357,"Users Score":0,"Answer":"Not sure about python but in the gui version you can use SpreadSubsample to reduce the class imbalance. If you feel that 'bad' is a a good representation of the class then you could experiment with different number of instances of 'good.'\nTo do this you need to select Filter ==> Supervised ==> Instance ==> SpreadSubsample ==> change the number of instances using 'max count'","Q_Score":1,"Tags":"python,weka","A_Id":40643572,"CreationDate":"2016-11-03T21:14:00.000","Title":"How to classify imbalanced data in weka?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an imbalanced training data and i am using logistic regression in weka to classify.\n\nThere are two classes good and bad. Good has 75000 instances and bad\n  3000.  My test data has 10000 good data.\n\nWhen i train it is more inclined to good data i.e it classifies almost all bad instances good. What should i do ?\nI tried to have 10000 good instances in training data instead of 75000 but still the problem is same.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1258,"Q_Id":40411357,"Users Score":0,"Answer":"There are a couple of things that you could try. \n\nUse Boosting (AdaBoostM1) so that the misclassified instances will be given extra weight.\nUse weka.classifiers.meta.CostSensitiveClassifier    and give the \"bad\" instances a higher weight than the \"good\" instances.   Note: This will probably reduce your overall accuracy, but make your classifier do a better job of identifying the \"bad\" instances.","Q_Score":1,"Tags":"python,weka","A_Id":40837930,"CreationDate":"2016-11-03T21:14:00.000","Title":"How to classify imbalanced data in weka?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to obtain the argmin of a numpy 2 dimensional array A which has nan values. Now the problem is:\n\nnumpy.nanargmin(A) returns only one index.\nnumpy.unravel_index(A.argmin(), A.shape) returns [0,0] because it has nan values. \nAnd...\nnumpy.unravel_index(A.nanargmin(), A.shape) throws the error: \n\nAttributeError                            Traceback (most recent call\n  last)  in ()\n  ----> 1 np.unravel_index(dist.nanargmin(), dist.shape)  AttributeError: 'numpy.ndarray' object has no attribute 'nanargmin'","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":471,"Q_Id":40411374,"Users Score":0,"Answer":"NumPy arrays only have the argmin() attribute, but no nanargmin() attribute. So A.nanargmin() does not exist.\nYou can use numpy.argmin(A) and numpy.nanargmin(A) instead.","Q_Score":1,"Tags":"python,python-3.x,numpy","A_Id":40411415,"CreationDate":"2016-11-03T21:15:00.000","Title":"Using numpy.nanargmin() in 2 dimensional matrix","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I was trying to use pandas (installed the binaries and dependencies using conda, then using pip, then built then using no-binaries option); still getting error.\nNumpy is available (1.11.2).\nI understand some interface is not provided by numpy anymore.\nPython version I am using is 2.7.11.\nList of packages installed are bellow.\nError message:\n\nC:.....Miniconda2\\lib\\site-packages\\numpy\\core__init__.py:14: Warning:\n  Numpy built with MINGW-W64 on Windows 64 bits is experimental, and\n  only available for testing. You are advised not to use it for\n  production.\nCRASHES ARE TO BE EXPECTED - PLEASE REPORT THEM TO NUMPY DEVELOPERS\n  from . import multiarray Traceback (most recent call last): File\n  \"io.py\", line 2, in  from data import support File\n  \"....\\support.py\", line 3, in  import pandas File\n  \"....Miniconda2\\lib\\site-packages\\pandas__init__.py\", line 18, in\n   raise ImportError(\"Missing required dependencies\n  {0}\".format(missing_dependencies)) ImportError: Missing required\n  dependencies ['numpy']","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":1990,"Q_Id":40433906,"Users Score":2,"Answer":"I would recommend not building from source on Windows unless you really know what you're doing.\nAlso, don't mix conda and pip for numpy; numpy is treated specially in conda and really should work out of the box. If you get an error on import pandas there's likely something wrong with your PATH or PYTHONPATH.\nI suggest that you just create an empty conda env, and install only pandas in it. That will pull in numpy. If that somehow does not work, let's see if we can help you debug that.","Q_Score":0,"Tags":"python,pandas,numpy","A_Id":40433924,"CreationDate":"2016-11-05T01:26:00.000","Title":"Numpy fails to serve as a dependency for pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Beginner here. Shouldnt the required variables be passed as arguments to the function. Why is it variable.function() in python?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":139,"Q_Id":40446650,"Users Score":0,"Answer":"It's simple:\nfoo.bar() does the same thing as foo.__class__.bar(foo)\nso it is a function, and the argument is passed to it, but the function is stored attached to the object via its class (type), so to say. The foo.bar() notation is just shorthand for the above.\nThe advantage is that different functions of the sams name can be attached to many objects, depending object type. So the caller of foo.bar() is calling whatever function is attached to the object by the name \"bar\".  This is called polymorphism and can be used for all sorts of things, such as generic programming. Such functions are called methods. \nThe style is called object orientation, albeit object orientation as well as generic programming can also be achieved using more familiar looking function (method) call notation (e.g. multimethods in Common Lisp and Julia, or classes in Haskell).","Q_Score":0,"Tags":"python","A_Id":40446697,"CreationDate":"2016-11-06T06:30:00.000","Title":"Why is it dataframe.head() in python and head(dataframe) in R? Why is python like this in general?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"i wrote one program to do the information retrieval and extraction. user enter the query in the search bar, the program can show the relevant txt result such as the relevant sentence and the article which consists the sentence.\nI did some research for how to evaluate the result. I might need to calculate the precision, recall, AP, MAP....\nHowever, I am new to that. How to calculate the result. Since my dataset is not labeled and i did not do the classification. The dataset I used was the article from BBC news. there were 200 articles. i named it as 001.txt, 002.txt ...... 200.txt \nIt would be good if u have any ideas how to do the evaluation in python. Thanks.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":6407,"Q_Id":40457331,"Users Score":1,"Answer":"Evaluation has two essentials. First one is a test resource with the ranking of documents or their relevancy tag (relevant or not-relevant) for specific queries, which is made with an experiment (like user click, etc. and is mostly used when you have a running IR system), or made through crowd-sourcing. The second essential part of evaluation is which formula to use for evaluating an IR system with the test collection. \nSo based on what you said, if you don't have a labeled test collection you cant evaluate your system.","Q_Score":3,"Tags":"python,information-retrieval,information-extraction","A_Id":40603239,"CreationDate":"2016-11-07T03:20:00.000","Title":"information retrieval evaluation python precision, recall, f score, AP,MAP","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've looked through the documentation over at scikit learn and I haven't seen a straightforward way to replace the matrix-vector product evaluations during propagation with a custom evaluation call.\nIs there a way to do this that's already part of the API...or are there any tricks that would allow me to inject a custom matrix vector product evaluator?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":38,"Q_Id":40465084,"Users Score":1,"Answer":"In short - no, it is not possible. Mostly because some aritchmetical operations are not even performed in python when you use scikit-learn - they are actually performed by C-based extensions (like libsvm library). You could monkey patch .dot of numpy to do what you want, but you do not have any guarantee that scikit-learn will still work, since it performs some operations using numpy and others using C-extensions.","Q_Score":0,"Tags":"python,matrix,vector,machine-learning,scikit-learn","A_Id":40473264,"CreationDate":"2016-11-07T12:22:00.000","Title":"Python & scikit Learn: replace matrix vector product during training with custom call","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I developed an algorithm that finds the minimum independent dominating set of a graph based on a distance constraint. (I used Python and NetworkX to generate graphs and get the pairs)\nThe algorithm uses a brute force approach:\n\nFind all possible pairs of edges\nCheck which nodes satisfy the distance constraint\nFind all possible independent dominating sets\nCompare the independent dominating sets found and find the minimum dominating set\n\nFor small number of nodes it wouldnt make a difference but for large numbers the program is really slow.\nIs there any way that I could make it run faster using a different approach?\nThanks","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":1579,"Q_Id":40471747,"Users Score":2,"Answer":"Unfortunately, the problem of finding the minimum independent dominating set is NP-complete. Hence, any known algorithm which is sound and complete will be inefficient.\nA possible approach is to use an incomplete algorithm (aka local search).\nFor example, the following algorithm is known to have a factor (1 + log|V|) approximation:\n 1. Choose a node with the maximal number of neighbors and add it to the dominating set.\n 2. Remove the node and all of it's neighbors from the graph.\n 3. Repeat until there are no more nodes in the graph.","Q_Score":1,"Tags":"python,algorithm,graph-theory,networkx,independent-set","A_Id":40473685,"CreationDate":"2016-11-07T18:10:00.000","Title":"Finding minimum indepedent dominating set using a greedy algorithm","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"The sample app given by google for tensorflow on android is written in C++.\nI have a tensorflow application written in python. This application currently runs on desktop. I want to move the application to android platform. Can I use bazel to build the application that is written in python directly for android? Thanks.\nAlso sample tensorflow app in python on android will be much appreciated.","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":534,"Q_Id":40472585,"Users Score":0,"Answer":"I tried to use python in my android application with some 3rd party terminals like SL4A and Qpython. Those will support to run the python files directly in our android application so we have to install SL4A apk's and we need to call that intent.But these will support for some level I guess.\nI tried to import tensorflow in that terminal it shows module not found. So I thought this tensorflow will not work in these terminals.\nSo I am trying to create one .pb file from the python files which are working in unix platform.So We need to include that output .pb file in our android application and we need to change the c++ code regarding that .pb file.I am thinking in this way.let see it will work or not.I will update soon if it working.","Q_Score":1,"Tags":"android,python,tensorflow","A_Id":40672019,"CreationDate":"2016-11-07T19:01:00.000","Title":"Tensorflow on android: directly build app in python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":1,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there a way to retrieve the list of feature names used for training of a classifier, once it has been trained with the fit method? I would like to get this information before applying to unseen data.\nThe data used for training is a pandas DataFrame and in my case, the classifier is a RandomForestClassifier.","AnswerCount":4,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":17091,"Q_Id":40485285,"Users Score":4,"Answer":"Based on the documentation and previous experience, there is no way to get a list of the features considered at least at one of the splitting.\nIs your concern that you do not want to use all your features for prediction, just the ones actually used for training? In this case I suggest to list the feature_importances_ after fitting and eliminate the features that does not seem relevant. Then train a new model with only the relevant features and use those features for prediction as well.","Q_Score":12,"Tags":"python,pandas,scikit-learn,random-forest","A_Id":40523772,"CreationDate":"2016-11-08T11:06:00.000","Title":"Retrieve list of training features names from classifier","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to calculate the confidence interval for the mean value using the method of bootstrap in python. Let say I have a vector a with 100 entries and my aim is to calculate the mean value of these 100 values and its 95% confidence interval using bootstrap. So far I have manage to resample 1000 times from my vector using the np.random.choice function. Then for each bootstrap vector with 100 entries I calculated the mean. So now I have 1000 bootstrap mean values and a single sample mean value from my initial vector but I'm not sure how to proceed from here. How could I use these mean values to find the confidence interval for the mean value of my initial vector? I'm relatively new in python and it's the first time I came across with the method of bootstrap so any help would be much appreciated.","AnswerCount":3,"Available Count":2,"Score":0.3215127375,"is_accepted":false,"ViewCount":15818,"Q_Id":40491298,"Users Score":5,"Answer":"I have a simple statistical solution : \nConfidence intervals are based on the standard error. \nThe standard error in your case is the standard deviation of your 1000 bootstrap means. Assuming a normal distribution of the sampling distribution of your parameter(mean), which should be warranted by the properties of the Central Limit Theorem, just multiply the equivalent z-score of the desired confidence interval with the standard deviation. Therefore:\nlower boundary = mean of your bootstrap means - 1.96 * std. dev. of your bootstrap means\nupper boundary = mean of your bootstrap means + 1.96 * std. dev. of your bootstrap means\n95% of cases in a normal distribution sit within 1.96 standard deviations from the mean\nhope this helps","Q_Score":9,"Tags":"python,statistics","A_Id":43329072,"CreationDate":"2016-11-08T15:57:00.000","Title":"How to calculate 95% confidence intervals using Bootstrap method","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to calculate the confidence interval for the mean value using the method of bootstrap in python. Let say I have a vector a with 100 entries and my aim is to calculate the mean value of these 100 values and its 95% confidence interval using bootstrap. So far I have manage to resample 1000 times from my vector using the np.random.choice function. Then for each bootstrap vector with 100 entries I calculated the mean. So now I have 1000 bootstrap mean values and a single sample mean value from my initial vector but I'm not sure how to proceed from here. How could I use these mean values to find the confidence interval for the mean value of my initial vector? I'm relatively new in python and it's the first time I came across with the method of bootstrap so any help would be much appreciated.","AnswerCount":3,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":15818,"Q_Id":40491298,"Users Score":8,"Answer":"You could sort the array of 1000 means and use the 50th and 950th elements as the 90% bootstrap confidence interval.\nYour set of 1000 means is basically a sample of the distribution of the mean estimator (the sampling distribution of the mean). So, any operation you could do on a sample from a distribution you can do here.","Q_Score":9,"Tags":"python,statistics","A_Id":40491405,"CreationDate":"2016-11-08T15:57:00.000","Title":"How to calculate 95% confidence intervals using Bootstrap method","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I use dbscan scikit-learn algorithm for clustering.\ndb = DBSCAN().fit(X) returns me 8 for example. My goal is to recover the cluster by cluster components. I said that X is a vector to vector and what I expect when I speak of cluster members, it is the sub-vectors of X.\nIs there anyone to help me?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":3915,"Q_Id":40491707,"Users Score":5,"Answer":"I believe you are asking for the cluster assignment of each item in your dataset, X. \nYou can use the labels_ attribute. db.labels_ Each index here corresponds to the same index in X, so you can see the assignments.","Q_Score":3,"Tags":"python,machine-learning,scikit-learn","A_Id":40492219,"CreationDate":"2016-11-08T16:17:00.000","Title":"Get cluster members\/elements clustering with scikit-learn DBSCAN","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to simulate multiple Modelica FMUs in parallel using python\/pyfmi and multiprocessing. However I am not able to return any pyfmi FMI objects from the subprocesses once the FMUs are initialized. It seems that pyfmi FMI objects (e.g. pyfmi.fmi.FMUModelCS2 or pyfmi.fmi.FMUState2) are not pickable. I also tried dill to pickle, which doesn't work for me eather. With dill the objects are picklable though, meaning no error, but somehow corrupted if I try to reload them afterwards. Does anyone have an idea of how to solve this issue? Thanks!","AnswerCount":3,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1150,"Q_Id":40511920,"Users Score":3,"Answer":"The problem is that pyfmi.fmiFMUModelCS2 is a Cython class dependent on external libraries which makes it unpickable. So it is not possible unfortunately.\nIf you want to use multiprocessing the only way forward that I see is that you first create the processes and then load the FMUs into the separate processes. In this way you do not need to pickle the classes.","Q_Score":4,"Tags":"python-2.7,modelica,fmi","A_Id":40543983,"CreationDate":"2016-11-09T16:40:00.000","Title":"Using pyfmi with multiprocessing for simulation of Modelica FMUs","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to run the Tensorflow for Poets sample.  I pass the following:\npython examples\/image_retraining\/retrain.py --bottlenext_dir=tf_files\/bottlenecks --how_many_training_steps 500 --model_dir=tf_files\/inception --output_graph=tf_files\/retrained_graph.pb --output_labels=tf_files\/retrained_labels.txt --image_dir tf_files\/flower_photos\nI get the error \nFile \"examples\/image_retraining\/retrain.py\", line 1013, in <module>\n    tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)\nTypeError: run() got an unexpected keyword argument 'argv'\nWhen I check the source of app.py it shows argv as an argument.  According to t.version I am running 0.11.0rc0\nAny ideas?","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":3375,"Q_Id":40513466,"Users Score":0,"Answer":"Please check your sample's version. I met the same problem and finally solved it. I found my tf version is 0.11, but I downloaded the master one,\nthen I compare the code asyntax difference.","Q_Score":3,"Tags":"python,tensorflow","A_Id":40789964,"CreationDate":"2016-11-09T18:15:00.000","Title":"tensorflow retrain.py app.run() got unexpected keyword argument 'argv'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to run the Tensorflow for Poets sample.  I pass the following:\npython examples\/image_retraining\/retrain.py --bottlenext_dir=tf_files\/bottlenecks --how_many_training_steps 500 --model_dir=tf_files\/inception --output_graph=tf_files\/retrained_graph.pb --output_labels=tf_files\/retrained_labels.txt --image_dir tf_files\/flower_photos\nI get the error \nFile \"examples\/image_retraining\/retrain.py\", line 1013, in <module>\n    tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)\nTypeError: run() got an unexpected keyword argument 'argv'\nWhen I check the source of app.py it shows argv as an argument.  According to t.version I am running 0.11.0rc0\nAny ideas?","AnswerCount":3,"Available Count":2,"Score":-0.0665680765,"is_accepted":false,"ViewCount":3375,"Q_Id":40513466,"Users Score":-1,"Answer":"You can also specifically checkout just the working fully_connected_feed.py file from the r0.11 branch by using the git command:\ngit checkout 5b18edb fully_connected_feed.py \nNOTE: You need to be in the mnist\/ directory to use this command","Q_Score":3,"Tags":"python,tensorflow","A_Id":40622206,"CreationDate":"2016-11-09T18:15:00.000","Title":"tensorflow retrain.py app.run() got unexpected keyword argument 'argv'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am clusterizing a bunch of feature vectors using scipy linkage with ward method. \nI want a predictive model that works in two steps:\n\nTraining data is clusterized\nA new vector comes, the distance between the vector and each cluster is computed, the new vector is assigned the \"nearest\" cluster's label\n\nHow can I compute the distances between a new vector and precomputed clusters?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":27,"Q_Id":40522186,"Users Score":0,"Answer":"Use the euclidean metric? I think the precomputed clusters have a center. So you can calculate the distanc between every center and the new vector.","Q_Score":0,"Tags":"python,numpy,vector,scipy","A_Id":40522400,"CreationDate":"2016-11-10T07:34:00.000","Title":"Scipy hierarchial clustering - clusterize new vector","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm working on a code to run it in abaqus. I need in my code to use numpy module. I have python 2.7.11 on my computer. I have installed it on windows 8.1.\nI have downloaded numpy-1.11.Zip already.\nI look for an easy detailed guide for installing it on my python\nThank You!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":45,"Q_Id":40527528,"Users Score":0,"Answer":"If you have pip installed on your system, execute \npip install numpy","Q_Score":0,"Tags":"python-2.7","A_Id":40527574,"CreationDate":"2016-11-10T12:21:00.000","Title":"installing numpy on a python 2.7 with system windows 8.1","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"What I want to do is to simulate the back-propagation process on different machines, from one machine, I get the gradient from layer3 d(layer3_output)\/d(layer2_output) as a numpy array, how am I able to get d(layer3_output)\/d(layer1_output) efficiently given the gradient I received and passed to the previous layer?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":351,"Q_Id":40534098,"Users Score":1,"Answer":"When you create your network and attach some loss you call minimize on the optimizer, which (under the hood) calls \"apply_gradients\". This function adds gradient computing ops to your graph. All you have to do is now request the op responsible for your partial derivative and pass the precomputed partial derivative through feed_dict option. Use tensorboard to visualize your graph and investigate names of gradients you are interested in. By default they will be in the \"gradient\" namescope, and naming of each op will be analogous to your operations, so something among the lines of gradient\/output_op:0 etc.","Q_Score":0,"Tags":"python,machine-learning,tensorflow,deep-learning","A_Id":40534978,"CreationDate":"2016-11-10T17:54:00.000","Title":"Can I apply calculated gradient in tensorflow?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using scikit-learn's KNN regressor to fit a model to a large dataset with n_neighbors = 100-500. Given the nature of the data, some parts (think: sharp delta-function like peaks) are better fit with fewer neighbors (n_neighbors ~ 20-50) so that the peaks are not smoothed out. The location of these peaks are known (or can be measured).\nIs there a way to vary the n_neighbors parameter? \nI could fit two models and stitch them together, but that would be inefficient. It would be preferable to either prescribe 2-3 values for n_neighbors or, worse, send in an list of n_neighbors.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":198,"Q_Id":40534871,"Users Score":1,"Answer":"I'm afraid not.  In part, this is due to some algebraic assumptions that the relationship is symmetric: A is a neighbour to B iff B is a neighbour to A.  If you give different k values, you're guaranteed to break that symmetry.\nI think the major reason is simply that the algorithm is simpler with a fixed quantity of neighbors, yielding better results in general.  You have a specific case that KNN doesn't fit so well.\nI suggest that you stitch together your two models, switching dependent on the imputed second derivative.","Q_Score":0,"Tags":"python,scikit-learn,regression,knn","A_Id":40537920,"CreationDate":"2016-11-10T18:45:00.000","Title":"Varying n_neighbors in scikit-learn KNN regression","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been working with anaconda3\/python3.5 and pandas for over a year and all of sudden when I run my script outside the console, I get a import error for pandas particularly the dependency email.parser. I get No module named 'email.parser';'email' is not a package. However importing in the console works fine. I'm not running any other environment","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":831,"Q_Id":40552453,"Users Score":0,"Answer":"I also had the same problem today. You're missing a specific path. Found that if you start your python interpreter and do import os, you can do os.environ. You'll notice that there are several paths set in the PATH variable. Copy\/paste the entire PATH line into your script. That worked for me. Also, remember to remove string single quotes (e.g., ').","Q_Score":0,"Tags":"python,email,pandas,import","A_Id":43399098,"CreationDate":"2016-11-11T16:41:00.000","Title":"Python - Importing pandas in console works but not when running script","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a table of shipment destinations in lat, long. I have one fixed origination point (also lat, long). I would like to find other optimal origin locations using clustering. In other words, I want to assign one cluster centroid (keep it fixed) and find 1, 2, 3 . . . N other cluster centroids. Is this possible with the scikit learn cluster module?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":512,"Q_Id":40552937,"Users Score":0,"Answer":"Rather than recycling clustering for this, treat it as a regular optimization problem. You don't want to \"discover structure\", but optimize cost.\nBeware that earth is not flat, and Euclidean distance (i.e. k-means) is a bad idea. 1 degree north is only at the equator approximately the same distance to 1 degree east. If your data is e.g. in New York, you have a non-neglibile distortion, and your solution will not even be a local optimum.\nIf you absolutely insist on abusing kmeans, it's easy to do.\nChoose n-1 centers at random and the predefined one.\nThen run 1 iteration of k-means only. Then replace that center with the desired center again. Repeat with the next iteration.","Q_Score":0,"Tags":"python-2.7,scikit-learn,cluster-analysis","A_Id":40555741,"CreationDate":"2016-11-11T17:10:00.000","Title":"Input one fixed cluster centroid, find N others (python)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Can we check the data in a pandas.core.groupby.SeriesGroupBy object?","AnswerCount":3,"Available Count":1,"Score":-0.1325487884,"is_accepted":false,"ViewCount":12663,"Q_Id":40630506,"Users Score":-2,"Answer":"Just adding list() should help but the structure isn't super useful in its raw form. It returns a list of tuples with index.","Q_Score":11,"Tags":"python,pandas","A_Id":62375979,"CreationDate":"2016-11-16T11:03:00.000","Title":"Can we see the group data in pandas.core.groupby.SeriesGroupBy object","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a python script that creates a csv file and I have a Hudson job that builds every day. I want to store the csv file as an artifact but when I start the job nothing is stored or created. The build is successful and all tests are done but no file is created. \nDo you know why this happens?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":42,"Q_Id":40632110,"Users Score":0,"Answer":"I ran the windows command line to execute the python script and it worked.","Q_Score":0,"Tags":"python,csv,jenkins,hudson,store","A_Id":40676992,"CreationDate":"2016-11-16T12:23:00.000","Title":"Store a csv file created by a python script on Hudson","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am having trouble filling a mask using cv2.drawContours, I dont know what could it be, or how to fix it.\nI have a list with three elements's sublists [coordX,coordY, contour], so for every pair of coordinates, there is an if\/else decision, each one with this stament: cv2.drawContours(mask,[l1[0][2]],0,c1[cla],-1)\n[l1[0][2]]: contour, like array([[[437, 497]],[[436, 498]],[[439, 498]],       [[438, 497]]])\nc1[cla]: tuple, like (25,250,138)\nThe script runs well without errors, but the resulting image is almost black, just having 4 green pixels.\nAny suggestion or advice?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":92,"Q_Id":40633399,"Users Score":0,"Answer":"Finally, I found what was the problem. I was passing the same contour to every step ([l1[0][2]]), so I need to change to operate with index, like [l1[i][2]]).\nSorry for that mistake and for your patience.\nHave a good day, Jaime","Q_Score":0,"Tags":"python-2.7","A_Id":40635363,"CreationDate":"2016-11-16T13:28:00.000","Title":"why python cv2.drawContours() just draw the first contours?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have python3.5.2 installed on a windows10 machine(Adding into the pythonpath is included in the setup with new python). I ,then, installed the Anaconda(4.2.0) version. At the command prompt when i run the python interpreter and import numpy it works fine. But when i save it as a script and try running from the IDLE, it gives\nTraceback (most recent call last):\n  File \"C:\\Users\\pramesh\\Desktop\\datascience code\\test.py\", line 1, in <module>\n    from numpy import *\nImportError: No module named 'numpy'\nI don't know what the problem is. I donot have any other python version installed.","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":6809,"Q_Id":40637774,"Users Score":0,"Answer":"Just use python -m before pip command, so that you won't get any problem while doing so in IDLE..\nex. python -m pip install scipy\/numpy","Q_Score":3,"Tags":"python,numpy","A_Id":63427934,"CreationDate":"2016-11-16T16:51:00.000","Title":"import numpy not working in IDLE","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have python3.5.2 installed on a windows10 machine(Adding into the pythonpath is included in the setup with new python). I ,then, installed the Anaconda(4.2.0) version. At the command prompt when i run the python interpreter and import numpy it works fine. But when i save it as a script and try running from the IDLE, it gives\nTraceback (most recent call last):\n  File \"C:\\Users\\pramesh\\Desktop\\datascience code\\test.py\", line 1, in <module>\n    from numpy import *\nImportError: No module named 'numpy'\nI don't know what the problem is. I donot have any other python version installed.","AnswerCount":3,"Available Count":2,"Score":0.1325487884,"is_accepted":false,"ViewCount":6809,"Q_Id":40637774,"Users Score":2,"Answer":"You do have two versions of python installed: the CPython 3.5.2 distribution you mention first, and the Anaconda 4.2.0 Python distribution you then mention. Anaconda packages a large number of 3rd party packages, including Numpy. However, the CPython 3.5.2 installation available on python.org only ships with the standard library.\nThese two python installs have separate package installations, so having Anaconda's numpy available doesn't make it available for the CPython install. Since you're starting the Idle with shipped with CPython, which doesn't have numpy, you're seeing this error. You have two options:\n\nInstall numpy for CPython. See numpy documentation for details on how to do this, but it may be difficult.\nUse the version of Idle included with Anaconda. This should be available in the Anaconda programs folder.","Q_Score":3,"Tags":"python,numpy","A_Id":40638108,"CreationDate":"2016-11-16T16:51:00.000","Title":"import numpy not working in IDLE","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have implemented 4 classifiers using scikit-learn in Python. But, the performance on all of them is not very good. I want to implement an ensemble of these classifiers. I looked up the ensembles on scikit-learn but it has Random Forests and Adaboost. How should I create an ensemble of my weak classifiers?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":513,"Q_Id":40642132,"Users Score":1,"Answer":"You have different ways to create an ensemble using your weak classifiers:\n-Bagging: You can average the output of the 4 classifiers.\n-Stacking: Your final output could be a linear combination of the 4 individual outputs. You can use the output of your 4 models as the input of a another algorithm or you can directly use different weights by choose the ones with better accuracy.","Q_Score":1,"Tags":"python,machine-learning,statistics,cross-validation,data-science","A_Id":40642637,"CreationDate":"2016-11-16T20:56:00.000","Title":"Ensembles in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In a regressions, I know instead of using fixed effects (say on country or year), you can specify these effects with dummy variables and this is a perfectly legitimate method. However, when the number of effects (e.g. countries) is large, then it becomes very computational difficult. Instead you would run the fixed effects model. I am curious what R's plm or Stata's xtreg,fe does behind the scenes. Specifically, I wanted to try custom rolling my own panel regression...looking for some likelihood function (or way to condition the likelihood) I can throw into optim and have some fun. Ideas?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":63,"Q_Id":40645689,"Users Score":0,"Answer":"how plm and xtreg,fedo behind the screen are well documented in their instructions, I didn't read them through but I think that when  you run pgmm or some thing like logit regression in plm,the algorithm actually is maximum likelihood given the nature of these problems. \nand it would be interesting to write all these by yourself.","Q_Score":0,"Tags":"python,r,statistics,regression","A_Id":41656919,"CreationDate":"2016-11-17T02:22:00.000","Title":"Custom roll fix effects in regressions","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to transform my python code into an Android app. The problem I saw is I'm using openCV in the code, and I didn't found anything related to generate an APK while using openCV.\nThere is a way to generate an APK from python and openCV?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":292,"Q_Id":40664845,"Users Score":1,"Answer":"There is SL4A \/ PythonForAndroid but unfortunately it uses hardcoded Java RMI invocations for anything os related. That means that there are no OpenCV bindings. \nI guess you'll have to learn Java","Q_Score":1,"Tags":"android,python,opencv,apk","A_Id":40665287,"CreationDate":"2016-11-17T20:55:00.000","Title":"Python and openCV to android APK","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":1,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I was wondering if there is an easy way to calculate the dot product of two vectors (i.e. 1-d tensors) and return a scalar value in tensorflow.\nGiven two vectors X=(x1,...,xn) and Y=(y1,...,yn), the dot product is\ndot(X,Y) = x1 * y1 + ... + xn * yn \nI know that it is possible to achieve this by first broadcasting the vectors X and Y to a 2-d tensor and then using tf.matmul. However, the result is a matrix, and I am after a scalar.\nIs there an operator like tf.matmul that is specific to vectors?","AnswerCount":9,"Available Count":2,"Score":0.0444152037,"is_accepted":false,"ViewCount":64462,"Q_Id":40670370,"Users Score":2,"Answer":"You can do tf.mul(x,y), followed by tf.reduce_sum()","Q_Score":28,"Tags":"python,tensorflow,dot-product","A_Id":40670426,"CreationDate":"2016-11-18T06:05:00.000","Title":"Dot product of two vectors in tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I was wondering if there is an easy way to calculate the dot product of two vectors (i.e. 1-d tensors) and return a scalar value in tensorflow.\nGiven two vectors X=(x1,...,xn) and Y=(y1,...,yn), the dot product is\ndot(X,Y) = x1 * y1 + ... + xn * yn \nI know that it is possible to achieve this by first broadcasting the vectors X and Y to a 2-d tensor and then using tf.matmul. However, the result is a matrix, and I am after a scalar.\nIs there an operator like tf.matmul that is specific to vectors?","AnswerCount":9,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":64462,"Q_Id":40670370,"Users Score":26,"Answer":"In addition to tf.reduce_sum(tf.multiply(x, y)), you can also do tf.matmul(x, tf.reshape(y, [-1, 1])).","Q_Score":28,"Tags":"python,tensorflow,dot-product","A_Id":40672159,"CreationDate":"2016-11-18T06:05:00.000","Title":"Dot product of two vectors in tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In a programming language like Python Which will have better efficiency? if i use a sorting algorithm like merge sort to sort an array or If I use a built in API like sort() to sort the array? If Algorithms are independent of programming languages, then what is the advantage of algorithms over built in methods or API's","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":200,"Q_Id":40671158,"Users Score":5,"Answer":"Why to use public APIs:\n\nThe built in methods were written and reviewed by very experienced and many coders, and a lot of effort was invested to optimize them to be as efficient as it gets.\nSince the built in methods are public APIs, it is also means they are constantly used, which means you get a massive \"free\" testing. You are much more likely to detect issues in public APIs than in private ones, and once something is discovered - it will be fixed for you.\nDon't reinvent the wheel. Someone already programmed it for you, use it. If your profiler says there is a problem, think about replacing it. Not before.\n\nWhy to use custom made methods:\n\nThat said, the public APIs are general case. If you need something\nvery specific for your scenario, you might find a solution that will\nbe more efficient, but it will take you quite some time to actually\nachieve better than the already optimize general purpose public API.\n\ntl;dr: Use public APIs unless you:\n\nNeed it and can afford a lot of time to replace it.\nKnow what you are doing pretty well.\nIntend to maintain it and do robust testing for it.","Q_Score":1,"Tags":"python,algorithm,api,data-structures","A_Id":40671334,"CreationDate":"2016-11-18T07:00:00.000","Title":"Use APIs for sorting or algorithm?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a classifier that has been trained using a given set of input training data vectors. There are missing values in the training data which is filled as numpy.Nan values and Using imputers to fill in the missing values.\nHowever, In case of my input vector for prediction, how do I pass in the input where the value is missing? should I pass the value as nan? Does imputer play a role in this.?\nIf I have to fill in the value manually, How do I fill in the value for such case will I need to calculate the mean\/median\/frequency from the existing data.\nNote : I am using sklearn.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":787,"Q_Id":40673629,"Users Score":0,"Answer":"You cannot use NaN values because the input vector will, for instance, be multiplied with a weight matrix. The result of such operations needs to be defined.\nWhat you typically do if you have gaps in your input data is, depending on the specific type and structure of the data, fill the gaps with \"artificial\" values. For instance, you can use the mean or median of the same column in the remaining training data instances.","Q_Score":0,"Tags":"python,numpy,machine-learning,scikit-learn","A_Id":40673779,"CreationDate":"2016-11-18T09:36:00.000","Title":"Pass a NAN in input vector for prediction","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am a python beginner and I would like some help with this. I am using Ubuntu and I had installed python using Anaconda, but then I tried to install it again using pip and now when I'm trying to run my code, at import numpy as np, I see this error \nImportError: \/home\/dev\/.local\/lib\/python2.7\/site-packages\/numpy\/core\/multiarray.so: undefined symbol: _PyUnicodeUCS4_IsWhitespace \nHow can I fix this?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":3200,"Q_Id":40678200,"Users Score":1,"Answer":"I also got this error. If you google for it, you will find lot's of similar issues. The problem can happen when you have multiple Python versions. In my case, I had the Ubuntu 16.04 Python 2.7 via \/usr\/bin\/python and another Python 2.7 via Linuxbrew. type python gave me \/u\/zeyer\/.linuxbrew\/bin\/python2, i.e. the Linuxbrew one. type pip2.7 gave me \/u\/zeyer\/.local\/bin\/pip2.7, and looking into that file, it had the shebang #!\/usr\/bin\/python, i.e. it was using the Ubuntu Python.\nSo, there are various solutions. You could just edit the pip2.7 file and change the shebang to #!\/usr\/bin\/env python2.7. Or reinstall pip in some way.\nIn my case, I found that the Python 2.7 via Linuxbrew was incompatible to a few packages I needed (e.g. Tensorflow), so I unlinked it and use only the Ubuntu 16.04 Python 2.7 now.","Q_Score":1,"Tags":"python,numpy","A_Id":47094209,"CreationDate":"2016-11-18T13:21:00.000","Title":"ImportError: undefined symbol: _PyUnicodeUCS4_IsWhitespace","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Consider I have an adjacency list of billion of nodes structured using hash table arranged in the following manner:\nkey = source node \nvalue = hash_table { node1,  node2, node3}\nThe input values are from text file in the form of \nfrom,to\n1,2\n1,5\n1,11\n... so on\neg.\nkey = '1' \nvalue = {'2','5','11'} \nmeans 1 is connected to nodes 2,5,11\nI want to know an algorithm or approach to find destination node from source node of exactly k edges in an undirected graph of billion nodes without cycle \nfor eg. from node 1 I want to find node 50 only till depth 3 or till 3 edges.\nMy assumption the algorithm finds 1 - 2 - 60 - 50 which is the shortest path but how would the traversing be efficient using the above adjacency list structure?\nI do not want to use Hadoop\/Map Reduce. \nI came up with naive solution as below in Python but is not efficient. Only thing is hash table searches key in O(1) so I could just search neighbours and their billion neighbours directly for the key. The following algo takes lot of time.\n\nstart with source node\nuse hash table search for finding key\ngo 1 level deeper with hash table of neighbor nodes and find their values for destination nodes until node found\nStop if node not found on k depth\n\n\n\n&nbsp1 \n\u00a0\u00a0| \n{2 \u00a0 \u00a0\u00a0\u00a0  \u00a0      5  \u00a0\u00a0\u00a0\u00a0\u00a0  \u00a0\u00a0\u00a0      11} \n\u00a0\u00a0 | \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0         |  \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0         | \n{3,6,7}  {nodes}     {nodes} .... connected nodes \n\u00a0 |\u00a0 | \u00a0| \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0     |     \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0      | \n{nodes}   {nodes}    {nodes} .... million more connected nodes. \nPlease suggest. The algorithm above implemented similar to BFS takes more than 3 hours to search for all the possible key value relationships. Can be it be reduced with other searching method?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":105,"Q_Id":40686773,"Users Score":0,"Answer":"As you've hinted, this will depend a lot on the data access characteristics of your system.  If you were restricted to single-element accesses, then you'd be truly stuck, as trincot observes.  However, if you can manage block accesses, then you have a chance of parallel operations.\nHowever, I think that would be outside your control: the hash function owns the adjacency characteristics -- and, in fact, will likely \"pessimize\" (opposite of \"optimize\") that characteristic.\nI do see one possible hope: use iteration instead of recursion, maintaining a list of nodes to visit.  When you place a new node on the list, get its hash value.  If you can organize the nodes clustered by location, you can perhaps do a block transfer, accessing several values in one read operation.","Q_Score":0,"Tags":"python,algorithm,graph,shortest-path,breadth-first-search","A_Id":40687262,"CreationDate":"2016-11-18T22:14:00.000","Title":"Algorithm\/Approach to find destination node from source node of exactly k edges in undirected graph of billion nodes without cycle","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have Keras installed with the Tensorflow backend and CUDA.  I'd like to sometimes on demand force Keras to use CPU.  Can this be done without say installing a separate CPU-only Tensorflow in a virtual environment?  If so how?  If the backend were Theano, the flags could be set, but I have not heard of Tensorflow flags accessible via Keras.","AnswerCount":7,"Available Count":1,"Score":0.0855049882,"is_accepted":false,"ViewCount":147257,"Q_Id":40690598,"Users Score":3,"Answer":"I just spent some time figure it out.\nThoma's answer is not complete.\nSay your program is test.py, you want to use gpu0 to run this program, and keep other gpus free.\nYou should write CUDA_VISIBLE_DEVICES=0 python test.py\nNotice it's DEVICES not DEVICE","Q_Score":113,"Tags":"python,machine-learning,tensorflow,keras","A_Id":44625370,"CreationDate":"2016-11-19T08:04:00.000","Title":"Can Keras with Tensorflow backend be forced to use CPU or GPU at will?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to opencv using python and trying to get the shape of a contour in an image.\nConsidering only regular shapes like square, rectangle, circle and triangle is there any way to get the contour shape using only numpy and cv2 libraries?\nAlso i want to find the colour inside a contour. How can I do it?\nFor finding area of a contour there is an inbuilt function: cv2.contourArea(cnt).\nAre there inbuilt functions for \"contour shape\" and \"color inside contour\" also?\nPlease help!\nNote : The images I am considering contains multiple regular shapes.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":5369,"Q_Id":40690675,"Users Score":1,"Answer":"For finding the shape of a particular contour we can draw a bounded rectangle around the contour.\nNow we can compare the area of contour with the area of bounded rectangle.\nIf area of contour is equal to half the area of bounded rectangle the shape is a triangle.\nIf the area of contour is less that area of bounded rectangle but is greater than half the area of bounded rectangle then its a circle.\nNote: This method is limited to regular triangle and circle. this doesnt apply to polygons like hexagon,heptagon etc.","Q_Score":2,"Tags":"python,opencv,colors,shape,contour","A_Id":40767562,"CreationDate":"2016-11-19T08:14:00.000","Title":"Detecting shape of a contour and color inside","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I wonder if it is possible to design ML experiments without using the drag&drop functionality (which is very nice btw)? I want to use Python code in the notebook (within Azure ML studio) to access the algorithms (e.g., matchbox recommender, regression models, etc) in the studio and design experiments? Is this possible?\nI appreciate any information and suggestion!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":236,"Q_Id":40703961,"Users Score":2,"Answer":"The algorithms used as modules in Azure ML Studio are not currently able to be used directly as code for Python programming.\nThat being said: you can attempt to publish the outputs of the out-of-the-box algorithms as web services, which can be consumed by Python code in the Azure ML Studio notebooks. You can also create your own algorithms and use them as custom Python or R modules.","Q_Score":0,"Tags":"python,azure,jupyter-notebook,azure-machine-learning-studio","A_Id":40713966,"CreationDate":"2016-11-20T12:22:00.000","Title":"Creating azure ml experiments merely using python notebook within azure ml studio","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm not sure of the rules to create a matrix for a word search puzzle game. I am able to create a matrix with initial values of 0.\nIs it correct that I'll randomly select a starting point(coordinates), and a random direction(horizontally,vertically,&,diagonally) for a word then manage if it would overlap with another word in the matrix? If it does then check if the characters are the same (although there's only a little chance) then if no I'll assign it there. The problem is it's like I lessen the chance of words to overlap. \nI have also read that I need to check first the words that have the same characters. But if that's the case, it seems like the words that I am going to put in the matrix are always overlapping.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":936,"Q_Id":40704339,"Users Score":0,"Answer":"I would rather look at words that are already there and then randomly select a word from the set of words that fit there.\nOf course you might not fill the whole matrix like this. If you have put  one word somewhere where it blocks all other words (no other word fits), you might have to backtrack, but that will kill the running time. \nIf you really want to fill the whole matrix, I would iterate over all possible starting positions, see how many words fit there, and then recurse over the possibilities of the starting position with the least number of candidates. That will cause your program recognize and leave \"dead ends\" early, which improves the running time drastically. That is a powerful technique from fixed parameter algorithms, which I like to call Branching-vector minimization.","Q_Score":1,"Tags":"python,string,matrix,coordinates,words","A_Id":40761500,"CreationDate":"2016-11-20T13:07:00.000","Title":"Generate a word search puzzle matrix","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm not sure of the rules to create a matrix for a word search puzzle game. I am able to create a matrix with initial values of 0.\nIs it correct that I'll randomly select a starting point(coordinates), and a random direction(horizontally,vertically,&,diagonally) for a word then manage if it would overlap with another word in the matrix? If it does then check if the characters are the same (although there's only a little chance) then if no I'll assign it there. The problem is it's like I lessen the chance of words to overlap. \nI have also read that I need to check first the words that have the same characters. But if that's the case, it seems like the words that I am going to put in the matrix are always overlapping.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":936,"Q_Id":40704339,"Users Score":0,"Answer":"Start with the longest word.\nFirst of all you must find all points and directions, where this word may fit. For example word 'WORD' may fit, when at the first pos there is NULL or W, on the second pos NULL or O, on the third NULL or R and on the fourth NULL or D. \nThen You should group it to positions with no NULLS, with one NULL, with two NULLs and so on. \nThen select randomly position from the group with the smallest ammount of NULLS. If there are no posible positions, skip the word.\nThis attempt will allow you to put more words and prevent situations, where random search can't find the proper place (when there is only a few of them).","Q_Score":1,"Tags":"python,string,matrix,coordinates,words","A_Id":40761650,"CreationDate":"2016-11-20T13:07:00.000","Title":"Generate a word search puzzle matrix","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Whats the difference between score() method in sklearn.naive_bayes.GaussianNB() module and accuracy_score method in sklearn.metrics module? Both appears to be same. Is that correct?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":12527,"Q_Id":40726899,"Users Score":15,"Answer":"In general, different models have score methods that return different metrics. This is to allow classifiers to specify what scoring metric they think is most appropriate for them (thus, for example, a least-squares regression classifier would have a score method that returns something like the sum of squared errors). In the case of GaussianNB the docs say that its score method:\n\nReturns the mean accuracy on the given test data and labels.\n\nThe accuracy_score method says its return value depends on the setting for the normalize parameter:\n\nIf False, return the number of correctly classified samples. Otherwise, return the fraction of correctly classified samples.\n\nSo it would appear to me that if you set normalize to True you'd get the same value as the GaussianNB.score method.\nOne easy way to confirm my guess is to build a classifier and call both score with normalize = True and accuracy_score and see if they match. Do they?","Q_Score":19,"Tags":"python,scikit-learn","A_Id":40727147,"CreationDate":"2016-11-21T18:22:00.000","Title":"Difference between score and accuracy_score in sklearn","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I recently came across tensorflow softmax_cross_entropy_with_logits, but I can not figure out what the difference on the implementation is compared to sigmoid_cross_entropy_with_logits.","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":1855,"Q_Id":40736440,"Users Score":1,"Answer":"the major difference between sigmoid and softmax is that softmax function return result in terms of probability which is kind of more inline with the ML philosophy. Sum of all outputs from softmax result to 1. This is turn tells you how confident the network is about the answer.\nWhereas, sigmoid outputs are discreet. Its either correct or incorrect. You would have to write code to calculate the probability yourself.\nAs far as performance of the network goes. Softmax generally gives better accuracy than sigmoid. But, it also highly dependent on other hyper parameters also.","Q_Score":4,"Tags":"python,tensorflow","A_Id":41664553,"CreationDate":"2016-11-22T07:51:00.000","Title":"Difference of implementation between tensorflow softmax_cross_entropy_with_logits and sigmoid_cross_entropy_with_logits","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have please two questions concerning the ImageDataGenerator:\n1) Are the same augmentations used on the whole batch or each image gets its own random transformation?\ne.g. for rotation, does the module rotates all the images in the batch with same angle or each image get a random rotation angle ?\n2) The data in ImageDataGenerator.flow is looped over (in batches) indefinitely. Is there a way to stop this infinite loop, i.e. doing the augmentation only for n number of time. Because I need to modify the batch_size in each step (not each epoch).\nThanks","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2135,"Q_Id":40737870,"Users Score":0,"Answer":"@Neal: Thank you for the prompt answer! You were right, I probably need to better explain my task. My work is somehow similar to classifying video sequences, but my data is saved in a database. I want my code to follow this steps for one epoch:\nFor i in (number_of_sequences):  \n\nGet N, the number of frames in the sequence i (I think that\u2019s\nequivalent to batch_size, the number of N of each sequence is\nalready saved in a list)\nFetch N successive frames from my database and their labels:\nX_train, y_train\nFor j in range(number_of_rotation):   -\n\n\nPerform (the same) data Augmentation on all frames of the sequence    (probably using datagen = ImageDataGenerator() and datagen.flow())\nTrain the network on X, y\n\n\nMy first thought was using model.fit_generator(generator = ImageDataGenerator().flow()) but this way, I can not modify my batch_size, and honestly I did not understand your solution.\nSorry for the long post, but I\u2019m still a novice in both python and NN, but I\u2019m really a big fan of Keras ;)\nThnx!","Q_Score":2,"Tags":"python,tensorflow,keras,conv-neural-network","A_Id":40791232,"CreationDate":"2016-11-22T09:17:00.000","Title":"Augementations in Keras ImageDataGenerator","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to find the best parameters for a RandomForest classifier (with scikit-learn) in a way that it generalises well to other datasets (which may not be iid).\nI was thinking doing grid search using the whole training dataset while evaluating the scoring function on other datasets.\nIs there an easy to do this in python\/scikit-learn?","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":1071,"Q_Id":40742172,"Users Score":1,"Answer":"If you can, you may simply merge the two datasets and perform GridSearchCV, this ensures the generalization ability to the other dataset.  If you are talking about generalization to future unknown dataset, then this might not work, because there isn't a perfect dataset from which we can train a perfect model.","Q_Score":0,"Tags":"python,machine-learning,scikit-learn,random-forest,data-science","A_Id":40745346,"CreationDate":"2016-11-22T12:41:00.000","Title":"How to do GridSearchCV with train and test being different datasets?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to find the best parameters for a RandomForest classifier (with scikit-learn) in a way that it generalises well to other datasets (which may not be iid).\nI was thinking doing grid search using the whole training dataset while evaluating the scoring function on other datasets.\nIs there an easy to do this in python\/scikit-learn?","AnswerCount":2,"Available Count":2,"Score":0.1973753202,"is_accepted":false,"ViewCount":1071,"Q_Id":40742172,"Users Score":2,"Answer":"I don't think you can evaluate on a different data set. The whole idea behind GridSearchCV is that it splits your training set into n folds, trains on n-1 of those folds and evaluates on the remaining one, repeating the procedure until every fold has been \"the odd one out\". This keeps you from having to set apart a specific validation set and you can simply use a training and a testing set.","Q_Score":0,"Tags":"python,machine-learning,scikit-learn,random-forest,data-science","A_Id":40744270,"CreationDate":"2016-11-22T12:41:00.000","Title":"How to do GridSearchCV with train and test being different datasets?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a flat Euclidean rectangular surface but when a point moves to the right boundary, it will appear at the left boundary (at the same y value). And visa versa for moving across the top and bottom boundaries. I'd like to be able to calculate the centroid of a set of points. The set of points in question are mostly clumped together.\nI know I can calculate the centroid for a set of points by averaging all the x and y values. How would I do it in a wrap-around map?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":116,"Q_Id":40744501,"Users Score":0,"Answer":"I've done this before for Ramachandran plots. I will check but I seem to remember the algorithm I used was:\n\nAssume that your Euclidian area has an origin at (0,0)\nShift all the points in a set so that they are fully within the area\nCalculate the centroid\nShift the centroid back by the reverse of the original\n\nSo, say your points are split across the X axis, you find the minimum X-coordinate of the point set that is larger than the midpoint axis of the area. Then find the distance d to the edge on the Y axis. Then shift the entire cluster by d.","Q_Score":1,"Tags":"python,algorithm,math","A_Id":40745068,"CreationDate":"2016-11-22T14:30:00.000","Title":"Centroid of a set of positions on a toroidally wrapped (x- and y- wrapping) 2D array?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I found model.predict and model.predict_proba both give an identical 2D matrix representing probabilities at each categories for each row. \nWhat is the difference of the two functions?","AnswerCount":4,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":47190,"Q_Id":40747679,"Users Score":2,"Answer":"Just a remark : In fact you have both predict and predict_proba in most classifiers (in Scikit for example). As already mentioned, the first one predicts the class, the second one provides probabilities for each class, classified in ascending order.","Q_Score":26,"Tags":"python,machine-learning,deep-learning,keras","A_Id":60201018,"CreationDate":"2016-11-22T17:06:00.000","Title":"keras: what is the difference between model.predict and model.predict_proba","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I found model.predict and model.predict_proba both give an identical 2D matrix representing probabilities at each categories for each row. \nWhat is the difference of the two functions?","AnswerCount":4,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":47190,"Q_Id":40747679,"Users Score":0,"Answer":"In the recent version of keras e.g. 2.6.0, predict and predict_proba is same i.e. both give probabilities. To get the class labels use predict_classes. The documentation is not updated","Q_Score":26,"Tags":"python,machine-learning,deep-learning,keras","A_Id":68835517,"CreationDate":"2016-11-22T17:06:00.000","Title":"keras: what is the difference between model.predict and model.predict_proba","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How to prevent pandas.read_csv() from inferring the data types. For example, its converting strings true and false to Bool: True and False. The columns are many for many files, therefore not feasible to do:\ndf['field_name'] = df['field_name'].astype(np.float64) for each of the columns in each file. I prefer pandas to just read file as it is, no type inferring.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":3832,"Q_Id":40750670,"Users Score":16,"Answer":"Use the parameter dtype=object for Pandas to keep the data as such at load time.","Q_Score":8,"Tags":"python,pandas","A_Id":40750775,"CreationDate":"2016-11-22T20:05:00.000","Title":"Prevent pandas.read_csv from inferring dtypes","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using tflearn for modeling CNN.\nHowever, my data has different number of rows in each input (but same number of columns).\nFor example, I have 100 inputs. \nThe first input's dimension is 4*9 but the second and third have 1*9. \nI do not sure how to feed and shape data by using input_data().","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":57,"Q_Id":40759257,"Users Score":0,"Answer":"First of all, you have to know what exactly is your training sample. I am not sure what you mean by \"input\", does one input means one sample? Or does one row in your input means one sample?\nIf one input means one sample, you are in some trouble, because almost all CNN (and almost any other machine learning algos) requires consistency in the shape of the data. Given that some sample have more data than others, it might be a solution to crop out the extra ones from which have more data, or to just ignore those with less rows (so as to maximize the data you use). A more complicated way would be to run a PCA over some of the samples which have more rows (and same number of rows), then use only the principal components for all samples, if possible.\nIf one row means one sample, you can just merge all the data into a big chunk and process it the usual way. You got it.","Q_Score":0,"Tags":"python,tensorflow,tflearn","A_Id":40759746,"CreationDate":"2016-11-23T08:24:00.000","Title":"tflearn: different number of rows of each input","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Does anybody have a suggestion on what's the best way to present overlapping lines on a plot? I have a lot of them, and I had the idea of having full lines of different colors where they don't overlap, and having dashed lines where they do overlap so that all colors are visible and overlapping colors are seen.\nBut still, how do I that.","AnswerCount":4,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":35477,"Q_Id":40766909,"Users Score":33,"Answer":"Just decrease the opacity of the lines so that they are see-through. You can achieve that using the alpha variable. Example:\nplt.plot(x, y, alpha=0.7)\nWhere alpha ranging from 0-1, with 0 being invisible.","Q_Score":28,"Tags":"python,matplotlib","A_Id":40767005,"CreationDate":"2016-11-23T14:18:00.000","Title":"Suggestions to plot overlapping lines in matplotlib?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a list of elements to sort and a comparison function cmp(x,y) which decides if x should appear before y or after y. The catch is that some elements do not have a defined order. The cmp function returns \"don't care\".\nExample: Input: [A,B,C,D], and C > D, B > D. Output: many correct answers, e.g. [D,C,B,A] or [A,D,B,C]. All I need is one output from all possible outputs..\nI was not able to use the Python's sort for this and my solution is the old-fashioned bubble-sort to start with an empty list and insert one element at a time to the right place to keep the list sorted all the time.\nIs it possible to use the built-in sort\/sorted function for this purpose? What would be the key?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":809,"Q_Id":40768575,"Users Score":0,"Answer":"The built-in sort method requires that cmp imposes a total ordering. It doesn't work if the comparisons are inconsistent. If it returns that A < B one time it must always return that, and it must return that B > A if the arguments are reversed.\nYou can make your cmp implementation work if you introduce an arbitrary tiebreaker. If two elements don't have a defined order, make one up. You could return cmp(id(a), id(b)) for instance -- compare the objects by their arbitrary ID numbers.","Q_Score":2,"Tags":"python,algorithm,sorting","A_Id":40768770,"CreationDate":"2016-11-23T15:40:00.000","Title":"How to sort with incomplete ordering?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to create an algorithm using the divide-and-conquer approach but using an iterative algorithm (that is, no recursion).\nI am confused as to how to approach the loops.\nI need to break up my problems into smaller sub problems, until I hit a base case. I assume this is still true, but then I am not sure how I can (without recursion) use the smaller subproblems to solve the much bigger problem.\nFor example, I am trying to come up with an algorithm that will find the closest pair of points (in one-dimensional space - though I intend to generalize this on my own to higher dimensions). If I had a function closest_pair(L) where L is a list of integer co-ordinates in \u211d, how could I come up with a divide and conquer ITERATIVE algorithm that can solve this problem?\n(Without loss of generality I am using Python)","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2428,"Q_Id":40770767,"Users Score":4,"Answer":"The cheap way to turn any recursive algorithm into an iterative algorithm is to take the recursive function, put it in a loop, and use your own stack. This eliminates the function call overhead and from saving any unneeded data on the stack. However, this is not usually the \"best\" approach (\"best\" depends on the problem and context.)\nThey way you've worded your problem, it sounds like the idea is to break the list into sublists, find the closest pair in each, and then take the closest pair out of those two results. To do this iteratively, I think a better way to approach this than the generic way mentioned above is to start the other way around: look at lists of size 3 (there are three pairs to look at) and work your way up from there. Note that lists of size 2 are trivial.\nLastly, if your coordinates are integers, they are in Z (a much smaller subset of R).","Q_Score":1,"Tags":"python,algorithm,computer-science","A_Id":40770869,"CreationDate":"2016-11-23T17:25:00.000","Title":"Iterative Divide and Conquer algorithms","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataset for which I need to fit the plot. I am using leastsq() for fitting. However, currently I need to give initial guess values manually which is really affecting the fitting. Is there any way to first calculate the initial guess values which I can pass in leastsq()?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":481,"Q_Id":40776967,"Users Score":1,"Answer":"No, you can't really calculate an initial guess.\nYou'll just have to make an educated guess, and that really depends on your data and model.\nIf the initial guess affects the fitting, there is likely something else going on; you're probably getting stuck in local minima. Your model may be too complex, or your data range may be so large that you run into floating point precision limits and the fitting algorithm can't detect any changes for parameter changes. The latter can often be avoided by normalizing your data (and model), or, for example, using log(-log) space instead of linear space.\nOr avoid leastsq altogether, and use a different minimization method (which will likely be much slower, but may produce overall better and more consistent results), such as the Nelder-Mead amoebe method.","Q_Score":0,"Tags":"python,numpy,least-squares,data-science","A_Id":40777110,"CreationDate":"2016-11-24T02:01:00.000","Title":"How to find initial guess for leastsq function in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How to determine if a new transformation in spark causes the creation of a stage? is there a list of them ?","AnswerCount":1,"Available Count":1,"Score":-0.1973753202,"is_accepted":false,"ViewCount":56,"Q_Id":40781351,"Users Score":-1,"Answer":"There are some common operator like reducebykey,groupbykey that can cause to shuffle.You can visit spark offical website to learn more.","Q_Score":0,"Tags":"python,scala,hadoop,apache-spark,rdd","A_Id":40782596,"CreationDate":"2016-11-24T08:33:00.000","Title":"In spark, what kind of transformations necessit a shuffling (can imply a new stage)?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How to use rv_continuous for multidimensional pdf?\nIs rv_continuous able to handle multidimensional pdf?\nMy goal is to use the rvs method to generate random values from an arbitrary 2d distribution. Do you know any other solutions?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":152,"Q_Id":40781553,"Users Score":1,"Answer":"rv_continuous only handles univariate distributions.","Q_Score":1,"Tags":"python,scipy","A_Id":40781778,"CreationDate":"2016-11-24T08:44:00.000","Title":"Generate random values given 2d pdf","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"A friend of mine had an idea to make a speed reading program that displays words one by one (much like currently existing speed reading programs). However, the program would filter out words that aren't completely necessary to the meaning (if you want to skim something). \nI have starting to implement this program, but I'm not quite sure on what the algorithm to get rid of \"unimportant\" words should be. \nMy idea is to parse the sentence (I'm currently using Stanford Parser) and somehow assign weights based on how important that word is to the sentence's meaning to each word then start removing words with the with the lowest weights. I will continue to do this, check how \"different\" the original tree and the new tree is. I will continue to remove the word with the lowest weight until the two trees are too different (I will determine some constant via a \"calibration\" process that each user goes through once). Finally, I will go through each word of the shortened sentence and try to replace it with a simpler or shorter synonym for that word (again while still trying to retain value).\nAs well, there will be special cases for very common words like \"the,\" \"a,\" and \"of.\"\nFor example:\n\"Billy said to Jane, 'Do you want to go out?'\"\nWould become:\n\"Billy told Jane 'want go out?'\"\nThis would retain basically all of the meaning of the sentence but shortened it significantly. \nIs this a good idea for an algorithm and if so how would I assign the weights, what tree comparison algorithm should I use, and is inserting the synonyms done in a good place (i.e. should it be done before I try to remove any words)?","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":3305,"Q_Id":40788494,"Users Score":1,"Answer":"Assigning weights is a million dollar question there. As a first step, I would identify parts of the sentence (subject-predicate-clause, etc) and the sentence structure (simple-compound-complex, etc) to find \"anchor\" words that would have highest weight. That should make the rest of the task easier.","Q_Score":3,"Tags":"python,algorithm,parsing,tree,nlp","A_Id":40793242,"CreationDate":"2016-11-24T14:09:00.000","Title":"An Algorithm to Determine How Similar Two Sentences Are","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there a way to index a python list like x = ['a','b','c'] using a numpy boolean array? I'm currently getting the following error: TypeError: only integer arrays with one element can be converted to an index","AnswerCount":4,"Available Count":1,"Score":0.1488850336,"is_accepted":false,"ViewCount":11235,"Q_Id":40793882,"Users Score":3,"Answer":"map(x.__getitem__,np.where(mask)[0]) \nOr if you want list comprehension\n[x[i] for i in np.where(mask)[0]]\nThis keeps you from having to iterate over the whole list, especially if mask is sparse.","Q_Score":5,"Tags":"python,numpy","A_Id":40800749,"CreationDate":"2016-11-24T20:08:00.000","Title":"Index Python List with Numpy Boolean Array","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a Bokeh plotting app, and I need to allow the user to upload a CSV file and modify the plots according to the data in it. \nIs it possible to do this with the available widgets of Bokeh?\nThank you very much.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":6493,"Q_Id":40794180,"Users Score":1,"Answer":"As far as I know there is no widget native to Bokeh that will allow a file upload.\nIt would be helpful if you could clarify your current setup a bit more. Are your plots running on a bokeh server or just through a Python script that generates the plots?\nGenerally though, if you need this to be exposed through a browser you'll probably want something like Flask running a page that lets the user upload a file to a directory which the bokeh script can then read and plot.","Q_Score":12,"Tags":"javascript,python,file,upload,bokeh","A_Id":40795462,"CreationDate":"2016-11-24T20:36:00.000","Title":"Upload a CSV file and read it in Bokeh Web app","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I am currently trying train a regression network using keras.  To ensure I proper training I've want to train using crossvalidation. \nThe Problem is that it seems that keras don't have any functions supporting crossvalidation or do they? \nThe only solution I seemed to have found is to use scikit test_train_split and run a model.fit for  for each k fold manually.  Isn't there a already an integrated solutions for this, rather than manually doing it ?","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":4491,"Q_Id":40819939,"Users Score":2,"Answer":"Nope... That seem to be the solution. (Of what I know of.)","Q_Score":4,"Tags":"python,scikit-learn,keras,cross-validation","A_Id":44195108,"CreationDate":"2016-11-26T15:31:00.000","Title":"Keras and cross validation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to take a part of values (say 500 values) of an array and perform some operation on it such as take sum of squares of those 500 values. and then proceed with the next 500 values of the same array.\nHow should I implement this? Would a blackman window be useful in this case or is another approach more suitable?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":410,"Q_Id":40821099,"Users Score":0,"Answer":"Assuming your array a is 1D and its length is a multiple a 500, a simple np.sum(a.reshape((-1, 500)) ** 2, axis=1) would suffice. If you want a more complicated operation, please edit your question accordingly.","Q_Score":1,"Tags":"python,arrays,numpy","A_Id":40821227,"CreationDate":"2016-11-26T17:33:00.000","Title":"How to use Blackman Window in numpy to take a part of values from an array?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have been told that when dealing with multivariate logistic regression, you want to add a column of 1's in the X matrix for the model intercept. When using the model from sklearn, does the module automatically add the column of 1?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2238,"Q_Id":40822563,"Users Score":0,"Answer":"This is the default behavior since fit_intercept defaults to True","Q_Score":3,"Tags":"python,scikit-learn","A_Id":41367703,"CreationDate":"2016-11-26T20:01:00.000","Title":"Does Sklearn.LogisticRegression add a column of 1?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to implement RNN in Tensorflow for text prediction. I am using BasicLSTMCell for this purpose, with sequence lengths of 100. \nIf I understand correctly, the output activation h_t and the activation c_t of the LSTM are reset each time we enter a new sequence (that is, they are updated 100 times along the sequence, but once we move to the next sequence in the batch, they are reset to 0).\nIs there a way to prevent this from happening using Tensorflow? That is, to continue using the updated c_t and h_t along all the sequences in the batch? (and then to reset them when moving to the next batch).","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":228,"Q_Id":40823341,"Users Score":3,"Answer":"I don't think you would want to do that since each example in the batch should be independent.  If they are not you should just have a batch size of 1 and a sequence of length 100 * batch_size.  Often you might want to save the state in between batches.  In this case you would need to save the RNN state to a variable or as I like to do allow the user to feed it in with a placeholder.","Q_Score":1,"Tags":"python,neural-network,tensorflow,recurrent-neural-network,lstm","A_Id":40829189,"CreationDate":"2016-11-26T21:27:00.000","Title":"resetting c_t and h_t in LSTM every couple of sequences in RNN","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm relatively new to Pandas and I wanted to know best practices around incremental vs. primary key indexes.\nIn particular, I'm wondering:\n\nwhat are the benefits of using a dataset's primary key as its DataFrame index, as opposed to just sticking with the default incremental integer index?\nare any potential pitfalls for replacing the default incremental integer index with a dataset's primary key?","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":1018,"Q_Id":40836932,"Users Score":2,"Answer":"It depends on your application.  The one \"best practice\" I know of for Pandas indexes is to keep their values unique.  As for which column to use as your index, that depends on what you will later do with the DataFrame (e.g. what other data you might merge it with).","Q_Score":0,"Tags":"python,pandas,dataframe","A_Id":40836970,"CreationDate":"2016-11-28T03:54:00.000","Title":"Using primary key column as index values in Pandas dataframe - Best practice?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Getting this error in python when trying to compute lda for a smaller size of corpus but works fine in other cases.\nThe size of corpus is 15 and I tried setting the number of topic to 5 then reduced it to 2 but it still gives the same error : ValueError: cannot compute LDA over an empty collection (no terms)\ngetting error at this line :     lda = models.LdaModel(corpus, num_topics=topic_number, id2word=dictionary, passes=passes)\nwhere corpus is corpus = [dictionary.doc2bow(text) for a, id, text, s_date, e_date, qd, qd_perc in texts]\nWhy is it giving no terms?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":5310,"Q_Id":40840731,"Users Score":4,"Answer":"Finally figured it out. The issue with small documents is that if you try to filter the extremes from dictionary, you might end up with empty lists in corpus.corpus = [dictionary.doc2bow(text)].\nSo the values of parameters in dictionary.filter_extremes(no_below=2, no_above=0.1) needs to be selected accordingly and carefully before corpus = [dictionary.doc2bow(text)]\nI just removed the filter extremes and lda model runs fine now. Though I will change the parameter values in filter extreme and use it later.","Q_Score":2,"Tags":"python,gensim,lda,topic-modeling","A_Id":40842347,"CreationDate":"2016-11-28T09:18:00.000","Title":"ValueError: cannot compute LDA over an empty collection (no terms)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Today I was installing the opencv by\nhomebrew install opencv\nand then I try to import it by:\npython\nimport cv2\nand it return: No moudule named cv2\nHowever, I try to import it by:\nPython3\nImport cv2\nit works well.\nI tried to install opencv again but homebrew said it has been installed.\nDont know what can I do now","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":30,"Q_Id":40840738,"Users Score":0,"Answer":"copy cv2.so and cv.py to \/System\/Library\/Frameworks\/Python.framework\/Versions\/2.7\/lib\/\nYou can find this two files in \/usr\/local\/Cellar\/opecv\/..\/lib\/python2.7","Q_Score":0,"Tags":"python,macos,opencv","A_Id":40842338,"CreationDate":"2016-11-28T09:18:00.000","Title":"Config the opencv in python 2.7 of MacOS","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":1,"Web Development":0},{"Question":"Is it required to install TF Slim externally after pulling a docker image latest-devel-gpu(r0.10) for GPU environment?\nwhen I do import tensorflow.contrib.slim as slim it gives me an error:\n\n\"No module named Slim\" through python.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":939,"Q_Id":40841872,"Users Score":0,"Answer":"OP self-solved problem by upgrading to the docker tag 0.10.0-devel-gpu (and it should work going forward).","Q_Score":0,"Tags":"python,tensorflow","A_Id":47368406,"CreationDate":"2016-11-28T10:20:00.000","Title":"is it required to install TF Slim externally after pulling a docker image latest-devel-gpu(r0.10)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have multiple images of same object taken at different angles  and has many such objects. I need to match a test image which is taken at a random angle later belongs to particular object with similar background,  by matching it with those images. The objects are light installations inside a building. Same object may be installed at different places, but backgrounds are different.\nI used mean shift error, template matching from opencv and Structural Similarity Index, but with less accuracy.\nHow about Image Fingerprinting or SIFT\/SURF","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1892,"Q_Id":40843794,"Users Score":2,"Answer":"The state of the art for such object recognition tasks are convolutional neural networks, but you will need a large labelled training set, which might rule that out. Otherwise SIFT\/SURF is probably what you are looking for. They are pretty robust towards most transformations.","Q_Score":0,"Tags":"python,opencv,image-processing","A_Id":40844025,"CreationDate":"2016-11-28T12:01:00.000","Title":"How to match images taken at different angles","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"There is question from one Quiz that I don't fully understand:\nSuppose you have a weighted directed graph and want to find a path between nodes A and B with the smallest total weight. Select the most accurate statement:\n\nIf some edges have negative weights, depth-first search finds a correct solution.\nIf all edges have weight 2, depth-first search guarantees that the first path found to be is the shortest path.\nIf some edges have negative weights, breadth-first search finds a correct solution.\nIf all edges have weight 2, breadth-first search guarantees that the first path found to be is the shortest path.\n\nAm I right that #1 is correct?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":826,"Q_Id":40852458,"Users Score":2,"Answer":"4 is the correct one!, because all edges have the same weight, so you need to find the node traversing the minimum quantity of edges.\n1 Is wrong because depth-first search doesn't consider edge weights, so any node could be reached first","Q_Score":1,"Tags":"python,algorithm,graph","A_Id":40852542,"CreationDate":"2016-11-28T19:58:00.000","Title":"Smallest total weight in weighted directed graph","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I was wondering what was the difference between Activation Layer and Dense layer in Keras.\nSince Activation Layer seems to be a fully connected layer, and Dense have a parameter to pass an activation function, what is the best practice ?\nLet's imagine a fictionnal network like this :\nInput -> Dense -> Dropout -> Final Layer\nFinal Layer should be : Dense(activation=softmax) or Activation(softmax) ?\nWhat is the cleanest and why ?\nThanks everyone!","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":11235,"Q_Id":40866124,"Users Score":46,"Answer":"Using Dense(activation=softmax) is computationally equivalent to first add Dense and then add Activation(softmax). However there is one advantage of the second approach - you could retrieve the outputs of the last layer (before activation) out of such defined model. In the first approach - it's impossible.","Q_Score":31,"Tags":"python,machine-learning,neural-network,deep-learning,keras","A_Id":40870126,"CreationDate":"2016-11-29T12:37:00.000","Title":"Difference between Dense and Activation layer in Keras","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I use GradientBoosting classifier to predict gender of users. The data have a lot of predictors and one of them is the country. For each country I have binary column. There are always only one column set to 1 for all country columns. But such desicion is very slow from computation point of view. Is there any way to represent country columns with only one column? I mean correct way.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":69,"Q_Id":40871371,"Users Score":1,"Answer":"You can replace the binary variable with the actual country name then collapse all of these columns into one column. Use LabelEncoder on this column to create a proper integer variable and you should be all set.","Q_Score":0,"Tags":"python,classification,gbm","A_Id":40871454,"CreationDate":"2016-11-29T16:52:00.000","Title":"GradientBoostingClassifier and many columns","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to install opencv in python on my windows machine but I am unable to do so. I have python 2.7.11::Anaconda 2.4.1 <32-bit> \nHere is what I have tried till now -\n\npip install cv2 on command line gives the error :\n\n\ncould not find a\nversion that satisfies the requirement cv2\n\n\nI downloaded the package from sourceforge site, followed the steps\nand pasted cv2.pyd in C:\\Python27\\Lib\\site-packages but still it is\nnot working. I get the following error message\n\n\nImportError: No\nmodule named cv2\n\n(I already have numpy installed and it works just fine).","AnswerCount":4,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":60085,"Q_Id":40872683,"Users Score":0,"Answer":"So I was using PyCharm, and what worked for me was to install it directly from file->settings, Project:your-project-name->Python Interpreter list","Q_Score":22,"Tags":"python,opencv","A_Id":72478813,"CreationDate":"2016-11-29T18:01:00.000","Title":"Unable to install cv2 on windows","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am getting the following error when I run mnist = input_data.read_data_sets(\"MNIST_data\", one_hot = True).\n\nEOFError: Compressed file ended before the end-of-stream marker was reached\n\nEven when I extract the file manually and place it in the MNIST_data directory, the program is still trying to download the file instead of using the extracted file. \nWhen I extract the file using WinZip which is the manual way, WinZip tells me that the file is corrupt. \nHow do I solve this problem? \nI can't even load the data set now, I still have to debug the program itself. Please help.\nI pip installed Tensorflow and so I don't have a Tensorflow example. So I went to GitHub to get the input_data file and saved it in the same directory as my main.py. The error is just regarding the .gz file. The program could not extract it.\n\nrunfile('C:\/Users\/Nikhil\/Desktop\/Tensor Flow\/tensf.py', wdir='C:\/Users\/Nikhil\/Desktop\/Tensor Flow')\n  Reloaded modules: input_data\n  Extracting MNIST_data\/train-images-idx3-ubyte.gz\n  C:\\Users\\Nikhil\\Anaconda3\\lib\\gzip.py:274: VisibleDeprecationWarning: converting an array with ndim > 0 to an index will result in an error in the future\n    return self._buffer.read(size)\n  Traceback (most recent call last):\nFile \"\", line 1, in \n      runfile('C:\/Users\/Nikhil\/Desktop\/Tensor Flow\/tensf.py', wdir='C:\/Users\/Nikhil\/Desktop\/Tensor Flow')\nFile \"C:\\Users\\Nikhil\\Anaconda3\\lib\\site-packages\\spyder\\utils\\site\\sitecustomize.py\", line 866, in runfile\n      execfile(filename, namespace)\nFile \"C:\\Users\\Nikhil\\Anaconda3\\lib\\site-packages\\spyder\\utils\\site\\sitecustomize.py\", line 102, in execfile\n      exec(compile(f.read(), filename, 'exec'), namespace)\nFile \"C:\/Users\/Nikhil\/Desktop\/Tensor Flow\/tensf.py\", line 26, in \n      mnist = input_data.read_data_sets(\"MNIST_data\/\", one_hot = True)\nFile \"C:\\Users\\Nikhil\\Desktop\\Tensor Flow\\input_data.py\", line 181, in read_data_sets\n      train_images = extract_images(local_file)\nFile \"C:\\Users\\Nikhil\\Desktop\\Tensor Flow\\input_data.py\", line 60, in extract_images\n      buf = bytestream.read(rows * cols * num_images)\nFile \"C:\\Users\\Nikhil\\Anaconda3\\lib\\gzip.py\", line 274, in read\n      return self._buffer.read(size)\nFile \"C:\\Users\\Nikhil\\Anaconda3\\lib_compression.py\", line 68, in readinto\n      data = self.read(len(byte_view))\nFile \"C:\\Users\\Nikhil\\Anaconda3\\lib\\gzip.py\", line 480, in read\n      raise EOFError(\"Compressed file ended before the \"\nEOFError: Compressed file ended before the end-of-stream marker was reached","AnswerCount":9,"Available Count":3,"Score":0.0665680765,"is_accepted":false,"ViewCount":52865,"Q_Id":40877781,"Users Score":3,"Answer":"It is very simple in windows :\nGo to : C:\\Users\\Username\\.keras\\datasets\nand then Delete the Dataset that you want to redownload or has the error","Q_Score":14,"Tags":"python,tensorflow","A_Id":58649705,"CreationDate":"2016-11-29T23:37:00.000","Title":"EOFError: Compressed file ended before the end-of-stream marker was reached - MNIST data set","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am getting the following error when I run mnist = input_data.read_data_sets(\"MNIST_data\", one_hot = True).\n\nEOFError: Compressed file ended before the end-of-stream marker was reached\n\nEven when I extract the file manually and place it in the MNIST_data directory, the program is still trying to download the file instead of using the extracted file. \nWhen I extract the file using WinZip which is the manual way, WinZip tells me that the file is corrupt. \nHow do I solve this problem? \nI can't even load the data set now, I still have to debug the program itself. Please help.\nI pip installed Tensorflow and so I don't have a Tensorflow example. So I went to GitHub to get the input_data file and saved it in the same directory as my main.py. The error is just regarding the .gz file. The program could not extract it.\n\nrunfile('C:\/Users\/Nikhil\/Desktop\/Tensor Flow\/tensf.py', wdir='C:\/Users\/Nikhil\/Desktop\/Tensor Flow')\n  Reloaded modules: input_data\n  Extracting MNIST_data\/train-images-idx3-ubyte.gz\n  C:\\Users\\Nikhil\\Anaconda3\\lib\\gzip.py:274: VisibleDeprecationWarning: converting an array with ndim > 0 to an index will result in an error in the future\n    return self._buffer.read(size)\n  Traceback (most recent call last):\nFile \"\", line 1, in \n      runfile('C:\/Users\/Nikhil\/Desktop\/Tensor Flow\/tensf.py', wdir='C:\/Users\/Nikhil\/Desktop\/Tensor Flow')\nFile \"C:\\Users\\Nikhil\\Anaconda3\\lib\\site-packages\\spyder\\utils\\site\\sitecustomize.py\", line 866, in runfile\n      execfile(filename, namespace)\nFile \"C:\\Users\\Nikhil\\Anaconda3\\lib\\site-packages\\spyder\\utils\\site\\sitecustomize.py\", line 102, in execfile\n      exec(compile(f.read(), filename, 'exec'), namespace)\nFile \"C:\/Users\/Nikhil\/Desktop\/Tensor Flow\/tensf.py\", line 26, in \n      mnist = input_data.read_data_sets(\"MNIST_data\/\", one_hot = True)\nFile \"C:\\Users\\Nikhil\\Desktop\\Tensor Flow\\input_data.py\", line 181, in read_data_sets\n      train_images = extract_images(local_file)\nFile \"C:\\Users\\Nikhil\\Desktop\\Tensor Flow\\input_data.py\", line 60, in extract_images\n      buf = bytestream.read(rows * cols * num_images)\nFile \"C:\\Users\\Nikhil\\Anaconda3\\lib\\gzip.py\", line 274, in read\n      return self._buffer.read(size)\nFile \"C:\\Users\\Nikhil\\Anaconda3\\lib_compression.py\", line 68, in readinto\n      data = self.read(len(byte_view))\nFile \"C:\\Users\\Nikhil\\Anaconda3\\lib\\gzip.py\", line 480, in read\n      raise EOFError(\"Compressed file ended before the \"\nEOFError: Compressed file ended before the end-of-stream marker was reached","AnswerCount":9,"Available Count":3,"Score":1.0,"is_accepted":false,"ViewCount":52865,"Q_Id":40877781,"Users Score":19,"Answer":"This is because for some reason you have an incomplete download for the MNIST dataset. \nYou will have to manually delete the downloaded folder which usually resides in ~\/.keras\/datasets or any path specified by you relative to this path, in your case MNIST_data.\nPerform the following steps in the terminal (ctrl + alt + t):\n\ncd ~\/.keras\/datasets\/\nrm -rf \"dataset name\"\n\nYou should be good to go!","Q_Score":14,"Tags":"python,tensorflow","A_Id":46372714,"CreationDate":"2016-11-29T23:37:00.000","Title":"EOFError: Compressed file ended before the end-of-stream marker was reached - MNIST data set","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am getting the following error when I run mnist = input_data.read_data_sets(\"MNIST_data\", one_hot = True).\n\nEOFError: Compressed file ended before the end-of-stream marker was reached\n\nEven when I extract the file manually and place it in the MNIST_data directory, the program is still trying to download the file instead of using the extracted file. \nWhen I extract the file using WinZip which is the manual way, WinZip tells me that the file is corrupt. \nHow do I solve this problem? \nI can't even load the data set now, I still have to debug the program itself. Please help.\nI pip installed Tensorflow and so I don't have a Tensorflow example. So I went to GitHub to get the input_data file and saved it in the same directory as my main.py. The error is just regarding the .gz file. The program could not extract it.\n\nrunfile('C:\/Users\/Nikhil\/Desktop\/Tensor Flow\/tensf.py', wdir='C:\/Users\/Nikhil\/Desktop\/Tensor Flow')\n  Reloaded modules: input_data\n  Extracting MNIST_data\/train-images-idx3-ubyte.gz\n  C:\\Users\\Nikhil\\Anaconda3\\lib\\gzip.py:274: VisibleDeprecationWarning: converting an array with ndim > 0 to an index will result in an error in the future\n    return self._buffer.read(size)\n  Traceback (most recent call last):\nFile \"\", line 1, in \n      runfile('C:\/Users\/Nikhil\/Desktop\/Tensor Flow\/tensf.py', wdir='C:\/Users\/Nikhil\/Desktop\/Tensor Flow')\nFile \"C:\\Users\\Nikhil\\Anaconda3\\lib\\site-packages\\spyder\\utils\\site\\sitecustomize.py\", line 866, in runfile\n      execfile(filename, namespace)\nFile \"C:\\Users\\Nikhil\\Anaconda3\\lib\\site-packages\\spyder\\utils\\site\\sitecustomize.py\", line 102, in execfile\n      exec(compile(f.read(), filename, 'exec'), namespace)\nFile \"C:\/Users\/Nikhil\/Desktop\/Tensor Flow\/tensf.py\", line 26, in \n      mnist = input_data.read_data_sets(\"MNIST_data\/\", one_hot = True)\nFile \"C:\\Users\\Nikhil\\Desktop\\Tensor Flow\\input_data.py\", line 181, in read_data_sets\n      train_images = extract_images(local_file)\nFile \"C:\\Users\\Nikhil\\Desktop\\Tensor Flow\\input_data.py\", line 60, in extract_images\n      buf = bytestream.read(rows * cols * num_images)\nFile \"C:\\Users\\Nikhil\\Anaconda3\\lib\\gzip.py\", line 274, in read\n      return self._buffer.read(size)\nFile \"C:\\Users\\Nikhil\\Anaconda3\\lib_compression.py\", line 68, in readinto\n      data = self.read(len(byte_view))\nFile \"C:\\Users\\Nikhil\\Anaconda3\\lib\\gzip.py\", line 480, in read\n      raise EOFError(\"Compressed file ended before the \"\nEOFError: Compressed file ended before the end-of-stream marker was reached","AnswerCount":9,"Available Count":3,"Score":0.022218565,"is_accepted":false,"ViewCount":52865,"Q_Id":40877781,"Users Score":1,"Answer":"I had the same issue when downloading datasets using torchvision on Windows. I was able to fix this by deleting all files from the following path:\n    C:\\Users\\UserName\\MNIST\\raw","Q_Score":14,"Tags":"python,tensorflow","A_Id":58121301,"CreationDate":"2016-11-29T23:37:00.000","Title":"EOFError: Compressed file ended before the end-of-stream marker was reached - MNIST data set","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I try to run  the following code but it gives the following error in recognistion of mglearn color map.\ngrr = pd.scatter_matrix( ...., cmap=mglearn.cm3)\nErrorName: name 'mglearn' is not defined\nI should add pd is Anaconda Pandas package imported as pd but does not recognize the color map mglearn.cm3\nAny suggestions?","AnswerCount":4,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":5864,"Q_Id":40878325,"Users Score":0,"Answer":"conda install pip(because conda install mglearn will give a error)\npip install mglearn\ngrr = pd.plotting.scatter_matrix( ...., cmap=mglearn.cm3)\nif you still can't see the output then you might have missed %matplotlib inline","Q_Score":1,"Tags":"python,pandas,anaconda","A_Id":60873472,"CreationDate":"2016-11-30T00:36:00.000","Title":"what is this color map? cmap=mglearn.cm3","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Disclaimer: I'm new to the field of Machine Learning, and even though I have done my fair share of research during the past month I still lack deep understanding on this topic.\nI have been playing around with the scikit library with the objective of learning how to predict new data based on historic information, and classify existing information.\nI'm trying to solve 2 different problems which may be correlated:\nProblem 1\nGiven a data set containing rows R1 ... RN with features F1 ... FN, and a target per each group of rows, determine in which group does row R(N+1) belongs to.\nNow, the target value is not singular, it's a set of values; The best solution I have been able to come up with is to represent those sets of values as a concatenation, this creates an artificial class and allows me to represent multiple values using only one attribute. Is there a better approach to this?\nWhat I'm expecting is to be able to pass totally new set of rows, and being told which are the target values per each of them.\nProblem 2\nGiven a data set containing rows R1 ... RN with features F1 ... FN, predict the values of R(N+1) based on the frequency of the features.\nA few considerations here:\n\nMost of the features are categorical in nature.\nSome of the features are dates, so when doing the prediction the date should be in the future relative to the historic data.\nThe frequency analysis needs to be done per row, because certain sets of values may be invalid.\n\nMy question here is: Is there any process\/ML algorithm, which given historic data would be able to predict a new set of values based on just the frequency of the parameters?\nIf you have any suggestions, please let me know.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":903,"Q_Id":40887631,"Users Score":0,"Answer":"Regarding Problem 1, if you expect the different components of the target value to be independent, you can approach the problem as building a classifier for every component. That is, if the features are F = (F_1, F_2, ..., F_N) and the targets Y = (Y_1, Y_2, ..., Y_N), create a classifier with features F and target Y_1, a second classifier with features F and target Y_2, etc.\nRegarding Problem 2, if you are not dealing with a time series, IMO the best you can do is simply predict the most frequent value for each feature.\nThat said, I believe your question fits better another stack exchange like cross-validated.","Q_Score":1,"Tags":"python,machine-learning,scikit-learn","A_Id":40947457,"CreationDate":"2016-11-30T11:48:00.000","Title":"Multi-Output Classification using scikit Decision Trees","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am implementing the Viterbi Algorithm for POS-Tagger using the Brown-corpus as my data set. Now an important aspect of this NLP task is finding the accuracy of the model. So I really need help as what to implement. It's easier using the nltk toolkit but since I am not using a toolkit, I am stuck on how to determine the accuracy of my model. Any help, code examples or referral links would be appreciated. Thanks","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":582,"Q_Id":40904368,"Users Score":0,"Answer":"To calculate accuracy on any dataset, count the number of words (excluding start and stop symbols) that you tagged correctly, and divide by the total number of words (excluding start and stop symbols).","Q_Score":1,"Tags":"python,machine-learning,nlp,pos-tagger","A_Id":43810812,"CreationDate":"2016-12-01T06:58:00.000","Title":"Finding the accuracy of an HMM model for POS-Tagger","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When you have two numPy matrix, you could call a dot function to multiply them. Or you could loop through each manually and multiply each value manually. Why and what is the speed difference ? Surely dot function still has to do that but Lower level?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":35,"Q_Id":40907731,"Users Score":1,"Answer":"Yes, both approaches will have to loop over the values in the two matrices. However, python is dynamically typed such that the body of the loop needs to check the types of the three indices used for iteration, ensure that indexing the two input matrices is supported, determine the type of the values extracted from the matrices, ...\nThe numpy implementation is, as you said, lower-level and makes stronger assumptions about the input and output. In particular, the matrix multiplication is implemented in a statically typed language (C or Fortran--I can't quite remember) such that the overhead of type checking disappears. Furthermore, indexing in lower-level languages is a relatively simple operation.","Q_Score":0,"Tags":"python,performance,numpy,processing-efficiency","A_Id":40907857,"CreationDate":"2016-12-01T10:07:00.000","Title":"What is the underlying difference between multiplying a matrix and looping through?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on my OpenCV project with Python which basically recognizes hand gestures. I want to do the same using Android. Is it possible? Is there any way to do so?\nI want to recognize very basic hand gestures using my Android device. Is it possible with Python & OpenCV with Android? Also share any other way possible.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":220,"Q_Id":40925079,"Users Score":1,"Answer":"This process can be done via NDK & OpenCV with Java support in Android Studio.\nYou need to compile NDK libraries and generate .so files. Then the project will work.","Q_Score":1,"Tags":"android,python,opencv,image-processing","A_Id":42197304,"CreationDate":"2016-12-02T04:57:00.000","Title":"How can I integrate my Python based OpenCV project in Android?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":1,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need some orientation for a problem I\u2019m trying to solve. Anything would be appreciated, a keyword to Google or some indication !\nSo I have a list of 5 items. All items share the same features, let\u2019s say each item has 3 features for the example.\nI pass the list to a ranking function which takes into account the features of every item in the list and returns an arbitrary ordered list of these items.\nFor example,  if I give the following list of items (a, b, c, d, e) to the ranking function, I get (e, a, b, d, c).\nHere is the thing, I don\u2019t know how the ranking function works. The only things I have is the list of 5 items (5 is for the example, it could be any number greater than 1), the features of every item and the result of the ranking function.\nThe goal is to train a model which outputs an ordered list of 5 items the same way the ranking function would have done it.\nWhat ML model can I use to support this notion of ranking ? Also, I can\u2019t determine if it is a classification or a regression problem. I\u2019m not trying to determine a continuous value or classify the items, I want to determine how they rank compared to each other by the ranking function.\nI have to my disposition an infinite number of items since I generate them myself. The ranking function could be anything but let\u2019s say it is :\nattribute a score = 1\/3 * ( x1 + x2 + x3 ) to each item and sort by descending score\nThe goal for the model is to guess as close as possible what the ranking function is by outputting similar results for the same batch of 5 items.\nThanks in advance !","AnswerCount":2,"Available Count":2,"Score":0.1973753202,"is_accepted":false,"ViewCount":1288,"Q_Id":40942391,"Users Score":2,"Answer":"Since there's an unknown function that generates the output, it's a regression problem. Neural network with 2 hidden layers and e.g. sigmoid can learn any arbitrary function.","Q_Score":2,"Tags":"python,machine-learning","A_Id":40942885,"CreationDate":"2016-12-02T23:25:00.000","Title":"ML model to predict rankings (arbitrary ordering of a list)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need some orientation for a problem I\u2019m trying to solve. Anything would be appreciated, a keyword to Google or some indication !\nSo I have a list of 5 items. All items share the same features, let\u2019s say each item has 3 features for the example.\nI pass the list to a ranking function which takes into account the features of every item in the list and returns an arbitrary ordered list of these items.\nFor example,  if I give the following list of items (a, b, c, d, e) to the ranking function, I get (e, a, b, d, c).\nHere is the thing, I don\u2019t know how the ranking function works. The only things I have is the list of 5 items (5 is for the example, it could be any number greater than 1), the features of every item and the result of the ranking function.\nThe goal is to train a model which outputs an ordered list of 5 items the same way the ranking function would have done it.\nWhat ML model can I use to support this notion of ranking ? Also, I can\u2019t determine if it is a classification or a regression problem. I\u2019m not trying to determine a continuous value or classify the items, I want to determine how they rank compared to each other by the ranking function.\nI have to my disposition an infinite number of items since I generate them myself. The ranking function could be anything but let\u2019s say it is :\nattribute a score = 1\/3 * ( x1 + x2 + x3 ) to each item and sort by descending score\nThe goal for the model is to guess as close as possible what the ranking function is by outputting similar results for the same batch of 5 items.\nThanks in advance !","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":1288,"Q_Id":40942391,"Users Score":1,"Answer":"It could be treated as a regression problem with the following trick: You are given 5 items with 5 feature vectors and the \"black box\" function outputs 5 distinct scores as [1, 2, 3, 4, 5]. Treat these as continuous values. So, you can think of your function as operating by taking five distinct input vectors x1, x2, x3, x4, x5 and outputting five scalar target variables t1, t2, t3, t4, t5 where the target variables for your training set are the scores the items get. For example, if the ranking for a single sample is (x1,4), (x2,5), (x3,3), (x4,1), (x5,2) then set t1=4, t2=5, t3=3, t4=1 and t5=2. MLPs have the \"universal approximation\" capability and given a black box function, they can approximate it arbitrarily close, dependent on the hidden unit count. So, build a 2 layer MLP with the inputs as the five feature vectors and the outputs as the five ranking scores. You are going to minimize a sum of squares error function, the classical regression error function. And don't use any regularization term, since you are going to try to mimic a deterministic black fox function, there is no random noise inherent in the outputs of that function, so you shouldn't be afraid of any overfitting issues.","Q_Score":2,"Tags":"python,machine-learning","A_Id":40947117,"CreationDate":"2016-12-02T23:25:00.000","Title":"ML model to predict rankings (arbitrary ordering of a list)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have built a system where the neural network can change size (number of and size of hidden layers, etc). When training it with a learning rate of 0.5, 1 hidden layer of 4 neurons, 2 inputs and 1 output, it successfully learns the XOR and AND problem (binary inputs, etc). Works really well. \nWhen I then make the structure 784 inputs, 1 hidden layer of 30 neurons, and 10 outputs, and apply the MNIST digit set, where each input is a pixel value, I simply cannot get good results (no better than random!). My question is quite theory based: If my code does seem to work with the other problems, should I assume i need to keep experimenting different learning rates, hidden layers, etc for this one? Or should decide theres a more underlying problem? \nHow do I find the right combo of layers, learning rate, etc? How would you go about this?\nTesting is also difficult as it takes about 2 hours to get to a point where it should have learnt... (on a mac)\nNo, I am not using TensorFlow, or other libraries, because I am challenging myself. Either way, it does work ...to a point!\nMany thanks. And apologies for the slightly abstract question - but I know its a problem many beginners have - so I hope it helps others too.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":24,"Q_Id":40949988,"Users Score":0,"Answer":"It is good that you have created your own program. I would suggest you to keep experimenting with basic problems, such as MNIST by adding more hidden layers, plotting variation of loss with training iterations using different learning rates, etc.\nIn general, the learning rate should not be kept high initially when the network weights are random and it is a good practice to keep decreasing the learning rate over training period. Plotting the values of loss or error function w.r.t training iterations will give you good insight regarding this. If learning rate is very high, loss will fluctuate and vary too much. If learning rate is very small, loss will decrease very slowly with training iterations. If you are interested, read about this in Andrew Ng's course or some blog.\nAbout your question regarding number of hidden layers and neurons, it better to start experimenting with lower number initially, such as 1 hidden layer and 30 neurons in your case. In your next experiment, you can have 2 hidden layers, however, keep track of number of learning parameters (weights and biases) compared to training samples you have because small training samples with large number of network parameters can overfit your network.\nAfter experimenting with small problems, you can try same thing with some framework, let's say Tensorflow, after which you can attempt more challenging problems.","Q_Score":1,"Tags":"python,machine-learning,computer-vision,neural-network","A_Id":45639855,"CreationDate":"2016-12-03T16:34:00.000","Title":"how to define an issue with neural networks","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have built a system where the neural network can change size (number of and size of hidden layers, etc). When training it with a learning rate of 0.5, 1 hidden layer of 4 neurons, 2 inputs and 1 output, it successfully learns the XOR and AND problem (binary inputs, etc). Works really well. \nWhen I then make the structure 784 inputs, 1 hidden layer of 30 neurons, and 10 outputs, and apply the MNIST digit set, where each input is a pixel value, I simply cannot get good results (no better than random!). My question is quite theory based: If my code does seem to work with the other problems, should I assume i need to keep experimenting different learning rates, hidden layers, etc for this one? Or should decide theres a more underlying problem? \nHow do I find the right combo of layers, learning rate, etc? How would you go about this?\nTesting is also difficult as it takes about 2 hours to get to a point where it should have learnt... (on a mac)\nNo, I am not using TensorFlow, or other libraries, because I am challenging myself. Either way, it does work ...to a point!\nMany thanks. And apologies for the slightly abstract question - but I know its a problem many beginners have - so I hope it helps others too.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":24,"Q_Id":40949988,"Users Score":0,"Answer":"A quick advice may be to solve an intermediate task (e.g. to use your own 5x5 ASCII \"pictures\" of digits), to have more neurons in the hidden layer, to reduce the data set for quicker simulation, to compare your implementation to other custom implementations in your programming language.","Q_Score":1,"Tags":"python,machine-learning,computer-vision,neural-network","A_Id":40950086,"CreationDate":"2016-12-03T16:34:00.000","Title":"how to define an issue with neural networks","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In tensorflow,we may see these codes.\nembeddings=tf.Variable(tf.random_uniform([vocabulary_size,embedding_size],-1.0,1.0))\nembed=tf.nn.embedding_lookup(embeddings,train_inputs)\nWhen tensorflow is training,does embedding matrix remain unchanged?\nIn a blog,it is said that embedding matrix can update.I wonder how does it work.Thanks a lot !","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":94,"Q_Id":40959177,"Users Score":0,"Answer":"Embedding matrix is similar to any other variable. If you set the trainable flag to True it will train it (see tf.Variable)","Q_Score":0,"Tags":"python,tensorflow,deep-learning","A_Id":40960903,"CreationDate":"2016-12-04T13:34:00.000","Title":"In tensorflow,does embedding matrix remain unchanged?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"searched for this, but cannot find an answer. \nSay I have a dataframe (apologies for formatting):\na   Dave   $400\na   Dave   $400\na   Dave   $400\nb   Fred   $220\nc   James  $150\nc   James  $150\nd   Harry  $50  \nAnd I want to filter the dataframe so it only shows the rows where the third column is the MAXIMUM value, could someone point me in the right direction?\ni.e. it would only show Dave's rows\nAll I can find is ways of showing the rows where its the maximum value for each separate index (the indexes being A, B, C etc)\nThank you in advance","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":327,"Q_Id":40959626,"Users Score":0,"Answer":"If your index is unique and you are OK with returning one row (in the case of multiple rows having the same max value) then you can use the idxmax method.\ndf.loc[df['money'].idxmax()]\nAnd if you want to add some flare you can highlight the max value in each column with:\ndf.loc[df['money'].idxmax()].style.highlight_max()","Q_Score":1,"Tags":"python,pandas","A_Id":40962551,"CreationDate":"2016-12-04T14:28:00.000","Title":"Python Pandas - Only showing rows in DF for the MAX values of a column","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using scikit-learn to construct regression trees, using tree.DecisionTreeRegression().\nI'm giving 56 data samples and it constructs me a Tree with 56 nodes (pruning=0).\nHow can I implement some pruning to the tree? Any help is appreciated!","AnswerCount":2,"Available Count":1,"Score":-0.0996679946,"is_accepted":false,"ViewCount":1207,"Q_Id":40960357,"Users Score":-1,"Answer":"You can't; use matlab.  Struggling with this at the moment.  Using a python based home-cooked decision tree is also an option.  However, there is no guarantee it will work properly (lots of places you can screw up). And you need to implement with numpy if you want any kind of reasonable run-time (also struggling with this now).  \nIf you still have this problem, I do have a decision tree working with node knowledge and am implementing pruning this weekend...\nIf I get it to run fast and the code isn't too embarrassingly complicated, I will post a GitHub up here if you are still interested, in exchange for endorsements of ML'ing and Python\/Numpy expertise on my LinkedIn.","Q_Score":0,"Tags":"python,tree,regression,cart","A_Id":41774573,"CreationDate":"2016-12-04T15:42:00.000","Title":"Python Decision Tree Regressor Pruning","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been researching the past few days on how to achieve this, to no avail.\nI have a JSON file with a large array of json objects like so:\n[{\n    \"tweet\": \"@SHendersonFreep @realDonaldTrump watch your portfolios go to the Caribbean banks and on to Switzerland. Speculation without regulation\",\n    \"user\": \"DGregsonRN\"\n},{\n    \"tweet\": \"RT @CodeAud: James Mattis Vs Iran.\\n\\\"The appointment of Mattis by @realDonaldTrump got the Iranian military leaders' more attention\\\". https:\\u2026\",\n    \"user\": \"American1765\"\n},{\n    \"tweet\": \"@realDonaldTrump the oyou seem to be only fraud I see is you, and seem scared since you want to block the recount???hmm cheater\",\n    \"user\": \"tgg216\"\n},{\n    \"tweet\": \"RT @realDonaldTrump: @Lord_Sugar Dopey Sugar--because it was open all season long--you can't play golf in the snow, you stupid ass.\",\n    \"user\": \"grepsalot\"\n},{\n    \"tweet\": \"RT @Prayer4Chandler: @realDonaldTrump Hello Mr. President, would you be willing to meet Chairman #ManHeeLee of #HWPL to discuss the #PeaceT\\u2026\",\n    \"user\": \"harrymalpoy1\"\n},{\n    \"tweet\": \"RT @realDonaldTrump: Thank you Ohio! Together, we made history \\u2013 and now, the real work begins. America will start winning again! #AmericaF\\u2026\",\n    \"user\": \"trumpemall\"\n}]\nAnd I am trying to access each key and value, and write them to a csv file. I believe using json.loads(json.dumps(file)) should work in normal json format, but because there is an array of objects, I can't seem to be able to access each individual one.\nconverter.py:\n\n\n    import json\n    import csv\n\n    f = open(\"tweets_load.json\",'r')\n    y = json.loads(json.dumps(f.read(), separators=(',',':')))\n    t = csv.writer(open(\"test.csv\", \"wb+\"))\n\n    # Write CSV Header, If you dont need that, remove this line\n    t.writerow([\"tweet\", \"user\"])\n\n    for x in y:\n       t.writerow([x[0],x[0]])\n\n\ngrab_tweets.py:\n\n\n    import tweepy\n    import json\n\n    def get_api(cfg):\n      auth = tweepy.OAuthHandler(cfg['consumer_key'], cfg['consumer_secret'])\n      auth.set_access_token(cfg['access_token'], cfg['access_token_secret'])\n      return tweepy.API(auth)\n\n    def main():\n\n      cfg = {\n        \"consumer_key\"        : \"xxx\",\n        \"consumer_secret\"     : \"xxx\",\n        \"access_token\"        : \"xxx\",\n        \"access_token_secret\" : \"xxx\"\n        }\n      api = get_api(cfg)\n      json_ret = tweepy.Cursor(api.search, q=\"@realDonaldTrump\",count=\"100\").items(100)\n      restapi =\"\"\n      for tweet in json_ret:\n          rest = json.dumps({'tweet' : tweet.text,'user' :str(tweet.user.screen_name)},sort_keys=True,indent=4,separators=(',',': '))\n          restapi = restapi+str(rest)+\",\"\n      f = open(\"tweets.json\",'a')\n      f.write(str(restapi))\n      f.close()\n\n    if __name__ == \"__main__\":\n      main()\n\n\nThe output so far is looking like: \ntweet,user^M\n{,{^M\n\"\n\",\"\n\"^M\n , ^M\n , ^M\n , ^M\n , ^M\n\"\"\"\",\"\"\"\"^M\nt,t^M\nw,w^M\ne,e^M\ne,e^M\nt,t^M\n\"\"\"\",\"\"\"\"^M\n:,:^M\n , ^M\n\"\"\"\",\"\"\"\"^M\nR,R^M\nT,T^M\n , ^M\n@,@^M\nr,r^M\ne,e^M\na,a^M\nl,l^M\nD,D^M\no,o^M\nn,n^M\na,a^M\nl,l^M\n\n\nWhat exactly am I doing wrong?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1675,"Q_Id":40976901,"Users Score":0,"Answer":"turns out it was the json.dumps(), should've read more into what it does! Thanks.","Q_Score":0,"Tags":"python,json,csv","A_Id":40977897,"CreationDate":"2016-12-05T14:46:00.000","Title":"Access key value from JSON array of objects Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a large data-set that I want save them in a npz file. But because the size of file is big for memory I cant save them in npz file.\nNow i want insert data in iterations into npz file.\nHow can I do this?\nAre HDF5 is better for this?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":528,"Q_Id":40995291,"Users Score":0,"Answer":"accepted\nlocals().update(npzfile)\na   # and\/or b\nIn the Ipython session, locals() is a large dictionary with variables the you've defined, the input history lines, and various outputs. Update adds the dictionary values of npzfile to that larger one.\nBy the way, you can also load and save MATLAB .mat files. Use scipy.io.loadmat and savemat. It handles v4 (Level 1.0), v6 and v7 to 7.2 files. But you have same issue - the result is a dictionary.\nOctave has an expression form of the load command, that loads the data into a structure\nS = load (\"file\", \"options\", \"v1\", \"v2\", ...)","Q_Score":0,"Tags":"python,numpy,hdf5","A_Id":40995578,"CreationDate":"2016-12-06T12:20:00.000","Title":"How can I update npz file in python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have been having some issues trying to open a simulation result output file (.mat) in Python. Upon loading the file I am faced with the following error:\n\nValueError: Not enough bytes to read matrix 'description'; is this a\n  badly-formed file? Consider listing matrices with whosmat and\n  loading named matrices with variable_names kwarg to loadmat\n\nHas anyone been successful in rectifying this error? I have heard there is a script DyMat which can manage mat files in Python but haven't had any luck with it so far.\nAny suggestions would be greatly appreciated.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":374,"Q_Id":40997813,"Users Score":1,"Answer":"You can reduce the size of the simulation result file by using variable selections in Dymola. That will restrict the output to states, parameters, and the variables that match your selection criteria.\nThe new Dymola 2017 FD01 has a user interface for defining variable selections.","Q_Score":1,"Tags":"python,dymola","A_Id":41197119,"CreationDate":"2016-12-06T14:33:00.000","Title":"Not enough memory to read .mat result file into Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm not sure whether I'm missing something really easy but I have a hard time trying to google something out.\nI can see there are cross_val_score and cross_val_predict functions in scikit-learn. However, I can't find a way to get both score and predictions at one go. Seems quite obvious as calling the functions above one after another is a waste of computing time. Is there a cross_val_score_predict function or similar?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":80,"Q_Id":41003897,"Users Score":1,"Answer":"If you run cross_val_predict then you can check the metric on the result. It is not a waste of compute time because cross_val_predict doesn't compute scores itself. \nThis won't give you per-fold scores though, only the aggregated score (which is not necessarily bad). I think you can workaround that by creating KFold \/ ... instance explicitly and then using it to split the cross_val_predict result.","Q_Score":1,"Tags":"python,scikit-learn,cross-validation","A_Id":41003996,"CreationDate":"2016-12-06T20:05:00.000","Title":"Scikit-learn - Cross validates score and predictions at one go?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Problem: calculation task can be paralleled easily. but it is needed real-time response.\nThere can be two approaches.\n1. using Celery: runs job in parallel from scratch\n2. using Spark: runs job in parallel with spark framework\nI think spark is better in scalability perspective. But is it OK Spark as backend of web-application?","AnswerCount":2,"Available Count":2,"Score":0.1973753202,"is_accepted":false,"ViewCount":3004,"Q_Id":41010560,"Users Score":2,"Answer":"Adding to the above answer, there are other areas also to identify.\n\nIntegration with the existing big data stack if you have.\nData pipeline for ingestion\nYou mentioned \"backend for web application\". I assume its for read operation. The response times for any batch application might not be a good fit for any web application. \n\nChoice of streaming can help you get the data into the cluster faster. But it will not guarantee the response times needed for web app. You need to look at HBase and Solr(if you are searching). \nSpark is undoubtedly better and faster than other batch frameworks. In streaming there may be few other. As I mentioned above, you should consider the parameters on which your choice is made.","Q_Score":2,"Tags":"python,apache-spark,celery,distributed,jobs","A_Id":41021060,"CreationDate":"2016-12-07T06:09:00.000","Title":"For distributing calculation task, which is better celery or spark","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":1},{"Question":"Problem: calculation task can be paralleled easily. but it is needed real-time response.\nThere can be two approaches.\n1. using Celery: runs job in parallel from scratch\n2. using Spark: runs job in parallel with spark framework\nI think spark is better in scalability perspective. But is it OK Spark as backend of web-application?","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":3004,"Q_Id":41010560,"Users Score":1,"Answer":"Celery :- is really a good technology for distributed streaming And its supports Python language . Which is it self strong in computation and easy to write. The streaming application in Celery supports so many features as well . Its little over head on CPU.\nSpark- Its supports various programming language Java,Scala,Python. its not pure streaming its micro batch streaming as per the Spark documentation \nIf your task can only be full filled by streaming and you dont need the SQl like feature . Then Celery will be the best. But you need various feature along with streaming then SPark will be better . In that case you can take scenario you application will generate the data in how many batches within second .","Q_Score":2,"Tags":"python,apache-spark,celery,distributed,jobs","A_Id":41012633,"CreationDate":"2016-12-07T06:09:00.000","Title":"For distributing calculation task, which is better celery or spark","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":1},{"Question":"Is it or will it be possible to add more Python libraries than pandas, numpy and numexpr to Azure Data Lake Analytics? Specifically, we need to use xarray, matplotlib, Basemap, pyresample and SciPy when processing NetCDF files using U-SQL.","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":360,"Q_Id":41045491,"Users Score":2,"Answer":"Assuming the libs work with the deployed Python runtime, try to upload the libraries into a location in ADLS and then use DEPLOY RESOURCE \"path to lib\"; in your script. I haven't tried it, but it should work.","Q_Score":2,"Tags":"python,azure-data-lake,u-sql","A_Id":41116628,"CreationDate":"2016-12-08T17:35:00.000","Title":"Add more Python libraries","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I am using Sympy Rational in an algorithm and I am getting enormous factions. Numerator and Denominator grow up to 10000 digits. \nI would like to stop the algorithm as soon as the fractions become unevaluable. So the question is, what is the maximum magnitude I can allow for sympy.Rational?","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":59,"Q_Id":41059520,"Users Score":2,"Answer":"The size of SymPy Rationals is limited only by your available memory. If you want an approximate but memory bounded number, use a Float. You can convert a Rational into a Float with evalf.","Q_Score":3,"Tags":"python,python-3.x,sympy","A_Id":41071087,"CreationDate":"2016-12-09T11:32:00.000","Title":"To what extent is it precise to evaluate Sympy enormous fractions (Rational)?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I installed opencv-python using pip install, in mac os. Now the cv2.imshow function giving following error\n\nOpenCV Error: Unspecified error (The function is not implemented. Rebuild the library with Windows, GTK+ 2.x or Carbon support. If you are on Ubuntu or Debian, install libgtk2.0-dev and pkg-config, then re-run cmake or configure script) in cvShowImage\n\nHow can I solve this issue? Why doesn't the pip check opencv dependencies?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2492,"Q_Id":41074980,"Users Score":0,"Answer":"The fix that worked best for me was using mathplotlib instead. \nSince you may have to remove all previous versions of OpenCV otherwise and reinstall from source!","Q_Score":6,"Tags":"python,macos,opencv","A_Id":45069376,"CreationDate":"2016-12-10T11:24:00.000","Title":"opencv-python imshow giving errors in mac","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am getting this error in Scikit learn. Previously I worked with K validation, and never encountered error. My data is sparse and training and testing set is divided in the ratio 90:10\nValueError: cannot use sparse input in 'SVC' trained on dense data\nIs there any straightforward reason and solution for this?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":960,"Q_Id":41078835,"Users Score":0,"Answer":"Since it cannot used sparse input on dense data, either convert your dense data to sparse data (recommended) or your sparse data to dense data. Use SciPy to create a sparse matrix from a dense one.","Q_Score":0,"Tags":"python-2.7,error-handling,scikit-learn,svm","A_Id":41079325,"CreationDate":"2016-12-10T18:26:00.000","Title":"Title: SVC-Scikit Learn issue","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am getting this error in Scikit learn. Previously I worked with K validation, and never encountered error. My data is sparse and training and testing set is divided in the ratio 90:10\nValueError: cannot use sparse input in 'SVC' trained on dense data\nIs there any straightforward reason and solution for this?","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":960,"Q_Id":41078835,"Users Score":3,"Answer":"This basically means that your testing set is not in the same format as your training set. \nA code snippet would have been great, but make sure you are using the same array format for both sets.","Q_Score":0,"Tags":"python-2.7,error-handling,scikit-learn,svm","A_Id":41079820,"CreationDate":"2016-12-10T18:26:00.000","Title":"Title: SVC-Scikit Learn issue","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataframe in which one of the row is filled with string \"Error\"\nI am trying to add rows of 2 different dataframe. However, since I have the string in one of the row, it is concatenating the 2 strings. \nSo, I am having the dataframe filled with a row \"ErrorError\". I would prefer leaving this row empty than concatenating the strings.\nAny idea how to do it ? \nThanks\nkiran","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":49,"Q_Id":41082675,"Users Score":1,"Answer":"You can fix resulting DataFrame using df.replace({'FieldName': {'ErrorError': ''}})","Q_Score":0,"Tags":"python,pandas,dataframe","A_Id":41082749,"CreationDate":"2016-12-11T03:25:00.000","Title":"Throw an exception while doing dataframe sum in Pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've got an imported csv file which has multiple columns with dates in the format \"5 Jan 2001 10:20\". (Note not zero-padded day)\nif I do df.dtype then it shows the columns as being a objects rather than a string or a datetime. I need to be able to subtract 2 column values to work out the difference so I'm trying to get them into a state where I can do that.\nAt the moment if I try the test subtraction at the end I get the error unsupported operand type(s) for -: 'str' and 'str'.\nI've tried multiple methods but have run into a problem every way I've tried.\nAny help would be appreciated. If I need to give any more information then I will.","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":3761,"Q_Id":41088840,"Users Score":1,"Answer":"I found that the problem was to do with missing values within the column. Using coerce=True so df[\"Date\"] = pd.to_datetime(df[\"Date\"], coerce=True) solves the problem.","Q_Score":0,"Tags":"python,csv,datetime,pandas,dataframe","A_Id":41088981,"CreationDate":"2016-12-11T17:18:00.000","Title":"Converting objects from CSV into datetime","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I just installed scikits.talkbox, and tried using it in my program. But I get the following error\n\n'ImportError: No module named 'tools'\n\nHow do I solve this problem?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1498,"Q_Id":41130126,"Users Score":0,"Answer":"I had the same problem\uff0cjust run pip install tools","Q_Score":0,"Tags":"python-3.x,machine-learning,signal-processing","A_Id":49290726,"CreationDate":"2016-12-13T20:37:00.000","Title":"No module named 'tools' while importing scikits.talkbox","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm making features for a machine learning model. I'm confused with dummy variable and one-hot encoding.For a instance,a category variable 'week' range 1-7.When using one-hot encoding, encode week = 1 as 1,000,000,week = 2 is 0,100,000... .But I can also make a dummy variable 'week_v',and in this way, I must set a \nhidden variable which means base variable,and feature week_v = 1 is 100,000,week_v = 2 is 010,000... and \ndoes not appear week_v = 7.So what's the difference between them? I'm using logistic model and then I'll try gbdt.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":14355,"Q_Id":41136853,"Users Score":11,"Answer":"In fact, there is no difference in the effect of the two approaches (rather wordings) on your regression.\nIn either case, you have to make sure that one of your dummies is left out (i.e. serves as base assumption) to avoid perfect multicollinearity among the set. \nFor instance, if you want to take the weekday of an observation into account, you only use 6 (not 7) dummies assuming the one left out to be the base variable. When using one-hot encoding, your weekday variable is present as a categorical value in one single column, effectively having the regression use the first of its values as the base.","Q_Score":14,"Tags":"python,machine-learning","A_Id":41137195,"CreationDate":"2016-12-14T07:15:00.000","Title":"What's the difference between dummy variable and one-hot encoding?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Python 3.5, I am trying to find command to install a Keras Deep Learning package for Anaconda. The command conda install -c keras does not work, can anyone answer Why it doesn't work?","AnswerCount":4,"Available Count":1,"Score":-0.0996679946,"is_accepted":false,"ViewCount":6149,"Q_Id":41163150,"Users Score":-2,"Answer":"Navigate to Anaconda installation folder\/Scripts and install with pip command","Q_Score":1,"Tags":"python,anaconda,python-3.5,packaging,keras","A_Id":41163228,"CreationDate":"2016-12-15T11:33:00.000","Title":"Package installation of Keras in Anaconda?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to make an ai following the alpha-beta pruning method for tic-tac-toe. I need to make checking a win as fast as possible, as the ai will goes through many different possible game states. Right now I have thought of 2 approaches, neither which is very efficient.\n\nCreate a large tuple for scoring every possible 4 in a row win conditions, and loop through that. \nUsing for loops, check horizontally, vertically, diag facing left, and diag facing right. This seems like it would be much slower that #1.\n\nHow would someone recommend doing it?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":812,"Q_Id":41185646,"Users Score":0,"Answer":"From your question, it's a bit unclear how your approaches would be implemented. But from the alpha-beta pruning, it seems as if you want to look at a lot of different game states, and in the recursion determine a \"score\" for each one. \nOne very important observation is that recursion ends once a 4-in-a-row has been found. That means that at the start of a recursion step, the game board does not have any 4-in-a-row instances. \nUsing this, we can intuitively see that the new piece placed in said recursion step must be a part of any 4-in-a-row instance created during the recursion step. This greatly reduces the search space for solutions from a total of 69 (21 vertical, 24 horizontal, 12+12 diagonals) 4-in-a-row positions to a maximum of 13 (3 vertical, 4 horizontal, 3+3 diagonal).\nThis should be the baseline for your second approach. It will require a maximum of 52 (13*4) checks for a naive implementation, or 25 (6+7+6+6) checks for a faster algorithm. \nNow it's pretty hard to beat 25 boolean checks for this win-check I'd say, but I'm guessing that your #1 approach trades some extra memory-usage to enable less calculation per recursion step. The simplest way of doing this would be to store 8 integers (single byte is fine for this application) which represent the longest chains of same-color chips that can be found in any of the 8 directions. \nUsing this, a check for win can be reduced to 8 boolean checks and 4 additions. Simply get the chain lengths on opposite sides of the newly placed chip, check if they're the same color as the chip, and if they are, add their lengths and add 1 (for the newly placed chip).\nFrom this calculation, it seems as if your #1 approach might be the most efficient. However, it has a much larger overhead of maintaining the data structure, and uses more memory, something that should be avoided unless you can pass by reference. Also (assuming that boolean checks and additions are similar in speed) the much harder approach only wins by a factor 2 even when ignoring the overhead. \nI've made some simplifications, and some explanations maybe weren't crystal clear, but ask if you have any further questions.","Q_Score":0,"Tags":"python,performance,python-3.x,artificial-intelligence","A_Id":41199608,"CreationDate":"2016-12-16T13:35:00.000","Title":"fastest Connect 4 win checking method","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using df= df.replace('No data', np.nan) on a csv file containing \u2018No data\u2019 instead of blank\/null entries where there is no numeric data. Using the head() method I see that the replace method does replace the \u2018No data\u2019 entries with NaN. When I use df.info() it says that the datatypes for each of the series is an object. \nWhen I open the csv file in Excel and manually edit the data using find and replace to change \u2018No data\u2019 to blank\/null entries, although the dataframes look exactly the same when I use df.head(), when I use df.info() it says that the datatypes are floats.\nI was wondering why this was and how can I make it so that the datatypes for my series are floats, without having to manually edit the csv files.","AnswerCount":3,"Available Count":1,"Score":0.1325487884,"is_accepted":false,"ViewCount":1782,"Q_Id":41198719,"Users Score":2,"Answer":"If the rest of the data in your columns is numeric then you should use pd.to_numeric(df, errors='coerce')","Q_Score":0,"Tags":"python,pandas,dataframe","A_Id":41200368,"CreationDate":"2016-12-17T12:47:00.000","Title":"Pandas replace method and object datatypes","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Hello guys i ve just installed anaconda3 in windows 8.1 and opencv 2.4.13 and 3.1.0\/ Ive copied from the file c:\/....\/opencv\/build\/python\/2.7\/x64\/cv2.pyd and i pasted it to C:\\Users.....\\Anaconda3\\Lib\\site-packages. I ve pasted both for opencv 2.4.13 as cv2.pyd and for opencv 3.1.0 as cv2(3)pyd in order to change it when i want to use any of them. My system is 64-bit and i use jupyter notebook. When i run the command import cv2 it write me\nImportError                               Traceback (most recent call last)\n in ()\n----> 1 import cv2\nIn anaconda3 i use python3.5 \nImportError: DLL load failed: The specified module could not be found.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1034,"Q_Id":41199408,"Users Score":0,"Answer":"I might be missing something, but i believe you are just missing seting up the envi. variables.\nSet Enviromental Variables\nRight-click on \"My Computer\" (or \"This PC\" on Windows 8.1) -> left-click Properties -> left-click \"Advanced\" tab -> left-click \"Environment Variables...\" button.\nAdd a new User Variable to point to the OpenCV (either x86 for 32-bit system or x64 for 64-bit system.) I am currently on a 64-bit machine.\n| 32-bit or 64 bit machine? | Variable     | Value                                |\n|---------------------------|--------------|--------------------------------------|\n| 32-bit                    | OPENCV_DIR | C:\\opencv\\build\\x86\\vc12           |\n| 64-bit                    | OPENCV_DIR | C:\\opencv\\build\\x64\\vc12           |\nAppend %OPENCV_DIR%\\bin to the User Variable PATH.\nFor example, my PATH user variable looks like this...\nBefore:\nC:\\Users\\Johnny\\Anaconda;C:\\Users\\Johnny\\Anaconda\\Scripts\nAfter:\nC:\\Users\\Johnny\\Anaconda;C:\\Users\\Johnny\\Anaconda\\Scripts;%OPENCV_DIR%\\bin","Q_Score":1,"Tags":"python,opencv,anaconda","A_Id":42058253,"CreationDate":"2016-12-17T14:08:00.000","Title":"Install opencv in anaconda3","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am performing parameter selection using GridSearchCv (sklearn package in python) where the model is an Elastic Net with a Logistic loss (i.e a logistic regression with L1- and L2- norm regularization penalties). I am using SGDClassifier to implement this model. There are two parameters I am interested in searching the optimal values for: alpha (the constant that multiplies the regularization term) and l1_ratio (the Elastic Net mixing parameter). My data set has ~300,000 rows. I initialize the model as follows:\nsgd_ela = SGDClassifier(alpha=0.00001, fit_intercept=True, l1_ratio=0.1,loss='log', penalty='elasticnet')\nand the searching fxn. as follows:\nGridSearchCV(estimator=sgd_ela, cv=8, param_grid=tune_para),\nwith tuning parameters:\ntune_para = [{'l1_ratio': np.linspace(0.1,1,10).tolist(),'alpha':[0.00001, 0.0001, 0.001, 0.01, 0.1, 1]}].  \nI get the best_params (of alpha and l1_ratio) upon running the code. However, in repeated runs, I do not get the same set of best parameters. I am interested to know why is this the case, and if possible, how can I overcome it?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":907,"Q_Id":41215169,"Users Score":1,"Answer":"Try setting the random seed if you want to get the same result each time.","Q_Score":1,"Tags":"python,machine-learning,scikit-learn,cross-validation,grid-search","A_Id":41252942,"CreationDate":"2016-12-19T02:55:00.000","Title":"Why does GridSearchCV give different optimums on repeated runs?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have been running in circles trying to get tensorflow to work in a jupyter notebook. I installed it via pip on ubuntu and also tried a conda environment (but unless I'm mistaken, getting that to work with ipython is beyond my ability). Tensorflow works fine in python3.4, but not python 3.5, which is used when I load ipython. I'm not sure if this question makes any sense, but can I make it so that ipython uses only python 3.4? The reason I need to use ipython instead of going through the python shell is that I am trying to use the kadenzie tutorial.  \nThank you.\nEdit: I'm not sure how applicable this is to other people with my problem,  but I solved it by changing my conda python version (conda install python=3.4.3), uninstalling ipython, and then reinstalling it.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":255,"Q_Id":41228983,"Users Score":0,"Answer":"Each major version of python has its own site-packages directory. It seems that you have both python 3.4 and 3.5 and you have jupyter installed in 3.5 and tensorflow in 3.4. The easy solution is to install tensorflow in 3.5 as well. This should allow you to use it with the 3.5 notebook kernel. You could attempt to add 3.4 as a kernel, but I am not sure how to do that.","Q_Score":0,"Tags":"python,ubuntu,tensorflow,pip,ipython","A_Id":41230349,"CreationDate":"2016-12-19T18:42:00.000","Title":"can import tensorflow in python 3.4 but not in ipython notebook","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have been running in circles trying to get tensorflow to work in a jupyter notebook. I installed it via pip on ubuntu and also tried a conda environment (but unless I'm mistaken, getting that to work with ipython is beyond my ability). Tensorflow works fine in python3.4, but not python 3.5, which is used when I load ipython. I'm not sure if this question makes any sense, but can I make it so that ipython uses only python 3.4? The reason I need to use ipython instead of going through the python shell is that I am trying to use the kadenzie tutorial.  \nThank you.\nEdit: I'm not sure how applicable this is to other people with my problem,  but I solved it by changing my conda python version (conda install python=3.4.3), uninstalling ipython, and then reinstalling it.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":255,"Q_Id":41228983,"Users Score":0,"Answer":"The best way to setup tensorflow with jupyter\n1.Install anaconda\n2.Create a environment named \"tensorflow\"\n3.activate that environment by the following command in command prompt\n\nInstall anaconda\nCreate a environment named \"Tensorflow\"\nactivate that environment by the following command in command prompt\nactivate tensorflow\nthen type conda install ipykernel\nthen when it is installed paste the following command\npython -m ipykernel install --user --name myenv --display-name \"Python[Tensorflow]\"\n\nThen run jupyter notebook in command prompt\nafter that when you are going to create a new notebook you will see two types of notebook just select the tensorflow one.","Q_Score":0,"Tags":"python,ubuntu,tensorflow,pip,ipython","A_Id":50797957,"CreationDate":"2016-12-19T18:42:00.000","Title":"can import tensorflow in python 3.4 but not in ipython notebook","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Can somebody please explain the following TensorFlow terms\n\ninter_op_parallelism_threads\nintra_op_parallelism_threads\n\nor, please, provide links to the right source of explanation. \nI have conducted a few tests by changing the parameters, but the results have not been consistent to arrive at a conclusion.","AnswerCount":3,"Available Count":1,"Score":0.1325487884,"is_accepted":false,"ViewCount":54304,"Q_Id":41233635,"Users Score":2,"Answer":"Tensorflow 2.0 Compatible Answer: If we want to execute in Graph Mode of Tensorflow Version 2.0, the function in which we can configure inter_op_parallelism_threads and intra_op_parallelism_threads is \ntf.compat.v1.ConfigProto.","Q_Score":86,"Tags":"python,parallel-processing,tensorflow,distributed-computing","A_Id":60222616,"CreationDate":"2016-12-20T01:33:00.000","Title":"Meaning of inter_op_parallelism_threads and intra_op_parallelism_threads","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I got the following error in OpenCV (python) and have googled a lot but have not been able to resolve.\nI would be grateful if anyone could provide me with some clue. \n\nOpenCV Error: Assertion failed (L.channels() == 1 && I.channels() == 1) \n     in connectedComponents_sub1, file \/home\/snoopy\/opencv-\n     3.1.0\/modules\/imgproc\/src\/connectedcomponents.cpp, line 341\n     Traceback (most recent call last):\n     File \"test.py\", line 30, in \n     plant = analyzeplant.analyzeSideView(plant)\n     File \"\/home\/snoopy\/Desktop\/Leaf-201612\/my-work-\n     editing\/ripps\/src\/analyzePlant.py\", line 229, in analyzeSideView\n     plant_img = self.__extractPlantArea(plant_img)\n     File \"\/home\/snoopy\/Desktop\/Leaf-201612\/my-work-\n     editing\/ripps\/src\/analyzePlant.py\", line 16, in __extractPlantArea\n     output = cv2.connectedComponentsWithStats(plant, 4, cv2.CV_32S)\n     cv2.error: \/home\/snoopy\/opencv-\n     3.1.0\/modules\/imgproc\/src\/connectedcomponents.cpp:341: error: (-215) >    L.channels() == 1 && I.channels() == 1 in function \n     connectedComponents_sub1","AnswerCount":1,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":9922,"Q_Id":41257336,"Users Score":11,"Answer":"Let us analyze it:\n\nAssertion failed (L.channels() == 1 && I.channels() == 1)\n\nThe images that you are passing to some function should be 1 channel (gray not color).\n\n__extractPlantArea(plant_img)\n\nThat happened in your code exactly at the function called __extractPlantArea.\n\ncv2.connectedComponentsWithStats\n\nWhile you are calling the OpenCV function called connectedComponentsWithStats.\nConclusion:\nDo not pass colorful (BGR) image to connectedComponentsWithStats","Q_Score":1,"Tags":"python,python-2.7,opencv,opencv3.1,connected-components","A_Id":41257567,"CreationDate":"2016-12-21T07:29:00.000","Title":"OpenCV Error: Assertion failed (L.channels() == 1 && I.channels() == 1) in connectedComponents_sub1","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I don't exactly understand how the while_loop parallelization works. Suppose I have a TensorArray having 10 Tensors all of same shape. Now suppose the computations in the loop body for the first 5 Tensors are independent of the computations in the remaining 5 Tensors. Would TensorFlow run these two in parallel? Also if I use a Tensor instead of a TensorArray and made the updates to it using scatter_update, would it pass the gradients properly during backprop?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":558,"Q_Id":41273756,"Users Score":0,"Answer":"You should probably get the parallel execution of the first 5 iterations and the second 5 iterations. I can say for sure if you provide a code sample.","Q_Score":0,"Tags":"python,tensorflow","A_Id":41295384,"CreationDate":"2016-12-21T23:47:00.000","Title":"TensorFlow while_loop parallelization TensorArray","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using NLTK in python. I understood that it uses regular expressions in its word tokenization functions, such as TreebankWordTokenizer.tokenize(), but it uses trained models (pickle files) for sentence tokenization. I don't understand why they don't use training for word tokenization? Does it imply that sentence tokenization is a harder task?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":198,"Q_Id":41276039,"Users Score":0,"Answer":"I'm not sure if you can say that sentence splitting is harder than (word) tokenisation. But tokenisation depends on sentence splitting, so errors in sentence splitting will propagate to tokenisation. Therefore you'd want to have reliable sentence splitting, so that you don't have to make up for it in tokenisation. And it turns out that once you have good sentence splitting, tokenisation works pretty well with regexes.\nWhy is that? \u2013 One of the major ambiguities in tokenisation (in Latin script languages, at least) is the period (\".\"): It can be a full stop (thus a token of its own), an abbreviation mark (belonging to that abbreviation token), or something special (like part of a URL, a decimal fraction, ...). Once the sentence splitter has figured out the first case (full stops), the tokeniser can concentrate on the rest. And identifying stuff like URLs is exactly what you would use a regex for, isn't it?\nThe sentence splitter's main job, on the other hand, is to find abbreviations with a period. You can create a list for that by hand \u2013 or you can train it on a big text collection. The good thing is, it's unsupervised training \u2013 you just feed in the plain text, and the splitter collects abbreviations. The intuition is: If a token almost always appears with a period, then it's probably an abbreviation.","Q_Score":0,"Tags":"python,nlp,nltk","A_Id":41279184,"CreationDate":"2016-12-22T04:59:00.000","Title":"Why NLTK uses regular expressions for word tokenization, but training for sentence tokenization?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm working on a new optimizer, and I managed to work out most of the process. Only thing I'm stuck on currently is finding gen_training_ops.\nApparently this file is crucial, because in both implementations of Gradient Descent, and Adagrad optimizers they use functions that are imported out of a wrapper file for gen_training_ops (training_ops.py in the python\/training folder).\nI can't find this file anywhere, so I suppose I don't understand something and search in the wrong place. Where can I find it? (Or specifically the implementations of apply_adagrad and apply_gradient_descent)\nThanks a lot :)","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":719,"Q_Id":41285440,"Users Score":1,"Answer":"If you find it, youll realize it just jumps to pyhon\/framework, where the actual update is just an assign operation and then gets grouped","Q_Score":2,"Tags":"python,optimization,tensorflow,deep-learning,jupyter-notebook","A_Id":54832398,"CreationDate":"2016-12-22T14:30:00.000","Title":"Can't find \"gen_training_ops\" in the tensorflow GitHub","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a python environment (on Windows 10) that uses OpenCV VideoCapture class to connect to multiple usb cameras.\nAs far as I know, there is no way to identify a specific camera in OpenCV other than the device parameter in the VideoCapture class constructor \/ open method.\nThe problem is that the device parameter changes depending on how many cameras are actually connected and to which usb ports.\nI want to be able to identify a specific camera and find its \"device index\" or \"camera index\" no matter how many cameras are connected and to which usb ports.\nCan somebody please suggest a way to achieve that functionality? python code is preferable but C++ will also do.","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":10992,"Q_Id":41298588,"Users Score":0,"Answer":"If you can differentiate the cameras by their serial number or device and vendor id, you can loop through all video devices before opening with opencv and search for the camera device you want to open.","Q_Score":3,"Tags":"python,c++,windows,opencv,usb","A_Id":41299177,"CreationDate":"2016-12-23T09:23:00.000","Title":"OpenCV VideoCapture device index \/ device number","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm working on this book and keep running error when i'm run \"Prop_cumsum\"\n> prop_cumsum = df.sort_index(by='prop', ascending=False).prop.cumsum()\n\n\/Users\/anaconda\/lib\/python3.5\/site-packages\/ipykernel\/main.py:1:\n  FutureWarning: by argument to sort_index is deprecated, pls use\n  .sort_values(by=...)   if name == 'main':\n  --------------------------------------------------------------------------- KeyError                                  Traceback (most recent call\n  last)\n  \/Users\/anaconda\/lib\/python3.5\/site-packages\/pandas\/indexes\/base.py in\n  get_loc(self, key, method, tolerance)    1944             try:\n  -> 1945                 return self._engine.get_loc(key)    1946             except KeyError:\npandas\/index.pyx in pandas.index.IndexEngine.get_loc\n  (pandas\/index.c:4154)()\npandas\/index.pyx in pandas.index.IndexEngine.get_loc\n  (pandas\/index.c:4018)()\npandas\/hashtable.pyx in pandas.hashtable.PyObjectHashTable.get_item\n  (pandas\/hashtable.c:12368)()\npandas\/hashtable.pyx in pandas.hashtable.PyObjectHashTable.get_item\n  (pandas\/hashtable.c:12322)()\nKeyError: 'prop'\nDuring handling of the above exception, another exception occurred:\nKeyError                                  Traceback (most recent call\n  last)  in ()\n  ----> 1 prop_cumsum = df.sort_index(by='prop', ascending=False).prop.cumsum()\n\/Users\/anaconda\/lib\/python3.5\/site-packages\/pandas\/core\/frame.py in\n  sort_index(self, axis, level, ascending, inplace, kind, na_position,\n  sort_remaining, by)    3237                 raise ValueError(\"unable\n  to simultaneously sort by and level\")    3238             return\n  self.sort_values(by, axis=axis, ascending=ascending,\n  -> 3239                                     inplace=inplace)    3240     3241         axis = self._get_axis_number(axis)\n\/Users\/anaconda\/lib\/python3.5\/site-packages\/pandas\/core\/frame.py in\n  sort_values(self, by, axis, ascending, inplace, kind, na_position)\n  3149     3150             by = by[0]\n  -> 3151             k = self[by].values    3152             if k.ndim == 2:    3153 \n\/Users\/anaconda\/lib\/python3.5\/site-packages\/pandas\/core\/frame.py in\n  getitem(self, key)    1995             return self._getitem_multilevel(key)    1996         else:\n  -> 1997             return self._getitem_column(key)    1998     1999     def _getitem_column(self, key):\n\/Users\/anaconda\/lib\/python3.5\/site-packages\/pandas\/core\/frame.py in\n  _getitem_column(self, key)    2002         # get column    2003         if self.columns.is_unique:\n  -> 2004             return self._get_item_cache(key)    2005     2006         # duplicate columns & possible reduce dimensionality\n\/Users\/anaconda\/lib\/python3.5\/site-packages\/pandas\/core\/generic.py in\n  _get_item_cache(self, item)    1348         res = cache.get(item)    1349         if res is None:\n  -> 1350             values = self._data.get(item)    1351             res = self._box_item_values(item, values)    1352\n  cache[item] = res\n\/Users\/anaconda\/lib\/python3.5\/site-packages\/pandas\/core\/internals.py\n  in get(self, item, fastpath)    3288     3289             if not\n  isnull(item):\n  -> 3290                 loc = self.items.get_loc(item)    3291             else:    3292                 indexer =\n  np.arange(len(self.items))[isnull(self.items)]\n\/Users\/anaconda\/lib\/python3.5\/site-packages\/pandas\/indexes\/base.py in\n  get_loc(self, key, method, tolerance)    1945                 return\n  self._engine.get_loc(key)    1946             except KeyError:\n  -> 1947                 return self._engine.get_loc(self._maybe_cast_indexer(key))    1948     1949\n  indexer = self.get_indexer([key], method=method, tolerance=tolerance)\npandas\/index.pyx in pandas.index.IndexEngine.get_loc\n  (pandas\/index.c:4154)()\npandas\/index.pyx in pandas.index.IndexEngine.get_loc\n  (pandas\/index.c:4018)()\npandas\/hashtable.pyx in pandas.hashtable.PyObjectHashTable.get_item\n  (pandas\/hashtable.c:12368)()\npandas\/hashtable.pyx in pandas.hashtable.PyObjectHashTable.get_item\n  (pandas\/hashtable.c:12322)()\nKeyError: 'prop'","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":187,"Q_Id":41298599,"Users Score":0,"Answer":"It seems that you invoked sort_index instead of sort_values.  The by='prop' doesn't make sense in such a context (you sort the index by the index, not by columns in the data frame).\nAlso, in my early release copy of the 2nd edition, this appears near the top of page 43.  But since this is early release, the page numbers may be fluid.","Q_Score":0,"Tags":"python,cumsum,prop","A_Id":41499342,"CreationDate":"2016-12-23T09:24:00.000","Title":"Python for Data Analysis: Chp 2 Pg 38 \"prop_cumsum\" error","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Does anyone have any idea whether Google collects data that one supplies to Tensorflow? I mean it is open source, but it falls under their licences.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":861,"Q_Id":41299126,"Users Score":0,"Answer":"Doesn't TF actually also uses google's models from cloud? I'm pretty sure google uses cloud data to provide better models for TF. \nI'd recommend you to stay away from it. Only by writing your models from scratch you will learn to do useful stuff with it long term. I can also recommend weka for java, it's open source and you can only look at the code of the models there an implement it yourself adjusting for your needs.","Q_Score":0,"Tags":"python,machine-learning,tensorflow,deep-learning,google-developer-tools","A_Id":50601700,"CreationDate":"2016-12-23T09:57:00.000","Title":"TensorFlow from Google - Data Security","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I see that a background class is used as a bonus class. So this class is used in case of not classifying an image in the other classes? In my case, I have a binary problem and I want to understand if an image contains food or not. I need to use 2 classes + 1 background class = 3 classes or only 2 classes?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":397,"Q_Id":41312197,"Users Score":2,"Answer":"Only two classes. \"Not food\" is your background class. If you were trying to detect food or dogs, you could have 3 classes: \"food\", \"dog\", \"neither food nor dog\".","Q_Score":1,"Tags":"python,computer-vision,tensorflow,conv-neural-network","A_Id":41315329,"CreationDate":"2016-12-24T09:54:00.000","Title":"Number of classes for inception network (Tensorflow)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to record a word beforehand and when the same password is spoken into the python script, the program should run if the spoken password matches the previously recorded file. I do not want to use the speech recognition toolkits as the passwords might not be any proper word but could be complete gibberish. I started with saving the previously recorded file and the newly spoken sound as numpy arrays. Now I need a way to determine if the two arrays are 'close' to each other. Can someone point me in the right direction for this?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":851,"Q_Id":41318435,"Users Score":1,"Answer":"It is not possible to compare to speech samples on a sample level (or time domain). Each part of the spoken words might vary in length, so they won't match up, and the levels of each part will also vary, and so on. Another problem is that the phase of the individual components that the sound signal consists of can change too, so that two signals that sound the same can look very different in the time domain. So likely the best solution is to move the signal into the frequency domain. One common way to do this is using the Fast Fourier Transform (FFT). You can look it up, there is a lot of material about this on the net, and good support for it in Python.\nThen could could proceed like this:\n\nDivide the sound sample into small segments of a few milliseconds.\nFind the principal coefficients of FFT of segments.\nCompare the sequences of some selected principal coefficients.","Q_Score":0,"Tags":"python-2.7,numpy,scipy,voice-recognition,voice","A_Id":41329052,"CreationDate":"2016-12-25T03:15:00.000","Title":"Voice activated password implementation in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Can a sample implementation code or a pointer be provided for implementing LSD with opencv 3.0 and python? HoughLines and HoughLinesP are not giving desired results in python and want to test LSD in python but am not getting anywhere.\nI have tried to do the following:\nLSD=cv2.createLineSegmentDetector(0)\n lines_std=LSD.detect(mixChl)\n  LSD.drawSegments(mask,lines_std) \nHowever when I draw lines on the mask I get an error which is:\n LSD.drawSegments(mask,lines_std) TypeError: lines is not a numerical tuple\nCan someone please help me with this? \nThanks in advance.","AnswerCount":4,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":18293,"Q_Id":41329665,"Users Score":2,"Answer":"old implementation is not available.\nNow it is available as follows:\nfld = cv2.ximgproc.createFastLineDetector()\nlines = fld.detect(image)","Q_Score":13,"Tags":"python-2.7,opencv3.0","A_Id":65705673,"CreationDate":"2016-12-26T10:15:00.000","Title":"LineSegmentDetector in Opencv 3 with Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have not used Linux\/Unix for more a decade. Why does the 'tensorflow' module import fail in Spyder and not in Jupyter Notebook and not in Python prompt?\nSCENARIO:\n\n[terminal] spyder\n[spyder][IPython console] Type 'import tensorflow as tf' in the IPython console\n\nCURRENT RESULT:\n\n[spyder][IPython console] Message error: 'ImportError: No module named 'tensorflow''\n\nADDITIONAL INFORMATION:\n\nOS: Ubuntu 14.04 (VMWare)\nPython: Python 3.5.2 :: Anaconda custom (64-bit)\nInstall of TensorFlow:\n\n\n[terminal] sudo -s\n[terminal] conda create --name=IntroToTensorFlow python=3 anaconda\n[terminal] source activate IntroToTensorFlow\n[terminal] conda install -c conda-forge tensorflow\n\nPATH = $PATH:\/home\/mo\/anaconda3\/envs\/IntroToTensorFlow\/bin\n\nCOMMENTS:\n\nWhen I replay the following scenario, it works fine:\n\n\n[terminal] sudo -s\n[terminal] source activate IntroToTensorFlow\n[terminal] python\n[python] import tensorflow as tf\n\nWhen I replay the tensorflow import in Jupyter Notebook, it works fine too\n\nWHAT I HAVE DONE SO FAR:\n\nI Googled it but I did not find a suitable anwser\nI searched in the Stack Overflow questions","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1193,"Q_Id":41344017,"Users Score":0,"Answer":"(Posted on behalf of the OP).\nIt is solved: I reinstalled spyder and it works properly now. Thank you.","Q_Score":0,"Tags":"python,ubuntu,anaconda,environment,spyder","A_Id":41346692,"CreationDate":"2016-12-27T11:07:00.000","Title":"Why does the 'tensorflow' module import fail in Spyder and not in Jupyter Notebook and not in Python prompt?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using scikit-learn's linear_model.LogisticRegression to perform multinomial logistic regress. I would like to initialize the solver's seed value, i.e. I want to give the solver its initial guess as the coefficients' values.\nDoes anyone know how to do that?  I have looked online and sifted through the code too, but haven't found an answer.\nThanks!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":493,"Q_Id":41346055,"Users Score":0,"Answer":"You can use the warm_start option (with solver not liblinear), and manually set coef_ and intercept_ prior to fitting.\n\nwarm_start : bool, default=False\nWhen set to True, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution. Useless for liblinear solver.","Q_Score":4,"Tags":"python,machine-learning,scikit-learn,logistic-regression","A_Id":66783030,"CreationDate":"2016-12-27T13:25:00.000","Title":"Feeding a seed value to solver in Python Logistic Regression","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to maintain a Pandas dataframe with 500 rows, and as the next row becomes available I want to push that new row in and throw out the oldest row from the dataframe. e.g. Let's say I maintain row 0 as newest, and row 500 as oldest. When I get a new data, I would push data to row 0, and it will shift row 0 to row 1, and so on until it pushes row 499 to row 500 (and row 500 gets deleted).\nIs there a way to do such a FIFO operation on Pandas? Thanks guys!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2326,"Q_Id":41360265,"Users Score":5,"Answer":"@JohnGalt posted an answer to this on the comments. Thanks a lot. I just wanted to put the answer here just in case if people are looking for similar information in the future.\ndf.shift(1) df.loc[0] = new_row\ndf.shift(n) will shift the rows n times, filling the first n rows with na and getting rid of last n rows. The number of rows of df will not change with df.shift.\nI hope this is helpful.","Q_Score":1,"Tags":"python,pandas","A_Id":41402234,"CreationDate":"2016-12-28T10:39:00.000","Title":"How to do a FIFO push-operation for rows on Pandas dataframe in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I`m trying to make a recommender system based on purchase history using trainImplicit. My input is in domain [1, +inf) (the sum of views and purchases). \nSo the element of my input RDD looks like this: [(user_id,item_id),rating] --> [(123,5564),6] - the user(id = 123) interacted with the item(id=5564) 6 times.\nShould I add to my RDD elements such as [(user_id,item_id),rating] --> [(123,2222),0], meaning that given user has never interacted with given item or the ALS.implicitTrain does this implicitly?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":363,"Q_Id":41361080,"Users Score":1,"Answer":"It it not necessary (for implicit) and shouldn't be done (for explicit) so in this case bass only data you actually have.","Q_Score":1,"Tags":"python,pyspark,collaborative-filtering","A_Id":41366334,"CreationDate":"2016-12-28T11:25:00.000","Title":"Understanding Spark MLlib ALS.trainImplicit input format","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to create multiple empty pandas dataframes in the following way:\ndfnames = ['df0', 'df1', 'df2']\nx = pd.Dataframes for x in dfnames\nThe above mentionned line returns error syntax.\nWhat would be the correct way to create the dataframes?","AnswerCount":3,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":3447,"Q_Id":41361151,"Users Score":2,"Answer":"the constructor pd.Dataframe must be called like a function, so followed by parentheses (). Now you are refering to the module pd.dataframes (also note the final 's').\nthe for x-construction you're using creates a sequence. In this form you can't assign it to the variable x. Instead, enclose everything right of the equal sign '=' in () or []\nit's usually not a good idea to use the same variable x both at the left hand side and at the right hand side of the assignment, although it won't give you a language error (but possibly much confusion!).\nto connect the names in fdnames to the dataframes, use e.g. a dict:\ndataFrames = {(name, pd.DataFrame()) for name in dfnames}","Q_Score":2,"Tags":"python,pandas","A_Id":41361442,"CreationDate":"2016-12-28T11:29:00.000","Title":"python pandas: create multiple empty dataframes","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to create multiple empty pandas dataframes in the following way:\ndfnames = ['df0', 'df1', 'df2']\nx = pd.Dataframes for x in dfnames\nThe above mentionned line returns error syntax.\nWhat would be the correct way to create the dataframes?","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":3447,"Q_Id":41361151,"Users Score":0,"Answer":"You can't have many data frames within a single variable name, here you are trying to save all empty data frames in x. Plus, you are using wrong attribute name, it is pd.DataFrame and not pd.Dataframes.\nI did this and it worked-\ndfnames = ['df0', 'df1', 'df2']\nx = [pd.DataFrame for x in dfnames]","Q_Score":2,"Tags":"python,pandas","A_Id":41361512,"CreationDate":"2016-12-28T11:29:00.000","Title":"python pandas: create multiple empty dataframes","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have two models trained with Tensorflow Python, exported to binary files named export1.meta and export2.meta. Both files will generate only one output when feeding with input, say output1 and output2.\nMy question is if it is possible to merge two graphs into one big graph so that it will generate output1 and output2 together in one execution.\nAny comment will be helpful. Thanks in advance!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2821,"Q_Id":41370987,"Users Score":7,"Answer":"I kicked this around with my local TF expert, and the brief answer is \"no\"; TF doesn't have a built-in facility for this.  However, you could write custom endpoint layers (input and output) with synch operations from Python's process management, so that they'd maintain parallel processing of each input, and concatenate the outputs.\nRationale\nI like the way this could be used to get greater accuracy with multiple features, where the features have little or no correlation.  For instance, you could train two character recognition models: one to identify the digit, the other to discriminate between left- and right-handed writers.\nThis would also allow you to examine the internal kernels that evolved for each individual feature, without interdependence with other features: the double-loop of an '8' vs the general slant of right-handed writing.\nI also expect that the models for individual features will converge measurably faster than one over-arching training session.\nFinally, it's quite possible that the individual models could be used in mix-and-match feature sets.  For instance, train another model to differentiate letters, while letting your previously-trained left\/right flagger would still have a pretty good guess at the writer's moiety.","Q_Score":9,"Tags":"python,machine-learning,tensorflow","A_Id":41371381,"CreationDate":"2016-12-28T23:09:00.000","Title":"Is it possible to merge multiple TensorFlow graphs into one?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to install the latest version (0.18.1) of sklearn for use in a web app\nI am hosting my webapp with apache web server and flask\nI have tried sudo apt-get -y install python3-sklearn and this works but installs an older version of sklearn (0.17)\nI have also tried pip3 and easy_install and these complete the install but are not picked up by flask or apache.\nI get the following error log on my apache server\n[Thu Dec 29 13:07:45.505294 2016] [wsgi:error] [pid 31371:tid 140414290982656] [remote 90.201.35.82:25030]     from sklearn.gaussian_process import GaussianProcessRegressor\n[Thu Dec 29 13:07:45.505315 2016] [wsgi:error] [pid 31371:tid 140414290982656] [remote 90.201.35.82:25030] ImportError: cannot import name 'GaussianProcessRegressor'\nThis is because I am trying to access some features of sklearn which are not present in 0.17 but are there in 0.18.1\nAny ideas?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":362,"Q_Id":41380710,"Users Score":0,"Answer":"Use anaconda. It will save you so much time with these annoying dependency issues.","Q_Score":0,"Tags":"python,apache,flask,scikit-learn","A_Id":41383430,"CreationDate":"2016-12-29T13:25:00.000","Title":"installing sklearn version 0.18.1 in Apache web server","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I have an numpy-array with 32 x 32 x 3 pictures with X_train.shape: (32, 32, 3, 73257). However, I would like to have the following array-shape: (73257, 32, 32, 3). \nHow can I accomplish this?","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":3420,"Q_Id":41382736,"Users Score":2,"Answer":"There are two ways to archive this either np.reshape(x, ndims) or np.transpose(x, dims). \nFor pictures I propose np.transpose(x, dims) which can be applied using \nX_train = np.transpose(X_train, (3,0,1,2)).","Q_Score":0,"Tags":"python,numpy","A_Id":41382785,"CreationDate":"2016-12-29T15:34:00.000","Title":"Numpy Array Change indices","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm looking for a way to split a dartboard image into polygons so that given an x,y coordinate I can find out which zone the dart fell within. I have found a working python script to detect if the coordinate falls within a polygon (stored as a list of x,y pairs), but I am lost as to how to generate the polygons as a list of points. I'm open to creating the \"shape map\" if that's the correct term, in whatever way necessary to get it done, I just don't know the correct technology or method to do this.\nAny advice is welcome!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":109,"Q_Id":41386463,"Users Score":0,"Answer":"Not sure what the issue is. Normally, x and y coordinates (of the dart)  will be given relative to the top-left corner of the image so you will need to add the radius of the dartboard to each to get your coordinates relative to the centre of the dartboard.\nThere are 20 segments on a dartboard, so each segment will subtend 360\/20 or 18 degrees around the centre. You can get the angle from the vertical using tan inverse x\/y and test which segment, and therefore, which number that corresponds to.\nThe distance from the centre will be sqrt(x^2 + y^2) and you can test if that is within the segment corresponding to a treble or a double.","Q_Score":0,"Tags":"python-2.7,opencv","A_Id":41387325,"CreationDate":"2016-12-29T20:04:00.000","Title":"Split Dartboard into Polygons","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a small project that I am tinkering with. I have a small box and I have attached my camera on top of it. I want to get a notification if anything is added or removed from it.\nMy original logic was to constantly take images and compare it to see the difference but that process is not good even the same images on comparison gives out a difference. I do not know why?\nCan anyone suggest me any other way to achieve this?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":619,"Q_Id":41404053,"Users Score":1,"Answer":"Well I can suggest a way of doing this. So basically what you can do is you can use some kind of Object Detection coupled with a Machine Learning Algo. So the way this might work is you first train your camera to recongnize the closed box. You can take like 10 pics of the closed box(just an example) and train your program to recognize that closed box. So the program will be able to detect when the box is closed. So when the box is not closed(i.e open or missing or something else) then you can code your program appropriately to fire off a signal or whatever it is you are trying to do. So the first obvious step is to write code for object detection. There are numerous ways of doing this alone like Haar Classification, Support Vector Machines. Once you have trained your program to look for the closed box you can then run this program to predict what's happening in every frame of the camera feed. Hope this answered your question! Cheers!","Q_Score":1,"Tags":"python,opencv,image-processing,raspberry-pi","A_Id":41408698,"CreationDate":"2016-12-30T23:39:00.000","Title":"Detect object in an image using openCV python on a raspberry pi","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Reviewing linear regressions via statsmodels OLS fit I see you have to use add_constant to add a constant '1' to all your points in the independent variable(s) before fitting. However my only understanding of intercepts in this context would be the value of y for our line when our x equals 0, so I'm not clear what purpose always just injecting a '1' here serves. What is this constant actually telling the OLS fit?","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":9060,"Q_Id":41404817,"Users Score":12,"Answer":"It doesn't add a constant to your values, it adds a constant term to the linear equation it is fitting.  In the single-predictor case, it's the difference between fitting an a line y = mx to your data vs fitting y = mx + b.","Q_Score":12,"Tags":"python,linear-regression,statsmodels","A_Id":41404825,"CreationDate":"2016-12-31T02:08:00.000","Title":"statsmodels add_constant for OLS intercept, what is this actually doing?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Reviewing linear regressions via statsmodels OLS fit I see you have to use add_constant to add a constant '1' to all your points in the independent variable(s) before fitting. However my only understanding of intercepts in this context would be the value of y for our line when our x equals 0, so I'm not clear what purpose always just injecting a '1' here serves. What is this constant actually telling the OLS fit?","AnswerCount":2,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":9060,"Q_Id":41404817,"Users Score":7,"Answer":"sm.add_constant in statsmodel is the same as sklearn's fit_intercept parameter in LinearRegression(). If you don't do sm.add_constant or when LinearRegression(fit_intercept=False), then both statsmodels and sklearn algorithms assume that b=0 in y = mx + b, and it'll fit the model using b=0 instead of calculating what b is supposed to be based on your data.","Q_Score":12,"Tags":"python,linear-regression,statsmodels","A_Id":43397319,"CreationDate":"2016-12-31T02:08:00.000","Title":"statsmodels add_constant for OLS intercept, what is this actually doing?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there any built-in pandas' method to find the cumulative correlation between two pandas series? \nWhat it should do is effectively fixing the left side of the window in pandas.rolling_corr(data, window) so that the width of the window increases and eventually the window includes all data points.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":506,"Q_Id":41406339,"Users Score":1,"Answer":"Just use rolling correlation, with a very large window, and min_period = 1.","Q_Score":1,"Tags":"python,pandas,rolling-computation","A_Id":62550444,"CreationDate":"2016-12-31T07:17:00.000","Title":"pandas \"cumulative\" rolling_corr","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"For example, when we compute a variable c as result = sess.run(c), does TF only compute the inputs required for computing c or updates all the variables of the complete computational graph?\nAlso, I don't seem to be able to do this:\nc = c*a*b\nas I am stuck with uninitialized variable error even after initializing c as tf.Variable(tf.constant(1)). Any suggestions?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1412,"Q_Id":41407241,"Users Score":0,"Answer":"Since Python code of TF only setups the graph, which is actually executed by native implementation of all ops, your variables need to be executed in this underlying environment. This happens by executing two ops - for local and global variables initialization:\n\nsession.run(tf.global_variables_initializer(), tf.local_variables_initializer())\n\nOn the original question - as far as I know - YES, it computes all the graph, and it requires you to feed placeholders, even if the executed op (in the session) is not dependent on them.","Q_Score":0,"Tags":"python,machine-learning,tensorflow","A_Id":45947287,"CreationDate":"2016-12-31T09:53:00.000","Title":"Does TensorFlow execute entire computation graph with sess.run()?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have two sparse scipy matrix's, title and paragraph whose dimensions are (284,183) and (284,4195) respectively. Each row of both matrix's are features from one instance of my dataset. I wish to cluster these without a predefined number of clusters and then plot them. \nI also have an array, Y that relates to each row. (284,1). One class is represented by 0, the other by 1. I would like to color the points using this. How can I do this using Python?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":156,"Q_Id":41416652,"Users Score":0,"Answer":"You can use the skikit-learn Affinity propagation or Mean-shift libraries for clustering. Those algorithms will output a number of clusters and centers. To use the Y seems to be a different question because you can't plot the multi dimensional point on a 3D plane unless you do some import some other libraries.","Q_Score":0,"Tags":"python,machine-learning,scikit-learn,cluster-analysis","A_Id":41417067,"CreationDate":"2017-01-01T15:44:00.000","Title":"Cluster two features in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'd like to minimize some objective function f(x1,x2,x3) in Python. Its quite a simple function but the problem is that the design vector x=[x1,x2,x3] constains integers only. \nSo for example I'd like to get the result: \n\"f is minimum for x=[1, 3, 2]\" and not: \n\"f is minimum for x=[1.12, 3.36, 2.24]\" since this would not make any sense for my problem.\nIs there any way to rig scipy.minimize to solve this kind of problem? Or is there any other Python library capable of doing this?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":267,"Q_Id":41447048,"Users Score":0,"Answer":"That is actually a way harder problem speaking math, the same algorithm will not be capable! This problem is np-hard. Maybe check out pyglpk... And check out mixed integer programming.","Q_Score":4,"Tags":"python,optimization,scipy,integer,minimum","A_Id":41447225,"CreationDate":"2017-01-03T15:38:00.000","Title":"Scipy.optimize.minimize using a design vector x that contains integers only","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Assuming that I have a pandas dataframe and I want to add thousand separators to all the numbers (integer and float), what is an easy and quick way to do it?","AnswerCount":6,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":20270,"Q_Id":41447383,"Users Score":2,"Answer":"If you want \".\" as thousand separator and \",\" as decimal separator this will works:\nData = pd.read_Excel(path)\nData[my_numbers] = Data[my_numbers].map('{:,.2f}'.format).str.replace(\",\", \"~\").str.replace(\".\", \",\").str.replace(\"~\", \".\")\nIf you want three decimals instead of two you change \"2f\" --> \"3f\"\nData[my_numbers] = Data[my_numbers].map('{:,.3f}'.format).str.replace(\",\", \"~\").str.replace(\".\", \",\").str.replace(\"~\", \".\")","Q_Score":10,"Tags":"python,pandas,number-formatting,separator","A_Id":59636728,"CreationDate":"2017-01-03T15:56:00.000","Title":"Easy way to add thousand separator to numbers in Python pandas DataFrame","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am quite new to neural networks and trying to use pybrain to build and train a network.\nI am building my network manually with full connections between all layers (input, two hidden layers, output) and then set some weights to zero using _SetParameters as I don't want connections between some specific nodes.\nMy problem is that the weights that are zero at the beginning are adapted in the same way as all other weights and therefore no more zero after training the network via backprop. How can I force the \"zero-weights\" to stay zero through the whole process?\nThanks a lot for your answers.\nFiona","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":562,"Q_Id":41459860,"Users Score":0,"Answer":"I am struggling with a similar problem.\nSo far I am using net._setParameters command to fix the weights after each training step, but there should be a better answer..\nIt might help for the meantime, I am waiting for the better answer as well :-)","Q_Score":1,"Tags":"python,python-2.7,neural-network,pybrain","A_Id":41730024,"CreationDate":"2017-01-04T09:02:00.000","Title":"Python\/Pybrain: How can I fix weights of a neural network during training?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"python beginner here with a simple question.  Been using Python-Docx to generate some reports in word from Python data (generated from excel sheets).  So far so good, but would like to add a couple of charts to the word document based on the data in question.  I've looked at pandas and matplotlib and all seem like they would work great for what I need (just a few bar charts, nothing crazy).  But can anyone tell me if it is possible to create the chart in python and have it output to the word document via docx?","AnswerCount":2,"Available Count":1,"Score":0.4621171573,"is_accepted":false,"ViewCount":10764,"Q_Id":41471887,"Users Score":5,"Answer":"The general approach that's currently supported is to export the chart from matplotlib or wherever as an image, and then add the image to the Word document.\nWhile Word allows \"MS Office-native\" charts to be created and embedded, that functionality is not in python-docx yet.","Q_Score":7,"Tags":"python,excel,matplotlib,python-docx","A_Id":41472883,"CreationDate":"2017-01-04T19:34:00.000","Title":"How Can I Write Charts to Python DocX Document","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Does Python's NLTK toolkit return different results for each iteration of:\n1) tokenization\n2) POS tagging?\nI am using NLTK to tag a large text file. The tokenized list of tuples has a different size every time. Why is this?","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":110,"Q_Id":41482733,"Users Score":0,"Answer":"Neither modify their logic or computation in any iterative loop.\nIn NLTK, tokenzation by default is rule based, using Regular Expressions, to split tokens from a sentence\nPOS tagging by default uses a trained model for English, and will therefore give the same POS tag per token for the given trained model. If that model is trained again, it will change. \nTherefore the basic answer to your question is no","Q_Score":0,"Tags":"python,python-2.7,nltk","A_Id":41486968,"CreationDate":"2017-01-05T10:35:00.000","Title":"Does NLTK return different results on each run?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Does Python's NLTK toolkit return different results for each iteration of:\n1) tokenization\n2) POS tagging?\nI am using NLTK to tag a large text file. The tokenized list of tuples has a different size every time. Why is this?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":110,"Q_Id":41482733,"Users Score":0,"Answer":"Both the tagger and the tokenizer are deterministic. While it's possible that iterating over a Python dictionary would return results in a different order in each execution of the program, this will not affect tokenization -- and hence the number of tokens (tagged or not) should not vary. Something else is wrong with your code.","Q_Score":0,"Tags":"python,python-2.7,nltk","A_Id":41487255,"CreationDate":"2017-01-05T10:35:00.000","Title":"Does NLTK return different results on each run?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a problem installing spark-python on CentOS.\nWhen I installed it using yum install spark-python, I get the following error message.\n\nError: Package: spark-python-1.6.0+cdh5.9.0+229-1.cdh5.9.0.p0.30.el5.noarch (cloudera-cdh5)\n             Requires: python26\nYou could try using --skip-broken to work around the problem\nYou could try running: rpm -Va --nofiles --nodigest\n\nI already installed other spark packages (spark-master, spark-worker...) but it only occurred installing spark-python.\nCan anyone help me?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":141,"Q_Id":41487708,"Users Score":0,"Answer":"You are installing spark Python 1.6 which depends on Python 2.6\nI think the current stable version is 2.x and the package for that is pyspark. Try installing that. It might require Python 3.0 but thats easy enough to install.\nYou'll probably need to reinstall the other spark packages as well to make sure they are the right version.","Q_Score":0,"Tags":"python,apache-spark,cloudera","A_Id":41497154,"CreationDate":"2017-01-05T14:38:00.000","Title":"What is the matter when I installing spark-python on CentOS","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Bokeh plots include a Bokeh favicon in the upper right of most plots.  Is it possible to replace this icon with another icon?  If so, how?","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":716,"Q_Id":41511597,"Users Score":2,"Answer":"As of Bokeh 0.12.4 it is only possible to remove it, not change it, directly from the python library. This can be done by setting the property logo=None on a plot.toolbar.","Q_Score":2,"Tags":"python,bokeh","A_Id":41511835,"CreationDate":"2017-01-06T18:03:00.000","Title":"How to change Bokeh favicon to another image","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have just started using NumPy. What is the difference between resize and reshape for arrays?","AnswerCount":5,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":29555,"Q_Id":41518351,"Users Score":7,"Answer":"reshape() is able to change the shape only (i.e. the meta info), not the number of elements.\nIf the array has five elements, we may use e.g. reshape(5, ), reshape(1, 5),\nreshape(1, 5, 1), but not reshape(2, 3).\n\nreshape() in general don't modify data themselves, only meta info about them,\n\nthe .reshape() method (of ndarray) returns the reshaped array, keeping the original array untouched.\n\n\n\nresize() is able to change both the shape and the number of elements, too.\nSo for an array with five elements we may use resize(5, 1), but also resize(2, 2) or resize(7, 9).\n\nThe .resize() method (of ndarray) returns None, changing only the original array (so it seems as an in-place change).","Q_Score":27,"Tags":"python,numpy","A_Id":61167164,"CreationDate":"2017-01-07T05:58:00.000","Title":"What is the difference between resize and reshape when using arrays in NumPy?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have some images i'm using for face recognition.\nSome of the images are very dark.\nI don't want to use Histogram equalisation on all the images only on the dark ones.\nHow can i determine if an image is dark?\nI'm using opencv in python.\nI would like to understand the theory and the implementation.\nThanks","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2224,"Q_Id":41526677,"Users Score":3,"Answer":"To determin if an image is dark, simply calculate the average intensity and judge it.\nThe problem for the recognition although is not that the image is dark, but that it has a low contrast. A bright image with the same contrast would yield the same bad results.\nHistogram equalization is a method that is used to improve images for human vision. Humans have difficulties to distinguish between very similar intensity values. A problem that a computer does not have, unless your algorithm is somehow made to mimic human vision with all its flaws.\nA low contrast image bears little information. There is no image enhancement algorithm in the world that will add any further information.\nI won't get into too much detail about image characterization. You'll find plenty of resources online or in text books.\nA simple measure would be to calculate the standard deviation of image regions you are interested in.","Q_Score":1,"Tags":"python,python-2.7,opencv,image-processing","A_Id":41527710,"CreationDate":"2017-01-07T21:19:00.000","Title":"How to determine if an image is dark?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to preallocate an integer matrix to store indices generated in iterations. In MATLAB this can be obtained by IXS = zeros(r,c) before for loops, where r and c are number of rows and columns. Thus all indices in subsequent for loops can be assigned into IXS to avoid dynamic assignment. If I accidentally select a 0 in my codes, for example, a wrong way to pick up these indices to select elements from a matrix, error can arise.\nBut in numpy, 0 or other minus values can also be used as indices. For example, if I preallocate IXS as IXS=np.zeros([r,c],dtype=int) in numpy. In a for loop, submatrix specified by the indices assigned into IXS previously can be obtained by X(:,IXS(IXS~=0)) in MATLAB, but the first row\/column may be lost if I perform the selection in the same way in numpy.\nFurther, in a large program with operations of large matrices, preallocation is important in speeding up the computation, and it is easy to locate the error raised by wrong indexing as 0 may be selected in MATLAB. In numpy, if I select an array by for example X[:,IXS[:n]] with wrong n, no error occurs. I have to pay lots of times to check where the error is. More badly, if the final results are not so strange, I may ignore this bug. This always occurs in my program. Thus I have to debug my codes again and again.\nI wonder is there a safe way to preallocate such index matrix in numpy?","AnswerCount":4,"Available Count":1,"Score":0.049958375,"is_accepted":false,"ViewCount":620,"Q_Id":41534489,"Users Score":1,"Answer":"If you really really want to catch errors that way, initialize your indices with NaN.\nIXS=np.full((r,c),np.nan, dtype=int)\nThat will always raise an IndexError.","Q_Score":1,"Tags":"python,numpy","A_Id":41543451,"CreationDate":"2017-01-08T15:45:00.000","Title":"How do I safely preallocate an integer matrix as an index matrix in numpy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm practicing programming and I would like to know what is the easiest way to solve linear system of equations over the field Z\/2Z? I found a problem where I managed to reduce the problem to solve a system of about 2200 linear equations over Z\/2Z but I'm not sure what is the easiest way to write a solver for the equations. Is there simpler solution that use nested lists to represent a matrix and then manually write the Gauss\u2013Jordan algorithm?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":612,"Q_Id":41557022,"Users Score":0,"Answer":"I'd use Sage if this were a quick hack, and maybe consider using something optimized for GF(2) if the matrices are really big, to ensure that only one bit is used for each entry and that addition of several elements can be accomplished using a single XOR operation. One benefit of working over a finite field is that you don't have to worry about numeric stability, so naive Gauss\u2013Jordan would sound like a good approach.","Q_Score":2,"Tags":"python-3.x,math,matrix","A_Id":41564872,"CreationDate":"2017-01-09T21:12:00.000","Title":"Is there an easy way to solve a system of linear equations over Z\/2Z in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I just installed numpy on my PC (running windows 10, running python 3.5.2) using WinPython, but when i try to import it in IDLE with:   import numpy I get the ImportError: Traceback (most recent call last):\n  File \"C:\\Users\\MY_USERNAME\\Desktop\\DATA\\dataScience1.py\", line 1, in <module>\n    import numpy\nImportError: No module named 'numpy'.\nDid I possibly install it incorrectly, or do I need to do something else before it can be used?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":3858,"Q_Id":41560796,"Users Score":0,"Answer":"Winpython has two size, and the smallest \"Zero\" size doesn't include numpy","Q_Score":1,"Tags":"python,numpy,python-3.5","A_Id":41577386,"CreationDate":"2017-01-10T03:46:00.000","Title":"Numpy not found after installation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on a project that involves a RandomWalk on a large graph(too big to fit in memory). I coded it in Python using networkx but soon, the graph became too big to fit in memory, and so I realised that I needed to switch to a distributed system. So, I understand the following:\n\nI will need to use a graph database as such(Titan, neo4j, etc)\nA graph processing framework such as Apache Giraph on hadoop\/ graphx on spark.\n\nFirstly, are there enough APIs to allow me to continue to code in Python, or should I switch to Java?\nSecondly, I couldn't find exact documentation on how I can write my custom function of traversal(in either Giraph or graphx) in order to implement the Random Walk algorithm.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":480,"Q_Id":41575620,"Users Score":0,"Answer":"My understanding is, you need to process large graphs which are stored on file systems. There are various distributed graph processing frameworks like Pregel, Pregel+, GraphX, GPS(Stanford), Mizan, PowerGraph etc. \nIt is worth taking a look at these frameworks. I will suggest coding in C, C++ using openMPI like which can help achieve better efficiency. \nFrameworks in Java are not very memory efficient. I am not sure of API of these frameworks in Python.\nIt is worth taking a look at blogs and papers which give a comparative analysis of these frameworks before deciding on implementing them.","Q_Score":0,"Tags":"python,hadoop,graph,random-walk,bigdata","A_Id":44357542,"CreationDate":"2017-01-10T18:15:00.000","Title":"Large graph processing on Hadoop","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm learning OpenCV and Python. I captured some images from my webcam and saved them. But they are being saved by default into the local folder. I want to save them to another folder from direct path. How do I fix it?","AnswerCount":7,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":137529,"Q_Id":41586429,"Users Score":6,"Answer":"Thank you everyone. Your ways are perfect. I would like to share another way I used to fix the problem. I used the function os.chdir(path) to change local directory to path. After which I saved image normally.","Q_Score":29,"Tags":"python,opencv,image-processing","A_Id":41626482,"CreationDate":"2017-01-11T08:55:00.000","Title":"OpenCV - Saving images to a particular folder of choice","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a project to detect ripeness of specific fruit, I will use phantom 2 with autopilot feature to fly  through fruit trees and capture images then I want to make real time image processing. \nI was searching a lot but didn't find the answers for the following questions. \ncan I use phantom 2 for real time image processing? can I connect \nraspberry pi to the phantom? and what I need? can I use python 2.7 +\nopencv lib to write image processing codes?","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":91,"Q_Id":41589611,"Users Score":2,"Answer":"I would be surprised if a off-the-shelf multicopter would comprise enough processing power to do any reasonable image processing on-board. It wouldn't make sense for the manufacturer.\nBut I guess it has some video or streaming capabilties or can be equipped with such. Then you can process the data on a remote computer, given that you are in transmission range.\nIf you have to process on a remote device it doesn't make any sense to request real-time processing. What for? I mean the multicopter can't do anything useful with real-time results and just for mapping or inspection purposes delay doesn't matter.\nIn general your question cannot be answered as no one can tell you if any hardware is capable of real-time processing without knowing how much there is to process.\nTo answer the rest of your questions:\nYou can connect a raspberry pi to the Phantom.\nYou can use pyhton 2.7 and opencv to write image processing code.\nThat you ask things like that makes me think that you might not be up to the job. So unless you have a team of talented people I guess it will take you years to come out with a usable and robust solution.","Q_Score":0,"Tags":"python-2.7,image-processing,raspberry-pi,phantom-types","A_Id":41591155,"CreationDate":"2017-01-11T11:23:00.000","Title":"use Phantom 2 for real time image processing","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to sort a dataframe with many columns by a specific column, but first I need to change type from object to int. How to change the data type of this specific column while keeping the original column positions?","AnswerCount":4,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":77099,"Q_Id":41590884,"Users Score":2,"Answer":"To simply change one column, here is what you can do:\ndf.column_name.apply(int)\nyou can replace int with the desired datatype you want e.g (np.int64), str, category.\nFor multiple datatype changes, I would recommend the following:\ndf = pd.read_csv(data, dtype={'Col_A': str,'Col_B':int64})","Q_Score":15,"Tags":"python,pandas","A_Id":56167288,"CreationDate":"2017-01-11T12:23:00.000","Title":"Change data type of a specific column of a pandas dataframe","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to sort a dataframe with many columns by a specific column, but first I need to change type from object to int. How to change the data type of this specific column while keeping the original column positions?","AnswerCount":4,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":77099,"Q_Id":41590884,"Users Score":26,"Answer":"df['colname'] = df['colname'].astype(int) works when changing from float values to int atleast.","Q_Score":15,"Tags":"python,pandas","A_Id":41591077,"CreationDate":"2017-01-11T12:23:00.000","Title":"Change data type of a specific column of a pandas dataframe","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new in robotics and I am working on a project where I need to pass the coordinates from the camera to the robot.\nSo the robot is just an arm, it is then stable in a fixed position. I do not even need the 'z' axis because the board or the table where everything is going on have always the same 'z' coordinates.\nThe webcam as well is always in a fixed position, it is not part of the robot and it does not move.\nThe problem I am having is in the conversion from 2D camera coordinates to a 3D robotic arm coordinates (2D is enough because as stated before the 'z' axis is not needed as is always in a fixed position).\nI'd like to know from you guys, which one is the best approach to face this kind of problems so I can start to research.\nI've found lot of information on the web but I am getting a lot of confusion, I would really appreciate if someone could address me to the right way. \nI don't know if this information are useful but I am using OpenCV3.2 with Python\nThank you in advance","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1200,"Q_Id":41599283,"Users Score":0,"Answer":"Define your 2D coordinate on the board, create a mapping from the image coordinate (2D) to the 2D board, and also create a mapping from the board to robot coordinate (3D). Usually, robot controller has a function to define your own coordinate (the board).","Q_Score":0,"Tags":"python,opencv,coordinates,robotics,coordinate-transformation","A_Id":41604196,"CreationDate":"2017-01-11T19:34:00.000","Title":"Translation from Camera Coordinates System to Robotic-Arm Coordinates System","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to install Tensorflow, and received the following error.\n\ntensorflow-0.12.1-cp35-cp35m-win_amd64.whl is not a supported wheel on this platform.\n\nBy reading through other questions, I think I've traced the issue to the cp35 tag not being supported by the version of pip I have installed. What's odd is that I believe I installed python 3.5 and the latest version of pip (9.0.1), but have the following supported tags:\n\n[('cp27', 'cp27m', 'win_amd64'), ('cp27', 'none', 'win_amd64'), ('py2', 'none', 'win_amd64'), ('cp27', 'none', 'any'), ('cp2', 'none', 'any'), ('py27', 'none', 'any'), ('py2', 'none', 'any'), ('py26', 'none', 'any'), ('py25', 'none', 'any'), ('py24', 'none', 'any'), ('py23', 'none', 'any'), ('py22', 'none', 'any'), ('py21', 'none', 'any'), ('py20', 'none', 'any')]\n\nHow can I go about modifying the supported tags, or is that even the right approach?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2301,"Q_Id":41605355,"Users Score":0,"Answer":"I have the same error when I run this command. I found error that the installed version of python was x86 and TensorFlow is for x64 versions. I reinstalled the python with x64 version and it works now! I hope this works for you too!","Q_Score":4,"Tags":"python,python-3.x,tensorflow,pip","A_Id":42087865,"CreationDate":"2017-01-12T04:53:00.000","Title":"Updating the supported tags for pip","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataset with a total of 58 samples. The dataset has two columns \"measured signals\" and \"people_in_area\". Due to it, I am trying to train a Linear Regression model using Scikit-learn. For the moment, I splited 75% of my dataset for training and 25% for testing. However, depending on the order in which the data was before the split, I obtain different R-squared values.\nI think that as the dataset is small, depending on the order in which the data was before being splited, different values would be kept as x_test and y_test. Due to it, I am thinking on using \"Cross-Validation\" on my Linear Regression model to divide the test and train data randomly several times, training it more and, also, being able to test more, obtaining in this way more reliable results. Is this a correct approach?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1054,"Q_Id":41619431,"Users Score":1,"Answer":"Yes, using cross validation will give you a better estimate of your model performance.\nSplitting randomly(cross validation) will however not work for time-series and\/or all distributions of data.\nThe \"final model\" will not be better only your estimate on model performance.","Q_Score":1,"Tags":"python,scikit-learn,linear-regression","A_Id":41620561,"CreationDate":"2017-01-12T17:28:00.000","Title":"Can I apply Cross Validation in a Linear Regression model?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using ipython notebook by connecting to a server\nI don't know how to download a thing (data frame, .csv file,... for example) programatically to my local computer. Because I can't specific declare the path like C:\/\/user\/\/... It will be downloaded to their machine not mine","AnswerCount":4,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":45980,"Q_Id":41627247,"Users Score":7,"Answer":"The download option did not appear for me.\nThe solution was to open the file (which could not be correctly read as it was a binary file), and to download it from the notebook's notepad.","Q_Score":24,"Tags":"python,download,ipython,jupyter-notebook,jupyter","A_Id":46266094,"CreationDate":"2017-01-13T04:08:00.000","Title":"Download data from a jupyter server","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm currently building my NGS pipeline using Snakemake and have an issue regarding the loading of R libraries. Several of the scripts, that my rules call, require the loading of R libraries. As I found no way of globally loading them, they are loaded inside of the R scripts, which of course is redundant computing time when I'm running the same set of rules on several individual input files. \nIs there a way to keep one R session for the execution of several rules and load all required libraries priorly?\nCheers,\nzuup","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":239,"Q_Id":41639782,"Users Score":1,"Answer":"I'm afraid not. This has performance reasons on (a) local systems (circumventing the Python GIL) and (b) cluster systems (scheduling to separate nodes).\nEven if there was a solution on local machines, it would need to take care that no sessions are shared between parallel jobs. If you really need to safe that time, I suggest to merge those scripts.","Q_Score":1,"Tags":"python,r,snakemake","A_Id":41672695,"CreationDate":"2017-01-13T17:01:00.000","Title":"Globally load R libraries in Snakemake","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am learning TensorFlow (as well as general deep learning). I am wondering when do we need to break the input training data into batches? And how do we determine the batch size? Is there a rule of thumb? Thanks!","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":211,"Q_Id":41668158,"Users Score":1,"Answer":"Generally Deep Learning algorithms are ran on GPUs which has limited memory and thus a limited number of input data samples (in the algorithm commonly defined as batch size) could be loaded at a time.\nIn general larger batch size reduces the overall computation time (as the internal matrix multiplications are done in a parallel manner in GPU, thus with large batch sizes the time gets saved in reading\/writing gradients and possibly some other operations output).\nAnother probable benefit of large batch size is: \nIn multi-class classification problems, if the number of classes are large, a \nlarger batch size makes algorithm generalize better(technically avoids over-fitting) over the different classes (while doing this a standard technique is to keep uniform distribution of classes in a batch).\nWhile deciding batch size there are some other factors which comes into play are: learning rate and the type of Optimization method.\nI hope this answers your question to certain extent!","Q_Score":0,"Tags":"python,python-3.x,tensorflow,deep-learning,data-science","A_Id":41763164,"CreationDate":"2017-01-16T01:09:00.000","Title":"TensorFlow: how to determine if we want to break the training dataset into batches","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"For a standard machine learning problem, e.g, image classification on MNIST, the loss function is fixed, therefor the optimization process can be accomplished simply by calling functions and feed the input into them. There is no need to derive gradients and code the descent procedure by hand. \nBut now I'm confused when I met some complicated formulation. Say we are solving a semi-supervised problem, and the loss function has two parts:Ls + lambda * Lu. The first part is a normal classification formulation, e.g, cross entropy loss. And the second part varies. In my situation, Lu is a matrix factorization loss, which in specific is:Lu = MF(D, C * W). And the total loss function can be written as:\nL = \\sum log p(yi|xi) + MF(D, C * W) \n= \\sum log p(yi|Wi) + MF(D, C * W)\n= \\sum log p(yi|T * Wi + b) + MF(D, C * W)\nWhere parameters are W, C, T and b. The first part is a classification loss, and the input xi is a raw of W, i.e. Wi, a vector of size (d, 1). And the label yi can be a one-hot vector of size (c, 1), so parameters T and b map the input to the label size. And the second part is a matrix factorization loss.\nNow I'm confused when I'm going to optimize this function using sgd. It can be solved by write down the formulation derive gradients then accomplish a training procedure from scratch. But I'm wondering if there is a simpler way? Because it's easy to use a deep learning tool like Tensorflow or Keras to train a classification model, all u need to do is build a network and feed the data. \nSo similarly, is there a tool that can automatically compute gradients after I defined the loss function? Because deriving gradients and achieve them from scratch is really annoying. Both the classification loss and matrix factorization loss is very common, so I think the combination can be achieved thoroughly.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":115,"Q_Id":41679182,"Users Score":0,"Answer":"Both Theano & Tensorflow have built-in differentiation for you. So you only need to form the loss.","Q_Score":0,"Tags":"python,optimization,machine-learning,tensorflow,deep-learning","A_Id":50435429,"CreationDate":"2017-01-16T14:57:00.000","Title":"Is there some way can accomplish stochastic gradient descent not from scratch","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a two-mode (grant X person) network in csv format. I would like to create personXperson projection of this network and calculate some network measures (including centrality measures of closeness and betweenness, etc.). \nWhat would be my first step? I am guessing creating 2 separate files for Nodes and Edges and run the analysis in R using igraph package?!\nHere is a super simplified version of my data (my_data.csv).\n\nGrant,   Person  \nA ,       1   \nA ,       2\nB ,       2\nB ,       3","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1405,"Q_Id":41682737,"Users Score":0,"Answer":"Decide how you want the graph to represent the data.  From what you've described one approach would be to have nodes in your graph represent People, and edges represent grants.  In that case, create a pairwise lis of people who are on the same grant.  Edges are bidirectional by default in iGraph, so you just need each pair once.","Q_Score":0,"Tags":"python,r,social-networking","A_Id":41684697,"CreationDate":"2017-01-16T18:22:00.000","Title":"network analysis: how to create nodes and edges files from csv","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an account on a supercomputing cluster where I've installed some packages using e.g. \"pip install --user keras\".\nWhen using qsub to submit jobs to the queue, I try to make sure the system can see my local packages by setting \"export PYTHONPATH=$PYTHONPATH:[$HOME]\/.local\/lib\/python2.7\/site-packages\/keras\" in the script.\nHowever, the resulting log file still complains that there is no package called keras. How can I make sure the system finds my packages?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":2181,"Q_Id":41689297,"Users Score":1,"Answer":"If you are using pbs professional then try to export PYTHONPATH in your environment and then submit job using \"-V\" option with qsub. This will make qsub take all of your environment variables and export it for the job.\nElse, try setting it using option \"-v\" (notice small v) and then put your environment variable key\/value pair with that option like qsub -v HOME=\/home\/user job.sh","Q_Score":3,"Tags":"python,cluster-computing,pbs,qsub,supercomputers","A_Id":41753582,"CreationDate":"2017-01-17T04:52:00.000","Title":"When using qsub to submit jobs, how can I include my locally installed python packages?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":1,"Web Development":0},{"Question":"Given a list A with n rows each having m columns each.\nIs there a one liner to create an empty list B with same structure (n rows each with m components)?\nNumpy lists can be created\/reshaped. Does the python in-built list type support such an argument?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1097,"Q_Id":41699897,"Users Score":0,"Answer":"You can use: B=[[None]*m]*n\nIt creates a list of n rows of m columns of None.","Q_Score":0,"Tags":"python,list","A_Id":41700079,"CreationDate":"2017-01-17T14:38:00.000","Title":"Initialize an empty list of the shape\/structure of a given list without numpy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using the Python SciKit OneClass SVM classifier to detect outliers in lines of text. The text is converted to numerical features first using bag of words and TF-IDF.\nWhen I train (fit) the classifier running on my computer, the time seems to increase exponentially with the number of items in the training set:\nNumber of items in training data and training time taken:\n10K: 1 sec, 15K: 2 sec, 20K: 8 sec, 25k: 12 sec, 30K: 16 sec, 45K: 44 sec. \nIs there anything I can do to reduce the time taken for training, and avoid that this will become too long when training data size increases to a couple of hundred thousand items ?","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":2038,"Q_Id":41715835,"Users Score":1,"Answer":"Hope I'm not too late. OCSVM, and SVM, is resource hungry, and the length\/time relationship is quadratic (the numbers you show follow this). If you can, see if Isolation Forest or Local Outlier Factor work for you, but if you're considering applying on a lengthier dataset I would suggest creating a manual AD model that closely resembles the context of these off-the-shelf solutions. By doing this then you should be able to work either in parallel or with threads.","Q_Score":4,"Tags":"python,scikit-learn,svm","A_Id":49909264,"CreationDate":"2017-01-18T09:36:00.000","Title":"SciKit One-class SVM classifier training time increases exponentially with size of training data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"For a small project I need to extract the features obtained from Doc2Vec object in gensim.\nI have used vector = model.infer_vector(words) is it correct?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":439,"Q_Id":41718767,"Users Score":1,"Answer":"Yes, if words is a list of word strings, preprocessed\/tokenized the same way as training data was fed to the model during training.","Q_Score":1,"Tags":"python,doc2vec","A_Id":41732675,"CreationDate":"2017-01-18T11:54:00.000","Title":"Extract the features from Doc2Vec in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am interested to find sentence vectors using word vectors.I  read that by multiplying each word's tf-idf weights with their vectors and finding their average we can get whole sentence vector.\nNow I want to know that how these tf-idf weights helps us to get sentence vectors i.e how these tf-idf and sentence vector are related?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":878,"Q_Id":41725993,"Users Score":0,"Answer":"Any aggregative operation on the word vectors can give you a sentence vector.\nYou should consider what do you want your representation to mean and choose the operation accordingly.\nPossible operations are summing the vectors, averaging them, concatenating, etc.","Q_Score":0,"Tags":"python,machine-learning","A_Id":41740148,"CreationDate":"2017-01-18T17:40:00.000","Title":"How tf-idf is relevant in calculating sentence vectors","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using the inception v3 model to retrain my own dataset. I have few folder which represent the classes which contain images for each class. What i would like to do is to 'attach' some text ids to these images so when they are retrained and used to run classification\/similarity-detection those ids are retrieved too. (basically its image similarity detection)\nFor instance, Image X is of class 'Teachers' and it belongs to John. When i retrain the model, and run a classification on the new model, i would like to get the Teachers class, but in addition to this i would like to know who is teacher (John).\nAny ideas how to go for it?\nRegards","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":596,"Q_Id":41745022,"Users Score":1,"Answer":"You have 3 main options - multiply your classes, multi-label learning or training several models.\nThe first option is the most straight forward - instead of having  teachers who belong to John and teachers who belong to Jane you can have teachers whose class is Teachers_John and class whose class is Teachers_John and learn to classify to those categories as you would learn any other set of categories, or use something like hierarchical softmax.\nThe second option is to have a set of categories that includes Teachers as well as John and Jane - now your target is not to correctly predict the one most accurate class (Teachers) but several (Teachers and John).\nYour last option is to create a hierarchy of models where the first learns to differentiate between John and Jane and others to classify the inner classes for each of them.","Q_Score":0,"Tags":"python,machine-learning,tensorflow,neural-network,deep-learning","A_Id":41792826,"CreationDate":"2017-01-19T15:02:00.000","Title":"Tensorflow Inception v3 retraining - attach text\/labels to individual images","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am doing MIT6.00.1x course on edX and the professor tells that \"If x<1, search space is 0 to x but cube root is greater than x and less than 1\".\nThere are two cases :\n\n 1. The number x is between 0 and 1\n\n2. The number x is less than 0 (negative)\n\nIn both the cases, the cube root of x will lie between x and 1. I understood that. But what about the search space? Will the initial search space lie between 0 and x? I think it is not that. I think the bold text as cited from the lecture is a flaw! Please enlighten me on this.","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":1193,"Q_Id":41748751,"Users Score":1,"Answer":"Yes, your instructor's one statement is a flaw.  For 0 < x < 1, the root will lie between x and 1.  This is true for any power in the range (0, 1) (roots > 1).\nYou can reflect the statement to the negative side, since this is an odd root.  The cube root of -1 <= x <= 0 will be in the range [-1, x].  For x < -1, your range is [x, -1].  It's the mirror-image of the positive cases.  I'm not at all clear why the instructor made that asymmetric partitioning.","Q_Score":0,"Tags":"python,algorithm,binary-search","A_Id":41749141,"CreationDate":"2017-01-19T18:07:00.000","Title":"Finding cube root of a number less than 1 using binary search","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am doing MIT6.00.1x course on edX and the professor tells that \"If x<1, search space is 0 to x but cube root is greater than x and less than 1\".\nThere are two cases :\n\n 1. The number x is between 0 and 1\n\n2. The number x is less than 0 (negative)\n\nIn both the cases, the cube root of x will lie between x and 1. I understood that. But what about the search space? Will the initial search space lie between 0 and x? I think it is not that. I think the bold text as cited from the lecture is a flaw! Please enlighten me on this.","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":1193,"Q_Id":41748751,"Users Score":1,"Answer":"I think I know the problem you're talking about. The only reason she put that is that she deals with the absolute difference: \nwhile abs(guess**3 - cube) >= epsilon\nHowever, the code will need another line to deal with negative cubes all together which will be something along the lines of:\nif cube<0: guess = -guess \nI hope this helps.","Q_Score":0,"Tags":"python,algorithm,binary-search","A_Id":44792766,"CreationDate":"2017-01-19T18:07:00.000","Title":"Finding cube root of a number less than 1 using binary search","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using matrix = np.array(docTermMatrix) to make DTM. But sometimes it will run into memory error problems at this line. How can I prevent this from happening?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":84,"Q_Id":41749448,"Users Score":0,"Answer":"I assume you are using 32bit python.  32bit python limits your program ram memory to 2 gb (all 32bit programs have this as a hard limit), some of this is taken up by python overhead, more of this is taken up by your program. normal python objects do not need contiguous memory and will map disparate regions of memory\nnumpy.arrays require contiguous memory allocation, this is much harder to allocate. aditionally np.array(a) + 1  creates a 2nd array and must allocate again a huge contiguous block (in fact most operations).\nsome possible solutions that come to mind\n\nuse 64 bit python ... this will give you orders of magnitude more ram to work with ... you will be unlikely to encounter a memory error with this unless you have a really really really big array (so much so that numpy is probably not the right solution)\nuse multiprocessing to create a new process with a new 2gb limit that just does the numpy processing stuff\nuse a different solution than numpy( ie a database)","Q_Score":0,"Tags":"python","A_Id":41749570,"CreationDate":"2017-01-19T18:47:00.000","Title":"Memory error with np array when making document term matrix in python 2.7","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"So recently I have found about a NEAT algorithm and wanted to give it a try using NEAT-Python(not sure if this is even the correct source :| ). So I created my virtual environment activated it and installed the neat-python using pip in the VE. When I then tried to run one of the examples from their GitHub page it threw an error like this: \n\nImportError: No module named visualize\n\nSo I checked my source files, and actually the neat-python doesn't include the visualize.py script, however it is in their GitHub repository. I then tried to add it myself by downloading just the visualize.oy script dragging it inside my VE and adding it to all the textfiles the NEAT brought with it, like the installed-filex.txt etc. However it still threw the same error. \nI'm still fairly new to VE and GitHub so please don't be too hard on me :] thanks in advance.\n-Jorge","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":8034,"Q_Id":41752291,"Users Score":6,"Answer":"I think you could simply copying the visualize.py into the same directory as the script you are running.\nIf you wanted it in your lib\/site-packages directory so you could import it with the neat module:\ncopy visualize.py into lib\/site-packages\/neat\/ and modify __init__.py to add the line import neat.visualize as visualize. Delete the __pycache__ directory. Make sure you have modules installed: Numpy, GraphViz, and Matplotlib. When you've done the above, you should be able to import neat and access neat.visualize. \nI don't recommend doing this though for several reasons:\n\nSay you wanted to update your neat module. Your visualize.py file is technically not part of the module. So it wouldn't be updated along with your neat module.\nthe visualize.py file seems to be written in the context of the examples as opposed to being for general use with the module, so contextually, it doesn't belong there.\nAt some point in the future, you might also forget that this wasn't a part of the module, but your code acts as if it was part of the API. So your code will break in some other neat installation.","Q_Score":5,"Tags":"python,importerror,iterm2,neat,virtual-environment","A_Id":41767302,"CreationDate":"2017-01-19T21:46:00.000","Title":"NEAT-Python not finding Visualize.py","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I used python scripting to do a series of complex queries from 3 different RDS's, and then exported the data into a CSV file. I am now trying to find a way to automate publishing a dashboard that uses this data into Tableau server on a weekly basis, such that when I run my python code, it will generate new data, and subsequently, the dashboard on Tableau server will be updated as well. \nI already tried several options, including using the full UNC path to the csv file as the live connection, but Tableau server had trouble reading this path. Now I'm thinking about just creating a powershell script that can be run weekly that calls the python script to create the dataset and then refreshes tableau desktop, then finally re-publishes\/overwrites the dashboard to tableau server. \nAny ideas on how to proceed with this?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1415,"Q_Id":41754825,"Users Score":0,"Answer":"Getting data from excel to Tableau Server:\n\nSetup the UNC path so it is accessible from your server.  If you do this, you can then set up an extract refresh to read in the UNC path at the frequency desired.\nCreate an extract with the Tableau SDK.\nUse the Tableau SDK to read in the CSV file and generate a file.\n\nIn our experience, #2 is not very fast.  The Tableau SDK seems very slow when generating the extract, and then the extract has to be pushed to the server. I would recommend transferring the file to a location accessible to the server.  Even a daily file copy to a shared drive on the server could be used if you're struggling with UNC paths. (Tableau does support UNC paths; you just have to be sure to use them rather than a mapped drive in your setup.)\nIt can be transferred as a file and then pushed (which may be fastest) or it can be pushed remotely.\nAs far as scheduling two steps (python and data extract refresh), I use a poor man's solution myself, where I update a csv file at one point (task scheduler or cron are some of the tools which could be used) and then setup the extract schedule at a slightly later point in time.  While it does not have the linkage of running the python script and then causing the extract refresh (surely there is a tabcmd for this), it works just fine for my purposes to put 30 minutes in between as my processes are reliable and the app is not mission critical.","Q_Score":2,"Tags":"python,powershell,scripting,server,tableau-api","A_Id":47515380,"CreationDate":"2017-01-20T01:52:00.000","Title":"Tableau: How to automate publishing dashboard to Tableau server","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"After upgrading to matplotlib 2.0 I have a hard time getting seaborn to plot a pairplot.  For example...\nsns.pairplot(df.dropna(), diag_kind='kde') returns the following error TypeError: slice indices must be integers or None or have an __index__ method.  My data doesn't have any Nans in it.  Infact, removing the kde option allows the function to run.\nAny idea what is happening?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1051,"Q_Id":41755950,"Users Score":0,"Answer":"Looks like the problem was with statsmodels (which seaborn uses to do KDE).  I reinstalled statsmodels and that cleared up the problem.","Q_Score":0,"Tags":"python,matplotlib,seaborn","A_Id":41767039,"CreationDate":"2017-01-20T04:07:00.000","Title":"Seaborn pairplot not showing KDE","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Does Fortify Supports Python, Scala, and Apache Spark? If it supports how to scan these codes using Fortify. \nWe need to have compiler to scan C++ code using Fortify. This can be done using Microsoft visual studio.\nSimilarly should we need to have some plugin to scan Python, Scala, and Spark codes?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":7798,"Q_Id":41772263,"Users Score":0,"Answer":"Fortify support python scan. Since it is not compiled, you can directly feed the code to python, it will detect the same, scan and give you the result.","Q_Score":2,"Tags":"python,scala,apache-spark,fortify","A_Id":49675309,"CreationDate":"2017-01-20T21:01:00.000","Title":"Does Fortify support Python, Scala, and Apache Spark?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Maybe a very naive question, but I am stuck in this: pandas.Series has a method sort_values and there is an option to do it \"in place\" or not. I have Googled for it a while, but I am not very clear about it. It seems that this thing is assumed to be perfectly known to everybody but me. Could anyone give me some illustrative explanation how these two options differ each other for dummies...?\nThank you for any assistance.","AnswerCount":4,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":21797,"Q_Id":41776801,"Users Score":0,"Answer":"\"inplace=True\" is more like a physical sort while \"inplace=False\" is more like logic sort. The physical sort means that the data sets saved in the computer is sorted based on some keys; and the logic sort means the data sets saved in the computer is still saved in the original (when it was input\/imported) way, and the sort is only working on the their index. A data sets have one or multiple logic index, but physical index is unique.","Q_Score":22,"Tags":"python,sorting,pandas,in-place","A_Id":60477227,"CreationDate":"2017-01-21T07:26:00.000","Title":"In-place sort_values in pandas what does it exactly mean?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Maybe a very naive question, but I am stuck in this: pandas.Series has a method sort_values and there is an option to do it \"in place\" or not. I have Googled for it a while, but I am not very clear about it. It seems that this thing is assumed to be perfectly known to everybody but me. Could anyone give me some illustrative explanation how these two options differ each other for dummies...?\nThank you for any assistance.","AnswerCount":4,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":21797,"Q_Id":41776801,"Users Score":0,"Answer":"inplace = True changes the actual list itself while sorting.\ninplace = False will return a new sorted list without changing the original.\nBy default, inplace is set to False if unspecified.","Q_Score":22,"Tags":"python,sorting,pandas,in-place","A_Id":71012398,"CreationDate":"2017-01-21T07:26:00.000","Title":"In-place sort_values in pandas what does it exactly mean?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to use theano gpu on my ubuntu, but each time after it running one time successfully, it will give me the error like this when I try to run next time. No idea why, could anyone help me ?\n\n\n\nimport theano\n      Traceback (most recent call last):\n        File \"\", line 1, in \n        File \"\/home\/sirius\/anaconda3\/lib\/python3.5\/site-packages\/theano\/init.py\", line 95, in \n          if hasattr(theano.tests, \"TheanoNoseTester\"):\n      AttributeError: module 'theano' has no attribute 'tests'","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1036,"Q_Id":41779922,"Users Score":0,"Answer":"For latest version of Theano (1.04) \nimport theano generates an error without the nose package installed \ninstall via conda or pip pip install nose \/ conda install nose","Q_Score":2,"Tags":"python-3.x,deep-learning,theano-cuda","A_Id":56411030,"CreationDate":"2017-01-21T13:25:00.000","Title":"AttributeError: module 'theano' has no attribute 'tests'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to use theano gpu on my ubuntu, but each time after it running one time successfully, it will give me the error like this when I try to run next time. No idea why, could anyone help me ?\n\n\n\nimport theano\n      Traceback (most recent call last):\n        File \"\", line 1, in \n        File \"\/home\/sirius\/anaconda3\/lib\/python3.5\/site-packages\/theano\/init.py\", line 95, in \n          if hasattr(theano.tests, \"TheanoNoseTester\"):\n      AttributeError: module 'theano' has no attribute 'tests'","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1036,"Q_Id":41779922,"Users Score":0,"Answer":"I met the same problem.I just fix it with conda install nose","Q_Score":2,"Tags":"python-3.x,deep-learning,theano-cuda","A_Id":50360800,"CreationDate":"2017-01-21T13:25:00.000","Title":"AttributeError: module 'theano' has no attribute 'tests'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to merge 2 pandas dataframes together on dates, but they currently have different date types. 1 is timestamp (imported from excel) and the other is datetime.date.\nAny advice?\nI've tried pd.to_datetime().date but this only works on a single item(e.g. df.ix[0,0]), it won't let me apply to the entire series (e.g. df['mydates']) or the dataframe.","AnswerCount":9,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":92271,"Q_Id":41783003,"Users Score":41,"Answer":"I got some help from a colleague. \nThis appears to solve the problem posted above \npd.to_datetime(df['mydates']).apply(lambda x: x.date())","Q_Score":33,"Tags":"python,date,datetime,pandas","A_Id":41796793,"CreationDate":"2017-01-21T18:36:00.000","Title":"How do I convert timestamp to datetime.date in pandas dataframe?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm doing my first tensorflow project.\nI need to get ctc probability (not ctc loss) for given input and my expected sequences.\nIs there any api or ways to do it in python or c++?\nI prefer python side, but c++ side is also okay.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":495,"Q_Id":41788924,"Users Score":1,"Answer":"according to Graves paper [1], the loss for a batch is defined as sum(log(p(z|x))) over all samples (x,z) in this batch.\nIf you use a batch size of 1, you get log(p(z|x)), that is the log-probability of seeing the labelling z given the input x. This can be achieved with the ctc_loss function from TensorFlow.\nYou can also implement the relevant parts of the Forward-Backward Algorithm described in Section 4.1 of the paper [1] yourself.\nFor small input sequences it is feasible to use a naive implementation by constructing the paths shown in Figure 3 and then sum over all that paths in the RNN output.\nI did this for a sequence of length 16 and for a sequence of length 100. For the former one the naive approach was sufficient while for the latter one the presented dynamic programming approach was needed.\n[1] Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks","Q_Score":1,"Tags":"python,c++,tensorflow","A_Id":47182528,"CreationDate":"2017-01-22T08:39:00.000","Title":"How to calculate ctc probability for given input and expected output?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to clearly understand for which type of data transformation the following functions in pandas should be used:\n\nreplace\nmap\ntransform\n\nCan anybody provide some clear examples so I can better understand them?\nMany thanks :)","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2423,"Q_Id":41790392,"Users Score":1,"Answer":"As far as I understand, Replace is used when working on missing values and transform is used while doing group_by operations.Map is used to change series or index","Q_Score":5,"Tags":"python,pandas","A_Id":41855351,"CreationDate":"2017-01-22T11:37:00.000","Title":"Python (Pandas) : When to use replace vs. map vs. transform?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there some way to add a global attribute to a netCDF file using xarray? When I do something like hndl_nc['global_attribute'] = 25, it just adds a new variable.","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":6426,"Q_Id":41794956,"Users Score":9,"Answer":"In Xarray, directly indexing a Dataset like hndl_nc['variable_name'] pulls out a DataArray object. To get or set attributes, index .attrs like hndl_nc.attrs['global_attribute'] or hndl_nc.attrs['global_attribute'] = 25.\nYou can access both variables and attributes using Python's attribute syntax like hndl_nc.variable_or_attribute_name, but this is a convenience feature that only works when the variable or attribute name does not conflict with a preexisting method or property, and cannot be used for setting.","Q_Score":5,"Tags":"python,netcdf,python-xarray","A_Id":41795121,"CreationDate":"2017-01-22T19:00:00.000","Title":"Adding global attribute using xarray","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there some way to add a global attribute to a netCDF file using xarray? When I do something like hndl_nc['global_attribute'] = 25, it just adds a new variable.","AnswerCount":2,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":6426,"Q_Id":41794956,"Users Score":6,"Answer":"I would add here that both Datasets and DataArrays can have attributes, both called with .attrs\ne.g.\nds.attrs['global attr'] = 25\nds.variable_2.attrs['variable attr'] = 10\nds.variable_2.attrs['variable attr'] = 10","Q_Score":5,"Tags":"python,netcdf,python-xarray","A_Id":46549251,"CreationDate":"2017-01-22T19:00:00.000","Title":"Adding global attribute using xarray","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a data frame which contains dates as index and a value column storing growth percentage between consecutive dates (i.e. dates in the index). Suppose I want to compute 'real' values by setting a 100 basis at the first date of the index and then iteratively applying the % of growth. It is easy with the cumprod method.\nNow, I want to set as 100 basis the laste date in the index. I thus need to compute for each date in the index the 'inverse' growth. Is there an easy way (and pythonic) to do this with pandas?\nRegards,\nAllia","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":2303,"Q_Id":41797071,"Users Score":1,"Answer":"Just in case anyone else ends up here, let me provide a more generic answer.\nSuppose your DataFrame column, Series, vector, whatever, X has n values. At an arbitrary position i you'd like to get \n(X[i])*(X[i+1])*...*(X[n]), \nwhich is equivalent to \n(X[1])*(X[2])*...*(X[n]) \/ (X[1])*(X[2])*...*(X[i-1]). \nTherefore, you may just do\ninverse_cumprod = (np.prod(X) \/ np.cumprod(X)) * X","Q_Score":4,"Tags":"python,pandas","A_Id":50193390,"CreationDate":"2017-01-22T22:54:00.000","Title":"'Inverse' cumprod in pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In Tensorflow, it seems that preprocessing could be done on either during training time, when the batch is created from raw images (or data), or when the images are already static. Given that theoretically, the preprocessing should take roughly equal time (if they are done using the same hardware), is there any practical disadvantage in doing data preprocessing (or even data augmentation) before training than during training in real-time?\nAs a side question, could data augmentation even be done in Tensorflow if was not done during training?","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":2142,"Q_Id":41806128,"Users Score":2,"Answer":"I have been wondering the same thing and have been disappointed with my during-training-time image processing performance. It has taken me a while to appreciate quite how big an overhead the image manipulation can be.\nI am going to make myself a nice fat juicy preprocessed\/augmented data file. Run it overnight and then come in the next day and be twice as productive!\nI am using a single GPU machine and it seems obvious to me that piece-by-piece model building is the way to go. However, the workflow-maths may look different if you have different hardware. For example, on my Macbook-Pro tensorflow was slow (on CPU) and image processing was blinding fast because it was automatically done on the laptop's GPU. Now I have moved to a proper GPU machine, tensorflow is running 20x faster and the image processing is the bottleneck.\nJust work out how long your augmentation\/preprocessing is going to take, work out how often you are going to reuse it and then do the maths.","Q_Score":4,"Tags":"python,machine-learning,tensorflow,computer-vision,deep-learning","A_Id":42111038,"CreationDate":"2017-01-23T12:14:00.000","Title":"Tensorflow: Is preprocessing on TFRecord files faster than real-time data preprocessing?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to speed up some algorithms working on NumPy arrays. They will use std::vector and some of the more advanced STL data structures.\nI've narrowed my choices down to Cython (which now wraps most STL containers) and Boost.Python (which now has built-in support for NumPy). \nI know from my experience as a programmer that sometimes it takes months of working with a framework to uncover its hidden issues (because they are rarely used as talking points by its disciples), so your help could potentially save me a lot of time.\nWhat are the relative advantages and disadvantages of extending NumPy in Cython vs Boost.Python?","AnswerCount":2,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":2636,"Q_Id":41813799,"Users Score":4,"Answer":"For small one shot problems, I tend to prefer cython, for larger integration with c++ code bases, prefer boost Python.\nIn part, it depends on the audience for your code.  If you're working with a team with significant experience in python, but little experience of using C++, Cython makes sense. If you have a fixed code base with complex types to inter operate with, the boost python can end up being a little cheaper to get running.\nCython encourages you to write incrementally, gradually adding types as required to get extra performance and solves many of the hard packaging problems. boost Python requires a substantial effort in getting a build setup, and it can be hard to produce packages that make sense on PyPI\nCython has good built in error messages\/diagnostics, but from what I've seen, the errors that come out of boost can be very hard to interpret - be kind to yourself and use a new-ish c++ compiler, preferably one known for producing readable error messages.\nDon't discount alternative tools like numba (similar performance to cython with code that is Python, not just something that looks similar) and pybind11 (boost Python without boost and with better error messages)","Q_Score":7,"Tags":"numpy,cython,boost-python","A_Id":41815502,"CreationDate":"2017-01-23T19:07:00.000","Title":"What are the relative advantages of extending NumPy in Cython vs Boost.Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am facing the following error on configuring Appium python test in AWS device farm:\n\nThere was a problem processing your file. We found at least one wheel file wheelhouse\/numpy-1.12.0-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl specified a platform that we do not support. Please unzip your test package and then open the wheelhouse directory, verify that names of wheel files end with -any.whl or -linux_x86_64.whl, and try again\n\nI require numpy and opencv-python packages to run my tests.\nHow to get this issue fixed?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":290,"Q_Id":41818382,"Users Score":0,"Answer":"(numpy-1.12.0-cp27-cp27m-manylinux1_x86_64.whl) is numpy wheel for ubuntu.\n But still Amazon device farm throws error while configuring tests with this wheel.\nBasically, Device farm is validating if the .whl file has prefix  -none-any.whl\nJust renaming the file to numpy-1.12.0-cp27-none-any.whl works in device farm.\nNote: This renamed file is non-universal python wheel. There might be few things which are not implemented in non-universal python wheel. This may cause somethings to break. So, test to ensure all your dependencies are working fine before using this.","Q_Score":0,"Tags":"python-2.7,opencv,numpy,aws-device-farm,python-appium","A_Id":42273559,"CreationDate":"2017-01-24T01:05:00.000","Title":"Amazon device farm - wheel file from macosx platform not supported","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I recently started experimenting with pymc and only just realised that the images being produced by pymc.Matplot.plot, which I use to diagnose whether the MCMC has performed well, are being saved to disk. This results in images appearing wherever I am running my scripts from, and it is time consuming to clear them up. Is there a way to stop figures being saved to disk? I can't see anything clearly in the documentation.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":60,"Q_Id":41831529,"Users Score":0,"Answer":"There is currently no way to plot them without being saved to disk. I would recommend only plotting a few diagnostic parameters, and specifying plot=False for the others. That would at least cut down on the volume of plots being generated. There probably should be a saveplot argument, however, I agree.","Q_Score":0,"Tags":"python,pymc","A_Id":41834290,"CreationDate":"2017-01-24T15:06:00.000","Title":"Stop images produced by pymc.Matplot.plot being saved","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When I launch ipython -i script_name or load the script with %load, it fails loading sklearn.ensamble.\nBut it succeed in loading and I am able to use it when I launch ipython alone and then from sklearn.ensamble import *.\nWhy?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":21,"Q_Id":41834141,"Users Score":0,"Answer":"Shame on me, it was just a typo: the correct module is named sklearn.ensemble.","Q_Score":0,"Tags":"python-3.x,ipython","A_Id":41849001,"CreationDate":"2017-01-24T17:04:00.000","Title":"ipython can't load module when using magic %load, but succeed when loading interactively","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"What will happen when I use df.corr()?  Will np.inf effect my results some how?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":72,"Q_Id":41836727,"Users Score":0,"Answer":"np.inf is treated the same way np.NaN.  \nI replaced the all the values of np.inf with np.NaN and the results were exactly the same.  If there is some subtle differences, please let me know.  I was looking for an answer on this and couldn't find one anywhere so I figured I would post this here.","Q_Score":1,"Tags":"python,pandas","A_Id":41836728,"CreationDate":"2017-01-24T18:50:00.000","Title":"I have a DataFrame with some values of np.inf. How does .corr() work?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I try to run this line: \nfrom sklearn.model_selection import GridSearchCV\nbut I get an ImportError (i.e. No module named model_selection)  although I have installed sklearn and I can import other packages. here is my python version : \n2.7.12 |Anaconda 4.2.0 (64-bit)| (default, Jun 29 2016, 11:07:13) [MSC v.1500 64 bit (AMD64)]\nis there a way to use \"sklearn.model_selection\" on my current version?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":238,"Q_Id":41838726,"Users Score":0,"Answer":"I just found the answer. the 0.18 sklearn has seen a number of updates. you may update your sklearn by typing \"conda update scikit-learn\" in your windows command line. \nIf it still didn't code you might want to update your conda\/Anaconda as well: \n\"conda update conda\" and \"conda update Anaconda\"","Q_Score":0,"Tags":"python-2.7,module,scikit-learn,grid-search","A_Id":41839189,"CreationDate":"2017-01-24T20:48:00.000","Title":"can't install model_selection on python 2.7.12","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Hello Friends,\nI want to pass data between MATLAB and Python, One way would be to use matlab.engine in Python or Call Python Libraries from MATLAB. But this approach requires MATLAB 2014 Version unlike mine which is MATLAB R2011b.\nSo I request you to please guide for a different Approach in order to comunicate between Python and MATLAB R2011b Version.\nThanks in advance","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":110,"Q_Id":41846630,"Users Score":0,"Answer":"Depending on what you want to do and your type of data, you could write it to a file and read from it in the other language. You could use numpy.fromfile for that in the python part.","Q_Score":0,"Tags":"python,matlab,python-2.7,parameter-passing,language-interoperability","A_Id":41846775,"CreationDate":"2017-01-25T08:26:00.000","Title":"Pass data between MATLAB R2011b and Python (Windows 7)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to know how scikit-learn put more emphasis on a class when we use the parameter class_weight. Is it an oversampling of the minority sampling ?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":420,"Q_Id":41850349,"Users Score":0,"Answer":"I'm not sure if there is a single method of treating class_weight for all the algorithms.\nThe way Decision Trees (and Forests) deals with this is by modifying the weights of each sample according to its class.\nYou can consider weighting samples as a more general case of oversampling all the minority class samples (using weights you can \"oversample\" fractions of samples).","Q_Score":0,"Tags":"python,scikit-learn","A_Id":41851421,"CreationDate":"2017-01-25T11:22:00.000","Title":"How class_weight emphasis a class in in scikit-learn","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have two tensors, prob_a and prob_b with shape [None, 1000], and I want to compute the KL divergence from prob_a to prob_b. Is there a built-in function for this in TensorFlow?  I tried using tf.contrib.distributions.kl(prob_a, prob_b), but it gives:\n\nNotImplementedError: No KL(dist_a || dist_b) registered for dist_a type Tensor and dist_b type Tensor\n\nIf there is no built-in function, what would be a good workaround?","AnswerCount":7,"Available Count":1,"Score":0.1418931938,"is_accepted":false,"ViewCount":17205,"Q_Id":41863814,"Users Score":5,"Answer":"I'm not sure why it's not implemented, but perhaps there is a workaround. The KL divergence is defined as:\nKL(prob_a, prob_b) = Sum(prob_a * log(prob_a\/prob_b))\nThe cross entropy H, on the other hand, is defined as:\nH(prob_a, prob_b) = -Sum(prob_a * log(prob_b))\nSo, if you create a variable y = prob_a\/prob_b, you could obtain the KL divergence by calling negative H(proba_a, y). In Tensorflow notation, something like:\nKL = tf.reduce_mean(-tf.nn.softmax_cross_entropy_with_logits(prob_a, y))","Q_Score":15,"Tags":"python,statistics,tensorflow,entropy","A_Id":41864069,"CreationDate":"2017-01-25T23:51:00.000","Title":"Is there a built-in KL divergence loss function in TensorFlow?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a real time data feed of health patient data that I connect to with python. I want to run some sklearn algorithms over this data feed so that I can predict in real time if someone is going to get sick. Is there a standard way in which one connects real time data to sklearn? I have traditionally had static datasets and never an incoming stream so this is quite new to me. If anyone has sort of some general rules\/processes\/tools used that would be great.","AnswerCount":2,"Available Count":2,"Score":0.1973753202,"is_accepted":false,"ViewCount":2016,"Q_Id":41899011,"Users Score":2,"Answer":"With most algorithms training is slow and predicting is fast. Therefore it is better to train offline using training data; and then use the trained model to predict each new case in real time.\nObviously you might decide to train again later if you acquire more\/better data. However there is little benefit in retraining after every case.","Q_Score":2,"Tags":"python,machine-learning,scikit-learn,real-time","A_Id":41911859,"CreationDate":"2017-01-27T16:49:00.000","Title":"Real time data using sklearn","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a real time data feed of health patient data that I connect to with python. I want to run some sklearn algorithms over this data feed so that I can predict in real time if someone is going to get sick. Is there a standard way in which one connects real time data to sklearn? I have traditionally had static datasets and never an incoming stream so this is quite new to me. If anyone has sort of some general rules\/processes\/tools used that would be great.","AnswerCount":2,"Available Count":2,"Score":0.1973753202,"is_accepted":false,"ViewCount":2016,"Q_Id":41899011,"Users Score":2,"Answer":"It is feasible to train the model from a static dataset and predict classifications for incoming data with the model. Retraining the model with each new set of patient data not so much. Also breaks the train\/test mode of testing a ML model.\nTrained models can be saved to file and imported in the code used for real time prediction. \nIn python scikit learn, this is via the pickle package.\nR programming saves to an rda object. saveRDS\nyay... my first answering a ML question!","Q_Score":2,"Tags":"python,machine-learning,scikit-learn,real-time","A_Id":43380457,"CreationDate":"2017-01-27T16:49:00.000","Title":"Real time data using sklearn","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"There is a 0-1 matrix, I need to sample M different entries of 1 value from this matrix. Are there any efficient Python implements for this kind of requirement?\nA baseline approach is having M iterations, during each iteration, randomly sample 1, if it is of value 1, then keep it and save its position, otherwise, continue this iteration until find entry with value 1; and continue to next iteration. It seems not a good heuristic at all.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":49,"Q_Id":41899930,"Users Score":0,"Answer":"We can do it in the following manner: first get all the (x,y) tuples (indices) of the matrix A where A[x,y]=1. Let there be k such indices. Now roll a k-sided unbiased dice M times (we can simulate by using function randint(1,k) drawing sample from uniform distribution). If you want samples with replacements (same position of the matrix can be chosen multiple times) then it can be done with M invocations of the function. Otherwise for samples with replacements (without repetitions allowed) you need to keep track of positions already selected and delete those indices from the array before throwing the dive next time.","Q_Score":1,"Tags":"python,algorithm","A_Id":41908820,"CreationDate":"2017-01-27T17:41:00.000","Title":"sample entries from a matrix while satisfying a given requirement","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Found on S.O. the following solution to plot multiple data frames:\n\nax = df1.plot()\ndf2.plot(ax=ax)\n\nBut what if I only want to plot where they overlap?\nSay that df1 index are timestamps that spans 24 hour and df2 index also are timestamps  that spans 12 hours within the 24 hours of df1 (but not exactly the same as df1).\nIf I only want to plot the 12 hours that both data frames covers. What's the easies way to do this?","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":3052,"Q_Id":41946758,"Users Score":1,"Answer":"To plot only the portion of df1 whose index lies within the index range of df2, you could do something like this:\nax = df1.loc[df2.index.min():df2.index.max()].plot()\nThere may be other ways to do it, but that's the one that occurs to me first.\nGood luck!","Q_Score":0,"Tags":"python,pandas,matplotlib,ipython,jupyter-notebook","A_Id":41947311,"CreationDate":"2017-01-30T22:45:00.000","Title":"Pandas plot ONLY overlap between multiple data frames","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a Series with Name as the index and a number in scientific notation such as 3.176154e+08. How can I convert this number to 317,615,384.61538464 with a thousands separator? I tried:\nformat(s, ',')\nBut it returns TypeError: non-empty format string passed to object.format\nThere are no NaNs in the data. \nThanks for your help!","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":6217,"Q_Id":41951160,"Users Score":0,"Answer":"You can also use\nSeriesName.map('{:,}'.format)","Q_Score":1,"Tags":"python,python-3.x,pandas","A_Id":63090168,"CreationDate":"2017-01-31T06:38:00.000","Title":"Converting scientific notation in Series to commas and thousands separator","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to design a neural network using Keras with priority on prediction performance, and I cannot get sufficiently high accuracy by further reducing the number of layers and nodes per layer. I have noticed that very large portion of my weights are effectively zero (>95%). Is there a way to prune dense layers in hope of reducing prediction time?","AnswerCount":4,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":9448,"Q_Id":41958566,"Users Score":0,"Answer":"If you set an individual weight to zero won't that prevent it from being updated  during back propagation? Shouldn't thatv weight remain zero from one epoch to the next? That's why you set the initial weights to nonzero values before training. If you want to \"remove\" an entire node, just set all of the weights on that node's output to zero and that will prevent that nodes from having any affect on the output throughout training.","Q_Score":21,"Tags":"python-3.x,neural-network,keras,pruning","A_Id":56069261,"CreationDate":"2017-01-31T13:14:00.000","Title":"Pruning in Keras","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am plotting both wedges and triangles on the same figure.  The wedges scale up as I zoom in (I like this), but the triangles do not (I wish they did), presumably because wedges are sized in data units (via radius property) and traingles are in screen units (via size property).\nIs it possible to switch the triangles to data units, so everything scales up during zoom in?\nI am using bokeh version 0.12.4 and python 3.5.2 (both installed via Anaconda).","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":49,"Q_Id":41961680,"Users Score":2,"Answer":"Markers (e.g. Triangle) are really meant for use as \"scatter\" plot markers. With the exception of Circle, they only accept screen dimensions (pixles) for size. If you need triangular regions that scale with data space range changes, your options are to use patch or patches to draw the triangles as polygons (either one at a time, or \"vectorized\", respectively)","Q_Score":1,"Tags":"python-3.x,bokeh","A_Id":41967371,"CreationDate":"2017-01-31T15:48:00.000","Title":"scaling glyphs in data units (not screen units)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using the Dask distributed scheduler, running a scheduler and 5 workers locally.  I submit a list of delayed() tasks to compute().\nWhen the number of tasks is say 20 (a number >> than the number of workers) and each task takes say at least 15 secs, the scheduler starts rerunning some of the tasks (or executes them in parallel more than once).\nThis is a problem since the tasks modify a SQL db and if they run again they end up raising an Exception (due to DB uniqueness constraints). I'm not setting pure=True anywhere (and I believe the default is False). Other than that, the Dask graph is trivial (no dependencies between the tasks).\nStill not sure if this is a feature or a bug in Dask. I have a gut feeling that this might be related to worker stealing...","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":864,"Q_Id":41965253,"Users Score":3,"Answer":"Correct, if a task is allocated to one worker and another worker becomes free it may choose to steal excess tasks from its peers.  There is a chance that it will steal a task that has just started to run, in which case the task will run twice.\nThe clean way to handle this problem is to ensure that your tasks are idempotent, that they return the same result even if run twice.  This might mean handling your database error within your task.\nThis is one of those policies that are great for data intensive computing workloads but terrible for data engineering workloads.  It's tricky to design a system that satisfies both needs simultaneously.","Q_Score":5,"Tags":"python,dask","A_Id":41965766,"CreationDate":"2017-01-31T18:48:00.000","Title":"Repeated task execution using the distributed Dask scheduler","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I'm a researcher studying animal behavior, and I'm trying to figure out the best way to structure my data. I present short musical tunes to animals and record their responses.\nThe Data\nEach tune consists of 1-10 notes randomly chosen from major + minor scales spanning several octaves. Each note is played for a fixed duration but played randomly within some short time window.\nI then record the animal's binary response to the tune (like \/ dislike).\nI play >500 tunes to the animal each day, for >300 days. I also combine data from >10 animals.\nI also need to store variables such as trial number on each day (was it the 1st tune presented? last? etc.), and date so that I know what data points to exclude due to external issues (e.g. animal stopped responding after 100 trials or for the entire day).\nThe Analysis\nI'm trying to uncover what sorts of musical structure in these randomly generated tunes will lead to likes\/dislikes from the animal. I do this in a mostly hypothesis-driven manner, based on previous research. The queries I need to perform on my dataset are of the form: \"does having more notes from the same octave increase likeability of the tune?\"\nI'm also performing analysis on the dataset throughout the year as data is being accumulated.\nWhat I've tried\nI combine data from all animals into a single gigantic list containing dicts. Each dict represents a single trial and its associated:\n\nanimal ID#\nsession ID#\ntrial ID#\nbinary response (like\/dislike)\ntune, which is defined by a dict. The keys are simply the notes played, and the values denote when the note is played. E.g. {'1A#':[30,100]} means a tune with just a single note, A# from 1st octave, played from 30ms to 100ms.\n\nI save this to a single pickle file. Every day after all the animals are done, I update the pickle file. I run my data analysis roughly once per week by loading the updated pickle file.\nI've been looking to re-structure my data into a database or Pandas DataFrame format because of speed of 1) serializing data and 2) querying, and 3) possible cleaner code instead of dealing with nested dicts. I initially thought that my data would naturally lend itself well to some table structure because of the trial-by-trial structure of my experiment. Unfortunately, the definition of tunes within the table seems tricky, as the tunes don't really have some fixed structure.\nWhat would be possible alternatives in structuring my data?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":65,"Q_Id":41967226,"Users Score":0,"Answer":"I think that the hard part of the problem is that you'll probably want the stimulus (tune) data formatted differently for different queries. What I would think about doing is making a relatively simple data structure for your stimuli (tunes) and add a unique identifier to each unique tune. You could probably get away with using your dictionary structures here if your structure can fit into memory.\nThen I would put your trials into a relational database with the corresponding stimulus IDs. Each trial entry in the database would have complete subject and session information. \nYour for each analysis permutation you will do two steps to get the relevant data: \n\nFilter the stimuli using the stimulus data structure and get a list of their corresponding IDs. \nPreform a query on your trials database to get the trials with this list IDs. You can add other parameters to your query, obviously, to filter based on subject, session, etc.\n\nI hope that helps","Q_Score":0,"Tags":"python,data-modeling","A_Id":41968970,"CreationDate":"2017-01-31T20:49:00.000","Title":"Database design for complex music analytics","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataframe containing 13 columns. Among 13 three columns are string. One string column is simple male and female which I converted to 1 and 0 using\n\npd.get_dummies()\n\n2nd column contains three different types of string so, easily converted to array using \n\nfrom sklearn.feature_extraction.text import CountVectorizer\n\nNo issue at all. Problem is my third and last column contains large number of names. if I try to convert using Countvectorizer it converts the names into long unreadable strings. \n\ndf['name']=Countvectorizer.fit_transform(df.name)\n\nif I try to convert back it to dataframe as shown in other examples on stackoverflow page in this case I get this\n\n245376      (0, 14297)\\t1\\n  (1, 5843)\\t1\\n  (1, 13365)...\n  245377      (0, 14297)\\t1\\n  (1, 5843)\\t1\\n  (1, 13365)...\n\nName: supplier_name, dtype: object\nand this next code results Memory Error\n\ndf['name'] =pd.DataFrame(CV.fit_transform(df.name).toarray(),columns=CV.get_feature_names())\n\nI have looked that issue as well. \nQuestion: is there any way best to use this name column in numeric forms except above mentioned. Or any other idea how to improve this so that data perfectly fit in Randomforest classifier. As, Dataframe is quit large containing 123790 rows. Thank you in advance for help or suggestion.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":498,"Q_Id":41970230,"Users Score":0,"Answer":"Well name is a unique thing and an id kind of use sklearn.preprocessing.LabelEncoder after storing the original to a separate list. It will automatically convert the names to a serial number. \nAlso, note if it's a unique thing you should remove names during predicting.","Q_Score":1,"Tags":"python,scikit-learn,random-forest,countvectorizer","A_Id":47134942,"CreationDate":"2017-02-01T01:01:00.000","Title":"How to deal with name column in Scikitlearn randomforest classifier. python 3","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to make a system that recognizes a single object using keras. In my case I will be detecting car wheels. How do I train my system just for 1 object? I did classification task before using cats and dogs, but now its a completely different task. Do I still \"classify\", with class 0= wheels, class = 1 non wheels(just random images of anything)? How do I do following steps in this problem?\n1) Train system for 1 object\n2) Detect object(sliding window or heatmap)","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":907,"Q_Id":42007591,"Users Score":1,"Answer":"Your task is a so-called binary classification. \nMake sure, that your final layer has got only one neuron (e.g. for Sequential model model.add(Dense(1, ... other parameters ... ))) and use the binary_crossentropy as loss function.\nHope this helps.","Q_Score":1,"Tags":"python,keras,object-detection,training-data","A_Id":42181934,"CreationDate":"2017-02-02T16:38:00.000","Title":"Single object detection keras","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have been at it for some time and tried everything. \nI need to find out whether the package GraphFrames is included in the spark installation at my office cluster. I am using Spark version 1.5.0. \nIs there a way to list all the installed packages in Spark?","AnswerCount":1,"Available Count":1,"Score":-0.1973753202,"is_accepted":false,"ViewCount":1593,"Q_Id":42026072,"Users Score":-1,"Answer":"include the package anyway to be sure eg via spak submit:\n  $SPARK_HOME\/bin\/spark-shell --packages graphframes:graphframes:0.1.0-spark1.6","Q_Score":1,"Tags":"python,apache-spark,pyspark","A_Id":42027081,"CreationDate":"2017-02-03T14:04:00.000","Title":"Finding out installed packages in Spark","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using a networkx weighted graph in order to model a transportation network. I am attempting to find the shortest path in terms of the sum of weighted edges. I have used Dijkstra path in order to find this path. My problem occurs when there is a tie in terms of weighted edges. When this occurs I would always like to choose from the set of paths that tied, the path that has the least number of edges. Dijkstra path does not seem to be doing this.\nIs there a way to ensure that I can choose the path with the least number of edges from a set of paths that are tied in terms of sum of weighted edges?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":711,"Q_Id":42029159,"Users Score":0,"Answer":"Instead of using floating points for weights, use tuples (weight, number_of_edges) with pairwise addition.  The lowest weight path using these new weights will have the lowest weight, and in the case of a tie, be the shortest path.\nTo define these weights I would make them a subclass of tuple with __add__ redefined.  Then you should be able to use your existing code.","Q_Score":1,"Tags":"python,algorithm,graph","A_Id":42029561,"CreationDate":"2017-02-03T16:51:00.000","Title":"Python: Shortest Weighted Path and Least Number of Edges","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":1,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Why doesn\u2019t the array class have a .sort()? I don't know how to sort an array directly.\nThe class array.array is a packed list which looks like a C array.\nI want to use it because only numbers are needed in my case, but I need to be able to sort it. Is there some way to do that efficiently?","AnswerCount":2,"Available Count":1,"Score":-1.0,"is_accepted":false,"ViewCount":144,"Q_Id":42040813,"Users Score":-7,"Answer":"A list is a data structure that has characteristics which make it easy to do some things. An array is a very well understood standard data structure and isn't optimized for sorting. An array is basically a standard way of storing the product of sets of data. There hasn't ever been a notion of sorting it.","Q_Score":1,"Tags":"python,arrays,python-3.x,sorting","A_Id":42040862,"CreationDate":"2017-02-04T13:15:00.000","Title":"Why doesn\u2019t 'array' have an in-place sort like list does?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"How to compute irfft if I have only amplitude and phase spectrum of signal? In numpy docs I've found only irfft which use fourier coefficients for this transformation.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":411,"Q_Id":42041151,"Users Score":1,"Answer":"If you have amplitude and phase vectors for a spectrum, you can convert them to a complex (IQ or Re,Im) vector by multiplying the cosine and sine of each phase value by its associated amplitude value (for each FFT bin with a non-zero amplitude, or vector-wise).","Q_Score":0,"Tags":"python,numpy,fft,ifft","A_Id":42046236,"CreationDate":"2017-02-04T13:50:00.000","Title":"numpy irfft by amplitude and phase spectrum","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to define ln2(x\/y) in Python, within NumPy.\nI can define ln(x) as np.log(x) but how I can define ln2(x\/y)?\nln2(x\/y); natural logarithm to the power of 2","AnswerCount":3,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":235,"Q_Id":42046184,"Users Score":3,"Answer":"You can use ** for exponentiation: np.log(x\/y) ** 2","Q_Score":0,"Tags":"python,numpy,logarithm","A_Id":42046234,"CreationDate":"2017-02-04T22:07:00.000","Title":"Define logarithmic power for NumPy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a data set of 1M+ observations of customer interactions with a call center. The text is free text written by the representative taking the call. The text is not well formatted nor is it close to being grammatically correct (a lot of short hand). None of the free text has a label on the data as I do not know what labels to provide. \nGiven the size of the data, would a random sample of the data  (to give a high level of confidence) be reasonable first step in determining what labels to create? Is it possible not to have to manually label 400+ random observations from the data, or is there no other method to pre-process the data in order to determine the a good set of labels to use for classification?\nAppreciate any help on the issue.","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":501,"Q_Id":42048725,"Users Score":1,"Answer":"Manual annotation is a good option since you have a very good idea of an ideal document corresponding to your label.\nHowever, with the large dataset size, I would recommend that you fit an LDA to the documents and look at the topics generated, this will give you a good idea of labels that you can use for text classification.\nYou can also use LDA for text classification eventually by finding out representative documents for your labels and then finding the closest documents to that document by a similarity metric(say cosine).\nAlternatively, once you have an idea of labels, you can also assign them without any manual intervention using LDA, but then you will get restricted to unsupervised learning.\nHope this helps!\nP.S. - Be sure to remove all the stopwords and use a stemmer to club together words of similar king example(managing,manage,management) at the pre-processing stage.","Q_Score":0,"Tags":"python,r,nlp,preprocessor,text-classification","A_Id":42048793,"CreationDate":"2017-02-05T05:01:00.000","Title":"Text Classification - Label Pre Process","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a data set of 1M+ observations of customer interactions with a call center. The text is free text written by the representative taking the call. The text is not well formatted nor is it close to being grammatically correct (a lot of short hand). None of the free text has a label on the data as I do not know what labels to provide. \nGiven the size of the data, would a random sample of the data  (to give a high level of confidence) be reasonable first step in determining what labels to create? Is it possible not to have to manually label 400+ random observations from the data, or is there no other method to pre-process the data in order to determine the a good set of labels to use for classification?\nAppreciate any help on the issue.","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":501,"Q_Id":42048725,"Users Score":1,"Answer":"Text Pre-Processing: \nConvert all text to lower case, tokenize into unigrams, remove all stop words, use stemmer to normalize a token to it's base word.\nThere are 2 approaches I can think of for classifying the documents a.k.a. the free text you spoke about. Each free text is a document:\n1) Supervised classification Take some time and randomly pick few samples of documents and assign them a category. Do this until you have multiple documents per category and all categories that you want to predict are covered. \nNext, create a Tf-Idf matrix from this text. Select the top K features (tune value of K to get best results). Alternatively, you can use SVD to reduce the number of features by combining correlated features into one. Please bare in mind that you can use other features like the department of the customer service executive and many others also as predictors. Now train a machine learning model and test it out. \n2) Unsupervised learning: If you know how many categories you have in your output variable, you can use that number as the number of clusters you want to create. Use the Tf-Idf vector from above technique and create k clusters. Randomly pick a few documents from each cluster and decide which category the documents belong to. Supposing you picked 5 documents and noticed that they belong to the category \"Wanting Refund\". Label all documents in this cluster to \"Wanting Refund\". Do this for all the remaining clusters. \nThe advantage of unsupervised learning is that it saves you the pain of pre-classification and data preparation, but beware of unsupervised learning. The accuracy might not be as good as supervised learning.\nThe 2 method explained are an abstract overview of what can be done. Now that you have an idea, read up more on the topics and use a tool like rapidminer to achieve your task much faster.","Q_Score":0,"Tags":"python,r,nlp,preprocessor,text-classification","A_Id":42063332,"CreationDate":"2017-02-05T05:01:00.000","Title":"Text Classification - Label Pre Process","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to find the accuracy of one class in MMNIST dataset .So how can i split it on the basis of classes?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":567,"Q_Id":42057667,"Users Score":1,"Answer":"Not sure exactly what you are asking. I will answer about what I understood. In case you want to predict only one class for example digit 5 and rest of the digits. Then first you need to label your vectors in such a way that maybe label all those vectors as 'one' which has ground truth 5 and 'zero' to those vectors whose ground truth is not 5. \nThen design your network with only two nodes in output, where first node will show the probability that the input vector belongs to class 'one' (or digit 5) and second node will show the probability of belonging to class 'zero'. Then just train your network. \nTo find accuracy, you can simple techniques like just count how many times you predict right i.e. if probability is higher than 0.5 for right class classify it as that class. \nI hope that helps, if not maybe it would be better if you could explain your question more precisely.","Q_Score":1,"Tags":"python,tensorflow","A_Id":42057766,"CreationDate":"2017-02-05T21:42:00.000","Title":"How can we use MNIST dataset one class as an input using tensorflow?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In case you want to predict only one class. Then first you need to label your vectors in such a way that maybe label all those vectors as 'one' which has ground truth 5 and 'zero' to those vectors whose ground truth is not 5. \nhow can I implement this in tensorflow using puthon","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":164,"Q_Id":42059103,"Users Score":0,"Answer":"while preparing the data you can use numpy to set all the data points in class 5 as 1 and the others will be set to as 0 using .\narr = np.where(arr!=5,arr,0)\narr = np.where(arr=5,arr,1) \nand then you can create a binary classifier using Tensorflow to classifiy them while using a binary_crossentropy loss to optimize the classifier","Q_Score":2,"Tags":"python,tensorflow,one-hot-encoding","A_Id":59105698,"CreationDate":"2017-02-06T01:03:00.000","Title":"how to predict only one class in tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am a little bit confused in the classification of dependent and independent variables in my dataset, on which I need to make a model for prediction. Any insights or how-to's would be very helpful here.\nSuppose my dataset have 40 variables. In this case, it would be very difficult to classify the variables as independent or dependent. Are there any tests in python which can help us in identifying these?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2782,"Q_Id":42061730,"Users Score":1,"Answer":"In any given data set, labeling variables as dependent or independent is arbitrary -- there is no fundamental reason that one column should be independent and another should be dependent.\nThat said, typically it's conventional to say that \"causes\" are independent variables and \"effects\" are dependent variables. But this business about causes and effects is arbitrary too -- often enough there are several interacting variables, with each of the them \"causing\" the others, and each of them \"affected\" by the others.\nThe bottom line is that you should assign dependent and independent according to what you're trying to achieve. What is the most interesting or most useful variable in your data? Typically if that one is missing or has an unknown value, you'll have to estimate it from the other variables. In that case the interesting variable is the dependent variable, and all others are independent.\nYou'll probably get more interest in this question on stats.stackexchange.com.","Q_Score":0,"Tags":"python,statistics","A_Id":42061940,"CreationDate":"2017-02-06T06:39:00.000","Title":"Statistics: How to identify dependent and independent variables in my dataset?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am studying a chess database with more than one million games. I am interested in identifying some characteristics of different players. The problem I have is that each single player appears with several identifications. \nFor example,\n\"Carlsen, M.\", \"Carlsen, Ma\", \"Carlsen, Magnus\" and \"Magnus Carlsen\" \nall correspond to player \"Magnus Carlsen\".\nFurthermore, there are other players which share Carlsen's last name, but have different names, such as \"Carlsen, Ingrid Oen\" and \"Carlsen, Jesper\".\nI need to identify all the different names in the database which correspond to each specific player and combine them. Is there any way to do that with Python?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":78,"Q_Id":42070138,"Users Score":0,"Answer":"@Ev.Kounis solution is simple and effective, I've used it myself successfully. Most of the time, we only care the top chess players. That's what I did:\n\nCreated a simple function like @Ev.Jounis suggests\nI also scanned the player rating. For example, there were several \"Carlsen\" players in my database, but they wouldn't have FIDE rating over 2700.\nI also search the other player in the game. If I'm interested in Garry Kasparov, he wouldn't be playing a club game with a 1600 rated opponent.\nGet a better database. Chessgames and TWIC have better quality than Chessbase.\n\nYou could try regular expression, but it's unnecessary. There's a simple pattern how a player name would differ:\n\n\"Carlsen, M\" == \"Magnus Carlsen\"\n\nThis applies to other players in the database. Save regular expression until you really have to do it.","Q_Score":1,"Tags":"python,regex,database,chess","A_Id":42104038,"CreationDate":"2017-02-06T14:28:00.000","Title":"Combining different names in a database","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have been working on designing REST api using springframework and deploying them on web servers like Tomcat. I have also worked on building Machine Learning model and use the model to make prediction using sklearn in Python.\nNow I have a use case where in I want to expose a REST api which builds Machine Learning Model, and another REST api which makes the prediction. What architecture should help me to achieve the same. (An example of the same maybe a Amazon Machine Learning. They have exposed REST api for generating model and making prediction)\nI searched round the internet and found following ways:\n\nWrite the whole thing in Java - ML model + REST api\nWrite the whole thing in Python - ML model + REST api\n\nBut playing around with Machine Learning, its models and predictions is really easier and more supported in python with libraries like sklearn, rather than Java. I would really like to use python for Machine Learning part.\nI was thinking about and approach wherein I write REST api using JAVA but use sub-process to make python ML calls. Will that work?\nCan someone help me regarding the probable architectural approaches that I can take. Also please suggest the most feasible solution.\nThanks in advance.","AnswerCount":6,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":5677,"Q_Id":42080598,"Users Score":0,"Answer":"I have been experimenting with this same task and would like to add another option, not using a REST API: The format of the Apache Spark models is compatible in both the Python and Jave implementations of the framework. So, you could train and build your model in Python (using PySpark), export, and import on the Java side for serving\/predictions. This works well.\nThere are, however, some downsides to this approach:\n\nSpark has two separate ML packages (ML and MLLib) for different data formats (RDD and dataframes)\nThe algorithms for training models in each of these packages are not the same (no model parity)\nThe models and training classes don't have uniform interfaces. So, you have to be aware of what the expected format is and might have to transform your data accordingly for both training and inference.\nPre-processing for both training and inference has to be the same, so you either need to do this on the Python side for both stages or somehow replicate the pre-processing on the Java side.\n\nSo, if you don't mind the downsides of a Rest API solution (availability, network latency), then this might be the preferable solution.","Q_Score":5,"Tags":"java,python,rest,machine-learning,scikit-learn","A_Id":69476803,"CreationDate":"2017-02-07T02:19:00.000","Title":"What is the best way to build and expose a Machine Learning model REST api?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I have been working on designing REST api using springframework and deploying them on web servers like Tomcat. I have also worked on building Machine Learning model and use the model to make prediction using sklearn in Python.\nNow I have a use case where in I want to expose a REST api which builds Machine Learning Model, and another REST api which makes the prediction. What architecture should help me to achieve the same. (An example of the same maybe a Amazon Machine Learning. They have exposed REST api for generating model and making prediction)\nI searched round the internet and found following ways:\n\nWrite the whole thing in Java - ML model + REST api\nWrite the whole thing in Python - ML model + REST api\n\nBut playing around with Machine Learning, its models and predictions is really easier and more supported in python with libraries like sklearn, rather than Java. I would really like to use python for Machine Learning part.\nI was thinking about and approach wherein I write REST api using JAVA but use sub-process to make python ML calls. Will that work?\nCan someone help me regarding the probable architectural approaches that I can take. Also please suggest the most feasible solution.\nThanks in advance.","AnswerCount":6,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":5677,"Q_Id":42080598,"Users Score":0,"Answer":"I'm using Node.js as my rest service and I just call out to the system to interact with my python that holds the stored model.  You could always do that if you are more comfortable writing your services in JAVA, just make a call to Runtime exec or use ProcessBuilder to call the python script and get the reply back.","Q_Score":5,"Tags":"java,python,rest,machine-learning,scikit-learn","A_Id":46918647,"CreationDate":"2017-02-07T02:19:00.000","Title":"What is the best way to build and expose a Machine Learning model REST api?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I have been working on designing REST api using springframework and deploying them on web servers like Tomcat. I have also worked on building Machine Learning model and use the model to make prediction using sklearn in Python.\nNow I have a use case where in I want to expose a REST api which builds Machine Learning Model, and another REST api which makes the prediction. What architecture should help me to achieve the same. (An example of the same maybe a Amazon Machine Learning. They have exposed REST api for generating model and making prediction)\nI searched round the internet and found following ways:\n\nWrite the whole thing in Java - ML model + REST api\nWrite the whole thing in Python - ML model + REST api\n\nBut playing around with Machine Learning, its models and predictions is really easier and more supported in python with libraries like sklearn, rather than Java. I would really like to use python for Machine Learning part.\nI was thinking about and approach wherein I write REST api using JAVA but use sub-process to make python ML calls. Will that work?\nCan someone help me regarding the probable architectural approaches that I can take. Also please suggest the most feasible solution.\nThanks in advance.","AnswerCount":6,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":5677,"Q_Id":42080598,"Users Score":0,"Answer":"Well it depends the situation you use python for ML.\nFor classification models like randomforest,use your train dataset to built tree structures and export as nested dict.Whatever the language you uesd,transform the model object to a kind of data structure then you can ues it anywhere.\nBUT if your situation is a large scale,real-timeing,distributional datesets,far as I know,maybe the best way is to deploy the whole ML process on severs.","Q_Score":5,"Tags":"java,python,rest,machine-learning,scikit-learn","A_Id":42127532,"CreationDate":"2017-02-07T02:19:00.000","Title":"What is the best way to build and expose a Machine Learning model REST api?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"Is it possible to import images from your Azure storage account from within a Python script module as opposed to using the Import Images module that Azure ML Studio provides. Ideally I would like to use cv2.imread(). I only want to read in grayscale data but the Import Images module reads in RGB. \nCan I use the BlockBlobService library as if I were calling it from an external Python script?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":775,"Q_Id":42081202,"Users Score":0,"Answer":"yes, you should be able to do that using Python. At the very least, straight REST calls should work.","Q_Score":0,"Tags":"python,azure,azure-blob-storage,azure-machine-learning-studio","A_Id":42081531,"CreationDate":"2017-02-07T03:27:00.000","Title":"Importing images Azure Machine Learning Studio","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Sorry about the vague title, but I didn't know how to word it. \nSo I have a pandas dataframe with 3 columns and any amount of rows. The first column is a person's name, the second column is their major (six possible majors, always written the same), and the third column is their gender (always 'Male' or 'Female'). \nI was told to print out the number of people in each major, which I was able to accomplish by saying table.Qmajor.value_counts() (table being my dataframe variable name). Now I am being asked to print the amount of males and females in each major, and I have no idea where to start. Any help is appreciated.\nThe column names are Qnames, Qmajor, and Qgender.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":5858,"Q_Id":42081790,"Users Score":2,"Answer":"altering @VaishaliGarg's answer a little, \nyou can use\ndf.groupby(['Qgender','Qmajor']).count()\nAlso if needed a dataframe out of it, we need to add .reset_index()\nsince it would be a groupbyObject.\ndf.groupby(['Qgender','Qmajor']).count().reset_index()","Q_Score":1,"Tags":"python,pandas,dataframe","A_Id":42081957,"CreationDate":"2017-02-07T04:33:00.000","Title":"Pandas dataframe: Listing amount of people per gender in each major","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When I ran a script in PyCharm, it exited with:\nI tensorflow\/stream_executor\/dso_loader.cc:128] successfully opened CUDA   library libcublas.so locally\nI tensorflow\/stream_executor\/dso_loader.cc:128] successfully opened CUDA library libcudnn.so locally\nI tensorflow\/stream_executor\/dso_loader.cc:128] successfully opened CUDA library libcufft.so locally\nI tensorflow\/stream_executor\/dso_loader.cc:128] successfully opened CUDA library libcuda.so.1 locally\nI tensorflow\/stream_executor\/dso_loader.cc:128] successfully opened CUDA library libcurand.so locally\nDEBUG:tm._add: \/joy, sensor_msgs\/Joy, sub\nFatal Python error: (pygame parachute) Segmentation Fault\nProcess finished with exit code 134 (interrupted by signal 6: SIGABRT)\n\nBut when I debug it in PyCharm, the program ran without any problem. \nAlso, if I ran the script in ubuntu terminal, no problem occurs, too. Why does this happen, or how can I debug this problem?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1056,"Q_Id":42086214,"Users Score":1,"Answer":"I have run into a similar error running caffe on pycharm. I think it's because of the version of Python. When I installed Python 2.7.13, it worked!","Q_Score":1,"Tags":"python,tensorflow,pycharm","A_Id":44600199,"CreationDate":"2017-02-07T09:32:00.000","Title":"pycharm cannot run script but can debug it","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I had trained my model on KNN classification algorithm , and I was getting around 97% accuracy. However,I later noticed that I had missed out to normalise my data and I normalised my data and retrained my model, now I am getting an accuracy of only 87%. What could be the reason? And should I stick to using data that is not normalised or should I switch to normalized version.","AnswerCount":3,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":8817,"Q_Id":42092448,"Users Score":6,"Answer":"That's a pretty good question, and is unexpected at first glance because usually a normalization will help a KNN classifier do better.  Generally, good KNN performance usually requires preprocessing of data to make all variables similarly scaled and centered.  Otherwise KNN will be often be inappropriately dominated by scaling factors.\nIn this case the opposite effect is seen:  KNN gets WORSE with scaling, seemingly.\nHowever, what you may be witnessing could be overfitting.  The KNN may be overfit, which is to say it memorized the data very well, but does not work well at all on new data. The first model might have memorized more data due to some characteristic of that data, but it's not a good thing. You would need to check your prediction accuracy on a different set of data than what was trained on, a so-called validation set or test set.  \nThen you will know whether the KNN accuracy is OK or not.  \nLook into learning curve analysis in the context of machine learning. Please go learn about bias and variance. It's a deeper subject than can be detailed here. The best, cheapest, and fastest sources of instruction on this topic are videos on the web, by the following instructors:\n\nAndrew Ng, in the online coursera course Machine Learning\nTibshirani and Hastie, in the online stanford course Statistical Learning.","Q_Score":7,"Tags":"python,machine-learning,scikit-learn,knn","A_Id":42093881,"CreationDate":"2017-02-07T14:32:00.000","Title":"Accuracy difference on normalization in KNN","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I had trained my model on KNN classification algorithm , and I was getting around 97% accuracy. However,I later noticed that I had missed out to normalise my data and I normalised my data and retrained my model, now I am getting an accuracy of only 87%. What could be the reason? And should I stick to using data that is not normalised or should I switch to normalized version.","AnswerCount":3,"Available Count":2,"Score":0.1325487884,"is_accepted":false,"ViewCount":8817,"Q_Id":42092448,"Users Score":2,"Answer":"If you use normalized feature vectors, the distances between your data points are likely to be different than when you used unnormalized features, particularly when the range of the features are different. Since kNN typically uses euclidian distance to find k nearest points from any given point, using normalized features may select a different set of k neighbors than the ones chosen when unnormalized features were used, hence the difference in accuracy.","Q_Score":7,"Tags":"python,machine-learning,scikit-learn,knn","A_Id":42093691,"CreationDate":"2017-02-07T14:32:00.000","Title":"Accuracy difference on normalization in KNN","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to know the which interpreter is good for Python to use features like Numpy, pandas and matplotlib with the feature of integrated Ipython note book.\nAlso I want to integrate this with Apache Spark. Is it possible? \nMy aim is I need to load different tables from different sources like Oracle, MS SQL, and HDFS files and need to transform them using Pyspark, SparkSQL. And then I want to use the pandas\/matplolib for manipulation and visualization.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":186,"Q_Id":42105716,"Users Score":0,"Answer":"Check out the hortonworks sandbox. It's a virtual machine with hadoop and all its components - such as spark ad hdfs - installed and configured. In a addition to that, there is a note book called Zeppelin notebook allowing you to write script in python or other languages. \nYou're also free to install python libs and access them through the notebook, even though i'm pretty it comes with it's own data visualisation. \nNote that the spark dataframe type is not compatible with the pandas one. you'll have to convert your data to a simple matrix and integrate back to spark or pandas type.","Q_Score":0,"Tags":"python-3.x,pandas,matplotlib,pyspark,apache-spark-sql","A_Id":42110000,"CreationDate":"2017-02-08T05:55:00.000","Title":"Integrate Spark SQL using Pyspark with python interpreter and pandas and Ipython notebook","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have trained my model on a data set and i used decision trees to train my model and it has 3 output classes - Yes,Done and No , and I got to know the feature that are most decisive in making a decision by checking feature importance of the classifier. I am using python and sklearn as my ML library. Now that I have found the feature that is most decisive I would like to know how that feature contributes, in the sense that if the relation is positive such that if the feature value increases the it leads to Yes and if it is negative It leads to No and so on and I would also want to know the magnitude for the same.\nI would like to know if there a solution to this and also would to know a solution that is independent of the algorithm of choice, Please try to provide solutions that are not specific to decision tree but rather general solution for all the algorithms. \nIf there is some way that would tell me like:\nfor feature x1 the relation is 0.8*x1^2\nfor feature x2 the relation is -0.4*x2 \njust so that I would be able to analyse the output depends based on input feature x1 ,x2  and so on\nIs it possible to find out the whether a high value for particular feature to a certain class, or a low value for the feature.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":998,"Q_Id":42108324,"Users Score":0,"Answer":"In general - no. Decision trees work differently that that. For example it could have a rule under the hood that if feature X > 100 OR X < 10 and Y = 'some value' than answer is Yes, if 50 < X < 70 - answer is No etc. In the instance of decision tree you may want to visualize its results and analyse the rules. With RF model it is not possible, as far as I know, since you have a lot of trees working under the hood, each has independent decision rules.","Q_Score":0,"Tags":"python,machine-learning,scikit-learn,decision-tree","A_Id":42115789,"CreationDate":"2017-02-08T08:40:00.000","Title":"How to know the factor by which a feature affects a model's prediction","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to integrate MATLAB and TensorFlow, although I can run TensorFlow native in python but I am required to use MATLAB for image processing. Can someone please help me out with this one?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1628,"Q_Id":42110293,"Users Score":0,"Answer":"I used a mex function for inference via the C++ API of TensorFlow once. That's pretty straight forward. I had to link the required TensorFlow libs statically from source though.","Q_Score":1,"Tags":"python,matlab,tensorflow","A_Id":54422825,"CreationDate":"2017-02-08T10:16:00.000","Title":"How will I integrate MATLAB to TensorFlow?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"im new to Keras in python, i got this warning message when after executing my code. I tried to search on google, but still didnt manage to solve this problem. Thank you in advance.\n\nUserWarning: he \"show_accuracy\" argument is deprecated, instead you\n  should pass the \"accurac \" metric to the model at compile time:\n  model.compile(optimizer, loss, metrics=[\"accuracy\"])\n  warnings.warn('The \"show_accuracy\" argument is deprecated, '","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":223,"Q_Id":42117777,"Users Score":1,"Answer":"In Keras < 1.0 (I believe), one would pass the show_accuracy argument to model.fit in order to display the accuracy during training.\nThis method has been replaced by metrics, as you can now define custom metrics that can help you during training. One of the metrics is of course, accuracy. The changes to your code to keep the same behavior are minimum:\n\nRemove show_accuracy from the model.fit call.\nAdd metrics = [\"accuracy\"] to the model.compile call.\n\nAnd that's it.","Q_Score":1,"Tags":"python,machine-learning,neural-network,theano,keras","A_Id":42120658,"CreationDate":"2017-02-08T15:54:00.000","Title":"Keras with Theano BackEnd","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"If I don't shuffle my files, I can get the file names with generator.filenames. But when the generator shuffles the images, filenames isn't shuffled, so I don't know how to get the file names back.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1974,"Q_Id":42118850,"Users Score":0,"Answer":"I think the only option here is to NOT shuffle the files. I have been wondering this myself and this is the only thing I could find in the docs. Seems odd and not correct...","Q_Score":7,"Tags":"python,machine-learning,neural-network,generator,keras","A_Id":46009804,"CreationDate":"2017-02-08T16:42:00.000","Title":"How to retrieve the filename of an image with keras flow_from_directory shuffled method?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Do RNNs learn different dependency patterns when the input is batch-major as opposed to time-major?","AnswerCount":2,"Available Count":1,"Score":0.4621171573,"is_accepted":false,"ViewCount":4154,"Q_Id":42130491,"Users Score":5,"Answer":"There is no difference in what the model learns. \nAt timestep t, RNNs need results from t-1, therefore we need to compute things time-major. If time_major=False, TensorFlow transposes batch of sequences from (batch_size, max_sequence_length) to (max_sequence_length, batch_size)*. It processes the transposed batch one row at a time: at t=0, the first element of each sequence is processed, hidden states and outputs calculated; at t=max_sequence_length, the last element of each sequence is processed.\nSo if your data is already time-major, use time_major=True, which avoids a transpose. But there isn't much point in manually transposing your data before feeding it to TensorFlow.\n*If you have multidimensional inputs (e.g. sequences of word embeddings: (batch_size, max_sequence_length, embedding_size)), axes 0 and 1 are transposed, leading to (max_sequence_length, batch_size, embedding_size)","Q_Score":8,"Tags":"python,tensorflow,deep-learning,lstm,recurrent-neural-network","A_Id":46544816,"CreationDate":"2017-02-09T07:18:00.000","Title":"Batch-major vs time-major LSTM","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a simple json in Django. I catch the file with this command data = request.body and i want to convert it to pandas datarame\nJSON:\n{ \"username\":\"John\", \"subject\":\"i'm good boy\", \"country\":\"UK\",\"age\":25}\nI already tried pandas read_json method and json.loads from json library but it didn't work.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":820,"Q_Id":42131205,"Users Score":2,"Answer":"You can also use pd.DataFrame.from_records() when you have json or dictonary\ndf = pd.DataFrame.from_records([ json ]) OR df = pd.DataFrame.from_records([ dict. ])\nor \nyou need to provide iterables for pandas dataframe:\ne.g. df = pd.DataFrame({'column_1':[ values ],'column_2':[ values ]})","Q_Score":2,"Tags":"python,json,django,pandas","A_Id":42133075,"CreationDate":"2017-02-09T08:06:00.000","Title":"Django JSON file to Pandas Dataframe","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":1},{"Question":"As I was checking out the Bokeh package I noticed that the tutorials use explicit import statements like from bokeh.plotting import figure and from numpy import linspace. I usually try to avoid these in favor of, e.g., import numpy as np, import matplotlib.pyplot as plt. I thought this is considered good practice as it helps to avoid namespace contamination.\nIs there any reason why Bokeh deviates from this practice, and\/or are there common aliases to use for Bokeh imports (e.g. import bokeh.plotting as bp)?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":545,"Q_Id":42145097,"Users Score":0,"Answer":"Importing individual names from a library isn't really \"contamination\".  What you want to avoid is doing from somelibrary import *.  This is different because you don't know which names will be imported, so you can't be sure there won't be a name clash.\nIn contrast, doing from numpy import linspace just creates one name linspace.  It's no different from doing creating an ordinary variable like linspace = 2 or defining your own function with def linspace.  There's no danger of unexpected name clashes because you know exactly which names you're creating in your local namespace.","Q_Score":3,"Tags":"python,bokeh","A_Id":42145160,"CreationDate":"2017-02-09T19:17:00.000","Title":"Why do bokeh tutorials use explicit imports rather than aliases?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"This is sort of a general question related to a specific implementation I have in mind, about whether it's safe to use python routines designed for use inside the GIL in a shared memory environment. Specifically what I'd like to do is use scipy.optimize.curve_fit on a large array inside a cython function.\nThe data can be expressed as a 2d numpy array (say, of floats) with the axis to be fit along and the other the serialized axis to be parallelized over. Then I'd just like to release the GIL and start looping through the data with a cython.parallel.prange (the idea being then that I can have all my cores working on fitting at once).\nThe main issue I can foresee is that curve_fit does not operate \"in place\"; it returns the fit values of the parameters (and optionally their covariance matrix) and so has to allocate that memory at some point. (Of course I also have no idea about any intermediate memory allocation the routine performs.) I'm worried about how this will operate outside the GIL with many threads working concurrently.\nI realize that the answer could just be \"it should work fine go try it,\" but I'm hoping to get some idea of what to look out for. I also realize that this question is similar to others about parallelizing scipy\/numpy routines, but I think this one is worded differently in that falls within the cython scope of a C environment for python.\nThanks for any help\/suggestions.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":312,"Q_Id":42148101,"Users Score":2,"Answer":"Not safe. If CPython could safely run that kind of code without the GIL, we wouldn't have the GIL in the first place.","Q_Score":2,"Tags":"python,scipy,cython,shared-memory,python-multithreading","A_Id":42148230,"CreationDate":"2017-02-09T22:30:00.000","Title":"Using scipy routines outside of the GIL","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"What I meant by the title is that I have two different programs and I want to plot data on one figure. In Matlab there is this definition for figure handle which eventually points to a specific plot. Let's say if I call figure(1) the first time, I get a figure named ''1'' created. The second I call figure(1), instead of creating a new one, Matlab simply just plot on the previous figure named ''1''. I wondered how I can go about and do that in Spyder. \nI am using Matplotlib in sypder. I would imagine this could be easily achieved. But I simply don't know much about this package to figure my problem out. :(\nAny suggestions are appreciated!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":54,"Q_Id":42149777,"Users Score":0,"Answer":"One way that I have figured out is to define a dictionary and then record the results you want individually. Apparently, this is not the most efficient way, but it works.","Q_Score":2,"Tags":"ipython,spyder","A_Id":50688040,"CreationDate":"2017-02-10T00:59:00.000","Title":"How to plot data from different runs on one figure in Spyder","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using TensorFlow to build a deep learning model. And new to TensorFlow.\nDue to some reason, my model has limited batch size, then this limited batch-size will make the model has a high variance.\nSo, I want to use some trick to make the batch size larger. My idea is to store the gradients of each mini-batch, for example 64 mini-batches, and then sum the gradients together, use the mean gradients of this 64 mini batches of training data to update the model's parameters.\nThis means that for the first 63 mini-batches, do not update the parameters, and after the 64 mini batch, update the model's parameters only once.\nBut as TensorFlow is graph based, do anyone know how to implement this wanted feature?\nThanks very much.","AnswerCount":6,"Available Count":1,"Score":-0.0333209931,"is_accepted":false,"ViewCount":8795,"Q_Id":42156957,"Users Score":-1,"Answer":"You can use Pytorch instead of Tensorflow as it allows the user to accumulate gradients during training","Q_Score":18,"Tags":"python,tensorflow,gradient","A_Id":56578297,"CreationDate":"2017-02-10T10:23:00.000","Title":"How to update model parameters with accumulated gradients?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there any way to get the current number of partitions of a DataFrame?\nI checked the DataFrame javadoc (spark 1.6) and didn't found a method for that, or am I just missed it? \n(In case of JavaRDD there's a getNumPartitions() method.)","AnswerCount":5,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":144311,"Q_Id":42171499,"Users Score":183,"Answer":"You need to call getNumPartitions() on the DataFrame's underlying RDD, e.g., df.rdd.getNumPartitions().  In the case of Scala, this is a parameterless method: df.rdd.getNumPartitions.","Q_Score":90,"Tags":"python,scala,dataframe,apache-spark,apache-spark-sql","A_Id":42171552,"CreationDate":"2017-02-11T02:24:00.000","Title":"Get current number of partitions of a DataFrame","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using tensorflow 0.12.1 on Python 3.5.2 on a Windows 10 64bit computer. For some reason, whenever I try to import legacy_seq2seq from tensorflow.contrib, it always occurs the error: ImportError: cannot import name 'legacy_seq2seq'.\nWhat causes the problem and how can I fix it?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2843,"Q_Id":42193779,"Users Score":0,"Answer":"I'm using tf.nn.seq2seq.sequence_loss_by_example - they've moved a lot of stuff from tf.contrib to main packages. This is because they updated their code, but not their examples - if you open github - you'll see a lot of requests to fix issues related to that!","Q_Score":0,"Tags":"python,tensorflow","A_Id":42195147,"CreationDate":"2017-02-12T22:00:00.000","Title":"Can't import legacy_seq2seq from tensorflow.contrib","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a table containing 200 columns out of which I need around 50 column mentioned in a list,\nand rows of last 24 months according to column 'timestamp'.\nI'm confused what comes under mapper and what under reducer?\nAs it is just transformation, will it only have mapper phase, or filtering of rows to last 24 months will come under reducer? I'm not sure if this exactly utilises \nwhat map-reduce was made for. \nI'm using python with hadoop streaming.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":165,"Q_Id":42204582,"Users Score":0,"Answer":"So, your have a table with 200 columns(say T), a separate list of entries(say L) to be picked from T and with the last 24-hours(from the timestamp in T).\nMapReduce, mapper does give entries from T sequentially. Before your mapper gets into map(), I.e in setup() put the block of code to read from the L and make it handy(use a feasible data structure to hold the list of data). Now, your code should hold two checks\/conditions 1) if the entry from T contains\/matches with L. If so, then check 2) if the data is within 24-hours range.\nDone. Your output is what you have expected. No, reducer is required here,  at least to do this much.\nHappy Mapreducing.","Q_Score":0,"Tags":"python,hadoop,mapreduce,hadoop-streaming","A_Id":42225141,"CreationDate":"2017-02-13T12:54:00.000","Title":"How to divide map-reduce tasks?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using Pandas to read data from some datafiles and generate a multiple-page pdf using PdfPages, in which each page contains the matplotlib figures from one datafile. It would be nice to be able to get a linked table of contents or bookmarks at each page, so that i can easily find figures corresponding to a given datafile . Is there a simple way to achieve this (for example by somehow inserting the name of the data file) in python 3.5?","AnswerCount":4,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1445,"Q_Id":42207211,"Users Score":0,"Answer":"What I do sometimes is generate a HTML file with my tables as I want and after I convert to PDF file. I know this is a little harder but I can control any element on my documents. Logically, this is not a good solution if you want write many files. Another good solution is make PDF from Jupyter Notebook.","Q_Score":11,"Tags":"python,python-3.x,pandas,pdf,matplotlib","A_Id":60818333,"CreationDate":"2017-02-13T15:06:00.000","Title":"A simple way to insert a table of contents in a multiple page pdf generated using PdfPages","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using Pandas to read data from some datafiles and generate a multiple-page pdf using PdfPages, in which each page contains the matplotlib figures from one datafile. It would be nice to be able to get a linked table of contents or bookmarks at each page, so that i can easily find figures corresponding to a given datafile . Is there a simple way to achieve this (for example by somehow inserting the name of the data file) in python 3.5?","AnswerCount":4,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1445,"Q_Id":42207211,"Users Score":0,"Answer":"It sounds like you want to generate fig{1, 2, ..., N}.pdf and then generate a LaTeX source file which mentions an \\includegraphics for each of them, and produces a ToC. If you do scratch this particular itch, consider packaging it up for others to use, as it is a pretty generic use case.","Q_Score":11,"Tags":"python,python-3.x,pandas,pdf,matplotlib","A_Id":51373915,"CreationDate":"2017-02-13T15:06:00.000","Title":"A simple way to insert a table of contents in a multiple page pdf generated using PdfPages","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using tensorflow with python 2.7. However, after updating python 2.7.10 to 2.7.13, I get an import error with tensorflow\nFile \"\", line 1, in \n  File \"\/Users\/usrname\/Library\/Python\/2.7\/lib\/python\/site-\n                        packages\/tensorflow\/__init__.py\", line 24, in \n    from tensorflow.python import *\n  File \"\/Users\/usrname\/Library\/Python\/2.7\/lib\/python\/site-\n                        packages\/tensorflow\/python\/__init__.py\", line 63, in \n    from tensorflow.core.framework.graph_pb2 import *\n  File \"\/Users\/usrname\/Library\/Python\/2.7\/lib\/python\/site-\n                        packages\/tensorflow\/core\/framework\/graph_pb2.py\", line 6, in \n    from google.protobuf import descriptor as _descriptor\nImportError: No module named google.protobuf\nOutput from pip install protobuf\nRequirement already satisfied: protobuf in \/usr\/local\/lib\/python2.7\/site-packages \nRequirement already satisfied: setuptools in \/Users\/usrname\/Library\/Python\/2.7\/lib\/\n    python\/site-packages (from protobuf) \nRequirement already satisfied: six>=1.9 in \/Library\/Python\/2.7\/site-packages\/\n    six-1.10.0-py2.7.egg (from protobuf) \nRequirement already satisfied: appdirs>=1.4.0 in \/usr\/local\/lib\/python2.7\/site-packages\n    (from setuptools->protobuf) \nRequirement already satisfied: packaging>=16.8 in \/usr\/local\/lib\/python2.7\/site-packages \n    (from setuptools->protobuf) \nRequirement already satisfied: pyparsing in \/usr\/local\/lib\/python2.7\/site-packages \n    (from packaging>=16.8->setuptools->protobuf) \nOutput from which python:\n\/Library\/Frameworks\/Python.framework\/Versions\/2.7\/bin\/python\nI believe this path changed after the python update, but not sure. A solution could possibly be to downgrade python, but this seems like a bad solution? As I work in a team, I would like to avoid reinstalling Tensorflow due to end up with different versions, but this is perhaps the way to go? Any advice?\n\nUpdate: I tired to install tensorflow all over, but the same error keeps popping up. Maybe the problem is the environment variables as which pipreturns \/usr\/local\/bin\/pip(which is different from which python)?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":781,"Q_Id":42224655,"Users Score":0,"Answer":"I had a similar problem. Make sure that pip and python has the same path when typing which pipand which python. If they differ, change your ~.bash_profile so that the python path match the pip path, and use source ~\\.bash_profile.\nIf that doesn't work, I would try to reinstall pip and tensorflow.\nI installed pip using this command:\nwget https:\/\/bootstrap.pypa.io\/get-pip.py\nsudo python2.7 get-pip.py","Q_Score":2,"Tags":"python-2.7,import,tensorflow,protocol-buffers,importerror","A_Id":42253328,"CreationDate":"2017-02-14T11:11:00.000","Title":"Tensorflow import error after python update","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using Anaconda 4.3.0 (64-bit) Python 3.6.0 on windows 7. I am getting the error \"ImportError: DLL load failed: The specified module could not be found.\" for importing the package import cv2. \nI have downloaded the OpenCV package and copy paste cv2.pyd into the Anaconda site package and updated my system path to point to OpenCV bin path to get the DLL. Still I am not able resolve this issue. \nI did another way to install using pip install opencv-python. Still  not working. \nPlease need suggestions. Thank you","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2068,"Q_Id":42232177,"Users Score":0,"Answer":"use python 2.7.1.0 instead of python 3, cv2 worked and dll load error fixed after using python 2.7","Q_Score":1,"Tags":"python,opencv,dll","A_Id":43529327,"CreationDate":"2017-02-14T17:11:00.000","Title":"Import cv2: ImportError: DLL load failed: windows 7 Anaconda 4.3.0 (64-bit) Python 3.6.0","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Suppose that I have an array of sensors that allows me to come up with an estimate of my pose relative to some fixed rectangular marker. I thus have an estimate as to what the contour of the marker will look like in the image from the camera. How might I use this to better detect contours?\nThe problem that I'm trying to overcome is that sometimes, the marker is occluded, perhaps by a line cutting across it. As such, I'm left with two contours that if merged, would yield the marker. I've tried opening and closing to try and fix the problem, but it isn't robust to the different types of lighting. \nOne approach that I'm considering is to use the predicted contour, and perform a local convolution with the gradient of the image, to find my true pose. \nAny thoughts or advice?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":68,"Q_Id":42232500,"Users Score":0,"Answer":"The obvious advantage of having a pose estimate is that it restricts the image region for searching your target. \nNext, if your problem is occlusion, you then need to model that explicitly, rather than just try to paper it over with image processing tricks: add to your detector objective function a term that expresses what your target may look like when partially occluded. This can be either an explicit \"occluded appearance\" model, or implicit - e.g. using an algorithm that is able to recognize visible portions of the targets independently of the whole of it.","Q_Score":0,"Tags":"python,computer-vision,opencv3.0,robotics,pose-estimation","A_Id":42255630,"CreationDate":"2017-02-14T17:27:00.000","Title":"Using external pose estimates to improve stationary marker contour tracking","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"What am I doing wrong here?\nI cannot add a path to my Jupyter Notebook. I am stuck. Any of my attempts did not work at all. \nhome_dir=\"\\C:\\Users\\User\\Desktop\\\"\ndata_dir=home_dir + \"\\C:\\Users\\User\\Desktop\\A Student's Guide to Python for Physical Modeling by KInder and Nelson\\code\"\ndata_set=np.loadtxt(data_dir + \"HIVseries.csv\", delimiter=',')","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":541,"Q_Id":42240124,"Users Score":1,"Answer":"It's easy question, modify \\C:\\Users\\User\\Desktop\\A Student's Guide to Python for Physical Modeling by KInder and Nelson\\code to C:\\\\Users\\\\User\\\\Desktop\\\\A Student's Guide to Python for Physical Modeling by KInder and Nelson\\\\code.","Q_Score":1,"Tags":"python,path,ipython-notebook","A_Id":42240221,"CreationDate":"2017-02-15T03:22:00.000","Title":"Add path in Python to a Notebook","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to TensorFlow and to Deep Learning.\nI am trying to recognize text in naturel scene images. I used to work with an OCR but I would like to use Deep Learning. The text has always the same format :\nABC-DEF 88:88. \nWhat I have done is recognize every character\/digit. It means that I cropped the image around every character (so each picture gives me 10 characters) to build my training and test set and they build a two conv neural networks. So my training set was a set of characters pictures and the labels were just characters\/digits.\nBut I want to go further. What I would like to do is just to give the full pictures and output the entire text (not one character such as in my previous model).\nThank you in advance for any help.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":13369,"Q_Id":42241038,"Users Score":6,"Answer":"The difficulty is that you don't know where the text is. The solution is, given an image, you need to use a sliding window to crop different part of the image, then use a classifier to decide if there are texts in the cropped area. If so, use your character\/digit recognizer to tell which characters\/digits they really are.\nSo you need to train another classifer: given a cropped image (the size of cropped images should be slightly larger than that of your text area), decide if there are texts inside.\nJust construct training set (positive samples are text areas, negative samples are other areas randomly cropped from the big images) and train it~","Q_Score":7,"Tags":"python,tensorflow,deep-learning,text-recognition","A_Id":42244824,"CreationDate":"2017-02-15T04:56:00.000","Title":"TensorFlow - Text recognition in image","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Python 2.7, Apache Spark 2.1.0, Ubuntu 14.04\nIn the pyspark shell I'm getting the following error:\n\n>>> from pyspark.mllib.stat import Statistics\nTraceback (most recent call last):\n  File \"\", line 1, in \nImportError: No module named stat\n\nSolution ?\nsimilarly\n\n\n>>> from pyspark.mllib.linalg import SparseVector\nTraceback (most recent call last):\n  File \"\", line 1, in \nImportError: No module named linalg\n\n\nI have numpy installed and\n\n >>> sys.path\n['', u'\/tmp\/spark-2d5ea25c-e2e7-490a-b5be-815e320cdee0\/userFiles-2f177853-e261-46f9-97e5-01ac8b7c4987', '\/usr\/local\/lib\/python2.7\/dist-packages\/setuptools-18.1-py2.7.egg', '\/usr\/local\/lib\/python2.7\/dist-packages\/pyspark-2.1.0+hadoop2.7-py2.7.egg', '\/usr\/local\/lib\/python2.7\/dist-packages\/py4j-0.10.4-py2.7.egg', '\/home\/d066537\/spark\/spark-2.1.0-bin-hadoop2.7\/python\/lib\/py4j-0.10.4-src.zip', '\/home\/d066537\/spark\/spark-2.1.0-bin-hadoop2.7\/python', '\/home\/d066537', '\/usr\/lib\/python2.7', '\/usr\/lib\/python2.7\/plat-x86_64-linux-gnu', '\/usr\/lib\/python2.7\/lib-tk', '\/usr\/lib\/python2.7\/lib-old', '\/usr\/lib\/python2.7\/lib-dynload', '\/usr\/local\/lib\/python2.7\/dist-packages', '\/usr\/lib\/python2.7\/dist-packages', '\/usr\/lib\/python2.7\/dist-packages\/PILcompat', '\/usr\/lib\/python2.7\/dist-packages\/gst-0.10', '\/usr\/lib\/python2.7\/dist-packages\/gtk-2.0', '\/usr\/lib\/python2.7\/dist-packages\/ubuntu-sso-client']","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1550,"Q_Id":42253981,"Users Score":0,"Answer":"I have the same problem. The Python file stat.py does not seem to be in Spark 2.1.x but in Spark 2.2.x. So it seems that you need to upgrade Spark with its updated pyspark (but Zeppelin 0.7.x does not seem to work with Spark 2.2.x).","Q_Score":4,"Tags":"python,pyspark","A_Id":45752337,"CreationDate":"2017-02-15T15:49:00.000","Title":"unable to import pyspark statistics module","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am currently working on the Dstl satellite kaggle challenge. There I need to create a submission file that is in csv format. Each row in the csv contains: \nImage ID, polygon class (1-10), Polygons\nPolygons are a very long entry with starts and ends and starts etc.\nThe polygons are created with an algorithm, for one class at a time, for one picture at a time (429 pictures, 10 classes each). \nNow my question is related to computation time and best practice: How do I best write the data of the polygons that I create into the csv? Do I open the csv at the beginning and then write each row into the file, as I iterate over classes and images? \nOr should I rather save the data in a list or dictionary or something and then write the whole thing into the csv file at once? \nThe thing is, I am not sure how fast the writing into a csv file is. Also, as the algorithm is already rather consuming computationally, I would like to save my pc the trouble of keeping all the data in the RAM. \nAnd I guess writing the data into the csv right away would result in less RAM used, right?\nSo you say that disc operations are slow. What exactly does that mean? When I write into the csv each row live as I create the data, does that slow down my program? So if I write a whole list into a csv file that would be faster than writing a row, then again calculating a new data row? So that would mean, that the computer waits for an action to finish before the next action gets started, right? But then still, what makes the process faster if I wait for the whole data to accumulate? Anyway the same number of rows have to be written into the csv, why would it be slower if I do it line by line?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":705,"Q_Id":42260538,"Users Score":3,"Answer":"How do I best write the data of the polygons that I create into the csv? Do I open the csv at the beginning and then write each row into the file, as I iterate over classes and images?\n\nI suspect most folks would gather the data in a list or perhaps dictionary and then write it all out at the end. But if you don't need to do additional processing to it, yeah -- send it to disk and release the resources. \n\nAnd I guess writing the data into the csv right away would result in less RAM used, right?\n\nYes, it would but it's not going to impact CPU usage; just reduce RAM usage though it does depend on when Python GC them. You really shouldn't worry about details like this. Get accurate output, first and foremost.","Q_Score":0,"Tags":"python,loops,csv,time,export-to-csv","A_Id":42260728,"CreationDate":"2017-02-15T21:36:00.000","Title":"Writing data into a CSV within a loop Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I just installed Tensorflow 1.0.0 using pip. When running, I get warnings like the one shown below.\nW tensorflow\/core\/platform\/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations.\nI get 5 more similar warning for SSE4.1, SSE4.2, AVX, AVX2, FMA.\nDespite these warnings the program seems to run fine.","AnswerCount":7,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":10737,"Q_Id":42270739,"Users Score":0,"Answer":"Those are simply warnings. \nThey are just informing you if you build TensorFlow from source it can be faster on your machine. \nThose instructions are not enabled by default on the builds available I think to be compatible with more CPUs as possible.","Q_Score":14,"Tags":"python,tensorflow","A_Id":42642759,"CreationDate":"2017-02-16T10:07:00.000","Title":"How do I resolve these tensorflow warnings?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I just installed Tensorflow 1.0.0 using pip. When running, I get warnings like the one shown below.\nW tensorflow\/core\/platform\/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations.\nI get 5 more similar warning for SSE4.1, SSE4.2, AVX, AVX2, FMA.\nDespite these warnings the program seems to run fine.","AnswerCount":7,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":10737,"Q_Id":42270739,"Users Score":0,"Answer":"It would seem that the PIP build for the GPU is bad as well as I get the warnings with the GPU version and the GPU installed...","Q_Score":14,"Tags":"python,tensorflow","A_Id":42539825,"CreationDate":"2017-02-16T10:07:00.000","Title":"How do I resolve these tensorflow warnings?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I try to write an script in python for analyse an .stl data file(3d geometry) and say which model is convex or concave and watertight and tell other properties...\nI would like to use and TensorFlow, scikit-learn or other machine learning library. Create some database with examples of objects with tags and in future add some more examples and just re-train model for better results.\nBut my problem is: I don\u00b4t know how to recalculate or restructure 3d data for working in ML libraries. I have no idea.\nThank you for your help.","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":1021,"Q_Id":42274756,"Users Score":2,"Answer":"You have to first extract \"features\" out of your dataset. These are fixed-dimension vectors. Then you have to define labels which define the prediction. Then, you have to define a loss function and a neural network. Put that all together and you can train a classifier.\nIn your example, you would first need to extract a fixed dimension vector out of an object. For instance, you could extract the object and project it on a fixed support on the x, y, and z dimensions. That defines the features.\nFor each object, you'll need to label whether it's convex or concave. You can do that by hand, analytically, or by creating objects analytically that are known to be concave or convex. Now you have a dataset with a lot of sample pairs (object, is-concave).\nFor the loss function, you can simply use the negative log-probability.\nFinally, a feed-forward network with some convoluational layers at the bottom is probably a good idea.","Q_Score":1,"Tags":"python,machine-learning,3d,tensorflow,scikit-learn","A_Id":42284733,"CreationDate":"2017-02-16T13:02:00.000","Title":"How to analyse 3d mesh data(in .stl) by TensorFlow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to implememt multitask Neural Network in tensorflow, for which I need my input as:\n\n[image label1 label2]\n\nwhich I can give to the neural network for training.\nMy question is, how can I associate more than one label with image in TFRecord file?\nI currently was using build_image_data.py file of inception model for genertrating TFRecord file but in that cases there is just one label per image.","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":249,"Q_Id":42290182,"Users Score":1,"Answer":"Do they have always two labels? If so try \"label1-label2\" as one label. \nOr simply build two networks, one for label 1 and the other for label 2.\nAre they hierarchical labels? Then, check out Hierarchical classifiers.","Q_Score":1,"Tags":"python,tensorflow,neural-network","A_Id":42292153,"CreationDate":"2017-02-17T05:36:00.000","Title":"How to give more than one labels with an image in tensorflow?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to implememt multitask Neural Network in tensorflow, for which I need my input as:\n\n[image label1 label2]\n\nwhich I can give to the neural network for training.\nMy question is, how can I associate more than one label with image in TFRecord file?\nI currently was using build_image_data.py file of inception model for genertrating TFRecord file but in that cases there is just one label per image.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":249,"Q_Id":42290182,"Users Score":0,"Answer":"I got this working. For any one looking for reference, you can modify Example proto of build_image_data.py file and associate it with two labels. :)","Q_Score":1,"Tags":"python,tensorflow,neural-network","A_Id":42298590,"CreationDate":"2017-02-17T05:36:00.000","Title":"How to give more than one labels with an image in tensorflow?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have created my own customised FacetedSearch class using Pythons Elasticsearch DSL library to perform search with additional filtering in def search(self). Now I would like to reuse my class to do some statistical aggregations. To stay DRY I want to reuse this class and for performance reason I would like to temporarily disable facets calculation when they are not necessary while preserving all the filtering. So question is how can I temporarily omit facets in FacetedSearch search?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":103,"Q_Id":42301719,"Users Score":1,"Answer":"So you just want to use the Search object's query, but not it's aggregations? In that case just call the object's search() method to get the Search object and go from there.\nIf you want the aggregations, but just want to skip the python-level facets calculation just use the build_search method to get the raw Search object including the aggregations.","Q_Score":0,"Tags":"python,elasticsearch,elasticsearch-dsl,elasticsearch-py","A_Id":42313679,"CreationDate":"2017-02-17T15:32:00.000","Title":"Temporarily disable facets in Python's FacetedSearch","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to classify paragraphs based on their sentiments. I have training data of 600 thousand documents. When I convert them to Tf-Idf vector space with words as analyzer and ngram range as 1-2 there are almost 6 million features. So I have to do Singular value decomposition (SVD) to reduce features.\nI have tried gensim and sklearn's SVD feature. Both work fine for feature reduction till 100 but as soon as I try for 200 features they throw memory error. \nAlso I have not used entire document (600 thousand) as training data, I have taken 50000 documents only. So essentially my training matrix is:\n50000 * 6 million and want to reduce it to  50000 * (100 to 500) \nIs there any other way I can implement it in python, or do I have to implement sparks mllib SVD(written for only java and scala) ? If Yes, how much faster will it be?\nSystem specification: 32 Gb RAM with 4 core processors on ubuntu 14.04","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":961,"Q_Id":42316431,"Users Score":1,"Answer":"I don't really see why using sparks mllib SVD would improve performance or avoid memory errors. You simply exceed the size of your RAM. You have some options to deal with that:\n\nReduce the dictionary size of your tf-idf (playing with max_df and min_df parameters of scikit-learn for example).\nUse a hashing vectorizer instead of tf-idf.\nGet more RAM (but at some point tf-idf + SVD is not scalable).\n\nAlso you should show your code sample, you might do something wrong in your python code.","Q_Score":1,"Tags":"python,scikit-learn,gensim,svd","A_Id":42317702,"CreationDate":"2017-02-18T14:30:00.000","Title":"SVD using Scikit-Learn and Gensim with 6 million features","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"currently i am looking for a way to filter specific classes out of my training dataset (MNIST) to train a neural network on different constellations, e.g. train a network only on classes 4,5,6 then train it on 0,1,2,3,4,5,6,7,8,9 to evaluate the results with the test dataset.\nI'd like to do it with an argument parser via console to chose which classes should be in my training dataset so i can split this into mini batches. I think i could do it with sorting out via labels but i am kinda stuck at this moment... would appreciate any tip!!!\nGreetings,\nAlex","AnswerCount":1,"Available Count":1,"Score":-0.1973753202,"is_accepted":false,"ViewCount":1514,"Q_Id":42317953,"Users Score":-1,"Answer":"Found the answer i guess... one hot=True transformed the scalar into a one hot vector :) thanks for your time anyway!","Q_Score":0,"Tags":"python,tensorflow,mnist","A_Id":47821847,"CreationDate":"2017-02-18T16:54:00.000","Title":"Select specific MNIST classes to train a neural network in TensorFlow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"does anyone know how to create or use 2 minibatch sources or inputs a sorted way? My problem is the following:\nI have images named from 0 to 5000 and images named 0_mask to 5000_mask. For each image x the coressponding image x_mask is the regression image for a deconvolution output. So i need a way to tell cntk that each x corresponds to x_match and that there is no regression done between x and y_mask.\nI'm well aware of the cntk convolution sample. I've seen it. The problem are the two input streams with x and x_mask. \nCan i combine them and make the reference, i need it in an easy way?\nThank you in advance.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":65,"Q_Id":42339941,"Users Score":1,"Answer":"You can create two minibatch sources, one for x and one for x_mask, both with randomize=False. Then the examples will be read in the order in which they are listed in the two map files. So as long as the map files are correct and the minibatch sizes are the same for both sources you will get the images and the masks in the order you want.","Q_Score":1,"Tags":"python,cntk","A_Id":42521000,"CreationDate":"2017-02-20T08:47:00.000","Title":"CNTK 2 sorted minibatch sources","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I installed Spark on Windows, and I'm unable to start pyspark. When I type in c:\\Spark\\bin\\pyspark, I get the following error:\n\nPython 3.6.0 |Anaconda custom (64-bit)| (default, Dec 23 2016, 11:57:41) [MSC v.1900 64 bit (AMD64)] on win32 Type \"help\", \"copyright\", \"credits\" or \"license\" for more information. Traceback (most recent call last): File \"c:\\Spark\\bin..\\python\\pyspark\\shell.py\", line 30, in import pyspark File \"c:\\Spark\\python\\pyspark__init__.py\", line 44, in from pyspark.context import SparkContext File \"c:\\Spark\\python\\pyspark\\context.py\", line 36, in from pyspark.java_gateway import launch_gateway File \"c:\\Spark\\python\\pyspark\\java_gateway.py\", line 31, in from py4j.java_gateway import java_import, JavaGateway, GatewayClient File \"\", line 961, in _find_and_load File \"\", line 950, in _find_and_load_unlocked File \"\", line 646, in _load_unlocked File \"\", line 616, in _load_backward_compatible File \"c:\\Spark\\python\\lib\\py4j-0.10.4-src.zip\\py4j\\java_gateway.py\", line 18, in File \"C:\\Users\\Eigenaar\\Anaconda3\\lib\\pydoc.py\", line 62, in import pkgutil File \"C:\\Users\\Eigenaar\\Anaconda3\\lib\\pkgutil.py\", line 22, in ModuleInfo = namedtuple('ModuleInfo', 'module_finder name ispkg') File \"c:\\Spark\\python\\pyspark\\serializers.py\", line 393, in namedtuple cls = _old_namedtuple(*args, **kwargs) TypeError: namedtuple() missing 3 required keyword-only arguments: 'verbose', 'rename', and 'module'\n\nwhat am I doing wrong here?","AnswerCount":5,"Available Count":1,"Score":0.0399786803,"is_accepted":false,"ViewCount":21967,"Q_Id":42349980,"Users Score":1,"Answer":"The Possible Issues faced when running Spark on Windows is, of not giving proper Path or by using Python 3.x to run Spark.\nSo,\n\nDo check Path Given for spark i.e \/usr\/local\/spark Proper or Not.\nDo set Python Path to Python 2.x (remove Python 3.x).","Q_Score":22,"Tags":"python,pyspark","A_Id":42718191,"CreationDate":"2017-02-20T16:45:00.000","Title":"Unable to run pyspark","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"i am using optical flow to track some features i am a begineer and was tol to follow these steps\n\nMatch good features to track \nDoing Lucas-Kanade Algorithm on them\nFind homography between 1-st frame and current frame\nDo camera calibration\nDecompose homography map\n\nNow what i don't understand is the homography part because you find the features and track them using Lucas-Kanade, now the homography is used to compute camera motion(rotation and translation\u2014between two images). but isn't that what the Lucas-Kanade does? or the Lucas-Kanade just tracks them and the homography makes the calculations? I am struggling to understand the difference between them, Thanks in advance.","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1959,"Q_Id":42350006,"Users Score":2,"Answer":"Optical flow: detect motions from one frame to the next. This is either sparse (few positions of interest are tracked, such as in the LKDemo.cpp example) or dense (one motion per position for many positions(e.g. all pixels) such as Farneback demos in openCV).\nRegardless of whether you have dense or sparse flow, there are different kinds of transforms that optical flow methods may try to estimate. The most common transform is translation. This is just the offset of postion from frame-to-frame. This can be visualized as vectors per frame, or as color when the flow is dense and high resolution.\nOne is not limited to only estimating translation per position. You can also estimate rotation for example (how a point is rotating, from frame to frame), or how it is skewed. In affine optical flow, you estimate a full affine transform per position (change translation, rotation, skew and scale). Affine flow is a classical and powerful technique that is much misunderstood, and probably used far less then it should.\nAffine transforms are given most economically by a 2x3 matrix: 6 degrees of freedom, compared to the regular 2 d.o.f. of regular translational optical flow.\nLeaving the topic of optical flow, An even more general family of transforms is called \"Homographies\" or \"projective transforms\". They require a 3x3 transform, and have 8 d.o.f. The affine family is not enough to describe the sort of deformation a plane undergoes, when you view it with projective distortion. \nHomographies are commonly estimated from many matched points between frames. In that sense, it uses the output of regular translational optical flow(but where the affine approach is often used under the hood to improve the results).\nAll of this only scratches the surface...","Q_Score":2,"Tags":"python-2.7,computer-vision,homography,opticalflow","A_Id":51124327,"CreationDate":"2017-02-20T16:46:00.000","Title":"Homography and Lucas Kanade what is the difference?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":1,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"This question is for a Windows 10 laptop. I'm currently trying to install tensorflow, however, when I run:\npip install --ignore-installed --upgrade https:\/\/storage.googleapis.com\/tensorflow\/windows\/cpu\/tensorflow-1.0.0-cp35-cp35m-win_x86_64.whl\nI get the following error:\ntensorflow-1.0.0-cp35-cp35m-win_x86_64.whl is not a supported wheel on this platform.\nI am trying to install the cpu-version only of tensorflow in an Anaconda 4.3.0 version. I had python 3.6.0 and then I downgraded to 3.5.0, none of them worked.","AnswerCount":5,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":3732,"Q_Id":42351728,"Users Score":0,"Answer":"So are you sure you correctly downgraded your python? Run this command on command line pip -V. This should print the pip version and the python version.","Q_Score":1,"Tags":"windows,python-3.x,cmd,tensorflow","A_Id":42352213,"CreationDate":"2017-02-20T18:21:00.000","Title":"Tensorflow installation on Windows 10, error 'Not a supported wheel on this platform'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using Scikit-learn's non-negative matrix factorization (NMF) to perform NMF on a sparse matrix where the zero entries are missing data. I was wondering if the Scikit-learn's NMF implementation views zero entries as 0 or missing data.\nThank you!","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":1903,"Q_Id":42357450,"Users Score":1,"Answer":"In your data matrix the missing values can be 0, but rather than storing a bunch of zeros for a very sparse matrix you would usually store a COO matrix instead, where each row is stored in CSR format.\nIf you are using NMF for recommendations, then you would be factorising your data matrix X by finding W and H such that W.H approximately equals X with the condition that all three matrices are non-negative. When you reconstruct this matrix X some of the missing values (where you would have stored zeros) may become non-zero and some may remain zero. At this point, in the reconstructed matrix, the values are your predictions.\nSo to answer your question, are they 0's or missing data in the NMF model? The NMF model once fit will contain your predicted values, so I would count them as zero. This is a method of predicting missing values in the data.","Q_Score":3,"Tags":"python,scikit-learn,nmf","A_Id":45426365,"CreationDate":"2017-02-21T02:09:00.000","Title":"Scikit-learn non-negative matrix factorization (NMF) for sparse matrix","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I know how to draw a rectangle in opencv.\nBut can I choose the length and breadth to be in centi meters?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":408,"Q_Id":42357801,"Users Score":0,"Answer":"Its dependent on the pixel-distance ratio. You can measure this by taking an image of a meter-stick and and measuring its pixel width (for this example say its 1000px). The ratio of pixels to distance is 1000px\/100cm, or 10. You can now use this constant as a multiplier, so for a given length and width in cm., you will just multiply by the ratio, and can get a pixel height and width, which can be passed into opencv's draw rectangle function.","Q_Score":0,"Tags":"python,opencv","A_Id":42357893,"CreationDate":"2017-02-21T02:51:00.000","Title":"Draw rectangle in opencv with length and breadth in cms?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to classify images using an Artificial Neural Network and the approach I want to try is:\n\nGet feature descriptors (using SIFT for now)\nClassify using a Neural Network\n\nI'm using OpenCV3 and Python for this.\nI'm relatively new to Machine Learning and I have the following question -\nEach image that I analyse will have different number of 'keypoints' and hence different dimensions of the 2D 'descriptor' array. How do I decide the input for my ANN. For example for one sample image the descriptor shape is  (12211, 128) so do I flatten this array and use it as an input, in which case I have to worry about varying input sizes for each image, or do I compute something else for the input?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":578,"Q_Id":42359440,"Users Score":0,"Answer":"It will be good if you apply Normalization on each image before getting the feature extractor.","Q_Score":0,"Tags":"python,opencv,neural-network,classification,sift","A_Id":42635609,"CreationDate":"2017-02-21T05:33:00.000","Title":"SIFT Input to ANN","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"My problem is the following: I have a tensor A of shape [None, None, None, 3] ([batch_size, height, width, num_channels]) and a tensor B. At runtime it is guaranteed that A and B will have the same shape. I would like to concatenate these two tensors along num_channels axis.\nPS. Note that I simplified my original problem - so having a tensor of shape [None, None, None, 6] in the first place is not an option.","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":857,"Q_Id":42386493,"Users Score":2,"Answer":"Before asking a question I should probably try to run the code :) Using tf.concat(values=[A, B], concat_dim=3) seems to be working.","Q_Score":2,"Tags":"python,tensorflow","A_Id":42386754,"CreationDate":"2017-02-22T08:41:00.000","Title":"Tensorflow: concatenating 2 tensors of shapes containing None","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a set of number that can get very small, from 1e-100, to 1e-700 and lower. The precision doesn't matter as much as the exponent.\nI can load such numbers just fine using Pandas by simply providing Decimal as a converter for all such numeric columns.\nThe problem is, even if I use Python's Decimal, I just can't use scipy.stats.chi2.isf and similar functions since their C code explicitly uses double.\nA possible workaround is that I can use log10 of the numbers. The problem here is that although there is logsf function, for chi2 it's implemented as just log(sf(...)), and will, therefore, fail when sf returns 0 where it should've returned something like 1e-600. And for isf there is no such log function at all.\nI wanted to know if there is any way to work with such numbers without resolving to writing all these functions myself for Decimal.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":105,"Q_Id":42400159,"Users Score":0,"Answer":"You could just write the expression for logsf directly using logs of gamma functions from scipy.special (gammaln, loggamma). And you could send a pull request implementing the logsf for the chi-square distribution.","Q_Score":2,"Tags":"python,pandas,scipy,double,precision","A_Id":42403483,"CreationDate":"2017-02-22T19:05:00.000","Title":"High exponent numbers with scipy.stats functions","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have numpy sparse matrix that I dump in a libsvm format. VC was created using CountVectorizer where the size of the vocabulary is 85731\nvc\n<1315689x85731 sparse matrix of type '<type 'numpy.int64'>'\n    with 38911625 stored elements in Compressed Sparse Row format>\nBut when I load libsvm file back I see that the shape is different. Two columns are gone:\ndata[0]\n<1315689x85729 sparse matrix of type '<type 'numpy.float64'>'\n    with 38911625 stored elements in Compressed Sparse Row format>\nI have no idea why this could be happening ? I also loaded the VC sparse matrix as dmatrix. Same issue 2 columns vanish.\nHope someone with more experience could point out the issue.\nThanks","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":251,"Q_Id":42401638,"Users Score":0,"Answer":"I suspect your last two columns consist of only 0's. When loading an libsvm file, it generally doesn't have anything indicating the number of columns. It's a sparse format of col_num:val and will learn the maximum number of columns by the highest column number observed. If you only have 0's in the last two columns, they'll get dropped in this transformation.","Q_Score":2,"Tags":"python,numpy,scikit-learn,libsvm,sklearn-pandas","A_Id":58574666,"CreationDate":"2017-02-22T20:29:00.000","Title":"Shape not the same after dumping to libsvm a numpy sparse matrix","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I generally use Pandas to extract data from MySQL into a dataframe. This works well and allows me to manipulate the data before analysis. This workflow works well for me.\nI'm in a situation where I have a large MySQL database (multiple tables that will yield several million rows). I want to extract the data where one of the columns matches a value in a Pandas series. This series could be of variable length and may change frequently. How can I extract data from the MySQL database where one of the columns of data is found in the Pandas series? The two options I've explored are:\n\nExtract all the data from MySQL into a Pandas dataframe (using pymysql, for example) and then keep only the rows I need (using df.isin()).\n\nor\n\nQuery the MySQL database using a query with multiple WHERE ... OR ... OR statements (and load this into Pandas dataframe). This query could be generated using Python to join items of a list with ORs.\n\nI guess both these methods would work but they both seem to have high overheads. Method 1 downloads a lot of unnecessary data (which could be slow and is, perhaps, a higher security risk) whilst method 2 downloads only the desired records but it requires an unwieldy query that contains potentially thousands of OR statements.\nIs there a better alternative? If not, which of the two above would be preferred?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":352,"Q_Id":42405493,"Users Score":0,"Answer":"I am not familiar with pandas but strictly speaking from a database point of view you could just have your panda values inserted in a PANDA_VALUES table and then join that PANDA_VALUES table with the table(s) you want to grab your data from.\nAssuming you will have some indexes in place on both PANDA_VALUES table and the table with your column the JOIN would be quite fast.\nOf course you will have to have a process in place to keep PANDA_VALUES tables updated as the business needs change.\nHope it helps.","Q_Score":0,"Tags":"python,mysql,sql,python-3.x,pandas","A_Id":42406043,"CreationDate":"2017-02-23T01:36:00.000","Title":"Selecting data from large MySQL database where value of one column is found in a large list of values","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on a personal project: gender classification (male | female) in python. I'm beginner in this domain\nI computed histograms for every image in training data.\nNow, to test if a test image is male or female is possible to make an average histogram for male | female and compare test histograms? Or I must compare all histograms with test histogram? \nIf there is possible to make an average. How should I do it?\nAlso, is ok to use SVM for classification?\nPS. I am looking for free faces databases. \nThanks","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":470,"Q_Id":42418948,"Users Score":1,"Answer":"If you want to use SVM as a classifier it does not make a lot of sense to make one average histogram for male and one for female because when you train you SVM classifier you can take all the histograms into account, but if you compute the average histograms you can use a nearest neighbor classifier instead.","Q_Score":1,"Tags":"python,pattern-matching,classification,svm,image-recognition","A_Id":42419250,"CreationDate":"2017-02-23T14:46:00.000","Title":"python lbp image classification","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When using estimator.Estimator in tensorflow.contrib.learn, after training and prediction there are these files in the modeldir: \n\ncheckpoint\nevents.out.tfevents.1487956647\nevents.out.tfevents.1487957016\ngraph.pbtxt\nmodel.ckpt-101.data-00000-of-00001\nmodel.ckpt-101.index\nmodel.ckpt-101.meta\n\nWhen the graph is complicated or the number of variables is big, the graph.pbtxt file and the events files can be very big. Is here a way to not write these files? Since model reloading only needs the checkpoint files removing them won't affect evaluation and prediction down the road.","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":4050,"Q_Id":42444796,"Users Score":0,"Answer":"I had the same issue and was not able to find any resolution for this while the events file kept on growing in size. My understanding is that this file stores the events generated by tensorflow. I went ahead and deleted this manually.\nInterestingly, it never got created again while the other files are getting updated when I run a train sequence.","Q_Score":8,"Tags":"python,tensorflow","A_Id":51049687,"CreationDate":"2017-02-24T17:30:00.000","Title":"How to turn off events.out.tfevents file in tf.contrib.learn Estimator","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am just starting out with tensorflow and I want to test something only on the 0's and 1's from the MNIST images. Is there a way to import only these images?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":98,"Q_Id":42458415,"Users Score":1,"Answer":"Assuming you are using \nfrom tensorflow.examples.tutorials.mnist import input_data\nNo, there is no function or argument in that file... What you can do is load all data, and select only the ones and zeros.","Q_Score":0,"Tags":"tensorflow,python-3.5,mnist","A_Id":42461595,"CreationDate":"2017-02-25T16:14:00.000","Title":"Is there any way to only import the MNIST images with 0's and 1's?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I did 1 nb_epoch with batch sizes of 10 and it successfully completed. The accuracy rate was absolutely horrible coming in at a whopping 27%. I want to make it run on more than one epoch to see if the accuracy will, ideally, be above 80% or so, but it keeps freezing my Jupyter Notebook if I try to make it do more than one epoch. How can I fix this?\nMy backend is Theano just for clarification.\nThere is definitely a correlation between performance and batch_size. I tried doing batch_size=1 and it took 12s of horrifying, daunting, unforgivable time out of my day to do 1 epoch.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":519,"Q_Id":42459726,"Users Score":0,"Answer":"You probably have to look into more factors.\nLook at the system resources, e.g CPU, Memory, Disk IO.  (If you use linux, run sar command)\nFor me, I had other problem with frozen notebook, and it turns out to be the issue of low memory.","Q_Score":0,"Tags":"python,keras","A_Id":53147988,"CreationDate":"2017-02-25T18:07:00.000","Title":"Why does my keras model terminate and freeze my notebook if I do more than one epoch?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I did 1 nb_epoch with batch sizes of 10 and it successfully completed. The accuracy rate was absolutely horrible coming in at a whopping 27%. I want to make it run on more than one epoch to see if the accuracy will, ideally, be above 80% or so, but it keeps freezing my Jupyter Notebook if I try to make it do more than one epoch. How can I fix this?\nMy backend is Theano just for clarification.\nThere is definitely a correlation between performance and batch_size. I tried doing batch_size=1 and it took 12s of horrifying, daunting, unforgivable time out of my day to do 1 epoch.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":519,"Q_Id":42459726,"Users Score":0,"Answer":"It takes time to run through the epochs and sometimes it looks like it freezes, but it still runs and if you wait long enough it will finish. Increasing the batch size makes it run through the epochs faster.","Q_Score":0,"Tags":"python,keras","A_Id":42460023,"CreationDate":"2017-02-25T18:07:00.000","Title":"Why does my keras model terminate and freeze my notebook if I do more than one epoch?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to subset a pandas dataframe based on values of two columns. I tried this code:\ndf[df['gold']>0, df['silver']>0, df['bronze']>0] but this didn't work.\nI  also tried:\ndf[(df['gold']>0 and df['silver']>0). This didn't work too. I got an error saying:\n\nValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().\n\nWhat would you suggest?","AnswerCount":1,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":6839,"Q_Id":42461086,"Users Score":11,"Answer":"I will answer my own question, hoping it will help someone. I tried this and it worked.\ndf[(df['gold']>0) & (df['silver']>0)]\nNote that I have used & instead of and and I have used brackets to separate the different conditions.","Q_Score":6,"Tags":"python,pandas,dataframe,subset","A_Id":42461103,"CreationDate":"2017-02-25T20:12:00.000","Title":"Subset pandas dataframe using values from two columns","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to implement Gaussian Mixture Model using keras with tensorflow backend. Is there any guide or example on how to implement it?","AnswerCount":1,"Available Count":1,"Score":0.537049567,"is_accepted":false,"ViewCount":2619,"Q_Id":42479954,"Users Score":3,"Answer":"Are you sure that it is what you want? you want to integrate a GMM into a neural network? \nTensorflow and Keras are libraries to create, train and use neural networks models. The Gaussian Mixture Model is not a neural network.","Q_Score":4,"Tags":"python-3.x,tensorflow,keras,gmm","A_Id":42481143,"CreationDate":"2017-02-27T07:24:00.000","Title":"Implement Gaussian Mixture Model using keras","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Does anybody know anything about NILM or power signature analysis? \nCan i do non-intrusive load monitoring using python?\nI got to know about one python toolkit known as NILMTK. But I need help for knowing about NILM.\nIf anybody know about NILM, then please guide me. Thank you.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":613,"Q_Id":42484305,"Users Score":1,"Answer":"The aim of non-intrusive load monitoring is to obtain a breakdown of the net energy consumption of a building in terms of individual appliance consumption. There has been work on multiple algorithms so as to get this done ( with varying performance) and as always these can be written in any programming language. \nNILMTK itself is written in python and is a good toolkit to describe, analyse and integrate nilm algorithms to compare them.","Q_Score":1,"Tags":"python,github,dataset,energy","A_Id":43356794,"CreationDate":"2017-02-27T11:10:00.000","Title":"What is Non-Intrusive Load Monitoring or energy disaggregation or power signature analysis?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Suppose I have an image of a car taken from my mobile camera and I have another image of the same car taken downloaded from the internet.\n(For simplicity please assume that both the images contain the same side view projection of the same car.)\nHow can I detect that both the images are representing the same object i.e. the car, in this case, using OpenCV?\nI've tried template matching, feature matching (ORB) etc but those are not working and are not providing satisfactory results.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":1709,"Q_Id":42499927,"Users Score":1,"Answer":"SIFT feature matching might produce better results than ORB. However, the main problem here is that you have only one image of each type (from the mobile camera and from the Internet. If you have a large number of images of this car model, then you can train a machine learning system using those images. Later you can submit one image of the car to the machine learning system and there is a much higher chance of the machine learning system recognizing it.\nFrom a machine learning point of view, using only one image as the master and matching another with it is analogous to teaching a child the letter \"A\" using only one handwritten letter \"A\", and expecting him\/her to recognize any handwritten letter \"A\" written by anyone.","Q_Score":1,"Tags":"python,opencv,image-processing,object-recognition","A_Id":42501574,"CreationDate":"2017-02-28T03:56:00.000","Title":"How to compare if two images representing the same object if the pictures of the object belongs from two different sources - in OpenCV?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Lets say I have two arrays in dataset:\n1) The first one is array classified as (0,1) - [0,1,0,1,1,1,0.....]\n2) And the second array costists of grey scale image vectors with 2500 elements in each(numbers from 0 to 300). These numbers are pixels from 50*50px images. - [[13 160 239 192 219 199 4 60..][....][....][....][....]]\nThe size of this dataset is quite significant (~12000 elements). \nI am trying to build bery basic binary classificator which will give appropriate results. Lets say I wanna choose non deep learning but some supervised method. \nIs it suitable in this case? I've already tried SVM of sklearn with various parameters. But the outcome is inappropriately inacurate and consists mainly of 1: [1,1,1,1,1,0,1,1,1,....]\nWhat is the right approach? Isnt a size of dataset enough to get a nice result with supervised algorithm?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":695,"Q_Id":42510042,"Users Score":2,"Answer":"You should probably post this on cross-validated:\nBut as a direct answer you should probably look into sequence to sequence learners as it has been clear to you SVM is not the ideal solution for this.\nYou should look into Markov models for sequential learning if you dont wanna go    the deep learning route, however, Neural Networks have  a very good track record   with image classification problems.  \nIdeally for a Sequential learning you should try to look into Long Short Term Memory Recurrent Neural Networks, and for your current dataset see if pre-training it on an existing data corpus (Say CIFAR-10) may help.\nSo my recomendation is give Tensorflow a try with a high level library such as Keras\/SKFlow.\nNeural Networks are just another tool in your machine learning repertoire and you might aswell give them a real chance.\nAn Edit to address your comment:\nYour issue there is not a lack of data for SVM,\nthe SVM will work  well, for a small dataset, as it will be easier for it to overfit\/fit a separating hyperplane on this dataset.\nAs you increase your data dimensionality, keep in mind that separating it using a separating hyperplane becomes increasingly difficult[look at the curse of dimensionality].\nHowever if you are set on doing it this way, try some dimensionality reduction\nsuch as PCA.  \nAlthough here you're bound to find another fence-off with Neural Networks,\nsince the Kohonen Self Organizing Maps do this task beautifully, you could attempt to\nproject your data in a lower dimension therefore allowing the SVM to separate it with greater accuracy.\nI still have to stand by saying you may be using the incorrect approach.","Q_Score":0,"Tags":"python,image,binary,classification,svm","A_Id":42510284,"CreationDate":"2017-02-28T13:27:00.000","Title":"What algorithm to chose for binary image classification","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am currently self-learning Spark programming and trying to recode an existing Python application in PySpark. However, I am still confused about how we use regular Python objects in PySpark. \nI understand the distributed data structure in Spark such as the RDD, DataFrame, Datasets, vector, etc. Spark has its own transformation operations and action operations such as .map(), .reduceByKey() to manipulate those objects. However, what if I create traditional Python data objects such as array, list, tuple, or dictionary in PySpark? They will be only stored in the memory of my driver program node, right? If I transform them into RDD, can i still do operations with typical Python function?\nIf I have a huge dataset, can I use regular Python libraries like pandas or numpy to process it in PySpark? Will Spark only use the driver node to run the data if I directly execute Python function on a Python object in PySpark? Or I have to create it in RDD and use Spark's operations?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":854,"Q_Id":42522654,"Users Score":0,"Answer":"You can create traditional Python data objects such as array, list, tuple, or dictionary in PySpark.\nYou can perform most of the operations using python functions in Pyspark.\nYou can import Python libraries in Pyspark and use them to process data in Pyspark\nYou can create a RDD and apply spark operations on them","Q_Score":1,"Tags":"python,python-2.7,apache-spark,pyspark","A_Id":42522820,"CreationDate":"2017-03-01T03:30:00.000","Title":"How Python data structure implemented in Spark when using PySpark?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Why am I not able to import sklearn?\nI downloaded Anaconda Navigator and it has scikit-learn in it. I even pip installed sklearn , numpy and scipy in Command Prompt and it shows that it has already been installed, but still when I import sklearn in Python (I use PyCharm for coding) it doesn't work. It says 'No module named sklearn'.","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":7012,"Q_Id":42539906,"Users Score":0,"Answer":"If someone is working with via bash here are the steps :\nFor ubunutu :\n\nsudo apt-get install python-sklearn","Q_Score":0,"Tags":"python,scikit-learn","A_Id":52912100,"CreationDate":"2017-03-01T19:02:00.000","Title":"Why can i not import sklearn","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Why am I not able to import sklearn?\nI downloaded Anaconda Navigator and it has scikit-learn in it. I even pip installed sklearn , numpy and scipy in Command Prompt and it shows that it has already been installed, but still when I import sklearn in Python (I use PyCharm for coding) it doesn't work. It says 'No module named sklearn'.","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":7012,"Q_Id":42539906,"Users Score":0,"Answer":"Problem solved! I didn't know that I was supposed to change my interpreter to Anaconda's interpreter(I am fairly new to Python). Thanks for the help!","Q_Score":0,"Tags":"python,scikit-learn","A_Id":42574660,"CreationDate":"2017-03-01T19:02:00.000","Title":"Why can i not import sklearn","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I just run across a problem when trying to ask help using Pandas DataFrames in Jupyper notebook. \nMore specifically my problem is what is the best way to embed iPython notebook input and output to StackOverflow question? \nSimply copy&paste breaks DataFrame output formatting so bad it becomes impossible to read. \nWhich would be preferred way to handle notebooks with StackOverflow: \n\nscreenshot\nlink to gist with the notebook\nconverting notebook to HTML and embedding it\nSomething else what?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":432,"Q_Id":42546701,"Users Score":1,"Answer":"A link to a gist is by far the superior option from those you have listed as that means helpers can run your code pretty easily and debug it from there.\nAn alternative option is to post the code that creates your DataFrame (or at least a minimal example of it) so that we can recreate it. This is advantageous over a gist since helpers don't have to look and download the gist because the code is in the body of the question. Also, this method is superior since you may later delete the gist and so the question is now useless for future reference, but if your code is in the body of the question then all future users can enjoy it as long as SO lives :)","Q_Score":1,"Tags":"python,python-3.x,ipython-notebook,jupyter-notebook","A_Id":42558220,"CreationDate":"2017-03-02T04:09:00.000","Title":"Best way to embed Jupyter\/IPython notebook information","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am creating a csv file in which i need to give hyperlinks to files in the same folder of csv file.\nI have tried with absolute url like =HYPERLINK(\"file:\/\/\/home\/user\/Desktop\/myfolder\/clusters.py\") and its working fine.But can i given the relative path like\n=HYPERLINK(\"file:\/\/\/myfolder\/clusters.py\") because that is what my project required.User will download this csv along with some other files into his machine.So i cant give the absolute path of other files in csv.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":215,"Q_Id":42550910,"Users Score":0,"Answer":"For HYPERLINK you need use only absolute url","Q_Score":0,"Tags":"python,csv,hyperlink","A_Id":42551702,"CreationDate":"2017-03-02T09:02:00.000","Title":"Give relative path of file in csv python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Within an interactive pyspark session you can import python files via sc.addPyFile('file_location').  If you need to make changes to that file and save them, is there any way to \"re-broadcast\" the updated file without having to shut down your spark session and start a new one?\nSimply adding the file again doesn't work.  I'm not sure if renaming the file works, but I don't want to do that anyways.\nAs far as I can tell from the spark documentation there is only a method to add a pyfile, not update one.  I'm hoping that I missed something!\nThanks","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1140,"Q_Id":42564069,"Users Score":1,"Answer":"I don't think it's feasible during an interactive session. You will have to restart your session to use the modified module.","Q_Score":8,"Tags":"python,apache-spark,pyspark","A_Id":50958242,"CreationDate":"2017-03-02T19:17:00.000","Title":"How can you update a pyfile in the middle of a PySpark shell session?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a very large dictionary with entries of the form {(Tuple) : [int, int]}. For example, dict = {(1.0, 2.1):[2,3], (2.0, 3.1):[1,4],...} that cannot fit in memory. \nI'm only interested in the top K values in this dictionary sorted by the first element in each key's value. If there a data structure that would allow me to keep only the largest K key-value pairs? As an example, I only want 3 values in my dictionary. I can put in the following key-value pairs; (1.0, 2.1):[2,3], (2.0, 3.1):[1,4], (3.1, 4.2):[8,0], (4.3, 4.1):[1,1] and my dictionary would be: (3.1, 4.2):[8,0], (1.0, 2.1):[2,3], (2.0, 3.1):[1,4] (in case of key-value pairs with the same first element, the second element will be checked and the largest key-value pair based on the second element will be kept)","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":698,"Q_Id":42592803,"Users Score":0,"Answer":"If your data will not fit in memory, you need to be particularly mindful of how it's stored. Is it in a database, a flat file, a csv file, JSON, or what?\nIf it is in a \"rectangular\" file format, you might do well to simply use a standard *nix sorting utility, and then just read in the first k lines.","Q_Score":2,"Tags":"python,dictionary,data-structures,heap","A_Id":42592884,"CreationDate":"2017-03-04T06:12:00.000","Title":"Data structure: Top K ordered dictionary keys by value","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to create a program that removes the background and get the foreground in color. For example if a face appears in front of my webcam i need to get the face only. I tried using BackgroundSubtractorMOG in opencv 3. But that didn't solve my problem. Can anyone tell me where to look or what to use. I'm a newbie in opencv. \nP.S i use opencv3 in python","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":713,"Q_Id":42606584,"Users Score":0,"Answer":"There is no way that your camera or software will be able to look at a flat image and decide what is foreground and what is background. Is that parrot sitting on a perch and staring at the camera or is it a picture of a parrot on the wall?\nIn the past I've made a collection of frames from the camera and formed a reference image by taking the median value of every pixel. Hopefully, this is now an imge that can be compared with every subsequent frame and now substracting the two can be used to isolate where change has occurred. The difference isn't what you want but can be turned into a mask that will select what you want from the frame in question.","Q_Score":0,"Tags":"python,opencv","A_Id":42611673,"CreationDate":"2017-03-05T09:22:00.000","Title":"Get the Foreground in opencv","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"According to the resources online \"train_test_split\" function from sklearn.cross_validation module returns data in a random state. \nDoes this mean if I train a model with the same data twice, I am getting two different models since the training data points used in the learning process is different in each case?\nIn practice can the accuracy of such two models differ a lot? Is that a possible scenario?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":86,"Q_Id":42610000,"Users Score":1,"Answer":"You can set random_state parameter to some constant value to reproduce data splits. On the other hand, it's generally a good idea to test exactly what you are trying to know - i.e. run your training at least twice with different randoms states and compare the results. If they differ a lot it's a sign that something is wrong and your solution is not reliable.","Q_Score":0,"Tags":"python,machine-learning,scikit-learn","A_Id":42610453,"CreationDate":"2017-03-05T15:10:00.000","Title":"model evaluation with \"train_test_split\" not static?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a web server using NodeJS - Express and I have a Scikit-Learn (machine learning) model pickled (dumped) in the same machine.\nWhat I need is to demonstrate the model by sending\/receiving data from it to the server. I want to load the model on startup of the web server and keep \"listening\" for data inputs. When receive data, executes a prediction and send it back.\nI am relatively new to Python. From what I've seen I could use a \"Child Process\" to execute that. I also saw some modules that run Python script from Node. \nThe problem is I want to load the model once and let it be for as long as the server is on. I don't want to keep loading the model every time due to it's size. How is the best way to perform that?\nThe idea is running everything in a AWS machine.\nThank you in advance.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":3643,"Q_Id":42610590,"Users Score":6,"Answer":"My recommendation: write a simple python web service (personally recommend flask) and deploy your ML model. Then you can easily send requests to your python web service from your node back-end. You wouldn't have a problem with the initial model loading. it is done once in the app startup, and then you're good to go\nDO NOT GO FOR SCRIPT EXECUTIONS AND CHILD PROCESSES!!! I just wrote it in bold-italic all caps so to be sure you wouldn't do that. Believe me... it potentially go very very south, with all that zombie processes upon job termination and other stuff. let's just simply say it's not the standard way to do that.\nYou need to think about multi-request handling. I think flask now has it by default\nI am just giving you general hints because your problem has been generally introduced.","Q_Score":11,"Tags":"python,node.js,scikit-learn,child-process","A_Id":62075227,"CreationDate":"2017-03-05T16:02:00.000","Title":"Sklearn Model (Python) with NodeJS (Express): how to connect both?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I have two, time value series (using pandas) and would like to represent the \"closeness\" of the last value in each series in regards to each other on a logarithmic scale between 0 and 1. 0 being very far away and 1 being the same.\nI am not sure how to approach this and any help would be appreciated.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":101,"Q_Id":42612439,"Users Score":0,"Answer":"So the solution I have is : \nlinear_closeness = 1 - (difference \/ max_deviation) \nexponential_closeness = 10^linear_closeness \/ 10 \nThis is suitable for me. I am open to better solutions.","Q_Score":0,"Tags":"python,math","A_Id":42655243,"CreationDate":"2017-03-05T18:43:00.000","Title":"Python - Closeness of two values on a logarithmic scale","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on a image recognition project in python. I have read in journals that if clustering performed by a self-organizing map (SOM) is input into a supervised neural network the accuracy of image recognition improves as opposed to the supervised network on its own. I have tried this myself by using the SOM to perform clustering and then using the coordinates of the winning neuron after each iteration as input to a multilayer perceptron from the keras. However the accuracy is very poor.\nWhat output of SOM should be used as input to a multilayer perceptron?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1430,"Q_Id":42625825,"Users Score":0,"Answer":"I have been wondering if there is any mileage to training a separate supervised neural network for the inputs which map to each node in the SOM. You'd then have separate supervised learning on the subset of the input data mapping to each SOM node. The networks attached to each node would perhaps be smaller and more easily trained than one large network which had to deal with the whole input space. There may also be benefit from including input vectors which map to the adjacent SOM nodes.\nIs anyone aware of this being the subject of research?","Q_Score":0,"Tags":"python,neural-network,cluster-analysis,image-recognition,self-organizing-maps","A_Id":47734394,"CreationDate":"2017-03-06T12:35:00.000","Title":"How to combine a Self-organising map and a multilayer perceptron in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on a image recognition project in python. I have read in journals that if clustering performed by a self-organizing map (SOM) is input into a supervised neural network the accuracy of image recognition improves as opposed to the supervised network on its own. I have tried this myself by using the SOM to perform clustering and then using the coordinates of the winning neuron after each iteration as input to a multilayer perceptron from the keras. However the accuracy is very poor.\nWhat output of SOM should be used as input to a multilayer perceptron?","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":1430,"Q_Id":42625825,"Users Score":1,"Answer":"Another way to use SOM is for vector quantisation. Rather than using the winning SOM coordinates, use the codebook values of the winning neuron. Not sure which articles you are reading, but I would have said that SOM into MLP will only provide better accuracy in certain cases. Also, you will need to choose parameters like dimensionality and map size wisely.\nFor image processing, I would have said that Autoencoders or Convolutional Neural Networks (CNNs) are more cutting-edge alternatives to SOM to investigate if you're not determined on the SOM + MLP architecture.","Q_Score":0,"Tags":"python,neural-network,cluster-analysis,image-recognition,self-organizing-maps","A_Id":42777643,"CreationDate":"2017-03-06T12:35:00.000","Title":"How to combine a Self-organising map and a multilayer perceptron in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Let's say I have a 360px by 240px image. Instead of cropping my (already small) image to 240x240, can I create a convolutional neural network that operates on the full rectangle? Specifically using the Convolution2D layer.\nI ask because every paper I've read doing CNNs seems to have square input sizes, so I wonder if what I propose will be OK, and if so, what disadvantages I may run into. Are all the settings (like border_mode='same') going to work the same?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2468,"Q_Id":42632411,"Users Score":4,"Answer":"No issues with a rectangle image... Everything will work properly as for square images.","Q_Score":2,"Tags":"python,image,keras,convolution","A_Id":42632755,"CreationDate":"2017-03-06T17:56:00.000","Title":"Can Convolution2D work on rectangular images?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using MXNet on IRIS dataset which has 4 features and it classifies the flowers as -'setosa', 'versicolor', 'virginica'. My training data has 89 rows. My label data is a row vector of 89 columns. I encoded the flower names into number -0,1,2 as it seems mx.io.NDArrayIter does not accept numpy ndarray with string values. Then I tried to predict using\nre = mod.predict(test_iter) \nI get a result which has the shape 14 * 10.\nWhy am I getting 10 columns when I have only 3 labels and how do I map these results to my labels. The result of predict is shown below:\n\n[[ 0.11760861 0.12082944 0.1207106 0.09154381 0.09155304 0.09155869\n  0.09154817 0.09155204 0.09154914 0.09154641] [ 0.1176083 0.12082954 0.12071151 0.09154379 0.09155323 0.09155825\n  0.0915481 0.09155164 0.09154923 0.09154641] [ 0.11760829 0.1208293 0.12071083 0.09154385 0.09155313 0.09155875\n  0.09154838 0.09155186 0.09154932 0.09154625] [ 0.11760861 0.12082901 0.12071037 0.09154388 0.09155303 0.09155875\n  0.09154829 0.09155209 0.09154959 0.09154641] [ 0.11760896 0.12082863 0.12070955 0.09154405 0.09155299 0.09155875\n  0.09154839 0.09155225 0.09154996 0.09154646] [ 0.1176089 0.1208287 0.1207095 0.09154407 0.09155297 0.09155882\n  0.09154844 0.09155232 0.09154989 0.0915464 ] [ 0.11760896 0.12082864 0.12070941 0.09154408 0.09155297 0.09155882\n  0.09154844 0.09155234 0.09154993 0.09154642] [ 0.1176088 0.12082874 0.12070983 0.09154399 0.09155302 0.09155872\n  0.09154837 0.09155215 0.09154984 0.09154641] [ 0.11760852 0.12082904 0.12071032 0.09154394 0.09155304 0.09155876\n  0.09154835 0.09155209 0.09154959 0.09154631] [ 0.11760963 0.12082832 0.12070873 0.09154428 0.09155257 0.09155893\n  0.09154856 0.09155177 0.09155051 0.09154671] [ 0.11760966 0.12082829 0.12070868 0.09154429 0.09155258 0.09155892\n  0.09154858 0.0915518 0.09155052 0.09154672] [ 0.11760949 0.1208282 0.12070852 0.09154446 0.09155259 0.09155893\n  0.09154854 0.09155205 0.0915506 0.09154666] [ 0.11760952 0.12082817 0.12070853 0.0915444 0.09155261 0.09155891\n  0.09154853 0.09155206 0.09155057 0.09154668] [ 0.1176096 0.1208283 0.12070892 0.09154423 0.09155267 0.09155882\n  0.09154859 0.09155172 0.09155044 0.09154676]]","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":158,"Q_Id":42641657,"Users Score":1,"Answer":"Using \"y = mod.predict(val_iter,num_batch=1)\" instead of \"y = mod.predict(val_iter)\", then you can get only one batch labels. For example,if you batch_size is 10, then you will only  get the 10 labels.","Q_Score":1,"Tags":"python,mxnet","A_Id":43152282,"CreationDate":"2017-03-07T06:29:00.000","Title":"mod.predict gives more columns than expected","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Here's the problem statement:\n\nI have m chocolate bars, of integer length, and n children who\n  want integer amounts of chocolate. Where the total chocolate needs of\n  the children are less than or equal to the total amount of chocolate\n  you have. You need to write an algorithm that distributes chocolate to\n  the children by making the least number of cuts to the bars.\n\nFor example, for M = {1,3,7}, N = {1,3,4}, the least number of cuts would be 1.\nI don't have any formal experience with algorithms, could anyone give me any hints on what I should start reading to tackle this problem in an efficient way?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":770,"Q_Id":42654075,"Users Score":0,"Answer":"This task can be reduced to solving several knapsack problems. To solve them, the principle of greedy search is usually used, and the number of cuts is the criterion of the search.\nThe first obvious step of the algorithm is checking the balance.\nThe second step is to arrange the arrays of bars and chocolate needs, which will simplify further calculations. This implements the principle of greedy search.\nThe third obvious step is to find and use all the bars, the sizes of which coincide with the needs.\nThe next step is to find and use all combinations of the bars what satisfy the needs. This task requires a \"greedy\" search in descending order of needs, which continues in the further calculations. This criterion is not optimal, but it allows to form a basic solution.\nIf not all the children have received chocolate, then the cuts become obvious. The search should be done according to the descending sizes of the tiles. First, one should check all possibilities to give the cut tiles to two children at once, then the same, but if one existing tile is used, etc.\n\u00a0\u00a0\u00a0\nAfter that there is an obvious variant \"one cut - one need\", allowing to form the base variant. But if there remain computational resources, they can be used first to calculate the options of the type \"two slits - three needs\", etc.\nFurther optimization consists in returning back to the steps and calculation for the following variants.","Q_Score":1,"Tags":"python,algorithm,dynamic-programming,knapsack-problem,bin-packing","A_Id":42657727,"CreationDate":"2017-03-07T17:04:00.000","Title":"Do I need to use a bin packing algorithm, or knapsack?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to work with a scatter plot within a FigureCanvasQTAgg. The scatter plot may have 50,000 or more data points. The user wants to draw a polygon in the plot to select the data points within the polygon. I've realized that by setting points via mouse clicks and connect them with lines using Axis.plot(). When the user has set all points the polygon is drawn. Each time I add a new point I call FigureCanvasQTAgg.draw() to render the current version of the plot. This is slow, because the scatter plot has so much data. \nIs there a way to make this faster?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":1682,"Q_Id":42656915,"Users Score":1,"Answer":"Two possible solutions:\n\nDon't show a scatter plot, but a hexbin plot instead.\nUse blitting.\n\n(In case someone wonders about the quality of this answer; mind that the questioner specifially asked for this kind of structure in the comments below the question.)","Q_Score":1,"Tags":"python,matplotlib,plot,pyqt4","A_Id":42697952,"CreationDate":"2017-03-07T19:40:00.000","Title":"How can I make matplotlib plot rendering faster","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I found pandas ewm function quite slow when applied to huge data. Is there any way to speed this up or use alternative functions for exponential weighted moving averages?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":416,"Q_Id":42658330,"Users Score":1,"Answer":"by definition, these are functions that are computationally intensive on huge datasets. \nSo there is very little hope to speed this up. Something you can try is to save the corresponding series as a .csv, do the smoothing in Pandas, and then merge back to your huge dataframe. \nSometimes that can help as carrying a large dataframe in memory back and forth is costly.","Q_Score":0,"Tags":"python,pandas","A_Id":42658405,"CreationDate":"2017-03-07T21:05:00.000","Title":"Speeding up exponential moving average in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"For example, using OpenCV and haarcascade_frontal_face.xml, we can predict if a face exists in an image. I would like to know if such a thing (detecting an object of interest) is possible with Tensorflow and if yes, how?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":540,"Q_Id":42664493,"Users Score":0,"Answer":"Yes and no. Tensorflow is a graph computation library mostly suited for neural networks. \nYou can create a neural network that determines if a face is in the image or not... You can even search for existing implementations that use Tensorflow...\nThere is no default Haar feature based cascade classifier in Tensorflow...","Q_Score":1,"Tags":"python,python-2.7,machine-learning,tensorflow","A_Id":42665677,"CreationDate":"2017-03-08T06:32:00.000","Title":"Can Tensorflow be used to detect if a particular feature exists in an image?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to construct clusters out of a set of data using the Kmeans algorithm from SkLearn. I want to know how one can determine whether the algorithm actually converged to a solution for one's data. \nWe feed in the tol parameter to define the tolerance for convergence but there is also a max_iter parameter that defines the number of iterations the algorithm will do for each run. I get that the algorithm may not always converge within the max_iter times of iterations. So is there any attribute or a function that I can access to know if the algorithm converged before the max_iter iterations ?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2975,"Q_Id":42666255,"Users Score":4,"Answer":"You have access to the n_iter_ field of the KMeans class, it gets set after you call fit (or other routines that internally call fit.\nNot your fault for overlooking that, it's not part of the documentation, I just found it by checking the source code ;)","Q_Score":2,"Tags":"python,scikit-learn,k-means","A_Id":42684721,"CreationDate":"2017-03-08T08:20:00.000","Title":"Sklearn K means Clustering convergence","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have to process a large volume of data ( feature maps of individual layers for around 4000 images) which sizes more than 50 GB after some point. The processing involves some calculation after which around 2MB file is written to the HDD.\nSince the free ram is around 40GB my process crashes after some point. Can anyone suggest a better approach to either divide or process this 50GB data such that the computation can be made within the available ram. For e.g. some in memory compression approach\nI am just looking for hints to the possible approaches to this problem.\nThanks","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":313,"Q_Id":42675835,"Users Score":0,"Answer":"This question makes me remember the early 80's. Memory used to be expensive and we invented swapping. The (high level part of the) OS sees more memory than actually present, and pages are copied on disk. When one is needed another one is swapped off, and the page is copied back into memory. Performances are awful, but at least it works.\nYour question is rather broad, but a rule of thumb says that if you can process your data in batches, explicitely loading batches of data is much more efficient, but if the algorythm is too complex or requires actions on any data at any moment, just let the swap care for it.\nSo add a swap file of significantly greater than the memory you think you need (with the given sizes, I would try 100 or 200 Gbytes), start the processing before leaving office, and you could have results next day in the morning.","Q_Score":0,"Tags":"python,memory-management","A_Id":42676591,"CreationDate":"2017-03-08T15:48:00.000","Title":"How to handle huge volume of data in limited RAM","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I downloaded the inception v3 model(a frozen graph) from website and imported it into a session then I found that the shape of inputs and outputs of all nodes in this graph_def are already fully known, but when I freeze my own graph contaning tf.Examples queues as inputs, the batch_size info seems to be lost and replaced with a ?, my question is how I can fix or change the unknown shape when I try to freeze a graph? \nedit:\nthe node.attr of some nodes in graph_def contains the shape info but why not all nodes?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":438,"Q_Id":42687327,"Users Score":0,"Answer":"each node in the graph_def doesn't contains the shape of output tensor, after importing graph_def into memory (with tf.import_graph_def), the shape of each tensor in the graph is automatically determined","Q_Score":1,"Tags":"python,tensorflow,deep-learning","A_Id":45650018,"CreationDate":"2017-03-09T05:19:00.000","Title":"How does graph_def store the shape info of input and output of a node","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using function: numpy.log(1+numpy.exp(z))\nfor small values of z (1-705) it gives identity result(1-705 {as expected}), \nbut for larger value of z from 710+ it gives infinity, and throw error \"runtimeWarning: overflow encountered in exp\"","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":3068,"Q_Id":42703506,"Users Score":0,"Answer":"What is happening is that you are overflowing the register such that it is overwriting itself. You have exceeded the maximum value that can be stored in the register. You will need to use a different datatype that will most likely not be compatible with exp(.). You might need a custom function that works with 64-bit Integers.","Q_Score":1,"Tags":"python-3.x,numpy","A_Id":42703588,"CreationDate":"2017-03-09T19:07:00.000","Title":"overflow in exp(x) in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Newbie to apache spark. What I want to do is to remove both the duplicated keys from two csv files. I have tried dropDuplicates() and distinct() but all the do is remove one value. For example if key = 1010 appears in both the csv files, I want both of them gone. How do I do this?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":368,"Q_Id":42708129,"Users Score":0,"Answer":"thx for @himanshulllTian's great answer. And I want so say some more. If you have several columns in your file; then you just want to remove record based on the key column. Also, I don't know whether your csv files have same schema.  Here is one way to deal with this situation. Let me borrow the example from himanshulllTian.\nFirst, Let's find the records that share some key: val dupKey = df1.join(df2, \"key\").select(\"key\"). Then we can find the part we hope to remove in each dataframe: val rmDF1 = df1.join(dupKey, \"key\"). Finally, same except action: val newDF1 = df1.except(rmDF). \nThis maybe trivial. But work. Hope this help.","Q_Score":0,"Tags":"python,csv,apache-spark,pyspark","A_Id":42713556,"CreationDate":"2017-03-10T00:40:00.000","Title":"How to remove both duplicated values from two csv files in apache spark?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using pandas read_csv to open a csv file 1327x11. The first 265 rows are only 4 columns wide. Here is row 1 to 5\nDWS_LENS1.converter,\"-300.0,5.5; -0.1,5.5; 10.0,-5.5; 300.0,-5.5\",(mass->volts),:  DWS_LENS1.mass_dependent,false,:\nDWS_LENS1.voltage.reading,-5.12642,V,:\nDWS_LENS1.voltage.target,-4.95000,V,: \nDWS_LENS2.converter,\"-300.0,20.0; -10.0,20.0; 10.0,-20.0; 300.0,-20.0\",(mass->volts),:\nand here are some other rows :   \n157955,SAMPLE,,,,1760.5388,,,,:\n,: \nSummary,:\n,: \nAnalyte,H3O+ (ppb),NO+ (ppb),O2+ (ppb),O- (ppb),OH- (ppb),: \ntoluene,1872.7367,,,,,: \nisobutane,,1945.7385,,,,:\nhexafluorobenzene,,,1951.0644,2121.6486,,:\ntetrafluorobenzene,,,,,1599.5802,:\nI receive Error tokenizing data. C error: Expected 4 fields in line 266, saw 11\nI tried \ndf=pd.read_csv(test,error_bad_lines=False)\nbut it skips most rows and returns a 491x4 table.\nIf I use pd.read_csv(test,delim_whitespace=True,error_bad_lines=False)\nI obtain a 1300x4 table but it fails splitting some data.\nHow can I have the 11 columns back?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1382,"Q_Id":42708388,"Users Score":0,"Answer":"add this : lineterminator = ':'","Q_Score":0,"Tags":"python,csv,pandas","A_Id":42708813,"CreationDate":"2017-03-10T01:10:00.000","Title":"Pandas read_csv fails","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to fit a curve with lmfit but the data set I'm working with does not contain a lot of points and this makes the resulting fit look jagged instead of curved.\nI'm simply using the line:\nout = mod.fit(SV, pars, x=VR)\nwere VR and SV are the coordinates of the points I'm trying to fit.\nI've tried using scipy.interpolate.UnivariateSpline and the fitted the resulting data but I want to know if there is a built-in or faster way to do this. \nThank you","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":424,"Q_Id":42711002,"Users Score":0,"Answer":"There is not a built-in way to automatically interpolate with lmfit.  With a lmfit Model, you provide the array on independent values at which the Model should be evaluated, and an array of data to compared to that model.\nYou're free to interpolate or smooth the data or perform some other transformation (I sometimes Fourier transform data and model to emphasize some frequencies), but you'll have to include that as part of the model.","Q_Score":2,"Tags":"python,interpolation,curve-fitting,lmfit","A_Id":42730875,"CreationDate":"2017-03-10T05:36:00.000","Title":"Interpolate with lmfit?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"If I have a diagonal matrix with diagonal 100Kx1 and how can I to get its pseudo inverse?\nI won't be able to diagonalise the matrix and then get the inverse like I would do for small matrix so this won't work\nnp.linalg.pinv(np.diag(D))","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":945,"Q_Id":42711310,"Users Score":2,"Answer":"Just take the reciprocals of the nonzero elements. You can check with a smaller diagonal matrix that this is what pinv does.","Q_Score":1,"Tags":"python,numpy,matrix","A_Id":42711798,"CreationDate":"2017-03-10T06:00:00.000","Title":"How to get the pseudo inverse of a huge diagonal matrix in python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a numpy array of strings, some duplicated, and I'd like to compare every element with every other element to produce a new vector of 1's and 0's indicating whether each pair (i,j) is the same or different.\ne.g. [\"a\",\"b\",\"a\",\"c\"] -> 12-element (4*3) vector [1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1]\nIs there a way to do this quickly in numpy without a double loop through all pairs of elements? My array has ~240,000 elements, so it's taking a terribly long time to do it the naive way.\nI'm aware of numpy.equal.outer, but apparently numpy.equal is not implemented on strings, so it seems like I'll need some more clever way to compare them.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":2678,"Q_Id":42724795,"Users Score":1,"Answer":"tl;dr\nYou don't want that.\nDetails\nFirst let's note that you're actually building a triangular matrix: for the first element, compare it to the rest of the elements, then repeat recursively to the rest. You don't use the triangularity, though. You just cut off the diagonal (each element is always equal to itself) and merge the rows into one list in your example.\nIf you sort your source list, you won't need to compare each element to the rest of the elements, only to the next element. You'd have to keep the position with element using a tuple, to keep track of it after sorting.\nYou would sort the list of pairs in O(n log n) time, then scan it and find all the matches if O(n) time. Both sorting and finding the matches are simple and quick in your case.\nAfter that, you'd have to create your 'bit vector', which is O(n^2) long. It would contain len(your vector) ** 2 elements, or 57600 million elements for a 240k-element vector. Even if you represented each element as one bit, it would take 53.6 Gbit, or 8.7 GBytes of memory.\nLikely you don't want that. I suggest that you find a list of pairs in O(n log n) time, sort it by both first and second position in O(n log n) time, too, and recreate any portion of your desired bitmap by looking at that list of pairs; binary search would really help. Provided that you have much fewer matches than pairs of elements, the result may even fit in RAM.","Q_Score":2,"Tags":"python,numpy,numpy-ufunc","A_Id":42725593,"CreationDate":"2017-03-10T17:53:00.000","Title":"Fastest way to compare every element with every other in np array of strings","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to use the pandas.DataFrame.apply function. My actual code performs similarly to the example below. At the end of the output it outputs \"None\" for each row in the dataframe. This behavior causes an error in the function I'm passing through apply.\ndf = pd.DataFrame({\"one\": range(0,5), \"two\": range(0,5)})\ndf.apply(print, axis=1)\n\nWhy does it behave this way? What is the None coming from?\nHow can I alter\/control this behavior?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1727,"Q_Id":42750479,"Users Score":0,"Answer":"The None occurs because the print() function doesn't return any value and apply() expects the function to return something.\nIf you want to print the data frame, just use print(df), or if you need some other format, tell us what your are trying to get at the printed output.","Q_Score":1,"Tags":"python,pandas,dataframe,apply","A_Id":42750534,"CreationDate":"2017-03-12T17:11:00.000","Title":"Understanding the None output when using pandas.DataFrame.apply","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When using tensorflow, the graph is logged in the summary file, which I \"abuse\" to keep track of the architecture modifications.\nBut that means every time I need to use tensorboard to visualise and view the graph.\nIs there a way to write out such a graph prototxt in code or export this prototxt from summary file from tensorboard?\nThanks for your answer!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":421,"Q_Id":42761359,"Users Score":0,"Answer":"The graph is available by calling tf.get_default_graph. You can get it in GraphDef format by doing graph.as_graph_def().","Q_Score":0,"Tags":"python,tensorflow","A_Id":42768183,"CreationDate":"2017-03-13T10:28:00.000","Title":"export graph prototxt from tensorflow summary","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I wanted to create a new \"build tool\" for sublime text, so that I can run my python scripts with an anaconda env with tensorflow. On my other machines this works without a problem, but on my ubuntu machine with GPU support I get an error. \nI think this is due to the missing paths. The path provided in the error message doesn't contain the cuda paths, although I've included them in .bashrc. \nUpdate\nI changed ~\/.profile to export the paths. But tensorflow still won't start from sublime. Running my script directly from terminal is no problem.\nI get ImportError: libcudart.so.8.0: cannot open shared object file: No such file or directory\nSo somehow the GPU stuff (cuda?) can not be found\nThanks","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":668,"Q_Id":42764539,"Users Score":2,"Answer":"Ok I got it:\nThe problem is that the LD_LIBRARY_PATH variable was missing. I only exported it in .bashrc.\nWhen I add \nexport LD_LIBRARY_PATH=\/usr\/local\/cuda-8.0\/lib64\\\n                         ${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}\nto ~\/.profile it's working (don't forget to restart). \nIt also works if I start sublime from terminal with subl which passes all the variables.","Q_Score":1,"Tags":"python,tensorflow,sublimetext2,sublimetext3,sublimetext","A_Id":42765548,"CreationDate":"2017-03-13T13:11:00.000","Title":"How to export PATH for sublime build tool?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I have tried to run the Holoviews examples from the Holoviews website.\nI have:\n\nbokeh 0.12.4.\nholoviews 1.6.2 py27_0 conda-forge\n\nHowever, following any of the tutorials I get an error such as the following and am unable to debug:\nAttributeError: 'Image' object has no attribute 'set'.\nIs anyone able to guide me as to how to fix this?\nCheers\nEd","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":997,"Q_Id":42771938,"Users Score":0,"Answer":"There are some changes in bokeh 0.12.4, which are incompatible with HoloViews 1.6.2. We will be releasing holoviews 1.7.0 later this month, until then you have the option to downgrading to bokeh 0.12.3 or upgrading to the latest holoviews dev release with:\nconda install -c ioam\/label\/dev holoviews\nor \npip install https:\/\/github.com\/ioam\/holoviews\/archive\/v1.7dev7.zip","Q_Score":0,"Tags":"python,bokeh,holoviews","A_Id":42772141,"CreationDate":"2017-03-13T19:31:00.000","Title":"Holoviews: AttributeError: 'Image' object has no attribute 'set'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Running on Mac Sierra, the autocompletion in Spyder (from Anaconda distribution), seems quite erratic. When used from the Ipython console, works as expected. However, when used from the editor (which is my main way of writing), is erratic. The autocompletion works (i.e. when pressing TAB a little box appears showing options) for some modules, such as pandas or matplotlib. So writing 'pd.' and hitting TAB, gets the box with options as expected. However, this does not happen with many other objects: for example, after defining a dataframe named 'df', typing 'df.' TAB shows nothing. In the Ipython console, 'df.' TAB would show the available procedures for that dataframe, such as groupby, and also its columns, etc..\nSo the question is threefold. First, is there any particular configuration that should be enabled to get this to work? I don't think so, given some time spent googling, but just wanna make sure. Second, could someone state what is the official word on what works and what doesn't in terms of autocompletion (e.g. what particular modules do work from the editor, and which ones doesn't?). Finally, what are the technical aspects of the differences between the editor and the Ipython console in the performance of the autocompletion with Spyder? I read something about Jedi vs. PsychoPy modules, so got curious (however, please keep in mind that although I have scientific experience, I am relatively new to computation, so please keep it reasonably simple for an educated but not expert person).\nUPDATE: As a side question, it would be great to know why is the autocompletion better in Rodeo (another IDE). It is more new, has way fewer overall options than Spyder, but the autocompletion works perfectly in the editor.","AnswerCount":3,"Available Count":2,"Score":0.3215127375,"is_accepted":false,"ViewCount":13872,"Q_Id":42777430,"Users Score":5,"Answer":"Autocomplete was not working for me at all. \nSo, I tried Tools -> Reset Sypder to factory defaults and it worked.","Q_Score":10,"Tags":"python,autocomplete,spyder","A_Id":50535897,"CreationDate":"2017-03-14T03:46:00.000","Title":"Why autocompletion options in Spyder 3.1 are not fully working in the Editor?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Running on Mac Sierra, the autocompletion in Spyder (from Anaconda distribution), seems quite erratic. When used from the Ipython console, works as expected. However, when used from the editor (which is my main way of writing), is erratic. The autocompletion works (i.e. when pressing TAB a little box appears showing options) for some modules, such as pandas or matplotlib. So writing 'pd.' and hitting TAB, gets the box with options as expected. However, this does not happen with many other objects: for example, after defining a dataframe named 'df', typing 'df.' TAB shows nothing. In the Ipython console, 'df.' TAB would show the available procedures for that dataframe, such as groupby, and also its columns, etc..\nSo the question is threefold. First, is there any particular configuration that should be enabled to get this to work? I don't think so, given some time spent googling, but just wanna make sure. Second, could someone state what is the official word on what works and what doesn't in terms of autocompletion (e.g. what particular modules do work from the editor, and which ones doesn't?). Finally, what are the technical aspects of the differences between the editor and the Ipython console in the performance of the autocompletion with Spyder? I read something about Jedi vs. PsychoPy modules, so got curious (however, please keep in mind that although I have scientific experience, I am relatively new to computation, so please keep it reasonably simple for an educated but not expert person).\nUPDATE: As a side question, it would be great to know why is the autocompletion better in Rodeo (another IDE). It is more new, has way fewer overall options than Spyder, but the autocompletion works perfectly in the editor.","AnswerCount":3,"Available Count":2,"Score":0.3215127375,"is_accepted":false,"ViewCount":13872,"Q_Id":42777430,"Users Score":5,"Answer":"Autocompletion  works correctly if there are NO white spaces in the project working directory path.","Q_Score":10,"Tags":"python,autocomplete,spyder","A_Id":46160256,"CreationDate":"2017-03-14T03:46:00.000","Title":"Why autocompletion options in Spyder 3.1 are not fully working in the Editor?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there any advantage in using tf.nn.* over tf.layers.*?\nMost of the examples in the doc use tf.nn.conv2d, for instance, but it is not clear why they do so.","AnswerCount":6,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":35607,"Q_Id":42785026,"Users Score":30,"Answer":"For convolution, they are the same. More precisely, tf.layers.conv2d (actually _Conv) uses tf.nn.convolution as the backend. You can follow the calling chain of: tf.layers.conv2d>Conv2D>Conv2D.apply()>_Conv>_Conv.apply()>_Layer.apply()>_Layer.\\__call__()>_Conv.call()>nn.convolution()...","Q_Score":66,"Tags":"python,tensorflow","A_Id":42932979,"CreationDate":"2017-03-14T11:41:00.000","Title":"tf.nn.conv2d vs tf.layers.conv2d","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there any advantage in using tf.nn.* over tf.layers.*?\nMost of the examples in the doc use tf.nn.conv2d, for instance, but it is not clear why they do so.","AnswerCount":6,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":35607,"Q_Id":42785026,"Users Score":7,"Answer":"All of these other replies talk about how the parameters are different, but actually, the main difference of tf.nn and tf.layers conv2d is that for tf.nn, you need to create your own filter tensor and pass it in. This filter needs to have the size of: [kernel_height, kernel_width, in_channels, num_filters]\nEssentially, tf.nn is lower level than tf.layers. Unfortunately, this answer is not applicable anymore is tf.layers is obselete","Q_Score":66,"Tags":"python,tensorflow","A_Id":53683545,"CreationDate":"2017-03-14T11:41:00.000","Title":"tf.nn.conv2d vs tf.layers.conv2d","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it really worth it to implement a C++ code for loading an already trained model and then fetch it instead of using Python?. \nI was wondering this because as far as I understand, Tensorflow for python is C++ behind the scenes (as it is for numpy). So if one ends up basically having a Python program fetching the model loaded with a Python module it would perform pretty similar than using a module in C++ right?\nIs there any benchmark? I wasn't able to find anything supporting this theory.\nThanks!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":789,"Q_Id":42810240,"Users Score":0,"Answer":"Write in the language that you are familiar with, in a way that you can maintain. \nIf it takes you a day longer to write it in the \"faster\" language, but only saves a minute of runtime, then it'll have to run 24*60 times to have caught up, and multiple times more than that to have been economical.","Q_Score":3,"Tags":"python,c++,tensorflow,benchmarking,inference","A_Id":42902517,"CreationDate":"2017-03-15T12:48:00.000","Title":"Python vs C++ Tensorflow inferencing","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a code which outputs an N-length Numpy array at every iteration. \nEg. -- theta = [ 0, 1, 2, 3, 4 ]\nI want to be able to save the arrays to a text file or .csv file dynamically such that I can load the data file later and extract appropriately which array corresponds to which iteration. Basically, it should be saved in an ordered fashion.\nI am assuming the data file would look something like this:-\n0 1 2 3 4\n1 2 3 4 5\n2 3 4 5 6 ... (Random output)\nI thought of using np.c_ but I don't want to overwrite the file at every iteration and if I simply save the terminal output as > output.txt, it saves as arrays including the brackets. I don't know how to read such a text file.\nIs there a proper method to do this, i.e. write and read the data?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":741,"Q_Id":42817337,"Users Score":0,"Answer":"How about ndarray's .tofile() method? To read use numpy.fromfile().","Q_Score":0,"Tags":"python,arrays,numpy,save","A_Id":42817461,"CreationDate":"2017-03-15T17:59:00.000","Title":"How do I save numpy arrays such that they can be loaded later appropriately?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using python's XGBRegressor and R's xgb.train with the same parameters on the same dataset and I'm getting different predictions. \nI know that XGBRegressor uses 'gbtree' and I've made the appropriate comparison in R, however, I'm still getting different results. \nCan anyone lead me in the right direction on how to differentiate the 2 and\/or find R's equivalence to python's XGBRegressor?\nSorry if this is a stupid question, thank you.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1321,"Q_Id":42819987,"Users Score":1,"Answer":"Since XGBoost uses decision trees under the hood it can give you slightly different results between fits if you do not fix random seed so the fitting procedure becomes deterministic.\nYou can do this via set.seed in R and numpy.random.seed in Python.\nNoting Gregor's comment you might want to set nthread parameter to 1 to achieve full determinism.","Q_Score":1,"Tags":"python,r,machine-learning,scikit-learn,xgboost","A_Id":42821370,"CreationDate":"2017-03-15T20:24:00.000","Title":"Python's XGBRegressor vs R's XGBoost","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"If I wanted to make a NLP Toolkit like NLTK, which features would I implement first after tokenisation and normalisation. POS Tagging or Lemmatisation?","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":519,"Q_Id":42835852,"Users Score":3,"Answer":"Part of speech is important for lemmatisation to work, as words which have different meanings depending on part of speech. And using this information, lemmatization will return the base form or lemma. So, it would be better if POS Tagging implementation is done first.\nThe main idea behind lemmatisation is to group different inflected forms of a word into one. For example, go, going, gone and went will become just one - go. But to derive this, lemmatisation would have to know the context of a word - whether the word is a noun or verb etc. \nSo, the lemmatisation function can take the word and the part of speech as input and return the lemma after processing the information.","Q_Score":1,"Tags":"python,nlp,nltk,pos-tagger,lemmatization","A_Id":42841898,"CreationDate":"2017-03-16T13:44:00.000","Title":"Which comes first in order of implementation: POS Tagging or Lemmatisation?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"If I wanted to make a NLP Toolkit like NLTK, which features would I implement first after tokenisation and normalisation. POS Tagging or Lemmatisation?","AnswerCount":2,"Available Count":2,"Score":0.1973753202,"is_accepted":false,"ViewCount":519,"Q_Id":42835852,"Users Score":2,"Answer":"Sure make the POS Tagger first. If you do lemmatisation first you could lose the best possible classification of words when doing the POS Tagger, especially in languages where ambiguity is commonplace, as it is in Portuguese.","Q_Score":1,"Tags":"python,nlp,nltk,pos-tagger,lemmatization","A_Id":42875622,"CreationDate":"2017-03-16T13:44:00.000","Title":"Which comes first in order of implementation: POS Tagging or Lemmatisation?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dask dataframe created from delayed functions which is comprised of randomly sized partitions. I would like to repartition the dataframe into chunks of size (approx) 10000.\nI can calculate the correct number of partitions with np.ceil(df.size\/10000) but that seems to immediately compute the result?\nIIUC to compute the result it would have had to read all the dataframes into memory which would be very inefficient. I would instead like to specify the whole operation as a dask graph to be submitted to the distributed scheduler so no calculations should be done locally.\nIs there some way to specify npartitions without having it immediately compute all the underlying delayed functions?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":298,"Q_Id":42849572,"Users Score":4,"Answer":"Short answer is probably \"no, there is no way to do this without looking at the data\".  The reason here is that the structure of the graph depends on the values of your lazy partitions.  For example we'll have a different number of nodes in the graph depending on your total datasize.","Q_Score":4,"Tags":"python,dataframe,dask","A_Id":42857362,"CreationDate":"2017-03-17T05:02:00.000","Title":"How to repartition a dataframe into fixed sized partitions?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I can use fbprophet (in python) to get some predicted data, but it just includes 't', 'yhat', 'yhat_upper', 'yhat_lower' and or so rather than 'y' which I also want to acquire.\nAt present I think I can't get the value of 'y' from the predicted data because Prophet doesn't work for predicting the future value like 'y'.\nAm I predicting by a wrong way?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":475,"Q_Id":42850344,"Users Score":0,"Answer":"If I'm not mistaken, 'y' is the data you're using yo fit with, i.e. the input to prophet. 'yhat' is the mean(or median?) of the predicted distribution.","Q_Score":1,"Tags":"python,facebook","A_Id":42853495,"CreationDate":"2017-03-17T06:06:00.000","Title":"How can i get 'y' rather than 'yhat' from predicted data by using fbprophet?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I a working on a text recognition project.\nI have built a classifier using TensorFlow to predict digits but I would like to implement a more complex algorithm of text recognition by using text localization and text segmentation (separating each character) but I didn't find an implementation for those parts of the algorithms.\nSo, do you know some algorithms\/implementation\/tips I, using TensorFlow, to localize text and do text segmentation in natural scenes pictures (actually localize and segmentation of text in the scoreboard for sports pictures)?\nThank you very much for any help.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":2116,"Q_Id":42868546,"Users Score":1,"Answer":"To group elements on a page, like paragraphs of text and images, you can use some clustering algo, and\/or blob detection with some tresholds.\nYou can use Radon transform to recognize lines and detect skew of a scanned page.\nI think that for character separation you will have to mess with fonts. Some polynomial matching\/fitting or something. (this is a very wild guess for now, don't take it seriously).\nBut similar aproach would allow you to get the character out of the line and recognize it in same step.\nAs for recognition, once you have a character, there is a nice trigonometric trick of comparing angles of the character to the angles stored in a database.\nWorks great on handwriting too.\nI am not an expert on how page segmentation exactly works, but it seems that I am on my way to become one. Just working on a project including it.\nSo give me a month and I'll be able to tell you more. :D\nAnyway, you should go and read Tesseract code to see how HP and Google did it there. It should give you pretty good ideas.\nGood luck!","Q_Score":0,"Tags":"python,tensorflow,deep-learning,text-classification,text-recognition","A_Id":42870291,"CreationDate":"2017-03-17T23:21:00.000","Title":"Text recognition and detection using TensorFlow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have no experience with Python. Just trying to run a program I downloaded from GitHub. Had a lot of problems trying to run it. After adding PATH, and installing a few modules(?), I got stuck:\nWhen I type in \"python filename.py\", I get the error: \n\nImportError: cannot import name: 'SequentialDataSet'\n\nI got the same error with different names. Just typed in \"pip install (name)\" to fix it. Got the same error again with another name, installed, but now I'm stuck with the error:\n\nCould not find a version that satisfies the requirement\n  SequentialDataSet (from versions: ). No matching distribution found\n  for SequentialDataSet.\n\nLet me know if there is any info you need\nThanks","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2259,"Q_Id":42881154,"Users Score":0,"Answer":"Met the same problem. Fixed by installing the pybrain from github:\npip install https:\/\/github.com\/pybrain\/pybrain\/archive\/0.3.3.zip","Q_Score":1,"Tags":"python,pip,installation,importerror","A_Id":43602549,"CreationDate":"2017-03-18T23:34:00.000","Title":"Cannot import name -> No matching distribution when trying to install","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a MxN array, where M is the number of observations and N is the dimensionality of each vector. From this array of vectors, I need to calculate the mean and minimum euclidean distance between the vectors. \nIn my mind, this requires me to calculate MC2 distances, which is an O(nmin(k, n-k)) algorithm. My M is ~10,000 and my N is ~1,000, and this computation takes ~45 seconds. \nIs there a more efficient way to compute the mean and min distances? Perhaps a probabilistic method? I don't need it to be exact, just close.","AnswerCount":3,"Available Count":1,"Score":-0.1325487884,"is_accepted":false,"ViewCount":1648,"Q_Id":42882604,"Users Score":-2,"Answer":"I had the same issue before, and it worked for me once I normalized the values. So try to normalize the data before calculating the distance.","Q_Score":15,"Tags":"python,algorithm,python-3.x,euclidean-distance","A_Id":52922702,"CreationDate":"2017-03-19T03:29:00.000","Title":"Efficient calculation of euclidean distance","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I fine tunes vgg-16 for binary classification. I used sigmoidLoss layer as the loss function.\nTo test the model, I coded a python file in which I loaded the model with an image and got the output using :\nout = net.forward()\nMy doubt is should I take the output from Sigmoid or SigmoidLoss layer.\nAnd What is the difference between 2 layers.\nMy output will actually be the probability of input image being class 1.**","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":329,"Q_Id":42883603,"Users Score":1,"Answer":"SigmoidWithLoss layer outputs a single number per batch representing the loss w.r.t the ground truth labels.\nOn the other hand, Sigmoid layer outputs a probability value for each input in the batch. This output does not require the ground truth labels to be computed.\nIf you are looking for the probability per input, you should be looking at the output of the Sigmoid layer","Q_Score":2,"Tags":"python,deep-learning,caffe,convolution,pycaffe","A_Id":42885380,"CreationDate":"2017-03-19T06:28:00.000","Title":"PyCaffe output layer for testing binary classification model","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have registered two images, let's say fixed and moving are Registered. After registration I want to measure overlap ratio etc.  \nThe SimpleITK has overlap measure filters and to use overlap_measures_filter.Execute(fixed, moving) and hausdroff_measures_filter.Execute() we need to segment the image and we need labels in input.  But the image is hard to segment using just thresholding or connected component filters.  \nNow the question is then how can we evaluate registration accuracy using SimpleITK with just fixed image and the registered image.(without segmentation ad labeling the image)","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":363,"Q_Id":42884375,"Users Score":0,"Answer":"If I understand your question correctly, you want the impossible: to have Hausdorff distance measure as if the image were segmented, but without segmenting it because the segmentation is hard.","Q_Score":1,"Tags":"python,image,itk,elastix,simpleitk","A_Id":42889299,"CreationDate":"2017-03-19T08:17:00.000","Title":"Image Registration accuracy evaluation (Hausdroff distance) using SimpleITK without segmenting the image","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":1,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Can anyone tell me commands to get contrib module for anaconda\nI need that module for \n matches = flann.knnMatch(des1,des2,k=2)\nto run correctly\nerror thrown is \ncv2.error: ......\\modules\\python\\src2\\cv2.cpp:163: error: (-215) The data should normally be NULL! in function NumpyAllocator::allocate\nAlso I am using Anaconda openCV version 3, and strictly dont want to switch to lower versions \nP.S. as suggested at many places to edit file cv2.cpp option is not available with anaconda.","AnswerCount":6,"Available Count":2,"Score":0.0333209931,"is_accepted":false,"ViewCount":29093,"Q_Id":42886286,"Users Score":1,"Answer":"The question is old but I thought to update the answer with the latest information. My Anaconda version is 2019.10 and build channel is py_37_0 . I  used pip install opencv-python==3.4.2.17 and pip install opencv-contrib-python==3.4.2.17. Now they are also visible as installed packages in Anaconda navigator and I am able to use patented methods like SIFT etc.","Q_Score":10,"Tags":"python,opencv,anaconda,conda","A_Id":62689216,"CreationDate":"2017-03-19T11:56:00.000","Title":"how to get opencv_contrib module in anaconda","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Can anyone tell me commands to get contrib module for anaconda\nI need that module for \n matches = flann.knnMatch(des1,des2,k=2)\nto run correctly\nerror thrown is \ncv2.error: ......\\modules\\python\\src2\\cv2.cpp:163: error: (-215) The data should normally be NULL! in function NumpyAllocator::allocate\nAlso I am using Anaconda openCV version 3, and strictly dont want to switch to lower versions \nP.S. as suggested at many places to edit file cv2.cpp option is not available with anaconda.","AnswerCount":6,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":29093,"Q_Id":42886286,"Users Score":14,"Answer":"I would recommend installing pip in your anaconda environment then just doing: pip install opencv-contrib-python. This comes will opencv and opencv-contrib.","Q_Score":10,"Tags":"python,opencv,anaconda,conda","A_Id":44329928,"CreationDate":"2017-03-19T11:56:00.000","Title":"how to get opencv_contrib module in anaconda","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Given A = [[1,2],[3,4],[5,6]]. How to use tf.diag() to construct a 3d tensor where each stack is a 2d diagonal matrix using the values from A? So the output should be B = [[[1,0],[0,2]],[[3,0],[0,4]],[[5,0],[0,6]]].  I want to use this as my Gaussian covariance matries.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":153,"Q_Id":42890105,"Users Score":0,"Answer":"Ok I figure it out. tf.matrix_diag() does the trick...","Q_Score":0,"Tags":"python,numpy,matrix,tensorflow,broadcast","A_Id":42890197,"CreationDate":"2017-03-19T17:58:00.000","Title":"construct 3d diagonal tensor using 2d tensor","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Suppose we have two large numpy arrays of the same data type and shape, of size on the order of GB's.  What is the fastest way to copy all the values from one into the other?\nWhen I do this using normal notation, e.g. A[:] = B, I see exactly one core on the computer at maximum effort doing the copy for several seconds, while the others are idle.  When I launch multiple workers using multiprocessing and have them each copy a distinct slice into the destination array, such that all the data is copied, using multiple workers is faster.  This is true regardless of whether the destination array is a shared memory array or one that becomes local to the worker.  I can get a 5-10x speedup in some tests on a machine with many cores.  As I add more workers, the speed does eventually level off and even slow down, so I think this achieves being memory-performance bound.  \nI'm not suggesting using multiprocessing for this problem; it was merely to demonstrate the possibility of better hardware utilization.\nDoes there exist a python interface to some multi-threaded C\/C++ memcpy tool?\nUpdate (03 May 2017)\nWhen it is possible, using multiple python processes to move data can give major speedup.  I have a scenario in which I already have several small shared memory buffers getting written to by worker processes.  Whenever one fills up, the master process collects this data and copies it into a master buffer.  But it is much faster to have the master only select the location in the master buffer, and assign a recording worker to actually do the copying (from a large set of recording processes standing by).  On my particular computer, several GB can be moved in a small fraction of a second by concurrent workers, as opposed to several seconds by a single process.\nStill, this sort of setup is not always (or even usually?) possible, so it would be great to have a single python process able to drop into a multi-threaded memcpy routine...","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1300,"Q_Id":42895292,"Users Score":1,"Answer":"If you are certain that the types\/memory layout of both arrays are identical, this might give you a speedup: memoryview(A)[:] = memoryview(B) This should be using memcpy directly and skips any checks for numpy broadcasting or type conversion rules.","Q_Score":2,"Tags":"python,arrays,multithreading,numpy,memcpy","A_Id":57453505,"CreationDate":"2017-03-20T03:19:00.000","Title":"faster numpy array copy; multi-threaded memcpy?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm currently part of a team working on a Hadoop application, parts of which will use Spark, and parts of which will use Java or Python (for instance, we can't use Sqoop or any other ingest tools included with Hadoop and will be implementing our own version of this). I'm just a data scientist so I'm really only familiar with the Spark portion, so apologies for the lack of detail or if this question just sucks in general - I just know that the engineering team needs both Java and Python support. I have been asked to look into using Cucumber (or any other BDD framework) for acceptance testing our app front to back once we're further along. I can't find any blogs, codebases, or other references where cucumber is being used in a polyglot app, and barely any where Hadoop is being used. Would it be possible to test our app using Cucumber or any other existing BDD framework? We already plan to do unit and integration testing via JUnit\/PyUnit\/etc as well.","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":994,"Q_Id":42909952,"Users Score":0,"Answer":"To use cucumber to test desktop applications you can use specflow which uses a framework in visual studio called teststack.white. Just google on cucumber specflow, teststack.white, etc and you should be able to get on track","Q_Score":0,"Tags":"java,python,hadoop,cucumber,cucumber-jvm","A_Id":43516507,"CreationDate":"2017-03-20T17:18:00.000","Title":"Can I use Cucumber to test an application that uses more than one language?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm currently part of a team working on a Hadoop application, parts of which will use Spark, and parts of which will use Java or Python (for instance, we can't use Sqoop or any other ingest tools included with Hadoop and will be implementing our own version of this). I'm just a data scientist so I'm really only familiar with the Spark portion, so apologies for the lack of detail or if this question just sucks in general - I just know that the engineering team needs both Java and Python support. I have been asked to look into using Cucumber (or any other BDD framework) for acceptance testing our app front to back once we're further along. I can't find any blogs, codebases, or other references where cucumber is being used in a polyglot app, and barely any where Hadoop is being used. Would it be possible to test our app using Cucumber or any other existing BDD framework? We already plan to do unit and integration testing via JUnit\/PyUnit\/etc as well.","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":994,"Q_Id":42909952,"Users Score":0,"Answer":"The feature files would be written using Gherkin. Gherkin looks the same if you are using Java or Python. So in theory, you are able to execute the same specifications from both Java end Python. This would, however, not make any sense. It would just be a way to implement the same behaviour in two different languages and therefore two different places. The only result would be duplication and miserable developers.\nWhat you can do is to use BDD and Gherkin to drive the implementation. But different behaviour in different languages. This will lead you to use two different sets of features. That is possible and probably a good idea given the context you describe.","Q_Score":0,"Tags":"java,python,hadoop,cucumber,cucumber-jvm","A_Id":42931219,"CreationDate":"2017-03-20T17:18:00.000","Title":"Can I use Cucumber to test an application that uses more than one language?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"My current data set is by Fiscal Week. It is in this format \"FY2013 WK 2\". How can I format it, so that I can use a regression model on it and predict a value for let's say \"FY2017 WK 2\".\nShould I treat Fiscal Week as a categorical value and use dmatrices?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":94,"Q_Id":42915249,"Users Score":1,"Answer":"You will need to convert the string describing the week into an integer you can use as the abscissa (x-coordinate, or independent variable).  Pick a \"zero point\", such as FY2012 WK 52, so that FY2013 WK 01 translates to the integer 1.\nI don't that DateTime handles this conversion; you might have to code the translation yourself: parse the string into year and week integers, and compute the abscissa from that: \n\n52*(year-2013) + week\n\nYou might also want to keep a dictionary of those translations, as well as a reverse list (week => FY_week) for output labelling.\nDoes that move you toward a solution you can implement?","Q_Score":0,"Tags":"python,date,format,regression","A_Id":42915731,"CreationDate":"2017-03-20T22:38:00.000","Title":"Week Number in python for regression","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I know how to create a hive udf with transform and using, but I can't use sklearn because not all the node in hive cluster has sklearn.\nI have an anaconda2.tar.gz with sklearn, What should I do ?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":314,"Q_Id":42927141,"Users Score":0,"Answer":"I recently started looking into this approach and I feel like the problem is not about to get all the 'hive nodes' having sklearn on them (as you mentioned above), I feel like it is rather a compatibility issue than 'sklearn node availability' one. I think sklearn is not (yet) designed to run as a parallel algorithm such that large amount of data can be processed in a short time.\n\nWhat I'm trying to do, as an approach, is to communicate python to 'hive' through 'pyhive' (for example) and implement the necessary sklearn libraries\/calls within that code. The rough assumption here that this 'sklearn-hive-python' code will run in each node and deal with the data at the 'map-reduce' level. \nI cannot say this is the right solution or correct approach (yet) but this is what I can conclude after searching for sometime.","Q_Score":0,"Tags":"python,hive,package,udf","A_Id":43289954,"CreationDate":"2017-03-21T12:26:00.000","Title":"How to create an udf for hive using python with 3rd party package like sklearn?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a SVM model trained in MATLAB (using 6 features) for which I have:\n\nSupport Vectors [337 x 6]\nAlpha [337 x 1]\nBias\nKernel Function: @rbf_kernel\nKernel Function Args = 0.9001\nGroupNames [781 x 1]\nSupport Vector Indices [337 x 1]\nScale Data containing:\n\n\nshift [1 x 6]\nscale factor [1 x 6]\n\n\nThese above are all data that I am able to load in python.\nNow I would like to use this model in python without retraining to perform classification in python. In particular I would like to create a SVM model in python from the support vector generated in MATLAB\nIs it possible? How? Any help would be very appreciated!\nI can't retrain it in python because I don't have the training data (and labels) anymore.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":519,"Q_Id":42931469,"Users Score":0,"Answer":"I guess you understand how SVM works, so what I would do is to train the model again in python just on the support vectors you found rather than on all the original training data and the result should remain the same (as if you trained it on the full data), since the support vectors are the \"interesting\" vectors in the data, that are sitting on the boundaries.","Q_Score":1,"Tags":"python,matlab,scikit-learn,svm","A_Id":42931849,"CreationDate":"2017-03-21T15:30:00.000","Title":"Use SVM model trained in Matlab for classification in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using OpenMDAO 1.7.3 for an optimization problem on a map.\nMy parameters are the coordinates on this map. The first thing I do is interpolating the height at this location from a map in one component. Then some more complex calculations follow in other components.\nIf OpenMDAO chooses a location outside the boundaries of the map I will get a height of NaN. I already know that the rest of there is no additional information to be gained from this optimization step. How can I make OpenMDAO move on to the next evaluation point as soon before doing the more complex calculations?\nIn my case the other calculations (in an external program) will even fail if they encounter a NaN, so I have to check the value before calling them in each of the components and assign NaN outputs for each of them. Is there a better way to do that?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":55,"Q_Id":42931794,"Users Score":0,"Answer":"Depending on your setup, you can raise an error inside the component that will kill the run. They you just change the input and start up the next run. Alternately, modify your wrapper for the subsequent code so that if it sees a NAN it skips running and just reports a garbage number thats easily identifiable.","Q_Score":0,"Tags":"python,openmdao","A_Id":42932214,"CreationDate":"2017-03-21T15:43:00.000","Title":"How can I stop OpenMDAO evaluating at a given location early","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using pyspark from a notebook and I do not handle the creation of the SparkSession.\nI need to load a jar containing some functions I would like to use while processing my rdds. This is something which you can easily do using --jars which I cannot do in my particular case. \nIs there a way to access the spark scala context and call the addJar method? I tried to use the JavaGateway (sparksession._jvm...) but have not been successful so far. Any idea?\nThanks\nGuillaume","AnswerCount":3,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":4578,"Q_Id":42967472,"Users Score":3,"Answer":"sparksession._jsc.addJar does the job.","Q_Score":2,"Tags":"python,apache-spark,jar","A_Id":42968126,"CreationDate":"2017-03-23T04:51:00.000","Title":"Adding a jar file to pyspark after context is created","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to install tensorflow with python 3.5 using anaconda but I don't know which anaconda version has python 3.5. When I go to anaconda download page am presented with Anaconda 4.3.1 which has either version 3.6 or 2.7 of python","AnswerCount":7,"Available Count":1,"Score":-0.057080742,"is_accepted":false,"ViewCount":123158,"Q_Id":42978349,"Users Score":-2,"Answer":"It is very simple, first, you need to be inside the virtualenv you created, then to install a specific version of python say 3.5, use Anaconda, conda install python=3.5\nIn general you can do this for any python package you want\nconda install package_name=package_version","Q_Score":47,"Tags":"python,anaconda","A_Id":62708019,"CreationDate":"2017-03-23T14:03:00.000","Title":"Anaconda version with Python 3.5","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a Bokeh document with many plots\/models, each of which has its own ColumnDataSource.  If I update one ColumnDataSource does that trigger updates to all of my models or only to the models to which the changed source is relevant?\nI ask because I have a few models, some of which are complex and change slowly and others which are simple and change quickly.  I want to know if it makes sense performance-wise to scale the update frequencies on a per-plot basis or if I have to actually have different documents for this to be effective.\nI am running a Bokeh server application","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":247,"Q_Id":42983784,"Users Score":0,"Answer":"As of Bokeh 0.12.5 update messages are:\n\ntriggered immediately for a given property change \ngranular (are not batched in any way)\n\nSo, updating model.foo triggers an immediate message sent to the browser, and that message only pertains to the corresponding model.foo in the browser. \nThere Bokeh protocol allows for batching updates, but this capability is not really used anywhere yet. It is an open feature request to allow for some kind of batching that would delay sending update messages until some grouping of them could be collected.","Q_Score":1,"Tags":"python,bokeh","A_Id":43007775,"CreationDate":"2017-03-23T18:08:00.000","Title":"Does updating one Bokeh ColumnDataSource affect the entire document?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am currently building a CNN with Keras and need to define a custom loss function. I would only like to consider specific parts of my data in the loss and ignore others based on a certain parameter value. But, I am having trouble iterating over the Tensor objects that the Keras loss function expects.\nIs there a simple way for me to compute the mean squared error between two Tensors, only looking at selected values in the Tensor?\nFor example, each Tensor in my case represents a 2D 16x16 grid, with each cell having 2 parameters - shape (16, 16, 2). I only want to compare cells where one of their parameters is equal to 1.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":707,"Q_Id":42986686,"Users Score":1,"Answer":"The general approach is to use binary masks. Tensorflow provides several boolean functions such as tf.equal and tf.not_equal. For selecting only enterings which are equal to a certain value, you could use tf.equal and then multiply the loss tensor by the obtained binary mask.","Q_Score":0,"Tags":"python,machine-learning,tensorflow,keras,conv-neural-network","A_Id":42996091,"CreationDate":"2017-03-23T20:48:00.000","Title":"Selectively Iterate over Tensor","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm fairly new to programming and thought I'd try writing a piecewise linear interpolation function. (perhaps which is done with numpy.interp or scipy.interpolate.interp1d)\nSay I am given data as follows: x= [1, 2.5, 3.4, 5.8, 6] y=[2, 4, 5.8, 4.3, 4]\nI want to design a piecewise interpolation function that will give the coefficents of all the Linear polynomial pieces between 1 and 2.5, 2.5 to 3.4 and so on using Python. \nof course matlab has the interp1 function which do this but im using python and i want to do exactly the same job as matlab but python only gives the valuse but not linear polynomials coefficient ! (in matlab we could get this with pp.coefs) .\nbut how to get pp.coefs in python numpy.interp ?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":4195,"Q_Id":42995027,"Users Score":1,"Answer":"If you're doing linear interpolation you can just use the formula that the line from point (x0, y0) to (x1, y1) the line that interpolates them is given by y - y0 = ((y0 - y1)\/(x0 - x1)) * (x - x0). You can take 2 element slices of your list using the slice syntax; for example to get [2.5, 3.4] you would use x[1:3].\nUsing the slice syntax you can then implement the linear interpolation formula to calculate the coefficients of the linear polynomial interpolations.","Q_Score":3,"Tags":"python,interpolation,coefficients","A_Id":42995646,"CreationDate":"2017-03-24T08:49:00.000","Title":"piecewise linear interpolation function in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a DataArray for which da.dims==().  I can assign a coordinate da.assign_coords(foo=42).  I would like to add a corresponding dimension with length one, such that da.dims==(\"foo\",) and the corresponding coordinate would be foo=[42].  I cannot use assign_coords(foo=[42]), as this results in the error message cannot add coordinates with new dimensions to a DataArray.\nHow do I assign a new dimension of length one to a DataArray? I could do something like DataArray(da.values.reshape([1]), dims=\"foo\", coords={\"foo\": [42]}) but I wonder if there is a method that does not require copying the entire object.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":281,"Q_Id":42997677,"Users Score":1,"Answer":"You can use xarray.concat to achieve this:\nda = xarray.DataArray(0, coords={\"x\": 42})\nxarray.concat((da,), dim=\"x\")","Q_Score":1,"Tags":"python,python-xarray","A_Id":42999562,"CreationDate":"2017-03-24T10:55:00.000","Title":"assign new dimension of length one","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Obviously the rfft2 function simply computes the discrete fft of the input matrix. However how do I interpret a given index of the output? Given an index of the output, which Fourier coefficient am I looking at?\nI am especially confused by the sizes of the output. For an n by n matrix, the output seems to be an n by (n\/2)+1 matrix (for even n). Why does a square matrix ends up with a non-square fourier transform?","AnswerCount":3,"Available Count":1,"Score":0.1325487884,"is_accepted":false,"ViewCount":5101,"Q_Id":43001729,"Users Score":2,"Answer":"Also note the ordering of the coefficients in the fft output:\nAccording to the doc: by default the 1st element is the coefficient for 0 frequency component (effectively the sum or mean of the array), and starting from the 2nd we have coeffcients for the postive frequencies in increasing order, and starts from n\/2+1 they are for negative frequencies in decreasing order. To have a view of the frequencies for a length-10 array:\n\nnp.fft.fftfreq(10)\n\nthe output is:\n\narray([ 0. ,  0.1,  0.2,  0.3,  0.4, -0.5, -0.4, -0.3, -0.2, -0.1])\n\nUse np.fft.fftshift(cf), where cf=np.fft.fft(array), the output is shifted so that it corresponds to this frequency ordering:\n\narray([-0.5, -0.4, -0.3, -0.2, -0.1,  0. ,  0.1,  0.2,  0.3,  0.4])\n\nwhich makes for sense for plotting.\nIn the 2D case it is the same. And the fft2 and rfft2 difference is as explained by others.","Q_Score":11,"Tags":"python,numpy,fft","A_Id":43012808,"CreationDate":"2017-03-24T14:12:00.000","Title":"How should I interpret the output of numpy.fft.rfft2?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a numpy array Z1, Z2, Z3:\n\u00a0\u00a0\u00a0\u00a0Z1 = [1,2,3]\n\u00a0\u00a0\u00a0\u00a0Z2 = [4,5]\n\u00a0\u00a0\u00a0\u00a0Z3 = [6,7,8,9]\nI want new numpy array Z that have Z1, Z2, Z3 as array like:\n\u00a0\u00a0\u00a0\u00a0Z = [[1,2,3],[4,5],[6,7,8,9]\n\u00a0\u00a0\u00a0\u00a0print(type(Z),type(Z[0]))\n\u00a0\u00a0\u00a0\u00a0>>> <class 'numpy.ndarray'> <class 'numpy.ndarray'>\nI used np.append, hstack, vstack, insert, concatenate ...but all I failed.\nThere is only 2 case:\n\u00a0\u00a0\u00a0\u00a0Z = [1,2,3,4,5,6,7,8,9]\nor ERROR\nso I made a list Z first, and append list Z1, Z2, Z3 and then convert list Z into numpy array Z.\nBUT\n\u00a0\u00a0\u00a0\u00a0Z = [[1,2,3],[4,5],[6,7,8,9]]\n\u00a0\u00a0\u00a0\u00a0print(type(Z),type(Z[0]))\n\u00a0\u00a0\u00a0\u00a0>>> <class 'numpy.ndarray'> <class 'list'> \nI want to do not use 'while' or 'for'. Help me please..","AnswerCount":4,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1326,"Q_Id":43018769,"Users Score":0,"Answer":"You can't make that array. Arrays is numpy are similar to matrices in math. They have to be mrows, each having n columns. Use a list of lists, or list of np.arrays","Q_Score":1,"Tags":"python,arrays,list,numpy,types","A_Id":43018809,"CreationDate":"2017-03-25T16:15:00.000","Title":"Python How to make array in array?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a numpy array Z1, Z2, Z3:\n\u00a0\u00a0\u00a0\u00a0Z1 = [1,2,3]\n\u00a0\u00a0\u00a0\u00a0Z2 = [4,5]\n\u00a0\u00a0\u00a0\u00a0Z3 = [6,7,8,9]\nI want new numpy array Z that have Z1, Z2, Z3 as array like:\n\u00a0\u00a0\u00a0\u00a0Z = [[1,2,3],[4,5],[6,7,8,9]\n\u00a0\u00a0\u00a0\u00a0print(type(Z),type(Z[0]))\n\u00a0\u00a0\u00a0\u00a0>>> <class 'numpy.ndarray'> <class 'numpy.ndarray'>\nI used np.append, hstack, vstack, insert, concatenate ...but all I failed.\nThere is only 2 case:\n\u00a0\u00a0\u00a0\u00a0Z = [1,2,3,4,5,6,7,8,9]\nor ERROR\nso I made a list Z first, and append list Z1, Z2, Z3 and then convert list Z into numpy array Z.\nBUT\n\u00a0\u00a0\u00a0\u00a0Z = [[1,2,3],[4,5],[6,7,8,9]]\n\u00a0\u00a0\u00a0\u00a0print(type(Z),type(Z[0]))\n\u00a0\u00a0\u00a0\u00a0>>> <class 'numpy.ndarray'> <class 'list'> \nI want to do not use 'while' or 'for'. Help me please..","AnswerCount":4,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1326,"Q_Id":43018769,"Users Score":0,"Answer":"everybody thanks! Answers are a little bit different with that I want, but eventually I solve that without use 'for' or 'while'.\nFirst, I made \"numpy array\" Z1, Z2, Z3 and put them into \"list\" Z. There are array in List.\nSecond, I convert \"list\" Z into \"numpy array\" Z. It is array in array that I want.","Q_Score":1,"Tags":"python,arrays,list,numpy,types","A_Id":43039088,"CreationDate":"2017-03-25T16:15:00.000","Title":"Python How to make array in array?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm creating a model to predict the probability that customers will buy product A in a department store that sells product A through Z. The store has it's own credit card with demographic and transactional information of 140,000 customers.\nThere is a subset of customers (say 10,000) who currently buy A. The goal is to learn from these customers 10,000 customers and score the remaining 130,000 with their probability to buy A, then target the ones with the highest scores with marketing campaigns to increase A sales.\nHow should I define my training and evaluation sets?\nTraining set: \nShould it be only the 10,000 who bought A or the whole 140k customers?\nEvaluation set: (where the model will be used in production)\nI believe this should be the 130k who haven't bought A.\nThe question about time:\nAnother alternative is to take a photograph of the database last year, use it as a training set, then take the database as it is today and evaluate all customer's with the model created with last year's info. Is this correct? When should I do this?\nWhich option is correct for all sets?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":55,"Q_Id":43021624,"Users Score":0,"Answer":"The training set and the evaluation set must be different. The whole point of having an evaluation set is guard against over-fitting. \nIn this case what you should do is take say 100,000 customers, picked at random. Then use the data to try and learn what is about customers that make them likely purchase A. Then use the remaining 40,000 to test how well you model works.","Q_Score":0,"Tags":"python,scikit-learn,anaconda,data-mining,prediction","A_Id":43021757,"CreationDate":"2017-03-25T20:29:00.000","Title":"Can training and evaluation sets be the same in predictive analytics?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"For DNN Classifier there is a method predict_proba to get the probabilities, whereas for DNN Regressor it is not there. Please help.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":827,"Q_Id":43029358,"Users Score":1,"Answer":"In terminal, type help(tf.contrib.learn.DNNRegressor. There you will see the object has methods such as predict() which returns predicted scores.\nDNNRegressor does regression, not classification, so you don't get a probability distribution over classes.","Q_Score":0,"Tags":"python,tensorflow","A_Id":43032478,"CreationDate":"2017-03-26T13:29:00.000","Title":"How to get confidence levels(probabilities) for DNN Regressor in Tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"What is the reason packages are distributed separately?\n\nWhy do we have separate 'add-on' packages like pandas, numpy?\nSince these modules seem so important, why are these not part of Python itself?\n\nAre the \"single distributions\" of Python to come pre-loaded?\n\nIf it's part of design to keep the 'core' separate from additional functionality, still in that case it should at least come 'pre-imported' as soon as you start Python.\n\nWhere can I find such distributions if they exist?","AnswerCount":4,"Available Count":2,"Score":0.1488850336,"is_accepted":false,"ViewCount":1040,"Q_Id":43034716,"Users Score":3,"Answer":"This is a bit like asking \"Why doesn't every motor come with a car around it?\"  \nWhile a car without a motor is pretty useless, the inverse doesn't hold: Most motors aren't even used for cars. Of course one could try selling a complete car to people who want to have a generator, but they wouldn't buy it.\nAlso the people designing cars might not be the best to build a motor and vice versa. \nSimilarly with python. Most python distributions are not used with numpy, scipy or pandas. Distributing python with those packages would create a massive overhead. \nHowever, there is of course a strong demand for prebuilt distributions which combine those modules with a respective python and make sure everything interacts smoothly. Some examples are Anaconda, Canopy, python(x,y), winpython, etc. So an end user who simply wants a car that runs, best chooses one of those, instead of installing everything from scratch. Other users who do want to always have the newest version of everything might choose to tinker them together themselves.","Q_Score":3,"Tags":"python,anaconda,package,python-packaging,canopy","A_Id":43035582,"CreationDate":"2017-03-26T21:24:00.000","Title":"Why doesn't Python come pre-built with required libraries like pandas, numpy etc","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"What is the reason packages are distributed separately?\n\nWhy do we have separate 'add-on' packages like pandas, numpy?\nSince these modules seem so important, why are these not part of Python itself?\n\nAre the \"single distributions\" of Python to come pre-loaded?\n\nIf it's part of design to keep the 'core' separate from additional functionality, still in that case it should at least come 'pre-imported' as soon as you start Python.\n\nWhere can I find such distributions if they exist?","AnswerCount":4,"Available Count":2,"Score":-0.049958375,"is_accepted":false,"ViewCount":1040,"Q_Id":43034716,"Users Score":-1,"Answer":"PyPi currently has over 100,000 libraries available. I'm sure someone thinks each of these is important.\nWhy do you need or want to pre-load libraries, considering how easy a pip install is especially in a virtual environment?","Q_Score":3,"Tags":"python,anaconda,package,python-packaging,canopy","A_Id":43035255,"CreationDate":"2017-03-26T21:24:00.000","Title":"Why doesn't Python come pre-built with required libraries like pandas, numpy etc","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I was trying to import sklearn.model_selection with Jupiter Notebook under anaconda environment with python 3.5, but I was warned that I didn't have \"model_selection\" module, so I did conda update scikit-learn. \nAfter that, I received a message of ImportError: cannot import name 'logsumexp' when importing sklearn.model_selection. \nI reinstalled sklearn and scipy, but still received the same error message. May I have some advice?","AnswerCount":5,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":18428,"Q_Id":43037903,"Users Score":0,"Answer":"Just closed the Spyder editor and restarted. This Issue got fixed.","Q_Score":7,"Tags":"python,scikit-learn","A_Id":44992636,"CreationDate":"2017-03-27T04:22:00.000","Title":"ImportError: cannot import name 'logsumexp' when importing sklearn.model_selection","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I was trying to import sklearn.model_selection with Jupiter Notebook under anaconda environment with python 3.5, but I was warned that I didn't have \"model_selection\" module, so I did conda update scikit-learn. \nAfter that, I received a message of ImportError: cannot import name 'logsumexp' when importing sklearn.model_selection. \nI reinstalled sklearn and scipy, but still received the same error message. May I have some advice?","AnswerCount":5,"Available Count":3,"Score":0.0399786803,"is_accepted":false,"ViewCount":18428,"Q_Id":43037903,"Users Score":1,"Answer":"The same error appeared when I tried to import hmm from hmmlearn, I reinstalled scipy and it worked. Hope this can be helpful.(I have tried updating all of the packages involved to solve the problem, but did not work. My computer system is ubuntu 16.04, with anaconda3 installed.)","Q_Score":7,"Tags":"python,scikit-learn","A_Id":55497890,"CreationDate":"2017-03-27T04:22:00.000","Title":"ImportError: cannot import name 'logsumexp' when importing sklearn.model_selection","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I was trying to import sklearn.model_selection with Jupiter Notebook under anaconda environment with python 3.5, but I was warned that I didn't have \"model_selection\" module, so I did conda update scikit-learn. \nAfter that, I received a message of ImportError: cannot import name 'logsumexp' when importing sklearn.model_selection. \nI reinstalled sklearn and scipy, but still received the same error message. May I have some advice?","AnswerCount":5,"Available Count":3,"Score":1.2,"is_accepted":true,"ViewCount":18428,"Q_Id":43037903,"Users Score":10,"Answer":"I came across exactly the same problem just now. After I updated scikit-learn and tried to import sklearn.model_selection, the ImportError appeared. \nI just restarted anaconda and ran it again. \nIt worked. Don't know why.","Q_Score":7,"Tags":"python,scikit-learn","A_Id":43158642,"CreationDate":"2017-03-27T04:22:00.000","Title":"ImportError: cannot import name 'logsumexp' when importing sklearn.model_selection","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to know were Quantopian gets data from? \nIf I want to do an analysis on a stock market other than NYSE, will I get the data? If not, can I manually upload the data so that I can run my algorithms on it.","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":312,"Q_Id":43041964,"Users Score":0,"Answer":"You can get data for non-NYSE stocks as well like Nasdaq securities.  Screens are also available by fundamentals(market, exchange, market cap).  These screens can limit stocks analyzed from the broad universe.","Q_Score":0,"Tags":"python-3.x,zipline","A_Id":53089089,"CreationDate":"2017-03-27T08:56:00.000","Title":"Using quantopian for data analysis","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am loading Spark dataframes into H2O (using Python) for building machine learning models. It has been recommended to me that I should allocate an H2O cluster with RAM 2-4x as big as the frame I will be training on, so that the analysis fits comfortably within memory. But I don't know how to precisely estimate the size of an H2O frame.\nSo supposing I have an H2O frame already loaded into Python, how do I actually determine its size in bytes? An approximation within 10-20% is fine.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":699,"Q_Id":43048126,"Users Score":0,"Answer":"This refers to 2-4 times the size of the file on disk, so rather than looking at the memory in Python, look at the original file size.  Also, the 2-4x recommendation varies by algorithm (GLM & DL will requires less memory than tree-based models).","Q_Score":0,"Tags":"python,h2o","A_Id":43051434,"CreationDate":"2017-03-27T13:42:00.000","Title":"How to determine size in bytes of H2O frame in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to save weights (and biases) from a CNN that I implemented and trained from scratch using Tensorflow (Python API).\nNow I would like to save these weights in a file and share it with someone so he can use my network. But since I have a lot of weights I don't know.  How can\/should I do that?  Is there a format recommended to do that?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":383,"Q_Id":43049673,"Users Score":0,"Answer":"Tensorflow provides a way to save your model: tensorflow.org\/api_docs\/python\/tf\/train\/Saver. Your friend should then also use Tensorflow to load them. The language you load \/ save with doesn't affect how it is saved - if you save them in Tensorflow in Python they can be read in Tensorflow in C++.","Q_Score":1,"Tags":"python,tensorflow,deep-learning","A_Id":43056493,"CreationDate":"2017-03-27T14:50:00.000","Title":"Best way to save a CNN's weights in order to reuse them","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"These questions about datetime parsing in pandas.read_csv() are all related.\nQuestion 1\nThe parameter infer_datetime_format is False by default. Is it safe to set it to True? In other words, how accurately can Pandas infer date formats? Any insight into its algorithm would be helpful.\nQuestion 2\nLoading a CSV file with over 450,000 rows took over 10 mins when I ran pd.read_csv(\"file.csv\", parse_dates = [\"Start\", \"End\"])\nHowever it took only 20 seconds when I added the parameters dayfirst = True and infer_datetime_format = True. Yet if either was False, it took over 10 mins.\nWhy must both be True in order to speed up datetime parsing? If one is False but not the other, shouldn't it take strictly between 20 sec and 10 mins? (The answer to this question may well be the algorithm, as in Question 1.)\nQuestion 3\nSince dayfirst = True, infer_datetime_format = True speeds up datetime parsing, why is it not the default setting? Is it because Pandas cannot accurately infer date formats?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":527,"Q_Id":43058703,"Users Score":0,"Answer":"and 2. In my experience, if processing time is not critical for your study (say you process the data once and then you run your analysis) then I would recommend you parse the dates using pd.to_datetime() and others after you have read in the data.\nanything that will help Pandas reduce the set of possibilities about the types in your data WILL speed up the processing. That make sense. The more precise you are, the faster will be the processing.","Q_Score":1,"Tags":"python,performance,csv,pandas,datetime","A_Id":43058780,"CreationDate":"2017-03-28T00:36:00.000","Title":"Slow datetime parsing in Pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I run ptb_word_lm.py provided by Tensorflow 1.0, but it shows this message:\n\nValueError: Attempt to have a second RNNCell use the weights of a variable scope that already has weights:\n  'Model\/RNN\/multi_rnn_cell\/cell_0\/basic_lstm_cell'; and the cell was\n  not constructed as BasicLSTMCell(..., reuse=True).  To share the\n  weights of an RNNCell, simply reuse it in your second calculation, or\n  create a new one with the argument reuse=True.\n\nThen I modify the code, add 'reuse=True' to BasicLSTMCell, but it show this message:\n\nValueError: Variable Model\/RNN\/multi_rnn_cell\/cell_0\/basic_lstm_cell\/weights does not\n  exist, or was not created with tf.get_variable(). Did you mean to set\n  reuse=None in VarScope?\n\nHow could I solve this problems?","AnswerCount":5,"Available Count":2,"Score":0.0399786803,"is_accepted":false,"ViewCount":3069,"Q_Id":43060827,"Users Score":1,"Answer":"Adding reuse = tf.get_variable_scope().reuse to BasicLSTMCell is OK to me.","Q_Score":2,"Tags":"python,tensorflow","A_Id":44095963,"CreationDate":"2017-03-28T04:50:00.000","Title":"ValueError: Attempt to have a second RNNCell use the weights of a variable scope that already has weights","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I run ptb_word_lm.py provided by Tensorflow 1.0, but it shows this message:\n\nValueError: Attempt to have a second RNNCell use the weights of a variable scope that already has weights:\n  'Model\/RNN\/multi_rnn_cell\/cell_0\/basic_lstm_cell'; and the cell was\n  not constructed as BasicLSTMCell(..., reuse=True).  To share the\n  weights of an RNNCell, simply reuse it in your second calculation, or\n  create a new one with the argument reuse=True.\n\nThen I modify the code, add 'reuse=True' to BasicLSTMCell, but it show this message:\n\nValueError: Variable Model\/RNN\/multi_rnn_cell\/cell_0\/basic_lstm_cell\/weights does not\n  exist, or was not created with tf.get_variable(). Did you mean to set\n  reuse=None in VarScope?\n\nHow could I solve this problems?","AnswerCount":5,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":3069,"Q_Id":43060827,"Users Score":0,"Answer":"You can trying to add scope='lstmrnn' in your tf.nn.dynamic_rnn() function.","Q_Score":2,"Tags":"python,tensorflow","A_Id":45557687,"CreationDate":"2017-03-28T04:50:00.000","Title":"ValueError: Attempt to have a second RNNCell use the weights of a variable scope that already has weights","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using sklearn's Linear Regression ML model in Python to predict. The predict function returns an array with a lot of floating point numbers, (which is correct) but I don't quite understand what the floating point numbers represent. Is it possible to map them back?\nFor context, I am trying to predict sales of a product (label) from stocks available. The predict function returns a large array of floating point numbers. How do I know what each floating point number represents? \nFor instance, the array is like [11.5, 12.0, 6.1,..]. It seems 6.1 is the sales qty but with what stock quantity is it associated with?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":4905,"Q_Id":43080722,"Users Score":2,"Answer":"i'th output is prediction for i'th input. Whatever you passed to .predict is a collection of objects, and the ordering of predictions is the same as the ordering of data passed in.","Q_Score":1,"Tags":"python,machine-learning,scikit-learn,scikits","A_Id":43080891,"CreationDate":"2017-03-28T22:00:00.000","Title":"Sklearn predict function","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"If I want to add new nodes to on of my tensorflow layers on the fly, how can I do that?\nFor example if I want to change the amount of hidden nodes from 10 to 11 after the model has been training for a while. Also, assume I know what value I want the weights coming in and out of this node\/neuron to be.\nI can create a whole new graph, but is there a different\/better way?","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2690,"Q_Id":43092454,"Users Score":5,"Answer":"Instead of creating a whole new graph you might be better off creating a graph which has initially more neurons than you need and mask it off by multiplying by a non-trainable variable which has ones and zeros. You can then change the value of this mask variable to allow effectively new neurons to act for the first time.","Q_Score":3,"Tags":"python,machine-learning,tensorflow,neural-network,artificial-intelligence","A_Id":43098199,"CreationDate":"2017-03-29T11:40:00.000","Title":"How to add new nodes \/ neurons dynamically in tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am about to install Apache Spark 2.1.0 on Ubuntu 16.04 LTS. My goal is a standalone cluster, using Hadoop, with Scala and Python (2.7 is active)\nWhilst downloading I get the choice: Prebuilt for Hadoop 2.7 and later (File is spark-2.1.0-bin-hadoop2.7.tgz)\n\nDoes this package actually include HADOOP 2.7 or does it need to be installed separately (first I assume)?\nI have Java JRE 8 installed (Needed for other tasks). As the JDK 8 also seems to be a pre requisite as well, I also did a ' sudo apt install default-jdk', which indeed shows as installed:\n\ndefault-jdk\/xenial,now 2:1.8-56ubuntu2 amd64 [installed]\nChecking java -version however doesn't show the JDK:\njava version \"1.8.0_121\"\nJava(TM) SE Runtime Environment (build 1.8.0_121-b13)\nJava HotSpot(TM) 64-Bit Server VM (build 25.121-b13, mixed mode)\nIs this sufficient for the installation? Why doesn't it also show the JDK?\n\nI want to use Scala 2.12.1. Does this version work well with the Spark2.1\/Hadoop 2.7 combination or is another version more suitable?\nIs the Scala SBT package also needed?\n\nBeen going back and forth trying to get everything working, but am stuck at this point.\nHope somebody can shed some light :)","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":730,"Q_Id":43099139,"Users Score":1,"Answer":"You need to install hadoop-2.7 more to whatever you are installing. \nJava version is fine. \nThe mentioned configuration should work with scala 2.12.1.","Q_Score":1,"Tags":"java,python,scala,ubuntu,hadoop","A_Id":43114845,"CreationDate":"2017-03-29T16:27:00.000","Title":"Apache Spark: Pre requisite questions","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to run a tensorflow python script in a google cloud vm instance with GPU enabled. I have followed the process for installing GPU drivers, cuda, cudnn and tensorflow. However whenever I try to run my program (which runs fine in a super computing cluster) I keep getting:\n\nundefined symbol: cudnnCreate\n\nI have added the next to my ~\/.bashrc\n\nexport LD_LIBRARY_PATH=\"$LD_LIBRARY_PATH:\/usr\/local\/cuda-8.0\/lib64:\/usr\/local\/cuda-8.0\/extras\/CUPTI\/lib64:\/usr\/local\/cuda-8.0\/lib64\"\nexport CUDA_HOME=\"\/usr\/local\/cuda-8.0\"\nexport PATH=\"$PATH:\/usr\/local\/cuda-8.0\/bin\"\n\nbut still it does not work and produces the same error","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":304,"Q_Id":43100290,"Users Score":1,"Answer":"Answering my own question: The issue was not that the library was not installed, the library installed was the wrong version hence it could not find it. In this case it was cudnn 5.0. However even after installing the right version it still didn't work due to incompatibilities between versions of driver, CUDA and cudnn. I solved all this issues by re-installing everything including the driver taking into account tensorflow libraries requisites.","Q_Score":0,"Tags":"python,tensorflow,ubuntu-16.04,cudnn","A_Id":43239777,"CreationDate":"2017-03-29T17:29:00.000","Title":"undefined symbol: cudnnCreate in ubuntu google cloud vm instance","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"i need to know why argument random_state in cross_validation.train_test_split is integer not Boolean, since it's role is to flag random allocation or not?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":280,"Q_Id":43102532,"Users Score":0,"Answer":"To expand a bit further on Kelvin's answer, if you want a random train-test split, then don't specify the random_state parameter. If you do not want a random train-test split (i.e. you want an identically-reproducible split each time), specify random_state with an integer of your choice.","Q_Score":0,"Tags":"python,machine-learning,cross-validation,sklearn-pandas","A_Id":43103742,"CreationDate":"2017-03-29T19:28:00.000","Title":"why argument random_state in cross_validation.train_test_split is integer not boolean","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"i need to know why argument random_state in cross_validation.train_test_split is integer not Boolean, since it's role is to flag random allocation or not?","AnswerCount":2,"Available Count":2,"Score":0.1973753202,"is_accepted":false,"ViewCount":280,"Q_Id":43102532,"Users Score":2,"Answer":"random_state is not only a flag of randomness or not, but which random seed to use. If you choose random_state = 3 you will \"randomly\" split the dataset, but you are able to reproduce the same split each time. I.e. each call with the same dataset will yield the same split, which is not the case if you don't specify the random_state parameter.\nThe reason why I use the quotation marks, is that it is actually pseudo random.\nWikipedia explains pseudorandomness like this:\n\nA pseudorandom process is a process that appears to be random but is\n  not. Pseudorandom sequences typically exhibit statistical randomness\n  while being generated by an entirely deterministic causal process.\n  Such a process is easier to produce than a genuinely random one, and\n  has the benefit that it can be used again and again to produce exactly\n  the same numbers - useful for testing and fixing software.","Q_Score":0,"Tags":"python,machine-learning,cross-validation,sklearn-pandas","A_Id":43103553,"CreationDate":"2017-03-29T19:28:00.000","Title":"why argument random_state in cross_validation.train_test_split is integer not boolean","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"If you make two concurrent calls to the same session, sess.run(...), how are variables concurrently accessed in tensorflow? \nWill each call see a snapshot of the variables as of the moment run was called, consistent throughout the call? Or will they see dynamic updates to the variables and only guarantee atomic updates to each variable?\nI'm considering running test set evaluation on a separate CPU thread and want to verify that it's as trivial as running the inference op on a CPU device in parallel. \nI'm having troubles figuring out exactly what guarantees are provided that make sessions \"thread safe\".","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":646,"Q_Id":43105148,"Users Score":3,"Answer":"After doing some experimentation it appears that each call to sess.run(...) does indeed see a consistent point-in-time snapshot of the variables. \nTo test this I performed 2 big matrix multiply operations (taking about 10 sec each to complete), and updated a single, dependent, variable before, between, and after. In another thread I grabbed and printed that variable every 1\/10th second to see if it picked up the change that occurred between operations while the first thread was still running. It did not, I only saw it's initial and final values. Therefore I conclude that variable changes are only visible outside of a specific call to sess.run(...) at the end of that run.","Q_Score":1,"Tags":"python,multithreading,tensorflow","A_Id":43107623,"CreationDate":"2017-03-29T22:15:00.000","Title":"How are variables shared between concurrent `session.run(...)` calls in tensorflow?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a two dataframes\nex:\n\ntest_1\nname1 name2\na1     b1\na1     b2\na2     b1\na2     b2\na2     b3\n\n\ntest_2\nname1 name2\na1     b1\na1     b2\na2     b1\n\nI need the difference of two dataframes like\n\nname1 name2\na2     b2\na2     b3","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":63,"Q_Id":43123378,"Users Score":1,"Answer":"df=pd.concat([a,b])\ndf = df.reset_index(drop=True)\ndf_gpby = df.groupby(list(df.columns))\nidx = [x[0] for x in df_gpby.groups.values() if len(x) == 1]\ndf1=df.reindex(idx)","Q_Score":1,"Tags":"python","A_Id":43189429,"CreationDate":"2017-03-30T16:24:00.000","Title":"Difference of two dataframes in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Basically, I am interested in solving a hypothesis problem, where I am only aware of the data distribution of a null hypothesis and don't know anything about the alternative case.\nMy concern is how should I train my deep neural network so that it can classify or recognise whether a particular sample data has a similar distribution as in null hypothesis case or it's from another class(An alternative Hypothesis case).\nAccording to my understanding, It's different from a binary classification (one vs all case), because in that case, we know what data we are going to tackle, but here in my case alternative hypothesis case can follow any data distribution.\nHere I am giving you an example situation, what I want exactly \n\nSuppose I want to predict that a person is likely to have cancer or not\ne.g\nI have a data set of the factors that cause cancer like, \nParameter A=1,Parameter B=3.87,Parameter C=5.6,Has cancer = yes \nBut I don't have a data set where \nParameter A=2,Parameter B=1.87,Parameter C=2.6,Has cancer = No\n\nCan be anything like this \nMeans I don't know about anything which leads to a conclusion of not having cancer, can I still train my model to recognise whether a person has cancer?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":103,"Q_Id":43139718,"Users Score":0,"Answer":"If you are sure enough that the alternative hypothesis data come from different distribution than the null hypothesis, you can try unsupervised learning algorithm. i.e a K-mean or a GMM with the right number of cluster could yield a great separation of the data. You can then assign label to the second class data and train a classifier using it. \nThis a general approach of semi-supervised learning.\nAnother idea would be to consider the alternative hypothesis data as outliers and use anomalie detection algorithm to find you second class data point. This is much more difficult to achieve and rely heavily on the supposition that data comes from really different distribution.","Q_Score":1,"Tags":"python,machine-learning,statistics,deep-learning","A_Id":43141030,"CreationDate":"2017-03-31T11:36:00.000","Title":"How can I create a deep neural network which has a capability to take a decision for hypothesis?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataframe in which one columns called 'label' holds values like 'b', 'm', 'n' etc.\nI want 'label' to instead hold the ascii equivalent of the letter.\nHow do I do it?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":3205,"Q_Id":43141160,"Users Score":0,"Answer":"e.g. \"a\"=97 in ascii}\nwrite print(ord(\"a\"))\n\nprint(ord(\"a\"))\n\nanswer would be 97","Q_Score":1,"Tags":"python-3.x,pandas","A_Id":43141287,"CreationDate":"2017-03-31T12:51:00.000","Title":"How to convert a column of a dataframe from char to ascii integers? [Pandas]","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am writing code and efficiency is very important.\nActually I need 2d array, that I am filling with 0 and 1 in for loop. What is better and why?\n\nMake empty array and fill it with \"0\" and \"1\". It's pseudocode, my array will be much bigger.\nMake array filled by zeros and make if() and if not zero - put one.\n\nSo I need information what is more efficiency:\n1. Put every element \"0\" and \"1\" to empty array \nor\n2. Make if() (efficiency of 'if') and then put only \"1\" element.","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":5613,"Q_Id":43145332,"Users Score":1,"Answer":"It is better to create array of zeros and fill it using if-else. Even conditions makes slow your code, reshaping empty array or concatenating it with new vectors each iteration of loop is more slower operation, because each time new array of new size is created and old array is copied there together with new vector value by value.","Q_Score":2,"Tags":"python,arrays,numpy","A_Id":43158973,"CreationDate":"2017-03-31T16:17:00.000","Title":"numpy array of zeros or empty","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am in an online course, and I find I do not understand this expression:\n\npredictors[training_indices,:]\n\npredictors is an np.array of floats.\ntraining_indices is a list of integers known to be indices of predictors, so 0=< i < len(training_indices)).\nIs this a special numpy expression?\nThanks!","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":802,"Q_Id":43160202,"Users Score":0,"Answer":"In your code predictors is a two dimensional array. You're taking a slice of the array. Your output will be all the values with training_indices as their index in the first axis. The : is slice notation, meaning to take all values along the second axis.\nThis kind of indexing is not common in Python outside of numpy, but it's not completely unique. You can write your own class that has a __getitem__ method, and interpret it however you want. The slice you're asking about will pass a 2-tuple to __getitem__. The first value in the tuple will be training_indices, and the second value will be a slice object.","Q_Score":0,"Tags":"python,python-3.x,numpy,comma","A_Id":43160280,"CreationDate":"2017-04-01T17:56:00.000","Title":"in Python 3.6 and numpy, what does the comma mean or do in \"predictors[training_indices,:]\"","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When I try to use  x = pandas.Series.from_csv('File_name.csv', header = None) \nIt throws an error saying IndexError: single positional indexer is out-of-bounds.\nHowever, If I read it as dataframe and then extract series, it works fine.\nx = pandas.read_csv('File_name.csv', header = None)[0]\nWhat could be wrong with first method?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":2872,"Q_Id":43166420,"Users Score":1,"Answer":"There is 2 option read series from csv file;\n\npd.Series.from_csv('File_name.csv')\npd.read_csv('File_name.csv', squeeze=True) \n\nMy prefer is; using squeeze=True with read_csv","Q_Score":4,"Tags":"python,pandas","A_Id":59593711,"CreationDate":"2017-04-02T08:48:00.000","Title":"How to read a csv file as series instead of dataframe in pandas?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I already know how to make a neural network using the mnist dataset. I have been searching for tutorials on how to train a neural network on your own dataset for 3 months now but I'm just not getting it. If someone can suggest any good tutorials or explain how all of this works, please help.\nPS. I won't install NLTK. It seems like a lot of people are training their neural network on text but I won't do that. If I would install NLTK, I would only use it once.","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":1168,"Q_Id":43169766,"Users Score":2,"Answer":"I suggest you use OpenCV library. Whatever you uses your MNIST data or PIL, when it's loaded, they're all just NumPy arrays. If you want to make MNIST datasets fit with your trained model, here's how I did it:\n1.Use cv2.imread to load all the images you want them to act as training datasets.\n2.Use cv2.cvtColor to convert all the images into grayscale images and resize them into 28x28.\n3.Divide each pixel in all the datasets by 255.\n4.Do the training as usual!\nI haven't tried to make it your own format, but theoratically it's the same.","Q_Score":4,"Tags":"python,tensorflow,neural-network,dataset","A_Id":43170260,"CreationDate":"2017-04-02T14:54:00.000","Title":"Using your own Data in Tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Suppose I have two placeholder quantities in tensorflow: placeholder_1 and placeholder_2. Essentially I would like the following computational functionality: \"if placeholder_1 is defined (ie is given a value in the feed_dict of sess.run()), compute X as f(placeholder_1), otherwise, compute X as g(placeholder_2).\" Think of X as being a hidden layer in a neural network that can optionally be computed in these two different ways. Eventually I would use X to produce an output, and I'd like to backpropagate error to the parameters of f or g depending on which placeholder I used. \nOne could accomplish this using the tf.where(condition, x, y) function if there was a way to make the condition \"placeholder_1 has a value\", but after looking through the tensorflow documentation on booleans and asserts I couldn't find anything that looked applicable.\nAny ideas? I have a vague idea of how I could accomplish this basically by copying part of the network, sharing parameters and syncing the networks after updates, but I'm hoping for a cleaner way to do it.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1190,"Q_Id":43175272,"Users Score":3,"Answer":"You can create a third placeholder variable of type boolean to select which branch to use and feed that in at run time.\nThe logic behind it is that since you are feeding in the placholders at runtime anyways you can determine outside of tensorflow which placeholders will be fed.","Q_Score":3,"Tags":"python,tensorflow,deep-learning","A_Id":43214452,"CreationDate":"2017-04-03T01:21:00.000","Title":"check if tensorflow placeholder is filled","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"We have a spark job running which consumes data from kafka stream , do some analytics and store the result.\nSince data is consumed as they are produced to kafka, if we want to get \n\ncount for the whole day,    count for an hour,      average for the whole\n  day\n\nthat is not possible with this approach. Is there any way which we should follow to accomplish such requirement\nAppreciate any help\nThanks and Regards\nRaaghu.K","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":35,"Q_Id":43176607,"Users Score":2,"Answer":"Your streaming job is not supposed to calculate the Daily count\/Avg.\nApproach 1 :\nYou can store the data consumer from Kafka into a persistent storage like DB\/HBase\/HDFS , and then you can run Daily batch which will calculate all the statistics for you like Daily count or avg.\nApproach 2 :\nIn order to get that information form streaming itself you need to use Accumulators which will hold the record count,sum. and calculate avg according.\nApproach 3 :\nUse streaming window, but holding data for a day doesn't make any sense. If you need 5\/10 min avg, you can use this.\nI think the first method is preferable as it will give you more flexibility to calculate all the analytics you want.","Q_Score":1,"Tags":"python-3.x,apache-spark,pyspark","A_Id":43182382,"CreationDate":"2017-04-03T04:42:00.000","Title":"spark consume from stream -- considering data for longer period","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I want to implement a classic Markov model problem: Train MM to learn English text patterns, and use that to detect English text vs. random strings.\nI decided to use hmmlearn so I don't have to write my own. However I am confused about how to train it. It seems to require the number of components in the HMM, but what is a reasonable number for English? Also, can I not do a simple higher order Markov model instead of hidden? Presumably the interesting property is is patterns of ngrams, not hidden states.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":764,"Q_Id":43178966,"Users Score":0,"Answer":"hmmlearn is designed for unsupervised learning of HMMs, while your problem is clearly supervised: given examples of English and random strings, learn to distinguish between the two. Also, as you've correctly pointed it out, the notion of hidden states is tricky to define for text data, therefore for your problem plain MMs would be more appropriate. I think you should be able to implement them in <100 lines of code in Python.","Q_Score":0,"Tags":"python-3.x,text-classification,markov-models,hmmlearn","A_Id":43355585,"CreationDate":"2017-04-03T07:32:00.000","Title":"How to use hmmlearn to classify English text?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Just a quick question, if I want to classify objects into either 0 or 1 but I would like the model to return me a 'likeliness' probability for example if an object is 0.7, it means it has 0.7 chance of being in class 1, do I do a regression or stick to classifiers and use the predict_proba function? \nHow is regression and predict_proba function different?\nAny help is greatly appreciated! \nThank you!","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2562,"Q_Id":43199108,"Users Score":4,"Answer":"Generally, for a qualitative problem that is to classify between categories or class, we prefer classification.\n\nfor example: to identify if it is night or day.\n\nFor Quantitative problems, we prefer regression to solve the problems.\n\nfor example: to identify if its 0th class or 1st class.\n\nBut in a special case, when we have only two classes. Then, we can use both classification and regression to solve two-class problems as in your case.\nPlease note that, this explanation is on the behalf of two-class point of view or multi-class problems. Though regression is to deal with real quantitative problems rather than classes.\nProbability has nothing to deal specifically with methods. Each method deduce a probability and on the basis of that, they predict the outcome.\n\nIt is better if you explain the reference to predict_proba from your\n  question.\n\nHope it helps!","Q_Score":1,"Tags":"python,machine-learning,scikit-learn,classification,regression","A_Id":43199249,"CreationDate":"2017-04-04T05:42:00.000","Title":"Regression vs Classifier predict_proba","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Im gensims latest version, loading trained vectors from a file is done using KeyedVectors, and dosent requires instantiating a new Word2Vec object. But now my code is broken because I can't use the model.vector_size property. What is the alternative to that? I mean something better than just kv[kv.index2word[0]].size.","AnswerCount":1,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":4245,"Q_Id":43202548,"Users Score":8,"Answer":"kv.vector_size still works; I'm using gensim 2.3.0, which is the latest as I write. (I am assuming kv is your KeyedVectors object.)  It appears object properties are not documented on the API page, but auto-complete suggests it, and there is no deprecated warning or anything.\nYour question helped me answer my own, which was how to get the number of words: len(kv.index2word)","Q_Score":6,"Tags":"python-3.x,gensim","A_Id":46034678,"CreationDate":"2017-04-04T08:56:00.000","Title":"gensim KeydVectors dimensions","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"We are trying to SET pickled object of size 2.3GB into redis through redis-py package. Encountered the following error.\n\nBrokenPipeError: [Errno 32] Broken pipe\nredis.exceptions.ConnectionError: Error 104 while writing to socket. Connection reset by peer.\n\nI would like to understand the root cause. Is it due to input\/output buffer limitation at server side or client side ? Is it due to any limitations on RESP protocol?  Is single value (bytes) of 2.3 Gb allowed to store into Redis ?\n\nimport redis\nr = redis.StrictRedis(host='10.X.X.X', port=7000, db=0)\npickled_object = pickle.dumps(obj_to_be_pickled)\nr.set('some_key', pickled_object)\n\nClient Side Error\n\nBrokenPipeError: [Errno 32] Broken pipe\n\/usr\/local\/lib\/python3.4\/site-packages\/redis\/connection.py(544)send_packed_command()\nself._sock.sendall(item)\n\nServer Side Error\n\n31164:M 04 Apr 06:02:42.334 - Protocol error from client: id=95 addr=10.2.130.144:36120 fd=11 name= age=0 idle=0 flags=N db=0 sub=0 psub=0 multi=-1 qbuf=16384 qbuf-free=16384 obl=42 oll=0 omem=0 events=r cmd=NULL\n31164:M 04 Apr 06:07:09.591 - Protocol error from client: id=96 addr=10.2.130.144:36139 fd=11 name= age=9 idle=0 flags=N db=0 sub=0 psub=0 multi=-1 qbuf=40 qbuf-free=32728 obl=42 oll=0 omem=0 events=r cmd=NULL\n\nRedis Version : 3.2.8 \/ 64 bit","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":9490,"Q_Id":43204496,"Users Score":8,"Answer":"Redis' String data type can be at most 512MB.","Q_Score":5,"Tags":"python,sockets,redis,redis-py","A_Id":43210008,"CreationDate":"2017-04-04T10:22:00.000","Title":"Broken Pipe Error Redis","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I'm fairly new to Python and I have a question related to the polynomials.\nLets have a  high-degree polynomial in GF(2), for example :\nx^n + x^m + ... + 1, where n, m could be up to 10000.\nI need to find inverse polynomial to this one. What will be the fastest way to do that in Python (probably using numpy) ?\nThanks","AnswerCount":3,"Available Count":1,"Score":-0.1325487884,"is_accepted":false,"ViewCount":3247,"Q_Id":43207222,"Users Score":-2,"Answer":"Try to use mathematical package sage","Q_Score":1,"Tags":"python,numpy,scipy,polynomials,inverse","A_Id":43208268,"CreationDate":"2017-04-04T12:26:00.000","Title":"Find inverse polynomial in python in GF2","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I trained 472 unique images for a person A for Face Recognition using \"haarcascade_frontalface_default.xml\".\nWhile I am trying to detect face for the same person A for the same images which I have trained getting 20% to 80% confidence, that's fine for me.\nBut, I am also getting 20% to 80% confidence for person B which I have not included in training the images. Why its happening for person B while I am doing face detection?\nI am using python 2.7 and OpenCV 3.2.0-dev version.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":402,"Q_Id":43207422,"Users Score":0,"Answer":"i guess here in your problem you are not actually referring to detection ,but recognition ,you must know the difference between these two things: \n1-detection does not distinguish between persons, it just detects the facial shape of a person based on the haarcascade previously trained\n2-recognition is the case where u first detect a person ,then try to distinguish that person from your cropped and aligned database of pics,i suggest you follow the philipp wagner tutorial for that matter.","Q_Score":0,"Tags":"python-2.7,opencv3.0,face-detection,face-recognition,opencv3.1","A_Id":43324614,"CreationDate":"2017-04-04T12:35:00.000","Title":"Why OpenCV face detection recognition the faces for untrained face?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"The sparse matrix has only 0 and 1 at each entry (i,j) (1 stands for sample i has feature j). How can I estimate the co-occurrence matrix for each feature given this sparse representation of data points? Especially, I want to find pairs of features that co-occur in at least 50 samples. I realize it might be hard to produce the exact result, is there any approximated algorithm in data mining that allows me to do that?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":101,"Q_Id":43209135,"Users Score":0,"Answer":"This can be solved reasonably easily if you go to a transposed matrix.\nOf any two features (now rows, originally columns) you compute the intersection. If it's larger than 50, you have a frequent cooccurrence.\nIf you use an appropriate sparse encoding (now of rows, but originally of columns - so you probably need not only to transpose the matrix, but also to reencode it) this operation using O(n+m), where n and m are the number of nonzero values.\nIf you have an extremely high number of features this make take a while. But 100000 should be feasible.","Q_Score":0,"Tags":"python,machine-learning,data-mining","A_Id":43216616,"CreationDate":"2017-04-04T13:48:00.000","Title":"Given a sparse matrix with shape (num_samples, num_features), how do I estimate the co-occurrence matrix?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"So basically I would like to be able to view two different databases within the same Grafana graph panel. The issue is that InfluxDB is a time series database, so it is not possible to see the trend between two databases in the same graph panel unless they have similar timestamps. The workaround is creating two panels in Grafana and adding a delay to one, but this doesn't give a good representation as the graphs are not on the same panel so it is more difficult to see the differences. I am currently working on a script to copy the databases in question and alter the timestamps so that the two newly created databases look like the data was taken at the same time. I am wondering if anyone has any idea how to change the timestamp, and if so, what would be the best way to to do so with a large amount of data points? Thanks.","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":569,"Q_Id":43215443,"Users Score":1,"Answer":"I believe this is currently available via kapacitor, but assume a more elegant solution will be readily accomplished using FluxQL. \nConsuming the influxdb measurements into kapacitor will allow you to force equivalent time buckets and present the data once normalized.","Q_Score":0,"Tags":"python,influxdb,grafana","A_Id":52570244,"CreationDate":"2017-04-04T18:55:00.000","Title":"InfluxDB and Grafana: comparing two databases with different timestamps on same graph panel","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"So basically I would like to be able to view two different databases within the same Grafana graph panel. The issue is that InfluxDB is a time series database, so it is not possible to see the trend between two databases in the same graph panel unless they have similar timestamps. The workaround is creating two panels in Grafana and adding a delay to one, but this doesn't give a good representation as the graphs are not on the same panel so it is more difficult to see the differences. I am currently working on a script to copy the databases in question and alter the timestamps so that the two newly created databases look like the data was taken at the same time. I am wondering if anyone has any idea how to change the timestamp, and if so, what would be the best way to to do so with a large amount of data points? Thanks.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":569,"Q_Id":43215443,"Users Score":0,"Answer":"I can confirm from my grafana instance that it's not possible to add a shift to one timeseries and not the other in one panel.\nTo change the timestamp, I'd just simply do it the obvious way. Load a few thousands of entries at a time to python, change the the timestamps and write it to a new measure (and indicate the shift in the measurement name).","Q_Score":0,"Tags":"python,influxdb,grafana","A_Id":43306424,"CreationDate":"2017-04-04T18:55:00.000","Title":"InfluxDB and Grafana: comparing two databases with different timestamps on same graph panel","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"By default the numerical values in data frame are stored up to 6 decimals only. How do I get the full precision.\nFor example\n34.98774564765 is stored as 34.987746. I do want the full value. \nand 0.00000565  is stored as 0. .\nApart from applying formats to each data frame is there any global setting that helps preserving the precision.\nThanks","AnswerCount":3,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":80695,"Q_Id":43217916,"Users Score":8,"Answer":"Your data is stored with the precision, corresponding to your dtype (np.float16, np.float32, np.float64).\npd.options.display.precision - allows you to change the precision for printing the data","Q_Score":24,"Tags":"python,pandas","A_Id":43217958,"CreationDate":"2017-04-04T21:22:00.000","Title":"Pandas data precision","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataset consisting of multiple columns and I want to calculate the average by using the groupby function in Python. However, since some of the values are NAN I get that the mean of that particular group is NAN when a NAN value is present. I would like to omit this value, not set it to zero or fill it with any statistical variable, just omit.\nAny idea how I can achieve this?\nThanks in advance!","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":266,"Q_Id":43219641,"Users Score":0,"Answer":"I get that the mean of that particular group is NAN when a NAN value\n  is present\n\nFALSE! :)\nthe mean will only consider non null values. You are safe my man.","Q_Score":0,"Tags":"python,pandas","A_Id":43219694,"CreationDate":"2017-04-04T23:54:00.000","Title":"How to Omit NaN values when applying groupyby in Pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have recently Installed Anaconda for Python 3.6 but it shows the error \"Segmentation fault\" whenever I try to run Anaconda-Navigator.\nI've tried just writting in the terminal Anaconda-Navigator and also going to my Anaconda3 folder and try to execute it inside bin. \nThe only solution that works so far is accessing the previously bin folder as root. My problem is that I need to activate TensorFlow before I run anything in my console, but that is imposible as a root user.\nI've already try to upgrade both, Anaconda and Navigator and reinstall them but nothing ocurrs\nAnyone here has any idea of what is happening?","AnswerCount":5,"Available Count":2,"Score":0.0399786803,"is_accepted":false,"ViewCount":9783,"Q_Id":43219679,"Users Score":1,"Answer":"I had the same issue when I install OpenCV library using conda.Most probably downgrading something makes this issue happen. Just type :\nconda update --all","Q_Score":6,"Tags":"python,ubuntu,anaconda,navigator","A_Id":53868885,"CreationDate":"2017-04-04T23:58:00.000","Title":"Segmentation fault when I try to run Anaconda Navigator","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":1,"Web Development":0},{"Question":"I have recently Installed Anaconda for Python 3.6 but it shows the error \"Segmentation fault\" whenever I try to run Anaconda-Navigator.\nI've tried just writting in the terminal Anaconda-Navigator and also going to my Anaconda3 folder and try to execute it inside bin. \nThe only solution that works so far is accessing the previously bin folder as root. My problem is that I need to activate TensorFlow before I run anything in my console, but that is imposible as a root user.\nI've already try to upgrade both, Anaconda and Navigator and reinstall them but nothing ocurrs\nAnyone here has any idea of what is happening?","AnswerCount":5,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":9783,"Q_Id":43219679,"Users Score":0,"Answer":"I had the same problem.I solved it by adding \/lib to mt LD_LIBRARY_PATH. \nNote: On my system Anaconda installation path is \/home\/pushyamik\/anaconda3.","Q_Score":6,"Tags":"python,ubuntu,anaconda,navigator","A_Id":47718983,"CreationDate":"2017-04-04T23:58:00.000","Title":"Segmentation fault when I try to run Anaconda Navigator","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":1,"Web Development":0},{"Question":"I have been told networkx library in python is the standard library to use for graph-theoretical applications, but I have found using it quite frustrating so far. \nWhat I want to do is this:\nGenerating an SIS epidemiological network, assigning initial contact rates and recovery rates and then following the progress of the disease. \nMore precisely, imagine a network of n individuals and an adjacency matrix A. Values of A are in [0,1] range and are contact rates. This means that the (i,j) entry shows the probability that disease is transferred from node i to node j. Initially, each node is assigned a random label, which can be either 1 (for Infective individuals) or 0 (for Susceptible, which are the ones which have not caught the disease yet). \nAt each time-step, if the node has a 0 label, then with a probability equal to the maximum value of weights for incoming edges to the node, it can turn into a 1. If the node has a  1 label then with a probability specified by its recovery rate, it can turn into a 0. Recovery rate is a value assigned to each node at the beginning of the simulation, and is in [0,1] range. \nAnd while the network evolves in each time step, I want to display the network with each node label coloured differently. \nIf somebody knows of any other library in python that can do such a thing more efficiently than netwrokx, be grateful if you let me know.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1096,"Q_Id":43260916,"Users Score":0,"Answer":"Do you use networkx for calculation or visualization?\nThere is no need to use it for calculation since your model is simple and it is easier to calculate it with matrix (vector) operations. That is suitable for numpy.\nMain part in a step is calculation of probability of switching from 0 to 1. Let N be vector that for each node stores 0 or 1 depending of state. Than probability that node n switch from 0 to 1 is numpy.amax(A[n,:] * N).\nIf you need visualization, than probably there are better libraries than networkx.","Q_Score":0,"Tags":"python,graph,graph-theory,networkx","A_Id":43304907,"CreationDate":"2017-04-06T16:32:00.000","Title":"Generating an SIS epidemilogical model using Python networkx","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there a way to do inverse z-transforms in Python?  (I don\u2019t see anything like this in NumPy or SciPy).","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":4204,"Q_Id":43264959,"Users Score":0,"Answer":"You can use scipy.signal.residuez for z^-n form of z-transform or scipy.signal.residue for z^n form of z-transform.","Q_Score":0,"Tags":"python,inverse-transform","A_Id":65609395,"CreationDate":"2017-04-06T20:14:00.000","Title":"Is there a way to do inverse z-transforms in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm training an autoencoder network which needs to read in three images per training sample (one input RGB image, two output RGB images). It was easy to make this work with python and numpy interop and reading the image files in myself.\nHow can I enable parallel\/distributed training with this? Do I have to use the training session construct? Do I have to use the image reader minibatch source with that?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":194,"Q_Id":43275431,"Users Score":1,"Answer":"There are the following options:\n1) Use distributed learner + training session - then you need to either use ImageDeserializer, or implement your own MinibatchSource (this extensibility only available starting RC2)\n2) Use distributed learner + write the training loop yourself. In that case you have to take care of splitting the data (each worker should only read images that correspond to its rank) and all conditions inside the loop should be based on trainer->TotalNumberOfSamples() (i.e. checkpointing if you do any).","Q_Score":0,"Tags":"python,cntk","A_Id":43362812,"CreationDate":"2017-04-07T10:10:00.000","Title":"Parallel training with CNTK and numpy interop","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I get a PEP8 complaint about numpy.where(mask == False) where mask is a boolean array. The PEP8 recommendation comparison should be either 'if condition is false' or 'if not condition'. What is the pythonic syntax for the suggested comparison inside numpy.where()?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":905,"Q_Id":43287990,"Users Score":1,"Answer":"Negating a boolean mask array in NumPy is ~mask.\nAlso, consider whether you actually need where at all. Seemingly the most common use is some_array[np.where(some_mask)], but that's just an unnecessarily wordy and inefficient way to write some_array[some_mask].","Q_Score":0,"Tags":"python,numpy","A_Id":43288022,"CreationDate":"2017-04-07T22:31:00.000","Title":"Pythonic array indexing with boolean masking array","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have huge data stored in cassandra and I wanted to process it using spark through python.\nI just wanted to know how to interconnect spark and cassandra through python.\nI have seen people using sc.cassandraTable but it isnt working and fetching all the data at once from cassandra and then feeding to spark doesnt make sense.\nAny suggestions?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1860,"Q_Id":43310597,"Users Score":0,"Answer":"I'll just give my \"short\" 2 cents. The official docs are totally fine for you to get started. You might want to specify why this isn't working, i.e. did you run out of memory (perhaps you just need to increase the \"driver\" memory) or is there some specific error that is causing your example not to work. Also it would be nice if you provided that example.\nHere are some of my opinions\/experiences that I had. Usually, not always, but most of the time you have multiple columns in partitions. You don't always have to load all the data in a table and more or less you can keep the processing (most of the time) within a single partition. Since the data is sorted within a partition this usually goes pretty fast. And didn't present any significant problem.\nIf you don't want the whole store in casssandra fetch to spark cycle to do your processing you have really a lot of the solutions out there. Basically that would be quora material. Here are some of the more common one:\n\nDo the processing in your application right away - might require some sort of inter instance communication framework like hazelcast of even better akka cluster this is really a wide topic \nspark streaming - just do your processing right away in micro batching and flush results for reading to some persistence layer - might be cassandra\napache flink - use proper streaming solution and periodically flush state of the process to i.e. cassandra\nStore data into cassandra the way it's supposed to be read - this approach is the most adviseable (just hard to say with the info you provided)\nThe list could go on and on ... User defined function in cassandra, aggregate functions if your task is something simpler.\n\nIt might be also a good idea that you provide some details about your use case. More or less what I said here is pretty general and vague, but then again putting this all into a comment just wouldn't make sense.","Q_Score":3,"Tags":"python,apache-spark,cassandra,pyspark","A_Id":43311283,"CreationDate":"2017-04-09T18:51:00.000","Title":"Spark and Cassandra through Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I keep getting this error whenever I try running this code, if I can get some insight in what is going on that would be a great help, since I'm pretty new to this coding environment I would really appreciate some help. The code is this:\n\nFile \"C:\\Users\\user\\Desktop\\Python\\pythonsimulation.py\", line 6, in \n      from scipy import *\n    File \"C:\\Python34\\lib\\site-packages\\scipy__init__.py\", line 61, in \n      from numpy._distributor_init import NUMPY_MKL  # requires numpy+mkl\n  ImportError: cannot import name 'NUMPY_MKL'\n\nAm I missing some package or module that python requires in order to run the code?","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":5133,"Q_Id":43317119,"Users Score":0,"Answer":"Actually, there is no problem. Just need to restart the Jupyter and you will see it is working well.","Q_Score":1,"Tags":"python,python-import,importerror","A_Id":45893216,"CreationDate":"2017-04-10T07:25:00.000","Title":"Python ImportError - NUMPY MKL","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I keep getting this error whenever I try running this code, if I can get some insight in what is going on that would be a great help, since I'm pretty new to this coding environment I would really appreciate some help. The code is this:\n\nFile \"C:\\Users\\user\\Desktop\\Python\\pythonsimulation.py\", line 6, in \n      from scipy import *\n    File \"C:\\Python34\\lib\\site-packages\\scipy__init__.py\", line 61, in \n      from numpy._distributor_init import NUMPY_MKL  # requires numpy+mkl\n  ImportError: cannot import name 'NUMPY_MKL'\n\nAm I missing some package or module that python requires in order to run the code?","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":5133,"Q_Id":43317119,"Users Score":0,"Answer":"If you are using Jupyter, try restarting the kernel. please click Restart in Kernel menu.","Q_Score":1,"Tags":"python,python-import,importerror","A_Id":45571159,"CreationDate":"2017-04-10T07:25:00.000","Title":"Python ImportError - NUMPY MKL","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Whenever I run a python script that uses tensorflow and for some reason decide to kill it before it finishes, there is the problem that ctrl-c doesn't work. I would use ctrl-z but it doesn't release the gpu memory, so when i try to re-run the script there is no memory left. Is there a solution for this in linux?","AnswerCount":4,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":6349,"Q_Id":43324788,"Users Score":2,"Answer":"Don't run this on your desktop, but for HPC\/remote machines with no display, this kills all left over GPU-using processes:\nnvidia-smi -q -d PIDS | grep -P \"Process ID +: [0-9]+\" | grep -Po \"[0-9]+\" | xargs kill -9","Q_Score":3,"Tags":"python,tensorflow","A_Id":50902599,"CreationDate":"2017-04-10T13:39:00.000","Title":"How to simply kill python-tensorflow process and release memory?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using scikit-learn library to perform a supervised classification (Support Vector Machine classifier) on a satellite image. My main issue is how to train my SVM classifier. I have watched many videos on youtube and have read a few tutorials on how to train an SVM model in scikit-learn. All the tutorials I have watched, they used the famous Iris datasets. In order to perform a supervised SVM classification in scikit-learn we need to have labels. For Iris datasets we have the Iris.target which is the labels ('setosa', 'versicolor', 'virginica') we are trying to predict. The procedure of training is straightforward by reading the scikit-learn documentation.\nIn my case, I have to train a SAR satellite image captured over an urban area and I need to classify the urban area, roads, river and vegetation (4 classes). This image has two bands but I do not have label data for each class I am trying to predict such as the Iris data. \nSo, my question is, do I have to manually create vector data (for the 4 classes) in order to train the SVM model? Is there an easier way to train the model than manually creating vector data? What do we do in this case?\nI am bit confused to be honest. I would appreciate any help","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":6775,"Q_Id":43331510,"Users Score":1,"Answer":"My Solution:-\nManual Processing:- \nIf the size of your dataset is small, you can manually create a vector data (also reliable, when it is created by yourself). If not, it is much difficult to apply SVM to classify the images. \nAutomatic Processing:- \nStep 1:-\nYou can use \"Unsupervised Image Clustering\" technique to group your images into those 4 categories, then label the images from 1 to 4 after clustering is done. (eg. K-Means Clustering Algorithm)\nStep 2:-\nCurrently, you are having a dataset of labeled images. Split them to train-test data.\nStep 3:-\nNow apply SVM to classify your test images and find out your model accuracy.","Q_Score":3,"Tags":"python,machine-learning,scikit-learn,svm,k-means","A_Id":43386604,"CreationDate":"2017-04-10T19:29:00.000","Title":"How to train an SVM classifier on a satellite image using Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to create a variable D by combining two other variable x and y.\nx has the shape [731] and y has the shape [146].\nAt the end D should be 2D so that D[0] contains all x-values and D[1] all y-values.\nI hope I explained it in a way someone can understand what I want to do.\nCan someone help me with this?","AnswerCount":4,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":792,"Q_Id":43337694,"Users Score":0,"Answer":"Is not possible to make arrays with different sizes as I understood you want to, and this is because a 2D-array is basically a table with rows and columns, and each row has the same number of columns, no matter what.\nBut, you can join the values in each variable and save the resulting strings in the array, and to use them again just split it back and parse the values to the type you need them.","Q_Score":0,"Tags":"python,variables,dimensions","A_Id":43337961,"CreationDate":"2017-04-11T05:50:00.000","Title":"python : create a variable with different dimension sizes","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to create a variable D by combining two other variable x and y.\nx has the shape [731] and y has the shape [146].\nAt the end D should be 2D so that D[0] contains all x-values and D[1] all y-values.\nI hope I explained it in a way someone can understand what I want to do.\nCan someone help me with this?","AnswerCount":4,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":792,"Q_Id":43337694,"Users Score":2,"Answer":"It is a simple as: D = [x, y]\nHope it helped :)","Q_Score":0,"Tags":"python,variables,dimensions","A_Id":43337785,"CreationDate":"2017-04-11T05:50:00.000","Title":"python : create a variable with different dimension sizes","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Currently I am testing the yolo 9000 model for object detection and in the Paper I understand that the image is splited in 13X13 boxes and in each boxes we calculate P(Object), but How can we calculate that ? how can the model know if there is an object in this boxe or not, please I need help to understand that\nI am using tensorflow \nThanks,","AnswerCount":4,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":1673,"Q_Id":43342433,"Users Score":0,"Answer":"There are 13x13 grid cells, true, but P(object) is calculated for each of 5x13x13 anchor boxes. From the YOLO9000 paper:\n\nWhen we move to anchor boxes we also decouple the class prediction mechanism from the spatial location and instead predict class and objectness for every anchor box.\n\nI can't comment yet because I'm new here, but if you're wondering about test time, it works kind of like an RPN. At each grid cell, the 5 anchor boxes each predict a bounding box, which can be larger than the grid cell, and then non-maximum suppression is used to pick the top few boxes to do classification on.\nP(object) is just a probability, the network doesn't \"know\" if there is really an object in there or not.\nYou can also look at the source code for the forward_region_layer method in region_layer.c and trace how the losses are calculated, if you're interested.","Q_Score":8,"Tags":"python,tensorflow,object-detection,yolo","A_Id":45476167,"CreationDate":"2017-04-11T09:48:00.000","Title":"How Yolo calculate P(Object) in the YOLO 9000","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Currently I am testing the yolo 9000 model for object detection and in the Paper I understand that the image is splited in 13X13 boxes and in each boxes we calculate P(Object), but How can we calculate that ? how can the model know if there is an object in this boxe or not, please I need help to understand that\nI am using tensorflow \nThanks,","AnswerCount":4,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":1673,"Q_Id":43342433,"Users Score":0,"Answer":"During test time the YOLO network gets the IOU from the default setted value. That is 0.5.","Q_Score":8,"Tags":"python,tensorflow,object-detection,yolo","A_Id":59667414,"CreationDate":"2017-04-11T09:48:00.000","Title":"How Yolo calculate P(Object) in the YOLO 9000","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Currently I am testing the yolo 9000 model for object detection and in the Paper I understand that the image is splited in 13X13 boxes and in each boxes we calculate P(Object), but How can we calculate that ? how can the model know if there is an object in this boxe or not, please I need help to understand that\nI am using tensorflow \nThanks,","AnswerCount":4,"Available Count":3,"Score":0.1973753202,"is_accepted":false,"ViewCount":1673,"Q_Id":43342433,"Users Score":4,"Answer":"They train for the confidence score = P(object) * IOU. For the ground truth box they take P(object)=1 and for rest of the grid pixels the ground truth P(object) is zero. You are training your network to tell you if some object in that grid location i.e. output 0 if not object, output IOU if partial object and output 1 if object is present. So at test time, your model has become capable of telling if there is an object at that location.","Q_Score":8,"Tags":"python,tensorflow,object-detection,yolo","A_Id":44433597,"CreationDate":"2017-04-11T09:48:00.000","Title":"How Yolo calculate P(Object) in the YOLO 9000","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using pretrained resnet50 and inception v3 networks to extract features from my images, which I then use with my ML algo. \nWhich layers are recommended for feature extraction? \nI am currently using: \"mixed10\" in Inception v3 and \"avg_pool\" in resent50. The features are modelling well in XGBoost though.\nThank you.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1958,"Q_Id":43343868,"Users Score":2,"Answer":"There is no general rules on how to choose the layer for feature extraction but you might use a easy rule of thumb. The deeper you go to the network - the less ImageNet specific semantic features you would have. But in the same time - you are getting less semantic features also.\nWhat I would do is to use the pool layers in both topologies - and if this didn't work well - then I would go deeper by setting the depth as metaparameter.","Q_Score":4,"Tags":"python,image-processing,computer-vision,deep-learning,keras","A_Id":43345587,"CreationDate":"2017-04-11T10:52:00.000","Title":"Feature extraction in Keras","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Currently, I am working to create a deep neural network for object detection, and i am also create my own dataset, and I use the bounding box to annotate my images, and my question is what are the rules to have the best bounding box for my images training. I mean if I wrap my object is it good to limit the background of my object or do I need t find a way to bound only my object.\nThanks,","AnswerCount":3,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":2423,"Q_Id":43345925,"Users Score":0,"Answer":"There is not any rule as such but yes the best practice of proper annotation is to keep certain pixel width while creating bounding boxes.\nSee, the background changes are the variations in the object which will make it robust but keep in mind to have enough samples to properly recognizing the patterns in object(edges, shapes, textures, etc.)\nHope i addressed your query!","Q_Score":1,"Tags":"python,tensorflow,deep-learning,object-detection","A_Id":60558628,"CreationDate":"2017-04-11T12:27:00.000","Title":"Best Way to create a bounding box for object detection","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Currently, I am working to create a deep neural network for object detection, and i am also create my own dataset, and I use the bounding box to annotate my images, and my question is what are the rules to have the best bounding box for my images training. I mean if I wrap my object is it good to limit the background of my object or do I need t find a way to bound only my object.\nThanks,","AnswerCount":3,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":2423,"Q_Id":43345925,"Users Score":0,"Answer":"You can reference YOLO algorithm- this is the best algorithm for object detection. The first, input image will divide into SxS grid cell, Yolo will predict 5 bounding box for each cell and with each bounding box, Yolo also predict the center coordinates of box, width, height of box and confidence score of having any object in that box along with the probabilities that object will belong to M classes. After that, we use Non Max Suppression and IOU to calculate the accuracy between bounding box with ground truth and get only the most exactly bounding box for the object in the input image.","Q_Score":1,"Tags":"python,tensorflow,deep-learning,object-detection","A_Id":53543602,"CreationDate":"2017-04-11T12:27:00.000","Title":"Best Way to create a bounding box for object detection","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Currently, I am working to create a deep neural network for object detection, and i am also create my own dataset, and I use the bounding box to annotate my images, and my question is what are the rules to have the best bounding box for my images training. I mean if I wrap my object is it good to limit the background of my object or do I need t find a way to bound only my object.\nThanks,","AnswerCount":3,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":2423,"Q_Id":43345925,"Users Score":0,"Answer":"I am not specialized in bounding box, but in general in deep learning, we try to obtain a network which will be robust against irrelevant variables, in your case, the background. Bounding should not be dependant of the background, so set your bounding box the way you want, it should be learned by the network how to replicate it. \nThe most important thing is more the size of the database and having consistent bounding than precise bounding.\nAlso if you want your network to be robust against changes of backgrounds, you should have as many backgrounds as possible, uncorrelated with the bounding.","Q_Score":1,"Tags":"python,tensorflow,deep-learning,object-detection","A_Id":43346462,"CreationDate":"2017-04-11T12:27:00.000","Title":"Best Way to create a bounding box for object detection","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a Python script set up where it instantiates Rserve, sets up a few R scripts and functions and then runs some data against the functions. However, I have been unable to create a multi-threaded instance of this same process. My core issue is that one thread always seems to dominate the processing and all of the other threads are ignored. \nI've made the assumption that pyRserve can be multi-threaded - is that a correct assumption? Are there any examples out there that show this as a multi-threaded app?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":290,"Q_Id":43351701,"Users Score":0,"Answer":"I kept on working with the code and it turns out that each thread needs its own port in order to work. I didn't find that documented anywhere, I was just trying out different idea. So:\n\nI set up as many instances of Rserve as a I wanted threads. Each one of those instances was its own port\nIn my python code, when I instantiated the pyRserve object, I assigned it a unique port number. \n\nMulti-threading is now working as desired and fast!","Q_Score":1,"Tags":"python,r,multithreading,rserve,pyrserve","A_Id":43352367,"CreationDate":"2017-04-11T16:39:00.000","Title":"R, Python and pyRserve - multi-threaded examples?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm doing a project that require cv2 and numpy in one of the scripts using choregraphe, but I get an error :\nNo module named cv2\/numpy.\nI think it is because choregraphe has its own python interpreter but I do not know how to install cv2 and numpy into the python of choregraphe.\nHow can I do it?","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1416,"Q_Id":43351742,"Users Score":2,"Answer":"It depends if you're using a real NAO or a simulated one.\n\nSimulated one: choregraphe use its own embedded python interpreter, even if you add library to your system it won't change anything\nReal NAO: the system python interpreter is used, you need to install those library to your robot (and not to the computer running choregraphe). As pip ofthen doesn't work fine in NAO, you'll have to manually copy library to \/home\/nao\/.local\/lib\/python2.7\/site-packages","Q_Score":1,"Tags":"python,opencv,numpy,nao-robot,choregraphe","A_Id":43369916,"CreationDate":"2017-04-11T16:42:00.000","Title":"how to import cv2 and numpy in Choregraphe for NAO robot?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need export geometric(stress or diffrential) stiffness matrix in linear buckling problem at abaqus software for use in matlab program, i find method export  of usual stiffness matrix, but i cant find any data about export geometric stiffness matrix in abaqus script, \nCan some one help me about it?\nfor example when i export stiffness matrix in buckling problem,the result is same as stiffness matrix in linear static problem","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":797,"Q_Id":43360854,"Users Score":0,"Answer":"Abaqus doesn't output stiffness matrices.  If you already know Abaqus and only want to do buckling, you might want to try CalculiX. I think it can output a stiffness matrix and uses Abaqus-style input files.","Q_Score":0,"Tags":"python,abaqus","A_Id":43388797,"CreationDate":"2017-04-12T05:18:00.000","Title":"export geometric stiffness matrix in abaqus software","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need export geometric(stress or diffrential) stiffness matrix in linear buckling problem at abaqus software for use in matlab program, i find method export  of usual stiffness matrix, but i cant find any data about export geometric stiffness matrix in abaqus script, \nCan some one help me about it?\nfor example when i export stiffness matrix in buckling problem,the result is same as stiffness matrix in linear static problem","AnswerCount":3,"Available Count":2,"Score":0.0665680765,"is_accepted":false,"ViewCount":797,"Q_Id":43360854,"Users Score":1,"Answer":"Let's discuss a little back ground on geometric nonlinear FEA. \nThere are many different methods used for nonlinear geometric analysis of structure. The most well-known and established ones are [1]: \nTotal Lagrangian (TL), Updated Lagrangian (UL). \nTL: Uses the full nonlinear definition of strains. \nUL: Uses the linear definition of strain but updates the reference configuration with previous deformation solutions. \nAbaqus's core solver uses a very specific type of geometric nonlinear algorithm called co-rotational in where the rotation and deformation with respect to reference configuration are treated separately. \nAbaqus's co-rotational formulation is propitiatory and I do not expect them to offer its output so easily. Even if you could see the output for nonlinear geometry matrices from Abaqus. These matrice(s) would be different than what you expect depending on the methods that you have used for nonlinear geometric modeling. \n[1] Reddy, Junuthula Narasimha. An Introduction to Nonlinear Finite Element Analysis: with applications to heat transfer, fluid mechanics, and solid mechanics. OUP Oxford, 2014.","Q_Score":0,"Tags":"python,abaqus","A_Id":43375546,"CreationDate":"2017-04-12T05:18:00.000","Title":"export geometric stiffness matrix in abaqus software","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am wondering if it's possible to calculate the distance\/similarity between two related words in Python (like \"fraud\" and \"steal\"). These two words are not synonymous per se but they are clearly related. Are there any concepts\/algorithms in NLP that can show this relationship numerically? Maybe via NLTK?\nI'm not looking for the Levenshtein distance as that relates to the individual characters that make up a word. I'm looking for how the meaning relates. \nWould appreciate any help provided.","AnswerCount":3,"Available Count":1,"Score":0.1325487884,"is_accepted":false,"ViewCount":2056,"Q_Id":43382857,"Users Score":2,"Answer":"My suggestion is as follows:\n\nPut each word through the same thesaurus, to get a list of synonyms.\nGet the size of the set of similar synonyms for the two words.\nThat is a measure of similarity between the words.\n\nIf you would like to do a more thorough analysis:\n\nAlso get the antonyms for each of the two words.\nGet the size of the intersection of the sets of antonyms for the two words.\n\nIf you would like to go further!...\n\nPut each word through the same thesaurus, to get a list of synonyms.\nUse the top n (=5, or whatever) words from the query result to initiate a new query.\nRepeat this to a depth you feel is adequate.\nMake a collection of synonyms from the repeated synonym queries.\nGet the size of the set of similar synonyms for the two words from the two collections of synonyms.\nThat is a measure of similarity between the words.","Q_Score":5,"Tags":"python,nlp,nltk","A_Id":43383011,"CreationDate":"2017-04-13T03:04:00.000","Title":"How to calculate the distance in meaning of two words in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it possible in keras to [Reshape][1] an array in eras by only specifying one of the two dimension, such that the last dimension fits accordingly?\nIn my case i have (30,1,2080) and i want to reshape it to (15,)","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":599,"Q_Id":43403560,"Users Score":1,"Answer":"Just put the last dim to -1.. \nSo Reshape(15,-1)","Q_Score":1,"Tags":"python,keras,reshape","A_Id":43403593,"CreationDate":"2017-04-14T00:17:00.000","Title":"Reshape array in keras","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'd like to understand why does the two functions tf.image.crop_to_bounding_box and tf.image.pad_to_bounding_box exists, since the behaviour of these two functions can be done really simply with respectively tf.slice and tf.pad.\nThey are not so much easier to understand, and their scope is narrow since they accept only 3D and 4D tensors. Furthermore, they tend to be slower in terms of time of execution. \nIs there something I miss here ?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":825,"Q_Id":43411738,"Users Score":3,"Answer":"Mostly you use them tf.image.* for easiness of use.\nBoth crop_to_bounding_box and pad_to_bounding_box use slice and padunderneath, but also add checkings and constraints to make sure you don't spend hours trying to debug your slice\/pad indices and offsets.","Q_Score":1,"Tags":"python,tensorflow","A_Id":43416019,"CreationDate":"2017-04-14T12:42:00.000","Title":"tf.image.pad_to_bounding_box VS tf.pad and tf.image.crop_to_bounding_box VS tf.slice","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am interested in the performance of Pyomo to generate an OR model with a huge number of constraints and variables (about 10e6). I am currently using GAMS to launch the optimizations but I would like to use the different python features and therefore use Pyomo to generate the model.\nI made some tests and apparently when I write a model, the python methods used to define the constraints are called each time the constraint is instanciated. Before going further in my implementation, I would like to know if there exists a way to create directly a block of constraints based on numpy array data ? From my point of view, constructing constraints by block may be more efficient for large models.\nDo you think it is possible to obtain performance comparable to GAMS or other AML languages with pyomo or other python modelling library ?\nThanks in advance for your help !","AnswerCount":2,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":4313,"Q_Id":43413067,"Users Score":6,"Answer":"While you can use NumPy data when creating Pyomo constraints, you cannot currently create blocks of constraints in a single NumPy-style command with Pyomo.  Fow what it's worth, I don't believe that you can in languages like AMPL or GAMS, either.  While Pyomo may eventually support users defining constraints using matrix and vector operations, it is not likely that that interface would avoid generating the individual constraints, as the solver interfaces (e.g., NL, LP, MPS files) are all \"flat\" representations that explicit represent individual constraints.  This is because Pyomo needs to explicitly generate representations of the algebra (i.e., the expressions) to send out to the solvers.  In contrast, NumPy only has to calculate the result: it gets its efficiency by creating the data in a C\/C++ backend (i.e., not in Python), relying on low-level BLAS operations to compute the results efficiently, and only bringing the result back to Python.  \nAs far as performance and scalability goes, I have generated raw models with over 13e6 variables and 21e6 constraints.  That said, Pyomo was designed for flexibility and extensibility over speed.  Runtimes in Pyomo can be an order of magnitude slower than AMPL when using cPython (although that can shrink to within a factor of 4 or 5 using pypy).  At least historically, AMPL has been faster than GAMS, so the gap between Pyomo and GAMS should be smaller.","Q_Score":10,"Tags":"python,mathematical-optimization,pyomo","A_Id":43420032,"CreationDate":"2017-04-14T13:59:00.000","Title":"Performance of pyomo to generate a model with a huge number of constraints","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am applying ScikitLearn's random forests on an extremely unbalanced dataset (ratio of 1:10 000). I can use the class_weigth='balanced' parameter. I have read it is equivalent to undersampling.\nHowever, this method seems to apply weights to samples and do not change the actual number of samples.\nBecause each tree of the Random Forest is built on a randomly drawn subsample of the training set, I am afraid the minority class will not be representative enough (or not representated at all) in each subsample. Is this true? This would lead to very biased trees.\nThus, my question is: does the class_weight=\"balanced\" parameter allows to build reasonably unbiased Random Forest models on extremely unbalanced datasets, or should I find a way to undersample the majority class at each tree or when building the training set?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":900,"Q_Id":43414689,"Users Score":0,"Answer":"I think you can split majority class in +-10000 samples and train the same model using each sample plus the same points of minority class.","Q_Score":3,"Tags":"python,scikit-learn,random-forest","A_Id":43507382,"CreationDate":"2017-04-14T15:33:00.000","Title":"Undersampling vs class_weight in ScikitLearn Random Forests","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I installed the new version python 3.6 with the anaconda package. However i am not able to install tensorflow. Always receive the error that \ntensorflow_gpu-1.0.0rc2-cp35-cp35m-win_amd64.whl is not a supported wheel on this platform.\nHow can I install tensorflow on anaconda (python 3.6)?","AnswerCount":13,"Available Count":4,"Score":-0.0153834017,"is_accepted":false,"ViewCount":191454,"Q_Id":43419795,"Users Score":-1,"Answer":"conda create -n tensorflow_gpuenv tensorflow-gpu\nOr \ntype the command pip install c:.*.whl in command prompt (cmd).","Q_Score":26,"Tags":"python,tensorflow,installation,python-wheel","A_Id":53412662,"CreationDate":"2017-04-14T22:01:00.000","Title":"how to install tensorflow on anaconda python 3.6","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I installed the new version python 3.6 with the anaconda package. However i am not able to install tensorflow. Always receive the error that \ntensorflow_gpu-1.0.0rc2-cp35-cp35m-win_amd64.whl is not a supported wheel on this platform.\nHow can I install tensorflow on anaconda (python 3.6)?","AnswerCount":13,"Available Count":4,"Score":0.0,"is_accepted":false,"ViewCount":191454,"Q_Id":43419795,"Users Score":0,"Answer":"Uninstall Python 3.7 for Windows, and only install Python 3.6.0\nthen you will have no problem or receive the error message:\nimport tensorflow as tf\nModuleNotFoundError: No module named 'tensorflow'","Q_Score":26,"Tags":"python,tensorflow,installation,python-wheel","A_Id":53520347,"CreationDate":"2017-04-14T22:01:00.000","Title":"how to install tensorflow on anaconda python 3.6","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I installed the new version python 3.6 with the anaconda package. However i am not able to install tensorflow. Always receive the error that \ntensorflow_gpu-1.0.0rc2-cp35-cp35m-win_amd64.whl is not a supported wheel on this platform.\nHow can I install tensorflow on anaconda (python 3.6)?","AnswerCount":13,"Available Count":4,"Score":0.0153834017,"is_accepted":false,"ViewCount":191454,"Q_Id":43419795,"Users Score":1,"Answer":"Well, conda install tensorflow worked perfect for me!","Q_Score":26,"Tags":"python,tensorflow,installation,python-wheel","A_Id":59495885,"CreationDate":"2017-04-14T22:01:00.000","Title":"how to install tensorflow on anaconda python 3.6","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I installed the new version python 3.6 with the anaconda package. However i am not able to install tensorflow. Always receive the error that \ntensorflow_gpu-1.0.0rc2-cp35-cp35m-win_amd64.whl is not a supported wheel on this platform.\nHow can I install tensorflow on anaconda (python 3.6)?","AnswerCount":13,"Available Count":4,"Score":-0.0153834017,"is_accepted":false,"ViewCount":191454,"Q_Id":43419795,"Users Score":-1,"Answer":"For Windows 10 with Anaconda 4.4 Python 3.6:\n1st step) conda create -n tensorflow  python=3.6\n2nd step) activate tensorflow \n3rd step) pip3 install --ignore-installed --upgrade https:\/\/storage.googleapis.com\/tensorflow\/windows\/cpu\/tensorflow-1.2.1-cp36-cp36m-win_amd64.whl","Q_Score":26,"Tags":"python,tensorflow,installation,python-wheel","A_Id":45116048,"CreationDate":"2017-04-14T22:01:00.000","Title":"how to install tensorflow on anaconda python 3.6","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a large dataset, 11 million rows, and I loaded the data into pandas.  I want to then build a spatial index, like rtree or quad tree, but when I push it into memory, it consumes a ton of RAM along with the already reading the large file.  \nTo help reduce the memory footprint, I was thinking of trying to push the index to disk.\nCan you store the tree in a table? Or even a dataframe and store it in hdf table?  Is there a better strategy?\nThanks","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":245,"Q_Id":43421508,"Users Score":0,"Answer":"Yes, r-trees can be stored on disk easily. (It's much harder with KD-trees and quad-trees)\nThat is why the index is block oriented - the block size is meant to be chosen to match hour drive.\nI don't use pandas, and will not give a library recommendation.","Q_Score":0,"Tags":"python,pandas,quadtree,r-tree","A_Id":43423664,"CreationDate":"2017-04-15T02:20:00.000","Title":"Hdf5 and spatial indexes","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am a newbie to TensorFlow (and the whole deep learning as well). I have a machine with dual boot, Windows 10 and Ubuntu 16.\nUnder which OS should I install and run TensorFlow? Windows or Ubuntu?\nAlso, what is the recommended Python environment? Anaconda or native pip?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":8589,"Q_Id":43425621,"Users Score":2,"Answer":"I think it's easier for you to use Ubuntu if you have the possibility. Getting lapack and blas libraries from sources is easier in linux (you can get precompiled packages for windows though). I prefer native pip, but for windows and for starting Anaconda should be the choice.","Q_Score":2,"Tags":"python,ubuntu,tensorflow,deep-learning","A_Id":43425655,"CreationDate":"2017-04-15T11:43:00.000","Title":"Is it recommended to use TensorFlow under Ubuntu or under Windows?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":1,"Web Development":0},{"Question":"I was wondering if anyone could show me a way of fitting multiple gaussian curves to a data set containing 6 peaks(data comes from a diffraction pattern from a copper gold alloy crystal). the way i have at the moment involves using multiple gaussian equations added together meaning i have to give multiple guesses of values when curve fitting.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":523,"Q_Id":43427707,"Users Score":0,"Answer":"I have successfully used a genetic algorithm to search error space and find initial parameter estimates for scipy's Levenberg-Marquardt nonlinear solver.  The most recent versions of scipy now include the Differential Evolution genetic algorithm which is what I had been using.  It will take a bit of experimenting on a previously fit data set to tune things like population size or genetic crossover strategy, but then you should have a way for the computer to find initial parameter estimates in this way.  It takes a while to run, so let it go overnight, but it can be automated.","Q_Score":0,"Tags":"python,curve-fitting,gaussian","A_Id":43436325,"CreationDate":"2017-04-15T15:15:00.000","Title":"Curve fitting a sum of Gaussian's to 6 peaks","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there any alorithm, which provides a chaotic permutation of a given list in a reasonable time? Im using Python, but I am concerned, that the given shuffle function will not provide a good solution, due a length of 1.1 Millions elements in the given list.\nI did some googling and did not find any usefull results, would be really surprised if there is anything like this, but I would really appreciate an answer","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":522,"Q_Id":43432439,"Users Score":1,"Answer":"Use shuffle. It is fast enough and adequately chaotic for any practical purpose.","Q_Score":2,"Tags":"python,algorithm","A_Id":43432668,"CreationDate":"2017-04-16T00:09:00.000","Title":"Chaotic permutations","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"How can I convert an image to an array of float numbers?\nimg = cv2.imread('img.png')\nand now convert img to float so I get for\nprint(img[0,0]) something like \"[ 4.0 2.0 0.0] instead of [4 2 0]\nDo you have an idea? Thank you very much!","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":27136,"Q_Id":43438653,"Users Score":2,"Answer":"You can convert the list of the integers to list of floats as [float(i) for i in values] with list comprehension.\nAn other option is to convert the img variable as numpy.ndarray to an other numpy.ndarray which contains float values:\nimg = img.astype(float)\nAfter this assignment the results will contain float values.","Q_Score":1,"Tags":"python","A_Id":43438949,"CreationDate":"2017-04-16T15:10:00.000","Title":"Convert image file to float array in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to understand the actual difference between float16 and float32 in terms of the result precision. For instance, Numpy allows you to choose the range of the datatype you want (np.float16, np.float32, np.float64). My concern is that if I decide to go with float 16 to reserve memory and avoid possible overflow, would that create a loss of the final results comparing with float32 for instance?\nThank you","AnswerCount":3,"Available Count":2,"Score":0.0665680765,"is_accepted":false,"ViewCount":53683,"Q_Id":43440821,"Users Score":1,"Answer":"float32 is less accurate but faster than float64, and flaot64 is more accurate than float32 but consumes more memory. If speed accuracy is more important, you can use float64, and if speed is more important than accuracy, you can use float32.","Q_Score":34,"Tags":"python,numpy,types","A_Id":72350771,"CreationDate":"2017-04-16T18:48:00.000","Title":"The real difference between float32 and float64","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to understand the actual difference between float16 and float32 in terms of the result precision. For instance, Numpy allows you to choose the range of the datatype you want (np.float16, np.float32, np.float64). My concern is that if I decide to go with float 16 to reserve memory and avoid possible overflow, would that create a loss of the final results comparing with float32 for instance?\nThank you","AnswerCount":3,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":53683,"Q_Id":43440821,"Users Score":37,"Answer":"float32 is a 32 bit number - float64 uses 64 bits.\nThat means that float64\u2019s take up twice as much memory - and doing operations on them may be a lot slower in some machine architectures.\nHowever, float64\u2019s can represent numbers much more accurately than 32 bit floats.\nThey also allow much larger numbers to be stored.\nFor your Python-Numpy project I'm sure you know the input variables and their nature.\nTo make a decision we as programmers need to ask ourselves\n\nWhat kind of precision does my output need?\nIs speed not an issue at all?\nwhat precision is needed in parts per million?\n\nA naive example would be if I store weather data of my city as\n[12.3, 14.5, 11.1, 9.9, 12.2, 8.2]\nNext day Predicted Output could be of 11.5 or 11.5164374 \ndo your think storing float 32 or float 64 would be necessary?","Q_Score":34,"Tags":"python,numpy,types","A_Id":52804163,"CreationDate":"2017-04-16T18:48:00.000","Title":"The real difference between float32 and float64","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Keras has model.predict which generates output predictions for the input samples. I am looking for this in tensorflow but cannot seem to find it or code it up.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":288,"Q_Id":43441827,"Users Score":0,"Answer":"You can use tf.contrib.Keras to get same behavior.","Q_Score":0,"Tags":"python,numpy,tensorflow,ipython,keras","A_Id":43813452,"CreationDate":"2017-04-16T20:30:00.000","Title":"How can I generate output predictions in Tensorflow just like model.predict in Keras?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have to translate a code from Octave to Python, among many things the program does something like this:\nload_image = imread('image.bmp')\nwhich as you can see its a bitmap, then if I do\nsize(load_image) that prints (1200,1600,3) which its ok, but, when I do:\nload_image\nit prints a one dimensional array, that does not make any sense to me, my question is how in Octave are these values interpreted because I have to load the same image in opencv and I couldn't find the way.\nthanks.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":201,"Q_Id":43459032,"Users Score":0,"Answer":"What you have is a 3D array in octave. Here in the x-dimension you seem to have RGB values for each pixel and Y and Z dimension are the rows and columns respectively. However when you print it you will see all the values in the array and hence it looks like a 1D array.","Q_Score":0,"Tags":"python,numpy,image-processing,octave","A_Id":43459094,"CreationDate":"2017-04-17T20:24:00.000","Title":"imread octave to imread cv2","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a project in which there is a for loop running about 14 times. In every iteration, a 2D array is created with this shape (4,3). I would like to concatenate those 2D arrays into one 3D array (with the shape of 4,3,14) so that every 2D array would be in different \"layer\". \nHow should that be implemented in Python?","AnswerCount":4,"Available Count":1,"Score":-0.0996679946,"is_accepted":false,"ViewCount":8835,"Q_Id":43466644,"Users Score":-2,"Answer":"If your arrays are only 'list', sumplt defines an empty list at the beginning and append item into it:\nfoo=[]\nfor i in range(14):\n    ...\n    foo.append(tab)","Q_Score":6,"Tags":"python,arrays","A_Id":43467467,"CreationDate":"2017-04-18T07:54:00.000","Title":"how to combine 2D arrays into a 3D array in python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Python 101 question. A pandas dataframe is created as:\ndf1=pandas.DataFrame(data, index=index, columns=columns) # takes up say 100 MB memory\nnow\ndf2=df1 # will memory usage be doubled?\nWhat is the effect in a script called something.py and executed as python something.py? Memory is unloaded after completion of execution. Please confirm.\nWhat is the effect when I run something.py in say Anaconda Spyder. The spyder memory will not be unloaded unless I disconnect from the kernel. Is that a true statement?\nThank you all for being patient with a Python newbie","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":844,"Q_Id":43473556,"Users Score":0,"Answer":"What is the effect in a script called something.py and executed as python something.py? Memory is unloaded after completion of execution. Please confirm.\n\nYes, I think memory is freed after execution.\n\nWhat is the effect when I run something.py in say Anaconda Spyder. The spyder memory will not be unloaded unless I disconnect from the kernel. Is that a true statement?\n\nSpyder doesn't hold memory, only the IPython kernel associated with a console does. To free that memory, you need to restart the kernel.","Q_Score":0,"Tags":"python,memory-management,anaconda,spyder","A_Id":43482677,"CreationDate":"2017-04-18T13:25:00.000","Title":"Python memory use for DataFrames in python script and in Anaconda Spyder","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Imagine that you working with a large dataset, distributed over a bunch of CSV files. You open an IPython notebook and explore stuff, do some transformations, reorder and clean up data.\nThen you start doing some experiments with the data, create some more notebooks and in the end find yourself heaped up with a bunch of different notebooks which have data transformation pipelines buried in them.\nHow to organize data exploration\/transformation\/learning-from-it process in such a way, that:\n\ncomplexity doesn't blow, raising gradually;\nkeep your codebase managable and navigable;\nbe able to reproduce and adjust data transformation pipelines?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":161,"Q_Id":43478908,"Users Score":1,"Answer":"Well, I have this problem now and then when working with a big set of data. Complexity is something I learned to live with, sometimes it's hard to keep things simple.\nWhat i think that help's me a lot is putting all in a GIT repository, if you manage it well and make frequent commits with well written messages you can track the transformation to your data easily.\nEvery time I make some test, I create a new branch and do my work on it. If it gets to nowhere I just go back to my master branch and keep working from there, but the work I done is still available for reference if I need it. \nIf it leads to something useful I just merge it to my master branch and keep working on new tests, making new branches, as needed.\nI don't think it answer all of your question and also don't know if you already use some sort version control in your notebooks, but that is something that helps me a lot and I really recommend it when using jupyter-notebooks.","Q_Score":1,"Tags":"python,ipython-notebook,code-organization,project-organization","A_Id":43481217,"CreationDate":"2017-04-18T17:43:00.000","Title":"How to manage complexity while using IPython notebooks?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am building a regression model with about 300 features using Python Sklearn. One of the features has over 100 categories and I end up having ~100 dummy columns for this feature.Now each of the dummy column has its own coefficient, or a feature ranking score (if using Random Forest or xgb) - which is something I dont like.  However, when I create the same model in SAS JMP, it gives me one single feature score for the feature with 100 categories -it apparently handles categories automatically. Can someone tell me how SAS JMP combines the coefficients\/feature importances of 100 dummy variables into one metric. And how can i can achieve the same in Python.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":284,"Q_Id":43483717,"Users Score":0,"Answer":"You may google SAS STAT manual \/User guide. Check out any major regression procedures there that support Class statement. Underneath the Class it details Reference... option. They all detail how a design matrix is fashioned out. The way you fed your 100 dummies must have been obvious enough to trigger JMP to roll back to a temp class variable that re-engineer back to one single variable. If you want to know how exactly JMP is triggered to do roll back, go to JMP website and open a technical support track. But mechanically I am confident this is what happens.","Q_Score":1,"Tags":"python,sas,regression,random-forest,dummy-variable","A_Id":48334045,"CreationDate":"2017-04-18T23:14:00.000","Title":"Combining effects of dummy variables in a regression model","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a pandas histogram that shows the frequency that specific years show up in a dataframe. The x axis contains 2006.0, 2006.5, 2007.0, 2007.5, etc. \nHowever I want my histogram x axis to only have 2006, 2007, etc. This will make my histogram clearer, especially since in my df I only have values for particular years in their integer form, not 2006.5, 2007.4, etc. \nHow would I go about doing this?","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":220,"Q_Id":43485972,"Users Score":2,"Answer":"There are two ways to do it.\n1) if you just want to change the display format, by using the {:,.0f} format to explicitly display (rounded) floating point values with no decimal numbers:\npd.options.display.float_format = '{:,.0f}'.format\n2) if you want to convert it to int : df.col = df.col.astype(int)","Q_Score":0,"Tags":"python,pandas","A_Id":43486149,"CreationDate":"2017-04-19T03:45:00.000","Title":"How to show only years on x axis of pandas histogram?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am getting this error when I am trying to import \"matplotlib.pyplot\". I cant even install matplotlib.pyplot through conda install.\nIt shows this:\n\nimport matplotlib.pyplot Traceback (most recent call last):   File\n  \"\", line 1, in  ModuleNotFoundError: No module named\n  'matplotlib.pyplot'","AnswerCount":7,"Available Count":2,"Score":0.057080742,"is_accepted":false,"ViewCount":25113,"Q_Id":43501102,"Users Score":2,"Answer":"Just open anaconda prompt and use either of the below command to install the package. This solved my issue.\n\nconda install -c plotly chart-studio \n\nor\n\nconda install -c plotly\/label\/test chart-studio","Q_Score":9,"Tags":"python","A_Id":57392720,"CreationDate":"2017-04-19T16:19:00.000","Title":"anaconda cannot import matplotlib.pyplot","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am getting this error when I am trying to import \"matplotlib.pyplot\". I cant even install matplotlib.pyplot through conda install.\nIt shows this:\n\nimport matplotlib.pyplot Traceback (most recent call last):   File\n  \"\", line 1, in  ModuleNotFoundError: No module named\n  'matplotlib.pyplot'","AnswerCount":7,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":25113,"Q_Id":43501102,"Users Score":0,"Answer":"Check if the .....\/python3.x\/site-packages is listed within sys.path. If not append it with sys.path.append('.....python3.8\/site-packages')","Q_Score":9,"Tags":"python","A_Id":70727396,"CreationDate":"2017-04-19T16:19:00.000","Title":"anaconda cannot import matplotlib.pyplot","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have data containing a mixture of numeric values and categorical values. I used K-prototype to cluster them.  \ninit       = 'Huang'\nn_clusters = 50\nmax_iter   = 100 \nkproto = kprototypes.KPrototypes(n_clusters=n_clusters,init=init,n_init=5,verbose=verbose)\nclusters = kproto.fit_predict(data_cats_matrix,categorical=categoricals_indicies)\nwhen I run the last code I'm getting an error as follows :  \nValueError: Clustering algorithm could not initialize. Consider assigning the initial clusters manually.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":2181,"Q_Id":43513662,"Users Score":1,"Answer":"Your data might not warrant a larger number of clusters.\nRun the algorithm for lesser number of k values and note the total cost at the end. If this stops decreasing, there is no need to increase k. It's called the elbow method, you can look it up.","Q_Score":1,"Tags":"algorithm,python-3.x","A_Id":62819510,"CreationDate":"2017-04-20T08:04:00.000","Title":"ValueError: Clustering algorithm could not initialize. Consider assigning the initial clusters manually","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"For one of my pet projects I would like to create an arbitrary number of different integer hashes. After a bit of research I figured that universal hashing is the way to go. However, I struggle with the (numpy) implementation. Say I'm trying to use the hash family h(a,b)(x) = ((a * x + b) mod p) mod m and my x can be anywhere in the range of uint32. Then choosing p >= max(x) and a, b < p means that in the worst case a * x + b overflows not only uint32 but also uint64. I tried to find an implementation which solves this problem but only found clever ways to speed up the modulo operation and nothing about overflow.\nAny suggestions for how to implement this are greatly appreciated. Thanks :-)","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":189,"Q_Id":43531607,"Users Score":1,"Answer":"(x + y) % z == ((x % z) + (y % z)) % z. So you could take the modulus before doing the sum:\n\nCast a and x to uint64. (Multiply two uint32 will never overflow uint64).\nCompute h = (a * x) % p + b\nReturn (h - p) if h > p else h. (Alternatively: return h % p)","Q_Score":1,"Tags":"python,numpy,hash","A_Id":43531728,"CreationDate":"2017-04-20T23:40:00.000","Title":"Integer overflow in universal hashing implementation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm new to machine learning and looking to do run a training \/ testing dataset through a few classifiers, but the problem I'm having is that I only have one label for my data (Legitimate, currently set as an int so 1 for legit, 0 for not). Ideally I'm looking for a classifier that is going to run with just one label and either confirm or deny if something falls into that label, without the need to specify a second label or class.\nAny help would be greatly appreciated!\nMany thanks.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":438,"Q_Id":43531951,"Users Score":2,"Answer":"The lack of negative examples does not make this unary classification; there is no such modelling, as one-class has no discrimination, and therefore derives no new information from the data set.\nAs you've pointed out, there are two classes: Legitimate and not.  That's binary.  Use any binary classifier from your research that's capable of deriving boundaries from positive data only.  For instance, so-called \"one-class\" SVM is one such classifier.","Q_Score":0,"Tags":"python,machine-learning","A_Id":43532000,"CreationDate":"2017-04-21T00:21:00.000","Title":"Python Machine Learning classifier for just one label","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a test data table (called \"eu\") with around 5 millions Observations, where each Observation belongs a country in the EU. Now I'd like to implement a country filter on my dashboard for further Analysis.\nThere already exists a filter widget, which does a query on my data set \"eu\" to get distinct Countries. However, I already know the Country names for the filter and I could skip (if possible) the query. Is there a possibility to set a filter without a query?\nThe filter would look like:\nDE (where Country='DE')\nFR (where Country='FR')\netc.\nEU without DE (where Country!='DE')\nRegards","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":1424,"Q_Id":43543469,"Users Score":2,"Answer":"I found a possibility to create a fast filter. \nA table 'fcountry' is created, where all countries (DE,FR, etc.) are stored. This table is used to create the filter widget, which is added to the Dashboard.\nHowever, I'm still looking for a handy solution for EU without DE (where Country!='DE'). At the Moment I need to select all the countries except DE.","Q_Score":1,"Tags":"python,superset","A_Id":43544802,"CreationDate":"2017-04-21T13:03:00.000","Title":"Dashboard Filter Superset","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have installed Tensorflow and Keras by Anaconda (on Windows 10), I have created an environment where I am using Python 3.5.2 (the original one in Anaconda was Python 3.6). \nWhen I try to execute import keras as ks, I get ModuleNotFoundError: No module named 'keras'.\nI have tried to solve this issue by sys.path.append(C:\\\\Users\\\\ ... \\\\Anaconda3\\\\python.exe)\nwith both notebook and console, but I continue to get the same error.\nHow could I solve this issue?","AnswerCount":9,"Available Count":5,"Score":0.0,"is_accepted":false,"ViewCount":27759,"Q_Id":43557881,"Users Score":0,"Answer":"I have realized that I had two different Jupyter's directories, so I have manually deleted one on them. Finally, I have reinstalled Anaconda. Now Keras works properly.","Q_Score":9,"Tags":"python,anaconda,keras,jupyter-notebook","A_Id":43576690,"CreationDate":"2017-04-22T10:03:00.000","Title":"Jupyter can't find keras' module","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have installed Tensorflow and Keras by Anaconda (on Windows 10), I have created an environment where I am using Python 3.5.2 (the original one in Anaconda was Python 3.6). \nWhen I try to execute import keras as ks, I get ModuleNotFoundError: No module named 'keras'.\nI have tried to solve this issue by sys.path.append(C:\\\\Users\\\\ ... \\\\Anaconda3\\\\python.exe)\nwith both notebook and console, but I continue to get the same error.\nHow could I solve this issue?","AnswerCount":9,"Available Count":5,"Score":0.022218565,"is_accepted":false,"ViewCount":27759,"Q_Id":43557881,"Users Score":1,"Answer":"(Not an answer but some troubleshooting hints)\nsys.path is not the path to your Python executable, but the path to the libraries. \n\nCheck where Keras is installed and check your sys.path. How exactly did you install Keras? \nTry to execute the same command from the Python interpreter. Do you have the same issue?\nHow did you install Jupiter, is the sys.path visible from there the same as sys.path visible from your Python interpreter?\nDo Jupiter and Keras use the same version of Python?\n\nYou might try to uninstall Jupiter and install it again, and hope that the new installation picks up the packages which are already installed. What could happen is that you have more than one Python installation and different libraries being installed to the different places. sys.path, when requested from different environments, might give you a hint if that's true.","Q_Score":9,"Tags":"python,anaconda,keras,jupyter-notebook","A_Id":43557975,"CreationDate":"2017-04-22T10:03:00.000","Title":"Jupyter can't find keras' module","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have installed Tensorflow and Keras by Anaconda (on Windows 10), I have created an environment where I am using Python 3.5.2 (the original one in Anaconda was Python 3.6). \nWhen I try to execute import keras as ks, I get ModuleNotFoundError: No module named 'keras'.\nI have tried to solve this issue by sys.path.append(C:\\\\Users\\\\ ... \\\\Anaconda3\\\\python.exe)\nwith both notebook and console, but I continue to get the same error.\nHow could I solve this issue?","AnswerCount":9,"Available Count":5,"Score":0.0,"is_accepted":false,"ViewCount":27759,"Q_Id":43557881,"Users Score":0,"Answer":"Here's how I solved this problem.\nFirst, the diagnosis. When I run which python in a terminal window on my Mac (the same terminal I used to launch jupyter, I get \/Users\/myusername\/.conda\/envs\/myenvname\/bin\/python, but when I run the same command from a terminal within Jupyter, I get \/usr\/bin\/python. So Jupyter isn't using the correct python executable; the version it's using doesn't have any of my packages installed.\nBut which jupyter returns \/usr\/bin\/jupyter; it's using a version of jupyter that isn't coming from within my conda environment. I ran conda install jupyter and now which jupyter returns \/Users\/myusername\/.conda\/envs\/myenvname\/bin\/jupyter (for some reason I had to restart the terminal window for this to take effect.) Then if I relaunch jupyter notebook, the notebook is using the correct version of Python and I have access to all my installed conda packages.","Q_Score":9,"Tags":"python,anaconda,keras,jupyter-notebook","A_Id":60808850,"CreationDate":"2017-04-22T10:03:00.000","Title":"Jupyter can't find keras' module","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have installed Tensorflow and Keras by Anaconda (on Windows 10), I have created an environment where I am using Python 3.5.2 (the original one in Anaconda was Python 3.6). \nWhen I try to execute import keras as ks, I get ModuleNotFoundError: No module named 'keras'.\nI have tried to solve this issue by sys.path.append(C:\\\\Users\\\\ ... \\\\Anaconda3\\\\python.exe)\nwith both notebook and console, but I continue to get the same error.\nHow could I solve this issue?","AnswerCount":9,"Available Count":5,"Score":0.0,"is_accepted":false,"ViewCount":27759,"Q_Id":43557881,"Users Score":0,"Answer":"If you are a windows\/mac user who are working on Jupyter notebook \u201cpip install keras\u201d doesn't help you .Try the below steps.It was solved for me \n1. In command prompt navigate to the \u201csite packages\u201d directory of your anaconda installed.\n2. Now use \u201cconda install tensorflow\u201d and after \u201cconda install keras\u201d \n3. Re-start your Jupyter notebook and run the packages.","Q_Score":9,"Tags":"python,anaconda,keras,jupyter-notebook","A_Id":51338496,"CreationDate":"2017-04-22T10:03:00.000","Title":"Jupyter can't find keras' module","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have installed Tensorflow and Keras by Anaconda (on Windows 10), I have created an environment where I am using Python 3.5.2 (the original one in Anaconda was Python 3.6). \nWhen I try to execute import keras as ks, I get ModuleNotFoundError: No module named 'keras'.\nI have tried to solve this issue by sys.path.append(C:\\\\Users\\\\ ... \\\\Anaconda3\\\\python.exe)\nwith both notebook and console, but I continue to get the same error.\nHow could I solve this issue?","AnswerCount":9,"Available Count":5,"Score":0.0,"is_accepted":false,"ViewCount":27759,"Q_Id":43557881,"Users Score":0,"Answer":"Acually, I did this command pip install keras and sudo -H pip3 install keras and pip3 install keras. None of them worked. I added the following command and everything worked like a charm:\npip install Keras. Yes a capital 'K'","Q_Score":9,"Tags":"python,anaconda,keras,jupyter-notebook","A_Id":56765580,"CreationDate":"2017-04-22T10:03:00.000","Title":"Jupyter can't find keras' module","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I was trying to do some exploratory analyses on a large (2.7 GB) JSON dataset using R, however, the file doesn't even load in the first place. When looking for solutions, I saw that I could process the data in smaller chunks, namely by iterating through the larger file or by down-sampling it. But I'm not really sure how to do that with a JSON dataset. I also thought of converting the original JSON data into .csv, but after having a look around that option didn't look that helpful.\nAny ideas here?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":609,"Q_Id":43563447,"Users Score":1,"Answer":"The jsonlite R package supports streaming your data. In that way there is no need to read all the json data into memory. See the documentation of jsonlite for more details, the stream_in function in particular.\n\nAlternatively:\nI would dump the json into a mongo database and process the data from that. You need to install mongodb, and start running mongod. After that you can use mongoimport to import the json file into the database. \nAfter that, you can use the mongolite package to read data from the database.","Q_Score":1,"Tags":"python,json,r,bigdata","A_Id":43563552,"CreationDate":"2017-04-22T19:05:00.000","Title":"How to iterate\/ loop through a large (>2GB) JSON dataset in R\/ Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I was wondering if anyone had this issue when running spark and trying to import numpy. Numpy imports properly in a standard notebook, but when I try importing it via a notebook running spark, I get this error. I have the most recent version of numpy and am running the most recent anaconda python 3.6.\nThanks!\n\n---------------------------------------------------------------------------\nAttributeError                            Traceback (most recent call last)\n in ()\n----> 1 import numpy\n\n\/Users\/michaelthomas\/anaconda\/lib\/python3.6\/site-packages\/numpy\/__init__.py in ()\n    144         return loader(*packages, **options)\n    145 \n--> 146     from . import add_newdocs\n    147     __all__ = ['add_newdocs',\n    148                'ModuleDeprecationWarning',\n\n\/Users\/michaelthomas\/anaconda\/lib\/python3.6\/site-packages\/numpy\/add_newdocs.py in ()\n     11 from __future__ import division, absolute_import, print_function\n     12 \n---> 13 from numpy.lib import add_newdoc\n     14 \n     15 ###############################################################################\n\n\/Users\/michaelthomas\/anaconda\/lib\/python3.6\/site-packages\/numpy\/lib\/__init__.py in ()\n      6 from numpy.version import version as __version__\n      7 \n----> 8 from .type_check import *\n      9 from .index_tricks import *\n     10 from .function_base import *\n\n\/Users\/michaelthomas\/anaconda\/lib\/python3.6\/site-packages\/numpy\/lib\/type_check.py in ()\n      9            'common_type']\n     10 \n---> 11 import numpy.core.numeric as _nx\n     12 from numpy.core.numeric import asarray, asanyarray, array, isnan, \\\n     13                 obj2sctype, zeros\n\nAttributeError: module 'numpy' has no attribute 'core'","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":22785,"Q_Id":43578533,"Users Score":0,"Answer":"Apart from upgrading and re-installing, sometimes it is caused by your Pandas. It might have dependency on older numpy so you may have to upgrade or reinstall pandas if upgrading numpy alone didn't resolve your problem.","Q_Score":7,"Tags":"python,numpy,pyspark,anaconda","A_Id":69907112,"CreationDate":"2017-04-24T01:41:00.000","Title":"AttributeError: module 'numpy' has no attribute 'core'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a pandas dataframe where I am plotting two columns out the 12, one as the x-axis and one as the y-axis. The x-axis is simply a time series and the y-axis are values are random integers between -5000 and 5000 roughly. \nIs there any way to make a scatter plot using only these 2 columns where the positive values of y are a certain color and the negative colors are another color?\nI have tried so many variations but can't get anything to go. I tried diverging color maps, colormeshs, using seaborn colormaps and booleans masks for neg\/positive numbers. I am at my wits end.","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":7728,"Q_Id":43579626,"Users Score":0,"Answer":"Make 2 separate dataframes by using boolean masking and the where keyword. The condition would be if >0 or not. Then plot both datframes one by one ,one top of the other, with different parameters for the color.","Q_Score":5,"Tags":"python,pandas,matplotlib,plot","A_Id":43579860,"CreationDate":"2017-04-24T04:05:00.000","Title":"Pandas Plot With Positive Values One Color And Negative Values Another","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"We have trained an Extra Tree model for some regression task. Our model consists of 3 extra trees, each having 200 trees of depth 30. On top of the 3 extra trees, we use a ridge regression.\nWe trained our model for several hours and pickled the trained model (the entire class object), for later use. However, the size of saved trained model is too big, about 140 GB!\nIs there a way to reduce the size of the saved model? Are there any configuration in pickle that could be helpful, or any alternative for pickle?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":9137,"Q_Id":43591621,"Users Score":4,"Answer":"In the best case (binary trees), you will have 3 * 200 * (2^30 - 1) = 644245094400 nodes or 434Gb assuming each one node would only cost 1 byte to store. I think that 140GB is a pretty decent size in comparision.","Q_Score":13,"Tags":"python,machine-learning,pickle,random-forest","A_Id":43592085,"CreationDate":"2017-04-24T15:04:00.000","Title":"Trained Machine Learning model is too big","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to make a single gaussian kernel density plot of a dataframe with multiple columns which includes all columns of the dataframe. Does anyone know how to do this?\nSo far I only found how to draw a gaussian kernel plot of a single column with seaborn. ax = sns.kdeplot(df['shop1'])\nHowever, neither ax = sns.kdeplot(df)norax = sns.kdeplot(df['shop1','shop2]) do not work. \nOtherwise is there a workaround where I could transform the dataframe with shape df.shape(544, 33) to (17952, 2), by appending each columns to eachother?\nThe dataframe includes normalized prices for one product, whereas each column represents a different seller and the rows indicate date and time of the prices.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":465,"Q_Id":43596345,"Users Score":0,"Answer":"I did find how to work around by transforming the dataframe's columns into one single column. \n\ndf.stack()","Q_Score":0,"Tags":"python,kernel-density","A_Id":43597467,"CreationDate":"2017-04-24T19:34:00.000","Title":"python: one kernel density plot which includes multiple columns in a single dataframe","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm looking for a lemmatization module\/lib that will transfer a sentence like:\n \"this is great\" to \"this is good\".\nI'm familiar with some of the tools available in nltk such as stemming and lemmatization, however it's not exactly what I'm looking for\nMy goal is to minimize the variety of ways saying the same thing.","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":593,"Q_Id":43611550,"Users Score":2,"Answer":"This is a bit more experimental, but another possibility is to use word embeddings.\nThe words great and good should have similar occurrence contexts, so their vectors should be similar, you cluster your words like that, and aggregate them into the same word\/concept.\nOf course this will greatly depend on the corpus and methods you use to generate the embeddings.","Q_Score":0,"Tags":"python,nlp,nltk","A_Id":43612292,"CreationDate":"2017-04-25T13:05:00.000","Title":"Python: linguistic normalization","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a corpus of around 6000 texts with comments from social network (FB, twitter), news content from general and regional news and magazines, etc. I have gone through first 300 of these texts and tag each of these 300 texts' content as either customer complaint or non-complaint. \nInstead of naive way of bag of words, I am wondering how can I accurately extract the features of these complaints and non-complaints texts? My goal is to use SVM or other classification algorithm\/ library such as Liblinear to most accurately classify the rest of these texts as either complaint or non-complaint with the current training set of 300 texts. Is this procedure similar to sentiment analysis? If not, where should I start?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":319,"Q_Id":43624308,"Users Score":0,"Answer":"I think you will find that bag-of-words is not so naive. It's actually a perfectly valid way of representing your data to give it to an SVM. If that's not giving you enough accuracy you can always include bigrams, i.e. word pairs, in your feature vector instead of just unigrams.","Q_Score":1,"Tags":"python,nlp,classification,feature-extraction,sentiment-analysis","A_Id":43625960,"CreationDate":"2017-04-26T03:07:00.000","Title":"How to extract COMPLAINT features from texts in order to classify complaints from non-complaints texts","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have set up a producer\/consumer model using python Queues. In one producer I'm reading images using scipy.misc.imread. \nReading images in one thread is not fast enough, it takes ~0.2s per image to read. About 20MB\/sec reading from an SSD.\nI tried adding another identical thread using python's threading module. However the time spent in scipy.misc.imread increased by approximately double, causing the 2 threads to read images approximately as fast as the 1 did.\nI'm sure my SSD can handle 40MB\/sec throughput, even with random reads. A dd write test shows 800MB+\/sec write speeds.\nI am left wondering if scipy.misc.imread runs as a critical region among threads? Would I expect multiprocessing to avoid the problem?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":186,"Q_Id":43624618,"Users Score":1,"Answer":"scipy.misc.imread is safe from multiple threads, but each call locks the global interpreter, so performance won't benefit from multithreading.\nIt works well from multiprocessing, no unexpected issues.","Q_Score":2,"Tags":"python,multithreading,scipy,multiprocessing,python-imaging-library","A_Id":44355165,"CreationDate":"2017-04-26T03:41:00.000","Title":"Is scipy.misc.imread safe\/efficient to run from multiple threads?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I ran the same code (with the same data) on CPU first using keras 1.2.0 and then keras 2.0.3 in both codes keras is with TensorFlow backend and also I used sklearn for model selection, plus pandas to read data. \nI was surprised when I got the MSE(Mean squared error) of 42 using keras 2.0.3 and 21 using keras 1.2.0. Can someone pls explain to me why this is happening? Why I am getting more error using keras 2? Thanks\nPS. This result is after editing the code to keras 2 standard. for example in Dense I change keras 1 code to keras 2 standard.","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":417,"Q_Id":43637515,"Users Score":0,"Answer":"Probably some default values that have changed from Keras 1.2.\nYou should check the default values for you 1.2 code, and set the same value for your new code.","Q_Score":4,"Tags":"python,keras,mse","A_Id":43637652,"CreationDate":"2017-04-26T14:47:00.000","Title":"Getting worse result using keras 2 than keras 1","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using numexpr for simple array addition on a remote cluster. My computer has 8 cores and the remote cluster has 28 cores. Numexpr documentation says that \"During initialization time Numexpr sets this number to the number of detected cores in the system\" But the cluster gives this output.\ndetect_number_of_cores() = 28\ndetect_number_of_threads()=8\nALthough when I try to set the number of threads manually to something else(set_num_threads=20) , array operation seems to run faster. But detect_number_of_threads() still gives 8 as output.    \nIs this a bug?","AnswerCount":1,"Available Count":1,"Score":-0.1973753202,"is_accepted":false,"ViewCount":5204,"Q_Id":43641247,"Users Score":-1,"Answer":"I\u00b4m not sure, how numexpr actually works internally, when detect_number_of_threads is called, but maybe it reads out the number of threads that is available to openmp and not the number of threads that were locally set.","Q_Score":2,"Tags":"python,arrays,cluster-computing,python-multithreading,numexpr","A_Id":46969923,"CreationDate":"2017-04-26T17:50:00.000","Title":"Numexpr detecting number of threads less than number of cores","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using Spacy 1.8.0 with Python and I would like to use Spacy to do analysis on Medical Documents. There is a way off adding new entity types to spacy's named entity recognizer. However, is it possible to add the names of medicines\/drugs as proper nouns to spcay's vocab? Or do they need to be added by training the spacy NER? \nThanks","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":806,"Q_Id":43650119,"Users Score":2,"Answer":"I'm not sure exactly what you want to do... but below are solutions for a few possibilities.\n\nYou have a complete list of the medicines\/drugs of interest...\ni. and you want a special rule for tokenisation of these strings: Would not recommend this approach but in principle you could add special cases to the Tokenizer.\nii. and you simply want to find them: Then you should use a Matcher to find them efficiently after you've created the Doc. Then you can do what you want with the spans that you find. Make them pronouns if you want.\nYou don't have a complete list of the medicines\/drugs of interest...\ni. but you want to generally identify them: Then you'll need to train an NER model to recognize them. To generate the training data you could take a whole bunch of papers and use a Matcher looking for known medicines\/drugs to grab a whole bunch of example sentences. Then you train...\n\nLater edit: To be clear, the Vocab only stores strings and assigns them a unique ID for working with under the hood. It's the Tagger which decides whether it's a PRON or not.","Q_Score":1,"Tags":"python,nlp,named-entity-recognition,spacy","A_Id":48454088,"CreationDate":"2017-04-27T06:26:00.000","Title":"How to add proper nouns as vocab to Spacy models?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have 2 columns df[year] and df[month]. It has values ranging from 2000 to 2017 and month values 1 - 12.\nHow to combine these to another column which would contain the combined output.\nEg: \nYear   Month   Y0M\n2000    1   200001\n2000    2   200002\n2000    3   200003\n2000    10  200010\n\nNote : there is a 0 added in between Year and Month in Y0M column, (only for single digit numbers and not double digit) \nCurrently I am able to only do this by converting to string, but I want to retain them as type numbers","AnswerCount":2,"Available Count":1,"Score":0.2913126125,"is_accepted":false,"ViewCount":3502,"Q_Id":43651802,"Users Score":3,"Answer":"Maybe something like df[year] * 100 + df[month] would help.","Q_Score":3,"Tags":"python,pandas,dataframe","A_Id":43651884,"CreationDate":"2017-04-27T07:54:00.000","Title":"How to Combine 2 integer columns in a dataframe and keep the type as integer itself in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been using py2exe to package some scripts as executable, which has worked well until this error.\nIn one script I need to solve a straightforward system of linear equations. I've been doing this with scipy.linalg.lstsq.\nThe problem is that any script I package with any scipy.linalg or numpy.linalg command crashes as soon as it gets to that line. The executable compiles with no errors. Everything runs as it should until it gets to a linalg command, then the executable just closes with no warning or error. No error log gets written.\nI've confirmed that it's always the linalg commands by manually writing log files with flags around the commands.\nI've used other scipy packages, primarily optimize, before with no issues.\nI've tried catching the error with a 'try' statement, but it crashes regardless.\nI can only think it's some kind of import error, but those normally throw errors when the executable is being built.\nThe complete lack of any error or warning messages has me at a loss as to what could be causing this.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":189,"Q_Id":43661027,"Users Score":0,"Answer":"I managed to solve this problem and wanted to post the solution.\nThe link posted by J.J. Hakala, showed that an error with the same symptoms can occur with matplotlib because py2exe does not transfer some necessary dll's to the new dist directory. I was already transferring the dll's mentioned in that post manually, however by manually testing I discovered that mkl_avx.dll was also necessary for numpy.linalg. By simply copying this to the dist directory this problem was resolved.","Q_Score":0,"Tags":"python,numpy,scipy,py2exe","A_Id":43688972,"CreationDate":"2017-04-27T14:45:00.000","Title":"Python py2exe executable silent crash with scipy.linalg or numpy.linalg","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a list of arrays (could be numpy arrays or just list of int) of different lengths and I would like to feed it into some TensorFlow queue for testing a larger input pipeline -- instead of serializing them and re-read from file system. Is it possible?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":119,"Q_Id":43661651,"Users Score":0,"Answer":"I assume you're using an RNN here, for an RNN to take a variable length array you're going to need to pad them with zeros to the batch length and pass in a value that tells the RNN how long each sequence is.\nI'd suggest taking the same approach here. Pad them before passing them into the TF queue and pass in another value that indicates the unpadded length.\nIf you're not using an RNN (what are you doing with variable length inputs then??) you must be processing them one-by-one (you didn't give us much detail there) so just break them up and send them into the queue one by one, read them off in TF as you need and recombine them with TF slicing and joining operations.","Q_Score":0,"Tags":"python,input,tensorflow,queue,pipeline","A_Id":43662378,"CreationDate":"2017-04-27T15:12:00.000","Title":"Feeding a queue from a list of arrays of different lengths","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an array, I attempt to find the n1 th smallest number until the n2 th smallest number in the array and store them in another array of size n2-n1+1. Here n2>n1 and both are realtively small compared to the size of the array (for example, the array size is 10000, n1=5, n2=20). \nI can sort this array first, and then retrieve the n1 th number, n1+1 th number until the n2 th number from the sorted array. But since n1 and n2 are usually relatively small compared to the size of the array, it is not necessary to sort the array completely. The algorithm should be able to stop in middle once reaches n2 I think. \nI wonder if there is any algorithm, maybe a modified version of certian sorting algorithm that is specifacally good (by good I mean fast) at this problem. You can either use Python code or pseudo code as an illustration, thanks!","AnswerCount":4,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":115,"Q_Id":43668307,"Users Score":0,"Answer":"If n2 (and therefore n1) are both small, then you can find the n2 smallest elements and ignore the first n1 ones.  These approaches are described by Arun Kumar and user448810 and will be efficient as long as n1 remains small.\nHowever, you may be describing a situation in which n1 (and therefore n2) can grow (perhaps even linearly with the overall list length) and it is only their difference n2-n1 which remains small.  In this case you need a selection algorithm such as quickselect which will remain O(N) in this case.","Q_Score":0,"Tags":"python,algorithm,sorting,columnsorting","A_Id":43733509,"CreationDate":"2017-04-27T21:35:00.000","Title":"Fast algorithm to find the n1 th smallest number to n2 th smallest number in an array","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an array, I attempt to find the n1 th smallest number until the n2 th smallest number in the array and store them in another array of size n2-n1+1. Here n2>n1 and both are realtively small compared to the size of the array (for example, the array size is 10000, n1=5, n2=20). \nI can sort this array first, and then retrieve the n1 th number, n1+1 th number until the n2 th number from the sorted array. But since n1 and n2 are usually relatively small compared to the size of the array, it is not necessary to sort the array completely. The algorithm should be able to stop in middle once reaches n2 I think. \nI wonder if there is any algorithm, maybe a modified version of certian sorting algorithm that is specifacally good (by good I mean fast) at this problem. You can either use Python code or pseudo code as an illustration, thanks!","AnswerCount":4,"Available Count":2,"Score":0.049958375,"is_accepted":false,"ViewCount":115,"Q_Id":43668307,"Users Score":1,"Answer":"Since, the N1 and N2 are really small compared to size of array  letus  say N.\nWe can have an implementation in O(N2 * LogN) using min heap data structures.\nSteps\n\nConstruct a min heap. Complexity of this operation will be O(N)\nLoop for a range of 0 to N2:\n     Get the root element and call heapify. Ignore first N1 elements and return rest of the elements. Complexity of this step is O(1)+O(logN)\nHence, overall we have O(N2 * logN)","Q_Score":0,"Tags":"python,algorithm,sorting,columnsorting","A_Id":43670278,"CreationDate":"2017-04-27T21:35:00.000","Title":"Fast algorithm to find the n1 th smallest number to n2 th smallest number in an array","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have written a piece of python code that scrapes the odds of horse races from a bookmaker's site. I wish to now:\n\nRun the code at prescribed increasingly frequent times as the race draws closer.\nStore the scraped data in a database fit for extraction and statistical analysis in R.\n\nApologies if the question is poorly phrased\/explained - I'm entirely self taught and so have no formal training in computer science. I don't know how to tell a piece of python code to run itself every say n-minutes and I also have no idea how to correctly approach building such a data base or what factors I should be considering. Can someone point me in the right direction for getting started on the above?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":73,"Q_Id":43670334,"Users Score":0,"Answer":"In windows, you can use Task Scheduler or in Linux crontab. You can configure these to run python with your script at set intervals of time. This way you don't have a python script continuously running preventing some hangup in a single call from impacting all subsequent attempts to scrape or store in database.\nTo store the data there are many options which could either be a flat file(write to a text file), save as a python binary(use shelf or pickle), or install an actual database like MySQL or PostgreSQL and many more. Google for info. Additionally an ORM like SQLAlchemy may make controlling, querying, and updating your database a little easier since it will handle all tables as objects and create the SQL itself so you don't need to code all queries as strings.","Q_Score":0,"Tags":"python,database-design,web-scraping","A_Id":43670482,"CreationDate":"2017-04-28T01:07:00.000","Title":"How to run python code at prescribed time and store output in database","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am kinda a newbie in machine learning. I have done lots of learning related to data analysis and ML algorithms, and I've got very good results and understanding of the algorithms. However, my approaches are normally getting datasets, writing scripts or notebooks to solve the problems and that's pretty much it. Which means, no matter how fancy I create a script, I have to run the script everytime for each update, this is not enough.\nI think that machine learning is more than that, and there are already smart systems that are constantly updating their systems automatically, and I am really interested in that, but can't really find any good materials that dive deep into that field.\n\nLet's just say, like the classic example, there's a website that has a ML system at the backend that identifies iris categories, and it's visitors constantly upload new observations of irises and the visitors get the category of the iris they upload, and the backend system continues to improve accordingly.\n\nHow can I get to learn thing like this? Would be super keen to dive into this field.\nI'd really appreciate all your opinions!\nThanks a lot!","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":405,"Q_Id":43671556,"Users Score":1,"Answer":"you are asking \"Incremental way of learning \" !!\nit's depends on what task are you doing. you can not apply incremental learning everywhere. the real problem is when we train , in the process of training , there are many other things that are related(inter dependent). which gets changed during a new cycle.\nso if you can make your algorithm independent , then you can do \"incremental way of learning\" !!","Q_Score":0,"Tags":"python,machine-learning,artificial-intelligence,data-analysis","A_Id":44968742,"CreationDate":"2017-04-28T03:36:00.000","Title":"How to build a machine learning system (smart system) that updates automatically?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using xgboost package in python and I'm going to extract tree structures after training. For instance, I'm going to know the feature and threshold that has been selected at each node to export the tree structures to a function.\nMoreover, I need to know the weight of each tree after training. (As we know, the result of trees will be combined like w1*h1+w2h2+...+wn*hn, in which wi is weigh and hi is the answer of ith tree in boosting). Actually, I need to have access to weights and inner trees. I'm doing classification.\nMy other question is that, when I use \"from xgboost import plot_tree\", I receive this error \"the package does not have this function\". How can I plot my trees?\nThanks","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1889,"Q_Id":43671704,"Users Score":1,"Answer":"You can dump the xgboost model into a text file, and parse it yourself. The file looks like this:\nbooster[0(<-tree id)]:\n0(<-node id):[f317(<-feature name)<0.187154] yes=1(<-child node id),no=2,missing=1\n    1:[f317<0.071172] yes=3,no=4,missing=3\n         ...\n            6379:leaf=0.125 (<-weight to that leaf)\nAt the end, it is the weighted sum of all leaves. This is in case of binary classification and regression.","Q_Score":1,"Tags":"python,algorithm,tree,random-forest,xgboost","A_Id":44010620,"CreationDate":"2017-04-28T03:55:00.000","Title":"Extracting weights and tree structures from xgboost - plot trees","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"sparse\narray objects will have a fixed size \u200bn \u200b set when they are created - attempting to set or get elements larger\nthan the size of the array should raise an \u200bIndexError","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":238,"Q_Id":43674393,"Users Score":0,"Answer":"I'd bet these are already implemented in numpy or scipy.","Q_Score":0,"Tags":"python,arrays,algorithm,linked-list","A_Id":43674438,"CreationDate":"2017-04-28T07:24:00.000","Title":"I need to implement a \u200bSparseArray\u200b with a \u200blinked list\u200b implementation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm a working a neural network that does face reconstruction from a single image using tensorflow.\nI'm trying to figure out how to render the output of the network (which is a 3D mesh) into an image in python.\nMost of the libraries I found do 3D rendering in real time, I'm only looking to render and single frame.\nI also need something that is fast, because this is potentially going to be part of the pre-processing for the network in real-time (apply the network to live video.\nThe mesh is quite simple, it's a simple face with about 30k vertices and 50k triangles.","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":11128,"Q_Id":43724600,"Users Score":0,"Answer":"nVidia's iRay is excellent however, 50k plus triangles is a serious task for any rendering engine.\nIf you're looking for photorealistic quality you'll be looking at several minutes per render at 50k triangles from a simple lighting source.\nThe more complex your lighting becomes the slower the process. A simple texture based shadow render is much quicker and can be done in real time, the results are limited to the quality of the UV mapped texture. \nDo you capture the surface material from the single image or do you apply a pre-mapped UV surface material to your standard 50k triangle model mesh?","Q_Score":5,"Tags":"python,3d,rendering,render","A_Id":54512055,"CreationDate":"2017-05-01T18:45:00.000","Title":"Rendering a 3D mesh into an image using python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a matrix where each row is padded to maxlen. If I apply softmax on the matrix, the padded indices are also considered. How to apply softmax on the matrix while considering only the non-padded indices?\nNote: padded length in each row of the matrix would differ.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":302,"Q_Id":43730740,"Users Score":1,"Answer":"You could set your padded cells to a value close to -Infinity (i.e. very small compared to your other logits), so that their contributions are just negligible.","Q_Score":1,"Tags":"python-3.x,tensorflow,deep-learning","A_Id":43735408,"CreationDate":"2017-05-02T05:33:00.000","Title":"How to avoid padded indices during calculation of softmax using tf.nn.softmax?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using OpenCV in windows. When I am using this command in windows command prompt then I am getting an error as opencv_annotation is not recognised as the internal or external command.\nopencv_annotation --annotations=\/C:\\Users\\harsh\\Desktop\/annotation.txt --images=C:\\Users\\harsh\\Desktop\/pos\/","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":743,"Q_Id":43739684,"Users Score":0,"Answer":"You need to build it using CMake.","Q_Score":1,"Tags":"windows,python-2.7,opencv","A_Id":65209790,"CreationDate":"2017-05-02T13:51:00.000","Title":"opencv_annotation not recognised by windows","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I have used:\npickle.dump(data, f, protocol=2)\nAnd try to open the pickle file with python 2.7, however, it still pop up with the error \"ValueError: unsupported pickle protocol: 3\".","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2769,"Q_Id":43769071,"Users Score":0,"Answer":"It is more likely to be a pickle file created with python 3. I guess you mainly use python 2.7. Either you re-create the pickle file with python 2.7 or you use python 3.","Q_Score":0,"Tags":"python,pickle","A_Id":44855960,"CreationDate":"2017-05-03T20:06:00.000","Title":"ValueError: unsupported pickle protocol: 3, python 2.7 cannot load pickle file even dump with protocol = 2","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to use Keras in Python for time series classification. \nI have made a simple CNN and was wondering if it is possible to randomly select a subsection of the full data set at each epoch to serve as the test set?\nI am quite new to neural nets so if there are any reasons this is impossible or impractical I would appreciate clarity.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":95,"Q_Id":43769357,"Users Score":0,"Answer":"Your method is not impossible, but it is impractical. A Neural Network learns from the training dataset. If you give the same data again in the test set, it will generate accurate predictions, which will have no relation with the network's predictive power.","Q_Score":2,"Tags":"python,deep-learning,classification,keras,conv-neural-network","A_Id":57201952,"CreationDate":"2017-05-03T20:24:00.000","Title":"Selecting test and train sets in Keras for Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a massive dataframe, and I'm getting the error:\nTypeError: (\"Empty 'DataFrame': no numeric data to plot\", 'occurred at index 159220')\nI've already dropped nulls, and checked dtypes for the DataFrame so I have no guess as to why it's failing on that row.\nHow do I print out just that row (at index 159220) of the data frame?\nThanks","AnswerCount":5,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":543751,"Q_Id":43772362,"Users Score":7,"Answer":"Sounds like you're calling df.plot(). That error indicates that you're trying to plot a frame that has no numeric data. The data types shouldn't affect what you print().\nUse print(df.iloc[159220])","Q_Score":106,"Tags":"python,python-3.x,pandas,indexing","A_Id":43772528,"CreationDate":"2017-05-04T01:22:00.000","Title":"How to print a specific row of a pandas DataFrame?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I will write an example so you can understand me :\nFor instance, I have these 3 points : (0, 1, 244) - (0, 1, 255) - (1, 2, 133)\nActually I need to average the Z when 2 points have the same (x, y). My idea is to store them in something (an array, a dictionary ?) with a double index\/key where the value is an array of the Z.\nSo I basically want to have this : [0, 1, [244, 255]] and [1, 2, [133]]\nMy problem is I don't know what to use to store them the simpliest way to do so...\nThanks in advance !","AnswerCount":3,"Available Count":1,"Score":0.1325487884,"is_accepted":false,"ViewCount":85,"Q_Id":43775108,"Users Score":2,"Answer":"Here are a couple of ways of doing this:\n\nAs Geotop suggested, use a dictionary indexed with a tuple.\nThis according to me is an elegant solution.  \nDepending on the purpose and ease of traverse, you may convert the same to a nested list as follows:  \na = [ [ (1,2), (3,4), (5,6) ],\n      [ [123,234,345], [567,678,543], [433,645,754] ] ]\n\nNow you may access the list of z values corresponding to (x,y) using a[1][i], where the tuple (x,y) is stored as a[0][i].\nThe second method seems much better to me as it avoids dictionary use and makes indexing much easier (as it is indexed with integers.). But at the end, it all depends on your purpose.\nStoring it randomly in a list and getting the values by a search is a bad idea if you look up the data frequently or if the data is huge.","Q_Score":1,"Tags":"python,arrays,list,dictionary,store","A_Id":43775529,"CreationDate":"2017-05-04T06:09:00.000","Title":"Store points by x, y and multiple z","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a pandas dataframe sorted by a number of columns. Now I'd like to split the dataframe in predefined percentages, so as to extract and name a few segments. \nFor example, I want to take the first 20% of rows to create the first segment, then the next 30% for the second segment and leave the remaining 50% to the third segment.\nHow would I achieve that?","AnswerCount":3,"Available Count":1,"Score":0.1325487884,"is_accepted":false,"ViewCount":22451,"Q_Id":43777243,"Users Score":2,"Answer":"Creating a dataframe with 70% values of original dataframe\npart_1 = df.sample(frac = 0.7)\n\nCreating dataframe with rest of the 30% values\npart_2 = df.drop(part_1.index)","Q_Score":14,"Tags":"python-3.x,pandas","A_Id":68590093,"CreationDate":"2017-05-04T08:06:00.000","Title":"How to split a DataFrame in pandas in predefined percentages?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to install Python 3.5 both 32 & 64 bit and also be able to transfer between the two as needed, but am not having any luck. Scipy will only install when I use the 32bit (Various issues when trying to install 64bit version even with physical .whl files).\nMeanwhile Tensorflow only works on x64. \nI'm using windows 7, and have tried various solutions I've found on Stackoverflow, but have had no luck.\nAlso, was thinking of just dual installing linux mint and running python off there. Would any of you recommend?\nThank you!","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":384,"Q_Id":43781633,"Users Score":0,"Answer":"Did you try using Anaconda or similar for the installation? - if this is an option in your case I would highly recommend it under Windows.","Q_Score":1,"Tags":"python,python-3.x,tensorflow,scipy,64-bit","A_Id":43782019,"CreationDate":"2017-05-04T11:28:00.000","Title":"Python 3.5 32 & 64 bit -- Scipy & Tensorflow issues","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to install Python 3.5 both 32 & 64 bit and also be able to transfer between the two as needed, but am not having any luck. Scipy will only install when I use the 32bit (Various issues when trying to install 64bit version even with physical .whl files).\nMeanwhile Tensorflow only works on x64. \nI'm using windows 7, and have tried various solutions I've found on Stackoverflow, but have had no luck.\nAlso, was thinking of just dual installing linux mint and running python off there. Would any of you recommend?\nThank you!","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":384,"Q_Id":43781633,"Users Score":0,"Answer":"Installing Linux (highly recommended)\nWhile your Issue with Scipy & Tensorflow.\nWhy don't you use the Anaconda Installer for Windows x64 bit(it contains alomost all major packges for Data Science already(Scipy too)) ,  and then install Tensorflow","Q_Score":1,"Tags":"python,python-3.x,tensorflow,scipy,64-bit","A_Id":43782112,"CreationDate":"2017-05-04T11:28:00.000","Title":"Python 3.5 32 & 64 bit -- Scipy & Tensorflow issues","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"A bit of a beginner question, but I was not able to find a relevant answer on this..\nEssentially my data about (7gb) is located on my local machine. I have distributed cluster running on the local network. How can I get this file onto the cluster? \nThe usual dd.read_csv() or read_parquet() fails, as the workers aren't able to locate the file in their own environments.\nWould I need to manually transfer the file to each node in the cluster?\nNote: Due to admin restrictions I am limited to SFTP...","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":2868,"Q_Id":43796774,"Users Score":1,"Answer":"Network solution : \n\nUnder Windows only it should works  with a shared forlder: dd.read_csv(\"\\\\server\\shared_dir\")\nUnder Unix\/Linux only it should works with HDFS: import hdfs3 and then hdfs.read_csv('\/server\/data_dir'...)\n\nBut if you want to use Windows AND Linux workers at the same time I don't know since dd.read_csv() with UNC does not seem to be supported under Linux (because of the file path '\\server\\data_dir') and HDFS with hdfs.read_csv is not working under Windows (import hdfs3 failed because the lib libhdfs3.so doesn't exist under Windows)\nDoes anyone have a Network solution for workers under Windows and Unix ?","Q_Score":6,"Tags":"python,python-3.x,dask","A_Id":46379569,"CreationDate":"2017-05-05T04:53:00.000","Title":"Loading local file from client onto dask distributed cluster","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I'm confused because it's going to be a problem if you first do OneHotEncoder and then StandardScaler because the scaler will also scale the columns previously transformed by OneHotEncoder. Is there a way to perform encoding and scaling at the same time and then concatenate the results together?","AnswerCount":4,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":26114,"Q_Id":43798377,"Users Score":0,"Answer":"Can't get your point as OneHotEncoder is used for nominal data, and StandardScaler is used for numeric data. So you shouldn't use them together for your data.","Q_Score":24,"Tags":"python,scikit-learn","A_Id":43798752,"CreationDate":"2017-05-05T06:54:00.000","Title":"One-Hot-Encode categorical variables and scale continuous ones simultaneouely","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Will the performance of the model be affected if I train the data on LINUX system and then use that model in the WINDOWS application or a python script?","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":496,"Q_Id":43804250,"Users Score":1,"Answer":"No, the model will be exactly the same. You'll only have to make sure that your TF versions on Linux and Windows are compatible ones, but this is not made more difficult by the different OS, it's only a matter of which versoin you install on which device.","Q_Score":1,"Tags":"linux,windows,python-3.x,tensorflow","A_Id":43805354,"CreationDate":"2017-05-05T11:51:00.000","Title":"Tensor-flow training and Testing on different OS","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Will the performance of the model be affected if I train the data on LINUX system and then use that model in the WINDOWS application or a python script?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":496,"Q_Id":43804250,"Users Score":0,"Answer":"Akshay, as gdelab said, make sure about the version. If you are running the same model on same data(training and testing) again & again, it might affect but using on different OS might not. I faced the same issue.","Q_Score":1,"Tags":"linux,windows,python-3.x,tensorflow","A_Id":43805554,"CreationDate":"2017-05-05T11:51:00.000","Title":"Tensor-flow training and Testing on different OS","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"So I know for a standard convolutional neural network you can provide the neural net (NN) a file with a list of labels or simply separate your classes by folders but for instance segmentation I imagine it's different right? \nFor example using a site like labelme2 you can annotate and segment images and then download them along with mask files and XML files for labels. Does one need to subsequently input the original image, mask image and XML file to the instance segmentation NN?  \nThanks in advance.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":667,"Q_Id":43821167,"Users Score":0,"Answer":"You'll want to train your NN in such as way that you'll be able to use it for prediction.  \n\nIf you want to just predict the classes from the image, then all you want to send to your NN is \n\nthe original image (probably color balanced) and\npredict the classes from the XML (convert that into a 1 hot class encoding)\n\nif you want to predict the mask also, then send\n\nthe original image (probably color balanced) and\npredict the mask and classes from the XML (convert that into a 1 hot class encoding)\n\n\nThe above objectives (just classes, or classes+mask prediction) drives the decision to store the classes or the classes + mask.","Q_Score":0,"Tags":"python,machine-learning,tensorflow,conv-neural-network,image-segmentation","A_Id":43895291,"CreationDate":"2017-05-06T13:43:00.000","Title":"How does one input images and labels for Semantic Instance Segmentation with neural networks?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have imported a CSV file as a Pandas dataframe. When I run df.dtypes I get most columns as \"object\", which is useless for taking into Bokeh for charts.\n\nI need to change a column as int, another column as date, and the rest as strings.\nI see the data types only once I import it.  Would you recommend changing it during import (how?), or after import?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":7317,"Q_Id":43835016,"Users Score":0,"Answer":"During reading a csv file:\nUse dtype or converters attribute in read_csv in pandas\nimport pandas as pd\nimport numpy as np\ndf = pd.read_csv('data.csv',dtypes = {'a':float64,'b':int32},headers=None)\nHere,automatically the types will be read as the datatype you specified.\nAfter having read the csv file:\nUse astype function to change the column types.\nCheck this code.\nConsider you have two columns\ndf[['a', 'b']] = df[['a', 'b']].astype(float)\nThe advantage of this is you change type of multiple columns at once.","Q_Score":1,"Tags":"python-3.x,pandas","A_Id":43835158,"CreationDate":"2017-05-07T18:15:00.000","Title":"How to change data types \"object\" in Pandas dataframe after importing a CSV?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to draw the plot result in an image and save it as an image in python.\nI have a method that could Harris method in result there are alot of point that I want to trace in a picture but win I save the figure is giving me just the point not the image also please give me an advice","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":40,"Q_Id":43838497,"Users Score":0,"Answer":"Use the command fig.savefig('line plot.jpg', bbox_inches='tight', dpi=150)","Q_Score":1,"Tags":"python,image,plot","A_Id":68993454,"CreationDate":"2017-05-08T01:33:00.000","Title":"Draw the plot result in an image and save it as an image in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I just got started with Keras and built a Q-learning example program. I created a tensorboard callback and I include it in the call to model.fit, but the only things that appear in TensorBoard are the scalar summary for the loss and the network graph. Interestingly, if I open up the dense layer in the graph, I see a little summary icon labeled \"bias_0\" and one labeled \"kernel_0\", but I don't see these appearing in the distributions or histograms tabs in TensorBoard like I did when I built a model in pure tensorflow.\nDo I need to do something else to enable these in Tensorboard? Do I need to look into the details of the model that Keras produces and add my own tensor_summary() calls?","AnswerCount":2,"Available Count":1,"Score":0.2913126125,"is_accepted":false,"ViewCount":17275,"Q_Id":43859133,"Users Score":3,"Answer":"I debugged this and found that the problem was I was not providing any validation data when I called fit(). The TensorBoard callback will only report on the weights when validation data is provided. That seems a bit restrictive, but I at least have something that works.","Q_Score":21,"Tags":"python,tensorflow,keras,tensorboard","A_Id":43879111,"CreationDate":"2017-05-09T00:18:00.000","Title":"Keras - is it possible to view the weights and biases of models in Tensorboard","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"After doing a bit of research I am finding it difficult to find out how to use mysql timestamps in matplotlib.\nMysql fields to plot\nX-axis:\nField: entered\nType: timestamp\nNull: NO\nDefault: CURRENT TIMESTAMP\nSample: 2017-05-08 18:25:10\nY-axis:\nField: value\nType: float(12,6)\nNull: NO\nSample: 123.332\nWhat date format is matplotlib looking for? How do I convert to this format? I found out how to convert from unix timestamp to a format that is acceptable with matplotlib, is unix timestamp better than the timestamp field type I am using? Should I convert my whole table to unix timestamps instead?\nWould appreciate any help!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":202,"Q_Id":43859988,"Users Score":0,"Answer":"you can use datetime module,although i use now() function to extract datetime from mysql,but i consider the format is the same\u3002\nfor instance\npython>import datetime as dt\ni put the datetime data into a list named datelist,and now you can use  datetime.strptime function to convert the date format to what you want\npython>dates = [dt.datetime.strptime(d,'%Y-%m-%d %H:%M:%S') for d in datelist\nAt last,you can put the list named dates into plot X-axis\nI hope it helps you.","Q_Score":0,"Tags":"python,mysql,matplotlib","A_Id":43860724,"CreationDate":"2017-05-09T02:17:00.000","Title":"Python - convert mysql timestamps type to matplotlib and graph","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"It seems that numpy.resize is not supported in numba.\nWhat is the best way to use dynamically growing arrays with numba.jit in nopython mode?\nSo far the best I could do is define and resize the arrays outside the jitted function, is there a better (and neater) option?","AnswerCount":3,"Available Count":2,"Score":0.0665680765,"is_accepted":false,"ViewCount":2018,"Q_Id":43869734,"Users Score":1,"Answer":"Typically the strategy I employ is to just allocate more than enough array storage to accommodate the calculation and then keep track of the final index\/indices used, and then slice the array down to the actual size before returning. This assumes that you know beforehand what the maximum size you could possibly grow the array to is. The thought is that in most of my own applications, memory is cheap but resizing and switching between python and jitted functions a lot is expensive.","Q_Score":2,"Tags":"python,numpy,dynamic-arrays,numba","A_Id":43870685,"CreationDate":"2017-05-09T12:21:00.000","Title":"dynamically growing array in numba jitted functions","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"It seems that numpy.resize is not supported in numba.\nWhat is the best way to use dynamically growing arrays with numba.jit in nopython mode?\nSo far the best I could do is define and resize the arrays outside the jitted function, is there a better (and neater) option?","AnswerCount":3,"Available Count":2,"Score":0.0665680765,"is_accepted":false,"ViewCount":2018,"Q_Id":43869734,"Users Score":1,"Answer":"To dynamically increase the size of an existing array (and therefore do it in-place), numpy.ndarray.resize must be used instead of numpy.resize. This method is NOT implemented in Python, and is not available in Numba, so it just cannot be done.","Q_Score":2,"Tags":"python,numpy,dynamic-arrays,numba","A_Id":68612285,"CreationDate":"2017-05-09T12:21:00.000","Title":"dynamically growing array in numba jitted functions","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to know how can I proceed in order to concatenate two csv files, here is the composition of this two files:\n\nThe first one contains some datas related to water chemical parameters, these measurements are taken in different dates.\nThe second one shows the different flow values of waste water, during a certain period of time.\n\nThe problem is that I am looking to assign each value of the second file (Flow values) to the right row in the first file (water chemical parameters) in such a way that the flow and the other chemical parameters are measured in the same moments.\nAny suggestions ?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":37,"Q_Id":43871444,"Users Score":0,"Answer":"The first file is smth like: \nTimestamp ;       Flow1 ;    Flow 2\n2017\/02\/17 00:05 ; 540 ;       0 \n2017\/02\/17 00:10 ; 535 ;       0 \n2017\/02\/17 00:15 ; 543 ;       0 \n2017\/02\/17 00:20 ; 539 ;       0 \nCSV file #2:\nTimestamp ; DOC ; Temperatute ; UV254; \n2017\/02\/17 00:14 ; 668.9 ; 15,13 ; 239,23 \n2017\/02\/17 00:15 ; 669,46 ; 15,14 ; 239,31 \n2017\/02\/17 00:19 ; 668 ; 15,13 ; 239,43 \n2017\/02\/17 00:20 ; 669,9 ; 15,14 ; 239,01 \nhe output file is supposed to be like : \n                                                                    Timestamp      ;  DOC  ;   Temperatute  ; UV254  ;   Flow1 ;    Flow2                                                   2017\/02\/17 00:15  ; 669,46 ; 15,14   ;     239,31  ;    543    ;       0\n2017\/02\/17 00:20  ; 669,9   ; 15,14   ;     239,01  ;    539   ;       0","Q_Score":0,"Tags":"python,csv,dataframe,merge","A_Id":43872949,"CreationDate":"2017-05-09T13:39:00.000","Title":"Merging two DataFrames (CSV files) with different dates using Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am currently aware that keras doesn't support list of list of numpys.. but I can't see other way to pass my input. \nMy input to my neural network is each columns (total 45 columns) of 33 different images. \nThe way I've currently stored it is as an \nlist of list in which the outer list has length 45, and the inner has length 33, and within this inner list I store a numpy.ndarray of shape (1,8,3).. \nI feed it this as I need to do 45 convolutions, one for each column in the image. The same convolution has to be applied on all images for their  respective column number.  \nSo convolution_1 has to be applied on every first column on all the 33 images.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":218,"Q_Id":43871607,"Users Score":0,"Answer":"If you want to create a 'list of numpys' you can do np.array(yourlist). \nIf you print result.shape you will see what the resulting shape is. Hope this helps!","Q_Score":0,"Tags":"python,numpy,keras","A_Id":43871677,"CreationDate":"2017-05-09T13:46:00.000","Title":"How do I pass my input to keras?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am currently aware that keras doesn't support list of list of numpys.. but I can't see other way to pass my input. \nMy input to my neural network is each columns (total 45 columns) of 33 different images. \nThe way I've currently stored it is as an \nlist of list in which the outer list has length 45, and the inner has length 33, and within this inner list I store a numpy.ndarray of shape (1,8,3).. \nI feed it this as I need to do 45 convolutions, one for each column in the image. The same convolution has to be applied on all images for their  respective column number.  \nSo convolution_1 has to be applied on every first column on all the 33 images.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":218,"Q_Id":43871607,"Users Score":0,"Answer":"You can use Input(batch_shape = (batch_size, height, width, channels)), where batch_size = 45, channels = 33 and use np.ndarray of shape (45, height, width, 33) if your backend is tensorflow","Q_Score":0,"Tags":"python,numpy,keras","A_Id":43872953,"CreationDate":"2017-05-09T13:46:00.000","Title":"How do I pass my input to keras?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"what are the equivalent flags of SURF in opencv C++ to python SURF flags extended and upright ?\n\nin python version upright flag decides whether to calculate orientation or not\nAnd extended flag gives option of whether to use 64 dim or 128 dim \n\nIs there a to do this similar operation in opencv C++ version of SURF function\nFYI I am using opencv version 2.4.13","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":175,"Q_Id":43878271,"Users Score":0,"Answer":"got it ! \nC++: SURF::SURF(double hessianThreshold, int nOctaves=4, int nOctaveLayers=2, bool extended=true, bool upright=false )","Q_Score":0,"Tags":"python,c++,opencv,image-processing,surf","A_Id":43878521,"CreationDate":"2017-05-09T19:23:00.000","Title":"extended and upright flags in SURF opencv c++ function","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm following the instructions on github.com\/jcjohnson\/torch-rnn and have it working until the training section. When I use th train.lua -input_h5 my_data.h5 -input_json my_data.jsonI get the error Error: unable to locate HDF5 header file at \/usr\/local\/Cellar\/hdf5\/1.10.0-patch1\/include;\/usr\/include;\/usr\/local\/opt\/szip\/include\/hdf5.h\n\nI'm new to luarocks and torch, so I'm not sure what's wrong. I installed torch-hd5f. Any advice would be very much appreciated.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":84,"Q_Id":43881941,"Users Score":1,"Answer":"Check that the header file exists and that you have the correct path. \nIf the header file is missing you skipped the preprocess step. If the header file exists it's likely in your data directory and not in the same directory as the sample.lua code:\nth train.lua -input_h5 data\/my_data.h5 -input_json data\/my_data.json","Q_Score":1,"Tags":"python-2.7,lua,torch,luarocks","A_Id":44149840,"CreationDate":"2017-05-10T00:51:00.000","Title":"Error using Torch RNN","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am a graduate student focusing on ML and NLP. I have a lot of data (8 million lines) and the text is usually badly written and contains so many spelling mistakes.\nSo i must go through some text cleaning  and vectorizing. To do so, i considered two approaches:\nFirst one:\ncleaning text by replacing bad words using hunspell package which is a spell checker and morphological analyzer\n+\ntokenization\n+\nconvert sentences to vectors using tf-idf\nThe problem here is that sometimes, Hunspell fails  to provide the correct word and changes the misspelled word with another word that don't have the same meaning. Furthermore, hunspell does not reconize acronyms or abbreviation (which are very important in my case) and tends to replace them.\nSecond approache:\ntokenization\n+\nusing some embeddings methode (like word2vec) to convert words into vectors without cleaning text\nI need to know if there is some (theoretical or empirical) way to compare this two approaches :)\nPlease do not hesitate to respond If you have any ideas to share, I'd love to discuss them with you.\nThank you in advance","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":842,"Q_Id":43896369,"Users Score":1,"Answer":"I post this here just to summarise the comments in a longer form and give you a bit more commentary. No sure it will answer your question. If anything, it should show you why you should reconsider it.\nPoints about your question\nBefore I talk about your question, let me point a few things about your approaches. Word embeddings are essentially mathematical representations of meaning based on word distribution. They are the epitome of the phrase \"You shall know a word by the company it keeps\". In this sense, you will need very regular misspellings in order to get something useful out of a vector space approach. Something that could work out, for example, is US vs. UK spelling or shorthands like w8 vs. full forms like wait. \nAnother point I want to make clear (or perhaps you should do that) is that you are not looking to build a machine learning model here. You could consider the word embeddings that you could generate, a sort of a machine learning model but it's not. It's just a way of representing words with numbers.\nYou already have the answer to your question\nYou yourself have pointed out that using hunspell introduces new mistakes. It will be no doubt also the case with your other approach. If this is just a preprocessing step, I suggest you leave it at that. It is not something you need to prove. If for some reason you do want to dig into the problem, you could evaluate the effects of your methods through an external task as @lenz suggested.\nHow does external evaluation work?\nWhen a task is too difficult to evaluate directly we use another task which is dependent on its output to draw conclusions about its success. In your case, it seems that you should pick a task that depends on individual words like document classification. Let's say that you have some sort of labels associated with your documents, say topics or types of news. Predicting these labels could be a legitimate way of evaluating the efficiency of your approaches. It is also a chance for you to see if they do more harm than good by comparing to the baseline of \"dirty\" data. Remember that it's about relative differences and the actual performance of the task is of no importance.","Q_Score":0,"Tags":"python-3.x,text,nlp,embedding,data-cleaning","A_Id":43917161,"CreationDate":"2017-05-10T15:07:00.000","Title":"Embeddings vs text cleaning (NLP)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to analyze the vectors looking for patterns and stuff, and use SVM on them to complete a classification task between class A and B, the task should be supervised. (I know it may sound odd but it's our homework.) so as a result I really need to know:\n1- how to extract the coded vectors of a document using a trained model?\n2- how to interpret them and how does word2vec code them?\nI'm using gensim's word2vec.","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":1519,"Q_Id":43904029,"Users Score":2,"Answer":"If you have trained word2vec model, you can get word-vector by __getitem__ method\nmodel = gensim.models.Word2Vec(sentences)\nprint(model[\"some_word_from_dictionary\"])\nUnfortunately, embeddings from word2vec\/doc2vec not interpreted by a person (in contrast to topic vectors from LdaModel)\n\nP\/S If you have texts at the object in your tasks, then you should use Doc2Vec model","Q_Score":1,"Tags":"python,machine-learning,gensim,word2vec,text-classification","A_Id":43976879,"CreationDate":"2017-05-10T23:09:00.000","Title":"How extract vocabulary vectors from gensim's word2vec?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"My Jupyter Notebook doesn't show in the Jupyter Dashboard in Windows 10.\nAdditionally, I get the following error in my Jupyter cmd line console:\n[W 00:19:39.638 NotebookApp] C:\\Users\\danie\\Documents\\Courses\\Python-Data-Science-and-Machine-Learning-Bootcamp Jupyter Notebooks\\Python-Data-Science-and-Machine-Learning-Bootcamp\\Machine Learning Sections\\Decision-Trees-and-Random-Forests\\Decision Trees and Random Forest Project - Solutions.ipynb doesn't exist","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":368,"Q_Id":43920802,"Users Score":0,"Answer":"The files path names are too long.  Reducing the path length by reducing the number of folders and\/or folder name lengths will solve the problem.","Q_Score":0,"Tags":"python,jupyter-notebook","A_Id":43920803,"CreationDate":"2017-05-11T16:17:00.000","Title":"Jupyter Notebook doesn't show in Dashboard (Windows 10)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a data-set that contains about 14,700 records. I wish to train it on ibm watson and currently i'm on trial version. What is the rough estimate about the time that the classifier will take to train? Each record of dataset contains a sentence and the second column contains the class-name.","AnswerCount":2,"Available Count":2,"Score":0.1973753202,"is_accepted":false,"ViewCount":222,"Q_Id":43920923,"Users Score":2,"Answer":"For NLC it depends on the type of data, and quantity. There is no fixed time to when it completes, but I have seen a classifier run a training session for nearly a day. \nThat said, normally anywhere from 30 minutes to a couple of hours. \nWatson conversation Intents is considerably faster (minutes). But both use different models, so I would recommend to test both and see the results. Also check how each is scoring when comparing (absolute\/relative).","Q_Score":3,"Tags":"python,ibm-cloud,ibm-watson,training-data,nl-classifier","A_Id":43935121,"CreationDate":"2017-05-11T16:23:00.000","Title":"IBM Watson nl-c training time","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a data-set that contains about 14,700 records. I wish to train it on ibm watson and currently i'm on trial version. What is the rough estimate about the time that the classifier will take to train? Each record of dataset contains a sentence and the second column contains the class-name.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":222,"Q_Id":43920923,"Users Score":0,"Answer":"If your operating system is UNIX, you can determine how long a query takes to complete and display results when executed using dbaccess. You can use the time command to report how much time is spent, from the beginning to the end of a query execution. Including the time to connect to the database, execute the query and write the results to an output device. \nThe time command uses another command or utility as an argument, and writes a message to standard error that lists timing statistics for that command. It reports the elapsed time between invocation of the command and its termination. The message includes the following information:\nThe elapsed (real) time between invocation and termination of the utility. The real time is divided in two components, based on the kind of processing:\nThe User CPU time, equivalent to the sum of the tms_utime and tms_cutime fields returned by the times function for the process in which utility is executed.\nor,\nThe System CPU time, equivalent to the sum of the tms_stime and tms_cstime fields returned by the times() function for the process in which utility is executed.","Q_Score":3,"Tags":"python,ibm-cloud,ibm-watson,training-data,nl-classifier","A_Id":43921011,"CreationDate":"2017-05-11T16:23:00.000","Title":"IBM Watson nl-c training time","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm doing some research in neuroscience and I'm using python's tinydb library for keeping track of all my model training runs and the data they generate. \nOne of the issues I realized might come up is when I try to train multiple models on a cluster. What could happen is that two threads might try to write to the tinydb json file at the same time. \nCan someone please let me know if this will be an issue?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":678,"Q_Id":43939316,"Users Score":0,"Answer":"Python processes, threads and coroutines offers synchronization primitives such as locks, rlocks, conditions and semaphores. If your threads access randomly one or more shared variables then every thread should acquire lock on this variable so that another thread couldn't access it.","Q_Score":0,"Tags":"python,file","A_Id":43939525,"CreationDate":"2017-05-12T13:44:00.000","Title":"File write collisions on parallelized python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to find out the distance between the camera and the people (detected using the HOG descriptor) in front of camera.I'm looking into more subtle approach rather than calibrating the camera and without knowing any distances before hand.\nThis can fall under the scenario of an autonomous car finding the distance between the car in front.\nCan someone help me out with a sample code or an explanation on how to do so","AnswerCount":1,"Available Count":1,"Score":-0.3799489623,"is_accepted":false,"ViewCount":2354,"Q_Id":43954187,"Users Score":-2,"Answer":"I am sorry but finding a distance is a metrology problem, so you need to calibrate your camera. Calibrating is a relatively easy process which is necessary for any measurements.\nLet's assume you only have one calibrated camera, if the orientation\/position of this camera is fixed relatively to the ground plane, it is possible to calculate the distance between the camera and the feet of somebody (assuming the feet are visible).","Q_Score":0,"Tags":"python,opencv,image-processing,distance","A_Id":43954917,"CreationDate":"2017-05-13T14:16:00.000","Title":"How to finding distance between camera and detected object using openCV in python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on a problem (doomsday_fuel) using python and I need to use matrices, so I would like to import numpy. I have solved the problem and it runs perfectly on my own computer, but Google returns the error: ImportError: No module named numpy [line 3].\nThe beginning of my code looks like:\nimport fractions\nfrom fractions import Fraction\nimport numpy as np\nI have checked constraints.txt and they do not seem to restrict numpy \n\"Your code will run inside a Python 2.7.6 sandbox. Standard libraries are supported except for bz2, crypt, fcntl, mmap, pwd, pyexpat, select, signal, termios, thread, time, unicodedata, zipimport, zlib.\"\nDoes anyone have any ideas how or why this would happen? Or do people have ideas as to what steps I could take to ask Google about this?","AnswerCount":2,"Available Count":1,"Score":0.2913126125,"is_accepted":false,"ViewCount":4751,"Q_Id":43954548,"Users Score":3,"Answer":"Math is a standard library but still it is not working with the Foobar.","Q_Score":5,"Tags":"python,numpy","A_Id":61691668,"CreationDate":"2017-05-13T14:56:00.000","Title":"Google foo.bar Challenge Issue: Can't import libraries","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Tagged this as Python because is the most pseudo-y-code language in my opinion\nI'll explain graphically and the answer can be graphical\/theorical too (maybe its the wrong site to post this?)\nLet's say I want to make an algorithm that solves a simple digital game for infants (this is not the actual context, its much more complex)\nThese are the rules :\n\nThere is a square grid seen from above, with a colored lego piece in\neach spot\nYou can drag pieces to try and stack on top of each other.\nIf their color match, they will stack, leaving the spot of the first\npiece you dragged empty.\nIf you move a piece to an empty spot, it will move to that spot\nIf their color don't match and you drag one of top of the other, they\nwill switch spots.\nThe amount of pieces of a same color is randomly generated when a new grid is started.\n\nThe goal of the game is to obviously drag pieces of the same color until you only have one stack of each color.\nNow here comes question, I want to make a script that solves the game, but it will be \"blind\", meaning it won't be able to see colors, or track when a match occurs. It will have to traverse in a way that it will ensure it tried all possible \"drags\"\nThe main problem for me to even start thinking about this comes from the fact that they swap positions if the script fails to guess the color, and there's no feedback to know that you failed.\nAlso is the complexity of this calculable? Is it too insane?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":58,"Q_Id":43967808,"Users Score":3,"Answer":"Let's try the following:\nSuppose we have an m times n grid grid with p different colors. First we work row by row with the following algorithm:\nColumn reduction\n\nDrag the piece at (1,1) to (1,2), then (1,2) to (1,3) and so on until you reach (1, n)\nDrag the piece at (1,1) the same way to (1,n-1).\nContinue till you reach (1, n-p) with the piece moved.\n\nThe first step is guaranteed to move the color that was originally at (1,1) to (1,n) and collect all pieces of the same color on its way. \nThe succeeding steps collect the remaining colors. After this part of the algorithm we are guaranteed to have only the columns p to n filled, each with a different color.\nThis we repeat for the remaining m-1 rows. After that the columns 1 to n-p-1 are guaranteed to be empty. \nRow reduction\nNow we repeat the same process with the columns, i.e. drag (1, j) to (m, j) for all j >= n-p and then drag (1,j) to (m-1, j). \nAfter this part we are guaranteed to have only filled a p times p subgrid. \nFull grid search\nNow we collect each different color by brute force:\nMove (p,p) to (p,p+1), (p, p+2), ... (p, n) and then to (p + 1, n), (p+1, n-1), ..., (p+1, p) and then to (p+2, p), ..., (p+2, n) and so on until we reach either (m, p) or (m,n), depending wether p is even or odd.\nThis step we repeat p times, only that we stop each time on field short of the last one. \nAs a result only the remaining p fields are filled and each contains a stack of the same color. Problem solved.\nTo estimate the complexity:\n\nThe row moving part requires n + n-1 + n-2 + ... + n-p= n*(n+1)\/2 - (n-p)*(n-p+1)\/2=np+(p^2+p)\/2=O(n^2) moves per row, hence O(mn^2).\nThe column moving part similarly requires O(nm^2) moves.\nThe final moving requires p^2 moves for each color, i.e. O(p^3).\n\nIf q = max(n,m,p) the complexity is O(q^3).\nNote: If we do not know p we could immediately start with the full grid search. We still remain in complexity O(q^3). If, however, p << n or p << m the column and row reduction will reduce the practical complexity greatly.","Q_Score":0,"Tags":"python,algorithm,sorting,theory","A_Id":43968199,"CreationDate":"2017-05-14T19:07:00.000","Title":"An algorithm for grouping by trying without feedback","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have some training images (more than 20 image with format .tif)that i want to plot their  histogram of width in python. I will be more than happy if any one can helps.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":194,"Q_Id":43971678,"Users Score":1,"Answer":"ok i will give you the steps, but the coding has to be done by you\nassuming you have python installed and pip in you machine\n\nInstall pillow using pip\nget the images in the script and calculate the width and store them in a list, you will get to know how to calculate width from the Pillow documentation\nInstall matplotlib using pip\nPass that list you created from the images to the plotting function of matplotlib.\nthe Histogram representation can be found in Matlpoltlib documentation\n\nhope it helps,","Q_Score":0,"Tags":"python,image-processing,computer-vision,ipython,opencv-python","A_Id":43971827,"CreationDate":"2017-05-15T04:39:00.000","Title":"How to plot the histogram of image width in python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm working on a project to classify 30 second samples of audio from 5 different genres (rock, electronic, rap, country, jazz). My dataset consists of 600 songs, exactly 120 for each genre. The features are a 1D array of 13 mfccs for each song and the labels are the genres.\nEssentially I take the mean of each set of 13 mfccs for each frame of the 30 second sample. This leads to 13 mfccs for each song. I then get the entire dataset, and use sklearn's scaling function.\nMy goal is to compare svm, knearest, and naive bayes classifiers (using the sklearn toolset). I have done some testing already but I've noticed that results vary depending on whether I do random sampling\/do stratified sampling.\nI do the following function in sklearn to get training and testing sets:\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=0, stratify=y)\nIt has the parameters \"random state\" and \"stratify\". When \"random state\" is ommitted, it randomly samples from the entire dataset; when it is set to 0, the training and test sets are guaranteed to be the same each time.\nMy question is, how do I appropriately compare different classifiers. I assume I should make the same identical call to this function before training and testing each classifer. My suspicion is that I should be handing the exact same split to each classifier, so it should not be random sampling, and stratifying as well.\nOr should I be stratifying (and random sampling)?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":894,"Q_Id":43972059,"Users Score":0,"Answer":"To evaluate a classifier's accuracy against another classifier, you need to randomly sample from the dataset for training and test. Use the test dataset to evaluate each classifier and compare the accuracy in one go. \nGiven a dataset stored in a dataframe , split it into training and test (random sampling is better to get an indepth understanding of how good your classifier is for all cases , stratified sampling can sometimes mask your true accuracy) Why? Let's take an example :\nIf you are doing stratified sampling on some particular category (and let's assume this category has an exceptionally large amount of data[skewed] and the classifier predicts that one category well , you might be led to believe that the classifier works well, even if doesn't perform better on categories with less information. Where does stratified sampling work better? When you know that the real world data will also be skewed and you will be satisifed if the most important categories are predicted correctly. (This definitely does not mean that your classifier will work bad on categories with less info, it can work well , it's just that stratified sampling sometimes does not present a full picture)\nUse the same training dataset to train all classifers and the same testing dataset to evaluate them. Also , random sampling would be better.","Q_Score":1,"Tags":"python,machine-learning,scikit-learn,statistical-sampling","A_Id":43972717,"CreationDate":"2017-05-15T05:25:00.000","Title":"Music genre classification with sklearn: how to accurately evaluate different models","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to get the x and y coordinates for which the area under the curve: y=-15.7log(x)+154.94 is maximum. I would like to compute this in R or Python. Can someone please help me to find it?\n\nBackground: I have data points of sales (y) vs prices (x). I tried fitting a log curve in R: lm(formula = y ~ log(x)) which gave me the above equation. I'm trying to increase the revenue which is the product of sales and prices. Hence the rectangular area under the curve should be maximized.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":161,"Q_Id":43977734,"Users Score":0,"Answer":"That is no programming question but a mathematics question and if I get the function in your question right, the answer is \"wherever the graph hits the x-axis\".\nBut I think that was not what you wanted. Maybe you want the rectangle between O(0,0) and P(x, y)?\nThan you still should simply use a cas and a-level mathematics:\nA = x*(-15.7log(x)+154.94)","Q_Score":1,"Tags":"python,r,python-2.7,equation,logarithm","A_Id":43977884,"CreationDate":"2017-05-15T10:58:00.000","Title":"How to maximize the area under -log(x) curve?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How do we edit a row in a datatable in spotfire?\nCan we do it using ironpython or R script?\nI have a requirement where I want to edit the values in spotfire datatable to see the effect in the respective visuals. The data table is populated using an information link (from a SQL database).","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1165,"Q_Id":43978775,"Users Score":1,"Answer":"This can be done.\n\n1-> Create function or packaged function which returns ref-cursor.\n   1.1-> In that update your value in table based on where clause. 2-> Once you have function ready, create informationlink on that\nobject    using parameter type single. 3-> Once you do that import\ninformation    link to spotfire using on demand values. 4-> create\ndocument property    and use that do property as parameter for on\ndemand. 5-> keep    datatable refresh manually(uncheck Auto\nRefresh). 6-> provide user    text box to have new values. 7->\nprovide 1 button and use    datatable.Refresh(). 8-> it will pass\nyour doproperty value to    database and your function will return\nref-cursor, in that you can    return the sql%rowcount or success or\nfailure msg.","Q_Score":3,"Tags":"ironpython,spotfire,rscript","A_Id":46858137,"CreationDate":"2017-05-15T11:50:00.000","Title":"Edit a row value in datatable in spotfire","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"1. Mattes Mutual Info Doubts\nIn SimpleITK Mattes Mutual information is a similarity metric measure, is this a maximizing function or minimizing function?\nI have tried a 3D registration(image size : 480*480*60) with\nMetric Mattes Mutual Info metric and Gradient Descent Optimizer\nOutput\n\nnumofbins = 30\nOptimizer stop condition: RegularStepGradientDescentOptimizerv4: Step too small after 24 iterations. Current step (7.62939e-06) is less than minimum step (1e-05).\nIteration: 25\nMetric value: -0.871268982129\n\nnumofbins = 4096\nOptimizer stop condition: RegularStepGradientDescentOptimizerv4: Step too small after 34 iterations. Current step (7.62939e-06) is less than minimum step (1e-05).\nIteration: 23\nMetric value: -1.7890\n\nIf it is a minimization function then the lower one is better, which I suspect.\n2. Transformation matrix final Output\nTranslationTransform (0x44fbd20)\nRTTI typeinfo: itk::TranslationTransform\nReference Count: 2\nModified Time: 5528423\nWhat is Modified Time?\n3. Final Metric is a registration accuracy measurement?\nIs metric a sign of registration accuracy? Is a higher metric value mean better registration? Or it is just a value at optimum point after optimization?\n4. Random sampling for registration\n10-20% of random sample points suffice for a registration. But the doubt arises whether the samples are taken from the main ROI or outside the ROI? Masking is an option, is there any other option in SimpleITK?\nThanks","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":770,"Q_Id":43985976,"Users Score":0,"Answer":"Similarity metrics in ITK usually give the cost, so the optimizers try to minimize them. Mutual information is an exception to this rule (higher MI is better), so in order to fit into the existing framework it has negative values -  bigger negative number is better than small negative number, while still following the logic that it should be minimized.\nModified time is used to check whether a certain filter should be updated or not.\nGenerally lower metric means better registration. But it is not comparable between different metrics, or even between different types of images using the same metric.\nRandom sampling will take 10-20% of samples in your RoI. I am not sure whether it picks randomly within RoI, or picks randomly within image and then checks whether it is in RoI.","Q_Score":0,"Tags":"python,image-processing,optimization,itk,image-registration","A_Id":43987786,"CreationDate":"2017-05-15T17:59:00.000","Title":"Mattes Mutual Info basic doubts on 3D image registration","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have RGB images (32 x 32 x 3) saved as 3D numpy arrays which I use as input for my Neural Net (using tensorflow). In order to use them as an input I reshape them to a 1D np array (1 x 3072) using reshape(1,-1). When I finish training my Net I want to reshape the output back, but using reshape(32,32,3) doesn't seem to provide the desired outcome.\nIs this the correct way to do it? How I can be sure that each datum will be back to the correct place?","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":11600,"Q_Id":44009244,"Users Score":8,"Answer":"If you are looking to create a 1D array, use .reshape(-1), which will create a linear version of you array. If you the use .reshape(32,32,3), this will create an array of 32, 32-by-3, arrays, which is the original format described. Using '-1' creates a linear array of the same size as the number of elements in the combined, nested array.","Q_Score":4,"Tags":"python,arrays,numpy,image-processing,tensorflow","A_Id":44009737,"CreationDate":"2017-05-16T18:43:00.000","Title":"Python: How can I reshape 3D Images (np array) to 1D and then reshape them back correctly to 3D?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have RGB images (32 x 32 x 3) saved as 3D numpy arrays which I use as input for my Neural Net (using tensorflow). In order to use them as an input I reshape them to a 1D np array (1 x 3072) using reshape(1,-1). When I finish training my Net I want to reshape the output back, but using reshape(32,32,3) doesn't seem to provide the desired outcome.\nIs this the correct way to do it? How I can be sure that each datum will be back to the correct place?","AnswerCount":2,"Available Count":2,"Score":0.1973753202,"is_accepted":false,"ViewCount":11600,"Q_Id":44009244,"Users Score":2,"Answer":"If M is (32 x 32 x 3), then .reshape(1,-1) will produce a 2d array (not 1d), of shape (1, 32*32*3).  That can be reshaped back to (32,32,3) with the same sort of reshape statement.\nBut that's reshaping the input to and from  But you haven't told us what the output of your Net is like.  What shape does it have?  How are you trying to reshape the output, and what is wrong with it?","Q_Score":4,"Tags":"python,arrays,numpy,image-processing,tensorflow","A_Id":44009566,"CreationDate":"2017-05-16T18:43:00.000","Title":"Python: How can I reshape 3D Images (np array) to 1D and then reshape them back correctly to 3D?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"All of my steps have worked very well up to this point. I am on a windows machine currently. I am in the root directory after using the command:\ndocker run -it gcr.io\/tensorflow\/tensorflow:latest-devel\nthen followed by a cd \/tensorflow, I am now in the directory and it is time to train the images so i jused:\n\/tensorflow# python tensorflow\/examples\/image_retraining\/retrain.py \\\n\n--bottleneck_dir=\/tf_files\/bottlenets \\\n  --how_many_training_steps 500 \\\n  --model_dir=\/tf_files\/retrained_graph.pb \\\n  --output_labels=\/tf_files\/retrained_labels.txt \\\n  --image_dir \/tf_files\/\n\nAnd i get this error:\n  File \"tensorflow\/examples\/image_retraining\/retrain.py\", line 77, in \n    import numpy as np\nImportError: No module named 'numpy'\nI DO already have numpy installed in my python35 folder and it is up to date. Thanks a lot for any help, I am really stuck on this!","AnswerCount":2,"Available Count":1,"Score":-0.0996679946,"is_accepted":false,"ViewCount":237,"Q_Id":44014764,"Users Score":-1,"Answer":"If you're planning to use Python 3, I'd recommend docker run -it gcr.io\/tensorflow\/tensorflow:latest-devel-py3 (Numpy is installed for python3 in that container). Not sure why Python 3 is partially installed in the latest-devel package.","Q_Score":0,"Tags":"windows,python-3.x,docker,tensorflow","A_Id":44054062,"CreationDate":"2017-05-17T02:58:00.000","Title":"Using Docker for Image training in Python (New to this)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I installed tensorflow via python3.5 pip, it is in the python3.5 lib folder and I can use it perfectly on shell IDLE.\nI have anaconda(jupyter notebook) on my computer at the same time, however, I couldn't import tensorflow on notebook.\nI guess notebook was using the anaconda lib folder, not python3.5 libs. is there any way to fix that instead of install again on anaconda folder?\nthanks","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":4674,"Q_Id":44017326,"Users Score":0,"Answer":"There is a package called nb_conda that helps manage your anaconda kernels. However, when you launch Jupyter make sure that you have jupyter installed inside your conda environment and that you are launching Jupyter from that activated environment.\nSo:\n\nActivate your conda environment that has Tensorflow installed. You can check by doing conda list. If Tensorflow is not installed within your environment then do so.\nInstall jupyter and nb_conda if you haven't already.\nFrom your activated environment, run jupyter notebook.\nYou should now be running in the correct kernel. You should see a kernel named Python [conda env:namehere] in the top right. You may also have a choice of kernels thanks to nb_conda if installed.\n\nSee if that works for you.","Q_Score":1,"Tags":"python-3.x,tensorflow,pip,installation,jupyter-notebook","A_Id":44020731,"CreationDate":"2017-05-17T06:38:00.000","Title":"How could I use TensorFlow in jupyter notebook? I install TensorFlow via python 3.5 pip already","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an issues with tensorflow on pycharm.\nWhenever I import tensorflow in the linux terminal, it works correctly. However, in PyCharm community 2017.1, it shows:\nImportError: libcudnn.so.5: cannot open shared object file: No such file or directory\nAny hint on how to tackle the issue.\nPlease note that I am using python 3.5.2, tensorflow 1.1.0, Cuda 8 and CuDnn 5.1\nEDIT: when printing sys.path, I get this in PyCharm:\n['\/home\/xxx\/pycharm-community-2017.1.2\/helpers\/pydev', '\/home\/xxx\/pycharm-community-2017.1.2\/helpers\/pydev', '\/usr\/lib\/python35.zip', '\/usr\/lib\/python3.5', '\/usr\/lib\/python3.5\/plat-x86_64-linux-gnu', '\/usr\/lib\/python3.5\/lib-dynload', '\/usr\/local\/lib\/python3.5\/dist-packages', '\/usr\/lib\/python3\/dist-packages', '\/usr\/local\/lib\/python3.5\/dist-packages\/IPython\/extensions', '\/home\/xxx\/xxx\/xxx']\nand this in the terminal:\n['', '\/usr\/local\/bin', '\/usr\/lib\/python35.zip', '\/usr\/lib\/python3.5', '\/usr\/lib\/python3.5\/plat-x86_64-linux-gnu', '\/usr\/lib\/python3.5\/lib-dynload', '\/usr\/local\/lib\/python3.5\/dist-packages', '\/usr\/lib\/python3\/dist-packages', '\/usr\/local\/lib\/python3.5\/dist-packages\/IPython\/extensions', '\/home\/xxx\/.ipython']","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":921,"Q_Id":44020050,"Users Score":3,"Answer":"The solution is:\n\nRun PyCharm from the console.\nOR\nadd the environment variable to the IDE settings: LD_LIBRARY_PATH=\/usr\/local\/cuda\/lib64:$LD_LIBRARY_PATH","Q_Score":1,"Tags":"python,tensorflow,pycharm,cudnn","A_Id":44022536,"CreationDate":"2017-05-17T08:54:00.000","Title":"Tensorflow and Pycharm","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I am new by tensorflow. I want to write a Neural network, that gets noisy images from a file and uncorrupted images from another file.\nthen I want to correct noisy images based on the other images.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":594,"Q_Id":44021777,"Users Score":0,"Answer":"Actually, I'm trying to train a NN that get corrupted images and based on them the grand truth, remove noise from that images.It must be Network in Network, an another word pixels independent.","Q_Score":0,"Tags":"python,tensorflow,neural-network,pixel,convolution","A_Id":44079737,"CreationDate":"2017-05-17T10:06:00.000","Title":"get pixel of image in tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to load a binary file using gensim.Word2Vec.load(fname) but I get the error:\n\nFile \"file.py\", line 24, in \n      model = gensim.models.Word2Vec.load('ammendment_vectors.model.bin')   \nFile \"\/home\/hp\/anaconda3\/lib\/python3.6\/site-packages\/gensim\/models\/word2vec.py\", line 1396, in load\n      model = super(Word2Vec, cls).load(*args, **kwargs) \nFile \"\/home\/hp\/anaconda3\/lib\/python3.6\/site-packages\/gensim\/utils.py\", line 271, in load\n      obj = unpickle(fname)  \nFile \"\/home\/hp\/anaconda3\/lib\/python3.6\/site-packages\/gensim\/utils.py\", line 933, in unpickle\n      return _pickle.load(f, encoding='latin1')\n_pickle.UnpicklingError: could not find MARK\n\nI googled but I am unable to figure out why this error is coming up. Please let me know if any other information is required.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":5562,"Q_Id":44022180,"Users Score":4,"Answer":"This would normally work, if the file was created by gensim's native .save(). \nAre you sure the file 'ammendment_vectors.model.bin' is complete and uncorrupted? \nWas it created using the same Python\/gensim versions as in use where you're trying to load() it? \nCan you try re-creating the file?","Q_Score":2,"Tags":"python,gensim,word2vec","A_Id":44037339,"CreationDate":"2017-05-17T10:23:00.000","Title":"Unpickling Error while using Word2Vec.load()","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am a newbie in python sorry for the simple question.\nIn the following code, I want to calculate the exponent and then take the log.\nY=numpy.log(1+numpy.exp(1000))\nThe problem is that when I take the exponent of 710 or larger numbers the numpy.exp() function returns 'inf' even if I print it with 64float it prints 'inf'.\nany help regarding the problem will be appreciated.","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2564,"Q_Id":44033533,"Users Score":4,"Answer":"You can use the function np.logaddexp() to do such operations. It computes logaddexp(x1, x2) == log(exp(x1) + exp(x2)) without explicitly computing the intermediate exp() values. This avoids the overflow. Since exp(0.0) == 1, you would compute np.logaddexp(0.0, 1000.0) and get the result of 1000.0, as expected.","Q_Score":2,"Tags":"python,numpy","A_Id":44035007,"CreationDate":"2017-05-17T19:43:00.000","Title":"How to deal with exponent overflow of 64float precision in python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using clustering algorithms like DBSCAN.\nIt returns a 'cluster' called -1 which are points that are not part of any cluster. For these points I want to determine the distance from it to the nearest cluster to get something like a metric for how abnormal this point is. Is this possible? Or are there any alternatives for this kind of metric?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":2660,"Q_Id":44041347,"Users Score":1,"Answer":"To be closer to the intuition of DBSCAN you probably should only consider core points.\nPut the core points into a nearest neighbor searcher. Then search for all noise points, use the cluster label of the nearest point.","Q_Score":2,"Tags":"python,machine-learning,scikit-learn,cluster-analysis,data-mining","A_Id":44055484,"CreationDate":"2017-05-18T07:31:00.000","Title":"sklearn: Get Distance from Point to Nearest Cluster","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I can't get tweepy filtering to quite work how I want to.\nstream.filter(track=['one two' , 'three four'])\nI want to retweet based on a specific two word set\ni.e. \"one two\"\nbut I'm getting retweets where the tweet has those two words, but not in order and separated\ni.e. \"three two one\" or \"one three two\" etc.\nI want tweets which contain my phrase but in order\ni.e. \"one two three\" or \"three one two\" or \"one two\" etc.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":990,"Q_Id":44044773,"Users Score":0,"Answer":"the twitter api doesn't allow that. you'll have to check for each returned tweet whether or not it actually contains one of your exact phrases.","Q_Score":0,"Tags":"python,tweepy","A_Id":44423577,"CreationDate":"2017-05-18T10:06:00.000","Title":"Filtering in tweepy - exact phrase","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Thanks for your help on this.\nThis feels like a silly question, and I may be overcomplicating things.  Some background information - I just recently learned some machine learning methodologies in Python (scikit and some statsmodels), such as linear regression, logistic regression, KNN, etc. I can work the steps of prepping the data in pandas data frames and transforming categorical data to 0's and 1's.  I can also load those into a model (like, logistic regression in scikit learn).  I know how to train and test it (using CV, etc.), and some fine tuning methods (gridscore, etc.). But this is all in the scope of predicting outcomes on new data.  I mainly focused on learning on building a model to predict on new X values, and testing that model to confirm accuracy\/precision.  \nHowever, now I'm having trouble identifying and executing the steps to the OTHER kinds of questions that say, a regression model, can answer, like:\nWhy did customer service calls drop last month?\nShould we go with this promotion model or another one?\nAssuming we have all our variables\/predictor sets, how would we determine those two questions using any supervised machine learning model, or just a stat model in the statsmodels package.\nHope this makes sense.  I can certainly go into more detail.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":200,"Q_Id":44045913,"Users Score":0,"Answer":"Why did customer service calls drop last month?\n\nIt depends on what type and features of data you have to analyze and explore the data. One of the basic things is to look at correlation between features and target variable to check if you can identify any feature that can correlate with the drop of calls. So exploring different statistic might help better to answer this question than prediction models.\nAlso it is always a good practice to analyze and explore the data before you even start working on prediction models as its often necessary to improve the data (scaling, removing outliers, missing data etc) depending on the prediction model you chose. \n\nShould we go with this promotion model or another one?\n\nThis question can be answered based on the regression or any other prediction models you designed for this data. These models would help you to predict the sales\/outcome for the feature if you can provide the input features of the promotion models.","Q_Score":0,"Tags":"python,machine-learning,statistics,regression,data-science","A_Id":44049390,"CreationDate":"2017-05-18T10:59:00.000","Title":"Answering business questions with machine learning models (scikit or statsmodels)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Trying to import Keras 2.0.4 with Tensorflow 1.0.1 on Windows10 as backend, but I got the following message:\n\nAttributeError: module 'pandas' has no attribute 'computation'\n\nI've recently upgraded my pandas into version 0.20.1, is it the reason why I failed to import keras?\nThere is a lot more information available on the error message. if you want to know about it, just let me know","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1406,"Q_Id":44047544,"Users Score":0,"Answer":"I had a similar problem, solved it by installing an older pandas version\n    pip install pandas==0.19.2","Q_Score":1,"Tags":"python","A_Id":44048354,"CreationDate":"2017-05-18T12:17:00.000","Title":"Trying to import keras but got an error","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using gensim version 0.12.4 and have trained two separate word embeddings using the same text and same parameters. After training I am calculating the Pearsons correlation between the word occurrence-frequency and vector-length.  One model I trained using save_word2vec_format(fname, binary=True) and then loaded using load_word2vec_format the other I trained using model.save(fname) and then loaded using Word2Vec.load(). I understand that the word2vec algorithm is non deterministic so the results will vary however the difference in the correlation between the two models is quite drastic. Which method should I be using in this instance?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":3611,"Q_Id":44051051,"Users Score":0,"Answer":"EDIT: this was intended as a comment. Don't know how to change it now, sorry\ncorrelation between the word occurrence-frequency and vector-length I don't quite follow - aren't all your vectors the same length? Or are you not referring to the embedding vectors?","Q_Score":1,"Tags":"python,nlp,gensim,word2vec","A_Id":44051350,"CreationDate":"2017-05-18T14:48:00.000","Title":"Gensim save_word2vec_format() vs. model.save()","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Let me start by saying that I know nothing about Python, but I am trying to learn(mostly through struggling it seems). I've looked around this site and tried to cobble together code to do what I need it to, but I keep running into problems. Firstly, I need to convert a file of 2 columns and 512 rows of strings to floats then put them in a 512x2 array. I check the first column (all rows) for negative values. If negative, add 512. Then I need to reorder the rows in numerical order and write\/save the new array. \nOn to my first problem, converting to floats and putting the floats into an array. I have this code, which I made from others' questions:\nwith open(\"binfixtest.composite\") as f:\n  f_values = map(lambda l: l.strip().split('   '), f)\n  print f_values\n  newarray = [map(float, v) for v in f_values]\nOriginal format of file: \n-91.  0.444253325\n-90.  0.883581936\n-89. -0.0912338793\nNew format of f_values:\n['-91.  0.444253325'], ['-90.  0.883581936'], ['-89. -0.0912338793']\nI'm getting the error:\nTraceback (most recent call last):\n  File \".\/binfix.py\", line 10, in <module>\n    newarray = [map(float, v) for v in f_values]\n ValueError: invalid literal for float(): -91.  0.444253325 \nwhich I can't seem to fix. If I don't convert to float, when I try to add 512.0 to negative rows it gives me the error TypeError: cannot concatenate 'str' and 'float' objects\nAny help is most definitely appreciated as I am completely clueless here.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":352,"Q_Id":44052893,"Users Score":1,"Answer":"First Part:\n@njzk2 is exactly right. Simply removing the literal spaces to change from l.strip().split('   ') to l.strip().split() will correct the error, and you will see the following output for f_values:\n\n[['-91.', '0.444253325'], ['-90.', '0.883581936'], ['-89.', '-0.0912338793']]\n\nAnd the output for newarray shows float values rather than strings: \n\n[[-91.0, 0.444253325], [-90.0, 0.883581936], [-89.0, -0.0912338793]]\n\nSecond Part:\nFor the second part of the question \"if negative, add 512\", a simple loop would be clear and simple, and I'm a big believer in clear, readable code.\nFor example the following is simple and straightforward: \nfor items in newarray:\n    if items[0] < 0:\n        items[0] += 512.00\nWhen we print newarray after the loop, we see the following: \n\n[[421.0, 0.444253325], [422.0, 0.883581936], [423.0, -0.0912338793]]","Q_Score":1,"Tags":"python,arrays,string","A_Id":44053256,"CreationDate":"2017-05-18T16:11:00.000","Title":"Python: Converting string to floats, reading floats into 2D array, if\/then, reordering of rows?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Looking to create a custom trainer for chatterbot, In the ubuntu corpus trainer, it looks as if the training is done based on all the conversation entries. \nI manually copy the ubuntu_dialogs.tgz to the 'data' folder.\nTrainer fails with error file could not be opened successfully\nhttps:\/\/github.com\/gunthercox\/ChatterBot\/blob\/master\/examples\/ubuntu_corpus_training_example.py\ncan i unzip all the data to ubuntu_dialogs and provide to trainer? \nedit: Yes, we can , the data folder is \".\\data\" , which is path from where you are invoking the ubuntu_corpus_training_example.py.\ncreate a folder ubuntu_dialogs and unzip all the folders, the trainer.py looks at .\\data\\ubuntu_dialogs***.tsv files","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":941,"Q_Id":44062679,"Users Score":1,"Answer":"Yes, we can , the data folder is \".\\data\" , which is path from where you are invoking the ubuntu_corpus_training_example.py. create a folder ubuntu_dialogs and unzip all the folders, the trainer.py looks at .\\data\\ubuntu_dialogs***.tsv files","Q_Score":0,"Tags":"python,chatbot","A_Id":44240013,"CreationDate":"2017-05-19T06:15:00.000","Title":"ChatterBot Ubuntu Corpus Trainer","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a large binary file (~4 GB) containing a series of image and time stamp data. I want to find the image that most closely corresponds to a user-given time stamp. There are millions of time stamps in the file, though. In Python 2.7, using seek, read, struct.unpack, it took over 900 seconds just to read all the time stamps into an array. Is there an efficient algorithm for finding the closest value that doesn't require reading all of the values? They monotonically increase, though at very irregular intervals.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":40,"Q_Id":44075041,"Users Score":0,"Answer":"First attempt. It works, seemingly every time, but I don't know if it's the most efficient way:\n\nTake first and last time stamps and number of frames to calculate an average time step.\nUse average time step and difference between target and beginning timestamps to find approximate index.\nCheck for approximate and 2 surrounding timestamps against target.\nIf target falls between, then take index with minimum difference.\n  If not, set approximate index as new beginning or end, accordingly, and repeat.","Q_Score":0,"Tags":"python","A_Id":44166939,"CreationDate":"2017-05-19T16:39:00.000","Title":"Finding closest value in a binary file","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am currently trying to use a \"simple\" LSTM network implemented through Keras for a summer project. Looking at the example code given, it appears the LSTM code wants a pre-generated 3D numpy array. As the dataset and the associated time interval I want to use are both rather large, it would be very prohibitive for me to load a \"complete array\" all at once. Is it possible to load the raw dataset and apply the sequencing transform to it as needed by the network (in this case construct the 3D array from x time-interval windows that then increment by 1 each time)? If so, how would you go about doing this?\nThanks for any help you can provide!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":160,"Q_Id":44076649,"Users Score":0,"Answer":"I found the answer to this on the Keras slack from user rocketknight. Use the model.fit_generator function. Define a generator function somewhere within your main python script that \"yields\" a batch of data. Then call this function in the arguments of the model.fit_generator function.","Q_Score":0,"Tags":"python,numpy,keras,lstm,training-data","A_Id":44091300,"CreationDate":"2017-05-19T18:22:00.000","Title":"How-To Generate a 3D Numpy Array On-Demand for an LSTM","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a large set (36k sentence) of sentences (text list) and their POS tags (POS list), and I'd like to group\/cluster the elements in the POS list using edit distance\/Levenshtein:\n(e.g Sentx POS tags= [CC DT VBZ RB JJ], Senty POS tags= [CC DT VBZ RB JJ] ) are in cluster edit distance =0, \nwhile ([CC DT VBZ RB JJ], [CC DT VB RB JJ]) are in cluster edit distance =1. \nI understand how the clustering algorithms work but I am confused how to approach such a problem in python and how to store the clusters in data structures so that I can retrieve them easily.\nI tried to create a matrix (measuring the distance of each sentence with all the sentences in the corpus) but it takes very long to be processed.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":520,"Q_Id":44099095,"Users Score":0,"Answer":"There is only a limited set of POS tags.\nRather than using edit distance, compute a POS-POS similarity matrix just once. You may even want to edit this matrixes desired, e.g. to make two POS tags effectively the same, or to increase the difference of two tags.\nStore that in a numpy array, convert all your vectors to indexes, and then compute similarities using that lookup table. For performance reasons, use numpy where possible, and write the performance critical code in cython because the Python interpreter is very slow.","Q_Score":2,"Tags":"python,machine-learning,nlp,cluster-analysis,edit-distance","A_Id":44106903,"CreationDate":"2017-05-21T16:17:00.000","Title":"How to group sentences by edit distance?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying different word embeddings methods, in order to pick the approache that works the best for me. I tried word2vec and FastText. Now, I would like to try Glove. In both word2vec and FastText, there is two versions: Skip-gram (predict context from word) and CBOW (predict word from context). But in Glove python package, there is no parameter that enables you to choose whether you want to use skipg-gram or Cbow. \nGiven that Glove does not work the same way as w2v, I'm wondering: Does it make sense to talk about skip-gram and cbow when using The Glove method ?\nThanks in Advance","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":341,"Q_Id":44113128,"Users Score":2,"Answer":"Not really, skip-gram and CBOW are simply the names of the two Word2vec models. They are shallow neural networks that generate word embeddings by predicting a context from a word and vice versa, and then treating the output of the hidden layer as the vector\/representation. GloVe uses a different approach, making use of the global statistics of a corpus by training on a co-occurrence matrix, rather than local context windows.","Q_Score":1,"Tags":"python-3.x,word2vec,word-embedding","A_Id":45963156,"CreationDate":"2017-05-22T12:40:00.000","Title":"Does it make sense to talk about skip-gram and cbow when using The Glove method?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"guys.\nI have a large data set (60k samples with 50 features). One of this features (which is really relevant for me) is job names. There are many jobs names that I'd like to encode to fit in some models, like linear regression or SVCs. However, I don't know how to handle them.\nI tried to use pandas dummy variables and Scikit-learn One-hot Encoding, but it generate many features that I may not be encounter on test set. I tried to use the scikit-learn LabelEncoder(), but I also got some errors when I was encoding the variables float() > str() error, for example.\nWhat would you guys recommend me to handle with this several categorical features? Thank you all.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1235,"Q_Id":44124471,"Users Score":0,"Answer":"One another solution is that, you can do a bivariate analysis of the categorical variable with the target variable. What yo will get is a result of how each level affects the target. Once you get this you can combine those levels that have a similar effect on the data. This will help you reduce number of levels, as well as each well would have a significant impact.","Q_Score":0,"Tags":"python,machine-learning,scikit-learn","A_Id":54380982,"CreationDate":"2017-05-23T01:40:00.000","Title":"How to encode categorical with many levels on scikit-learn?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am currently in the phase of data preparation and have a certain issue I would like to make easy. \nThe content of my columns: 10 MW \/ color. All the columns which have this content are named with line nr. [int] or a [str]\nWhat I want to display and which is the data of interest is the color. What I did was following: \ndf['columnname'] = df['columnname'].str.split('\/').str[-1]\nThe problem which occurs is that this operation should be done on all columns which names start with the string \"line\". How could I do this?\nI thought about doing it with a for-loop or a while-loop but I am quite unsure how to do the referencing in the data frame then since I am a nooby in python for now. \nThanks for your help!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":50,"Q_Id":44133280,"Users Score":0,"Answer":"Ok just solved the question: \nwith df.shape I found out what the dimensions are and then started a for loop:\nfor i in range(1,x):\ndf[df.columns[i]]= df[df.columns[i]].str.split('\/').[-1]\nIf you have any more efficient ways let me know :)","Q_Score":0,"Tags":"python,string,pandas,split","A_Id":44134880,"CreationDate":"2017-05-23T11:17:00.000","Title":"Strip certain content of columns in multiple columns","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a DataFrame with about 6 million rows of daily data that I will use to find how certain technical markers affected their respective stocks\u2019 long term performance. I have 2 approaches, which one is recommended?\n\nMake 2 different tables, one of raw data and one (a filtered copy) containing the technical markers, then do \u201clookups\u201d on the master table to get the subsequent performance.\nUse 1 big table, containing both the markers and the performance data.\n\nI\u2019m not sure what is more computationally expensive \u2013 calculating the technical markers for all the rows, even the unneeded ones, or doing the lookups against the master table. Thanks.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":320,"Q_Id":44140675,"Users Score":1,"Answer":"I think the simplest and most efficient path would be to have two tables. The reason being is that with the 1 big table your algorithm can take O(n^2) since you have to iterate n number of times for each element in your markers and then matching for each element n times for each performance.\nIf you did the 2 table approach your complexity goes to O(n * m) where n is the number of technical markers and then m is the number of records in performance. In this use case I'd imagine your n to be based on whichever set you want to look at and not the whole set so that means your n < m and therefore you can simple apply a short circuit to make the algorithm much more efficient.\nAlternatively if you were able to build a master look up table to capture all the relationships between a performance and a technical marker then your complexity is essentially a hash look up or O(1).","Q_Score":1,"Tags":"python,pandas","A_Id":44140770,"CreationDate":"2017-05-23T16:46:00.000","Title":"Python pandas Several DataFrames Best Practice","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using PyMC3 to cluster my grouped data. Basically, I have g vectors and would like to cluster the g vectors into m clusters. However, I have two problems.\nThe first one is that, it seems PyMC3 could only deal with one-dimensional data but not vectors. The second problem is, I do not know how to extract the cluster id for the raw data. I do extract the number of components (k) and corresponding weights. But I could not extract the id that indicating the which cluster that each point belongs to.\nAny ideas or comments are welcomed!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":95,"Q_Id":44141059,"Users Score":0,"Answer":"If I understand you correctly, you're trying to extract which category (1 through k) a data point belongs to. However, a Dirichlet random variable only produces a probability vector. This should be used as a prior for a Categorical RV, and when that is sampled from, it will result in a numbered category.","Q_Score":0,"Tags":"python,process,cluster-computing,pymc3,dirichlet","A_Id":44144746,"CreationDate":"2017-05-23T17:08:00.000","Title":"How to extract cluster id from Dirichlet process in PyMC3 for grouped data?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"After preprocessing the pyspark dataframe , I am trying to apply pipeline to it but I am getting below error:\n\njava.lang.AssertionError: assertion failed: No plan for\n  MetastoreRelation.\n\nWhat is the meaning of this and how to solve this.\nMy code has become quite large, so I will explain the steps 1. I have 8000 columns and 68k rows in my spark dataframe. Out of 8k columns, 500 are categorical to which I applied pyspark.ml one hot encoding as a stage in ml.pipeline  encoders2 = [OneHotEncoder(inputCol=c,\n              outputCol=\"{0}_enc\".format(c)) for c in cat_numeric[i:i+2]]\nbut this is very slow and even after 3 hours it was not complete. I am using 40gb memory on each of 12 nodes!.\n2. So I am reading 100 columns from pyspark dataframe , creating pandas dataframe from that and doing one hot encoding. Then I transform pandas daaframe back into pyspark data and merge it with original dataframe.\n3. Then I try to apply pipeline with stages of string indexer and OHE for categorical string features which are just 5 and then assembler to create 'features' and 'labels' . But in this stage I get the above error.\n4. Please let me know if my approach is wrong or if I am missing anything. Also let me know if you want more information. Thanks","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1261,"Q_Id":44144421,"Users Score":1,"Answer":"This error was due to the order of joining the 2 pyspark dataframes.\nI tried changing the order of join from say a.join(b) to b.join(a) and its working.","Q_Score":0,"Tags":"python,apache-spark,machine-learning,pyspark,spark-dataframe","A_Id":44207434,"CreationDate":"2017-05-23T20:28:00.000","Title":"PySpark dataframe pipeline throws No plan for MetastoreRelation Error","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm new to open source so appreciate any\/all help. \nI've got notebook server 4.2.3 running on: Python 3.5.2 |Anaconda 4.2.0 (64-bit) on my Windows10 machine. \nWhen trying to update scikit-learn from 0.17 to 0.18, I get below error which I believe indicates one of the dependency files is outdated. I can't understand how or why since I just (<1 month ago) installed Python through anaconda. Note I get the same error when I try \nconda update scikit-learn \nconda install scikit-learn=0.18\npip install -U scikit-learn\nImportError: cannot import name '_remove_dead_weakref'\nHow do I fix it? Should I try to uninstall and re-install? If so what's the safest (meaning will cleanly remove all bits) way to do this?\nThanks in advance.\nI'm trying to update to 0.18. I'm running","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":3110,"Q_Id":44146146,"Users Score":2,"Answer":"After spending a couple hours to no avail, deleted the python anaconda folder and reinstalled. Have the latest bits now and problem solved :)","Q_Score":3,"Tags":"python,scikit-learn","A_Id":44147287,"CreationDate":"2017-05-23T22:37:00.000","Title":"'_remove_dead_weakref' error when updating scikit-learn on Win10 machine","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am looking to do either a multivariate Linear Regression or a Logistic Regression using Python on some data that has a large number of categorical variables. I understand that with one Categorical variable I would need to translate this into a dummy and then remove one type of dummy so as to avoid colinearity however is anyone familiar with what the approach should be when dealing with more than one type of categorical variable?\nDo I do the same thing for each? e.g translate each type of record into a dummy variable and then for each remove one dummy variable so as to avoid colinearity?","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":1175,"Q_Id":44160324,"Users Score":0,"Answer":"In a case where there is more than one categorical variable that needs to be replaced for a dummy. The approach should be to encode each of the variables for a dummy (as in the case for a single categorical variable) and then remove one instance of each dummy that exists for each variable in order to avoid colinearity.\nBasically, each categorical variable should be treated the same as a single individual one.","Q_Score":1,"Tags":"python,machine-learning,dummy-variable","A_Id":44366690,"CreationDate":"2017-05-24T13:47:00.000","Title":"Using dummy variables for Machine Learning with more than one categorical variable","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am looking to do either a multivariate Linear Regression or a Logistic Regression using Python on some data that has a large number of categorical variables. I understand that with one Categorical variable I would need to translate this into a dummy and then remove one type of dummy so as to avoid colinearity however is anyone familiar with what the approach should be when dealing with more than one type of categorical variable?\nDo I do the same thing for each? e.g translate each type of record into a dummy variable and then for each remove one dummy variable so as to avoid colinearity?","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":1175,"Q_Id":44160324,"Users Score":1,"Answer":"If there are many categorical variables and also in these variables, if there are many levels, using dummy variables might not be a good option.\nIf the categorical variable has data in form of bins, for e.g, a variable age having data in form 10-18, 18-30, 31-50, ... you can either use Label Encoding or create a new numerical feature using mean\/median of the bins or create two features for lower age and upper age\nIf you have timestamps from initiation of a task to end, for e.g, starting time of machine to the time when the machine was stopped, you can create a new feature by calculating the duration in terms of hours or minutes.\nGiven many categorical variables but with few number of levels, the obvious and only way out in such cases would be to apply One-Hot Encoding on the categorical variables.\nBut when a categorical variable has many levels, there may be certain cases which are too rare or too frequent. Applying One-Hot Encoding on such data would affect the model performance badly. In such cases, it'd be recommended to apply certain business logic\/feature engineering and thereby reduce the number of levels first. Thereafter you can use One-Hot Encoding on the new feature if it is still categorical.","Q_Score":1,"Tags":"python,machine-learning,dummy-variable","A_Id":44163726,"CreationDate":"2017-05-24T13:47:00.000","Title":"Using dummy variables for Machine Learning with more than one categorical variable","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to use keras to create a CNN, but I keep getting this warning which I do not understand how to fix.\n\nUpdate your Conv2D call to the Keras 2 API: Conv2D(64, (3, 3), activation=\"relu\") after removing the cwd from sys.path.\n\nCan anyone give any ideas about fixing this?","AnswerCount":4,"Available Count":1,"Score":0.2449186624,"is_accepted":false,"ViewCount":6588,"Q_Id":44170581,"Users Score":5,"Answer":"As it says, it's not an issue. It still works fine although they might change it any day and the code will not work.\nIn Keras 2 Convolution2D has been replaced by Conv2d along with some changes in the parameters.\n\nConvolution* layers are renamed Conv*.\nConv2D(10, 3, 3) becomes Conv2D(10, (3, 3))","Q_Score":7,"Tags":"python,keras","A_Id":44213304,"CreationDate":"2017-05-25T00:43:00.000","Title":"Warning from keras: \"Update your Conv2D call to the Keras 2 API\"","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have used im2rec.py to convert \"caltech101 images\" into record io format:\nI have created \"caltech.lst\" succesfully using\nos.system('python %s\/tools\/im2rec.py --list=1 --recursive=1 --shuffle=1 data\/caltech data\/101_ObjectCategories'%MXNET_HOME)\nThen, when I run this :\nos.system(\"python %s\/tools\/im2rec.py --train-ratio=0.8 --test-ratio=0.2 --num-thread=4 --pass-through=1 data\/caltech data\/101_ObjectCategories\"%MXNET_HOME)\nI have this error : attributeError:'module' object has no attribute 'MXIndexedRecordIO'\nPlease, someone has an idea to fix this error ?\nThanks in advance.\nEnvironment info\nOperating System:Windows 8.1\nMXNet version:0.9.5","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":375,"Q_Id":44175700,"Users Score":0,"Answer":"@user3824903 I think to create a bin directory, you have to compile MXNet from source with option USE_OPENCV=1","Q_Score":1,"Tags":"python-2.7,cpu,mxnet","A_Id":44424599,"CreationDate":"2017-05-25T08:20:00.000","Title":"attributeError:'module' object has no attribute 'MXIndexedRecordIO'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm making a recommender system, and I'd like to have a matrix of ratings (User\/Item). My problem is there are only 9066 unique items in the dataset, but their IDs range from 1 to 165201. So I need a way to map the IDs to be in the range of 1 to 9066, instead of 1 to 165201. How do I do that?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":210,"Q_Id":44183927,"Users Score":0,"Answer":"I would go through and find the item with the smallest id in the list, set it to 1, then find the next smallest, set it to 2, and so on.\nedit: you are right. That would take way too long. I would just go through and set one of them to 1, the next one to 2, and so on. It doesn't matter what order the ids are in (I am guessing). When a new item is added just set it to 9067, and so on.","Q_Score":2,"Tags":"python,pandas,numpy,ipython,jupyter-notebook","A_Id":44183963,"CreationDate":"2017-05-25T15:11:00.000","Title":"Normalize IDs column","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Does anyone know of a Python package to connect to SSAS multidimensional and\/or SSAS tabular that supports MDX and\/or DAX queries. I know of olap.xmla but that requires an HTTP connection. I am looking for a Python equivalent of olapR in R. Thanks","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":6435,"Q_Id":44204086,"Users Score":0,"Answer":"Seems Python does not support to include .net dll, but IronPython does, we had a MS BI automation project before with IronPython to connect SSAS, it is a nice experience. \nwww.mdx-helper.com","Q_Score":0,"Tags":"python,ssas,olap","A_Id":44204330,"CreationDate":"2017-05-26T14:27:00.000","Title":"SSAS connection from Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm attempting to create an autonomous RC car and my Python program is supposed to query the live stream on a given interval and add it to a training dataset. The data I want to collect is the array of the current image from OpenCV and the current speed and angle of the car. I would then like it to be loaded into Keras for processing.\nI found out that numpy.save() just saves one array to a file. What is the best\/most efficient way of saving data for my needs?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":744,"Q_Id":44212063,"Users Score":1,"Answer":"As with anything regarding performance or efficiency, test it yourself. The problem with recommendations for the \"best\" of anything is that they might change from year to year.\nFirst, you should determine if this is even an issue you should be tackling. If you're not experiencing performance issues or storage issues, then don't bother optimizing until it becomes a problem. What ever you do, don't waste your time on premature optimizations.\nNext, assuming it actually is an issue, try out every method for saving to see which one yields the smallest results in the shortest amount of time. Maybe compression is the answer, but that might slow things down? Maybe pickling objects would be faster? Who knows until you've tried.\nFinally, weigh the trade-offs and decide which method you can compromise on; You'll almost never have one silver bullet solution. While your at it, determine if just adding more CPU, RAM or disk space at the problem would solve it. Cloud computing affords you a lot of headroom in those areas.","Q_Score":0,"Tags":"python,opencv,numpy,keras","A_Id":44212992,"CreationDate":"2017-05-27T01:30:00.000","Title":"How to save large Python numpy datasets?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Does Python have a random number generator that returns only one random integer number each time when next() function is called? Numbers should not repeat and the generator should return random integers in the interval [1, 1 000 000] that are unique.\nI need to generate more than million different numbers and that sounds as if it is very memory consuming in case all the number are generated at same time and stored in a list.","AnswerCount":7,"Available Count":1,"Score":0.0285636566,"is_accepted":false,"ViewCount":5066,"Q_Id":44215505,"Users Score":1,"Answer":"For a large number of non-repeating random numbers use an encryption.  With a given key, encrypt the numbers: 0, 1, 2, 3, ...  Since encryption is uniquely reversible then each encrypted number is guaranteed to be unique, provided you use the same key.  For 64 bit numbers use DES.  For 128 bit numbers use AES.  For other size numbers use some Format Preserving Encryption.  For pure numbers you might find Hasty Pudding cipher useful as that allows a large range of different bit sizes and non-bit sizes as well, like [0..5999999].\nKeep track of the key and the last number you encrypted.  When you need a new unique random number just encrypt the next number you haven't used so far.","Q_Score":4,"Tags":"python,python-3.x,random,generator","A_Id":44216018,"CreationDate":"2017-05-27T10:04:00.000","Title":"Random number generator that returns only one number each time","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"What is the difference between tf.nn_conv2d and tf.nn.depthwise_conv2d in Tensorflow?","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":8789,"Q_Id":44226932,"Users Score":22,"Answer":"I am no expert on this, but as far as I understand the difference is this:\nLets say you have an input colour image with length 100, width 100. So the dimensions are 100x100x3. For both examples we use the same filter of width and height 5. Lets say we want the next layer to have a depth of 8.\nIn tf.nn.conv2d you define the kernel shape as [width, height, in_channels, out_channels]. In our case this means the kernel has shape [5,5,3,out_channels].\nThe weight-kernel that is strided over the image has a shape of 5x5x3, and it is strided over the whole image 8 times to produce 8 different feature maps.\nIn tf.nn.depthwise_conv2d you define the kernel shape as [width, height, in_channels, channel_multiplier]. Now the output is produced differently. Separate filters of 5x5x1 are strided over each dimension of the input image, one filter per dimension, each producing one feature map per dimension. So here, a kernel size [5,5,3,1] would produce an output with depth 3. The channel_multiplier tells you how many different filters you want to apply per dimension. So the original desired output of depth 8 is not possible with 3 input dimensions. Only multiples of 3 are possible.","Q_Score":10,"Tags":"python,tensorflow,deep-learning,conv-neural-network","A_Id":45108482,"CreationDate":"2017-05-28T11:43:00.000","Title":"Difference between tf.nn_conv2d and tf.nn.depthwise_conv2d","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've used multiple ways of splitting and stripping the strings in my pandas dataframe to remove all the '\\n'characters, but for some reason it simply doesn't want to delete the characters that are attached to other words, even though I split them. I have a pandas dataframe with a column that captures text from web pages using Beautifulsoup. The text has been cleaned a bit already by beautifulsoup, but it failed in removing the newlines attached to other characters.  My strings look a bit like this:\n\"hands-on\\ndevelopment of games. We will study a variety of software technologies\\nrelevant to games including programming languages, scripting\\nlanguages, operating systems, file systems, networks, simulation\\nengines, and multi-media design systems. We will also study some of\\nthe underlying scientific concepts from computer science and related\\nfields including\"\nIs there an easy python way to remove these \"\\n\" characters? \nThanks in advance!","AnswerCount":5,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":88228,"Q_Id":44227748,"Users Score":8,"Answer":"in messy data it might to be a good idea to remove all whitespaces df.replace(r'\\s', '', regex = True, inplace = True).","Q_Score":34,"Tags":"python,string,pandas,split","A_Id":47000213,"CreationDate":"2017-05-28T13:18:00.000","Title":"removing newlines from messy strings in pandas dataframe cells?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am getting different score values from different estimators from scikit.\n\nSVR(kernel='rbf', C=1e5, gamma=0.1) 0.97368549023058548\nLinear regression 0.80539997869990632\nDecisionTreeRegressor(max_depth = 5) 0.83165426563946387\n\nSince all regression estimators should use R-square score, I think they are comparable, i.e. the closer the score is to 1, the better the model is trained. However, each model implements score function separately so I am not sure. Please clarify.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":94,"Q_Id":44227908,"Users Score":4,"Answer":"If you have a similar pipeline to feed the same data into the models, then the metrics are comparable. You can choose the SVR Model without any doubt.\nBy the way, it could be really interesting for you to \"redevelop\" this \"R_squared\" Metric, it could be a nice way to learn the underlying mechanic.","Q_Score":1,"Tags":"python,scikit-learn,regression","A_Id":44228127,"CreationDate":"2017-05-28T13:37:00.000","Title":"Is it correct to compare score of different estimators?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"i'm trying to impute missing values with KNN in python so i have downloaded a package named fancyimpute that contain the methode KNN then when i want to import it i get this Error ImportError: cannot import name 'KNN'\nplease help me","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":427,"Q_Id":44228698,"Users Score":0,"Answer":"I had seen this same exact error message, and it was because python was confused about some other file names in the same folder that is was loading instead of library files. Try cleaning the folder, renaming your files, etc.","Q_Score":0,"Tags":"python-3.x","A_Id":46943022,"CreationDate":"2017-05-28T14:58:00.000","Title":"Impute Missing Values Using K-Nearest Neighbors","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've got a model in Keras that I need to train, but this model invariably blows up my little 8GB memory and freezes my computer.\nI've come to the limit of training just one single sample (batch size = 1) and still it blows up.\n\nPlease assume my model has no mistakes or bugs and this question is not about \"what is wrong with my model\". (Yes, smaller models work ok with the same data, but aren't good enough for the task).\n\nHow can I split my model in two and train each part separately, but propagating the gradients between them?\nIs there a possibility? (There is no limitation about using theano or tensorflow)\nUsing CPU only, no GPU.","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":957,"Q_Id":44233042,"Users Score":2,"Answer":"You can do this thing, but it will cause your training time to approach sizes that will only make the results useful for future generations.\nLet's consider what all we have in our memory when we train with a batch size of 1 (assuming you've only read in that one sample into memory):\n1) that sample\n2) the weights of your model\n3) the activations of each layer #your model stores these for backpropogation\nNone of this stuff is unnecessary for training.  However, you could, theoretically, do a forward pass on the first half of the model, dump the weights and activations to disk, load the second half of the model, do a forward pass on that, then the backward pass on that, dump those weights and activations to disk, load back the weights and activations of the first half, then complete the backward pass on that.  This process could be split up even more to the point of doing one layer at a time.\nOTOH, this is akin to what swap space does, without you having to think about it.  If you want a slightly less optimized version of this (which, optimization is clearly moot at this point), you can just increase your swap space to 500GB and call it a day.","Q_Score":2,"Tags":"python,memory-management,tensorflow,keras,theano","A_Id":51102921,"CreationDate":"2017-05-28T23:46:00.000","Title":"Can I train a model in steps in Keras?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to detect the difference between a spade, club, diamond and hart. the number on the card is irrelevant, just the suit matters.\ni've tried color detection by looking at just the red or black colors, but that still leaves me with two results per color. how could i make sure i can detect each symbol individually? \nfor instance: i have a picture of a red hart, a red diamond, a black spade and a black club. i want to draw the contours of each symbol in a different color. \nI'm using my webcam as a camera.","AnswerCount":4,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":4838,"Q_Id":44242207,"Users Score":0,"Answer":"As the card symbol is at fixed positions, you may try below (e.g. in OpenCV 3.2 Python):\n\nCrop the symbol at top left corner, image = image[h1:h2,w1:w2] \nThreshold the symbol colors to black, the rest to white, thresh = mask = cv2.inRange(image,(0,0,0),(100,100,100)) \nPerform a find contour detection, _, contours, hierarchy = cv2.findContours()\nGet the area size of the contour. area = cv2.contourArea(contour)\nCompare the area to determine which one of the 4 symbols it belongs to.\n\nIn which you have to build the area thresholds of each symbol for step 4 to compare. All the cv2 functions above are just for reference.\nHope this help.","Q_Score":0,"Tags":"python,opencv","A_Id":44246824,"CreationDate":"2017-05-29T12:12:00.000","Title":"detect card symbol using opencv python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to use cv2.VideoCapture to capture image from a docker container.\nimport cv2\nvid = cv2.VideoCapture('path\\to\\video')\nret, frame = vid.read()\nIn terms of the video file,\nI have tried \neither mount the file with docker -v\nor docker cp to copy the video file into container,\nbut both with no luck (ret returns False).\nShould I add any command when launching the container?\nThanks in advance.","AnswerCount":1,"Available Count":1,"Score":-0.1973753202,"is_accepted":false,"ViewCount":1491,"Q_Id":44242760,"Users Score":-1,"Answer":"There might be 2 problems:\n1) In your container opencv is not installed properly. To check that do print(ret, frame). If they come (false, None). Then opencv has not installed properly. \n2) The file you are using is corrupted. To check that try to copy any image file (jpg) into the container and use cv2.imread to read the image and then print it. If numpy array comes then while copying your file is not getting corrupted. \nThe best option is pull any opencv + python image and then create a container with it. The better option is use dockerfiles of these images to create the container.","Q_Score":2,"Tags":"python,opencv,docker,video-capture","A_Id":44807718,"CreationDate":"2017-05-29T12:40:00.000","Title":"cv2.VideoCapture doesn't work within docker container","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"This question seems so trivial but I didn't find any suitable answer so I am asking!\nLets say I have a two column data(say, {x, sin(x)} ) \nX Y(X)\n0.0 0.0\n0.1 0.099\n0.2 0.1986\nHow do I find the period of the function Y(X); \nI have some experience in Mathematica where(roughly) \n\nI just interpolate the data as a function say y(x), then \nCalculate y'(x) and set y'(x_p)=0; \ncollect all (x_p+1 - x_p)'s and take average to get the period.\n\nIn python, however I am stuck after step 1 as I can find out x_p for a particular guess value but not all the x_p's. Also this procedure doesn't seem very elegant to me. Is there a better way to do things in python?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1459,"Q_Id":44242810,"Users Score":0,"Answer":"For calculating periods I would just find the peak of the Fourier transformed data, to do that in python look into script.fft. Could be computationally intensive though.","Q_Score":3,"Tags":"python,fft,dft","A_Id":44309273,"CreationDate":"2017-05-29T12:42:00.000","Title":"Python: Finding period of a two column data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have binary classification problem where one class represented 99.1% of all observations (210 000). As a strategy to deal with the imbalanced data, I choose sampling techniques. But I don't know what to do: undersampling my majority class or oversampling the less represented class. \nIf anybody have an advise?\nThank you.\nP.s. I use random forest algorithm from sklearn.","AnswerCount":3,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":4671,"Q_Id":44244711,"Users Score":1,"Answer":"oversampling or \nunder sampling or \nover sampling the minority and under sampling the majority \n\nis a hyperparameter. Do cross validation which ones works best.\nBut use a Training\/Test\/Validation set.","Q_Score":1,"Tags":"python,machine-learning,classification,random-forest,supervised-learning","A_Id":44250976,"CreationDate":"2017-05-29T14:17:00.000","Title":"Imbalanced data: undersampling or oversampling?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have binary classification problem where one class represented 99.1% of all observations (210 000). As a strategy to deal with the imbalanced data, I choose sampling techniques. But I don't know what to do: undersampling my majority class or oversampling the less represented class. \nIf anybody have an advise?\nThank you.\nP.s. I use random forest algorithm from sklearn.","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":4671,"Q_Id":44244711,"Users Score":0,"Answer":"Undersampling:\nUndersampling is typically performed when we have billions (lots) of data points and we don\u2019t have sufficient compute or memory(RAM) resources to process the data. Undersampling may lead to worse performance as compared to training the data on full data or on oversampled data in some cases. In other cases, we may not have a significant loss in performance due to undersampling.\nUndersampling is mainly performed to make the training of models more manageable and feasible when working within a limited compute, memory and\/or storage constraints.\nOversampling:\noversampling tends to work well as there is no loss of information in oversampling unlike undersampling.","Q_Score":1,"Tags":"python,machine-learning,classification,random-forest,supervised-learning","A_Id":51599898,"CreationDate":"2017-05-29T14:17:00.000","Title":"Imbalanced data: undersampling or oversampling?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using python Gensim to train doc2vec. Is there any possibility to allow this code to be distributed on AWS (s3). \nThank you in advance","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":632,"Q_Id":44253840,"Users Score":1,"Answer":"Gensim's Doc2Vec is not designed to distribute training over multiple-machines. It'd be a significant and complex project to adapt its initial bulk training to do that. \nAre you sure your dataset and goals require such distribution? You can get a lot done on a single machine with many cores & 128GB+ RAM. \nNote that you can also train a Doc2Vec model on a smaller representative dataset, then use its .infer_vector() method on the frozen model to calculate doc-vectors for any number of additional texts. Those frozen models can be spun up on multiple machines \u2013 allowing arbitrarily-distributed calculation of doc-vectors. (That would be far easier than distributing initial training.)","Q_Score":0,"Tags":"python-2.7,amazon-s3,aws-lambda,doc2vec","A_Id":44291881,"CreationDate":"2017-05-30T05:06:00.000","Title":"How to train doc2vec on AWS cluster using spark","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am very new to the keras. currently working with CNN in keras using theano as backend. I would like to train my network with own images( around 25000 images),which all are in same folder and test it. How could I do that? (please help me, i am not familiar with deep learning)","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":70,"Q_Id":44260553,"Users Score":0,"Answer":"Create a folder dataset and then create two sub-folders train and test. Then inside Train if you wish create sub-folders with images labels (e.g. fish - holds all fish images, lion - holds lion images etc) and in test you can populate with some images. Finally train the model pointing to Dataset - > Train.","Q_Score":0,"Tags":"python-2.7","A_Id":44584830,"CreationDate":"2017-05-30T11:07:00.000","Title":"CNN in keras using theano as backend","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"guys.\nI was developing an ML model and I got a doubt. Let's assume that my train data has the following data:\nID | Animal | Age | Habitat\n0  | Fish   | 2   | Sea\n1  | Hawk   | 1   | Mountain\n2  | Fish   | 3   | Sea\n3  | Snake  | 4   | Forest\nIf I apply One-hot Encoding, it will generate the following matrix:\nID | Animal_Fish | Animal_Hawk | Animal_Snake | Age | ...\n0  | 1           | 0           | 0            | 2   | ...\n1  | 0           | 1           | 0            | 1   | ...\n2  | 1           | 0           | 0            | 3   | ...\n3  | 0           | 0           | 1            | 4   | ...\nThat's beautiful and work in most of the cases. But, what if my test set contains fewer (or more) features than the train set? What if my test set doesn't contain \"Fish\"? It will generate one less category.\nCan you guys help me how can I manage this kind of problem?\nThank you","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":5636,"Q_Id":44266677,"Users Score":0,"Answer":"The train set determines what features you can use for recognition. If you're lucky, your recognizer will just ignore unknown features (I believe NaiveBayes does), otherwise you'll get an error. So save the set of feature names you created during training, and use them during testing\/recognition.\nSome recognizers will treat a missing binary feature the same as a zero value. I believe this is what the NLTK's NaiveBayesClassifier does, but other engines might have different semantics. So for binary present\/absent features, I would write my feature extraction function to always put the same keys in the feature dictionary.","Q_Score":3,"Tags":"python,machine-learning","A_Id":44267916,"CreationDate":"2017-05-30T15:47:00.000","Title":"Machine Learning - test set with fewer features than the train set","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a pandas dataframe with a variable, which, when I print it, shows up as mostly containing NaN. It is of dtype object. However, when I run the isnull function, it returns \"FALSE\" everywhere. I am wondering why the NaN values are not encoded as missing, and if there is any way of converting them to missing values that are treated properly. \nThank you.","AnswerCount":2,"Available Count":1,"Score":-0.0996679946,"is_accepted":false,"ViewCount":1285,"Q_Id":44273555,"Users Score":-1,"Answer":"Building on from piRSquared, a possible method to treating NaN values (if applicable to your problem) is to convert the NaN inputs to the median of the column.\ndf = df.fillna(df.mean())","Q_Score":2,"Tags":"python,pandas","A_Id":44274041,"CreationDate":"2017-05-30T23:44:00.000","Title":"Unable to handle NaN in pandas dataframe","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I get a ximgproc_DisparityWLSFilter from cv2.ximgproc.createDisparityWLSFilter(left_matcher),\nbut I cannot get ximgproc_DisparityWLSFilter.filter() to work.\nThe error I get is \n\nOpenCV Error: Assertion failed (!disparity_map_right.empty() && (disparity_map_right.depth() == CV_16S) && (disparity_map_right.channels() == 1)) in cv::ximgproc::DisparityWLSFilterImpl::filter, file ......\\opencv_contrib\\modules\\ximgproc\\src\\disparity_filters.cpp, line 262\n\nIn general, how do I figure out how to use this, when there isn't a single google result for \"ximgproc_DisparityWLSFilter\"?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2604,"Q_Id":44276962,"Users Score":0,"Answer":"Unlike c++, Python doesn't work well with pointers. So the arguments are \nFiltered_disp = ximgproc_DisparityWLSFilter.filter(left_disp,left, None,right_disp)\nNote that it's no longer a void function in Python!\nI figured this out through trial and error though.","Q_Score":2,"Tags":"python,opencv,disparity-mapping","A_Id":44306111,"CreationDate":"2017-05-31T06:10:00.000","Title":"What are the ximgproc_DisparityWLSFilter.filter() Arguments?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":1,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there functionality in Machine Learning (using Python) that I could use to feed it a group of inputs, tell it what the end product should be, then have the code figure out what function it should use in order to reach the end product? \nThanks.","AnswerCount":8,"Available Count":4,"Score":0.1243530018,"is_accepted":false,"ViewCount":1870,"Q_Id":44290736,"Users Score":5,"Answer":"question is really vague one.\nstill as you mentioned machine learning TAG. i take it as machine learning problem. in this case there is no specific model or algorithm available to decide which algorithm\/function best suits to your data !!. \nit's hit & trial method to decide which model should be best for your data. so you are going to write a wrapper program which going to test your data with all possible model and based on their Accuracy Score your code will decide which model best suits. !!","Q_Score":6,"Tags":"python,machine-learning","A_Id":44968506,"CreationDate":"2017-05-31T17:01:00.000","Title":"Python Machine Learning Functions","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there functionality in Machine Learning (using Python) that I could use to feed it a group of inputs, tell it what the end product should be, then have the code figure out what function it should use in order to reach the end product? \nThanks.","AnswerCount":8,"Available Count":4,"Score":0.024994793,"is_accepted":false,"ViewCount":1870,"Q_Id":44290736,"Users Score":1,"Answer":"I don't think this is a perfect place to ask this kind of questions. There are some other websites where you can ask this kind of questions.\nFor learning Machine Learning (ML), do a basic ML course and follow blogs.","Q_Score":6,"Tags":"python,machine-learning","A_Id":49795930,"CreationDate":"2017-05-31T17:01:00.000","Title":"Python Machine Learning Functions","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there functionality in Machine Learning (using Python) that I could use to feed it a group of inputs, tell it what the end product should be, then have the code figure out what function it should use in order to reach the end product? \nThanks.","AnswerCount":8,"Available Count":4,"Score":0.024994793,"is_accepted":false,"ViewCount":1870,"Q_Id":44290736,"Users Score":1,"Answer":"If you have just started learning ML then you should first get the ideas about different scientific libraries which Python provides. Most important thing is that you have to start with basic understanding of machine learning modelling from various online material available or by doing ML course.\nFYI.. there is no such functionality available in python which gives you information about the model which is perfect for a particular set of data. It solely depends on how much analytical you are to choose a good model for your dataset by checking different statistical\/model parameters.","Q_Score":6,"Tags":"python,machine-learning","A_Id":52089251,"CreationDate":"2017-05-31T17:01:00.000","Title":"Python Machine Learning Functions","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there functionality in Machine Learning (using Python) that I could use to feed it a group of inputs, tell it what the end product should be, then have the code figure out what function it should use in order to reach the end product? \nThanks.","AnswerCount":8,"Available Count":4,"Score":0.024994793,"is_accepted":false,"ViewCount":1870,"Q_Id":44290736,"Users Score":1,"Answer":"From your question, I garner that you have a result and are trying to find the optimal algorithm to reach there. Unfortunately, as far as I'm aware, you have to compare the different algorithms in itself to understand which one has better performance. \nHowever if you only wish to obtain a suitable algorithm for your use-case but are unsure what broad classification to start looking for an algorithm in, then I'd suggest you read up on the different types of machine learning (classification\/regression) and then establish relations between your use case and how the algorithm performs its tasks. With that as a fundamental, you can fine tune your search.","Q_Score":6,"Tags":"python,machine-learning","A_Id":56123050,"CreationDate":"2017-05-31T17:01:00.000","Title":"Python Machine Learning Functions","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have tensorflow's gpu version installed, as soon as I create a session, it shows me this log:\n\nI tensorflow\/core\/common_runtime\/gpu\/gpu_device.cc:885] Found device 0\n  with properties:  name: GeForce GTX TITAN Black major: 3 minor: 5\n  memoryClockRate (GHz) 0.98 pciBusID 0000:01:00.0 Total memory: 5.94GiB\n  Free memory: 5.31GiB I\n  tensorflow\/core\/common_runtime\/gpu\/gpu_device.cc:906] DMA: 0  I\n  tensorflow\/core\/common_runtime\/gpu\/gpu_device.cc:916] 0:   Y  I\n  tensorflow\/core\/common_runtime\/gpu\/gpu_device.cc:975] Creating\n  TensorFlow device (\/gpu:0) -> (device: 0, name: GeForce GTX TITAN\n  Black, pci bus id: 0000:01:00.0)\n\nAnd when I check my GPU memory usage, around 90% of it gets consumed.\nTensorflow documentation does not say anything about this. Does it take control of the gpu ? Why does it consume most of the memory ?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":526,"Q_Id":44306765,"Users Score":1,"Answer":"TensorFlow sessions allocate ~all GPU memory on startup, so they can bypass the cuda allocator.\nDo not run more than one cuda-using library in the same process or weird things (like this stream executor error) will happen.","Q_Score":4,"Tags":"python,machine-learning,tensorflow,gpu","A_Id":44316105,"CreationDate":"2017-06-01T11:51:00.000","Title":"What does a tensorflow session exactly do?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I understand that matplotlib.figure.suptitle() adds a title to a figure. \nBut what does the \"sup\" stand for?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":370,"Q_Id":44308195,"Users Score":7,"Answer":"It is an abbreviation indicating a \"super\" title. It is a title which appears at the top of the figure, whereas a normal title only appears above a particular axes. If you only have one axes object, then there's unlikely an appreciable difference, but the difference happens when you have multiple subplots on the same figure and you would like a title at the top of the figure not on each of the axes objects.","Q_Score":5,"Tags":"python,matplotlib","A_Id":44308255,"CreationDate":"2017-06-01T12:58:00.000","Title":"matplotlib.figure.suptitle(), what does 'sup' stand for?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have about 30 word2vec models. When loading them in a python script each consumes a few GB of RAM so it is impossible to use all of them at once. Is there any way to use the models without loading the complete model into RAM?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":467,"Q_Id":44323816,"Users Score":0,"Answer":"I am not intimately familiar with the word2vec implementation in gensim but the model, once trained, should basically boil down to a dictionary of (word -> vector) pairs. This functionality is provided by the gensim.models.KeyedVectors class and is independent of the training algorithm used to derive the vectors.\nYou could extend that class such that it loads the vectors from a database (SQLite for instance) on demand instead of into memory upon creation. \nProbably best if you open an issue on github and start a discussion with the core developers on the matter.","Q_Score":0,"Tags":"python,gensim,word2vec","A_Id":44324291,"CreationDate":"2017-06-02T08:05:00.000","Title":"word2vec - reduce RAM consumption when loading model","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying out pandas for the first time. I have a dataframe with two columns: user_id and string. Each user_id may have several strings, thus showing up in the dataframe multiple times. I want to derive another dataframe from this; one where only those user_ids are listed that have at least 2 or more strings associated to them.\nI tried df[df['user_id'].value_counts()> 1], which I thought was the standard way to do this, but it yields IndexingError: Unalignable boolean Series key provided. Can someone clear out my concept and provide the correct alternative?","AnswerCount":4,"Available Count":1,"Score":-0.049958375,"is_accepted":false,"ViewCount":9412,"Q_Id":44329734,"Users Score":-1,"Answer":"l2 = ((df.val1.loc[df.val== 'Best'].value_counts().sort_index()\/df.val1.loc[df.val.isin(l11)].value_counts().sort_index())).loc[lambda x : x>0.5].index.tolist()","Q_Score":4,"Tags":"python,pandas","A_Id":57430035,"CreationDate":"2017-06-02T13:06:00.000","Title":"Filtering dataframe based on column value_counts (pandas)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How to specify multiple target_column in tflearn.data_utils.load_csv method.\nAccording to Tflearn docs load_csv takes target_column as integer.\nTried passing my target_columns as a list in the load_csv method and as expected got a TypeError: 'list' object cannot be interpreted as an integer traceback.\nAny solutions for this. \nThanks","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":443,"Q_Id":44351248,"Users Score":0,"Answer":"That's not how regression works. You must have only one column as a target. That's why the tensorflow API only allows one column to be the target of regression, specified with an integer.","Q_Score":0,"Tags":"python,regression,tflearn","A_Id":44433959,"CreationDate":"2017-06-04T06:08:00.000","Title":"Specifying Multiple targets for regression in TFLearn","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"The Operation : transforming a rgb image  numpy array to string gives an output that if saved to a file also results in a bigger file than the original image, why?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":15,"Q_Id":44355163,"Users Score":1,"Answer":"This is likely due to the fact that typical image formats are compressed. If you open an image using e.g. scipy.ndimage.imread, the file will be decompressed and the result will be a numpy array of size (NxMx3), where N and M are the dimensions of the image and 3 represents the [R, G, B] channels. Transforming this to a string does not perform any compression, so the result will be larger than the original file.","Q_Score":0,"Tags":"python-3.x,numpy","A_Id":45017647,"CreationDate":"2017-06-04T14:27:00.000","Title":"Numpy array to string gives an output that if saved to a file results in a bigger file than the original image, why?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"My tests show that Tensorflow GPU operations are ~6% slower on Python 3 compared to Python 2. Does anyone have any insight on this?\nPlatform:\n\nUbuntu 16.04.2 LTS\nVirtualenv 15.0.1\nPython 2.7.12\nPython 3.6.1\nTensorFlow 1.1\nCUDA Toolkit 8.0.44\nCUDNN 5.1\nGPU: GTX 980Ti\nCPU: i7 4 GHz\nRAM: 32 GB","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":1834,"Q_Id":44360273,"Users Score":2,"Answer":"When operating Tensorflow from python most code to feed the computational engine with data resides in python domain. There are known differences between python 2\/3 when it comes to performance on various tasks. Therefore, I'd guess that the python code you use to feed the net (or TF python layer, which is quite thick) makes heavy use of python features that are (by design) a bit slower in python 3.","Q_Score":3,"Tags":"python,python-2.7,python-3.x,tensorflow","A_Id":44362389,"CreationDate":"2017-06-05T00:43:00.000","Title":"Tensorflow Slower on Python 3 vs. Python 2","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to manage problems from Scipy. So does Scipy provide techniques to import and export model files?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":95,"Q_Id":44370237,"Users Score":0,"Answer":"No as Sascha mentioned in the comment. Use other alternatives such as cvxpy\/cvxopt.","Q_Score":0,"Tags":"python,import,scipy,export,linear-programming","A_Id":44379161,"CreationDate":"2017-06-05T13:35:00.000","Title":"Does Scipy have techniques to import&export optimisation model files such as LP?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a 100x100 Matrix with Zeros. I want to add a 10x20 ellipsis around a specific point in the Matrix - lets say at position 40,60. The Ellipsis should be filled with values from 0 to 1. (1 in the center - 0 at the edge) - The numbers should be gaussian-distributed.\nMaybe someone can give me a clue, how to start with this problem..","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":221,"Q_Id":44387854,"Users Score":0,"Answer":"You need to draw samples from a multi-variate gaussian distribution. The function you can use is numpy.random.multivariate_normal\nYou mean value matrix should be [40, 60]. The covariance C matrix should be 2X2. Regarding its values:\nC[1, 1], C[2, 2]: decides the width of the ellipse along each axis. Choose it so that 3*C[i,i] is almost equal to the width of the ellipse along this axis.\nThe diagonal values are zero if you want the ellipse to be along the axes, otherwise put larger values (keep in mind that C[2, 1]==C[1, 2]\nHowever, keep in mind that, since it is a Gaussian distribution, the output values will be close to 0 at distance 3*C[i,i] from the center, but they will never be truly zero.","Q_Score":0,"Tags":"python,matrix,ellipse,gaussianblur","A_Id":44388121,"CreationDate":"2017-06-06T10:43:00.000","Title":"Create Matrix with gaussian-distributed ellipsis in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm running pandas read_sql_query and cx_Oracle 6.0b2 to retrieve data from an Oracle database I've inherited to a DataFrame.\nA field in many Oracle tables has data type NUMBER(15, 0) with unsigned values. When I retrieve data from this field the DataFrame reports the data as int64 but the DataFrame values have 9 or fewer digits and are all signed negative. All the values have changed - I assume an integer overflow is happening somewhere.\nIf I convert the database values using to_char in the SQL query and then use pandas to_numeric on the DataFrame the values are type int64 and correct.\nI'm using Python 3.6.1 x64 and pandas 0.20.1. _USE_BOTTLENECK is False.\nHow can I retrieve the correct values from the tables without using to_char?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":577,"Q_Id":44392676,"Users Score":1,"Answer":"Removing pandas and just using cx_Oracle still resulted in an integer overflow so in the SQL query I'm using:\nCAST(field AS NUMBER(19))\nAt this moment I can only guess that any field between NUMBER(11) and NUMBER(18) will require an explicit CAST to NUMBER(19) to avoid the overflow.","Q_Score":0,"Tags":"python,sql,oracle,pandas,dataframe","A_Id":44519380,"CreationDate":"2017-06-06T14:24:00.000","Title":"pandas read_sql_query returns negative and incorrect values for Oracle Database number field containing positive values","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Let's say I have a dataframe df with columns 'A', 'B', 'C'\nNow I just want to extract row 2 of df and only columns 'B' and 'C'. What is the most efficient way to do that?\nCan you please tell me why df.ix[2, ['B', 'C']] didn't work?\nThank you!","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":52,"Q_Id":44397034,"Users Score":2,"Answer":"row_2 = df[['B', 'C']].iloc[1]\nOR\n# Convert column to 2xN vector, grab row 2\nrow_2 = list(df[['B', 'C']].apply(tuple, axis=1))[1]","Q_Score":2,"Tags":"python,pandas,dataframe,indexing","A_Id":44397071,"CreationDate":"2017-06-06T18:13:00.000","Title":"Get a subset of data from one row of Dataframe","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am studying deep learning and trying to implement it using CAFFE- Python. can anybody tell that how we can assign the weights to each node in input layer instead of using weight filler in caffe?","AnswerCount":1,"Available Count":1,"Score":0.761594156,"is_accepted":false,"ViewCount":166,"Q_Id":44403745,"Users Score":5,"Answer":"There is a fundamental difference between weights and input data: the training data is used to learn the weights (aka \"trainable parameters\") during training. Once the net is trained, the training data is no longer needed while the weights are kept as part of the model to be used for testing\/deployment.\nMake sure this difference is clear to you before you precede.\nLayers with trainable parameters has a filler to set the weights initially.\nOn the other hand, an input data layer does not have trainable parameters, but it should supply the net with input data. Thus, input layers has no filler.\nBased on the type of input layer you use, you will need to prepare your training data.","Q_Score":4,"Tags":"python,machine-learning,neural-network,deep-learning,caffe","A_Id":44403857,"CreationDate":"2017-06-07T04:50:00.000","Title":"Deep learning using Caffe - Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a set of thermal images which are encoded with different types of color maps.\nI want to use a constant color map to make fault intelligence easier.\nPlease guide me on how to go about this.","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":942,"Q_Id":44409981,"Users Score":4,"Answer":"I think it is a little more complicated than what is suggested in the comments, since you are dealing with temperatures. You need to revert the color mapping to a temperature value image, then apply one colormap with OpenCV that you like.\nGoing back to greyscale is not so straightforward as converting the image from BGR to greyscale, because you have colors like dark red that will may be mapped as dark grey as well as dark blue colors, however they are in totally opposite parts of the scale.\nBoth of your images are in different scale (temperature wise) as well, so, if you pass them back to grey scale, black is not the same temperature as the other one, so it is not possible to compare them directly.\nTo get a proper scale value you can try to get the upper rectangle (the one that shows the scale) and separate them in equal pieces and divide the temperature range with the same number of divisions. This will give you a color for each temperature. Then transform both images to cv::Mat double and each pixel will have the temperature value. \nFinally you must decide what will be your temperature range to decide the colors for all the images you have. For example you can choose 25-45. Then normalize the images with temperatures (the one with doubles) with the range you selected and normalize them to greyscale images (0 will be 25 and 255 will be 45) and apply the color map to this images. \nI hope this helps.","Q_Score":1,"Tags":"python,opencv","A_Id":44412918,"CreationDate":"2017-06-07T10:24:00.000","Title":"How to convert between different color maps on OpenCV?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a set of thermal images which are encoded with different types of color maps.\nI want to use a constant color map to make fault intelligence easier.\nPlease guide me on how to go about this.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":942,"Q_Id":44409981,"Users Score":0,"Answer":"You can use cvtcolor to HSV, and then manually change Hue. After you change hue, you can cvt color back to rbg.","Q_Score":1,"Tags":"python,opencv","A_Id":44412645,"CreationDate":"2017-06-07T10:24:00.000","Title":"How to convert between different color maps on OpenCV?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"This is my first question on stackoverflow, please bear with me as I will do my best to provide as much info as possible.\nI have a windows 10, 6-bit processor.  My end goal is to use keras within spyder.  The first thing I did was update python to 3.6 and install tensorflow, which seemed to work.  When I attempted to get keras, however, it wasn't working, and I read that keras worked on python 3.5.  I successfully installed keras on python 3.5, which automatically installed theano as the backend.  \nBut now I have two spyder environments, one running off of python 3.5, one off of 3.6.  The 3.5 reads keras but doesn't go through with any modules because it cannot find tensorflow.  The 3.6 can read tensorflow, but cannot find keras.  \nPlease let me know what you would recommend.  Thank you!","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2643,"Q_Id":44419017,"Users Score":0,"Answer":"I had some issues with my tensorflow's installation too.\nI personnaly used anaconda to solve the problem.\nAfter installing anaconda (Maybe uninstall the old one if you already have one), launch an anaconda prompt and input conda create -n tensorflow python=3.5, afther that, you must activate it with activate tensorflow.\nOnce it's done, you have to install tensorflow on your python 3.5.\nFor that, use:\npip install --ignore-installed --upgrade https:\/\/storage.googleapis.com\/tensorflow\/windows\/cpu\/tensorflow-1.2.0rc1-cp35-cp35m-win_amd64.whl\nfor cpu version\npip install --ignore-installed --upgrade https:\/\/storage.googleapis.com\/tensorflow\/windows\/gpu\/tensorflow_gpu-1.2.0rc1-cp35-cp35m-win_amd64.whl for gpu version\nYou now have the r1.2 version of tensorflow.\nThen, just use pip install keras and keras will be installed.\nNow, all you have to do is launch anaconda navigator, select tensorflow on the scrolling menu and launch spyder\/jupyter.\nYou can now use Keras with a tensorflow backend in Python 3.5\nHope it helped someone ! (It take me so much time to find it by myself)","Q_Score":3,"Tags":"python,tensorflow,keras","A_Id":44433064,"CreationDate":"2017-06-07T17:17:00.000","Title":"Switching from tensorflow on python 3.6 to python 3.5","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"can the tensorflow read a file contain a normal images for example in JPG, .... or the tensorflow just read the .bin file contains images\nwhat is the difference between .mat file and .bin file\nAlso when I rename the .bin file name to .mat, does the data of the file changed??\nsorry maybe my language not clear because I cannot speak English very well","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":574,"Q_Id":44420434,"Users Score":0,"Answer":"A file-name suffix is just a suffix (which sometimes help to get info about that file; e.g. Windows decides which tool is called when double-clicked). A suffix does not need to be correct. And of course, changing the suffix will not change the content.\nEvery format will need their own decoder. JPG, PNG, MAT and co.\nTo some extent, these are automatically used by reading out metadata (giving some assumptions!). Many image-tools have some imread-function which works for jpg and png, even if there is no suffix (because there is checking for common and supported image-formats).\nI'm not sure what tensorflow does automatically, but:\n\njpg, png, bmp should be no problem\n\n\nworst-case: use scipy to read and convert\n\nmat is usually a matrix (with infinite different encodings) and often matlab-based\n\n\nscipy can read many matlab-based formats\n\nbin can be anything (usually stands for binary; no clear mapping like the above)\n\nDon't get me wrong, but i expect someone trying to use tensorflow (not a small, not a simple tool) to know that changing a suffix should never magically transform the content to the new format (especially in the lossless\/lossy case like png, jpg). I hope you evaluated this decision and you are not running blindly into using a popular tool.","Q_Score":1,"Tags":"python,image-processing,tensorflow","A_Id":44421256,"CreationDate":"2017-06-07T18:40:00.000","Title":"the difference between .bin file and .mat files","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using tensorflow for a classification problem.\nI have some utility for saving and loading my network. When I restore the network, I can specify a different batch size than for training.\nMy problem is that I am getting different test results when I restore with a different batch size. However, there is no difference when using the same batch size.\nEDIT: Please note that I am not using dropout.\nThe difference is between 0% and 1% (0.5% on average).\nMy network is a fully connected layer that predicts two different outputs. I did not have the issue when I only had one task to predict.\nMy loss op is a sum of both losses.\nWhat could be the issue? Does it have to do with Tensorflow's parallelization strategy?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":361,"Q_Id":44436899,"Users Score":0,"Answer":"This normally means that you did not set the phase_train parameter back to false after testing.","Q_Score":0,"Tags":"python,machine-learning,tensorflow,neural-network","A_Id":50867766,"CreationDate":"2017-06-08T13:19:00.000","Title":"Is it normal to obtain different test results with different batch sizes with tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"What is label_keys parameter good for in a Classifier.\nCan you visualize the labeled data on Tensorboard at the Embeddings section?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":65,"Q_Id":44437307,"Users Score":1,"Answer":"Not in tensorboard, but the predict method can return the class names instead of numbers if you provide label_keys.","Q_Score":0,"Tags":"python,tensorflow,tensorboard","A_Id":44444164,"CreationDate":"2017-06-08T13:38:00.000","Title":"What is label_keys parameter good for in a Classifier - Tensorflow?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a numpy 2d array (8000x7200). I want to count the number of cells having a value greater than a specified threshold. I tried to do this using a double loop, but it takes a lot of time.\nIs there a way to perform this calculation quickly?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1618,"Q_Id":44439375,"Users Score":3,"Answer":"Your best bet is probably something like np.count_nonzero(x > threshold), where x is your 2-d array.\nAs the name implies, count_nonzero counts the number of elements that aren't zero. By making use of the fact that True is 1-ish, you can use it to count the number of elements that are True.","Q_Score":4,"Tags":"python,numpy","A_Id":44439439,"CreationDate":"2017-06-08T15:05:00.000","Title":"Conditional summation in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to use SVM from sklearn for a classification problem. I got a highly sparse dataset with more than 50K rows and binary outputs.\nThe problem is I don't know quite well how to efficiently choose the parameters, mainly the kernel, gamma anc C.   \nFor the kernels for example, am I supposed to try all kernels and just keep the one that gives me the most satisfying results or is there something related to our data that we can see in the first place before choosing the kernel ?\nSame goes for C and gamma.\nThanks !","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":726,"Q_Id":44441002,"Users Score":0,"Answer":"Yes, this is mostly a matter of experimentation -- especially as you've told us very little about your data set: separability, linearity, density, connectivity, ... all the characteristics that affect classification algorithms.\nTry the linear and Gaussian kernels for starters.  If linear doesn't work well and Gaussian does, then try the other kernels.\nOnce you've found the best 1 or 2 kernels, then play with the cost and gamma parameters.  Gamma is a \"slack\" parameter: it gives the kernel permission to make a certain proportion of raw classification errors as a trade-off for other benefits: width of the gap, simplicity of the partition function, etc.\nI haven't yet had an application that got more than trivial benefit from altering the cost.","Q_Score":1,"Tags":"python,machine-learning,scikit-learn,svm","A_Id":44441810,"CreationDate":"2017-06-08T16:24:00.000","Title":"How to choose parameters for svm in sklearn","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have about 3 million images and need to calculate a histogram for each one. Right now I am using python but it is taking of lot of time. Is there any way to process the images in batches? I have NVIDIA 1080 Ti GPU cards, so maybe if there is a way to process on the GPU? \nI can't find any code or library to process the images in parallel. Any kind of help to boost up the speed is appreciated","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":528,"Q_Id":44443999,"Users Score":0,"Answer":"Python is among the slowest production-ready languages you can use.\nAs you haven't posted any code, I can only provide general suggestions. They are listed in order of practicality below:\n\nUse a compiled version of python, such as pypy or cpython\nUse existing software with your desired functionality. There's nothing wrong with finding free software online.\nUse a more efficient (or perhaps even lossy) algorithm to skip computation\nUse a faster language such as Rust, C++, C#, or Java","Q_Score":0,"Tags":"python-3.x,numpy,histogram","A_Id":44444173,"CreationDate":"2017-06-08T19:23:00.000","Title":"Faster calculation histogram of a set of images","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to run keras on anaconda for convolution neural network using mnist handwriting recognition. A day before everything worked fine but as I try to run the same program, i get the following error in the first line:\n\nfrom keras.datasets import mnist (first line of code)\nModuleNotFoundError: No module named 'keras.datasets'; 'keras' is not\n  a package\n\nI also created virtual environment to use python 3.5 as my python version is 3.6. I have installed both keras and tensorflow. How do i fix the above error? Perhaps it is related to path and not error with keras. My anaconda is installed in E: whearas working environment is C:\\Users\\Prashant Mahato.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1591,"Q_Id":44444634,"Users Score":0,"Answer":"Do you get an error message if you just import keras? I was getting a similar error in the command line and then implemented in Spyder (using Anaconda) and it worked fine.","Q_Score":1,"Tags":"python,machine-learning,virtualenv,keras,mnist","A_Id":45195580,"CreationDate":"2017-06-08T20:04:00.000","Title":"Cannot run keras","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a list of numpy arrays which are actually input images to my CNN. However size of each of my image is not cosistent, and my CNN takes only images which are of dimension 224X224. How do I reshape each of my image into the given dimension?\nprint(train_images[key].reshape(224, 224,3))\ngives me an output\nValueError: total size of new array must be unchanged\n\nI would be very grateful if anybody could help me with this.","AnswerCount":3,"Available Count":2,"Score":0.0665680765,"is_accepted":false,"ViewCount":2121,"Q_Id":44451227,"Users Score":1,"Answer":"The standard way is to resize the image such that the smaller side is equal to 224 and then crop the image to 224x224. Resizing the image to 224x224 may distort the image and can lead to erroneous training. For example, a circle might become an ellipse if the image is not a square. It is important to maintain the original aspect ratio.","Q_Score":2,"Tags":"python,numpy,deep-learning,conv-neural-network","A_Id":44454289,"CreationDate":"2017-06-09T06:55:00.000","Title":"How to reshape a 3D numpy array?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a list of numpy arrays which are actually input images to my CNN. However size of each of my image is not cosistent, and my CNN takes only images which are of dimension 224X224. How do I reshape each of my image into the given dimension?\nprint(train_images[key].reshape(224, 224,3))\ngives me an output\nValueError: total size of new array must be unchanged\n\nI would be very grateful if anybody could help me with this.","AnswerCount":3,"Available Count":2,"Score":0.0665680765,"is_accepted":false,"ViewCount":2121,"Q_Id":44451227,"Users Score":1,"Answer":"Here are a few ways I know to achieve this:\n\nSince you're using python, you can use cv2.resize(), to resize the image to 224x224. The problem here is going to be distortions.\nScale the image to adjust to one of the required sizes (W=224 or H=224) and trim off whatever is extra. There is a loss of information here.\nIf you have the larger image, and a bounding box, use some delta to bounding box to maintain the aspect ratio and then resize down to the required size.\n\nWhen you reshape a numpy array, the produce of the dimensions must match. If not, it'll throw a ValueError as you've got. There's no solution using reshape to solve your problem, AFAIK.","Q_Score":2,"Tags":"python,numpy,deep-learning,conv-neural-network","A_Id":44451381,"CreationDate":"2017-06-09T06:55:00.000","Title":"How to reshape a 3D numpy array?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I think,I understood well the function \"cv2.findContours(image, mode, method).\nBut I got this thing contours,hierarchy = cv2.findContours(thresh,2,1) in one of the documents of opencv. I am not getting what is the meaning of 2,1 here and why have they been used. Someone please explain it.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":777,"Q_Id":44456932,"Users Score":1,"Answer":"The mode and method parameter of findContours() are enum with integer values.  One can use either the keywords or the integer values assigned to it. This detail can be viewed as an intellisense in visual studio when opencv is included in a project.\nBelow are the associated values with each enum.\nMODES\n\nCV_RETR_EXTERNAL : 0\nCV_RETR_LIST : 1\nCV_RETR_CCOMP : 2\nCV_RETR_TREE : 3 \n\nMETHODS\n\nCV_CHAIN_APPROX_NONE : 1\nCV_CHAIN_APPROX_SIMPLE : 2\nCV_CHAIN_APPROX_TC89_L1 : 3\nCV_CHAIN_APPROX_TC89_KCOS : 4","Q_Score":0,"Tags":"python-3.x,opencv,contour","A_Id":49260218,"CreationDate":"2017-06-09T11:44:00.000","Title":"Finding contour in using opencv in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Say I want to process a CSV file. I know in Python I can call the read() function to open the file and read it in a byte at a time, from the first field in the file (i.e. the field in the top left of the file) to the last field (the field in the bottom right).\nMy question is how I can determine the orientation of a file in memory. That is, if I view the contents of the file as a single binary number and process it as bit stream, how can I know if the first field (the field the read() first returns to us) is stored in the least significant positions of the binary number or the most significant positions? Would that be determined by the endianness of the machine my program is running on?\nHere's one (contrived) instance where this distinction would matter. Say I first scanned the binary representation of the file from least significant position to most significant position to determine the widths of each of the CSV values. If I were to then call read(), the first field width I calculated would correspond to the first field read() returns if and only if the first field of the CSV file is stored at the least significant bit positions when we view the file as a single binary number. If the first field was instead stored at the most significant positions, I'd first have to reverse my list of calculated field widths before I could use it.\nHere's a more concrete example:\nCSV file: abc,12345\nScanned field widths: either [3, 5] or [5, 3] depending on how the CSV file is laid out in memory.\nNow, if I call read(), the first field I'll process is abc. If abc happened to be the first field I scanned through when calculating the field widths, I'm good. I'll know that I've scanned the entire first field after reading 3 characters. However, if I first scanned 12345 when calculating the field widths, I've got a problem.\nHow can I determine how a file is laid out in memory? Is the first field of a file stored in the least significant bit positions, or the most significant bit positions?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":29,"Q_Id":44464315,"Users Score":1,"Answer":"Endiannes is a problem of binary files. CSV file is a text file. The numbers are not binary numbers but ASCII characters. There is no endiannes in it.","Q_Score":0,"Tags":"python,file,io,bit-manipulation","A_Id":44464380,"CreationDate":"2017-06-09T18:12:00.000","Title":"Determining the orientation of a file in memory","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a column called amount with holds values that look like this: $3,092.44 when I do dataframe.dtypes() it returns this column as an object how can i convert this column to type int?","AnswerCount":15,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":12214,"Q_Id":44469313,"Users Score":0,"Answer":"This will also work:  dframe.amount.str.replace(\"$\",\"\").astype(int)","Q_Score":4,"Tags":"python,pandas,ipython-notebook","A_Id":59876018,"CreationDate":"2017-06-10T03:50:00.000","Title":"Price column object to int in pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"How can we create an array with n elements. zeros function can create only arrays of dimensions greater than or equal to 2? zeros(4), zeros([4]) and zeros([4 4]) all create 2D zero matrix of dimensions 4x4.\nI have a code in Python where I have used numpy.zeros(n). I wish to do something similar in Octave.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":7005,"Q_Id":44471853,"Users Score":3,"Answer":"zeros(n,1) works well for me in Octave.","Q_Score":0,"Tags":"python,numpy,matrix,octave","A_Id":44471880,"CreationDate":"2017-06-10T09:42:00.000","Title":"Creating 1D zero array in Octave","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Using the Stanford NLP, I want my text to go through lemmatization and coreference resolution.\nSo for an input.txt:     \"Stanford is located in California.  It is a great University, founded in 1891.\"\nI would want the output.txt:      \"Stanford be located in California. Stanford be a great University, found in 1891.\"\nI am also looking to get a table where the first column consists of the name-entities that were recognized in the text, and the second column is the name class they were identified as. Thus, for the example sentence above, it would be something like:\n1st Column                    2nd Column\nStanford                      Location, Organization\nCalifornia                    Location\nThus, in the table, the name-entities would occur only once. \nThere's nothing I was able to find online about manipulating the default xml output or making direct changes to the input text file using the NLP.  Could you give me any tips on how to go about this?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":784,"Q_Id":44487269,"Users Score":0,"Answer":"If you are using the command line you can use -outputFormat text to get a human readable version or -outputFormat json to get a json version.  In Java code you can use edu.stanford.nlp.pipeline.StanfordCoreNLP.prettyPrint() or edu.stanford.nlp.pipeline.StanfordCoreNLP.jsonPrint() to print out an Annotation.","Q_Score":0,"Tags":"java,python,stanford-nlp","A_Id":44512629,"CreationDate":"2017-06-11T18:29:00.000","Title":"Stanford NLP Output Formatting","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Let's day I have a pandas dataframe df where the column names are the corresponding indices, so 1, 2, 3,....len(df.columns). How do I loop through all but the last column, so one before len(df.columns). My goal is to ultimately compare the corresponding element in each row for each of the columns with that of the last column. Any code with be helpful! Thank you!","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":5300,"Q_Id":44491067,"Users Score":0,"Answer":"A simple way would be to use slicing with iloc\nall but last column would be:\n\ndf.iloc[:,:-1]\n\nall but first column would be:\n\ndf.iloc[:,1:]","Q_Score":4,"Tags":"python,pandas,dataframe,indexing","A_Id":50835528,"CreationDate":"2017-06-12T03:35:00.000","Title":"How to loop over all but last column in pandas dataframe + indexing?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have written a Variational Auto-Encoder in Keras using Tensorflow as backend. As optimizer I use Adam, with a learning rate of 1e-4 and batch size 16. When I train the net on my Macbook's CPU (Intel Core i7), the loss value after one epoch (~5000 minibatches) is a factor 2 smaller than after the first epoch on a different machine running Ubuntu. For the other machine I get the same result on both CPU and GPU (Intel Xeon E5-1630 and Nvidia GeForce GTX 1080). Python and the libraries I'm using have the same version number. Both machines use 32 bit floating points. If I use a different optimizer (eg rmsprop), the significant difference between machines is still there. I'm setting np.random.seed to eliminate randomness. \nMy net outputs logits (I have linear activation in the output layer), and the loss function is tf.nn.sigmoid_cross_entropy_with_logits. On top of that, one layer has a regularizer (the KL divergence between its activation, which are params of a Gaussian distribution, and a zero mean Gauss).\nWhat could be the cause of the major difference in loss value?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":203,"Q_Id":44500526,"Users Score":0,"Answer":"The dataset I used was a single .mat file, created by using scipy's savemat and loaded with loadmat. It was created on my Macbook and distributed via scp to the other machines. It turned out that the issue was with this .mat file (I do not know exactly what though). I have switched away from the .mat file and everything is fine now.","Q_Score":2,"Tags":"python,machine-learning,tensorflow,keras,autoencoder","A_Id":44530760,"CreationDate":"2017-06-12T13:20:00.000","Title":"Tensorflow major difference in loss between machines","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In the cntk.layers package we have the option to do zero padding:\npad (bool or tuple of bools, defaults to False) \u2013 if False, then the filter will be shifted over the \u201cvalid\u201d area of input, that is, no value outside the area is used. If pad=True on the other hand, the filter will be applied to all input positions, and positions outside the valid region will be considered containing zero. Use a tuple to specify a per-axis value.\nBut how can I use other types of padding like reflect or symmetric padding?\nIs it possible to integrate my own padding criterion in the cntk.layers?\nI'm a beginner in cntk and really grateful for every help.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":177,"Q_Id":44504140,"Users Score":0,"Answer":"There is a new pad operation (in master; will be released with CNTK 2.2) that supports reflect and symmetric padding.","Q_Score":1,"Tags":"python,padding,cntk","A_Id":45828029,"CreationDate":"2017-06-12T16:16:00.000","Title":"CNTK & Python: How to do reflect or symmetric padding instead of zero padding?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to python. I have to build a chatbot using python nltk -- my use case and expected output is this:\nI have a custom dictionary of some categories (shampoo,hair,lipstick,face wash),\nsome brands (lakme,l'oreal,matrix), some entities ((hair concern: dandruff, hair falling out), (hair type: oily hair, dry hair), (skin type: fair skin, dark skin, dusky skin), etc.).\n\nI want to buy shampoo for hair falling out and dry hair \n\nor\n\nShow me best lipsticks for fair skin and office wear\n\nHow do I extract values by category: shampoo, hair concern: hair falling out, hair type: dry hair\nI am using python nltk.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":203,"Q_Id":44514898,"Users Score":0,"Answer":"I hope  this is what you are looking for   https:\/\/github.com\/sujitpal\/nltk-examples\/tree\/master\/src\/cener","Q_Score":0,"Tags":"python-3.x,dictionary,nltk","A_Id":44608170,"CreationDate":"2017-06-13T07:25:00.000","Title":"How to tokenize and tag those tokenized strings from my own custom dictionary using python nltk?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to call tf.image.resize_image_with_crop_or_pad(images,height,width) to resize my input images. As my input images are all in form as 2-d numpy array of pixels, while the image input of resize_image_with_crop_or_pad must be 3-d or 4-d tensor, it will cause an error. What should I do?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2981,"Q_Id":44515532,"Users Score":2,"Answer":"Let's suppose that you got images that's a [n, W, H] numpy nd-array, in which n is the number of images and W and H are the width and the height of the images.\n\nConvert images to a tensor, in order to be able to use tensorflow functions:\ntf_images = tf.constant(images)\nConvert tf_images to the image data format used by tensorflow (thus from n, W, H to n, H, W)\ntf_images = tf.transpose(tf_images, perm=[0,2,1])\nIn tensorflow, every image has a depth channell, thus altough you're using grayscale images, we have to add the depth=1 channell.\ntf_images = tf.expand_dims(tf_images, 2)\nNow you can use tf.image.resize_image_with_crop_or_pad to resize the batch (that how has a shape of [n, H, W, 1] (4-d tensor)):\nresized = tf.image.resize_image_with_crop_or_pad(tf_images,height,width)","Q_Score":1,"Tags":"python,tensorflow","A_Id":44518795,"CreationDate":"2017-06-13T07:58:00.000","Title":"Tensorflow resize_image_with_crop_or_pad","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have trained a model in CNTK. Then I clone it and change some parameters; when I try to test the quantized model, I get\n\nRuntimeError: Block Function 'softplus:  -> Unknown': Inputs\n'Constant('Constant70738', [], []), Constant('Constant70739', [], []),\nParameter('alpha', [], []), Constant('Constant70740', [], [])' of the\nnew clone do not match the cloned inputs 'Constant('Constant70738',\n[], []), Constant('Constant70739', [], []), Constant('Constant70740',\n[], []), Parameter('alpha', [], [])' of the clonee Block Function.\n\nI have no idea what this error means or how to fix it. Do you have any ideas?\nP.S. I clone and edit the model by doing\n\nclonedModel = model.clone(cntk.ops.CloneMethod.clone)\ncloneModel.parameters[0].value = cloneModel.parameters[0].value*4\n\nthen when I try to use cloneModel I get that error above.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":115,"Q_Id":44517122,"Users Score":0,"Answer":"This line\n\ncloneModel.parameters[0] = cloneModel.parameters[0]*4\n\ntries to replace the first parameter with an expression (a CNTK graph) that multiplies the parameter by 4. I don't think that's the intent here. Rather, you want to do the above on the .value attribute of the parameter. Try this instead:\n\ncloneModel.parameters[0].value = cloneModel.parameters[0].value*4","Q_Score":1,"Tags":"python,runtime-error,cntk","A_Id":44535536,"CreationDate":"2017-06-13T09:13:00.000","Title":"CNTK: The new clone do not match the cloned inputs of the clonee Block Function","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have two given points (3.0, 3.2) and (7.0, 4.59) .\nMy job here is very simple but I don't even know how to start. I just need to plot 4 nonlinear functions that go through these two points.\nDid somebody have a similar problem before? How does one even start?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":591,"Q_Id":44526642,"Users Score":0,"Answer":"It looks more like a math problem to me here, since you ask \"how to start\".\nyou know that a function's plot is just a lot of points (x, y) where y=f(x).\nAnd I know that for any two pairs of points (not vertically aligned), I have an infinity of second-degree functions (parabolas) going through these two points.\nthey are given by y=ax^2+bx+c \nYou want the parabola to go through your 2 points, so you can substitute x and y for each of the 2 points, that will give you 2 equations (where a, b and c are the unknown) .\nThen you can add a random point (I would suggest on the y-axis :  (0; r) ).\nThis will give you a third equation.\nWith these 3 equations, solve for a, b and c. (in function of r)\nnow, for any value of r, you will have some a, b and c that define a parabola going through your 2 known points.\nOnce you understand how to solve this math problem, the python part is completely independant.","Q_Score":0,"Tags":"python-2.7,nonlinear-functions","A_Id":44526860,"CreationDate":"2017-06-13T16:10:00.000","Title":"Generate a random nonlinear function going through given points in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm building a text classifier that will classify text into topics.\nIn the first phase of my program as a part of cleaning the data, I remove all the non-English words. For this I'm using the nltk.corpus.words.words() corpus. The problem with this corpus is that it removes 'modern' English words such as Facebook, Instagram etc. Does anybody know another, more 'modern' corpus which I can replace or union with the present one?\nI prefer nltk corpus but I'm open to other suggestions.\nThanks in advance","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":507,"Q_Id":44550004,"Users Score":1,"Answer":"Rethink your approach. Any collection of English texts will have a \"long tail\" of words that you have not seen before. No matter how large a dictionary you amass, you'll be removing words that are not \"non-English\". And to what purpose? Leave them in, they won't spoil your classification.\nIf your goal is to remove non-English text, do it at the sentence or paragraph level using a statistical approach, e.g. ngram models. They work well and need minimal resources.","Q_Score":0,"Tags":"python,nltk,corpus","A_Id":44553514,"CreationDate":"2017-06-14T16:21:00.000","Title":"Find 'modern' nltk words corpus","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a 1 column excel file. I want to import all the values it has in a variable x (something like x=[1,2,3,4.5,-6.....]), then use this variable to run numpy.correlate(x,x,mode='full') to get autocorrelation, after I import numpy.\nWhen I manually enter x=[1,2,3...], it does the job fine, but when I try to copy paste all the values in x=[], it gives me a NameError: name 'NO' is not defined.\nCan someone tell me how to go around doing this?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":57,"Q_Id":44554135,"Users Score":0,"Answer":"You can use Pandas to import a CSV file with the pd.read_csv function.","Q_Score":0,"Tags":"python","A_Id":44554231,"CreationDate":"2017-06-14T20:25:00.000","Title":"Import a column from excel into python and run autocorrelation on it","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"A newbie question, as I get increasingly confused with pyspark. I want to scale an existing python data preprocessing and data analysis pipeline. I realize if I partition my data with pyspark, I can't treat each partition as a standalone pandas data frame anymore, and need to learn to manipulate with pyspark.sql row\/column functions, and change a lot of existing code, plus I am bound to spark mllib libraries and can't take full advantage of more mature scikit-learn package. Then why would I ever need to use Spark if I can use multiprocessing tools for cluster computing and parallelize tasks on existing dataframe?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":2667,"Q_Id":44555485,"Users Score":1,"Answer":"True, Spark does have the limitations you have mentioned, that is you are bounded in the functional spark world (spark mllib, dataframes etc). However, what it provides vs other multiprocessing tools\/libraries is the automatic distribution, partition and rescaling of parallel tasks. Scaling and scheduling spark code becomes an easier task than having to program your custom multiprocessing code to respond to larger amounts of data + computations.","Q_Score":6,"Tags":"python,scikit-learn,multiprocessing,pyspark,cluster-computing","A_Id":55951963,"CreationDate":"2017-06-14T22:06:00.000","Title":"Python multiprocessing tool vs Py(Spark)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying port a python code into java and am stuck at one place. Is there any method in java that is equivalent to numpy.random.uniform() in python?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":674,"Q_Id":44559717,"Users Score":0,"Answer":"Just use \n\nRandom rnd = new Random();\nrnd.nextInt(int boundary);\nrnd.nextDouble(double boundary);\nrnd.next();\n\nIf you want a list of randoms the best way is probably ti write your own little method, just use an array and fill it with a for loop.","Q_Score":0,"Tags":"java,python,random,distribution,uniform-distribution","A_Id":44561811,"CreationDate":"2017-06-15T06:02:00.000","Title":"Equivalent method in Java for np.random.uniform()","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"for some common reasons and solutions.  Include the entire stack trace\nabove this error message when asking for help.\nProcess finished with exit code 1","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":223,"Q_Id":44569033,"Users Score":1,"Answer":"Since you have in the log\n\nLibrary not loaded: @rpath\/libcublas.8.0.dylib\n\nI would say you've installed TF with CUDA support but didn't install CUDA libraries properly. Try to install TF CPU only.","Q_Score":1,"Tags":"python-3.x,tensorflow,pycharm,importerror","A_Id":45875329,"CreationDate":"2017-06-15T13:37:00.000","Title":"Problems using Tensorflow in PyCharm-keep getting ImportError","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am very new to ML using Big Data and I have played with Keras generic convolutional examples for the dog\/cat classification before, however when applying a similar approach to my set of images, I run into memory issues.\nMy dataset consists of very long images that are 10048 x1687 pixels in size. To circumvent the memory issues, I am using a batch size of 1, feeding in one image at a time to the model. \nThe model has two convolutional layers, each followed by max-pooling which together make the flattened layer roughly 290,000 inputs right before the fully-connected layer.\nImmediately after running however, Memory usage chokes at its limit (8Gb).\nSo my questions are the following:\n1) What is the best approach to process computations of such size in Python locally (no Cloud utilization)? Are there additional python libraries that I need to utilize?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":841,"Q_Id":44569938,"Users Score":0,"Answer":"While using train_generator(), you should also set the max_q_size parameter. It's set at 10 by default, which means you're loading in 10 batches while using only 1 (since train_generator() was designed to stream data from outside sources that can be delayed like network, not to save memory). I'd recommend setting max_q_size=1for your purposes.","Q_Score":2,"Tags":"python,memory,computer-vision,keras,convolution","A_Id":44624518,"CreationDate":"2017-06-15T14:18:00.000","Title":"Memory Issues Using Keras Convolutional Network","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Using matplotlib.pyplot, I plotted multiple wave functions w.r.t time series, showing the waves in multiple vertical axes, and output the graph in jpg using savefig. I want to know the easiest way in which I can output all wave functions into a single output data file maybe in CSV or DAT in rows and columns.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2703,"Q_Id":44582210,"Users Score":0,"Answer":"If you plotted the data using numpy array, you can use numpy.savetxt.","Q_Score":0,"Tags":"python,csv,matplotlib,output","A_Id":44589585,"CreationDate":"2017-06-16T06:25:00.000","Title":"How to Save Plotted Graph Data into Output Data File in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to replicate a neural network for depth estimation. The original authors have taken a pre-trained network and added between the fully connected layer and the convolutional layer a 'Superpixel Pooling Layer'. In this layer, the convolutional feature maps are upsampled and the features per superpixel are averaged. \nMy problem is that in order to successfully achieve this, I need to calculate the superpixels per image. How can I access the data being used by keras\/tensorflow during batch processing to perform SLIC oversegmentation?\nI considered splitting the tasks and working by pieces i.e. feed the images into the convolutional network. Process the outputs separately and then feed them into a fully connected layer. However, this makes further training of the network impossible.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":486,"Q_Id":44587813,"Users Score":0,"Answer":"At the time it seems to be impossible to actually access the data within the symbolic tensor. It also seems unlikely that such functionality will be added in the future since in the Tensorflow page it says:\n\nA Tensor object is a symbolic handle to the result of an operation, but \n  does not actually hold the values of the operation's output. \n\nKeras allows for the creation of personalized layers. However, these are limited by the available backend operations. As such, it is simply not possible to access the batch data.","Q_Score":0,"Tags":"python,tensorflow,deep-learning,keras,keras-layer","A_Id":44651045,"CreationDate":"2017-06-16T11:10:00.000","Title":"Accessing Input Layer data in Tensorflow\/Keras","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When and how should I use fit_generator?\nWhat is the difference between fit and fit_generator?","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":3009,"Q_Id":44597555,"Users Score":2,"Answer":"They are useful for on-the-fly augmentations, which the previous poster mentioned. This however is not neccessarily restricted to generators, because you can fit for one epoch and then augment your data and fit again.\nWhat does not work with fit is using too much data per epoch though. This means that if you have a dataset of 1 TB and only 8 GB of RAM you can use the generator to load the data on the fly and only hold a couple of batches in memory. This helps tremendously on scaling to huge datasets.","Q_Score":4,"Tags":"python,deep-learning","A_Id":44609082,"CreationDate":"2017-06-16T20:33:00.000","Title":"How to use model.fit_generator in keras","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When using TF-IDF to compare Document A, B\nI know that length of document is not important.\nBut compared to A-B, A-C \nin this case, I think the length of document B, C should be the same length.\nfor example\nLog : 100 words\nDocument A : 20 words\nDocument B : 30 words\nLog - A 's TF-IDF score : 0.xx\nLog - B 's TF-IDF score : 0.xx\nShould I do normalization of document A,B?\n(If the comparison target is different, it seems to be a problem or wrong result)","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2290,"Q_Id":44600170,"Users Score":4,"Answer":"Generally you want to do whatever gives you the best cross validated results on your data.  \nIf all you are doing to compare them is taking cosine similarity then you have to normalize the vectors as part of the calculation but it won't affect the score on account of varying document lengths.  Many general document retrieval systems consider shorter documents to be more valuable but this is typically handled as a score multiplier after the similarities have been calculated.\nOftentimes ln(TF) is used instead of raw TF scores as a normalization feature because differences between seeing a term 1and 2 times is way more important than the difference between seeing a term 100 and 200 times; it also keeps excessive use of a term from dominating the vector and is typically much more robust.","Q_Score":2,"Tags":"python,normalization,word,tf-idf","A_Id":44600391,"CreationDate":"2017-06-17T02:15:00.000","Title":"tf-idf : should I do normalization of documents length","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Pytorch have Dynamic Neural Networks (defined-by-run) as opposed to Tensorflow which have to compile the computation graph before run.\nI see that both Torch7 and PyTorch depend on TH, THC, THNN, THCUNN (C library). Does Torch7 have Dynamic Neural Networks (defined-by-run) feature ?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":79,"Q_Id":44614977,"Users Score":1,"Answer":"No, Torch7 use static computational graphs, as in Tensorflow. It is one of the major differences between PyTorch and Torch7.","Q_Score":0,"Tags":"python,lua,torch,pytorch","A_Id":44638348,"CreationDate":"2017-06-18T12:29:00.000","Title":"Is Torch7 defined-by-run like Pytorch?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a matrix X which has len(X) equal to 13934 and len(X[i]), for all i, equal to 74, and I have an array Y which has len(Y) equal to 13934 and len(Y[i]) equal to TypeError: object of type 'numpy.int64' has no len() for all i.\nWhen I try np.vstack((X,Y)) or result = np.concatenate((X, Y.T), axis=1)\nI get ValueError: all the input array dimensions except for the concatenation axis must match exactly\nWhat is the problem?\nWhen I print out Y it says array([1,...], dtype=int64) and when I print out X it says array([data..]) with no dtype. Could this be the problem?\nI tried converting them both to float32 by doing X.view('float32') and this did not help.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":203,"Q_Id":44617331,"Users Score":1,"Answer":"Since X is a numpy array, you can do X.shape instead of the repeated len.  I expect it to show (13934, 74).\nI expect Y.shape to be (13934,). It's a 1d array, which is why Y[0] is a number, numpy.int64.  And since it is 1d, transpose (swapping axes) doesn't do anything.  (this isn't MATLAB where everything has at least 2 dimensions.)\nIt looks like you want to create an array that has shape (13934, 75).  To do that you'll need to add a dimension to Y.  Y[:,None] is a concise way of doing that.  The shape of that is (13934,1), which will concatenate with X.  If that None syntax is puzzling, try, Y.reshape(-1,1) (or reshape(13934,1)).","Q_Score":0,"Tags":"python,numpy","A_Id":44617764,"CreationDate":"2017-06-18T16:51:00.000","Title":"Python vertical stack not working","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In Keras there is a feature called pop() that lets you remove the bottom layer of a model. Is there any way to remove the top layer of a model? \nI have a fully saved pre-trained Variational Autoencoder and am trying to only load the decoder (the bottom four layers).\nI am using Keras with a Tensorflow backend.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":1067,"Q_Id":44620403,"Users Score":1,"Answer":"Keras pop() removes the last (aka top) layer, not the bottom one. \nI suggest you use model.summary() to print out the list of layers and than subsequently use pop() until only the necessary layers are left.","Q_Score":2,"Tags":"python,tensorflow,keras","A_Id":44623114,"CreationDate":"2017-06-18T23:14:00.000","Title":"Keras: Is there any way to \"pop()\" the top layers?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a n*m tensor that basically represents m points in n dimensional euclidean space. I wanted calculate the pairwise euclidean distance between each consecutive point.\nThat is, if my column vectors are the points a, b, c, etc., I want to calculate euc(a, b), euc(b, c), etc.\nThe result would be an m-1 length 1D-tensor with each pairwise euclidean distance.\nAnyone know who this can be performed in TensorFlow?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2501,"Q_Id":44635695,"Users Score":0,"Answer":"Define a function to calculate distances\ncalc_distance = lambda f, g: tf.norm(f-g, axis=1, ord='euclidean')\nPass your n*m vector to the function, example:\nP = tf.constant([[1, 2], [3, 4], [2, 1], [0, 2], [2, 3]], dtype=tf.float32)\ndistances = calc_distance(P[:-1:], P[1::])\nprint(distances)\n<tf.Tensor: shape=(4,), dtype=float32, numpy=array([2.8284273, 3.1622777, 2.2360682, 2.2360682], dtype=float32)>","Q_Score":1,"Tags":"python,tensorflow","A_Id":70358084,"CreationDate":"2017-06-19T16:34:00.000","Title":"Tensorflow - Euclidean Distance of Points in Matrix","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using Keras to build a convolutional neural net to perform regression from microscopic images to 2D label data (for counting). I'm looking into training the network on smaller patches of the microscopic data (where the patches are the size of the receptive field). The problem is, the fit() method requires validation data to be of the same size as the input. Instead, I'm hoping to be able to validate on entire images (not patches) so that I can validate on my entire validation set and compare the results to other methods I've used so far.\nOne solution I found was to alternate between fit() and evaluate() each epoch. However, I was hoping to be able to observe these results using Tensorboard. Since evaluate() doesn't take in callbacks, this solution isn't ideal. Does anybody have a good way validating on full-resolution images while training on patches?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":888,"Q_Id":44636877,"Users Score":0,"Answer":"You need to make sure that your network input is of shape (None,None,3), which means your network accepts an input color image of arbitrary size.","Q_Score":5,"Tags":"python,validation,machine-learning,neural-network,keras","A_Id":49830684,"CreationDate":"2017-06-19T17:47:00.000","Title":"Training and validating on images with different resolution in Keras","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using Keras to build a convolutional neural net to perform regression from microscopic images to 2D label data (for counting). I'm looking into training the network on smaller patches of the microscopic data (where the patches are the size of the receptive field). The problem is, the fit() method requires validation data to be of the same size as the input. Instead, I'm hoping to be able to validate on entire images (not patches) so that I can validate on my entire validation set and compare the results to other methods I've used so far.\nOne solution I found was to alternate between fit() and evaluate() each epoch. However, I was hoping to be able to observe these results using Tensorboard. Since evaluate() doesn't take in callbacks, this solution isn't ideal. Does anybody have a good way validating on full-resolution images while training on patches?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":888,"Q_Id":44636877,"Users Score":0,"Answer":"You could use fit generator instead of fit and provide a different generator for validation set. As long as the rest of your network is agnostic to the image size, (e.g, fully convolutional layers), you should be fine.","Q_Score":5,"Tags":"python,validation,machine-learning,neural-network,keras","A_Id":45889288,"CreationDate":"2017-06-19T17:47:00.000","Title":"Training and validating on images with different resolution in Keras","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a tensor of shape (10, 100, 20, 3). Basically, it can be thought of as a batch of images. So the image height is 100 and width is 20 and channel depth is 3.\nI have run some computations to generate a set of 10*50 indices corresponding to 50 columns I would like to keep per image in the batch. The indices are stored in a tensor of shape (10, 50). I would like to end up with a tensor of shape (10, 50, 20, 3).\nI have looked into tf.batch_nd() but I can't figure out the semantics for how indices are actually used.\nAny thoughts?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":78,"Q_Id":44639106,"Users Score":0,"Answer":"I can't comment on the question because of low rep, so using an answer instead.\nCan you clarify your question a bit, maybe with a small concrete example using very small tensors?\nWhat are the \"columns\" you are referring to? You say that you want to keep 50 columns (presumably 50 numbers) per image. If so, the (10, 50) shape seems like what you want - it has 50 numbers for each image in the batch. The (10, 50, 20, 3) shape you mention would allocate 50 numbers to each \"image_column x channel\". That is 20*3*50 = 3000 numbers per image. How do you want to construct them from the 50 that you have?\nAlso, can you give a link to tf.batch_nd(). I did not find anything similar and relevant.","Q_Score":0,"Tags":"python,tensorflow","A_Id":44937787,"CreationDate":"2017-06-19T20:10:00.000","Title":"TensorFlow extracting columns","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I attempted to install Opencv for python two ways, \nA) Downloading the opencv zip, then copying cv2.pyd to \/Python36\/lib\/site-packages.\nB) undoing that, and using \"pip install opencv-python\"\n\/lib\/site-packages is definitly the place where python is loading my modules, as tensorflow and numpy are there, but any attempt to \"import cv2\" leads to \"ImportError: DLL Load Failed: The specified module could not be found\"\nI am at a loss, any help appreciated. And yes i have tried reinstalling VC redist 2015","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":4066,"Q_Id":44662278,"Users Score":0,"Answer":"Use the zip, extract it, and run sudo python3 setup.py install if you are on Mac or Linux. If on Windows, open cmd or Powershell in Admin mode and then run py -3.6 setup.py install, after cding to the path of the zip. If on Linux, you also have to run sudo apt-get install python-opencv. Maybe on Mac you have to use Homebrew, but I am not sure.","Q_Score":3,"Tags":"python,opencv,dll,pip","A_Id":44662310,"CreationDate":"2017-06-20T20:18:00.000","Title":"Pip and\/or installing the .pyd of library to site-packages leads \"import\" of library to DLL load faliure","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm working with multidimensional matrices (~100 dimensions or so, see below why). My matrix are NumPy arrays and I mainly multiply them with each other.\nDoes NumPy care (with respect to speed or accuracy) in what form I ask it to multiply these matrices? I.e. would it make sense to reshape them into a linear array before performing the multiplication? I did some own test with random matrices, and it seemed to be irrelevant, but would like to have some theoretical insight into this.\nI guess there is a limit to how large matrices can be and how large they can be, before Python becomes slow handling them. Is there a way to find this limit?\n\nI have several species (biology) and want to assign each of these species a fitness. Then I want to see how these different finesses affect the outcome of competition. And I want to check for all possible fitness combinations of all species. My matrices have many dimensions, but all dimensions are quite small.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":115,"Q_Id":44698632,"Users Score":4,"Answer":"For elementwise multiplication it does not matter, and flattening the array does not change a thing. Remember: Arrays, no matter their dimension, are saved linearly in RAM. If you flatten the array before multiplication, you are only changing the way NumPy presents the data to you, the data in RAM is never touched. Multiplying the 1D or the 100D data is exactly the same operation.","Q_Score":1,"Tags":"python,arrays,numpy,vectorization","A_Id":44698955,"CreationDate":"2017-06-22T11:51:00.000","Title":"What are the limits of vectorization?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to perform features selection (for regression tasks) by XGBRegressor().\nMore precisely, I would like to know: \n\nIf there is something like the method\nfeature_importances_, utilized with XGBClassifier, which I could use for regression.\nIf the XGBoost's method plot_importance() is reliable when it is used with XGBRegressor()","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2264,"Q_Id":44699889,"Users Score":1,"Answer":"Finally I have solved this issue by:\nmodel.booster().get_score(importance_type='weight')","Q_Score":0,"Tags":"python,xgboost","A_Id":45310833,"CreationDate":"2017-06-22T12:49:00.000","Title":"XGBoost - Feature selection using XGBRegressor","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I previously installed tensorflow-gpu 0.12.0rc0 with Winpython-3.5.2, and when I tried to upgrade or uninstall it to install the newer version using both the Winpython Control Panel and pip, I got the following error:\nFileNotFoundError: [WinError 3] The system cannot find the path specified:\n'c:\\\\users\\\\moliang\\\\downloads\\\\winpython-64bit-3.5.2.3qt5\\\\python-3.5.2.amd64\\\\lib\\\\site-packages\\\\tensorflow\\\\contrib\\\\session_bundle\\\\testdata\\\\saved_model_half_plus_two\\\\variables\\\\__pycache__\\\\__init__.cpython-35.pyc'\nI installed the tensorflow-gpu 0.12.0rc0 through Winpython-3.5.2 pip, and the __init__.cpython-35.pyc does exist at the correct directory. So I don't understand how this error could happen? And it prevents me from getting the new version.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":508,"Q_Id":44711726,"Users Score":0,"Answer":"Posting my own answer (alternative) here in case someone overlooked comment:\nI forced to delete the package in the python\/lib\/site-packages\/ and reinstalled the tensorflow-gpu, and it seems working well. \nThough I solve this problem via such alternate I would still like to know the root cause and long-term fix for this.","Q_Score":3,"Tags":"python,tensorflow,installation","A_Id":61737353,"CreationDate":"2017-06-23T01:31:00.000","Title":"Uninstall\/upgrade tensorflow failed: __init__.cpython-35.pyc not found","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"After applying KernelPCA to my data and passing it to a classifier (SVC) I'm getting the following error:\n\nValueError: Input contains NaN, infinity or a value too large for\n  dtype('float64').\n\nand this warning while performing KernelPCA:\n\nRuntimeWarning: invalid value encountered in sqrt X_transformed =\n  self.alphas_ * np.sqrt(self.lambdas_)\n\nLooking at the transformed data I've found several nan values.\nIt makes no difference which kernel I'm using. I tried cosine, rbf and linear.\nBut what's interesting:\n\nMy original data only contains values between 0 and 1 (no inf or nan), it's scaled with MinMaxScaler\nApplying standard PCA works, which I thought to be the same as KernelPCA with linear kernel.\n\nSome more facts:\n\nMy data is high dimensional ( > 8000 features) and mostly sparse.\nI'm using the newest version of scikit-learn, 18.2 \n\nAny idea how to overcome this and what could be the reason?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":622,"Q_Id":44716368,"Users Score":3,"Answer":"The NaNs are produced because the eigenvalues (self.lambdas_) of the input matrix are negative which provoke the ValueError as the square root does not operate with negative values.\nThe issue might be overcome by setting KernelPCA(remove_zero_eig=True, ...) but such action would not preserve the original dimensionality of the data. Using this parameter is a last resort as the model's results may be skewed.\nActually, it has been stated negative eigenvalues indicate a model misspecification, which is obviously bad. Possible solution for evading that fact without corroding the dimensionality of the data with remove_zero_eig parameter might be reducing the quantity of the original features, which are greatly correlated. Try to build the correlation matrix and see what those values are. Then, try to omit the redundant features and fit the KernelPCA() again.","Q_Score":2,"Tags":"python,machine-learning,scikit-learn","A_Id":44736370,"CreationDate":"2017-06-23T08:14:00.000","Title":"KernelPCA produces NaNs","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I was trying to train CIFAR10 and MNIST dataset on VGG16 network. In my first attempt, I got an error which says shape of input_2 (labels) must be (None,2,2,10). What information does this structure hold in 2x2x10 array because I expect input_2 to have shape (None, 10) (There are 10 classes in both my datasets).\nI tried to expand dimensions of my labels from (None,10) to (None,2,2,10). But I am sure this is not the correct way to do it since I obtain a very low accuracy (around 0.09)\n(I am using keras, Python3.5)","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":212,"Q_Id":44723464,"Users Score":0,"Answer":"Low accuracy is caused by the problem in layers. I just modified my network and obtained .7496 accuracy.","Q_Score":1,"Tags":"python,machine-learning,deep-learning,keras","A_Id":45011256,"CreationDate":"2017-06-23T14:08:00.000","Title":"VGG16 Training new dataset: Why VGG16 needs label to have shape (None,2,2,10) and how do I train mnist dataset with this network?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have 45000 text records in my dataframe. I wanted to convert those 45000 records into word vectors so that I can train a classifier on the word vector. I am not tokenizing the sentences. I just split the each entry into list of words.\nAfter training word2vec model with 300 features, the shape of the model resulted in only 26000. How can I preserve all of my 45000 records ? \nIn the classifier model, I need all of those 45000 records, so that it can match 45000 output labels.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":277,"Q_Id":44740161,"Users Score":2,"Answer":"If you are splitting each entry into a list of words, that's essentially 'tokenization'. \nWord2Vec just learns vectors for each word, not for each text example ('record') \u2013 so there's nothing to 'preserve', no vectors for the 45,000 records are ever created. But if there are 26,000 unique words among the records (after applying min_count), you will have 26,000 vectors at the end. \nGensim's Doc2Vec (the '\nParagraph Vector' algorithm) can create a vector for each text example, so you may want to try that. \nIf you only have word-vectors, one simplistic way to create a vector for a larger text is to just add all the individual word vectors together. Further options include choosing between using the unit-normed word-vectors or raw word-vectors of many magnitudes; whether to then unit-norm the sum; and whether to otherwise weight the words by any other importance factor (such as TF\/IDF). \nNote that unless your documents are very long, this is a quite small training set for either Word2Vec or Doc2Vec.","Q_Score":0,"Tags":"python-3.x,nlp,word2vec","A_Id":44740700,"CreationDate":"2017-06-24T19:25:00.000","Title":"how to preserve number of records in word2vec?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to python (coming from R), and I am trying to understand how I can convert a timestamp series in a pandas dataframe (in my case this is called df['timestamp']) into what I would call a string vector in R.  is this possible?  How would this be done?\nI tried df['timestamp'].apply('str'), but this seems to simply put the entire column df['timestamp'] into one long string.  I'm looking to convert each element into a string and preserve the structure, so that it's still a vector (or maybe this a called an array?)","AnswerCount":3,"Available Count":1,"Score":0.2605204458,"is_accepted":false,"ViewCount":102326,"Q_Id":44741587,"Users Score":4,"Answer":"Following on from VinceP's answer, to convert a datetime Series in-place do the following: \ndf['Column_name']=df['Column_name'].astype(str)","Q_Score":32,"Tags":"python,arrays,pandas,vector","A_Id":51706173,"CreationDate":"2017-06-24T22:39:00.000","Title":"pandas timestamp series to string?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have thousands of documents with associated tag information. However i also have many documents without tags.\nI want to train a model on the documents WITH tags and then apply the trained classifier to the UNTAGGED documents; the classifier will then suggest the most appropriate tags for each UNTAGGED document.\nI have done quite a lot of research and there doesn't seem to be a SUPERVISED implementation to document tag classification.\nI know NLTK, gensim, word2vec and other libraries will be useful for this problem.\nI will be coding the project in Python.\nAny help would be greatly appreciated.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":461,"Q_Id":44763743,"Users Score":0,"Answer":"I'm currently working on something similar,\nbesides what @Joonatan Samuel suggested I would encourage you to do careful preprocessing and considerations.\n\nIf you want two or more tags for documents you could train several model : one model per tag. You need to consider if there will be enough cases for each model (tag)\nIf you have a lot of tags, you could run into a problem with document-tag cases like above.\nStick to most common tag prediction don't try to predict all tags.","Q_Score":0,"Tags":"python,machine-learning,nlp,text-classification","A_Id":44765840,"CreationDate":"2017-06-26T15:55:00.000","Title":"supervised tag suggestion for documents","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am passing in a hardcoded list \/ tuple (tried both) when initialising the OneHotEncoder and I get this error during fit_transform , not using numpy types anywhere (well except for the data matrix itself).\nThe only thing is that some of the values in that array are None because I am also using categorical_features to specify a mask (as in some of the features are real-valued and I want them to stay real-valued.\nMy n_values looks like [1, 2, 3, None, 5] or (1, 2, 3, None, 5) and\nmy categorical_features looks like [0, 1, 2, 4] though I have also tried:\n[True, True, True, False, True].\nThe documentation does not present any actual examples with the mask on.\nEDIT:\nSo, I tried replacing None with zeroes and this issue went away but now I get:\nValueError: Shape mismatch: if n_values is an array, it has to be of shape (n_features,).\nWhether I wrap my mask array with np.array or not (and when I do the shape is indeed the same as (n_features,)) I get this same error (though interestingly it does not complain about it being a numpy array anymore as long as there are no None values in it.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1561,"Q_Id":44776786,"Users Score":0,"Answer":"n_values should only contain domain sizes for categorical values completely skipping out the non-categorical columns in the data matrix.\nTherefore if [True, False, True] format is used, the size should correspond to the number of True values in the array or if indices are used the two arrays should be of the same size.\nSo there should be no None values but also no 0s, -1s or any other ways to encode real-valued variables in the n_values array.","Q_Score":0,"Tags":"python,arrays,numpy,scikit-learn","A_Id":44777825,"CreationDate":"2017-06-27T09:30:00.000","Title":"sklearn::TypeError: Wrong type for parameter `n_values`. Expected 'auto', int or array of ints, got","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I was planning to teach np.einsum to colleagues, by hoping to show how it would be reduced to multiplications and summations.\nSo, instead of numerical data, I thought to use alphabet chars. in the arrays.\nSay, we have A (2X2) as [['a', 'b'], ['c', 'd']] and B (2X1) as [['e'], ['f']]\nWe could use einsum to create a matrix C, say like: np.einsum('ab , bc -> ac', A, B). \nWhat I'd like to see is: it return the computation graph: something like: a*c + ..., etc.\nOfcourse, np.einsum expects numerical data and would give an error if given the above code to run.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":193,"Q_Id":44780195,"Users Score":1,"Answer":"First, why do you need B to be 2-dim? Why not just np.einsum('ab , b -> a', A, B)?\nNow the actual question:\nIt's not exactly what you want, but by using smart choices for A and B you can make this visible. e.g. A = [[1,10],[100,1000]] and B = [1,2], which gives np.einsum('ab , b -> a', A, B) = [21,2100] and it's quite obvious what has happend. \nMore general versions are a little bit more complicated (but hopefully not necessary). The idea is to use different potences of primes (especially useful are 2 and 5, as they align to easy readyble number in dezimal system). In case you want to sum over more than one dimesion you might consider taking primes (2,3,5,7 etc) and then convert the result into another number system. \nIn case you sume over two dims-> 30-ary system\n3 dims (2,3,5,7)-> 210-ary system","Q_Score":1,"Tags":"python,numpy,scipy,numpy-einsum","A_Id":45102844,"CreationDate":"2017-06-27T12:21:00.000","Title":"Generating np.einsum evaluation graph","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Every time I run a tensorflow file on terminal, this warning pops up before the file runs. I have checked my version of sklearn and it is 0.18.1. How do you make this message to not appear? Thank you.\nanaconda2\/envs\/tensorflow\/lib\/python2.7\/site-packages\/sklearn\/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n  \"This module will be removed in 0.20.\", DeprecationWarning)","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":187,"Q_Id":44782916,"Users Score":1,"Answer":"It is not an error message, it is simply a warning that a module cross_validation has been transmitted from sklearn.cross_validation to sklearn.model_selection.. It is not a problem at all.\nIf you are still eager to fix it, then you should find out what snippet of code tries to import sklearn.cross_validation and alter it to sklearn.model_selection.\n\nIf you check both sklearn.cross_validation and sklearn.model_selection, you will see that they contain the same methods. Again, it is not an error.","Q_Score":0,"Tags":"python,scikit-learn","A_Id":44783384,"CreationDate":"2017-06-27T14:29:00.000","Title":"sklearn warning message whenever I run tensorflow on terminal","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using Apache Pyspark with Jupyter notebook. In one of the machine learning tutorials, the instructors were using seaborn with pyspark. How can we install and use third party libraries like Seaborn on the Apache Spark (rather Pyspark)?","AnswerCount":1,"Available Count":1,"Score":0.537049567,"is_accepted":false,"ViewCount":824,"Q_Id":44794347,"Users Score":3,"Answer":"Generally, for plotting, you need to move all the data points to the master node (using functions like collect() ) before you can plot. PLotting is not possible while the data is still distributed in memory.","Q_Score":0,"Tags":"python-2.7,pyspark,seaborn","A_Id":46108215,"CreationDate":"2017-06-28T05:34:00.000","Title":"Installing seaborn on Pyspark","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I think it's some sort of bug.\nThe problem is quite simple:\n\nlaunch ipython\nimport Tensorflow and run whatever session\ntype nvidia-smi in bash (see really high gpu memory usage, related process name, etc)\ncontrol+z quit ipython\ntype nvidia-smi in bash (still! really high GPU memory usage, and the same process name, strangely, these processes are not killed!)\n\nI guess iPython failed to clean Tensorflow variables or graphs when exiting.\nIs there any way I can clean the GPU memory without restart my machine? \nSystem: Ubuntu 14.04\nPython: Python3.5\nIPython: IPython6.0.0","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":449,"Q_Id":44799200,"Users Score":3,"Answer":"Control+Z doesn't quit a process, it stops it (use fg to bring it back up). If some computation is running in a forked process, it may not stop with the main process (I'm no OS guy, this is just my intuition).\nIn any case, properly quitting iPython (e.g. by Control+D or by running exit()) should solve the problem. If you need to interrupt a running command, first hit Control+C, then run exit().","Q_Score":0,"Tags":"python,tensorflow,ipython","A_Id":44799700,"CreationDate":"2017-06-28T09:53:00.000","Title":"When run a tensorflow session in iPython, GPU memory usage remain high when exiting iPython","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have one program that downloads time series (ts) data from a remote database and saves the data as csv files. New ts data is appended to old ts data. My local folder continues to grow and grow and grow as more data is downloaded. After downloading new ts data and saving it, I want to upload it to a Google BigQuery table. What is the best way to do this? \nMy current work-flow is to download all of the data to csv files, then to convert the csv files to gzip files on my local machine and then to use gsutil to upload those gzip files to Google Cloud Storage. Next, I delete whatever tables are in Google BigQuery and then manually create a new table by first deleting any existing table in Google BigQuery and then creating a new one by uploading data from Google Cloud Storage. I feel like there is room for significant automation\/improvement but I am a Google Cloud newbie. \nEdit: Just to clarify, the data that I am downloading can be thought of downloading time series data from Yahoo Finance. With each new day, there is fresh data that I download and save to my local machine. I have to uploading all of the data that I have to Google BigQUery so that I can do SQL analysis on it.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":642,"Q_Id":44804051,"Users Score":1,"Answer":"Consider breaking up your data into daily tables (or partitions). Then you only need to upload the CVS from the current day.\nThe script you have currently defined otherwise seems reasonable.\n\nExtract your new day of CSVs from your source of timeline data.\nGzip them for fast transfer.\nCopy them to GCS.\nLoad the new CVSs into the current daily table\/partition.\n\nThis avoids the need to delete existing tables and reduces the amount of data and processing that you need to do. As a bonus, it is easier to backfill a single day if there is an error in processing.","Q_Score":0,"Tags":"python,pandas,google-bigquery,google-cloud-platform,gsutil","A_Id":44814853,"CreationDate":"2017-06-28T13:34:00.000","Title":"Python\/Pandas\/BigQuery: How to efficiently update existing tables with a lot of new time series data?","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have 4 separate CSV files that I wish to read into Pandas. I want to merge these CSV files into one dataframe.\nThe problem is that the columns within the CSV files contain the following: , ; | and spaces. Therefore I have to use different delimiters when reading the different CSV files and do some transformations to get them in the correct format.\nEach CSV file contains an 'ID' column. When I merge my dataframes, it is not done correctly and I get 'NaN' in the column which has been merged.\nDo you have to use the same delimiter in order for the dataframes to merge properly?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":891,"Q_Id":44804235,"Users Score":1,"Answer":"In short : no, you do not need similar delimiters within your files to merge pandas Dataframes - in fact, once data has been imported (which requires setting the right delimiter for each of your files), the data is placed in memory and does not keep track of the initial delimiter (you can see this by writing down your imported dataframes to csv using the .to_csv method : the delimiter will always be , by default).\nNow, in order to understand what is going wrong with your merge, please post more details about your data and the code your are using to perform the operation.","Q_Score":0,"Tags":"python,csv,pandas,merge,delimiter","A_Id":44804374,"CreationDate":"2017-06-28T13:42:00.000","Title":"Pandas: Reading CSV files with different delimiters - merge error","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I noticed that some numpy operations take an argument called shape, such as np.zeros, whereas some others take an argument called size, such as np.random.randint. To me, those arguments have the same function and the fact that they have different names is a bit confusing. Actually, size seems a bit off since it really specifies the .shape of the output.\nIs there a reason for having different names, do they convey a different meaning even though they both end up being equal to the .shape of the output?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":17837,"Q_Id":44804965,"Users Score":1,"Answer":"Because you are working with a numpy array, which was seen as a C array, size refers to how big your array will be. Moreover, if you can pass np.zeros(10) or np.zeros((10)). While the difference is subtle, size passed this way will create you a 1D array. You can give size=(n1, n2, ..., nn) which will create an nD array.\nHowever, because python users want multi-dimensional arrays, array.reshape allows you to get from 1D to an nD array. So, when you call shape, you get the N dimension shape of the array, so you can see exactly how your array looks like. \nIn essence, size is equal to the product of the elements of shape.\nEDIT: The difference in name can be attributed to 2 parts: firstly, you can initialise your array with a size. However, you do not know the shape of it. So size is only for total number of elements. Secondly, how numpy was developed, different people worked on different parts of the code, giving different names to roughly the same element, depending on their personal vision for the code.","Q_Score":19,"Tags":"python,numpy","A_Id":44805286,"CreationDate":"2017-06-28T14:11:00.000","Title":"numpy: \"size\" vs. \"shape\" in function arguments?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a pandas dataframe with Datetime as index. The index is generally monotonically increasing however there seem to be a few rows don't follow this tread. Any quick way to identify these unusual rows?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":759,"Q_Id":44828905,"Users Score":0,"Answer":"\"Quick\" in terms of what resource?  If you want programming ease, then simply make a new frame resulting from subtracting adjacent columns.  Any entry of zero or negative value is your target.\nIf you need execution speed, do note that adjacent differences are still necessary: all you can save is the overhead of finding multiple violations in a given row.  However, unless you have a particularly wide data frame, it's likely that you'll lose more in short-circuiting than you'll gain by the saved subtractions.  Also note that a processor with matrix operations or other parallelism will be fast enough with the whole data frame, that the checking will cost you significant time.","Q_Score":2,"Tags":"python,pandas","A_Id":44830094,"CreationDate":"2017-06-29T15:09:00.000","Title":"find non-monotonical rows in dataframe","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a huge file csv file with around 4 million column and around 300 rows. File size is about 4.3G. I want to read this file and run some machine learning algorithm on the data. \nI tried reading the file via pandas read_csv in python but it is taking long  time for reading even a single row ( I suspect due to large number of columns ). I checked few other options like numpy fromfile, but nothing seems to be working. \nCan someone please suggest some way to load file with many columns in python?","AnswerCount":3,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":1206,"Q_Id":44835126,"Users Score":2,"Answer":"csv is very inefficient for storing large datasets. You should convert your csv file into a better suited format. Try hdf5 (h5py.org or pytables.org), it is very fast and allows you to read parts of the dataset without fully loading it into memory.","Q_Score":1,"Tags":"python,file-handling","A_Id":44835396,"CreationDate":"2017-06-29T21:16:00.000","Title":"Reading file with huge number of columns in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a huge file csv file with around 4 million column and around 300 rows. File size is about 4.3G. I want to read this file and run some machine learning algorithm on the data. \nI tried reading the file via pandas read_csv in python but it is taking long  time for reading even a single row ( I suspect due to large number of columns ). I checked few other options like numpy fromfile, but nothing seems to be working. \nCan someone please suggest some way to load file with many columns in python?","AnswerCount":3,"Available Count":2,"Score":0.1973753202,"is_accepted":false,"ViewCount":1206,"Q_Id":44835126,"Users Score":3,"Answer":"Pandas\/numpy should be able to handle that volume of data no problem. I hope you have at least 8GB of RAM on that machine. To import a CSV file with Numpy, try something like\ndata = np.loadtxt('test.csv', dtype=np.uint8, delimiter=',')\nIf there is missing data, np.genfromtext might work instead. If none of these meet your needs and you have enough RAM to hold a duplicate of the data temporarily, you could first build a Python list of lists, one per row using readline and str.split. Then pass that to Pandas or numpy, assuming that's how you intend to operate on the data. You could then save it to disk in a format for easier ingestion later. hdf5 was already mentioned and is a good option. You can also save a numpy array to disk with numpy.savez or my favorite the speedy bloscpack.(un)pack_ndarray_file.","Q_Score":1,"Tags":"python,file-handling","A_Id":44835474,"CreationDate":"2017-06-29T21:16:00.000","Title":"Reading file with huge number of columns in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've done a lot of searching and can't find anything related. Is there a built-in function to automatically generate a list of Pandas dataframes that I've created?\nFor example, I've created three dataframes: df1 df2 df3\nNow I want a list like: df_list = [df1, df2, df3] so I can iterate through it.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":5562,"Q_Id":44835358,"Users Score":1,"Answer":"%who_ls DataFrame\nThis is all dataframes loaded in memory as a list\nall_df_in_mem = %who_ls DataFrame","Q_Score":3,"Tags":"python,list,pandas","A_Id":60381721,"CreationDate":"2017-06-29T21:35:00.000","Title":"Pandas - List of Dataframe Names?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to use some R packages requiring R version 3.4 and above. I want to access these packages in python (3.6.1) through rpy2 (2.8).\nI have R version 3.4 installed, and it is located in \/Library\/Frameworks\/R.framework\/Resources However, when I use pip3 install rpy2 to install and use the python 3.6.1 in \/Library\/Frameworks\/Python.framework\/Versions\/3.6\/bin\/python3.6) as my interpreter, I get the error:\n\nTraceback (most recent call last):\n    File \"\/Users\/vincentliu\/PycharmProjects\/magic\/rpy2tester.py\", line 1, in \n      from rpy2 import robjects\n    File \"\/Library\/Frameworks\/Python.framework\/Versions\/3.6\/lib\/python3.6\/site-packages\/rpy2\/robjects\/init.py\", line 16, in \n      import rpy2.rinterface as rinterface\n    File \"\/Library\/Frameworks\/Python.framework\/Versions\/3.6\/lib\/python3.6\/site-packages\/rpy2\/rinterface\/init.py\", line 92, in \n      from rpy2.rinterface._rinterface import (baseenv,\n  ImportError: dlopen(\/Library\/Frameworks\/Python.framework\/Versions\/3.6\/lib\/python3.6\/site-packages\/rpy2\/rinterface\/_rinterface.cpython-36m-darwin.so, 2): Library not loaded: @rpath\/libiconv.2.dylib\n    Referenced from: \/Library\/Frameworks\/Python.framework\/Versions\/3.6\/lib\/python3.6\/site-packages\/rpy2\/rinterface\/_rinterface.cpython-36m-darwin.so\n    Reason: Incompatible library version: _rinterface.cpython-36m-darwin.so requires version 8.0.0 or later, but libiconv.2.dylib provides version 7.0.0\n\nWhich first seemed like a problem caused by Anaconda, and so I remove all Anaconda-related files but the problem persists.\nI then uninstalled rpy2, reinstalled Anaconda and used conda install rpy2 to install, which also installs R version 3.3.2 through Anaconda. I can then change the interpreter to \/anaconda\/bin\/python and can use rpy2 fine, but I couldn't use the R packages I care about because they need R version 3.4 and higher. Apparently, the oldest version Anaconda can install is 3.3.2, so is there any way I can use rpy2 with R version 3.4?\nI can see two general solutions to this problem. One is to install rpy2 through conda and then somehow change its depending R to the 3.4 one in the system. Another solution is to resolve the error\n\nIncompatible library version: _rinterface.cpython-36m-darwin.so requires version 8.0.0 or later, but libiconv.2.dylib provides version 7.0.0\n\nAfter much struggling, I've found no good result with either.","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":1095,"Q_Id":44836123,"Users Score":1,"Answer":"I uninstalled rpy2 and reinstalled with --verborse. I then found  \n\nld: warning: ignoring file \/opt\/local\/lib\/libpcre.dylib, file was built for x86_64 which is not the architecture being linked (i386): \/opt\/local\/lib\/libpcre.dylib\n      ld: warning: ignoring file \/opt\/local\/lib\/liblzma.dylib, file was built for x86_64 which is not the architecture being linked (i386): \/opt\/local\/lib\/liblzma.dylib\n      ld: warning: ignoring file \/opt\/local\/lib\/libbz2.dylib, file was built for x86_64 which is not the architecture being linked (i386): \/opt\/local\/lib\/libbz2.dylib\n      ld: warning: ignoring file \/opt\/local\/lib\/libz.dylib, file was built for x86_64 which is not the architecture being linked (i386): \/opt\/local\/lib\/libz.dylib\n      ld: warning: ignoring file \/opt\/local\/lib\/libiconv.dylib, file was built for x86_64 which is not the architecture being linked (i386): \/opt\/local\/lib\/libiconv.dylib\n      ld: warning: ignoring file \/opt\/local\/lib\/libicuuc.dylib, file was built for x86_64 which is not the architecture being linked (i386): \/opt\/local\/lib\/libicuuc.dylib\n      ld: warning: ignoring file \/opt\/local\/lib\/libicui18n.dylib, file was built for x86_64 which is not the architecture being linked (i386): \/opt\/local\/lib\/libicui18n.dylib\n      ld: warning: ignoring file \/opt\/local\/Library\/Frameworks\/R.framework\/R, file was built for x86_64 which is not the architecture being linked (i386): \/opt\/local\/Library\/Frameworks\/R.framework\/R\n\nSo I supposed the reason is the architecture incompatibility of the libiconv in opt\/local, causing make to fall back onto the outdate libiconv in usr\/lib. This is strange because my machine should be running on x86_64 not i386. I then tried export ARCHFLAGS=\"-arch x86_64\" and reinstalled libiconv using port. This resolved the problem.","Q_Score":2,"Tags":"r,conda,python-3.6,rpy2,libiconv","A_Id":44935654,"CreationDate":"2017-06-29T22:49:00.000","Title":"Installing rpy2 to work with R 3.4.0 on OSX","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to use some R packages requiring R version 3.4 and above. I want to access these packages in python (3.6.1) through rpy2 (2.8).\nI have R version 3.4 installed, and it is located in \/Library\/Frameworks\/R.framework\/Resources However, when I use pip3 install rpy2 to install and use the python 3.6.1 in \/Library\/Frameworks\/Python.framework\/Versions\/3.6\/bin\/python3.6) as my interpreter, I get the error:\n\nTraceback (most recent call last):\n    File \"\/Users\/vincentliu\/PycharmProjects\/magic\/rpy2tester.py\", line 1, in \n      from rpy2 import robjects\n    File \"\/Library\/Frameworks\/Python.framework\/Versions\/3.6\/lib\/python3.6\/site-packages\/rpy2\/robjects\/init.py\", line 16, in \n      import rpy2.rinterface as rinterface\n    File \"\/Library\/Frameworks\/Python.framework\/Versions\/3.6\/lib\/python3.6\/site-packages\/rpy2\/rinterface\/init.py\", line 92, in \n      from rpy2.rinterface._rinterface import (baseenv,\n  ImportError: dlopen(\/Library\/Frameworks\/Python.framework\/Versions\/3.6\/lib\/python3.6\/site-packages\/rpy2\/rinterface\/_rinterface.cpython-36m-darwin.so, 2): Library not loaded: @rpath\/libiconv.2.dylib\n    Referenced from: \/Library\/Frameworks\/Python.framework\/Versions\/3.6\/lib\/python3.6\/site-packages\/rpy2\/rinterface\/_rinterface.cpython-36m-darwin.so\n    Reason: Incompatible library version: _rinterface.cpython-36m-darwin.so requires version 8.0.0 or later, but libiconv.2.dylib provides version 7.0.0\n\nWhich first seemed like a problem caused by Anaconda, and so I remove all Anaconda-related files but the problem persists.\nI then uninstalled rpy2, reinstalled Anaconda and used conda install rpy2 to install, which also installs R version 3.3.2 through Anaconda. I can then change the interpreter to \/anaconda\/bin\/python and can use rpy2 fine, but I couldn't use the R packages I care about because they need R version 3.4 and higher. Apparently, the oldest version Anaconda can install is 3.3.2, so is there any way I can use rpy2 with R version 3.4?\nI can see two general solutions to this problem. One is to install rpy2 through conda and then somehow change its depending R to the 3.4 one in the system. Another solution is to resolve the error\n\nIncompatible library version: _rinterface.cpython-36m-darwin.so requires version 8.0.0 or later, but libiconv.2.dylib provides version 7.0.0\n\nAfter much struggling, I've found no good result with either.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1095,"Q_Id":44836123,"Users Score":0,"Answer":"I had uninstall the version pip installed and install from source python setup.py install on the download https:\/\/bitbucket.org\/rpy2\/rpy2\/downloads\/. FWIW not using Anaconda at all either.","Q_Score":2,"Tags":"r,conda,python-3.6,rpy2,libiconv","A_Id":53839320,"CreationDate":"2017-06-29T22:49:00.000","Title":"Installing rpy2 to work with R 3.4.0 on OSX","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an h2o random forest in Python. How to extract for each tree the threshold of each features ?\nMy aim is to implement this random forest in c++\nThanks !","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":235,"Q_Id":44843175,"Users Score":0,"Answer":"you could download and take a look at the POJO which lists all the thresholds used for the model h2o.download_pojo(model, path=u'', get_jar=True, jar_name=u'')","Q_Score":0,"Tags":"python,random-forest,h2o","A_Id":44852474,"CreationDate":"2017-06-30T09:30:00.000","Title":"How to get the random forest threshold from an h2o random forest object","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to use the tf.fill() method to create a tensor of different data types(float16,float32,float64) similar to what you can do with numpy.full(). would tf.constant() be a suitable substitution? or should I create my fill values to be of the data type I want them to be then plug it into the value holder inside tf.fill()","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":2486,"Q_Id":44852137,"Users Score":1,"Answer":"You can either provide the value of the datatype you want your resulting tensor to be or to cast a tensor afterwards. \n\ntf.fill((3, 3), 0.0) # will be a float 32\ntf.cast(tf.fill((3, 3)), tf.float32) # also float 32\n\nThe first one is better because you use less operations in the graph","Q_Score":1,"Tags":"python,numpy,tensorflow,tensor","A_Id":44855284,"CreationDate":"2017-06-30T17:37:00.000","Title":"using tensorflow fill method to create a tensor of certain datatype","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm working on a binary classification problem and I'm using the tf.contrib.learn.DNNClassifier class within TensorFlow. When invoking this estimator for only 2 classes, it uses a threshold value of 0.5 as the cutoff between the 2 classes. I'd like to know if there's a way to use a custom threshold value since this might improve the model's accuracy.\nI've searched all around the web and apparently there isn't a way to do this.\nAny help will be greatly appreciated, thank you.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":476,"Q_Id":44856964,"Users Score":1,"Answer":"The tf.contrib.learn.DNNClassifier class has a method called predict_proba which returns the probabilities belonging to each class for the given inputs. Then you can use something like, tf.round(prob+thres) for binary thresholding with the custom parameter thres.","Q_Score":0,"Tags":"python,machine-learning,tensorflow,neural-network","A_Id":44857779,"CreationDate":"2017-07-01T02:56:00.000","Title":"Using a custom threshold value with tf.contrib.learn.DNNClassifier?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a binary image of large size (2000x2000). In this image most of the pixel values are zero and some of them are 1. I need to get only 100 randomly chosen pixel coordinates with value 1 from image. I am beginner in python, so please answer.","AnswerCount":4,"Available Count":1,"Score":0.1488850336,"is_accepted":false,"ViewCount":3545,"Q_Id":44857970,"Users Score":3,"Answer":"I'd suggest making a list of coordinates of all non-zero pixels (by checking all pixels in the image), then using random.shuffle on the list and taking the first 100 elements.","Q_Score":4,"Tags":"python,random,pixel","A_Id":44858027,"CreationDate":"2017-07-01T06:19:00.000","Title":"how to get random pixel index from binary image with value 1 in python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an xlsx file, with columns with various coloring. \nI want to read only the white columns of this excel in python using pandas, but I have no clues on hot to do this.\nI am able to read the full excel into a dataframe, but then I miss the information about the coloring of the columns and I don't know which columns to remove and which not.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":7328,"Q_Id":44861989,"Users Score":1,"Answer":"This can not be done in pandas. You will need to use other library to read the xlsx file and determine what columns are white. I'd suggest using openpyxl library. \nThen your script will follow this steps:\n\nOpen xlsx file\nRead and filter the data (you can access the cell color) and save the results\nCreate pandas dataframe\n\nEdit: Switched xlrd to openpyxl as xlrd is no longer actively maintained","Q_Score":5,"Tags":"python,excel,pandas","A_Id":44862175,"CreationDate":"2017-07-01T14:24:00.000","Title":"Reading an excel with pandas basing on columns' colors","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using matplotlib.rc('text', usetex=True); matplotlib.rc('font', family='serif') to set my font to serif with LaTeX. This works for the tick labels, however the plot title and axis lables are typeset using the sans-serif CMS S 12 computer modern variant.\nFrom what I have found on the web, most people seem to have trouble using the sans-serif font. For me the opposite is the case, I cannot get the serif font to work properly.\nI have tried a hacky solution of setting the sans-serif font to Computer Modern, which unfortunately does not work either.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":184,"Q_Id":44863610,"Users Score":1,"Answer":"I was importing the seaborn package after setting the matplotlib rcParams, which overwrote values such as the font family. Calling rcParams.update(params) after importing seaborn fixes the problem.","Q_Score":0,"Tags":"python,matplotlib,latex","A_Id":44864201,"CreationDate":"2017-07-01T17:18:00.000","Title":"Matplotlib is incorrectly rendering axis labels in sans-serif when using LaTeX","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Hi on running a code in the console I am getting the display as:\n\n\nrunfile('C:\/Users\/DX\/Desktop\/me template\/Part 1 - Data Preprocessing\/praCTICE.py', wdir='C:\/Users\/DX\/Desktop\/me template\/Part 1 - Data Preprocessing')\n\n\nand on viewing a small matrix it is showing up as \n\narray([['France', 44.0, 72000.0],\n         ['Spain', 27.0, 48000.0],\n         ['Germany', 30.0, 54000.0],\n         ..., \n         ['France', 48.0, 79000.0],\n         ['Germany', 50.0, 83000.0],\n         ['France', 37.0, 67000.0]], dtype=object)\n\nEven though the matrix is pity small how to change it to get the default view when i cun my codes in ipython console \nI installed the latest version of anaconda","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":272,"Q_Id":44871312,"Users Score":1,"Answer":"(Spyder developer here) Please use the Variable Explorer to visualize Numpy arrays and Pandas DataFrames. That's its main purpose.","Q_Score":0,"Tags":"python-3.x,anaconda,spyder","A_Id":44871723,"CreationDate":"2017-07-02T13:26:00.000","Title":"In spyder how to get back default view of running a code in Ipython console","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"In Tensorflow, I'm getting outputs like 0.602129 or 0.663941. It appears that values closer to 0 imply a better model, but it seems like perplexity is supposed to be calculated as 2^loss, which implies that loss is negative. This doesn't make any sense.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":298,"Q_Id":44873156,"Users Score":1,"Answer":"This does not make a lot of sense to me. Perplexity is calculated as 2^entropy. And the entropy is from 0 to 1. So your results which are < 1 do not make sense.\nI would suggest you to take a look at how your model calculate the perplexity because I suspect there might be an error.","Q_Score":0,"Tags":"python,tensorflow,language-model,sequence-to-sequence,perplexity","A_Id":44875134,"CreationDate":"2017-07-02T16:56:00.000","Title":"How can the perplexity of a language model be between 0 and 1?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Before I state my question, let me put my constraint - I can't post the code as it is related to my job and they don't allow it. So this is just a survey query to see if somebody has seen similar issues.\nI have a python multiprocessing set up where the workers do the work and put the result in a queue. A special writer worker then accumulates the results from the queue. These results are simple pandas Series. The accumulator puts the results into a pandas dataframe and writes it to a pytable on the disk.\nThe issue is that I randomly see that sometimes a few results are missing in the dataframe, e.g. out of 268 expected columns I will get 267. This has happened around 10 out of 80 times in the last three months. The cure is - simply rerun the code (which means recalculate everything) and it works 100% the second time. I have ensured that there is no error in the calculations, so my guess is that it is related to multiprocessing or pytable data writing.\nAny hints are appreciated. Sorry for not being able to put the code.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":54,"Q_Id":44883116,"Users Score":0,"Answer":"It's really hard to help you without code. But I just think if you want to find \"thin\" places in your code you have to write log of it.\nAs I understood one iteration of your worker has to create 268 Series that are made as columns in final dataframe. If these Series are the same shape, then it seems that the issue in queue-worker \u2014 and you have to log it in all steps that you can.","Q_Score":0,"Tags":"python,queue,multiprocessing,pytables","A_Id":44884458,"CreationDate":"2017-07-03T10:19:00.000","Title":"python multiprocessing (using pytable) misses some results from the queue in the final output","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Wonder if anyone can help.\nI have a set of lists of numbers, around 300 lists in the set, each list of around 200 numbers. What I wish to calculate is the \"relative stability\" of each list.\nFor example: \nList A: 100,101,103,99,98 - the range is v small - so stable.\nList B: 0.3, 0.1, -0.2, 0.1 - again, v small range, so stable.\nList C: 0.00003, 0.00002, 0.00007, 0.00008 - stable.\nNow, I could use standard deviation - but the values returned by the standard deviation will be relative to the values within each list. So std for list C would be tiny in comparison to std for list A - and therefore numerically not give me a comparable measure of stability\/volatility enabling me to meaningfully ask: if list A more or less stable than list C?\nSo, I wondered if anyone had any suggestions for a measure that will be comparable across such lists?\nMany thanks for any help.\nR","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":361,"Q_Id":44894250,"Users Score":2,"Answer":"You could use the standard deviation of the list divided by the mean of the list.\nThose measures have the same units so their quotient will be a pure number, without a unit. This scales the variability (standard deviation) to the size of the numbers (mean).\nThe main difficulty with this is for lists that have both positive and negative numbers, like your List B. The mean could end up being an order of magnitude less that the numbers, exaggerating the stability measure. Worse, the mean could end up being zero, making the measure undefined. I cannot think of any correction that would work well in all cases. The \"stability\" of a list with both positive and negative numbers is very doubtful and would depend on the context, so I doubt that any general stability measure would work well in all such cases. You would need a variety for different situations.","Q_Score":2,"Tags":"python,math,statistics,volatility","A_Id":44894389,"CreationDate":"2017-07-03T21:47:00.000","Title":"Measure Volatility or Stability Of Lists of Floating Point Numbers","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Sorry I have a very simple question. So I have two dataframes that look like\nDataframe 1:\ncolumns: a b c d e f g h\nDataframe 2:\ncolumns: e ef\nI'm trying to join Dataframe 2 on Dataframe 1 at column e, which should yield\ncolumns: a b c d e ef g h\nor\ncolumns: a b c d e f g h ef\nHowever: \ndf1.merge(df2, how = 'inner', on = 'e') yields a blank dataframe when I print it out.\n'outer' merge only extends the dataframe vertically (like using an append function).\nWould appreciate some help thank you!","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":87,"Q_Id":44899119,"Users Score":0,"Answer":"Instead of \ndf1.merge(...) \ntry:\npd.merge(left=df1, right=df2, on ='e', how='inner')","Q_Score":1,"Tags":"python,pandas","A_Id":44899478,"CreationDate":"2017-07-04T07:04:00.000","Title":"Merging 2 dataframes on Pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to install python 3.5 packages so they would be available in Jupyter notebook with pyspark3 kernel.\nI've tried to run the following script action:\n#!\/bin\/bash\nsource \/usr\/bin\/anaconda\/envs\/py35\/bin\/activate py35\nsudo \/usr\/bin\/anaconda\/envs\/py35\/bin\/conda install -y keras tensorflow theano gensim\n\nbut the packages get installed on python 2.7 and not in 3.5","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2656,"Q_Id":44902885,"Users Score":0,"Answer":"Have you tried installing using pip? \nIn some cases where you have both Python 2 and Python 3, you have to run pip3 instead of just pip to invoke pip for Python 3.","Q_Score":1,"Tags":"python,azure,pyspark,jupyter-notebook,azure-hdinsight","A_Id":44903656,"CreationDate":"2017-07-04T10:02:00.000","Title":"how to install python package on azure hdinsight pyspark3 kernel?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":1,"Web Development":0},{"Question":"I've been using scikit learn, very neat.\nFitting a polynomial curve through points (X-Y) is pretty easy. If I have N points, I can chose a polynomial of order N-1 and the curve will fit the points perfectly.\nIf my X vector has several dimension, in scikit-learn I can build a pipeline with a PolynomialFeatures and a LinearRegression.\nWhere I am stuck is having a bit more flexibility with the numbers of features created by PolynomialFeatures (which is not an input); I'd like to be able to specify the total amount of features, with the end goal to have a polynomial that goes through all the points.\nE.g. in 3D (X has 3 columns), if I have 27 points (square matrix of 3X3X3); I'd like to limit the number of features to 27.\n(PolynomialFeatures does have an attribute of powers_, but it can't be set. Exploring the source also does not seem to show anything specific).\nAny ideas?\nCheers,\nN","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":300,"Q_Id":44915500,"Users Score":1,"Answer":"Your question is ill defined. If you want, say, 14 features of 34 possible, which 14 should that be? \nIn your place, I would generate a redundant number of features and then would use a feature selection algorithm. It would be a sparse model (like Lasso) or a feature elemination algorithm (like RFE).","Q_Score":1,"Tags":"python,scikit-learn,regression,polynomials","A_Id":47966038,"CreationDate":"2017-07-05T00:27:00.000","Title":"Python - Fitting a polynomial (multi-dimension) through X points","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to use tensorflow with gpu and installed CUDA 8.0 toolkit and cuDNn v5.1 libraries as described in nvidia website. But when I try to import tensorflow as module in python3.5, it does not load cuDNn libraries (outputs nothing, just loads tensorflow module). And I do not observe speed in processing (same speed I obtained when I use CPU) with GPU.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":784,"Q_Id":44923993,"Users Score":0,"Answer":"Fresh install is the key but there are some important points:\n1. Install CUDA 8.0 toolkit\n2. Install cuDNn 5.1 version (not 6.0)\n3. Install from source(bazel) and configure to use ensorflow with CUDA\nAbove steps worked for me! Hope it helps anyone.","Q_Score":0,"Tags":"python,python-3.x,tensorflow,gpu","A_Id":44964036,"CreationDate":"2017-07-05T10:42:00.000","Title":"Tensorflow GPU cuDNn: How do I load cuDDN libraries?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When working with groupby on a pandas DataFrame instance, I have never not used either as_index=False or reset_index(). I cannot actually think of any reason why I wouldn't do so. Because my behavior is not the pandas default (indeed, because the groupby index exists at all), I suspect that there is some functionality of pandas that I am not taking advantage of.\nCan anyone describe cases where it would be advantageous to not reset the index?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":40,"Q_Id":44937573,"Users Score":3,"Answer":"When you perform a groupby\/agg operation, it is natural to think of the result as a mapping from the groupby keys to the aggregated scalar values. If we were using plain Python, a dict would be the natural data structure to hold such a mapping from keys to values. Since we are using Pandas, a Series is the natural data structure. Its index would hold the keys, and the Series values would be the aggregrated scalars. If there is more than one aggregated value for each key, then the natural data structure to use would be a DataFrame. \nThe advantage of holding the keys in an index rather than a column is that looking up values based on index labels is an O(1) operation, whereas looking up values based on a value in a column is an O(n) operation. \nSince the result of a groupby\/agg operation fits naturally into a Series or DataFrame with groupby keys as the index, and since indexes have this special fast lookup property, it is better to return the result in this form by default.","Q_Score":1,"Tags":"python,pandas","A_Id":44937815,"CreationDate":"2017-07-05T23:35:00.000","Title":"What are use cases for *not* resetting a groupby index in pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there an OpenCV-python, dlib or any python 3 implementation of a face shape detector (diamond, oblong, square)?","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":2005,"Q_Id":44938737,"Users Score":2,"Answer":"Dear past version of self. With Deep learning; Convolutional Neural Networks,  This becomes a trivial problem. You can retrain Google's inception V3 Model to classify faces into the the 5 face shapes you mentioned in your question. With Just 500 training images you can attain an accuracy of 98%.","Q_Score":0,"Tags":"python-3.x,computer-vision,opencv3.0,dlib","A_Id":46657731,"CreationDate":"2017-07-06T02:12:00.000","Title":"Is there a python implementation of a Face shape detector?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using TensorFlow for training model which has 1 output for the 4 inputs. The problem is of regression.\nI found that when I use RandomForest to train the model, it quickly converges and also runs well on the test data. But when I use a simple Neural network for the same problem, the loss(Random square error) does not converge. It gets stuck on a particular value. \nI tried increasing\/decreasing number of hidden layers, increasing\/decreasing learning rate. I also tried multiple optimizers and tried to train the model on both normalized and non-normalized data. \nI am new to this field but the literature that I have read so far vehemently asserts that the neural network should marginally and categorically work better than the random forest.\nWhat could be the reason behind non-convergence of the model in this case?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2520,"Q_Id":44939210,"Users Score":0,"Answer":"A useful rule for beginning training models, is not to begin with the more complex methods, for example, a Linear model, which you will be able to understand and debug more easily.\nIn case you continue with the current methods, some ideas:\n\nCheck the initial weight values (init them with a normal distribution)\nAs a previous poster said, diminish the learning rate\nDo some additional checking on the data, check for NAN and outliers, the current models could be more sensitive to noise. Remember, garbage in, garbage out.","Q_Score":1,"Tags":"python,machine-learning,tensorflow,neural-network,random-forest","A_Id":44950403,"CreationDate":"2017-07-06T03:16:00.000","Title":"TensorFlow RandomForest vs Deep learning","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How to download necessary python packages for data analysis (e.g. pandas,scipy,numpy etc) and machine learning packages (sci-kit learn for starter, tensorflow for deeplearning if possible etc) without using github or anaconda?\nOur client has permitted us to install python 3.6 and above (32-bit) in our terminals for data analysis and machine learning projects but we cannot access github due to security restrictions and also cannot download anaconda bundle.\nPlease provide suitable weblinks and instructions.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":931,"Q_Id":44945850,"Users Score":0,"Answer":"as suggested by @DavidG, the following solution worked:\nDownload the whl file\nuse cmd window and go to the download folder and then install like below:\n \nC:\\Users\\XXXXXXXX>cd C:\\Users\\XXXXXXXX\\Documents\\Python Packages\nC:\\Users\\XXXXXXXX\\Documents\\Python Packages>pip install numpy-1.13.0+mkl-cp36-cp36m-win32.whl\nProcessing c:\\users\\XXXXXXXX\\documents\\python packages\\numpy-1.13.0+mkl-cp36-cp\n36m-win32.whl\nInstalling collected packages: numpy\n  Found existing installation: numpy 1.13.0\n    Uninstalling numpy-1.13.0:\n      Successfully uninstalled numpy-1.13.0\nSuccessfully installed numpy-1.13.0+mkl\nC:\\Users\\XXXXXXXX\\Documents\\Python Packages>pip install scipy-0.19.1-cp36-cp36m\n-win32.whl\nProcessing c:\\users\\XXXXXXXX\\documents\\python packages\\scipy-0.19.1-cp36-cp36m-\nwin32.whl\nRequirement already satisfied: numpy>=1.8.2 in c:\\users\\XXXXXXXX\\appdata\\local\\\nprograms\\python\\python36-32\\lib\\site-packages (from scipy==0.19.1)\nInstalling collected packages: scipy\nSuccessfully installed scipy-0.19.1\nC:\\Users\\XXXXXXXX\\Documents\\Python Packages>","Q_Score":0,"Tags":"python,machine-learning,data-science","A_Id":44947979,"CreationDate":"2017-07-06T10:05:00.000","Title":"Installing data science packages to vanilla python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am currently working on an artificial neural network model with Keras for image recognition and I want to convert it using CoreML.\nUnfortunately, I have been working with Python3 and CoreML only works with Python 2.7 at the moment. Moreover, Tensorflow for Python 2.7 does not seem to be supported by Windows... So my only hope is to find a way to install it.\nI saw some tips using Docker Toolbox but I did not catch it and I failed when trying this solution, even though it looks like the only thing that works.\nSo, is there any quite simple way to install Tensorflow for Python 2.7 on Windows 10?\nThank you very much!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1130,"Q_Id":44946737,"Users Score":1,"Answer":"A non-optimal solution (the only one I found) in my opinion, is to install a Linux virtual machine. I used VitualBox for it.\nThen, it is very easy to download Anaconda and Python 2, as well as the right versions of the packages. For example, you can download Tensorflow 1.1.0 using the following command $ pip install -I tensorflow==1.1.0.","Q_Score":0,"Tags":"python-2.7,tensorflow,windows-10,keras,coreml","A_Id":45007258,"CreationDate":"2017-07-06T10:44:00.000","Title":"Installing Tensorflow for Python 2.7 for Keras and CoreML conversion on Windows 10","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"My input data has 10 features and it is taken at 25 different timestamps. My output data consists of class labels. So, basically, I am having a many to one classification problem. \nI want to implement an LSTM for this problem. Total training data consists of 10000 data points. How should the input and output format (shape) for this LSTM network be?","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":248,"Q_Id":44959636,"Users Score":2,"Answer":"In the first layer of the model you should define input_shape=(n_timesteps,n_features). So in your case input_shape = (25,10).\nYour actual input to the model will have shape (1000,25,10). \nYou should also use keras.np_utils.to_categorical to convert your labels to one-hot-encoded vectors, so that they will become vectors with length X, where X is your class number. Every element will be equal to zero, except the one corresponding to the corresponding class.\nHope this helps!","Q_Score":0,"Tags":"python-3.x,keras,lstm","A_Id":44966279,"CreationDate":"2017-07-06T22:07:00.000","Title":"Many to one LSTM input shape","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to finetune ResNet50 ImageNet pretrained model, and I have a few question about image preprocessing of finetune.   \n\nIn ImageNet preprocessing, we need to subtract the mean of pixel ([103.939, 116.779, 123.68]). When I use my dataset to finetune, should I subtract mean of ImageNet or subtract the mean of my data.\nI do see many people rescale the data to [0,1], but the pretrained model(ImageNet) use image scale in [0,255]. Why do people do that? Is it reasonable?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":2606,"Q_Id":44962433,"Users Score":0,"Answer":"In my honest opinion people overstate the impact of image preprocessing. The only truly important thing is that the test data is similar in value scale to the training data. There are some theoretical benefits of having a pre normalized dataset, with the usage of batch normalization, but in practice it never made much of a difference (2-4% Accuracy).\nIf you have a running model and you are trying to get those last few % better accuracy without having to increase the amount of parameter than I would suggest tweaking this to your use-case. \nIn my opinion there is no single method that works for every use-case, but a good starting point is to use the same preprocessing as ImageNet, because the features will be similar to the ones produced for the imagenet classification.","Q_Score":0,"Tags":"python,deep-learning","A_Id":44965968,"CreationDate":"2017-07-07T04:11:00.000","Title":"Image preprocessing of finetune in ResNet","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to finetune ResNet50 ImageNet pretrained model, and I have a few question about image preprocessing of finetune.   \n\nIn ImageNet preprocessing, we need to subtract the mean of pixel ([103.939, 116.779, 123.68]). When I use my dataset to finetune, should I subtract mean of ImageNet or subtract the mean of my data.\nI do see many people rescale the data to [0,1], but the pretrained model(ImageNet) use image scale in [0,255]. Why do people do that? Is it reasonable?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":2606,"Q_Id":44962433,"Users Score":0,"Answer":"I would try both. Subtracting your mean makes sense because generally one tries to get mean 0. Subtracting image net mean makes sense because you want the network as a feature extractor. If you change something that early in the feature extractor it could be that it doesn't work at all. \nJust like the mean 0 thing, it is generally seen as a desirable property to have features within a fixed range or with a fixed standard deviation. Again, I can't really tell you what is better but you can easily try it. My guess is that there aren't too big differences.\n\nMost important: Make sure you apply the same preprocessing steps to your training \/ testing \/ evaluation data.","Q_Score":0,"Tags":"python,deep-learning","A_Id":44983413,"CreationDate":"2017-07-07T04:11:00.000","Title":"Image preprocessing of finetune in ResNet","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a model build in Python using keras and tensorflow. I want to export the model and use it for training in C++. I am using TF1.2 and used the tf.train.export_metagraph to export my graph. I am not exactly sure on how to proceed in using the model in C++ for training. Thanks :)","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":192,"Q_Id":44967751,"Users Score":0,"Answer":"Why would want to train the model in C++? Tensorflows core libraries are in c++. I think you mean use the trained model in C++? Once you've trained a model and exported it (assuming you have the .pb file) you use the model for predicting .Theres no way to retrain an exported model.","Q_Score":0,"Tags":"python,c++,tensorflow","A_Id":44968146,"CreationDate":"2017-07-07T09:49:00.000","Title":"Tensorflow:Using a trained model in C++","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using numpy's polyfit to find a best fit curve for a set of data. However, numpy's polyfit returns an array of float64 and because the calculated coefficients are so large\/small (i.e. 1e-200), it's returning an overflow error that's encountered in multiply :\nRuntimeWarning: overflow encountered in multiply\n  scale = NX.sqrt((lhs*lhs).sum(axis=0))\nI've tried casting the initial array to be float128, but that does not seem to work. Is there any way around this overflow issue \/ any way to handle such large coefficients?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":281,"Q_Id":44982378,"Users Score":0,"Answer":"You may use logarithmic version of your variables (np.log10), so when dealing with something like 1e-200 you will have -200, less memory and more efficiency.","Q_Score":0,"Tags":"python,arrays,numpy","A_Id":44982417,"CreationDate":"2017-07-08T04:05:00.000","Title":"Numpy Float128 Polyfit","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to use numpy module for solving problems on hackerrank. But, when I imported numpy, it gave me the following error.\n\nImportError: No module named 'numpy'.\n\nI understand that this might be a very trivial question. But, I am a beginner in programming. Any help is highly appreciated.","AnswerCount":1,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":19903,"Q_Id":44983165,"Users Score":22,"Answer":"I have run into the same issue on HackerRank. A number of their challenges do support NumPy--indeed, a handful require it. Either import numpy or the idiomatic import numpy as np will work just fine on those.\nI believe you're simply trying to use numpy where they don't want you to. Because it's not part of the standard library, HackerRank would need to intentionally provide it. Where they do not, you will need to substitute lower-level, non-numpy code as a result.","Q_Score":11,"Tags":"python,numpy","A_Id":44983209,"CreationDate":"2017-07-08T06:16:00.000","Title":"importing numpy in hackerrank competitions","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am practicing the nltk examples from the \"Natural language processing in Python\" book. While trying to get the words that start with syllable \"p\" and of syllable length 3 from cmu dictionary (one of the examples provided in chapter 2), I am not getting any values returned. I am using Python 3. Below is the code:\n\n\n\nentries = nltk.corpus.cmudict.entries()\np3 = [(pron[0] + '-' + pron[2], word)\n            for pron, word in entries if pron[0] == 'P' and len(pron) == 3]\n\n\n\nBut no value returned: \n\n\n\np3 = \n      []\n\n\n\nHowever, I know that the value exist. See below:\n\n\n\n[(word, pron[0] + '-' + pron[2]) for word, pron in entries if word == 'perch']\n[('perch', 'P-CH')]","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":414,"Q_Id":44986375,"Users Score":0,"Answer":"My bad, I realized the mistake. I had swapped the position of \"pron\" with \"word\" thereby causing this problem. The corrected code is:\n\n\n\np3 = [(pron[0] + '-' + pron[2], word)\n            for word, pron in entries if pron[0] == 'P' and len(pron) == 3]\n      \"","Q_Score":0,"Tags":"python-3.x,nltk","A_Id":44986475,"CreationDate":"2017-07-08T12:46:00.000","Title":"NLTK: No value returned when searching the CMU dictionary based on syllable value","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"We have a 1.5BM records spread out in several csv files. We need to groupby on several columns in order to generate a count aggregate.\nOur current strategy is to:   \n\nLoad them into a dataframe (using Dask or pyspark)  \nAggregate columns in order to generate 2 columns as key:value (we are not sure if this is worthwhile)\nSave file as Parquet\nRead the Parquet file (Dask or pyspark) and run a groupby on the index of the dataframe.\n\nWhat is the best practice for an efficient groupby on a Parquet file?\nHow beneficial is it to perform the groupby on the index rather then on a column (or a group of columns)?\nWe understand that there is a partition that can assist - but in our case we need to groupby on the entire dataset - so we don't think it is relevant.","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":1447,"Q_Id":44999814,"Users Score":2,"Answer":"If you are doing a groupby-aggregation with a known aggregation like count or mean then your partitioning won't make that much of a difference.  This should be relatively fast regardless.\nIf you are doing a groupby-apply with a non-trivial apply function (like running an sklearn model on each group) then you will have a much faster experience if you store your data so that the grouping column is sorted in parquet.\nEdit:\nThat being said, even though groupby-count doesn't especially encourage smart partitioning it's still nice to switch to Parquet.  You'll find that you can read the relevant columns much more quickly.  \nAs a quick disclaimer, dask.dataframe doesn't currently use the count statistics within parquet to accelerate queries, except by filtering within the read_parquet function and to help identify sorted columns.","Q_Score":2,"Tags":"python,pyspark,parquet,dask","A_Id":45001380,"CreationDate":"2017-07-09T18:45:00.000","Title":"Best practice for groupby on Parquet file","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to pyspark. I want to plot the result using matplotlib, but not sure which function to use. I searched for a way to convert sql result to pandas and then use plot.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":30940,"Q_Id":45003301,"Users Score":1,"Answer":"For small data, you can use .select() and .collect() on the pyspark DataFrame. collect will give a python list of pyspark.sql.types.Row, which can be indexed. From there you can plot using matplotlib without Pandas, however using Pandas dataframes with df.toPandas() is probably easier.","Q_Score":15,"Tags":"python,pandas,matplotlib,pyspark-sql","A_Id":66233233,"CreationDate":"2017-07-10T03:15:00.000","Title":"How to use matplotlib to plot pyspark sql results","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to classify sentiment on movie review and predict the genres of that movie based on the review itself. Now Sentiment is a Binary Classification problem where as Genres can be Multi-Label Classification problem.\nAnother example to clarify the problem is classifying Sentiment of a sentence and also predicting whether the tone of the sentence is happy, sarcastic, sad, pitiful, angry or fearful.\nMore to that is, I want to perform this classification using Tensorflow CNN. My problem is in structuring the y_label and training the data such that the output helps me retrieve Sentiment as well as the genres.\nEg Data Y Label: [[0,1],[0,1,0,1,0]] for sentiment as Negative and mood as sarcastic and angry\nHow do you suggest I tackle this?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":254,"Q_Id":45004514,"Users Score":0,"Answer":"You can treat this as a multi-label problem, and append the sentiment and the tone labels together. \nNow since the network has to predict multiple outputs (2 in this case) you need to use an activation function like sigmoid and not softmax. And your prediction can be made using tf.round(logits).","Q_Score":0,"Tags":"python-3.x,tensorflow,neural-network,deep-learning,data-science","A_Id":45005490,"CreationDate":"2017-07-10T05:46:00.000","Title":"How to classify both sentiment and genres from movie reviews using CNN Tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm a Beginner in bigdata.\nI had learned Python.\nI want get Reverse a matrix with tensorflow (matrix n*n in input), but office boss will to do it with tensorflow, so i wanna do it without Adjoining matrix.\nhelp me, please.\nthank you In advance. <3","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":435,"Q_Id":45030827,"Users Score":0,"Answer":"You mean you need to swap rows and columns? If that's the case then you might use tf.transpose.","Q_Score":0,"Tags":"python,tensorflow,matrix-inverse,bigdata","A_Id":45031046,"CreationDate":"2017-07-11T09:39:00.000","Title":"Reverse a matrix with tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it possible to somehow override import so that I can do some more sophisticated operations on a module before it gets imported?\nAs an example: I have a larger application that uses matplotlib for secondary features that are not vital for the overall functionality of the application. In case that matplotlib is not installed I just want to mock the functionality so that the import and all calls to matplotlib functions appear to be working, just without actually doing anything. A simple warning should then just indicate that the module is not installed, though that fact would not impair the core functionality of the application. I already have an import function that, in the case that matplotlib is not installed, returns a MagicMock object instead of the actual module which just mimics the behavior of the matplotlib API. \nSo, all import matplotlib... or from matplotlib import... should then be automatically overridden by the corresponding function call. I could replace all import and from ... import expressions by hand but I'd like to make but there are a lot of them. I'd rather like to have this functionality automatically by overriding import.\nIs that possible?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2180,"Q_Id":45034266,"Users Score":0,"Answer":"Short answer is NO... But you could and should catch ImportError for when the module is not there, and handle it then. Otherwise replacing all import statements with something else is the clever thing to do.","Q_Score":7,"Tags":"python,import","A_Id":45034487,"CreationDate":"2017-07-11T12:11:00.000","Title":"Override `import` for more sophisticated module import","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am creating a new dataframe in pandas as below:\ndf = pd.read_excel(zipfile.open('zipfilename 2017-06-28.xlsx'), header=1, names=cols)\nThe single .xlsx within the .zip is dynamically named (so changes based on the date).\nThis means I need to change the name of the .xlsx in my code each time I open the .zip to account for the dynamically named .xlsx.\nIs there a way to make pandas read the file within the .zip, regardless of the name of the file? Or to return the name of the .xlsx within the line of code somehow?\nThanks","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":71,"Q_Id":45045043,"Users Score":0,"Answer":"Read your file using,\ndf = pd.read_excel('zipfilename 2017-06-28.xlsx',compression='zip', header=1, names=cols)","Q_Score":1,"Tags":"python,pandas","A_Id":45047193,"CreationDate":"2017-07-11T21:51:00.000","Title":"pandas - reading dynamically named file within a .zip","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I trained a linear regression model using pyspark ml and save it.now i want to re-train it on the bases of new data batch.. is it possible??","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":126,"Q_Id":45050839,"Users Score":0,"Answer":"I don't think so, you use pyspark.ml.regression.GeneralizedLinearRegression to train, and then you get a pyspark.ml.regression.GeneralizedLinearRegressionModel, that is what you have saved.\nAFIK, the model can't be refitted, you have to use the regression fit again to get a new model.","Q_Score":1,"Tags":"python,machine-learning,pyspark","A_Id":45052125,"CreationDate":"2017-07-12T07:15:00.000","Title":"how to re-train Saved linear regression ML model in pyspark when new data is coming","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've always used virtualenv(wrapper) for my python needs, but now I'm considering trying conda for new projects, mainly because theano docs \"strongly\" recommend it, and hoping that it will save me some hassle with pygpu config. I'm on linux mint 16( I guess, kernel in uname is from ubuntu 14.04) and there are no system packages for conda\/miniconda so I'll have to use their shell script for installation.\nNow I have a dilemma - should I install as my user or as root? What is likely to give me less hassle in the future (given that I'm going to use (nvidia) GPU for computation).","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":94,"Q_Id":45056037,"Users Score":1,"Answer":"Anaconda and miniconda are designed to be installed by each user individually, into each users $HOME\/miniconda directory. If you installed it as a shared install as root, all users would need to access \/root\/miniconda.\nAlso, environments will be created in $HOME\/miniconda\/envs, so environments of several people will interfere with each other (plus the whole issue of permissions, file ownership etc.).\nBottom line: Don't install it as root, install it as yourself.\nAny third party dependencies you'd still install as root using apt-get, but once they're installed they're accessible by everyone, no matter if they use miniconda or not.","Q_Score":0,"Tags":"python,conda","A_Id":45056341,"CreationDate":"2017-07-12T11:10:00.000","Title":"Installing miniconda for theano with gpuarray: as root or as user?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to translate some matlab code to python. In the matlab code, I have a radon transform function. I start with a 146x146 image, feed it into the radon() function, and get a 211x90 image. When I feed the same image into my python radon() function, I get a 146x90 image. The documentation for the python radon () function says it is a circular radon transform. Is the matlab function also circular? Why are these returning different shaped images and how can I get the outputs to match?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":269,"Q_Id":45060419,"Users Score":0,"Answer":"Matlab's radon() function is not circular. This was the problem. Although the output image sizes do still differ, I am getting essentially the result I want.","Q_Score":0,"Tags":"python,matlab","A_Id":45061095,"CreationDate":"2017-07-12T14:24:00.000","Title":"is the Matlab radon() function a \"circular\" radon transform?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Currently we are uploading the data retrieved from vendor APIs into Google Datastore. Wanted to know what is the best approach with data storage and querying the data. \nI will be need to query millions of rows of data and will be extracting custom engineered features from the data. So wondering whether I should load the data into BigQuery directly and query it for faster processing or store it in Datastore and then move it to BigQuery for querying?. I will be using pandas for performing statistics on stored data.","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":577,"Q_Id":45061306,"Users Score":0,"Answer":"As far as I can tell there is no support for Datastore in Pandas. This might affect your decision.","Q_Score":0,"Tags":"python,pandas,google-cloud-datastore,google-bigquery,google-cloud-platform","A_Id":45395282,"CreationDate":"2017-07-12T15:00:00.000","Title":"Is Google Cloud Datastore or Google BigQuery better suited for analytical queries?","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a pandas df with 5 columns, one of them being State. I want to find the number of times each state appears in the State column. I'm guessing I might need to use groupby, but I haven't been able to figure out the exact command.","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":354,"Q_Id":45063425,"Users Score":2,"Answer":"Use df['your column name'].value_counts()['your value name'].","Q_Score":0,"Tags":"python,pandas","A_Id":45063449,"CreationDate":"2017-07-12T16:45:00.000","Title":"Using pandas, how can I return the number of times an element appears in a column?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"What I'm trying to do is to ask the user to input a company name, for example Microsoft, and be able to predict that it is in the Computer Software industry. I have around 150 000 names and 60+ industries. Some of the names are not English company names.\nI have tried training a Word2Vec model using Gensim based on company names only and averaged up the word vectors before feeding it into SKlearn's logistic regression but had terrible results. My questions are:\n\nHas anyone tried these kind of tasks? Googling on short text classification shows me results on classifying short sentences instead of pure names. If anyone had tried this before, mind sharing a few keywords or research papers regarding this task?\nWould it be better if I have a brief description for each company instead of only using their names? How much would it help for my Word2Vec model rather than using only the company names?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2136,"Q_Id":45070186,"Users Score":0,"Answer":"Not sure what you want.\nIf the point is to use just company names, maybe break names into syllables\/phonemes, and train on that data.\nIf the point is to use Word2Vec, I'd recommend pulling the Wikipedia page for each company (easier to automate than an 'about me').","Q_Score":2,"Tags":"python,machine-learning,text-classification,multilabel-classification","A_Id":45070245,"CreationDate":"2017-07-13T01:49:00.000","Title":"Machine learning to classify company names to their industries","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'd like to know the differences between tf.train.Saver().restore() and \ntf.saved_model.loader().\nAs far as I know, tf.train.Saver().restore() restores the previously saved variables from the checkpoint file; and tf.saved_model.loader() loads the graph def from the pb file.\nBut I have no idea about when I should choose restore() or loader()?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":323,"Q_Id":45075568,"Users Score":0,"Answer":"During training, restoring from checkpoints, etc, you want to use the saver. You only want to use the saved model if you're loading your exported model for inference.","Q_Score":4,"Tags":"python,tensorflow","A_Id":45084990,"CreationDate":"2017-07-13T08:40:00.000","Title":"what the differences between tf.train.Saver().restore() and tf.saved_model.loader","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a glyph that's a series of Circles. I want to click on one point and change the colour \/ alpha of the unselected glyphs such that each unselected glyph has a custom colour based on it's relationship with the selected point. \nFor example, I'd want the closest points to the selected point to have alpha near to 1 and the furthest to have alpha near to 0.\nI've seen other questions where the unselected glyphs have different alphas, but the alphas are independent of what is selected. Is it possible to do this without using JavaScript?\nEdited for more details:\nThe specific dataset I'm working on is a dataset of a bike sharing system, with data on trips made between specific stations. When I click on a specific station, I want to show the destination stations to which users go to when they start from the station selected. For n stations, the data thus has a n * n format: for each station, we have the probability of going to every other station. Ideally, this probability will be the alpha of the unselected stations, such the most popular destinations would have alpha near to 1, and the less popular ones an alpha near to 0.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":195,"Q_Id":45090562,"Users Score":0,"Answer":"In order to avoid tripling (or quintupling) memory usage in the browser, Bokeh only supports setting \"single values\" for non-selection colors and alphas. That is, non-selection properties can't be vectorized by pointing them at a ColumnDataSource column. So there's only two options I can think of:\n\nSplit the glyphs into different groups of glyphs, that each have a different different nonselection_color. This might be feasible if you only have a few groups. Of course now you have to partition your data to have e.g. five calls to p.circle instead of one, but it would entirely avoid JS. \nUse a tiny amount of JavaScript in a CustomJS callback. You can have an additional column in the CDS that provides the non-selected colors. When a selection happens, the CustomJS callback switches the glyph's normal color field to point to the other column and when a selection is cleared, changes it back to the \"normal\" field.","Q_Score":0,"Tags":"python,bokeh,glyph","A_Id":45105205,"CreationDate":"2017-07-13T20:43:00.000","Title":"Visual properties of unselected glyphs in Bokeh based on what is selected","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"If I am given an array of indices but I don't know whether it is a regular index array or a boolean mask, what is the best way to determine which it is?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":57,"Q_Id":45106240,"Users Score":1,"Answer":"You can check the dtype, or iterate through and check if the values are not in the set {True, False} as well as checking if the values are not in the set {0,1}\nBoolean masks must be the same shape as the array they are intended to index into, so that's another check. \nBut there's no hard and fast way to distinguish a priori whether an array of consisting of only values in {0,1} is one or the other without additional knowledge.","Q_Score":0,"Tags":"python,numpy","A_Id":45106390,"CreationDate":"2017-07-14T15:06:00.000","Title":"Best way to differentiate an array of indices vs a boolean mask","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When you select \"View as DataFrame\" in the variables pane it has a nice spreadsheet like view of the DataFrame. That said, as the DataFrame itself changes, the Data View does not auto update and you need to reclick the View as DataFrame to see it again. Is there a way to make PyCharm autoupdate this? Seems like such a basic feature.","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":835,"Q_Id":45106431,"Users Score":1,"Answer":"If you put a cursor on the text field just below the displayed dataframe and hit Enter, it'll update itself.","Q_Score":17,"Tags":"python,pycharm","A_Id":69032680,"CreationDate":"2017-07-14T15:15:00.000","Title":"PyCharm: Is there a way to make the \"data view\" auto update when dataframe is changed?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When you select \"View as DataFrame\" in the variables pane it has a nice spreadsheet like view of the DataFrame. That said, as the DataFrame itself changes, the Data View does not auto update and you need to reclick the View as DataFrame to see it again. Is there a way to make PyCharm autoupdate this? Seems like such a basic feature.","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":835,"Q_Id":45106431,"Users Score":1,"Answer":"Unfortunately, no. The only thing you can do is use 'watches' to watch the variable and open when you want it. It requires a lot of background processing and memory usage to display the dataframe.","Q_Score":17,"Tags":"python,pycharm","A_Id":66154095,"CreationDate":"2017-07-14T15:15:00.000","Title":"PyCharm: Is there a way to make the \"data view\" auto update when dataframe is changed?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"can anyone let me know what is the method of estimating the parameters in fractional logit model in statsmodel package of python? \nAnd can anyone refer me the specific part of the source code of fractional logit model?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1645,"Q_Id":45111640,"Users Score":1,"Answer":"I assume fractional Logit in the question refers to using the Logit model to obtain the quasi-maximum likelihood for continuous data within the interval (0, 1) or [0, 1].\nThe discrete models in statsmodels like GLM, GEE, and Logit, Probit, Poisson and similar in statsmodels.discrete, do not impose an integer condition on the response or endogenous variable. So those models can be used for fractional or positive continuous data. \nThe parameter estimates are consistent if the mean function is correctly specified. However, the covariance for the parameter estimates are not correct under quasi-maximum likelihood. The sandwich covariance is available with the fit argument, cov_type='HC0'. Also available are robust sandwich covariance matrices for cluster robust, panel robust or autocorrelation robust cases.\neg.\nresult = sm.Logit(y, x).fit(cov_type='HC0')\nGiven that the likelihood is not assumed to be correctly specified, the reported statistics based on the resulting maximized log-likelihood, i.e. llf, ll_null and likelihood ratio tests are not valid.\nThe only exceptions are multinomial (logit) models which might impose the integer constraint on the explanatory variable, and might or might not work with compositional data. (The support for compositional data with QMLE is still an open question because there are computational advantages to only support the standard cases.)","Q_Score":0,"Tags":"python,python-2.7,statistics","A_Id":45115173,"CreationDate":"2017-07-14T21:10:00.000","Title":"statsmodel fractional logit model","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When I use model.predict_generator() on my test_set (images) I am getting a different prediction and when I  use mode.predict() on the same test_Set I am getting a different set of predictions.\nFor using model.predict_generator I followed the below steps to create a generator:\n\nImagedatagenerator(no arguments here) and used flow_from_directory \nwith shuffle = False.\nThere are no augmentations nor preprocessing of \nimages(normalization,zero-centering etc) while training the model.\n\nI am working on a binary classification problem involving dogs and cats (from kaggle).On the test set, I have 1000 cat images.\nand by using model.predict_generator() I am able to get 87% accuracy()i.e 870 images are classified correctly.\nBut while using model.predict I am getting 83% accuracy.\nThis is confusing because both should give identical results right?\nThanks in advance :)","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":5757,"Q_Id":45115582,"Users Score":2,"Answer":"@petezurich Thanks for your comment. Generator.reset() before model.predict_generator() and turning off the shuffle in predict_generator() fixed the problem","Q_Score":10,"Tags":"python,keras,prediction","A_Id":46335988,"CreationDate":"2017-07-15T07:16:00.000","Title":"difference in predictions between model.predict() and model.predict_generator() in keras","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to build a web application that requires intensive mathematical calculations. Can I use Django to populate python charts and pandas dataframe?","AnswerCount":1,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":7691,"Q_Id":45117857,"Users Score":6,"Answer":"You can use any framework to do so. If you worked with Python before I can recommend using Django since you have the same (clear Python) syntax along your project. This is good because you keep the same logic everywhere but should not be your major concern when it comes to choosing the right framework for your needs. So for example if you are a top Ruby-On-Rails developer I would not suggest to learn Django just because of Pandas. \nIn general: A lot of packages\/libraries are written in other languages but you will still be able to use them in Django\/Python. So for example the famous \"Elasticsearch\" Searchbackend has its roots in JAVA but is still used in a lot of Django apps. \nBut it also goes the other way around \"Celery\" is written in Python but can be used in Node.js or PHP. There are hundreds of examples but I think you get the Point. \nI hope that I brought some light into the darkness. If you have questions please leave them in the comments.","Q_Score":1,"Tags":"python,django,pandas,numpy","A_Id":45118234,"CreationDate":"2017-07-15T11:49:00.000","Title":"Can Django work well with pandas and numpy?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"Trying to understand some Scala's code on network training in MXNet.\nI believe you can access gradient on the executor in Scala by calling networkExecutor.gradDict(\"data\"), what would be equivalent of it in Python MXNet?\nThanks!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":64,"Q_Id":45125919,"Users Score":1,"Answer":"How about grad_dict in executor?  It returns a dictionary representation of the gradient arrays.","Q_Score":0,"Tags":"python,scala,mxnet","A_Id":45171965,"CreationDate":"2017-07-16T06:56:00.000","Title":"MXNet - what is python equivalent of getting scala's mxnet networkExecutor.gradDict(\"data\")","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to train a GAN in Keras. My final target is BEGAN, but I'm starting with the simplest one. Understanding how to freeze weights properly is necessary here and that's what I'm struggling with.\nDuring the generator training time the discriminator weights might not be updated. I would like to freeze and unfreeze discriminator alternately for training generator and discriminator alternately. The problem is that setting trainable parameter to false on discriminator model or even on its' weights doesn't stop model to train (and weights to update). On the other hand when I compile the model after setting trainable to False the weights become unfreezable. I can't compile the model after each iteration because that negates the idea of whole training.\nBecause of that problem it seems that many Keras implementations are bugged or they work because of some non-intuitive trick in old version or something.","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":7178,"Q_Id":45154180,"Users Score":0,"Answer":"Can you use tf.stop_gradient to conditionally freeze weights?","Q_Score":8,"Tags":"python,tensorflow,neural-network,keras,theano","A_Id":45154694,"CreationDate":"2017-07-17T21:47:00.000","Title":"How to dynamically freeze weights after compiling model in Keras?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to train a GAN in Keras. My final target is BEGAN, but I'm starting with the simplest one. Understanding how to freeze weights properly is necessary here and that's what I'm struggling with.\nDuring the generator training time the discriminator weights might not be updated. I would like to freeze and unfreeze discriminator alternately for training generator and discriminator alternately. The problem is that setting trainable parameter to false on discriminator model or even on its' weights doesn't stop model to train (and weights to update). On the other hand when I compile the model after setting trainable to False the weights become unfreezable. I can't compile the model after each iteration because that negates the idea of whole training.\nBecause of that problem it seems that many Keras implementations are bugged or they work because of some non-intuitive trick in old version or something.","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":7178,"Q_Id":45154180,"Users Score":0,"Answer":"Maybe your adversarial net(generator plus discriminator) are wrote in 'Model'.\nHowever, even you set the d.trainable=False, the independent d net are set non-trainable, but the d in the whole adversarial net is still trainable. \nYou can use the d_on_g.summary() before then after set d.trainable=False and you would know What I mean(pay attention to the trainable variables).","Q_Score":8,"Tags":"python,tensorflow,neural-network,keras,theano","A_Id":47122897,"CreationDate":"2017-07-17T21:47:00.000","Title":"How to dynamically freeze weights after compiling model in Keras?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"According to the API, it seems like the normal xgboost interface allows for this option: \nxgboost.train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None, maximize=False, early_stopping_rounds=None, evals_result=None, verbose_eval=True, xgb_model=None, callbacks=None, learning_rates=None).\nIn this option, one can input xgb_model to allow continued training on the same model. \nHowever, I'm using the scikit learn API of xgboost so I can put the classifier in a scikit pipeline, along with other nice tools such as random search for hyperparameter tuning.\nSo does anyone know of any (albeit hacky) way of allowing online training for the scikitlearn api for xgboost?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":571,"Q_Id":45154751,"Users Score":0,"Answer":"I don't think the sklearn wrapper has an option to incrementally train a model. The feat can be achieved to some extent using the warm_start parameter. But, the sklearn wrapper for XGBoost doesn't have that parameter.\nSo, if you want to go for incremental training you might have to switch to the official API version of xgboost.","Q_Score":4,"Tags":"python,machine-learning,scikit-learn,xgboost","A_Id":54628350,"CreationDate":"2017-07-17T22:41:00.000","Title":"Scikit learn API xgboost allow for online training?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've got a script that uses the Canny method and probabilistic Hough transform to identify line segments in an image. \nI need to be able to filter out all line segments that are NOT connected to a specific pixel. How would one tackle this problem?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":276,"Q_Id":45154854,"Users Score":0,"Answer":"The result of Hough Line transform is an array of (rho,theta) parameter pairs. The equation of the line represented by the pair is \ny + x\/tan(theta) + rho\/sin(theta) = 0\nYou can check whether the (x, y) coordinates of the point satisfy this condition, to find lines that pass throught the point (practically, use a small value instead of 0).","Q_Score":0,"Tags":"python,opencv,edge-detection","A_Id":45161655,"CreationDate":"2017-07-17T22:53:00.000","Title":"OpenCV Python, filter edges to only include those connected to a specific pixel","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a python script that execute a gbq job to import a csv file from Google cloud storage to an existing table on BigQuery.\nHow can I set the job properties to import to the right columns provided in the first row of the csv file? \nI set parameter 'allowJaggedRows' to TRUE, but it import columns in order regardless of column names in the header of csv file.","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":3297,"Q_Id":45155117,"Users Score":2,"Answer":"When you import a CSV into BigQuery the columns will be mapped in the order the CSV presents them - the first row (titles) won't have any effect in the order the subsequent rows are read. \nTo be noted, if you were importing JSON files, then BigQuery would use the name of each column, ignoring the order.","Q_Score":0,"Tags":"python,google-bigquery,import-from-csv","A_Id":45156763,"CreationDate":"2017-07-17T23:23:00.000","Title":"How to import CSV to an existing table on BigQuery using columns names from first row?","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Python 3.5.2 is installed, and I need to ensure it doesn't upgrade to 3.6 due to some other dependencies.\nWhen I install OpenCV 3 via brew (see below), brew invokes python3 and upgrades to Python 3.6, the latest build:\nbrew install opencv3 --with-python3\nHow can I install OpenCV 3 without changing my Python build?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":150,"Q_Id":45156570,"Users Score":0,"Answer":"Try using pip, \nYou can create a pip-reqs.txt file and pin things to a specific version\nThen just run \n\npip -r pip-reqs.txt\n\npip will then take care of installing opencv for you for the python version that is currently configured","Q_Score":0,"Tags":"python,opencv3.0","A_Id":45159452,"CreationDate":"2017-07-18T02:37:00.000","Title":"Install OpenCV 3 Without Upgrading Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":1,"Web Development":0},{"Question":"Matrix exponentiation can be performed in python using functions within the scipy.linalg library, namely expm, expm2, expm3. expm makes use of a Pade approximation; expm2 uses the eigenvalue decomposition approach and expm3 makes use of a Taylor series with a default number of terms of 20.\nIn SciPy 0.13.0 release notes it is stated that: \n\nThe matrix exponential functions scipy.linalg.expm2 and scipy.linalg.expm3 are deprecated. All users should use the numerically more robust scipy.linalg.expm function instead.\n\nAlthough expm2 and expm3 are deprecated since release version SciPy 0.13.0, I have found that in many situations these implementations are faster than expm.\nFrom this, some questions arise: \nIn what situations could expm2 and expm3 result in numerical instabilities? \nIn what situations (e.g. sparse matrices, symmetric, ...) is each of the algorithms faster\/more precise?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1827,"Q_Id":45167237,"Users Score":5,"Answer":"This will depend a lot on the detail of the implementation of these different ways of exponentiating the matrix.\nIn general terms, I would expect the eigen-decomposition (expm2) to be poorly suited to sparse matrices, because it is likely to remove the sparseness. It will also be more difficult to apply to non-symmetric matrices, because this will require the use of complex arithmetic and more expensive algorithms to compute the eigen-decomposition.\nFor the Taylor-series approach (expm3), this sounds risky if there are a fixed number of terms independent of the norm of the matrix. When computing e^x for a scalar x, the largest terms in the Taylor series are around that for which n is close to x.\nHowever, the implementation details of these (deprecated) functions may use tricks like diagonally loading the matrix so as to improve the stability of these series expansion.","Q_Score":12,"Tags":"python,scipy,sparse-matrix","A_Id":49570892,"CreationDate":"2017-07-18T12:46:00.000","Title":"Matrix exponentiation with scipy: expm, expm2 and expm3","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In deep learning experiments,there is a consensus that mean subtraction from the data set could improve the accuracy.For example,the mean value of ImageNet is [104.0 117.0 124.0],so before feeding the network,the mean value will be subtracted from the image. My question is \n\nHow the mean value is calculated?\nShould I calculate the mean value on training and testing data set separately?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2860,"Q_Id":45168747,"Users Score":1,"Answer":"The mean value of the dataset is the mean value of the pixels of all the images across all the colour channels (e.g. RBG). Grey scale images will have just one mean value and colour images like ImageNet will have 3 mean values. \nUsually mean is calculated on the training set and the same mean is used to normalize both training and test images.","Q_Score":2,"Tags":"python,statistics,deep-learning","A_Id":45189807,"CreationDate":"2017-07-18T13:49:00.000","Title":"Calculate mean value of an image dataset","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a question about regression model in machine learning and I am wondering if my way is correct or not.\nI have built my regression model and already trained it with my data, but my model always predict 1.5 times more than actual values.\nI understood that this is my model's habit, consider as it is predict alway 1.5 times.\nAfter considering as it is, I divided predicted value by 1.5 times.\nLet's say, my model predict 100 in some case, and I calculated 100\/1.5 and get approximately 66.6 in a result.\nActually 66.6 is not predicted value and I manipulated it.\nIs this manipulation acceptable for regression?\nCan I supply this 66.6 to my customer?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":64,"Q_Id":45170586,"Users Score":1,"Answer":"No you shouldn't do it. If your mode always predict 1.5 times more than the actual values then that means your model is just not performing well and the data cannot be linearly fitted. To prevent this, you should look at other models that is able to capture the structure of your data or you might have outliers and removing them would help the linear regression model.","Q_Score":0,"Tags":"python,machine-learning,regression","A_Id":45170697,"CreationDate":"2017-07-18T15:05:00.000","Title":"Way to predict with regression model","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When training, what will word2vec do to cope with the words at the end of a sentence . Will it use the exact words at the beginning of another sentence as the context words of the center words which is \nat the end of last sentence.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":438,"Q_Id":45170589,"Users Score":1,"Answer":"The window is trimmed to the edges of the current text example. So, the first word of a text only gets its context words from subsequent words in the same text. (No words are retained from previous examples.) Similarly, the last word in a text only gets its context words from previous words in the same text. (No words are pulled in from the next text example.) Each text example (aka sentence) stands alone.","Q_Score":0,"Tags":"python,gensim,word2vec","A_Id":45172785,"CreationDate":"2017-07-18T15:05:00.000","Title":"How word2vec deal with the end of a sentence","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using dynamic time warping (DTW) as a similarity metric to cluster ~3500 time series using the k-means algorithm in Python. \nI am looking for a similar metric to the popular silhouette score used in sklearn.metrics.silhouette_score but relevant to DTW. \nWondering if anyone can provide any help?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":240,"Q_Id":45173476,"Users Score":1,"Answer":"You can use Silhouette with DTW as distance function.\nBut don't forget that this just a heuristic. A different k can be better by subjective use.","Q_Score":1,"Tags":"python,cluster-analysis","A_Id":45178600,"CreationDate":"2017-07-18T17:29:00.000","Title":"Metric for appropriate number of DTW clusters","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to download the latest version of OpenCV using anaconda, but Anaconda only has version 3.1.0. I ended up installing it with pip, but can someone explain why anaconda does not have 3.2.0 version of OpenCV. Also, I am using Python 2.7.\nThanks","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":355,"Q_Id":45178197,"Users Score":0,"Answer":"Anaconda is maintained by Continuum so it seems like they have not had a chance to update to the newer version of OpenCV. I will try to see if I can bring it to their attention.","Q_Score":0,"Tags":"python-2.7,opencv,anaconda","A_Id":45392458,"CreationDate":"2017-07-18T22:33:00.000","Title":"Anaconda and OpenCV using old version","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have nearly 60-70 timing log files(all are .csv files, with a total size of nearly 100MB). I need to analyse these files at a single go. Till now, I've tried the following methods :\n\nMerged all these files into a single file and stored it in a DataFrame (Pandas Python) and analysed them.\nStored all the csv files in a database table and analysed them.\n\nMy doubt is, which of these two methods is better? Or is there any other way to process and analyse these files?\nThanks.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":160,"Q_Id":45181769,"Users Score":0,"Answer":"For me I usually merge the file into a DataFrame and save it as a pickle but if you merge it the file will pretty big and used up a lot of ram when you used it but it is the fastest way if your machine have a lot of ram.\nStoring the database is better in the long term but you will waste your time uploading the csv to the database and then waste even more of your time retrieving it from my experience you use the database if you want to query specific things from the table such as you want a log from date A to date B however if you use pandas to query all of that than this method is not very good.\nSometime for me depending on your use case you might not even need to merge it use the filename as a way to query and get the right log to process (using the filesystem) then merge the log files you are concern with your analysis only and don't save it you can save that as pickle for further processing in the future.","Q_Score":1,"Tags":"python,database,pandas,csv","A_Id":45181917,"CreationDate":"2017-07-19T05:38:00.000","Title":"How to analyse multiple csv files very efficiently?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm implementing a UNet for binary segmentation while using Sigmoid and BCELoss. The problem is that after several iterations the network tries to predict very small values per pixel  while for some regions it should predict values close to one (for ground truth mask region). Does it give any intuition about the wrong behavior?\nBesides, there exist NLLLoss2d which is used for pixel-wise loss. Currently, I'm simply ignoring this and I'm using MSELoss() directly. Should I use NLLLoss2d with Sigmoid activation layer?\nThanks","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":4348,"Q_Id":45184741,"Users Score":2,"Answer":"Seems to me like that your Sigmoids are saturating the activation maps. The images are not properly normalised or some batch normalisation layers are missing. If you have an implementation that is working with other images check the image loader and make sure it does not saturate the pixel values. This usually happens with 16-bits channels. Can you share some of the input images?\nPS Sorry for commenting in the answer. This is a new account and I am not allowed to comment yet.","Q_Score":1,"Tags":"python,deep-learning,pytorch","A_Id":45187882,"CreationDate":"2017-07-19T08:17:00.000","Title":"BCELoss for binary pixel-wise segmentation pytorch","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Good morning to all.\nI want to generate an array for example with 10 columns and 100 rows with the vector a = np.arange (1,1001), but I do not want to use loop since my web page gets saturated if I put a loop.\nSomeone knows of some numpy or math command or another.\nThank you very much for your attention.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":86,"Q_Id":45188890,"Users Score":3,"Answer":"a = np.arange(1000).reshape(100, 10) should do it.","Q_Score":0,"Tags":"python,loops,numpy,matrix","A_Id":45188959,"CreationDate":"2017-07-19T11:13:00.000","Title":"How do I generate a matrix with x dimension and a vector and without using loops? Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there are more direct way to access the RandomState object created on import other than np.random.<some function>.__self__? Both np.random._rand and getattr(np.random, \"_rand\") raise AttributeError. The former works fine but doesn't seem very transparent\/Pythonic, though the most transparent might just be creating a separate RandomState object. The purpose is passing the interal_state variable to a cython function that calls randomkit functions directly.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":238,"Q_Id":45190558,"Users Score":0,"Answer":"Found an answer thanks to kazemakase: _rand is accessible directly, I'd just need to import mtrand. But __self__ may be more future proof, if syntax doesn't change.","Q_Score":3,"Tags":"python,numpy,cython,numpy-random","A_Id":45191351,"CreationDate":"2017-07-19T12:25:00.000","Title":"Direct way to access Numpy RandomState object","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to train InceptionV3 network with my custom dataset (36 classes 130 samples per each). And some parameters fro my network:","AnswerCount":3,"Available Count":3,"Score":0.1325487884,"is_accepted":false,"ViewCount":3006,"Q_Id":45190707,"Users Score":2,"Answer":"Given just this information it is hard to tell what might be the underlying problem. In general, the machine learning engineer is always working with a direct trade-off between overfitting and model complexity. If the model isn't complex enough, it may not be powerful enough to capture all of the useful information necessary to solve a problem. However, if our model is very complex (especially if we have a limited amount of data at our disposal), we run the risk of overfitting. Deep learning takes the approach of solving very complex problems with complex models and taking additional countermeasures to prevent overfitting.\nThree of the most common ways to do that are\n\nRegularization\nDropout\nData augmentation\n\nIf your model is not complex enough:\n\nMake it bigger (easy)\nMake it smarter (hard)","Q_Score":0,"Tags":"python,deep-learning,keras","A_Id":45191429,"CreationDate":"2017-07-19T12:31:00.000","Title":"Keras deep learning why validation accuracy stuck in a value every time?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to train InceptionV3 network with my custom dataset (36 classes 130 samples per each). And some parameters fro my network:","AnswerCount":3,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":3006,"Q_Id":45190707,"Users Score":0,"Answer":"Be more specific on the example, post the code you used to build the Sequential Model.\nAt the moment I can say that your problem could be in the initial dataset.\nYou have 130 sample for 36 classes that means 3.6 example for each class?","Q_Score":0,"Tags":"python,deep-learning,keras","A_Id":45191017,"CreationDate":"2017-07-19T12:31:00.000","Title":"Keras deep learning why validation accuracy stuck in a value every time?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to train InceptionV3 network with my custom dataset (36 classes 130 samples per each). And some parameters fro my network:","AnswerCount":3,"Available Count":3,"Score":0.0665680765,"is_accepted":false,"ViewCount":3006,"Q_Id":45190707,"Users Score":1,"Answer":"It could mean that the model has learned everything possible and can't improve further.\nOne of the possible ways to improve accuracy is to get new data. You have ~4 samples per class, which is rather low. Try to get more samples or use data augmentation technics.","Q_Score":0,"Tags":"python,deep-learning,keras","A_Id":45190981,"CreationDate":"2017-07-19T12:31:00.000","Title":"Keras deep learning why validation accuracy stuck in a value every time?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have brew install python2 for OsX, then I install numpy, scipy using pip install.\nI also need python3, so I brew install python3, but when I import numpy under python3, import error occurs.\nI known I can fix this by install numpy using pip3 install numpy, but do I have to do this? Since I have the package already installed for python2, can I just tell python3 where it is and then use it?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":494,"Q_Id":45191428,"Users Score":3,"Answer":"Yes you need to install them using pip3 as well as python3.4 bundles pip in along side of python","Q_Score":2,"Tags":"python,python-2.7,python-3.x,numpy","A_Id":45191580,"CreationDate":"2017-07-19T13:00:00.000","Title":"Do I have to install numpy, spicy again for python3?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to do Segmentation in Customer Data in Python using Pandas. I have a customer ID variable in my dataset. I am confused over here, even though it won't be considered as a variable that affects the Output variable. How do we actually treat this variable if needed, a Categorical or a numerical ? \nAlso, Is there a business case that you could think of where the customerID will be considered?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":655,"Q_Id":45193502,"Users Score":1,"Answer":"I don't think you should use customerID as a variable. This is an unique value for each customer. It can be used as an index - to know for what customer belongs the prediction.\nSo you'de better drop this column from training\/test data.","Q_Score":0,"Tags":"python,pandas,data-science","A_Id":45193581,"CreationDate":"2017-07-19T14:24:00.000","Title":"Unique Key - CustomerID, A Categorical or a Numerical Variable?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I encountered the following irregularities and wanted to share my solution.\nI'm reading a sql table from Microsoft SQL Server in Python using Pandas and SQLALCHEMY. There is a column called \"occurtime\" with the following format: \"2017-01-01 01:01:11.000\". Using SQLAlchemy to read the \"occurtime\" column, everything was returned as NaN. I tried to set the parse_date parameter in the pandas.read_sql() method but with no success. \nIs anyone else encountering issue reading a datetime column from a SQL table using SQLAlchemy\/Pandas?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":752,"Q_Id":45197851,"Users Score":0,"Answer":"I had to work around the datetime column from my SQL query itself just so SQLAlchemy\/Pandas can stop reading it as a NaN value. \nIn my SQL query, I used CONVERT() to convert the datetime column to a string. This was ready with no issue, and then I used pandas.to_datetime() to convert it back into datetime. \nAnyone else with a better solution or know what's really going on, please share your answer, I'd really appreciate it!!!","Q_Score":0,"Tags":"python,sql-server,pandas,sqlalchemy","A_Id":45197852,"CreationDate":"2017-07-19T17:59:00.000","Title":"Pandas read_sql","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have frozen the tensorflow graph and I have noticed that the using the graph in realtime for prediction is too slow. so I would like to do graph optimization.\nWithin my project file, I have got a folder called model which contains the model file (pb file).\nNow I tried running the following command inside the model dir\n\n bazel build tensorflow\/python\/tools:strip_unused\n\nBut it is firing the following error,\n\nbazel build tensorflow\/python\/tools:strip_unused\n  ERROR: no such package 'tensorflow\/python\/tools': BUILD file not found on package path.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":170,"Q_Id":45197995,"Users Score":0,"Answer":"You need to build the tool inside the tensorflow directory, not the model directory, and then run the tool on the model.","Q_Score":0,"Tags":"python,tensorflow","A_Id":45219444,"CreationDate":"2017-07-19T18:07:00.000","Title":"Optimizing the frozen graph","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a column in my dataframe (call it 'FY') which has financial year values in the format: 2015\/2016 or 2016\/2017.\nI want to convert the whole column so it says 15\/16 or 16\/17 etc instead.\nI presume you somehow only take the 3rd, 4th and 5th character from the string, as well as the 8th and 9th, but haven't got a clue how to do it.\nCould anyone help me? Thank you.","AnswerCount":5,"Available Count":1,"Score":0.0399786803,"is_accepted":false,"ViewCount":138,"Q_Id":45198564,"Users Score":1,"Answer":"If you have a string you can always just choose parts of it by writing:\nfoo = 'abcdefg'\nfoo2 = foo[2:4]\nprint foo2\nthen the output would be:\ncd","Q_Score":1,"Tags":"python,pandas,dataframe","A_Id":45198690,"CreationDate":"2017-07-19T18:37:00.000","Title":"Python Pandas - Dataframe column - Convert FY in format '2015\/2016' to '15\/16'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"In some OpenAI gym environments, there is a \"ram\" version. For example: Breakout-v0 and Breakout-ram-v0. \nUsing Breakout-ram-v0, each observation is an array of length 128.\nQuestion: How can I transform an observation of Breakout-v0 (which is a 160 x 210 image) into the form of an observation of Breakout-ram-v0 (which is an array of length 128)?\nMy idea is to train a model on the Breakout-ram-v0 and display the trained model playing using the Breakout-v0 environment.","AnswerCount":4,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":5417,"Q_Id":45207569,"Users Score":15,"Answer":"There's a couple ways of understanding the ram option.\nLet's say you wanted to learn pong.  If you train from the pixels, you'll likely use a convolutional net of several layers.  interestingly, the final output of the convnet is a a 1D array of features.  These you pass to a fully connected layer and maybe output the correct 'action' based on the features the convnet recognized in the image(es). Or you might use a reinforcement layer working on the 1D array of features.\nNow let's say it occurs to you that pong is very simple, and could probably be represented in a 16x16 image instead of 160x160. straight downsampling doesn't give you enough detail, so you use openCV to extract the position of the ball and paddles, and create your mini version of 16x16 pong.  with nice, crisp pixels. The computation needed is way less than your deep net to represent the essence of the game, and your new convnet is nice and small.  Then you realize you don't even need your convnet any more. you can just do a fully connected layer to each of your 16x16 pixels.\nSo, think of what you have. Now you have 2 different ways of getting a simple representation of the game, to train your fully-connected layer on. (or RL algo)\n\nyour deep convnet goes through several layers and outputs a 1D array, say of 256 features in the final layer. you pass that to the fully connected layer.\nyour manual feature extraction extracts the blobs (pattles\/ball) with OpenCV, to make a 16x16 pong. by passing that to your fully connected layer, it's really just a set of 16x16=256 'extracted features'.\n\nSo the pattern is that you find a simple way to 'represent' the state of the game, then pass that to your fully connected layers. \nEnter option 3.  The RAM of the game may just be a 256 byte array.  But you know this contains the 'state' of the game, so it's like your 16x16 version of pong. it's most likely a 'better' representation than your 16x16  because it probably has info about the direction of the ball etc.\nSo now you have 3 different ways to simplify the state of the game, in order to train your fully connected layer, or your reinforcment algorithm.\nSo, what OpenAI has done by giving you the RAM is helping you avoid the task of learning a 'representation' of the game, and that let's you move directly to learning a 'policy' or what to do based on the state of the game.\nOpenAI may provide a way to 'see' the visual output on the ram version. If they don't, you could ask them to make that available.  But that's the best you will get.  They are not going to reverse engineer the code to 'render' the RAM, nor are they going to reverse engineer the code to 'generate' 'RAM' based on pixels, which is not actually possible, since pixels are only part of the state of the game.\nThey simply provide the ram if it's easily available to them, so that you can try algorithms that learn what to do assuming there is something giving them a good state representation.  \nThere is no (easy) way to do what you asked, as in translate pixels to RAM, but most likely there is a way to ask the Atari system to give you both the ram, and the pixels, so you can work on ram but show pixels.","Q_Score":6,"Tags":"python,openai-gym","A_Id":45640420,"CreationDate":"2017-07-20T07:03:00.000","Title":"How to interpret the observations of RAM environments in OpenAI gym?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've followed the pet detector tutorial, i have exported the model using \"export_inference_graph.py\".\nHowever when I try to freeze the graph using the provided \"freeze_graph.py\" but now sure what --output_node_names to use.\nDoes anyone know which I should use, or more importantly how I find out what to use for when I train my own model.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1483,"Q_Id":45218374,"Users Score":0,"Answer":"To find what to use for output_node_names, just checkout the graph.pbtxt file. In this case it was Softmax","Q_Score":1,"Tags":"python,tensorflow,object-detection","A_Id":45222284,"CreationDate":"2017-07-20T14:57:00.000","Title":"freezing the \"tensorflow object detection api pet detector\" graph","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a symbol, x, in my sympy code and have been computing a number of expressions with it. Ultimately the expression I have is very long and I am only interested in its expectation under the assumption E(x) = 0, E(x^2) = 1. Is there some way to set the expectation and variance of x in advance and then ask sympy to compute expectation of my entire expression?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":93,"Q_Id":45219177,"Users Score":0,"Answer":"Let x=stats.Bernoulli('x', 1\/2, succ=1, fail=-1). Then the E and variance are what I want. So if I compute any expression with only the first two moments and take E and variance, I get the answer I want.","Q_Score":1,"Tags":"python,probability,sympy","A_Id":45219608,"CreationDate":"2017-07-20T15:31:00.000","Title":"sympy set expectation and variance of random variable","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on an exploratory data analysis using python on a huge Dataset (~20 Million records and 10 columns). I would be segmenting, aggregating data and create some visualizations, I might as well create some decision trees liner regression models using that dataset. \nBecause of the large data set I need to use a data-frame that allows out of core data storage. Since I am relatively new to Python and working with large data-sets, i want to use a method which would allow me to easily use sklearn on  my data-sets. I'm confused weather to use Py-tables, Blaze or s-Frame  for this exercise. If someone could help me understand what are their pros and cons. What are the factors that are important in this kind of decision making that would be much appreciated.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":170,"Q_Id":45225500,"Users Score":1,"Answer":"good question! one option you may consider is to not use any of the libraries aformentioned, but instead read and process your file chunk-by-chunk, something like this:\ncsv=\"\"\"\\path\\to\\file.csv\"\"\"\npandas allows to read data from (large) files chunk-wise via a file-iterator:\nit = pd.read_csv(csv, iterator=True, chunksize=20000000 \/ 10)\nfor i, chunk in enumerate(it):\n    ...","Q_Score":0,"Tags":"python-3.x,pandas,hdf5,pytables,blaze","A_Id":45231429,"CreationDate":"2017-07-20T21:27:00.000","Title":"Py-tables vs Blaze vs S-Frames","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"On doing K means fit on some vectors with 3 clusters, I was able to get the labels for the input data. \nKMeans.cluster_centers_ returns the coordinates of the centers and so shouldn't there be some vector corresponding to that? How can I find the value at the centroid of these clusters?","AnswerCount":2,"Available Count":1,"Score":0.2913126125,"is_accepted":false,"ViewCount":17090,"Q_Id":45234336,"Users Score":3,"Answer":"The cluster centre value is the value of the centroid. At the end of k-means clustering, you'll have three individual clusters and three centroids, with each centroid being located at the centre of each cluster. The centroid doesn't necessarily have to coincide with an existing data point.","Q_Score":6,"Tags":"python,scikit-learn,k-means","A_Id":45234643,"CreationDate":"2017-07-21T09:42:00.000","Title":"Value at KMeans.cluster_centers_ in sklearn KMeans","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How do I apply scikit-learn to a numpy array with 4 columns each representing a different attribute? \nBasically, I'm wanting to teach it how to recognize a healthy patient from these 4 characteristics and then see if it can identify an abnormal one.\nThanks in advance!","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":773,"Q_Id":45238671,"Users Score":0,"Answer":"Look into the pandas package which allows you to import CSV files into a dataframe. pandas is supported by scikit-learn.","Q_Score":0,"Tags":"python,arrays,scikit-learn","A_Id":45238795,"CreationDate":"2017-07-21T13:15:00.000","Title":"Using Scikit-learn on a csv dataset","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When I was trying to get the rows of my dataset belonging to column of userid =1 through graphlab's sframe datastructure, sf[sf['userid'] == 1],\nI got the rows,however I also got this message, [? rows x 6 columns]\nNote: Only the head of the SFrame is printed. This SFrame is lazily evaluated.\nYou can use sf.materialize() to force materialization.\nI have gone through the documention, yet I can't understand what sf.materialize() do! could someone help me out here.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":661,"Q_Id":45256140,"Users Score":0,"Answer":"The note tells you that the operation (filtering in your case) isn't applied to whole date set right away, but only to some portion of it. This is to save resources -- in case the operation doesn't do what you intended, resources want be wasted by applying the operation on whole possibly large data set but only on the needed portion (head in your case, that is output by default). Materialization forces propagation of the operation on the whole data set.","Q_Score":1,"Tags":"python,graphlab,sframe","A_Id":45256348,"CreationDate":"2017-07-22T15:20:00.000","Title":"What is use of SFrame.materialize() in Graphlab?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to do symbolic manipulations to very large systems of equations, and end up with well over 200 variables that I need to do computations with. The problem is, one would usually name their variables x, y, possibly z when solving small system of equations. Even starting at a, b, ... you only get 26 unique variables this way.\nIs there a nice way of fixing this problem? Say for instance I wanted to fill up a 14x14 matrix with a different variable in each spot. How would I go about doing this?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":109,"Q_Id":45259985,"Users Score":0,"Answer":"I think the most straightfoward way to do this is to use sympy.symarray, like so:\nx = sympy.symarray(\"x\",(5,5,5))\n\nThis creates an accordingly sized (numpy) array - here the size is 5x5x5 - that contains sympy variables, more specifically these variables are prefixed with whatever you chose - here \"x\"- and have as many indices as you provided dimensions, here 3. Of course you can make as many of these arrays as you need - perhaps it makes sense to use different prefixes for different groups of variables for readability etc.\nYou can then use these in your code by using e.g. x[i,j,k]:\n\nIn [6]: x[0,1,4]\nOut[6]: x_0_1_4\n\n(note that you can not access the elements via x_i_j_k - I found this a bit counterintuitive when I started using sympy, but once you get the hang on python vs. sympy variables, it makes perfect sense.)\nYou can of course also use slicing on the array, e.g. x[:,0,0].\nIf you need a python list of your variables, you can use e.g. x.flatten().tolist().\nThis is in my opinion preferable to using sympy.MatrixSymbol because (a) you get to decide the number of indices you want, and (b) the elements are \"normal\" sympy.Symbols, meaning you can be sure you can do anything with them you could also do with them if you declared them as regular symbols.\n(I'm not sure this is still the case in sympy 1.1, but in sympy 1.0 it used to be that not all functionality was implemented for MatrixElement.)","Q_Score":2,"Tags":"python,sympy","A_Id":45288121,"CreationDate":"2017-07-22T22:54:00.000","Title":"Is there a good way to keep track of large numbers of symbols in scipy?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to spark and want to know about optimisations on spark jobs.\nMy job is a simple transformation type job merging 2 rows based on a condition. What are the various types of optimisations one can perform over such jobs?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":49,"Q_Id":45261395,"Users Score":0,"Answer":"More information about the job would help:\nSome of the generic suggestions:\n\nArrangement operators is very important. Not all arrangements will result in the same performance. Arrangement of operators should be such to reduce the number of shuffles and the amount of data shuffled. Shuffles are fairly expensive operations; all shuffle data must be written to disk and then transferred over the network.\n\n\nrepartition , join, cogroup, and any of the *By or *ByKey transformations can result in shuffles. \n\n\nrdd.groupByKey().mapValues(_.sum) will produce the same results as rdd.reduceByKey(_ + _). However, the former will transfer the entire dataset across the network, while the latter will compute local sums for each key in each partition and combine those local sums into larger sums after shuffling.\n\n\nYou can avoid shuffles when joining two datasets by taking advantage\n  of broadcast variables.\n\n\nAvoid the flatMap-join-groupBy pattern.\nAvoid reduceByKey When the input and output value types are different.\n\nThis is not exhaustive. And you should also consider tuning your configuration of Spark.\nI hope it helps.","Q_Score":0,"Tags":"python,apache-spark,pyspark","A_Id":45265597,"CreationDate":"2017-07-23T04:07:00.000","Title":"Optimisations on spark jobs","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I understand reating convolutional nerural network for 32 x 32 x 3 image, but i am planning to use larger image with different pixels. How can I reduce the image size to the required size ? does the pixel reduction impact accuracy in tensor flow ?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":971,"Q_Id":45263156,"Users Score":2,"Answer":"Theoretically there is no limit on the size of images being fed into a CNN. The most significant problem with larger image sizes is the increased memory footprint, especially with large batches. Moreover, you would need to use more convolutional layers to down sample the input image. Downsizing an image is a possibility of course, but you will lose discriminative information, naturally. For downsampling you can use scipy's scipy.misc.imresize.","Q_Score":1,"Tags":"python,machine-learning,tensorflow,neural-network,conv-neural-network","A_Id":45263297,"CreationDate":"2017-07-23T08:40:00.000","Title":"Building Convolutional Neural Network using large images?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I can't find an appropriate question title, sorry.\nI have a graph composed by two main data flow: image classification and label cleaning. I have two type of data:\n\n(image_data, noisy_label, verified_label) from validation set\n(image_data, noisy_label) from train set\n\nThe first is used to train the label cleaning part of the graph.\nThe second is used to train the image classification after its noisy label is cleaned.\nEvery batch need to have a ratio of 1:9.\nHow can i create this type of batch?? is it possible in tensorflow??","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":258,"Q_Id":45266976,"Users Score":0,"Answer":"I solved the ratio problem!! I create two batch, one for validation, one for train. Then i concatenate them with image_batch = tf.concat([image_validation_batch, image_train_batch], 0). This is only for image batch, i will investigate on the label.","Q_Score":1,"Tags":"python,tensorflow,batch-processing","A_Id":45868691,"CreationDate":"2017-07-23T15:46:00.000","Title":"Tensorflow: how to create batch with different type of data from different source (folder)?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I was wondering if anybody either had experience or suggestions regarding the\npossibility of storing a neural network (nodes, synapses) on an external hard-drive, and using a computer to run it. I do not know whether this is possible.\nI would like to be able to run a convolutional Neural Network while not loading my\ncomputer up. Thanks.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":180,"Q_Id":45276669,"Users Score":1,"Answer":"A hard drive could simply store a file that represents your network. Perhaps a JSON file with the thousands of optimized weight values, etc. Or if it is optimized, simply a template on layers and neurons, depending on what you hope to do (test or train?). Then the program you have on your computer can load this file, and test\/train it. The fact it is on a hard drive makes no difference.","Q_Score":0,"Tags":"python,neural-network","A_Id":45276971,"CreationDate":"2017-07-24T09:17:00.000","Title":"Neural Network on an external harddrive","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have generated a word2vec model using gensim for  a huge corpus and I need to cluster the vocabularies using k means clustering for that i need:\n\ncosine distance matrix (word to word, so the size of the matrix the number_of_words x number_of_words )\nfeatures matrix (word to features, so the size of the matrix is the number_of_words x number_of_features(200) )\n\nfor the feature matrix i tried to use x=model.wv and I got the object type as gensim.models.keyedvectors.KeyedVectors and its much smaller than what I expected a feature matrix will be \nis there a way to use this object directly to generate the k-means clustering ?","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":1198,"Q_Id":45280020,"Users Score":2,"Answer":"In gensim's Word2Vec model, the raw number_of-words x number_of_features numpy array of word vectors is in model.wv.vectors. (In older Gensim versions, the .vectors property was named .syn0 matching the original Google word2vec.c naming).\nYou can use the model.wv.key_to_index dict (previously .vocab) to learn the string-token-to-array-slot assignment, or the model.wv.index_to_key list (previously .index2word) to learn the array-slot-to-word assignment.\nThe pairwise distances aren't pre-calculated, so you'd have to create that yourself. And with typical vocabulary sizes, it may be impractically large. (For example, with a 100,000 word vocabulary, storing all pairwise distances in the most efficient way possible would require roughly 100,000^2 * 4 bytes\/float \/ 2 = 20GB of addressable space.","Q_Score":0,"Tags":"python,k-means,gensim,word2vec","A_Id":45289131,"CreationDate":"2017-07-24T11:56:00.000","Title":"getting distance matrix and features matrix from word2vec model","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am doing some sentiment analysis with Tensorflow, but there is a problem I can't solve:\nI have one tensor (input) shaped as [?, 38] [batch_size, max_word_length] and one (prediction) shaped as [?, 3] [batch_size, predicted_label].\nMy goal is to combine both tensors into a single tensor with the shape of [?, 38, 3].\nThis tensor is used as the input of my second stage.\nSeems easy, but i can't find a way of doing it.\nCan (and will) you tell me how to do this?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":834,"Q_Id":45282194,"Users Score":0,"Answer":"This is impossible. You have tensor, which contains batch_size * max_word_length \n elements and tensor which contains batch_size * predicted_label elements. Hence there are \nbatch_size * (max_word_length + predicted_label) \nelements. And now you want to create new tensor [batch_size, max_word_length, predicted_label] with \nbatch_size * max_word_length * predicted_label \nelements. You don't have enough elements for this.","Q_Score":0,"Tags":"python,tensorflow","A_Id":45282483,"CreationDate":"2017-07-24T13:39:00.000","Title":"Tensorflow: combining two tensors with dimension X into one tensor with dimension X+1","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to learning Python, and some of its libraries (numpy, pandas).\nI have found a lot of documentation on how numpy ndarrays, pandas series and python dictionaries work. \nBut owing to my inexperience with Python, I have had a really hard time determining when to use each one of them. And I haven't found any best-practices that will help me understand and decide when it is better to use each type of data structure.\nAs a general matter, are there any best practices for deciding which, if any, of these three data structures a specific data set should be loaded into?\nThanks!","AnswerCount":5,"Available Count":1,"Score":0.0399786803,"is_accepted":false,"ViewCount":27323,"Q_Id":45285743,"Users Score":1,"Answer":"Numpy is very fast with arrays, matrix, math.\nPandas series have indexes, sometimes it's very useful to sort or join data.\nDictionaries is a slow beast, but sometimes it's very handy too.\nSo, as it was already was mentioned, it depends on use case which data types and tools to use.","Q_Score":48,"Tags":"python,pandas,numpy","A_Id":45285878,"CreationDate":"2017-07-24T16:32:00.000","Title":"When to use pandas series, numpy ndarrays or simply python dictionaries?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I use two GTX 980 gpu.\nWhen I am dealing with slim in tensorflow.\nUsually, i have a problem so called 'Out of Memory'.\nSo, I want to use two gpu at the same time.\nHow can I use 2 gpu?\nOh, sorry for my poor english skill. :(","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":599,"Q_Id":45294801,"Users Score":0,"Answer":"By default, if the GPU version of TensorFlow is installed, TensorFlow will use all the GPU available.\nTo control the GPU memory allocation, you can use the tf.ConfigProto().gpu_options.","Q_Score":2,"Tags":"python,tensorflow","A_Id":45297247,"CreationDate":"2017-07-25T05:44:00.000","Title":"How can I use 2 gpu to calculate in tensorflow?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using tensorflow to scale images by a factor of 2. But since the tensor (batchsize, height, width, channels) determines the resolution it only accepts images of only one resolution for inference and training.\nFor other resolutions I have to modify the code and retrain the model. Is it possible to make my code resolution independent? In theory convolutions of images are resolution independent, I don't see a reason why this wouldn't be possible.\nI have no idea how to do this in tensorflow though. Is there anything out there to help me with this?\nThanks","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":208,"Q_Id":45299561,"Users Score":0,"Answer":"Okay so here is what I did:\ninput and output tensors now have the shape (batchsize, None, None, channels)\nThe training images now have to be resized outside of the network.\nImportant reminder: training images have to be the same size since they are in batches! Images in one batch have to have the same size. When inferencing the batch size is 1 so the size does not matter.","Q_Score":1,"Tags":"python,tensorflow,resolution,inference,tensor","A_Id":45456995,"CreationDate":"2017-07-25T09:43:00.000","Title":"Variable Resolution with Tensorflow for Superresolution","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How can I change G_h1 = tf.nn.relu(tf.matmul(z, G_W1) + G_b1) to leaky relu? I have tried looping over the tensor using max(value, 0,01*value) but I get TypeError: Using a tf.Tensor as a Python bool is not allowed. \nI also tried to find the source code on relu on Tensorflow github so that I can modify it to leaky relu but I couldn't find it..","AnswerCount":4,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":19787,"Q_Id":45307072,"Users Score":24,"Answer":"If alpha < 1 (it should be), you can use tf.maximum(x, alpha * x)","Q_Score":16,"Tags":"python,tensorflow,neural-network","A_Id":45308393,"CreationDate":"2017-07-25T15:02:00.000","Title":"using leaky relu in Tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to create a python script that zooms in and out of matplotlib graphs along the horizontal axis. My plot is a set of horizontal bar graphs.\nI also want to make that able to take any generic matplotlib graph. \nI do not want to just load an image and zoom into that, I want to zoom into the graph along the horizontal axis. (I know how to do this)\nIs there some way I can save and load a created graph as a data file or is there an object I can save and load later?\n(typically, I would be creating my graph and then displaying it with the matplotlib plt.show, but the graph creation takes time and I do not want to recreate the graph every time I want to display it)","AnswerCount":2,"Available Count":1,"Score":0.2913126125,"is_accepted":false,"ViewCount":6630,"Q_Id":45310481,"Users Score":3,"Answer":"@Cedric's Answer.\nAdditionally, if you get the pickle error for pickling functions, add the 'dill' library to your pickling script. You just need to import it at the start, it will do the rest.","Q_Score":4,"Tags":"python,matplotlib","A_Id":45312185,"CreationDate":"2017-07-25T17:55:00.000","Title":"python matplotlib save graph as data file","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I try to install TensorFlow via pip (pip install tensorflow) but get this error\n\ncould not find a version that satisfies the requirement tensorflow (from versions: )\n\nIs there a solution to this problem? I still wish to install it via pip","AnswerCount":5,"Available Count":1,"Score":0.0399786803,"is_accepted":false,"ViewCount":88521,"Q_Id":45316569,"Users Score":1,"Answer":"If you are using windows:\nIf you take a gander at TensorFlow website under windows PIP installation first line says.\n\"Pip installation on Windows\nTensorFlow supports only 64-bit Python 3.5 on Windows. We have tested the pip packages with the following distributions of Python:\"\nNow either install python 3.5, or use the unofficial version of Tensorflow from ANACONDA.\nother way is to Download and install docker toolbox for windows https:\/\/www.docker.com\/docker-toolbox Open a cmd window, and type: docker run -it b.gcr.io\/tensorflow\/tensorflow This should bring up a linux shell. Type python and I think all would be well!","Q_Score":31,"Tags":"python,tensorflow,module,pip,installation","A_Id":45316714,"CreationDate":"2017-07-26T02:46:00.000","Title":"How to install Tensorflow on Python 2.7 on Windows?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataset with 330 samples and 27 features for each sample, with a binary class problem for Logistic Regression.\nAccording to the \"rule if ten\" I need at least 10 events for each feature to be included. Though, I have an imbalanced dataset, with 20% o positive class and 80% of negative class.\nThat gives me only 70 events, allowing approximately only 7\/8 features to be included in the Logistic model.\nI'd like to evaluate all the features as predictors, I don't want to hand pick any features.\nSo what would you suggest? Should I make all possible 7 features combinations? Should I evaluate each feature alone with an association model and then pick only the best ones for a final model?\nI'm also curious about the handling of categorical and continuous features, can I mix them? If I have a categorical [0-1] and a continuous [0-100], should I normalize?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":772,"Q_Id":45369106,"Users Score":0,"Answer":"Your best choice would be to use L1 regularized logistic regression (aka Lasso regression). In case you're not familiar with it, the algorithm automatically selects some of the features by penalizing those that do not lead to increased accuracy (in layman terms).\nYou can increase\/decrease this regularization strength (it's just a parameter) till your model achieved the highest accuracy (or some other metric) on a test set or in a cross-validation procedure.","Q_Score":0,"Tags":"python,machine-learning,statistics,logistic-regression,prediction","A_Id":45370757,"CreationDate":"2017-07-28T08:57:00.000","Title":"Performing Logistic Regression with a large number of features?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm having data with around 60 features and most will be zeros most of the time in my training data only 2-3 cols may have values( to be precise its perf log data). however, my test data will have some values in some other columns. \nI've done normalization\/standardization(tried both separately) and feed it to PCA\/SVD(tried both separately). I used these features in to fit my model but, it is giving very inaccurate results.  \nWhereas, if I skip normalization\/standardization step and directly feed my data to PCA\/SVD and then to the model, its giving accurate results(almost above 90% accuracy).\nP.S.: I've to do anomaly detection so using Isolation Forest algo. \nwhy these results are varying?","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":2142,"Q_Id":45370442,"Users Score":1,"Answer":"Any features that only have zeros (or any other constant value) in the training set, are not and cannot be useful for any ML model. You should discard them. The model cannot learn any information from them so it won't matter that the test data do have some non-zero values.\nGenerally, you should do normalization or standardization before feeding data for PCA\/SVD, otherwise these methods will catch wrong patterns in the data (e.g. if features are on a different scale between each other).\nRegarding the reason behind such a difference in the accuracy, I'm not sure. I guess it has to do with some peculiarities of the dataset.","Q_Score":2,"Tags":"python,machine-learning,pca,svd,normalize","A_Id":45370664,"CreationDate":"2017-07-28T09:58:00.000","Title":"Is it good to normalization\/standardization data having large number of features with zeros","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm having data with around 60 features and most will be zeros most of the time in my training data only 2-3 cols may have values( to be precise its perf log data). however, my test data will have some values in some other columns. \nI've done normalization\/standardization(tried both separately) and feed it to PCA\/SVD(tried both separately). I used these features in to fit my model but, it is giving very inaccurate results.  \nWhereas, if I skip normalization\/standardization step and directly feed my data to PCA\/SVD and then to the model, its giving accurate results(almost above 90% accuracy).\nP.S.: I've to do anomaly detection so using Isolation Forest algo. \nwhy these results are varying?","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":2142,"Q_Id":45370442,"Users Score":3,"Answer":"Normalization and standarization (depending on the source they sometimes are used equivalently, so I'm not sure what you mean exactly by each one in this case, but it's not important) are a general recommendation that usually works well in problems where the data is more or less homogeneously distributed. Anomaly detection however is, by definition, not that kind of problem. If you have a data set where most of the examples belong to class A and only a few belong to class B, it is possible (if not necessary) that sparse features (features that are almost always zero) are actually very discriminative for your problem. Normalizing them will basically turn them to zero or almost zero, making it hard for a classifier (or PCA\/SVD) to actually grasp their importance. So it is not unreasonable that you get better accuracy if you skip the normalization, and you shouldn't feel you are doing it \"wrong\" just because you are \"supposed to do it\"\nI don't have experience with anomaly detection, but I have some with unbalanced data sets. You could consider some form of \"weighted normalization\", where the computation of the mean and variance of each feature is weighted with a value inversely proportional to the number of examples in the class (e.g. examples_A ^ alpha \/ (examples_A ^ alpha + examples_B ^ alpha), with alpha some small negative number). If your sparse features have very different scales (e.g. one is 0 in 90% of cases and 3 in 10% of cases and another is 0 in 90% of cases and 80 in 10% of cases), you could just scale them to a common range (e.g. [0, 1]).\nIn any case, as I said, do not apply techniques just because they are supposed to work. If something doesn't work for your problem or particular dataset, you are rightful not to use it (and trying to understand why it doesn't work may yield some useful insights).","Q_Score":2,"Tags":"python,machine-learning,pca,svd,normalize","A_Id":45371303,"CreationDate":"2017-07-28T09:58:00.000","Title":"Is it good to normalization\/standardization data having large number of features with zeros","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Gridsearhcv uses StratifiedKFold or KFold. So my question is that should I split my data into train and test before using gridsearch, then do fitting only for test data? I am not sure whether it is necessary because cv method already splits the data but I have seen some examples which split data beforehand. \nThank you.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":8338,"Q_Id":45394527,"Users Score":23,"Answer":"GridSearchCV will take the data you give it, split it into Train and CV set and train algorithm searching for the best hyperparameters using the CV set. You can specify different split strategies if you want (for example proportion of split).\nBut when you perform hyperparameter tuning information about dataset still 'leaks' into the algorithm.\nHence I would advice to take the following approach:\n1) Take your original dataset and hold out some data as a test set (say, 10%)\n2) Use grid search on remaining 90%. Split will be done for you by the algorithm here.\n3) After you got optimal hyperparameters, test it on the test set from #1 to get final estimate of the performance you can expect on new data.","Q_Score":12,"Tags":"python,machine-learning,scikit-learn,grid-search","A_Id":45394598,"CreationDate":"2017-07-29T22:08:00.000","Title":"Do I need to split data when using GridSearchCV?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there any real difference between the math functions performed by numpy and tensorflow. For example, exponential function, or the max function? \nThe only difference I noticed is that tensorflow takes input of tensors, and not numpy arrays.\nIs this the only difference, and no difference in the results of the function, by value?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":2450,"Q_Id":45410644,"Users Score":1,"Answer":"Of course there is a real difference. Numpy works on arrays which can use highly optimized vectorized computations and it's doing pretty well on CPU whereas tensorflow's math functions are optimized for GPU where many matrix multiplications are much more important. So the question is where you want to use what. For CPU, I would just go with numpy whereas for GPU, it makes sense to use TF operations.","Q_Score":6,"Tags":"python,numpy,tensorflow","A_Id":45410830,"CreationDate":"2017-07-31T08:18:00.000","Title":"Tensorflow vs Numpy math functions","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I built and trained my nn and now it is time to make predictions for the given input data. But I don't know the proper way to make fast predictions with the trained nn. What I am currently doing is loading model every time and making predictions on it. I wonder if there is a way to load the model on memory permanently (for a session) and then make predictions.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":998,"Q_Id":45413651,"Users Score":4,"Answer":"One way would be to use a Jupyter notebook, load your model in one cell and do continouus predictions in subsequent cells. \nAnother way is to setup a server with Flask and run predictions against a simple API.","Q_Score":3,"Tags":"python-3.x,deep-learning,keras","A_Id":45414023,"CreationDate":"2017-07-31T10:40:00.000","Title":"Keras: How to make fast predictions with trained network?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a pandas GroupBy object. I am using head(k) to extract the first k elements of each group into a dataframe and I want to also extract the complement. Each group has a nonconstant size.\nIs there any straightforwards way of doing this?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":433,"Q_Id":45428631,"Users Score":0,"Answer":"Yesy, You can use reindex the new dataframe using the reset_index() method.","Q_Score":4,"Tags":"python,pandas,pandas-groupby","A_Id":45428708,"CreationDate":"2017-08-01T03:47:00.000","Title":"Getting all but the first k rows from a group in a GroupBy object","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to get Matlab's python engine to work with my Anaconda installation on Linux. But I'm not quite getting it right.\nAnaconda's Python version: 3.6 (created a virtual-env for python 3.4)\nMatlab Version: 2016b\nPath to matlab root: \/usr\/local\/MATLAB\nPath to Anaconda: \/home\/fire-trail\/anaconda3\nVirtual env: py34\nI installed matlab engine via official documentation from mathworks but it installs it in the default Linux Python installation and that too in Python 2.7\nI want Anaconda 3.4 virtual env (py34) to find matlab engine.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":594,"Q_Id":45431931,"Users Score":1,"Answer":"Did it myself. Just copied the matlab folder which was formed in matlab directory for  py2.7 to my anaconda's virtual-env's site-packages.\nAccording to the paths mentioned above in question, you need to do this on linux terminal.\ncp \/usr\/local\/MATLAB\/R2016a\/extern\/engines\/python\/build\/lib.linux-x86_64-2.7\/matlab \/home\/fire-trail\/anaconda3\/envs\/py34\/lib\/python3.4\nand it will work with py34 in anaconda.\nremember that min requirement for matlab engine in linux is, matlab 2014b and python 2.7\nhope this helps other.","Q_Score":2,"Tags":"linux,matlab,python-3.x,anaconda,ubuntu-16.04","A_Id":45432298,"CreationDate":"2017-08-01T07:49:00.000","Title":"Linux[Ubuntu 16.04]-Installing MATLAB engine for Anaconda Python3","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am currently reading \"Reinforcement Learning\" from Sutton & Barto and I am attempting to write some of the methods myself.\nPolicy iteration is the one I am currently working on. I am trying to use OpenAI Gym for a simple problem, such as CartPole or continuous mountain car. \nHowever, for policy iteration, I need both the transition matrix between states and the Reward matrix. \nAre these available from the 'environment' that you build in OpenAI Gym. \nI am using python. \nIf not, how do I calculate these values, and use the environment?","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1540,"Q_Id":45437357,"Users Score":2,"Answer":"No, OpenAI Gym environments will not provide you with the information in that form. In order to collect that information you will need to explore the environment via sampling: i.e. selecting actions and receiving observations and rewards. With these samples you can estimate them.\nOne basic way to approximate these values is to use LSPI (least square policy iteration), as far as I remember, you will find more about this in Sutton too.","Q_Score":3,"Tags":"python,machine-learning,reinforcement-learning,openai-gym","A_Id":45468144,"CreationDate":"2017-08-01T12:05:00.000","Title":"Implementing Policy iteration methods in Open AI Gym","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have been struggling to understand the use of size parameter in the gensim.models.Word2Vec\nFrom the Gensim documentation, size is the dimensionality of the vector. Now, as far as my knowledge goes, word2vec creates a vector of the probability of closeness with the other words in the sentence for each word. So, suppose if my vocab size is 30 then how does it create a vector with the dimension greater than 30? Can anyone please brief me on the optimal value of Word2Vec size? \nThank you.","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":14983,"Q_Id":45444964,"Users Score":21,"Answer":"size is, as you note, the dimensionality of the vector.\nWord2Vec needs large, varied text examples to create its 'dense' embedding vectors per word. (It's the competition between many contrasting examples during training which allows the word-vectors to move to positions that have interesting distances and spatial-relationships with each other.)\nIf you only have a vocabulary of 30 words, word2vec is unlikely an appropriate technology. And if trying to apply it, you'd want to use a vector size much lower than your vocabulary size \u2013 ideally much lower. For example, texts containing many examples of each of tens-of-thousands of words might justify 100-dimensional word-vectors.\nUsing a higher dimensionality than vocabulary size would more-or-less guarantee 'overfitting'. The training could tend toward an idiosyncratic vector for each word \u2013 essentially like a 'one-hot' encoding \u2013 that would perform better than any other encoding, because there's no cross-word interference forced by representing a larger number of words in a smaller number of dimensions. \nThat'd mean a model that does about as well as possible on the Word2Vec internal nearby-word prediction task \u2013 but then awful on other downstream tasks, because there's been no generalizable relative-relations knowledge captured. (The cross-word interference is what the algorithm needs, over many training cycles, to incrementally settle into an arrangement where similar words must be similar in learned weights, and contrasting words different.)","Q_Score":9,"Tags":"python,gensim,word2vec","A_Id":45453040,"CreationDate":"2017-08-01T18:12:00.000","Title":"Python: What is the \"size\" parameter in Gensim Word2vec model class","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have been struggling to understand the use of size parameter in the gensim.models.Word2Vec\nFrom the Gensim documentation, size is the dimensionality of the vector. Now, as far as my knowledge goes, word2vec creates a vector of the probability of closeness with the other words in the sentence for each word. So, suppose if my vocab size is 30 then how does it create a vector with the dimension greater than 30? Can anyone please brief me on the optimal value of Word2Vec size? \nThank you.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":14983,"Q_Id":45444964,"Users Score":0,"Answer":"It's equal to vector_size.\nTo make it easy, it's a uniform size of dimension of the output vectors for each word that you trained with word2vec.","Q_Score":9,"Tags":"python,gensim,word2vec","A_Id":65432085,"CreationDate":"2017-08-01T18:12:00.000","Title":"Python: What is the \"size\" parameter in Gensim Word2vec model class","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"During the process of making a lmdb file,we are supposed to make a train.txt and val.txt file,i have already made a train.txt file which consists of the image name space its corresponding label.Ex image1.JPG 0.\nNow that i have to make the val.txt file im confused as to how do i give it its corresponding values since it is my test data and i am hoping to predict those.Can anyone tell me what thisval.txt file is and what is it supposed to be doing.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":427,"Q_Id":45446829,"Users Score":0,"Answer":"You are confusing test and validation sets. A validation set is a set where you know the labels (like in training) but you do not train on it. The validation set is used to make sure you are not overfitting the training data.\nAt test time you may present your model with unlabeled data and make prediction for these samples.","Q_Score":1,"Tags":"python,caffe","A_Id":45447380,"CreationDate":"2017-08-01T20:08:00.000","Title":"Caffe LMDB train and val.txt","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm new to opencv and using ubuntu 14.04, I'm confused of the difference with opencv, python-opencv, and libopencv, as I have libopencv and python-opencv installed in my system, but I there is no cv interface accessible, so I have to install opencv which is much hard than python-opencv and libopencv.","AnswerCount":2,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":16239,"Q_Id":45450706,"Users Score":4,"Answer":"libopencv is the debian\/ubuntu package while python-opencv is the python wrapper and can be accessed using cv2 interface like COLDSPEED mentioned","Q_Score":9,"Tags":"python,opencv,ubuntu-14.04","A_Id":45450859,"CreationDate":"2017-08-02T02:57:00.000","Title":"What's the difference with opencv, python-opencv, and libopencv?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have developed a desktop application. It includes a neural network as a part of the application. Now I'm confused what to do after training. Can I make an executable out of it as usual method? \nPlease someone explain what should do, because I have no idea how to pass this milestone. I've tried searching neural network tutorials. But none of them helped me with this problem.         \nIf someone wants to know, I have used numpy and openCV only.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":248,"Q_Id":45462207,"Users Score":0,"Answer":"Neural network weights are just data. You can store this any way you like along with the distributed application. As you have used Numpy to create the weights and biases array, you can probably just use pickle - add save_network function or similar name (used in training program only) and load_network function. If your weights and biases are just a bunch of local variables, you will want put them into a structure like a dict first.","Q_Score":0,"Tags":"python,numpy,neural-network","A_Id":45462707,"CreationDate":"2017-08-02T13:26:00.000","Title":"How to deploy a desktop application that includes a neural network?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using an MLPRegressor to solve a problem and would like to plot the loss function, i.e., by how much the loss decreases in each training epoch. However, the attribute model.loss_ available for the MLPRegressor only allows access to the last loss value. Is there any possibility to access the whole loss history?","AnswerCount":2,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":7290,"Q_Id":45488558,"Users Score":4,"Answer":"Very simple actually: model.loss_curve_ gives you the values of the loss for each epoch. You can then easily plot the learning curve by putting the epochs on the x axis and the values mentioned above on the y axis","Q_Score":5,"Tags":"python,scikit-learn","A_Id":48011076,"CreationDate":"2017-08-03T15:26:00.000","Title":"Loss history for MLPRegressor","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm working on a machine learning problem in which there are many missing values in the features. There are 100's of features and I would like to remove those features that have too many missing values (it can be features with more than 80% missing values). How can I do that in Python?\nMy data is a Pandas dataframe.","AnswerCount":9,"Available Count":2,"Score":0.0444152037,"is_accepted":false,"ViewCount":35470,"Q_Id":45515031,"Users Score":2,"Answer":"The fastest way to find the sum of NaN or the percentage by columns is:\n\nfor the sum: df.isna().sum()\nfor the percentage: df.isna().mean()","Q_Score":9,"Tags":"python,pandas,dataframe,scikit-learn,missing-data","A_Id":57315631,"CreationDate":"2017-08-04T20:30:00.000","Title":"How to remove columns with too many missing values in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm working on a machine learning problem in which there are many missing values in the features. There are 100's of features and I would like to remove those features that have too many missing values (it can be features with more than 80% missing values). How can I do that in Python?\nMy data is a Pandas dataframe.","AnswerCount":9,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":35470,"Q_Id":45515031,"Users Score":0,"Answer":"One thing about dropna() according to the documentation, thresh argument specifies the number of non-NaN to keep.","Q_Score":9,"Tags":"python,pandas,dataframe,scikit-learn,missing-data","A_Id":66210039,"CreationDate":"2017-08-04T20:30:00.000","Title":"How to remove columns with too many missing values in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm working on a Raspberry PI, an embedded linux platform with Raspbian Jessie where Python 2.7 is already installed, and I have OpenCV algorithms that must run in real-time and must apply several HAAR Cascade classifiers on the same image. Is there any method to reduce the time of these operations? such as multithreading for example?\nI also hear about GPU calculations but I didn't know from where I can start.\nThank you for the help.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":669,"Q_Id":45517925,"Users Score":0,"Answer":"If you haven't already done so, you should consider the following:\n\nReduce image size to the minimum required size for recognizing the target object for each classifier. If different objects require different resolutions, you can even use a set of copies of the original image, with different sizes.\nIdentify search regions for each classifier and thereby reduce the search area. For example, if you are searching for face landmarks, you can define search regions for the left eye, right eye, nose, and mouth after running the face detector and finding the rectangle that contains the face.\n\nI am not very sure if optimization is going to be very helpful, because OpenCv already does some hardware optimization.","Q_Score":0,"Tags":"python,opencv,raspberry-pi,raspbian,opencv3.0","A_Id":45788917,"CreationDate":"2017-08-05T03:37:00.000","Title":"How to run openCV algorithms in real-time on Raspberry PI3","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to Spark and using python to write jobs using pyspark. I wanted to run my script on a yarn cluster and remove the verbose logging by sending a log4j.properties for setting logging level to WARN using --files tag. I have a local csv file that the script uses and i need to include this as well. How do I use --files tag to include both the files?\nI am using the following command:\n\/opt\/spark\/bin\/spark-submit --master yarn --deploy-mode cluster --num-executors 50 --executor-cores 2 --executor-memory 2G --files \/opt\/spark\/conf\/log4j.properties .\/list.csv  .\/read_parquet.py\nBut I get the following error:\nError: Cannot load main class from JAR file:\/opt\/spark\/conf\/.\/list.csv\n`","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":4088,"Q_Id":45519339,"Users Score":1,"Answer":"you can send comma separated files in a string like this through file paths  :\n--files \"filepath1,filepath2,filepath3\" \\\nworked for me!!","Q_Score":0,"Tags":"python,pyspark","A_Id":66559845,"CreationDate":"2017-08-05T07:16:00.000","Title":"Pyspark: How to use --files tag for multiple files while running job on Yarn cluster","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When slicing a dataframe using loc, \ndf.loc[start:end]\nboth start and end are included. Is there an easy way to exclude the end when using loc?","AnswerCount":6,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":11186,"Q_Id":45523749,"Users Score":17,"Answer":"Easiest I can think of is df.loc[start:end].iloc[:-1]. \nChops off the last one.","Q_Score":20,"Tags":"python,pandas,indexing","A_Id":49416048,"CreationDate":"2017-08-05T15:49:00.000","Title":"Pandas slicing excluding the end","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am having some trouble with NLTK's FreqDist. Let me give you some context first:\n\nI have built a web crawler that crawls webpages of companies selling wearable products (smartwatches etc.). \nI am then doing some linguistic analysis and for that analysis I am also using some NLTK functions - in this case FreqDist.\nnltk.FreqDist works fine in general - it does the job and does it well; I don't get any errors etc.\n\nMy only problem is that the word \"heart rate\" comes up often and because I am generating a list of the most frequently used words, I get heart and rate separately to the tune of a few hundred occurrences each.\nNow of course rate and heart can both occur without being used as \"heart rate\" but how do I count the occurrences of \"heart rate\" instead of just the 2 words separately and I do mean in an accurate way. I don't want to subtract one from the other in my current Counters or anything like that.\nThank you in advance!","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1205,"Q_Id":45531514,"Users Score":0,"Answer":"This is a well-known problem in NLP and it is often referred to Tokenization. I can think about two possible solutions:\n\ntry different NLTK tokenizers (e.g. twitter tokenizer), which maybe will be able to cover all of your cases\nrun a Name Entity Recognition (NER) on your sentences. This allows you to recognise entity present in the text. This could work because it can recognise Heart rate as a single entity, thus as a single token.","Q_Score":0,"Tags":"python,nltk","A_Id":45533385,"CreationDate":"2017-08-06T11:23:00.000","Title":"NLTK FreqDist counting two words as one","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"To be clear I have no idea what I'm doing here and any help would be useful.\nI have a number of saved files, Keras neural network models and dataframes. I want to create a program that loads all of these files so that the data is there and waiting for when needed.\nAny data sent to the algorithm will be standardised and fed into the neural networks.\nThe algorithm may be called hundreds of times in quick succession and so I don't want to have to import the model and standardisation parameters every time as it will slow everything down.\nAs I understand it the plan is to have this program running in the background on a server and then somehow call it when required.\nHow would I go about setting up something like this? I'm asking here first because I've never attempted anything like this before and I don't even know where to start. I'm really hoping you can help me find some direction or maybe provide an example of something similar. Even a  search term that would help me research would be useful.\nMany thanks","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":28,"Q_Id":45537958,"Users Score":0,"Answer":"Wrap it in a Python based web server listening on some agreed-on port.  Hit it with HTTP requests when you want to supply a new file or retrieve results.","Q_Score":0,"Tags":"python,scripting,neural-network","A_Id":45537986,"CreationDate":"2017-08-07T00:35:00.000","Title":"Python have program running and ready for when called","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have 300 million rows and 3 columns in pandas.\nI want to pivot this to a wide format. \nI estimate that the total memory of in the \nCurrent long format is 9.6 GB.\nI arrived at this by doing\n300,000,000 * 3 * 8 bytes per \"cell\". \nI want to convert to a wide format with\n1.9 million rows * 1000 columns. \nI estimate that it should take 15.2 GB. \nWhen I pivot, the memory usage goes to 64gb (Linux resource monitor) and the swap gets used to 30gb and then\nThe ipython kernel dies, which I am assuming is an out of memory related death. \nAm I correct that during the generation of a pivot table the RAM usage will spike to more than the  64 GB of RAM that my desktop has?  Why does generating a pivot table  exceed system RAM?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":528,"Q_Id":45543799,"Users Score":0,"Answer":"If you're using DataFrame.pivot_table(), try using DataFrame.pivot() instead, it has much smaller memory consumption and is also faster.\nThis solution is only possible if you're not using a custom aggregation function to construct you pivot table and if the tuple of columns you're pivoting on don't have redundant combinations.","Q_Score":1,"Tags":"python,pandas,pivot-table","A_Id":49675393,"CreationDate":"2017-08-07T09:39:00.000","Title":"Pandas: calculating how much RAM is needed to generate a pivot table?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"This might sound silly but I'm just wondering about the possibility of modifying a neural network to obtain a probability density function rather than a single value when you are trying to predict a scalar. I know that when you are trying to classify images or words you can get a probability for each class, so I'm thinking there might be a way to do something similar with a continuous value and plot it. (Similar to the posterior plot with bayesian optimisation)\nSuch details could be interesting when deploying a model for prediction and could provide more flexibility than a single value.\nDoes anyone knows a way to obtain such an output?\nThanks!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":117,"Q_Id":45551212,"Users Score":0,"Answer":"Ok So I found a solution to this issue, though it adds a lot of overhead.\nInitially I thought the keras callback could be of use but despite the fact that it provided the flexibility that I wanted i.e.: train only on test data or only a subset and not for every test. It seems that callbacks are only given summary data from the logs.\nSo the first step what to create a custom metric that would do the same calculation as any metric with the 2 arrays ( the true value and the predicted value) and once those calculations are done, output them to a file for later use.\nThen once we found a way to gather all the data for every sample, the next step was to implement a method that could give a good measure of error. I'm currently implementing a handful of methods but the most fitting one seem to be bayesian bootstraping ( user lmc2179 has a great python implementation). I also implemented ensemble methods and gaussian process as alternatives or to use as other metrics and some other bayesian methods.\nI'll try to find if there are internals in keras that are set during the training and testing phases to see if I can set a trigger for my metric. The main issue with using all the data is that you obtain a lot of unreliable data points at the start since the network is not optimized. Some data filtering could be useful to remove a good amount of those points to improve the results of the error predictors.\nI'll update if I find anything interesting.","Q_Score":0,"Tags":"python,tensorflow,neural-network,keras","A_Id":45798320,"CreationDate":"2017-08-07T16:03:00.000","Title":"NN: outputting a probability density function instead of a single value","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have been looking now for a few hours and I can't find and answer to if can I rescale a image without using a imaging library. I am using python 3 don't really know if that matters. Thank you","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":39,"Q_Id":45551570,"Users Score":1,"Answer":"Images has so many formats, compressed, uncompressed, black & white or colored, they may be flat or layered, the may be constructed as raster or vector ones, so the answer is generally NO.","Q_Score":1,"Tags":"python,image,python-3.x","A_Id":45551702,"CreationDate":"2017-08-07T16:24:00.000","Title":"Can I rescale an image without using an imaging library?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"The main purpose of the LSTM is to utilize its memory property. Based on that what is the point of a stateless LSTM to exist? Don\u2019t we \u201cconvert\u201d it into a simple NN by doing that?\nIn other words.. Does the stateless use of LSTM aim to model the sequences (window) in the input data  - if we apply shuffle = False in the fit layer in keras - (eg. for a window of 10 time steps capture any pattern between 10-character words)? If yes why don\u2019t we convert the initial input data to match the form of the sequencers under inspection and then use a plain NN?\nIf we choose to have shuffle = True then we are losing any information that could be found in our data (e.g. time series data - sequences), don't we? In that case I would expect in to behave similarly to a plain NN and get the same results between the two by setting the same random seed.\nAm I missing something in my thinking?\nThanks!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":408,"Q_Id":45555122,"Users Score":0,"Answer":"Data for keras LSTM models is always in the form of (batch_size, n_steps, n_features). When you use shuffle=True, you are going to shuffle on the batch_size argument, thereby retaining the natural order of the sequence that is n_steps long.\nIn cases that each of your batch_size number of sequences are unrelated to each other, it would be natural to use a stateless model. Each array still contains order (a time-series), but does not depend on the others in your batch.","Q_Score":1,"Tags":"python,keras,lstm,rnn","A_Id":45555309,"CreationDate":"2017-08-07T20:22:00.000","Title":"What is the point of stateless LSTM to exists?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How do you print (in the terminal) a subset of columns from a pandas dataframe?\nI don't want to remove any columns from the dataframe; I just want to see a few columns in the terminal to get an idea of how the data is pulling through.\nRight now, I have  print(df2.head(10)) which prints the first 10 rows of the dataframe, but how to I choose a few columns to print?  Can you choose columns by their indexed number and\/or name?","AnswerCount":1,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":31744,"Q_Id":45577630,"Users Score":25,"Answer":"print(df2[['col1', 'col2', 'col3']].head(10)) will select the top 10 rows from columns 'col1', 'col2', and 'col3' from the dataframe without modifying the dataframe.","Q_Score":11,"Tags":"python,python-3.x,pandas","A_Id":45577693,"CreationDate":"2017-08-08T20:38:00.000","Title":"Print sample set of columns from dataframe in Pandas?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"A machine learning model has been trained to recognize the name of Animals and Plants. If suppose an automobile name is given, is it possible to say that the given name doesn't belong to the category animals or plants. If possible, kindly mention the methodology or algorithm which achieves this scenario.\nE.g. If 'Lion' or 'Coconut Tree' is given the model will be predicting either 'Animals' or 'Trees' category. If suppose, 'Audi' is given, is it possible to say that the given item belongs neither to  'Animals' or 'Plants'. (Note : I have heard that the machine learning model will try to fit into either one the category).","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":48,"Q_Id":45582182,"Users Score":1,"Answer":"A classifier actually gives you a probability of item belonging to a category, unless you add a final layer or post-processing that translates those probabilities to one and zeros. So, you can define a certain confidence threshold for probabilities and if classifier does not output probabilities above the threshold then call the output undecided.\nAn \"audi\" can still have features that make network believe it is tree for example.","Q_Score":0,"Tags":"python,machine-learning,neural-network,deep-learning,artificial-intelligence","A_Id":45587277,"CreationDate":"2017-08-09T05:18:00.000","Title":"Possibility of identification of non trained item in Machine Learning","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"My problem is \nI have full names with concatenated names, like \"davidrobert jones\". I want to split it to be \"david robert jones\". \nI tested the solutions using longest prefix matching algorithm with a names dictionary, but it's not that simple because a name could be written in many ways.\nI added phonetic matching algorithm too, but also there are many names that could have same pronunciation and so they're very ambiguous.\nWhat is the best solution to do so?, i believe machine learning could have an answer, but i don't know much about machine learning.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":180,"Q_Id":45610370,"Users Score":0,"Answer":"One possible algorithmic solution is to create a longer compositional dictionary representing all possible first_name last_name. Then for any given list of tokens as a name (words separated with space), for each token, find all dictionary enteries which have shortest edit distance to that token","Q_Score":0,"Tags":"python,algorithm,machine-learning,nlp","A_Id":45646885,"CreationDate":"2017-08-10T09:54:00.000","Title":"An algorithm to split concatenated names","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"This question may appear similar to previously asked questions, but it is not.\nI have a Python script with a single line: \nimport matplotlib\nThis fails with the error: \n'module' object is not callable\nrandom.py - print a random integer between 1 and 100\n(followed by 3 more lines of usage of random.py)\nIf I start python from the command line, then type\n\nimport matplotlib\n\nThat works. I can instantiate classes from the module, plot figures and so on.\nI am completely lost as to what is going on. Any clue appreciated.\nPython version 2.6.6 on 64 bit x86 Linux machine.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1820,"Q_Id":45618524,"Users Score":0,"Answer":"Matplotlib is an entire library, so if you are using import matplotlib as plt in your code, it might not work. Use 'import matplotlib.plyplot as plt' instead.","Q_Score":0,"Tags":"python,matplotlib,import,module","A_Id":72499535,"CreationDate":"2017-08-10T16:02:00.000","Title":"import matplotlib fails with \"'module' object not callable\" error","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a list of dataframes in R, with which I'm trying to select a particular dataframe as follows:\nx = listOfdf$df1$df2$df3\nNow, trying hard to find an equivalent way to do so in Python. Like, the syntax on how a particular DataFrame be selected from a list of DataFrames in Pandas Python.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":4311,"Q_Id":45628665,"Users Score":1,"Answer":"Found a solution to select a particular dataframe\/dataframe_column from a list of dataframes.\nIn R : x = listOfdf$df1$df2$df3\nIn Python : x = listOfdf['df1']['df2']['df3']\nThank you :)","Q_Score":1,"Tags":"r,python-3.x,list,pandas,dataframe","A_Id":45633068,"CreationDate":"2017-08-11T06:46:00.000","Title":"How to select a particular dataframe from a list of dataframes in Python equivalent to R?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to convert each workfront api object (Projects, Isues, Tasks, etc) output to a csv using Python.  Is there a recommended way to do this with the nested objects?  For example: json to csv, list to csv, etc?\nThanks,\nVictor","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":88,"Q_Id":45641038,"Users Score":0,"Answer":"Parsing nested objects is a challenge with Python. I would suggest opting for multiple CSVs for each set of objects referenced in the collection of another object (tasks contained in a project, for example). Ensure that you have a key that can be used to link entries between the files, such as the tasks' project ID.","Q_Score":0,"Tags":"python,workfront-api","A_Id":45641584,"CreationDate":"2017-08-11T17:47:00.000","Title":"workfront api output to csv","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there a parameter like \"scale_pos_weight\" in catboost package as we used to have in the xgboost package in python ?","AnswerCount":3,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":14128,"Q_Id":45644200,"Users Score":7,"Answer":"CatBoost also has scale_pos_weight parameter starting from version 0.6.1","Q_Score":7,"Tags":"python-3.x,catboost","A_Id":51193723,"CreationDate":"2017-08-11T21:44:00.000","Title":"for Imbalanced data dealing with cat boost","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to implement a feed-forward neural network, with the only difference from a usual one that I'd manually control the correspondence between input features and the first hidden layer neurons. For example, in the input layer I have features f1, f2, ..., f100, and in the first hidden layer I have h1, h2, ..., h10. I want the first 10 features f1-f10 fed into h1, and f11-f20 fed into h2, etc. \nGraphically, unlike the common deep learning technique dropout which is to prevent over-fitting by randomly omit hidden nodes for a certain layer, here what I want is to statically (fixed) omit certain hidden edges between input and hidden.\nI am implementing it using Tensorflow and didn't find a way of specifying this requirement. I also looked into other platforms such as pytourch and theano, but still haven't got an answer. Any idea of implementation using Python would be appreciated!","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":75,"Q_Id":45644367,"Users Score":0,"Answer":"I finally implemented the requirement by forcing certain blocks of the weight matrix corresponding to the first layer to be constant zero. That is, rather than just define w1 = tf.Variables(tf.random_normal([100,10])), I define ten 10 by 1 weight vectors and concatenate them with zeros to form a block diagonal matrix as final w1.","Q_Score":0,"Tags":"python,tensorflow,neural-network,deep-learning","A_Id":45657010,"CreationDate":"2017-08-11T22:02:00.000","Title":"How to implement a neural network model, with fixed correspondence between the input layer and the first hidden layer specified?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it possible to initialize a numpy.ndarray in a parallel fashion such that the corresponding pages will be distributed among the NUMA-nodes on the system?\nThe ndarray will later be passed to a multi-threaded C function which yields much better performance if the passed data is allocated in parallel (adhering to the first-touch policy)","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":167,"Q_Id":45644840,"Users Score":1,"Answer":"I'd initialize the array with np.empty and then pass the buffer to the C function. That should allow each core to grab whatever pages from the array it needs during the initialization.","Q_Score":0,"Tags":"python,multithreading,numpy,ctypes","A_Id":45644861,"CreationDate":"2017-08-11T22:58:00.000","Title":"NUMA-friendly initialization of numpy.ndarray","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there a hack so that I can increase the maximum recursion depth allowed? I only need it to be 2-3 times as big.\nI have a tensorflow graph with many tensors that are lazily constructed because they depend on other tensors (which may or may not be constructed yet). I can guarantee that this process terminates, and that I will not run out of memory. However, I run into this recursion depth error.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":252,"Q_Id":45645507,"Users Score":0,"Answer":"I had made a coding error that referenced a tensor in the construction of the same tensor. I don't know if changing recursion depth would solve similar, but unbugged, situations.","Q_Score":0,"Tags":"python-2.7,recursion,tensorflow","A_Id":45714701,"CreationDate":"2017-08-12T00:53:00.000","Title":"increase maximum recursion depth tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a list of ten 1-dimension-ndarrays, where each on hold a string, and I would like to one long list where every item will be a string (without using ndarrays anymore). How should I implement it?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":136,"Q_Id":45661124,"Users Score":0,"Answer":"I found a code that accomplishes my request:\n    x = [str(i[0]) for i in the_list]","Q_Score":1,"Tags":"python,list,numpy","A_Id":45661326,"CreationDate":"2017-08-13T13:57:00.000","Title":"How to convert a list of ndarray arrays into list in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm running a Keras model, with a submission deadline of 36 hours, if I train my model on the cpu it will take approx 50 hours, is there a way to run Keras on gpu?\nI'm using Tensorflow backend and running it on my Jupyter notebook, without anaconda installed.","AnswerCount":7,"Available Count":1,"Score":0.057080742,"is_accepted":false,"ViewCount":296920,"Q_Id":45662253,"Users Score":2,"Answer":"Of course. if you are running on Tensorflow or CNTk backends, your code will run on your GPU devices defaultly.But if Theano backends, you can use following \n\nTheano flags: \n\n\"THEANO_FLAGS=device=gpu,floatX=float32 python my_keras_script.py\"","Q_Score":154,"Tags":"python,tensorflow,keras,jupyter","A_Id":57688939,"CreationDate":"2017-08-13T15:58:00.000","Title":"Can I run Keras model on gpu?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a large dataset with 100 million rows of user online activities.  Each row includes a timestamp, user id, and site domain name. I would like to transform the dataset into a matrix of unique domain and user id, in order to perform some matrix operations.  The number of unique domains is about 100K and the number of unique user is about 10 million.  The matrix is very sparse.  \nWhat's the best packages or technologies to use? I realize my question is very broad.  I am using python and Google Cloud Platform, so I am hoping the solutions would be on those lines.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":48,"Q_Id":45666183,"Users Score":0,"Answer":"From python perspective, I'm currently using h5py to handle this big data. And it is fast too. You should check it out. However, I believe that Google might have provide something to handle this type of data.","Q_Score":0,"Tags":"python,google-cloud-platform,sparse-matrix,bigdata","A_Id":45666230,"CreationDate":"2017-08-14T00:58:00.000","Title":"How to load large dataset to python and perform matrix operations","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"OpenCV uses BGR encoding, and img[...,::-1] swaps the red and blue axes of img for when an image needs to be in the more common RGB. I've been using it for several months now but still don't understand how it works.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":207,"Q_Id":45680851,"Users Score":2,"Answer":"The slice operator works as with 3 params. Start(inclusive), end(exclusive) and step.\nIf start is not specified then it gets the start of the array, same with the end but with the last element. If step not specified the default is 1.\nThis way, if you do [1, 2, 3, 4][0:2] it will return [1, 2]\nIf you do [1, 2, 3, 4][1:] it will return [2, 3, 4]\nIf you do [1, 2, 3, 4][1::2] it will return [2, 4]\nFor negative indexes, that means iterate backwards so [1, 2, 3, 4][::-1] says, from the starting element until the last element iterate the list backwards 1 element at a time, returning [4, 3, 2, 1].\nAs the question is not entirely clear I hope this clears up the functioning and make you get to an answer.","Q_Score":1,"Tags":"python,opencv,slice","A_Id":45681068,"CreationDate":"2017-08-14T18:34:00.000","Title":"How does [...,::-1] work in slice notation?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using quantlib in python. In order to construct a DiscountCurve object, I need to pass a vector of Dates and corresponding discount factors. The problem is that, when I change the evaluation date to account for settlement days, the curve object is not shifted\/adjusted properly and the NPV of the bond does not change as a function of evaluation date.\nIs there any way around this? Do I have to construct a different DiscountCurve by shifting the dates whenever I change the number of settlement days?\nIdeally, instead of passing a vector of dates, I should be able to pass a vector of distances between consecutive dates but the very first date should be allowed to be the evaluation date.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":667,"Q_Id":45683889,"Users Score":1,"Answer":"No, unfortunately there's no way around this.  For this particular class, you'll have to recreate an instance when your settlement date changes.\nWriting a version of the class that takes distances between dates can be done, but it's not currently available.  If you write it, please consider creating a pull request for inclusion in the library.","Q_Score":0,"Tags":"python,quantitative-finance,quantlib","A_Id":45896146,"CreationDate":"2017-08-14T22:23:00.000","Title":"DiscountCurve is not aware of evaluation date in QuantLib python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to cntk and python. I have created a python program based on TrainResNet_CIFAR10.py to train 4736 of (64x64x3) images and test 2180 images with 4 classes.  After train 160 epochs, I got loss = 0.663 and metric = 29.37%.  Finished evaluation metric = 18.94%.  When I evaluate the train model based on CNTKLibraryCSEvalExamples.cs to test 2180 images, almost all 2180 are classified as one class (second class). My questions are:\n\nI assume loss is calculated from cross_entropy_with_softmax(z, label_var) and metric is using classification_error(z, label_var).  Am I correct and how are they actually determined? \nWhat does mean of metric = 29.37% and evaluation metric = 18.94%? Are they from train and test images, respectively?\nwhat could cause totally wrong evaluate results?\n\nAny help will be greatly appreciated.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":401,"Q_Id":45687374,"Users Score":1,"Answer":"(1) Yes. \n(2) 29.37% mean that 29.37% of the classification are correct. Evaluation is on the test data, assuming you are reading both training and test data. \n(3) Make sure that the input is the same format, by that I mean do you normalize or subtract the mean in your python, if so then you need to do the same in C#. Can you run the eval first using Python and see what result do you get?","Q_Score":3,"Tags":"python,cntk,resnet","A_Id":45702226,"CreationDate":"2017-08-15T06:13:00.000","Title":"how loss and metric are calculated in cntk","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm working a lot with Excel xlsx files which I convert using Python 3 into Pandas dataframes, wrangle the data using Pandas and finally write the modified data into xlsx files again. \nThe files contain also text data which may be formatted. While most modifications (which I have done) have been pretty straight forward, I experience problems when it comes to partly formatted text within a single cell:\nExample of cell content: \"Medical device whith remote control and a Bluetooth module for communication\"\nThe formatting in the example is bold and italic but may also be a color. \nSo, I have two questions:\n\nIs there a way of preserving such formatting in xlsx files when importing the file into a Python environment?\nIs there a way of creating\/modifying such formatting using a specific python library?\n\nSo far I have been using Pandas, OpenPyxl, and XlsxWriter but have not succeeded yet. So I shall appreciate your help!\nAs pointed out below in a comment and the linked question OpenPyxl does not allow for this kind of formatting:\nAny other ideas on how to tackle my task?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":190,"Q_Id":45688168,"Users Score":0,"Answer":"i have been recently working with openpyxl. Generally if one cell has the same style(font\/color), you can get the style from cell.font:    cell.font.bmeans bold andcell.font.i means italic, cell.font.color contains color object.\nbut if the style is different within one cell, this cannot help. only some minor indication on cell.value","Q_Score":0,"Tags":"python,excel,pandas,openpyxl,xlsxwriter","A_Id":45689273,"CreationDate":"2017-08-15T07:22:00.000","Title":"Modifying and creating xlsx files with Python, specifically formatting single words of a e.g. sentence in a cell","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"(I have already resolved this issue but it cost me two weeks of my time and my employer a couple of grand, so I'm sharing it here to save some poor soul.)\nMy company is converting our application from 32-bit to 64-bit. We create an executable using py2exe, using the bundle=2 option. The executable started crashing as soon as it tried to render a matplotlib plot.\nVersions:\npython==2.7.13,\nmatplotlib==2.0.0,\nnumpy==1.13.1,\npy2exe==0.6.10a1","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":69,"Q_Id":45702870,"Users Score":0,"Answer":"I tracked the error to the numpy library. Numpy calls numpy.linalg._umath_linalg.inv() and the program abruptly exits with no error message, warning, or traceback.\n_umath_linalg is a .pyd file and I discovered that this particular .pyd file doesn't like being called from library.zip, which is where py2exe puts it when using bundle option 2 or 1.\nThe solution is to exclude numpy in the py2exe setup script and copy the entire package folder into the distribution directory and add that directory to the system path at the top of the main python script.","Q_Score":0,"Tags":"python-2.7,numpy,matplotlib,64-bit,py2exe","A_Id":45702871,"CreationDate":"2017-08-15T23:14:00.000","Title":"Bundled executable crashes without warning when rendering plots","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm not sure where exactly to start with this one. I know I can do bit-wise logical combinations of masks like so: (mask1 & mask2) | mask3 | (mask4 & (mask5 | mask6))\nNow, if I had a user input a string like: '(criteria1 & criteria2) | criteria3 | (criteria4 & (criteria5 | criteria6))', but needed to interpret each criteria through a function to determine and return a mask, how can I retain the parentheses and logic and then combine the masks?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":25,"Q_Id":45717860,"Users Score":0,"Answer":"After some work I was able to do this with regular expressions and eval(). \nUsing regex, I extracted both the 'template' and the 'criteria'. The 'template' would look something like 1 & 2 | (3 & 4 & (5 or 6)), and the associated 'criteria' would be something like ['criteria1', 'criteria2', ..., 'criteria6']. Then I could manipulate the criteria however I wanted, then substitute the manipulated values back into the template string. Finally, I could just run eval(template) or whatever name of the final string to be executed.","Q_Score":0,"Tags":"python,dataframe,logical-operators,interpretation","A_Id":45743561,"CreationDate":"2017-08-16T15:41:00.000","Title":"interpreting a string to do and\/or combinations of dataframe masks","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to achieve a 3D-reconstruction algorithm with sfm,\nBut how should i set the parameters of the Camera Matrix?\nI have double cameras,both know their focal length.\nAnd how about Rotation Matrix and Translation Matrix from world view?\ni use python","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":574,"Q_Id":45725440,"Users Score":1,"Answer":"You already have a code for camera calibration and printing a camera matrix in your OpenCV installation. Go to this path if you are on windows -\nC:\\opencv\\sources\\samples\\python\nThere you have  a file called calibrate","Q_Score":1,"Tags":"python,opencv,matrix,camera,3d-reconstruction","A_Id":45727419,"CreationDate":"2017-08-17T01:51:00.000","Title":"How can i obtain Camera Matrix in 3Dreconstruction?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm after advice on how to debug what on Tensorflow is struggling with   when it hangs.\nI have a multi layer CNN which hangs upon global_variables_initializer() is run in the session. I am getting no errors or messages on the console output.\nIs there an intelligent way of debugging what Tensorflow is struggling with when it hangs instead of repeatedly commenting out lines of code that makes the graph, and re-running to see where it hangs. Would TensorFlow debugger (tfdbg) help? What options do I have?\nIdeally it would be great to just to break current execution and look at some stack or similar to see where the execution is hanging during the init.\nI'm currently running Tensorflow 0.12.1 with Python 3 inside a Jupiter notebook.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":944,"Q_Id":45729494,"Users Score":1,"Answer":"I managed to solve the problem. The tip from @amo-ej1 to run in a regular file was a step in the correct direction. This uncovered that the tensor flow process was killing itself off with a SIGKILL and returning an error code of 137.\nI tried Tensorflow Debugger tfdbg though this did not provide any further details as the problem was the graph did not initialize. I started to think the graph structure was incorrect, so I dumped out the graph structure using:\ntf.summary.FileWriter('.\/logs\/traing_graph', graph)\nI then used up Tensorboard to inspect the resultant summary graph structure data dumped out the the directory and found that the tensor dimensions of the Fully Connected layer was wrong , having a width of 15million !!?! (wrong)\nIt turned out that one of the configurable parameters of the graph was incorrect. It was picking the dimension of the layer 2 tensor shape  incorrectly from an incorrect addressing the previous tf.shape type property and it exploded the dimensions of the graph.\nThere were no OOM error messages in \/var\/log\/system.log so I am unsure why the graph initialisation caused the python tensorflow script process to die.\nI fixed the dimensions of the graph and graph initialization worked just fine!\nMy top tip is visualise your graph with Tensorboard before initialisation and training to do a quick check the resultant graph structure you coded it what you expected it to be. You probably will save yourself a lot of time! :-)","Q_Score":2,"Tags":"python,tensorflow","A_Id":45788066,"CreationDate":"2017-08-17T07:54:00.000","Title":"Debugging Tensorflow hang on global variables initialisation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"When running this example\uff1a\" python mnist_with_summaries.py \", it has\noccurred the following error:\ndetailed errors\uff1a\n\nTraceback (most recent call last):\n    File \"mnist_with_summaries.py\", line 214, in \n      tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)\n    File \"D:\\ProgramData\\Anaconda2\\envs\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\platform\\app.py\"\n  , line 48, in run\n      _sys.exit(main(_sys.argv[:1] + flags_passthrough))\n    File \"mnist_with_summaries.py\", line 186, in main\n      tf.gfile.MakeDirs(FLAGS.log_dir)\n    File \"D:\\ProgramData\\Anaconda2\\envs\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\lib\\io\\file_io.p\n  y\", line 367, in recursive_create_dir\n      pywrap_tensorflow.RecursivelyCreateDir(compat.as_bytes(dirname), status)\n    File \"D:\\ProgramData\\Anaconda2\\envs\\Anaconda3\\lib\\contextlib.py\", line 89, in exit\n      next(self.gen)\n    File \"D:\\ProgramData\\Anaconda2\\envs\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\errors\n  _impl.py\", line 466, in raise_exception_on_not_ok_status\n      pywrap_tensorflow.TF_GetCode(status))\n  tensorflow.python.framework.errors_impl.NotFoundError: Failed to create a directory: \/tmp\\tensorflow\n\nRunning environment\uff1awindows7+Anaconda3+python3.6+tensorflow1.3.0\nWhy?Any idea on how to resolve this problem?Thank you\uff01","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":249,"Q_Id":45731787,"Users Score":1,"Answer":"I have solved this problem. \nI changed line 204 and line 210 of mnist_with_summaries.py to the local directories, and I created some folders.\nOR, don't change the code, and I created some folders in the local disk where is the running environment according to the code. \n\nline 204: create \/tmp\/tensorflow\/mnist\/input_data \nline 210: create \/tmp\/tensorflow\/mnist\/logs\/mnist_with_summaries","Q_Score":1,"Tags":"python-3.x,tensorflow,mnist","A_Id":45747350,"CreationDate":"2017-08-17T09:47:00.000","Title":"When running \" python mnist_with_summaries.py \", it has occurred the error","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to visualize data computed by my program (neural network), by showing images while the program is working, creating a video that shows the progress in real time.\nIt should be pretty basic, but I'm new to Python, and I'm struggling to find the good framework to do this. It seems that with most libraries (Tkinter, graphics, matplotlib, etc), displaying a video stops the computation, and the user has to interact with the GUI (like close the window) to go back to the program. For now I use PIL.show() to display a single image without stopping the program, but it does not seem suited to video, because I cannot replace the displayed image by another, as the window is not handled by the program anymore.\nI'm using Linux Mint and Python 2.7.6\nSo what is the simplest way to do that ? Is there a library that is well-suited ? Or where can I find an example code doing that ?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":436,"Q_Id":45735688,"Users Score":0,"Answer":"Write a multithreading script that runs both, your computation script and a script for the images(where each of them can act as one frame for the video). Keep closing the image window each time the next image is computed. \nThis solution is makeshift but will work","Q_Score":0,"Tags":"python,matplotlib,graphics,real-time","A_Id":45740933,"CreationDate":"2017-08-17T12:53:00.000","Title":"Real-time animation in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":1,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I do not see any documentation on pandas explaining the parameter False passed into loc. Can anyone explain how () and [] differ in this case?","AnswerCount":3,"Available Count":1,"Score":0.1325487884,"is_accepted":false,"ViewCount":971,"Q_Id":45736874,"Users Score":2,"Answer":"For any python object, () invokes the __call__ method, whereas [] invokes the __getitem__ method (unless you are setting a value, in which case it invokes __setitem__). In other words () and [] invoke different methods, so why would you expect them to act the same?","Q_Score":14,"Tags":"python,pandas,indexing","A_Id":45737266,"CreationDate":"2017-08-17T13:45:00.000","Title":"Why can you do df.loc(False)['value'] in pandas?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I do wonder what is the parameter 'eval_steps' in learn.experiment in tensorflow ? Why would you run over the evaluation set several times every time you want to evaluate your model ?\nThanks!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":629,"Q_Id":45743894,"Users Score":2,"Answer":"If your evaluation is a whole epoch, you're right that it doesn't make much sense. eval_steps is more for the case when you're doing mini-batch evaluation and want to evaluate on multiple mini-batches.","Q_Score":2,"Tags":"python,tensorflow","A_Id":45804171,"CreationDate":"2017-08-17T19:51:00.000","Title":"What is eval_step in Experiment Tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to fit a custom function to some data points using curve_fit. I have tried 1 or two free parameters. I have used it other times. Now I am struggling to make a fit, because the algorithm returns always the initial input values, with infinite sigma, no matter what the initial values are. I have also tried to print the internal parameters with which my custom function is called, and I don't understand, my custom function is called just 4 times, the first three with always the same parameters and the last with a relative change of the parameter of 10^-8. this doesn't look right","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":985,"Q_Id":45761067,"Users Score":6,"Answer":"It is normal for the objective function to be called initially with very small (roughly 1e-8) changes in parameter values in order to calculate the partial derivatives to decide which way to go in parameter space.  If the result of the objective function does not change at all (not even at 1e-8 level) the fit will give up: changing the parameter values did not change the result.  \nI would first look into whether the result of your objective function is really sensitive to the parameters. If the changes to your result really are not sensitive to a 1e-8 change, but would be sensitive to a larger change, you may want to increase the value of epsfcn passed to scipy.optimize.leastsq.","Q_Score":3,"Tags":"python-3.x,scipy,curve-fitting","A_Id":45766815,"CreationDate":"2017-08-18T16:21:00.000","Title":"python 3.3 : scipy.optimize.curve_fit doesn't update the value of point","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"i have a chart pattern recognition program using neural networks written in python .instead of porting the whole code to C# ,i decided it was better to only send certain bits indicating the following :\nBuy=1,Sell=-1,Do nothing=0\nonce they are in C-sharp ,i could relay them to a third party program (multicharts) which would continuously call the C# dll function and receive these values after a certain time interval .\nmy question is ,is there a way to relay these bit's to C# and pack all of this in a dll ,which gets read by the 3rd party program ?\nthe whole reason i want to port to C# is because multicharts only reads in dll's and i dont think python has them.\nsorry for being naive ,i don't have very good grip on C# .","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":612,"Q_Id":45788655,"Users Score":1,"Answer":"Your options are as follow,\nUse a TCP socket, bind it to a port and listen for data in C# while the python application sends all data to it. C# has some great features for sockets such as System.Net.TcpClient\n and \nSystem.Net.TcpServer.\nYour other option is that if the C# application only needs to be run once it receives information from the python program and then it can die, you could have the python program start the C# one and pass it parameters containing the information that you need transmitted.\nBy the looks of it your question only asked if there was a way to communicate, these are probably the best two. Please let me know if I can help anymore.","Q_Score":0,"Tags":"c#,python,dll","A_Id":45789539,"CreationDate":"2017-08-21T02:39:00.000","Title":"sending data from python to c#","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am consistently getting this error under normal conditions. I am using the Python Cassandra driver (v3.11) to connect locally with RPC enabled. The issue presents itself after a period of time. y assumption was that it was related to max number of connections or queries. Any pointers on where to begin troubleshooting would be greatly appreciated.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":203,"Q_Id":45803795,"Users Score":0,"Answer":"Please check if your nodes are really listening by opening up a separate connection from say cqlsh terminal, as you say it is running locally so probably a single node. If that connects, you might want to see how many file handles are open, maybe it is running out of those. We had a similar problem couple of years back, that was attributed to available file handles.","Q_Score":1,"Tags":"python,python-3.x,cassandra,cassandra-3.0,datastax-python-driver","A_Id":45809411,"CreationDate":"2017-08-21T18:47:00.000","Title":"DataStax Cassandra cassandra.cluster.NoHostAvailable","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm currently using anaconda and python 3.6 on windows bash. Every time i want to use matplotlib I have to paste a copy of the matplotlibrc file into my working directory otherwise my code won't run or plot and I get the warning - \/home\/computer\/anaconda3\/lib\/python3.6\/site-packages\/matplotlib\/init.py:1022: UserWarning: could not find rc file;returning defaults\nmy matplotlibrc file is located at C:\\Users\\user\\AppData\\Local\\lxss\\home\\puter\\anaconda3\\lib\\python3.6\\site-packages\\matplotlib\\mpl-data\nI thought to fix this I could edit my .condarc file and set it to look for matplotlibrc in the correct directory. Could anyone tell me how to do this?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":950,"Q_Id":45813527,"Users Score":0,"Answer":"It looks like when anaconda or matplotlib was installed it's created the matplotlibrc file in C:\\Users\\user\\AppData\\Local\\lxss\\home\\puter\\anaconda3\\lib\\py\u200c\u200bthon3.6\\site-package\u200c\u200bs\\matplotlib\\mpl-dat\u200c\u200ba using the windows environment. This has caused the file not to be recognised in WSL.\nTo fix this create another matplotlibrc file in bash or whatever shell you're using. In the directory listed above copy the contents of the previously created matplotlibrc file into your new matplotlibrc file. Make sure you don't create this file in the windows environment otherwise it won't be recognised.","Q_Score":0,"Tags":"python,matplotlib,anaconda,windows-subsystem-for-linux","A_Id":45832556,"CreationDate":"2017-08-22T09:16:00.000","Title":"how to change matplotlibrc default directory","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I installed Tensorflow on my macOS Sierra using pip install tensorflow. \nIm getting the following error:\nOSError: [Errno 1] Operation not \npermitted:'\/var\/folders\/zn\/l9gmn4613677f6mlrh6prtb00000gn\/T\/pip-xv3AU6-uninstall\/System\/Library\/Frameworks\/Python.framework\/Versions\/2.7\/Extras\/lib\/python\/numpy-1.8.0rc1-py2.7.egg-info'\nIs there anyway to resolve this?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":109,"Q_Id":45824737,"Users Score":0,"Answer":"Use Python EasyInstall, is super easy:\nsudo easy_install pip","Q_Score":0,"Tags":"python-2.7,tensorflow","A_Id":45825713,"CreationDate":"2017-08-22T18:39:00.000","Title":"Error in installing Tensorflow on Mac","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"When selecting a data source for a graph in Excel, you can specify how the graph should treat empty cells in your data set (treat as zero, connect with next data point, leave gap). \nThe option to set this behavior is available in xlsxwriter with chart.show_blanks_as(), but I can't find it in openpyxl. If anyone knows where to find it or can confirm that it's not present, I'd appreciate it.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":101,"Q_Id":45825401,"Users Score":0,"Answer":"Asked the dev about it - \n\nThere is a dispBlanksAs property of the ChartContainer but this currently isn't accessible to client code.\n\nI looked through the source some more using that answer to guide me. The option is definitely in there, but you'd have to modify source and build locally to get at it.\nSo no, it's not accessible at this time.","Q_Score":0,"Tags":"python,excel,openpyxl","A_Id":45868428,"CreationDate":"2017-08-22T19:20:00.000","Title":"How to replicate the \"Show empty cells as\" functionality of Excel graphs","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I saw in tutorial (there were no further explanation) that we can process data to zero mean with x -= np.mean(x, axis=0) and normalize data with x \/= np.std(x, axis=0). Can anyone elaborate on these two pieces on code, only thing I got from documentations is that np.mean calculates arithmetic mean calculates mean along specific axis and np.std does so for standard deviation.","AnswerCount":4,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":42882,"Q_Id":45834276,"Users Score":2,"Answer":"Key here are the assignment operators. They actually performs some operations on the original variable.\na += c is actually equal to a=a+c.  \nSo indeed a (in your case x) has to be defined beforehand. \nEach method takes an array\/iterable (x) as input and outputs a value (or array if a multidimensional array was input), which is thus applied in your assignment operations.\nThe axis parameter means that you apply the mean or std operation over the rows. Hence, you take values for each row in a given column and perform the mean or std.\nAxis=1 would take values of each column for a given row.  \nWhat you do with both operations is that first you remove the mean so that your column mean is now centered around 0. Then, when you divide by std, you happen to reduce the spread of the data around this zero, and now it should roughly be in a [-1, +1] interval around 0.  \nSo now, each of your column values is centered around zero and standardized.\nThere are other scaling techniques, such as removing the minimal or maximal value and dividing by the range of values.","Q_Score":16,"Tags":"python,numpy,image-preprocessing","A_Id":45834527,"CreationDate":"2017-08-23T08:16:00.000","Title":"Numpy:zero mean data and standardization","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am looking for an elegant way to sample a dataset in a specific way. I found a few solutions, but I was wondering if any of you know a better way.\nHere is the task I am looking at:\nI want to balance my dataset, so that I have the same amount of instances for class 0 as for class 1, so in the example below we have 5 instances of class 1 and 11 instances of class 0:\n\nid | class\n------ | ------ \n1 | 1\n1 | 0\n1 | 0\n1 | 0\n1 | 0\n2 | 1\n2 | 1\n2 | 0\n2 | 0 \n2 | 0\n3 | 1\n3 | 1\n3 | 0\n3 | 0\n3 | 0\n3 | 0\n\nSofar I have just deleted randomly 6 instances of class 0, but I would like to prevent that all instances of one id could get deleted. I tried doing a stratified \"split\", with sklearn, but it does not work, because not every id contains more than 1 item. The desired output should look similar to this:\n\nid | class\n------ | ------ \n1 | 1\n1 | 0\n2 | 1\n2 | 1\n2 | 0\n2 | 0 \n3 | 1\n3 | 1\n3 | 0\n3 | 0\n\nAny good ideas?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":749,"Q_Id":45845425,"Users Score":1,"Answer":"My best guess: 'protect' one random row from each id (create separate dataframe with those rows), then delete from original dataframe until satisfied (including the fact that the classes in the 'protected' dataframe will line up flush with what remains) and concatenate the two dataframes?","Q_Score":1,"Tags":"python,pandas,dataframe,sampling,balance","A_Id":45845595,"CreationDate":"2017-08-23T16:55:00.000","Title":"Python Pandas Dataframe sampling","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm developing a line chart. The data is being generated by a sensor and is a tuple (timestamp, value). Sensor creates a new datapoint every 60 seconds or so.\nNow I want to display it in a graph and my limitation is about 900 points on then graph. In a daily view of that graph, I'd get about 1440 points and that's too much.\nI'm looking for a general way how to shrink my dataset of any size to fixed size (in my case 900) while it keeps the timestamp distribution linear.\nThanks","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1337,"Q_Id":45855209,"Users Score":1,"Answer":"I believe you are trying to resample your data. Your current sample rate is 1\/60 samples per second and you are trying to get to 1\/96 samples per second (900 \/ (24*60*60)). The ratio between the two rates is 5\/8.\nIf you search for \"python resample\" you will find other similar questions and articles involving numpy and pandas which have built in routines for it.\nTo do it manually you can first upsample by 5 to get to 7200 samples per second and then downsample by 8 to get down to 900 samples per second.\nTo upsample you can make a new list five times as long and fill in every fifth element with your existing data. Then you can do, say, linear interpolation to fill in the gaps.\nOne you do that you can downsample by simply taking every eighth element.","Q_Score":0,"Tags":"python,math,statistics","A_Id":45855698,"CreationDate":"2017-08-24T07:13:00.000","Title":"Reduce dataset to smaller size, keep the gist of information in the dataset","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to validate a system to detect more than 2 cluster in a  network graph. For this i need to create a synthetic graph with some cluster. The graph should be very large, more than 100k nodes at least. I s there any system to do this? Any known dataset with more than 2 cluster would also be suffice.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":182,"Q_Id":45857602,"Users Score":0,"Answer":"If you stick to networkx, you can generate two large complete graphs with nx.complete_graph(), merge them, and then add some edges connecting randomly chosen nodes in each graph. If you want a more realistic example, build dense nx.erdos_renyi_graph()s instead of the complete graphs.","Q_Score":0,"Tags":"python,graph,networkx","A_Id":45891842,"CreationDate":"2017-08-24T09:15:00.000","Title":"Synthetic network graph","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":1,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to find optimal parameters for doing classification using Catboost.\nI have training data and test data. I want to run the algorithm for say 500 iterations and then make predictions on test data. Next, I want to repeat this for 600 iterations and then 700 iterations and so on. I don't want to start from iteration 0 again. So, is there any way I can do this in Catboost algorithm?\nAny help is highly appreciated!","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":970,"Q_Id":45866292,"Users Score":0,"Answer":"You can run the algorithm for the maximum number of iterations and then use CatBoost.predict() with ntree_limit parameter or CatBoost.staged_predict() to try different number of iterations.","Q_Score":3,"Tags":"python-3.x,machine-learning,data-analysis,catboost","A_Id":45957039,"CreationDate":"2017-08-24T16:02:00.000","Title":"Use the previously trained model for further prediction in catboost","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I just installed tensorflow for Ubuntu 16.04 through Anaconda3.  I can run the test tensorflow script with python and have no issues.  But when I run it with ipython, it cannot find the modules.  How can I link ipython to read the same libraries as the python ones?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":50,"Q_Id":45873077,"Users Score":0,"Answer":"Did you notice your anaconda environment? Did you launch your ipython in the same environment as you installed tensorflow?","Q_Score":0,"Tags":"python,ubuntu,tensorflow,ipython,anaconda","A_Id":45873121,"CreationDate":"2017-08-25T01:22:00.000","Title":"Tensorflow install--python can import but ipython cannot","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm totally new with neural networks (NN) in python, and I do not know if NN can run in raspberry pi 3? for I think the problem is that NN requires good CPU\/GPU performance for training , data transferring and calculation. \nSo is it possible to train a NN with single class training data? inorder to save CPU\/GPU?. \nFor example I want the system to detect only the sea cucumber in an image.\nA good answer\/explanation or link to any example will be very appreciated. \nTHANKYOU PO","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":1350,"Q_Id":45891271,"Users Score":1,"Answer":"I'm not sure if I understand your question well.\nSingle class training data maybe not exist at all. If you want to detect only the sea cucumber, it is the two-classes classification problem right? It is the sea cucumber or not. Yes or no are two classes.\nYes right cool people implement the NN on raspberry pi. But to some extent it's just possible but not efficient. A good GPU will speed up much for training.\nA PC is enable to train some small NN.","Q_Score":1,"Tags":"python,neural-network","A_Id":45891349,"CreationDate":"2017-08-26T02:01:00.000","Title":"Neural network to detect one class of object only","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to ask about a multi parameters linear regression model.\nThe question is as following:\nWe have now data of 100 companies, and for each company, I have the data for parameter A,B,C,D for 3 seasons.(we can call it A1,A2,A3,B1,B2,B3..etc)\nWe assume that there is some relationship(which we do not know yet, and need to find)between A and BCD, and now we need to predict A for season 4 which is A4...\nMy method is calculate the relation using the formula of Ordinary least squares and get a final formula in the form as A4=x1*B4+x2*C4+x3*D4.\nI get B4, C4, D4 by simply do linear regression on B,C,D\nBut the problem is the A4 I get in this way is worse than just do linear regression for A...\nCan someone tell me a better solution for the problem?\nThanks","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":85,"Q_Id":45899846,"Users Score":0,"Answer":"I am also a rookie for data analysis and modeling. \nIf I faced this sort of problems, I might consider some questions like:\n\nWhether there is really a significant linear or generalized linear relationship between independent and dependent variables? Should I pre-process or transfer them before regression?\nWhether it is necessary to involve interactions among predictor variables?\nHow the quality of the data set used to train the model? Whether it is good enough for the true underlying relationship between Factors and Responses?\nShould I select a more suitable method to create the prediction model? For example, we usually choose Partial Least Squares Regression (PLS), other than Ordinary Least Squares Regression (OLS), to solve multicollinearity in my work area.\nHope these could be helpful for you.","Q_Score":0,"Tags":"python,regression","A_Id":45911480,"CreationDate":"2017-08-26T21:33:00.000","Title":"How to build multi parameters linear regression in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using LightGBM 2.0.6 Python API. My training data has around 80K samples and 400 features, and I am training a model with ~2000 iterations, and the model is for multi-class classification (#classes = 10). When the model is trained, and when I called model.feature_importance(), I encountered segmentation fault.\nI tried to generate artificial data to test (with the same number of samples, classes, iterations and hyperparameters), and I can successfully obtain the list of feature importance. Therefore I suspect whether the problem occurs depends on the training data.\nI would like to see if someone else has encountered this problem and if so how was it overcome. Thank you.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":639,"Q_Id":45913233,"Users Score":0,"Answer":"This is a bug in LightGBM; 2.0.4 doesn't have this issue. It should be also fixed in LightGBM master. So either downgrade to 2.0.4, wait for a next release, or use LightGBM master.\nThe problem indeed depends on training data; feature_importances segfault only when there are \"constant\" trees in the trained ensemble, i.e. trees with a single leaf, without any splits.","Q_Score":1,"Tags":"python,lightgbm","A_Id":45913568,"CreationDate":"2017-08-28T06:52:00.000","Title":"Encountered segmentation fault when calling feature_importance in LightGBM python API","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How can we join\/combine two models in Transfer Leaning in KERAS? \nI have two models:\nmodel 1 = My Model\nmodel 2 = Trained Model\nI can combine these models by putting the model 2 as input and then passed its output to the model 1, which is the conventional way.  \nHowever, I am doing it in other way. I want to put the model 1 as input and then passed its output to the model 2 (i.e. trained model one).","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2275,"Q_Id":45924742,"Users Score":0,"Answer":"The issue has been resolved.\nI used the model.add()function and then added all the required layers of both Model 1 and Model 2.\nThe following code would add the first 10 layers of Model 2 just after the Model 1.\nfor i in model2.layers[:10]:\n    model.add(i)","Q_Score":1,"Tags":"python,tensorflow,deep-learning,keras,pre-trained-model","A_Id":45928049,"CreationDate":"2017-08-28T17:50:00.000","Title":"Joining\/Combining two models for Transfer Leaning in KERAS","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I'm pretty new to ML and Datascience, so my question may be a little silly.\nI have a dataset, each row is a vector [a1,a2,a3,a3,...,an]. Those vectors are different not only in their measurements but also in number of n and the sum A = a1 + a2 + a3 +...+ an.\nMost of the vectors have 5-6 dimensions, with some exception at 15-20 dimensions. On average, their components often have value of 40-50.\nI have tried Kmeans, DBSCAN and GMM to cluster them:\n\nKmeans overall gives the best result, however, for vectors with 2-3 dimensions and vectors with low A, it often misclassifies.\nDBSCAN can only separate vector with low dimension and low A from the dataset, the rest it treats as noise.\nGMM separates the vectors with 5-10 dimension, low A, very good, but performs poorly on the rest.\n\nNow I want to include the information of n and A into the process. Example:\n-Vector 1 [0,1,2,1,0] and Vector 2 [0,2,4,5,3,2,1,0], they are differents in both n and A, they can't be in the same cluster. Each cluster only contains vectors with similar(close value) A and n, before taking their components into account.\nI'm using sklearn on Python, I'm glad to hear suggestion and advice on this problem.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":91,"Q_Id":45931835,"Users Score":1,"Answer":"Your main problem is how to measure similarity.\nI'm surprised you got the algorithms to run at all, because usually they would expect all vectors to have exactly the same length for computing distances. Maybe you had them automatically filled up with 0 values - and that is likely why the long vectors end up being very far away from all others.\nDon't use the algorithms as black boxes\nYou need to understand what they are doing or the result will likely be useless. In your case, they are using a bad distance, so of course the result can't be very good.\nSo first, you'll need to find a better way of computing the distance of two points with different length. How similar should [0,1,2,1,0] and [30,40,50,60,50,40,30] be. To me, this is a highly similar pattern (ramp up, ramp down).","Q_Score":0,"Tags":"python,vector,machine-learning,k-means,dbscan","A_Id":46190984,"CreationDate":"2017-08-29T06:11:00.000","Title":"Add a criteria to the dataset in clustering","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have created some subplots using matplotlib librairies (pyplot and gridspec). I am trying to put some text in front of the graphs, but sometimes they are located below, in the background so I can't see them.\nI don't know if I should used plt.text or annonate or rather use methods of sublplots?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":207,"Q_Id":45935606,"Users Score":0,"Answer":"If you change the command window to white you should be able to see the text. If you are using the command %matplotlib inline, together with a black command window, the text will not always be visible.","Q_Score":0,"Tags":"python,matplotlib","A_Id":45935693,"CreationDate":"2017-08-29T09:36:00.000","Title":"Matplolib, put text in front of graphs","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a python script that runs perfectly in my IDE on my local machine, but when I run it on Google Datalab, it throws this error:\nValueError: could not convert string to float: '80,354'\nThe code is simple, and the graph prints in my Pycharm IDE, but not on GoogleDatalab. \nplt.plot(new_df['Volume'])\nplt.show()\nThe error is related to the last line in the data. I'm using the date as an index. Here's what the data looks like? Is there a slash missing somehwere? What am I doing wrong or missing?\n'               Micro Market   Volume\\nMonth\/Year                          \\n2014-01-01  DALLAS-FT WORTH   63,974\\n2014-02-01  DALLAS-FT WORTH   68,482\\n2014-03-01  DALLAS-FT WORTH   85,866\\n2014-04-01  DALLAS-FT WORTH   79,735\\n2014-05-01  DALLAS-FT WORTH   75,339\\n2014-06-01  DALLAS-FT WORTH   71,739\\n2014-07-01  DALLAS-FT WORTH   85,893\\n2014-08-01  DALLAS-FT WORTH   83,694\\n2014-09-01  DALLAS-FT WORTH   87,567\\n2014-10-01  DALLAS-FT WORTH   87,389\\n2014-11-01  DALLAS-FT WORTH   68,340\\n2014-12-01  DALLAS-FT WORTH   74,805\\n2015-01-01  DALLAS-FT WORTH   68,568\\n2015-02-01  DALLAS-FT WORTH   61,924\\n2015-03-01  DALLAS-FT WORTH   56,885\\n2015-04-01  DALLAS-FT WORTH   68,101\\n2015-05-01  DALLAS-FT WORTH   52,806\\n2015-06-01  DALLAS-FT WORTH   79,918\\n2015-07-01  DALLAS-FT WORTH   92,134\\n2015-08-01  DALLAS-FT WORTH   88,047\\n2015-09-01  DALLAS-FT WORTH   91,377\\n2015-10-01  DALLAS-FT WORTH   91,307\\n2015-11-01  DALLAS-FT WORTH   65,415\\n2015-12-01  DALLAS-FT WORTH   81,456\\n2016-01-01  DALLAS-FT WORTH   82,820\\n2016-02-01  DALLAS-FT WORTH   91,688\\n2016-03-01  DALLAS-FT WORTH   81,495\\n2016-04-01  DALLAS-FT WORTH   87,872\\n2016-05-01  DALLAS-FT WORTH   82,031\\n2016-06-01  DALLAS-FT WORTH  100,783\\n2016-07-01  DALLAS-FT WORTH   99,285\\n2016-08-01  DALLAS-FT WORTH   99,179\\n2016-09-01  DALLAS-FT WORTH   93,939\\n2016-10-01  DALLAS-FT WORTH   99,663\\n2016-11-01  DALLAS-FT WORTH   86,751\\n2016-12-01  DALLAS-FT WORTH   84,551\\n2017-01-01  DALLAS-FT WORTH   81,890\\n2017-02-01  DALLAS-FT WORTH   90,212\\n2017-03-01  DALLAS-FT WORTH   97,798\\n2017-04-01  DALLAS-FT WORTH   89,338\\n2017-05-01  DALLAS-FT WORTH   96,891\\n2017-06-01  DALLAS-FT WORTH   86,613\\n2017-07-01  DALLAS-FT WORTH   80,354'","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":68,"Q_Id":45944697,"Users Score":0,"Answer":"I was loading the data inappropriately. I was using pandas load_csv on my local machine, and BytesIO on in Datalab. The comma in the numberical value was throwing off the import of the data. I had to say that the delimiter is a \",\" and the thousand separator is also a \",\"","Q_Score":0,"Tags":"python,google-cloud-datalab","A_Id":46005947,"CreationDate":"2017-08-29T17:07:00.000","Title":"Google Datalab and Python Issue","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a readonly very large dataframe and I want to do some calculation so I do a multiprocessing.map and set the dataframe as global. However, does this imply that for each process, the program will copy the dataframe separately(so it will be fast then a shared one)?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":843,"Q_Id":45950381,"Users Score":0,"Answer":"Every sub-process will have its own resources, so this imply does. More precisely, every sub-process will copy a part of original dataframe, determined by your implementation.\nBut will it be fast then a shared one? I'm not sure. Unless your dataframe implement a w\/r lock, read a shared one or read separated ones are the same. But why a dataframe need to lock read operation? It doesn't make sense.","Q_Score":0,"Tags":"python,multithreading,multiprocessing","A_Id":45950446,"CreationDate":"2017-08-30T01:03:00.000","Title":"multiprocessing.map in python with very large read-only object?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been scouring the stackexchange archives and can not seem to come across the right answer... should reshape be used, should resize be used, but both fail...\nsetup: 3 netCDF files of two resolutions... 1 500 meter, 2 1000 meter\nneed to resize or decrease resolution or reshape or whatever the right word is the higher resolution file :)\nusing either gdalinfo or \"print (np.shape(array))\" we know that the higher resolution file has a shape or size of (2907, 2331)  and the lower resolution array has the size of (1453, 1166)\nSo i have tried both np.resize (array, (1453,1166)) and np.reshape (array, (1453,1166)) and receive errors such as: \nValueError: cannot reshape array of size 6776217 into shape (1453,1166)\nSurely I'm using the wrong terms \/ lingo and I apologize for that... on the command line to do what I would need done it would be as simple as gdal_translate -outsize x y -of GTiff infile outfile \nPlease help!","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1755,"Q_Id":45985692,"Users Score":2,"Answer":"Neither.\nReshape only changes the shape of the data, but not the total size, so you can for example reshape an array of shape 1x9 into one which is 3x3, but not into 2x4. \nResize does similar thing, but lets you increase the size, in which case it will fill new space with elements of array which is being resized.\nYou have two choices: write your function which does resizing in the manner you want it to do so, or use one of Python image libraries (PIL, Pillow...) to apply common image resizing functions.","Q_Score":0,"Tags":"python,arrays,numpy,resize,reshape","A_Id":45985888,"CreationDate":"2017-08-31T15:58:00.000","Title":"Numpy resize or Numpy reshape","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I know for integers one can use htonl and ntohl but what about pickle byte streams?\nIf I know that the next 150 bytes that are received are a pickle object, do I still have to reverse byte-order just in case one machine uses big-endian and the other is little-endian?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":358,"Q_Id":45992329,"Users Score":0,"Answer":"So I think I figured out the answer.\n\nBy default, the pickle data format uses a printable ASCII\n  representation.\n\nsince ASCII is a single byte representation the endianess does not matter.","Q_Score":0,"Tags":"python,sockets","A_Id":45992812,"CreationDate":"2017-09-01T01:52:00.000","Title":"Does Pickle.dumps and loads used for sending network data require change in byte order?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I'm developing a python program to receive a live streaming video from android device via RTMP. I created a server and also I'm capable of transmitting a videoStream from android device. But the problem is I can't access that stream in opencv. Can anyone tell me a way to access it via opencv. It is better if you can post any python code snippets.","AnswerCount":1,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":9717,"Q_Id":45993828,"Users Score":9,"Answer":"Just open the address instead of your cam :\nmyrtmp_addr = \"rtmp:\/\/myip:1935\/myapp\/mystream\"\ncap = cv2.VideoCapture(myrtmp_addr)\nframe,err = cap.read()\nFrom there you can handle your frames like when you get it from your cam.\nif it still doesn't work, check if you have a valid version of ffmpeg linked with your opencv. You can check it with \nprint(cv2.getBuildInformation())\nHope I could help","Q_Score":5,"Tags":"python,opencv,video-streaming,rtmp","A_Id":50102832,"CreationDate":"2017-09-01T05:33:00.000","Title":"Get videoStream from RTMP to opencv","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"On the tensorflow\/models repo on GitHub, they supply five pre-trained models for object detection.\nThese models are trained on the COCO dataset and can identify 90 different objects.\nI need a model to just detect people, and nothing else. I can modify the code to only print labels on people, but it will still look for the other 89 objects, which takes more time than just looking for one object.  \nI can train my own model, but I would rather be able to use a pre-trained model, instead of spending a lot of time training my own model.\nSo is there a way, either by modifying the model file, or the TensorFlow or Object Detection API code, so it only looks for a single object?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1270,"Q_Id":45995076,"Users Score":1,"Answer":"Either you finetune your model, with just a few thousands steps, on pedestrians (a small dataset to train would be enough) or you look in your in your label definition file (.pbtx file), search for the person label, and do whatever stuff you want with the others.","Q_Score":1,"Tags":"python,machine-learning,tensorflow,object-detection","A_Id":45997098,"CreationDate":"2017-09-01T07:15:00.000","Title":"Is there a way to limit an existing TensorFlow object detection model?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I wrote a Monte Carlo (MC) code in Python with a Fortran extension (compiled with f2py). As it is a stochastic integration, the algorithm relies heavily on random numbers, namely I use ~ 10^8 - 10^9 random numbers for a typical run. So far, I didn't really mind the 'quality' of the random numbers - this is, however, something that I want to check out. \nMy question is: does the Mersenne-Twister used by numpy suffice or are there better random number generators out there that one should (could) use? (better in the sense of runtime as well as quality of the generated sequence)\nAny suggestions\/experiences are most definitely welcome, thanks!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":859,"Q_Id":45996727,"Users Score":2,"Answer":"I do not think anyone can tell you if this algorithm suffices without knowing how the random numbers are being used.\nWhat I would do is to replace the numpy random numbers by something else, certainly there are other modules already available that provide different algorithms.\nIf your simulation results are not affected by the choice of random number generator, it is already a good sign.","Q_Score":1,"Tags":"python,numpy,random,montecarlo,mersenne-twister","A_Id":45996951,"CreationDate":"2017-09-01T09:04:00.000","Title":"numpy.random and Monte Carlo","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Relatively new to model building with sklearn. I know cross validation can be parallelized via the n_jobs parameter, but if I'm not using CV, how can I utilize my available cores to speed up model fitting?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":3044,"Q_Id":46015426,"Users Score":1,"Answer":"There are alternatives like XGboost or LigtGMB that are distributed (i.e., can run parallel). These are well documented and popular boosting algorithms.","Q_Score":6,"Tags":"python,machine-learning,scikit-learn","A_Id":68411615,"CreationDate":"2017-09-02T15:38:00.000","Title":"How can I parallelize fitting a gradient boosting model with sklearn?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataset and there are missing values which are encoded as ?. My problem is how can I change the missing values, ?, to NaN? So I can drop any row with NaN. Can I just use .replace() ?","AnswerCount":4,"Available Count":2,"Score":0.049958375,"is_accepted":false,"ViewCount":8652,"Q_Id":46016838,"Users Score":1,"Answer":"You can also do like this,\n\ndf[df == '?'] = np.nan","Q_Score":2,"Tags":"python,pandas,dataframe","A_Id":58381427,"CreationDate":"2017-09-02T18:14:00.000","Title":"How can I convert '?' to NaN","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataset and there are missing values which are encoded as ?. My problem is how can I change the missing values, ?, to NaN? So I can drop any row with NaN. Can I just use .replace() ?","AnswerCount":4,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":8652,"Q_Id":46016838,"Users Score":2,"Answer":"You can also read the data initially by passing \ndf = pd.read_csv('filename',na_values = '?')\nIt will automatically replace '?' to NaN","Q_Score":2,"Tags":"python,pandas,dataframe","A_Id":52961763,"CreationDate":"2017-09-02T18:14:00.000","Title":"How can I convert '?' to NaN","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"The data exists out of Date, Open, High, Low, Close, Volume and it's currently stored in a .csv file. It's currently updating every minute and when time goes by the file keeps growing and growing. A problem is when I need 500 observations from the data, I need to import the whole .csv file and that is a problem yes. Especially when I need to access the data fast.\nIn Python I use the data mostly in a data frame or panel.","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":841,"Q_Id":46017576,"Users Score":0,"Answer":"I would also suggest to use DB, it is much more convenient to update tables in DB than a csv file, moreover if you have a substantial amount of observations, you will be able to access\/manipulate your data much more faster.\nAnother solution is to keep separate updates in separate .csv files.\nYou can still keep your major file (the one which is regularly updated), and at the same time create separate files for each update.","Q_Score":2,"Tags":"python,database,csv,panel","A_Id":46018254,"CreationDate":"2017-09-02T19:46:00.000","Title":"Most efficient way to store financial data (Python)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"The data exists out of Date, Open, High, Low, Close, Volume and it's currently stored in a .csv file. It's currently updating every minute and when time goes by the file keeps growing and growing. A problem is when I need 500 observations from the data, I need to import the whole .csv file and that is a problem yes. Especially when I need to access the data fast.\nIn Python I use the data mostly in a data frame or panel.","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":841,"Q_Id":46017576,"Users Score":0,"Answer":"You maybe wanna check RethinkDB, it gives you fastness, reliability and also flexible searching ability, it has a good python driver. I also recommend to use docker, because in that case, regardless of which database you want to use, you can easily store the data of your db inside a folder, and you can anytime change that folder(when you have a 1TB hard, now you want to change it to 4TB hard). maybe using docker in your project is more important than DB.","Q_Score":2,"Tags":"python,database,csv,panel","A_Id":70355115,"CreationDate":"2017-09-02T19:46:00.000","Title":"Most efficient way to store financial data (Python)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Very basic question:\nSuppose I have a 1D numpy array (A) containing  5 elements:\nA = np.array([ -4.0,  5.0,  -3.5,  5.4,  -5.9])\nI need to add, 5 to all the elements of A that are lesser than zero. What is the numpy way to do this without for-looping ?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":4001,"Q_Id":46022688,"Users Score":0,"Answer":"I found another answer:\nA = np.where(A < 0, A + 5, A)","Q_Score":0,"Tags":"python,numpy,vectorization","A_Id":46022740,"CreationDate":"2017-09-03T10:52:00.000","Title":"Change values of a 1D numpy array based on certain condition","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am facing the below error while running the code for LinearClassifier in tensorflow.\nAttributeError: module 'tensorflow.python.estimator.estimator_lib' has no attribute 'LinearRegressor'\nMy current version for tensorflow is 1.2.1. I tried to update the version of the package from ANACONDA environment, its not showing for an upgrade.\nI tried to upgrade it from command prompt by using below command, it is successfully updating the package however it is not reflecting to the actual library when I am using it.\npip install --upgrade tensorflow==1.3.0\nFYI, I am using Jupyter Notebook and have created a separate environment for tensorflow.\nPlease let me know if I have missed anything.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":5131,"Q_Id":46032658,"Users Score":0,"Answer":"That should work. Check if you are using any environment but you are not updating the tensorflow version within the environment.\nAlso, please restart the notebook after saving it and run the cells and try. That should work.\nVerify in the notebook : run - print(tf.__version__). Please mark the answer if it resolves.","Q_Score":1,"Tags":"python,tensorflow","A_Id":46032724,"CreationDate":"2017-09-04T07:58:00.000","Title":"Not able to update the version of tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a project that use Deep CNN to classify parking lot. My idea is to classify every space whether there is a car or not. and my question is, how do i prepare my image dataset to train my model ?\ni have downloaded PKLot dataset for training included negative and positive image.\nshould i turn all my data training image to grayscale ? should i rezise all my training image to one fix size? (but if i resize my training image to one fixed size, i have landscape and portrait image). Thanks :)","AnswerCount":3,"Available Count":2,"Score":0.0665680765,"is_accepted":false,"ViewCount":822,"Q_Id":46038671,"Users Score":1,"Answer":"First detect the cars present in the image, and obtain their size and alignment. Then go for segmentation and labeling of the parking lot by fixing a suitable size and alignment.","Q_Score":0,"Tags":"python,computer-vision,artificial-intelligence,keras,training-data","A_Id":46039296,"CreationDate":"2017-09-04T13:59:00.000","Title":"how to prepare image dataset for training model?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a project that use Deep CNN to classify parking lot. My idea is to classify every space whether there is a car or not. and my question is, how do i prepare my image dataset to train my model ?\ni have downloaded PKLot dataset for training included negative and positive image.\nshould i turn all my data training image to grayscale ? should i rezise all my training image to one fix size? (but if i resize my training image to one fixed size, i have landscape and portrait image). Thanks :)","AnswerCount":3,"Available Count":2,"Score":0.0665680765,"is_accepted":false,"ViewCount":822,"Q_Id":46038671,"Users Score":1,"Answer":"as you want use pklot dataset for training your machine and test with real data, the best approach is to make both datasets similar and homological, they must be normalized , fixed sized , gray-scaled and  parameterized shapes. then you can use Scale-invariant feature transform (SIFT) for image feature extraction as basic method.the exact definition often depends on the problem or the type of application. Since features are used as the starting point and main primitives for subsequent algorithms, the overall algorithm will often only be as good as its feature detector. you can use these types of image features based on your problem:\n\nCorners \/ interest points\nEdges\nBlobs \/ regions of interest points\nRidges\n...","Q_Score":0,"Tags":"python,computer-vision,artificial-intelligence,keras,training-data","A_Id":46153830,"CreationDate":"2017-09-04T13:59:00.000","Title":"how to prepare image dataset for training model?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"What is the reason of Pandas to provide two different correlation functions?\n\nDataFrame.corrwith(other, axis=0, drop=False): \n  Correlation between rows or columns of two DataFrame objectsCompute pairwise\n\nvs.\n\nDataFrame.corr(method='pearson', min_periods=1): Compute pairwise\n  correlation of columns, excluding NA\/null values\n\n(from pandas 0.20.3 documentation)","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":22525,"Q_Id":46041148,"Users Score":14,"Answer":"The first one computes correlation with another dataframe:\n\nbetween rows or columns of two DataFrame objects\n\nThe second one computes it with itself\n\nCompute pairwise correlation of columns","Q_Score":16,"Tags":"python,pandas","A_Id":46041308,"CreationDate":"2017-09-04T16:32:00.000","Title":"Pandas corr() vs corrwith()","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to import 'sklearn' using Python 3.4.3 on a Raspberry Pi 3 running Raspian.\nI downloaded microconda3, which includes all the necessary modules to use scikit.\nHowever, when I attempt to import 'sklearn' in IDLE, I receive an error stating that there is no module named 'sklearn'.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":4166,"Q_Id":46045916,"Users Score":1,"Answer":"Try starting fresh and reinstall all of the necessary modules through miniconda3. Maybe the scikit-learn included on that install did not work. \nYou can then try\nsudo apt-get install python3-scipy python3-sklearn","Q_Score":0,"Tags":"python,python-3.x,scikit-learn","A_Id":46290419,"CreationDate":"2017-09-05T01:46:00.000","Title":"No Module named \"sklearn\"","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"So I would like to generate a 50 X 50 covariance matrix for a random variable X given the following conditions: \n\none variance is 10 times larger than the others \nthe parameters of X are only slightly correlated \n\nIs there a way of doing this in Python\/R etc? Or is there a covariance matrix that you can think of that might satisfy these requirements?\nThank you for your help!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1796,"Q_Id":46055886,"Users Score":3,"Answer":"OK, you only need one matrix and randomness isn't important. Here's a way to construct a matrix according to your description. Start with an identity matrix 50 by 50. Assign 10 to the first (upper left) element. Assign a small number (I don't know what's appropriate for your problem, maybe 0.1? 0.01? It's up to you) to all the other elements. Now take that matrix and square it (i.e. compute transpose(X) . X where X is your matrix). Presto! You've squared the eigenvalues so now you have a covariance matrix. \nIf the small element is small enough, X is already positive definite. But squaring guarantees it (assuming there are no zero eigenvalues, which you can verify by computing the determinant -- if the determinant is nonzero then there are no zero eigenvalues).\nI assume you can find Python functions for these operations.","Q_Score":0,"Tags":"python,r,statistics,covariance","A_Id":46061009,"CreationDate":"2017-09-05T13:12:00.000","Title":"How to generate a random covariance matrix in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I was trying to add a column in dask dataframe, but it's not letting me to add columns of type list, so I reached a little bit and found that it would add a dask series. However I'm unable to convert my list to a dask series. Can you help me out?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1854,"Q_Id":46062523,"Users Score":0,"Answer":"dask.dataframe.from_pandas(pandas.Series(my_data), npartitions=n) is what you need. from_pandas accepts both pandas.DataFrame\/Series.","Q_Score":2,"Tags":"python,pandas,dask","A_Id":57173648,"CreationDate":"2017-09-05T19:59:00.000","Title":"Initialize a dask series","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am interested in using the tensorflow slim library (tf.contrib.slim) to do evaluation of a model performance on a(n) (entire) test set periodically during training. The documentation is pretty clear that slim.evaluation.evaluation_loop is the way to go, and it looks promising. The issue is that I don't have a second gpu to spare, this model parameters take up an entire gpu's worth of memory, and I would like to do concurrent evaluation.\nFor example, if I had 2 GPUs, I could run a python script that terminated with \"slim.learning.train()\" on the first gpu, and another that terminated with \"slim.evaluation.evaluation_loop()\" on the second gpu.\nIs there an approach that can manage 1 gpu's resources for both tasks? tf.train.Supervisor comes to mind, but I don't honestly know.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":215,"Q_Id":46062649,"Users Score":1,"Answer":"You can partition the GPU usage using the following code.\nYou can set the fraction of the GPU to be used for training and evaluation separately. The code below means that the process is given 30% of the memory.\ngpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.3000)\n  sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))\n  sess.run(tf.app.run())","Q_Score":1,"Tags":"python-2.7,tensorflow,tensorflow-gpu,tf-slim","A_Id":49581528,"CreationDate":"2017-09-05T20:08:00.000","Title":"tensorflow slim concurrent train and evaluation loops; single device","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Assume that I have set of points scattered on the XY plane, and i have two points say start and end point any where in XY plane. I want to find the shortest path between start and end point without touching scattered points. The path has to maintain certain offset ( i.e assume path has some width ). \nHow to approach this kind of problems in programming, Are there any algorithms in machine learning.","AnswerCount":4,"Available Count":2,"Score":0.049958375,"is_accepted":false,"ViewCount":1861,"Q_Id":46069364,"Users Score":1,"Answer":"This is not a machine learning problem but an optimization problem. \n\nSo you need a greedy algorithm for the shortest path\n\nIndeed it could be solved this way but the challenge is to represent your grid as a graph... \nFor example, decomposing the grid in a n x n matrix. In your shortest path algorithm, a node is an element of your matrix (so you exclude the elements of the matrice that contains the scattered points) and the weight of the arcs are the distance. \nHowever n must be small since shortest path algotithms are np-hard problems...\nMaybe other algorithms exist for this specific problem but I'm not aware of.","Q_Score":0,"Tags":"algorithm,python-3.x,machine-learning,artificial-intelligence","A_Id":46070137,"CreationDate":"2017-09-06T07:34:00.000","Title":"Shortest root using machine learning\/AI","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Assume that I have set of points scattered on the XY plane, and i have two points say start and end point any where in XY plane. I want to find the shortest path between start and end point without touching scattered points. The path has to maintain certain offset ( i.e assume path has some width ). \nHow to approach this kind of problems in programming, Are there any algorithms in machine learning.","AnswerCount":4,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1861,"Q_Id":46069364,"Users Score":0,"Answer":"Like others already stated: this is not a typical \"Artificial Intelligence\" problem. It is kind of a path planning problem.\nThere are different algorithms available. If your path doesn't neet to satisfy any constraints like .g. smoothness, you can use an A*-Algorithm with distance as heuristic.\nYou have to represent your XYZ-space as a Graph where each node has a coordinate. Further you need to take into account, that no nodes lie near the points you want to avoid.  \nIf your path needs to satisfy constraints, this turns into a more complicated path planning problem where you could apply optimization or RRTs.","Q_Score":0,"Tags":"algorithm,python-3.x,machine-learning,artificial-intelligence","A_Id":46078672,"CreationDate":"2017-09-06T07:34:00.000","Title":"Shortest root using machine learning\/AI","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How can I plot a bunch of 2-D points X (say, using matplotlib) with color labels from scipy.cluster.hierarchy.linkage(X, method=\"single\") (say, k = 3)?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":340,"Q_Id":46086401,"Users Score":0,"Answer":"fcluster from scipy.cluster.hierarchy will do.","Q_Score":1,"Tags":"python,matplotlib,scipy,cluster-analysis,hierarchical-clustering","A_Id":52784797,"CreationDate":"2017-09-07T01:12:00.000","Title":"How to Plot with Label from scipy.cluster.hierarchy.linkage?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there any way to check whether Gurobi is adding an extra redundant constraint to the model? \nI tried model.write() at every iteration and it seems fine. But still, my output is not what I'm expecting. \nI want to check whether there are any unwanted constraints added by Gurobi when solving.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":249,"Q_Id":46090908,"Users Score":0,"Answer":"Your statements are somewhat contradictory: If gurobi was to add redundant constraints in the beginning, the presolve step would remove them again and second them being redundant would mean that they wont influence the solution. Thus, there would be a modelling error on your side. \nAs sascha mentioned it is extremely unlikely that any constraint-generating routine\/presolve step would be falsely implemented - such errors would show up immediately, that is before patching into the published versions. \nAnd from a theoretical side: These methods require mathematical proofs that guarantee they do not cut-off feasible solutions. What is overseen sometimes, I did that only recently, is the default lower bound of 0 on all variables which stems from the LP-notation according to standard form. \nFurther investigation can be done if you were to post the .lp and .mps files of your initial problem with your question. That way people could investigate themselves.","Q_Score":0,"Tags":"python-3.x,gurobi","A_Id":59344648,"CreationDate":"2017-09-07T07:53:00.000","Title":"Check Gurobi redundant constraints","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to know is there any way to see \"under the hood\" on other python sklearn algorithms. For example, I have created a decision tree classifier using sklearn and have been able to export the exact structure of the tree but would like to also be able to do this with other algorithms, for example KNN classification. Is this possible?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":60,"Q_Id":46095249,"Users Score":1,"Answer":"I would recommend looking up this book. \nIntroduction to Machine Learning with Python A Guide for Data Scientists by Andreas C. Mu\u0308ller, Sarah Guido\nThe book has code written to visualise various outputs for Machine Learning algorithms.","Q_Score":0,"Tags":"python,scikit-learn,knn","A_Id":46101630,"CreationDate":"2017-09-07T11:29:00.000","Title":"Output from scikit learn ML algorithms","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I was wondering how to handle not labeled parts of an image in image segmentation using TensorFlow. For example, my input is an image of height * width * channels. The labels are too of the size height * width, with one label for every pixel.\nSome parts of the image are annotated, other parts are not. I would wish that those parts have no influence on the gradient computation whatsoever. Furthermore, I am not interested in the network predicting this \u201cvoid\u201d label.\nIs there a label or a function for this? At the moment I am using tf.nn.sparse_softmax_cross_entropy_with_logits.","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":3236,"Q_Id":46097968,"Users Score":2,"Answer":"If I understand correctly you have a portion of each image with label void in which you are not interested at all. Since there is not a easy way to obtain the real value behind this void spots, why don't you map these points to background label and try to get results for your model? I would try in a preprocessing state to clear the data labels from this void label and substitute them with background label. \nAnother possible strategy ,if you don's simply want to map void labels to background, is to run a mask (with a continuous motion from top to bottom from right to left) to check the neigthbooring pixels from a void pixel (let's say an area of 5x5 pixels) and assign to the void pixels the most common label besides void.\nAlso you can always keep a better subset of the data, filtering data where the percentage of void labels is over a threshold. You can keep only images with no void labels, or more likeley you can keep images that have only under a threshold (e.g. 5%) of non-labeled points. In this images you can implement the beforementioned strategies for replacing the void labels.","Q_Score":21,"Tags":"python,tensorflow,neural-network,deep-learning,image-segmentation","A_Id":46203664,"CreationDate":"2017-09-07T13:42:00.000","Title":"TensorFlow: How to handle void labeled data in image segmentation?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am compiling my current program using pyinstaller and it seems to not be able to handle all required files in plotly. It runs fine on its own, and without plotly it can compile and run as well. \nIt seems be to failing to find a file \"default-schema.json\" that I cannot even locate anywhere on my drive.\n\n\nTraceback (most recent call last):   File \"comdty_runtime.py\", line\n  17, in    File \"\", line 2237, in\n  _find_and_load   File \"\", line 2226, in _find_and_load_unlocked   File \"\", line 1191, in _load_unlocked   File \"\", line\n  1161, in _load_backward_compatible   File\n  \"d:\\users\\ktehrani\\appdata\\local\\continuum\\anaconda3\\envs\\py34\\lib\\site-p\n  ackages\\PyInstaller\\loader\\pyimod03_importers.py\", line 389, in\n  load_module\n      exec(bytecode, module.dict)   File \"actual_vs_mai.py\", line 12, in    File \"\", line 2237, in\n  _find_and_load   File \"\", line 2226, in _find_and_load_unlocked   File \"\", line 1191, in _load_unlocked   File \"\", line\n  1161, in _load_backward_compatible   File\n  \"d:\\users\\ktehrani\\appdata\\local\\continuum\\anaconda3\\envs\\py34\\lib\\site-p\n  ackages\\PyInstaller\\loader\\pyimod03_importers.py\", line 389, in\n  load_module\n      exec(bytecode, module.dict)   File \"site-packages\\plotly__init__.py\", line 31, in    File\n  \"\", line 2237, in _find_and_load   File\n  \"\", line 2226, in _find_and_load_unlocked\n  File \"\", line 1191, in _load_unlocked\n  File \"\", line 1161, in\n  _load_backward_compatible   File \"d:\\users*\\appdata\\local\\continuum\\anaconda3\\envs\\py34\\lib\\site-p\n  ackages\\PyInstaller\\loader\\pyimod03_importers.py\", line 389, in\n  load_module\n      exec(bytecode, module.dict)   File \"site-packages\\plotly\\graph_objs__init__.py\", line 14, in \n  File \"\", line 2237, in _find_and_load\n  File \"\", line 2226, in\n  _find_and_load_unlocked   File \"\", line 1191, in _load_unlocked   File \"\", line\n  1161, in _load_backward_compatible   File\n  \"d:\\users*\\appdata\\local\\continuum\\anaconda3\\envs\\py34\\lib\\site-p\n  ackages\\PyInstaller\\loader\\pyimod03_importers.py\", line 389, in\n  load_module\n      exec(bytecode, module.dict)   File \"site-packages\\plotly\\graph_objs\\graph_objs.py\", line 34, in  \n  File \"\", line 2237, in _find_and_load\n  File \"\", line 2226, in\n  _find_and_load_unlocked   File \"\", line 1191, in _load_unlocked   File \"\", line\n  1161, in _load_backward_compatible   File\n  \"d:\\users*\\appdata\\local\\continuum\\anaconda3\\envs\\py34\\lib\\site-p\n  ackages\\PyInstaller\\loader\\pyimod03_importers.py\", line 389, in\n  load_module\n      exec(bytecode, module.dict)   File \"site-packages\\plotly\\graph_reference.py\", line 578, in \n  File \"site-packages\\plotly\\graph_reference.py\", line 70, in\n  get_graph_referenc e   File\n  \"site-packages\\setuptools-27.2.0-py3.4.egg\\pkg_resources__init__.py\",\n  li ne 1215, in resource_string   File\n  \"site-packages\\setuptools-27.2.0-py3.4.egg\\pkg_resources__init__.py\",\n  li ne 1457, in get_resource_string   File\n  \"site-packages\\setuptools-27.2.0-py3.4.egg\\pkg_resources__init__.py\",\n  li ne 1530, in _get   File\n  \"d:\\users*\\appdata\\local\\continuum\\anaconda3\\envs\\py34\\lib\\site-p\n  ackages\\PyInstaller\\loader\\pyimod03_importers.py\", line 474, in\n  get_data\n      with open(path, 'rb') as fp: FileNotFoundError: [Errno 2] No such file or directory: 'H:\\Python\\Commodity_M\n  AI_Trade_List\\Code\\dist\\comdty_runtime\\plotly\\package_data\\default-schema.\n  json' Failed to execute script comdty_runtime","AnswerCount":5,"Available Count":2,"Score":0.1973753202,"is_accepted":false,"ViewCount":6865,"Q_Id":46099695,"Users Score":5,"Answer":"it's seem like plotly isn't fully supported by PyInstaller.\nI used an work around solution that worked for me.\n\nDon't use the one file option\nCompletely copy the plotly package(for me it was  Lib\\site-packages\\plotly) for the python installation directory into the \/dist\/{exe name}\/ directory","Q_Score":9,"Tags":"python,plotly,pyinstaller","A_Id":47979807,"CreationDate":"2017-09-07T15:04:00.000","Title":"pyinstaller fails with plotly","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am compiling my current program using pyinstaller and it seems to not be able to handle all required files in plotly. It runs fine on its own, and without plotly it can compile and run as well. \nIt seems be to failing to find a file \"default-schema.json\" that I cannot even locate anywhere on my drive.\n\n\nTraceback (most recent call last):   File \"comdty_runtime.py\", line\n  17, in    File \"\", line 2237, in\n  _find_and_load   File \"\", line 2226, in _find_and_load_unlocked   File \"\", line 1191, in _load_unlocked   File \"\", line\n  1161, in _load_backward_compatible   File\n  \"d:\\users\\ktehrani\\appdata\\local\\continuum\\anaconda3\\envs\\py34\\lib\\site-p\n  ackages\\PyInstaller\\loader\\pyimod03_importers.py\", line 389, in\n  load_module\n      exec(bytecode, module.dict)   File \"actual_vs_mai.py\", line 12, in    File \"\", line 2237, in\n  _find_and_load   File \"\", line 2226, in _find_and_load_unlocked   File \"\", line 1191, in _load_unlocked   File \"\", line\n  1161, in _load_backward_compatible   File\n  \"d:\\users\\ktehrani\\appdata\\local\\continuum\\anaconda3\\envs\\py34\\lib\\site-p\n  ackages\\PyInstaller\\loader\\pyimod03_importers.py\", line 389, in\n  load_module\n      exec(bytecode, module.dict)   File \"site-packages\\plotly__init__.py\", line 31, in    File\n  \"\", line 2237, in _find_and_load   File\n  \"\", line 2226, in _find_and_load_unlocked\n  File \"\", line 1191, in _load_unlocked\n  File \"\", line 1161, in\n  _load_backward_compatible   File \"d:\\users*\\appdata\\local\\continuum\\anaconda3\\envs\\py34\\lib\\site-p\n  ackages\\PyInstaller\\loader\\pyimod03_importers.py\", line 389, in\n  load_module\n      exec(bytecode, module.dict)   File \"site-packages\\plotly\\graph_objs__init__.py\", line 14, in \n  File \"\", line 2237, in _find_and_load\n  File \"\", line 2226, in\n  _find_and_load_unlocked   File \"\", line 1191, in _load_unlocked   File \"\", line\n  1161, in _load_backward_compatible   File\n  \"d:\\users*\\appdata\\local\\continuum\\anaconda3\\envs\\py34\\lib\\site-p\n  ackages\\PyInstaller\\loader\\pyimod03_importers.py\", line 389, in\n  load_module\n      exec(bytecode, module.dict)   File \"site-packages\\plotly\\graph_objs\\graph_objs.py\", line 34, in  \n  File \"\", line 2237, in _find_and_load\n  File \"\", line 2226, in\n  _find_and_load_unlocked   File \"\", line 1191, in _load_unlocked   File \"\", line\n  1161, in _load_backward_compatible   File\n  \"d:\\users*\\appdata\\local\\continuum\\anaconda3\\envs\\py34\\lib\\site-p\n  ackages\\PyInstaller\\loader\\pyimod03_importers.py\", line 389, in\n  load_module\n      exec(bytecode, module.dict)   File \"site-packages\\plotly\\graph_reference.py\", line 578, in \n  File \"site-packages\\plotly\\graph_reference.py\", line 70, in\n  get_graph_referenc e   File\n  \"site-packages\\setuptools-27.2.0-py3.4.egg\\pkg_resources__init__.py\",\n  li ne 1215, in resource_string   File\n  \"site-packages\\setuptools-27.2.0-py3.4.egg\\pkg_resources__init__.py\",\n  li ne 1457, in get_resource_string   File\n  \"site-packages\\setuptools-27.2.0-py3.4.egg\\pkg_resources__init__.py\",\n  li ne 1530, in _get   File\n  \"d:\\users*\\appdata\\local\\continuum\\anaconda3\\envs\\py34\\lib\\site-p\n  ackages\\PyInstaller\\loader\\pyimod03_importers.py\", line 474, in\n  get_data\n      with open(path, 'rb') as fp: FileNotFoundError: [Errno 2] No such file or directory: 'H:\\Python\\Commodity_M\n  AI_Trade_List\\Code\\dist\\comdty_runtime\\plotly\\package_data\\default-schema.\n  json' Failed to execute script comdty_runtime","AnswerCount":5,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":6865,"Q_Id":46099695,"Users Score":0,"Answer":"Before using pyinstaller, make sure that the imports that you are using in your program are specifically listed in the requirements file.\npip freeze->requirements.txt\nquickly creates a requirements file in your program.\nI am not sure if this is the solution? I had similar issues that seem to go away once I updated the requirements file then making an executable.","Q_Score":9,"Tags":"python,plotly,pyinstaller","A_Id":70137929,"CreationDate":"2017-09-07T15:04:00.000","Title":"pyinstaller fails with plotly","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have images of a big size (6000x4000). I want to train FasterRCNN to detect quite small object (tipycally between 50 150 pixels). So for memory purpose I crop the images to 1000x1000. The training is ok. When I test the model on the 1000x1000 the results are really good. When I test the model on images of 6000x4000 the result are really bad...\nI guess it is the region proposal step, but I don't know what I am doing wrong (the keep_aspect_ratio_resizer max_dimension is fix to 12000)...\nThanks for your help !","AnswerCount":3,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":2633,"Q_Id":46143492,"Users Score":0,"Answer":"I want to know what is your min_dimension which should be larger than 4000 in your case, otherwise the image will be scale down.\n\nobject_detection-> core-> preprocessor.py \n\ndef _compute_new_dynamic_size(image, min_dimension, max_dimension):\n    \"\"\"Compute new dynamic shape for resize_to_range method.\"\"\"\n    image_shape = tf.shape(image)\n    orig_height = tf.to_float(image_shape[0])\n    orig_width = tf.to_float(image_shape[1])\n    orig_min_dim = tf.minimum(orig_height, orig_width)\n    # Calculates the larger of the possible sizes\n    min_dimension = tf.constant(min_dimension, dtype=tf.float32)\n    large_scale_factor = min_dimension \/ orig_min_dim\n    # Scaling orig_(height|width) by large_scale_factor will make the smaller\n    # dimension equal to min_dimension, save for floating point rounding errors.\n    # For reasonably-sized images, taking the nearest integer will reliably\n    # eliminate this error.\n    large_height = tf.to_int32(tf.round(orig_height * large_scale_factor))\n    large_width = tf.to_int32(tf.round(orig_width * large_scale_factor))\n    large_size = tf.stack([large_height, large_width])\n    if max_dimension:\n        # Calculates the smaller of the possible sizes, use that if the larger\n        # is too big.\n        orig_max_dim = tf.maximum(orig_height, orig_width)\n        max_dimension = tf.constant(max_dimension, dtype=tf.float32)\n        small_scale_factor = max_dimension \/ orig_max_dim\n        # Scaling orig_(height|width) by small_scale_factor will make the larger\n        # dimension equal to max_dimension, save for floating point rounding\n        # errors. For reasonably-sized images, taking the nearest integer will\n        # reliably eliminate this error.\n        small_height = tf.to_int32(tf.round(orig_height * small_scale_factor))\n        small_width = tf.to_int32(tf.round(orig_width * small_scale_factor))\n        small_size = tf.stack([small_height, small_width])\n        new_size = tf.cond(\n            tf.to_float(tf.reduce_max(large_size)) > max_dimension,\n            lambda: small_size, lambda: large_size)\n    else:\n        new_size = large_size\n    return new_size","Q_Score":2,"Tags":"python,tensorflow,size,object-detection,region","A_Id":47131499,"CreationDate":"2017-09-10T17:22:00.000","Title":"Faster RCNN tensorflow object detection API : dealing with big images","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have images of a big size (6000x4000). I want to train FasterRCNN to detect quite small object (tipycally between 50 150 pixels). So for memory purpose I crop the images to 1000x1000. The training is ok. When I test the model on the 1000x1000 the results are really good. When I test the model on images of 6000x4000 the result are really bad...\nI guess it is the region proposal step, but I don't know what I am doing wrong (the keep_aspect_ratio_resizer max_dimension is fix to 12000)...\nThanks for your help !","AnswerCount":3,"Available Count":3,"Score":1.2,"is_accepted":true,"ViewCount":2633,"Q_Id":46143492,"Users Score":3,"Answer":"You need to keep training images and images to test on of roughly same dimension. If you are using random resizing as data augmentation, you can vary the test images by roughly that factor.\nBest way to deal with this problem is to crop large image into images of same dimension as used in training and then use Non-maximum suppression on crops to merge the prediction.\nThat way, If your smallest object to detect is of size 50px, you can have training images of size ~500px.","Q_Score":2,"Tags":"python,tensorflow,size,object-detection,region","A_Id":46399506,"CreationDate":"2017-09-10T17:22:00.000","Title":"Faster RCNN tensorflow object detection API : dealing with big images","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have images of a big size (6000x4000). I want to train FasterRCNN to detect quite small object (tipycally between 50 150 pixels). So for memory purpose I crop the images to 1000x1000. The training is ok. When I test the model on the 1000x1000 the results are really good. When I test the model on images of 6000x4000 the result are really bad...\nI guess it is the region proposal step, but I don't know what I am doing wrong (the keep_aspect_ratio_resizer max_dimension is fix to 12000)...\nThanks for your help !","AnswerCount":3,"Available Count":3,"Score":0.0665680765,"is_accepted":false,"ViewCount":2633,"Q_Id":46143492,"Users Score":1,"Answer":"It looks to me like you are training on images with a different aspect ratio than what you are testing on (square vs not square) --- this could lead to a significant degradation in quality.  \nThough to be honest I'm a bit surprised that the results could be really bad, if you are just visually evaluating, maybe you also have to turn down the score thresholds for visualization.","Q_Score":2,"Tags":"python,tensorflow,size,object-detection,region","A_Id":46143853,"CreationDate":"2017-09-10T17:22:00.000","Title":"Faster RCNN tensorflow object detection API : dealing with big images","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am solving a large sparse quadratic problem. My objective function has only quadratic terms and the coefficients of all terms are the same and equal to 1 and it includes all of the variables. \nI use objective.set_quadratic_coefficients function in python to create my objective function. For small problems (10000 variables), the objective function is generated quickly but it gets much slower for larger problems (100000 variables) and does return anything for my main problem that has 1000000 variables. \nIs there an alternative to objective.set_quadratic_coefficients to speed up the creating the problem?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":119,"Q_Id":46147245,"Users Score":1,"Answer":"After asking my question in the IBM forum, I received and answer and it works. The fastest way to create the quadratic objective function is to use objective.set_quadratic() with only a list that contains the coefficient values (they can vary and don't need to be all equal to 1.0)","Q_Score":0,"Tags":"python,cplex,quadratic","A_Id":46158131,"CreationDate":"2017-09-11T01:58:00.000","Title":"Performance issue with setting quadratic objective in CPLEX","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How to initialize a matrix to a very large number, say to infinity.\nSimilar to initializing all elements to zero:\nsample = np.matrix((np.zeros(50,50))\nI want to initalize to infinity \nHow to do it in python?","AnswerCount":2,"Available Count":1,"Score":0.2913126125,"is_accepted":false,"ViewCount":14126,"Q_Id":46151461,"Users Score":3,"Answer":"Numpy has infinity object, you can call it by np.inf.","Q_Score":4,"Tags":"python,infinity","A_Id":46151523,"CreationDate":"2017-09-11T08:40:00.000","Title":"Initializing a matrix to infinity in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to build a MCMC model to simulate a changing beavior over time. I have to simulate one day with a time interval of 10-minutes. I have several observations of one day from N users in 144 intervals. So I have  U_k=U_1,...,U_N  U users with k ranging from 1 to N and for each user I have X_i=X_1,...X_t samples. Each user has two possible states, 1 and 0. I have understood that I have to build a transition probability matrix for each time step and then run the MCMC model. Is it right? But I did not understood how to build it in pyMC can anybody provided me suggestion?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":245,"Q_Id":46152636,"Users Score":1,"Answer":"Perhaps, assuming each user behaves the same way in a particular time interval, at each interval t we can get the matrix\n[ Pr 0->0 , Pr 1->0;\n  Pr 1->0 , Pr 1->0]\nwhere Pr x ->y = (the number of people in interval t+1 who are in state y AND who were in state x in interval t) divided by (the number of people who were in state x in interval t), i.e. the probability based on the sample that someone in the given time interval in state x (0 or 1) will transition to state y (0 or 1) in the next time interval.","Q_Score":1,"Tags":"python,pymc,markov-chains,mcmc","A_Id":46322688,"CreationDate":"2017-09-11T09:44:00.000","Title":"Monte Carlo Marcov Chain with pymc","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to execute a simple nltk code: nltk.sent_tokenize(text) and am getting error LookupError: unknown encoding: cp0. I tried typing in chcp in my IPython Console and I am getting the same error. \nI am working on Windows10 desktop, executing Python code over Miniconda > Spyder IDE. I have Python 2.7 installed.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2069,"Q_Id":46159517,"Users Score":0,"Answer":"I was able to work around this issue, but had to uninstall Miniconda and Python. I reinstalled Anaconda, launched Spyder from Anaconda-Navigator and its all working fine now. But I still don't understand the cause of this issue. It will be great if someone is able to explain.","Q_Score":0,"Tags":"python,encoding,nltk,spyder,miniconda","A_Id":46161737,"CreationDate":"2017-09-11T15:43:00.000","Title":"Python: LookupError: unknown encoding: cp0","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am supposed to determine the direction a windmill is facing from aerial images (with respect to True North - 0 to 359 degrees).\nMy question is, how can I determine the correct direction of the windmill and calculate its angle relative to the y-axis? Thanks!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1499,"Q_Id":46173428,"Users Score":4,"Answer":"Do not know if this works in general, but given your sample images I'd find the middle point of the short edges of the bounding box and get two rectangles for the two halves of the big BBox.\nI would then compute the sum of the mask pixels in the two separate half-BBoxes, assuming white is 1 and black is 0. Since the white area is bigger on the half of the rectangle where the \"front\" of the turbine is, pick the direction according to which of the two half-BBoxes has a higher sum","Q_Score":6,"Tags":"python,opencv,image-processing,computer-vision","A_Id":46173790,"CreationDate":"2017-09-12T09:58:00.000","Title":"How to determine object orientation in binary image? (Python, OpenCV)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"So .loc and .iloc are not your typical functions. They somehow use [ and ] to surround the arguments so that it is comparable to normal array indexing. However, I have never seen this in another library (that I can think of, maybe numpy as something like this that I'm blanking on), and I have no idea how it technically works\/is defined in the python code.\nAre the brackets in this case just syntactic sugar for a function call? If so, how then would one make an arbitrary function use brackets instead of parenthesis? Otherwise, what is special about their use\/defintion Pandas?","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":3012,"Q_Id":46176656,"Users Score":2,"Answer":"Underneath the covers, both are using the __setitem__ and __getitem__ functions.","Q_Score":19,"Tags":"python,pandas,pandas-loc","A_Id":46176863,"CreationDate":"2017-09-12T12:40:00.000","Title":"Why\/How does Pandas use square brackets with .loc and .iloc?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am learning to deal with python and lasagne. I have following installed on my pc:\n\npython 3.4.3\ntheano 0.9.0\nlasagne 0.2.dev1\n\nand also six, scipy and numpy. I call net.fit(), and the stacktrace tries to call train_split(X, y, self), which, I guess, should split the samples into training set and validation set (both the inputs X as well as the outputs Y).\nBut there is no method like train_split(X, y, self) , there is only a float field train_split - I assume, the ratio between training and validation set sizes. Then I get following error:\n\nTraceback (most recent call last):\nFile \"...\\workspaces\\python\\cnn\\dl_tutorial\\lasagne\\Test.py\", line\n  72, in \n      net = net1.fit(X[0:10,:,:,:],y[0:10])\nFile \"...\\Python34\\lib\\site-packages\\nolearn\\lasagne\\base.py\", line\n  544, in fit\n      self.train_loop(X, y, epochs=epochs)\nFile \"...\\Python34\\lib\\site-packages\\nolearn\\lasagne\\base.py\", line\n  554, in train_loop\n      X_train, X_valid, y_train, y_valid = self.train_split(X, y, self) \nTypeError: 'float' object is not callable\n\nWhat could be wrong or missing? Any suggestions? Thank you very much.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":47,"Q_Id":46179209,"Users Score":0,"Answer":"SOLVED\nin previous versions, the input parameter train_split has been a number, that was used by the same-named method. In nolearn 0.6.0, it's a callable object, that can implement its own logic to split the data. So instead of providing a float number to the input parameter train_split, I have to provide a callable instance (the default one is TrainSplit), that will be executed in each training epoch.","Q_Score":1,"Tags":"python-3.x,theano,lasagne","A_Id":46185735,"CreationDate":"2017-09-12T14:34:00.000","Title":"Missing method NeuralNet.train_split() in lasagne","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a Python application that needs to load the same large\narray (~4 GB) and do a perfectly parallel function on \nchunks of this array. The array starts off saved to disk.\nI typically run this application on a cluster computer with\nsomething like, say, 10 nodes, each node of which has 8\ncompute cores and a total RAM of around 32GB.\nThe easiest approach (which doesn't work) is to do\nn=80 mpi4py. The reason it doesn't work is that\neach MPI core will load the 4GB map, and this will exhaust\nthe 32GB of RAM resulting in a MemoryError.\nAn alternative is that rank=0 is the only process that loads\nthe 4GB array, and it farms out chunks of the array to the rest\nof the MPI cores -- but this approach is slow because of network\nbandwidth issues.\nThe best approach would be if only 1 core in each node loads\nthe 4GB array and this array is made available as shared memory\n(through multiprocessing?)  for the remaining 7 cores on each\nnode.\nHow can I achieve this? How can I have MPI be aware of nodes\nand make it coordinate with multiprocessing?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":981,"Q_Id":46180292,"Users Score":1,"Answer":"MPI-3 has a shared memory facility for precisely your sort of scenario. And you can use MPI through mpi4py....\nUse MPI_Comm_split_type to split your communicator into groups that live on a node. Use MPI_Win_allocate_sharedfor a window on the node; specify nonzero size only on one rank. Use MPI_Win_shared_query to get pointers to that window.","Q_Score":2,"Tags":"python,multiprocessing,mpi4py","A_Id":55276764,"CreationDate":"2017-09-12T15:27:00.000","Title":"Python hybrid multiprocessing \/ MPI with shared memory in the same node","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there a way to compile the entire Python script with my trained model for faster inference? Seems like loading the Python interpreter, all of Tensorflow, numpy, etc. takes a non-trivial amount of time. When this has to happen at a server responding to a non-trivial frequency of requests, it seems slow.\nEdit\nI know I can use Tensorflow serving, but don't want to because of the costs associated with it.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":372,"Q_Id":46187056,"Users Score":2,"Answer":"How do you set up a server? If you are setting up a server using python framework like django, flask or tornado, you just need to preload your model and keep it as a global variable, and then use this global variable to predict.\nIf you are using some other server. You can also make the entire python script you use to predict as a local server, and transform request or response between python server and web server.","Q_Score":2,"Tags":"python,machine-learning,tensorflow,inference","A_Id":46187418,"CreationDate":"2017-09-13T00:25:00.000","Title":"Compiling model as executable for faster inference?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to generate vector arrows that conform to the topography\/slope of a raster dataset of a river catchment area. \nI have created a Fishnet grid of points in ArcGIS and I would like to create a single arrow for each point of a set length that will follow the shape of the slope i.e. follow the path of least resistance, the line will follow progressively small numbers in a 3 x 3 grid.\nI think I can generate the vector arrows using vector plot. Is it possible to achieve the lines conforming to the raster?\nUPDATE: I have ~200,000 lines that I generated from a grid of points. I am going to turn these into a raster using R and set it to the same resolution as my slope raster. \nAny ideas on how to layer the raster lines on the slope so I can get the lines to follow the lowest values of the slope?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":322,"Q_Id":46187988,"Users Score":0,"Answer":"This is for display\/mapping purposes only? Use a DEM or TIN and display your arrow lines in ArcScene.\nEDIT: given your update about your data and software not working-\nTry this:\n1) Make a raster surface covering the extent of your data with a cell size of 100m (or smaller or larger if that doesn't suit)\n2) Convert that raster to a polygon layer e.g. 'area_grid100m'\n3) Do a spatial join and assign all points a polygon cell id from one of the unique id fields in 'area_grid100m'\n4) Use Summarize to get the mean lat\/long of the start points and mean lat\/long of the end points for each polygon. Summarize on the polygon id field and get select mean for both the lat and long fields\n5) Add summary table to ArcMap, right click and select Display XY Data (set X Field as longitude and y Field as latitude). Right right the result and select Data > Export Data to make it permanent. You will now have two points per 'area_grid100m' cell.\n5) Recreate your lines using this new file, which will give you one line per cell\nIf the res is not small enough, make the 'area_grid' cells smaller.","Q_Score":0,"Tags":"r,python-2.7,arcgis,r-raster","A_Id":46215900,"CreationDate":"2017-09-13T02:39:00.000","Title":"How to generate vector arrows that conforms to a raster slope layer?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How to find degree of fit in K-means++ clustering such that it shows how much percentage the inputs are aligned to each clusters. For instance, input A is in cluster 1 for 0.4 and in cluster 2 for 0.6.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":121,"Q_Id":46194025,"Users Score":0,"Answer":"There are \"soft\" variants of k-means that allow this.\nIn particular, fuzzy-c-means (don't ask me why they use c instead of k...)\nBut beware that the resulting soft assignment is far from a statistical probability. It's just a number that gives some relative weight based on the squared distance, without any strong statistical model.","Q_Score":0,"Tags":"python,machine-learning,cluster-analysis,k-means","A_Id":46212289,"CreationDate":"2017-09-13T09:39:00.000","Title":"How to find degree of fit in Kmeans++ clustering in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am currently working on a 2x2 subplot figure. In each subplot, I have 3 groups on the X axis. I want to decrease the spacing between each of these groups on the X-axis. Currently, the last value on 221 subplot is very close to the first value of 222 subplot.\nI have changed the spacing between subplots but I would like each of the subplots to be more compact by decreasing the spacing between the X-axis values. The variable on the X-axis is non-numeric.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":452,"Q_Id":46209466,"Users Score":1,"Answer":"Assign or use numeric values to the x axis then label the x axis ticks with the non-numeric information. Then you can play around with x-axis scaling and limits to move the plot around.","Q_Score":1,"Tags":"python,matplotlib","A_Id":46209673,"CreationDate":"2017-09-14T02:16:00.000","Title":"Scatter plot: Decreasing spacing between scatter points\/x-axis ticks","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a data set of consumer reviews. From these reviews I will like to extract most frequently occurring aspects. The process I am applying includes \n - Step 1: Tokenizing reviews into sentences\n- Step 2: Tokenizing sentences into words after basic NLP pre-processing. Pre-processing removes punctuation and English stop words. \n- Step 3: Pos_tagging and extracting all words with pos tag of 'NN','NNP','NNS','NNPS'\n- Step 4: Combining all the words across all reviews to find the most frequently occuring words \n- Step 5: Using top 40 terms as my aspects\nIs this a good approach or do you recommend doing something different?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":696,"Q_Id":46210282,"Users Score":0,"Answer":"yes .. but u need to specify aspects by yourself by choosing the most essential specification for the product","Q_Score":0,"Tags":"python,text,nltk","A_Id":51272932,"CreationDate":"2017-09-14T03:57:00.000","Title":"Python, extracting key aspects from consumer reviews","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would really like to get access to some of the updated functions in pandas 0.19, but Azure ML studio uses pandas 0.18 as part of the Anaconda 4.0 bundle. Is there a way to update the version that is used within the \"Execute Python Script\" components?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1862,"Q_Id":46222606,"Users Score":0,"Answer":"The Azure Machine Learning Workbench allows for much more flexibility with setting up environments using Docker. I moved to using that tool.","Q_Score":5,"Tags":"python,pandas,azure,anaconda,azure-machine-learning-studio","A_Id":46853504,"CreationDate":"2017-09-14T15:07:00.000","Title":"Updating pandas to version 0.19 in Azure ML Studio","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"For python dataframe, info() function provides memory usage.\nIs there any equivalent in pyspark ?\nThanks","AnswerCount":5,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":18349,"Q_Id":46228138,"Users Score":0,"Answer":"You can persist dataframe in memory and take action as df.count(). You would be able to check the size under storage tab on spark web ui.. let me know if it works for you.","Q_Score":13,"Tags":"python,apache-spark,dataframe,pyspark","A_Id":53010988,"CreationDate":"2017-09-14T20:59:00.000","Title":"How to find pyspark dataframe memory usage?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"For python dataframe, info() function provides memory usage.\nIs there any equivalent in pyspark ?\nThanks","AnswerCount":5,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":18349,"Q_Id":46228138,"Users Score":8,"Answer":"I have something in mind, its just a rough estimation. as far as i know spark doesn't have a straight forward way to get dataframe memory usage, But Pandas dataframe does. so what you can do is.\n\nselect 1% of data sample = df.sample(fraction = 0.01)\npdf = sample.toPandas()\nget pandas dataframe memory usage by pdf.info()\nMultiply that values by 100, this should give a rough estimate of your whole spark dataframe memory usage.\nCorrect me if i am wrong :|","Q_Score":13,"Tags":"python,apache-spark,dataframe,pyspark","A_Id":60070654,"CreationDate":"2017-09-14T20:59:00.000","Title":"How to find pyspark dataframe memory usage?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to create a program\/script for the creation of a high numbers of random sequences (20 letter long sequence based on 4 different letters) with a minimum edit distance between all sequences. \"High\" would here be a minimum of 100k sequences, but if possible up to 1 million. \nI started with a naive approach of just generating random 20 letter sequences, and for each sequence, calculate the edit distance between the sequence and all other sequences already created and stored. If the new sequence pass my threshold value, store it, otherwise discard. \nAs you understand, this scales very badly for higher number of sequences. Up to 10k is reasonably fine, but trying to get 100k this starts to get troublesome. \nI really only need to create the sequences once and store the output, so I'm really not that fussy about speed, but making 1 million at this rate today is just no possible. \nBeen trying to think of alternatives to speed up the process, like building the sequences is \"blocks\" of minimal ED and then combining, but haven't come up with any solution. \nWondering, do anyone have any smart idea\/method that could be implemented to create such high number of sequences with minimal ED more time efficient? \nCheers,\nJB","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":79,"Q_Id":46235675,"Users Score":0,"Answer":"It seems, from wikipedia, that edit distance is one of three operations insertion, deletion, substitution; performed on a starting string. Why not systematically generate all strings up to N edits from a starting string then stop  when you reach your limit?\nThere would be no need to check for the actual edit distance as they would be correct by generation. For randomness could you generate a number then shuffle them.","Q_Score":0,"Tags":"python,algorithm,performance,edit-distance","A_Id":46241767,"CreationDate":"2017-09-15T09:00:00.000","Title":"Create high nr of random sequences with min Edit Distance time efficient","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to estimate a logit model with individual fixed effects in a panel data setting, i.e. a conditional logit model, with python.\nI have found the pylogit library. However, the documentation I could find, explained how to use the conditional logit model for multinomial models with varying choice attributes. This model does not seem to be the same use case as a simple binary panel model.\nSo my questions are: \nDoes pylogit allow to estimate conditional logits for panel data? \nIf so, is there documentation? \nIf not, are there other libraries that allow you to estimate this type of model?\nAny help would be much appreciated.","AnswerCount":1,"Available Count":1,"Score":0.6640367703,"is_accepted":false,"ViewCount":2528,"Q_Id":46244095,"Users Score":4,"Answer":"I'm the creator of pylogit.\nI don't have built in utilities for estimating conditional logits with fixed effects. However, you can use pylogit to estimate this model. Simply\n\nCreate dummy variables for each decision maker. Be sure to leave out one decision maker for identification.\nFor each created dummy variable, add dummy variable's column name to the one's utility specification.","Q_Score":1,"Tags":"python","A_Id":48470963,"CreationDate":"2017-09-15T16:34:00.000","Title":"conditional logit for panel data in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I understand that scaling means centering the mean(mean=0) and making unit variance(variance=1).\nBut, What is the difference between preprocessing.scale(x)and preprocessing.StandardScalar() in scikit-learn?","AnswerCount":2,"Available Count":1,"Score":-0.0996679946,"is_accepted":false,"ViewCount":9544,"Q_Id":46257627,"Users Score":-1,"Answer":"My understanding is that scale will transform data in min-max range of the data, while standardscaler will transform data in range of [-1, 1].","Q_Score":21,"Tags":"python,scikit-learn,scale","A_Id":49274464,"CreationDate":"2017-09-16T19:21:00.000","Title":"Scikit-learn: preprocessing.scale() vs preprocessing.StandardScalar()","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How can I extract a mapping of numbers to labels from a .sav file without access to SPSS?\nI am working with a non-profit who uses SPSS, but I don't have access to SPSS (and they are not technical). They've sent me some SPSS files, and I was able to extract these into csv files which have correct information with an R package called foreign. \nHowever for some files the R package extracts textual labels and for other files the R package extracts numbers. The files are for parallel case studies of different individuals, and when I count the labels vs. the numbers they don't even match exactly (say 15 labels vs. 18 enums because the underlying records were made across many years and by different personnel, so I assume the labels probably don't match in any case). So I really need to see the number to label matching in the underlying enum. How can I do this without access to SPSS?\n(p.s. I also tried using scipy.io to read the .sav file and got the error Exception: Invalid SIGNATURE: b'$F' when testing on multiple files before giving up so that seems like a non-starter)","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":102,"Q_Id":46257848,"Users Score":1,"Answer":"For R, you can perhaps use the haven package. Of the course the results will depend on the files being imported, but the package does included functions for dealing with\/viewing labels (presuming the labels actually exist).","Q_Score":0,"Tags":"python,r,spss","A_Id":46259970,"CreationDate":"2017-09-16T19:47:00.000","Title":"How to extract enumerated labels and corresponding numerical values from a .sav file?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When I import matplotlib.pyplot in any python 3.6 program, I get the following error:\n$ python kernel1.py\nTraceback (most recent call last):\n  File \"kernel1.py\", line 13, in <module>\n    import matplotlib.pyplot as plt\n  File \"\/home\/atul\/anaconda3\/lib\/python3.6\/site-packages\/matplotlib\/pyplot.py\", line 115, in <module>\n    _backend_mod, new_figure_manager, draw_if_interactive, _show = pylab_setup()\n  File \"\/home\/atul\/anaconda3\/lib\/python3.6\/site-packages\/matplotlib\/backends\/__init__.py\", line 32, in pylab_setup\n    globals(),locals(),[backend_name],0)\n  File \"\/home\/atul\/anaconda3\/lib\/python3.6\/site-packages\/matplotlib\/backends\/backend_qt5agg.py\", line 16, in <module>\n    from .backend_qt5 import QtCore\n  File \"\/home\/atul\/anaconda3\/lib\/python3.6\/site-packages\/matplotlib\/backends\/backend_qt5.py\", line 26, in <module>\n    import matplotlib.backends.qt_editor.figureoptions as figureoptions\n  File \"\/home\/atul\/anaconda3\/lib\/python3.6\/site-packages\/matplotlib\/backends\/qt_editor\/figureoptions.py\", line 20, in <module>\n    import matplotlib.backends.qt_editor.formlayout as formlayout\n  File \"\/home\/atul\/anaconda3\/lib\/python3.6\/site-packages\/matplotlib\/backends\/qt_editor\/formlayout.py\", line 56, in <module>\n    from matplotlib.backends.qt_compat import QtGui, QtWidgets, QtCore\n  File \"\/home\/atul\/anaconda3\/lib\/python3.6\/site-packages\/matplotlib\/backends\/qt_compat.py\", line 137, in <module>\n    from PyQt4 import QtCore, QtGui\nModuleNotFoundError: No module named 'PyQt4'\nHowever, if I use python 3.5, matplotlib.pyplot works perfectly. \nI have tried using sudo apt-get install python-qt4. Still I get the same error.\nI am using Ubuntu 16.04.","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":4436,"Q_Id":46281845,"Users Score":1,"Answer":"For python 3.6(since i had that in my computer), you go to command line , and type this :\nconda install -c anaconda pyqt=5.6.0\nIf you are unsure about the python and pyqt version. Then type :\nconda info pyqt\nThis will output the relevant pyqt version. Hence you can check your pyqt version and install from command mentioned at first.","Q_Score":0,"Tags":"python,matplotlib,pyqt4,ubuntu-16.04,python-3.6","A_Id":46406790,"CreationDate":"2017-09-18T14:28:00.000","Title":"No module named PyQt4 in python 3.6 when I use matplotlib.pyplot","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to install Tensorflow on a Windows 7 laptop in order to use jupyter notebook to play around with the object detection notebook in Github. I am facing this error:\n\nImportError                               Traceback (most recent call\n  last)  in ()\n        4 import sys\n        5 import tarfile\n  ----> 6 import tensorflow as tf\n        7 import zipfile\n        8 \nImportError: No module named tensorflow\n\nI am getting the above error when I start the Jupyter Notebook from inside the Conda environment in Windows 7. I have installed Python 3.5.4 & within conda environment, tensorflow as well.\nI am also getting ... not recognized as an internal\/external... for $ command while giving $ python and sometimes also for pip3 I have included several file paths in Environment Variables. Can you please suggest me what to do. I am using the Conda env as I feel I have a problem in having Windows Service Pack 1.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":820,"Q_Id":46286077,"Users Score":0,"Answer":"Make sure your Tensorflow folder is somewhere that the environment will look, such as [Python install directory]\/Lib\/Site-packages","Q_Score":0,"Tags":"python,tensorflow","A_Id":46287762,"CreationDate":"2017-09-18T18:37:00.000","Title":"Error installing Tensorflow in Windows 7","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"What I'm trying to do is conceptually similar to the infamous NMIST classification example. Except that each digit is a computer generated sound wave.\nI'll be adding some background noise to the data in order to improve real world accuracy.\nMy question is; considering the sequential data what model is best suited for this? Am I right in assuming a convolutional net would work?\nI favour simpler model in exchange for a few percentage performance points and preferably it could be written with the Scikit Learn library.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":58,"Q_Id":46299246,"Users Score":0,"Answer":"Yes, \nyou can use 1D convolutional neural network. The convolutional filters can exploit consecutive parts of the signal and therefore it can be useful.\nYou can also try to look into recurrent neural networks which are more complex.","Q_Score":0,"Tags":"python-3.x,machine-learning,scikit-learn,sound-recognition","A_Id":46299601,"CreationDate":"2017-09-19T11:27:00.000","Title":"Sound digit classification","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have two CSV's, each with about 1M lines, n number of columns, with identical columns. I want the most efficient way to compare the two files to find where any difference may lie. I would prefer to parse this data with Python rather than use any excel-related tools.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":147,"Q_Id":46303776,"Users Score":1,"Answer":"An efficient way would be to read each line from the first file(with less number of lines) and save in an object like Set or Dictionary, where you can access using O(1) complexity. \nAnd then read lines from the second file and check if it exists in the Set or not.","Q_Score":0,"Tags":"python,algorithm,csv,search","A_Id":46303902,"CreationDate":"2017-09-19T14:59:00.000","Title":"Most efficient way to compare two near identical CSV's in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to all the tensorflow and Python programming and have installed tensorflow through pip and virtualenv but now I read that in order to use Spyder for Python it is best to use Anaconda. I know that tf can be installed through conda as well but how do I go about it now? Do I have to completely remove the existing installations first and if yes, can someone explain in detail which and how I can do it?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":633,"Q_Id":46307874,"Users Score":0,"Answer":"Yes, after a short reading into the topic, I simply unistalled tf using sudo pip uninstall tensorflow within my virtualenv and then deactivated the virtualenv. Don't know how to really uninstall the virtualenv as well but I guess that is already enough then and I can proceed with the installation of Anaconda?\nI also have installed some additional packages like mathplotlib, ipython etc. but I can keep them as well without problems?\nThanks","Q_Score":1,"Tags":"python,tensorflow,pip,anaconda,virtualenv","A_Id":46308361,"CreationDate":"2017-09-19T18:54:00.000","Title":"Completely removing Tensorflow, pip and virtualenv","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to all the tensorflow and Python programming and have installed tensorflow through pip and virtualenv but now I read that in order to use Spyder for Python it is best to use Anaconda. I know that tf can be installed through conda as well but how do I go about it now? Do I have to completely remove the existing installations first and if yes, can someone explain in detail which and how I can do it?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":633,"Q_Id":46307874,"Users Score":0,"Answer":"Just install Anaconda it will take care of everything. Uninstalling the existing ones is up to you, they wont harm anything.","Q_Score":1,"Tags":"python,tensorflow,pip,anaconda,virtualenv","A_Id":46308280,"CreationDate":"2017-09-19T18:54:00.000","Title":"Completely removing Tensorflow, pip and virtualenv","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there a way to see how the categorical features are encoded when we allow h2o to automatically create categorical data by casting a column to enum type? \nI am implementing holdout stacking where my underlying training data differs for each model. I have a common feature that I want to make sure is encoded the same way across both sets. The feature contains names (str). It is guaranteed that all names that appear in one data set will be appear in the other.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":246,"Q_Id":46328579,"Users Score":0,"Answer":"The best way to see inside a model is to export the pojo, and look at the java source code. You should see how it is processing enums.\nBut, if I understand the rest of your question correctly, it should be fine. As long as the training data contains all possible values of a category it will work as you expect. If a categorical value not see in training is presented in production it will be treated as an NA.","Q_Score":1,"Tags":"python,h2o,categorical-data","A_Id":46359347,"CreationDate":"2017-09-20T17:42:00.000","Title":"Encoded categorical features in h2o in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"What is the easiest way to use packages such as NumPy and Pandas within the new ETL tool on AWS called Glue? I have a completed script within Python I would like to run in AWS Glue that utilizes NumPy and Pandas.","AnswerCount":13,"Available Count":3,"Score":0.0153834017,"is_accepted":false,"ViewCount":29917,"Q_Id":46329561,"Users Score":1,"Answer":"As of now, You can use Python extension modules and libraries with your AWS Glue ETL scripts as long as they are written in pure Python. C libraries such as pandas are not supported at the present time, nor are extensions written in other languages.","Q_Score":15,"Tags":"python,pandas,amazon-web-services,aws-lambda,aws-glue","A_Id":51174183,"CreationDate":"2017-09-20T18:42:00.000","Title":"Use AWS Glue Python with NumPy and Pandas Python Packages","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"What is the easiest way to use packages such as NumPy and Pandas within the new ETL tool on AWS called Glue? I have a completed script within Python I would like to run in AWS Glue that utilizes NumPy and Pandas.","AnswerCount":13,"Available Count":3,"Score":0.0307595242,"is_accepted":false,"ViewCount":29917,"Q_Id":46329561,"Users Score":2,"Answer":"when you click run job you have a button  Job parameters (optional) that is collapsed by default , when we click on it  we have the following options which we can use to save the libraries in s3 and this works for me :  \nPython library path\ns3:\/\/bucket-name\/folder-name\/file-name\nDependent jars path\ns3:\/\/bucket-name\/folder-name\/file-name\nReferenced files path\ns3:\/\/bucket-name\/folder-name\/file-name","Q_Score":15,"Tags":"python,pandas,amazon-web-services,aws-lambda,aws-glue","A_Id":46416040,"CreationDate":"2017-09-20T18:42:00.000","Title":"Use AWS Glue Python with NumPy and Pandas Python Packages","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"What is the easiest way to use packages such as NumPy and Pandas within the new ETL tool on AWS called Glue? I have a completed script within Python I would like to run in AWS Glue that utilizes NumPy and Pandas.","AnswerCount":13,"Available Count":3,"Score":0.0153834017,"is_accepted":false,"ViewCount":29917,"Q_Id":46329561,"Users Score":1,"Answer":"If you go to edit a job (or when you create a new one) there is an optional section that is collapsed called \"Script libraries and job parameters (optional)\". In there, you can specify an S3 bucket for Python libraries (as well as other things). I haven't tried it out myself for that part yet, but I think that's what you are looking for.","Q_Score":15,"Tags":"python,pandas,amazon-web-services,aws-lambda,aws-glue","A_Id":46414546,"CreationDate":"2017-09-20T18:42:00.000","Title":"Use AWS Glue Python with NumPy and Pandas Python Packages","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an Ubuntu 16.04 system with an Anaconda installation. I want to compile and install OpenCV 3.3 and use also the Python bindings. I used the following CMake command:\n\ncmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=\/usr\/local -D WITH_CUDA=ON -D D WITH_FFMPEG=1 -D WITH_CUBLAS=ON -D WITH_TBB=ON -D WITH_V4L=ON -D WITH_QT=ON -D WITH_OPENGL=ON -D INSTALL_PYTHON_EXAMPLES=ON -D INSTALL_C_EXAMPLES=OFF -D OPENCV_EXTRA_MODULES_PATH=~\/opencv_contrib-3.3.0\/modules -D BUILD_EXAMPLES=ON -D BUILD_TIFF=ON -D PYTHON_EXECUTABLE=\/home\/guel\/anaconda2\/envs\/py27\/bin\/python -D PYTHON2_LIBRARIES=\/home\/guel\/anaconda2\/envs\/py27\/lib\/libpython2.7.so -D PYTHON2_PACKAGES_PATH=\/home\/guel\/anaconda2\/envs\/py27\/lib\/python2.7\/site-packages -DWITH_EIGEN=OFF -D BUILD_opencv_cudalegacy=OFF ..\n\nThe command does the job but then, of course, OpenCV is installed only for a specific conda environment that I created. However, I want to be able to use it also from different environments without having to go through the compilation for each and every environment. Is there a way to achieve that in a simple way? Since the OpenCv libraries are actually installed in \/usr\/local, I can imagine that there must be a simple way to link the libraries to each new conda enviroment but I couldn't figure out exactly how.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2609,"Q_Id":46339134,"Users Score":0,"Answer":"So you are providing the python package and library path to environment specific location, in order to make it available environment try using the anaconda\/bin and lib path. Can't make it as a comment due to low reputation.","Q_Score":3,"Tags":"python,opencv,anaconda,conda","A_Id":46645511,"CreationDate":"2017-09-21T08:34:00.000","Title":"Installing OpenCV for all conda environments","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I using jupyter notebook and graphlab \/ turi for tfidf-nearest_neighbors model, which works fine so far.\nHowever, when I query the model like \ntfidf_model.query(Test_AD)\nI always just get the head - [5 rows x 4 columns]\nI am supposed to use \"print_rows(num_rows=m, num_columns=n)\" to print more rows and columns like:\ntfidf_model.query(Test_AD).print_rows(num_rows=50, num_columns=4)\nhowever, when I used it, I dont get any rows anymore, only the summary field:\nStarting pairwise querying.\n+--------------+---------+-------------+--------------+\n| Query points | # Pairs | % Complete. | Elapsed Time |\n+--------------+---------+-------------+--------------+\n| 0            | 1       | 0.00519481  | 13.033ms     |\n| Done         |         | 100         | 106.281ms    |\n+--------------+---------+-------------+--------------+\nThat's it. No error message, nothing. Any Ideas, how to get all\/ more rows?\nI tried to convert into pandas or .show() command etc., didnt help.","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1900,"Q_Id":46347289,"Users Score":0,"Answer":"ok, well, seems like I have to define the number or neighbours with:\ntfidf_model.query(Test_AD, k=100).show()\nso I can get a list of first 100 in the canvass.","Q_Score":0,"Tags":"python-2.7,jupyter-notebook,rows,nearest-neighbor,graphlab","A_Id":46347870,"CreationDate":"2017-09-21T15:00:00.000","Title":"print_rows(num_rows=m, num_columns=n) in graphlab \/ turi not working","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"i have written a little csv parser based on pandas.\nIt works like a charm in Spyder 3. \nYesterday i tried to put it into production and run it with a .bat file, like:\npython my_parser.py\nIn the console it doesn't work at all.\nPandas behaves different: The read_csv method lost the \"quotechar\" keyword argument, for example. \nEspecially date comparisons break all the time. \nI read the dates with pandas as per\npd.read_csv(parse_dates=[col3, col5, col8])\nThen i try a date calculation by substracting pd.to_datetime('now')\nI tested everything, and as said, in Spyder no failure is thrown, it works and produces results as it should be.\nAs soon as i start it in the console, he throws type errors. \nThe most often one of the two dates is a mere string and the other stays a datetime, so the minus operation fails.\nI could now rewrite the code and find a procedure that works in both, Spyder and console.\nHowever, i prefer to ask you guys here: \nWhat could be a possible reason that spyder and the console python behave completely different from each other? \nIt's really annoying to debug code that does not throw any failures, so i really would like to understand the cause.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":164,"Q_Id":46356186,"Users Score":0,"Answer":"The problem was related to having several python installations on my PC. After removing all of it and installing a single instance, it was working well. Thanks for the tipp, Carlos Cordoba!","Q_Score":0,"Tags":"python,pandas,datetime,spyder","A_Id":46450459,"CreationDate":"2017-09-22T03:04:00.000","Title":"Python: Date comparing works in Spyder but not in Console","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"In the book \"Spark: The definitive guide\" (currently early release, text might change), the authors advise against the use of Pyspark for user-defined functions in Spark:\n\"Starting up this Python process is expensive but the real cost is in serializing the data to Python. This is costly for two reasons, it is an expensive computation but also once the data enters Python, Spark cannot manage the memory of the worker. This means that you could potentially cause a worker to fail if it becomes resource constrained (because both the JVM and python are competing for memory on the same machine).\"\nI understand that the competition for worker node resources between Python and the JVM can be a serious problem. But doesn't that also apply to the driver? In this case, it would be an argument against using Pyspark at all. Could anyone please explain what makes the situation different on the driver?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":134,"Q_Id":46356893,"Users Score":0,"Answer":"In your driver application, you don't necessarily have to collect a ton of records. Maybe you're just doing a reduce down to some statistics.\nThis is just typical behavior: Drivers usually deal with statistical results. Your mileage may vary.\nOn the other hand, Spark applications typically use the executors to read in as much data as their memory allows and process it. So memory management is almost always a concern.\nI think this is the distinction the book is getting at.","Q_Score":4,"Tags":"python,scala,apache-spark,pyspark,user-defined-functions","A_Id":46358100,"CreationDate":"2017-09-22T04:29:00.000","Title":"Spark: Dangers of using Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm focusing on the special case where A is a n x d matrix (where k < d) representing an orthogonal basis for a subspace of R^d and b is known to be inside the subspace.\nI thought of using the tools provided with numpy, however they only work with square matrices. I had the approach of filling in the matrix with some linearly independent vectors to \"square\" it and then solve, but I could not figure out how to choose those vectors so that they will be linearly independent of the basis vectors, plus I think it's not the only approach and I'm missing something that can make this easier.\nis there indeed a simpler approach than the one I mentioned? if not, how indeed do I choose those vectors that would complete Ainto a square matrix?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":23050,"Q_Id":46377331,"Users Score":0,"Answer":"Can use:\n\nnp.linalg.lstsq(x, y)\nnp.linalg.pinv(X) @ y\nLinearRegression().fit(X, y)\n\nWhere 1 and 2 are from numpy and 3 is from sklearn.linear_model.\nAs a side note you will need to concatenate ones(use np.ones_like) in both 1 and 2 to represent the bias from the equation y = ax + b","Q_Score":8,"Tags":"python,numpy,matrix","A_Id":72120529,"CreationDate":"2017-09-23T07:49:00.000","Title":"solving Ax =b for a non-square matrix A using python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a matrix or a multiple array written in python, each element in the array is an integer ranged from 0 to 7, how would I randomly initalize this matrix or multiple array, so that for each element holds a value, which is different from the values of its 4 neighbours(left,right, top, bottom)? can it be implemented in numpy?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":62,"Q_Id":46385250,"Users Score":1,"Answer":"You can write your own matrix initializer. \nGo through the array[i][j] for each i, j pick a random number between 0 and 7. \nIf the number equals to either left element: array[i][j-1] or to the upper one: array[i-1][j] regenerate it once again. \nYou have 2\/7 probability to encounter such a bad case, and 4\/49 to make it twice in a row, 8\/343 for 3 in a row, etc.. the probability dropes down very quickly. \nThe average case complexity for n elements in a matrix would be O(n).","Q_Score":0,"Tags":"python,arrays,numpy","A_Id":46385537,"CreationDate":"2017-09-23T23:41:00.000","Title":"how to init a array with each element holding the value different from its neighbours","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Windows 10\nPython 2.7\nAnaconda\npip\nI am having big problems installing SciKit. \nI have tried every installation option I can find. \ntried installing with pip and anaconda. It says it is successfully installed but I can't import it to my script - I get error -\nTraceback (most recent call last):\n  File \"C:\/Python27\/trash.py\", line 1, in \n    from sklearn import datasets\n  File \"C:\\Python27\\lib\\site-packages\\sklearn__init__.py\", line 134, in \n    from .base import clone\n  File \"C:\\Python27\\lib\\site-packages\\sklearn\\base.py\", line 10, in \n    from scipy import sparse\nImportError: No module named scipy\nI have installed numpy, pandas, ipython, sympy, scipy etc .... everything that any post or forum says is needed. My pc says I already have scipy installed. I was told the easiest option was to do it with Anaconda. Anaconda also says it is all already installed. \n\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\nIf I try install it with pip install scipy or pip -U install scipy I get this error ---\nCommand \"c:\\python27\\python.exe -u -c \"import setuptools, tokenize;file='c:\\users\\james\\appdata\\local\\temp\\pip-build-g1vohj\\scipy\\setup.py';f=getattr(tokenize, 'open', open)(file);code=f.read().replace('\\r\\n', '\\n');f.close();exec(compile(code, file, 'exec'))\" install --record c:\\users\\james\\appdata\\local\\temp\\pip-xjacl_-record\\install-record.txt --single-version-externally-managed --compile\" failed with error code 1 in c:\\users\\james\\appdata\\local\\temp\\pip-build-g1vohj\\scipy\\\n\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\nAnaconda using conda install scipy I get --\n(C:\\Users\\james\\Anaconda2) C:\\Users\\james>conda install scipy                                                           Fetching package metadata ...........                                                                                   Solving package specifications: .                                                                                                                                                                                                               # All requested packages already installed.                                                                             # packages in environment at C:\\Users\\james\\Anaconda2:                                                                  #                                                                                                                       scipy                     0.19.1              np113py27_0\nI get the same response when installing all the stuff that is required like numpy.\n\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\nI am trying to get started on machine learning but this is just a nightmare.\nplease help me...","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":507,"Q_Id":46385689,"Users Score":0,"Answer":"It looks to me like you may have two versions of Python installed. In your original stack trace, you can see that the version of Python that is complaining about scipy is coming from \"C:\/Python27\/\". However, your install of Anaconda looks like it's coming from \"C:\/Users\/james\/Anaconda2\".\nI would recommend putting Anaconda's python.exe first in your PATH.","Q_Score":2,"Tags":"python,python-2.7,numpy,scikit-learn,anaconda","A_Id":46430717,"CreationDate":"2017-09-24T01:24:00.000","Title":"PYTHON: I can't get scipy\/sklearn to work. No scipy module","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I happen to have a list y = [y1, y2, ... yn]. I need to generate random numbers ai such that 0<=ai<=c (for some constant c) and sum of all ai*yi = 0. Can anyone help me out on how to code this in python? Even pseudocode\/logic works, I am having a conceptual problem here. While the first constraint is easy to satisfy, I cannot get anything for the second constraint. \nEDIT: Take all yi = +1 or -1, if that helps, but a general solution to this would be interesting.","AnswerCount":5,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1846,"Q_Id":46392625,"Users Score":0,"Answer":"If you need random integer values between 0 and c use random.randint(0, c). For random floating point values between 0 anc c use random.uniform(0, c).","Q_Score":1,"Tags":"python,random","A_Id":46392727,"CreationDate":"2017-09-24T17:04:00.000","Title":"Generating Random Numbers Under some constraints","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I happen to have a list y = [y1, y2, ... yn]. I need to generate random numbers ai such that 0<=ai<=c (for some constant c) and sum of all ai*yi = 0. Can anyone help me out on how to code this in python? Even pseudocode\/logic works, I am having a conceptual problem here. While the first constraint is easy to satisfy, I cannot get anything for the second constraint. \nEDIT: Take all yi = +1 or -1, if that helps, but a general solution to this would be interesting.","AnswerCount":5,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1846,"Q_Id":46392625,"Users Score":0,"Answer":"I like splitting this problem up. Note that there must be some positive and some negative values of y (otherwise sum(ai*yi) can't equal zero). \n\nGenerate random positive coefficients ai for the negative values of y, and construct the sum of ai*yi over only the negative values of y (let's say this sum is -R). \nAssuming there are \"m\" remaining positive values, choose random numbers for the first m-1 of the ai coefficients for positive yi values, according to ai = uniform(R\/(m*max(y)).\nUse your constraint to determine am = (R-sum(aiyi | yi> 0))\/ym. Notice that, by construction, all ai are positive and the sum of aiyi = 0.\nAlso note that multiplying all ai by the same amount k will also satisfy the constraint. Therefore, find the largest ai (let's call it amax), and if amax is greater than c, multiply all values of ai by c\/(amax + epsilon), where epsilon is any number greater than zero.\n\nDid I miss anything?","Q_Score":1,"Tags":"python,random","A_Id":46440005,"CreationDate":"2017-09-24T17:04:00.000","Title":"Generating Random Numbers Under some constraints","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to use the histogram of the output from a CNN to compute the loss. I am wondering whether tf.histogram_fixed_width() supports the gradient to flow back to its former layer. Only it works, I can add a loss layer after calculating the histogram.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":670,"Q_Id":46394659,"Users Score":0,"Answer":"I had the similar problem. There are two ways you can try: 1. After the output layer, add an extra layer to produce the histogram; 2. Use something like tf.RegisterGradient or tf.custom_gradient to define your own gradients for the operations.","Q_Score":3,"Tags":"python,tensorflow","A_Id":68513454,"CreationDate":"2017-09-24T20:42:00.000","Title":"Does tf.histogram_fixed_width() support back propagation?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to spark and have to write a streaming application that has to perform tasks like fast fourier transformations and some machine learning stuff like classification\/regression with svms etc. I want to do this in pyspark, because of python's huge variety of modules like numpy, scikit-learn etc. My question is, is it possible to do such stuff in a streaming application? As far as I know, spark uses dstreams. Are these streams convertible to something like numpy arrays or anything similar that can serve as an input for python functions?\nThx","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":189,"Q_Id":46404243,"Users Score":0,"Answer":"Pyspark is used to run program\/code\/algorithm in spark, which are coded in python language.\nFor machine leaning, spake has MLlib library packages.\nFor streaming purpose, spark has Spark streaming lib packages\nYou can explore Storm as well for real time streaming.","Q_Score":0,"Tags":"python,numpy,pyspark,spark-streaming","A_Id":46404352,"CreationDate":"2017-09-25T11:35:00.000","Title":"Is pyspark streaming suitable for machine learning\/ scientific computing?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to spark and have to write a streaming application that has to perform tasks like fast fourier transformations and some machine learning stuff like classification\/regression with svms etc. I want to do this in pyspark, because of python's huge variety of modules like numpy, scikit-learn etc. My question is, is it possible to do such stuff in a streaming application? As far as I know, spark uses dstreams. Are these streams convertible to something like numpy arrays or anything similar that can serve as an input for python functions?\nThx","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":189,"Q_Id":46404243,"Users Score":0,"Answer":"Machine learning is process of learn from data. First you train your model and then use that on top of data stream.\nData can be processed as mini, micro or even real time, depends on amount of data it is generating in a particular time.\nFlume and Kafka are used to fetch data on real time and store on HDFS or can be fed to Spark with Spark streaming pointing to flume sink.","Q_Score":0,"Tags":"python,numpy,pyspark,spark-streaming","A_Id":46480253,"CreationDate":"2017-09-25T11:35:00.000","Title":"Is pyspark streaming suitable for machine learning\/ scientific computing?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to reduce dimension of RGB images using PCA on python. But it seems to me that all codes I found only work on a greyscale image. Is there any way to do PCA on RGB image using any python library like sklearn or opencv?\nThanks","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1645,"Q_Id":46406527,"Users Score":0,"Answer":"Separate three channels i.e., Red, Blue, Green and apply PCA on each. After applying PCA on each channel again join them.","Q_Score":0,"Tags":"python,rgb,pca","A_Id":53309575,"CreationDate":"2017-09-25T13:32:00.000","Title":"PCA for RGB image in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to resize a tensor (between layers) of size say (None, 2, 7, 512) to (None, 2, 8, 512), by interpolating it (say using nearest neighbor), similar to this function tf.image.resize_nearest_neighbor available in Tensorflow. \nIs there any way to do that? \nI tried directly using the Tensorflow function tf.image.resize_nearest_neighbor and the pass the tensors to the next Keras layer, but with the next layer this error was thrown:\n\nAttributeError: 'Tensor' object has no attribute '_keras_history' \n\nI believe this is due to some attributes that are missing in Tensorflow tensors, which makes sense as the layer expects Keras tensors to be passed.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":5492,"Q_Id":46418373,"Users Score":2,"Answer":"I would use Repeat to add one element and implement the interpolation as a new lambda layer. I don't think there's an existing layer for this in keras.","Q_Score":1,"Tags":"python,tensorflow,deep-learning,keras,keras-2","A_Id":46418511,"CreationDate":"2017-09-26T05:21:00.000","Title":"How to resize (interpolate) a tensor in Keras?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"The official documentation for tf.contrib.legacy_seq2seq.embedding_rnn_seq2seq has the following explanation for the output_projection argument:\n\noutput_projection: None or a pair (W, B) of output projection weights and biases; W has shape [output_size x num_decoder_symbols] and B has shape [num_decoder_symbols]; if provided and feed_previous=True, each fed previous output will first be multiplied by W and added B.\nI don't understand why the B argument should have the size of [num_decoder_symbols]? Since the output is first multiplied by W and then the biases are added, Shouldn't it be [output_size]?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":141,"Q_Id":46424912,"Users Score":0,"Answer":"Alright! So, I have found the answer to the question. \nThe main source of confusion was in the dimensions \n[output_size x num_decoder_symbols] of the W matrix itself. \nThe output_size here doesn't refer to the output_size that you want, but is the output_size (same as the size of the hidden vector) of the LSTM cell. Thus the matrix multiplication u x W will result in a vector of size num_decoder_symbols that can be considered as the logits for the output symbols.","Q_Score":1,"Tags":"python,tensorflow,lstm,embedding,rnn","A_Id":46461915,"CreationDate":"2017-09-26T11:13:00.000","Title":"tf.contrib.legacy_seq2seq.embedding_rnn_seq2seq output projection","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've recently noticed that a function where I iterate over a DataFrame rows using .iloc is very slow. I found out that there's a faster method called .iat, that's said to be equivalent to .iloc. I tried it and it cut the run time down by about 75%. \nBut I'm a little hesitant: why is there an \"equivalent\" method that's faster? There must be some difference between the inner workings of these two and a reason why they both exist and not just the faster one. I've tried looking everywhere but even the pandas documentation just states that\n\nDataFrame.iat\n  Fast integer location scalar accessor. \nSimilarly to iloc, iat provides integer based lookups. You can also set using these indexers.\n\nAnd that doesn't help. \nAre there limits to using .iat? Why is faster; is it sloppier? Or do I just switch to using .iat and happily forget .iloc ever existed?","AnswerCount":2,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":5846,"Q_Id":46426875,"Users Score":6,"Answer":"iat and at gives only a single value output, while iloc and loc can give multiple row output.  Example:  iloc[1:2,5:8] is valid but iat[1:2,5:8] will throw error","Q_Score":13,"Tags":"python,pandas,dataframe","A_Id":57285250,"CreationDate":"2017-09-26T12:41:00.000","Title":"Difference between pandas .iloc and .iat?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working with a medical data set that contains many variables with discrete outputs. For example: type of anesthesia, infection site, Diabetes y\/n. And to deal with this I have just been converting them into multiple columns with ones and zeros and then removing one to make sure there is not a direct correlation between them but I was wondering if there was a more efficient way of doing this","AnswerCount":1,"Available Count":1,"Score":-0.1973753202,"is_accepted":false,"ViewCount":89,"Q_Id":46439814,"Users Score":-1,"Answer":"It depends on the purpose of the transformation. Converting categories to numerical labels may not make sense if the ordinal representation does not correspond to the logic of the categories. In this case, the \"one-hot\" encoding approach you have adopted is the best way to go, if (as I surmise from your post) the intention is to use the generated variables as the input to some sort of regression model. You can achieve what you are looking to do using pandas.get_dummies.","Q_Score":1,"Tags":"python,pandas,dataframe,machine-learning","A_Id":46443938,"CreationDate":"2017-09-27T05:01:00.000","Title":"Machine Learning dataset with many discrete features","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"So I've read the manual - but the structure still comes confusing to me. Specifically, what is the relationship between:\n\nnd-array and Python list?\nnd-array and Python dictionary?\nnd-array and Pandas DataFrame?\nnd-arrays and Numpy \"structured arrays\"?\n\nAlso, is nd-array just like a sequence of lists? \nWhere does the \"n-dimension\" come into the picture? Because it looks just like a matrix, which is just two dimensions.\nThanks!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":184,"Q_Id":46459441,"Users Score":3,"Answer":"ndarray vs list: both can hold a 1-dimensional collection of elements; however, in an ndarray the elements would usually all be of the same type (e.g., 64-bit floating point numbers), and numpy provides operators (and behind-the-scenes optimizations) for calculations on these vectors. For example, you can (quickly) add elements in nda1 and nda2 via nda3 = nda1 + nda2. With lists, you would need to do lst3 = [a + b for (a, b) in zip(lst1, lst2)]. On the other hand, you can easily insert and remove items in lists. ndarrays are designed for high-performance computations on vectors of numbers; lists are designed for ad hoc operations on arbitrary collections of objects.\nndarray vs dictionary: these are quite dissimilar. Dictionaries allow you to select objects from an arbitrary collection by name; ndarrays usually only hold numbers, and only allow lookup via index number (unless you get into recarrays, which you didn't ask about).  \nndarray vs Pandas dataframe: dataframes are somewhat similar to multidimensional ndarrays, in that they are designed to hold similar types of data in each column. However, different columns of a dataframe would often hold different types of data, while all the elements in a multidimensional ndarray would usually be numbers of the same type. In addition, dataframes provide name-based indexing across rows and columns. I like to think of dataframes as something like a dictionary of 1-dimensional ndarrays, i.e., each column of the dataframe is like a 1-dimensional ndarray, and you can retrieve the column by name and then manipulate it. But Pandas provides additional indexing goodness, so you can also give a name to each row, and then pull elements out of the table based on both their row and column names. Pandas also provides operators for element-wise operations (e.g., adding two columns together), similar to numpy. But it generally does this by matching index terms, not row\/column numbers. So data manipulations in Pandas are slower but more reliable.\nndarrays vs structured arrays: structured arrays are somewhat like the rows of a Pandas dataframe (you can have different standardized types of data in each column). But the semantics for manipulating them are more like standard numpy operations -- you have to make sure the right data is in the right spot in the array before you operate on it. (Pandas will re-sort the tables so the row-names match if needed.)\nndarray vs sequence of lists: ndarrays are initialized and displayed like sequences of lists, and if you take the nth element of a 2D array, you get a list (the row). But internally, in an ndarray, every element has the same datatype (unlike lists), and the values are packed tightly and uniformly in memory. This allows processors to quickly perform operations on all the values together. Lists are more like pointers to values stored elsewhere, and mathematical computations on lists or lists-of-lists are not optimized. Also, you can't use 2D or 3D indexing with lists-of-lists (you have to say lst[1][2][3], not lst[1, 2, 3]), nor can you easily do elementwise operations (lst1+lst2 does not do elementwise addition like nda1+nda2).\nhigher dimensions: you can create an ndarray with any number of dimensions. This is sort of similar to a list of lists of lists. e.g., this makes a 3D array: np.array([[[1, 2], [3, 4]], [[5, 6], [7,8]]])","Q_Score":1,"Tags":"python,arrays,numpy,data-structures","A_Id":46460039,"CreationDate":"2017-09-28T01:11:00.000","Title":"Intuitive understanding of Numpy nd-array","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to model a neural network using tensorflow.\nBut the matrices are in the order of 800000x300000.When I initialize the variables using global variable initializer in tensorflow, the system freezes. How to do deal with this problem?\nCould tensorflow with gpu support will be able to handle this large matrix?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":125,"Q_Id":46509601,"Users Score":0,"Answer":"You can  divide data set into batches and then process you model or you can use tensor flow queue","Q_Score":0,"Tags":"python-3.x,matrix,tensorflow,ubuntu-16.04,tensorflow-gpu","A_Id":46509800,"CreationDate":"2017-10-01T05:47:00.000","Title":"Tensorflow hangs when initializing large matrix with variable.Whats the best solution for handling large matrix multiplication in tensorflow?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a high-dimensional dataset which is categorical in nature and I have used Kmodes to identify clusters, I want to visualize the clusters, what would be the best way to do that? PCA doesn't seem to be a recommended method for dimensionality reduction in a categorical dataset, how to visualize in such a scenario?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1437,"Q_Id":46516325,"Users Score":0,"Answer":"Agreeing with @DIMKOIM, Multiple Correspondence Analysis is your best bet. PCA is mainly used for continuous variables. To visualize your data, you can build a scatter plot from scratch.","Q_Score":0,"Tags":"python,machine-learning,cluster-analysis,categorical-data","A_Id":51921509,"CreationDate":"2017-10-01T19:56:00.000","Title":"How to plot a cluster in python prepared using categorical data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a motion detection program in OpenCV, and I want it to play a sound when it detects motion. I use winsound. However, OpenCV still seems to be gathering frames while the sound is playing, so I want to know a way to stop all OpenCV processes for about 17 seconds. I tried time.sleep and running it with the -u tag. Neither worked. Any ideas? Thanks.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":32,"Q_Id":46516782,"Users Score":0,"Answer":"I was able to solve the problem by putting what I want to run before resuming processes (playing the sound) in another script, performing import sound and breaking out of the loop to stop the program, and I can't figure out how to start it again. For my purposes, I can restart it manually.","Q_Score":0,"Tags":"python,opencv,wait,python-2.x","A_Id":46517378,"CreationDate":"2017-10-01T20:52:00.000","Title":"Stop OpenCV for seventeen seconds? python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a loss function implemented that uses numpy and opencv methods. This function also uses the input image and the output of the network.\nIs it possible to convert the input and the output layers to numpy arrays, compute the loss and use it to optimize the network?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":228,"Q_Id":46517118,"Users Score":3,"Answer":"No, gradients are needed to perform gradient descent, so if you only have a numerical loss, it cannot be differentiated, in contrast to a symbolic loss that is required by Keras.\nYour only chance is to implement your loss using keras.backend functions or to use another Deep Learning framework that might let you specify the gradient manually. You still would need to compute the gradient somehow.","Q_Score":2,"Tags":"python,numpy,keras,loss","A_Id":46517906,"CreationDate":"2017-10-01T21:33:00.000","Title":"Loss layer on Keras using two input layers and numpy operations","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to learn Python by converting a crude bash script of mine. \nIt runs every 5 minutes, and basically does the the following:\n\nLoops line-by-line through a .csv file, grabs first element ($URL),\nFetches $URL with wget and extracts $price from the page,\nAdds $price to the end of the same line in the .csv file,\nContinues looping for the remaining lines.\n\nI use it to keep track of products prices on eBay and similar websites. It notifies me when a lower price is found and plots graphs with the product's price history.\nIt's simple enough that I could just replicate the algorithm in Python, however, as I'm trying to learn it, it seems there are several types of objects (lists, dicts, etc.) that could do the storing much more efficiently. My plan is using pickle or even a simple DB solution (like dataset) from the beggining, instead of messing around with .csv files and extracting the data via sketchy string manipulations.\nOne of the improvements I would also like to make is store the absolute time of each fetch alongside its price, so I can plot a \"true timed\" graph, instead of assuming each cycle is 5 minutes away from each other (which it never is).\n\nTherefore, my question sums to...\nAssuming I need to work with the following data structure:\n\nList of Products, each with its respective\n\n\nURL\nAnd its list of Time<->Prices pairs\n\n\nWhat would be the best strategy in Python for doing it so?\nShould I use dictionaries, lists, sets or maybe even creating a custom class for products?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":34,"Q_Id":46526112,"Users Score":0,"Answer":"Firstly, if you plan on accessing the data structure which holds the URL-(time,Price) for specific URLs - use dictionary, since URLs are unique (the URL will be the key in the dictionary).\nOtherwise u can keep a list of (URL,(time,Price)) tuples.\nSecondly, a list of (Time, Price) Tuples since you don't need to sort them (they will be sorted already by the way you insert them).\n{} - Dictionary\n[] - List\n() - tuple\nOption 1:\n[(URL, [(time, Price)])]\nOption 2:\n{URL, [(time, Price)]}","Q_Score":0,"Tags":"python,bash,datetime,plot,storing-data","A_Id":46526690,"CreationDate":"2017-10-02T13:08:00.000","Title":"Best strategy for storing timed data and plotting afterwards?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"When i'm running my tensorflow training module in pycharm IDE in Ubuntu 16.04, it doesn't show any training with GPU and it trains usually with CPU. But When i run the same python script using terminal it runs using GPU training. I want to know how to configure GPU training in Pycharm IDE.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":691,"Q_Id":46536893,"Users Score":0,"Answer":"Actually the problem was, the python environment for the pycharm project is not the same as which is in run configurations. This issue was fixed by changing the environment in run configurations.","Q_Score":1,"Tags":"python,ubuntu,tensorflow,pycharm,tensorflow-gpu","A_Id":50614889,"CreationDate":"2017-10-03T03:44:00.000","Title":"Tensorflow GPU doesn't work in Pycharm","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have wound several examples of equal-mean binning, using scipy, but I wondering if it is possible to use library for equal-width or -depth binning.\nActually, I'm fine using other libraries, not only scipy","AnswerCount":2,"Available Count":1,"Score":-0.0996679946,"is_accepted":false,"ViewCount":1881,"Q_Id":46543779,"Users Score":-1,"Answer":"Don't expect everything to require a library.\nBoth bombings can be implemented in 1 or 2 lines of Python code if you think about them for a minute. It probably takes you longer to find\/install\/study a library than to just write this code yourself.","Q_Score":1,"Tags":"python,scipy,data-mining,binning","A_Id":46578602,"CreationDate":"2017-10-03T11:42:00.000","Title":"Equal-width and equal-depth binning, using scipy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to upload data from certain fields in a CSV file to an already existing table.\nFrom my understanding, the way to do this is to create a new table and then append the relevant columns of the newly created table to the corresponding columns of the main table.\nHow exactly do I append certain columns of data from one table to another?\nAs in, what specific commands?\nI am using the bigquery api and the python-client-library.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":4039,"Q_Id":46546388,"Users Score":0,"Answer":"You can use pandas library for that.\n\nimport pandas as pd \ndata = pd.read_csv('input_data.csv')\nuseful_columns = [col1, col2, ... ] # List the columns you need data[useful_columns].to_csv('result_data.csv', index=False) # index=False is to prevent creating extra column","Q_Score":1,"Tags":"python,csv,google-api,google-bigquery,google-python-api","A_Id":46546554,"CreationDate":"2017-10-03T13:57:00.000","Title":"How to Skip Columns of CSV file","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to create a numpy.polynomial by the roots of the polynomial.\nI could only find a way to do that by the polynomial's a's\nThe way it works now, for the polynomial x^2 - 3x + 2 I can create it like that:\npoly1d([1, -3, 2])\nI want to create it by its roots, which are -1, -2","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":852,"Q_Id":46558735,"Users Score":0,"Answer":"For that purpose you will need to implement the multiplication of polynomial, that is, you need to make sure your product is able to generate the product of\n(am * x^m + ... + a0) * (bn * x^n + ... + b0)\nIf your product is able to do this, then knowing the roots of\nr1, ..., rk\nYou can write this as \n(x - r1) * ... * (x - rk)\nand you need to repeatedly calculate the product here.","Q_Score":1,"Tags":"python,numpy","A_Id":46559545,"CreationDate":"2017-10-04T06:55:00.000","Title":"numpy - create polynomial by its roots","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Let's say I would like to generate n > 10 ^ 20 numbers using random.seed(SEED) and n subsequent calls to random.random(). Is the generated sequence guaranteed to be uniformly distributed regardless of the chosen value of SEED?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":124,"Q_Id":46569943,"Users Score":3,"Answer":"Let's say I would like to generate n > 10 ^ 20 numbers\n\nLet's say not.  If you could generate a billion values per second, that would require 1E20 values \/ 1E9 values per second \/ 3600 seconds per hour \/ 24 hours per day \/ 365.25 days per year, which is more than 3000 years. Even if you have hardware and energy sources that reliable, you won't be there to see the outcome.\n\nusing random.seed(SEED) and n subsequent calls to random.random()\n\nThe results would be statistically indistinguishable from uniform because the underlying algorithm, Mersenne Twister, is designed to produce that behavior.","Q_Score":0,"Tags":"python,random,seed","A_Id":46573367,"CreationDate":"2017-10-04T16:37:00.000","Title":"Does every chosen seed for random.seed() guarantee that random will generate a uniformly distributed sequence?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to create a program, which automates excel reporting including various graphs in colours. The program needs to be able to read an excel dataset. Based on this dataset, the program then has to create report pages and graphs and then export to an excel file as well as pdf file.\nI have done some research and it seems this is possible using python with pandas - xlsxWriter or xlswings as well as Ruby gems - axlsx or win32ole. \nWhich is the user-friendlier and easy to learn alternative? What are the advantages and disadvantages? Are there other options I should consider (I would like to avoid VBA - as this is how the reports are currently produced)?\nAny responses and comments are appreciated. Thank you!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":388,"Q_Id":46575847,"Users Score":0,"Answer":"If you already have VBA that works for your project, then translating it to Ruby + WIN32OLE is probably your quickest path to working code. Anything you can do in VBA is doable in Ruby (if you find something you can't do, post here to ask for help).\nI prefer working with Excel via OLE since I know the file produced by Excel will work anywhere I open it. I haven't used axlsx but I'm sure it's a fine project; I just wouldn't trust that it would produce working Excel files every time.","Q_Score":0,"Tags":"python,ruby,excel,xlsxwriter,axlsx","A_Id":46669389,"CreationDate":"2017-10-04T23:52:00.000","Title":"Automating excel reporting and graphs - Python xlsxWriter\/xlswings or Ruby axlsx\/win32ole","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using python 3.6 on my pc(windows 10)\nI wanted to install tensor flow package (using pip),\nSO opened the cmd and typed the following as specified in the tensorflow website,\ni want to install the cpu package not the gpu package\nC:\\Users\\rahul>C:\\Windows.old\\Users\\rahul\\AppData\\Local\\Programs\\Python\\Python36-32\\Scripts\\ pip3.exe install --upgrade tensorflow\nbut i get this error \nCollecting tensorflow\n  Could not find a version that satisfies the requirement tensorflow (from versions: )\nNo matching distribution found for tensorflow,\nHow do i overcome this.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":7333,"Q_Id":46579249,"Users Score":5,"Answer":"You need a 64 bit version of Python.","Q_Score":3,"Tags":"python-3.x,tensorflow","A_Id":48451660,"CreationDate":"2017-10-05T06:34:00.000","Title":"Could not find a version that satisfies the requirement tensorflow (from versions: ) No matching distribution found for tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a data frame with one column full of string values. They need to be converted into categories. Due to huge amount it would be inconvenient to define categories in dictionary. Is there any other way in pandas to do that?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":34,"Q_Id":46581018,"Users Score":0,"Answer":"I applied below command and it works:\ndf['kategorie']=action['kategorie'].astype('category')","Q_Score":0,"Tags":"python,pandas","A_Id":46582120,"CreationDate":"2017-10-05T08:24:00.000","Title":"Converting many string values to categories","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm writing some image processing routines for a micro-controller that supports MicroPython. The bad news is that it only has 0.5 MB of RAM. This means that if I want to work with relatively big images\/matrices like 256x256, I need to treat it as a collection of smaller matrices (e.g. 32x32) and perform the operation on them. Leaving at aside the fact of reconstructing the final output of the orignal (256x256) matrix from its (32x32) submatrices, I'd like to focus on how to do the loading\/saving from\/to disk (an SD card in this case) of this smaller matrices from a big image.\nGiven that intro, here is my question: Assuming I have a 256x256 on disk that I'd like to apply some operation onto (e.g. convolution), what's the most convenient way of storing that image so it's easy to load it into 32x32 image patches? I've seen there is a MicroPython implementation of the pickle module, is this a good idea for my problem?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":52,"Q_Id":46583487,"Users Score":0,"Answer":"Sorry, but your question contains the answer - if you need to work with 32x32 tiles, the best format is that which represents your big image as a sequence of tiles (and e.g. not as one big 256x256 image, though reading tiles out of it is also not a rocket science and should be fairly trivial to code in MicroPython, though 32x32 tiles would be more efficient of course).\nYou don't describe the exact format of your images, but I wouldn't use pickle module for it, but store images as raw bytes and load them into array.array() objects (using inplace .readinto() operation).","Q_Score":0,"Tags":"python,data-structures,microcontroller,micropython","A_Id":47052669,"CreationDate":"2017-10-05T10:31:00.000","Title":"Load portions of matrix into RAM","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've installed Tensorflow using Python 3 (pip3 install). Now, since Jupyter Notebook is using Python 2, thus the python command is linked to python2.7, all the codes in Jupyter Notebook get error (ImportError: No module named tensorflow).\nQuestion: Can I install Tensorflow running side by side for both Python 2 and 3?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":948,"Q_Id":46584556,"Users Score":1,"Answer":"Yes you can do. Easy step is install python anaconda then create environment with python 2.7 and python 3. Install Tensorflow for both environment","Q_Score":0,"Tags":"python,tensorflow","A_Id":46584984,"CreationDate":"2017-10-05T11:31:00.000","Title":"Can I install Tensorflow on both Python 2 and 3?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have recently been working on a slackbot and I have the basic functionality down, I am able to take simple commands and make have the bot answer. But, I want to know if there is anyway to have to bot store some data given by a user, such as \"@slackbot 5,4,3,2,1\" and then have the bot sort it and return it like \"1,2,3,4,5\". Also, is there anyway to have the bot read an external .csv file and have it return some type of information? for example I want the bot to tell me what the first row of a .csv file says.\nThank you! any help would be appreciated","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":158,"Q_Id":46592760,"Users Score":0,"Answer":"you can solve using pandas from python\npandas is data processing framework\npandas framework can processing EXCEL, TXT as well as csv file. \nThe following links pandas documentation","Q_Score":0,"Tags":"python,csv,slack,slack-api","A_Id":46597662,"CreationDate":"2017-10-05T18:46:00.000","Title":"Programming an interactive slackbot - python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have recently been working on a slackbot and I have the basic functionality down, I am able to take simple commands and make have the bot answer. But, I want to know if there is anyway to have to bot store some data given by a user, such as \"@slackbot 5,4,3,2,1\" and then have the bot sort it and return it like \"1,2,3,4,5\". Also, is there anyway to have the bot read an external .csv file and have it return some type of information? for example I want the bot to tell me what the first row of a .csv file says.\nThank you! any help would be appreciated","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":158,"Q_Id":46592760,"Users Score":0,"Answer":"Whatever you have mentioned in your question, can easily be done using slackbot. You can develop slackbot as Django server. \nIf you want bot to store data, you can connect your django server to any database or to any cache (eg: Redis, Memecache).\nYou can write sorting logic in python and send sorted list back to slack using Slackclient library.\nAnd based on your input to slackbot, you can perform action in python and send response back to slack.\nHope this answers!","Q_Score":0,"Tags":"python,csv,slack,slack-api","A_Id":56807453,"CreationDate":"2017-10-05T18:46:00.000","Title":"Programming an interactive slackbot - python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"So the output of my network is a list of propabilities, which I then round using tf.round() to be either 0 or 1, this is crucial for this project. \nI then found out that tf.round isn't differentiable so I'm kinda lost there.. :\/","AnswerCount":7,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":6120,"Q_Id":46596636,"Users Score":4,"Answer":"Rounding is a fundamentally nondifferentiable function, so you're out of luck there. The normal procedure for this kind of situation is to find a way to either use the probabilities, say by using them to calculate an expected value, or by taking the maximum probability that is output and choose that one as the network's prediction. If you aren't using the output for calculating your loss function though, you can go ahead and just apply it to the result and it doesn't matter if it's differentiable. Now, if you want an informative loss function for the purpose of training the network, maybe you should consider whether keeping the output in the format of probabilities might actually be to your advantage (it will likely make your training process smoother)- that way you can just convert the probabilities to actual estimates outside of the network, after training.","Q_Score":6,"Tags":"python,tensorflow","A_Id":46597146,"CreationDate":"2017-10-06T00:31:00.000","Title":"Differentiable round function in Tensorflow?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there is way to create sheet 2 in same csv file by using python code","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":4260,"Q_Id":46600652,"Users Score":0,"Answer":"You can do this by using multiple CSV files - one CSV file per sheet.\nA comma-separated value file is a plain text format. It is only going to be able to represent flat data, such as a table (or a \"sheet\")\nWhen storing multiple sheets, you should use separate CSV files. You can write each one separately and import\/parse them individually into their destination.","Q_Score":0,"Tags":"python,csv","A_Id":46600856,"CreationDate":"2017-10-06T07:37:00.000","Title":"How can I create sheet 2 in a CSV file by using Python code?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've built some tools that create front-end list boxes for users that reference dynamic Redshift tables. New items in the table, they appear automatically in the list.\nI want to put the list in alphabetical order in the database so the dynamic list boxes will show the data in that order. \nAfter downloading the list from an API, I attempt to sort the list alphabetically in a Pandas dataframe before uploading. This works perfectly:\ndf.sort_values(['name'], inplace=True, ascending=True, kind='heapsort')\nBut then when I try to upload to Redshift in that order, it loses the order while it uploads. The data appears in chunks of alphabetically ordered segments.\ndb_conn = create_engine('<redshift connection>')\nobj.to_sql('table_name', db_conn, index = False, if_exists = 'replace')\nBecause of the way the third party tool (Alteryx) works, I need to have this data in alphabetical order in the database. \nHow can I modify to_sql to properly upload the data in order?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":725,"Q_Id":46608223,"Users Score":0,"Answer":"While ingesting data into redshift, data gets distributed between slices on each node in your redshift cluster. \nMy suggestion would be to create a sort key on a column which you need to be sorted. Once you have sort key on that column, you can run Vacuum command to get your data sorted.\nSorry! I cannot be of much help on Python\/Pandas\nIf I\u2019ve made a bad assumption please comment and I\u2019ll refocus my answer.","Q_Score":0,"Tags":"python,sorting,amazon-redshift,pandas-to-sql","A_Id":46610485,"CreationDate":"2017-10-06T14:36:00.000","Title":"Sorting and loading data from Pandas to Redshift using to_sql","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there a way to share memory to share an openCV image (MAT in C+++ and numpy in python) image between a C\/C++ and python? Multiplataform is not needed, I'm doing it in linux, I've thought share between mmap or similar think.\nI have two running processes one is written in C and the other is python, and I need to share an image between them. \nI will call from the c process to python via socket but I need to send and image and via memory.\nAnother alternative could be write in memory file, not sure if it could be more time consuming.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":5634,"Q_Id":46619531,"Users Score":4,"Answer":"OK, this is not exactly a memory sharing in its real sense. What you want is IPC to send image data from one process to another.\nI suggestthat you use Unix named pipes. You will have to get the raw data in a string format in C\/C++, send it through pipe or Unix socket to Python and there get a numpy array from the sent data. Perhaps using np.fromstring() function.\nDo not worry about the speed, pipes are pretty fast. Local and Unix sockets as well. Most time will be lost on getting the string representation and turning it back to matrix.\nThere is a possibility that you can create real shared memory space and get the data from OpenCV in C\/C++ directly into Python, and then use OpenCV in Python to get out numpy array, but it would be complicated. If you don't need speed of light your best bet are named pipes.","Q_Score":7,"Tags":"python,c++,linux,opencv","A_Id":46619774,"CreationDate":"2017-10-07T11:16:00.000","Title":"Share memory between C\/C++ and Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Start with a one dimensional space of length m, where m = 2 * n + 1. Take a step either to the left or to the right at random, with equal probability. Continue taking random steps until you go off one edge of the space, for which I'm using while 0 <= position < m. \nWe have to write a program that executes the random walk. We have to create a 1D space using size n = 5 and place the marker in the middle. Every step, move it either to the left or to the right using the random number generator. There should be an equal probability that it moves in either direction.  \nI have an idea for the plan but do not know how to write it in python:\n\nInitialize n = 1, m = 2n + 1, and j = n + 1.\nLoop until j = 0 or j = m + 1 as shown. At each step:\n\n\nMove j left or right at random.\nDisplay the current state of the walk, as shown.\n\nMake another variable to count the total number of steps.\n\n\nInitialize this variable to zero before the loop.\nHowever j moves always increase the step count.\n\nAfter the loop ends, report the total steps.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":327,"Q_Id":46620657,"Users Score":1,"Answer":"1 - Start with a list initialized with 5 items (maybe None?) \n2 - place the walker at index 2\n3 - randomly chose a direction (-1 or + 1)\n4 - move the walker in the chosen direction  \n5 - maybe print the space and mark the location of the walker\n6 - repeat at step 3 as many times as needed","Q_Score":0,"Tags":"python,random","A_Id":46620696,"CreationDate":"2017-10-07T13:23:00.000","Title":"How to use random numbers that executes a one dimensional random walk in python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"What does the term epochs mean in the neural network.\nHow does it differ from pass and iteration","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":971,"Q_Id":46621774,"Users Score":1,"Answer":"Epoch:\nOne round  forward Propagation and backward Propagation into the neural network at once.(dataset )\nExample :\nOne round of throwing the ball into the basket  and finding out the error and come back and changing the weights.(f = ma)\nForward propagation:\nThe Process of initizing the mass and acceleration with random values and predicting the output is called the forward propagation.\nBackward propagation:\nChanging the values and again predicting the output .(By finding out the gradient)\nGradient:\nIf i change the input of X(Independent variable) then what the value of y(Dependent variable) is changed out is called the gradient. \nActually there is no answer for that . And the epochs are based on the dataset but you can say that the numbers of epochs is related to how different your data is. With an example, Do you have the only white tigers in your dataset or is it much more different dataset.\nIteration:\nIteration is the number of batches needed to complete one epoch.\nExample:\nWe can divide the dataset of 1000 examples into batches of 250 then it will take 4 iterations to complete 1 epoch. (Here Batch size = 250, iteration = 4)","Q_Score":3,"Tags":"python,machine-learning,deep-learning,neural-network,epoch","A_Id":58870147,"CreationDate":"2017-10-07T15:14:00.000","Title":"Epochs Vs Pass Vs Iteration","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"What does the term epochs mean in the neural network.\nHow does it differ from pass and iteration","AnswerCount":2,"Available Count":2,"Score":0.1973753202,"is_accepted":false,"ViewCount":971,"Q_Id":46621774,"Users Score":2,"Answer":"There are many neural networks algorithms in unsupervised learning. As long as a cost function can be defined, so can \"neural networks\" be used.\nFor instance, there are for instance autoencoders, for dimensionality reduction, or Generative Adversarial Networks (so 2 networks, one generating new samples). All these are unsupervised learning, and still using neural networks.","Q_Score":3,"Tags":"python,machine-learning,deep-learning,neural-network,epoch","A_Id":53937484,"CreationDate":"2017-10-07T15:14:00.000","Title":"Epochs Vs Pass Vs Iteration","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have roughly 200 documents that need to have IBM Watson NLU analysis done.  Currently, processing is performed one at a time.  Will NLU be able preform a batch analysis? What is the correct python code or process to batch load the files and then response results?  The end goal is to grab results to analyze which documents are similar in nature.  Any direction is greatly appreciated as IBM Support Documentation does not cover batch processing.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":445,"Q_Id":46624822,"Users Score":0,"Answer":"NLU can be \"manually\" adapted to do batch analysis. But the Watson service that provides what you are asking for is Watson Discovery. It allows to create Collections (set of documents) that will be enriched thru an internal NLU function and then queried.","Q_Score":0,"Tags":"python,ibm-cloud,ibm-watson,watson-nlu","A_Id":46929384,"CreationDate":"2017-10-07T20:31:00.000","Title":"IBM Watson Natural Language Understanding uploading multiple documents for analysis","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I was working on a numeric dataset and apparently it is a multi variable output regression. I wanted to know if you can have a multi-label classification in a numeric dataset or it is strictly for text based.\nFor Eg: Stackoverflow an categorize every text\/code into multiple tags like python,flask, python2.7 ... But can something like that be done with numbers. Sorry I know that this is a noob question but I wanted to know the answer. Thanks in Advance.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":36,"Q_Id":46646141,"Users Score":2,"Answer":"Of course it can be done with numbers. After all, the text itself is converted to numbers to be classified. But you should not use regression for that. It is clearly a case for classification.\nA regular classifier (for example, a neural network) usually has multiple outputs, one for each class. Each output returns the probability that the input vector belongs to that particular class.\nIn standard classification, you assign it to the class with the maximum probability. In your case, just assign it to all the classes for which p > 0.5 (assuming that the output is in [0, 1].\nRegarding the question of whether your problem is a multi-regression or multi-classification problem, you can't know that just by looking at the inputs. You decide it based on what you are trying to find. Choose regression if you are trying to find numeric values in a continuous range (for example, predict the price and number of sales for a given product). Choose classification if you have a number of attributes that the input has or doesn't have.","Q_Score":0,"Tags":"python,machine-learning,multilabel-classification","A_Id":46646311,"CreationDate":"2017-10-09T12:10:00.000","Title":"is multi-label clasification for text only","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been learning a lot about the uses of Machine Learning and Google's Tensorflow. Mostly, developers use Python when developing with Tensorflow. I do realize that other languages can be used with Tensorflow as well, i.e. Java and C++. I see that Google s about to launch Tensorflow Lite that is supposed to be a game changer for mobile devices. My question; can I be safe by learning Tensorflow using Python and still be able to develop mobile apps using this service?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":249,"Q_Id":46676738,"Users Score":0,"Answer":"Short answer is : yes. You will be safe with python since it\u2019s the main front end language for tensorflow. Also I agree with BHawk\u2019s answer above.","Q_Score":0,"Tags":"java,android,python,mobile,tensorflow","A_Id":46680237,"CreationDate":"2017-10-10T22:02:00.000","Title":"Using Tensorflow on smartphones","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":1,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been learning a lot about the uses of Machine Learning and Google's Tensorflow. Mostly, developers use Python when developing with Tensorflow. I do realize that other languages can be used with Tensorflow as well, i.e. Java and C++. I see that Google s about to launch Tensorflow Lite that is supposed to be a game changer for mobile devices. My question; can I be safe by learning Tensorflow using Python and still be able to develop mobile apps using this service?","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":249,"Q_Id":46676738,"Users Score":0,"Answer":"In short, yes. It would be safe to learn implementing TensorFlow using python and still comfortably develop machine learning enabled mobile apps.\nLet me elaborate. Even with TensorFlow Lite, training the data can only happen on the server side; only the prediction, or the inference happens on the mobile device. So typically, you would create your models on TensorFlow, often using python, and then leverage TensorFlow Lite to package that model into your app.","Q_Score":0,"Tags":"java,android,python,mobile,tensorflow","A_Id":46817475,"CreationDate":"2017-10-10T22:02:00.000","Title":"Using Tensorflow on smartphones","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":1,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to generate a size 1000 random number list according to Poisson distribution and with a fixed mean. Since the size is fixed to 1000, so the sum is also fixed. \nThe first idea I get is to use numpy.random.Poisson(lambda,size), but it can not set a fixed mean for the list. So I am really confused.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":561,"Q_Id":46680795,"Users Score":1,"Answer":"So in a Poisson distribution lambda is the mean and variance at the same time. And if you draw infinitely often you will see it is true. \nWhat you are asking for is like expecting to roll a dice 10 times and get an average of 3.5 since thats the expected mean.\nNevertheless you could generate a list with numpy.random.poisson, check if the mean is what you want or draw another 1000 times and check again.","Q_Score":1,"Tags":"python,python-2.7","A_Id":46681839,"CreationDate":"2017-10-11T05:56:00.000","Title":"How to generate a size 1000 random number list according to Poisson distribution and with a fixed mean(size)?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"My text contains text=\"Ravi beated Ragu\"\nMy Question will be \"Who beated Ragu?\"\nThe Answer Should come \"Ravi\" Using NLP\nHow to do this by natural language processing.\nKindly guide me to proceed with this by syntactic,semantic and progmatic analysis using python","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":78,"Q_Id":46681209,"Users Score":0,"Answer":"I would suggest that you should read an introductory book on NLP to be familiar with the chain processes you are trying to achieve. You are trying to do question-answering , aren't you? If it is the case, you should read about question-answering systems. The above sentence has to be morphologically analyzed (so read about morphological analyzers), syntactically parsed (so read about syntactic parsing) and semantically understood (so read about anaphora resolution and , in linguistics, theta theory). Ravi is called agent and Ragu is called patient or experiencer. Only then, you can proceed to pursue your objectives.\nI hope this helps you!","Q_Score":0,"Tags":"python-3.x,nlp,stanford-nlp","A_Id":46719538,"CreationDate":"2017-10-11T06:28:00.000","Title":"Natural Language Processing(syntatctic,semantic,progmatic) Analysis","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"The data consists of text parameters as well as integer parameters. The problem is to train machine with both data. Hashing Vectorizer is used for text parameters training.\nThanks in advance....","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":207,"Q_Id":46696478,"Users Score":0,"Answer":"Three are several ways to do that:\n\nsimply concatenate Hashing Vectors with integers and train on that bigger features vector. It will work.\n\nIt would be more reasonable to do so using different classifier, beacuse MultinominalNB can't model the interactions between the features. But if you want nothing else but MultinominalNB, you can do it. You can also:\n\ntrain two of them - one on HV, one on integers, and weigth the output, or\nuse MultinominalNB on text and different classifier on integers, or\nuse MultinominalNB on text, and take the output as a features both with integers.","Q_Score":0,"Tags":"python,machine-learning,naivebayes","A_Id":46697151,"CreationDate":"2017-10-11T19:55:00.000","Title":"How to use multinomial naive bayes for both text and non text data using python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using the minimize function from scipy.optimize library. \nIs there a way to print some values during the optimization procedure? Values like the current x, objective function value, number of iterations and number of gradient evaluations.\nI know there are options to save these values and return them after the optimization is over. But can I see them at each step?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":311,"Q_Id":46698519,"Users Score":0,"Answer":"The minimize function takes an options dict as a keyword argument. Accepted keys for this dict inlude, disp, which should be set to True to print the progress of the minimization.","Q_Score":0,"Tags":"python,optimization,scipy","A_Id":46698554,"CreationDate":"2017-10-11T22:27:00.000","Title":"scipy optimize - View steps during procedure","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am currently learning python and playing around with tensorflow.\nI have a bunch of images where I have obtained the landmarks (pixel points) of a person's facial features such as ears and eyes. In addition, it also provides me with a box (4 coordinates) where the face exists.\nMy goal is to normalise all the data from different images into a standard sized rectangle \/ square and calculate the position of the landmarks relative to the normalised size.\nIs there an API that allows me to do this already or should I get cracking and calculate the points myself?\nThanks in advance.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":339,"Q_Id":46698570,"Users Score":0,"Answer":"Actually I think I have figured it out, pretty simple maths actually, here is what i am going to do\n\nTake every point and take away the first box point values - this will give me the points as if the box starts at [ 0,0 ]\nApply the box\/normalised size ratio to every point","Q_Score":0,"Tags":"python,image-processing","A_Id":46699385,"CreationDate":"2017-10-11T22:33:00.000","Title":"Normalise face landmark data using python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using model.fit_generator() to train a neural network with Keras. During the fitting process I've set the steps_per_epoch to 16 (len(training samples)\/batch_size). \nIf the mini batch size is set to 12, and the total number of training samples is 195, does it mean that 3 samples won't be used in the training phase?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":198,"Q_Id":46701216,"Users Score":2,"Answer":"No, because it is a generator the model does not know the total number of training samples. Therefore, it finishes an epoch when it reaches the final step defined with the steps_per_epoch argument. In your case it will indeed train 192 samples per epoch.\nIf you want to use all samples in your model you can shuffle the data at the start of every epoch with the argument shuffle.","Q_Score":1,"Tags":"python,tensorflow,machine-learning,keras,neural-network","A_Id":46704606,"CreationDate":"2017-10-12T04:03:00.000","Title":"Are all train samples used in fit_generator in Keras?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to find the length of a 1-D float array and convert it into a 2-d array in python. Also when I am trying to print the elements of the float array the following error is coming:-\n'float' object is not iterable","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":608,"Q_Id":46701431,"Users Score":0,"Answer":"Length of array: len(array). Try to do 2 cycles to spread all values to 2-d array.","Q_Score":0,"Tags":"python,arrays","A_Id":46701588,"CreationDate":"2017-10-12T04:29:00.000","Title":"How to find the number of elements of a float array in python and how to convert it to a 2-dimensional float array?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"What is the best way to resize a 2D array (the array has a thermal data contents values between 20 to 30) from size 173X151 to size 146X121 without losing too much information. \nI understand it is possible to reduce the size of images with some function(images of intensity values 0 to 255) but my understanding that these functions are for images and not other types of arrays.\nIs there a function for reducing the size for any type of array? something like compressing the 2D array to different sizes?\nThanks","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":40,"Q_Id":46736521,"Users Score":0,"Answer":"n-dimensional arrays can be many things, aside from being an image.\none example would be a geo-spatial representation that would consolidate (roll-up_ whenever you zoom out and drill down whenever you zoom in.\narray resizing technique should rely on the context of such resize takes place, and hence there is no best answer. \nBut typically resizing arrays or tensors is by consolidation whenever you reduce number of entries, and by interpolation whenever you increase them","Q_Score":1,"Tags":"arrays,python-3.x,compression","A_Id":46737626,"CreationDate":"2017-10-13T19:04:00.000","Title":"Resizing 2D arrays to a different size (e.,g. reduction or comression)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Im creating Excel file from pandas and I'm using worksheet.hide_gridlines(2)\nthe problem that all gridlines are hide in my current worksheet.I need to hide a range of cells, for example A1:I80.How can I do that?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":2139,"Q_Id":46745120,"Users Score":1,"Answer":"As far as I know that isn't possible in Excel to hide gridlines for a range. Gridlines are either on or off for the entire worksheet.\nAs a workaround you could turn the gridlines off and then add a border to each cell where you want them displayed.\nAs a first step you should figure out how you would do what you want to do in Excel and then apply that to an XlsxWriter program.","Q_Score":3,"Tags":"excel,python-2.7,xlsxwriter","A_Id":46747332,"CreationDate":"2017-10-14T13:29:00.000","Title":"Set worksheet.hide_gridlines(2) to certain range of cells","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Interestingly, I see a lot of different answers about this both on stackoverflow and other sites:\nWhile working on my training data set, I imputed missing values of a certain column using a decision tree model. So here's my question. Is it fair to use ALL available data (Training & Test) to make a model for imputation (not prediction) or may I only touch the training set when doing this? Also, once I begin work on my Test set, must I use only my test set data, impute using the same imputation model made in my training set, or can I use all the data available to me to retrain my imputation model?\nI would think so long as I didn't touch my test set for prediction model training, using the rest of the data for things like imputations would be fine. But maybe that would be breaking a fundamental rule. Thoughts?","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":2913,"Q_Id":46749037,"Users Score":1,"Answer":"The philosophy behind splitting data into training and test sets is to have the opportunity of validating the model through fresh(ish) data, right?\nSo, by using the same imputer on both train and test sets, you are somehow spoiling the test data, and this may cause overfitting.\nYou CAN use the same approach to impute the missing data on both sets (in your case, the decision tree), however, you should instantiate two different models, and fit each one with its own related data.","Q_Score":7,"Tags":"python-2.7,data-science,imputation","A_Id":57548526,"CreationDate":"2017-10-14T20:28:00.000","Title":"Can I use Train AND Test data for Imputation?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"The sklearn DecisionTreeClassifier has a attribute called \"splitter\" , it is set to \"best\" by default, what does setting it to \"best\" or \"random\" do? I couldn't find enough information from the official documentation.","AnswerCount":4,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":8211,"Q_Id":46756606,"Users Score":2,"Answer":"Short ans:\nRandomSplitter initiates a **random split on each chosen feature**, whereas BestSplitter goes through **all possible splits on each chosen feature**.\n\nLonger explanation: \nThis is clear when you go thru _splitter.pyx.\nRandomSplitter calculates improvement only on threshold that is randomly initiated (ref. lines 761 and 801). BestSplitter goes through all possible splits in a while loop (ref. lines 436 (which is where loop starts) and 462). [Note: Lines are in relation to version 0.21.2.]\nAs opposed to earlier responses from 15 Oct 2017 and 1 Feb 2018, RandomSplitter and BestSplitter both loop through all relevant features. This is also evident in _splitter.pyx.","Q_Score":11,"Tags":"python,python-3.x,machine-learning,scikit-learn","A_Id":56999837,"CreationDate":"2017-10-15T15:18:00.000","Title":"What does \"splitter\" attribute in sklearn's DecisionTreeClassifier do?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"The sklearn DecisionTreeClassifier has a attribute called \"splitter\" , it is set to \"best\" by default, what does setting it to \"best\" or \"random\" do? I couldn't find enough information from the official documentation.","AnswerCount":4,"Available Count":2,"Score":0.1973753202,"is_accepted":false,"ViewCount":8211,"Q_Id":46756606,"Users Score":4,"Answer":"The \"Random\" setting selects a feature at random, then splits it at random and calculates the gini. It repeats this a number of times, comparing all the splits and then takes the best one. \nThis has a few advantages: \n\nIt's less computation intensive than calculating the optimal split of every feature at every leaf. \nIt should be less prone to overfitting. \nThe additional randomness is useful if your decision tree is a component of an ensemble method.","Q_Score":11,"Tags":"python,python-3.x,machine-learning,scikit-learn","A_Id":48555365,"CreationDate":"2017-10-15T15:18:00.000","Title":"What does \"splitter\" attribute in sklearn's DecisionTreeClassifier do?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"To create RNN cells, there are classes like GRUCell and LSTMCell which can be used later to create RNN layers.\nAnd also there are 2 other classes as CudnnGRU and CudnnLSTM which can be directly used to create RNN layers.\nIn the documentation they say that the latter classes have cuDNN implementation. Why should I use or not use this cuDNN implemented classes over classical RNN implementations when I'm creating a RNN model..?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1107,"Q_Id":46767001,"Users Score":0,"Answer":"In short: cudnnGRU and cudnnLSTM can\/ must be used on GPU, normal rnn implementations not. So if you have tensorflow-gpu, cudnn implementation of RNN cells would run faster.","Q_Score":0,"Tags":"python-3.x,tensorflow,cudnn","A_Id":48411184,"CreationDate":"2017-10-16T09:28:00.000","Title":"What is cuDNN implementation of rnn cells in Tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When I run classifier.py in the openface demos directory using: \nclassifier.py train .\/generated-embeddings\/\nI get the following error message:\n\n--> from sklearn.lda import LDA\nModuleNotFoundError: No module named 'sklearn.lda'.\n\nI think to have correctly installed sklearn.\nWhat could be the reason for this message?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":15902,"Q_Id":46775155,"Users Score":0,"Answer":"In case you are using new version and using \nfrom sklearn.qda import QDA\nit will give error, try\nfrom sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis","Q_Score":8,"Tags":"python,machine-learning,scikit-learn,lda","A_Id":58364407,"CreationDate":"2017-10-16T16:41:00.000","Title":"ImportError: No module named 'sklearn.lda'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using CPU based tensorflow (on non GPU platform) from python. I want to use functionality like load_dataset, random_mini_batches, convert_to_one_hot etc. from tf_utils package. However the one from neuroailab github has dependency on tensorflow-gpu. Is there any other CPU based (for non GPU platform) equivalent package for this?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":462,"Q_Id":46782575,"Users Score":0,"Answer":"pandas already has quite a bit of functionality that tf_utils provides. E.g. \n\nread_csv could capture what I was looking for from load_dataset.\nget_dummies does what convert_to_one_hot does.\nI could not find any direct functionality in pandas that came close to random_mini_batches but it can be achieved with sampling and random numbers.","Q_Score":0,"Tags":"python,tensorflow,deep-learning","A_Id":46807104,"CreationDate":"2017-10-17T04:38:00.000","Title":"Is there non-GPU equivalent of tf_utils package?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working with large Pandas dataframes in Spyder. Occasionally I accidentally click the large dataframes in the Variable Explorer window and Spyder will very hang for very long periods while it tries to open. \nThe only way I have found to stop this process is to close Spyder completely and then reopen. \nIs it possible to: \n\nCancel the request to view the variable without closing the program?\nSet a default so Spyder will only display the first 1000 rows of very large objects such as dataframes?","AnswerCount":1,"Available Count":1,"Score":0.6640367703,"is_accepted":false,"ViewCount":890,"Q_Id":46790782,"Users Score":4,"Answer":"(Spyder developer here) My answers:\n\nCancel the request to view the variable without closing the program?\n\nNo, that's not possible, sorry.\n\nSet a default so Spyder will only display the first 1000 rows of very large objects such as dataframes?\n\nThat's already in place. The problem is the size in memory of your dataframes because Spyder needs to make a copy of them to graphically display them.\nTo fix this problem, we are planning to use more efficient serialization libraries (e.g. pyarrow) in Spyder 5, to be released in 2021.","Q_Score":5,"Tags":"python-3.x,spyder","A_Id":46813155,"CreationDate":"2017-10-17T12:59:00.000","Title":"Spyder IDE - Cancel opening large variables without restarting the entire program","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to shift a polynomial. I'm currently using numpy.poly1d() to make a quadratic equation.\n\nexample: 2x^2 + 3x +4\n\nbut I need to shift the function by tau, such that \n\n2(x-tau)^2 + 3(x-tau) + 4\n\nTau is a value that will change base on some other variables in my code.","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":487,"Q_Id":46792940,"Users Score":2,"Answer":"Create a burner variable, store x-tau into it, and feed that into your function","Q_Score":3,"Tags":"python,polynomial-math,coordinate-transformation","A_Id":46792992,"CreationDate":"2017-10-17T14:45:00.000","Title":"polynomial transformation in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have just started exploring bokeh and here is a small issue I am stuck with. This is in regards with live graphs. \nThe problem is with the axis values. Initially if I start with say 10, till 90 it shows correct values but while printing 100, it only show 10 and the last zero(0) is hidden. It's not visible.\nThat is when it switches from 2 digit number to a 3 or more digit number only the first two digits are visible.\nIs there any figure property I am missing or what I am not sure of.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":601,"Q_Id":46817031,"Users Score":1,"Answer":"This is a known bug with current versions (around 0.12.10) for now the best workaround is to increase plot.min_border (or p.min_border_left, etc) to be able to accommodate whatever the longest label you expect is. Or to rotate the labels to be parallel to the axis so that they always take up the same space, e.g. p.yaxis.major_label_orientation = \"vertical\"","Q_Score":1,"Tags":"python-3.x,bokeh","A_Id":46832946,"CreationDate":"2017-10-18T18:37:00.000","Title":"Bokeh Plots Axis Value don't show completely","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'am reading a data off a csv file. The columns are as below:\nDate    , Buy, Sell, Price\n1\/10\/2011, 1 , 5,    500\n1\/15\/2011, 4,  2,    500\nWhen I tried to pull data based on index like df[\"2011-01-10\"], I got an error KeyError: '2011-01-10'\nAnyone know what this is might be the case?\nThanks,","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":15,"Q_Id":46823445,"Users Score":0,"Answer":"I think the way you are passing your argument is wrong, try passing it in the same pattern as its in csv. Like [\"1\/10\/2011\"], it should work. Good luck :)","Q_Score":1,"Tags":"python,dataframe","A_Id":46823775,"CreationDate":"2017-10-19T05:44:00.000","Title":"Error when trying to pull row based on index value in the dataframe","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to classify pages, in particular search for a page, in documents based on bag of words, page layout, contain tables or not, has bold titles, etc. With this premise I have created a pandas.DataFrame like this, for each document:\n\n    page  totalCharCount  matchesOfWordX  matchesOfWordY          hasFeaturesX     hasFeaturesY   hasTable      score\n0    0.0           608.0             0.0             2.0                   0.0              0.0        0.0        0.0\n1    1.0          3292.0             1.0            24.0                   7.0              0.0        0.0        0.0\n2    2.0          3302.0             0.0            15.0                   1.0              0.0        1.0        0.0\n3    3.0            26.0             0.0             0.0                   0.0              1.0        1.0        1.0\n4    4.0          1851.0             3.0            25.0                  20.0              7.0        0.0        0.0\n5    5.0          2159.0             0.0            27.0                   6.0              0.0        0.0        0.0\n6    6.0          1906.0             0.0             9.0                  15.0              3.0        0.0        0.0\n7    7.0          1825.0             0.0            24.0                   9.0              0.0        0.0        0.0\n8    8.0          2053.0             0.0            20.0                  10.0              2.0        0.0        0.0\n9    9.0          2082.0             2.0            16.0                   3.0              2.0        0.0        0.0\n10  10.0          2206.0             0.0            30.0                   1.0              0.0        0.0        0.0\n11  11.0          1746.0             3.0            31.0                   3.0              0.0        0.0        0.0\n12  12.0          1759.0             0.0            38.0                   3.0              1.0        0.0        0.0\n13  13.0          1790.0             0.0            21.0                   0.0              0.0        0.0        0.0\n14  14.0          1759.0             0.0            11.0                   6.0              0.0        0.0        0.0\n15  15.0          1539.0             0.0            20.0                   3.0              0.0        0.0        0.0\n16  16.0          1891.0             0.0            13.0                   6.0              1.0        0.0        0.0\n17  17.0          1101.0             0.0             4.0                   0.0              1.0        0.0        0.0\n18  18.0          2247.0             0.0            16.0                   5.0              5.0        0.0        0.0\n19  19.0           598.0             2.0             3.0                   1.0              1.0        0.0        0.0\n20  20.0          1014.0             2.0             1.0                  16.0              3.0        0.0        0.0\n21  21.0           337.0             1.0             2.0                   1.0              1.0        0.0        0.0\n22  22.0           258.0             0.0             0.0                   0.0              0.0        0.0        0.0\n\nI'm taking a look to Naive Bayes and SVM algorithms but I'm not sure which one fits better with the problem. The variables are independent. Some of them must be present to increase the score, and some of them matches the inverse document frequency, like totalCharCount.\nAny help? \nThanks a lot!","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":258,"Q_Id":46828118,"Users Score":0,"Answer":"Because of the continuous score, which i assume is your label, it's a regression problem. SVMs are more common for classification problems. There are lots of possible algorithms out there. Logistic Regression would be pretty common to solve something like this.\nEdit \nNow that you edited your post your problem became a classification problem :-)\nClassification = some classes you want your data to classify as like boolean(True, False) or multinomial(Big, Middle, Small, Very Small)\nRegression = continuous values(all real numbers between 0 and 1)\nNow you can try your SVM and see if it works well enough for your data.\nSee @Maxim's answer he has some good points (balancing, scaling)","Q_Score":0,"Tags":"python,machine-learning,svm,naivebayes,document-classification","A_Id":46828277,"CreationDate":"2017-10-19T10:44:00.000","Title":"What classification algorithm should I use for document classification with this variables?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am exporting a table from a pandas script into a .tex and would like to add a caption.\nwith open('Table1.tex','w') as tf:\n    tf.write(df.to_latex(longtable=True))\n(I invoke the longtable argument to span the long table over multiple pages.)\nThe Table1.tex file gets imported into a bigger LaTeX document via the \\import command. I would like to add a caption to the table, ideally on its top. Do you think there would be any way for doing that?\nI cannot manually edit the Table1.tex, it gets updated regularly using a Python script.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1703,"Q_Id":46837459,"Users Score":1,"Answer":"As of version 1.0.0, released on 29 January 2020, to_latex accepts caption and label arguments.","Q_Score":3,"Tags":"python,pandas,latex","A_Id":61232784,"CreationDate":"2017-10-19T19:27:00.000","Title":"pandas: create a caption with to_latex?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a data set which contains 12 years of weather data. For first 10 years, the data was recorded per day. For last two years, it is now being recorded per week. I want to use this data in Python Pandas for analysis but I am little lost on how to normalize this for use.\nMy thoughts\n\nConvert first 10 years data also into weekly data using averages. Might work but so much data is lost in translation.\nWeekly data cannot be converted to per day data.\nIgnore daily data - that is a huge loss\nIgnore weekly data - I lose more recent data.\n\nAny ideas on this?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":205,"Q_Id":46841117,"Users Score":0,"Answer":"First, you need to define what output you need, then, deduce how to treat the input to get the desired output.\nRegarding daily data for the first 10 years, it could be a possible option to keep only one day per week. Sub-sampling does not always mean loosing information, and does not always change the final result. It depends on the nature of the collected data: speed of variations of the data, measurement error, noise.\nSpeed of variations: Refer to Shannon to decide whether no information is lost by sampling once every week instead of every day. Given that for the 2 last year, some people had decided to sample only once every week, it seems to say that they have observed that data does not vary much every day and that a sample every week is enough information. That provides a hint to vote for a final data set that would include one sample every week for the total 12 years. Unless they reduced the sampling for cost reason, making a compromise between accuracy and cost of doing the sampling. Try to find in the literature a what speed your data is expected to vary.\nMeasurement error: If the measurement error contains a small epsilon that is randomly positive or negative, then, taking the average of 7 days to make a \"one week\" data will be better because it will increase the chances to cancel this variation. Otherwise, it is enough to do a sub-sampling taking only 1 day per week and throwing other days of the week. I would try both methods, averaging, and sub-sampling, and see if the output is significantly different.","Q_Score":0,"Tags":"python,pandas,periodicity","A_Id":46842124,"CreationDate":"2017-10-20T01:26:00.000","Title":"Data Periodicity - How to normalize?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am having a ML language identification project (Python) that requires a multi-class classification model with high dimension feature input. \nCurrently, all I can do to improve accuracy is through trail-and-error. Mindlessly combining available feature extraction algorithms and available ML models and see if I get lucky. \nI am asking if there is a commonly accepted workflow that find a ML solution systematically. \nThis thought might be naive, but I am thinking if I can somehow visualize those high dimension data and the decision boundaries of my model. Hopefully this visualization can help me to do some tuning. In MATLAB, after training, I can choose any two features among all features and MATLAB will give a decision boundary accordingly. Can I do this in Python? \nAlso, I am looking for some types of graphs that I can use in the presentation to introduce my model and features. What are the most common graphs used in the field? \nThank you","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":211,"Q_Id":46841795,"Users Score":1,"Answer":"Feature engineering is more of art than technique. That might require domain knowledge or you could try adding, subtracting, dividing and multiplying different columns to make features out of it and check if it adds value to the model. If you are using Linear Regression then the adjusted R-squared value must increase or in the Tree models, you can see the feature importance, etc.","Q_Score":0,"Tags":"python,machine-learning,visualization","A_Id":46845150,"CreationDate":"2017-10-20T03:02:00.000","Title":"Machine Learning, What are the common techniques for feature engineering and presenting the model?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I use python cv2 module to join jpg frames into video, but I can't add audio to it. Is it possible to add audio to video in python without ffmpeg?\nP.S. Sorry for my poor English","AnswerCount":4,"Available Count":1,"Score":0.049958375,"is_accepted":false,"ViewCount":19102,"Q_Id":46864915,"Users Score":1,"Answer":"You can use pygame for audio.\nYou need to initialize pygame.mixer module\nAnd in the loop, add pygame.mixer.music.play()\nBut for that, you will need to choose audio file as well.\nHowever, I have found better idea! You can use webbrowser module for playing videos (and because it would play on browser, you can hear sounds!)\nimport webbrowser\nwebbrowser.open(\"video.mp4\")","Q_Score":8,"Tags":"python-3.x,opencv,ffmpeg,opencv-python","A_Id":61983790,"CreationDate":"2017-10-21T15:35:00.000","Title":"Python add audio to video opencv","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to build a neural network with shared input weights. \nGiven pk inputs in the form X=[x_1, ..., x_p, v_1,...,v_p,z1,...,z_p,...] and a weight matrix w  of shape (p, layer1_size),  I want the  the first layer to be defined as  sum(w, x_.) + sum(w, v_.) + ....\nIn other words the input and the first layer should be fully connected where weights are shared across the different groups of inputs.  l = tf.matmul(X, W) where each row of W must have a structure like: (w1, ... ,wp, w1, ..., wp, ...)\nHow can I do that in tensorflow?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":547,"Q_Id":46886770,"Users Score":1,"Answer":"A simple option would be to replicate W. If the original W is, lets say p X q, you can just do l = tf.matmul(X, tf.tile(W, (k, 1))).","Q_Score":0,"Tags":"python,tensorflow,neural-network","A_Id":46887660,"CreationDate":"2017-10-23T10:08:00.000","Title":"Tensorflow share weights across input placeholder","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using OpenCV to do some image processing on Python. I'm trying to overlay an outline on an image where the outline was made from a mask. I'm using cv2.Canny() to get the outline of the mask, then changing that to a color using cv2.cvtColor() then finally converting that edge to cyan using outline[np.where((outline == [255,255,255]).all(axis=2))] = [180,105,255]. My issue now is that this is a one pixel thick line and can barely be seen on large images. This outline is all [0,0,0] except on the points I apply as a mask onto my color image using cv2.bitwise_or(img, outline. \nI'm currently thickening this outline by brute forcing and checking every single pixel in the bitmap to check if any of its neighbors are [180,105,255] and if so, that pixel will also change. This is very slow. Is there any way using numpy or openCV to do this automatically? I was hoping for some conditional indexing with numpy, but can't find anything.","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":7385,"Q_Id":46895772,"Users Score":1,"Answer":"I'd take a look at morphological operations.  Dilation sounds closest to what you want.  You might need to work on a subregion with your line if you don't want to dilate the rest of the image.","Q_Score":8,"Tags":"python,image,numpy,opencv,image-processing","A_Id":58123095,"CreationDate":"2017-10-23T18:05:00.000","Title":"Thicken a one pixel line","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to opencv and am trying to extract keypoints of a gesture image by ORB algorithm in python interface. The image input is binary and has many curvatures. So ORB gives too many points as keypoints (which are actually not). I am trying to increase the threshold of ORB algorithm so that the unnesessary points dont get detected. I have searched for ORB algorithms and haven't found any use of threshold except for in c++ function description.\n     So my question is what are the input parameters for ORB detection algorithm  and what is the actual syntax in python.\n  Thanks in advance.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":258,"Q_Id":46911160,"Users Score":0,"Answer":"The python docstring of ORB_create actually contains information about the parameter nfeatures, which is the maximum number of features to return. Could that solve your problem?","Q_Score":0,"Tags":"python,opencv,threshold,orb","A_Id":46911651,"CreationDate":"2017-10-24T12:53:00.000","Title":"orb opencv variable inputs","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to opencv and am trying to extract keypoints of a gesture image by ORB algorithm in python interface. The image input is binary and has many curvatures. So ORB gives too many points as keypoints (which are actually not). I am trying to increase the threshold of ORB algorithm so that the unnesessary points dont get detected. I have searched for ORB algorithms and haven't found any use of threshold except for in c++ function description.\n     So my question is what are the input parameters for ORB detection algorithm  and what is the actual syntax in python.\n  Thanks in advance.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":258,"Q_Id":46911160,"Users Score":0,"Answer":"After looking at the ORB() function in Opencv C++ description, I realized that the input parameters can be passed into function in Python as nfeatures=200,mask=img etc. (not sure about C++ though).","Q_Score":0,"Tags":"python,opencv,threshold,orb","A_Id":47732243,"CreationDate":"2017-10-24T12:53:00.000","Title":"orb opencv variable inputs","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Currently I'm trying to make Keras binary classify a set of (x,y) plots.\nAs a newbie, I can't figure out the proper way to give a correct input, since I've got these plots with app 3400 pairs each one and a set of 8 aditional features (local minimae locations) for every plot. What I tried is to give keras a 3400 + 3400 + 8 input layer, but it just feels wrong to do, and so far isn't making any progress.\nAs x variable is almost a correlative order, \u00bfshould I ignore it?\n\u00bfIs it possible to ask keras to distinguish: \"Hey these 3400 numbers are a plot, and these other 8 are some features about it\"?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":128,"Q_Id":46916554,"Users Score":1,"Answer":"If x coordinates for all plots are same you could (and in fact should) ignore it. Because in this case this data do not introduce any additional information. Their use will only lead to a more complex neural network, worse convergence and as result to increasing of training time and performance degradation.\nAbout second question - it is not necessary to do it. During training neural network will automatically identify which features are the most important.","Q_Score":1,"Tags":"python-3.x,tensorflow,keras","A_Id":46918492,"CreationDate":"2017-10-24T17:26:00.000","Title":"Keras. Correct way to give a (x,y) plot + features as input to a NN","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to python pandas, and I am trying to find the strongest month within a given series of timestamped sales data. The question to answer for n products is: when is the demand for the given product the highest?\nI am not looking for a complete solution but rather some ideas, how to approach this problem.\nI already looked into seasonal_decomposition to get some sort of seasonality indication but I feel that this might be a bit too complicated of an approach.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":324,"Q_Id":46919360,"Users Score":0,"Answer":"Well, I am not sure about how is your data so not sure if the answer will help but from what you said is that you are trying to check the month of the highest sales, so giving the product you will probably want to use the pandas groupby using the month and you will have a DataFrame with every month grouped.\nimagine a DF named Data:\nmean_buy = Data.groupby(months).mean()\nwith months = np.array([1,2,3,4,5,6,7,8,9,10,11,12]*number_of_years)","Q_Score":0,"Tags":"python,pandas","A_Id":46919591,"CreationDate":"2017-10-24T20:20:00.000","Title":"How to use Pandas to find the strongest month of sale for a product","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to python pandas, and I am trying to find the strongest month within a given series of timestamped sales data. The question to answer for n products is: when is the demand for the given product the highest?\nI am not looking for a complete solution but rather some ideas, how to approach this problem.\nI already looked into seasonal_decomposition to get some sort of seasonality indication but I feel that this might be a bit too complicated of an approach.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":324,"Q_Id":46919360,"Users Score":0,"Answer":"I don't have 50 reputation to add comment hence adding answer section. Some insight about your required solution would be great, because to me it's not clear about your requirement. BTW Coming to the idea, if your can split and load the time series data as the timestamp and demand then you can easily do it using regular python methods like max and then getting the time stamp value where the max demand occurred.","Q_Score":0,"Tags":"python,pandas","A_Id":46919474,"CreationDate":"2017-10-24T20:20:00.000","Title":"How to use Pandas to find the strongest month of sale for a product","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've written a custom loss function for my neural network but it can't compute any gradients. I thinks it is because I need the index of the highest value and are therefore using argmax to get this index.\nAs argmax is not differentiable I to get around this but I don't know how it is possible.\nCan anyone help?","AnswerCount":4,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":10784,"Q_Id":46926809,"Users Score":0,"Answer":"tf.argmax is not differentiable because it returns an integer index.  tf.reduce_max and tf.maximum are differentiable","Q_Score":18,"Tags":"python,tensorflow","A_Id":47045367,"CreationDate":"2017-10-25T07:49:00.000","Title":"Getting around tf.argmax which is not differentiable","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to train a very large number of Neural Nets using Tensorflow with Python. My neural nets (MLP) are ranging from very small ones (~ 2 Hidden Layers with ~30 Neurons each) to large ones (3-4 Layers with >500 neurons each).\nI am able to run all of them sequencially on my GPU, which is fine. But my CPU is almost idling. Additionally I found out, that my CPU is quicker than the GPU for my very small nets (I assume because of the GPU-Overhead etc...). Thats why I want to use both my CPU and my GPU in parallel to train my nets. The CPU should process the smaller networks to the larger ones, and my GPU should process from the larger to the smaller ones, until they meet somewhere in the middle... I thought, this is a good idea :-)\nSo I just simply start my consumers twice in different processes. The one with device = CPU, the other one with device = GPU. Both are starting and consuming the first 2 nets as expected. But then, the GPU-consumer throws an Exception, that his tensor is accessed\/violated by another process on the CPU(!), which I find weird, because it is supposed to run on the GPU...\nCan anybody help me, to fully segregate my to processes?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1450,"Q_Id":46929145,"Users Score":1,"Answer":"Do any of your networks share operators? \nE.g. they use variables with the same name in the same variable_scope which is set to variable_scope(reuse=True)\nThen multiple nets will try to reuse the same underlying Tensor structures. \nAlso check it tf.ConfigProto.allow_soft_placement is set to True or False in your tf.Session. If True you can't be guaranteed that the device placement will be actually executed in the way you intended in your code.","Q_Score":0,"Tags":"python,tensorflow,gpu,cpu","A_Id":48502090,"CreationDate":"2017-10-25T09:46:00.000","Title":"Running Python Tensorflow on CPU and GPU in parallel","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Suppose there is a list a[i] which stores index value v, v is the index value of  another list b[v]. I want to according to the values of list b to sort the list a.\nFor example\na=[0,2,3,1]  b=[7,10,8,6] \nI want the list a become a=[1,2,0,3], is there some concise way to sort list a?","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":48,"Q_Id":46945113,"Users Score":0,"Answer":"You can transform the items in a using a key.  That key is a function of each element of a.\nTry this:\na = sorted(a, key=lambda i: b[i])\nNote that if any value in a is outside the range of b, this would fail and raise an IndexError: list index out of range.\nBased on your description, however, you want the list to be sorted in reverse order, so:\na = sorted(a, key=lambda i: b[i], reverse=True)","Q_Score":0,"Tags":"python,sorting","A_Id":46945235,"CreationDate":"2017-10-26T02:53:00.000","Title":"If the value of list 1 is the index of list 2, how can I sort list 1 according to the list 2 value","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Suppose there is a list a[i] which stores index value v, v is the index value of  another list b[v]. I want to according to the values of list b to sort the list a.\nFor example\na=[0,2,3,1]  b=[7,10,8,6] \nI want the list a become a=[1,2,0,3], is there some concise way to sort list a?","AnswerCount":3,"Available Count":2,"Score":-0.0665680765,"is_accepted":false,"ViewCount":48,"Q_Id":46945113,"Users Score":-1,"Answer":"Simply solution would be sort the list b first and then get the indexes for list b after sorting, finally get the values of a list in the order of b indexes that were taken after sorting b list","Q_Score":0,"Tags":"python,sorting","A_Id":46945183,"CreationDate":"2017-10-26T02:53:00.000","Title":"If the value of list 1 is the index of list 2, how can I sort list 1 according to the list 2 value","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"So my understanding is that you perform one hot encoding to convert categorical features as integers to fit them to scikit learn machine learning classifier.\nSo let's say we have two choices\na. Splitting all the features into one hot encoded features (if A is say a categorical features that takes values 'a', 'b' and 'c', then it becomes A_a, A_b and A_c with binary values in each of its rows with binary value '1' meaning that the observation has the feature and binary value '0' meaning it does not possess the feature!). I would then fit a DecisionTreeClassifier on this.\nb. Not splitting all the features, but converting each category into an integer value WITHOUT performing one hot encoding (if A is say a categorical features that takes values 'a', 'b' and 'c', then 'a', 'b' and 'c' are renamed as 1, 2, 3 and no new columns are created, 'A' remains a single column with integer values 1, 2, 3 by using pandas.factorize or something which you an then fit a DecisionTreeClassifier.\nMy question is, when you fit DecisionTreeClassifier on the one hot encoded dataset, with multiple columns, will each of the new columns be treated as a separate feature?\nAlso, if you fit the DecisionTreeClassifier on the dataset where the categorical features are simply converted to an integer and kept in a single column; will it produce the same node splits, as the one where the DecisionTreeClassifier was fit on the dataset with the one-hot encoded features?\nLike, when you visualize the tree in both cases, \nis the interpretation given below the right way to look at it?\nfor DecisionTreeClassifier with one-hot-encoding\nif attribute == A_a, then yes\nif attribute == A_b, then no\nfor DecisionTreeClassifier without one-hot-encoding ('a' represented by integer value 1 and 'b' by value 2)\nif attribute == 1 then yes\nif attribute == 2, then no","AnswerCount":1,"Available Count":1,"Score":-0.1973753202,"is_accepted":false,"ViewCount":325,"Q_Id":46961091,"Users Score":-1,"Answer":"The two options you are describing do two very different things. \nIf you choose to binarize (one-hot encode) the values of the variable, there is no order to them. The decision tree at each split, considers a binary split on each of the new binary variables and chooses the most informative one. So yes, each binary feature is now treated as an independent feature.\nThe second choice puts the values in an order. Implicitly you are saying that a < b < c if transforming a=1, b=2, c=3. If you use this variable in a decision tree, the algorithm considers the splits 1 vs 2,3 and 1,2 vs 3 (but not 3 vs 1,2). \nSo the meaning of the variables is very different and I don't think you can expect equivalent results.","Q_Score":0,"Tags":"python,pandas,scikit-learn,decision-tree,one-hot-encoding","A_Id":46961237,"CreationDate":"2017-10-26T18:10:00.000","Title":"One hot encoding and its combination with DecisionTreeClassifier","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Date   Revenue\n9-Jan   $943,690.00 \n9-Feb   $1,062,565.00 \n9-Mar   $210,079.00 \n9-Apr   -$735,286.00\n9-May   $842,933.00 \n9-Jun   $358,691.00 \n9-Jul   $914,953.00 \n9-Aug   $723,427.00 \n9-Sep   -$837,468.00\n9-Oct   -$146,929.00\n9-Nov   $831,730.00 \n9-Dec   $917,752.00 \n10-Jan  $800,038.00 \n10-Feb  $1,117,103.00 \n10-Mar  $181,220.00 \n10-Apr  $120,968.00 \n10-May  $844,012.00 \n10-Jun  $307,468.00 \n10-Jul  $502,341.00 \n\n# This is what I did so far...\n\n# Dependencies\nimport csv\n\n# Files to load (Remember to change these)\nfile_to_load = \"raw_data\/budget_data_2.csv\"\n\n\ntotalrev = 0\ncount = 0\n\n# Read the csv and convert it into a list of dictionaries\nwith open(file_to_load) as revenue_data:\n    reader = csv.reader(revenue_data)\n\n    next(reader)  \n\n\n\n    for row in reader:\n\n        count += 1\n        revenue = float(row[1])     \n        totalrev += revenue\n\n    for i in range(1,revenue):\n         revenue_change = (revenue[i+1] - revenue[i])\n\navg_rev_change = sum(revenue_change)\/count\n\nprint(\"avg rev change: \", avg_rev_change)         \n\nprint (\"budget_data_1.csv\")\nprint (\"---------------------------------\")\nprint (\"Total Months: \", count)\nprint (\"Total Revenue:\", totalrev)\n\n\n\n\n\n\nI have above data in CSV file. I am having problem in finding revenue change, which is Revenue of row 1 - row 0 , row 2 - row 1 and so on... finally, I want sum of total revenue change. I tried with loop but I guess there is some silly mistake. Please suggest me codes so I can compare my mistake. I am new to python and coding.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":5951,"Q_Id":46965192,"Users Score":0,"Answer":"import csv\n\n# Files to load (Remember to change these)\nfile_to_load = \"raw_data\/budget_data_2.csv\"\n\n\n# Read the csv and convert it into a list of dictionaries\nwith open(file_to_load) as revenue_data:\n    reader = csv.reader(revenue_data)\n\n    # use of next to skip first title row in csv file\n    next(reader) \n    revenue = []\n    date = []\n    rev_change = []\n\n    # in this loop I did sum of column 1 which is revenue in csv file and counted total months which is column 0 \n    for row in reader:\n\n        revenue.append(float(row[1]))\n        date.append(row[0])\n\n    print(\"Financial Analysis\")\n    print(\"-----------------------------------\")\n    print(\"Total Months:\", len(date))\n    print(\"Total Revenue: $\", sum(revenue))\n\n\n    #in this loop I did total of difference between all row of column \"Revenue\" and found total revnue change. Also found out max revenue change and min revenue change. \n    for i in range(1,len(revenue)):\n        rev_change.append(revenue[i] - revenue[i-1])   \n        avg_rev_change = sum(rev_change)\/len(rev_change)\n\n        max_rev_change = max(rev_change)\n\n        min_rev_change = min(rev_change)\n\n        max_rev_change_date = str(date[rev_change.index(max(rev_change))])\n        min_rev_change_date = str(date[rev_change.index(min(rev_change))])\n\n\n    print(\"Avereage Revenue Change: $\", round(avg_rev_change))\n    print(\"Greatest Increase in Revenue:\", max_rev_change_date,\"($\", max_rev_change,\")\")\n    print(\"Greatest Decrease in Revenue:\", min_rev_change_date,\"($\", min_rev_change,\")\")\n\n\n\nOutput I got\n\n\nFinancial Analysis\n-----------------------------------\nTotal Months: 86\nTotal Revenue: $ 36973911.0\nAvereage Revenue Change: $ -5955\nGreatest Increase in Revenue: Jun-2014 ($ 1645140.0 )\nGreatest Decrease in Revenue: May-2014 ($ -1947745.0 )","Q_Score":0,"Tags":"python,python-3.x,csv","A_Id":46979942,"CreationDate":"2017-10-26T23:13:00.000","Title":"Python - How can I find difference between two rows of same column using loop in CSV file?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have two lists\ny_test = array('B', [1, 2, 3, 4, 5])\nand \nlabs = [1, 2, 3, 4, 5]\nIn sklearn, when i do print accuracy_score(y_test,labs), i get error\n\nValueError: Expected array-like (array or non-string sequence), got array('B', [1, 2, 3, 4, 5]).\n\nI tried to compare it using print accuracy_score(y_test['B'],labs) but it is showing \n\nTypeError: array indices must be integers","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2073,"Q_Id":46967312,"Users Score":0,"Answer":"You have to convert the array to list to make it work\nThis should do for you\naccuracy_score(y_test.tolist(),labs)","Q_Score":0,"Tags":"python,numpy,scikit-learn","A_Id":46967516,"CreationDate":"2017-10-27T04:01:00.000","Title":"python sklearn accuracy score for two different list","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using keras for CNN on 2D images for regression with mean squared error as the loss function. The loss values are of the range 100. To know average error at each pixel, should I divide it by total number of pixels? Or the loss values displayed are for pixels?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":246,"Q_Id":46989998,"Users Score":0,"Answer":"The way how mse is defined in Keras make it compute an average pixel error. So you could simply take a loss value as an average pixel error.","Q_Score":1,"Tags":"python,keras","A_Id":47003117,"CreationDate":"2017-10-28T12:59:00.000","Title":"Is the loss value computed by keras for 2D CNN regression by keras point wise?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataset that includes 7 different covariates and an output variable, the 'success rate'. \nI'm trying to find the important factors that predict the success rate. One of the covariates in my dataset is a categorical variable that takes on 700 values (0- 700), each representing the ID of the district they're from. \nHow should I deal with this variable while performing logistic regression?\nIf I make 700 dummy columns, how can I make it easier to interpret the results? \nI'm using Python and statsmodels.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":755,"Q_Id":46998234,"Users Score":1,"Answer":"You can apply grouping and then do logistic regression on each group. Or you treat it as multilabel classifier and do \"Softmax regression\".","Q_Score":0,"Tags":"python,pandas,regression,statsmodels","A_Id":46998392,"CreationDate":"2017-10-29T08:29:00.000","Title":"Logistic Regression- Working with categorical variable in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"So I am using a genetic algorithm to train a feedforward neural network, tasked with recognizing a function given to the genetic algorithm. I.e x = x**2 or something more complicated obviously.\nI realized I am using random inputs in my fitness function, which causes the fitness to be somewhat random for a member of the population, however, still in line with how close it is to the given function obviously. A colleague remarked that it is stranged that the same member of the population doesnt always get the same fitness, which I agree is a little unconventional. However, it got me thinking, is there any reason why this would be bad for the genetic algorithm? I actually think it might be quite good because it enables me to have a rather small testset, speeding up number of generations while still avoiding overfitting to any given testest.\nDoes anyone have experience with this?\n(fitness function is MSE compared to given function, for a randomly generated testset of 10 iterations)","AnswerCount":2,"Available Count":1,"Score":-0.0996679946,"is_accepted":false,"ViewCount":495,"Q_Id":46999584,"Users Score":-1,"Answer":"Normally you use a seed for genetic algorithms, which should be fixed. It will always generate the same \"random\" childs sequentially, which makes your approach reproducible. So the genetic algorithm is kind of pseudo-random. That is state of art how to perform genetic algorithms.","Q_Score":0,"Tags":"python,neural-network,artificial-intelligence,genetic-algorithm","A_Id":46999668,"CreationDate":"2017-10-29T11:24:00.000","Title":"Random element in fitness function genetic algorithm","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"So with Catboost you have parameters to tune and also iterations to tune. So for iterations you can tune using cross validation with the overfit detector turned on. And for the rest of the parameters you can use Bayesian\/Hyperopt\/RandomSearch\/GridSearch. My question is which order to tune Catboost in. Should I tune the number of iterations first or the other parameters first. A lot of the parameters are kind of dependent on number of iterations, but also the number of iterations could be dependent on the parameters set. So any idea of which order is the proper way?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1861,"Q_Id":47006642,"Users Score":0,"Answer":"You have essentially answered your own question already. Any variable that depends on something else x you must first define x.\nOne thing to keep in mind is you can define a function before the variables you need to pass into it since its only when you call the function that you need the input variables, defining the function is just setting the process you will use. Calling a function and defining the variable it returns is what you have to do in order.\nThe order you would use is:\nInclude any remote librarys or functions, define any initial variables that dont depend on anything, define your local functions.\nNext in your main you first need to generate the variables that your iteration function requires, then itterate with these variables, then generate the ones that depend on the itteration.","Q_Score":1,"Tags":"python,machine-learning,cross-validation,hyperparameters,catboost","A_Id":47006813,"CreationDate":"2017-10-30T00:25:00.000","Title":"Catboost tuning order?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there a way in tensorflow to transform a one-hot vector into a softmax-like distribution?\nFor example, I have the following one-hot vector:\n\n[0 0 0 0 1 0]\n\nI want to have a vector with probabilities where the one value is the most likely number, like:\n\n[0.1 0.1 0.1 0.1 0.5 0.1]\n\nThis vector should always be random, but with the true class having the highest probability.\nHow can I reach this?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":359,"Q_Id":47013937,"Users Score":0,"Answer":"A solution could be to keep your one-hot vector ;). \nAnother one, more general, is to make a random positive vector, then compute the difference between the highest score d and the score of your true class, then add a random number between d and +infinity to the true class's score, then normalize to get a valid distribution. (note that you can force the true class's initial score to be 0, but that will probably be a tiny bit longer to code).\nThe choice of the distributions for the initial random vector and for the quantity to add to the true class's score will change the output distribution, but I don't know which one you want or why you want to do that...","Q_Score":0,"Tags":"python,tensorflow","A_Id":47014211,"CreationDate":"2017-10-30T11:17:00.000","Title":"One-hot vector to softmax-like distribution in tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am training a neural net on a set of images with heterogeneous dimensions. Of course, they all have to have the same dimensions to be fed to the NN, and it is simple enough to use scipy.misc.imresize() for this. But, how should I choose width and height? My first instinct was to plot histograms of both and eyeball values around the 75th percentile. I also thought maybe I should scale all images up to the max values for both height and width, so that no details are discarded from the higher-pixel images. Is there a best practice for addressing this problem? Thanks!\nFor reference, I am using python 2.7 and keras with theano backend and dimension ordering.","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":44,"Q_Id":47025896,"Users Score":2,"Answer":"I don't think there is a standard approach on this. In machine learning, in many cases we have to try and see.\n\nIf I were you, if I had to build a custom neural network, I would start with mean image size and then I would gradually increase the size until reaching optimum score.\nIf you are using a pretrained neural network then just resize your images to network's default.","Q_Score":1,"Tags":"python-2.7,image-processing,machine-learning,keras,conv-neural-network","A_Id":47031545,"CreationDate":"2017-10-30T23:49:00.000","Title":"Is there a heuristic for homogenizing image dimensions before using them to train neural net?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I frequently need to enter large integers for indexing and creating numpy arrays, such as 3500000 or 250000.  Normally I'd enter these using scientific notation, 3.5e6 or .25e6 or such.  This is quicker, and much less likely to have errors.\nUnfortunately, python expects integer datatypes for indexing.  The obvious solution is to convert datatypes.  So [5e5:1e6] becomes [int(5e5):int(1e6)], but this decreases readability and is somewhat longer to type. Not to mention, it's easy to forget what datatype an index is until an indexing operation fails on a list or numpy.ndarray.\nIs there a way to have numpy or python interpret large floats as integers, or is there an easy way to create large integers in python?","AnswerCount":6,"Available Count":1,"Score":0.0333209931,"is_accepted":false,"ViewCount":330,"Q_Id":47037150,"Users Score":1,"Answer":"I can propose to use such notation [5*10**5:1*10**6] but it's not so clear as in case of 5e5 and 1e6. And even worse in case of 3.5e6 = 35*10**5","Q_Score":1,"Tags":"python,numpy","A_Id":47037520,"CreationDate":"2017-10-31T13:53:00.000","Title":"Using large index in python (numpy or lists)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am writing a program in Python which should import *.dat files, subtract a specific value from certain columns and subsequently save the file in *.dat format in a different directory. \nMy current tactic is to load the datafiles in a numpy array, perform the calculation and then save it. I am stuck with the saving part. I do not know how to save a file in python in the *.dat format. Can anyone help me? Or is there an alternative way without needing to import the *.dat file as a numpy array? Many thanks!","AnswerCount":5,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":41290,"Q_Id":47038101,"Users Score":0,"Answer":"Correct me if I'm wrong, but opening, writing to, and subsequently closing a file should count as \"saving\" it. You can test this yourself by running your import script and comparing the last modified dates.","Q_Score":0,"Tags":"python,database,save","A_Id":47038338,"CreationDate":"2017-10-31T14:42:00.000","Title":"Save data as a *.dat file?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I use Pandas with Jupyter notebook a lot. After I ingest a table in from using pandas.read_sql, I would preview it by doing the following:\ndata = pandas.read_sql(\"\"\"blah\"\"\")\n data\nOne problem that I have been running into is that all my preview tables will disappear if I reopen my .ipynb\nIs there a way to prevent that from happening?\nThanks!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":71,"Q_Id":47042689,"Users Score":0,"Answer":"Are you explicitly saving your notebook before you re-open it? A Jupyter notebook is really just a large json object, eventually rendered as a fancy html object. If you save the notebook, illustrations and diagrams should be saved as well. If that doesn't do the trick, try putting the one-liner \"data\" in a different cell than read_sql().","Q_Score":0,"Tags":"python,ipython,jupyter-notebook,ipython-notebook","A_Id":47042891,"CreationDate":"2017-10-31T18:53:00.000","Title":"How to prevent charts or tables to disappear when I re-open Jupyter Notebook?","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've searched through other questions but have not found anything that has helped (most just suggest you do install pandas with conda or pip). In my jupyter notebook I'm trying to import pandas (import pandas as pd)\nbut I'm getting the following error:\n\nModuleNotFoundError: No module named 'pandas'\n\nSome pertinent information:\n\nI'm using python3\nI've installed pandas using conda install pandas \nMy conda environment has pandas installed correctly. After activating the environment, I type python into the terminal and from there I can successfully import pandas and use it appropriately. This leads me to believe that it is an issue with my jupyter notebook.","AnswerCount":5,"Available Count":4,"Score":0.0399786803,"is_accepted":false,"ViewCount":7502,"Q_Id":47043407,"Users Score":1,"Answer":"Try this for python3\nsudo pip3 install pandas","Q_Score":5,"Tags":"python,python-3.x,pandas,jupyter-notebook","A_Id":55322568,"CreationDate":"2017-10-31T19:44:00.000","Title":"Jupyter Notebook: no module named pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've searched through other questions but have not found anything that has helped (most just suggest you do install pandas with conda or pip). In my jupyter notebook I'm trying to import pandas (import pandas as pd)\nbut I'm getting the following error:\n\nModuleNotFoundError: No module named 'pandas'\n\nSome pertinent information:\n\nI'm using python3\nI've installed pandas using conda install pandas \nMy conda environment has pandas installed correctly. After activating the environment, I type python into the terminal and from there I can successfully import pandas and use it appropriately. This leads me to believe that it is an issue with my jupyter notebook.","AnswerCount":5,"Available Count":4,"Score":0.1586485043,"is_accepted":false,"ViewCount":7502,"Q_Id":47043407,"Users Score":4,"Answer":"You can try: which conda and which python to see the exact location where conda and python was installed and which was launched.\nAnd try using the absolute path of conda to launch jupyter.\nFor example, \/opt\/conda\/bin\/jupyter notebook","Q_Score":5,"Tags":"python,python-3.x,pandas,jupyter-notebook","A_Id":47049051,"CreationDate":"2017-10-31T19:44:00.000","Title":"Jupyter Notebook: no module named pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've searched through other questions but have not found anything that has helped (most just suggest you do install pandas with conda or pip). In my jupyter notebook I'm trying to import pandas (import pandas as pd)\nbut I'm getting the following error:\n\nModuleNotFoundError: No module named 'pandas'\n\nSome pertinent information:\n\nI'm using python3\nI've installed pandas using conda install pandas \nMy conda environment has pandas installed correctly. After activating the environment, I type python into the terminal and from there I can successfully import pandas and use it appropriately. This leads me to believe that it is an issue with my jupyter notebook.","AnswerCount":5,"Available Count":4,"Score":0.0,"is_accepted":false,"ViewCount":7502,"Q_Id":47043407,"Users Score":0,"Answer":"The default kernel in jupyter notebook points the python that is different to the python used inside the terminal.\nYou could check using which python\nSo the packages installed by conda lives in different place compared to the python that is used by the jupyter notebook at default.\nTo fix the issue, both needs to be same.\nFor that create a new kernel using ipykernel. syntax: python -m ipykernel install --user --name custom_name --display-name \"Python (custom_name)\"\nAfter that, check the custom kernel and the path of the python used. jupyter kernel list --json\nFinally, Restart the jupyter notebook. And change the kernel to the new custom_kernel.","Q_Score":5,"Tags":"python,python-3.x,pandas,jupyter-notebook","A_Id":72239426,"CreationDate":"2017-10-31T19:44:00.000","Title":"Jupyter Notebook: no module named pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've searched through other questions but have not found anything that has helped (most just suggest you do install pandas with conda or pip). In my jupyter notebook I'm trying to import pandas (import pandas as pd)\nbut I'm getting the following error:\n\nModuleNotFoundError: No module named 'pandas'\n\nSome pertinent information:\n\nI'm using python3\nI've installed pandas using conda install pandas \nMy conda environment has pandas installed correctly. After activating the environment, I type python into the terminal and from there I can successfully import pandas and use it appropriately. This leads me to believe that it is an issue with my jupyter notebook.","AnswerCount":5,"Available Count":4,"Score":0.0,"is_accepted":false,"ViewCount":7502,"Q_Id":47043407,"Users Score":0,"Answer":"Its seems using homebrew installs for packages dependancies of home brew formulas are not handled by home brew well. Mostly path issues as installs are in different locations vs pip3, I also had tried installing pandas thru nb with !pip3, but I got errors that is was already satisfied meaning it was already installed just not importing. I uninstalled homebrew jupyterlab and used pip3 instead and all worked proper as a work around.","Q_Score":5,"Tags":"python,python-3.x,pandas,jupyter-notebook","A_Id":64191121,"CreationDate":"2017-10-31T19:44:00.000","Title":"Jupyter Notebook: no module named pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to read a Stata (.dta) file in Python with pandas.read_stata, But I'm getting this error:\n\nValueError: Version of given Stata file is not 104, 105, 108, 111 (Stata 7SE), 113 (Stata 8\/9), 114 (Stata 10\/11), 115 (Stata 12), 117 (Stata 13), or 118 (Stata 14)\n\nPlease advise.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":3485,"Q_Id":47051326,"Users Score":1,"Answer":"Just use the read_table() of Pandas then make sure to include delim_whitespace=True and header=None.","Q_Score":3,"Tags":"python,pandas,stata","A_Id":47129061,"CreationDate":"2017-11-01T09:01:00.000","Title":"unable to read stata .dta file in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have built model which gives me 84% accuracy for random forest and support vector machine but giving very low auc of 13% only. I am building this in python and I am new to machine learning and data science.\nI am predicting 0 and 1 labels on dataset. My overall dataset is having records of 30744.\nLabel 1 - 6930\nLabel 0 - 23814\nCould you please advise if this is fine? Is model is getting overfitted?\nAppreciate any suggestion on improving auc?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1723,"Q_Id":47066314,"Users Score":0,"Answer":"No, your model is not fine. \nIn your dataset around 88% records belong to \"Label 0\", which makes your model bias to \"Label 0\". Thus, even though your AUC is low, it shows 84% accuracy as most of the data belongs to \"Label 0\".\nYou can undersample records belong to \"Label 0\" or oversample records belong to \"Label 1\" to make your model more accurate. \nHope it helps.","Q_Score":0,"Tags":"python,machine-learning,random-forest","A_Id":47236204,"CreationDate":"2017-11-02T01:47:00.000","Title":"why model is giving high accuracy of 84% but very low AUC 13%?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Here is a problem I am stuck with presently.\nRecently I have been exploring bokeh for plotting and kafka for streaming.\nAnd I thought of making a sample live dashboard using both of them.\nBut the problem is I use bokeh with python and kafka stream api's with Java. Is there a way to use them together by any chance.\nThe only way I can see is both of them can be used with scala. But presently I don't want to get into scala.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":824,"Q_Id":47069678,"Users Score":0,"Answer":"Try kafka-python. You can set up a simple consumer to read the data from your cluster.","Q_Score":0,"Tags":"java,python,apache-kafka,bokeh,apache-kafka-streams","A_Id":50312611,"CreationDate":"2017-11-02T07:20:00.000","Title":"Using bokeh plotting with kafka streaming","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"So, I have close to 2000 reports and each report has an associated short description of the problem. My goal is to cluster all of these so that we can find distinct trends within these reports. \nOne of the features I'd like to use some sort of contextual text vector. Now, I've used Word2Vec and think this would be a good option but I also so Doc2Vec and I'm not quite sure what would be a better option for this use case. \nAny feedback would be greatly appreciated.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":380,"Q_Id":47081149,"Users Score":0,"Answer":"They're very similar, so just as with a single approach, you'd try tuning parameters to improve results in some rigorous manner, you should try them both, and compare the results. \nYour dataset sounds tiny compared to what either needs to induce good vectors \u2013 Word2Vec is best trained on corpuses of many millions to billions of words, while Doc2Vec's published results rely on tens-of-thousands to millions of documents. \nIf composing some summary-vector-of-the-document from word-vectors, you could potentially leverage word-vectors that are reused from elsewhere, but that will work best if the vectors' original training corpus is similar in vocabulary\/domain-language-usage to your corpus.  For example, don't expect words trained on formal news writing to work well with, or even cover the same vocabulary as, informal tweets, or vice-versa.\nIf you had a larger similar-text corpus of documents to train a Doc2Vec model, you could potentially train a good model on the full set of documents, but then just use your small subset, or re-infer vectors for your small subset, and get better results than a model that was only trained on your subset. \nStrictly for clustering, and with your current small corpus of short texts, if you have good word-vectors from elsewhere, it may be worth looking at the \"Word Mover's Distance\" method of calculating pairwise document-to-document similarity. It can be expensive to calculate on larger docs and large document-sets, but might support clustering well.","Q_Score":1,"Tags":"python,machine-learning,nlp,word2vec,doc2vec","A_Id":47099951,"CreationDate":"2017-11-02T17:20:00.000","Title":"Looking to cluster short descriptions of reports. Should I use Word2Vec or Doc2Vec","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to construct Hammersley and Halton quasi random sequences. I have for example three variables x1, x2 and x3. They all have integer values. x1 has a range from 2-4, x2 from 2-4 and x3 from 1-7. Is there any python package which can create those sequences? I saw that there are some procject like sobol or SALib, but they do not implemented Halton and Hammersley.\nBest regards","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1801,"Q_Id":47094705,"Users Score":0,"Answer":"Most library methods offering low discrepancy methods for arbitrary dimensions,  won\u2019t include arguments that allow you to define arbitrary intervals for each of the separate dimensions\/components. However,  in virtually all of these cases,   you can adapt the exisiting method to suit your requirements with the addition of a single line of code. Understanding this will dramatically increase number of librbaries you can choose to use!\nFor nearly all low discrepancy (quasirandom) sequences, each term is equidistributed in the half open range [0,1).\nSimilarly, for d-dimensional sequences, each component of each term falls in [0,1).\nThis includes the Halton sequence ( which is a generalization if the van der Corput), Hammersley, Weyl\/Kronecker, Sobol, and  Niederreiter sequences. \nConverting a value from [0,1) to [a,b) can be achieved, via the linear transformation x = a + (b-a) z.\nThus if the n-th term of the canonical low discrepancy sequence is (z_1,z_2,z,z_3), then you desired sequence is (2+2*z1, 2+2*z2, 1+6*z3).","Q_Score":3,"Tags":"python,numpy,scipy","A_Id":51358087,"CreationDate":"2017-11-03T11:28:00.000","Title":"Python : Halton and Hammersley quasi random sequences","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am teaching myself data science and something peculiar has caught my eyes. In a sample DNN tutorial I was working on, I found that the Keras layer.get_weights() function returned empty list for my variables. I've successfully cross validated and used model.fit() function to compute the recall scores.\nBut as I'm trying to use the get_weights() function on my categorical variables, it returns empty weights for all.\nI'm not looking for a solution to code but I am just curious about what would possibly cause this. I've read through the Keras API but it did not provide me with the information I was hoping to see. What could cause the get_weights() function in Keras to return empty list except for of course the weights not being set?","AnswerCount":2,"Available Count":1,"Score":0.2913126125,"is_accepted":false,"ViewCount":2799,"Q_Id":47106830,"Users Score":3,"Answer":"Maybe you are asking for weights before they are created. \n\nWeights are created when the Model is first called on inputs or\n  build() is called with an input_shape.\n\nFor example, if you load weights from checkpoint but you don't give an input_shape to the model, then get_weights() will return an empty list.","Q_Score":8,"Tags":"python,machine-learning,tensorflow,keras","A_Id":56950297,"CreationDate":"2017-11-04T02:37:00.000","Title":"Why does get_weights return an empty list?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am data scientist in beijing and working with anaconda in win7\nbut after I pip installed tensorflow v1.4,code completion of my IDE spyder in anaconda not work, before anything of code completion function is work perfectly. \nNow even I uninstall tensorflow,code completion function of spyder still not work. Any help?\nmy envirment:\nwin7\nanaconda3 v5.0 for win64 (py3.6)\ntensorflow v1.4 for win (tf_nightly-1.4.0.dev20171006-cp36-cp36m-win_amd64.whl)\nSo two question:\n1 How can i fix it so to make anaconda3 spyder code completion work again?\n2 After uninstall tensorflow, anaconda3 spyder code completion still not work, what can I do?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":487,"Q_Id":47109343,"Users Score":0,"Answer":"Now that I have to use a temporary alternative, I installed anaconda version without an installed tensorflow in anaconda's envs. And I use it when I don't use tensorflow. I hope this question can be complement answered, please attent my answer.","Q_Score":0,"Tags":"python,tensorflow,anaconda,spyder,code-completion","A_Id":47526695,"CreationDate":"2017-11-04T09:36:00.000","Title":"how can i make anaconda spyder code completion work again after installing tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am data scientist in beijing and working with anaconda in win7\nbut after I pip installed tensorflow v1.4,code completion of my IDE spyder in anaconda not work, before anything of code completion function is work perfectly. \nNow even I uninstall tensorflow,code completion function of spyder still not work. Any help?\nmy envirment:\nwin7\nanaconda3 v5.0 for win64 (py3.6)\ntensorflow v1.4 for win (tf_nightly-1.4.0.dev20171006-cp36-cp36m-win_amd64.whl)\nSo two question:\n1 How can i fix it so to make anaconda3 spyder code completion work again?\n2 After uninstall tensorflow, anaconda3 spyder code completion still not work, what can I do?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":487,"Q_Id":47109343,"Users Score":0,"Answer":"I try pip rope_py3k\u3001jedi and readline, and reset the set of tool, but all are not useful.\nand my Spyder code editing area also can not be automatically completed after the installation of tensorflow, I have re-installed again and found the same problem.\nHowever,when I re-installed all envs except tensorflow,it can work!! \n My environment is win10, anaconda3.5, python3.6.3, tensorflow1.4.\nDid you resolve it?And I hope you can teach me.","Q_Score":0,"Tags":"python,tensorflow,anaconda,spyder,code-completion","A_Id":47525945,"CreationDate":"2017-11-04T09:36:00.000","Title":"how can i make anaconda spyder code completion work again after installing tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I found 2 ways to replace nan values in pythons,\nOne using sklearn's imputer class and the other using df.fillnan()\nthe later seems easy with less code.\nBut efficiency wise which is better.\nCan anyone explain the use cases of each.?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2979,"Q_Id":47114021,"Users Score":10,"Answer":"I feel imputer class has its own benefits because you can just simply mention mean or median to perform some action unlike in fillna where you need to supply values. But in imputer you need to fit and transform the dataset which means more lines of code. But it may give you better speed over fillna but unless really big dataset it doesn\u2019t matter. \nBut fillna has something which is really cool. You can fill the na even with a custom value which you may sometime need. This makes fillna better IMHO even if it may perform slower.","Q_Score":5,"Tags":"python,pandas,dataframe,scikit-learn","A_Id":47115148,"CreationDate":"2017-11-04T18:20:00.000","Title":"Sklearn's imputer v\/s df.fillnan to replace nan values with mean of the column","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have two dataframes both of which have the same basic schema. (4 date fields, a couple of string fields, and 4-5 float fields). Call them df1 and df2. \nWhat I want to do is basically get a \"diff\" of the two - where I get back all rows that are not shared between the two dataframes (not in the set intersection). Note, the two dataframes need not be the same length.\nI tried using pandas.merge(how='outer') but I was not sure what column to pass in as the 'key' as there really isn't one and the various combinations I tried were not working. It is possible that df1 or df2 has two (or more) rows that are identical. \nWhat is a good way to do this in pandas\/Python?","AnswerCount":5,"Available Count":1,"Score":0.0399786803,"is_accepted":false,"ViewCount":32562,"Q_Id":47131361,"Users Score":1,"Answer":"Set df2.columns = df1.columns\nNow, set every column as the index: df1 = df1.set_index(df1.columns.tolist()), and similarly for df2.\nYou can now do df1.index.difference(df2.index), and df2.index.difference(df1.index), and the two results are your distinct columns.","Q_Score":7,"Tags":"python,pandas,merge,compare,diff","A_Id":47131429,"CreationDate":"2017-11-06T06:55:00.000","Title":"Diff between two dataframes in pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"On a windows machine with Anaconda installed. Script B runs correctly and produces the correct result. Script B is called from a Windows console app. \nWhen script A imports script B, script B fails with the error \"ModuleNotFoundError: No module named 'numpy'\". \nWhen script B is passed directly to Python executable, script B works and executes without error. (I'm new to python) Any help pointing me in the right direction would be appreciated. \nThanks","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":52,"Q_Id":47142323,"Users Score":0,"Answer":"basteflp, thanks for your response. I managed to solve it. The module not found error was due to running the script outside of the a specific Anaconda environment. Running the script after loading the Anaconda environment resolved the error.","Q_Score":0,"Tags":"python,windows,numpy,anaconda","A_Id":47259321,"CreationDate":"2017-11-06T17:14:00.000","Title":"ModuleNotFound Error in python script but only when imported into a parent script","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am comfortable with basic filtering and querying using Pandas. For example, if I have a dataframe called df I can do df[df['field1'] < 2] or df[df['field2'] < 3]. I can also chain multiple criteria together, for example:\ndf[(df['field1'] < 3) & (df['field2'] < 2)].\nWhat if I don't know in advance how many criteria I will need to use? Is there a way to \"chain\" an arbitrary number of these operations together? I would like to pass a list of filters such as [('field1', 3), ('field2', 2), ('field3', 4)] which would result in the chaining of these 3 conditions together.\nThanks!","AnswerCount":6,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":813,"Q_Id":47147414,"Users Score":0,"Answer":"I believe that reduce( (lambda x, y: x & (df[y[0]]<y[1])), list_of_filters ) will do it.","Q_Score":4,"Tags":"python,pandas","A_Id":47147531,"CreationDate":"2017-11-06T23:13:00.000","Title":"Filter Pandas Dataframe using an arbitrary number of conditions","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"This is my first time using Doc2Vec\nI'm trying to classify works of an author. I have trained a model with Labeled Sentences (paragraphs, or strings of specified length), with words = the list of words in the paragraph, and tags = author's name. In my case I only have two authors.\nI tried accessing the docvecs attribute from the trained model but it only contains two elements, corresponding to the two tags I have when I trained the model. I'm trying to get the doc2vec numpy representations of each paragraph I fed in to the training so I can use that as training data later on. How can I do this?\nThanks.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":773,"Q_Id":47148615,"Users Score":0,"Answer":"Bulk training only creates vectors for tags you supplied. If you want to read out a bulk-trained vector per paragraph (as if by model.docvecs['paragraph000']), you have to give each paragraph a unique tag during training (like 'paragraph000'). You can give docs other tags as well - but bulk training only creates remembers doc-vectors for supplied tags. \nAfter training, you can infer vectors for any other texts you supply to infer_vector() - and of course you could supply the same paragraphs that were used during training.","Q_Score":0,"Tags":"python-3.x,nlp,gensim,doc2vec","A_Id":47172267,"CreationDate":"2017-11-07T01:36:00.000","Title":"Getting numpy vector from a trained Doc2Vec model for each document","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I already know \"xgboost.XGBRegressor is a Scikit-Learn Wrapper interface for XGBoost.\"\nBut do they have any other difference?","AnswerCount":3,"Available Count":1,"Score":0.3215127375,"is_accepted":false,"ViewCount":17925,"Q_Id":47152610,"Users Score":5,"Answer":"From my opinion the main difference is the training\/prediction speed.\nFor further reference I will call the xgboost.train - 'native_implementation' and XGBClassifier.fit - 'sklearn_wrapper'\nI have made some benchmarks on a dataset shape (240000, 348)\nFit\/train time:\nsklearn_wrapper time = 89 seconds\nnative_implementation time = 7 seconds\nPrediction time:\nsklearn_wrapper = 6 seconds\nnative_implementation = 3.5 milliseconds\nI believe this is reasoned by the fact that sklearn_wrapper is designed to use the pandas\/numpy objects as input where the native_implementation needs the input data to be converted into a xgboost.DMatrix object.\nIn addition one can optimise n_estimators using a native_implementation.","Q_Score":30,"Tags":"python,machine-learning,scikit-learn,regression,xgboost","A_Id":64742353,"CreationDate":"2017-11-07T07:54:00.000","Title":"What is the difference between xgb.train and xgb.XGBRegressor (or xgb.XGBClassifier)?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am already having tensorflow in my anaconda.Still when i run the ipython notebook ,it shows No module named tensorflow.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":263,"Q_Id":47172525,"Users Score":0,"Answer":"Its done...tried installing within the environment Tensorflow.","Q_Score":0,"Tags":"python,tensorflow,jupyter","A_Id":47173103,"CreationDate":"2017-11-08T05:56:00.000","Title":"No module named tensorflow even after it is present in the local","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am already having tensorflow in my anaconda.Still when i run the ipython notebook ,it shows No module named tensorflow.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":263,"Q_Id":47172525,"Users Score":0,"Answer":"Are you using Virtual Environment?\nIf yes, there might be difference in version.\ntry \"pip install ipython\", and then import tensorflow.\nMay it works.","Q_Score":0,"Tags":"python,tensorflow,jupyter","A_Id":47172877,"CreationDate":"2017-11-08T05:56:00.000","Title":"No module named tensorflow even after it is present in the local","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am deploying a Jupyter notebook(using python 2.7 kernel) on client side which accesses data on a remote and does processing in a remote Spark standalone cluster (using pyspark library). I am deploying spark cluster in Client mode. The client machine does not have any Spark worker nodes.\nThe client does not have enough memory(RAM). I wanted to know that if I perform a Spark action operation on dataframe like df.count()on client machine, will the dataframe be stored in Client's RAM or will it stored on Spark worker's memory?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":187,"Q_Id":47173286,"Users Score":0,"Answer":"If i understand correctly, then what you will get on the client side is an int. At least should be, if setup correctly. So the answer is no, the DF is not going to hit your local RAM.\nYou are interacting with the cluster via SparkSession (SparkContext for earlier versions). Even though you are developing -i.e. writing code- on the client machine, the actual computation of spark operations -i.e. running pyspark code-  will not be performed on your local machine.","Q_Score":0,"Tags":"python,pyspark,apache-spark-sql,spark-dataframe","A_Id":47173911,"CreationDate":"2017-11-08T06:50:00.000","Title":"Where is RDD or Spark SQL dataframe stored or persisted in client deploy mode on a Spark 2.1 Standalone cluster?","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to perform sentiment analysis on a dataset of 2 classes (Binary Classification). Dataset is heavily imbalanced about 70% - 30%. I am using LightGBM and Python 3.6 for making the model and predicting the output.\nI think imbalance in dataset effect performance of my model. I get about 90% accuracy but it doesn't increase further even though I have performed fine-tuning of the parameters. I don't think this the maximum possible accuracy as there are others who scored better than this.\nI have cleaned the dataset with Textacy and nltk. I am using CountVectorizer for encoding the text. \nI have tried up-sampling the dataset but it resulted in poor model (I haven't tuned that model) \nI have tried using the is_unbalance parameter of LightGBM, but it doesn't give me a better model.\nAre there any approaches to follow to handle this type of datasets that are so imbalanced.? How can I further improve my model.? Should I try down-sampling.? Or is it the maximum possible accuracy.? How can I be sure of it.?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":3502,"Q_Id":47187750,"Users Score":6,"Answer":"Are there any approaches to follow to handle this type of datasets\n  that are so imbalanced.?\n\nYour dataset is almost balanced. 70\/30 is close to equal. With gratient boosted trees it is possible to train on much more unbalanced data, like credit scoring, fraud detection, and medical diagnostics, where percentage of positives may be less that 1%. \nYour problem might be not in class imbalance, but in the wrong metric you use. When you calculate accuracy, you implicitly penalize your model equally for false negatives and false positives. But is it really the case? When classes are imbalanced, or just uncomparable from the business or physical point of view, other metrics, like precision, recall, or ROC AUC might be of more use than accuracy. For your problem I would recommend ROC AUC.\nMaybe, what you really want is probabilistic classification. And if you want to keep it binary, play with the threshold used for the classification.\n\nHow can I further improve my model.?\n\nBecause it is analysis of text, I would suggest more accurate data cleaning. Some directions to start with:\n\nDid you try different regimes of lemmatization\/stemming?\nHow did you preprocess special entities, like numbers, smileys, abbreviations, company names, etc.?\nDid you exploit collocations, by including bigrams or even trigrams into your model along with words?\nHow did you handle negation? One single \"no\" could change the meaning dramatically, and CountVectorizer catches that poorly.\nDid you try to extract semantics from the words, e.g. match the synonyms or use word embeddins from a pretrained model like word2vec or fastText?\n\nMaybe tree-based models is not the best choice. In my own experience, best sentiment analysis was performed by linear models like logistic regression or a shallow neural network. But you should heavily regularize them, and you should scale your features wisely, e.g. with TF-IDF.\nAnd if your dataset is large, you can try deep learning and train a RNN on your data. LSTM is often the best model for many text-related problems.\n\nShould I try down-sampling.?\n\nNo, you should never down-sample, unless you have too much data to process on your machine. Down-sampling creates biases in your data.\nIf you really really want to increase the relative importance of the minority class for your classifier, you can just reweight the observations. As far as I know, in LightGBM you can change class weights with the scale_pos_weight parameter.\n\nOr is it the maximum possible accuracy.? How can I be sure of it.?\n\nYou can never know. But you can do an experiment: ask several humans to label your test samples, and compare them with each other. If only 90% of labels coincide, then even humans cannot relaibly classify the rest 10% of samples, so you have reached the maximum. \nAnd again, don't focus on accuracy too much. Maybe, for your business application it is okay if you incorrectly label some positive reviews as negative, as long as all the negative reviews are successfully identified.","Q_Score":2,"Tags":"python-3.x,machine-learning,nlp,sentiment-analysis,lightgbm","A_Id":47379830,"CreationDate":"2017-11-08T19:09:00.000","Title":"Sentiment Analysis with Imbalanced Dataset in LightGBM","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"We have a Python code which involves expensive linear algebra computations. The data is stored in NumPy arrays. The code uses numpy.dot, and a few BLAS and LAPACK functions which are currently accessed through scipy.linalg.blas and scipy.linalg.lapack. The current code is written for CPU. We want to convert the code so that some of the NumPy, BLAS, and LAPACK operations are performed on a GPU. \nI am trying to determine the best way to do this is. As far as I can tell, Numba does not support BLAS and LAPACK functions on the GPU. It appears that PyCUDA may the best route, but I am having trouble determining whether PyCUDA allows one to use both BLAS and LAPACK functions.\nEDIT: We need the code to be portable to different GPU architectures, including AMD and Nvidia. While PyCUDA appears to offer the desired functionality, CUDA (and hence, PyCUDA) cannot run on AMD GPUs.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2168,"Q_Id":47209532,"Users Score":0,"Answer":"Another option is ArrayFire. While this package does not contain a complete BLAS and LAPACK implementation, it does offer much of the same functionality. It is compatible with OpenCL and CUDA, and hence, is compatible with AMD and Nvidia architectures. It has wrappers for Python, making it easy to use.","Q_Score":4,"Tags":"python,numpy,lapack,blas","A_Id":48100987,"CreationDate":"2017-11-09T18:55:00.000","Title":"NumPy + BLAS + LAPACK on GPU (AMD and Nvidia)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a working anaconda environment on my ubuntu 17.10 machine and installed opencv3 using conda install -c menpo opencv3\nWhen I try to import cv2 the following error shows up\n\nimport cv2\nImportError: \/usr\/lib\/x86_64-linux-gnu\/libpangoft2-1.0.so.0: undefined symbol: hb_font_funcs_set_variation_glyph_func","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2740,"Q_Id":47230690,"Users Score":4,"Answer":"you can try conda install -c conda-forge opencv, this one also will install opencv version 3.\nFor your error, you can fix it by install pango using conda install -c conda-forge pango","Q_Score":2,"Tags":"python,opencv,anaconda","A_Id":50078794,"CreationDate":"2017-11-10T20:32:00.000","Title":"OpenCV - undefined symbol: hb_font_funcs_set_variation_glyph_func","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Hi everyone,\n              I am trying to convert a contour to a blob in an image .There are several blobs in image ; the proper one is extracted by applying contour feature. The blob is required to mask a grayscale image.\nI have tried extracting each non-zero pixels and pointPolygontest() in order to find the BLOB points, but it requires >70ms to complete the proccess. The application is in 30 fps videos, so I need to convert them within 30ms. I am using OpenCV in python.  Is there a way to convert a contour into a Blob within 30ms in opencv?\nThanks in advance.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":532,"Q_Id":47251937,"Users Score":1,"Answer":"After a lot of programming, I realized the procedure is:\n\nAfter a contour is extracted, create a black image with same size as the original image.\nDraw the contour in black image. \nFind the coordinate of the contour center by contour feature : moments, x0 and y0.\nUse Floodfill() to white-fill the interior of the contour using (x0,y0) as seed point.\n\nThe resulting image contains the white BLOB to the corresponding contour.","Q_Score":0,"Tags":"python,opencv,blob,contour","A_Id":50935626,"CreationDate":"2017-11-12T17:45:00.000","Title":"Convert Contour into BLOB OpenCV","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am coding in Python trying to decide whether I should return a numpy array (the result of a diff on some other array) or return numpy.where(diff)[0], which is a smaller array but requires that little extra work to create. Let's call the method where this happens methodB.\nI call methodB from methodA. The rub is that I won't necessarily always need the where() result in methodA, but I might. So is it worth doing this work inside methodB, or should I pass back the (much larger memory-wise) diff itself and then only process it further in methodA if needed? That would be the more efficient choice assuming methodA just gets a reference to the result.\nSo, are function results ever not copied when they are passed back the the code that called that function?\nI believe that when methodB finishes, all the memory in its frame will be reclaimed by the system, so methodA has to actually copy anything returned by methodB in to its own frame in order to be able to use it. I would call this \"return by value\". Is this correct?","AnswerCount":4,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2293,"Q_Id":47253169,"Users Score":0,"Answer":"I see what I missed now: Objects are created on the heap, but function frames are on the stack. So when methodB finishes, its frame will be reclaimed, but that object will still exist on the heap, and methodA can access it with a simple reference.","Q_Score":2,"Tags":"python,function,numpy,heap-memory","A_Id":47270384,"CreationDate":"2017-11-12T19:45:00.000","Title":"Are all objects returned by value rather than by reference?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am still getting this error: An error occurred while starting the kernel\nThings I tried: \n\nsetuptools command\nupdating spyder\nUninstalled everything that had word python in it from Uninstall or change progam panel  \nUninstalling and reinstalling anaconda\nReading people's response on how they tried to fix it\nTried not to get frustrated. \n\nThis started occurring after I updated the spyder which I shouldn't have, but now I am stuck with the issue. I will share the complete message that's coming up on my IPhython Console screen. \n\nTraceback (most recent call last):\nFile \"C:\\Users\\afsan\\Anaconda3\\lib\\site\u2011packages\\spyder\\utils\\ipython\\start_kernel.py\", line 245, in \nmain()\nFile \"C:\\Users\\afsan\\Anaconda3\\lib\\site\u2011packages\\spyder\\utils\\ipython\\start_kernel.py\", line 213, in main\nfrom ipykernel.kernelapp import IPKernelApp\nFile \"C:\\Users\\afsan\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site\u2011packages\\ipykernel\\__init__.py\", line 2, in \nfrom .connect import *\nFile \"C:\\Users\\afsan\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site\u2011packages\\ipykernel\\connect.py\", line 18, in \nimport jupyter_client\nFile \"C:\\Users\\afsan\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site\u2011packages\\jupyter_client\\__init__.py\", line 4, in \nfrom .connect import *\nFile \"C:\\Users\\afsan\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site\u2011packages\\jupyter_client\\connect.py\", line 22, in \nimport zmq\nFile \"C:\\Users\\afsan\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site\u2011packages\\zmq\\__init__.py\", line 34, in \nfrom zmq import backend\nFile \"C:\\Users\\afsan\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site\u2011packages\\zmq\\backend\\__init__.py\", line 40, in \nreraise(*exc_info)\nFile \"C:\\Users\\afsan\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site\u2011packages\\zmq\\utils\\sixcerpt.py\", line 34, in reraise\nraise value\nFile \"C:\\Users\\afsan\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site\u2011packages\\zmq\\backend\\__init__.py\", line 27, in \n_ns = select_backend(first)\nFile \"C:\\Users\\afsan\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site\u2011packages\\zmq\\backend\\select.py\", line 26, in select_backend\nmod = __import__(name, fromlist=public_api)\nFile \"C:\\Users\\afsan\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site\u2011packages\\zmq\\backend\\cython\\__init__.py\", line 6, in \nfrom . import (constants, error, message, context,\nImportError: cannot import name 'constants'\nTraceback (most recent call last):\nFile \"C:\\Users\\afsan\\Anaconda3\\lib\\site\u2011packages\\spyder\\utils\\ipython\\start_kernel.py\", line 245, in \nmain()\nFile \"C:\\Users\\afsan\\Anaconda3\\lib\\site\u2011packages\\spyder\\utils\\ipython\\start_kernel.py\", line 213, in main\nfrom ipykernel.kernelapp import IPKernelApp\nFile \"C:\\Users\\afsan\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site\u2011packages\\ipykernel\\__init__.py\", line 2, in \nfrom .connect import *\nFile \"C:\\Users\\afsan\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site\u2011packages\\ipykernel\\connect.py\", line 18, in \nimport jupyter_client\nFile \"C:\\Users\\afsan\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site\u2011packages\\jupyter_client\\__init__.py\", line 4, in \nfrom .connect import *\nFile \"C:\\Users\\afsan\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site\u2011packages\\jupyter_client\\connect.py\", line 22, in \nimport zmq\nFile \"C:\\Users\\afsan\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site\u2011packages\\zmq\\__init__.py\", line 34, in \nfrom zmq import backend\nFile \"C:\\Users\\afsan\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site\u2011packages\\zmq\\backend\\__init__.py\", line 40, in \nreraise(*exc_info)\nFile \"C:\\Users\\afsan\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site\u2011packages\\zmq\\utils\\sixcerpt.py\", line 34, in reraise\nraise value\nFile \"C:\\Users\\afsan\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site\u2011packages\\zmq\\backend\\__init__.py\", line 27, in \n_ns = select_backend(first)\nFile \"C:\\Users\\afsan\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site\u2011packages\\zmq\\backend\\select.py\", line 26, in select_backend\nmod = __import__(name, fromlist=public_api)\nFile \"C:\\Users\\afsan\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site\u2011packages\\zmq\\backend\\cython\\__init__.py\", line 6, in \nfrom . import (constants, error, message, context,\nImportError: cannot import name 'constants'\nTraceback (most recent call last):\nFile \"C:\\Users\\afsan\\Anaconda3\\lib\\site\u2011packages\\spyder\\utils\\ipython\\start_kernel.py\", line 245, in \nmain()\nFile \"C:\\Users\\afsan\\Anaconda3\\lib\\site\u2011packages\\spyder\\utils\\ipython\\start_kernel.py\", line 213, in main\nfrom ipykernel.kernelapp import IPKernelApp\nFile \"C:\\Users\\afsan\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site\u2011packages\\ipykernel\\__init__.py\", line 2, in \nfrom .connect import *\nFile \"C:\\Users\\afsan\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site\u2011packages\\ipykernel\\connect.py\", line 18, in \nimport jupyter_client\nFile \"C:\\Users\\afsan\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site\u2011packages\\jupyter_client\\__init__.py\", line 4, in \nfrom .connect import *\nFile \"C:\\Users\\afsan\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site\u2011packages\\jupyter_client\\connect.py\", line 22, in \nimport zmq\nFile \"C:\\Users\\afsan\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site\u2011packages\\zmq\\__init__.py\", line 34, in \nfrom zmq import backend\nFile \"C:\\Users\\afsan\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site\u2011packages\\zmq\\backend\\__init__.py\", line 40, in \nreraise(*exc_info)\nFile \"C:\\Users\\afsan\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site\u2011packages\\zmq\\utils\\sixcerpt.py\", line 34, in reraise\nraise value\nFile \"C:\\Users\\afsan\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site\u2011packages\\zmq\\backend\\__init__.py\", line 27, in \n_ns = select_backend(first)\nFile \"C:\\Users\\afsan\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site\u2011packages\\zmq\\backend\\select.py\", line 26, in select_backend\nmod = __import__(name, fromlist=public_api)\nFile \"C:\\Users\\afsan\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site\u2011packages\\zmq\\backend\\cython\\__init__.py\", line 6, in \nfrom . import (constants, error, message, context,\nImportError: cannot import name 'constants'\nTraceback (most recent call last):\nFile \"C:\\Users\\afsan\\Anaconda3\\lib\\site\u2011packages\\spyder\\utils\\ipython\\start_kernel.py\", line 245, in \nmain()\nFile \"C:\\Users\\afsan\\Anaconda3\\lib\\site\u2011packages\\spyder\\utils\\ipython\\start_kernel.py\", line 213, in main\nfrom ipykernel.kernelapp import IPKernelApp\nFile \"C:\\Users\\afsan\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site\u2011packages\\ipykernel\\__init__.py\", line 2, in \nfrom .connect import *\nFile \"C:\\Users\\afsan\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site\u2011packages\\ipykernel\\connect.py\", line 18, in \nimport jupyter_client\nFile \"C:\\Users\\afsan\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site\u2011packages\\jupyter_client\\__init__.py\", line 4, in \nfrom .connect import *\nFile \"C:\\Users\\afsan\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site\u2011packages\\jupyter_client\\connect.py\", line 22, in \nimport zmq\nFile \"C:\\Users\\afsan\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site\u2011packages\\zmq\\__init__.py\", line 34, in \nfrom zmq import backend\nFile \"C:\\Users\\afsan\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site\u2011packages\\zmq\\backend\\__init__.py\", line 40, in \nreraise(*exc_info)\nFile \"C:\\Users\\afsan\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site\u2011packages\\zmq\\utils\\sixcerpt.py\", line 34, in reraise\nraise value\nFile \"C:\\Users\\afsan\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site\u2011packages\\zmq\\backend\\__init__.py\", line 27, in \n_ns = select_backend(first)\nFile \"C:\\Users\\afsan\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site\u2011packages\\zmq\\backend\\select.py\", line 26, in select_backend\nmod = __import__(name, fromlist=public_api)\nFile \"C:\\Users\\afsan\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site\u2011packages\\zmq\\backend\\cython\\__init__.py\", line 6, in \nfrom . import (constants, error, message, context,\nImportError: cannot import name 'constants'\nTraceback (most recent call last):\nFile \"C:\\Users\\afsan\\Anaconda3\\lib\\site\u2011packages\\spyder\\utils\\ipython\\start_kernel.py\", line 245, in \nmain()\nFile \"C:\\Users\\afsan\\Anaconda3\\lib\\site\u2011packages\\spyder\\utils\\ipython\\start_kernel.py\", line 213, in main\nfrom ipykernel.kernelapp import IPKernelApp\nFile \"C:\\Users\\afsan\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site\u2011packages\\ipykernel\\__init__.py\", line 2, in \nfrom .connect import *\nFile \"C:\\Users\\afsan\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site\u2011packages\\ipykernel\\connect.py\", line 18, in \nimport jupyter_client\nFile \"C:\\Users\\afsan\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site\u2011packages\\jupyter_client\\__init__.py\", line 4, in \nfrom .connect import *\nFile \"C:\\Users\\afsan\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site\u2011packages\\jupyter_client\\connect.py\", line 22, in \nimport zmq\nFile \"C:\\Users\\afsan\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site\u2011packages\\zmq\\__init__.py\", line 34, in \nfrom zmq import backend\nFile \"C:\\Users\\afsan\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site\u2011packages\\zmq\\backend\\__init__.py\", line 40, in \nreraise(*exc_info)\nFile \"C:\\Users\\afsan\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site\u2011packages\\zmq\\utils\\sixcerpt.py\", line 34, in reraise\nraise value\nFile \"C:\\Users\\afsan\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site\u2011packages\\zmq\\backend\\__init__.py\", line 27, in \n_ns = select_backend(first)\nFile \"C:\\Users\\afsan\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site\u2011packages\\zmq\\backend\\select.py\", line 26, in select_backend\nmod = __import__(name, fromlist=public_api)\nFile \"C:\\Users\\afsan\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site\u2011packages\\zmq\\backend\\cython\\__init__.py\", line 6, in \nfrom . import (constants, error, message, context,\nImportError: cannot import name 'constants'","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":19126,"Q_Id":47267716,"Users Score":6,"Answer":"The problem is that you have two Python versions installed:\n\nC:\\Users\\afsan\\Anaconda3\\\nC:\\Users\\afsan\\AppData\\Local\\Programs\\Python\\Python36\n\nGiven that it seems you want to use Spyder with Anaconda, please remove your second Python version (manually, if necessary). That should fix your problem.","Q_Score":3,"Tags":"python,installation,anaconda,spyder","A_Id":47269281,"CreationDate":"2017-11-13T15:21:00.000","Title":"Spyder :An error ocurred while starting the kernel","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Recently I have been developing some code to read a csv file and store key data columns in a dataframe. Afterwards I plan to have some mathematical functions performed on certain columns in the dataframe. \nI've been fairly successful in storing the correct columns in the dataframe. I have been able to have it do whatever maths is necessary such as summations, additions of dataframe columns, averaging etc. \nMy problem lies in accessing specific columns once they are stored in the dataframe. I was working with a test file to get everything working and managed this no problem. The problems arise when I open a different csv file, it will store the data in the dataframe, but the accessing the column I want no longer works and it stops at the calculation part. \nFrom what I can tell the problem lies with how it reads the column name. The column names are all numbers. For example, df['300'], df['301'] etc. When accessing the column df['300'] works fine in the testfile, while the next file requires df['300.0']. If I switch to a different file it may require df['300'] again. All the data was obtained in the same way so I am not certain why some are read as 300 and the others 300.0. \nShort of constantly changing the column labels each time I open a different file, is there anyway to have it automatically distinguish between '300' and '300.0' when opening the file, or force '300.0' = '300'? \nThanks","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":213,"Q_Id":47274094,"Users Score":0,"Answer":"Some solutions: \n\nGo through all the files, change the columns names, then save the result in a new folder. Now when you read a file, you can go to the new folder and read it from there.\nWrap the normal file read function in another function that automatically changes the column names, and call that new function when you read a file.\nWrap column selection in a function. Use a try\/except block to have the function try to access the given column, and if it fails, use the other form.","Q_Score":1,"Tags":"python,pandas,csv,dataframe","A_Id":47274239,"CreationDate":"2017-11-13T21:46:00.000","Title":"python distinguish between '300' and '300.0' for a dataframe column","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using OpenCV's Optical Flow module. I understand the examples in the documentation but those take the entire image and then get the optical flow over the image.\nI only want to pass it over some parts of an image. Is it possible to do that? If yes, how do I go about it?\nThanks!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":279,"Q_Id":47277332,"Users Score":0,"Answer":"Yes, it's possible. cv2.calcOpticalFlowPyrLK() will be the optical flow function you need. Before you make that function call, you will have to create an image mask. I did a similar project, but in C++, though I can outline the steps for you:\n\nCreate an empty matrix with same width and height of your images\nUsing the points from your ROI, create a shape out of it (I did mine using cv2.fillPoly()) and fill the inside of the shape with white (Your image mask should only be comprised of black and white color)\nIf you are planning on using corners as features, then call cv2.goodFeaturesToTrack() and pass in the mask you've made as one of its arguments. \nIf you're using the Feature2D module to detect features, you can use the same mask to only extract the features in that masked area.   \nBy this step, you should now have a collection of features\/points that are only within the bounds of the shape! Call the optical flow function and then process the results.\n\nI hope that helps.","Q_Score":0,"Tags":"python,opencv,opticalflow,cv2","A_Id":47689288,"CreationDate":"2017-11-14T03:49:00.000","Title":"cv2 running optical flow on particular rectangles","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using tensorflow v1.4. I want to use early stopping using the validation set with a patience of 5 epochs. \nI have searched on the web and found out that there used to be a function called ValidationMonitor but it is depreciated now. So is there a way to achieve this  ?","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":1059,"Q_Id":47282399,"Users Score":0,"Answer":"Recently I have come across this function in tensorflow API. \ntf.keras.callbacks.EarlyStopping. tf version is r1.9. \nArguments:\n\nmonitor: quantity to be monitored\nmin_delta: minimum change in the monitored quantity to qualify as an improvement, i.e. an absolute change of less than min_delta, will count as no improvement.\npatience: number of epochs with no improvement after which training\nwill be stopped.\nverbose: verbosity mode.\nmode: one of {auto, min, max}. In min mode, training will stop when\nthe quantity monitored has stopped decreasing; in max mode it will\nstop when the quantity monitored has stopped increasing; in auto\nmode, the direction is automatically inferred from the name of the\nmonitored quantity.","Q_Score":5,"Tags":"python,tensorflow","A_Id":51707973,"CreationDate":"2017-11-14T09:46:00.000","Title":"Early stopping using tensorflow tf.estimator ?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using tensorflow v1.4. I want to use early stopping using the validation set with a patience of 5 epochs. \nI have searched on the web and found out that there used to be a function called ValidationMonitor but it is depreciated now. So is there a way to achieve this  ?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1059,"Q_Id":47282399,"Users Score":0,"Answer":"There doesn't seem to be a good way of doing this, unfortunately. One method to consider is to save checkpoints quite often during training, and then later iterate over them and evaluating them. Then you can discard the checkpoints that does not have the best eval performance. This doesn't help you in saving time during training, but at least the resulting model you are left with is an early stopping model.","Q_Score":5,"Tags":"python,tensorflow","A_Id":49161168,"CreationDate":"2017-11-14T09:46:00.000","Title":"Early stopping using tensorflow tf.estimator ?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have some data in csv file.\nBecause it is collected from machine,all lines should be number but some NaN values exists in some lines.And the machine can auto replace these NaN values with a string '-'.\nMy question is how to set params of pd.read_csv() to auto replace '-'values with zero from csv file?","AnswerCount":5,"Available Count":1,"Score":0.0399786803,"is_accepted":false,"ViewCount":31191,"Q_Id":47286547,"Users Score":1,"Answer":"Putting this into the read_csv function does work:\ndtype={\"count\": pandas.Int64Dtype()}\ni.e.\ndf = pd.read_csv('file.csv')\nThis type supports both integers and pandas.NA values so you can import without the floats becoming integers.\nIf necessary, you can then use regular DataFrame commands to clean up the missing values, as described in other answers here.\nBTW, my first attempt to solve this changes the integers into strings. If that's of interest:\ndf = pd.read_csv('file.csv', na_filter= False)\n(It reads the file without replacing any missing values with NaN).","Q_Score":14,"Tags":"python,pandas,csv","A_Id":64614856,"CreationDate":"2017-11-14T12:58:00.000","Title":"Is there a way in pd.read_csv to replace NaN value with other character?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am on a virtual machine running on Ubuntu 16.04. I have installed pandas, sklearn, and conda using pip3. When I try to run a python3 program using these packages, I get the error \"Illegal instruction (core dump).\"\nNot sure how to fix this. Simple python3 programs (aka no imports) run fine. I also tried importing but not using these packages, and that works.\nThe only other thing I have done on this VM is opencv development with C++.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2258,"Q_Id":47312023,"Users Score":0,"Answer":"Although I'm not sure the source of the issue, I did a full purge of python3 and reinstalled it and all packages with it and that fixed the issue!","Q_Score":4,"Tags":"python,linux,virtual-machine,python-3.5","A_Id":51295721,"CreationDate":"2017-11-15T16:02:00.000","Title":"python3 Illegal instruction (core dump)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have been using R Studio for quite some time, and I find View() function very helpful for viewing my datasets.\nIs there a similar View() counterpart in Spyder?","AnswerCount":1,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":18421,"Q_Id":47324077,"Users Score":26,"Answer":"(Spyder maintainer here) There's no function similar to view() in Spyder. To view the contents of a Dataframe, you need to double-click on it in the Variable Explorer.","Q_Score":16,"Tags":"python,r,rstudio,spyder","A_Id":47341200,"CreationDate":"2017-11-16T07:39:00.000","Title":"Viewing dataframes in Spyder using a command in its console","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using C# CNTK 2.2.0 API for training.\nI have installed Nuget package CNTK.CPUOnly and CNTK.GPU.\nI am looking for following learners in C#.\n1. AdaDelta\n2. Adam\n3. AdaGrad\n4. Neterov\nLooks like Python supports these learners but C#\npackage is not showing them.\nI can see only SGD and SGDMomentun learners in C# there.\nAny thoughts, how to get and set other learners in C#.\nDo I need to install any additional package to get these learners?\nAppreciate your help.","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":447,"Q_Id":47324511,"Users Score":0,"Answer":"Checked that CNTKLib is providing those learners in CPUOnly package.\nNestrov is missing in there but present in python.\nThere is a difference while creating the trainer object \nwith CNTKLib learner function vs Learner class.\nIf a learner class is used, \nnet parameters are provided as a IList.\nThis can be obtained using netout.parameter() ;\nIf CNTKLib is used, \nparameters are provided as ParameterVector. \nBuild ParameterVector while building the network.\nand provide it while creating Trainer object.\nParameterVector pv = new ParameterVector () \npv.Add(weightParameter)\npv.Add(biasParameter)\nThanks everyone for your answers.","Q_Score":0,"Tags":"c#,python,cntk","A_Id":47371621,"CreationDate":"2017-11-16T08:07:00.000","Title":"Learners in CNTK C# API","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using C# CNTK 2.2.0 API for training.\nI have installed Nuget package CNTK.CPUOnly and CNTK.GPU.\nI am looking for following learners in C#.\n1. AdaDelta\n2. Adam\n3. AdaGrad\n4. Neterov\nLooks like Python supports these learners but C#\npackage is not showing them.\nI can see only SGD and SGDMomentun learners in C# there.\nAny thoughts, how to get and set other learners in C#.\nDo I need to install any additional package to get these learners?\nAppreciate your help.","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":447,"Q_Id":47324511,"Users Score":0,"Answer":"Download the NCCL 2 app to configure in c# www.nvidia. com or google NCCL download","Q_Score":0,"Tags":"c#,python,cntk","A_Id":47324718,"CreationDate":"2017-11-16T08:07:00.000","Title":"Learners in CNTK C# API","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to solve Optimisation problem with pyomo (Pyomo 5.3 (CPython 2.7.13 on Linux 3.10.0-514.26.2.el7.x86_64)) using CBC solver (Version: 2.9.8) and specifying a time limit in solver of 60 sec. The solver is getting a feasible solution (-1415.8392) but apparently not yet optimal (-1415.84) as you can see below.\nAfter time limit ends model seemingly exits with an error code. I want to print or get values of all variables of feasible solution using CBC in specified time limit. Or is there any other way by which I can set, if Model gets 99% value of an Optimal solution, to exit and print the feasible solution.\nThe error code is posted below.\n\nCbc0004I Integer solution of -1415.8392 found after 357760 iterations and 29278 nodes (47.87 seconds)\nCbc0010I After 30000 nodes, 6350 on tree, -1415.8392 best solution, best possible -1415.84 (48.87 seconds)\nCbc0010I After 31000 nodes, 6619 on tree, -1415.8392 best solution, best possible -1415.84 (50.73 seconds)\nCbc0010I After 32000 nodes, 6984 on tree, -1415.8392 best solution, best possible -1415.84 (52.49 seconds)\nCbc0010I After 33000 nodes, 7384 on tree, -1415.8392 best solution, best possible -1415.84 (54.31 seconds)\nCbc0010I After 34000 nodes, 7419 on tree, -1415.8392 best solution, best possible -1415.84 (55.73 seconds)\nCbc0010I After 35000 nodes, 7824 on tree, -1415.8392 best solution, best possible -1415.84 (57.37 seconds)\nTraceback (most recent call last):\n  File \"model_final.py\", line 392, in \n    solver.solve(model, timelimit = 60*1, tee=True)\n  File \"\/home\/aditya\/0r\/lib\/python2.7\/site-packages\/pyomo\/opt\/base\/solvers.py\", line 655, in solve\n    default_variable_value=self._default_variable_value)\n  File \"\/home\/aditya\/0r\/lib\/python2.7\/site-packages\/pyomo\/core\/base\/PyomoModel.py\", line 242, in load_from\n    % str(results.solver.status))\nValueError: Cannot load a SolverResults object with bad status: error\n\nWhen I run the model generated by pyomo manually using the same command-line parameters as pyomo \/usr\/bin\/cbc -sec 60 -printingOptions all -import \/tmp\/tmpJK1ieR.pyomo.lp -import -stat=1 -solve -solu \/tmp\/tmpJK1ieR.pyomo.soln it seems to exit normally and also writes the solution as shown below.\n\nCbc0010I After 35000 nodes, 7824 on tree, -1415.8392 best solution, best possible -1415.84 (57.06 seconds)\nCbc0038I Full problem 205 rows 289 columns, reduced to 30 rows 52 columns\nCbc0010I After 36000 nodes, 8250 on tree, -1415.8392 best solution, best possible -1415.84 (58.73 seconds)\nCbc0020I Exiting on maximum time\nCbc0005I Partial search - best objective -1415.8392 (best possible -1415.84), took 464553 iterations and 36788 nodes (60.11 seconds)\nCbc0032I Strong branching done 15558 times (38451 iterations), fathomed 350 nodes and fixed 2076 variables\nCbc0035I Maximum depth 203, 5019 variables fixed on reduced cost\nCbc0038I Probing was tried 31933 times and created 138506 cuts of which 0 were active after adding rounds of cuts (4.431 seconds)\nCbc0038I Gomory was tried 30898 times and created 99534 cuts of which 0 were active after adding rounds of cuts (4.855 seconds)\nCbc0038I Knapsack was tried 30898 times and created 12926 cuts of which 0 were active after adding rounds of cuts (8.271 seconds)\nCbc0038I Clique was tried 100 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\nCbc0038I MixedIntegerRounding2 was tried 30898 times and created 13413 cuts of which 0 were active after adding rounds of cuts (3.652 seconds)\nCbc0038I FlowCover was tried 100 times and created 4 cuts of which 0 were active after adding rounds of cuts (0.019 seconds)\nCbc0038I TwoMirCuts was tried 30898 times and created 15292 cuts of which 0 were active after adding rounds of cuts (2.415 seconds)\nCbc0038I Stored from first was tried 30898 times and created 15734 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\nCbc0012I Integer solution of -1411.9992 found by Reduced search after 467825 iterations and 36838 nodes (60.12 seconds)\nCbc0020I Exiting on maximum time\nCbc0005I Partial search - best objective -1411.9992 (best possible -1415.4522), took 467825 iterations and 36838 nodes (60.12 seconds)\nCbc0032I Strong branching done 476 times (1776 iterations), fathomed 1 nodes and fixed 18 variables\nCbc0035I Maximum depth 21, 39 variables fixed on reduced cost\nCuts at root node changed objective from -1484.12 to -1415.45\nProbing was tried 133 times and created 894 cuts of which 32 were active after adding rounds of cuts (0.060 seconds)\nGomory was tried 133 times and created 1642 cuts of which 0 were active after adding rounds of cuts (0.047 seconds)\nKnapsack was tried 133 times and created 224 cuts of which 0 were active after adding rounds of cuts (0.083 seconds)\nClique was tried 100 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.001 seconds)\nMixedIntegerRounding2 was tried 133 times and created 163 cuts of which 0 were active after adding rounds of cuts (0.034 seconds)\nFlowCover was tried 100 times and created 5 cuts of which 0 were active after adding rounds of cuts (0.026 seconds)\nTwoMirCuts was tried 133 times and created 472 cuts of which 0 were active after adding rounds of cuts (0.021 seconds)\nImplicationCuts was tried 25 times and created 41 cuts of which 0 were active after adding rounds of cuts (0.003 seconds)\n\nResult - Stopped on time limit\n\nObjective value:                -1411.99922848\nLower bound:                    -1415.452\nGap:                            0.00\nEnumerated nodes:               36838\nTotal iterations:               467825\nTime (CPU seconds):             60.13\nTime (Wallclock seconds):       60.98\n\nTotal time (CPU seconds):       60.13   (Wallclock seconds):       61.01\n\nThe top few lines of the CBC solution file are:\n\nStopped on time - objective value -1411.99922848\n      0 c_e_x1454_                         0                       0\n      1 c_e_x1455_                         0                       0\n      2 c_e_x1456_                         0                       0\n      3 c_e_x1457_                         0                       0\n      4 c_e_x1458_                         0                       0\n      5 c_e_x1459_                         0                       0\n      6 c_e_x1460_                         0                       0\n      7 c_e_x1461_                         0                       0\n      8 c_e_x1462_                         0                       0\n\nCan anyone tell me how can I get these values without generating any error?\nThanks in advance.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1241,"Q_Id":47334254,"Users Score":0,"Answer":"You could try to set the bound gap tolerance such that it will accept the other answer. I'm surprised that the solver status is coming back with error if there is a feasible solution found. Could you print out the whole results object?","Q_Score":0,"Tags":"python-2.7,pyomo,coin-or-cbc","A_Id":47336205,"CreationDate":"2017-11-16T16:08:00.000","Title":"Error reported while running pyomo optimization with cbc solver and using timelimit","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to fit a generalized ordered logit model to some data I have. I first tried to use the ordered logit model using the MASS package from R, but it seems that the proportional odds assumption is violated by the data. Indeed, not all independent variables do exert the same effect across all categories of the dependent variable. Hence I am a bit blocked. I say that I could use the generalized ordered logit model instead, but could not find how to use it. Indeed, I can not find any package on either R or python that coud help me on that. \nIf someone has any hints on packages I could use, it would be of great help!\nThank you very much!","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1939,"Q_Id":47346321,"Users Score":0,"Answer":"Try the 'VGAM' package. There is a function called vglm.\nexample: vglm(Var2~factor(Var1),cumulative(parallel = F),data) generalized order model\ncumulative(parallel=T) will perform a proportional odds model.\nparallel=F is the default.","Q_Score":3,"Tags":"python,r,logistic-regression","A_Id":59307014,"CreationDate":"2017-11-17T08:33:00.000","Title":"generalized ordered logit in R or python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am creating a tuple of two objects and pickling them. And when I try to unpickle them on the server side after passing it through sockets. It gives the above mentioned error.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":862,"Q_Id":47349740,"Users Score":0,"Answer":"Actually in my case this was appearing due to unexpectedly something else was also getting in the data after getting pickled and before being sent to other end. So I was trying to unpickle some garbage data. Thats why this contained some unidentified character.","Q_Score":1,"Tags":"python,io,stream","A_Id":47747060,"CreationDate":"2017-11-17T11:35:00.000","Title":"_pickle.UnpicklingError: invalid load key, 'xff'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to move from using stan in R (I've mainly used the brms package) to pystan, but having trouble locating any methods in pystan that will give me predictive posterior distributions for new data. In R, I've been using brms::posterior_linpred() to do this. Does anyone know if this is possible in pystan?\nIf not, can it be bolted on without too much problem?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":174,"Q_Id":47351601,"Users Score":1,"Answer":"As far as I know, they are not any Python libraries that do what the brms and rstanarm R packages do, i.e. come with precompiled Stan models that allow users to draw from the predictive distribution in the high-level language. There are things like prophet and survivalstan but those are for a pretty niche set of models.","Q_Score":0,"Tags":"python,r,stan","A_Id":47353749,"CreationDate":"2017-11-17T13:20:00.000","Title":"how to obtain predictive posteriors in pystan?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to know if the validation set influences in any way the training of the network, or if it's sole purpose is to monitor the training progress (with the tensorflow for poets project in question).\nIf doesn't, is it okay\/good-practice to reduce the validation and training sets to zero once I know the accuracy for my dataset? So in that way I can get a model trained with the most amount of data I can provide.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":31,"Q_Id":47353479,"Users Score":0,"Answer":"Very briefly, you have three sets of data: training, testing, and validation.  These inputs should be distinct; any duplication between data sets will reduce some desirable aspect of your final model.\nTraining data is used just the way you think: these are the inputs that are matched with the expected outputs (after filtering through your model).  The model weights are adjusted until you've converged at the greatest accuracy you can manage.\nTesting data is used during training to determine when training is done.  If the testing accuracy is still dropping, then your model is still learning things; when it starts to rise, you're over-fitting.\nValidation data is the final check on whether your training phase got to a useful point.  This data set is entirely independent of the previous two, as is representative of not-previously-seen, real-world input.\nThe short answer is no.  If you fold your testing and validation data into your training set (which is what I think you're trying to ask) and re-train the model, you've changed the training conditions, and you have no evidence that the resulting training is still valid.  How do you know ho many epochs you need to train with this larger data set?  When you're ready to deploy the trained model, how do you know your new accuracy?","Q_Score":0,"Tags":"python,machine-learning,tensorflow,deep-learning,data-science","A_Id":47355283,"CreationDate":"2017-11-17T14:55:00.000","Title":"Is the validation set used for anything besides monitoring progress on the Tensorflow for poets google code lab's project?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'd like to take a subset of rows of a Dask dataframe based on a set of index keys. (Specifically, I want to find rows of ddf1 whose index is not in the index of ddf2.)\nBoth cache.drop([overlap_list]) and diff = cache[should_keep_bool_array] either throw a NotImplementedException or otherwise don't work.\nWhat is the best way to do this?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1328,"Q_Id":47361570,"Users Score":0,"Answer":"I'm not sure this is the \"best\" way, but here's how I ended up doing it:\n\nCreate a Pandas DataFrame with the index be the series of index keys I want to keep (e.g., pd.DataFrame(index=overlap_list))\nInner join the Dask Dataframe","Q_Score":3,"Tags":"python,dask","A_Id":47420745,"CreationDate":"2017-11-18T01:31:00.000","Title":"Dask: subset (or drop) rows from Dataframe by index","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"tf.data.* has dataset classes.  There is a TextLineDataset, but what I need is one for multiline text (between start\/end tokens). Is there a way to use a different line-break delimiter for tf.data.TextLineDataset?\nI am an experienced developer, but a python neophyte. I can read but my writing is limited.  I am bending an existing Tensorflow NMT tutorial to my own dataset.  Most TFRecord tutorials involve jpgs or other structured data.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":805,"Q_Id":47367180,"Users Score":0,"Answer":"You can try two options:\n\nWrite a generator and then use Dataset.from_generator: In your generator you can read your file line by line, append to your example while doing that and then yield when you encounter your custom delimiter.\nFirst parse your file, create tf.train.SequenceExample with multiple lines and then store your dataset as a TFRecordDataset (more cumbersome option in my opinion)","Q_Score":1,"Tags":"python,tensorflow,tensorflow-datasets","A_Id":52067484,"CreationDate":"2017-11-18T14:31:00.000","Title":"Multi-line text dataset in Tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a csv file containing numerical values such as 1524.449677. There are always exactly 6 decimal places.\nWhen I import the csv file (and other columns) via pandas read_csv, the column automatically gets the datatype object. My issue is that the values are shown as 2470.6911370000003 which actually should be 2470.691137. Or the value 2484.30691 is shown as 2484.3069100000002.\nThis seems to be a datatype issue in some way. I tried to explicitly provide the data type when importing via read_csv by giving the dtype argument as {'columnname': np.float64}. Still the issue did not go away.\nHow can I get the values imported and shown exactly as they are in the source csv file?","AnswerCount":2,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":30458,"Q_Id":47368296,"Users Score":14,"Answer":"I realise this is an old question, but maybe this will help someone else:\nI had a similar problem, but couldn't quite use the same solution. Unfortunately the float_precision option only exists when using the C engine and not with the python engine. So if you have to use the python engine for some other reason (for example because the C engine can't deal with regex literals as deliminators), this little \"trick\" worked for me:\nIn the pd.read_csv arguments, define dtype='str' and then convert your dataframe to whatever dtype you want, e.g. df = df.astype('float64') .\nBit of a hack, but it seems to work. If anyone has any suggestions on how to solve this in a better way, let me know.","Q_Score":24,"Tags":"python,pandas,csv,floating-point,rounding","A_Id":57801832,"CreationDate":"2017-11-18T16:24:00.000","Title":"Pandas read csv file with float values results in weird rounding and decimal digits","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Suppose I have training data X, Y where X has shape (n,m,p)\nI want to set up a neural network which applies a RNN (followed by a dense layer) given by f  to each i-slice (i,a,b) and outputs f(m,x) which has shape (p') then concatenates all the output slices (presumably using tf.map_fn(f,X)) to form a vector of dimension (n,p') then runs the next neural network on (n,p').\nEssentially something similar to:\nX' = tf.map_fn(f,X)\nY= g(X')\nI am having difficulty getting my head around how to prepare my training data, which shape should X, X' (and later Z) should be.\nFurther more what if I wanted to merge X' with another dataset, say Z? \nY = g(X' concat Z)","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":39,"Q_Id":47383336,"Users Score":0,"Answer":"I think you don't need map_fn, you need tf.dynamic_rnn instead. It takes an RNN cell (so it knows what's the output and what's the state) and returns the concatenated outputs and concatenated states.","Q_Score":0,"Tags":"python,tensorflow,tflearn","A_Id":47400198,"CreationDate":"2017-11-19T23:58:00.000","Title":"How do I use the output of one RNN applied to slices as input of the next?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to reconstruct a condensed distance matrix from the linkage output when using scipy.cluster.hierarchy.linkage. \nThe input is an m by n observation matrix. I understand therefore, that the distances are computed internally and then finally output in the linkage table.\nDoes anybody know of a solution to obtain the condensed distance matrix from the linkage output?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":284,"Q_Id":47390019,"Users Score":0,"Answer":"The linkage output clearly does not contain the entire distance matrix. The output is (n-1 x 4), but the entire distance matrix is much larger: n*(n-1)\/2.\nThe contents of the matrix are well described in the Scopus documentation.","Q_Score":0,"Tags":"python,scipy,cluster-analysis,hierarchical-clustering,linkage","A_Id":47400793,"CreationDate":"2017-11-20T10:37:00.000","Title":"Obtain distance matrix from scipy `linkage` output","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to get a list of indices (row,col) for all raster cells that fall within or are intersected by a polygon feature. Looking for a solution in python, ideally with gdal\/ogr modules.\nOther posts have suggested rasterizing the polygon, but I would rather have direct access to the cell indices if possible.","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":3761,"Q_Id":47404898,"Users Score":0,"Answer":"if you want to do this manually you'll need to test each cell for: \nSquare v Polygon intersection and\nSquare v Line intersection.  \nIf you treat each square as a 2d point this becomes easier - it's now a Point v Polygon problem.  Check in Game Dev forums for collision algorithms.\nGood luck!","Q_Score":1,"Tags":"python,gdal,ogr","A_Id":47405311,"CreationDate":"2017-11-21T03:52:00.000","Title":"Find indices of raster cells that intersect with a polygon","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"The input of my network is n sequential pixels of a NxN image (where n is small compared to N) and the output is 1 pixel.\nThe loss is defined as the squared difference between the ouput and the desired output.\nI want to use the optimizer on the average loss after iterating over the whole image.\nBut if I try to collect the loss in a list and average those losses after all the iterations are done, feeding this to my optimizer, causes an error because Tensorflow does not know where this loss comes from since it is not on the computational graph.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":42,"Q_Id":47417060,"Users Score":0,"Answer":"Apparently, feeding an array [x,n] (where x is the number of inputs, I would otherwise have to feed seperatley in each iteration and n is the amount of sequential pixels) to my network and then optimizing the loss computed for this input, is exactly what I was looking for.","Q_Score":1,"Tags":"python,tensorflow,neural-network,perceptron,multi-layer","A_Id":47534280,"CreationDate":"2017-11-21T15:45:00.000","Title":"Is it possible to optimize a Tensorflow MLP with the averaged loss after n iterations instead of every iteration?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a large file, imported into a single dataframe in Pandas.\nI'm using pandas to split up a file into many segments, by the number of rows in the dataframe.\neg:  10 rows:\nfile 1 gets [0:4]\nfile 2 gets [5:9]\nIs there a way to do this without having to create more dataframes?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":8708,"Q_Id":47421880,"Users Score":0,"Answer":"iterating over iloc's arguments will do the trick.","Q_Score":2,"Tags":"python-3.x,pandas","A_Id":47422098,"CreationDate":"2017-11-21T20:13:00.000","Title":"pandas: split dataframe into multiple csvs","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I know it might be old debate, but out of pandas.drop and python del function which is better in terms of performance over large dataset?\nI am learning machine learning using python 3 and not sure which one to use. My data is in pandas data frame format. But python del function is in built-in function for python.","AnswerCount":4,"Available Count":1,"Score":0.049958375,"is_accepted":false,"ViewCount":10359,"Q_Id":47426089,"Users Score":1,"Answer":"In drop method using \"inplace=False\" you have option to create Subset DF and keep un-touch the original DF, But in del I believe this option is not available.","Q_Score":20,"Tags":"python,python-3.x,pandas","A_Id":59068008,"CreationDate":"2017-11-22T02:57:00.000","Title":"python del vs pandas drop","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In Colaboratory, is there a way to save library installations across sessions? If so how, can I do that? I'd like to learn more about Colaboratory session management in general.\nCurrently, everytime I have to import, for e.g. a cv2 module(as this is not available by default), I need to reinstall the module with !pip install opencv-python along with it's dependencies that provide for shared objects, through a !apt=-get package-name.","AnswerCount":1,"Available Count":1,"Score":0.761594156,"is_accepted":false,"ViewCount":309,"Q_Id":47428195,"Users Score":5,"Answer":"VMs time out after a period of inactivity, so you'll want to structure your notebooks to install custom dependencies if needed. \nA typical pattern is to have a cell at the top of your notebook that executes apt and pip install commands as needed.\nIn the case of opencv, that would look something like:\n!apt install -y -qq libsm6 libxext6 && pip install -q -U opencv-python\n(Takes ~7 seconds for me on a fresh VM.)","Q_Score":1,"Tags":"python-2.7,session,google-colaboratory","A_Id":47440890,"CreationDate":"2017-11-22T06:32:00.000","Title":"How to save installations across sessions?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"In this:\ncross_val_score(GaussianNB(),features,target, cv=10)\nAre we splitting the data randomly into 10 or is it done sequentially?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":992,"Q_Id":47431661,"Users Score":2,"Answer":"This depends on what you specify in the cv parameter. \nIf the independent variable is binary or multiclass it will use StratifiedKFold, else it will use KFold. You can also override the options by specifying a function (sklearn or otherwise) to perform the splits. \nThe KFold function will divide the data into sequential folds. If you want it to do a random split, you can set the shuffle parameter to True. If you want to fix the random shuffle you can set a value for the random_state. If you do not, it will take a random value and the folds will be different every time you run the function.\nFor StratifiedKFold, it will split the data while attempting to keep the same ratio of classes of the dependent variable in each split. Because of this, there can be slight changes every time you call the function. i.e. It will not be sequential by default.","Q_Score":2,"Tags":"python,machine-learning,scikit-learn,cross-validation","A_Id":47432646,"CreationDate":"2017-11-22T10:00:00.000","Title":"Does cross_val_score take sequential samples or random samples?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Put another way I want the filter to cover the image across one dimension and only move in the other direction.\nIf I have an input with shape (ignoring batch size and number of input channels) (h, w), I want to have a filter to have shape (x, w) where x<h. Would creating a filter with shape (x, w) and using padding of 'VALID' be the correct thing to do (the idea being VALID will force that the filter is only convolved across the image when the filter 'fits' across the image in the width dimension)?\nMight there be another, better way to do this?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":20,"Q_Id":47444039,"Users Score":2,"Answer":"Yes, you could do it that way.  Another is to use the stride parameter to specify the stride in each dimension.  You can set the other dimension's stride to 0.  stride=(1, 0) is the proper syntax, I think.","Q_Score":1,"Tags":"python,tensorflow,convolution","A_Id":47444089,"CreationDate":"2017-11-22T21:20:00.000","Title":"How to create a conv layer where the filter doesn't move across one dimension","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I know this has been asked 1000 times before but what i am experiencing is really weird and I cant troubleshoot it.\nI have a date column structured like this:\n\n24JUN2017:14:46:57\n\nI use:\n\npd.to_datetime('24JUN2017:14:46:57', format=\"%d%b%Y:%H:%M:%S\")\n\nand it works fine. \nBut when I try to input the whole column:\n\npd.to_datetime(df['date_column'], format=\"%d%b%Y:%H:%M:%S\")\n\nI get the error of the title. Anyone knows why I might be experiencing this?\nps:I have checked for any empty spaces and there are none","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":111,"Q_Id":47455323,"Users Score":0,"Answer":"As pointed by jezrael and EdChum, i have bd data in my column. errors='coerce' option solved this problem.","Q_Score":1,"Tags":"python,pandas,python-datetime","A_Id":47455441,"CreationDate":"2017-11-23T12:11:00.000","Title":"Weird behavior with datetime: error: time data '0' does not match format '%d%b%Y:%H:%M:%S'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on a NLP problem of rewriting job responsibilities and requirements to skill sets ready to be mentioned in the resume.\nExample:\n1:responsibility:\n\"Train, supervise, motivate and develop sales personnel to attain or exceed their sales territory sales goals;\"  \n1:skill:\n\"Have provided training,guidance and motivation to \nsales representatives to achieve or exceed their sales area and target.\" \nI have tried to do this using paraphrasing techniques,but the narration of sentence and some words are not appropriate to be mentioned in resume.\nAny help and guidance will be appreciable.\nThanks","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":53,"Q_Id":47455914,"Users Score":0,"Answer":"Use POS tagging \/ Parse Trees to take the corpus of job responsibility sentences that you have accumulated and split it into some broad categories.\n(e.g. sentences that start out with a bunch of comma-separated verbs, sentences that have a single verb but are in the past tense (i.e. requiring the person to have some specific type of experience), etc).  \nNow try to create a transformation rule for each of those parts.  \nThe rule for the sentence you put above might be to prefix the sentence with the word \"Can\".  \nSome rules might require to change the tense of some verb, convert some noun from singular to plural, swap the order of some parts of the sentence, etc. All these things should be doable using standard NLP tools.","Q_Score":0,"Tags":"python,nlp,deep-learning,nltk","A_Id":47459041,"CreationDate":"2017-11-23T12:46:00.000","Title":"conversion of job responsibilites and requirements from job descriptions to skillset Using Nlp","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am asking this for mgx4:\nI imported a .uff file containing FRF from Experimental Modal Analysis (EMA). I generated the mesh (geometry) of the tested structure with lines and triangle surfaces. I succeed to realize the analysis and extract eigen frequencies and modal damping. What I am still unable to do is to animate the geometry according to eigen shapes as a function of eigen frequencies. When I am on the tab \"Measurement\" on the tab menu \"ANIMATION\" and I put the frequency cursor on one peak of the FRF and I click on \"Play\", I can just see one node moving. I would like to observe all the nodes moving in order to identify the eigen shape of the mode observed. Do you please have an idea on how to do that? Thank you for your help!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":45,"Q_Id":47469591,"Users Score":0,"Answer":"in animation tab try a right click on model and under \"roving type\" select\n\"response roved\".","Q_Score":0,"Tags":"python,open-source","A_Id":47469592,"CreationDate":"2017-11-24T08:56:00.000","Title":"Animating modes in openmodal: roving type","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to calculate distance from camera to depth image pixel. I searched through internet but I found stereo image related info and code example where I need info for depth image.\nHere, I defined depth image in gray scale(0-255) and I defined a particular value( let range defined 0 pixel value is equal to 5m and 255 pixel value is equal to 500m in gray scale). \ncamera's intrinsic (focal length, image sensor format) and extrinsic (rotation and transition matrix) is given. I need to calculate distance from different camera orientation and rotation. \nI want to do it using opencv python. Is there any specific documentation and code example regarding this?\nOr any further info is necessary to find this.","AnswerCount":1,"Available Count":1,"Score":-0.1973753202,"is_accepted":false,"ViewCount":569,"Q_Id":47475206,"Users Score":-1,"Answer":"The content of my research is the same as yours, but I have a problem now. I use stereocalibrate() to calibrate the binocular camera, and found that the obtained translation matrix is very different from the actual baseline distance. In addition, the parameters used in stereocalibrate() are obtained by calibrating the two cameras with calibrate().","Q_Score":0,"Tags":"python,opencv,object,depth","A_Id":63786602,"CreationDate":"2017-11-24T14:22:00.000","Title":"distance calculation between camera and depth pixel?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am running an Anaconda installation of Python3 64bit on Windows. I have no idea how to put those words in a proper sentence, but I hope it gives enough information. I am taking an Udacity course which wants me to run %matplotlib inline. This gives the following error:   \n\nAttributeError: module 'matplotlib' has no attribute 'colors'   \n\nI get the same error when I run from matplotlib import pylab, but i get no error from import matplotlib.  \nI installed matplotlib as follows: conda install -n tensorflow -c conda-forge matplotlib.    \nHow do I solve this error?  \nKind regards\nPer request:\nconda list gives  \n\nmatplotlib                2.1.0                    py36_1    conda-forge  \n\nand a list of other modules.","AnswerCount":2,"Available Count":2,"Score":0.3799489623,"is_accepted":false,"ViewCount":15407,"Q_Id":47497097,"Users Score":4,"Answer":"The notebook needs to be restarted for the new installations to take effect.","Q_Score":3,"Tags":"python-3.x,matplotlib,anaconda","A_Id":48661572,"CreationDate":"2017-11-26T14:17:00.000","Title":"Module 'matplotlib' has no attribute 'colors'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am running an Anaconda installation of Python3 64bit on Windows. I have no idea how to put those words in a proper sentence, but I hope it gives enough information. I am taking an Udacity course which wants me to run %matplotlib inline. This gives the following error:   \n\nAttributeError: module 'matplotlib' has no attribute 'colors'   \n\nI get the same error when I run from matplotlib import pylab, but i get no error from import matplotlib.  \nI installed matplotlib as follows: conda install -n tensorflow -c conda-forge matplotlib.    \nHow do I solve this error?  \nKind regards\nPer request:\nconda list gives  \n\nmatplotlib                2.1.0                    py36_1    conda-forge  \n\nand a list of other modules.","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":15407,"Q_Id":47497097,"Users Score":1,"Answer":"You just need to upgrade matplotlib.\n\npip3 install -U matplotlib","Q_Score":3,"Tags":"python-3.x,matplotlib,anaconda","A_Id":53349221,"CreationDate":"2017-11-26T14:17:00.000","Title":"Module 'matplotlib' has no attribute 'colors'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a non-lenear optimization problem with a constraint and upper\/lower bounds, so with scipy I have to use SLSQP. The problem is clearly not convex.\nI got the jacobian fo both the objective and constraint functions to work correctly (results are good\/fast up to 300 input vector). All functions are vectorized and tuned to run very fast. The problem is that using 1000+ input vector takes ages though I can see the minimizer is not calling my functions a lot (objective\/constraint\/gradients) and seems to spend most of its processing time internally. I read somewhere perf of SLSQP is O(n^3).\nIs there a better\/faster SLSQP implementation or another method for this type of problem for python ? I tried nlopt and somehow returns wrong results given the exact same functions I use in scipy (with a wrapper to adapt to its method signature). I also failed to use ipopt with pyipopt package, cannot get working ipopt binaries to work with the python wrapper.\nUPDATE: if it helps, my input variable is basically a vector of (x,y) tuples or points in 2D surface representing coordinates. With 1000 points, I end up with a 2000 dim input vector. The function I want to optimize calculates optimum position of the points between each other taking into consideration their relationships and other constraints. So the problem is not sparse.\nThanks...","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":8691,"Q_Id":47497552,"Users Score":1,"Answer":"Surprisingly, I found a relatively ok solution using an optimizer from for a deep learning framework, Tensorflow, using basic gradient descent (actually RMSProp, gradient descent with momentum) after I changed the cost function to include the inequality constraint and the bounding constraints as penalties (I suppose this is same as lagrange method). It trains super fast and converges quickly with proper lambda parameters on the constraint penalties. I didn't even have to rewrite the jacobians as TF takes care of that without much speed impact apparently.\nBefore that, I managed to get NLOPT to work and it is much faster than scipy\/SLSQP but still slow on higher dimensions. Also NLOPT\/AUGLANG is super fast but converges poorly.\nThis said, at 20k variables, it is stillslow. Partly due to memory swapping and the cost function being at least O(n^2) from the pair-wise euclidean distance (I use (x-x.t)^2+(y-y.t)^2 with broadcasting). So still not optimal.","Q_Score":3,"Tags":"python,optimization,nonlinear-optimization","A_Id":47608446,"CreationDate":"2017-11-26T15:09:00.000","Title":"scipy.optimize.minimize('SLSQP') too slow when given 2000 dim variable","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"My main function loads a largish dataframe from a csv supplied by the user (along with several other data objects), and then instantiates an object that forms a bunch of equations as part of a mathematical programming problem. Many of the equations' components are returned by calls to about 5 helper functions that I define in a utils file (most importantly, outside of the class that stores the optimization problem). These helper functions make reference to the data loaded in main, but I want their calls to show up in the equations as parameterized only by a time index t (not by the dataframe), for readability.  Is the best way to accomplish this to define those functions in utils.py with the data as explicit parameters, and then use functools.partial before generating the equations to make them implicit?\nThis seems like a verbose approach to me, but the other options seem worse: to define the helper functions inside of main, or to give up on the idea of a main function loading the data, which basically means giving up on having a main function. And possibly having confusing circular imports.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":36,"Q_Id":47501275,"Users Score":0,"Answer":"I ended up going with my proposed answer:\n\ndefine those functions in utils.py with the data as explicit\n  parameters, and then use functools.partial before generating the\n  equations to make them implicit\n\nand with that codebase having been in production for  over a year, it seems reasonable enough to me.","Q_Score":0,"Tags":"python,main,functools","A_Id":55925583,"CreationDate":"2017-11-26T21:37:00.000","Title":"Best way to make data loaded in main() an implicit argument of a function in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it possible to start the index from n in a pandas dataframe?\nI have some datasets saved as csv files, and would like to add the column index with the row number starting from where the last row number ended in the previous file.\nFor example, for the first file I'm using the following code which works fine, so I got an output csv file with rows starting at 1 to 1048574, as expected:\nyellow_jan['index'] = range(1, len(yellow_jan) + 1)\nI would like to do same for the yellow_feb file, but starting the row index at 1048575 and so on.\nAppreciate any help!","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1626,"Q_Id":47515644,"Users Score":0,"Answer":"you may just reset the index at the end or define a local variable and use it in `arange' function.  update the variable with the numbers of rows for each file you read.","Q_Score":0,"Tags":"python,pandas,csv,dataframe,indexing","A_Id":47516794,"CreationDate":"2017-11-27T16:32:00.000","Title":"How to index a pandas data frame starting at n?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When I update the dataframe, I should check if the value exists in the dataframe index, but I want to know which way is faster, thanks!\n\n1. if value in set(dataframe.index)\n2. if value in dataframe.index","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2481,"Q_Id":47526059,"Users Score":0,"Answer":"Another way could be.\na=time.time()\nif value in set(dataframe.index)\nb=time.time()\ntimetaken=b-a","Q_Score":1,"Tags":"python,pandas,dataframe","A_Id":47526928,"CreationDate":"2017-11-28T07:23:00.000","Title":"How to quickly check if a value exists in pandas DataFrame index?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"What routine or algorithm should I use to provide eps and minPts parameters to DBSCAN algorithm for efficient results?","AnswerCount":1,"Available Count":1,"Score":0.761594156,"is_accepted":false,"ViewCount":4405,"Q_Id":47533930,"Users Score":5,"Answer":"The DBSCAN paper suggests to choose minPts based on the dimensionality, and eps based on the elbow in the k-distance graph.\nIn the more recent publication\n\nSchubert, E., Sander, J., Ester, M., Kriegel, H. P., & Xu, X. (2017). DBSCAN Revisited, Revisited: Why and How You Should (Still) Use DBSCAN. ACM Transactions on Database Systems (TODS), 42(3), 19.\n\nthe authors suggest to use a larger minpts for large and noisy data sets, and to adjust epsilon depending on whether you get too large clusters (decrease epsilon) or too much noise (increase epsilon). Clustering requires iterations.\nThat paper was an interesting read, because it shows what can go wrong if you don't look at your data. People are too obsesses with performance metrics, and forget to look at the actual data.","Q_Score":3,"Tags":"python,cluster-analysis,dbscan","A_Id":47536805,"CreationDate":"2017-11-28T14:25:00.000","Title":"How can I choose eps and minPts (two parameters for DBSCAN algorithm) for efficient results?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to implement a custom layer. The 2 inputs of my custom layer are 2 tensors, which come from 2 seperate 2D convolution layers, is there an example?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":406,"Q_Id":47536066,"Users Score":0,"Answer":"Since you don't need it to be trainable, a lambda function will also do. Or you can keep the custom layer as you have it, and set trainable to False. The weights will never be updated for this layer, and whatever you do here will forward propagate to the next layer in the model and as mentioned in the comments, backprop will impact the other layers with weights. So, definitely your model will learn something.\nI personally recommend using a custom layer, if you decide later to add some learning to this layer and check your results. You cannot do this in a Lambda function. If you add one (kernel), you will have to use in the 'call' method. Your model will throw an error during training otherwise.","Q_Score":0,"Tags":"python,neural-network,keras,layer","A_Id":47536983,"CreationDate":"2017-11-28T16:10:00.000","Title":"Define a custom layer with 2 tensors inputs in Keras","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How can I compute the product sum of the first three elements of two vectors \nA = [a1, a2, a3, a4, a5, a6]andB = [b1, b2, b3, b4, b5, b6] (i.e. [a1b1 + a2b2 + a3b3])in python and tensorflow.","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":52,"Q_Id":47562817,"Users Score":0,"Answer":"If it's just that simple do:\nsum([A[i]*B[i] for i in range(3)])\nThis sums the products of the first three values with each other.\nHope this helps!","Q_Score":0,"Tags":"python,tensorflow","A_Id":47562880,"CreationDate":"2017-11-29T22:21:00.000","Title":"How to compute the product some of parts of two vectors","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"This question already has an answer here:\nHow to change the font size on a matplotlib plot","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":445,"Q_Id":47566009,"Users Score":0,"Answer":"You may use tick_params function like plt.tick_params(axis='y', which='major', labelsize=10)","Q_Score":2,"Tags":"python,matplotlib,plot","A_Id":47566035,"CreationDate":"2017-11-30T04:41:00.000","Title":"How to change a font size within a plot figure","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"My goal is that by given  a value on a row (let's say 3), look for the value of a given column 3 rows below. Currently I am perfoming this using for loops but it is tremendously inefficient.\nI have read that vectorizing can help to solve this problem but I am not sure how.\nMy data is like this:\nDate DaysToReception Quantity QuantityAtTheEnd\n20\/03     3             102\n21\/03     -             88\n22\/03     -             57\n23\/03     5             178\n24\/03\nAnd I want to obtain:\nDate DaysToReception Quantity QuantityAtReception\n20\/03     3              102         178\n21\/03     -              88\n22\/03     -              57\n23\/03     5              178\n24\/03\n...\nThanks for your help!","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":19,"Q_Id":47578774,"Users Score":0,"Answer":"If you have unique date or DaysToReception, you can actually use Map\/HashMap where the key will be the date or DaysToReception and the values will be other information which you can store using a list or any other appropriate data structure. \nThis will definitely improve the efficiency. \nAs you pointed out that \"number of rows I search the value below depends on the value \"DaysToReception\", I believe \"DaysToReception\" will not be unique. In that case, the key to your Map will be the date.","Q_Score":0,"Tags":"python,pandas,dataframe","A_Id":47578933,"CreationDate":"2017-11-30T17:01:00.000","Title":"Python: How to efficiently do operations using different rows of the same column?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am loading a cascade classifier from a file in OpenCV using python. Since the CascadeClassifier() method requires the filename to be the absolute filename, I have to load the absolute filename.\nHowever, I am deploying and using it on Heroku and I can't seem to get the absolute path of the file. I've tried using os.getcwd() + '\\cascader_file.xml', but that still does not work.\nI would like to know how to load the classifier on the Heroku deployment","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":174,"Q_Id":47585666,"Users Score":0,"Answer":"I figured it out. All I needed to do was use the os.path.abspath() method to convert the relative path to an absolute path","Q_Score":0,"Tags":"python,opencv,heroku,cascade-classifier","A_Id":47585710,"CreationDate":"2017-12-01T02:13:00.000","Title":"Load classifier on Heroku Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am currently working on vehicle platooning for which I need to design a code in python opencv for counting the number of vehicles based on the classification.The input is a real time traffic video.\nThe aim is finding an average size \"x\" for the bounding box and say that for cars its \"x\", for buses its \"3x\" and so on.Based on size of \"x\" or multiples of \"x\", determine the classification.Is there any possible way I can approach this problem statement?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1211,"Q_Id":47586587,"Users Score":0,"Answer":"I have worked on almost similar problem.\nEasiest way is to train a Haar-cascade on the vehicles of similar size. \nYou will have to train multiple cascades based on the number of categories.\nData for the cascade can be downloaded from any used car selling site using some browser plugin.\nThe negative sets pretty much depend on the context in which this solution will be used. \nThis also brings the issue that, if you plan to do this on a busy street, there are going to be many unforeseen scenarios. For example, pedestrian walking in the FoV. Also, FoV needs to be fixed, especially the distance from which objects are observed. Trail and error is the only way to achieve sweet-spot for the thresholds, if any. \nNow I am going to suggest something outside the scope of the question you asked.\nThough this is purely image processing based approach, you can turn the problem on its face, and ask a question  'Why' classification is needed? Depending on the use-case, more often than not, it will be possible to train a deep reinforcement learning agent. It will solve the problem without getting into lot of manual work.\nLet me know in case of a specific issues.","Q_Score":0,"Tags":"python,opencv,image-processing,bounding-box","A_Id":47586784,"CreationDate":"2017-12-01T04:19:00.000","Title":"Vehicle counting based on classification (cars,buses,trucks)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am going to install commit framework (Convex Optimization Modeling for Microstructure Informed Tractography) on anaconda 3. \nHere I explained the process that I did: 1st, I downloaded commit and then opened an anaconda prompt and went to the location that I downloaded the folder but when I run pip install commit I faced with this error: \n\nCould not find a version that satisfies the requirement commit. No\n  matching distribution found for commit.\n\nI am grateful for any suggestion to solve this error.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":30,"Q_Id":47591513,"Users Score":1,"Answer":"If you already have the binary downloaded on the folder why you need to run pip install commit That would download and install it again right? \nJust run pip install . on the folder where you downloaded the package.","Q_Score":0,"Tags":"python,anaconda,commit","A_Id":47591883,"CreationDate":"2017-12-01T10:36:00.000","Title":"error for installing COMMIT package in anaconda 3","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"When I try to import sklearn, I get the following error message:\n\nImportError: DLL load failed: Das angegebene Modul wurde nicht gefunden.\n\n(In English: The specified module was not found)\nI'm working on Windows 10, 64-Bit. I'm using Python 3.6.1. (and no other version), Anaconda and PyCharm. I installed scikit-learn using \n\nconda install scikit-learn\n\nand I can find it in\n\nconda list\n\nas well as in File | Settings | Project Interpreter with version 0.19.1. I also have numpy 1.13.3 and scipy 1.0.0.\nI know this error message has been discussed multiple, but none of these discussions could help me ... \nI tried uninstalling and re-installing numpy, scipy and scikit-learn. I also tried installing scikit-learn using pip.\nIf I randomly try to load other packages, that are in my conda list, they all work perfectly fine, but not scikit-learn.\nI don't even know where my error is. Can anyone give me a hint in the right direction, or a suggestion what I could try?\nThanks!","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":606,"Q_Id":47592516,"Users Score":0,"Answer":"Have you tried to import sklearn directly from a Python interpreter ?\nAlso, try to check in your Project Settings that sklearn is recognized as a package of the Python interpreter associated (Settings --> Project --> Project Interpreter)","Q_Score":0,"Tags":"python,import,installation,scikit-learn","A_Id":47598201,"CreationDate":"2017-12-01T11:35:00.000","Title":"Cannot load scikit-learn","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've used YOLO detection with trained model using my GPU - Nvidia 1060 3Gb, and everything worked fine. \nNow I am trying to generate my own model, with param --gpu 1.0. Tensorflow can see my gpu, as I can read at start those communicates:\n\"name: GeForce GTX 1060 major: 6 minor: 1 memoryClockRate(GHz): 1.6705\"\n\"totalMemory: 3.00GiB freeMemory: 2.43GiB\"\nAnyway, later on, when program loads data, and is trying to start learning i got following error:\n\"failed to allocate 832.51M (872952320 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY\"\nI've checked if it tries to use my other gpu (Intel 630) , but it doesn't.\nAs i run the train process without \"--gpu\" mode option, it works fine, but slowly. \n( I've tried also --gpu 0.8, 0.4 ect.)\nAny idea how to fix it?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1053,"Q_Id":47606161,"Users Score":0,"Answer":"Problem solved. Changing batch size and image size in config file didn't seem to help as they didn't load correctly. I had to go to defaults.py file and change them up there to lower, to make it possible for my GPU to calculate the steps.","Q_Score":1,"Tags":"python-3.x,tensorflow,darkflow","A_Id":47685678,"CreationDate":"2017-12-02T08:53:00.000","Title":"YOLO - tensorflow works on cpu but not on gpu","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've used YOLO detection with trained model using my GPU - Nvidia 1060 3Gb, and everything worked fine. \nNow I am trying to generate my own model, with param --gpu 1.0. Tensorflow can see my gpu, as I can read at start those communicates:\n\"name: GeForce GTX 1060 major: 6 minor: 1 memoryClockRate(GHz): 1.6705\"\n\"totalMemory: 3.00GiB freeMemory: 2.43GiB\"\nAnyway, later on, when program loads data, and is trying to start learning i got following error:\n\"failed to allocate 832.51M (872952320 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY\"\nI've checked if it tries to use my other gpu (Intel 630) , but it doesn't.\nAs i run the train process without \"--gpu\" mode option, it works fine, but slowly. \n( I've tried also --gpu 0.8, 0.4 ect.)\nAny idea how to fix it?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1053,"Q_Id":47606161,"Users Score":0,"Answer":"Look like your custom model use to much memory and the graphic card cannot support it. You only need to use the --batch option to control the size of memory.","Q_Score":1,"Tags":"python-3.x,tensorflow,darkflow","A_Id":50273011,"CreationDate":"2017-12-02T08:53:00.000","Title":"YOLO - tensorflow works on cpu but not on gpu","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to calculate the semantic similarity between two bi-grams and I need to use fasttext's pre-trained word vectors to accomplish this task. \nFor ex :\nThe b-grams are python lists of two elements:\n[his, name] and [I, am] \nThey are two tuples and I need to calculate the similarity between these two tuples by any means necessary.\nI'm hoping there's a score which can give me a good approximation of similarity.\nFor ex - If there are methods which can tell me that [His, name] is more similar to [I, am] than [An, apple].\nRight now I only made use of cosine similarity which does include any semantic similarity.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":567,"Q_Id":47618217,"Users Score":1,"Answer":"Cosine similarity might be useful if you average both word vectors in a bi-gram first. So you want to take the vector for 'his' and 'name', average them into one vector. Then take the vector for 'I' and 'am' and average them into one vector. Finally, calculate cosine similarity for both resulting vectors and it should give you a rough semantic similarity.","Q_Score":1,"Tags":"python,fasttext,sentence-similarity","A_Id":47619440,"CreationDate":"2017-12-03T12:11:00.000","Title":"How do I calculate the semantic similarity between two n-grams?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"As title states I'm trying to use my Keras (tf backend) pretrained model for predicitions in c#. What's the best approach? \nI've tried IronPython but it gave me errors, after search I found it isn't supported. Simply calling python script won't work since target Windows devices won't have python interpreters installed.","AnswerCount":3,"Available Count":1,"Score":-1.0,"is_accepted":false,"ViewCount":11563,"Q_Id":47624488,"Users Score":-9,"Answer":"it does not work like that, since you wont even be able to install tensorflow in a C# project. Abandon C# stack and learn framework in python stack instead, ex. if if you need to consume the prediction result in a web app, learn Flask or Django","Q_Score":6,"Tags":"c#,python,.net,tensorflow,keras","A_Id":47719572,"CreationDate":"2017-12-03T23:27:00.000","Title":"Getting Keras trained model predictions in c#","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to tensorflow, \nOS: Mac 10.13.1\nPython: 2.7\ntensorflow: 1.4.0(install with pip)  \nI want to use tensorflow from Pycharm for a project, and when I open:\n\"Pycharm\" - \"Preferences\" - \"Project Interpreter\", There are only two local:  \n2.7.13\/(Library\/Frameworks\/Python.framework.Versions.2.7\/bin\/python2.7)\n\/System\/Library\/Frameworks.Python.framwork.Versions.2,7.bin.python2.7  \nI can't find tensorflow, what should I do?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":184,"Q_Id":47626431,"Users Score":0,"Answer":"Click on the python 2.7 that you see. Then after you select you python you will see all the packages that come with it. One of the packages will be Tensorflow.","Q_Score":0,"Tags":"python,tensorflow,pycharm,macos-sierra","A_Id":47655652,"CreationDate":"2017-12-04T04:18:00.000","Title":"can't find tensorflow local in Pycharm project Interpreters","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"In TensorFlow I have a graph that has in it a string_input_producer that is used in it's input pipeline.\nI am loading the graph from a checkpoint file, so I do not have access to the original object when it was created. Nonetheless, I need to run the enqueue method of this object.\nI have tried getting the FIFOQueue object using get_operation_by_name and get_tensor_by_name, which obviously did not work because the queue is neither an operation nor a tensor. Is there any function like the mentioned that would do what I want? (fake e.g. get_queue_by_name) How can I solve my problem otherwise?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":79,"Q_Id":47644110,"Users Score":0,"Answer":"I think that as of today that is not possible. However, you can call methods of the queue object by getting them using get_operation_by_name. For example get_operation_by_name(\"queue_name\/enqueue\"). You can also make your own Queue object which internally simply calls the corresponding graph operations.","Q_Score":0,"Tags":"python,tensorflow","A_Id":51676877,"CreationDate":"2017-12-05T00:17:00.000","Title":"How to get a Queue object from a TensorFlow graph?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"For Tensorflow feature columns contain boolean value 0 and 1.\nShould I be using tf.feature_column.numeric_column\nor tf.feature_column.categorical_column_with_vocabulary_list?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1012,"Q_Id":47646159,"Users Score":0,"Answer":"I would recommend tf.feature_column.categorical_column_with_vocabulary_list.\nI would think you would want to treat the data as categories, rather than as scalars.","Q_Score":3,"Tags":"python-3.x,tensorflow","A_Id":48959163,"CreationDate":"2017-12-05T04:34:00.000","Title":"Tensorflow Feature Column for 0 and 1","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to apply SMOTE to unbalanced dataset which contains binary, categorical and continuous data.  Is there a way to apply SMOTE to binary and categorical data?","AnswerCount":3,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":22277,"Q_Id":47655813,"Users Score":3,"Answer":"So as per documentation SMOTE doesn't support Categorical data in Python yet, and provides continuous outputs. \nYou can instead employ a workaround where you convert the categorical variables to integers and use SMOTE.\nThen use np.round(X_train[categorical_variables]) to convert them back to the respective categorical values.","Q_Score":16,"Tags":"python-3.x,imputation","A_Id":48248566,"CreationDate":"2017-12-05T14:20:00.000","Title":"Oversampling: SMOTE for binary and categorical data in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am having troubles understanding the meaning and usages for Tensorflow Tensors and Sparse Tensors. \nAccording to the documentation\nTensor\n\nTensor is a typed multi-dimensional array. For example, you can represent a mini-batch of images as a 4-D array of floating point numbers with dimensions [batch, height, width, channels].\n\nSparse Tensor\n\nTensorFlow represents a sparse tensor as three separate dense tensors: indices, values, and shape. In Python, the three tensors are collected into a SparseTensor class for ease of use. If you have separate indices, values, and shape tensors, wrap them in a SparseTensor object before passing to the ops below.\n\nMy understandings are Tensors are used for operations, input and output. And Sparse Tensor is just another representation of a Tensor(dense?). Hope someone can further explain the differences, and the use cases for them.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":13330,"Q_Id":47662143,"Users Score":24,"Answer":"The difference involves computational speed. If a large tensor has many, many zeroes, it's faster to perform computation by iterating through the non-zero elements. Therefore, you should store the data in a SparseTensor and use the special operations for SparseTensors. \nThe relationship is similar for matrices and sparse matrices. Sparse matrices are common in dynamic systems, and mathematicians have developed many special methods for operating on them.","Q_Score":22,"Tags":"python,tensorflow","A_Id":47662958,"CreationDate":"2017-12-05T20:29:00.000","Title":"What is the difference between tensors and sparse tensors?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am a new learner to machine learning and I want to do a 2-class classification with only a few attributes. I have learned by researching online that two-class averaged perceptron algorithm is good for two-class classification with a linear model.\nHowever, I have been reading through the documentation of Scikit-learn, and I am a bit confused if Scikit-learn is providing a averaged perceptron algorithm.\nI wonder if the sklearn.linear_model.Perceptron class can be implemented as the two-class averaged perceptron algorithm by setting up the parameters correctly.\nI appreciate it very much for your kind help.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":635,"Q_Id":47665910,"Users Score":-1,"Answer":"I'm sure someone will correct me if I'm wrong but I do not believe Averaged Perceptron is implemented in sklearn.  If I recall correctly, Perceptron in sklearn is simply SGD with certain default parameters.\nWith that said, have you tried good old logistic regression?  While it may not be the sexiest algorithm around, it often does provide good results and can serve as a baseline to see if you need to explore more complicated methods.","Q_Score":2,"Tags":"python-2.7,machine-learning,scikit-learn,classification,perceptron","A_Id":47674949,"CreationDate":"2017-12-06T02:32:00.000","Title":"scikit learn averaged perceptron classifier","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"This is my genetic algorithm, step by step:\n\nGenerate two initial population's randomly, and select the fittest tour from both.\nPerform an ordered crossover, which selects a random portion of the first fit tour and fills in the rest from the second, in order.\nMutates this tour by randomly swapping two cities if the tour is only 1.3 times as good as the top 10% tour in the initial population (which I have literally just done by induction, singling out poor tours that are produced) - I would love to change this but can't think of a better way.\n\nThe mutation is selected from a population of several mutations. \n\nReturns the tour produced. \n\nThe mutation, however is almost ALWAYS worse, if not the same as the crossover. \nI'd greatly appreciate some help. Thanks!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":469,"Q_Id":47679966,"Users Score":0,"Answer":"A problem in GA's is narrowing your search space too quickly and reaching a local maxima solution. You need to ensure that you are not leading your solution in any way other than in the selection\/fitness function. So when you say,\n\nwhy would you take a good solution and then\n  perform a function that will most likely make it a worse solution\n\n,the reason is that you WANT a chance for the solution to take a step back, it will likely need to get worse before it gets better. So really you should remove any judgement logic from your genetic operators, leave this to the selection process.\nAlso, crossover and mutation should be seen as 2 different ways of generating a child individual, you should use one or the other. In practice, this means you have a chance of performing either a mutation of a single parent or a crossover between 2 parents. Usually the mutation chance is only 5% with crossover being used to generate the other 95%.\nA crossover keeps the genetic information from both parents (children are mirror images), so one child will be worse than the parents and the other better (or both the same). So in this sense with crossover, if there is a change, you will always get a better individual.\nMutation on the other hand offers no guarantee of a better individual, yet it exists for the purpose of introducing new data, helping to move the GA from the local maxima scenario. If the mutation fails to improve the individual and makes it worse, then it has less chance of being selected for parenting a child anyway (i.e. you don't need this logic in the mutation operator itself).\n\nyou select the best one to mutate\n\nThis is not strictly true, good individuals should have a higher CHANCE of being selected. In here there is a subtle difference that BAD individuals may also be selected for parents. Again this helps reduce the chance of reaching a local maxima solution. This also means that the best individual for a generation could (and often does) actually get worse. To solve this we usually implement 'elitism', whereby the best individual is always copied to the next generation (at it is\/undergoing no operations).\nIt would also be beneficial if I could comment on which genetic operators you are using. I have found cycle crossover and inversion mutation to work well in my experience.","Q_Score":0,"Tags":"python,genetic-algorithm,traveling-salesman,mutation,crossover","A_Id":48290905,"CreationDate":"2017-12-06T17:17:00.000","Title":"How can I improve this genetic algorithm for the TSP?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How do I finetune ResNet50 Keras to only classify images in 2 classes (cats vs. dogs) instead of all 1000 imagenet classes? I used Python and was able to classify random images with ResNet50 and keras with the 1000 ImageNet classes. Now I want to fine tune my code so that it only classifies cats vs. dogs, using the Kaggle images instead of ImageNet. How do I go about doing that?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":544,"Q_Id":47685816,"Users Score":0,"Answer":"There are several ways of applying Transfer Learning and it's trial & error what works best. However, ImageNet includes multiple types of cats and dogs in its 1000 classes, which is why I would do the following:\n\nAdd a single Dense layer to your model with 2 outputs\nSet only the last layer to trainable\nRetrain the network using only images of cats and dogs\n\nThis will get solid results rather quickly because you're only training one layer. Meaning, you don't have to backpropagate through the entire network. In addition, your model only has to learn a rather linear mapping from the original cats and dogs subclasses to this binary output.","Q_Score":0,"Tags":"python,classification,imagenet","A_Id":47710171,"CreationDate":"2017-12-07T00:42:00.000","Title":"How do I finetune ResNet50 Keras to only classify images in 2 classes (cats vs. dogs) instead of all 1000 imagenet classes?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"My xgboost model trained for a regression task in python using the xgboost package version 0.6 is using strange values for splits. Some values used as a splitting criteria are not present in the training dataset at all.\nExample:\n- there's a variable 'text' with values in the train set of [Missing,1,2]\n- yet, a derived splitting criteria of a node in the trained model is 'text < 4'\nWhat could be a possible reason of such a split when no such value (-> 4) can be found in the data set?The split does not increase the information gain, since all samples follow one branch after this decision node.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":579,"Q_Id":47692738,"Users Score":1,"Answer":"A possible explanation: you have all non-missing values going to the 'text < 4' branch, and all missing values to the other - 'text > 4' - branch. Can you verify?","Q_Score":1,"Tags":"python,machine-learning,deep-learning,xgboost","A_Id":47720583,"CreationDate":"2017-12-07T10:20:00.000","Title":"XGBoost unreasonable splitting value in nodes","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"It might be a naive question but I couldn't find a reasonable answer. Why the median is not included in the return of stats.describe ? Even kurtosis is included but why not median ?\nThanks","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":573,"Q_Id":47704158,"Users Score":4,"Answer":"I cannot speak for the scipy stats.describe people, but the general answer is this: mean, variance, and even kurtosis can be computed in one or two O(n) passes through the data, while median requires an O(n*log(n)) sort.","Q_Score":2,"Tags":"python,scipy,statistics","A_Id":47704296,"CreationDate":"2017-12-07T21:33:00.000","Title":"Why does scipy stats describe does not have median?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I just started to study OpenCV with Python and was trying to build my first game bot. My thought is: capture the game window frame by frame and analyze the pixels in some specific locations, if the color of those pixels has changed, then press a key to do some automatic operations.\nThis game needs quick reactions so the FPS is quite important, I have tried the mss and PIL, but the fps in both methods are not enough (30+ with mss and 10+ with PIL's ImageGrab), so I'm wondering if there is any better way to deal with pixels in real-time. Thanks!","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1166,"Q_Id":47740206,"Users Score":0,"Answer":"Want to continue Jakubs great answer:\nUsing C++ or something might be a good idea in some cases.\nI created a csgo aimbot with opencv, pil, pyautogui and numpy and few other modules, watching sentdex's videos on this subject might give u some idea(link above).\nThere might be a custom module for GPU cv2\ntips for py:\n1) lower imagegrab resolution.\n2) in imagegrab.grab bbox you can give coordinates on which the grabs will happen instead of full screen.\n3) grayscale is 0-255 so it will speed up the process, if u know which colors are in this reaction thingy just pass them to grayscale so u know in which colors to respond to.","Q_Score":0,"Tags":"python","A_Id":47741851,"CreationDate":"2017-12-10T15:16:00.000","Title":"Dealing with pixels in real-time using OpenCV","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":1,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I don't know how this happens or why, \nbut I'll be in a jupyter notebook grouping by things and I will very conciously type in dataframe.groupby, write some other code and hit ctrl+ enter\nand there will be that damn error. Every single time, I will go back and delete the 'groupyouby' and type in groupby. \nI doubt that anyone has run into this error,and I don't know how long it will be until someone else creates the mess of libraries that I have that resulted in this chinese water tourture like nightmare. I am here, to let you know, that you are not alone. \nAlso if someone has a fix that would be great. I got nothing for you other than that description above.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":39,"Q_Id":47757753,"Users Score":1,"Answer":"Grammarly was the cause of this. \nIf you use jupyter notebooks and have the grammarly extension. It will cause problems.","Q_Score":0,"Tags":"python,jupyter","A_Id":49182635,"CreationDate":"2017-12-11T17:00:00.000","Title":"Jupyternotebook corrects groupby to groupyouby Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"C:\\Users\\Om>pip install opencv_python-3.2.0-cp36-cp36m-win32.whl\nRequirement 'opencv_python-3.2.0-cp36-cp36m-win32.whl' looks like a filename, but the file does not exist\nProcessing c:\\users\\om\\opencv_python-3.2.0-cp36-cp36m-win32.whl\nException:\n  Traceback (most recent call last):\n    File \"c:\\users\\om\\appdata\\local\\programs\\python\\python36-32\\lib\\site-packages\\pip\\basecommand.py\", line 215, in main\n      status = self.run(options, args)\nFile \"c:\\users\\om\\appdata\\local\\programs\\python\\python36-32\\lib\\site-packages\\pip\\commands\\install.py\", line 324, in run\n      requirement_set.prepare_files(finder)\nFile \"c:\\users\\om\\appdata\\local\\programs\\python\\python36-32\\lib\\site-packages\\pip\\req\\req_set.py\", line 380, in prepare_files\n      ignore_dependencies=self.ignore_dependencies))\nFile \"c:\\users\\om\\appdata\\local\\programs\\python\\python36-32\\lib\\site-packages\\pip\\req\\req_set.py\", line 620, in _prepare_file\n      session=self.session, hashes=hashes)\nFile \"c:\\users\\om\\appdata\\local\\programs\\python\\python36-32\\lib\\site-packages\\pip\\download.py\", line 809, in unpack_url\n      unpack_file_url(link, location, download_dir, hashes=hashes)\nFile \"c:\\users\\om\\appdata\\local\\programs\\python\\python36-32\\lib\\site-packages\\pip\\download.py\", line 715, in unpack_file_url\n      unpack_file(from_path, location, content_type, link)\nFile \"c:\\users\\om\\appdata\\local\\programs\\python\\python36-32\\lib\\site-packages\\pip\\utils__init__.py\", line 599, in unpack_file\n      flatten=not filename.endswith('.whl')\nFile \"c:\\users\\om\\appdata\\local\\programs\\python\\python36-32\\lib\\site-packages\\pip\\utils__init__.py\", line 482, in unzip_file\n      zipfp = open(filename, 'rb')\nFileNotFoundError: [Errno 2] No such file or directory: 'C:\\Users\\Om\\opencv_python-3.2.0-cp36-cp36m-win32.whl'\n\nwhat is the problem ?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":266,"Q_Id":47774652,"Users Score":0,"Answer":"It looks like you have not given a valid file location.\nThe error is saying that it thinks you have given it a whl file to install but it can't find it.\nI suggest you check you file path again.\nMake sure it is located at C:\\Users\\Om and it is called exactly the same as the filename you have put down.  It should match opencv_python-3.2.0-cp36-cp36m-win32.whl exactly.  \nEnsure you do not rename the whl file.\nCorrect that and it will attempt an install and if that does not work you will get another error.","Q_Score":0,"Tags":"python,opencv","A_Id":47775119,"CreationDate":"2017-12-12T14:09:00.000","Title":"Python package installation error for openCV","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In the chapter seven of this book \"TensorFlow Machine Learning Cookbook\" the author in pre-processing data uses fit_transform function of scikit-learn to get the tfidf features of text for training. The author gives all text data to the function before separating it into train and test. Is it a true action or we must separate data first and then perform fit_transform on train and transform on test?","AnswerCount":3,"Available Count":2,"Score":0.1325487884,"is_accepted":false,"ViewCount":12402,"Q_Id":47778403,"Users Score":2,"Answer":"As we are talking about text data, we have to make sure that the model is trained only on the vocabulary of the training set as when we will deploy a model in real life, it will encounter words that it has never seen before so we have to do the validation on the test set keeping that in mind.\nWe have to make sure that the new words in the test set are not a part of the vocabulary of the model.\nHence we have to use fit_transform on the training data and transform on the test data.\nIf you think about doing cross validation, then you can use this logic across all the folds.","Q_Score":20,"Tags":"python,machine-learning,scikit-learn,nlp,tf-idf","A_Id":57437066,"CreationDate":"2017-12-12T17:34:00.000","Title":"Computing TF-IDF on the whole dataset or only on training data?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In the chapter seven of this book \"TensorFlow Machine Learning Cookbook\" the author in pre-processing data uses fit_transform function of scikit-learn to get the tfidf features of text for training. The author gives all text data to the function before separating it into train and test. Is it a true action or we must separate data first and then perform fit_transform on train and transform on test?","AnswerCount":3,"Available Count":2,"Score":0.2605204458,"is_accepted":false,"ViewCount":12402,"Q_Id":47778403,"Users Score":4,"Answer":"Author gives all text data before separating train and test to\n  function. Is it a true action or we must separate data first then\n  perform tfidf fit_transform on train and transform on test?\n\nI would consider this as already leaking some information about the test set into the training set.\nI tend to always follow the rule that before any pre-processing first thing to do is to separate the data, create a hold-out set.","Q_Score":20,"Tags":"python,machine-learning,scikit-learn,nlp,tf-idf","A_Id":55300936,"CreationDate":"2017-12-12T17:34:00.000","Title":"Computing TF-IDF on the whole dataset or only on training data?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have setup Tensorflow and Keras on Mac OS. I also have Jupyter that came as part of my Anaconda installation. \nWhen I try to import Tensoflow or Keras in a Jupyter notebook, I get \"a no module named <...>\" error. \nAm I missing a step ?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":661,"Q_Id":47783481,"Users Score":0,"Answer":"If you are a Windows\/Mac user who is working on Jupyter notebook, pip install keras doesn't help you. Instead, try the steps below:\n\nIn command prompt navigate to the \u201csite packages\u201d directory of your anaconda installed.\nNow use conda install tensorflow and after conda install keras\nRestart your Jupyter notebook and run the packages.","Q_Score":0,"Tags":"python,tensorflow,keras,jupyter-notebook","A_Id":51338424,"CreationDate":"2017-12-12T23:56:00.000","Title":"Can import TensorFlow and Keras in Jupyter, even though I have them installed?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it possible to decrease the debug startup time of the python script in visual studio code? If I a have a lot of import library (like opencv, numpy, pandas and so on) every time i start the debug of the script, pressing the F5 button, the environment wait for seconds to reload them. Is it possible to reduce this time? Thanks.","AnswerCount":1,"Available Count":1,"Score":-0.1973753202,"is_accepted":false,"ViewCount":80,"Q_Id":47789968,"Users Score":-1,"Answer":"There's no explicit technique to improving the import time short of using lazy loading, but there are technical considerations which have to be taken into account before going down that route (e.g. some modules simply don't work when loaded lazily).","Q_Score":1,"Tags":"python,pandas,numpy,opencv,visual-studio-code","A_Id":47800231,"CreationDate":"2017-12-13T09:50:00.000","Title":"Decrease the debug startup time of the python code in Visual Studio Code","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am performing some perspective transforms on camera images, however in certain cases the roll and pitch of the camera are not zero. In other words, the camera is not level and I would like to be able to correct for this.\nI have some questions:\n1) Can the transformation matrix (from M = cv2.getPerspectiveTransform(...) ) be corrected for the pitch and roll angles?\n2) Should I just transform the source points and get a new transformation matrix? Roll seems like a simple enough correction since it's analogous to rotating the image, but how can I get the proper transformation for both roll and pitch?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":715,"Q_Id":47794365,"Users Score":0,"Answer":"If the camera motion is approximately a rotation about its optical center \/ lens entrance pupil (for example, pan-tilt-roll on a tripod with the subject distance much larger than the translation of the optical center), then images taken from rotated viewpoints are related by a homography.\nIf you know the 3D rotation (pan\/tilt\/roll), then you can explicitly compute the homography and apply it to the image. If not, but you have two images upon which you can identify 4 corresponding points or more, then you can estimate the homography directly from those correspondences.","Q_Score":1,"Tags":"python,opencv,graphics,computer-vision","A_Id":47808839,"CreationDate":"2017-12-13T13:37:00.000","Title":"OpenCV perspective transform with camera roll and pitch correction","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"my task is to assign tags (descriptive words) to documents or posts from the list of available tags. I'm working with Doc2vec available in Gensim. I read that doc2vec can be used for document tagging. But i could not get the suitable parameter values for this task. Till now, i have tested it by changing value of parameters named 'size' and 'window'. The results i'm getting are too nonsense and also by changing values of these parameters i haven't find any trend in results i.e. at some values results got little bit improved and at some values results fall down. Can anyone suggest what should be suitable parameter values for this task? I found that 'size'(defines size if feature vector) should be large if we have enough training data. But about the rest of parameters, i am not getting sure!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":374,"Q_Id":47799657,"Users Score":0,"Answer":"Which parameters are best can vary with the quality & size of your training data, and exactly what your downstream goals are. (There's no one set of best-for-everything parameters.) \nStarting with the gensim defaults is reasonable first guess, or other values you've see someone else having used successfully on a similar dataset\/problem. \nBut really you'll need to experiment, ideally by creating an automated evaluation based on some held-back testing set, then meta-optimizing the Doc2Vec parameters by searching over many small adjustments to the parameters for the best ranges\/combinations.","Q_Score":0,"Tags":"python,gensim,doc2vec","A_Id":47806250,"CreationDate":"2017-12-13T18:13:00.000","Title":"Parameter values of Doc2vec for Document Tagging - Gensim","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using distributed Tensorflow on aws using gpus. When I train the model on my local machine, I indicate ps_host\/workers_host as something like 'localhost:2225'.  What are the ps\/workers host I need to use in case of aws?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":186,"Q_Id":47804792,"Users Score":0,"Answer":"When a distributed TF code is run on the cluster, other nodes could be accessed through \"private ip: port number\". \nBut the problem with AWS is that the other nodes can not be easily launched and it needs extra configuration.","Q_Score":4,"Tags":"python,tensorflow,tensorflow-gpu","A_Id":54758338,"CreationDate":"2017-12-14T01:56:00.000","Title":"Distributed Tensorflow: ps\/workers hosts on aws ?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I am working in python with the numba library and wondered if there is a solution to write a parallel version of a previous work. I have a function f(X, S, F) where X and S are scalar arrays, and F is a list of functions. \nI am almost sure that passing an array of functions is not possible with numba (and cuda in general?). What would be an alternative solution to this? If there is one.\nThanks in advance for your help","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":227,"Q_Id":47810891,"Users Score":2,"Answer":"The Numba CUDA Python implementation presently doesn't support any sort of function pointer or objects within kernels. So what you would have ambitions to do is not possible.","Q_Score":0,"Tags":"python,cuda,numba","A_Id":47820153,"CreationDate":"2017-12-14T10:19:00.000","Title":"Passing functions to CUDA blocks with numba","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Can I integrate scipy.optimize.minimize solver with method=SLSQP inside pyomo? Modeling in pyomo is much faster than in scipy but pyomo documentation does not seem to say explicitly if this is feasible.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":588,"Q_Id":47821346,"Users Score":1,"Answer":"At the moment (Dec 2017), there is no built-in support for passing a Pyomo model to scipy.optimize.  That said, it would not be a very difficult task to write a reasonably general purpose object that could generate the necessary (value, Jacobian, Hessian) evaluation functions to pass to scipy.optimize.minimize().","Q_Score":1,"Tags":"python,scipy,pyomo","A_Id":47821531,"CreationDate":"2017-12-14T20:14:00.000","Title":"Call scipy.optimize inside pyomo","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to do textual analysis on a bunch (about 140 ) of textual documents. Each document, after preprocessing and removing unnecessary words and stopwords, has about 7000 sentences (as determined by nlkt's sentence tokenizer) and each sentence has about 17 words on average. My job is to find hidden themes in those documents. \nI have thought about doing topic modeling. However, I cannot decide if the data I have is enough to obtain meaningful results via LDA or is there anything else that I can do. \nAlso, how do I divide the texts into different documents? Is 140 documents (each with roughly 7000 x 17 words) enough ? or should I consider each sentence as a document. But then each document will have only 17 words on average; much like tweets. \nAny suggestions would be helpful. \nThanks in advance.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":66,"Q_Id":47827130,"Users Score":0,"Answer":"I have worked on similar lines. This approach can work till 300 such documents. But, taking it to higher scale you need to replicate the approach using spark.\nHere it goes:\n1) Prepare TF-IDF matrix: Represent documents in terms Term Vectors. Why not LDA because you need to supply number of themes first which you don't know first. You can use other methods of representing documents if want to be more sophisticated (better than semantics) try word2Vec, GloVe, Google News Vectors etc.\n2) Prepare a Latent Semantic Space from the above TF-IDF. Creation of LSA uses SVD approach (one can choose the kaiser criteria to choose the number of dimensions). \nWhy we do 2)?\na) TF-IDF is very sparse. Step 3 (tSne) which is computationally expensive. \nb) This LSA can be used to create a semantic search engine\nYou can bypass 2) when your TF-IDF size is very small but i don't think given your situation that would be the case and also, you don't have other needs like having semantic search on these documents.\n3) Use tSne (t-stochastic nearest embedding) to represent the documents in 3 dimensions. Prepare a spherical plot from the euclidean cordinates.\n4) Apply K-means iteratively to find the optimal number of clusters.\nOnce decided. Prepare word clouds for each categories. Have your themes.","Q_Score":0,"Tags":"python-3.x,nlp,gensim,text-analysis","A_Id":47830720,"CreationDate":"2017-12-15T06:44:00.000","Title":"Suggestion on LDA","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I loaded google's news vector -300 dataset.  Each word is represented with a 300 point vector.  I want to use this in my neural network for classification.  But 300 for one word seems to be too big.  How can i reduce the vector from 300 to say 100 without compromising on the quality.","AnswerCount":2,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":2005,"Q_Id":47838719,"Users Score":7,"Answer":"tl;dr Use a dimensionality reduction technique like PCA or t-SNE.\nThis is not a trivial operation that you are attempting. In order to understand why, you must understand what these word vectors are.\nWord embeddings are vectors that attempt to encode information about what a word means, how it can be used, and more. What makes them interesting is that they manage to store all of this information as a collection of floating point numbers, which is nice for interacting with models that process words. Rather than pass a word to a model by itself, without any indication of what it means, how to use it, etc, we can pass the model a word vector with the intention of providing extra information about how natural language works.\nAs I hope I have made clear, word embeddings are pretty neat. Constructing them is an area of active research, though there are a couple of ways to do it that produce interesting results. It's not incredibly important to this question to understand all of the different ways, though I suggest you check them out. Instead, what you really need to know is that each of the values in the 300 dimensional vector associated with a word were \"optimized\" in some sense to capture a different aspect of the meaning and use of that word. Put another way, each of the 300 values corresponds to some abstract feature of the word. Removing any combination of these values at random will yield a vector that may be lacking significant information about the word, and may no longer serve as a good representation of that word.\nSo, picking the top 100 values of the vector is no good. We need a more principled way to reduce the dimensionality. What you really want is to sample a subset of these values such that as much information as possible about the word is retained in the resulting vector. This is where a dimensionality reduction technique like Principle Component Analysis (PCA) or t-distributed Stochastic Neighbor Embeddings (t-SNE) come into play. I won't describe in detail how these methods work, but essentially they aim to capture the essence of a collection of information while reducing the size of the vector describing said information. As an example, PCA does this by constructing a new vector from the old one, where the entries in the new vector correspond to combinations of the main \"components\" of the old vector, i.e those components which account for most of the variety in the old data.\nTo summarize, you should run a dimensionality reduction algorithm like PCA or t-SNE on your word vectors. There are a number of python libraries that implement both (e.g scipy has a PCA algorithm). Be warned, however, that the dimensionality of these word vectors is already relatively low. To see how this is true, consider the task of naively representing a word via its one-hot encoding (a one at one spot and zeros everywhere else). If your vocabulary size is as big as the google word2vec model, then each word is suddenly associated with a vector containing hundreds of thousands of entries! As you can see, the dimensionality has already been reduced significantly to 300, and any reduction that makes the vectors significantly smaller is likely to lose a good deal of information.","Q_Score":2,"Tags":"python-3.x,gensim","A_Id":51581196,"CreationDate":"2017-12-15T19:25:00.000","Title":"reducing word2vec dimension from Google News Vector Dataset","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a set of about 2000 files that look like: 10_20.txt, 10_21.txt, 10_21.txt, ... ,10_50.txt, ... , 11.20.txt, ... , 11.50.txt , ... , 20_50.txt\nThe first value in the file name, we'll call x, goes from 10-20 in steps of 1, and the second value in the file name, we'll call y, and goes from 20-50 in steps of 1.\nWithin these files, there is a load of values and another value I want to extract, which we'll call z.\nI have written a program to cycle through the files and extract z from each file and add it to a list.\nMy question now is, if I have 2 numpy arrays that look like:\nx = np.arange(10,20,1)\n y = np.arange(20,50,1)\nand a z list that has ~2000 floats in it, what is the best way to plot how z depends on x and y? Is there standard way to do this?\nI had been thinking it would be best to extract x, y and z from a file, and add them to a multidimensional array. If this is the case could anyone point me in the right direction of how to extract the x and y values out of the file name.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":470,"Q_Id":47838811,"Users Score":0,"Answer":"use x and y as coordinates and put a dot sized for the energy z.\na table would work as well, since you haven't stated that x and y geometries have any numeric purpose other than as lablels.","Q_Score":0,"Tags":"python,arrays,numpy,matplotlib,multidimensional-array","A_Id":47839249,"CreationDate":"2017-12-15T19:30:00.000","Title":"Plotting a 3d surface in Python from known values","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have python 3.6, Mac OS X El Capitan.\nI installed scipy by pip install scipy. But when I import scipy, I get the following error:\n\n\/Library\/Frameworks\/Python.framework\/Versions\/3.6\/lib\/python3.6\/site-packages\/scipy\/init.py in ()\n      116     del _NumpyVersion\n      117 \n  --> 118     from scipy._lib._ccallback import LowLevelCallable\n      119 \n      120     from scipy._lib._testutils import PytestTester\n\/Library\/Frameworks\/Python.framework\/Versions\/3.6\/lib\/python3.6\/site-packages\/scipy\/_lib\/_ccallback.py in ()\n  ----> 1 from . import _ccallback_c\n        2 \n        3 import ctypes\n        4 \n        5 PyCFuncPtr = ctypes.CFUNCTYPE(ctypes.c_void_p).bases[0]\nImportError: dlopen(\/Library\/Frameworks\/Python.framework\/Versions\/3.6\/lib\/python3.6\/site-packages\/scipy\/_lib\/_ccallback_c.cpython-36m-darwin.so, 2): no suitable image found.  Did find:\n      \/Library\/Frameworks\/Python.framework\/Versions\/3.6\/lib\/python3.6\/site-packages\/scipy\/_lib\/_ccallback_c.cpython-36m-darwin.so: mach-o, but wrong architecture\n\nI don't get this error in Python2.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":5874,"Q_Id":47858150,"Users Score":0,"Answer":"Looks like I am the only one on earth to have this issue. Fortunately, I got it to work with endless attempts. In case someone in the future gets the same error, you can try this:python -m pip install scipy. I have no idea why pip install scipy doesn't work.","Q_Score":3,"Tags":"scipy,python-3.6","A_Id":48814986,"CreationDate":"2017-12-17T18:23:00.000","Title":"scipy ImportError: dlopen no suitable image found in Python 3","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have python 3.6, Mac OS X El Capitan.\nI installed scipy by pip install scipy. But when I import scipy, I get the following error:\n\n\/Library\/Frameworks\/Python.framework\/Versions\/3.6\/lib\/python3.6\/site-packages\/scipy\/init.py in ()\n      116     del _NumpyVersion\n      117 \n  --> 118     from scipy._lib._ccallback import LowLevelCallable\n      119 \n      120     from scipy._lib._testutils import PytestTester\n\/Library\/Frameworks\/Python.framework\/Versions\/3.6\/lib\/python3.6\/site-packages\/scipy\/_lib\/_ccallback.py in ()\n  ----> 1 from . import _ccallback_c\n        2 \n        3 import ctypes\n        4 \n        5 PyCFuncPtr = ctypes.CFUNCTYPE(ctypes.c_void_p).bases[0]\nImportError: dlopen(\/Library\/Frameworks\/Python.framework\/Versions\/3.6\/lib\/python3.6\/site-packages\/scipy\/_lib\/_ccallback_c.cpython-36m-darwin.so, 2): no suitable image found.  Did find:\n      \/Library\/Frameworks\/Python.framework\/Versions\/3.6\/lib\/python3.6\/site-packages\/scipy\/_lib\/_ccallback_c.cpython-36m-darwin.so: mach-o, but wrong architecture\n\nI don't get this error in Python2.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":5874,"Q_Id":47858150,"Users Score":0,"Answer":"What I did find on MacOS 10.14.2 is that I had installed Scipy 1.1. After executing python -m pip install scipy I got Scipy 1.2 and get rid of \"ImportError: dlopen\".","Q_Score":3,"Tags":"scipy,python-3.6","A_Id":54552819,"CreationDate":"2017-12-17T18:23:00.000","Title":"scipy ImportError: dlopen no suitable image found in Python 3","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to import tensorflow (with GPU) and keep getting the following error:\n\nImportError: Could not find 'cudart64_80.dll'. TensorFlow requires that this DLL be installed in a directory that is named in your %PATH% environment variable\n\nSetup:\n\nNVIDIA GTX 1080\nCUDA Development Tool v8.0\ncuDNN 6.0\ntensorflow-gpu 1.4\n\nEnvironment variables:\n\nCUDA_HOME: C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v8.0\nCUDA_PATH: C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v8.0\nCUDA_PATH_V8.0: C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v8.0\n\nI have also added the following to the %PATH% variable:\n\nC:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v8.0\\bin\nC:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v8.0\\libnvvp\nC:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v8.0\\extras\\CUPTI\\libx64\nC:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v8.0\\lib\\x64\n\nWhat am I missing? Why it can't find cudart64_80.dll despite its location is explicitly specified in %PATH%?\nAny help would be much appreciated.","AnswerCount":3,"Available Count":3,"Score":0.1325487884,"is_accepted":false,"ViewCount":12211,"Q_Id":47860803,"Users Score":2,"Answer":"I had a similar issue, but with the version 9.1 which I had on my machine.\nThe one which was missing  'cudart64_90.dll', whereas there was  'cudart64_91.dll'. So I did a 'downgrade' from CUDA 9.1 to 9.0 and it solved my problem. Hope it helps.","Q_Score":5,"Tags":"python,tensorflow,tensorflow-gpu","A_Id":49210718,"CreationDate":"2017-12-18T00:34:00.000","Title":"Error loading tensorflow - Could not find \"cudart64_80.dll\"","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to import tensorflow (with GPU) and keep getting the following error:\n\nImportError: Could not find 'cudart64_80.dll'. TensorFlow requires that this DLL be installed in a directory that is named in your %PATH% environment variable\n\nSetup:\n\nNVIDIA GTX 1080\nCUDA Development Tool v8.0\ncuDNN 6.0\ntensorflow-gpu 1.4\n\nEnvironment variables:\n\nCUDA_HOME: C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v8.0\nCUDA_PATH: C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v8.0\nCUDA_PATH_V8.0: C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v8.0\n\nI have also added the following to the %PATH% variable:\n\nC:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v8.0\\bin\nC:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v8.0\\libnvvp\nC:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v8.0\\extras\\CUPTI\\libx64\nC:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v8.0\\lib\\x64\n\nWhat am I missing? Why it can't find cudart64_80.dll despite its location is explicitly specified in %PATH%?\nAny help would be much appreciated.","AnswerCount":3,"Available Count":3,"Score":1.2,"is_accepted":true,"ViewCount":12211,"Q_Id":47860803,"Users Score":1,"Answer":"In certain cases you may need to restart the computer to propagate all the changes. \nIf you are using intellij or pycharm, make sure to restart that as it may not take the correct path environment variables otherwise.","Q_Score":5,"Tags":"python,tensorflow,tensorflow-gpu","A_Id":47864449,"CreationDate":"2017-12-18T00:34:00.000","Title":"Error loading tensorflow - Could not find \"cudart64_80.dll\"","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to import tensorflow (with GPU) and keep getting the following error:\n\nImportError: Could not find 'cudart64_80.dll'. TensorFlow requires that this DLL be installed in a directory that is named in your %PATH% environment variable\n\nSetup:\n\nNVIDIA GTX 1080\nCUDA Development Tool v8.0\ncuDNN 6.0\ntensorflow-gpu 1.4\n\nEnvironment variables:\n\nCUDA_HOME: C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v8.0\nCUDA_PATH: C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v8.0\nCUDA_PATH_V8.0: C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v8.0\n\nI have also added the following to the %PATH% variable:\n\nC:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v8.0\\bin\nC:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v8.0\\libnvvp\nC:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v8.0\\extras\\CUPTI\\libx64\nC:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v8.0\\lib\\x64\n\nWhat am I missing? Why it can't find cudart64_80.dll despite its location is explicitly specified in %PATH%?\nAny help would be much appreciated.","AnswerCount":3,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":12211,"Q_Id":47860803,"Users Score":0,"Answer":"I just changed cudart64_90 to cudart64_80. It worked","Q_Score":5,"Tags":"python,tensorflow,tensorflow-gpu","A_Id":57690869,"CreationDate":"2017-12-18T00:34:00.000","Title":"Error loading tensorflow - Could not find \"cudart64_80.dll\"","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Pandas .to_sql is not inserting any records for a dataframe I want to send to sql. Are there any generic reasons why this might be the case? \n\nI am not getting any error messages. The column names appear fine, but the table is entirely empty. \nWhen I try to send over a single column (i.e. data.ix[2]), it actually works. \nHowever, if I try to send over more than one column (data.ix[1:3]), I again get a completely blank table in sql. \n\nI have been using this code for other dataframes and have never encountered this problem. It still runs for other dataframes in my set.","AnswerCount":2,"Available Count":2,"Score":-0.0996679946,"is_accepted":false,"ViewCount":2183,"Q_Id":47878076,"Users Score":-1,"Answer":"I was also facing same issue because dot was added in header. remove dot then it will work.","Q_Score":1,"Tags":"python,sql,python-3.x,postgresql,pandas","A_Id":55399149,"CreationDate":"2017-12-18T23:46:00.000","Title":"Pandas .to_sql is not inserting any records for a dataframe I want to send to sql. Are there any generic reasons why this might be the case?","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Pandas .to_sql is not inserting any records for a dataframe I want to send to sql. Are there any generic reasons why this might be the case? \n\nI am not getting any error messages. The column names appear fine, but the table is entirely empty. \nWhen I try to send over a single column (i.e. data.ix[2]), it actually works. \nHowever, if I try to send over more than one column (data.ix[1:3]), I again get a completely blank table in sql. \n\nI have been using this code for other dataframes and have never encountered this problem. It still runs for other dataframes in my set.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":2183,"Q_Id":47878076,"Users Score":0,"Answer":"I fixed this problem - it was becomes some of the column headers had '%' in it. \nI accidentally discovered this reason for the empty tables when I tried to use io and copy_from a temporary csv, instead of to_sql. I got a transaction error based on a % placeholder error. \nAgain, this is specific to passing to PSQL; it went through to SQL Server without a hitch.","Q_Score":1,"Tags":"python,sql,python-3.x,postgresql,pandas","A_Id":47896038,"CreationDate":"2017-12-18T23:46:00.000","Title":"Pandas .to_sql is not inserting any records for a dataframe I want to send to sql. Are there any generic reasons why this might be the case?","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"ModuleNotFoundError                       Traceback (most recent call last)\n in ()\n     11 import numpy as np\n     12 \n---> 13 import tensorflow as tf\n     14 \n     15 \nModuleNotFoundError: No module named 'tensorflow'","AnswerCount":3,"Available Count":1,"Score":0.1325487884,"is_accepted":false,"ViewCount":9109,"Q_Id":47892038,"Users Score":2,"Answer":"I was looking for a similar issue (unable to import tensorflow in jupyter) and found that maybe most answers are outdated because now conda installs tf in its own environment. \nThe most useful thing I found is:\nhttps:\/\/docs.anaconda.com\/anaconda\/user-guide\/tasks\/tensorflow\/\nwhich explains in very few steps how to install tf or tf-gpu in its own environment. \nThen my problem was that the jupyter notebook is in its own base environment, not in the tf-gpu environment. How to work with that from a jupyter notebook based on the base environment?\nThe solution comes from the very helpful answer from Nihal Sangeeth to this question \nhttps:\/\/stackoverflow.com\/questions\/53004311\/how-to-add-conda-environment-to-jupyter-lab\nconda activate tf-gpu\n(tf-gpu)$ conda install ipykernel\n(tf-gpu)$ ipython kernel install --user --name=<any_name_you_like_for_kernel>\n(tf-gpu)$ conda deactivate\nClose and reopen your jupyter notebook.\nThen in your jupyter notebook you will find the option, under \"kernel\" of \"change kernel\". Change kernel to your newly created kernel and you will be able to import tensorflow as tf and go on from there.\nHope it helps somebody","Q_Score":2,"Tags":"python","A_Id":58295830,"CreationDate":"2017-12-19T17:13:00.000","Title":"import tensorflow in Anaconda prompt","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to ask about how I reshape an array with a different number of elements, For example, the size of the array is (1000,). I want to reshape it to be (1000, None,None,1) to be the input to CNN, I'm using keras.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":373,"Q_Id":47898566,"Users Score":1,"Answer":"I don't think 'None' can be accepted in any reshape function. Reshape functions usually require numeric size. But you can reshape \n'None' part to 1 to fit the model you are using.\nFor reshaping, \nconsidering x is the variable that needs to be reshaped,\n\nUsing Numpy,\nx = np.reshape(x, (1000, 1, 1, 1))\nUsing Tensorflow,\nx = tf.reshape(x, (1000, 1, 1, 1))","Q_Score":0,"Tags":"python,tensorflow,keras","A_Id":47898790,"CreationDate":"2017-12-20T03:56:00.000","Title":"Variable length array reshape for input to CNN","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am planning to classify emails. I am using tfidf vectorizer and logistic regression algorithm to do this. I took very small training and testing sets. My training set consists of 150 emails( 3 classes, 50 emails\/class) and testing set consists of 6 emails. Now my classifier is predicting 4 out of 6 correctly. Now my doubt is, can I tell the classifier that this document belongs to class X not class Y? If yes, What is this process called?\nThank you.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":422,"Q_Id":47905139,"Users Score":0,"Answer":"If you have 3 classes and labelled data and have trained the model, then you have \"told the classifier\" everything you can (ie trained).\nIf you're saying you want to tell the classifier about the 2\/6 test cases that failed, you then no its not possible with Logistic Regression (maybe some other feedback model?).  What you need is to train the model more, or add more test cases.  You could add those 2 failed cases to training and try different test data.\nYou may have an underfit model you can try to tune, but with the experiments I've done with text similar to yours it can be difficult to obtain really high accuracy with limited data and just tf-idf since the \"model\" is just word frequencies.","Q_Score":0,"Tags":"python,text-classification","A_Id":50867759,"CreationDate":"2017-12-20T11:36:00.000","Title":"text classification using logistic regression","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have created document vectors for a large corpus using Gensim's doc2vec.\nsentences=gensim.models.doc2vec.TaggedLineDocument('file.csv')\nmodel = gensim.models.doc2vec.Doc2Vec(sentences,size = 10, window = 800, min_count = 1, workers=40, iter=10, dm=0)\nNow I am using Gensim's infer_vector() using those document vectors to create document vectors for another sample corpus\nEg: model.infer_vector('This is a string')\nIs there a way to pass the entire DataFrame through infer_vector and get the output vectors for each line in the DataFrame?","AnswerCount":2,"Available Count":2,"Score":0.1973753202,"is_accepted":false,"ViewCount":1915,"Q_Id":47905576,"Users Score":2,"Answer":"Doc2Vec infer_vector() only takes individual text examples, as lists-of-word-tokens. So you can't pass in a batch of examples. (And, you shouldn't be passing in non-tokenized strings \u2013 but lists-of-tokens, preprocessed in the same manner as your training data was preprocessed.)\nBut, you might be able to use a function that multiply-applies infer_vector() for you, as the @COLDSPEED comment suggests. Still, the column should have lists-of-tokens, rather than strings-of-characters, if you want meaningful results. \nAlso, most users find infer_vector() works much better using non-default values for its steps parameter (much larger than its default of 5), and perhaps smaller values for its starting alpha parameter (such as more like the training default of 0.025 than the inference default of 0.1).","Q_Score":2,"Tags":"python,gensim,doc2vec","A_Id":47918220,"CreationDate":"2017-12-20T11:59:00.000","Title":"How to use Gensim Doc2vec infer_vector() for large DataFrame?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have created document vectors for a large corpus using Gensim's doc2vec.\nsentences=gensim.models.doc2vec.TaggedLineDocument('file.csv')\nmodel = gensim.models.doc2vec.Doc2Vec(sentences,size = 10, window = 800, min_count = 1, workers=40, iter=10, dm=0)\nNow I am using Gensim's infer_vector() using those document vectors to create document vectors for another sample corpus\nEg: model.infer_vector('This is a string')\nIs there a way to pass the entire DataFrame through infer_vector and get the output vectors for each line in the DataFrame?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1915,"Q_Id":47905576,"Users Score":0,"Answer":"@gojomo: Thanks for the answer, but I tried inferring using both tokenized rows as well as raw strings, and got the same document vector.\nIs there a way to know if the document vector which are getting created are meaningful or not?","Q_Score":2,"Tags":"python,gensim,doc2vec","A_Id":47925223,"CreationDate":"2017-12-20T11:59:00.000","Title":"How to use Gensim Doc2vec infer_vector() for large DataFrame?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to tensorflow and I am working on shared linux (Ubuntu 16.04), it means I don't have root access. Cuda 8.0 and Cudnn 8 are already installed by admin as root. I have installed python 3.5 using anaconda and then installed tensorflow using pip. I have added the cuda-8.0\/bin and cuda-8.0\/lib64 to PATH and LD_PATH_LIBRARY using following exports.\nexport PATH=\"$PATH:\/usr\/local\/cuda-8.0\/bin\"\nexport LD_LIBRARY_PATH=\"\/usr\/local\/cuda-8.0\/lib64\"\nBut when I try to run the program it gives the following error.\nImportError: libcublas.so.8.0: cannot open shared object file: No such file or directory\nHowever these files exist in LD_LIBRARY_PATH, and nvcc -V is also working.\nIs it even possible to refer to the system installed Cuda and CuDnn ? If yes, can you help to clear the above error. Thanks in advance.","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":1016,"Q_Id":47928371,"Users Score":2,"Answer":"A likely explanation is that your path is not set up correctly. \nTry echo $LD_LIBRARY_PATH and let us know what you get. \nAnother explanation is that it is not in that directory. Yes, libcublas.so should normally be in \/usr\/local\/cuda-8.0\/lib64 but double check if it is there or another directory by using find.","Q_Score":3,"Tags":"python,ubuntu,tensorflow,cublas","A_Id":47928522,"CreationDate":"2017-12-21T15:48:00.000","Title":"ImportError: libcublas.so.8.0: cannot open shared object file: No such file or directory (Shared Linux)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Does anyone know of an efficient way to create a new dataframe based off of two dataframes in Python\/Pandas?\nWhat I am trying to do is check if a value from df1 is in df2, then do not add the row to df3. I am working with student IDS, and if a student ID from df1 is in df2, I do not want to include it in the new dataframe, df3.\nSo does anybody know an efficient way to do this? I have googled and looked on SO, but found nothing that works so far.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":226,"Q_Id":47934376,"Users Score":0,"Answer":"If you have both dataframes of same length you can also use:\nprint df1.loc[df1['ID'] != df2['ID']]\nassign it to a third dataframe.","Q_Score":1,"Tags":"python,pandas,dataframe","A_Id":47934883,"CreationDate":"2017-12-22T00:10:00.000","Title":"Create a dataframe by discarding intersections of two dataframes (Pandas)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using online library and able to fetch words from an image with their locations.\nNow I want to form sentences exactly like which are in image.\nAny idea how can I do that?\nEarlier i used the distance between two words and if there are pretty close then it means it is a part of a sentence but this approach is not working fine\nPlease help\nThis is the json I am receiving I have...\n\"WordText\": \"Word 1\",\n                                \"Left\": 106,\n                                \"Top\": 91,\n                                \"Height\": 9,\n                                \"Width\": 11\n                                },\n                                {\n                                \"WordText\": \"Word 2\",\n                                \"Left\": 121,\n                                \"Top\": 90,\n                                \"Height\": 13,\n                                \"Width\": 51\n                                }\n                                .\n                                .\n                                .\n                                More Words","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":41,"Q_Id":47941250,"Users Score":0,"Answer":"I don't if I am understanding your problem correctly, but I'm assuming each dictionary in your json is giving you the coordinates for a word.\nMy approach would be to first find the difference in pixels for the space between any 2 words, and you use this value to detect the sequence of words.\nFor example:\nimg1 = {'coordinates': 'information'}\nimg2 = {'coordinates': 'information'}\nspace_value = 10  # for example\nif img1['Height'] == img2['Height'] and (img1['Left'] + img1['Width'] + space_value) == img2['Left']:\nnext_word = True","Q_Score":0,"Tags":"javascript,python,image-processing,ocr,image-conversion","A_Id":47942101,"CreationDate":"2017-12-22T12:06:00.000","Title":"Able to fetch text with their locations from an image...How can I form sentence?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Any ideas on how to export the yearly seasonal trend using fbprophet library?\nThe plot_components() function plots the trend, yearly, and weekly.\nI want to obtain the values for yearly only.","AnswerCount":2,"Available Count":1,"Score":0.2913126125,"is_accepted":false,"ViewCount":3166,"Q_Id":47946518,"Users Score":3,"Answer":"There is a simple solution in the current version of the library. You can use from the predicted model fc. What you want for the value of yearly can be found with fc['yearly'] without using the functions in the above solution.\nMoreover, if you want all the other components like trend, you can use fc['trend'].","Q_Score":7,"Tags":"python-3.x,time-series,facebook-prophet","A_Id":51340436,"CreationDate":"2017-12-22T19:34:00.000","Title":"Python fbprophet - export values from plot_components() for yearly","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have been trying to use OpenCV to display my camera stream in the same window as another (GUI) window. However, the imshow() method opens its own window. Is there another way to do this so that it displays in the same window as my GUI?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":223,"Q_Id":47947718,"Users Score":0,"Answer":"I think the GUI you're talking about is Tkinter, but opencv's cv.imshow is just playing frame.I don't think it's necessary to use GUI to show frame.Because cv2.imshow is good at handling.","Q_Score":0,"Tags":"python,opencv,imshow","A_Id":47976344,"CreationDate":"2017-12-22T21:43:00.000","Title":"How to display OpenCV camera stream in the same window as another file?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there anyway of saving OneHotencoder object in python? . Reason is being I used that object in preprocessing of training data and test data and we are building a API containing the same trained model and that will be injected by real data from the website when user created. So first that data needs to be preprocessed and then model can predict o\/p for the same.\nThanks","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1953,"Q_Id":47958697,"Users Score":-1,"Answer":"simple use of pickel for OHE will do for me.","Q_Score":4,"Tags":"python,scikit-learn,one-hot-encoding","A_Id":47958997,"CreationDate":"2017-12-24T07:01:00.000","Title":"Save OneHot Encoder object python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've tried to create a custom object detector in the paperspace cloud desktop, then I tried it on Jupyter Notebook and it works. \nNow, I've uploaded the whole models-master folder and downloaded it on my local machine. \nI ran it using Jupyter Notebook and it now gives an InvalidArgumentError. I've tried re-exporting the inference graph on my local machine using the same ckpt that was trained on cloud but it is still not working.\n\nInvalidArgumentError                      Traceback (most recent call\n  last)\n  \/usr\/local\/lib\/python3.5\/dist-packages\/tensorflow\/python\/client\/session.py\n  in _do_call(self, fn, *args)    1322     try:\n  -> 1323       return fn(*args)    1324     except errors.OpError as e:\n\/usr\/local\/lib\/python3.5\/dist-packages\/tensorflow\/python\/client\/session.py\n  in _run_fn(session, feed_dict, fetch_list, target_list, options,\n  run_metadata)    1301                                    feed_dict,\n  fetch_list, target_list,\n  -> 1302                                    status, run_metadata)    1303 \n\/usr\/local\/lib\/python3.5\/dist-packages\/tensorflow\/python\/framework\/errors_impl.py\n  in exit(self, type_arg, value_arg, traceback_arg)\n      472             compat.as_text(c_api.TF_Message(self.status.status)),\n  --> 473             c_api.TF_GetCode(self.status.status))\n      474     # Delete the underlying status object from memory otherwise it stays alive\nInvalidArgumentError: NodeDef mentions attr 'T' not in Op index:int64>; NodeDef:\n  Postprocessor\/BatchMultiClassNonMaxSuppression\/map\/while\/MultiClassNonMaxSuppression\/FilterGreaterThan\/Where\n  = WhereT=DT_BOOL, _device=\"\/job:localhost\/replica:0\/task:0\/device:GPU:0\".\n  (Check whether your GraphDef-interpreting binary is up to date with\n  your GraphDef-generating binary.).     [[Node:\n  Postprocessor\/BatchMultiClassNonMaxSuppression\/map\/while\/MultiClassNonMaxSuppression\/FilterGreaterThan\/Where\n  = WhereT=DT_BOOL, _device=\"\/job:localhost\/replica:0\/task:0\/device:GPU:0\"]]\nDuring handling of the above exception, another exception occurred:\nInvalidArgumentError                      Traceback (most recent call\n  last)  in ()\n       20       (boxes, scores, classes, num) = sess.run(\n       21           [detection_boxes, detection_scores, detection_classes, num_detections],\n  ---> 22           feed_dict={image_tensor: image_np_expanded})\n       23       # Visualization of the results of a detection.\n       24       vis_util.visualize_boxes_and_labels_on_image_array(\n\/usr\/local\/lib\/python3.5\/dist-packages\/tensorflow\/python\/client\/session.py\n  in run(self, fetches, feed_dict, options, run_metadata)\n      887     try:\n      888       result = self._run(None, fetches, feed_dict, options_ptr,\n  --> 889                          run_metadata_ptr)\n      890       if run_metadata:\n      891         proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)\n\/usr\/local\/lib\/python3.5\/dist-packages\/tensorflow\/python\/client\/session.py\n  in _run(self, handle, fetches, feed_dict, options, run_metadata)\n  1118     if final_fetches or final_targets or (handle and\n  feed_dict_tensor):    1119       results = self._do_run(handle,\n  final_targets, final_fetches,\n  -> 1120                              feed_dict_tensor, options, run_metadata)    1121     else:    1122       results = []\n\/usr\/local\/lib\/python3.5\/dist-packages\/tensorflow\/python\/client\/session.py\n  in _do_run(self, handle, target_list, fetch_list, feed_dict, options,\n  run_metadata)    1315     if handle is None:    1316       return\n  self._do_call(_run_fn, self._session, feeds, fetches, targets,\n  -> 1317                            options, run_metadata)    1318     else:    1319       return self._do_call(_prun_fn, self._session,\n  handle, feeds, fetches)\n\/usr\/local\/lib\/python3.5\/dist-packages\/tensorflow\/python\/client\/session.py\n  in _do_call(self, fn, *args)    1334         except KeyError:    1335 \n  pass\n  -> 1336       raise type(e)(node_def, op, message)    1337     1338   def _extend_graph(self):\nInvalidArgumentError: NodeDef mentions attr 'T' not in Op index:int64>; NodeDef:\n  Postprocessor\/BatchMultiClassNonMaxSuppression\/map\/while\/MultiClassNonMaxSuppression\/FilterGreaterThan\/Where\n  = WhereT=DT_BOOL, _device=\"\/job:localhost\/replica:0\/task:0\/device:GPU:0\".\n  (Check whether your GraphDef-interpreting binary is up to date with\n  your GraphDef-generating binary.).     [[Node:\n  Postprocessor\/BatchMultiClassNonMaxSuppression\/map\/while\/MultiClassNonMaxSuppression\/FilterGreaterThan\/Where\n  = WhereT=DT_BOOL, _device=\"\/job:localhost\/replica:0\/task:0\/device:GPU:0\"]]\nCaused by op\n  'Postprocessor\/BatchMultiClassNonMaxSuppression\/map\/while\/MultiClassNonMaxSuppression\/FilterGreaterThan\/Where',\n  defined at:   File \"\/usr\/lib\/python3.5\/runpy.py\", line 184, in\n  _run_module_as_main\n      \"main\", mod_spec)   File \"\/usr\/lib\/python3.5\/runpy.py\", line 85, in _run_code\n      exec(code, run_globals)   File \"\/home\/ryan\/.local\/lib\/python3.5\/site-packages\/ipykernel_launcher.py\",\n  line 16, in \n      app.launch_new_instance()   File \"\/home\/ryan\/.local\/lib\/python3.5\/site-packages\/traitlets\/config\/application.py\",\n  line 658, in launch_instance\n      app.start()   File \"\/home\/ryan\/.local\/lib\/python3.5\/site-packages\/ipykernel\/kernelapp.py\",\n  line 477, in start\n      ioloop.IOLoop.instance().start()   File \"\/home\/ryan\/.local\/lib\/python3.5\/site-packages\/zmq\/eventloop\/ioloop.py\",\n  line 177, in start\n      super(ZMQIOLoop, self).start()   File \"\/home\/ryan\/.local\/lib\/python3.5\/site-packages\/tornado\/ioloop.py\",\n  line 888, in start\n      handler_func(fd_obj, events)   File \"\/home\/ryan\/.local\/lib\/python3.5\/site-packages\/tornado\/stack_context.py\",\n  line 277, in null_wrapper\n      return fn(*args, **kwargs)   File \"\/home\/ryan\/.local\/lib\/python3.5\/site-packages\/zmq\/eventloop\/zmqstream.py\",\n  line 440, in _handle_events\n      self._handle_recv()   File \"\/home\/ryan\/.local\/lib\/python3.5\/site-packages\/zmq\/eventloop\/zmqstream.py\",\n  line 472, in _handle_recv\n      self._run_callback(callback, msg)   File \"\/home\/ryan\/.local\/lib\/python3.5\/site-packages\/zmq\/eventloop\/zmqstream.py\",\n  line 414, in _run_callback\n      callback(*args, **kwargs)   File \"\/home\/ryan\/.local\/lib\/python3.5\/site-packages\/tornado\/stack_context.py\",\n  line 277, in null_wrapper\n      return fn(*args, **kwargs)   File \"\/home\/ryan\/.local\/lib\/python3.5\/site-packages\/ipykernel\/kernelbase.py\",\n  line 283, in dispatcher\n      return self.dispatch_shell(stream, msg)   File \"\/home\/ryan\/.local\/lib\/python3.5\/site-packages\/ipykernel\/kernelbase.py\",\n  line 235, in dispatch_shell\n      handler(stream, idents, msg)   File \"\/home\/ryan\/.local\/lib\/python3.5\/site-packages\/ipykernel\/kernelbase.py\",\n  line 399, in execute_request\n      user_expressions, allow_stdin)   File \"\/home\/ryan\/.local\/lib\/python3.5\/site-packages\/ipykernel\/ipkernel.py\",\n  line 196, in do_execute\n      res = shell.run_cell(code, store_history=store_history, silent=silent)   File\n  \"\/home\/ryan\/.local\/lib\/python3.5\/site-packages\/ipykernel\/zmqshell.py\",\n  line 533, in run_cell\n      return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)   File\n  \"\/home\/ryan\/.local\/lib\/python3.5\/site-packages\/IPython\/core\/interactiveshell.py\",\n  line 2728, in run_cell\n      interactivity=interactivity, compiler=compiler, result=result)   File\n  \"\/home\/ryan\/.local\/lib\/python3.5\/site-packages\/IPython\/core\/interactiveshell.py\",\n  line 2850, in run_ast_nodes\n      if self.run_code(code, result):   File \"\/home\/ryan\/.local\/lib\/python3.5\/site-packages\/IPython\/core\/interactiveshell.py\",\n  line 2910, in run_code\n      exec(code_obj, self.user_global_ns, self.user_ns)   File \"\", line 7, in \n      tf.import_graph_def(od_graph_def, name='')   File \"\/usr\/local\/lib\/python3.5\/dist-packages\/tensorflow\/python\/framework\/importer.py\",\n  line 313, in import_graph_def\n      op_def=op_def)   File \"\/usr\/local\/lib\/python3.5\/dist-packages\/tensorflow\/python\/framework\/ops.py\",\n  line 2956, in create_op\n      op_def=op_def)   File \"\/usr\/local\/lib\/python3.5\/dist-packages\/tensorflow\/python\/framework\/ops.py\",\n  line 1470, in init\n      self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access\nInvalidArgumentError (see above for traceback): NodeDef mentions attr\n  'T' not in Op index:int64>;\n  NodeDef:\n  Postprocessor\/BatchMultiClassNonMaxSuppression\/map\/while\/MultiClassNonMaxSuppression\/FilterGreaterThan\/Where\n  = WhereT=DT_BOOL, _device=\"\/job:localhost\/replica:0\/task:0\/device:GPU:0\".\n  (Check whether your GraphDef-interpreting binary is up to date with\n  your GraphDef-generating binary.).     [[Node:\n  Postprocessor\/BatchMultiClassNonMaxSuppression\/map\/while\/MultiClassNonMaxSuppression\/FilterGreaterThan\/Where\n  = WhereT=DT_BOOL, _device=\"\/job:localhost\/replica:0\/task:0\/device:GPU:0\"]]","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":665,"Q_Id":47964765,"Users Score":0,"Answer":"Are the cloud and local machines running the same Python\/Tensorflow versions? Sometimes checkpoints produced by a specific Tensorflow version are not backward compatible due to internal variables renaming.","Q_Score":0,"Tags":"python,tensorflow,machine-learning,deep-learning","A_Id":48297553,"CreationDate":"2017-12-24T23:52:00.000","Title":"Tensorflow trained model works on cloud machine but gives an error when used in my local pc","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I keep getting this error\nOpenCV Error: Assertion failed (_img.rows * _img.cols == vecSize) in get, file \/build\/opencv-SviWsf\/opencv-2.4.9.1+dfsg\/apps\/traincascade\/imagestorage.cpp, line 157\nterminate called after throwing an instance of 'cv::Exception'\n  what():  \/build\/opencv-SviWsf\/opencv-2.4.9.1+dfsg\/apps\/traincascade\/imagestorage.cpp:157: error: (-215) _img.rows * _img.cols == vecSize in function get\nAborted (core dumped)\nwhen running opencv_traincascade.  I run with these arguments: opencv_traincascade -data data -vec positives.vec -bg bg.txt -numPos 1600 -numNeg 800 -numStages 10 -w 20 -h 20.  \n\nMy project build is as follows: \nworkspace\n|__bg.txt\n|__data\/ # where I plan to put cascade\n|__info\/\n      |__ # all samples\n      |__info.lst\n|__jersey5050.jpg\n|__neg\/\n   |__ # neg images\n|__opencv\/\n|__positives.vec\n\n\nbefore I ran opencv_createsamples -img jersey5050.jpg -bg bg.txt -info info\/info.lst -maxxangle 0.5 - maxyangle 0.5 -maxzangle 0.5 -num 1800\n\nNot quite sure why I'm getting this error.  The images are all converted to greyscale as well.  The neg's are sized at 100x100 and jersey5050.jpg is sized at 50x50.  I saw someone had a the same error on the OpenCV forums and someone suggested deleting the backup .xml files that are created b OpenCV in case the training is \"interrupted\".  I deleted those and nothing.  Please help! I'm using python 3 on mac.  I'm also running these commands on an ubuntu server from digitalocean with 2GB of ram but I don't think that's part of the problem.\nEDIT\nForgot to mention, after the opencv_createsamples command, i then ran opencv_createsamples -info info\/info.lst -num 1800 -w 20 -h20 -vec positives.vec","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":2068,"Q_Id":47965021,"Users Score":1,"Answer":"I solved it haha.  Even though I specified in the command the width and height to be 20x20, it changed it to 20x24.  So the opencv_traincascade command was throwing an error.  Once I changed the width and height arguments in the opencv_traincascade command it worked.","Q_Score":1,"Tags":"python-3.x,opencv,cascade,assertion","A_Id":47965130,"CreationDate":"2017-12-25T01:08:00.000","Title":"OpenCV Error: Assertion failed (_img.rows * _img.cols == vecSize)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I installed the module factor-analyzer-0.2.2 but cannot import the function. My code is from factor_analyzer import FactorAnalyzer. I'm getting an error ModuleNotFoundError: No module named 'factor_analyzer'.\nI'm using Python 3.6 and Jupyter notebook. Do you know why this does not work? \nThank you!!","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":5421,"Q_Id":47973216,"Users Score":0,"Answer":"Can you try to run the import command on your Python Shell and let us know if you are able to import it successfully?","Q_Score":2,"Tags":"python,python-3.x","A_Id":47973889,"CreationDate":"2017-12-26T01:43:00.000","Title":"Cannot import FactorAnalyzer from module factor-analyzer-0.2.2","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I installed the module factor-analyzer-0.2.2 but cannot import the function. My code is from factor_analyzer import FactorAnalyzer. I'm getting an error ModuleNotFoundError: No module named 'factor_analyzer'.\nI'm using Python 3.6 and Jupyter notebook. Do you know why this does not work? \nThank you!!","AnswerCount":3,"Available Count":2,"Score":0.0665680765,"is_accepted":false,"ViewCount":5421,"Q_Id":47973216,"Users Score":1,"Answer":"Use the anaconda prompt to run the pip install factor-analyzer rather than termainal or powershell. I had the same problem and doing that solved it for me.","Q_Score":2,"Tags":"python,python-3.x","A_Id":58283301,"CreationDate":"2017-12-26T01:43:00.000","Title":"Cannot import FactorAnalyzer from module factor-analyzer-0.2.2","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am engaged in analysing HDF5 format data for scientific research purposes. I'm using Python's h5py library.\nNow, the HDF file I want to read is so large. Its file size is about 20GB and the main part of its data is 400000*10000 float matrix. I tried to read the data once, but my development environment Spyder was terminated by compulsion because of the shortage of the memory. Then is there any method to read it partially and avoid this problem?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1280,"Q_Id":47978664,"Users Score":0,"Answer":"You can slice h5py datasets like numpy arrays, so you could work on a number of subsets instead of the whole dataset (e.g. 4 100000*10000 subsets).","Q_Score":1,"Tags":"python,hdf5,h5py","A_Id":47985532,"CreationDate":"2017-12-26T12:27:00.000","Title":"How to read data in HDF5 format file partially when the data is too large to read fully","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using Keras for a project and I don't understand how Keras uses data input, that is to say how Keras reads our input data when creating the first layer. \nFor example: \n\nmodel = Sequential()\nmodel.add(Dense(10, activation='sigmoid', input_dim=3,name='layer1'))\n\nIn this model, what does it mean to have 10 neurons and an input with 3 dimensions? If the input data has 100 examples (number of lines in the matrix data), how does Keras use them?\nThank you.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1223,"Q_Id":47988983,"Users Score":2,"Answer":"input_dim=3 means that your data have 3 features which will be used to determine final result eg. if you want to determine what animal data refer to you could put width, height and color as data.\n100 examples of different animals widths, heights and colors combinations allow neural network to adjust its parameters (learn) what width, height and color refers to what kind of animal. Keras starts with random weights for neurons and goes one by one 100 times using provided samples to adjust network weights. Keras actually uses batches which means 100 samples are divided into smaller groups for better learning rate and general performance (less data to store in memory at once).\n10 neurons are 10 'places' where network is able to store multiplications results between neuron weights and input data. You could imagine neuron as a bulb which lights a bit brighter or darker depending whether data shows some useful data feature eg. if animal is above 3 meters tall. Each neuron has its own set of weights which are changed just a bit when network examines next sample from your data. At the end you should have 10 neurons (bulbs) which react in more or less intense way depending on presence of different features in your data eg. if animal is very tall. \nThe more neurons you have the more possible features you can track eg. if animal is tall, hairy, orange, spotted etc. But the more neurons you have there is also the higher risk that your network will be too exact and will learn features which are unique for your training example (it's called overfitting) but do not help you to recognize animal samples which were not included in your training data (it's called ability to generalize and is most important point of actually training neural network). Selecting number of neurons is then a bit of practical exercise where you search for one which works for your need.\nI hope it clarifies your doubts. If you would like to get deeper in this field there are a lot of good resources online explaining neural networks training process in details including features, neurons and learning.","Q_Score":0,"Tags":"python,machine-learning,neural-network,keras,keras-layer","A_Id":47990202,"CreationDate":"2017-12-27T08:43:00.000","Title":"How does Keras read input data?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to make a neural network that has one single output neuron on the last layer that tells me the probability of there being a car on an image (the probability going from 0 - 1).\nI'm used to making neural networks for classification problems, with multiple output neurons, using the tf.nn.softmax_cross_entropy_with_logits() and tf.nn.softmax() methods. But these methods don't work when there is only one column for each sample in the labels matrix since the softmax() method will always return 1.\nI tried replacing tf.nn.softmax() with tf.nn.sigmoid() and tf.nn.softmax_cross_entropy_with_logits() with tf.nn.sigmoid_cross_entropy_with_logits(), but that gave me some weird results, I might have done something wrong in the implementation.\nHow should I define the loss and the predictions on a neural network with only one output neuron?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":430,"Q_Id":48003535,"Users Score":1,"Answer":"Just use the sigmoid layer as the final layer. There's no need for any cross entropy when you have a single output, so just let the loss function work on the sigmoid output which is limited to the output range you want.","Q_Score":0,"Tags":"python,tensorflow,machine-learning,computer-vision,deep-learning","A_Id":49556042,"CreationDate":"2017-12-28T07:39:00.000","Title":"Neural network with a single out with tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to do a neural network to predict who is going to win a soccer game. I have several features (like the team, the physical shape of the team, etc.) and an output, telling which team won (or if there was a die).\nIn my training, I want to add features like the number of goals these teams scored. The problem is that I won't be able to include these features when predicting the final result of a future game. Is there a way to do it ?\nI'm using Keras in Python as a library to easily build neural networks.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":26,"Q_Id":48015101,"Users Score":0,"Answer":"The number of goals would be an output, not an input. For example, you could build a model to predict how many goals each team would score...\nIf you used goals scored as an input, the model train to be incredibly simple and useless- just comparing the goals, ignoring all other inputs.","Q_Score":0,"Tags":"python,neural-network,keras,recurrent-neural-network","A_Id":48015138,"CreationDate":"2017-12-28T22:20:00.000","Title":"Neural Network: Extra features used for training but not for predicting new data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have created a Glue job that copies data from S3 (csv file) to Redshift.  It works and populates the desired table.\nHowever, I need to purge the table during this process as I am left with duplicate records after the process completes.\nI'm looking for a way to add this purge to the Glue process.  Any advice would be appreciated.\nThanks.","AnswerCount":5,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":4214,"Q_Id":48026111,"Users Score":0,"Answer":"You need to modify the auto generated code provided by Glue. Connect to redshift using spark jdbc connection and execute the purge query.\nTo spin up Glue containers in redshift VPC; specify the connection in glue job, to gain access for redshift cluster.\nHope this helps.","Q_Score":4,"Tags":"python,amazon-web-services,pyspark,amazon-redshift,aws-glue","A_Id":50180152,"CreationDate":"2017-12-29T17:21:00.000","Title":"AWS Glue Truncate Redshift Table","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have created a Glue job that copies data from S3 (csv file) to Redshift.  It works and populates the desired table.\nHowever, I need to purge the table during this process as I am left with duplicate records after the process completes.\nI'm looking for a way to add this purge to the Glue process.  Any advice would be appreciated.\nThanks.","AnswerCount":5,"Available Count":2,"Score":0.0798297691,"is_accepted":false,"ViewCount":4214,"Q_Id":48026111,"Users Score":2,"Answer":"The link @frobinrobin provided is out of date, and I tried many times that the preactions statements will be skiped even you provide a wrong syntax, and came out with duplicated rows(insert action did executed!)\nTry this:\njust replace the syntax from\nglueContext.write_dynamic_frame.from_jdbc_conf() in the link above to glueContext.write_dynamic_frame_from_jdbc_conf() will works!\nAt least this help me out in my case(AWS Glue job just insert data into Redshift without executing Truncate table actions)","Q_Score":4,"Tags":"python,amazon-web-services,pyspark,amazon-redshift,aws-glue","A_Id":65486258,"CreationDate":"2017-12-29T17:21:00.000","Title":"AWS Glue Truncate Redshift Table","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Does anyone know how I can see the final acceptance-rate in PyMC3 (Metropolis-Hastings) ? Or in general, how can I see all the information that pymc3.sample() returns ? \nThanks","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":551,"Q_Id":48036741,"Users Score":0,"Answer":"Let step = pymc3.Metropolis() be our sampler, we can get the final acceptance-rate through\n\"step.accepted\"\nJust for beginners (pymc3) like myself, after each variable\/obj. put a \".\" and hit the tab key; you will see some interesting suggestions ;)","Q_Score":0,"Tags":"python,pymc3","A_Id":48042507,"CreationDate":"2017-12-30T18:28:00.000","Title":"Acceptance-rate in PyMC3 (Metropolis-Hastings)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I pretty much understand the concept of Variable in Tensorflow (I think) but I still haven't found them very useful. The main reason I read it is interesting to use them it's to later restore them, which might be handy in some cases, but you could achieve similar results using numpy.save for saving matrices and values, or even writing them out in a log file.\nVariables are used to save Tensors but you can use a Python variable to save them, avoiding the Tensorflow's Variable extra wrapper.\nVariables become nice when used using get_variable function since the code will be much cleaner than using dictionary to store weights, however, in terms of functionality, I don't get why are they important.\nMy conclusion about TF Variables is that they help us to write nicer code but they are not essential. Any thoughts about them?","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":52,"Q_Id":48047793,"Users Score":2,"Answer":"The main benefit of using tf.Variables is you don't have to explicitly state what to optimize when you train a neural network. From the perspective of Machine Learning, Variables are network parameters (or weights) which have some initial value before training but get optimized during training (The gradients of the loss are calculated with respect to the Variables and using an Optimization Algorithm, their Variables are updated. (the famous equation w = w - alpha*dL_dw for SGD Algorithm).\nIn contrast, tf.constant is used to store those network parameters for which gradients of the loss are not intended. During training, the gradients of the loss with respect to constants are not calculated and hence their values are not updated. In order to feed the network with inputs, we use tf.placeholder. \nAre they essential? Yes. A deep neural network has millions of parameters  across dozens of Variables and though one can\n\ndefine tensors with some initial value (based on some heuristics, like Glorot initialization)\ncalculate the gradient of the loss with respect to each of them (maybe using a for loop)\nupdate their values based on some optimization algorithm (e.g., SGD)\ntaking extra care not to leave behind any of them,\n\na wise person will just use Variables and let the magic happen. After all, more and messy code means more chances of error.","Q_Score":0,"Tags":"python,tensorflow","A_Id":48048843,"CreationDate":"2018-01-01T07:06:00.000","Title":"Are Tensorflow Variables essential?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have two columns, one is a string field customer containing customer names and the other is a numeric field sales representing sales.\nWhat I want to do is to group data by customer and then sort sales within group.\nIn SQL or Pandas, this is normally achieved by something like order by customer, sales on the table. But I am just curious about this implementation. Instead first sorting on customer and then sorting on sales, why not first group customer and sort sales. I don't really care about the order of the different customers since I only care about records of same customers being grouped together.\nGrouping is essentially mapping and should run faster than sorting. \nWhy isn't there such implementation in SQL? Am I missing something? \nExample data\n\nname,sales\njohn,1\nAmy,1\njohn,2\nAmy,3\nAmy,4\n\nand I want it to group by name and then sort by sales:\n\nname,sales\njohn,1\njohn,2\nAmy,1\nAmy,3\nAmy,4\nIn SQL you probably would do select * from table order by name,sales\nThis would definitely do the job. But my confusion is since I don't care about the order of name, I should be able to do some kind of grouping (which should be cheaper than sorting) first and do sorting only on the numeric field. Am I able to do this? Why do a lot of examples from google simply uses sorting on the two fields? Thanks!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":119,"Q_Id":48065753,"Users Score":0,"Answer":"Here is the answer to it-\nGrouping is done when you want to pull out the conclusion based on the entire group , like total of sales done,for each of the groups(in this case John and Amy) . It is used mostly with an aggregate function or sometimes to select distinct records only. What you wrote above is sorting the data in the order of name and sales , there is no grouping involved at all. Since the operation is sorting , its obvious that the command written for it would be sorting .","Q_Score":0,"Tags":"python,sql,pandas,sorting,group-by","A_Id":48071714,"CreationDate":"2018-01-02T18:09:00.000","Title":"Sort by two columns, why not do grouping first?","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am having trouble running a python script file that contains import opencv command. Upon running the file, it gives the following error:\nImportError: No module named cv2\nI then ran pip install python-opencv but it gave Could not find a version that satisfies the requirement python-opencv (from versions: )\nNo matching distribution found for python-opencv error.\nDoes anyone know what the issue might be? I was able to run this python file at first but I haven't been able to run after that.","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":542,"Q_Id":48076009,"Users Score":1,"Answer":"Package name is wrong pip install opencv-python Should work.","Q_Score":0,"Tags":"python,macos,opencv","A_Id":48076093,"CreationDate":"2018-01-03T10:58:00.000","Title":"ImportError: No module named cv2 error upon running .py file in terminal","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am having trouble running a python script file that contains import opencv command. Upon running the file, it gives the following error:\nImportError: No module named cv2\nI then ran pip install python-opencv but it gave Could not find a version that satisfies the requirement python-opencv (from versions: )\nNo matching distribution found for python-opencv error.\nDoes anyone know what the issue might be? I was able to run this python file at first but I haven't been able to run after that.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":542,"Q_Id":48076009,"Users Score":0,"Answer":"I also had this issue. Tried different things. But finally\nconda install opencv\nsolved the issue for me.","Q_Score":0,"Tags":"python,macos,opencv","A_Id":48076121,"CreationDate":"2018-01-03T10:58:00.000","Title":"ImportError: No module named cv2 error upon running .py file in terminal","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am a new in Python, is there any function that can do normalizing a data? \nFor example, I have set of list in range 0 - 1 example : [0.92323, 0.7232322, 0,93832, 0.4344433]\nI want to normalize those all values to range 0.25 - 0.50 \nThank you,","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":18367,"Q_Id":48109228,"Users Score":1,"Answer":"You can use sklearn.preprocessing for a lot of types of pre-processing tasks including normalization.","Q_Score":6,"Tags":"python,normalize","A_Id":48109501,"CreationDate":"2018-01-05T07:42:00.000","Title":"Normalizing data to certain range of values","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a sorted set in Redis with priorities starting from 0 up to 3. I would like to traverse this sorted set from highest to lowest priority using the python iterator zscan_iter. However, using zscan_iter gives me the items starting from 0. Is there a way to reverse the order? Unfortunately, reverse() only works on iterators and not on python generators.\nI see two solutions:\n\nUse negative priorities (so instead of 3 use -3)\nPaginate through slices of keys using ZREVRANGEBYSCORE, however I would prefer to use an iterator.\n\nAre there any other ways of doing this?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":884,"Q_Id":48115753,"Users Score":5,"Answer":"Scanning the Sorted Set with an iterator does not guarantee any order. Use ZREVRANGEBYSCORE for that.","Q_Score":1,"Tags":"python,redis,iterator,redis-py","A_Id":48117192,"CreationDate":"2018-01-05T14:37:00.000","Title":"How to traverse a sorted set in Redis in reverse order using zscan?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to Convolutional Neural Networks. I am reading some tutorial and testing some sample codes using Keras. To add a convolution layer, basically I just need to specify the number of kernels and the size of the kernel. \nMy question is what each kernel looks like? Are they generic to all computer vision applications?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":605,"Q_Id":48116484,"Users Score":0,"Answer":"The actual kernel values are learned during the learning process, that's why you only need to set the number of kernels and their size.\nWhat might be confusing is  that the learned kernel values actually mimic things like Gabor and edge detection filters. These are generic to many computer vision applications, but instead of being engineered manually, they are learned from a big classification dataset (like ImageNet).\nAlso the kernel values are part of a feature hierarchy that can be used directly as features for a variety of computer vision problems. In that terms they are also generic.","Q_Score":1,"Tags":"python,neural-network,keras,kernel,conv-neural-network","A_Id":48118131,"CreationDate":"2018-01-05T15:21:00.000","Title":"Does convolution kernel need to be designed in CNN (Convolutional Neural Networks)?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to calculate intersection of two planes in form of AX+BY+CZ+D=0 and get a line in form of two (x,y,z) points. I know how to do the math, but I want to avoid inventing a bicycle and use something effective and tested. Is there any library which already implements this? Tried to search opencv and google, but with no success.","AnswerCount":3,"Available Count":1,"Score":-0.1325487884,"is_accepted":false,"ViewCount":8472,"Q_Id":48126838,"Users Score":-2,"Answer":"This is solved by elementary vector computation and is not substantial\/useful enough to deserve a library implementation. Work out the math with Numpy.\nThe line direction is given by the cross product of the two normal vectors (A, B, C), and it suffices to find a single point, say the intersection of the two given planes and the plane orthogonal to the line direction and through the origin (by solving a 3x3 system).\n\nComputation will of course fail for parallel planes, and be numerically unstable for nearly parallel ones, but I don't think there's anything you can do.","Q_Score":8,"Tags":"python,computational-geometry,intersection,plane","A_Id":48129094,"CreationDate":"2018-01-06T11:25:00.000","Title":"Plane-plane intersection in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm new to coding and I've been guided to start with Python because it is good for a beginner and very versatile. I've been watching some tutorials online on how to  create a neural network with Python, however I've just got stuck in this example.\nI've seen and worked out tutorials where you have the learning rate and the error threshold which are constant variables. For example learning rate = 0.1 and error threshold = 0.1, however in this particular example there are no constant learning rate and error threshold variables that I can see. \nCan someone explain why the learning rate and error threshold aren't being used?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":90,"Q_Id":48126924,"Users Score":1,"Answer":"Learning rate: that code does not use a learning rate, or rather it uses a learning rate of 1.  Lines 48,49 just add the adjustment (gradient) value without any rate.  That is an uncommon choice and can sometimes work in practice, though in general it is not advised.  Technically this is the simplest version of gradient descent, and there are many elaborations to simple gradient descent, most of which involve a learning rate.  I won't elaborate further as this comment answers the question that you are asking, but you should know that building an optimizer is a big area of research, and there are lots of ways to do this more elaborately or with more sophistication (in the hopes of better performance).\nError threshold: instead of stopping optimization when an error threshold is reached, this algorithm stops after a fixed number of iterations (60,000).  That is a common choice, particularly when using something like stochastic gradient descent (again, another big topic).  The basic choice here is valid though: instead of optimizing until a performance threshold is reached (error threshold), optimize until a computational budget is reached (60,000 iterations).","Q_Score":1,"Tags":"python,neural-network,deep-learning","A_Id":48127014,"CreationDate":"2018-01-06T11:34:00.000","Title":"Trying to understand neural networks","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am implementing a reinforcement agent that takes actions based on classes.\nso it can take action 1 or 2 or 3 or 4.\nSo my question is can I use negative loss in tensorflow to stop it from outputting an action.\nExample:\nLet's say the agent outputs action 1 I want to very strongly dissuade it from taking action 1 in that situation again.  but there is not a known action that it should have taken instead.  So I can't just choose a different action to make it learn that.\nSo my question is:\ndoes tensorflow gradient computation handle negative values for loss.\nAnd if it does will it work the way I describe?","AnswerCount":1,"Available Count":1,"Score":0.537049567,"is_accepted":false,"ViewCount":1647,"Q_Id":48140133,"Users Score":3,"Answer":"A gradient descent minimizer will typically try to find the minimum loss irrespective of the sign of the loss surface.  It sounds like you either want to a) assign a large loss to encourage your model to pick something else or b) assign a fifth no-action category.","Q_Score":1,"Tags":"python,tensorflow,machine-learning","A_Id":48141696,"CreationDate":"2018-01-07T18:12:00.000","Title":"use negative loss in tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to make a fuction that takes in 2 (x,y) coordinates and returns and array of coordinates for where the mouse should be every 0.05 second (or about that)...\n(The whole aim of the program is to have random\/not smooth mouse movement to mimic a human's mouse movement)\nE.G. INPUTS : [(800,600), (300,400)]\n    OUTPUTS : [(800,600),(780,580),...,(300,400)]\nI would like to know which type of neural network I should use to get the correct outputs.\nI'm kind of new to the subject of neural nets but I have a decent understanding and will research the suggestions given.\nA pointer in the right direction and some links would be greatly appreciated!","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":2533,"Q_Id":48142421,"Users Score":1,"Answer":"Using a neural network for this task seems like total over kill to me. It seems like you have 2 inputs, each which have an X and a Y coordinate with one representing the initial position and one representing the final position of the mouse. \nThere are a ton of ways you can introduce randomness into this path in hard to detect ways that are much simpler than a neural network. Use some weird random number generator with bizarre personal logic in if statements to determine the amounts to add within some range to the current value on each iteration. You could use a neural net, but again i think it's overkill. \nAs far as what type of neural net you need to use, i would just start with an out of the box one from a tutorial online (tensorflow and sklearn are what i've used) and tweak the hyper parameters to see what makes the model better.","Q_Score":3,"Tags":"python,tensorflow,machine-learning,neural-network,mouse","A_Id":48142657,"CreationDate":"2018-01-07T23:11:00.000","Title":"What Neural Network to use for AI Mouse Movement","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to make a fuction that takes in 2 (x,y) coordinates and returns and array of coordinates for where the mouse should be every 0.05 second (or about that)...\n(The whole aim of the program is to have random\/not smooth mouse movement to mimic a human's mouse movement)\nE.G. INPUTS : [(800,600), (300,400)]\n    OUTPUTS : [(800,600),(780,580),...,(300,400)]\nI would like to know which type of neural network I should use to get the correct outputs.\nI'm kind of new to the subject of neural nets but I have a decent understanding and will research the suggestions given.\nA pointer in the right direction and some links would be greatly appreciated!","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":2533,"Q_Id":48142421,"Users Score":0,"Answer":"If you're trying to predict where the mouse should be based on the position of something else, a simple ANN will do the job. \nAre you trying to automate tasks, like have a script that controls a game? A Recurrent Neural Network like an LSTM or GRU will take history into account. \nI know you're doing this as a learning exercise, but if you're just trying to smooth mouse movement a simple interpolation algorithm might work.","Q_Score":3,"Tags":"python,tensorflow,machine-learning,neural-network,mouse","A_Id":48143029,"CreationDate":"2018-01-07T23:11:00.000","Title":"What Neural Network to use for AI Mouse Movement","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been working with NLTK in Python for a few days for sentiment analysis and it's a wonderful tool. My only concern is the sentiment it has for the word 'Quick'. Most of the data that I am dealing with has comments about a certain service and MOST refer to the service as being 'Quick' which clearly has Positive sentiments to it. However, NLTK refers to it as being Neutral. I want to know if it's even possible to retrain NLTK to now refer to the Quick adjective as having positive annotations?","AnswerCount":2,"Available Count":2,"Score":0.1973753202,"is_accepted":false,"ViewCount":915,"Q_Id":48145777,"Users Score":2,"Answer":"The models used for sentiment analysis are generally the result of a machine-learning process. You can produce your own model by running the model creation on a training set where the sentiments are tagged the way you like, but this is a significant undertaking, especially if you are unfamiliar with the underpinnings.\nFor a quick and dirty fix, maybe just make your code override the sentiment for an individual word, or (somewhat more challenging) figure out how to change its value in the existing model.  Though if you can get a hold of the corpus the NLTK maintainers trained their sentiment analysis on and can modify it, that's probably much simpler than figuring out how to change an existing model.  If you have a corpus of your own with sentiments for all the words you care about, even better.\nIn general usage, \"quick\" is not superficially a polarized word -- indeed, \"quick and dirty\" is often vaguely bad, and a \"quick assessment\" is worse than a thorough one; while of course in your specific context, a service which delivers quickly will dominantly be a positive thing. There will probably be other words which have a specific polarity in your domain, even though they cannot be assigned a generalized polarity, and vice versa -- some words with a polarity in general usage will be neutral in your domain. Thus, training your own model may well be worth the effort, especially if you are exploring utterances in a very specific register.","Q_Score":2,"Tags":"python,nlp,nltk,sentiment-analysis","A_Id":48146211,"CreationDate":"2018-01-08T07:15:00.000","Title":"Change sentiment of a single word","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been working with NLTK in Python for a few days for sentiment analysis and it's a wonderful tool. My only concern is the sentiment it has for the word 'Quick'. Most of the data that I am dealing with has comments about a certain service and MOST refer to the service as being 'Quick' which clearly has Positive sentiments to it. However, NLTK refers to it as being Neutral. I want to know if it's even possible to retrain NLTK to now refer to the Quick adjective as having positive annotations?","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":915,"Q_Id":48145777,"Users Score":3,"Answer":"I have fixed the problem. Found the vader Lexicon file in AppData\\Roaming\\nltk_data\\sentiment. Going through the file I found that the word Quick wasn't even in it. The format of the file is as following:\nToken Mean-sentiment StandardDeviation [list of sentiment score collected from 10 people ranging from -4 to 4]\nI edited the file. Zipped it. Now NLTK refers to Quick as having positive sentiments.","Q_Score":2,"Tags":"python,nlp,nltk,sentiment-analysis","A_Id":48154073,"CreationDate":"2018-01-08T07:15:00.000","Title":"Change sentiment of a single word","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on Stanford NER, My question is regarding ambiguous entities.\nFor example, I have 2 sentences:\n\n\nI love oranges.\nOrange is my dress code for tomorrow.\n\n\nHow can i train these 2 sentences to give out, \n\nfirst orange as Fruit,  second orange as Color.\n\nThanks","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":154,"Q_Id":48149281,"Users Score":0,"Answer":"Scrape data from sites like wikipedia,etc. and create a scoring model and then use it for context prediction.","Q_Score":1,"Tags":"python-3.x,stanford-nlp,named-entity-recognition","A_Id":53168687,"CreationDate":"2018-01-08T11:21:00.000","Title":"Ambiguous Entity in stanfors NER","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"How do I check if pytorch is using the GPU? It's possible to detect with nvidia-smi if there is any activity from the GPU during the process, but I want something written in a python script.","AnswerCount":14,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":569617,"Q_Id":48152674,"Users Score":18,"Answer":"Query\nCommand\n\n\n\n\nDoes PyTorch see any GPUs?\ntorch.cuda.is_available()\n\n\nAre tensors stored on GPU by default?\ntorch.rand(10).device\n\n\nSet default tensor type to CUDA:\ntorch.set_default_tensor_type(torch.cuda.FloatTensor)\n\n\nIs this tensor a GPU tensor?\nmy_tensor.is_cuda\n\n\nIs this model stored on the GPU?\nall(p.is_cuda for p in my_model.parameters())","Q_Score":375,"Tags":"python,memory-management,gpu,nvidia,pytorch","A_Id":66533975,"CreationDate":"2018-01-08T14:50:00.000","Title":"How to check if pytorch is using the GPU?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Looking to calculate Mean and STD per channel over a batch efficiently.\n\nDetails:\n\nbatch size: 128\nimages: 32x32\n3 channels (RGB)\n\nSo each batch is of size [128, 32, 32, 3].\nThere are lots of batches (naive method takes ~4min over all batches).\nAnd I would like to output 2 arrays: (meanR, meanG, meanB) and (stdR, stdG, stdB)\n\n(Also if there is an efficient way to perform arithmetic operations on the batches after calculating this, then that would be helpful. For example, subtracting the mean of the whole dataset from each image)","AnswerCount":3,"Available Count":1,"Score":0.1325487884,"is_accepted":false,"ViewCount":3580,"Q_Id":48159562,"Users Score":2,"Answer":"Assume you want to get the mean of multiple axis(if I didn't get you wrong). numpy.mean(a, axis=None) already supports multiple axis mean if axis is a tuple.\nI'm not so sure what you mean by naive method.","Q_Score":2,"Tags":"python,numpy,tensorflow,batch-processing,channel","A_Id":48159738,"CreationDate":"2018-01-08T23:34:00.000","Title":"Calculating Mean & STD for Batch [Python\/Numpy]","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to execute spark job from on demand HD Insight cluster using Azure datafactory.\nDocumentation indicates clearly that ADF(v2) does not support datalake linked service for on demand HD insight cluster and one have to copy data onto blob from copy activity and than execute the job. BUT this work around seems to be a hugely resource expensive in case of a billion files on a datalake. Is there any efficient way to access datalake files either from python script that execute spark jobs or any other way to directly access the files.\nP.S Is there a possiblity of doing similar thing from v1, if yes then how? \"Create on-demand Hadoop clusters in HDInsight using Azure Data Factory\" describe on demand hadoop cluster that access blob storage but I want on demand spark cluster that access datalake.\nP.P.s Thanks in advance","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":343,"Q_Id":48165947,"Users Score":1,"Answer":"Currently, we don't have support for ADLS data store with HDI Spark cluster in ADF v2. We plan to add that in the coming months. Till then, you will have to contiue using the workaround as you mentioned in your post above. Sorry for the inconvenience.","Q_Score":1,"Tags":"python,pyspark,azure-hdinsight,azure-data-factory,azure-data-lake","A_Id":48221294,"CreationDate":"2018-01-09T10:18:00.000","Title":"Access datalake from Azure datafactory V2 using on demand HD Insight cluster","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I am trying to execute spark job from on demand HD Insight cluster using Azure datafactory.\nDocumentation indicates clearly that ADF(v2) does not support datalake linked service for on demand HD insight cluster and one have to copy data onto blob from copy activity and than execute the job. BUT this work around seems to be a hugely resource expensive in case of a billion files on a datalake. Is there any efficient way to access datalake files either from python script that execute spark jobs or any other way to directly access the files.\nP.S Is there a possiblity of doing similar thing from v1, if yes then how? \"Create on-demand Hadoop clusters in HDInsight using Azure Data Factory\" describe on demand hadoop cluster that access blob storage but I want on demand spark cluster that access datalake.\nP.P.s Thanks in advance","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":343,"Q_Id":48165947,"Users Score":0,"Answer":"The Blob storage is used for the scripts and config files that the On Demand cluster will use. In the scripts you write and store in the attached Blob storage they can write from ADLS to SQLDB for example.","Q_Score":1,"Tags":"python,pyspark,azure-hdinsight,azure-data-factory,azure-data-lake","A_Id":49116105,"CreationDate":"2018-01-09T10:18:00.000","Title":"Access datalake from Azure datafactory V2 using on demand HD Insight cluster","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"We have the x and y values, and I am taking their log, by logx = np.log10(x) and logy = np.log10(y). I am trying to compute the derivative of logy w.r.t logx, so dlogy\/dlogx. I used to do this successfully using numpy gradient, more precisely \nderivy = np.gradient(logy,np.gradient(logx)) \nbut for some strange reason it doesn't seem to work anymore yielding the error: \"Traceback (most recent call last):\n  File \"derivlog.py\", line 79, in <module>\n    grady = np.gradient(logy,np.gradient(logx))\n  File \"\/usr\/lib\/python2.7\/dist-packages\/numpy\/lib\/function_base.py\", line 1598, in gradient\n    raise ValueError(\"distances must be scalars\")\nValueError: distances must be scalars\"\nContext: When trying to detect power-laws, of the kind y ~ x^t, given the values of y as a function of x, one wants to exctract essentially the power t, so we take logs which gives log y ~ t*log x and then take the derivative in order to extract t.\nHere's a minimal example for recreating the problem: x=[  3.   4.   5.   6.   7.   8.   9.  10.  11.]\ny = [ 1.05654  1.44989  1.7939   2.19024  2.62387  3.01583  3.32106  3.51618\n  3.68153]\nAre there other (more suited) methods in python for taking such numerical derivatives?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1101,"Q_Id":48171283,"Users Score":1,"Answer":"Thanks to the discussions in the comments the problem with np.gradient has been solved by updating the numpy package from version 1.12.1 to 1.13.3. This update is specially relevant if you are also getting the ValueError \"distances must be scalars\" when using gradient. Thus, in order to extract the order of the power-law, computing np.gradient(logy,logx) remains a valid option of going about it.","Q_Score":0,"Tags":"python,numpy,derivative","A_Id":48172836,"CreationDate":"2018-01-09T15:16:00.000","Title":"Derivative of log plot in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to use CNTK python to train a CNN model and then use  the trained model when i am programming in c# or c++. Is it possible to use CNTK python \ntrained model in C# or c++?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":149,"Q_Id":48173038,"Users Score":1,"Answer":"Yes, this is completely possible. The CNTK framework itself is written in C++ and the C# as well as the Python interface are only wrappers onto the C++ code. So, as long you use versions which are compatible to each other, you can do that. \nFor instance, if you use CNTK 2.3.1 with python and also CNTK 2.3.1 with C# there is of course nothing which should get in your way. If the versions are different, it depends if there have been breaking changes.\nJust for your information: There will be two formats in the near future: CNTK V2 model format and the new ONNX format.","Q_Score":2,"Tags":"c#,python,c++,cntk","A_Id":48258953,"CreationDate":"2018-01-09T16:52:00.000","Title":"is it possible to use cnn models trained in cntk python in c#?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying machine learning for the first time, and am playing around with a handwriting recognition NN (in Python). I just wanted to know whether or not I need to train the model every time I run it, or if it 'saves' the training. Thanks in advance.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":966,"Q_Id":48177771,"Users Score":0,"Answer":"If you're not using an already ready library it 'saves' the training only if you write a part of code to save it. The simplest way is to generate a TXT with a list of all the weights after the training and load it with a specific function.","Q_Score":1,"Tags":"python,neural-network,training-data","A_Id":48188480,"CreationDate":"2018-01-09T22:47:00.000","Title":"Do I need to train my neural network everytime I run it?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Suppose both x and y are very small numbers, but I know that the true value of x \/ y is reasonable. \nWhat is the best way to compute x\/y?\nIn particular, I have been doing np.exp(np.log(x) - np.log(y) instead, but I'm not sure if that would make a difference at all?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":6827,"Q_Id":48180177,"Users Score":0,"Answer":"Division, like other IEEE-754-specified operations, is computed at infinite precision and then (with ordinary rounding rules) rounded to the closest representable float. The result of calculating x\/y will almost certainly be a lot more accurate than the result of calculating np.exp(np.log(x) - np.log(y) (and is guaranteed not to be less accurate).","Q_Score":2,"Tags":"python,floating-point,precision,underflow","A_Id":48187560,"CreationDate":"2018-01-10T04:10:00.000","Title":"Prevent underflow in floating point division in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am developing a solution using a comercial computer vision software called Halcon. I am thinking on migrating or convert my solution to OpenCV in Python. I will like to start developing my other computer vision solution in Halcon because the IDE is incredible, and them generate a script to migrate them to OpenCV.\nDoes anyone know any library for this task? \nI will like to start developing an open source SDK to convert Halcon to OpenCV. I and thinking to start developing all internal function from Halcon to Python. Any advice?","AnswerCount":3,"Available Count":2,"Score":0.0665680765,"is_accepted":false,"ViewCount":4998,"Q_Id":48188940,"Users Score":1,"Answer":"It depends on which Halcon functionalities are you using and why you want to do it. The question appears to be very general. I would recommend you to convert your Halcon Program to C++ and write a wrapper function to pass arguments to\/from your openCV program.This would be the simplest option to provide interaction between your opencv and halcon program. Hope it helps.","Q_Score":1,"Tags":"python,opencv,computer-vision,halcon","A_Id":48302888,"CreationDate":"2018-01-10T13:51:00.000","Title":"Migrate Halcon code to OpenCV","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am developing a solution using a comercial computer vision software called Halcon. I am thinking on migrating or convert my solution to OpenCV in Python. I will like to start developing my other computer vision solution in Halcon because the IDE is incredible, and them generate a script to migrate them to OpenCV.\nDoes anyone know any library for this task? \nI will like to start developing an open source SDK to convert Halcon to OpenCV. I and thinking to start developing all internal function from Halcon to Python. Any advice?","AnswerCount":3,"Available Count":2,"Score":0.0665680765,"is_accepted":false,"ViewCount":4998,"Q_Id":48188940,"Users Score":1,"Answer":"This is unfortunately not possible because Halcon itself is not an open source library and every single function is locked.\nThe reason behind is runtime licencing.","Q_Score":1,"Tags":"python,opencv,computer-vision,halcon","A_Id":48305091,"CreationDate":"2018-01-10T13:51:00.000","Title":"Migrate Halcon code to OpenCV","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Coming from a programming background where you write code, test, deploy, run.. I'm trying to wrap my head around the concept of \"training a model\" or a \"trained model\" in data science, and deploying that trained model. \nI'm not really concerned about the deployment environment, automation, etc.. I'm trying to understand the deployment unit.. a trained model. What does a trained model look like on a file system, what does it contain? \nI understand the concept of training a model, and splitting a set of data into a training set and testing set, but lets say I have a notebook (python \/ jupyter) and I load in some data, split between training\/testing data, and run an algorithm to \"train\" my model. What is my deliverable under the hood? While I'm training a model I'd think there'd be a certain amount of data being stored in memory.. so how does that become part of the trained model? It obviously can't contain all the data used for training; so for instance if I'm training a chatbot agent (retrieval-based), what is actually happening as part of that training after I'd add\/input examples of user questions or \"intents\" and what is my deployable as far as a trained model? Does this trained model contain some sort of summation of data from training or array of terms, how large (deployable size) can it get?\nWhile the question may seem relatively simple \"what is a trained model\", how would I explain it to a devops tech in simple terms? This is an \"IT guy interested in data science trying to understand the tangible unit of a trained model in a discussion with a data science guy\". \nThanks","AnswerCount":3,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":225,"Q_Id":48197227,"Users Score":0,"Answer":"It depends on the model. For example linear regression, training will give you the coefficients of the slope and the intercept (generally). These are the \"model parameters\". When deployed, traditionally, these coefficients get fed into a different algorithm (literally y=mx+b), and then when queried \"what should y be, when I have x\", it responds with the appropriate value.\nKmeans clustering on the other hand the \"parameters\" are vectors, and the predict algorithm calculates distance from a vector given to the algorithm, and then returns the closest cluster - note often times these clusters are post processed, so the predict algorithm will say \"shoes\" not \"[1,2,3,5]\", which is again an example of how these things change in the wild.\nDeep learning returns a list of edge weights for a graph, various parametric systems (as in maximum likelihood estimation), return the coefficients to describe a particular distribution, for example uniform distribution is number of buckets, Gaussian\/Normal distribution is mean and variance, other more complicated ones have even more, for example skew and conditional probabilities.","Q_Score":0,"Tags":"python,machine-learning,artificial-intelligence,jupyter-notebook,data-science","A_Id":48197823,"CreationDate":"2018-01-10T22:36:00.000","Title":"Data Science Model and Training - Understanding","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Coming from a programming background where you write code, test, deploy, run.. I'm trying to wrap my head around the concept of \"training a model\" or a \"trained model\" in data science, and deploying that trained model. \nI'm not really concerned about the deployment environment, automation, etc.. I'm trying to understand the deployment unit.. a trained model. What does a trained model look like on a file system, what does it contain? \nI understand the concept of training a model, and splitting a set of data into a training set and testing set, but lets say I have a notebook (python \/ jupyter) and I load in some data, split between training\/testing data, and run an algorithm to \"train\" my model. What is my deliverable under the hood? While I'm training a model I'd think there'd be a certain amount of data being stored in memory.. so how does that become part of the trained model? It obviously can't contain all the data used for training; so for instance if I'm training a chatbot agent (retrieval-based), what is actually happening as part of that training after I'd add\/input examples of user questions or \"intents\" and what is my deployable as far as a trained model? Does this trained model contain some sort of summation of data from training or array of terms, how large (deployable size) can it get?\nWhile the question may seem relatively simple \"what is a trained model\", how would I explain it to a devops tech in simple terms? This is an \"IT guy interested in data science trying to understand the tangible unit of a trained model in a discussion with a data science guy\". \nThanks","AnswerCount":3,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":225,"Q_Id":48197227,"Users Score":0,"Answer":"A trained model(pickled) or whatever you want to use, contains the features on which it has been trained at least. Take for example a simple distance based model, you design a model based on the fact that (x1,x2,x3,x4) features are important and if any point if comes into contact with the model should give back the calculated distance based on which you draw insights or conclusions. \nSimilarly for chatbots, you train based on ner-crf, whatever features you want. As soon as a text comes into contact with the model, the features are extracted based on the model and insights\/conclusions are drawn. Hope it was helpful !! I tried explaining the Feyman way.","Q_Score":0,"Tags":"python,machine-learning,artificial-intelligence,jupyter-notebook,data-science","A_Id":50946421,"CreationDate":"2018-01-10T22:36:00.000","Title":"Data Science Model and Training - Understanding","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Coming from a programming background where you write code, test, deploy, run.. I'm trying to wrap my head around the concept of \"training a model\" or a \"trained model\" in data science, and deploying that trained model. \nI'm not really concerned about the deployment environment, automation, etc.. I'm trying to understand the deployment unit.. a trained model. What does a trained model look like on a file system, what does it contain? \nI understand the concept of training a model, and splitting a set of data into a training set and testing set, but lets say I have a notebook (python \/ jupyter) and I load in some data, split between training\/testing data, and run an algorithm to \"train\" my model. What is my deliverable under the hood? While I'm training a model I'd think there'd be a certain amount of data being stored in memory.. so how does that become part of the trained model? It obviously can't contain all the data used for training; so for instance if I'm training a chatbot agent (retrieval-based), what is actually happening as part of that training after I'd add\/input examples of user questions or \"intents\" and what is my deployable as far as a trained model? Does this trained model contain some sort of summation of data from training or array of terms, how large (deployable size) can it get?\nWhile the question may seem relatively simple \"what is a trained model\", how would I explain it to a devops tech in simple terms? This is an \"IT guy interested in data science trying to understand the tangible unit of a trained model in a discussion with a data science guy\". \nThanks","AnswerCount":3,"Available Count":3,"Score":0.0665680765,"is_accepted":false,"ViewCount":225,"Q_Id":48197227,"Users Score":1,"Answer":"A trained model will contain the value of its parameters. If you tuned only a few parameters, then only they will contain the new adjusted value. Unchanged parameters will store the default value.","Q_Score":0,"Tags":"python,machine-learning,artificial-intelligence,jupyter-notebook,data-science","A_Id":54176655,"CreationDate":"2018-01-10T22:36:00.000","Title":"Data Science Model and Training - Understanding","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"What is the difference in the functionalities of tf.layers.conv1d and tf.layers.conv2d in tensorflow and how to decide which one to choose?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":4923,"Q_Id":48219121,"Users Score":8,"Answer":"tf.layers.conv1d is used when you slide your convolution kernels along 1 dimensions (i.e. you reuse the same weights, sliding them along 1 dimensions), whereas tf.layers.conv2d is used when you slide your convolution kernels along 2 dimensions (i.e. you reuse the same weights, sliding them along 2 dimensions).\nSo the typical use case for tf.layers.conv2d is if you have a 2D image. And possible use-cases for tf.layers.conv1d are, for example:\n\nConvolutions in Time\nConvolutions on Piano notes","Q_Score":2,"Tags":"python,tensorflow,neural-network,conv-neural-network","A_Id":48223162,"CreationDate":"2018-01-12T03:24:00.000","Title":"Difference between tf.layers.conv1d vs tf.layers.conv2d","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"To my understanding, in order to update model parameters through gradient descend, the algorithm needs to calculate at some point the derivative of the error function E with respect of the output y: dE\/dy. Nevertheless, I've seen that if you want to use a custom loss function in Keras, you simply need to define E and you don't need to define its derivative. What am I missing?\nEach lost function will have a different derivative, for example:\nIf loss function is the mean square error: dE\/dy = 2(y_true - y)\nIf loss function is cross entropy: dE\/dy = y_true\/y\nAgain, how is it possible that the model does not ask me what the derivative is? How does the model calculate the gradient of the loss function with respect of parameters from just the value of E?\nThanks","AnswerCount":1,"Available Count":1,"Score":0.6640367703,"is_accepted":false,"ViewCount":1747,"Q_Id":48219296,"Users Score":4,"Answer":"To my understanding, as long as each operator that you will use in your Error function has already a predefined gradient. the underlying framework will manage to calculate the gradient of you loss function.","Q_Score":8,"Tags":"python,python-3.x,tensorflow,deep-learning,keras","A_Id":50122396,"CreationDate":"2018-01-12T03:54:00.000","Title":"Why doesn't Keras need the gradient of a custom loss function?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Scenerio: A word2vec model is trained on corpus C1 with vocabulary V1. If we want to re-train the same model with another corpus C2 having vocabulary V2 using train() API, what will happen out of these two:\n\nFor model, weights for V1 intersection V2 will be reset and re-training for with corpus C2 will come up with all together new weights\nFor model, re-training with corpus C2 will be continued with the existing weights for vocabulary V1 intersection V2.\n\nWhich one is correct hypothesis out of the above two?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":224,"Q_Id":48220414,"Users Score":0,"Answer":"If I understand this option correctly, you are resetting all the weights of the shared words and then train them on the C2 data... This would mean that all the information on the shared words from C1 is lost, which would seem like a big loss to me. (I dont know the corpus sizes). Also, how different are the two corpora? How big is this intersection? Do the corpora cover similar topics\/areas or not? This could also influence your decision on whether losing all the info from the C1 corpus is okay or not okay.\nThis seems like a more logical flow to me... but again the difference in corpora\/vocabulary is important here. If a lot of words from C2 are left out because of the intersection, you can think of ways to add unknown words one way or another. \n\nBut in order to asses which option is truly 'best' in your case, create a case where you can measure how 'good' one approach is according to the other. In most cases this involves some similarity measure... but maybe your case is different..","Q_Score":0,"Tags":"python,word2vec","A_Id":48220586,"CreationDate":"2018-01-12T06:01:00.000","Title":"Word2vec Re-training with new corpus, how the weights will be updated for the existing vocabulary?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Scenerio: A word2vec model is trained on corpus C1 with vocabulary V1. If we want to re-train the same model with another corpus C2 having vocabulary V2 using train() API, what will happen out of these two:\n\nFor model, weights for V1 intersection V2 will be reset and re-training for with corpus C2 will come up with all together new weights\nFor model, re-training with corpus C2 will be continued with the existing weights for vocabulary V1 intersection V2.\n\nWhich one is correct hypothesis out of the above two?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":224,"Q_Id":48220414,"Users Score":0,"Answer":"Why not initiate each of the word2vec parameters with random generated numbers for each run? I could do this and with careful selection of the random numbers for each parameter (numFeatures, contextWindow, seed) I was able to get random similarity tuples which I wanted for my usecase. Simulating an ensemble architecture.\nWhat do others think of it? Pls do reply.","Q_Score":0,"Tags":"python,word2vec","A_Id":51941579,"CreationDate":"2018-01-12T06:01:00.000","Title":"Word2vec Re-training with new corpus, how the weights will be updated for the existing vocabulary?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm just starting with deep learning, and I've been told that Keras would be the best library for beginners.\nBefore that, for the sake of learning, I built a simple feed forward network using only numpy so I could get the feel of it.\nIn this case, the shape of the weight matrix was (len(X[0]), num_neurons). The number of features and the number of neurons. And it worked.\nNow, I'm trying to build a simple RNN using Keras. My data has 7 features and the size of the layer would be 128.\nBut if I do something like model.add(Dense(128, input_dim=(7, 128)))it says it's wrong.\nSo I have no idea what this input_dim should be.\nMy data has 5330 data points and 7 features (shape is (5330, 7)).\nCan someone tell me what the input_dim should be and why?\nThank you.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":328,"Q_Id":48231233,"Users Score":1,"Answer":"The input_dim is just the shape of the input you pass to this layer. So:\n\ninput_dim = 7\n\nThere are other options, such as:\n\ninput_shape=(7,) -- This argument uses tuples instead of integers, good when your input has more than one dimension     \nbatch_input_shape=(batch_size,7) -- This is not usually necessary, but you use it in cases you need a fixed batch size (there are a few layer configurations that demand that)\n\nNow, the size of the output in a Dense layer is the units argument. Which is 128 in your case and should be equal to num_neurons.","Q_Score":0,"Tags":"python,machine-learning,deep-learning,keras,rnn","A_Id":48231802,"CreationDate":"2018-01-12T17:26:00.000","Title":"What's the input_size for the RNN Model in Keras","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Hello I'm working with images with Python. I want to convolve an image with a gaussian filter.\nThe image is an array that it have the shape (64,64,3) 64x64 pixels and 3 channels of colour. How will it be the gaussian filter? which dimension? Do you know a function to define it and make the convolution with the image?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1583,"Q_Id":48233118,"Users Score":0,"Answer":"You have to create a gaussian filter of the dimension you want e.g. 3x3 or 11x11. \nThen do the convolution on each channel of colour.\nIf you want to do it using Fourir, you have to apply psf2otf (a matlab function that it is also in Python by users) and multiply both matrixs pointwise (on each channel).","Q_Score":0,"Tags":"python,kernel,convolution,gaussian,gaussianblur","A_Id":50582786,"CreationDate":"2018-01-12T19:48:00.000","Title":"Python - Gaussian Kernel for colour images","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to get the summary statistics of the columns of a data frame with data type: Boolean.\nWhen I run:df.describe() it only gives me the summary statistics for numerical (in this case float) data types. When I change it to df.describe(include=['O']), it gives me only the object data type. \nIn either case, the summary statistics for Boolean data types are not provided.\nAny suggestion is highly appreciated.\nThanks","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":5123,"Q_Id":48236383,"Users Score":0,"Answer":"Try using this:\ndf.describe(include=['object','bool']).T\nthe T here is used for the transpose purpose","Q_Score":2,"Tags":"python,pandas","A_Id":62427224,"CreationDate":"2018-01-13T02:22:00.000","Title":"How to perform .describe() method on variables that have boolean data type in pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have two column dataset where each element of first column corresponds to an array. So basically my second column elements are arrays and first column elements are just numbers. I need to write it in a file using Python and later read it in C. I know HDF5 is the best way to store arrays but I was wondering if there is any other effective way of writing it in .csv\/.dat\/.txt file. Since I have to read it in C I can't use things like numpy.savez.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":135,"Q_Id":48242064,"Users Score":0,"Answer":"I'd say write it to a text file. Per line put one first column number, followed by the second column's list of floats. Put a space between each element.\nAssuming you know the maximum number of floats in the second column's array, and the maximum character length of each float, you can use fgets() to read the file line by line.","Q_Score":1,"Tags":"python,c,arrays,data-files","A_Id":48242730,"CreationDate":"2018-01-13T16:49:00.000","Title":"How to write arrays as column elements in a Data file in python and read it later in C?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"So I have computers all running on ubuntu and only one of them has python tensorflow module. I want to install tensorflow to all of them but it would be inefficient to connect every computer to the internet and install them all over again.so is there a possible efficient way to just copy paste some files from the computer to another to use this python module? thanks in advance.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":160,"Q_Id":48251568,"Users Score":0,"Answer":"You could copy the contents of your python site-packages across. But if you are generally in the situation where internet access is expensive, you might find it more practical to use a caching proxy for all of your internet traffic.","Q_Score":0,"Tags":"python,ubuntu,tensorflow","A_Id":48259989,"CreationDate":"2018-01-14T16:16:00.000","Title":"how could I copy tensorflow module from one computer to another?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've just started to experiment with AWS SageMaker and would like to load data from an S3 bucket into a pandas dataframe in my SageMaker python jupyter notebook for analysis.\nI could use boto to grab the data from S3, but I'm wondering whether there is a more elegant method as part of the SageMaker framework to do this in my python code?\nThanks in advance for any advice.","AnswerCount":8,"Available Count":1,"Score":0.1243530018,"is_accepted":false,"ViewCount":73507,"Q_Id":48264656,"Users Score":5,"Answer":"Do make sure the Amazon SageMaker role has policy attached to it to have access to S3. It can be done in IAM.","Q_Score":53,"Tags":"python,amazon-web-services,amazon-s3,machine-learning,amazon-sagemaker","A_Id":48278872,"CreationDate":"2018-01-15T14:07:00.000","Title":"Load S3 Data into AWS SageMaker Notebook","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"Is there a way to get the number of layers (not parameters) in a Keras model?\nmodel.summary() is very informative, but it is not straightforward to get the number of layers from it.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":16915,"Q_Id":48265926,"Users Score":44,"Answer":"model.layers will give you the list of all layers. The number is consequently len(model.layers)","Q_Score":24,"Tags":"python,machine-learning,keras,deep-learning,keras-layer","A_Id":48267194,"CreationDate":"2018-01-15T15:25:00.000","Title":"Keras: find out the number of layers","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am performing Sequential Rule Mining using Apriori Algorithm and FPA, I have the dataset in excel as shown below, I want to know, how should I load my data into pandas dataframe what I am using is the following read_excel command, but the data contains ---> between items and lies in single column as shown below.\nHow should I load and perform Pattern Mining.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":683,"Q_Id":48279902,"Users Score":1,"Answer":"message is string type, and elif \"what is\" in message: seems to be correct in syntax.\nHave you checked whether the indentation is correct? Sometimes the bug can be a very simple thing..","Q_Score":0,"Tags":"python,pandas,apriori","A_Id":48280113,"CreationDate":"2018-01-16T11:12:00.000","Title":"Sequential Rule Mining using Apriori Algorithm and Pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to sort a data table that is well over the size of the physical memory of the machine I am using. Pandas cannot handle it because it needs to read the entire data into memory. Can dask handle that?\nThanks!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1393,"Q_Id":48287766,"Users Score":3,"Answer":"Yes, by calling set_index on the column that you wish to sort.  On a single machine it uses your hard drive intelligently for excess space.","Q_Score":4,"Tags":"python,pandas,dataframe,sorting,dask","A_Id":48287827,"CreationDate":"2018-01-16T18:36:00.000","Title":"sort very large data with dask?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to use regex to match patterns in paragraphs like the following:\n\n\u00a92016 Rina Foygel Barber and Emil Y. Sidky. Many optimization problems arising in high-dimensional statistics decompose naturally into a sum of several terms, where the individual terms are relatively simple but the composite objective function can only be optimized with iterative algorithms. In this paper, we are interested in optimization problems of the form F(Kx) + G(x), where K is a fixed linear transformation, while F and G are functions that may be nonconvex and\/or nondifferentiable. In particular, if either of the terms are nonconvex, existing alternating minimization techniques may fail to converge; other types of existing approaches may instead be unable to handle nondifferentiability. We propose the mocca (mirrored convex\/concave) algorithm, a primal\/dual optimization approach that takes a local convex approximation to each term at every iteration. Inspired by optimization problems arising in computed tomography (CT) imaging, this algorithm can handle a range of nonconvex composite optimization problems, and offers theoretical guarantees for convergence when the overall problem is approximately convex (that is, any concavity in one term is balanced out by convexity in the other term). Empirical results show fast convergence for several structured signal recovery problems.\n\nSo that the first line with human names, year, and copyright (\u00a92016 Rina Foygel Barber and Emil Y. Sidky.) can be removed.\nThe only I can come up now was to use ^\u00a9 ?[0-9][0-9][0-9][0-9].+\\.. However, this can hardly match things like the above paragraph due to the . in human names. Any suggestions? Thanks!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":179,"Q_Id":48288763,"Users Score":2,"Answer":"If you are ok with the following assertions:  \n\nNames and surnames always begin with a capital letter\nFor names reduced to one capital letter, this letter is always immediately followed by a dot\nNames can be separated with either a comma or the \"and\" word\nThese names end with a final dot\n\nThen you can use this regex: ^\u00a9[0-9]{4} +(([A-Z][a-z]+|[A-Z]\\.|and|,) *)*\\. *","Q_Score":0,"Tags":"python,regex","A_Id":48289327,"CreationDate":"2018-01-16T19:47:00.000","Title":"Python regex match human names with abbr (dots) in text","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm facing a weird issue on Pandas now, not sure if a pandas pitfall or just something I'm missing...\nMy pd.Series is just\nfoo\nFalse\nFalse\nFalse\n\n> a.foo.dtype\ndtype('bool')\n\nWhen I use a dataframe.set_value(index, col, None), my whole Series is converted to dtype('float64') (same thing applies to a.at[index, col] = None).\nNow my Series is\n\nfoo\nNaN\nNaN\nNaN\n\nDo you have any idea on how this happens and how to fix it?\nThanks in advance. :)\nEdit:\nUsing 0.20.1.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":37,"Q_Id":48302876,"Users Score":1,"Answer":"I think the problem is related to the fact that I was trying to assign a None to a bool Series, then it just tries to convert to a different type (why not object?)\nFixed changing the dtype to object first: dataframe.foo = dataframe.foo.astype(object).\nWorks like a charm now.","Q_Score":0,"Tags":"python,pandas","A_Id":48303455,"CreationDate":"2018-01-17T13:57:00.000","Title":"dtype changes after set_value\/at","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I try to get number of rows, columns and nonzeros values after presolve. I know about getAttr(GRB.Attr.NumVars), getAttr(GRB.Attr.NumConstrs)...but they give the statics before presolve. Can any one help me.\nThanks","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":71,"Q_Id":48302947,"Users Score":0,"Answer":"If m is the model, i create new model for presolve (mpre = m.presolve()), and then i use mpre.getAttr(GRB.Attr.NumVars), mpre.getAttr(GRB.Attr.NumConstrs) and mpre.getAttr(GRB.Attr.NumConstrs), mpre.getAttr(GRB.Attr.NumNZs).","Q_Score":0,"Tags":"python,attributes,gurobi","A_Id":48479898,"CreationDate":"2018-01-17T14:01:00.000","Title":"Python Gurobi, report of statistics presolve","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am python newbie. I am doing 1 project testing of Keras. I already installed opencv by pip install opencv-python but i can't find opencv in interpreter of Pycharm and have an error when i import cv2. My interpreter is usr\/bin\/python2.7","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":335,"Q_Id":48314549,"Users Score":1,"Answer":"Go to project interpreter settings in preferences in Pycharm, and use the + sign there to add the module to your interpreter. \nThis can happen when pip is installing to a directory that is not part of your project's python interpreter's PATH.\nInstalling via the Pycharm preferences menu always solves for me, although there is a deeper issue of pip not installing to the correct directory...","Q_Score":0,"Tags":"python,opencv,pycharm,interpreter","A_Id":48314822,"CreationDate":"2018-01-18T05:43:00.000","Title":"I have trouble with Interpreter in Pycharm","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to build decision trees on categorical data. \nI understood that scikit-learn was only able to deal with numerical values, and the recommended approach is then to use on-hot encoding, preferrably using the Panda Dummies. \nSo, I build a sample dataset where all attributes and labels are categorical. At this stage, I try to understand how to 'one-hot' encode to be able to use sklearn, but the documentation does not address this case. \nCould eventually give me a quick example or a link to some material for beginners ?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":792,"Q_Id":48322232,"Users Score":0,"Answer":"Based on my tests, One-hot encoding results in continuous variables (amounts, in my case) having been assigned higher feature importance. \nAlso, a single level of a categorical variable must meet a very high bar in order to be selected for splitting early in the tree building. This apparently degrades predictive performance (I didn't see this consequence mentioned in any post, unfortunately). \nI will investigate for other approaches.","Q_Score":0,"Tags":"python-3.x,decision-tree,one-hot-encoding","A_Id":48341100,"CreationDate":"2018-01-18T13:13:00.000","Title":"Use of one-hot encoder to build decision trees","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I installed opencv in python3 using pip. It runs well in terminal, but when I try to run it in idle, it cannot import cv2. What could be the solution?\nI am using vim as my python idle.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":787,"Q_Id":48324322,"Users Score":0,"Answer":"It could be an issue of having multiple python versions on your machine, you should select the python interpreter that is global to your system \"that which utilises\" pip in terminal.","Q_Score":0,"Tags":"python-3.x,opencv3.0","A_Id":48517556,"CreationDate":"2018-01-18T15:04:00.000","Title":"Use opencv in python idle","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"the error message \nerror: Microsoft Visual C++ 10.0 is required. Get it with \"Microsoft Windows SDK 7.1\": www.microsoft.com\/download\/details.aspx?id=8279","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":244,"Q_Id":48326786,"Users Score":0,"Answer":"It's telling you that you need Microsoft Visual C++ 10.0 as pandas library has dependencies that refer to that version of Microsoft Visual C++. It provides you the url to download and install it. Use it, then import pandas library.","Q_Score":0,"Tags":"python-3.x,pycharm","A_Id":48326851,"CreationDate":"2018-01-18T17:08:00.000","Title":"While trying to import pandas library to pycharm there is an error message","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to use OpenCV Python to do SIFT feature detection on remote sensing images. These images are high resolution and can be thousands of pixels wide (7000 x 6000 or bigger). I am having trouble with insufficient memory, however. As a reference point, I ran the same 7000 x 6000 image in Matlab (using VLFEAT) without memory error, although larger images could be problematic. Does anyone have suggestions for processing this kind of data set using OpenCV SIFT?\nOpenCV Error: Insufficient memory (Failed to allocate 672000000 bytes) in cv::OutOfMemoryError, file C:\\projects\\opencv-python\\opencv\\modules\\core\\src\\alloc.cpp, line 55\nOpenCV Error: Assertion failed (u != 0) in cv::Mat::create, file \n(I'm using Python 2.7 and OpenCV 3.4 in the Spyder IDE on a Windows 64-bit with 32 GB of RAM.)","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":649,"Q_Id":48331004,"Users Score":1,"Answer":"I would split the image into smaller windows. So long as you know the windows overlap (I assume you have an idea of the lateral shift) the match in any window will be valid.\nYou can even use this as a check, the translation between feature points in any part of the image must be the same for the transform to be valid","Q_Score":1,"Tags":"python,opencv,image-processing,feature-detection,sift","A_Id":48331105,"CreationDate":"2018-01-18T21:58:00.000","Title":"Errors processing large images with SIFT OpenCV","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to use OpenCV Python to do SIFT feature detection on remote sensing images. These images are high resolution and can be thousands of pixels wide (7000 x 6000 or bigger). I am having trouble with insufficient memory, however. As a reference point, I ran the same 7000 x 6000 image in Matlab (using VLFEAT) without memory error, although larger images could be problematic. Does anyone have suggestions for processing this kind of data set using OpenCV SIFT?\nOpenCV Error: Insufficient memory (Failed to allocate 672000000 bytes) in cv::OutOfMemoryError, file C:\\projects\\opencv-python\\opencv\\modules\\core\\src\\alloc.cpp, line 55\nOpenCV Error: Assertion failed (u != 0) in cv::Mat::create, file \n(I'm using Python 2.7 and OpenCV 3.4 in the Spyder IDE on a Windows 64-bit with 32 GB of RAM.)","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":649,"Q_Id":48331004,"Users Score":0,"Answer":"There are a few flavors how to process SIFT corner detection in this case:\n\nprocess single image per unit\/time one core;\nmultiprocess 2 or more images \/unit time on single core;\nmultiprocess 2 or more images\/unit time on multiple cores.\n\nRead cores as either cpu or gpu. Threading result in serial processing instead of parallel.\nAs stated Rebecca has at least 32gb internal memory on her PC at her disposal which is more than sufficient for option 1 to process at once.\nSo in that light.. splitting a single image as suggested by Martin... should be a last resort in my opinion. \n\nWhy should you avoid splitting a single image in multiple windows during feature detection (w\/o running out of memory)?\n\nAnswer:\nIf a corner is located at the spilt-side of the window and thus becomes unwillingly two more or less polygonal straight-line-like shapes you won't find the corner you're looking for, unless you got a specialized algorithm to search for those anomalies. \nIn casu:\nIn Rebecca's case its crucial to know which approach she took on processing the image(s)... Was it one, two, or many more images loaded simultaneously into memory?\nIf hundreds or thousands of images are simultaneously loaded into memory... you're basically choking the system by taking away its breathing space (in the form of free memory). In addition, we're not talking about other programs that are loaded into memory and claim (reserve) or consume memory space for various background programs. That comes on top of the issue at hand.\nOverthinking:\nIf as suggested by Martin there is an issue with the Opencv lib in handling such amount of images as described by Rebecca.. do some debugging and then report your findings to Opencv, post a question here at SO as she did... but post also code that shows how you deal with the image processing at the start; as explained above why that is important. And yes as Martin stated... don't post wrappers... totally pointless to do so. A referral link to it (with possible version number) is more than enough... or a tag ;-)","Q_Score":1,"Tags":"python,opencv,image-processing,feature-detection,sift","A_Id":48355204,"CreationDate":"2018-01-18T21:58:00.000","Title":"Errors processing large images with SIFT OpenCV","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on copying csv file content into postgresql database.\nWhile copying into the database, I get this error:\n\ninvalid input syntax for type numeric: \"inf\"\n\nMy question is:\nI think \"inf\" means \"infinitive\" value, is it right? what does \"inf\" correctly mean? If it is kinda error, is it possible to recover original value?\nAnd, Should I manually correct these values to copy it into the database?\nIs there any good solution to fix this problem without manually correcting or setting exceptions in copying codebase?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1132,"Q_Id":48333572,"Users Score":3,"Answer":"inf (meaning infinity) is a correct value for floating point values (real and double precision), but not for numeric.\nSo you will either have to use one of the former data types or fix the input data.","Q_Score":2,"Tags":"python,postgresql,csv","A_Id":48336589,"CreationDate":"2018-01-19T03:11:00.000","Title":"What mean \"Inf\" in csv?","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"My question is, \n\nDoes the machine learning algorithm takes care of selecting the best features in my data ? or shall I do feature selection and scaling prior to my machine learning algorithm.\n\nI am aware of few supervised classification machine learning algorithms such as kNN, Neural Networks, Adaboast etc. \nBut is there some you recommend me looking at ?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":124,"Q_Id":48335994,"Users Score":0,"Answer":"There is no definite answer to this. The current trend is not do feature selection and let the classifier decide which features to use. Take current image datasets for example which also have 1000+ features (depending on the image resolution). They are fed to a CNN usually without any preprocessing. However, this is not generally true. If for example you assume to have a lot of correlations in the data, feature selection might help.","Q_Score":0,"Tags":"python,algorithm,classification,knn,supervised-learning","A_Id":48701185,"CreationDate":"2018-01-19T07:23:00.000","Title":"Do I have to do feature selection prior to applying my machine learning algorithm?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using the PYGMO package to solve some nasty non-linear minimization problems, and am very interested in using their simulated_annealing algorithm, however it has a lot of hyper-parameters for which I don't really have any good intuition. These include:\nTs (float) \u2013 starting temperature\n Tf (float) \u2013 final temperature\n n_T_adj (int) \u2013 number of temperature adjustments in the annealing schedule\n n_range_adj (int) \u2013 number of adjustments of the search range performed at a constant temperature\n bin_size (int) \u2013 number of mutations that are used to compute the acceptance rate\n start_range (float) \u2013 starting range for mutating the decision vector\nLet's say I have a 4 dimensional geometric registration (homography) problem with variables and search ranges:\nx1: [-10,10] (a shift in x)\n x2: [10,30]  (a shift in y)\n x3: [-45,0]  (rotation angle)\n x4: [0.5,2]  (scaling\/magnification factor)\nAnd the cost function for a random (bad) choice of values is 50. A good value is around zero. \nI understand that Ts and Tf are for the Metropolis acceptance criterion of new solutions. That means Ts should be about the expected size of the initial changes in the cost function, and Tf small enough that no more changes are expected. \nIn Corana's paper, there are many hyperparameters listed that make sense: N_s is the number of evaluation cycles before changing step sizes, N_T are the number of step-size changes before changing the temperature, and r_T is the factor by which the temp is reduced each time. However, I can't figure out how these correlate to pygmo's parameters of n_T_adj, n_range_adj, bin_size, and start_range.\nI'm really curious if anyone can explain how pygmo's hyperparameters are used, and how they relate to the original paper by Corana et al?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":175,"Q_Id":48344081,"Users Score":0,"Answer":"Gonna answer my own question here. I climbed into the actual .cpp code and found the answers.\nIn Corana's method, you select how many total iterations N of annealing you want. Then the minimization is a nested series of loops where you vary the step sizes, number of step-size adjustments, and temperature values at user-defined intervals. In PAGMO, they changed this so you explicitly specify how many times you will do these. Those are the n_* parameters and bin_size. I don't think bin_size is a good name here, because it isn't actually a size. It is the number of steps taken through a bin range, such that N=n_T_adj * n_range_adj * bin_range. I think just calling it n_bins or n_bins_adj makes more sense. Every bin_size function evaluations, the stepsize is modified (see below for limits). \nIn Corana's method you specify the multiplicative factor to decrease the temperature each time it is needed; it could be that you reach the minimum temp before running out of iterations, or vice versa. In PAGMO, the algorithm automatically computes the temperature-change factor so that you reach Tf at the end of the iteration sequence: r_t=(Tf\/Ts)**(1\/n_T_adj). \nThe start_range is, I think, a bad name for this variable. The stepsize in the alorithm is a fraction between 0 and start_range which defines the width of the search bins between the upper and lower bounds for each variable. So if stepsize=0.5, width=0.5*(upper_bound-lower_bound). At each iteration, the step size is adjusted based on how many function calls were accepted. If the step size grows larger than start_range, it is reset to that value. I think I would call it step_limit instead. But there you go.","Q_Score":0,"Tags":"python,minimization,simulated-annealing","A_Id":48346637,"CreationDate":"2018-01-19T15:09:00.000","Title":"PAGMO\/PYGMO: Anyone understand the options for Corana\u2019s Simulated Annealing?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"data = [{'info': ('orange', 400000, 'apple'), 'photo': None}, {'info': ('grape', 485000, 'watermelon'), 'photo': None}]\nI want to sort data by the 2nd element (400000, 485000) in the tuple in the dictionary.  How do I do this?\nI followed another answer and my closest attempt was data.sort(key=lambda tup: tup[1]), but that produces this error:\n\nKeyError: 1","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":55,"Q_Id":48351902,"Users Score":0,"Answer":"For each dict item in the list you want to sort, you want to take the item's value keyed by 'info', which is a list, and sort on the second item (addressed as [1], counting from zero.\nSo: data.sort(key=lambda item: item['info'][1])","Q_Score":1,"Tags":"python","A_Id":48351965,"CreationDate":"2018-01-20T01:58:00.000","Title":"Python - Sort a list by a certain element in a tuple in a dictionary","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Within AWS Glue how do I deal with files from S3 that will change every week.\nExample:\nWeek 1: \u201cfilename01072018.csv\u201d\nWeek 2: \u201cfilename01142018.csv\u201d\nThese files are setup in the same format but I need Glue to be able to change per week to load this data into Redshift from S3. The code for Glue uses native Python as the backend.","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":292,"Q_Id":48353544,"Users Score":0,"Answer":"AWS Glue should be able to process all the files in a folder irrespective of the name in a single job. If you don\u2019t want the old file to be processed again move it using boto3 api for s3 to another location after each run.","Q_Score":1,"Tags":"python,python-3.x,amazon-redshift,aws-glue","A_Id":54622059,"CreationDate":"2018-01-20T07:00:00.000","Title":"Aws Glue - S3 - Native Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Say I calculated the correlations of prices of 500 stocks, and stored them in a 500x500 correlation matrix, with 1s on the diagonal. \nHow can I cluster the correlations into smaller correlation matrices (in Python), such that the correlations of stocks in each matrix is maximized? Meaning to say, I would like to cluster the stocks such that in each cluster, the stock prices are all highly correlated with one another.\nThere is no upper bound to how many smaller matrices I can cluster into, although preferably, their sizes are similar i.e it is better to have 3 100x100 matrices and 1 200x200 matrix than say a 10x10 matrix, 90x90 matrix and 400x400 matrix. (i.e minimize standard deviation of matrix sizes).\nPreferably to be done in Python. I've tried to look up SciPy's clustering libraries but have not yet found a solution (I'm new to SciPy and such statistical programming problems).\nAny help that points me in the right direction is much appreciated!","AnswerCount":1,"Available Count":1,"Score":-0.1973753202,"is_accepted":false,"ViewCount":1454,"Q_Id":48365313,"Users Score":-1,"Answer":"The obvious choice here is hierarchical agglomerative clustering.\nBeware that most tools (e.g., sklearn) expect a distance matrix. But it can be trivially implemented for a similarity matrix instead. Then you can use correlation. This is textbook stuff.","Q_Score":0,"Tags":"python,numpy,scipy,cluster-analysis,correlation","A_Id":48372244,"CreationDate":"2018-01-21T09:14:00.000","Title":"Clustering a correlation matrix into multiple matrices of maximum correlations in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I just upgraded from tf 1.1 to tf 1.4 for python 2.7 and I got the following problem: \nI have a graph which I am putting all its OPs in the specific device by using tf.device('device') command. But, one of the OPs is only allowed to be in CPU device, so I am using allow_soft_placement=True and it was working correctly in tf 1.1 (it put only the OPs without GPU implementation in CPU and other OPs in GPU). But now (in tf1.4) when I am running my network it is putting all the OPs in the CPU (not just the one which has not GPU implementation). \nAny help is appreciated.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":518,"Q_Id":48369319,"Users Score":0,"Answer":"This error probably occurs because you are using python 2.7. Where as tensorflow is for use with python 3.5 and python 3.6","Q_Score":0,"Tags":"python-2.7,tensorflow","A_Id":48369514,"CreationDate":"2018-01-21T16:56:00.000","Title":"Allow soft placement in tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to RL and the best I've done is CartPole in openAI gym. In cartPole, the API automatically provides the reward given the action taken. How am I supposed to decide the reward when all I have is pixel data and no \"magic function\" that could tell the reward for a certain action.\nSay, I want to make a self driving bot in GTA San Andreas. The input I have access to are raw pixels. How am I supposed to figure out the reward for a certain action it takes?","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":322,"Q_Id":48370121,"Users Score":2,"Answer":"You need to make up a reward that proxies the behavior you want - and that is actually no trivial business.\nIf there is some numbers on a fixed part of the screen representing score, then you can use old fashioned image processing techniques to read the numbers and let those be your reward function.\nIf there is a minimap in a fixed part of the screen with fixed scale and orientation, then you could use minus the distance of your character to a target as reward.\nIf there are no fixed elements in the UI you can use to proxy the reward, then you are going to have a bad time, unless you can somehow access the internal variables of the console to proxy the reward (using the position coordinates of your PC, for example).","Q_Score":0,"Tags":"python,machine-learning,artificial-intelligence,reinforcement-learning,openai-gym","A_Id":48370179,"CreationDate":"2018-01-21T18:14:00.000","Title":"Reinforcement Learning - How to we decide the reward to the agent when the input to the game is only pixels?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to implement an adaptive filter for noise cancellation, in particular a RLS filter to remove motion artifacts from a signal. To do this I am reading some literature, there is one thing I don't understand and every book or article I found just asumes I already now this.\nI have a reference signal represented as a list in Python of about 8000 elements, or samples. I need to input this to the RLS filter, but every algorithm I find always talks about the input vector as\n\nX[n] = [x1[n], x2[n], x3[n], ........, xM[n]]T\n\nWhere X is the input vector, and n is a time instant. And here is where I get lost. If n is a time instant, it would mean x[n] is an element in the list, a salmple. But if that is the case, what are x1, x2, ...., xM???.\nI realise this is not strictly a coding problem, but I hope someone can help! \nThanks...","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":345,"Q_Id":48382873,"Users Score":0,"Answer":"Your explanation is correct.  The X input vector is multiplied recursively by the filter coefficients.  It's been some time since I wrote an adaptive filter, but if I remember correctly you're multiplying M filter coefficients by the latest M input values to get an update.  \nSo M is the order of your filter, or the number of filter coefficients, and n is the length of the signal you are filtering.  And as you note your recursive filter will look at a 'window' of those input values for each filtered output calculation.","Q_Score":0,"Tags":"python,algorithm,filtering,signal-processing,perceptron","A_Id":48451376,"CreationDate":"2018-01-22T13:36:00.000","Title":"Adaptive Filter input vectors and iteration","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm working on a competitive programming problem where we're trying to find the median of two sorted arrays. The optimal algorithm is to perform a binary search and identify splitting points, i and j, between the two arrays.\nI'm having trouble deriving the solution myself. I don't understand the initial logic.  I will follow how I think of the problem so far.\nThe concept of the median is to partition the given array into two sets.  Consider a hypothetical left array and a hypothetical right array after merging the two given arrays. Both these arrays are of the same length.\nWe know that the median given both those hypothetical arrays works out to be [max(left) + min(right)]\/2. This makes sense so far. But the issue here is now knowing how to construct the left and right arrays. \nWe can choose a splitting point on ArrayA as i and a splitting point on ArrayB as j. Note that len(ArrayB[:j] + ArrayB[:i]) == len(ArrayB[j:] +ArrayB[i:]).\nNow we just need to find the cutting points. We could try all splitting points i, j such that they satisfy the median condition. However this works out to be O(m*n) where M is size of ArrayB and where N is size of ArrayA. \nI'm not sure how to get where I am to the binary search solution using my train of thought. If someone could give me pointers - that would be awesome.","AnswerCount":1,"Available Count":1,"Score":0.537049567,"is_accepted":false,"ViewCount":711,"Q_Id":48386293,"Users Score":3,"Answer":"Here is my approach that I managed to come up with.\nFirst of all we know that the resulting array will contain N+M elements, meaning that the left part will contain (N+M)\/2 elements, and the right part will contain (N+M)\/2 elements as well. Let's denote the resulting array as Ans, and denote the size of one of its parts as PartSize.\nPerform a binary search operation on array A. The range of such binary search will be [0, N]. This binary search operation will help you determine the number of elements from array A that will form the left part of the resulting array.\nNow, suppose we are testing the value i. If i elements from array A are supposed to be included in the left part of the resulting array, this means that j = PartSize - i elements must be included from array B in the first part as well. We have the following possibilities:\n\nj > M this is an invalid state. In this case it means we still need to choose more elements from array A, so our new binary search range becomes [i + 1, N].\nj <= M & A[i+1] < B[j] This is a tricky case. Think about it. If the next element in array A is smaller than the element j in array B, this means that element A[i+1] is supposed to be in the left part rather than element B[j]. In this case our new binary search range becomes [i+1, N].\nj <= M & A[i] > B[j+1] This is close to the previous case. If the next element in array B is smaller than the element i in array A, the means that element B[j+1] is supposed to be in the left part rather than element A[i]. In this case our new binary search range becomes [0, i-1].\nj <= M & A[i+1] >= B[j] & A[i] <= B[j+1] this is the optimal case, and you have finally found your answer.\n\nAfter the binary search operation is finished, and you managed to calculate both i and j, you can now easily find the value of the median. You need to handle a few cases here depending on whether N+M is odd or even.\nHope it helps!","Q_Score":1,"Tags":"python,arrays,algorithm","A_Id":48389275,"CreationDate":"2018-01-22T16:37:00.000","Title":"How to find the median between two sorted arrays?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a neural net with two loss functions, one is binary cross entropy for the 2 classes, and another is a regression. Now I want the regression loss to be evaluated only for class_2, and return 0 for class_1, because the regressed feature is meaningless for class_1.\nHow can I implement such an algorithm in Keras?\nTraining it separately on only class_1 data doesn't work because I get nan loss. There are more elegant ways to define the loss to be 0 for one half of the dataset and mean_square_loss for another half?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2069,"Q_Id":48387602,"Users Score":4,"Answer":"This is a question that's important in multi-task learning where you have multiple loss functions, a shared neural network structure in the middle, and inputs that may not all be valid for all loss functions.\nYou can pass in a binary mask which are 1 or 0 for each of your loss functions, in the same way that you pass in the labels. Then multiply each loss by its corresponding mask. The derivative of 1x is just dx, and the derivative of 0x is 0. You end up zeroing out the gradient in the appropriate loss functions. Virtually all optimizers are additive optimizers, meaning you're summing the gradient, adding a zero is a null operation. Your final loss function should be the sum of all your other losses.\nI don't know much about Keras. Another solution is to change your loss function to use the labels only: L = cross_entropy * (label \/ (label + 1e-6)). That term will be almost 0 and almost 1. Close enough for government work and neural networks at least. This is what I actually used the first time before I realized it was as simple as multiplying by an array of mask values. \nAnother solution to this problem is to us tf.where and tf.gather_nd to select only the subset of labels and outputs that you want to compare and then pass that subset to the appropriate loss function. I've actually switched to using this method rather than multiplying by a mask. But both work.","Q_Score":0,"Tags":"python,tensorflow,keras","A_Id":48448962,"CreationDate":"2018-01-22T17:59:00.000","Title":"Multi-Task Learning: Train a neural network to have different loss functions for the two classes?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working with python3 and tensorflow to generate image patches by using numpy view_as_windows but because of numpy can't run on GPU, is there any way to do it with tensorflow?\nex: view_as_windows(array2d, window_shape, stride)\nThanks","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":133,"Q_Id":48400191,"Users Score":2,"Answer":"Note: This answer does not answer the OP's exact question, but addresses the actual need of the OP as clarified in the comments (i.e., generate image patches, quickly). I just thought this would fit better here than in a badly-formatted comment.\nIf all you need to do is generating image patches, Tensorflow (and generally GPU acceleration) is not the right tool for this, because the actual computation is trivial (extract a sub-area of an image) and the bottleneck would be memory transfer between GPU and CPU.\nMy suggestion is, then, to write CPU-only code that uses view_as_windows and parallelize it via multiprocessing to split the workload on all your CPU cores.\nShould you need to feed those patches to a Tensorflow graph afterwards, the way to go would be to first generate the patches on the CPU (with whatever input pipeline you like), batch them and then feed them to the GPU for the graph computation.","Q_Score":1,"Tags":"python-3.x,numpy,tensorflow","A_Id":48401126,"CreationDate":"2018-01-23T11:07:00.000","Title":"Is there any tensorflow version of numpy.view_as_windows?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've read some tutorials and documentation on MPI for python. However, I'm still not clear on how it is supposed to be used for sending jobs to separate nodes in a cluster, then combining\/processing the results. It seems that you only specify the number of different processes.\nIs it possible to use MPI for sending versions of the same script to separate nodes which run separately with multiprocessing, then combine the combine the results later? If this is an inappropriate use for MPI, what could do something like this?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":207,"Q_Id":48415348,"Users Score":0,"Answer":"Actually, you may submit one script to some nodes, specified in a given file. But the results are given based on each script. You cannot combine the results of more than one script on run-time, since each result is saved in some particular file (if any job scheduler used).","Q_Score":0,"Tags":"python,parallel-processing,cluster-computing,hpc,mpi4py","A_Id":48505541,"CreationDate":"2018-01-24T05:25:00.000","Title":"How to use mpi for python for parallel cluster computing\/ hpc?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to initial the value for weight and bias in BasicLSTMcell in Tensorflow with my pre-trained value (I get them by .npy). But as I use get_tensor_by_name to get the tensor, it seems that it just returns a copy for me and the raw value is never changed. I need your help!","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":488,"Q_Id":48417764,"Users Score":0,"Answer":"In the first iteration add some tf.assign operations to assign the values you want to the internal variables.\nMake sure this only happens in the first iteration otherwise you'll overwrite any training you do.\nThe cell has a method called get_trainable_variables to help you if you want.","Q_Score":1,"Tags":"python,tensorflow,lstm,recurrent-neural-network","A_Id":48419553,"CreationDate":"2018-01-24T08:21:00.000","Title":"How to modify the initial value in BasicLSTMcell in tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Assume that I have five columns in my dataset (A,B,C,D,E) and I want to build an LSTM model by training just on A,B,D,E (i.e. I want to exclude C)\nMy problem is that I still want to use this model to predict C. Is it possible if I didn't train my model with this variable? How can I do that?\nEDIT 1\nI'm working with categorical and numerical data modeled as time series. In this specific case, C is a categorical time series (given in a one-hot representation).","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":686,"Q_Id":48420005,"Users Score":2,"Answer":"I'd say that one way for it to do this is for your network to simply predict C or have C as the label\nI have been seeing this again and again. Don't confuse a NN with something more than it actually is. You simply approximate the output Y given an input X by learning a function F. That is your NN.\nIn your case the output could very easily be C + Other_Output\nDepending on what that other output is, your network could converge and have good results. It could very well not, so your question is simply at this point incomplete.You have to ask yourself some questions like:\n\nDoes C + Ohter_Output make sense for the give input.\nIs there a good way for me serialize the C + Other_Output ? Like having the first K out of N output array elements describing C and the rest N-K describing Other_Output ?\nIs C a multiclass problem and if so is Other_Output a different kind of problem or could potentially be turned into a multiclass of the same kind, problem that could converge along with C or make them both multilabel problem ? \n\nThese are at least some of the questions you need to ask yourself before even choosing the architecture.\nThat being said, no, unless you train your network to learn about patterns between A B D and C it will not be able to predict a missing input.\nGood luck,\nGabriel","Q_Score":1,"Tags":"python,keras,lstm","A_Id":48605766,"CreationDate":"2018-01-24T10:20:00.000","Title":"Predict a variable that is not in the input sequences with LSTM-Keras","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Hi I have a python script that uses dask library to handle a very large data frame, larger than the physical memory. I notice that the job get killed in the middle of a run if the memory usage stays at 100% of the computer for some time. \nIs it expected? I would thought the data would be spilled to disk and there are plenty of disk space left.\nIs there a way to limit its total memory usage? Thanks\nEDIT:\nI also tried:\ndask.set_options(available_memory=12e9)\nIt did not work. It did not seemed to limit its memory usage. Again, when memory usage reach 100%, the job gets killed.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1009,"Q_Id":48424813,"Users Score":0,"Answer":"Try going through the data in chunks with:\nchunksize = 10 ** 6\nfor chunk in pd.read_csv(filename, chunksize=chunksize):\nprocess(chunk)","Q_Score":3,"Tags":"python,performance,dask","A_Id":48425028,"CreationDate":"2018-01-24T14:26:00.000","Title":"dask job killed because memory usage?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm fairly new to python but I'm pretty sure I didn't get this behaviour before.\nA couple of days back I've noticed that if I open a new python console and simply do:\nimport pandas as pd\nThen python.exe ram usage grows steadily in about 5 seconds to reach about 96% utilisation (ie about 15.5G of my 16G total ram).\nThat's not normal, right?\nI'm using anaconda3 python 3.5 on windows 10....I've updated my conda and pandas but to no avail...\nCheers","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":61,"Q_Id":48432038,"Users Score":0,"Answer":"Ok, so starting from a brand new environment with latest python 3.5.4 and latest panda seems to cut it....so I think I'll close this wonderful thread for now and re-open if after reinstalling all other needed libs I end up with the same problem.","Q_Score":0,"Tags":"python,pandas,ram,conda","A_Id":48432486,"CreationDate":"2018-01-24T21:39:00.000","Title":"Why would importing pandas use up almost all my ram?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have to read thousands of images in memory.This has to be done.When i extract frames using ffmpeg from a video,the disk space for the 14400 files =92MB and are in JPG format.When I read those images in python and append in a python list using libraries like opencv,scipy etc the same 14400 files=2.5 to 3GB.Guess the decoding is the reason?any thoughts on this will be helpful?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":420,"Q_Id":48447814,"Users Score":1,"Answer":"You are exactly right, JPEG images are compressed (this is even a lossy compression, PNG would be a format with lossless compression), and JPEG files are much smaller than the data in uncompressed form.\nWhen you load the images to memory, they are in uncompressed form, and having several GB of data with 14400 images is not surprising.\nBasically, my advice is don't do that. Load them one at a time (or in batches), process them, then load the next images. If you load everything to memory beforehand, there will be a point when you run out of memory.\nI'm doing a lot of image processing, and I have trouble imagining a case where it is necessary to have that many images loaded at once.","Q_Score":0,"Tags":"python,numpy,ffmpeg","A_Id":48448182,"CreationDate":"2018-01-25T16:35:00.000","Title":"Can I use ffmpeg to output jpgs to a numpy array in python without writing the files to disk etc?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I installed (with pip) MatPlotLib and Pandas, and they are both not working properly in programs. Here is the strange thing... \nWhen I type the following into the interactive environment of IDLE\nimport pandas as pd\npd.Series([1, 2, 3, 4, 5])\nI get this as output: (indicating that it works properly)\n0    1\n1    2\n2    3\n3    4\n4    5\ndtype: int64\nBut when I use that very same code in a python program, it crashes and says \"AttributeError: module 'pandas' has no attribute 'Series'\" \nCan anyone tell me what's going on?\nI can also successfully import matplotlib in the interactive environment but get errors when I do it in a program and run it.\nEDIT: There is no shebang because I am running the program through IDLE. I am using python 3.6, the only python I have on my computer. I am executing this file by clicking run in IDLE.\nCurrently In my command prompt paths I have \nC:\\Users\\Karl\\AppData\\Local\\Programs\\Python\\Python36\nC:\\Users\\Karl\\AppData\\Local\\Programs\\Python\\Python36\\Scripts\\\nEDIT 2: I think we are closer to finding out the problem. If I run a python program with the code above in the command line (this time with the proper shebang(I forgot one before)), it works! So this must be an idle issue. \nCurrently, one of IDLE's paths is to \nC:\\Users\\Karl\\AppData\\Local\\Programs\\Python\\Python36\\lib\\site-packages\nwhich contains all of the libraries for python.\nEDIT FINAL: SOLVED. So when I did:\nimport sys\nsys.path.append(path to pandas library) it worked! so now I can fully use pandas. I guess I will just have to do this anytime I download a new library and it doesn't work. Thank you for all the help","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":53,"Q_Id":48455129,"Users Score":0,"Answer":"SOLVED. So when I did:\nimport sys\nsys.path.append(path to pandas library) \nit worked! so now I can fully use pandas. I guess I will just have to do this anytime I download a new library and it doesn't work. Thank you for all the help","Q_Score":0,"Tags":"python,python-3.x,pandas,python-import","A_Id":48455544,"CreationDate":"2018-01-26T02:48:00.000","Title":"Pip is correctly installing libraries to the proper directory, but I cannot import those packages properly in program","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have web scraped data in a column of a pandas dataframe that represents when different pieces of art were created. This data was entered in as strings by various people in many many different formats. Some examples:\n\n1998\ncirca 1995\nc. 2003-5\nMarch 2, 1904\n1st quarter of 19th century\n19th to 20th century\nca. late 19th and early 20th Century\nBCE 500\n206 BCE-240 CE\n1995-99\ndesigned 1950, produced 1969\ndesigned 1935, produced circa 1946-1968\n1990; and 1989\n1975\/97\n618-907 CE\n2001; 2006 and 2008\n1937-42\/48\nno date\nn.d.\nmid 1900s\nlate 1940's\n\nI've spent a couple days writing a long transformer class that attempts to handle every combination in my current dataset, which is semi-successful, but I figured this must be something people have done in the past.\nSo does there exist any way in Python to handle date information that is extremely fuzzy in this way?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":290,"Q_Id":48470828,"Users Score":0,"Answer":"The problem you're describing is categorically known as \"Natural Language Parsing\", or NLP.\nGoogling for Python NLP Date Parsing libraries yields several results.  You should do that, and evaluate them for your needs.","Q_Score":2,"Tags":"python-3.x,date,fuzzy-comparison","A_Id":48471254,"CreationDate":"2018-01-26T23:07:00.000","Title":"How to parse EXTREMELY fuzzy dates?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am implementing some variants of FCN for Segmentation. In particular, I have implemented a U-net architecture. Within the architecture, I am applying valid convolution with a 3x3 kernel and then I apply transposed convolution for upsampling with a 2x2 kernel and stride of 2. \nMy question is, if using valid or same padding for the convolution, does this determine whether we use valid or same padding for the transposed convolution?\nCurrently I use valid padding for convolution and same padding for transposed convolution.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":330,"Q_Id":48473417,"Users Score":0,"Answer":"No, it's your decision how you calculate the kernel-map in every single convolutional layer. It's a matter of designing your model.","Q_Score":2,"Tags":"python,tensorflow,deep-learning,convolution,image-segmentation","A_Id":48475567,"CreationDate":"2018-01-27T06:55:00.000","Title":"Transposed convolution TensorFlow padding for FCN style networks","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying out NiftyNet and got stuck at the first step.\nTrying to run the quickstart command\n\npython net_download.py dense_vnet_abdominal_ct_model_zoo\n  python net_segment.py inference -c  ~\/niftynet\/extensions\/dense_vnet_abdominal_ct\/config.ini\n\ngives me \n\nKeyError: \"Registering two gradient with name 'FloorMod' !(Previous registration was in _find_and_load_unlocked :955)\"\n\nCould any one help? I'm using Ubuntu 16.04 with Nvidia GPU. Tried tensorflow:1.4.1-py3 docker image, Anaconda with CPU version of tensorflow \nand native python with CPU version of tensorflow and I get the same error.\nI'm pretty sure it's something I did wrong because I get the same error from those different environment but I'm not sure what...\nThanks!","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":599,"Q_Id":48481327,"Users Score":0,"Answer":"Please Install tensorflow using this command\npip install tensorflow\nAfter that install nifty net using below command \n'''\npip install niftynet\n'''\nreinstall the python\n'''\npip install python\n'''\nif the problem is still than please mentioned your problem in more details\nplease make sure your environment variable is set before executing the command from niftynet page.","Q_Score":3,"Tags":"python,niftynet","A_Id":58831150,"CreationDate":"2018-01-27T22:44:00.000","Title":"Error while trying to run NiftyNet quick start command","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"is there a way that I can turn off stride in tensor flow when using: tf.layers.conv2d()? According to the docs, the default is (1,1) but when I try to change this to (0,0) I get an error telling me that it has to be a positive number.\nThanks.","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":336,"Q_Id":48481873,"Users Score":2,"Answer":"A unity stride is the same as not having a stride (turning it off), as its the normal way a convolution works.\nAs the stride is the amount of pixels the sliding window moves, one is the minimum value, and zero would not be valid as then the sliding window wouldn't move at all.","Q_Score":0,"Tags":"python,tensorflow,neural-network,conv-neural-network","A_Id":48482421,"CreationDate":"2018-01-28T00:08:00.000","Title":"how to set stride to zero when using tf.layers.conv2d","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a python program and am trying to plot something using the mplot3d from mpl toolkits,  but whenever I try to import the Axes3D from mpl_toolkits from mpl_toolkits.mplot3d import Axes3D\n I get the following error: ImportError: No module named mpl_toolkits","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":11673,"Q_Id":48509766,"Users Score":6,"Answer":"I solved this by running the following imports in the following order:\nimport matplotlib.pyplot as plt \nimport mpl_toolkits\nfrom mpl_toolkits.mplot3d import Axes3D\nNote: This only worked for me on python3. So first, I had to install python3 and pip3. Then I did \"pip3 install matplotlib\" in Terminal. If you already have matplotlib then try \"pip3 install --upgrade matplotlib\"","Q_Score":2,"Tags":"python,matplotlib","A_Id":53142990,"CreationDate":"2018-01-29T20:52:00.000","Title":"ImportError: No module named mpl_toolkits","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have some data in memory that I want to store in a HDF file.\nMy data are not huge (<100 MB, so they fit in memory very comfortably), so for performance it seems to make sense to keep them there. At the same time, I also want to store it on disk. It is not critical that the two are always exactly in sync, as long as they are both valid (i.e. not corrupted), and that I can trigger a synchronization manually.\nI could just keep my data in a separate container in memory, and shovel it into an HDF object on demand. If possible I would like to avoid writing this layer. It would require me to keep track of what parts have been changed, and selectively update those. I was hoping HDF would take care of that for me.\nI know about the driver='core' with backing store functionality, but it AFAICT, it only syncs the backing store when closing the file. I can flush the file, but does that guarantee to write the object to storage?\nFrom looking at the HDF5 source code, it seems that the answer is yes. But I'd like to hear a confirmation.\nBonus question: Is driver='core' actually faster than normal filesystem back-ends? What do I need to look out for?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":870,"Q_Id":48517784,"Users Score":0,"Answer":"If you need consistency and avoid corrupted hdf5 files, you may like to:\n1) use write-ahead-log, use append logs to write what's being added\/updated each time, no need to write to hdf5 this moment.\n2) periodically, or at the time you need to shutdown, you replay the logs to apply them one by one, write to the hdf5 file\n3) if your process down during 1), you won't lose data, after you startup next time, just replay the logs and writes them to hdf5 file\n4) if your process is down during 2), you will not lose data, just remove the corrupted hdf5 file, replay the logs and write it again.","Q_Score":2,"Tags":"python,hdf5,h5py","A_Id":49185722,"CreationDate":"2018-01-30T09:18:00.000","Title":"Fine control over h5py buffering","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am executing a Python-Tensorflow script on Amazon Sagemaker.  I need to checkpoint my model to the S3 instance I am using, but I can't find out how to do this without using the Sagemake Tensorflow version.\nHow does one checkpoint to an S3 instance without using the Sagemaker TF version?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":895,"Q_Id":48525733,"Users Score":0,"Answer":"Create an object in S3 and enable versioning to the bucket. Everytime you change the model and save it to S3, it will be automatically versioned and stored in the bucket. \nHope it helps.","Q_Score":0,"Tags":"python,tensorflow,amazon-s3,amazon-sagemaker","A_Id":48531968,"CreationDate":"2018-01-30T16:03:00.000","Title":"TensorFlow Checkpoints to S3","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"Is there any way to see the previous output without rerunning the program? For example, I left my ML algorithm to work overnight and in the morning I got the results. But, for some reason, when I pressed Enter on the original code, it started to run again and the original output disappeared.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":3493,"Q_Id":48527451,"Users Score":1,"Answer":"usually, yes, so long as the kernel is still up.  the return values of all expressions evaluated are stored in the Out global list.  If you are now executing statement number n, then Out[n-1] will have the last thing you successfully finished.\nif your output was not returned, but rather printed. You're out of luck...","Q_Score":3,"Tags":"python,jupyter-notebook","A_Id":48527617,"CreationDate":"2018-01-30T17:42:00.000","Title":"Jupyter Notebook and previous output","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to import curdoc. I have tried from bokeh.io import curdoc and from bokeh.plotting import curdocbut neither works.\nI've tried pip install -U bokeh and pip install bokeh but it still returns no module named 'bokeh.plotting; 'bokeh' is not a package'. What is happening?\nI have reverted back to 0.12.1 currently.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":6323,"Q_Id":48527785,"Users Score":0,"Answer":"Renaming the bokeh.py file and deleting bokeh.pyc file solved it for me.","Q_Score":8,"Tags":"python,bokeh","A_Id":63316299,"CreationDate":"2018-01-30T18:02:00.000","Title":"No module named 'bokeh.plotting'; bokeh is not a package","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to import curdoc. I have tried from bokeh.io import curdoc and from bokeh.plotting import curdocbut neither works.\nI've tried pip install -U bokeh and pip install bokeh but it still returns no module named 'bokeh.plotting; 'bokeh' is not a package'. What is happening?\nI have reverted back to 0.12.1 currently.","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":6323,"Q_Id":48527785,"Users Score":18,"Answer":"Check your folder if any of the program named bokeh.py please rename it because it's picking bokeh.plotting from your program bokeh.py not from the library.","Q_Score":8,"Tags":"python,bokeh","A_Id":52453673,"CreationDate":"2018-01-30T18:02:00.000","Title":"No module named 'bokeh.plotting'; bokeh is not a package","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am training my siamese network for nlp. I have used lstm in it. and BCELoss. My loss is increasing from the first epoch. The first 36 epoch loss is \nerror after  0 is \n 272.4357\n[torch.FloatTensor of size 1]\nerror after  1 is \n 271.8972\n[torch.FloatTensor of size 1]\nerror after  2 is \n 271.5598\n[torch.FloatTensor of size 1]\nerror after  3 is \n 271.6979\n[torch.FloatTensor of size 1]\nerror after  4 is \n 271.7315\n[torch.FloatTensor of size 1]\nerror after  5 is \n 272.3965\n[torch.FloatTensor of size 1]\nerror after  6 is \n 273.3982\n[torch.FloatTensor of size 1]\nerror after  7 is \n 275.1197\n[torch.FloatTensor of size 1]\nerror after  8 is \n 275.8228\n[torch.FloatTensor of size 1]\nerror after  9 is \n 278.3311\n[torch.FloatTensor of size 1]\nerror after  10 is \n 277.1054\n[torch.FloatTensor of size 1]\nerror after  11 is \n 277.8418\n[torch.FloatTensor of size 1]\nerror after  12 is \n 279.0189\n[torch.FloatTensor of size 1]\nerror after  13 is \n 278.4090\n[torch.FloatTensor of size 1]\nerror after  14 is \n 281.8813\n[torch.FloatTensor of size 1]\nerror after  15 is \n 283.4077\n[torch.FloatTensor of size 1]\nerror after  16 is \n 286.3093\n[torch.FloatTensor of size 1]\nerror after  17 is \n 287.6292\n[torch.FloatTensor of size 1]\nerror after  18 is \n 297.2318\n[torch.FloatTensor of size 1]\nerror after  19 is \n 307.4176\n[torch.FloatTensor of size 1]\nerror after  20 is \n 304.6649\n[torch.FloatTensor of size 1]\nerror after  21 is \n 328.9772\n[torch.FloatTensor of size 1]\nerror after  22 is \n 300.0669\n[torch.FloatTensor of size 1]\nerror after  23 is \n 292.3902\n[torch.FloatTensor of size 1]\nerror after  24 is \n 300.8633\n[torch.FloatTensor of size 1]\nerror after  25 is \n 305.1822\n[torch.FloatTensor of size 1]\nerror after  26 is \n 333.9984\n[torch.FloatTensor of size 1]\nerror after  27 is \n 346.2062\n[torch.FloatTensor of size 1]\nerror after  28 is \n 354.6148\n[torch.FloatTensor of size 1]\nerror after  29 is \n 341.3568\n[torch.FloatTensor of size 1]\nerror after  30 is \n 369.7580\n[torch.FloatTensor of size 1]\nerror after  31 is \n 366.1615\n[torch.FloatTensor of size 1]\nerror after  32 is \n 368.2455\n[torch.FloatTensor of size 1]\nerror after  33 is \n 391.4102\n[torch.FloatTensor of size 1]\nerror after  34 is \n 394.3190\n[torch.FloatTensor of size 1]\nerror after  35 is \n 401.0990\n[torch.FloatTensor of size 1]\nerror after  36 is \n 422.3723\n[torch.FloatTensor of size 1]","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1124,"Q_Id":48527808,"Users Score":1,"Answer":"Probably your learning rate is too high. Try decreasing your learning rate. A too large learning rate is the most common reason for loss increasing from the first epoch. \nAlso your loss is very high. It is unusual to have such a high lost. You probably have a sum in your loss function, it might be wiser to replace that sum with a mean. While this makes no difference if you use the Adam optimizer, if you use simple SGD with or without momentum using a sum instead of a mean, means that you will need to tune your learning rate differently if the dimensions (or the length of your sequence processed by your lstm) of your system changes.","Q_Score":0,"Tags":"python,optimization,neural-network,deep-learning,pytorch","A_Id":48529697,"CreationDate":"2018-01-30T18:04:00.000","Title":"Loss is increasing from first epoch itself","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on a multiclass classification problem with an unbalanced dataset of images(different class). I tried imblearn library, but it is not working on the image dataset.\nI have a dataset of images belonging to 3 class namely A,B,C. A has 1000 data, B has 300 and C has 100. I want to oversample class B and C, so that I can avoid data imbalance. Please let me know how to oversample the image dataset using python.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":2828,"Q_Id":48532069,"Users Score":1,"Answer":"Thanks for the clarification.  In general, you don't oversample with Python.  Rather, you pre-process your data base, duplicating the short-handed classes.  In the case you cite, you might duplicate everything in class B, and make 5 copies of everything in class C.  This gives you a new balance of 1000:600:500, likely more palatable to your training routines.  Instead of the original 1400 images, you now shuffle 2100.\nDoes that solve your problem?","Q_Score":3,"Tags":"python-3.x,machine-learning,deep-learning,computer-vision,imblearn","A_Id":48550016,"CreationDate":"2018-01-30T23:20:00.000","Title":"How to oversample image dataset using Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am training neural networks using the great Keras library for Python. I got curious about one behaviour I don't understand. \nOften even slighly bigger models converge to bigger error than smaller ones.\nWhy does this happen? I would expect bigger model just to train longer, but converge to smaller or same error.\nI hyperoptimized the model, tried different amounts of dropout regularization and let it train for sufficient time. I experimented with models about 10-20k parameters, 5 layers, 10M data samples and 20-100 epochs with decreasing LR. Models contained Dense and sometimes LSTM layers.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":407,"Q_Id":48539256,"Users Score":1,"Answer":"What I noticed in my tests is that increasing the number of parameters require sometime to review how you prepare your input data or how you initialize your weights. I found that often increasing the number of parameteres requires to initialize the weights differently (meaning initializing with smaller values) or you need to normalize the input data (I guess you have done that), or even dividing them by a constant factor to make them smaller. \nSometime reducing the learning rate helps, since your cost function will become more complex with more parameters and it may happen that the learning rate that before was working fine is too big for your new case. But is very difficult to give a precise answer. \nSomething else: what do you mean with bigger error? Are you doing classification or regression? In addition are you talking about error on the train set or the dev\/test sets? That is a big difference. It may well be that (if you are talking about the dev\/test sets) that you are overfitting your data and therefore gets a bigger error on the dev\/tests sets (bias-variance tradeoff)... Can you give us more details?","Q_Score":0,"Tags":"python,machine-learning,neural-network,keras","A_Id":48560107,"CreationDate":"2018-01-31T09:56:00.000","Title":"Bigger neural network converges to bigger error than smaller","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have been working with Anaconda, Python 3.5 and Win 7. Having been obliged to work with TensorFlow, I installed and run it. For the first time it worked but for the second time, I got the error : \"tensor flow module not found\". I uninstalled and installed it again and got the same error. Switching to windows 10, python 2 or using another system have not been useful.\nIs there any other way to try (except using Linux)?\nThanks a lot in advance.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":39,"Q_Id":48542418,"Users Score":0,"Answer":"Just change this instruction: conda create -n tensorflow pip python=3.5\nto: conda create -n tensorflow pip python=3.5 -spyder.","Q_Score":0,"Tags":"python,import,packages","A_Id":49105522,"CreationDate":"2018-01-31T12:37:00.000","Title":"Unsuccessfully Running TensorFlow Package on Windows and Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Oversampled data as in those created by the SMOTHE and ADASYN method. \nSimulated data as in those created by statistics, using extreme mean values, bootstrapping and measuring performance with the p-value.\nThe application is to increase the minority class of underrepresented data using python.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":39,"Q_Id":48543257,"Users Score":0,"Answer":"So after some research, here is my answer.\nBootstrapping is for either 1D array or 2D (also referred to as pairs bootstrap) of any columns of data (only columns) while oversampling is for the minority class in the dataset (rows + columns). \nBootstrapping is less popular for binary classification with 100+ features as it does not maintain the underlying relationship between the features while oversampling (depending on the technique) does maintain the underlying relationship between the features in the data. Moreover, Bootstrap works best for continuous data in a single column in case of 1D or 2d, as then you can produce a range of data for the whole column regardless of minority or majority class as if the measurements where taken n numbers of times (n is is any integer 1+)\nThus, in this question scenario, oversampling is preferred as it maintains the underlying structure of the data of the minority class.","Q_Score":0,"Tags":"python,statistics","A_Id":48544648,"CreationDate":"2018-01-31T13:23:00.000","Title":"What is the difference between oversampling minority data and simulating minority data?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I saw a sample of code (too big to paste here) where the author used model.train_on_batch(in, out) instead of model.fit(in, out). The official documentation of Keras says: \n\nSingle gradient update over one batch of samples.\n\nBut I don't get it. Is it the same as fit(), but instead of doing many feed-forward and backprop steps, it does it once? Or am I wrong?","AnswerCount":3,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":13182,"Q_Id":48550201,"Users Score":15,"Answer":"Yes, train_on_batch trains using a single batch only and once. \nWhile fit trains many batches for many epochs. (Each batch causes an update in weights).\nThe idea of using train_on_batch is probably to do more things yourself between each batch.","Q_Score":12,"Tags":"python,tensorflow,machine-learning,keras,artificial-intelligence","A_Id":48550825,"CreationDate":"2018-01-31T19:41:00.000","Title":"What does train_on_batch() do in keras model?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I saw a sample of code (too big to paste here) where the author used model.train_on_batch(in, out) instead of model.fit(in, out). The official documentation of Keras says: \n\nSingle gradient update over one batch of samples.\n\nBut I don't get it. Is it the same as fit(), but instead of doing many feed-forward and backprop steps, it does it once? Or am I wrong?","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":13182,"Q_Id":48550201,"Users Score":0,"Answer":"The method fit of the model train the model for one pass through the data you gave it, however because of the limitations in memory (especially GPU memory), we can't train on a big number of samples at once, so we need to divide this data into small piece called mini-batches (or just batchs). The methode fit of keras models will do this data dividing for you and pass through all the data you gave it.\nHowever, sometimes we need more complicated training procedure we want for example to randomly select new samples to put in the batch buffer each epoch (e.g. GAN training and Siamese CNNs training ...), in this cases we don't use the fancy an simple fit method but instead we use the train_on_batch method. To use this methode we generate a batch of inputs and a batch of outputs(labels) in each iteration and pass it to this method and it will train the model on the whole samples in the batch at once and gives us the loss and other metrics calculated with respect to the batch samples.","Q_Score":12,"Tags":"python,tensorflow,machine-learning,keras,artificial-intelligence","A_Id":62452563,"CreationDate":"2018-01-31T19:41:00.000","Title":"What does train_on_batch() do in keras model?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to write a pandas dataframe to csv.  One of the columns of the df has entries which are lists, e.g. [1, 2], [3, 4], ...\nWhen I use df.to_csv('output.csv') and I open the output csv file, the commas are gone.  That is, the corresponding column of the csv has entries [1  2], [3  4], ....\nIs there a way to write a dataframe to csv without removing the commas in these lists?  I've also tried using csv.writer.","AnswerCount":3,"Available Count":1,"Score":0.1325487884,"is_accepted":false,"ViewCount":7416,"Q_Id":48553303,"Users Score":2,"Answer":"df.to_csv('output.tsv', sep='\\t')\nWill separate the values with tabs instead of commas.  \n.tsv is tab separated value file","Q_Score":3,"Tags":"python,python-3.x,pandas,csv","A_Id":48553586,"CreationDate":"2018-01-31T23:47:00.000","Title":"Writing dataframe to csv WITHOUT removing commas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm reading in a large amount of data via parquet files into dataframes. I noticed a vast amount of the columns either have 1,0,-1 as values and thus could be converted from Ints to Byte types to save memory. \nI wrote a function to do just that and return a new dataframe with the values casted as bytes, however when looking at the memory of the dataframe in the UI, I see it saved as just a transformation from the original dataframe and not as a new dataframe itself, thus taking the same amount of memory.\nI'm rather new to Spark and may not fully understand the internals, so how would I go about initially setting those columns to be of ByteType?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":339,"Q_Id":48555860,"Users Score":0,"Answer":"In order to see whether there is any impact, you can try two things:\n\nWrite the data back to the file system. Once with the original type and anther time with your optimisation. Compare size on disk.\nTry calling collect on the dataframe and look at the driver memory in your OS's system monitor, make sure to induce a garbage collection to get a cleaner indication. Again- do this once w\/o the optimisation and another time with the optimisation. \n\nuser8371915 is right in the general case but take into account that the optimisations may or may not kick in based on various parameters like row group size and dictionary encoding threshold. \nThis means that even if you do see impact, there is a good chance you could get the same compression by tuning spark.","Q_Score":5,"Tags":"python,apache-spark,pyspark,spark-dataframe","A_Id":50587505,"CreationDate":"2018-02-01T05:22:00.000","Title":"PySpark casting IntegerTypes to ByteType for optimization","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm reading in a large amount of data via parquet files into dataframes. I noticed a vast amount of the columns either have 1,0,-1 as values and thus could be converted from Ints to Byte types to save memory. \nI wrote a function to do just that and return a new dataframe with the values casted as bytes, however when looking at the memory of the dataframe in the UI, I see it saved as just a transformation from the original dataframe and not as a new dataframe itself, thus taking the same amount of memory.\nI'm rather new to Spark and may not fully understand the internals, so how would I go about initially setting those columns to be of ByteType?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":339,"Q_Id":48555860,"Users Score":0,"Answer":"TL;DR It might be useful, but in practice impact might be much smaller than you think.\nAs you noticed:\n\nthe memory of the dataframe in the UI, I see it saved as just a transformation from the original dataframe and not as a new dataframe itself, thus taking the same amount of memory.\n\nFor storage, Spark uses in-memory columnar storage, which applies a number of optimizations, including compression. If data has low cardinality, then column can be easily compressed using run length encoding or dictionary encoding, and  casting won't make any difference.","Q_Score":5,"Tags":"python,apache-spark,pyspark,spark-dataframe","A_Id":48645854,"CreationDate":"2018-02-01T05:22:00.000","Title":"PySpark casting IntegerTypes to ByteType for optimization","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have some problem when detecting anomaly from time series data. \nI use LSTM model to predict value of next time as y_pred, true value at next time of data is y_real, so I have er = |y_pred - y_t|, I use er to compare with threshold = alpha * std and get anomaly data point. But sometime, our data is effected by admin or user for example number of player of a game on Sunday will higher than Monday. \nSo, should I use another model to classify anomaly data point or use \"If else\" to classify it?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1811,"Q_Id":48558085,"Users Score":4,"Answer":"I think you are using batch processing model(you didn't using any real-time processing frameworks and tools) so there shouldn't be any problem when making your model or classification. The problem may occur a while after you make model, so after that time your predicted model is not valid.\nI suggest some ways maybe solve this problem:\nUse Real-time or near real-time processing(like apache spark, flink, storm, etc).\nUse some conditions to check your data periodically for any changes if any change happens run your model again.\nDelete instances that you think they may cause problem(may that changed data known as anomaly itself) but before make sure that data is not so important.\nChange you algorithm and use algorithms that not very sensitive to changes.","Q_Score":2,"Tags":"python,machine-learning,keras,time-series","A_Id":48558428,"CreationDate":"2018-02-01T08:13:00.000","Title":"Real-time anomaly detection from time series data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How can one generate random sparse orthogonal matrix?\nI know there is a sparse matrices in scipy library but they are generally non-orthogonal. One can exploit QR-factorization, but it is not necessarily preserves sparsity.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":989,"Q_Id":48561974,"Users Score":2,"Answer":"As a preliminary thought you could partition the matrix into diagonal blocks, fill those blocks with QR and then permute rows\/columns. The resulting matrices will remain orthagonal. Alternatively, you could define some sparsity pattern for Q and try to minimize f(Q, xi)  subject to QQ^T=I  where f is some (preferably) convex function that adds entropy through the random variable xi. Can't say anything about the efficacy of either method since I haven't actually tried them.\nEDIT: A bit more about the second method. f can really be any function. One choice might be similarity of the non-zero elements to a random gaussian vector (or any other random variate): f = ||vec(Q) - x||_2^2, x ~ N(0, sigma * I). You could handle this using any general constrained optimizer. The problem of course, is that not every pattern S is guaranteed to have a (full rank) orthogonal filling. If you have the memory, L1 regularization (or a smooth approximation) could encourage sparsity in a dense matrix variable: g(Q) = f(Q) + P(Q) where P is any sparsity-inducing penalty function. Check out Wen & Yen (2010) \"A feasible Method for Optimization with Orthogonality Constraints\" for an algorithm specifically designed for optimization of general (differentiable) functions over (dense) orthogonal matrices and Liu, Wu, So (2015) \"Quadratic Optimization with Orthogonality Constraints\" for more theorical evaluation of several line\/arc search algorithms for quadratic functions. If memory is a problem, you could generate each row\/column separately using sparse basis pursuit, for which there are many algorithms depending on the nature of your problem. See Qu, Sun and Wright (2015) \"Finding a sparse vector in a subspace: linear sparsity using alternate directions\" and Bian et al (2015) \"Sparse null space basis pursuit and analysis dictionary learning for high-dimensional data analysis\" for algorithm details, though in both cases you will have to incorporate\/replace constraints to promote orthogonality to all previous vectors. \nIt's also worth noting there are sparse QR algorithms that return Q as the product of sparse\/structured matrices. If you are concerned about storage space alone, this might be the simplest method to create large, efficient orthogonal operators.","Q_Score":2,"Tags":"python,sparse-matrix,orthogonal","A_Id":48800308,"CreationDate":"2018-02-01T11:45:00.000","Title":"How to generate sparse orthogonal matrix in python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Tried googling up, but could not find how to implement Sklearn metrics like cohen kappa, roc, f1score in keras as a metric for imbalanced data.\nHow to implement Sklearn Metric in Keras as Metric?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2808,"Q_Id":48567012,"Users Score":5,"Answer":"Metrics in Keras and in Sklearn mean different things.  \nIn Keras metrics are almost same as loss. They get called during training at the end of each batch and each epoch for reporting and logging purposes. Example use is having the loss 'mse' but you still would like to see 'mae'. In this case you can add 'mae' as a metrics to the model.  \nIn Sklearn metric functions are applied on predictions as per the definition \"The metrics module implements functions assessing prediction error for specific purposes\". While there's an overlap, the statistical functions of Sklearn doesn't fit to the definition of metrics in Keras. Sklearn metrics can return float, array, 2D array with both dimensions greater than 1. There is no such object in Keras by the predict method.   \nAnswer to your question:\nIt depends where you want to trigger:\n\nEnd of each batch or each epoch\nYou can write a custom callback that is fired at the end of batch.  \nAfter prediction\nThis seems to be easier. Let Keras predict on the entire dataset, capture the result and then feed the y_true and y_pred arrays to the respective Sklearn metric.","Q_Score":2,"Tags":"python,machine-learning,scikit-learn,keras","A_Id":48573176,"CreationDate":"2018-02-01T16:05:00.000","Title":"How to implement Sklearn Metric in Keras as Metric?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to Python.\nI am confused as to what is happening with the following:\nB = np.array([A[..., n:n+5] for n in (5*4, 5*5)])\nWhere A.shape = (60L, 128L, 128L)\nand B.shape = (2L, 60L, 128L, 5L)\nI believe it is supposed to make some sort of image patch. Can someone explain to me what this does? This example is in the context of applying neural networks to images.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":80,"Q_Id":48572999,"Users Score":1,"Answer":"The shape of A tells me that A is most likely an array of 60 grayscale images (batch size 60), with each image having a size of 128x128 pixels.\nWe have: B = np.array([A[..., n:n+5] for n in (5*4, 5*5)]). To better understand what's happening here, let's unpack this line in reverse:\nfor n in (5*4, 5*5): This is the same as for n in (20, 25). The author probably chose to write it in this way for some intuitive reason related to the data or the rest of the code. This gives us n=20 and n=25.\nA[..., n:n+5]: This is the same as A[:, :, n:n+5]. This gives us all the rows from all the images of in A, but only the 5 columns at n:n+5. The shape of the resulting array is then (60, 128, 5).\nn=20 gives us A[:, :, 20:25] and n=25 gives us A[:, :, 25:30]. Each of these arrays is therefore of size (60, 128, 5).\nTogether, [A[..., n:n+5] for n in (5*4, 5*5)] gives us a list (thanks list comprehension!) with two elements, each a numpy array of size (60, 128, 5). np.array() converts this list into a numpy array of shape (2, 60, 128, 5).\nThe result is that B contains 2 patches of each image, each a 5 pixel column wide subset of the original image- one starting at column 20 and the second one starting at column 25.\nI can't speculate to the reason for this crop without further information about the network and its purpose.\nHope this helps!","Q_Score":0,"Tags":"python,neural-network","A_Id":48574103,"CreationDate":"2018-02-01T22:47:00.000","Title":"Python np.array example","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Sorry for some grammatical mistakes and misuse of words.\nI am currently working with text classification, trying to classify the email.\nAfter my research, i found out Multinomial Naive Bayes and Bernoulli Naive Bayes is more often used for text classification.\nBernoulli just cares about whether the word happens or not.\nMultinomial cares about the number of occurrence of the word.\nFor Gaussian Naive Bayes, it's usually been used for continuous data and data with normal distribution, eg: height,weight\nBut what is the reason that we don't use Gaussian Naive Bayes for text classification?\nAny bad things will happen if we apply it to text classification?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1887,"Q_Id":48580762,"Users Score":0,"Answer":"We use algorithm based on the kind of dataset we have.Bernoulli Naive bayes is good at handling boolean\/binary attributes,while Multinomial Naive bayes is good at handling discrete values and Gaussian naive bayes is good at handling continuous values.\nConsider three scenarios\n1)consider a datset which has columns like has_diabetes,has_bp,has_thyroid and then you classify the person as healthy or not.In such a scenario,Bernoulli NB will work well.\n2)consider a dataset that has marks of various students of various subjects and you want to predict,whether the student is clever or not.Then in this case multinomial NB will work fine.\n3)consider a dataset that has weight of students and you are predicting height of them,then GaussiaNB will well in this case.","Q_Score":1,"Tags":"python,machine-learning,naivebayes","A_Id":68209345,"CreationDate":"2018-02-02T10:32:00.000","Title":"Difference of three Naive Bayes classifiers","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am transitioning from R and would like to use ggplot in the yhat Rodeo IDE. \nI have tried installing ggplot for python 3.6.3 (Anaconda 5.0.1 64-bit) in a number of ways. \nWhen trying to install any kind of ggplot via conda i get this:\n\"UnsatisfiableError: The following specifications were found to be in conflict:\n-ggplot\n-zict\" \nI am aware that ggplot does not work with the most recent version of python, so i tried installing an older version and received the same error.\nI then thought to try pip, and ensured that pip was installed and up to date via conda.\nIn rodeo when i try to use \"! pip install ggplot\" i get the \"pip.exe has stopped working\" error. Same for easy_install, etc.\nAny ideas? It seems that i can't install it in any way.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":2135,"Q_Id":48589399,"Users Score":1,"Answer":"Try making a fresh conda environment and only request the installation of ggplot:\nconda create --name my_new_env -c conda-forge ggplot\nThis installed ggplot v0.11.5 and python v3.6.3 for me just now (Ubuntu 17.10)","Q_Score":1,"Tags":"python,ggplot2,pip,conda","A_Id":48620666,"CreationDate":"2018-02-02T19:11:00.000","Title":"Installing ggplot in python 3.6.3","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"With python 2.7 on a mac with tensorflow and running \ntensorboard --logdir= directory\/wheremylog\/fileis produces the following error ImportError: cannot import name weakref\nI've seen several folks remedy the issue with pip install backports.weakref\n but that requirement is already satisfied for me. Requirement already satisfied: backports.weakref in \/usr\/local\/lib\/python2.7\/site-packages\n\nI am out of ideas and am really keen to get tensorboard working. \nThanks","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":917,"Q_Id":48593014,"Users Score":2,"Answer":"For those with the same problem I was able to fix it through the following:\n1) find where your tensorflow lives pip show tensorflow and look at the location line, copy it.\n2) For me it was cd \/usr\/local\/lib\/python2.7\/site-packages\/\n3) cd tensorflow\/python\/lib\n4) open tf_should_use.py\n5) In your python editor replace line 28 from backports import weakref with import weakref and save the file.","Q_Score":0,"Tags":"python,tensorflow,tensorboard","A_Id":48633697,"CreationDate":"2018-02-03T01:18:00.000","Title":"Launching tensorboard error - ImportError: cannot import name weakref","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Suppose I have a tensor, x = [1, 2, 6, 6, 4, 2, 3, 2]\nI want to find the index of the first occurrence of every unique element in x.\nThe output should be [0, 1, 6, 4, 2].\nI basically want the second output of numpy.unique(x,return_index=True). This functionality doesn't seem to be supported in tf.unique. \nIs there a workaround to this in tensorflow, without using any loops?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":778,"Q_Id":48595802,"Users Score":0,"Answer":"One possibilty would be to use x.eval() which gives you a numpy array back,\nthen use numpy.unique(...)","Q_Score":1,"Tags":"python,tensorflow","A_Id":48595998,"CreationDate":"2018-02-03T09:18:00.000","Title":"Tensorflow: Finding index of first occurrence of elements in a tensor","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a task to find similar parts based on numeric dimensions--diameters, thickness--and categorical dimensions--material, heat treatment, etc. I have a list of 1 million parts. My approach as a programmer is to put all parts on a list, pop off the first part and use it as a new \"cluster\" to compare the rest of the parts on the list based on the dimensions. As a part on the list matches the categorical dimensions and numerical dimensions--within 5 percent--I will add that part to the cluster and remove from the initial list. Once all parts in the list are compared with the initial cluster part's dimensions, I will pop the next part off the list and start again, populating clusters until no parts remain on the original list. This is a programmatic approach. I am not sure if this is most efficient way of categorizing parts into \"clusters\" or if k-means clustering would be a better approach.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":161,"Q_Id":48600277,"Users Score":0,"Answer":"Define \"better\".\nWhat you do seems to be related to \"leader\" clustering. But that is a very primitive form of clustering that will usually not yield competitive results. But with 1 million points, your choices are limited, and kmeans does not handle categorical data well.\nBut until you decide what is 'better', there probably is nothing 'wrong' with your greedy approach.\nAn obvious optimization would be to first split all the data based on the categorical attributes (as you expect them to match exactly). That requires just one pass over the data set and a hash table. If your remaining parts are small enough, you could try kmeans (but how would you choose k), or DBSCAN (probably using the same threshold you already have) on each part.","Q_Score":0,"Tags":"python,r,statistics,cluster-analysis","A_Id":48607299,"CreationDate":"2018-02-03T17:54:00.000","Title":"Difference between k-means clustering and creating groups by brute force approach","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Now I can plot the map using osmnx, but I don't know the node id of nodes in the figure. That is how can I associate the node id and the node plot in the figure? Or, Can I plot the map with node id? Thanks!","AnswerCount":1,"Available Count":1,"Score":0.6640367703,"is_accepted":false,"ViewCount":469,"Q_Id":48609095,"Users Score":4,"Answer":"Just set the annotate parameter to True in the osmnx.plot.plot_graph() module.","Q_Score":2,"Tags":"python,plot,openstreetmap,networkx","A_Id":48799838,"CreationDate":"2018-02-04T14:26:00.000","Title":"Get specific node id from the figure plot by osmnx","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to have you sharing the template in Dash python where it generate a scatter charter with the help of plot.ly stunning graphics.\nI am unable to make my Dash framework running but I do the same flawlessly in Jupyter Notebook. Just was wondering if I could run it on Dash.\nYour help is much appreciated.\nRegards.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":458,"Q_Id":48612105,"Users Score":0,"Answer":"I have solved it.\nIt was due to miscoding. The code was not in the right place.\nThanks.","Q_Score":2,"Tags":"python,plotly-dash","A_Id":48779917,"CreationDate":"2018-02-04T19:22:00.000","Title":"How to render a plot.ly Jupyter Notebook file into Dash python Dashboard","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I have an array which has intensity values from -3000 to 1000,I have thresholded all values which are less then -100 to -100 and all values greater than 400 to 400,After which i convert it to datatype np.uint8,Which makes all the values in the array to have intensity values from 0 to 255,\nWhat i am confused about is,is there an operation i can run which will give the array from 0-255 intensity range to the earlier one -100 to 400?\nAny suggestions will be useful,Thanks in advance","AnswerCount":2,"Available Count":1,"Score":-0.1973753202,"is_accepted":false,"ViewCount":1021,"Q_Id":48644272,"Users Score":-2,"Answer":"Try array_name.encode(\"uint8\").decode(\"uint8\") if this worked, then you can use the decode() method","Q_Score":0,"Tags":"python","A_Id":48644352,"CreationDate":"2018-02-06T13:31:00.000","Title":"Encode array to uint8 and then back","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am interested in training a Keras CNN and I have some data in the form of 2D matrices (e.g. width x height). I normally represent, or visualize the data like a heatmap, with a colorbar.\nHowever, in training the CNN and formatting the data input, I'm wondering if I should keep this matrix as a 2D matrix, or convert it into an RGB image that is essentially a 3D matrix?\nWhat is the best practice and some considerations people should take into account?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":691,"Q_Id":48664649,"Users Score":2,"Answer":"In fact - you need to reshape a single data point to have a 3D shape - as keras expects your dataset to have shape (number examples, width, height, channels). If you don't wont to make your image RGB - you can simply leave it with only one channel (and interpret it as greyscale channel).","Q_Score":1,"Tags":"python,tensorflow,keras,conv-neural-network,data-processing","A_Id":48674801,"CreationDate":"2018-02-07T13:07:00.000","Title":"Keras Training a CNN - Should I Convert Heatmap Data As Image or 2D Matrix","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to Tensorflow and to implementing deep learning. I have a dataset of images (images of the same object). \nI want to train a Neural Network model using python and Tensorflow for object detection.\nI am trying to import the data to Tensorflow but I am not sure what is the right way to do it. \nMost of the tutorials available online are using public datasets (i.e. MNIST), which importing is straightforward but not helpful in the case where I need to use my own data.\nIs there a procedure or tutorial that i can follow?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":864,"Q_Id":48668031,"Users Score":1,"Answer":"There are many ways to import images for training, you can use Tensorflow but these will be imported as Tensorflow objects, which you won't be able to visualize until you run the session.\nMy favorite tool to import images is skimage.io.imread. The imported images will have the dimension (width, height, channels)\nOr you can use importing tool from scipy.misc.\nTo resize images, you can use skimage.transform.resize.\nBefore training, you will need to normalize all the images to have the values between 0 and 1. To do that, you simply divide the images by 255.\nThe next step is to one hot encode your labels to be an array of 0s and 1s.\nThen you can build and train your CNN.","Q_Score":0,"Tags":"python,tensorflow,neural-network,deep-learning,classification","A_Id":48668122,"CreationDate":"2018-02-07T15:54:00.000","Title":"How to import data into Tensorflow?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using Apache-Beam with the Python SDK.\nCurrently, my pipeline reads multiple files, parse them and generate pandas dataframes from its data.\nThen, it groups them into a single dataframe.\nWhat I want now is to retrieve this single fat dataframe, assigning it to a normal Python variable.\nIs it possible to do?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1669,"Q_Id":48668686,"Users Score":4,"Answer":"PCollection is simply a logical node in the execution graph and its contents are not necessarily actually stored anywhere, so this is not possible directly.\nHowever, you can ask your pipeline to write the PCollection to a file (e.g. convert elements to strings and use WriteToText with num_shards=1), run the pipeline and wait for it to finish, and then read that file from your main program.","Q_Score":2,"Tags":"python,apache-beam","A_Id":48674833,"CreationDate":"2018-02-07T16:27:00.000","Title":"How to retrieve the content of a PCollection and assign it to a normal variable?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a problem with reading in a csv with an id field with mixed dtypes from the original source data, i.e. the id field can be 11, 2R399004, BL327838, 7 etc. but the vast majority of them being 8 characters long.\nWhen I read it with multiple versions of pd.read_csv and encoding='iso-8859-1' it always converts the 7 and 11 to 00000007 or the like. I've tried using utf-8 but I get the following error:\nUnicodeDecodeError: 'utf-8' codec can't decode byte 0xc9 in position 40: unexpected end of data\nI have tried setting the dtype={'field': object} and string and various iterations of latin-1 and the like but it will continually do this.\nIs there any way to get around this error, without going through every individual file and fixing the dtypes?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":85,"Q_Id":48670780,"Users Score":0,"Answer":"Basically the column looks like this\nColumn_ID\n10\nHGF6558\n059\nKP257\n0001","Q_Score":0,"Tags":"python,pandas,csv,encoding,iso-8859-1","A_Id":48681568,"CreationDate":"2018-02-07T18:20:00.000","Title":"Pandas read csv adds zeros","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have been training OpenCV classifier for recognition of books.The requirement is recognize book from an image. I have used 1000+ images and OpenCV is able to detect books with no rotation. However, when I try to detect books with rotations it does not work properly.So I am wondering if their anyway to detect objects with rotations in images using OpenCV?","AnswerCount":3,"Available Count":1,"Score":0.1325487884,"is_accepted":false,"ViewCount":5762,"Q_Id":48673104,"Users Score":2,"Answer":"What features are you using to detect your books? Are you training a CNN and deploying it with OpenCV? In that case adding rotation image augmentation to your training would make it easy to detect rotated books. \nIf you are using traditional computer vision techniques instead, you can try to use some rotation invariant feature extractors like SURF, however, the results will not be as good as using CNNs which are now the state of the art for this kind of problems.","Q_Score":5,"Tags":"c#,python,c++,opencv,object-detection","A_Id":48678366,"CreationDate":"2018-02-07T20:57:00.000","Title":"How to detect rotated object from image using OpenCV?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using gensim lda for topic modeling and getting the results like so:\nTopic 1: word1 word2 word3 word4\nTopic 2: word4 word1 word2 word5\nTopic 3: word1 word4 word5 word6\nHowever using mallet on same lda does not produce duplicate words across topics. I have ~20 documents with >1000 words each that I train the lda on. How to get rid of words appearing across multiple topics?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":675,"Q_Id":48674084,"Users Score":0,"Answer":"In LDA all words are part of all topics, but with a different probability. You could define a minimum probability for your words to print, but I would be very surprised if mallet didn't come up with at least a couple of \"duplicate\" words across topics as well. Make sure to use the same parameters for both gensim and mallet.","Q_Score":0,"Tags":"python,gensim,lda,topic-modeling","A_Id":49749946,"CreationDate":"2018-02-07T22:08:00.000","Title":"Words appearing across all topics in lda","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a multivariate probability density function P(x,y,z), and I want to sample from it. Normally, I would use numpy.random.choice() for this sort of task, but this function only works for 1-dimensional probability densities. Is there an equivalent function for multivariate PDFs?","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":965,"Q_Id":48675954,"Users Score":2,"Answer":"There a few different paths one can follow here.\n(1) If P(x,y,z) factors as P(x,y,z) = P(x) P(y) P(z) (i.e., x, y, and z are independent) then you can sample each one separately.\n(2) If P(x,y,z) has a more general factorization, you can reduce the number of variables that need to be sampled to whatever's conditional on the others. E.g. if P(x,y,z) = P(z|x, y) P(y | x) P(x), then you can sample x, y given x, and z given y and x in turn.\n(3) For some particular distributions, there are known ways to sample. E.g. for multivariate Gaussian, if x is a sample from a mean 0 and identity covariance Gaussian (i.e. just sample each x_i as N(0, 1)), then y = L x + m has mean m and covariance S = L L' where L is the lower-triangular Cholesky decomposition of S, which must be positive definite.\n(4) For many multivariate distributions, none of the above apply, and a more complicated scheme such as Markov chain Monte Carlo is applied. \nMaybe if you say more about the problem, more specific advice can be given.","Q_Score":2,"Tags":"python,random,statistics,probability,probability-density","A_Id":48676209,"CreationDate":"2018-02-08T01:13:00.000","Title":"Sampling from a multivariate probability density function in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Assume we are reading and loading an image using OpenCV from a specific location on our drive and then we read some pixels values and colors, and lets assume that this is a scanned image.\nUsually if we open scanned image we will notice some differences  between the printed image (before scanning) and the image if we open it and see it on the display screen.\nThe question is:\nThe values of the pixels colors that we get from OpenCV. Are they according to our display screen color space or we get exactly the same colors we have in the scanned image (printed version) ??","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":135,"Q_Id":48683621,"Users Score":0,"Answer":"As long as you don't change the extension of the image file, the pixel values don't change because they're stored in memory and your display or printer are just the way you want to see the image and often you don't get the same thing because it depends on the technology and different filters applied to you image before they're displayed or printed..","Q_Score":0,"Tags":"python,opencv,image-processing","A_Id":48683822,"CreationDate":"2018-02-08T10:47:00.000","Title":"RGB in OpenCV. What does it mean?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to convert X,Y position of a tracked object in an image to 3D coordinates.\nI got the distance to the object based on the size of the tracked object (A marker) but now I need to convert all of this to a 3D coordinate in the space. I have been reading a lot about this but all of the methods I found require a calibration matrix to achieve this.\nIn my case I don't need a lot of precision but I need this to work with multiple cameras without calibration. Is there a way to achieve what I'm trying to do?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":630,"Q_Id":48693266,"Users Score":0,"Answer":"The \"without calibration\" bit dooms you, sorry. \nWithout knowing the focal length (or, equivalently, the field of view) you cannot \"convert\" a pixel into a ray.\nNote that you can sometimes get an approximate calibration directly from the camera - for example, it might write a focal length for its lens into the EXIF header of the captured images.","Q_Score":0,"Tags":"python,c++,opencv","A_Id":48694488,"CreationDate":"2018-02-08T19:23:00.000","Title":"Get 3D coordinates in OpenCV having X,Y and distance to object","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to convert X,Y position of a tracked object in an image to 3D coordinates.\nI got the distance to the object based on the size of the tracked object (A marker) but now I need to convert all of this to a 3D coordinate in the space. I have been reading a lot about this but all of the methods I found require a calibration matrix to achieve this.\nIn my case I don't need a lot of precision but I need this to work with multiple cameras without calibration. Is there a way to achieve what I'm trying to do?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":630,"Q_Id":48693266,"Users Score":0,"Answer":"If you're using some sort of micro controller, it may be possible to point a sensor towards that object that's seen through the camera to get the distance.\nYou would most likely have to have a complex algorithm to get multiple cameras to work together to return the distance. If there's no calibration, there would be no way for those cameras to work together, as Francesco said.","Q_Score":0,"Tags":"python,c++,opencv","A_Id":48694562,"CreationDate":"2018-02-08T19:23:00.000","Title":"Get 3D coordinates in OpenCV having X,Y and distance to object","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"i have a dataset (for compbio people out there, it's a FASTA) that is littered with newlines, that don't act as a delimiter of the data.\nIs there a way for pandas to ignore newlines when importing, using any of the pandas read functions?\nsample data:\n\n>ERR899297.10000174\n  TGTAATATTGCCTGTAGCGGGAGTTGTTGTCTCAGGATCAGCATTATATATCTCAATTGCATGAATCATCGTATTAATGC\n  TATCAAGATCAGCCGATTCT\n\nevery entry is delimited by the \">\"\ndata is split by newlines (limited to, but not actually respected worldwide \nwith 80 chars per line)","AnswerCount":5,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":9964,"Q_Id":48694790,"Users Score":0,"Answer":"There is no good way to do this.\nBioPython alone seems to be sufficient, over a hybrid solution involving iterating through a BioPython object, and inserting into a dataframe","Q_Score":1,"Tags":"python,pandas,biopython","A_Id":49092434,"CreationDate":"2018-02-08T21:07:00.000","Title":"pandas read csv ignore newline","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to match text strings (gene names) in a column from one file to text strings in a column of another, in order to create a subset the second. \nFor simplicity, the first will look more or less like this:\nhits = [\"IL1\", \"NRC31\", \"AR\", etc.]\nHowever, the column of interest in the second df looks like this:\n68           NFKBIL1;NFKBIL1;ATP6V1G2;NFKBIL1;NFKBIL1;NFKBI\n236                                                  BARHL2\n272                                             ARPC2;ARPC2\n324                                                  MARCH5\n...\n11302        NFKBIL1;NFKBIL1;ATP6V1G2;NFKBIL1;NFKBIL1;NFKBI\n426033                                        ABC1;IL1;XYZ2\n...\n425700                                                IL17D\n426295                                              RAB3IL1\n426474                                        IL15RA;IL15RA\nI came up with:\ndf2[df2.UCSC_RefGene_Name.str.contains('|'.join(hits), na=False)]\nBut I need to match the gene IL1 if it falls in the middle of the string (e.g.row 426033 above) but not similar genes (e.g. row 426295 above). \nHow do I use regular expressions to say:\n\"Match the any of the strings in hits when they have ';' or 'a blankspace' at either the beginning or the end of the gene name, but not when they have other letters or numbers on either side (which indicate a different gene with a similar name)? \nI also need to exclude any rows with NA in dataframe 2.\nYes, I know there are regex syntax documents, but there are too many moving parts here for me to make sense of them.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":271,"Q_Id":48696556,"Users Score":0,"Answer":"For character handling at such micro level the query will end up being clunky with high response time \u2014 if you're lucky to write a working one. \nThis's more of a script kind of operation.","Q_Score":0,"Tags":"python,regex,subset","A_Id":48696674,"CreationDate":"2018-02-08T23:36:00.000","Title":"Matching genes in string in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to normalize MR image.\nThere is a negative value in the MR image.\nSo the MR image was normalized using the Gaussian method, resulting in a negative area. \nBut i don't want to get negative area.\nMy question:\n     What is the normalization method so that there is no negative value?\nThanks in advance","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":424,"Q_Id":48697967,"Users Score":1,"Answer":"Without more information about your source code and the packages you're using, this is really more of a data science question than a python question.\nTo answer your question, a more than satisfactory method in most circumstances it min-max scaling. Simply normalize each coordinate of your images between 0 and 1. Whether or not that is appropriate for your circumstance depends on what you intend to do next with that data.","Q_Score":0,"Tags":"python,normalization,image-preprocessing","A_Id":48697994,"CreationDate":"2018-02-09T02:41:00.000","Title":"What is the normalization method so that there is no negative value?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have fitted a curve using lmfit but the trendline\/curve is short. Please how do I extend the trendline\/curve in both directions because the trendline\/curve is hanging. Sample codes are warmly welcome my senior programmers. Thanks.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":213,"Q_Id":48699357,"Users Score":0,"Answer":"More detail about what you are actually doing would be helpful.  That is, vague questions can really only get vague answers.\nAssuming you are doing curve fitting with lmfit's Model class, then once you have your Model and a set of Parameters (say, after a fit has refined them to best match some data), then you can use those to evaluate the Model (with the model.eval() method) for any values of the independent variable (typically called x).  That allows on a finer grid or extending past the range of the data you actually used in the fit.\nOf course, predicting past the end of the data range assumes that the model is valid outside the range of the data.  It's hard to know when that assumption is correct, especially when you have no data ;). \"It's tough to make predictions, especially about the future.\"","Q_Score":0,"Tags":"python,lmfit","A_Id":48707014,"CreationDate":"2018-02-09T05:38:00.000","Title":"Extending a trendline in a lmfit plot","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to chatbot application and RASA as well, can anyone please help me to understand how should i use RASA NLU with RASA CORE.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1115,"Q_Id":48700554,"Users Score":14,"Answer":"RASA NLU is the natural language understanding piece, which is used for taking examples of natural language and translating them into \"intents.\"  For example: \"yes\", \"yeah\", \"yep\" and \"for sure\" would all be translated into the \"yes\" intent.\nRASA CORE on the other hand is the engine that processes the flow of conversation after the intent of the user has already been determined.  RASA CORE can use other natural language translators as well, so while it pairs very nicely with RASA NLU they don't both have to be used together.\nAs an example if you were using both:\n\nUser says \"hey there\" to RASA core bot\nRasa core bot calls RASA NLU to understand what \"hey there\" means\nRASA NLU translates \"hey there\" into intent = hello (with 85% confidence)\nRasa core receives \"hello\" intent\nRasa core runs through it's training examples to guess what it should do when it receives the \"hello\" intent\nRasa core predicts (with 92% confidence) that it should respond with the \"utter_hello\" template\nRasa core responds to user \"Hi, I'm your friendly Rasa bot\"\n\nHope this helps.","Q_Score":2,"Tags":"python,rasa-nlu,rasa-core","A_Id":49782296,"CreationDate":"2018-02-09T07:15:00.000","Title":"How to use RASA NLU with RASA CORE","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to create a multiplicative group of order q. \nThis code generates an additive cyclic group of order 5\nfrom sympy.combinatorics.generators import cyclic\nlist(cyclic(5))\n\n[(4), (0 1 2 3 4), (0 2 4 1 3), (0 3 1 4 2), (0 4 3 2 1)]\nAny help ?","AnswerCount":1,"Available Count":1,"Score":-0.1973753202,"is_accepted":false,"ViewCount":463,"Q_Id":48708133,"Users Score":-1,"Answer":"after reading more on the subject ,\n\nMultiplicative group Z * p\nIn classical cyclic group gryptography we usually use multiplicative group Z p * , where p is prime.\nZ p *\n= { 1, 2, .... , p - 1} combined with multiplication of integers mod p\n\nSo it is simply\nG2=  [ i for i in range(1, n-1 )] #G2 multiplicativ Group of ordern","Q_Score":1,"Tags":"python,sympy","A_Id":48713292,"CreationDate":"2018-02-09T14:33:00.000","Title":"multiplicative group using SymPy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataset of peak load for a year. Its a simple two column dataset with the date and load(kWh). \nI want to train it on the first 9 months and then let it predict the next three months .  I can't get my head around how to implement SVR. I understand my 'y' would be predicted value in kWh but what about my X values?\nCan anyone help?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2181,"Q_Id":48715867,"Users Score":0,"Answer":"given multi-variable regression, y = \nRegression is a multi-dimensional separation which can be hard to visualize in ones head since it is not 3D.\nThe better question might be, which  are consequential to the output value `y'.\nSince you have the code to the loadavg in the kernel source, you can use the input parameters.","Q_Score":0,"Tags":"python,scikit-learn,time-series,svm","A_Id":48715905,"CreationDate":"2018-02-10T00:08:00.000","Title":"support vector regression time series forecasting - python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I installed the latest version of Python (3.6.4 64-bit) and the latest version of PyCharm (2017.3.3 64-bit). Then I installed some modules in PyCharm (Numpy, Pandas, etc), but when I tried installing Tensorflow it didn't install, and I got the error message: \n\nCould not find a version that satisfies the requirement TensorFlow (from versions: )\n      No matching distribution found for TensorFlow.\n\nThen I tried installing TensorFlow from the command prompt and I got the same error message.\nI did however successfully install tflearn. \nI also installed Python 2.7, but I got the same error message again. I googled the error and tried some of the things which were suggested to other people, but nothing worked (this included installing Flask). \nHow can I install Tensorflow? Thanks.","AnswerCount":23,"Available Count":13,"Score":1.0,"is_accepted":false,"ViewCount":663737,"Q_Id":48720833,"Users Score":9,"Answer":"Uninstalling Python and then reinstalling solved my issue and I was able to successfully install TensorFlow.","Q_Score":305,"Tags":"python,python-3.x,python-2.7,tensorflow,pip","A_Id":48735246,"CreationDate":"2018-02-10T12:35:00.000","Title":"Could not find a version that satisfies the requirement tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I installed the latest version of Python (3.6.4 64-bit) and the latest version of PyCharm (2017.3.3 64-bit). Then I installed some modules in PyCharm (Numpy, Pandas, etc), but when I tried installing Tensorflow it didn't install, and I got the error message: \n\nCould not find a version that satisfies the requirement TensorFlow (from versions: )\n      No matching distribution found for TensorFlow.\n\nThen I tried installing TensorFlow from the command prompt and I got the same error message.\nI did however successfully install tflearn. \nI also installed Python 2.7, but I got the same error message again. I googled the error and tried some of the things which were suggested to other people, but nothing worked (this included installing Flask). \nHow can I install Tensorflow? Thanks.","AnswerCount":23,"Available Count":13,"Score":1.2,"is_accepted":true,"ViewCount":663737,"Q_Id":48720833,"Users Score":228,"Answer":"As of October 2020:\n\nTensorflow only supports the 64-bit version of Python\n\nTensorflow only supports Python 3.5 to 3.8\n\n\nSo, if you're using an out-of-range version of Python (older or newer) or a 32-bit version, then you'll need to use a different version.","Q_Score":305,"Tags":"python,python-3.x,python-2.7,tensorflow,pip","A_Id":51831928,"CreationDate":"2018-02-10T12:35:00.000","Title":"Could not find a version that satisfies the requirement tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I installed the latest version of Python (3.6.4 64-bit) and the latest version of PyCharm (2017.3.3 64-bit). Then I installed some modules in PyCharm (Numpy, Pandas, etc), but when I tried installing Tensorflow it didn't install, and I got the error message: \n\nCould not find a version that satisfies the requirement TensorFlow (from versions: )\n      No matching distribution found for TensorFlow.\n\nThen I tried installing TensorFlow from the command prompt and I got the same error message.\nI did however successfully install tflearn. \nI also installed Python 2.7, but I got the same error message again. I googled the error and tried some of the things which were suggested to other people, but nothing worked (this included installing Flask). \nHow can I install Tensorflow? Thanks.","AnswerCount":23,"Available Count":13,"Score":1.0,"is_accepted":false,"ViewCount":663737,"Q_Id":48720833,"Users Score":7,"Answer":"(as of Jan 1st, 2021)\nAny over version 3.9.x there is no support for TensorFlow 2. If you are installing packages via pip with 3.9, you simply get a \"package doesn't exist\" message. After reverting to the latest 3.8.x. Thought I would drop this here, I will update when 3.9.x is working with Tensorflow 2.x","Q_Score":305,"Tags":"python,python-3.x,python-2.7,tensorflow,pip","A_Id":65537792,"CreationDate":"2018-02-10T12:35:00.000","Title":"Could not find a version that satisfies the requirement tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I installed the latest version of Python (3.6.4 64-bit) and the latest version of PyCharm (2017.3.3 64-bit). Then I installed some modules in PyCharm (Numpy, Pandas, etc), but when I tried installing Tensorflow it didn't install, and I got the error message: \n\nCould not find a version that satisfies the requirement TensorFlow (from versions: )\n      No matching distribution found for TensorFlow.\n\nThen I tried installing TensorFlow from the command prompt and I got the same error message.\nI did however successfully install tflearn. \nI also installed Python 2.7, but I got the same error message again. I googled the error and tried some of the things which were suggested to other people, but nothing worked (this included installing Flask). \nHow can I install Tensorflow? Thanks.","AnswerCount":23,"Available Count":13,"Score":1.0,"is_accepted":false,"ViewCount":663737,"Q_Id":48720833,"Users Score":71,"Answer":"I installed it successfully by pip install https:\/\/storage.googleapis.com\/tensorflow\/mac\/cpu\/tensorflow-1.8.0-py3-none-any.whl","Q_Score":305,"Tags":"python,python-3.x,python-2.7,tensorflow,pip","A_Id":55988352,"CreationDate":"2018-02-10T12:35:00.000","Title":"Could not find a version that satisfies the requirement tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I installed the latest version of Python (3.6.4 64-bit) and the latest version of PyCharm (2017.3.3 64-bit). Then I installed some modules in PyCharm (Numpy, Pandas, etc), but when I tried installing Tensorflow it didn't install, and I got the error message: \n\nCould not find a version that satisfies the requirement TensorFlow (from versions: )\n      No matching distribution found for TensorFlow.\n\nThen I tried installing TensorFlow from the command prompt and I got the same error message.\nI did however successfully install tflearn. \nI also installed Python 2.7, but I got the same error message again. I googled the error and tried some of the things which were suggested to other people, but nothing worked (this included installing Flask). \nHow can I install Tensorflow? Thanks.","AnswerCount":23,"Available Count":13,"Score":0.0434508851,"is_accepted":false,"ViewCount":663737,"Q_Id":48720833,"Users Score":5,"Answer":"Looks like the problem is with Python 3.8. Use Python 3.7 instead. Steps I took to solve this.\n\nCreated a python 3.7 environment with conda \nList item Installed rasa using pip install rasa within the environment.\n\nWorked for me.","Q_Score":305,"Tags":"python,python-3.x,python-2.7,tensorflow,pip","A_Id":59836416,"CreationDate":"2018-02-10T12:35:00.000","Title":"Could not find a version that satisfies the requirement tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I installed the latest version of Python (3.6.4 64-bit) and the latest version of PyCharm (2017.3.3 64-bit). Then I installed some modules in PyCharm (Numpy, Pandas, etc), but when I tried installing Tensorflow it didn't install, and I got the error message: \n\nCould not find a version that satisfies the requirement TensorFlow (from versions: )\n      No matching distribution found for TensorFlow.\n\nThen I tried installing TensorFlow from the command prompt and I got the same error message.\nI did however successfully install tflearn. \nI also installed Python 2.7, but I got the same error message again. I googled the error and tried some of the things which were suggested to other people, but nothing worked (this included installing Flask). \nHow can I install Tensorflow? Thanks.","AnswerCount":23,"Available Count":13,"Score":0.008695433,"is_accepted":false,"ViewCount":663737,"Q_Id":48720833,"Users Score":1,"Answer":"using pip install tensorflow --user did it for me","Q_Score":305,"Tags":"python,python-3.x,python-2.7,tensorflow,pip","A_Id":69111030,"CreationDate":"2018-02-10T12:35:00.000","Title":"Could not find a version that satisfies the requirement tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I installed the latest version of Python (3.6.4 64-bit) and the latest version of PyCharm (2017.3.3 64-bit). Then I installed some modules in PyCharm (Numpy, Pandas, etc), but when I tried installing Tensorflow it didn't install, and I got the error message: \n\nCould not find a version that satisfies the requirement TensorFlow (from versions: )\n      No matching distribution found for TensorFlow.\n\nThen I tried installing TensorFlow from the command prompt and I got the same error message.\nI did however successfully install tflearn. \nI also installed Python 2.7, but I got the same error message again. I googled the error and tried some of the things which were suggested to other people, but nothing worked (this included installing Flask). \nHow can I install Tensorflow? Thanks.","AnswerCount":23,"Available Count":13,"Score":1.0,"is_accepted":false,"ViewCount":663737,"Q_Id":48720833,"Users Score":36,"Answer":"I am giving  it for Windows\nIf you are using python-3\n\nUpgrade pip to the latest version using py -m pip install --upgrade pip\nInstall package using py -m pip install <package-name>\n\nIf you are using python-2\n\nUpgrade pip to the latest version using py -2 -m pip install --upgrade pip\nInstall package using py -2 -m pip install <package-name>\n\nIt worked for me","Q_Score":305,"Tags":"python,python-3.x,python-2.7,tensorflow,pip","A_Id":49432863,"CreationDate":"2018-02-10T12:35:00.000","Title":"Could not find a version that satisfies the requirement tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I installed the latest version of Python (3.6.4 64-bit) and the latest version of PyCharm (2017.3.3 64-bit). Then I installed some modules in PyCharm (Numpy, Pandas, etc), but when I tried installing Tensorflow it didn't install, and I got the error message: \n\nCould not find a version that satisfies the requirement TensorFlow (from versions: )\n      No matching distribution found for TensorFlow.\n\nThen I tried installing TensorFlow from the command prompt and I got the same error message.\nI did however successfully install tflearn. \nI also installed Python 2.7, but I got the same error message again. I googled the error and tried some of the things which were suggested to other people, but nothing worked (this included installing Flask). \nHow can I install Tensorflow? Thanks.","AnswerCount":23,"Available Count":13,"Score":1.0,"is_accepted":false,"ViewCount":663737,"Q_Id":48720833,"Users Score":42,"Answer":"if you are using anaconda, python 3.7 is installed by default, so you have to downgrade it to 3.6:\n\nconda install python=3.6\n\nthen: \n\npip install tensorflow\n\nit worked for me in Ubuntu.","Q_Score":305,"Tags":"python,python-3.x,python-2.7,tensorflow,pip","A_Id":53488421,"CreationDate":"2018-02-10T12:35:00.000","Title":"Could not find a version that satisfies the requirement tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I installed the latest version of Python (3.6.4 64-bit) and the latest version of PyCharm (2017.3.3 64-bit). Then I installed some modules in PyCharm (Numpy, Pandas, etc), but when I tried installing Tensorflow it didn't install, and I got the error message: \n\nCould not find a version that satisfies the requirement TensorFlow (from versions: )\n      No matching distribution found for TensorFlow.\n\nThen I tried installing TensorFlow from the command prompt and I got the same error message.\nI did however successfully install tflearn. \nI also installed Python 2.7, but I got the same error message again. I googled the error and tried some of the things which were suggested to other people, but nothing worked (this included installing Flask). \nHow can I install Tensorflow? Thanks.","AnswerCount":23,"Available Count":13,"Score":0.0,"is_accepted":false,"ViewCount":663737,"Q_Id":48720833,"Users Score":0,"Answer":"This issue also happens with other libraries such as matplotlib(which doesn't support Python > 3.9 for some functions) let's just use COLAB.","Q_Score":305,"Tags":"python,python-3.x,python-2.7,tensorflow,pip","A_Id":67496288,"CreationDate":"2018-02-10T12:35:00.000","Title":"Could not find a version that satisfies the requirement tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I installed the latest version of Python (3.6.4 64-bit) and the latest version of PyCharm (2017.3.3 64-bit). Then I installed some modules in PyCharm (Numpy, Pandas, etc), but when I tried installing Tensorflow it didn't install, and I got the error message: \n\nCould not find a version that satisfies the requirement TensorFlow (from versions: )\n      No matching distribution found for TensorFlow.\n\nThen I tried installing TensorFlow from the command prompt and I got the same error message.\nI did however successfully install tflearn. \nI also installed Python 2.7, but I got the same error message again. I googled the error and tried some of the things which were suggested to other people, but nothing worked (this included installing Flask). \nHow can I install Tensorflow? Thanks.","AnswerCount":23,"Available Count":13,"Score":0.0,"is_accepted":false,"ViewCount":663737,"Q_Id":48720833,"Users Score":0,"Answer":"use python version 3.6 or 3.7 but the important thing is you should install the python version of 64-bit.","Q_Score":305,"Tags":"python,python-3.x,python-2.7,tensorflow,pip","A_Id":60302029,"CreationDate":"2018-02-10T12:35:00.000","Title":"Could not find a version that satisfies the requirement tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I installed the latest version of Python (3.6.4 64-bit) and the latest version of PyCharm (2017.3.3 64-bit). Then I installed some modules in PyCharm (Numpy, Pandas, etc), but when I tried installing Tensorflow it didn't install, and I got the error message: \n\nCould not find a version that satisfies the requirement TensorFlow (from versions: )\n      No matching distribution found for TensorFlow.\n\nThen I tried installing TensorFlow from the command prompt and I got the same error message.\nI did however successfully install tflearn. \nI also installed Python 2.7, but I got the same error message again. I googled the error and tried some of the things which were suggested to other people, but nothing worked (this included installing Flask). \nHow can I install Tensorflow? Thanks.","AnswerCount":23,"Available Count":13,"Score":-0.0173895512,"is_accepted":false,"ViewCount":663737,"Q_Id":48720833,"Users Score":-2,"Answer":"I solved the same problem with python 3.7 by installing one by one all the packages required \nHere are the steps:\n\nInstall the package\nSee the error message:\n\ncouldn't find a version that satisfies the requirement -- the name of the module required\n\nInstall the module required.\nVery often, installation of the required module requires the installation of another module, and another module - a couple of the others and so on. \n\nThis way I installed more than 30 packages and it helped. Now I have tensorflow of the latest version in Python 3.7 and didn't have to downgrade the kernel.","Q_Score":305,"Tags":"python,python-3.x,python-2.7,tensorflow,pip","A_Id":61057983,"CreationDate":"2018-02-10T12:35:00.000","Title":"Could not find a version that satisfies the requirement tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I installed the latest version of Python (3.6.4 64-bit) and the latest version of PyCharm (2017.3.3 64-bit). Then I installed some modules in PyCharm (Numpy, Pandas, etc), but when I tried installing Tensorflow it didn't install, and I got the error message: \n\nCould not find a version that satisfies the requirement TensorFlow (from versions: )\n      No matching distribution found for TensorFlow.\n\nThen I tried installing TensorFlow from the command prompt and I got the same error message.\nI did however successfully install tflearn. \nI also installed Python 2.7, but I got the same error message again. I googled the error and tried some of the things which were suggested to other people, but nothing worked (this included installing Flask). \nHow can I install Tensorflow? Thanks.","AnswerCount":23,"Available Count":13,"Score":0.0260810405,"is_accepted":false,"ViewCount":663737,"Q_Id":48720833,"Users Score":3,"Answer":"For version TensorFlow 2.2:\n\nMake sure you have python 3.8\n\ntry:\npython --version\nor\npython3 --version\nor\npy --version\n\nUpgrade the pip of the python which has version 3.8\n\ntry:\npython3 -m pip install --upgrade pip\nor\npython -m pip install --upgrade pip\nor\npy -m pip install --upgrade pip\n\nInstall TensorFlow:\n\ntry:\npython3 -m pip install TensorFlow\nor python -m pip install TensorFlow\nor py -m pip install TensorFlow\n\nMake sure to run the file with the correct python:\n\ntry:\npython3 file.py\nor python file.py\nor py file.py","Q_Score":305,"Tags":"python,python-3.x,python-2.7,tensorflow,pip","A_Id":62932939,"CreationDate":"2018-02-10T12:35:00.000","Title":"Could not find a version that satisfies the requirement tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I installed the latest version of Python (3.6.4 64-bit) and the latest version of PyCharm (2017.3.3 64-bit). Then I installed some modules in PyCharm (Numpy, Pandas, etc), but when I tried installing Tensorflow it didn't install, and I got the error message: \n\nCould not find a version that satisfies the requirement TensorFlow (from versions: )\n      No matching distribution found for TensorFlow.\n\nThen I tried installing TensorFlow from the command prompt and I got the same error message.\nI did however successfully install tflearn. \nI also installed Python 2.7, but I got the same error message again. I googled the error and tried some of the things which were suggested to other people, but nothing worked (this included installing Flask). \nHow can I install Tensorflow? Thanks.","AnswerCount":23,"Available Count":13,"Score":0.0,"is_accepted":false,"ViewCount":663737,"Q_Id":48720833,"Users Score":0,"Answer":"In case you are using Docker, make sure you have\nFROM python:x.y.z\ninstead of\nFROM python:x.y.z-alpine.","Q_Score":305,"Tags":"python,python-3.x,python-2.7,tensorflow,pip","A_Id":64305430,"CreationDate":"2018-02-10T12:35:00.000","Title":"Could not find a version that satisfies the requirement tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am having problems trying to install the following packages on Ubuntu:\n\nscipy\nnumpy\nmatplotlib\npandas\nsklearn\n\nWhen I execute the command: \nsudo apt-get install python-numpy python-scipy python-matplotlib ipython ipython-notebook python-pandas python-sympy python-nose\nI get the following message: \nReading package lists... Done\nBuilding dependency tree\nReading state information... Done\nbuild-essential is already the newest version (12.1ubuntu2).\nYou might want to run 'apt-get -f install' to correct these:\nThe following packages have unmet dependencies.\n libatlas-dev : Depends: libblas-dev but it is not going to be installed\n libatlas3-base : Depends: libgfortran3 (>= 4.6) but it is not going to be installed\n                  Depends: libblas-common but it is not going to be installed\n python-dev : Depends: libpython-dev (= 2.7.11-1) but it is not going to be installed\n              Depends: python2.7-dev (>= 2.7.11-1~) but it is not going to be installed\n python-scipy : Depends: python-decorator but it is not going to be installed\n                Depends: libgfortran3 (>= 4.6) but it is not going to be installed\n                Recommends: python-imaging but it is not going to be installed\n python-setuptools : Depends: python-pkg-resources (= 20.7.0-1) but it is not going to be installed\n rstudio : Depends: libjpeg62 but it is not going to be installed\n           Depends: libgstreamer0.10-0 but it is not going to be installed\n           Depends: libgstreamer-plugins-base0.10-0 but it is not going to be installed\n           Recommends: r-base (>= 2.11.1) but it is not going to be installed\nE: Unmet dependencies. Try 'apt-get -f install' with no packages (or specify a solution).\n\n\nSo the packages are failing to install, but I really need these packages to begin a new project, how can I successfully install these packages?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":428,"Q_Id":48729174,"Users Score":0,"Answer":"U cannot install it using apt-get. u need to install pip first. After you install pip, just google about how to install different packages using pip","Q_Score":0,"Tags":"python,numpy,ubuntu,matplotlib,installation","A_Id":48729205,"CreationDate":"2018-02-11T07:03:00.000","Title":"How do I install packages for python ML on ubuntu?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"Currently google colaboratory uses tensorflow 1.4.1. I want to upgrade it to 1.5.0 version. Each time when i executed !pip install --upgrade tensorflow command, notebook instance succesfully upgrades the tensorflow version to 1.5.0. But after upgrade operation tensorflow instance only supports \"CPU\".\nWhen i have executed this command it shows nothing :\nfrom tensorflow.python.client import device_lib\ndevice_lib.list_local_devices()\nShould there be another way for upgrading tensorflow ? such as upgrading to tensorflow-gpu package ? Also when will notebooks will come with upgraded tensorflows ?","AnswerCount":4,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":15191,"Q_Id":48731124,"Users Score":4,"Answer":"Even if you will install gpu version !pip install tensorflow-gpu==1.5.0 it will still fail to import it because of the cuda libraries. Currently I have not found a way to use 1.5 version with GPU. So I would rather use 1.4.1 with gpu than 1.5 without gpu.\nYou can send them a feedback ( Home  -  Send Feedback ) and hopefully if enough people will send something similar, they will update the new gpu version.","Q_Score":5,"Tags":"python,tensorflow,google-colaboratory","A_Id":48739426,"CreationDate":"2018-02-11T11:44:00.000","Title":"How to upgrade tensorflow with GPU on google colaboratory","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to train my deep learning code using Keras with tensorflow backend on a remote server with GPU.  However, even the GPU server states OOM. \nThis was the output:\n2018-02-09 14:19:28.918619: I tensorflow\/core\/common_runtime\/bfc_allocator.cc:685] Stats: \nLimit: 10658837300 \nInUse: 10314885120 \nMaxInUse: 10349312000 \nNumAllocs: 8762 \nMaxAllocSize: 1416551936\n2018-02-09 14:19:28.918672: W tensorflow\/core\/common_runtime\/bfc_allocator.cc:277] ************__********************************************************************************xxxxxx 2018-02-09 14:19:28.918745: W tensorflow\/core\/framework\/op_kernel.cc:1182] Resource exhausted: OOM when allocating tensor of shape [13772,13772] and type float 2018-02-09 14:19:29.294784: E tensorflow\/core\/common_runtime\/executor.cc:643] Executor failed to create kernel. Resource exhausted: OOM when allocating tensor of shape [13772,13772] and type float [[Node: training_4\/RMSprop\/zeros = Constdtype=DT_FLOAT, value=Tensor, _device=\"\/job:localhost\/replica:0\/task:0\/device:GPU:0\"]]\nWould there be any ways to resolve this issue? I tried adjusting for batch size, it initially worked when batch size with 100 but when i reduced it to 50, it showed this error. Afterwhich i tried batch size 100 but it displayed this same error again. \nI tried to search on how to suspend training binary while running evaluation but did not get much. \nWould greatly appreciate your help in this! Thank you!!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":102,"Q_Id":48738979,"Users Score":0,"Answer":"You can try using model.fit_generator instead.","Q_Score":0,"Tags":"python-2.7,tensorflow,out-of-memory,gpu","A_Id":51146080,"CreationDate":"2018-02-12T03:01:00.000","Title":"OOM when training on GPU external server","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have developed few modules in python and I want to import them to rstudio RMarkdown file. However, I am not sure how I can do it.\nFor example, I can't do from code.extract_feat.cluster_blast import fill_df_by_blast as fill_df as I am used to do it in pycharm.\nAny hint?\nThanks.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":378,"Q_Id":48753128,"Users Score":0,"Answer":"First I had to make a setup.py file for my project.\nactivate the virtual environment corresponding to my project source activate, then run python setup.py develop \nNow, I can import my own python library from R as I installed it in my environment.","Q_Score":0,"Tags":"python,rstudio,r-markdown,python-import","A_Id":49017753,"CreationDate":"2018-02-12T18:28:00.000","Title":"Import my python module to rstudio","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have been trying to upload a csv file using pandas .read() function. But as you can see from my title this is what I get \n\"'utf-8' codec can't decode byte 0x92 in position 16: invalid start byte\"\nAnd it's weird because from the same folder I was able to upload a different csv file without problems. \nSomething that might have caused this is that the file was previously xlsx and I manually converted it to cvs?\nPlease Help\nPython3","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":603,"Q_Id":48754469,"Users Score":0,"Answer":"Try the below:\npd.read_csv(\"filepath\",encoding='cp1252'). \nThis one should work as it worked for me.","Q_Score":2,"Tags":"python-3.x","A_Id":49515251,"CreationDate":"2018-02-12T19:52:00.000","Title":"Uploading CSV - 'utf-8' codec can't decode byte 0x92 in position 16: invalid start byte","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"tensorflow works using python in a virtualenv I created, but tensorflow doesn't work in the same virtualenv with ipython. This is the error I get:\nException: Versioning for this project requires either an sdist tarball, or access to an upstream git repository. It's also possible that there is a mismatch between the package name in setup.cfg and the argument given to pbr.version.VersionInfo. Project name mock was given, but was not able to be found.\nI have tried installing ipython within the virtual environment. This is the message I get:\nRequirement already satisfied: ipython in \/opt\/local\/Library\/Frameworks\/Python.framework\/Versions\/2.7\/lib\/python2.7\/site-packages\nIf I try to uninstall ipython within the virtual environment. I get this message:\nNot uninstalling ipython at \/opt\/local\/Library\/Frameworks\/Python.framework\/Versions\/2.7\/lib\/python2.7\/site-packages\nAny ideas on how to get this to work? I don't know how to force the install of ipython to be inside the virtual environment. I've tried deleting the virtual environment and making a new one from scratch, but I get the same error.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":213,"Q_Id":48757970,"Users Score":0,"Answer":"I think I figured out the problem. pip was pointing to \/Library\/Frameworks\/Python.framework\/Versions\/3.4\/bin\/pip\nMy ipython was pointing to \/opt\/local\/bin\/ipython\nI re-installed tensorflow within my virtual environment by calling \/opt\/local\/bin\/pip-2.7 install --upgrade tensorflow\nNow I can use tensorflow within ipython.","Q_Score":0,"Tags":"tensorflow,ipython","A_Id":48777636,"CreationDate":"2018-02-13T00:58:00.000","Title":"Running tensorflow in ipython","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I read the document about the function and I understood how NMS works. What I'm not clear is scores argument to this function. I think NMS first look at bottom right coordinate and sort according to it and calculate IoU then discard some boxes which have IoU greater than the threshold that you set. In this theory scores argument does absolutely nothing and the document doesn't tell much about scores arguments. I want to know how the argument affect the function. Thank you.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":537,"Q_Id":48759535,"Users Score":0,"Answer":"The scores argument decides the sorting order. The method tf.image.non_max_suppression goes through (greedily, so all input entries are covered) input bounding boxes in order decided by this scores argument, selects only those bounding boxes from them which are not overlapping (more than iou_threshold) with boxes already selected.\n\nNMS first look at bottom right coordinate and sort according to it and calculate IoU...\n\nThis is not correct, can you site any resource which made you think this way?","Q_Score":1,"Tags":"python,tensorflow,computer-vision","A_Id":48761331,"CreationDate":"2018-02-13T04:35:00.000","Title":"What does tensorflow nonmaximum suppression function's argument \"score\" do to this function?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am doing transfer-learning\/retraining using Tensorflow Inception V3 model. I have 6 labels. A given image can be one single type only, i.e, no multiple class detection is needed. I have three queries:\n\nWhich activation function is best for my case? Presently retrain.py file provided by tensorflow uses softmax? What are other methods available? (like sigmoid etc)\nWhich Optimiser function I should use? (GradientDescent, Adam.. etc)\nI want to identify out-of-scope images, i.e. if users inputs a random image, my algorithm should say that it does not belong to the described classes. Presently with 6 classes, it gives one class as a sure output but I do not want that. What are possible solutions for this?\n\nAlso, what are the other parameters that we may tweak in tensorflow. My baseline accuracy is 94% and I am looking for something close to 99%.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":124,"Q_Id":48761144,"Users Score":1,"Answer":"Since you're doing single label classification, softmax is the best loss function for this, as it maps your final layer logit values to a probability distribution. Sigmoid is used when it's multilabel classification. \nIt's always better to use a momentum based optimizer compared to vanilla gradient descent. There's a bunch of such modified optimizers like Adam or RMSProp. Experiment with them to see what works best. Adam is probably going to give you the best performance.\nYou can add an extra label no_class, so your task will now be a 6+1 label classification. You can feed in some random images with no_class as the label. However the distribution of your random images must match the test image distribution, else it won't generalise.","Q_Score":1,"Tags":"python-3.x,tensorflow,computer-vision,softmax,sigmoid","A_Id":48762415,"CreationDate":"2018-02-13T07:00:00.000","Title":"Image Classification using Tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an image and and am transforming it with a nonlinear spatial transformation. I have a written a function that, for every pixel (i, j) in the destination image array, returns a coordinate (y, x) in the source array. \nThe returned coordinate is a floating point value, meaning that it corresponds to a point that lies between the pixels in the source image. \nDoes anyone know if there an established method in PIL or opencv to interpolate the value of this subpixel, or should I roll my own? Thanks!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1342,"Q_Id":48767750,"Users Score":0,"Answer":"There are two common methods:\n\nbilinear interpolation,\nbicubic interpolation.\n\nThese evaluate an intermediate value, based on the values at four or sixteen neighboring pixels, using weighting functions based on the fractional parts of the coordinates.\nLookup these expressions.\nFrom my experience, the bilinear quality is often sufficient.","Q_Score":1,"Tags":"python,opencv,image-processing,python-imaging-library","A_Id":48771960,"CreationDate":"2018-02-13T13:18:00.000","Title":"estimation of subpixel values from images in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm working on a project from the Berkeley AI curriculum, and they require me to use stacks, queues, and priority queues in my Depth First Graph Search implementation. I stored my fringe in a priority queue and my already visited states in a set. What am I supposed to use stacks and queues for in this assignment?\nI'm not a student at Berkeley and I'm just using their curriculum for an independent study in high school and I got permission from my instructor to ask this online, so this is not a case of cheating on homework.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":86,"Q_Id":48769149,"Users Score":0,"Answer":"I realized that I misread the assignment. It said: \n\"Important note: Make sure to use the Stack, Queue and PriorityQueue data structures provided to you in util.py! These data structure implementations have particular properties which are required for compatibility with the autograder.\"\nI had misread it as saying that I need to use all of them, when it is really saying that if I want to use them I should use their version.","Q_Score":1,"Tags":"python,search,data-structures","A_Id":48769576,"CreationDate":"2018-02-13T14:31:00.000","Title":"Why would I need stacks and queues for Depth First Search?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am learning python pandas.\nI see a tutorial which shows two ways to save a pandas dataframe.\n\npd.to_csv('sub.csv') and to open pd.read_csv('sub.csv')\npd.to_pickle('sub.pkl') and to open pd.read_pickle('sub.pkl')\n\nThe tutorial says to_pickle is to save the dataframe to disk. I am confused about this. Because when I use to_csv, I did see a csv file appears in the folder, which I assume is also save to disk right?\nIn general, why we want to save a dataframe using to_pickle rather than save it to csv or txt or other format?","AnswerCount":2,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":25209,"Q_Id":48770542,"Users Score":36,"Answer":"csv\n\n\u2705human readable\n\u2705cross platform\n\u26d4slower\n\u26d4more disk space\n\u26d4doesn't preserve types in some cases\n\npickle\n\n\u2705fast saving\/loading\n\u2705less disk space\n\u26d4non human readable\n\u26d4python only\n\nAlso take a look at parquet format (to_parquet, read_parquet)\n\n\u2705fast saving\/loading\n\u2705less disk space than pickle\n\u2705supported by many platforms\n\u26d4non human readable","Q_Score":31,"Tags":"python,pandas,csv,pickle","A_Id":62222676,"CreationDate":"2018-02-13T15:46:00.000","Title":"What is the difference between save a pandas dataframe to pickle and to csv?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm learning python and numpy. The docstring of numpy.linspace says\n\nReturn evenly spaced numbers over a specified interval.\nReturns num evenly spaced samples, calculated over the interval\n  [start, stop].\n\nSo I guess the \"space\" part of linspace means \"space\". But what does \"lin\" stand for?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":672,"Q_Id":48770786,"Users Score":4,"Answer":"A linear space. So in other words, from a straight line over an interval we take n samples.","Q_Score":4,"Tags":"python,numpy","A_Id":48770832,"CreationDate":"2018-02-13T15:59:00.000","Title":"Why linspace was named like that in numpy?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"What does seed=1 is doing in the following code:\nW3 = tf.get_variable(\"W3\", [L3, L2], initializer = tf.contrib.layers.xavier_initializer(seed=1))","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":160,"Q_Id":48787340,"Users Score":0,"Answer":"It's to define the random seed. By this means, the weight values are always initialized by the same values.\nFrom Wiki: A random seed is a number (or vector) used to initialize a pseudo-random number generator.","Q_Score":0,"Tags":"python,tensorflow","A_Id":48787920,"CreationDate":"2018-02-14T12:33:00.000","Title":"seed=1, TensorFlor- Xavier_initializer","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to use scipy.optimize.least_squares(fun= my_fun, jac=my_jac, max_nfev= 1000) with two callable functions: my_fun and my_jac\nboth fuctions: my_fun and my_jac, use an external software to evaluate their value, this task is much time consuming, therefore I prefer to control the number of evaluations for both\nthe trf method uses the my_fun function for evaluating if trust region is adequate and the my_jac function for determine both the cost function and the jacobian matrix\nThere is an input parameter max_nfev. does this parameter count only for the fun evaluations? does it consider also the jac evaluations?\nmoreover, in matlab there are two parameters for the lsqnonlin function, MaxIterations  and MaxFunctionEvaluations. does it exist in scipy.optimize.least_squares? \nThanks\nAlon","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":675,"Q_Id":48789406,"Users Score":0,"Answer":"According to the help of scipy.optimize.least_squares, max_nfev is the number of function evaluations before the program exits :\n\nmax_nfev : None or int, optional\n      Maximum number of function evaluations before the termination.\n      If None (default), the value is chosen automatically:\n\nAgain, according to the help, there is no MaxIterations argument but you can define the tolerance in f (ftol) that is the function you want to minimize or x (xtol) the solution, before exiting the code.\nYou can also use scipy.optimize.minimize(). In it, you can define a maxiter argument which will be in the options dictionary.\nIf you do so, beware that the function you want to minimize must be your cost function, meaning that you will have to code your least square function.\nI hope it will be clear and useful to you","Q_Score":0,"Tags":"python,optimization,scipy,least-squares","A_Id":48790015,"CreationDate":"2018-02-14T14:20:00.000","Title":"scipy.optimize.least_squares - limit number of jacobian evaluations","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a 128-length (s) array cube with unique values held at each point inside. At the center of this cube is the meat of the data (representing an object), while on the inner borders of the cube, there are mostly zero values. \nI need to shift this entire array such that the meat of the data is actually at the origin (0,0) instead of at (s\/2, s\/2, s\/2)... such that my new coordinate origin is actually at (-s\/2, -s\/2, -s\/2). What is the best way to tackle this problem?\n\nEdit: Sorry for the lack of data - I'm using a .mrc file. This is all to circumvent a plotting issue in mayaVI using its contour3d method. Perhaps I should be finding a way to translate my plotted object (with mayaVI) instead of translating my raw data array? But aren't these two technically the same thing?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":297,"Q_Id":48795574,"Users Score":0,"Answer":"My initial question was very misleading - my apologies for the confusion. \nI've since solved the problem by translating my local array (data cube) within a global array.\nTo accomplish this, I needed to first plot my data within a larger array (such as a Mayavi scene, which I did). Then, within this scene, I moved my data (eg. using actors in Mayavi) to be centered at the global array's origin.  Pretty simple actually - the point here being that my initial question was flawed; thank you all for the help and advice.","Q_Score":0,"Tags":"python,arrays,numpy,coordinates,translation","A_Id":49395441,"CreationDate":"2018-02-14T20:22:00.000","Title":"Translating entire coordinates of array to new origin","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I wanted to train on multiple CPU so i run this command\n\nC:\\Users\\solution\\Desktop\\Tensorflow\\research>python\n  object_detection\/train.py --logtostderr\n  --pipeline_config_path=C:\\Users\\solution\\Desktop\\Tensorflow\\myFolder\\power_drink.config --train_dir=C:\\Users\\solution\\Desktop\\Tensorflow\\research\\object_detection\\train\n  --num_clones=2 --clone_on_cpu=True\n\nand i got the following error\n\nTraceback (most recent call last):   File \"object_detection\/train.py\",\n  line 169, in \n      tf.app.run()   File \"C:\\Users\\solution\\AppData\\Local\\Programs\\Python\\Python35\\lib\\site-packages\\tensorflow\\python\\platform\\app.py\",\n  line 124, in run\n      _sys.exit(main(argv))   File \"object_detection\/train.py\", line 165, in main\n      worker_job_name, is_chief, FLAGS.train_dir)   File \"C:\\Users\\solution\\Desktop\\Tensorflow\\research\\object_detection\\trainer.py\",\n  line 246, in train\n      clones = model_deploy.create_clones(deploy_config, model_fn, [input_queue])   File\n  \"C:\\Users\\solution\\Desktop\\Tensorflow\\research\\slim\\deployment\\model_deploy.py\",\n  line 193, in create_clones\n      outputs = model_fn(*args, **kwargs)   File \"C:\\Users\\solution\\Desktop\\Tensorflow\\research\\object_detection\\trainer.py\",\n  line 158, in _create_losses\n      train_config.merge_multiple_label_boxes) ValueError: not enough values to unpack (expected 7, got 0) \n\nIf i set num_clones to 1 or omitted it, it works normally.\nI also tries setting --ps_tasks=1 which doesn't help\nany advice would be appreciated","AnswerCount":2,"Available Count":1,"Score":0.2913126125,"is_accepted":false,"ViewCount":2859,"Q_Id":48795950,"Users Score":3,"Answer":"You don't mention which type of model you are training - if like me you were using the default model from the TensorFlow Object Detection API example (Faster-RCNN-Inception-V2) then num_clones should equal the batch_size. I was using a GPU however, but when I went from one clone to two, I saw a similar error and setting batch_size: 2 in the training config file was the solution.","Q_Score":8,"Tags":"python,tensorflow,object-detection","A_Id":54771885,"CreationDate":"2018-02-14T20:50:00.000","Title":"[Tensorflow][Object detection] ValueError when try to train with --num_clones=2","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using Tensorflow retraining model for Image Classification. I am doing single label classification. \nI want to set a threshold for correct classification. \nIn other words, if the highest probability is less than a given threshold, I can say that the image is \"unknown\" i.e. if np.max(results) < 0.5 -> set label as \"unknown\". \nSo, is there any industry standard to set this threshold. I can set a random value say 60%, but is there any literature to back this threshold ?\nAny links or references will be very helpful. \nThanks a lot.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":288,"Q_Id":48822796,"Users Score":0,"Answer":"Single label classification is not something Neural Networks can do \"off-the-shelf\". \nHow do you train it ? With only data relevant to your target domain ? Your model will only learn to output one.\nYou have two strategies:\n\nyou use the same strategy as in the \"HotDog or Not HotDog app\", you put the whole imagenet in two different folders, one with the class you want, the other one containing everything else.\nYou use the convnet as feature extractor and then use a second model like a One-Class SVM.\n\nYou have to understand that doing one class classification is not a simple and direct problem like binary classification could be.","Q_Score":0,"Tags":"python,image-processing,tensorflow","A_Id":48822919,"CreationDate":"2018-02-16T08:30:00.000","Title":"Probability for correct Image Classification in Tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a task to train neural networks using tensorflow and opencv-python on HPC nodes via Torque. \nI have made privatemodule with python virtualenv and installed tensorflow and  opencv-python modules in it.\nIn the node I can load my python module. \nBut when I try to run training script I get following error:\nTraceback (most recent call last):\n  File \"tensornetwork\/train_user_ind_single_subj2.py\", line 16, in <module>\n    from reader_user_ind_single_subj import MyData\n  File \"\/home\/trig\/tensornetwork\/reader_user_ind_single_subj.py\", line 10, in <module>\n    import cv2\n  File \"\/home\/trig\/privatemodules\/venv_python275\/lib\/python2.7\/site-packages\/cv2\/__init__.py\", line 4, in <module>\n    from .cv2 import *\nImportError: libSM.so.6: cannot open shared object file: No such file or directory\nThe training script can run on head node, but cant on compute node.\nCan you suggest how to modify my module or add a new module to make training run on compute node using Torque.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":388,"Q_Id":48824675,"Users Score":2,"Answer":"The Python module uses a system library (namely libSM.so.6 : library support for the freedesktop.org version of X) that is present on the head node, but not on the compute nodes (which is not very surprising)\nYou can either:\n\nask the administrators to have that library installed systemwide on the compute nodes through the package manager ;\nor locate the file on the head node (probably in \/usr\/lib or \/usr\/lib64 or siblings), and copy it in \/home\/trig\/privatemodules\/venv_python275\/lib\/python2.7\/site-packages\/cv2\/, where Python should find it. If Python still does not find it, run export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:\/home\/trig\/privatemodules\/venv_python275\/lib\/python2.7\/site-packages\/cv2\/ in your Torque script after you load the module.\nor you can search for the source for libSM and compile it in your home directory","Q_Score":2,"Tags":"python-2.7,opencv,hpc,torque,environment-modules","A_Id":48829716,"CreationDate":"2018-02-16T10:24:00.000","Title":"create environment module to work with opencv-python on hpc nodes","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I work at an ecommerce company and I'm responsible for clustering our customers based on their transactional behavior. I've never worked with clustering before, so I'm having a bit of a rough time.\n1st) I've gathered data on customers and I've chosen 12 variables that specify very nicely how these customers behave. Each line of the dataset represents 1 user, where the columns are the 12 features I've chosen.\n2nd) I've removed some outliers and built a correlation matrix in order to check of redundant variables. Turns out some of them are highly correlated ( > 0.8 correlation)\n3rd) I used sklearn's RobustScaler on all 12 variables in order to make sure the variable's variability doesn't change much (StandardScaler did a poor job with my silhouette)\n4th) I ran KMeans on the dataset and got a very good result for 2 clusters (silhouette of >70%)\n5th) I tried doing a PCA after scaling \/ before clustering to reduce my dimension from 12 to 2 and, to my surprise, my silhouette started going to 30~40% and, when I plot the datapoints, it's just a big mass at the center of the graph.\nMy question is:\n1) What's the difference between RobustScaler and StandardScaler on sklearn? When should I use each?\n2) Should I do : Raw Data -> Cleaned Data -> Normalization -> PCA\/TSNE -> Clustering ? Or Should PCA come before normalization?\n3) Is a 12 -> 2 dimension reduction through PCA too extreme? That might be causing the horrible silhouette score.\nThank you very much!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":214,"Q_Id":48825248,"Users Score":0,"Answer":"Avoid comparing Silhouettes of different projections or scalings. Internal measures tend to be too sensitive.\nDo not use tSNE for clustering (Google for the discussion on stats.SE, feel free to edit the link into this answer). It will cause false separation and false adjacency; it is a visualization technique.\nPCA will scale down high variance axes, and scale up low variance directions. It is to be expected that this overall decreases the quality if the main axis is what you are interested in (and it is expected to help if it is not). But if PCA visualization shows only one big blob, then a Silhouette of 0.7 should not be possible. For such a high silhouette, the clusters should be separable in the PCA view.","Q_Score":2,"Tags":"python,cluster-analysis,customer","A_Id":48833452,"CreationDate":"2018-02-16T10:55:00.000","Title":"Clustering Customers with Python (sklearn)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have developed a rest API using Flask to expose a Python Keras Deep Learning model (CNN for text classification). I have a very simple script that loads the model into memory and outputs class probabilities for a given text input. The API works perfectly locally.\nHowever, when I git push heroku master, I get Compiled slug size: 588.2M is too large (max is 500M). The model is 83MB in size, which is quite small for a Deep Learning model. Notable dependencies include Keras and its tensorflow backend. \nI know that you can use GBs of RAM and disk space on Heroku. But the bottleneck seems to be the slug size. Is there a way to circumvent this? Or is Heroku just not the right tool for deploying Deep Learning models?","AnswerCount":7,"Available Count":1,"Score":0.0855049882,"is_accepted":false,"ViewCount":7474,"Q_Id":48840025,"Users Score":3,"Answer":"A lot of these answers are great for reducing slug size but if anyone still has problems with deploying a deep learning model to heroku it is important to note that for whatever reason tensorflow 2.0 is ~500MB whereas earlier versions are much smaller. Using an earlier version of tensorflow can greatly reduce your slug size.","Q_Score":10,"Tags":"python,tensorflow,heroku,keras,deep-learning","A_Id":61806979,"CreationDate":"2018-02-17T09:52:00.000","Title":"Heroku: deploying Deep Learning model","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I have both Python 3.5 and Python 3.6 on my laptop. I am using Ubuntu 16.04. I used pip3 to install numpy. It is working with Python3.5 but not with Python3.6. Please help.","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2139,"Q_Id":48840282,"Users Score":0,"Answer":"Cannot comment since I don't the rep. \nIf your default python is 3.5 when you check python --version, the way to go would be to find the location of the python executable for the desired version (here 3.6).\ncd to that folder and then run the command given by Mike.","Q_Score":0,"Tags":"python,python-3.5,python-3.6","A_Id":48840524,"CreationDate":"2018-02-17T10:21:00.000","Title":"numpy got installed in Python3.5 but not in Python3.6","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I went through the docs but I'm not able to interpret correctly\nIN my code, I wanted to find a line that goes through 2 points(x1,y1), (x2,y2), so I've used \n    np.polyfit((x1,x2),(y1,y2),1)\nsince its a 1 degree polynomial(a straight line)\nIt returns me [ -1.04 727.2 ]\nThough my code (which is a much larger file) runs properly, and does what it is intended to do - i want to understand what this is returning\nI'm assuming polyfit returns a line(curved, straight, whatever) that satisfies(goes through) the points given to it, so how can a line be represented with 2 points which it is returning?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":6890,"Q_Id":48856497,"Users Score":0,"Answer":"These are essentially the beta and the alpha values for the given data.\nWhere beta necessarily demonstrates the degree of volatility or the slope","Q_Score":3,"Tags":"python,numpy","A_Id":64853941,"CreationDate":"2018-02-18T20:57:00.000","Title":"What does np.polyfit do and return?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a data set with continuous label ranging from one to five with nine different features. So I wanted to give weight to each feature manually because some of the features have very less dependency on the label so I wanted to give more weight to those features which have more dependency on the label. How can I manually give weight to each feature? Will it be possible to give weight like this?\nI went through different documentations I can only find how to give weight to the label. What I only find is eliminating features ranking features etc. But I wanted to give weight to each feature manually also I wanted to tune these weights (Sometimes the feature weight will be different for different scenario so I wanted to tune the weight according to that)\nWill it be possible ?","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":839,"Q_Id":48860824,"Users Score":1,"Answer":"The whole point of using machine learning is to let it decide on its own how much weight should be given to which predictor based on its importance in predicting the label correctly. \nIt just doesn't makes any sense trying to do this on your own and then also use machine learning.","Q_Score":0,"Tags":"python,regression,jupyter-notebook,decision-tree","A_Id":48877243,"CreationDate":"2018-02-19T06:47:00.000","Title":"how to manually give weight to features using python in machine learning","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a data set with continuous label ranging from one to five with nine different features. So I wanted to give weight to each feature manually because some of the features have very less dependency on the label so I wanted to give more weight to those features which have more dependency on the label. How can I manually give weight to each feature? Will it be possible to give weight like this?\nI went through different documentations I can only find how to give weight to the label. What I only find is eliminating features ranking features etc. But I wanted to give weight to each feature manually also I wanted to tune these weights (Sometimes the feature weight will be different for different scenario so I wanted to tune the weight according to that)\nWill it be possible ?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":839,"Q_Id":48860824,"Users Score":0,"Answer":"Don't assign weights manually, let the model learn the weights itself.\nIt will automatically decide which features are more important.","Q_Score":0,"Tags":"python,regression,jupyter-notebook,decision-tree","A_Id":48877568,"CreationDate":"2018-02-19T06:47:00.000","Title":"how to manually give weight to features using python in machine learning","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there a quick and simple way to convert HDF5 files to netcdf(4) from the command line in bash?  Alternatively a simple script that handle such a conversion automatically in R, NCL or python ?","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":5482,"Q_Id":48864357,"Users Score":6,"Answer":"with netcdf-c library you can: $ nccopy in.h5 out.nc","Q_Score":2,"Tags":"python,r,hdf5,netcdf4,ncl","A_Id":48870492,"CreationDate":"2018-02-19T10:43:00.000","Title":"Convert hdf5 to netcdf4 in bash, R, python or NCL?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I'm trying to build a multilabel-classifier to predict the probabilities of some input data being either 0 or 1. I'm using a neural network and Tensorflow + Keras (maybe a CNN later).\nThe problem is the following: \nThe data is highly skewed. There are a lot more negative examples than positive maybe 90:10. So my neural network nearly always outputs very low probabilities for positive examples. Using binary numbers it would predict 0 in most of the cases.\nThe performance is > 95% for nearly all classes, but this is due to the fact that it nearly always predicts zero...\nTherefore the number of false negatives is very high.\nSome suggestions how to fix this?\nHere are the ideas I considered so far:\n\nPunishing false negatives more with a customized loss function (my first attempt failed). Similar to class weighting positive examples inside a class more than negative ones. This is similar to class weights but within a class.\nHow would you implement this in Keras? \nOversampling positive examples by cloning them and then overfitting the neural network such that positive and negative examples are balanced.\n\nThanks in advance!","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1027,"Q_Id":48880273,"Users Score":2,"Answer":"You're on the right track.\nUsually, you would either balance your data set before training, i.e. reducing the over-represented class or generate artificial (augmented) data for the under-represented class to boost its occurrence.\n\nReduce over-represented class\nThis one is simpler, you would just randomly pick as many samples as there are in the under-represented class, discard the rest and train with the new subset. The disadvantage of course is that you're losing some learning potential, depending on how complex (how many features) your task has.\nAugment data\nDepending on the kind of data you're working with, you can \"augment\" data. That just means that you take existing samples from your data and slightly modify them and use them as additional samples. This works very well with image data, sound data. You could flip\/rotate, scale, add-noise, in-\/decrease brightness, scale, crop etc.\nThe important thing here is that you stay within bounds of what could happen in the real world. If for example you want to recognize a \"70mph speed limit\" sign, well, flipping it doesn't make sense, you will never encounter an actual flipped 70mph sign. If you want to recognize a flower, flipping or rotating it is permissible. Same for sound, changing volume \/ frequency slighty won't matter much. But reversing the audio track changes its \"meaning\" and you won't have to recognize backwards spoken words in the real world.\n\nNow if you have to augment tabular data like sales data, metadata, etc... that's much trickier as you have to be careful not to implicitly feed your own assumptions into the model.","Q_Score":8,"Tags":"python,tensorflow,neural-network,keras,multilabel-classification","A_Id":49065611,"CreationDate":"2018-02-20T07:57:00.000","Title":"Classification: skewed data within a class","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Python terminal getting abort with following msg:\n\/grid\/common\/\/pkgs\/python\/v2.7.6\/bin\/python\nPython 2.7.6 (default, Jan 17 2014, 04:05:53)\n[GCC 4.1.2 20080704 (Red Hat 4.1.2-48)] on linux2\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\n\n\n\nimport tensorflow as tf\n      2018-02-20 01:40:11.268134: F tensorflow\/core\/platform\/cpu_feature_guard.cc:36] The TensorFlow library was compiled to use FMA instructions, but these aren't available on your machine.\n      Abort","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":230,"Q_Id":48882088,"Users Score":0,"Answer":"Your computer seems to be incompatible with the library tensorflow.\nYour computer needs to be able to use FMA instructions but can't.","Q_Score":0,"Tags":"python,tensorflow","A_Id":48882154,"CreationDate":"2018-02-20T09:41:00.000","Title":"import tensorflow with python 2.7.6","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Python terminal getting abort with following msg:\n\/grid\/common\/\/pkgs\/python\/v2.7.6\/bin\/python\nPython 2.7.6 (default, Jan 17 2014, 04:05:53)\n[GCC 4.1.2 20080704 (Red Hat 4.1.2-48)] on linux2\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\n\n\n\nimport tensorflow as tf\n      2018-02-20 01:40:11.268134: F tensorflow\/core\/platform\/cpu_feature_guard.cc:36] The TensorFlow library was compiled to use FMA instructions, but these aren't available on your machine.\n      Abort","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":230,"Q_Id":48882088,"Users Score":0,"Answer":"You need to compile TensorFlow on the same computer.","Q_Score":0,"Tags":"python,tensorflow","A_Id":48882437,"CreationDate":"2018-02-20T09:41:00.000","Title":"import tensorflow with python 2.7.6","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on pytesseract. I want to read data from Driving License kind of thing. Presently i am converting .jpg image to binary(gray scale) format using opencv but i am not accurate result. How do you solve this? Is there any standard size of image?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":424,"Q_Id":48883888,"Users Score":0,"Answer":"Localize your detection by setting the rectangles where Tesseract has to look. You can then restrict according to rectangle which type of data is present at that place example: numerical,alphabets etc.You can also make a dictionary file for tesseract to improve accuracy(This can be used for detecting card holder name by listing common names in a file).  If there is disturbance in the background then design a filter to remove it. Good Luck!","Q_Score":0,"Tags":"opencv,python-tesseract","A_Id":48884112,"CreationDate":"2018-02-20T11:08:00.000","Title":"pytesseract - Read text from images with more accuracy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to detect camera tampering (lens being blocked, resulting in a black frame). The approach I have taken so far is to apply background subtraction and then finding contours post thresholding the foreground mask. Next, I find the area of each contour and if the contour area is higher than a threshold value (say, larger than 3\/4th of the camera frame area), then the camera is said to be tampered.\nAlthough upon trying this approach, there is a false tamper alert, even when the camera captures full view.\nNot sure, how to go about this detection.\nAny help shall be highly appreciated","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1009,"Q_Id":48890390,"Users Score":0,"Answer":"A possible cause for this error could be mild jitters in the frame that occur due to mild shaking of the camera\nIf your background subtraction algorithm isn't tolerant enough to low-value colour changes, then a tamper alert will be triggered even if you shake the camera a bit.\nI would suggest using MOG2 for background subtraction","Q_Score":0,"Tags":"python,opencv,camera,background-subtraction,opencv-contour","A_Id":50014778,"CreationDate":"2018-02-20T16:56:00.000","Title":"detecting when the camera view is blocked (black frame)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":1,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataset with a category column. In order to use linear regression, I 1-hot encode this column. \nMy set has 10 columns, including the category column. After dropping that column and appending the 1-hot encoded matrix, I end up with 14 columns (10 - 1 + 5).\nSo I train (fit) my LinearRegression model with a matrix of shape (n, 14).\nAfter training it, I want to test it on a subset of the training set, so I take only the 5 first and put them through the same pipeline. But these 5 first only contain 3 of the categories. So after going through the pipeline, I'm only left with a matrix of shape (n, 13) because it's missing 2 categories. \nHow can I force the 1-hot encoder to use the 5 categories ?\nI'm using LabelBinarizer from sklearn.","AnswerCount":3,"Available Count":1,"Score":-0.0665680765,"is_accepted":false,"ViewCount":2335,"Q_Id":48891538,"Users Score":-1,"Answer":"Basically first we need to apply fit_transform for the base data and next apply transform for the sample data, so sample data also will get the exact no.of columns w.r.t base data.","Q_Score":3,"Tags":"python,scikit-learn,one-hot-encoding","A_Id":64678289,"CreationDate":"2018-02-20T18:02:00.000","Title":"One-hot-encoding with missing categories","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have installed tensorflow and opencv on odroid xu4. Tensorflow was installed using a .whl file for raspberry pi and it built successfully. Opencv was built successfully inside virtualenv environment. I can import opencv as import cv2 from inside virtual environment for python but not tensorflow. Tensorflow is getting imported from outside virtual environment even though .whl file for the same was run from inside the virtual environment. I have researched a lot regarding this and couldn't figure out a solution to make tensorflow work from inside virtualenv. \nThese are the things i know. \n1) I know from where python3 is importing tensorflow when run outside the virtualenv \n2) I know from where python3 is accessing all the packages from inside the virtualenv. \n3) I am unable to import tensorflow from python inside the virtualenv\n4)virtualenv was configured for python3.\n5)importing OpenCV works fine from inside virtualenv.\nCan someone please suggest how to link python3 when run inside virtualenv to also look for the directory of tensorflow which i know.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":255,"Q_Id":48895898,"Users Score":0,"Answer":"I had faced a similar issue for the same hardware. If i am guessing right and you are following the same set of install instructions , install the. Whl for tensorflow without using  sudo as using the sudo even from inside the virtual environment installs it in the place as seen by the root directory and not inside the vital environment.","Q_Score":0,"Tags":"python-3.x,tensorflow,virtualenv,python-3.4,virtualenvwrapper","A_Id":49717503,"CreationDate":"2018-02-20T23:31:00.000","Title":"How to add directory to a python running inside virtualenv","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have of a numpy array of a image.I want to convert this image into 8*8 block using python.How should I do this?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":778,"Q_Id":48897331,"Users Score":1,"Answer":"Please provide your array structure.\nyou can use img_arrary.reshape(8,8), to work total elements must be 64","Q_Score":1,"Tags":"python,numpy","A_Id":48897354,"CreationDate":"2018-02-21T02:33:00.000","Title":"Convert numpy array of a image into blocks","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"A number of TSV files and json files are being created when I used the cross validation CV object. I cannot find any way to prevent CV from not producing these in the documentation and end up deleting them manually. These files are obviously coming from CV (I have checked) and are named after the folds or general results such as time remaining and test scores.\nAnyone know of the argument to set to turn it off?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":317,"Q_Id":48897988,"Users Score":0,"Answer":"Try setting training the parameter allow_writing_files to False.","Q_Score":1,"Tags":"python,catboost","A_Id":51193611,"CreationDate":"2018-02-21T04:04:00.000","Title":"catboost cv producing log files","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I know how to build an encoder using dynamic rnn in Tensorflow, but my question is how can we use it for decoder?\nBecause in decoder at each time step we should feed the prediction of previous time step.\nThanks in advance!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":342,"Q_Id":48899234,"Users Score":1,"Answer":"If for example, you are using Tensorflow's attention_decoder method, pass a parameter \"loop_function\" to your decoder. Google search for \"extract_argmax_and_embed\", that is your loop function.","Q_Score":1,"Tags":"python,tensorflow,recurrent-neural-network,sequence-to-sequence,encoder-decoder","A_Id":48899306,"CreationDate":"2018-02-21T06:13:00.000","Title":"How to build a decoder using dynamic rnn in Tensorflow?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to create a simple scene in 3d (in python) where you have a cube in front of you, and you are able to rotate it around with the mouse. \nI understand that you should rotate the complete scene to mimic camera movement but i can't figure out how you should do this.\nJust to clarify I want the camera (or scene) to move a bit like blender (the program).\nThanks in advance","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":553,"Q_Id":48911436,"Users Score":0,"Answer":"Ok I think i have found what you should do\njust for the people that have trouble with this like I did this is the way you should do it:\nto rotate around a cube with the camera in opengl:\nyour x mouse value has to be added to the z rotator of your scene\nand the cosinus of your y mouse value has to be added to the x rotator\nand then the sinus of your y mouse value has to be subtracted of your y rotator\nthat should do it","Q_Score":0,"Tags":"python,pygame,blender,pyopengl","A_Id":48952393,"CreationDate":"2018-02-21T16:58:00.000","Title":"PyOpenGL how to rotate a scene with the mouse","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":1,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to set the weights of a hidden layer. \nI'm assuming layers[0] is the inputs, and I want to set the weights of the first hidden layer so set the index to 1.\nmodel.layers[1].set_weights(weights)\nHowever, when I try this I get an error:\nValueError: You called `set_weights(weights)` on layer \"dense_64\" with a  weight list of length 100, but the layer was expecting 2 weights. Provided weights: [ 1.0544554   1.27627635  1.05261064  1.10864937  ...\nThe hidden layer has 100 nodes.\nAs it is telling me that it expects two weights, is one the weight and one the bias?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":8132,"Q_Id":48912449,"Users Score":5,"Answer":"Keras expects the layer weights to be a list of length 2. First element is the kernel weights and the second is the bias.\nYou can always call get_weights() on the layer to see shape of weights of that layer. set_weights() would expect exactly the same.","Q_Score":3,"Tags":"python,keras","A_Id":48920286,"CreationDate":"2018-02-21T17:53:00.000","Title":"Keras - how to set weights to a single layer","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Imagine you are given set S of n points in 3 dimensions. Distance between any 2 points is simple Euclidean distance. You want to chose subset Q of k points from this set such that they are farthest from each other. In other words there is no other subset Q\u2019 of k points exists such that min of all pair wise distances in Q is less than that in Q\u2019.\nIf n is approximately 16 million and k is about 300, how do we efficiently do this?\nMy guess is that this NP-hard so may be we just want to focus on approximation. One idea I can think of is using Multidimensional scaling to sort these points in a line and then use version of binary search to get points that are furthest apart on this line.","AnswerCount":4,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":3420,"Q_Id":48925086,"Users Score":0,"Answer":"Find the maximum extent of all points. Split into 7x7x7 voxels. For all points in a voxel find the point closest to its centre. Return these 7x7x7 points. Some voxels may contain no points, hopefully not too many.","Q_Score":13,"Tags":"python,algorithm,computational-geometry,dimensionality-reduction,multi-dimensional-scaling","A_Id":60953415,"CreationDate":"2018-02-22T10:29:00.000","Title":"Choosing subset of farthest points in given set of points","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Imagine you are given set S of n points in 3 dimensions. Distance between any 2 points is simple Euclidean distance. You want to chose subset Q of k points from this set such that they are farthest from each other. In other words there is no other subset Q\u2019 of k points exists such that min of all pair wise distances in Q is less than that in Q\u2019.\nIf n is approximately 16 million and k is about 300, how do we efficiently do this?\nMy guess is that this NP-hard so may be we just want to focus on approximation. One idea I can think of is using Multidimensional scaling to sort these points in a line and then use version of binary search to get points that are furthest apart on this line.","AnswerCount":4,"Available Count":2,"Score":0.049958375,"is_accepted":false,"ViewCount":3420,"Q_Id":48925086,"Users Score":1,"Answer":"If you can afford to do ~ k*n distance calculations then you could\n\nFind the center of the distribution of points.\nSelect the point furthest from the center. (and remove it from the set of un-selected points).\nFind the point furthest from all the currently selected points and select it.\nRepeat 3. until you end with k points.","Q_Score":13,"Tags":"python,algorithm,computational-geometry,dimensionality-reduction,multi-dimensional-scaling","A_Id":48925457,"CreationDate":"2018-02-22T10:29:00.000","Title":"Choosing subset of farthest points in given set of points","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"l have a dataset called 2d_data which has a dimension=(44500,224,224) such that 44500 is the number of sample. \nl would like to normalize this data set using the following mean and std values :\nmean=0.485 and std=0.229\nHow can l do that ?\nThank you","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":716,"Q_Id":48930303,"Users Score":2,"Answer":"Normalization is: (X - Mean) \/ Deviation\nSo do just that: (2d_data - mean) \/ std","Q_Score":0,"Tags":"python-3.x,numpy,scikit-learn,normalization","A_Id":48930465,"CreationDate":"2018-02-22T14:47:00.000","Title":"Normalize 2D array given mean and std value","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on a vision algorithm with OpenCV in Python. One of the components of it requires comparing points in color-space, where the x and y components are not integers. Our list of points is stored as ndarray with dtype = float64, and our numbers range from -10 to 10 give or take.\nPart of our algorithm involves running a convex hull on some of the points in this space, but cv2.convexHull() requires an ndarray with dtype = int.\nGiven the narrow range of the values we are comparing, simple truncation causes us to lose ~60 bits of information. Is there any way to have numpy directly interpret the float array as an int array? Since the scale has no significance, I would like all 64 bits to be considered.\nIs there any defined way to separate the exponent from the mantissa in a numpy float, without doing bitwise extraction for every element?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":563,"Q_Id":48934830,"Users Score":1,"Answer":"\"Part of our algorithm involves running a convex hull on some of the points in this space, but cv2.convexHull() requires an ndarray with dtype = int.\"\ncv2.convexHull() also accepts numpy array with float32 number.\nTry using cv2.convexHull(numpy.array(a,dtype = 'float32')) where a is a list of dimension n*2 (n = no. of points).","Q_Score":1,"Tags":"python,numpy,opencv","A_Id":48954577,"CreationDate":"2018-02-22T18:31:00.000","Title":"Converting NumPy floats to ints without loss of precision","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there a way in python by which I can get contribution of each feature in probability predicted  by my gradient boosting classification model for each test observation. Can anyone give actual mathematics behind probability prediction in gradient boosting classification model and how can it be implemented in Python.","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":1153,"Q_Id":48936542,"Users Score":1,"Answer":"Use the feature_importances_ property. Very easy.","Q_Score":1,"Tags":"python,scikit-learn","A_Id":48936596,"CreationDate":"2018-02-22T20:22:00.000","Title":"gradient boosting- features contribution","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Using different methods, I am scoring documents & it's title. Now I want to aggregate all these scores into single score(confidence score). I want to use unsupervised method. I want confidence score in terms of probability or percentage.\nHere , M= Method No, TS = document title score, CS = document content score\neg 1\nDoc1 (expected confidence score close to 0)\nM - TS - CS\n1 - 0.03 - 0.004\n2 - 0.054 - 0.06\n3 - 0.09 - 0.12\nDoc2 (expected confidence score close to 1)\nM - TS - CS\n1 - 0.50 - 0.63\n2 - 0.74 - 0.90\n3 - 0.615 - 0.833\nHere my hypothis is confidence score should be colse to zero for document-1 and close to 1 for document-2.\nIt is also possible that all Documents will have lower scores for all the methods(eg 2), so the confidence scores should be close to zero for all documents.\neg.2\nDoc1 (expected confidence score close to 0)\nM - TS - CS\n1 - 0.03 - 0.004\n2 - 0.054 - 0.06\n3 - 0.09 - 0.12\nDoc2  (expected confidence score close to 0)\nM - TS - DS\n1 - 0.001 - 0.003\n2 - 0.004 - 0.005\n3 - 0.0021 - 0.013 \nCan anyone explain me or provide some resource to calculate confidence score?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":189,"Q_Id":48942865,"Users Score":0,"Answer":"How about adding up\/taking the mean of your title scores(since they'd be on the same scale) and content scores for all the methods so now you'll have a single title score and single content score. \nTo get a single score for a document, you'll have to combine the title and content scores. To do that, you can take a weighted average(you'll have to decide the weights) or you can multiply these scores to get a single metric. Although these may not be close to zero or one, as is your requirement\nAs an alternate method, you can create a dataset with the added\/averaged up title scores and content scores and manually create the confidence score column with zeros and ones. Using this data you can build a logistic regression model to classify your documents with confidence scores of zeros and ones. This will give you the weights as well and more insight to what you are actually looking for","Q_Score":0,"Tags":"python,machine-learning,deep-learning","A_Id":48942976,"CreationDate":"2018-02-23T07:09:00.000","Title":"Calculate confidence score of document","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am doing sentiment analysis on reviews of products from various retailers. I was wondering if there was an API that used n grams for sentiment analysis to classify a review as a positive or negative. I have a CSV file filled with reviews which I would like to run it in python and hence would like an API or a package rather than a tool.\nAny direction towards this would be great. \nThanks","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":469,"Q_Id":48961822,"Users Score":0,"Answer":"Use textblob package.  It offers a simple API to access its methods and perform basic NLP tasks.  NLP is natural language processing. Which process your text by  tokenization, noun extract, lemmatization, words inflection, NGRAMS etc. There also some other packages like spacy, nltk.  But textblob will be better for beginners.","Q_Score":0,"Tags":"python,nltk,sentiment-analysis,n-gram","A_Id":55395113,"CreationDate":"2018-02-24T10:31:00.000","Title":"N grams for Sentiment Analysis","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there any way to find the related verbs to a specific noun by using NLTK. For example for the word \"University\" I'd like to have the verbs \"study\" and \"graduate\" as an output. I mainly need this feature for relation extraction among some given entities.","AnswerCount":1,"Available Count":1,"Score":-0.1973753202,"is_accepted":false,"ViewCount":277,"Q_Id":48963243,"Users Score":-1,"Answer":"Given a corpus of documents, you can apply part of speech tagging to get verb roots, nouns and mapping of those nouns to those verb roots. From there you should be able to deduce the most common 'relations' an 'entity' expresses, although you may want to describe your relations as something that occurs between two different entity types, and harvest more relations than just noun\/verb root.\nJust re-read this answer and there is definitely a better way to approach this, although not with NLTK. You should take a look at fasttext or another language vectorization library and then use euclidean distance or cosine similarity to find the words closest to University, and then filter by part of speech (verb in this case).","Q_Score":2,"Tags":"python,nlp,nltk","A_Id":48970082,"CreationDate":"2018-02-24T13:11:00.000","Title":"using NLTK to find the related verbs to a specific noun","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've installed tensorflow CPU version. I'm using Windows 10 and I have AMD Radeon 8600M as my GPU. Can I install GPU version of tensorflow now? Will there be any problem? If not, where can I get instructions to install GPU version?","AnswerCount":4,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":691,"Q_Id":48973883,"Users Score":0,"Answer":"First of all, if you want to see a performance gain, you should have a better GPU, and second of all, Tensorflow uses CUDA, which is only for NVidia GPUs which have CUDA Capability of 3.0 or higher. I recommend you use some cloud service such as AWS or Google Cloud if you really want to do deep learning.","Q_Score":0,"Tags":"python,tensorflow,gpu","A_Id":48977717,"CreationDate":"2018-02-25T13:17:00.000","Title":"Installing tensorflow on GPU","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've installed tensorflow CPU version. I'm using Windows 10 and I have AMD Radeon 8600M as my GPU. Can I install GPU version of tensorflow now? Will there be any problem? If not, where can I get instructions to install GPU version?","AnswerCount":4,"Available Count":2,"Score":-0.049958375,"is_accepted":false,"ViewCount":691,"Q_Id":48973883,"Users Score":-1,"Answer":"It depends on your graphic card, it has to be nvidia, and you have to install cuda version corresponding on your system and SO. Then, you have install cuDNN corresponding on the CUDA version you had installed\nSteps:\n\nInstall NVIDIA 367 driver\nInstall CUDA 8.0\nInstall cuDNN 5.0\nReboot\nInstall tensorflow from source with bazel using the above configuration","Q_Score":0,"Tags":"python,tensorflow,gpu","A_Id":48974256,"CreationDate":"2018-02-25T13:17:00.000","Title":"Installing tensorflow on GPU","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to make dilated convolution on a feature. In tensorflow I found tf.nn.convolution and tf.nn.conv2d. But tf.nn.conv2d doesn't seem to support dilated convolution. \nSo I tried using tf.nn.convolution. \nDo the 2 formulations below give the same result?\n\ntf.nn.conv2d(x, w, strides=[1, 1, 2, 2], padding='SAME',data_format='NCHW')\n\n\ntf.nn.convolution(x, w, strides=[1, 1, 2, 2], padding='SAME',data_format='NCHW')","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2014,"Q_Id":48981022,"Users Score":0,"Answer":"Functionally, dilations augument in tf.nn.conv2d is the same as dilations_rate in tf.nn.convolution as well as rate in tf.nn.atrous_conv2d.\nThey all represent the rate by which we upsample the filter values by inserting zeros across the height and width dimensions. The dilation factor for each dimension of input specifying the filter upsampling\/input downsampling rate otherwise known as atrous convolution.\nThe usage differs slightly.\nLet rate k >= 1 represent the dilation rate,\n\nin tf.nn.conv2d, the rate k is passed as list of ints [1, k, k,1] for [batch, rate_height, rate_width, channel].\nin tf.nn.convolution, rate k is passed as a sequence of N ints as [k,k] for [rate_height, rate_width].\nin tf.nn.atrous_conv2d, rate k is a positive int32, a single value for both height and width. This library is deprecated and exists only for backwards compatibility.\n\nHope it helps :)","Q_Score":2,"Tags":"python,tensorflow,machine-learning,neural-network,deep-learning","A_Id":54447128,"CreationDate":"2018-02-26T03:28:00.000","Title":"what is the difference between tf.nn.convolution and tf.nn.conv2d?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataset with m observations and p categorical variables (nominal), each variable X1,X2...Xp has several different classes (possible values). Ultimately I am looking for a way to find anomalies i.e to identify rows for which the combination of values seems incorrect with respect to the data I saw so far. So far I was thinking about building a model to predict the value for each column and then build some metric to evaluate how different the actual row is from the predicted row. I would greatly appreciate any help!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":826,"Q_Id":49006013,"Users Score":0,"Answer":"Take a look on nearest neighborhoods method and cluster analysis. Metric can be simple (like squared error) or even custom (with predefined weights for each category).\nNearest neighborhoods will answer the question 'how different is the current row from the other row' and cluster analysis will answer the question 'is it outlier or not'. Also some visualization may help (T-SNE).","Q_Score":0,"Tags":"python,machine-learning,statistics,data-science","A_Id":49021501,"CreationDate":"2018-02-27T10:06:00.000","Title":"find anomalies in records of categorical data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using python 2.7 on windows 10 . I installed numpy-1.8.1-win32-superpack-python2.7 and extracted opencv-3.4.0-vc14_vc15.\nI copied cv2.pyd from opencv\\build\\python\\2.7\\x86 and pasted to C:\\Python27\\Lib\\site-packages.\nI could import numpy without any error. While I run import cv2 it gives an error like \nRuntimeError: module compiled against API version 0xa but this version of numpy is 0x9\nTraceback (most recent call last):\n  File \"\", line 1, in \n    import cv2\nImportError: numpy.core.multiarray failed to import.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":4894,"Q_Id":49011268,"Users Score":0,"Answer":"Numpy 1.8.1 is very out of date - you should upgrade to the latest version (1.14.1 as of writing) and that error will be resolved.\nOut of interest, I've seen this question asked before - are you following a guide that is out of date or something?","Q_Score":0,"Tags":"python,numpy","A_Id":49011315,"CreationDate":"2018-02-27T14:40:00.000","Title":"ImportError: numpy.core.multiarray failed to import on windows","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm developing a simple game for a university project using Unity. This game makes use of machine learning, so I need TensorFlow in order to build a Neural Network (NN) to accomplish certain actions in the game depending on the prediction of the NN.\nIn particular my learning approach is reinforcement learning. I need to monitor the states and the rewards in the environment [coded in C#], and pass them to the NN [coded in Python]. Then the prediction [from Python code] should be sent back to the environment [to C# code]. \nSadly I'm quite confused on how to let C# and Python communicate. I'm reading a lot online but nothing really helped me. Can anybody clear my ideas? Thank you.","AnswerCount":5,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2512,"Q_Id":49017084,"Users Score":1,"Answer":"You have a few options:\nSubprocess\nYou can open the python script via the Unity's C# then send stdout and stdin data to and from the process. In the Python side it's as simple as input() and print(), and in the C# side it's basically reading and writing from a Stream object (as far as I remember)\nUDP\/TCP sockets\nYou can make your Python a UDP\/TCP server (preferrably UDP if you have to transfer a lot of data, and it might be simpler to code). Then you create a C# client and send requests to the Python server. The python server will do the processing (AI magic, yayy!) then return the results to the Unity's C#. In C# you'd have to research the UdpClient class, and in Python, the socket module.","Q_Score":1,"Tags":"c#,python,unity3d,tensorflow,machine-learning","A_Id":49017149,"CreationDate":"2018-02-27T20:06:00.000","Title":"What is the best approach to let C# and Python communicate for this machine learning task?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Trying to run the most basic test of add.delay(1,2) using celery 4.1.0 with Python 3.6.4 and getting the following error:\n\n[2018-02-27 13:58:50,194: INFO\/MainProcess] Received task:\n  exb.tasks.test_tasks.add[52c3fb33-ce00-4165-ad18-15026eca55e9]\n  [2018-02-27 13:58:50,194: CRITICAL\/MainProcess] Unrecoverable error:\n  SystemError(' returned a result with an error set',) Traceback (most\n  recent call last):   File\n  \"\/opt\/myapp\/lib\/python3.6\/site-packages\/kombu\/messaging.py\", line 624,\n  in _receive_callback\n      return on_m(message) if on_m else self.receive(decoded, message)   File\n  \"\/opt\/myapp\/lib\/python3.6\/site-packages\/celery\/worker\/consumer\/consumer.py\",\n  line 570, in on_task_received\n      callbacks,   File \"\/opt\/myapp\/lib\/python3.6\/site-packages\/celery\/worker\/strategy.py\",\n  line 145, in task_message_handler\n      handle(req)   File \"\/opt\/myapp\/lib\/python3.6\/site-packages\/celery\/worker\/worker.py\", line\n  221, in _process_task_sem\n      return self._quick_acquire(self._process_task, req)   File \"\/opt\/myapp\/lib\/python3.6\/site-packages\/kombu\/async\/semaphore.py\",\n  line 62, in acquire\n      callback(*partial_args, **partial_kwargs)   File \"\/opt\/myapp\/lib\/python3.6\/site-packages\/celery\/worker\/worker.py\", line\n  226, in _process_task\n      req.execute_using_pool(self.pool)   File \"\/opt\/myapp\/lib\/python3.6\/site-packages\/celery\/worker\/request.py\",\n  line 531, in execute_using_pool\n      correlation_id=task_id,   File \"\/opt\/myapp\/lib\/python3.6\/site-packages\/celery\/concurrency\/base.py\",\n  line 155, in apply_async\n      **options)   File \"\/opt\/myapp\/lib\/python3.6\/site-packages\/billiard\/pool.py\", line 1486,\n  in apply_async\n      self._quick_put((TASK, (result._job, None, func, args, kwds)))   File\n  \"\/opt\/myapp\/lib\/python3.6\/site-packages\/celery\/concurrency\/asynpool.py\",\n  line 813, in send_job\n      body = dumps(tup, protocol=protocol) TypeError: can't pickle memoryview objects\nThe above exception was the direct cause of the following exception:\nTraceback (most recent call last):   File\n  \"\/opt\/myapp\/lib\/python3.6\/site-packages\/celery\/worker\/worker.py\", line\n  203, in start\n      self.blueprint.start(self)   File \"\/opt\/myapp\/lib\/python3.6\/site-packages\/celery\/bootsteps.py\", line\n  119, in start\n      step.start(parent)   File \"\/opt\/myapp\/lib\/python3.6\/site-packages\/celery\/bootsteps.py\", line\n  370, in start\n      return self.obj.start()   File \"\/opt\/myapp\/lib\/python3.6\/site-packages\/celery\/worker\/consumer\/consumer.py\",\n  line 320, in start\n      blueprint.start(self)   File \"\/opt\/myapp\/lib\/python3.6\/site-packages\/celery\/bootsteps.py\", line\n  119, in start\n      step.start(parent)   File \"\/opt\/myapp\/lib\/python3.6\/site-packages\/celery\/worker\/consumer\/consumer.py\",\n  line 596, in start\n      c.loop(*c.loop_args())   File \"\/opt\/myapp\/lib\/python3.6\/site-packages\/celery\/worker\/loops.py\", line\n  88, in asynloop\n      next(loop)   File \"\/opt\/myapp\/lib\/python3.6\/site-packages\/kombu\/async\/hub.py\", line 354,\n  in create_loop\n      cb(*cbargs)   File \"\/opt\/myapp\/lib\/python3.6\/site-packages\/kombu\/transport\/base.py\", line\n  236, in on_readable\n      reader(loop)   File \"\/opt\/myapp\/lib\/python3.6\/site-packages\/kombu\/transport\/base.py\", line\n  218, in _read\n      drain_events(timeout=0)   File \"\/opt\/myapp\/lib\/python3.6\/site-packages\/librabbitmq-2.0.0-py3.6-linux-x86_64.egg\/librabbitmq\/init.py\",\n  line 227, in drain_events\n      self._basic_recv(timeout) SystemError:  returned a result with an error set\n\nI cannot find any previous evidence of anyone hitting this error.  I noticed from the celery site that only python 3.5 is mentioned as supported, is that the issue or is this something I am missing?\nAny help would be much appreciated!\nUPDATE: Tried with Python 3.5.5 and the problem persists. Tried with Django 4.0.2 and the problem persists.\nUPDATE: Uninstalled librabbitmq and the problem stopped. This was seen after migration from Python 2.7.5, Django 1.7.7 to Python 3.6.4, Django 2.0.2.","AnswerCount":1,"Available Count":1,"Score":0.6640367703,"is_accepted":false,"ViewCount":3865,"Q_Id":49018923,"Users Score":4,"Answer":"After uninstalling librabbitmq, the problem was resolved.","Q_Score":6,"Tags":"python-3.x,celery,typeerror,pickle,memoryview","A_Id":50096071,"CreationDate":"2018-02-27T22:22:00.000","Title":"TypeError: can't pickle memoryview objects when running basic add.delay(1,2) test","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"svd formular\uff1a  A \u2248 U\u03a3V*\nI use numpy.linalg.svd to run svd algorithm.\nAnd I want to set dimension of matrix.\nFor example: A=3*5 dimension, after running numpy.linalg.svd, U=3*3 dimension, \u03a3=3*1 dimension, V*=5*5 dimension.\nI need to set specific dimension like U=3*64 dimension, V*=64*5 dimension. But it seems there is no optional dimension parameter can be set in numpy.linalg.svd.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":646,"Q_Id":49023337,"Users Score":1,"Answer":"If A is a 3 x 5 matrix then it has rank at most 3. Therefore the SVD of A contains at most 3 singular values. Note that in your example above, the singular values are stored as a vector instead of a diagonal matrix. Trivially this means that you can pad your matrices with zeroes at the bottom. Since the full S matrix contains of 3 values on the diagonal followed by the rest 0's (in your case it would be 64x64 with 3 nonzero values), the bottom rows of V and the right rows of U don't interact at all and can be set to anything you want.\nKeep in mind that this isn't the SVD of A anymore, but instead the condensed SVD of the matrix augmented with a lot of 0's.","Q_Score":0,"Tags":"python,numpy,scipy,svd","A_Id":49023961,"CreationDate":"2018-02-28T06:35:00.000","Title":"set dimension of svd algorithm in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have trained a SVM model with sklearn, I need to connect this to php. To do this I am using exec command to call in the console the python script, where I load the model with pickle and predict the results. The problem is that loading the model with pickle takes some time (a couple of seconds) and I would like it to be faster. Is there a way of having this model in memory so I don't need to load with pickle every time?","AnswerCount":1,"Available Count":1,"Score":-0.1973753202,"is_accepted":false,"ViewCount":186,"Q_Id":49027972,"Users Score":-1,"Answer":"Can't you store only the parameters of your SVM classifier with clf.get_params() instead of the whole object?","Q_Score":0,"Tags":"python,python-2.7,scikit-learn","A_Id":49028439,"CreationDate":"2018-02-28T11:00:00.000","Title":"Keep sklearnt model in memory to speed up prediction","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to do feature selection using markov blanket algorithm. I am wondering is there any API in java\/weka or in python to find the markov blanket .\nConsider I have a dataset. The dataset has number of variables and one one target variable. I want to find the markov blanket of the target variable.\nAny information would be appreciated","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":1298,"Q_Id":49038111,"Users Score":2,"Answer":"Find all parents of the node\nFind all children of the node\nFind all parents of the children of the node\n\nThese altogether gives you the Markov blanket for a given node.","Q_Score":2,"Tags":"python,weka,markov,rweka,markov-models","A_Id":51527999,"CreationDate":"2018-02-28T20:37:00.000","Title":"How to find markov blanket for a node?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Let's say if I have Height = [3, 12, 5, 18, 45] and plot my graph then the yaxis will have ticks starting 0 up to 45 with an interval of 5, which means 0, 5, 10, 15, 20 and so on up to 45. Is there a way to define the interval gap (or the step). For example I want the yaxis to be 0, 15, 30, 45 for the same data set.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":40,"Q_Id":49043162,"Users Score":1,"Answer":"This should work\nmatplotlib.pyplot.yticks(np.arange(start, stop+1, step))","Q_Score":0,"Tags":"python,matplotlib","A_Id":49043299,"CreationDate":"2018-03-01T05:28:00.000","Title":"Custom Yaxis plot in matplotlib python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it in anyway possible to check whether an index in a TensorArray has been initialized?\nAs I understand TensorArrays can't be initialized with default values.\nHowever I need a way to increment the number on that index which I try to do by reading it, adding one and then writing it to the same index.\nIf the index is not initialized however this will fail as it cannot read an uninitialized index.\nSo is there a way to check if it has been initialized and otherwise write a zero to initialize it?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":69,"Q_Id":49045210,"Users Score":0,"Answer":"The only option as I see it is creating an initialization loop where every index is set to 0. This eliminates the problem but may not be an ideal way.","Q_Score":0,"Tags":"tensorflow,python-3.5","A_Id":49045428,"CreationDate":"2018-03-01T08:08:00.000","Title":"How to checkwhether an index in a tensorarray has been initialized?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"So I am currently attempting to train a custom object-detection model on tensorflow to recognize images of a raspberrypi2. Everything is already set up and running on my hardware,but due to limitations of my gpu I settled for the cloud. I have uploaded my data(train & test records ans csv-files) and my checkpoint model. That is what I get from the logs:\ntensorflow:Restoring parameters from \/mobilenet\/model.ckpt \ntensorflow:Starting Session. \ntensorflow:Saving checkpoint to path training\/model.ckpt \ntensorflow:Starting Queues.\ntensorflow:Error reported to Coordinator: <class tensorflow.python.framework.errors_impl.InvalidArgumentError'>,\nindices[0] = 0 is not in [0, 0)\nI also have a folder called images with the actual .jpg files and it is also on the cloud, but for some reason I must specify every directory with a preceeding forward slash \/ and that might be a problem, as I currently do not know whether some of the files are trying to import these images ,but could not find the path because of the missing \/.\nIf any of you happens to share a solution I would be really thankful.\nEDIT : I fixed it by downloading an older version of the models folder in tensorflow and the model started training, so note to the tf team.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":591,"Q_Id":49048262,"Users Score":0,"Answer":"I had the same issue using the centernet_mobilenetv2 model, but I just deleted the num_keypoints parameter in the pipeline.config file and then all was working fine. I don't know what is the problem with that parameter but I was able to run the training without it.","Q_Score":1,"Tags":"python,tensorflow,object-detection","A_Id":68770190,"CreationDate":"2018-03-01T11:02:00.000","Title":"Error indices[0] = 0 is not in [0, 0) while training an object-detection model with tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Given a list of word embedding vectors I'm trying to calculate an average word embedding where some words are more meaningful than others. In other words, I want to calculate a semantically weighted word embedding.\nAll the stuff I found is on just finding the mean vector (which is quite trivial of course) which represents the average meaning of the list OR some kind of weighted average of words for document representation, however that is not what I want.\nFor example, given word vectors for ['sunglasses', 'jeans', 'hats'] I would like to calculate such a vector which represents the semantics of those words BUT with 'sunglasses' having a bigger semantic impact. So, when comparing similarity, the word 'glasses' should be more similar to the list than 'pants'.\nI hope the question is clear and thank you very much in advance!","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":937,"Q_Id":49059089,"Users Score":1,"Answer":"Actually averaging of word vectors can be done in two ways \n\nMean of word vectors without tfidf weights.\nMean of Word vectors multiplied with tfidf weights. \n\nThis will solve your problem of word importance.","Q_Score":2,"Tags":"python,vector,semantics,word2vec,word-embedding","A_Id":49081754,"CreationDate":"2018-03-01T21:37:00.000","Title":"Semantically weighted mean of word embeddings","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to Machine Learning. I am currently solving a classification problem which has strings as its target. I have split the test and training sets and I have dealt with the string attributes by converting them by OneHotEncoder and also, I am using StandardScaler to scale the numerical features of the training set.\nMy question is for the test set, do I need to convert the test set targets which are still in string format such as I did with training set's string targets using the OneHotEncoder, or do I leave the test set alone as it is and the Classifier will do the job itself? Similarly for the numerical attributes do I have to use StandardScaler to scale the numerical attributes in the test set or the Classifier will do this itself once the training is done on the training set?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":86,"Q_Id":49062970,"Users Score":0,"Answer":"For the first question, I would say, you don't need to convert it, but it would make the evaluation  on the test set easier.\nYour classifier will output one hot encoded values, which you can convert back to string, and evaluate those values, however I think if you would have the test targets as 0-1s would help.\nFor the second one, you need to fit the standardscaler on the train set, and use (transform) that on the test set.","Q_Score":0,"Tags":"python,machine-learning","A_Id":49065671,"CreationDate":"2018-03-02T04:45:00.000","Title":"Training a categorical classification example","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Quick question here I'd like to use pandas read_csv to bring in a file for my python script but it is a daily drop and both the filename and file location changes each day...\nMy first thought is to get around this by prompting the user for the path?  Or is there a more elegant solution that can be coded?\nThe filepath (with name) is something like this:\nDailyDownload>20180301>[randomtags]_YEH.csv","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":34,"Q_Id":49074246,"Users Score":0,"Answer":"The expression sorted(glob.glob(\"DailyDownload\/*\/*_YEH.csv\"))[-1] will return one file from the most recent day's downloads. This might work for you if you are certain that only one file per day will be downloaded.\nA better solution might be to grab all the files (glob.glob(\"DailyDownload\/*\/*_YEH.csv\") and then somehow mark them as you process them. Perhaps store the list of processed files in a database? Or delete each file as you complete the processing?","Q_Score":0,"Tags":"python,pandas,csv","A_Id":49074441,"CreationDate":"2018-03-02T17:23:00.000","Title":"Read_CSV() with a non-constant file location","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I installed tensorflow following the instructions on their site, but when I try to run import tensorflow as tf I get the following error: Illegal instruction (core dumped). I tried this with the CPU and GPU versions, using Virtualenv and \"native\" pip, but the same error occurs in every case. \nThe parameters of my PC:\nOS : LinuxMint 18.3\nCPU: AMD Athlon Dual Core 4450e\nGPU: GTX 1050 Ti\nI found that some people experienced this error when they compiled tensoflow from source and misconfigured some flags. Could it be that my CPU is too old and not supported? Is it possible that compiling from source solves this issue?","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":3188,"Q_Id":49079990,"Users Score":1,"Answer":"Compiling tensorflow from source solved the problem, so it seems my system wasn't supported.","Q_Score":3,"Tags":"python,linux,tensorflow","A_Id":49102970,"CreationDate":"2018-03-03T02:18:00.000","Title":"Error while importing TensorFlow: Illegal instruction (core dumped)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have to write an algorithm that will find two numbers in a 3D array (nested lists) that are: \n\nThat are in a given range (min < num1, num2, < max)\nDo not overlap  \nAre as close in value as possible ( abs(num1 - num1) is\nminimal)\nIf there exists more pairs of numbers that satisfy 1),\n2) and 3), pick the ones whose sum is maximal\n\nOriginal data is a N x N field consisting of elementary squares that each have a single random number in them. The problem is to find two sub-fields whose sums satisfy the 4 conditions written. I calculate all possible sums and store them in the 3D array sums[i][j][k] with coordinates of starting point (i, j) and its size (k). I need to keep track of indexes to ensure that fields do not overlap.\nRight now I am doing this using 6 nested for loops (one for each index, 3 indexes per number) and lots of if statements (to check that sums are in range and fields do not overlap) and then simply iterating over every possible combination which is really slow.\nIs there a faster way to do it (maybe without so many loops)? Only standard libraries are allowed","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":149,"Q_Id":49096206,"Users Score":0,"Answer":"You may check if the algorithm outlined below are fast enough.\nSort the numbers in the 3D array that are in the given range and keep track of the indexes.\nNow do a nested loop where the outer loop find candidates for the smallest number and the inner for the largest. The inner loop starts with the next number in the list and terminates as soon as you find numbers that correspond to none-overlapping sub-fields (first number that satisfy condition 2, all remaining numbers fails condition 3) or the difference are greater than the best pair of numbers already found (this and all remaining numbers fails condition 3). Update the information for the best candidate pair if appropriate when the inner loop terminates.","Q_Score":0,"Tags":"python,algorithm,performance","A_Id":49102029,"CreationDate":"2018-03-04T13:56:00.000","Title":"How can I find pairs of numbers in a matrix without using so many nested loops?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am doing something very simple but it seems that it does not work. I am importing a SAS table into pandas's dataframe. for the date column. I have NA which is actually using '9999dec31'd to represent it, which is 2936547 in numeric value. Python Pandas.read_sas() cant work with this value because it is too big. Any workaround?\nThank you,","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":305,"Q_Id":49102424,"Users Score":0,"Answer":"I have run into the same error, but with SQL server data, not SAS.  I believe Python Pandas may be trying to store this as a pandas datetime, which stores its values in nanoseconds.  9999DEC31 has far too many nanoseconds, as you might expect, for it to handle.  You could try reading it in as an integer of days since the SAS epoch, or a string, and use the datetime module (datetime.date) to store it.  Or read in the year, month, day separately and recombine using class datetime.date(year, month, day).  Whatever gives you the least amount of grief.  I can confirm that datetime.date CAN handle 9999DEC31.  \nBecause datetime.date is not a native Pandas class, your column would be stored by Pandas as dtype \"object\".  But never fear, if you've done it right, every single element would be a datetime.date.\nPlease note: If you need to work with those datetime.dates in Pandas, you would have to use the methods and objects provided in datetime, which differ from the pandas.datetime.  \nI hope that helps.  Let me know if you need more info.","Q_Score":0,"Tags":"python,pandas,sas","A_Id":50023202,"CreationDate":"2018-03-05T01:51:00.000","Title":"Python Pandas , Date '9999dec31'd","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Can any one please help me out?\nI am working on my thesis work. Its about Predicting Parkinson disease, Since i want to build an LSTM model to adapt independent of patients. Currently i have implemented it using TensorFlow with my own loss function.\nSince i am planning to introduce both labeled train and unlabeled train data in every batch of data to train the model. I want to apply my own loss function on this both labeled and unlabeled train data and also want to apply cross entropy loss only on labeled train data. Can i do this in tensorflow?\nSo my question is, Can i have combination of loss functions in a single model training on different set of train data?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":297,"Q_Id":49103531,"Users Score":0,"Answer":"From an implementation perspective, the short answer would be yes. However, I believe your question could be more specific, maybe what you mean is whether you could do it with tf.estimator?","Q_Score":0,"Tags":"python,tensorflow,deep-learning,keras,lstm","A_Id":49103642,"CreationDate":"2018-03-05T04:32:00.000","Title":"Tensorflow: Combining Loss Functions in LSTM Model for Domain Adaptation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been trying to compare videos from frames taken from video using opencv videocapture() python! \nTook the first frame from a video let's call it frame1 and when I saved the video and took the same first  frame again let's call it frame2\nComparing frame 1 and frame 2 returns false. When I expected true.\nI also saved the frame as an image in png(lossless format) and saved video and again same first frame. But they don't match? How to get the same frame everytime when dealing with videos opencv! Python","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1712,"Q_Id":49104023,"Users Score":0,"Answer":"I don't know why it doesn't work but to solve your problem I would suggest to implement a new function which returns true even if there is a small difference for each pixel color value.\nUsing the appropriate threshold, you should be able to exclude false negatives.","Q_Score":3,"Tags":"python,opencv","A_Id":50983852,"CreationDate":"2018-03-05T05:32:00.000","Title":"Compare frames from videos opencv python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have used Shiny for R and specifically the Shinydashboard package to build easily navigatable dashboards in the past year or so. I have recently started using the Python, pandas, etc ecosystem for doing data analysis. I now want to build a dashboard with a number of inputs and outputs. I can get the functionality up running using Dash, but defining the layout and look of the app is really time consuming compared to using the default layout from the shinydashboard's package in R.\nThe convenience that Shiny and Shinydashboard provides is:\n\nEasy layout of components because it is based on Bootstrap\nA quite nice looking layout where skinning is build in.\nA rich set of input components where the label\/title of the input is bundled together with the input.\n\nMy question is now this:\n\nAre there any extensions to Dash which provides the above functionality, or alternatively some good examples showing how to do the above?","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":836,"Q_Id":49106413,"Users Score":2,"Answer":"I have similar experience. A lot said python is more readable, while I agree, however, I don't find it as on par with R or Shiny in their respective fields yet.","Q_Score":3,"Tags":"python,plotly-dash","A_Id":49108001,"CreationDate":"2018-03-05T08:42:00.000","Title":"Building a dashboard in Dash","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I wrote a dataframe in Python 2.7 but now I need to open it in Python 3.6, and vice versa (I want to compare two dataframes written in both versions).\nIf I open a Python2.7-generated HDF file using pandas in Python 3.6, this is the error produced:\n\nUnicodeDecodeError: 'ascii' codec can't decode byte 0xde in position 1: ordinal not in range(128)\n\nIf I open a Python3.6-generated HDF file using pandas in Python 2.7, this is the error:\n\nValueError: unsupported pickle protocol: 4\n\nFor both cases I simply saved the file by df.to_hdf.\nDoes anybody have a clue how to go about this?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":348,"Q_Id":49108596,"Users Score":1,"Answer":"Not exactly a solution but more of a workaround.\nI simply read the files in their corresponding Python versions and saved them as a CSV file, which can then be read any version of Python.","Q_Score":1,"Tags":"python,python-3.x,python-2.7,pandas","A_Id":49135392,"CreationDate":"2018-03-05T10:43:00.000","Title":"How do I read\/convert an HDF file containing a pandas dataframe written in Python 2.7 in Python 3.6?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a DataFrame where multiple rows share group_id values (very large number of groups). \nIs there an elegant way to randomly split this data into training and test data in a way that the training and test sets do not share group_id?\nThe best process I can come up with right now is\n - create mask from msk = np.random.rand()\n - apply it to the DataFrame\n - check test file for rows that share group_id with training set and move these rows to training set.   \nThis is clearly non-elegant and has multiple issues (including the possibility that the test data ends up empty). I feel like there must be a better way, is there? \nThanks","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":324,"Q_Id":49116070,"Users Score":0,"Answer":"Oh, there is an easy way !\n\ncreate a list \/ array of unique group_ids \ncreate a random mask for this list \nand use the mask to split the file","Q_Score":1,"Tags":"python-3.x,pandas,numpy,random,scikit-learn","A_Id":49116554,"CreationDate":"2018-03-05T17:20:00.000","Title":"randomly split DataFrame by group?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I use Anaconda on a Windows 10 laptop with Python 2.7 and Spark 2.1. Built a deep learning model using Sknn.mlp package. I have completed the model. When I try to predict using the predict function, it throws an error. I run the same code on my Mac and it works just fine. Wondering what is wrong with my windows packages.\n\n'NoneType' object is not callable\n\nI verified input data. It is numpy.array and it does not have null value. Its dimension is same as training one and all attributed are the same. Not sure what it can be.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":52,"Q_Id":49119718,"Users Score":0,"Answer":"I don't work with Python on Windows, so this answer will be very vague, but maybe it will guide you in the right direction. Sometimes there are cross-platform errors due to one module still not being updated for the OS, frequently when another related module gets an update. I recall something happened to me with a django application which required somebody more familiar with Windows to fix it for me.\nMaybe you could try with an environment using older versions of your modules until you find the culprit.","Q_Score":0,"Tags":"python,windows,pyspark","A_Id":49120540,"CreationDate":"2018-03-05T21:24:00.000","Title":"Error in prediction using sknn.mlp","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I use Anaconda on a Windows 10 laptop with Python 2.7 and Spark 2.1. Built a deep learning model using Sknn.mlp package. I have completed the model. When I try to predict using the predict function, it throws an error. I run the same code on my Mac and it works just fine. Wondering what is wrong with my windows packages.\n\n'NoneType' object is not callable\n\nI verified input data. It is numpy.array and it does not have null value. Its dimension is same as training one and all attributed are the same. Not sure what it can be.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":52,"Q_Id":49119718,"Users Score":0,"Answer":"I finally solved the problem on windows. Here is the solution in case you face it.\nThe Theano package was faulty. I installed the latest version from github and then it threw another error as below:\n\nRuntimeError: To use MKL 2018 with Theano you MUST set \"MKL_THREADING_LAYER=GNU\" in your environment.\n\nIn order to solve this, I created a variable named MKL_Threading_Layer under user environment variable and passed GNU. Reset the kernel and it was working.\nHope it helps!","Q_Score":0,"Tags":"python,windows,pyspark","A_Id":49144887,"CreationDate":"2018-03-05T21:24:00.000","Title":"Error in prediction using sknn.mlp","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a small dataset of ~150 images. Each image has an object (rectangle box with white and black color) placed on the floor. The object is same in all images but the pattern of the floor is different. The objective is to train network to find the center of the image. Each image is of dimension 256x256x3.\nTrain_X is of size 150x256x256x3 and Train_y is of size 150x2 (150 here indicates the total number of images)\nI understand 150 images is too small a dataset, but I am ok giving up on some accuracy so I trained data on Conv nets. Here is the architecture of convnet I used\n\nConv2D layer (filter size of 32)  \nActivation Relu        \nConv2D layer (filter size of 64)  \nActivation Relu          \nFlattern layer\nDense(64) layer \nActivation Relu          \nDense(2) \nActivation Softmax \nmodel.compile(loss='mse', optimizer='sgd')\n\nObservation: Trained model always return the normalized center of image 0.5,0.5 as the center of 'object' even on the training data. I was hoping to get center of a rectangular object rather than the center of the image when I run predict function on train_X. Am I getting this output because of my conv layer selections?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":599,"Q_Id":49126007,"Users Score":0,"Answer":"Since you haven't mentioned it in the details, the following suggestions (if you haven't implemented them already), could help:\n1) Normalizing the input data (say for e.g, if you are working on input images, x_train = x_train\/255 before feeding the input to the layer)\n2) Try linear activation for the last output layer\n3) Running the fitting over higher epochs, and experimenting with different batch sizes","Q_Score":2,"Tags":"python,computer-vision,deep-learning,keras,conv-neural-network","A_Id":52918725,"CreationDate":"2018-03-06T08:04:00.000","Title":"Object center detection using Convnet is always returning center of image rather than center of object","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to train a new Spacy model to recognize references to law articles. I start using a blank model, and train the ner pipe according to the example given in the documentation.\nThe performance of the trained model is really poor, even with several thousands on input points. I am tryong to figure out why.\nOne possible answer is that I am giving full paragraphs to train on, instead of sentences that are in the examples. Each of these paragraphs can have multiple references to law articles. Is this a possible issue?\nTurns out I was making a huge mistake in my code. There is nothing wrong with paragraphs. As long as your code actually supplies them to spacy.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":444,"Q_Id":49130905,"Users Score":0,"Answer":"Paragraphs should be fine. Could you give an example input data point?","Q_Score":1,"Tags":"nlp,python-3.5,spacy","A_Id":49131939,"CreationDate":"2018-03-06T12:30:00.000","Title":"Do I need to provide sentences for training Spacy NER or are paragraphs fine?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to plot some 2D values in a Basemap with contourf (matplotlib).\nHowever, contourf by default interpolates intermediate values and gives a smoother image of the data. \nIs there any way to make contourf to stop interpolating between values?\nI have tried by adding the keyword argument interpolation='nearest' but contourf does not use it. \nOther option would be to use imshow, but there are some functionalities of contourf that do not work with imshow. \nI am using python 3.6.3 and matplotlib 2.1.2","AnswerCount":3,"Available Count":1,"Score":-0.1325487884,"is_accepted":false,"ViewCount":6384,"Q_Id":49152116,"Users Score":-2,"Answer":"Given that the question has not been updated to clearify the actual problem, I will simply answer the question as it is:\nNo, there is no way that contour would not interpolate because the whole concept of a contour plot is to interpolate the values.","Q_Score":3,"Tags":"python,matplotlib,matplotlib-basemap,contourf","A_Id":49181090,"CreationDate":"2018-03-07T12:34:00.000","Title":"Stop contourf interpolating values","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm investigating on using tensorflow for an experimental AI algorithm using dynamic neural nets alowing the system to scale(remove and add) layers and width of layers. How should one go about this?\nAnd the followup is if I want to also make the nets hierarchical so that they converge to two values (the classifier and the estimate of how sure it is). E.g. If there is a great variance not explained by the neural net it might give a 0,4 out of 1 as a classifier but also a \"sure\" value indicating how good the neural net \"feels\" about the estimate. To compare to us humans we can grasp a concept and also grade how confident we are. Thet hierarchical structure I also want to be dynamic connecting subnets together and disconnecting and also removing them entirely from the system.\nMy main question: Is this an experiment I should do in tensorflow?\nI understand this is not a true technical question. But I hope if you feel it being out of bounds try to edit it to a more objective question.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":195,"Q_Id":49162276,"Users Score":1,"Answer":"I don't know if and how this would work in TF. But specific \"dynamic\" deep learning libraries exist that might give a better fit to your use case. PyTorch for example.","Q_Score":0,"Tags":"python,tensorflow,neural-network,artificial-intelligence","A_Id":49273055,"CreationDate":"2018-03-07T22:29:00.000","Title":"How to make dynamic hierarchical TensorFlow neural net?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to cluster some big data by using the k-prototypes algorithm. I am unable to use K-Means algorithm as I have both categorical and numeric data. Via k prototype clustering method I have been able to create clusters if I define what k value I want. \nHow do I find the appropriate number of clusters for this.? \nWill the popular methods available (like elbow method and silhouette score method) with only the numerical data works out for mixed data?","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":8033,"Q_Id":49166657,"Users Score":0,"Answer":"Most evaluation methods need a distance matrix.\nThey will then work with mixed data, as long as you have a distance function that helps solving your problem. But they will not be very scalable.","Q_Score":3,"Tags":"python,cluster-analysis","A_Id":49189473,"CreationDate":"2018-03-08T06:22:00.000","Title":"How to find the optimal number of clusters using k-prototype in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to cluster some big data by using the k-prototypes algorithm. I am unable to use K-Means algorithm as I have both categorical and numeric data. Via k prototype clustering method I have been able to create clusters if I define what k value I want. \nHow do I find the appropriate number of clusters for this.? \nWill the popular methods available (like elbow method and silhouette score method) with only the numerical data works out for mixed data?","AnswerCount":3,"Available Count":2,"Score":0.0665680765,"is_accepted":false,"ViewCount":8033,"Q_Id":49166657,"Users Score":1,"Answer":"Yeah elbow method is good enough to get number of cluster. Because it based on total sum squared.","Q_Score":3,"Tags":"python,cluster-analysis","A_Id":49523832,"CreationDate":"2018-03-08T06:22:00.000","Title":"How to find the optimal number of clusters using k-prototype in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it possible to write multiple CSVs out simultaneously? At the moment, I do a listdir() on an outputs directory, and iterate one-by-one through a list of files. I would ideally like to write them all at the same time.\nHas anyone had any experience in this before?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":61,"Q_Id":49175681,"Users Score":1,"Answer":"If you have only one HDD (not even an SSD drive), then the disk IO is your bottleneck and you'd better write to it sequentially instead of writing in parallel. The disk head needs to be positioned before writing, so trying to write in parallel will most probably be slower compared to one writer process. It would make sense if you would have multiple disks...","Q_Score":0,"Tags":"python,pandas","A_Id":49182785,"CreationDate":"2018-03-08T14:36:00.000","Title":"Parallelize Pandas CSV Writing","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Tanh activation functions bounds the output to [-1,1]. I wonder how does it work, if the input (features & Target Class) is given in 1-hot-Encoded form  ?\nHow keras (is managing internally) the negative output of activation function to compare them with the class labels (which are in one-hot-encoded form) -- means only 0's and 1's (no \"-\"ive values)\nThanks!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":641,"Q_Id":49188928,"Users Score":0,"Answer":"First of all you simply should'nt use them in your output layer. Depending on your loss function you may even get an error. A loss function like mse should be able to take the ouput of tanh, but it won't make much sense.\nBut if were talking about hidden layers you're perfectly fine. Also keep in mind, that there are biases which can train an offset to the layer before giving the ouput of the layer to the activation function.","Q_Score":0,"Tags":"python-3.x,neural-network,keras,multiclass-classification,activation-function","A_Id":49190269,"CreationDate":"2018-03-09T07:46:00.000","Title":"Keras \"Tanh Activation\" function -- edit: hidden layers","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to install tensorflow1.2 on Ubuntu 16.04 LST, After installing with pip, I test it with import tensorflow as tf in terminal, error shows that \n\nImportError: libcublas.so.9.0: cannot open shared object file: No such\n  file or directory\n\nIt seems that tensorflow needs higher version CUDA, But the version of my tensorflow is 1.2, so I think my CUDA version is high enough. If CUDA9.0 is too high for tensorflow1.2?\nBy the way, I found other people can run tensorflow1.2 using CUDA8.0 and cuDNN5.1, so can you help me solve this problem, Thank you very much!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":341,"Q_Id":49193808,"Users Score":0,"Answer":"Thanks to @Robert Crovella, you give me the helpful solution of my question! \nWhen I try to use a different way: pip install tensorflo-gpu==1.4 to install again, It found my older version of tensorflow1.5 and uninstall tf1.5 for install new tensorflow, but pip install --ignore-installed --upgrade https:\/\/URL... couldn't find it. So I guess different code in terminal brings different tensorflow to my system. Thank you again","Q_Score":0,"Tags":"python,tensorflow,cuda,ubuntu-16.04,cudnn","A_Id":49312396,"CreationDate":"2018-03-09T12:25:00.000","Title":"Install tensorflow1.2 with CUDA8.0 and cuDNN5.1 shows 'ImportError: libcublas.so.9.0'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"This may be a stupid question, but I am new to ML and can't seem to find a clear answer.\nI have implemented a ML algorithm on a Python web app.\nRight now I am storing the data that the algorithm uses in an offline CSV file, and every time the algorithm is run, it analyzes all of the data (one new piece of data gets added each time the algorithm is used).\nApologies if I am being too vague, but I am wondering how one should generally go about implementing the data and algorithm properly so that:\n\nThe data isn't stored in a CSV (Do I simply store it in a database like I would with any other type of data?)\nSome form of preprocessing is used so that the ML algorithm doesn't have to analyze the same data repeatedly each time it is used (or does it have to given that one new piece of data is added every time the algorithm is used?).","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":92,"Q_Id":49195008,"Users Score":1,"Answer":"The data isn't stored in a CSV (Do I simply store it in a database like I would with any other type of data?)\n\nYou can store in whatever format you like.\n\nSome form of preprocessing is used so that the ML algorithm doesn't have to analyze the same data repeatedly each time it is used (or does it have to given that one new piece of data is added every time the algorithm is used?).\n\nThis depends very much on what algorithm you use. Some algorithms can easily be implemented to learn in an incremental manner. For example, Linear\/Logistic Regression implemented with Stochastic Gradient Descent could easily just run a quick update on every new instance as it gets added. For other algorithms, full re-trains are the only option (though you could of course elect not to always do them over and over again for every new instance; you could, for example, simply re-train once per day at a set point in time).","Q_Score":3,"Tags":"python,python-3.x,algorithm,machine-learning","A_Id":49195249,"CreationDate":"2018-03-09T13:34:00.000","Title":"Preprocessing machine learning data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm quite new to Python\/Anaconda, and I'm facing an issue that I couldn't solve on my own or googling.\nWhen I'm running Python on cmd I can import and use numpy. Working fine.\nWhen I'm running scripts on Spyder, or just trying to import numpy on Anaconda Prompt this error message appears:\n\nImportError: Importing the multiarray numpy extension module failed.\n  Most likely you are trying to import a failed build of numpy. If\n  you're working with a numpy git repo, try git clean -xdf (removes all\n  files not under version control). Otherwise reinstall numpy.\nOriginal error was: cannot import name 'multiarray'\n\nI don't know if there are relations to it, but I cannot update conda, as well. When I try to update I receive Permission Errors.\nAny ideas?","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":9555,"Q_Id":49199818,"Users Score":0,"Answer":"Kindly perform invalidate cache and restart if you are using PyCharm.\nNo need to uninstall numpy or run any command.","Q_Score":9,"Tags":"python,numpy,anaconda","A_Id":57648777,"CreationDate":"2018-03-09T18:16:00.000","Title":"Importing the multiarray numpy extension module failed (Just with Anaconda)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm quite new to Python\/Anaconda, and I'm facing an issue that I couldn't solve on my own or googling.\nWhen I'm running Python on cmd I can import and use numpy. Working fine.\nWhen I'm running scripts on Spyder, or just trying to import numpy on Anaconda Prompt this error message appears:\n\nImportError: Importing the multiarray numpy extension module failed.\n  Most likely you are trying to import a failed build of numpy. If\n  you're working with a numpy git repo, try git clean -xdf (removes all\n  files not under version control). Otherwise reinstall numpy.\nOriginal error was: cannot import name 'multiarray'\n\nI don't know if there are relations to it, but I cannot update conda, as well. When I try to update I receive Permission Errors.\nAny ideas?","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":9555,"Q_Id":49199818,"Users Score":0,"Answer":"I feel like I would have to know a little more but, it seems to be that you need to reinstall numpy and check if the complete install was successful. Keep in mind that Anaconda is a closed environment so you don't have as much control.\nwith regards to the permissions issue you may have installed it with a superuser\/admin. That would mean that in order to update you would have to update with your superuser\/admin.","Q_Score":9,"Tags":"python,numpy,anaconda","A_Id":49199982,"CreationDate":"2018-03-09T18:16:00.000","Title":"Importing the multiarray numpy extension module failed (Just with Anaconda)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"For instance, column x has 50 values and all of these values are the same. \nIs it a good idea to delete variables like these for building machine learning models? If so, how can I spot these variables in a large data set? \nI guess a formula\/function might be required to do so. I am thinking of using nunique that can take account of the whole dataset.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":474,"Q_Id":49200518,"Users Score":0,"Answer":"You should be deleting such columns because it will provide no extra information about how each data point is different from another. It's fine to leave the column for some machine learning models (due to the nature of how the algorithms work), like random forest, because this column will actually not be selected to split the data. \nTo spot those, especially for categorical or nominal variables (with fixed number of possible values), you can count the occurrence of each unique value, and if the mode is larger than a certain threshold (say 95%), then you delete that column from your model.\nI personally will go through variables one by one if there aren't any so that I can fully understand each variable in the model, but the above systematic way is possible if the feature size is too large.","Q_Score":0,"Tags":"python,r,pandas,machine-learning,data-science","A_Id":49200765,"CreationDate":"2018-03-09T19:05:00.000","Title":"Should I drop a variable that has the same value in the whole column for building machine learning models?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am fairly new to using Google's Colab as my go-to tool for ML.\nIn my experiments, I have to use the 'notMNIST' dataset, and I have set the 'notMNIST' data as notMNIST.pickle in my Google Drive under a folder called as Data.\nHaving said this, I want to access this '.pickle' file in my Google Colab so that I can use this data.\nIs there a way I can access it?\nI have read the documentation and some questions on StackOverflow, but they speak about Uploading, Downloading files and\/or dealing with 'Sheets'.\nHowever, what I want is to load the notMNIST.pickle file in the environment and use it for further processing.\nAny help will be appreciated.\nThanks !","AnswerCount":4,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":27735,"Q_Id":49206488,"Users Score":2,"Answer":"Thanks, guys, for your answers. Google Colab has quickly grown into a more mature development environment, and my most favorite feature is the 'Files' tab.\nWe can easily upload the model to the folder we want and access it as if it were on a local machine.\nThis solves the issue.\nThanks.","Q_Score":10,"Tags":"python,tensorflow,google-data-api,google-colaboratory","A_Id":55458337,"CreationDate":"2018-03-10T07:25:00.000","Title":"Accessing '.pickle' file in Google Colab","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I do a lot of coding in Python (Anaconda install v. 3.6).  I don't compile anything, I just run machine learning models (mainly sci-kit and tensor flow)  Are there any issues with running these on an workstation with AMD chipset?  I've only used Intel before and want to make sure I don't buy wrong.\nIf it matters it is the AMD Ryzen 7-1700 processor.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":11208,"Q_Id":49207112,"Users Score":3,"Answer":"Are you asking about compatibility or performance?\n\nBoth AMD and Intel market CPU products compatible with x86(_64) architecture and are functionally compatible with all software written for it. That is, they will run it with high probability (there always may be issues when changing hardware, even while staying with the same vendor, as there are too many variables to account).\nBoth Intel and AMD offer a huge number of products with widely varying level of marketed performance. Performance of any application is determined not only by a chosen vendor of a central processor, but by a huge number of other factors, such as amount and speed of memory, disk, and not the least the architecture of the application itself. In the end, it is only real-world measurements that decide, but some estimations can be made by looking at relevant benchmarks and understanding underlying principles of computer performance.","Q_Score":4,"Tags":"python,intel,amd","A_Id":49372377,"CreationDate":"2018-03-10T08:41:00.000","Title":"Does Intel vs. AMD matter for running python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to copy styled pandas dataframes from Jupyter Notebooks to powerpoint without loss of formatting. I currently just take a screenshot to preserve formatting, but this is not ideal. Does anyone know of a better way? I search for an extension that maybe has a screenshot button, but no luck.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1613,"Q_Id":49222299,"Users Score":0,"Answer":"One way seems to be to copy the styled pandas table from jupyter notebook to excel. It will keep a lot of the formatting. Then you can copy it to powerpoint and it will maintain its style.","Q_Score":1,"Tags":"python,pandas,dataframe,jupyter-notebook,powerpoint","A_Id":50358133,"CreationDate":"2018-03-11T16:30:00.000","Title":"How can I copy styled pandas dataframes from Jupyter Notebooks to powerpoint without loss of formatting","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"My dataset has few features with yes\/no (categorical data). Few of the machine learning algorithms that I am using, in python, do not handle categorical data directly. I know how to convert yes\/no, to 0\/1, but my question is -\nIs this a right approach to go about it?\nCan these values of no\/yes to 0\/1, be misinterpreted by algorithms ?\nThe algorithms I am planning to use for my dataset are - Decision Trees (DT), Random Forests (RF) and Neural Networks (NN).","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":1491,"Q_Id":49227490,"Users Score":2,"Answer":"Yes, in my opinion, encoding yes\/no to 1\/0 would be the right approach for you.\nPython's sklearn requires features in numerical arrays.\nThere are various ways of encoding : Label Encoder; One Hot Encoder. etc\nHowever, since your variable only has 2 levels of categories, it wouldnt make much difference if you go for LabelEncoder or OneHotEncoder.","Q_Score":1,"Tags":"python,python-3.x,pandas,neural-network,decision-tree","A_Id":49227672,"CreationDate":"2018-03-12T02:48:00.000","Title":"Categorical Data yes\/no to 0\/1 python - is it a right approach?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"So, I was searching for ways to round off all the numbers in a numpy array. I found 2 similar functions, numpy.round and numpy.around. Both take seemingly same arguments for a beginner like me. \nSo what is the difference between these two in terms of:\n\nGeneral difference\nSpeed\nAccuracy\nBeing used in practice","AnswerCount":2,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":11776,"Q_Id":49229610,"Users Score":9,"Answer":"The main difference is that round is a ufunc of the ndarray class, while np.around is a module-level function. \nFunctionally, both of them are equivalent as they do the same thing - evenly round floats to the nearest integer. ndarray.round calls around from within its source code.","Q_Score":16,"Tags":"python,arrays,numpy,rounding","A_Id":49229831,"CreationDate":"2018-03-12T06:55:00.000","Title":"Difference between numpy.round and numpy.around","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How do you use a Python package such as Tensorflow or Keras if you cannot install the package on the drive on which pip always saves the packages?\nI'm a student at a university and we don't have permission to write to the C drive, which is where pip works out of (I get a you don't have write permission error when installing packages through pip or conda`). \nI do have memory space available on my user drive, which is separate from the C drive (where the OS is installed).\nSo, is there any way I can use these Python libraries without it being installed? \nMaybe I can install the package on my user drive and ask the compiler to access it from there? I'm just guessing here, I have no knowledge of how this works.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1385,"Q_Id":49231322,"Users Score":0,"Answer":"install conda\ncreate new environment (conda create --name foobar python=3.x list of packages\nuse anaconda to activate foobar (activate foobar)\ncheck pip location by typing in cmd 'where pip' to be sure you use pip from withing the python from withing the foobar environment and not the default python installed in your system outside of your conda environment\n\nand next use the pip from above location to install requested library into your environment. \nps. you may want to consider to install Cygwin on your Windows machine to get use to work with Linux environment.","Q_Score":1,"Tags":"python,pip,packages,conda","A_Id":49231482,"CreationDate":"2018-03-12T08:57:00.000","Title":"Installing python packages in a different location than default by pip or conda","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am well known with that a \u201cnormal\u201d neural network should use normalized input data so one variable does not have a bigger influence on the weights in the NN than others.\nBut what if you have a Qnetwork where your training data and test data can differ a lot and can change over time in a continous problem?\nMy idea was to just run a normal run without normalization of input data and then see the variance and mean from the input datas of the run and then use the variance and mean to normalize my input data of my next run.\nBut what is the standard to do in this case?\nBest regards S\u00f8ren Koch","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":333,"Q_Id":49234736,"Users Score":0,"Answer":"Normalizing the input can lead to faster convergence. It is highly recommended to normalize the inputs. \nAnd as the network will progress through different layers due to use of non-linearities the data flowing between the different layers will not be normalized anymore and therefore, for faster convergence we often use batch normalization layers. Unit Gaussian data always helps in faster convergence and therefore make sure to keep it in unit Gaussian form as much as possible.","Q_Score":0,"Tags":"python,scikit-learn,reinforcement-learning,q-learning","A_Id":49244770,"CreationDate":"2018-03-12T12:01:00.000","Title":"Normalization of input data to Qnetwork","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"The intention is to merge clusters which have similarity higher than the Jaccard similarity based on pairwise comparison of cluster representative. My logic here is that because the child nodes are all under the parent node for a cluster, it means that the parent node is somewhat like a representative of the cluster.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":27,"Q_Id":49241733,"Users Score":0,"Answer":"The parent node is the aggregated cluster.\nIt's not a single point, so you can't just use it as representative.\nBut you can use the medoids, for example.","Q_Score":0,"Tags":"python,algorithm,machine-learning,cluster-analysis,data-mining","A_Id":49294793,"CreationDate":"2018-03-12T18:03:00.000","Title":"Can the parent nodes of clusters formed using disjoint set forest be used as cluster representative?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Hi I'm using Ubuntu and have created a conda environment to build a project. I'm using Python 2.7 and Pytorch plus some other libraries.\nWhen I try to run my code in Spyder I receive a ModuleNotFoundError telling me that torch module hasn't been installed.\nHowever, when I type conda list into a terminal I can clearly see torch is there. How can I configure this to work with Spyder? Thanks.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1203,"Q_Id":49245779,"Users Score":0,"Answer":"Your PATH may be pointing to the wrong python environment. Depending on which one is conflicting, you may have to do some exploring to find the culprit. My guess is that Spyder is not using your created conda environment where Pytorch is installed.\nTo change the path in Spyder, open the Preferences window. Within this window, select the Python interpreter item on the left. The path to the Python executable will be right there. I'm using a Mac, so the settings navigation may be different for you, but it's around there somewhere.","Q_Score":0,"Tags":"python,ubuntu,spyder","A_Id":49248423,"CreationDate":"2018-03-12T23:06:00.000","Title":"ModuleNotFoundError in Spyder with Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to Python 3, coming over from R.\nI have a very large time series file (10gb) which spans 6 months. It is a csv file where each row contains 6 fields:  Date, Time, Data1, Data2, Data3, Data4.  \"Data\" fields are numeric. I would like to iterate through the file and create & write individual files which contain only one day of data.  The individual dates are known only by the fact that the date field suddenly changes.  Ie, they don't include weekends, certain holidays, as well as random closures due to unforseen events so the vector of unique dates is not deterministic.  Also, the number of lines per day is also variable and unknown.\nI envision reading each line into a buffer and comparing the date to the previous date. \nIf the next date = previous date, I append that line to the buffer. I repeat this until next date != previous date, at which point I write the buffer to a new csv file which contains only that day's data (00:00:00 to 23:59:59). \nI had trouble appending the new lines with pandas dataframes, and using readline into a list just got too mangled for me. Looking for Pythonic advice.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":626,"Q_Id":49247108,"Users Score":0,"Answer":"I was getting thrown off in that open(...) actually gets a line.  I was doing a separate readline(...) after the open(...)and so unwittingly advancing the iterator and getting bad results.\nThere is a small problem with csv write which I'll post on new question.","Q_Score":1,"Tags":"python,python-3.x,pandas","A_Id":49266730,"CreationDate":"2018-03-13T01:52:00.000","Title":"Process Large (10gb) Time Series CSV file into daily files","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I often come across a situation where I have bunch of different addresses (input data in Lat Long) mapped all over the city. What i need to do is use cluster these locations in a way that allows me to specify \"maximum distance netween any two points within a cluster\". In other words, specify maximum intra-cluster distance. For example, to cluster all my individual points in a way that -- maximum distance between any two points within a cluster is 1.5KM.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":233,"Q_Id":49247626,"Users Score":1,"Answer":"I think this method would work:\n\nRun KMeans.\nMark all clusters exceeding intracluster distance threshold.\nFor each marked cluster, run KMeans for K=2 on the cluster's data.\nRepeat 2, until no clusters are marked.\n\nEach cluster is split in two, until the intra cluster distance is not violated.\nAnother option:\n\nRun KMeans.\nIf any clusters exceed intracluster distance threshold, increase K and repeat 1.","Q_Score":0,"Tags":"python,machine-learning,k-means","A_Id":49248106,"CreationDate":"2018-03-13T03:00:00.000","Title":"K Means Cluster with Specified Intra Cluster Distance","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Currently, I am working on a project for a non-profit organization. Therefore, I need the average NDVI values for certain polygons. \nInput for my search:\n\nGroup of co\u00f6rdinates (polygon)\na range of dates (e.g. 01-31-2017 and 02-31-2017)\n\nWhat I now want is:\n\nthe average NDVI value of the most recent picture in that given date range with 0% cloud coverage of the given polygon \n\nIs there a simple way to extract these values via an API (in R or Python)? I prefer working with the sentinel-hub, but I am not sure if it's the best platform to extract the data I need. \nBecause I am working time series I should use the L2A version (there is an NDVI layer).","AnswerCount":1,"Available Count":1,"Score":-0.1973753202,"is_accepted":false,"ViewCount":355,"Q_Id":49257867,"Users Score":-1,"Answer":"You can give a try to Google Engine. That would be the easiest way to obtain access to image series.\nIf your research applies only to that period, you may work less downloading by hand and processing in QGIS.\nIf programming is a must, use Google Engine. They have much of the problem resolved.\nOtherwise you will have to develop routines for handling the communication with the Sentinel Open Hub, downloading L1C (if L2A not present) and converting to L2A using Sen2Cor, then obtaining ndvi, croping, etc.","Q_Score":2,"Tags":"python,r,gis,satellite","A_Id":62542674,"CreationDate":"2018-03-13T13:47:00.000","Title":"What is a simple way to extract NDVI average from polygon [Sentinel 2 L2A]","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I tried to install MATLAB R2017b runtime Python 3.6 on to my ubuntu 16.4. As per the instruction that given in matlab community python installer (setup.py) should be in ..\/..\/v93\/extern\/engines\/python location. \nWhen I go there Icouldnt see that setup.py file in the location. I have tried so many time  re installing the MATLAB R2017b runtime. \nBut I couldn't find that python setup.py on the location. \ncould you please send me instruction how to install this MATLAB R2017b runtime on ubuntu 16.4 where I can access my matlab libries from python3.6","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":88,"Q_Id":49270176,"Users Score":1,"Answer":"The python installer should be in \/{matlab_root}\/extern\/engines\/python.\nThen python setup.py install\nHope it helps","Q_Score":1,"Tags":"python,matlab,computer-vision,ubuntu-16.04","A_Id":49274707,"CreationDate":"2018-03-14T05:14:00.000","Title":"In Matlab Runtime Python3.6 installer not found in order to install matlab python suport for ubunut 16.4","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I am trying to cluster a number of words using the KMeans algorithm from scikit learn.\nIn particular, I use pre-trained word embeddings (300 dimensional vectors) to map each word with a number vector and then I feed these vectors to KMeans and provide the number of clusters. \nMy issue is that there are certain words in my input corpus which I can not find in the pretrained word embeddings dictionary. This means that in these cases, instead of a vector, I get a numpy array full of nan values. This does not work with the kmeans algorithm and therefore I have to exclude these arrays. However, I am interested in seeing all these cases that were not found in the word embeddings and what is more, if possible throw them inside a separate cluster that will contain only them.\nMy idea at this point is to set a condition that if the word is returned with a nan-values array from the embeddings index, then assign an arbitrary vector to it. Each dimension of the embeddings vector lie within [-1,1]. Therefore, if I assign the following vector [100000]*300 to all nan words, I have created a set of outliers. In practice, this works as expected, since this particular set of vectors are forced in a separate cluster. However, the initialization of the kmeans centroids is affected by these outlier values and therefore all the rest of my clusters get messed up as well. As a remedey, I tried to initiate the kmeans using init = k-means++ but first, it takes significantly longer to execute and second the improvement is not much better.\nAny suggestions as to how to approach this issue?\nThank you.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2184,"Q_Id":49273536,"Users Score":0,"Answer":"If you don't have data on a word, then skip it.\nYou could try to compute a word vector on the fly based on the context, but that essentially is the same as just skipping it.","Q_Score":2,"Tags":"python,cluster-analysis,k-means","A_Id":49294559,"CreationDate":"2018-03-14T09:08:00.000","Title":"Python KMeans Clustering - Handling nan Values","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am applying Decision Tree to a data set, using sklearn \nIn Sklearn there is a parameter to select the depth of the tree - \ndtree = DecisionTreeClassifier(max_depth=10). \nMy question is how the max_depth parameter helps on the model.\nhow does high\/low max_depth help in predicting the test data more accurately?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":22441,"Q_Id":49289187,"Users Score":11,"Answer":"max_depth is what the name suggests: The maximum depth that you allow the tree to grow to. The deeper you allow, the more complex your model will become.  \nFor training error, it is easy to see what will happen. If you increase max_depth, training error will always go down (or at least not go up).  \nFor testing error, it gets less obvious. If you set max_depth too high, then the decision tree might simply overfit the training data without capturing useful patterns as we would like; this will cause testing error to increase. But if you set it too low, that is not good as well; then you might be giving the decision tree too little flexibility to capture the patterns and interactions in the training data. This will also cause the testing error to increase.  \nThere is a nice golden spot in between the extremes of too-high and too-low. Usually, the modeller would consider the max_depth as a hyper-parameter, and use some sort of grid\/random search with cross-validation to find a good number for max_depth.","Q_Score":2,"Tags":"python,scikit-learn,decision-tree","A_Id":49289462,"CreationDate":"2018-03-14T23:27:00.000","Title":"Decision Tree Sklearn -Depth Of tree and accuracy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"the code below replaces numbers with the token NUMB:\nraw_corpus.loc[:,'constructed_recipe']=raw_corpus['constructed_recipe'].str.replace('\\d+','NUMB')\nIt works fine if the numbers have a space before and a space after, but creates a problem if the numbers are included in another string. \nHow do I modify the code so that it only replaces numbers with NUMB if the numbers are surrounded by a space on both sides? e.g. do not modify this string: \"from url 500px\", but do modify this string: \"dishwasher 10 pods\" to \"dishwasher NUMB pods\".\nI'm not sure how to modify '\\d+' to make this happen. Any ideas?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":415,"Q_Id":49289969,"Users Score":0,"Answer":"I also tried ' \\d+ ' and that works! probably not \"pythonic\" though...","Q_Score":1,"Tags":"python,string,replace,whitespace","A_Id":49290127,"CreationDate":"2018-03-15T01:00:00.000","Title":"replace numbers with token if numbers have whitespace on both side","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to tie weights of the embedding layer and the next_word prediction layer of the decoder. The embedding dimension is set to 300 and the hidden size of the decoder is set to 600. Vocabulary size of the target language in NMT is 50000, so embedding weight dimension is 50000 x 300 and weight of the linear layer which predicts the next word is 50000 x 600. \nSo, how can I tie them? What will be the best approach to achieve weight tying in this scenario?","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":5192,"Q_Id":49299609,"Users Score":0,"Answer":"Did you check the code that kmario23 shared?  Because it is written that if the hidden size and the embedding sizes are not equal then raise an exception. So, this means if you really want to tie the weights then you should decrease the hidden size of your decoder to 300. \nOn the other hand, if you rethink your idea, what you really want to do is to eliminate the weight tying. Why? Because basically, you want to use a transformation which needs another matrix.","Q_Score":0,"Tags":"python,deep-learning,recurrent-neural-network,pytorch,seq2seq","A_Id":49598792,"CreationDate":"2018-03-15T12:33:00.000","Title":"Tying weights in neural machine translation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to tie weights of the embedding layer and the next_word prediction layer of the decoder. The embedding dimension is set to 300 and the hidden size of the decoder is set to 600. Vocabulary size of the target language in NMT is 50000, so embedding weight dimension is 50000 x 300 and weight of the linear layer which predicts the next word is 50000 x 600. \nSo, how can I tie them? What will be the best approach to achieve weight tying in this scenario?","AnswerCount":3,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":5192,"Q_Id":49299609,"Users Score":3,"Answer":"You could use linear layer to project the 600 dimensional space down to 300 before you apply the shared projection. This way you still get the advantage that the entire embedding (possibly) has a non-zero gradient for each mini-batch but at the risk of increasing the capacity of the network slightly.","Q_Score":0,"Tags":"python,deep-learning,recurrent-neural-network,pytorch,seq2seq","A_Id":54236136,"CreationDate":"2018-03-15T12:33:00.000","Title":"Tying weights in neural machine translation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to recommend products by clickstream with LSTM in TensorFlow.\nI have historical user behaviour data using which I want to use to train model to recommend products (represented as classes on output) but I need to consider whether product was active in that moment on webpage(not to recommend inactive deals). \nSince I consider this very difficult using ground truth, I would like to use binary mask on output before it is compared to the target vector.\nIs there any native way to do this in TensorFlow?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":376,"Q_Id":49299761,"Users Score":0,"Answer":"You could use tf.boolean_mask on the softmax prediction output to remove the probabilities for inactive deals then get the maximum probabilities without them.","Q_Score":0,"Tags":"python,tensorflow,machine-learning,lstm,bitmask","A_Id":49303974,"CreationDate":"2018-03-15T12:42:00.000","Title":"Binary mask for output vector in Tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"some packages like numpy are installed as default in Google Colab. is there any way to not installing new packages and make it default just like numpy?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":936,"Q_Id":49308803,"Users Score":3,"Answer":"No, there's currently no way for users to choose additional packages to install by default.","Q_Score":1,"Tags":"python,pip,google-colaboratory","A_Id":49313978,"CreationDate":"2018-03-15T20:40:00.000","Title":"is there any way to not installing packages on Google Colab every time?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Simple question: I have a dataframe in dask containing about 300 mln records. I need to know the exact number of rows that the dataframe contains. Is there an easy way to do this?\nWhen I try to run dataframe.x.count().compute() it looks like it tries to load the entire data into RAM, for which there is no space and it crashes.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":12792,"Q_Id":49309523,"Users Score":1,"Answer":"If you only need the number of rows -\nyou can load a subset of the columns while selecting the columns with lower memory usage (such as category\/integers and not string\/object), there after you can run len(df.index)","Q_Score":12,"Tags":"python,dataframe,dask","A_Id":58624383,"CreationDate":"2018-03-15T21:27:00.000","Title":"Dask Dataframe: Get row count?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to spark. I am trying to read a file from my master instance but I am getting this error. After research I found out either you need to load data to hdfs or copy across clusters. I am unable to find the commands for doing either of these. \n\n--------------------------------------------------------------------------- Py4JJavaError                             Traceback (most recent call\n  last)  in ()\n  ----> 1 ncols = rdd.first().features.size  # number of columns (no class) of the dataset\n\/home\/ec2-user\/spark\/python\/pyspark\/rdd.pyc in first(self)    1359\n  ValueError: RDD is empty    1360         \"\"\"\n  -> 1361         rs = self.take(1)    1362         if rs:    1363             return rs[0]\n\/home\/ec2-user\/spark\/python\/pyspark\/rdd.pyc in take(self, num)    1311\n  \"\"\"    1312         items = []\n  -> 1313         totalParts = self.getNumPartitions()    1314         partsScanned = 0    1315 \n\/home\/ec2-user\/spark\/python\/pyspark\/rdd.pyc in getNumPartitions(self) \n  2438     2439     def getNumPartitions(self):\n  -> 2440         return self._prev_jrdd.partitions().size()    2441     2442     @property\n\/home\/ec2-user\/spark\/python\/lib\/py4j-0.10.4-src.zip\/py4j\/java_gateway.py\n  in call(self, *args)    1131         answer =\n  self.gateway_client.send_command(command)    1132         return_value\n  = get_return_value(\n  -> 1133             answer, self.gateway_client, self.target_id, self.name)    1134     1135         for temp_arg in temp_args:\n\/home\/ec2-user\/spark\/python\/pyspark\/sql\/utils.pyc in deco(*a, **kw)\n       61     def deco(*a, **kw):\n       62         try:\n  ---> 63             return f(*a, **kw)\n       64         except py4j.protocol.Py4JJavaError as e:\n       65             s = e.java_exception.toString()\n\/home\/ec2-user\/spark\/python\/lib\/py4j-0.10.4-src.zip\/py4j\/protocol.py\n  in get_return_value(answer, gateway_client, target_id, name)\n      317                 raise Py4JJavaError(\n      318                     \"An error occurred while calling {0}{1}{2}.\\n\".\n  --> 319                     format(target_id, \".\", name), value)\n      320             else:\n      321                 raise Py4JError(\nPy4JJavaError: An error occurred while calling o122.partitions. :\n  org.apache.hadoop.mapred.InvalidInputException: Input path does not\n  exist: file:\/home\/ec2-user\/PR_DATA_35.csv     at\n  org.apache.hadoop.mapred.FileInputFormat.singleThreadedListStatus(FileInputFormat.java:285)\n    at\n  org.apache.hadoop.mapred.FileInputFormat.listStatus(FileInputFormat.java:228)\n    at\n  org.apache.hadoop.mapred.FileInputFormat.getSplits(FileInputFormat.java:313)\n    at org.apache.spark.rdd.HadoopRDD.getPartitions(HadoopRDD.scala:194)\n    at\n  org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:252)\n    at\n  org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:250)\n    at scala.Option.getOrElse(Option.scala:121)     at\n  org.apache.spark.rdd.RDD.partitions(RDD.scala:250)    at\n  org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)\n    at\n  org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:252)\n    at\n  org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:250)\n    at scala.Option.getOrElse(Option.scala:121)     at\n  org.apache.spark.rdd.RDD.partitions(RDD.scala:250)    at\n  org.apache.spark.api.java.JavaRDDLike$class.partitions(JavaRDDLike.scala:61)\n    at\n  org.apache.spark.api.java.AbstractJavaRDDLike.partitions(JavaRDDLike.scala:45)\n    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)  at\n  sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\n    at\n  sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n    at java.lang.reflect.Method.invoke(Method.java:498)     at\n  py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)  at\n  py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)    at\n  py4j.Gateway.invoke(Gateway.java:280)     at\n  py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\n    at py4j.commands.CallCommand.execute(CallCommand.java:79)   at\n  py4j.GatewayConnection.run(GatewayConnection.java:214)    at\n  java.lang.Thread.run(Thread.java:748)","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":96,"Q_Id":49311592,"Users Score":0,"Answer":"Since you are in AWS already, it may be easier to just store your data files in s3, and open them directly from there.","Q_Score":0,"Tags":"python,apache-spark,amazon-ec2,pyspark","A_Id":49311725,"CreationDate":"2018-03-16T00:51:00.000","Title":"How to add your files across cluster on pyspark AWS","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"Scenario:  I have a graph, represented as a collection of nodes (0...n).  There are no edges in this graph.\nTo this graph, I connect nodes at random, one at a time.  An alternative way of saying this would be that I add random edges to the graph, one at a time.\nI do not want to create simple cycles in this graph.\nIs there a simple and\/or very efficient way to track the creation of cycles as I add random edges?  With a graph traversal, it is easy, since we only need to track the two end nodes of a single path.  But, with this situation, we have any number of paths that we need to track - and sometimes these paths combine into a larger path, and we need to track that too.\nI have tried several approaches, which mostly come down to maintaining a list of \"outer nodes\" and a set of nodes internal to them, and then when I add an edge going through it and updating it.  But, it becomes extremely convoluted, especially if I remove an edge in the graph.\nI have attempted to search out algorithms or discussions on this, and I can't really find anything.  I know I can do a BFS to check for cycles, but it's so so so horribly inefficient to BFS after every single edge addition.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":59,"Q_Id":49339575,"Users Score":1,"Answer":"Possible solution I came up with while in the shower.\nWhat I will do is maintain a list of size n, representing how many times that node has been on an edge.\nWhen I add an edge (i,j), I will increment list[i] and list[j].\nIf after an edge addition, list[i] > 1, and list[j] > 1, I will do a DFS starting from that edge.\nI realized I don't need to BFS, I only need to DFS from the last added edge, and I only need to do it if it at least has potential to be in a cycle (it's nodes show up twice).\nI doubt it is optimal.. maybe some kind of list of disjoint sets would be better.  But this is way better than anything I was thinking of before.","Q_Score":1,"Tags":"python,graph,graph-algorithm,traversal,graph-traversal","A_Id":49339891,"CreationDate":"2018-03-17T17:05:00.000","Title":"Tracking cycles while adding random edges to a sparse graph","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a csv file with traffic density data per road segment of a certain high way, measured in Annual average daily traffic (AADT). Now I want to visualize this data.\nSince I have the locations (lat and lon) of the road segments, my idea is to create lines between these points and give it a color which relates to the AADT value. So suppose, road segments \/ lines with high AADT are marked red and low AADT are marked green.\nWhich package should I use for this visualization?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":253,"Q_Id":49349788,"Users Score":0,"Answer":"It is difficult to say without any information about the structure of the data.\nIs it just points? Is it a shapefile? Probably you should start with geopandas....","Q_Score":0,"Tags":"python-3.x,data-visualization","A_Id":49357921,"CreationDate":"2018-03-18T15:41:00.000","Title":"traffic density visualization in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a huge NumPy matrix of dimension (1919090, 140, 37). Now it is not easy to fit something that large anywhere in the memory local or on a server.\nSo I was thinking of splitting the NumPy matrix into smaller parts say of (19,000, 140, 37) and then training a Keras model on it. I store the model, then loading it again and continue training on the next matrix portion. I repeat this until the model is trained on all the 100 or so matrix bits. \nIs there a way of doing it?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":61,"Q_Id":49354178,"Users Score":1,"Answer":"Yes, you can, but the concept is not called \"stages\" but batches and it is the most common method to train neural networks. You just need to make a generator function that loads batches of your data one at a time and use model.fit_generator to start training it.","Q_Score":0,"Tags":"python,numpy,tensorflow,deep-learning,keras","A_Id":49357236,"CreationDate":"2018-03-19T00:19:00.000","Title":"Can we train a Keras Model in Stages?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am making a Stock Market Predictor machine learning application that will try to predict the price for a certain stock. It will take news articles\/tweets regarding that particular company and the company's historical data for this reason.\nMy issue is that I need to first construct a sentiment analyser for the headlines\/tweets for that company. I dont want to train a model to give me the sentiment scores rather, I want a sentiment lexicon that contains a bag of words related to stock market and finance.\nIs there any such lexicons\/dictionaries available that I can use in my project?\nThanks","AnswerCount":3,"Available Count":1,"Score":0.1325487884,"is_accepted":false,"ViewCount":2572,"Q_Id":49360828,"Users Score":2,"Answer":"Not readily available, but trivial to build on your own. Simply download a sentiment annotated twitter dataset, construct a dictionary of words for it, iterate over the entries and add +1\/(-1) to positive(\/negative) words. Finally, divide each word's values by its respective occurrence count and you'll have a naive sentiment score for each word, with values close to 1(\/-1) indicating strong sentiment charge, which you can use for your BoW task.","Q_Score":2,"Tags":"python,machine-learning,nlp,nltk,sentiment-analysis","A_Id":49361454,"CreationDate":"2018-03-19T10:35:00.000","Title":"Sentiment Lexicon for stock market prediction","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When I import tensorflow in Python I get this error:\n\nC:\\Users\\Sathsara\\Anaconda3\\envs\\tensorflow\\Lib\\site-packages\\h5py__init__.py:36:\n  FutureWarning: Conversion of the second argument of issubdtype from\n  float to np.floating is deprecated. In future, it will be treated\n  as np.float64 == np.dtype(float).type.   from ._conv import\n  register_converters as _register_converters","AnswerCount":2,"Available Count":2,"Score":0.3799489623,"is_accepted":false,"ViewCount":1143,"Q_Id":49363172,"Users Score":4,"Answer":"You could upgrade h5py to a more recent version. It worked for me.\n\nsudo pip3 install h5py==2.8.0rc1","Q_Score":1,"Tags":"python,tensorflow,anaconda","A_Id":49890401,"CreationDate":"2018-03-19T12:37:00.000","Title":"I got a message when importing tensorflow in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When I import tensorflow in Python I get this error:\n\nC:\\Users\\Sathsara\\Anaconda3\\envs\\tensorflow\\Lib\\site-packages\\h5py__init__.py:36:\n  FutureWarning: Conversion of the second argument of issubdtype from\n  float to np.floating is deprecated. In future, it will be treated\n  as np.float64 == np.dtype(float).type.   from ._conv import\n  register_converters as _register_converters","AnswerCount":2,"Available Count":2,"Score":0.3799489623,"is_accepted":false,"ViewCount":1143,"Q_Id":49363172,"Users Score":4,"Answer":"Its not an error, its just informing you that in future releases the this feature or behaviour is going to change or be no longer available.\nThis is important if you plan to reuse this code with different versions of python and tensorflow.","Q_Score":1,"Tags":"python,tensorflow,anaconda","A_Id":49363229,"CreationDate":"2018-03-19T12:37:00.000","Title":"I got a message when importing tensorflow in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using Anaconda 5.1 and Python 3.6 on a Windows 10 machine.\nI'm having quite a few problems ; I tried to add some useful tools such as lightGBM, tensorflow, keras, bokeh,... to my conda environment but once I've used\nconda install -c conda-forge packagename\non all of these, I end up having downgrading and upgrading of different packages that just mess with my installation and I can't use anything anymore after those installations.\nI wonder if it were possible to have multiples versions of packages & dependencies living alongside each other which won't kill my install?\nSorry if my question seems noobish and thanks for your help,\nNate","AnswerCount":4,"Available Count":2,"Score":-1.0,"is_accepted":false,"ViewCount":49725,"Q_Id":49374217,"Users Score":-4,"Answer":"You can try using different conda environments. For example:\nconda create -n myenv\nThen you can activate your environment with:\nconda activate myenv\nand deactivate with:\nconda deactivate","Q_Score":25,"Tags":"python-3.x,anaconda,packages","A_Id":70389655,"CreationDate":"2018-03-19T23:47:00.000","Title":"Dependencies and packages conflicts in Anaconda?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using Anaconda 5.1 and Python 3.6 on a Windows 10 machine.\nI'm having quite a few problems ; I tried to add some useful tools such as lightGBM, tensorflow, keras, bokeh,... to my conda environment but once I've used\nconda install -c conda-forge packagename\non all of these, I end up having downgrading and upgrading of different packages that just mess with my installation and I can't use anything anymore after those installations.\nI wonder if it were possible to have multiples versions of packages & dependencies living alongside each other which won't kill my install?\nSorry if my question seems noobish and thanks for your help,\nNate","AnswerCount":4,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":49725,"Q_Id":49374217,"Users Score":6,"Answer":"You could try disabling transitive deps updates by passing --no-update-dependencies or --no-update-deps to conda install command. Ex: \nconda install --no-update-deps pandas.","Q_Score":25,"Tags":"python-3.x,anaconda,packages","A_Id":49374371,"CreationDate":"2018-03-19T23:47:00.000","Title":"Dependencies and packages conflicts in Anaconda?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"What is meant by Activation function in Machine learning. I go through with most of the articles and videos, everyone states or compare that with neural network. I'am a newbie to machine learning and not that much familiar with deep learning and neural networks. So, can any one explain me what exactly an Activation function is ? instead of explaining with neural networks. I struck with this ambiguity while I learning Sigmoid function for logistic regression.","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":317,"Q_Id":49391576,"Users Score":0,"Answer":"Simply put, an activation function is a function that is added into an artificial neural network in order to help the network learn complex patterns in the data. When comparing with a neuron-based model that is in our brains, the activation function is at the end deciding what is to be fired to the next neuron. That is exactly what an activation function does in an ANN as well. It takes in the output signal from the previous cell and converts it into some form that can be taken as input to the next cell.","Q_Score":2,"Tags":"python,math,machine-learning,calculus,sigmoid","A_Id":62830161,"CreationDate":"2018-03-20T18:21:00.000","Title":"Activation Function in Machine learning","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I installed tensorflow on my mac and now I can't seem to open anaconda-navigator. When I launch the app, it appears in the dock but disappears quickly. When I launch anaconda-navigator the terminal I get the following error(s).\nKeyError: 'pip._vendor.urllib3.contrib'","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":155,"Q_Id":49393300,"Users Score":0,"Answer":"I fixed the issue by downgrading to pip version 9.0.1. It appears anaconda doesn't like pip version 9.0.2 I ran: pip install pip==9.0.1","Q_Score":0,"Tags":"python,macos,tensorflow,terminal,anaconda","A_Id":49401566,"CreationDate":"2018-03-20T20:11:00.000","Title":"I installed tensorflow on mac and now I can't open Anaconda","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":1,"Web Development":0},{"Question":"I would like to incorporate a particular performance metric into my portfolio managing software. This metric should be one where I can measure \n\"how much of the potential gains from the selected assets have been captured by the selected portfolio composition\". \nConsider the following table reporting a portfolio's performance with key metrics between dates 2017\/10 and 2018\/03  \nnetpeq: net $ profit gained over the period\naroc: annualized rate of change in asset's price over the period\ncagr: compounded annualized growth  of portfolio over the period  \nI need a metric which penalizes divergence between cagr (or netpeq) and aroc. Namely, positive aroc says these asset could have produced growth (as in BA, MSFT, CSCO) but the portfolio manager failed to make money out of these or even lost money. \nI would like to measure the extent the portfolio manager missed to capture\n a. the growth potential due to each asset in the portfolio\n b. the overall growth potential w.r.t portfolio as a whole.\n\n+---------+----------+----------+---------+-------+--------+--------+--------+\n| name    | netpeq   | draw     | aroa    | cagr  | sharpe | rvalue | aroc   |\n+---------+----------+----------+---------+-------+--------+--------+--------+\n| BA      | -555.71  | 3439.15  | -36.54  | -1.25 | -0.17  | 0.42   | 64.58  |\n+---------+----------+----------+---------+-------+--------+--------+--------+\n| DWDP    | 0        | 0        | 0       | 0     | 0      | 0      | -13.18 |\n+---------+----------+----------+---------+-------+--------+--------+--------+\n| CAT     | -447.66  | 1361.54  | -74.36  | -1.01 | -0.66  | -0.17  | 39.91  |\n+---------+----------+----------+---------+-------+--------+--------+--------+\n| WMT     | 363.25   | 448.09   | 183.34  | 0.82  | 1.1    | 0.66   | 4.73   |\n+---------+----------+----------+---------+-------+--------+--------+--------+\n| UTX     | 0        | 0        | 0       | 0     | 0      | 0      | 18.96  |\n+---------+----------+----------+---------+-------+--------+--------+--------+\n| NKE     | 690.34   | 498.24   | 313.36  | 1.57  | 1.21   | 0.84   | 67.19  |\n+---------+----------+----------+---------+-------+--------+--------+--------+\n| VZ      | -76      | 76       | -226.16 | -0.17 | -2.18  | -0.63  | 4.73   |\n+---------+----------+----------+---------+-------+--------+--------+--------+\n| XOM     | -272.87  | 555.36   | -111.12 | -0.62 | -0.65  | -0.46  | -18.69 |\n+---------+----------+----------+---------+-------+--------+--------+--------+\n| GE      | 0        | 0        | 0       | 0     | 0      | 0      | -85.61 |\n+---------+----------+----------+---------+-------+--------+--------+--------+\n| MCD     | 1025.63  | 731.44   | 317.12  | 2.33  | 1.09   | 0.64   | -6.02  |\n+---------+----------+----------+---------+-------+--------+--------+--------+\n| CSCO    | -313.88  | 313.88   | -226.16 | -0.71 | -1.81  | -0.39  | 75.23  |\n+---------+----------+----------+---------+-------+--------+--------+--------+\n| JPM     | 961.69   | 267.33   | 813.59  | 2.19  | 1.72   | 0.86   | 45.46  |\n+---------+----------+----------+---------+-------+--------+--------+--------+\n| V       | 3261.55  | 1969.88  | 374.46  | 7.53  | 1.76   | 0.9    | 31.18  |\n+---------+----------+----------+---------+-------+--------+--------+--------+\n| GS      | 0        | 0        | 0       | 0     | 0      | 0      | 24.24  |\n+---------+----------+----------+---------+-------+--------+--------+--------+\n| HD      | -32.32   | 960.59   | -7.61   | -0.07 | -0.06  | 0.09   | 20     |\n+---------+----------+----------+---------+-------+--------+--------+--------+\n| PFE     | 0        | 0        | 0       | 0     | 0      | 0      | 4.12   |\n+---------+----------+----------+---------+-------+--------+--------+--------+\n| KO      | 0        | 0        | 0       | 0     | 0      | 0      | -10.66 |\n+---------+----------+----------+---------+-------+--------+--------+--------+\n| MMM     | 0        | 0        | 0       | 0     | 0      | 0      | 17.01  |\n+---------+----------+----------+---------+-------+--------+--------+--------+\n| DIS     | 0        | 0        | 0       | 0     | 0      | 0      | 11.43  |\n+---------+----------+----------+---------+-------+--------+--------+--------+\n| CVX     | 357.2    | 1415.09  | 57.09   | 0.81  | 0.37   | 0.33   | -5.8   |\n+---------+----------+----------+---------+-------+--------+--------+--------+\n| INTC    | 1632.52  | 599.42   | 615.95  | 3.73  | 1.4    | 0.63   | 67.32  |\n+---------+----------+----------+---------+-------+--------+--------+--------+\n| PG      | -197.12  | 314.7    | -141.66 | -0.45 | -1.25  | -0.72  | -32.05 |\n+---------+----------+----------+---------+-------+--------+--------+--------+\n| TRV     | -348.86  | 348.86   | -226.16 | -0.79 | -1.55  | -0.79  | 26.49  |\n+---------+----------+----------+---------+-------+--------+--------+--------+\n| MSFT    | -205.86  | 680.29   | -68.44  | -0.46 | -0.42  | 0.25   | 47.6   |\n+---------+----------+----------+---------+-------+--------+--------+--------+\n| AAPL    | 0        | 0        | 0       | 0     | 0      | 0      | 28.32  |\n+---------+----------+----------+---------+-------+--------+--------+--------+\n| JNJ     | 17.55    | 64.8     | 61.25   | 0.04  | 0.33   | 0.43   | -7.55  |\n+---------+----------+----------+---------+-------+--------+--------+--------+\n| AXP     | -1366.89 | 1492.43  | -207.14 | -3.06 | -1.69  | -0.77  | 5.65   |\n+---------+----------+----------+---------+-------+--------+--------+--------+\n| IBM     | 0        | 0        | 0       | 0     | 0      | 0      | 20.59  |\n+---------+----------+----------+---------+-------+--------+--------+--------+\n| UNH     | 877.04   | 676.82   | 293.06  | 1.99  | 1.13   | 0.79   | 39.98  |\n+---------+----------+----------+---------+-------+--------+--------+--------+\n| MRK     | 0        | 0        | 0       | 0     | 0      | 0      | -27.88 |\n+---------+----------+----------+---------+-------+--------+--------+--------+\n| RunPort | 5369.6   | 10091.44 | 120.34  | 12.56 | 0.65   | 0.73   | -1     |\n+---------+----------+----------+---------+-------+--------+--------+--------+","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":595,"Q_Id":49394773,"Users Score":0,"Answer":"A reasonable intensive measure of how much a market instrument within a portfolio captured its potential would be the geometric difference between its IRR for the period and its annualized market return for the same period.\nFor this you would need the cash flow amounts and dates into and out of the instrument, its actual opening and closing market value and the total market return of the component.\nIf the instrument or its IRR-implied value is ever short at any time during the period, then you would need to employ an advanced version of IRR instead of the standard calculations provided in Excel.\nWhat is usually the more relevant is the extensive measure of the achieved controllable relative success of the management of a portfolio. It is best captured by a well-formulated value-based decision attribution analysis using daily data.\nAndre Mirabelli","Q_Score":0,"Tags":"python,performance,portfolio,metric,attribution","A_Id":49500435,"CreationDate":"2018-03-20T21:56:00.000","Title":"Portfolio Performance Attribution Metrics","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using the scikit-learn KNeighborsClassifier for classification on a dataset with 4 output classes. The following is the code that I am using:\nknn = neighbors.KNeighborsClassifier(n_neighbors=7, weights='distance', algorithm='auto', leaf_size=30, p=1, metric='minkowski')\nThe model works correctly. However, I would like to provide user-defined weights for each sample point. The code currently uses the inverse of the distance for scaling using the metric='distance' parameter.\nI would like to continue to keep the inverse distance scaling but for each sample point, I have a probability weight as well. I would like to apply this as a weight in the distance calculation. For example, if x is the test point and y,z are the two nearest neighbors for which distance is being calculated, then I would like the distance to be calculated as (sum|x-y|)*wy and (sum|x-z|)*wz respectively.\nI tried to define a function that was passed into the weights argument but then I also would like to keep the inverse distance scaling in addition to the user defined weight and I do not know the inverse distance scaling function. I could not find an answer from the documentation.\nAny suggestions?","AnswerCount":2,"Available Count":2,"Score":0.1973753202,"is_accepted":false,"ViewCount":1542,"Q_Id":49420191,"Users Score":2,"Answer":"KNN in sklearn doesn't have sample weight, unlike other estimators, e.g. DecisionTree.\nPersonally speaking, I think it is a disappointment. It is not hard to make KNN support sample weight, since the predicted label is the majority voting of its neighbours.\nA stupid walk around, is to generate samples yourself based on the sample weight. E.g., if a sample has weight 2, then make it appear twice.","Q_Score":4,"Tags":"python,scikit-learn,knn,nearest-neighbor","A_Id":53602315,"CreationDate":"2018-03-22T03:47:00.000","Title":"Providing user defined sample weights for knn classifier in scikit-learn","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using the scikit-learn KNeighborsClassifier for classification on a dataset with 4 output classes. The following is the code that I am using:\nknn = neighbors.KNeighborsClassifier(n_neighbors=7, weights='distance', algorithm='auto', leaf_size=30, p=1, metric='minkowski')\nThe model works correctly. However, I would like to provide user-defined weights for each sample point. The code currently uses the inverse of the distance for scaling using the metric='distance' parameter.\nI would like to continue to keep the inverse distance scaling but for each sample point, I have a probability weight as well. I would like to apply this as a weight in the distance calculation. For example, if x is the test point and y,z are the two nearest neighbors for which distance is being calculated, then I would like the distance to be calculated as (sum|x-y|)*wy and (sum|x-z|)*wz respectively.\nI tried to define a function that was passed into the weights argument but then I also would like to keep the inverse distance scaling in addition to the user defined weight and I do not know the inverse distance scaling function. I could not find an answer from the documentation.\nAny suggestions?","AnswerCount":2,"Available Count":2,"Score":-0.0996679946,"is_accepted":false,"ViewCount":1542,"Q_Id":49420191,"Users Score":-1,"Answer":"sklearn.neighbors.KNeighborsClassifier.score() has a sample_weight parameter. Is that what you're looking for?","Q_Score":4,"Tags":"python,scikit-learn,knn,nearest-neighbor","A_Id":63655345,"CreationDate":"2018-03-22T03:47:00.000","Title":"Providing user defined sample weights for knn classifier in scikit-learn","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to build a plot using matplotlib.pyplot as plt.\nFor example plt.hist(df.amount, bins = 30)\nBut when I hit enter in the console it generates the graph.\nI want to apply xlim, ylim and title functions of plt but can't do this.\nAnyone familiar with this behavior? Should I change Spyder settings?\nSame behavior with Seaborn package.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":44,"Q_Id":49425805,"Users Score":1,"Answer":"try typing %matplotlib or %matplotlib qt before doing plt.hist(df.amount, bins=30). This will switch the console out of \"inline\" mode.","Q_Score":0,"Tags":"python,matplotlib,spyder","A_Id":49432623,"CreationDate":"2018-03-22T10:08:00.000","Title":"Python 3.6 matplotlib.pyplot shows graph immediately without letting me apply other functions?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using Tensorflow to generate a transformation matrix for a set of input vectors (X) to target vectors (Y). To minimize the error between the transformed input and the target vector samples I'm using a gradient descent algorithm. Later on I want to use the generated matrix to transform vectors coming from the same source as the training input vectors so that they look like the corresponding target vectors. Linear regression, pretty much, but with 3-dimensional targets. \nI can assume that the input and target vectors are in cartesian space. Thus, the transformation matrix should consist of a rotation and a translation. I'm working solely with unit vectors, so I can also safely assume that there's no translation, only rotation. \nSo, in order to get a valid rotation matrix that I can turn into a rotation quaternion I understand that I have to make sure the matrix is orthogonal. \nThus, the question is, is it possible to give Tensorflow some kind of constraint so that the matrix it tries to converge to is guaranteed to be orthogonal? Can be a parameter, a mathematical constraint, a specific optimizer, whatever. I just need to make sure the algorithm converges to a valid rotation matrix.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":729,"Q_Id":49430178,"Users Score":0,"Answer":"It should be possible.\nI see two solutions.\nIf you don't care that the transformation is a perfect rotation, you can take the matrix, adjust it to what you think it's a good matrix (make it a perfect rotation) then compute the difference between the one you like and the original and add it as a loss.  With this approach you can push the model to do what you want but the model might not converge on a perfect rotation, especially if a perfect rotation is not a very good solution.\nAnother approach is to start with a proper rotation matrix and train the parameters of the rotation. In this case you would have the x,y,z of the rotation axis and the amount of rotation. So declare these as variables, compute the rotation matrix and use that in your model. The operations are derivable so gradient descent should work, but depending on the data itself you might depend on the start position (so try a few times).\nHard to say which one will work best in your case, so it might be worth trying both.","Q_Score":1,"Tags":"python,tensorflow","A_Id":49430423,"CreationDate":"2018-03-22T13:38:00.000","Title":"Is it possible to force Tensorflow to generate orthogonal matrix?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"TF has tf.variable_scope() that allows users to access tf.Variable() anywhere in the code. Basically every variable in TF is a global variable.\nIs there a similar way to access class objects like tf.nn.rnn_cell.LSTMCell() or tf.layers.Dense()? To be more specific, can i create a new class object, let's say lstm_cell_2 (used for prediction) that uses the same weights and biases in lstm_cell_1 (used during training).\nI am building an RNN to do language modeling. What i am doing right now is to return the lstm_cell_1 then pass it onto the prediction function. This works, but i want to in the end use separate tf.Graph() and tf.Session() for training, inference and prediction. Hence comes the problem of sharing Tensorflow objects.\nAlso, my lstm_cell is an instance of tf.nn.rnn_cell.MultiRNNCell() which doesn't take a name argument.\nThanks","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":27,"Q_Id":49432175,"Users Score":0,"Answer":"You can share final state that is one of the outputs of for example consider dynamic rnn. You get outputs,cell_final_state which you can share. You probably know about cell_final_state.c and cell_final_state.h. you can set that final state as initial state. lemme know if I cleared your question or not.\nJust noticed it's such an old question. Hopefully helps future people.","Q_Score":0,"Tags":"python,tensorflow,lstm,rnn","A_Id":50856659,"CreationDate":"2018-03-22T15:10:00.000","Title":"Sharing class objects in Tensorlfow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to implement a Q Learning agent to learn an optimal policy for playing against a random agent in a game of Tic Tac Toe.\nI have created a plan that I believe will work. There is just one part that I cannot get my head around. And this comes from the fact that there are two players within the environment. \nNow, a Q Learning agent should act upon the current state, s, the action taken given some policy, a, the successive state given the action, s', and any reward received from that successive state, r. \nLets put this into a tuple (s, a, r, s')\nNow usually an agent will act upon every state it finds itself encountered in given an action, and use the Q Learning equation to update the value of the previous state. \nHowever, as Tic Tac Toe has two players, we can partition the set of states into two. One set of states can be those where it is the learning agents turn to act. The other set of states can be where it is the opponents turn to act. \nSo, do we need to partition the states into two? Or does the learning agent need to update every single state that is accessed within the game?\nI feel as though it should probably be the latter, as this might affect updating Q Values for when the opponent wins the game. \nAny help with this would be great, as there does not seem to be anything online that helps with my predicament.","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":2280,"Q_Id":49451366,"Users Score":1,"Answer":"Q-Learning is an algorithm from the MDP (Markov Decision Process) field, i.e the MDP and Learning in practically facing a world that being act upon. and each action change the state of the agent (with some probability)\nthe algorithm build on the basis that for any action, the world give a feedback (reaction).\nQ-Learning works best when for any action there is a somewhat immediate and measurable reaction\nin addition this method looks at the world from one agent perspective \nMy Suggestion is to implement the agent as part of the world. like a be bot which plays with various strategies e.g random, best action, fixed layout or even a implement it's logic as q-learning \nfor looking n steps forward and running all the states (so later you can pick the best one) you can use monte-carlo tree search if the space size is too large (like did with GO) \nthe Tic-Tac-Toe game is already solved, the player can achieve win or draw if follows the optimal strategy, and 2 optimal players will achieve draw, the full game tree is fairly easy to build","Q_Score":7,"Tags":"python,tic-tac-toe,reinforcement-learning,q-learning","A_Id":49451732,"CreationDate":"2018-03-23T13:59:00.000","Title":"Q Learning Applied To a Two Player Game","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to implement a Q Learning agent to learn an optimal policy for playing against a random agent in a game of Tic Tac Toe.\nI have created a plan that I believe will work. There is just one part that I cannot get my head around. And this comes from the fact that there are two players within the environment. \nNow, a Q Learning agent should act upon the current state, s, the action taken given some policy, a, the successive state given the action, s', and any reward received from that successive state, r. \nLets put this into a tuple (s, a, r, s')\nNow usually an agent will act upon every state it finds itself encountered in given an action, and use the Q Learning equation to update the value of the previous state. \nHowever, as Tic Tac Toe has two players, we can partition the set of states into two. One set of states can be those where it is the learning agents turn to act. The other set of states can be where it is the opponents turn to act. \nSo, do we need to partition the states into two? Or does the learning agent need to update every single state that is accessed within the game?\nI feel as though it should probably be the latter, as this might affect updating Q Values for when the opponent wins the game. \nAny help with this would be great, as there does not seem to be anything online that helps with my predicament.","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":2280,"Q_Id":49451366,"Users Score":7,"Answer":"In general, directly applying Q-learning to a two-player game (or other kind of multi-agent environment) isn't likely to lead to very good results if you assume that the opponent can also learn. However, you specifically mentioned\n\nfor playing against a random agent\n\nand that means it actually can work, because this means the opponent isn't learning \/ changing its behaviour, so you can reliably treat the opponent as ''a part of the environment''.\nDoing exactly that will also likely be the best approach you can take. Treating the opponent (and his actions) as a part of the environment means that you should basically just completely ignore all of the states in which the opponent is to move. Whenever your agent takes an action, you should also immediately generate an action for the opponent, and only then take the resulting state as the next state. \nSo, in the tuple (s, a, r, s'), we have:\n\ns = state in which your agent is to move\na = action executed by your agent\nr = one-step reward\ns' = next state in which your agent is to move again\n\nThe state in which the opponent is to move, and the action they took, do not appear at all. They should simply be treated as unobservable, nondeterministic parts of the environment. From the point of view of your algorithm, there are no other states in between s and s', in which there is an opponent that can take actions. From the point of view of your algorithm, the environment is simply nondeterministic, which means that taking action a in state s will sometimes randomly lead to s', but maybe also sometimes randomly to a different state s''.\n\nNote that this will only work precisely because you wrote that the opponent is a random agent (or, more importantly, a non-learning agent with a fixed policy). As soon as the opponent also gains the ability to learn, this will break down completely, and you'd have to move on to proper multi-agent versions of Reinforcement Learning algorithms.","Q_Score":7,"Tags":"python,tic-tac-toe,reinforcement-learning,q-learning","A_Id":49451735,"CreationDate":"2018-03-23T13:59:00.000","Title":"Q Learning Applied To a Two Player Game","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"If I use GridSearchCV in scikit-learn library to find the best model, what will be the final model it returns? That said, for each set of hyper-parameters, we train the number of CV (say 3) models. In this way, will the function return the best model in those 3 models for the best setting of parameters?","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":5388,"Q_Id":49455806,"Users Score":2,"Answer":"This is given in sklearn:\n\u201cThe refitted estimator is made available at the best_estimator_ attribute and permits using predict directly on this GridSearchCV instance.\u201d\nSo, you don\u2019t need to fit the model again. You can directly get the best model from best_estimator_ attribute","Q_Score":5,"Tags":"python,machine-learning,scikit-learn","A_Id":68944912,"CreationDate":"2018-03-23T17:59:00.000","Title":"GridSearchCV final model","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to create a dashboard using R. However, all the data that I need to connect is from TM1.\nThe easiest way that I found is using an python library called TM1py to connect to tm1 data.\nI would like to know what is the easist to access to access TM1py library from R ?\nThanks","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":412,"Q_Id":49459591,"Users Score":1,"Answer":"It seems as if you only want to read data from tm1. Therefore a \"simple\" mdx query should be fine. Have a look at the package \"httr\" how to send POST-Requests. Then it's pretty staright forward to port the relevant parts from tm1py to R.","Q_Score":1,"Tags":"python,r","A_Id":49622289,"CreationDate":"2018-03-23T23:29:00.000","Title":"Tm1 to python to R","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Suppose I have a pandas dataframe looks like this:\n\n    col1    col2\n0   A       A60\n1   B       B23\n2   C       NaN\n\nThe data from is read from a csv file. Suppose I want to change each non-missing value of 'col2' to its prefix (i.e. 'A' or 'B'). How could I do this without writing a for loop?\nThe expected output is \n\n    col1    col2\n0   A       A\n1   B       B\n2   C       NaN","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":489,"Q_Id":49460990,"Users Score":0,"Answer":"You may need to use isnull():\n\ndf['col2'] = df['col2'].apply(lambda x: str(x)[0] if not pd.isnull(x) else x)","Q_Score":0,"Tags":"python,pandas","A_Id":49461632,"CreationDate":"2018-03-24T03:43:00.000","Title":"How to change values in certain columns according to certain rule in pandas dataframe","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying out the different optimization methods of tf.contrib.opt.ScipyOptimizerInterface and some of them (e.g. trust-exact) require the hessian of the objective function.\nHow can I use tf.hessians as hessian for tf.contrib.opt.ScipyOptimizerInterface? \nI tried to provide it with hess=tf.hessians(loss,variable) (which returns a list of tensors) but it needs a callable object. And if I input directly hess=tf.hessians I get TypeError: hessians() takes at least 2 arguments (1 given). \nIf you have any examples of tf.gradients or tf.hessians used with ScipyOptimizerInterface it would already be super useful!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":182,"Q_Id":49468976,"Users Score":0,"Answer":"This is not directly supported by TensorFlow's ScipyOptimizerInterface  but you should be able to build a hessian function that will be passed through the interface and work over its head. scipy.optimize.minimize expects a function that recieves a candidate solution p (in the form of a 1D numpy vector) and returns the Hessian.\nTherefore, define a Python function that receives a numpy vector p and evaluates the hessian for the solution defined by p using sess.run(hess,feed_dict=feed_dict). \nhess is a tensor defined once by hess=tf.hessians(loss,variable) outside this function (so you won't define another tf.hessians operation with each function call).\nfeed_dict is a dictionary with your tensorflow variable as a key and a numpy array p reshaped to fit your variable's shape as a value. In case you optimize more than one variable in parallel, dig in external_optimizer.py to see how it packs and unpacks multiple tensors into a single vector (look for _pack and _make_eval_func).\nNote that for most deep learning problems, calculating the Hessian is not practical because it's a matrix of the dimensions size(variable)xsize(variable) - which is typically too large. Hence the LBFGS optimizer is a more feasible second-order optimizer.","Q_Score":1,"Tags":"python,tensorflow","A_Id":49472921,"CreationDate":"2018-03-24T19:29:00.000","Title":"Tensoflow: Providing jacobians and hessians to ScipyOptimizerInterface","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there a way to choose a group of columns in a dask dataframe? \nThe slice df [['col_1', 'col_2']] does not seem to work.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1478,"Q_Id":49473649,"Users Score":0,"Answer":"It was my mistake. I passed to the slicing operator a list of strings in a numpy array receiving a \"not implemented error\", passing a python list instead works correctly.","Q_Score":0,"Tags":"python,dataframe,dask","A_Id":49477081,"CreationDate":"2018-03-25T07:54:00.000","Title":"How to choose a group of columns in a Dask Dataframe?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I get the following error when trying to use tensorflow\n\nimportError                               Traceback (most recent call\n  last)  in ()\n  ----> 1 import tensorflow as tf\n~\\Anaconda3\\lib\\site-packages\\tensorflow__init__.py in ()\n       22 \n       23 # pylint: disable=wildcard-import\n  ---> 24 from tensorflow.python import *\n       25 # pylint: enable=wildcard-import\n       26 \n~\\Anaconda3\\lib\\site-packages\\tensorflow\\python__init__.py in\n  ()\n       54 # imported using tf.load_op_library() can access symbols defined in\n       55 # _pywrap_tensorflow.so.\n  ---> 56 import numpy as np\n       57 try:\n       58   if hasattr(sys, 'getdlopenflags') and hasattr(sys, 'setdlopenflags'):\n~\\Anaconda3\\lib\\site-packages\\numpy__init__.py in ()\n      140         return loader(*packages, **options)\n      141 \n  --> 142     from . import add_newdocs\n      143     all = ['add_newdocs',\n      144                'ModuleDeprecationWarning',\n~\\Anaconda3\\lib\\site-packages\\numpy\\add_newdocs.py in ()\n       11 from future import division, absolute_import, print_function\n       12 \n  ---> 13 from numpy.lib import add_newdoc\n       14 \n       15 ###############################################################################\n~\\Anaconda3\\lib\\site-packages\\numpy\\lib__init__.py in ()\n        6 from numpy.version import version as version\n        7 \n  ----> 8 from .type_check import *\n        9 from .index_tricks import *\n       10 from .function_base import *\n~\\Anaconda3\\lib\\site-packages\\numpy\\lib\\type_check.py in ()\n        9            'common_type']\n       10 \n  ---> 11 import numpy.core.numeric as _nx\n       12 from numpy.core.numeric import asarray, asanyarray, array, isnan, zeros\n       13 from .ufunclike import isneginf, isposinf\n~\\Anaconda3\\lib\\site-packages\\numpy\\core__init__.py in ()\n       36 from . import numerictypes as nt\n       37 multiarray.set_typeDict(nt.sctypeDict)\n  ---> 38 from . import numeric\n       39 from .numeric import *\n       40 from . import fromnumeric\n~\\Anaconda3\\lib\\site-packages\\numpy\\core\\numeric.py in ()\n  1818     1819 # Use numarray's printing function\n  -> 1820 from .arrayprint import array2string, get_printoptions, set_printoptions    1821     1822 \n~\\Anaconda3\\lib\\site-packages\\numpy\\core\\arrayprint.py in ()\n       42 from .umath import absolute, not_equal, isnan, isinf, isfinite, isnat\n       43 from . import multiarray\n  ---> 44 from .multiarray import (array, dragon4_positional, dragon4_scientific,\n       45                          datetime_as_string, datetime_data, dtype, ndarray,\n       46                          set_legacy_print_mode)\n\nThis error occured after I tried to upgrade TF from version 1.1 to the latest version. So I dont know what current TF version I am using.\nI am using Windows 10 without a GPU.\nDo you know how to fix it?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1449,"Q_Id":49477640,"Users Score":0,"Answer":"This looks like it is an issue with Numpy, which is a dependency of Tensorflow. Did you try upgrading your version of numpy using pip or conda?\nLike such: pip install --ignore-installed --upgrade numpy","Q_Score":0,"Tags":"python,python-3.x,tensorflow","A_Id":52206018,"CreationDate":"2018-03-25T15:41:00.000","Title":"TensorFlow: ImportError: cannot import name 'dragon4_positional'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"import cv2\n\nOn executing the above code, it shows the following error.\nError:\n\nTraceback (most recent call last)  in\n  ()\n  ----> 1 import cv2\nImportError: DLL load failed: The specified module could not be found.\n\nUnable to import cv2 in python\nI have installed OpenCV 2.4.13 and Anaconda3 with python 3.6.4.\nOpenCV location:C:\\Users\\harsh\\Anaconda3\nAnaconda location:C:\\Users\\harsh\\opencv.\nI have also added cv2.pyd in C:\\Users\\harsh\\Anaconda3\\Lib\\site-packages.","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":2644,"Q_Id":49479145,"Users Score":1,"Answer":"Try pip install opencv-python instead of pip install cv2. Although the name of the package changes, you can still import it as import cv2, It will work.","Q_Score":0,"Tags":"python,opencv,computer-vision,anaconda","A_Id":51741148,"CreationDate":"2018-03-25T18:07:00.000","Title":"Unable to import cv2 OpenCV 2.4.13 in python 3.6","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have never worked with audio before.\nFor a monophonic wav file read() returns an 1-D array of integers. What do these integers represent? Are they the frequencies? If not how do I use them to get the frequencies?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":152,"Q_Id":49481114,"Users Score":1,"Answer":"wavfile.read() returns two things:\ndata: This is the data from your wav file which is the amplitude of the audio taken at even intervals of time.\nsample rate: How many of those intervals make up one second of audio.","Q_Score":0,"Tags":"python-3.x,audio,scipy","A_Id":49957782,"CreationDate":"2018-03-25T21:26:00.000","Title":"What is returned by scipy.io.wavefile.read()?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have using nltk packages and train a model using Naive Bayes. I have save the model to a file using pickle package. Now i wonder how can i use this model to test like a random text not in the dataset and the model will tell if the sentence belong to which categorize?\nLike my idea is i have a sentence : \" Ronaldo have scored 2 goals against Egypt\" And pass it to the model file and return categorize \"sport\".","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":147,"Q_Id":49484820,"Users Score":0,"Answer":"Just saving the model will not help. You should also save your VectorModel (like tfidfvectorizer or countvectorizer what ever you have used for fitting the train data). You can save those the same way using pickle. Also save all those models you used for pre-processing the train data like normalization\/scaling models, etc. For the test data repeat the same steps by loading the pickle models that you saved and transform the test data in train data format that you used for model building and then you will be able to classify.","Q_Score":0,"Tags":"python,nltk,text-mining,naivebayes","A_Id":49485045,"CreationDate":"2018-03-26T05:57:00.000","Title":"Text Categorization Test NLTK python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm training some models with my own NER pipe. I need to run spacy in lxc container so I can run it with python3.6 (which allow multi thread on training).\nBut.. on my 7 core authorized to run on my container only 1 run at 100% others run at 40-60% (actually they start at 100% but decrease after fews minutes). I would really like to improve this % core usage. Any idea to where to look ? Could it be a problem of Producer \/ Consumer ?\nEnv:\n- spaCy version 2.0.8\n- Location \/root\/.env\/lib\/python3.6\/site-packages\/spacy\n- Platform Linux-3.14.32-xxxx-grs-ipv6-64-x86_64-with-debian-buster-sid\n- Python version 3.6.4","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2172,"Q_Id":49492038,"Users Score":4,"Answer":"The only things that are multi-threaded are the matrix multiplications, which in v2.0.8 are done via numpy, which delegates them to a BLAS library. Everything else is single-threaded.\nYou should check what BLAS library your numpy is linked to, and also make sure that the library has been compiled appropriately for your machine. On my machine the numpy I install via pip comes with a copy of OpenBLAS that thinks my machine has a Prescott CPU. This prevents it from using AVX instructions. So if I install default numpy from pip on my machine, it runs 2-3x slower than it should.\nAnother problem is that OpenBLAS might be launching more threads than it should. This seems especially common in containers.\nFinally, the efficiency of parallelism very much depends on batch-size. On small batches, the matrices are small and the per-update routines such as the Adam optimiser take more of the time.\nI usually disable multi-threading and train on a single core, because this is the most efficient (in the sense of dollars-for-work) --- I then have more models training as separate processes (usually on separate  GCE VMs).\nWhen writing spaCy I haven't assumed that the goal is to use lots of cores. The goal is efficiency. It's not a virtue to use your whole machine to perform the same work that could be done on a single core. A lot of papers are very misleading in this respect. For instance, it might feel satisfying to launch 12 training processes across a cloud and optimize using an asynchronous SGD strategy such as Hogwild!. This is an efficient way to burn up a bunch of energy, but doesn't necessarily train your models any faster: using Adam and smaller batch sizes, training is more stable and often reaches the same accuracy in many fewer iterations. Similarly, we can make the network larger so the machines get their workout...But why? The goal is to train the model. Multiplying a bunch of matrices is a means, not an end.\nThe problem I've been most concerned with is the terrible BLAS linkage situation. This will be much improved in v2.1, as we'll be bringing our own OpenBLAS kernel. The kernel will be single-threaded by default.\nA simple thing to try if you suspect your BLAS is bad is to try installing numpy using conda. That will give you a copy linked against intel's MKL library.","Q_Score":2,"Tags":"python,multithreading,nlp,spacy","A_Id":49493806,"CreationDate":"2018-03-26T12:57:00.000","Title":"Spacy training multithread CPU usage","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to build a Random Forest Regressor to model count data (Poisson distribution). The default 'mse' loss function is not suited to this problem. Is there a way to define a custom loss function and pass it to the random forest regressor in Python (Sklearn, etc..)? \nIs there any implementation to fit count data in Python in any packages?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":10061,"Q_Id":49493331,"Users Score":0,"Answer":"If the problem is that the counts c_i arise from different exposure times t_i, then indeed one cannot fit the counts, but one can still fit the rates r_i = c_i\/t_i using MSE loss function, where one should, however, use weights proportional to the exposures, w_i = t_i.\nFor a true Random Forest Poisson regression, I've seen that in R there is the rpart library for building a single CART tree, which has a Poisson regression option. I wish this kind of algorithm would have been imported to scikit-learn.","Q_Score":10,"Tags":"python-3.x,scikit-learn,random-forest,statsmodels,poisson","A_Id":51396901,"CreationDate":"2018-03-26T14:00:00.000","Title":"Random Forest Regressor using a custom objective\/ loss function (Python\/ Sklearn)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I was wondering if there was a function in xlsxwriter that lets you sort the contents in the column from greatest to least or least to greatest? thanks!","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":847,"Q_Id":49501501,"Users Score":2,"Answer":"Sorting isn't a feature of the xlsx file format. It is something Excel does at runtime.\nSo it isn't something XlsxWriter can replicate. A workaround would be to to sort your data using Python before you write it.","Q_Score":3,"Tags":"python,xlsxwriter","A_Id":49507909,"CreationDate":"2018-03-26T22:15:00.000","Title":"Is there a function in xlsxwriter that lets you sort a column?","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a set of fairly complicated models that I am training and I am looking for a way to save and load the model optimizer states. The \"trainer models\" consist of different combinations of several other \"weight models\", of which some have shared weights, some have frozen weights depending on the trainer, etc. It is a bit too complicated of an example to share, but in short, I am not able to use model.save('model_file.h5') and keras.models.load_model('model_file.h5') when stopping and starting my training. \nUsing model.load_weights('weight_file.h5') works fine for testing my model if the training has finished, but if I attempt to continue training the model using this method, the loss does not come even close to returning to its last location. I have read that this is because the optimizer state is not saved using this method which makes sense. However, I need a method for saving and loading the states of the optimizers of my trainer models. It seems as though keras once had a model.optimizer.get_sate() and model.optimizer.set_sate() that would accomplish what I am after, but that does not seem to be the case anymore (at least for the Adam optimizer). Are there any other solutions with the current Keras?","AnswerCount":6,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":21170,"Q_Id":49503748,"Users Score":2,"Answer":"upgrading Keras to 2.2.4 and using pickle solved this issue for me. with keras release 2.2.3 Keras models can now be safely pickled.","Q_Score":22,"Tags":"python,tensorflow,machine-learning,keras","A_Id":52691745,"CreationDate":"2018-03-27T03:06:00.000","Title":"Save and load model optimizer state","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it possible to do clustering without providing any input apart from the data? The clustering method\/algorithm should decide from the data on how many logical groups the data can be divided, even it doesn't require me to input the threshold eucledian distance on which the clusters are built, this also needs to be learned from the data.\nCould you please suggest me what is closest solution for my problem?","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":231,"Q_Id":49504271,"Users Score":1,"Answer":"Clustering is an explorative technique.\nThis means it must always be able to produce different results, as desired by the user. Having many parameters is a feature. It means the method can be adapted easily to very different data, and to user preferences.\nThere will never be a generally useful parameter-free technique. At best, some parameters will have default values or heuristics (such as Euclidean distance, such as standardizing the input prior to clusterings such as the gap statistic for choosing k) that may give a reasonable first try in 80% of cases. But after that first try, you'll need to understand the data, and try other parameters to learn more about your data.\nMethods that claim to be \"parameter free\" usually just have some hidden parameters set so it works on the few toy example it was demonstrated on.","Q_Score":0,"Tags":"python,cluster-analysis","A_Id":49528355,"CreationDate":"2018-03-27T04:15:00.000","Title":"clustering in python without number of clusters or threshold","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it possible to do clustering without providing any input apart from the data? The clustering method\/algorithm should decide from the data on how many logical groups the data can be divided, even it doesn't require me to input the threshold eucledian distance on which the clusters are built, this also needs to be learned from the data.\nCould you please suggest me what is closest solution for my problem?","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":231,"Q_Id":49504271,"Users Score":1,"Answer":"Why not code your algorithm to create a list of clusters ranging from size 1 to n (which could be defined in a config file so that you can avoid hard coding and just fix it once). \nOnce that is done, compute the clusters of size 1 to n. Choose the value which gives you the smallest Mean Square Error. \nThis would require some additional work by your machine to determine the optimal number of logical groups the data can be divided (bounded between 1 and n).","Q_Score":0,"Tags":"python,cluster-analysis","A_Id":49504632,"CreationDate":"2018-03-27T04:15:00.000","Title":"clustering in python without number of clusters or threshold","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have started learning Tensorflow recently and I am wondering if it is worth using in simple optimization problems (least squares, maximum likelihood estimation, ...) instead of more traditional libraries (scikit-learn, statsmodel)?\nI have implemented a basic AR model estimator using Tensorflow with MLE and the AdamOptimizer and the results are not convincing either performance or computation speed wise.\nWhat do you think?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":343,"Q_Id":49512935,"Users Score":6,"Answer":"This is somewhat opinion based, but Tensorflow and similar frameworks such as PyTorch are useful when you want to optimize an arbitrary, parameter-rich non-linear function (e.g., a deep neural network). For a 'standard' statistical model, I would use code that was already tailored to it instead of reinventing the wheel. This is true especially when there are closed-form solutions (as in linear least squares) - why go into to the murky water of local optimization when you don't have to? Another advantage of using existing statistical libraries is that they usually provide you with measures of uncertainty about your point estimates.\nI see one potential case in which you might want to use Tensorflow for a simple linear model: when the number of variables is so big the model can't be estimated using closed-form approaches. Then gradient descent based optimization makes sense, and tensorflow is a viable tool for that.","Q_Score":1,"Tags":"python,tensorflow","A_Id":49514082,"CreationDate":"2018-03-27T12:31:00.000","Title":"Is Tensorflow worth using for simple optimization problems?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Does python have a function similar to the excel function IFERROR(value, value_if_error)\nCan I use np.where?\nMany Thanks","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":4595,"Q_Id":49514486,"Users Score":1,"Answer":"No sure what you mean by an error in data in Python. \nDo you mean NA? Then try the fillna function in pandas.","Q_Score":1,"Tags":"python,pandas","A_Id":49514624,"CreationDate":"2018-03-27T13:39:00.000","Title":"Excel function IFERROR(value, value_if_error). Does it have a python equivalent?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to call the R function predict(fit_hs, type=\"quantile\", se.fit=True, p=0.5) where predict refers to survreg in library survival. \nIt gives an error about the se.fit parameter saying it's a keyword that can't be used.\nCould you please help finding a way to call this R function from Python?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":54,"Q_Id":49514535,"Users Score":0,"Answer":"In Python you can not use \".\" as part of keywords, replacing \".\" with \"_\", so \"se_fit\", should work.","Q_Score":1,"Tags":"python,r,prediction,rpy2","A_Id":67007173,"CreationDate":"2018-03-27T13:42:00.000","Title":"Calling R function with se.fit parameter with rpy2 from Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have recently begun looking at Dask for big data. \nI have a question on efficiently applying operations in parallel.\nSay I have some sales data like this:\ncustomerKey    productKey    transactionKey    grossSales  netSales      unitVolume    volume transactionDate\n-----------  --------------  ----------------  ----------  --------      ----------    ------ --------------------\n    20353           189            219548     0.921058     0.921058              1         1  2017-02-01 00:00:00\n  2596618           189            215015     0.709997     0.709997              1         1  2017-02-01 00:00:00\n 30339435           189            215184     0.918068     0.918068              1         1  2017-02-01 00:00:00\n 32714675           189            216656     0.751007     0.751007              1         1  2017-02-01 00:00:00\n 39232537           189            218180     0.752392     0.752392              1         1  2017-02-01 00:00:00\n 41722826           189            216806     0.0160143    0.0160143             1         1  2017-02-01 00:00:00\n 46525123           189            219875     0.469437     0.469437              1         1  2017-02-01 00:00:00\n 51024667           189            215457     0.244886     0.244886              1         1  2017-02-01 00:00:00\n 52949803           189            215413     0.837739     0.837739              1         1  2017-02-01 00:00:00\n 56526281           189            220261     0.464716     0.464716              1         1  2017-02-01 00:00:00\n 56776211           189            220017     0.272027     0.272027              1         1  2017-02-01 00:00:00\n 58198475           189            215058     0.805758     0.805758              1         1  2017-02-01 00:00:00\n 63523098           189            214821     0.479798     0.479798              1         1  2017-02-01 00:00:00\n 65987889           189            217484     0.122769     0.122769              1         1  2017-02-01 00:00:00\n 74607556           189            220286     0.564133     0.564133              1         1  2017-02-01 00:00:00\n 75533379           189            217880     0.164387     0.164387              1         1  2017-02-01 00:00:00\n 85676779           189            215150     0.0180961    0.0180961             1         1  2017-02-01 00:00:00\n 88072944           189            219071     0.492753     0.492753              1         1  2017-02-01 00:00:00\n 90233554           189            216118     0.439582     0.439582              1         1  2017-02-01 00:00:00\n 91949008           189            220178     0.1893       0.1893                1         1  2017-02-01 00:00:00\n 91995925           189            215159     0.566552     0.566552              1         1  2017-02-01 00:00:00\nI want to do a few different groupbys, first a groupby-apply on customerKey.\nThen another groupby-sum on customerKey, and a column which will be the result of the previos groupby apply.\nThe most efficient way I can think of doing this would be do split this dataframe into partitions of chunks of customer keys. \nSo, for example I could split the dataframe into 4 chunks with a partition scheme for example like (pseudocode)\npartition by customerKey % 4\nThen i could use map_partitions to do these group by applies for each partition, then finally returning the result. However it seems dask forces me to do a shuffle for each groupby I want to do.\nIs there no way to repartition based on the value of a column?\nAt the moment this takes ~45s with 4 workers on a dataframe of only ~80,000 rows. I am planning to scale this up to a dataframe of trillions of rows, and already this seems like it is going to scale horribly.\nAm I missing something fundamental to Dask?","AnswerCount":2,"Available Count":1,"Score":-0.0996679946,"is_accepted":false,"ViewCount":9214,"Q_Id":49532824,"Users Score":-1,"Answer":"Setting index to the required column and map_partitions works much efficient compared to groupby","Q_Score":14,"Tags":"python,pandas,dataframe,dask,dask-distributed","A_Id":55154657,"CreationDate":"2018-03-28T11:02:00.000","Title":"Dask dataframe split partitions based on a column or function","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Goal: how to convert (111, 222, 333) to ('111', '222', '333') for an sql query in Python?\nWhat I have done so far: \nI am calling a csv file to a df:\ndataset = pd.read_csv('simple.csv')\nprint(dataset)\nLIST\n0     111\n1     222\n2     333\nList11 = dataset.LIST.apply(str)\nprint(List1)\n0    111\n1    222\n2    333\nName: OPERATION, dtype: object\nmyString = \",\".join(List1)\nprint(myString)\n111,222,333\nsql = \"SELECT * FROM database WHERE list IN (%s)\" %myString\nThis does not work. Could you please help?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1305,"Q_Id":49541070,"Users Score":0,"Answer":"Please try this and verify if it helps-\nsql = \"SELECT * FROM database WHERE list IN (%s)\" % \",\".join(map(myString,List1))","Q_Score":2,"Tags":"python,sql,arrays,string,quote","A_Id":49542244,"CreationDate":"2018-03-28T17:49:00.000","Title":"(Sql + Python) df array to string with single quotes?","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"1) I try to rename a model and the layers in Keras with TF backend, since I am using multiple models in one script.\nClass Model seem to have the property model.name, but when changing it I get \"AttributeError: can't set attribute\".\nWhat is the Problem here?\n2) Additionally, I am using sequential API and I want to give a name to layers, which seems to be possibile with Functional API, but I found no solution for sequential API. Does anonye know how to do it for sequential API?\nUPDATE TO 2): Naming the layers works, although it seems to be not documented. Just add the argument name, e.g. model.add(Dense(...,...,name=\"hiddenLayer1\"). Watch out, Layers with same name share weights!","AnswerCount":9,"Available Count":2,"Score":-1.0,"is_accepted":false,"ViewCount":40090,"Q_Id":49550182,"Users Score":-4,"Answer":"for 1), I think you may build another model with right name and same structure with the exist one. then set weights from layers of the exist model to layers of the new model.","Q_Score":27,"Tags":"python,keras","A_Id":51157225,"CreationDate":"2018-03-29T07:20:00.000","Title":"Keras rename model and layers","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"1) I try to rename a model and the layers in Keras with TF backend, since I am using multiple models in one script.\nClass Model seem to have the property model.name, but when changing it I get \"AttributeError: can't set attribute\".\nWhat is the Problem here?\n2) Additionally, I am using sequential API and I want to give a name to layers, which seems to be possibile with Functional API, but I found no solution for sequential API. Does anonye know how to do it for sequential API?\nUPDATE TO 2): Naming the layers works, although it seems to be not documented. Just add the argument name, e.g. model.add(Dense(...,...,name=\"hiddenLayer1\"). Watch out, Layers with same name share weights!","AnswerCount":9,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":40090,"Q_Id":49550182,"Users Score":10,"Answer":"To rename a keras model in TF2.2.0:\nmodel._name = \"newname\"\nI have no idea if this is a bad idea - they don't seem to want you to do it, but it does work. To confirm, call model.summary() and you should see the new name.","Q_Score":27,"Tags":"python,keras","A_Id":63853924,"CreationDate":"2018-03-29T07:20:00.000","Title":"Keras rename model and layers","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Let's say we have some method foo we call during graph construction time that returns some tf.Tensors or a nested structure of them every time is called, and multiple other methods that make use of foo's result. For efficiency and to avoid spamming the TF graph with unnecessary repeated operations, it might be tempting to make foo cache its result (to reuse the subgraph it produces) the first time is called. However, that will fail if foo is ever used in the context of a control flow, like tf.cond, tf.map_fn or tf.while_loop.\nMy questions are:\n\nWhen is it safe to cache tf.Tensor objects in such a way that does not cause problems with control flows? Perhaps is there some way to retrieve the control flow under which a tf.Tensor was created (if any), store it and compare it later to see if a cached result can be reused?\nHow would the answer to the question above apply to tf.Operations?\n\n(Question text updated to make clearer that foo creates a new set of tensors every time is called)","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":321,"Q_Id":49550723,"Users Score":0,"Answer":"TL;DR: TF already caches what it needs to, don't bother with it yourself.\nEvery time you call sess.run([some_tensors]) TF's engine find the minimum subgraph needed to compute all tensors in [some_tensors] and runs it from top to bottom (possibly on new data, if you're not feeding it the same data).\nThat means, caching of results in-between sess.run calls is useless towards saving computation, because they will be recomputed anyway.\nIf, instead, you're concerned with having multiple tensors using the same data as input in one call of sess.run, don't worry, TF is smart enough. if you have input A and B = 2*A, C = A + 1, as long as you do one sess.run call as sess.run([B,C]) A will be evaluated only once (and then implicitly cached by the TF engine).","Q_Score":1,"Tags":"python,tensorflow","A_Id":49551180,"CreationDate":"2018-03-29T07:51:00.000","Title":"When is it safe to cache tf.Tensors?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have just started learning Python, Pandas and NumPy and I want to find out what is the cleanest and most efficient way to solve the following problem.\nI have data which holds CarManufacturer, Car, TotalCarSales, bearing in mind that the data is not small:\nCarManufacturer Car TotalCarSales\nVolkswagen Polo 100 \nVolkswagen Golf 50 \nHonda Jazz 40 \nHonda Civic 100\nQuestion:\nWhich manufacturer sold the most cars according to it's top 3 best sellers?\nI'm struggling to solve this efficiently. I want to avoid iterating over the data. \nMy thoughts:\n- Load Data into DataFrame\n- Index data according to CarManufacturer, Car, TotalCarSales\n- Do I want to do a sort here? That would be slow? \n- Create a new DataFrame which has CarManufacturer, TotalSales. For each CarManufacturer I would need to get the top 3 TotalCarSales and take their sum - Is there a way of doing this without iterating over all records in DataFrame? What is best way to fetch the top 3? \n- Then if I sort the TotalSales and take the top 3, wouldn't the sort be slow? Is there a more efficient way?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":91,"Q_Id":49553357,"Users Score":0,"Answer":"The best way to do when you are learning is to try it. \nIt's very unlikely your data will be too large (there aren't millions of car models), but in any case, you can use df.head(N) to take the top N rows to try your method and see if it's slow.\nOther useful functions include df.groupby, df.nlargest, df.sort_values","Q_Score":1,"Tags":"python,python-3.x,pandas","A_Id":49553542,"CreationDate":"2018-03-29T10:09:00.000","Title":"Python Pandas DataFrames Sorting, Summing and Fetching Max Data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to add a data augmentation on the WiderFace dataset and I would like to know, how is it possible to random crop an image and only keep the bouding box of faces with the center inside the crop using tensorflow ?\nI have already try to implement a solution but I use TFRecords and the TfExampleDecoder and the shape of the input image is set to [None, None, 3] during the process, so no way to get the shape of the image and do it by myself.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":972,"Q_Id":49560347,"Users Score":0,"Answer":"You can get the shape, but only at runtime - when you call sess.run and actually pass in the data - that's when the shape is actually defined.\nSo do the random crop manually in tesorflow, basically, you want to reimplement tf.random_crop so you can handle the manipulations to the bounding boxes.\nFirst, to get the shape, x = your_tensor.shape[0] will give you the first dimension. It will appear as None until you actually call sess.run, then it will resolve to the appropriate value. Now you can compute some random crop parameters using tf.random_uniform or whatever method you like. Lastly you perform the crop with tf.slice.\nIf you want to choose whether to perform the crop or not you can use tf.cond.\nBetween those components, you should be able to implement what you want using only tensorflow constructs. Try it out and if you get stuck along the way post the code and error you run into.","Q_Score":1,"Tags":"python,tensorflow","A_Id":49565369,"CreationDate":"2018-03-29T15:54:00.000","Title":"Random crop and bounding boxes in tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I understand how random state is used to randomly split data into training and test set. As Expected, my algorithm gives different accuracy each time I change it. Now I have to submit a report in my university and I am unable to understand the final accuracy to mention there. Should I choose the maximum accuracy I get? Or should I run it with different RandomStates and then take its average? Or something else?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":5696,"Q_Id":49561882,"Users Score":1,"Answer":"For me personally, I set random_state to a specific number (usually 42) so if I see variation in my programs accuracy I know it was not caused by how the data was split.\nHowever, this can lead to my network over fitting on that specific split.  I.E. I tune my network so it works well with that split, but not necessarily on a different split.  Because of this, I think it's best to use a random seed when you submit your code so the reviewer knows you haven't over fit to that particular state.\nTo do this with sklearn.train_test_split you can simply not provide a random_state and it will pick one randomly using np.random.","Q_Score":5,"Tags":"python,pandas,machine-learning,scikit-learn,svm","A_Id":49562017,"CreationDate":"2018-03-29T17:21:00.000","Title":"How to choose RandomState in train_test_split?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to import dask.distributed package, but I keep getting this error: ImportError: cannot import name 'collections_to_dsk'. Help is appreciated.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":311,"Q_Id":49564542,"Users Score":0,"Answer":"I resolved the error, there were some outdated packages including imageio which needed upgrade to work with dask.distributed and dask.dataframe.","Q_Score":1,"Tags":"python-3.x,importerror,dask-distributed","A_Id":49571075,"CreationDate":"2018-03-29T20:31:00.000","Title":"ImportError with dask.distributed","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on a problem in which I have to predict whether a sent email from a company is opened or not and if it is opened, I have to predict whether the recipient clicked on the given link or not.\nI have a data set with the following features:\n\nTotal links inside the emai`\nTotal internal links inside the\nemail \nNumber of images inside the email \nNumber of sections\ninside the email \nEmail_body \nEmail Subject\n\nFor the email body and subject I can use a CountVectorizer but how can I include my other feature to that sparse matrix created by said CountVectorizer.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":82,"Q_Id":49570046,"Users Score":0,"Answer":"You could easily just concatenate these matrices and other feature columns to build one very large matrix.\nHowever, be aware that concatenating the matrix from email body and email subject will probably create an incredibly sparse matrix. When you then add other features you might risk to \"water down\" your other features. This depends mostly on the algorithm you choose to use for your prediction. \nIn all cases you would benefit from reducing the dimensionality of the two matrices for email subject and body or directly use a different approach than CountVectorizer - for example TFIDF.","Q_Score":0,"Tags":"python,tf-idf,text-classification,countvectorizer,tfidfvectorizer","A_Id":49902116,"CreationDate":"2018-03-30T07:13:00.000","Title":"CountVectorizer in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on a problem in which I have to predict whether a sent email from a company is opened or not and if it is opened, I have to predict whether the recipient clicked on the given link or not.\nI have a data set with the following features:\n\nTotal links inside the emai`\nTotal internal links inside the\nemail \nNumber of images inside the email \nNumber of sections\ninside the email \nEmail_body \nEmail Subject\n\nFor the email body and subject I can use a CountVectorizer but how can I include my other feature to that sparse matrix created by said CountVectorizer.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":82,"Q_Id":49570046,"Users Score":0,"Answer":"Your problem is you have two large sparse feature vectors (email body and subject) and also small dense feature vectors. Here is my simple suggestion:\n\n(Jerome's idea) Reduce the dimension of email body and subject (via PCA, AutoEncoder, CBOW, Doc2Vec, PLSA, or LDA) so that you will end up with a dense feature vector. Then, concatenate it with other meta information.\nI think concatenating the matrix with other features are okay. If you use a simple linear model, you can put more weights on meta information and scale down all weights learned from subject and body of the emails.\n\nThe real problem is when you use a bag-of-word representation (either term-frequency or TFIDF), your feature vector will be extremely sparse for a very short email. The model might not perform well.\nBy the way, I think the author information could be a good indicator whether the email will be opened or not.","Q_Score":0,"Tags":"python,tf-idf,text-classification,countvectorizer,tfidfvectorizer","A_Id":49936932,"CreationDate":"2018-03-30T07:13:00.000","Title":"CountVectorizer in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am writing a sql query using pandas within python. In the where clause I need to compare a date column (say review date 2016-10-21) with this value '2016Q4'. In other words if the review dates fall in or after Q4 in 2016 then they will be selected. Now how do I convert the review date to something comparable to 'yyyyQ4' format. Is there any python function for that ? If not, how so I go about writing one for this purpose ?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":798,"Q_Id":49576487,"Users Score":1,"Answer":"Once you are able to get the month out into a variable: mon\nyou can use the following code to get the quarter information:\nfor mon in range(1, 13):\n  print (mon-1)\/\/3 + 1,\nprint\nwhich would return: \n\nfor months 1 - 3    : 1\nfor months 4 - 6    : 2\nfor months 7 - 9    : 3\nfor months 10 - 12  : 4","Q_Score":1,"Tags":"sql,python-3.x,pandas","A_Id":49577047,"CreationDate":"2018-03-30T14:45:00.000","Title":"How to compare date (yyyy-mm-dd) with year-Quarter (yyyyQQ) in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have millions of images, and I am able to use OCR with pytesseract to perform descent text extraction, but it takes too long to process all of the images.  \nThus I would like to determine if an image simply contains text or not, and if it doesn't, i wouldn't have to perform OCR on it. Ideally this method would have a high recall. \nI was thinking about building a SVM or some machine learning model to help detect, but I was hoping if anyone new of a method to quickly determine if an object contains text or not.","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":285,"Q_Id":49576528,"Users Score":2,"Answer":"Unfortunately there is no way to tell if an image has text in it, without performing OCR of some kind on it.\nYou could build a machine learning model that handles this, however keep in mind it would still need to process the image as well.","Q_Score":0,"Tags":"python,classification,ocr,tesseract,text-extraction","A_Id":49577580,"CreationDate":"2018-03-30T14:47:00.000","Title":"Quick way to classify if an image contains text or not","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to implement an objective function that minimize the overlap of 2 irregular shaped 3d objects. While the most accurate measurement of the overlap is the intersection volume, it's too computationally expensive as I am dealing with complex objects with 1000+ faces and are not convex.\nI am wondering if there are other measurements of intersection between 3d objects that are much faster to compute? 2 requirements for the measurement are: 1. When the measurement is 0, there should be no overlap; 2. The measurement should be a scalar(not a boolean value) indicating the degree of overlapping, but this value doesn't need to be very accurate.\nPossible measurements I am considering include some sort of 2D surface area of intersection, or 1D penetration depth. Alternatively I can estimate volume with a sample based method that sample points inside one object and test the percentage of points that exist in another object. But I don't know how computational expensive it is to sample points inside a complex 3d shape as well as to test if a point is enclosed by such a shape.\nI will really appreciate any advices, codes, or equations on this matter. Also if you can suggest any libraries (preferably python library) that accept .obj, .ply...etc files and perform 3D geometry computation that will be great! I will also post here if I find out a good method.\nUpdate:\nI found a good python library called Trimesh that performs all the computations mentioned by me and others in this post. It computes the exact intersection volume with the Blender backend; it can voxelize meshes and compute the volume of the co-occupied voxels; it can also perform surface and volumetric points sampling within one mesh and test points containment within another mesh. I found surface point sampling and containment testing(sort of surface intersection) and the grid approach to be the fastest.","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":1361,"Q_Id":49584153,"Users Score":0,"Answer":"A sample-based approach is what I'd try first. Generate a bunch of points in the unioned bounding AABB, and divide the number of points in A and B by the number of points in A or B. (You can adapt this measure to your use case -- it doesn't work very well when A and B have very different volumes.) To check whether a given point is in a given volume, use a crossing number test, which Google. There are acceleration structures that can help with this test, but my guess is that the number of samples that'll give you reasonable accuracy is lower than the number of samples necessary to benefit overall from building the acceleration structure.\nAs a variant of this, you can check line intersection instead of point intersection: Generate a random (axis-aligned, for efficiency) line, and measure how much of it is contained in A, in B, and in both A and B. This requires more bookkeeping than point-in-polyhedron, but will give you better per-sample information and thus reduce the number of times you end up iterating through all the faces.","Q_Score":3,"Tags":"python,3d,computational-geometry,bin-packing","A_Id":49597211,"CreationDate":"2018-03-31T04:10:00.000","Title":"Measurement for intersection of 2 irregular shaped 3d object","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to implement an objective function that minimize the overlap of 2 irregular shaped 3d objects. While the most accurate measurement of the overlap is the intersection volume, it's too computationally expensive as I am dealing with complex objects with 1000+ faces and are not convex.\nI am wondering if there are other measurements of intersection between 3d objects that are much faster to compute? 2 requirements for the measurement are: 1. When the measurement is 0, there should be no overlap; 2. The measurement should be a scalar(not a boolean value) indicating the degree of overlapping, but this value doesn't need to be very accurate.\nPossible measurements I am considering include some sort of 2D surface area of intersection, or 1D penetration depth. Alternatively I can estimate volume with a sample based method that sample points inside one object and test the percentage of points that exist in another object. But I don't know how computational expensive it is to sample points inside a complex 3d shape as well as to test if a point is enclosed by such a shape.\nI will really appreciate any advices, codes, or equations on this matter. Also if you can suggest any libraries (preferably python library) that accept .obj, .ply...etc files and perform 3D geometry computation that will be great! I will also post here if I find out a good method.\nUpdate:\nI found a good python library called Trimesh that performs all the computations mentioned by me and others in this post. It computes the exact intersection volume with the Blender backend; it can voxelize meshes and compute the volume of the co-occupied voxels; it can also perform surface and volumetric points sampling within one mesh and test points containment within another mesh. I found surface point sampling and containment testing(sort of surface intersection) and the grid approach to be the fastest.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1361,"Q_Id":49584153,"Users Score":0,"Answer":"By straight voxelization:\nIf the faces are of similar size (if needed triangulate the large ones), you can use a gridding approach: define a regular 3D grid with a spacing size larger than the longest edge and store one bit per voxel.\nThen for every vertex of the mesh, set the bit of the cell it is included in (this just takes a truncation of the coordinates). By doing this, you will obtain the boundary of the object as a connected surface. You will obtain an estimate of the volume by means of a 3D flood filling algorithm, either from an inside or an outside pixel. (Outside will be easier but be sure to leave a one voxel margin around the object.)\nEstimating the volumes of both objects as well as intersection or union is straightforward with this machinery. The cost will depend on the number of faces and the number of voxels.","Q_Score":3,"Tags":"python,3d,computational-geometry,bin-packing","A_Id":49688037,"CreationDate":"2018-03-31T04:10:00.000","Title":"Measurement for intersection of 2 irregular shaped 3d object","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"It is a common practice to normalize input values (to a neural network) to speed up the learning process, especially if features have very large scales.\nIn its theory, normalization is easy to understand. But I wonder how this is done if the training data set is very large, say for 1 million training examples..? If # features per training example is large as well (say, 100 features per training example), 2 problems pop up all of a sudden:\n- It will take some time to normalize all training samples\n- Normalized training examples need to be saved somewhere, so that we need to double the necessary disk space (especially if we do not want to overwrite the original data).\nHow is input normalization solved in practice, especially if the data set is very large?\nOne option maybe is to normalize inputs dynamically in the memory per mini batch while training.. But normalization results will then be changing from one mini batch to another. Would it be tolerable then?\nThere is maybe someone in this platform having hands on experience on this question. I would really appreciate if you could share your experiences.\nThank you in advance.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1045,"Q_Id":49593985,"Users Score":1,"Answer":"A large number of features makes it easier to parallelize the normalization of the dataset. This is not really an issue. Normalization on large datasets would be easily GPU accelerated, and it would be quite fast. Even for large datasets like you are describing. One of my frameworks that I have written can normalize the entire MNIST dataset in under 10 seconds on a 4-core 4-thread CPU. A GPU could easily do it in under 2 seconds. Computation is not the problem. While for smaller datasets, you can hold the entire normalized dataset in memory, for larger datasets, like you mentioned, you will need to swap out to disk if you normalize the entire dataset. However, if you are doing reasonably large batch sizes, about 128 or higher, your minimums and maximums will not fluctuate that much, depending upon the dataset. This allows you to normalize the mini-batch right before you train the network on it, but again this depends upon the network. I would recommend experimenting based on your datasets, and choosing the best method.","Q_Score":0,"Tags":"python,tensorflow,machine-learning,neural-network,deep-learning","A_Id":49594057,"CreationDate":"2018-04-01T01:28:00.000","Title":"Neural Network - Input Normalization","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to read the dat file using pyspark csv reader and it contains newline character (\"\\n\") as part of the data. Spark is unable to read this file as single column, rather treating it as new row. \nI tried using the \"multiLine\" option while reading , but still its not working. \nspark.read.csv(file_path, schema=schema, sep=delimiter,multiLine=True)\nData is something like this. Here $ is CRLF for newline shown in vim.\n\nname,test,12345,$\n$\n,desc$\nname2,test2,12345,$\n$\n,desc2$\n\nSo pyspark is treating desc as next record. \nHow to read such data in pyspark . \nTried this in both spark2.2 and spark2.3 versions.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":648,"Q_Id":49603834,"Users Score":0,"Answer":"I created my own hadoop Custom Record Reader and was able to read it by invoking the api . \n\nspark.sparkContext.newAPIHadoopFile(file_path,'com.test.multi.reader.CustomFileFormat','org.apache.hadoop.io.LongWritable','org.apache.hadoop.io.Text',conf=conf)\nAnd in the Custom Record Reader implemented the logic to handle the newline characters encountered .","Q_Score":1,"Tags":"python-3.x,apache-spark,pyspark,spark-dataframe","A_Id":50748457,"CreationDate":"2018-04-02T00:11:00.000","Title":"pyspark read csv file multiLine option not working for records which has newline spark2.3 and spark2.2","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a few basic questions about using Numpy with GPU (nvidia GTX 1080 Ti).  I'm new to GPU, and would like to make sure I'm properly using the GPU to accelerate Numpy\/Python.  I searched on the internet for a while, but didn't find a simple tutorial that addressed my questions.  I'd appreciate it if someone can give me some pointers:\n1) Does Numpy\/Python automatically detect the presence of GPU and utilize it to speed up matrix computation (e.g. numpy.multiply, numpy.linalg.inv, ... etc)?  Or do I have code in a specific way to exploit the GPU for fast computation?\n2) Can someone recommend a good tutorial\/introductory material on using Numpy\/Python with GPU (nvidia's)?\nThanks a lot!","AnswerCount":4,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":49124,"Q_Id":49605231,"Users Score":50,"Answer":"Does Numpy\/Python automatically detect the presence of GPU and utilize\nit to speed up matrix computation (e.g. numpy.multiply,\nnumpy.linalg.inv, ... etc)?\n\nNo.\n\nOr do I have code in a specific way to exploit the GPU for fast\ncomputation?\n\nYes. Search for Numba, CuPy, Theano, PyTorch or PyCUDA for different paradigms for accelerating Python with GPUs.","Q_Score":52,"Tags":"python,numpy,gpu","A_Id":49605405,"CreationDate":"2018-04-02T04:14:00.000","Title":"Does Numpy automatically detect and use GPU?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Suppose I have an array A of dtype int32, and I want to cast it into float64.\nThe standard way to do this (that I know) is A.astype('float64').\nBut this allocates a new array for the result. If I run this command repeatedly (with different arrays of the same shape),  each time using the result and discarding it shortly after, then the overhead from these allocations can be non-negligible.\nSuppose I pre-allocated an array B, with the same shape of A and of type float64. Is there a way to use the memory of B for the result of the casting, instead of allocating new memory each time?\nufuncs and numpy.dot have an 'out' argument for this, but astpye does not.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":76,"Q_Id":49607824,"Users Score":0,"Answer":"As Paul Panzer commented, this can be done simply by B[...] = A.","Q_Score":0,"Tags":"python,numpy","A_Id":49608006,"CreationDate":"2018-04-02T08:27:00.000","Title":"Casting a numpy array into a (different) pre-allocated array","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I wish to use classification metrics like matthews_corrcoef as a metric to a neural network built with CNTK. The way I could find as of now was to evaluate the value by passing the predictions and label as shown\nmatthews_corrcoef(cntk.argmax(y_true, axis=-1).eval(), cntk.argmax(y_pred, axis=-1).eval())\nIdeally I'd like to pass the metric to the trainer object while building my network.\nOne of the ways would be to create own custom metric and pass that to the trainer object. Although possible, it'll be better to be able to reuse the already existing metrics present in other libraries.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":68,"Q_Id":49612908,"Users Score":0,"Answer":"Unless this metric is already implemented in CNTK, implement your own custom \"metric\" function in whatever format CNTK requires, and have it pass the inputs on to scikit-learn's metric function.","Q_Score":1,"Tags":"python,neural-network,cntk","A_Id":49613345,"CreationDate":"2018-04-02T14:12:00.000","Title":"How to use scikit-learn metrics in CNTK?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Trying to take data from a csv like this:\ncol1   col2\neggs   sara\nbacon  john\nham    betty\nThe number of items in each column can vary and may not be the same. Col1 may have 25 and col2 may have 3. Or the reverse, more or less.\nAnd loop through each entry so its output into a text file like this\nbreakfast_1\nbreakfast_item eggs\nperson sara\nbreakfast_2\nbreakfast_item bacon\nperson sara\nbreakfast_3\nbreakfast_item ham\nperson sara\nbreakfast_4\nbreakfast_item eggs\nperson john\nbreakfast_5\nbreakfast_item bacon\nperson john\nbreakfast_6\nbreakfast_item ham\nperson john\nbreakfast_7\nbreakfast_item eggs\nperson betty\nbreakfast_8\nbreakfast_item bacon\nperson betty\nbreakfast_9\nbreakfast_item ham\nperson betty\nSo the script would need to add the \"breakfast\" number and loop through each breakfast_item and person.\nI know how to create one combo but not how to pair up each in a loop?\nAny tips on how to do this would be very helpful.","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":50,"Q_Id":49619655,"Users Score":0,"Answer":"First, get a distinct of all breakfast items.\nA pseudo code like below \nIterate through each line\nCollect item and person in 2 different lists\nDo a set on those 2 lists\nSay persons, items\n\nCounter = 1\nfor  person in persons:\n  for  item in items:\n    Print \"breafastitem\", Counter\n    Print person, item","Q_Score":0,"Tags":"python,csv,text","A_Id":49623125,"CreationDate":"2018-04-02T22:28:00.000","Title":"python pair multiple field entries from csv","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have n and m binary vectors(of length 1500) from set A and B respectively.\nI need a metric that can say how similar (kind of distance metric) all those n vectors and m vectors are.\nThe output should be total_distance_of_n_vectors and total_distance_of_m_vectors.\nAnd if total_distance_of_n_vectors > total_distance_of_m_vectors, it means Set B have more similar vectors than Set A.\nWhich metric should I use? I thought of Jaccard similarity. But I am not able to put it in this context. Should I find the distance of each vector with each other to find the total distance or something else ?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1262,"Q_Id":49627823,"Users Score":0,"Answer":"There are two concepts relevant to your question, which you should consider separately.\nSimilarity Measure:\nIndependent of your scoring mechanism, you should find a similarity measure which suits your data best. It can be an Euclidean distance (not suitable for a 1500 dimensional space), a cosine (dot product based) distance, or a Hamiltonian distance (assuming your input features are completely independent, which rarely is the case).\nA lot can go on in your distance function, and you should find one which makes sense for your data.\nScoring Mechanism:\nYou mention total_distance_of_vectors in your question, which probably is not what you want. If n >> m, almost certainly the total sum of distances for n vectors is more than the total distance for m vectors.\nWhat you're looking for is most probably an average of the distances between the members of your sets. Then, depending on weather you want your average to be sensitive to outliers or not, you can go for average of the distances or average of squared distances.\nIf you want to dig deeper, you can also get the mean and variance of the distances within the two sets and compare the distributions.","Q_Score":1,"Tags":"python,machine-learning,similarity,cosine-similarity","A_Id":49630619,"CreationDate":"2018-04-03T10:30:00.000","Title":"Distance metric for n binary vectors","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Getting few \"package missing\" errors while installing ipython on High Sierra.\nmatplotlib 1.3.1 has requirement numpy>=1.5, but you'll have numpy 1.8.0rc1 which is incompatible.","AnswerCount":2,"Available Count":1,"Score":0.2913126125,"is_accepted":false,"ViewCount":1097,"Q_Id":49638201,"Users Score":3,"Answer":"I just met the same problem. It's a issue about numpy preinstall in Python has a version number issue(required >=1.5, but found 1.8.0rc1).\nTry running brew install python2 to upgrade your python which may solve this issue.","Q_Score":0,"Tags":"macos,numpy,matplotlib,ipython,homebrew","A_Id":49713880,"CreationDate":"2018-04-03T19:58:00.000","Title":"numpy version creating issue. python 2.7 already installed","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"In numpy, we use ndarray.reshape() for reshaping an array.\nI noticed that in pytorch, people use torch.view(...) for the same purpose, but at the same time, there is also a torch.reshape(...) existing.\nSo I am wondering what the differences are between them and when I should use either of them?","AnswerCount":5,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":88597,"Q_Id":49643225,"Users Score":16,"Answer":"Tensor.reshape() is more robust. It will work on any tensor, while Tensor.view() works only on tensor t where t.is_contiguous()==True.\nTo explain about non-contiguous and contiguous is another story, but you can always make the tensor t contiguous if you call t.contiguous() and then you can call view() without the error.","Q_Score":173,"Tags":"python,pytorch","A_Id":55976519,"CreationDate":"2018-04-04T05:09:00.000","Title":"What's the difference between reshape and view in pytorch?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In numpy, we use ndarray.reshape() for reshaping an array.\nI noticed that in pytorch, people use torch.view(...) for the same purpose, but at the same time, there is also a torch.reshape(...) existing.\nSo I am wondering what the differences are between them and when I should use either of them?","AnswerCount":5,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":88597,"Q_Id":49643225,"Users Score":0,"Answer":"I would say the answers here are technically correct but there's another reason for existing of reshape. pytorch is usually considered more convenient than other frameworks because it closer to python and numpy. It's interesting that the question involves numpy.\nLet's look into size and shape in pytorch. size is a function so you call it like x.size(). shape in pytorch is not a function. In numpy you have shape and it's not a function - you use it x.shape. So it's handy to get both of them in pytorch. If you came from numpy it would be nice to use the same functions.","Q_Score":173,"Tags":"python,pytorch","A_Id":69676338,"CreationDate":"2018-04-04T05:09:00.000","Title":"What's the difference between reshape and view in pytorch?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"df.to_sql(name='hourly', con=engine, if_exists='append', index=False)\n\nIt inserts data not only to table 'hourly', but also to table 'margin' - I execute this particular line only.\nIt's Postgresql 10.\nWhile Creating table 'hourly', I inherited column names and dtypes from table 'margin'.\nIs it something wrong with the db itself or is it Python code?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":588,"Q_Id":49651442,"Users Score":0,"Answer":"Removing\n\nINHERITS (tablename);\n\non the slave table (creating it again without INHERITS)\nseems to have done the trick.\nOnly a matter of curiosity:\nWhy did it matter? I thought inheritance only gets columns and dtypes not the actual data.","Q_Score":1,"Tags":"python-3.x,postgresql,pandas","A_Id":49651837,"CreationDate":"2018-04-04T12:47:00.000","Title":"Pandas Dataframe.to_sql wrongly inserting into more than one table (postgresql)","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on a excel file with large text data. 2 columns have lot of text data. Like descriptions, job duties. \nWhen i import my file in python df=pd.read_excel(\"form1.xlsx\"). It shows the columns with text data as NaN. \nHow do I import all the text in the columns ?\nI want to do analysis on job title , description and job duties. Descriptions and Job Title are long text. I have over 150 rows.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":2378,"Q_Id":49652693,"Users Score":1,"Answer":"Try converting the file from .xlsx to .CSV \nI had the same problem with text columns so i tried converting to CSV (Comma Delimited) and it worked. Not very helpful, but worth a try.","Q_Score":0,"Tags":"excel,python-3.x,pandas,import","A_Id":49656081,"CreationDate":"2018-04-04T13:46:00.000","Title":"how to read text from excel file in python pandas?","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Currently I have multiple trained models for regression task, each model is of the same architecture but while training, I have dropout layer, to improve the performance, is that still possible for me to combine those trained models and calculate the mean of the weights as the combined, new model? I just heard that there is an ensemble prediction method which allow us to do, but I am not sure whether I can still do this because I already have random dropout layer.\nAny hint is much appreciated!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":137,"Q_Id":49659892,"Users Score":0,"Answer":"I think the presence of dropout is irrelevant to what you want to do. Ensembling should work just fine with dropout.","Q_Score":0,"Tags":"python,tensorflow,regression,prediction,robust","A_Id":49678287,"CreationDate":"2018-04-04T20:30:00.000","Title":"Ensemble (Combine) multiple deep learning regression models which already have dropout layers","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a model that starts with a Conv2D layer and so it must take input of shape (samples, rows, cols, channels) (and the model must ultimately output a shape of (1)). However, for my purposes one full unit of input needs to be some (fixed) number of samples, so the overall input shape sent into this model when given a batch of input ends up being (batch_size, samples, rows, cols, channels) (which is expected and correct, but...). How do I send each item in the batch through this model so that I end up with an output of shape (batch_size, 1)?\nWhat I have tried so far:\nI tried creating an inner model containing the Conv2D layer et al then wrapping the entire thing in a TimeDistributed wrapper, followed by a Dense(units=1) layer. This compiled, but resulted in an output shape of (batch_size, samples, 1). I feel like I am missing something simple...","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":587,"Q_Id":49662869,"Users Score":1,"Answer":"At the moment you are returning a 3D array.\nAdd a Flatten() layer to convert the array to 2D, and then add a Dense(1). This should output (batch_size, 1).","Q_Score":1,"Tags":"python,tensorflow,keras","A_Id":49662938,"CreationDate":"2018-04-05T01:37:00.000","Title":"In Keras, how to send each item in a batch through a model?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to train a neural net on a GPU using Keras and am getting a \"Resource exhausted: OOM when allocating tensor\" error.  The specific tensor it's trying to allocate isn't very big, so I assume some previous tensor consumed almost all the VRAM.  The error message comes with a hint that suggests this:\n\nHint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.\n\nThat sounds good, but how do I do it?  RunOptions appears to be a Tensorflow thing, and what little documentation I can find for it associates it with a \"session\".  I'm using Keras, so Tensorflow is hidden under a layer of abstraction and its sessions under another layer below that.\nHow do I dig underneath everything to set this option in such a way that it will take effect?","AnswerCount":4,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":26528,"Q_Id":49665757,"Users Score":2,"Answer":"OOM means out of memory. May be it is using more memory at that time.\nDecrease batch_size significantly. I set to 16, then it worked fine","Q_Score":27,"Tags":"python,tensorflow,keras,gpu","A_Id":64231036,"CreationDate":"2018-04-05T06:45:00.000","Title":"How to add report_tensor_allocations_upon_oom to RunOptions in Keras","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"It is the first time that I am working with the LSTM networks. I have a video with a frame rate of 30 fps. I have a CNN network (AlexNet based) and I want to feed the last layer of my CNN network into the recurrent network (I am using tensorflow). Supposing that my batch_size=30, so equal to the fps, and I want to have a timestep of 1 second (so, every 30 frames). The output of the last layer of my network will be [bast_size, 1000], so in my case [30, 1000], now do I have to reshape the size of my output to [batch_size, time_steps, features] (in my case: [30, 30, 1000])? Is that correct? or am I wrong?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2027,"Q_Id":49668169,"Users Score":0,"Answer":"If you merge several small sequences from different videos to form a batch, the output of the last layer of your model (the RNN) should already be [batch_size, window_size, num_classes]. Basically, you want to wrap your CNN with reshape layers which will concatenate the frames from each batch:\n\ninput -> [batch_size, window_size, nchannels, height, width]\nreshape -> [batch_size * window_size, nchannels, height, width]\nCNN -> [batch_size * window_size, feat_size]\nreshape -> [batch_size, window_size, feats_size]\nRNN -> [batch_size, window_size, num_outputs]  (assuming frame-wise predictions)\n\nBut this will take a lot of memory, so you can set batch size to 1, which is what you seem to be doing if I understood correctly. In this case you can spare the first reshape.\nI'm not sure about the order of the axes above, but the general logic remains the same.\nAs a side note: if you plan on using Batch Normalization at some point, you may want to raise the batch size because consecutive frames from a single segment might not contain a lot of variety by themselves. Also give a double check on the batch normalization axes which should cover both time and batch axes.","Q_Score":0,"Tags":"python,tensorflow,deep-learning,lstm","A_Id":49669130,"CreationDate":"2018-04-05T08:57:00.000","Title":"Feed the output of a CNN in a LSTM","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"The docs say\n\nh_n of shape (num_layers * num_directions, batch, hidden_size): tensor containing the hidden state for t = seq_len\n\nNow, the batch and hidden_size dimensions are pretty much self-explanatory. The first dimension remains a mystery, though.\nI assume, that the hidden states of all \"last cells\" of all layers are included in this output. But then what is the index of, for example, the hidden state of the \"last cell\" in the \"uppermost layer\"? h_n[-1]? h_n[0]? \nIs the output affected by the batch_first option?","AnswerCount":1,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":1829,"Q_Id":49674079,"Users Score":8,"Answer":"The implementation of LSTM and GRU in pytorch automatically includes the possibility of stacked layers of LSTMs and GRUs.\nYou give this with the keyword argument nn.LSTM(num_layers=num_layers). num_layers is the number of stacked LSTMs (or GRUs) that you have. The default value is 1, which gives you the basic LSTM. \nnum_directions is either 1 or 2. It is 1 for normal LSTMs and GRUs, and it is 2 for bidirectional RNNs.\nSo in your case, you probably have a simple LSTM or GRU so the value of num_layers * num_directions would then be one.\nh_n[0] is the hidden state of the bottom-most layer (the one which takes in the input), and h_n[-1] of the top-most layer (the one which outputs the output of the network).  \nbatch_first puts the batch dimension before the time dimension (the default being the time dimension before the batch dimension), because the hidden state doesn't have a time dimension, batch_first has no effect on the hidden state shape.","Q_Score":5,"Tags":"python,neural-network,deep-learning,lstm,pytorch","A_Id":49686565,"CreationDate":"2018-04-05T13:50:00.000","Title":"How is the output h_n of an RNN (nn.LSTM, nn.GRU, etc.) in PyTorch structured?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"We have a hadoop cluster of 625 nodes. But some of them are centos 6 (python 2.6) and some are centos 7 (python 2.7). So how can I resolve this as I am getting this error constantly.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":562,"Q_Id":49676701,"Users Score":0,"Answer":"Install at least Python 2.7 on each node and configure SPARK_PYTHON environment variable to point to the required installation. Spark doesn't support mixed environments and doesn't support Python 2.6 anymore.","Q_Score":0,"Tags":"python,apache-spark,pyspark","A_Id":49676768,"CreationDate":"2018-04-05T15:58:00.000","Title":"Exception: Python in worker has different version 2.7 than that in driver 2.6, PySpark cannot run with different minor versions","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have multiple rosbag files with a ton of data, and what I would like to do is analyze these bag files using Jupyter Notebook, but the problem is that each bag has a different set of data parameters. So I have created msg files to subscribe to data from each bag file. Some msg files have the same variables since those variables are used in multiple plots.\nCan someone walk me through the process of getting the plots on the notebook file one at a time (from the data obtained from the bag files), for every bag file I have? Even if multiple files use the same topics?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":690,"Q_Id":49685635,"Users Score":0,"Answer":"Found this answer in one of the older .ipynb files. Plots can be obtained with Plotly library.  It really doesn't matter how many bag files are being scanned for topics and are plotted. Each bag file can be separately converted using the 'bag_to_dataframe' function from the \"rosbag_pandas\" package. \nEven in case of similar topics between bag files, it is possible to differentiate their values in the plots. Hope this helps, I'll share a sample bag file soon :)","Q_Score":0,"Tags":"python,jupyter-notebook,ros","A_Id":49817588,"CreationDate":"2018-04-06T05:00:00.000","Title":"Using Jupyter Notebook to plot data from rosbag files","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to produce a graph from two sets of data files (txt and csv) and I have encountered a couple of issues using either R or Python and would be super super grateful if somebody could help :)\n\nHow to import to sets of data and how to choose specific column in each of them (would it be exactly the same as if dealing with one file)? \nOne of the file have much more data points than the other (3 600 000 vs 80 000). How can I select every nth row in the csv file? \n\nI would be grateful for any help in either R or python","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":52,"Q_Id":49713065,"Users Score":1,"Answer":"Ok, addressing the points as best I can without an example of what you tried that didn't work...\n\nHow to import to sets of data and how to choose specific column in each of them (would it be exactly the same as if dealing with one file)?\n\nYou'd import each file separately. Assuming your file has headers, something like\n\nd1 <- read.csv(\"your_file1_name.csv\", headers=TRUE)\nd2 <- read.csv(\"your_file2_name.csv\", headers=TRUE)\n\nIf your headers name useful names (e.g. \"Time\", \"88height\", \"number of octopus\", etc), your data frame will have the same column names, after running the headers through make.names(), which coverts the titles to legal R data frame column names.\ne.g.\n\nd1$Time\nd1$number.of.octopus\nd2$X88height\n\nIf you want the data frames merged into one big data frame, use rbind().\nIf you want a vector of all the data from a particular column from each data frame, you'd use c()\ne.g. \n\ntotal.octopus <- c(d1$number.of.octopus, d2$number.of.octopus)\n\n\nOne of the file have much more data points than the other (3 600 000 vs 80 000). How can I select every nth row in the csv file?\n\nTo select every 9th row of, say, d1, you'd index:\n\nidx <- seq(1, nrow(d1), by=9)\nd1_samp <- d1[idx,]  #note the comma and blank - means \"every column\"\n\nBecause of the lack of examples, this is only my interpretation of your needs. If it doesn't answer your question, you'll get there faster if you post a sample or toy example of code we can run that shows what you tried. For example, what kind of graph are you trying to make? Scatterplot? Trend? Barchart? And what kind of data? Time series? Number-vs-category? etc.","Q_Score":0,"Tags":"python,r,pandas,matplotlib,graph","A_Id":49714303,"CreationDate":"2018-04-07T23:44:00.000","Title":"Graphs from two sets of data files with different number of datapoints","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am currently working a large graph, with 1.5 Million Nodes and 11 Million Edges.\nFor the sake of speed, I checked the benchmarks of the most popular graph libraries: iGraph, Graph-tool, NetworkX and Networkit. And it seems iGraph, Graph-tool and Networkit have similar performance. And I eventually used iGraph.\nWith the directed graph built with iGraph, the pagerank of all vertices can be calculated in 5 secs. However, when it came to Betweenness and Closeness, it took forever for the calculation.\nIn the documentation, it says that by specifying \"CutOff\", iGraph will ignore all path with length < CutOff value.\nI am wondering if there a rule of thumb to choose the best CutOff value to choose?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":676,"Q_Id":49713991,"Users Score":0,"Answer":"The cutoff really depends on the application and on the netwrok parameters (# nodes, # edges).\n\nIt's hard to talk about closeness threshold, since it depends greatly on other parameters (# nodes, # edges,...). \nOne thing you can know for sure is that every closeness centrality is somewhere between 2\/[n(n-1)] (which is minimum, attained at path) and 1\/(n-1) (which is maximum, attained at clique or star).\nPerhaps better question would be about Freeman centralization of closeness (which is somehow normalized version of closeness that you can better compare between various graphs).\n\n\nSuggestion:\nYou can do a grid search for different cutoff values and then choose the one that makes more sense based on your application.","Q_Score":1,"Tags":"python,igraph","A_Id":51268892,"CreationDate":"2018-04-08T03:04:00.000","Title":"Cutoff in Closeness\/Betweenness Centrality in python igraph","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":1,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am building my own Tensor class in Rust, and I am trying to make it like PyTorch's implementation. \nWhat is the most efficient way to store tensors programmatically, but, specifically, in a strongly typed language like Rust? Are there any resources that provide good insights into how this is done?\nI am currently building a contiguous array, so that, given dimensions of 3 x 3 x 3, my array would just have 3^3 elements in it, which would represent the tensor. However, this does make some of the mathematical operations and manipulations of the array harder.\nThe dimension of the tensor should be dynamic, so that I could have a tensor with n dimensions.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1685,"Q_Id":49724954,"Users Score":6,"Answer":"Contiguous array\nThe commonly used way to store such data is in a single array that is laid out as a single, contiguous block within memory. More concretely, a 3x3x3 tensor would be stored simply as a single array of 27 values, one after the other. \nThe only place where the dimensions are used is to calculate the mapping between the (many) coordinates and the offset within that array. For example, to fetch the item [3, 1, 1] you would need to know if it is a 3x3x3 matrix, a 9x3x1 matrix, or a 27x1x1 matrix - in all cases the \"storage\" would be 27 items long, but the interpretation of \"coordinates\" would be different. If you use zero-based indexing, the calculation is trivial, but you need to know the length of each dimension.\nThis does mean that resizing and similar operations may require copying the whole array, but that's ok, you trade off the performance of those (rare) operations to gain performance for the much more common operations, e.g. sequential reads.","Q_Score":14,"Tags":"python,python-3.x,rust,pytorch,tensor","A_Id":49734613,"CreationDate":"2018-04-09T02:59:00.000","Title":"How are PyTorch's tensors implemented?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to convert a list of tuple like a = [(1,2),(2,49),(3,45)]\nto numpy matrix : [[1,2],[2,49],[3,45]]\nCan anyone help me?\nthanks in advance","AnswerCount":1,"Available Count":1,"Score":-0.1973753202,"is_accepted":false,"ViewCount":1397,"Q_Id":49730922,"Users Score":-1,"Answer":"try this:\na = [(1,2),(2,49),(3,45)]\nprint map(list,a)","Q_Score":0,"Tags":"python,numpy,tuples","A_Id":49731077,"CreationDate":"2018-04-09T10:35:00.000","Title":"convert list of tuple () to numpy matrix","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to crop and save images from tensorflow object detection API output with following code I created using previous stack overflow questions.\nBut after saving the image seemed to have high density of blue color.\nThe original image has normal color. Since both cropping and encoding refer image channel for format I am not sure about the source of the issue.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":197,"Q_Id":49732469,"Users Score":1,"Answer":"Your problem here is not related to Tensorflow at all but to OpenCV.\nFor some reason, OpenCV loads images in a BGR format and not RGB as Tensorflow expects them to be.\nInvert your first and third channels in your image before passing it to Tensorflow and everything should be fine.","Q_Score":0,"Tags":"python,tensorflow,computer-vision","A_Id":49732736,"CreationDate":"2018-04-09T12:00:00.000","Title":"Image becomes blue masked after applying tf.image.crop_to_bounding_box and tf.image.encode_jpeg","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"So I am facing the following problem:\nI have a ; separated csv, which has ; enclosed in quotes, which is corrupting the data.\nSo like abide;acdet;\"adds;dsss\";acde\nThe ; in the \"adds;dsss\" is moving \" dsss\" to the next line, and corrupting the results of the ETL module which I am writing. my ETL is taking such a csv from the internet, then transforming it (by first loading it in Pandas data frame, doing pre-processing and then saving it), then loading it in sql server. But corrupted files are breaking the sql server schema. \nIs there any solution which I can use in conjunction with Pandas data frame which allows me to fix this issue either during the read(pd.read_csv) or writing(pd.to_csv)( or both) part using Pandas dataframe?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2337,"Q_Id":49733229,"Users Score":0,"Answer":"The sep parameter of pd.read_csv allows you to specify which character is used as a separator in your CSV file. Its default value is ,. Does changing it to ; solve your problem?","Q_Score":2,"Tags":"python-3.x,pandas,csv,data-cleaning,quoting","A_Id":49733409,"CreationDate":"2018-04-09T12:41:00.000","Title":"Solving csv files with quoted semicolon in Pandas data frame","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"sorry for very basic question but i can't find a direct answer.\nI want to check a column to find the maximum value.  for now I assume there is only one maximum value, its unique.\nI then want to find the index name for that row.\nIs there a way to do this?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":66,"Q_Id":49733632,"Users Score":0,"Answer":"Just df.index[df[your_column_name].argmax()] :)","Q_Score":0,"Tags":"python,pandas,dataframe","A_Id":49733693,"CreationDate":"2018-04-09T13:02:00.000","Title":"finding the maximum value of a column in pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Using matplotlib is it possible to take a 2D image of something and place it in a 3D figure? I'd like to take a 2D image and place it at z position of 0. I want to then move the other pixels in the image along the z-axis separately based on a calculation I am making.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1911,"Q_Id":49743901,"Users Score":0,"Answer":"If your image is a coloured image you must first ensure that it is an indexed image. This means that you can only have 2d matrix (and not 3 matricies for the RGB components). Command rgb2ind can help.\nThen you can directly show you image in a 3D way. Use the mesh or surf command.\nYou can also adjust perspective with angle and azimuth.","Q_Score":1,"Tags":"python,matplotlib","A_Id":49743935,"CreationDate":"2018-04-10T01:27:00.000","Title":"Plot 2D image in 3D axes","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When I unpickle cifar-10 by python, I get a dictionary with 4 keys:'batch_label','data','filenames','labels'. But I don't konw what the key 'batch_label' represent for. It's a 'bytes' type data, length is 21. I konw I needn't to know it when I train a network. But I'm still curious about it. Thanks for any reply. ^_^","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":233,"Q_Id":49750513,"Users Score":1,"Answer":"The cifar10 dataset you are donwloading is splitted in serveral batches.\nEach batch has its own id (batch_label).\ndata is the actual batch if images, while filenames is the name of the images encoded. labels, of course, is the set of labels associated with the data","Q_Score":0,"Tags":"python,machine-learning","A_Id":49800463,"CreationDate":"2018-04-10T09:49:00.000","Title":"What does the key \" b'batch_label' \" in cifar-10 unpickle by python use for?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a problem.\nI need numpy 1.14.2, and my machine (Linux Mint 17.3) has only 1.8.2 installed.\nI then installed 1.14.2 through pip. But when I load it in ipython, it still says that it is 1.8.2.\nUsing yolk I saw that 1.14.2 is actually installed, but marked as non-active.\n\nnumpy           - 1.14.2       - non-active development\n  (\/usr\/local\/lib\/python2.7\/dist-packages)\n\nWhile for 1.8.2 it says \"active\".\nAlso, 1.8.2 is located in \"\/usr\/lib\/python2.7\/dist-packages\/numpy\".\nWhy is there a difference? I dont see a reason why there are two libraries.\nI read that python loads the libraries by the order in $PATH.\nAnd indeed, when I look at sys.path, I see that \/usr\/lib... is listet before \/usr\/local ....\nHow can I change that? I dont have anything in .bashrc, \/etc\/profile or \/etc\/rc.local which would set this specific order.\nThx.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":343,"Q_Id":49756369,"Users Score":0,"Answer":"You should use a virtual environment. Your system most likely will default to the most up to date version. \nTry installing:\npip install virtualenv \nthen run:\nvirtualenv -p python2.7 environment_name. \nSource the virtualenv with:\nsource environment_name\/bin\/activate \nthen pip install numpy=1.14.2.\nThen you will have a mini environment with the exact version you want and wont update. This way you can have multiple versions all contained in the same system.","Q_Score":0,"Tags":"python,linux,path","A_Id":49756505,"CreationDate":"2018-04-10T14:34:00.000","Title":"python not using latest numpy version","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have the following data frame: \nParameters Year 2016 Year 2017 Year 2018.... \n0)   X           10          12      13\n1)    Y           12          12      45\n2)    Z           89          23      97\n3\n.\n.\n.\nI want to make a line chart with the column headers starting from Year 2016 to be on the x-axis and each line on the chart to represent each of the parameters - X, Y, Z\nI am using the matplotlib library to make the plot but it is throwing errors.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":403,"Q_Id":49762381,"Users Score":0,"Answer":"If you have a pandas DataFrame, let's call it df, for which your columns are X, Y, Z, etc. and your rows are the years in order, you can simply call df.plot() to plot each column as a line with the y axis being the values and the row name giving the x-axis.","Q_Score":1,"Tags":"python,pandas,matplotlib,visualization","A_Id":49762442,"CreationDate":"2018-04-10T20:29:00.000","Title":"Manipulate Python Data frame to plot line charts","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to Python programming and stumbled across this feature of subtracting in python that I can't figure out.  I have two 0\/1 arrays, both of size 400.  I want to subtract each element of array one from its corresponding element in array 2.  \nFor example say you have two arrays A = [0, 1, 1, 0, 0] and B = [1, 1, 1, 0, 1].\nThen I would expect A - B = [0 - 1, 1 - 1, 1 - 1, 0 - 0, 0 - 1] = [-1, 0, 0, 0, -1]\nHowever in python I get [255, 0, 0, 0, 255].\nWhere does this 255 come from and how do I get -1 instead?\nHere's some additional information:\nThe real variables I'm working with are Y and LR_predictions.\nY = array([[0, 0, 0, ..., 1, 1, 1]], dtype=uint8)\nLR_predictions = array([0, 1, 1, ..., 0, 1, 0], dtype=uint8)\nWhen I use either Y - LR_predictions or numpy.subtract(Y, LR_predictions) \nI get: array([[  0, 255, 255, ...,   1,   0,   1]], dtype=uint8)\nThanks","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":5861,"Q_Id":49784499,"Users Score":0,"Answer":"I can't replicate this but it looks like the numbers are 8 bit and wrapping some how","Q_Score":0,"Tags":"python,arrays,math,subtraction","A_Id":49784641,"CreationDate":"2018-04-11T21:08:00.000","Title":"Python - Subtracting the Elements of Two Arrays","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I recently started to use Google Colab to train my CNN model. It always needs about 10+ hours to train once. But I cannot stay in the same place during these 10+ hours, so I always poweroff my notebook and let the process keep going.\nMy code will save models automatically. I figured out that when I disconnect from the Colab, the process are still saving models after disconnection.\nHere are the questions:\n\nWhen I try to reconnect to the Colab notebook, it always stuck at \"INITIALIZAING\" stage and can't connect. I'm sure that the process is running. How do I know if the process is OVER?\nIs there any way to reconnect to the ongoing process? It will be nice to me to  observe the training losses during the training.\n\nSorry for my poor English, thanks alot.","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":3496,"Q_Id":49790025,"Users Score":0,"Answer":"It seems there's no normal way to do this. But you can save your model to Google Drive with current training epoch number, so when you see something like \"my_model_epoch_1000\" on your google drive, you will know that the process is over.","Q_Score":5,"Tags":"python,deep-learning,keras,jupyter-notebook,google-colaboratory","A_Id":49856665,"CreationDate":"2018-04-12T07:02:00.000","Title":"How to reconnect to the ongoing process on GoogleColab","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I recently started to use Google Colab to train my CNN model. It always needs about 10+ hours to train once. But I cannot stay in the same place during these 10+ hours, so I always poweroff my notebook and let the process keep going.\nMy code will save models automatically. I figured out that when I disconnect from the Colab, the process are still saving models after disconnection.\nHere are the questions:\n\nWhen I try to reconnect to the Colab notebook, it always stuck at \"INITIALIZAING\" stage and can't connect. I'm sure that the process is running. How do I know if the process is OVER?\nIs there any way to reconnect to the ongoing process? It will be nice to me to  observe the training losses during the training.\n\nSorry for my poor English, thanks alot.","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":3496,"Q_Id":49790025,"Users Score":0,"Answer":"first question: restart runtime from runtime menu\nsecond question: i think you can use tensorboard to monitor your work.","Q_Score":5,"Tags":"python,deep-learning,keras,jupyter-notebook,google-colaboratory","A_Id":49854222,"CreationDate":"2018-04-12T07:02:00.000","Title":"How to reconnect to the ongoing process on GoogleColab","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How can I eliminate a dummy dimension in python numpy ndarray?\nFor example, suppose that A.shape = (0, 1325, 3),\nthen how can eliminate '0' dimension so that A.shape = (1325,3).  \nBoth 'np.sqeeze(A)' or 'A.reshape(A.shape[1:])' don't work.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":118,"Q_Id":49797656,"Users Score":3,"Answer":"You can't eliminate that 0 dimension.  A dimension of length 0 is not a \"dummy\" dimension.  It really means length 0.  Since the total number of elements in the array (which you can check with a.size) is the product of the shape attribute, an array with shape (0, 1325, 3) contains 0 elements, while an array with shape (1325, 3) contains 3975 elements.  If there was a way to eliminate the 0 dimension, where would that data come from?  \nIf your array is supposed to contain data, then you probably need to look at how that array was created in the first place.","Q_Score":1,"Tags":"python,numpy","A_Id":49797871,"CreationDate":"2018-04-12T13:18:00.000","Title":"How to eliminate a dummy dimension of ndarray?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying the run the TF object detection locally on my own dataset. Every step is happening perfectly except the Visualization of the BB on a test image.\nFirst I have run the Pascal VOC dataset on the Faster R-CNN Inception ResNet v2, modified the scripts as per the VOC dataset and Then followed the instructions from G3doc, everything worked perfectly, for the visualization, I am using the ipython jupyter notebook given in the objection detection. The visualization was awesome.\nThen I tried to do the same for my own dataset. Repeated all the steps same but no BB is showing on the image.\nCan someone help what might be going wrong?\nP.S. I am using ubuntu 16.04, 64GB ram system.","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":1054,"Q_Id":49805206,"Users Score":2,"Answer":"Try passing the bounding boxes to a print statement. Do you get any valid output? If so then it is probably nothing to do with your model and your bounding boxes are created just fine. What is your mAP? If it is very early on in the training process then your accuracy is probably too poor and do not meet a minimum threshold.\nTake a look at the visualize_boxes_and_labels_on_image_array() function from object_detection\/utils\/visualization_utils.py, note the default value for min_score_thresh. You can either change this default value or pass in min_score_thres=0 as an argument when you call the function.","Q_Score":0,"Tags":"python,tensorflow,ubuntu-16.04,object-detection","A_Id":51196523,"CreationDate":"2018-04-12T20:17:00.000","Title":"tensorflow Object detection, Bounding Boxes are not visible on the own dataset","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Trying to install quandl and need pandas, so I tried pip install pandas and get:\n\nCould not find a version that satisfies the requirement numpy==1.9.3 (from versions: 1.10.4, 1.11.0, 1.11.1rc1, 1.11.1, 1.11.2rc1, 1.11.2, 1.11.3, 1.12.0b1, 1.12.0rc1, 1.12.0rc2, 1.12.0, 1.12.1rc1, 1.12.1, 1.13.0rc1, 1.13.0rc2, 1.13.0, 1.13.1, 1.13.3, 1.14.0rc1, 1.14.0, 1.14.1, 1.14.2)\nNo matching distribution found for numpy==1.9.3.\n\nI'm using python 3.4, win32","AnswerCount":11,"Available Count":2,"Score":0.0363476168,"is_accepted":false,"ViewCount":57246,"Q_Id":49805576,"Users Score":2,"Answer":"Just Install Python 3.5 or higher.","Q_Score":9,"Tags":"python,python-3.x,pandas,numpy,quandl","A_Id":51225714,"CreationDate":"2018-04-12T20:45:00.000","Title":"Could not find a version that satisfies the requirement numpy == 1.9.3","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Trying to install quandl and need pandas, so I tried pip install pandas and get:\n\nCould not find a version that satisfies the requirement numpy==1.9.3 (from versions: 1.10.4, 1.11.0, 1.11.1rc1, 1.11.1, 1.11.2rc1, 1.11.2, 1.11.3, 1.12.0b1, 1.12.0rc1, 1.12.0rc2, 1.12.0, 1.12.1rc1, 1.12.1, 1.13.0rc1, 1.13.0rc2, 1.13.0, 1.13.1, 1.13.3, 1.14.0rc1, 1.14.0, 1.14.1, 1.14.2)\nNo matching distribution found for numpy==1.9.3.\n\nI'm using python 3.4, win32","AnswerCount":11,"Available Count":2,"Score":0.0181798149,"is_accepted":false,"ViewCount":57246,"Q_Id":49805576,"Users Score":1,"Answer":"Just use this command :\npip install pandas==0.19.0 .\nAn older version which will not cry with this error !!","Q_Score":9,"Tags":"python,python-3.x,pandas,numpy,quandl","A_Id":51056520,"CreationDate":"2018-04-12T20:45:00.000","Title":"Could not find a version that satisfies the requirement numpy == 1.9.3","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have around 20TB of time series data stored in big query.\nThe current pipeline I have is:\nraw data in big query => joins in big query to create more big query datasets => store them in buckets\nThen I download a subset of the files in the bucket:\nWork on interpolation\/resampling of data using Python\/SFrame, because some of the time series data have missing times and they are not evenly sampled.\nHowever, it takes a long time on a local PC, and I'm guessing it will take days to go through that 20TB of data.\n\nSince the data are already in buckets, I'm wondering what would the best Google tools for interpolation and resampling?\nAfter resampling and interpolation I might use Facebook's Prophet or Auto ARIMA to create some forecasts. But that would be done locally.\n\nThere's a few services from Google that seems are like good options.\n\nCloud DataFlow: I have no experience in Apache Beam, but it looks like the Python API with Apache Beam have missing functions compared to the Java version? I know how to write Java, but I'd like to use one programming language for this task.\nCloud DataProc: I know how to write PySpark, but I don't really need any real time processing or stream processing, however spark has time series interpolation, so this might be the only option?\nCloud Dataprep: Looks like a GUI for cleaning data, but it's in beta. Not sure if it can do time series resampling\/interpolation.\n\nDoes anyone have any idea which might best fit my use case?\nThanks","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":522,"Q_Id":49809084,"Users Score":0,"Answer":"I would use PySpark on Dataproc, since Spark is not just realtime\/streaming but also for batch processing. \nYou can choose the size of your cluster (and use some preemptibles to save costs) and run this cluster only for the time you actually need to process this data. Afterwards kill the cluster.\nSpark also works very nicely with Python (not as nice as Scala) but for all effects and purposes the main difference is performance, not reduced API functionality.\nEven with the batch processing you can use the WindowSpec for effective time serie interpolation\nTo be fair: I don't have a lot of experience with DataFlow or DataPrep, but that's because out use case is somewhat similar to yours and Dataproc works well for that","Q_Score":1,"Tags":"python,apache-spark,google-cloud-platform,google-cloud-dataflow,google-cloud-dataproc","A_Id":49812019,"CreationDate":"2018-04-13T03:56:00.000","Title":"Google Cloud - What products for time series data cleaning?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I already have installed tensorflow 1.2, CUDA 8.0 and CuDNN 5.1 for python2.7. Now I want to use it for python3.5 but importing tensorflow fails. How do I install tensorflow for python3 again. And do I have to do the CUDA and CuDNN episodes again?","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1667,"Q_Id":49811510,"Users Score":2,"Answer":"If you have already installed tensorflow 1.2, CUDA 8.0 and CuDNN 5.1 for python2.7. Then you can:\n\nyum install python3-pip\n\n(now you have python3 and pip3, however the python version may not be 3.5)\n\npython3 -m pip install --upgrade tensorflow-gpu==1.2\n\n(make sure the installed version is exactly same as that of python2)\nI made it after these two steps.","Q_Score":0,"Tags":"python-3.x,tensorflow","A_Id":57368807,"CreationDate":"2018-04-13T07:24:00.000","Title":"How to install tensorflow-gpu for both python2 and python3","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to use tensorflow-gpu. Using Anaconda, I installed the libraries and active both (tensorflow and tensorflow-gpu) I have also installed Keras in this anaconda environment as well\nNext I launch Spider IDE within my Anaconda environment and run my py script, that is when I get the following error:\ncannot import tensorflow - Import Error: cannot import name 'self_check'\nI'm totally lost. Any recommendations? \nUsing Python 3.5\nThanks","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":2441,"Q_Id":49819253,"Users Score":2,"Answer":"pip install --upgrade tensorflow\nor\nyou can uninstall the tensorflow and re-install it.","Q_Score":2,"Tags":"python,tensorflow,anaconda","A_Id":50134947,"CreationDate":"2018-04-13T14:26:00.000","Title":"cannot import tensorflow - \"Import Error: cannot import name 'self_check' \"","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have been trying to implement l1-regularization in Tensorflow using the l1_regularization_strength parameter in the ProximalAdagradOptimizer function from Tensorflow. (I am using this optimizer specifically to get a sparse solution.) I have two questions regarding the regularization.\n\nDoes the l1-regularization used in the optimizer apply to forward and backward propagation for a neural network or only the back propagation?\nIs there a way to break down the optimizer so the regularization only applies to specific layers in the network?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":425,"Q_Id":49824281,"Users Score":1,"Answer":"Regularization applies neither to forward or backpropagation but to the weight updates.\nYou can use different optimizers for different layers by explicitly passing the variables to minimize to each optimizer.","Q_Score":1,"Tags":"python,optimization,tensorflow,neural-network,regularized","A_Id":49908382,"CreationDate":"2018-04-13T19:58:00.000","Title":"Regularization Application in the Proximal Adagrad Optimizer in Tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a object with many fields. Each field has different range of values. I want to use hypothesis to generate different instances of this object.\nIs there a limit to the number of combination of field values Hypothesis can handle?  Or what does the search tree hypothesis creates look like? I don't need all the combinations but I want to make sure that I get a fair number of combinations where I test many different values for each field. I want to make sure Hypothesis is not doing a DFS until it hits the max number of examples to generate","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":75,"Q_Id":49825415,"Users Score":1,"Answer":"TLDR: don't worry, this is a common use-case and even a naive strategy works very well.\nThe actual search process used by Hypothesis is complicated (as in, \"lead author's PhD topic\"), but it's definitely not a depth-first search!  Briefly, it's a uniform distribution layered on a psudeo-random number generator, with a coverage-guided fuzzer biasing that towards less-explored code paths, with strategy-specific heuristics on top of that.\nIn general, I trust this process to pick good examples far more than I trust my own judgement, or that of anyone without years of experience in QA or testing research!","Q_Score":1,"Tags":"python-hypothesis","A_Id":49838147,"CreationDate":"2018-04-13T21:32:00.000","Title":"Hypothesis search tree","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'd like to create a strategy C that, 90% of the time chooses strategy A, and 10% of the time chooses strategy B.\nThe random python library does not work even if I seed it since each time the strategy produces values, it generates the same value from random.\nI looked at the implementation for OneOfStrategy and they use \n            i = cu.integer_range(data, 0, n - 1)\nto randomly generate a number\ncu is from the internals\nimport hypothesis.internal.conjecture.utils as cu\nWould it be fine for my strategy to use cu.integer_range or is there another implementation?","AnswerCount":1,"Available Count":1,"Score":0.537049567,"is_accepted":false,"ViewCount":172,"Q_Id":49827010,"Users Score":3,"Answer":"Hypothesis does not allow users to control the probability of various choices within a strategy.  You should not use undocumented interfaces either - hypothesis.internal is for internal use only and could break at any time!\nI strongly recommend using C = st.one_of(A, B) and trusting Hypothesis with the details.","Q_Score":0,"Tags":"python-hypothesis","A_Id":49838288,"CreationDate":"2018-04-14T01:27:00.000","Title":"Have a Strategy that does not uniformly choose between different strategies","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have to handle sparse matrix that can occasionally be very big, nearing or exceeding RAM capacity. I also need to support mat*vec and mat*mat operations.\nSince internally a csr_matrix is 3 arrays data, indices and indptr is it possible to create a csr matrix from numpy memmap.","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":517,"Q_Id":49835342,"Users Score":1,"Answer":"This can partially work, until you try to do much with the array. There's a very good chance the subarrays will be fully read into memory if you subset, or you'll get an error.\nAn important consideration here is that the underlying code is written assuming the arrays are typical in-memory numpy arrays. Cost of random access is very different for mmapped arrays and in memory arrays. In fact, much of the code here is (at time of writing) in Cython, which may not be able to work with more exotic array types.\nAlso most of this code can change at any time, as long as the behaviour is the same for in-memory arrays. This has personally bitten me when some I learned some code I worked with was doing this, but with h5py.Datasets for the underlying arrays. It worked surprisingly well, until a bug fix release of scipy completely broke it.","Q_Score":1,"Tags":"python,numpy,scipy,sparse-matrix","A_Id":62990659,"CreationDate":"2018-04-14T19:26:00.000","Title":"Using memory mapped buffers for scipy sparse","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have to handle sparse matrix that can occasionally be very big, nearing or exceeding RAM capacity. I also need to support mat*vec and mat*mat operations.\nSince internally a csr_matrix is 3 arrays data, indices and indptr is it possible to create a csr matrix from numpy memmap.","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":517,"Q_Id":49835342,"Users Score":-2,"Answer":"This works without any problems.","Q_Score":1,"Tags":"python,numpy,scipy,sparse-matrix","A_Id":50985982,"CreationDate":"2018-04-14T19:26:00.000","Title":"Using memory mapped buffers for scipy sparse","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"After I instantiate a scikit model (e.g. LinearRegression), if I call its fit() method multiple times (with different X and y data), what happens? Does it fit the model on the data like if I just re-instantiated the model (i.e. from scratch), or does it keep into accounts data already fitted from the previous call to fit()?\nTrying with LinearRegression (also looking at its source code) it seems to me that every time I call fit(), it fits from scratch, ignoring the result of any previous call to the same method. I wonder if this true in general, and I can rely on this behavior for all models\/pipelines of scikit learn.","AnswerCount":2,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":28564,"Q_Id":49841324,"Users Score":6,"Answer":"You can use term fit() and train() word interchangeably in machine learning. Based on classification model you have instantiated, may be a clf = GBNaiveBayes() or clf = SVC(),  your model uses specified machine learning technique.\nAnd as soon as you call clf.fit(features_train, label_train) your model starts training using the features and labels that you have passed.\nyou can use clf.predict(features_test) to predict.\nIf you will again call clf.fit(features_train2, label_train2) it will start training again using passed data and will remove the previous results. Your model will reset the following inside model:\n\nWeights\nFitted Coefficients\nBias\nAnd other training related stuff...\n\nYou can use partial_fit() method as well if you want your previous calculated stuff to stay and additionally train using next data","Q_Score":58,"Tags":"python,machine-learning,scikit-learn","A_Id":49841568,"CreationDate":"2018-04-15T11:19:00.000","Title":"What does calling fit() multiple times on the same model do?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When I call any function in python3.6, I get the error below; however, it works fine in python3.4. Any idea? \nimport tensorflow as tf \ntf.Session()\nTraceback (most recent call last):\n  File \"\", line 1, in \nAttributeError: module 'tensorflow' has no attribute 'Session'\n\nHere is my  System information\n\n\nOS Platform and Distribution (e.g., Linux Ubuntu 16.04): Debian 8.7 \nTensorFlow installed from (source or binary): by pip3\nTensorFlow version (use command below): 1.7.0\nPython version: 3.6.5\nCUDA\/cuDNN version: cuda 9.0 and cudnn 7.0\nGPU model and memory: K80, 12 GB \nExact command to reproduce:\nimport tensorflow as tf \ntf.Session()","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":18023,"Q_Id":49846106,"Users Score":0,"Answer":"With the new version of python3.6 my problem is solved. \nI removed my previous python3.6 and installed the newest version. Then installed tensorflow through pip and it works.","Q_Score":3,"Tags":"tensorflow,python-3.6","A_Id":54985155,"CreationDate":"2018-04-15T19:47:00.000","Title":"AttributeError: module 'tensorflow' has no attribute 'Session'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I built a image classification model using convolutional neural network for 5 classes.It is giving the training accuracy as 100% and testing accuracy as 82%. But when I gave an image manually for predicting, the model is not able to classify them correctly.For 10 images the model is classifying only 3-4 images correctly.What is the mistake? What should I do??","AnswerCount":4,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":116,"Q_Id":49849715,"Users Score":0,"Answer":"This means that you are clearly overfitting.\nYour model is not able to generalize well to the images that you enter manually.\nThere are a few things that you could do.\n\nYou could try regularization so that your model does not overfit.\nTry getting more data\nData augmentation\n\nI hope that by introducing regularization, your model would work better on manually entered images.","Q_Score":0,"Tags":"python,tensorflow,classification,conv-neural-network","A_Id":49849810,"CreationDate":"2018-04-16T04:34:00.000","Title":"Regarding image classification using CNN","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I built a image classification model using convolutional neural network for 5 classes.It is giving the training accuracy as 100% and testing accuracy as 82%. But when I gave an image manually for predicting, the model is not able to classify them correctly.For 10 images the model is classifying only 3-4 images correctly.What is the mistake? What should I do??","AnswerCount":4,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":116,"Q_Id":49849715,"Users Score":0,"Answer":"Your model is not able to generalize properly. the training has gone very quickly through all the images in the training set and even started making predictions on the same images.  \n\nTry adding images and of different sizes.   \nTry flipping the    original images horizontally to create transformed images that might  also help in accurately training your model.","Q_Score":0,"Tags":"python,tensorflow,classification,conv-neural-network","A_Id":51365356,"CreationDate":"2018-04-16T04:34:00.000","Title":"Regarding image classification using CNN","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I built a image classification model using convolutional neural network for 5 classes.It is giving the training accuracy as 100% and testing accuracy as 82%. But when I gave an image manually for predicting, the model is not able to classify them correctly.For 10 images the model is classifying only 3-4 images correctly.What is the mistake? What should I do??","AnswerCount":4,"Available Count":3,"Score":-0.049958375,"is_accepted":false,"ViewCount":116,"Q_Id":49849715,"Users Score":-1,"Answer":"Besides that, probably your training set has way easier images than your test set (those image that you give manually). That can happen even if you are feeding the network from the same dataset.\nTo have better results on unseen images, you must have to reach a good generalisation level. Try what is said previously above...","Q_Score":0,"Tags":"python,tensorflow,classification,conv-neural-network","A_Id":70926699,"CreationDate":"2018-04-16T04:34:00.000","Title":"Regarding image classification using CNN","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How to select the inverse index in pd.DataFrame by using loc or iloc?\nI tried df.loc[!my_index,my_feature] but fail. \nAnd df.loc[[ind for ind in df.index.tolist() if ind not in my_index],my_feature] looks too dull. Any better idea?","AnswerCount":3,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":12631,"Q_Id":49854796,"Users Score":3,"Answer":"Assuming my_index are the row indices you want to ignore, you could drop these where they exist in the dataframe df:\ndf = df.drop(my_index, errors='ignore')","Q_Score":17,"Tags":"python,pandas,dataframe,indexing","A_Id":63833958,"CreationDate":"2018-04-16T10:13:00.000","Title":"Select the inverse index in pd.Dataframe","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm reading through the Numpy docs, and it appears that the functions np.prod(...), np.product(...) and the ndarray method a.prod(...) are all equivalent. \nIs there a preferred version to use, both in terms of style\/readability and performance? Are there different situations where different versions are preferable? If not, why are there three separate but very similar ways to perform the same operation?","AnswerCount":2,"Available Count":1,"Score":0.4621171573,"is_accepted":false,"ViewCount":13184,"Q_Id":49863633,"Users Score":5,"Answer":"This is what I could gather from the source codes of NumPy 1.14.0. For the answer relevant to the current Master branch (NumPy 1.15.0), see the answer of miradulo.\n\nFor an ndarray, prod() and product() are equivalent. \nFor an ndarray, prod() and product() will both call um.multiply.reduce().\nIf the object type is not ndarray but it still has a prod method, then prod() will return prod(axis=axis, dtype=dtype, out=out, **kwargs) whereas product will try to use um.multiply.reduce.\nIf the object is not an ndarray and it does not have a prod method, then it will behave as product(). \nThe ndarray.prod() is equivalent to prod().\n\nI am not sure about the latter part of your question regarding preference and readability.","Q_Score":16,"Tags":"python,numpy,numpy-ndarray","A_Id":49864136,"CreationDate":"2018-04-16T18:04:00.000","Title":"numpy.product vs numpy.prod vs ndarray.prod","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to train an RNN with different input size of sentence X, without padding. The logic used for this is that I am using Global Variables and for every step, I take an example, write the forward propagation i.e. build the graph, run the optimizer and then repeat the step again with another example. The program is extremely slow as compared to the numpy implementation of the same thing where I have implemented forward and backward propagation and using the same logic as above. The numpy implementation takes a few seconds while Tensorflow is extremely slow. Can running the same thing on GPU will be useful or I am doing some logical mistake ?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":42,"Q_Id":49869622,"Users Score":0,"Answer":"As a general guideline, GPU boosts performance only if you have calculation intensive code and little data transfer. In other words, if you train your model one instance at a time (or on small batch sizes) the overhead for data transfer to\/from GPU can even make your code run slower! But if you feed in a good chunk of samples, then GPU will definitely boost your code.","Q_Score":0,"Tags":"python,numpy,tensorflow,machine-learning,rnn","A_Id":49869744,"CreationDate":"2018-04-17T03:51:00.000","Title":"Training changing input size RNN on Tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Trying to read a matlab file into python. I am using the function loadmat from scipy.io to read it, and I'm getting back the error ValueError: negative dimensions are not allowed.\nHow can I fix this issue?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":131,"Q_Id":49879324,"Users Score":0,"Answer":"I found out that this was a binary file (was written with Matlab's fwrite).\nThe solution was saving the file as text, and then loading it in python not using loadmat.","Q_Score":0,"Tags":"python,scipy,mat","A_Id":49905936,"CreationDate":"2018-04-17T13:23:00.000","Title":"python: loadmat - ValueError: negative dimensions are not allowed","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"What is the difference between plot() and iplot() in displaying a figure in Jupyter Notebook?","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":28800,"Q_Id":49880314,"Users Score":1,"Answer":"Correct answer provided.I tried to run this code in pycharm IDE but could not. jupyter notebook is required to graph iplot.","Q_Score":10,"Tags":"python,pandas,matplotlib,plotly","A_Id":63543545,"CreationDate":"2018-04-17T14:07:00.000","Title":"What is difference between plot and iplot in Pandas?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Library: Keras, backend:Tensorflow\nI am training a single class\/binary classification problem, wherein my final layer has a single node, with activation of sigmoid type. I am compiling my model with a binary cross entropy loss. When I run the code to train my model, I notice that the loss is a value greater than 1. Is that right, or am I going wrong somewhere? I have checked the labels. They're all 0s and 1s.  \nIs it possible to have the binary cross entropy loss greater than 1?","AnswerCount":2,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":10194,"Q_Id":49882424,"Users Score":6,"Answer":"Yes, its correct, the cross-entropy is not bound in any specific range, its just positive (> 0).","Q_Score":5,"Tags":"python,tensorflow,keras","A_Id":49885159,"CreationDate":"2018-04-17T15:47:00.000","Title":"Keras Tensorflow Binary Cross entropy loss greater than 1","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm new to python, so apologies if this is a silly question. \nI'm trying to use mlxtend, and have installed it using pip. Pip confirms that it is installed (when I type \"pip install mlxtend\" it notes that the requirement is already satisfied). However, when I try and import mlxtend in python using \"import mlxtend as ml\", I get the error: \"ModuleNotFoundError: No module named 'mlxtend'\". I used the same process for installing and importing pandas and numpy, and they both worked. Any advice?\nI should note that I have resorted to dropping in the specific code I need from mlxtend (apriori and association rules), which is working, but hardly a good long term strategy!\nI'm using python version 3.6.5. \nThanks!","AnswerCount":5,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":19607,"Q_Id":49889524,"Users Score":0,"Answer":"Try this:\nconda install -c conda-forge mlxtend\nDon't mix pip and Conda environments. Last time I did this I broke my Python and needed to reinstall everything again.\nIf you must deal with different environments using pip and conda because of many projects, use pyenv.","Q_Score":8,"Tags":"python,python-import,importerror,mlxtend","A_Id":64196727,"CreationDate":"2018-04-18T00:56:00.000","Title":"Issues importing mlxtend python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm new to python, so apologies if this is a silly question. \nI'm trying to use mlxtend, and have installed it using pip. Pip confirms that it is installed (when I type \"pip install mlxtend\" it notes that the requirement is already satisfied). However, when I try and import mlxtend in python using \"import mlxtend as ml\", I get the error: \"ModuleNotFoundError: No module named 'mlxtend'\". I used the same process for installing and importing pandas and numpy, and they both worked. Any advice?\nI should note that I have resorted to dropping in the specific code I need from mlxtend (apriori and association rules), which is working, but hardly a good long term strategy!\nI'm using python version 3.6.5. \nThanks!","AnswerCount":5,"Available Count":2,"Score":0.1194272985,"is_accepted":false,"ViewCount":19607,"Q_Id":49889524,"Users Score":3,"Answer":"I had the same issue while using Anaconda, I tried to install it with Anaconda, however, Notebook didn't see it installed. You can also try to install it in CMD by just typing\npip install mlxtend --user or pip3 install mlxtend --user\nAfter installing it with CMD, Notebook didn't give error for my case. Just reply if this helps. Good luck all.","Q_Score":8,"Tags":"python,python-import,importerror,mlxtend","A_Id":58758122,"CreationDate":"2018-04-18T00:56:00.000","Title":"Issues importing mlxtend python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm translating a Python program which uses OpenCV to C# with Java bindings. I tested both Python and C# programs using the same image, and I realized the findContours method returns different contours between the 2 programs.\nPython: _, contours, hierarchy = cv2.findContours(edges, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\nC#: Imgproc.FindContours(edges, contours, hierarchy, Imgproc.RetrTree, Imgproc.ChainApproxSimple);\nFor Python, I checked using len(contours), for C# contours.Count, and they return the values 94 and 106 respectively. I think this may be the cause to many discrepancies in my translated program, and I'm not sure why. What am I doing wrong here?\n\nAdd-on: The Canny method below is called before calling findContours. Anything before is just reading the image, and converting the image into a gray one, thus the grayImg variable.\n\nC#: Imgproc.Canny(grayImg, edges, 100, 200, 3, false);\nPython: edges = cv2.Canny(gray, 100, 200, apertureSize = 3)\nI previously thought it was because of differing OpenCV versions, but I realized both are using OpenCV 3. Unless the FindContours method is different in 3.1 than 3.4, I'm back to square one again as I don't know the cause of the problem.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":194,"Q_Id":49890287,"Users Score":0,"Answer":"I've finally found the answer to this phenomenon. The python program is using OpenCV 3.4, while the Java bindings of my C# program is using the older OpenCV 3.1 . The FindContours method may be found in both versions, but apparently returns different values.","Q_Score":0,"Tags":"c#,python,opencv,opencv-contour","A_Id":49896647,"CreationDate":"2018-04-18T02:37:00.000","Title":"Different return from Java and Python cv2.findContours","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":1,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"tensorflow version 1.5.0rc1 \npython version:3.5\nWhen reshape a rectangular image to [height,width]\nby using tf.reshape(x,[-1,x,y,1]) \n\neg. tf.reshape(x,[-1,14,56,1]) run conv2d returns:\n  InvalidArgumentError (see above for traceback): Input to reshape is a\n  tensor with 358400 values, but the requested shape requires a multiple\n  of 3136    [[Node: Reshape_1 = Reshape[T=DT_FLOAT, Tshape=DT_INT32,\n  _device=\"\/job:localhost\/replica:0\/task:0\/device:GPU:0\"](MaxPool_1, Reshape_1\/shape)]]\n\nwhich 3136 is the square of 56.\nthe tensor treats the reshape as 56x56 instead of 14*56 matrix.\nIs there a way to get rid of it and set my CNN to a non square image?\nThanks","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1803,"Q_Id":49893741,"Users Score":0,"Answer":"I don't exactly agree with reshaping a rectangular picture as you destroy the relationship between neighbour pixels. Instead, you have several options to apply CNNs on a non-quadratic image:\n1.) Use padding. During preprocessing, you could fill in pixels to get a quadratic image. Through this you can apply the quadratic filter.\n2.) Use different quadratic windows of that image for training. For example, create a quadratic window and run it over the image to get a number of sub-pictures.\n3.) You could use different strides for the dimensions of your picture.\n4.) You could stretch the picture in the needed direction, although I'm not exactly sure how this affects the performance later on. I would only try this as a last resort solution.","Q_Score":0,"Tags":"python,tensorflow","A_Id":49894197,"CreationDate":"2018-04-18T07:32:00.000","Title":"tensorflow: CNN for non square image","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to write a script that imports a file, then does something with the file and outputs the result into another file.\ndf = pd.read_csv('somefile2018.csv')\nThe above code works perfectly fine. However, I'd like to avoid hardcoding the file name in the code.\nThe script will be run in a folder (directory) that contains the script.py and several csv files.\nI've tried the following:\nsomefile_path = glob.glob('somefile*.csv')\ndf = pd.read_csv(somefile_path)\nBut I get the following error:\nValueError: Invalid file path or buffer object type: <class 'list'>","AnswerCount":5,"Available Count":1,"Score":0.0399786803,"is_accepted":false,"ViewCount":14334,"Q_Id":49898742,"Users Score":1,"Answer":"Loop over each file and build a list of DataFrame, then assemble them together using concat.","Q_Score":8,"Tags":"python,pandas","A_Id":49898817,"CreationDate":"2018-04-18T11:36:00.000","Title":"Pandas reading csv files with partial wildcard","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a large (9.3 GB) .npy file containing a uint8 values in an (67000, 9, 128, 128) ndarray. I created it using np.save() and when loading it using x = np.memmap('file.npy', \"uint8\", shape=(67000, 9, 128, 128), mode=\"r\"), np.sum(x[0,0,:,0]) returns 13783. The \"problem\" is that when I try loading it with np.load(\"file.npy\") and run the same function, I get the sum to 13768.\nSince np.load() loads the whole file in memory, I'd assume that the sum computed on its ndarray is correct, while the one returned by the ndarray loaded with memmap is wrong, but why are they different ? If it was a reading error the sum should be really off, so why is it off by only 15 ??! \nI have no clue why that is the case. This won't affect my computation per say but it could be significant for other tasks.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":379,"Q_Id":49908788,"Users Score":4,"Answer":"numpy.memmap is for treating raw data in a file as a numpy array.  Your filename is 'file.npy', so that is not \"raw\" data.  It is a NPY file, which has a header containing meta-information about the array stored in it.\nTo memory-map a NPY file, use the mmap_mode argument of numpy.load().","Q_Score":0,"Tags":"python,numpy,memory","A_Id":49908880,"CreationDate":"2018-04-18T20:48:00.000","Title":"Sum of values accessed through numpy memmap is wrong","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been searching for a bit now and haven't been able to find anything similar to my question. Maybe i'm just not searching correctly. Anyways this is a question from my exam review. Given a binary tree, I need to output a list such that each item in the list is the number of nodes on a level in a binary tree at the items list index. What I mean, lst = [1,2,1] and the 0th index is the 0th level in the tree and the 1 is how many nodes are in that level. lst[1] will represent the number of nodes (2) in that binary tree at level 1. The tree isn't guaranteed to be balanced. We've only been taught preorder,inorder and postorder traversals, and I don't see how they would be useful in this question. I'm not asking for specific code, just an idea on how I could figure this out or the logic behind it. Any help is appreciated.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1773,"Q_Id":49909109,"Users Score":1,"Answer":"The search ordering doesn't really matter as long as you only count each node once. A depth-first search solution with recursion would be:\n\nCreate a map counters to store a counter for each level. E.g. counters[i] is the number of nodes found so far at level i. Let's say level 0 is the root.\nDefine a recursive function count_subtree(node, level): Increment counters[level] once. Then for each child of the given node, call count_subtree(child, level + 1) (the child is at a 1-deeper level).\nCall count_subtree(root_node, 0) to count starting at the root. This will result in count_subtree being run exactly once on each node because each node only has one parent, so counters[level] will be incremented once per node. A leaf node is the base case (no children to call the recursive function on).\nBuild your final list from the values of counters, ordered by their keys ascending.\n\nThis would work with any kind of tree, not just binary. Running time is O(number of nodes in tree). Side note: The depth-first search solution would be easier to divide and run on parallel processors or machines than a similar breadth-first search solution.","Q_Score":3,"Tags":"python,python-3.x,tree,binary-tree,tree-traversal","A_Id":49911007,"CreationDate":"2018-04-18T21:14:00.000","Title":"Count number of nodes per level in a binary tree","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using Keras for a regression task and want to restrict my output to a range (say between 1 and 10)\nIs there a way to ensure this?","AnswerCount":2,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":8004,"Q_Id":49911206,"Users Score":6,"Answer":"Normalize your outputs so they are in the range 0, 1. Make sure your normalization function lets you transform them back later.\nThe sigmoid activation function always outputs between 0, 1. Just make sure your last layer has sigmoid activation to restrict your output into that range. Now you can take your outputs and transform them back into the range you wanted.\nYou could also look into writing your own activation function to transform your data.","Q_Score":8,"Tags":"python,neural-network,keras,regression","A_Id":49911299,"CreationDate":"2018-04-19T01:13:00.000","Title":"How to restrict output of a neural net to a specific range?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"This is the error : \nSolving environment: failed\nUnsatisfiableError: The following specifications were found to be in conflict:\n  - numba -> numpy[version='>=1.14,<1.15.0a0']\n  - tensorflow\nUse \"conda info \" to see the dependencies for each package.","AnswerCount":2,"Available Count":1,"Score":0.2913126125,"is_accepted":false,"ViewCount":1294,"Q_Id":49914882,"Users Score":3,"Answer":"You have to run the conda info tensorflow and conda info numba to see each dependencies for each package and then you have to install those package like conda install package=version to fix the problem.","Q_Score":0,"Tags":"python,tensorflow,anaconda,jupyter-notebook,conda","A_Id":49915763,"CreationDate":"2018-04-19T07:12:00.000","Title":"I get the following error whenever I try to run conda install tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an numpy array in python that represent an image its size is 28x28x3 while the max value of it is 0.2 and the min is -0.1. I want to scale that image between 0-255. How can I do so?","AnswerCount":2,"Available Count":1,"Score":0.2913126125,"is_accepted":false,"ViewCount":18784,"Q_Id":49922460,"Users Score":3,"Answer":"You can also use uint8 datatype while storing the image from numpy array.\nimport numpy as np\n from PIL import Image\n img = Image.fromarray(np.uint8(tmp))\ntmp is my np array of size 255*255*3.","Q_Score":10,"Tags":"python,image,numpy","A_Id":52469364,"CreationDate":"2018-04-19T13:34:00.000","Title":"Scale a numpy array with from -0.1 - 0.2 to 0-255","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am looking for a good approach using python libraries to tackle the following problem:\nI have a dataset with a column that has product description. The values in this column can be very messy and would have a lot of other words that are not related to the product. I want to know which rows are about the same product, so I would need to tag each description sentence with its main topics. For example, if I have the following: \n\"500 units shoe green sport tennis import oversea plastic\", I would like the tags to be something like: \"shoe\", \"sport\". So I am looking to build an approach for semantic tagging of sentences, not part of speech tagging. Assume I don't have labeled (tagged) data for training. \nAny help would be appreciated.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":184,"Q_Id":49929066,"Users Score":2,"Answer":"Lack of labeled data means you cannot apply any semantic classification method using word vectors, which would be the optimal solution to your problem. An alternative however could be to construct the document frequencies of your token n-grams and assume importance based on some smoothed variant of idf (i.e. words that tend to appear often in descriptions probably carry some semantic weight). You can then inspect your sorted-by-idf list of words and handpick(\/erase) words that you deem important(\/unimportant). The results won't be perfect, but it's a clean and simple solution given your lack of training data.","Q_Score":1,"Tags":"python-2.7,nlp,nltk,gensim,semantic-analysis","A_Id":49935903,"CreationDate":"2018-04-19T19:44:00.000","Title":"Clear approach for assigning semantic tags to each sentence (or short documents) in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Idea behind nullifying\/ignoring a feature from test set is to understand how important is it considered by the model to predict the target variable (by comparing the evaluation metric's value). For numerical variables, I thought of setting them to 0, assuming the multiplication (with weights) would be 0 and thus it would get eliminated from the set. Is this approach right, else what should be done?\nI am using tensorflow's DNNRegressor for modelling.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":46,"Q_Id":49935491,"Users Score":0,"Answer":"For deep models there's no general input-independent way to do this kind of feature ablation you want (take a pretrained model and just change that feature's representation on the test set).\nInstead I recommend you do training time ablation: train different variations of your model with different feature combinations and compare their validation set performances. This will actually tell you how much each feature helps.","Q_Score":0,"Tags":"python,tensorflow,machine-learning,deep-learning,regression","A_Id":50009358,"CreationDate":"2018-04-20T06:37:00.000","Title":"Is there a way to nullify a specific feature in test set while evaluating a tensorflow model?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I created several Keras models (Model_1, Model_2...Model_N) in one code. I would like to clear only one specified model (e.g. Model_1).\nI guess K.clear_session(), which will clear all models from the memory, is not useful in this case. Is there any solution?\nThank you in advance.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":36,"Q_Id":49936467,"Users Score":0,"Answer":"you can use del[] to explicitly delete the variables you no longer need, this triggers python's garbage collector. so put all the variables associated with that model inside del[] and python would free the memory.","Q_Score":2,"Tags":"python,keras","A_Id":49936800,"CreationDate":"2018-04-20T07:36:00.000","Title":"How can a specific model be cleared from the memory?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I have a question concerning tuning hyperparameters for the Inception ResNet V2 model (or any other DL model), which I can't really wrap my head around.\nRight now, I have certain set certain hyperparameters, such as learning_rate, decay_factor and decay_after_nr_epochs. My model saves checkpoints, so it can continue at these points later on. \nIf I run the model again, with more epochs, it logically continues at the last checkpoint to continue training.\nHowever, if I would set new hyperparameters, such as learning_rate = 0.0001 instead of learning_rate = 0.0002, does it make sense to continue on the checkpoints, or is it better to use new hyperparameters on the initial model? \nThe latter sounds more logical to me, but I'm not sure whether this is necessary. \nThanks in advance.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":470,"Q_Id":49941265,"Users Score":1,"Answer":"Both the methods are okay but you have to see your training loss after adjusting them. If they are converging in both the cases then it's fine otherwise adjust accordingly.\nHowever, people adopt these two methods as far as I know 1. Keep a higher learning rate initially and keep a decay factor, thus reducing your learning rate slowly as it starts converging. 2. You can keep an eye on loss function and do early stopping if you think you can adjust to better learning rate.","Q_Score":0,"Tags":"python,tensorflow,deep-learning","A_Id":49943047,"CreationDate":"2018-04-20T12:04:00.000","Title":"Tuning hyperparameters Inception model with checkpoints","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using lightgbm.train with valid_sets, early_stopping_rounds and feval function for multiclass problem with \"objective\": \"multiclass\". I want to find best_iteration and best_score for my custom evaluation function. But it finds them for multi_logloss metrics, which is corresponding to specified objective. So the question is can I find in LightGBM best_iteration and best_score for my feval function and how?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":472,"Q_Id":49943689,"Users Score":0,"Answer":"You can use objective:\"multi_error\", or also you can combine objectives as\nobjective: \"multi_error\", \"multi_logloss\"\nMulti_error will directly focus on the accuracy.","Q_Score":0,"Tags":"python-3.x,lightgbm","A_Id":52770137,"CreationDate":"2018-04-20T14:13:00.000","Title":"LightGBM Python API. Best_iteration and best_score for custom evaluation function (feval)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have built a predictive model  in Windows to train the dataset using python 3.6.1. I have used pickle to save the train model as a Pickle file\nNow I have written a another python script to read and load the train pickle file to predict the test data.\nI have got results successful in Windows.\nNow I want to move the Train pickle file and the Python script (that used the Train pickle file to predict the model using test data) to Linux environment which has the similar version of python installation as that of windows.But the python installation in Linux doesn't include machine learning libraries.\nIn such a case will the Train pickle file of windows behave as a exe file having all the machine learning libraries?\nWill the python script that loads and read the train pickle file to predict the data fail in Linux as it does not any machine learning libraries installed.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":984,"Q_Id":49947850,"Users Score":1,"Answer":"Pickling is just the python form of serialization.  Serialization only preserves raw data, such as strings, integers, floats, lists, and similar things.\nIf you pickle a model, you can unpickle it and use it later, but if you don't have the libraries (keras, tensorflow, or whatever you need), you cannot use it on that machine.","Q_Score":1,"Tags":"python","A_Id":49947979,"CreationDate":"2018-04-20T18:38:00.000","Title":"Python pickle for Machine Learning","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using cartopy to plot images on geographic axes. I used the Mercator projection to plot my data. However I realized that the equal aspect ratio is based on degrees and not in km. How can I do to force the map to respect a 1 km x 1 km aspect ratio instead of a 1\u00b0 x 1\u00b0 aspect ratio.\nThanks","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":160,"Q_Id":49950672,"Users Score":1,"Answer":"The aspect ratio of a Mercator projection isn't actually 1\u00b0:1\u00b0, but is instead 1:1 in cartesian \"Mercator units\". The Mercator projection doesn't preserve distance along the meridians, and therefore the higher (or lower) you go in latitude in the Mercator projection, the longer (in physical distance) 1 Mercator unit is in the y direction.\nAs 1\u00b0 has approximately the same length anywhere on the Earth (assuming a perfect sphere), you are actually looking for a projection that does preserve the 1\u00b0 x 1\u00b0 aspect ratio. At high-resolution, many map projections are good representations of 1km:1km, but at global scale there are only a few good choices.\nEssentially, you are looking for a projection that is equidistant along both parallels and meridians, and the most obvious projection with that property is Plate Carree \/ Equirectangular (all cylindrical projections are equidistant along parallels, but Plate Carree is equidistant along meridians too).","Q_Score":0,"Tags":"python-3.x,cartopy","A_Id":51285200,"CreationDate":"2018-04-20T22:54:00.000","Title":"Using Cartopy by respecting 1 to 1 km aspect ratio","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a very large HDF5 file created using Python\/ h5py. I cannot open the file on HDFView 2.14, when I try to open the file nothing happens. Any suggestions on how I can open\/ view the file? It contains just 5 datasets, but each dataset has 778 million rows.. hence the problem. \nThank you!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":626,"Q_Id":49950725,"Users Score":0,"Answer":"you can edit your hdfview.bat and add more memory on the switch\nthis is the line on .bat file:\nstart \"HDFView\" \"%JAVABIN%\\javaw.exe\" %JAVAOPTS% -Xmx1024M -Djava.library.path=\"%INSTALLDIR%\\lib;%INSTALLDIR%\\lib\\ext\" -Dhdfview.root=\"%INSTALLDIR%\" -cp \"%INSTALLDIR%\\lib\\fits.jar;%INSTALLDIR%\\lib\\netcdf.jar;%INSTALLDIR%\\lib\\jarhdf-3.3.2.jar;%INSTALLDIR%\\lib\\jarhdf5-3.3.2.jar;%INSTALLDIR%\\lib\\slf4j-api-1.7.5.jar;%INSTALLDIR%\\lib\\extra\\slf4j-nop-1.7.5.jar;%INSTALLDIR%\\lib\\HDFView.jar\" hdf.view.HDFView %*\nyou can edit -Xmx1024M to more like: -Xmx4024M\nit add more memory to java machine","Q_Score":1,"Tags":"python,out-of-memory,hdf5,h5py,hdf","A_Id":50278726,"CreationDate":"2018-04-20T23:01:00.000","Title":"Viewing a large (12.5GB) HDF5 file written using h5py on HDFView 2.14","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am going to implement RNN using Pytorch . But , before that , I am having some difficulties in understanding the character level one-hot encoding which is asked in the question .\nPlease find below the question \n\nChoose the text you want your neural network to learn, but keep in mind that your\ndata set must be quite large in order to learn the structure! RNNs have been trained\non highly diverse texts (novels, song lyrics, Linux Kernel, etc.) with success, so you\ncan get creative. As one easy option, Gutenberg Books is a source of free books where\nyou may download full novels in a .txt format.\nWe will use a character-level representation for this model. To do this, you may use\nextended ASCII with 256 characters. As you read your chosen training set, you will\nread in the characters one at a time into a one-hot-encoding, that is, each character\nwill map to a vector of ones and zeros, where the one indicates which of the characters\nis present:\nchar \u2192 [0, 0, \u00b7 \u00b7 \u00b7 , 1, \u00b7 \u00b7 \u00b7 , 0, 0]\nYour RNN will read in these length-256 binary vectors as input.\n\nSo , For example , I have read a novel in python. Total unique characters is 97. and total characters is somewhere around 300,000 .\nSo , will my input be 97 x 256 one hot encoded matrix ?\nor will it be 300,000 x 256 one hot encoded matrix ?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":147,"Q_Id":49954852,"Users Score":1,"Answer":"One hot assumes each of your vector should be different in one place. So if you have 97 unique character then i think you should use a 1-hot vector of size ( 97 + 1 = 98). The extra vector maps all the unknown character to that vector. But you can also use a 256 length vector. So you input will be:\nB x N x V ( B = batch size, N = no of characters , V = one hot vector size).\nBut if you are using libraries they usually ask the index of characters in vocabulary and they handle index to one hot conversion. Hope that helps.","Q_Score":0,"Tags":"python-3.x,recurrent-neural-network,pytorch,rnn","A_Id":49956444,"CreationDate":"2018-04-21T10:32:00.000","Title":"RNN Implementation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I Have data-set for which consist 2000 lines in a text file.\nEach line represents x,y,z (3D coordinates location) of 20 skeleton joint points  of human body (eg: head, shoulder center, shoulder left, shoulder right,......, elbow left, elbow right). I want to do k-means clustering of this data.\nData is separated by  'spaces ', each joint is represented by 3 values (Which represents x,y,z coordinates). Like head and shoulder center represented by \n.0255...  .01556600 1.3000...  .0243333 .010000 .1.3102000 .... \nSo basically I have 60 columns in each row, which which represents 20 joints and each joins consist of three points.  \nMy question is how do I format or use this data for k-means clustering,","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":394,"Q_Id":49961977,"Users Score":0,"Answer":"You don't need to reformat anything.\nEach row is a 60 dimensional vector of continous values with a comparable scale (coordinates), as needed for k-means.\nYou can just run k-means on this.\nBut assuming that the measurements were taken in sequence, you may observe a strong correlation between rows, so I wouldn't expect the data to cluster extremely well, unless you set up the use to do and hold certain poses.","Q_Score":0,"Tags":"python,cluster-analysis,k-means,data-science","A_Id":49968114,"CreationDate":"2018-04-22T02:32:00.000","Title":"k-means clustering multi column data in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an RNN and way too much training data. Iterating through the data in its entirety will take years. Right now I have two options for training:\n1. Do one pass over as much data as possible\n2. Find a select portion of the data and train for multiple epochs on it\nWhich one is better and why?","AnswerCount":2,"Available Count":2,"Score":0.2913126125,"is_accepted":false,"ViewCount":418,"Q_Id":49968000,"Users Score":3,"Answer":"Option 1 is far superior, supposing the data has the same quality throughout.\nThe reason being is that it helps avoid overfitting. One of the biggest issues in ML is that you have a limited amount of data to train on, and the networks tend to overfit that is learn the specific of the set of data instead of generalizing to the problem intended.\nNever repeating the data is actually the ideal situation for training.\nThe only caveat being is if you have the labels very accurately set in certain parts and more sloppily done in others - in that case sticking with the better quality data might be better.","Q_Score":0,"Tags":"python,tensorflow,machine-learning,neural-network,keras","A_Id":49968205,"CreationDate":"2018-04-22T16:07:00.000","Title":"How to train a model when there is too much training data?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an RNN and way too much training data. Iterating through the data in its entirety will take years. Right now I have two options for training:\n1. Do one pass over as much data as possible\n2. Find a select portion of the data and train for multiple epochs on it\nWhich one is better and why?","AnswerCount":2,"Available Count":2,"Score":0.1973753202,"is_accepted":false,"ViewCount":418,"Q_Id":49968000,"Users Score":2,"Answer":"The question is asked in general terms, so the following is also in general terms.  I hope this contributes something to helping you with your specific problem.\nOne approach would be a variant of cross validation.  You randomly select some portion of the data, and evaluate the result on a second portion of the data. Repeat the entire process until you satisfy your convergence criteria or exhaust your count.  The convergence criteria might be something like getting the same or similar network at some rate.  You can do this in either of two modes, allowing or not allowing reuse of the data.\nA second point to keep in mind is that your execution time depends on the length of your \"feature vectors\" or whatever serves that function in your application.  Selecting the important components of your feature vectors, both reduces the processing time and also can help the training be more successful. Scikit learn has a function SelectKBest() that may be helpful for this.","Q_Score":0,"Tags":"python,tensorflow,machine-learning,neural-network,keras","A_Id":49968311,"CreationDate":"2018-04-22T16:07:00.000","Title":"How to train a model when there is too much training data?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"can I convert all the images to an array saved in one file csv then use those arrays for clustering,\nor do I have to convert one by one image into array form then merge them for used in clustering?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":122,"Q_Id":49983588,"Users Score":0,"Answer":"If you want save data for subsequent uses other than this one you should use csv. Otherwise you could load images directly in arrays, in this case take a look at spark.","Q_Score":0,"Tags":"python,arrays,image,cluster-analysis","A_Id":51364465,"CreationDate":"2018-04-23T14:25:00.000","Title":"Python : Image Clustering","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm creating an image detection module, and I do a lot of math calculations around arrays.\nI know that C\/C++\u2019s array iterates faster than Python\u2019s\nI can't move my project to C\/C++, so I wanted to create an array module in C\/C++ and call it in Python.\nWhat I want to know:\n1) Is this viable? Or calling a module from another interpreter will slow down my program more than it will speed it up?\n2) Is there some Python package that does what I want?\nI feel like I haven\u2019t written enough info, but I can't think of anything else important.\n[EDIT] So I just went with numpy and it has everything I need :p, thanks everyone","AnswerCount":4,"Available Count":2,"Score":0.1488850336,"is_accepted":false,"ViewCount":111,"Q_Id":49984220,"Users Score":3,"Answer":"Both the array and the low level operations on it would have to be in C++; switching on a per element basis will have little benefit.\nThere are many python modules that have internal C\/C++ implementations.  Simply wrapping a C or C++ style array would be pointless, as the built in python data types can basically be that.","Q_Score":0,"Tags":"python,c++,c,arrays","A_Id":49984333,"CreationDate":"2018-04-23T15:01:00.000","Title":"C++ Vector in Python for Perfomance","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":1,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm creating an image detection module, and I do a lot of math calculations around arrays.\nI know that C\/C++\u2019s array iterates faster than Python\u2019s\nI can't move my project to C\/C++, so I wanted to create an array module in C\/C++ and call it in Python.\nWhat I want to know:\n1) Is this viable? Or calling a module from another interpreter will slow down my program more than it will speed it up?\n2) Is there some Python package that does what I want?\nI feel like I haven\u2019t written enough info, but I can't think of anything else important.\n[EDIT] So I just went with numpy and it has everything I need :p, thanks everyone","AnswerCount":4,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":111,"Q_Id":49984220,"Users Score":0,"Answer":"Try boost.python.\nIf you can port all the computational heavy stuff to C++, it'll be quite fast but if you need to switch continuously between C++ and python, you won't get much improvement.","Q_Score":0,"Tags":"python,c++,c,arrays","A_Id":49984679,"CreationDate":"2018-04-23T15:01:00.000","Title":"C++ Vector in Python for Perfomance","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":1,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"General python question- \nI have built a script using numpy and pandas libraries. I have now been told that I cannot use any libraries- only base python to code. This is because apparently open source libraries are not approved. \n\nDoes this restriction make sense? Isn't base python as open source as pandas\/numpy libraries are?\nIs it possible to convert pandas\/numpy code to base python? Does this sound like a simple exercise or does it require learning a lot of new functions? Majority of the code is reading tables and then using if\/then type statements and looking up values from other tables to generate and populate new tables.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":53,"Q_Id":49984571,"Users Score":0,"Answer":"I'm only going to address the 2nd point. Reimplementing all of numpy\/pandas is certainly a very large and useless task. But you're not reimplementing all of it, you only need some parts, and if it's only a few functions, than it's certainly possible.\nI'd start from a working script, replace arrays by python lists, and implement the needed fucntions one by one. For SO specifically, I suspect you're better off asking specific questions, e.g. how to implement an analog of a function X in pure python etc.","Q_Score":0,"Tags":"python,pandas,numpy","A_Id":49994600,"CreationDate":"2018-04-23T15:21:00.000","Title":"Feasibility of converting all python pandas\/numpy code to base python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have two identical neural networks running on two separate computers (to reduce the time taken to train the network), each having a subset of a complete data set (MNIST).\nMy question is; can I combine the two weight matrices of both networks into one matrix, while remaining a proper accuracy? I have seen several articles about 'batching' or 'stochastic gradient descent', but I don't think this is applicable to my situation.\nIf this is possible, could you also provide me with some pseudo code?\nAny input is valuable!\nThank you,","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":3859,"Q_Id":49988009,"Users Score":8,"Answer":"In general if you combine the weights \/ biases entirely after training, this is unlikely to produce good results.  However, there are ways to make it work.\nIntuition about combining weights  Consider the following simple example:  You have a MLP with one hidden layer.  Any two instances of the MLP can produce identical outputs for identical inputs if the nodes in the hidden layer are permuted, the weights input->hidden are permuted the same way, and the weights hidden->output are permuted using the inverse permutation.  In other words, even if there was no randomness in what the final network you end up with does, which hidden node corresponds to a particular feature would be random (and determined from the noise of the initializations).  \nIf you train two MLPs like that on different data (or random subsets of the same data), they would likely end up with different permutations of the hidden nodes even if the initializations are all the same, because of the noise of the gradients during training.\nNow, if that a certain property of the input activates most strongly the i-th node of network A and the j-th node of network B (and in general i != j), averaging the weights between the i-th node of A and i-th node of B (which corresponds to a different feature) is likely to decrease performance, or even produce a network that produces nonsense outputs.\nThere are two possible fixes here - you can use either one, or both together.  The idea is to either figure out which nodes match between the two networks, or to force the nodes to match.\nSolution A:  Train both networks on different data for a few iterations.  Average the weights of both, and replace both networks with the average weights.  Repeat.  This makes the i-th node of each network learn the same feature as the matching node of the other network, since they can't ever diverge too far.  They are frequently re-initialized from the average weights, so once the permutation is determined it is likely to stay stable.  \nA reasonable value for how often to average is somewhere between once per epoch and once every few minibatches. Learning is still faster than training one network on all the data sequentially, although not quite 2x faster with 2 networks.  Communication overhead is a lot lower than averaging weights (or gradients) after every minibatch.  This can be run on different machines in a cluster: transferring weights is not prohibitive since it is relatively infrequent.  Also, any number of networks trained at the same time (and splits of the data) can be more than two: up to 10-20 works okay in practice.\n(Hint: for better results, after every epoch, do a new random split of the data between the networks you're training)\nThis is similar in effect to \"gradient aggregation\" that was mentioned here, but aggregates a lot less often.  You can think of it as \"lazy aggregation\". \nSolution B: Try to figure out which hidden layer nodes match before averaging.  Calculate some similarity metric on the weights (could be L2 or anything along those lines), and average the weights of pairs of most-similar nodes from the two networks.  You can also do a weighted average of more than just a pair of nodes; for example you can average all nodes, or the k-most similar nodes, where the weights used are a function of the similarity.  \nFor deep networks, you have to keep track of the pairings all the way up from the input, and permute the weights according to the highest-similarity pairings of the lower level before calculating similarity on the next level (or if doing weighted averaging, propagate the weights).\nThis probably works for a networks with a few layers but I think for very deep networks this is unlikely to work perfectly.  It will still work okay for the first few layers, but tracking the permutations will likely fail to find good matching nodes by the time you get to the top of the network.  \nAnother way to deal with deep networks (other that tracking the permutations up from the bottom) is to run both networks on a test dataset and record the activations of all nodes for each input, then average the weights of nodes which have similar activation pattern (ie which tend to be activated strongly by the same inputs).  Again this could be based on just averaging the most similar pair from A and B, or a suitable weighted average of more than two nodes.\nYou can use this technique together with \"Solution A\" above, to average weights somewhat less often.  You can also use the weighted averaging by node similarity to speed the convergence of \"Solution A\".  In that case it is okay if the method in \"Solution B\" doesn't work perfectly, since the networks are replaced with the combined network once in a while - but the combined network could be better if it is produced by some matching method rather than simple averaging.  Whether the extra calculations are worth it vs the reduced communication overhead in a cluster and faster convergence depends on your network architecture etc.","Q_Score":9,"Tags":"python,matrix,machine-learning,neural-network,mnist","A_Id":50057446,"CreationDate":"2018-04-23T18:49:00.000","Title":"How do I merge two trained neural network weight matrices into one?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Currently, we need a interpolation in a Image. I have used the scipy.misc.imresize. This function has two drawback:\n\nit can only output interger matrix, but I need a float result. \nthe speed of scipy.misc.imresize is a little slow","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":259,"Q_Id":49998519,"Users Score":0,"Answer":"cv2.resize is slightly faster than scipy.misc.imresize","Q_Score":0,"Tags":"python,image,performance,interpolation","A_Id":52720246,"CreationDate":"2018-04-24T09:50:00.000","Title":"which imresize is fastest in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a model already trained by dynet library. But i forget the --dynet-seed parameter when training this model. \nDoes anyone know how to read back this parameter from the saved model?\nThank you in advance for any feedback.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":78,"Q_Id":50003397,"Users Score":0,"Answer":"You can't read back the seed parameter. Dynet model does not save the seed parameter. The obvious reason is, it is not required at testing time. Seed is only used to set fixed initial weights, random shuffling etc. for different experimental runs. At testing time no parameter initialisation or shuffling is required. So, no need to save seed parameter.\nTo the best of my knowledge, none of the other libraries like tensorflow, pytorch etc. save the seed parameter as well.","Q_Score":0,"Tags":"python,lstm,dynet","A_Id":50051554,"CreationDate":"2018-04-24T13:49:00.000","Title":"How to read back the \"random-seed\" from a saved model of Dynet","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"vsample_data = credit_card.sample(n=100, replace='False')\nprint(vsample_data)\nHere, I was trying to sample 100 data points from a data set but not able to get correct sample data such that it preserves the original distribution of the credit card fraud data-set i.e Class-0( Non- Fraud) and Class-1(Fraud).","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":537,"Q_Id":50005578,"Users Score":0,"Answer":"Increase your sample size (n>>100). The data you are sampling from is itself a random sample. Creating a subset through random selection is itself a random process. If one of the data classes has a low frequency then the problem is that your sample size (100) is too low.\nIf you change the replace flag to 'True' and do repeated samples, you are doing something called bootstrapping. Assuming the complete data set represents the true population distribution this resampling will give you examples of what kind of measurements you might get for lower values of n (n=100).\nThe alternative is a stratification strategy as suggested by some above. However, you are not creating random subsets when you do this, and the assumption of distribution is now built into your smaller data sets. Note that you can only achieve this after having looked at the entire data set to determine its distribution. Probably not what you want.\nIf you are creating a (supervised) training data set from the data you can repeat under-represented data to manipulate the bias.","Q_Score":0,"Tags":"python-3.x,pandas","A_Id":50011442,"CreationDate":"2018-04-24T15:30:00.000","Title":"Sampling data such that distribution is preserved","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Trying to Import 200 contacts from CSV file to telegram using Python3 Code. It's working with first 50 contacts and then stop and showing below:\ntelethon.errors.rpc_error_list.FloodWaitError: A wait of 101 seconds is required\nAny idea how I can import all list without waiting?? Thanks!!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":287,"Q_Id":50012489,"Users Score":0,"Answer":"You can not import a large number of people in sequential. \u064fThe telegram finds you're sperm.\nAs a result, you must use \u200dsleep between your requests","Q_Score":0,"Tags":"python,csv,telegram,telethon","A_Id":50310718,"CreationDate":"2018-04-25T00:38:00.000","Title":"can't import more than 50 contacts from csv file to telegram using Python3","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":1,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'am using Spacy 2.0.11 with Dutch language model nl_core_news_sm (nl).  How can I add the lemmatization lookup similar to the implementation for German (de)?\nI tried the following steps:\n\nadd lookup to init.py in the language folder (nl)\nadd lemmatizer.py in the language folder (nl)   \n\nThis resulted in the following error after 'nlp = nl_core_news_sm.load()' or 'from spacy.lang.nl import Dutch':\nModuleNotFoundError: No module named 'spacy.lang.nl.lemmatizer'\nImportError: [E048] Can't import language nl from spacy.lang","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":1755,"Q_Id":50016956,"Users Score":1,"Answer":"In theory, your approach is correct\u00a0\u2013 if you copy exactly how it's implemented in German and other languages that implement the lookup, it should work.\nI suspect your problem here is actually a different one: According to the error message, it can't actually find the spacy.lang.nl.lemmatizer module, so spaCy now fails to import the Dutch language class. Are you sure the lemmatizer.py file exists in the correct place, and is imported correctly? (If you're not doing it already, I'd also recommend running your development installation in a separate environment and build spaCy from source, to make sure there are no weird conflicts).","Q_Score":2,"Tags":"python,spacy,lemmatization","A_Id":50215479,"CreationDate":"2018-04-25T07:59:00.000","Title":"spacy: add lemmatizer lookup for Dutch (nl) language","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Suppose we have a joint distribution p(x_1,x_2), and we know x_1,x_2,p. Both are discrete, (x_1,x_2) is scatter, its contour could be drawn, marginal as well. I would like to show the area of 95% quantile (a scale of 95% data will be contained) of the joint distribution, how can I do that?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":305,"Q_Id":50017251,"Users Score":0,"Answer":"If you are interested in finding a pair x_1, x_2 of real numbers such that\nP(X_1<=x_1, X_2<=x_2) = 0.95 and your distribution is continuous then there will be infinitely many of these pairs. You might be better of just fixing one of them and then finding the other","Q_Score":0,"Tags":"python,numpy,scipy,confidence-interval,credible-interval","A_Id":50017530,"CreationDate":"2018-04-25T08:16:00.000","Title":"How to calculate a 95 credible region for a 2D joint distribution?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Suppose we have a joint distribution p(x_1,x_2), and we know x_1,x_2,p. Both are discrete, (x_1,x_2) is scatter, its contour could be drawn, marginal as well. I would like to show the area of 95% quantile (a scale of 95% data will be contained) of the joint distribution, how can I do that?","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":305,"Q_Id":50017251,"Users Score":1,"Answer":"As the other points out, there are infinitely many solutions to this problem. A practical one is to find the approximate center of the point cloud and extend a circle from there until it contains approximately 95% of the data. Then, find the convex hull of the selected points and compute its area.\nOf course, this will only work if the data is sort of concentrated in a single area. This won't work if there are several clusters.","Q_Score":0,"Tags":"python,numpy,scipy,confidence-interval,credible-interval","A_Id":50017700,"CreationDate":"2018-04-25T08:16:00.000","Title":"How to calculate a 95 credible region for a 2D joint distribution?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a non trivial input pipeline, which consists of reading ground truth and raw data and performing preprocessing on them, written in Python. It takes a long time to run the input pipeline for a single sample so I have multiple processes (from python multiprocessing package) running in parallel and queues to perform the operation quickly and prefetch data. The output is then fed to my network using feed_dict. The overhead of this process in my training loop is 2 orders of magnitude less than the actual tf.Session.run() time.\nI'm trying to move to the tf.data API, by wrapping with tf.py_func my read+preprocess functions but it runs slowly, probably due to GIL, even when increasing the number of multiple calls.\nI want to scale up my training to multiple machines and am not sure how data fetching behaves in such a case, also there's the performance issue for a single machine as well :)\nSo, basically my question is:\nHow to run python functions in tf.data api input pipeline in parallel on multiple CPU cores?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":932,"Q_Id":50022168,"Users Score":0,"Answer":"Recently Google has released Tensorflow Extended (TFX). It essentially consists of:\n\nA set of operators which each use Apache Beam to do data distribution (They call them components).\nA standardization of both data and parameter format (what they call protobuf)\nAutomated dependency management of operators (workflow\/orchestration)\nTracking of runs.  This allows the system to skip operations that have already been performed under the same conditions.\n\nI would suggest either take a look at TFX.  Or, for a more modest leap, look at Apache Beam.","Q_Score":5,"Tags":"python,tensorflow,tensorflow-datasets","A_Id":57815167,"CreationDate":"2018-04-25T12:18:00.000","Title":"How to parallelise python input pipeline in Distributed Tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I looked into different cases but could not understand what I have to choose. My code is also working but my prediction looks strange only having 2 parallel lines. I have an LSTM Autoencoder for regression of time series. Autoencoder because I need to reduce the dimensions. Mydata looks like: 400 samples with each of them containing 5.000 rows.I concatenated them into an array. (Between the samples in real I have 5 minutes). How do I have to choose time steps for the model ? is it like (400,10,5000) ?can anyone please give me an advise with an example also in regarding to the batchsize?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":484,"Q_Id":50028127,"Users Score":1,"Answer":"Sample: an individual \"sequence\", not connected or related to any other sequence    \nTimesteps: the length of your sequences, each sequence has a start and an end, between them the sequence has steps     \nFeatures (the last dimension): different parallel variables measured in a same sequence.   \n\nOnly you can organize your data based on what you know about it. Number of rows and columns don't say anything about it. You must know what they mean and organize them in a shape (samples, timesteps, features) according to those definitions above. \nExample:\nYou measured vital signs in patients for 10 hours. \nYou had 5 patients, each one was measured for ten hours, and you have the features: body temperature, heartbeat frequency, breath frequency, every five minutes. \nThen you have\n\n5 individual sequences (they're not related to each other)    \n10 hours in steps for each 5 minutes (120 steps)    \n3 features","Q_Score":0,"Tags":"python,keras","A_Id":50028462,"CreationDate":"2018-04-25T17:27:00.000","Title":"lstm autoencoder time series data input shape","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to construct a variational autoencoder where one sample is an N*M matrix where for each row, there are M categories. Essentially one sample is a list of categorical data where only one category can be selected - a list of one-hot vectors.\nCurrently, I have a working autoencoder for this type of data - I use a softmax on the last dimension to create this constraint and it works (reconstruction cross entropy is low).\nNow, I want to use tf.distributions to create a variational autoencoder. I was wondering what kind of distribution would be appropriate.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":794,"Q_Id":50037919,"Users Score":2,"Answer":"Does tf.contrib.distributions.Categorical satisfy your needs? Samples should be from (0 to n - 1), where n represents the category.\nExample:\n# logits has shape [N, M], where M is the number of classes\ndist = tf.contrib.distributions.Categorical(logits=logits) \n# Sample 20 times. Should give shape [20, N].\nsamples = dist.sample(20)\n# depth is the number of categories.\none_hots = tf.one_hot(samples, depth=M)","Q_Score":0,"Tags":"python,tensorflow,machine-learning","A_Id":50339467,"CreationDate":"2018-04-26T08:08:00.000","Title":"What tensorflow distribution to represent a list of categorical data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"While learning Doc2Vec library, I got stuck on the following question.\nDo gensim Doc2Vec distinguish between the same Sentence with positive and negative context?\nFor Example:\nSentence A: \"I love Machine Learning\"\nSentence B: \"I do not love Machine Learning\"\nIf I train sentence A and B with doc2vec and find cosine similarity between their vectors:\n\nWill the model be able to distinguish the sentence and give a cosine similarity very less than 1 or negative?\nOr Will the model represent both the sentences very close in vector space and give cosine similarity close to 1, as mostly all the words are same except the negative word (do not).\n\nAlso, If I train only on sentence A and try to infer Sentence B, will both vectors be close to each other in vector space.?\nI would request the NLP community and Doc2Vec experts for helping me out in understanding this.\nThanks in Advance !!","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":806,"Q_Id":50038347,"Users Score":0,"Answer":"use textblob and set the sentiment and polarity for each sentence. tokenize the sentences using nlp","Q_Score":4,"Tags":"python,nlp,gensim,doc2vec","A_Id":65922663,"CreationDate":"2018-04-26T08:31:00.000","Title":"Do gensim Doc2Vec distinguish between same Sentence with positive and negative context.?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am actually new to python scripting and I have a requirement where we have a line chart (each line shows tasks and y axis the time it took to complete).\nUsing lines and curves I have added average, upper control and lower control limits to the line chart. I also have a filter which shows the tasks.\nwhenever a task is selected i want these lines(average and control lines) to appear and whenever I select more than one task or None these lines should disappear.\nI saw an example which shows to create a property control for filter and then trigger the functions. Can we trigger the same functions when there is a change in the filter values instead of property control?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":339,"Q_Id":50042009,"Users Score":1,"Answer":"there isn't a way to react to filter changes as far as I know, but you can \"recreate\" the filter as a property control and execute the script when that property value changes. \ntypical example is for a listbox\/dropdown property control representing a listbox filter.","Q_Score":0,"Tags":"python,line,spotfire,tibco","A_Id":50044405,"CreationDate":"2018-04-26T11:37:00.000","Title":"spotfire - Disappear lines when value in filters are selected","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"While I installed properly the matplotlib and seaborn. I was able to import matplotlib but when I was trying to import the seaborn I got the following error message. ModuleNotFoundError: No module named 'matplotlib._path' . Same if I was trying to import matplotlib.pyplot.\nAfter spending a lot of time googling and trying this and that, installing and unistaling, finally, I\nfirst checked out the \nimport sys\nsys.path to see what are the folders that it searches for the installed packages.\nmy result was something like this.\n['',\n 'C:\\\\Users\\\\gsotiropoulos\\\\AppData\\\\Local\\\\conda\\\\conda\\\\envs\\\\py36\\\\python36.zip',\n 'C:\\\\Users\\\\gsotiropoulos\\\\AppData\\\\Local\\\\conda\\\\conda\\\\envs\\\\py36\\\\DLLs',\n 'C:\\\\Users\\\\gsotiropoulos\\\\AppData\\\\Local\\\\conda\\\\conda\\\\envs\\\\py36\\\\lib',\n 'C:\\\\Users\\\\gsotiropoulos\\\\AppData\\\\Local\\\\conda\\\\conda\\\\envs\\\\py36',\n 'C:\\\\Users\\\\gsotiropoulos\\\\AppData\\\\Roaming\\\\Python\\\\Python36\\\\site-packages',\n 'C:\\\\Users\\\\gsotiropoulos\\\\AppData\\\\Local\\\\conda\\\\conda\\\\envs\\\\py36\\\\lib\\\\site-packages',\n 'C:\\\\Users\\\\gsotiropoulos\\\\AppData\\\\Local\\\\conda\\\\conda\\\\envs\\\\py36\\\\lib\\\\site-packages\\\\win32',\n 'C:\\\\Users\\\\gsotiropoulos\\\\AppData\\\\Local\\\\conda\\\\conda\\\\envs\\\\py36\\\\lib\\\\site-packages\\\\win32\\\\lib',\n 'C:\\\\Users\\\\gsotiropoulos\\\\AppData\\\\Local\\\\conda\\\\conda\\\\envs\\\\py36\\\\lib\\\\site-packages\\\\Pythonwin',\n 'C:\\\\Users\\\\gsotiropoulos\\\\AppData\\\\Local\\\\conda\\\\conda\\\\envs\\\\py36\\\\lib\\\\site-packages\\\\IPython\\\\extensions',\n 'C:\\\\Users\\\\gsotiropoulos\\\\.ipython']\nthen as suggested I import matplotlib as mpl and mpl.__path__ seeing that I import matplotlib from the folder 'C:\\\\Users\\\\gsotiropoulos\\\\AppData\\\\Roaming\\\\Python\\\\Python36\\\\site-packages' Which is not the one from anaconda and it is older. I am not sure if it would be better to just remove this folder completely.\nHowever, as I understand, python first searched there and found a matplotlib package which was outdated. I simply changed the name of the `matplotlib' to something like 'matplotlib_test' and then the library is installed from one of the anaconda folders and the problem is solved.\nAs I understand I installed in the past python but the 'roaming' folder did not get unistalled. Is that right?\nI wonder if I should delete the \"roaming\" folder to avoid similar other problems.","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":925,"Q_Id":50045758,"Users Score":1,"Answer":"A temporary solution is to add the line sys.path.append(\/path\/to\/located\/package).\nA permanent solution is to add the path to .bashrc","Q_Score":1,"Tags":"python,matplotlib,anaconda","A_Id":50046198,"CreationDate":"2018-04-26T14:36:00.000","Title":"ModuleNotFoundError: No module named 'matplotlib._path'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"While I installed properly the matplotlib and seaborn. I was able to import matplotlib but when I was trying to import the seaborn I got the following error message. ModuleNotFoundError: No module named 'matplotlib._path' . Same if I was trying to import matplotlib.pyplot.\nAfter spending a lot of time googling and trying this and that, installing and unistaling, finally, I\nfirst checked out the \nimport sys\nsys.path to see what are the folders that it searches for the installed packages.\nmy result was something like this.\n['',\n 'C:\\\\Users\\\\gsotiropoulos\\\\AppData\\\\Local\\\\conda\\\\conda\\\\envs\\\\py36\\\\python36.zip',\n 'C:\\\\Users\\\\gsotiropoulos\\\\AppData\\\\Local\\\\conda\\\\conda\\\\envs\\\\py36\\\\DLLs',\n 'C:\\\\Users\\\\gsotiropoulos\\\\AppData\\\\Local\\\\conda\\\\conda\\\\envs\\\\py36\\\\lib',\n 'C:\\\\Users\\\\gsotiropoulos\\\\AppData\\\\Local\\\\conda\\\\conda\\\\envs\\\\py36',\n 'C:\\\\Users\\\\gsotiropoulos\\\\AppData\\\\Roaming\\\\Python\\\\Python36\\\\site-packages',\n 'C:\\\\Users\\\\gsotiropoulos\\\\AppData\\\\Local\\\\conda\\\\conda\\\\envs\\\\py36\\\\lib\\\\site-packages',\n 'C:\\\\Users\\\\gsotiropoulos\\\\AppData\\\\Local\\\\conda\\\\conda\\\\envs\\\\py36\\\\lib\\\\site-packages\\\\win32',\n 'C:\\\\Users\\\\gsotiropoulos\\\\AppData\\\\Local\\\\conda\\\\conda\\\\envs\\\\py36\\\\lib\\\\site-packages\\\\win32\\\\lib',\n 'C:\\\\Users\\\\gsotiropoulos\\\\AppData\\\\Local\\\\conda\\\\conda\\\\envs\\\\py36\\\\lib\\\\site-packages\\\\Pythonwin',\n 'C:\\\\Users\\\\gsotiropoulos\\\\AppData\\\\Local\\\\conda\\\\conda\\\\envs\\\\py36\\\\lib\\\\site-packages\\\\IPython\\\\extensions',\n 'C:\\\\Users\\\\gsotiropoulos\\\\.ipython']\nthen as suggested I import matplotlib as mpl and mpl.__path__ seeing that I import matplotlib from the folder 'C:\\\\Users\\\\gsotiropoulos\\\\AppData\\\\Roaming\\\\Python\\\\Python36\\\\site-packages' Which is not the one from anaconda and it is older. I am not sure if it would be better to just remove this folder completely.\nHowever, as I understand, python first searched there and found a matplotlib package which was outdated. I simply changed the name of the `matplotlib' to something like 'matplotlib_test' and then the library is installed from one of the anaconda folders and the problem is solved.\nAs I understand I installed in the past python but the 'roaming' folder did not get unistalled. Is that right?\nI wonder if I should delete the \"roaming\" folder to avoid similar other problems.","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":925,"Q_Id":50045758,"Users Score":0,"Answer":"I finally started using anaconda and installing everything in one environment. In this way, I save and import everything that I want in this environment without any confusion.","Q_Score":1,"Tags":"python,matplotlib,anaconda","A_Id":52482106,"CreationDate":"2018-04-26T14:36:00.000","Title":"ModuleNotFoundError: No module named 'matplotlib._path'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"SciPy can read only read unsymmetric matrices with hb_read.  Anyone know if it is possible to read symmetric matrices in Python (3)?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":202,"Q_Id":50046865,"Users Score":0,"Answer":"I found a solution using BeBOP Sparse Matrix Converter to convert to MarketMatrix and then importing this into Python.","Q_Score":0,"Tags":"python,scipy,sparse-matrix,eigenvalue,finite-element-analysis","A_Id":52635273,"CreationDate":"2018-04-26T15:29:00.000","Title":"Reading symmetrical Harwell-Boeing matrix in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am a newbie in machine learning and trying to make a segmentation with clustering algorithms. However, Since my dataset has both categorical variables (such as gender, marital status, preferred social media platform etc) as well as numerical variables ( average expenditure, age, income etc.), I could not decide which algorithms worth to focus on.\nWhich one should I try: fuzzy c means, k-medoids, or latent class to compare with k-means++? which ones would yield better results for these type of mixed datasets?\nBonus question: Should I try to do clustering without dimensionality reduction? or should I use PCA or K-PCA in any case to decrease dimensions? Also, how can I understand and interpret results without visualization if the dataset has more than 3 dimensions ?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":330,"Q_Id":50046876,"Users Score":1,"Answer":"The best thing to try is hierarchical agglomerative clustering with a distance metric such as Gower's.\nMixed data with different scales usually does not work in any statistical meaningful way. You have too many weights to choose, so no result will be statistically well founded, but largely a result of your weighting. So it's impossible to argue that some result is the \"true\" clustering. Don't expect the results to be very good thus.","Q_Score":0,"Tags":"python,machine-learning,cluster-analysis,dimensionality-reduction","A_Id":50056349,"CreationDate":"2018-04-26T15:30:00.000","Title":"Deciding to the clustering algorithm for the dataset containing both categorical and numerical variables","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"What is the benefit in applying tf.image.per_image_standardization() before the first layer of the deep neural network over adding the Batch_Norm layer as the first layer? \nIn order to normalize the [0.0, 255.0] float value image pixels before feeding into the network, which method would be suitable?\n\ntf.image.per_image_standardization()\nBatch_Norm - layer","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":661,"Q_Id":50047909,"Users Score":0,"Answer":"They both do similar things, and one could indeed use batch norm to normalize input images.\nHowever, I would not use batch norm for this purpose:\n\nIt is clearer to use tf.image.per_image_standardization for image normalization than batch norm.\nBatch normalization is a broader concept than a per-channel normalization. Libraries like tensorflow let you normalize along any axis you want.\nBatch normalization is usually paired with streaming statistics of the means and variances used for normalization, that are meant to be used for testing and deployement. You don't need those statistics when you normalize your input image per sample.","Q_Score":0,"Tags":"python,tensorflow,deep-learning","A_Id":50048886,"CreationDate":"2018-04-26T16:27:00.000","Title":"Benefit of applying tf.image.per_image_standardization() over batch_norm layer in Tensorflow?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to build a recommender system using collaborative filtering. The issues I am facing are :\n\nThe User-Item dataset has mostly categorical variables, so cant find the best way to calculate similarity matrix. Euclidean \/ Cosine distance will not work here, trying with Jaccard distance.\nThe dataset does not have User rating for items, instead, we have classifiers - \"did not buy\", \"buy\", \"added to cart but did not buy\".\n\nWe have used XGB to get the likelihood to buy a particular item by a particular user, but this kind of dataset is not helping for the recommendation.\nCan you please suggest any recommendation algorithm (preferably in python) which handles classification and categorical data?\nThanks in advance.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":895,"Q_Id":50059020,"Users Score":0,"Answer":"Association rule mining will be helpful here. It calculates the relative likelihood that items will appear together in a user's history. It's different from, and differently useful from, collaborative filtering recommendation techniques.","Q_Score":1,"Tags":"python,recommendation-engine,collaborative-filtering,multiclass-classification","A_Id":50177881,"CreationDate":"2018-04-27T09:10:00.000","Title":"Collaborative Filtering using categorical features","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have looked for this everywhere and already installed a few softwares that did not help me.\nThe thing is, I am studying and researching image and video quality analysis and part of the process is applying my work to different types of lossy compression formats.\nRegarding the image part of the work, to do so for JPEG was the simplest thing. I was using something like this command line:\nmogrify -format jpg -quality 15 img01.bmp\nHowever, when I tried to move the study to other lossy compression formats, I could not find anything that allowed me to compress an image to different values of quality with a format that was not JPG. Just a few examples would be PGF, CPC, Fractual Compression etc.\nIt's important to say that Lossless Compression, such as PNG, won't help me.\nI want to ask if anyone knows a software, a package or a library for python that allows me to compress an image to a lossy compression format (other that JPEG) and change the quality of it, even to the point of where the image is really terrible (5% quality on JPEG, for example).\nThank you very much for the help and attention.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":305,"Q_Id":50062696,"Users Score":0,"Answer":"There is no \"quality\" setting in JPEG. Some encoders use quality as a simplified interface method for selecting quantization tables. You mentioned PNG. You can control the compression settings. The difference is that in JPEG compression setting trade off variations from the original against the output stream size. In PNG the compression settings trade off the time to do the compression against the output stream size.\nYou are unlikely to find compression values analogous to encoder-specific quality settings among various compression formats.","Q_Score":0,"Tags":"python,compression,jpeg","A_Id":50067974,"CreationDate":"2018-04-27T12:43:00.000","Title":"Image Lossy Compression to Different Formats","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm working on a Dataframe with 1116 columns, how could I select just the columns in a period of 17 ? \nMore clearly select the 12th, 29th,46th,63rd... columns","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":81,"Q_Id":50062936,"Users Score":0,"Answer":"df.iloc[:,[i*17 for i in range(0,65)]]","Q_Score":1,"Tags":"python,pandas,dataframe","A_Id":50063200,"CreationDate":"2018-04-27T12:58:00.000","Title":"Select columns periodically on pandas DataFrame","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a text file that contains 3 columns of useful data that I would like to be able to extract in python using numpy.  The file type is a *.nc and is NOT a netCDF4 filetype.  It is a standard file output type for CNC machines.  In my case it is sort of a CMM (coordinate measurement machine).  The format goes something like this:\nX0.8523542Y0.0000000Z0.5312869\nThe X,Y, and Z are the coordinate axes on the machine.  My question is, can I delimit an array with multiple delimiters?  In this case: \"X\",\"Y\", and \"Z\".","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":84,"Q_Id":50065295,"Users Score":0,"Answer":"I ended up using the Pandas solution provided by Scott.  For some reason I am not 100% clear on, I cannot simply convert the array from string to float with float(array).  I created an array of equal size and iterated over the size of the array, converting each individual element to a float and saving it to the other array.\nThanks all","Q_Score":0,"Tags":"python-3.x,numpy,csv","A_Id":50069637,"CreationDate":"2018-04-27T15:05:00.000","Title":"Delimit array with different strings","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"There are bunch of text files in a directory. I want to add each text file to the last column in a row. Can this be achieved using pandas?\nEg:\nDirectory1>\n\nFile1.txt\nFile2.txt\nFile3.txt.... (Size of these files are close to 70KB)\n\nDataframe:\nColumn1      Column2      column3\nValue1       Value2       (Trying to add File1.txt content here)\nIs it possible using pandas? Thank you","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":149,"Q_Id":50068665,"Users Score":0,"Answer":"You can concatenate the files to one file with a delimiter of your choice (make sure that the delimiter is not included in any of the text files), and then use pd.read_csv() to read the concatenated file.","Q_Score":0,"Tags":"python,pandas","A_Id":50068729,"CreationDate":"2018-04-27T18:54:00.000","Title":"How to append a contents of text file to a column in dataframe (pandas python)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to tensorflow. When I am using import tensorflow.contrib.learn.python.learn   for using the DNNClassifier it is giving me an error: module object has no attribute python \nPython version 3.4\nTensorflow 1.7.0","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1041,"Q_Id":50070398,"Users Score":0,"Answer":"It looks like you have an error in your import statement. Try\nfrom tensorflow.contrib.learn import DNNClassifier","Q_Score":1,"Tags":"python,python-3.x,tensorflow,scikit-learn,deep-learning","A_Id":50070463,"CreationDate":"2018-04-27T21:14:00.000","Title":"Import tensorflow contrib learn python learn","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm currently developing a keyword-spotting system that recognizes digits from 0 to 9 using deep neural networks. I have a dataset of people saying the numbers(namely the TIDIGITS dataset, collected at Texas Instruments, Inc), however the data is not prepared to be fed into a neural network, because not all the audio data have the same audio length, plus some of the files contain several digits being spoken in sequence, like \"one two three\".\nCan anyone tell me how would I transform these wav files into 1 second wav files containing only the sound of one digit? Is there any way to automatically do this? Preparing the audio files individually would be time expensive.\nThank you in advance!","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":3097,"Q_Id":50087271,"Users Score":1,"Answer":"I would split each wav by the areas of silence. Trim the silence from beginning and end. Then I'd run each one through a FFT for different sections. Smaller ones at the beginning of the sound. Then I'd normalise the frequencies against the fundamental. Then I'd feed the results into the NN as a 3d array of volumes, frequencies and times.","Q_Score":3,"Tags":"python,audio,machine-learning,deep-learning,speech-recognition","A_Id":50088667,"CreationDate":"2018-04-29T13:47:00.000","Title":"How to preprocess audio data for input into a Neural Network","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I was wondering how to generate a random 4 digit number that has no duplicates in python 3.6\nI could generate 0000-9999 but that would give me a number with a duplicate like 3445, Anyone have any ideas\nthanks in advance","AnswerCount":1,"Available Count":1,"Score":-0.1973753202,"is_accepted":false,"ViewCount":537,"Q_Id":50091226,"Users Score":-1,"Answer":"Generate a random number\ncheck if there are any duplicates, if so go back to 1\nyou have a number with no duplicates\n\nOR\nGenerate it one digit at a time from a list, removing the digit from the list at each iteration.\n\nGenerate a list with numbers 0 to 9 in it.\nCreate two variables, the result holding value 0, and multiplier holding 1.\nRemove a random element from the list, multiply it by the multiplier variable, add it to the result.\nmultiply the multiplier by 10\ngo to step 3 and repeat for the next digit (up to the desired digits)\nyou now have a random number with no repeats.","Q_Score":1,"Tags":"python,python-3.x,random","A_Id":50091280,"CreationDate":"2018-04-29T20:58:00.000","Title":"How would i generate a random number in python without duplicating numbers","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have this doubt when I fit a neural network in a regression problem. I preprocessed the predictors (features) of my train and test data using the methods of Imputers and Scale from sklearn.preprocessing,but I did not preprocessed the class or target of my train data or test data.\nIn the architecture of my neural network all the layers has relu as activation function except the last layer that has the sigmoid function. I have choosen the sigmoid function for the last layer because the values of the predictions are between 0 and 1.\ntl;dr: In summary, my question is: should I deprocess the output of my neuralnet? If I don't use the sigmoid function, the values of my output are < 0 and > 1. In this case, how should I do it?\nThanks","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":99,"Q_Id":50103377,"Users Score":0,"Answer":"Usually, if you are doing regression you should use a linear' activation in the last layer. A sigmoid function will 'favor' values closer to 0 and 1, so it would be harder for your model to output intermediate values. \nIf the distribution of your targets is gaussian or uniform I would go with a linear output layer. De-processing shouldn't be necessary unless you have very large targets.","Q_Score":0,"Tags":"python,scikit-learn,neural-network,keras","A_Id":53323802,"CreationDate":"2018-04-30T15:12:00.000","Title":"Keras Neural Network. Preprocessing","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Conscious that dbscan clusters don't necessarily have cluster centres, but for an anomaly detection task, I want to spot data that are outliers\/away from the normal clusters. Is there a way to do this using sklearn's dbscan?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":62,"Q_Id":50116265,"Users Score":0,"Answer":"For anomaly detection use an anomaly detection algorithm instead.\nThere are siblings derived from DBSCAN for exactly this purpose. For example, LOF and LoOP and kNN and ...","Q_Score":0,"Tags":"python,scikit-learn,dbscan","A_Id":50125190,"CreationDate":"2018-05-01T12:12:00.000","Title":"Using DBSCAN to find data that are far from high density clusters?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I realize there's another question with a similar title, but my dataset is very different.\nI have nearly 40 million rows and about 3 thousand labels. Running a simply sklearn train_test_split takes nearly 20 minutes.\nI initially was using multi-class classification models as that's all I had experience with, and realized that since I needed to come up with all the possible labels a particular record could be tied to, I should be using a multi-label classification method.\nI'm looking for recommendations on how to do this efficiently. I tried binary relevance, which took nearly 4 hours to train. Classifier chains errored out with a memory error after 22 hours. I'm afraid to try a label powerset as I've read they don't work well with a ton of data. Lastly, I've got adapted algorithm, MlkNN and then ensemble approaches (which I'm also worried about performance wise).\nDoes anyone else have experience with this type of problem and volume of data? In addition to suggested models, I'm also hoping for advice on best training methods, like train_test_split ratios or different\/better methods.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":450,"Q_Id":50117450,"Users Score":2,"Answer":"20 minutes for this size of a job doesn't seem that long, neither does 4 hours for training. \nI would really try vowpal wabbit. It excels at this sort of multilabel problem and will probably give unmatched performance if that's what you're after. It requires significant tuning and will still require quality training data, but it's well worth it. This is essentially just a binary classification problem. An ensemble will of course take longer so consider whether or not it's necessary given your accuracy requirements.","Q_Score":2,"Tags":"python,scikit-learn,multilabel-classification","A_Id":50117662,"CreationDate":"2018-05-01T13:33:00.000","Title":"Multi-label classification methods for large dataset","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How would one refer to p2 in :\nlayout1=layout(row(p1,p2), p3)\nwhere p1, p2, and p3 are plots?\nI've attempted layout1.children[0][1] but to no avail","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":7,"Q_Id":50123144,"Users Score":0,"Answer":"Oftentimes, posting a question quickly boosts my research ability, and it is no different here. To refer to p2 in:\nlayout1=layout(row(p1,p2), p3)\nit is:\nlayout1.children[0].children[1]\nNow I can speak to all my grandchildren!","Q_Score":0,"Tags":"python,reference,bokeh","A_Id":50123218,"CreationDate":"2018-05-01T20:11:00.000","Title":"Bokeh- refer to child within a row within a layout","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Tensorflow provides all sorts of nice streaming operations to aggregate statistics along batches, such as tf.metrics.mean.\nHowever I find that accumulating all values since the beginning often does not make a lot of sense. For example, one could rather want to have statistics per epoch, or any other time window that makes sense in a given context.\nIs there any way to restrict the history of such streaming statistics, for example by reseting streaming operations so that they start over the accumulation?\nWork-arounds:\n\naccumulate by hand accross batch\nuse a \"soft\" sliding window using EMA","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":275,"Q_Id":50137369,"Users Score":0,"Answer":"The metrics in tf.contrib.eager.metrics (which work both with and without eager execution) have a init_variable() op you can call if you want to reset their internal variables.","Q_Score":0,"Tags":"python,tensorflow","A_Id":50219125,"CreationDate":"2018-05-02T14:41:00.000","Title":"How to window or reset streaming operations in tensorflow?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am quite new in Python coding, and I am dealing with a big dataframe for my internship.\nI had an issue as sometimes there are wrong values in my dataframe. For example I find string type values (\"broken leaf\") instead of integer type values as (\"120 cm\") or (NaN).\nI know there is the df.replace() function, but therefore you need to know that there are wrong values. So how do I find if there are any wrong values inside my dataframe?\nThank you in advance","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1435,"Q_Id":50138110,"Users Score":0,"Answer":"\"120 cm\" is a string, not an integer, so that's a confusing example.  Some ways to find \"unexpected\" values include:\nUse \"describe\" to examine the range of numerical values, to see if there are any far outside of your expected range.\nUse \"unique\" to see the set of all values for cases where you expect a small number of permitted values, like a gender field.\nLook at the datatypes of columns to see whether there are strings creeping in to fields that are supposed to be numerical.\nUse regexps if valid values for a particular column follow a predictable pattern.","Q_Score":0,"Tags":"python,pandas,dataframe","A_Id":50140995,"CreationDate":"2018-05-02T15:18:00.000","Title":"How to find if there are wrong values in a pandas dataframe?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a pandas dataframe (wrapped in holoviews typically) that has three columns.  Col1 is a datetime, Col2 are categorical strings (i.e. one of 'Cat', 'Dog', 'Bird'), and Col3 being an integer count.  \nI'm trying to find a way to use the bokeh library to downsample the datetime to months, quarters, years, etc. similar to that available in pandas.DataFrame.resample or pandas.DataFrame.groupby([pd.Grouper(key='Date', freq=sample)]).  \nDoes anyone know if there is a native bokeh ability to do this, or do I need to provide all the data already sampled from pandas?  Thanks!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":151,"Q_Id":50139193,"Users Score":1,"Answer":"You will need to do your own downsampling. Bokeh does not generally do any sort of data aggregations itself. The few exceptions are: \n\nhexbin to generate data binned on a hex tile grid\npassing a pandas GroupBy to initialize a CDS will create a CDS with columns for the aggregates\n\nAnd even in the second case, arguably it's not Bokeh itself doing the aggragation.","Q_Score":0,"Tags":"python-3.x,pandas,bokeh,holoviews","A_Id":50139776,"CreationDate":"2018-05-02T16:17:00.000","Title":"Bokeh or Holoviews: BarChart by downsampling date time to months\/years\/etc","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm new using Keras and I'm building a model to classify medical images. \nThe dataset is very large and I am using fit_generator() function to optimize RAM space. \nWhen the model trains by batches, it shows statistics of each batch such as loss, precision, etc; and finally when it ends with all the batches at the end of the epoch, it gives me what I suppose is the average of these previous values, now the problem is: when I write a callback to save the training history, I get different values, close to those shown in the console but definitely different.\nCould this be a floating point error or something like that?\nThis did not happen to me when I used the function fit () that showed me the same information as the one I got in the history object. \nI would appreciate any help on the subject, thank you for your time.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":172,"Q_Id":50144314,"Users Score":0,"Answer":"Even the metrics shown in verbose mode (val_loss, val_acc, etc...) has rounding and sometimes the rounding conflict with EarlyStopping or ModelCheckPoint callbacks behaviour.\nI think that this isn't really a problem, just a summary point of view. If you really need these numbers with all decimals places, the history is the right place to get it.","Q_Score":1,"Tags":"python,tensorflow,machine-learning,keras","A_Id":55172665,"CreationDate":"2018-05-02T22:13:00.000","Title":"Keras: Getting different values shown by fit_generator (verbose=1) and the metrics in the history object","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have created a DLAMI (Deep Learning AMI (Amazon Linux) Version 8.0 - ami-9109beee on g2.8xlarge) and installed jupyter notebook to create a simple Keras LSTM. When I try to turn my Keras model into a GPU model using the multi_gpu_model function, I see the following error logged:\n\nIgnoring visible gpu device (device: 0, name: GRID K520, pci bus id:\n  0000:00:03.0, compute capability: 3.0) with Cuda compute capability\n  3.0. The minimum required Cuda capability is 3.5.\n\nI've tried reinstalling tensorflow-gpu to no avail. Is there any way to align the compatibilities on this AMI?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":683,"Q_Id":50146392,"Users Score":2,"Answer":"This was resolved by uninstalling, then reinstalling tensorflow-gpu through the conda environment provided by the AMI.","Q_Score":0,"Tags":"python,amazon-web-services,tensorflow,amazon-ec2,keras","A_Id":50218148,"CreationDate":"2018-05-03T03:16:00.000","Title":"AWS DLAMI - GPUs not showing up for Keras","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Can someone provide the steps and the necessary links of the dependencies to install external python package \"opencv-python\" for image processing?\nI tried installing it in pycharm, but it was not able to import cv2(opencv) and was throwing version mismatch with numpy!\nPlease help!","AnswerCount":3,"Available Count":3,"Score":0.0665680765,"is_accepted":false,"ViewCount":9045,"Q_Id":50147385,"Users Score":1,"Answer":"The good thing with anaconda is that it should make your life easier without getting your hands too dirty. If you use only basic libs, only install packages with anaconda commands to be sure not to corrupt your python environment.\nif you're on linux, use conda install -c conda-forge opencv\non windows, do the same with the \"anaconda prompt\" terminal.\nIf you still have some trouble with you numpy version, try conda update numpy","Q_Score":1,"Tags":"python-2.7,opencv,image-processing,ide,anaconda","A_Id":50984130,"CreationDate":"2018-05-03T05:14:00.000","Title":"Install opencv python package in Anaconda","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Can someone provide the steps and the necessary links of the dependencies to install external python package \"opencv-python\" for image processing?\nI tried installing it in pycharm, but it was not able to import cv2(opencv) and was throwing version mismatch with numpy!\nPlease help!","AnswerCount":3,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":9045,"Q_Id":50147385,"Users Score":0,"Answer":"Create a virtual env. Following which run following instals\npip install numpy scipy matplotlib scikit-learn jupyter\npip install opencv-contrib-python\npip install dlib\nYou may verify the install via this script in Python environment- \nimport cv2\ncv2.version","Q_Score":1,"Tags":"python-2.7,opencv,image-processing,ide,anaconda","A_Id":54570301,"CreationDate":"2018-05-03T05:14:00.000","Title":"Install opencv python package in Anaconda","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Can someone provide the steps and the necessary links of the dependencies to install external python package \"opencv-python\" for image processing?\nI tried installing it in pycharm, but it was not able to import cv2(opencv) and was throwing version mismatch with numpy!\nPlease help!","AnswerCount":3,"Available Count":3,"Score":-0.0665680765,"is_accepted":false,"ViewCount":9045,"Q_Id":50147385,"Users Score":-1,"Answer":"Remove all previous\/current (if any) python installation\nInstall Anaconda and add anaconda to PATH(Envirnoment variables:: Adavanced system setting->Environment variables->under system variables go to variable PATHand click edit to add new envirnomental variables)\n(During installation check box involve PATH)\nOpen anaconda prompt with admin access. Type and enter:-\nconda update --all\nconda install -c conda-forge opencv\nconda install spyder=4.0.0  (spyder updation)\nconda update python (for python updation)\n\nTo install this package with conda run one of the following:\nconda install -c conda-forge opencv   \nconda install -c conda-forge\/label\/gcc7 opencv\nconda install -c conda-forge\/label\/broken opencv\nconda install -c conda-forge\/label\/cf201901 opencv","Q_Score":1,"Tags":"python-2.7,opencv,image-processing,ide,anaconda","A_Id":59453381,"CreationDate":"2018-05-03T05:14:00.000","Title":"Install opencv python package in Anaconda","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to do create plot using columns on pandas. But the number of columns are many and some of the columns are not shown on Jupyter notebook. And when I use the columns which are not shown on Jupyter Notebook, the plot cannot be created corrctly. Like when I do this, sns.pairplot(data[['col1', 'col13(cannot see on Jupyter Notebook)']]) col1 is used as y-label instead of col13(cannot see on Jupyter Notebook). \nHow can I fix this?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":64,"Q_Id":50148502,"Users Score":0,"Answer":"You can try: pandas.set_option('display.max_columns', None) in order to display all columns. \nTo reset it, type: pandas.reset_option('display.max_columns'). \nYou may change None to whatever number of columns you wish to have.","Q_Score":0,"Tags":"python,pandas,jupyter-notebook","A_Id":50148675,"CreationDate":"2018-05-03T06:43:00.000","Title":"Jupyter Notebook; Cannot use columns which are not shown on Jupyter notebook","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to build a relation extraction system for drug-drug interactions using a CNN and need to make embeddings for the words in my sentences. The plan is to represent each word in the sentences as a combination of 3 embeddings: (w2v,dist1,dist2) where w2v is a pretrained word2vec embedding and dist1 and dist2 are the relative distances between each word in the sentence and the two drugs that are possibly related.\nI am confused about how I should approach the issue of padding so that every sentence is of equal length. Should I pad the tokenised sentences with some series of strings(what string?) to equalise their lengths before any embedding?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":354,"Q_Id":50154465,"Users Score":0,"Answer":"You can compute the maximal separation between\nentity mentions linked by a relation and choose an\ninput width greater than this distance. This will ensure\nthat every input (relation mention) has same length\nby trimming longer sentences and padding shorter\nsentences with a special token.","Q_Score":1,"Tags":"python,nlp,keras,word2vec,word-embedding","A_Id":50159650,"CreationDate":"2018-05-03T11:52:00.000","Title":"embedding word positions in keras","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm doing an opencv project which needs to detect a image on the screen, which will disappear after some time it shows up. It needs to save the most amount of frames possible while the image is showing, and stop when it disappears. I plan to use the data collected to do a ConvNet, so the more frames I can capture, the better.\nI was using template matching full-screen to search for the image and to identify when it disappears, but I was only capturing about 30% of the total frames, with the screen at 30FPS.  \nWanting to increase frame capture rate, I changed to searching full-screen with template matching until the image was found, and then the area for searching was reduced to the coordinates found of the image with a little margin, so the program could identify when the image disapeared using a lot less resources (because of a very smaller \u00e1rea to check if the image was still there). This allowed me to capture 60% of the frames.  \nHowever I want to know, can I do something else to allow me to optimize my program? I feel like doing template matching for every frame is overkill. Is object tracking better in this case, or it won't even work because the image disapears?\nPS: the image stays for about 7~10 seconds on the screen and takes about the same time to pop up again.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":118,"Q_Id":50165274,"Users Score":1,"Answer":"I ended using numpy to save the captured frames and reached 99% efficiency with the reduced area, no resizing of the images or multiprocessing.","Q_Score":1,"Tags":"python,python-3.x,opencv","A_Id":50185377,"CreationDate":"2018-05-03T23:53:00.000","Title":"How to identify when a certain image disappear","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I created a keras model (tensorflow) and want to store it in my MS SQL Server database. What is the best way to do that? pyodbc.Binary(model) throws an error. I would prefer a way without storing the model in the file system first.\nThanks for any help","AnswerCount":3,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":3587,"Q_Id":50174189,"Users Score":3,"Answer":"It seems that there is no clean solution to directly store a model incl. weights into the database. I decided to store the model as h5 file in the filesystem and upload it from there into the database as a backup. For predictions I load anyway the model from the filesystem as it is much faster than getting it from the database for each prediction.","Q_Score":2,"Tags":"sql-server,database,python-3.x,tensorflow,keras","A_Id":50363845,"CreationDate":"2018-05-04T11:46:00.000","Title":"Save keras model to database","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I created a keras model (tensorflow) and want to store it in my MS SQL Server database. What is the best way to do that? pyodbc.Binary(model) throws an error. I would prefer a way without storing the model in the file system first.\nThanks for any help","AnswerCount":3,"Available Count":2,"Score":0.0665680765,"is_accepted":false,"ViewCount":3587,"Q_Id":50174189,"Users Score":1,"Answer":"The best approach would be to save it as a file in the system and just save the path in the database. This technique is usually used to store large files like images since databases usually struggle with them.","Q_Score":2,"Tags":"sql-server,database,python-3.x,tensorflow,keras","A_Id":57933840,"CreationDate":"2018-05-04T11:46:00.000","Title":"Save keras model to database","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm wondering, which is better to use with GridSearchCV( ..., n_jobs = ... ) to pick the best parameter set for a model, n_jobs = -1 or n_jobs with a big number, like n_jobs = 30 ?\nBased on Sklearn documentation:\n\nn_jobs = -1 means that the computation will be dispatched on all the\n  CPUs of the computer.\n\nOn my PC I have an Intel i3 CPU, which has 2 cores and 4 threads, so does that mean if I set n_jobs = -1, implicitly it will be equal to n_jobs = 2 ?","AnswerCount":2,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":4600,"Q_Id":50183080,"Users Score":4,"Answer":"An additional simpler answer by Prof. Kevyn Collins-Thompson, from course Applied Machine Learning in Python:\nIf I have 4 cores in my system, n_jobs = 30 (30 as an example) will be the same as n_jobs = 4. So no additional effect\n\nSo the maximum performance that can be obtained always is using n_jobs = -1","Q_Score":6,"Tags":"python,machine-learning,parallel-processing,scikit-learn,parallelism-amdahl","A_Id":50494869,"CreationDate":"2018-05-04T21:09:00.000","Title":"How to find an optimum number of processes in GridSearchCV( ..., n_jobs = ... )?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have been self-learning machine learning lately, and I am now trying to solve a binary classification problem (i.e: one label which can either be true or false). I was representing this as a single column which can be 1 or 0 (true or false).\nNonetheless, I was researching and read about how categorical variables can reduce the effectiveness of an algorithm, and how one should one-hot encode them or translate into a dummy variable thus ending with 2 labels (variable_true, variable_false).\nWhich is the correct way to go about this? Should one predict a single variable with two possible values or 2 simultaneous variables with a fixed unique value?\nAs an example, let's say we want to predict whether a person is a male or female:\nShould we have a single label Gender and predict 1 or 0 for that variable, or Gender_Male and Gender_Female?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":381,"Q_Id":50198094,"Users Score":0,"Answer":"it's basically the same, when talking about binary classification, you can think of a final layer for each model that adapt the output to other model\ne.g if the model output 0 or 1 than the final layer will translate it to vector like [1,0] or [0,1] and vise-versa by a threshold criteria, usually is >= 0.5\na nice byproduct of 2 nodes in the final layer is the confidence level of the model in it's predictions [0.80, 0.20] and [0.55, 0.45] will both yield [1,0] classification but the first prediction has more confidence\nthis can be also extrapolate from 1 node output by the distance of the output from the fringes 1 and 0 so 0.1 will be considered with more confidence than 0.3 as a 0 prediction","Q_Score":0,"Tags":"python,machine-learning","A_Id":50198173,"CreationDate":"2018-05-06T09:13:00.000","Title":"Predicting binary classification","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to create a classifier to identify some aphids.\nMy project has two parts, one with a computer vision (OpenCV), which I already conclude. The second part is with Machine Learning using TensorFlow. But I have no idea how to do it.\nI have these data below that have been removed starting from the use of OpenCV, are HuMoments (I believe that is the path I must follow), each line is the HuMoments of an aphid (insect), I have 500 more data lines that I passed to one CSV file.\nHow can I make a classifier from a CSV file using TensorFlow?\n\nHuMoments (in CSV file):\n  0.27356047,0.04652453,0.00084231,7.79486673,-1.4484489,-1.4727380,-1.3752532\n  0.27455502,0.04913969,3.91102408,1.35705980,3.08570234,2.71530819,-5.0277362\n  0.20708829,0.01563241,3.20141907,9.45211423,1.53559373,1.08038279,-5.8776765\n  0.23454372,0.02820523,5.91665789,6.96682467,1.02919203,7.58756583,-9.7028848","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":102,"Q_Id":50206222,"Users Score":0,"Answer":"You can start with this tutorial, and try it first without changing anything; I strongly suggest this unless you are already familiar with Tensorflow so that you gain some familiarity with it.\nNow you can modify the input layer of this network to match the dimensions of the HuMoments. Next, you can give a numeric label to each type of aphid that you want to recognize, and adjust the size of the output layer to match them.\nYou can now read the CSV file using python, and remove any text like \"HuMoments\". If your file has names of aphids, remove them and replace them with numerical class labels. Replace the training data of the code in the above link, with these data.\nNow you can train the network according to the description under the title \"Train the Model\".\nOne more note. Unless it is essential to use Tensorflow to match your project requirements, I suggest using Keras. Keras is a higher level library that is much easier to learn than Tensorflow, and you have more sample code online.","Q_Score":0,"Tags":"python,csv,tensorflow","A_Id":50206803,"CreationDate":"2018-05-07T02:06:00.000","Title":"Tensorflow How can I make a classifier from a CSV file using TensorFlow?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I developed a gomory cut for a LP problem (based on 'test-gomory.py' test file) which I could not manage to run. Finally, I copied the test file to check whether I'd the same trouble. Indeed I got the same message:\nif not scip.isLPSolBasic():\nAttributeError: 'pyscipopt.scip.Model' object has no attribute 'isLPSolBasic'\nI have downloaded SCIPOptSuite 5.0.1 win64, set up path and installed pyscipiopt using pip on conda. \nI cannot figure what is wrong, except that I may have failed to install pyscipopt properly? Thank you for pointing me in the right direction.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":215,"Q_Id":50209928,"Users Score":0,"Answer":"Your assumption is correct: The pip version of PySCIPOpt was outdated and did not yet include the latest updates with respect to cutting plane separators. I just triggered a new release build (v.1.4.6) that should be available soon.\nWhen in doubt, you can always build PySCIPOpt from source by running python setup.py install from within the project directory.","Q_Score":0,"Tags":"python,discrete-mathematics,scip","A_Id":50210208,"CreationDate":"2018-05-07T08:23:00.000","Title":"PySCIPOpt\/SCIP - isLPSolBasic() not in pyscipopt.scip.Model","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm working on something using gensim.\nIn gensim, var index usually means an object of gensim.similarities.<cls>.\nAt first, I use gensim.similarities.Similarity(filepath, ...) to save index as a file, and then loads it by gensim.similarities.Similarity.load(filepath + '.0'). Because gensim.similarities.Similarity default save index to shards file like index.0.\nWhen index file becoming larger, it automatically seperate into more shards, like index.0,index.1,index.2......\nHow can I load these shards file? gensim.similarities.Similarity.load() can only load one file.\nBTW: I have try to find the answer in gensim's doc, but failed.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":473,"Q_Id":50213754,"Users Score":0,"Answer":"shoresh's answer is correct. The key part that OP was missing was \n    index.save(output_fname)\nWhile just creating the object appears to save it, it's really only saving the shards, which require saving a sort of directory file (via index.save(output_fname) to be made accessible as a whole object.","Q_Score":1,"Tags":"python,gensim","A_Id":58696815,"CreationDate":"2018-05-07T12:01:00.000","Title":"How to load index shards by gensim.similarities.Similarity\uff1f","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Background: I am working on classifying data from a ticketing system data into a failed or successful requests. A request goes into various stages before getting completed. Each request is assigned to different teams and individuals before being marked as complete. \nMaking use of historical data I want to create predictions for these tickets at a final state x before they are marked as complete(success or fail).\nAmongst the various features, individual's name who work on the records & team names are very important factors in analysing this data. Being a huge organization I expect 5-10 new names being added every day.\nHistorical data \n60k records (used for training, validation and testing)\nHas 10k unique individual names \nCurrent data\nOveral 1k records\n- Has 200 individual names\nI'm facing a challenge due to high cardinality data like individual names whose number is not fixed and keeps on growing.\n1. Challenge while making actual predictions - The no. of columns for the current data will be different every time and would never match the feature length of training data. \n- So I have to train my model every single time, I want to make predictions.\n2. Challenge while data prep -  The above also presents a challenge for data prep as now I always have to encode the complete data and the query encoded data to split into current and future data.\nSorry for the long story. \nWhat am I looking for?\nIs there a better way to approach? \nThese high & constantly changing dimensions is a pain. Any suggestions on how can I handle them, to avoid training every time?\nNote: I tried using PCA and Autoencoders for dim red. (The results were not great for my highly unbalanced dataset so I'm working on the data with high dimensions only)","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":254,"Q_Id":50219738,"Users Score":0,"Answer":"You can use replace all id numbers and names in the data with a standard token like <ID> or <NAME>. This should be done during preprocessing. Next you should pick a fixed vocabulary. Like all words that occur at least 5 times in the training data.","Q_Score":1,"Tags":"python,machine-learning,data-science,dimensionality-reduction,cardinality","A_Id":50225476,"CreationDate":"2018-05-07T17:42:00.000","Title":"Challenges with high cardinality data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Background: I am working on classifying data from a ticketing system data into a failed or successful requests. A request goes into various stages before getting completed. Each request is assigned to different teams and individuals before being marked as complete. \nMaking use of historical data I want to create predictions for these tickets at a final state x before they are marked as complete(success or fail).\nAmongst the various features, individual's name who work on the records & team names are very important factors in analysing this data. Being a huge organization I expect 5-10 new names being added every day.\nHistorical data \n60k records (used for training, validation and testing)\nHas 10k unique individual names \nCurrent data\nOveral 1k records\n- Has 200 individual names\nI'm facing a challenge due to high cardinality data like individual names whose number is not fixed and keeps on growing.\n1. Challenge while making actual predictions - The no. of columns for the current data will be different every time and would never match the feature length of training data. \n- So I have to train my model every single time, I want to make predictions.\n2. Challenge while data prep -  The above also presents a challenge for data prep as now I always have to encode the complete data and the query encoded data to split into current and future data.\nSorry for the long story. \nWhat am I looking for?\nIs there a better way to approach? \nThese high & constantly changing dimensions is a pain. Any suggestions on how can I handle them, to avoid training every time?\nNote: I tried using PCA and Autoencoders for dim red. (The results were not great for my highly unbalanced dataset so I'm working on the data with high dimensions only)","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":254,"Q_Id":50219738,"Users Score":0,"Answer":"Since you have a dynamic data like you said, you can use neural net to identify and merge updating variables and data.\nAlso you should use classifiers like\n\nCVParameterSelection : For cross validation parameter selection.\nPART : For making a decision tree, great utility as it works on divide and conquer rule.\nREP Tree (Pruned) : For reduced error in output by splitting error values\n\nAnd finally when you have the systems in place, you can run the prediction model!","Q_Score":1,"Tags":"python,machine-learning,data-science,dimensionality-reduction,cardinality","A_Id":50226408,"CreationDate":"2018-05-07T17:42:00.000","Title":"Challenges with high cardinality data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to train a classifier, and my target variable has 300 unique values, and its type is character\/string\nIs there an automated process with pandas that can automatically trnsform each string into a number?\nThanks a lot","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":486,"Q_Id":50232939,"Users Score":0,"Answer":"You can use the pandas factorize method for converting strings into numbers.  numpy.unique can also be used but will be comparatively slower.","Q_Score":1,"Tags":"python-3.x","A_Id":50233334,"CreationDate":"2018-05-08T11:51:00.000","Title":"action when target variable is character\/string","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I hope to know a general approach when do data engineering.\nI have a data set with some variables with too many categories, and including these variables into a predictive model definitely would increase the complexity of the model, thus leads to overfit.\nWhile normally I would group those categories into fewer groups by drawing plots to see if the response variable is significantly different among these groups. Is there a more efficient way dealing with this issue, like automatically carry out some statistical test?\nADDED: In a nutshell, I hope to group or bin values in a variable  so that the response variable in each group has very different distribution.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":339,"Q_Id":50248547,"Users Score":0,"Answer":"I am not sure if I understand by \"automatically\". However, instead of plotting (which can be a hard task if you have many attributes for each sample), you can try to automatically group your samples using clustering techniques such as K-Means, Hierarchical clustering, SOM (or any clustering technique that fits to your problem). Then, for each group, you may extract any statistical information of interest.","Q_Score":0,"Tags":"python,r","A_Id":50252474,"CreationDate":"2018-05-09T08:13:00.000","Title":"Data with too many categories","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm currently looking over the Eager mode in Tensorflow and wanted to know if I can extract the graph to use in Tensorboard. I understand that Tensorflow Eager mode does not really have a graph or session system that the user has to create. However, from my understanding there is one under the hood. Could this hidden Graph and Session be exported to support a visual graph view in Tensorboard? Or do I need to redo my model into a Graph\/Session form of execution?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":4878,"Q_Id":50257614,"Users Score":10,"Answer":"No, by default there is no graph nor sessions in eager executing, which is one of the reasons why it is so appealing. You will need to write code that is compatible with both graph and eager execution to write your net's graph in graph mode if you need to.\nNote that even though you can use Tensorboard in eager mode to visualize summaries, good ol' tf.summary.FileWriter is incompatible with eager execution: you need to use tf.contrib.summary.create_file_writer instead (works in graph mode too, so you won't have to change your code).","Q_Score":9,"Tags":"python,tensorflow,machine-learning","A_Id":50260668,"CreationDate":"2018-05-09T16:01:00.000","Title":"Tensorflow Eager and Tensorboard Graphs?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"We are having n number of documents. Upon submission of new document by user, our goal is to inform him about possible duplication of existing document (just like stackoverflow suggests questions may already have answer).\nIn our system, new document is uploaded every minute and mostly about the same topic (where there are more chance of duplication).\nOur current implementation includes gensim doc2vec model trained on documents (tagged with unique document ids). We infer vector for new document and find most_similar docs (ids) with it. Reason behind choosing doc2vec model is that we wanted to take advantage of semantics to improve results. As far as we know, it does not support online training, so we might have to schedule a cron or something that periodically updates the model. But scheduling cron will be disadvantageous as documents come in a burst. User may upload duplicates while model is not yet trained for new data. Also given huge amount of data, training time will be higher.\nSo i would like to know how such cases are handled in big companies. Are there any better alternative? or better algorithm for such problem?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":418,"Q_Id":50264369,"Users Score":2,"Answer":"You don't have to take the old model down to start training a new model, so despite any training lags, or new-document bursts, you'll always have a live model doing the best it can.\nDepending on how much the document space changes over time, you might find retraining to have a negligible benefit. (One good model, built on a large historical record, might remain fine for inferring new vectors indefinitely.)\nNote that tuning inference to use more steps (especially for short documents), or a lower starting alpha (more like the training default of 0.025) may give better results. \nIf word-vectors are available, there is also the \"Word Mover's Distance\"  (WMD) calculation of document similarity, which might be ever better at identifying close duplicates. Note, though, it can be quite expensive to calculate \u2013 you might want to do it only against a subset of likely candidates, or have to add many parallel processors, to do it in bulk. There's another newer distance metric called 'soft cosine similarity' (available in recent gensim) that's somewhere between simple vector-to-vector cosine-similarity and full WMD in its complexity, that may be worth trying. \nTo the extent the vocabulary hasn't expanded, you can load an old Doc2Vec model, and continue to train() it \u2013 and starting from an already working model may help you achieve similar results with fewer passes. But note: it currently doesn't support learning any new words, and the safest practice is to re-train with a mix of all known examples interleaved. (If you only train on incremental new examples, the model may lose a balanced understanding of the older documents that aren't re-presented.)\n(If you chief concern is documents that duplicate exact runs-of-words, rather than just similar fuzzy topics, you might look at mixing-in other techniques, such as breaking a document into a bag-of-character-ngrams, or 'shingleprinting' as in common in plagiarism-detection applications.)","Q_Score":3,"Tags":"python,machine-learning,nlp,gensim,doc2vec","A_Id":50265639,"CreationDate":"2018-05-10T01:53:00.000","Title":"Document similarity in production environment","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a reference image captured from a cc-camera and need to capture current image from the cc-camera to check if the camera is shifted or not at regular intervals. If shifted, the shift angle must also be needed. How can I achieve this using python?   Is the cv2.phaseCorrelate() function be helpful for this. Please give me some suggestions.\nThank you.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":352,"Q_Id":50273650,"Users Score":1,"Answer":"Use of hanning window will be more accurate for getting the phase values. Use phase correlate function with the window will give you a right phase shift.","Q_Score":1,"Tags":"python,opencv,image-processing,scipy,computer-vision","A_Id":50737007,"CreationDate":"2018-05-10T13:06:00.000","Title":"python: Find camera shift angle using a reference image and current image from the camera","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How can I quickly switch between running tensorflow code with my CPU and my GPU?\nMy setup:\nOS = Windows 10\nPython = 3.5\nTensorflow-gpu = 1.0.0\nCUDA = 8.0.61\ncuDNN = 5.1\nI saw a post suggesting something about setting CUDA_VISIBLE_DEVICES=0 but I don't have this variable in my environment (not sure if it's because I'm running windows or what) but if I do set it using something like os.environ it doesn't effect how tensorflow runs code.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2716,"Q_Id":50282567,"Users Score":4,"Answer":"If you set the environment variable CUDA_VISIBLE_DEVICES=-1 you will use the CPU only. If you don't set that environment variable you will allocate memory to all GPUs but by default only use GPU 0. You can also set it to the specific GPU you want to use. CUDA_VISIBLE_DEVICES=0 will only use GPU 0. \nThis environment variable is created by the user, it won't exist until you create it. You need to set the variable before tensorflow is imported (usually that is before you start your script).","Q_Score":3,"Tags":"python-3.x,tensorflow","A_Id":50282794,"CreationDate":"2018-05-10T22:49:00.000","Title":"Tensorflow: switch between CPU and GPU [Windows 10]","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using SVM for text classification (tf-idf score based classification).\nIs it possible to plot SVM having more than 100 attributes and 10 labels. Is there any way to reduce the features and then plot the same multiclass SVM.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":150,"Q_Id":50289947,"Users Score":1,"Answer":"For input data of higher dimensionality, I think that there is not a direct way to render a SVM. You should apply a dimensionality reduction, in order to have something to plot in 2-d or 3-d.","Q_Score":1,"Tags":"python,machine-learning,plot,svm,text-classification","A_Id":50290731,"CreationDate":"2018-05-11T10:08:00.000","Title":"Visualize a SVM model having100 attributes in 2D plot python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Algo (Source: Elements of Programming Interviews, 5.16) \n\nYou are given n numbers as well as probabilities p0, p1,.., pn-1\n  which sum up to 1. Given a rand num generator that produces values in\n  [0,1] uniformly, how would you generate one of the n numbers according\n  to their specific probabilities.\n\nExample\n\nIf numbers are 3, 5, 7, 11, and the probabilities are 9\/18, 6\/18,\n  2\/18, 1\/18, then in 1000000 cals to the program, 3 should appear\n  500000 times, 7 should appear 111111 times, etc.\n\nThe book says to create intervals p0, p0 + p1, p0 + p1 + p2, etc so in the example above the intervals are [0.0, 5.0), [0.5, 0.0.8333), etc and combining these intervals into a sorted array of endpoints could look something like [1\/18, 3\/18, 9\/18, 18\/18]. Then run the random function generator, and find the smallest element that is larger than the generated element - the array index that it corresponds to maps to an index in the given n numbers.\nThis would require O(N) pre-processing time and then O(log N) to binary search for the value.\nI have an alternate solution that requires O(N) pre-processing time and O(1) execution time, and am wondering what may be wrong with it.\nWhy can't we iterate through each number in n, multiplying [n] * 100 * probability that matches with n. E.g [3] * (9\/18) * 100. Concatenate all these arrays to get, at the end, a list of 100 elements, with the number of elements for each mapping to how likely it is to occur. Then, run the random num function and index into the array, and return the value.\nWouldn't this be more efficient than the provided solution?","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":556,"Q_Id":50296427,"Users Score":2,"Answer":"Your number 100 is not independent of the input; it depends on the given p values. Any parameter that depends on the magnitude of the input values is really exponential in the input size, meaning you are actually using exponential space. Just constructing that array would thus take exponential time, even if it was structured to allow constant lookup time after generating the random number.\nConsider two p values, 0.01 and 0.99. 100 values is sufficient to implement your scheme. Now consider 0.001 and 0.999. Now you need an array of 1,000 values to model the probability distribution. The amount of space grows with (I believe) the ratio of the largest p value and the smallest, not in the number of p values given.","Q_Score":1,"Tags":"python,arrays,algorithm","A_Id":50296509,"CreationDate":"2018-05-11T16:20:00.000","Title":"Generate non-uniform random numbers","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Algo (Source: Elements of Programming Interviews, 5.16) \n\nYou are given n numbers as well as probabilities p0, p1,.., pn-1\n  which sum up to 1. Given a rand num generator that produces values in\n  [0,1] uniformly, how would you generate one of the n numbers according\n  to their specific probabilities.\n\nExample\n\nIf numbers are 3, 5, 7, 11, and the probabilities are 9\/18, 6\/18,\n  2\/18, 1\/18, then in 1000000 cals to the program, 3 should appear\n  500000 times, 7 should appear 111111 times, etc.\n\nThe book says to create intervals p0, p0 + p1, p0 + p1 + p2, etc so in the example above the intervals are [0.0, 5.0), [0.5, 0.0.8333), etc and combining these intervals into a sorted array of endpoints could look something like [1\/18, 3\/18, 9\/18, 18\/18]. Then run the random function generator, and find the smallest element that is larger than the generated element - the array index that it corresponds to maps to an index in the given n numbers.\nThis would require O(N) pre-processing time and then O(log N) to binary search for the value.\nI have an alternate solution that requires O(N) pre-processing time and O(1) execution time, and am wondering what may be wrong with it.\nWhy can't we iterate through each number in n, multiplying [n] * 100 * probability that matches with n. E.g [3] * (9\/18) * 100. Concatenate all these arrays to get, at the end, a list of 100 elements, with the number of elements for each mapping to how likely it is to occur. Then, run the random num function and index into the array, and return the value.\nWouldn't this be more efficient than the provided solution?","AnswerCount":2,"Available Count":2,"Score":0.1973753202,"is_accepted":false,"ViewCount":556,"Q_Id":50296427,"Users Score":2,"Answer":"If you have rational probabilities, you can make that work.  Rather than 100, you must use a common denominator of the rational proportions.  Insisting on 100 items will not fulfill the specs of your assigned example, let alone more diabolical ones.","Q_Score":1,"Tags":"python,arrays,algorithm","A_Id":50296535,"CreationDate":"2018-05-11T16:20:00.000","Title":"Generate non-uniform random numbers","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Suppose I clustered a data set using sklearn's K-means. \nI can see the centroids easily using KMeans.cluster_centers_ but I need to get the clusters as I get centroids.\nHow can I do that?","AnswerCount":3,"Available Count":1,"Score":0.1325487884,"is_accepted":false,"ViewCount":5157,"Q_Id":50297142,"Users Score":2,"Answer":"You probably look for the attribute labels_.","Q_Score":2,"Tags":"python-3.x,scikit-learn,k-means,data-science","A_Id":50298062,"CreationDate":"2018-05-11T17:11:00.000","Title":"Get cluster points after KMeans in a list format","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have trained a model successfully and now I want to continue training it with new data. If a given data with the same amount of classes it works fine. But having more data then initially it will give me the error:\n\nValueError: Shapes (?, 14) and (?, 21) are not compatible\n\nHow can I dynamically increase the number of classes in my trained model or how to make the model accept a lesser number of classes? Do I need to save the classes in a pickle file?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":168,"Q_Id":50305294,"Users Score":0,"Answer":"Best thing to do is to train your network from scratch with the output layers adjusted to the new output class size.\nIf retraining is an issue, then keep the trained network as it is and only drop the last layer. Add a new layer with the proper output size, initialized to random weights and then fine-tune (train) the entire network.","Q_Score":1,"Tags":"python-3.x,tensorflow,machine-learning,rnn","A_Id":50305713,"CreationDate":"2018-05-12T10:36:00.000","Title":"How to continue to train a model with new classes and data?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to work with the object detection api by tensorflow but was unable to install that properly. I looked up every solution in the internet and everything was in vain. Below is the error message I am getting: \n\n\u201cC:\\Program Files\\protoc-3.5.0-win32\\bin\\protoc.exe\u201d object_detection\/protos\/*.proto --python_out=.\nobject_detection\/protos\/*.proto: No such file or directory","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":353,"Q_Id":50307347,"Users Score":0,"Answer":"Currently you are in which directory?\nYou can set the Path variable to point to protoc.exe and run the command from object detection directory","Q_Score":0,"Tags":"python,tensorflow,object-detection,protoc","A_Id":50632788,"CreationDate":"2018-05-12T14:33:00.000","Title":"protoc executable unable to find object detection .protos file in tensorflow models","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Moving an image to an array then flattening it and shuffling with given x seed it should be easy to unshuffle it with the given seed and indexes from the shuffling process.\n\nread image IMG.jpg\nrandom.seed(x) and shuffle -> indexes, shuffle_img.jpg\n\n\nunshuffle\n\n\nHowever, this RESULT shows that the resulting IMG is simmilar but not 1:1 as the input image with this grain noise. \nWhy the unshuffling gives so much noise if it is not the RNG, only PRNG?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":287,"Q_Id":50308623,"Users Score":2,"Answer":"This has nothing to do with your random numbers.\nNotice that you use the random number generator only once when you create the shuffled indices. When you load the indices from the file, the random number generator is not used, since only a file is read.\nYour issue occurs at a different place: You save the scrambled Lena as a .jpg. Thereby, poor Lena's scrambled image gets compressed and the colour values change a little bit. When you load the image again and reorder the indices, you do not get the original colours back but only the values after the compression.\nSolution: Save your images as a *.png and everything works out.\nIf you run into problems with an alpha channel, just convert the image back to RGB: scrambled_img = Image.open(img_path).convert(\"RGB\")","Q_Score":0,"Tags":"python,numpy,random,pillow","A_Id":50309756,"CreationDate":"2018-05-12T16:54:00.000","Title":"How to revert shuffled array in python with default PRNG and indexes?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm implementing a custom sklearn Transformer, which requires an optimization step which has been coded in Tensorflow. TF requires a Session, which should be used as a context manager or explicitly closed. The question is: adding a close() method to the Transformer would be odd (and unexpected for a user), what is the best place to close the session? Should I open and close a new session for a every call to fit()? Or should I keep it open and leave the session.close() to the __del__ method of the transformer? Any other options?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":143,"Q_Id":50310515,"Users Score":1,"Answer":"Don't open a session at each function call, that could be very inefficient if the function is called many times.\nIf for some reason, you don't want to expose a context manager, then you need to open the session yourself, and leave it open. It is perhaps a bit simpler for the user, but sharing the tf.Session with other objects or libraries might be more difficult. Also trying to hide the fact that you are using tensorflow may be a bit vain, as it is potentially incompatible with other libraries also relying on the GPU. (Also the user will need to install tensorflow to use the library, s\/he will definitely know that you are using it).\nSo I would not try to encapsulate things that can't or shouldn't (in my opinion) and use a context manager for the tf.Session (maybe even using directly a tf.Session itself if I don't mind exposing tensorflow, otherwise wrapping it in my own context manager).","Q_Score":1,"Tags":"python,tensorflow,scikit-learn","A_Id":50333204,"CreationDate":"2018-05-12T20:43:00.000","Title":"How to efficiently use a Session from within a python object to keep tensorflow as an implementation detail?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"My goal is to make an easy neural network fit by providing 2 verticies of a certain Graph and 1 if there's a link or 0 if there's none.\nI fit my model, it gets loss of about 0.40, accuracy of about 83% during fitting. I then evaluate the model by providing a batch of all positive samples and several batches of negative ones (utilising random.sample). My model gets loss of ~0.35 and 1.0 accuracy for positive samples and ~0.46 loss 0.68 accuracy for negative ones.\nMy understanding of neural networks if extremely limited, but to my understanding the above means it theoretically always is right when it outputs 0 when there's no link, but can sometimes output 1 even if there is none.\nNow for my actual problem: I try to \"reconstruct\" the original graph with my neural network via model.predict. The problem is I don't understand what the predict output means. At first I assumed values above 0.5 mean 1, else 0. But if that's the case the model doesn't even come close to rebuilding the original.\nI get that it won't be perfect, but it simply returns value above 0.5 for random link candidates.\nCan someone explain to me how exactly model.predict works and how to properly use it to rebuild my graph?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":343,"Q_Id":50319873,"Users Score":0,"Answer":"The model that you trained is not directly optimized w.r.t. the graph reconstruction. Without loss of generality, for a N-node graph, you need to predict N choose 2 links. And it may be reasonable to assume that the true values of the most of these links are 0.\nWhen looking into your model accuracy on the 0-class and 1-class, it is clear that your model is prone to predict 1-class, assuming your training data is balanced. Therefore, your reconstructed graph contains many false alarm links. This is the exact reason why the performance of your reconstruction graph is poor.\nIf it is possible to retrain the model, I suggest you do it and use more negative samples.\nIf not, you need to consider applying some post-processing. For example, instead of finding a threshold to decide which two nodes have a link, use the raw predicted link probabilities to form a node-to-node linkage matrix, and apply something like the minimum spanning tree to further decide what are appropriate links.","Q_Score":2,"Tags":"python,tensorflow,machine-learning,keras,predict","A_Id":50326237,"CreationDate":"2018-05-13T19:28:00.000","Title":"Need help using Keras' model.predict","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I build two graphs in my code, graph1 and graph2. \nThere is a tensor, named embedding, in graph1. I tied to use it in graph2 by using get_variable, while the error is tensor must be from the same graph as Tensor. I found that this error occurs because they are in different graphs.\nSo how can I use a tensor in graph1 to graph2?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":39,"Q_Id":50323744,"Users Score":0,"Answer":"expanding on @jdehesa's comment,\nembedding could be trained initially, saved from graph1 and restored to graph2 using tensorflows saver\/restore tools. for this to work you should assign embedding to a name\/variable scope in graph1 and reuse the scope in graph2","Q_Score":0,"Tags":"python,tensorflow,graph","A_Id":50329741,"CreationDate":"2018-05-14T05:48:00.000","Title":"How to used a tensor in different graphs?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to identify the type of feature in a dataset which can be either categorical\/bag of words\/ floats. \nHowever I am unable to reach to a accurate solution to distinguish between categorical and bag of words due to following reasons.\n\nCategorical data can either be object or float. Counting the unique values in a feature does not ensure the accurate solution as different samples can have the same feature value which necessarily may not be categorical.\nFor bag or words, I thought of counting the number of words but again this is not the correct way as text can be written in a single word or may be missing.\n\nwhat can be the best way to identify the type of features?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":183,"Q_Id":50326774,"Users Score":1,"Answer":"Well you're confused between those two terms:\nCategorical Data is the kind of data which can be categorized between different categories especially more than two classes or multi-class. Search for 20 Newsgroup Dataset.\nWhereas,\nBag of Words is a technique of storing features. Identification of features is done on the basis of what outcome is required. There are techniques to extract features like TF-IDF Vectorizer from sklearn, Word2Vec, Doc2Vec, etc. But identification of features is solely based on the dataset you use and the application it is used for. Always remember, if you convert textual data to numerical form or whatsoever, the column names are your Features or Dimensions whereas the rows are your samples or instances or records.","Q_Score":1,"Tags":"python,pandas,machine-learning","A_Id":50327148,"CreationDate":"2018-05-14T09:11:00.000","Title":"identifying feature type in a dataset : categorical or bag of words","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When trying to execute the following command:\n\nimport matplotlib.pyplot as plt\n\nThe following error occurs:\n\nfrom _bz2 import BZ2Compressor, BZ2Decompressor ImportError: No module\n  named '_bz2'\n\nSo, I was trying to install bzip2 module in Ubuntu using :\n\nsudo pip3 install bzip2\n\nBut, the following statement pops up in the terminal:\n\nCould not find a version that satisfies the requirement bzip2 (from\n  versions: ) No matching distribution found for bzip2\n\nWhat can I do to solve the problem?","AnswerCount":5,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":17593,"Q_Id":50335503,"Users Score":16,"Answer":"If you compiling python yourself, you need to install libbz2 headers and .so files first, so that python will be compiled with bz2 support.\nOn ubuntu, apt-get install libbz2-dev then compile python.","Q_Score":15,"Tags":"python,python-3.x,matplotlib,importerror,bzip2","A_Id":53698659,"CreationDate":"2018-05-14T17:00:00.000","Title":"No module named '_bz2' in python3","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"When trying to execute the following command:\n\nimport matplotlib.pyplot as plt\n\nThe following error occurs:\n\nfrom _bz2 import BZ2Compressor, BZ2Decompressor ImportError: No module\n  named '_bz2'\n\nSo, I was trying to install bzip2 module in Ubuntu using :\n\nsudo pip3 install bzip2\n\nBut, the following statement pops up in the terminal:\n\nCould not find a version that satisfies the requirement bzip2 (from\n  versions: ) No matching distribution found for bzip2\n\nWhat can I do to solve the problem?","AnswerCount":5,"Available Count":2,"Score":0.0399786803,"is_accepted":false,"ViewCount":17593,"Q_Id":50335503,"Users Score":1,"Answer":"I found a pattern in those issue.\nIt happens mostly if you are missing dev tools and other important libraries important to compile code and install python.\nFor me most of those step did not work. But I had to do the following :\n\nRemove my python installation\n\npyenv uninstall python_version\n\nThen install all the build tools to make sure I am not missing any\n\nsudo apt-get install -y make build-essential libssl-dev zlib1g-dev libbz2-dev \\ libreadline-dev libsqlite3-dev wget curl llvm libncurses5-dev libncursesw5-dev \\ xz-utils tk-dev libffi-dev liblzma-dev python-openssl git\n\nReinstall the new python version\n\npyenv install python_version\nI hope that solves your issues.","Q_Score":15,"Tags":"python,python-3.x,matplotlib,importerror,bzip2","A_Id":71457141,"CreationDate":"2018-05-14T17:00:00.000","Title":"No module named '_bz2' in python3","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I created a CNN model on tensorflow with input placeholder - [None, 32, 32, 3]tf.placeholder(tf.float32, [None, 24, 24, 3]). Then I want to use that model in Android application and for that reason I froze the model. When I include the library I noticed that I can feed only with two dimensional array or ByteBuffer. How can I feed the bitmap from the camera? Do I have to change the input size of the placeholder and what should the size be? \nThanks!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":86,"Q_Id":50355139,"Users Score":0,"Answer":"tensorInferenceInterface.feed(\"the_input\", pixels, batch_size, width, height, dims); \nhope this will work for you","Q_Score":0,"Tags":"android,python,tensorflow","A_Id":54786075,"CreationDate":"2018-05-15T16:29:00.000","Title":"Tensorflow android feed with specific array","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Performing .shape is giving me the following error. \n\nAttributeError: 'DataFrame' object has no attribute 'shape'\n\nHow should I get the shape instead?","AnswerCount":6,"Available Count":1,"Score":0.0333209931,"is_accepted":false,"ViewCount":16817,"Q_Id":50355598,"Users Score":1,"Answer":"To get the shape we can try this way:\n dask_dataframe.describe().compute()  \n\"count\" column of the index will give the number of rows\n len(dask_dataframe.columns) \nthis will give the number of columns in the dataframe","Q_Score":25,"Tags":"python,dask","A_Id":53350392,"CreationDate":"2018-05-15T16:57:00.000","Title":"How should I get the shape of a dask dataframe?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using scipy.optimize.least_squares to solve an interval constrained nonlinear least squares optimization problem.  The form of my particular problem is that of finding a0, a1, b0, and b1 such that the cost function:\n\\sum^N_{n=1} ( g_n - (y_n - b0 e^-(tn\/b1)) \/ a0 e^-(tn\/a1) )^2\nis minimized where g_n, y_n and t_n are known and there are interval constraints on a0, a1, b0, and b1.\nThe four unknown parameters span approximately four orders of magnitude (e.g, a0 = 2e-3, a1 = 30, similar for b0 and b1).  I have heard that a high dynamic range of unknown parameters can be numerically problematic for optimization routines.   \nMy first question is whether four or so orders of magnitude range would be problematic for scipy.optimize.minimize. The routine appears to converge on the data I've applied so far. \nMy second question relates to the form of the cost function.  I can equivalently write it as:\n\\sum^N_{n=1} ( g_n - ( 1\/a0 e^(tn\/a1) y_n - b0\/a0 e^-(tn\/b1) +tn\/a1) \/ )^2\n=\n\\sum^N_{n=1} ( g_n - ( a0' e^(tn\/a1) y_n - b0' e^-(tn*b1')) )^2\nwhere the new parameters are simple transformations of the original parameters.  Is there any advantage to doing this in terms of numerical stability or the avoidance of local minima?  I haven't proven it, but I wonder whether this new cost function would be convex as opposed to the original cost function.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":176,"Q_Id":50358434,"Users Score":0,"Answer":"Most solvers are designed for variables in the 1-10 range. A large range can cause numerical problems, but it is not guaranteed to be problematic. Numerical problems sometimes stem from the matrix factorization step of the linear algebra for solving the Newton step, which is more dependent of the magnitude of the derivatives. You may also encounter challenges with termination tolerances for values outside the 1-10 range. Overall, if it looks like it's working, it's probably fine. You could get a slightly better answer by normalizing values.\nDivision by a degree of freedom can cause difficulties in three ways:\n\ndivision by zero\ndiscontinuous derivatives around 0\nvery steep derivatives near 0, or very flat derivatives far from 0\n\nFor these reasons, I would recommend \\sum^N_{n=1} ( g_n - ( a0' e^(tn\/a1) y_n - b0' e^-(tn*b1')) )^2. However, as previously stated, if it's already working it may not be worth the effort to reformulate your problem.","Q_Score":0,"Tags":"python,scipy,mathematical-optimization,least-squares,nonlinear-optimization","A_Id":50400053,"CreationDate":"2018-05-15T20:09:00.000","Title":"Dynamic range of unknown parameters and form of cost function in scipy.optimize.least_squares","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In tensorflow Object Detection API we are using ssd_mobilenet_v1_coco_2017_11_17 model to detect 90 general objects. I want to use this model for detection.\nNext, I have trained faster_rcnn_inception_v2_coco_2018_01_28 model to detect a custom object. I wish to use this in the same code where I will be able to detect those 90 objects as well as my new trained custom object. How to achieve this with single code?","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":2781,"Q_Id":50364281,"Users Score":2,"Answer":"You cant combine both the models. Have two sections of code which will load one model at a time and identify whatever it can see in the image. \nOther option is to re-train a single model that can identify all objects you are interested in","Q_Score":7,"Tags":"python,tensorflow,object-detection","A_Id":50632652,"CreationDate":"2018-05-16T06:58:00.000","Title":"How to use two models in Tensorflow object Detection API","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"With training & validation through a dataset for nearly 24 epochs, intermittently 8 epochs at once and saving weights cumulatively after each interval. \nI observed a constant declining train & test-loss for first 16 epochs, post which the training loss continues to fall whereas test loss rises so i think it's the case of Overfitting.\nFor which i tried to resume training with weights saved after 16 epochs with change in hyperparameters say increasing dropout_rate a little.\nTherefore i reran the dense & transition blocks with new dropout to get identical architecture with same sequence & learnable parameters count.\nNow when i'm assigning previous weights to my new model(with new dropout) with model.load_weights() and compiling thereafter.\ni see the training loss is even higher, that should be initially (blatantly with increased inactivity of random nodes during training) but later also it's performing quite unsatisfactory,\nso i'm suspecting maybe compiling after loading pretrained weights might have ruined the performance? \nwhat's reasoning & recommended sequence of model.load_weights() & model.compile()? i'd really appreciate any insights on above case.","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":1304,"Q_Id":50367540,"Users Score":2,"Answer":"The model.compile() method does not touch the weights in any way. \nIts purpose is to create a symbolic function adding the loss and the optimizer to the model's existing function. \nYou can compile the model as many times as you want, whenever you want, and your weights will be kept intact. \nPossible consequences of compile\nIf you got a model, well trained for some epochs, it's optimizer (depending on what type and parameters you chose for it) will also be trained for that specific epochs. \nCompiling will make you lose the trained optimizer, and your first training batches might experience some bad results due to learning rates not suited to the current state of the model. \nOther than that, compiling doesn't cause any harm.","Q_Score":1,"Tags":"python,keras,deep-learning","A_Id":50370068,"CreationDate":"2018-05-16T09:47:00.000","Title":"should model.compile() be run prior to using model.load_weights(), if model has been only slightly changed say dropout?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I run my pyspark program in vscode, and get error:\nPicklingError: Could not serialize object: ImportError: No module named visualstudio_py_debugger .\nI suppose it has something to do with my vscode setting?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":185,"Q_Id":50367947,"Users Score":0,"Answer":"Somehow your code is trying to pickle the debugger itself. Do make sure you are running the debugger using the PySpark configuration.","Q_Score":0,"Tags":"python,pyspark,visual-studio-code","A_Id":50518560,"CreationDate":"2018-05-16T10:05:00.000","Title":"get vscode python error when run pyspark program using vs code","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I've been researching like forever, but couldn't find an answer. I'm using OpenCV to detect faces, now I want to calculate the distance to the face. When I detect a face, I get a matofrect (which I can visualize with a rectangle). Pretty clear so far. But now: how do I get the width of the rectangle in the real world? There has to be some average values that represent the width of the human face. If I have that value (in inch, mm or whatever), I can calculate the distance using real width, pixel width and focal length. Please, can anyone help me?\nNote: I'm comparing the \"simple\" rectangle solution against a Facemark based distance measuring solution, so no landmark based answers. I just need the damn average face \/ matofrectwidth :D\nThank you so much!","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":138,"Q_Id":50375489,"Users Score":2,"Answer":"OpenCV's facial recognition is slightly larger than a face, therefore an average face may not be helpful. Instead, just take a picture of a face at different distances from the camera and record the distance from the camera along with the pixel width of the face for several distances. After plotting the two variables on a graph, use a trendline to come up with a predictive model.","Q_Score":1,"Tags":"python,opencv,distance,face-detection,measure","A_Id":50375996,"CreationDate":"2018-05-16T16:07:00.000","Title":"Real width of detected face","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Could some one please explain with an example about how we can send the pivot table output from pandas to word document without losing format.","AnswerCount":2,"Available Count":1,"Score":-0.0996679946,"is_accepted":false,"ViewCount":2260,"Q_Id":50376081,"Users Score":-1,"Answer":"I suggest exporting it to csv using pandas.to_csv(), and then read the csv to create a table in a word document.","Q_Score":1,"Tags":"python,python-2.7,pandas,ms-word,pivot-table","A_Id":50376159,"CreationDate":"2018-05-16T16:42:00.000","Title":"How can I print pandas pivot table to word document","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've going on a little competition with friends: We're writing models for the upcoming worldcup to see whoms model gets the most points out of a tip game.\nSo my approach would be to write a neural network and train it with previous worldcup results regarding to the anticipated wining rates (back then), to maximize the score of the tip game (e.g. 6 points for the exact score, 4 correct goal difference, 3 correct winner).\nScrap rates from various sites (bwin etc.) and let the network tip for me.\nI'm familiar with linear algebra, probability calculus etc., but have never programmed a neural network yet.\nSince I have not much time left, could someone help me by picking the best approach (like which concept\/algorithm I should use) or link me a tutorial to a similar problem or the approach that would fit?\nBest,\n  Hannes","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":968,"Q_Id":50382706,"Users Score":1,"Answer":"I did this for the 2017 UK Premier league season. However I accumulated the first 19 (out of 38 Games), in order to try and help with my predictions.\nI would attack this in the following manner\n\nGet Data on the Teams (What you consider data I will leave up to you)\nGet the History of previous matches (Personally I think this is not going to help, as teams change so much)\nPython\n\n\nPandas\n\n\nCreate New Features\n\nKeras\nModel away\n\n\nWhen I was playing with the UK Premier league, got a prediction accuracy of aprox 62%. So how to make it better ?\n\nDistance travelled\n\n\nImproved it by 1.2%. Some teams do not like traveling it seems.\n\nWeather\n\n\nI got the weather forecast per ground @ Kick-Off-Time (What a pain)\nAccuracy improved by 0.5%\n\n\nWhat I did not do - was get a list of Players who were playing per game, per day. And attribute things like distance run, passing percentage, goals, fouls, yellow cards etc","Q_Score":0,"Tags":"python,c,tensorflow,neural-network","A_Id":50382763,"CreationDate":"2018-05-17T02:58:00.000","Title":"Neural Network Predict Soccer Results","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to solve a regression problem, where in one of my features can take up two values ('1','0') in the train set but can be valued only '1' in the test data. Intuitively, including this feature seems wrong to me but I am unable to find a concrete logic to support my assumption.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":52,"Q_Id":50385511,"Users Score":0,"Answer":"well, It depends on how many features you have in total. If very few (say less than five), that single feature will most likely play an important role in your classification. In this case, I would say you have \"Data Mismatch\" problem; meaning that your training and test data are coming from different distributions. One simple way to solve it is to put the two sets together, shuffle the whole set, and split your data again.","Q_Score":0,"Tags":"python,machine-learning,regression,data-science,feature-selection","A_Id":50385903,"CreationDate":"2018-05-17T07:15:00.000","Title":"What if a categorical column has multiple values in the train set but only one in test data? Would such a feature be useful in model training at all?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to train CNN where image-dimension is 128*512, then I want to use this weight file to train other data which has 128*1024 dimension. That means I want to use pre-trained weight file during the training time of different data(128*1024).\nIs it possible or How can I do it?\nI want to do this because I have only 300 images which have 128*1024 dimension, while I have 5000 images which have 128*512 dimension and both datasets are different.\nThank you","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":97,"Q_Id":50392211,"Users Score":1,"Answer":"If your model is fully CNN, there is absolutely no need to have different models. \nA CNN model can take images of any size. Just make sure the input_shape=(None,None,channels)\nYou will need separate numpy arrays though, one for the big images, another for the small images, and you will have to call a different fit method for each.\n(But probably you will be working with a generator anyway)","Q_Score":2,"Tags":"python,keras,deep-learning,convolutional-neural-network","A_Id":50392683,"CreationDate":"2018-05-17T13:04:00.000","Title":"Weight file use for different size image","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to perform image recognition thing on real time camera feed. The video is embedded in shtml hosted on a IP. How do I access the video and process things using openCV.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":378,"Q_Id":50406354,"Users Score":0,"Answer":"First,you need to find the actual path to camera stream. It is mjpeg or rtsp stream. Use developer option in this page to find the stream, like http:\/\/ip\/video.mjpg or rtsp:\/\/ip\/live.sdp. When you find the stream url,create python script which will use stream like capture = cv2.VideoCapture(stream_url). But,as noticed,this is too broad which mean you are asking for full tutorial. This is some directions,and when you have some code,ask a question about issues with your code and you'll get an answer.","Q_Score":0,"Tags":"python,opencv,camera,video-streaming","A_Id":50410930,"CreationDate":"2018-05-18T07:49:00.000","Title":"How do I capture a live IP Camera feed (eg: http:\/\/61.60.112.230\/view\/viewer_index.shtml?id=938427) to python application?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":1,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a CNN trained on a classification task (Network is simple, 2 convolution + pooling layers and 2x fully connected layers). I would like to use this to reconstruct an image if I input a classification label. \nIs this possible to achieve? \nIs it possible to share weights between corresponding layers in the 2 networks?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":25,"Q_Id":50412204,"Users Score":0,"Answer":"You should have a look to cGAN implementation and why not DeepDream from Google ;)\nThe answer is yes it is possible, however it's not straight forward","Q_Score":0,"Tags":"python,tensorflow,convolutional-neural-network,deconvolution","A_Id":50412247,"CreationDate":"2018-05-18T13:15:00.000","Title":"Reconstructing a classified image from a simple Convolution Neural Network","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am coming from a C++ programming background and am wondering if there is a pass by reference equivalent in python. The reason I am asking is that I am passing very large arrays into different functions and want to know how to do it in a way that does not waste time or memory by having copy the array to a new temporary variable each time I pass it. It would also be nice if, like in C++, changes I make to the array would persist outside of the function.\nThanks in advance,\nJared","AnswerCount":1,"Available Count":1,"Score":0.6640367703,"is_accepted":false,"ViewCount":507,"Q_Id":50414041,"Users Score":4,"Answer":"Python handles function arguments in the same manner as most common languages: Java, JavaScript, C (pointers), C++ (pointers, references).\nAll objects are allocated on the heap.  Variables are always a reference\/pointer to the object.  The value, which is the pointer, is copied.  The object remains on the heap and is not copied.","Q_Score":0,"Tags":"python,python-3.x","A_Id":50414131,"CreationDate":"2018-05-18T14:53:00.000","Title":"Effective passing of large data to python 3 functions","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to convert the kitti dataset into the tensorflow .record. After I typed the command:\n\npython object_detection\/dataset_tools\/create_kitti_tf_record.py \n  --lable_map_path=object_detection\/data\/kitti_label_map.pbtxt  --data_dir=\/Users\/zhenglyu\/Graduate\/research\/DataSet\/kitti\/data_object_image_2\/testing\/image_2\n  --output_path=\/Users\/zhenglyu\/Graduate\/research\/DataSet\/kitti2tf\/train.record\n  validation_set_size=1000\n\nI got this error:\n\nTraceback (most recent call last):   File\n  \"object_detection\/dataset_tools\/create_kitti_tf_record.py\", line 310,\n  in \n      tf.app.run()   File \"\/Users\/zhenglyu\/tensorflow\/lib\/python3.6\/site-packages\/tensorflow\/python\/platform\/app.py\",\n  line 126, in run\n      _sys.exit(main(argv))   File \"object_detection\/dataset_tools\/create_kitti_tf_record.py\", line 307,\n  in main\n      validation_set_size=FLAGS.validation_set_size)   File \"object_detection\/dataset_tools\/create_kitti_tf_record.py\", line 94,\n  in convert_kitti_to_tfrecords\n      label_map_dict = label_map_util.get_label_map_dict(label_map_path)   File\n  \"\/Users\/zhenglyu\/Graduate\/research\/TensorFlow\/model\/research\/object_detection\/utils\/label_map_util.py\",\n  line 152, in get_label_map_dict\n      label_map = load_labelmap(label_map_path)   File \"\/Users\/zhenglyu\/Graduate\/research\/TensorFlow\/model\/research\/object_detection\/utils\/label_map_util.py\",\n  line 132, in load_labelmap\n      label_map_string = fid.read()   File \"\/Users\/zhenglyu\/tensorflow\/lib\/python3.6\/site-packages\/tensorflow\/python\/lib\/io\/file_io.py\",\n  line 120, in read\n      self._preread_check()   File \"\/Users\/zhenglyu\/tensorflow\/lib\/python3.6\/site-packages\/tensorflow\/python\/lib\/io\/file_io.py\",\n  line 80, in _preread_check\n      compat.as_bytes(self.name), 1024 * 512, status)   File \"\/Users\/zhenglyu\/tensorflow\/lib\/python3.6\/site-packages\/tensorflow\/python\/framework\/errors_impl.py\",\n  line 519, in __exit\n      c_api.TF_GetCode(self.status.status)) tensorflow.python.framework.errors_impl.NotFoundError:\n  data\/kitti_label_map.pbtxt; No such file or directory\n\nThe file exists for sure. And I don't know why as I set the label_map_path to another one (object_detection\/data\/kitti_label_map.pbtxt), the path still remains the default setting (data\/kitti_label_map.pbtxt).\nI know there's a lot of related problem but none of the solutions that I found works for me. I used Virtualenv to install the tensorflow and using python 3.6. Could these be the problem? Thanks!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":4898,"Q_Id":50417709,"Users Score":0,"Answer":"I don't have a definitive solution to this but here is what resolved it.\nFirst, I copied the kitti_label_map.pbtxt into the data_dir. Then I also copied create_kitti_tf_record.py into the data_dir. And now I copied(this is what made it run in the end) the name and absolute path of the kitti_label_map.pbtxt and pasted it as label_map_path\nI have no idea why but it worked.","Q_Score":1,"Tags":"python,python-3.x,tensorflow","A_Id":52691471,"CreationDate":"2018-05-18T18:57:00.000","Title":"tensorflow.python.framework.errors_impl.NotFoundError: data\/kitti_label_map.pbtxt; No such file or directory","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an array of integers(nodes or destinations) i.e array[2,3,4,5,6,8] that need to be visited in the given sequence.\nWhat I want is, to get the shortest distance using pgr_dijkstra. But the pgr_dijkstra finds the shortest path for two points, therefore I need to find the distance of each pair using pgr_dijkstra and adding all distances to get the total distance.\nThe pairs will be like\n2,3\n3,4 \n4,5 \n5,6 \n6,8.\nIs there any way to define a function that takes this array and finds the shortest path using pgr_dijkstra.\nQuery is:\nfor 1st pair(2,3)\nSELECT * FROM pgr_dijkstra('SELECT gid as id,source, target, rcost_len AS cost FROM finalroads',2,3, false);\nfor 2nd pair(3,4)\nSELECT * FROM pgr_dijkstra('SELECT gid as id,source, target, rcost_len AS cost FROM finalroads'***,3,4,*** false)\nfor 3rd pair(4,5)\nSELECT * FROM pgr_dijkstra('SELECT gid as id,source, target, rcost_len AS cost FROM finalroads'***,4,5,*** false);\nNOTE: The array size is not fixed, it can be different.\nIs there any way to automate this in postgres sql may be using a loop etc?\nPlease let me know how to do it.\nThank you.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":708,"Q_Id":50429760,"Users Score":0,"Answer":"If you want all pairs distance then use\nselect * from pgr_apspJohnson ('SELECT gid as id,source, target, rcost_len AS cost FROM finalroads)","Q_Score":1,"Tags":"mysql,sql,postgresql,mysql-python,pgrouting","A_Id":51161622,"CreationDate":"2018-05-19T21:46:00.000","Title":"how to get the distance of sequence of nodes in pgr_dijkstra pgrouting?","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using the tf.nn.dynamic_rnn class to create an LSTM. I have trained this model on some data, and now I want to inspect what are the values of the hidden states of this trained LSTM at each time step when I provide it some input.\nAfter some digging around on SO and on TensorFlow's GitHub page, I saw that some people mentioned that I should write my own LSTM cell that returns whatever I want printed as part of the output of the LSTM. However, this does not seem straight forward to me since the hidden states and the output of the LSTM do not have the same shapes.\nMy output tensor from the LSTM has shape [16, 1] and the hidden state is a tensor of shape [16, 16]. Concatenating them results in a tensor of shape [16, 17]. When I tried to return it, I get an error saying that some TensorFlow op required a tensor of shape [16,1].\nDoes anyone know an easier work around to this situation? I was wondering if it is possible to use tf.Print to just print the required tensors.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":217,"Q_Id":50447786,"Users Score":0,"Answer":"Okay, so the issue was that I was modifying the output but wasn't updating the output_size of the LSTM itself. Hence the error. It works perfectly fine now. However, I still find this method to be extremely annoying. Not accepting my own answer with the hope that somebody will have a cleaner solution.","Q_Score":0,"Tags":"python,debugging,tensorflow,lstm","A_Id":50448522,"CreationDate":"2018-05-21T11:22:00.000","Title":"TensorFlow: Print Internal State of RNN at at Every Time Step","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When I try to build Linear Regression model with training data in Jupyter notebook, python has stopped working, with error as shown below. I am using Anaconda 3.5 on windows7, Python 3.6 version.\nProblem signature:\n  Problem Event Name:   APPCRASH\n  Application Name: python.exe\n  Application Version:  3.6.4150.1013\n  Application Timestamp:    5a5e439a\n  Fault Module Name:    mkl_core.dll\n  Fault Module Version: 2018.0.1.1\n  Fault Module Timestamp:   59d8a332\n  Exception Code:   c000001d\n  Exception Offset: 0000000001a009a3\n  OS Version:   6.1.7600.2.0.0.256.48\n  Locale ID:    1033\n  Additional Information 1: 7071\n  Additional Information 2: 70718f336ba4ddabacde4b6b7fbe73e3\n  Additional Information 3: de32\n  Additional Information 4: de328b4df988a86fd2d750fb0942dbd1\nI am not able to get any help from google when I search with this error, even I tried\n1 Uninstalled and again installed\n2. Ran below commands but no use\n   conda update conda\n   conda update ipython ipython-notebook ipython-qtconsole\nI suspect this error related to Windows, but not sure how to fix it. \nThanks,\nSagar.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2067,"Q_Id":50463731,"Users Score":0,"Answer":"Updated all the libraries to latest version in Anaconda and try.\nI was facing a similar situation when I was running the code for Convolutional Neural Network in Spyder under Anaconda environment (Windows 7) I was getting following error\n\nProblem Event Name:  APPCRASH\n    Application Name: pythonw.exe\n    Fault Module Name:    StackHash\n    Exception Code:   c0000005\n    OS Version:   6.1.7601.2.1.0.256.1  \n\nI updated all the libraries to latest version in Anaconda and the problem is resolved.","Q_Score":0,"Tags":"python,anaconda","A_Id":53102357,"CreationDate":"2018-05-22T09:01:00.000","Title":"Python has stopped working(APPCRASH) Anaconda","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have 2 data sets with different variables.  But both includes a variable, say NUM, that helps to identify the occurrence of an event. With the NUM, I was able to identify the event, by labelling it. How can one run RF to effectively include considerations of the 2 datasets? I am not able to append them (column wise) as the number of records for each NUM differs.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":120,"Q_Id":50469243,"Users Score":0,"Answer":"You could try to put NUM as a single column, and the first and second datasets would use completely independent columns, with the non-matching cells containing empty data. Whether the results will be any good, will depend much on your data.","Q_Score":0,"Tags":"python,random-forest","A_Id":50487464,"CreationDate":"2018-05-22T13:41:00.000","Title":"How to use classifier random forest in Python for 2 different data sets?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to solve a linear system spanning somewhat between hundred thousand and two hundred thousand equations with numpy.linalg.lstsq but is taking waaaaay too long. What can I do to speed this up?\nThe matrix is sparse with hundreds of columns (the dimensions are approximately 150 000 x 140) and the system is overdetermined.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2001,"Q_Id":50472095,"Users Score":0,"Answer":"If your coefficient matrix is sparse, use \"spsolve\" from \"scipy.sparse.linalg\".","Q_Score":0,"Tags":"python,performance,numpy,math,computer-science","A_Id":56719666,"CreationDate":"2018-05-22T16:06:00.000","Title":"How can I solve a system of linear equations in python faster than with numpy.linalg.lstsq?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm testing Apache superset Dashboards, It s a great tool.\nI added an external Database source (Oracle), and I created nice Dashboards very easily.\nI would like to see my Dashboards updated regularly and automatically (3 times a day) in superset.\nBut my Dashboards are not updated.\nI mean when a row is inserted into the Oracle Tables, if I refresh the Dashboard, I cannot view the new data in the Dashboard.\nWhat is the best way to do it ?\n=> Is there a solution \/ an option to force the Datasource to be automatically updated regularly ? in a frequency ? What is the parameter \/ option ? \n=> is there a solution to import in batch csv files (for instance in python), then this operation will update the Dashboard ?\n=> other way ?\nIf you have examples to share... :-)\nMy environment: \nSuperset is Installed on ubuntu 16.04 and Python 2.7.12.\nOracle is installed on another Linux server.\nI connect from  google chrome to Superset.\nMany thanks for your help","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1509,"Q_Id":50472282,"Users Score":0,"Answer":"You could set the auto-refresh interval for a dashboard if you click on the arrow next to the Edit dashboard-button.","Q_Score":1,"Tags":"python,oracle,superset","A_Id":59359358,"CreationDate":"2018-05-22T16:16:00.000","Title":"superset dashboards - dynamic updates","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":1},{"Question":"I'm testing Apache superset Dashboards, It s a great tool.\nI added an external Database source (Oracle), and I created nice Dashboards very easily.\nI would like to see my Dashboards updated regularly and automatically (3 times a day) in superset.\nBut my Dashboards are not updated.\nI mean when a row is inserted into the Oracle Tables, if I refresh the Dashboard, I cannot view the new data in the Dashboard.\nWhat is the best way to do it ?\n=> Is there a solution \/ an option to force the Datasource to be automatically updated regularly ? in a frequency ? What is the parameter \/ option ? \n=> is there a solution to import in batch csv files (for instance in python), then this operation will update the Dashboard ?\n=> other way ?\nIf you have examples to share... :-)\nMy environment: \nSuperset is Installed on ubuntu 16.04 and Python 2.7.12.\nOracle is installed on another Linux server.\nI connect from  google chrome to Superset.\nMany thanks for your help","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":1509,"Q_Id":50472282,"Users Score":1,"Answer":"I just found the origin of my error... :-)\nIn fact I added records in the future (tomorow, the day after, ...)... \nAnd My dashboard was only showing all Records to the today date...\nI inserted a record before, I refreshed and It appeared.\nThanks to having read me...","Q_Score":1,"Tags":"python,oracle,superset","A_Id":50473011,"CreationDate":"2018-05-22T16:16:00.000","Title":"superset dashboards - dynamic updates","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":1},{"Question":"I am making graphs using bokeh in python and for that I was calling an HTML page to fetch data inside my dataframe but now I want to fetch data directly from database inside my dataframe. So which method is more efficient?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":44,"Q_Id":50506046,"Users Score":0,"Answer":"This depends on a lot of things, namely:\n\nis your database in the same local network as your application server?\nis the website used in the same local network as your application server?\nhow large is the table you have in the website?\nhow large is your database?\ncan a user do some trusted changes on the webpage that is not inside the database yet?\n\nAll in all, the most common case is that your database is in the same local network as your application server, but there is a lot of data stored in there. So, it is difficult to answer in terms of efficiency, it might be quicker to load data from your local network, but selecting items from the database can be very time-consuming, to such an extent that it will be less efficient than loading data from the webpage. So, to compare efficiency, you will have to do a lot of tests and estimations for the future.\nHowever, there are some other aspects\nServer load\nIf you always load from the database, then you have higher server load, which will affect scalability and even performance when longer database tasks are running, for instance some maintenance cron jobs.\nSecurity\nIf a hacker sends some valid requests but with wrong data, you might be misled, so you will need to validate any input from users you do not trust. And you should not trust the users generally, trusted users should be the exception.\nData structure\nYou might cache data for this purpose if you do this with the database and you may use JSON when you send the content of the table to the server, so network communication will not increase.\nNetwork burden\nIf data is sent from the webpage back, then in the case of usage spikes a lot of data might be sent, which might affect the responsiveness of your application.","Q_Score":0,"Tags":"python,database,pandas,api,dataframe","A_Id":50506830,"CreationDate":"2018-05-24T09:42:00.000","Title":"Which is more efficient ? Fetching data directly from databse or from an HTML page in pandas dataframe?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I need to generate a matrix of N random integers between 1 and K, where each number appears at least once, having K \u2264 N.\nI have no problem using a call to numpy.random.random_integers() and checking the number of distinct elements, when K is much less than N, but it's harder to get a valid array when K approximates to N.\nIs there any nice way to get this done? Any hint would be appreciated.","AnswerCount":4,"Available Count":2,"Score":0.049958375,"is_accepted":false,"ViewCount":152,"Q_Id":50517089,"Users Score":1,"Answer":"Fill the matrix iteratively with numbers 1-K so if K was 2, and N was 4, [1,2,1,2]. Then randomly generate 2 random numbers between 1-N where the numbers don't equal, and swap the numbers at those positions.","Q_Score":0,"Tags":"python,numpy,random","A_Id":50517141,"CreationDate":"2018-05-24T19:47:00.000","Title":"Generate an array of N random integers, between 1 and K, but containing at least one of each number","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to generate a matrix of N random integers between 1 and K, where each number appears at least once, having K \u2264 N.\nI have no problem using a call to numpy.random.random_integers() and checking the number of distinct elements, when K is much less than N, but it's harder to get a valid array when K approximates to N.\nIs there any nice way to get this done? Any hint would be appreciated.","AnswerCount":4,"Available Count":2,"Score":0.049958375,"is_accepted":false,"ViewCount":152,"Q_Id":50517089,"Users Score":1,"Answer":"Fill K numbers using xrange(k) and then fill (n-k) number using random number generator","Q_Score":0,"Tags":"python,numpy,random","A_Id":50517145,"CreationDate":"2018-05-24T19:47:00.000","Title":"Generate an array of N random integers, between 1 and K, but containing at least one of each number","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to create a model that has an LSTM layer of 100 units with input dimensions (16,48,12) (16 is the batch size as it takes input through a generator function). The generator function produces an expected output of (16, 1, 2) (16 is the batch size) and I want to use as output a dense layer with a softmax activation fucntion. What would be the best way to do that? I am fairly new to keras and I can't quiet get the grasp of using generator functions...","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":240,"Q_Id":50518710,"Users Score":0,"Answer":"Personally, it is recommended to use the PReLU activation function before the fully connected dense layer.\nFor example\uff1a\nmodel.add(LSTM(128,input_shape=(train_X.shape[1],train_X.shape[2])))\nmodel.add(BatchNormalization())\nmodel.add(Dropout(.2))\nmodel.add(Dense(64))\nmodel.add(PReLU())","Q_Score":0,"Tags":"python,neural-network,keras,lstm,recurrent-neural-network","A_Id":50520768,"CreationDate":"2018-05-24T21:53:00.000","Title":"LSTM using generator function","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"After learning a bit about encoder\/decoder models in deep learning (mostly in Keras), i still cannot understand where the learning takes place.\nDoes the encoder just create the feature map and then the decoder tries to get as close as possible as the result with BackProp, or does the encoder learn as well when the model is trained?\nOne last question: if i understood correctly, the filters are the equivalent of the nodes in a classic machine learning model, changing its weights to learn, am i right?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":135,"Q_Id":50524927,"Users Score":0,"Answer":"The encoder learns a compressed representation of the input data and the decoder tries to learn how to use just this compressed representation to reconstruct the original input data as best as possible. Let's say that the initial weights (usually randomly set) produce a reconstruction error of e. During training, both the encoder and decoder layer weights are adjusted so that e is reduced. \nLater on, usually, the decoder layer is removed and the output of the encoder layer (the compressed representation) is used as a feature map of the input.\nWhat does compressed representation mean? If your input is an image of size 20 * 20 = 400 elements, your encoder layer might be of size 100 with a compression factor of 4. In other words, you are learning how to capture the essence of the data with 400 elements in only 100 while still being able to reconstruct the 400 element data with minimum error.\nYou are correct about filters being equivalent of nodes and changing the weights to learn the best representation for the input during training.","Q_Score":0,"Tags":"python,tensorflow,machine-learning,keras","A_Id":50525619,"CreationDate":"2018-05-25T08:36:00.000","Title":"How do Encoder\/Decoder models learn in Deep Learning?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"i'm trying to cluster some documents with word2vec and numpy.\nw2v = W2VTransformer()\nX_train = w2v.fit_transform(X_train)\nWhen I run the fit or fit_transform I get this error:\n\nException in thread Thread-8:\n  Traceback (most recent call last):\n    File \"C:\\Users\\lperona\\AppData\\Local\\Continuum\\anaconda3\\lib\\threading.py\", line 916, in _bootstrap_inner\n      self.run()\n    File \"C:\\Users\\lperona\\AppData\\Local\\Continuum\\anaconda3\\lib\\threading.py\", line 864, in run\n      self._target(*self._args, **self._kwargs)\n    File \"C:\\Users\\lperona\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\gensim\\models\\base_any2vec.py\", line 99, in _worker_loop\n      tally, raw_tally = self._do_train_job(data_iterable, job_parameters, thread_private_mem)\n    File \"C:\\Users\\lperona\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\gensim\\models\\word2vec.py\", line 539, in _do_train_job\n      tally += train_batch_cbow(self, sentences, alpha, work, neu1, self.compute_loss)\n    File \"gensim\/models\/word2vec_inner.pyx\", line 458, in gensim.models.word2vec_inner.train_batch_cbow\n  ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()\n\n(X_train is a 2D numpy array of strings)\nDoes anyone know a solution?\nThank you","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":380,"Q_Id":50531181,"Users Score":0,"Answer":"It seems that gensim's word2vec has some problems when working with numpy arrays. Converting data to python lists helped me.","Q_Score":1,"Tags":"python,arrays,python-3.6,word2vec,gensim","A_Id":52078671,"CreationDate":"2018-05-25T14:13:00.000","Title":"Fit method of gensim.sklearn_api.w2vmodel.W2VTransformer throws error when inputed 2-dimensional array of strings","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a program with mainly OpenCV and NumPy, with some SciPy as well. The system needs to be a real-time system with a frame rate close to 30 fps but right now only about 10 fps. Will using Cython help speed this up? I ask because OpenCV is already written in C++ and should already be quite optimized, and NumPy, as far as I understand, is also quite optimized. So will the use of Cython help improve the processing time of my program?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":7781,"Q_Id":50535498,"Users Score":0,"Answer":"It all depends on what your program is doing.\nIf you program is just stringing together large operations that are implemented in C++ then cython isn't going to help you much if at all.\nIf you are writing code that works directly on individual pixels then cython could be a big help.","Q_Score":8,"Tags":"python,numpy,opencv,cython","A_Id":61811370,"CreationDate":"2018-05-25T19:09:00.000","Title":"Cython + OpenCV and NumPy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been trying to use tensorlayer.prepro.threading_data, but I'm getting a different return type for different inputs. Sometimes it returns an ndarray and sometimes it returns a list. The documentation doesn't specify what's the reason for the different return types.\nCan anyone shed some light on this?\nAnswer:\nIt seems that what was causing this problem was having items with different shapes in the list. In this instance, PNG images with 3 and 4 channel.\nRemoving the alpha channel (the fourth channel) from all PNG images solved this for me.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":51,"Q_Id":50545324,"Users Score":0,"Answer":"It seems that what was causing this problem was having items with different shapes in the list. In this instance, PNG images with 3 and 4 channel. Removing the alpha channel (the fourth channel) from all PNG images solved this for me.","Q_Score":1,"Tags":"python,list,types,return,numpy-ndarray","A_Id":50557193,"CreationDate":"2018-05-26T17:23:00.000","Title":"What affects tensorlayer.prepro.threading_data's return type?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have two CSV file one contain points for polygon around 2000 point (lat, long). another file has more than 1 billion row ( id, lat, long). how to extract only the points intersect(inside) the polygon by pyspark","AnswerCount":1,"Available Count":1,"Score":-0.1973753202,"is_accepted":false,"ViewCount":222,"Q_Id":50548575,"Users Score":-1,"Answer":"Insert csv on map and polygons layer for selection.\ntake Select by Location tools and insert to processing.\nTarget layer is point (csv), Source Layer is Polygon layer.\nSpatial selection for target layer feature(s) is completely contain the source layer feature selected.\nAnd click apply.\nNotes: You must configure porjection same this two layers.","Q_Score":1,"Tags":"python,pyspark,arcgis,pyspark-sql,point-in-polygon","A_Id":68709535,"CreationDate":"2018-05-27T02:35:00.000","Title":"Extract only points inside polygon","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Can you write video to memory? I use Raspberry Pi and I don't want to keep writing and deleting videowriter objects created on sd card (or is it ok to do so?).\nIf conditions are not met I would like to discard written video every second. I use type of motion detector recording and I would like to capture moment (one second in this case) before movement has been detected, because otherwise written video loses part of what happened. I use opencv in python environment.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1432,"Q_Id":50559105,"Users Score":1,"Answer":"Video files will often, or even generally, be too big to fit in your main memory so you will be unlikely to be able to just keep the entire video there.\nIt also worth noting that your OS itself may decide to move data between fast memory, slower memory, disk etc as it manages multiple processes but that is likely not important in this discussion.\nIt will depend on your use case but a typical video scenario might be:\n\nreceive video frame from camera or source stream\ndo some processing on the video frame - e.g. transcode it, detect an object, add text or images etc\nstore or send the updated frame someplace\ngo back to first step\n\nIn a flow like this, there is obviously advantage in keeping the frame in memory while working on it, but once done then it is generally not an advantage any more so its fine to move it to disk, or send it to its destination.\nIf you use cases requires you to work on groups of frames, as some encoding algorithms do, then it may make sense to keep the group of frames in memory until you are finished with them and then write them to disk.\nI think the best answer for you will depend on your exact use case, but whatever that it is it is unlikely that keeping the entire video in memory would be necessary or even possible.","Q_Score":0,"Tags":"python,python-3.x,opencv,video,video-capture","A_Id":50564217,"CreationDate":"2018-05-28T04:25:00.000","Title":"How to write video to memory in OpenCV","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have 6 points in each row and have around 20k such rows. Each of these row points are actually points on a curve, the nature of curve of each of the rows is same (say a sigmoidal curve or straight line, etc). These 6 points may have different x-values in each row.I also know a point (a,b) for each row which that curve should pass through. How should I go about in finding the rows which may be anomalous or show an unexpected behaviour than other rows? I was thinking of curve fitting but then I only have 6 points for each curve, all I know is that majority of the rows have same nature of curve, so I can perhaps make a general curve for all the rows and have a distance threshold for outlier detection.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":185,"Q_Id":50561180,"Users Score":0,"Answer":"What happens if you just treat the 6 points as a 12 dimensional vector and run any of the usual outlier detection methods such as LOF and LoOP?\nIt's trivial to see the relationship between Euclidean distance on the 12 dimensional vector, and the 6 Euclidean distances of the 6 points each. So this will compare the similarities of these curves.\nYou can of course also define a complex distance function for LOF.","Q_Score":0,"Tags":"python-3.x,cluster-analysis,curve-fitting,outliers,lmfit","A_Id":50597931,"CreationDate":"2018-05-28T07:27:00.000","Title":"Unsupervised Outlier detection","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When creating an AWS lambda package I noticed that the ZIP became a lot smaller when I updated from numpy 1.14.0 to 1.14.3. From 24.6MB to 8.4MB.\nThe directory numpy\/random went from 4.3MB to 1.2MB, according to Ubuntus Disc Usage analyzer. When I, however, compare the directories with meld they seem to be identical. So I had a closer look at this and found that only one file (mtrand.cpython-36m-x86_64-linux-gnu.so) differs that much. I guess it is a similar reason why the core became smaller.\nCould somebody explain why this became so much smaller?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":60,"Q_Id":50565322,"Users Score":0,"Answer":"It appears that the difference is in the debug symbols.  Perhaps one was built with a higher level of debug symbols than the other, or perhaps the smaller one was built with compressed debug info (a relatively new feature).  One way to find out more would be to inspect the compiler and linker flags used during each build, or try to build it yourself.","Q_Score":1,"Tags":"python,numpy,shared-libraries","A_Id":50574746,"CreationDate":"2018-05-28T11:26:00.000","Title":"Why did the numpy core size shrink from 1.14.0 to 1.14.1?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"i have an unbalanced data set which has two categorical values. one has around 500 values of a particular class and other is only one single datapoint with another class.Now i would like to split this data into test train with 80-20 ratio. but since this is unbalanced , i would like to have the second class to be present in both the test and train data.\nI tried using the test-train-split from sklearn, but it does not give the second class data to be present in both of them. I even tried the stratified shuffle split, but that was also not giving data as i thought.\nIs there any way we can split the data from a data frame forcing both the test and train datasets to have the single datapoint?. I am new to python so having difficulty figuring it out.\nthe data looks like:\na  b  c  d  label\n1  0  0  1   1\n1  1  1  0   1 \n..........\n........\n1  0  0   1  0.\nthe label has only 1 and 0 but the 0 is only one single observation but the rest of the 500 data points are having label as 1","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":61,"Q_Id":50569782,"Users Score":0,"Answer":"Try to do oversampling as you have less amount of data points. Or else you can use neural network preferably MLP, That works fine with unbalanced data.","Q_Score":0,"Tags":"python,machine-learning","A_Id":50583288,"CreationDate":"2018-05-28T15:43:00.000","Title":"splitting data in to test train data when there is unbalance of data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Suppose, I have a model that is already trained with 100 epoch. I tested the model with test data and the performance is not satisfactory. So I decide to train for another 100 epoch. How can I do that?\nI trained with \nmodel.fit(data, target, epochs=100, batch_size=batch_size)\nNow I want to train the same model without adding new data for another 100 epoch. How can I do that?","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1201,"Q_Id":50572884,"Users Score":2,"Answer":"Simply call model.fit(data, target, epochs=100, batch_size=batch_size) again to carry on training the same model. model needs to be the same model object as in the initial training, not re-compiled.","Q_Score":1,"Tags":"python-3.x,tensorflow,keras,deep-learning","A_Id":50573045,"CreationDate":"2018-05-28T20:07:00.000","Title":"How to increasing Number of epoch in keras Conv Net","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm working on a project that requires the recognition of just people in a video or a live stream from a camera. I'm currently using the tensorflow object recognition API with python, and i've tried different pre-trained models and frozen inference graphs. I want to recognize only people and maybe cars so i don't need my neural network to recognize all 90 classes that come with the frozen inference graphs, based on mobilenet or rcnn, as it seems this slows the process, and 89 of this 90 classes are not needed in my project. Do i have to train my own model or is there a way to modify the inference graphs and the existing models? This is probably a noob question for some of you, but mind that i've worked with tensorflow and machine learning for just one month.\nThanks in advance","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":980,"Q_Id":50572962,"Users Score":0,"Answer":"Shrinking the last layer to output 1 or two classes is not likely to yield large speed ups. This is because most of the computation is in the intermediate layers. You could shrink the intermediate layers, but this would result in poorer accuracy.","Q_Score":1,"Tags":"python,python-3.x,tensorflow,object-detection,object-detection-api","A_Id":50594107,"CreationDate":"2018-05-28T20:14:00.000","Title":"Managing classes in tensorflow object detection API","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In Python when using np.empty(), for example np.empty((3,1)) we get an array that is of size (3,1) but, in reality, it is not empty and it contains very small values (e.g., 1.7*(10^315)). Is possible to create an array that is really empty\/have no values but have given dimensions\/shape?","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":4649,"Q_Id":50579027,"Users Score":1,"Answer":"I am guessing that by empty, you mean an array filled with zeros.\nUse np.zeros() to create an array with zeros. np.empty() just allocates the array, so the numbers in there are garbage. It is provided as a way to even reduce the cost of setting the values to zero. But it is generally safer to use np.zeros().","Q_Score":2,"Tags":"python,numpy","A_Id":50579077,"CreationDate":"2018-05-29T07:48:00.000","Title":"Creating an empty multidimensional array","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a sparse matrix and another vector and I want to multiply the matrix and vector so that each column of the vector where it's equal to zero it'll zero the entire column of the sparse matrix. \nHow can I achieve that?","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":357,"Q_Id":50580459,"Users Score":0,"Answer":"If you don't like the speed of matrix multiplication, then you have to consider modification of the matrix attributes directly.  But depending on the format that may be slower.\nTo zero-out columns of a csr, you can find the relevant nonzero elements, and set the data values to zero.  Then run the eliminate_zeros method to remove those elements from the sparsity structure.\nSetting columns of a csc format may be simpler - find the relevant value in the indptr.  At least the elements that you want to remove will be clustered together.  I won't go into the details.\nZeroing rows of a lil format should be fairly easy - replace the relevant lists with [].\nAnyways with familiarity of the formats it should possible to work out alternatives to matrix multiplication.  But without doing so, and doing sometimes, I could say which ones are faster.","Q_Score":1,"Tags":"python,numpy,scipy,sparse-matrix,linear-algebra","A_Id":50711224,"CreationDate":"2018-05-29T09:08:00.000","Title":"For a given sparse matrix, how can I multiply it with a given vector of binary values","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a sparse matrix and another vector and I want to multiply the matrix and vector so that each column of the vector where it's equal to zero it'll zero the entire column of the sparse matrix. \nHow can I achieve that?","AnswerCount":3,"Available Count":2,"Score":0.0665680765,"is_accepted":false,"ViewCount":357,"Q_Id":50580459,"Users Score":1,"Answer":"The main problem is the size of your problem and the fact you're using Python which is on the order of 10-100x slower for matrix multiplication than some other languages. Unless you use something like Cython I don't see you getting an improvement.","Q_Score":1,"Tags":"python,numpy,scipy,sparse-matrix,linear-algebra","A_Id":50589936,"CreationDate":"2018-05-29T09:08:00.000","Title":"For a given sparse matrix, how can I multiply it with a given vector of binary values","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want tensor-flow-on-spark programs(for learning purpose),& I don't have a gpu support . Is it possible to execute tensor-flow on spark program without GPU support? \nThank you","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":149,"Q_Id":50580534,"Users Score":0,"Answer":"Yes, it is possible to use CPU. Tensorflow will automatically use CPU if it doesn't find any GPU on your system.","Q_Score":1,"Tags":"python,apache-spark,tensorflow,pyspark","A_Id":50580610,"CreationDate":"2018-05-29T09:11:00.000","Title":"Is it possible to execute tensorflow-on-spark program without gpu suppport?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have measurements of channel impulse responses as complex CSI's. There are two transmitters Alice and Bob and the measurements look like\n[real0], [img0], [real1], [img1], ..., [real99], [img99] (100 complex values).\nAmplitude for the Nth value is ampN = sqrt(realN^2 + imgN^2)\nHow do I get the frequency and phase values out of the complex CSI's?\nAny help would be appreciated.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":364,"Q_Id":50587784,"Users Score":0,"Answer":"In Matlab, you do abs(csi) to get the amplitude. To get the phase, angle(csi). Search for similar functions in python","Q_Score":1,"Tags":"python,frequency,phase,amplitude,csi","A_Id":52261793,"CreationDate":"2018-05-29T15:14:00.000","Title":"How to get phase and frequency of complex CSI for channel impulse responses?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have measurements of channel impulse responses as complex CSI's. There are two transmitters Alice and Bob and the measurements look like\n[real0], [img0], [real1], [img1], ..., [real99], [img99] (100 complex values).\nAmplitude for the Nth value is ampN = sqrt(realN^2 + imgN^2)\nHow do I get the frequency and phase values out of the complex CSI's?\nAny help would be appreciated.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":364,"Q_Id":50587784,"Users Score":0,"Answer":"complex-valued Channel State Information ?\npython has cmath, a standard lib for complex number math\nbut numpy and scipy.signal will probably ultimately be more useful to you","Q_Score":1,"Tags":"python,frequency,phase,amplitude,csi","A_Id":50593481,"CreationDate":"2018-05-29T15:14:00.000","Title":"How to get phase and frequency of complex CSI for channel impulse responses?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I installed spyder onto my computer a few months ago and it has worked fine until I needed to produce a map with station plots and topography. I simply tried to import matplotlib-basemap and get the following error:\n\nFile \"<ipython-input-12-6634632f8d36>\", line 1, in \n  runfile('C:\/Users\/Isa\/Documents\/Freedman\/2018\/ENVIROCOMP\/Stationplots.py', wdir='C:\/Users\/Isa\/Documents\/Freedman\/2018\/ENVIROCOMP')\nFile \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\spyder\\utils\\site\\sitecustomize.py\", line 710, in runfile\n      execfile(filename, namespace)\nFile \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\spyder\\utils\\site\\sitecustomize.py\", line 101, in execfile\n      exec(compile(f.read(), filename, 'exec'), namespace)\nFile \"C:\/Users\/Isa\/Documents\/Freedman\/2018\/ENVIROCOMP\/Stationplots.py\", line 15, in \n      from mpl_toolkits.basemap import Basemap, shiftgrid, cm\nFile \"<frozen importlib._bootstrap>\", line 971, in _find_and_load\nFile \"<frozen importlib._bootstrap>\", line 951, in _find_and_load_unlocked\nFile \"<frozen importlib._bootstrap>\", line 890, in _find_spec\nFile \"<frozen importlib._bootstrap>\", line 864, in _find_spec_legacy\nFile \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\pyximport\\pyximport.py\", line 253, in find_module\n      fp, pathname, (ext,mode,ty) = imp.find_module(fullname,package_path)\nFile \"C:\\ProgramData\\Anaconda3\\lib\\imp.py\", line 271, in find_module\n      \"not {}\".format(type(path)))\nRuntimeError: 'path' must be None or a list, not <class '_frozen_importlib_external._NamespacePath'>\n\nIf anyone has gone through this or understands this type of error suggest a way to make basemap work on spyder?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":453,"Q_Id":50591637,"Users Score":0,"Answer":"(Spyder maintainer here) This error was fixed in our 3.2.8 version, released in March\/2018.\nSince you're using Anaconda, please open the Anaconda prompt and run there\nconda update spyder\nto get the fix.","Q_Score":0,"Tags":"python,runtime-error,spyder,matplotlib-basemap","A_Id":50591996,"CreationDate":"2018-05-29T19:29:00.000","Title":"Runtime error when importing basemap into spyder","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"When I am using the below line in my code\nvocab_processor = learn.preprocessing.VocabularyProcessor(max_document_length, vocabulary=bow)\nI get theses warnings. How do I eliminate them ?\nWARNING:tensorflow:From \/usr\/local\/lib\/python3.5\/dist-packages\/tensorflow\/contrib\/learn\/python\/learn\/datasets\/base.py:198: retry (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\nInstructions for updating:\nUse the retry module or similar alternatives.\nWARNING:tensorflow:From \/tmp\/anyReader-376H566fJpAUSEt\/anyReader-376qtSRQxT2gOiq.tmp:67: VocabularyProcessor.__init__ (from tensorflow.contrib.learn.python.learn.preprocessing.text) is deprecated and will be removed in a future version.\nInstructions for updating:\nPlease use tensorflow\/transform or tf.data.\nWARNING:tensorflow:From \/usr\/local\/lib\/python3.5\/dist-packages\/tensorflow\/contrib\/learn\/python\/learn\/preprocessing\/text.py:154: CategoricalVocabulary.__init__ (from tensorflow.contrib.learn.python.learn.preprocessing.categorical_vocabulary) is deprecated and will be removed in a future version.\nInstructions for updating:\nPlease use tensorflow\/transform or tf.data.\nWARNING:tensorflow:From \/usr\/local\/lib\/python3.5\/dist-packages\/tensorflow\/contrib\/learn\/python\/learn\/preprocessing\/text.py:170: tokenizer (from tensorflow.contrib.learn.python.learn.preprocessing.text) is deprecated and will be removed in a future version.\nInstructions for updating:\nPlease use tensorflow\/transform or tf.data.","AnswerCount":2,"Available Count":1,"Score":-0.1973753202,"is_accepted":false,"ViewCount":1335,"Q_Id":50602085,"Users Score":-2,"Answer":"All of these warning have instructions for updating. Follow the instructions: switch to tf.data for preprocessing.","Q_Score":0,"Tags":"python,tensorflow,machine-learning,deprecation-warning","A_Id":50611284,"CreationDate":"2018-05-30T10:14:00.000","Title":"Tensorflow contrib learn deprecation warnings","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm new (obviously) to python, but not so new to TensorFlow\nI've been trying to debug my program using breakpoint, but everytime I try to check the content of a tensor in the variable view of my Visual Studio Code debugger, the content doesn't show I get this warning in the console:\n\nWARNING:tensorflow:Tensor._shape is private, use Tensor.shape instead. Tensor._shape will eventually be removed.\n\nI'm a bit confused on how to fix this issue. Do I have to wait for an update of TensorFlow before it works?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1348,"Q_Id":50609002,"Users Score":0,"Answer":"You can simply stop at the break point, and switch to DEBUG CONSOLE panel, and type var.shape. It's not that convenient, but at least you don't need to write any extra debug code in your code.","Q_Score":0,"Tags":"python,debugging,tensorflow,visual-studio-code","A_Id":51165112,"CreationDate":"2018-05-30T15:56:00.000","Title":"TensorFlow debug: WARNING:tensorflow:Tensor._shape is private, use Tensor.shape instead. Tensor._shape will eventually be removed","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm new (obviously) to python, but not so new to TensorFlow\nI've been trying to debug my program using breakpoint, but everytime I try to check the content of a tensor in the variable view of my Visual Studio Code debugger, the content doesn't show I get this warning in the console:\n\nWARNING:tensorflow:Tensor._shape is private, use Tensor.shape instead. Tensor._shape will eventually be removed.\n\nI'm a bit confused on how to fix this issue. Do I have to wait for an update of TensorFlow before it works?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1348,"Q_Id":50609002,"Users Score":0,"Answer":"Probably yes you may have to wait. In the debug mode a deprecated function is being called. \nYou can print out the shape explicitly by calling  var.shape() in the code as a workaround. I know not very convenient.","Q_Score":0,"Tags":"python,debugging,tensorflow,visual-studio-code","A_Id":50609100,"CreationDate":"2018-05-30T15:56:00.000","Title":"TensorFlow debug: WARNING:tensorflow:Tensor._shape is private, use Tensor.shape instead. Tensor._shape will eventually be removed","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Instead of iterating through each element in a matrix and checking if random() returns lower than the rate of mutation, does it work if you generate a certain amount of random indices that match the rate of mutation or is there some other method?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":54,"Q_Id":50610831,"Users Score":0,"Answer":"Yes. \nSuppose your gene length is 100 and your mutation rate is 0.1, then picking 100*0.1=10 random indices and mutating them is faster than generating & checking 100 numbers.","Q_Score":0,"Tags":"python,numpy,statistics,genetic-algorithm","A_Id":50734768,"CreationDate":"2018-05-30T17:55:00.000","Title":"Mutation algorithm efficiency","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to implement Kernel PCA to my dataset which has both categorical (encoded with one hot encoder) and numeric features and decreases the number of dimensions from 22 to 3 dimensions in total. After that, I will continue with clustering implementation. I use Spyder as IDE.\nIn order to understand the structure of my yielded clusters from the algorithm, I want to interpret which features affect the derived principal components and how they affect them.\nIs it possible? If so, how can I interpret this, is there any method?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":91,"Q_Id":50613294,"Users Score":0,"Answer":"Since you are applying PCA in the kernel space, there is a strictly nonlinear relation with your original features and the features of the reduced data; the eigenvectors you calculate are in the kernel space to begin with. This obstructs a straightforward approach, but maybe you can do some kind of sensitivity analysis. Apply small perturbations to the original features and measure how the final, reduced features react to them. The Jacobian of the final features with respect to the original features can be also a good place to start.","Q_Score":1,"Tags":"python,machine-learning,cluster-analysis,pca,kernel-density","A_Id":50645446,"CreationDate":"2018-05-30T21:00:00.000","Title":"Finding original features' effect to the principal components used as inputs in Kernel PCA","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am fairly new to OpenCV 3+ in Python. It looks to me that FileStorage under Python does not support, for example, a writeObj() method. Is it possible to save the SimpleBlobDetector_create to an XML or YAML file using OpenCV 3+ in Python?  Another way to put it is this: using Python OpenCV, can I save XML\/YAML data that is not a numpy array or a scalar (e.g. an object)?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":362,"Q_Id":50630168,"Users Score":0,"Answer":"I have lately had some troubles when using FileStorage with XML or YAML (It appears to be some kind of bug in the OpenCV sourcecode). I would recommend you to try it with JSON. In oder to do so, just change the name of the file to XXXX.json. If you are saving self-constructed structures as well, just construct the structure as if it was a YAML and change the filename to .json.\nI hope this helps you further. \nRegards, David","Q_Score":0,"Tags":"python-3.x,opencv,file-storage","A_Id":50633400,"CreationDate":"2018-05-31T17:56:00.000","Title":"Will OpenCV 3.2+ FileStorage save the SimpleBlobDetector_create object in XML or YML?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I installed miniconda for Windows10 successfully and then I could install numpy, scipy, sklearn successfully, but when I run import sklearn in python IDLE I receive No module named 'sklearn' in anaconda prompt. It recognized my python version, which was 3.6.5, correctly. I don't know what's wrong, can anyone tell me how do I import modules in IDLE ?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1095,"Q_Id":50633488,"Users Score":0,"Answer":"Why bot Download the full anaconda and this will install everything you need to start which includes Spider IDE, Rstudio, Jupyter and all the needed modules..\nI have been using anaconda without any error and i will recommend you try it out.","Q_Score":2,"Tags":"python,import,scikit-learn","A_Id":50634001,"CreationDate":"2018-05-31T22:09:00.000","Title":"import sklearn in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I cannot install tensorflow in pycharm on windows 10, though I have tried many different things:\n\nwent to settings > project interpreter and tried clicking the green plus button to install it, gave me the error: non-zero exit code (1) and told me to try installing via pip in the command line, which was successful, but I can't figure out how to make Pycharm use it when it's installed there\ntried changing to a Conda environment, which still would not allow me to run tensorflow since when I input into the python command line: pip.main(['install', 'tensorflow']) it gave me another error and told me to update pip\nupdated pip then tried step 2 again, but now that I have pip 10.0.1, I get the error 'pip has no attribute main'. I tried reverted pip to 9.0.3 in the command line, but this won't change the version used in pycharm, which makes no sense to me. I reinstalled anaconda, as well as pip, and deleted and made a new project and yet it still says that it is using pip 10.0.1 which makes no sense to me\n\nSo in summary, I still can't install tensorflow, and I now have the wrong version of pip being used in Pycharm. I realize that there are many other posts about this issue but I'm pretty sure I've been to all of them and either didn't get an applicable answer or an answer that I understand.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":16417,"Q_Id":50634751,"Users Score":0,"Answer":"what worked for is this;\n\nI installed TensorFlow on the command prompt as an administrator using this command pip install tensorflow\nthen I jumped back to my pycharm and clicked the red light bulb pop-up icon, it will have a few options when you click it, just select the one that says install tensor flow. This would not install in from scratch but basically, rebuild and update your pycharm workspace to note the newly installed tensorflow","Q_Score":3,"Tags":"python,tensorflow,pip,pycharm,conda","A_Id":69603564,"CreationDate":"2018-06-01T01:04:00.000","Title":"Pycharm Can't install TensorFlow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Suppose I want to select a range of columns from a dataframe: Call them 'column_1' through 'column_60'. I know I could use loc like this:\ndf.loc[:, 'column_1':'column_60']\nThat will give me all rows in columns 1-60. \nBut what if I wanted that range of columns plus 'column_81'. This doesn't work:\ndf.loc[:, 'column_1':'column_60', 'column_81']\nIt throws a \"Too many indexers\" error. \nIs there another way to state this using loc? Or is loc even the best function to use in this case? \nMany thanks.","AnswerCount":4,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":4458,"Q_Id":50647832,"Users Score":0,"Answer":"You can use pandas.concat():\npd.concat([df.loc[:,'column_1':'columns_60'],df.loc[:,'column_81']],axis=1)","Q_Score":9,"Tags":"python,python-3.x,pandas,dataframe","A_Id":50648721,"CreationDate":"2018-06-01T16:44:00.000","Title":"Can you use loc to select a range of columns plus a column outside of the range?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"from wordcloud import WordCloud\nWhile importing WordCloud I get below issue.Can you help with this?\nRuntimeWarning: The _imaging extension was built for another version of Pillow or PIL:\nCore version: 5.1.0\nPillow version: 4.2.1\n  \"The _imaging extension was built for Python with UCS2 support; \"\n\nImportError                               Traceback (most recent call last)\n in ()\n----> 1 from wordcloud import WordCloud\n:\\Users\\jhaas\\Anaconda2\\lib\\site-packages\\wordcloud__init__.py in ()\n----> 1 from .wordcloud import (WordCloud, STOPWORDS, random_color_func,\n      2                         get_single_color_func)\n      3 from .color_from_image import ImageColorGenerator\n      4 \n      5 all = ['WordCloud', 'STOPWORDS', 'random_color_func',\nC:\\Users\\jhaas\\Anaconda2\\lib\\site-packages\\wordcloud\\wordcloud.py in ()\n     17 from operator import itemgetter\n     18 \n---> 19 from PIL import Image\n     20 from PIL import ImageColor\n     21 from PIL import ImageDraw\nC:\\Users\\jhaas\\Anaconda2\\lib\\site-packages\\PIL\\Image.py in ()\n     65                           \"Pillow version: %s\" %\n     66                           (getattr(core, 'PILLOW_VERSION', None),\n---> 67                            PILLOW_VERSION))\n     68 \n     69 except ImportError as v:\nImportError: The _imaging extension was built for another version of Pillow or PIL:\nCore version: 5.1.0\nPillow version: 4.2.1","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":270,"Q_Id":50655939,"Users Score":0,"Answer":"This is a Pillow problem, rather than a WordCloud problem. As it says, your Pillow installation has somehow become part 4.2.1, part 5.1.0. The simplest solution would be to reinstall Pillow.","Q_Score":0,"Tags":"python,python-imaging-library,python-import,word-cloud","A_Id":51454563,"CreationDate":"2018-06-02T10:28:00.000","Title":"Wordcloud-Pillow issue","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am developing a convolution neural network (CNN) model to predict whether a patient in category 1,2,3 or 4. I use Keras on top of TensorFlow.\nI have 64 breast cancer patient data, classified into four category (1=no disease, 2= \u2026., 3=\u2026.., 4=progressive disease). In each patient's data, I have 3 set of MRI scan images taken at different dates and inside each MRI folder, I have 7 to 8 sub folders containing MRI images in different plane (such as coronal plane\/sagittal plane etc). \nI learned how to deal with basic \u201cCat-Dog-CNN-Classifier\u201d, it was easy as I put all the cat & dog images into a single folder to train the network. But how do I tackle the problem in my breast cancer patient data? It has multiple folders and sub-solders.\nPlease suggest.","AnswerCount":3,"Available Count":1,"Score":-0.0665680765,"is_accepted":false,"ViewCount":1045,"Q_Id":50664485,"Users Score":-1,"Answer":"Use os.walk to access all the files in sub-directories recursively and append to the dataset.","Q_Score":0,"Tags":"python,tensorflow,keras","A_Id":55304529,"CreationDate":"2018-06-03T08:14:00.000","Title":"Train CNN model with multiple folders and sub-folders","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a very large data set and am using Keras' fit_generator to train a Keras model (tensorflow backend).  My data needs to be normalized across the entire data set however when using fit_generator, I have access to relatively small batches of data and normalization of the data in this small batch is not representative of normalizing the data across the entire data set.  The impact is quite large (I tested it and the model accuracy is significantly degraded).\nMy question is this:  What is the correct practice of normalizing data across entire data set when using Keras' fit_generator?  One last point:  my data is a mix of text and numeric data and not images, and hence I am not able to use some of the capabilities in Keras' provided image generator which may address some of the issues for image data.\nI have looked at normalizing the full data set prior to training (\"brute-force\" approach, I suppose) but I am wondering if there is a more elegant way of doing this.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2686,"Q_Id":50682119,"Users Score":3,"Answer":"The generator does allow you to do on-the-fly processing of data but pre-processing the data prior to training is the preferred approach:\n\nPre-process and saving avoids processing the data for every epoch, you should really just do small operations that can be applied to batches. One-hot encoding for example is a common one while tokenising sentences etc can be done offline.\nYou probably will tweak, fine-tune your model. You don't want to have the overhead of normalising the data and ensure every model trains on the same normalised data.\n\nSo, pre-process once offline prior to training and save it as your training data. When predicting you can process on-the-fly.","Q_Score":3,"Tags":"python,tensorflow,machine-learning,keras,keras-2","A_Id":50683342,"CreationDate":"2018-06-04T13:46:00.000","Title":"How to normalize data when using Keras fit_generator","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a script that I am trying to execute every 2 seconds.. to begin it reads a .csv with pd.read_csv. Then executes modifications on the df and finally overwrites the original .csv with to_csv.\nI'm running into a PermissionError: [Errno 13] Permission denied: and from my searches I believe it's due to trying to open\/write too often to the same file though I could be wrong.\n\nAny suggestions how to avoid this?\nNot sure if relevant but the file is stored in one-drive folder.\nIt does save on occasion, seemingly randomly.\nIncreasing the timeout so the script executes slower helps but I want it running fast!\n\nThanks","AnswerCount":2,"Available Count":1,"Score":-0.0996679946,"is_accepted":false,"ViewCount":1596,"Q_Id":50692295,"Users Score":-1,"Answer":"Close the file that you are trying to read and write and then try running your script.\nHope it helps","Q_Score":0,"Tags":"python,pandas,csv,io","A_Id":50692350,"CreationDate":"2018-06-05T04:53:00.000","Title":"Pandas - Read\/Write to the same csv quickly.. getting permissions error","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am training with some documents with gensim's Doc2vec.  \nI have two types of inputs:  \n\nWhole English Wikipedia: Each article of Wikipedia text is considered as one \ndocument for doc2vec training. (Total around 5.5 million articles or documents)\nSome documents related to my project that are manually prepared and collected from some websites. (around 15000 documents).\nWhere each document the size is around 100 sentences.\n\nFurther, I want to use this model to infer sentences of size (10~20 words).\nI request some clarification on my approach.\nIs the method of training over documents(size of each document approx. 100 sentences each) and then inferring over new sentence correct. ?\nOr, should I train over only sentences and not documents and then infer over the new sentence.?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":332,"Q_Id":50692739,"Users Score":1,"Answer":"Every corpus and project goals are different. Your approach of training on larger docs but then inferring on shorter sentences could plausibly work, but you have to try it to see how well, and then iteratively test whether perhaps shorter training docs (as single sentences or groups-of-sentences) work better, for your specific goal. \nNote that gensim Doc2Vec inference often gains from non-default parameters \u2013 especially more steps (than the tiny default 5) or a smaller starting alpha (more like the training default of 0.025), especially on shorter documents. And, that inference also may work better or worse depending on original model metaparameters.\nNote also that an implementation limit means that texts longer than 10,000 tokens are silently truncated in gensim Word2Vec\/Doc2Vec training. (If you have longer docs, you can split them into less-than-10K-token subdocuments, but then repeat the tags for each subdocument, to closely simulate what effect training with a longer document would have had.)","Q_Score":0,"Tags":"python,gensim,training-data,doc2vec","A_Id":50709075,"CreationDate":"2018-06-05T05:38:00.000","Title":"Can doc2vec be useful if training on Documents and inferring on sentences only","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"It seems I have a conflict when trying to install pytrends via anaconda. After submitting \"pip install pytrends\" the following error arises:\ntensorflow-tensorboard 1.5.1 has requirement bleach==1.5.0, but you'll have bleach 2.0.0 which is incompatible.\ntensorflow-tensorboard 1.5.1 has requirement html5lib==0.9999999, but you'll have html5lib 0.999999999 which is incompatible.\nI also have tensorflow but don't necessarily need it. But I'd prefer a means to operate with both.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":333,"Q_Id":50712246,"Users Score":1,"Answer":"Try upgrading your version of tensorflow. I tried it with Tensorflow 1.6.0 ,tensorboard 1.5.1 and it worked fine. I was able to import pytrends.","Q_Score":1,"Tags":"python,tensorflow,anaconda","A_Id":50713079,"CreationDate":"2018-06-06T04:26:00.000","Title":"Pytrends anaconda install conflict with TensorFlow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have been working on creating and training a Deep Learning model for the first time. I did not have any knowledge about the subject prior to the project and therefor my knowledge is limited even now.\nI used to run the model on my own laptop but after implementing a well working OHE and SMOTE I simply couldnt run it on my own device anymore due to MemoryError (8GB of RAM). Therefor I am currently running the model on a 30GB RAM RDP which allows me to do so much more, I thought. \nMy code seems to have some horribly inefficiencies of which I wonder if they can be solved. One example is that by using pandas.concat my model's RAM usages skyrockets from 3GB to 11GB which seems very extreme, afterwards I drop a few columns making the RAm spike to 19GB but actually returning back to 11GB after the computation is completed (unlike the concat). I also forced myself to stop using the SMOTE for now just because the RAM usage would just go up way too much. \nAt the end of the code, where the training happens the model breaths its final breath while trying to fit the model. What can I do to optimize this?\nI have thought about splitting the code into multiple parts (for exmaple preprocessing and training) but to do so I would need to store massive datasets in a pickle which can only reach 4GB (correct me if I'm wrong). I have also given thought about using pre-trained models but I truely did not understand how this process goes to work and how to use one in Python. \nP.S.: I would also like my SMOTE back if possible \nThank you all in advance!","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1499,"Q_Id":50719405,"Users Score":0,"Answer":"Slightly orthogonal to your actual question, if your high RAM usage is caused by having entire dataset in memory for the training, you could eliminate such memory footprint by reading and storing only one batch at a time: read a batch, train on this batch, read next batch and so on.","Q_Score":0,"Tags":"python,deep-learning,ram,rdp","A_Id":50723414,"CreationDate":"2018-06-06T11:33:00.000","Title":"Optimizing RAM usage when training a learning model","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Say that I'm training a (Gensim) Word2Vec model with min_count=5. The documentation learns us what min_count does:\n\nIgnores all words with total frequency lower than this.\n\nWhat is the effect of min_count on the context? Lets say that I have a sentence of frequent words (min_count > 5) and infrequent words (min_count < 5), annotated with f and i:\n\nThis (f) is (f) a (f) test (i) sentence (i) which (f) is (f) shown (i) here (i)\n\nI just made up which word is frequently used and which word is not for demonstration purposes.\nIf I remove all infrequent words, we get a completely different context from which word2vec is trained. In this example, your sentence would be \"This is a which is\", which would then be a training sentence for Word2Vec. Moreover, if you have a lot of infrequent words, words that were originally very far away from each other are now placed within the same context.\nIs this the correct interpretation of Word2Vec? Are we just assuming that you shouldn't have too many infrequent words in your dataset (or set a lower min_count threshold)?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":6773,"Q_Id":50723303,"Users Score":8,"Answer":"Words below the min_count frequency are dropped before training occurs. So, the relevant context window is the word-distance among surviving words. \nThis de facto shrinking of contexts is usually a good thing: the infrequent words don't have enough varied examples to obtain good vectors for themselves. Further, while individually each infrequent word is rare, in total there are lots of them, so these doomed-to-poor-vector rare-words intrude on most other words' training, serving as a sort of noise that makes those word-vectors worse too.\n(Similarly, when using the sample parameter to down-sample frequent words, the frequent words are randomly dropped \u2013 which also serves to essentially \"shrink\" the distances between surviving words, and often improves overall vector quality.)","Q_Score":3,"Tags":"python,word2vec,gensim","A_Id":50730108,"CreationDate":"2018-06-06T14:46:00.000","Title":"How is Word2Vec min_count applied","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I was trying to install spyder in the virtual environment on anaconda, but ended up with this debugging error.\n\nExecuting transaction: \n  \\ DEBUG menuinst_win32:init(199): Menu: name: 'Anaconda${PY_VER} ${PLATFORM}', prefix: 'C:\\Users\\Public\\Anaconda\\envs\\tensorflow', env_name: 'tensorflow', mode: 'None', used_mode: 'user'\n  DEBUG menuinst_win32:create(323): Shortcut cmd is C:\\Users\\Public\\Anaconda\\pythonw.exe, args are ['C:\\Users\\Public\\Anaconda\\cwp.py', 'C:\\Users\\Public\\Anaconda\\envs\\tensorflow', 'C:\\Users\\Public\\Anaconda\\envs\\tensorflow\\pythonw.exe', 'C:\\Users\\Public\\Anaconda\\envs\\tensorflow\\Scripts\\spyder-script.py']\n  | DEBUG menuinst_win32:create(323): Shortcut cmd is C:\\Users\\Public\\Anaconda\\python.exe, args are ['C:\\Users\\Public\\Anaconda\\cwp.py', 'C:\\Users\\Public\\Anaconda\\envs\\tensorflow', 'C:\\Users\\Public\\Anaconda\\envs\\tensorflow\\python.exe', 'C:\\Users\\Public\\Anaconda\\envs\\tensorflow\\Scripts\\spyder-script.py', '--reset']\n\nI have also tried to clear the debugging errors using \nconda config --set quiet True , but no use.\nCan anyone help me with this?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":777,"Q_Id":50728057,"Users Score":1,"Answer":"You should activate your virtual environment and then type: conda install spyder.  That should install spyder for that particular virtual environment. If you used pip or pip3 you may have problems.","Q_Score":1,"Tags":"python,python-3.x,anaconda,spyder","A_Id":50787466,"CreationDate":"2018-06-06T19:21:00.000","Title":"Not able to install Spyder in the virtual environment on Anaconda","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to modelize the service of selling seats on an airplane as an MDP( markov decision process) to use reinforcement learning for airline revenues optimization, for that I needed to define what would be: states, actions, policy, value and reward. I thought a little a bit about it, but i think there is still something missing.\nI modelize my system this way:\n\nStates = (r,c) where r is the number of passengers and c the number of seats bought so r>=c.\nActions = (p1,p2,p3) that are the 3 prices. the objective is to decide which one of them give more revenues.\nReward: revenues.\n\nCould you please tell me what do u think and help me?\nAfter the modelization, I have to implement all of that wit Reinforcement Learning. Is there a package that do the work ?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":232,"Q_Id":50737705,"Users Score":0,"Answer":"I think the biggest thing missing in your formulation is the sequential part. Reinforcement learning is useful when used sequentially, where the next state has to be dependent on the current state (thus the \"Markovian\"). In this formulation, you have not specified any Markovian behavior at all. Also, the reward is a scalar which is dependent on either the current state or the combination of current state and action. In your case, the revenue is dependent on the price (the action), but it has no correlation to the state (the seat). These are the two big problems that I see with your formulation, there are others as well. I will suggest you to go through the RL theory (online courses and such) and write a few sample problems before trying to formulate your own.","Q_Score":0,"Tags":"python,optimization,reinforcement-learning,markov-decision-process","A_Id":50748029,"CreationDate":"2018-06-07T09:27:00.000","Title":"Reinforcement Learning with MDP for revenues optimization","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am reading a file using\n file=pd.read_csv('file_1.csv')\nwhich is taking a long time on CPU.\nIs there any method to read this using GPU.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":810,"Q_Id":50738058,"Users Score":3,"Answer":"No. The biggest bottleneck is IO and that\u2019s handled by the CPU.","Q_Score":1,"Tags":"python-3.x,gpu,h2o,h2o4gpu","A_Id":50739411,"CreationDate":"2018-06-07T09:44:00.000","Title":"How can I read a csv file using panda dataframe from GPU?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been doing a lot of Image Processing recently on Python using OpenCV and I've worked all this while with 2-D Images in the generic BGR style.\nNow, I'm trying to figure out how to incorporate depth and work with depth information as well.\nI've seen the documentation on creating simple point clouds using the Left and Right images of a Stereocamera, but I was hoping to gain some intuition on Depth-based cameras themselves like Kinect.\nWhat kind of camera should I use for this purpose, and more importantly: how do I process these images in Python - as I can't find a lot of documentation on handling RGBD images in OpenCV.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":280,"Q_Id":50743476,"Users Score":0,"Answer":"If you want to work with depth based cameras you can go for Time of Flight(ToF) cameras like picoflexx and picomonstar cameras. They will give you X,Y and Z values. Where your x and y values are distances from camera centre of that point (like in 2D space) and Z will five you the direct distance of that point (not perpendicular) from camera centre. \nFor this camera and this 3d data processing you can use Point Cloud Library fro processing.","Q_Score":1,"Tags":"python,opencv","A_Id":50776543,"CreationDate":"2018-06-07T14:13:00.000","Title":"Processing Images with Depth Information","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a time series data which looks something like this\nLoan_id Loan_amount Loan_drawn_date\n  id_001    2000000    2015-7-15\n  id_003     100       2014-7-8\n  id_009     78650     2012-12-23\n  id_990      100      2018-11-12\nI am trying to build a Arima forecasting model on this data which has round about 550  observations. These are the steps i have followed     \n\nConverted the time series data into daily data and replaced NA values with 0. the data look something like this           \nLoan_id Loan_amount Loan_drawn_date \nid_001    2000000    2015-7-15\nid_001    0          2015-7-16\nid_001    0          2015-7-17\nid_001    0          2015-7-18\nid_001    0          2015-7-19\nid_001    0          2015-7-20\n....\nid_003     100       2014-7-8\nid_003     0       2014-7-9\nid_003     0       2014-7-10\nid_003     0        2014-7-11\nid_003     0       2014-7-12\nid_003     0       2014-7-13\n....\nid_009     78650     2012-12-23\nid_009     0     2012-12-24\nid_009     0     2012-12-25\nid_009     0     2012-12-26\nid_009     0     2012-12-27\nid_009     0     2012-12-28\n...\nid_990      100      2018-11-12\nid_990      0      2018-11-13\nid_990      0      2018-11-14\nid_990      0      2018-11-15\nid_990      0      2018-11-16\nid_990      0      2018-11-17\nid_990      0      2018-11-18\nid_990      0      2018-11-19\nCan Anyone please suggest me how do i proceed ahead with these 0 values now?\nSeeing the variance in the loan amount numbers i would take log of the of the loan amount. i am trying to build the ARIMA model for the first time and I have read about all the methods of imputation but there is nothing i can find. Can anyone please tell me how do i proceed ahead in this data","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":148,"Q_Id":50747097,"Users Score":0,"Answer":"I don't know exactly about your specific domain problem, but these things apply usually in general:\n\nIf the NA values represent 0 values for your domain specific problem, then replace them with 0 and then fit the ARIMA model (this would for example be the case if you are looking at daily sales and on some days you have 0 sales)\nIf the NA values represent unknown values for your domain specific problem then do not replace them and fit your ARIMA model. (this would be the case, if on a specific day the employee forgot to write down the amount of sales and it could be any number). \n\nI probably would not use imputation at all. There are methods to fit an ARIMA model on time series that have missing values. Usually these algorithms should probably also implemented somewhere in python. (but I don't know since I am mostly using R)","Q_Score":0,"Tags":"python,time-series,missing-data,arima","A_Id":50774913,"CreationDate":"2018-06-07T17:31:00.000","Title":"ARIMA Forecasting","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm pretty new the LightGBM, and when I try to apply lightgbm.train on my dataset, I got this error:\nLightGBMError: Length of label is not same with #data\nI'm not sure where I made a mistake. I tried\nmodel = lightgbm.train(params, train_data, valid_sets=test_data, early_stopping_rounds=150, verbose_eval=200)\nThanks in advance.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":4332,"Q_Id":50747460,"Users Score":0,"Answer":"It simply means that the dimensions of your training examples and the respective list of labels do not match. In other words, if you have 10 training instances you need exactly 10 labels. (For a multi-label scenarios a better formulation would be to replace label by labelling, or refer to the size of the array.)","Q_Score":3,"Tags":"python,lightgbm","A_Id":50774153,"CreationDate":"2018-06-07T17:57:00.000","Title":"what does the error \"Length of label is not same with #data\" when I call lightgbm.train","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a python script which uses readlines and numpy.loadtxt to load a csv file. It works perfectly fine on my desktop running ubuntu 16.04. On my laptop running 18.04 I get (loading the same file) the following error: UnicodeDecodeError: 'utf8' codec can't decode byte 0xb5 in position 446: invalid start byte\nWhat can I do to make the script working?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":322,"Q_Id":50748564,"Users Score":1,"Answer":"Adding encoding='ISO-8859-1' to readlines and loadtxt did the trick.","Q_Score":0,"Tags":"python,numpy,ubuntu","A_Id":50753840,"CreationDate":"2018-06-07T19:07:00.000","Title":"readlines and numpy loadtxt gives UnicodeDecodeError after upgrade 18.04","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"The arrays are of following dimensions:\ndists: (500,5000)\ntrain: (5000,)\ntest:(500,)\nWhy does the first two statements throw an error whereas the third one works fine?\n\ndists += train + test\n\nError: ValueError: operands could not be broadcast together with shapes (5000,) (500,) \n\ndists += train.reshape(-1,1) + test.reshape(-1,1)\n\nError: ValueError: operands could not be broadcast together with shapes (5000,1) (500,1)\n\ndists += train + test.reshape(-1,1)\nThis works fine!\n\nWhy does this happen?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":13229,"Q_Id":50758165,"Users Score":6,"Answer":"It's to do with NumPy's broadcasting rules. Quoting the NumPy manual:\n\nWhen operating on two arrays, NumPy compares their shapes element-wise. It starts with the trailing dimensions, and works its way forward. Two  dimensions are compatible when\n\nthey are equal, or\none of them is 1\n\n\nThe first statement throws an error because NumPy looks at the only dimension, and (5000,) and (500,) are inequal and cannot be broadcast together.\nIn the second statement, train.reshape(-1,1) has the shape (5000,1) and test.reshape(-1,1) has the shape (500,1). The trailing dimension (length one) is equal, so that's ok, but then NumPy checks the other dimension and 5000 != 500, so the broadcasting fails here.\nIn the third case, your operands are (5000,) and (500,1). In this case NumPy does allow broadcasting. The 1D-array is extended along the trailing length-1 dimension of the 2D-array.\nFWIW, the shape and broadcasting rules can be a bit tricky sometimes, and I've often been confused with similar matters.","Q_Score":6,"Tags":"python,python-3.x,numpy,array-broadcasting","A_Id":50758844,"CreationDate":"2018-06-08T10:03:00.000","Title":"Why the following operands could not be broadcasted together?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"1.I already have nvcuda.dll in system32.\n2.I have path C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v9.2\\bin.\n3.The program already upgraded tensorflow and GPU tensorflow.\nI check import still have the error. \n\nImportError: Could not find 'nvcuda.dll'. TensorFlow requires that\n  this DLL be installed in a directory that is named in your %PATH%\n  environment variable. Typically it is installed in\n  'C:\\Windows\\System32'. If it is not present, ensure that you have a\n  CUDA-capable GPU with the correct driver installed.\n\nWhy?\nHow to fix this problem shown by the message error ?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2669,"Q_Id":50758472,"Users Score":0,"Answer":"As the comment mentioned, you need to ensure the time you import the tensorflow, the path environment points to c:\\windows\\system32 and as you said you have nvcuda.dll, ensure the file is there too. There is no need to set the libraries.","Q_Score":0,"Tags":"python-3.x,tensorflow,spyder,dllimport","A_Id":57155250,"CreationDate":"2018-06-08T10:20:00.000","Title":"How to fix import error 'nvcuda.dll' in spyder for python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"i am facing issue with my inception model during the performance testing with Apache JMeter.\n\nError: OOM when allocating tensor with shape[800,1280,3] and type\n  float on \/job:localhost\/replica:0\/task:0\/device:GPU:0 by allocator\n  GPU_0_bfc      [[Node: Cast = CastDstT=DT_FLOAT, SrcT=DT_UINT8,\n  _device=\"\/job:localhost\/replica:0\/task:0\/device:GPU:0\"]]\n  Hint: If you want to see a list of allocated tensors when OOM happens,\n  add report_tensor_allocations_upon_oom to RunOptions for current\n  allocation info.","AnswerCount":2,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":34640,"Q_Id":50760543,"Users Score":19,"Answer":"OOM stands for Out Of Memory.  That means that your GPU has run out of space, presumably because you've allocated other tensors which are too large.  You can fix this by making your model smaller or reducing your batch size.  By the looks of it, you're feeding in a large image (800x1280) you may want to consider downsampling.","Q_Score":16,"Tags":"python-3.x,tensorflow,gpu,gunicorn","A_Id":50764934,"CreationDate":"2018-06-08T12:20:00.000","Title":"Error: OOM when allocating tensor with shape","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am writing a custom conda package for tensorflow. When I name the package \"tensorflow\" it takes it more than 3 minutes to get past the \"solving environment\" part but if I change the package name even a little bit, to \"tensorflowp3\" it loads in around 10 seconds. \nI am using the commands - \nconda install -c <my_channel> tensorflow\nconda install -c <my_package> tensorflowp3\nI am not sure why setting a slightly different package name causes such a significant time change. I am specifying which channel the package should be loaded from in the command as well. I have tried doing the same with locally stored packages using the --use-local tag as well but it still behaves the same way as with the channel name. Any help would be very appreciated.","AnswerCount":1,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":21062,"Q_Id":50765892,"Users Score":6,"Answer":"I solved by doing this\n\nopen Anaconda Navigator application \nselect Environment from menu\nchoose the environment you wanna use (base enviroment if you don't use multiple enviroments)\nUpdate index\nClick on channels and remove all eventual channel, but default\n\nNow, for me it takes a reasonable abount of time to install a new packet.","Q_Score":14,"Tags":"python,tensorflow,anaconda,conda","A_Id":53469361,"CreationDate":"2018-06-08T17:40:00.000","Title":"\"Solving Environment\" during `conda install -c  tensorflow` takes 3+ min but changing the name a bit reduces the time significantly","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a problem with rasa core, let's suppose that I have a rasa-nlu able to detect time  \neg \"let's start tomorrow\" would get the entity time: 2018-06-10:T18:39:155Z\nOk, now I want next branches, or decisions to be conditioned by:  \n\ntime is in the past  \ntime before one month from now \ntime is beyond 1\nmonth\n\nI do not know how to do that. I do not know how to convert it to a slot able to influence the dialog. My only idea would be to have an action that converts the date to a categorical slot right after detecting time, but I see two problems with that approach:  \n\none it would already be too late, meaning that if I do it with a\nposterior action it means the rasa-core has already decided what\ndecision to take without using the date\nand secondly, I do know how to save it, because if I have a\nstories.md that compares a detecting date like in the example with\nthe current time, maybe in the time of the example it was beyond one\nmonth but now it is in the past, so the reset of that story would be\nwrong.\n\nI am pretty lost and I do not know how to deal with this, thanks a lot!!!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":568,"Q_Id":50776518,"Users Score":0,"Answer":"I think you could have a validation in the custom form.\nWhere it perform validation on the time and perform next action base on the decision on the time.\nYour story will have to train to handle different action paths.","Q_Score":0,"Tags":"python,date,rasa-nlu,rasa-core","A_Id":54032632,"CreationDate":"2018-06-09T16:51:00.000","Title":"Rasa-core, dealing with dates","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I used tf.layers.dense to build a fully connected autoencoder. and I want to save it and restore only the encoder to get the embedding output.\nHow to use tf.train.saver to restore only the encoder? Because I want to set different batch size of the restored model, to input only one data into it. \nI saw many tutorials but there is no tutorials about this.\nIs there any standard solution about this\nThank you very much","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":341,"Q_Id":50778593,"Users Score":0,"Answer":"If you don't care about memory space the easiest way is by saving the whole graph (encoder and decoder) and when using it for prediction, you can pass the last layer of the encoder as the fetch argument. Tensorflow will only calculate to this point and you don't have any computational difference compared to only saving the encoder. \nOtherwise you can create two graphs (one for the encoder, one for the decoder) an train them at the same time and train them together. But this is I bit more complex.","Q_Score":0,"Tags":"python,tensorflow","A_Id":50781250,"CreationDate":"2018-06-09T21:13:00.000","Title":"tensorflow save and restore autoencoder","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am building a recommender system which does Multi Criteria based ranking of car alternatives. I just need to do ranking of the alternatives in  a meaningful way. I have ways of asking user questions via a form.\nEach car will be judged on the following criteria: price, size, electric\/non electric, distance etc. As you can see its a mix of various data types, including ordinal, cardinal(count) and quantitative dat.\nMy question is as follows:\n\nWhich technique should I use for incorporating all the models into a single score Which I can rank. I looked at normalized Weighted sum model, but I have a hard time assigning weights to ordinal(ranked) data. I tried using the SMARTER approach for assigning numerical weights to ordinal data but Im not sure if it is appropriate. Please help!\nAfter someone can help me figure out answer to finding the best ranking method, what if the best ranked alternative isnt good enough on an absolute scale? how do i check that so that enlarge the alternative set further?\n\n3.Since the criterion mention above( price, etc) are all on different units, is there a good method to normalized mixed data types belonging to different scales? does it even make sense to do so, given that the data belongs to many different types?\nany help on these problems will be greatly appreciated! Thank you!","AnswerCount":2,"Available Count":1,"Score":-0.1973753202,"is_accepted":false,"ViewCount":972,"Q_Id":50784441,"Users Score":-2,"Answer":"I am happy to see that you are willing to use multiple criteria decision making tool. You can use Analytic Hierarchy Process (AHP), Analytic Network Process (ANP), TOPSIS, VIKOR etc. Please refer relevant papers. You can also refer my papers. \nKrishnendu Mukherjee","Q_Score":1,"Tags":"python,statistics,ranking,recommendation-engine,economics","A_Id":50991998,"CreationDate":"2018-06-10T13:57:00.000","Title":"Multi crtieria alterative ranking based on mixed data types","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am writing a machine learning toolkit to run algorithm with different settings in parallel (each process run the algorithm for one setting). I am thinking about either to use mpi4py or python's build-in multiprocessing ? \nThere are a few pros and cons I am considering about. \n\nEasy-to-use:\n\nmpi4py: It seems more concepts to learn and a bit more tricks to make it work well\nmultiprocessing: quite easy and clean API\n\nSpeed:\n\nmpi4py: people say it is more low level, so I am expect it can be faster than python multiprocessing ?\nmultiprocessing: compared with mpi4py, much slower ?\n\nClean and short code:\n\nmpi4py: seems more code to write\nmultiprocessing: preferred, easy to use API\n\n\nThe working context is I am aiming at running the code basically in one computer or a GPU server. Not really targeting at running in different machines in the network (which only MPI can do it).\nAnd since the main goal is doing machine learning, so the parallelization is not really required to be very optimal, the key goal I want to have is to balance easy, clean and quick to maintain code base but at the same time like to exploit the benefits of parallelization. \nWith the background described above, is it recommended that using multiprocessing should just be enough ? Or is there a very strong reason to use mpi4py ?","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":3083,"Q_Id":50787392,"Users Score":2,"Answer":"By using mpi4py you can divide the task into multiple threads, but with a single computer with limited performance or number of cores the usability will be limited. However you might find it handy during training.\nmpi4py is constructed on top of the MPI-1\/2 specifications and provides an object oriented interface which closely follows MPI-2 C++ bindings.\nMPI for Python provides MPI bindings for the Python language, allowing programmers to exploit multiple processor computing systems.\nMPI for Python supports convenient, pickle-based communication of generic Python object as well as fast, near C-speed, direct array data communication of buffer-provider objects","Q_Score":8,"Tags":"mpi,python-multiprocessing","A_Id":58026950,"CreationDate":"2018-06-10T19:38:00.000","Title":"mpi4py or multiprocessing in Python ?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a huge file (>400MB) of NDJson formatted data and like to flatten it into a table format for further analysis. \nI started iterate through the various objects manually but some are rather deep and might even change over time, so I was hoping for a more general approach.\nI was certain pandas lib would offer something but could not find anything that would help my case. Also, the several other libs I found seem to not \u2018fully\u2019 provide what I was hoping for (flatten_json). It all seems very early on. \nIs it possible that there is not good (fast and easy) solve for this at this time?\nAny help is appreciated","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":521,"Q_Id":50787438,"Users Score":2,"Answer":"pandas read_json has a bool param lines, set this to True to read ndjsons\ndata_frame = pd.read_json('ndjson_file.json', lines=True)","Q_Score":1,"Tags":"python,ndjson","A_Id":63611068,"CreationDate":"2018-06-10T19:43:00.000","Title":"How to best flatten NDJson data in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"After installing Tensorflow with cpu support, I am getting some problems in the verification of Tensorflow.\nI don't have any GPU in my laptop and used pip3  for installation","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":163,"Q_Id":50796923,"Users Score":0,"Answer":"Pls, explain a little more where you're getting that error also it's quite possible that code you're using for verification is using codes which involve GPU. Hit me back on this one.","Q_Score":2,"Tags":"python-3.x","A_Id":50798156,"CreationDate":"2018-06-11T11:42:00.000","Title":"No module named tensorflow found in Windows 10 64bit version","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using Facebook's faiss index with custom indexes using the add_with_ids method. In inference time I use distance, ID = model.search() which returns the custom ID it was trained with. Is it possible to return also a unique id without retraining? Or just return the actual closest vector?\nThank you!","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":1900,"Q_Id":50798515,"Users Score":2,"Answer":"Since you provided the actual vectors, you presumably know how to map ids to vectors. \nMost Faiss indexes in do not store the vectors, because they need to be compressed to fit in RAM.","Q_Score":1,"Tags":"python,knn","A_Id":51878585,"CreationDate":"2018-06-11T13:08:00.000","Title":"How to return 'faiss' unique vector id on 'add_with_ids' trained index?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I want to find the distribution that best fit some data. This would typically be some sort of measurement data, for instance force or torque.\nIdeally I want to run Anderson-Darling with multiple distributions and select the distribution with the highest p-value. This would be similar to the 'Goodness of fit' test in Minitab. I am having trouble finding a python implementation of Anderson-Darling that calculates the p-value.\nI have tried scipy's stats.anderson() but it only returns the AD-statistic and a list of critical values with the corresponding significance levels, not the p-value itself.\nI have also looked into statsmodels, but it seems to only support the normal distribution. I need to compare the fit of several distributions (normal, weibull, lognormal etc.).\nIs there an implementation of the Anderson-Darling in python that returns p-value and supports nonnormal distributions?","AnswerCount":3,"Available Count":1,"Score":0.1325487884,"is_accepted":false,"ViewCount":2819,"Q_Id":50811061,"Users Score":2,"Answer":"I would just rank distributions by the goodness-of-fit statistic and not by p-values. We can use the Anderson-Darling, Kolmogorov-Smirnov or similar statistic just as distance measure to rank how well different distributions fit.\nbackground:\np-values for Anderson-Darling or Kolmogorov-Smirnov depend on whether the parameters are estimated or not. In both cases the distribution is not a standard distribution.\nIn some cases we can tabulate or use a functional approximation to tabulated values. This is the case when parameters are not estimated and if the distribution is a simple location-scale family without shape parameters.\nFor distributions that have a shape parameter, the distribution of the test statistic that we need for computing the p-values depends on the parameters. That is we would have to compute different distributions or tabulated p-values for each set of parameters, which is impossible.\nThe only solution to get p-values in those cases is either by bootstrap or by simulating the test statistic for the specific parameters.\nThe technical condition is whether the test statistic is asymptotically pivotal which means that the asymptotic distribution of the test statistic is independent of the specific parameters.\nUsing chisquare test on binned data requires fewer assumption, and we can compute it even when parameters are estimated. (Strictly speaking this is only true if the parameters are estimated by MLE using the binned data.)","Q_Score":4,"Tags":"python,statistics,p-value,hypothesis-test,goodness-of-fit","A_Id":50819970,"CreationDate":"2018-06-12T07:00:00.000","Title":"Is there an Anderson-Darling implementation for python that returns p-value?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using Hec-Ras for 2D unsteady modeling of a river delta. My model is simulated for one year. I need to extract the velocities and\/or discharges and compare them with the velocities from a already done 1DSA model. I wanted to do it in python but I'm new in programming and I wanted to see if anyone has experience with this kind of problem or has any idea on how I can compare the results the easiest because there is a big amount of data and manually doing it from the ras mapper would take a lot of time.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":319,"Q_Id":50813889,"Users Score":0,"Answer":"Maybe can use vector data, first with a classified and get different values between areas, after a statistical analysis.","Q_Score":1,"Tags":"python,data-modeling,data-extraction","A_Id":58000976,"CreationDate":"2018-06-12T09:36:00.000","Title":"Data extraction from HEC-RAS","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I routinely use pickle.dump() to save large files in Python 2.7. In my code, I have one .pickle file that I continually update with each iteration of my code, overwriting the same file each time.\nHowever, I occasionally encounter crashes (e.g. from server issues). This may happen in the middle of the pickle dump, rendering the pickle incomplete and the pickle file unreadable, and I lose all my data from the past iterations.\nI guess one way I could do it is to save one .pickle file for each iteration, and combine all of them later. Are there any other recommended methods, or best practices in writing to disk that is robust to crashing?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":175,"Q_Id":50822127,"Users Score":0,"Answer":"You're effectively doing backups, as your goal is the same: disaster recovery, lose as little work as possible.\nIn backups, there are these standard practices, so choose whatever fits you best:\n\nbacking up \n\n\nfull backup (save everything each time)\nincremental backup (save only what changed since the last backup)\ndifferential backup (save only what changed since the last full backup)\n\ndealing with old backups\n\n\ncircular buffer\/rotating copies (delete or overwrite backups older than X days\/iterations, optionally change indices in others' names)\nconsolidatating old incremental\/differential copies into the preceding full backup (as a failsafe, consolidate into a new file and only then delete the old ones)","Q_Score":1,"Tags":"python,pickle","A_Id":50822329,"CreationDate":"2018-06-12T16:53:00.000","Title":"How to perform a pickling so that it is robust against crashing?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I create up to 100 keras models in separated script an save them localy with model.save().\nFor Training them, I use multiprocessing.pool. In those processes I load each model separately. Because of occuring Memory Errors I used keras.backend.clear_session(). This seems to work but I have also read that it deletes the weights of models.\nSo to come back to my question, if I import \"from keras import backend as K\" in each process of the pool and at the end, after I saved the models, I use K.clear_session(), do I clear important data of parallel running processes or just data of this process?\nIf it deletes important data of parallel running processes. Is there any possibility of creating a local tensorflow session inside the process. Then assign the needed model to this session and then clear_session() this local one?\nI\u00b4m thankful for any input. \nIn adition it would be helpful if anyone knows the exact functionality of clear_session(). The explanation of this function is not very informative especially for beginners like me.\nThank you :)","AnswerCount":1,"Available Count":1,"Score":0.537049567,"is_accepted":false,"ViewCount":1899,"Q_Id":50823233,"Users Score":3,"Answer":"I faced similar kind of issue but I am not running models in parallel but alternatively i;e; either of the models (in different folders but same model file names) will run. \nWhen I run the models directly without clear_session it was conflicting with the previously loaded model and cannot switch to other model. After including clear_session at the beginning of statements (which loads the model) it was working, however it was also deleting global variables declared at the beginning of the program which are necessary for prediction activity. \nlesson learnt:\nclear_session will not only \"Destroys the current TF graph and creates a new one.\" as mentioned in the documentation but also deletes global variables defined in the program. \nSo I defined the global variables just after the clear_session statement\n** feedback appreciated","Q_Score":5,"Tags":"python,tensorflow,keras,multiprocessing","A_Id":53845674,"CreationDate":"2018-06-12T18:05:00.000","Title":"Does keras.backend.clear_session() deletes sessions in a process or globally?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am experimenting with python and data analytics. I collected tweets, counted the distinct users, and summed them ,grouped by their locations. Then i have calculated the percentage of users per country population. To make my graphs look better i have standardised my data using the z-score formula. Now i observe that i have a few outliers that ruin my graphs, so i will exclude them. My question is, do i have to exlude them from the original dataset and then re standardise my data, or is it correct to just exclude the standardised form from my analysis and proceed with the values i have already calculated?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":29,"Q_Id":50824847,"Users Score":0,"Answer":"As part of the Exploratory Data Analysis(EDA) Process, you'll want to visualize your data with all data points, identify outliers and then further investigate those outliers to figure out what to do with them. Are these outliers inaccurate values that need to be corrected? Perhaps erroneous entries in the raw data? Or are they valid data points that might point to something interesting? You can also assess the distribution of your data with df.describe()\nIf they are errors, correct them in your dataset and don't delete them. If they are accurate, valid outliers, just exclude them from the visualization to have a better picture of the rest of your data. Does this help?","Q_Score":0,"Tags":"python,data-visualization,data-analysis","A_Id":50825599,"CreationDate":"2018-06-12T19:51:00.000","Title":"Re- standardise data after excluding outliers?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a neural network with 3 consecutive linear layers (convolution), with no activation functions in between. After training the network and obtaining the weights, I would like to collapse all 3 layers into one layer. \nHow can this be done in practice, when each layer has different kernel size and stride?\nThe layers are as follows:\n\nConvolution layer with a 3x3 kernel, 5 input channels and 5 output channels (a tensor of size 3x3x5x5), with stride 1 and padding \"same\"\nConvolution layer with a 5x5 kernel, 5 input channels and 50 output channels (a tensor of size 5x5x5x50), with stride 2 and padding \"same\"\nConvolution layer with a 3x3 kernel, 50 input channels and 50 output channels (a tensor of size 3x3x50x50), with stride 1 and padding \"same\"\n\nThanks in advance","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":223,"Q_Id":50835327,"Users Score":1,"Answer":"Permute the dimensions of the first-layer kernels such that input channels are in the \"mini-batch\" dimension and output channels are in the \"channels\" dimension. Apply the second layer to that as if that were an image. Then apply the third layer to the result of that. The final result are kernels of the \"collapsed\" layer.\nUse \"full\" padding all these operations. If that works roughly correctly (apart from padding), try fixing the padding (probably it should be \"same\" in the last operation).","Q_Score":1,"Tags":"python,machine-learning,neural-network,convolution,convolutional-neural-network","A_Id":50840894,"CreationDate":"2018-06-13T10:49:00.000","Title":"Collapsing consecutive linear layers","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am optimizing the hyper-parameters of my neural-network, for which I am recursively training the network using different hyper-parameters. It works as expected until after some iterations, when creating a new network for training, it dies with the error \"Segmentation fault (core dumped)\".\nFurthermore, I am using GPU for training and I am doing this on a Nvidia Jetson TX2 and Python3.5. Also, I am using Keras with TensorFlow backend.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1251,"Q_Id":50840749,"Users Score":2,"Answer":"If you run K.clearsession() on a GPU with Keras 2, you may get a segmentation fault.\nIf you have this in your code, try removing it!","Q_Score":2,"Tags":"python-3.x,tensorflow,segmentation-fault,keras,nvidia-jetson","A_Id":51275732,"CreationDate":"2018-06-13T15:15:00.000","Title":"Segmentation fault (core dumped) when training more than one Keras NN models","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am doing a Kaggle competition which requires imputing some missing data. I have installed latest Anaconda(4.5.4) with all relevant dependencies (i.e scikit-learn (0.19.1)).\nWhen I try to import the modules I am getting the following error:\n\nModuleNotFoundError: No module named 'sklearn.impute'\n\nI have tried to import different sklearn modules without any problems. It seems that only sklearn.impute is missing.","AnswerCount":7,"Available Count":4,"Score":0.0,"is_accepted":false,"ViewCount":17981,"Q_Id":50843757,"Users Score":0,"Answer":"Another option is SimpleImputer, it works fine:\nfrom sklearn.impute import SimpleImputer","Q_Score":12,"Tags":"python-3.x,scikit-learn,anaconda","A_Id":64878625,"CreationDate":"2018-06-13T18:17:00.000","Title":"Getting \"ModuleNotFoundError: No module named 'sklearn.impute'\" despite having latest sklearn installed (0.19.1)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am doing a Kaggle competition which requires imputing some missing data. I have installed latest Anaconda(4.5.4) with all relevant dependencies (i.e scikit-learn (0.19.1)).\nWhen I try to import the modules I am getting the following error:\n\nModuleNotFoundError: No module named 'sklearn.impute'\n\nI have tried to import different sklearn modules without any problems. It seems that only sklearn.impute is missing.","AnswerCount":7,"Available Count":4,"Score":1.2,"is_accepted":true,"ViewCount":17981,"Q_Id":50843757,"Users Score":10,"Answer":"As BallpointBen pointed out, sklearn.impute is not yet released in the latest stable release (0.19.1). Currently it's supported only in 0.20.dev0.","Q_Score":12,"Tags":"python-3.x,scikit-learn,anaconda","A_Id":50844299,"CreationDate":"2018-06-13T18:17:00.000","Title":"Getting \"ModuleNotFoundError: No module named 'sklearn.impute'\" despite having latest sklearn installed (0.19.1)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am doing a Kaggle competition which requires imputing some missing data. I have installed latest Anaconda(4.5.4) with all relevant dependencies (i.e scikit-learn (0.19.1)).\nWhen I try to import the modules I am getting the following error:\n\nModuleNotFoundError: No module named 'sklearn.impute'\n\nI have tried to import different sklearn modules without any problems. It seems that only sklearn.impute is missing.","AnswerCount":7,"Available Count":4,"Score":0.0285636566,"is_accepted":false,"ViewCount":17981,"Q_Id":50843757,"Users Score":1,"Answer":"It's a version error. Here's a fix that worked for me while working in Jupyter Notebook. From your Terminal:\nconda update anaconda\nconda update scikit-learn\n\nThen restart your jupyter kernal","Q_Score":12,"Tags":"python-3.x,scikit-learn,anaconda","A_Id":54895196,"CreationDate":"2018-06-13T18:17:00.000","Title":"Getting \"ModuleNotFoundError: No module named 'sklearn.impute'\" despite having latest sklearn installed (0.19.1)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am doing a Kaggle competition which requires imputing some missing data. I have installed latest Anaconda(4.5.4) with all relevant dependencies (i.e scikit-learn (0.19.1)).\nWhen I try to import the modules I am getting the following error:\n\nModuleNotFoundError: No module named 'sklearn.impute'\n\nI have tried to import different sklearn modules without any problems. It seems that only sklearn.impute is missing.","AnswerCount":7,"Available Count":4,"Score":0.0,"is_accepted":false,"ViewCount":17981,"Q_Id":50843757,"Users Score":0,"Answer":"you can use \nfrom sklearn.preprocessing import Imputer\nit works.","Q_Score":12,"Tags":"python-3.x,scikit-learn,anaconda","A_Id":57499632,"CreationDate":"2018-06-13T18:17:00.000","Title":"Getting \"ModuleNotFoundError: No module named 'sklearn.impute'\" despite having latest sklearn installed (0.19.1)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using Google's Word2vec and I'm wondering how to get the top words that are predicted by a skipgram model that is trained using hierarchical softmax, given an input word?\nFor instance, when using negative sampling, one can simply multiply an input word's embedding (from the input matrix) with each of the vectors in the output matrix and take the one with the top value. However, in hierarchical softmax, there are multiple output vectors that correspond to each input word, due to the use of the Huffman tree. \nHow do we compute the likelihood value\/probability of an output word given an input word in this case?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":342,"Q_Id":50860649,"Users Score":0,"Answer":"I haven't seen any way to do this, and given the way hierarchical-softmax (HS) outputs work, there's no obviously correct way to turn the output nodes' activation levels into a precise per-word likelihood estimation. Note that:\n\nthe predict_output_word() method that (sort-of) simulates a negative-sampling prediction doesn't even try to handle HS mode\nduring training, neither HS nor negative-sampling modes make exact predictions \u2013 they just nudge the outputs to be more like the current training example would require\n\nTo the extent you could calculate all output node activations for a given context, then check each word's unique HS code-point node values for how close they are to \"being predicted\", you could potentially synthesize relative scores for each word \u2013 some measure of how far the values are from a \"certain\" output of that word. But whether and how each node's deviation should contribute to that score, and how that score might be indicative of a interpretable liklihood, is unclear. \nThere could also be issues because of the way HS codes are assigned strictly by word-frequency \u2013 so 'neighbor' word sharing mostly-the-same-encoding may be very different semantically. (There were some hints in the original word2vec.c code that it could potentially be beneficial to assign HS-encodings by clustering related words to have similar codings, rather than by strict frequency, but I've seen little practice of that since.)\nI would suggest sticking to negative-sampling if interpretable predictions are important. (But also remember, word2vec isn't mainly used for predictions, it just uses the training-attempts-at-prediction to bootstrap a vector-arrangment that turn out to be useful for other tasks.)","Q_Score":2,"Tags":"python,c++,nlp,word2vec,gensim","A_Id":50868421,"CreationDate":"2018-06-14T15:07:00.000","Title":"How to predict word using trained skipgram model?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am currently working on building a neural net model that targets to predict success\/failure of server update. \nHowever, the existing data is highly imbalanced. I.e. only 3 % of the records are failures, the rest is all success record.\nI am now trying to do some data exploration using confusion matrix. In this case, should I assign 'positive(1)' label to 'failure' label? or does it even matter which label that I assign to ?\nThanks in advances.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":39,"Q_Id":50864233,"Users Score":0,"Answer":"It is a binary classification problem. Usually the classes labeled as positive =1, and negative =0","Q_Score":0,"Tags":"python,neural-network","A_Id":50864355,"CreationDate":"2018-06-14T19:02:00.000","Title":"Defining label in confusion matrix with highly imbalanced dataset","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I use numpy.memmap to load only the parts of arrays into memory that I need, instead of loading an entire huge array. I would like to do the same with bool arrays.\nUnfortunately, bool memmap arrays aren't stored economically: according to ls, a bool memmap file requires as much space as a uint8 memmap file of the same array shape.\nSo I use numpy.unpackbits to save space. Unfortunately, it seems not lazy: It's slow and can cause a MemoryError, so apparently it loads the array from disk into memory instead of providing a \"bool view\" on the uint8 array.\nSo if I want to load only certain entries of the bool array from file, I first have to compute which uint8 entries they are part of, then apply numpy.unpackbits to that, and then again index into that.\nIsn't there a lazy way to get a \"bool view\" on the bit-packed memmap file?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":163,"Q_Id":50865421,"Users Score":2,"Answer":"Not possible. The memory layout of a bit-packed array is incompatible with what you're looking for. The NumPy shape-and-strides model of array layout does not have sub-byte resolution. Even if you were to create a class that emulated the view you want, trying to use it with normal NumPy operations would require materializing a representation NumPy can work with, at which point you'd have to spend the memory you don't want to spend.","Q_Score":0,"Tags":"python,numpy,boolean,mmap,numpy-memmap","A_Id":50865504,"CreationDate":"2018-06-14T20:26:00.000","Title":"Lazy version of numpy.unpackbits","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using the VADER sentiment lexicon in Python's nltk library to analyze text sentiment.  This lexicon does not suit my domain well, and so I wanted to add my own sentiment scores to various words.  So, I got my hands on the lexicon text file (vader_lexicon.txt) to do just that.  However, I do not understand the architecture of this file well.  For example, a word like obliterate will have the following data in the text file: \n    obliterate  -2.9    0.83066 [-3, -4, -3, -3, -3, -3, -2, -1, -4, -3]\nClearly the -2.9 is the average of sentiment scores in the list.  But what does the 0.83066 represent?\nThanks!","AnswerCount":2,"Available Count":1,"Score":0.2913126125,"is_accepted":false,"ViewCount":1281,"Q_Id":50882838,"Users Score":3,"Answer":"The vader_lexicon.txt file has four tab delimited columns as you said.\n\nColumn 1: The Token\nColumn 2: It is the Mean of the human Sentiment ratings\nColumn 3: It is the Standard Deviation of the token assuming it follows Normal Distribution\nColumn 4: It is the list of 10 human ratings taken during experiments\n\nThe actual code or sentiment calculation does not use the 3rd and 4th columns. So if you want to update the lexicon according to your requirement you can leave the last two columns blank or fill in with a random number and a list.","Q_Score":3,"Tags":"python,nltk,lexicon,vader","A_Id":54122776,"CreationDate":"2018-06-15T21:23:00.000","Title":"Python VADER lexicon Structure for sentiment analysis","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"My question is trivial, nevertheless I need your help. It's not a problem to take a np.log(x) of an array. But in my case this array could be N-dimensional\/Tensor (N=2..1024 and 100 samples in each dimension). \nFor N=4 calculation of element-wise np.log(x) takes 10 seconds. I need to take this log(x) in a cost function for optimization, thus, all process of optimization takes roughly 2 hours.\nSo, the question is how this log(x) can be implemented in faster way for N-dimensional arrays. Is it really possible?  \nThanks in advance.","AnswerCount":3,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":101,"Q_Id":50892030,"Users Score":0,"Answer":"The numpy log function is implemented in C and optimised for handling arrays, so although you may be able to scrape a bit of overhead off by writing your own custom log function in a lower-level language, this will still remain the bottleneck. If you want to see a big speed increase, you'll need to implement your algorithm differently.\nIs it really necessary to take the log of all these elements? You mention that each dimension can have 100 samples, do you plan on averaging these samples? If so you can reduce the number of logarithms you need to compute by using the fact that log(a) + log(b) = log(ab), and so the average [log(a0) + log(a1) + ... + log(aN)]\/(a0 + a1 + ... + aN) is the same as log(a0*a1*...*aN)\/(a0 + a1 + ... + aN).","Q_Score":0,"Tags":"python,arrays","A_Id":50897886,"CreationDate":"2018-06-16T21:47:00.000","Title":"Python faster way to take logarithm of N-dimensional array","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"My question is trivial, nevertheless I need your help. It's not a problem to take a np.log(x) of an array. But in my case this array could be N-dimensional\/Tensor (N=2..1024 and 100 samples in each dimension). \nFor N=4 calculation of element-wise np.log(x) takes 10 seconds. I need to take this log(x) in a cost function for optimization, thus, all process of optimization takes roughly 2 hours.\nSo, the question is how this log(x) can be implemented in faster way for N-dimensional arrays. Is it really possible?  \nThanks in advance.","AnswerCount":3,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":101,"Q_Id":50892030,"Users Score":0,"Answer":"Thanks guys,\nthe problem was a big amount of entries that I had to go through for processing. I just found another cost function for my optimization.\nBut, to speed up exactly this code - I think the idea with self made log table exactly for my type of signal can make it work.","Q_Score":0,"Tags":"python,arrays","A_Id":51058038,"CreationDate":"2018-06-16T21:47:00.000","Title":"Python faster way to take logarithm of N-dimensional array","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"My question is trivial, nevertheless I need your help. It's not a problem to take a np.log(x) of an array. But in my case this array could be N-dimensional\/Tensor (N=2..1024 and 100 samples in each dimension). \nFor N=4 calculation of element-wise np.log(x) takes 10 seconds. I need to take this log(x) in a cost function for optimization, thus, all process of optimization takes roughly 2 hours.\nSo, the question is how this log(x) can be implemented in faster way for N-dimensional arrays. Is it really possible?  \nThanks in advance.","AnswerCount":3,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":101,"Q_Id":50892030,"Users Score":0,"Answer":"Maybe multiprocessing can help you on this situation","Q_Score":0,"Tags":"python,arrays","A_Id":50892434,"CreationDate":"2018-06-16T21:47:00.000","Title":"Python faster way to take logarithm of N-dimensional array","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In feature extraction and detection using SIFT, I could extract features from 2 image. But I have 400 frames in video and want to have features from all 400 images in python. Can someone help me out with this?\nThank you.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":520,"Q_Id":50904849,"Users Score":0,"Answer":"Key-points extracted from SIFT describe numerous features. If you wish to compare all 400 frames from a video to an image that you have, you will have to make a loop over your process and run SIFT iteratively. This will be computationally expensive.\nOne method to make this fast would be to read all key-points of these 400 frames and store them into a file so that you don't have to detect them each time you want to compare them with a test image.\nThis is what I've made from the question that you've asked.","Q_Score":0,"Tags":"python-3.x,image-processing","A_Id":50905356,"CreationDate":"2018-06-18T07:48:00.000","Title":"Feature extraction from multiple images in python using SIFT","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a large pretrained Word2Vec model in gensim from which I want to use the pretrained word vectors for an embedding layer in my Keras model. \nThe problem is that the embedding size is enormous and I don't need most of the word vectors (because I know which words can occure as Input). So I want to get rid of them to reduce the size of my embedding layer.\nIs there a way to just keep desired wordvectors (including the coresponding indices!), based on a whitelist of words?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2182,"Q_Id":50914729,"Users Score":0,"Answer":"There's no built-in feature that does exactly that, but it shouldn't require much code, and could be modeled on existing gensim code. A few possible alternative strategies:\n\nLoad the full vectors, then save in an easy-to-parse format - such as via .save_word2vec_format(..., binary=False). This format is nearly self-explanatory; write your own code to drop all lines from this file that aren't on your whitelist (being sure to update the leading line declaration of entry-count). The existing source code for load_word2vec_format() & save_word2vec_format() may be instructive. You'll then have a subset file. \nOr, pretend you were going to train a new Word2Vec model, using your corpus-of-interest (with just the interesting words). But, only create the model and do the build_vocab() step. Now, you have untrained model, with random vectors, but just the right vocabulary. Grab the model's wv property - a KeyedVectors instance with that right vocabulary. Then separately load the oversized vector-set, and for each word in the right-sized KeyedVectors, copy over the actual vector from the larger set. Then save the right-sized subset.\nOr, look at the (possibly-broken-since-gensim-3.4) method on Word2Vec intersect_word2vec_format(). It more-or-less tries to do what's described in (2) above: with an in-memory model that has the vocabulary you want, merge in just the overlapping words from another word2vec-format set on disk. It'll either work, or provide the template for what you'd want to do.","Q_Score":8,"Tags":"python,keras,word2vec,gensim,word-embedding","A_Id":50916669,"CreationDate":"2018-06-18T17:32:00.000","Title":"Gensim Word2Vec select minor set of word vectors from pretrained model","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have been implementing some deep nets in Keras, but have eventually gotten frustrated with some limitations (for example: setting floatx to float16 fails on batch normalization layers, and the only way to fix it is to actually edit the Keras source; implementing custom layers requires coding them in backend code, which destroys the ability to switch backends), there appear to be no parallel training mechanisms [unlike tf.Estimator], and even vanilla programs run 30% slower in Keras than in tf (if one is to trust the interwebs), and was grumbling about moving to tensorflow, but was pleased to discover that TensorFlow (especially if you use tf.layers stuff) is not actually any longer for anything imaginable you might want to do. Is this a failure of my imagination, or is tf.layers basically a backporting of Keras into core TensorFlow, and is there any actual use case for Keras?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":423,"Q_Id":50920425,"Users Score":3,"Answer":"Keras used to have an upper hand on TensorFlow in the past but ever since the author is now affiliated with Google all the features that made it attractive are being implemented into TensorFlow you can check version 1.8, like you rightfully pointed out tf.layers is one such example.","Q_Score":1,"Tags":"python,tensorflow,keras","A_Id":50926356,"CreationDate":"2018-06-19T03:56:00.000","Title":"Keras vs TensorFlow - does Keras have any actual benefits?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I use VGG-16 pre-trained model and fine-tune the last 3 FC layers. \nBut in my case, I only use 3 classes as my classification.\nI want to ask how to choose the perceptron of FC layers.\nShould I visualize the Conv5_3 layer, then making a decision?\nBTW, VGG-16 official model is 4096, 4096, 1000 perceptron in FC layers.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":50,"Q_Id":50922606,"Users Score":0,"Answer":"The easiest way to adapt your network is to add another FC layer on top of the VGG (with weight kernel of size 1000x3).\nAlternatively, replace the last FC layer (of size 4096x1000) with an FC layer of size 4096x3.\nDon't forget to properly initialize your newly added layers.","Q_Score":0,"Tags":"python,tensorflow","A_Id":50923449,"CreationDate":"2018-06-19T07:17:00.000","Title":"How to choose number of perceptron in fine-tuning FC layer?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to know if there's a technique to simply undo a change that was done using Pandas. \nFor example, I did a string replacement on a few thousand rows of Pandas Dataframe, where, every occurrence of \"&\" in its string be replaced with \"and\". However after performing the replacement, I found out that I've made a mistake in the changes and would want to revert back to the Dataframe's most latest form before that string replacement was done.\nIs there a way to do this?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":4868,"Q_Id":50931697,"Users Score":0,"Answer":"Yes, there is a way to do this. If you're using the newest iteration of python and pandas you could do it this way:\ndf.replace(to_replace='and', value='&', inplace=true)\nThis is the way I learned it!","Q_Score":6,"Tags":"python,pandas","A_Id":64109376,"CreationDate":"2018-06-19T15:10:00.000","Title":"Undo a change that was performed using Pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to know if there's a technique to simply undo a change that was done using Pandas. \nFor example, I did a string replacement on a few thousand rows of Pandas Dataframe, where, every occurrence of \"&\" in its string be replaced with \"and\". However after performing the replacement, I found out that I've made a mistake in the changes and would want to revert back to the Dataframe's most latest form before that string replacement was done.\nIs there a way to do this?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":4868,"Q_Id":50931697,"Users Score":0,"Answer":"If you have cells structured in step, and the mess is because of running a couple of cells that have affected the dataset, you can stop the kernel and run all the cells from the beginning.","Q_Score":6,"Tags":"python,pandas","A_Id":67360151,"CreationDate":"2018-06-19T15:10:00.000","Title":"Undo a change that was performed using Pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Given I trained a simple neural network using Tensorflow and Python on my laptop and I want to use this model on my phone in C++ app.\nIs there any compatibility format I can use? What is the minimal framework to run neural networks (not to train)?\nUDP. I'm also interested in Tensorflow to NOT-Tensorflow compatibility. Do I need to build it up from scratch or there're any best practices?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":117,"Q_Id":50933736,"Users Score":1,"Answer":"Technically you don't need a framework at all. A conventional fully connected neural network is simple enough that you can implement it in straight C++. It's about 100 lines of code for the matrix multiplication and a dozen or so for the non-linear activation function. \nThe biggest part is figuring out how to parse a serialized Tensorflow model, especially given that there are quite a few ways to do so. You probably will want to freeze your TensorFlow model; this inserts the weights from the latest training into the model.","Q_Score":0,"Tags":"python,c++,tensorflow,neural-network","A_Id":50947050,"CreationDate":"2018-06-19T17:17:00.000","Title":"How to use trained neural network in different platform\/technology?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a script called \"RiskTemplate.py\" which generates a pandas dataframe consisting of 156 columns.  I created two additional columns which gives me a total count of 158 columns.  However, when I run this \"RiskTemplate.py\" script in another script using the below code, the dataframe only pulls the original 156 columns I had before the two additional columns were added.\nexec(open(\"RiskTemplate.py\").read()) \nhow can I get the reference script to pull in the revised dataframe from the underlying script \"RiskTemplate.py\"?\nhere are the lines creating the two additional dataframe columns, they work as intended when I run it directly in the \"RiskTemplate.py\" script.  The original dataframe is pulling from SQL via df = pd.read_sql(query,connection)\ndf['LMV % of NAV'] = df['longmv']\/df['End of Month NAV']*100\ndf['SMV % of NAV'] = df['shortmv']\/df['End of Month NAV']*100","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":21,"Q_Id":50934946,"Users Score":1,"Answer":"I figured it out, sorry for the confusion.  I did not save the risktemplate that I updated the dataframe to in the same folder that the other reference script was looking at!  Newbie!","Q_Score":0,"Tags":"python,dataframe,reference","A_Id":50935236,"CreationDate":"2018-06-19T18:36:00.000","Title":"dataframe from underlying script not updating","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I say to dataframes.\ndf_A has columns A__a, B__b, C. (shape 5,3)\ndf_B has columns A_a, B_b, D. (shape 4,3)\nHow can I unify them (without having to iterate over all columns) to get one df with columns A,B ? (shape 9,2) - meaning A__a and A_a should be unified to the same column.\nI need to use merge with applying the function lambda x: x.replace(\"_\",\"\"). Is it possible?","AnswerCount":4,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":200,"Q_Id":50941528,"Users Score":0,"Answer":"A simple concatenation will do \npd.concat([df_A, df_B], join='outer')[['A', 'B']].copy().\nor\n'pd.concat([df_A, df_B], join='inner')","Q_Score":0,"Tags":"python-3.x,pandas,dataframe,merge,concat","A_Id":50941849,"CreationDate":"2018-06-20T06:23:00.000","Title":"Pandas dataframe merge by function on column names","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I just updated my nvidia GPU driver and got this error when i import tensorflow like that:\nimport tensorflow as tf\nConfig:\nUbuntu 16.04\nNVIDIA Corporation GM204M [GeForce GTX 970M]\n16GB RAM\ni7 6700HQ\nPython 3.5.2\nGCC 5.4.0\nCuda 9.0.176\nTensorflow 1.8\nCudNN 7\nThis error had no result on Google ...\nMaybe i should downgrade any version like my GPU driver or update CudNN ?\nThanks for any help","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":798,"Q_Id":50955454,"Users Score":0,"Answer":"Running\nexport LD_LIBRARY_PATH=\/usr\/lib\/nvidia-396 fixed it\nnow i have another error","Q_Score":0,"Tags":"python,tensorflow","A_Id":50955970,"CreationDate":"2018-06-20T19:12:00.000","Title":"ImportError: libnvidia-fatbinaryloader.so.396.24.02: cannot open shared object file: No such file or directory","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Given a model that has to classify 10 table-cloth items (spoons, forks, cups, plate etc,) and must be tested on an image of a table with all the table-cloth items in it (test_model_accuracy,) which is the best approach for training:\n\nA: Train the model on individual items then test on test_model_accuracy \nB: Train the model on an entire table with bounding boxes then test on test_model_accuracy\nC: Start with A, then B or vice-versa, then test on test_model_accuracy","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":140,"Q_Id":50964401,"Users Score":1,"Answer":"The strategy that you will choose depends mainly on the structure of the CNN that you are going to create.\nIf you train a model that is able to recognize if an image contains a spoon or a fork, you will not be able to test on a table with several table-cloth items (e.g. both a fork and a spoon) because the network will try to answer if in the image there is a spoon or a fork indeed.\nAnyway, it is still possible to train the network to classify several features (strategy \"A\") but in that case you need a model that is able to do Multi-label classification.\nFinally, I would suggest to go for the \"B\" strategy because, in my humble opinion, it fits good the application domain.\nHope this answer is clear and helpful!\nCheers.","Q_Score":2,"Tags":"python,machine-learning,classification,conv-neural-network","A_Id":50964829,"CreationDate":"2018-06-21T08:56:00.000","Title":"Image classification: Best approach to training the model","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In order to do proper CV it is advisable to use pipelines so that same transformations can be applied to each fold in the CV. I can define custom  transformations by using either sklearn.preprocessing.FunctionTrasformer or by subclassing sklearn.base.TransformerMixin. Which one is the recommended approach? Why?","AnswerCount":2,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":10493,"Q_Id":50965004,"Users Score":11,"Answer":"The key difference between FunctionTransformer and a subclass of TransformerMixin is that with the latter, you have the possibility that your custom transformer can learn by applying the fit method.\nE.g. the StandardScaler learns the means and standard deviations of the columns during the fit method, and in the transform method these attributes are used for transformation. This cannot be achieved by a simple FunctionTransformer, at least not in a canonical way as you have to pass the train set somehow.\nThis possibility to learn is in fact the reason to use custom transformers and pipelines - if you just apply an ordinary function by the usage of a FunctionTransformer, nothing is gained in the cross validation process. It makes no difference whether you transform before the cross validation once or in each step of the cross validation (except that the latter will take more time).","Q_Score":14,"Tags":"python,machine-learning,scikit-learn,cross-validation","A_Id":62225211,"CreationDate":"2018-06-21T09:24:00.000","Title":"Sklearn custom transformers: difference between using FunctionTransformer and subclassing TransformerMixin","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I know that question is really simple, but I didn't find how to bypass the issue:\nI'm processing images, the output pixels are float32, and values are in range [-1; 1]. The thing is, when saving using openCV, all negative data and float values are lost (I only get images with 0 or 1 values)\nSo I need to convert those images to [0; 255] (Int8)\nI've tried\n\nimg * 255, but doing this does not help with negative values. \n(img + 1) * 255, I'm removing the negative values, but I'm creating\nan overflow\n\nIs there a (clean) way to do it ?\nI'm using Python35, OpenCV2 and Numpy, but I think it's more a math problem than a library thing","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":30202,"Q_Id":50966204,"Users Score":12,"Answer":"As you have found, img * 255 gives you a resulting range of  [-255:255], and (img + 1) * 255 gives you a result of [0:510]. You're on the right track.\nWhat you need is either: int((img + 1) * 255 \/ 2) or round((img + 1) * 255 \/ 2). This shifts the input from [-1:1] to [0:2] then multiplies by 127.5 to get [0.0:255.0]. \nUsing int() will actually result in [0:254]","Q_Score":11,"Tags":"python,numpy,opencv","A_Id":50966711,"CreationDate":"2018-06-21T10:25:00.000","Title":"convert images from [-1; 1] to [0; 255]","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to get the maximum (2 or more) indexes set to 1 from the output of tf.nn.softmax(). \ngiven tf.nn.softmax's  outputs as [0.1, 0.4, 0.2, 0.1, 0.8] I want to get something like [0,1,0,0,1] since those indexes have the maximum numbers (in this case I chose just the maximum 2). Thank you in advance!","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":445,"Q_Id":50973687,"Users Score":0,"Answer":"tf.nn.softmax forces everything to add up to 1.0 to make a valid probability distribution. If you want multiple values in the vector to be ones then you should use tf.nn.sigmoid instead. \nIf you want to retrieve the maximum numbers in the vector use tf.nn.top_k.","Q_Score":0,"Tags":"python,tensorflow,softmax","A_Id":50974355,"CreationDate":"2018-06-21T16:43:00.000","Title":"How to get two or more maximum indexes values set to 1 from tf.softmax's output","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am creating scatterplots of data with integer targets.  Naturally, I represent the targets as color in the scatterplot.  However, sometimes my models, because of the nature of the model, predict targets that are not in the original set.  I.e., my training targets are chosen from [0,1,2], and my model occasionally predicts 3, because it is not very bright.\nThe problem is that when I scatterplot my data, and then separately scatterplot the predictions, the target 2 gets mapped to a different color in each scatter, which makes for a bad picture.  This is because matplotlib by default scales the values in my color list to be between some given values.  I would like to override this default behavior, and have my color list (which are integers), always map to the same color, e.g. 1 maps to green, regardless of how many different classes are in my c=targets parameter.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":174,"Q_Id":50975047,"Users Score":1,"Answer":"Create a list of colors, say colors = ['blue', 'red', 'green', 'purple'], that has as many colors as you have different targets. Then, set c=colors[target] with target being the integer your model popped out. This means you will need to plot each point one at a time unless you sort all the targets and plot at the end.","Q_Score":0,"Tags":"python,matplotlib","A_Id":50975222,"CreationDate":"2018-06-21T18:06:00.000","Title":"matplotlib colormap without normalization","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Some of the standard matplotlib cmaps, such as viridis or jet show dark colors in small values. While this is what I need, I like them to show nothing, i.e. white background if the value is exactly zero. For non zero values the usual colors of that color map are fine.\nIs it possible to do this?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":4611,"Q_Id":50976177,"Users Score":2,"Answer":"You would rather mask zero out of your data, e.g. setting those values to nan or use a masked array. Then you can just set_bad(\"white\") for your colormap.","Q_Score":3,"Tags":"python,matplotlib,colormap","A_Id":50977040,"CreationDate":"2018-06-21T19:25:00.000","Title":"matplotlib modified color map with white as zero","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a whole bunch of code interacting with hdf files through h5py.  The code has been working for years. Recently, with a change in python environments, I am receiving this new error message.  \nIOError: Unable to open file (unable to lock file, errno = 11, error message = 'Resource temporarily unavailable')\nWhat is interesting is the error occurs intermittently in some places and persistently in others. In places where it is occuring routinely, I have looked at my code and confirm that there is no other h5py instance connected to the file and that the last connection was properly flushed and closed.  Again this was all working fine prior to the environment change.  \nHeres snippets from my conda environment:\nh5py                      2.8.0            py27h470a237_0    conda-forge\nhdf4                      4.2.13                        0    conda-forge\nhdf5                      1.10.1                        2    conda-forge","AnswerCount":5,"Available Count":4,"Score":0.0,"is_accepted":false,"ViewCount":7784,"Q_Id":50977839,"Users Score":0,"Answer":"with h5py.File(), the same .h5 file can be open for read (\"r\") multiple times. But h5py doesn't support more than a single thread. You can experience bad data with multiple concurrent readers.","Q_Score":3,"Tags":"python-2.7,hdf5,h5py,hdf","A_Id":57412703,"CreationDate":"2018-06-21T21:46:00.000","Title":"Locking of HDF files using h5py","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a whole bunch of code interacting with hdf files through h5py.  The code has been working for years. Recently, with a change in python environments, I am receiving this new error message.  \nIOError: Unable to open file (unable to lock file, errno = 11, error message = 'Resource temporarily unavailable')\nWhat is interesting is the error occurs intermittently in some places and persistently in others. In places where it is occuring routinely, I have looked at my code and confirm that there is no other h5py instance connected to the file and that the last connection was properly flushed and closed.  Again this was all working fine prior to the environment change.  \nHeres snippets from my conda environment:\nh5py                      2.8.0            py27h470a237_0    conda-forge\nhdf4                      4.2.13                        0    conda-forge\nhdf5                      1.10.1                        2    conda-forge","AnswerCount":5,"Available Count":4,"Score":0.0,"is_accepted":false,"ViewCount":7784,"Q_Id":50977839,"Users Score":0,"Answer":"Similar as with the other answers I had already opened the file, but for me it was in a separate HDF5 viewer.","Q_Score":3,"Tags":"python-2.7,hdf5,h5py,hdf","A_Id":71661095,"CreationDate":"2018-06-21T21:46:00.000","Title":"Locking of HDF files using h5py","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a whole bunch of code interacting with hdf files through h5py.  The code has been working for years. Recently, with a change in python environments, I am receiving this new error message.  \nIOError: Unable to open file (unable to lock file, errno = 11, error message = 'Resource temporarily unavailable')\nWhat is interesting is the error occurs intermittently in some places and persistently in others. In places where it is occuring routinely, I have looked at my code and confirm that there is no other h5py instance connected to the file and that the last connection was properly flushed and closed.  Again this was all working fine prior to the environment change.  \nHeres snippets from my conda environment:\nh5py                      2.8.0            py27h470a237_0    conda-forge\nhdf4                      4.2.13                        0    conda-forge\nhdf5                      1.10.1                        2    conda-forge","AnswerCount":5,"Available Count":4,"Score":0.0798297691,"is_accepted":false,"ViewCount":7784,"Q_Id":50977839,"Users Score":2,"Answer":"My issue! Failed to close the file in an obscure method.  Interesting thing is that unlocking the file in some cases just took a restart of ipython, other times took a full reboot.","Q_Score":3,"Tags":"python-2.7,hdf5,h5py,hdf","A_Id":51071193,"CreationDate":"2018-06-21T21:46:00.000","Title":"Locking of HDF files using h5py","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a whole bunch of code interacting with hdf files through h5py.  The code has been working for years. Recently, with a change in python environments, I am receiving this new error message.  \nIOError: Unable to open file (unable to lock file, errno = 11, error message = 'Resource temporarily unavailable')\nWhat is interesting is the error occurs intermittently in some places and persistently in others. In places where it is occuring routinely, I have looked at my code and confirm that there is no other h5py instance connected to the file and that the last connection was properly flushed and closed.  Again this was all working fine prior to the environment change.  \nHeres snippets from my conda environment:\nh5py                      2.8.0            py27h470a237_0    conda-forge\nhdf4                      4.2.13                        0    conda-forge\nhdf5                      1.10.1                        2    conda-forge","AnswerCount":5,"Available Count":4,"Score":0.0399786803,"is_accepted":false,"ViewCount":7784,"Q_Id":50977839,"Users Score":1,"Answer":"I had other process running that I did not realize. How did I solved my problem:\n\nUsed ps aux | grep myapp.py to find the process number that was running myapp.py.\nKill the process usill the kill command\nRun again","Q_Score":3,"Tags":"python-2.7,hdf5,h5py,hdf","A_Id":65687853,"CreationDate":"2018-06-21T21:46:00.000","Title":"Locking of HDF files using h5py","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to cluster Object A with Object B or Object C. But value of Cosine Similarity Object A with Object B is 0 and Cosine Similarity Object A with Object C is 0. Before it directly clustered, I need to cluster those object step by stem, which one should be combined first Object A with B or Object A with C?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":18,"Q_Id":50980199,"Users Score":0,"Answer":"With Cosine similarity, you'll probably want to stop at 0...\nBut of course the problem of ties can arise with any distance function, too.\nBut there obviously is no mathematical answer. They are all equally good. Usually, one hopes that the order does not matter. For a it doesn't, but for all other it does. Don't forget that HAC cannot guarantee to find the best solution (except for single link).\nSo just choose any, or even all at once. It's fairly common to choose the first found. This allows getting different versions by shuffling the data.","Q_Score":0,"Tags":"python-2.7,cluster-analysis,hierarchical-clustering,cosine-similarity","A_Id":50995396,"CreationDate":"2018-06-22T03:23:00.000","Title":"How to cluster some object in HAC but they have same value of Cosine Similarity","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have been successfully using PyCharm for my python work.All the packages can be easily installed by going to settings and then project interpreter but tensorflow installation is showing error.In suggestions it asked me to upgrade pip module.But even after that it shows error with following message: \n\"   Could not find a version that satisfies the requirement tensorflow (from versions: ) No matching distribution found for tensorflow\"\nI am able to install all other packages but error is with tensorflow only.I am using windows.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":182,"Q_Id":50993189,"Users Score":0,"Answer":"Please run pip install tensorflow in command line and post the output here. Tensorflow can be installed on Windows but the process is often annoying.","Q_Score":0,"Tags":"python-3.x,tensorflow,pycharm","A_Id":51001967,"CreationDate":"2018-06-22T17:44:00.000","Title":"Tensor flow package installation in PyCharm","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have been successfully using PyCharm for my python work.All the packages can be easily installed by going to settings and then project interpreter but tensorflow installation is showing error.In suggestions it asked me to upgrade pip module.But even after that it shows error with following message: \n\"   Could not find a version that satisfies the requirement tensorflow (from versions: ) No matching distribution found for tensorflow\"\nI am able to install all other packages but error is with tensorflow only.I am using windows.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":182,"Q_Id":50993189,"Users Score":0,"Answer":"You could also tried anaconda. It is has very nice UI and you could switch between different version.","Q_Score":0,"Tags":"python-3.x,tensorflow,pycharm","A_Id":51021687,"CreationDate":"2018-06-22T17:44:00.000","Title":"Tensor flow package installation in PyCharm","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Quite simple, \nIf I perform t-SNE in Python for high-dimensional data then I get 2 or 3 coordinates that reflect each new point. \nBut how do I map these to the original IDs? \nOne way that I can think of is if the indices are kept fixed the entire time, then I can do: \n\nPick a point in t-SNE\nSee what row it was in t-SNE (e.g. index 7)\nGo to original data and pick out row\/index 7.\n\nHowever, I don't know how to check if this actually works. My data is super high-dimensional and it is very hard to make sense of it with a normal \"sanity check\".\nThanks a lot!\nBest,","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":660,"Q_Id":50993934,"Users Score":1,"Answer":"If you are using sklearn's t-SNE, then your assumption is correct. The ordering of the inputs match the ordering of the outputs. So if you do y=TSNE(n_components=n).fit_transform(x) then y and x will be in the same order so y[7] will be the embedding of x[7]. You can trust scikit-learn that this will be the case.","Q_Score":3,"Tags":"python,mapping","A_Id":50994909,"CreationDate":"2018-06-22T18:41:00.000","Title":"Getting IDs from t-SNE plot?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am running Tensorflow 1.5.0 on a docker container because i need to use a version that doesn't use the AVX bytecodes because the hardware i am running on is too old to support it.\nI finally got tensorflow-gpu to import correctly (after downgrading the docker image to tf 1.5.0) but now when i run any code to detect the GPU it says the GPU is not there.\nI looked at the docker log and Jupyter is spitting out this message\nIgnoring visible gpu device (device: 0, name: GeForce GTX 760, pci bus id: 0000:01:00.0, compute capability: 3.0) with Cuda compute capability 3.0. The minimum required Cuda capability is 3.5.\nThe tensorflow website says that GPUs with compute capability of 3.0 is supported so why does it says it needs compute capability 3.5?\nIs there any way to get a docker image for tensorflow and jupyter that uses tf 1.5.0 but supports GPUs with compute capability?","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":9306,"Q_Id":50995707,"Users Score":1,"Answer":"I just spent a day trying to build this thing from source and what worked for me finally is quite surprising: the pre-built wheel for TF 1.5.0 does not complain about this anymore, while pre-built wheel for TF 1.14.0 does complain. It seems you have used the same version, so it's quite interesting, but I thought I would share, so if anyone struggles with this, there seems to be an easy way out.\nConfigs:\n\nVisual Studio version: 2017\nCuda compute capabilitz: 3.0\nGPU: two Geforce GPU 755M\nOS: Windows 10\nPython: 3.6.8\nCuda Toolkit: 9.0\nCuDNN: 7.0 (the earliest available is needed from, but it will complain anyway)","Q_Score":4,"Tags":"python,docker,tensorflow","A_Id":56987465,"CreationDate":"2018-06-22T21:16:00.000","Title":"Ignoring visible gpu device with compute capability 3.0. The minimum required Cuda capability is 3.5","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I had installed opencv in python 3.6 32 bit version using the command 'pip install opencv-python', which I successfully used. Later when I upgraded my version to 64 bit as to use tensorflow as well, and ran the same command 'pip install opencv-python', opencv was already present, yet when I tried to import cv2 module, it showed the error that any module named cv2 is not found.\nNow I don't understand what to do next. Hope someone can help.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":433,"Q_Id":51001356,"Users Score":0,"Answer":"You may uninstall previous opencv installation via pip uninstall opencv-python then do the pip install opencv-python.","Q_Score":0,"Tags":"python,opencv","A_Id":51002024,"CreationDate":"2018-06-23T12:54:00.000","Title":"Unable to import cv2 in 64 bit version of python 3.6.5","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I just installed Canopy because I had some issues running code in Jupyter Notebook.\nI have an Anaconda distribution installed.\nI installed OpenCV through anaconda and can easily import cv2 in Jupyter Notebook.\nHowever, when I import cv2 in Canopy IDE it says \"No module named cv2\".\nHow can I safely fix this?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":327,"Q_Id":51006130,"Users Score":1,"Answer":"Each Python environment is independent. Installing a package into an anaconda Python environment does not install it into a Canopy Python environment (nor into a different anaconda Python environment). This is a feature, not a bug; it allows different Python environments to be configured differently, even incompatibly.\nTo use OpenCV in Canopy User Python environment, first install it using the Canopy Package Manager.","Q_Score":0,"Tags":"python,jupyter,canopy","A_Id":51012003,"CreationDate":"2018-06-24T00:45:00.000","Title":"importing library Jupyter Notebook vs Canopy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have many numpy arrays of shape (Ni,227,227,3), where Ni of each array is different.\nI want to join them and make array of shape (N1+N2+..+Nk,227,227,3) where k is the number of arrays.\nI tried numpy.concatenate and numpy.append but they ask for same dimension in axis 0. I am also confused on what is axis 1 and axis 2 in my arrays.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1321,"Q_Id":51017203,"Users Score":0,"Answer":"So, the main problem here was with the one of the arrays of shape (0,) instead of (0,227,227,3). \nnp.concatenate(alist,axis=0) works.","Q_Score":0,"Tags":"python,numpy,concatenation","A_Id":51192080,"CreationDate":"2018-06-25T06:08:00.000","Title":"numpy concatenate multiple arrays arrays","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an empty data frame with about 120 columns, I want to fill it using data I have in a file.\nI'm iterating over a file that has about 1.8 million lines.\n(The lines are unstructured, I can't load them to a dataframe directly)\nFor each line in the file I do the following:\n\nExtract the data I need from the current line\nCopy the last row in the data frame and append it to the end df = df.append(df.iloc[-1]). The copy is critical, most of the data in the previous row won't be changed.\nChange several values in the last row according to the data I've extracted df.iloc[-1, df.columns.get_loc('column_name')] = some_extracted_value\n\nThis is very slow, I assume the fault is in the append.\nWhat is the correct approach to speed things up ? preallocate the dataframe ? \nEDIT:\nAfter reading the answers I did the following:\n\nI preallocated the dataframe (saved like 10% of the time)\nI replaced this : df = df.append(df.iloc[-1]) with this : df.iloc[i] = df.iloc[i-1] (i is the current iteration in the loop).(save like 10% of the time).\nDid profiling, even though I removed the append the main issue is copying the previous line, meaning : df.iloc[i] = df.iloc[i-1] takes about 95% of the time.","AnswerCount":4,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":88,"Q_Id":51018628,"Users Score":0,"Answer":"The Fastest way would be load to dataframe directly via pd.read_csv()\nTry separating the logic to clean out unstructured to structured data and then use pd.read_csv to load the dataframe.\nYou can share the sample unstructured line and logic to take out the structured data, So that might share some insights on the same.","Q_Score":0,"Tags":"python,pandas,dataframe","A_Id":51018770,"CreationDate":"2018-06-25T07:56:00.000","Title":"How to add rows to pandas dataframe with reasonable performance","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an empty data frame with about 120 columns, I want to fill it using data I have in a file.\nI'm iterating over a file that has about 1.8 million lines.\n(The lines are unstructured, I can't load them to a dataframe directly)\nFor each line in the file I do the following:\n\nExtract the data I need from the current line\nCopy the last row in the data frame and append it to the end df = df.append(df.iloc[-1]). The copy is critical, most of the data in the previous row won't be changed.\nChange several values in the last row according to the data I've extracted df.iloc[-1, df.columns.get_loc('column_name')] = some_extracted_value\n\nThis is very slow, I assume the fault is in the append.\nWhat is the correct approach to speed things up ? preallocate the dataframe ? \nEDIT:\nAfter reading the answers I did the following:\n\nI preallocated the dataframe (saved like 10% of the time)\nI replaced this : df = df.append(df.iloc[-1]) with this : df.iloc[i] = df.iloc[i-1] (i is the current iteration in the loop).(save like 10% of the time).\nDid profiling, even though I removed the append the main issue is copying the previous line, meaning : df.iloc[i] = df.iloc[i-1] takes about 95% of the time.","AnswerCount":4,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":88,"Q_Id":51018628,"Users Score":0,"Answer":"Where you use append you end up copying the dataframe which is inefficient. Try this whole thing again but avoiding this line: \ndf = df.append(df.iloc[-1]) \nYou could do something like this to copy the last row to a new row (only do this if the last row contains information that you want in the new row): \ndf.iloc[...calculate the next available index...] = df.iloc[-1]\nThen edit the last row accordingly as you have done \ndf.iloc[-1, df.columns.get_loc('column_name')] = some_extracted_value","Q_Score":0,"Tags":"python,pandas,dataframe","A_Id":51018824,"CreationDate":"2018-06-25T07:56:00.000","Title":"How to add rows to pandas dataframe with reasonable performance","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've spent a good deal of time trying to find out what these \"subsample\", \"colsample_by_tree\", and \"colsample_bylevel\" actually did in XGBClassifier() but I can't exactly find out what they do. Can someone please explain briefly what it is they do?\nThanks!","AnswerCount":3,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":13307,"Q_Id":51022822,"Users Score":14,"Answer":"The idea of \"subsample\", \"colsample_by_tree\", and \"colsample_bylevel\" comes from Random Forests. \nIn it, you build an ensemble of many trees and then group them together when making a prediction. \nThe \"random\" part happens through random sampling of the training samples for each tree (bootstrapping), and building each tree (actually each tree's node) only considering a random subset of the attributes.\nIn other words, for each tree in a random forest you:\n\nSelect a random sample from the dataset to train this tree;\nFor each node of this tree, use a random subset of the features.\nThis avoids overfitting and decorrelates the trees.\n\nSimilarly to random forests, XGB is an ensemble of weak models that when put together give robust and accurate results.\nThe weak models can be decision trees, which can be randomized in the same way as random forests.\nIn this case:\n\n\"subsample\" is the fraction of the training samples (randomly selected) that will be used to train each tree.\n\"colsample_by_tree\" is the fraction of features (randomly selected) that will be used to train each tree.\n\"colsample_bylevel\" is the fraction of features (randomly selected) that will be used in each node to train each tree.","Q_Score":8,"Tags":"python-3.x,xgboost","A_Id":56691091,"CreationDate":"2018-06-25T11:50:00.000","Title":"subsample, colsample_bytree, colsample_bylevel in XGBClassifier() Python 3.x","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Please suggest a way to execute SQL statement and pandas dataframe .to_sql() in one transaction\nI have the dataframe and want to delete some rows on the database side before insertion\nSo basically I need to delete and then insert in one transaction using .to_sql of dataframe\nI use sqlalchemy engine with pandas.df.to_sql()","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1173,"Q_Id":51023642,"Users Score":0,"Answer":"After further investigation I realized that it is possible to do only with sqllite3, because to_sql supports both sqlalchemy engine and plain connection object as conn parameter, but as a connection it is supported only for sqllite3 database\nIn other words you have no influence on connection which will be created by to_sql function of dataframe","Q_Score":1,"Tags":"python,pandas,dataframe,transactions,sqlalchemy","A_Id":51035810,"CreationDate":"2018-06-25T12:36:00.000","Title":"Python pandas dataframe transaction","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When talking about imbalanced datasets, most articles would refer to maximising the prediction of the minority class (e.g. for fraud detection). I have an imbalanced dataset (ratio approximately 1:20). where I am interested to achieve the highest prediction accuracy for the majority class. My work is in Python. Possible solutions I have researched are:\n\nOversampling of the minority class\nChanging the loss\/cost matrix for some classification models\n\nWhat are the pros and cons of using each method? Are there any other methods I could try?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":129,"Q_Id":51025178,"Users Score":0,"Answer":"You are thinking about this the wrong way. If all you cared about was the majority class, you could just predict everything as belonging to the majority class. You'd get 100% of them right. You would have lots of false positives, but you don't care about those right? \nAh, if you do care about the false positives, then that means you actually care about the minority class after all. The more things in the minority class you predict correctly, the less false positives you have. \nThis is two sides of the same coin.","Q_Score":0,"Tags":"python,optimization,classification,data-science","A_Id":51275886,"CreationDate":"2018-06-25T13:53:00.000","Title":"Maximising prediction accuracy of the majority class in an imbalanced dataset","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I recently found an article that indicates that the conventional methods for downloading python machine learning modules such as tensorflow and keras are not optimized for computers with a cpu.  How can I configure tensorflow and keras to make it most compatible with my processor on MacOSX in python 2.7?  \nIf it helps, I use pycharm to download most of my libraries and for my coding interface.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":784,"Q_Id":51026983,"Users Score":0,"Answer":"I think what you read meant that tensorflow programs work much faster if your computer has a GPU. You need a Nvidia GPU in your computer to install tensorflow with GPU support on your Mac and as far as I know, after version 1.2 tensorflow no longer provides GPU support for MacOS","Q_Score":1,"Tags":"python,macos,python-2.7,tensorflow,cpu","A_Id":51027093,"CreationDate":"2018-06-25T15:25:00.000","Title":"Install Keras\/Tensorflow on Mac with cpu python2.7","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using dlib's correaltion tracker and would like to restart it on some que. When \nI pass None as the image it crashes. How can I tell the tracker a new video is starting?\nI'm using multiple thread and would not like to open a new tracker every time. \nThank you!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":213,"Q_Id":51037016,"Users Score":0,"Answer":"Call the correlation_tracker's start_track() member function.","Q_Score":0,"Tags":"python,tracking,dlib","A_Id":51041372,"CreationDate":"2018-06-26T07:17:00.000","Title":"How to restart dlib's correlation tracker","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"If the dataset contains features some of which are Categorical Variables and some of the others are continuous variable Decision Tree is better than Linear Regression,since Trees can accurately divide the data based on Categorical Variables. Is there any situation where Linear regression outperforms Random Forest?","AnswerCount":2,"Available Count":2,"Score":0.2913126125,"is_accepted":false,"ViewCount":9961,"Q_Id":51037363,"Users Score":3,"Answer":"There for sure have to be situations where Linear Regression outperforms Random Forests, but I think the more important thing to consider is the complexity of the model.\nLinear Models have very few parameters, Random Forests a lot more. That means that Random Forests will overfit more easily than a Linear Regression.","Q_Score":5,"Tags":"python,data-science","A_Id":51039032,"CreationDate":"2018-06-26T07:35:00.000","Title":"Linear Regression vs Random Forest performance accuracy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"If the dataset contains features some of which are Categorical Variables and some of the others are continuous variable Decision Tree is better than Linear Regression,since Trees can accurately divide the data based on Categorical Variables. Is there any situation where Linear regression outperforms Random Forest?","AnswerCount":2,"Available Count":2,"Score":0.1973753202,"is_accepted":false,"ViewCount":9961,"Q_Id":51037363,"Users Score":2,"Answer":"Key advantages of linear models over tree-based ones are:\n\nthey can extrapolate (e.g. if labels are between 1-5 in train set, tree based model will never predict 10, but linear will)\ncould be used for anomaly detection because of extrapolation\ninterpretability (yes, tree based models have feature importance, but it's only a proxy, weights in linear model are better)\nneed less data to get good results\nhave strong online learning implementations (Vowpal Wabbit), which is crucial to work with giant datasets with a lot of features (e.g. texts)","Q_Score":5,"Tags":"python,data-science","A_Id":51062800,"CreationDate":"2018-06-26T07:35:00.000","Title":"Linear Regression vs Random Forest performance accuracy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to figure out the minimum amount of room I will need to train neural networks on my machine. Often times (image) data sets are relatively small in their raw forms, but when we transform them (in keras w\/ flow_from_dir) we augment the images and kind of multiply the size of the data set to our desire. \nMy question is: how does flow_from_directory work with storing the augmented images? If I don't specify that the images need to be stored (parameter of the class) does keras augment the image, train with it and discard it or does it save it for a period of time, weighing down my computer until training is over?\nAlso, would those images be in persistent memory or RAM? Thank you in  advance.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":369,"Q_Id":51048421,"Users Score":0,"Answer":"By default, ImageDataGenerator does data augmentation on the fly and does not store the augmented images anywhere. As you mention, doing so would require too much space. So you should only worry about having enough RAM to fit a certain number of augmented batches, not the whole dataset.","Q_Score":0,"Tags":"python-3.x,tensorflow,machine-learning,neural-network,keras","A_Id":51051880,"CreationDate":"2018-06-26T17:21:00.000","Title":"how does Keras flow_from_directory affect computer storage?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am building a machine learning model to forecast future prices in scikit-learn. The dependent variable price is not normally distributed, thus, I will perform log transformation on only dependent variable price using np.log(price).  After this, I will split complete data-set into train and test sets. Thus y_train and y_test both are now log transformed prices. After machine learning model fitting, I have to calculate forecasting performance metrics like MAPE error for the fitted model.\n\nShould the data (price) be transformed back to its original scale before calculating MAPE using np.exp() for both model.predict method and y_test set ?  \nOr we should first split the data into train and test, apply log transformation on training set y_train only, after this apply inverse transform on model.prediction set. Thus, y_test set (original) and np.exp(model.predict()) would then be used to calculate MAPE  \nOr the values of MAPE or MAE metric is independent of scaling of response variable y and MAPE can be reported using transformed log values of dependent variable price?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":514,"Q_Id":51057993,"Users Score":0,"Answer":"I would say that in your circumstance it is necessary to scale back to true prices. This is not an absolute statement, but really depends on the setup of your problem: if you have a true price that is \"1\", then its log will be \"0\" and, whatever you predict for that single point, you'll get undefined \/ infinite MAPE. So I'd say yes, at least scale it back to exp before doing it.\nAlso I don't understand the difference between \"1\" and \"2\": they seem identical to me, in \"1\" you're just taking the log of the price for the test set and then taking the exp again, in \"2\" you're just avoiding doing the two operations...\nAs for \"3\", no, definitely they aren't independent on all transformations - in particular not log. MAPE is only independent to rescaling data by a constant factor, MAE by shifting it by a constant addend.\nOn this point, beware that no measure gives a perfect truth and you might get very bad results just applying them. For example, using MAPE, if you have something with price of 1 cent and you're estimating it at 1$ you'll give it the same (huge) error as if it had price 1000$ and you're estimating it at 100k. On the other hand, since you're taking logs in training, that's basically the same rule you're using to train your model, so it might not be catastrophic. Just beware if you have true prices that are very close to, or worse exactly, 0.\n(MAE is probably worse in this case, because it will basically give all the weight to the few very expensive items in your database, but I can't say for sure from here)","Q_Score":2,"Tags":"python,logging,machine-learning,transformation,metrics","A_Id":51058681,"CreationDate":"2018-06-27T08:24:00.000","Title":"Evaluation of Forecasting performance Metric on original or transformed Dependent Variable","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to run a function written in matlab in a python script using matlab.engine.\nThe first time I run the script everything works fine, but when I try to run the script again I get the error \"ImportError: cannot import name 'TimeoutError'\" on importing the matlab engine. Restarting the kernel allows me to run the script again. I am also using import matlab.engine and not from matlab.engine import to avoid circular importing.\nAny suggestions on how I can solve the issue?\nI am using Ubuntu 16.04 and working with spyder.\nMany Thanks!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":429,"Q_Id":51064328,"Users Score":0,"Answer":"I get a similar running error. But after trying several times, I found that for the same *.py manuscript, the phrase import matlab.engine and eng = matlab.engine.start_matlab() should be implemented only once\nI commented them, by doing this I can re-run the script *.py again. \nOtherwise, it will post the error\n\nImportError: cannot import name 'TimeoutError'.\n\nI think using the import matlab.engine to start a new MATLAB\u00ae process is just like to open a door before we close the door, everyone can enter the room or go out again and again, but do not need open the door again because it is not closed yet. \n\"Restarting the kernel\" is just like close the door automatically (the Python stops the engine and its MATLAB process).","Q_Score":1,"Tags":"python,matlab,matlab-engine","A_Id":55392343,"CreationDate":"2018-06-27T13:40:00.000","Title":"TimeoutError import error when using matlab engine with python 3.5","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"In python, what is the best to run fft using cuda gpu computation?\nI am using pyfftw to accelerate the fftn, which is about 5x faster than numpy.fftn.\nI want to use pycuda to accelerate the fft. I know there is a library called pyculib, but I always failed to install it using conda install pyculib.\nIs there any suggestions?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":1084,"Q_Id":51066245,"Users Score":1,"Answer":"I want to use pycuda to accelerate the fft\n\nYou can't. PyCUDA has no built-in FFT support of any kind.","Q_Score":1,"Tags":"python,cuda,cufft","A_Id":51066356,"CreationDate":"2018-06-27T15:12:00.000","Title":"the best way to conduct fft using GPU accelaration with cuda","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"X: a list of lists, where each list element corresponds to a label in Y\nY: a binary list of labels (values are either 1 or 0)\nI want to extract the elements in X according to the value at the corresponding index in Y, as follows:\ngood = values of X where the label\/value in Y is 1\nbad = values of X where the label\/value in Y is 0\nI am still fairly new to sub-setting in Python and not really sure of a good way to do this.","AnswerCount":4,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":123,"Q_Id":51071467,"Users Score":0,"Answer":"list(itertools.compress(X, Y)) will get you the list of good lists. list(itertools.compress(X, [not a for a in Y])) will get you the list of bad lists.","Q_Score":0,"Tags":"python","A_Id":51071525,"CreationDate":"2018-06-27T21:11:00.000","Title":"Separate a list based on values in a second list","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataset where over 5 years, for each person, i have 3 stats (for, against, neutral) which are represented as percentage.\nDo you have any ideas on how to plot this over time for each person ?\nI tought of a pie chart for each year, is it good idea ?\nyear|x|y|z|uniq_key\n2011|0.005835365238989241|0.7761263149278178|0.21803831983319283|P1\n2012|0.009289549431275945|0.7361277645833669|0.2545826859853564|P1\n2013|0.008078032727124805|0.7392018809692821|0.2527200863035934|P1\n2011|0.0069655450457009405|0.7275518410866034|0.26548261386769734|P2\n2012|0.008719188605542267|0.7855644106925562|0.20571640070190217|P2\n2013|0.011938649525870312|0.8028230127919557|0.1852383376821743|P2","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":28,"Q_Id":51076577,"Users Score":0,"Answer":"A line chart for each stat with year on the x-axis, percentage on the y-axis and a line for each user.","Q_Score":0,"Tags":"python,matplotlib,time,statistics","A_Id":51078286,"CreationDate":"2018-06-28T07:01:00.000","Title":"how to plot different stats for every year","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Just out of curiosity, if dask enables both len() and size, why is there not shape as well?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":660,"Q_Id":51080681,"Users Score":2,"Answer":"This has been discussed in dask. First I'll point out that in the python spec, len() is always supposed to return a concrete integer. Dask respects this, and so len(df) blocks, unlike most operations on a data-frame. There is no such constraint on .size, which is therefore lazy.\nThe metadata of the dataframe is immediately available, however, the number, names and types of the columns are known without computing any of the data. Therefore, .shape would be a combination of a known value and either a lazy or a slowly-computed concrete value. This doesn't seem necessary.","Q_Score":0,"Tags":"python,dataframe,dask","A_Id":51085292,"CreationDate":"2018-06-28T10:33:00.000","Title":"Why doesn't Dask dataframe have a shape attribute?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using on-line data augmentation of images I feed into my Convolutional Neural Network. I am using the Keras ImageDataGenerator for this. The images are augmented in each batch and then the model is trained on these images.\nI am comparing different models, but since the images are augmented on the fly, is this really fair, since each models is getting slightly different images?","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":424,"Q_Id":51081439,"Users Score":2,"Answer":"If I understand you correct you are wondering whether the randomness caused by the data augmentation affects the result?\nThe randomness of the augmentation does not affect the result (at least not to a degree that makes a difference anyway) if you train long enough. The other options you have are (as I think about it):\n\nAugment your data deterministically applying the same transformation to your images before inserting them to your model. Those transformation could be (a) either random ones, e.g. rotate your images by a random degree between some limits, or (b) predetermined ones, e.g. rotate all your images by 1, 3 and 5 degrees.\nDon't augment your data at all. Use your initial data to train your model.\n\nThe effect of those choices are:\n\nThe number of transformation you would apply is limited and even if choice 1a is chosen would be predefined set. If you are willing to increase this dramatically other issues arise like where are you going to store all this data, how are you going to handle it during training etc. So, on the fly has the advantage that the storage of your data does not change, neither is the way you deal with your data. The disadvantage of course being a slower procedure is used (which depending on the transformation could make quite a difference).\nThis choice to be valid means that you have a lot of data. And by meaning a lot (depending on the problem of course) sometimes a lot is not enough. Since your are (probably) using different data for testing differences appear between your training and testing data in many aspects. For example for human detection (arbitrary choice) differences in poses, colors, light conditions, image clarity, image size, aspect ratio are common. How do you deal with that? You either collect a super huge collection of data or (probably) use data augmentation, right?\n\nTo sum it up, it's fair because on the long run it does not make a big difference. Consider the option of early stopping for your model for example. Is it fair to compare models that have stopped their training not in the best iteration? Well, it's not completely fair but it does not make a difference.","Q_Score":2,"Tags":"python,tensorflow,machine-learning,keras,convolutional-neural-network","A_Id":51082005,"CreationDate":"2018-06-28T11:11:00.000","Title":"Is the usage of on-line data augmentation a fair comparison between CNN models","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using on-line data augmentation of images I feed into my Convolutional Neural Network. I am using the Keras ImageDataGenerator for this. The images are augmented in each batch and then the model is trained on these images.\nI am comparing different models, but since the images are augmented on the fly, is this really fair, since each models is getting slightly different images?","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":424,"Q_Id":51081439,"Users Score":1,"Answer":"In my opinion, you already give part of the answer within your question:\n\nimages are augmented on the fly, is this really fair, since each models is getting slightly different images?\n\n\nFor Evaluation \/ Validation I usually try to provide situations as similar as possible over the different architectures - otherwise you might induce unnecessary bias you are not able to account for.\nAlso you could reduce computational effort by offline-Augmentation and then directly handing over the same augmented Training samples","Q_Score":2,"Tags":"python,tensorflow,machine-learning,keras,convolutional-neural-network","A_Id":51081966,"CreationDate":"2018-06-28T11:11:00.000","Title":"Is the usage of on-line data augmentation a fair comparison between CNN models","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am getting the following error when using import cv2:\nModuleNotFoundError: No module named 'cv2'\nMy version of Python is 3.6 64 bit. I have downloaded the whl file to install it via pip manually, and have also installed it with pip install opencv-python however I still get ModuleNotFoundError. \npip outputs Requirement already satisfied: opencv-python in c:\\...\\python36\\site-packages\nHelp would be much appreciated.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1891,"Q_Id":51082504,"Users Score":0,"Answer":"Use pip install -U opencv-python","Q_Score":0,"Tags":"python,module,pip","A_Id":51082709,"CreationDate":"2018-06-28T12:06:00.000","Title":"OpenCV install on Python 3.6: ModuleNotFoundError","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am getting the following error when using import cv2:\nModuleNotFoundError: No module named 'cv2'\nMy version of Python is 3.6 64 bit. I have downloaded the whl file to install it via pip manually, and have also installed it with pip install opencv-python however I still get ModuleNotFoundError. \npip outputs Requirement already satisfied: opencv-python in c:\\...\\python36\\site-packages\nHelp would be much appreciated.","AnswerCount":2,"Available Count":2,"Score":0.1973753202,"is_accepted":false,"ViewCount":1891,"Q_Id":51082504,"Users Score":2,"Answer":"Your download must have been corrupted. It happened to me too. Simply uninstall the package and use \nsudo apt-get install python open-cv","Q_Score":0,"Tags":"python,module,pip","A_Id":51082582,"CreationDate":"2018-06-28T12:06:00.000","Title":"OpenCV install on Python 3.6: ModuleNotFoundError","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have tried to run a script with pandas in python3, but it appears to have an terrible error I even don't understand. Note: my script is \"gh.py\" and has an error in line 1, i can't import pandas.\nFile \"gh.py\", line 1\n    from pandas as pd\n                 ^\nSyntaxError: invalid syntax","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":23,"Q_Id":51105431,"Users Score":0,"Answer":"Maybe you\u2019re using python2 instead of python3. Use the appropiate syntax to import pandas and also try \n\npip install pandas","Q_Score":2,"Tags":"python-3.x,pandas","A_Id":51105559,"CreationDate":"2018-06-29T15:50:00.000","Title":"Error by running script with python3","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a classification problem which requires some optimization, as my results are not quite adequate.\nI'm using the DNNClassifier for a huge dataset in order to classify items in 5 different classes (labels). I have over 2000 distinct items (in a hashbucket column with size 2000 and dims 6 - is this adequate?) and multiple numeric columns for said classification.\nMy problem is the following: the amount of items belonging in each class is very variable. Class 1 is very common, class 2 is common but classes 3 4 and 5 are highly uncommon (under 2% of the dataset) but they are the most interesting ones in my test case. Even if I tweak the network size\/number of neurons or the training epoch, I get close to no results in classes 3, 4 and 5, so class 1 and 2 are clearly overfitted.\nI saw the weight_column option in the documentation - would that be a good idea to change the learning weight of these three class to \"normalize\" the weight in each class ? Is there a more efficient way to get better results on rarer cases without losing the detection precision on the common classes?\nMany thanks!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":84,"Q_Id":51106402,"Users Score":0,"Answer":"What you said is considered one approach to solving this issue, as this prevents your computed gradient from being dominated by Classes 1 and 2. Depending on your data, it may be effective to add some Gaussian noise to the underrepresented samples to create similar samples. Another good idea in general to avoid overfitting is to apply a dropout if you have not already.","Q_Score":0,"Tags":"python,tensorflow,machine-learning,artificial-intelligence","A_Id":51106833,"CreationDate":"2018-06-29T16:57:00.000","Title":"Tensorflow 1.9 DNNClassifier unequal output labels optimization","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In pandas, how do I replace &amp; with '&' from all columns where &amp could be in any position in a string?\nFor example, in column Title if there is a value 'Good &amp; bad', how do I replace it with 'Good & bad'?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":11751,"Q_Id":51121170,"Users Score":0,"Answer":"Try this\ndf['Title'] = titanic_df['Title'].replace(\"&amp;\", \"&\")","Q_Score":5,"Tags":"python,python-3.x,pandas","A_Id":51121201,"CreationDate":"2018-07-01T07:10:00.000","Title":"How to replace all string in all columns using pandas?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I created one task, where I have white background and black digits.\nI need to take the largest by thickness digit. I have made my picture bw, recognized all symbols, but I don't  understand, how to scale thickness. I have tried arcLength(contours), but it gave me the largest by size. I have tried morphological operations, but as I undestood, it helps to remove noises and another mistakes in picture, right? And I had a thought to check the distance between neighbour points of contours, but then I thought that it would be hard because of not exact and clear form of symbols(I draw tnem on paint). So, that's all Ideas, that I had. Can you help me in this question by telling names of themes in Comp. vision and OpenCV, that could help me to solve this task? I don't need exact algorithm of solution, only themes. And if that's not OpenCV task, so  which is? What library? Should I learn some pack of themes and basics before the solution of my task?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":2238,"Q_Id":51133962,"Users Score":1,"Answer":"One possible solution that I can think of is to alternate erosion and find contours till you have only one contour left (that should be the thicker). This could work if the difference in thickness is enough, but I can also foresee many particular cases that can prevent a correct identification, so it depends very much on how is your original image.","Q_Score":2,"Tags":"python,image,opencv,computer-vision","A_Id":51134741,"CreationDate":"2018-07-02T10:22:00.000","Title":"How can i scale a thickness of a character in image using python OpenCV?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Regarding word2vec with gensim,\nSuppose you already trained a model on a big corpus, and you want to update it with new words from new sentences, but not update the words which already have a vector.\nIs it possible to freeze the vectors of some words and update only some chosen words (like the new words) when calling model.train ?\nOr maybe is there a trick to do it ?\nThanks.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":436,"Q_Id":51135118,"Users Score":1,"Answer":"There is! But it's an experimental feature with little documentation \u2013 you'd need to read the source to fully understand it, and directly mutate your model to make use of it.\nLook through the word2vec.py source for properties ending _lockf \u2013 specifically in the latest code, one named vectors_lockf. It's a sort of mask which either allows, weakens, or stops training of certain words. For each word, if it's value is 1.0, normal full backpropagated updates are applied. Any lower value weakens the update \u2013 so 0.0 freezes a word against updates. (The potential update is still calculated \u2013 so there's no net speedup \u2013 it's just multiplied-by-0.0 before final application to particular frozen words.)","Q_Score":0,"Tags":"python,word2vec,gensim","A_Id":51139393,"CreationDate":"2018-07-02T11:26:00.000","Title":"Gensim Word2vec Freeze some wordvectors and Update others","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"If I had a column in a dataframe, and that column contained two possible categorical variables, how do I count how many times each variable appeared? \nSo e.g, how do I count how many of the participants in the study were male or female?\nI've tried value_counts, groupby, len etc, but seem to be getting it wrong. \nThanks","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2617,"Q_Id":51140765,"Users Score":0,"Answer":"You could use len([x for x in df[\"Sex\"] if x == \"Male\"). This iterates through the Sex column of your dataframe and determines whether an element is \"Male\" or not. If it is, it is appended to a list via list comprehension. The length of that list is the number of Males in your dataframe.","Q_Score":0,"Tags":"python,python-3.x,pandas","A_Id":51140808,"CreationDate":"2018-07-02T17:04:00.000","Title":"Count Specific Values in Dataframe","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to Kafka. I am sending a request to REST server and send the response of the request to kafka server as messages.\nWhen i consume the data from consumer the message is split into multiple smaller messages. How do i avoid this. The response is a JSON row. I want each json row to be one message. Any help would be appreciated. The size of the Json is also not very big. A json with over 1500 rows is about 2 MB\nFor eg \n\nConsumerRecord(topic='meetup_sample', partition=0, offset=445386, >timestamp=1530554568191, timestamp_type=0, >key=b'5fa4964b035c072a81fedb93cfca8f0ecb562cf913c69f63efbbf4e799871f05', >value=b':\"food-and-drink\",\"topic_name\":\"Food and Drink\"},{\"urlkey\":\"newintown\",\"topic_name\":\"New In Town\"},{\"urlkey\":\"beer\",\"topic_name\"', >checksum=None, serialized_key_size=64, serialized_value_size=128)\n  \n  ConsumerRecord(topic='meetup_sample', partition=0, offset=445387, >timestamp=1530554568192, timestamp_type=0, >key=b'1f170ceabf91335d332487ebb0890f0bd0ed69018c618ecc2c789a260d561f43', >value=b':\"Beer\"},{\"urlkey\":\"game-night\",\"topic_name\":\"Game Night\"},{\"urlkey\":\"happy-hours\",\"topic_name\":\"Happy Hour\"},{\"urlkey\":\"water-s', >checksum=None, serialized_key_size=64, serialized_value_size=128)","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":556,"Q_Id":51141942,"Users Score":0,"Answer":"Once use fetch.message.max.bytes=2000000 in consumer side","Q_Score":0,"Tags":"python-3.x,apache-kafka,streaming","A_Id":71822982,"CreationDate":"2018-07-02T18:32:00.000","Title":"Kafka Messages are split in consumer","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I am facing error while importing Keras.\nBelow is the error trace:\nUsing TensorFlow backend.\nTraceback (most recent call last):\n  File \"recognize.py\", line 8, in <module>\n    import keras\n  File \"\/home\/pi\/.local\/lib\/python2.7\/site-packages\/keras\/__init__.py\", line 3, in <module>\n    from . import utils\n  File \"\/home\/pi\/.local\/lib\/python2.7\/site-packages\/keras\/utils\/__init__.py\", line 6, in <module>\n    from . import conv_utils\n  File \"\/home\/pi\/.local\/lib\/python2.7\/site-packages\/keras\/utils\/conv_utils.py\", line 9, in <module>\n    from .. import backend as K\n  File \"\/home\/pi\/.local\/lib\/python2.7\/site-packages\/keras\/backend\/__init__.py\", line 87, in <module>\n    from .tensorflow_backend import *\n  File \"\/home\/pi\/.local\/lib\/python2.7\/site-packages\/keras\/backend\/tensorflow_backend.py\", line 5, in <module>\n    import tensorflow as tf\n  File \"\/home\/pi\/.local\/lib\/python2.7\/site-packages\/tensorflow\/__init__.py\", line 24, in <module>\n    from tensorflow.python import pywrap_tensorflow  # pylint: disable=unused-import\n  File \"\/home\/pi\/.local\/lib\/python2.7\/site-packages\/tensorflow\/python\/__init__.py\", line 63, in <module>\n    from tensorflow.python.framework.framework_lib import *  # pylint: disable=redefined-builtin\n  File \"\/home\/pi\/.local\/lib\/python2.7\/site-packages\/tensorflow\/python\/framework\/framework_lib.py\", line 25, in <module>\n    from tensorflow.python.framework.ops import Graph\n  File \"\/home\/pi\/.local\/lib\/python2.7\/site-packages\/tensorflow\/python\/framework\/ops.py\", line 54, in <module>\n    from tensorflow.python.platform import app\n  File \"\/home\/pi\/.local\/lib\/python2.7\/site-packages\/tensorflow\/python\/platform\/app.py\", line 24, in <module>\n    from tensorflow.python.platform import flags\n  File \"\/home\/pi\/.local\/lib\/python2.7\/site-packages\/tensorflow\/python\/platform\/flags.py\", line 33, in <module>\n    disclaim_key_flags()  # pylint: disable=undefined-variable\nNameError: name 'disclaim_key_flags' is not defined\n\nThis was found in RaspberryPi 3 Model B. OS: Raspbian Strech","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":588,"Q_Id":51142979,"Users Score":1,"Answer":"pip install --upgrade pip setuptools  work for me.","Q_Score":1,"Tags":"python,tensorflow,keras","A_Id":52422681,"CreationDate":"2018-07-02T19:59:00.000","Title":"Error while importing Keras","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"What is the difference between tensor.permute() and tensor.view()? \nThey seem to do the same thing.","AnswerCount":4,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":25972,"Q_Id":51143206,"Users Score":2,"Answer":"tensor.permute() permutes the order of the axes of a tensor.\ntensor.view() reshapes the tensor (analogous to numpy.reshape) by reducing\/expanding the size of each dimension (if one increases, the others must decrease).","Q_Score":27,"Tags":"python,multidimensional-array,deep-learning,pytorch,tensor","A_Id":66550315,"CreationDate":"2018-07-02T20:18:00.000","Title":"Difference between tensor.permute and tensor.view in PyTorch?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to know the difference between importing sklearn.model_estimation and sklearn.cross_validation when I run Python code for linear regression.\nI found out that sklearn.model_estimation calls a method called next(ShuffleSplit().split(X, y)) and sklearn.cross_validation calls a method called next(iter(ShuffleSplit(n_samples))) but I am still in darkness as to what is the difference between what these two methods actually perform. \nLooking for help.\nThanks in advance.","AnswerCount":1,"Available Count":1,"Score":0.537049567,"is_accepted":false,"ViewCount":819,"Q_Id":51149995,"Users Score":3,"Answer":"cross_validation is an older package used previously in scikit. model_selection is newer replacement of the cross_validation (and some others too). It has some structural changes in the classes defined in it. \nSo same class which was previously in cross_validation is now present in model_selection but with changed behaviour (input params, output type, attributes etc).\nSo you should always use classes from model_selection.","Q_Score":0,"Tags":"python,machine-learning,scikit-learn","A_Id":51153467,"CreationDate":"2018-07-03T08:31:00.000","Title":"What is the difference between sklearn.cross_validation and sklearn.model_estimation?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have 6 dimension tables, all in the form of csv files. I have to form a star schema using Python. I'm not sure how to create the fact table using Python. The fact table (theoretically) has at least one column that is common with a dimension table. \nHow can I create the fact table, keeping in mind that quantities from multiple dimension tables should correspond correctly in the fact table?\nI am not allowed to reveal the code or exact data, but I'll add a small example. File 1 contains the following columns: student_id, student_name. File 2 contains : student_id, department_id, department_name, sem_id. Lastly File 3 contains student_id, subject_code, subject_score. The 3 dimension tables are in the form of csv files. I now need the fact table to contain: student_id, student_name, department_id, subject_code. How can I form the fact table in that form? Thank you for your help.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1539,"Q_Id":51151263,"Users Score":0,"Answer":"Reading certain blogs look like it is not a good way to handle such cases in python in memory but still if the below post make sense you cn use it\nFact Loading\nThe first step in DW loading is dimensional conformance. With a little cleverness the above processing can all be done in parallel, hogging a lot of CPU time. To do this in parallel, each conformance algorithm forms part of a large OS-level pipeline. The source file must be reformatted to leave empty columns for each dimension's FK reference. Each conformance process reads in the source file and writes out the same format file with one dimension FK filled in. If all of these conformance algorithms form a simple OS pipe, they all run in parallel. It looks something like this.\nsrc2cvs source | conform1 | conform2 | conform3 | load\nAt the end, you use the RDBMS's bulk loader (or write your own in Python, it's easy) to pick the actual fact values and the dimension FK's out of the source records that are fully populated with all dimension FK's and load these into the fact table.","Q_Score":0,"Tags":"python,csv,star-schema","A_Id":51152822,"CreationDate":"2018-07-03T09:37:00.000","Title":"Creating star schema from csv files using Python","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to Dask library.I wanted to know if we implement parallel computation using dask on two systems ,then is the data frame on which we apply the computation stored on both the systems ? How actually does the parallel computation takes place,it is not clear from the documentation.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":52,"Q_Id":51155513,"Users Score":0,"Answer":"Dask dataframes are chunked, so in general you have one big dataframe made up of smaller dataframes spread across your cluster.  Computations apply to each chunk individually with shuffling of results where required (such as groupby, sum and other aggregate tasks).","Q_Score":0,"Tags":"python-2.7,parallel-processing,dask,dask-distributed","A_Id":51211614,"CreationDate":"2018-07-03T13:11:00.000","Title":"How is dask implemented on multiple systems?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"We receive a .tar.gz file from a client every day and I am rewriting our import process using SSIS. One of the first steps in my process is to unzip the .tar.gz file which I achieve via a Python script.\nAfter unzipping we are left with a number of CSV files which I then import into SQL Server. As an aside, I am loading using the CozyRoc DataFlow Task Plus.\nMost of my CSV files load without issue but I have five files which fail. By reading the log I can see that the process is reading the Header and First line as though there is no HeaderRow Delimiter (i.e. it is trying to import the column header as ColumnHeader1ColumnValue1\nI took one of these CSVs, copied the top 5 rows into Excel, used Text-To-Columns to delimit the data then saved that as a new CSV file.\nThis version imported successfully.\nThat makes me think that somehow the original CSV isn't using {CR}{LF} as the row delimiter but I don't know how to check. Any suggestions?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2081,"Q_Id":51160071,"Users Score":0,"Answer":"Seeing that you have EmEditor, you can use EmEditor to find the eol character in two ways:\n\nUse View > Character Code Value... at the end of a line to display a dialog box showing information about the character at the current position.\nGo to View > Marks and turn on Newline Characters and CR and LF with Different Marks to show the eol while editing. LF is displayed with a down arrow while CRLF is a right angle.\n\nSome other things you could try checking for are: file encoding, wrong type of data for a field and an inconsistent number of columns.","Q_Score":2,"Tags":"python,csv,ssis,delimiter,eol","A_Id":51195993,"CreationDate":"2018-07-03T17:27:00.000","Title":"Which newline character is in my CSV?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I built an image classifier using CNTK. The images are grayscale. Therefore, I entered the number of channels as 1. So, the model requires (1x64x64) data (64 being the image height and width).\nThe problem is, when I try to predict the class of a new image, it is seen as (64x64) only. So, the code errors out due to data mismatch.\nTherefore, I reshaped the image using:\nimage_data = image_data.reshape((1, image_data.shape[0], image_data.shape[1]))\nThis generated (1x64x64) - which worked. Though the predictions are coming the same class for every image I select. I wonder if it is because of this reshaping or not. Can someone chime in? Thanks!","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":72,"Q_Id":51160298,"Users Score":1,"Answer":"Reshaping your input would not affect the output of the model.  If it is only predicting one class for every image, it is an issue with model training.  I would suggest you try predicting on your training data to see if it only predicts one class on the training data.  If that is the case, it is definitely a model training issue.","Q_Score":1,"Tags":"python,image,classification,cntk","A_Id":51160557,"CreationDate":"2018-07-03T17:43:00.000","Title":"CNTK evaluation for image classification","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to crop the circular image attached below according to the following:\n\ncrop input circular image to the unique square inscribed in the circle.\ncrop the square image down to the circle inscribed in the square\ncrop the circular image from the previous step down to square that inscribes image.\n\nI am using python, opencv, and PIL. I have tried cropping with hard coding coordinates, but this obviously causes problems when applied to other images, so I would like a universal solution.\nI have included a rough visualization of how I would like to crop it:\nIn addition, I have included the original image:\nA code snippet with brief explanation would be greatly appreciated.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":577,"Q_Id":51164645,"Users Score":2,"Answer":"The image you want to crop to is, geometrically, a square centered on the input image, half as large. This is because you're inscribing twice, each time the square shrinks by the square root of two, and dividing by SQRT(2) twice is the same thing as dividing by 2.\nSo if you have an input square of side D (or a circular image of diameter D), what you need to do is crop with center (D\/2, D\/2) and a side of D\/2.","Q_Score":0,"Tags":"python,image,opencv,image-processing,python-imaging-library","A_Id":51180855,"CreationDate":"2018-07-04T01:22:00.000","Title":"How to crop a circular image to inscribed square, then crop to inscribed circle, and finally crop to inscribed square?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have read some journals and paper of HMM and MFCC but i still got confused on how it works step by step with my dataset (audio of sentences dataset).\nMy data set Example (Audio Form) :\n\nhello good morning\ngood luck for you exam\netc\nabout 343 audio data and 20 speaker (6800 audio data)\n\nAll i know :\n\nMy sentences datasets is used to get the transition probabilty\nHmm states is the phonemes\n39 MFCC features is used to train the HMM models\n\nMy Questions :\n\nDo i need to cut my sentences into words or just use sentences for train \nHMM models?\nDo I need phonemes dataset for train ? if yes do i need to train it use HMM too ? if not how my program recognize the phonemes for HMM predict input?\nWhat steps i must do first ?\n\nNote : Im working with python and i used hmmlearn and python_speech_features as my library.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":795,"Q_Id":51165305,"Users Score":1,"Answer":"Do i need to cut my sentences into words or just use sentences for train HMM models?\n\nTheoretically you just need sentences and phonemes. But having isolated words may be useful for your model (it increases the size of your training data)\n\nDo I need phonemes dataset for train ? if yes do i need to train it use HMM too ? if not how my program recognize the phonemes for HMM predict input?\n\nYou need phonemes, otherwise it will be too hard for your model to find the right phoneme segmentation if it does not have any example of isolated phonemes. You should first train your HMM states on the isolated phonemes and then add the rest of the data. If you have enough data, your model may be able to learn without the isolated phoneme examples, but I wouldn't beat on this.\n\nWhat steps i must do first ?\n\nBuild your phoneme examples and use them to train a simple HMM model you don't model the transition between phonemes. Once your hidden states have some information about phonemes, you may continue the training on isolated words and sentences.","Q_Score":3,"Tags":"python,tensorflow,speech-recognition,mfcc,hmmlearn","A_Id":51167856,"CreationDate":"2018-07-04T03:22:00.000","Title":"How to train HMM with audio senteces dataset for speech recognition?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have multiple csv files in the form of a star schema. To perform analytics using Python, is it better to combine all these csv files into one csv file, or to extract data from each csv file and then do analytics? People online have almost always combined all files into one and have then performed analytics. However, combining all csv files would eliminate my star schema.  I currently have approximately 25,000 rows and 10 columns in each csv file. The size of each csv file is around 7 MB. Thank you in advance for your help.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":143,"Q_Id":51165589,"Users Score":1,"Answer":"I feel you can leave the fact tables as is and combine the rest of the data with which you can reduce the amount of data your dealing with and have the star schema intact too..\nThanks,\nRam","Q_Score":0,"Tags":"python,data-analysis","A_Id":51165703,"CreationDate":"2018-07-04T04:04:00.000","Title":"Data analytics using Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to use pytorch in MSVS2017.  I started a pytorch project, have anaconda environment set using python3.6, but when I run the debugger, I get a \"module not found\" error on the first import statement \"import torch\".  I've tried various methods for installing pytorch in a way that allows MSVS2017 to use it, including command line and Anaconda command line installations (using tips from other closely related StackOverflow questions), but I cannot clear the error.  This is a native MSVS2017 project type that came with their AI Tools module.  What am I doing wrong?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":92,"Q_Id":51173695,"Users Score":1,"Answer":"Probably, at the date of our MSVS2017 installation (esp. if prior to April 2018), there were no official .whl files for Windows pytorch (this has since changed).  Also, given the default installation pathway, permissions on Windows (or file lock access) may be a problem (for example, when attempting to install to the \"c:\\ProgramData\" folder).  \nThe solution is to 1) ensure all pytorch requisites are installed first (for example, if, during your failed pytorch installation you get a \"_____ requires _____ which is not installed, for example cython, then install cython) 2) avoid permission errors by using the --user switch, and 3) install directly from the online repository.  \nSo, at the environment command line (top right corner in the \"Python Environments\" tool) provide --user  http:\/\/download.pytorch.org\/whl\/cpu\/torch-0.4.0-cp36-cp36m-win_amd64.whl.  \nThis operation will create and execute the command: pip install --user  http:\/\/download.pytorch.org\/whl\/cpu\/torch-0.4.0-cp36-cp36m-win_amd64.whl.\nIncidentally, you can install all packages at this environmental command line simply by typing the package name (e.g., cython, torchvision, scipy, etc...).","Q_Score":1,"Tags":"python,installation,anaconda,pytorch","A_Id":51173697,"CreationDate":"2018-07-04T12:26:00.000","Title":"How do I install and run pytorch in MSVS2017 (to avoid \"module not found\" error on \"import torch\" statement)?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Let's say I have 100x200 numpy array of random numbers and wish to average blocks of 5x5 in the this array, that is I need the operation to be done on all 800 distinct blocks that are 5x5. I wonder if there is an efficient way to do this without nested loop and possibly without any loop.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":26,"Q_Id":51180854,"Users Score":0,"Answer":"You can iterate over the array and just call numpy.average on the 5x5 blocks ?","Q_Score":0,"Tags":"python,vectorization","A_Id":51180929,"CreationDate":"2018-07-04T21:19:00.000","Title":"operation on blocks of a matrix efficiently in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I get the error: \n\nmodule 'matplotlib.colors' has no attribute 'to_rgba',\n\nwhen i import chainer in ipynb.\nI am using python 2 ,anaconda 4.1.1 ,chainer 4 and matplotlib 1.5.1.could anyone asses the problem","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":322,"Q_Id":51184116,"Users Score":0,"Answer":"Try restarting your text editor and trying it, sometimes it needs to be restarted for changes to take effect.","Q_Score":0,"Tags":"python,anaconda,chainer","A_Id":51184234,"CreationDate":"2018-07-05T05:46:00.000","Title":"Import chainer in python throws error","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a corpus consisting of around 160,000 documents. I want to do a topic modeling on it using LDA in R (specifically the function lda.collapsed.gibbs.sampler in lda package). \nI want to determine the optimal number of topics. It seems the common procedure is to have a vector of topic numbers, e.g., from 1 to 100, then run the model for 100 times and the find the one has the largest harmonic mean or samllest perplexity. \nHowever, given the large amount of documents, the optimal number of topics can easily go to several hundreds or even thousands. I find that as the number of topic increases, the computation time grows significantly. Even if I use parallel computing, it will several days or weeks. \nI wonder is there a better (time-efficient) way to choose the optimal number of topics? or is there any suggestion to reduce the computation time?\nAny suggestion is welcomed.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":826,"Q_Id":51185358,"Users Score":0,"Answer":"Start with some guess in middle. decrease and increase the number of topics by say 50 or 100 instead of 1. Check in which way Coherence Score is increasing. I am sure it will converge.","Q_Score":1,"Tags":"python,r,lda,topic-modeling","A_Id":51226434,"CreationDate":"2018-07-05T07:15:00.000","Title":"Fast way to determine the optimal number of topics for a large corpus using LDA","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to create column with sequential numbers in pyspark dataframe starting from specified number. For instance, I want to add column A to my dataframe df which will start from 5 to the length of my dataframe, incrementing by one, so 5, 6, 7, ..., length(df). \nSome simple solution using pyspark methods?","AnswerCount":3,"Available Count":1,"Score":0.1325487884,"is_accepted":false,"ViewCount":11579,"Q_Id":51200217,"Users Score":2,"Answer":"Three simple steps:\nfrom pyspark.sql.window import Window\nfrom pyspark.sql.functions import monotonically_increasing_id,row_number\ndf =df.withColumn(\"row_idx\",row_number().over(Window.orderBy(monotonically_increasing_id())))","Q_Score":2,"Tags":"python,dataframe,pyspark,sequential-number","A_Id":63504519,"CreationDate":"2018-07-05T21:37:00.000","Title":"How to create sequential number column in pyspark dataframe?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm currently running an airflow (1.9.0) instance on python 3.6.5. I have a manual workflow that I'd like to move to a DAG. This manual workflow now requires code written in python 2 and 3. Let's simplify my DAG to 3 steps:\n\nDataflow job that processes data and sets up data for Machine\nLearning Training \nTensorflow ML training job\nOther\nPythonOperators that I wrote using python 3 code\n\nThe dataflow job is written in python 2.7 (required by google) and the tensorflow model code is in python 3. Looking at \"MLEngineTrainingOperator\" in airflow 1.9.0 there is a python_version parameter which sets the \"The version of Python used in training\".\nQuestions:\n\nCan I dynamically specify a specific python version in a worker\nenvironment?\nDo I have to just install airflow on python 2.7 to make step 1) run?\nCan I have tensorflow model code in python 3 that just gets packaged up and submitted via MlEngineTraining running on python 2?\nDo I have to rewrite my 3) operators in python 2?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":4405,"Q_Id":51201188,"Users Score":4,"Answer":"There isn't a way specify the python version dynamically on a worker. However if you are using the the Celery executor, you can run multiple workers either on difference servers\/vms or in different virtual environments.  \nYou can have one worker running python 3, and one running 2.7, and have each listening to different queues.  This can be done three different ways:\n\nWhen starting the worker you can add a -q [queue-name] flag\nset an env of AIRFLOW__CELERY__DEFAULT_QUEUE\nupdating default_queue under [celery] in the airflow.cfg. \n\nThen in your task definitions specify a queue parameter, changing the queue up depending on which python version the task needs to run.\nI'm not familiar with the MLEngineOperator, but you can specify a python_version in the PythonOperator which should run it in a virtualenv of that version.  Alternative you can use the BashOperator, write the code to run in a different file and specify the python command to run it using the absolute path the version of python you want to use.\nRegardless of how the task is run, you just need to ensure the DAG itself is compatible with python version you are running it as it. ie. if you are going to start an airflow worker in different python versions, the DAG file itself needs to be python 2 & 3 compatible. The DAG can have addition file dependencies that it uses have version incompatibilities.","Q_Score":4,"Tags":"python,tensorflow,google-cloud-dataflow,airflow","A_Id":51209553,"CreationDate":"2018-07-05T23:32:00.000","Title":"Python 3.6 airflow with a Operator that requires 2.7","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I am trying to learn ML using Kaggle datasets. In one of the problems (using Logistic regression) inputs and parameters matrices are of size (1110001, 8) & (2122640, 8) respectively.\nI am getting memory error while doing it in python. This would be same for any language I guess since it's too big. My question is how do they multiply matrices in real life ML implementations (since it would usually be this big)?\nThings bugging me :\n\nSome ppl in SO have suggested to calculate dot product in parts and then combine. But even then matrix would be still too big for RAM (9.42TB? in this case)\n\nAnd If I write it to a file wouldn't it be too slow for optimization algorithms  to read from file and minimize function?\n\nEven if I do write it to file how would fmin_bfgs(or any opt. function) read from file?\n\nAlso Kaggle notebook shows only 1GB of storage available. I don't think anyone would allow TBs of storage space.\n\nIn my input matrix many rows have similar values for some columns. Can I use it my advantage to save space? (like sparse matrix for zeros in matrix)\nCan anyone point me to any real life sample implementation of such cases. Thanks!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":102,"Q_Id":51205149,"Users Score":0,"Answer":"I have tried many things. I will be mentioning these here, if anyone needs them in future:\n\nI had already cleaned up data like removing duplicates and\nirrelevant records depending on given problem etc. \nI have stored large matrices which hold mostly 0s as sparse matrix.  \nI implemented the gradient descent using mini-batch method instead of plain old Batch method (theta.T dot X).\n\nNow everything is working fine.","Q_Score":0,"Tags":"python,numpy,machine-learning,scipy,logistic-regression","A_Id":51456740,"CreationDate":"2018-07-06T07:29:00.000","Title":"How to find dot product of two very large matrices to avoid memory error?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"This is most likely a dumb question but being a beginner in Python\/Numpy I will ask it anyways. I have come across a lot of posts on how to Normalize an array\/matrix in numpy. But I am not sure about the WHY. Why\/When does an array\/matrix need to be normalized in numpy? When is it used?\nNormalize can have multiple meanings in difference context. My question belongs to the field of Data Analytics\/Data Science. What does Normalization mean in this context? Or more specifically in what situation should I normalize an array?\nThe second part to this question is - What are the different methods of Normalization and can they be used interchangeably in all situations? \nThe third and final part - can Normalization be used for Arrays of any dimensions?\nLinks to any reference material (for beginners) will be appreciated.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":229,"Q_Id":51208733,"Users Score":2,"Answer":"Consider trying to cluster objects with two numerical attributes A and B. Both are equally important. Attribute A can range from 0 to 1000 and attribute B can range from 0 to 5. \nIf you did not normalize A and B you would end up with attribute A completely overpowering attribute B when applying any standard distance metric.","Q_Score":0,"Tags":"python,arrays,numpy,normalization","A_Id":51208845,"CreationDate":"2018-07-06T10:48:00.000","Title":"Python, Numpy - Normalize a matrix\/array","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a 2D array of ~200,000 points, and wish to \"jitter\" these points such that the distance between any point and its nearest neighbor is >= some minimum value.\nBefore writing this algorithm from scratch, I wanted to ask: Are there any canonical approaches or often-used algorithms to implement this behavior? I thought it would make sense to start by reviewing those algorithms before setting out on this.\nAny suggestions others can offer on this question would be greatly appreciated.","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":625,"Q_Id":51214496,"Users Score":0,"Answer":"compute pairwise distances between all points -\nsklearn.metrics.pairwise.euclidean_distances \ncompute minimum of all pairwise distances\nif minimum is small enough, exit\ncompute pairwise vectors between each two points so they point away\nfrom each other\ndivide by pairwise distances so they are unit\nvectors\nfor each point, compute how far to nudge it as sum over\nother points of: pairwise vector\/pairwise distance squared * some\nconstant (the force - constant should be set so that when points are at the minimum distance force is small but not infinitesimal)\nnudge all the points by amount\ncomputed in 6. should also cap the nudge at maybe minimum\/10, so if 2 points end up in same spot, they don't get nudged an infinite distance apart\nrepeat until minimum is small enough\n\nShould converge in all cases but will expand canvas as necessary. Also it is memory and compute intensive for e.g. 200,000 points, but a sparse matrix that ignores large vectors\/small forces makes it more tractable.","Q_Score":4,"Tags":"python,algorithm,numpy","A_Id":54048062,"CreationDate":"2018-07-06T16:32:00.000","Title":"Python: Ensure each pairwise distance is >= some minimum distance","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to make a program where I detect red. However sometimes it is darker than usual so I can't just use one value.\nWhat is a good range for detecting different shades of red?\nI am currently using the range 128, 0, 0 - 255, 60, 60 but sometimes it doesn't even detect a red object I put in front of it.","AnswerCount":4,"Available Count":1,"Score":0.049958375,"is_accepted":false,"ViewCount":18158,"Q_Id":51229126,"Users Score":1,"Answer":"Please, use HSV or HSL (hue, saturation, luminance) instead of RGB, in HSV the red color can be easily detected using the value of hue within some threshold.","Q_Score":4,"Tags":"python,opencv,colors","A_Id":51229167,"CreationDate":"2018-07-08T05:32:00.000","Title":"How to find the RED color regions using OpenCV?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a txt file to import in pandas but the data contains characters like L\\E9on, which translates to L\u00e9on. How can I import this kind of data in pandas? I have tried using encoding as utf-8 and raw_unicode_escape. It still gives out an error multiple repeat at position 2.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":114,"Q_Id":51230915,"Users Score":0,"Answer":"To process accented character please try encoding='iso-8859-1'.","Q_Score":0,"Tags":"python,pandas","A_Id":51231004,"CreationDate":"2018-07-08T10:09:00.000","Title":"escaped character in data for pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a txt file to import in pandas but the data contains characters like L\\E9on, which translates to L\u00e9on. How can I import this kind of data in pandas? I have tried using encoding as utf-8 and raw_unicode_escape. It still gives out an error multiple repeat at position 2.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":114,"Q_Id":51230915,"Users Score":0,"Answer":"Interesting!!!\nTo re-produce this issue at my end, I have created dummy data with consists of the text specified by you and saved it as a .txt file\nI am able to import this txt file content into pandas data frame without any issues using read_csv method\ndf=pd.read_csv('spcl.txt')","Q_Score":0,"Tags":"python,pandas","A_Id":51232652,"CreationDate":"2018-07-08T10:09:00.000","Title":"escaped character in data for pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a TensorFlow model in which I want to pass an image to it in order for it to determine the object within the image.\nHowever, the model is complaining of the shape of the image saying it wants it in the form (1, 1, 1, 2048) however it's receiving (1, 7, 7, 2048).\nI have tried doing a numpy.reshape() on the image by doing either numpy.reshape(myObj, (1, 1, 1, 2048)) or numpy.reshape(myObj, (1, 1, 1, -1)). However, the former just complains that it can't reshape array of size 100352 to (1, 1, 1, 2048) and the latter resizes the last element of the array to the multiple of (7, 7, 2048), i.e. 100352.\nHow would one go about reshaping an odd array size, or is this not how Numpy shapes\/reshapes work? Is there an alternative way to do what I'm asking for if not possible with Numpy?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1912,"Q_Id":51231925,"Users Score":0,"Answer":"You cannot reshape an array of n elements into an array of m elements if n and m are not equal. The operation of reshaping an array is really just the operation of obtaining a new view of the same array.\nYour input array has n=1*7*7*2048=100353 elements while you're trying to reshape to an array with m=1*1*1*2048=2048 elements.\nAs pointed out by hpaulj, your problem lies in your input shape. From the shapes you mention, (1, 7, 7, 2048) and (1, 1, 1, 2048), it looks like your expected input should probably be extracted from a later layer (maybe after a global pooling stage) but we cannot say much more without more details on your models.","Q_Score":0,"Tags":"python,arrays,numpy","A_Id":51233015,"CreationDate":"2018-07-08T12:17:00.000","Title":"Numpy - How to reshape odd array sizes?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"What is the difference between MinMaxScaler and standard scaler.\nMMS= MinMaxScaler(feature_range = (0, 1)) ( Used in Program1)\nsc = StandardScaler() ( In another program they used Standard scaler and not minMaxScaler)","AnswerCount":3,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":48259,"Q_Id":51237635,"Users Score":6,"Answer":"Many machine learning algorithms perform better when numerical input variables are scaled to a standard range.\nScaling the data means it helps to  Normalize the data within a particular range.\nWhen MinMaxScaler is used the it is also known as Normalization and it transform all the values in range between (0 to 1)\nformula is x = [(value - min)\/(Max- Min)]\nStandardScaler comes under Standardization and its value ranges between (-3 to +3)\nformula is z = [(x - x.mean)\/Std_deviation]","Q_Score":31,"Tags":"python,data-science","A_Id":64356403,"CreationDate":"2018-07-09T02:42:00.000","Title":"Difference between Standard scaler and MinMaxScaler","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"What is the difference between MinMaxScaler and standard scaler.\nMMS= MinMaxScaler(feature_range = (0, 1)) ( Used in Program1)\nsc = StandardScaler() ( In another program they used Standard scaler and not minMaxScaler)","AnswerCount":3,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":48259,"Q_Id":51237635,"Users Score":71,"Answer":"MinMaxScaler(feature_range = (0, 1)) will transform each value in the column proportionally within the range [0,1]. Use this as the first scaler choice to transform a feature, as it will preserve the shape of the dataset (no distortion).\nStandardScaler() will transform each value in the column to range about the mean 0 and standard deviation 1, ie, each value will be normalised by subtracting the mean and dividing by standard deviation. Use StandardScaler if you know the data distribution is normal.\nIf there are outliers, use RobustScaler(). Alternatively you could remove the outliers and use either of the above 2 scalers (choice depends on whether data is normally distributed)\nAdditional Note: If scaler is used before train_test_split, data leakage will happen. Do use scaler after train_test_split","Q_Score":31,"Tags":"python,data-science","A_Id":58850139,"CreationDate":"2018-07-09T02:42:00.000","Title":"Difference between Standard scaler and MinMaxScaler","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"when using the dask dataframe to_parquet method is there any way to set the default parquet file size like in spark ?\nmy problem is that when I save it with the partition_on kwarg i get several small files per partition dir and thus resulting very slow queries using \"Amazon Athena\".\nThe intermediate desired result (if file size control is not available) is n files (right now 1 will suffice) per partition dir.\nThe only way i thought of guaranteeing 1 file per partition dir is repartitioning to one partition and then using the to_parquet method (however this is highly inefficient).\nis there a better way ?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":940,"Q_Id":51249295,"Users Score":1,"Answer":"The current behaviour is by design, allowing each worker to process a partition independently, and write to files which no other process is writing to. Otherwise, there would need to be some kind of lock, or some consolidation step after writing for each directory.\nWhat you could do, is to use set_index to shuffle the data into one partition for each value of the column you want to partition by (perhaps using the divisions= keyword); now a to_parquet would result in a file for each of these values. If you wanted the files to automatically end up in the correct directories, and have the now-redundant index trimmed, you would want to use to_delayed() and create a delayed function, which takes one partition (a pandas dataframe) and writes it to the correct location.","Q_Score":2,"Tags":"python,parquet,filesize,dask","A_Id":51252563,"CreationDate":"2018-07-09T15:39:00.000","Title":"set size of Parquet output files in dask","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to learn machine learning. \nI had a doubt about one hot encoding:\nI have a data set split into 2 excel sheets of data. One sheet has train and other has test data. I first trained my model by importing the train data sheet with pandas. There are categorical features in the data set that have to be encoded. I one hot encoded them.  \nAfter importing the test dataset , if I one hot encode it, will the encoding be the same as of the train data set or will it be different. If so, how can I solve this issue?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1910,"Q_Id":51256927,"Users Score":0,"Answer":"you have 2 seperate sheets ( for test and train data set). you have to one-hot encode both the sheets seperately after importing it into the pandas data frame.\nand YES one hot encoding will be the same for the same data set no matter you apply on different data sheets, make sure you have same categorical values in that column in each of your data sheet","Q_Score":1,"Tags":"python,pandas,machine-learning","A_Id":52307049,"CreationDate":"2018-07-10T03:41:00.000","Title":"One hot encoding in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Gensim's Word2Vec model takes as an  input a list of lists with the inner list containing individual tokens\/words of a sentence. As I understand Word2Vec is used to \"quantify\" the context of words within a text using vectors. \nI am currently dealing with a corpus of text that has already been split into individual tokens and no longer contains an obvious sentence format (punctuation has been removed). I was wondering how should I input this into the Word2Vec model? \nSay if I simply split the corpus into \"sentences\" of uniform length (10 tokens per sentence for example), would this be a good way of inputting the data into the model? \nEssentially, I am wondering how the format of the input sentences (list of lists) affects the output of Word2Vec?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":98,"Q_Id":51269058,"Users Score":1,"Answer":"That sounds like a reasonable solution. If you have access to data that is similar to your cleaned data you could get average sentence length from that data set. Otherwise, you could find other data in the language you are working with (from wikipedia or another source) and get average sentence length from there.\nOf course your output vectors will not be as reliable as if you had the correct sentence boundaries, but it sounds like word order was preserved so there shouldn't be too much noise from incorrect sentence boundaries.","Q_Score":0,"Tags":"python,nlp,gensim,word2vec,word-embedding","A_Id":51270563,"CreationDate":"2018-07-10T15:26:00.000","Title":"Use proxy sentences from cleaned data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I did this command to check results of tensorflow training.\n\n\n    python label_image.py --image xxx.jpg --graph retrained_graph.pb --labels retrained_labels.txt\n\n\nAnd found following errors.\n\n\n    \/Users\/xxx\/anaconda3\/lib\/python3.6\/site-packages\/h5py\/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n      from ._conv import register_converters as _register_converters\n    2018-07-11 00:39:22.028051: I tensorflow\/core\/platform\/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA\n    Traceback (most recent call last):\n      File \"label_image.py\", line 131, in \n        input_operation = graph.get_operation_by_name(input_name)\n      File \"\/Users\/xxx\/tensorFlow\/lib\/python3.6\/site-packages\/tensorflow\/python\/framework\/ops.py\", line 3718, in get_operation_by_name\n        return self.as_graph_element(name, allow_tensor=False, allow_operation=True)\n      File \"\/Users\/xxx\/tensorFlow\/lib\/python3.6\/site-packages\/tensorflow\/python\/framework\/ops.py\", line 3590, in as_graph_element\n        return self._as_graph_element_locked(obj, allow_tensor, allow_operation)\n      File \"\/Users\/xxx\/tensorFlow\/lib\/python3.6\/site-packages\/tensorflow\/python\/framework\/ops.py\", line 3650, in _as_graph_element_locked\n        \"graph.\" % repr(name))\n    KeyError: \"The name 'import\/Mul' refers to an Operation not in the graph.\"\n\n\nlabel_image.py is following lines.\n\n\n      input_height = 299\n      input_width = 299\n      input_mean = 0\n      input_std = 255\n      input_layer = \"Mul\"\n      output_layer = \"final_result\"","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1478,"Q_Id":51269683,"Users Score":0,"Answer":"python label_image.py --image cat3.jpg --graph retrained_graph.pb --labels retrained_labels.txt --input_layer=Placeholder \nThis command, the problem was solved.","Q_Score":2,"Tags":"python,python-3.x,tensorflow,machine-learning","A_Id":51269893,"CreationDate":"2018-07-10T15:57:00.000","Title":"tensorflow error KeyError: \"The name 'import\/Mul' refers to an Operation not in the graph.\"","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've recently begun using Tensorflow via Keras and Python 3.5 to analyze company data, and I am by no means an expert and only recently built my first \"real-world\" model. \nWith my experimental data I used Tensorboard to visualiza how my neural network was working, and I would like to do the same with my real data. However, my company is extremely strict about company data leaving our servers -  so my question is this:\nDoes tensorboard take the raw data used in the model and upload it off-site to generate its reports\/visuals or does it only use processed data\/results from my model?\nI've done several google searches already, and I haven't found anything conclusive one way or the other.\nIf I'm not asking this question correctly, please let me know - I'm new to all of this.\nThank you.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":81,"Q_Id":51271790,"Users Score":0,"Answer":"No, Tensorboard does not upload the data to \"the cloud\" or anywhere outside the computer where it is running, it just interprets data produced by the model.","Q_Score":2,"Tags":"python,tensorflow,keras,tensorboard,privacy","A_Id":51272378,"CreationDate":"2018-07-10T18:17:00.000","Title":"Data Privacy with Tensorboard","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have this issue: \n\nContextualVersionConflict: (pandas 0.22.0 (...),\n  Requirement.parse('pandas<0.22,>=0.19'), {'scikit-survival'})\n\nI have even tried to uninstall pandas and install scikit-survival + dependencies via anaconda. But it still does not work....\nAnyone with a suggestion on how to fix?\nThanks!","AnswerCount":1,"Available Count":1,"Score":0.761594156,"is_accepted":false,"ViewCount":2454,"Q_Id":51272642,"Users Score":5,"Answer":"Restarting jupyter notebook fixed it. But I am unsure why this would fix it?","Q_Score":3,"Tags":"python,pandas,scikit-learn","A_Id":51272663,"CreationDate":"2018-07-10T19:19:00.000","Title":"Python: ContextualVersionConflict: pandas 0.22.0; Requirement.parse('pandas<0.22,>=0.19'), {'scikit-survival'})","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using the sample() method for a KernelDensity that is fitted to my data's percentage changes. Are the samples from the sample method in the same units as my input data? I ask because documentation says that for the score_sample() method it actually returns the log density and I just want to make sure the same doesn't happen with the sample method or if there's a need to adjust the output.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":118,"Q_Id":51287512,"Users Score":0,"Answer":"Answer: yes, the sample is in the same unit as my input data. I checked carefully and with a cold head :)","Q_Score":0,"Tags":"python-3.x,scikit-learn,kernel-density","A_Id":51949438,"CreationDate":"2018-07-11T14:02:00.000","Title":"sampling from sklearn Kernel Density estimation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to divide a city into n squares. \nRight now, I'm calculating the coordinates for all square centres and using the ox.graph_from_point function to extract the OSM data for each of them.\nHowever, this is getting quite long at high n due to the API pausing times.\nMy question:\nIs there a way to download all city data from OSM, and then divide the cache file into squares (using ox.graph_from_point or other) without making a request for each?\nThanks","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":161,"Q_Id":51287813,"Users Score":1,"Answer":"Using OSMnx directly - no, there isn't. You would have to script your own solution using the existing tools OSMnx provides.","Q_Score":1,"Tags":"python-3.x,osmnx","A_Id":51637340,"CreationDate":"2018-07-11T14:15:00.000","Title":"OSMnx: Divide a cache ox.graph into equal squares without redownloading each","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":1,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to write a program in Python (with OpenCV) that compares 2 images, shows the difference between them, and then informs the user of the percentage of difference between the images.  I have already made it so it generates a .jpg showing the difference, but I can't figure out how to make it calculate a percentage.  Does anyone know how to do this?\nThanks in advance.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":12248,"Q_Id":51288756,"Users Score":0,"Answer":"You will need to calculate this on your own. You will need the count of diferent pixels and the size of your original image then a simple math: (diferentPixelsCount \/ (mainImage.width * mainImage.height))*100","Q_Score":6,"Tags":"python,opencv","A_Id":51289142,"CreationDate":"2018-07-11T15:01:00.000","Title":"How do I calculate the percentage of difference between two images using Python and OpenCV?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I noticed that the de facto standard for array manipulation in Python is through the excellent numpy library. However, I know that the Python Standard Library has an array module, which seems to me to have a similar use-case as Numpy.\nIs there any actual real-world example where array is desirable over numpy or just plain list?\nFrom my naive interpretation, array is just memory-efficient container for homogeneous data, but offers no means of improving computational efficiency.\n\nEDIT\nJust out of curiosity, I searched through Github and import array for Python hits 186'721 counts, while import numpy hits 8'062'678 counts.\nHowever, I could not find a popular repository using array.","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2898,"Q_Id":51290791,"Users Score":0,"Answer":"Yes, if you don't want another dependency in your code.","Q_Score":7,"Tags":"python,arrays,numpy","A_Id":51290834,"CreationDate":"2018-07-11T16:59:00.000","Title":"Numpy arrays vs Python arrays","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a severe space limitation on my deployment device that mean I cannot fit the entire \"Keras\" package.\nIs there a way to get a prediction from a trained keras model without the entire keras package?\nI have noted that the Keras docker is ~1GB. I would need it to be < 650MB","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":137,"Q_Id":51297972,"Users Score":0,"Answer":"I was able to get my docker container small enough by using a different backend when predicting ( theano ) as opposed to tensorflow.\nThe other solutions I investigated are some C++ libraries, e.g. ( frugally deep, kerasify, keras2cpp ) but the preprocessng of my data became much harder ( for me anyway ). So I went with the theano backend as above!\nOf course you could manually do the convolutions for each filter etc in python.. but that would be even harder","Q_Score":3,"Tags":"python,tensorflow,keras","A_Id":51536067,"CreationDate":"2018-07-12T05:34:00.000","Title":"\"Mini Keras\" Is there a way get a prediction form a trained keras model without the entire keras package?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I was trying to pack and release a project which uses tensorflow-gpu. Since my intention is to make the installation as easy as possible, I do not want to let the user compile tensorflow-gpu from scratch so I decided to use pipenv to install whatsoever version pip provides.\nI realized that although everything works in my original local version, I can not import tensorflow in the virtualenv version.\nImportError: libcublas.so.9.0: cannot open shared object file: No such file or directory\nAlthough this seems to be easily fixable by changing local symlinks, that may break my local tensorflow and is against the concept of virtualenv and I will not have any idea on how people installed CUDA on their instances, so it doesn't seems to be promising for portability.\nWhat can I do to ensure that tensorflow-gpu works when someone from internet get my project only with the guide of \"install CUDA X.X\"? Should I fall back to tensorflow to ensure compatibility, and let my user install tensorflow-gpu manually?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":124,"Q_Id":51309596,"Users Score":1,"Answer":"Having a working tensorflow-gpu on a machine does involve a series of steps including installation of cuda and cudnn, the latter requiring an NVidia approval. There are a lot of machines that would not even meet the required config for tensorflow-gpu, e.g. any machine that doesn't have a modern nvidia gpu. You may want to define the tensorflow-gpu requirement and leave it to the user to meet it, with appropriate pointers for guidance. If the  project can work acceptably on tensorflow-cpu, that would be a much easier fallback option.","Q_Score":0,"Tags":"python,tensorflow","A_Id":51310630,"CreationDate":"2018-07-12T15:34:00.000","Title":"Can I let people use a different Tensorflow-gpu version above what they had installed with different CUDA dependencies?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have the following problem:\nI'm working on a formula to calculate some network effects. The idea is that I have 450 \"red users\" and 6550 \"blue users\" which sums up to 7000 users in total. Now I would like to plot \"picking x users (the same user cannot be picked twice, so this is sampling without replacement) and calculate the probability that at least 1 user is red\". \nE.g for x = 3, that means I'm picking 3 random users out of 7000 and check if any of these are \"red users\"\nThe probability for having at least 1 red user is p = 1 - the probability all 3 picks are blue users and the probability for a blue user is equal to p = 6550\/7000, right? \nResulting in a probability for at least 1 red user:\n* p = 1 - 6550\/7000 * 6549\/6999 * 6548\/6998 *\nTherefore i came up with the formula:\nf(x) = e^-(1- sum of (6500-i)\/(7000-i)); for i = 0, till x)\nWhat I've realized is that the curve is pretty edgy since it's just going from a value in \u2115 to the next value in \u2115. \nAlthough adding decimal numbers wouldn't make that much sense since \"picking 0,5 users or even 0,01 users\" is just stupid, I would like to see the full graph in order to be able to compare the formula to some others. \nIs there any way I can implement this in python?\nBest regards,\nKorbi","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":206,"Q_Id":51312546,"Users Score":0,"Answer":"In your formula, wouldn't it be the product instead of the sum? Anyways, my original thought was to use the Poisson distribution, but that wouldn't work since it's without replacement. The problem is that the factorial function is only defined for whole numbers, so you'd need to use the gamma function.","Q_Score":0,"Tags":"python,numpy,matplotlib,probability-theory","A_Id":51313130,"CreationDate":"2018-07-12T18:47:00.000","Title":"Plot Probability Curve with Summation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have like 3 columns in a data frame for example\nColumn_A has 2 categorical values like A,B \nColumn_B also has 3 categorical values like Type1, Type2, Type3    \nDate column has values like 2010-06-13,2010-06-10\nThere are about 20,000 rows so the Categorical Column A,B's values keep repeating.\nSo  I need the find min date where Column_A='A' and Column_B='type 1' using Python(Pandas,Numpy)","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":201,"Q_Id":51314716,"Users Score":0,"Answer":"Use boolean index with loc and min:\ndf.loc[(df['Column_A'] == 'A') & (df['Column_B'] == 'type 1'), 'Date'].min()","Q_Score":0,"Tags":"python-3.x,pandas,numpy,jupyter-notebook","A_Id":51314759,"CreationDate":"2018-07-12T21:27:00.000","Title":"Groupby to find min date with conditions in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have Parcels_All.csv which I compiled by merging many smaller\nparcel files, using the command line copy *.csv\nI have WKT_Revisions_Combined.csv that needs to go into\nParcels_All.csv to update certain sections of data.\nI have vlookup.py script that can print the data from\nWKT_Revisions_Combined.csv into Parcels_All.csv by referencing row\nID's and column headers.\n\nVlookup.py has not been able to successfully run because it seems there is an error within Parcels_All.csv. When I open the file in EmEditor (a big data text editor) it gives the error message \"Inconsistent number of columns detected\" for a few rows\nThe interesting thing is when I open Parcels_All.csv in Excel the data is organized and in place; no inconsistent columns.\nAny thoughts? Could it be an encoding issue?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1552,"Q_Id":51329549,"Users Score":0,"Answer":"When a CSV file with different number of columns are imported to Excel, the differences in row lengths are not evident. Excel has an infinitely wide table by default, so the real size of the CSV file is not shown. I suspect there are empty fields in the last column somewhere, because these would not be evident in Excel.","Q_Score":1,"Tags":"python,csv,encoding,emeditor","A_Id":51385960,"CreationDate":"2018-07-13T16:40:00.000","Title":"Inconsistent number of columns","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Unity3D has a package for Reinforcement Learning called ML-agents that I am playing with to understand its components. For my project, I am in the situation that I need to write my own logic to set the reward out of Unity3D (not 'addReward' using C# logic, but write a Python code to set the reward out of Unity).\nI wonder if I can use the Python API given by the ML-agents package for using the env observations and update the reward with a custom logic set out of Unity (and send back to Unity)? And where to look for doing so?\nIn other words (example). In the 3DBall example, a reward logic is set in Unity3D as such if the ball stays on the platform gets a positive reward and if it falls from the platform it receives a negative reward. This logic is implemented in Unity3D by using C# and determine the position of the Ball (vector position) compare to the platform. For every action, the agent calls the env.step(action) and get the tuple of (reward, state...). What if I want to write the logic outside Unity? For example, if I want to write a python program that reads the observation (from Unity3D) and update the reward without using the Unity reward logic? Is this possible? I cannot understand where this option is in the Python API of ML-agents.\nAt the moment I am thinking to run an external python program in-between the line where I set the reward in C# in Unity3D, but I wonder if this is overcomplicated and that there is an easier solution.\nAny help would be really appreciated.\nRegards\nGuido","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":464,"Q_Id":51337634,"Users Score":0,"Answer":"According to my Reinforcement Learning understanding, the reward is handled by the environment and the agent just get it together with the next observation. You could say it's part of the observation. \nTherefore the logic which rewards to get when is part of the environment logic, i.e. in case of Unity-ML the environment lives in Unity, so you have to implement the reward function in Unity (C#). \nSo in order to keep the clear separation between environment (Unity) and agent (Python). I think its best to keep the reward logic in Unity\/C# and don't tinker with it in Python.\ntl;dr: I think it's intended that you cannot set the reward via the Python API to keep a clear environment-agent separation.","Q_Score":2,"Tags":"c#,python,unity3d,reinforcement-learning,ml-agent","A_Id":51348285,"CreationDate":"2018-07-14T10:36:00.000","Title":"Reward Logic out of Unity3D in ml-agents package","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I use Keras for a CNN and have two types of Inputs: Images of objects, and one or two more parameters describing the object (e.g. weight). How can I train my network with both data sources? Concatenation doesn't seem to work because the inputs have different dimensions. My idea was to concatenate the output of the image analysis and the parameters somehow, before sending it into the dense layers, but I'm not sure how. Or is it possible to merge two classifications in Keras, i.e. classifying the image and the parameter and then merging the classification somehow?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1717,"Q_Id":51341613,"Users Score":1,"Answer":"You can use Concatenation layer to merge two inputs. Make sure you're converting multiple inputs into same shape; you can do this by adding additional Dense layer to either of your inputs, so that you can get equal length end layers. Use those same shape outputs in Concatenation layer.","Q_Score":2,"Tags":"python,tensorflow,keras,concatenation,conv-neural-network","A_Id":51341789,"CreationDate":"2018-07-14T17:06:00.000","Title":"Multiple Inputs for CNN: images and parameters, how to merge","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm clustering data (trying out multiple algorithms) and trying to evaluate the coherence\/integrity of the resulting clusters from each algorithm. I do not have any ground truth labels, which rules out quite a few metrics for analysing the performance. \nSo far, I've been using Silhouette score as well as calinski harabaz score (from sklearn). With these scores, however, I can only compare the integrity of the clustering if my labels produced from an algorithm propose there to be at minimum, 2 clusters - but some of my algorithms propose that one cluster is the most reliable.\nThus, if you don't have any ground truth labels, how do you assess whether the proposed clustering by an algorithm is better than if all of the data was assigned in just one cluster?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1769,"Q_Id":51343116,"Users Score":0,"Answer":"Don't just rely on some heuristic, that someone proposed for a very different problem.\nKey to clustering is to carefully consider the problem that you are working on. What is the proper way of proposing the data? How to scale (or not scale)? How to measure the similarity of two records in a way that it quantifies something meaningful for your domain.\nIt is not about choosing the right algorithm; your task is to do the math that relates your domain problem to what the algorithm does. Don't treat it as a black box. Choosing the approach based on the evaluation step does not work: it is already too late; you probably did some bad decisions already in the preprocessing, used the wrong distance, scaling, and other parameters.","Q_Score":1,"Tags":"python-3.x,machine-learning,scikit-learn,cluster-analysis,silhouette","A_Id":51346452,"CreationDate":"2018-07-14T20:27:00.000","Title":"How to analyse the integrity of clustering with no ground truth labels?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to run DBSCAN clustering on about 14M users, each one has 1k data points. Each user is a different clustering case which is completely separate from other users. basically I have many small clustering tasks.\nRunning it on a single machine doesn't work for me, even when paralleling the tasks using python multiprocessing module, as IO and clustering take ages.\nI thought about using Spark to manage a parallel run on a cluster, but decided it might not fit my case, since DBSCAN is not implemented in MLlib and the fact that I don't need to run each clustering task in parallel, but run each one separately. whenever I try to use anything outside of Spark native RDD or Dataframes it obviously has to collect all data to the driver node.\nmy question is weather there is a smarter solution to my problem than simply run many isolated processes on different nodes, when each one will select a subset of the users?\nthanks","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":893,"Q_Id":51347015,"Users Score":0,"Answer":"Since your users are all independent, this clearly is an embarrassingly parallel problem. You want to run the same task (DBSCAN) millions of times. There are many ways to achieve this. You can probably use Spark (although I would consider using a Java based tool with it, such as ELKI - and you probably need to make sure you parallelize on the users, not within each user), MapReduce, or even Makefiles with locking, if you have a network file system with locking.\nThe key factor is how your data is organized. It makes a huge difference whether you can read in parallel for all workers, or route all your data through a master node (bad). You need to get the data efficiently to the workers, and need to store the clustering results.","Q_Score":1,"Tags":"python,apache-spark,cluster-analysis,dbscan","A_Id":51351211,"CreationDate":"2018-07-15T09:21:00.000","Title":"DBSCAN clustering python - parallel run on multiple clustering tasks","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to python programming. I am using spyder to run my code. I have generated a very big data in python after 3 hours of computation. Now I want to save my data. One way of doing this is calling the pickle.dump() function, but in order to do this I have to run my program again and this will take another 3 hours. In what way I can store my data when data has already been generated in Spyder?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":135,"Q_Id":51351472,"Users Score":0,"Answer":"What form does the data take? If it's a database in pandas, you can export it to a csv or xlsx","Q_Score":0,"Tags":"python,python-3.x,python-2.7,python-requests,spyder","A_Id":51351532,"CreationDate":"2018-07-15T19:19:00.000","Title":"Storing already generated data in spyder","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working with tracking data that tracks tennis players in a local match. The data is provided to me in a json file and every 100 milliseconds (so 10 times a second), it knows where the players on both sides of the court are and where the ball is and provides other match data as well. Using this tracking data, I created a bunch of custom python classes and subclasses to create a \"match\" object and I am loading all of the tracking data into this \"match object.\" Can I create a pandas df holding instances of custom classes\/does this make sense? \n\nBackstory\/thought process (in case my question doesn't make sense which is highly possible...)\nThe first time I implemented this I basically created a \"match\" object where I used python OOP and had the match broken into games, sets, points, players, etc. The players part is a little confusing because due to calculations on my end we created a player object and a new instance of a player every 100 milliseconds (it's hard to wrap your head around because one player is the same throughout the game, but think of it as that player at that exact moment in time). I'm not sure if it makes more sense to change these \"player\" objects instead into rows in a pandas dataframe (they're are a ton of them, think about a 3 hour match) or instead if I can just create a pandas df and have a player be a column. Players make up points and then points make up frames, so if I did change the player objects to a pandas df it would be hard because than I would have a bunch of rows in the dataframe making up a point and then a bunch of points making up a game.. and whatnot\nBecause there is so much tracking data, efficiency considerations are important to me (although I prefer to do things something somewhat slower but not drastically but it helps me ensure\/check all data)","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2039,"Q_Id":51353218,"Users Score":8,"Answer":"A DataFrame's columns (Series) can have any of the NumPy dtypes, including object, which can hold arbitrary Python objects.\nDoing so gives up most of the speed and space benefits of using NumPy\/Pandas in the first place. And also the type checking\u2014if you accidentally insert an object that isn't an instance of a match subclass, it will just work. And many convenience features. \nBut you do still get some convenience features, and sometimes that's a more than good enough reason to use Pandas.\nIf the performance isn't acceptable, though, you will have to rethink things. For example, if you flatten the object out into a set of attributes (maybe some of them NaN or N\/A for some of the subclasses) that you can store as a row, especially if some of those attributes are things like floats or ints that you want to do a lot of computation on, you'll get a lot more out of Pandas\u2014but at the cost of losing the OO benefits of your classes, of course.\nOccasionally, it's worth building a hybrid to get the best of both worlds: a DataFrame whose rows hold the storage for your match objects, but then also a match class hierarchy that holds an index or even a single-row DataFrame (a slice from the main one) and provides an OO view onto the same information. But more often, it isn't worth the work to do this, as either almost all of your code ends up being Pandas or almost all of it ends up being OO.\nOne last possibility, if there are huge numbers of these things, would be an ORM that uses a relational database, or an old-school hierarchical database,1 for storage and indexing.\n\n1. In fact, building a hierarchical database out of a bunch of DataFrames and then wrapping it in an object model is basically the same thing as the previous paragraph, but the idea here is that you use stuff that's already built to do all the hard and\/or tedious stuff, not that you build a database just to hide it from yourself.","Q_Score":4,"Tags":"python,pandas,class,oop,dataframe","A_Id":51353269,"CreationDate":"2018-07-16T00:19:00.000","Title":"Can I (\/does it make sense to) create a pandas dataframe to hold custom class instances?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I can show rgba image using pyplot.imshow(image,alpha=0.8).\nI tried to save image using pyplot.savefig(), but image included padding is saved.\nI want to save only image which not have padding.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":505,"Q_Id":51353755,"Users Score":1,"Answer":"can you use: pyplot.savefig() instead of pyplot.saveconfig()?\nif yes, then you can use bbox_inches='tight' to remove or reduce margins and padding around the image:\nsolution one:\npyplot.savefig('test.png', bbox_inches='tight')","Q_Score":2,"Tags":"python,image,matplotlib,rgba","A_Id":51353814,"CreationDate":"2018-07-16T02:12:00.000","Title":"How can save rgba image on pyplot?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Now i am facing a problem in tensorflow:\nI have a network consisting of 6 convolutional layers (each with batch normalization, and the last convolution is followed by an average pooling to make the output shape Nx1x1xC), aiming to classify one image into a category. Everything is fine during training: \n- training samples are about 150000\n- validation samples during training are about 12000\nI have trained totally 50000 iterations with mini-batch size of 6.\n- During training, the training loss is getting lower always (from about 2.6 at beginning to about 0.3 at iteration 50000), \n- and the validation accuracy is getting higher and saturated after about 40000 iterations (from 60% at beginning to 72% at iteration 50000)\nBUT when I use the learned weights of iteration 50000 on the same validation samples to test, the overall accuracy comes at only about 40%. I have googled if there someone who have faced similar problems. Some said the decay of moving average in batch normalization may be the cause.\nThe default decay in tf.contrib.layers.batch_norm is 0.999. Then I have trained with decay of 0.9, 0.99, 0.999. the result of OA on validation samples during test are 70%, 30%, 39%. Although decay of 0.9 have the best result, it is still lower than the OA on validation during training.\nI am writing to ask if anyone have such similar problems, and do you have any idea what could be the cause?\nbest wishes,","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":517,"Q_Id":51360966,"Users Score":1,"Answer":"I have two suggestions:\n\nIf you aren't using a bool isTraining and passing it to the Batch Normalization layers, do so! this should be a placeholder, and set before each session (in training it will be set to true, in test\/validation to false).\nCheck that during test\/validation you don't shuffle your tet\/validation dataset (there might be some kind of shuffle=True in some import\/management of the batche sof your data).\nThe first one is key, the second shouldn't make that much of a difference, but it ensures exact numberical values each time.","Q_Score":0,"Tags":"python,tensorflow,machine-learning","A_Id":51361104,"CreationDate":"2018-07-16T11:39:00.000","Title":"In tensorflow training and test shows different results","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've searched for working mechanism of tensorflow object detection in google. I've searched how tensorflow train models with dataset. It give me suggestion about how to implement rather than how it works.\nCan anyone explain how dataset are trained in fit into models?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":27,"Q_Id":51362567,"Users Score":0,"Answer":"You can't \"simply\" understand how Tensorflow works without a good background on Artificial Intelligence and Machine Learning.\nI suggest you start working on those topics. Tensorflow will get much easier to understand and to handle after that.","Q_Score":0,"Tags":"python,tensorflow,object-detection,tensorflow-datasets","A_Id":51362949,"CreationDate":"2018-07-16T13:07:00.000","Title":"Brief explanation on tensorflow object detection working mechanism","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Say I have a high dimensional dataset which I assume to be well separable by some kind of clustering algorithm. And I run the algorithm and end up with my clusters.\nIs there any sort of way (preferable not \"hacky\" or some kind of heuristic) to explain \"what features and thresholds were important in making members of cluster A (for example) part of cluster A?\"\nI have tried looking at cluster centroids but this gets tedious with a high dimensional dataset. \nI have also tried fitting a decision tree to my clusters and then looking at the tree to determine which decision path most of the members of a given cluster follow. I have also tried fitting an SVM to my clusters and then using LIME on the closest samples to the centroids in order to get an idea of what features were important in classifying near the centroids. \nHowever, both of these latter 2 ways require the use of supervised learning in an unsupervised setting and feel \"hacky\" to me, whereas I'd like something more grounded.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1372,"Q_Id":51364165,"Users Score":-1,"Answer":"Do not treat the clustering algorithm as a black box.\nYes, k-means uses centroids. But most algorithms for high-dimensional data don't (and don't use k-means!). Instead, they will often select some features, projections, subspaces, manifolds, etc. So look at what information the actual clustering algorithm provides!","Q_Score":1,"Tags":"python,scikit-learn,cluster-analysis,k-means","A_Id":51369003,"CreationDate":"2018-07-16T14:30:00.000","Title":"How to explain clustering results?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to use sklearn to build an Isolation Forest machine learning program to go through a ton of data. I can only store the past 10 days of data, so I was wondering:\nWhen I use the \"fit\" function on new data that comes in, does it refit the model considering the hyper-parameters from the old data without having had access to that old data anymore? Or is it completely recreating the model?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":295,"Q_Id":51368416,"Users Score":0,"Answer":"In general, only the estimators implementing the partial_fit method are able to do this. Unfortunately, IsolationForest is not one of them.","Q_Score":0,"Tags":"python,python-3.x,machine-learning,scikit-learn,data-fitting","A_Id":51368477,"CreationDate":"2018-07-16T18:53:00.000","Title":"Do you need to store your old data to refit a model in sklearn?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am developing an application (Python 3.x) in which I need to collect the first 13,000 results of a CSE query using one search keyword (from result index 1 to 13,000). For a free version of CSE JSON API (I have tried it), I can only get the first 10 results per query or 100 results per day (by repeating the same query while incrementing the index) otherwise it gives an error (HttpError 400.....returned Invalid Value) when the result index exceeds 100. Is there any option (paid\/free) that I can deploy to achieve the objective?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":116,"Q_Id":51369427,"Users Score":0,"Answer":"Custom Search JSON API is limited to a max depth of 100 results per query, so you'll need to find a different API or devise some solution to modify the query to divide up the result set into smaller parts","Q_Score":0,"Tags":"python-3.x,google-search,google-custom-search,google-search-api","A_Id":51387763,"CreationDate":"2018-07-16T20:10:00.000","Title":"Extracting the first 13,000 results of a search query with Custom Search Engine JSON API","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"Does anyone know the equation\/algorithm\/theorem used by MATLAB's 'box' interpolation kernel in the imresize function? Or (even better) know a numpy equivelant?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":806,"Q_Id":51369489,"Users Score":0,"Answer":"A \"box\" kernel is an averaging kernel with uniform weights. If it is an interpolation kernel, then it corresponds to nearest neighbor interpolation (it always takes the average of one input sample).\nA bit of theory: an interpolating kernel is one that has a value of 1 at the origin, and a value of 0 at integer distances from the origin. In between it can do different things. Thus, to make from \"box\" an interpolating kernel, we'd make its width somewhere in between infinitesimally thin and just under 2 sample spacings. That makes it fit the definition of an interpolating kernel. However, if it is thinner than 1 sample spacing, it will generate an output of 0 for some displacements -- not desirable. And if it is wider than 1 sample spacing, there will be displacements where the output is the addition of two input samples, twice as large as it should be -- not desirable either. Thus, making it exactly 1 sample spacing wide is the only useful width here. With this width, at any displacement it always covers exactly one input sample -- hence it does linear interpolation.","Q_Score":3,"Tags":"python,matlab,numpy,interpolation,image-resizing","A_Id":51370046,"CreationDate":"2018-07-16T20:14:00.000","Title":"What Is Matlab's 'box' Interpolation Kernel","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Does anyone know the equation\/algorithm\/theorem used by MATLAB's 'box' interpolation kernel in the imresize function? Or (even better) know a numpy equivelant?","AnswerCount":2,"Available Count":2,"Score":0.1973753202,"is_accepted":false,"ViewCount":806,"Q_Id":51369489,"Users Score":2,"Answer":"box interpolation is simply averaging pixels within the specified window size.\nYou may check the matlab function smooth3 etc for detail.","Q_Score":3,"Tags":"python,matlab,numpy,interpolation,image-resizing","A_Id":51369992,"CreationDate":"2018-07-16T20:14:00.000","Title":"What Is Matlab's 'box' Interpolation Kernel","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to Pytorch and I am confused about the difference between nn.Parameter  and autograd.Variable. I know that the former one is the subclass of Variable and has the gradient. But I really don't understand why we introduce Parameter and when we should use it?\n\nSUMMARY:\nThanks for the explanation of iacolippo, i finally understand the difference between parameter and variable. In a summary, variable in pytorch is NOT same as in  the variable in tensorflow, the former one is not attach to the model's trainable parameters while the later one will. Attaching to the model means that using model.parameters() will return the certain parameter to you, which is useful in training phase to specify the variable needed to train. The 'variable' is more helpful as a cache in some network.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":5351,"Q_Id":51373919,"Users Score":5,"Answer":"From the documentation:\nParameters are Tensor subclasses, that have a very special property when used with Modules - when they\u2019re assigned as Module attributes they are automatically added to the list of its parameters, and will appear e.g. in parameters() iterator. Assigning a Tensor doesn\u2019t have such effect. This is because one might want to cache some temporary state, like last hidden state of the RNN, in the model. If there was no such class as Parameter, these temporaries would get registered too.\nThink for example when you initialize an optimizer:\noptim.SGD(model.parameters(), lr=1e-3)\nThe optimizer will update only registered Parameters of the model.\nVariables are still present in Pytorch 0.4 but they are deprecated. From the docs:\nThe Variable API has been deprecated: Variables are no longer necessary to use autograd with tensors. Autograd automatically supports Tensors with requires_grad set to True.\n\nPytorch pre-0.4\nIn Pytorch before version 0.4 one needed to wrap a Tensor in a torch.autograd.Variable in order to keep track of the operations applied to it and perform differentiation. From the docs of Variable in 0.3:\nWraps a tensor and records the operations applied to it.\nVariable is a thin wrapper around a Tensor object, that also holds the gradient w.r.t. to it, and a reference to a function that created it. This reference allows retracing the whole chain of operations that created the data. If the Variable has been created by the user, its grad_fn will be None and we call such objects leaf Variables.\nSince autograd only supports scalar valued function differentiation, grad size always matches the data size. Also, grad is normally only allocated for leaf variables, and will be always zero otherwise.\nThe difference wrt Parameter was more or less the same. From the docs of Parameters in 0.3:\nA kind of Variable that is to be considered a module parameter.\nParameters are Variable subclasses, that have a very special property when used with Modules - when they\u2019re assigned as Module attributes they are automatically added to the list of its parameters, and will appear e.g. in parameters() iterator. Assigning a Variable doesn\u2019t have such effect. This is because one might want to cache some temporary state, like last hidden state of the RNN, in the model. If there was no such class as Parameter, these temporaries would get registered too.\nAnother difference is that parameters can\u2019t be volatile and that they require gradient by default.","Q_Score":5,"Tags":"python,neural-network,deep-learning,pytorch","A_Id":51383465,"CreationDate":"2018-07-17T05:38:00.000","Title":"The purpose of introducing nn.Parameter in pytorch","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have model saved in graph (.pb file). But now the model is inaccurate and I would like to develop it. I have pictures of additional data to learn, but I don't if it's possible or if it's how to do it? The result must be the modified of new data pb graph.","AnswerCount":2,"Available Count":1,"Score":0.2913126125,"is_accepted":false,"ViewCount":1718,"Q_Id":51379506,"Users Score":3,"Answer":"It's a good question. Actually it would be nice, if someone could explain how to do this. But in addition i can say you, that it would come to \"catastrophic forgetting\", so it wouldn't work out. You had to train all your data again.\nBut anyway, i also would like to know that espacially for ssd, just for test reasons.","Q_Score":2,"Tags":"python,tensorflow","A_Id":51383561,"CreationDate":"2018-07-17T10:48:00.000","Title":"How to retrain model in graph (.pb)?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How can I know whether or not I should use a sparse or dense representation in PySpark? I understand the differences between them (sparse saves memory by only storing the non-zero indices and values), but performance-wise, are there any general heuristics that describe when to use sparse vectors over dense ones?\nIs there a general \"cutoff\" dimension and percent of 0 values beyond which it is generally better to use sparse vectors? If not, how should I go about making the decision? Thanks.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1280,"Q_Id":51385657,"Users Score":2,"Answer":"The thing to remember is that pyspark.ml.linalg.Vector and pyspark.mllib.linalg.Vector are just compatibility layer between Python and Java API. There are not full featured or optimized linear algebra utilities and you shouldn't use them as such. The available operations are either not designed for performance or just convert to standard NumPy array under the covers.\nWhen used with other ml \/ mllib tools there will be serialized and converted to Java equivalents so Python representation performance is mostly inconsequential. \nThis means that the biggest real concern is storage and a simple rule of thumb is:\n\nIf on average half of the entries is zero it is better to use SparseVector.\nOtherwise it is better to use DenseVector.","Q_Score":4,"Tags":"python,apache-spark,machine-learning,pyspark,sparse-matrix","A_Id":51386373,"CreationDate":"2018-07-17T15:52:00.000","Title":"Sparse vs. Dense Vectors PySpark","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"We have Anaconda 4.3.1 installed on our hosts and recently we have installed several packages for data science use. All the imports were fine except for gensim.\nI am getting \"Intel MKL FATAL ERROR: Cannot load libmkl_avx2.so or libmkl_def.so.\" and getting out of python shell.\nIt sounds like a duplicate but the weird part is, when I import tensorflow or seaborn before importing gensim, I am not getting that error and gensim is being imported. I would also like to know if there is any dependency between these packages. And I do have the latest version of numpy which is 1.14.5. I have looked at various solutions proposed about installing few packages and uninstalling few. I would like to know the reason why we should be doing it before actually doing it.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":732,"Q_Id":51388707,"Users Score":2,"Answer":"Here is my theory on your question:\nIs there any dependency between gensim, tensoflow, seaborn and such packages?\n When you try to install these packages one by one using conda, you might have already seen conda prompting that some of the dependencies will be DOWNGRADED\/UPDATED\/INSTALLED. Hence there is dependency between the dependencies of these packages.\nWhy import error is thrown only on certain cases?\nLooks like a dependency issue. When you try to import gensim, it tries to load certain lib files, which its not able to find. However, when tensorflow or seaborn is imported the mentioned lib files might have already loaded, hence importing gensim did not show an error.\nWhy installing few packages and uninstalling few, help to solve the problem?\nThis might help to have the correct dependencies for the packages to work properly.\nHaving said that, I tried to recreate the error that you got, however gensim is importing fine for me. If you could give the result of \"conda list\", will try to recreate the problem and would be able to give a better insight.","Q_Score":0,"Tags":"python,tensorflow,anaconda,seaborn,gensim","A_Id":52216299,"CreationDate":"2018-07-17T19:14:00.000","Title":"Intel MKL FATAL ERROR: while trying to import gensim package","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Suppose I use DataFrame.ix in some code.\nDoes the fact that it's deprecated mean that at some point in the future, I'm going to update pandas, and then a little bit later, the stuff using that code will mysteriously start to break because they decided that finally, they were going to actually remove ix?","AnswerCount":3,"Available Count":2,"Score":0.0665680765,"is_accepted":false,"ViewCount":646,"Q_Id":51390464,"Users Score":1,"Answer":"Yes, deprecated here means that the attribute or method has been removed from the newer versions. Hence, it is advised to avoid them in your code to avoid future issues.","Q_Score":3,"Tags":"python,pandas,deprecated","A_Id":51390517,"CreationDate":"2018-07-17T21:30:00.000","Title":"What does it mean for DataFrame.ix to be deprecated?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Suppose I use DataFrame.ix in some code.\nDoes the fact that it's deprecated mean that at some point in the future, I'm going to update pandas, and then a little bit later, the stuff using that code will mysteriously start to break because they decided that finally, they were going to actually remove ix?","AnswerCount":3,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":646,"Q_Id":51390464,"Users Score":6,"Answer":"That's the basic idea of deprecation. The library's maintainers are letting you know now that they plan to stop supporting ix (e.g., fixing bugs in it), and may very well remove it in the near future. As long as it's deprecated, you have a window of opportunity to change your code to use other alternatives (such as loc and iloc), on your own terms, before you're forced to do so when pandas breaks \"under your feet\".","Q_Score":3,"Tags":"python,pandas,deprecated","A_Id":51390526,"CreationDate":"2018-07-17T21:30:00.000","Title":"What does it mean for DataFrame.ix to be deprecated?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Currently I am researching about viable approaches to identify a certain object with the image processing techniques. However I ams struggling finding them. For example, I have a CNN capable of detecting certain objects, like a person, then I can track the person as well. However, my issue is that I want the identify the detected and tracked person like saving its credentials and giving an id. I do not want something like who is he\/she. Just giving an id in that manner.\nAny help\/resource will be appreciated.","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":331,"Q_Id":51395132,"Users Score":2,"Answer":"Create a database, Store the credentials you needed for later use e.g object type and some usable specifications, by giving them some unique ID. CNN already recognized the object so just need to store it in database and later on you can perform more processing on the generated data. Simple solution is that to the problem you are explaining.\nOkay I got your problem that you want to identify what kind of object is being tracked because cnn is only tracking not identifying. For that purpose you have to train your CNN on some specific features and give them some identity like objectA has [x,y,z] features. Then CNN will help you in finding the identity of the object.\nYou can use openCv to do this as well, store some features of some specific objects, then use some distance matching technique to match the live feature with stored features. \nThanks.","Q_Score":0,"Tags":"python,opencv,image-processing,computer-vision","A_Id":51396146,"CreationDate":"2018-07-18T06:39:00.000","Title":"Object Identification Using OpenCV","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a working app with TFlite using tensorflow for poets. It works with a labels.txt and graph.lite pair files. I have downloaded another model in .tflite file format and wanted to use in my application. I wanted to ask what are the differences between .lite and .tflite files and are there any ways to convert tflite format to lite?\nThanks","AnswerCount":1,"Available Count":1,"Score":0.537049567,"is_accepted":false,"ViewCount":477,"Q_Id":51396671,"Users Score":3,"Answer":"There is no difference in \".lite\" and \".tflite\" format (as long as they can be correctly consumed by Tensonflow Lite). And there is no need to convert them.","Q_Score":0,"Tags":"python,tensorflow","A_Id":51565917,"CreationDate":"2018-07-18T08:03:00.000","Title":"Convert TFLite to Lite","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm currently working on a neural network that evaluates students' answers to exam questions. Therefore, preprocessing the corpora for a Word2Vec network is needed. Hyphenation in german texts is quite common. There are mainly two different types of hyphenation:\n1) End of line:  \nThe text reaches the end of the line so the last word is sepa-\nrated.\n2) Short form of enumeration:\nin case of two \"elements\": \nGeistes- und Sozialwissenschaften\nmore \"elements\": \nWirtschafts-, Geistes- und Sozialwissenschaften\nThe de-hyphenated form of these enumerations should be:\nGeisteswissenschaften und Sozialwissenschaften\nWirtschaftswissenschaften, Geisteswissenschaften und Sozialwissenschaften\nI need to remove all hyphenations and put the words back together. I already found several solutions for the first problem.\nBut I have absoluteley no clue how to get the second part (in the example above \"wissenschaften\") of the words in the enumeration problem. I don't even know if it is possible at all.\nI hope that I have pointet out my problem properly.\nSo has anyone an idea how to solve this problem?\nThank you very much in advance!","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":50,"Q_Id":51430249,"Users Score":1,"Answer":"It's surely possible, as the pattern seems fairly regular. (Something vaguely analogous is sometimes seen in English. For example: The new requirements applied to under-, over-, and average-performing employees.)\nThe rule seems to be roughly, \"when you see word-fragments with a trailing hyphen, and then an und, look for known words that begin with the word-fragments, and end the same as the terminal-word-after-und \u2013 and replace the word-fragments with the longer words\". \nNot being a German speaker and without language-specific knowledge, it wouldn't be possible to know exactly where breaks are appropriate. That is, in your Geistes- und Sozialwissenschaften example, without language-specific knowledge, it's unclear whether the first fragment should become Geisteszialwissenschaften or Geisteswissenschaften or Geistesenschaften or Geiestesaften or any other shared-suffix with Sozialwissenschaften. But if you've got a dictionary of word-fragments, or word-frequency info from other text that uses the same full-length word(s) without this particular enumeration-hyphenation, that could help choose. \n(If there's more than one plausible suffix based on known words, this might even be a possible application of word2vec: the best suffix to choose might well be the one that creates a known-word that is closest to the terminal-word in word-vector-space.)\nSince this seems a very German-specific issue, I'd try asking in forums specific to German natural-language-processing, or to libraries with specific German support. (Maybe, NLTK or Spacy?)\nBut also, knowing word2vec, this sort of patch-up may not actually be that important to your end-goals. Training without this logical-reassembly of the intended full words may still let the fragments achieve useful vectors, and the corresponding full words may achieve useful vectors from other usages. The fragments may wind up close enough to the full compound words that they're \"good enough\" for whatever your next regression\/classifier step does. So if this seems a blocker, don't be afraid to just try ignoring it as a non-problem. (Then if you later find an adequate de-hyphenation approach, you can test whether it really helped or not.)","Q_Score":0,"Tags":"python,nlp,word2vec,preprocessor,hyphenation","A_Id":51439004,"CreationDate":"2018-07-19T19:12:00.000","Title":"Python3 remove multiple hyphenations from a german string","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Does it call forward() in nn.Module? I thought when we call the model, forward method is being used.\nWhy do we need to specify train()?","AnswerCount":6,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":130339,"Q_Id":51433378,"Users Score":15,"Answer":"There are two ways of letting the model know your intention i.e do you want to train the model or do you want to use the model to evaluate.\nIn case of model.train() the model knows it has to learn the layers and when we use model.eval() it indicates the model that nothing new is to be learnt and the model is used for testing.\nmodel.eval() is also necessary because in pytorch if we are using batchnorm and during test if we want to just pass a single image, pytorch throws an error if model.eval() is not specified.","Q_Score":161,"Tags":"python,pytorch","A_Id":51596850,"CreationDate":"2018-07-20T00:10:00.000","Title":"What does model.train() do in PyTorch?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am attempting to perform outlier detection and I have 15 different test sets and 3 different models (a PCA-based classifier, One Class SVM and Isolation Forest).  \nFor PCA-based classification, I have written my own code for generating ROC curves. I have 2 lists pcafprs and pcatprs, each of which has 15 sublists, each sublist representing the False Positive Ratios and True Positive Ratios, required to plot the ROC curve.  \nFor One-Class SVM and Isolation Forest, I can get the (fpr, tpr) from sklearn.metrics.roc_curve. Similar to PCA, I have ocsvmfprs and ocsvmtprs for One-Class SVM, and isoforestfprs and isoforesttprs for Isolation Forest.  \nFor each test set, I can iterate over the FPR and TPR lists and plot the ROC curve. The code might look like:\nfor i in range(len(pcafprs)):\n    plt.plot(pcafprs[i], pcatprs[i]) #Plot the ROC curve\n    plt.show()\n\nFor each of the 3 models, I want to be able to plot the average of all 15 ROC curves for the 15 test sets in one graph. I cannot simply do np.mean over the arrays containing the TPRs and FPRs because the FPRs returned by sklearn.metrics.roc_curve are all different points for each test set.\nFor PCA, I have tried using np.mean(pcatprs, axis=0) and np.mean(pcafprs, axis=0) to average out all the TPRs and FPRs so that I can plot a single graph which represents the mean of all the test sets. This works because for PCA I have generated the same number of FPRs and TPRs for each test set.\nHowever, I am unable to control the no. of FPRs and TPRs returned for each test set by sklearn.metrics.roc_curve, and it turns out that it returns different number of values for each test set. Due to this, I cannot use np.mean to find the average ROC curve.  \ntl,dr: Is there a way to plot the average of multiple lines on a graph without having the equations and only having some points that lie on the lines, where we have a different number of points available for each line?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1603,"Q_Id":51434567,"Users Score":0,"Answer":"Solved by choosing a fixed set of FPRs, then using no.interp for each pair of (fpr,tpr) returned by sklearn.metrics.roc_curve to get the corresponding TPRs, then averaged all ROCs using np.mean","Q_Score":1,"Tags":"python,matplotlib,plot,scikit-learn,roc","A_Id":51436328,"CreationDate":"2018-07-20T03:18:00.000","Title":"How to plot the average of ROC curves?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"This question is related to convolutionals neural networks (especially YoloV3)\nSince one epoch is one forward pass and one backward pass of all the training examples, for the model to converge properly, is it the same (in terms of precision and time to converge) to :\n\ntrain with n*k images during m epochs ?\ntrain with n images during m*k epochs ?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":794,"Q_Id":51437817,"Users Score":0,"Answer":"No, they are not the same. \n*The number of examples you show the network defines what it will be looking for - a network with more examples will tend to be more general. If there are, for example, 1000 pictures with different dogs in it, and you only show 300\/300000 pictures, the network (on average) will only recognize one specific kind of dog, and be unable to pick out common traits of all dogs.\n*An epoch is basically modifying the network in a small step, and the key word here is small - taking too big steps risk overshooting our target values for the network parameters. Since we\u00b4re taking small steps, we have to take several of them to get where we want.","Q_Score":1,"Tags":"python,deep-learning,epoch,yolo","A_Id":51438139,"CreationDate":"2018-07-20T07:59:00.000","Title":"CNN: Is it better to train 300.000 images during 1 epoch or 300 images during 1000 epoch?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Does anyone know how to properly install tensorflow on Windows?\nI'm currently using Python 3.7 (also tried with 3.6) and every time I get the same \"Could not find a version that satisfies the requirement tensorflow-gpu (from versions: )\nNo matching distribution found for tensorflow-gpu\" error\nI tried installing using pip and anaconda, both don't work for me.\n\nFound a solution, seems like Tensorflow doesn't support versions of python after 3.6.4. This is the version I'm currently using and it works.","AnswerCount":16,"Available Count":5,"Score":0.0,"is_accepted":false,"ViewCount":44333,"Q_Id":51440475,"Users Score":0,"Answer":"Not Enabling the Long Paths can be the potential problem.To solve that,\nSteps include:\n\nGo to Registry Editor on the Windows Laptop\n\nFind the key \"HKEY_LOCAL_MACHINE\"->\"SYSTEM\"->\"CurrentControlSet\"->\n\"File System\"->\"LongPathsEnabled\" then double click on that option and change the value from 0 to 1.\n\n\n3.Now try to install the tensorflow it will work.","Q_Score":5,"Tags":"python,tensorflow","A_Id":68421682,"CreationDate":"2018-07-20T10:26:00.000","Title":"Can't install tensorflow with pip or anaconda","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Does anyone know how to properly install tensorflow on Windows?\nI'm currently using Python 3.7 (also tried with 3.6) and every time I get the same \"Could not find a version that satisfies the requirement tensorflow-gpu (from versions: )\nNo matching distribution found for tensorflow-gpu\" error\nI tried installing using pip and anaconda, both don't work for me.\n\nFound a solution, seems like Tensorflow doesn't support versions of python after 3.6.4. This is the version I'm currently using and it works.","AnswerCount":16,"Available Count":5,"Score":0.0,"is_accepted":false,"ViewCount":44333,"Q_Id":51440475,"Users Score":0,"Answer":"As of July 2019, I have installed it on python 3.7.3 using py -3 -m pip install tensorflow-gpu\npy -3 in my installation selects the version 3.7.3. \nThe installation can also fail if the python installation is not 64 bit. Install a 64 bit version first.","Q_Score":5,"Tags":"python,tensorflow","A_Id":56944215,"CreationDate":"2018-07-20T10:26:00.000","Title":"Can't install tensorflow with pip or anaconda","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Does anyone know how to properly install tensorflow on Windows?\nI'm currently using Python 3.7 (also tried with 3.6) and every time I get the same \"Could not find a version that satisfies the requirement tensorflow-gpu (from versions: )\nNo matching distribution found for tensorflow-gpu\" error\nI tried installing using pip and anaconda, both don't work for me.\n\nFound a solution, seems like Tensorflow doesn't support versions of python after 3.6.4. This is the version I'm currently using and it works.","AnswerCount":16,"Available Count":5,"Score":0.0,"is_accepted":false,"ViewCount":44333,"Q_Id":51440475,"Users Score":0,"Answer":"Actually the easiest way to install tensorflow is:\ninstall python 3.5 (not 3.6 or 3.7) you can check wich version you have by typing \"python\" in the cmd.\nWhen you install it check in the options that you install pip with it and you add it to variables environnement.\nWhen its done just go into the cmd and tipe \"pip install tensorflow\"\nIt will download tensorflow automatically.\nIf you want to check that it's been installed type \"python\" in the cmd then some that \">>>\" will appear, then you write \"import tensorflow\" and if there's no error, you've done it!","Q_Score":5,"Tags":"python,tensorflow","A_Id":53177064,"CreationDate":"2018-07-20T10:26:00.000","Title":"Can't install tensorflow with pip or anaconda","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Does anyone know how to properly install tensorflow on Windows?\nI'm currently using Python 3.7 (also tried with 3.6) and every time I get the same \"Could not find a version that satisfies the requirement tensorflow-gpu (from versions: )\nNo matching distribution found for tensorflow-gpu\" error\nI tried installing using pip and anaconda, both don't work for me.\n\nFound a solution, seems like Tensorflow doesn't support versions of python after 3.6.4. This is the version I'm currently using and it works.","AnswerCount":16,"Available Count":5,"Score":1.2,"is_accepted":true,"ViewCount":44333,"Q_Id":51440475,"Users Score":5,"Answer":"Tensorflow or Tensorflow-gpu is supported only for 3.5.X versions of Python. Try installing with any Python 3.5.X version. This should fix your problem.","Q_Score":5,"Tags":"python,tensorflow","A_Id":51706227,"CreationDate":"2018-07-20T10:26:00.000","Title":"Can't install tensorflow with pip or anaconda","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Does anyone know how to properly install tensorflow on Windows?\nI'm currently using Python 3.7 (also tried with 3.6) and every time I get the same \"Could not find a version that satisfies the requirement tensorflow-gpu (from versions: )\nNo matching distribution found for tensorflow-gpu\" error\nI tried installing using pip and anaconda, both don't work for me.\n\nFound a solution, seems like Tensorflow doesn't support versions of python after 3.6.4. This is the version I'm currently using and it works.","AnswerCount":16,"Available Count":5,"Score":0.0,"is_accepted":false,"ViewCount":44333,"Q_Id":51440475,"Users Score":0,"Answer":"You mentioned Anaconda. Do you run your python through there?\nIf so check in Anaconda Navigator --> Environments, if your current environment have got tensorflow installed. \nIf not, install tensorflow and run from that environment. \nShould work.","Q_Score":5,"Tags":"python,tensorflow","A_Id":51440570,"CreationDate":"2018-07-20T10:26:00.000","Title":"Can't install tensorflow with pip or anaconda","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a pandas Series like the following:\na = pd.Series([a1, a2, a3, a4, ...]) \nand I want to create another pandas Series based on the following rule:\nb = pd.Series(a1, a2+a1**0.8, a3 + (a2 + a1**0.8)**0.8, a4 + (a3 + (a2 + a1**0.8)**0.8)**0.8, ...).\nThis is doable using iteration,  but I have a large dataset (millions of records) and I must perform operation for thousands of times (for optimization purposes). I need to do this operation very fast. Is there any possible way for me to realize this by using pandas or numpy built-in functions?","AnswerCount":4,"Available Count":1,"Score":0.049958375,"is_accepted":false,"ViewCount":210,"Q_Id":51449260,"Users Score":1,"Answer":"The important point about these kinds of problems that you need to know is that you're on a paradox spot right now. This means that you're on a point that you want to take advantage of both vectorization and non-vectorization like threading or parallelization.\nIn such situation you can try one\/some of the following options:\n\nChange the type of your data structure.\nRethink your problem and see if it's possible to solve this entirely in a Vectorized way (preferably)\nSimply use a non-vectorized-based approach but sacrifice something else like memory.","Q_Score":7,"Tags":"python,performance,pandas,numpy,iteration","A_Id":51450250,"CreationDate":"2018-07-20T19:30:00.000","Title":"pandas iteratively update column values","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a python script which opens an image file (.png or .ppm) using OpenCV, then loads all the RGB values into a multidimensional Python array (or list), performs some pixel by pixel calculations solely on the Python array (OpenCV is not used at all for this stage), then uses the newly created array (containing new RGB values) to write a new image file (.png here) using OpenCV again. Numpy is not used at all in this script. The program works fine.\nThe question is how to do this without using any external libraries, regardless whether they are for image processing or not (e.g. OpenCV, Numpy, Scipy, Pillow etc.). To summarize, I need to use bare bones Python's internal modules to: 1. open image and read the RGB values and 2. write a new image from pre-calculated RGB values. I will use Pypy instead of CPython for this purpose, to speed things up.\nNote: I use Windows 10, if that matters.","AnswerCount":4,"Available Count":1,"Score":0.049958375,"is_accepted":false,"ViewCount":6169,"Q_Id":51457993,"Users Score":1,"Answer":"Working with bare-bones .ppm files is trivial: you have three lines of text (P6, \"width height\", 255), and then you have the 3*width*height bytes of RGB.  As long as you don't need more complicated variants of the .ppm format, you can write a loader and a saver in 5 lines of code each.","Q_Score":2,"Tags":"python,image-processing,pypy","A_Id":51458710,"CreationDate":"2018-07-21T16:20:00.000","Title":"How to open\/create images in Python without using external modules","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"What is the best way to implement a multi process based consumer producer pattern with pyarrow as a fast memory store for pandas dataframes?\nCurrently I am using redis pub sub but I think there might be a more efficient (faster) solution? Could you provide an example?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":229,"Q_Id":51465320,"Users Score":1,"Answer":"Solution with lists:\nProducer puts data into a list with LPUSH\nConsumer takes data from this list with RPOP or BRPOP (blocking).\nLimitations: only one consumer reads the message. If you have 2, only one of them will see the message.\nSpeed: for one pair of consumer-producer it will have the same speed. The more consumers (for this or other lists), the faster it will be than pub\/sub.","Q_Score":1,"Tags":"python,pandas,redis,pyarrow","A_Id":51479161,"CreationDate":"2018-07-22T12:51:00.000","Title":"Consumer-producer pattern with pyarrow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there a way to only use the base decision tree that is used in the XGBoost algorithm?\nI know that Sklearn's GBT just uses a Sklearn Decision Tree as their base but XGBoost builds trees differently (e.g. regularization of the leaf weights).\nI looked at the code for XGBoost but I wasn't able to figure out how they built their base tree.\nThanks!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":113,"Q_Id":51467350,"Users Score":0,"Answer":"There are 2 solutions for you:\n\nBuild only 1 tree (meaning use only 1 iteration), that means that when you train use num_boost_round=1\nWhen you predict use ntree_limit=1\n\nEither one will solve your problem. You either train only 1 tree or you predict using only 1 tree.","Q_Score":0,"Tags":"python,machine-learning,scikit-learn,xgboost","A_Id":51473577,"CreationDate":"2018-07-22T16:56:00.000","Title":"Use Base Tree from XGBoost","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have images of vehicles . I need to predict the price of the vehicle based on image extraction. \nWhat I have learnt is , I can use CNN to extract the image features but what I am not able to get is, How to predict the prices of vehicles. \nI know that the I need to train my CNN model before it predicts the price. \nI don't know how to train the model with images along with prices .\nIn the end what I expect is , I will input an vehicle image and I need to get price of the vehicle. \nCan any one provide the approach for this ?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":268,"Q_Id":51484727,"Users Score":0,"Answer":"I would use the CNN to predict the model of the car and then using a list of all the car prices it's easy enough to get the price, or if you dont care about the car model just use the prices as lables","Q_Score":0,"Tags":"python,tensorflow","A_Id":67088725,"CreationDate":"2018-07-23T17:57:00.000","Title":"CNN image extraction to predict a continuous value","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using numpy to analyze graphs.  One type of analysis I am doing is traversing, while enumerating: \"how often is node K in a path with node J, up to this point\". In this analysis, many of my values are ratios, or percentages, or however you want to think of it.\nSince graphs often branch, they are often exponential when it comes to combinations or permutations.  And so, some of the time, my ratios become very small.  And, numpy loses accuracy.  And, eventually, numpy says that the ratio is zero even though it should still be greater than zero.\nTo elaborate a bit more, I use rows of the matrix to represent depth of my search, and I use columns to represent nodes.  The value of the [row,column] is the ratio of said node, at said depth, to whatever other node I am comparing it to.  And so it's the case, that depending upon the graph, that ratio may be cut in half at every next level.  From 1, to .5, to .25, to ..... 1.369^(-554) and suddenly it's zero next iteration.  Not to mention, when it gets small enough I lose accuracy in all my other calculations as well.\nIf I want extreme accuracy even on large graphs, what options do I have?  I suppose I could enumerate in the opposite direction, getting total counts and doing division to recalculate ratios when necessary (it is necessary at time in my program).  But if I do this, I still expect I would lose a ton of accuracy when I divide one huge number by another huge number, yes?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":59,"Q_Id":51487174,"Users Score":2,"Answer":"All your ratios are rational numbers, so you could use the Fraction class in the fractions module. Each \"fraction\" is the ratio of two integers. And since Python's integers have no upper limit, neither do the fractions. You can treat them much like float values--add, subtract, multiply, divide, and print them.\nI did something very much like your operations in a previous project of mine, to track ratios in the tree structure of Windows Registry. I did this project in Delphi but have started redoing it in Python. I have already decided to use fractions.\nThe problem is that the values will be kept exactly, as well as the operations on them. But if you plot them on a graph, the values may still be overwhelmed by other values.","Q_Score":1,"Tags":"python,numpy,math,types,numbers","A_Id":51487294,"CreationDate":"2018-07-23T21:00:00.000","Title":"Maintaining accuracy of ratios in numpy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Question 1\nDoes the rotation_range: Int. Degree range for random rotations refer to the range [0, rotation_range] or [-rotation_range, rotation_range]. If I set rotation_range=40, will my images be randomly rotated between [-40, 40] or [0, 40]?\nQuestion 2\nDoes ImageDataStore.flow randomly generate different augmentations of an input image at every epoch or is a single augmentation generated at the start and used for all epochs. \nFor example, let's say I have some image A that is part of my inputs into the flow method. Is image A augmented only once before training, and this augmented version used for all epochs? Or is image A randomly augmented every epoch?\nQuestion 3\nWhen the param shuffle is set to True in the flow method, does this mean the batches are shuffled every epoch, or the images within the batches are shuffled every epoch? \nFor example, lets say our training data consists of 15 images (labeled I1 - I15) is divided into 3 batches\/mini-batches before epoch 1 starts (labeled B1, B2, B3). \nLets say before epoch 1, the images were assigned to the batches as follows:\n\nB1 = {I1, I2, I3, I4, I5}\nB2 = {I6, I7, I8, I9, I10}\nB3 = {I11, I12, I13, I14, I15}\n\nNow in epoch 1, the batches are trained in the order B1, B2, B3. \nWhen epoch 2 starts, will the images in B1, B2, B3 be shuffled so that each batch will not contain the same set of 5 images?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1238,"Q_Id":51489390,"Users Score":1,"Answer":"Question 1\nThe rotation range is [-rotation_range, rotation_range]. I suggest you to check your augmented images by using the parameter save_to_dir in the method flow. This way you can be sure that the images are being augmented as you expect.\nQuestion 2\nWhen calling next, a random augmentation is applied to every image right after it's being loaded, according to the parameters you gave to the constructor of ImageDataGenerator. I.e. an image can be left rotated in one epoch and in the next epoch the same image can be right rotated. That's what makes augmentation so efficient- you artificially increase the size of your data.\nQuestion 3 \nThe list of images is shuffled before each epoch. A batch of images will never repeat itself (well... you can calculate the odds)","Q_Score":0,"Tags":"python,keras,image-preprocessing","A_Id":51494847,"CreationDate":"2018-07-24T01:59:00.000","Title":"Understanding parameters in ImageDataGenerator and flow in Keras","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to train a new model using my own dataset. I will be\n  using Darkflow\/Tensorflow for it.\n\nRegarding my doubts:\n(1) Should we resize our training images for a specific size?\n(2) I think smaller images might save time, but can smaller images harm the accuracy?\n(3) And what about the images to be predicted, should we resize them as well or is it not necessary?","AnswerCount":3,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":4420,"Q_Id":51505729,"Users Score":3,"Answer":"(1) It already resize it with random=1 in .cfg file.The answer is \"yes\".The input resolution of images are same.You can resize it by yourself or Yolo can do it.\n(2)If your hardware is good enough,I suggest you to use big sized images.Also as a suggest,If you will use webcam,use images as the same resolutions as your webcam uses.\n(3)Yes, same as training.","Q_Score":4,"Tags":"python,tensorflow,yolo,darknet,darkflow","A_Id":57576484,"CreationDate":"2018-07-24T18:55:00.000","Title":"Is image resizing needed to training a new Yolo model?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I used to work in Matlab and it is really convenient (when working with big arrays\/matrices and nested functions) to visualize intermediate results during debugging using plot function. \nIn Python I cannot plot anything in debug mode: a window with figure plot is never loaded (I am using Spyder IDE for coding and matplotlib.pyplot for plotting).\nThis is really annoying when debugging nested function and classes. \nDoes anyone know a good solution? Of course, I can always output intermediate results, however it is not convenient. \nThanks,\nMikhail","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2174,"Q_Id":51506354,"Users Score":6,"Answer":"Ok, I found a way to show the plot without breaking the debugging process.\nAll you need to do is to issue plt.pause(1) command, which will display the plots, and then one can continue the debugging process.","Q_Score":3,"Tags":"python,debugging,matplotlib","A_Id":60745616,"CreationDate":"2018-07-24T19:37:00.000","Title":"Plot a function during debugging in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am currently working on a program that would take the previous 4000 days of stock data about a particular stock and predict the next 90 days of performance.\nThe way I've elected to do this is with an RNN that makes use of LSTM layers to use the previous 90 days to predict the next day's performance (when training, the previous 90 days are the x-values and the next day is used as the y-value). What I would like to do however, is use the previous 90-180 days to predict all the values for the next 90 days. However, I am unsure of how to implement this in Keras as all the examples I have seen only predict the next day and then they may loop that prediction into the next day's 90 day x-values.\nIs there any ways to just use the previous 180 days to predict the next 90? Or is the LSTM restricted to only predicting the next day?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":299,"Q_Id":51506404,"Users Score":0,"Answer":"I don't have the rep to comment, but I'll say here that I've toyed with a similar task. One could use a sliding window approach for 90 days (I used 30, since 90 is pushing LSTM limits), then predict the price appreciation for next month (so your prediction is for a single value). @Digital-Thinking is generally right though, you shouldn't expect great performance.","Q_Score":0,"Tags":"python,tensorflow,keras,lstm,rnn","A_Id":51526725,"CreationDate":"2018-07-24T19:41:00.000","Title":"How to make RNN time-forecast multiple days using Keras?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using keras-rl to train my network with the D-DQN algorithm. I am running my training on the GPU with the model.fit_generator() function to allow data to be sent to the GPU while it is doing backprops. I suspect the generation of data to be too slow compared to the speed of processing data by the GPU.\nIn the generation of data, as instructed in the D-DQN algorithm, I must first predict Q-values with my models and then use these values for the backpropagation. And if the GPU is used to run these predictions, it means that they are breaking the flow of my data (I want backprops to run as often as possible).\nIs there a way I can specify on which device to run specific operations? In a way that I could run the predictions on the CPU and the backprops on the GPU.","AnswerCount":2,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":8487,"Q_Id":51507285,"Users Score":7,"Answer":"Maybe you can save the model at the end of the training. Then start another python file and write os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"-1\"before you import any keras or tensorflow stuff. Now you should be able to load the model and make predictions with your CPU.","Q_Score":5,"Tags":"python,tensorflow,keras,keras-rl","A_Id":51570129,"CreationDate":"2018-07-24T20:47:00.000","Title":"Keras with Tensorflow backend - Run predict on CPU but fit on GPU","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm not sure if the Tensorflow ObjectDetection API automatically normalizes the input images (my own dataset). It seems to have an option called 'NormalizeImage' in the DataAugmentations. So far, I haven't specified it, and my models are doing reasonably well. Am I missing image normalization, or does Tensorflow do it automatically for me, or is it just not needed for this Object Detection API?\nMy models have used Faster RCNN and RetinaNet so far.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":372,"Q_Id":51509083,"Users Score":0,"Answer":"Depending on what configuration you specified it uses an image resizer to normalize your dataset.\nimage_resizer {\n  fixed_shape_resizer {\n    height: 300\n    width: 300\n  }\n}\nThis will either downsample or upsample your images using bilinear interpolation.","Q_Score":1,"Tags":"python,tensorflow,object-detection,object-detection-api","A_Id":51544489,"CreationDate":"2018-07-24T23:52:00.000","Title":"Need to NormalizeImage if using Tensorflow's ObjectDetection API?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to plot an array of temperatures for different location during one day in python and want it to be graphed in the format (time, temperature_array). I am using matplotlib and currently only know how to graph 1 y value for an x value.\nThe temperature code looks like this:\nTemperatures = [[Temp_array0] [Temp_array1] [Temp_array2]...], where each numbered array corresponds to that time and the temperature values in the array are at different latitudes and longitudes.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1722,"Q_Id":51527766,"Users Score":0,"Answer":"You can simply repeat the X values which are common for y values\nSuppose\n[x,x,x,x],[y1,y2,y3,y4]","Q_Score":0,"Tags":"python,matplotlib,graph","A_Id":51527863,"CreationDate":"2018-07-25T21:15:00.000","Title":"Python: How to plot an array of y values for one x value in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How can I extract video into frames using python only. I got plenty of solutions but they all are using OpenCV. But in my case I want to do it using python only.\nYour help will be highly appreciated.","AnswerCount":1,"Available Count":1,"Score":0.537049567,"is_accepted":false,"ViewCount":520,"Q_Id":51539813,"Users Score":3,"Answer":"First you should understand that there are many different video codecs and even different video containers in common use, currently. Any library that offers video decoding usually has a multitude of different sub-libraries, to be able to read all the codecs.\nBut even for single codec\/container variants you will not find any Python implementations, beyond toy or research projects. Video decoders are written in C, C++ or similar languages, as the process is computationally very expensive.\nThe video decoding in OpenCV is a relatively thin wrapper of ffmpeg\/libav functionality. All the heavy lifting is done by ffmpeg. So if you want to do without OpenCV, that's possible by finding another video decoding library wrapper in Python. But you will not find a pure-Python implementation of video decoding for common video files.","Q_Score":2,"Tags":"python,python-3.x","A_Id":51541112,"CreationDate":"2018-07-26T13:17:00.000","Title":"Extract video into frames using pure python(without using openCV)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I understand that sklearn requires categorical features to be encoded to dummy variables or one-hot encoded when running the sklearn.ensemble.RandomForestRegressor method, and that XGBoost requires the same, but h2o permitted raw categorical features to be used in its h2o.estimators.random_forest.H2ORandomForestEstimator method. Since h2o4gpu's implementation of random forest is built on top of XGBoost, does this mean support for raw categorical features is not included?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":190,"Q_Id":51543158,"Users Score":1,"Answer":"There is no native support for categorical columns in h2o4gpu (at least yet), so you will have to one-hot encode (or label encode) your categorical columns like you do in sklearn and xgboost.","Q_Score":2,"Tags":"python,scikit-learn,h2o,h2o4gpu","A_Id":51547413,"CreationDate":"2018-07-26T16:02:00.000","Title":"Does h2o4gpu handle categorical features like sklearn or like h2o?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have exported a list of AD Users out of AD and need to validate their login times.\nThe output from the powershell script give lastlogin as LDAP\/FILE time\nEXAMPLE 130305048577611542 \nI am having trouble converting this to readable time in pandas \nIm using the following code:\ndf['date of login'] = pd.to_datetime(df['FileTime'], unit='ns')\nThe column FileTime contains time formatted like the EXAMPLE above. \nIm getting the following output in my new column date of login \nEXAMPLE 1974-02-17 03:50:48.577611542\nI know this is being parsed incorrectly as when i input this date time on a online converter i get this output\nEXAMPLE:\nEpoch\/Unix time: 1386031258\nGMT: Tuesday, December 3, 2013 12:40:58 AM\nYour time zone: Monday, December 2, 2013 4:40:58 PM GMT-08:00\nAnyone have an idea of what occuring here why are all my dates in the 1970'","AnswerCount":3,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":8553,"Q_Id":51547064,"Users Score":6,"Answer":"I know this answer is very late to the party, but for anyone else looking in the future.\nThe 18-digit Active Directory timestamps (LDAP), also named 'Windows NT time format','Win32 FILETIME or SYSTEMTIME' or NTFS file time. These are used in Microsoft Active Directory for pwdLastSet, accountExpires, LastLogon, LastLogonTimestamp and LastPwdSet. The timestamp is the number of 100-nanoseconds intervals (1 nanosecond = one billionth of a second) since Jan 1, 1601 UTC.\nTherefore, 130305048577611542 does indeed relate to December 3, 2013.\nWhen putting this value through the date time function in Python, it is truncating the value to nine digits. Therefore the timestamp becomes 130305048 and goes from 1.1.1970 which does result in a 1974 date!\nIn order to get the correct Unix timestamp you need to do: \n(130305048577611542 \/ 10000000) - 11644473600","Q_Score":1,"Tags":"python,datetime","A_Id":54131481,"CreationDate":"2018-07-26T20:29:00.000","Title":"Convert 18-digit LDAP\/FILETIME timestamps to human readable date","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"This code is for running my trained weights the folder ckpt contains 1050 step train data and this file is in outside of cfg folder in darkflow main folder.\nimport cv2\nfrom darkflow.net.build import TFNet\nimport numpy as np\nimport time\noptions = {\n    'model': 'cfg\/tiny-yolo-voc-1c.cfg',\n    'load': 1050,\n    'threshold': 0.2,\n    'gpu': 1.0\n}\nafter running this code in atom editor below error showing\nParsing cfg\/\/tiny-yolo-voc-1c.cfg\nTraceback (most recent call last):\n  File \"C:\\Users\\amard\\Desktop\\Hotel\\darkflow\\test.py\", line 13, in <module>\n    tfnet = TFNet(options)\n  File \"C:\\Users\\amard\\Desktop\\Hotel\\darkflow\\darkflow\\net\\build.py\", line 58, in __init__\n    darknet = Darknet(FLAGS)\n  File \"C:\\Users\\amard\\Desktop\\Hotel\\darkflow\\darkflow\\dark\\darknet.py\", line 17, in __init__\n    src_parsed = self.parse_cfg(self.src_cfg, FLAGS)\n  File \"C:\\Users\\amard\\Desktop\\Hotel\\darkflow\\darkflow\\dark\\darknet.py\", line 68, in parse_cfg\n    for i, info in enumerate(cfg_layers):\n  File \"C:\\Users\\amard\\Desktop\\Hotel\\darkflow\\darkflow\\utils\\process.py\", line 66, in cfg_yielder\n    layers, meta = parser(model); yield meta;\n  File \"C:\\Users\\amard\\Desktop\\Hotel\\darkflow\\darkflow\\utils\\process.py\", line 17, in parser\n    with open(model, 'rb') as f:\nFileNotFoundError: [Errno 2] No such file or directory: 'cfg\/\/tiny-yolo-voc-1c.cfg'\n[Finished in 4.298s]","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":778,"Q_Id":51549279,"Users Score":0,"Answer":"Oftenly I was also used  get something similar to this error \nmy first mistake was not to open Jupyter notebook at the directory where I kept these folders\nso, try to open the notebook from that directory \n(in windows simply type cmd on location bar of shift+right-click)\n(in MacOS there is some setting first enable that then right-click on the folder)\nlater I used this options \noptions={\n    'model':'cfg\/yolo.cfg',\n    'load':'bin\/yolov2.weights',\n    'threshold':0.3,\n    'gpu':1.0\n}\ntfNet=TFNet(options)\nand everything work as expected\nhope this will help you","Q_Score":0,"Tags":"python,yolo,darkflow","A_Id":54362653,"CreationDate":"2018-07-27T00:46:00.000","Title":"while running YOLO for test custom object cfg file path error the path is correct but even though its showing this error","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using the 2d interpolation function in scipy to smooth a 2d image.  As I understand it, interpolate will return z = f(x,y).  What I want to do is find x with known values of y and z.  I tried something like this;\nf = interp2d(x,y,z)\nindex = (np.abs(f(:,y) - z)).argmin()\nHowever the interp2d object does not work that way.  Any ideas on how to do this?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":102,"Q_Id":51549293,"Users Score":0,"Answer":"I was able to figure this out.  yvalue, zvalue, xmin, and xmax are known values.  By creating a linspace out of the possible values x can take on, a list can be created with all of the corresponding function values.  Then using argmin() we can find the closest value in the list to the known z value.  \nf = interp2d(x,y,z)\nxnew = numpy.linspace(xmin, xmax)\nfnew = f(xnew, yvalue)\nxindex = (numpy.abs(fnew - zvalue)).argmin()\nxvalue = xnew(xindex)","Q_Score":1,"Tags":"python,numpy,scipy","A_Id":51565999,"CreationDate":"2018-07-27T00:49:00.000","Title":"Scipy interp2d function produces z = f(x,y), I would like to solve for x","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a minimization problem, that is modeled to be solved in Gurobi, via python.\nBesides, I can calculate a \"good\" initial solution for the problem separately, that can be used as an upper bound for the problem.\nWhat I want to do is to set Gurobi use this upper bound, to enhance its efficiency. I mean, if this upper bound can help Gurobi for its search. The point is that I just have the objective value, but not a complete solution.\nCan anybody help me how to set this upper bound in the Gurobi?\nThanks.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":438,"Q_Id":51550870,"Users Score":0,"Answer":"I think that if you can calculate a good solution, you can also know some bound for your variable even you dont have the solution exactly ?","Q_Score":0,"Tags":"python,initialization,gurobi,upperbound","A_Id":51712765,"CreationDate":"2018-07-27T04:42:00.000","Title":"How to set an start solution in Gurobi, when only objective function is known?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an Excel workbook with close to 90 columns. I would like to create column alias for each column in the workbook, so that it will be easier for me to use the respective columns in formulas.\nNormally, I would select each column in the workbook and type in my alias for the column into the Cell Reference Bar at the top\nIs there a way to do this automatically, because i have a lot of columns? Especially in Python ?\nI tried the pandas.Series.to_excel function which has the header attribute. However all it does is change the column names to the string specified and does not modify the alias for all the cells in the column.\nThanks a lot for your help","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":632,"Q_Id":51554503,"Users Score":0,"Answer":"In Excel, is your data in a table, or named range? \nI'm kind of assuming a table (which could make this a snap) because, as a column heading in a (named) range, the 'header' (or alias, if I understand) isn't \"connected\" to the underlying data, as it would be in a table...\nCan you provide an example of how you would (or expect to) use the 'column alias' in a formula?","Q_Score":0,"Tags":"python,excel","A_Id":51567400,"CreationDate":"2018-07-27T09:05:00.000","Title":"How to create common alias for all cells in a column in Excel automatically through scripting","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an Excel workbook with close to 90 columns. I would like to create column alias for each column in the workbook, so that it will be easier for me to use the respective columns in formulas.\nNormally, I would select each column in the workbook and type in my alias for the column into the Cell Reference Bar at the top\nIs there a way to do this automatically, because i have a lot of columns? Especially in Python ?\nI tried the pandas.Series.to_excel function which has the header attribute. However all it does is change the column names to the string specified and does not modify the alias for all the cells in the column.\nThanks a lot for your help","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":632,"Q_Id":51554503,"Users Score":0,"Answer":"If I understand you correctly...\nTo name each of the columns something slightly different you can use a for-loop which contains an incrementing number that's added to the column name.\nThere are loads of examples of this available online, here's a really rough illustrative example:\nnum = 0\nfor header in column:\n    num +=1\n    header = header+str(num)\nI don't think you need to program this for just 90 columns in one book though tbh. \nYou could name the first 3 columns, select the three named cells, and then drag right when you see the + symbol in the bottom right corner of the most rightern cell of the three selected.\nDragging across 90 cells should only take one second.\nOnce you've named the 90 columns, you can always select row#1 and do some ctrl+h on it to change the header names later.","Q_Score":0,"Tags":"python,excel","A_Id":51556893,"CreationDate":"2018-07-27T09:05:00.000","Title":"How to create common alias for all cells in a column in Excel automatically through scripting","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to evaluate the performance of a regressor by means of GridSearchCV. In my implementation cv is an int, so I'm applying the K-fold validation method. Looking at cv_results_['mean_test_score'],\n the best mean score on the k-fold unseen data is around 0.7, while the train scores are much higher, like 0.999. This is very normal, and I'm ok with that.\nWell, following the reasoning behind this concept, when I apply the best_estimator_ on the whole data set, I expect to see at least some part of the data predicted not perfectly, right? Instead, the numerical deviations between the predicted quantities and the real values are near zero for all datapoints. And this smells of overfitting.\nI don't understand that, because if I remove a small part of the data and apply GridSearchCV to the remaining part, I find almost identical results as above, but the best regressor applied to the totally unseen data predicts with much higher errors, like 10%, 30% or 50%. Which is what I expected, at least for some points, fitting GridSearchCV on the whole set, based on the results of k-fold test sets.\nNow, I understand that this forces the predictor to see all datapoints, but the best estimator is the result of k fits, each of them never saw 1\/k fraction of data. Being the mean_test_score the average between these k scores, I expect to see a bunch of predictions (depending on cv value) which show errors distributed around a mean error that justifies a 0.7 score.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":234,"Q_Id":51558872,"Users Score":1,"Answer":"The refit=True parameter of GridSearchCV makes the estimator with the found best set of hyperparameters be refit on the full data. So if your training error is almost zero in the CV folds, you would expect it to be near zero in the best_estimator_ as well.","Q_Score":3,"Tags":"python-3.x,scikit-learn,grid-search","A_Id":51559013,"CreationDate":"2018-07-27T13:21:00.000","Title":"GridSearchCV: based on mean_test_score results, predict should perform much worse, but it does not","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to implement pbft algorithm(3f+1 systems;f=1) in python. But what is the channel should use for sending and receiving from replicas. I have tried python multicast but it seems something going wrong while receiving. So please suggest any solution that can put me forward. \nThanks in Advance.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":305,"Q_Id":51569086,"Users Score":0,"Answer":"You need to use authenticated point-to-point communication channel to implement any sorts of BFT algorithm. Because PBFT assumes all participant's identity are established in prior, you don't need to assume multicast communication primitive. Even though broadcast is executed in PBFT protocol, each message is encrypted by its private key. So you don't need to use multicast or broadcast.","Q_Score":0,"Tags":"python,algorithm,blockchain,consensus","A_Id":51963250,"CreationDate":"2018-07-28T07:25:00.000","Title":"pbft implementation in python when f=1","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"the question is:\nIn the keras tutorial it use an input x_train = np.random.random((100, 100, 100, 3)), it should means that there's 100 images each has size of [100,100,3] right?\nSo i thought that x_train[0][0] should represent the first channel of the first img (which should be [100, 100]), but x_train[0][0] in fact has a size of [100,3]... so i'm confused, how can keras take this [100,100,100,3] numpy array as a set of imgs? please help me out, thank in advance.\nAnother question is:\nhow can I construct a input like this ? Cause when I do np.array([[100,100],[100,100]]), it becomes to array of [2,100,100]","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":1287,"Q_Id":51569542,"Users Score":2,"Answer":"Here is an explanation on how you can access your images.\nX is four dimensional tensor. In mathematics tensors are generalization of vectors and metrics into higher dimensional arrays.\nAssuming \"channels last\" data-format\n\n1st Axis = Number of images \n2nd Axis = Number of rows in single image\n3rd Axis = Number of columns in single row \n4th Axis = Number of channels of certain pixel  \n\nNow you can access image,row,column, and channels using indexing as follows. \n\nx[0] Represents first image  \nx[0][0] Represents First row of first image  \nx[0][0][0] Represents First column of first row of first image  \nx[0][0][0][0] Represents Red channel of First column of first row of first image","Q_Score":0,"Tags":"python,numpy,tensorflow,keras","A_Id":51569613,"CreationDate":"2018-07-28T08:31:00.000","Title":"How to properly use numpy in keras","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to run the Transformer (speech2text) model on windows (till I get my linux machine).\nwhen I'm running the entire command from the cmd:\n\"python transformer_main.py --data_dir=$DATA_DIR --model_dir=$MODEL_DIR --params=$PARAMS\"\nI'm getting an error :\"ModuleNotFoundError: No module named 'tensorflow'\"\nBut I know that tf is installed and also when I'm using pycharm, first, I can see the package installed(File -> Settings ->Project interpreter).\nsecond, when I'm running the code it's passing that fall site...\nI can run through pycharm, but I think it's important to understand what I'm missing, Is it something with the interpreter?\nThanks.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":95,"Q_Id":51572338,"Users Score":0,"Answer":"Most likely you are simply using a different interpreter from your command line than from your PyCharm project. This will happen, for example, if you have set up your PyCharm project using a fresh conda environment.\nTo see which one you are using on the command-line, simply run where python. Then compare this with what you found in PyCharm.","Q_Score":0,"Tags":"python,tensorflow","A_Id":51572366,"CreationDate":"2018-07-28T14:36:00.000","Title":"Getting:\"ModuleNotFoundError: No module named 'tensorflow'\", only when running from the command line","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there any way to create a pandas dataframe consisting of two columns. The first columns will of datatype int and the second one will be of dictionary type. Then insert data into the dataframe iteratively.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":175,"Q_Id":51574236,"Users Score":0,"Answer":"No, this isn't possible.\nPandas dtypes(*) are closely related to NumPy dtypes. There are some differences and additions, e.g. for datetime and category, but in the general the rule holds. Often these additional dtypes are wrappers around NumPy dtypes. The key here is series with these specifically defined dtypes are held in contiguous memory blocks. They can manipulated with vectorised computations.\nA series which cannot be held in the fashion described above gets labelled with dtype object. This is nothing more than a sequence of pointers to arbitrary Python types. You should not consider this as an \"array of dictionaries\" in any vectorised sense. You can compare such a series to a list. You would never say that a list has \"dtype dict\" just because it contains dictionaries. Similarly, the fact that an object series only contains dictionaries doesn't make it a series of dtype dict.\n(*) Notice I use \"dtype\" instead of \"type\". This is intentional. \"dtype\" has a specific and important meaning in relation to Pandas \/ NumPy, as the rest of my answer should demonstrate.","Q_Score":0,"Tags":"python,pandas","A_Id":51574299,"CreationDate":"2018-07-28T18:44:00.000","Title":"How to create a pandas dataframe where the datatype of a column will be a dictionary?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there any python package that could random generate connected graph (there is a path between every pair of vertices) on which each vertex has degree at most 4? \nThank you!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":275,"Q_Id":51584121,"Users Score":0,"Answer":"A simple algorithm I can think to is:\n\nStart with one vertex\nRepeat one of two random moves:\n2A) Pick a random vertex with degree less than 4 and add a new vertex connected to it\n2B) Pick two random vertices with degree less than 4 that are not connected and add an edge between them\n\nuntil you've enough vertices\/edges.","Q_Score":0,"Tags":"python,graph","A_Id":51584354,"CreationDate":"2018-07-29T20:06:00.000","Title":"python generate random connected graph with certain constraint on the vertex degree","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I pip installed OpenCV-python. The installation seems to be fine and I tested it out on the python IDLE. It ran without any problems. I've been trying to run it on VS Code but it doesn't seem to work. The autocomplete recognizes the imread function but when I type it in it throws up an error saying cv2 has no imread member. I am using the most updated version of python\nI am calling it like this:img2 = cv2.imread(\"C:\\Biometric\\min.jpg\", 0)","AnswerCount":7,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":33464,"Q_Id":51593147,"Users Score":53,"Answer":"Since you are trying to executing this with VS Code, try following steps\n\nOpen palette on VS Code (use specifies command): CTRL + Shift + P\nThen select \"Preferences > Open Settings (JSON)\" option in the palette dropdown\nThen add the following line in the opened settings.json file\npython.linting.pylintArgs\": [\"--generate-members\"]\n\nthis must work","Q_Score":19,"Tags":"python-3.x,opencv","A_Id":59050229,"CreationDate":"2018-07-30T11:38:00.000","Title":"cv2 python has no imread member","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I pip installed OpenCV-python. The installation seems to be fine and I tested it out on the python IDLE. It ran without any problems. I've been trying to run it on VS Code but it doesn't seem to work. The autocomplete recognizes the imread function but when I type it in it throws up an error saying cv2 has no imread member. I am using the most updated version of python\nI am calling it like this:img2 = cv2.imread(\"C:\\Biometric\\min.jpg\", 0)","AnswerCount":7,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":33464,"Q_Id":51593147,"Users Score":0,"Answer":"Go to the terminal and type pylint --extension-pkg-whitelist=cv2\nit has worked for me.","Q_Score":19,"Tags":"python-3.x,opencv","A_Id":56180952,"CreationDate":"2018-07-30T11:38:00.000","Title":"cv2 python has no imread member","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to install tensorflow for their image recognition program thing, but I get the error in the title, it does work when i go to C:\\Users\\Diederik\\AppData\\Local\\Programs\\Python\\Python36\\Scripts, and type pip3 install --upgrade tensorflow there, but I am not sure if that is how I am supposed to do it, since I tried that before and I got errors while running the classify_image.py, so I thought maybe I should try it the way tensorflow told me to, but that didn't work, Please help me, I am happy to provide any extra information you need. I am on windows 10","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":857,"Q_Id":51604098,"Users Score":0,"Answer":"Try .\/pip3 install --upgrade tensorflow from that directory. I think on windows, you need to be explicit that the command is in your working directory. Alternatively you could try \nC:\\Users\\Diederik\\AppData\\Local\\Programs\\Python\\Python36\\Scripts\\pip3 install --upgrade tensorflow. That's how I do it on my windoze machine.","Q_Score":0,"Tags":"python,windows,tensorflow,artificial-intelligence","A_Id":51604459,"CreationDate":"2018-07-31T00:56:00.000","Title":"Installing tensorflow, getting error 'pip3' is not recognized as an internal or external command, operable program or batch file\"","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there a way in python (by using NLTK or SpaCy or any other library) that I can predict the POS tag of the word that are likely to follow the words so far I have entered.\nEg- If i input\n\nI am going to\n\nIt shows the POS tag of the next most likely word\neg NN, becuase college can come after this","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":345,"Q_Id":51605036,"Users Score":2,"Answer":"You may train a simple language model on POS tag data using LSTMs. That is, say, using Spacy, convert your corpus to POS tag corpus. Train the model using the new corpus. Predict the POS on evaluation. Another way to do it is by building a language model on your data, generate the next word and find its POS.","Q_Score":1,"Tags":"python,nlp,nltk","A_Id":51608308,"CreationDate":"2018-07-31T03:26:00.000","Title":"Predicting the POS tag of the upcoming word","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"My question is, I think, too simple, but it's giving me headaches. I think I'm missing either something conceptually in Neural Networks or Tensorflow is returning some wrong layer. \nI have a network in which last layer outputs 4800 units. The penultimate layer has 2000 units. I expect my weight matrix for last layer to have the shape (4800, 2000) but when I print out the shape in Tensorflow I see (2000, 4800). Please can someone confirm which shape of weight matrix the last layer should have? Depending on the answer, I can further debug the issue. Thanks.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1570,"Q_Id":51608879,"Users Score":4,"Answer":"Conceptually, a neural network layer is often written like y = W*x where * is matrix multiplication, x is an input vector and y an output vector. If x has 2000 units and y 4800, then indeed W should have size (4800, 2000), i.e. 4800 rows and 2000 columns.\nHowever, in implementations we usually work on a batch of inputs X. Say X is (b, 2000) where b is your batch size. We don't want to transform each element of X individually by doing W*x as above since this would be inefficient.\nInstead we would like to transform all inputs at the same time. This can be done via Y = X*W.T where W.T is the transpose of W. You can work out that this essentially applies W*x to each row of X (i.e. each input). Y is then a (b, 4800) matrix containing all transformed inputs.\nIn Tensorflow, the weight matrix is simply saved in this transposed state, since it is usually the form that is needed anyway. Thus, we have a matrix with shape (2000, 4800) (the shape of W.T).","Q_Score":1,"Tags":"python-3.x,tensorflow,conv-neural-network","A_Id":51611176,"CreationDate":"2018-07-31T08:32:00.000","Title":"Weights Matrix Final Fully Connected Layer","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a few linear regression models and due to underlying reasons need to need have the weights for a set regression to be user-defined. is it possible to get an OLS summary based user-defined weights rather than the linear regression weights it finds itself?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":476,"Q_Id":51615868,"Users Score":0,"Answer":"If you're doing a linear regression, and you know some of the weights, you can essentially mathematically reduce it to a simpler regression. For instance assume you have the following regression\nz = ax + by\nGiven z, x, y it's relatively easy to set up a linear regression. If you want the user to specify (a) rather than having the system solve for it, then your expression becomes\nz' = by where z' = z - ax, where a is a coefficient that is specified by your end user.\nmodel = sm.OLS(Predicted_Z, Actual_Z)\nresults = model.fit()\nprint(results.summary())","Q_Score":1,"Tags":"python,statistics,regression,linear-regression,summary","A_Id":51615949,"CreationDate":"2018-07-31T14:32:00.000","Title":"python specify weights for linear regression","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've got a content-based recommender that works... fine. I was fairly certain it was the right approach to take for this problem (matching established \"users\" with \"items\" that are virtually always new, but contain known features similar to existing items).\nAs I was researching, I found that virtually all examples of content-based filtering use articles\/movies as an example and look exclusively at using encoded tf-idf features from blocks of text. That wasn't exactly what I was dealing with, but most of my features were boolean features, so making a similar vector and looking at cosine distance was not particularly difficult. I also had one continuous feature, which I scaled and included in the vector. As I said, it seemed to work, but was pretty iffy, and I think I know part of the reason why...\nThe continuous feature that I'm using is a rating (let's call this \"deliciousness\"), where, in virtually all cases, a better score would indicate an item more favorable for the user. It's continuous, but it also has a clear \"direction\" (not sure if this is the correct terminology). Error in one direction is not the same as error in another.\nI have cases where some users have given high ratings to items with mediocre \"deliciousness\" scores, but logically they would still prefer something that was more delicious. That user's vector might have an average deliciousness of 2.3. My understanding of cosine distance is that in my model, if that user encountered two new items that were exactly the same except that one had a deliciousness of 1.0 and the other had a deliciousness of 4.5, it would actually favor the former because it's a shorter distance between vectors.\nHow do I modify or incorporate some other kind of distance measure here that takes into account that deliciousness error\/distance in one direction is not the same as error\/distance in the other direction?\n(As a secondary question, how do I decide how to best scale this continuous feature next to my boolean features?)","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":98,"Q_Id":51618103,"Users Score":1,"Answer":"There are two basic approaches to solve this:\n(1) Write your own distance function.  The obvious approach is to remove the deliciousness element from each vector, evaluating that difference independently.  Use cosine similarity on the rest of the vector.  Combine that figure with the taste differential as desired.\n(2) Transform your deliciousness data such that the resulting metric is linear.  This will allow a \"normal\" distance metric to do its job as expected.","Q_Score":1,"Tags":"python,machine-learning,cosine-similarity,recommender-systems","A_Id":51618254,"CreationDate":"2018-07-31T16:30:00.000","Title":"Handling Error for Continuous Features in a Content-Based Filtering Recommender System","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"After fitting a GradientBoostingClassifier in SciKit-Learn, some of the features have zero importance.\nMy understanding is that zero importance would mean that no splits are made on this feature.\nIf I try to predict using a data set that does not include the feature then it throws an error for not having all the features.\nOf course I realize I can remove the zero importance features, but I would rather not alter the already fit model. (If I remove the zero importance features and refit I get a slightly different model.) \nIs this a bug that the model requires zero importance features to make predictions or is there something about the zero importance features I'm not thinking about? Is there a work around to get the exact same model?\n(I'm forseeing a question about why this matters -- it's because requiring zero importance features means pulling more columns from a very very large database and it looks sloppy to include a feature in the model that does nothing.)","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":414,"Q_Id":51619180,"Users Score":0,"Answer":"This is not a bug and is the expected behavior.  Scikit will not make assumptions after the model has been trained about what features should have been included or not.  \nInstead, when you call fit for a model there is an implicit assumption being made that you have already performed feature selection to remove features that will not be important to the model.  Once fit the expectation is that you will provide a dataset of the same size that was used to fit the model regardless of whether the features are important or not.","Q_Score":2,"Tags":"python,machine-learning,scikit-learn","A_Id":51620347,"CreationDate":"2018-07-31T17:43:00.000","Title":"Zero Importance Feature Removal Without Refit in SciKit-Learn GradientBoostingClassifier","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm doing some work on the data in an excel sheet using python pandas. When I write and save the data it seems that pandas only saves and cares about the raw data on the import. Meaning a lot of stuff I really want to keep such as cell colouring, font size, borders, etc get lost. Does anyone know of a way to make pandas save such things?\nFrom what I've read so far it doesn't appear to be possible. The best solution I've found so far is to use the xlsxwriter to format the file in my code before exporting. This seems like a very tedious task that will involve a lot of testing to figure out how to achieve the various formats and aesthetic changes I need. I haven't found anything but would said writer happen to in any way be able to save the sheet format upon import? \nAlternatively, what would you suggest I do to solve the problem that I have described?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":468,"Q_Id":51626502,"Users Score":0,"Answer":"Separate data from formatting. Have a sheet that contains only the data \u2013 that's the one you will be reading\/writing to \u2013 and another that has formatting and reads the data from the first sheet.","Q_Score":1,"Tags":"python,excel,pandas,xlsxwriter","A_Id":51628570,"CreationDate":"2018-08-01T06:16:00.000","Title":"Any way to save format when importing an excel file in Python?","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am currently testing gradient-based optimisation in OpenMDAO with high-fidelity flow solvers (SU2) but the shape parameterisation method I am using appears to be highly sensitive to the step size of the finite difference approximation. This is probably due to the objective function being more sensitive to some design variables than others, and so I have been using relative step sizes instead of absolute. However, I expected the step size to be relative to each design variable in the vector, but this appears not to be the case with a constant step size applied to all the design variables. \nFor example, a relative step size of 1e-5 produced an actual step size of 4.2e-5 (constant) with a vector of 28 design variables that vary in magnitude.\ni.e. design_variables = [0, 1e-2, 1e-1...]\nQuestion: How is the relative step size calculated for a vector of design variables that vary in magnitude and include zero?\nNotes: the design variables are scaled (equally) and share the same (%) upper and lower bounds. Also, this number does appear to vary with the lower and upper bounds? \n\nUPDATE: Issue partially resolved after reviewing the finite_difference.py script. The norm of the input is taken and multiplied with the step size. However, the codes suggests (step *= scale) that the scaled value is also a scalar and so constant across all design variables, is this correct?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":80,"Q_Id":51630972,"Users Score":0,"Answer":"Yes, that is how the step size is calculated for vectors -- for relative finite difference stepping, the stepsize is scaled by the norm of the vector. However, you bring up a good point that a vector may have wildly different magnitudes in its elements, so maybe we need to add support for specifying a vector of fd step sizes.","Q_Score":0,"Tags":"python,openmdao","A_Id":51655116,"CreationDate":"2018-08-01T10:24:00.000","Title":"openmdao: how is the 'rel' step size calculated for a vector input of design variables?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In the pandas documentation, it says that the optional dependencies for Excel I\/O are:\n\nxlrd\/xlwt: Excel reading (xlrd) and writing (xlwt)\nopenpyxl: openpyxl > version 2.4.0 for writing .xlsx files (xlrd >= 0.9.0)\nXlsxWriter: Alternative Excel writer\n\nI can't install any external modules.  Is there any way to create an .xlsx file with just a pandas installation?\nEdit: My question is - is there any built-in pandas functionality to create Excel workbooks, or is one of these optional dependencies required to create any Excel workbook at all?\nI thought that openpyxl was part of a pandas install, but turns out I had XlsxWriter installed.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":831,"Q_Id":51635233,"Users Score":3,"Answer":"The pandas codebase does not duplicate Excel reading or writing functionality provided by the external libraries you listed.  \nUnlike the csv format, which Python itself provides native support for, if you don't have any of those libraries installed, you cannot read or write Excel spreadsheets.","Q_Score":1,"Tags":"python,pandas,pandas.excelwriter","A_Id":51635610,"CreationDate":"2018-08-01T13:58:00.000","Title":"Can I create Excel workbooks with only Pandas (Python)?","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to use tensorflow to run classify_image.py, but I keep getting the same error: \nTraceback (most recent call last):\n  File \"classify_image.py\", line 46, in <module>\n    import tensorflow as tf\n  File \"C:\\Users\\Diederik\\AppData\\Local\\Programs\\Python\\Python36\\lib\\site-packages\\tensorflow\\__init__.py\", line 22, in <module>\n    from tensorflow.python import pywrap_tensorflow  # pylint: disable=unused-import\nModuleNotFoundError: No module named 'tensorflow.python'\nSomeone asked me to do a pip3 list, so I did: \nC:\\Users\\Diederik\\AppData\\Local\\Programs\\Python\\Python36\\Scripts>pip3 list Package Version ----------- ------- absl-py 0.3.0 astor 0.7.1 gast 0.2.0 grpcio 1.13.0 Markdown 2.6.11 numpy 1.15.0 pip 10.0.1 protobuf 3.6.0 setuptools 39.0.1 six 1.11.0 tensorboard 1.9.0 tensorflow 1.9.0** termcolor 1.1.0 Werkzeug 0.14.1 wheel 0.31.1 You are using pip version 10.0.1, however version 18.0 is available. You should consider upgrading via the 'python -m pip install --upgrade pip' command.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":437,"Q_Id":51648278,"Users Score":0,"Answer":"try pip install --upgrade --ignore-installed tensorflow again.\nIf it said some packages can't be installed\/upgrade for missing some other packages, pip installthose packages too, and try again the first code.\nThat's how I solved my problem.","Q_Score":0,"Tags":"python,tensorflow,artificial-intelligence,image-recognition","A_Id":52934117,"CreationDate":"2018-08-02T07:49:00.000","Title":"No module named tensorflow.python on windows 10 when I run classify_image.py","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to use tensorflow to run classify_image.py, but I keep getting the same error: \nTraceback (most recent call last):\n  File \"classify_image.py\", line 46, in <module>\n    import tensorflow as tf\n  File \"C:\\Users\\Diederik\\AppData\\Local\\Programs\\Python\\Python36\\lib\\site-packages\\tensorflow\\__init__.py\", line 22, in <module>\n    from tensorflow.python import pywrap_tensorflow  # pylint: disable=unused-import\nModuleNotFoundError: No module named 'tensorflow.python'\nSomeone asked me to do a pip3 list, so I did: \nC:\\Users\\Diederik\\AppData\\Local\\Programs\\Python\\Python36\\Scripts>pip3 list Package Version ----------- ------- absl-py 0.3.0 astor 0.7.1 gast 0.2.0 grpcio 1.13.0 Markdown 2.6.11 numpy 1.15.0 pip 10.0.1 protobuf 3.6.0 setuptools 39.0.1 six 1.11.0 tensorboard 1.9.0 tensorflow 1.9.0** termcolor 1.1.0 Werkzeug 0.14.1 wheel 0.31.1 You are using pip version 10.0.1, however version 18.0 is available. You should consider upgrading via the 'python -m pip install --upgrade pip' command.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":437,"Q_Id":51648278,"Users Score":0,"Answer":"I simply needed to add a python path, that was the only problem","Q_Score":0,"Tags":"python,tensorflow,artificial-intelligence,image-recognition","A_Id":51657493,"CreationDate":"2018-08-02T07:49:00.000","Title":"No module named tensorflow.python on windows 10 when I run classify_image.py","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am training a model that incrementally learns new classes, e.g. n target classes during the first 70 or so epochs, then the original n classes plus m new target classes, etc. When training the model on n+m target classes, the loss function requires predictions from the model trained on n target classes. How can I restore the old model efficiently? \nIt seems I can do this by creating two separate sessions for each batch and loading the old model in one before training the new model in the other, but this is terribly inefficient and makes training go from taking hours to days.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":35,"Q_Id":51654259,"Users Score":0,"Answer":"Would it be possible to use the weights to do it?\nPut the weigh of your targets to 1 and the weights of your not-targets to 0\nAt first, you'd have your weight tensor at [1]*n + [0]*m (+ as in concat).\nThen you'd assign it to [1]*(n+m) when you want to add you m new targets\nand so forth.","Q_Score":1,"Tags":"python,tensorflow","A_Id":51654678,"CreationDate":"2018-08-02T12:56:00.000","Title":"Make predictions with an old model without losing the current model","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"df_filter = df.filter(~(col('word').isin(stop_words_list)))\ndf_filter.count()\n\n\n\n27781\n\n\n\ndf.count()\n\n\n\n31240\nWhile submitting the same code to Spark cluster using spark-submit, the filter function is not working properly, the rows with col('word') in the stop_words_list are not filtered. \nWhy does this happen?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":312,"Q_Id":51661079,"Users Score":0,"Answer":"The filtering is working now after the col('word') is trimmed.\n    df_filter = df.filter(~(trim(col(\"word\")).isin(stop_words_list)))\nI still don't know why it works in pyspark shell, but not spark-submit. The only difference they have is: in pyspark shell, I used spark.read.csv() to read in the file, while in spark-submit, I used the following method.\n    from pyspark.sql import SparkSession\nfrom pyspark.sql import SQLContext\nsession = pyspark.sql.SparkSession.builder.appName('test').getOrCreate()\nsqlContext = SQLContext(session)\ndf = sqlContext.read.format(\"com.databricks.spark.csv\").option('header','true').load()\nI'm not sure if two different read-in methods are causing the discrepancy. Someone who is familiar with this can clarify.","Q_Score":0,"Tags":"python-3.x,apache-spark,filter,pyspark,apache-spark-sql","A_Id":51681014,"CreationDate":"2018-08-02T19:42:00.000","Title":"filter working in pyspark shell not spark-submit","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am recently studying about GAN model and thought that it would be useful for my system in which I am going to predict whether a given image is car or not. I understand the part that the \"discriminator\" gets input from the \"generator\". The generator generates image from a random vector which is then passed to the discriminator for authenticity check. But what exactly is that vector that is used by the generator ? Is it in an image converted to pixels and we pass it as an vector of pixels ?\nCan anyone please explain me this \nOr is there any other method that I should follow to build a system that can classify images to car or not\nThanks in advance","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":177,"Q_Id":51666513,"Users Score":0,"Answer":"The input to the generator is a z-dimensional vector of completely random values. In case of DCGAN, the input is from gaussian distribution source. GANs' theory is based on the fact that these random values are learnt to be distotred by the network in such a way that Discriminator\/ Critic is fooled by the image that is produced by the generator. Both generator and discriminator are adversaries of each other making each other better epoch by epoch, and hence the name Adversarial networks.\nFor classifying an image into cars and not car, you can actually solve this problem fairly easily and accurately using a simple ConvNet by training it on multiple car images and \"not car\" images. There are already pretty complex object detection networks out there trained on Imagenet data, so you might as well start by referring the architecture of those networks.\nHope my answer was of some help to you. :)","Q_Score":0,"Tags":"python-3.x,image-processing,generative-adversarial-network","A_Id":51686316,"CreationDate":"2018-08-03T06:28:00.000","Title":"What is the input to the generator in Generative Adversarial Network","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I use JanusGraph, HBase and Python (through gremlin_python) to create and store a sample graph. Now I'd like to do some graph anaysis (eg. page rank), and wish to stick to Python.  I'm wondering if it's possible to query a graph from JanusGraph in DataFrame format, then ingest into GraphFrame to calculate pageRank? \nThe key questions are how can I bridge between JanusGraph + GraphFrame using Python, i.e.\n1. query a graph which returns in one format\n2. call GraphFrame API to caluclate PageRank.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":234,"Q_Id":51667488,"Users Score":1,"Answer":"As far as I am concerned, there is not official connector available for transforming query results of JanusGraph into Spark DataFrame (and then Graph in GraphFrame).\nThus, you must manually write code to performe the transformation. You can use gremlin-python package to query JanusGraph and then using Spark to feed the result set into Spark DataFrame.","Q_Score":0,"Tags":"python-2.7,apache-spark,apache-spark-sql,gremlin,janusgraph","A_Id":53847802,"CreationDate":"2018-08-03T07:33:00.000","Title":"Janusgraph query returns dataframe to perform analysis using GraphFrame","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"Consider a array A of shape (n x 1). The transpose of A will have the shape (1 x n). Why is that the sum of A and A.T (i.e A + A.T) give back an (n x n) array in Python?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":62,"Q_Id":51670148,"Users Score":0,"Answer":"Your result will need n rows to comply to the structure of one of the arrays and n columns to comply to the structure of the other array.","Q_Score":0,"Tags":"python,arrays","A_Id":51670317,"CreationDate":"2018-08-03T10:00:00.000","Title":"Sum of an array and it's transpose","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to triangulate points from stereo images.On the web I find a lot of confusion, because there are many different methods.\nI have two images of the same subject in different angles, with manually defined landmarks,(for example , 2 faces where I scored 3 points: nose and pupils).\nI would like to go back to the three-dimensional position of these points through their triangulation.\nIt could be listed the various steps to be performed to get this result in OpenCv for python?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":510,"Q_Id":51674917,"Users Score":0,"Answer":"To triangulate you need to now position of cameras.\nThose position can be presented in different forms, translate, rotation, or affine, depend of needs.\nConstruct two rays from each camera thru the same landmark.\nIn theory find intersection of those two rays.\nIn practice find point that is closest to both rays.\nThat point is what you want.","Q_Score":0,"Tags":"python,image,opencv,point,triangulation","A_Id":51685128,"CreationDate":"2018-08-03T14:33:00.000","Title":"Opencv step for Triangulate point from stereo image","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm currently working on a program that can do binary image classification with machine learning. I have a list of labels and a list of images that i'm using as inputs which are then fed into the Inception V3 model. \nWill inputting of the dataset this way work with the inception V3 architecture? Is it necessary to organize the images with labeled folders before feeding it into the model?\nThanks for your help!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":145,"Q_Id":51679311,"Users Score":2,"Answer":"In your example, you have all the images in memory. You can simply call model.fit(trainX, trainY) to train your model. No need to organize the images in specific folder structures.\nWhat you are referring to, is the flow_from_directory() method of the ImageDataGenerator. This is an object that will yield images from the directories, and automatically infer the labels from the folder structure. In this case, your images should be arranged in one folder per label. Since the ImageDataGenerator is a generator, you should use it in combination with model.fit_generator().\nAs a third option, you can write your own custom generator that yields both images and labels. This is advised in case you have a more complex label structure than one label per images; for instance in multi-label classification, object detection or semantic segmentation, where the outputs are also images. A custom generator should also be used with model.fit_generator().","Q_Score":2,"Tags":"python,tensorflow,machine-learning,keras,classification","A_Id":51680611,"CreationDate":"2018-08-03T19:53:00.000","Title":"Is Dataset Organization for Image Classification Necessary?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Im using python 3.6 tensorflow 1.5\nim following the link\nBut got the error:\ndoe@doe:~\/anaconda3\/envs\/tensorflow\/models\/research\/object_detection$ python3 train.py --logtostderr --train_dir=training\/ --pipeline_config_path=training\/ssd_mobilenet_v1_coco.config\nWARNING:tensorflow:From \/home\/doe\/anaconda3\/lib\/python3.6\/site-packages\/tensorflow\/python\/platform\/app.py:124: main (from main) is deprecated and will be removed in a future version.\nInstructions for updating:\nUse object_detection\/model_main.py.\nTraceback (most recent call last):\n  File \"\/home\/doe\/.local\/lib\/python3.6\/site-packages\/google\/protobuf\/text_format.py\", line 1500, in _ParseAbstractInteger\n    return int(text, 0)\nValueError: invalid literal for int() with base 0: '03'\nDuring handling of the above exception, another exception occurred:\nTraceback (most recent call last):\n  File \"\/home\/doe\/.local\/lib\/python3.6\/site-packages\/google\/protobuf\/text_format.py\", line 1449, in _ConsumeInteger\n    result = ParseInteger(tokenizer.token, is_signed=is_signed, is_long=is_long)\n  File \"\/home\/doe\/.local\/lib\/python3.6\/site-packages\/google\/protobuf\/text_format.py\", line 1471, in ParseInteger\n    result = _ParseAbstractInteger(text, is_long=is_long)\n  File \"\/home\/doe\/.local\/lib\/python3.6\/site-packages\/google\/protobuf\/text_format.py\", line 1502, in _ParseAbstractInteger\n    raise ValueError('Couldn\\'t parse integer: %s' % text)\nValueError: Couldn't parse integer: 03\nDuring handling of the above exception, another exception occurred:\nTraceback (most recent call last):\n  File \"train.py\", line 184, in \n    tf.app.run()\n  File \"\/home\/doe\/anaconda3\/lib\/python3.6\/site-packages\/tensorflow\/python\/platform\/app.py\", line 124, in run\n    _sys.exit(main(argv))\n  File \"\/home\/doe\/anaconda3\/lib\/python3.6\/site-packages\/tensorflow\/python\/util\/deprecation.py\", line 136, in new_func\n    return func(*args, **kwargs)\n  File \"train.py\", line 93, in main\n    FLAGS.pipeline_config_path)\n  File \"\/home\/doe\/anaconda3\/envs\/tensorflow\/models\/research\/object_detection\/utils\/config_util.py\", line 94, in get_configs_from_pipeline_file\n    text_format.Merge(proto_str, pipeline_config)\n  File \"\/home\/doe\/.local\/lib\/python3.6\/site-packages\/google\/protobuf\/text_format.py\", line 536, in Merge\n    descriptor_pool=descriptor_pool)\n  File \"\/home\/doe\/.local\/lib\/python3.6\/site-packages\/google\/protobuf\/text_format.py\", line 590, in MergeLines\n    return parser.MergeLines(lines, message)\n  File \"\/home\/doe\/.local\/lib\/python3.6\/site-packages\/google\/protobuf\/text_format.py\", line 623, in MergeLines\n    self._ParseOrMerge(lines, message)\n  File \"\/home\/doe\/.local\/lib\/python3.6\/site-packages\/google\/protobuf\/text_format.py\", line 638, in _ParseOrMerge\n    self._MergeField(tokenizer, message)\n  File \"\/home\/doe\/.local\/lib\/python3.6\/site-packages\/google\/protobuf\/text_format.py\", line 763, in _MergeField\n    merger(tokenizer, message, field)\n  File \"\/home\/doe\/.local\/lib\/python3.6\/site-packages\/google\/protobuf\/text_format.py\", line 837, in _MergeMessageField\n    self._MergeField(tokenizer, sub_message)\n  File \"\/home\/doe\/.local\/lib\/python3.6\/site-packages\/google\/protobuf\/text_format.py\", line 763, in _MergeField\n    merger(tokenizer, message, field)\n  File \"\/home\/doe\/.local\/lib\/python3.6\/site-packages\/google\/protobuf\/text_format.py\", line 837, in _MergeMessageField\n    self._MergeField(tokenizer, sub_message)\n  File \"\/home\/doe\/.local\/lib\/python3.6\/site-packages\/google\/protobuf\/text_format.py\", line 763, in _MergeField\n    merger(tokenizer, message, field)\n  File \"\/home\/doe\/.local\/lib\/python3.6\/site-packages\/google\/protobuf\/text_format.py\", line 871, in _MergeScalarField\n    value = _ConsumeInt32(tokenizer)\n  File \"\/home\/doe\/.local\/lib\/python3.6\/site-packages\/google\/protobuf\/text_format.py\", line 1362, in _ConsumeInt32\n    return _ConsumeInteger(tokenizer, is_signed=True, is_long=False)\n  File \"\/home\/doe\/.local\/lib\/python3.6\/site-packages\/google\/protobuf\/text_format.py\", line 1451, in _ConsumeInteger\n    raise tokenizer.ParseError(str(e))\ngoogle.protobuf.text_format.ParseError: 9:18 : Couldn't parse integer: 03","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1370,"Q_Id":51684220,"Users Score":0,"Answer":"I faced a similar problem relating to label_map_path when running on local machine. Solved by removing spaces between lines in the label map pbtxt file. Please check config file as well.","Q_Score":0,"Tags":"python-3.x,ubuntu,tensorflow,video-processing,object-detection-api","A_Id":51758333,"CreationDate":"2018-08-04T09:24:00.000","Title":"google.protobuf.text_format.ParseError: 9:18 : Couldn't parse integer: 03","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"from sklearn import svm\nImportError: cannot import name __check_build","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":4987,"Q_Id":51694076,"Users Score":1,"Answer":"I've been able to install scikit-learn on a fresh virtualenv here but it seems you need to do some extra job to get it up-and-running:\nBy doing just pip install scikit-learn and then from sklearn import svm you'll get import errors, it seems the library requires numpy and scipy. So you need to do:\n\npip install numpy scipy\n\nAfter that it should work fine.","Q_Score":0,"Tags":"python,scikit-learn,sklearn-pandas","A_Id":51694410,"CreationDate":"2018-08-05T11:54:00.000","Title":"ImportError: cannot import name __check_build","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Given a bounding box for an image in OpenCV, is there a way to return all the colors inside the box? Ideally, the output would give the information along the lines of \"In the bounding box, it is 50% white, 25% blue, 15% green, 10% red.\" \nIs there any way to do this? At the very least, is there some function that could tell me all the colors in the box? (without percentages or how much of it there is). Any indication would be huge help\nThanks in advance","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":672,"Q_Id":51695023,"Users Score":0,"Answer":"It can be done by using a grayscale convertion of your image or of your ROI.\nYou can weather process the histogram of the ROI extract from the gray scale (or convert to grayscale if extracted from the colour image) and count the non zero value. \nOr you can copy all the gray-scale value of your ROI into a set container, then the lenght of the set is the number of colours presents in your ROI.","Q_Score":0,"Tags":"python,opencv,colors","A_Id":51696373,"CreationDate":"2018-08-05T14:03:00.000","Title":"Determine all colors in bounding box in OpenCV\/Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataset of [~16k] and am doing binary classsfication [0\/1].When i am doing hyperparameter grid search in random forest my train and test accuracy increase as increase the depth[optimum is coming out to be 25].I am getting a test accuracy of as high as 97% and no there is no data leakage as there are three sets [train,test,and valid].My data has ~10 features.Is this fine since usually i have seen trees have 10 depth at max.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":102,"Q_Id":51706535,"Users Score":0,"Answer":"This is extremely dependent on the dataset at hand. For such a small dataset tree depth of 25 is indeed on the high side and I would say somewhat unusual. But it is not impossible. What it can depend on:\n\nrelation between features and the target\ncategorical vs numerical features\nimbalance of 0 vs 1 target\nyour code implementation (bugs, data leakage, other parameters)\n\nIf I were you i would look deeper to understand why such a deep tree is required. For example, you can start by identifying while feature gets the most splits: extract from the best model feature importances with type split and get the top-5 features. Look how do those correlate with the target.","Q_Score":0,"Tags":"python,random-forest,cross-validation,xgboost,test-data","A_Id":51729004,"CreationDate":"2018-08-06T11:25:00.000","Title":"Height of a random forest decison tree increasing till 25 and the test accuracy also increases","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there a way to update the xaxis of a bar chart using a columndatasource object? I have a chart that updates the bars based off of what is selected, but I want the tick marks in the xaxis to only represent the values that are being displayed. So, I figured the best option would be to dynamically update the xrange as each new value is selected, similar to how the overall plot is changed based on this.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1385,"Q_Id":51730321,"Users Score":0,"Answer":"No, FactorRange objects are only configurable by setting the factors property of the range. It is not possible to link the factors property to a CDS directly. (Factor ranges support multi-level hierarchical levels, and that does not map naturally to CDS columns.) However, you can change the value of factors to whatever you need, in a CustomJS callback, or Python callback (if are making a Bokeh server application).","Q_Score":0,"Tags":"python,bokeh","A_Id":51730840,"CreationDate":"2018-08-07T15:22:00.000","Title":"Bokeh Dynamically Update xrange based on a ColumnDataSource","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a huge array with coordinates describing a 3D curves, ~20000 points. I am trying to use less points, by ignoring some, say take 1 every 2 points. When I do this and I plot the reduced number of points the shape looks the same. However I would like to compare the two curves properly, similar to the chi squared test to see how much the reduced plot differs from the original.\nIs there an easy, built-in way of doing this or does anyone have any ideas on how to approach the problem.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":1324,"Q_Id":51730532,"Users Score":1,"Answer":"If you subsample the original curve, a simple way to assess the approximation error is by computing the maximum distance between the original curve and the line segments between the resampled vertices. The maximum distance occurs at the original vertices and it suffices to evaluate at these points only.\nBy the way, this provides a simple way to perform the subsampling by setting a maximum tolerance and decimating until the tolerance is exceeded.\nYou can also think of computing the average distance, but this probably involves nasty integrals and might give less visually pleasing results.","Q_Score":3,"Tags":"python,computational-geometry,curve-fitting","A_Id":51743453,"CreationDate":"2018-08-07T15:33:00.000","Title":"How to compare two 3D curves in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Currently i am using the standard Tensorflow object detection script which is custom trained, i would like to change the colors of the bound boxes to suit the nature of my application. However i cannot seem to find a way to do so. For example instead of drawing a green box id like to draw a red box around a detected object. Thank you in advance!","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":1764,"Q_Id":51738546,"Users Score":1,"Answer":"I sort of found a way - after much trouble. I found nothing documenting how to do this. \nSort of, as some colors don't seem to work.\nOpen \"visualizations_utils.py\". Should be in Lib\\site-packages\\utils. \nRows 41 to 63 are your colors. \nDirectly under row 164,\ndraw = ImageDraw.Draw(image), \nenter a new row \ncolor = 'Pink'\nSave it, and you have now changed the color to a pinkish color. Row 175, you can make the label text smaller. \nSome colors don't seem to work, like \"Red\".","Q_Score":0,"Tags":"python,tensorflow,computer-vision,data-science,object-detection","A_Id":52075500,"CreationDate":"2018-08-08T04:19:00.000","Title":"How to add custom colors to Tensorflow object detection python model bound boxes?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"So, I have 3 different data frames:\nall_options = {'free': free, 'paid': paid, 'all': df}\nWhat is the best way to iterate over to get the values for each one?\nI want to make a plot in dash. So when user will click on \"free\" data frame it will show him the graph with free items only?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":32,"Q_Id":51741482,"Users Score":0,"Answer":"it was easy as [v.columns for k, v in all_options.items()]\nThank you :)","Q_Score":1,"Tags":"python-3.x,pandas,plotly,plotly-dash","A_Id":51748887,"CreationDate":"2018-08-08T07:59:00.000","Title":"Best way to get the values from dict or list for 3 different dataframes","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Columns L,M,N of my dataframe are populated with 'true' and 'false' statements(1000 rows). I would like to create a new column 'count_false' that will return the number of times 'false' statement occurred in columns L,M and N.\nAny tips appreciated!\nThank you.","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1777,"Q_Id":51745679,"Users Score":0,"Answer":"Try this :  df[df==false].count()","Q_Score":0,"Tags":"python,pandas,countif","A_Id":51745774,"CreationDate":"2018-08-08T11:32:00.000","Title":"Python | count number of False statements in 3 rows","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to invert a classifier on MNIST dataset on keras, thus the determinant of weight matrix must not be zero. Then I can use x=W-1f-1(y) \nBut there is no custom constraint in keras.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":61,"Q_Id":51748305,"Users Score":1,"Answer":"By far the easiest way to ensure that is to check wether the trained W has determinant 0. If it doesn't then you're OK to go, if it does you can add a small constant (1e-7) to any weight in the matrix and solve the issue. I can tell you anyway that the odds of zero determinant happenning are almost negligible.","Q_Score":0,"Tags":"python,keras","A_Id":51748484,"CreationDate":"2018-08-08T13:44:00.000","Title":"custom constraint of weight matrix","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to do image classification with the Inception V3 model. Does ImageDataGenerator from Keras create new images which are added onto my dataset? If I have 1000 images, will using this function double it to 2000 images which are used for training? Is there a way to know how many images were created and now fed into the model?","AnswerCount":7,"Available Count":2,"Score":0.057080742,"is_accepted":false,"ViewCount":20435,"Q_Id":51748514,"Users Score":2,"Answer":"Also note that: These augmented images are not stored in the memory, they are generated on the fly while training and lost after training. You can't read again those augmented images. \nNot storing those images is a good idea because we'd run out of memory very soon storing huge no of images","Q_Score":39,"Tags":"python,tensorflow,machine-learning,keras,computer-vision","A_Id":51754972,"CreationDate":"2018-08-08T13:54:00.000","Title":"Does ImageDataGenerator add more images to my dataset?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to do image classification with the Inception V3 model. Does ImageDataGenerator from Keras create new images which are added onto my dataset? If I have 1000 images, will using this function double it to 2000 images which are used for training? Is there a way to know how many images were created and now fed into the model?","AnswerCount":7,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":20435,"Q_Id":51748514,"Users Score":0,"Answer":"Let me try and tell u in the easiest way possible with the help of an example.\nFor example:\n\nyou have a set of 500 images\nyou applied the ImageDataGenerator to the dataset with batch_size = 25\nnow you run your model for lets say 5 epochs with\nsteps_per_epoch=total_samples\/batch_size\nso , steps_per_epoch will be equal to 20\nnow your model will run on all 500 images (randomly transformed according to instructions provided to ImageDataGenerator) in each epoch","Q_Score":39,"Tags":"python,tensorflow,machine-learning,keras,computer-vision","A_Id":68196196,"CreationDate":"2018-08-08T13:54:00.000","Title":"Does ImageDataGenerator add more images to my dataset?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Trying to load CSV file while doing simple linear regression . When I try to run , the error is coming as - \"File name\" is not exist as file\/directory . Do I need to save the file in a particular folder or directory ?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":220,"Q_Id":51750841,"Users Score":0,"Answer":"try to use the full qualified path, or in the same directory of the main programm","Q_Score":3,"Tags":"python-3.x,jupyter-notebook,linear-regression","A_Id":51751064,"CreationDate":"2018-08-08T15:50:00.000","Title":"CSV file unable to upload","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to record the training accuracy of the model at the end of each epoch, but I wasn't sure if the training accuracy is calculated on the entire training data or the last mini-batch used for training. For example, if I have 10000 training data and I'm training with mini-batches of 100, would I calculate the training accuracy using the last mini-batch of 100 (the 100th and last mini-batch in that epoch) or use the entire 10000 training data?","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":1493,"Q_Id":51756558,"Users Score":1,"Answer":"Ideally training accuracy should be calculated including every sample, but practically, it's all right to use a subset of the data as long as its representative of all data.  Whether a random batch of 100 samples is representative of your data or not, depends on the problem at hand.  For instance if you were classifying a sample as one of one thousand possible classes, this is clearly not enough.\nI generally keep a running average of the loss over a given epoch during training.  Since the training loss is almost always monotonically decreasing, this will tend to overestimate loss (and underestimate accuracy.  However, the discrepancy should disappear as the model converges.","Q_Score":3,"Tags":"python,tensorflow,machine-learning","A_Id":51757163,"CreationDate":"2018-08-08T22:31:00.000","Title":"Is training accuracy of a model calculated on the mini-batch?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to record the training accuracy of the model at the end of each epoch, but I wasn't sure if the training accuracy is calculated on the entire training data or the last mini-batch used for training. For example, if I have 10000 training data and I'm training with mini-batches of 100, would I calculate the training accuracy using the last mini-batch of 100 (the 100th and last mini-batch in that epoch) or use the entire 10000 training data?","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":1493,"Q_Id":51756558,"Users Score":1,"Answer":"I agree with @Cory that ideally training accuracy should be calculated on every training sample. Instead of using an running average (exponential, perhaps), I keep the sum of last, let's say, 50 mini-batches. Then I report the average loss across these mini-batches. You can easily track the model's recent performance (you can report this number much frequently than every epoch).","Q_Score":3,"Tags":"python,tensorflow,machine-learning","A_Id":51757886,"CreationDate":"2018-08-08T22:31:00.000","Title":"Is training accuracy of a model calculated on the mini-batch?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Let's say I have two instances of a keras model model0 and model1 and datasets data0 and data1. If I have two or more GPUs, is there a way that I can train model0 on data0 on GPU0 and model1 on data1 on GPU1 in parallel? All of the methods I have found so far split the training of a single model over multiple gpus.\nThanks!","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":196,"Q_Id":51757355,"Users Score":1,"Answer":"How about multiprocessing?\nYou just execute you function in a multiprocessing pool twice:\nWhat you need to consider:\n\nYour model have to be defined or loaded inside the function\nYou need a parameter which mask the GPUs. Masking is possible by setting the env variable CUDA_VISIBLE_DEVICES(you also have to do this inside the function)\nYou could pass the different training data via a parameter\nit would be best to save the resulting models into different files and then load it from your main program \n\nSo basically passing keras\/tensorflow sessions between your main programm and the functions in the multiprocessing tool is a nogo. But if you keep everything keras\/tensorflow related inside the function and mask the GPUs differently then you're good to go.","Q_Score":0,"Tags":"python,tensorflow,parallel-processing,keras","A_Id":51760289,"CreationDate":"2018-08-09T00:35:00.000","Title":"How to run same Keras model on different GPUs in parallel independently with different data?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I was writing a code to read a csv file using pandas and I saw some weird functioning of the package. My file has column names which I want to ignore, so I use header = 0 or 'infer' instead of None. But I see something weird.\nWhen I use None and I want to get a specific column, I just need to do df[column_index] but when I use 0 or 'infer', I need to do df.ix[:,column_index] to get the column otherwise, for df[column_index] I get the following error:\n\nTraceback (most recent call last):   File\n  \"\/home\/sarvagya\/anaconda3\/envs\/tf\/lib\/python3.6\/site-packages\/pandas\/core\/indexes\/base.py\",\n  line 2525, in get_loc\n      return self._engine.get_loc(key)   File \"pandas\/_libs\/index.pyx\", line 117, in pandas._libs.index.IndexEngine.get_loc   File\n  \"pandas\/_libs\/index.pyx\", line 139, in\n  pandas._libs.index.IndexEngine.get_loc   File\n  \"pandas\/_libs\/hashtable_class_helper.pxi\", line 1265, in\n  pandas._libs.hashtable.PyObjectHashTable.get_item   File\n  \"pandas\/_libs\/hashtable_class_helper.pxi\", line 1273, in\n  pandas._libs.hashtable.PyObjectHashTable.get_item KeyError: column_index\nDuring handling of the above exception, another exception occurred:\nTraceback (most recent call last):   File \"\", line 1, in\n     File\n  \"\/home\/sarvagya\/anaconda3\/envs\/tf\/lib\/python3.6\/site-packages\/pandas\/core\/frame.py\",\n  line 2139, in getitem\n      return self._getitem_column(key)   File \"\/home\/sarvagya\/anaconda3\/envs\/tf\/lib\/python3.6\/site-packages\/pandas\/core\/frame.py\",\n  line 2146, in _getitem_column\n      return self._get_item_cache(key)   File \"\/home\/sarvagya\/anaconda3\/envs\/tf\/lib\/python3.6\/site-packages\/pandas\/core\/generic.py\",\n  line 1842, in _get_item_cache\n      values = self._data.get(item)   File \"\/home\/sarvagya\/anaconda3\/envs\/tf\/lib\/python3.6\/site-packages\/pandas\/core\/internals.py\",\n  line 3843, in get\n      loc = self.items.get_loc(item)   File \"\/home\/sarvagya\/anaconda3\/envs\/tf\/lib\/python3.6\/site-packages\/pandas\/core\/indexes\/base.py\",\n  line 2527, in get_loc\n      return self._engine.get_loc(self._maybe_cast_indexer(key))   File \"pandas\/_libs\/index.pyx\", line 117, in\n  pandas._libs.index.IndexEngine.get_loc   File\n  \"pandas\/_libs\/index.pyx\", line 139, in\n  pandas._libs.index.IndexEngine.get_loc   File\n  \"pandas\/_libs\/hashtable_class_helper.pxi\", line 1265, in\n  pandas._libs.hashtable.PyObjectHashTable.get_item   File\n  \"pandas\/_libs\/hashtable_class_helper.pxi\", line 1273, in\n  pandas._libs.hashtable.PyObjectHashTable.get_item KeyError: column_index\n\nCan someone help with this? Why is this happening?","AnswerCount":3,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":23961,"Q_Id":51759122,"Users Score":3,"Answer":"The difference pops up when working with a dataframe with header, so lets say your DataFrame df has header!\n\nheader=None pandas automatically assign the first row of df (which is the actual column names) to the first row, hence your columns no longer have names\nheader=0, pandas first deletes column names(header) and then assign new column names to them (only if you pass names = [........] while loading your file).\nread_csv( filepath, header = 0 , names = ['....' , '....' ...])\n\nhope it helps!","Q_Score":0,"Tags":"python-3.x,pandas,csv,dataframe","A_Id":54832068,"CreationDate":"2018-08-09T04:57:00.000","Title":"difference between `header = None` and `header = 0` in pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Python scipy.optimize.minimize function supports the following methods:\n\nNelder-Mead\nPowell\nCG\nBFGS\nNewton-CG\nL-BFGS-B  \nTNC\nCOBYLA\nSLSQP\ntrust-constr\ndogleg\ntrust-ncg\ntrust-exact\ntrust-krylov\n\nWhich method is closest to R's nlminb?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":761,"Q_Id":51759606,"Users Score":4,"Answer":"nlminb is an unconstrained and bounds-constrained quasi-Newton method optimizer. This code is based on a FORTRAN PORT library by David Gay in the Bell Labs. As for Pyhon quasi-Newton menthods are:\n\nUnconstrained minimization\nMethod BFGS uses the quasi-Newton method of Broyden, Fletcher, Goldfarb, > > and Shanno (BFGS) [5] pp. 136. \nBound-Constrained minimization\nMethod L-BFGS-B uses the L-BFGS-B algorithm [6], [7] for bound\n  constrained minimization.\n\nL-BFGS-B  & BFGS, being a member of quasi-Newton family methods, are the closest analogs of nlminb.","Q_Score":2,"Tags":"r,python-3.x,scipy,nonlinear-optimization","A_Id":52239819,"CreationDate":"2018-08-09T05:45:00.000","Title":"What is the closest function to R's nlminb in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"so I am using a convolutional layer as the first layer of a neural network for deep reinforcement learning to get the spatial features out of a simulation I built. The simulation gives different maps that are of different lengths and heights to process. If I understand convolutional networks, this should not matter since the channel size is kept constant. In between the convolutional network and the fully connected layers there is a spatial pyramid pooling layer so that the varying image sizes does not matter. Also the spatial data is pretty sparse. Usually it is able to go through a few states and sometimes a few episodes before the first convolutional layer spits out all Nans. Even when I fix the map size this happens. I do not know where the problem lies, where can the problem lie?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1443,"Q_Id":51759882,"Users Score":0,"Answer":"Try to initialize your weights with random numbers between 0 and 1 and then try different learning rates for your network training. (I suggest test it with learning rates equal to 10, 1, 0.1, 0.01, ...)","Q_Score":0,"Tags":"python,neural-network,artificial-intelligence,conv-neural-network,pytorch","A_Id":51861681,"CreationDate":"2018-08-09T06:07:00.000","Title":"Pytorch Convolutional Layer returning Nan","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Suppose I have built a model to predict the final positions of horses in horse racing using data of races and horses from the past 10 years with the following features:\nhorse_age, horse_weight, race_distance, jockey\nNow, I would like to predict positions in horse racing for the upcoming season. However, during those 10 years, many of the jockeys have retired, and new ones have joined, so there will be jockeys who were unseen by the trained model, unlike horse age, horse weight and race distance.\nI would like to know what is a good way to tackle this problem. I realize that one thing I can do is to continually retrain my model to incorporate the new data, but would like to hear more interesting ideas!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":126,"Q_Id":51761031,"Users Score":0,"Answer":"I don't think that's possible. \nHowever, you can do the following:\nAssume that for each race there are 10 horses. Number them from 0 to 9.\nInclude all the features you want for all the horses, and predict which horse number will win.\nFor example: \nhorse 0: horse_age0, horse_weight0, race_distance0, jockey0 \nhorse 1: horse_age1, horse_weight1, race_distance1, jockey1 \n...\nhorse 9: horse_age9, horse_weight9, race_distance9, jockey9 \na total of 4*10=40 feature. \nThe model should predict one of the classes 0 to 9, indicating the horse number. \nBe careful with this approach though, the ordering of the horses doesn't matter, you should take that into account when training\/choosing the model. \nYou can simplify the problem by considering a one-vs-one approach, where you evaluate each pair of horses to see which one would win.","Q_Score":0,"Tags":"python,machine-learning,scikit-learn","A_Id":51777618,"CreationDate":"2018-08-09T07:22:00.000","Title":"How to build a predictive model where there will be unseen categorical variables in the future","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I ran a clustering exercise to identify my top customers based on 12 distinct features, using K-Means (on 3 PCA dimensions and 5 PCA dimensions) and GMM (using 5 PCA dimensions) methodologies. Both the outputs from the K-Means produced almost similar customers as the best set (1182 customers in each case with an overlap of 1156) while the GMM approach gave me 660 customers as my top customers. These 660 customers were present in both the K-Means approaches. \nNow i want to identify who my top customers are from among this list. Could you please suggest any statistical approaches that i could use to say that these X number of customers are truly my best set and run some A\/B tests on them? I do not want to go with the full identified set as it might cost me more to do what is planned to such a large set of customers.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":50,"Q_Id":51761810,"Users Score":1,"Answer":"Try some good old filtering! Select one or several features, create your own metric (maybe top customers are those who buy the most, or those who are more loyal\/have stayedlonger with the company, or a weighted sum of those two factors), sort the 660 customers in your cluster and pick only the N first customers, N being your maximum allowed number of customers.","Q_Score":0,"Tags":"python,cluster-analysis,unsupervised-learning","A_Id":51762007,"CreationDate":"2018-08-09T08:05:00.000","Title":"After clustering, How do i choose the best customers (subset) from the top cluster?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"The eval() method under sklearn.mixture.GMM from the scikit-learn version 0.11 is deprecated. Is there a similar method or a workaround in the newest version 0.19.2 ?","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":515,"Q_Id":51765325,"Users Score":1,"Answer":"Eval(X)\nParameters : \nX: array_like, shape (n_samples, n_features) : List of n_features-dimensional data points. Each row corresponds to a single data point.\nReturns : \nlogprob: array_like, shape (n_samples,) : Log probabilities of each data point in X\nresponsibilities: array_like, shape (n_samples, n_components) :\nPosterior probabilities of each mixture component for each observation\nClearly , there is no workaround in the newer definition , it is the same and quite concise definition","Q_Score":0,"Tags":"python,scikit-learn,gmm","A_Id":51766044,"CreationDate":"2018-08-09T11:06:00.000","Title":"scikit-learn deprecated GMM.eval() replacement","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new in Python.\nI have an image of size 8*8, and when flattened = 64. Is there any function that measures the correlation between the pixel i and all the other pixels in this image ? \nSo at the end I want to plot a 64*64 matrix of correlation. \nThanks !","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1074,"Q_Id":51771344,"Users Score":2,"Answer":"Correlation is a measure to evaluate mutual relationship or connection between two or more things, usually vectors, not single point. Pixel is a single point with a single value. So, in your case, I would subtract pixel value form each pixel value of the image, looking at the difference in this case.","Q_Score":1,"Tags":"python,image,correlation,cross-correlation","A_Id":51771528,"CreationDate":"2018-08-09T15:51:00.000","Title":"Pixel correlation \/ similarity in an image Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to build an Agent that can play Pocket Tanks using RL. The problem I'm facing now is that how can I train a neural network to output the correct Power and Angle. so instead of actions classification. and I want a regression.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":748,"Q_Id":51774091,"Users Score":1,"Answer":"In order to output the correct power and angle, all you need to do is go into your neural network architecture and change the activation of your last layer.  \nIn your question, you stated that you are currently using an action classification output, so it is most likely a softmax output layer.  We can do two things here:\n\nIf the power and angle has hard constraints, e.g. the angle cannot be greater than 360\u00b0, or the power cannot exceed 700 kW, we can change the softmax output to a TanH output (hyperbolic tangent) and multiply it by the constraint of power\/angle.  This will create a \"scaling effect\" because tanh's output is between -1 and 1.  Multiplying the tanh's output by the constraint of the power\/angle ensures that the constraints are always satisfied and the output is the correct power\/angle.\nIf there are no constraints on your problem.  We can simply just delete the softmax output all together.  Removing the softmax allows for the output to no longer be constrained between 0 and 1.  The last layer of the neural network will simply act as a linear mapping, i.e., y = Wx + b. \n\nI hope this helps!\nEDIT: In both cases, your reward function to train your neural network can simply be a MSE loss.  Example: loss = (real_power - estimated_power)^2 + (real_angle - estimated_angle)^2","Q_Score":1,"Tags":"python,neural-network,reinforcement-learning","A_Id":51794321,"CreationDate":"2018-08-09T19:00:00.000","Title":"Regression through reinforcement learning","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm facing this issue while trying to use pandas package. I have installed numpy 1.9.0 and installed dateutil 2.5.0 using the command \npip install python-dateutil==2.5.0. Still I see this error. \nIs there any other way to install dateutil? And this is related only to pandas package\n\nTraceback (most recent call last):    import pandas as pd   File\n  \"\/Users\/xyz\/Library\/Python\/2.7\/lib\/python\/site-packages\/pandas\/init.py\",\n  line 23, in \n      from pandas.compat.numpy import *   File \"\/Users\/xyz\/Library\/Python\/2.7\/lib\/python\/site-packages\/pandas\/compat\/init.py\",\n  line 422, in \n      raise ImportError('dateutil 2.5.0 is the minimum required version') ImportError: dateutil 2.5.0 is the minimum required version","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":4264,"Q_Id":51776103,"Users Score":0,"Answer":"The issue was resolved by installing python 3.6. There is a version compatibility issue between pandas package and earlier version of Python","Q_Score":0,"Tags":"python,pandas,pip,easy-install,python-dateutil","A_Id":51957181,"CreationDate":"2018-08-09T21:36:00.000","Title":"ImportError: dateutil 2.5.0 is the minimum required version","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm facing this issue while trying to use pandas package. I have installed numpy 1.9.0 and installed dateutil 2.5.0 using the command \npip install python-dateutil==2.5.0. Still I see this error. \nIs there any other way to install dateutil? And this is related only to pandas package\n\nTraceback (most recent call last):    import pandas as pd   File\n  \"\/Users\/xyz\/Library\/Python\/2.7\/lib\/python\/site-packages\/pandas\/init.py\",\n  line 23, in \n      from pandas.compat.numpy import *   File \"\/Users\/xyz\/Library\/Python\/2.7\/lib\/python\/site-packages\/pandas\/compat\/init.py\",\n  line 422, in \n      raise ImportError('dateutil 2.5.0 is the minimum required version') ImportError: dateutil 2.5.0 is the minimum required version","AnswerCount":3,"Available Count":2,"Score":0.0665680765,"is_accepted":false,"ViewCount":4264,"Q_Id":51776103,"Users Score":1,"Answer":"I had this same issue using the newest pandas version, try downgrading to pandas 0.22.0 which fixed my issue","Q_Score":0,"Tags":"python,pandas,pip,easy-install,python-dateutil","A_Id":53266794,"CreationDate":"2018-08-09T21:36:00.000","Title":"ImportError: dateutil 2.5.0 is the minimum required version","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"As we know, when using tensorflow to save checkpoint, we have 3 files, for e.g.:\nmodel.ckpt.data-00000-of-00001\nmodel.ckpt.index\nmodel.ckpt.meta\nI check on the faster rcnn and found that they have an evaluation.py script which helps evaluate the pre-trained model, but the script only accept .ckpt file (as they provided some pre-trained models above).\nI have run some finetuning from their pre-trained model\nAnd then I wonder if there's a way to convert all the .data-00000-of-00001, .index and .meta into one single .ckpt file to run the evaluate.py script on the checkpoint?\n(I also notice that the pre-trained models they provided in the repo do have only 1 .ckpt file, how can they do that when the save-checkpoint function generates 3 files?)","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":658,"Q_Id":51782871,"Users Score":0,"Answer":"These \n{\nmodel.ckpt.data-00000-of-00001\nmodel.ckpt.index\nmodel.ckpt.meta\n}\nare the more recent checkpoint format\nwhile \n{model.ckpt} \nis a previous checkpoint format\nIt will be in the same concept as to convert a Nintendo Switch to NES ... Or a 3 pieces CD bundle to a single ROM cartridge...","Q_Score":0,"Tags":"python,tensorflow","A_Id":59889359,"CreationDate":"2018-08-10T09:07:00.000","Title":"how to convert tensorflow .meta .data .index to .ckpt file?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using sns.distplot with hist=True and kde=True. This works fine but for some datasets (e.g. if they contain only discrete values) the kernel density estimation line is zig-zagging which looks very odd given that the histogram underneath is smooth. A manual adjustment of the kde bandwidth should fix this but how can I set this for sns.distplot? The documentation does not say anything and the \"bw\" parameter that works for sns.kdeplot does not exist. How can I stop it from zig-zagging?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":3950,"Q_Id":51784529,"Users Score":2,"Answer":"You can use the bandwidth option(bw) with the optional parameter \"kde_kws\" in the seaborn distplot to set the desired bandwidth.\neg :\ng = g.map(sns.distplot, \"value\", kde_kws={'bw':0.1})","Q_Score":0,"Tags":"python,plot,seaborn,kernel-density","A_Id":62967486,"CreationDate":"2018-08-10T10:31:00.000","Title":"How can I hand a to bandwidth for the kde to seaborn's distplot?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a use case where I need to classify some images as grey scale or color. My initial step was based on the feature that grey scale images should have r,g,b values at a pixel, the same values as it is single channel. Were as for color images, r,g,b values at the same pixel may not be the same.\nSo I am checking by getting the difference between (r,g), (b,g) and (r,b) and if all three has only zero, its grey scale else, its color.\nThis approach helped me to identify many grey scale images but still there are some images which does not follow this logic. Can anyone specify some good features on which we can classify an image as color or grey scale using opencv?\nDo not ask me to check the number of channels and classify, it gives 3 for both the classes as we are loading it in .jpg format.\nThanks in advance","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":1021,"Q_Id":51784719,"Users Score":1,"Answer":"I suspect, some never were grey-scale images after digitizing (e.g. a color scan of gray-scale picture). Due to noise, there are minimal differences in the RGB values. A low threshold greater than perfect zero should do the trick.\nPlease note that JPEG totally has a gray-scale option. However, when storing the picture, you request that mode. Compressors usually do not pick it up automatically. Also, you explicitly need to set the flag IMREAD_UNCHANGED while reading with OpenCV's imread.","Q_Score":1,"Tags":"python,opencv,image-processing,classification,opencv-python","A_Id":51784896,"CreationDate":"2018-08-10T10:41:00.000","Title":"How to classify between a color image and grey scale image using opencv?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a wrf output netcdf file.File have variables temp abd prec.Dimensions keys are time, south-north and west-east. So how I select different lat long value in region. The problem is south-north and west-east are not variable. I have to find index value of four lat long value","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":26,"Q_Id":51807792,"Users Score":0,"Answer":"1) Change your Registry files (I think it is Registry.EM_COMMON) so that you print latitude and longitude in your wrfout_d01_time.nc files.\n2) Go to your WRFV3 map.\n3) Clean, configure and recompile.\n4) Run your model again the way you are used to.","Q_Score":0,"Tags":"python,netcdf4","A_Id":51878303,"CreationDate":"2018-08-12T10:05:00.000","Title":"Data extraction from wef output file","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm working on a model that would predict an exam schedule for a given course and term. My input would be the term and the course name, and the output would be the date. I'm currently done with the data cleaning and preprocessing step, however, I can't wrap my head around a way to make a model whose input is two strings and the output is two numbers (the day and month of exam). One approach that I thought of would be encoding my course names, and writing the term as a binary list. I.E input: encoded(course), [0,0,1] output: day, month. and then feeding to a regression model. \nI hope someone who's more experienced could tell me a better approach.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":21,"Q_Id":51816305,"Users Score":0,"Answer":"Create dummy variables or use RandomForest. They accept text input and numerical output.","Q_Score":0,"Tags":"python,machine-learning,deep-learning,regression","A_Id":51826257,"CreationDate":"2018-08-13T06:27:00.000","Title":"Text Input and Numerical Output issue for Exam Schedule Predictor","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Currently I have a large amount of numbers that I am copy and pasting from excel from a column into python and they paste as such:\n12\n13\n14\n15\n16\n...\nEach number is on a seperate line.\nCurrently I am having to backspace in front of a number so that it jumps to the previous line then I insert commas between each number so that I can use this array in my code. These numbers come in quantities of hundreds and it is easier currently to copy and paste into excel and then into python versus saving an excel file with these numbers and referencing the excel file in my script. I am using visual studio code for my editor and I tried alt+v and that didnt work(read that as a shot). Let me know if there is an easy way to get my copy and pasted column into a useable array. Thanks","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":597,"Q_Id":51833351,"Users Score":0,"Answer":"If I\u2019m reading what you are trying to do correctly, in VS Code, you can do essentially what @sundance suggested by holding Alt and dragging your cursor down the different lines to get a multiline cursor.","Q_Score":0,"Tags":"python,excel","A_Id":51833595,"CreationDate":"2018-08-14T03:34:00.000","Title":"Pasting numbers into python so that they may be used as an array","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am coding specifically in python, but right now in the phase of designing psuedocode for an algorithm that will take all the data points in a data set that has n values, n being very large and pick out the 10 smallest values (or finite number m << n, where m is the m smallest numbers). I wish to have an optimally efficient algorithm for the requirements. \nMy idea: \n1) Heapsort the data then pick the smallest 10 values. O(nlog(n))\n2) Alternatively,use a loop to identify a 'champion' that runs 10 times. With the first 'champion' determined remove from the dataset and then repeat this loop. O(n) (given m is small)\nWhich suggestion or if there is another would be best?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":250,"Q_Id":51848078,"Users Score":2,"Answer":"One approach among many possible:\nGrab 10 values and sort them. Now compare the largest with the 11th through nth values one at a time. Whenever the new value is smaller replace the 10th smallest with it and resort your 10 values. \nThe list of 10 values, sorting them etc will all be in cache so fast even with rough code. The whole list will be accessed once through so will be fast as well.","Q_Score":1,"Tags":"python-3.x,performance,pseudocode","A_Id":51848259,"CreationDate":"2018-08-14T18:54:00.000","Title":"Find the 10 smallest numbers in a large dataset?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have CSV files that I need to feed to a Deep-Learning network. Currently my CSV files are of size 360*480, but the network restricts them to be of size 224*224. I am using Python and Keras for the deep-learning part. So how can I resize the matrices?\nI was thinking that since aspect ratio is 3:4, so if I resize them to 224:(224*4\/3) = 224:299, and then crop the width of the matrix to 224, it could serve the purpose. But I cannot find a suitable function to do that. Please suggest.","AnswerCount":4,"Available Count":1,"Score":0.049958375,"is_accepted":false,"ViewCount":1515,"Q_Id":51848406,"Users Score":1,"Answer":"You could also convert the csv file to a list, truncate the list, and then convert the list to a numpy array and then use np.reshape.","Q_Score":4,"Tags":"python,python-3.x,python-2.7,keras,deep-learning","A_Id":51848498,"CreationDate":"2018-08-14T19:17:00.000","Title":"Resize CSV data using Python and Keras","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to install cv2 for a script that has been written for me. I tried  pip install cv2 and pip install open_cv and got the same problem - a warning message from dist.py and complains about zlib being not found. No cv2 installed. I also tried pyopenvc and pip install opencv-python.\nSo, I went to the opencv site and downloaded the relevant exe. Ran it - generated a heap of subdirectories and a make file and stuff. \nWhat do I do now?","AnswerCount":24,"Available Count":4,"Score":0.0166651236,"is_accepted":false,"ViewCount":506854,"Q_Id":51853018,"Users Score":2,"Answer":"Open terminal\nRun the following command\npip install --trusted-host=pypi.org --trusted-host=files.pythonhosted.org opencv-python.\nHope it will work.","Q_Score":178,"Tags":"python,opencv","A_Id":57182351,"CreationDate":"2018-08-15T04:34:00.000","Title":"How do I install opencv using pip?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to install cv2 for a script that has been written for me. I tried  pip install cv2 and pip install open_cv and got the same problem - a warning message from dist.py and complains about zlib being not found. No cv2 installed. I also tried pyopenvc and pip install opencv-python.\nSo, I went to the opencv site and downloaded the relevant exe. Ran it - generated a heap of subdirectories and a make file and stuff. \nWhat do I do now?","AnswerCount":24,"Available Count":4,"Score":0.024994793,"is_accepted":false,"ViewCount":506854,"Q_Id":51853018,"Users Score":3,"Answer":"Open anaconda command prompt and type in below command.\nconda install -c conda-forge opencv\n\nOnce the 'Solving environment' is done. It will ask to download dependencies. Type 'y'.\n\nIt will install all the dependencies and then you are ready to code.","Q_Score":178,"Tags":"python,opencv","A_Id":54848727,"CreationDate":"2018-08-15T04:34:00.000","Title":"How do I install opencv using pip?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to install cv2 for a script that has been written for me. I tried  pip install cv2 and pip install open_cv and got the same problem - a warning message from dist.py and complains about zlib being not found. No cv2 installed. I also tried pyopenvc and pip install opencv-python.\nSo, I went to the opencv site and downloaded the relevant exe. Ran it - generated a heap of subdirectories and a make file and stuff. \nWhat do I do now?","AnswerCount":24,"Available Count":4,"Score":0.0166651236,"is_accepted":false,"ViewCount":506854,"Q_Id":51853018,"Users Score":2,"Answer":"->pip install opencv-python you can use this.\nBut if this code does not working then you can check python version on cmd and anaconda because they are different. So you type command in anaconda prompt and cmd, it will work. You can check this -> pip list","Q_Score":178,"Tags":"python,opencv","A_Id":71060884,"CreationDate":"2018-08-15T04:34:00.000","Title":"How do I install opencv using pip?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to install cv2 for a script that has been written for me. I tried  pip install cv2 and pip install open_cv and got the same problem - a warning message from dist.py and complains about zlib being not found. No cv2 installed. I also tried pyopenvc and pip install opencv-python.\nSo, I went to the opencv site and downloaded the relevant exe. Ran it - generated a heap of subdirectories and a make file and stuff. \nWhat do I do now?","AnswerCount":24,"Available Count":4,"Score":0.0,"is_accepted":false,"ViewCount":506854,"Q_Id":51853018,"Users Score":0,"Answer":"if you are using Pycharm navigate settings > Project:name > Project interpreter just search the module by name(in this case OpenCV-python) and install it. worked for me","Q_Score":178,"Tags":"python,opencv","A_Id":60336465,"CreationDate":"2018-08-15T04:34:00.000","Title":"How do I install opencv using pip?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"The size of my pandas dataframe is 6844 and is named all_data_frame.\nWhen I type print(str(all_ssis_data.tail)) I get rows in the index range of 0-29 and 3633-3662.\nWhy doesn't it show me rows around the 6800 range?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1158,"Q_Id":51853350,"Users Score":0,"Answer":"tail is a method of dataframe. You can do this:\nprint(str(all_ssis_data.tail()))\nif you want to get page around 6800\nyou can add 50 as parameter\n.tail(50) will show the last 50 rows","Q_Score":1,"Tags":"python,pandas","A_Id":51853382,"CreationDate":"2018-08-15T05:21:00.000","Title":"Pandas Dataframe Tail Not Working","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have been playing around the tensorflow object detection API for instance segmentation. In the pipeline configuration file, as a part of the train_config we specify the num_steps value. So it is the total number of steps that network is trained on. \nSo let us say, I have 10,000 images in training dataset and batch_size is set to 1. In this case, does the data gets shuffled after every 10000 steps? Does it make sure automatically, that entire dataset is seen by the network before shuffling occurs?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":544,"Q_Id":51860849,"Users Score":1,"Answer":"The framework does not actually shuffle your data, it's memory intensive and time consuming.\nInstead, there's an array with random indices that are used to access your data. Usually, indices cover the whole dataset, so, you're right, all data is seen by your network, however in random order.","Q_Score":1,"Tags":"python,tensorflow,object-detection-api","A_Id":51861092,"CreationDate":"2018-08-15T14:38:00.000","Title":"How does shuffling work while training in tensorflow object detection API","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"On 2 distinct independent systems, I have Anaconda\/Miniconda. On my default virtual environment on either one, since 2018-8-15, when I import pandas or matplotlib, I get errors module 'numpy' has no attribute '__version__' and module 'numpy' has no attribute 'square', respectively.\nAn update to conda or one of the modules may have done damage, but I haven't been able to figure out. The computers, one is a Mac and one a Windows PC. The only common features are that I use the Jupyter Notebook environment on both systems, both are default environments, both may have deep learning packages installed on them (as opposed to the other environments which are still functioning well), and I've been using the Jupyter Notebook environment to code on both.\nHas anyone come across this problem? Is there a solution? Is it waiting for a fix update?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":244,"Q_Id":51864008,"Users Score":0,"Answer":"I had this issue and I think it is related to a broken install. I fixed mine with conda update --all. But I guess you can fix it with uninstalling numpy and reinstalling it back.","Q_Score":1,"Tags":"python,numpy,anaconda,jupyter,miniconda","A_Id":52075651,"CreationDate":"2018-08-15T17:58:00.000","Title":"Numpy no module\/attribute when importing other packages depending on it","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I read the data from oracle database to panda dataframe, then, there are some columns with type 'object', then I write the dataframe to hive table, these 'object' types are converted to 'binary' type, does any one know how to solve the problem?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":42,"Q_Id":51869125,"Users Score":0,"Answer":"When you read data from oracle to dataframe it's created columns with object datatypes.\nYou can ask pandas dataframe try to infer better datatypes (before saving to Hive) if it can:\ndataframe.infer_objects()","Q_Score":0,"Tags":"python-2.7,hive","A_Id":51877123,"CreationDate":"2018-08-16T03:16:00.000","Title":"Why there is binary type after writing to hive table","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a series of points in two 3D systems. With them, I use np.linalg.lstsq to calculate the affine transformation matrix (4x4) between both. However, due to my project, I have to \"disable\" the shear in the transform. Is there a way to decompose the matrix into the base transformations? I have found out how to do so for Translation and Scaling but I don't know how to separate Rotation and Shear. \nIf not, is there a way to calculate a transformation matrix from the points that doesn't include shear?\nI can only use numpy or tensorflow to solve this problem btw.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1921,"Q_Id":51876622,"Users Score":1,"Answer":"I'm not sure I understand what you're asking.\nAnyway If you have two sets of 3D points P and Q, you can use Kabsch algorithm to find out a rotation matrix R and a translation vector T such that the sum of square distances between (RP+T) and Q is minimized.\nYou can of course combine R and T into a 4x4 matrix (of rotation and translation only. without shear or scale).","Q_Score":1,"Tags":"python-3.x,numpy,transformation,affinetransform,matrix-decomposition","A_Id":51876979,"CreationDate":"2018-08-16T12:03:00.000","Title":"How to decompose affine matrix?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on the Movielens dataset and I wanted to apply K-Means algorithm on it. I would like to know what columns to choose for this purpose and How can I proceed further Or Should I directly use the KNN algorithm.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":927,"Q_Id":51877919,"Users Score":0,"Answer":"Well, K-Means is a clustering Algorithm so you can use it to cluster the movies based on the genres.\nColumns: movieId, genres\nThere's a new dataset which includes relevant tags for each movie, so you can also cluster them based on those tags.\nHowever, you can also make use of K-Nearest Neighbors to find out how users are similar to each other.\nIn the reviews dataset, you would use the following columns:\nColumns: userId, rating\nWhile in the movies dataset you would use the movieId.\nThe idea is to figure out which movies every user has viewed, then use those movies to compute the similarity index of users (eg; user1, user2)","Q_Score":1,"Tags":"python,k-means,data-science,scikit-image","A_Id":56354392,"CreationDate":"2018-08-16T13:11:00.000","Title":"KMeans Clustering on Movielens Dataset","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I read a list of float values of varying precision from a csv file into a Pandas Series and need the number of digits after the decimal point. So, for 123.4567 I want to get 4.\nI managed to get the number of digits for randomly generated numbers like this:\n\ndf = pd.Series(np.random.rand(100)*1000)\nprecision_digits = (df - df.astype(int)).astype(str).str.split(\".\", expand=True)[1].str.len().max()\n\nHowever, if I read data from disk using pd.read_csv where some of the rows are empty (and thus filled with nan), I get the following error:\n\nTraceback (most recent call last):\n  File \"<input>\", line 1, in <module>\n  File \"\/home\/tgamauf\/workspace\/mostly-sydan\/venv\/lib\/python3.6\/site-packages\/pandas\/core\/generic.py\", line 4376, in __getattr__\n    return object.__getattribute__(self, name)\nAttributeError: 'DataFrame' object has no attribute 'str'\n\nWhat is going wrong here?\nIs there a better way to do what I need?","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1611,"Q_Id":51879063,"Users Score":1,"Answer":"pd.read_csv() typically returns a DataFrame object.  The StringMethods object returned by using .str is only defined for a Series object.  Try using pd.read_csv('your_data.csv' , squeeze=True) to have it return a Series object; then you will be able to use .str","Q_Score":1,"Tags":"python,pandas,dataframe","A_Id":51882505,"CreationDate":"2018-08-16T14:08:00.000","Title":"How to get the maximum number of digits after the decimal point in a Pandas series","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Can you download OpenCV with Python 3.5.2? I want to set up OpenCV. But I don't know if I need 2.7 or can I use my already downloaded Python 3.5.2 version. Will it be compatible? I want learn computer vision so I can make some projects. Like a automated waste sorter. But I need to download OpenCV first. Just need some guidance. It's not like it's a straight download but a process. So if anyone can tell be if my version (on windows 10) Python 3.5.2 will work with the current OpenCV that would be great. Thanks","AnswerCount":3,"Available Count":1,"Score":0.1325487884,"is_accepted":false,"ViewCount":482,"Q_Id":51881545,"Users Score":2,"Answer":"yes, OpenCV 3 works with Python 2.7, 3.4, 3.5, 3.6 and 3.7","Q_Score":0,"Tags":"python,opencv,python-3.5","A_Id":51881699,"CreationDate":"2018-08-16T16:24:00.000","Title":"Can you download OpenCV with Python 3.5.2?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I recently came to know about Modelcheckpoint Callback in Keras. But it can only monitor Validation Accuracy or Training Acc. It can save best model weights when validation accuracy is maximum. But the model may be overfitted to validation data. Thus, I feel a need to monitor training accuracy as well. I want to save that model for which Validation Accuracy is maximum and Training Accuracy is also good.\nIs there way or callback for it?\nThanks","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":202,"Q_Id":51886718,"Users Score":0,"Answer":"But the model may be overfitted to validation data.\n\nThats basically not possible. If you split your traing data into validation and training correctly, your model does never see the validation data, before it validates the hypothesis, it learned. \nYou can overfit your training data, but you can not overfit your validation data, because the model does not learn, while validating. Thats the idea behind validation split.","Q_Score":0,"Tags":"python,callback,keras","A_Id":51891386,"CreationDate":"2018-08-16T23:52:00.000","Title":"Keras Callback to Monitor Both Validation and Training Accuracy at the Same Time","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"My network was achieving 96% accuracy on my dataset (edit: on predicting 9 classes). I saved the entire model for every epoch (weights included) whenever I ran it. I ran it 3 times, each time testing different hyperparameters, each time achieving around 96% accuracy.\nWhen I try to load any of these tests now and run it again, it achieves around 50% accuracy. I'm extremely confident I'm running it on the same dataset. \nHere's the interesting thing: if I train a new network instance of the exact same architecture, same size, shape, etc., it only ever reaches a max of 85% accuracy. Additionally, saving and loading these new training models works correctly, as in the model will reach the same 85% accuracy.\nSo, there's no problem with loading and no problem with my dataset. The only way this could happen is if something is wrong in my custom layer, or something else is happening.\nUnfortunately I haven't been committing all of my changes to the custom layer to git. While I can't guarantee that my custom layer is the exact same, I'm almost completely confident it is.\nAny ideas to what could be causing this discrepancy?\nEdit: To add more context, the layer is ripped from the ConvLSTM2d class but I replaced the call() function to simply be a vanilla RNN that uses convolution instead of the dot product. I am confident that the call() function is the same as before, but I am not confident that the rest of the class is the same. Is there anything else in the class that could affect performance? I've checked activation function already.","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":1125,"Q_Id":51887958,"Users Score":1,"Answer":"I finally found the answer.\nI have implemented a custom Lambda layer to handle reshaping. This layer has difficulties loading. Specifically, it reshapes one dimension into two dimensions on an arbitrary interval. The interval was defaulting to one specific value every time I loaded the model, even though it was incorrect. When I force it to be the correct interval, it works.","Q_Score":2,"Tags":"python,tensorflow,machine-learning,keras,keras-layer","A_Id":51921611,"CreationDate":"2018-08-17T03:21:00.000","Title":"Loading a saved model in Keras with a custom layer and prediction results are different?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"My network was achieving 96% accuracy on my dataset (edit: on predicting 9 classes). I saved the entire model for every epoch (weights included) whenever I ran it. I ran it 3 times, each time testing different hyperparameters, each time achieving around 96% accuracy.\nWhen I try to load any of these tests now and run it again, it achieves around 50% accuracy. I'm extremely confident I'm running it on the same dataset. \nHere's the interesting thing: if I train a new network instance of the exact same architecture, same size, shape, etc., it only ever reaches a max of 85% accuracy. Additionally, saving and loading these new training models works correctly, as in the model will reach the same 85% accuracy.\nSo, there's no problem with loading and no problem with my dataset. The only way this could happen is if something is wrong in my custom layer, or something else is happening.\nUnfortunately I haven't been committing all of my changes to the custom layer to git. While I can't guarantee that my custom layer is the exact same, I'm almost completely confident it is.\nAny ideas to what could be causing this discrepancy?\nEdit: To add more context, the layer is ripped from the ConvLSTM2d class but I replaced the call() function to simply be a vanilla RNN that uses convolution instead of the dot product. I am confident that the call() function is the same as before, but I am not confident that the rest of the class is the same. Is there anything else in the class that could affect performance? I've checked activation function already.","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":1125,"Q_Id":51887958,"Users Score":1,"Answer":"Well, let\u00b4s think about it, there are basically two possible reasons for your problem. Your custom layer is not the same and therefore some weights are not set up correctly, which leads to missclassification. In case of a binary classification you will end up in 50% accuracy on balanced data.\nThe second possible reason is, that threre is a bug in keras serialization. In this case your new trained and saved models should also show this issue, after deserialization.\n\nWhile I can't guarantee that my custom layer is the exact same, I'm almost completely confident it is.\n\nSo the problem is not reproducable with a new model, you accuracy is 50% and you cannot guarantee that the custom layer matches. -> I guess it is you custom layer.","Q_Score":2,"Tags":"python,tensorflow,machine-learning,keras,keras-layer","A_Id":51891264,"CreationDate":"2018-08-17T03:21:00.000","Title":"Loading a saved model in Keras with a custom layer and prediction results are different?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When using keras for machine learning, model.fit() is used when training data is small. When training data is too big, model.fit_generator() is recommended instead of model.fit(). How does one know when data size has become too large?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":309,"Q_Id":51895278,"Users Score":6,"Answer":"The moment you run into memory errors when trying to take the training data into memory, you'll have to switch to fit_generator(). There is extra overhead associated with generating data on the fly (and reading from disk to do so), so training a model on a dataset that lives in memory will always be faster.","Q_Score":3,"Tags":"python-3.x,machine-learning,keras","A_Id":51895476,"CreationDate":"2018-08-17T12:16:00.000","Title":"How to know when to use fit_generator() in keras when training data gets too big for fit()?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a variable target_test (for machine learning) and I'd like to display just one element of target_test.\ntype(target_test) print the following statement on the terminal : \nclass 'pandas.core.series.Series'\nIf I do print(target_test) then I get the entire 2 vectors that are displayed. \nBut I'd like to print just the second element of the first column for example.\nSo do you have an idea how I could do that ?\nI convert target_test to frame or to xarray but it didn't change the error I get.\nWhen I write something like : print(targets_test[0][0]) \nI got the following output : \nTypeError: 'instancemethod' object has no attribute '__getitem__'","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1594,"Q_Id":51898845,"Users Score":1,"Answer":"For the first column, you can use targets_test.keys()[i], for the second one targets_test.values[i] where i is the row starting from 0.","Q_Score":0,"Tags":"python,pandas","A_Id":51898927,"CreationDate":"2018-08-17T15:42:00.000","Title":"How to display a pandas Series in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to create Faster RCNN like model. I get stuck when it comes to the ROI pooling from the feature map. I know here billinear sampling can be used but, it may not help for end to end training. How to implement this ROI pooling layer in tensorflow?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":506,"Q_Id":51905199,"Users Score":1,"Answer":"Bilinear sampling - as the name suggests - can actually be used even with end-to-end training as it's basically a linear operation. However, the disadvantage would be that your local maxima (i.e. strong excitations or certain units) could vanish because your sampling points just happen to be close to the minima. To remedy this, you can instead apply a max_pool(features, kernel, stride) operation where kernel and stride are adjusted such that the final output of this max pool operation does always have the same dimensions.\nAn example: your features have size 12x12 and you would like to pool to 4x4, then setting kernel=(3,3) and stride=(3,3) would help you achieve that and for each 3x3 patch, the strongest excitations in the respective feature maps will be contained in the output.","Q_Score":0,"Tags":"python,tensorflow,neural-network,artificial-intelligence","A_Id":51905252,"CreationDate":"2018-08-18T04:15:00.000","Title":"How to implement Region of Interest Pooling layer in tensorflow?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"OS: Windows 10\nUsing anaconda3 - 5.2, running commands in Anaconda command prompt. \nHere's the commands I used to locate the word-rnn and activate the conda enviroment, plus the error messages when I attempt to train the model. I am also going off of a Mac based tutorial, so please correct me if \"python train.py\" is the incorrect command to being training. \n(base) C:\\Users\\mower>conda activate tensorflow\n(tensorflow) C:\\Users\\mower>cd \"C:\\Users\\mower\\Downloads\\word-rnn-tensorflow-master\"\n(tensorflow) C:\\Users\\mower\\Downloads\\word-rnn-tensorflow-master>python train.py\nTraceback (most recent call last):\n  File \"train.py\", line 2, in \n    import numpy as np\nImportError: No module named 'numpy'\n(tensorflow) C:\\Users\\mower\\Downloads\\word-rnn-tensorflow-master>","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":701,"Q_Id":51926059,"Users Score":0,"Answer":"You can use python's pip installer or anaconda's own installer to install numpy. In pip you use:\npip install numpy\nOR\nyou can use anaconda like below:\nconda install numpy","Q_Score":0,"Tags":"python,numpy,tensorflow,cmd,anaconda","A_Id":51926152,"CreationDate":"2018-08-20T07:38:00.000","Title":"When running command \"python train.py\" to train word-rnn, getting \"no module named numpy\" error.","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using object detection from tensorflow. \nSome background information about the problem\nI was facing a challenge since I am interested in training a model which will among other things detect also weapons. My regular point of view is from a drone but I couldn't find any publicly available dataset from this point of view containing weapons. So, I collected images with weapons and tried to imitate as much as possible the drone point of view. \n\nOne of my first thought was to pad my images (to make smaller my actual bounding boxes inside them in respect with the image itself). \nA second though was to also rotate my images. Since tensorflow has not implemented this feature I made a simple code to create prerotated images. This has limitation but anyway I gave it a shot.\n\nI experimented with images of people at the beach and tried to imitate the actual problem: \n\nI am using relatively close images of people at the beach for training\nAnd at the same time using relatively distant images of people at the beach for testing purposes.\n\nThe actual problem\nAt one of my experiments I trained my model for longer than usual and noticed that it really blew it. To be more specific: \n\nI have an initial 140 \"close frame\" images for training. \nFrom them I had created 4x140=560 images with small padding (resulting image is up to 1.6 times the original in the biggest version) and rotation (up to 5 degrees rotation) resized version.\nI used this dataset of 560 images to train my model for almost 800k iterations using Faster R-CNN model.\nI exported the model in various iterations:\nAt 150k, 300k, 500k, 700k and used to test it on my test images.\n\nI have also used for comparison models which was trained with the original resized images to check the differences.\nUn-expected results\n\nThe first peculiar observation is that my model at 500k and above it does not detect any person at all. (First thought was overtraining)\nI tried it on the actual training images to check this and here also did not detect anything at my testing images. \nAlso, I would expect my \"enhanced\" dataset to perform better than the simply resized one but this does not seem the case also. (This though is a minor problem which seem scientifically rational).\n\nSo, my question is how is that even possible? Training for too long can lead to being unable to detect training samples also? Is there any theoretical explanation for this? (Apart from errors in implementation etc which I think is not really probable but cannot be excluded in any case).\nP.S.1\nMy resized version of the image follows the same resize conditions of Faster R-CNN with min. dim. being 600 pixels and max. one 1024 pixels. So, this should not be any resizing problem.\nP.S.2\nI am using tensorflow but I don't think this is tensorflow specific issue. So, I did not include it in tags.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":245,"Q_Id":51931564,"Users Score":0,"Answer":"After some research (and a lot of experiments) I have come to some conclusions which might help others facing the same problem.\n\nFailing to detect any object probably means that the model has collapsed in some part of training. A possible indication of this in my case was some extremely high values of loss values when training. While I have models that this happened and does not seem to affect the model too much (at least not too much to make it useless) an increasing amount of these values is a good sign that something is fatally wrong. Notice that the frequency of appearance of these values is not necessarily increasing all the time.\nMore classes the better: After the first period I was conducting my experiments with just 1 class. While this is legitimate and nothing has been mentioned (to my knowledge) about avoiding it (I even read tutorials using 1 class object detection) this also intensified the crack rate. Using 2 classes seems to partially alleviate the problem. When I trained my model with 11 classes the problem seems to vanish.\nSmall variation in bbox's sizes helps: Experimenting with bounding boxes varying a lot in size I came to the conclusion that largely homogeneous bbox's sizes help to also alleviate the issue.\nMore classes may increase performance: I have noticed also that while 11 classes problem seem quite heavier task than 1 class due to the limitations in training the model with 1 class performance is some cases was higher in the former case.\n\nMy final conclusion is that my model for some reason was experiencing at some point an overflow on some weights that prevent it from returning to the normal again. The fact that I have augmented my data (maybe similar images were fed one after the other and intensified certain weights for example) maybe increased the frequency of the problem occurrence but the problem was still present in non-augmented dataset.","Q_Score":0,"Tags":"python,object-detection","A_Id":52290741,"CreationDate":"2018-08-20T13:13:00.000","Title":"Training object detection model too long causes model to crack","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When building a model for machine learning, is it okay to have non-trainable params? Or does this create errors in the model? I'm confused as to what non-trainable params actually are and how to fix your model based on that.","AnswerCount":2,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":3816,"Q_Id":51935097,"Users Score":4,"Answer":"Usually, non-trainable params does not mean some weights to which you assign random or predefined values (there would be no way to know the correct values, as they depend on the other weight values).\nIt means some architectural decisions that you have made a priori. For example, in a neural network , such parameters would be the number of layers and the number of nodes per layer. These can be decided by educated guesses, or, usually, by trial-and-error (cross-validation).\nHaving such non-trainable parameters is not only ok, but unavoidable with most training algorithms.","Q_Score":3,"Tags":"python,machine-learning,keras,hyperparameters","A_Id":51935221,"CreationDate":"2018-08-20T16:35:00.000","Title":"Is it okay to have non-trainable params in machine learning?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I had a question, For a given data set X with two classes {0,1}. If I train two separate neural networks NN0 and NN1 for each class 0 and 1 respectively. Can NN0 predict points in the dataset from class 1, even though it was trained on class 0?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":75,"Q_Id":51938859,"Users Score":0,"Answer":"When you test a case from X with NN0, and it tells you \"I'm a 0\", that's the exact same thing as it telling you \"I'm not a 1\", because your class set has size two, like a boolean.\nTherefor, your NN0 network predicts 1s as well as 0s, so does your NN1.","Q_Score":0,"Tags":"python-3.x,tensorflow,neural-network,keras,regression","A_Id":51938930,"CreationDate":"2018-08-20T21:40:00.000","Title":"Neural Network Regression","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I came across multiple implementations of a CNN in Keras and noticed that some people use Conv1D from from keras.layers.convolutional import Conv1D and others use Convolution1D from from keras.layers import Convolution1D. Is there a difference or an advantage to either one or are they possibly simply just different versions of Keras.","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":2001,"Q_Id":51939468,"Users Score":9,"Answer":"They are just for different keras versions, before Keras 2.0 the layers were called ConvolutionalND for N = 1, 2, 3, and since Keras 2.0 they are just called ConvND.","Q_Score":6,"Tags":"python,keras","A_Id":51939645,"CreationDate":"2018-08-20T22:50:00.000","Title":"Keras Conv1D or Convolution1D","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I came across multiple implementations of a CNN in Keras and noticed that some people use Conv1D from from keras.layers.convolutional import Conv1D and others use Convolution1D from from keras.layers import Convolution1D. Is there a difference or an advantage to either one or are they possibly simply just different versions of Keras.","AnswerCount":2,"Available Count":2,"Score":-1.0,"is_accepted":false,"ViewCount":2001,"Q_Id":51939468,"Users Score":-4,"Answer":"It's not about the versions. The n stands for the dimension of data, for e.g. if you are using a text data you use n=1 i.e. convolution1d. The value of n=2 is used for images. The value for n=3 goes for mri or ct scan. So, depending on the dimensions we use the value.","Q_Score":6,"Tags":"python,keras","A_Id":53997197,"CreationDate":"2018-08-20T22:50:00.000","Title":"Keras Conv1D or Convolution1D","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"i have two series X and Y. X contains time values and Y contains a real function values for those times. I wish to perform FFT of the Y signal in python. The problem is that X is unevenly spaced: X(n+1)-X(n) \/= constant. Is there a known way to apply FFT to such data ?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":386,"Q_Id":51949595,"Users Score":0,"Answer":"You can try creating a new evenly spaced set of samples, Y', by interpolation.  Then FFT Y'.  How well this works depends on well the form of interpolation you choose matches the physics that produced your original Y,X data.","Q_Score":1,"Tags":"python-3.x,fft","A_Id":51961029,"CreationDate":"2018-08-21T13:16:00.000","Title":"Is there a way to apply FFT to an unevenly spaced time series?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"If python,numpy,pandas are installed in local dir,how to set env ,so that ,import numpy as np,import pandas as pd can work.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":37,"Q_Id":51959670,"Users Score":0,"Answer":"If you install in a virtualenv, then activating the virtualenv will take care of that for you. Otherwise, add the dir to your PYTHONPATH environment variable. (How this is done depends on your operating system.)","Q_Score":0,"Tags":"python,pandas","A_Id":51960145,"CreationDate":"2018-08-22T03:59:00.000","Title":"how to set env if python,pandas are installed in local dir","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Environment: Python-PCL, WIndows 10, Python 3.6\nI need to downsample point clouds to a specific number of points. These point clouds vary in size and hence I am stuck. From going through documentation, I understand there are only \nVoxelGrid, ConditionalOutlierRemoval ,StatisticalOutlierRemoval and RadiusOutlierRemoval   are the options available. \nIn VoxelGrid, the leaf size doesnt guarantee the number of points, Radius removal doesnt help in retaining the shape, and statistical outlier mostly helps in removing noise. \nIs there any solution to this? It is possible to use numpy.random.choice()and hoping for a miracle? \nEDIT: numpy.random.choice works but only if you don't care about the geometric shape of your point cloud, which I do. Anyway to use VoxelGrid dynamically?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":3390,"Q_Id":51964798,"Users Score":1,"Answer":"Unfortunately, the answer is no. The number of output points using PCL's VoxelGrid is always going to be a function of the number of occupied voxels. The only way to control the number of occupied voxels is by altering the leaf size, and there is no dynamic way of doing this.\nAlternately, you may have luck using Farthest Point Sampling, as it would allow you to select N points and has nice blue noise properties, but to the best of my knowledge this is not available in PCL.","Q_Score":3,"Tags":"python,numpy,point-clouds,downsampling","A_Id":51976804,"CreationDate":"2018-08-22T10:25:00.000","Title":"Downsampling point clouds to specific number of points while retaining shape","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"i want to do something as a parametric study in Abaqus, where the parameter i am changing is a part of the assembly\/geometry. \nImagine the following:\nA cube is hanging on 8 ropes. Each two of the 8 ropes line up in one corner of a room. the other ends of the ropes merge with the room diagonal of the cube. It's something like a cable-driven parallel robot\/rope robot.\nNow, i want to calculate the forces in the ropes in different positions of the cube, while only 7 of the 8 ropes are actually used. That means i have 8 simulations for each position of my cube. \nI wrote a matlab script to generate the nodes and wires of the cube in different positions and angle of rotations so i can copy them into an input file for Abaqus. \nSince I'm new to Abaqus scripting etc, i wonder which is the best way to make this work.\nwould you guys generate 8 input files for one position of the cube and calculate \nthem manually or is there a way to let abaqus somehow iterate different assemblys?\nI guess i should wright a python script, but i don't know how to make the ropes the parameter that is changing.\nAny help is appreciated!\nThanks, Tobi","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":854,"Q_Id":51966721,"Users Score":0,"Answer":"In case someon is interested, i was able to do it the following way:\nI created a model in abaqus till the point, i could have started the job. Then i took the .jnl file (which is created automaticaly by abaqus) and saved it as a .py file. Then i modified this script by defining every single point as a variable and every wire for the parts as tuples, consisting out of the variables. Than i made for loops and for every 9 cases unique wire definitions, which i called during the loop. During the loop also the constraints were changed and the job were started. I also made a field output request for the endnodes of the ropes (representing motors) for there coordinates and reaction force (the same nodes are the bc pinned)\nThen i saved the fieldoutput in a certain simple txt file which i was able to analyse via matlab.\nThen i wrote a matlab script which created the points, attached them to the python script, copied it to a unique directory and even started the job. \nThis way, i was able to do geometric parametric studies in abaqus using matlab and python.\nCode will be uploaded soon","Q_Score":0,"Tags":"python,abaqus","A_Id":52174746,"CreationDate":"2018-08-22T12:17:00.000","Title":"Abaqus: parametric geometry\/assembly in Inputfile or Python script?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to make my display tables bigger so users can see the tables better when that are used in conjunction with Jupyter RISE (slide shows).  \nHow do I do that? \nI don't need to show more columns, but rather I want the table to fill up the whole width of the Jupyter RISE slide.\nAny idea on how to do that?\nThanks","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":509,"Q_Id":51967445,"Users Score":0,"Answer":"If df is a pandas.DataFrame object.\nYou can do: \ndf.style.set_properties(**{'max-width': '200px', 'font-size': '15pt'})","Q_Score":2,"Tags":"python,pandas,jupyter-notebook","A_Id":56978954,"CreationDate":"2018-08-22T12:57:00.000","Title":"Pandas DataFrame Display in Jupyter Notebook","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using keras\/tensorflow on google colaboratory and I need to go back to previous versions of them. \nThe problem is when I run !pip install q keras==1.2.2 , the kernel shows keras 1.2.2 installed but when I check it using keras.__version_ it shows 2.1.6 . And same case is with tensorflow. \nHow do I fix this?","AnswerCount":3,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":28843,"Q_Id":51981095,"Users Score":3,"Answer":"In the latest colab, when you upgrade or downgrade a module, in the output cell you will automatically see the statement:\n\"WARNING: The following packages were previously imported in this runtime:\n  [scipy]\nYou must restart the runtime in order to use newly installed versions.\n\" asking you to restart the runtime along with a button for it.","Q_Score":15,"Tags":"python,tensorflow,keras,google-colaboratory","A_Id":58063570,"CreationDate":"2018-08-23T08:10:00.000","Title":"How to change Keras\/tensorflow version in Google colab?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to run this python script below:\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.metrics import confusion_matrix \nIt gives me the error below.\n\nWarning (from warnings module):   File\n  \"C:\\Users\\Dipali\\AppData\\Local\\Programs\\Python\\Python37-32\\lib\\site-packages\\sklearn\\ensemble\\weight_boosting.py\",\n  line 29     from numpy.core.umath_tests import inner1d\n  DeprecationWarning: numpy.core.umath_tests is an internal NumPy module\n  and should not be imported. It will be removed in a future NumPy\n  release.\n\nWhat I need to do?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":10943,"Q_Id":51986414,"Users Score":1,"Answer":"Alright so this is a deprecation warning on Python 3.x. \nSince, this is warning your code will run fine. It is not an error (When the code stops running by graceful degradation). \nThe solution to remove this error is as follow:\n\nAs I can see you have Scikit-Learn version 0.19.2 installed you need to get the latest version. To do so enter the following command  pip3 install --force-reinstall scikit-learn==0.20rc1  This will install the latest versions of scikit-learn, scipy and numpy. Your deprecation warnings will now not exist. \nAlthough you'll next get a new warning. This time regarding the file in the scikit-learn library called cloudpickle \\sklearn\\externals\\joblib\\externals\\cloudpickle\\cloudpickle.py.  To overcome this warning you got to edit the code which python shows us.  Just do a sudo idle3 onto the file and edit the lines which says :  import imp  from imp import find_module to import importlib  Next go to the funtion find_module and change the line  file, path, description = find_module(path) to file, path, description = importlib.utils.find_spec(path).\n\nThis must solve the deprecation warnings in the scikit-learn libraries.","Q_Score":7,"Tags":"python,scikit-learn","A_Id":52438763,"CreationDate":"2018-08-23T12:58:00.000","Title":"DeprecationWarning: numpy.core.umath_tests","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am getting this error\n\n-\"module 'cv2' has no attribute 'version'\n\nwhen I try to find version of cv2, but my import is executing fine without any error.\nI am confused about whether my openCV is working correctly or not?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":94,"Q_Id":51987825,"Users Score":0,"Answer":"Your openCV is probably installed in another python version and pylint is having problem identifying. Try to locate where your openCV is installed and manually change it.","Q_Score":0,"Tags":"python,opencv,anaconda","A_Id":51989369,"CreationDate":"2018-08-23T14:10:00.000","Title":"python-openCV module not found error","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to create an array with the form [1, 2, 4, 8, 16, 32, ...]. Not always with that spacing between numbers.\nIs there a linspace\/arange type function which allows me to create vector of non equally spaced numbers?\nThanks","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":663,"Q_Id":51993428,"Users Score":4,"Answer":"Yes, there is also logspace and geomspace in NumPy.\nnp.geomspace(1,32,6) gives you array([ 1.,  2.,  4.,  8., 16., 32.])","Q_Score":1,"Tags":"python,numpy","A_Id":51993668,"CreationDate":"2018-08-23T19:57:00.000","Title":"Function to create a non equally spaced array in numpy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to use the numpy.argsort function (or a code that makes the same) but ignoring a concrete value, for example -1, and keeping it in the resulted array\nIf this is the list: [5,8,1,-1,2,4], I need a result like [3,4,0,-1,1,2]\nHow can I do it?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":203,"Q_Id":51994112,"Users Score":2,"Answer":"Locate and remove each of the values to ignore, remembering their positions.  For instance, in this case, you might store the index and value as ignore = (3, -1).\nSort the remaining list as usual.\nReinsert the missing element: mylist.insert(ignore[0], ignore[1])\nDoes that get you moving?","Q_Score":0,"Tags":"python,arrays,sorting","A_Id":51994159,"CreationDate":"2018-08-23T20:53:00.000","Title":"Sort a list, but leave a specified value in place","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"We made an Image recognition API that accepts image URL and responds an image description. This process takes about 5-20 seconds.\nI have a huge CSV file with 200+ million rows of image URLs scraped from different sources. I found that the CSV file has duplicate image URLs from different sources. So I don't need to send all the URLs to image recognition API, I need to send only unique URLs to the API but I need to populate response from API to all the rows.\nI read the CSV file in chunks of 100K rows and created a set of unique Image URLs and process them in API and again populate the results back to the CSV file. But It ended up in memory issues (16 GB RAM), I can't able to create a set of unique Image URLs with 200+ million rows.\nAny suggestion?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":995,"Q_Id":52000223,"Users Score":0,"Answer":"Two possible approaches:\nSQL Select Distinct\nLoad the CSV file into a database and us SQL Select Distinct and export the query result as a Csv file. Some database let you define a CSV file as a table and let you run SQL queries on the CSV file\nSelect distinct URL from UrlTable\nSort the File\nYou could \n\nSort the file on the URL using your operating systems sort command (both Windows and Linux have Sort commands).\nRead the file and check if the URL is different from the last URL. You only need check the URL when it changes","Q_Score":0,"Tags":"python,pandas,csv,dataframe,duplicates","A_Id":52016745,"CreationDate":"2018-08-24T08:18:00.000","Title":"Python - How to remove duplicates in huge CSV file (200+ Million rows)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"After the training is finished and I did the prediction on my network, I want to calculate \"precision\" and \"recall\" of my model, and then send it to log file of \"tensorboard\" to show the plot.\nwhile training, I send \"tensorboard\" function as a callback to keras. but after training is finished, I dont know how to add some more data to tensorboard to be plotted.\nI use keras for coding and tensorflow as its backend.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":289,"Q_Id":52006745,"Users Score":0,"Answer":"I believe that you've already done that work: it's the same process as the validation (prediction and check) step you do after training.  You simply tally the results of the four categories (true\/false pos\/neg) and plug those counts into the equations (ratios) for precision and recall.","Q_Score":1,"Tags":"python,tensorflow,keras,tensorboard,precision-recall","A_Id":52006897,"CreationDate":"2018-08-24T14:36:00.000","Title":"how to add the overall \"precision\" and \"recall\" metrics to \"tensorboard\" log file, after training is finished?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have two databases (each with 1000's of tables) which are supposed to reflect the same data but they come from two different sources. I compared two tables to see what the differences were, but to do that I joined the two on a common ID key. I checked the table manually to see what the ID key was, but when I have to check 1000's of tables its not practical to do so. \nIs there a way in pandas to find what column (or columns) in a table have only unique values?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":64,"Q_Id":52007555,"Users Score":0,"Answer":"Use the Python library that allows you to query your database (pymysql, psycopg2, etc). Programmatically use the metadata available from the DB to iterate over the tables and columns. Dynamically create the SQL queries to compare \"select count(field) - count(distinct field) from table\". \nOr you could also potentially use the metadata to see which columns in each table are indexed.\nThe SQL query to pull the relevant metadata will vary based on the kind of DBMS.","Q_Score":0,"Tags":"python,sql,pandas,dataframe","A_Id":52007874,"CreationDate":"2018-08-24T15:27:00.000","Title":"Pandas find columns with unique values","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In jupyter notebook, we can use %matplotlib inline to guide inline the image created by python script. Can anyone give a 5 sentence explaining what is happening under the hood to make that happen? If i'd like to have some web page to host the matplotlib created plot, is that possible or not? thanks~","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":53,"Q_Id":52018374,"Users Score":0,"Answer":"The inline backend essentially calls fig.savefig(buffer, format=\"png\", bbox_inches=\"tight\") where fig is the figure to show. It'll then supply the content of buffer inside a <img> tag in the jupyter's output section.","Q_Score":2,"Tags":"python,matplotlib,jupyter-notebook","A_Id":52547503,"CreationDate":"2018-08-25T14:48:00.000","Title":"how matplotlib create and deliver plot to jupyter notebook?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"My question is quite a simple one: My intention is to minimize loss=a-b where loss is ideally as big a negative number as possible, ie. b much bigger than a.\nSince in all the examples, the loss is positive, I wanted to ask whether I can just input my loss into compute_gradients and get the desired result.\nCheers","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":143,"Q_Id":52029879,"Users Score":1,"Answer":"Yes you can. As long as it is a minimization instead of maximization, everything should be exactly the same as in the examples.","Q_Score":2,"Tags":"python,tensorflow,loss","A_Id":52029914,"CreationDate":"2018-08-26T20:08:00.000","Title":"Does the loss in tensorflow.train.AdamOptimizer have to be positive?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a directed bigraph that contains directed and undirected edges.\nnetworkx_Graph = nx.read_adjlist('graph.txt', create_using=nx.DiGraph())\nI was able to find the number of directed edges using: len(list(networkx_Graph.in_edges(data=False)))\nBut I am trying to find the number of undirected edges.\nThis is quite easy using the python package snap, but I am not finding anything like this in networkx's documentation?\nWhat is the networkx equivalent of snap.CntUniqDirEdges()?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":372,"Q_Id":52030344,"Users Score":0,"Answer":"You have to convert the graph to an undirected graph the calculate the size. NetworkX does not have a function that does this in one shot like snap does.\n\nnetworkx_Graph.to_undirected().size()","Q_Score":0,"Tags":"python,graph,networkx","A_Id":52031095,"CreationDate":"2018-08-26T21:19:00.000","Title":"How can I find the number of unique undirected edges of a graph using networkx?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":1,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I wanted to know if the squared error depends also on the number of hidden layers and the number of neurons on each hidden layer , because I've created a neuron network with one hidden layer but I can't reach a small squared error , so maybe the function is not convex ? Can I optimize weights by adding more hidden layers ?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":34,"Q_Id":52032541,"Users Score":0,"Answer":"Though this is not a programming question, I'll try my best to answer it here.\nThe squared error, i.e. the 'loss' of your neural network, depends on your neural network prediction and the ground truth. And it is convex from its definition.\nThe reasons that you're not getting low losses could be:\n\nYou're not normalizing your inputs. For example, if you got a series of house prices as input, which is around 500k to 1m, and you didn't normalize them, your prediction will be the linear combination of the prices, which is about the same order of magnitude, then pass through the activation function. This could result in large losses.\nYou're not initializing your weights and biases correctly. Similar to above, you could have large weights\/biases which lead to large prediction values.\nYou didn't choose the proper activation function. When you're doing classification, your labels are generally one hot encoded, so your activation functions should limit the prediction to [0,1] or similar, so relu won't be a proper option. Also you don't want sigmoid as activation for regression problems.\nYour labels are not predictable or have too much noise. Or maybe your network is not complex enough to capture important patterns, in that case you could try adding more layers and more nodes per layer.\nYour learning rate is too small, this leads to slow convergence.\n\nThat's all I have in mind. You probably need more work to find out the reason to your problem.","Q_Score":0,"Tags":"python,neural-network","A_Id":52032920,"CreationDate":"2018-08-27T04:24:00.000","Title":"Does the squared error depends on the number of hidden layers?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am very new to image recognition with CNNs and currently using several standard (pre-trained) architectures available within Keras (VGG and ResNet) for image classification tasks. I am wondering how one can generalise the number of input channels to more than 3 (instead of standard RGB). For example, I have an image which was taken through 5 different (optic) filters and I am thinking about passing these 5 images to the network.\nSo, conceptually, I need to pass as an input (Height, Width, Depth) = (28, 28, 5), where 28x28 is the image size and 5 - the number of channels.\nAny easy way to do it with ResNet or VGG please?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":315,"Q_Id":52047126,"Users Score":2,"Answer":"If you retrain the models, that's not a problem. Only if you want to use a trained model, you have to keep the input the same.","Q_Score":7,"Tags":"python,tensorflow,image-processing,keras,conv-neural-network","A_Id":54873735,"CreationDate":"2018-08-27T21:20:00.000","Title":"Convolutional neural network architectures with an arbitrary number of input channels (more than RGB)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"What is difference between arima.predict() and arima.forecast(). \nWhich function should use for forecasting time series in python?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1974,"Q_Id":52051348,"Users Score":1,"Answer":"They will give you the same answers. But the combination of Arima (not arima) and forecast from the forecast package are enhanced versions with additional functionality.\nArima calls stats::arima for the estimation, but stores more information in the returned object. It also allows some additional model functionality such as including a drift term in a model with a unit root.\nforecast calls stats::predict to generate the forecasts. It will automatically handle the drift term from Arima. It returns a forecast object (rather than a simple list) which is useful for plotting, displaying, summarizing and analysing the results.","Q_Score":1,"Tags":"python,machine-learning,time-series,arima","A_Id":52051400,"CreationDate":"2018-08-28T06:22:00.000","Title":"Difference between arima.predict() and arima.forecast()","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to detect upright and upside-down faces, however faces weren't recognized in upside-down images.\nI used the dlib library in Python with shape_predictor_68_face_landmarks.dat. \nIs there a library that can recognize upright and upside-down faces?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":855,"Q_Id":52059560,"Users Score":8,"Answer":"You could use the same library to detect upside down faces. If the library is unable to detect the face initially, transform it 180\u00b0 and check again. If it is recognized in this condition, you know it was an upside down face.","Q_Score":0,"Tags":"python,opencv,computer-vision,face-detection,dlib","A_Id":52059673,"CreationDate":"2018-08-28T13:52:00.000","Title":"how to detect upside down face?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Let's say I have a square matrix with 20 lines and 20 columns. Using NumPy, what should I do to transform this matrix into a 1D array with a single line and 400 columns (that is, 20.20 = 400, all in one line)?\nSo far, I've tried:\n1) array = np.ravel(matrix)\n2) array = np.squeeze(np.asarray(matrix))\nBut when I print array, it's still a square matrix.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":679,"Q_Id":52061828,"Users Score":3,"Answer":"Use the reshape method:\narray = matrix.reshape((1,400)).\nThis works for both Numpy Array and Matrix types.\nUPDATE: As sacul noted, matrix.reshape(-1) is more general in terms of dimensions.","Q_Score":0,"Tags":"python,arrays,numpy,matrix","A_Id":52061923,"CreationDate":"2018-08-28T15:48:00.000","Title":"Transform square matrix into 1D array with NumPy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In Anaconda Navigator, I switched to running applications on tensorflow and installed jupyter 5.6.0. I then open up a Python3 notebook. I then import tensorflow, keras and numpy without issue. Then when I try to import matplotlib, the notebook says ImportError: No module named 'matplotlib'. \nI tried running the following command in my anaconda prompt in both base and after activating tensorflow: pip3 install matplotlib\nAnd it says: \n(tensorflow) C:\\Users\\danie>pip3 install matplotlib\nRequirement already satisfied \nfor 7 different lines. What am I doing wrong?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":940,"Q_Id":52082814,"Users Score":1,"Answer":"Add import sys and sys.executable to the top of your notebook, then run it. This shows you the directory of the running kernel. With this information, from a new command-line (not a Python console) run C:\\path\\to\\python.exe -m pip install matplotlib","Q_Score":0,"Tags":"python,matplotlib,tensorflow,jupyter-notebook","A_Id":52083402,"CreationDate":"2018-08-29T17:09:00.000","Title":"Matplotlib in Tensorflow Jupyter Notebook","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"How can I generate an image using buildroot with opencv2 in it?\nIn \"Target Packages->Libraries->Graphics->opencv2.4\" says that python_opencv depends on numpy, which isn't available.\nAn self made app(which is in the built image as well) uses python3.6 and opencv2.\nBuildroot Version: 2017.08-rc3","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":753,"Q_Id":52083346,"Users Score":4,"Answer":"Back when opencv was introduced in Buildroot, there was indeed no numpy package, so support for doing OpenCV in Python could not be added, hence this comment. But since then, a python-numpy package was added, and it is even used in the opencv3 package to provide Python support.\nSo, the opencv package simply needs to be updated to leverage the python-numpy package to provide Python support.\nAs a side note: why are you using 2017.08-rc3 ? It is a release candidate, not even a final release. And why this version ? Why not using the LTS version 2018.02.x, which we maintain during one year with security updates and bug fixes ?","Q_Score":0,"Tags":"python-3.x,opencv,buildroot","A_Id":52091386,"CreationDate":"2018-08-29T17:41:00.000","Title":"Add python_opencv in buildroot image","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Pandas has both isna() and isnull(). I usually use isnull() to detect missing values and have never met the case so that I had to use other than that.\nSo, when to use isna()?","AnswerCount":3,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":28252,"Q_Id":52086574,"Users Score":7,"Answer":"They both are same. As a best practice, always prefer to use isna() over isnull(). \nIt is easy to remember what isna() is doing because when you look at numpy method np.isnan(), it checks NaN values. In pandas there are other similar method names like dropna(), fillna() that handles missing values and it always helps to remember easily.","Q_Score":69,"Tags":"python,pandas","A_Id":57507051,"CreationDate":"2018-08-29T21:52:00.000","Title":"Pandas isna() and isnull(), what is the difference?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"One more question:\nIf they are tied biases, how can I implement untied biases?\nI am using tensorflow 1.10.0 in python.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":213,"Q_Id":52088059,"Users Score":0,"Answer":"tied biases is used in tf.layers.conv2d.\nIf you want united biases, just turn off use_bias and create bias variable manually with tf.Variable or tf.get_variable same shape with following feature map, finally sum them up.","Q_Score":0,"Tags":"python,tensorflow","A_Id":52089011,"CreationDate":"2018-08-30T01:29:00.000","Title":"In tf.layers.conv2d, with use_bias=True, are the biases tied or untied?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Yesterday I began to read about using bootstrapping to determine confidence intervals (CIs) in many situations. My current situation is that I am trying to estimate three parameters in a model via maximum likelihood estimation (MLE). This I have done, and now I need to define my CIs. This can obviously be done via profile likelihood, but bootstrapping will give a more broad CI interval as far as I can read. My problem is that I am unsure on how to actually perform bootstrapping ? I have written my own code for the parameter estimation, so no build-in MLE calculators.\nBasically the observed data I have is binary data, so 1 or 0. And it's from those data (put into a model with three parameters) that I have tried to estimate the parameter values.\nSo let's say my cohort is 500, is the idea then that I take a sample from my cohort, maybe 100, and then expand it to 500 again by just multiplying the sample 5 times, and run the simulation once again, which in turn should result in some new parameter estimates, and then just do this 1000-2000 times in order to get a series of parameter values, which can then be used to define the CI ?\nOr am I missing something here ?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":763,"Q_Id":52104841,"Users Score":4,"Answer":"This question isn't related to Python. I think you need to read an intro to bootstrapping. \"An Introduciton to Statistical Learning\" provides a good one. The idea is not to sample 100 -- you must sample with replacement and taking the same sample size (500). Yes, then you reestimate your parameter many times. And then there's several ways of taking all of these estimates and turning them into a confidence interval. For example, you can use them to estimate the standard error (the standard deviation of the sampling distribution), and then use +\/- 2*se.","Q_Score":0,"Tags":"python,statistics-bootstrap","A_Id":52104907,"CreationDate":"2018-08-30T20:23:00.000","Title":"Confidence intervals via bootstrapping","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a problem when training a neural net with Keras in Jupyter Notebook. I created a sequential model with several hidden layers. After training the model and saving the results, I want to delete this model and create a new model in the same session, as I have a for loop that checks the results for different parameters. But as I understand the errors I get, when changing the parameters, when I loop over, I am just adding layers to the model (even though I initialise it again with network = Sequential() inside the loop). So my question is, how can I completely clear the previous model or how can I initialise a completely new model in the same session?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":47667,"Q_Id":52133347,"Users Score":0,"Answer":"I know it is a bit old thread, but I was looking for something to clear the session. For TensorFlow 2.8 I think you need to use tf.keras.backend.clear_session()","Q_Score":35,"Tags":"python,tensorflow,keras,jupyter-notebook","A_Id":71774912,"CreationDate":"2018-09-02T01:38:00.000","Title":"How can I clear a model created with Keras and Tensorflow(as backend)?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on the following project and I am having really difficulties in finding the right way of doing that. I would like to build in Python (but I am open to other possibilities) a very basic interface that allows the user to draw with the mouse (or the pen if used on a surface laptop) something and then save the image. Ideally I would like this to work on a website or at least in a jupyter notebook (at least I imagine this to be utterly difficult).\nAnyone can point me in the right direction? The goal would be to use the images as input to a neural network model to demonstrate its result with real life examples.\nI am looking at tk but I don't seem to find much in terms of examples.\nThanks in advance, Umberto","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":1360,"Q_Id":52140339,"Users Score":1,"Answer":"I'd take a look at pyautogui to capture the mouse location then \"draw\" it in matplotlib -- should be able to do this in a loop. You'll want to watch the tkinter window size to sync the mouse coordinates with the relative location.\nWhy not just have your script open create a new blank img and automatically open it with paint - then read it on close? Seems easier than creating a drawing GUI.","Q_Score":1,"Tags":"python,image,user-interface,draw","A_Id":52140437,"CreationDate":"2018-09-02T19:25:00.000","Title":"Create a basic Python interface to save images drawn with a mouse","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":1,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it possible that a flat numpy 1d array's size (nbytes) is 16568 (~16.5kb) but when saved to disk, has a size of >2 mbs? \nI am saving the array using numpy's numpy.save method. Dtype of array is 'O' (or object).\nAlso, how do I save that flat array to disk such that I get approx similar size to nbytes when saved on disk? Thanks","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":138,"Q_Id":52142524,"Users Score":0,"Answer":"For others references, From numpy documentation:\n\nnumpy.ndarray.nbytes attribute\nndarray.nbytes Total bytes consumed by the elements of the array.\nNotes\nDoes not include memory consumed by non-element attributes of the\narray object.\n\nSo, the nbytes just considers elements of the array.","Q_Score":0,"Tags":"python,numpy","A_Id":71867816,"CreationDate":"2018-09-03T02:29:00.000","Title":"Numpy array size different when saved to disk (compared to nbytes)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"My problem is related to building a recommender system. I am using tensorflow to train the model with user_features, item_features and interactions. \nHow can i handle new data?\nFor example, whenever a use clicks on an item, i wanna record that interaction immediately and learn from that interaction (training the model for just that interaction). How can i do this?\nCurrent implementation\nRight now, i am following the traditional method of training the model first and then making predictions from that. In order to handle new interactions, i have to re-initialize the graph and retrain with all the data to make it work.\n\nI hope there is some solution for this.\n\nThanks","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":4565,"Q_Id":52144642,"Users Score":2,"Answer":"You don't need to re-initialize the graph. Tensorflow is not explicitly aware of whether you're doing the training or testing phase when you request the output tensor. Rather, back propagation happens whenever you evaluate the optimizer's (e.g. tf.train.AdamOptimizer, tf.train.GradientDescentOptimizer, etc) minimize operation.\nNormally you don't do this during testing, since you're trying to predict how well it performs on unseen data.  But if you want to add that data to the training set after making the initial prediction, you're free to do so as long as you have a way to get the true value so it can compute the error tensor.\nEven in non-online learning, it's common to intersperse training and testing passes: if you use a validation set to determine early stopping, you alternate between training batches, where you do do backprop, and validation batches, where you don't.","Q_Score":4,"Tags":"python,tensorflow,machine-learning,reinforcement-learning","A_Id":52145089,"CreationDate":"2018-09-03T07:02:00.000","Title":"Online Learning with Tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an tensorflow model that I'm planning to deploy using tensorflow serving for model inference. \nJust wondering if there is a way to add some custom logic after the service receives the request and before it goes to the tensorflow model. Specifically, I need a lookup step from an external database, that would enrich the data the application receives through the REST call. \nAlternatively, is that possible as part of the tensorflow graph?\nIt sounds like an overkill to create a separate service just for that, if it can be bundled in the serving layer.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":57,"Q_Id":52153548,"Users Score":0,"Answer":"I'd say that you could wrap the TF server in something like a flask app, which takes in a REST request, applies the additional logic (the DB lookup), and populates and sends a request to the TF server (either via HTTP or gRPC). I don't think it would be possible to do it as 'part of the TF graph'.","Q_Score":0,"Tags":"python,tensorflow,tensorflow-serving","A_Id":52182820,"CreationDate":"2018-09-03T16:33:00.000","Title":"External database lookup in TF serving or part of the TF graph","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"everyone\nI'm currently running a research using Multilayer perceptron.\nI previously run this using WEKA but I have migrated to scikit-learn in python for various reason.\nI tried to reproduce my prediction results in WEKA to scikit-learn so I made an exact perceptron with the exact same learning rate, momentum, hidden layer, etc\nHowever, the prediction results turns out differently for these both platforms. Can anybody gives me some explanation regarding this matter?\nThe only thing that I notice is that scikit learn uses either lbfgs, adam, and sgd to optimize its connection weight while WEKA uses backpropagation. But could it be the only reason? or is there another reason?\nBest regards","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":318,"Q_Id":52159019,"Users Score":1,"Answer":"If you train a neural net with a different optimizer, it will certainly give different results. This difference could be slight or tremendous. All NN optimization algorithms use backpropagation - i.e., LBFGS, Adam, and SGD all use backpropagation. Also - in general - training the same NN twice will likely never give you the exact same result - this is because most optimizers (at least the ones worth your time) take partially stochastic steps.","Q_Score":1,"Tags":"python,scikit-learn,weka","A_Id":52159197,"CreationDate":"2018-09-04T04:53:00.000","Title":"WEKA and Scikit-Learn Multilayer Perceptron Gives Different Result","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to apply a Keras' image classifier to my project, but down the road I got stuck with this. Though previously, with the same code I could use OpenCV to read and train images, but after switching to a new batch of images it got caught with the error. So my speculation is that there's something wrong with my file type:\nThis is from the batch that got the error:\n\ntraf.204.jpg: JPEG image data, JFIF standard 1.01, aspect ratio,\ndensity 1x1, segment length 16, baseline, precision 8, 480x294, frames\n1\n\nThis is from the batch that didn't get caught with the error:\n\nbear.290.jpg: JPEG image data, JFIF standard 1.01, aspect ratio,\ndensity 1x1, segment length 16, baseline, precision 8, 224x224, frames\n3\n\nBut the file type seems to be exactly the same (except for the resolution). How can I fix this problem?","AnswerCount":9,"Available Count":5,"Score":0.0444152037,"is_accepted":false,"ViewCount":78798,"Q_Id":52162004,"Users Score":2,"Answer":"Just enter the precise image format. .jpg instead of .jpeg.","Q_Score":20,"Tags":"python,opencv,keras","A_Id":57736414,"CreationDate":"2018-09-04T08:28:00.000","Title":"I am having trouble with this error (-215:Assertion failed) !ssize.empty() in function 'resize' in OpenCV","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to apply a Keras' image classifier to my project, but down the road I got stuck with this. Though previously, with the same code I could use OpenCV to read and train images, but after switching to a new batch of images it got caught with the error. So my speculation is that there's something wrong with my file type:\nThis is from the batch that got the error:\n\ntraf.204.jpg: JPEG image data, JFIF standard 1.01, aspect ratio,\ndensity 1x1, segment length 16, baseline, precision 8, 480x294, frames\n1\n\nThis is from the batch that didn't get caught with the error:\n\nbear.290.jpg: JPEG image data, JFIF standard 1.01, aspect ratio,\ndensity 1x1, segment length 16, baseline, precision 8, 224x224, frames\n3\n\nBut the file type seems to be exactly the same (except for the resolution). How can I fix this problem?","AnswerCount":9,"Available Count":5,"Score":0.022218565,"is_accepted":false,"ViewCount":78798,"Q_Id":52162004,"Users Score":1,"Answer":"In my case there were special characters in the file name. Like \"\u00fc\", \"\u00e4\", \"\/\", etc.\nI had to make a choice: Renaming automatically or just raise an error for the user to know, that the pictures have bad names.","Q_Score":20,"Tags":"python,opencv,keras","A_Id":67135915,"CreationDate":"2018-09-04T08:28:00.000","Title":"I am having trouble with this error (-215:Assertion failed) !ssize.empty() in function 'resize' in OpenCV","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to apply a Keras' image classifier to my project, but down the road I got stuck with this. Though previously, with the same code I could use OpenCV to read and train images, but after switching to a new batch of images it got caught with the error. So my speculation is that there's something wrong with my file type:\nThis is from the batch that got the error:\n\ntraf.204.jpg: JPEG image data, JFIF standard 1.01, aspect ratio,\ndensity 1x1, segment length 16, baseline, precision 8, 480x294, frames\n1\n\nThis is from the batch that didn't get caught with the error:\n\nbear.290.jpg: JPEG image data, JFIF standard 1.01, aspect ratio,\ndensity 1x1, segment length 16, baseline, precision 8, 224x224, frames\n3\n\nBut the file type seems to be exactly the same (except for the resolution). How can I fix this problem?","AnswerCount":9,"Available Count":5,"Score":-0.022218565,"is_accepted":false,"ViewCount":78798,"Q_Id":52162004,"Users Score":-1,"Answer":"Put double backslashes in your path inside the imread function.","Q_Score":20,"Tags":"python,opencv,keras","A_Id":63875303,"CreationDate":"2018-09-04T08:28:00.000","Title":"I am having trouble with this error (-215:Assertion failed) !ssize.empty() in function 'resize' in OpenCV","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to apply a Keras' image classifier to my project, but down the road I got stuck with this. Though previously, with the same code I could use OpenCV to read and train images, but after switching to a new batch of images it got caught with the error. So my speculation is that there's something wrong with my file type:\nThis is from the batch that got the error:\n\ntraf.204.jpg: JPEG image data, JFIF standard 1.01, aspect ratio,\ndensity 1x1, segment length 16, baseline, precision 8, 480x294, frames\n1\n\nThis is from the batch that didn't get caught with the error:\n\nbear.290.jpg: JPEG image data, JFIF standard 1.01, aspect ratio,\ndensity 1x1, segment length 16, baseline, precision 8, 224x224, frames\n3\n\nBut the file type seems to be exactly the same (except for the resolution). How can I fix this problem?","AnswerCount":9,"Available Count":5,"Score":0.0,"is_accepted":false,"ViewCount":78798,"Q_Id":52162004,"Users Score":0,"Answer":"I was facing a similar problem because I was reading multiple images from a directory and that directory had a pdf file(silly), so make sure your specified paths have only images.","Q_Score":20,"Tags":"python,opencv,keras","A_Id":67938301,"CreationDate":"2018-09-04T08:28:00.000","Title":"I am having trouble with this error (-215:Assertion failed) !ssize.empty() in function 'resize' in OpenCV","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to apply a Keras' image classifier to my project, but down the road I got stuck with this. Though previously, with the same code I could use OpenCV to read and train images, but after switching to a new batch of images it got caught with the error. So my speculation is that there's something wrong with my file type:\nThis is from the batch that got the error:\n\ntraf.204.jpg: JPEG image data, JFIF standard 1.01, aspect ratio,\ndensity 1x1, segment length 16, baseline, precision 8, 480x294, frames\n1\n\nThis is from the batch that didn't get caught with the error:\n\nbear.290.jpg: JPEG image data, JFIF standard 1.01, aspect ratio,\ndensity 1x1, segment length 16, baseline, precision 8, 224x224, frames\n3\n\nBut the file type seems to be exactly the same (except for the resolution). How can I fix this problem?","AnswerCount":9,"Available Count":5,"Score":0.0,"is_accepted":false,"ViewCount":78798,"Q_Id":52162004,"Users Score":0,"Answer":"For me, I had my camera app open in Windows\u00a010, and that's why I was getting that error. I shut it down and reran my code and it worked.","Q_Score":20,"Tags":"python,opencv,keras","A_Id":65369786,"CreationDate":"2018-09-04T08:28:00.000","Title":"I am having trouble with this error (-215:Assertion failed) !ssize.empty() in function 'resize' in OpenCV","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I build a cpython locally (Windows) with a fix to a problem with multiprocessing that I have, but I also need data science stack of packages like numpy, pandas, scipy, matplotlib, statsmodels and few others. When I try to install them the process appears quite cubersome for many packages and for scipy I wasn't able to resolve it after 3 days of trying.\nI was thinking that it would be amazing if I can just replace python in my anaconda env, and use conda to install the packages I need. Is it possible to easily replace python with binaries I have or I need to wait while my fix is released with new Python version?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":322,"Q_Id":52162907,"Users Score":1,"Answer":"I was able to resolve my issue by replacing only python DLL that I had in, and the conda environment just worked with it","Q_Score":1,"Tags":"python,scipy,anaconda,conda,cpython","A_Id":52321037,"CreationDate":"2018-09-04T09:18:00.000","Title":"Is it possible to use custom build cpython in Anaconda environment?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a pandas series with boolean entries.  I would like to get a list of indices where the values are True.\nFor example the input pd.Series([True, False, True, True, False, False, False, True])\nshould yield the output [0,2,3,7].\nI can do it with a list comprehension, but is there something cleaner or faster?","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":70848,"Q_Id":52173161,"Users Score":1,"Answer":"Also works:\ns.where(lambda x: x).dropna().index, and\nit has the advantage of being easy to chain pipe - if your series is being computed on the fly, you don't need to assign it to a variable.\nNote that if s is computed from r: s = cond(r)\nthan you can also use: r.where(lambda x: cond(x)).dropna().index.","Q_Score":67,"Tags":"python,pandas,boolean,series,boolean-indexing","A_Id":70050317,"CreationDate":"2018-09-04T19:53:00.000","Title":"Getting a list of indices where pandas boolean series is True","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I can't import Keras in PyCharm IDE on a Mac. I have tried installing and uninstalling Keras using both pip, pip3, conda, and easy install, but none worked. I have tried changing interpreters (Python 2.7 and 3.6) but neither worked.\nIn a terminal, when I run:\n\npip3 list | grep -i keras\n\nI get:\n\nKeras                              2.2.2\n  Keras-Applications                 1.0.4\n  Keras-Preprocessing                1.0.2 \n\nI think this means that my Keras installation was successful. I have also checked my environment with:\n\npython3 -c 'import sys, pprint; pprint.pprint(sys.path)'\n\nI get:\n\n'\/anaconda3\/lib\/python36.zip',\n   '\/anaconda3\/lib\/python3.6',\n   '\/anaconda3\/lib\/python3.6\/lib-dynload',\n   '\/anaconda3\/lib\/python3.6\/site-packages',\n   '\/anaconda3\/lib\/python3.6\/site-packages\/aeosa']\n\nI have tried running:\n\npython -c \"import keras\"\n\nI get:\n\nUsing TensorFlow backend.\n\nBut when I run\/import Keras on the PyCharm IDE, I get:\n\nModuleNotFoundError: No module named 'keras'\n\nWhat should I do to run Keras on a Mac with PyCharm 3.6?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":14633,"Q_Id":52174530,"Users Score":1,"Answer":"When you start a project in PyCharm, there is an option to use an existing virtual environment or create a new virtual environment. If nothing mentioned while creating the project, new virtual environment will be created. If that is the case, there is no Keras in the new environment which is possibly your issue.\n It will be better to do this installation in some virtual environments and use it accordingly in Pycharm.","Q_Score":4,"Tags":"python,tensorflow,keras","A_Id":60923830,"CreationDate":"2018-09-04T22:00:00.000","Title":"ModuleNotFoundError: No module named 'keras'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"First off I would like to say that I am aware that this question has been asked before, however, none of the other posts have offered a solution that resolves the problem.\nI am trying to use pandas-datareader to grab stock prices from the internet. I am using windows with python version 3.6. I first installed pandas-datareader from the console using \npip install pandas-datareader.\nI then wrote a code which used the line \nimport pandas_datareader.data as web\nIt came back with the error\n`ModuleNotFoundError: No module named 'pandas_datareader'\nI tried to redownload pandas-datareader, just in case it didn't work the first time, but the console came back with \"Requirement already satisfied\" so that wasn't the problem.\nFrom there I uninstalled pandas-datareader, and reinstalled it with\npip3 install pandas-datareader\nI still got the same error message.\nI was worried that it might have something to do with old versions of python installed on my computer so I deleted all files for python 2.7, but this did not help the issue. I restarted spyder and my computer and this did not help. I tried Jupiter notebook and this did not help either.\nNow to take my investigation one step further, I looked in my file folders at the hidden files to see where pandas-datareader could be hiding. When I go to C:\\Users\\J.Shepard\\Anaconda3\\pkgs and C:\\Users\\J.Shepard\\Anaconda3\\pkgs I see that pandas-0.23.0-py36h830ac7b_0 is installed but I cannot find anything that looks like pandas-datareader. In fact, when I search for \"pandas-datareader\" in my file search, I only see 2 text files with one line of code each. I do not know what to make of this discovery but I thought it might be helpful to someone else.\nI hope that I have made a good case to prove that I have genuinely tried and failed to solve this problem on my own. Based on the number of other unresolved posts related to this same question, I believe that this is a question that deserves to be asked again.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2865,"Q_Id":52175718,"Users Score":1,"Answer":"I tried conda install pandas-datareader in Anaconda Prompt. It was installed and after my computer restarted, pandas-datareader worked in spyder 3.6.","Q_Score":1,"Tags":"python,pandas,module,spyder,datareader","A_Id":71438963,"CreationDate":"2018-09-05T01:01:00.000","Title":"Spyder cannot find module named 'pandas_datareader'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In TensorFlow's Dataset API, we can use dataset.prefetch(buffer_size=xxx) to preload other batches' data while GPU is processing the current batch's data, therefore, I can make full use of GPU.\nI'm going to use Keras, and wonder if keras has a similar API for me to make full use of GPU, instead of serial execution: read batch 0->process batch 0->read batch 1-> process batch 1-> ...\nI briefly looked through the keras API and did not see a description of the prefetch.","AnswerCount":1,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":2301,"Q_Id":52176792,"Users Score":6,"Answer":"If you call fit_generator with workers > 1, use_multiprocessing=True, it will prefetch queue_size batches.\nFrom docs: max_queue_size: Integer. Maximum size for the generator queue. If unspecified, max_queue_size will default to 10.","Q_Score":13,"Tags":"python,tensorflow,keras,dataset","A_Id":56251858,"CreationDate":"2018-09-05T03:51:00.000","Title":"Could Keras prefetch data like tensorflow Dataset?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"From tensorflow documentation about tf.contrib.rnn.RNNCell: \"This definition of cell differs from the definition used in the literature. In the literature, 'cell' refers to an object with a single scalar output. This definition refers to a horizontal array of such units.\"\nIt seems, that rnn cell only accepts vectors as inputs. However I would like to feed images\/videos to an rnn (e.g. [batch size, steps, height, width, channels]). Is there a way to do this using rnn cell and dynamic rnn, or do I have to manually construct an rnn?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":155,"Q_Id":52180502,"Users Score":0,"Answer":"As you have said RNN only accept as input a Tensor like [batch_size, sequence_lentgh, features].\nIn order to use RNN from tensorflow you will have to extract the features with a CNN for each frame and convert your CNN output data to a tensor that follows [batch_size, sequence_lentgh, features] shape in order to feed it to the RNN.","Q_Score":1,"Tags":"python,tensorflow,deep-learning,computer-vision,rnn","A_Id":52183726,"CreationDate":"2018-09-05T08:25:00.000","Title":"Multi-dimensional tensors as input to rnn in tensorflow (tf.contrib.rnn.RNNCell)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"We are going to handle Big Data (~50 million records) in our organization. We are partitioning data on the basis of date and other some parameters, but data is not equally partitioned. Can we do repartition on it for good performance?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":64,"Q_Id":52191056,"Users Score":0,"Answer":"Usually, partitioning is a good idea and as @Karthik already said, often the date is not the best idea. In my experience it always made sense to partition your data based on the amount of workers you have. So ideally your partition size is a multiple of your workers. We normally use 120 partitions, as we have 24 workers in our spark environment and end up with code like:\nnew_df = spark.read.csv(\"some_csv.csv\", header=\"true\", escape=\"\\\"\", quote=\"\\\"\").repartition(100)\nWe also experienced way better performance in working with parquet instead of csv, which is a tradeoff, as the data has to be read, repartitioned and stored again, but it paid off in the analysis steps. So maybe you should also consider this conversion.","Q_Score":0,"Tags":"python,database,dataframe,pyspark,hadoop2","A_Id":52200967,"CreationDate":"2018-09-05T18:03:00.000","Title":"Is it good idea to repartition 50 million records data in dataframe? If yes then someone please tell me the appropriate way of doing this","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"We are going to handle Big Data (~50 million records) in our organization. We are partitioning data on the basis of date and other some parameters, but data is not equally partitioned. Can we do repartition on it for good performance?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":64,"Q_Id":52191056,"Users Score":0,"Answer":"Depending on your machine try maintaining a fixed number of partitions. It is always a good idea to partition but in most cases, it's not a good idea to partition based on date(Not sure because I don't know the nature of your data).","Q_Score":0,"Tags":"python,database,dataframe,pyspark,hadoop2","A_Id":52194219,"CreationDate":"2018-09-05T18:03:00.000","Title":"Is it good idea to repartition 50 million records data in dataframe? If yes then someone please tell me the appropriate way of doing this","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a general question on a specific topic. \nI am using the vectors generated by Word2Vec to feed as features into my Distributed Random Forest model for classifying some records. I have millions of records and am receiving new records on a daily basis. Because of the new records coming in I want the new records to be encoded with the same vector model as the previous records. Meaning that the word \"AT\" will be the same vector now and in the future. \nI know that Word2Vec uses a random seed to generate the vectors for the words in the corpus but I want to turn this off. I need to set the seed such that if I train a model on a section of the data today and then again on the same data in the future, I want it to generate the same model with the exact same vectors for each word.\nThe problem with generating new models and then encoding is that it takes a great deal of time to encode these records and then on top of that my DRF model for classification isn't any good anymore because the vector for the words have changed. So I have to retrain a new DRF.\nNormally this would not be an issue since I could just train one model each and then use that forever;However I know that a good practice is to update your packages on the regular. This is a problem for h2o since once you update there is no backward comparability with model generated on previous version.\nAre there any sources that I could read on how to set the seed on the Word2Vec model for h2o in python? I am using Python version 3 and h2o version 3.18","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":140,"Q_Id":52210521,"Users Score":1,"Answer":"word2vec in h2o-3 uses hogwild implementation - the model parameters are updated concurrently from multiple threads and it is not possible to guarantee the reproducibility in this implementation.\nHow big is your text corpus? At the cost of a slowdown of the model training you could get reproducible result with limiting the algo to use just a single thread (h2o start-up parameter -nthread).","Q_Score":0,"Tags":"python,word2vec,h2o","A_Id":52212217,"CreationDate":"2018-09-06T18:54:00.000","Title":"H2O Word2Vec inconsistent vectors","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have saved my text vectors by using gensim library which consists of some negative numbers. will it effect the training? \nIf not then why am i getting nan loss value first for discriminator and then for both discriminator and generator after certain steps of training?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":5113,"Q_Id":52211665,"Users Score":4,"Answer":"There are several reasons for a NaN loss and why models diverge. Most common ones I've seen are:\n\nYour learning rate is too high. If this is the case, the loss increases and then diverges to infinity.\nYou are getting a division by zero error. If this is the case, you can add a small number like 1e-8 to your output probability.\nYou have bad inputs. If this is the case, make sure that you do not feed your model with NaNs. i.e. use assert not np.any(np.isnan(x)) on the input data.\nYour labels are not in the same domain of your objective function. If this is the case, check the range of your labels and make sure they match.\n\nIf none of the above helps, try to check the activation function, the optimizer, the loss function, the size and the shape of the network.\nFinally, though less likely, there might be a bug with the framework you are using. Check the repo of the framework if there are others having the same issue.","Q_Score":0,"Tags":"python,tensorflow,generative-adversarial-network","A_Id":52211776,"CreationDate":"2018-09-06T20:24:00.000","Title":"why do i get nan loss value in training discriminator and generator of GAN?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to read an Excel file containing the Swedish characters \u00e5\u00e4\u00f6. \nI am importing the Excel file with pd.read_excel(path, sheetname, encoding='utf8')\nWorks fine to import it and I can see the \u00e5\u00e4\u00f6 characters, but when I work with the data for example creating a new variable df['East'] = df['\u00d6st'] + 50 I receive an error message \nAscii codec can't decode byte 0xc3 in position 33: ordinal not in range \nAnyone that can help me solve this issue?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":865,"Q_Id":52220676,"Users Score":0,"Answer":"Double Check if Excel is saved as UTF-8\nIn Excel 2016 When saving as: click More Options > Tools > Web Options > Encoding > Save this document as ... (pick UTF-8 from the list)\nSaving Excel as csv or even txt helps in many cases too.\nIf csv or txt exported from Excel also doesn't open\/work properly\nopen it in notepad and save it again selecting proper UTF-8 encoding","Q_Score":0,"Tags":"python-2.7,pandas","A_Id":52221695,"CreationDate":"2018-09-07T10:33:00.000","Title":"Python Pandas reading UTF-8 characters","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"When I try to translate a project written in python to C++. I have to implement the function SimilarityTransform in the package scikit-image. I find estimateRigidTransform in OpenCV will do the same thing. But estimateRigidTransform will return empty matrix somtimes. So, Is there some method that which will works better than that and alway return a matrix?\nThanks in advance.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":748,"Q_Id":52222864,"Users Score":0,"Answer":"I found a function in eigen3, can do the same thing as the python code does.","Q_Score":0,"Tags":"python,c++,image,opencv","A_Id":52454922,"CreationDate":"2018-09-07T12:44:00.000","Title":"How to use C++ to implement SimilarityTransform in scikit-image without using estimateRigidTransform in OpenCV?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have Three dataframes. All of them have a common column and I need to merge them based on the common column without missing any data\nInput\n\n>>>df1\n0 Col1  Col2  Col3\n1 data1  3      4\n2 data2  4      3\n3 data3  2      3\n4 data4  2      4\n5 data5  1      4\n\n>>>df2\n0 Col1  Col4  Col5\n1 data1  7      4\n2 data2  6      9\n3 data3  1      4\n\n>>>df3\n0 Col1  Col6  Col7\n1 data2  5      8\n2 data3  2      7\n3 data5  5      3\n\n\nExpected Output\n\n>>>df\n0 Col1  Col2  Col3  Col4 Col5  Col6  Col7\n1 data1  3      4    7    4\n2 data2  4      3    6    9     5     8\n3 data3  2      3    1    4     2     7\n4 data4  2      4\n5 data5  1      4               5     3","AnswerCount":4,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":29412,"Q_Id":52223045,"Users Score":4,"Answer":"You can do\ndf1.merge(df2, how='left', left_on='Col1', right_on='Col1').merge(df3, how='left', left_on='Col1', right_on='Col1')","Q_Score":21,"Tags":"python,pandas,dataframe,merge,concat","A_Id":52223093,"CreationDate":"2018-09-07T12:55:00.000","Title":"Merge multiple dataframes based on a common column","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there anyway to assert the presence of a detection box in an image and obtain the classification score of said hypothetical box? \nI am working with a tensorflow object detection graph and want to refine it's accuracy with a little trickery; by making the claim that there are more (N) objects in a given image than it is detecting, asserting there are image objects in multiple areas in the image, and evaluating each hypothetical image object based on it's classification score between 0 and 1.\nIn other words:\nI want to say \"Hey, TensorFlow, I think there is an image object with rectangular coordinates (x1, y1), (x2, y2) in this image. What would the classification score of a hypothetical detection box defined by that rectangle be?\" Is this possible?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":32,"Q_Id":52231108,"Users Score":0,"Answer":"With tensorflow you cannot do that. What you are saying is almost like a region proposal and rest of the pipeline on which different platforms like tensorflow, yolo are built to arrive at object detection. You are proposing to a built a different platform by asking what you are asking.","Q_Score":0,"Tags":"python,tensorflow,deep-learning","A_Id":52245736,"CreationDate":"2018-09-08T00:53:00.000","Title":"Get classification score of hypothetical detection box","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Let\u2019s say you have a set\/list\/collection of numbers: [1,3,7,13,21,19] (the order does not matter). Let\u2019s say for reasons that are not important, you run them through a function and receive the following pairs:\n(1, 13), (1, 19), (1, 21), (3,19), (7, 3), (7,13), (7,19), (21, 13), (21,19). Again order does not matter. My question involves the next part: how do I find out the minimum amount of numbers that can be part of a pair without being repeated? For this particular sequence it is all six. For [1,4,2] the pairs are (1,4), (1,2), (2,4). In this case any one of the numbers could be excluded as they are all in pairs, but they each repeat, therefore it would be 2 (which 2 do not matter). \nAt first glance this seems like a graph traversal problem - the numbers are nodes, the pairs edges. Is there some part of mathematics that deals with this? I have no problem writing up a traversal algorithm, I was just wondering if there was a solution with a lower time complexity. Thanks!","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":81,"Q_Id":52231442,"Users Score":0,"Answer":"If you really intended to find the minimum amount, the answer is 0, because you don't have to use any number at all.\nI guess you meant to write \"maximal amount of numbers\". \nIf I understand your problem correctly, it sounds like we can translated it to the following problem:\nGiven a set of n numbers (1,..,n), what is the maximal amount of numbers I can use to divide the set into pairs, where each number can appear only once. \nThe answer to this question is:\n\nwhen n = 2k    f(n) = 2k for k>=0\nwhen n = 2k+1  f(n) = 2k for k>=0\n\nI'll explain, using induction.\n\nif n = 0 then we can use at most 0 numbers to create pairs.\nif n = 2 (the set can be [1,2]) then we can use both numbers to\ncreate one pair (1,2)\nAssumption: if n=2k lets assume we can use all 2k numbers to create 2k pairs and prove using induction that we can use 2k+2 numbers for n = 2k+2.\nProof: if n = 2k+2, [1,2,..,k,..,2k,2k+1,2k+2], we can create k pairs using 2k numbers (from our assomption). without loss of generality, lets assume out pairs are (1,2),(3,4),..,(2k-1,2k). we can see that we still have two numbers [2k+1, 2k+2] that we didn't use, and therefor we can create a pair out of two of them, which means that we used 2k+2 numbers.\n\nYou can prove on your own the case when n is odd.","Q_Score":0,"Tags":"python,algorithm,set,graph-algorithm,graph-traversal","A_Id":52236009,"CreationDate":"2018-09-08T02:24:00.000","Title":"Graph traversal, maybe another type of mathematics?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Let\u2019s say you have a set\/list\/collection of numbers: [1,3,7,13,21,19] (the order does not matter). Let\u2019s say for reasons that are not important, you run them through a function and receive the following pairs:\n(1, 13), (1, 19), (1, 21), (3,19), (7, 3), (7,13), (7,19), (21, 13), (21,19). Again order does not matter. My question involves the next part: how do I find out the minimum amount of numbers that can be part of a pair without being repeated? For this particular sequence it is all six. For [1,4,2] the pairs are (1,4), (1,2), (2,4). In this case any one of the numbers could be excluded as they are all in pairs, but they each repeat, therefore it would be 2 (which 2 do not matter). \nAt first glance this seems like a graph traversal problem - the numbers are nodes, the pairs edges. Is there some part of mathematics that deals with this? I have no problem writing up a traversal algorithm, I was just wondering if there was a solution with a lower time complexity. Thanks!","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":81,"Q_Id":52231442,"Users Score":0,"Answer":"In case anyone cares in the future, the solution is called a blossom algorithm.","Q_Score":0,"Tags":"python,algorithm,set,graph-algorithm,graph-traversal","A_Id":52249740,"CreationDate":"2018-09-08T02:24:00.000","Title":"Graph traversal, maybe another type of mathematics?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Suppose I have numpy array with shape = [y,z,21] where y = image_width, z= image_height. The above array represents image with 21 channels. How should I convert it to size = [ y,z,3 ] ?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":355,"Q_Id":52239164,"Users Score":0,"Answer":"You should set a score threshold to map every pixel in the image to one class, and every class has a color (which has RGB channels), so every pixel is a RGB value for its class.","Q_Score":0,"Tags":"python-3.x,numpy","A_Id":52291702,"CreationDate":"2018-09-08T20:30:00.000","Title":"How to change channel dimension of an image?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a set of data with 50 features (c1, c2, c3 ...), with over 80k rows.\nEach row contains normalised numerical values (ranging 0-1). It is actually a normalised dummy variable, whereby some rows have only few features, 3-4 (i.e. 0 is assigned if there is no value). Most rows have about 10-20 features.\nI used KMeans to cluster the data, always resulting in a cluster with a large number of members. Upon analysis, I noticed that rows with fewer than 4 features tends to get clustered together, which is not what I want.\nIs there anyway balance out the clusters?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":3624,"Q_Id":52253787,"Users Score":5,"Answer":"It is not part of the k-means objective to produce balanced clusters. In fact, solutions with balanced clusters can be arbitrarily bad (just consider a dataset with duplicates). K-means minimizes the sum-of-squares, and putting these objects into one cluster seems to be beneficial.\nWhat you see is the typical effect of using k-means on sparse, non-continuous data. Encoded categoricial variables, binary variables, and sparse data just are not well suited for k-means use of means. Furthermore, you'd probably need to carefully weight variables, too.\nNow a hotfix that will likely improve your results (at least the perceived quality, because I do not think it makes them statistically any better) is to normalize each vector to unit length (Euclidean norm 1). This will emphasize the ones of rows with few nonzero entries. You'll probably like the results more, but they are even much harder to interpret.","Q_Score":3,"Tags":"python,cluster-analysis,k-means,data-science,feature-engineering","A_Id":52258663,"CreationDate":"2018-09-10T08:26:00.000","Title":"KMeans clustering unbalanced data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a DataFrame series with day resolution. I want to transform the series to a series of monthly averages. Ofcourse I can apply rolling mean and select only every  30th of means but it would not precise. I want to get series which contains mean value from the previous month on every first day of a month. For example, on February 1 I want to have daily average for the January. How can I do this in pythonic way?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":63,"Q_Id":52254994,"Users Score":0,"Answer":"data.resample('M', how='mean')","Q_Score":0,"Tags":"python,pandas,dataframe","A_Id":52255420,"CreationDate":"2018-09-10T09:41:00.000","Title":"How to apply avg function to DataFrame series monthly?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to decode one character (represented as c-dimensional one hot vectors) at a time with tensorflow seq2seq model implementations. I am not using any embedding in my case. \nNow I am stuck with tf.contrib.seq2seq.GreedyEmbeddingHelper. It requires \"embedding: A callable that takes a vector tensor of ids (argmax ids), or the params argument for embedding_lookup. The returned tensor will be passed to the decoder input.\"\nHow I will define callable? What are inputs (vector tensor if ids(argmax ids)) and outputs of this callable function? Please explain using examples.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":267,"Q_Id":52256809,"Users Score":0,"Answer":"embedding = tf.Variable(tf.random_uniform([c-dimensional ,\nEMBEDDING_DIM]))\nhere you can create the embedding for you own model.\nand this will be trained during your training process to give a vector for your own input.\nif you don't want to use it you just can create a matrix where is every column of it is one hot vector represents the character and pass it as embedding.\nit will be some thing like that:\n[[1,0,0],[0,1,0],[0,0,1]]\nhere if you have vocabsize of 3 .","Q_Score":0,"Tags":"python,tensorflow","A_Id":55653445,"CreationDate":"2018-09-10T11:25:00.000","Title":"how to use Tensorflow seq2seq.GreedyEmbeddingHelper first parameter Embedding in case of using normal one hot vector instead of embedding?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Hi I tried to use pdfimages to extract ID images from my pdf resume files. However for some files they return also the icon, table lines, border images which are totally irrelevant.\nIs there anyway I can limit it to only extract person photo? I am thinking if we can define a certain size constraints on the output?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":194,"Q_Id":52271908,"Users Score":0,"Answer":"You need a way of differentiating images found in the PDF in order to extract the ones of interest.\nI believe you have the options of considering:\n\nImage characteristics such as Width, Height, Bits Per Component, ColorSpace\nMetadata information about the image (e.g. a XMP tag of interest)\nFacial recognition of the person in the photo or Form recognition of the structure of the ID itself.\nExtracting all of the images and then use some image processing code to analyze the images to identify the ones of interest.\n\nI think 2) may be the most reliable method if the author of the PDF included such information with the photo IDs.  3) may be difficult to implement and get a reliable result from consistently.  1) will only work if that is a reliable means of identifying such photo IDs for your PDF documents.\nThen you could key off of that information using your extraction tool (if it lets you do that). Otherwise you would need to write your own extraction tool using a PDF library.","Q_Score":0,"Tags":"python,image,pdf,extract,pypdf","A_Id":52279819,"CreationDate":"2018-09-11T08:36:00.000","Title":"How to extract only ID photo from CV with pdfimages","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it possible to write binary numpy array containing 0 and 1 to file using opencv (cv2) or Pillow? I was using scipy.misc.imsave and it worked well, but i read it's depreciated so i wanted to switch to other modules, but when trying to write such an array i see only black image. I need to have 0\/1 values, and not 0\/255 for further processing.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":486,"Q_Id":52273313,"Users Score":0,"Answer":"Since you are writing pixels with values (0, 0, 0) or (1, 1, 1) to the image you are seeing an image that is entirely black and almost-black, so it looks black.\nYou can multiply your array by 255 to get an array of { (0, 0, 0), (255, 255, 255) } which would be black and white. When you read the image you can convert back to 0s and 1s.","Q_Score":0,"Tags":"python-3.x,python-imaging-library,cv2","A_Id":52287805,"CreationDate":"2018-09-11T09:54:00.000","Title":"Write binary numpy array of zeros and ones to file using cv2 or Pillow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am making a machine learning program for time series data analysis and using NEAT could help the work. I started to learn TensorFlow not long ago but it seems that the computational graphs in TensorFlow are usually fixed. Is there tools in TensorFlow to help build a dynamically evolving neural network? Or something like Pytorch would be a better alternative? Thanks.","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":2641,"Q_Id":52287254,"Users Score":1,"Answer":"One way to make an evolving tensorflow network would be to use either hyperneat or the es-hyperneat algorithms instead of running the evolution on the individual networks in the species this instead evolves a \"genome\" that is actually cppn that encodes the phenotype neural nets. For the cppn you can use a feed forward tensorflow network with the caveat of having different activation functions that can be used at each node, this lets the cppn evolve to be able to be queried for the structure and weights of the \"phenotype\" neural network for which you can use a generic tensorflow net(or whatever net you so choose)  \nI would look into the neat-python and peas libraries and look at the networks they use and replicate those classes with tensorflow nets.","Q_Score":6,"Tags":"python,tensorflow,pytorch,neat","A_Id":53675625,"CreationDate":"2018-09-12T03:53:00.000","Title":"Can neuroevolution of augmenting topologies (NEAT) neural networks be built in TensorFlow?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to know if the sklearn.model_selection learning_curve can use or does use sklearn.preprocessing StandardScaler.  I've looked over the implementation, but my skill level isn't up to par to come to a conclusion on my own.  All tutorials on using learning_curve have you pass the entire data set to the learning_curve and learning_curve will split the data into training and testing sets.\nAll tutorials for any estimators have you split the data into training and test then scale only the training data and transform the test data using the training data scale.  Which completely understand.\nShould I scale the entire data set before passing it to learn_curve.  I do know learning_curve will use either k-folds or some other cross validation method, so does it even matter because it will all get averaged out with cross validation?\nThanks,","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":374,"Q_Id":52302047,"Users Score":0,"Answer":"learning_curve does not implement StandardScaler on its own.  You could create a Pipeline as your estimator where the first step is StandardScaler then whatever your estimator you're using as the next step.  This way when you call learning_curve during each cv iteration you are training both the scaler and estimator on the training folds and performance is being validated against the testing fold in each iteration.\nYou would not want to scale the entire dataset before calling learning_curve.  The reason is when you scale the entire set before training your model you are introducing bias because you are using data that will be used for validation to train the model, which can cause over-fitting.","Q_Score":2,"Tags":"python,scikit-learn","A_Id":52302920,"CreationDate":"2018-09-12T19:22:00.000","Title":"sklearn learning_curve and StandardScaler","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am fairly new to deep learning and TensorFlow, and in the set of lectures from the course I am taking they go over two methods of employing TensorFlow: using estimators and using sessions. It seems like the estimators method is much easier to understand and simpler as it is similar to what I have done using the sklearn classifier packages. Is there any particular reason why one would use sessions instead of estimators? Or will it depend on the type of dataset I am dealing with?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":471,"Q_Id":52302352,"Users Score":1,"Answer":"A simple answer would be:\nEstimator hides some TensorFlow concepts, such as Graph and\nSession, from the user. This is best for newbies since it makes new learners be able to get started much easier (this is nothing to do with the type of dataset, just use tf.dataset API to write an input_fn is sufficient to provide input data for an estimator). \nOnce you have played with tensorflow for a while, understanding how Estimator works and maybe start to use low-level APIs is definitely needed to make you an expert.","Q_Score":2,"Tags":"python,tensorflow,deep-learning,tensorflow-estimator","A_Id":52302923,"CreationDate":"2018-09-12T19:44:00.000","Title":"TensorFlow estimators vs manual\/session approach","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am installing python on windows10 and trying to install the opencv and numpy extentions in the command window. I get no error installing them and it says it is successfully installed. But when I try to check the installation and import cv2 it does not recognize it and give me the error: no module named cv2.\ncan anybody help me with this problem? Is there something wrong in installation process or do I need to install something else? \nI checked the newest version of each and used the compatible one with my system.\nThanks.","AnswerCount":4,"Available Count":2,"Score":0.049958375,"is_accepted":false,"ViewCount":3551,"Q_Id":52316354,"Users Score":1,"Answer":"I removed the Anaconda version on my machine, so I just have python 3.7 installed. I removed the python interpreter(Pycharm) and installed it again and the problem got fixed somehow!","Q_Score":0,"Tags":"python,numpy,opencv","A_Id":52318766,"CreationDate":"2018-09-13T14:52:00.000","Title":"problem installing and importing modules in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am installing python on windows10 and trying to install the opencv and numpy extentions in the command window. I get no error installing them and it says it is successfully installed. But when I try to check the installation and import cv2 it does not recognize it and give me the error: no module named cv2.\ncan anybody help me with this problem? Is there something wrong in installation process or do I need to install something else? \nI checked the newest version of each and used the compatible one with my system.\nThanks.","AnswerCount":4,"Available Count":2,"Score":0.049958375,"is_accepted":false,"ViewCount":3551,"Q_Id":52316354,"Users Score":1,"Answer":"Is it possible that you have 2 versions of python on your machine and your native pip is pointing to the other one? (e.g. you pip install opencv which installs opencv for python 2, but you are using python 3). If this is so, then use pip3 install opencv","Q_Score":0,"Tags":"python,numpy,opencv","A_Id":52316565,"CreationDate":"2018-09-13T14:52:00.000","Title":"problem installing and importing modules in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Previously I installed pytorch,PIL,numpy... using pip. After that I installed python3. Thus ipython switched from python2 to python3. I have to use ipython2 to start python2 kernel. These modules still works well in ipython2, but when I run a python script using python, python2, python2.7, they all raise ImportError:\n\nImportError: No module named PIL(numpy,torch...)\n\nWhen run this command: sudo pip install numpy\nreturn: \n\nRequirement already satisfied: numpy in\n  \/usr\/local\/lib\/python3.5\/dist-packages (1.15.1)\n\nwhen running this command: sudo pip2 install numpy\nreturn: Requirement already satisfied (use --upgrade to upgrade): numpy in \/usr\/lib\/python2.7\/dist-packages\nWhen I run: python, import sys, sys.path\nit shows :\n\n['', '\/home\/szy\/miniconda2\/lib\/python27.zip',\n  '\/home\/szy\/miniconda2\/lib\/python2.7',\n  '\/home\/szy\/miniconda2\/lib\/python2.7\/plat-linux2',\n  '\/home\/szy\/miniconda2\/lib\/python2.7\/lib-tk',\n  '\/home\/szy\/miniconda2\/lib\/python2.7\/lib-old',\n  '\/home\/szy\/miniconda2\/lib\/python2.7\/lib-dynload',\n  '\/home\/szy\/.local\/lib\/python2.7\/site-packages',\n  '\/home\/szy\/miniconda2\/lib\/python2.7\/site-packages']\n\nThe location of numpy is not among them.\nand the sys.path in ipython2:\n\n['',  '\/usr\/local\/bin',  '\/usr\/lib\/python2.7', \n  '\/usr\/lib\/python2.7\/plat-x86_64-linux-gnu', \n  '\/usr\/lib\/python2.7\/lib-tk',  '\/usr\/lib\/python2.7\/lib-old', \n  '\/usr\/lib\/python2.7\/lib-dynload', \n  '\/home\/szy\/.local\/lib\/python2.7\/site-packages', \n  '\/usr\/local\/lib\/python2.7\/dist-packages', \n  '\/usr\/lib\/python2.7\/dist-packages', \n  '\/usr\/local\/lib\/python2.7\/dist-packages\/IPython\/extensions', \n  '\/home\/szy\/.ipython']\n\nWhat's wrong?\nPrevious I could run scripts with python and import these modules.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":62,"Q_Id":52323907,"Users Score":0,"Answer":"Make sure the python path that you given in bashrc is correct.\nAlso it will be good to use conda environment to try out the same since there is confusion in python environments. For that you can follow the below steps:\nCreate the environment and activate it using following commands:\nconda create -n test_env python=2.7\nconda activate test_env\nconda install ipykernel\nipython kernel install --name test_env --user\nThen install the required packages in the environment that you created and try to import it within the created environment.","Q_Score":0,"Tags":"python,linux,python-2.7,numpy","A_Id":52332078,"CreationDate":"2018-09-14T01:42:00.000","Title":"Some modules can be imported in python previously but now can only be imported in ipython2","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"RuntimeError:\n\nCMake must be installed to build the following extensions: dlib\n\n\nFailed building wheel for dlib\n  Running setup.py clean for dlib\nFailed to build dlib","AnswerCount":4,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":35770,"Q_Id":52332268,"Users Score":10,"Answer":"I ran into this issue as well. I am using windows and have a python environment that I am installing the requirements to.\nI ran pip install cmake , and then pip install dlib. I no longer received the error and successfully installed dlib.","Q_Score":0,"Tags":"python,face-recognition","A_Id":52564257,"CreationDate":"2018-09-14T12:41:00.000","Title":"pip install face_recognition giving error","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have made a plot, and I don't want extra whitespaces in my plot; the question is: \nHow can I strip extra whitespaces from a plot?\nI know you can strip extra whitespaces from a plot when you save it; Then you just do this: plt.savefig('file_name.png', bbox_inches='tight')\nBut I can't find any similar arguments you can pass to plt.plot() to have no extra whitespaces. Is it possible to pass an argument to plt.plot()?","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":76,"Q_Id":52334185,"Users Score":1,"Answer":"The easiest way imho is to click on the button \"configure subplots\" and adjust the sliders because you see the result immediately. You could although call the tight_layout() function directly on plt bevor  show()","Q_Score":2,"Tags":"python,matplotlib,whitespace","A_Id":52334301,"CreationDate":"2018-09-14T14:37:00.000","Title":"Matplotlib - How to strip extra whitespaces from a plot without needing to save it?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to create a predictive curve using 12 different datasets of empirical data. Essentially I want to write a function that passes 2 variables (Number of Applications, Days) and generates a predictive curve based on the 12 datasets that i have. The datasets all have 60 days and have Number of Applications from 500 to 100,000.\nI'm not really sure of what the best approach would be, I was thinking maybe taking the average percentage of total applications at each day (ex: at day 1 on average there are 3% of total applications issued, day 10 on average there are 10%, etc)would be a good place to start but i'm not sure if that's the best approach.\nI have python, SQL, and excel at my disposal but I'm not necessarily looking for a specific solution as much as just a general suggestion on approach. Any help would be much appreciated!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":23,"Q_Id":52334490,"Users Score":0,"Answer":"It sounds like you want to break it all out into (60*12) rows with 3 columns: one recording the application number, another recording the time, and another recording the location. Then a model could dummy out each location as a predictor, and you could generate 12 simulated predictions, with uncertainty. Then, to get your one overall prediction, average those predictions instead - bootstrap and then pool the predictions if you're fancy. Model time however you want - autoregression, Kalman filter, nearest-neighbor (probably not enough data for that one though). Just don't dummy out each time point individually or you'll have a perfect-fitting model.\nBut be aware of the possible universe of interactions between the locations that you could model here. Dummying them all out assumes no interactions between them, or at least one you care about, or that relate to anything you care about. It just accounts for fixed effects, i.e. you're assuming that the time dynamic within each location is the same, it's just that some locations tend overall and on average to have higher application numbers than others. You could derive tons of predictors pertaining to any given location based on the application number(s) in other location(s) - current number, past number, etc. All depends on what you consider to be possible and informative to account for.","Q_Score":0,"Tags":"python,sql,excel,statistics","A_Id":52335074,"CreationDate":"2018-09-14T14:55:00.000","Title":"Python\/SQL\/Excel I have 12 datasets and I want to combine them to one representative set","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm running my session on a GPU and I'm wondering if the 'session.run()' piece of code is running in parallel to my other code in my script. \nI use batch processing on the CPU prior to running 'session.run()' in a loop and would like to pipeline this processing with the execution on the GPU. Is this already satisfied in this setting or do I need to manually start threads?","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":354,"Q_Id":52335065,"Users Score":0,"Answer":"After some research I found out that 'session.run' is not running concurrently to your other code. Indeed, as Ujjwal suggested, the 'tf.data.Dataset' API is the best choice for pipelining batch preprocessing and GPU execution.","Q_Score":0,"Tags":"python,multithreading,tensorflow,parallel-processing,batch-processing","A_Id":52596253,"CreationDate":"2018-09-14T15:29:00.000","Title":"Is tensorflow session running in parallel to the rest of my code?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm running my session on a GPU and I'm wondering if the 'session.run()' piece of code is running in parallel to my other code in my script. \nI use batch processing on the CPU prior to running 'session.run()' in a loop and would like to pipeline this processing with the execution on the GPU. Is this already satisfied in this setting or do I need to manually start threads?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":354,"Q_Id":52335065,"Users Score":0,"Answer":"It entirely depends upon how you have written your code. This should be trivial to check, by checking out your CPU  and GPU utilization simultanously\nI normally make use of tf.data.Dataset API. I use the get_next() method of an iterator to feed data to a network. CPU and GPU work in parallel in this case.","Q_Score":0,"Tags":"python,multithreading,tensorflow,parallel-processing,batch-processing","A_Id":52335207,"CreationDate":"2018-09-14T15:29:00.000","Title":"Is tensorflow session running in parallel to the rest of my code?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on Glue since january, and have worked multiple POC, production data lakes using AWS Glue \/ Databricks \/ EMR, etc.  I have used AWS Glue to read data from S3 and perform ETL before loading to Redshift, Aurora, etc.\nI have a need now to read data from a source table which is on SQL SERVER, and fetch data, write to a S3 bucket in a custom (user defined) CSV file, say employee.csv.\nAm looking for some pointers, to do this please.\nThanks","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1571,"Q_Id":52336996,"Users Score":0,"Answer":"This task fits AWS DMS (Data Migration Service) use case. DMS is designed to either migrate data from one data storage to another or keep them in sync. It can certainly keep in sync as well as transform your source (i.e., MSSQL) to your target (i.e., S3).\nThere is one non-negligible constraint in your case thought. Ongoing sync with MSSQL source only works if your license is the Enterprise or Developer Edition and for versions 2016-2019.","Q_Score":3,"Tags":"python,python-2.7,amazon-web-services,amazon-s3,aws-glue","A_Id":66705862,"CreationDate":"2018-09-14T17:50:00.000","Title":"AWS Glue - read from a sql server table and write to S3 as a custom CSV file","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a Python list (numpy array) and another list which contains the indices for the location of values from the first array which I want to keep.\nIs there a Pythonic way to do this?\nI know numpy.delete, but I want to keep the elements and not delete them.","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":178,"Q_Id":52342187,"Users Score":1,"Answer":"Why don't you use just c=a[b] as this is the Python way to take the values from array a.","Q_Score":1,"Tags":"python,python-2.7,list,numpy","A_Id":52344172,"CreationDate":"2018-09-15T06:18:00.000","Title":"Pythonic way to cut specific elements from numpy array","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm looking to program a Python function that takes in 6 variables, a, b, c, d, e, f, where a, b is the interval to compute on (e.g. [1, 3], all real numbers), and c, d, e, f are the coefficients of the cubic polynomial, i.e. f(x) = cx^3 + dx^2 + ex + f, and returns the local min\/max on the interval [a, b].\nI have a rough idea (although the computing time would be bad) of how to program this, where I create a new list of steps 0.01 or something similarly small from a to b, evaluate f at each value, then simply return the min\/max of the list. This would take very long for a, b values that are very far apart.\nWhat is the best way to go about making this? Are there any outside libraries for scientific\/mathematical computing? Thank you.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1730,"Q_Id":52360672,"Users Score":3,"Answer":"For cubic function you can find positions of potential minumum\/maximums without optimization but using differentiation:\n\nget the first and the second derivatives\nfind zeros of the first derivative (solve quadratic equation)\ncheck the second derivative in found points - sign tells whether that point is min, max or saddle point\n\nI think that  differentiation should be in sympy package\nAlso check whether problem statement assumes accounting for boundary values (as @Lakshay Garg notices in comments)","Q_Score":0,"Tags":"python,math,scientific-computing","A_Id":52360778,"CreationDate":"2018-09-17T04:15:00.000","Title":"Finding local min\/max of a cubic function","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"How to find the centre point of clusters of DBSCAN clustering algorithm in sklearn.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":6728,"Q_Id":52364959,"Users Score":10,"Answer":"DBSCAN doesn't have centers.\nYou can compute then yourself, but they may be outside of the cluster if it is not convex.","Q_Score":3,"Tags":"python-3.x,scikit-learn,dbscan","A_Id":52438930,"CreationDate":"2018-09-17T09:49:00.000","Title":"How to find center points of DBSCAN clusrering in sklearn","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm running a NN training on my GPU with pytorch.\nBut the GPU usage is strangely \"limited\" at about 50-60%.\nThat's a waste of computing resources but I can't make it a bit higher.\nI'm sure that the hardware is fine because running 2 of my process at the same time,or training a simple NN (DCGAN,for instance) can both occupy 95% or more GPU.(which is how it supposed to be)\nMy NN contains several convolution layers and it should use more GPU resources.\nBesides, I guess that the data from dataset has been feeding fast enough,because I used workers=64 in my dataloader instance and my disk works just fine.\nI just confused about what is happening.\nDev details: \nGPU : Nvidia GTX 1080 Ti\nos:Ubuntu 64-bit","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":795,"Q_Id":52374287,"Users Score":1,"Answer":"I can only guess without further research but it could be that your network is small in terms of layer-size (not number of layers) so each step of the training is not enough to occupy all the GPU resources. Or at least the ratio between the data size and the transfer speed (to the gpu memory) is bad and the GPU stays idle most of the time.\ntl;dr: the gpu jobs are not long enough to justify the memory transfers","Q_Score":0,"Tags":"python,machine-learning,pytorch","A_Id":52387459,"CreationDate":"2018-09-17T19:08:00.000","Title":"Why will GPU usage run low in NN training?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I split my dataset in X_train, Y_train, X_test and Y_test, and then I used the symbolicRegressor...\nI've already convert the string values from Dataframe in float values.\nBut by applying the symbolicRegressor I get this error: \n\nValueError: could not convert string to float: 'd'\n\nWhere 'd' is a value from Y.\nSince all my values in Y_train and Y_test are alphabetic character because they are the \"labels\", I can not understand why the symbolicRegressor tries to get a float number ..\nAny idea?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":302,"Q_Id":52381949,"Users Score":0,"Answer":"Sorry for the late replay. gplearn supports regression (numeric y) with the SymbolicRegressor estimator, and with the newly released gplearn 0.4.0 we also support binary classification (two labels in y) using the SymbolicClassifier. From the sounds of things though, you have a multi-label problem which gplearn does not currently support. It may be something we look to support in the future.","Q_Score":0,"Tags":"python-3.x,genetic,gplearn","A_Id":55842872,"CreationDate":"2018-09-18T08:14:00.000","Title":"Y_train values for symbolicRegressor","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I split my dataset in X_train, Y_train, X_test and Y_test, and then I used the symbolicRegressor...\nI've already convert the string values from Dataframe in float values.\nBut by applying the symbolicRegressor I get this error: \n\nValueError: could not convert string to float: 'd'\n\nWhere 'd' is a value from Y.\nSince all my values in Y_train and Y_test are alphabetic character because they are the \"labels\", I can not understand why the symbolicRegressor tries to get a float number ..\nAny idea?","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":302,"Q_Id":52381949,"Users Score":0,"Answer":"According to the https:\/\/gplearn.readthedocs.io\/en\/stable\/index.html - \"Symbolic regression is a machine learning technique that aims to identify an underlying mathematical expression that best describes a relationship\". Pay attention to mathematical. I am not good at the topic of the question and gplearn's description does not clearly define area of applicability \/ restrictions. \nHowever, according to the source code https:\/\/gplearn.readthedocs.io\/en\/stable\/_modules\/gplearn\/genetic.html method fit() of BaseSymbolic class contains line X, y = check_X_y(X, y, y_numeric=True) where check_X_y() is sklearn.utils.validation.check_X_y(). Argument y_numeris means: \"Whether to ensure that y has a numeric type. If dtype of y is object, it is converted to float64. Should only be used for regression algorithms\".\nSo y values must be numeric.","Q_Score":0,"Tags":"python-3.x,genetic,gplearn","A_Id":52389432,"CreationDate":"2018-09-18T08:14:00.000","Title":"Y_train values for symbolicRegressor","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to create an array of powers of 10 as a label for the y axis of a plot.\nI am using the plt.yticks() with matplotlib imported as plt but this does not matter here anyway.\nI have plots where as the y axis is varying from 1e3 to 1e15. Those are log plots.\nMatplotlib is automatically displaying those with ticks with 1e2 steps and I want to have a step of 10 instead (in order to be able to use the minorticks properly).\nI want to use the plt.yticks(numpy.arange(1e3, 1e15, 10)) command as said but numpy.arange(1e3, 1e15, 10) result in a MemoryError. Isn't it supposed to output an array of length 13? Why does the memory gets full?\nHow to overpass this issue and not build the array manually?\nI also tried using built-in range but it won't work with floats.\nThank you.","AnswerCount":4,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":911,"Q_Id":52383129,"Users Score":0,"Answer":"In this case the function logspace from numpy is more suitable.\nThe answer to the example is\nnp.logspace(3,15,num=15-3+1, endpoint=True)","Q_Score":5,"Tags":"python,numpy,matplotlib,out-of-memory","A_Id":52383425,"CreationDate":"2018-09-18T09:18:00.000","Title":"MemoryError with numpy arange","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working with a relatively large CSV file in Python. I am using the pandas read_csv function to import it. The data is on a shared folder at work and around 25 GB.\nI have 2x8 GB RAM and an Intel Core i5 processor and using the juypter notebook. While loading the file the RAM Monitoring goes up to 100%. It stays at 100% or 96% for some minutes and then my computer clock stopped and my screen is frozen. Even if I wait 2 hours my computer is not able to use any more, so I have to restart.\nMy question is:\nDo I need to split the data? Would it help? Or is it a general performance problem with my laptop?\nIt is the first time that I am working with such a 'large' dataset (I still think 25 GB is not too much.)","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":593,"Q_Id":52387191,"Users Score":1,"Answer":"You're probably loading all of the data in your RAM, thus allocating all memory available, forcing your system to rely on swap memory (writing temporary data to the disk, which is MUCH slower).\nIt should solve the issue if you split the data into chunks that fit in your memory. Maybe 1 GB each?","Q_Score":0,"Tags":"python,pandas,csv","A_Id":52387276,"CreationDate":"2018-09-18T13:00:00.000","Title":"Reading large CSV file with Pandas freezes computer","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I try to Fit Multiple Linear Regression Model\nY= c + a1.X1 + a2.X2 + a3.X3 + a4.X4 +a5X5 +a6X6 \nHad my model had only 3 variable I would have used 3D plot to plot.\nHow can I plot this . I basically want to see how the best fit line looks like or should I plot multiple scatter plot and see the effect of individual variable \nY = a1X1 when all others are zero and see the best fit line.\nWhat is the best approach for these models. I know it is not possible to visualize higher dimensions want to know what should be the best approach. I am desperate to see the best fit line","AnswerCount":3,"Available Count":1,"Score":0.1325487884,"is_accepted":false,"ViewCount":24001,"Q_Id":52404857,"Users Score":2,"Answer":"You can use Seaborn's regplot function, and use the predicted and actual data for comparison. It is not the same as plotting a best fit line, but it shows you how well the model works.\nsns.regplot(x=y_test, y=y_predict, ci=None, color=\"b\")","Q_Score":5,"Tags":"python,matplotlib,machine-learning,regression,linear-regression","A_Id":65549840,"CreationDate":"2018-09-19T11:29:00.000","Title":"How do I plot for Multiple Linear Regression Model using matplotlib","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a pandas DataFrame with a column representing a categorical variable. How can I get a list of the categories? I tried .values on the column but that does not return the unique levels.\nThanks!","AnswerCount":5,"Available Count":2,"Score":0.1194272985,"is_accepted":false,"ViewCount":67005,"Q_Id":52404971,"Users Score":3,"Answer":"Try executing the below code.\nList_Of_Categories_In_Column=list(df['Categorical Column Name'].value_counts().index)","Q_Score":28,"Tags":"python,pandas,categorical-data","A_Id":59574232,"CreationDate":"2018-09-19T11:36:00.000","Title":"Get a list of categories of categorical variable (Python Pandas)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a pandas DataFrame with a column representing a categorical variable. How can I get a list of the categories? I tried .values on the column but that does not return the unique levels.\nThanks!","AnswerCount":5,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":67005,"Q_Id":52404971,"Users Score":0,"Answer":"df.column name.value_counts() # to see total number of values for each categories in a column\ndf.column name.value_counts().index # to see only the categories name\ndf.column name .value_counts().count() # to see how many categories in a column (only number)","Q_Score":28,"Tags":"python,pandas,categorical-data","A_Id":67443900,"CreationDate":"2018-09-19T11:36:00.000","Title":"Get a list of categories of categorical variable (Python Pandas)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Apply returns indices of leafs. \nCould anyone explain which indices does it return? Related fucntion in Matlab?\nThanks","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":33,"Q_Id":52408980,"Users Score":2,"Answer":"It gives you the indices of the leaf your data point is for every tree of your forest.\nThis is what is then used to predict the class of your point.","Q_Score":0,"Tags":"python-3.x,scikit-learn,random-forest,sklearn-pandas","A_Id":52409256,"CreationDate":"2018-09-19T15:06:00.000","Title":"RandomForestClassifiers sklearn apply(X)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When I set the hoverinfo of a Plotly object to \"x+text\", I can modify what is shown in the hover tooltip using the hovertext attribute.\nI haven't found a way to modify the hover text along the x-axis though. I would like to modify it to be more than the default x-axis value at that location.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":130,"Q_Id":52409641,"Users Score":0,"Answer":"I think maybe you want to change the ticklabels of x-axis instead of the hoverinfo.\nThe meaning of x in hoverinfo is the x-coordinated of the points. So if you truly want to revise the x, maybe you should change the x-coordinated of the points. Maybe changed into string or some special case.\nOf course, you could also use hovertext to fully present all the information which you want to present. If you are worried about the \\n symbol is not working, you could use <Br> directly.","Q_Score":0,"Tags":"python,plotly","A_Id":52435565,"CreationDate":"2018-09-19T15:42:00.000","Title":"Is it possible to customize Plotly x-axis hoverinfo in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a Python script that calculates a few, quite small, NumPy arrays and I have a Java service on a separate machine that needs to use these arrays. \nThese arrays sometimes need to be recalculated and then used afterwards by the Java service. What is the best way to dump a NumPy array to the disk and load it in Java as float[][]? \nI know that I could use JSON to do this (Python script dumps the NumPy array to a JSON file and Java service recovers float[][] from this), but is there any other, \"preferred\" way?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":57,"Q_Id":52421822,"Users Score":1,"Answer":"Saving a file to disk to exchange the data between different applications sounds like a hacky approach to me. \nDepending on your structure and complexity, I would consider implementing a messaging queue (i.e. redis) or a document database (i.e. mongo or prefered alternative) with respective clients to do the data exchange between the apps.\nOn the data structure itself, I would choose json or csv for the task. If you need human readability or strict structure, json is your tool. If the data is meant only for the machine to read, csv takes less space to store the same amount of data.","Q_Score":0,"Tags":"java,python,arrays","A_Id":52422031,"CreationDate":"2018-09-20T09:29:00.000","Title":"Move array of doubles from Python to Java","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I was doing a frame slicing from the OpenCV Library in Python, and I am successfully able to create frames from the video being tested on. \nI am doing it on a CCTV Camera installed at a parking entry gateway where the video plays 24x7, and at times the car is standing still for good number of minutes, leading to having consecutive frames of the same vehicle. \nMy question is how can I create a frame only when a new vehicle enters the parking lot?","AnswerCount":4,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":810,"Q_Id":52422060,"Users Score":0,"Answer":"Do you need to detect license plates, etc? Or just notice if something happens? For the latter, you could use a very simple approach. Take an average of say the frames of the last 30 seconds and subtract that from a current frame. If the mean absolute average of the delta image is above a threshold, that could be the change you are looking for.","Q_Score":0,"Tags":"python,opencv","A_Id":52422304,"CreationDate":"2018-09-20T09:40:00.000","Title":"Open CV to Capture Unique Objects from a Video","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In numpy, if a is an ndarray, then, something like \nnp.sin(a) takes sin of all the entries of ndarray. What if I need to define my own function (for a stupid example, f(x) = sin(x) if x<1 else cos(x)) with broadcasting behavior?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":134,"Q_Id":52436499,"Users Score":2,"Answer":"You could define your own function f = lambda x: sin(x) if x<1 else cos(x) and then use numpy's builtin vectorizer f_broadcasting = np.vectorize(f).\nThis doesn't offer any speed improvements (and the additional overhead can slow down small problems), but it gives you the desired broadcasting behavior.","Q_Score":1,"Tags":"python,python-3.x,numpy","A_Id":52436519,"CreationDate":"2018-09-21T04:11:00.000","Title":"numpy Broadcasting for user functions","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"For some integer K around 100, I have 2 * K (n, n) arrays: X_1, ..., X_K and Y_1, ..., Y_K.\nI would like to perform K least squares simultaneously, i.e. find the n by n matrix A minimizing the sum of squares over k: \\sum_k norm(Y_k - A.dot(X_k), ord='fro') ** 2 (A must not depend on k).\nI am looking for an easy way to do this with numpy or scipy.\nI know the function I want to minimize is a quadratic form in A so I could do it by hand, but I'm looking for an off-the-shelf way of doing it. Is there one?","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":834,"Q_Id":52439564,"Users Score":0,"Answer":"In fact the answer was simple, I just needed to create bigger matrices Y and X by horizontally stacking the Y_k (to create Y) and the  X_k (to create X). Then I can just solve a regular 2d least squares problem: minimize norm(Y - A.dot(X))","Q_Score":0,"Tags":"python,numpy,scipy,linear-algebra,least-squares","A_Id":52504486,"CreationDate":"2018-09-21T08:22:00.000","Title":"Solve 3D least squares in numpy\/scipy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have created a virtual machine in Google Compute us-east-1c region with the following specifications: n1-standard-2 (2 vCPU, 7.5 GB memory), 1 NVIDIA Tesla K80 GPU, boot disk: Deep Learning Image Tensorflow 1.10.1 m7 CUDA 9.2.\nWhen I first logged in to the machine, it asked me to install the drivers and I agreed.  It gave me some warning messages which I did not save.\nI tried to train a model written entirely in Keras with TF backend.\nHowever, judging by the speed and CPU utilization (both similar to what it does on my laptop, slow and using almost all CPU available), GPU is not used.\nThis is also confirmed by the TF output:\n\n2018-09-21 08:39:48.602158: I\n  tensorflow\/core\/platform\/cpu_feature_guard.cc:141] Your CPU supports\n  instructions that this TensorFlow binary was not compiled to use: AVX2\n  FMA\n\nIt did not mention any GPU.  (Thanks @Matias Valdenegro !)\nIn my model I did not relate to GPU with an understanding that TF takes care of it automatically.\nAny ideas?\nMany thanks.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":985,"Q_Id":52440606,"Users Score":0,"Answer":"The problem was with my use of requirements.txt.\nI created it on my laptop with pip freeze, uploaded it to the VM and used pip to install all the requirements.\nIn this way my requirements.txt included tensorflow.  As the result, pip installed the repository version that did not include GPU support, replacing pre-installed tensorflow with GPU support.\nI was able to figure this out by repeating my steps and checking GPU per second suggestion of @Shintlor (thanks!) along the way.\nI have created another VM and did not use requirements.txt; rather, I installed all the missing packages one by one.  After that my model trains now 20 times faster than on my laptop.","Q_Score":0,"Tags":"python,tensorflow,keras,google-compute-engine","A_Id":52460262,"CreationDate":"2018-09-21T09:24:00.000","Title":"NVIDIA K-80 GPU does not run with Deep Learning Image Tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When running keras model inside Jupyter notebook with \"verbose=1\" option, I started getting not single line progress status updates as before, but a flood of status lines updated at batch. See attached picture.  Restarting jupyter or the browser is not helping. \nJupyter notebook server is: 5.6.0, keras is 2.2.2, Python is Python 3.6.5\nPlease help. \ncell content:\nhistory = model.fit(x=train_df_scaled, y=train_labels, batch_size=BATCH_SIZE, epochs=EPOCHS, verbose=1, validation_data=(validation_df_scaled, validation_labels), shuffle=True)\noutput flood example: (it is thousands of lines like this)\nloss: 217.5794 - mean_absolute_error: 11.166 - ETA: 32:42 - loss: 216.9500 - mean_absolute_error: 11.165 - ETA: 32:21 - loss: 216.6378 - mean_absolute_error: 11.164 - ETA: 32:00 - loss: 216.0345 - mean_absolute_error: 11.164 - ETA: 31:41 - loss: 215.6621 - mean_absolute_error: 11.166 - ETA: 31:21 - loss: 215.4639 - mean_absolute_error: 11.171 - ETA: 31:02 - loss: 215.1654 - mean_absolute_error: 11.173 - ETA: 30:44 - loss: 214.6583 - mean_absolute_error: 11.169 - ETA: 30:27 - loss: 213.8844 - mean_absolute_error: 11.164 - ETA: 30:10 - loss: 213.3308 - mean_absolute_error: 11.163 - ETA: 29:54 - loss: 213.1179 - mean_absolute_error: 11.167 - ETA: 29:37 - loss: 212.8138 - mean_absolute_error: 11.169 - ETA: 29:25 - loss: 212.7157 - mean_absolute_error: 11.174 - ETA: 29:11 - loss: 212.5421 - mean_absolute_error: 11.177 - ETA: 28:56 - loss: 212.1867 - mean_absolute_error: 11.178 - ETA: 28:42 - loss: 211.8032 - mean_absolute_error: 11.180 - ETA: 28:28 - loss: 211.4079 - mean_absolute_error: 11.179 - ETA: 28:15 - loss: 211.2733 - mean_absolute_error: 11.182 - ETA: 28:02 - loss: 210.8588 - mean_absolute_error: 11.179 - ETA: 27:50 - loss: 210.4498 - mean_absolute_error: 11.178 - ETA: 27:37 - loss: 209.9327 - mean_absolute_error: 11.176 - ETA: 27:","AnswerCount":3,"Available Count":3,"Score":0.0665680765,"is_accepted":false,"ViewCount":2733,"Q_Id":52443200,"Users Score":1,"Answer":"verbose=2 should be used for interactive outputs.","Q_Score":7,"Tags":"python,keras,jupyter-notebook,jupyter,tqdm","A_Id":52443366,"CreationDate":"2018-09-21T11:53:00.000","Title":"Keras floods Jupyter cell output during fit (verbose=1)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When running keras model inside Jupyter notebook with \"verbose=1\" option, I started getting not single line progress status updates as before, but a flood of status lines updated at batch. See attached picture.  Restarting jupyter or the browser is not helping. \nJupyter notebook server is: 5.6.0, keras is 2.2.2, Python is Python 3.6.5\nPlease help. \ncell content:\nhistory = model.fit(x=train_df_scaled, y=train_labels, batch_size=BATCH_SIZE, epochs=EPOCHS, verbose=1, validation_data=(validation_df_scaled, validation_labels), shuffle=True)\noutput flood example: (it is thousands of lines like this)\nloss: 217.5794 - mean_absolute_error: 11.166 - ETA: 32:42 - loss: 216.9500 - mean_absolute_error: 11.165 - ETA: 32:21 - loss: 216.6378 - mean_absolute_error: 11.164 - ETA: 32:00 - loss: 216.0345 - mean_absolute_error: 11.164 - ETA: 31:41 - loss: 215.6621 - mean_absolute_error: 11.166 - ETA: 31:21 - loss: 215.4639 - mean_absolute_error: 11.171 - ETA: 31:02 - loss: 215.1654 - mean_absolute_error: 11.173 - ETA: 30:44 - loss: 214.6583 - mean_absolute_error: 11.169 - ETA: 30:27 - loss: 213.8844 - mean_absolute_error: 11.164 - ETA: 30:10 - loss: 213.3308 - mean_absolute_error: 11.163 - ETA: 29:54 - loss: 213.1179 - mean_absolute_error: 11.167 - ETA: 29:37 - loss: 212.8138 - mean_absolute_error: 11.169 - ETA: 29:25 - loss: 212.7157 - mean_absolute_error: 11.174 - ETA: 29:11 - loss: 212.5421 - mean_absolute_error: 11.177 - ETA: 28:56 - loss: 212.1867 - mean_absolute_error: 11.178 - ETA: 28:42 - loss: 211.8032 - mean_absolute_error: 11.180 - ETA: 28:28 - loss: 211.4079 - mean_absolute_error: 11.179 - ETA: 28:15 - loss: 211.2733 - mean_absolute_error: 11.182 - ETA: 28:02 - loss: 210.8588 - mean_absolute_error: 11.179 - ETA: 27:50 - loss: 210.4498 - mean_absolute_error: 11.178 - ETA: 27:37 - loss: 209.9327 - mean_absolute_error: 11.176 - ETA: 27:","AnswerCount":3,"Available Count":3,"Score":0.0665680765,"is_accepted":false,"ViewCount":2733,"Q_Id":52443200,"Users Score":1,"Answer":"Two things I would recommend:\n\nTry restarting Jupyter Notebook server. \nTry different browser other\nthan what you're using; perhaps your browser got some update and it's\nbreaking stuff! (usually, chrome is bad with notebooks!)","Q_Score":7,"Tags":"python,keras,jupyter-notebook,jupyter,tqdm","A_Id":52445923,"CreationDate":"2018-09-21T11:53:00.000","Title":"Keras floods Jupyter cell output during fit (verbose=1)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When running keras model inside Jupyter notebook with \"verbose=1\" option, I started getting not single line progress status updates as before, but a flood of status lines updated at batch. See attached picture.  Restarting jupyter or the browser is not helping. \nJupyter notebook server is: 5.6.0, keras is 2.2.2, Python is Python 3.6.5\nPlease help. \ncell content:\nhistory = model.fit(x=train_df_scaled, y=train_labels, batch_size=BATCH_SIZE, epochs=EPOCHS, verbose=1, validation_data=(validation_df_scaled, validation_labels), shuffle=True)\noutput flood example: (it is thousands of lines like this)\nloss: 217.5794 - mean_absolute_error: 11.166 - ETA: 32:42 - loss: 216.9500 - mean_absolute_error: 11.165 - ETA: 32:21 - loss: 216.6378 - mean_absolute_error: 11.164 - ETA: 32:00 - loss: 216.0345 - mean_absolute_error: 11.164 - ETA: 31:41 - loss: 215.6621 - mean_absolute_error: 11.166 - ETA: 31:21 - loss: 215.4639 - mean_absolute_error: 11.171 - ETA: 31:02 - loss: 215.1654 - mean_absolute_error: 11.173 - ETA: 30:44 - loss: 214.6583 - mean_absolute_error: 11.169 - ETA: 30:27 - loss: 213.8844 - mean_absolute_error: 11.164 - ETA: 30:10 - loss: 213.3308 - mean_absolute_error: 11.163 - ETA: 29:54 - loss: 213.1179 - mean_absolute_error: 11.167 - ETA: 29:37 - loss: 212.8138 - mean_absolute_error: 11.169 - ETA: 29:25 - loss: 212.7157 - mean_absolute_error: 11.174 - ETA: 29:11 - loss: 212.5421 - mean_absolute_error: 11.177 - ETA: 28:56 - loss: 212.1867 - mean_absolute_error: 11.178 - ETA: 28:42 - loss: 211.8032 - mean_absolute_error: 11.180 - ETA: 28:28 - loss: 211.4079 - mean_absolute_error: 11.179 - ETA: 28:15 - loss: 211.2733 - mean_absolute_error: 11.182 - ETA: 28:02 - loss: 210.8588 - mean_absolute_error: 11.179 - ETA: 27:50 - loss: 210.4498 - mean_absolute_error: 11.178 - ETA: 27:37 - loss: 209.9327 - mean_absolute_error: 11.176 - ETA: 27:","AnswerCount":3,"Available Count":3,"Score":1.2,"is_accepted":true,"ViewCount":2733,"Q_Id":52443200,"Users Score":4,"Answer":"After a few tests I found that the error is related to tqdm import. Tqdm was used in a piece of code which was later rewritten withoout it. Even though I was not using tqdm in this notebook, just having it imported affected the keras output.\nTo fix it I just commented out this line:\nfrom tqdm import tqdm\nand everything went fine, with nice keras progress bars. Not sure how exactly it conflicted with keras though...","Q_Score":7,"Tags":"python,keras,jupyter-notebook,jupyter,tqdm","A_Id":52505253,"CreationDate":"2018-09-21T11:53:00.000","Title":"Keras floods Jupyter cell output during fit (verbose=1)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there a way I can train an autoencoder model using a pre-trained model like ResNet?\nI'm trying to train an autoencoder model with input as an image and output as a masked version of that image.\nIs it possible to use weights from a pretrained model here?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1464,"Q_Id":52454090,"Users Score":0,"Answer":"From what I know, there is no proven method to do this. I'd train the autoencoder from scratch. \nIn theory, if you find a pre-trained CNN which does not use max pooling, you can use those weights and architecture for the encoder stage in your autoencoder. You can also extract features from a pre-trained model and concatenate\/merge them to your autoencoder. But the value add is not clear, and the architecture might become overly complex.","Q_Score":1,"Tags":"python-3.x,keras,computer-vision,autoencoder,resnet","A_Id":52463724,"CreationDate":"2018-09-22T06:21:00.000","Title":"Autoencoder with Transfer Learning?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"so if I have two labels \"dogs\" and \"cats\" and I want to create multi classification neural network. \nnow if I provided a new random image which is not a dog or a cat, is there a way I can teach the classifier to tell me that this image is not a dog or a cat instead of saying how much percent it maybe cat or dog?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":67,"Q_Id":52459748,"Users Score":1,"Answer":"The best way to accomplish this is to create a new class in addition to dog and cat to handle images you have no interest in. So now, your labels would be [\"dogs\", \"cats\", \"other\"].\nIn your current architecture, your model is forced to predict a random image as either a dog or cat as those are the only two options it has. Adding a new class to deal with other images is generally the easiest way to make your classifier more robust to incorrect predictions.","Q_Score":0,"Tags":"python,neural-network,keras,multilabel-classification","A_Id":52461491,"CreationDate":"2018-09-22T18:39:00.000","Title":"image multi classification with keras","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a python pandas dataframe on my local machine, and have access to a remote mongodb server that has additional data that I can query via pymongo.\nIf my local dataframe is large, say 40k rows with 3 columns in each row, what's the most efficient way to check for the intersection of my local dataframe's features and a remote collection containing millions of documents?\nI'm looking for general advice here.  I thought I could just take a distinct list of values from each of the 3 features, and use each of these in an $or find statement, but if I have 90k distinct values for one of the 3 features it seems like a bad idea.\nSo any opinion would be very welcome.  I don't have access to insert my local dataframe into the remote server, I only have select\/find access.\nthanks very much!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":112,"Q_Id":52460327,"Users Score":1,"Answer":"As you already explained that you won't be able to insert data. So only thing is possible is first take the unique values to a list.df['column_name'].unique(). Then you can use the $in operator in .find() method and pass your list as a parameter. If it takes time or it is too much. Then break your list in equal chunks, I mean list of list [[id1, id2, id3], [id4, id5, id6] ... ] and do a for loop for sub-list in list: db.xyz.find({'key':{'$in': sublist}}, {'_id': 1}) and use the sub list as parameter in $in operator. Then for each iteration if the value exist in the db it will return the _id and we can easily store that in a empty list and append it and we will be able to get all the id's in such cases where the value exist in the collection.\nSo it's just the way I would do. Not necessarily the best possible.","Q_Score":0,"Tags":"python,mongodb,pandas,pymongo","A_Id":52462006,"CreationDate":"2018-09-22T19:51:00.000","Title":"Efficient Intersection of pandas dataframe with remote mongodb?","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When I read other people's python code, like, spark.read.option(\"mergeSchema\", \"true\"), it seems that the coder has already known what the parameters to use. But for a starter, is there a place to look up those available parameters? I look up the apche documents and it shows parameter undocumented. \nThanks.","AnswerCount":4,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":38801,"Q_Id":52472993,"Users Score":8,"Answer":"Annoyingly, the documentation for the option method is in the docs for the json method. The docs on that method say the options are as follows (key -- value -- description):\n\nprimitivesAsString -- true\/false (default false) -- infers all primitive values as a string type\n\nprefersDecimal -- true\/false (default false) -- infers all floating-point values as a decimal type. If the values do not fit in decimal, then it infers them as doubles.\n\nallowComments -- true\/false (default false) -- ignores Java\/C++ style comment in JSON records\n\nallowUnquotedFieldNames -- true\/false (default false) -- allows unquoted JSON field names\n\nallowSingleQuotes -- true\/false (default true) -- allows single quotes in addition to double quotes\n\nallowNumericLeadingZeros -- true\/false (default false) -- allows leading zeros in numbers (e.g. 00012)\n\nallowBackslashEscapingAnyCharacter -- true\/false (default false) -- allows accepting quoting of all character using backslash quoting mechanism\n\nallowUnquotedControlChars -- true\/false (default false) -- allows JSON Strings to contain unquoted control characters (ASCII characters with value less than 32, including tab and line feed characters) or not.\n\nmode -- PERMISSIVE\/DROPMALFORMED\/FAILFAST (default PERMISSIVE) -- allows a mode for dealing with corrupt records during parsing.\n\nPERMISSIVE : when it meets a corrupted record, puts the malformed\nstring into a field configured by columnNameOfCorruptRecord, and sets\nother fields to null. To keep corrupt records, an user can set a\nstring type field named columnNameOfCorruptRecord in an user-defined\nschema. If a schema does not have the field, it drops corrupt records\nduring parsing. When inferring a schema, it implicitly adds a\ncolumnNameOfCorruptRecord field in an output schema.\nDROPMALFORMED : ignores the whole corrupted records.\nFAILFAST : throws an exception when it meets corrupted records.","Q_Score":26,"Tags":"python,python-3.x,apache-spark","A_Id":64562864,"CreationDate":"2018-09-24T05:11:00.000","Title":"Available options in the spark.read.option()","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a data set which contains site usage behavior of users over a period of six months. It contains data about:\n\nNumber of pages viewed \nNumber of unique cookies associated with each user\nDifferent number of OS, Browsers used \nDifferent number of cities visited\n\nEverything over here is collected on a six month time frame. I have used this data to train a model to predict a target variable 'y'. Everything is numeric in format.\nNow I know since its a six month data, and the model is built upon this 6 months of data, I can use this to predict on the next six month data to get target variable y.\nMy question is that if instead of using it to predict on six month time frame, I use the model to predict on monthly time frame, will it give me incorrect results?\nMy logic tells me yes, as for example, I used tree method such as Decision tree and Random forest, these algorithms kind of makes thresholds to give output \"0\/1\". Now the variables I mentioned above such as number of cookies associated, OS, Browser etc would have different values if we look at it from  one month stand point and if we look at it from 6 months standpoint. For example, number of unique cookies associated with a user would be less if seen over a month where as it will be more if seen from 6 months standpoint. \nBut I am confused as to if the model will automatically adjust these values while running on monthly data or not. Request you to help me understand the if I am thinking this right or wrong. Also please provide logical explanation if possible. \nThanks.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":421,"Q_Id":52487832,"Users Score":0,"Answer":"Is your minimum unit of mesurement 6 months ? I hope not, but if yes, then I would sugges that you dont try to predict the next 1 month.\nSeasonality within a year aside, you would need daily volume measurements.. I would be very worried to build anything on monthly or even weekly numbers.\nIn terms of modelling techniques, please stick to simple regression methods like kungphu suggests.","Q_Score":0,"Tags":"python,machine-learning,logic,data-science,prediction","A_Id":52604844,"CreationDate":"2018-09-24T21:49:00.000","Title":"Prediction problem- Build model using 6 months data and predict on one month data?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"The documentation of pandas.Timestamp states a concept well-known to every pandas user:\n\nTimestamp is the pandas equivalent of python\u2019s Datetime and is interchangeable with it in most cases.\n\nBut I don't understand why are pandas.Timestamps needed at all.\nWhy is, or was, it useful to have a different object than python's Datetime? Wouldn't it be cleaner to simply build pandas.DatetimeIndex out of Datetimes?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":416,"Q_Id":52492996,"Users Score":3,"Answer":"You can go through Pandas documentation for the details:\n\n\"pandas.Timestamp\" is a replacement for python datetime.datetime for\n  Padas usage.\nTimestamp is the pandas equivalent of python\u2019s Datetime and is\n  interchangeable with it in most cases. It\u2019s the type used for the\n  entries that make up a DatetimeIndex, and other timeseries oriented\n  data structures in pandas.\nNotes\nThere are essentially three calling conventions for the constructor.\n  The primary form accepts four parameters. They can be passed by\n  position or keyword.\nThe other two forms mimic the parameters from datetime.datetime. They\n  can be passed by either position or keyword, but not both mixed\n  together.\nTimedeltas are differences in times, expressed in difference units,\n  e.g. days, hours, minutes, seconds. They can be both positive and\n  negative.\nTimedelta is a subclass of datetime.timedelta, and behaves in a\n  similar manner, but allows compatibility with np.timedelta64 types\n  as well as a host of custom representation, parsing, and attributes.\n\nI would say as pandas works better with Time Series data hence its been a kind of warper on the original built-in datetime module.\n\nThe weaknesses of Python's datetime format inspired the NumPy team to\n  add a set of native time series data type to NumPy. The datetime64\n  dtype encodes dates as 64-bit integers, and thus allows arrays of\n  dates to be represented very compactly.","Q_Score":7,"Tags":"python,pandas,timestamp","A_Id":52493234,"CreationDate":"2018-09-25T07:39:00.000","Title":"Why pandas has its own datetime object Timestamp?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a binary (0-1) 3D numpy array, which I plan to use for masking a 3D image. The mask at the moment consists in the area of a cylinder. The two centres of the faces are two arbitrary points, and the axis is not parallel to x, y or z.\nHow can I fill the cylinder with a pure numpy solution?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":792,"Q_Id":52497995,"Users Score":1,"Answer":"If you have surface cells marked and there si no additional information, then scan array layer by layer to get the first marked cell (or get some surface cell if they are known).\nWhen you have marked A[z,y,x] surface cell, fill line in the last dimension (x) 1d array until new marked cell is met.\nThen find neighbor marked cell in the same top-level layer (same z, close y ans x) and repeat to fill lines until whole section (ellipse or cutted ellipse) is filled, then continue with the next z layer\nEdit\nPerhaps I overcomplicate the problem, and FloodFill algo is simple solution.","Q_Score":0,"Tags":"python-2.7,numpy,geometry","A_Id":52499867,"CreationDate":"2018-09-25T12:13:00.000","Title":"Filled 3D numpy mask","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm working with Concept Drift, but when trying to run my code i get this error\n\"ImportError: No module named detector_classifier\" been trying to install the module with pip install, but all i get is no match found. Anyone had this problem before?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":908,"Q_Id":52499573,"Users Score":0,"Answer":"I think a little more information might help. Which python version and which pip version are you using? I just googled \"detector_classifier\" and couldn't find anything. What library does \"detector_classifier\" belong to? \nWithout much background to go off of, I would recommended making sure you have updated pip. Depending on what operating system you're using, your configuration might need some tinkering so your system knows where to look.","Q_Score":0,"Tags":"python,ubuntu","A_Id":52499750,"CreationDate":"2018-09-25T13:36:00.000","Title":"ImportError: No module named detector_classifier","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to tensorflow and it\u2019s object detection API. In its tutorial, it\u2019s said that the images must be separated into train\/ and test\/ folders. Actually I am working on a server where my entire data is kept in a folder called \u2018images\u2019 and I don\u2019t want to either change it\u2019s structure or create another copy of it. \nHowever, I have created separate train.record and test.record files as well and it\u2019s just that I want all my images to be in one folder together. Is it possible? If yes , then which of files need to be modified? Thanks","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":192,"Q_Id":52500185,"Users Score":0,"Answer":"In case you already have separate record files for train and eval (validation\/test), then it's okay.\nYou simply put the pathes of the corresponding records in \ntf_record_input_reader {\n    input_path: \"\/path\/to\/record\/record_name.record\"\n    }\nonce for train_input_reader and once for eval_input_reader. In case the records are split into shards, than use the format of \ninput_path: \"\/path\/to\/record\/record_name.record-?????-of-00010\" (instead of 10 - number of shards).","Q_Score":0,"Tags":"python,tensorflow","A_Id":52628707,"CreationDate":"2018-09-25T14:07:00.000","Title":"Is it possible to keep all the images in one folder for tensorflow object detection API","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"if all(data_Window['CI']!=np.nan):\nI have used the all() function with if so that if column CI has no NA values, then it will do some operation. But i got syntax error.","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":77,"Q_Id":52529669,"Users Score":0,"Answer":"This gives you all a columns and how many null values they have.\ndf = pd.DataFrame({0:[1,2,None,],1:[2,3,None])\ndf.isnull().sum()","Q_Score":0,"Tags":"python,pandas","A_Id":52529791,"CreationDate":"2018-09-27T04:54:00.000","Title":"how can i check all the values of dataframe whether have null values in them without a loop","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to build a multidimensional gaussian model using scipy.stats.multivariate_normal. I am trying to use the output of scipy.stats.multivariate_normal.pdf() to figure out if a test value fits reasonable well in the observed distribution.\nFrom what I understand, high values indicate a better fit to the given model and low values otherwise.\nHowever, in my dataset, I see extremely large PDF(x) results, which lead me to question if I understand things correctly. The area under the PDF curve must be 1, so very large values are hard to comprehend.\nFor e.g., consider:\nx = [-0.0007569417915494715, -0.01394295997613827, 0.000982078369890444, -0.03633664354397629, -0.03730583036106844, 0.013920453054506978, -0.08115836865224338, -0.07208494497398354, -0.06255237023298793, -0.0531888840386906, -0.006823760545565131] \nmean = [0.01663645201261102, 0.07800335614699873, 0.016291452384234965, 0.012042931155488702, 0.0042637244100103885, 0.016531331606477996, -0.021702714746699842, -0.05738646649459681, 0.00921296058625439, 0.027940994009345254, 0.07548111758006244]\ncovariance = [[0.07921927017771506, 0.04780185747873293, 0.0788086850274493, 0.054129466248481264, 0.018799028456661045, 0.07523731808137141, 0.027682748950487425, -0.007296954729572955, 0.07935165417756569, 0.0569381100965656, 0.04185848489472492], [0.04780185747873293, 0.052300105044833595, 0.047749467098423544, 0.03254872837949123, 0.010582358713999951, 0.045792252383799206, 0.01969282984717051, -0.006089301208961258, 0.05067712814145293, 0.03146214776997301, 0.04452949330387575], [0.0788086850274493, 0.047749467098423544, 0.07841809405745602, 0.05374461924031552, 0.01871005609017673, 0.07487015790787396, 0.02756781074862818, -0.007327131572569985, 0.07895548129950304, 0.056417456686115544, 0.04181063355048408], [0.054129466248481264, 0.03254872837949123, 0.05374461924031552, 0.04538801863296238, 0.015795381235224913, 0.05055944754764062, 0.02017033995851422, -0.006505939129684573, 0.05497361331950649, 0.043858860182247515, 0.029356699144606032], [0.018799028456661045, 0.010582358713999951, 0.01871005609017673, 0.015795381235224913, 0.016260640022897347, 0.015459548918222347, 0.0064542528152879705, -0.0016656858963383602, 0.018761682220822192, 0.015361512546799405, 0.009832025009280924], [0.07523731808137141, 0.045792252383799206, 0.07487015790787396, 0.05055944754764062, 0.015459548918222347, 0.07207012779105286, 0.026330967917717253, -0.006907504360835279, 0.0753380831201204, 0.05335128471397023, 0.03998397595850863], [0.027682748950487425, 0.01969282984717051, 0.02756781074862818, 0.02017033995851422, 0.0064542528152879705, 0.026330967917717253, 0.020837940236441078, -0.003320408544812026, 0.027859582829638897, 0.01967636950969646, 0.017105000942890598], [-0.007296954729572955, -0.006089301208961258, -0.007327131572569985, -0.006505939129684573, -0.0016656858963383602, -0.006907504360835279, -0.003320408544812026, 0.024529061074105817, -0.007869287828047853, -0.006228903058681195, -0.0058974553248417995], [0.07935165417756569, 0.05067712814145293, 0.07895548129950304, 0.05497361331950649, 0.018761682220822192, 0.0753380831201204, 0.027859582829638897, -0.007869287828047853, 0.08169291677188911, 0.05731196406065222, 0.04450058445993234], [0.0569381100965656, 0.03146214776997301, 0.056417456686115544, 0.043858860182247515, 0.015361512546799405, 0.05335128471397023, 0.01967636950969646, -0.006228903058681195, 0.05731196406065222, 0.05064023101024737, 0.02830810316675855], [0.04185848489472492, 0.04452949330387575, 0.04181063355048408, 0.029356699144606032, 0.009832025009280924, 0.03998397595850863, 0.017105000942890598, -0.0058974553248417995, 0.04450058445993234, 0.02830810316675855, 0.040658283674780395]]\nFor this, if I compute y = multivariate_normal.pdf(x, mean, cov);\nthe result is 342562705.3859754.\nHow could this be the case? Am I missing something?\nThanks.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1012,"Q_Id":52536206,"Users Score":0,"Answer":"This is fine. The probability density function can be larger than 1 at a specific point. It's the integral than must be equal to 1. \nThe idea that pdf < 1 is correct for discrete variables. However, for continuous ones, the pdf is not a probability. It's a value that is integrated to a probability. That is, the integral from minus infinity to infinity, in all dimensions, is equal to 1.","Q_Score":0,"Tags":"python,scipy","A_Id":52536297,"CreationDate":"2018-09-27T11:42:00.000","Title":"Understanding the output of scipy.stats.multivariate_normal","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using holoviews and bokeh with python 3 to create an interactive network graph fro mNetworkx. I can't manage to set the edge color to blank. It seems that the edge_color option does not exist. Do you have any idea how I could do that?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":284,"Q_Id":52539639,"Users Score":1,"Answer":"Problem solved, the option to change edges color is edge_line_color and not edge_color.","Q_Score":0,"Tags":"python,networkx,bokeh,holoviews","A_Id":52539914,"CreationDate":"2018-09-27T14:44:00.000","Title":"Holoviews - network graph - change edge color","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How can we parse the output from DataFrame.describe()? When we print the result of DataFrame.describe() as shown in examples, it is in string format, which is why it is difficult to parse it.\nI understand that the print function might be converting the output into a displayable and readable form. However, it is not easily parseable. How can we achieve this?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":110,"Q_Id":52544301,"Users Score":1,"Answer":"print always prints in string format. \nBut if you check type(df.describe()) then you'll see that it is a dataframe. \nSo you can treat it like one. :)","Q_Score":0,"Tags":"python,pandas,dataframe,sklearn-pandas","A_Id":52544455,"CreationDate":"2018-09-27T19:59:00.000","Title":"How can we parse DataFrame.describe()?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to find the number of rows that have certain values such as None or \"\" or NaN (basically empty values) in all columns of a DataFrame object. How can I do this?","AnswerCount":3,"Available Count":1,"Score":0.1325487884,"is_accepted":false,"ViewCount":875,"Q_Id":52544340,"Users Score":2,"Answer":"Use df.isnull().sum() to get number of rows with None and NaN value.\nUse df.eq(value).sum() for any kind of values including empty string \"\".","Q_Score":1,"Tags":"python,pandas,dataframe,sklearn-pandas","A_Id":52544615,"CreationDate":"2018-09-27T20:02:00.000","Title":"In Python DataFrame how to find out number of rows that have valid values of columns","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In which deployment mode can we Not add Nodes\/workers to a cluster in Apache Spark 2.3.1\n1.Spark Standalone\n2.Mesos\n3.Kubernetes\n4.Yarn\n5.Local Mode\ni have installed Apache Spark 2.3.1 on my machine and have run it in Local Mode\nin Local Mode can we add Nodes\/workers to Apache Spark?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":45,"Q_Id":52544955,"Users Score":0,"Answer":"When master is Local, your program will run on single machine that is your edge node.\nTo run it in distributed environment i.e. on cluster you need to select master as \"Yarn\".\nWhen deployment mode is \"client\" (default) your edge node will become the master (where driver program will run).\nWhen deployment mode is \"cluster\", any of the healthy node from the cluster becomes master","Q_Score":1,"Tags":"python-2.7,apache-spark,cluster-computing,worker","A_Id":52555122,"CreationDate":"2018-09-27T20:52:00.000","Title":"In which deployment mode can we \"Not\" add nodes to a cluster in Apache Spark 2.3.1","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I was working on matching company names of two sets. I was trying to code it in Python with Levenstien's distance. I was having issues with short names of companies, and their trailing part like Pvt,Ltd. I have ran the same set with Excel Fuzzy lookup and was getting good results. I there a way that i can see how excel fuzzy lookup is coded and use the same implementation in python.","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":2872,"Q_Id":52553735,"Users Score":2,"Answer":"The following is an excerpt from Microsoft Fuzzy Lookup Add-In for Excel, Readme.docx. I hope that helps.\n\nAdvanced Concepts\nFuzzy Lookup technology is based upon a very simple, yet flexible\n  measure of similarity between two records. Jaccard similarity  Fuzzy\n  Lookup uses Jaccard similarity, which is defined as the size of the\n  set intersection divided by the size of the set union for two sets of\n  objects. For example, the sets {a, b, c} and {a, c, d} have a Jaccard\n  similarity of 2\/4 = 0.5 because the intersection is {a, c} and the\n  union is {a, b, c, d}. The more that the two sets have in common, the\n  closer the Jaccard similarity will be to 1.0. \nWeighted Jaccard similarity and tokenization of records  With Fuzzy\n  Lookup, you can assign weights to each item in a set and define the\n  weighted Jaccard similarity as the total weight of the intersection\n  divided by the total weight of the union. For the weighted sets {(a,\n  2), (b, 5), (c, 3)}, {(a, 2), (c, 3), (d, 7)}, the weighted Jaccard\n  similariyt is (2 + 3)\/(2 + 3 + 5 +7) = 5\/17 = .294. \nBecause Jaccard similarity is defined over sets, Fuzzy Lookup must\n  first convert data records to sets before it calculates the Jaccard\n  similarity. Fuzzy Lookup converts the data to sets using a Tokenizer.\n  For example, the record {\u201cJesper Aaberg\u201d, \u201c4567 Main Street\u201d} might be\n  tokenized into the set, {\u201c Jesper\u201d, \u201cAaberg\u201d, \u201c4567\u201d, \u201cMain\u201d,\n  \u201cStreet\u201d}. The default tokenizer is for English text, but one may\n  change the LocaleId property in Configure=>Global Settings to specify\n  tokenizers for other languages.\nToken weighting  Because not all tokens are of equal importance, Fuzzy\n  Lookup assigns weights to tokens. Tokens are assigned high weights if\n  they occur infrequently in a sample of records and low weights if they\n  occur frequently. For example, frequent words such as \u201cCorporation\u201d\n  might be given lower weight, while less frequent words such as\n  \u201cAbracadabra\u201d might be given a higher weight. One may override the\n  default token weights by supplying their own table of token weights.\nTransformations Transformations greatly increase the power of Jaccard\n  similarity by allowing tokens to be converted from one string to\n  another. For instance, one might know that the name \u201cBob\u201d can be\n  converted to \u201cRobert\u201d; that \u201cUSA\u201d is the same as \u201cUnited States\u201d; or\n  that \u201cMissispi\u201d is a misspelling of \u201cMississippi\u201d. There are many\n  classes of such transformations that Fuzzy Lookup handles\n  automatically such as spelling mistakes (using Edit Transformations\n  described below), string prefixes, and string merge\/split operations.\n  You can also specify a table containing your own custom\n  transformations.\nJaccard similarity under transformations  The Jaccard similarity under\n  transformations is the maximum Jaccard similarity between any two\n  transformations of each set. Given a set of transformation rules, all\n  possible transformations of the set are considered. For example, for\n  the sets {a, b, c} and {a, c, d} and the transformation rules {b=>d,\n  d=>e}, the Jaccard similarity is computed as follows: Variations of\n  {a, b, c}: {a, b, c}, {a, d, c} Variations of {a, c, d}: {a, c, d},\n  {a, c, e} Maximum Jaccard similarity between all pairs: J({a, b, c},\n  {a, c, d}) = 2\/4 = 0.5 J({a, b, c}, {a, c, e}) = 2\/4 = 0.5 J({a, d,\n  c}, {a, c, d}) = 3\/3 = 1.0 J({a, d, c}, {a, c, e}) = 2\/4 = 0.5 The\n  maximum is 1.0. Note: Weghted Jaccard similiary under transformations\n  is simply the maximum weighted Jaccard similarity across all pairs of\n  transformed sets. \nEdit distance  Edit distance is the total number of character\n  insertions, deletions, or substitutions that it takes to convert one\n  string to another. For example, the edit distance between \u201cmisissipi\u201d\n  and \u201cmississippi\u201d is 2 because two character insertions are required.\n  One of the transformation providers that\u2019s included with Fuzzy Lookup\n  is the EditTransformationProvider, which generates specific\n  transformations for each input record and creates a transformation\n  from the token to all words in its dictionary that are within a given\n  edit distance. The normalized edit distance is the edit distance\n  divided by the length of the input string. In the previous example,\n  the normalized edit distance is 2\/9 = .222.","Q_Score":2,"Tags":"python,excel,levenshtein-distance,fuzzy-logic","A_Id":57938416,"CreationDate":"2018-09-28T10:49:00.000","Title":"Algorithm used in Excel Fuzzy Lookup","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am connecting the mongodb database via pymongo and achieved the expected result of fetching it outside the db in json format . but my task is that i need to create a hive table via pyspark , I found that mongodb provided json (RF719) which spark is not supporting .when i tried to load the data in pyspark (dataframe) it is showing as corrupted record. . and if any possible ways of converting the json format in python is also fine ..Please suggest a response","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":208,"Q_Id":52559131,"Users Score":0,"Answer":"mport json with open('D:\/json\/aaa.json') as f: d = f.read() da = ''.join(d.split()) print(type(da)) print(da) daa=da.replace('u'','') daaa= json.loads(daa) print(daaa)\nsatisfied with the answer. Hence closing this question","Q_Score":1,"Tags":"python,mongodb,hive,pymongo,pyspark-sql","A_Id":63618206,"CreationDate":"2018-09-28T16:10:00.000","Title":"unable to read the mongodb data (json) in pyspark","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am an R user currently learning python.\nIn RStudio, when I type a piece of code, it automatically gives me predictions of the functions I am looking for - like an autocomplete.\nI would like to have something similar in Jupyter Lab. Is it possible?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":200,"Q_Id":52568135,"Users Score":0,"Answer":"Auto-completion is supported in Jupyter already. You could try type enu and then hit tab. This enumerate will prompt out automatically.","Q_Score":0,"Tags":"python,rstudio,jupyter,jupyter-lab","A_Id":53070860,"CreationDate":"2018-09-29T11:57:00.000","Title":"Predicting python script in Jupyter Lab","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I intent to implement image captioning. Would it be possible to transfer learning for LSTM? I have used pretrained VGG16(transfer learning) to Extract features as input of the LSTM.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":289,"Q_Id":52568209,"Users Score":-1,"Answer":"As I have discovered, we can't use Transfer learning on the LSTM weights. I think the causation is infra-structure of LSTM networks.","Q_Score":0,"Tags":"python-3.x,conv-neural-network,lstm","A_Id":52700217,"CreationDate":"2018-09-29T12:08:00.000","Title":"how can I use Transfer Learning for LSTM?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to save a series of images to CSV file so they can be used as a training dataset for Alexnet Keras machine learning. The shape is (15,224,224,3).\nSo far I am having issue doing this. I have managed to put all data into a numpy array but now I cannot save it to a file.\nPlease help.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":8759,"Q_Id":52576617,"Users Score":1,"Answer":"You can try using pickle to save the data. It is much more diverse and easy to handle compare to np.save.","Q_Score":3,"Tags":"python,csv,numpy,keras","A_Id":52579026,"CreationDate":"2018-09-30T09:32:00.000","Title":"How to save Numpy 4D array to CSV?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to understand the clear difference between Datashader and other graphing libraries eg plotly\/matplotlib etc.\n\nI understand that in order to plot millions\/billions of data points, we need datashader as other plotting libraries will hung up the browser.\nBut what exactly is the reason which makes datashader fast and does not hung up the browser and how exactly the plotting is done which doesnt put any load on the browser ????\n\nAlso, datashader doesnt put any load on browser because in the backend datashader will create a graph on the basis of my dataframe and send only the image to the browser which is why its fast??\nPlz explain i am unable to understand the in and out clearly.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":901,"Q_Id":52584339,"Users Score":3,"Answer":"It may be helpful to first think of Datashader not in comparison to Matplotlib or Plotly, but in comparison to numpy.histogram2d.  By default, Datashader will turn a long list of (x,y) points into a 2D histogram, just like histogram2d. Doing so only requires a simple increment of a grid cell for each new point, which is easily accellerated to machine-code speeds with Numba and is trivial to parallelize with Dask. The resulting array is then at most the size of your display screen, no matter how big your dataset is.  So it's cheap to process in a separate program that adds axes, labels, etc., and it will never crash your browser.\nBy contrast, a plotting program like Plotly will need to convert each data point into a JSON or other serialized representation, pass that to JavaScript in the browser, have JavaScript draw a shape into a graphics buffer, and make each such shape support hover and other interactive features. Those interactive features are great, but it means Plotly is doing vastly more work per data point than Datashader is, and requires that the browser can hold all those data points. The only computation Datashader needs to do with your full data is to linearly scale the x and y locations of each point to fit the grid, then increment the grid value, which is much easier than what Plotly does.\nThe comparison to Matplotlib is slightly more complicated, because with an Agg backend, Matplotlib is also pre-rendering to a fixed-size graphics buffer before display (somewhat like Datashader). But Matplotlib was written before Numba and Dask (making it more difficult to speed up), it still has to draw shapes for each point (not just a simple increment), it can't fully parallelize the operations (because later points overwrite earlier ones in Matplotlib), and it provides anti-aliasing and other nice features not available in Datashader.  So again Matplotlib is doing a lot more work than Datashader.\nBut if what you really want to do is see the faithful 2D distribution of billions of data points, Datashader is the way to go, because that's really all it is doing. :-)","Q_Score":2,"Tags":"python,matplotlib,plotly,datashader","A_Id":52630545,"CreationDate":"2018-10-01T03:49:00.000","Title":"difference between datashader and other plotting libraries","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to ensemble the three different models (FastText, SVM, NaiveBayes).\nI thought of using python to do this. I'm sure that we can ensemble NaiveBayes as well as SVM models. But, can we ensemble fastText using python ?\nCan anyone please suggest me regarding the same ...","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":780,"Q_Id":52585975,"Users Score":0,"Answer":"In your use case you can as you're dealing with 3 models you should keep in mind that:\n\nThe models have different mechanics to use the predict() method: \n\nFastText uses an internal file (serialized model with .bin extension, for example) with all embeddings and wordNGrams and you can pass raw text directly;\nSVM and NaiveBayes you're obligated to pre-process the data using CountVectorizer, TfidfVectorizer LabelEncoder, get the result, repass for the LabelEncoder and deliver the result. \nYou will need at the end deal with different probabilities (if you're predicting with k > 1) and probably you need to take care of this\n\nIf you're going to serialize it to production you'll need to pickle the SVM and NB models and use .bin for FastText model and of course the embeddings from the former ones need to be instantiated too. This can be a little pain in your response time if you need to predict in near real time.","Q_Score":0,"Tags":"python,machine-learning,scikit-learn,ensemble-learning","A_Id":55257524,"CreationDate":"2018-10-01T06:56:00.000","Title":"Can we ensemble fastText along with SVM?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to present my data in various graphs. Usually what I do is to take a screenshot of my graph (I almost exclusively make them with matplotlib) and paste it into my PowerPoint.\nUnfortunately my direct superior seems not to be happy with the way I present them. Sometimes he wants certain things in log scale and sometimes he dislike my color palette. The data is all there, but because its an image there's no way I can change that in the meeting. \nMy superior seems to really care about those things and spend quite a lot of time telling me how to make plots in every single meeting. He (usually) will not comment on my data before I make a plot the way he wants.\nThat's where my question becomes relevant. Right now what I have in my mind is to have an interactive canvas embedded in my PowerPoint such that I can change the range of the axis, color of my data point, etc in real time. I have been searching online for such a thing but it comes out empty. I wonder if that could be done and how can it be done?\nFor some simple graphs Excel plot may work, but usually I have to present things in 1D or 2D histograms\/density plots with millions of entries. Sometimes I have to fit points with complicated mathematical formulas and that's something Excel is incapable of doing and I must use scipy and pandas. \nThe closest thing to this I found online is rise with jupyter which convert a jupyter notebook into a slide show. I think that is a good start which allows me to run python code in real time inside the presentation, but I would like to use PowerPoint related solutions if possible, mostly because I am familiar with how PowerPoint works and I still find certain PowerPoint features useful. \nThank you for all your help. While I do prefer PowerPoint, any other products that allows me to modify plots in my presentation in real time or alternatives of rise are welcomed.","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":8150,"Q_Id":52586506,"Users Score":3,"Answer":"When putting a picture in PowerPoint you can decide whether you want to embed it or link to it. If you decide to link to the picture, you would be free to change it outside of powerpoint. This opens up the possibility for the following workflow:\nNext to your presentation you have a Python IDE or Juypter notebook open with the scripts that generate the figures. They all have a savefig command in them to save to exactly the location on disc from where you link the images in PowerPoint. If you need to change the figure, you make the changes in the python code, run the script (or cell) and switch back to PowerPoint where the newly created image is updated.\nNote that I would not recommend putting too much effort into finding a better solution to this, but rather spend the time thinking about good visual reprentations of the data, due to the following reasons: 1. If your instrutor's demands are completely unreasonable (\"I like blue better than green, so you need to use blue.\") than it's not worth spending effort into satisfying their demands at all. 2. If your instrutor's demands are based on the fact that the current reprentation does not allow to interprete the data correctly, this can be prevented by spending more thoughts on good plots prior to the presentation. This is a learning process, which I guess your instructor wants you to internalize. After all, you won't get a degree in computer science for writing a PowerPoint backend to matplotlib, but rather for being able to present your research in a way suited for your subject.","Q_Score":2,"Tags":"python,matplotlib,powerpoint,jupyter,rise","A_Id":52589887,"CreationDate":"2018-10-01T07:38:00.000","Title":"Possible ways to embed python matplotlib into my presentation interactively","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Suppose I have several dataframes: df1, df2, df3, etc. The label with each dataframes is A1, A2, A3 etc. I want to use this information as a whole, so that I can pass them. Three methods came into my mind:\nmethod 1\nuse a label list: labels=[\"A1\", \"A2\", \"A3\"...] and a list of dataframes dfs=[df1, df2, df3...].\nmethod 2\nuse a dictionary: d={\"A1\": df1, \"A2\": df2, \"A3\": df3}.\nmethod 3\nuse a pandas series: s=pd.Series([df1, df2, df3], index=[\"A1\", \"A2\", \"A3\"]).\nI will use the label and dataframes sequentially, therefore I think method1 and method3 should be my choice. However, using method 1 will need me to pass two items, while using method 3 I only need to keep one object. Is it a common practice to put the dataframes in a series? I seldom see people do this, is it against best practice? Is there any better suggestions?","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":117,"Q_Id":52591696,"Users Score":2,"Answer":"Method 2 also works. Since Python 3.6 it remembers the order it is created too.","Q_Score":3,"Tags":"python,pandas,dataframe,panel","A_Id":52592108,"CreationDate":"2018-10-01T12:57:00.000","Title":"Should I use the dictionary or the series to hold a bunch of dataframe?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a web scraper which iteratively retrieves data from web pages, and I would like to add the attributes pulled to a pandas dataframe (eventually) for running simple statistics and analysis. The current script returns a dictionary every time a new page is scraped.\nI understand adding a new row or column to an existing pandas dataframe is slow, so my thought was to add the dictionary's as they are retrieved to a csv, and then convert this csv all at once to a dataframe when the data collection is finished (possibly months from now). I will be dealing with up to 100,000 dicts, with 18 key value pairs. \nIs there a more efficient method or datatype to use in this scenario?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":16,"Q_Id":52592761,"Users Score":0,"Answer":"In your question you say when the data collection is finished (possibly months from now). It is enormous amount of time in comparison with efficiency of python or pandas or any other programming tool I can imagine. I just created 100k random dictionaries of length 18 containing floats, saved them into text file (csv format) and loaded with pandas into dataframe. Took 2 seconds to save and 0.5 seconds to load. So just add every new record to the file and periodically create backups of your choice.","Q_Score":1,"Tags":"python-3.x,data-science","A_Id":52592997,"CreationDate":"2018-10-01T13:56:00.000","Title":"Most efficient datatype for iteratively adding to?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a pandas dataframe with rows as records (patients) and 105 columns as features.(properties of each patient)\nI would like to cluster, not the patients, not the rows as is customary, but the columns so I can see which features are similar or correlated to which other features. I can already calculate the correlation each feature with every other feature using df.corr(). But how can I cluster these into k=2,3,4... groups using sklearn.cluster.KMeans?\nI tried KMeans(n_clusters=2).fit(df.T) which does cluster the features (because I took the transpose of the matrix) but only with a Euclidian distance function, not according to their correlations. I prefer to cluster the features according to correlations. \nThis should be very easy but I would appreciate your help.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":1423,"Q_Id":52612841,"Users Score":1,"Answer":"Create a new matrix by taking the correlations of all the features df.corr(), now use this new matrix as your dataset for the k-means algorithm.\nThis will give you clusters of features which have similar correlations.","Q_Score":1,"Tags":"python,machine-learning,scikit-learn,k-means,sklearn-pandas","A_Id":67233384,"CreationDate":"2018-10-02T16:45:00.000","Title":"How to cluster *features* based on their correlations to each other with sklearn k-means clustering","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"This may be a straight-up unwise idea so I'd best explain the context. I am finding that some of my functions have multiple and sometimes mutually exclusive or interdependent keyword arguments - ie, they offer the user the ability to input a certain piece of data as (say) a numpy array or a dataframe. And then if a numpy array, an index can be separately passed, but not if it it's a dataframe.\nWhich has led me to wonder if it's worth creating some kind of keyword parser function to handle these exclusivities\/dependencies. One issue with this is that the keyword parser function would then need to return any variables created (and ex-ante, we would not know their number or their names) into the namespace of the function that called it. I'm not sure if that's possible, at least in a reasonable way (I imagine it could be achieved by directly changing the local dict but that's sometimes said to be a bad idea).\nSo my question is:\n1. Is this a bad idea in the first place? Would creating separate functions depending on whether the input was a dataframe or ndarray be more sensible and simpler?\n2. Is it possible without too much hacking to have a function return an unspecified number of variables into the local namespace?\nApologies for the slightly vague nature of this question but any thoughts gratefully received.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":30,"Q_Id":52619262,"Users Score":0,"Answer":"A dict is a good way to package a variable number of named values.  If the parser returns a dict, then there is a single object that can be queried to get those names and values, avoiding the problem of needing to know the number and names ahead of time.\nAnother possibility would be to put the parser into a class, either as a factory method (classmethod or staticmethod returning an instance) or as a regular method (invoked during or after __init__), where the class instance holds the parsed values.","Q_Score":0,"Tags":"python","A_Id":52775839,"CreationDate":"2018-10-03T03:22:00.000","Title":"Have a keyword parser function return variables into local namespace","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to Python and am trying to pip install the pandas, numpy and a few other libraries, but it won't work. \nMy method is: \ngo to command prompt and type python -m pip install pandas --user - I have also tried every other way like pip install etc. Each time i do it it just says syntax error. Solutions? \nThank you.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":721,"Q_Id":52642130,"Users Score":1,"Answer":"You should not use pip in python CLI. You must use pip in your system CLI like Windows powershell.\nuse command below to install packages :\npip install pachakge-name\nfor example:\npip install numpy scipy matplotlib pandas \nOr you can do this one by one. Each package in single line of pip install","Q_Score":1,"Tags":"python,pandas,numpy","A_Id":52642522,"CreationDate":"2018-10-04T08:18:00.000","Title":"pip install not working for pandas + numpy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Hi have my own corpus and I train several Word2Vec models on it.\nWhat is the best way to evaluate them one against each-other and choose the best one? (Not manually obviously - I am looking for various measures).\nIt worth noting that the embedding is for items and not word, therefore I can't use any existing benchmarks.\nThanks!","AnswerCount":3,"Available Count":3,"Score":1.2,"is_accepted":true,"ViewCount":6758,"Q_Id":52645459,"Users Score":7,"Answer":"There's no generic way to assess token-vector quality, if you're not even using real words against which other tasks (like the popular analogy-solving) can be tried. \nIf you have a custom ultimate task, you have to devise your own repeatable scoring method. That will likely either be some subset of your actual final task, or well-correlated with that ultimate task. Essentially, whatever ad-hoc method you may be using the 'eyeball' the results for sanity should be systematized, saving your judgements from each evaluation, so that they can be run repeatedly against iterative model improvements. \n(I'd need more info about your data\/items and ultimate goals to make further suggestions.)","Q_Score":7,"Tags":"python,nlp,word2vec,embedding,word-embedding","A_Id":52675950,"CreationDate":"2018-10-04T11:22:00.000","Title":"How to evaluate Word2Vec model","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Hi have my own corpus and I train several Word2Vec models on it.\nWhat is the best way to evaluate them one against each-other and choose the best one? (Not manually obviously - I am looking for various measures).\nIt worth noting that the embedding is for items and not word, therefore I can't use any existing benchmarks.\nThanks!","AnswerCount":3,"Available Count":3,"Score":0.1973753202,"is_accepted":false,"ViewCount":6758,"Q_Id":52645459,"Users Score":3,"Answer":"One way to evaluate the word2vec model is to develop a \"ground truth\" set of words.   Ground truth will represent words that should ideally be closest together in vector space.  For example if your corpus is related to customer service, perhaps the vectors for \"dissatisfied\" and \"disappointed\" will ideally have the smallest euclidean distance or largest cosine similarity.  \nYou create this table for ground truth, maybe it has 200 paired words.  These 200 words are the most important paired words for your industry \/ topic.   To assess which word2vec model is best, simply calculate the distance for each pair, do it 200 times, sum up the total distance, and the smallest total distance will be your best model.   \nI like this way better than the \"eye-ball\" method, whatever that means.","Q_Score":7,"Tags":"python,nlp,word2vec,embedding,word-embedding","A_Id":55913014,"CreationDate":"2018-10-04T11:22:00.000","Title":"How to evaluate Word2Vec model","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Hi have my own corpus and I train several Word2Vec models on it.\nWhat is the best way to evaluate them one against each-other and choose the best one? (Not manually obviously - I am looking for various measures).\nIt worth noting that the embedding is for items and not word, therefore I can't use any existing benchmarks.\nThanks!","AnswerCount":3,"Available Count":3,"Score":0.0665680765,"is_accepted":false,"ViewCount":6758,"Q_Id":52645459,"Users Score":1,"Answer":"One of the ways of evaluating the Word2Vec model would be to apply the K-Means algorithm on the features generated by the Word2Vec. Along with that create your own manual labels\/ground truth representing the instances\/records. You can calculate the accuracy of the model by comparing the clustered result tags with the ground truth label. \nEg: CLuter 0 - Positive -{\"This is a good restaurant\", \"Good food here\", \"Not so good dinner\"}\n    Cluster 1 - Negative - {\"This is a fantastic hotel\", \"food was stale\"}\nNow, compare the tags\/labels generated by the clusters with the ground truth values of the instances\/sentences in the clusters and calculate the accuracy.","Q_Score":7,"Tags":"python,nlp,word2vec,embedding,word-embedding","A_Id":58868796,"CreationDate":"2018-10-04T11:22:00.000","Title":"How to evaluate Word2Vec model","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am going through the painful process of learning how to manage packages\/ different (virtual) environments in Python\/Anaconda. I was told that Anaconda is basically a python installation with all the packages I need (e.g. numpy, scipy, sci-kit learn etc). \nHowever, when I create a new environment, none of these packages is readily available. I cannot import them when using PyCharm with the newly created environment. When I check the Pycharm project interpreter, or the anaconda navigator environments tab, It seems that indeed none of these packages are installed in my new environments. Why is this? It doesn't make sense to me to provide all these packages, but then not make them ready for use when creating new environments. Do I have to install all these packages manually in new env's or am I missing something?\nKindest regards, and thanks in advance.","AnswerCount":4,"Available Count":2,"Score":-0.049958375,"is_accepted":false,"ViewCount":12607,"Q_Id":52648520,"Users Score":-1,"Answer":"You can check the packages you have in your environment with the command:\n\nconda list\n\nIf packages are not listed you just have to add it, with the command:\n\nconda install numpy","Q_Score":13,"Tags":"python,package,anaconda,conda","A_Id":52648696,"CreationDate":"2018-10-04T13:53:00.000","Title":"Why doesn't a new Conda environment come with packages like numpy?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am going through the painful process of learning how to manage packages\/ different (virtual) environments in Python\/Anaconda. I was told that Anaconda is basically a python installation with all the packages I need (e.g. numpy, scipy, sci-kit learn etc). \nHowever, when I create a new environment, none of these packages is readily available. I cannot import them when using PyCharm with the newly created environment. When I check the Pycharm project interpreter, or the anaconda navigator environments tab, It seems that indeed none of these packages are installed in my new environments. Why is this? It doesn't make sense to me to provide all these packages, but then not make them ready for use when creating new environments. Do I have to install all these packages manually in new env's or am I missing something?\nKindest regards, and thanks in advance.","AnswerCount":4,"Available Count":2,"Score":0.1488850336,"is_accepted":false,"ViewCount":12607,"Q_Id":52648520,"Users Score":3,"Answer":"I don't know about \"conda\" environments but in general virtual environments are used to provide you a \"unique\" environment. This might include different packages, different environment variables etc. \nThe whole point of making a new virtual environment is to have a separate place where you can install all the binaries ( and other resources ) required for your project. If you have some pre-installed binaries in the environment, doesn't it defeat the purpose of creating one in the first place?\nThe fact that you can create multiple environments helps you to separate binaries that might be needed by one and not by the other. \nFor instance, if you are creating a project which requires numpy:1.1 but you have numpy:2.1 installed , then you have to change it. So basically, by not installing any other packages, they are not making assumptions about your project's requirements.","Q_Score":13,"Tags":"python,package,anaconda,conda","A_Id":52648738,"CreationDate":"2018-10-04T13:53:00.000","Title":"Why doesn't a new Conda environment come with packages like numpy?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to install rasa_core in my python by using  !pip install rasa_core; command.\nBut i am getting an error :\nBelow is the error :\nFailed building wheel for Twisted\n  The scripts freeze_graph.exe, saved_model_cli.exe, tensorboard.exe, tflite_convert.exe, toco.exe and toco_from_protos.exe are installed in 'C:\\Users\\user\\AppData\\Roaming\\Python\\Python36\\Scripts' which is not on PATH.\n  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\nCommand \"c:\\programdata\\anaconda3\\python.exe -u -c \"import setuptools, tokenize;__file__='C:\\\\Users\\\\user\\\\AppData\\\\Local\\\\Temp\\\\pip-install-fot9mu3e\\\\Twisted\\\\setup.py';f=getattr(tokenize, 'open', open)(__file__);code=f.read().replace('\\r\\n', '\\n');f.close();exec(compile(code, __file__, 'exec'))\" install --record C:\\Users\\user\\AppData\\Local\\Temp\\pip-record-vp3wq_7u\\install-record.txt --single-version-externally-managed --compile --user --prefix=\" failed with error code 1 in C:\\Users\\user\\AppData\\Local\\Temp\\pip-install-fot9mu3e\\Twisted\\\nCould anyone please help me.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":868,"Q_Id":52651437,"Users Score":0,"Answer":"I faced the same issue and able to install rasa_core after resolving dependencies fast.\nPlease try below:\n\nFirst install twisted\npip install Twisted\nThen, install rasa_core     \npip install rasa_core","Q_Score":0,"Tags":"python,installation,chatbot,rasa-nlu,rasa-core","A_Id":53033952,"CreationDate":"2018-10-04T16:23:00.000","Title":"Facing issues while installing rasa_core","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to find an autoregressive model on some data stored in a dataframe and I have 96 data points per day. The data is the value of solar irradiance in some region and I know it has a 1-day seasonality. I want to obtain a simple linear model using scikit LinearRegression and I want to specify which lagged data points to use. I would like to use the last 10 data points, plus the data point that has a lag of 97, which corresponds to the data point of 24 hour earlier. How can I specify the lagged coefficients that I want to use? I don't want to have 97 coefficients, I just want to use 11 of them: the previous 10 data points and the data point 97 positions back.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":38,"Q_Id":52661107,"Users Score":1,"Answer":"Just make a dataset X with 11 columns [x0-97, x0-10, x0-9,...,x0-1]. Then series of x0 will be your target Y.","Q_Score":0,"Tags":"python,scikit-learn,autoregressive-models","A_Id":52665360,"CreationDate":"2018-10-05T08:09:00.000","Title":"How to choose coefficients with scikit LinearRegression","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am bit puzzled by how to read and understand a simple line of code:\n\nI have a tensor input of shape (19,4,64,64,3).\nThe line of code input[:, None] returns a tensor of shape (19, 1,\n4, 64, 64, 3).\n\nHow should I understand the behavior of that line? It seems that None is adding a dimension, with a size of 1. But why is this added on that specific position (between 19 and 4)?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":59,"Q_Id":52662727,"Users Score":2,"Answer":"Indeed, None adds a new dimension. You can also use tf.newaxis for this which is a bit more explicit IMHO. \nThe new dimension is added in axis 1 because that's where it appears in the index. E.g. input[:, :, None] should result in shape (19, 4, 1, 64, 64, 3) and so on.\nIt might get clearer if we write all the dimensions in the slicing: input[:, None, :, :, :, :]. In slicing, : simply means taking all elements of the dimension. So by using one :, we take all elements of dimension 0 and then \"move on\" to dimension 1. Since None appears here, we know that the new size-1 axis should be in dimension 1. Accordingly, the remaining dimensions get \"pushed back\".","Q_Score":1,"Tags":"python,tensorflow","A_Id":52665472,"CreationDate":"2018-10-05T09:42:00.000","Title":"Tensor shape modification using slicing and None","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using the object detection API from the models\/research python repo on Ubuntu 16.04, and I wanted to fine-tune a pre-trained model (at the moment I'm interested in SSD with MobileNet or Inception backbones) on the UA-DETRAC dataset.\nThe problem is that there are specific regions, with their bounding boxes, which are marked as \"ignored regions\" and I wouldn't want the model to train on what he thinks are some false positives which are true, just not annotated (included in those regions).\nI thought of cropping the images to exclude those regions, but I would lose some information.\nIs there the built-in possibility to mark them as \"don't care\" boxes or should I modify the code?\nThanks","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":811,"Q_Id":52668857,"Users Score":1,"Answer":"If those regions to ignore remain static, as in, the contents of the region doesn't change throughout the dataset, then the model can be learnt to ignore those regions. \nIf you really want the model to ignore them during training, then mask them with a constant value.","Q_Score":4,"Tags":"python,tensorflow,object-detection,tensorflow-serving,object-detection-api","A_Id":52670172,"CreationDate":"2018-10-05T15:23:00.000","Title":"Tensorflow Object Detection API: How to ignore regions during training?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am totally new to Jupyter Notebook.\nCurrently, I am using the notebook with R and it is working well.\nNow, I tried to use it with Python and I receive the following error.\n\n[I 09:00:52.947 NotebookApp] KernelRestarter: restarting kernel (4\/5),\n  new random ports\nTraceback (most recent call last):\nFile \"\/usr\/lib\/python3.6\/runpy.py\", line 193, in _run_module_as_main\n  \"main\", mod_spec)\nFile \"\/usr\/lib\/python3.6\/runpy.py\", line 85, in _run_code exec(code,\n  run_globals)\nFile\n  \"\/home\/frey\/.local\/lib\/python3.6\/site-packages\/ipykernel_launcher.py\",\n  line 15, in  from ipykernel import kernelapp as app\nFile\n  \"\/home\/frey\/.local\/lib\/python3.6\/site-packages\/ipykernel\/init.py\",\n  line 2, in  from .connect import *\nFile\n  \"\/home\/frey\/.local\/lib\/python3.6\/site-packages\/ipykernel\/connect.py\",\n  line 13, in  from IPython.core.profiledir import ProfileDir\nFile \"\/home\/frey\/.local\/lib\/python3.6\/site-packages\/IPython\/init.py\",\n  line 55, in  from .terminal.embed import embed\nFile\n  \"\/home\/frey\/.local\/lib\/python3.6\/site-packages\/IPython\/terminal\/embed.py\",\n  line 16, in  from IPython.terminal.interactiveshell import\n  TerminalInteractiveShell\nFile\n  \"\/home\/frey\/.local\/lib\/python3.6\/site-packages\/IPython\/terminal\/interactiveshell.py\",\n  line 20, in  from prompt_toolkit.formatted_text import PygmentsTokens\n  ModuleNotFoundError: No module named 'prompt_toolkit.formatted_text'\n[W 09:00:55.956 NotebookApp] KernelRestarter: restart failed [W\n  09:00:55.956 NotebookApp] Kernel 24117cd7-38e5-4978-8bda-d1b84f498051\n  died, removing from map.\n\nHopefully, someone can help me.","AnswerCount":5,"Available Count":2,"Score":-0.0399786803,"is_accepted":false,"ViewCount":13255,"Q_Id":52676660,"Users Score":-1,"Answer":"Check your environment variable Path!\nIn the system variable Path add the following line\n\nC:\\Users\\\\AppData\\Roaming\\Python\\Python37\\Scripts","Q_Score":1,"Tags":"python,jupyter-notebook,jupyter","A_Id":59511989,"CreationDate":"2018-10-06T07:18:00.000","Title":"No module named 'prompt_toolkit.formatted_text'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am totally new to Jupyter Notebook.\nCurrently, I am using the notebook with R and it is working well.\nNow, I tried to use it with Python and I receive the following error.\n\n[I 09:00:52.947 NotebookApp] KernelRestarter: restarting kernel (4\/5),\n  new random ports\nTraceback (most recent call last):\nFile \"\/usr\/lib\/python3.6\/runpy.py\", line 193, in _run_module_as_main\n  \"main\", mod_spec)\nFile \"\/usr\/lib\/python3.6\/runpy.py\", line 85, in _run_code exec(code,\n  run_globals)\nFile\n  \"\/home\/frey\/.local\/lib\/python3.6\/site-packages\/ipykernel_launcher.py\",\n  line 15, in  from ipykernel import kernelapp as app\nFile\n  \"\/home\/frey\/.local\/lib\/python3.6\/site-packages\/ipykernel\/init.py\",\n  line 2, in  from .connect import *\nFile\n  \"\/home\/frey\/.local\/lib\/python3.6\/site-packages\/ipykernel\/connect.py\",\n  line 13, in  from IPython.core.profiledir import ProfileDir\nFile \"\/home\/frey\/.local\/lib\/python3.6\/site-packages\/IPython\/init.py\",\n  line 55, in  from .terminal.embed import embed\nFile\n  \"\/home\/frey\/.local\/lib\/python3.6\/site-packages\/IPython\/terminal\/embed.py\",\n  line 16, in  from IPython.terminal.interactiveshell import\n  TerminalInteractiveShell\nFile\n  \"\/home\/frey\/.local\/lib\/python3.6\/site-packages\/IPython\/terminal\/interactiveshell.py\",\n  line 20, in  from prompt_toolkit.formatted_text import PygmentsTokens\n  ModuleNotFoundError: No module named 'prompt_toolkit.formatted_text'\n[W 09:00:55.956 NotebookApp] KernelRestarter: restart failed [W\n  09:00:55.956 NotebookApp] Kernel 24117cd7-38e5-4978-8bda-d1b84f498051\n  died, removing from map.\n\nHopefully, someone can help me.","AnswerCount":5,"Available Count":2,"Score":0.0399786803,"is_accepted":false,"ViewCount":13255,"Q_Id":52676660,"Users Score":1,"Answer":"It's more stable to create a kernel with an Anaconda virtualenv.\nFollow these steps.\n\nExecute Anaconda prompt.\nType conda create --name $ENVIRONMENT_NAME R -y\nType conda activate $ENVIRONMENT_NAME\nType python -m ipykernel install\nType ipython kernel install --user --name $ENVIRONMENT_NAME\n\nThen, you'll have a new jupyter kernel named 'R' with R installed.","Q_Score":1,"Tags":"python,jupyter-notebook,jupyter","A_Id":52676845,"CreationDate":"2018-10-06T07:18:00.000","Title":"No module named 'prompt_toolkit.formatted_text'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to Python and I have been stuck on a problem for some time now. I recently installed the module pandas and at first, it worked fine. However, for some reason it keeps saying \n\nAttributeError(\"module 'pandas' has no attribute 'read_csv'\").\n\nI have looked all over StackOverflow and the consensus is that there is likely another file in my CWD with the same name but I believe I don't.  \nEven if I create a new project and call it, for example, Firstproject.py, and immediately import pandas as pd, I get the error.  \nI would appreciate the help. I can provide more info if required.","AnswerCount":5,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":9258,"Q_Id":52677658,"Users Score":0,"Answer":"There might be possibility that you are using this name for your script as read_csv.py  hence pandas itself confused what to import, if or csv.py then you can rename it to something else like test_csv_read.py.\nalso remove any files in the path naming read_csv.pyc  or csv.pyc .","Q_Score":0,"Tags":"python,pandas,attributeerror","A_Id":52677839,"CreationDate":"2018-10-06T09:34:00.000","Title":"AttributeError(\"module 'pandas' has no attribute 'read_csv'\")","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to Python and I have been stuck on a problem for some time now. I recently installed the module pandas and at first, it worked fine. However, for some reason it keeps saying \n\nAttributeError(\"module 'pandas' has no attribute 'read_csv'\").\n\nI have looked all over StackOverflow and the consensus is that there is likely another file in my CWD with the same name but I believe I don't.  \nEven if I create a new project and call it, for example, Firstproject.py, and immediately import pandas as pd, I get the error.  \nI would appreciate the help. I can provide more info if required.","AnswerCount":5,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":9258,"Q_Id":52677658,"Users Score":0,"Answer":"Here is the solution \nwhen you downloaded python its automatically download 32 you need to delete if you don't have 32 and go download 64 and then problem solved :)","Q_Score":0,"Tags":"python,pandas,attributeerror","A_Id":55653559,"CreationDate":"2018-10-06T09:34:00.000","Title":"AttributeError(\"module 'pandas' has no attribute 'read_csv'\")","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to Python and I have been stuck on a problem for some time now. I recently installed the module pandas and at first, it worked fine. However, for some reason it keeps saying \n\nAttributeError(\"module 'pandas' has no attribute 'read_csv'\").\n\nI have looked all over StackOverflow and the consensus is that there is likely another file in my CWD with the same name but I believe I don't.  \nEven if I create a new project and call it, for example, Firstproject.py, and immediately import pandas as pd, I get the error.  \nI would appreciate the help. I can provide more info if required.","AnswerCount":5,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":9258,"Q_Id":52677658,"Users Score":0,"Answer":"In my case, I had installed module \"panda\" instead of \"pandas\". I was getting this error, even when there was no conflicting .py files were present in working folder. \nThen I recognized my mistake, and then installed package \"pandas and problem got resolved.","Q_Score":0,"Tags":"python,pandas,attributeerror","A_Id":60574804,"CreationDate":"2018-10-06T09:34:00.000","Title":"AttributeError(\"module 'pandas' has no attribute 'read_csv'\")","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on a machine learning project and I am wondering whether it is possible to change the loss function while the network is training. I'm not sure how to do it exactly in code.\nFor example, start training with cross entropy loss and then halfway through training, switch to 0-1 loss.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":783,"Q_Id":52682979,"Users Score":0,"Answer":"You have to implement your own algorithm. This is mostly possible with Tensorflow.","Q_Score":0,"Tags":"python,tensorflow,machine-learning","A_Id":52695712,"CreationDate":"2018-10-06T20:14:00.000","Title":"Is it possible to change the loss function dynamically during training?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am searching for a way to make my training and testing data smaller in file size.\nThe model I want to end up with\nI want to train a model that predicts whether or not a crypto coin price is making and x% (0.4 or so) jump within the next 10 minutes (i.e. I want the model to answer with a Yes or No). \nEvery minute I will the model with the last 3 hours of price and volume data (that means 180 datapoints each containing 5 values open, close, high, low prices and volume).\nMy current training and testing sets are BIG\nMy training and testing sets are therefore row in a csv file where each row contains 5 x 180 = 900 numbers plus one label (Yes or No) and with about 100k rows I guess this is a very huge dataset.\nBut each row in csv contains mostly redundant data\nBut each \"neighbor\" row in the csv file only contain 1 new data point since every next row is only 1 minute \"older\" and therefore only has skipped the data point of oldest minute and instead introduced a new point of the next minute.\nIs it possible to setup up the traing code so the csv file only needs to have the latest minute data point in each row?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":133,"Q_Id":52685768,"Users Score":0,"Answer":"If you use Data API then you can cache the input. Also maybe TF's support for Kafka might be a help here as you could model it as a stream of data. \nAnother approach would be to reuse some data between session calls. Then you would have to use resource variable (in the current Variable() spec it means using flag use_resource in the constructor). This way your csv could contain only minute level data and you would just add it to the variable - create a kind of circle-buffer from it.","Q_Score":0,"Tags":"python,tensorflow,dataset,tensorflow-datasets","A_Id":52807275,"CreationDate":"2018-10-07T05:39:00.000","Title":"Tensorflow: Each row in the training dataset contains 99% of the previous rows data - can I optimize it before running the training?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have seen multiple questions for the same issue. I went through all the answers and tried all of them. I updated pip, tensorflow, python etc to the latest versions or as suggested in the answers and still I am facing this issue.\nPip version 18.0,\nPython 3.7","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":45,"Q_Id":52692622,"Users Score":1,"Answer":"Please downgrade Python to 3.6.x and try again. I had faced similar issue while using Python 3.7.x. Once I downgraded it, it worked. Make sure you adjust your path variable accordingly. \"Pip\" also may have to be modified and the corresponding path variable.","Q_Score":1,"Tags":"python,tensorflow","A_Id":52692667,"CreationDate":"2018-10-07T20:28:00.000","Title":"Error - No module named '_pywrap_tensorflow'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"It seems we can achieve same goal using pivot_table from both libraries, but which one is more efficient in performance for large dataset?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":515,"Q_Id":52694289,"Users Score":1,"Answer":"Definitely Dask. The way pandas work is, it processes everything as a monolithic block in memory and is not parallelizable, while Dask is made to break the data frame into chunks that can be processed in parallel.","Q_Score":1,"Tags":"python,python-3.x,pandas,pivot-table,dask","A_Id":52694329,"CreationDate":"2018-10-08T01:18:00.000","Title":"Difference between dask pivot_table and pandas pivot_table python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am making a random forest multi-classifier model. Basically there are hundred of households which have 200+ features, and based on these features I have to classify them in one of the classes {1,2,3,4,5,6}. \nThe problem I am facing is I cannot improve the accuracy of the model how much ever I can try. I have used RandomSearchCV and also GridSearchCV but I can only achieve accuracy of around 68%.\nSome points to note\n\nThe sample points are unbalanced. This is the order of classes in decreasing order {1,4,2,7,6,3}. I have used class_weight = \"balanced\" but it does improve the accuracy.\nI have tried number of estimators ranging from 50-450\nI have also calculated the f1 score and not only going by accuracy to compare the models\n\nWhat else do you guys suggest to improve the accuracy\/f1-score? I am stuck with this problem from a long time. Any help will be highly appreciated.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":118,"Q_Id":52703577,"Users Score":0,"Answer":"Try to tune below parameters\nn_estimators\nThis is the number of trees you want to build before taking the maximum voting or averages of predictions. Higher number of trees give you better performance but makes your code slower.\nmax_features\nThese are the maximum number of features Random Forest is allowed to try in individual tree. There are multiple options available in Python to assign maximum features.\nmin_sample_leaf\nLeaf is the end node of a decision tree. A smaller leaf makes the model more prone to capturing noise in train data. You can start with some minimum value like 75 and gradually increase it. See which value your accuracy is coming high.\nOtherwise:\n\nYou can try XGBoost, LightGBM or Adaboost, they often perform better than Random Forest\n\nTry do not remove missing values, complex ensemble models such as RF and GBM treats it well, may be you lost some useful information doing so, especially if you have large percent of your data missing in some features\n\nTry to increase n_estimators and max_depth, may be your trees not deep enough to catch all data properties","Q_Score":0,"Tags":"python,random-forest,multiclass-classification","A_Id":70800730,"CreationDate":"2018-10-08T13:37:00.000","Title":"Random Forest Multi Class Python does not improve accuracy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am making a random forest multi-classifier model. Basically there are hundred of households which have 200+ features, and based on these features I have to classify them in one of the classes {1,2,3,4,5,6}. \nThe problem I am facing is I cannot improve the accuracy of the model how much ever I can try. I have used RandomSearchCV and also GridSearchCV but I can only achieve accuracy of around 68%.\nSome points to note\n\nThe sample points are unbalanced. This is the order of classes in decreasing order {1,4,2,7,6,3}. I have used class_weight = \"balanced\" but it does improve the accuracy.\nI have tried number of estimators ranging from 50-450\nI have also calculated the f1 score and not only going by accuracy to compare the models\n\nWhat else do you guys suggest to improve the accuracy\/f1-score? I am stuck with this problem from a long time. Any help will be highly appreciated.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":118,"Q_Id":52703577,"Users Score":0,"Answer":"You can check if the features are on different scales. If they are, it is  suggested to use some type of normalization. This step is essential for many linear-based models to perform well. You can take a quick look at the distributions of each numeric feature to decide what type of normalization to use.","Q_Score":0,"Tags":"python,random-forest,multiclass-classification","A_Id":70800413,"CreationDate":"2018-10-08T13:37:00.000","Title":"Random Forest Multi Class Python does not improve accuracy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"but I have been trying to play around with it for awhile. I've seen a lot of guides on how Keras is used to build LSTM models and how people feed in the inputs and get expected outputs. But what I have never seen yet is, for example stock data, how we can make the LSTM model understand patterns between different dimensions, say close price is much higher than normal because volume is low.\nPoint of this is that I want to do a test with stock prediction, but make it so that each dimensions are not reliant on previous time steps, but also reliant on other dimensions it haves as well.\nSorry if I am not asking the question correctly, please ask more questions if I am not explaining it clearly.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":51,"Q_Id":52706996,"Users Score":0,"Answer":"First: Regressors will replicate if you input a feature that gives some direct intuition about the predicted input might be to secure the error is minimized, rather than trying to actually predict it. Try to focus on binary classification or multiclass classification, whether the closing price go up\/down or how much. \nSecond: Always engineer the raw features to give more explicit patterns to the ML algorithm. Think on inputs as Volume(t) - Volume(t-1), close(t)^2 - close(t-1)^2, technical indicators(RSI, CCI, OBV etc.) Create your own features. You can use the pyti library for technical indicators.","Q_Score":0,"Tags":"python,machine-learning,keras,lstm,rnn","A_Id":52786576,"CreationDate":"2018-10-08T17:02:00.000","Title":"Keras LSTM Input Dimension understanding each other","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"We've built a large python repo that uses lots of libraries (numpy, scipy, tensor flow, ...) And have managed these dependencies through a conda environment. Basically we have lots of developers contributing and anytime someone needs a new library for something they are working on they 'conda install' it. \nFast forward to today and now we need to deploy some applications that use our repo. We are deploying using docker, but are finding that these images are really large and causing some issues, e.g. 10+ GB. However each individual application only uses a subset of all the dependencies in the environment.yml. \nIs there some easy strategy for dealing with this problem? In a sense I need to know the dependencies for each application, but I'm not sure how to do this in an automated way. \nAny help here would be great. I'm new to this whole AWS, Docker, and python deployment thing... We're really a bunch of engineers and scientists who need to scale up our software. We have something that works, it just seems like there has to be a better way .","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2376,"Q_Id":52719729,"Users Score":3,"Answer":"First see if there are easy wins to shrink the image, like using Alpine Linux and being very careful about what gets installed with the OS package manager, and ensuring you only allow installing dependencies or recommended items when truly required, and that you clean up and delete artifacts like package lists, big things you may not need like Java, etc.\nThe base Anaconda\/Ubuntu image is ~ 3.5GB in size, so it's not crazy that with a lot of extra installations of heavy third-party packages, you could get up to 10GB. In production image processing applications, I routinely worked with Docker images in the range of 3GB to 6GB, and those sizes were after we had heavily optimized the container.\nTo your question about splitting dependencies, you should provide each different application with its own package definition, basically a setup.py script and some other details, including dependencies listed in some mix of requirements.txt for pip and\/or environment.yaml for conda.\nIf you have Project A in some folder \/ repo and Project B in another, you want people to easily be able to do something like pip install <GitHub URL to a version tag of Project A> or conda env create -f ProjectB_environment.yml or something, and voila, that application is installed.\nThen when you deploy a specific application, have some CI tool like Jenkins build the container for that application using a FROM line to start from your thin Alpine \/ whatever container, and only perform conda install or pip install for the dependency file for that project, and not all the others.\nThis also has the benefit that multiple different projects can declare different version dependencies even among the same set of libraries. Maybe Project A is ready to upgrade to the latest and greatest pandas version, but Project B needs some refactoring before the team wants to test that upgrade. This way, when CI builds the container for Project B, it will have a Python dependency file with one set of versions, while in Project A's folder or repo of source code, it might have something different.","Q_Score":5,"Tags":"python,amazon-web-services,docker","A_Id":52719901,"CreationDate":"2018-10-09T11:15:00.000","Title":"Deploying python with docker, images too big","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":1,"Web Development":0},{"Question":"I have a database of images that contains identity cards, bills and passports.\nI want to classify these images into different groups (i.e identity cards, bills and passports).\nAs I read about that, one of the ways to do this task is clustering (since it is going to be unsupervised).\nThe idea for me is like this: the clustering will be based on the similarity between images (i.e images that have similar features will be grouped together).\nI know also that this process can be done by using k-means.\nSo the problem for me is about features and using images with K-means.\nIf anyone has done this before, or has a clue about it, please would you recommend some links to start with or suggest any features that can be helpful.","AnswerCount":3,"Available Count":3,"Score":0.0665680765,"is_accepted":false,"ViewCount":4082,"Q_Id":52721662,"Users Score":1,"Answer":"I have implemented Unsupervised Clustering based on Image Similarity using Agglomerative Hierarchical Clustering.\nMy use case had images of People, so I had extracted the Face Embedding (aka Feature) Vector from each image. I have used dlib for face embedding and so each feature vector was 128d. \nIn general, the feature vector of each image can be extracted. A pre-trained VGG or CNN network, with its final classification layer removed; can be used for feature extraction.\nA dictionary with KEY as the IMAGE_FILENAME and VALUE as the FEATURE_VECTOR can be created for all the images in the folder. This will make the co-relation between the filename and it\u2019s feature vector easier.\nThen create a single feature vector say X, which comprises of individual feature vectors of each image in the folder\/group which needs to be clustered. \nIn my use case, X had the dimension as : NUMBER OF IMAGE IN THE FOLDER, 128 (i.e SIZE OF EACH FEATURE VECTOR). For instance, Shape of X : 50,128\nThis feature vector can then be used to fit an Agglomerative Hierarchical Cluster. One needs to fine tune the distance threshold parameter empirically.\nFinally, we can write a code to identify which IMAGE_FILENAME belongs to which cluster.\nIn my case, there were about 50 images per folder so this was a manageable solution. This approach was able to group image of a single person into a single clusters. For example, 15 images of PERSON1 belongs to CLUSTER 0, 10 images of PERSON2 belongs to CLUSTER 2 and so on\u2026","Q_Score":7,"Tags":"python,computer-vision,cluster-analysis,k-means,unsupervised-learning","A_Id":62167763,"CreationDate":"2018-10-09T12:58:00.000","Title":"Clustering images using unsupervised Machine Learning","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a database of images that contains identity cards, bills and passports.\nI want to classify these images into different groups (i.e identity cards, bills and passports).\nAs I read about that, one of the ways to do this task is clustering (since it is going to be unsupervised).\nThe idea for me is like this: the clustering will be based on the similarity between images (i.e images that have similar features will be grouped together).\nI know also that this process can be done by using k-means.\nSo the problem for me is about features and using images with K-means.\nIf anyone has done this before, or has a clue about it, please would you recommend some links to start with or suggest any features that can be helpful.","AnswerCount":3,"Available Count":3,"Score":1.2,"is_accepted":true,"ViewCount":4082,"Q_Id":52721662,"Users Score":3,"Answer":"Label a few examples, and use classification.\nClustering is as likely to give you the clusters \"images with a blueish tint\", \"grayscale scans\" and \"warm color temperature\". That is a quote reasonable way to cluster such images.\nFurthermore, k-means is very sensitive to outliers. And you probably have some in there.\nSince you want your clusters correspond to certain human concepts, classification is what you need to use.","Q_Score":7,"Tags":"python,computer-vision,cluster-analysis,k-means,unsupervised-learning","A_Id":52735568,"CreationDate":"2018-10-09T12:58:00.000","Title":"Clustering images using unsupervised Machine Learning","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a database of images that contains identity cards, bills and passports.\nI want to classify these images into different groups (i.e identity cards, bills and passports).\nAs I read about that, one of the ways to do this task is clustering (since it is going to be unsupervised).\nThe idea for me is like this: the clustering will be based on the similarity between images (i.e images that have similar features will be grouped together).\nI know also that this process can be done by using k-means.\nSo the problem for me is about features and using images with K-means.\nIf anyone has done this before, or has a clue about it, please would you recommend some links to start with or suggest any features that can be helpful.","AnswerCount":3,"Available Count":3,"Score":0.3215127375,"is_accepted":false,"ViewCount":4082,"Q_Id":52721662,"Users Score":5,"Answer":"Most simple way to get good results will be to break down the problem into two parts :\n\nGetting the features from the images: Using the raw pixels as features will give you poor results. Pass the images through a pre trained CNN(you can get several of those online). Then use the last CNN layer(just before the fully connected) as the image features.\nClustering of features : Having got the rich features for each image, you can do clustering on these(like K-means).\n\nI would recommend implementing(using already implemented) 1, 2 in Keras and Sklearn respectively.","Q_Score":7,"Tags":"python,computer-vision,cluster-analysis,k-means,unsupervised-learning","A_Id":52721794,"CreationDate":"2018-10-09T12:58:00.000","Title":"Clustering images using unsupervised Machine Learning","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"My intent is to cluster document vectors from doc2vec using HDBSCAN. I want to find tiny clusters where there are semantical and textual duplicates.\nTo do this I am using gensim to generate document vectors. The elements of the resulting docvecs are all in the range [-1,1].\nTo compare two documents I want to compare the angular similarity. I do this by calculating the cosine similarity of the vectors, which works fine. \nBut, to cluster the documents HDBSCAN requires a distance matrix, and not a similarity matrix. The native conversion from cosine similarity to cosine distance in sklearn is 1-similarity. However, it is my understanding that using this formula can break the triangle inequality preventing it from being a true distance metric. When searching and looking at other people's code for similar tasks, it seems that most people seem to be using sklearn.metrics.pairwise.pairwise_distances(data, metric='cosine') which is defines cosine distance as 1-similarity anyway. It looks like it provides appropriate results.\nI am wondering if this is correct, or if I should use angular distance instead, calculated as np.arccos(cosine similarity)\/pi. I have also seen people use Euclidean distance on l2-normalized document vectors; this seems to be equivalent to cosine similarity.\nPlease let me know what is the most appropriate method for calculating distance between document vectors for clustering :)","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":1238,"Q_Id":52722423,"Users Score":1,"Answer":"I believe in practice cosine-distance is used, despite the fact that there are corner-cases where it's not a proper metric. \nYou mention that \"elements of the resulting docvecs are all in the range [-1,1]\". That isn't usually guaranteed to be the case \u2013 though it would be if you've already unit-normalized all the raw doc-vectors. \nIf you have done that unit-normalization, or want to, then after such normalization euclidean-distance will always give the same ranked-order of nearest-neighbors as cosine-distance. The absolute values, and relative proportions between them, will vary a little \u2013 but all \"X is closer to Y than Z\" tests will be identical to those based on cosine-distance. So clustering quality should be nearly identical to using cosine-distance directly.","Q_Score":4,"Tags":"python,cluster-analysis,distance,doc2vec,hdbscan","A_Id":52728282,"CreationDate":"2018-10-09T13:35:00.000","Title":"What is the appropriate distance metric when clustering paragraph\/doc2vec vectors?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"My intent is to cluster document vectors from doc2vec using HDBSCAN. I want to find tiny clusters where there are semantical and textual duplicates.\nTo do this I am using gensim to generate document vectors. The elements of the resulting docvecs are all in the range [-1,1].\nTo compare two documents I want to compare the angular similarity. I do this by calculating the cosine similarity of the vectors, which works fine. \nBut, to cluster the documents HDBSCAN requires a distance matrix, and not a similarity matrix. The native conversion from cosine similarity to cosine distance in sklearn is 1-similarity. However, it is my understanding that using this formula can break the triangle inequality preventing it from being a true distance metric. When searching and looking at other people's code for similar tasks, it seems that most people seem to be using sklearn.metrics.pairwise.pairwise_distances(data, metric='cosine') which is defines cosine distance as 1-similarity anyway. It looks like it provides appropriate results.\nI am wondering if this is correct, or if I should use angular distance instead, calculated as np.arccos(cosine similarity)\/pi. I have also seen people use Euclidean distance on l2-normalized document vectors; this seems to be equivalent to cosine similarity.\nPlease let me know what is the most appropriate method for calculating distance between document vectors for clustering :)","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":1238,"Q_Id":52722423,"Users Score":1,"Answer":"The proper similarity metric is the dot product, not cosine.\nWord2vec etc. are trained using the dot product, not normalized by the vector length. And you should exactly use what was trained.\nPeople use the cosine all the time because it worked well for bag of words. The choice is not based on a proper theoretical analysis for all I know.\nHDBSCAN does not require a metric. The 1-sim transformation assumes that x is bounded by 1, so that won't reliably work.\nI suggest to try the following approaches:\n\nuse negative distances. That may simply work. I.e., d(x,y)=-(x dot y)\nuse max-sim transformation. Once you have the dot product matrix it is easy to get the maximum value.\nimplement HDBSCAN* with a similarity rather than a metric","Q_Score":4,"Tags":"python,cluster-analysis,distance,doc2vec,hdbscan","A_Id":52735502,"CreationDate":"2018-10-09T13:35:00.000","Title":"What is the appropriate distance metric when clustering paragraph\/doc2vec vectors?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I just discovered rasterio for easy raster handling in Python. I am working with multi-dimensional climate data (4D and 5D). I was successful to open and read my 4D-NetCDF file with rasterio (lat: 180, lon: 361, time: 6, number: 51). However, the rasterio dataset object shows me three dimensions (180, 361, 306), whereby dimension 3 and 4 were combined. Can rasterio dataset objects only store 3 dimensions?\nIf yes, how does rasterio combine dimensions 3 and 4, to know what layer of the 306 resembles the original?\nThanks.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":162,"Q_Id":52723483,"Users Score":0,"Answer":"rasterio is really not the tool of choice for multi-dimensional netCDF data. It excels at handling 3D (band, y, x) data where band is some relatively short, unlabeled axis.\nLook into xarray instead, which is built around the netCDF model and supports labeled axes and many dimensions, plus lazy loading, out-of-memory computation, plotting, indexing, ...","Q_Score":0,"Tags":"python-3.x,rasterio","A_Id":53180151,"CreationDate":"2018-10-09T14:30:00.000","Title":"rasterio - load multi-dimensional data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on building a Random Forest Regression model for predicting ETA. I am saving the model in pickle format by using pickle package. I have also used joblib to save the model. But the size of file is really large (more than 100 GB). I would like to ask the data science experts that is it the correct format to save the model or is there any other efficient method to do so? Any insights on this will be appreciated.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":234,"Q_Id":52729048,"Users Score":0,"Answer":"Aim of saving complete model is modifying the model in the future. If you are not planning to modify your model, you can just save the weights and use them for the prediction. This will save a huge space for you.","Q_Score":0,"Tags":"python,machine-learning,pickle,random-forest,data-science-experience","A_Id":52730991,"CreationDate":"2018-10-09T20:44:00.000","Title":"Right way to serialize a Random Forest Regression File","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm writing a program that gathers tweets from Twitter, and evaluates the text to find trending topics. I'm planning on using NLTK to stem the terms and do some other operations on the data. \nWhat I need is a tool that can determine if two adjacent words in a tweet should be treated as a single term. For example, if \"fake news\" is trending on Twitter, I don't want to treat those two words as different. Another example, if everyone is tweeting about 'Computer Science', it wouldn't make sense to treat computer and science as two different terms since they refer to the same topic. Does a tool exist that can find such terms?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":681,"Q_Id":52729565,"Users Score":0,"Answer":"Interesting problem to play with, assuming there's not already a lexicon of meaningful compound words that you could leverage. And I'd love to see \"computer science\" as a trending topic.\nLet's take the approach that we know nothing about compound words in English, whether \"stop sign\" is as meaningfully distinct from \"stop\" and \"sign\" as \n\"does better\" is from \"does\" and \"better\"\nBreaking it down, you want to build a process that:\n\nIdentifies co-located pairs\nDrops any that are clearly not related as compound words (ie parts of    speech, proper names or punctuation)\nSaves candidate pairs       \nAnalyzes candidate pairs for frequency\nTeaches your system to look for the most valuable candidate pairs\n\nIs that an accurate description?\nIf so, I think the tool you ask for would be in (4) or (5). For 4), consider the Associative Rule in Python's Orange library as a start. You could also use TF-IDF from scikit-learn. For 5) you could expose the output from 4) as a list, set or dictionary of strings with counts.","Q_Score":0,"Tags":"python,nltk,natural-language-processing","A_Id":52729697,"CreationDate":"2018-10-09T21:27:00.000","Title":"Python tool to find meaningful pairs of words in a document","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am currently using TensorFlow tutorial's first_steps_with_tensor_flow.ipynb  notebook to learn TF for implementing ML models. In the notebook, they have used Stochastic Gradient Descent (SGD) to optimize the loss function. Below is the snippet of the my_input_function:\ndef my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\nHere, it can be seen that the batch_size is 1. The notebook uses a housing data set containing 17000 labeled examples for training. This means for SGD, I will be having 17000 batches.\nLRmodel = linear_regressor.train(input_fn = lambda:my_input_fn(my_feature, \n           targets), steps=100)\nI have three questions - \n\nWhy is steps=100 in linear_regressor.train method above? Since we have 17000 batches and steps in ML means the count for evaluating one batch, in linear_regressor.train method steps = 17000 should be initialized, right?\nIs number of batches equal to the number of steps\/iterations in ML?\nWith my 17000 examples, if I keep my batch_size=100, steps=500, and num_epochs=5, what does this initialization mean and how does it correlate to 170 batches?","AnswerCount":1,"Available Count":1,"Score":-0.1973753202,"is_accepted":false,"ViewCount":185,"Q_Id":52738335,"Users Score":-1,"Answer":"step is the literal meaning: means you refresh the parameters in your batch size; so for linear_regessor.train, it will train 100 times for this batch_size 1.\nepoch means to refresh the whole data, which is 17,000 in your set.","Q_Score":0,"Tags":"python,tensorflow,machine-learning","A_Id":52904530,"CreationDate":"2018-10-10T10:39:00.000","Title":"TensorFlow: Correct way of using steps in Stochastic Gradient Descent","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to expand the following list\n[(1, [('a', '12'), ('b', '64'), ('c', '36'), ('d', '48')]), (2, [('a', '13'), ('b', '26'), ('c', '39'), ('d', '52')])] \nto   \n[(1,a,12),(1,b,24),(1,c,36),(1,d,48),(2,a,13),(2,b,26),(2,c,39),(2,d,52)] \nI used zip(itertools.cycle()) in python 3, but instead get a zip object reference. Is there any other way I can do it? This worked for python 2","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":66,"Q_Id":52746519,"Users Score":0,"Answer":"Provided that the zip object is created correctly, you can either do list(zip_object) or [*zip_object] to get the list.","Q_Score":1,"Tags":"python,python-3.x","A_Id":52746594,"CreationDate":"2018-10-10T18:20:00.000","Title":"Correct usage of itertools in python 3","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I try to pass a Keras model (as a function) to KerasClassifier wrapper from scikit_learn, and then use GridSearchCV to create some settings, and finally fit the train and test datasets (both are numpy array)\nI then, with the same python script, got different exceptions, some of them are:\n_1.\n\nTraceback (most recent call last):   File \"mnist_flat_imac.py\", line\n  63, in \n      grid_result = validator.fit(train_images, train_labels)   File \"\/home\/longnv\/PYTHON_ENV\/DataScience\/lib\/python3.5\/site-packages\/sklearn\/model_selection\/_search.py\",\n  line 626, in fit\n      base_estimator = clone(self.estimator)   File \"\/home\/longnv\/PYTHON_ENV\/DataScience\/lib\/python3.5\/site-packages\/sklearn\/base.py\",\n  line 62, in clone\n      new_object_params[name] = clone(param, safe=False)   File \"\/home\/longnv\/PYTHON_ENV\/DataScience\/lib\/python3.5\/site-packages\/sklearn\/base.py\",\n  line 53, in clone\nsnipped here\nin _deepcopy_dict\n      y[deepcopy(key, memo)] = deepcopy(value, memo)   File \"\/home\/longnv\/PYTHON_ENV\/DataScience\/lib\/python3.5\/copy.py\", line 174,\n  in deepcopy\n      rv = reductor(4) TypeError: can't pickle SwigPyObject objects Exception ignored in: > Traceback (most recent call last):   File\n  \"\/home\/longnv\/PYTHON_ENV\/DataScience\/lib\/python3.5\/site-packages\/tensorflow\/python\/framework\/c_api_util.py\",\n  line 52, in __del__\n      c_api.TF_DeleteGraph(self.graph) AttributeError: 'ScopedTFGraph' object has no attribute 'graph'\n\n_2. \n\nTraceback (most recent call last):   File \"mnist_flat_imac.py\", line\n  63, in \n      grid_result = validator.fit(train_images, train_labels)   File \"\/home\/longnv\/PYTHON_ENV\/DataScience\/lib\/python3.5\/site-packages\/sklearn\/model_selection\/_search.py\",\n  line 626, in fit\n      base_estimator = clone(self.estimator)   File \"\/home\/longnv\/PYTHON_ENV\/DataScience\/lib\/python3.5\/site-packages\/sklearn\/base.py\",\n  line 62, in clone\n      new_object_params[name] = clone(param, safe=False)   File \"\/home\/longnv\/PYTHON_ENV\/DataScience\/lib\/python3.5\/site-packages\/sklearn\/base.py\",\n  line 53, in clone\n      return copy.deepcopy(estimator)   File \"\/home\/longnv\/PYTHON_ENV\/DataScience\/lib\/python3.5\/copy.py\", line 182,\n  in deepcopy\n      y = _reconstruct(x, rv, 1, memo)   File \"\/home\/longnv\/PYTHON_ENV\/DataScience\/lib\/python3.5\/copy.py\", line 297,\n  in _reconstruct\nsnipped here\nin deepcopy\n      y = _reconstruct(x, rv, 1, memo)   File \"\/home\/longnv\/PYTHON_ENV\/DataScience\/lib\/python3.5\/copy.py\", line 297,\n  in _reconstruct\n      state = deepcopy(state, memo)   File \"\/home\/longnv\/PYTHON_ENV\/DataScience\/lib\/python3.5\/copy.py\", line 155,\n  in deepcopy\n      y = copier(x, memo)   File \"\/home\/longnv\/PYTHON_ENV\/DataScience\/lib\/python3.5\/copy.py\", line 243,\n  in _deepcopy_dict\n      y[deepcopy(key, memo)] = deepcopy(value, memo)   File \"\/home\/longnv\/PYTHON_ENV\/DataScience\/lib\/python3.5\/copy.py\", line 174,\n  in deepcopy\n      rv = reductor(4) TypeError: can't pickle SwigPyObject objects\n\n_3. \n\nTraceback (most recent call last):   File \"mnist_flat_imac.py\", line\n  63, in \n      grid_result = validator.fit(train_images, train_labels)   File \"\/home\/longnv\/PYTHON_ENV\/DataScience\/lib\/python3.5\/site-packages\/sklearn\/model_selection\/_search.py\",\n  line 626, in fit\n      base_estimator = clone(self.estimator)   File \"\/home\/longnv\/PYTHON_ENV\/DataScience\/lib\/python3.5\/site-packages\/sklearn\/base.py\",\n  line 62, in clone\n      new_object_params[name] = clone(param, safe=False)   File \"\/home\/longnv\/PYTHON_ENV\/DataScience\/lib\/python3.5\/site-packages\/sklearn\/base.py\",\n  line 53, in clone\nsnipped here\nin _deepcopy_dict\n      y[deepcopy(key, memo)] = deepcopy(value, memo)   File \"\/home\/longnv\/PYTHON_ENV\/DataScience\/lib\/python3.5\/copy.py\", line 182,\n  in deepcopy\n      y = _reconstruct(x, rv, 1, memo)   File \"\/home\/longnv\/PYTHON_ENV\/DataScience\/lib\/python3.5\/copy.py\", line 306,\n  in _reconstruct\n      y.dict.update(state) AttributeError: 'NoneType' object has no attribute 'update'\n\nWhy did it output different errors with the same python script? \nAnd how can I fix this?\nThank you so much!\nP.S.\n\npython: 3.5 \ntensorflow: 1.10.1\npandas: 0.23.4\nUbuntu: 4.4.0-124-generic","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":196,"Q_Id":52751040,"Users Score":0,"Answer":"Found it.\nIt should be:\nclf = KerasClassifier(build_fn=get_model)\nInstead of:\nclf = KerasClassifier(build_fn=get_model())","Q_Score":0,"Tags":"python,tensorflow,machine-learning,scikit-learn,keras","A_Id":52752228,"CreationDate":"2018-10-11T01:55:00.000","Title":"Different exceptions happened when running Keras and scikit-learn","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on a data set and after performing the bucketing operation over two columns, I ended up with two buckets that have maximum number of data points.\nFor those two buckets, I have created two separate data frames, which is of different shapes (number of columns are same and the number of rows are different) so as to compare them. \nI need to know which transformation I can use to perform a correlation of two data frames possible. How can I do that?\nAny other suggestions for comparing data frames are appreciated.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":48,"Q_Id":52760769,"Users Score":0,"Answer":"Bin them both to vectors of equal length, with bin or window sizes dependent on the shapes of the input frames, then calculate correlation on the vectors.","Q_Score":0,"Tags":"python,correlation","A_Id":52772728,"CreationDate":"2018-10-11T13:04:00.000","Title":"Finding correlation of two data frames using python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am very new to this and struggling with the basics. I have a csv file \/home\/emily\/Downloads\/Roger-Federer.csv The textbook says that I need to \"extract the file and download it to my current directory\" on JupyterLab (I am using Python). What does this mean? How do I do this? Thank you","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":628,"Q_Id":52766928,"Users Score":2,"Answer":"Every running program, including JupyterLab, has a \"working directory\" which is where it thinks it is on your computer's file system. What exactly this directory is usually depends on how you launched it (e.g., when you run a program from terminal, its working directory is initially the folder your terminal was in when you ran the command, but it's possible for a program to change its own working directory later).\nYour file path indicates you're on Linux, so I'd suggest opening a terminal in your JupyterLab and running pwd to have it print out its current directory. (You can also run !pwd in any open notebook if that's easier.) You should then copy your CSV file to that directory.\nIf you do that, then from your Python code, you can just open the file locally, like open('Roger-Federer.csv') or pandas.read_csv('Roger-Federer.csv'). You don't have to move the file to open it from Python, though, you can just give it the entire file path, like open('\/home\/emily\/Downloads\/Roger-Federer.csv'), and that'll work just fine too.","Q_Score":0,"Tags":"python,jupyter-notebook","A_Id":52767061,"CreationDate":"2018-10-11T18:40:00.000","Title":"How to get a file from the files on my computer to JupyterLab?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Tensorflow was working fine when I had 1.4 but when I upgraded it, it stopped working.\nThe version that I installed is 1.11 with CUDA 9 and cuDNN 7. \n\nTraceback (most recent call last):   File\n  \"C:\\Users\\anime\\AppData\\Local\\Programs\\Python\\Python36\\lib\\site-packages\\tensorflow\\python\\pywrap_tensorflow.py\",\n  line 58, in \n      from tensorflow.python.pywrap_tensorflow_internal import *   File \"C:\\Users\\anime\\AppData\\Local\\Programs\\Python\\Python36\\lib\\site-packages\\tensorflow\\python\\pywrap_tensorflow_internal.py\",\n  line 28, in \n      _pywrap_tensorflow_internal = swig_import_helper()   File \"C:\\Users\\anime\\AppData\\Local\\Programs\\Python\\Python36\\lib\\site-packages\\tensorflow\\python\\pywrap_tensorflow_internal.py\",\n  line 24, in swig_import_helper\n      _mod = imp.load_module('_pywrap_tensorflow_internal', fp, pathname, description)   File\n  \"C:\\Users\\anime\\AppData\\Local\\Programs\\Python\\Python36\\lib\\imp.py\",\n  line 243, in load_module\n      return load_dynamic(name, filename, file)   File \"C:\\Users\\anime\\AppData\\Local\\Programs\\Python\\Python36\\lib\\imp.py\",\n  line 343, in load_dynamic\n      return _load(spec) ImportError: DLL load failed: The specified module could not be found.\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):   File \"\", line 1, in\n     File\n  \"C:\\Users\\anime\\AppData\\Local\\Programs\\Python\\Python36\\lib\\site-packages\\tensorflow__init__.py\",\n  line 22, in \n      from tensorflow.python import pywrap_tensorflow  # pylint: disable=unused-import   File\n  \"C:\\Users\\anime\\AppData\\Local\\Programs\\Python\\Python36\\lib\\site-packages\\tensorflow\\python__init__.py\",\n  line 49, in \n      from tensorflow.python import pywrap_tensorflow   File \"C:\\Users\\anime\\AppData\\Local\\Programs\\Python\\Python36\\lib\\site-packages\\tensorflow\\python\\pywrap_tensorflow.py\",\n  line 74, in \n      raise ImportError(msg) ImportError: Traceback (most recent call last):   File\n  \"C:\\Users\\anime\\AppData\\Local\\Programs\\Python\\Python36\\lib\\site-packages\\tensorflow\\python\\pywrap_tensorflow.py\",\n  line 58, in \n      from tensorflow.python.pywrap_tensorflow_internal import *   File \"C:\\Users\\anime\\AppData\\Local\\Programs\\Python\\Python36\\lib\\site-packages\\tensorflow\\python\\pywrap_tensorflow_internal.py\",\n  line 28, in \n      _pywrap_tensorflow_internal = swig_import_helper()   File \"C:\\Users\\anime\\AppData\\Local\\Programs\\Python\\Python36\\lib\\site-packages\\tensorflow\\python\\pywrap_tensorflow_internal.py\",\n  line 24, in swig_import_helper\n      _mod = imp.load_module('_pywrap_tensorflow_internal', fp, pathname, description)   File\n  \"C:\\Users\\anime\\AppData\\Local\\Programs\\Python\\Python36\\lib\\imp.py\",\n  line 243, in load_module\n      return load_dynamic(name, filename, file)   File \"C:\\Users\\anime\\AppData\\Local\\Programs\\Python\\Python36\\lib\\imp.py\",\n  line 343, in load_dynamic\n      return _load(spec) ImportError: DLL load failed: The specified module could not be found.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":358,"Q_Id":52767007,"Users Score":0,"Answer":"you can uninstall the tensorflow and re-install it.","Q_Score":1,"Tags":"python,python-3.x,tensorflow","A_Id":58498554,"CreationDate":"2018-10-11T18:47:00.000","Title":"Importing tensorflow not working when upgraded","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have the next problem, I'm trying to classify one of four hand position, I'm using SVM and this positions will be used to make commands in my program, the predict function works fine, but for example if I made some other gestures (none of the 4 that I use for commands) the predict function try to classify this gesture in one of the original four, I want to know if is possible to say \"this gesture is none of the four that I know\".\nThe final function of this is similar to the behaivor of some commands of the kinect in Xbox for example, you can move your hands but the Xbox will only react to a especifict gesture.\nI'm using Python, pandas, scikit-learn and leap motion, I training my network with data collected from four gestures.\nThanks in advance.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":87,"Q_Id":52772906,"Users Score":0,"Answer":"Instead of treating this as a classification problem with 4 levels, treat it as a classification problem with 5 levels. 4 levels would correspond to one of the original four and the 5th level can be used for all others.","Q_Score":0,"Tags":"python,python-3.x,machine-learning,scikit-learn,leap-motion","A_Id":52773168,"CreationDate":"2018-10-12T05:36:00.000","Title":"How to add a none option in scikit learn predict","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I wanted to read some input from the csv file and then modify the input and replace it with the new value. For this purpose, I first read the value but then I'm stuck at this point as I want to modify all the values present in the file.\nSo is it possible to open the file in r mode in one for loop and then immediately in w mode in another loop to enter the modified data?\nIf there is a simpler way to do this please help me out\nThank you.","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":9000,"Q_Id":52773491,"Users Score":1,"Answer":"You can do open(\"data.csv\", \"rw\"), this allows you to read and write at the same time.","Q_Score":1,"Tags":"python,python-3.x","A_Id":52773601,"CreationDate":"2018-10-12T06:25:00.000","Title":"Reading and Writing into CSV file at the same time","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there a way in Keras to cross-validate the early stopping metric being monitored EarlyStopping(monitor = 'val_acc', patience = 5)? Before allowing training to proceed to the next epoch, could the model be cross-validated to get a more robust estimate of the test error? What I have found is that the early stopping metric, say the accuracy on a validation set, can suffer from high variance. Early-stopped models often do not perform nearly as well on unseen data, and I suspect this is because of the high variance associated with the validation set approach.\nTo minimize the variance in the early stopping metric, I would like to k-fold cross-validate the early stopping metric as the model trains from epoch i to epoch i + 1. I would like to take the model at epoch i, divide the training data into 10 parts, learn on 9 parts, estimate the error on the remaining part, repeat so that all 10 parts have had a chance to be the validation set, and then proceed with training to epoch i + 1 with the full training data as usual. The average of the 10 error estimates will hopefully be a more robust metric that can be used for early stopping.\nI have tried to write a custom metric function that includes k-fold cross-validation but I can't get it to work. Is there a way to cross-validate the early stopping metric being monitored, perhaps through a custom function inside the Keras model or a loop outside the Keras model? \nThanks!!","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":960,"Q_Id":52788635,"Users Score":0,"Answer":"I imagine that using a callback as suggested by @VincentPakson would be cleaner and more efficient, but the level of programming required is beyond me. I was able to create a for loop to do what I wanted by:\n\nTraining a model for a single epoch and saving it using model.save().\nLoading the saved model and training the model for a single epoch for each of the 10 folds (i.e. 10 models), then averaging the 10 validation set errors.\nLoading the saved model and training for a single epoch using all of the training data, and the overwriting the saved model with this model.\nRepeating steps 1-3 until the estimate from 2 stops improving for a given patience.\n\nI'd love a better answer but this seems to work. Slowly.","Q_Score":6,"Tags":"python,keras,prediction,cross-validation","A_Id":52843229,"CreationDate":"2018-10-13T01:01:00.000","Title":"Early Stopping with a Cross-Validated Metric in Keras","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am currently setting the vector size by using model.add(LSTM(50)) i.e setting the value in units attribute but I highly doubt its correctness(In keras documentation, units is explained as dimensionality of the output space). Anyone who can help me here?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":567,"Q_Id":52789325,"Users Score":0,"Answer":"If by vector size you mean the number of nodes in a layer, then yes you are doing it right. The output dimensionality of your layer \nis the same as the number of nodes. The same thing applies to convolutional layers, number of filters and output dimensionality along the last axis, aka number of color channels is the same.","Q_Score":0,"Tags":"python,keras,lstm","A_Id":52846778,"CreationDate":"2018-10-13T03:42:00.000","Title":"How to set size of hidden state vector in LSTM, keras?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have been using pandas for a while and it worked fine but out of nowhere, it decided to give me this error \n\nAttributeError(\"module 'pandas' has no attribute 'read_csv'\")\n\nNow I have spent many many hours trying to solve this issue viewing every StackOverflow forum but they don't help. \n\nI know where both my cvs + python files are located.\nMy script is not called cvs.py or anything such.\nMy code can literally just be `'import pandas as pd' and I get the\nno attribute error.\n\nI would appreciate if someone could spare the time + assist me in solving this problem.","AnswerCount":5,"Available Count":5,"Score":0.0,"is_accepted":false,"ViewCount":5427,"Q_Id":52791477,"Users Score":0,"Answer":"Since I had this problem just now, and on the whole internet no answer covered my issue:\nYou may have pandas installed (Like I did), but in the wrong environment. Especially when you just start out in Python and use an IDE like PyCharm, you don't realise that you may create a new Environment (Called \"pythonProject\", \"pythonProject1\", pythonProject2\",... by default), and installing a package does not mean it is installed in all Environments.\nIf you have AnacondaNavigator installed, you can easily look up which Environment has which packages.\n\nThis is a very cruel oversight, as PyCharm doesn't warn you of the misplaced package; it just looks up if it is somewhere; so you don't get the error at import.","Q_Score":4,"Tags":"python,pandas,csv","A_Id":64389564,"CreationDate":"2018-10-13T09:27:00.000","Title":"'pandas' has no attribute 'read_csv'\"","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have been using pandas for a while and it worked fine but out of nowhere, it decided to give me this error \n\nAttributeError(\"module 'pandas' has no attribute 'read_csv'\")\n\nNow I have spent many many hours trying to solve this issue viewing every StackOverflow forum but they don't help. \n\nI know where both my cvs + python files are located.\nMy script is not called cvs.py or anything such.\nMy code can literally just be `'import pandas as pd' and I get the\nno attribute error.\n\nI would appreciate if someone could spare the time + assist me in solving this problem.","AnswerCount":5,"Available Count":5,"Score":0.0798297691,"is_accepted":false,"ViewCount":5427,"Q_Id":52791477,"Users Score":2,"Answer":"I've just been spinning my wheels on the same problem. \nTL\/DR: try renaming your python files\nI think there must be a number of other naming conflicts besides some of the conceivably obvious ones like csv.py and pandas.py mentioned in other posts on the topic.\nIn my case, I had a single file called inspect.py. Running on the command line gave me the error, as did running import pandas from within a python3 shell, but only when launching the shell from the same directory as inspect.py. I renamed inspect.py, and now it works just fine!!","Q_Score":4,"Tags":"python,pandas,csv","A_Id":53273547,"CreationDate":"2018-10-13T09:27:00.000","Title":"'pandas' has no attribute 'read_csv'\"","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have been using pandas for a while and it worked fine but out of nowhere, it decided to give me this error \n\nAttributeError(\"module 'pandas' has no attribute 'read_csv'\")\n\nNow I have spent many many hours trying to solve this issue viewing every StackOverflow forum but they don't help. \n\nI know where both my cvs + python files are located.\nMy script is not called cvs.py or anything such.\nMy code can literally just be `'import pandas as pd' and I get the\nno attribute error.\n\nI would appreciate if someone could spare the time + assist me in solving this problem.","AnswerCount":5,"Available Count":5,"Score":0.0,"is_accepted":false,"ViewCount":5427,"Q_Id":52791477,"Users Score":0,"Answer":"I have faced the same problem when I update my python packages using conda update --all.\nThe error:\nAttributeError: module 'pandas' has no attribute 'read_csv'\nI believe it is a pandas path problem.\nThe solution:\nprint(pd)\nTo see where are your pandas come from. I was getting\n<module 'pandas' (namespace)>\nThen I used to print(np), for example, to see where my numpy is, then I got\n<module 'numpy' from 'C:\\\\Users\\\\name\\\\Anaconda3\\\\envs\\\\eda_env\\\\lib\\\\site-packages\\\\numpy\\\\__init__.py'>\nI used the same path to find my pandas path. I found out that the folder lib named Lib with an uppercase letter. I change it to lowercase lib, and it solves my problem.\nChange Lib to lib or check a working module and make sure pandas have the same.","Q_Score":4,"Tags":"python,pandas,csv","A_Id":68730346,"CreationDate":"2018-10-13T09:27:00.000","Title":"'pandas' has no attribute 'read_csv'\"","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have been using pandas for a while and it worked fine but out of nowhere, it decided to give me this error \n\nAttributeError(\"module 'pandas' has no attribute 'read_csv'\")\n\nNow I have spent many many hours trying to solve this issue viewing every StackOverflow forum but they don't help. \n\nI know where both my cvs + python files are located.\nMy script is not called cvs.py or anything such.\nMy code can literally just be `'import pandas as pd' and I get the\nno attribute error.\n\nI would appreciate if someone could spare the time + assist me in solving this problem.","AnswerCount":5,"Available Count":5,"Score":0.0399786803,"is_accepted":false,"ViewCount":5427,"Q_Id":52791477,"Users Score":1,"Answer":"After spending 2 hours researching a solution to this question, running pip uninstall pandas and then pip install pandas in your terminal will work.","Q_Score":4,"Tags":"python,pandas,csv","A_Id":69326903,"CreationDate":"2018-10-13T09:27:00.000","Title":"'pandas' has no attribute 'read_csv'\"","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have been using pandas for a while and it worked fine but out of nowhere, it decided to give me this error \n\nAttributeError(\"module 'pandas' has no attribute 'read_csv'\")\n\nNow I have spent many many hours trying to solve this issue viewing every StackOverflow forum but they don't help. \n\nI know where both my cvs + python files are located.\nMy script is not called cvs.py or anything such.\nMy code can literally just be `'import pandas as pd' and I get the\nno attribute error.\n\nI would appreciate if someone could spare the time + assist me in solving this problem.","AnswerCount":5,"Available Count":5,"Score":0.0399786803,"is_accepted":false,"ViewCount":5427,"Q_Id":52791477,"Users Score":1,"Answer":"I had the same issue and it is probably cause of writing\ndataframe = pd.read.csv(\"dataframe.csv\")\ninstead of\ndataframe = pd.read_csv(\"dataframe.csv\")\nthat little \"_\" is the problem.\nHope this helps somebody else too.","Q_Score":4,"Tags":"python,pandas,csv","A_Id":64144515,"CreationDate":"2018-10-13T09:27:00.000","Title":"'pandas' has no attribute 'read_csv'\"","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there any workaround how to use cv2.imshow() with a specific framerate? Im capturing the video via VideoCapture and doing some easy postprocessing on them (both in a separeted thread, so it loads all frames in Queue and the main thread isn't slowed by the computation). I tryed to fix the framerate by calculating the time used for \"reading\" the image from the queue and then substract that value from number of miliseconds avalible for one frame:\nif I have as input video with 50FPS and i want to playback it in real-time i do 1000\/50 => 20ms per frame. \nAnd then wait that time using cv2.WaitKey()\nBut still I get some laggy output. Which is slower then the source video","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2282,"Q_Id":52806175,"Users Score":1,"Answer":"I don't believe there is such a function in opencv but maybe you could improve your method by adding a dynamic wait time using timers? timeit.default_timer()\ncalculate the time taken to process and subtract that from the expected framerate and maybe add a few ms buffer.\neg cv2.WaitKey((1000\/50) - (time processing finished - time read started) - 10)\nor you could have a more rigid timing eg script start time + frame# * 20ms - time processing finished\nI haven't tried this personally so im not sure if it will actually work, also might be worth having a check so the number isnt below 1","Q_Score":0,"Tags":"python,opencv","A_Id":52806830,"CreationDate":"2018-10-14T19:10:00.000","Title":"imshow() with desired framerate with opencv","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to implement neurons freezing in CNN for a deep learning research,\nI tried to find any function in the Tensorflow docs, but I didn't find anything.\nHow can I freeze specific neuron when I implemented the layers with tf.nn.conv2d?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":473,"Q_Id":52814880,"Users Score":0,"Answer":"A neuron in a dense neural network layer simply corresponds to a column in a weight matrix. You could therefore redefine your weight matrix as a concatenation of 2 parts\/variables, one trainable and one not. Then you could either:\n\nselectively pass only the trainable part in the var_list argument of the minimize function of your optimizer, or\nUse tf.stop_gradient on the vector\/column corresponding to the neuron you want to freeze.\n\nThe same concept could be used for convolutional layers, although in this case the definition of a \"neuron\" becomes unclear; still, you could freeze any column(s) of a convolutional kernel.","Q_Score":0,"Tags":"python,tensorflow,deep-learning","A_Id":52816856,"CreationDate":"2018-10-15T10:41:00.000","Title":"Neuron freezing in Tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I cannot install package in Python.For example the package numpy or pandas.I download the python today.\nI press import numpy as np and nothing","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":40,"Q_Id":52839205,"Users Score":1,"Answer":"You have to install it first. Search \u201cPython Pip\u201d on google and download Pip. Then use that to open CMD and type \u201cpip install (Module)\u201d. Then it should import with no errors.","Q_Score":0,"Tags":"python,package","A_Id":52839277,"CreationDate":"2018-10-16T15:37:00.000","Title":"Cannot import python pack","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to deploy my flask app on Pythonanywhere but am getting an error i have no idea what to do about. I've looked online and people haven't been getting similar errors like mine. \nMy app loads a bunch of pretrained ML models. \nWould love some help!\n\n2018-10-16 20:52:38 \/home\/drdesai\/.virtualenvs\/flask-app-env\/lib\/python3.6\/site-packages\/sklearn\/base.py:251: UserWarning: Trying to unpickle estimator LinearRegression from version 0.19.1 when using version 0.20.0. This might lead to breaking code or invalid results. Use at your own risk.#012  UserWarning)\n2018-10-16 20:52:38 !!! uWSGI process 1 got Segmentation Fault !!!\n2018-10-16 20:52:38 * backtrace of 1 *#012\/usr\/local\/bin\/uwsgi(uwsgi_backtrace+0x2c) [0x46529c]#012\/usr\/local\/bin\/uwsgi(uwsgi_segfault+0x21) [0x465661]#012\/lib\/x86_64-linux-gnu\/libc.so.6(+0x36cb0) [0x7f6ed211ccb0]#012\/home\/drdesai\/.virtualenvs\/flask-app-env\/lib\/python3.6\/site-packages\/sklearn\/neighbors\/kd_tree.cpython-36m-x86_64-linux-gnu.so(+0x404b6) [0x7f6ead1d54b6]#012\/usr\/lib\/x86_64-linux-gnu\/libpython3.6m.so.1.0(_PyCFunction_FastCallDict+0x105) [0x7f6ed0e80005]#012\/usr\/lib\/x86_64-linux-gnu\/libpython3.6m.so.1.0(+0x16b5fa) [0x7f6ed0f195fa]#012\/usr\/lib\/x86_64-linux-gnu\/libpython3.6m.so.1.0(_PyEval_EvalFrameDefault+0x2f3c) [0x7f6ed0f1ccfc]#012\/usr\/lib\/x86_64-linux-gnu\/libpython3.6m.so.1.0(+0x16a890) [0x7f6ed0f18890]#012\/usr\/lib\/x86_64-linux-gnu\/libpython3.6m.so.1.0(+0x16b7b4) [0x7f6ed0f197b4]#012\/usr\/lib\/x86_64-linux-gnu\/libpython3.6m.so.1.0(_PyEval_EvalFrameDefault+0x2f3c) [0x7f6ed0f1ccfc]#012\/usr\/lib\/x86_64-linux-gnu\/libpython3.6m.so.1.0(+0x16a890) [0x7f6ed0f18890]#012\/usr\/lib\/x86_\n2018-10-16 20:52:38 chdir(): No such file or directory [core\/uwsgi.c line 1610]\n2018-10-16 20:52:38 VACUUM: unix socket \/var\/sockets\/drdesai.pythonanywhere.com\/socket removed.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1381,"Q_Id":52844037,"Users Score":0,"Answer":"uWSGI is a C\/C++ compiled app and segmentation fault is its internal error that means that there is some incorrect behavior in uWSGI logic: somewhere in its code it's trying to get access to area of memory it's not allowed to access to, so OS kills this process and returns \"segfault\" error. So make sure you have the latest stable version of uwsgi installed. Also make sure you installed it properly either using a package manager or via manual compiling. It's recommended to install it using a package manager since it's much more easy than via manual compiling. Also, make sure you use it properly.","Q_Score":0,"Tags":"python,flask,deployment,wsgi,pythonanywhere","A_Id":52859940,"CreationDate":"2018-10-16T21:04:00.000","Title":"uWSGI process 1 got Segmentation Fault _ Fail to deploy Flask App on Pythonanywhere","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I am execute a python script in Azure machine learning studio. I am including other python scripts and python library, Theano. I can see the Theano get loaded and I got the proper result after script executed. But I saw the error message:\n\nWARNING (theano.configdefaults): g++ not detected ! Theano will be unable to execute optimized C-implementations (for both CPU and GPU) and will default to Python implementations. Performance will be severely degraded. To remove this warning, set Theano flags cxx to an empty string.\n\nDid anyone know how to solve this problem? Thanks!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":96,"Q_Id":52844431,"Users Score":1,"Answer":"I don't think you can fix that - the Python script environment in Azure ML Studio is rather locked down, you can't really configure it (except for choosing from a small selection of Anaconda\/Python versions). \nYou might be better off using the new Azure ML service, which allows you considerably more configuration options (including using GPUs and the like).","Q_Score":1,"Tags":"python,theano,azure-machine-learning-studio","A_Id":52850290,"CreationDate":"2018-10-16T21:43:00.000","Title":"Azure Machine Learning Studio execute python script, Theano unable to execute optimized C-implementations (for both CPU and GPU)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am attempting to import an Excel file into a new table in a Teradata database, using SQLAlchemy and pandas.\nI am using the pandas to_sql function. I load the Excel file with pandas and save it as a dataframe named df. I then use df.to_sql and load it into the Teradata database.\nWhen using the code:\ndf.to_sql('rt_test4', con=td_engine, schema='db_sandbox')\nI am prompted with the error:\nDatabaseError: (teradata.api.DatabaseError) (3534, '[42S11] [Teradata][ODBC Teradata Driver][Teradata Database] Another index already exists, using the same columns and the same ordering. ') [SQL: 'CREATE INDEX ix_db_sandbox_rt_test4_index (\"index\") ON db_sandbox.rt_test4']\nWhen I try this and use Teradata SQL Assistant to see if the table exists, I am prompted with selecting txt or unicode for each column name, and to pick a folder directory. A prompt titled LOB information pops open and I have to select if it's UTF or unicode, and a file directory. Then it loads and all the column titles populate, but they are left as empty fields. Looking for some direction here, I feel I've been spinning my wheels on this.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":780,"Q_Id":52847985,"Users Score":1,"Answer":"I solved it! Although I do not know why, I'm hoping someone can explain:\ntf.to_sql('rt_test4', con=td_engine, schema='db_sandbox', index = False, dtype= {'A': CHAR, 'B':Integer})","Q_Score":1,"Tags":"python,pandas,sqlalchemy,teradata","A_Id":52848240,"CreationDate":"2018-10-17T05:46:00.000","Title":"Importing a pandas dataframe into Teradata database","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I built tensorflow from source and got a *.whl file that I could install on my pc with pip install *.whl. Now in the virtualenv where I installed it I can open python and do import tensorflow without a problem and also use tf. Now I tried to install this same wheel on an other pc in a virtualenv and it worked successfully, but when I try to use import tensorflow in python I get: \nImportError: libnvidia-fatbinaryloader.so.390.48: cannot open shared object file: No such file or directory\nNow I actually do not have that file on the other pc, but after checking my own pc I also don't have it here. I have on both pcs libnvidia-fatbinaryloader.so.390.87. On both pcs the LD_LIBRARY_PATH points to the directory with that version. \nHow can it be that tensorflow searches for version 48 on the remote pc while searching for 87 and finding it on my pc, even though they are both installed with the same whl file? Is there a config that I need to adjust what version it should search for?","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":48,"Q_Id":52854983,"Users Score":1,"Answer":"I would say you have a broken CUDA installation somewhere in the library path. It is libcuda.so that has a dependency on libnvidia-fatbinaryloader.so, so maybe the symbolic links point to a library that no longer exists but was installed before.\nYou can find this information by running the ldd command on the libcuda.so file.","Q_Score":0,"Tags":"python,tensorflow","A_Id":52855692,"CreationDate":"2018-10-17T12:33:00.000","Title":"Why are different libraries searched for in tensorflow, even though both were installed the same way?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I built tensorflow from source and got a *.whl file that I could install on my pc with pip install *.whl. Now in the virtualenv where I installed it I can open python and do import tensorflow without a problem and also use tf. Now I tried to install this same wheel on an other pc in a virtualenv and it worked successfully, but when I try to use import tensorflow in python I get: \nImportError: libnvidia-fatbinaryloader.so.390.48: cannot open shared object file: No such file or directory\nNow I actually do not have that file on the other pc, but after checking my own pc I also don't have it here. I have on both pcs libnvidia-fatbinaryloader.so.390.87. On both pcs the LD_LIBRARY_PATH points to the directory with that version. \nHow can it be that tensorflow searches for version 48 on the remote pc while searching for 87 and finding it on my pc, even though they are both installed with the same whl file? Is there a config that I need to adjust what version it should search for?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":48,"Q_Id":52854983,"Users Score":0,"Answer":"The building process is related to the computer environment.Could building tensorflow in the same machine and installing it on the same machine help?Building on one machine and generating the *.whl,but installing on other machines may cause problem.","Q_Score":0,"Tags":"python,tensorflow","A_Id":52855249,"CreationDate":"2018-10-17T12:33:00.000","Title":"Why are different libraries searched for in tensorflow, even though both were installed the same way?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I created an R kernel to use in a Jupyter notebook with:\nconda create -n myrenv r-essentials -c r\nAnd when running Jupyter, in the menu to create a new notebook, i can see the choice of my new kernel new --> R [conda env:myrenv] but I also have the choice (among others) of new --> Python [conda env:myrenv]. \nHow can I remove the latter environment from the list? I do not even know why python would be in my R environment.\nAdditional info:\nconda 4.5.11","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":86,"Q_Id":52857461,"Users Score":2,"Answer":"r-essentials comes with python as well as the jupyter_client and the ipykernel packages which enables your jupyter to propose this R and thus the python installed as kernels in a notebook. ipykernel is mandatory for the jupyter to propose the R as a kernel and python is a dependency to ipykernel so...\nI don't think you can remove python from the list of the kernels proposed. If you remove python from the conda environment, it also removes the ipykernel and the jupyter_client packages. All you can do is ignore it.\nEDIT: found more infos\nAfter looking into this since I wanted to do the same thing, it seems jupyter has a nice built-in program to do this:\nRun\njupyter-kernelspec list\nto list all available kernels. Then you can remove one with\njupyter-kernelspec remove <kernel_to_remove>\nif you want to remove the kernel.\nHOWEVER, it seems that you CANNOT remove the python3 kernel. Even though I ran:\njupyter-kernelspec remove python3\npython3 still appears in the list and is still an available kernel in the notebook...","Q_Score":0,"Tags":"python,r,jupyter-notebook,conda","A_Id":52859799,"CreationDate":"2018-10-17T14:35:00.000","Title":"Installing R kernel with conda creates an unwanted addtional python kernel in Jupyter","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Normally in a jupyter notebook I would use %matplotlib notebook magic to display an interactive window, however this doesn't seem to work with google colab. Is there a solution, or is it not possible to display interactive windows in google colab?","AnswerCount":5,"Available Count":1,"Score":0.0399786803,"is_accepted":false,"ViewCount":44387,"Q_Id":52859983,"Users Score":1,"Answer":"In addition to @Nilesh Ingle excellent answer, in order to solve the problem of axes and title not displaying :\nyou should change the link https:\/\/cdn.plot.ly\/plotly-1.5.1.min.js?noext (which refers to an older version of plotly, thus not displaying axes labels) by https:\/\/cdn.plot.ly\/plotly-1.5.1.min.js?noext when calling the script in the function configure_plotly_browser_state().\nHope this would help !","Q_Score":50,"Tags":"python,matplotlib,google-colaboratory","A_Id":61304539,"CreationDate":"2018-10-17T16:54:00.000","Title":"Interactive matplotlib figures in Google Colab","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I used one set of data to learn a Random Forest Regressor and right now I have another dataset with smaller number of features (the subset of the previous set).\nIs there a function which allows to get the list of names of columns used during the training of the Random Forest Regressor model?\nIf not, then is there a function which for the missing columns would assign Nulls?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":216,"Q_Id":52861742,"Users Score":0,"Answer":"Is there a function which allows to get the list of names of columns\n  used during the training of the Random Forest Regressor model?\n\nRF uses all features from your dataset. Each tree may contain sqrt(num_of_features) or log2(num_of_features) or whatever but these columns are selected at random. So usually RF covers all columns from your dataset.\nThere may be edge case when you use a small number of estimators in RF and some features may not be considered. I suppose, RandomForestRegressor.feature_importances_ (zero or nan value may be indicators here) or dive into each tree in RandomForestRegressor.estimators_ may help.\n\nIf not, then is there a function which for the missing columns would\n  assign Nulls?\n\nRF does not accept missing values. Either you need to code missing value as the separate class (and use it for learning too) or XGBoost (for example) is your choice.","Q_Score":0,"Tags":"python,pandas,scikit-learn,random-forest","A_Id":52873525,"CreationDate":"2018-10-17T18:52:00.000","Title":"Getting the list of features used during training of Random Forest Regressor","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Assuming I have an excel sheet already open, make some changes in the file and use pd.read_excel to create a dataframe based on that sheet, I understand that the dataframe will only reflect the data in the last saved version of the excel file. I would have to save the sheet first in order for pandas dataframe to take into account the change.\nIs there anyway for pandas or other python packages to read an opened excel file and be able to refresh its data real time (without saving or closing the file)?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1261,"Q_Id":52862768,"Users Score":1,"Answer":"There is no way to do this. The table is not saved to disk, so pandas can not read it from disk.","Q_Score":10,"Tags":"python,excel,pandas","A_Id":72310873,"CreationDate":"2018-10-17T20:03:00.000","Title":"How do I use python pandas to read an already opened excel sheet","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a gazillion CSV files and in column 2 is the x-data and column 3 is the y-data. Each CSV file is a different time stamp. The x-data is slightly different in each file, but the number of rows is constant. I'm happy to assume the x-data is in fact identical.\nI am persuaded that Tableau is a good interface for me to do some visualization and happily installed tabpy and \"voila\", I can call python from Tableau... except...  to return an array I will need to return a string with comma separated values for each time stamp, and then one of those strings per x-axis and then.... Hmm, that doesnt sound right.\nI tried telling Tableau just open them all and I'd join them later, but gave up after 30 mins of it crunching.\nSo what do you reckon? I am completely agnostic. Install an SQL server and create a database? Create a big CSV file that has a time-stamp for each column? Google? JSON?\nOr maybe there is some clever way in Tableau to loop through the CSV files?","AnswerCount":3,"Available Count":2,"Score":0.0665680765,"is_accepted":false,"ViewCount":1928,"Q_Id":52863882,"Users Score":1,"Answer":"I would suggest doing any data prep outside of Tableau. Since you seem to be familiar with Python, try Pandas to combine all the csv files into one dataframe then output to a database or a single csv. Then connect to that single source.","Q_Score":0,"Tags":"python,csv,tableau-api","A_Id":52864018,"CreationDate":"2018-10-17T21:28:00.000","Title":"Load thousands of CSV files into tableau","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a gazillion CSV files and in column 2 is the x-data and column 3 is the y-data. Each CSV file is a different time stamp. The x-data is slightly different in each file, but the number of rows is constant. I'm happy to assume the x-data is in fact identical.\nI am persuaded that Tableau is a good interface for me to do some visualization and happily installed tabpy and \"voila\", I can call python from Tableau... except...  to return an array I will need to return a string with comma separated values for each time stamp, and then one of those strings per x-axis and then.... Hmm, that doesnt sound right.\nI tried telling Tableau just open them all and I'd join them later, but gave up after 30 mins of it crunching.\nSo what do you reckon? I am completely agnostic. Install an SQL server and create a database? Create a big CSV file that has a time-stamp for each column? Google? JSON?\nOr maybe there is some clever way in Tableau to loop through the CSV files?","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1928,"Q_Id":52863882,"Users Score":0,"Answer":"If you are using Windows, you can combine all the csv files into a single csv, then import that into Tableau. This of course assumes that all of your csv files have the same data structure.\n\nOpen the command prompt\nNavigate to the directory where the csv files are (using the cd command)\nUse the command copy *.csv combined-file.csv. The combined-file.csv can be whatever name you want.","Q_Score":0,"Tags":"python,csv,tableau-api","A_Id":52864473,"CreationDate":"2018-10-17T21:28:00.000","Title":"Load thousands of CSV files into tableau","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I know how to import and manipulate data from csv, but I always need to save to xlsx or so to see the changes. Is there a way to see 'live changes' as if I am already using Excel? \nPS using pandas\nThanks!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":47,"Q_Id":52871447,"Users Score":0,"Answer":"This is not possible using pandas. This lib creates copy of your .csv \/ .xls file and stores it in RAM. So all changes are applied to file stored in you memory not on disk.","Q_Score":0,"Tags":"python,pandas","A_Id":52871555,"CreationDate":"2018-10-18T09:53:00.000","Title":"Is it possible to manipulate data from csv without the need for producing a new csv file?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using Anaconda3-5.3.0-Windows-x86_64 release and experienced the following problem:\nWhile running import command (e.g. - import numpy as np), from Jupyter notebook,  I receive the following error -\n\nThe ordinal 241 could not be located in the dynamic link library path:\\mkl_intel_thread.dll>\n\nWhere 'path' is the path to anaconda directory in my Win10 PC.\nI tried the following in order to overcome this issue -\n\nReinstall anaconda from scratch\nUpdate the .dll to latest one\nUpdate Win10 path to search the right folders\n\nUnfortunately - non of the above methods worked for me.\nCan someone offer some solution \/ new ideas to check?\nThank you all in advance!","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":930,"Q_Id":52871597,"Users Score":1,"Answer":"Eventually, I came to a conclusion that there is a problem with adaptation of this Anaconda version with my Win 8.1.\nSo, I downgraded Anaconda version to Anaconda3-5.2.0-Windows-x86_64 and that solved the issue.","Q_Score":1,"Tags":"python,anaconda,jupyter","A_Id":52928244,"CreationDate":"2018-10-18T10:03:00.000","Title":"Ordinal 241 could not be located","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"When I append elements to a list that have the following format and type:\ndata.append([float, float, string]) \nThen stack the list using:\ndata = np.hstack(data)\nAnd then finally reshape the array and transpose using:\ndata = np.reshape(data,(-1,3)).T\nAll the array elements in data are changed to strings. I want (and expected) the first and second columns in data to be of type float and the third of type string, but instead they are all of type string. [Interestingly, if I do not append the string elements to data and adjust the newshape to (-1,2), both columns are floats.] I cannot figure this one out. Any help would be appreciated.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":397,"Q_Id":52876380,"Users Score":1,"Answer":"Because of the mix of numbers and strings, np.array will use the common format: string. The solution here is to convert data to type object which supports mixed element types. This is performed by using: \ndata = np.array(data, dtype=object)\nprior to hstack.","Q_Score":1,"Tags":"python,arrays,append,reshape","A_Id":52880527,"CreationDate":"2018-10-18T14:31:00.000","Title":"Elements in array change data types from float to string","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I read the following sentence in the TensorFlow documentation:\n\nWith the exception of tf.Variable, the value of a tensor is immutable,\n  which means that in the context of a single execution tensors only\n  have a single value. However, evaluating the same tensor twice can\n  return different values; for example that tensor can be the result of\n  reading data from disk, or generating a random number.\n\nCan someone elaborate a little bit on the \"immutable\" aspect of a Tensor?\n\nWhat is the \"scope of the immutability\" since evaluating a tensor twice could return different results? \nWhat does it mean \"the context of a single execution\"?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2114,"Q_Id":52879126,"Users Score":6,"Answer":"Tensors, differently from variables, can be compared to a math equation.\nWhen you say a tensor equals 2+2, it's value is not actually 4, it's the computing instructions that leads to the value of 2+2 and when you start a session an execute it, TensorFlow runs the computations needed to return the value of 2+2 and gives you the output. And because of the tensor beeing the computations, rather than the the result, a tensor is immutable\nNow for your questions:\n\nBy saying the tensor can be evaluated with different values it means that if you for example say that a tensor equals to a random number, when you run it different times, you will have different values (as the equation itself is a random one), but the value of the tensor itself as mentioned before, is not the value, is the steps that leads to it (in this case a random formula)\nThe context of a single execution means that when you run a tensor, it will only output you one value. Think executing a tensor like applying the equation i mentioned. If i say a tensor equals random + 1, when you execute the tensor a single time, it will return you a random value +1, nothing else. But since the tensor contains a randomic output, if you run it multiple times, you will most likely get different values","Q_Score":4,"Tags":"python,tensorflow","A_Id":52879296,"CreationDate":"2018-10-18T17:03:00.000","Title":"How are tensors immutable in TensorFlow?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have data frame with a object column lets say col1, which has values likes:\n1.00,\n1,\n0.50,\n1.54\nI want to have the output like the below:\n1,\n1,\n0.5,\n1.54\nbasically, remove zeros after decimal values if it does not have any digit after zero. Please note that i need answer for dataframe. pd.set_option and round don't work for me.","AnswerCount":5,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":5418,"Q_Id":52889130,"Users Score":0,"Answer":"A quick-and-dirty solution is to use \"%g\" % value, which will convert floats 1.5 to 1.5 but 1.0 to 1 and so on. The negative side-effect is that large numbers will be represented in scientific notation like 4.44e+07.","Q_Score":2,"Tags":"python,pandas","A_Id":52889192,"CreationDate":"2018-10-19T09:04:00.000","Title":"how to remove zeros after decimal from string remove all zero after dot","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to track a moving deformable object in a video (but only 2D space). How do I find the paths (subpaths) revisited by the object in the span of its whole trajectory? For instance, if the object traced a path, p0-p1-p2-...-p10, I want to find the number of cases the object traced either p0-...-p10 or a sub-path like p3-p4-p5. Here, p0,p1,...,p10 represent object positions (in (x,y) pixel coordinates at the respective instants). Also, how do I know at which frame(s) these paths (subpaths) are being revisited?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":77,"Q_Id":52901800,"Users Score":0,"Answer":"I would first create a detection procedure that outputs a list of points visited along with their video frame number. Then use list exploration functions to know how many redundant suites are found and where.\nAs you see I don't write your code. If you need anymore advise please ask!","Q_Score":1,"Tags":"python,python-2.7,video-tracking","A_Id":52943459,"CreationDate":"2018-10-20T01:58:00.000","Title":"How to find redundant paths (subpaths) in the trajectory of a moving object?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have data that contains English text messages.\nI want to detect messages that are \"written in English letters\", but aren't English words. (For example with codes based rules, but I don't want to hard coded the rules).\nPlease note that the computer being used does not have an active internet connection (so I cannot check against online dictionary).\nExample Data\n\n\"hello how are you\"\n\"fjrio kjfdelf ejfe\" <-- code (let's say is means \"how are you\" in spanish)\n\"i am fine thanks\"\n\"10x man\"\n\"jfrojf feoif\" <-- code (let's say it means \"hello world\" in japanish)\n\nI'm new to machine learning, so for my understanding, maybe one approach could \nbe using nlp?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":440,"Q_Id":52912553,"Users Score":1,"Answer":"Character frequency scanning is one way to do this.\nFor example for each language obtain a list of character frequencies,\nA: 3%\nB: 1%\nC: 0.5%\nD: 0.7%\nE: 4%\netc..\nThen evaluate your string's character frequency against your static map. You can obtain a probabilistic model of the likelihood of the string being one of your languages.\nOf course this works best for longer strings where there is enough statistical data to capture the true frequency. You would also need to train your frequencies on samples from your target source, e.g. English tweets likely have a different letter frequency to works of Shakespeare. \nAnother option is to find the most likely n-grams in a language, e.g, 'we' is a common 2-gram in english. If you scan your code for how often these most likely n-grams occur you can generally detect if something is in a specific language or not.\nI'm sure there are also other ideas or combinations of classifiers, but this gives you a start. Don't underestimate the power of an ensemble of classifiers either. For example suppose you came up with 3 different models that were all different and uncorrelated, and say each model could detect english correctly 3 times out of 4 (75%). If you then used all 3 models with an equally weighted vote, so if 3 of 3 or 2 of 3 voted english it was classed as english then your error improves to about 3.4 times correct from 4 (85%) (=0.75^3 + 3*0.75^2*0.25)","Q_Score":1,"Tags":"python,tensorflow,machine-learning","A_Id":52912811,"CreationDate":"2018-10-21T06:05:00.000","Title":"How To Detect English Language Words Using Machine Learning From Data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a huge number of 128-bit unsigned integers that need to be sorted for analysis (around a trillion of them!).\nThe research I have done on 128-bit integers has led me down a bit of a blind alley, numpy doesn't seem to fully support them and the internal sorting functions are memory intensive (using lists).\nWhat I'd like to do is load, for example, a billion 128-bit unsigned integers into memory (16GB if just binary data) and sort them. The machine in question has 48GB of RAM so should be OK to use 32GB for the operation. If it has to be done in smaller chunks that's OK, but doing as large a chunk as possible would be better. Is there a sorting algorithm that Python has which can take such data without requiring a huge overhead? \nI can sort 128-bit integers using the .sort method for lists, and it works, but it can't scale to the level that I need. I do have a C++ version that was custom written to do this and works incredibly quickly, but I would like to replicate it in Python to accelerate development time (and I didn't write the C++ and I'm not used to that language).\nApologies if there's more information required to describe the problem, please ask anything.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1852,"Q_Id":52920727,"Users Score":0,"Answer":"I was probably expecting too much from Python, but I'm not disappointed. A few minutes of coding allowed me to create something (using built-in lists) that can process the sorting a hundred million uint128 items on an 8GB laptop in a couple of minutes.\nGiven a large number of items to be sorted (1 trillion), it's clear that putting them into smaller bins\/files upon creation makes more sense than looking to sort huge numbers in memory. The potential issues created by appending data to thousands of files in 1MB chunks (fragmentation on spinning disks) are less of a worry due to the sorting of each of these fragmented files creating a sequential file that will be read many times (the fragmented file is written once and read once).\nThe benefits of development speed of Python seem to outweigh the performance hit versus C\/C++, especially since the sorting happens only once.","Q_Score":0,"Tags":"python,sorting,numpy,int128","A_Id":52934432,"CreationDate":"2018-10-21T23:20:00.000","Title":"How can I sort 128 bit unsigned integers in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I pickled a model and want to expose only the prediction api written in Flask. However when I write a dockerfile to make a image without sklearn in it, I get an error ModuleNotFoundError: No module named 'sklearn.xxxx' where xxx refers to sklearn's ML algorithm classes, at the point where I am loading the model using pickle like classifier = pickle.load(f).  \nWhen I rewrite the dockerfile to make an image that has sklearn too, then I don't get the error even though in the API I never import sklearn.\nMy concept of pickling is very simple, that it will serialize the classifier class with all of its data. So when we unpickle it, since the classifier class already has a predict attribute, we can just call it. Why do I need to have sklearn in the environment?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1171,"Q_Id":52929649,"Users Score":0,"Answer":"The pickle is just the representation of the data inside the model. You still need the code to use it, that's why you need to have sklearn inside the container.","Q_Score":3,"Tags":"python,python-3.x,docker,scikit-learn,pickle","A_Id":52929724,"CreationDate":"2018-10-22T12:42:00.000","Title":"Why do I need sklearn in docker container if I already have the model as a pickle?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I pickled a model and want to expose only the prediction api written in Flask. However when I write a dockerfile to make a image without sklearn in it, I get an error ModuleNotFoundError: No module named 'sklearn.xxxx' where xxx refers to sklearn's ML algorithm classes, at the point where I am loading the model using pickle like classifier = pickle.load(f).  \nWhen I rewrite the dockerfile to make an image that has sklearn too, then I don't get the error even though in the API I never import sklearn.\nMy concept of pickling is very simple, that it will serialize the classifier class with all of its data. So when we unpickle it, since the classifier class already has a predict attribute, we can just call it. Why do I need to have sklearn in the environment?","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":1171,"Q_Id":52929649,"Users Score":1,"Answer":"You have a misconception of how pickle works.\nIt does not seralize anything, except of instance state (__dict__ by default, or custom implementation). When unpickling, it just tries to create instance of corresponding class (here goes your import error) and set pickled state.\nThere's a reason for this: you don't know beforehand what methods will be used after load, so you can not pickle implementation. In addition to this, in pickle time you can not build some AST to see what methods\/modules will be needed after deserializing, and main reason for this is dynamic nature of python \u2014 your implementation can actually vary depending on input.\nAfter all, even assuming that theoretically we'd have smart self-contained pickle serialization, it will be actual model + sklearn in single file, with no proper way to manage it.","Q_Score":3,"Tags":"python,python-3.x,docker,scikit-learn,pickle","A_Id":52929910,"CreationDate":"2018-10-22T12:42:00.000","Title":"Why do I need sklearn in docker container if I already have the model as a pickle?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataset with 53 independent variables (X) and 1 dependent (Y).\nThe dependent variable is a boolean (either 1 or 0), while the independent set is made of both continuous and discrete variables. \nI was planning to use pandas.DataFrame.corr() to list the most influencing variables for the output Y. \ncorr can be:\n\npearson regression\nkendall regression\nspearman regression\n\nI get different results for 3 approaches.\nDo you have suggestions on which one would be the most suitable given the shape (discrete+continuos) of the dataset?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":339,"Q_Id":52932510,"Users Score":0,"Answer":"Correlation is used only for numeric data, discrete \/ binary data need to be treated differently. Have a look at Phi coefficient for binary.\nAs for correlation coefficient (for numeric data), it depends on the relationship between the variables. If these are linear then Pearson is preferred, otherwise Spearman (or something else).","Q_Score":0,"Tags":"python,regression","A_Id":52942231,"CreationDate":"2018-10-22T15:08:00.000","Title":"python - multivariate regression with discrete and continuous","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using grid search to optimize the hyper-parameters of a Random Forest fit on a balanced data set, and I am struggling with which model evaluation metric to choose. Given the real-world context of this problem, false negatives are more costly than false positives. I initially tried optimizing recall but I was ending up with extremely high numbers of false positives. My solution is to instead optimize an f-beta score with beta > 1. My question is, how best to choose beta? If I can calculate the cost of a false negative and false positive, can I set beta = Cost of False Negative\/Cost of False Positive? Does this approach make sense?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1826,"Q_Id":52934864,"Users Score":1,"Answer":"To give more weight to the Precision, we pick a Beta value in the\ninterval 0 < Beta < 1 \nTo give more weight to the Recall, we pick a Beta Value in the interval 1 < Beta\n\nWhen you set beta = Cost of False Negative\/Cost of False Positive then you'll give more weight to recall, in case of the cost of False negative is higher than that of False positive, so it will work, but this doesn't mean that this is the optimum solution for your problem.\nOptimizing Beta is relevant to the shape of your data, so it would be better to try different values of Beta on your data until you get the best value.","Q_Score":3,"Tags":"python-3.x,machine-learning,scikit-learn,random-forest,grid-search","A_Id":56162459,"CreationDate":"2018-10-22T17:39:00.000","Title":"How to choose beta in F-beta score","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using google cloud VM with 4 Tesla K80 GPU's. \nI am running a keras model using multi_gpu_model with gpus=4(since i have 4 gpu's). But, i am getting the following error\n\nValueError: To call multi_gpu_model with gpus=4, we expect the\n  following devices to be available: ['\/cpu:0', '\/gpu:0', '\/gpu:1',\n  '\/gpu:2', '\/gpu:3']. However this machine only has: ['\/cpu:0',\n  '\/xla_cpu:0', '\/xla_gpu:0', '\/gpu:0']. Try reducing gpus.\n\nI can see that there are only two gpu's here namely '\/xla_gpu:0', '\/gpu:0'. so, i tried with gpus = 2 and again got the following error\n\nValueError: To call multi_gpu_model with gpus=2, we expect the\n  following devices to be available: ['\/cpu:0', '\/gpu:0', '\/gpu:1'].\n  However this machine only has: ['\/cpu:0', '\/xla_cpu:0', '\/xla_gpu:0',\n  '\/gpu:0']. Try reducing gpus.\n\ncan anyone help me out with the error. Thanks!","AnswerCount":6,"Available Count":2,"Score":0.0333209931,"is_accepted":false,"ViewCount":7993,"Q_Id":52950449,"Users Score":1,"Answer":"TensorFlow is only seeing one GPU (the gpu and xla_gpu devices are two backends over the same physical device). Are you setting CUDA_VISIBLE_DEVICES? Does nvidia-smi show all GPUs?","Q_Score":3,"Tags":"python,tensorflow,keras,google-cloud-platform,gpu","A_Id":52957261,"CreationDate":"2018-10-23T13:34:00.000","Title":"valueError when using multi_gpu_model in keras","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using google cloud VM with 4 Tesla K80 GPU's. \nI am running a keras model using multi_gpu_model with gpus=4(since i have 4 gpu's). But, i am getting the following error\n\nValueError: To call multi_gpu_model with gpus=4, we expect the\n  following devices to be available: ['\/cpu:0', '\/gpu:0', '\/gpu:1',\n  '\/gpu:2', '\/gpu:3']. However this machine only has: ['\/cpu:0',\n  '\/xla_cpu:0', '\/xla_gpu:0', '\/gpu:0']. Try reducing gpus.\n\nI can see that there are only two gpu's here namely '\/xla_gpu:0', '\/gpu:0'. so, i tried with gpus = 2 and again got the following error\n\nValueError: To call multi_gpu_model with gpus=2, we expect the\n  following devices to be available: ['\/cpu:0', '\/gpu:0', '\/gpu:1'].\n  However this machine only has: ['\/cpu:0', '\/xla_cpu:0', '\/xla_gpu:0',\n  '\/gpu:0']. Try reducing gpus.\n\ncan anyone help me out with the error. Thanks!","AnswerCount":6,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":7993,"Q_Id":52950449,"Users Score":0,"Answer":"I had the same issue. Tensorflow-gpu 1.14 installed, CUDA 10.0, and 4 XLA_GPUs were displayed with device_lib.list_local_devices().\nI have another conda environement and there is just Tensorflow 1.14 installed and no tensorflow-gpu, and i don't know why, but i can run my multi_gpu model on all gpus with that environment.","Q_Score":3,"Tags":"python,tensorflow,keras,google-cloud-platform,gpu","A_Id":58273653,"CreationDate":"2018-10-23T13:34:00.000","Title":"valueError when using multi_gpu_model in keras","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to run the PVWatts model (concretely to get pvwatts_dc) on an Amerisolar 315 module which doesn't seem to appear. What I am trying to do is to replicate the steps in the manual, which only requires system DC size. \nWhen I go into the power model, the formula says g_poa_effective must be already angle-of-incidence-loss corrected. How do I do this correction? I've thought about using the physical correction formula pvlib.pvsystem.physicaliam(aoi), but is this the right track?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":77,"Q_Id":52957433,"Users Score":1,"Answer":"Yes, use an incident angle modifier function such as physicaliam to calculate the AOI loss, apply the AOI loss to the in-plane direct component, then add the in-plane diffuse component.","Q_Score":0,"Tags":"python,pvlib","A_Id":52973320,"CreationDate":"2018-10-23T20:32:00.000","Title":"running PVWatts for module system not in Sandia DB (python library)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to use the first three convolution layers of vgg-16 to generate feature maps.\nBut i want to use it with variable image size,i.e not imagenet size of 224x224 or 256x256. Such as 480x640or any other randome image dimension. \nAs convolution layer are independent of image spatial size, how can I use the weights for varying image sizes?\nSo how do we use the pre-trained weights of vgg-16 upto the first three convolution layers.\nKindly let me know if that is possible.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":129,"Q_Id":52965773,"Users Score":0,"Answer":"As convolution layer are independent of image size\nActually it's more complicated than that. The kernel itself is independent of the image size because we apply it on each pixel. And indeed, the training of these kernels can be reused.\nBut this means that the output size is dependent on the image size, because this is the number of nodes that are fed out of the layer for each input pixel. So the dense layer is not adapted to your image, even if the feature extractors are independent.\nSo you need to preprocess your image to fit into the size of the first layer or you retrain your dense layers from scratch.\nWhen people talk about \"transfer-learning\" is what people have done in segmentation for decades. You reuse the best feature extractors and then you train a dedicated model with these features.","Q_Score":0,"Tags":"python,tensorflow,deep-learning","A_Id":52965846,"CreationDate":"2018-10-24T09:41:00.000","Title":"Using convolution layer trained weights for different image size","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've installed tensorflow over pip3 and python3, and am working on it. While using the colum function, the commonly experienced error AttributeError: module 'tensorflow' has no attribute 'feature_column'. \nIt might look like a duplicate question, but I've looked at the other occurrences of the same question, but, after updating the file (pip3 install --upgrade tensorflow), I checked the version. The version 0.12.0 is shown. So why does pip still show its completely new. Is 0.12.0 the newest version?\nWhen I attempted to uninstall tensorflow and re-install it, it refuses to re-install. I'm using python3 -m pip install tensorflow. The error thrown here is Could not find a version that satisfies the requirement tensorflow (from versions: ) No matching distribution found for tensorflow\nThanks in advance for your help","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":194,"Q_Id":52977453,"Users Score":0,"Answer":"As mentioned in the comments, the most probable solution to the attribute error is the update problem. However, if you're encountering the Could not find a version that satisfies the requirement tensorflow (from versions: ) No matching distribution found for tensorflow, the easiest solution is to use following code.\npip install --upgrade  https:\/\/storage.googleapis.com\/tensorflow\/mac\/cpu\/tensorflow-1.1.0-py2-none-any.whl (for python 2)\nor\npip install --upgrade  https:\/\/storage.googleapis.com\/tensorflow\/mac\/cpu\/tensorflow-1.1.0-py3-none-any.whl (for python 3)","Q_Score":0,"Tags":"python,tensorflow,pip,artificial-intelligence","A_Id":53006871,"CreationDate":"2018-10-24T20:27:00.000","Title":"Why is pip not updating tensorflow correctly, and, if it is, why is the 'attrib error' still thrown?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Well I start learning Tensorflow but I notice there's so much confusion about how to use this thing..\nFirst, some tutorials present models using low level API tf.varibles, scopes...etc, but other tutorials use Keras instead and for example to use tensor board to invoke callbacks.\nSecond, what's the purpose of having ton of duplicate API, really what's the purpose behind  using high level API like Keras when you have low  level to build  model like Lego blocks?\nFinally, what's the true purpose of using eager execution?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":241,"Q_Id":52980583,"Users Score":0,"Answer":"You can use these APIs all together. E.g. if you have a regular dense network, but with an special layer you can use higher level API for dense layers (tf.layers and tf.keras) and low level API for your special layer.  Furthermore, it is complex graphs are easier to define in low level APIs, e.g. if you want to share variables, etc. \nEager execution helps you for fast debugging, it evaluates tensors directly without a need of invoking a session.","Q_Score":0,"Tags":"python,python-3.x,tensorflow,tensorboard","A_Id":52981417,"CreationDate":"2018-10-25T02:44:00.000","Title":"How to use Tensorflow Keras API","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using keras's data generator with flow_from_dataframe. for training it works just fine, but when using model.predict_generator on the test set, I discovered that the ordering of the generated results is different than the ordering of the \"id\" column in my dataframe.\nshuffle=False does make the ordering of the generator consistent, but it is a different ordering than the dataframe. I also tried different batch sizes and the corresponding correct steps for the predict_generator function. (for example: batch_Size=1, steps=len(data))\nhow can I make sure the labels predicted for my test set are ordered in the same way of my dataframe \"id\" column?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":403,"Q_Id":52987835,"Users Score":0,"Answer":"While I haven't found a way to decide the order in which the generator produces data, the order can be obtained with the generator.filenames property.","Q_Score":0,"Tags":"python,keras","A_Id":53030878,"CreationDate":"2018-10-25T11:08:00.000","Title":"Keras flow_from_dataframe wrong data ordering","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I just updated all conda packages, as Jupyter had a kernel error. Had been working in Pycharm for a while, but wanted to continue in Jupyter now that the code was working. Updating fixed my jupyter kernel error, but now the script won't work, in jupyter, pycharm, or from console. I get same error in each case:\n\nFile \"myscript.py\", line 58, in \n      myFunction(path, out)   File \"myscript.py\", line 7, in myFunction\n      from sklearn.feature_extraction.text import TfidfTransformer, CountVectorizer   File\n  \"C:\\Anaconda\\lib\\site-packages\\sklearn__init__.py\", line 134, in\n  \n      from .base import clone   File \"C:\\Anaconda\\lib\\site-packages\\sklearn\\base.py\", line 12, in \n      from .utils.fixes import signature   File \"C:\\Anaconda\\lib\\site-packages\\sklearn\\utils__init__.py\", line 11, in\n  \n      from .validation import (as_float_array,   File \"C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\validation.py\", line 18,\n  in \n      from ..utils.fixes import signature   File \"C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\fixes.py\", line 144, in\n  \n      from scipy.sparse.linalg import lsqr as sparse_lsqr  # noqa   File \"C:\\Anaconda\\lib\\site-packages\\scipy\\sparse\\linalg__init__.py\", line\n  113, in \n      from .isolve import *   File \"C:\\Anaconda\\lib\\site-packages\\scipy\\sparse\\linalg\\isolve__init__.py\",\n  line 6, in \n      from .iterative import *   File \"C:\\Anaconda\\lib\\site-packages\\scipy\\sparse\\linalg\\isolve\\iterative.py\",\n  line 7, in \n      from . import _iterative ImportError: DLL load failed\n\nAny ideas?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":392,"Q_Id":52989115,"Users Score":0,"Answer":"Ended up unstalling anaconda and re-installing. Seems to work again now","Q_Score":0,"Tags":"python,scikit-learn","A_Id":53005078,"CreationDate":"2018-10-25T12:18:00.000","Title":"Python DLL load fail after updating all packages","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have list of numbers ( 1 to 30 ) most probably. I need to arrange the list in such a way that the absolute difference between two successive elements is not more than 2 or 3 or 4, and the sum of absolute differences of all the successive elements is minimum.\nI tried generating all possible permutations of list ranging upto 10 and 11 and then sorting them according to the cost value, but for large nmbers it takes too long.\nIt would take ages to get list for 30 numbers.\nIs there any way I could perform the constraints while generating the permutations itself ? \nCurrently I'm using itertools library for python to generate permutations.\nAny help is greatly apprecited!\nThank you\nEDIT 1: Here are the results I got on small numbers like 10 and 12.\nArranged Array -> Cost (Cost is the sum of absolute difference between two successive elements)\n\nFor 10 numbers\n[1, 3, 5, 2, 4, 6, 8, 10, 7, 9] 20\n[2, 4, 1, 3, 5, 7, 9, 6, 8, 10] 20\nFor 12.\n[1, 3, 5, 2, 4, 6, 8, 11, 9, 7, 10, 12] 25\n[1, 3, 5, 7, 10, 12, 9, 11, 8, 6, 4, 2] 25\n\nI need to arrange 30 such numbers where 2 <= difference <= 4 and overall cost is minimum.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":343,"Q_Id":52990024,"Users Score":0,"Answer":"Calculating all permutations for a list of size 30 is impossible, irrespective of the implementation approach as there will be a total of 30! permutations.\nIt seems to me that the permutation you require can simply be achieved by sorting the the given list though by using arr.sort() and then calculating the difference between consecutive elements. Am I missing something?","Q_Score":1,"Tags":"python,constraints,permutation,itertools","A_Id":52990768,"CreationDate":"2018-10-25T13:05:00.000","Title":"Generate Permutations of a large number( probably 30) with constraints","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been struggling with this problem in various guises for a long time, and never managed to find a good solution.\nBasically if I want to write a function that performs an operation over a given, but arbitrary axis of an arbitrary rank array, in the style of (for example) np.mean(A,axis=some_axis), I have no idea in general how to do this.\nThe issue always seems to come down to the inflexibility of the slicing syntax; if I want to access the ith slice on the 3rd index, I can use A[:,:,i], but I can't generalise this to the nth index.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":68,"Q_Id":52992767,"Users Score":1,"Answer":"numpy functions use several approaches to do this:\n\ntranspose axes to move the target axis to a known position, usually first or last; and if needed transpose the result\nreshape (along with transpose) to reduce the problem simpler dimensions.  If your focus is on the n'th dimension, it might not matter where the (:n) dimension are flattened or not.  They are just 'going along for the ride'.\nconstruct an indexing tuple.  idx = (slice(None), slice(None), j); A[idx] is the equivalent of A[:,:,j].  Start with a list or array of the right size, fill with slices, fiddle with it, and then convert to a tuple (tuples are immutable).\nConstruct indices with indexing_tricks tools like np.r_, np.s_ etc.  \n\nStudy code that provides for axes.  Compiled ufuncs won't help, but functions like tensordot, take_along_axis, apply_along_axis, np.cross are written in Python, and use one or more of these tricks.","Q_Score":0,"Tags":"python,numpy,multidimensional-array,indexing","A_Id":52993813,"CreationDate":"2018-10-25T15:16:00.000","Title":"Write python functions to operate over arbitrary axes","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I develop an application that will be used for running simulation and optimization over graphs (for instance Travelling salesman problem or various other problems).\nCurrently I use 2d numpy array as graph representation and always store list of lists and after every load\/dump from\/into DB I use function np.fromlist, np.tolist() functions respectively.\nIs there supported way how could I store numpy ndarray into psql? Unfortunately, np arrays are not JSON-serializable by default.\nI also thought to convert numpy array into scipy.sparse matrix, but they are not json serializable either","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":975,"Q_Id":52993954,"Users Score":2,"Answer":"json.dumps(np_array.tolist()) is the way to convert a numpy array to json. np_array.fromlist(json.loads(json.dumps(np_array.tolist()))) is how you get it back.","Q_Score":1,"Tags":"python,numpy,psql","A_Id":52994178,"CreationDate":"2018-10-25T16:21:00.000","Title":"How to store np arrays into psql database and django","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Let me first describe the setup:\nWe have an autonomous agent in Unity, whose decisions are based on the perceived environment(level) and some pre-defined parameters for value mapping. Our aim is to pre-train the agents' parameters in a DNN. So the idea is basically to define an error metric to evaluate the performance of the agent in a Unity simulation (run the level, e.g. measure the deviation from the optimal trajectory = ground truth in unity). So based on the input level into the DNN, the network should train to output the params, the simulation is performed and the error is passed back to the network as the error value, like accuracy, so the network could train based on that error\/performance.\nIs there any way to perform the evaluation(comparison to the ground truth) during the training outside of Keras? Usually, one passes X data to the network, train stuff and compare it to the ground truth Y. This works fine for predictions, but I don't want to predict something. What I do want is to measure the deviation from the ground truth inside the simulation.\nI know there is Unity ML Agents, but as far as I could read, the 'brain' controls the agent on runtime, i.e. update it on every frame and control the movement. What I want is to perform the whole simulation to update the params\/weights of the network.\nBest wishes.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":78,"Q_Id":52995288,"Users Score":0,"Answer":"After some talks at my university:\nthe setup won't work this way since I need to split the process.\nI need the parameters of working agents to train the network based only on the level description(e.g. matrix like video game description language). To obtain the parametrized agents based on the actual level and the ground truth data(e.g. deviation from trajectory), one need to use reinforcement deep learning with a score function to obtain these parameters. Therefore Unity ML Agents might be useful. Afterwards, I can use the parameters settings and the correlating level data to train a network to yield the desired parameters based only on the level description.","Q_Score":0,"Tags":"python,unity3d,keras,deep-learning,ml-agent","A_Id":53008270,"CreationDate":"2018-10-25T17:48:00.000","Title":"Training in Keras with external evaluation function","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have been working on creating a convolutional neural network from scratch, and am a little confused on how to treat kernel size for hidden convolutional layers. For example, say I have an MNIST image as input (28 x 28) and put it through the following layers.\nConvolutional layer with kernel_size = (5,5) with 32 output channels\n\nnew dimension of throughput = (32, 28, 28)\n\nMax Pooling layer with pool_size (2,2) and step (2,2)\n\nnew dimension of throughput = (32, 14, 14)\n\nIf I now want to create a second convolutional layer with kernel size = (5x5) and 64 output channels, how do I proceed? Does this mean that I only need two new filters (2 x 32 existing channels) or does the kernel size change to be (32 x 5 x 5) since there are already 32 input channels? \nSince the initial input was a 2D image, I do not know how to conduct convolution for the hidden layer since the input is now 3 dimensional (32 x 14 x 14).","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1046,"Q_Id":52997810,"Users Score":0,"Answer":"you need 64 kernel, each with the size of (32,5,5) . \ndepth(#channels) of kernels, 32 in this case, or 3 for a RGB image, 1 for gray scale etc, should always match the input depth, but values are all the same.\ne.g. if you have a 3x3 kernel like this : [-1 0 1; -2 0 2; -1 0 1]  and now you want to convolve it with an input with N as depth or say channel, you just copy this 3x3 kernel N times in 3rd dimension, the following math is just like the 1 channel case, you sum all values in all N channels which your kernel window is currently on them after multiplying the kernel values with them and get the value of just 1 entry or pixel. so what you get as output in the end is a matrix with 1 channel:) how much depth you want your matrix for next layer to have? that's the number of kernels you should apply. hence in your case it would be a kernel with this size (64 x 32 x 5 x 5) which is actually 64 kernels with 32 channels for each and same 5x5 values in all cahnnels.\n(\"I am not a very confident english speaker hope you get what I said, it would be nice if someone edit this :)\")","Q_Score":0,"Tags":"python,tensorflow,neural-network,conv-neural-network,convolution","A_Id":52998511,"CreationDate":"2018-10-25T20:43:00.000","Title":"Kernel size change in convolutional neural networks","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I can get the dimensions of tensors at graph construction time via manually printing shapes of tensors(tf.shape()) but how to get the shape of these tensors at session runtime? \nThe reason that I want shape of tensors at runtime is because at graph construction time shape of some tensors is coming as (?,8) and I cannot deduce the first dimension then.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":885,"Q_Id":53003231,"Users Score":0,"Answer":"You have to make the tensors an output of the graph. For example, if showme_tensor is the tensor you want to print, just run the graph like that : \n_showme_tensor = sess.run(showme_tensor) \nand then you can just print the output as you print a list. If you have different tensors to print, you can just add them like that :\n_showme_tensor_1, _showme_tensor_2 = sess.run([showme_tensor_1, showme_tensor_2])","Q_Score":0,"Tags":"python,tensorflow","A_Id":53003804,"CreationDate":"2018-10-26T07:04:00.000","Title":"How to get the dimension of tensors at runtime?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"So, I know this might not be the place where to ask, but I can simply not figure it out! When im using Spyder and say numpy (np) when I type np. a lot of options pop up - I know most of them are functions related to np, but I kinda struggling to figure out exactly what the different calls are; they all have one letter to the very left of the name, e.g \"(f) all\", \"(a) base\".\nSo what is the difference between (f), (a), (c) ? My first guess would be \"function\", \"attributes\", \"class\" but I'm not entirely sure","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":419,"Q_Id":53005691,"Users Score":0,"Answer":"So what is the difference between (f), (a), (c) ? My first guess would be \"function\", \"attributes\", \"class\" but I'm not entirely sure\n\n(Spyder maintainer here) This is the right interpretation.","Q_Score":0,"Tags":"python,function,class,methods,spyder","A_Id":53010613,"CreationDate":"2018-10-26T09:32:00.000","Title":"What does \"a\",\"c\",\"f\" mean in Spyder-methods (Python)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"In Andrew Ng's machine learning course it is recommended that you plot the learning curve (training set size vs cost) to determine if your model has a high bias or variance. \nHowever, I am training my model using Tensorflow and see that my validation loss is increasing while my training loss is decreasing. It's my understanding that this means my model is overfitting and so I have high variance. Is there still a reason to plot the learning curve?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":141,"Q_Id":53015550,"Users Score":1,"Answer":"Yes, there is, but it's not for spotting overfitting only. But anyway, plotting is just fancy way to see numbers, and sometimes it gives you insights. If you are monitoring loss on train\/validation simultaneously \u2013 you're looking at same data, obviously.\nRegarding Andrew's ideas \u2013 I suggest looking into Deep Learning course by him, he clarifies that in modern applications (DL + a lot of data, and I believe, this is your case) bias is not an opposite of variance.","Q_Score":0,"Tags":"python,tensorflow,machine-learning,neural-network","A_Id":53015631,"CreationDate":"2018-10-26T19:58:00.000","Title":"Why is a learning curve necessary to determine if a neural network has high bias or variance?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"For example, if I define my array as my_array = np.array([1,2,3]),\nWhat is the difference between \nmax(my_array)and \nmy_array.max() ? \nIs this one of those cases where one is 'syntactic sugar' for the other? Also, why does the first one work with a Python list, but the second doesn't?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":79,"Q_Id":53015623,"Users Score":1,"Answer":"As people have stated in the comments of your question, they are referencing two different functions.\nmax(my_array) is a built in Python function available to any sequence data in python.\nWhereas, my_array.max() is referencing a function within the object. In this case my_array is from the Numpy array class. Numpy does not recognize the list data type for this function. However, this method will provide a speed improvement over the max(my_array) whenever you are using numpy data sequences. \nAs a rule of thumb if the variable has a variable.someMethod() the function is a method and is specific to the class of object. If the function is called as function(variable) then the function is either a part of the python distribution or the file\/class you are working with.","Q_Score":0,"Tags":"python,numpy,methods,syntax","A_Id":53015879,"CreationDate":"2018-10-26T20:05:00.000","Title":"What is the difference between max(my_array) and my_array.max()","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am fairly new to tensorflow and I am interested in developing a DeseNet Architecture. I have found implementations from scratch on Github. I was wondering if the tensorflow API happen to implement the dense blocks. Is tensorflow's tf.layers.dense the same as the dense blocks in DenseNet?\nThanks!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":354,"Q_Id":53016653,"Users Score":2,"Answer":"No, tf.layers.dense implements what is more commonly known as a fully-connected layer, i.e. the basic building block of multilayer perceptrons. If you want dense blocks, you will need to to write your own implementation or use one of those you found on Github.","Q_Score":1,"Tags":"python,tensorflow","A_Id":53020115,"CreationDate":"2018-10-26T21:45:00.000","Title":"DenseNet in Tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a pandas dataframe result which stores a result obtained from a sql query. I want to paste this result onto the chart backend of a specified chart in the selected presentation. Any idea how to do this?\nP.S. The presentation is loaded using the module python-pptx","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1226,"Q_Id":53021158,"Users Score":0,"Answer":"you will need to read a bit about python-pptx.\nYou need chart's index and slide index of the chart. Once you know them\nget your chart object like this->\nchart = presentation.slides[slide_index].shapes[shape_index].chart\nreplacing data\nchart.replace_data(new_chart_data)\nreset_chart_data_labels(chart)\nthen when you save your presentation it will have updated the data.\nusually, I uniquely name all my slides and charts in a template and then I have a function that will get me the chart's index and slide's index. (basically, I iterate through all slides, all shapes, and find a match for my named chart).\nHere is a screenshot where I name a chart->[![screenshot][1]][1]. Naming slides is a bit more tricky and I will not delve into that but all you need is slide_index just count the slides 0 based and then you have the slide's index.\n[1]: https:\/\/i.stack.imgur.com\/aFQwb.png","Q_Score":2,"Tags":"python,python-3.x,pandas,powerpoint","A_Id":69335685,"CreationDate":"2018-10-27T10:53:00.000","Title":"python - pandas dataframe to powerpoint chart backend","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Scikit documentation states that:\n\nMethod for initialization:\n\u2018k-means++\u2019 : selects initial cluster centers for k-mean clustering in a smart way to speed up convergence. See section Notes in k_init for more details.\nIf an ndarray is passed, it should be of shape (n_clusters, n_features) and gives the initial centers.\n\nMy data has 10 (predicted) clusters and 7 features. However, I would like to pass array of 10 by 6 shape, i.e. I want 6 dimensions of centroid of be predefined by me, but 7th dimension to be iterated freely using k-mean++.(In another word, I do not want to specify initial centroid, but rather control 6 dimension and only leave one dimension to vary for initial cluster)\nI tried to pass 10x6 dimension, in hope it would work, but it just throw up the error.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":1975,"Q_Id":53045859,"Users Score":1,"Answer":"That is a very nonstandard variation of k-means. So you cannot expect sklearn to be prepared for every exotic variation. That would make sklearn slower for everybody else.\nIn fact, your approach is more like certain regression approaches (predicting the last value of the cluster centers) rather than clustering. I also doubt the results will be much better than simply setting the last value to the average of all points assigned to the cluster center using the other 6 dimensions only. Try partitioning your data based on the nearest center (ignoring the last column) and then setting the last column to be the arithmetic mean of the assigned data.\nHowever, sklearn is open source.\nSo get the source code, and modify k-means. Initialize the last component randomly, and while running k-means only update the last column. It's easy to modify it this way - but it's very hard to design an efficient API to allow such customizations through trivial parameters - use the source code to customize at his level.","Q_Score":0,"Tags":"python,machine-learning,scikit-learn,cluster-analysis,k-means","A_Id":53070814,"CreationDate":"2018-10-29T12:47:00.000","Title":"partially define initial centroid for scikit-learn K-Means clustering","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to calculate the mean of a certain column in DataFrame, so that means for each row is calculated excluding the value of the row for which it's calculated. \nI know I can iterate each row by index, dropping each row by index in every iteration, and then calculating mean. I wonder if there's a more efficient way of doing it.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":336,"Q_Id":53062585,"Users Score":0,"Answer":"you can dataframe[\"ColumnName\"].mean() for single column, or dataframe.describe() for all columns","Q_Score":0,"Tags":"python,pandas,dataframe,machine-learning,data-science","A_Id":53062724,"CreationDate":"2018-10-30T10:45:00.000","Title":"Calculate mean of column for each row excluding the row for which mean is calculated","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on a system to simplify our image library which grows anywhere from 7k to 20k new pictures per week.  The specific application is identifying which race cars are in pictures (all cars are similar shapes with different paint schemes).  I plan to use python and tensorflow for this portion of the project.\nMy initial thought was to use image classification to classify the image by car; however, there is a very high probability of the picture containing multiple cars.  My next thought is to use object detection to detect the car numbers (present in fixed location on all cars [nose, tail, both doors, and roof] and consistent font week to week).  Lastly there is the approach of object recognition of the whole car. This, on the surface, seems to be the most practical; however, the paint schemes change enough that it may not be.\nWhich approach will give me the best results?  I have pulled a large number of images out for training, and obviously the different methods require very different training datasets.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":43,"Q_Id":53063944,"Users Score":1,"Answer":"The best approach would be to use all 3 methods as an ensamble. You train all 3 of those models, and pass the input image to all 3 of them. Then, there are several ways how you can evaluate output. \n\nYou can sum up the probabilities for all of the classes for all 3 models and then draw a conclusion based on the highest probability.\nYou can get prediction from every model and decide based on number of votes: 1. model - class1, 2. model - class2, 3. model - class2 ==> class2\nYou can do something like weighted decision making. So, let's say that first model is the best and the most robust one but you don't trust it 100% and want to see what other models will say. Than you can weight the output of the first model with 0.6, and output of other two models with weight of 0.2.\n\nI hope this helps :)","Q_Score":0,"Tags":"python,tensorflow,computer-vision,object-detection,image-recognition","A_Id":53064651,"CreationDate":"2018-10-30T12:06:00.000","Title":"Racecar image tagging","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a general tensorflow question about when to use estimators. I feel sometimes estimators are not convenient to build something, since we need to meet some fixed requirements when building the graph. On the other hand, using lower level api can be tedious sometimes. Therefore, I want to ask when it is proper to use estimators and when it is not. Thanks!","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":52,"Q_Id":53066376,"Users Score":1,"Answer":"This is a very opinionated answer but I will still write it:\nThe Estimator-API was developed to simplify building and sharing models. You could compare it with Keras and in fact Estimators is built with tf.keras.layers so one could say it is a simplification of a simplification.\nThis is obviously good for beginners or people who come from other fields (as people working with ML often do) but can also delimit the things you can do.\nAs a general rule of thumb I would use Estimators if you want to work on or share a model with people that do not have a good CS background but want to get going either way.","Q_Score":2,"Tags":"python,tensorflow","A_Id":53066538,"CreationDate":"2018-10-30T14:18:00.000","Title":"When to use tensorflow estimators?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'd like to compare variables in a Keras model with those from a TensorFlow checkpoint. I can get the TF variables like this:\nvars_in_checkpoint = tf.train.list_variables(os.path.join(\".\/model.ckpt\"))\nHow can I get the Keras variables to compare from my model?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":2810,"Q_Id":53070199,"Users Score":0,"Answer":"To get the variable's name you need to access it from the weight attribute of the model's layer. Something like this:\nnames = [weight.name for layer in model.layers for weight in layer.weights]\nAnd to get the shape of the weight:\nweights = [weight.shape for weight in model.get_weights()]","Q_Score":2,"Tags":"python-3.x,tensorflow,keras","A_Id":66114558,"CreationDate":"2018-10-30T17:56:00.000","Title":"Get list of Keras variables","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'd like to compare variables in a Keras model with those from a TensorFlow checkpoint. I can get the TF variables like this:\nvars_in_checkpoint = tf.train.list_variables(os.path.join(\".\/model.ckpt\"))\nHow can I get the Keras variables to compare from my model?","AnswerCount":2,"Available Count":2,"Score":0.1973753202,"is_accepted":false,"ViewCount":2810,"Q_Id":53070199,"Users Score":2,"Answer":"You can get the variables of a Keras model via model.weights (list of tf.Variable instances).","Q_Score":2,"Tags":"python-3.x,tensorflow,keras","A_Id":53163169,"CreationDate":"2018-10-30T17:56:00.000","Title":"Get list of Keras variables","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"We are getting an extra column 'int64_field_0' while loading data from CSV to BigTable in GCP. Is there any way to avoid this first column. We are using the method load_table_from_file and setting option AutoDetect Schema as True. Any suggestions please. Thanks.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":182,"Q_Id":53077155,"Users Score":0,"Answer":"Per the comments, using Pandas Data Frame's pd.to_csv(filename, index=false) resolved the issue.","Q_Score":0,"Tags":"python,csv","A_Id":55940953,"CreationDate":"2018-10-31T05:57:00.000","Title":"Google Cloud Platform int64_field_0","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have some code that just makes some random noise using the numpy random normal distribution function and then I add this to a numpy array that contains an image of my chosen object. I then have to clip the array to between values of -1 and 1.\nI am just trying to get my head round whether I should be adding this to the array and clipping or multiplying the array by the noise and clipping?\nI can't conceptually understand which I should be doing. Could someone please help?\nThanks","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":424,"Q_Id":53079698,"Users Score":1,"Answer":"Well as you have said it yourself, the problem is that you don't know what you want.\nBoth methods will increase the entropy of the original data.\nWhat is the purpose of your task?\nIf you want to simulate something like sensor noise, the addition will do just fine.\nYou can try both and observe what happens to the distribution of your original data set after the application.","Q_Score":0,"Tags":"python,numpy,noise,conceptual","A_Id":53079992,"CreationDate":"2018-10-31T09:02:00.000","Title":"adding noise to an array. Is it addition or multiplication?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have some code that just makes some random noise using the numpy random normal distribution function and then I add this to a numpy array that contains an image of my chosen object. I then have to clip the array to between values of -1 and 1.\nI am just trying to get my head round whether I should be adding this to the array and clipping or multiplying the array by the noise and clipping?\nI can't conceptually understand which I should be doing. Could someone please help?\nThanks","AnswerCount":2,"Available Count":2,"Score":0.1973753202,"is_accepted":false,"ViewCount":424,"Q_Id":53079698,"Users Score":2,"Answer":"It depends what sort of physical model you are trying to represent; additive and multiplicative noise do not correspond to the same phenomenon. Your image can be considered a variable that changes through time. Noise is an extra term that varies randomly as time passes. If this noise term depends on the state of the image in time, then the image and the noise are correlated and noise is multiplicative. If the two terms are uncorrelated, noise is additive.","Q_Score":0,"Tags":"python,numpy,noise,conceptual","A_Id":53080222,"CreationDate":"2018-10-31T09:02:00.000","Title":"adding noise to an array. Is it addition or multiplication?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am training a neural network model, and my model fits the training data well. The training loss decreases stably. Everything works fine. However, when I output the weights of my model, I found that it didn't change too much since random initialization (I didn't use any pretrained weights. All weights are initialized by default in PyTorch). All dimension of the weights only changed about 1%, while the accuracy on training data climbed from 50% to 90%.\nWhat could account for this phenomenon? Is the dimension of weights too high and I need to reduce the size of my model? Or is there any other possible explanations?  \nI understand this is a quite broad question, but I think it's impractical for me to show my model and analyze it mathematically here. So I just want to know what could be the general \/ common cause for this problem.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":157,"Q_Id":53086166,"Users Score":5,"Answer":"There are almost always many local optimal points in a problem so one thing you can't say specially in high dimensional feature spaces is which optimal point your model parameters will fit into. one important point here is that for every set of weights that you are computing for your model to find a optimal point, because of real value weights,  there are infinite set of weights for that optimal point, the proportion of weights to each other is the only thing that matters, because you are trying to minimize the cost, not finding a unique set of weights with loss of 0 for every sample. every time you train you may get different result based on initial weights. when weights change very closely with almost same ratio to each others this means your features are highly correlated(i.e. redundant) and since you are getting very high accuracy just with a little bit of change in weights, only thing i can think of is that your data set classes are far away from each other. try to remove features one at a time, train and see results if accuracy was good continue to remove another one till you hopefully reach to a 3 or 2 dimensional space which you can plot your data and visualize it to see how data points are distributed and make some sense out of this.\nEDIT: Better approach is to use PCA for dimensionality reduction instead of removing one by one","Q_Score":1,"Tags":"python,machine-learning,neural-network,torch","A_Id":53091954,"CreationDate":"2018-10-31T14:50:00.000","Title":"What's the reason for the weights of my NN model don't change a lot?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"On my package manager for canopy, every time I try to download opencv it downgrades several other important packages. I am then not able to upgrade those same packages or run my code. How can I download opencv without downgrading my other packages?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":113,"Q_Id":53088354,"Users Score":1,"Answer":"You haven't provided any version or platform information. But perhaps you are using an old Canopy version (current is 2.1.9), or perhaps you are using the subscriber-only \"full\" installer, which is only intended for airgapped or other non-updateable systems. Otherwise, the currently supported version of opencv is 3.2.0 (build 3.2.0-4) which depends on numpy 1.13.3, which is the currently supported version of numpy.","Q_Score":0,"Tags":"python-2.7,opencv,package,enthought,canopy","A_Id":53088607,"CreationDate":"2018-10-31T16:44:00.000","Title":"Why does opencv on Canopy downgrade numpy, scipy, and other packages when I try to install it?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am making a convolutional network model with which I want to classify EEG data. The data is an experiment where participants are evoked with images of 3 different classes with 2 subclasses each. To give a brief explanation about the dataset size, a subclass has \u00b1300 epochs of a given participant (this applies for all the subclasses).\n\nObject \nColor\nNumber\n\nNow my question is:\nI have 5 participants in my training dataset, I took 15% of each participants' data and put it in the testing dataset. Can I consider the 15% as unseen data even though the same participant was used to train the model on?\nAny input is welcome!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":174,"Q_Id":53093576,"Users Score":2,"Answer":"It depends on what you want to test. A test set is used to estimate the generalization (i.e. performance on unseen data). So the question is:\n\nDo want to estimate the generalization to unseen data from the same participants (whose data was used to train the classifier)?\nOr do you want to estimate the generalization to unseen participants (the general population)?\n\nThis really depends on you goal or the claim you are trying to make. I can think of situations for both approaches:\n\nThink of BCIs which need to be retrained for every user. Here, you would test on data from the same individual.\nOn the other hand, if you make a very general claim (e.g. I can decode some relevant signal from a certain brain region across the population) then having a test set consisting of participants which were not included in the training set would lend much stronger support to your claim. (The question is whether this works, though.)","Q_Score":0,"Tags":"python,deep-learning,neuroscience","A_Id":53114849,"CreationDate":"2018-11-01T00:13:00.000","Title":"Deeplearning with electroencephalography (EEG) data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am current working on a real time face detection project.\nWhat I have done is that I capture the frame using cv2, do detection and then show result using cv2.imshow(), which result in a low fps. \nI want a high fps video showing on the screen without lag and a low fps detection bounding box overlay.\nIs there a solution to show the real time video stream (with the last detection result bounding box), and once a new detection is finished, show the new bounding box and the background was not delayed by the detection function.\nAny help is appreciated!\nThanks!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":413,"Q_Id":53094695,"Users Score":0,"Answer":"A common approach would be to create a flag that allows the detection algorithim to only run once every couple of frames and save the predicted reigons of interest to a list, whilst creating bounding boxes for every frame.\nSo for example you have a face detection algorithim, process every 15th frame to detect faces, but in every frame create a bounding box from the predictions. Even though the predictions get updated every 15 frames.\nAnother approach could be to add an object tracking layer. Run your heavy algorithim to find the ROIs and then use the object tracking library to hold on to them till the next time it runs the detection algorithim.\nHope this made sense.","Q_Score":0,"Tags":"python,cv2","A_Id":53094747,"CreationDate":"2018-11-01T03:08:00.000","Title":"cv2 show video stream & add overlay after another function finishes","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using keras sequential model for binary classification. But My data is unbalanced. I have 2 features column and 1 output column(1\/0). I have 10000 of data. Among that only 20 results in output 1, all others are 0. Then i have extended the data size to 40000. Now also only 20 results in output 1, all others are 0. Since the data is unbalanced(0 dominates 1), which neural network will be better for correct prediction?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":63,"Q_Id":53095061,"Users Score":0,"Answer":"First of all, two features is a really small amount. Neural Networks are highly non-linear models with a really really high amount of freedom degrees, thus if you try to train a network with more than just a couple of networks it will overfit even with balanced classes. You can find more suitable models for a small dimensionality like Support Vector Machines in scikit-learn library.\nNow about unbalanced data, the most common techniques are Undersampling and Oversampling. Undersampling is basically training your model several times with a fraction of the dataset, that contains the non dominant class and a random sample of the dominant so that the ratio is acceptable, where as oversampling consist on generating artificial data to balance the classes. In most cases undersampling works better.\nAlso when working with unbalanced data it's quite important to choose the right metric based on what is more important for the problem (is minimizing false positives more important than false negatives, etc).","Q_Score":0,"Tags":"python-3.x,keras","A_Id":53096595,"CreationDate":"2018-11-01T04:04:00.000","Title":"How to choose the right neural network in a binary classification problem for a unbalanced data?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to do a groupby of my MODELS by CITYS with keeping all the columns where i can print the percentage of each MODELS IN THIS CITY. \nI put my dataframe in PHOTO below.\nAnd i have written this code but i don\"t know how to do ??\nfor name,group in d_copy.groupby(['CITYS'])['MODELS']:","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":60,"Q_Id":53110240,"Users Score":0,"Answer":"Did you try this : d_copy.groupby(['CITYS','MODELS']).mean() to have the average percentage of a model by city.\nThen if you want to catch the percentages you have to convert it in DF and select the column : pd.DataFrame(d_copy.groupby(['CITYS','MODELS']).mean())['PERCENTAGE']","Q_Score":0,"Tags":"python,pandas,dataframe,group-by","A_Id":53112148,"CreationDate":"2018-11-01T22:25:00.000","Title":"GROUPBY with showing all the columns","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to build an AutoEncoder, where I am trying to de-noise the signal.\nNow, for example, the amplitude range of my input signal varies in between -47 to +32. But while I am getting the decoded signal (reconstructed one), that signal only ranges in between 0 to +1 amplitude. \nHow can I get my reconstructed signal with the same amplitude range of  -47 to +32?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":38,"Q_Id":53119402,"Users Score":2,"Answer":"I guess the question is whether your returned signal is a faithful representation of the input signal (but it's just constrained to the range 0 to 1)?\nIf so, you could simply multiply it by 79, and then subtract 47.\nWe'd need to see code if it's more than just a scaling issue.","Q_Score":0,"Tags":"python,tensorflow,autoencoder","A_Id":53119543,"CreationDate":"2018-11-02T13:24:00.000","Title":"Autoencoder Decoded Output","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm currently using sklearn to compute all the k-nearest neighbors from a dataset. Say k = 10. The problem I'm having is sklearn will only return the 10 nearest neighbors and none of the other data points that may tie for the 10th nearest neighbor in terms of distance. I was wondering is there any efficient way to find any other points that may tie the kth nearest neighbor  in terms of distance?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":203,"Q_Id":53124843,"Users Score":1,"Answer":"In theory, all points in the set may tie, making the problem a different one. Indeed, the K nearest neighbors can be reported in time O(Log N + K) in the absence of ties, whereas ties can imply K = O(N) making any solution O(N).\nIn practice, if the coordinates are integer, the ties will be a rare event, unless the problem has a special structure. And in floating-point, ties are virtually impossible.\nIMO, handling ties will kill efficiency, for no benefit.","Q_Score":1,"Tags":"python,algorithm,pandas,numpy,scikit-learn","A_Id":53125246,"CreationDate":"2018-11-02T19:35:00.000","Title":"K-Nearest Neighbors find all ties","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a data set that contains information about whether medium-budget companies can get loans. There are data on the data set that approximately 38,000 different companies will receive loans. And based on this data, I'm trying to estimate each company's credit score. What would be your suggestion?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":155,"Q_Id":53134930,"Users Score":1,"Answer":"Do you have credit scores? Without labeled data I think you might consider reformulating the problem.\nIf you do, then you can implement any number of regression algorithms from OLS all the way up to an ANN. Rather than look for the \"one true\" algorithm, many projects implement TPOT or grid search as part of model selection.","Q_Score":0,"Tags":"python,machine-learning,classification","A_Id":53135661,"CreationDate":"2018-11-03T19:48:00.000","Title":"Which classification model do you suggest for predicting a credit score?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataset consisting in 15 columns and 3000 rows to train a model for a binary classification. \nThere is a imbalance for y (1:2). Both outcomes (0,1) are equally important.\nAfter downsampling (because the parameter class_weight = balanced didn't work well), I used the parameter scoring = \"f1\", because I read that this was next to the ROC curve the best measurement of the performance. \nThe question is: \nDo I treat my data after downsampling still as unbalanced and therefore apply f1 or can I go back to normal accuracy?\nf1 = 2 * (precision * recall) \/ (precision + recall)\nCheers in advance! :)","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":122,"Q_Id":53145190,"Users Score":0,"Answer":"If you've rebalanced your data, then it's not unbalanced anymore and I see no problem with using accuracy as the success metric.\nAccuracy can mislead you in very skewed datasets but since it isn't skewed anymore, it should work.","Q_Score":0,"Tags":"python,classification","A_Id":53145276,"CreationDate":"2018-11-04T20:33:00.000","Title":"f1 or accuracy scoring after downsampling - classification, svm - Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"On their website they say 'gradient boosting' but it seems people here compare it to other 'xgboost' algorithm. I would like to know whether it is a real extreme gradient boosting algorithm. \nthanks","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":102,"Q_Id":53149072,"Users Score":1,"Answer":"Gradient boosting is a meta-algorithm. There is no simple gradient boosting. Each boosting library uses their own unique algorithm to search for regression trees, as a result, we obtain different results.\nExtreme gradient boosting is just an implementation of standard gradient boosting on decision trees from xgboost with some heuristics\/regularizations to improve models quality and special scheme to learn regression trees\nCatBoost is another implementation of standard gradient boosting with another set of regularizations\/heuristics.\nSo this are different algorithms.","Q_Score":0,"Tags":"python,xgboost,catboost","A_Id":53565096,"CreationDate":"2018-11-05T05:57:00.000","Title":"Does catboost implements xgboost (extreme gradient boosting) or a simple gradient boosting?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a hyperspectral image having dimension S * S * L where S*S is the spatial size and L denotes the number of spectral bands.\nNow the shape of my X (image array) is: (1, 145, 145, 200) where 1 is the number of examples, 145 is the length and width of the image and 200 is no. of channels of the image.\nI want to input this small windows of this image (having dimension like W * W * L; W < S) into a 3D CNN, but for that, I need to have 5 dimensions in the following format: (batch, length, width, depth, channels).\nIt seems to me I am missing one of the spatial dimensions, how do I convert my image array into a 5-dimensional array without losing any information?\nI am using python and Keras for the above.","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":138,"Q_Id":53159930,"Users Score":1,"Answer":"If you want to convolve along the dimension of your channels, you should add a singleton dimension in the position of channel.  If you don't want to convolve along the dimension of your channels, you should use a 2D CNN.","Q_Score":1,"Tags":"python,keras,conv-neural-network","A_Id":53159976,"CreationDate":"2018-11-05T18:14:00.000","Title":"What should be the 5th dimension for the input to 3D-CNN while working with hyper-spectral images?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a hyperspectral image having dimension S * S * L where S*S is the spatial size and L denotes the number of spectral bands.\nNow the shape of my X (image array) is: (1, 145, 145, 200) where 1 is the number of examples, 145 is the length and width of the image and 200 is no. of channels of the image.\nI want to input this small windows of this image (having dimension like W * W * L; W < S) into a 3D CNN, but for that, I need to have 5 dimensions in the following format: (batch, length, width, depth, channels).\nIt seems to me I am missing one of the spatial dimensions, how do I convert my image array into a 5-dimensional array without losing any information?\nI am using python and Keras for the above.","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":138,"Q_Id":53159930,"Users Score":1,"Answer":"What you want is a 2D CNN, not a 3D one. A 2D CNN already supports multiple channels, so you should have no problem using it with a hyperspectral image.","Q_Score":1,"Tags":"python,keras,conv-neural-network","A_Id":53163769,"CreationDate":"2018-11-05T18:14:00.000","Title":"What should be the 5th dimension for the input to 3D-CNN while working with hyper-spectral images?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"So I've recently started looking into network flows (Max flow, min cuts, etc) and the general problems for network flow always involve assigning \"n\" of something to \"k\" of another thing. For example, how would I set up a network flow for \"n\" children in a city that has \"k\" schools such that the children's homes are within x kilometres of the school (for simplicity, let's just say 1km)? \nWhat if I were to further add limitations, for example, say each school cannot have more than 100 students? Or 300 students? Could someone help me with how I would initially set up my algorithm to approach problems like these (would appreciate any references too)? They tend to show up on past midterms\/exams, so I just wanted to be prepared","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":146,"Q_Id":53160428,"Users Score":0,"Answer":"Create vertices for each student and each school. Draw an edge with capacity 1 from each student to each school that they can attend according to your distance constraint. Create a source vertex with edges to each student with a capacity of 1. Create a sink vertex with edges coming in from each school with capacities equal to each school's maximum capacity.\nRunning a standard max-flow algorithm will match as many students as possible to schools. Not every student is guaranteed to get to go to school, of course, given the constraints.\nThis is basically a modification of the standard maximum bipartite matching algorithm. The main difference is that the sinks have capacities greater than 1, which allows multiple students to be matched to a school.","Q_Score":0,"Tags":"java,python,algorithm,computer-science,network-flow","A_Id":53161713,"CreationDate":"2018-11-05T18:52:00.000","Title":"Applying Network flows","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to take a dataframe df and return a new dataframe excluding any columns with the word 'job' in its name, excluding any columns with the string 'birth' in its name, and excluding these columns: name, userID, lgID.\nHow can I do that?","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":149,"Q_Id":53161078,"Users Score":1,"Answer":"To build on @sven-harris answer.  \nList the columns:\nremove = [x for x in df.columns if 'job' in x or 'birth' in x]\nremove += ['name', 'userID', 'IgID']\ndf = df.drop(remove, axis=1)  # axis=1 to drop columns, 0 for rows.","Q_Score":2,"Tags":"python,pandas,numpy,dataframe,indexing","A_Id":53161823,"CreationDate":"2018-11-05T19:38:00.000","Title":"How to return a new dataframe excluding certain columns?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"There is good documentation on persisting Numpy arrays in Joblib using a memory-mapped file.\nIn recent versions, Joblib will (apparently) automatically persist and share Numpy arrays in this fashion.\nWill Pandas data frames also be persisted, or would the user need to implement persistence manually?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":488,"Q_Id":53162741,"Users Score":1,"Answer":"Since Pandas data frames are built on Numpy arrays, yes, they will be persisted.\nJoblib implements its optimized persistence by hooking in to the pickle protocol. Anything that includes numpy arrays in its pickled representation will benefit from Joblib's optimizations.","Q_Score":1,"Tags":"python,python-3.x,pandas,parallel-processing,joblib","A_Id":55496917,"CreationDate":"2018-11-05T21:51:00.000","Title":"Joblib persistence and Pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am a little confused with the difference between conv2d and conv3d functions. \nFor example, if I have a stack of N images with H height and W width, and 3 RGB channels. The input to the network can be two forms\nform1: (batch_size, N, H, W, 3) this is a rank 5 tensor\nform2: (batch_size, H, W, 3N ) this is a rank 4 tensor\nThe question is\uff0c if I apply conv3d with M filters with size (N,3,3) to form1 and apply conv2d with M filters with size (3,3)\nDo they have basicly the same feature operations? I think both of these forms convolve in temporal and spatial dimension.\nI really appreciate if anyone can help me figure this out.","AnswerCount":1,"Available Count":1,"Score":0.537049567,"is_accepted":false,"ViewCount":10046,"Q_Id":53164733,"Users Score":3,"Answer":"If you have a stack of images, you have a video. You can not have two input forms. You have either images or videos. For the video case you can use 3D convolution and 2D convolution is not defined for it. If you stack the channels as you mentioned it (3N) the 2D convolution will interpret the stack as one image with a lot of channels, but not as stack.\nNote here that a 2D convolution with (batch, H, W, Channels) is the same as an 3D convolution with (batch, H, W, Channels, 1).","Q_Score":5,"Tags":"python,tensorflow,neural-network,deep-learning,convolution","A_Id":53169648,"CreationDate":"2018-11-06T01:51:00.000","Title":"Difference between Conv3d vs Conv2d","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to model a four generational family tree starting with a couple. After that if I input a name of a person and a relation like 'brother' or 'sister' or 'parent' my code should output the person's brothers or sisters or parents. I have a fair bit of knowledge of python and self taught in DSA. I think I should model the data as a dictionary and code for a tree DS with two root nodes(i.e, the first couple). But I am not sure how to start. I just need to know how to start modelling the family tree and the direction of how to proceed to code. Thank you in advance!","AnswerCount":1,"Available Count":1,"Score":0.761594156,"is_accepted":false,"ViewCount":6151,"Q_Id":53166322,"Users Score":5,"Answer":"There's plenty of ways to skin a cat, but I'd suggest to create:\n\nA Person class which holds relevant data about the individual (gender) and direct relationship data (parents, spouse, children).\nA dictionary mapping names to Person elements.\n\nThat should allow you to answer all of the necessary questions, and it's flexible enough to handle all kinds of family trees (including non-tree-shaped ones).","Q_Score":0,"Tags":"python,algorithm,family-tree","A_Id":53166406,"CreationDate":"2018-11-06T05:41:00.000","Title":"Family tree in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am really new to Tensorflow as well as gaussian mixture model.\nI have recently used tensorflow.contrib.distribution.MixtureSameFamily class for predicting probability density function which is derived from gaussian mixture of 4 components.\nWhen I plotted the predicted density function using \"prob()\" function as Tensorflow tutorial explains, I found the plotted pdf with only one mode. I expected to see 4 modes as the mixture components are 4.\nI would like to ask whether Tensorflow uses any global mode predicting algorithm in their MixtureSameFamily class. If not, I would also like to know how MixtureSameFamily class forms the pdf with statistical values.  \nThank you very much.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":339,"Q_Id":53167161,"Users Score":0,"Answer":"I found an answer for above question thanks to my collegue. \nThe 4 components of gaussian mixture have had very similar means that the mixture seems like it has only one mode.\nIf I put four explicitly different values as means to the MixtureSameFamily class, I could get a plot of gaussian mixture with 4 different modes.\nThank you very much for reading this.","Q_Score":0,"Tags":"python,tensorflow,gmm","A_Id":53184428,"CreationDate":"2018-11-06T07:03:00.000","Title":"Tensorflow MixtureSameFamily and gaussian mixture model","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Currently the integration between Spark structures and Dask seems cubersome when dealing with complicated nested structures. Specifically dumping a Spark Dataframe with nested structure to be read by Dask seems to not be very reliable yet although the parquet loading is part of a large ongoing effort (fastparquet, pyarrow);\nso my follow up question - Let's assume that I can live with doing a few transformations in Spark and transform the DataFrame into an RDD that contains custom class objects; Is there a way to reliably dump the data of an Spark RDD with custom class objects and read it in a Dask collection? Obviously you can collect the rdd into a python list, pickle it and then read it as a normal data structure but that removes the opportunity to load larger than memory datasets. Could something like the spark pickling be used by dask to load a distributed pickle?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":460,"Q_Id":53169690,"Users Score":0,"Answer":"I solved this by doing the following\nHaving a Spark RDD with a list of custom objects as Row values I created a version of the rdd where I serialised the objects to strings using cPickle.dumps. Then converted this RDD to a simple DF with string columns and wrote it to parquet. Dask is able to read parquet files with simple structure. Then deserialised with cPickle.loads to get the original objects","Q_Score":1,"Tags":"python,pyspark,dask,dask-distributed,fastparquet","A_Id":53173320,"CreationDate":"2018-11-06T10:07:00.000","Title":"Is there any good way to read the content of a Spark RDD into a Dask structure","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"As we all Know, if we want to train a LSTM network, we must reshape the train dataset by the function numpy.reshape(), and reshaping result is like [samples,time_steps,features]. However, the new shape is influenced by the original one. I have seen some blogs teaching LSTM programming taking 1 as time_steps, and if time_steps is another number, samples will change relevently. My question is that does the samplesequal to batch_size?\nX = X.reshape(X.shape[0], 1, X.shape[1])","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":74,"Q_Id":53182773,"Users Score":0,"Answer":"No, samples is different from batch_size. samples is the total number of samples you would have. batch_size would be the size of each batch or the number of samples per each batch used in training, like by .fit. \nFor example, if samples=128 and batch_size=16, then your data would be divided into 8 batches with each having 16 samples inside during .fit call. \nAs another note, time_steps is the total time steps or observations within each sample. It does not make much sense to have it as 1 with LSTM as the main advantage of RNN's in general is to learn the temporal patterns. With time_step=1, there won't be any history to leverage. Here as an example that might help:\nAssume that your job is to determine if someone is active or not every hour by looking at they breathing rate and heart rate provided every minute, i.e. 2 features measured at 60 samples per hour. (This is just an example, use accelerometers if you really wanted to do this :)) Let's say you have 128 hours of labeled data. Then your input data would be of shape (128, 60, 2) and your output would be of shape (128, 1).\nHere, you have 128 samples, 60 time steps or observations per sample, and two features.\nNext you split the data into train, validation, and testing according to the samples. For example, your train, validation, and test data would be of shapes (96, 60, 2), (16, 60, 2), and (16, 60, 2), respectively. \nIf you use batch_size=16, your training, validation, and test data would have 6, 1, and 1 batches, respectively.","Q_Score":0,"Tags":"python-3.x,tensorflow,keras,lstm,rnn","A_Id":53183497,"CreationDate":"2018-11-07T02:17:00.000","Title":"Pit in LSTM programming by python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"As we all Know, if we want to train a LSTM network, we must reshape the train dataset by the function numpy.reshape(), and reshaping result is like [samples,time_steps,features]. However, the new shape is influenced by the original one. I have seen some blogs teaching LSTM programming taking 1 as time_steps, and if time_steps is another number, samples will change relevently. My question is that does the samplesequal to batch_size?\nX = X.reshape(X.shape[0], 1, X.shape[1])","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":74,"Q_Id":53182773,"Users Score":0,"Answer":"No. Samples are not equal to batch size. Samples means the number of rows in your data-set. Your training data-set is divided into number of batches and pass it to the network to train. \nIn simple words,\nImagine your data-set has 30 samples, and you define your batch_size as 3. \nThat means the 30 samples divided into 10 batches(30 divided by you defined batch_size = 10). When you train you model, at a time only 3 rows of data will be be pushed to the neural network and then next 3 rows will be push to the neural network. Like wise whole data-set will push to the neural network.\nSamples\/Batch_size = Number of batches\nRemember that batch_size and number of batches are two different things.","Q_Score":0,"Tags":"python-3.x,tensorflow,keras,lstm,rnn","A_Id":53186754,"CreationDate":"2018-11-07T02:17:00.000","Title":"Pit in LSTM programming by python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using Tensorflow with python for object detection.\nI want to start training and leave it for a while and keep all training nodes (model-cpk). Standard Tensorflow training seems to delete nodes and only keep the last few nodes. How do I prevent that?\nPlease excuse me if this is the wrong place to ask such questions. I would be oblidged if been told a proper place. Thank you.","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":69,"Q_Id":53195482,"Users Score":1,"Answer":"You can use the keep_checkpoint_max flag to tf.estimator.RunConfig in model_main.py.\nYou can set it to a very large number to practically save all checkpoints.\nYou should be warned though that depending on the model size and saving frequency, it might fill up your disk (and therefore crash during training).\nYou can change saving frequency by the flags save_checkpoints_steps or save_checkpoints_secs of RunConfig. The default is to use save_checkpoints_secs, with a default value of 600 (10 minutes).","Q_Score":0,"Tags":"python,tensorflow,machine-learning","A_Id":53204495,"CreationDate":"2018-11-07T18:20:00.000","Title":"Tensorflow training, how to prevent training node deletion","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using Tensorflow with python for object detection.\nI want to start training and leave it for a while and keep all training nodes (model-cpk). Standard Tensorflow training seems to delete nodes and only keep the last few nodes. How do I prevent that?\nPlease excuse me if this is the wrong place to ask such questions. I would be oblidged if been told a proper place. Thank you.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":69,"Q_Id":53195482,"Users Score":0,"Answer":"You can save modelcheckpoints as .hdf5 files are load them again when wanting to predict on test data.\nHope that helps.","Q_Score":0,"Tags":"python,tensorflow,machine-learning","A_Id":53205258,"CreationDate":"2018-11-07T18:20:00.000","Title":"Tensorflow training, how to prevent training node deletion","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a model for object detection (Faster RCNN from Tensorflow's Object Detection API) running on Google Cloud ML. I also have some code to filter the resulting bounding boxes based on size, aspect ratio etc.\n\nIs it possible to run this code as part of the prediction process so I don't need to run a separate process to do it afterwards.\nIs it possible to limit the number of bounding boxes predicted by the model based on some confidence threshold, as currently outputting a lot of extraneous data.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":81,"Q_Id":53196467,"Users Score":0,"Answer":"I'll answer (1): we have an Alpha API that will permit this. Please contact cloudml-feedback@google.com for more information.","Q_Score":1,"Tags":"python,tensorflow,google-cloud-ml","A_Id":53202686,"CreationDate":"2018-11-07T19:29:00.000","Title":"Can you post process results from Cloud ML's prediction output?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to track down the docs for the various distributions in scipy.stats.  It is easy enough to google around for them, but I like to use the built-in help function for kicks sometimes.  Through a series of help calls, can find that scipy has a stats module and that scipy.stats has a binom distribution.  However, at that point, using help becomes tricky.  help(scipy.stats.binom) actually returns a help document for a class named binom_gen which inherits methods from some parent abstract class whose __init__ method is utterly uninformative.  However, it does provide the following hint:  \"See help(type(self)) for accurate signature.\"  Okay.  Since I do not have access to self from outside of the class code itself, I assume that this means \"go ahead and instantiate an object, and then call help.\"  After some trial and error on getting literally any old parameters to not raise an Exception (specifically, scipy.stats.binom(0.5,0.5) returns successfully), we can call help on that thing.\nBoth help(scipy.stats.binom(0.5,0.5)) and help(type(scipy.stats.binom(0.5,0.5)) give the docs for class rv_frozen, which is equally uninformative, and actually gives the same suggestion to: \"See help(type(self)) for accurate signature.\"\nHow do I access the help for distributions in scipy.stats?  More generally, is there a meaningful way to navigate an abstract class morass via successive calls to the help function, or must I simply know a priori the class ultimately returned by these factories?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":70,"Q_Id":53198927,"Users Score":1,"Answer":"If you use ipython then I believe scipy.stats.binom? achieves this.","Q_Score":0,"Tags":"python,scipy","A_Id":53199041,"CreationDate":"2018-11-07T22:35:00.000","Title":"scipy stats distributions documentation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Tensorflow used to work on my computer. But now when I try to import tensorflow python itself errors out. I am not given a traceback call to tell me what the error  is. I get a window's prompt that says \"Python has stopped working\". When I click \"debug\" all I get is \"An unhandled win32 exception occurred in python.exe\". I've never had a python package actually error out python itself for me, I've always just had a traceback error thrown by python if I didn't install something right. \nI've tried uninstalling and reinstalling tensorflow (effectively updating from 1.7.0 to 1.12.0) but that has not helped. I'm not sure how to search for a solution to this problem either since I'm not given a traceback or an error code or an error message aside from the very generic one above.\nI'm currently using python 3.6.5 with tensorflow 1.12.0 (CPU only) installed. My OS is Windows 7 Enterprise 64 bit.  \nAny ideas? \nEDIT: The python distro I am using is through Anaconda and I'm trying to run python directly through the anaconda prompt (command line interface). \nEDIT2: I used the faulthandler module to see if I can get a stack trace out of it, and I got a Windows fatal exception: code 0xc0000139 and a  Windows fatal exception: access violation, along with a bunch of lines linking to various frozen importlib._bootstrap lines of code in various __init__.py modules.\nEDIT3: For a bit more context, this is on a workplace machine with a lot of security software installed on it.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2939,"Q_Id":53199675,"Users Score":9,"Answer":"I have solved the issue. The following procedure was used to find and fix the problem:\nI used the faulthandler module to force python to print out a stack trace and recieved a Windows fatal exception: access violation error which seems to suggest the problem was indeed a segfault caused by some module used by tensorflow.\nI tried to fix dependencies by doing a conda update --all and then a conda clean --all which didn't fix the problem. \nI noticed though that the problems seems to arise from the h5py and keras modules so I did pip install --upgrade h5py and pip install --upgrade keras and pip install --upgrade tensorflow and the problem was fixed. I am now using tensorflow version 1.12.0, keras version 2.2.4, and h5py version 2.8.0.\nThe key to solving this problem seems to be the faulthander module which showed me which modules (h5py and keras) were leading to the segfault.","Q_Score":12,"Tags":"python,python-3.x,tensorflow","A_Id":53286280,"CreationDate":"2018-11-07T23:57:00.000","Title":"Importing tensorflow makes python 3.6.5 error","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"i am working on NLP using python and nltk. \nI was wondering whether is there any dataset which have bags of words which shows keywords relating to emotions such as happy, joy, anger, sadness and etc\nfrom what i dug up in the nltk corpus, i see there are some sentiment analysis corpus which contain positive and negative review which doesn't exactly related to keywords showing emotions.\nIs there anyway which i could build my own dictionary containing words which shows emotion for this purpose? is so, how do i do it and is there any collection of such words?\nAny help would be greatly appreciated","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":822,"Q_Id":53200934,"Users Score":0,"Answer":"I'm not aware of any dataset that associates sentiments to keywords, but you can easily built one starting from a generic sentiment analysis dataset.\n1) Clean the datasets from the stopwords and all the terms that you don't want to associate to a sentiment. \n2)Compute the count of each words in the two sentiment classes and normalize it. In this way you will associate a probability to each word to belong to a class. Let's suppose that you have 300 times the word \"love\" appearing in the positive sentences and the same word appearing 150 times in the negative sentences. Normalizing you have that the word \"love\" belongs with a probability of 66% (300\/(150+300)) to the positive class and 33% to the negative one.\n3) In order to make the dictionary more robust to the borderline terms you can set a threshold to consider neutral all the words with the max probability lower than the threshold.   \nThis is an easy approach to build the dictionary that you are looking for. You could use more sophisticated approach as Term Frequency-Inverse Document Frequency.","Q_Score":0,"Tags":"python,nlp,nltk","A_Id":53211031,"CreationDate":"2018-11-08T02:59:00.000","Title":"nltk bags of words showing emotions","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How do I save a '.txt' file as a '.mat' file, using either MATLAB or Python?\nI tried using textscan() (in MATLAB), and scipy.io.savemat() (in Python). Both didn't help.\nMy text file is of the format: value1,value2,value3,valu4 (each row) and has over 1000 rows.\nAppreciate any help is appreciated. Thanks in advance.","AnswerCount":3,"Available Count":1,"Score":-0.1973753202,"is_accepted":false,"ViewCount":880,"Q_Id":53203507,"Users Score":-3,"Answer":"if what you need is to change file format:\nmv example.mat example.txt","Q_Score":0,"Tags":"python-2.7,matlab,text-files,mat-file","A_Id":53203802,"CreationDate":"2018-11-08T08:00:00.000","Title":"How to save a text file to a .mat file?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am looking for a technique which could help us find the relation in between a categorical dependent variable and combination of independent variables (Y ~ X1*X2+X2*X3+X3*X4), here among X1 to X4 we have few categorical columns and few continuous columns. \nI am working on a classification problem and I want to check what combination of independent columns are highly related to dependent columns.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":97,"Q_Id":53204674,"Users Score":0,"Answer":"I am not sure if I correctly understand your question, but from what I understand:\nYou can try to convert your continuous columns to buckets, which means effectively converting them as categorical as well and then find correlation between them.","Q_Score":0,"Tags":"python,machine-learning,statistics,analysis","A_Id":53205313,"CreationDate":"2018-11-08T09:18:00.000","Title":"relation in between a categorical dependent variable and combination of independent variables","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When I ran pip install hypothesis[pandas] I got the following:\nCollecting hypothesis[pandas]\n  Using cached https:\/\/files.pythonhosted.org\/packages\/36\/58\/222aafec5064d12c2b6123c69e512933b1e82a55ce49015371089d216f89\/hypothesis-3.82.1-py3-none-any.whl\n  hypothesis 3.82.1 does not provide the extra 'pandas'\n\npip install hypothesis[django] seemed to work and hypothesis.extra has django but not pandas. Any idea what is going on with the pip install for pandas and numpy extras?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":432,"Q_Id":53221061,"Users Score":0,"Answer":"fixed my problem with \n\npip install hypothesis[all]\n\nand also realizing that hypothesis.extra tab completion only showed django, and that pandas and numpy extras seem to need to be imported explicitly.","Q_Score":2,"Tags":"python-hypothesis","A_Id":53235950,"CreationDate":"2018-11-09T06:50:00.000","Title":"pip install hypothesis[pandas] says hypothesis3.82.1 does not provide the extra 'pandas'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to use Keras Sequential, however, my jupyter notebook is flooded with error as it's not able to import tensorflow in the backend (i think). Later I found that, its not with Keras, but I am not able to do 'import tensorflow as tf' as well.\nAny suggestions, please?\nI am using python 3.5.6\ntensorflow 1.12\nI did, pip install tensorflow for installation.\nImportError                               Traceback (most recent call last)\n~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\tensorflow\\python\\pywrap_tensorflow.py in swig_import_helper()\n     17         try:\n---> 18             fp, pathname, description = imp.find_module('_pywrap_tensorflow', [dirname(file)])\n     19         except ImportError:\n~\\AppData\\Local\\Continuum\\anaconda3\\lib\\imp.py in find_module(name, path)\n    295     else:\n--> 296         raise ImportError(_ERR_MSG.format(name), name=name)\n    297 \nImportError: No module named '_pywrap_tensorflow'\nDuring handling of the above exception, another exception occurred:\nModuleNotFoundError                       Traceback (most recent call last)\n~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\tensorflow\\python__init__.py in \n     53     # use dlopen() for dynamic loading.\n---> 54     from tensorflow.python import pywrap_tensorflow\n     55 except ImportError:\n~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\tensorflow\\python\\pywrap_tensorflow.py in \n     27             return _mod\n---> 28     _pywrap_tensorflow = swig_import_helper()\n     29     del swig_import_helper\n~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\tensorflow\\python\\pywrap_tensorflow.py in swig_import_helper()\n     19         except ImportError:\n---> 20             import _pywrap_tensorflow\n     21             return _pywrap_tensorflow\nModuleNotFoundError: No module named '_pywrap_tensorflow'\nDuring handling of the above exception, another exception occurred:\nImportError                               Traceback (most recent call last)\n in \n----> 1 import tensorflow as tf\n~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\tensorflow__init__.py in \n     22 \n     23 # pylint: disable=wildcard-import\n---> 24 from tensorflow.python import *\n     25 # pylint: enable=wildcard-import\n     26 \n~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\tensorflow\\python__init__.py in \n     58 please exit the tensorflow source tree, and relaunch your python interpreter\n     59 from there.\"\"\" % traceback.format_exc()\n---> 60   raise ImportError(msg)\n     61 \n     62 # Protocol buffers\nImportError: Traceback (most recent call last):\n  File \"C:\\Users\\ritesh.kankonkar\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\tensorflow\\python\\pywrap_tensorflow.py\", line 18, in swig_import_helper\n    fp, pathname, description = imp.find_module('_pywrap_tensorflow', [dirname(file)])\n  File \"C:\\Users\\ritesh.kankonkar\\AppData\\Local\\Continuum\\anaconda3\\lib\\imp.py\", line 296, in find_module\n    raise ImportError(_ERR_MSG.format(name), name=name)\nImportError: No module named '_pywrap_tensorflow'\nDuring handling of the above exception, another exception occurred:\nTraceback (most recent call last):\n  File \"C:\\Users\\ritesh.kankonkar\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\tensorflow\\python__init__.py\", line 54, in \n    from tensorflow.python import pywrap_tensorflow\n  File \"C:\\Users\\ritesh.kankonkar\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\tensorflow\\python\\pywrap_tensorflow.py\", line 28, in \n    _pywrap_tensorflow = swig_import_helper()\n  File \"C:\\Users\\ritesh.kankonkar\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\tensorflow\\python\\pywrap_tensorflow.py\", line 20, in swig_import_helper\n    import _pywrap_tensorflow\nModuleNotFoundError: No module named '_pywrap_tensorflow'\nError importing tensorflow.  Unless you are using bazel,\nyou should not try to import tensorflow from its source directory;\nplease exit the tensorflow source tree, and relaunch your python interpreter\nfrom there.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1593,"Q_Id":53227954,"Users Score":0,"Answer":"Well, I am answering my own question since the error seems to have multiple causes.\nI am not sure what was the cause, however, after downgrading python to 3.5 and installing tensorflow with pip (pip install tensorflow), resolved the issue.\nNote: I uninstalled everything before installing Anaconda again.","Q_Score":0,"Tags":"python,tensorflow","A_Id":53377498,"CreationDate":"2018-11-09T14:45:00.000","Title":"Unable to import tensorflow, error for importing pywrap_tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Hi I am using win 7 64 bit and tensorflow version 1.5 I've tried 1.9 and higher but isnt work and I've tried tensorflow-gpu version but again isnt work all the error this","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":764,"Q_Id":53250360,"Users Score":1,"Answer":"I encountered same issue using tf 1.8. Tensorflow versions < 1.9 did not support the score_threshold param.\nNeed to be sure you're using version 1.9 or newer.","Q_Score":0,"Tags":"python,python-3.x,tensorflow","A_Id":54779304,"CreationDate":"2018-11-11T15:43:00.000","Title":"TypeError: non_max_suppression() got an unexpected keyword argument 'score_threshold'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm gonna use data from a .csv to train a model to predict user activity on google ads (impressions, clicks) in relation to the weather for a given day. And I have a .csv that contains 6000+ recordings of this info and want to parse it into a database using Python. \nI tried making a df in pandas but for some reason the whole table isn't shown. The middle columns (there's about 7 columns I think) and rows (numbered over 6000 as I mentioned) are replaced with '...' when I print the table so I'm not sure if the entirety of the information is being stored and if this will be usable.\nMy next attempt will possible be SQLite but since it's local memory, will this interfere with someone else making requests to my API endpoint if I don't have the db actively open at all times? \nThanks in advance.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":270,"Q_Id":53253610,"Users Score":1,"Answer":"If you used pd.read_csv() i can assure you all of the info is there, it's just not displaying it.\nYou can check by doing something like print(df['Column_name_you_are_interested_in'].tolist()) just to make sure though. You can also use the various count type methods in pandas to make sure all of your lines are there.\nPanadas is pretty versatile so it shouldn't have trouble with 6000 lines","Q_Score":1,"Tags":"python,sql,database,pandas,sqlite","A_Id":53253685,"CreationDate":"2018-11-11T21:51:00.000","Title":"Parsing a CSV into a database for an API using Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have been building some variations of CNN's off of Keras\/Tensorflow examples that use the MNIST data images (ubyte files) for feature extraction. My eventual goal is to do a similar thing but with a collection (~10000) 2D FFT arrays of signal data that I have made (n x m ~ 1000 x 50)(32 bite float data)\nI have been looking for an example that uses something other than image files but can not seem to find any.\nMy questions are: Is this possible to do without converting them to images. Can the dataset be exported to a pickle or some other file that I could input? Whats the best way to achieve this?\nThanks!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":440,"Q_Id":53266491,"Users Score":1,"Answer":"Yes, you can use CNN for data other than images like sequential\/time-series data(1D convolution but you can use 2D convolution as well).\nCNN does its job pretty good for these types of data.\nYou should provide your input as an image matrix i.e a window on which CNN can perform convolution on.\nAnd you can store those input matrices\/window in a numpy array and then load those file and train your CNN on it.","Q_Score":1,"Tags":"python,tensorflow,keras,conv-neural-network,mnist","A_Id":53294036,"CreationDate":"2018-11-12T16:39:00.000","Title":"Input numerical arrays instead of images into Keras\/TF CNN","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using a bit of code that is derived from inception v3 as distributed by the Google folks, but it's now complaining that the queue runners used to read the data are deprecated (tf.train.string_input_producer in image_processing.py, and similar).  Apparently I'm supposed to switch to tf.data for this kind of stuff.\nUnfortunately, the documentation on tf.data isn't doing much to relieve my concern that I've got too much data to fit in memory, especially given that I want to batch it in a reusable way, etc. I'm confident that the tf.data stuff can do this; I just don't know how to do it. Can anyone point me to a full example of code that uses tf.data to deal with batches of data that won't all fit in memory?  Ideally, it would simply be an updated version of the inception-v3 code, but I'd be happy to try and work with anything.  Thanks!","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":58,"Q_Id":53272508,"Users Score":1,"Answer":"Well, I eventually got this working.  The various documents referenced in the comment on my question had what I needed, and I gradually figured out which parameters passed to queuerunners corresponded to which parameters in the tf.data stuff.\nThere was one gotcha that took a while for me to sort out.  In the inception implementation, the number of examples used for validation is rounded up to be a multiple of the batch size; presumably the validation set is reshuffled and some examples are used more than once.  (This does not strike me as great practice, but generally the number of validation instances is way larger than the batch size, so only a relative few are double counted.)\nIn the tf.data stuff, enabling shuffling and reuse is a separate thing and I didn't do it on the validation data.  Then things broke because there weren't enough unique validation instances, and I had to track that down.\nI hope this helps the next person with this issue.  Unfortunately, my code has drifted quite far from Inception v3 and I doubt that it would be helpful for me to post my modification.  Thanks!","Q_Score":1,"Tags":"python,tensorflow","A_Id":53452436,"CreationDate":"2018-11-13T01:35:00.000","Title":"inception v3 using tf.data?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I apologize for my lengthy title name. I have two questions, where the second question is based on the first one. \n(1). Suppose I have a matrix, whose entries are either 0 or 1. Now, I pick an arbitrary 0 entry. Is there an efficient algorithm that searches the nearest entry with label 1 or calculates the distance between the chosen 0 entry and its nearest entry with label 1?\n(2). Suppose now the distribution of entries 1 has a geometric property. To make this statement clearer, imagine this matrix as an image. In this image, there are multiple continuous lines (not necessarily straight). These lines form several boundaries that partition the image into smaller pieces. Assume the boundaries are labeled 1, whereas all the pixels in the partitioned area are labeled 0. Now, similar to (1), I pick a random pixel labeled as 0, and I hope to find out the coordinate of the nearest pixel labeled as 1 or the distance between them.\nA hint\/direction for part (1) is enough for me. If typing up an answer takes too much time, it is okay just to tell me the name of the algorithm, and I will look it up.\np.s.: If I post this question in an incorrect section, please let me know. I will re-post it to an appropriate section. Thank you!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":80,"Q_Id":53292326,"Users Score":0,"Answer":"I think that if you have a matrix, you can run a BFS version where the matrix A will be your graph G and the vertex v will be the arbitrary pixel you chose.\nThere is an edge between any two adjacent cells in the matrix.","Q_Score":0,"Tags":"python,algorithm,distance,distance-matrix","A_Id":53301201,"CreationDate":"2018-11-14T02:26:00.000","Title":"An algorithm that efficiently computes the distance of one labeled pixel to its nearest differently labeled pixel","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Why are there two different docker files for tensorflow serving - Dockerfile & Dockerfile.devel - for both CPU and GPUs?\nWhich one is necessary for deploying and testing?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":125,"Q_Id":53294415,"Users Score":1,"Answer":"a Dockerfile is a file where your write the configurations to create a docker image. \nThe tensorflow\/serving cpu and gpu are docker images which means they are already configured to work with tensorflow, tensorflow_model_server and, in the case of gpu, with CUDA. \nIf you have a GPU, then you can use a tensorflow\/serving gpu version which would reduce the latency of your predictions. If you don't have a GPU, then you can use a tensorflow\/serving cpu version which would do exactly the same but will be slower.","Q_Score":0,"Tags":"python,docker,tensorflow,dockerfile,tensorflow-serving","A_Id":53308093,"CreationDate":"2018-11-14T06:38:00.000","Title":"What is the difference between tensorflow serving Dockerfile and Dockerfile.devel?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I know that in gensims KeyedVectors-model, one can access the embedding matrix by the attribute model.syn0. There is also a syn0norm, which doesn't seem to work for the glove model I recently loaded. I think I also have seen syn1 somewhere previously. \nI haven't found a doc-string for this and I'm just wondering what's the logic behind this?\nSo if syn0 is the embedding matrix, what is syn0norm? What would then syn1 be and generally, what does syn stand for?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":6897,"Q_Id":53301916,"Users Score":7,"Answer":"These names were inherited from the original Google word2vec.c implementation, upon which the gensim Word2Vec class was based. (I believe syn0 only exists in recent versions for backward-compatbility.)\nThe syn0 array essentially holds raw word-vectors. From the perspective of the neural-network used to train word-vectors, these vectors are a 'projection layer' that can convert a one-hot encoding of a word into a dense embedding-vector of the right dimensionality. \nSimilarity operations tend to be done on the unit-normalized versions of the word-vectors. That is, vectors that have all been scaled to have a magnitude of 1.0. (This makes the cosine-similarity calculation easier.) The syn0norm array is filled with these unit-normalized vectors, the first time they're needed. \nThis syn0norm will be empty until either you do an operation (like most_similar()) that requires it, or you explicitly do an init_sims() call. If you explicitly do an init_sims(replace=True) call, you'll actually clobber the raw vectors, in-place, with the unit-normed vectors. This saves the memory that storing both vectors for every word would otherwise require. (However, some word-vector uses may still be interested in the original raw vectors of varying magnitudes, so only do this when you're sure most_similar() cosine-similarity operations are all you'll need.)\nThe syn1 (or syn1neg in the more common case of negative-sampling training) properties, when they exist on a full model (and not for a plain KeyedVectors object of only word-vectors), are the model neural network's internal 'hidden' weights leading to the output nodes. They're needed during model training, but not a part of the typical word-vectors collected after training. \nI believe the syn prefix is just a convention from neural-network variable-naming, likely derived from 'synapse'.","Q_Score":6,"Tags":"python,deep-learning,nlp,gensim,word-embedding","A_Id":53333072,"CreationDate":"2018-11-14T13:56:00.000","Title":"Python\/Gensim - What is the meaning of syn0 and syn0norm?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to understand the security implications of serializing a scikit-learn\/keras fitted model (using pickle\/joblib etc). \nSpecifically, if I work on data that I don't want to be revealed, would there be anyway for someone to reverse engineer what data a model was fitted on? Or is the data, just a way for the algorithm to update the relevant coefficients\/weights for the algorithm? (If I train the model against \"This movie is great\" and store it as a foo.pkl file, would I also be able to load the foo.pkl and say it was trained on \"This movie is great\" if all I have access to is the pkl file and not the data)","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":450,"Q_Id":53304675,"Users Score":-1,"Answer":"No, you cant (in principle, anyway) reverse engineer the data based on a model. You can obviously derive the trained model weights\/etc and start to get a good understanding of what it might have been trained over, but directly deriving the data, I'm not aware of any possible way of doing that, providing you're pickling the trained model.","Q_Score":0,"Tags":"python,machine-learning,scikit-learn,pickle,joblib","A_Id":53306686,"CreationDate":"2018-11-14T16:25:00.000","Title":"Reverse engineer scikit-learn serialized model","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I tried to compare the two, one is pandas.unique() and another one is numpy.unique(), and I found out that the latter actually surpass the first one.\nI am not sure whether the excellency is linear or not.\nCan anyone please tell me why such a difference exists, with regards to the code implementation? In what case should I use which?","AnswerCount":1,"Available Count":1,"Score":0.537049567,"is_accepted":false,"ViewCount":1666,"Q_Id":53310547,"Users Score":3,"Answer":"np.unique() is treating the data as an array, so it goes through every value individually then identifies the unique fields.\nwhereas, pandas has pre-built metadata which contains this information and pd.unique() is simply calling on the metadata which contains 'unique' info, so it doesn't have to calculate it again.","Q_Score":7,"Tags":"python,pandas,numpy,data-science,data-analysis","A_Id":62402653,"CreationDate":"2018-11-14T23:57:00.000","Title":"Why is pd.unique() faster than np.unique()?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using Python to try and do some macroeconomic analysis of different stock markets. I was wondering about how to properly compare indices of varying sizes. For instance, the Dow Jones is around 25,000 on the y-axis, while the Russel 2000 is only around 1,500. I know that the website tradingview makes it possible to compare these two in their online charter. What it does is shrink\/enlarge a background chart so that it matches the other on a new y-axis. Is there some statistical method where I can do this same thing in Python?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":318,"Q_Id":53312182,"Users Score":0,"Answer":"I know that the website tradingview makes it possible to compare these two in their online charter. What it does is shrink\/enlarge a background chart so that it matches the other on a new y-axis.  \n\nThese websites rescale them by fixing the initial starting points for both indices at, say, 100. I.e. if Dow is 25000 points and S&P is 2500, then Dow is divided by 250 to get to 100 initially and S&P by 25. Then you have two indices that start at 100 and you then can compare them side by side.  \nThe other method (works good only if you have two series) - is to set y-axis on the right hand side for one series, and on the left hand side for the other one.","Q_Score":0,"Tags":"python,plot,statistics,correlation","A_Id":53312488,"CreationDate":"2018-11-15T03:51:00.000","Title":"Compare stock indices of different sizes Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have data with the shape of (3000, 4), the features are (product, store, week, quantity). Quantity is the target. \nSo I want to reconstruct this matrix to a tensor, without blending the corresponding quantities.\nFor example, if there are 30 product, 20 stores and 5 weeks, the shape of the tensor should be (5, 20, 30), with the corresponding quantity. Because there won't be an entry like (store A, product X, week 3) twice in entire data, so every store x product x week pair should have one corresponding quantity.\nAny suggestions about how to achieve this, or there is any logical error? Thanks.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":626,"Q_Id":53313913,"Users Score":0,"Answer":"You can first go through each of your first three columns and count the number of different products, stores and weeks that you have. This will give you the shape of your new array, which you can create using numpy. Importantly now, you need to create a conversion matrix for each category. For example, if product is 'XXX', then you want to know to which row of the first dimension (as product is the first dimension of your array) 'XXX' corresponds; same idea for store and week. Once you have all of this, you can simply iterate through all lines of your existing array and assign the value of quantity to the correct location inside your new array based on the indices stored in your conversion matrices for each value of product, store and week. As you said, it makes sense because there is a one-to-one correspondence.","Q_Score":1,"Tags":"python,tensorflow","A_Id":53314237,"CreationDate":"2018-11-15T06:53:00.000","Title":"How to convert 2D matrix to 3D tensor without blending corresponding entries?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm new to Python and because I couldn't find a solution for my problem after some researches in google, I'm creating a new question, where I'm sure someone for 100% asked for it already. I have installed miniconda with numpy and pandas, which I want to use. It's located at ~\/miniconda. I've created new python file in ~\/Desktop, where I have imported those two libraries:\nimport numpy as np\nimport pandas as pd\nWhen I run my code, I got an error message:\nModuleNotFoundError: No module named 'numpy'\n\nHow can I make miniconda libraries visible in console for the python command?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":53,"Q_Id":53319860,"Users Score":0,"Answer":"In Anaconda Navigator, I have already installed those libraries. What I have done was to delete them and install once again. Now it works for me from both: console and Jupyter Notebook.","Q_Score":0,"Tags":"python,macos","A_Id":53320092,"CreationDate":"2018-11-15T12:49:00.000","Title":"Python - No module found","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm new to Python and because I couldn't find a solution for my problem after some researches in google, I'm creating a new question, where I'm sure someone for 100% asked for it already. I have installed miniconda with numpy and pandas, which I want to use. It's located at ~\/miniconda. I've created new python file in ~\/Desktop, where I have imported those two libraries:\nimport numpy as np\nimport pandas as pd\nWhen I run my code, I got an error message:\nModuleNotFoundError: No module named 'numpy'\n\nHow can I make miniconda libraries visible in console for the python command?","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":53,"Q_Id":53319860,"Users Score":1,"Answer":"conda has its own version of the Python interpreter. It is located in the Miniconda directory (It's called \"Python.exe\"). If you are using an IDE you need to switch the interpreter to use this version of Python rather than the default one you may have installed on the internet from the Python website itself.","Q_Score":0,"Tags":"python,macos","A_Id":53320029,"CreationDate":"2018-11-15T12:49:00.000","Title":"Python - No module found","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using XGBClassifier to train in python and there are a handful of categorical variables in my training dataset. Originally, I planed to convert each of them into a few dummies before I throw in my data, but then the feature importance will be calculated for each dummy, not the original categorical ones. Since I also need to order all of my original variables (including numerical + categorical) by importance, I am wondering how to get importance of my original variables? Is it simply adding up?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1226,"Q_Id":53327334,"Users Score":0,"Answer":"You could probably get by with summing the individual categories' importances into their original, parent category. But, unless these features are high-cardinality, my two cents would be to report them individually. I tend to err on the side of being more explicit with reporting model performance\/importance measures.","Q_Score":0,"Tags":"python,xgboost,categorical-data","A_Id":53327378,"CreationDate":"2018-11-15T20:21:00.000","Title":"xgboost feature importance of categorical variable","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataset that is too large to store locally and I want to train a neural network.\nWhich would be faster? or are they the same? \n1) All files are stored on the external hard drive. The python file is run in the directory of the hard drive that loads the data and trains the network\n2) Python files are saved locally and the loading of the dataset during training is done by pointing it to the dataset on the external hard drive \nI'd assume that the execution speed and loading of the dataset will be equal in both these cases, but I'm not sure","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1158,"Q_Id":53332468,"Users Score":1,"Answer":"It depends on the read speed of your Hard drive and External hard drive.\nIs your hard drive a SSD? If it is, then It sure gonna be way faster than your external hard drive.\nIf the read speed of your hard disk drive and external is same or similar, then its doesn't matter where you store your dataset.\n1) Your python file will be \"loaded\" into RAM, and executed. So your internal hard disk plays no major role.\n2)Again, if your External HDD and internal HDD has similar read speed, then it doesn't matter.","Q_Score":0,"Tags":"python,neural-network","A_Id":53332661,"CreationDate":"2018-11-16T06:20:00.000","Title":"Running python script on external hard disk (training neural network)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to use dask to parallelize a numbercrunching task.\nThis task utilizes only one of the cores in my computer. \nAs a result of that task I would like to add an entry to a DataFrame via shared_df.loc[len(shared_df)] = [x, 'y']. This DataFrame should be populized by all the (four) paralllel workers \/ threads in my computer. \nHow do I have to setup dask to perform this?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":48,"Q_Id":53333644,"Users Score":0,"Answer":"The right way to do something like this, in rough outline:\n\nmake a function that, for a given argument, returns a data-frame of some part of the total data\nwrap this function in dask.delayed, make a list of calls for each input argument, and make a dask-dataframe with dd.from_delayed\nif you really need the index to be sorted and the index to partition along different lines than the chunking you applied in the previous step, you may want to do set_index \n\nPlease read the docstrings and examples for each of these steps!","Q_Score":0,"Tags":"python,pandas,python-multiprocessing,python-multithreading,dask","A_Id":53363572,"CreationDate":"2018-11-16T07:58:00.000","Title":"How to use dask to populate DataFrame in parallelized task?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"What does Tensorflow really do when the Gradient descent optimizer is applied to a \"loss\" placeholder that is not a number (a tensor of size 1) but rather a vector (a 1-dimensional tensor of size 2, 3, 4, or more)?\nIs it like doing the descent on the sum of the components?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":358,"Q_Id":53333794,"Users Score":0,"Answer":"Tensorflow first reduces your loss to a scalar and then optimizes that.","Q_Score":2,"Tags":"python,tensorflow,gradient-descent","A_Id":53416420,"CreationDate":"2018-11-16T08:10:00.000","Title":"Multidimensional gradient descent in Tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am pretty new to neural networks. I am training a network in tensorflow, but the number of positive examples is much much less than negative examples in my dataset (it is a medical dataset). \nSo, I know that F-score calculated from precision and recall is a good measure of how well the model is trained. \nI have used error functions like cross-entropy loss or MSE before, but they are all based on accuracy calculation (if I am not wrong). But how do I use this F-score as an error function? Is there a tensorflow function for that? Or I have to create a new one?\nThanks in advance.","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":8429,"Q_Id":53354176,"Users Score":0,"Answer":"the loss value and accuracy is a different concept. The loss value is used for training the NN. However, accuracy or other metrics is to value the training result.","Q_Score":6,"Tags":"python,tensorflow,loss-function,precision-recall","A_Id":61325048,"CreationDate":"2018-11-17T18:20:00.000","Title":"How to use F-score as error function to train neural networks?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there an algorithm that can automatically calculate a numerical rating of the degree of abstractness of a word. For example, the algorithm rates purvey as 1, donut as 0, and immodestly as 0.5 ..(these are example values)\nAbstract words in the sense words that refer to ideas and concepts that are distant from immediate perception, such as economics, calculating, and disputable. Other side Concrete words refer to things, events, and properties that we can perceive directly with our senses, such as trees, walking, and red.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":77,"Q_Id":53364314,"Users Score":1,"Answer":"There's no definition of abstractness that I know of, neither any algorithm to calculate it.\nHowever, there are several directions I would use as proxies\n\nFrequency - Abstract concepts are likely to be pretty rare in a common speech, so a simple idf should help identify rare words.\nEtymology - Common words in English, are usually decedent from Germanic origin, while more technical words are usually borrowed from French \/ Latin.\nSupervised learning - If you have Wikipedia articles you find abstract, then the common phrases or word would probably also describe similar abstract concepts. Training a classifier can be a way to score.\n\nThere's no ground truth as to what is abstract, and what is concrete, especially if you try to quantify it.\nI suggest aggregating these proxies to a metric you find useful for your needs.","Q_Score":2,"Tags":"python,nlp,wordnet","A_Id":53365423,"CreationDate":"2018-11-18T18:45:00.000","Title":"Is there an algorithm to calculate a numerical rating of the degree of abstractness of a word in NLP?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to build a neural network to output a probabilistic distribution over set of whole action space. \nMy action space is a vector of 3 individual actions : [a,b,c] \na can have 3 possible actions within itself a1,a2,a3 and similarly b has b1,b2,b3, c has c1,c2,c3. So In total i can have 27 different combinations of these actions 3^3 = 27. Ultimately the neural network should output 27 combinations of these actions (which is a matrix of 27 x 3) : [[a1,b1,c1],[a2,b2,c2],[a3,b3,c3],[a1,b1,c2],[a1,b1,c3],.....] and so on for all 27 combinations. Just to mention the input to my network is a state which is a vector of 5 elements. \nI want a probability associated to each of these 27 combinations. \nI know I can associate probability by using  a softmax with 27 outputs but I don't understand how the network can output a matrix in this case where every row has a probability associated to it.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":57,"Q_Id":53378284,"Users Score":0,"Answer":"Is there any reason you want it to return a matrix of these actions? Why not just map each of the 27 combinations to integers 0-26? So your architecture could look like [Linear(5, n), ReLU, Linear(n, .) ... Softmax(Linear(., 27))]. Then when you need to evaluate, you can just map it back to the action sequence. This is similar to how in NLP tasks you map multidimensional word vectors to integers via stoi for training and bring them back via itos.\nI should point out that if your training paradigm involves some further use of these discrete choices (say you use the argmax of this upstream of another net), then the nondifferentiability of argmax means that this architechture will not learn anything. I only mention this because you use the phrase \"action space\", which is typical in DRL. If this is the case, you may want to consider algorithms like REINFORCE where action sequences can be learned discretely and used upstream via policy gradient.","Q_Score":0,"Tags":"python,tensorflow,neural-network,probability,reinforcement-learning","A_Id":53378514,"CreationDate":"2018-11-19T15:52:00.000","Title":"Return distribution over set of action space from Neural Network","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Using dask I have defined a long pipeline of computations; at some point given constraints in apis and version I need to compute some small result (not lazy) and feed it in the lazy operations. My problem is that at this point the whole computation graph will be executed so that I can produce an intermediate results. Is there a way to not loose the work done at this point and have to recompute everything from scratch when in a following step I am storing the final results to disk?\nIs using persist supposed to help with that?\nAny help will be very appreciated.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":210,"Q_Id":53392067,"Users Score":0,"Answer":"Yes, this is the usecase that persist is for. The trick is figuring out where to apply it - this decision is usually influenced by:\n\nThe size of your intermediate results. These will be kept in memory until all references to them are deleted (e.g. foo in foo = intermediate.persist()).\nThe shape of your graph. It's better to persist only components that would need to be recomputed, to minimize the memory impact of the persisted values. You can use .visualize() to look at the graph.\nThe time it takes to compute the tasks. If the tasks are quick to compute, then it may be more beneficial just to recompute them rather than keep them around in memory.","Q_Score":1,"Tags":"python,dask,dask-distributed","A_Id":53711345,"CreationDate":"2018-11-20T11:30:00.000","Title":"Dask how to avoid recomputing things","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataset with more than 20 columns. I want to find out which two variables contributes towards highest importance. How to do it?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":635,"Q_Id":53396792,"Users Score":1,"Answer":"The brute force approach is to try all different 380 possibilities.\nThe non brute force approach could be try to do your clustering with 19 features (all 20 solutions) and keeping the best one, then dropping one more, selecting the best of the 19... up to two classes.","Q_Score":0,"Tags":"python-3.x,cluster-analysis,k-means","A_Id":53396837,"CreationDate":"2018-11-20T15:53:00.000","Title":"Feature selection in K means clustering","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Just I am curious why I have to scale the testing set on the testing set, and not on the training set when I\u2019m training a model on, for example, CNN?!\nOr am I wrong? And I still have to scale it on the training set.\nAlso, can I train a dataset in the CNN that contents positive and negative elements as the first input of the network?\nAny answers with reference will be really appreciated.","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":245,"Q_Id":53404679,"Users Score":0,"Answer":"We usually have 3 types of datasets for getting a model trained,\n\nTraining Dataset\nValidation Dataset\nTest Dataset\n\nTraining Dataset\nThis should be an evenly distributed data set which covers all varieties of data. If your train with more epochs, the model will get used to the training dataset and will only give proper proper prediction on the training dataset and this is called Overfitting. Only way to keep a check on overfitting is by having other datasets which the model has never been trained on.\nValidation Dataset\nThis can be used fine tune model hyperparameters\nTest Dataset\nThis is the dataset which the model has not been trained on has never been a part of deciding the hyperparameters and will give the reality of how the model is performing.","Q_Score":0,"Tags":"python,tensorflow,conv-neural-network","A_Id":53404984,"CreationDate":"2018-11-21T03:00:00.000","Title":"How do I train the Convolutional Neural Network with negative and positive elements as the input of the first layer?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Just I am curious why I have to scale the testing set on the testing set, and not on the training set when I\u2019m training a model on, for example, CNN?!\nOr am I wrong? And I still have to scale it on the training set.\nAlso, can I train a dataset in the CNN that contents positive and negative elements as the first input of the network?\nAny answers with reference will be really appreciated.","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":245,"Q_Id":53404679,"Users Score":0,"Answer":"Scaling data depends upon the requirement as well the feed\/data you got. Test data gets scaled with Test data only, because Test data don't have the Target variable (one less feature in Test data). If we scale our Training data with new Test data, our model will not be able to correlate with any target variable and thus fail to learn. So the key difference is the existence of Target variable.","Q_Score":0,"Tags":"python,tensorflow,conv-neural-network","A_Id":53404823,"CreationDate":"2018-11-21T03:00:00.000","Title":"How do I train the Convolutional Neural Network with negative and positive elements as the input of the first layer?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am creating a questionnaire on Qualtrics and I have 3 different blocks of questions. Let's call them A, B, and C. Each of the blocks has 100 questions each. I want to randomly pick 15 questions from each of the blocks. That part is easy. I have used Randomization options available for each block. \nHowever, I want to mix questions across these blocks. Currently, what I can get is \nA1 A2 ... A15   B1, B2, ... B15   C1, C2, ... C15\nAll the questions from one block appear together.\nI want to randomize this specific ordering as well. My requirements are:\n\n15 questions from each block selected randomly from a pool of 100 in each block.\nRandomly displaying this pool of 45 questions to the user. \n\nHow can I do this? I've been stuck at this for hours now. Thanks for your help in advance.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":30,"Q_Id":53404770,"Users Score":0,"Answer":"There isn't any easy way to do this.\nYou could put all 300 questions in the same block.  Then in a block before the 300 question block have 3 multiple choice questions (QA, QB, QC) where you have placeholders for the 100 questions as choices (QA: A1, A2, ..., A100; QB: B1, B2, ... , B100; QC: C1, C2, ..., C100).  For each of those questions used Advanced Randomization to randomly display 15 choices. Hide the questions with JavaScript. Then in your 300 question block add display logic to each question based on whether its corresponding choice was displayed in the applicable QA, QB, QC question.","Q_Score":0,"Tags":"python,machine-learning,deep-learning,qualtrics","A_Id":53413823,"CreationDate":"2018-11-21T03:13:00.000","Title":"Mixing questions between Qualtrics Blocks","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"in below code .\ni didn't understand the meaning of workers parameter .\nmodel = Word2Vec(sentences, size=300000, window=2, min_count=5, workers=4)","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":5032,"Q_Id":53417258,"Users Score":3,"Answer":"workers = use this many worker threads to train the model (=faster training with multicore machines).\nIf your system is having 2 cores, and if you specify workers=2, then data will be trained in two parallel ways.\nBy default , worker = 1 i.e,  no parallelization","Q_Score":0,"Tags":"python,machine-learning,nlp,word2vec","A_Id":53417517,"CreationDate":"2018-11-21T17:07:00.000","Title":"what is workers parameter in word2vec in NLP","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to create a speaker recognition with a neural network using Keras and also the Fourier transformation to process the voice samples. The voice samples are me and my friend saying 'eeeee' for 3 seconds. Now the problem is if we give the neural network an input of someone else doing that ('ee' for 3 seconds), it still gives an output that indicates that it's  100% one of us. I output data using softmax so it gives about [1, 0] for my friend and [0, 1] for me. Sometimes it's like [0.95, 0.05]. \nIt's working well except for if we input the data from another person like I said, it still gives like [1,0] although I would expect it to give something like [0.6, 0.4] because it's another voice. Now I have also tried using 2 features of MFCC but it doesn't seem to work either. Would making a third output and train it with random samples work (I myself don't really think so because it can't train for all different inputs)? Or how can I try to face this issue otherwise? I've been struggling with this issue for quite a while now so any help would be much appreciated!","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":74,"Q_Id":53430205,"Users Score":0,"Answer":"A neural-net is - in essence - nothing more than a fancy feature-extractor and interpolator.\nThere is no reason to expect anything specific for data that it's never seen, and this doesn't have much to do with working with the DTFT, MFCC, or I-Vectors, it's a basic principle of data-driven algorithms.\nJust as a methodological explanation, not to be taken literally, finding a differentiation between two speakers going \"eeeeeeeee\" can be done by finding the  mean pitch and just deciding upon that one not-so-informative feature.\nThen what do you think would happen when introducing a new utterance?\nOne last point - there are so many ways to solve such a degenerate case, that you will most probably get some overfit. Which can also lead to unexpected results for out-of-sample data.  \n\nRegarding the general problem, there are a few different approaches, but I'd recommend having two stages:  \n\n1-Class SVM or something similar, to identify that the utterance is in the class you or your friend \nThe NN you trained.  \n\nAnother option would be to get enough \"general speaker\" samples and add them as a third class. This is known in some contexts as the OOS (out of sample) category for classification. That can get you some good googling material :-)","Q_Score":0,"Tags":"python,machine-learning,keras","A_Id":53430651,"CreationDate":"2018-11-22T11:40:00.000","Title":"How to process a voice input in Keras that is not one of the two speaker outputs?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to create a speaker recognition with a neural network using Keras and also the Fourier transformation to process the voice samples. The voice samples are me and my friend saying 'eeeee' for 3 seconds. Now the problem is if we give the neural network an input of someone else doing that ('ee' for 3 seconds), it still gives an output that indicates that it's  100% one of us. I output data using softmax so it gives about [1, 0] for my friend and [0, 1] for me. Sometimes it's like [0.95, 0.05]. \nIt's working well except for if we input the data from another person like I said, it still gives like [1,0] although I would expect it to give something like [0.6, 0.4] because it's another voice. Now I have also tried using 2 features of MFCC but it doesn't seem to work either. Would making a third output and train it with random samples work (I myself don't really think so because it can't train for all different inputs)? Or how can I try to face this issue otherwise? I've been struggling with this issue for quite a while now so any help would be much appreciated!","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":74,"Q_Id":53430205,"Users Score":0,"Answer":"It is not a simple matter. You need a lot more training examples and to do some tests.\nYou COULD try to train something to be you-and-your-friend vs all, but it won't be that easy and (again) you will need lots of examples.\nIt's very broad as a question, there are a few different approaches and i'm not sure Keras and neural networks are the correct answer for this specific task.","Q_Score":0,"Tags":"python,machine-learning,keras","A_Id":53430299,"CreationDate":"2018-11-22T11:40:00.000","Title":"How to process a voice input in Keras that is not one of the two speaker outputs?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When i try to use tensorflow to train model, i get this error message. \nFile \"\/Users\/ABC\/anaconda3\/lib\/python3.6\/site-packages\/keras\/utils\/layer_utils.py\", line 7, in \n    from .conv_utils import convert_kernel\nImportError: cannot import name 'convert_kernel'\ni have already install Keras","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1678,"Q_Id":53456874,"Users Score":1,"Answer":"I got the same issue. The filename of my python code was \"tensorflow.py\". After I changed the name to \"test.py\". The issue was resolved.\nI guess there is already a \"tensorflow.py\" in the tensorflow package. If anyone uses the same name, it may lead to the conflict.\nIf your python code is also called \"tensorflow.py\", you can try to use other names and see if it helps.","Q_Score":0,"Tags":"python,tensorflow","A_Id":54192123,"CreationDate":"2018-11-24T09:33:00.000","Title":"ImportError: cannot import name 'convert_kernel'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Propagation model: \nP = 10 * n * log10 (d\/do)\nP = path loss (dB)\nn = the path loss distance exponent\nd = distance (m)\ndo = reference distance (m)\nThe initial idea is to make the loss measurements 'P' with respect to a distance 'd', and to determine the value of 'n'\nmy question: is this implementation possible using multi-layer Perceptron?\nBut what could be my inputs and outputs? I thought of something like:\ninput: distance 'd'\noutput: Loss \"P\"\nBut I could not think of a solution to determine 'n' from these parameters\nthe idea is that it is something simple, only for study and later improved","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":33,"Q_Id":53464452,"Users Score":0,"Answer":"I believe you need a data for the response (PL) and data for the independent variables in order to find n.\nyou can find n using that data in SPSS, excel, Matlab etc.\nGood luck.","Q_Score":1,"Tags":"python,networking,perceptron,propagation","A_Id":53636929,"CreationDate":"2018-11-25T03:39:00.000","Title":"propagation model using neural network (I am beginner)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am currently testing an LSTM network. I print the loss of its prediction on a training example before back-propagation and after back-propagation. It would make sense that the after loss should always be less than the before loss because the network was just trained on that example. \nHowever, I am noticing that around the 100th training example, the network begins to give a more inaccurate prediction after back-propagation than before back-propagating on a training example. \nIs a network expected to always have the before loss be higher than the after loss? If so, are there any reasons this happens?\nTo be clear, for the first hundred examples, the network seems to be training correctly and doing just fine.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":28,"Q_Id":53464933,"Users Score":0,"Answer":"Is your dataset shuffled? \nOtherwise it could be the case that it was predicting one class for the first 99 examples.\nIf not then LSTM can be tricky to train. Try changing hyper parameters and also I would recommend starting with SimpleRNN, GRU and then LSTM as sometimes a simple network might just do the trick.","Q_Score":0,"Tags":"python,machine-learning,neural-network,lstm,recurrent-neural-network","A_Id":53465899,"CreationDate":"2018-11-25T05:33:00.000","Title":"Is it Normal for a Neural Network Loss to Increase after being trained on an example?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have just built Tensorflow r1.12 from source in Ubuntu 16.04. The installation is successful.\nWhen I run a certain script in Spyder at the 1st time, everything flows smoothly.\nHowever, when I continue to run another script, following errors occur (which didn't happen previously):\n\nFile \"\/home\/haohua\/tf_env\/lib\/python3.6\/site-packages\/tensorflow\/init.py\", line 24, in \n      from tensorflow.python import pywrap_tensorflow  # pylint: disable=unused-import\nFile \"\/home\/haohua\/tf_env\/lib\/python3.6\/site-packages\/tensorflow\/python\/init.py\", line 70, in \n      from tensorflow.python.framework.framework_lib import *  # pylint: disable=redefined-builtin\nFile \"\/home\/haohua\/tf_env\/lib\/python3.6\/site-packages\/tensorflow\/python\/framework\/framework_lib.py\", line 30, in \n      from tensorflow.python.framework.sparse_tensor import SparseTensor\nFile \"\/home\/haohua\/tf_env\/lib\/python3.6\/site-packages\/tensorflow\/python\/framework\/sparse_tensor.py\", line 248, in \n      pywrap_tensorflow.RegisterType(\"SparseTensorValue\", SparseTensorValue)\nTypeError: Type already registered for SparseTensorValue \n\nThe temporary solution to avoid such TypeError is to restart the kernel.\nBut I don't want to restart kernel at every single step of running a script.\nThus, I would like to ask for a critical solution for such kind of issue. Thank you in advance.","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2669,"Q_Id":53477005,"Users Score":2,"Answer":"(Spyder maintainer here) This error was fixed in Spyder 3.3.3, released on February\/2019.","Q_Score":1,"Tags":"python-3.x,tensorflow,spyder","A_Id":54864247,"CreationDate":"2018-11-26T08:15:00.000","Title":"Tensorflow r1.12: TypeError: Type already registered for SparseTensorValue when running a 2nd script","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on a very huge dataset with 20 million+ records. I am trying to save all that data into a feathers format for faster access and also append as I proceed with me analysis.\nIs there a way to append pandas dataframe to an existing feathers format file?","AnswerCount":1,"Available Count":1,"Score":0.761594156,"is_accepted":false,"ViewCount":1519,"Q_Id":53488351,"Users Score":5,"Answer":"Feather files are intended to be written at once. Thus appending to them is not a supported use case.\nInstead I would recommend to you for such a large dataset to write the data into individual Apache Parquet files using pyarrow.parquet.write_table or pandas.DataFrame.to_parquet and read the data also back into Pandas using pyarrow.parquet.ParquetDataset or pandas.read_parquet. These functions can treat a collection of Parquet files as a single dataset that is read at once into a single DataFrame.","Q_Score":4,"Tags":"python,pandas,feather","A_Id":53508471,"CreationDate":"2018-11-26T20:13:00.000","Title":"Is it possible to append to an existing Feathers format file?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to detect camera is capturing frozen frames or black frame. Suppose a camera is capturing video frames and suddenly same frame is capturing again and again. I spend long time to get any idea about this problem statement but i failed. So how we detect it or any idea\/steps\/procedure for this problem.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":805,"Q_Id":53493527,"Users Score":1,"Answer":"This was my approach to solve this issue.\nFrozen frames: calculate absolute difference over HSV\/RGB per every pixel in two consecutive frames np.arrays and determine max allowed diff that is valid for detecting frozen frames. \nBlack frames have naturally very low (or zero) V-value sum over the frame. Determine max V-sum of whole frame to determine, below which the frame is \"black\".","Q_Score":1,"Tags":"python,opencv,ubuntu-16.04","A_Id":53493633,"CreationDate":"2018-11-27T05:53:00.000","Title":"frozen frames detection openCV python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to do calculation on my graph neglecting some edges (as if they don't exist). Like calculation of degree, closeness, or betweenness. \nany ideas ! \nPython","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":32,"Q_Id":53504630,"Users Score":1,"Answer":"You could simply remove the edges you want to ignore before running the computations, and keep a record of what edges you have to put back when you're done.","Q_Score":0,"Tags":"python,networkx,igraph","A_Id":53504769,"CreationDate":"2018-11-27T17:01:00.000","Title":"Ignore edges when calculating betweenness or closeness of the graph","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to get a column of (x,y) coordinate pairs in my pandas data frame. I want to be able to access each part of the coordinate. For example, if the title of the column is 'pair1' I want to be able to call pair1[0] and pair1[1] to access the x and y integers respectively. Ultimately, I'd be passing these into a distance function to get the distance between 'pair1' and 'pair2'. Thanks!","AnswerCount":2,"Available Count":1,"Score":-0.0996679946,"is_accepted":false,"ViewCount":854,"Q_Id":53509923,"Users Score":-1,"Answer":"Implementing (x,y) coordinates in 1 column would be unnecessarily complex and hacky. I strongly recommend you make two columns, for example pair1_x and pair1_y. Is there a particular reason you need one column?","Q_Score":0,"Tags":"python,pandas","A_Id":53509981,"CreationDate":"2018-11-27T23:39:00.000","Title":"How to create a column of (x,y) pairs in a data frame","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"The docs for Xgboost imply that the output of a model trained using the Cox PH loss will be exponentiation of the individual persons predicted multiplier (against the baseline hazard). Is there no way to extract from this model the baseline hazard in order to predict the entire survival curve per person?\n\nsurvival:cox: Cox regression for right censored survival time data\n  (negative values are considered right censored). Note that predictions\n  are returned on the hazard ratio scale (i.e., as HR =\n  exp(marginal_prediction) in the proportional hazard function h(t) =\n  h0(t) * HR)","AnswerCount":1,"Available Count":1,"Score":0.537049567,"is_accepted":false,"ViewCount":2074,"Q_Id":53521427,"Users Score":3,"Answer":"No, I think not. A workaround would be to fit the baseline hazard in another package e.g. from sksurv.linear_model import CoxPHSurvivalAnalysis or in R by require(survival). Then you can use the predicted output from XGBoost as multiplyers to the fitted baseline. Just remember that if the baseline is on the log scale then use output_margin=True and add the predictions.\nI hope the authors of XGBoost soon will provide some examples of how to use this function.","Q_Score":3,"Tags":"python,xgboost","A_Id":54064392,"CreationDate":"2018-11-28T14:13:00.000","Title":"XGBoost (Python) Prediction for Survival Model","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm working on a graph with (x,y) node coordinates randomly picked from 0-100. If I simply plot the graph using nx.draw() and passing the original coordinates it looks ok, but if I try to plot some node sizes in a way it relates to coordinates it looks clearly inconsistent.\nLooks like the nodes position parameter in draw() is not in the same unit of the node sizes, which are in pixels.Sadly there's nothing about position units in NetworkX documentation...","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":307,"Q_Id":53527307,"Users Score":0,"Answer":"Networkx uses matplotlib to plot things.  It does not use pixels for its coordinates, and for good reason.\nIf you you have coordinates whose values range from -0.01 to 0.01, it will produce a plot that scales the upper and lower bounds on the coordinates to be large enough to hold this, but not so large that everything is in a tiny little bit of the plot.  If you now add points with coordinate values around 100, it will rescale the plot to show these as well.\nThink about how it would look to plot the line y = x+1 for x in (-0.5, 0.5) if matplotlib insisted that 1 had to correspond to a pixel.","Q_Score":0,"Tags":"python,matplotlib,networkx","A_Id":53529130,"CreationDate":"2018-11-28T20:06:00.000","Title":"NetworkX plotting: different units\/scale between node positions and sizes?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm working on a graph with (x,y) node coordinates randomly picked from 0-100. If I simply plot the graph using nx.draw() and passing the original coordinates it looks ok, but if I try to plot some node sizes in a way it relates to coordinates it looks clearly inconsistent.\nLooks like the nodes position parameter in draw() is not in the same unit of the node sizes, which are in pixels.Sadly there's nothing about position units in NetworkX documentation...","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":307,"Q_Id":53527307,"Users Score":1,"Answer":"Ok, I figured it out... \nPosition parameter for nodes are relative, from 0.0 to 1.0 times whatever your plot size is, while size parameter is absolute, in pixels","Q_Score":0,"Tags":"python,matplotlib,networkx","A_Id":53529069,"CreationDate":"2018-11-28T20:06:00.000","Title":"NetworkX plotting: different units\/scale between node positions and sizes?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a system I am reading from that implemented a time tracking function in a pretty poor way - It shows the tracked working time as [hh]:mm in the cell.  Now this is problematic when attempting to read this data because when you click that cell the data bar shows 11:00:00 PM, but what that 23:00 actually represents is 23 hours of time spent and not 11PM.  So whenever the time is 24:00 or more you end up with 1\/1\/1900 12:00:00 AM and on up ( 25:00 = 1\/1\/1900 01:00:00 AM).\nSo pandas picks up the 11:00:00 AM or 1\/1\/1900 01:00:00 AM when it comes into the dataframe.  I am at a loss as to how I would put this back into an INT for and get the number of hours in a whole number format 24, 25, 32, etc.\nCan anyone help me figure out how to turn this horribly formatted data into the number of hours in int format?","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":470,"Q_Id":53528583,"Users Score":1,"Answer":"pandas is not at fault its the excel that is interpreting the data wrongly, \nSet the data to text in that column and it wont interpret as date.\nthen save the file and open through pandas and it should work fine.\nother wise export as CSV and try to open in pandas.","Q_Score":0,"Tags":"python,pandas","A_Id":53528742,"CreationDate":"2018-11-28T21:44:00.000","Title":"Pandas read and parse Excel data that shows as a datetime, but shouldn't be a datetime","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am confused now about the loss functions used in XGBoost. Here is how I feel confused:\n\nwe have objective, which is the loss function needs to be minimized; eval_metric: the metric used to represent the learning result. These two are totally unrelated (if we don't consider such as for classification only logloss and mlogloss can be used as eval_metric). Is this correct? If I am, then for a classification problem, how you can use rmse as a performance metric?\ntake two options for objective as an example, reg:logistic and binary:logistic. For 0\/1 classifications, usually binary logistic loss, or cross entropy should be considered as the loss function, right? So which of the two options is for this loss function, and what's the value of the other one? Say, if binary:logistic represents the cross entropy loss function, then what does reg:logistic do?\nwhat's the difference between multi:softmax and multi:softprob? Do they use the same loss function and just differ in the output format? If so, that should be the same for reg:logistic and binary:logistic as well, right?\n\nsupplement for the 2nd problem\nsay, the loss function for 0\/1 classification problem should be\nL = sum(y_i*log(P_i)+(1-y_i)*log(P_i)). So if I need to choose binary:logistic here, or reg:logistic to let xgboost classifier to use L loss function. If it is binary:logistic, then what loss function reg:logistic uses?","AnswerCount":2,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":19483,"Q_Id":53530189,"Users Score":11,"Answer":"'binary:logistic' uses -(y*log(y_pred) + (1-y)*(log(1-y_pred)))\n'reg:logistic' uses (y - y_pred)^2\nTo get a total estimation of error we sum all errors and divide by number of samples.\n\nYou can find this in the basics. When looking on Linear regression VS Logistic regression.\nLinear regression uses (y - y_pred)^2 as the Cost Function\nLogistic regression uses -(y*log(y_pred) + (y-1)*(log(1-y_pred))) as the Cost function\n\nEvaluation metrics are completely different thing. They design to evaluate your model. You can be confused by them because it is logical to use some evaluation metrics that are the same as the loss function, like MSE in regression problems. However, in binary problems it is not always wise to look at the logloss. My experience have thought me (in classification problems) to generally look on AUC ROC.\nEDIT\n\naccording to xgboost documentation:\n\nreg:linear: linear regression\n\n\nreg:logistic: logistic regression\n\n\nbinary:logistic: logistic regression for binary classification, output\nprobability\n\nSo I'm guessing:\nreg:linear: is as we said, (y - y_pred)^2\nreg:logistic is -(y*log(y_pred) + (y-1)*(log(1-y_pred))) and rounding predictions with 0.5 threshhold\nbinary:logistic is plain -(y*log(y_pred) + (1-y)*(log(1-y_pred))) (returns the probability)\nYou can test it out and see if it do as I've edited. If so, I will update the answer, otherwise, I'll just delete it :<","Q_Score":15,"Tags":"python,machine-learning,xgboost,xgbclassifier","A_Id":53535742,"CreationDate":"2018-11-29T00:38:00.000","Title":"The loss function and evaluation metric of XGBoost","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am currently diving deeper into tensorflow and I am a bit puzzled with the proper use of tf.nn.Conv2d(input, filter, strides, padding). Although it looks simple at first glance I cannot get my hear around the following issue:\nThe use of filter, strides, padding is clear to me. However what is not clear is the correct application of the input. \nI am coming from a reinforcement learning Atari (Pong) problem in which I want to use the network for batch training AND (with a certain probability) also for predictions in each step. That means, for training I am feeding the network a full batch of let's say 100 , each single unit consisting of 3 frames with size 160, 128. Using the NHWC format of tensorflow my input to input would be a tf.placeholder of shape (100,160,128,3). So for training I am feeding 100 160x128x3 packages.\nHowever, when predicting outputs from my network (going up or down with the pong paddle) in a certain situation I am only feeding the network one package of 160x128x3 (i.e. one package of three frames). Now this is where tensorflow crashes. It expects (100,160,128,3) but receives (1,160,128,3). \nNow I am puzzled. I obviously do not want to set the batch size to 1 and always feed only one package for training. But how can I proceed here? How shall this be implemented with tf.nn.conv2d? \nVery much appreciated if someone cann steer me into the right direction here\nThank you in advance for your help!\nKevin","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":221,"Q_Id":53540424,"Users Score":0,"Answer":"You need to set your Placeholder like follows\n tf.placeholder(shape=(None,160,128,3) ....) , with having None in the first dimension , your placeholder will be flexible to any value you feed either 1 or 100.","Q_Score":1,"Tags":"python,tensorflow,neural-network,deep-learning,reinforcement-learning","A_Id":53540809,"CreationDate":"2018-11-29T13:43:00.000","Title":"How to use Tensorflow tf.nn.Conv2d simultaneously for training and prediction?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Can I run a normal python code using regular ML libraries (e.g., Tensorflow or sci-kit learn) in a Spark cluster? If yes, can spark distribute my data and computation across the cluster? if no, why?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":238,"Q_Id":53542955,"Users Score":2,"Answer":"Spark use RDD(Resilient distributed dataset) to distribute work among workers or slaves , I dont think you can use your existing code in python without dramatically adapting the code to spark specification , for tensorflow there are many options to distribute computing over multiple gpus.","Q_Score":0,"Tags":"python,apache-spark,distributed-computing","A_Id":53543239,"CreationDate":"2018-11-29T15:58:00.000","Title":"Using regular python code on a Spark cluster","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to normalize custom dataset of images. For that i need to compute mean and standard deviation by iterating over the dataset. How can I normalize my entire dataset before creating the data set?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":3508,"Q_Id":53542974,"Users Score":1,"Answer":"What normalization tries to do is mantain the overall information on your dataset, even when there exists differences in the values, in the case of images it tries to set apart some issues like brightness and contrast that in certain case does not contribute to the general information that the image has. There are several ways to do this, each one with pros and cons, depending on the image set you have and the processing effort you want to do on them, just to name a few:\n\nLinear Histogram stetching: where you do a linear map on the current\nrange of values in your image and stetch it to match the 0 and 255\nvalues in RGB \nNonlinear Histogram stetching: Where you use a\nnonlinear function to map the input pixels to a new image. Commonly\nused functions are logarithms and exponentials. My favorite function\nis the cumulative probability function of the original histogram, it\nworks pretty well.\nAdaptive Histogram equalization: Where you do a linear\nhistogram stretching in certain places of your image to avoid doing\nan identity mapping where you have the max range of values in your original\nimage.","Q_Score":1,"Tags":"python,deep-learning,computer-vision,conv-neural-network,pytorch","A_Id":53543608,"CreationDate":"2018-11-29T15:59:00.000","Title":"Pytorch: Normalize Image data set","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to design an algorithm that takes in a resistance value and outputs the minimum number of resistors and the values associated with those resistors. I would like the algorithm to iterate through a set of resistor values and the values in the set can be used no more than n times. I would like some direction on where to start.\nSeries: Req = R1 + R2 +...\nParallel: (1\/Req) = (1\/R1) + (1\/R2)  +...\nInput:\n100000 (100k)\nSet: {30k, 50k, 80k, 200k}\nOutput: \n2 resistors in series: 50k + 50k \n2 resistors in parallel: 200k || 200k","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":241,"Q_Id":53543066,"Users Score":1,"Answer":"This is actually quite hard, best I can do is propose an idea for an algorithm for solving the first part, the concept of including parallels looks harder as well, but maybe the algorithm can be extended.\nIf you define a function \"best\", which takes a target resistance as input and outputs the minimal set of resistors that generates that resistance. E.G: if you only had 10K resistors, best(50K)=5*10K.\nThis function \"best\" has the following properties for a set of available resistors [A, B, C,...]:\n\nbest(A) = A for any A in the set.\nbest(Target) = min(best(Target-A) + A), best(Target-B) + B,...)\nbest(0)=0\nbest(x)=nonsense, if x<0 (remove these cases)\n\nThis can be used to reductively solve the problem. (I'd probably recommend storing the variables down the tree as you go along.\nHere's an example to illustrate a bit:\n\nAvailableSet = [10K, 100K]\nTarget = 120K\nFirst iteration:\nbest(120K) = min[ best(110K) + 10K, best(20K) + 100K]\ncalculate each subtree:\nbest(110K) = min[best(100K) + 10K, best(10K) + 100K]\nThis is now finalised as we can calculate everything in the min(_) by using the properties, so work back up the tree:\nbest(110K) = 100K + 10K ( I suppose if there is a tie like in this case pick a permuation randomly)\nbest(120K) = min[best(110K) + 10K , best(20K) + 100K] = ... = 100K + 10K + 10K\nThat should work as a solution to the first half of the problem, you may be able to extend this by adding extra properties, but it will make it harder to solve the problem reductively in this way.\nProbably the best way is to solve this first half of the problem and use a different algorithm to find the best solution using parallels and decide which is minimal in each case.","Q_Score":3,"Tags":"python,c++,algorithm","A_Id":53544202,"CreationDate":"2018-11-29T16:03:00.000","Title":"Algorithm to find minimum number of resistors from a set of resistor values. (C++ or Python)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I intend to use posenet in python and not in browser, for that I need the model as a frozen graph to do inference on. Is there a way to do that?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":2638,"Q_Id":53548976,"Users Score":1,"Answer":"We currently do not have the frozen graph for inference publicly, however you could download the assets and run them in a Node.js environment.","Q_Score":1,"Tags":"python,tensorflow,neural-network,deep-learning,tensorflow.js","A_Id":53709777,"CreationDate":"2018-11-29T23:14:00.000","Title":"is there a way to download the frozen graph ( .pb file ) of PoseNet?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Currently in R, once you load a dataset (for example with read.csv), Rstudio saves it as a variable in the global environment. This ensures you don't have to load the dataset every single time you do a particular test or change. \nWith Python, I do not know which text editor\/IDE will allow me to do this. E.G - I want to load a dataset once, and then subsequently do all sorts of things with it, instead of having to load it every time I run the script. \nAny points as to how to do this would be very useful","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":119,"Q_Id":53557674,"Users Score":0,"Answer":"It depends how large your data set is. \nFor relatively smaller datasets you could look at installing Anaconda Python Jupyter notebooks. Really great for working with data and visualisation once the dataset is loaded. For larger datasets you can write some functions \/ generators to iterate efficiently through the dataset.","Q_Score":0,"Tags":"python,global-variables,spyder","A_Id":53557733,"CreationDate":"2018-11-30T12:32:00.000","Title":"not having to load a dataset over and over","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a data set that have 43.462.904 milions of records. I try to do a group by with two variables and do an average of the third one. \nThe fnuction is: df1 = df.groupby([\"var1\", pd.Grouper(key=\"var2\"freq=\"MS\")]).mean()\nThe error that exit is the follow: IndexError: index 43462904 is out of bounds for size 43462904\nThe error is because I have a long dataset? The function with a few data function","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":162,"Q_Id":53558877,"Users Score":0,"Answer":"An array of length N can be indexed with 0 ... N-1:\n\narr = [0,1,2]\narr[0]: 0\narr[1]: 1\narr[2]: 2\nlen(arr): 3\n\nIn this example you try to access arr[3] which is invalid as it's the N+1st entry in the array.","Q_Score":0,"Tags":"python-3.x,group-by,compiler-errors,index-error","A_Id":53558970,"CreationDate":"2018-11-30T13:52:00.000","Title":"IndexError: index 43462904 is out of bounds for size 43462904","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm wondering if the result of the test set is used to make the optimization of model's weights. I'm trying to make a model but the issue I have is I don't have many data because they are medical research patients. The number of patient is limited in my case (61) and I have 5 feature vectors per patient. What I tried is to create a deep learning model by excluding one subject and I used the exclude subject as the test set. My problem is there is a large variability in subject features and my model fits well the training set (60 subjects) but not that good the 1 excluded subject. \nSo I'm wondering if the testset (in my case the excluded subject) could be used in a certain way to make converge the model to better classify the exclude subject?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":204,"Q_Id":53559147,"Users Score":0,"Answer":"No , you souldn't use your test set for training to prevent overfitting  ,  if you use cross-validation principles you need exactly to split your data into three datasets  a train set which you'll use to train your model , a validation set to test different value of your  hyperparameters , and a test set to finally test your model , if you use all your data for training, your model will overfit obviously.\nOne thing to remember deep learning work well if you have a large and very rich datasets","Q_Score":0,"Tags":"python,keras,deep-learning","A_Id":53559275,"CreationDate":"2018-11-30T14:10:00.000","Title":"Does the test set is used to update weight in a deep learning model with keras?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I downloaded a dataset which contains a MATLAB file called 'depths.mat' which contains a 3-dimensional matrix with the dimensions 480 x 640 x 1449. These are actually 1449 images, each with the dimension 640 x 480. I successfully loaded it into python using the scipy library but the problem is the unusual order of the dimensions. This makes Python think that there are 480 images with the dimensions 640 x 1449. I tried to reshape the matrix in python, but a simple reshape operation did not solve my problem.\nAny suggestions are welcome. Thank you.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":42,"Q_Id":53560638,"Users Score":2,"Answer":"You misunderstood. You do not want to reshape, you want to transpose it. In MATLAB, arrays are A(x,y,z) while in python they are P[z,y,x]. Make sure that once you load the entire matrix, you change the first and last dimensions. \nYou can do this with the swapaxes function, but beware! it does not make a copy nor change the the data, just changes how the higher level indices of nparray access the internal memory. Your best chances if you have enough RAM is to make a copy and dump the original.","Q_Score":0,"Tags":"python-3.x,matlab","A_Id":53560972,"CreationDate":"2018-11-30T15:43:00.000","Title":"Unusual order of dimensions of an image matrix in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I finnally managed to join two big DataFrames on a big machine of my school (512G memory). At the moment we re two people using the same machine, the other one is using about 120G of the memory, after I called the garbage collecter we get to 420G.\nI want to save the DataFrame to memory so I then I can reuse it easily and move it on another machine, I have tried to export it to a parquet file, but I get a memory error...\nSo how can I manage to dump that Dataframe on the hard drive in purpose of reusing it without running into memory error when memory is already near full ?\nThank you","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1372,"Q_Id":53564278,"Users Score":0,"Answer":"There are several options. You can pickle the dataframe or you can use hdf5 format. \nThese will occupy less memory. Also when you load it next time, it would be quicker then other formats.","Q_Score":1,"Tags":"python,pandas","A_Id":53564337,"CreationDate":"2018-11-30T20:12:00.000","Title":"Pandas memory error when saving DataFrame to file","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am currently developing a Twitter content-based recommender system and have a word2vec model pre-trained on 400 million tweets.\nHow would I go about using those word embeddings to create a document\/tweet-level embedding and then get the user embedding based on the tweets they had posted? \nI was initially intending on averaging those words in a tweet that had a word vector representation and then averaging the document\/tweet vectors to get a user vector but I wasn't sure if this was optimal or even correct. Any help is much appreciated.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":312,"Q_Id":53565271,"Users Score":0,"Answer":"You are on the right track with averaging the word vectors in a tweet to get a \"tweet vector\" and then averaging the tweet vectors for each user to get a \"user vector\". Whether these average vectors will be useful or not depends on your learning task. Hard to say if this average method will work or not without trying since it depends on how diverse is your data set in terms of variation between the words used in tweets by each user.","Q_Score":1,"Tags":"python,twitter,nlp,word2vec,word-embedding","A_Id":53566219,"CreationDate":"2018-11-30T21:40:00.000","Title":"word2vec: user-level, document-level embeddings with pre-trained model","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am currently developing a Twitter content-based recommender system and have a word2vec model pre-trained on 400 million tweets.\nHow would I go about using those word embeddings to create a document\/tweet-level embedding and then get the user embedding based on the tweets they had posted? \nI was initially intending on averaging those words in a tweet that had a word vector representation and then averaging the document\/tweet vectors to get a user vector but I wasn't sure if this was optimal or even correct. Any help is much appreciated.","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":312,"Q_Id":53565271,"Users Score":2,"Answer":"Averaging the vectors of all the words in a short text is one way to get a summary vector for the text. It often works OK as a quick baseline. (And, if all you have, is word-vectors, may be your main option.) \nSuch a representation might sometimes improve if you did a weighted average based on some other measure of relative term importance (such as TF-IDF), or used raw word-vectors (before normalization to unit-length, as pre-normalization raw magnitudes can sometimes hints at strength-of-meaning). \nYou could create user-level vectors by averaging all their texts, or by (roughly equivalently) placing all their authored words into a pseudo-document and averaging all those words together. \nYou might retain more of the variety of a user's posts, especially if their interests span many areas, by first clustering their tweets into N clusters, then modeling the user as the N centroid vectors of the clusters. Maybe even the N varies per user, based on how much they tweet or how far-ranging in topics their tweets seem to be. \nWith the original tweets, you could also train up per-tweet vectors using an algorithm like 'Paragraph Vector' (aka 'Doc2Vec' in a library like Python gensim.) But, that can have challenging RAM requirements with 400 million distinct documents. (If you have a smaller number of users, perhaps they can be the 'documents', or they could be the predicted classes of a FastText-in-classification-mode training session.)","Q_Score":1,"Tags":"python,twitter,nlp,word2vec,word-embedding","A_Id":53612424,"CreationDate":"2018-11-30T21:40:00.000","Title":"word2vec: user-level, document-level embeddings with pre-trained model","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there a way to see an updated version of my Dataframe every time I run the code in Spyder? I can see the name and size of the Dataframe in \"Variable explorer\" but I don't like that I have to double click it to open it.\nOr is there a way to have the Dataframe (that I have already earlier opened by double clicking it) update after I run the code again?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":219,"Q_Id":53571920,"Users Score":4,"Answer":"(Spyder maintainer here) Unfortunately this is not possible as of September 2020. However, we'll try to implement this functionality in Spyder 5, to be released in 2021.","Q_Score":4,"Tags":"python,pandas,dataframe,spyder","A_Id":53572155,"CreationDate":"2018-12-01T14:48:00.000","Title":"How to see updated Dataframe after I run the code again in Spyder (without doubleclicking from Variable explorer after EVERY run)?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an array of data points of dimension n by 2, where the second dimension $2$ corresponds to the real part and imaginary part of a complex number. \nNow I know the data points will intersect the unit circle on the plane for a couple of times. What I want to implement is: suppose the path starts will some color, it changes to another color when it touches the unit circle on the plane and changes color again if it intersects the unit circle again. I am not sure whether there is an easy to implement this.","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1989,"Q_Id":53577532,"Users Score":0,"Answer":"The simplest way to do this is probably to implement the logic outside the plot, by assigning a different group to each point defined by your circle-crossing concept.\nOnce you have these group-indexes it's a simple plt.scatter, using the c (stands for \"color\") input.  \nGood luck!","Q_Score":0,"Tags":"python,matplotlib","A_Id":53577656,"CreationDate":"2018-12-02T04:56:00.000","Title":"How do I alternate the color of a scatter plot in matplotlib?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to generate three different sized output vectors namely 25d, 50d and 75d. I am trying to do so by training the same dataset using the word2vec model. I am not sure how I can get three vectors of different sizes using the same training dataset. Can someone please help me get started on this? I am very new to machine learning and word2vec. Thanks","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":25,"Q_Id":53586254,"Users Score":1,"Answer":"You run the code for one model three times, each time supplying a different vector_size parameter to the model initialization.","Q_Score":0,"Tags":"python,vector,word2vec","A_Id":53586551,"CreationDate":"2018-12-03T01:15:00.000","Title":"Different sized vectors in word2vec","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I just installed anaconda ver5.3.1 which uses python 3.7.\nI encountered the following error;\n\n\"Missing required dependencies {0}\".format(missing_dependencies))\n   ImportError: Missing required dependencies ['numpy']\n\nI have upgraded numpy, pandas to the latest version using conda but the same error appears. To fix this problem, I have to downgrade to an older anaconda version which uses python 3.6\nI am using Windows 10.\nEDIT: I just discovered this problem is more related to pycharm than anaconda. I got this error while running pycharm in debug mode. However, when I run the same python script in Anaconda prompt console, there is no error. \nWhat are some possible pycharm settings I should check to fix this problem? Are there ways to configure pycharm to output more verbose error messages?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":5309,"Q_Id":53587001,"Users Score":1,"Answer":"Your pycharm created a new environment for your project I suspect. Maybe it copied across the anaconda python.exe but not all the global packages.\nIn pycharm you can go to the project properties where you can see a list of all the packages available, and add additional packages. Here you can install Numpy.\nFile --> Settings --> Project:  --> Project Interpreter","Q_Score":1,"Tags":"python,python-3.x,pycharm,anaconda,conda","A_Id":53609235,"CreationDate":"2018-12-03T03:16:00.000","Title":"Missing required dependencies ['numpy'] for anaconda ver5.3.1 on pycharm","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Assume that we have 1000 words (A1, A2,..., A1000) in a dictionary. As fa as I understand, in words embedding or word2vec method, it aims to represent each word in the dictionary by a vector where each element represents the similarity of that word with the remaining words in the dictionary. Is it correct to say there should be 999 dimensions in each vector, or the size of each word2vec vector should be 999?\nBut with Gensim Python, we can modify the value of \"size\" parameter for Word2vec, let's say size = 100 in this case. So what does \"size=100\" mean? If we extract the output vector of A1, denoted (x1,x2,...,x100), what do x1,x2,...,x100 represent in this case?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2261,"Q_Id":53587960,"Users Score":4,"Answer":"It is not the case that \"[word2vec] aims to represent each word in the dictionary by a vector where each element represents the similarity of that word with the remaining words in the dictionary\". \nRather, given a certain target dimensionality, like say 100, the Word2Vec algorithm gradually trains word-vectors of 100-dimensions to be better and better at its training task, which is predicting nearby words. \nThis iterative process tends to force words that are related to be \"near\" each other, in rough proportion to their similarity - and even further the various \"directions\" in this 100-dimensional space often tend to match with human-perceivable semantic categories. So, the famous \"wv(king) - wv(man) + wv(woman) ~= wv(queen)\" example often works because \"maleness\/femaleness\" and \"royalty\" are vaguely consistent regions\/directions in the space. \nThe individual dimensions, alone, don't mean anything. The training process includes randomness, and over time just does \"whatever works\". The meaningful directions are not perfectly aligned with dimension axes, but angled through all the dimensions. (That is, you're not going to find that a v[77] is a gender-like dimension. Rather, if you took dozens of alternate male-like and female-like word pairs, and averaged all their differences, you might find some 100-dimensional vector-dimension that is suggestive of the gender direction.)\nYou can pick any 'size' you want, but 100-400 are common values when you have enough training data.","Q_Score":2,"Tags":"python,gensim,word2vec,word-embedding","A_Id":53601635,"CreationDate":"2018-12-03T05:29:00.000","Title":"What is the meaning of \"size\" of word2vec vectors [gensim library]?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to implement machine learning on hardware platform s which can learning by itself Is there any way to by which machine learning on hardware works seamlessly?","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":68,"Q_Id":53592665,"Users Score":0,"Answer":"Python supports wide range of platforms, including arm-based. \nYou raspberry pi supports Linux distros, just install Python and go on.","Q_Score":0,"Tags":"python,machine-learning,raspberry-pi,artificial-intelligence,robotics","A_Id":53592746,"CreationDate":"2018-12-03T11:15:00.000","Title":"How to embed machine learning python codes in hardware platform like raspberry pi?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to implement machine learning on hardware platform s which can learning by itself Is there any way to by which machine learning on hardware works seamlessly?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":68,"Q_Id":53592665,"Users Score":0,"Answer":"First, you may want to be clear on hardware - there is wide range of hardware with various capabilities. For example raspberry by is considered a powerful hardware. EspEye and Arduio Nano 33 BLE considered low end platforms.\nIt also depends which ML solution you are deploying. I think the most widely deployed method is neural network. Generally, the work flow is to train the model on a PC or on Cloud using lots of data. It is done on PC due to large amount of resources needed to run back prop. The inference is much lighter which can be done on the edge devices.","Q_Score":0,"Tags":"python,machine-learning,raspberry-pi,artificial-intelligence,robotics","A_Id":67797110,"CreationDate":"2018-12-03T11:15:00.000","Title":"How to embed machine learning python codes in hardware platform like raspberry pi?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to train SqueezeNet 1.1 model using MNIST dataset instead of ImageNet dataset. \nCan i have the same model as torchvision.models.squeezenet? \nThanks!","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1212,"Q_Id":53593363,"Users Score":0,"Answer":"The initialization of the pretrained weights is possible but you'll get trouble with the strides and kernel sizes since MNIST images are 28X28 pixels. Most probably the reduction will lead to  (batch_sizex1x1xchannel) feature maps before the net is at its infernece layer which will then cause an error.","Q_Score":1,"Tags":"python,neural-network,pytorch,mnist,torchvision","A_Id":53610813,"CreationDate":"2018-12-03T11:56:00.000","Title":"Train SqueezeNet model using MNIST dataset Pytorch","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm currently using the Fourier transformation in conjunction with Keras for voice recogition (speaker identification). I have heard MFCC is a better option for voice recognition, but I am not sure how to use it.\nI am using librosa in python (3) to extract 20 MFCC features. My question is: which MFCC features should I use for speaker identification?\nIn addition to this I am unsure on how to implement these features. What I would do is to get the necessary features and make one long vector input for a neural network. However, it is also possible to display colors, so could image recognition also be possible, or is this more aimed at speech, and not speaker recognition?\nIn short, I am unsure where I should start, as I am not very experienced with image recognition and have no idea where to start.\nThanks in advance!!","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1526,"Q_Id":53601892,"Users Score":0,"Answer":"You can use MFCCs with dense layers \/ multilayer perceptron, but probably a Convolutional Neural Network on the mel-spectrogram will perform better, assuming that you have enough training data.","Q_Score":0,"Tags":"python,keras,neural-network,voice-recognition,mfcc","A_Id":53806861,"CreationDate":"2018-12-03T21:09:00.000","Title":"Using MFCC's for voice recognition","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am extracting a road network from satellite imagery. Herein the pixel classification is binary ( 0 = non-road, 1 = road). Hence, the mask of the complete satellite image which is 6400 x 6400 pixels shows one large road network where each road is connected to another road. For the implementation of the U-net I divided that large image in 625 images of 256 x 256 pixels. \nMy question is: Can a neural network easier find structure with an increase in batch size (thus can it find structure between different batches), or can it only find structure if the input image size is enlarged?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":955,"Q_Id":53613681,"Users Score":2,"Answer":"If your model is a regular convolutional network (without any weird hacks), the samples in a batch will not be connected to each other. \nDepending on which loss function you use, the batch size might be important too. For regular functions (available 'mse', 'binary_crossentropy', 'categorical_crossentropy', etc.), they all keep the samples independent from each other. But some losses might consider the entire batch. (F1 metrics, for instance). If you're using a loss function that doesn't treat samples independently, then the batch size is very important.\nThat said, having a bigger batch size may help the net to find its way more easily, since one image might push weights towards one direction, while another may want a different direction. The mean results of all images in the batch should then be more representative of a general weight update.\nNow, entering an experimenting field (we never know everything about neural networks until we test them), consider this comparison:\n\na batch with 1 huge image versus   \na batch of patches of the same image\n\nBoth will have the same amount of data, and for a convolutional network, it wouldn't make a drastic difference. But for the first case, the net will probably be better at finding connections between roads, maybe find more segments where the road might be covered by something, while the small patches, being full of borders might be looking more into textures and be not good at identifying these gaps.\nAll of this is, of course, a guess. Testing is the best.\nMy net in a GPU cannot really use big patches, which is bad for me...","Q_Score":1,"Tags":"python,keras,image-segmentation","A_Id":53613988,"CreationDate":"2018-12-04T13:06:00.000","Title":"Increasing accuracy by changing batch-size and input image size","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an Excel file where A1,A2,A3 are empty but A4:A53 contains column names.\nIn \"R\" when you were to read that data, the columns names for A1,A2,A3 would be \"X_1,X_2,X_3\" but when using pandas.read_excel it simply skips the first three columns, thus ignoring them. The problem is that the number of columns in each file is dynamic thus I cannot parse the column range, and I cannot edit the files and adding \"dummy names\" for A1,A2,A3","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":6260,"Q_Id":53615545,"Users Score":0,"Answer":"A quick fix would be to pass header=None to pandas' read_excel() function, manually insert the missing values into the first row (it now will contain the column names), then assign that row to df.columns and drop it after. Not the most elegant way, but I don't know of a builtin solution to your problem\nEDIT: by \"manually insert\" I mean some messing with fillna(), since this appears to be an automated process of some sort","Q_Score":3,"Tags":"python-3.x,pandas","A_Id":53616756,"CreationDate":"2018-12-04T14:50:00.000","Title":"Pandas read_excel removes columns under empty header","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I create a pyspark dataframe and i want to see it in the SciView tab in PyCharm when i debug my code (like I used to do when i have worked with pandas).\nIt says \"Nothing to show\" (the dataframe exists, I can see it when I use the show() command).\nsomeone knows how to do it or maybe there is no integration between pycharm and pyspark dataframe in this case?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1310,"Q_Id":53627818,"Users Score":5,"Answer":"Pycharm does not support spark dataframes, you should call the toPandas() method on the dataframe. As @abhiieor mentioned in a comment, be aware that you can potentially collect a lot of data, you should first limit() the number of rows returned.","Q_Score":5,"Tags":"python,pyspark,pycharm","A_Id":53633844,"CreationDate":"2018-12-05T08:13:00.000","Title":"DataFrame view in PyCharm when using pyspark","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been trying to set up an LSTM model but I'm a bit confused about batch_size. I'm using the Keras module in Tensorflow.\nI have 50,000 samples, each has 200 time steps and each time step has three features. So I've shaped my training data as (50000, 200, 3). \nI set up my model with four LSTM layers, each having 100 units. For the first layer I specified the input shape as (200, 3). The first three layers have return_sequences=True, the last one doesn't. Then I do some softmax classification.\nWhen I call model.fit with batch_size='some_number' do Tensorflow\/Keras take care of feeding the model with batches of the specified size? Do I have to reshape my data somehow in advance? What happens if the number of samples is not evenly divisible by 'some_number'?\nThanks for your help!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1170,"Q_Id":53630041,"Users Score":2,"Answer":"If you provide your data as numpy arrays to model.fit() then yes, Keras will take care of feeding the model with the batch size you specified. If your dataset size is not divisible by the batch size, Keras will have the final batch be smaller and equal to dataset_size mod batch_size.","Q_Score":0,"Tags":"python,tensorflow,keras,lstm","A_Id":53630217,"CreationDate":"2018-12-05T10:22:00.000","Title":"batch size for LSTM","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on a multi class classification for segmenting customers into 3 different classes based on their purchasing behavior and demographics. I cannot disclose the data set completely but in general it contains around 300 features and  50000 rows. I have tried the following methods but I am unable to achieve accuracy above 50% :\n\nTuning the hyperparameters ( I am using tuned hyperparameters after doing GridSearchCV)\nNormalizing the dataset and then running my models\nTried different classification methods : OneVsRestClassifier, RandomForestClassification, SVM, KNN and LDA\nI have also removed irrelevant features and tried running my models\nMy classes were imbalanced, so I have also tried using class_weight = balanced, oversampling using SMOTE, downsampling and resampling.\n\nIs there something else I can try to improve my accuracy ( and by accuracy I mean f-score, precision and recall ).\nAny help will be appreciated.","AnswerCount":3,"Available Count":3,"Score":0.0665680765,"is_accepted":false,"ViewCount":4038,"Q_Id":53634808,"Users Score":1,"Answer":"Try doing a feature selection first using PCA or Random forest and then fit a chained classifier where first do a oneversesall and then a random forest or a decision tree. You should get a slightly better accuracy.","Q_Score":2,"Tags":"python,machine-learning,random-forest","A_Id":53749834,"CreationDate":"2018-12-05T14:43:00.000","Title":"How to improve accuracy of random forest multiclass classification model?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on a multi class classification for segmenting customers into 3 different classes based on their purchasing behavior and demographics. I cannot disclose the data set completely but in general it contains around 300 features and  50000 rows. I have tried the following methods but I am unable to achieve accuracy above 50% :\n\nTuning the hyperparameters ( I am using tuned hyperparameters after doing GridSearchCV)\nNormalizing the dataset and then running my models\nTried different classification methods : OneVsRestClassifier, RandomForestClassification, SVM, KNN and LDA\nI have also removed irrelevant features and tried running my models\nMy classes were imbalanced, so I have also tried using class_weight = balanced, oversampling using SMOTE, downsampling and resampling.\n\nIs there something else I can try to improve my accuracy ( and by accuracy I mean f-score, precision and recall ).\nAny help will be appreciated.","AnswerCount":3,"Available Count":3,"Score":1.2,"is_accepted":true,"ViewCount":4038,"Q_Id":53634808,"Users Score":2,"Answer":"Try to tune below parameters\nn_estimators\nThis is the number of trees you want to build before taking the maximum voting or averages of predictions. Higher number of trees give you better performance but makes your code slower. You should choose as high value as your processor can handle because    this makes your predictions stronger and more stable. As your data size is bigger so it will take more time for each iteration but try this.\nmax_features\nThese are the maximum number of features Random Forest is allowed to try in individual tree. There are multiple options available in Python to assign maximum features. Few of them are  :\n\nAuto\/None : This will simply take all the features which make sense\nin every tree.Here we simply do not put any restrictions on the\nindividual tree.\nsqrt : This option will take square root of the total number of\nfeatures in individual run. For instance, if the    total number of\nvariables are 100, we can only take 10 of them in    individual\ntree.\u201dlog2\u2033 is another similar type of option for max_features.\n0.2 : This option allows the random forest to take 20% of variables in individual run. We can assign and value in a format \u201c0.x\u201d where we\nwant x% of features to be considered.\n\n\nmin_sample_leaf\nLeaf is the end node of a decision tree. A smaller leaf makes the model more prone to capturing noise in train data. You can start with some minimum value like 75 and gradually increase it. See which value your accuracy is coming high.","Q_Score":2,"Tags":"python,machine-learning,random-forest","A_Id":53755232,"CreationDate":"2018-12-05T14:43:00.000","Title":"How to improve accuracy of random forest multiclass classification model?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on a multi class classification for segmenting customers into 3 different classes based on their purchasing behavior and demographics. I cannot disclose the data set completely but in general it contains around 300 features and  50000 rows. I have tried the following methods but I am unable to achieve accuracy above 50% :\n\nTuning the hyperparameters ( I am using tuned hyperparameters after doing GridSearchCV)\nNormalizing the dataset and then running my models\nTried different classification methods : OneVsRestClassifier, RandomForestClassification, SVM, KNN and LDA\nI have also removed irrelevant features and tried running my models\nMy classes were imbalanced, so I have also tried using class_weight = balanced, oversampling using SMOTE, downsampling and resampling.\n\nIs there something else I can try to improve my accuracy ( and by accuracy I mean f-score, precision and recall ).\nAny help will be appreciated.","AnswerCount":3,"Available Count":3,"Score":0.0665680765,"is_accepted":false,"ViewCount":4038,"Q_Id":53634808,"Users Score":1,"Answer":"How is your training acc? I assume that your acc is your validation. If your training acc is way to high, som normal overfitting might be the case. Random forest normally handles overfitting very well.\nWhat you could try is PCA of your data, and then try classify on that. This gives you the features which accounts for most variation in the data, thus can be a good idea to try, if you cannot classify on the original data (and also it reduces your features).\nA side note: remember, that the fitting of SVM is quadratic in the number of points, thus reducing your data to around 10-20000 for tuning parameters and then fit a SVM on the full dataset with the optimal parameter for the subset, might also speed up the process.\nAlso remember to consider trying different kernels for the SVM.","Q_Score":2,"Tags":"python,machine-learning,random-forest","A_Id":53635051,"CreationDate":"2018-12-05T14:43:00.000","Title":"How to improve accuracy of random forest multiclass classification model?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using keras with Tensorflow-gpu backend in Python. I'm trying to put the correct number of nb_train_samplesn nb_validation_ samples and epochs.\nI am using the fit-generator-method.\n\nnb_train_samples has to be the same number that images that i have for training? Can be higher?\nnb_validation_samples has to be the same number that images that i have for validation? Can be higher?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":454,"Q_Id":53637278,"Users Score":1,"Answer":"Yes, they have to be the same.  These are the parameters you use to tell the process how many you have of each type of image.  For instance, if you tell it that you have 5_000 validation samples, but there are only 3_000 in the data set, you will crash the run.","Q_Score":0,"Tags":"python,tensorflow,keras,deep-learning","A_Id":53637348,"CreationDate":"2018-12-05T17:01:00.000","Title":"How to use a good use of nb_train_samples in keras?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want a column in a dataframe including datetime in the format \"M1,D1,H1\" (January 1st, 1.00 am). I have a dataframe of the size with 8760 elements. How do I populate it?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":73,"Q_Id":53638858,"Users Score":0,"Answer":"df.insert(0, 'time', [dt.datetime(self.MODEL_YEAR, 1, 1, 0) + dt.timedelta(hours=int(x))\n                                      for x in df['hour'].values])","Q_Score":0,"Tags":"python-3.x,pandas","A_Id":53842312,"CreationDate":"2018-12-05T18:46:00.000","Title":"Datetime in the format \"M1,D1,H1\" (January 1st, 1.00 am)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Hi I am trying to write a python code for reading NFL Stats \nI converted stats into a excel Csv file,\nI am wondering if anyone could help me plan out my code\nLike how would I go about getting whos got the most touchdowns, sacks, passing yards, and etc.\nI know this is kinda of beginner stuff but much help would be appreciated!","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":92,"Q_Id":53644166,"Users Score":1,"Answer":"What I suggest is:\n\nRead the CVS file and set it in an array of two dimensions. Where columns are touchdowns, sacks, passing yards, etc. and rows are specific values for each player.\nTo determinate for example which player got the most touchdown, go through the column 1 \"column for touchdowns\" and compare the maximum value of that column. \nTo continue with the other stats you have to repeat the previous process with another column.\n\nI hope this helps you.","Q_Score":0,"Tags":"python,csv","A_Id":53644466,"CreationDate":"2018-12-06T03:24:00.000","Title":"Csv Python reader","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"One can apply recurrent dropout onto basic LSTM or GRU layers in Keras by passing its value as a parameter of the layer.\nCuDNNLSTM and CuDNNGRU are LSTM and GRU layers that are compatible with CUDA. The main advantage is that they are 10 times faster during training. However they lack some of the beauty of the LSTM or GRU layers in Keras, namely the possibility to pass dropout or recurrent dropout values.\nWhile we can add Dropout layers directly in the model, it seems we cannot do that with Recurrent Dropout.\nMy question is then the following : How to add recurrent dropout to CuDNNGRU or CuDNNLSTM in Keras ?","AnswerCount":2,"Available Count":1,"Score":0.2913126125,"is_accepted":false,"ViewCount":4528,"Q_Id":53656220,"Users Score":3,"Answer":"You can use kernel_regularizer and recurrent_regularizer for prevent overfitting, i am using L2 regularizers and i am having good results.","Q_Score":5,"Tags":"python,tensorflow,keras,lstm","A_Id":58120534,"CreationDate":"2018-12-06T16:55:00.000","Title":"How to add recurrent dropout to CuDNNGRU or CuDNNLSTM in Keras","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm a student in medical imaging. I have to construct a neural network for image segmentation. I have a data set of 285 subjects, each with 4 modalities (T1, T2, T1ce, FLAIR) + their respective segmentation ground truth. Everything is in 3D with resolution of 240x240x155 voxels (this is BraTS data set).\nAs we know, I cannot input the whole image on a GPU for memory reasons. I have to preprocess the images and decompose them in 3D overlapping patches (sub-volumes of 40x40x40) which I do with scikit-image view_as_windows and then serialize the windows in a TFRecords file. Since each patch overlaps of 10 voxels in each direction, these sums to 5,292 patches per volume. The problem is, with only 1 modality, I get sizes of 800 GB per TFRecords file. Plus, I have to compute their respective segmentation weight map and store it as patches too. Segmentation is also stored as patches in the same file. \nAnd I eventually have to include all the other modalities, which would take nothing less than terabytes of storage. I also have to remember I must also sample equivalent number of patches between background and foreground (class balancing).\nSo, I guess I have to do all preprocessing steps on-the-fly, just before every training step (while hoping not to slow down training too). I cannot use tf.data.Dataset.from_tensors() since I cannot load everything in RAM. I cannot use tf.data.Dataset.from_tfrecords() since preprocessing the whole thing before takes a lot of storage and I will eventually run out.\nThe question is : what's left for me for doing this cleanly with the possibility to reload the model after training for image inference ?\nThank you very much and feel free to ask for any other details.\nPierre-Luc","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":680,"Q_Id":53662978,"Users Score":1,"Answer":"Finally, I found a method to solve my problem.\nI first crop a subject's image without applying the actual crop. I only measure the slices I need to crop the volume to only the brain. I then serialize all the data set images into one TFRecord file, each training example being an image modality, original image's shape and the slices (saved as Int64 feature). \nI decode the TFRecords afterward. Each training sample are reshaped to the shape it contains in a feature. I stack all the image modalities into a stack using tf.stack() method. I crop the stack using the previously extracted slices (the crop then applies to all images in the stack). I finally get some random patches using tf.random_crop() method that allows me to randomly crop a 4-D array  (heigh, width, depth, channel).\nThe only thing I still haven't figured out is data augmentation. Since all this is occurring in Tensors format, I cannot use plain Python and NumPy to rotate, shear, flip a 4-D array. I would need to do it in the tf.Session(), but I would rather like to avoid this and directly input the training handle.\nFor the evaluation, I serialize in a TFRecords file only one test subject per file. The test subject contains all modalities too, but since there is no TensorFLow methods to extract patches in 4-D, the image is preprocessed in small patches using Scikit-Learn extract_patches() method. I serialize these patches to the TFRecords.\nThis way, training TFRecords is a lot smaller. I can evaluate the test data using batch prediction.\nThanks for reading and feel free to comment !","Q_Score":1,"Tags":"python,tensorflow","A_Id":54430444,"CreationDate":"2018-12-07T03:51:00.000","Title":"TensorFlow tf.data.Dataset API for medical imaging","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have some 1000 news articles related to science and technology. I need to train a classifier which will predict say 3(computer science, electronics, electrical) confidence scores for each article. \nEach score represents how much the article belongs to each field.\nThe confidence score will be a value between zero and one.\nBut the data set doesn't have a training label.  \nHow do I proceed from here? What kind of data do I need?\nHow do I train such a model?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":68,"Q_Id":53668101,"Users Score":1,"Answer":"It is not super clear what is your main idea, but articles typically do have tags or categories and you may use that for the classification labels.\nHumans are pretty good at articles tagging.","Q_Score":2,"Tags":"python,machine-learning,deep-learning,text-classification","A_Id":53672476,"CreationDate":"2018-12-07T10:55:00.000","Title":"MutiLabel classification","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a big dbf file, converting it to a pandas dataframe is taking a lot of time.\nIs there a way to convert the file into a dask dataframe?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":235,"Q_Id":53669318,"Users Score":0,"Answer":"Dask does not have a dbf loading method.\nAs far as I can tell, dbf files do not support random-access to the data, so it is not possible to read from sections of the file in separate workers, in parallel. I may be wrong about this, but certainly dbfreader makes no mention of jumping through to an arbitrary record.\nTherefore, the only way you could read from dbf in parallel, and hope to see a speed increase, would be to split your original data into multiple dbf files, and use dask.delayed to read each of them.\nIt is worth mentioning, that probably the reason dbfreader is slow (but please, do your own profiling!) is that it's doing byte-by-byte manipulations and making python objects for every record before passing the records to pandas. If you really wanted to speed things up, this code should be converted to cython or maybe numba, and a pre-allocated dataframe assigned into.","Q_Score":0,"Tags":"python,dataframe,dask,dbf","A_Id":53673661,"CreationDate":"2018-12-07T12:12:00.000","Title":"How to convert a dbf file to a dask dataframe?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using a Jupiter Notebook for making a machine learning model using turicreate. When ever I upload a big .csv file, I get a message: kernel died. Since I am new in python is there an other way to Batch loading file or anyone nows how to fix this issue ?\nThe csv file is 52.9 MB\nThanks","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":3050,"Q_Id":53669913,"Users Score":0,"Answer":"53MB is not a big file ! You should try to load this in a ipython terminal to test.\nLoad as you would in a Jupyter to see if you have any issue. If there is no issue, This could be a bad installation of Jupyter.\nNote : The kernel dies mostly when you're out of RAM. But 53MB is not that big, assuming you have at least 2 or 4G or RAM on your laptop, an error like this shouldn't happend","Q_Score":1,"Tags":"python,python-3.x,machine-learning,jupyter-notebook,turi-create","A_Id":53670039,"CreationDate":"2018-12-07T12:49:00.000","Title":"Jupyter notebook : kernel died msg when loading big CSV file","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I have a weighted Graph using networkx and the topology is highly meshed. I would like to extract a number of paths between two nodes with distance minimization.\nTo clarify, the dijkstra_path function finds the weighted shortest path between two nodes, I would like to get that as well as the second and third best option of shortest weighted paths between two nodes.\nI tried using all_simple_paths and then ordering the paths in distance minimization order but it is extremely time consuming when the network is meshed with 500 nodes or so.\nAny thoughts on the matter? Thank you for your help!","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":270,"Q_Id":53674393,"Users Score":1,"Answer":"Try networkx's shortest_simple_paths.","Q_Score":0,"Tags":"python-3.x,networkx,dijkstra","A_Id":53677838,"CreationDate":"2018-12-07T17:35:00.000","Title":"Descending order of shortest paths in networkx","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":1,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a script, parts of which at some time able to run in parallel. Python 3.6.6\nThe goal is to decrease execution time at maximum. \nOne of the parts is connection to Redis, getting the data for two keys, pickle.loads for each and returning processed objects. \nWhat\u2019s the best solution for such a tasks?\nI\u2019ve tried Queue() already, but Queue.get_nowait() locks the script, and after {process}.join() it also stops execution even though the task is done. Using pool.map raises TypeError: can't pickle _thread.lock objects. \nAll I could achieve is parallel running of all parts but still cannot connect the results","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":95,"Q_Id":53688617,"Users Score":0,"Answer":"cPickle.load() will release the GIL so you can use it in multiple threads easily. But cPickle.loads() will not, so don't use that.\nBasically, put your data from Redis into a StringIO then cPickle.load() from there.  Do this in multiple threads using concurrent.futures.ThreadPoolExecutor.","Q_Score":1,"Tags":"python-3.x,python-multiprocessing,python-multithreading","A_Id":53688653,"CreationDate":"2018-12-09T01:25:00.000","Title":"processing data in parallel python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have been trying to install 'Keras' library from Anaconda on my laptop. I have the latest version of Anaconda. After that, I tried \n\nconda update conda\nconda update --all\n\nThe above two succeeds. After that I tried\n\nconda install -c conda-forge keras \nconda install keras\n\nBoth of the above fails with the below error.\n\nERROR conda.core.link:_execute(502): An error occurred while installing package >'::automat-0.7.0-py_1'.\n  CondaError: Cannot link a source that does not exist. >C:\\Users\\Anaconda3\\Scripts\\conda.exe\n\nI downloaded \"automat-0.7.0-py_1\" from anaconda site into one local folder and tried conda install from there. It works. However when I try to install Keras again, that again fails. I am clueless now what to do.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1214,"Q_Id":53689684,"Users Score":0,"Answer":"I guess the issue is that tensorflow is not yet released for Python3.7 (you have mentioned latest version of Anaconda). To overcome this, you may create a new environment with Python3.6 and simultaneously install Keras.\nconda create -n p360 python=3.6 anaconda tensorflow keras\nHere p360 is the name of the environment I chose.","Q_Score":1,"Tags":"python,tensorflow,keras,anaconda,theano","A_Id":54559052,"CreationDate":"2018-12-09T05:34:00.000","Title":"Issue with installing Keras library from Anaconda","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm having some issues with sorting a pandas dataframe.\nsort_index(axis=0) results in the dataframe sorting the index as 1 10 11 12 13... etc.\nWhile sort_index(axis=1) seems to work for the first couple of rows and then it gets completely disordered.\nI simply cannot wrap my head around what is going on. I want a simply numerical sorting of my indices, it seems that it ought to be the standard setting for sort_index.","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":719,"Q_Id":53693429,"Users Score":0,"Answer":"You might have the index as a string type.\nI was having this issue after using the groupby() function. I fixed the problem by changing the column that later became my index to an int() with:\ndf['col_name'] = df['col_name'].astype(int) .","Q_Score":1,"Tags":"python,pandas,dataframe","A_Id":61193906,"CreationDate":"2018-12-09T14:40:00.000","Title":"Pandas sort_index numerically, not lexicographically","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm having some issues with sorting a pandas dataframe.\nsort_index(axis=0) results in the dataframe sorting the index as 1 10 11 12 13... etc.\nWhile sort_index(axis=1) seems to work for the first couple of rows and then it gets completely disordered.\nI simply cannot wrap my head around what is going on. I want a simply numerical sorting of my indices, it seems that it ought to be the standard setting for sort_index.","AnswerCount":3,"Available Count":2,"Score":0.1325487884,"is_accepted":false,"ViewCount":719,"Q_Id":53693429,"Users Score":2,"Answer":"you have 2 types of index, either row index (axis=0) or columns index (axis=1)\nyou are just arranging columns by name when you use axis=1. it does not reorder each row by values. check your columns names after your sort_index(axis=1) and you will understang","Q_Score":1,"Tags":"python,pandas,dataframe","A_Id":53693470,"CreationDate":"2018-12-09T14:40:00.000","Title":"Pandas sort_index numerically, not lexicographically","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an astropy.table.table object holding stars data. One row per star with columns holding data such as Star Name, Max Magnitude, etc.\nI understand an astropy table's internal representation is a dict for each column, with the rows being returned on the fly as slices across the dict objects.\nI need to convert the astropy table to a Python list of dict objects, with one dict per star. Essentially this is both a transposition of the table and a conversion.\nI can obviously iterate through the table, by column within row, to build the dicts and add them to the list, but I was hoping there was a more efficient way?'","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":885,"Q_Id":53694408,"Users Score":0,"Answer":"I ended up iterating, slicing and copying as list which worked fine on the relatively small dataset.","Q_Score":1,"Tags":"python,list,dictionary,astropy","A_Id":56693911,"CreationDate":"2018-12-09T16:39:00.000","Title":"Convert astropy table to list of dictionaries","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to write a GA in Python to solve TSP. I would like to speed it up. Because right now, it takes 24 seconds to run 200 generations with a population size of 200.\nI am using a map with 29 cities. Each city has an id and (x,y) coordinates.\nI tried implementing a distance matrix, which calculates all the distances once and stores it in a list. So instead of calculating the distance using the sqrt() function 1M+ times, it only uses the function 406 times. Every time a distance between two cities is required, it is just retrieved from the matrix using the id of the two cities as the index.\nBut even with this, it takes just as much time. I thought sqrt() would be more expensive than just indexing a list. Is it not? Would a dictionary make it faster?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":177,"Q_Id":53696084,"Users Score":0,"Answer":"The short answer:\nYes. Dictionary would make it faster.\nThe long answer:\nLets say, you pre-processing and calculates all distances once - Great! Now, lets say I want to find the distance between A and B. So, all I have to do now is to find that distance where I put it - it is in the list!\nWhat is the time complexity to find something in the list? Thats right - O(n)\nAnd how may times I'm going to use it? My guess according to your question: 1M+ times\nNow, that is a huge problem. I suggest you to use a dictionary so you could search in the pre-calculated distace between any two cities in O(1).","Q_Score":0,"Tags":"python,list,dictionary,time-complexity,sqrt","A_Id":53696362,"CreationDate":"2018-12-09T19:51:00.000","Title":"Solving TSP with GA: Should a distance matrix speed up run-time?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When in Jupyter I Shift+TAB on pandas.DataFrame, it displays e.g.\nTwo-dimensional size-mutable, potentially heterogeneous tabular data\nstructure with labeled axes (rows and columns). Arithmetic operations\nalign on both row and column labels. Can be thought of as a dict-like\ncontainer for Series objects. The primary pandas data structure.\nIs there any way to display this in Pycharm as well? Quick documentation (Ctrl+Q) doesnt display this.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":62,"Q_Id":53696405,"Users Score":1,"Answer":"What you want here is help(pandas.DataFrame). Prints the same information as shift+TAB does in Jupyter.","Q_Score":0,"Tags":"python,pycharm,jupyter-notebook","A_Id":53709894,"CreationDate":"2018-12-09T20:32:00.000","Title":"How to display same detailed help in Pycharm as in Jupyter?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"the version of python is 3.6\nI tried to execute my code but, there are still some errors as below:\nTraceback (most recent call last):\n\nFile\n  \"C:\\Users\\tmdgu\\Desktop\\NLP-master1\\NLP-master\\Ontology_Construction.py\",\n  line 55, in \n      , binary=True)\nFile \"E:\\Program\n  Files\\Python\\Python35-32\\lib\\site-packages\\gensim\\models\\word2vec.py\",\n  line 1282, in load_word2vec_format\n      raise DeprecationWarning(\"Deprecated. Use gensim.models.KeyedVectors.load_word2vec_format instead.\")\nDeprecationWarning: Deprecated. Use\n  gensim.models.KeyedVectors.load_word2vec_format instead.\n\nhow to fix the code? or is the path to data wrong?","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":1129,"Q_Id":53697450,"Users Score":2,"Answer":"This is just a warning, not a fatal error. Your code likely still works.\n\"Deprecation\" means a function's use has been marked by the authors as no longer encouraged. \nThe function typically still works, but may not for much longer \u2013 becoming unreliable or unavailable in some future library release. Often, there's a newer, more-preferred way to do the same thing, so you don't trigger the warning message. \nYour warning message points you at the now-preferred way to load word-vectors of that format: use KeyedVectors.load_word2vec_format() instead. \nDid you try using that, instead of whatever line of code (not shown in your question) that you were trying before seeing the warning?","Q_Score":0,"Tags":"python,gensim,word2vec","A_Id":53714251,"CreationDate":"2018-12-09T22:47:00.000","Title":"Error for word2vec with GoogleNews-vectors-negative300.bin","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"If I have a rainfall map which has three dimensions (latitude, longitude and rainfall value), if I put it in an array, do I need a 2D or 3D array? How would the array look like?\nIf I have a series of daily rainfall map which has four dimensions (lat, long, rainfall value and time), if I put it in an array, do I need a 3D or 4D array?\nI am thinking that I would need a 2D and 3D arrays, respectively, because the latitude and longitude can be represented by a 1D array only (but reshaped such that it has more than 1 rows and columns). Enlighten me please.","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":77,"Q_Id":53714557,"Users Score":0,"Answer":"I believe that the rainfall value shouldn't be a dimension. Therefore, you could use 2D array[lat][lon] = rainfall_value or 3D array[time][lat][lon] = rainfall_value respectively.\nIf you want to reduce number of dimensions further, you can combine latitude and longitude into one dimension as you suggested, which would make arrays 1D\/2D.","Q_Score":2,"Tags":"python,arrays","A_Id":53714817,"CreationDate":"2018-12-10T22:20:00.000","Title":"physical dimensions and array dimensions","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there any library to save images in binary (1bit per pixel) .tiff compressed file?\nopencv and pillow cannot do that","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1104,"Q_Id":53729889,"Users Score":0,"Answer":"You can try using libtiff.\nInstall using pip install libtiff","Q_Score":0,"Tags":"python,tiff","A_Id":53730003,"CreationDate":"2018-12-11T18:03:00.000","Title":"How to save image as binary compressed .tiff python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to perform cross-validation on NMF to find the best parameters to use. I tried using the sklearn cross-validation but get an error that states the NMF does not have a scoring method. Could anyone here help me with that? Thank you all","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":657,"Q_Id":53732904,"Users Score":1,"Answer":"A property of nmf is that it is an unsupervised (machine learning) method. This generally means that there is no labeled data that can serve as a 'golden standard'. \nIn case of NMF you can not define what is the 'desired' outcome beforehand.\nThe cross validation in sklearn is designed for supervised machine learning, in which you have labeled data by definition.\nWhat cross validation does, it holds out sets of labeled data, then trains a model on the data that is leftover and evaluates this model on the held out set. For this evaluation any metric can be used. For example: accuracy, precision, recall and F-measure, and for computing these measures it needs labeled data.","Q_Score":0,"Tags":"python,scikit-learn,nmf","A_Id":55491090,"CreationDate":"2018-12-11T21:55:00.000","Title":"How to perform cross validation on NMF Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've produced a few Bokeh output files as the result of a fairly time intensive process. It would be really cool to pull the plots together from their respective files and build a new output file where i could visualize them all in a column. I know I should have thought to do this earlier on before producing all the individual plots, but, alas, i did not. \nIs there a way to import the preexisting figures so that I can aggregate them together into new multi-multi plot output file?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":90,"Q_Id":53746686,"Users Score":1,"Answer":"As of Bokeh 1.0.2, there is not any existing API for this, and I don't think there is any simple technique that could accomplish this either. I think the only options are: some kind of (probably somewhat fragile) text scraping of the HTML files, or distributing all the HTML files and using something like <iframe> to collect the individual subplot files into one larger view. \nGoing forward, for reference there is autoload_static that allows plots to be encapsulated in \"sidecar\" JS files that can be individually distributed and embedded, or there is json_item that produces and isolated JSON representation of the document that can also be individually distributed and embedded.","Q_Score":1,"Tags":"python-3.x,plot,import,bokeh","A_Id":53747543,"CreationDate":"2018-12-12T15:51:00.000","Title":"Is it possible to import bokeh figures from the html file they have been saved in?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I have made a comparison between time of execution only for watershed functions in OpenCV, Skimage(SciPy) and BoofCV. Although OpenCV appears to be much faster than the other two (average time: 0.0031 seconds on 10 samples), Skimage time of execution varies significantly (from 0.03 to 0.554 seconds). I am wondering why this happens? Isn\u00b4t it supposed to be a native python function?","AnswerCount":1,"Available Count":1,"Score":0.537049567,"is_accepted":false,"ViewCount":276,"Q_Id":53746868,"Users Score":3,"Answer":"It's hard to know without more details why your particular application runs slowly. In general, though, the scikit-image code is not as optimized as OpenCV, but covers many more use cases. For example, it can work with floating point values as input, rather than just uint8, and it can work with 3D or even higher-dimension images.\nAbout the performance: OpenCV is coded in highly optimized C\/C++, while scikit-image is coded in Cython, a hybrid language that compiles Python code to C, achieving C-performance. However, several optimizations are not available in Cython, and as I mentioned above, there are differences in what is actually implemented, resulting in a performance difference.","Q_Score":2,"Tags":"python,time,native,scikit-image,watershed","A_Id":53771189,"CreationDate":"2018-12-12T16:00:00.000","Title":"Why is watershed function from SciKit too slow?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have two different text which I want to compare using tfidf vectorization.\nWhat I am doing is:\n\ntokenizing each document\nvectorizing using TFIDFVectorizer.fit_transform(tokens_list)\n\nNow the vectors that I get after step 2 are of different shape.\nBut as per the concept, we should have the same shape for both the vectors. Only then the vectors can be compared.\nWhat am I doing wrong? Please help.\nThanks in advance.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2848,"Q_Id":53748236,"Users Score":3,"Answer":"As G. Anderson already pointed out, and to help the future guys on this, when we use the fit function of TFIDFVectorizer on document D1, it means that for the D1, the bag of words are constructed.\nThe transform() function computes the tfidf frequency of each word in the bag of word. \nNow our aim is to compare the document D2 with D1. It means we want to see how many words of D1 match up with D2. Thats why we perform fit_transform() on D1 and then only the transform() function on D2 would apply the bag of words of D1 and count the inverse frequency of tokens in D2. \nThis would give the relative comparison of D1 against D2.","Q_Score":4,"Tags":"python,nltk,cosine-similarity,tfidfvectorizer","A_Id":59012601,"CreationDate":"2018-12-12T17:20:00.000","Title":"how to compare two text document with tfidf vectorizer?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Hey guys so I've been studying for an upcoming test and I came across this question:\n\nIf you had an unsorted list of one million unique items, and knew that you would only search it once for a value, which of the following algorithms would be the fastest?\n\nUse linear search on the unsorted list\nUse insertion sort to sort the list and then binary search on the sorted list\n\n\nWouldn't the second choice be the fastest? Sorting the list and then looking for the value than only using linear search?","AnswerCount":4,"Available Count":4,"Score":0.049958375,"is_accepted":false,"ViewCount":1753,"Q_Id":53748254,"Users Score":1,"Answer":"Alternative 1 of course, since that only requires you to go through the list once. If you are to sort the list, you have to traverse the list at least once for the sorting, and then some for the search.","Q_Score":2,"Tags":"python,algorithm,time-complexity","A_Id":53748320,"CreationDate":"2018-12-12T17:21:00.000","Title":"Unsorted Lists vs Linear and Binary Search","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Hey guys so I've been studying for an upcoming test and I came across this question:\n\nIf you had an unsorted list of one million unique items, and knew that you would only search it once for a value, which of the following algorithms would be the fastest?\n\nUse linear search on the unsorted list\nUse insertion sort to sort the list and then binary search on the sorted list\n\n\nWouldn't the second choice be the fastest? Sorting the list and then looking for the value than only using linear search?","AnswerCount":4,"Available Count":4,"Score":0.0996679946,"is_accepted":false,"ViewCount":1753,"Q_Id":53748254,"Users Score":2,"Answer":"sorting a list has a O(log(N)*N) complexity at best.\nLinear search has O(N) complexity.\nSo if you have to search more than once, you begin to gain time after some searches.\nIf objects are hashable (ex: integers) a nice alternative (when searching more than once only) to sorting+bisection search is to put them in a set. Then complexity is down to O(1) because of hashing, but still O(N) to create it, and the hashing adds to the toll.\nIf you need only to search once, linear search is the best choice.","Q_Score":2,"Tags":"python,algorithm,time-complexity","A_Id":53748319,"CreationDate":"2018-12-12T17:21:00.000","Title":"Unsorted Lists vs Linear and Binary Search","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Hey guys so I've been studying for an upcoming test and I came across this question:\n\nIf you had an unsorted list of one million unique items, and knew that you would only search it once for a value, which of the following algorithms would be the fastest?\n\nUse linear search on the unsorted list\nUse insertion sort to sort the list and then binary search on the sorted list\n\n\nWouldn't the second choice be the fastest? Sorting the list and then looking for the value than only using linear search?","AnswerCount":4,"Available Count":4,"Score":1.2,"is_accepted":true,"ViewCount":1753,"Q_Id":53748254,"Users Score":3,"Answer":"Linear search takes just O(n), while sorting a list first takes O(n log n). Since you are going to search the list only once for a value, the fact that subsequent searches in the sorted list with a binary search takes only O(log n) does not help overcome the overhead of the O(n log n) time complexity involved in the sorting, and hence a linear search would be more efficient for the task.","Q_Score":2,"Tags":"python,algorithm,time-complexity","A_Id":53748316,"CreationDate":"2018-12-12T17:21:00.000","Title":"Unsorted Lists vs Linear and Binary Search","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Hey guys so I've been studying for an upcoming test and I came across this question:\n\nIf you had an unsorted list of one million unique items, and knew that you would only search it once for a value, which of the following algorithms would be the fastest?\n\nUse linear search on the unsorted list\nUse insertion sort to sort the list and then binary search on the sorted list\n\n\nWouldn't the second choice be the fastest? Sorting the list and then looking for the value than only using linear search?","AnswerCount":4,"Available Count":4,"Score":0.049958375,"is_accepted":false,"ViewCount":1753,"Q_Id":53748254,"Users Score":1,"Answer":"For solving these types of questions, it is simply necessary to see where you'd spend more time. For a million elements:\n\nInsertion sort with 'n' inversions would take O(n) and then it would take an additional O(log(n)) time.\nWhereas linear search would take only O(n) time.\n\nSince there is only a single query method 1 would be a better alternative but for multiple queries(search element in the list) there will be a point where bin-srch*x<lin-srch*x where x is the  number of queries.","Q_Score":2,"Tags":"python,algorithm,time-complexity","A_Id":53748430,"CreationDate":"2018-12-12T17:21:00.000","Title":"Unsorted Lists vs Linear and Binary Search","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've built Machine Learning Models Random Forest and XGBOOST on Python or R\nHow can I implement that my model work in mobile phone IOS \/ Android? Not for training, just to predict the probability for users by properties and events.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":525,"Q_Id":53756524,"Users Score":1,"Answer":"I think I'm qualified to answer this because it was yesterday that I viewed Google's \"DevFestOnAir 2018\". There was an \"End to End Machine Learning\" talk where the speaker mentioned what TensorFlow(TF) has to support AI in mobile devices. \nNow, TF is available for JS , Java and many other languages, so this captures the entirety of the model that runs on your PC, uses other functionalities to make it run on lesser RAM and Processors. Do check this out. If I'm not wrong TF has a feature that would do the conversion for you.","Q_Score":1,"Tags":"android,python,ios,machine-learning","A_Id":53756944,"CreationDate":"2018-12-13T06:58:00.000","Title":"How to implement machine learning models on mobile phones?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When I train a sequential model with keras using the method fit_generator, I see this output\n\nEpoch 1\/N_epochs\nn\/N [====================>..............] - ETA xxxx - loss: yyyy\n\nI noticed that the loss decreased gradually with the number of steps, as expected. My problem is that I also noticed that when one epoch finishes and another one starts, the value of the loss is quite different from the one that I see at the end of the previous epoch.\nWhy so? I thought that epoch and number of steps per epochs was arbitrary values and using, for instance, 10 epochs with 1000 steps should be the same of 1000 epochs with 10 steps. But what does exactly happen between one epoch and the next one in Keras 2.0?\nDisclaimer: I know the definition of epoch and how the number of steps should be decided using a batch generator, but I have too many data and I cannot apply this rule.","AnswerCount":3,"Available Count":2,"Score":0.0665680765,"is_accepted":false,"ViewCount":740,"Q_Id":53758399,"Users Score":1,"Answer":"As far as I can tell the output of the keras function is a running average loss and the loss is quite a lot larger at the beginning of the epoch, than in the end. The loss is reset after each epoch and a new running average is formed. Therefore, the old running average is quite a bit higher (or at least different), than the beginning loss in the next epoch.","Q_Score":2,"Tags":"python,machine-learning,keras,deep-learning","A_Id":53759191,"CreationDate":"2018-12-13T09:12:00.000","Title":"Could you explain me the output of keras at each iteration?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When I train a sequential model with keras using the method fit_generator, I see this output\n\nEpoch 1\/N_epochs\nn\/N [====================>..............] - ETA xxxx - loss: yyyy\n\nI noticed that the loss decreased gradually with the number of steps, as expected. My problem is that I also noticed that when one epoch finishes and another one starts, the value of the loss is quite different from the one that I see at the end of the previous epoch.\nWhy so? I thought that epoch and number of steps per epochs was arbitrary values and using, for instance, 10 epochs with 1000 steps should be the same of 1000 epochs with 10 steps. But what does exactly happen between one epoch and the next one in Keras 2.0?\nDisclaimer: I know the definition of epoch and how the number of steps should be decided using a batch generator, but I have too many data and I cannot apply this rule.","AnswerCount":3,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":740,"Q_Id":53758399,"Users Score":4,"Answer":"The loss that Keras calculates during the epoch is accumulated and estimated online. So it includes the loss from the model after different weight updates.\nLet we clarify with an easy case: assume for a second that the model is only improving (every weight update results in better accuracy and loss), and that each epoch contains 2 weight updates (each min-batch is half the training dataset).\nAt epoch X, the first mini-batch is processed and the result is a loss score 2.0.\nAfter updating the weights, the model runs its second mini-batch which results in a loss score of 1.0 (for just the mini-batch). however you will see a loss of 2.0 change to 1.5 (average over all the dataset).\nNow we start epoch X+1, but it happens after another weight update which leads to a loss of 0.8 over the first mini-batch, which is shown to you. And so on and on...\nThe same thing happens during your training, only that obviously, not all changes are positive.","Q_Score":2,"Tags":"python,machine-learning,keras,deep-learning","A_Id":53759336,"CreationDate":"2018-12-13T09:12:00.000","Title":"Could you explain me the output of keras at each iteration?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I used sklearn.preprocessing.KBinsDiscretizer(n_bins=10, encode='ordinal') to discretize my continuous feature. \nThe strategy is 'quantile', by defalut. But my data distribution is actually not uniformly, like 70% of rows is 0. \nThen I got KBinsDiscretizer.bins_edges=[0.,0.,0.,0.,0.,0.,0.,256.,602., 1306., 18464.]. \nThere're many duplicate bins. So, is there a method to drop the duplicates in KBinsDiscretizer's bins? \nKBinsDiscretizer calculates the quantile of input. If the most samples of input are zero, the 10-quantiles will have multiple zeros. The result I expected is a discretizer with unique bins. For the example I mentioned, is [0.,256.,602., 1306., 18464.].","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":858,"Q_Id":53773352,"Users Score":1,"Answer":"That will not be possible. Set strategy='uniform' to achieve your goal.","Q_Score":1,"Tags":"python-2.7,scikit-learn","A_Id":53821015,"CreationDate":"2018-12-14T03:59:00.000","Title":"Can sklearn.preprocessing.KBinsDiscretizer with strategy='quantile' drop the duplicated bins?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I just want to know the effect of the value of alpha in gensim word2vec and fasttext word-embedding models? I know that alpha is the initial learning rate and its default value is 0.075 form Radim blog.\nWhat if I change this to a bit higher value i.e. 0.5 or 0.75? What will be its effect? Does it is allowed to change the same? However, I have changed this to 0.5 and experiment on a large-sized data with D = 200, window = 15, min_count = 5, iter = 10, workers = 4 and results are pretty much meaningful for the word2vec model. However, using the fasttext model, the results are bit scattered, means less related and unpredictable high-low similarity scores.\nWhy this imprecise result for same data with two popular models with different precision? Does the value of alpha plays such a crucial role during building of the model?\nAny suggestion is appreciated.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2594,"Q_Id":53815402,"Users Score":5,"Answer":"The default starting alpha is 0.025 in gensim's Word2Vec implementation. \nIn the stochastic gradient descent algorithm for adjusting the model, the effective alpha affects how strong of a correction to the model is made after each training example is evaluated, and will decay linearly from its starting value (alpha) to a tiny final value (min_alpha) over the course of all training. \nMost users won't need to adjust these parameters, or might only adjust them a little, after they have a reliable repeatable way of assessing whether a change improves their model on their end tasks. (I've seen starting values of 0.05 or less commonly 0.1, but never as high as your reported 0.5.)","Q_Score":4,"Tags":"python-3.x,gensim,word2vec,word-embedding,fasttext","A_Id":53821335,"CreationDate":"2018-12-17T12:36:00.000","Title":"Value of alpha in gensim word-embedding (Word2Vec and FastText) models?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to make a series of figures with 3x3 subplots using matplotlib. I can make the first figure fine (9 total subplots), but when I try to make a tenth subplot I get this error: ValueError: num must be 1 <= num <= 9, not 10. What I think I want to do is plot the first 9 subplots, clear the figure, and then plot the next 9 subplots. I haven't been able to get this approach to work so far though. If anyone could offer some suggestions I would really appreciate it! \nThanks!","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":84,"Q_Id":53817735,"Users Score":0,"Answer":"Call plt.show() before the 10th chart, then start over with plt.subplot(3, 3, 1), followed by the code to plot the 10th chart","Q_Score":0,"Tags":"python,matplotlib","A_Id":53818650,"CreationDate":"2018-12-17T14:56:00.000","Title":"Matplotlib erase figure and plot new series of subplots","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"So I've gotten myself a little confused. \nAt the moment, I've got a dataset of about 800 instances. I've split it into a training and validation set because there were missing values so I used SimpleImputer from sklearn and fit_transform-ed the training set and transformed the testing set. I did that because if I want to predict for new instances, if there's missing values then I'll need to impute it the same way I imputed the test set. \nNow I want to use cross validation to train and score models, but that would involve using the whole dataset and splitting it up into different training and testing sets, so then I'm worried about leakage from the training set because of the imputed values being fitted?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":726,"Q_Id":53825586,"Users Score":1,"Answer":"Generally, you'll want to split your data into three sets- a training set, testing set, and validation set. The testing set should be completely left out of training (your concern is correct.) When using cross validation, you don't need to worry about splitting your training and validation set- that's what cross validation does for you! Simply pass the training set to the cross validator, allow it to split into training and validation behind the scenes, and test the final model on your testing set (which has been completely left out of the training process.)","Q_Score":4,"Tags":"python,cross-validation,imputation","A_Id":53825670,"CreationDate":"2018-12-18T02:24:00.000","Title":"How to use cross validation after imputing on a training and validation set?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using scipy.signal.fft.rfft() to calculate power spectral density of a signal. The sampling rate is 1000Hz and the signal contains 2000 points. So frequency bin is (1000\/2)\/(2000\/2)=0.5Hz. But I need to analyze the signal in [0-0.1]Hz. \nI saw several answers recommending chirp-Z transform, but I didn't find any toolbox of it written in Python. \nSo how can I complete this small-bin analysis in Python? Or can I just filter this signal to [0-0.1]Hz using like Butterworth filter?\nThanks a lot!","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":279,"Q_Id":53830329,"Users Score":1,"Answer":"Even if you use another transform, that will not make more data.\nIf you have a sampling of 1kHz and 2s of samples, then your precision is 0.5Hz. You can interpolate this with chirpz (or just use sinc(), that's the shape of your data between the samples of your comb), but the data you have on your current point is the data that determines what you have in the lobes (between 0Hz and 0.5Hz).\nIf you want a real precision of 0.1Hz, you need 10s of data.","Q_Score":0,"Tags":"python,signal-processing,fft","A_Id":53830482,"CreationDate":"2018-12-18T09:50:00.000","Title":"How to obtain small bins after FFT in python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using scipy.signal.fft.rfft() to calculate power spectral density of a signal. The sampling rate is 1000Hz and the signal contains 2000 points. So frequency bin is (1000\/2)\/(2000\/2)=0.5Hz. But I need to analyze the signal in [0-0.1]Hz. \nI saw several answers recommending chirp-Z transform, but I didn't find any toolbox of it written in Python. \nSo how can I complete this small-bin analysis in Python? Or can I just filter this signal to [0-0.1]Hz using like Butterworth filter?\nThanks a lot!","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":279,"Q_Id":53830329,"Users Score":1,"Answer":"You can't get smaller frequency bins to separate out close spectral peaks unless you use more (a longer amount of) data.\nYou can't just use a narrower filter because the transient response of such a filter will be longer than your data.\nYou can get smaller frequency bins that are just a smooth interpolation between nearby frequency bins, for instance to plot the spectrum on wider paper or at a higher dpi graphic resolution, by zero-padding the data and using a longer FFT.  But that won't create more detail.","Q_Score":0,"Tags":"python,signal-processing,fft","A_Id":53860082,"CreationDate":"2018-12-18T09:50:00.000","Title":"How to obtain small bins after FFT in python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In OpenCV under Python, is there no better way to compute the centroid of the inside a contour than with the function cv2.moments, which computes all moments up to order 3 (and is overkill) ?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":153,"Q_Id":53830357,"Users Score":0,"Answer":"You can use cv2.connectedComponentsWithStats it return the centroid and size of contour.","Q_Score":0,"Tags":"python,opencv","A_Id":53830550,"CreationDate":"2018-12-18T09:51:00.000","Title":"Centroid of a contour","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am quite new to Python and therefore this might seem easy but I am really stuck here.\nI have a CSV file with values in a [525599 x 74] matrix. For each column of the 74 columns I would like to have the total sum of all 525599 values saved in one list.\nI could not figure out the right way to iterate over each column and save the sum of each column in a list.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":106,"Q_Id":53831555,"Users Score":1,"Answer":"Why don't you :\n\ncreate a columnTotal integer array (one index for each column).\nread the file line by line, per line:\n\n\nSplit the line using the comma as separator\nConvert the splitted string parts to integers\nAdd the value of each column to the columnTotal array's colum index.","Q_Score":0,"Tags":"python,csv","A_Id":53831640,"CreationDate":"2018-12-18T11:01:00.000","Title":"Sum of different CSV columns in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I wanted to install the numpy package for python 3.5 on my Mac OS High Sierra, but I can't seem to make it work.\nI have it on python2.7, but I would also like to install it for the next versions. \nCurrently, I have installed python 2.7, python 3.5, and python 3.7.\nI tried to install numpy using:\n\nbrew install numpy --with-python3 (no error)\nsudo port install py35-numpy@1.15.4 (no error)\nsudo port install py37-numpy@1.15.4 (no error)\npip3.5  install numpy (gives \"Could not find a version that satisfies the requirement numpy (from versions: )\nNo matching distribution found for numpy\" )\n\nI can tell that it is not installed because when I type python3 and then import numpy as np gives \"ModuleNotFoundError: No module named 'numpy'\"\nAny ideas on how to make it work?\nThanks in advance.","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":3167,"Q_Id":53842426,"Users Score":1,"Answer":"First, you need to activate the virtual environment for the version of python you wish to run. After you have done that then just run \"pip install numpy\" or \"pip3 install numpy\".\nIf you used Anaconda to install python then, after activating your environment, type conda install numpy.","Q_Score":2,"Tags":"python,python-3.x,macos,numpy","A_Id":53844061,"CreationDate":"2018-12-18T23:09:00.000","Title":"install numpy on python 3.5 Mac OS High sierra","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I wanted to install the numpy package for python 3.5 on my Mac OS High Sierra, but I can't seem to make it work.\nI have it on python2.7, but I would also like to install it for the next versions. \nCurrently, I have installed python 2.7, python 3.5, and python 3.7.\nI tried to install numpy using:\n\nbrew install numpy --with-python3 (no error)\nsudo port install py35-numpy@1.15.4 (no error)\nsudo port install py37-numpy@1.15.4 (no error)\npip3.5  install numpy (gives \"Could not find a version that satisfies the requirement numpy (from versions: )\nNo matching distribution found for numpy\" )\n\nI can tell that it is not installed because when I type python3 and then import numpy as np gives \"ModuleNotFoundError: No module named 'numpy'\"\nAny ideas on how to make it work?\nThanks in advance.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":3167,"Q_Id":53842426,"Users Score":0,"Answer":"If running pip3.5 --version or pip3 --version works, what is the output when you run pip3 freeze? If there is no output, it indicates that there are no packages installed for the Python 3 environment and you should be able to install numpy with pip3 install numpy.","Q_Score":2,"Tags":"python,python-3.x,macos,numpy","A_Id":53928674,"CreationDate":"2018-12-18T23:09:00.000","Title":"install numpy on python 3.5 Mac OS High sierra","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"This is for Python\/PySpark using Spark 2.3.2. \nI am looking for best practice approach for copying columns of one data frame to another data frame using Python\/PySpark for a very large data set of 10+ billion rows (partitioned by year\/month\/day, evenly). Each row has 120 columns to transform\/copy. The output data frame will be written, date partitioned, into another parquet set of files.\nExample schema is: \ninput DFinput (colA, colB, colC) and \noutput DFoutput (X, Y, Z)\nI want to copy DFInput to DFOutput as follows (colA => Z, colB => X, colC => Y).\nWhat is the best practice to do this in Python Spark 2.3+ ?\nShould I use DF.withColumn() method for each column to copy source into destination columns? \nWill this perform well given billions of rows each with 110+ columns to copy? \nThank you","AnswerCount":5,"Available Count":2,"Score":0.0399786803,"is_accepted":false,"ViewCount":9487,"Q_Id":53843406,"Users Score":1,"Answer":"Use dataframe.withColumn() which Returns a new DataFrame by adding a column or replacing the existing column that has the same name.","Q_Score":5,"Tags":"python,apache-spark,pyspark","A_Id":59532530,"CreationDate":"2018-12-19T01:32:00.000","Title":"Spark copying dataframe columns best practice in Python\/PySpark?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"This is for Python\/PySpark using Spark 2.3.2. \nI am looking for best practice approach for copying columns of one data frame to another data frame using Python\/PySpark for a very large data set of 10+ billion rows (partitioned by year\/month\/day, evenly). Each row has 120 columns to transform\/copy. The output data frame will be written, date partitioned, into another parquet set of files.\nExample schema is: \ninput DFinput (colA, colB, colC) and \noutput DFoutput (X, Y, Z)\nI want to copy DFInput to DFOutput as follows (colA => Z, colB => X, colC => Y).\nWhat is the best practice to do this in Python Spark 2.3+ ?\nShould I use DF.withColumn() method for each column to copy source into destination columns? \nWill this perform well given billions of rows each with 110+ columns to copy? \nThank you","AnswerCount":5,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":9487,"Q_Id":53843406,"Users Score":0,"Answer":"Bit of a noob on this (python), but might it be easier to do that in SQL (or what ever source you have)  and then read it into a new\/separate dataframe?","Q_Score":5,"Tags":"python,apache-spark,pyspark","A_Id":53884751,"CreationDate":"2018-12-19T01:32:00.000","Title":"Spark copying dataframe columns best practice in Python\/PySpark?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying put my optimization problem into Pyomo, but it is strongly dependent upon standard linear algebra operations - qr, inverse, transpose, product. Actually, this is Kalman filter problem; recursive linear algebra for long time series. I failed to find pyomo functions to implement it like I could in tensor flow. Is it possible?\nConnected questions:\n\nAm I right that numpy target function is practically not usable in pyomo? \nIs there a better free optimization solution for the purpose? (scipy cannot approach efficiency of Matlab by far, tensor flow is extremely slow for particular problem, though I do not see why, algorithmic differentiation in Matlab was reasonably fast though not fast enough)\n\nMany thanks,\nVladimir","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":600,"Q_Id":53851982,"Users Score":1,"Answer":"Pyomo is mainly a package for optimization. i.e. specifying data -> building problem -> sending to the solver -> wait for solver's results -> retrieving solution. Even if it can handle matrix-like data, it cannot manipulate it with matrix operations. This should be done using a good external library, before you send your data to Pyomo. Once you have all your matrixes ready to be used as data in your optimization model, then you can use Pyomo for optimization. \nThat being said, you should look into finding a library that fits your needs to build your data, since your data values must be static, once you provide it as an input to your model. \nAlso, keep in mind that Pyomo, like any optimization tools, is deterministic. It is not meant to do data analysis or data description, but to provide a way to find one optimal solution of a mathematical problem. In your case, Pyomo is not meant to do the Kalman filter problem, but to give you the solution of minimizing the mean square error.","Q_Score":0,"Tags":"python,optimization,pyomo","A_Id":53875465,"CreationDate":"2018-12-19T13:11:00.000","Title":"Linear algebra with Pyomo","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am in 10th grade and I am looking to use a machine learning model on patient data to find a correlation between the time of week and patient adherence. I have separated the week into 21 time slots, three for each time of day (1 is Monday morning, 2 is monday afternoon, etc.). Adherence values will be binary (0 means they did not take the medicine, 1 means they did). I will simulate training, validation and test data for my model. From my understanding, I can use a logistic regression model to output the probability of the patient missing their medication on a certain time slot given past data for that time slot. This is because logistic regression outputs binary values when given a threshold and is good for problems dealing with probability and binary classes, which is my scenario. In my case, the two classes I am dealing with are yes they will take their medicine, and no they will not. But the major problem with this is that this data will be non-linear, at least to my understanding. To make this more clear, let me give a real life example. If a patient has yoga class on Sunday mornings, (time slot 19) and tends to forget to take their medication at this time, then most of the numbers under time slot 19 would be 0s, while all the other time slots would have many more 1s. The goal is to create a machine learning model which can realize given past data that the patient is very likely going to miss their medication on the next time slot 19. I believe that logistic regression must be used on data that still has an inherently linear data distribution, however I am not sure. I also understand that neural networks are ideal for non-linear distributions, but neural networks require a lot of data to function properly, and ideally the goal of my model is to be able to function decently with simply a few weeks of data. Of course any model becomes more accurate with more data, but it seems to me that generally neural networks need thousands of data sets to truly become decently accurate. Again, I could very well be wrong.\nMy question is really what model type would work here. I do know that I will need some form of supervised classification. But can I use logistic regression to make predictions when given time of week about adherence?\nReally any general feedback on my project is greatly appreciated! Please keep in mind I am only 15, and so certain statements I made were possibly wrong and I will not be able to fully understand very complex replies.\nI also have to complete this within the next two weeks, so please do not hesitate to respond as soon as you can! Thank you so much!","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":78,"Q_Id":53862029,"Users Score":0,"Answer":"It's true that you need a lot of data for applying neural networks. \nIt would have been helpful if you could be more precise about your dataset and the features. You can also try implementing K-Means-Clustering for your project. If your aim is to find out that did the patient took medicine or not then it can be done using logistic regression.","Q_Score":0,"Tags":"python,machine-learning,neural-network,classification,logistic-regression","A_Id":53903711,"CreationDate":"2018-12-20T03:25:00.000","Title":"Would a Logistic Regression Machine Learning Model Work Here?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am in 10th grade and I am looking to use a machine learning model on patient data to find a correlation between the time of week and patient adherence. I have separated the week into 21 time slots, three for each time of day (1 is Monday morning, 2 is monday afternoon, etc.). Adherence values will be binary (0 means they did not take the medicine, 1 means they did). I will simulate training, validation and test data for my model. From my understanding, I can use a logistic regression model to output the probability of the patient missing their medication on a certain time slot given past data for that time slot. This is because logistic regression outputs binary values when given a threshold and is good for problems dealing with probability and binary classes, which is my scenario. In my case, the two classes I am dealing with are yes they will take their medicine, and no they will not. But the major problem with this is that this data will be non-linear, at least to my understanding. To make this more clear, let me give a real life example. If a patient has yoga class on Sunday mornings, (time slot 19) and tends to forget to take their medication at this time, then most of the numbers under time slot 19 would be 0s, while all the other time slots would have many more 1s. The goal is to create a machine learning model which can realize given past data that the patient is very likely going to miss their medication on the next time slot 19. I believe that logistic regression must be used on data that still has an inherently linear data distribution, however I am not sure. I also understand that neural networks are ideal for non-linear distributions, but neural networks require a lot of data to function properly, and ideally the goal of my model is to be able to function decently with simply a few weeks of data. Of course any model becomes more accurate with more data, but it seems to me that generally neural networks need thousands of data sets to truly become decently accurate. Again, I could very well be wrong.\nMy question is really what model type would work here. I do know that I will need some form of supervised classification. But can I use logistic regression to make predictions when given time of week about adherence?\nReally any general feedback on my project is greatly appreciated! Please keep in mind I am only 15, and so certain statements I made were possibly wrong and I will not be able to fully understand very complex replies.\nI also have to complete this within the next two weeks, so please do not hesitate to respond as soon as you can! Thank you so much!","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":78,"Q_Id":53862029,"Users Score":1,"Answer":"In my opinion a logistic regression won't be enough for this as u are going to use a single parameter as input. When I imagine a decision line for this problem, I don't think it can be achieved by a single neuron(a logistic regression). It may need few more neurons or even few layers of them to do so. And u may need a lot of data set for this purpose.","Q_Score":0,"Tags":"python,machine-learning,neural-network,classification,logistic-regression","A_Id":53902459,"CreationDate":"2018-12-20T03:25:00.000","Title":"Would a Logistic Regression Machine Learning Model Work Here?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"As a started with GPU programming, CUDA and Python I decided to install the latest version of CUDA (10) in order to experiment with ML.\nAfter spending considerable time installing (huge downloads) I ended up with a version that isn't supporting Tensorflow.\nI discovered the tensorflow-gpu meta package using Anaconda though! Now unfortunately I have two versions installed and I am not sure how can I uninstall the version 10! Any ideas?\nThanks!","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":1375,"Q_Id":53866591,"Users Score":2,"Answer":"If you installed via conda install tensorflow-gpu all dependencies are in the Conda environment (e.g., CUDA dlls are in the lib subfolder in the environment), so yes you can safely uninstall CUDA 10.\nNote: at least on Ubuntu I saw that XLA JIT optimization of code (which is an experimental feature still) requires CUDA to be installed properly in the system (it looks from some binaries in the CUDA install dir and it seems to be hardcoded that way), but for normal TF execution the Conda setup is perfectly fine.","Q_Score":0,"Tags":"python,tensorflow,cuda,anaconda,gpu","A_Id":53866818,"CreationDate":"2018-12-20T10:15:00.000","Title":"I installed CUDA10 but Anaconda installs CUDA9. Can I remove the former?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I found there are a lot of same names in keras.backend or keras.layers, for example keras.backend.concatenate and keras.layers.Concatenate. I know vaguely that one is for tensor while the other is for layer. But when the code is so big, so many function made me confused that which is tensor or which is layer. Anybody has a good idea to solve this problem?\nOne way I found is to define all placeholders in one function at first, but the function take it as variable may return layers at end, while another function take this layer as variable may return another variable.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":523,"Q_Id":53871303,"Users Score":5,"Answer":"You should definitely use keras.layers if there is a layer that achieves what you want to do. That's because, when building a model, Keras layers only accept Keras Tensors (i.e. the output of layers) as the inputs. However, the output of methods in keras.backend.* is not a Keras Tensor (it is the backend Tensor, such as TensorFlow Tensor) and therefore you can't pass them directly to a layer.\nAlthough, if there is an operation that could not be done with a layer, then you can use keras.backned.* methods in a Lambda layer to perform that custom operation\/computation.\nNote: Keras Tensor is actually the same type as the backend Tensor (e.g. tf.Tensor); however, it has been augmented with some additional Keras-specific attributes which Keras needs when building a model.","Q_Score":0,"Tags":"python,tensorflow,keras,deep-learning,keras-layer","A_Id":53871442,"CreationDate":"2018-12-20T15:10:00.000","Title":"How to choose between keras.backend and keras.layers?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using MS-D or UNet network for image segmentation. My image has three classes: noise, signal and empty. The class empty is easy to find because the pixel values for the empty class is mainly -1 while for the two other classes is between 0-1. \nIs there a way that I only ask the network to find noise and signal class and not bother the network about the easy one? Or any other clue that can help? I am seeing that the network sometimes is confused when predicting the signal pixels and gives about the same score but with higher to the signal class (e.g. empty0.0001, noise0.0003, signal0.0005) to all three classes. I want to make it easier for the network to figure it out.\nJust more information about my image, around 25% of pixels are signal, 40% noise, and 35% are empty. I am using dice_coef for the metric and loss function.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":158,"Q_Id":53872418,"Users Score":0,"Answer":"I'm not sure if you can do that. I think You should apply some regularization and\/or dropout to the network and feed it more data.\nBut what you could do is to label all the empty pixels as noise as signal is usually in the middle and noise is on the outer side of the signal graph. Then you train the network that way. You will have to set network outputs to 2: noise or signal. From the original image you know which pixels were empty and then you can set those pixels from noise to empty. Then you will have the result you wanted. \nThe only thing that can happen here is that the  network will perform bad because of the imbalanced classes as you will have much more noise than signal pixels.","Q_Score":0,"Tags":"python-2.7,keras,neural-network,image-segmentation","A_Id":53885612,"CreationDate":"2018-12-20T16:20:00.000","Title":"Image segmentation with 3 classes but one of them is easy to find, How can I write the network to not train on the easy one?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on a project for my Linear Algebra class. I am stuck with a small problem. I could not find any method for finding the row-echelon matrix form (not reduced) in Python (not MATLAB).\nCould someone help me out?\nThank you.\n(I use python3.x)","AnswerCount":2,"Available Count":1,"Score":0.2913126125,"is_accepted":false,"ViewCount":3252,"Q_Id":53875432,"Users Score":3,"Answer":"Bill M's answer is correct. When you find the LU decomposition the U matrix is a  correct way of writing M in REF (note that REF is not unique so there are multiple possible ways to write it). To see why, remember that the LU decomposition finds P,L,U such that PLU = M. When L is full rank we can write this as U = (PL)-1M. So (PL)-1 defines the row operations that you have to preform on M to change it into U.","Q_Score":2,"Tags":"python,python-3.x,matrix","A_Id":53992065,"CreationDate":"2018-12-20T20:15:00.000","Title":"How to find row-echelon matrix form (not reduced) in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Before I ask the question, let me preface this by stating that this question has been answered in many articles, but I still struggle to understand the basic format of word embeddings. \nLet's start with the sentence \"I like dogs\". Assuming a simple hashing approach, \"I like dogs\" can be represented in the vector [1, 4, 6] where the elements of the vector correspond to the hash of each word (assuming these aren't the only words in the vocabulary). From what I understand, this vector is fed into an embedding layer which adds an extra embedding dimension onto the input tensor of the RNN (doesn't have to be vanilla RNN).\nThe embedding tensor (with lets say an embedding dimension of 2) will look something like this for a single entry in the batch:\n[[4.55, 6.78], -> I \n[3.12, 8.17],  -> like\n[1.87, 10.95]] -> dogs\nThis tensor has the shape (1, 3, 2). Does the length of the second axis (3 in this case) always equal the length of the input vector and therefore represent each individual word in the sequence or do I have a fundamental misconception of how the tensorflow embeddings work?\nTo clarify: say I had a much longer sentence with 600 words, would each word after embedding be remembered in their original order and be represented by a vector of whatever size was chosen for the embedding dimension (we'll say 15), thus making the shape of the embedded tensor (batch_size, 600, 15)?\nNote: these are just random numbers and don't represent anything in particular.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":85,"Q_Id":53875910,"Users Score":1,"Answer":"Yes and yes. So, if you have \"I\" [4.55, 6.78], \"like\" [3.12, 8.17], and \"dogs\" [1.87, 10.95], each embedded representation roughly equates directly to each word, and thus the order isn't lost when the embedding is done. And yes, the shape would be (batch_size, 600, 15) for batches of 600-word-sentences and embedding dimension 15. I think the question you're indirectly asking is something like \"Does each word directly correlate to a single embedding vector of length embedding_dimension?\" aka \"Does 'I' directly correlate to [4.55, 6.78] independent of the other words\/embedding vectors?\" For the most part, the answer is yes.\nFor what it's worth, it's been useful to think of it like languages. Doing a hash representation (excluding the duplicate values) or a categorical column with a unique value for each word is somewhat akin to how classic Chinese is, with a unique character for every word. Whereas embedded representations are more akin to the English language, with a \"word\" being a fixed number (embedding dimension) of letters (floats). The advantage is similar to how we gain advantages in the English language. For example \"dog\" vs \"dogs\" has 3 similar characters because they are very related concepts. Similarly, you can take advantage of embedding by representing \"dog\" as [1.23 4.56 7.89 1.12] and \"dogs\" as [1.23 4.56 7.89 9.87] or some such.\nRandom but I hope this helped. Good luck~~ =)","Q_Score":2,"Tags":"python,tensorflow","A_Id":53876998,"CreationDate":"2018-12-20T20:55:00.000","Title":"Word Embedding Interpretation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to develop a trend following strategy via back-testing a universe of stocks; lets just say all NYSE or S&P500 equities. I am asking this question today because I am unsure how to handle the storage\/organization of the massive amounts of historical price data. \nAfter multiple hours of research I am here, asking for your experience and awareness. I would be extremely grateful for any information\/awareness you can share on this topic\n\nPersonal Experience background:\n-I know how to code. Was a Electrical Engineering major, not a CS major.\n-I know how to pull in stock data for individual tickers into excel. \nFamiliar with using filtering and custom studies on ThinkOrSwim.\nApplied Context: \nFrom 1995 to today lets evaluate the best performing equities on a relative strength\/momentum basis. We will look to compare many technical characteristics to develop a strategy. The key to this is having data for a universe of stocks that we can run backtests on using python, C#, R, or any other coding language. We can then determine possible strategies by assesing the returns, the omega ratio, median excess returns, and Jensen's alpha (measured weekly) of entries and exits that are technical driven. \n\nHere's where I am having trouble figuring out what the next step is:\n-Loading data for all S&P500 companies into a single excel workbook is just not gonna work. Its too much data for excel to handle I feel like. Each ticker is going to have multiple MB of price data. \n-What is the best way to get and then store the price data for each ticker in the universe? Are we looking at something like SQL or Microsoft access here? I dont know; I dont have enough awareness on the subject of handling lots of data like this. What are you thoughts? \n\nI have used ToS to filter stocks based off of true\/false parameters over a period of time in the past; however the capabilities of ToS are limited. \nI would like a more flexible backtesting engine like code written in python or C#. Not sure if Rscript is of any use. - Maybe, there are libraries out there that I do not have awareness of that would make this all possible? If there are let me know.\nI am aware that Quantopia and other web based Quant platforms are around. Are these my best bets for backtesting? Any thoughts on them? \n\nAm I making this too complicated? \nBacktesting a strategy on a single equity or several equities isnt a problem in excel, ToS, or even Tradingview. But with lots of data Im not sure what the best option is for storing that data and then using a python script or something to perform the back test. \n\nRandom Final thought:-Ultimately would like to explore some AI assistance with optimizing strategies that were created based off parameters. I know this is a thing but not sure where to learn more about this. If you do please let me know.\n\nThank you guys. I hope this wasn't too much. If you can share any knowledge to increase my awareness on the topic I would really appreciate it.\nTwitter:@b_gumm","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":603,"Q_Id":53878551,"Users Score":0,"Answer":"The amout of data is too much for EXCEL or CALC. Even if you want to screen only 500 Stocks from S&P 500, you will get 2,2 Millions of rows (approx. 220 days\/year * 20 years * 500 stocks). For this amount of data, you should use a SQL Database like MySQL. It is performant enough to handle this amount of data. But you have to find a way for updating. If you get the complete time series daily and store it into your database, this process can take approx. 1 hour. You could also use delta downloads but be aware of corporate actions (e.g. splits). \nI don't know Quantopia, but I know a similar backtesting service where I have created a python backtesting script last year. The outcome was quite different to what I have expected. The research result was that the backtesting service was calculating wrong results because of wrong data. So be cautious about the results.","Q_Score":0,"Tags":"python,excel,stocks,universe,back-testing","A_Id":53965459,"CreationDate":"2018-12-21T02:43:00.000","Title":"Backtesting a Universe of Stocks","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Let's say I've trained and saved 6 different networks where all of the values for hidden layer counts, neuron counts, and learn rates differ.\nFor example:\n\n1 with 8 hidden layers with 16 neurons in each trained at .1 learn rate.\n1 with 4 hidden layers with 4 neurons in each trained at .01 learn rate.\n1 with 4 hidden layers with 4 neurons in each trained at .03 learn rate.\n1 with 4 hidden layers with 8 neurons in each trained at .01 learn rate.\n1 with 4 hidden layers with 8 neurons in each trained at .001 learn rate.\n1 with 6 hidden layers with 4 neurons in each trained at .01 learn rate.\n\nHow could I create a new network where each of these saved networks acts basically as a neuron? While training this combined network, I don't want to affect the saved network weights and biases, but rather would like to essentially determine which one is more accurate for a given input.\nI've achieved this in practice by loading each network, running the data through each network, and then storing all of the outputs which then later feed into the new network, but I feel like there must be a simpler and most importantly, a faster way of doing this.\nAn example might be two networks: image detection at night and image detection during the day. Each trained and saved separately. I would want another network which essentially takes an image and says \"oh we're somewhere in the middle here so let's use 50\/50, or oh it's closer to night, but not night completely, use 90% night data and 10% day.\" So I would want to feed loads of images where it tries each model, but then weights out how valued the data was based on night vs day to create a network which works for either night or day.\nAny help highly appreciated. In reality the network I'm shooting for is far bigger and more complicated, but I'm looking for a strategy.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":36,"Q_Id":53882317,"Users Score":1,"Answer":"I am not sure if I got your point correctly, but Block Based Neural Networks might be what you are searching for. In BBNN each node can be a neural network and w.r.t what you describe one layer BBNN is what you need.","Q_Score":0,"Tags":"python,python-3.x,tensorflow","A_Id":53882507,"CreationDate":"2018-12-21T09:36:00.000","Title":"How to create a tensorflow network of two saved tensorflow networks?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've got an array of 4 images, each image lets say is NxM (all images share this same size)\n(I'm implementing a Harris Corner detector by the way.)\nNow I made a matrix M = ([Ix^2, Ixy],[Ixy, Iy^2]).reshape(2,2)\nand now I'd like to compute my response. \nwhich is usually Det(RM) - k*(trace(RM)**2) \nRM being a 2x2 Matrix each point in this matrix is derived from the same coordinate location for each image in M.\nHow can I slice M to create RM? \nIn other words how can I slice the Matrix M to create a smaller matrix 2x2 RM for every pixel in the NxM images?\nFor example the first RM matrix should be a 2x2 matrix taking the 0,0 coordinate from each image in M.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":54,"Q_Id":53902843,"Users Score":0,"Answer":"It's not really clear what RM and M are based on your description.\nIs M the ndarray containing all 4 images, and RM the 2x2 array for a given pixel containing the data from the 4 images? \nYou can put the 4 images into the same ndarray so it has shape (4,N,M) and then reshape slices.\nFor example, to get the (0,0) entry you would do A[:,0,0] to get the 4 pixels, and then reshape it to get a 2x2 array.","Q_Score":0,"Tags":"python,numpy,numpy-slicing","A_Id":53905344,"CreationDate":"2018-12-23T10:29:00.000","Title":"Slicing Array of four matrices","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I having trouble running relplot function in colab notebook but it works fine in jupyter notebook.\nGetting the following error in colab\n\n\nAttributeError                            Traceback (most recent call\n  last)  in ()\n  ----> 1 sns.relplot(x=\"total_bill\", y=\"tip\", \n        2             col=\"time\", # Categorical variables that will determine the faceting of the grid.\n        3             hue=\"smoker\", # Grouping variable that will produce elements with different colors.\n        4             style=\"smoker\", # Grouping variable that will produce elements with different styles.\n        5             size=\"size\", # Grouping variable that will produce elements with different sizes.\nAttributeError: module 'seaborn' has no attribute 'relplot'","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":11284,"Q_Id":53910548,"Users Score":1,"Answer":"Change directory to where pip3.exe is located:\nfor me: cd C:\\Users\\sam\\AppData\\Local\\Programs\\Python\\Python37-32\\Scripts \nuse .\\\n.\\pip3 install seaborn==0.9.0","Q_Score":2,"Tags":"python,seaborn,google-colaboratory","A_Id":56282819,"CreationDate":"2018-12-24T07:37:00.000","Title":"module 'seaborn' has no attribute 'relplot'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I just started with AWS and I want to train my own model with own dataset. I have my model as keras model with tensorflow backend in Python. I read some documentations, they say I need a Docker image to load my model. So, how do I convert keras model into Docker image. I searched through internet but found nothing that explained the process clearly. How to make docker image of keras model, how to load it to sagemaker. And also how to load my data from a h5 file into S3 bucket for training? Can anyone please help me in getting clear explanation?","AnswerCount":4,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2089,"Q_Id":53921454,"Users Score":0,"Answer":"You can convert your Keras model to a tf.estimator and train using the TensorFlow framework estimators in Sagemaker.\nThis conversion is pretty basic though, I reimplemented my models in TensorFlow using the tf.keras API which makes the model nearly identical and train with the Sagemaker TF estimator in script mode.\nMy initial approach using pure Keras models was based on bring-your-own-algo containers similar to the answer by Matthew Arthur.","Q_Score":0,"Tags":"python,amazon-web-services,tensorflow,keras,amazon-sagemaker","A_Id":54089983,"CreationDate":"2018-12-25T10:26:00.000","Title":"How to train your own model in AWS Sagemaker?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I have a quiz app where students can take tests. There is  ranking based on every test. It's implemented with simple lists (Every new score is inserted into the list and then sorted (index+1 is the rank)). \nBut I want to add another abstraction. ie. Suppose 1000 students took the test and my ranking was 890. But those 1000 students should automatically be divided into 10 groups ie. group 1 of ranking 1 to 99, group2 of ranking 100 to 199 and so on. So if my overall ranking is 890. I should be subscribed to group 9 with 90th rank in that group. \nHow should this be implemented?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":27,"Q_Id":53922685,"Users Score":0,"Answer":"Just save the ranking score for every student. Calculate their group when you displaying them.","Q_Score":0,"Tags":"django,python-3.x,data-structures","A_Id":53925094,"CreationDate":"2018-12-25T13:11:00.000","Title":"What data structure to use for ranking system which divides itself in groups?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using scipy curve_fit to curve a line for retention. however, I found the result line may produce negative number. how can i add some constrain?\nthe 'bounds' only constrain parameters not the results y","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":149,"Q_Id":53942983,"Users Score":0,"Answer":"One of the simpler ways to handle negative value in y, is to make a log transformation. Get the best fit for log transformed y, then do exponential transformation for actual error in the fit or for any new value prediction.","Q_Score":0,"Tags":"python,scipy","A_Id":53944352,"CreationDate":"2018-12-27T09:49:00.000","Title":"how to constrain scipy curve_fit in positive result","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it possible to read an Excel file from an Outlook attachment without saving it, and return a pandas dataframe from the attached file? The file will always be in the same format.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2158,"Q_Id":53950601,"Users Score":1,"Answer":"Attachments are MIME-encoded and have to be decoded back into the original format (which essentially means making a disk copy) for programs that are expecting that format.\nWhat you want is to give pandas the identifier of the email, the name of the attachment, the details of the message store, and suitable authentication, and have pandas read the attachment directly. This would entail extending the function pandas.read_csv() or maybe adding a new function read_csv_attachment(). \nWhile I am sure this is possible, it is a more ambitious project than I (as one unfamiliar with pandas internals) would want to tackle myself. And certainly much more work than saving the attachments manually, unless you have thousands of them.","Q_Score":2,"Tags":"python,excel,pandas,outlook","A_Id":53962197,"CreationDate":"2018-12-27T20:37:00.000","Title":"Create dataframe from Excel attachment in Outlook","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How to get most frequent context words from pretrained fasttext model?\nFor example:\nFor word 'football' and corpus [\"I like playing football with my friends\"]\nGet list of context words: ['playing', 'with','my','like']\nI try to use \nmodel_wiki = gensim.models.KeyedVectors.load_word2vec_format(\"wiki.ru.vec\")\nmodel.most_similar(\"\u0431\u043b\u043e\u043a\")\nBut it's not satisfied for me","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":473,"Q_Id":53955958,"Users Score":0,"Answer":"The plain model doesn't retain any such co-occurrence statistics from the original corpus. It just has the trained results: vectors per word. \nSo, the ranked list of most_similar() vectors \u2013 which isn't exactly words that appeared-together, but strongly correlates to that \u2013 is the best you'll get from that file. \nOnly going back to the original training corpus would give you exactly what you've requested.","Q_Score":2,"Tags":"python,gensim,word2vec,fasttext","A_Id":53964532,"CreationDate":"2018-12-28T09:02:00.000","Title":"How to get list of context words in Gensim","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I change a new computer and install Python3.6 and matplotlib\uff0cWhen I run the code last month in the old computer, I get the following error:\nax.hold(True)\nAttributeError: 'AxesSubplot' object has no attribute 'hold'","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":5619,"Q_Id":53957042,"Users Score":1,"Answer":"The API Changes document says:\nSetting or unsetting hold (deprecated in version 2.0) has now been completely removed. Matplotlib now always behaves as if hold=True. To clear an axes you can manually use cla(), or to clear an entire figure use clf().","Q_Score":4,"Tags":"python-3.x","A_Id":63126102,"CreationDate":"2018-12-28T10:28:00.000","Title":"AttributeError: 'AxesSubplot' object has no attribute 'hold'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Suppose i have a data-frame object named as df, head() is a method that can be applied to df to see the first 5 records of the data-frame and df.size is a property to get the size of the data-frame. \nFor the property we are not using '()' as we used for a method. This was little confusing initially. \nCould anyone explain whats basic difference between a property and a method in python. I mean why we had to define size as a property for a dataframe, why not it was defined as a method which would have just returned the size of the data-frame.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":311,"Q_Id":53957850,"Users Score":0,"Answer":"Here in the above example you mentioned, you can pass argument to df.head() function, where as you cannot pass arguments for properties.\nfor same above example, if you have written df.head(20) it would return first 20 rows.","Q_Score":0,"Tags":"python,methods,properties","A_Id":53958035,"CreationDate":"2018-12-28T11:27:00.000","Title":"What is the difference between Methods and Properties for an object in python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have some calculations roughly looking like this: trip_count = DF_Trip.groupby([target], as_index=False)['Delivery'].count()\nAll my DF could possibly be grouped by Shop, Route and Driver. When I enter a single value for target, f.e. target = 'Route' it works fine.\nBut when I want to enter multiple values, f.e. target = 'Shop', 'Route' it only works when I enter it directly in the place where the variable is, f.e. trip_count = DF_Trip.groupby(['Shop', 'Route'], as_index=False)['Delivery'].count() but when I set the variable to target = 'Shop', 'Route', it gives me a ton of errors.\nI've realized from debugging, that target = 'Shop', 'Route' is treated as a tuple and read in the pandas.df.groupby documentation, that tuples are treated as a single value. Is there any workaround for this?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":35,"Q_Id":53968141,"Users Score":0,"Answer":"First I had to use a list as suggested by Gennady Kandaurov, and to later rename the columns I just had to add the two lists.\n\ntarget = ['Shop', 'Route']\nDF1.columns = target + ['static columns']","Q_Score":0,"Tags":"python,pandas","A_Id":53968421,"CreationDate":"2018-12-29T08:57:00.000","Title":"Tuple treated as single value in group by statement, any workaround?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Trying to figure out a good way of solving this problem but wanted to ask for the best way of doing this.\nIn my project, I am looking at multiple instrument note pairs for a neural network. The only problem is that there are multiple instruments with the same name and just because they have the same name doesn't mean that they are the same instrument 100% of the time. (It should be but I want to be sure.)\nI personally would like to analyze the instrument itself (like metadata on just the instrument in question) and not the notes associated with it. Is that possible?\nI should also mention that I am using pretty-midi to collect the musical instruments.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":431,"Q_Id":53971287,"Users Score":1,"Answer":"In MIDI files, bank and program numbers uniquely identity instruments.\nIn General MIDI, drums are on channel 10 (and, in theory, should not use a Program Change message).\nIn GM2\/GS\/XG, the defaults for drums are the same, but can be changed with bank select messages.","Q_Score":0,"Tags":"python,artificial-intelligence,midi,music21,midi-instrument","A_Id":53976520,"CreationDate":"2018-12-29T16:27:00.000","Title":"Analyze just Pretty_Midi Instruments","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Here\u2019s a general question that maybe someone could point me in the right direction. \nI\u2019m getting into Reinforcement Learning with Python 3.6\/Tensorflow and I have found\/tweaked my own model to train on historical data from a particular stock. My question is, is it possible to train this model on more than just one stock\u2019s dataset? Every single machine learning article I\u2019ve read on time series prediction and RL uses one dataset for training and testing, but my goal is to train a model on a bunch of tickers with varying prices in the hopes that the model can recognize similar price patterns, regardless of the price or ticker so that I could apply the trained model to a new dataset and it\u2019ll work. \nRight now it trains on one ticker and it\u2019s prices, but when I try to add a new dataset for added training, it performs horribly because it doesn\u2019t know the new prices, if that makes sense. \nThis is a basic question and I don\u2019t necessarily expect a coded answer, just somewhere I could learn how to train a model using multiple datasets. I\u2019m using OpenAI gym environment if that helps anything. \nThanks!","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":296,"Q_Id":53974005,"Users Score":0,"Answer":"Thanks to @Primusa I normalized my separate datasets by dividing each value by their respective maximums, then combined the datasets into one for training. Thanks!","Q_Score":1,"Tags":"python-3.x,tensorflow,reinforcement-learning,stocks,openai-gym","A_Id":53976226,"CreationDate":"2018-12-29T23:04:00.000","Title":"Reinforcement Learning Using Multiple Stock Ticker\u2019s Datasets?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Here\u2019s a general question that maybe someone could point me in the right direction. \nI\u2019m getting into Reinforcement Learning with Python 3.6\/Tensorflow and I have found\/tweaked my own model to train on historical data from a particular stock. My question is, is it possible to train this model on more than just one stock\u2019s dataset? Every single machine learning article I\u2019ve read on time series prediction and RL uses one dataset for training and testing, but my goal is to train a model on a bunch of tickers with varying prices in the hopes that the model can recognize similar price patterns, regardless of the price or ticker so that I could apply the trained model to a new dataset and it\u2019ll work. \nRight now it trains on one ticker and it\u2019s prices, but when I try to add a new dataset for added training, it performs horribly because it doesn\u2019t know the new prices, if that makes sense. \nThis is a basic question and I don\u2019t necessarily expect a coded answer, just somewhere I could learn how to train a model using multiple datasets. I\u2019m using OpenAI gym environment if that helps anything. \nThanks!","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":296,"Q_Id":53974005,"Users Score":0,"Answer":"I think normalizing the dataset with % change from previous close on all datasets could be a good start. in that way, any stock with any price seems normalized.","Q_Score":1,"Tags":"python-3.x,tensorflow,reinforcement-learning,stocks,openai-gym","A_Id":62697169,"CreationDate":"2018-12-29T23:04:00.000","Title":"Reinforcement Learning Using Multiple Stock Ticker\u2019s Datasets?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to figure out some of the hyperparamters used for training some old keras models I have.  They were saved as .h5 files.  When using model.summary(), I get the model architecture, but no additional metadata about the model.\nWhen I open this .h5 file in notepad++, most of the file is not human readable, but there are bits that I can understand, for instance;\n\n{\"loss_weights\": null, \"metrics\": [\"accuracy\"], \"sample_weight_mode\":\n  null, \"optimizer_config\": {\"config\": {\"decay\": 0.0, \"momentum\":\n  0.8999999761581421, \"nesterov\": false, \"lr\": 9.999999747378752e-05}, \"class_name\": \"SGD\"}, \"loss\": \"binary_crossentropy\"}\n\nwhich is not present in the output printed by model.summary().\nIs there a way to make these files human readable or to get a more expanded summary that includes version information and training parameters?","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":3271,"Q_Id":53988984,"Users Score":1,"Answer":"Configuration - model.get_config()\nOptimizer config - model.optimizer.get_config()\nTraining Config  model.history.params (this will be empty, if model is saved and reloaded)\nLoss Fuction - model.loss","Q_Score":6,"Tags":"python,keras,hdf5","A_Id":67994767,"CreationDate":"2018-12-31T15:23:00.000","Title":"Get training hyperparameters from a trained keras model","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"If num_workers is 2, Does that mean that it will put 2 batches in the RAM and send 1 of them to the GPU or Does it put 3 batches in the RAM then sends 1 of them to the GPU?\nWhat does actually happen when the number of workers is higher than the number of CPU cores? I tried it and it worked fine but How does it work? (I thought that the maximum number of workers I can choose is the number of cores).\nIf I set num_workers to 3 and during the training there were no batches in the memory for the GPU, Does the main process waits for its workers to read the batches or Does it read a single batch (without waiting for the workers)?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":54707,"Q_Id":53998282,"Users Score":71,"Answer":"When num_workers>0, only these workers will retrieve data, main process won't. So when  num_workers=2 you have at most 2 workers simultaneously putting data into RAM, not 3.\nWell our CPU can usually run like 100 processes without trouble and these worker processes aren't special in anyway, so having more workers than cpu cores is ok. But is it efficient? it depends on how busy your cpu cores are for other tasks, speed of cpu, speed of your hard disk etc. In short, its complicated, so setting workers to number of cores is a good rule of thumb, nothing more.\nNope. Remember DataLoader doesn't just randomly return from what's available in RAM right now, it uses batch_sampler to decide which batch to return next. Each batch is assigned to a worker, and main process will wait until the desired batch is retrieved by assigned worker.\n\nLastly to clarify, it isn't DataLoader's job to send anything directly to GPU, you explicitly call cuda() for that. \nEDIT: Don't call cuda() inside Dataset's __getitem__() method, please look at @psarka's comment for the reasoning","Q_Score":70,"Tags":"python,memory-management,deep-learning,pytorch,ram","A_Id":54002191,"CreationDate":"2019-01-01T19:23:00.000","Title":"How does the \"number of workers\" parameter in PyTorch dataloader actually work?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to see the entire row for a dask dataframe without the fields being cutoff, in pandas the command is pd.set_option('display.max_colwidth', -1), is there an equivalent for dask? I was not able to find anything.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2155,"Q_Id":54002006,"Users Score":0,"Answer":"Dask does not normally display the data in a dataframe at all, because it represents lazily-evaluated values. You may want to get a specific row by index, using the .loc accessor (same as in Pandas, but only efficient if the index is known to be sorted).\nIf you meant to get the whole list of columns only, you can get this by the .columns attribute.","Q_Score":3,"Tags":"python-3.x,dask","A_Id":54008906,"CreationDate":"2019-01-02T06:13:00.000","Title":"Dask Dataframe View Entire Row","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using TensorFlow for training my own dataset using capsule network. While training mnist dataset, it contains function mnist.train.next_batch(batch size). How to replace this function for training own dataset using TensorFlow?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":472,"Q_Id":54002301,"Users Score":0,"Answer":"The function sample batch_size number of samples from a shuffled training dataset, then return the batch for training.\nYou could write your own next_batch() method that does the same thing, or modify it as you wish. Then use it similarly when you're training your model.","Q_Score":0,"Tags":"python,tensorflow","A_Id":54002342,"CreationDate":"2019-01-02T06:46:00.000","Title":"What is use of function mnist.train.next_batch() in training dataset?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it possible to display documentation of numpy functions from jupyter notebook? \nhelp(linspace) did not work for me","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":55,"Q_Id":54019510,"Users Score":1,"Answer":"Highlight and press SHIFT + TAB.","Q_Score":1,"Tags":"python,jupyter-notebook","A_Id":54019576,"CreationDate":"2019-01-03T09:31:00.000","Title":"Documentations for Numpy Functions in Jupyter","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"trying to install tensorflow using conda package manager\nusing following command\n\nconda install -c conda-forge tensorflow\n\nbut it gives following error while executing transaction\n\nCondaError: Cannot link a source that does not exist.\n  C:\\ProgramData\\Anaconda3\\Scripts\\conda.exe","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2069,"Q_Id":54024671,"Users Score":0,"Answer":"Try to run conda clean --all --yes and conda update anaconda.\nDo you have a conda.exe file in the following folder C:\\ProgramData\\Anaconda3\\Scripts\\?\nDo you use the latest Conda?\nAnother solution could be to create a conda environments conda create -n name_environment pip python=3.5 and using pip to install tensorflow pip install tensorflow inside the new environment \nafter having activated it (activate name_environment).\nP.S. I can not write a comment because I do not have enough reputation.\nEDIT - Now i can!","Q_Score":3,"Tags":"python,tensorflow,anaconda,conda","A_Id":54025044,"CreationDate":"2019-01-03T14:52:00.000","Title":"error while installing tensorflow in conda environment (CondaError: Cannot link a source that does not exist.)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Suppose I have 20,000 features on map, and each feature have many attributes (as well as the latitude and longitude). One of the attributes called population.\nI want to split these 20,000 features into 3 clusters where the total sum of population of each cluster are equal to specific value 90,000 and features in each cluster should be near each others(ie will take locations in our consideration)\nSo, the output clusters should have the following conditions:\n\nSum(population) of all points\/items\/features in cluster 1=90,000\nSum(population) of all points\/items\/features in cluster 2=90,000\nSum(population) of all points\/items\/features in cluster 3=90,000\n\nI tried to use the k-mean clustering algorithm which gave me 3 clusters, but how to force the above constraint (sum of population should equal 90,000)\nAny idea is appreciated.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":791,"Q_Id":54031283,"Users Score":1,"Answer":"A turnkey solution will not work for you.\nYou'll have to formulate this as a standard constraint optimization problem and run a silver to optimize this. It's fairly straightforward: take the k-means objective and add your constraints...","Q_Score":0,"Tags":"python,cluster-analysis,mean,arcgis","A_Id":54032226,"CreationDate":"2019-01-03T23:38:00.000","Title":"k means clustering with fixed constraints (sum of specific attribute should be less than or equal 90,000)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm wondering about how a dash app works in terms of loading data, parsing and doing initial calcs when serving to a client who logs onto the website.\nFor instance, my app initially loads a bunch of static local csv data, parses a bunch of dates and loads them into a few pandas data frames. This data is then displayed on a map for the client.\nDoes the app have to reload\/parse all of this data every time a client logs onto the website? Or does the dash server load all the data only the first time it is instantiated and then just dish it out every time a client logs on?\nIf the data reloads every time, I would then use quick parsers like udatetime, but if not, id prefer to use a convenient parser like pendulum which isn't as efficient (but wouldn't matter if it only parses once).\nI hope that question makes sense. Thanks in advance!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":50,"Q_Id":54035114,"Users Score":2,"Answer":"The only thing that is called on every page load is the function you can assign to app.layout. This is useful if you want to display dynamic content like the current date on your page.\nEverything else is just executed once when the app is starting. \nThis means if you load your data outside the app.layout (which I assume is the case) everything is loaded just once.","Q_Score":0,"Tags":"python,performance,plotly-dash","A_Id":54173014,"CreationDate":"2019-01-04T08:03:00.000","Title":"Do Dash apps reload all data upon client log in?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I have n lists each of length m. assume n*m is even. i want to get a randomly shuffled list with all elements, under the constraint that the elements in locations i,i+1 where i=0,2,...,n*m-2 never come from the same list. edit: other than this constraint i do not want to bias the distribution of random lists. that is, the solution should be equivalent to a complete random choice that is reshuffled until the constraint hold.\nexample:\nlist1: a1,a2\nlist2: b1,b2\nlist3: c1,c2\nallowed: b1,c1,c2,a2,a1,b2\ndisallowed: b1,c1,c2,b2,a1,a2","AnswerCount":4,"Available Count":2,"Score":0.049958375,"is_accepted":false,"ViewCount":247,"Q_Id":54041705,"Users Score":1,"Answer":"A possible solution is to think of your number set as n chunks of item, each chunk having the length of m. If you randomly select for each chunk exactly one item from each lists, then you will never hit dead ends. Just make sure that the first item in each chunk (except the first chunk) will be of different list than the last element of the previous chunk.\nYou can also iteratively randomize numbers, always making sure you pick from a different list than the previous number, but then you can hit some dead ends.\nFinally, another possible solution is to randomize a number on each position sequentially, but only from those which \"can be put there\", that is, if you put a number, none of the constraints will be violated, that is, you will have at least a possible solution.","Q_Score":2,"Tags":"python,shuffle","A_Id":54041995,"CreationDate":"2019-01-04T15:20:00.000","Title":"Shuffling with constraints on pairs","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have n lists each of length m. assume n*m is even. i want to get a randomly shuffled list with all elements, under the constraint that the elements in locations i,i+1 where i=0,2,...,n*m-2 never come from the same list. edit: other than this constraint i do not want to bias the distribution of random lists. that is, the solution should be equivalent to a complete random choice that is reshuffled until the constraint hold.\nexample:\nlist1: a1,a2\nlist2: b1,b2\nlist3: c1,c2\nallowed: b1,c1,c2,a2,a1,b2\ndisallowed: b1,c1,c2,b2,a1,a2","AnswerCount":4,"Available Count":2,"Score":0.049958375,"is_accepted":false,"ViewCount":247,"Q_Id":54041705,"Users Score":1,"Answer":"A variation of b above that avoids dead ends: At each step you choose twice. First, randomly chose an item. Second, randomly choose where to place it. At the Kth step there are k optional places to put the item (the new item can be injected between two existing items). Naturally, you only choose from allowed places.\nMoney!","Q_Score":2,"Tags":"python,shuffle","A_Id":54088978,"CreationDate":"2019-01-04T15:20:00.000","Title":"Shuffling with constraints on pairs","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an extremely simple cnn which i will be trying to bind to an rnn (but that in the future). For now, all I have is conv2D->maxpool>conv2d->maxpool->dense->dense. The CNN works well, no problems, compiles, runs.\n'model.add(TimeDistributed(Conv2D(..., input_shape=(32,32,1))\nRuntimeError: You must compile your model before using it.\nAnd of course, model.compile() is immediately after the model definition and .fit following the compile...\nHence, is it me not getting something right or it is really an issue with the current Keras build?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":74,"Q_Id":54042532,"Users Score":0,"Answer":"The problem was that input_shape must be specified outside Conv2D and inside TimeDistributed. Keep in mind it must be 4D '(batch_size, width, height, channels)'","Q_Score":0,"Tags":"python,tensorflow,video,keras","A_Id":54049978,"CreationDate":"2019-01-04T16:12:00.000","Title":"TimeDistribution Wrapper Fails the Compilation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using the tensorflow object detection API. \nI have performed the training on the remote server GPU and saved the frozen model and checkpoints. \nAfter that i took that frozen model along with checkpoints and copied to my local machine and then performed the testing on my test data using the the script \"object_detection_tutorial.ipnyb\" \n\nWhen i run this cell \"Load a (frozen) Tensorflow model into memory\", it gives the invalid argument error. \nCan you please explain what is the issue while running the save model on my local machine ? Is it necessary that that the training and testing should be on same machine? I was encountered with the following error:\n\nInvalidArgumentError                      Traceback (most recent call\n  last)\n  ~\/anaconda3\/lib\/python3.6\/site-packages\/tensorflow\/python\/framework\/importer.py\n  in import_graph_def(graph_def, input_map, return_elements, name,\n  op_dict, producer_op_list)\n      417         results = c_api.TF_GraphImportGraphDefWithResults(\n  --> 418             graph._c_graph, serialized, options)  # pylint: disable=protected-access\n      419         results = c_api_util.ScopedTFImportGraphDefResults(results)\nInvalidArgumentError: NodeDef mentions attr 'T' not in\n  Op\n  selected_indices:int32>; NodeDef: {{node\n  Postprocessor\/BatchMultiClassNonMaxSuppression\/map\/while\/MultiClassNonMaxSuppression\/non_max_suppression\/NonMaxSuppressionV3}}\n  = NonMaxSuppressionV3[T=DT_FLOAT](Postprocessor\/BatchMultiClassNonMaxSuppression\/map\/while\/MultiClassNonMaxSuppression\/unstack,\n  Postprocessor\/BatchMultiClassNonMaxSuppression\/map\/while\/MultiClassNonMaxSuppression\/Reshape,\n  Postprocessor\/BatchMultiClassNonMaxSuppression\/map\/while\/MultiClassNonMaxSuppression\/Minimum,\n  Postprocessor\/BatchMultiClassNonMaxSuppression\/map\/while\/MultiClassNonMaxSuppression\/non_max_suppression\/iou_threshold,\n  Postprocessor\/BatchMultiClassNonMaxSuppression\/map\/while\/MultiClassNonMaxSuppression\/non_max_suppression\/score_threshold).\n  (Check whether your GraphDef-interpreting binary is up to date with\n  your GraphDef-generating binary.).\nDuring handling of the above exception, another exception occurred:\nValueError                                Traceback (most recent call\n  last)  in \n        5     serialized_graph = fid.read()\n        6     od_graph_def.ParseFromString(serialized_graph)\n  ----> 7     tf.import_graph_def(od_graph_def, name='')\n~\/anaconda3\/lib\/python3.6\/site-packages\/tensorflow\/python\/util\/deprecation.py\n  in new_func(*args, **kwargs)\n      486                 'in a future version' if date is None else ('after %s' % date),\n      487                 instructions)\n  --> 488       return func(*args, **kwargs)\n      489     return tf_decorator.make_decorator(func, new_func, 'deprecated',\n      490                                        _add_deprecated_arg_notice_to_docstring(\n~\/anaconda3\/lib\/python3.6\/site-packages\/tensorflow\/python\/framework\/importer.py\n  in import_graph_def(graph_def, input_map, return_elements, name,\n  op_dict, producer_op_list)\n      420       except errors.InvalidArgumentError as e:\n      421         # Convert to ValueError for backwards compatibility.\n  --> 422         raise ValueError(str(e))\n      423 \n      424     # Create _DefinedFunctions for any imported functions.\nValueError: NodeDef mentions attr 'T' not in\n  Op\n  selected_indices:int32>; NodeDef: {{node\n  Postprocessor\/BatchMultiClassNonMaxSuppression\/map\/while\/MultiClassNonMaxSuppression\/non_max_suppression\/NonMaxSuppressionV3}}\n  = NonMaxSuppressionV3[T=DT_FLOAT](Postprocessor\/BatchMultiClassNonMaxSuppression\/map\/while\/MultiClassNonMaxSuppression\/unstack,\n  Postprocessor\/BatchMultiClassNonMaxSuppression\/map\/while\/MultiClassNonMaxSuppression\/Reshape,\n  Postprocessor\/BatchMultiClassNonMaxSuppression\/map\/while\/MultiClassNonMaxSuppression\/Minimum,\n  Postprocessor\/BatchMultiClassNonMaxSuppression\/map\/while\/MultiClassNonMaxSuppression\/non_max_suppression\/iou_threshold,\n  Postprocessor\/BatchMultiClassNonMaxSuppression\/map\/while\/MultiClassNonMaxSuppression\/non_max_suppression\/score_threshold).\n  (Check whether your GraphDef-interpreting binary is up to date with\n  your GraphDef-generating binary.).","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":300,"Q_Id":54050290,"Users Score":0,"Answer":"I had a similar issue. The solution for me was to take my GPU training files from TF1.9 and move them to my local TF1.5 CPU environment (which doesn't support AVX instructions). I then created the frozen model on the local environment from the training files and was successfully able to use it.","Q_Score":1,"Tags":"python,python-3.x,tensorflow,object-detection-api","A_Id":59746722,"CreationDate":"2019-01-05T08:26:00.000","Title":"Invalid Argument error:Load a (frozen) Tensorflow model into memory (While testing the model on local machine)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I just got a new computer, and I was installing some Python libraries. When I tried to install numpy, I got a message on the console saying numpy was already downloaded. I went into the library folder, and not only was numpy there, but scipy, matplotlib, and a bunch of other libraries as well. How is this possible, considering this computer is brand new? I had installed Python the previous evening, so does installing Python automatically install these libraries as well?","AnswerCount":4,"Available Count":3,"Score":0.049958375,"is_accepted":false,"ViewCount":379,"Q_Id":54056362,"Users Score":1,"Answer":"If you copied your data from your previous computer to this one, you may have copied the python installation (and thereby the libraries you had installed before) in your appdata folder.\nAnother possibility is that you have install Anaconda, which is targeted especially at scientific things, and comes with numpy, scipy and some other things preinstalled.","Q_Score":1,"Tags":"python","A_Id":54056410,"CreationDate":"2019-01-05T21:18:00.000","Title":"Does installing Python also install libraries like scipy and numpy?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I just got a new computer, and I was installing some Python libraries. When I tried to install numpy, I got a message on the console saying numpy was already downloaded. I went into the library folder, and not only was numpy there, but scipy, matplotlib, and a bunch of other libraries as well. How is this possible, considering this computer is brand new? I had installed Python the previous evening, so does installing Python automatically install these libraries as well?","AnswerCount":4,"Available Count":3,"Score":0.049958375,"is_accepted":false,"ViewCount":379,"Q_Id":54056362,"Users Score":1,"Answer":"Python does not ship with these libraries unless you are using a pre-packaged distribution such as Anaconda.","Q_Score":1,"Tags":"python","A_Id":54056390,"CreationDate":"2019-01-05T21:18:00.000","Title":"Does installing Python also install libraries like scipy and numpy?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I just got a new computer, and I was installing some Python libraries. When I tried to install numpy, I got a message on the console saying numpy was already downloaded. I went into the library folder, and not only was numpy there, but scipy, matplotlib, and a bunch of other libraries as well. How is this possible, considering this computer is brand new? I had installed Python the previous evening, so does installing Python automatically install these libraries as well?","AnswerCount":4,"Available Count":3,"Score":0.049958375,"is_accepted":false,"ViewCount":379,"Q_Id":54056362,"Users Score":1,"Answer":"Although this is not the place for these types of questions, yes, there is no need to install libraries, as most of the times when you download Python in a distribution, such as Anaconda, they are also included.","Q_Score":1,"Tags":"python","A_Id":54056384,"CreationDate":"2019-01-05T21:18:00.000","Title":"Does installing Python also install libraries like scipy and numpy?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to start by saying that my knowledge of clustering techniques is extremely limited, please don\u2019t shoot me down too harshly.\nI have a sizable set of 3D points (around 8,000) - think of a X, Y, Z triplets, for which the Z coordinate represents a point in the earth underground (negative). I would like to cluster these points using the absolute minimum number of clusters, with the following constraints:\n\nUse the least number of clusters\nAll points should be included in the clustering, which means that any point should at least belong to one cluster\nThe maximum distance between any point and the cluster centroid (shifted at Z=0, on the earth surface) should not exceed a certain fixed distance d.\n\nI was thinking to use scikit-learn k-means approach, by iteratively incrementing the number of clusters and then, for all points in the dataset calculate if the distance between the point and the cluster centroid (at Z=0) is less than the specific distance provided.\nOf course, I am open to better\/more efficient suggestions - the clusters, for example, do not need to be circular as the ones returned by k-means. They can be ellipses or anything else, as long as the constraints above are satisfied.\nI welcome any suggestion, thank you for your insights.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1912,"Q_Id":54060208,"Users Score":0,"Answer":"Use hierarchical clustering.\nWith complete linkage.\nFinding the true minimum cover is NP hard. So you don't want to do this. But this should produce a fairly good approximation in \"just\" O(n\u00b3).\nThis is basic knowledge. When looking for a clustering algorithm, at least read the Wikipedia article. Better even some book, to get an overview. There is not just k-means...","Q_Score":1,"Tags":"python,scikit-learn,cluster-analysis","A_Id":54060749,"CreationDate":"2019-01-06T09:25:00.000","Title":"Python K-Means clustering and maximum distance","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"while uploading csv file on BigQuery through storage , I am getting below error:\nCSV table encountered too many errors, giving up. Rows: 5; errors: 1. Please look into the error stream for more details.\nIn schema , I am using all parameter as string.\nIn csv file,I have below data:\nIt's Time. Say \"I Do\" in my style.\nI am not able upload csv file in BigQuery containing above sentence","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1178,"Q_Id":54071304,"Users Score":0,"Answer":"Thanks to all for a response.\nHere is my solution to this problem:\n\nwith open('\/path\/to\/csv\/file', 'r') as f:\n      text = f.read()\nconverted_text = text.replace('\"',\"'\") print(converted_text)\nwith open('\/path\/to\/csv\/file', 'w') as f:\n      f.write(converted_text)","Q_Score":0,"Tags":"python,google-cloud-platform,google-bigquery,google-cloud-storage","A_Id":54090809,"CreationDate":"2019-01-07T09:06:00.000","Title":"How to fix upload csv file in bigquery using python","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"If I have a vector space spanned by five vectors v1....v5, to find the orthogonal basis for A where A=[v1,v2...v5] and A is 5Xn\nshould I use np.linalg.qr(A) or scipy.linalg.orth(A)?? \nThanks in advance","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":4408,"Q_Id":54081800,"Users Score":1,"Answer":"Note that sp.linalg.orth uses the SVD while np.linalg.qr uses a QR factorization. Both factorizations are obtained via wrappers for LAPACK functions.\nI don't think there is a strong preference for one over the other. The SVD will be slightly more stable but also a bit slower to compute. In practice I don't think you will really see much of a difference.","Q_Score":3,"Tags":"python,numpy,matrix,vector","A_Id":54081952,"CreationDate":"2019-01-07T20:59:00.000","Title":"np.linalg.qr(A) or scipy.linalg.orth(A) for finding the orthogonal basis (python)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been doing Andrew Ng's DeepLearning AI course (course 2).\nFor the exercise in gradient checking, he implements a function converting a dictionary containing all of the weights (W) and constants (b) into a single, one-hot encoded vector (of dimensions 47 x 1). \nThe starter code then iterates through this vector, adding epsilon to each entry in the vector.\nDoes gradient checking generally include adding epsilon\/subtracting to the constant as well? Or is it simply for convenience, as constants play a relatively small role in the overall calculation of the cost function?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":222,"Q_Id":54087106,"Users Score":1,"Answer":"You should do it regardless, even for constants. The reason is simple: being constants, you know their gradient is zero, so you still want to check you \"compute\" it correctly. You can see it as an additional safety net","Q_Score":1,"Tags":"python,neural-network,backpropagation,gradient-descent","A_Id":54087388,"CreationDate":"2019-01-08T07:32:00.000","Title":"In gradient checking, do we add\/subtract epsilon (a tiny value) to both theta and constant parameter b?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataset of ~10,000 rows of vehicles sold on a portal similar to Craigslist. The columns include price, mileage, no. of previous owners, how soon the car gets sold (in days), and most importantly a body of text that describes the vehicle (e.g. \"accident free, serviced regularly\"). \nI would like to find out which keywords, when included, will result in the car getting sold sooner. However I understand how soon a car gets sold also depends on the other factors especially price and mileage. \nRunning a TfidfVectorizer in scikit-learn resulted in very poor prediction accuracy. Not sure if I should try including price, mileage, etc. in the regression model as well, as it seems pretty complicated. Currently am considering repeating the TF-IDF regression on a particular segment of the data that is sufficiently huge (perhaps Toyotas priced at $10k-$20k).\nThe last resort is to plot two histograms, one of vehicle listings containing a specific word\/phrase and another for those that do not. The limitation here would be that the words that I choose to plot will be based on my subjective opinion.\nAre there other ways to find out which keywords could potentially be important? Thanks in advance.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":406,"Q_Id":54097067,"Users Score":1,"Answer":"As you mentioned you could only so much with the body of text, which signifies the amount of influence of text on selling the cars. \nEven though the model gives very poor prediction accuracy, you could ahead to see the feature importance, to understand what are the words that drive the sales. \nInclude phrases in your tfidf vectorizer by setting ngram_range parameter as (1,2)\nThis might gives you a small indication of what phrases influence the sales of a car. \nIf would also suggest you to set norm parameter of tfidf as None, to check if has influence. By default, it applies l2 norm. \nThe difference would come based the classification model, which you are using. Try changing the model also as a last option.","Q_Score":1,"Tags":"python,scikit-learn,nlp,regression,prediction","A_Id":54102609,"CreationDate":"2019-01-08T17:44:00.000","Title":"TF-IDF + Multiple Regression Prediction Problem","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Hi so i'm trying to get started on machine learning by installing tensorflow, however it's only supported by Python 3.6.x as of now.\nI guess you can say this was a failed attempt to downgrade python.\nMy installed version of python is 3.7.2 which has all my modules installed.\nI just installed Python 3.6.8. \nThe IDE i use is Visual Studio Code \nHowever now when i use Python 3.7.2 in Visual Studio Code, I get an error saying no module named... was found","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":61,"Q_Id":54102868,"Users Score":0,"Answer":"tensorflow is not suport by python 3.7 its only supported by 3.6 use a virtual environment to deal with multiple python versions.","Q_Score":1,"Tags":"python","A_Id":54102957,"CreationDate":"2019-01-09T03:32:00.000","Title":"No module named... Issue","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have seen the following statement in a number of docstrings when help()ing a class: \"See help(type(self)) for accurate signature.\"\nNotably, it is in the help() for scipy.stats.binom.__init__ and for stockfish.Stockfish.__init__ at the very least.  I assume, therefore, that it is some sort of stock message.\nIn any case, I can't figure out what the heck it means.  Is this useful information?  Note that, being \"outside\" of the class, so to speak, I never have access to self.  Furthermore, it is impossible to instantiate a class if I cannot access the signature of the __init__ method, and can therefore not even do help(type(my_object_instantiated)).  Its a catch 22.  In order to use __init__, I need the signature for __init__, but in order to read the signature for __init__, I need to instantiate an object with __init__.  This point is strictly academic however, for even when I do manage to instantiate a scipy.stats.binom, it actually returns an object of an entirely different class, rv_frozen, with the exact same message in its __init__ docstring, but whose signature is entirely different and entirely less useful.  In other words, help(type(self)) actually does not give an accurate signature.  It is useless.\nDoes anyone know where this message comes from, or what I'm supposed to make of it?  Is it just stock rubbish from a documentation generator, or am I user-erroring?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1623,"Q_Id":54114270,"Users Score":4,"Answer":"There is a convention that the signature for constructing a class instance is put in the __doc__ on the class (since that is what the user calls) rather than on __init__ (or __new__) which determines that signature.  This is especially true for extension types (written in C) whose __init__ cannot have its signature discovered via introspection.\nThe message that you see is part of the type class (see help(type.__init__)) and is thus inherited by metaclasses by default.\nIn some versions, scipy.stats.binom confuses the matter by not actually being a type; it is merely an instance of another class that (like type) is callable.  So asking for help on it merely gives the help for that class (just like help(1) gets you help(int))\u2014you have to look at its __call__ for further information (if any).  And asking for help on the result of calling it gives you help for the actual class of whatever it returns, as you observed.","Q_Score":8,"Tags":"python,documentation,docstring","A_Id":55400526,"CreationDate":"2019-01-09T16:18:00.000","Title":"Python \"See help(type(self)) for accurate signature.\"","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I already installed it with pip3 install pandas and using python3.7 but when I try to import pandas and run the code error popping up. \n\nTraceback (most recent call last):   File\n  \"\/Users\/barbie\/Python\/Test\/test.py\", line 1, in \n      import pandas as pd ModuleNotFoundError: No module named 'pandas'\n\nand if I try to install again.. it says this. \n\npip3 install \n  pandas Requirement already satisfied: pandas in\n  \/usr\/local\/lib\/python3.7\/site-packages (0.23.4) Requirement already\n  satisfied: pytz>=2011k in \/usr\/local\/lib\/python3.7\/site-packages (from\n  pandas) (2018.9) Requirement already satisfied: numpy>=1.9.0 in\n  \/usr\/local\/lib\/python3.7\/site-packages (from pandas) (1.15.4)\n  Requirement already satisfied: python-dateutil>=2.5.0 in\n  \/usr\/local\/lib\/python3.7\/site-packages (from pandas) (2.7.5)\n  Requirement already satisfied: six>=1.5 in\n  \/usr\/local\/lib\/python3.7\/site-packages (from\n  python-dateutil>=2.5.0->pandas) (1.12.0)","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":6513,"Q_Id":54129321,"Users Score":1,"Answer":"If you're using pycharm you can go to File -> Settings -> Project -> Project Interpreter.\nThere you'll get a list of all the packages installed with the current python that pycharm is using. There is a '+' sign on the right of the window that you can use to install new packages, just enter pandas there.","Q_Score":3,"Tags":"python-3.x,pandas","A_Id":54129738,"CreationDate":"2019-01-10T13:02:00.000","Title":"installed pandas but still can't import it","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been using Matlab to fit data to a Weibull distribution using [paramhat, paramci] = wblfit(data, alpha).  This gives the shape and scale parameters for a Weibull distribution as well as the confidence intervals for each value.\nI'm trying to use Scipy to accomplish the sane task and can easily get the parameters with scipy.stats.weibull_min.fit but I cannot figure out a way to get the confidence intervals on the vlauee.  Does Scipy offer this functionality? Or do I need to write the MLE confidence intervals estimation myself?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":496,"Q_Id":54130419,"Users Score":0,"Answer":"You could use scipy.optimize.curve_fit to fit the weibull distribution to your data. This will also give you the covariance and thus you can estimate the error of the fitted parameters.","Q_Score":2,"Tags":"python,scipy,weibull","A_Id":54131475,"CreationDate":"2019-01-10T14:05:00.000","Title":"Scipy Weibull parameter confidence intervals","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have some difficulty with understanding the role of activation functions and cost functions. Lets take a look at a simple example. Lets say I am building a neural network (artificial neural network). I have 5 \u201ex\u201c variables and one \u201ey\u201c variable. \nIf I do usual feature scaling and then apply, for example, Relu activation function in hidden layer, then this activation function does the transformation and as a result we get our predicted output value (y hat) between 0 and lets say M. Then the next step is to calculate the cost function. \nIn calculating the cost function, however, we need to compare the output value (y hat) with the actual value (y).\nThe question is how we can compare transformed output value (y hat) which is lets say between 0 and M with the untransformed actual value (y) (which can be any number as it is not been subjected to the Relu activation function) to calculate the cost function? There can be a large mismatch as one variable has been exposed to transformation and the other has not been.\nThank you for any help.","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":179,"Q_Id":54130706,"Users Score":0,"Answer":"-A cost function is a measure of error between what value your model predicts and what the value actually is. For example, say we wish to predict the value  yi  for data point  xi . Let  f\u03b8(xi)  represent the prediction or output of some arbitrary model for the point  xi  with parameters  \u03b8  . One of many cost functions could be\n\u2211ni=1(yi\u2212f\u03b8(xi))2\nthis function is known as the L2 loss. Training the hypothetical model we stated above would be the process of finding the  \u03b8  that minimizes this sum.\n-An activation function transforms the shape\/representation of the data going into it. A simple example could be  max(0,xi) , a function which outputs 0 if the input  xi  is negative or  xi  if the input  xi  is positive. This function is known as the \u201cReLU\u201d or \u201cRectified Linear Unit\u201d activation function. The choice of which function(s) are best for a specific problem using a particular neural architecture is still under a lot of discussion. However, these representations are essential for making high-dimensional data linearly separable, which is one of the many uses of a neural network.\nI hope this gave a decent idea of what these things are. If you wish to learn more, I suggest you go through Andrew Ng\u2019s machine learning course on Coursera. It provides a wonderful introductory look into the field.","Q_Score":0,"Tags":"python,neural-network,activation-function","A_Id":62532493,"CreationDate":"2019-01-10T14:20:00.000","Title":"Role of activation function in calculating the cost function for artificial neural networks","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have some difficulty with understanding the role of activation functions and cost functions. Lets take a look at a simple example. Lets say I am building a neural network (artificial neural network). I have 5 \u201ex\u201c variables and one \u201ey\u201c variable. \nIf I do usual feature scaling and then apply, for example, Relu activation function in hidden layer, then this activation function does the transformation and as a result we get our predicted output value (y hat) between 0 and lets say M. Then the next step is to calculate the cost function. \nIn calculating the cost function, however, we need to compare the output value (y hat) with the actual value (y).\nThe question is how we can compare transformed output value (y hat) which is lets say between 0 and M with the untransformed actual value (y) (which can be any number as it is not been subjected to the Relu activation function) to calculate the cost function? There can be a large mismatch as one variable has been exposed to transformation and the other has not been.\nThank you for any help.","AnswerCount":3,"Available Count":2,"Score":-0.0665680765,"is_accepted":false,"ViewCount":179,"Q_Id":54130706,"Users Score":-1,"Answer":"The value you're comparing your actual results to for the cost function doesn't (intrinsically) have anything to do with the input you used to get the output. It doesn't get transformed in any way.\nYour expected value is [10,200,3] but you used Softmax on the output layer and RMSE loss? Well, too bad, you're gonna have a high cost all the time (and the model probably won't converge).\nIt's just on you to use the right cost functions to serve as a sane heuristic for evaluating the model performance and the right activations to be able to get sane outputs for the task at hand.","Q_Score":0,"Tags":"python,neural-network,activation-function","A_Id":54130955,"CreationDate":"2019-01-10T14:20:00.000","Title":"Role of activation function in calculating the cost function for artificial neural networks","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"my data looks like this.\n0 199 1028 251 1449 847 1483 1314 23 1066 604 398 225 552 1512 1598\n1 1214 910 631 422 503 183 887 342 794 590 392 874 1223 314 276 1411\n2 1199 700 1717 450 1043 540 552 101 359 219 64 781 953\n10 1707 1019 463 827 675 874 470 943 667 237 1440 892 677 631 425\nHow can I read this file structure in python? I want to extract a specific column from rows. For example, If I want to extract value in the second row, second column, how can I do that? I've tried 'loadtxt' using data type string. But it requires string index slicing, so that I could not proceed because each column has different digits. Moreover, each row has a different number of columns. Can you guys help me?\nThanks in advance.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":55,"Q_Id":54142589,"Users Score":0,"Answer":"Use something like this to split it\nsplit2=[]\nsplit1=txt.split(\"\\n\")\nfor item in split1:\n    split2.append(item.split(\" \"))","Q_Score":0,"Tags":"python,jupyter-notebook","A_Id":54142820,"CreationDate":"2019-01-11T08:09:00.000","Title":"How can I read a file having different column for each rows?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a spark dataframe which has a column 'X'.The column contains elements which are in the form:\n\nu'[23,4,77,890,455,................]'\n\n. How can I convert this unicode to list.That is my output should be\n\n[23,4,77,890,455...................]\n\n.  I have apply it for each element in the 'X' column.\nI have tried df.withColumn(\"X_new\", ast.literal_eval(x)) and got the error \n\n\"Malformed String\"\n\nI also tried \n\ndf.withColumn(\"X_new\", json.loads(x)) and got the error \"Expected\n  String or Buffer\"\n\nand \n\ndf.withColumn(\"X_new\", json.dumps(x)) which says JSON not\n  serialisable.\n\nand also \n\ndf_2 = df.rdd.map(lambda x: x.encode('utf-8')) which says rdd has no\n  attribute encode.\n\nI dont want to use collect and toPandas() because its memory consuming.(But if thats the only way please do tell).I am using Pyspark\nUpdate: cph_sto gave the answer using UDF.Though it worked well,I find that it is Slow.Can Somebody suggest any other method?","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":8294,"Q_Id":54144887,"Users Score":1,"Answer":"Since it's a string, you could remove the first and last characters:\nFrom '[23,4,77,890,455]' to '23,4,77,890,455'\nThen apply the split() function to generate an array, taking , as the delimiter.","Q_Score":1,"Tags":"python,apache-spark,pyspark,pyspark-sql,unicode-string","A_Id":69165691,"CreationDate":"2019-01-11T10:43:00.000","Title":"How can I convert unicode to string of a dataframe column?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a data set from telecom company having lots of categorical features. I used the pandas.get_dummies method to convert them into one hot encoded format with drop_first=True option. Now how can I use the predict function, test input data needs to be encoded in the same way, as the drop_first=True option also dropped some columns, how can I ensure that encoding takes place in similar fashion. \nData set shape before encoding : (7043, 21)\nData set shape after encoding : (7043, 31)","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1013,"Q_Id":54145226,"Users Score":1,"Answer":"When not using drop_first=True you have two options:\n\nPerform the one-hot encoding before splitting the data in training and test set. (Or combine the data sets, perform the one-hot encoding, and split the data sets again).\nAlign the data sets after one-hot encoding: an inner join removes the features that are not present in one of the sets (they would be useless anyway). train, test = train.align(test, join='inner', axis=1)\n\nYou noted (correctly) that method 2 may not do what you expect because you are using drop_first=True. So you are left with method 1.","Q_Score":0,"Tags":"python,machine-learning,sklearn-pandas,one-hot-encoding","A_Id":54145335,"CreationDate":"2019-01-11T11:02:00.000","Title":"How to align training and test set when using pandas `get_dummies` with `drop_first=True`?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm setting up Dask Python cluster at work (30 machines, 8 cores each in average). People use only a portion of their CPU power, so dask-workers will be running on background at low priority. All workers are listening to dask-scheduler on my master node. It works perfect if only I who use it, however it's gonna be used by several people in a concurrent manner - so i need to be able to admin this cluster:\n\nAuthenticate users, reject unknowns\nIdentify who submitted which jobs\nRestrict number of submitted jobs per user\nRestrict timeout for computation per job\nKill any job as admin\n\ndask.distributed out of box provides a little of functionality described above. Could you please advice on some solution (may be hybrid Dask + something)?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":101,"Q_Id":54147096,"Users Score":0,"Answer":"Usually people use a cluster manager like Kubernetes, Yarn, SLURM, SGE, PBS or something else.  That system handles user authentication, resource management, and so on.  A user then uses the one of the Dask-kubernetes, Dask-yarn, Dask-jobqueue projects to create their own short-lived scheduler and workers on the cluster on an as-needed basis.","Q_Score":0,"Tags":"python,dask,dask-distributed","A_Id":54777689,"CreationDate":"2019-01-11T13:02:00.000","Title":"Dask.distributed cluster administration","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I'm trying to use the python dlib.train_shape_predictor function to train using a very large set of images (~50,000). \nI've created an xml file containing the necessary data, but it seems like train_shape_predictor loads all the referenced images into RAM before it starts training. This leads to the process getting terminated because it uses over 100gb of RAM. Even trimming down the data set uses over 20gb (machine only has 16gb physical memory).\nIs there some way to get train_shape_predictor to load images on demand, instead of all at once?\nI'm using python 3.7.2 and dlib 19.16.0 installed via pip on macOS.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":738,"Q_Id":54155910,"Users Score":0,"Answer":"I posted this as an issue on the dlib github and got this response from the author:\n\nIt's not reasonable to change the code to cycle back and forth between disk and ram like that. It will make training very slow. You should instead buy more RAM, or use smaller images.\n\nAs designed, large training sets need tons of RAM.","Q_Score":0,"Tags":"python,computer-vision,face-recognition,dlib","A_Id":54161780,"CreationDate":"2019-01-12T01:03:00.000","Title":"How can I train dlib shape predictor using a very large training set","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a code that takes in a video then constructs a list of frames from that video. then does something with each frame then put the frames back together into cv2 video writer. However, when the video is constructed again, it loses all its audio.","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":2044,"Q_Id":54161418,"Users Score":2,"Answer":"Stephen Meschke is right ! Use FFMPEG to extract and import audio.\nType in cmd:\nExtract audio:\nffmpeg -i yourvideo.avi -f mp3 -ab 192000 -vn sound.mp3\nImport audio:\nffmpeg -i yourvideo.avi -i sound.mp3 -c copy -map 0:v:0 -map 1:a:0 output.avi","Q_Score":2,"Tags":"python-3.x,opencv,audio,video-processing,cv2","A_Id":57248756,"CreationDate":"2019-01-12T16:15:00.000","Title":"Python3.6 add audio to cv2 processed video","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Keras 2.0 has removed keras.layers.merge and now we should use keras.layers.Concatenate, \nI was wonder what is the equivalent to having the 'cos' and 'dot_axis=0' arg, for example\nsimilarity = keras.layers.merge([target, context], mode='cos', dot_axes=0) \nHow would I write that in keras 2.0?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":787,"Q_Id":54165333,"Users Score":3,"Answer":"I tried with:\nsimilarity = dot([target, context], axes=1, normalize=True)","Q_Score":1,"Tags":"python,tensorflow,keras","A_Id":56676328,"CreationDate":"2019-01-13T01:16:00.000","Title":"What is the Keras 2.0 equivalent of `similarity = keras.layers.merge([target, context], mode='cos', dot_axes=0)`","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm making a form using reportlab and its in two columns. The second columns is just a copy of the first column. \nI used Frame() function to create two columns and I used a Spacer() function to separate the original form from the copied form into two columns. \nMy expected result is to make the data from the second column stay in place. But the result that I'm getting is when the data from the first columns gets shorter the second columns starts shifting up and moves to the first column.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":44,"Q_Id":54166726,"Users Score":2,"Answer":"If I get your question correct, the problem is that you use a spacer to control the contents' visual placement in two columns\/frames. By this, you see it as a single long column split in two, meanwhile you need to see it as two separate columns (two separate frames).\nTherefore you will get greater control if you end the first frame (with FrameBreak() before start filling the other and only use the spacer to control any visual design within the same frame.\nTools you need to be aware of are:\nFrameBreak(), if you search for it you will find many code examples.\ne.g. you fill frame 1 with with 10 lines of text, then you insert a FramBreak() and instruct the script to start filling the second column.\nAnother tool you should be aware of is the settings used e.g for BaseDocTemplate:\nallowSplitting: If set to 1, flowables (eg, paragraphs) may be split across frames or pages. If 0, you force content into the same frame. (default: 1, disabled with 0).","Q_Score":1,"Tags":"python,reportlab","A_Id":54192306,"CreationDate":"2019-01-13T06:55:00.000","Title":"Make the data from the second column stay at the second column","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"We know that elimination requires roughly 1\/3 n^3 operations, and if we use LU decomposition stored in memory, it is reduced to n^2 operations. If we have a band matrix with w upper and lower diagonals, we can skip the zeros and bring it down to about nw^2 operations, and if we use LU decomposition, it can be done in about 2nw operations.\nIn scipy.linalg, we have lu_factor and lu_solve, but they do not seem to be optimized for band matrices. We also have solve_banded, but it directly solves Ax=b. How can we do an efficient LU decomposition for banded matrices and efficiently perform forward and backward elimination with banded triangular L and U?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":407,"Q_Id":54175192,"Users Score":0,"Answer":"Lapack's *gbsv routine computes the LU decomp of an input banded matrix. \nFrom python, you can use either its f2py wrapper (see e.g. the source of scipy.linalg.solve_banded for example usage) or drop to Cython and use scipy.linalg.cython_lapack bindings.","Q_Score":0,"Tags":"python,scipy,linear-algebra","A_Id":54260769,"CreationDate":"2019-01-14T02:41:00.000","Title":"Can we use scipy to do faster LU decomposition for band matrices?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataset of medical images in grayscale Png format which must be converted to RGB format. Tried many solutions but in vain.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":1046,"Q_Id":54182675,"Users Score":1,"Answer":"GIMP, Menu image -> Mode -> RGB mode","Q_Score":0,"Tags":"python,rgb,grayscale,medical,image-preprocessing","A_Id":54182847,"CreationDate":"2019-01-14T13:41:00.000","Title":"Convert grayscale png to RGB png image","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using multiprocessing pool to speed up the title extraction process on a text corpus. At one stage of the code, I am using wordnet path similarity module to determine the similarity of two words. \nIf i run my code sequentially i.e. without the use of multiprocessing pool, I get normal times in calculating this path similarity. However, when I use multiprocessing to process multiple documents simultaneously, I observe great time delays in computing this path similarity as compared to sequential.\nQuestion: Does NLTK show any problems with multiprocessing module ?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":173,"Q_Id":54187798,"Users Score":1,"Answer":"It is very likely that the module in separate processes attempts to access the very same file with Wordnet data. This would result in dependence on GIL to access the file or OS-level file locks use. Both cases would explain the behaviour you are observing.","Q_Score":0,"Tags":"python,nltk,python-multiprocessing,pool,wordnet","A_Id":54188845,"CreationDate":"2019-01-14T19:17:00.000","Title":"Python multiprocesing and NLTK wordnet path similarity","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Whenever I submit a dask task, I can specify the requisite resources for that task.  e.g. client.submit(process, d, resources={'GPU': 1})\nHowever, If I abstract my dask scheduler away as a joblib.parallel_backend, it is not clear how to specify resources when I do so.\nHow do I call joblib.parallel_backend('dask') and still specify requisite resources?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":193,"Q_Id":54188251,"Users Score":1,"Answer":"As of 2019-02-19, there is no way to do this.","Q_Score":0,"Tags":"python,dask,joblib","A_Id":54777735,"CreationDate":"2019-01-14T19:53:00.000","Title":"joblib parallel_backend with dask resources","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"My question is not one of coding per say, but of understanding the algorithm. \nConceptually I understand how the column transposition deciphers text with a constant key value for example 10.\nMy confusion occurs, when the key is a permutation. For example key = [2,4,6,8,10,1,3,5,7,9] and a message like \"XOV EK HLYR NUCO HEEEWADCRETL CEEOACT KD\". The part where I'm confused is writing the cipher text into rows, then permuting the row according to the key. \nCan someone please provide some clarification on this.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1074,"Q_Id":54190370,"Users Score":0,"Answer":"I figured it out. Once you know the number of rows and columns, you can write the ciphertext into the rows, then permute the rows according to the key. Please correct if my explanation is wrong.  The plain text is \"execlent work you have cracked the code\"","Q_Score":1,"Tags":"python,encryption,cryptography","A_Id":54190671,"CreationDate":"2019-01-14T22:49:00.000","Title":"How to decrypt a columnar transposition cipher","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using the yolov3 model running on several surveillance cameras. Besides this I also run tensorflow models on these surveillaince streams. I feel a little lost when it comes to using anything but opencv for rtsp streaming.\nSo far I haven't seen people use anything but opencv in python. Are there any places I should be looking into. Please feel free to chime in.\nSorry if the question is a bit vague, but I really don't know how to put this better. Feel free to edit mods.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":51,"Q_Id":54193968,"Users Score":1,"Answer":"Of course are the alternatives to OpenCV in python if it comes to video capture but in my experience none of them preformed better","Q_Score":0,"Tags":"python,opencv,video,video-streaming,video-processing","A_Id":54197292,"CreationDate":"2019-01-15T06:52:00.000","Title":"Good resources for video processing in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In s3 bucket daily new JSON files are dumping , i have to create solution which pick the latest file when it arrives PARSE the JSON and load it to Snowflake Datawarehouse. may someone please share your thoughts how can we achieve","AnswerCount":4,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1762,"Q_Id":54193979,"Users Score":0,"Answer":"There are some aspects to be considered such as is it a batch or streaming data , do you want retry loading the file in case there is wrong data or format or do you want to make it a generic process to be able to handle different file formats\/ file types(csv\/json) and stages. \nIn our case we have built a generic s3 to Snowflake load using Python and Luigi and also implemented the same using SSIS but for csv\/txt file only.","Q_Score":2,"Tags":"python,amazon-s3,snowflake-cloud-data-platform","A_Id":54209309,"CreationDate":"2019-01-15T06:54:00.000","Title":"Automate File loading from s3 to snowflake","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"TLDR: Is there a way to freeze a Tensorflow model during runtime at time t1, such that running the network from time 0 to t2>t1 would lead to exactly the same results as running it from t1 to t2?\nI have searched this quite a lot and couldn't find this exact scenario:\nI have a tensorflow model which is receiving inputs through Datasets API from a list of TFRecords. At very random moments I get an error regarding tensor shape incompatibility and I'm trying to figure out why. I have changed the seeds, so that the code is reproducible, but it takes about 30 minutes for the reproducible error to occur. What is the best strategy in such situations to debug the code faster?\nWhat I have been trying has been to save a checkpoint at every iteration, hoping that by restoring the last one (right before the error) I'd be able to quickly reproduce the error later on and troubleshoot it. Unfortunately the random state and dataset api pointer get reset when I do this. Is there any way to fully store the state of a network during runtime (including its random number generator state and the Dataset API pointer), so that when it is restored the same outputs get reproduced?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":163,"Q_Id":54205857,"Users Score":1,"Answer":"From my personal experience I would approach it in the following ways.\n\nRunning the code with -i flag (python -i) which takes you to the interpreter with preserved state at the moment the script stops OR (even better) calling problematic parts of code from jupyter notebook which will also preserve the state after the exception is raised and you could investigate what the problem is more easily. If the problem is inside a function you could catch the exception and return all relevant objects. Or you could also put your functions inside the class to have a single object, instantiate and run it from jupyter and when the problem occurs you will have all variables inside that class object.\nAdding assert's statements for the shapes of your data and for the shapes of your model variables\/placeholders. For example, if you have some preprocessing\/augmentation add assert's before and after preprocessing\/augmentation to be sure that the shapes are as expected.\nTaking a break. Sometimes you spend a lot of time and effort on something without success, but after having a rest you solve the problem immediately.\n\nGood luck!","Q_Score":0,"Tags":"python,tensorflow","A_Id":54207491,"CreationDate":"2019-01-15T19:46:00.000","Title":"Save the exact state of Tensorflow model, random state, and Datasets API pointer for debugging","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Specifications:\nSystem: Ubuntu 18.0.4\nTensorflow:1.9.0,\ncudnn=7.2.1\nInterpreter project: anaconda environment.\nWhen I run the script on terminal with the same anaconda env, it works fine. Using pycharm, it does not work!! What is the issue ?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":94,"Q_Id":54207221,"Users Score":0,"Answer":"Go to File -> Settings -> Project Interpreter and set the same python environment used by Anaconda.","Q_Score":0,"Tags":"python-3.x,tensorflow,pycharm","A_Id":54207278,"CreationDate":"2019-01-15T21:38:00.000","Title":"Tensorflow does not see gpu on pycharm","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"When running\npd.read_hdf('myfile.h5')\nI get the following traceback error:\n\n[[...some longer traceback]]\n~\/.local\/lib\/python3.6\/site-packages\/pandas\/io\/pytables.py in\n  read_array(self, key, start, stop)    2487     2488         if\n  isinstance(node, tables.VLArray):\n  -> 2489             ret = node[0][start:stop]    2490         else:    2491             dtype = getattr(attrs, 'value_type', None)\n~\/.local\/lib\/python3.6\/site-packages\/tables\/vlarray.py in\n  getitem(self, key)\n~\/.local\/lib\/python3.6\/site-packages\/tables\/vlarray.py in read(self,\n  start, stop, step)\ntables\/hdf5extension.pyx in tables.hdf5extension.VLArray._read_array()\nValueError: cannot set WRITEABLE flag to True of this array\n\nNo clue what's going on. I've tried reinstalling tables, pandas everything basically, but doesn't want to read it.","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":15113,"Q_Id":54210073,"Users Score":0,"Answer":"It seems that time-date strings were causing the problem and when I converted these from text to numpy (pd.to_datetime()) and stored the table and the problem went away so perhaps it has something to do with text data?","Q_Score":26,"Tags":"python,pandas,pytables,hdf","A_Id":56453634,"CreationDate":"2019-01-16T03:37:00.000","Title":"pd.read_hdf throws 'cannot set WRITABLE flag to True of this array'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"As I am new in Big Data Platform, I would like like to do some feature engineering work with my data. The Database size is about 30-50 Gb. Is is possible to load the full data (30-50Gb) in a data frame like pandas data frame? \nThe Database used here is Oracle. I tried to load it but I am getting out of memory error. Furthermore I like to work in Python.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":224,"Q_Id":54232066,"Users Score":1,"Answer":"pandas is not good if you have GBS of data it would be better to use distributed architecture to improve speed and efficiency. There is a library called DASK that can load large data and use distributed architecture.","Q_Score":0,"Tags":"python-3.x,oracle,jupyter-notebook,bigdata","A_Id":68094492,"CreationDate":"2019-01-17T08:50:00.000","Title":"Big Data Load in Pandas Data Frame","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a few basic questions on Dask:\n\nIs it correct that I have to use Futures when I want to use dask for distributed computations (i.e. on a cluster)?\nIn that case, i.e. when working with futures, are task graphs still the way to reason about computations. If yes, how do I create them.\nHow can I generally, i.e. no matter if working with a future or with a delayed, get the dictionary associated with a task graph?\n\nAs an edit:\nMy application is that I want to parallelize a for loop either on my local machine or on a cluster (i.e. it should work on a cluster).\nAs a second edit:\nI think I am also somewhat unclear regarding the relation between Futures and delayed computations.\nThx","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1864,"Q_Id":54232080,"Users Score":16,"Answer":"1) Yup. If you're sending the data through a network, you have to have some way of asking the computer doing the computing for you how's that number-crunching coming along, and Futures represent more or less exactly that.\n2) No. With Futures, you're executing the functions eagerly - spinning up the computations as soon as you can, then waiting for the results to come back (from another thread\/process locally, or from some remote you've offloaded the job onto). The relevant abstraction here would be a Queque (Priority Queque, specifically).\n3) For a Delayed instance, for instance, you could do some_delayed.dask, or for an Array, Array.dask; optionally wrap the whole thing in either dict() or vars(). I don't know for sure if it's reliably set up this way for every single API, though  (I would assume so, but you know what they say about what assuming makes of the two of us...).\n4) The simplest analogy would probably be: Delayed is essentially a fancy Python yield wrapper over a function; Future is essentially a fancy async\/await wrapper over a function.","Q_Score":12,"Tags":"python,distributed-computing,dask","A_Id":54235046,"CreationDate":"2019-01-17T08:51:00.000","Title":"Dask: delayed vs futures and task graph generation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I have a dataset in csv file which contains one of the column as list(or dict which further includes several semi colons and commas because of key, value pair). Now trouble is accessing with Pandas and it is return mixed values because of the reason that it has several commas in the list which is in fact a single column. \nI have seen several solutions such as use \"\" or ; as delimiter, but problem is I already have the data, find and replace will completely change my dataset.\nexample of csv is :\ndata_column1, data_column2, [{key1:value1},{key2:value2}], data_column3\nPlease advise any faster way to access specific columns of the data with out any ambiguity.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":105,"Q_Id":54235643,"Users Score":0,"Answer":"You can only set the delmiter to one character so you can't use square brackets in this Way. You would need to use a single character such as \" so that it knows to ignore the commas between the delmieters.","Q_Score":0,"Tags":"python,python-3.x,pandas","A_Id":54235779,"CreationDate":"2019-01-17T12:10:00.000","Title":"Access dict columns of a csv file in python pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm already installed numpy and it works in cmd.\nmy Python version is 3.7.2 and numpy version is 1.16.0\n\nWhen I use numpy in windows cmd, It works.\nimport numpy is working well in the python interactive console.\nBut in pyCharm, it doesn't work and errors with No module named 'numpy'.\nHow can I solve it?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":229,"Q_Id":54241710,"Users Score":5,"Answer":"You probably arent using the same python installation in pycharm and in your console. Did you double-check in project settings ?\nIf you just want to install numpy, you can create a requirements.txt file and add numpy in it, pycharm will suggest to install it if not already done. \nAlternatively, you could use a venv","Q_Score":0,"Tags":"python,numpy","A_Id":54241747,"CreationDate":"2019-01-17T17:59:00.000","Title":"I get an ImportError: No module named 'numpy' when trying to use numpy in PyCarm, but it works fine in the interactive console","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am iterating through a few thousand lines of some really messy data from a csv file using pandas.  I'm iterating through one of the dataframe columns which contains generally fairly short strings of disparate, concatenated customer information (name, location, customer numbers, telephone numbers, etc).\nThere's not a lot of identifiable difference between customer numbers and telephone numbers, though most of the rows in the df column contain a TEL identifier within the string text for a telephone number, as demonstrated below (where 0123456 is a customer number, and 55555 5555 is the TEL number):\nJERRY 0123456 TEL: 55555 5555 LOCATION CITY\nI can clear the whitespace from the digits following the TEL: indicator, but can't seem to formulate a regular expression that only pulls the text following the TEL: indicator.  My ideal output in my new df[\"TEL\"] column could be 555555555.\nSo far the regular expression I have is (note, some of the phone numbers are different lengths to deal with international callers, some of which include country code, and some of which do not):\nre.match(r'(TEL)?:? ?[0-9 ]{9-12}').group()\nHowever, the above regular expression still pulls the TEL piece of the string I am matching against.  How do I fix this error?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":51,"Q_Id":54247079,"Users Score":0,"Answer":"re.match(r'(?:TEL)?:? ?([0-9 ]{9-12})').group(1)\n(?:...) makes it a non-capturing group\n([0-9 ]{9-12}) captures that part as the group(1)","Q_Score":0,"Tags":"python,regex,pandas","A_Id":54247129,"CreationDate":"2019-01-18T02:42:00.000","Title":"finding a string after identifying group","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it possible to run machine learning through R (RStudio) or Python in a Netezza server? More specifically, can I train models and make predictions using the Netezza server? Has anybody been able to install TensorFlow, Keras or Pytorch in the Netezza server for these ML tasks?\nI appreciate any feedback whether this is feasible or not.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":159,"Q_Id":54261531,"Users Score":0,"Answer":"It is possible to install R and To my knowledge all kinds of R-packages can be installed. Some of the code will only run on the HOST but all the basics (like Apply and filtering) runs on all the SPU\u2019s","Q_Score":0,"Tags":"python,r,machine-learning,netezza","A_Id":54271206,"CreationDate":"2019-01-18T21:17:00.000","Title":"Machine learning through R\/Python in the Netezza server","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"If i've an networkx graph from a python dataframe and i've generated the adjacency matrix from it. \nSo basically, how to get labels of that adjacency matrix ?","AnswerCount":2,"Available Count":2,"Score":-0.0996679946,"is_accepted":false,"ViewCount":910,"Q_Id":54262904,"Users Score":-1,"Answer":"If the adjacency matrix is generated without passing the nodeList, then you can call G.nodes to obtain the default NodeList, which should correspond to the rows of the adjacency matrix.","Q_Score":0,"Tags":"python-3.x,networkx,adjacency-matrix","A_Id":66045559,"CreationDate":"2019-01-19T00:00:00.000","Title":"Python how to get labels of a generated adjacency matrix from networkx graph?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"If i've an networkx graph from a python dataframe and i've generated the adjacency matrix from it. \nSo basically, how to get labels of that adjacency matrix ?","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":910,"Q_Id":54262904,"Users Score":0,"Answer":"Assuming you refer to nodes' labels, networkx only keeps the the indices when extracting a graph's adjacency matrix. Networkx represents each node as an index, and you can add more attributes if you wish. All node's attributes except for the index are kept in a dictionary. When generating graph's adjacency matrix only the indices are kept, so if you only wish to keep a single string per node, consider indexing nodes by that string when generating your graph.","Q_Score":0,"Tags":"python-3.x,networkx,adjacency-matrix","A_Id":54274980,"CreationDate":"2019-01-19T00:00:00.000","Title":"Python how to get labels of a generated adjacency matrix from networkx graph?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to solve an lp optimization problem where the upper bounds of a few variables are not an integer, instead of a function of another variable. As an example, i, j and k are three variables and bounds are 0<=i<=100, 0<=j<=i-1 and 0<=k<=j-1. How can we represent such noninteger bounds in scipy lp solver?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":145,"Q_Id":54267193,"Users Score":0,"Answer":"You can try in the below way.\nfor i in range(0,100):\n    for j in range(0,int(i)):\n        for k in range(0,int(j)):\n            print(k)","Q_Score":1,"Tags":"python,scipy","A_Id":54268086,"CreationDate":"2019-01-19T12:40:00.000","Title":"How to represent bounds of variables in scipy.optimization where bound is function of another variable","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to solve an lp optimization problem where the upper bounds of a few variables are not an integer, instead of a function of another variable. As an example, i, j and k are three variables and bounds are 0<=i<=100, 0<=j<=i-1 and 0<=k<=j-1. How can we represent such noninteger bounds in scipy lp solver?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":145,"Q_Id":54267193,"Users Score":0,"Answer":"Currently none of scipy's methods allows for applying dynamic bounds. You can make a non standard extension to scipy.optimize.minimize or fsolve or implement your own optimiser with dynamic bounds. \nNow on whether it is a good idea to do so: NO!\nThat is because for a well formulated optimisation problem you want the design variables and their bounds to be orthogonally independent. If the bounds are changing based on other design variables then the problem is not orthogonally independent.","Q_Score":1,"Tags":"python,scipy","A_Id":54267964,"CreationDate":"2019-01-19T12:40:00.000","Title":"How to represent bounds of variables in scipy.optimization where bound is function of another variable","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I loaded a csv file into 'dataset' and tried to execute dataset.head(), but it reports an error. How to check the head or tail of a numpy array? without specifying specific lines?","AnswerCount":3,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":65506,"Q_Id":54269647,"Users Score":82,"Answer":"For a head-like function you can just slice the array using dataset[:10].\nFor a tail-like function you can just slice the array using dataset[-10:].","Q_Score":54,"Tags":"python,numpy","A_Id":54303968,"CreationDate":"2019-01-19T17:33:00.000","Title":"Is there a head and tail method for Numpy array?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Background\nI have a school project that has this specific item I want to construct an object detector for. I was planning on making a custom dataset, labeling the image, and then using tensorflow to train my classifier.\nI was reading an article and it said if I didn't have a gpu with 2GB or more it would be better to train the dataset on AWS or google cloud.\nQuestion\nMy question is could I still write the code and configuration files on my laptop, which has no GPU, and then outsource the training to AWS or google cloud?\nI'm very new to this so I'm not sure if I'm overlooking some sort of dependencies that will prevent me from moving forward. \nIf I can't move forward with my given hardware what are my other options I guess?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":78,"Q_Id":54282272,"Users Score":0,"Answer":"Use Kaggle kernel to run and save the file in system thats a easy and smart way out.","Q_Score":0,"Tags":"python,amazon-web-services,tensorflow,google-cloud-platform,gpu","A_Id":55568832,"CreationDate":"2019-01-21T00:41:00.000","Title":"Can I use tensor flow GPU without a graphics card? Outsource training to AWS\/Cloud?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have access to a dataframe of 100 persons and how they performed on a certain motion test. This frame contains about 25,000 rows per person since the performance of this person is kept track of (approximately) each centisecond (10^-2). We want to use this data to predict a binary y-label, that is to say, if someone has a motor problem or not. \nTrained neural networks on mean's and variances of certain columns per person classified +-72% of the data correctly. \nNaive bayes classifier on mean's and variances of certain columns per person classified +-80% correctly.\nNow since this is time based data, 'performance on this test through time', we were suggested to use Recurrent Neural Networks. I've looked into this and I find that this is mostly used to predict future events, i.e. the events happening in the next centiseconds. \nQuestion is, is it in general feasible to use RNN's on (in a way time-based) data like this to predict a binary label? If not, what is?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":37,"Q_Id":54288421,"Users Score":1,"Answer":"Yes it definitely is feasible and also very common. Search for any document classification tasks (e.g. sentiment) for examples of this kind of tasks.","Q_Score":0,"Tags":"python,machine-learning,neural-network,recurrent-neural-network","A_Id":54288478,"CreationDate":"2019-01-21T10:54:00.000","Title":"Classifier for time based data to binary label","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a problem with importing numpy for python3, I'm new in coding and followed all steps.  I want to import numpy in python3 but not in python2 which is set by defaultn my mac mojave 10.14.2.\nI have Python 2.7.10 by default on my mac and can switch now to Python 3.7.2 in shell. I installed pip 18.1 as described in PyPA and now I can import numpy only in Python 2. what should I do to be able to import numpy in Python 3? Is it better to set Python 3 by default on my mac mojave 10.14.2?","AnswerCount":3,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":876,"Q_Id":54296469,"Users Score":3,"Answer":"You can install numpy in python3 with help of pip3 command instead of pip.\nYes you should change python3 as default as python2 support is going to end this year.","Q_Score":0,"Tags":"python,numpy","A_Id":54296537,"CreationDate":"2019-01-21T19:15:00.000","Title":"importing numpy in different python versions","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"When i run the coding in this yolo_opencv.py file :\nThe console shows this error:\n\nFile \"\", line 40, in \n     Width = image.shape[1]\n  AttributeError: 'NoneType' object has no attribute 'shape'","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2273,"Q_Id":54297834,"Users Score":0,"Answer":"Just to give some advice. You could\/should describe your problem a bit more clear. I just Googled the file you mentioned yolo_opencsv.py and its part of Object detection (YOLO, SSD, Faster R-CNN) with OpenCV and Python library. It always a good idea to mention the tool\/library you are using (and are having problems with) in the title. Also you could\/describe the procedure and environment you are using when the error occurs. By doing so, people can try to replicate you problem and have to ask less additional questions trying to help you solve the problem.\nYour error seems to indicate that the image object has not loaded properly, i.e. the return of cv2.imread(args.image) has returned None. Do you run the command from the command line as described in the README. And are you sure you image path and image format is correct?","Q_Score":0,"Tags":"python","A_Id":54297972,"CreationDate":"2019-01-21T21:11:00.000","Title":"AttributeError: no attribute 'shape'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a super basic machine learning question. I've been working through various tutorials and online classes on machine learning and the various techniques to learning how to use it, but what I'm not seeing is the persistent application piece.\nSo, for example, I train a network to recognize what a garden gnome looks like, but, after I run the training set and validate with test data, how do I persist the network so that I can feed it an individual picture and have it tell me whether the picture is of a garden gnome or not? Every tutorial seems to have you run through the training\/validation sets without any notion as of how to host the network in a meaningful way for future use.\nThanks!","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":309,"Q_Id":54297846,"Users Score":0,"Answer":"Use python pickle library to dump your trained model on your hard drive and load model and test for persistent results.","Q_Score":0,"Tags":"python,machine-learning","A_Id":54303388,"CreationDate":"2019-01-21T21:13:00.000","Title":"Persistent Machine Learning","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have some Python image processing code that uses Keras and TensorFlow Libs, which is needed to be converted to Java Framework.\nMy main problems are:\na. TensorFlow for java is not stable yet, so I have to find another  equivalent Java Lib.\nb. I haven't found Java image processing library that covers the Keras image processing functions and utility (e.g. the class \"ImageDataGenerator\")\nc. The program intends to be cross-platform for dummies (biologists....) so it shall not use any native parts that may cause realtime compiling problems for the clients, means it needs to be written in pure Java.\nThanks!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":63,"Q_Id":54324148,"Users Score":0,"Answer":"Tensorflow themselves say: \n\nCaution: The TensorFlow Java API is not covered by the TensorFlow API\n  stability guarantees.\n\nThis however doesn't mean that your application will not be stable. The key take-away here is that whatever you will build is not guaranteed to work out of the box when new versions are released. However, if you pin down the version of the Tensorflow API that you use, you should be alright.","Q_Score":1,"Tags":"java,python,tensorflow,image-processing,keras","A_Id":54324410,"CreationDate":"2019-01-23T09:38:00.000","Title":"Python to java image proccesing translation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I'm trying to run a Monte-Carlo control algorithm, and keep getting frustrated when setting the number of steps in the code. It needs to be a large number (thousands, millions...) and it is hard to read it at a glance so I need to count zeros whenever I change it. I tried entering it as \nN_EPISODES = 1 000 000 \nor \nN_EPISODES = int('1 000 000')\nor with commas instead of spaces, but none of these work. Is there a way to set large numbers in the code in a human-readable way?","AnswerCount":1,"Available Count":1,"Score":-0.1973753202,"is_accepted":false,"ViewCount":459,"Q_Id":54325854,"Users Score":-1,"Answer":"You can try writing your number in a scientific-style way?\nLike 1e6 instead of 1000000 or 1,000,000...","Q_Score":0,"Tags":"python","A_Id":54325913,"CreationDate":"2019-01-23T11:08:00.000","Title":"Is it possible to write large numbers with spaces in the code","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm so much newbie in openCV\/Python tasks. I use Python 3.7 and openCV 4 running by a JNotebook. The question: I wanna save just 1,000 images from a dataset with 10,000 pictures, extracting them from it and write only those 1,000.jpeg in a new folder, is it possible using openCV package in Python? I've already had a list of names (1,000 images).","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":3167,"Q_Id":54328126,"Users Score":2,"Answer":"Assuming you have OpenCV correctly installed on your machine, you can first read the images with img = cv.imread(filename) and then write them with cv.imwrite(filename, img).","Q_Score":0,"Tags":"python,opencv,computer-vision","A_Id":54328218,"CreationDate":"2019-01-23T13:14:00.000","Title":"How to save (write) a list of images from a dataset into a new folder - openCV Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"what does it mean to use K-means clustering on a data set that indicates relative distances from one item to another?\nFor example, each item is given a relative distance to every other item.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":26,"Q_Id":54338377,"Users Score":0,"Answer":"K-means clustering assigns items to clusters such that the total intra-cluster distance is minimized. Hence, the only input that is needed is a distance function d(a,b). Often, the items are vectors in a normed vector space and d is chosen as d(a,b) = ||a-b|| for some norm ||x||. But in general, any distance function that fulfills d(a,a) = 0 (distance from a point to itself is zero), d(a,b) <= d(a,c) + d(c,b) (triangle inequality), and d(a,b) = d(b,a) (symmetry) can be used. It can even be a matrix where the entry M(i,j) gives the distance between items i and j, as long as the entries obey the rules above.","Q_Score":0,"Tags":"python","A_Id":54338545,"CreationDate":"2019-01-24T02:02:00.000","Title":"What does it mean to use K-means clustering on a data set that indicates relative distances from one item to another?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"(py1) C:\\Users\\XXXXX>pip list\n        Version\n        ----------------- ----------\n        certifi           2018.11.29\n        chardet           3.0.4\n        idna              2.8\n        lxml              4.3.0\n        numpy             1.16.0\n        pandas            0.23.4\n        pandas-datareader 0.7.0\n        pip               18.1\n        python-dateutil   2.7.5\n        pytz              2018.9\n        requests          2.21.0\n        scipy             1.2.0\n        setuptools        40.6.3\n        six               1.12.0\n        urllib3           1.24.1\n        wheel             0.32.3\n        wrapt             1.11.1\nand it also works when I type python on my cmd:\n`        (py1) C:\\Users\\XXXXX>python\n        Python 3.7.2 (tags\/v3.7.2:9a3ffc0492, Dec 23 2018, 22:20:52) [MSC v.1916 32 bit\n        (Intel)] on win32\n        Type \"help\", \"copyright\", \"credits\" or \"license\" for more information.\n\n\n\nimport pandas_datareader\n      `\n\n\n\nHowever, when I try importing it in Jupyter Notebook (launching from the same venv) it gives me the error below:\n(py1) C:\\Users\\XXXXX>python\n`        Python 3.7.2 (tags\/v3.7.2:9a3ffc0492, Dec 23 2018, 22:20:52) [MSC         v.1916 32 bit\n        (Intel)] on win32\n        Type \"help\", \"copyright\", \"credits\" or \"license\" for more information.\n\n\n\nimport pandas_datareader\n      `","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":546,"Q_Id":54340532,"Users Score":1,"Answer":"I could resolve this, I went to my venv and executed this command: python -m pip install ipykernel and then upgraded pip and it started working.\nThank you,\nMohit","Q_Score":0,"Tags":"python,jupyter-notebook","A_Id":54392629,"CreationDate":"2019-01-24T06:26:00.000","Title":"I have installed pandas_datareader in pip but when I try to import the same in Jupyter Notebook it says Module not found","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"How can i calculate Imbalance Ratio for a dataset which is imbalanced? I came across a way in which it defined (it's taken from a paper):\n\ngiven by the imbalance ratio (IR), defined as the ratio of the number of instances in the majority class to the number of examples in the minority class\n\nNow, is this one of the right ways to calculate?\nThanks","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1656,"Q_Id":54342353,"Users Score":0,"Answer":"IR = (negative_class\/positive_class)\nWhere positive_class is the number of minority class samples and negative_class is the number of majority class samples.","Q_Score":0,"Tags":"python,dataset,data-mining,data-science","A_Id":58307961,"CreationDate":"2019-01-24T08:33:00.000","Title":"Calculate Imbalance Ratio for a multi-class dataset","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have coco dataset(19 gb), the dataset upload googledrive but colab is not find this data despite use this code import sys\nsys.path.insert(0, 'content\/gdrive\/My Drive\/caption').How can I find this file?\nimport sys\nsys.path.insert(0, 'content\/gdrive\/My Drive\/caption')","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1305,"Q_Id":54343491,"Users Score":1,"Answer":"I suspect you need a leading \/ in your path. (Typically, the Drive FUSE mount path is \/content\/...)","Q_Score":1,"Tags":"python,google-colaboratory","A_Id":54351092,"CreationDate":"2019-01-24T09:40:00.000","Title":"How to install coco dataset in colab?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using google colab on a dataset with 4 million rows and 29 columns. When I run the statement sns.heatmap(dataset.isnull()) it runs for some time but after a while the session crashes and the instance restarts. It has been happening a lot and I till now haven't really seen an output. What can be the possible reason ? Is the data\/calculation too much ? What can I do ?","AnswerCount":5,"Available Count":4,"Score":0.0,"is_accepted":false,"ViewCount":32699,"Q_Id":54344101,"Users Score":0,"Answer":"I would first suggest closing your browser and restarting the notebook. Look at the run time logs and check to see if cuda is mentioned anywhere. If not then do a factory runtime reset and run the notebook. Check your logs again and you should find cuda somewhere there.","Q_Score":16,"Tags":"python,data-visualization,google-colaboratory","A_Id":69872171,"CreationDate":"2019-01-24T10:11:00.000","Title":"Why does my google colab session keep crashing?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using google colab on a dataset with 4 million rows and 29 columns. When I run the statement sns.heatmap(dataset.isnull()) it runs for some time but after a while the session crashes and the instance restarts. It has been happening a lot and I till now haven't really seen an output. What can be the possible reason ? Is the data\/calculation too much ? What can I do ?","AnswerCount":5,"Available Count":4,"Score":0.0798297691,"is_accepted":false,"ViewCount":32699,"Q_Id":54344101,"Users Score":2,"Answer":"This error mostly comes if you enable the GPU but do not using it. Change your runtime type to \"None\". You will not face this issue again.","Q_Score":16,"Tags":"python,data-visualization,google-colaboratory","A_Id":69688669,"CreationDate":"2019-01-24T10:11:00.000","Title":"Why does my google colab session keep crashing?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using google colab on a dataset with 4 million rows and 29 columns. When I run the statement sns.heatmap(dataset.isnull()) it runs for some time but after a while the session crashes and the instance restarts. It has been happening a lot and I till now haven't really seen an output. What can be the possible reason ? Is the data\/calculation too much ? What can I do ?","AnswerCount":5,"Available Count":4,"Score":0.0,"is_accepted":false,"ViewCount":32699,"Q_Id":54344101,"Users Score":0,"Answer":"For me, passing specific arguments to the tfms augmentation failed the dataloader and crahed the session.\nWasted lot of time checking the images not coruppt and clean the gc and more...","Q_Score":16,"Tags":"python,data-visualization,google-colaboratory","A_Id":63649402,"CreationDate":"2019-01-24T10:11:00.000","Title":"Why does my google colab session keep crashing?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using google colab on a dataset with 4 million rows and 29 columns. When I run the statement sns.heatmap(dataset.isnull()) it runs for some time but after a while the session crashes and the instance restarts. It has been happening a lot and I till now haven't really seen an output. What can be the possible reason ? Is the data\/calculation too much ? What can I do ?","AnswerCount":5,"Available Count":4,"Score":0.1973753202,"is_accepted":false,"ViewCount":32699,"Q_Id":54344101,"Users Score":5,"Answer":"Another cause - if you're using PyTorch and assign your model to the GPU, but don't assign an internal tensor to the GPU (e.g. a hidden layer).","Q_Score":16,"Tags":"python,data-visualization,google-colaboratory","A_Id":61336224,"CreationDate":"2019-01-24T10:11:00.000","Title":"Why does my google colab session keep crashing?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am training Random Forests with two sets of \"true\" y values (empirical). I can easy tell which one is better.\nHowever, I was wondering if there is a simple method, other than brute force, to pick up the values from each set that would produce the best model. In other words, I would like to automatically mix both y sets to produce a new ideal one.\nSay, for instance, biological activity. Different experiments and different databases provide different values. This is a simple example showing two different sets of y values on columns 3 and 4.\n\n4a50,DQ7,47.6,45.4\n3atu,ADP,47.7,30.7\n5i9i,5HV,47.7,41.9\n5jzn,GUI,47.7,34.2\n4bjx,73B,48.0,44.0\n4a6c,QG9,48.1,45.5\n\nI know that column 3 is better because I have already trained different models against each of them and also because I checked a few articles to verify which value is correct and 3 is right more often than 4. However, I have thousands of rows and cannot read thousands of papers.\nSo I would like to know if there is an algorithm that, for instance, would use 3 as a base for the true y values but would pick values from 4 when the model improves by so doing.\nIt would be useful it it would report the final y column and be able to use more than 2, but I think I can figure out that.\nThe idea now is to find out if there is already a solution out there so that I don't need to reinvent the wheel.\nBest,\nMiro\nNOTE: The features (x) are in a different file.","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":314,"Q_Id":54345579,"Users Score":2,"Answer":"The problem is that an algorithm alone doesn't know which label is better. \nWhat you could do: Train a classifier on data which you know is correct. Use the clasifier to predcit a value for each datapoint. Compare this value to the two list of labels which you already have and choose the label which is closer. \nThis solution obviously isn't perfect since the results depends on quality of the classfier which predicts the value and you still need enough labeled data to train the classifier. Additionaly there is also a chance that the classifier itself predicts a better value compared to your two lists of labels.","Q_Score":0,"Tags":"python,scikit-learn,random-forest","A_Id":54346332,"CreationDate":"2019-01-24T11:28:00.000","Title":"Scikit-Learn Random Forest regression: mix two sets of true values (y)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataframe with around 20000 rows and 98 features (all the features are numerical) and a target feature with binary values: 0 and 1. Basically, there are two population (first population with target value 1 --50%--, and the second with target value 0 -50%- balanced data). In a classification problem, I tried to predict the target value given the data. So, I have implanted a supervised learning algorithm (e.g., SVM) to predict the target value, and could obtain a very good result with around 0.95 accuracy. This result gives me a point that there is a considerable difference between the features. So, in the next step, I have to know what are the important features which made this difference, and what is best way to quantify this difference in the features between these two group of population. Any idea?","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":203,"Q_Id":54350062,"Users Score":0,"Answer":"Can you try to use KS-test for your features? for example, feature 1, split by it's class. then you get two groups. Then test if they come from different distribution or just record the p-value.\nwhen you have all the test result or p-value, make another model with the samples that comes from different distribution \/ very low p-value. see if the new model is better or similar.\nnot sure if this achieves anything.\nwanted to comment but couldn't do so.","Q_Score":0,"Tags":"python,machine-learning,statistics,data-mining,feature-selection","A_Id":54356646,"CreationDate":"2019-01-24T15:26:00.000","Title":"How to Measure the difference between features in dataframe?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataframe with around 20000 rows and 98 features (all the features are numerical) and a target feature with binary values: 0 and 1. Basically, there are two population (first population with target value 1 --50%--, and the second with target value 0 -50%- balanced data). In a classification problem, I tried to predict the target value given the data. So, I have implanted a supervised learning algorithm (e.g., SVM) to predict the target value, and could obtain a very good result with around 0.95 accuracy. This result gives me a point that there is a considerable difference between the features. So, in the next step, I have to know what are the important features which made this difference, and what is best way to quantify this difference in the features between these two group of population. Any idea?","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":203,"Q_Id":54350062,"Users Score":0,"Answer":"Aside from using the coefficients of the support vectors from your model, you could try build other models.\nA decision tree approach will explicitly show you which input features split the data - those nearer the root being more important, for some definition of important.\nIf you try a feature reduction technique, like PCA, and rebuild you model, the coefficients of the components here will tell you which contribute most.\nOr you could be completely thug-headed, and build lots of models leaving out some features and see which are better.\nOr you could be lateral, and consider what's so different about the few points that your model doesn't accurately classify.","Q_Score":0,"Tags":"python,machine-learning,statistics,data-mining,feature-selection","A_Id":54350594,"CreationDate":"2019-01-24T15:26:00.000","Title":"How to Measure the difference between features in dataframe?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm working on a python script whose goal is to detect if a point is out of a row of points (gps statement from an agricultural machine). \nInput data are shapefile and I use Geopandas library for all geotreatments.\nMy first idea was to make a buffer around the 2 points around considered point. After that, I watch if my point is in the buffer. But results aren't good.\nSo I ask myself if there is a mathematical smart method, maybe with Scikit lib... Somebody is able to help me?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":136,"Q_Id":54350293,"Users Score":0,"Answer":"try arcgis.\nbuild two new attributes in arcgis with their X and Y coordinate,then calculate the distance between the points you want","Q_Score":0,"Tags":"python,math,row,geospatial,point","A_Id":54350414,"CreationDate":"2019-01-24T15:38:00.000","Title":"Smart way to detect too far away point from a row of points?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to train a model for sentiment analysis and below is my trained Multinomial Naive Bayes Classifier returning an accuracy of 84%.\nI have been unable to figure out how to use the trained model to predict the sentiment of a sentence. For example, I now want to use the trained model to predict the sentiment of the phrase \"I hate you\".\nI am new to this area and any help is highly appreciated.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":119,"Q_Id":54362232,"Users Score":1,"Answer":"I don't know the dataset and what is semantic of individual dictionaries, but you are training your model on a dataset which has form as follows:\n[[{\"word\":True, \"word2\": False}, 'neg'], [{\"word\":True, \"word2\": False}, 'pos']]\n\nThat means your input is in form of a dictionary, and output in form of 'neg' label. If you want to predict you need to input a dictionary in a form:\n\n{\"I\": True, \"Hate\": False, \"you\": True}.\n\nThen:\n\nMNB_classifier.classify({\"love\": True})\n>> 'neg'\nor \nMNB_classifier.classify_many([{\"love\": True}])\n>> ['neg']","Q_Score":0,"Tags":"python,python-3.x,classification,sentiment-analysis","A_Id":54363252,"CreationDate":"2019-01-25T09:21:00.000","Title":"Predicting values using trained MNB Classifier","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a cuda kernel written in numba-cuda that processes large arrays that do not fit in GPU memory at once. So, I have to call the kernel multiple times to process the entire arrays. The kernel is called in a loop and, inside the loop, after GPU is done the computation, I copy and aggregate the results back to a host array. \nMy questions: \n\nWhat is the lifetime of a device array and an array that is copied to GPU memory? Are their value preserved from one kernel call to another?\nDo I need to put the device arrays definitions inside the loop (before I call the kernel) or  do I just do it once before I enter the loop? \nDo I need to free\/delete the device arrays manually in the code or the CUDA memory manager will do it at the end of the program? \n\nThanks.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":476,"Q_Id":54370090,"Users Score":1,"Answer":"What is the lifetime of a device array and an array that is copied to GPU memory? Are their value preserved from one kernel call to\n  another?\n\n\nIn Numba, global memory allocations are preserved until they are freed.\n\n\nDo I need to put the device arrays definitions inside the loop (before I call the kernel) or  do I just do it once before I enter the\n  loop? \n\n\nThe latter.\n\n\nDo I need to free\/delete the device arrays manually in the code or the CUDA memory manager will do it at the end of the program?\n\n\nThe first thing to realize is that there is no CUDA memory manager in the way you imagine. Memory allocations are automatically freed when a context is destroyed, otherwise they are not under any circumstances. The only exception to this is if you use a Numba device_array, which may be garbage collected by Python if it falls out of scope. But you should, in general, assume that anything you allocate remains in memory until you explicitly free it, and always include explicit memory deallocation in your code.","Q_Score":1,"Tags":"python,cuda,gpgpu,numba","A_Id":54380618,"CreationDate":"2019-01-25T17:25:00.000","Title":"Device memory array's life time","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a single time series that has multiple input features and a single output (classification). I would like to create a model that would predict the output at every step.\nMy data looks like this:\nx: [[0.4, 0.8, -1], [1, 1.6, -0.2], ....]\ny: [[1], [0], ...]\nSo when given a single feature vector, I would like my model to output 0 or 1. The feature vector is not a sequence of any kind, as this is just a straight-forward time series.\nI have the following questions:\n\nSince Keras requires data to be formatted in (batch_size, time_steps, num_features) and I have fixed num_features = 3, what should I put for batch_size and time_steps?\nWhat do I put for the value of stateful? Since my data is in a specific order in that the first event in my list came first, followed by the second and third etc., I'm assuming I put stateful = True?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":108,"Q_Id":54374608,"Users Score":0,"Answer":"I'm not sure, but your batch_size could be set automatically, time_steps is equal to number of feature sets so for [[0.4, 0.8, -1]] its 1 for [[0.4, 0.8, -1],[1, 1.6, -0.2]] its 2 etc. Try to reshape your data to (shape[0],3).","Q_Score":0,"Tags":"python,keras,lstm,recurrent-neural-network","A_Id":54374671,"CreationDate":"2019-01-26T00:38:00.000","Title":"Keras LSTM Paradigm","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When using the Python PREDICT method in lightGBM with predict_contrib = TRUE, I get an array of [n_samples, n_features +1]. What does the n_feature+1 correspond to? I thought first that it could be the log odds of class 1 but the value does not correspond to the right probability.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":862,"Q_Id":54374866,"Users Score":0,"Answer":"Suppose that you are doing regression from X \u03f5 Rn to y \u03f5 R1. For a given point, x, You can view the model prediction f(x) as a linear function of the feature contributions such as : f(x) = contribution(x1) + contribution (x2) + ... + contribution(xn) + mean(y)\nThe first n columns of your array correspond to the contribution of your n features respectively and the last column is the prediction without any information, here the mean of your output y. If you sum up those n+1 values, you will obtain the prediction f(x).\nFor classification, you obtain the \"raw prediction\". Depending on the objective you selected, you will have to convert it to probability (the \"prediction\") using softmax or logit function.\nPS: Hope its clear despite my poor editing!","Q_Score":0,"Tags":"python-3.x,predict,lightgbm","A_Id":54446294,"CreationDate":"2019-01-26T01:26:00.000","Title":"Understanding \"PREDICT\" function output in python lightGBM implementation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I successfully trained the network but got this error during validation:\n\nRuntimeError: CUDA error: out of memory","AnswerCount":10,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":201166,"Q_Id":54374935,"Users Score":34,"Answer":"The error occurs because you ran out of memory on your GPU.\nOne way to solve it is to reduce the batch size until your code runs without this error.","Q_Score":55,"Tags":"python,pytorch","A_Id":54376403,"CreationDate":"2019-01-26T01:39:00.000","Title":"How to fix this strange error: \"RuntimeError: CUDA error: out of memory\"","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using cv2 version 4.0.0 and python version 3.7.2.\nI am trying to subtract Background using this method cv2.createBackgroundSubtractorMOG2() and its working well.\nBut when I use cv2.createBackgroundSubtractorMOG() its not working its showing me \n\nAttributeError: module 'cv2.cv2' has no attribute\n  'createBackgroundSubtractorMOG '.\n\nI also tried cv2.BackgroundSubtractorMOG() but i got same error \n\nAttributeError: module 'cv2.cv2' has no attribute\n  'BackgroundSubtractorMOG'.\n\nand another subtraction method cv2.createBackgroundSubtractorGMG() also not working. \nI also refer other stackoverflow answers but I didn't get solution.","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":3478,"Q_Id":54376883,"Users Score":2,"Answer":"there are two subtraction packages in opencv. BackgroundSubtractorMOG() it's at cv2.bgsegm.BackgroundSubtractorMOG(),to use you must install opencv-contrib-python","Q_Score":1,"Tags":"python-3.x,opencv,background-subtraction","A_Id":63150254,"CreationDate":"2019-01-26T08:40:00.000","Title":"OpenCV2 createBackgroundSubtractorMOG Attribute not found","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a sparse large matrix (linear dimension of 2*10^6) for which I want to calculate its trace.\nCalculating it brute force takes 16 seconds to access each diagonal element (hence I could do it in a YEAR!). \nI was thinking of saving it to the disk using scipy.io.mmwrite and reading it with a c++ code which should be much faster. However I cannot find any package that could help me do that.\nAny suggestions would be much appreciated!\nThanks.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":476,"Q_Id":54379162,"Users Score":0,"Answer":"The solution was as simple as: np.array(Mat.diagonal()).sum()\nThanks @hpaulj !","Q_Score":1,"Tags":"python,c++,scipy","A_Id":54395217,"CreationDate":"2019-01-26T14:10:00.000","Title":"Calculating the trace of a large sparse matrix","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on classification of brain tumors. The dataset is comprised of brain images from various angles, with a border and mask of the tumor position. I have cropped the rectangle that contains the tumor, as the other parts of the image are irrelevant and vary due to the different angles from which the image is taken. Now I am left with a series of images of the tumor, each belonging to 1 of 3 possible cases of tumor. But to train this data for classification, I need to have the 2d image array in a uniform shape I believe.\nPossible approaches:\n1) Crop each image to a fixed size (say 100x100). But this would result in loss of data and also depending on position of the tumor in image, I could be facing uneven crops due to reaching the edge of the image.\n2) Pad the image to a fixed shape, bigger than the biggest cropped image shape (say 350x350). But again this would introduce noise in the data I suppose, and I'm not sure how can I pad the image uniformly across all 4 sides of the rectangle.\nAs these don't seem viable, I'm looking for other solutions to tackle this problem.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":236,"Q_Id":54387850,"Users Score":1,"Answer":"The common approach is to crop the tumor on each image. You will obtain different tumor image size. Then rescale the tumor images to the smallest one. Choose wisely the resampling technic for the rescale depending on the algorithm you will use for classification. The fastest one would be nearest neighborhood resampling, a smoother one would be linear interpolation, the ultimate one can be spline interpolation.","Q_Score":1,"Tags":"python,image-processing,machine-learning,classification,multiclass-classification","A_Id":54387971,"CreationDate":"2019-01-27T11:55:00.000","Title":"How to approach variable image sizes for Image Classification?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Say for example I have a multi-column dataframe. I want to arrange my data by sorting column a ascending first, then by column b ascending also.\nI am able to achieve this by the ff. code: df.sort_values(['b','a']). Note the reversed order of the arguments.\nMy question is, why doesn't df.sort_values('a').sort_values('b') work?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":78,"Q_Id":54405063,"Users Score":2,"Answer":"When you use .sort_values(['a', 'b']) you are first sorting the dataframe by the column a, and then within those sortings, sorting by b. Think of it almost as grouping by the first sort, then sorting within those groupings. If there was a c, it would sort the c column within the a b groups.\nIf you do .sort_values('a').sort_values('b') you are sorting the dataframe by a and then taking that returned dataframe, and \"starting over\" by sorting it by b basically destroying the previous sorting of a. using ['a', 'b'] keeps each of the sorts and sorts within.","Q_Score":0,"Tags":"python,pandas,data-analysis","A_Id":54405935,"CreationDate":"2019-01-28T15:21:00.000","Title":"Why can't I apply sort_values to a dataframe more than once?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am fairly new to python (and Stackflow) so apologies if this is a repost. I am currently run Pycharm on my iMac (with Mac OS Mojave), python install from Anaconda. \nI am having some issues with Matplotlib as everytime I plot a graph I don't see anything. The backend currently uses TkAgg. I googled the issue and came across many links and tried to change the backend in matplotlibrc, but that just gave me errors. \nIt is Mojave specific problem? I have another Mac with High Sierra (which uses MacOSX in the backend) and Matplotlib runs fine on it. I tried changing the backend on my iMac to MacOSX but that doesn't work. Any help will be really appreciated as I have already spend more than 3-4 hours (in vain!) to fix the problem\nIf I need to change the backend in matplotlibrc, could you advise what should I be using?\nthanks,\nPK","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1210,"Q_Id":54410776,"Users Score":1,"Answer":"if you use virtualenv in Mojave, you have to install matplotliv (v1.3.1) at virtualenv. ** NOT VERSION 2.2.3 **. so, try the below command (btw, in your code, 'TkAgg' is needless):\n$pip install --no-cache-dir matplotlib==1.3.1\neverything will be fine.","Q_Score":1,"Tags":"python-3.x,matplotlib,backend","A_Id":54614230,"CreationDate":"2019-01-28T21:49:00.000","Title":"Mac OS (Mojave) - Pycharm backend : TkAgg not working, graphs are blank","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a column of data that is in the following format:\nbla bla bla, bla bla bla, bla bla bla, bla bla bla bla bla bla, 23\/09\/2012\nIs there an easy way to extract the date at the end of the string from all rows? It is in the same DD\/MM\/YYYY format every time.\nI could split on the last , but it would be good if I could specify that I want the date in case of errors in the data.","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":96,"Q_Id":54411374,"Users Score":1,"Answer":"row.split(\", \")[-1] is the string 23\/09\/2012, now you can use the date module to extract that into a Date object. And use try\/except for error handling.","Q_Score":0,"Tags":"python,string,pandas,datetime,dataframe","A_Id":54411400,"CreationDate":"2019-01-28T22:44:00.000","Title":"Extracting a date at the end of a pandas dataframe","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"My goal is to disable the downloading of csv data from my JupyterHub environment. Is there any way to do this?","AnswerCount":4,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":3699,"Q_Id":54425431,"Users Score":0,"Answer":"I have achieved this by putting Jupyter Hub behind ALB (Application load balancer), AWS and have a rule that basically routes every request with path \"\/files\" to a 503 error page. This can even be achieved using nginx as well. Just to bear that in mind, the download request path is \"\/files\".","Q_Score":4,"Tags":"python,security,jupyter","A_Id":61452667,"CreationDate":"2019-01-29T16:22:00.000","Title":"Disabling Downloads on Jupyter Notebooks","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"C:\\WINDOWS\\system32>pip install tensorflow\n\nCollecting tensorflow\n\n  Could not find a version that satisfies the requirement tensorflow (from versions: )\nNo matching distribution found for tensorflow\n\nI installed the Python (3.6 64-bit), and wanna install tensorflow in Anaconda3.\nAnd I upgraded pip to the latest version, 19.0.1.\nRequirement already up-to-date: pip in c:\\anaconda3\\lib\\site-packages (19.0.1)\nSo, how can I solve this problem?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":450,"Q_Id":54434432,"Users Score":0,"Answer":"if you have anaconda do conda install tensorflow. anaconda version of tensorflow is faster than pip anyways.","Q_Score":0,"Tags":"tensorflow,pip,installation,python-3.6","A_Id":54437112,"CreationDate":"2019-01-30T06:21:00.000","Title":"with python 3.6, Could not find a version that satisfies the requirement tensorflow (from versions: ) No matching distribution found for tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"How to use NLTK DependencyGrammar package in Named Entity Recognition (NER)?\nEg. sentence that I am looking is:\n\u201cwhat is the status of my invoice P1234U?\u201d\nAnd I would like to extract P1234U as the invoice number.\nWithout much training I would like to do this, so no SPACY solution please.\nI would prefer to go in a knowledge graph way.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":68,"Q_Id":54447937,"Users Score":1,"Answer":"NER algorithms are normally trained to recognize well known enities such as places,names etc. P1234U does not fall into any of the well known categories. I would suggest a simple regex search with constraints on max length of the invoice id and check the accuracy on that.","Q_Score":0,"Tags":"python,nlp,nltk","A_Id":54451861,"CreationDate":"2019-01-30T19:13:00.000","Title":"How to use NLTK DependencyGrammar package In NER","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have trained and tested a KNN model on a small supervised dataset of about 200 samples in Python. I would like to apply these results to a much larger unsupervised dataset of several thousand samples.\nMy question is: is there a way to fit the KNN model using the small supervised dataset, and then change the K-value for the large unsupervised dataset? I do not want to overfit the model by using the low K-value from the smaller dataset but am unsure how to fit the model and then change the K-value in Python.\nIs this possible using KNN? Is there some other way to apply KNN to a much larger unsupervised dataset?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":388,"Q_Id":54450495,"Users Score":0,"Answer":"In machine learning there are two broad types of learners, namely eager learners (Decision trees, neural nets, svms...) and lazy learners such as KNN. In fact, KNN doesn't do any learning at all. It just stores the \"labeled\" data you have and then uses it to perform inference such that it computes how similar the new sample (unlabeled) is, to all of the samples in the data that it has stored (labeled data). Then based on majority voting of the K nearest instances (K nearest neighbours hence the name) of the new sample, it will infer it's class\/value. \nNow to get to your question, \"training\" the KNN has nothing to do with the K itself, so when performing inference feel free to use whatever K gives the best result for you.","Q_Score":0,"Tags":"python,scikit-learn,knn","A_Id":54451152,"CreationDate":"2019-01-30T22:26:00.000","Title":"Apply KNN from small supervised dataset to large unsupervised dataset in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When you run a function that returns a Pandas data frame in a Jupyter cell, it prints out this very aesthetic table.  When you give an explicit command to print, it looks much worse.  I have a list of data frames and I'd like to print each.  Is there a way to get the nice version of the print using a for-loop?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":176,"Q_Id":54452598,"Users Score":1,"Answer":"As mentioned in the comment, using display instead of print does the job.","Q_Score":2,"Tags":"python,pandas,jupyter,pretty-print","A_Id":54996195,"CreationDate":"2019-01-31T02:48:00.000","Title":"Jupyter: How can you pretty-print many data frames from the code in one cell?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Given scores = { 0.0: \"bob\", 5.2: \"alex\", 2.8: \"carl\"}\nTo get the output [ \"bob\", \"carl\", \"alex\" ]\nI can do print([ scores[key] for key in sorted(scores.keys()) ])\nIs this the best (most \"pythonic\") way? I was thinking I could use scores.items() in conjunction with sorted(key=...) to avoid the dictionary lookup, but not sure what that key parameter would be.","AnswerCount":5,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":928,"Q_Id":54468377,"Users Score":0,"Answer":"Another approach would be to create a generator object yielding the values of the dict,\ncast them to a list and then print it.\nprint(list(val for val in scores.values()))","Q_Score":4,"Tags":"python,python-3.x,sorting,dictionary","A_Id":54469052,"CreationDate":"2019-01-31T19:59:00.000","Title":"Sort dict by key and retrieve value","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am doing kaggle inclass challege of bosten hosing prices and learnt that XGBoost is faster than RandomForest but when implemented was slower.i Want to ask when XGBoost becomes faster and when RandomForest??.I am new to machine learning and need your help.Thanking in advance","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1525,"Q_Id":54468877,"Users Score":0,"Answer":"Mainly, the parameters you choose have strong impact in the speed of your algorithm, (e.g learning rate, depth of the tree, number of features etc.), there's a trade-off between accuracy and speed, so i suggest you put the parameters you've chosen for every model and see how to change it to get faster performance with reasonable accuracy.","Q_Score":0,"Tags":"python-3.x,machine-learning","A_Id":54469054,"CreationDate":"2019-01-31T20:39:00.000","Title":"XGBOOST faster than random forest?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"So I'm finished one part of this assignment I have to do. There's only one part of the assignment that doesn't make any sense to me. \nI'm doing a LinearRegression model and according to others I need to apply ans[i,:] = y_poly at the very end, but I never got an answer as to why. \nCan someone please explain to me what [i,:] means? I haven't found any explanations online.","AnswerCount":3,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":7360,"Q_Id":54469789,"Users Score":3,"Answer":"I guess you are also using numpy to manipulate data (as matrix) ?\nIf based on numpy,  ans[i,:] means to pick the ith 'row' of ans with all of its 'columns'. \nNote,when dealing with numpy arrays, we should (almost) always use [i, j] instead of [i][j]. This might be counter-intuitive if you used Python or Java to manipulate matrix before.","Q_Score":6,"Tags":"python,python-3.x,numpy,scikit-learn,linear-regression","A_Id":54469843,"CreationDate":"2019-01-31T21:52:00.000","Title":"What does [i,:] mean in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have tensorflow-gpu and keras installed in Python 3.6.8. But when I tried importing keras, it is giving the the error of Module not found. Also the error statements show the error is at a line inside keras at import tensorflow as tf. How to make keras import tensorflow-gpu instead of tensorflow? Do I have to go to the library code and change it? Or what else is the solution?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1130,"Q_Id":54471160,"Users Score":0,"Answer":"I just reinstalled both tf and keras and now everything is working fine. Don't know what had went wrong. Maybe the installation was not proper first time.","Q_Score":0,"Tags":"python,tensorflow,keras","A_Id":54477319,"CreationDate":"2019-02-01T00:17:00.000","Title":"Keras giving error \"ModuleNotFoundError: No module named 'tensorflow'\" even though tensorflow-gpu is installed","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Are there any Deep Learning literature\/references where they performed clustering in structured data?\nI know it can be done using Kmeans, GMM etc. But is there any chance that cluster analysis to be done using Deep Neural Nets and the like? Thanks.","AnswerCount":4,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":490,"Q_Id":54476162,"Users Score":0,"Answer":"Yes.\nIf you do a little bit of literature research yourself you will find that people have repeatedly published clustering with deep neural networks.\nExcept that it doesn't seem to work anywhere but on MNIST data...","Q_Score":0,"Tags":"python,deep-learning,cluster-analysis,data-science","A_Id":54485647,"CreationDate":"2019-02-01T09:06:00.000","Title":"Clustering structured data in Deep learning","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Are there any Deep Learning literature\/references where they performed clustering in structured data?\nI know it can be done using Kmeans, GMM etc. But is there any chance that cluster analysis to be done using Deep Neural Nets and the like? Thanks.","AnswerCount":4,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":490,"Q_Id":54476162,"Users Score":0,"Answer":"Two other Potential Methods:\n\nKMeans + Autoencoder (a simple deep learning architecture with\nkmeans and reducing the dimensionality of the data using autoencoders). \nDeep Embedded Clustering algorithm (advanced deep learning)","Q_Score":0,"Tags":"python,deep-learning,cluster-analysis,data-science","A_Id":55927272,"CreationDate":"2019-02-01T09:06:00.000","Title":"Clustering structured data in Deep learning","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I just reinstalled python into my system with all necessary modules. But when I run a program with the import statement from keras.utils import HDF5Matrix, it is giving attribute error. The place where the error is coming is: File \"h5py\\h5r.pxd\", line 21, in init h5py._conv\n  File \"h5py\\h5r.pyx\", line 145, in init h5py.h5r. But when I run the same statement in the IDLE directly, it is not giving any error. So, what is the cause of error. I have not posted my code because most of it is irrelevant as the Interpreter is not going past the import statement. Also, before I reinstalled Python, the code was working fine. So, what can be the problem.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":3723,"Q_Id":54477273,"Users Score":0,"Answer":"I got the solution. It was because I was importing both HDF5Matrix and tables in same program. Due to some reason both cannot be imported in  same program. I think it is because both make use of h5py in backend, so importing both is causing some conflict.","Q_Score":2,"Tags":"python,tensorflow,keras,hdf5","A_Id":54650272,"CreationDate":"2019-02-01T10:12:00.000","Title":"AttributeError: type object 'h5py.h5r.Reference' has no attribute '__reduce_cython__' on using \"from keras.utils import HDF5Matrix\"","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to run clustering algorithm on a dataset with 14 features. Of those features, except for one, every feature lies between 0 to 1. The remaining one feature is a continuous variable between 0 to 8000. Do i need to use the standard scaler on all the features or just this one feature ( 0 - 8000) before clustering?? OR do i just use the min-max scaler for this one feature alone??","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":20,"Q_Id":54479965,"Users Score":0,"Answer":"You don't \"have\" to use either.\nStandard scaling as well as [0;1] scaling (which is not the same) are just two popular heuristics for weighting variables. Neither is \"correct*.\nIf you have a better reason to weight variables differently, you may as well just do that. Things don't get more \"correct\" by scaling.\nReducing the weight of that 8000 variable likely is a good idea. But if that question arises, it probably means you have not understood the problem that you are trying to solve... You are just using a huge, without a plan.","Q_Score":0,"Tags":"python-3.x,cluster-analysis","A_Id":54485609,"CreationDate":"2019-02-01T12:53:00.000","Title":"Clustering: if my features are all in 0-1 range do i still have to use standard scaling","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"From what I see, most people seem to be initializing an entire model, and sending the whole thing to the GPU. But I have a neural net model that is too big to fit entirely on my GPU. Is it possible to keep the model saved in ram, but run all the operations on the GPU?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":162,"Q_Id":54485815,"Users Score":1,"Answer":"I do not believe this is possible. However, one easy work around would be to split you model into sections that will fit into gpu memory along with your batch input. \n\nSend the first part(s) of the model to gpu and calculate outputs\nRelease the former part of the model from gpu memory, and send the next section of the model to the gpu. \nInput the output from 1 into the next section of the model and save outputs.\n\nRepeat 1 through 3 until you reach your models final output.","Q_Score":0,"Tags":"python,gpu,cpu,pytorch","A_Id":54485944,"CreationDate":"2019-02-01T19:12:00.000","Title":"PyTorch: Is there a way to store model in CPU ram, but run all operations on the GPU for large models?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"On my Anaconda navigator, matplotlib is installed however when I run import matplotlib it will give me an error message saying: \"import is not recognized as internal or external command, operable program or batch file.\"\nDid I not download it? How do I check that I've successfully installed it?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":323,"Q_Id":54488308,"Users Score":0,"Answer":"You are not running import matplotlib in a python interpreter.\nRun python.exe first","Q_Score":0,"Tags":"python,anaconda","A_Id":54488348,"CreationDate":"2019-02-01T23:10:00.000","Title":"Error when trying to import matplotlib: \"import is not recognized as internal or external command\"","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have two large tables with one of them is relatively small ~8Million rows and one column. Other is large 173Million rows and one column. The index of the first data frame is IntervalIndex (eg (0,13], (13, 20], (20, 23], ...) and the second one is ordered numbers (1,2,3, ...). Both DataFrame are sorted so \nDF1     category\n(0,13]  1\n(13 20] 2\n....\nDf2 Value\n1   5.2\n2   3.4\n3   7.8\nDesired\nDf3\nindex value  category\n1  5.2      1\n2  3.4      1\n3 7.8       1\nI want two obtain inner join (faster algorithm) that returns inner join similar to MySQL on data_frame2.index\n\nI would like to be able to perform it in an elaborate way in a cluster because when I PRODUCED THE INNERJOIN WITH SMALLER SECOND DATASET THE RESULT ARE SO MEMORY CONSUMING IMAGINE 105MEGABYTE for 10 rows using map_partitions.\nAnother problem is that I cannot use scatter twice, given if first DaskDF=client.scatter(dataframe2) followed by DaskDF=client.submit(fun1,DaskDF) I am unable to do sth like client.submit(fun2,DaskDF).","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":343,"Q_Id":54488890,"Users Score":0,"Answer":"You might try using smaller partitions.  Recall that the memory use of joins depend on how many shared rows there are.  Depending on your data the memory use of an output partition may be much larger than the memory use of your input partitions.","Q_Score":0,"Tags":"python,pandas,distributed-computing,dask","A_Id":54777333,"CreationDate":"2019-02-02T00:39:00.000","Title":"innerjoin between two large pandas dataframe using dask","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I trained a model in TensorFlow using the tf.estimator API, more specifically using tf.estimator.train_and_evaluate. I have the output directory of the training. How do I load my model from this and then use it?\nI have tried using the tf.train.Saver class by loading the most recent ckpt file and restoring the session. However, then to call sess.run() I need to know what the name of the output node of the graph is so I can pass this to the fetches argument. What is the name\/how can I access this output node? Is there a better way to load and use the trained model?\nNote that I have already trained and saved the model in a ckpt file, so please do not suggest that I use the simple_save function.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":215,"Q_Id":54489497,"Users Score":0,"Answer":"(Answering my own question) I realized that the easiest way to do this was to use the tf.estimator API. By initializing an estimator that warm starts from the model directory, it's possible to just call estimator.predict and pass the correct args (predict_fn) and get the predictions immediately. It's not required to deal with the graph variables in any way.","Q_Score":0,"Tags":"python,tensorflow","A_Id":54505574,"CreationDate":"2019-02-02T02:41:00.000","Title":"Loading and using a trained TensorFlow model in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a 2D array of 0's and 1's where the 1's represent the edges and inside of a quadrilateral, and 0's are everything outside. What is the best way to find the coordinates of the four corners?\nI'm using Python so any libraries that can help are fine, but an algorithm in any language would be helpful as well.","AnswerCount":2,"Available Count":2,"Score":0.1973753202,"is_accepted":false,"ViewCount":815,"Q_Id":54490347,"Users Score":2,"Answer":"i have an idea about how u can make a function for this by your self. according to what i understand only the for corners can have more than one \"0\" next to it. \nif you take array[x,y] to be a point. array[x-1,y],array[x+1,y],array[x,y+1],array[x,y-1] are the 4 point close to it. you can make a loop to go through the array checking the four neighbors if the value is \"1\" and count the number of neighbors which has value \"0\". those point that has more than one(has two) neighbors with value \"0\" are you four corners.","Q_Score":0,"Tags":"python","A_Id":54490809,"CreationDate":"2019-02-02T05:43:00.000","Title":"Finding corners of a polygon represented in a 2D array","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a 2D array of 0's and 1's where the 1's represent the edges and inside of a quadrilateral, and 0's are everything outside. What is the best way to find the coordinates of the four corners?\nI'm using Python so any libraries that can help are fine, but an algorithm in any language would be helpful as well.","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":815,"Q_Id":54490347,"Users Score":1,"Answer":"In the absolutely general case (e.g. allowing for concave hulls or darn-near-triangular shapes) this will involve some uncertainty. But since you are dealing with a restricted subclass, it's easier.\nYou might start with kernel convolution. Consider overlapping squares of (say) 3x3 pixels: you can design a function that tells you if the center is inside (all ones), outside (all zeros), on an edge, or at a corner, based on patterns of points.\nOn the other hand, you could take the \"convex hull\" approach: the leftmost and rightmost 1-pixels are probably corners; if several appear at the same column, take the uppermost\/lowermost respectively. Using the line between them, you can tell if the bulk of 1-pixels are above, below, or on both sides of the line; this tells you where to look for the other corners.","Q_Score":0,"Tags":"python","A_Id":54490817,"CreationDate":"2019-02-02T05:43:00.000","Title":"Finding corners of a polygon represented in a 2D array","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am looking for a way to set a fixed step size for solving my initial value problem by Runge-Kutta method in Python. Accordingly, how I can tell the scipy.integrate.RK45 to keep a constant update (step size) for its integration procedure?\nThank you very much.","AnswerCount":6,"Available Count":4,"Score":0.1325487884,"is_accepted":false,"ViewCount":5393,"Q_Id":54494770,"Users Score":4,"Answer":"Scipy.integrate is usually used with changeable step method by controlling the TOL(one step error) while integrating numerically. The TOL is usually computed by checking with another numerical method. For example RK45 uses the 5th order Runge-Kutta to check the TOL of the 4th order Runge-Kutta method to determine the integrating step. \nHence if you must integrate ODEs with fixed step, just turn off the TOL check by setting atol, rtol with a rather large constant. For example, like the form:\nsolve_ivp(your function, t_span=[0, 10], y0=..., method=\"RK45\", max_step=0.01, atol = 1, rtol = 1)\nThe TOL check is set to be so large that the integrating step would be the max_step you choose.","Q_Score":3,"Tags":"python,scipy,integrate,runge-kutta","A_Id":57942410,"CreationDate":"2019-02-02T15:59:00.000","Title":"How to set fixed step size with scipy.integrate?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am looking for a way to set a fixed step size for solving my initial value problem by Runge-Kutta method in Python. Accordingly, how I can tell the scipy.integrate.RK45 to keep a constant update (step size) for its integration procedure?\nThank you very much.","AnswerCount":6,"Available Count":4,"Score":0.0665680765,"is_accepted":false,"ViewCount":5393,"Q_Id":54494770,"Users Score":2,"Answer":"If you are interested in data-wise fix step size, then I highly recommend you to use the scipy.integrate.solve_ivp function and its t_eval argument.\nThis function wraps up all of the scipy.integrate ode solvers in one function, thus you have to choose the method by giving value to its method argument. Fortunately, the default method is the RK45, so you don't have to bother with that.\nWhat is more interesting for you is the t_eval argument, where you have to give a flat array. The function samples the solution curve at t_eval values and only returns these points. So if you want a uniform sampling by the step size then just give the t_eval argument the following: numpy.linspace(t0, tf, samplingResolution), where t0 is the start and tf is the end of the simulation.\nThusly you can have uniform sampling without having to resort fix step size that causes instability for some ODEs.","Q_Score":3,"Tags":"python,scipy,integrate,runge-kutta","A_Id":64495201,"CreationDate":"2019-02-02T15:59:00.000","Title":"How to set fixed step size with scipy.integrate?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am looking for a way to set a fixed step size for solving my initial value problem by Runge-Kutta method in Python. Accordingly, how I can tell the scipy.integrate.RK45 to keep a constant update (step size) for its integration procedure?\nThank you very much.","AnswerCount":6,"Available Count":4,"Score":0.0333209931,"is_accepted":false,"ViewCount":5393,"Q_Id":54494770,"Users Score":1,"Answer":"You've said you want a fixed-time step behaviour, not just a fixed evluation time step. Therefore, you have to \"hack\" your way through that if you not want to reimplement the solver yourself. Just set the integration tolerances atol and rtol to 1e90, and max_step and first_step to the value dt of the time step you want to use. This way the estimated integration error will always be very small, thus tricking the solver into not shrinking the time step dynamically.\nHowever, only use this trick with EXPLICIT algortithms (RK23,RK45,DOP853) !\nThe implicit algorithms from \"solve_ivp\" (Radau, BDF, maybe LSODA as well) adjust the precision of the nonlinear Newton solver according to atol and rtol, therefore you might end up having a solution which does not make any sense...","Q_Score":3,"Tags":"python,scipy,integrate,runge-kutta","A_Id":64773397,"CreationDate":"2019-02-02T15:59:00.000","Title":"How to set fixed step size with scipy.integrate?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am looking for a way to set a fixed step size for solving my initial value problem by Runge-Kutta method in Python. Accordingly, how I can tell the scipy.integrate.RK45 to keep a constant update (step size) for its integration procedure?\nThank you very much.","AnswerCount":6,"Available Count":4,"Score":0.0333209931,"is_accepted":false,"ViewCount":5393,"Q_Id":54494770,"Users Score":1,"Answer":"I suggest to write your own rk4 fixed step program in py. There are many internet examples to help. That guarantees that you know precisely how each value is being computed. Furthermore, there will normally be no 0\/0 calculations and if so they will be easy to trace and prompt another look at the ode's being solved.","Q_Score":3,"Tags":"python,scipy,integrate,runge-kutta","A_Id":66190287,"CreationDate":"2019-02-02T15:59:00.000","Title":"How to set fixed step size with scipy.integrate?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm reading a basic csv file where the columns are separated by commas. However, the body column is a string which may contain commas and quotations.\nFor example, there are some cells like \"Bahamas\\\", The\" and \"Germany, West\"\nI have tried \ntext = pd.read_table(\"input.txt\", encoding = 'utf-16', quotechar='\"', sep = ','),\ntext = pd.read_table(\"input.txt\", encoding = 'utf-16', quotechar='\"', delimiter = ','). But they both cannot work.\nIs there a way to go around this problem?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":372,"Q_Id":54509495,"Users Score":1,"Answer":"Are you able to regenerate the csv? If yes, change the delimit character to a pipe, I.e | . If not, you may be forced to take the long route... because there is no way for any code to figure out which characters are delimiting\/quoting and which are part of the value if you have both commas and quotes lurking inside the value.\nA workaround could involve leveraging the column position where this problem occurs... I.e first you could isolate the columns to the left of the troubled column, isolate all columns to the right, then all characters remaining are your troubled column. Can you post a few example rows? It would be good to see a few rows that have this issue, and a few that work fine","Q_Score":0,"Tags":"python,pandas","A_Id":54509633,"CreationDate":"2019-02-04T02:26:00.000","Title":"pandas read csv with extra commas and quotations in column","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In the SURF technique, and more precisely within the feature description stage, the authors have stated (if I understand correctly) that the description will be performed in a area of 20 times sigma. Sigma represents the scale on which the keypoint was detected.\nSigma = 0.4 x L where L = 2^Octave  x level+1. If we use the OpenCV implementation, the DetectAndCompute function computes, with the value of Keypoint.size, the radius of the circle surrounding the keypoint. \nMy question is : How could we get the value of sigma from the radius value  ?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":95,"Q_Id":54517035,"Users Score":0,"Answer":"This value s = size*1.2f\/9.0f is not montioned in the bay's article scale= L*0.4 or \nscale= L* 1.2\/3 any one can explain me this part??","Q_Score":0,"Tags":"opencv,image-processing,computer-vision,surf,opencv-python","A_Id":54530692,"CreationDate":"2019-02-04T13:19:00.000","Title":"SURF: How could we get the value of sigma from the keypoint radius","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I write pyspark code to deal with some spark-sql data. \nLast month, it worked perfectly when I ran spark-submit --master local[25]. From top command, I could see 25 python threads.\nHowever, nothing change but today the spark-submit only create one thread. I wonder what kind of things can cause such problem.\nThis is on a ubuntu server on AWS, which has 16 CPU cores. The Spark version is 2.2.1 and Python is 3.6","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":68,"Q_Id":54521867,"Users Score":1,"Answer":"Just find the problem: there is another user running his own spark task on the same instance which occupying resources.","Q_Score":0,"Tags":"python,apache-spark,pyspark","A_Id":54541372,"CreationDate":"2019-02-04T18:03:00.000","Title":"spark-submit --master local[n] cannot create multi-threads","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a simple binary classification problem, and I want to assess the learning feasibility using Hoeffding's Inequality and also if possible VC dimension. \nI understand the theory but, I am still stuck on how to implement it in Python.\nI understand that In-sample Error (Ein) is the training Error. Out of sample Error(Eout) is the error on the test subsample I guess.\nBut how do I plot the difference between these two errors with the Hoeffdings bound?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":172,"Q_Id":54534664,"Users Score":0,"Answer":"Well here is how I handled it : I generate multiple train\/test samples, run the algorithm on them, calculate Ein as the train set error, Eout estimated by the test set error, calculate how many times their differnces exceeds the value of epsilon (for a range of epsilons). And then I plot the curve of these rates of exceeding epsilon and the curve of the right side of the Hoeffding's \/VC inequality so I see if the differences curve is always under the Hoeffding\/VC's Bound curve, this informs me about the learning feasiblity.","Q_Score":0,"Tags":"python,machine-learning","A_Id":54809376,"CreationDate":"2019-02-05T12:40:00.000","Title":"How to check learning feasibility on a binary classification problem with Hoeffding's inequality\/VC dimension with Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"As the title say, let's assume I have a plane equation:\n\nax+by+cz=d    (e.g. -4x + 5y + 9z = -9)\n\nI want to draw n random points (let's say 500) that belong to that plane.\nCan somebody help me with that?\nI saw that from mpl_toolkits import mplot3d has the function plot_surface(x,y,z) but (as it says) it plots the surface equation and this is not what I need.\nAny help is really appreciated.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":446,"Q_Id":54540676,"Users Score":1,"Answer":"For a plane a simple approach that would work is\n\npick a coefficient that's not 0 (let's suppose is c)\npick random values for the other two coordinates (x and y in this case)\ncompute z with (d - ax - by)\/c","Q_Score":1,"Tags":"python,matplotlib,linear-algebra,mplot3d","A_Id":54540745,"CreationDate":"2019-02-05T18:17:00.000","Title":"Python: given a plane equation draw a subset of points that belong to it","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm working on a multivariate (100+ variables) multi-step (t1 to t30) forecasting problem where the time series frequency is every 1 minute. The problem requires to forecast one of the 100+ variables as target.\nI'm interested to know if it's possible to do it using FB Prophet's Python API. I was able to do it in a univariate fashion using only the target variable and the datetime variable. Any help and direction is appreciated. Please let me know if any further input or clarity is needed on the question.","AnswerCount":6,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":28022,"Q_Id":54544285,"Users Score":0,"Answer":"This might be late, however if you are reading this in 2019, you can implement multivariate time series using LSTM, Keras.","Q_Score":31,"Tags":"python,machine-learning,time-series,forecasting,facebook-prophet","A_Id":57832051,"CreationDate":"2019-02-05T22:54:00.000","Title":"Is it possible to do multivariate multi-step forecasting using FB Prophet?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a data-frame of size (140000,22) dimensions.\ni have to create 2d array of equal dimensions to pass it into convolution neural network .\nCan you please guide how to transform on this dataframe","AnswerCount":2,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":21926,"Q_Id":54549284,"Users Score":15,"Answer":"You just need to call .values on the DataFrame.\nIf for example your dataframe is called df, then you can pass df.values to your convolutional neural network.","Q_Score":9,"Tags":"python,dataframe,conv-neural-network,convolution","A_Id":54551132,"CreationDate":"2019-02-06T08:25:00.000","Title":"convert Dataframe to 2d Array","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to build a logistic regression model in python 3 using sklearn library.\nLet's stick to below short versions going forward,\ndv - dependent variable \nidv - independent variable\nNow I have idv1, idv2, idv3, idv4, idv5, idv6, idv7, idv8 & idv9.\nOut of which idv6 to idv9 are categorical variables (idv6 & idv7 has 3 categories..where as idv8 & idv9 are boolean variables..yes or no kind of variables [0 or 1])\nAnd dv is again a boolean variable (yes or no kind of variable).\nNow, I have created a dummies for all idv6 to idv9 for the final model data...i.e idv6_c1, idv6_c2, idv_c3 and followed similar for the remaining..like idv8_c1, idv8_c2 for idv8 & idv9.\nNow, after fitting the model and finding the metrics of the predicted values...\nI am getting let's say accuracy_score of 76.7415479670124 % and  precision_score of 76.7415479670124 %\nI have calculated using sklearn.metrics.accuracy_score and sklearn.metrics.precision_score libraries.\nI am wondering..is this correct or am I missing something...??\nCan this happen ??...accuracy & precision to be equal to almost 13 decimals ???....I am sure...I am doing something wrong...can anyone please help me ??","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":780,"Q_Id":54557082,"Users Score":2,"Answer":"Precision = True Positive \/ (True Positive + False Positive)\nAccuracy = (True Positive + True Negative) \/ (True Positive + False Positive + True Negative + False Negative)\nTherefore, if there are no negative predictions, these two values will be equal.","Q_Score":1,"Tags":"python,scikit-learn,logistic-regression,sklearn-pandas","A_Id":54557235,"CreationDate":"2019-02-06T15:27:00.000","Title":"Can accuracy_score and precision_score be equal?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In MATLAB, binofit returns the maximum likelihood estimate of the success of binomial probability distribution and confidence intervals. \nstatsmodels.stats.proportion.proportion_confint returns confidence intervals as well, but couldn't find a function for maximum likelihood estimate of the binomial probability distribution. Is there any function that you can suggest as a binofit function in MATLAB for python?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":319,"Q_Id":54559209,"Users Score":1,"Answer":"I think the function you suggested is good enough. I ran some test comparing Matlab binofit and Python statsmodels.stats.proportion.proportion_confint. The test was empyrical like testin 100K experiments like [phat,pci] = binofit(x,n,alpha) with min_conf,max_conf = proportion_confint(x,n,alpha=alpha,method='beta').\nThe RMSE bewteen confidence interval limits from Matlab and Python are below 5e-6 for values values of x and n between 0 and 10000. Tested with alpha=0.05 and 0.01.\nI know this is not strict demonstrations but for my project I decided to consider the two estimates of confidence intervals as equivalent.","Q_Score":0,"Tags":"python,python-2.7","A_Id":56294966,"CreationDate":"2019-02-06T17:21:00.000","Title":"Python equivalent for MATLAB binofit","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to get into deep learning. I installed Anaconda to use jupyter and generally not to care about installing all of those packages like matplotlib etc myself. But I cannot install tensorflow as it works only with Python  3.4, 3.5, or 3.6 but I have 3.7. After I read about it I installed python 3.6.8. I uninstalled Anaconda and installed it again, nothing changed. After that, I used this command \n\nconda install python=3.6.8\n\nto presumably install python 3.6.8 for it (I found this solution somewhere on the web). The command worked but didn't change anything. Please help","AnswerCount":6,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":88747,"Q_Id":54559566,"Users Score":0,"Answer":"You just can change the python version by creating a new environment in anaconda.\nIt will ask for the python version when you create an environment","Q_Score":18,"Tags":"python,tensorflow,anaconda,jupyter-notebook,jupyter","A_Id":66913904,"CreationDate":"2019-02-06T17:41:00.000","Title":"How to change python version in Anaconda?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"After training the trained model will be saved as H5 format. But I didn't know how that H5 file can be used as classifier to classifying new data. How H5 model works in theory when classifying new data?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":1209,"Q_Id":54566249,"Users Score":1,"Answer":"When you save your model as h5-file, you save the model structure, all its parameters and further informations like state of your optimizer and so on. It is just an efficient way to save huge amounts of information. You could use json or xml file formats to do this as well. \nYou can't classifiy anything only using this file (it is not executable). You have to rebuild the graph as a tensorflow graph from this file. To do so you simply use the load_model() function from keras, which returns a keras.models.Model object. Then you can use this object to classifiy new data, with keras predict() function.","Q_Score":2,"Tags":"python,python-3.x,keras,deep-learning,conv-neural-network","A_Id":54573011,"CreationDate":"2019-02-07T04:21:00.000","Title":"How keras model H5 works in theory","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an array of thousands of doc2vec vectors with 90 dimensions. For my current purposes I would like to find a way to \"sample\" the different regions of this vector space, to get a sense of the diversity of the corpus. For example, I would like to partition my space into n regions, and get the most relevant word vectors for each of these regions.\nI've tried clustering with hdbscan (after reducing the dimensionality with UMAP) to carve the vector space at its natural joints, but it really doesn't work well.\nSo now I'm wondering whether there is a way to sample the \"far out regions\" of the space (n vectors that are most distant from each other). \n\nWould that be a good strategy?\nHow could I do this?\n\nMany thanks in advance!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":244,"Q_Id":54579473,"Users Score":1,"Answer":"Wouldn't a random sample from all vectors necessarily encounter any of the various 'regions' in the set? \nIf there are \"natural joints\" and clusters to the documents, some clustering algorithm should be able to find the N clusters, then the smaller number of NxN distances between each cluster's centroid to each other cluster's centroid might identify those \"furthest out\" clusters. \nNote for any vector, you can use the Doc2Vec doc-vectors most_similar() with a topn value of 0\/false-ish to get the (unsorted) similarities to all other model doc-vectors. You could then find the least-similar vectors in that set. If your dataset is small enough for it to be practical to do this for \"all\" (or some large sampling) of doc-vectors, then perhaps other docs that appear in the \"bottom N\" least-similar, for the most number of other vectors, would be the most \"far out\". \nWhether this idea of \"far out\" is actually shown in the data, or useful, isn't clear. (In high-dimensional spaces, everything can be quite \"far\" from everything else in ways that don't match our 2d\/3d intuitions, and slight differences in some vectors being a little \"further\" might not correspond to useful distinctions.)","Q_Score":1,"Tags":"python,vector,cluster-analysis,doc2vec,dimensionality-reduction","A_Id":54581374,"CreationDate":"2019-02-07T17:57:00.000","Title":"Is there a way to find the n most distant vectors in an array?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm running my Jupyter Notebook using Pytorch on Google Colab. After I received the 'Cuda assert fails: device-side assert triggered' I am unable to run any other code that uses my pytorch module. Does anyone know how to reset my code so that my Pytorch functions that were working before can still run?\nI've already tried implementing CUDA_LAUNCH_BLOCKING=1but my code still doesn't work as the Assert is still triggered!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":11545,"Q_Id":54585685,"Users Score":9,"Answer":"You need to reset the Colab notebook. To run existing Pytorch modules that used to work before, you have to do the following:\n\nGo to 'Runtime' in the tool bar\nClick 'Restart and Run all'\n\nThis will reset your CUDA assert and flush out the module so that you can have another shot at avoiding the error!","Q_Score":3,"Tags":"python,pytorch,google-colaboratory,tensor","A_Id":54585712,"CreationDate":"2019-02-08T03:38:00.000","Title":"How to reset Colab after the following CUDA error 'Cuda assert fails: device-side assert triggered'?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I`m trying to filter the rows of a Data Frame, but since the index name of the column has white spaces, I've not been able to do it\nThe DDTS Number is the name of the column \nIt worked when there is no spaces\ndata[data3.(DDTS Number) != null]\nI've tried different syntax but I don't if there is way to do it without to rename the column name","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":36,"Q_Id":54586159,"Users Score":0,"Answer":"Hi everyone I found the solution. The problem with the method I used was it did not work when the index had spaces in the name so there is another way to get rid of the null values using the following structure:\ndf1 = df[df[\"ColumnName With Spaces\"].notnull()]\nFrom here you will filter all the rows in the \"df\" with index \"ColumnName With Spaces\" thet contain null values.","Q_Score":0,"Tags":"python,pandas,filter,row,spaces","A_Id":54720270,"CreationDate":"2019-02-08T04:55:00.000","Title":"I\u00b4m trying to filter data of colums from a Data Frame, but the index names contain white spaces","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"For Topic Modelling ,\nWhy random_state parameter is used in NMF and LDA algorithm ? What are the benefits of using random topics generated every time ?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":545,"Q_Id":54587051,"Users Score":1,"Answer":"The algorithms for both are stochastic - meaning they use randomness as a part of estimating a good answer. It's done that way to make it tractable, and in the case of LDA, the whole model is stochastic, providing you ideally with a probabilistic distribution (called \"the posterior distribution\") of answers, but instead providing a single, likely answer as an estimate. \nSo the answer is that using randomness in the algorithms makes a tremendously difficult problem much simpler and feasible to calculate in less than a hundred years.\nIf you're going to use them, I think it would do you well to study them, learn something of how they work, why they work. Using a tool that you don't understand is risky, as you don't really know what the result the tool provides actually means. One example is the numerors words in all \"topics\" with very low probability. The differences in these probabilities are actually meaningless - given a different sample from the posterior, you'd get different probabilities, ranked differently between words.","Q_Score":1,"Tags":"python,lda,topic-modeling,nmf","A_Id":54623195,"CreationDate":"2019-02-08T06:28:00.000","Title":"Why random_state parameter is used in NMF and LDA algorithm ? What are the benefits of using random topics generated every time?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have two tensors x and y that have equal shape in the first k dimensions. The second tensor contains indices to retrieve values from the first along to the last dimension. For a rank of 3, then the output z should be such that z[i_1, i_2,...,i_k, j] = x[i_1, i_2,...,i_k, y[i_1, i_2, ...,i_k, j]].\nI currently have a method that requires reshaping the x and y, appending row indices of y, using gather_nd and finally returning to the original shape. Is there is a more elegant method? Is there a way to get the tensor of indices (like np.indices), preferably that does not require knowledge of the rank or shape beyond that they satisfy the above condition?","AnswerCount":1,"Available Count":1,"Score":0.537049567,"is_accepted":false,"ViewCount":46,"Q_Id":54602610,"Users Score":3,"Answer":"Found it! tf.batch_gather and tf.batch_scatter.","Q_Score":4,"Tags":"python,tensorflow","A_Id":54603217,"CreationDate":"2019-02-09T02:31:00.000","Title":"Simplest way to index within a dimension","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"As title, I know there're some model supporting streaming learning like classification model. And the model has function partial_fit()\nNow I'm studying regression model like SVR and RF regressor...etc in scikit.\nBut most of regression models doesn't support partial_fit .\nSo I want to reach the same effect in neural network. If in tensorflow, how to do like that? Is there any keyword?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":29,"Q_Id":54607881,"Users Score":0,"Answer":"There is no some special function for it in TensorFlow. You make a single training pass over a new chunk of data. And then another training pass over another new chunk of data, etc till you reach the end of the data stream (which, hopefully, will never happen).","Q_Score":0,"Tags":"python,tensorflow,machine-learning","A_Id":54608871,"CreationDate":"2019-02-09T15:50:00.000","Title":"How to reach streaming learning in Neural network?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In tensorflow, there is well-known batch normalization which adds the weights update ops to tf.GraphKeys.UPDATE_OPS. But in the case of instance normalization there is no update op added. When using tf.contrib.layer.batch_norm, I can specify the is_training parameter which adds the update op to collection.\nBut for tf.contrib.layer.instance_norm and tf.contrib.layer.group_norm there is no such parameter, and also no op is added to tf.GraphKeys.UPDATE_OPS. \nIs this correct behaviour, or bug in tensorflow? How does the update op in instance normalization work then?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":918,"Q_Id":54609375,"Users Score":8,"Answer":"You should understand why does batch_norm need to add ops in UPDATE_OPS.\ntf.layers.batch_normalization function will generate four variables, namely gamma, beta, moving_mean and moving_variance, and only gamma, beta are in tf.GraphKeys.TRAINABLE_VARIABLES. \nWhen it is in the training phase, moving_mean and moving_variance also need to be updated, so that if we set training parameter as 1 for tf.layers.batch_normalization, the function will add corresponding ops into tf.GraphKeys.UPDATE_OPS for updating moving_mean and moving_variance, whereas if we set training parameter as 0 for tf.layers.batch_normalization, the function will not do anything to tf.GraphKeys.UPDATE_OPS, since it doesn't need to update moving_mean and moving_variance in the test phase.\nAs for the group_norm and instance_norm, they only have beta and gamma variables, which have already been in the tf.GraphKeys.TRAINABLE_VARIABLES, so that there is no need to add any ops in tf.GraphKeys.UPDATE_OPS.","Q_Score":3,"Tags":"python,tensorflow","A_Id":54611524,"CreationDate":"2019-02-09T18:33:00.000","Title":"Instance normalization and group normalization missing update","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to machine learning my question is: \n\nDo we need to encode dependent variable y if it contains three class\n  segments 1,2,3 and I want to know if there is need to encode the\n  dependent variable when it contains no","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1860,"Q_Id":54617771,"Users Score":0,"Answer":"OneHotEncoder will create k number of columns if there are k classes for a single variable.\nFor example : it will create 2 variables if  gender values in that dataset are Male\/Female,\nIt will create 3 Variables if gender values are male\/Female\/PreferNotToSay\nNow, You don't want multiple variables in your predicate y, So better go with LabelEncoder(from sklearn.preprocessing) or some mechanism that keeps the dimensionality intact.","Q_Score":2,"Tags":"python,machine-learning","A_Id":54617927,"CreationDate":"2019-02-10T15:10:00.000","Title":"dependent variable One hot encoder","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"My question is about forward and backward propagation for deep neural networks when the number of hidden units is greater than 1. \nI know what I have to do if I have a single hidden layer. In case of a single hidden layer, if my input data X_train has n samples, with d number of features (i.e. X_train is a (n, d) dimensional matrix, y_train is a (n,1)  dimensional vector) and if I have h1 number of hidden units in my first hidden layer, then I use Z_h1 = (X_train * w_h1) + b_h1 (where w_h1 is a weight matrix with random number entries which has the shape (d, h1) and b_h1 is a bias unit with shape (h1,1). I use sigmoid activation A_h1 = sigmoid(Z_h1) and find that both A_h1 and Z_h1 have shapes (n, h1). If I have t number of output units, then I use a weight matrix w_out with dimensions (h1, t) and b_out with shape (t,1) to get the output Z_out = (A_h1 * w_h1) + b_h1. From here I can get A_out = sigmoid(Z_out) \n which has shape (n, t). If I have a 2nd hidden layer (with h2 number of units) after the 1st hidden layer and before the output layer, then what steps must I add to the forward propagation and which steps should I modify?\nI also have idea about how to tackle backpropagation in case of single hidden layer neural networks. For the single hidden layer example in the previous paragraph, I know that in the first backpropagation step (output layer -> hidden layer1), I should do Step1_BP1: Err_out = A_out - y_train_onehot (here y_train_onehot is the onehot representation of y_train. Err_out has shape (n,t). This is followed by Step2_BP1: delta_w_out = (A_h1)^T * Err_out and delta_b_out = sum(Err_out). The symbol (.)^T denotes the transpose of matrix. For the second backpropagation step (hidden layer1 -> input layer), we do the following Step1_BP2: sig_deriv_h1 = (A_h1) * (1-A_h1). Here sig_deriv_h1 has shape (n,h1). In the next step, I do Step2_BP2: Err_h1 = \\Sum_i \\Sum_j [ ( Err_out * w_out.T)_{i,j} * sig_deriv_h1__{i,j} )]. Here, Err_h1 has shape (n,h1). In the final step, I do Step3_BP2: delta_w_h1 = (X_train)^T  * Err_h1 and delta_b_h1 = sum(Err_h1). What backpropagation steps should I add if I have a 2nd hidden layer (h2 number of units) after the 1st hidden layer and before the output layer? Should I modify the backpropagation steps for the one hidden layer case that I have described here?","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":1732,"Q_Id":54624562,"Users Score":6,"Answer":"\u25cf Let X be a matrix of samples with shape (n, d), where n denotes number of samples, and d denotes number of features.   \n\u25cf Let wh1 be the matrix of weights - of shape (d, h1) , and \n\u25cf Let bh1 be the bias vector of shape (1, h1).  \nYou need the following steps for forward and backward propagations:\n\u25ba FORWARD PROPAGATION:\n\u26f6 Step 1:\nZh1 \u00a0 \u00a0 \u00a0  = \u00a0   \u00a0 \u00a0 [ X \u00a0  \u2022 \u00a0   wh1 ] \u00a0 \u00a0  + \u00a0 \u00a0  bh1\n\u2193 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0  \u00a0 \u2193 \u00a0 \u00a0 \u00a0  \u00a0  \u2193 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u2193  \n(n,h1) \u00a0 \u00a0 (n,d) \u00a0 (d,h1) \u00a0 \u00a0 (1,h1)\nHere, the symbol \u2022 represents matrix multiplication, and the h1 denotes the number of hidden units in the first hidden layer.\n\u26f6 Step 2:\nLet \u03a6() be the activation function. We get.\nah1 \u00a0 \u00a0 = \u00a0 \u00a0  \u03a6 (Zh1) \n\u00a0 \u2193 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u2193  \n(n,h1) \u00a0 \u00a0 \u00a0 (n,h1)\n\u26f6 Step 3:\nObtain new weights and biases:\n\u25cf wh2 of shape (h1, h2), and   \n\u25cf bh2 of shape (1, h2).\n\u26f6 Step 4:\nZh2 \u00a0 \u00a0 \u00a0  = \u00a0   \u00a0 \u00a0 [ ah1 \u00a0  \u2022 \u00a0   wh2 ] \u00a0 \u00a0  + \u00a0 \u00a0  bh2 \n\u2193 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0  \u00a0 \u2193 \u00a0 \u00a0 \u00a0  \u00a0 \u00a0  \u2193 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u2193\n(n,h2) \u00a0 \u00a0 (n,h1) \u00a0 (h1,h2) \u00a0 \u00a0 (1,h2)\nHere, h2 is the number of hidden units in the second hidden layer.\n\u26f6 Step 5:\nah2 \u00a0 \u00a0 = \u00a0 \u00a0  \u03a6 (Zh2) \n\u00a0 \u2193 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u2193  \n(n,h2) \u00a0 \u00a0 \u00a0 (n,h2)\n\u26f6 Step 6:\nObtain new weights and biases:\n\u25cf wout of shape (h2, t), and   \n\u25cf bout of shape (1, t).\nHere, t is the number of classes.\n\u26f6 Step 7:\nZout \u00a0 \u00a0 \u00a0  = \u00a0   \u00a0 \u00a0 [ ah2 \u00a0  \u2022 \u00a0   wout ] \u00a0 \u00a0  + \u00a0 \u00a0  bout \n\u2193 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0  \u00a0 \u00a0 \u2193 \u00a0 \u00a0 \u00a0  \u00a0 \u00a0 \u2193 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u2193\n(n,t) \u00a0 \u00a0 \u00a0 (n,h2) \u00a0 (h2,t) \u00a0 \u00a0 (1,t)\n\u26f6 Step 8:\naout \u00a0 \u00a0 = \u00a0 \u00a0  \u03a6 (Zout) \n\u00a0 \u2193 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u2193 \n(n,t) \u00a0 \u00a0 \u00a0 (n,t)\n\u25ba BACKWARD PROPAGATION:\n\u26f6 Step 1:\nConstruct the one-hot encoded matrix of the unique output classes ( yone-hot ).\nErrorout \u00a0 \u00a0 = \u00a0 \u00a0 aout \u00a0 - \u00a0 yone-hot\n\u00a0 \u00a0 \u2193 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0  \u2193 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u2193\n(n,t) \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 (n,t) \u00a0 \u00a0 \u00a0 (n,t)\n\u26f6 Step 2:\n\u0394wout \u00a0 \u00a0 = \u00a0 \u00a0 \u03b7 ( ah2T \u00a0 \u2022 \u00a0 Errorout ) \n\u00a0 \u00a0 \u2193 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u2193 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u2193 \n(h2,t) \u00a0 \u00a0 \u00a0 \u00a0 (h2,n) \u00a0 \u00a0 (n,t)\n\u0394bout \u00a0 \u00a0 = \u00a0 \u00a0 \u03b7 [ \u2211 i=1n\u00a0 (Errorout,i) ]\n\u00a0 \u00a0 \u2193 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u2193\n(1,t) \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 (1,t)\nHere \u03b7 is the learning rate. \nwout = wout - \u0394wout \u00a0 \u00a0 \u00a0 \u00a0 (weight update.)\nbout = bout - \u0394bout \u00a0 \u00a0 \u00a0 \u00a0 (bias update.)\n\u26f6 Step 3:\nError2 \u00a0 \u00a0 = \u00a0 \u00a0 [Errorout \u00a0 \u2022 \u00a0   woutT] \u00a0 \u2734 \u00a0 \u03a6\/ (ah2)\n\u00a0 \u00a0 \u2193 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u2193 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u2193 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u2193\n(n,h2) \u00a0 \u00a0 \u00a0 \u00a0 (n,t) \u00a0 \u00a0 \u00a0 \u00a0 (t,h2) \u00a0 \u00a0 \u00a0 (n,h2)\nHere, the symbol \u2734 denotes element wise matrix multiplication. The symbol \u03a6\/ represents derivative of sigmoid function.  \n\u26f6 Step 4:\n\u0394wh2 \u00a0 \u00a0 = \u00a0 \u00a0 \u03b7 ( ah1T \u00a0 \u2022 \u00a0 Error2 ) \n\u00a0 \u00a0 \u2193 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u2193 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u2193 \n(h1,h2) \u00a0 \u00a0 \u00a0 \u00a0 (h1,n) \u00a0 \u00a0 (n,h2)\n\u0394bh2 \u00a0 \u00a0 = \u00a0 \u00a0 \u03b7 [ \u2211 i=1n\u00a0 (Error2,i) ]\n\u00a0 \u00a0 \u2193 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u2193\n(1,h2) \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 (1,h2)\nwh2 = wh2 - \u0394wh2 \u00a0 \u00a0 \u00a0 \u00a0 (weight update.)\nbh2 = bh2 - \u0394bh2 \u00a0 \u00a0 \u00a0 \u00a0 (bias update.)\n\u26f6 Step 5:\nError3 \u00a0 \u00a0 = \u00a0 \u00a0 [Error2 \u00a0 \u2022 \u00a0   wh2T] \u00a0 \u2734 \u00a0 \u03a6\/ (ah1)\n\u00a0 \u00a0 \u2193 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u2193 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u2193 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u2193\n(n,h1) \u00a0 \u00a0 \u00a0 (n,h2) \u00a0 \u00a0 (h2,h1) \u00a0 \u00a0 \u00a0 (n,h1)\n\u26f6 Step 6:\n\u0394wh1 \u00a0 \u00a0 = \u00a0 \u00a0 \u03b7 ( XT \u00a0 \u2022 \u00a0 Error3 )\n\u00a0 \u00a0 \u2193 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u2193 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u2193 \n(d,h1) \u00a0 \u00a0 \u00a0 \u00a0 (d,n) \u00a0 \u00a0 (n,h1)\n\u0394bh1 \u00a0 \u00a0 = \u00a0 \u00a0 \u03b7 [ \u2211 i=1n\u00a0 (Error3,i) ]\n\u00a0 \u00a0 \u2193 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u2193\n(1,h1) \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 (1,h1)\nwh1 = wh1 - \u0394wh1 \u00a0 \u00a0 \u00a0 \u00a0 (weight update.)\nbh1 = bh1 - \u0394bh1 \u00a0 \u00a0 \u00a0 \u00a0 (bias update.)","Q_Score":3,"Tags":"python,neural-network,deep-learning","A_Id":56425972,"CreationDate":"2019-02-11T05:44:00.000","Title":"Back-propagation and forward-propagation for 2 hidden layers in neural network","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"My question is about forward and backward propagation for deep neural networks when the number of hidden units is greater than 1. \nI know what I have to do if I have a single hidden layer. In case of a single hidden layer, if my input data X_train has n samples, with d number of features (i.e. X_train is a (n, d) dimensional matrix, y_train is a (n,1)  dimensional vector) and if I have h1 number of hidden units in my first hidden layer, then I use Z_h1 = (X_train * w_h1) + b_h1 (where w_h1 is a weight matrix with random number entries which has the shape (d, h1) and b_h1 is a bias unit with shape (h1,1). I use sigmoid activation A_h1 = sigmoid(Z_h1) and find that both A_h1 and Z_h1 have shapes (n, h1). If I have t number of output units, then I use a weight matrix w_out with dimensions (h1, t) and b_out with shape (t,1) to get the output Z_out = (A_h1 * w_h1) + b_h1. From here I can get A_out = sigmoid(Z_out) \n which has shape (n, t). If I have a 2nd hidden layer (with h2 number of units) after the 1st hidden layer and before the output layer, then what steps must I add to the forward propagation and which steps should I modify?\nI also have idea about how to tackle backpropagation in case of single hidden layer neural networks. For the single hidden layer example in the previous paragraph, I know that in the first backpropagation step (output layer -> hidden layer1), I should do Step1_BP1: Err_out = A_out - y_train_onehot (here y_train_onehot is the onehot representation of y_train. Err_out has shape (n,t). This is followed by Step2_BP1: delta_w_out = (A_h1)^T * Err_out and delta_b_out = sum(Err_out). The symbol (.)^T denotes the transpose of matrix. For the second backpropagation step (hidden layer1 -> input layer), we do the following Step1_BP2: sig_deriv_h1 = (A_h1) * (1-A_h1). Here sig_deriv_h1 has shape (n,h1). In the next step, I do Step2_BP2: Err_h1 = \\Sum_i \\Sum_j [ ( Err_out * w_out.T)_{i,j} * sig_deriv_h1__{i,j} )]. Here, Err_h1 has shape (n,h1). In the final step, I do Step3_BP2: delta_w_h1 = (X_train)^T  * Err_h1 and delta_b_h1 = sum(Err_h1). What backpropagation steps should I add if I have a 2nd hidden layer (h2 number of units) after the 1st hidden layer and before the output layer? Should I modify the backpropagation steps for the one hidden layer case that I have described here?","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":1732,"Q_Id":54624562,"Users Score":1,"Answer":"For Forward Propagation, the dimension of the output from the first hidden layer must cope up with the dimensions of the second input layer.\nAs mentioned above, your input has dimension (n,d). The output from hidden layer1 will have a dimension of (n,h1). So the weights and bias for the second hidden layer must be (h1,h2) and (h1,h2) respectively.\nSo w_h2 will be of dimension (h1,h2) and b_h2 will be (h1,h2).\nThe dimensions for the weights and bias for the output layer will be w_output will be of dimension (h2,1) and b_output will be (h2,1).\nThe same you have to repeat in Backpropagation.","Q_Score":3,"Tags":"python,neural-network,deep-learning","A_Id":54625251,"CreationDate":"2019-02-11T05:44:00.000","Title":"Back-propagation and forward-propagation for 2 hidden layers in neural network","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to perform a summation of the kind i<j on symmetric matrices. This is equivalent to sum over the upper triangular elements of a matrix, diagonal excluded.\nGiven A a symmetric N x N array, the simplest solution is np.triu(A,1).sum() however I was wondering if faster methods exist that require less memory.\nIt seems that (A.sum() - np.diag(A).sum())\/2 is faster on large array, but how to avoid creating even the N x 1 array from np.diag?\nA doubly nested for loop would require no additional memory, but it is clearly not the way to go in Python.","AnswerCount":4,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":1810,"Q_Id":54629601,"Users Score":3,"Answer":"You can replace np.diag(A).sum() with np.trace(A); this will not create the temporary Nx1 array","Q_Score":3,"Tags":"python,arrays,performance,numpy","A_Id":54629669,"CreationDate":"2019-02-11T11:28:00.000","Title":"Fastest way in numpy to sum over upper triangular elements with the least memory","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to perform a summation of the kind i<j on symmetric matrices. This is equivalent to sum over the upper triangular elements of a matrix, diagonal excluded.\nGiven A a symmetric N x N array, the simplest solution is np.triu(A,1).sum() however I was wondering if faster methods exist that require less memory.\nIt seems that (A.sum() - np.diag(A).sum())\/2 is faster on large array, but how to avoid creating even the N x 1 array from np.diag?\nA doubly nested for loop would require no additional memory, but it is clearly not the way to go in Python.","AnswerCount":4,"Available Count":2,"Score":0.049958375,"is_accepted":false,"ViewCount":1810,"Q_Id":54629601,"Users Score":1,"Answer":"The fastest method with the least memory, in pure numpy is going to be to sum the entire thing and subtract the diagonal.\nIt may feel wasteful in terms of FLOPS, but note that the theoretical savings relative to that implementation are only a factor 2. If that means anything to you, you probably should not be using numpy in the first place. \nAlso, numpy fundamentally deals with blocks of memory addressable as strided views. If you could get a single strided view onto your triangle, it might lead to an efficient numpy implementation. But you cant (proof left as exercise to the reader), so you can safely forget about any true numpy solution that isnt a call to an optimized C-routine that solves your problem for you. And none exist that I am aware.\nBut even that 'optimized' C loop may in practice get its ass kicked by A.sum(). If A is contiguous, that sum has the potential to dispatch a maximally cache-optimized and SIMD-optimized codepath. Likely, any vanilly-C youd write yourself would get absolutely demolished by A.sum() in a benchmark.","Q_Score":3,"Tags":"python,arrays,performance,numpy","A_Id":54630624,"CreationDate":"2019-02-11T11:28:00.000","Title":"Fastest way in numpy to sum over upper triangular elements with the least memory","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to solve an NLP multilabel classification problem. I have a huge amount of documents that should be classified into 29 categories. \nMy approach to the problem was, after cleaning up the text, stop word removal, tokenizing etc., is to do the following:\nTo create the features matrix I looked at the frequency distribution of the terms of each document, I then created a table of these terms (where duplicate terms are removed), I then calculated the term frequency for each word in its corresponding text (tf). So, eventually I ended up with around a 1000 terms and their respected frequency in each document. \nI then used selectKbest to narrow them down to around 490. and after scaling them I used OneVsRestClassifier(SVC) to do the classification. \nI am getting an F1 score around 0.58 but it is not improving at all and I need to get 0.62. \nAm I handling the problem correctly? \nDo I need to use tfidf vectorizer instead of tf, and how? \nI am very new to NLP and I am not sure at all what to do next and how to improve the score. \nAny help in this subject is priceless. \nThanks","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":240,"Q_Id":54636433,"Users Score":0,"Answer":"Tf method can give importance to common words more than necessary rather use Tfidf method which gives importance to words that are rare and unique in the particular document in the dataset.\nAlso before selecting Kbest rather train on the whole set of features and then use feature importance to get the best features.\nYou can also try using Tree Classifiers or XGB ones to better model but SVC is also very good classifier.\nTry using Naive Bayes as the minimum standard of f1 score and try improving your results on other classifiers with the help of grid search.","Q_Score":4,"Tags":"python,nlp,tf-idf,multilabel-classification,tfidfvectorizer","A_Id":54661633,"CreationDate":"2019-02-11T17:56:00.000","Title":"nlp multilabel classification tf vs tfidf","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am running a polynomial regression for order p. To make it simple, we use order p = 2 in this question.\nSuppose we have X with two feature x1, x2 and y. And I am trying to run a polynomial regression of\ny = \u03b5 + \u03b1 + \u03b21\u2022x1 + \u03b22\u2022x2 + \u03b23\u2022x1^2 + \u03b24\u2022x2^2\nI find that the sklearn have a sklearn.preprocessing.PolynomialFeatures. However, if I use order p = 2 and it automatically gives the combination of features. It will result in a regression of:\ny = \u03b5 + \u03b1 + \u03b21\u2022x1 + \u03b22\u2022x2 + \u03b23\u2022x1^2 + \u03b24\u2022x2^2 + \u03b25\u2022x1x2\nHowever, I do not want the combination of the features, i.e. things like x1x2. Is there any package that can do the polynomial regress as I wished?\nThanks!","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":843,"Q_Id":54639963,"Users Score":1,"Answer":"Tbh, this seems like very weird idea to me. A lot of strength of poly features come out of interaction features.\nI'm pretty sure there's nothing built-in, but\n\nyou can take a look at PolynomialFeatures pre-processor, and make your own with modification. You'll need to \"revert\" logic of interaction_only parameter\njust build your pre-processor, your case is quite simple, .fit will do smth like concat(a, a**2)","Q_Score":2,"Tags":"python,scikit-learn,regression,polynomials,non-linear-regression","A_Id":54640039,"CreationDate":"2019-02-11T22:14:00.000","Title":"Run a polynomial regression without combinations of the features","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"So I have a .mat file\nIt is a little over 1 GB but I don't know how much data or lines of code is on it. I want to convert this .mat file to a NumPy file in Python so I can look at the data and see what is in it. How do I do this conversion?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":3092,"Q_Id":54641709,"Users Score":1,"Answer":"I think you have two options to read it.\n\nReading it in python:\nimport scipy.io\nmat = scipy.io.loadmat('fileName.mat')\n\nConverting it to .csv in MATLAB in order to read it in python later:\nFileData = load('FileName.mat');\ncsvwrite('FileName.csv', FileData.M);","Q_Score":1,"Tags":"python-3.x","A_Id":54641758,"CreationDate":"2019-02-12T01:46:00.000","Title":"How can I convert .mat files to NumPy files in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm a newbie to python, i need to complete a project on IMDB Review sentiment analysis. I did not quiet understand how to train the imdb dataset aclImdb_v1.tar to a model.  Please show me how to train a model from this dataset.\nBelow is the method I need to implement for my project.\nText Reviews -> Embedded Layer -> Word vector generation ->\nCNN -> Feature Maps -> LSTM -> Classification layer\nThe result I expect is to find if a review taken as input is either positive or negative.\nPlease help me out and pardon my unknowledgeable words, if any..","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":84,"Q_Id":54647276,"Users Score":0,"Answer":"If the size of your dataset is quite large you can easily employ Deep Learning to complete your project. \nFirst, you need to create a pipeline to convert your data into well representable form, and then feed into the model.\nAnd do not worry about computational costs if you are writing in TensorFlow, the Google Cloud TPU offers free service on Google Colab.","Q_Score":0,"Tags":"python,nltk,word2vec,sentiment-analysis","A_Id":54647401,"CreationDate":"2019-02-12T09:55:00.000","Title":"How to create a word2vector model from imdb dataset and get it's featuremap using CNN","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using pandas to read a file inside a rest service. The file is huge with more than 100 columns. But I only want to read just first two columns. I know I can use usecols in read_csv but I was wondering how exactly it works?\nDoes pandas read the whole file and filter out the required columns?\nOr does it only read the required columns?\nI'm asking because I don't want to overload the memory.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":454,"Q_Id":54649847,"Users Score":8,"Answer":"According to the documentation, it will read the whole file (no way to only read columns from disk), but will only parse and store the columns given in the use_cols variable (emphasize mine):\n\nusecols : list-like or callable, optional\nReturn a subset of the columns... Using this parameter results in much faster parsing time and lower memory usage.","Q_Score":1,"Tags":"python,pandas","A_Id":54650088,"CreationDate":"2019-02-12T12:10:00.000","Title":"Does pandas read the whole file even when usecols is used?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"The SASData object has to_csv and to_df_CSV methods, but both of these write to locations on the host machine (where the SAS session is running). Is there a way to write a large SAS data table to .CSV on my local machine from a remote connection? The option on the saspy Github page was to get the DataFrame, via to_df, and then write that to .CSV, but this won't work in my case because the SAS table is bigger than memory. Must I read and write in chunks?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1164,"Q_Id":54653191,"Users Score":0,"Answer":"With saspy V2.4.3, you could try to_csv() to create the csv file on the SAS server, then download(), which is new in 2.4.3, to pull the csv over to your local filesystem.","Q_Score":2,"Tags":"python,csv,sas,saspy","A_Id":54711863,"CreationDate":"2019-02-12T15:13:00.000","Title":"saspy: write large SAS table to local csv","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When I tried to read a pickle file that saved by a former version of pandas, it yielded an ImportError.\n\nImportError: No module named 'pandas.core.internals.managers';\n  'pandas.core.internals' is not a package\n\nThere was no hit on stackoverflow so i would like to share my solution for this particular problem.","AnswerCount":5,"Available Count":3,"Score":1.2,"is_accepted":true,"ViewCount":62757,"Q_Id":54665527,"Users Score":70,"Answer":"This error comes off due to encoding of formerly saved pickle file. If you updated pandas to newly amended version, it produces this import error.","Q_Score":66,"Tags":"python,pandas,pickle","A_Id":54665528,"CreationDate":"2019-02-13T08:24:00.000","Title":"ImportError: No module named 'pandas.core.internals.managers'; 'pandas.core.internals' is not a package","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"When I tried to read a pickle file that saved by a former version of pandas, it yielded an ImportError.\n\nImportError: No module named 'pandas.core.internals.managers';\n  'pandas.core.internals' is not a package\n\nThere was no hit on stackoverflow so i would like to share my solution for this particular problem.","AnswerCount":5,"Available Count":3,"Score":1.0,"is_accepted":false,"ViewCount":62757,"Q_Id":54665527,"Users Score":10,"Answer":"I had the same problem, but for me, it seemed to come from the pickle package \/ interaction with the pandas package. \nI had Pandas version 0.23.4.\nI saved some pickle files with pandas.Dataframe.to_pickle, with python 3.6.6 & Pandas version 0.23.4.\nThen I upgraded to python 3.7.2 (Pandas version 0.23.4), and was enabled to read thoses pickle files with pandas.Dataframe.read_pickle.\nNext, I upgraded pandas to pandas 0.24.1, and it worked for me. I can read those files again.","Q_Score":66,"Tags":"python,pandas,pickle","A_Id":54785333,"CreationDate":"2019-02-13T08:24:00.000","Title":"ImportError: No module named 'pandas.core.internals.managers'; 'pandas.core.internals' is not a package","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"When I tried to read a pickle file that saved by a former version of pandas, it yielded an ImportError.\n\nImportError: No module named 'pandas.core.internals.managers';\n  'pandas.core.internals' is not a package\n\nThere was no hit on stackoverflow so i would like to share my solution for this particular problem.","AnswerCount":5,"Available Count":3,"Score":0.0798297691,"is_accepted":false,"ViewCount":62757,"Q_Id":54665527,"Users Score":2,"Answer":"conda update pandas\nIf you use conda package manager.","Q_Score":66,"Tags":"python,pandas,pickle","A_Id":58784510,"CreationDate":"2019-02-13T08:24:00.000","Title":"ImportError: No module named 'pandas.core.internals.managers'; 'pandas.core.internals' is not a package","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have installed Ancaconda3 and Tensorflow. When I try to import Tensorflow in python shell I receive the following error:\n\nModuleNotFoundError: No module named 'numpy.core._multiarray_umath'\n  ImportError: numpy.core.multiarray failed to import\nThe above exception was the direct cause of the following exception:\nTraceback (most recent call last):   File \"\", line 980, in _find_and_load SystemError:\n   returned a result with\n  an error set ImportError: numpy.core._multiarray_umath failed to\n  import ImportError: numpy.core.umath failed to import\n\nI am not sure what the problem is as numpy is installed on my system and can be successfully imported in python.\nI am using Windows10.\nThanks in advance.","AnswerCount":3,"Available Count":3,"Score":1.2,"is_accepted":true,"ViewCount":76204,"Q_Id":54665842,"Users Score":102,"Answer":"I also had the same issue.\nIt got resloved once i upgraded the numpy from 1.15.4 to 1.16.1.\nIf you're using pip:\npip install numpy --upgrade\nNumpy that came with Anaconda3 is of version 1.15.4. so i upgraded and it worked.\n\nSide note: if you're also using scikit-image in your script, be aware that numpy 1.16.3 has a conflict with old versions of scikit-image (e.g. you may get ImportError: cannot import name '_validate_lengths'). In that case, pip install --upgrade scikit-image from terminal solved the issue for me.\nHope it helps!!","Q_Score":71,"Tags":"python-3.x,numpy,tensorflow,anaconda","A_Id":54708388,"CreationDate":"2019-02-13T08:43:00.000","Title":"When importing tensorflow, I get the following error: No module named 'numpy.core._multiarray_umath'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have installed Ancaconda3 and Tensorflow. When I try to import Tensorflow in python shell I receive the following error:\n\nModuleNotFoundError: No module named 'numpy.core._multiarray_umath'\n  ImportError: numpy.core.multiarray failed to import\nThe above exception was the direct cause of the following exception:\nTraceback (most recent call last):   File \"\", line 980, in _find_and_load SystemError:\n   returned a result with\n  an error set ImportError: numpy.core._multiarray_umath failed to\n  import ImportError: numpy.core.umath failed to import\n\nI am not sure what the problem is as numpy is installed on my system and can be successfully imported in python.\nI am using Windows10.\nThanks in advance.","AnswerCount":3,"Available Count":3,"Score":0.1325487884,"is_accepted":false,"ViewCount":76204,"Q_Id":54665842,"Users Score":2,"Answer":"You can use two options in python 3.6\n\nInstall\n\n\npy pip -m install numpy==1.14.5\n\n\nUpgrade \n\n\npy pip install numpy --upgrade\n\nNote: the version most recently is 1.14.5","Q_Score":71,"Tags":"python-3.x,numpy,tensorflow,anaconda","A_Id":56179177,"CreationDate":"2019-02-13T08:43:00.000","Title":"When importing tensorflow, I get the following error: No module named 'numpy.core._multiarray_umath'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have installed Ancaconda3 and Tensorflow. When I try to import Tensorflow in python shell I receive the following error:\n\nModuleNotFoundError: No module named 'numpy.core._multiarray_umath'\n  ImportError: numpy.core.multiarray failed to import\nThe above exception was the direct cause of the following exception:\nTraceback (most recent call last):   File \"\", line 980, in _find_and_load SystemError:\n   returned a result with\n  an error set ImportError: numpy.core._multiarray_umath failed to\n  import ImportError: numpy.core.umath failed to import\n\nI am not sure what the problem is as numpy is installed on my system and can be successfully imported in python.\nI am using Windows10.\nThanks in advance.","AnswerCount":3,"Available Count":3,"Score":0.1325487884,"is_accepted":false,"ViewCount":76204,"Q_Id":54665842,"Users Score":2,"Answer":"Kindly check whether you have installed the numpy package from pip. Because if you are running on conda evironment, then all packages need to be downloaded from there.\nPlease use the below mentioned statement for this purpose\nconda install -c anaconda numpy\nAlso make sure that the numpy version supports the Python version you are using.","Q_Score":71,"Tags":"python-3.x,numpy,tensorflow,anaconda","A_Id":56356627,"CreationDate":"2019-02-13T08:43:00.000","Title":"When importing tensorflow, I get the following error: No module named 'numpy.core._multiarray_umath'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I run conda 4.6.3 with python 3.7.2 win32. In python, when I import numpy, i see the RAM usage increase by 80MB. Since I am using multiprocessing, I wonder if this is normal and if there is anyway to avoid this RAM overhead? Please see below all the versions from relevant packages (from conda list):\n\npython...........3.7.2 h8c8aaf0_2\nmkl_fft...........1.0.10 py37h14836fe_0\nmkl_random..1.0.2 py37h343c172_0\nnumpy...........1.15.4 py37h19fb1c0_0\nnumpy-base..1.15.4 py37hc3f5095_0\n\nthanks!","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1002,"Q_Id":54675983,"Users Score":2,"Answer":"You can't avoid this cost, but it's likely not as bad as it seems. The numpy libraries (a copy of C only libopenblasp, plus all the Python numpy extension modules) occupy over 60 MB on disk, and they're all going to be memory mapped into your Python process on import; adding on all the Python modules and the dynamically allocated memory involved in loading and initializing all of them, and 80 MB of increased reported RAM usage is pretty normal.\nThat said:\n\nThe C libraries and Python extension modules are memory mapped in, but that doesn't actually mean they occupy \"real\" RAM; if the code paths in a given page aren't exercised, the page will either never be loaded, or will be dropped under memory pressure (not even written to the page file, since it can always reload it from the original DLL).\nOn UNIX-like systems, when you fork (multiprocessing does this by default everywhere but Windows) that memory is shared between parent and worker processes in copy-on-write mode. Since the code itself is generally not written, the only cost is the page tables themselves (a tiny fraction of the memory they reference), and both parent and child will share that RAM.\n\nSadly, on Windows, fork isn't an option (unless you're running Ubuntu bash on Windows, in which case it's only barely Windows, effectively Linux), so you'll likely pay more of the memory costs in each process. But even there, libopenblasp, the C library backing large parts of numpy, will be remapped per process, but the OS should properly share that read-only memory across processes (and large parts, if not all, of the Python extension modules as well).\nBasically, until this actually causes a problem (and it's unlikely to do so), don't worry about it.","Q_Score":3,"Tags":"python,numpy,conda,ram","A_Id":54677347,"CreationDate":"2019-02-13T17:18:00.000","Title":"RAM usage after importing numpy in python 3.7.2","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using PyCharm with a remote interpreter. My code is showing a scatter figure using matplotlib. I can see the figure normally and interact with (zoom and rotate), but the interaction is too slow. I think this is because it's done through the SSH X-server.\nMy question is how to manually make the figure interactions graphically (using mouse, like zooming, panning, rotation, ...) execute faster? I think this should be via allowing the figure interactions to happen on local machine (machine running PyCharm), not the remote server.\nOne workaround is to save a .fig file, then copy it to my local machine, then load it. But I don't prefer this workaround.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":815,"Q_Id":54679917,"Users Score":0,"Answer":"Interacting with visualisations on separate windows is slow, not just on PyCharm but any IDE that you specify visualisations to be loaded separately. This is because they are loaded on either tkinter or any of the available modules like Qt5 and if you're on a mac, you can also use the inbuilt OSX rendering. Especially if your dataset is large then interaction becomes slower. It just seems that visualisations in Python are not exactly that optimised as you witness on Tableau or even Orange\/Glue.\nThat's why I personally prefer visualising data inline through the IPython console (for that I use Spyder). You cannot interact with plots though, but the purpose suffices.","Q_Score":4,"Tags":"python,matplotlib,ssh,pycharm,xserver","A_Id":54861503,"CreationDate":"2019-02-13T21:43:00.000","Title":"Remote interpreter and local plot in PyCharm?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to get OpenCV working, but the issue with this is that my Python27 Numpp version is too old. Every time I write \"install --upgrade numpy\", I am told that Numpy is already up to date in C:Python37. How can I update Numpy in C:Python27 rather than 37? Any help would be much appreciated.","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":14288,"Q_Id":54683455,"Users Score":0,"Answer":"Extending the first answer by JCutrer, after installing numpy on the specified version, you need to run the python.exe interpreter of the specified version. \nSo, assuming  you did\nc:\\python27\\Scripts\\pip.exe install --upgrade numpy, the libraries have been installed for python27, now you can run python from the below path, or set this path as interpreter in your IDE. \nc:\\python27\\bin\\python27.exe (or python.exe)","Q_Score":2,"Tags":"python,python-2.7,numpy,opencv,pip","A_Id":54683752,"CreationDate":"2019-02-14T04:58:00.000","Title":"How can I upgrade Numpy in just one version of Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I was trying to run some commands that needed me to update pandas, and then numpy, and so I did. Problem is now, when I try to look into a DataFrame in the variable explorer, they don't open and I get this error instead:\nImportError: No module named 'pandas.core.internals.managers'; 'pandas.core.internals' is not a package\nDo you know what is happening?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":788,"Q_Id":54699473,"Users Score":1,"Answer":"I had the same problem. I solved it installing the pandas version 0.24.2\npip install pandas==0.24.2","Q_Score":1,"Tags":"python,pandas,spyder","A_Id":57377131,"CreationDate":"2019-02-14T21:39:00.000","Title":"Getting ImportError on spyder when trying to open a DataFrame after updating pandas and numpy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to make my first neural network in keras (python) that takes in the x and y distances to the next pipe and outputs whether or not the bird should flap. How would I go about creating an input data set from the game and then turning that into something keras can use for training?\nI don't have very much knowledge in this area and my high school computer science teachers don't know either, therefore I'm not quite sure where to start. I have a very, very basic understanding of Keras and NN concepts. I have tried using .csv files with pandas but I am not sure how to turn that into useable data.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":117,"Q_Id":54701027,"Users Score":0,"Answer":"Sir even i'm new to neural network  but i have some knowledge \n if you want to do this in exactly this manner then i'm no help but\nyour can try doing this by genetic algo which will surely work for this","Q_Score":0,"Tags":"python,pandas,tensorflow,keras,neural-network","A_Id":54706020,"CreationDate":"2019-02-15T00:17:00.000","Title":"How to create a 2 value data table for keras","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am writing a simple implementation of AlexNet. I tried with using tf.nn.conv2d and tf.layers.conv2d, and the results turn out that the loss dropped faster when using tf.nn.conv2d, even the structure is exactly the same. Does anyone know any explanation for that?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":92,"Q_Id":54701429,"Users Score":1,"Answer":"If you try to follow the chain of function calls, you will find that tf.layers.conv2D() makes calls to tf.nn.conv2D() so no matter what you use, tf.nn.conv2d() will be called, it will be just faster if you call it yourself. You can use traceback.print_stack() method to verify that for yourself.\nNOTE This does not mean that they are one and the same, select the function based on your need as there are various other tasks undertaken by tf.layers.conv2D().","Q_Score":0,"Tags":"python,tensorflow","A_Id":54702197,"CreationDate":"2019-02-15T01:18:00.000","Title":"Tensorflow: why tf.nn.conv2d runs faster than tf.layers.conv2d?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I do not understand why we also need the function tf.name_scope when we already have tf.variable_scope. From the Tensorflow official API, I see that the tf.variable_scope is more powerful because it can have an effect on tf.get_variable. When we create layers and want to share variables, we always use tf.variable_scope and tf.name_scope. However, I try to learn something new from code released by Nvidia on GitHub. I found that it is frequent for coders to use tf.name_scope. Why do we still need this function?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":53,"Q_Id":54703473,"Users Score":0,"Answer":"You can use tf.variable_scope to add a prefix on both variables created with tf.get_variable and operations: as you said, this allows also variable sharing but it also makes the first call to tf.get_variable the definition of new variable under this scope.\ntf.name_scope adds a prefix only at the operations: variables defined outside the tf.name_scope using tf.get_variable are not prefixed thus the tf.name_scope is ignored completely for this variable: you're not declarning a variable prefixed in any way.\nThis can be useful when you want to create an operation block (using tf.name_scope) that uses a variable declared outside of it. This variable can be even used by multiple operation blocks at the same time.","Q_Score":0,"Tags":"python,tensorflow","A_Id":54704727,"CreationDate":"2019-02-15T06:03:00.000","Title":"TensorFlow why we still use tf.name_scope when we already have the function tf.variable_scope","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"What is difference between SGD classifier and SGD regressor in python sklearn?  Also can we set batch size for faster performance in them?","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":1733,"Q_Id":54704015,"Users Score":1,"Answer":"Well, it's in the name. SGD Classifier is a model that is optimized (trained) using SGD (taking the gradient of the loss of each sample at a time and the model is updated along the way) in classification problems. It can represent a variety of classification models (SVM, logistic regression...) which is defined with the loss parameter. By default, it represents linear SVM. SGD Regressor is a model that is optimized (trained) using SGD  for regression tasks. It's basically a linear model that is updated along the way with a decaying learning rate.","Q_Score":1,"Tags":"python-3.x,machine-learning,scikit-learn","A_Id":54704764,"CreationDate":"2019-02-15T06:50:00.000","Title":"What is difference between SGD classifier and SGD regressor in python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to insert a new row of totals after groupby sum.\nI can get the groupby sums of my table. I can also compute the sum of groupby sums with a second groupby sum. I tried and searched here to find a solution to insert\/append each sum of sums as a new row (e.g. named Total) after each group, but to no avail.\nFirst groupby sum\ndf.groupby(['Col1', 'Col2']).sum()\nSecond groupby sum\ndf.groupby(['Col1', 'Col2']).sum().groupby(level=[0]).sum()","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":93,"Q_Id":54708714,"Users Score":0,"Answer":"after the groupby use df.reset_index() to convert the df to a dataframe again","Q_Score":0,"Tags":"python,pandas,sum,pandas-groupby","A_Id":54735778,"CreationDate":"2019-02-15T11:48:00.000","Title":"How to insert a sum row of groupby sums","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to get data from an a endoscope into my opencv code in python. The endoscope machine has a dvi output and I have a Datapath vision capture card which helps read it into my system. I understand that opencv can only read from usb cameras or ip webcams. I was wondering if anyone has done this kind of a read into opencv?\nPointers greatly appreciated.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":838,"Q_Id":54709269,"Users Score":0,"Answer":"It might be possible to have the DVI stream written to a file, FFMPEG\/gstreamer read the file stream, and then pipe the input into OpenCV.","Q_Score":0,"Tags":"python,c++,opencv","A_Id":54800843,"CreationDate":"2019-02-15T12:19:00.000","Title":"How to access data from the vision capture cards to opencv?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":1,"Web Development":0},{"Question":"I am trying to run jupyter notebook and getting following error.\nI am using Win 7 with anaconda python 3.7.\n\nImportError: Something is wrong with the numpy installation. While importing we detected an older version of numpy in ['c:\\users\\paperspace\\anaconda3\\envs\\tensorflow10\\lib\\site-packages\\numpy']. One method of fixing this is to repeatedly uninstall numpy until none is found, then reinstall this version.\n\nI have followed the steps mentioned in the error but still not working.","AnswerCount":11,"Available Count":6,"Score":1.0,"is_accepted":false,"ViewCount":64552,"Q_Id":54715835,"Users Score":10,"Answer":"I was getting the error when I was trying to use Keras. This can be fixed by removing the numpy package continuously by running pip3 uninstall numpy. And checking the successful un-installation by opening a python terminal and importing numpy package.","Q_Score":33,"Tags":"python,numpy,tensorflow","A_Id":54793162,"CreationDate":"2019-02-15T19:21:00.000","Title":"Numpy is installed but still getting error","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to run jupyter notebook and getting following error.\nI am using Win 7 with anaconda python 3.7.\n\nImportError: Something is wrong with the numpy installation. While importing we detected an older version of numpy in ['c:\\users\\paperspace\\anaconda3\\envs\\tensorflow10\\lib\\site-packages\\numpy']. One method of fixing this is to repeatedly uninstall numpy until none is found, then reinstall this version.\n\nI have followed the steps mentioned in the error but still not working.","AnswerCount":11,"Available Count":6,"Score":-0.0363476168,"is_accepted":false,"ViewCount":64552,"Q_Id":54715835,"Users Score":-2,"Answer":"For me it was about the consoles used. \nMy cygwin terminal - NOPE, dos - NOPE, but console opened from Anaconda or Spyder etc... all commands (pip install etc) worked.","Q_Score":33,"Tags":"python,numpy,tensorflow","A_Id":56572030,"CreationDate":"2019-02-15T19:21:00.000","Title":"Numpy is installed but still getting error","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to run jupyter notebook and getting following error.\nI am using Win 7 with anaconda python 3.7.\n\nImportError: Something is wrong with the numpy installation. While importing we detected an older version of numpy in ['c:\\users\\paperspace\\anaconda3\\envs\\tensorflow10\\lib\\site-packages\\numpy']. One method of fixing this is to repeatedly uninstall numpy until none is found, then reinstall this version.\n\nI have followed the steps mentioned in the error but still not working.","AnswerCount":11,"Available Count":6,"Score":1.0,"is_accepted":false,"ViewCount":64552,"Q_Id":54715835,"Users Score":22,"Answer":"Run\npip3 uninstall numpy \nuntil you receive a message stating no files available with numpy to uninstall and then you can freshly install numpy using\npip install numpy\nand that will fix the issue.","Q_Score":33,"Tags":"python,numpy,tensorflow","A_Id":55652317,"CreationDate":"2019-02-15T19:21:00.000","Title":"Numpy is installed but still getting error","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to run jupyter notebook and getting following error.\nI am using Win 7 with anaconda python 3.7.\n\nImportError: Something is wrong with the numpy installation. While importing we detected an older version of numpy in ['c:\\users\\paperspace\\anaconda3\\envs\\tensorflow10\\lib\\site-packages\\numpy']. One method of fixing this is to repeatedly uninstall numpy until none is found, then reinstall this version.\n\nI have followed the steps mentioned in the error but still not working.","AnswerCount":11,"Available Count":6,"Score":0.072599319,"is_accepted":false,"ViewCount":64552,"Q_Id":54715835,"Users Score":4,"Answer":"This Means a Duplicated. \nTry pip uninstall numpy or pip3 uninstall numpy then sudo apt-get install python3-numpy\nFOR (DEBIAN DIST)","Q_Score":33,"Tags":"python,numpy,tensorflow","A_Id":56306185,"CreationDate":"2019-02-15T19:21:00.000","Title":"Numpy is installed but still getting error","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to run jupyter notebook and getting following error.\nI am using Win 7 with anaconda python 3.7.\n\nImportError: Something is wrong with the numpy installation. While importing we detected an older version of numpy in ['c:\\users\\paperspace\\anaconda3\\envs\\tensorflow10\\lib\\site-packages\\numpy']. One method of fixing this is to repeatedly uninstall numpy until none is found, then reinstall this version.\n\nI have followed the steps mentioned in the error but still not working.","AnswerCount":11,"Available Count":6,"Score":0.0,"is_accepted":false,"ViewCount":64552,"Q_Id":54715835,"Users Score":0,"Answer":"Your issue maybe similar to mine below.\nHow to reproduce it\n\nStarted a virtual environment named \"jupyter_ve\"  with numpy=1.21.0   where I launched jupyter.\nOpened a notebook using a virtual environment named \"project_ve\"   with numpy=1.20.0   where I launched my notebook kernel using \"project_ve\"\nIn Notebook import fails on from numba import njit giving errors install numpy 1.20.0 or lower\n\nSolution\nMake sure you install  numpy version in the virtual environment you used to start the jupyter.\nIn my case above I had to install \/ downgrade numpy in the virtual envrionment where I launched jupyter from.  It doesn't matter that I had installed notebook virtual environment kernel.","Q_Score":33,"Tags":"python,numpy,tensorflow","A_Id":70216694,"CreationDate":"2019-02-15T19:21:00.000","Title":"Numpy is installed but still getting error","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to run jupyter notebook and getting following error.\nI am using Win 7 with anaconda python 3.7.\n\nImportError: Something is wrong with the numpy installation. While importing we detected an older version of numpy in ['c:\\users\\paperspace\\anaconda3\\envs\\tensorflow10\\lib\\site-packages\\numpy']. One method of fixing this is to repeatedly uninstall numpy until none is found, then reinstall this version.\n\nI have followed the steps mentioned in the error but still not working.","AnswerCount":11,"Available Count":6,"Score":0.0544914242,"is_accepted":false,"ViewCount":64552,"Q_Id":54715835,"Users Score":3,"Answer":"Use conda update --all \nThis works.","Q_Score":33,"Tags":"python,numpy,tensorflow","A_Id":55599428,"CreationDate":"2019-02-15T19:21:00.000","Title":"Numpy is installed but still getting error","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"What is the correct way to perform gradient clipping in pytorch?\nI have an exploding gradients problem.","AnswerCount":5,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":99166,"Q_Id":54716377,"Users Score":0,"Answer":"Well, I met with same err. I tried to use the clip norm but it doesn't work.\nI don't want to change the network or add regularizers. So I change the optimizer to Adam, and it works.\nThen I use the pretrained model from Adam to initate the training and use SGD + momentum for fine tuning. It is now working.","Q_Score":71,"Tags":"python,machine-learning,deep-learning,pytorch,gradient-descent","A_Id":70205626,"CreationDate":"2019-02-15T20:09:00.000","Title":"How to do gradient clipping in pytorch?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have 2 Python environments (Python 3.6 by Visual studio 2017 and Python 3.7 by Anaconda)\nWhen I run a script I get an error that import numpy\n\nTraceback (most recent call last):   File\n  \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\core__init__.py\",\n  line 16, in \n      from . import multiarray ImportError: DLL load failed: The specified module could not be found.\nDuring handling of the above exception, another exception occurred:\nTraceback (most recent call last):   File\n  \"D:\\projects\\UdacityNanoDegreeCourse\\UdacityNanoDegreeCourse\\LearningCurves\\LearningCurves.py\",\n  line 2, in \n      import numpy as np   File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy__init__.py\", line\n  142, in \n      from . import add_newdocs   File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\add_newdocs.py\",\n  line 13, in \n      from numpy.lib import add_newdoc   File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\lib__init__.py\",\n  line 8, in \n      from .type_check import *   File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\lib\\type_check.py\",\n  line 11, in \n      import numpy.core.numeric as _nx   File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\core__init__.py\",\n  line 26, in \n      raise ImportError(msg) ImportError: Importing the multiarray numpy extension module failed.  Most likely you are trying to import a\n  failed build of numpy. If you're working with a numpy git repo, try\n  git clean -xdf (removes all files not under version control). \n  Otherwise reinstall numpy.\nOriginal error was: DLL load failed: The specified module could not be\n  found.\n\nAs a temporary fix I run Visual Studio from Anaconda prompt.\nHow can I fix the environment so that I could run Visual Studio as usually by double click? Do I need to change PATH somehow?\nRight click-> Activate Environment didn't help either.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":469,"Q_Id":54724459,"Users Score":0,"Answer":"Resolved it myself\nIn the PATH variable\nC:\\ProgramData\\Anaconda3\nC:\\ProgramData\\Anaconda3\\Library\\mingw-w64\\bin\nC:\\ProgramData\\Anaconda3\\Library\\usr\\bin\nC:\\ProgramData\\Anaconda3\\Library\\bin\nC:\\ProgramData\\Anaconda3\\Scripts\nshould go first","Q_Score":2,"Tags":"python,python-3.x,visual-studio,numpy,anaconda","A_Id":55090778,"CreationDate":"2019-02-16T15:09:00.000","Title":"Visual studio can't see Anaconda's modules","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Ive started experimenting with tensorflow lately and there is one thing I'm not quite sure i get right:\n  If i have a set of training data, train the model  on it for N number of epochs and than use model.save(\"Multi_stage_test.model\") to save the model.\nAfter running the program again with the same training data and with the previously trained model loaded with model=load_model(\"Multi_stage_test.model\"). Am I continuing to train the model (the 1 epoch of the training session is a N+1 epoch for the model) or am I retraining the model (the 1 epoch of the training session is a 1 epoch for the model)?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":84,"Q_Id":54726275,"Users Score":1,"Answer":"If you load your model before the training instructions, then you are continuing to train the same model. Tensorflow will just load the pre-trained weights of your model and continue updating them during the new training session.","Q_Score":0,"Tags":"python,tensorflow","A_Id":54726295,"CreationDate":"2019-02-16T18:25:00.000","Title":"Retraining or Continuing Training","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to use featuretools to generate features to help me predict the number of museum visits next month.\nCan featuretools generate features for time series? Should I changed the data so that the id is the month or can featuretools do it automatically?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":468,"Q_Id":54730480,"Users Score":0,"Answer":"Featuretools can certainly help with this. Can you provide more information on the dataset and specific prediction problem so we can help?","Q_Score":1,"Tags":"python,featuretools","A_Id":54730825,"CreationDate":"2019-02-17T05:40:00.000","Title":"Automated feature generation for time series problems - Featuretools","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm pretty new to object detection. I'm using tensorflow object detection API and I'm now collecting datasets for my project \nand model_main.py to train my model.\nI have found and transformed two quite large datasets of cars and traffic lights with annotations. And made two tfrecords from them. \nNow I want to train a pretrained model however, I'm just curious will it work? When it is possible that an image for example \"001.jpg\" will have of course some annotated bounding boxes of cars (it is from the car dataset) but if there is a traffic light as well it wouldn't be annotated -> will it lead to bad learning rate? (there can be many of theese \"problematic\" images) How should I improve this? Is there any workaround? (I really don't want to annotate the images again)\nIf its stupid question I'm sorry, thanks for any response - some links with this problematic would be the best ! \nThanks !","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1404,"Q_Id":54737739,"Users Score":1,"Answer":"The short answer is yes, it might be problematic, but with some effort you can make it possible.\nIf you have two urban datasets, and in one you only have annotations for traffic lights, and in the second you only have annotations for cars, then each instance of car in the first dataset will be learned as false example, and each instance of traffic light in the second dataset will be learned as false example.\nThe two possible outcomes I can think of are:\n\nThe model will not converge, since it tries to learn opposite things.\nThe model will converge, but will be domain specific. This means that the model will only detect traffic lights on images from the domain of the first dataset, and cars on the second.\nIn fact I tried doing so myself in a different setup, and got this outcome.\n\nIn order to be able to learn your objective of learning traffic lights and cars no matter which dataset they come from, you'll need to modify your loss function. You need to tell the loss function from which dataset each image comes from, and then only compute the loss on the corresponding classes (\/zero out the loss on the classes do not correspond to it). So returning to our example, you only compute loss and backpropagate traffic lights on the first dataset, and cars on the second.\nFor completeness I will add that if resources are available, then the better option is to annotate all the classes on all datasets in order to avoid the suggested modification, since by only backpropagating certain classes, you do not enjoy using actual false examples for other classes.","Q_Score":0,"Tags":"python,tensorflow,object-detection-api","A_Id":54782462,"CreationDate":"2019-02-17T21:13:00.000","Title":"Is it possible to train model from multiple datasets for each class?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have many qualitative variables in one column1 and corresponding qualitative values in another column which have blank values. I want to replace the blanks with the MODE value of each qualitative variable in column 1. For example if I have varibles such as Accountant, Engineer, Manager, etc. in column 1 and if I have Bachelor's degree as MODE for accountant, Master's for Engineer in column 2, I want to replace the coressponding blanks with Bachelor's and Master's Correctly. How can I achieve this in pandas?","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":90,"Q_Id":54741230,"Users Score":2,"Answer":"df.loc[df[\"columnname\"]==0,\"columnname\"]=np.nan\ndf[\"columnname\"]=df.groupby(\"groupbycolumnname\").columnname.transform(lambda x: x.fillna(x.mode()))","Q_Score":1,"Tags":"python,pandas","A_Id":54742112,"CreationDate":"2019-02-18T05:59:00.000","Title":"Filtering and filling Nan with mode in Pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to implement doc2vec, but I am not sure how the input for the model should look like if I have pretrained word2vec vectors.\nThe problem is, that I am not sure how to theoretically use pretrained word2vec vectors for doc2vec. I imagine, that I could prefill the hidden layer with the vectors and the rest of the hidden layer fill with random numbers\nAnother idea is to use the vector as input for word instead of a one-hot-encoding but I am not sure if the output vectors for docs would make sense.\nThank you for your answer!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1101,"Q_Id":54762478,"Users Score":4,"Answer":"You might think that Doc2Vec (aka the 'Paragraph Vector' algorithm of Mikolov\/Le) requires word-vectors as a 1st step. That's a common belief, and perhaps somewhat intuitive, by analogy to how humans learn a new language: understand the smaller units before the larger, then compose the meaning of the larger from the smaller. \nBut that's a common misconception, and Doc2Vec doesn't do that. \nOne mode, pure PV-DBOW (dm=0 in gensim), doesn't use conventional per-word input vectors at all. And, this mode is often one of the fastest-training and best-performing options. \nThe other mode, PV-DM (dm=1 in gensim, the default) does make use of neighboring word-vectors, in combination with doc-vectors in a manner analgous to word2vec's CBOW mode \u2013 but any word-vectors it needs will be trained-up simultaneously with doc-vectors. They are not trained 1st in a separate step, so there's not a easy splice-in point where you could provide word-vectors from elsewhere. \n(You can mix skip-gram word-training into the PV-DBOW, with dbow_words=1 in gensim, but that will train word-vectors from scratch in an interleaved, shared-model process.)\nTo the extent you could pre-seed a model with word-vectors from elsewhere, it wouldn't necessarily improve results: it could easily send their quality sideways or worse. It might in some lucky well-managed cases speed model convergence, or be a way to enforce vector-space-compatibility with an earlier vector-set, but not without extra gotchas and caveats that aren't a part of the original algorithms, or well-described practices.","Q_Score":0,"Tags":"python,machine-learning,nlp,word2vec,doc2vec","A_Id":54777057,"CreationDate":"2019-02-19T09:11:00.000","Title":"How to use pretrained word2vec vectors in doc2vec model?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"All, I am pursuing a path of manual installation of python libraries, one that unfortunately, I cannot deviate from and it has become challenging because some of the libraries are just not easily found from pypi.org.    This is a Windows 10 set up using Anaconda for python 3.7\nMy goal is to install SPACY and I have tried this version: spacy-2.0.18-cp37-cp37m-win_amd64.whl\nWhich Requires Collecting thinc<6.13.0,>=6.12.1 (from spacy==2.0.18)\nNow I can't seem to find 6.13.0   But was able to find 6.12.1 also found thinc-7.0.0.   \nso I installed thinc-7.0.0 but spacy does not recognize that as >= 6.12.1  not sure if I am interpreting it correctly. \nSo instead I install thinc-6.12.0-cp37-cp37m-win_amd64.whl \nwhich fails because it is looking for.. Collecting msgpack-numpy<0.4.4.0 (from thinc==6.12.0)\nHowever msgpack-numpy<0.4.4.0  seems undiscoverable.   \nI have found msgpack_numpy-0.4.4.2-py2.py3-none-any.whl\nI also Found msgpack_numpy-0.4.4-py2.py3-none-any.whl\nof which neither will be accepted by thinc-6.12.0 as valid.\nSo have I chosen the wrong spacy version to start with for 3.7? \nI tried this path in 3.6 and I think I was able to get it all to work, is the python 3.7 path just broken?  \nIf someone knows the path and the location of the files to get spacy to work, that would be great.   Unfortunately, I cannot just issue pip commands at this time..\nbest regards","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":132,"Q_Id":54766199,"Users Score":0,"Answer":"After many tries, I figured out the simple mistake I was making...\nCollecting msgpack-numpy<0.4.4.0 (from thinc==6.12.0)    Does not mean, I need 0.4.4.0  it means I need a package less than 0.4.4.0.\nSo I found msgpack_numpy-0.4.3.2-py2.py3-none-any.whl   \nIt is confusing to list packages as a less than or equal requirement that do not actually exist.   \nI eventually was able to install spacy after many tries of different combinations of thinc, regex, spacy \nregex==2018.01.10\nmsgpack-numpy 0.4.3.2\nthinc-6.12.1\nSuccessfully installed spacy-2.0.18","Q_Score":1,"Tags":"python,numpy","A_Id":54767990,"CreationDate":"2019-02-19T12:19:00.000","Title":"Python - Manual - libary installation Failure on Windows with Spacy, Thinc and msgpack-numpy python 3.7","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am having a trouble picking the right data structure as\/library . I lack in experience in the area of image processing \/ pattern recognition . The aim is to building a simple prototype to learn recognizing particular shapes from construction plans. I would be great full for any indication about the data structure as I know It will be hard to switch it later on during the project and thus I am not entirely sure which one to pick. \nThe problem is , I plan to use a kind of neural network \/ algorithm later on so the performance of processing of the data structure may happen to be my bottle neck.\nI was thinking about NumPy \/ SciPy \/  PIL \/ MatPlotLib\nI will be extremely grateful for expertise of anyone who has tackled similar problem","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":47,"Q_Id":54767421,"Users Score":0,"Answer":"If you're planning on using something like PyTorch later on (which would tie in to the sort of neural-network functionality you're pursuing), I'd become familiar with how NumPy operates, as it bridges pretty well into Torch data structures. If it helps, a lot of SciPy and Matplotlib functions work beautifully with Numpy structures right out of the box.\nIt's hard to tell exactly what you're looking for in these non-neural data structures; where are you worried about performance concerns and bottlenecks?\nI'd recommend starting out with some PyTorch (or other deep learning framework) tutorials regarding image recognition and classification; it will get you closer to where you want to end up, and you'll be better able to make decisions about what your eventual program structure needs will be.","Q_Score":1,"Tags":"python,algorithm,image-processing,data-structures","A_Id":54767624,"CreationDate":"2019-02-19T13:27:00.000","Title":"data structures to recognize particular elements \/ shapes with similar patterns on the image in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to build a machine learning system with large amount of historical trading data for machine learning purpose (Python program). \nTrading company has an API to grab their historical data and real time data. Data volume is about 100G for historical data and about 200M for daily data. \nTrading data is typical time series data like price, name, region, timeline, etc. The format of data could be retrieved as large files or stored in relational DB.  \nSo my question is, what is the best way to store these data on AWS and what'sthe best way to add new data everyday (like through a cron job, or ETL job)? Possible solutions include storing them in relational database like Or NoSQL databases like DynamoDB or Redis, or store the data in a file system and read by Python program directly. I just need to find a solution to persist the data in AWS so multiple team can grab the data for research. \nAlso, since it's a research project, I don't want to spend too much time on exploring new systems or emerging technologies. I know there are Time Series Databases like InfluxDB or new Amazon Timestream. Considering the learning curve and deadline requirement, I don't incline to learn and use them for now. \nI'm familiar with MySQL. If really needed, i can pick up NoSQL, like Redis\/DynamoDB. \nAny advice? Many thanks!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":82,"Q_Id":54770900,"Users Score":0,"Answer":"If you want to use AWS EMR, then the simplest solution is probably just to run a daily job that dumps data into a file in S3. However, if you want to use something a little more SQL-ey, you could load everything into Redshift. \nIf your goal is to make it available in some form to other people, then you should definitely put the data in S3. AWS has ETL and data migration tools that can move data from S3 to a variety of destinations, so the other people will not be restricted in their use of the data just because of it being stored in S3. \nOn top of that, S3 is the cheapest (warm) storage option available in AWS, and for all practical purposes, its throughout is unlimited. If you store the data in a SQL database, you significantly limit the rate at which the data can be retrieved. If you store the data in a NoSQL database, you may be able to support more traffic (maybe) but it will be at significant cost. \nJust to further illustrate my point, I recently did an experiment to test certain properties of one of the S3 APIs, and part of my experiment involved uploading ~100GB of data to S3 from an EC2 instance. I was able to upload all of that data in just a few minutes, and it cost next to nothing. \nThe only thing you need to decide is the format of your data files. You should talk to some of the other people and find out if Json, CSV, or something else is preferred. \nAs for adding new data, I would set up a lambda function that is triggered by a CloudWatch event. The lambda function can get the data from your data source and put it into S3. The CloudWatch event trigger is cron based, so it\u2019s easy enough to switch between hourly, daily, or whatever frequency meets your needs.","Q_Score":0,"Tags":"mysql,database,amazon-web-services,amazon-dynamodb,mysql-python","A_Id":54779427,"CreationDate":"2019-02-19T16:30:00.000","Title":"What is a good AWS solution (DB, ETL, Batch Job) to store large historical trading data (with daily refresh) for machine learning analysis?","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"i have problems rounding Decimals() inside a Pandas Dataframe. The round() method does not work and using quantize() neither. I've searched for a solution  with no luck so far.\nround() does nothing, i asume it is meant for float numbers\nquantize() won't work because it is not a DataFrame function\nAny thoughts?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":881,"Q_Id":54773447,"Users Score":0,"Answer":"To round your decimal to 2 significant figures for example:\nround(df['yourSeries'].astype('float'), 2)\nOr if you just want an int:\nround(df['yourSeries'].astype('float'))","Q_Score":2,"Tags":"python,pandas,decimal","A_Id":59461549,"CreationDate":"2019-02-19T19:16:00.000","Title":"Decimal class rounding in Pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Scenario: four users are annotating images with one of four labels each. These are stored in a fairly complex format - either as polygons or as centre-radius circles. I'm interested in quantifying, for each class, the area of agreement between individual raters \u2013 in other words, I'm looking to get an m x n matrix, where M_i,j will be some metric, such as the IoU (intersection over union), between i's and j's ratings (with a 1 diagonal, obviously). There are two problems I'm facing.\nOne, I don't know what works best in Python for this. Shapely doesn't implement circles too well, for instance.\nTwo, is there a more efficient way for this than comparing it annotator-by-annotator?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":21,"Q_Id":54789340,"Users Score":0,"Answer":"IMO the simplest is to fill the shapes using polygon filling \/ circle filling (this is simple, you can roll your own) \/ path filling (from a seed). Then finding the area of overlap is an easy matter.","Q_Score":0,"Tags":"python,computational-geometry","A_Id":54805518,"CreationDate":"2019-02-20T15:02:00.000","Title":"Calculating object labelling consensus area","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am currently working on IMDB 5000 movie dataset for a class project. The budget variable has a lot of zero values.\nThey are missing entries. I cannot drop them because they are 22% of my entire data. \nWhat should I do in Python? Some suggested binning? Could you provide more details?","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":1215,"Q_Id":54796340,"Users Score":2,"Answer":"Well there are a few options.\n\nTake an average of the non zero values and fill all the zeros with the average. This yields 'tacky' results and is not best practice a few outliers can throw off the whole. \nUse the median of the non zero values, also not a super option but less likely to be thrown off by outliers.\nBinning would be taking the sum of the budgets then say splitting the movies into a certain number of groups like say budgets over or under a million, take the average budget then divide that by the amount of groups you want then use the intervals created from the average if they fall in group 0 give them a zero if group 1 a one etc. \nI think finding the actual budgets for the movies and replacing the bad itemized budgets with the real budget would be a good option depending on the analysis you are doing. You could take the median or average of each feature column of the budget make that be the percent of each budget for a movie then fill in the zeros with the percent of budget the median occupies. If median value for the non zero actor_pay column is budget\/actor_pay=60% then filling the actor_pay column of a zeroed value with 60 percent of the budget for that movie would be an option.\nHard option create a function that takes the non zero values of a movies budgets and attempts to interpolate the movies budget based upon the other movies data in the table. This option is more like its own project and the above options should really be tried first.","Q_Score":2,"Tags":"python,data-science,mining","A_Id":58782823,"CreationDate":"2019-02-20T22:36:00.000","Title":"Dataset with lots of zero value as missing value. What should I do?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm currently learning deep-learning from two lectures. What gets me confused is that there is a notation difference between two lectures when they shape an input matrix X. \nIn Coursera's lecture, they make a matrix X in shape of (number of features, number of samples), so that they stack the samples vertically. Otherwise, the other lecture stacks the samples horizontally, so that every row represents one sample.\nWhat makes this difference and which one should I follow?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":39,"Q_Id":54798437,"Users Score":0,"Answer":"Both ways are fine, it only matters to be consistent, i.e., that all matrix operations are correct. Depending on the shape of the matrix you might have matrix*vector or vector_transposed*matrix, or some variety along these lines.\nPlaying around with different representations might actually help the understanding in the long run. So I would recommend to follow both lectures and appreciate the differences in how they represent the data etc.","Q_Score":0,"Tags":"python,machine-learning,deep-learning","A_Id":54799272,"CreationDate":"2019-02-21T02:43:00.000","Title":"Matrix shapes difference between lectures","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataset which has several Variables. \nI want to determine that how can we judge for a variable if it is categorical or numerical other than the method of unique value counts, as for instance one of my variable Disease Type has 31 Unique Values whereas other Variable Distance from Office has 25 Unique Values, both in the form of numbers.","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":1458,"Q_Id":54800350,"Users Score":3,"Answer":"The short answer, is: your knowledge of the problem domain \/ application domain will tell you.\nThere are some differences that you look for, but to apply these differences, you will still have to use some domain knowledge (sometimes common sense).\nThe following are some differences which will help you distinguish:\n\nFor categorical variables, the set of permitted values is usually fixed, and rarely changes, if at all. In contrast, for numeric variable, the set of values can change, for example, when you receive a new record for the same dataset.\nNumeric variables can potentially have values that are not round integers. In your example, even though \"distance from office\" happens to have integer values, that could be purely incidental, or could have been a choice made by some one about how much numeric precision they want in the data.\nFor categorical variables, it usually doesn't make sense to talk of averages. For example, there are 2 types of diabetes called Type 1, Type 2, but it just doesn't make sense to talk of an average of these types (Type 1.2357?).\nAsk yourself this thumb-rule question: When I perform my data analysis can I express my inferences in terms of specific values of this variable? How about ranges of this variable (\"0 to 5 km\", \"5 to 10 km\", etc). For example, can I report any inferences from my data analysis that says \"Those whose distance from office is 123, are prone to be successful in their career\"? That specific value sounds silly, right? In contrast, if it were a categorical variable such as Type 2 Diabetes, you can always make inferences in terms of the specific value.","Q_Score":1,"Tags":"python,pandas,dataframe,statistics,data-analysis","A_Id":54801198,"CreationDate":"2019-02-21T06:09:00.000","Title":"How to know if the Variable is Categorical or Numerical if all it contains is Digits?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataset which has several Variables. \nI want to determine that how can we judge for a variable if it is categorical or numerical other than the method of unique value counts, as for instance one of my variable Disease Type has 31 Unique Values whereas other Variable Distance from Office has 25 Unique Values, both in the form of numbers.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1458,"Q_Id":54800350,"Users Score":0,"Answer":"<dataframename>.info() will give the total count of each variable along with whether it is non-null and its datatype like float64,object,int64 etc","Q_Score":1,"Tags":"python,pandas,dataframe,statistics,data-analysis","A_Id":54801115,"CreationDate":"2019-02-21T06:09:00.000","Title":"How to know if the Variable is Categorical or Numerical if all it contains is Digits?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am coming over from Matlab, and while everything was mostly ported over really well (the community has to be thanked for this, a Matlab license costs way over $1000). There is one thing that I cannot for the life of me find out.\nIn Matlab, all arrays are 2D (until recently, where they gave you other options). Such that, when I define a scalar, array, matrix they are all considered 2D. This is pretty useful when doing matrix multiplication!\nIn Python, when using numpy. Unfortunately, I find myself having to use the reshape command quite frequently.\nIs there anyway that I can globally set that all array's have 2D dimensions unless stated otherwise?\nEdit:\nAccording to the numpy documentation numpy.matrix may be removed in the coming future. What I want to do in essence is that have all output of any numpy operation have the function np.atleast_2d applied to them automatically.","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":114,"Q_Id":54821206,"Users Score":2,"Answer":"As noted above, the np.matrix class has semantics quite similar to a matlab array.\nHowever, if you goal is to learn numpy as a marketable skill, I would strongly recommend you fully embrace the concept of an ndarray; while there is some historical truth to calling numpy a port of matlab, it is a bit of an insult, as the ndarray is one of the most compelling objective conceptual improvements of numpy over matlab, other than its price.\nTLDR; you will have a hard time not getting your application tossed by me if you claim to know numpy, but your code samples smell like ported matlab in any way.","Q_Score":1,"Tags":"python,arrays,numpy","A_Id":54821940,"CreationDate":"2019-02-22T06:24:00.000","Title":"I want all numpy arrays to be forced to be 2 dimensional","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've installed opencv from source using a virtualenv, however I faced some errors and needed to reinstall it. I tried removing all the files with sudo find \/ -name \"opencv\" -exec rm {} \\; and checked if the package was removed with pkg-config --modversion opencv, and it said it could not be found, but when I open the terminal with python3 and enter import cv2 then print(cv2.__version__), the terminal returns 4.0.0. How can I completely remove opencv? I'm on Ubuntu 18.04 LTS.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":362,"Q_Id":54826979,"Users Score":0,"Answer":"I found out that I hadn't uninstalled in the build folder, problem solved!","Q_Score":1,"Tags":"python,opencv,ubuntu","A_Id":54832931,"CreationDate":"2019-02-22T12:17:00.000","Title":"OpenCV can't be found but can be imported","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a data set for which I use Sklearn Decision Tree regression machine learning package to build a model for prediction purposes. Subsequently, I am trying to utilize scipy.optimize package to solve for the minimized solution based on a given constraint. \nHowever, I am not sure if I can take the decision tree model as the objective function for the optimization problem. What should be the approach in a situation like this? I have tried linear regression models such as LarsCV in the past and they worked just fine. But in a linear regression model, you can essentially extract the coefficients and interception point from the model.","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":904,"Q_Id":54833739,"Users Score":2,"Answer":"Yes; a linear regression model is a straightforward linear function of coefficients (one of which is the \"intercept\" or \"bias\").\nThe problem you have now is that a more complex model isn't quite so simple.  You need to load the model into an appropriate engine.  To \"call\" the model, you feed that engine the input vector (the cognate of a list of arguments), and wait for the model to return the prediction.\nYou need to wrap this process in a function call, perhaps one that issues the model load and processing as external system \/ shell commands, and returns the results to your main program.  Some applications are large enough that it makes sense to implement a full-bore data stream with listener and reporter to handle the throughput.\nDoes that get you moving?","Q_Score":6,"Tags":"python,machine-learning,optimization,scikit-learn,scipy","A_Id":54833867,"CreationDate":"2019-02-22T19:18:00.000","Title":"Can I use a machine learning model as the objective function in an optimization problem?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am running C# project which use result of python file. I'm using IronPython to run python file in visual studio. But the error above come up. Please help me. Many thanks!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1617,"Q_Id":54839602,"Users Score":0,"Answer":"Sorry you cant access third party functionalities\/packages (like Pandas here)\nThe reason:\nPandas \/ numpy use great parts of c code, that is a no good  for IronPython (.NET).!!!!\nIf you need to use the power of pandas, i suggest you to create a dialog (send\/reveive datas or by file or by calling an external python prog) between your IronPython Project and a program python including pandas.","Q_Score":0,"Tags":"c#,python,pandas","A_Id":54839647,"CreationDate":"2019-02-23T08:18:00.000","Title":"IronPython.Runtime.Exceptions.ImportException: 'No module named pandas'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I try to understand Keras and LSTMs step by step. Right now I build an LSTM there the input is a sentence and the output is an array of five values which can each be 0 or 1.\nExample:\nInput sentence: 'I hate cookies'\nOutput example: [0,0,1,0,1]\nFor this, I am using keras library.\nNow I am not sure which loss function I should use. Right now I just know two predefined loss functions a little bit better and both seem not to be good for my example:\nBinary cross entropy: Good if I have a output of just 0 or 1\nCategorical cross entropy: Good if I have an output of an array with one 1 and all other values being 0.\nBoth functions would not make any sense for my example. What would you use and why?\nEdit\nAnother Question: Which Activation function would you use in Keras?","AnswerCount":5,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":7333,"Q_Id":54842219,"Users Score":0,"Answer":"You'll want to use a logistic activation. This pushes each logit between 0 and 1, which represents the probability of that category. \nThen use categorical cross entropy. This will not make your model a single class classifier since you are using the logistic activation rather than the softmax activation. \nAs a rule of thumb: \n\nlogistic activation pushes values between 0 and 1\nsoftmax pushes values between 0 and 1 AND makes them a valid probability distribution (sum to 1)\ncross entropy calculates the difference between distributions of any type.","Q_Score":0,"Tags":"python,python-3.x,tensorflow,keras","A_Id":54842514,"CreationDate":"2019-02-23T13:41:00.000","Title":"Which loss function should I use in my LSTM and why?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I try to understand Keras and LSTMs step by step. Right now I build an LSTM there the input is a sentence and the output is an array of five values which can each be 0 or 1.\nExample:\nInput sentence: 'I hate cookies'\nOutput example: [0,0,1,0,1]\nFor this, I am using keras library.\nNow I am not sure which loss function I should use. Right now I just know two predefined loss functions a little bit better and both seem not to be good for my example:\nBinary cross entropy: Good if I have a output of just 0 or 1\nCategorical cross entropy: Good if I have an output of an array with one 1 and all other values being 0.\nBoth functions would not make any sense for my example. What would you use and why?\nEdit\nAnother Question: Which Activation function would you use in Keras?","AnswerCount":5,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":7333,"Q_Id":54842219,"Users Score":0,"Answer":"When it comes to regression problem in deep learning mean square error MSE is the most preferred loss function but when it comes to categorical problem where you want your output to be 1 or 0, true or false the cross binary entropy is preferable","Q_Score":0,"Tags":"python,python-3.x,tensorflow,keras","A_Id":70772878,"CreationDate":"2019-02-23T13:41:00.000","Title":"Which loss function should I use in my LSTM and why?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a little problem using Gurobi in Python. I have a .lp-file, where my linear programm is saved. To these constraints I want to add some additional constraints. Loading and optimizing works without any problems, but I just cannot add new constraints to my model. I do not know what I am doing wrong...\nI hope there is someone, who finds my mistake!\nThanks!\nMy code looks like below (I made it a bit easier)\nSI is a two-dimensional array containing the data for every variable.\nfrom gurobipy import *\nmodel = read(\"testdatei.lp\")\nfor j in range(len(SI)):\n    model.addConstr(x[SI[j][0], SI[j][1], SI[j][2], SI[j][3], SI[j][4], SI[j][5]] == 1)","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":392,"Q_Id":54842821,"Users Score":0,"Answer":"Without knowing your error message, I assume the problem is that you have not defined x.\nThe quickest way to fix this would probably be to reconstruct the variable name (that you defined earlier when building the model) from the SI values and then  access the variables with getVarByName.\nIf this is slow because the model is big and you are accessing many variables, you could instead get the array of all variables with model.getVars(), then iterate over this and rebuild your multi-dimensional array (or tuple_dict) x by parsing the names of the variables.","Q_Score":1,"Tags":"python,gurobi","A_Id":54928019,"CreationDate":"2019-02-23T14:52:00.000","Title":"Read existing gurobi .lp-file and add constraints to it","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have my own precomputed data for running AP or Kmeans in python. However when I go to run predict() as I would like to run a train() and test() on the data to see if the clusterings have a good accuracy on the class or clusters, Python tells me that predict() is not available for \"precomputed\" data. \nIs there another way to run a train \/ test on clustered data in python?","AnswerCount":1,"Available Count":1,"Score":0.537049567,"is_accepted":false,"ViewCount":439,"Q_Id":54842990,"Users Score":3,"Answer":"Most clustering algorithms, including AP, have no well-defined way to \"predict\" on new data. K-means is one of the few cases simple enough to allow a \"prediction\" consistent with the initial clusters.\nNow sklearn has this oddity of trying to squeeze everything into a supervised API. Clustering algorithms have a fit(X, y) method, but ignore y, and are supposed to have a predict method even though the algorithms don't have such a capability.\nFor affinity propagation, someone at some point decided to add a predict based on k-means: It always predicts the nearest center. Computing the mean only is possible with coordinate data, and hence the method fails with metric=precomputed.\nIf you want to replicate this behavior, computer the distances to all cluster centers, and choose the argmin, that's all. You can't fit this into the sklearn API easily with \"precomputed\" metrics. You could require the user to pass a distance vector to all \"training\" examples for the precomputed metric, but only few of them are needed...\nIn my opinion, I'd rather remove this method altogether:\n\nIt is not in published research on affinity propagation that I know\nAffinity propagation is based on concepts of similarity (\"affinity\") not on distance or means\nThis predict will not return the same results as the points were labeled by AP, because AP is labeling points using a \"propagated responsibility\", rather than the nearest \"center\". (The current sklearn implementation may be losing this information...)\nClustering methods don't have a consistent predict anyway - it's not a requirement to have this.\nIf you want to do this kind of prediction, just pass the cluster centers to a nearest neighbor classifier. That is what is re-implemented here, a hidden NN classifier. So you get more flexibility if you make prediction a second (classification) step.\n\nNote that it clustering it is not common to do any test-train split, because you don't use the labels anyway, and use only unsupervised evaluation methods (if any at all, because these have their own array of issues) if any at all - you cannot reliably do \"hyperparameter optimization\" here, but have to choose parameters based on experience and humans looking at the data.","Q_Score":2,"Tags":"python,cluster-analysis","A_Id":54850244,"CreationDate":"2019-02-23T15:12:00.000","Title":"How to run predict() on \"precomputed\" data for clustering in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to install tensorflow in python using pip command as\npip install tensorflow\n, but unfortunately, I received the following error:\nCould not find a version that satisfies the requirement tensorflow (from versions: ) No matching distribution found for tensorflow\nI have also tried to install tensorflow using the following command \npip install --upgrade\nhttps:\/\/storage.googleapis.com\/tensorflow\/windows\/cpu\/tensorflow-0.12.0rc0-cp35-cp35m-win_amd64.whl, but was again faced with the following error:\ntensorflow-0.12.0rc0-cp35-cp35m-win_amd64.whl is not a supported wheel on this platform. Additionally, I have checked the same commands against Python Version 3.5x 3.6x and obviously 3.7 as well, but those didn't work.","AnswerCount":6,"Available Count":3,"Score":0.0333209931,"is_accepted":false,"ViewCount":10204,"Q_Id":54843623,"Users Score":1,"Answer":"I had the same issue on windows 10, I find out that tensorflow work with python x64 installation and the command I used is the follow: \n\npip install --upgrade tf","Q_Score":0,"Tags":"python,tensorflow,object-detection-api","A_Id":58692607,"CreationDate":"2019-02-23T16:30:00.000","Title":"Pip Install Tensorflow Failed","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to install tensorflow in python using pip command as\npip install tensorflow\n, but unfortunately, I received the following error:\nCould not find a version that satisfies the requirement tensorflow (from versions: ) No matching distribution found for tensorflow\nI have also tried to install tensorflow using the following command \npip install --upgrade\nhttps:\/\/storage.googleapis.com\/tensorflow\/windows\/cpu\/tensorflow-0.12.0rc0-cp35-cp35m-win_amd64.whl, but was again faced with the following error:\ntensorflow-0.12.0rc0-cp35-cp35m-win_amd64.whl is not a supported wheel on this platform. Additionally, I have checked the same commands against Python Version 3.5x 3.6x and obviously 3.7 as well, but those didn't work.","AnswerCount":6,"Available Count":3,"Score":0.0333209931,"is_accepted":false,"ViewCount":10204,"Q_Id":54843623,"Users Score":1,"Answer":"Are you sure that you are capitalizing\/spelling properly? The command prompt is case-sensitive. The input I use is:\ncd C:\\path\\to\\the\\directory\\python\\is\\installed\\in (cd, space, the path to the directory) then:\npython -m pip install TensorFlow\nIt should work afterwards.","Q_Score":0,"Tags":"python,tensorflow,object-detection-api","A_Id":54843717,"CreationDate":"2019-02-23T16:30:00.000","Title":"Pip Install Tensorflow Failed","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to install tensorflow in python using pip command as\npip install tensorflow\n, but unfortunately, I received the following error:\nCould not find a version that satisfies the requirement tensorflow (from versions: ) No matching distribution found for tensorflow\nI have also tried to install tensorflow using the following command \npip install --upgrade\nhttps:\/\/storage.googleapis.com\/tensorflow\/windows\/cpu\/tensorflow-0.12.0rc0-cp35-cp35m-win_amd64.whl, but was again faced with the following error:\ntensorflow-0.12.0rc0-cp35-cp35m-win_amd64.whl is not a supported wheel on this platform. Additionally, I have checked the same commands against Python Version 3.5x 3.6x and obviously 3.7 as well, but those didn't work.","AnswerCount":6,"Available Count":3,"Score":0.0665680765,"is_accepted":false,"ViewCount":10204,"Q_Id":54843623,"Users Score":2,"Answer":"I think Tensorflow does not currently have support for Python 3.7 and if you have Python 3.7 currently installed this might be the cause of the error message Could not find a version that satisfies the requirement tensorflow (from versions: ) No matching distribution found for tensorflow\nYou can downgrade to Python 3.6.x and install tensorflow using pip then.","Q_Score":0,"Tags":"python,tensorflow,object-detection-api","A_Id":54843877,"CreationDate":"2019-02-23T16:30:00.000","Title":"Pip Install Tensorflow Failed","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am doing a binary classification task using linear SVM in scikit learn. I use nominal features and word vectors. I obtained the word vectors using the pretrained Google word2vec, however, I am not sure how SVM can handle word vectors as a feature.\nIt seems that I need to \"split\" each vector in 300 separate features (=300 vector dimensions), because I can't pass the vector as a whole to SVM. But that doesn't seem right, as the vector should be treated as one feature.\nWhat would be the correct way to represent a vector in this case?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":947,"Q_Id":54846314,"Users Score":1,"Answer":"Vector of many features\nFrom the perspective of an SVM, each dimension of a word vector would be a separate numeric feature - each dimension in that vector represents a numeric metric representing something different.\nThe same applies for non-SVM classifiers. For example, if you'd have a neural network, and your input features were that word vector of length 300 and (for the sake of a crude example) a bit stating whether that word was capitalized, then you'd concatenate those things and would have 301 numbers as your input; you'd treat that feature just as each of the 300 dimensions.","Q_Score":3,"Tags":"python,classification,svm,word2vec","A_Id":54846520,"CreationDate":"2019-02-23T21:20:00.000","Title":"What is the input format for word2vec features in SVM classification task?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I recently installed and compiled openCV on my raspberrypi.\nNow if I use the command import cv2 in my raspian OS (either in the python shell or the IDE) I get an error \n\nno module named cv2 found but the same command works in the raspberrypi shell.    \n\nHow do I resolve this??\n\nFile  \"home\/pi\/Desktop\/FR.py\",line 2,in module\nimport cv2\nFile \"\/usr\/lib\/python3\/dist-packages\/thonny\/backend.py\",line\n305,in_custom_import\nmodule=self_original_import(*args,**kw) ImportError:no module named\n'cv2'","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":252,"Q_Id":54849139,"Users Score":0,"Answer":"Please try to run your code after executing the below command.\npython -m pip install opencv-contrib-python","Q_Score":0,"Tags":"python","A_Id":54921745,"CreationDate":"2019-02-24T05:49:00.000","Title":"Why am I getting cv2 error in raspbianOS and not raspberrypi shell","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on a data set of approximately 3000 questions and I want to perform intent classification. The data set is not labelled yet, but from the business perspective, there's a requirement of identifying approximately 80 various intent classes. Let's assume my training data has approximately equal number of each classes and is not majorly skewed towards some of the classes. I am intending to convert the text to word2vec or Glove and then feed into my classifier.\nI am familiar with cases in which I have a smaller number of intent classes, such as 8 or 10 and the choice of machine learning classifiers such as SVM, naive bais or deeplearning (CNN or LSTM).\nMy question is that if you have had experience with such large number of intent classes before, and which of machine learning algorithm do you think will perform reasonably? do you think if i use deep learning frameworks, still large number of labels will cause poor performance given the above training data?\nWe need to start labelling the data and it is rather laborious to come up with 80 classes of labels and then realise that it is not performing well, so I want to ensure that I am making the right decision on how many classes of intent maximum I should consider and what machine learning algorithm do you suggest?\nThanks in advance...","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1227,"Q_Id":54850657,"Users Score":2,"Answer":"First, word2vec and GloVe are, almost, dead. You should probably consider using more recent embeddings like BERT or ELMo (both of which are sensitive to the context; in other words, you get different embeddings for the same word in a different context). Currently, BERT is my own preference since it's completely open-source and available (gpt-2 was released a couple of days ago which is apparently a little bit better. But, it's not completely available to the public).\nSecond, when you use BERT's pre-trained embeddings, your model has the advantage of seeing a massive amount of text (Google massive) and thus can be trained on small amounts of data which will increase it's performance drastically.\nFinally, if you could classify your intents into some coarse-grained classes, you could train a classifier to specify which of these coarse-grained classes your instance belongs to. Then, for each coarse-grained class train another classifier to specify the fine-grained one. This hierarchical structure will probably improve the results. Also for the type of classifier, I believe a simple fully connected layer on top of BERT would suffice.","Q_Score":2,"Tags":"python,tensorflow,nlp,text-classification","A_Id":54855155,"CreationDate":"2019-02-24T09:50:00.000","Title":"Intent classification with large number of intent classes","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm having trouble resetting my keras model to the weights it had in the previous epoch after I hit a train_on_batch update that makes some of the weights nans.\nI have tried to save the model weights after each training step and then to load the \"good\" (non-nan) weights back into the keras model after a nan training update. \nThis seems to work fine - when I print the result of model.get_weights() after loading the old weights file into the model, the resulting weights contain no nans (and predict using them also gives a non-nan output).\nHowever, now when I try to train_on_batch again, this time using a new batch, I get a nan update again immediately. I've tried with multiple randomly chosen batches and the nan update happens each time. \nIs there something (maybe a parameter) that changes in the model or optimizer configuration when a nan train_on_batch update occurs that needs to be reset for training to continue once I change out the weights?\nI would also like to avoid using model.save() and load_model() in the solution.\n(keras 2.2.4, tensorflow 1.12.0)\nAny thoughts are appreciated!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":896,"Q_Id":54856754,"Users Score":0,"Answer":"since you have not pasted your code and weights I can't tell you much, but I suspect this problem may be due to dropout or regularisation, if you are using any of the two techniques set the parameters or percentage of dropouts properly as per your network, a high percentage in a small network will lead this sort of problem same with regularization. \nand for reverting and saving models use checkpoints.","Q_Score":1,"Tags":"python,tensorflow,keras,deep-learning,nan","A_Id":54856783,"CreationDate":"2019-02-24T21:29:00.000","Title":"How to revert keras model to previous epoch weights after train_on_batch nan update","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using lucas-kanade opencv implementation to track objects between frames. I want to be able to do the following two things:\n\nCalculate the distance moved by each point between frames\nTrack bounding boxes for each object across frames\n\nI have obtained the features to track using cv2.goodFeaturesToTrack(). I also add the bounding boxes of objects to the features to be tracked. Right now I am using the following to calculate distance between the points\nnp.sqrt(np.square(new_pts - old_pts).sum(axis=1).sum(axis=1)). I am not quite sure if this is the correct way to do this because the indices of the points might be different in the new_pts. \nIs the assumption that every index in old_pts corresponds to  the same feature in new_pts array correct?\nSecondly, is there a way to track bounding boxes across frames using lucas kanade?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":241,"Q_Id":54856954,"Users Score":0,"Answer":"In new_pts points have the same index. But they can be not founded - see to the status array: if status[i] == 1 then new_pts[i] contains a new coordinates of the old_pts[i].\nFor the more robustness it can to search direct flow (goodFeaturesToTrack(frame1) -> LK flow), backward flow (goodFeaturesToTrack(frame2) -> LK flow) and leave the points whose coordinates are equal in both directions.","Q_Score":1,"Tags":"python,opencv,computer-vision,computer-science","A_Id":54862189,"CreationDate":"2019-02-24T21:57:00.000","Title":"Lucas Kanade: How to calculate distance between tracked points","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there some way to a constraint on the data generated by tensor flow, for example if my  model produced two outputs can you impose some sort of constraint on these, like if a and b where the outputs could you pre-enforce something like (a+b)\/2<10? So the model wouldn't break this rule?\nThanks in advance","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":236,"Q_Id":54864100,"Users Score":1,"Answer":"If by \"generated by TensorFlow\" you mean generated by a neural network, I don't think it is possible to do that in general. You can't really guarantee that the output of a neural network never violates such hard constraints in general, especially at test time.\nHere's what you could do:\n\nAdd a loss term, something like max(0, (a+b)\/2 - 10). This will not guarantee that your constraint is not violated (the optimization of the NN is \"best-effort\"). This loss function is btw very similar to the hinge loss used in support vector machines.\nUse an appropriate activation function. E.g. if you know your data must lie between [0, 1], use the sigmoid activation on the output.\n\"Project\" the output back to the allowed range if it is outside of it.\n\nWhile the last two options guarantee feasibility, it is not always possible to do that or it is not clear how to do it and - even worse - how this will affect the learning. For example, if you see that (a+b)\/2 >= 10 what will you do? Will you decrease b until the constraint is fulfilled, or both trade-off a and b somehow? Sometimes it is possible to define the \"closest feasible point\" w.r.t. some metric, but not in general.","Q_Score":3,"Tags":"python-3.x,tensorflow,constraints","A_Id":54870187,"CreationDate":"2019-02-25T10:27:00.000","Title":"Is there a way to impose a constraint in tensor flow, could I enforce some rule along the way?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When I am installing the new package in my deep-learning environment it gives me this error:\n\nCould not install packages due to an EnvironmentError: [WinError 32]\n  The process cannot access the file because it is being used by another\n  process:  Consider using the --user option or check the permissions.\n\nPlease help to resolve this","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":170,"Q_Id":54865526,"Users Score":0,"Answer":"terminal one as administrator if using Windows and use ubuntu before command use sudo.","Q_Score":1,"Tags":"python,pip,deep-learning","A_Id":54865791,"CreationDate":"2019-02-25T11:49:00.000","Title":"Python packages installing error in deep-learning environment","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have the following problem and am not quite sure if it is solvable by image recognition (and convolutional neural networks).\nI have a dataset of 400k pictures divided into 5 classes. The pictures are screenshots of apps, which are put into the 5 classes depending on what age rating they received.\nFor example: I have 200k labeled as class 0, which means they are suitable all ages (according to the age rating); I have 50k pictures labeled as class 1 (suitable for children aged 6+) and so on.\nWith this data I want to train a neural network, that can tell me, which age rating a screenshot (and therefore the corresponding game) likely has.\nIs this a problem, which is manageable by image recognition? \nI've looked into examples (mostly Keras tutorials) for image recognition and all of them deal with problems, which are distinctly visible (like \"does the image show a cat or a dog\"). Browsing through my dataset I realized, that some of the pictures are pretty similar, although belonging to different classes.\nCan a convolutional neural network (or any other type of image recognition algorithm) deal with classes, where the deciding factor is not directly visible? Is this just a problem of how deep the network is?\nI'd be very thankful, if someone could point me in the general direction on where to look for further information.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":36,"Q_Id":54871092,"Users Score":1,"Answer":"It could and also could not. For a CNN to provide good results with no contextual input, it would mean there must be some for of correlation between the input and the output. So lets say some apps have designs that correlate with age-rating, then yes its possible, otherwise its not unless you give the network something more to work with.\nThis could actually set you up for a cool experiment to check yourself, run this through some run-of-the-mill CNN, and if it evaluates well (through cross-validation) then youve probably shown the correlation exists \n(Note: if the model does not test well, that is not evidence that correlation isnt there, probably isnt likely, but not gauranteed)","Q_Score":1,"Tags":"python,keras,image-recognition","A_Id":54871249,"CreationDate":"2019-02-25T16:55:00.000","Title":"Can Image Recognition deal with classes, where the deciding quality is not directly visible?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataset in csv format which contains dates in a column. I have imported this dataset into python pandas, and this date column is shown as an object. I need to convert this column to date time but i have a problem. This date column has date format in two formats\n1. 11\/7\/2013  11:51\n2. 13-07-2013 08:33:16\nI need to convert one format to another one in order to have a standard date format in my python to do analysis. How can i do this?\nThere are many rows of date in both these formats, so when i try to convert second format to first format using the below code\n\nprint(df['date'].apply(lambda x: pd.to_datetime(x, format='%d\/%m\/%Y\n  %H:%M')))\n\ni get the below error\n\nValueError: time data '13-07-2013 08:33:16' does not match format\n  '%d\/%m\/%Y %H:%M' (match)\n\nso what would be the best method to standardise this column in one format?","AnswerCount":4,"Available Count":1,"Score":0.049958375,"is_accepted":false,"ViewCount":2154,"Q_Id":54871446,"Users Score":1,"Answer":"Try removing the format parameter and setting infer_datetime_format=Truein the arguments you pass to pd.to_datetime","Q_Score":1,"Tags":"python,pandas","A_Id":54871548,"CreationDate":"2019-02-25T17:17:00.000","Title":"How to standardise different date formats in pandas?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm looking for a way to plot a density histogram with Plotly. As a density=True with a numpy histogram. My variable is a continuous one from 0 to 20. I already have a count on yaxis with bins. So I'm looking for replace theses counts by percentage (o density).","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":207,"Q_Id":54872488,"Users Score":0,"Answer":"Try this:\ngo.Histogram(x=some_vec, histnorm=\"probability density\")","Q_Score":0,"Tags":"python,plotly","A_Id":68398101,"CreationDate":"2019-02-25T18:23:00.000","Title":"Plot a density histogram with Plotly","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is auc better in handling imbalenced data. As in most of the cases if I am dealing with imbalenced data accuracy is not giving correct idea. Even though accuracy is high, model has poor perfomance. If it's not auc which is the best measure to handle imbalenced data.","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":1508,"Q_Id":54879340,"Users Score":1,"Answer":"Neither are good for imbalanced datasets. Use the area under the precision recall curve instead.","Q_Score":0,"Tags":"python,machine-learning,artificial-intelligence,roc,auc","A_Id":54880674,"CreationDate":"2019-02-26T06:07:00.000","Title":"Is AUC a better metric than accuracy in case of imbalenced datasets in machine learning,If not which is the best metric?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm having low fps for real-time object detection on my raspberry pi\nI trained the yolo-darkflow object detection on my own data set using my laptop windows 10 .. when I tested the model for real-time detection on my laptop with webcam it worked fine with high fps \nHowever when trying to test it on my raspberry pi, which runs on Raspbian OS, it gives very low fps rate that is about 0.3 , but when I only try to use the webcam without the yolo it works fine with fast frames.. also when I use Tensorflow API for object detection with webcam on pi it also works fine with high fps\ncan someone suggest me something please? is the reason related to the yolo models or opencv or phthon? how can I make the fps rate higher and faster for object detection with webcam?","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1551,"Q_Id":54881654,"Users Score":0,"Answer":"The raspberry pi not have the GPU procesors and because of that is very hard for it to do image recognition at a high fps .","Q_Score":0,"Tags":"python,opencv,raspberry-pi,object-detection,yolo","A_Id":55558787,"CreationDate":"2019-02-26T08:57:00.000","Title":"how to increase fps for raspberry pi for object detection","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm having low fps for real-time object detection on my raspberry pi\nI trained the yolo-darkflow object detection on my own data set using my laptop windows 10 .. when I tested the model for real-time detection on my laptop with webcam it worked fine with high fps \nHowever when trying to test it on my raspberry pi, which runs on Raspbian OS, it gives very low fps rate that is about 0.3 , but when I only try to use the webcam without the yolo it works fine with fast frames.. also when I use Tensorflow API for object detection with webcam on pi it also works fine with high fps\ncan someone suggest me something please? is the reason related to the yolo models or opencv or phthon? how can I make the fps rate higher and faster for object detection with webcam?","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1551,"Q_Id":54881654,"Users Score":0,"Answer":"My detector on raspberry pi without any accelerator can reach 5 FPS.\nI used SSD mobilenet, and quantize it after training.\nTensorflow Lite supplies a object detection demo can reach about 8 FPS on raspberry pi 4.","Q_Score":0,"Tags":"python,opencv,raspberry-pi,object-detection,yolo","A_Id":66489611,"CreationDate":"2019-02-26T08:57:00.000","Title":"how to increase fps for raspberry pi for object detection","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am stuck at a problem using OpenVINO. I am trying to run the facenet after converting model using OpenVINO toolkit but I am unable to use .npy and .pickle for complete face recognition. I am successful in converting .pb file to .bin and .xml file using OpenVino toolkit.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":636,"Q_Id":54891713,"Users Score":0,"Answer":"You can use Python APIs to integrate OV inference into your Python application. Please see inference_engine\/samples\/python_samples folder for existing Python samples.","Q_Score":0,"Tags":"python,tensorflow,openvino","A_Id":55735532,"CreationDate":"2019-02-26T18:14:00.000","Title":"Running Facenet using OpenVINO","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am stuck at a problem using OpenVINO. I am trying to run the facenet after converting model using OpenVINO toolkit but I am unable to use .npy and .pickle for complete face recognition. I am successful in converting .pb file to .bin and .xml file using OpenVino toolkit.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":636,"Q_Id":54891713,"Users Score":0,"Answer":"openvino converts the model to intermediate representation, which is compatible across multiple hardwares. It can improve the performance of your model too. Since you have already mentioned that yopu were ableto convert your model to IR format, the next phase is inference for which you can use the .xml and .bin files.\nCan you please state what exactly do you intend to carryout with .npy or .pickle files?","Q_Score":0,"Tags":"python,tensorflow,openvino","A_Id":55158313,"CreationDate":"2019-02-26T18:14:00.000","Title":"Running Facenet using OpenVINO","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to find the largest subset sum of a particular data set, where the average of a field in the data set matches predetermined criteria.\nFor example, say I have a people's weights (example below) and my goal is to find the largest weight total where the average weight of the resulting group is between 200 and 201 pounds.\n\n210\n201\n190\n220\n188\n\nUsing the above, the largest sum of weights where the average weight is between 200 and 201 pounds is from persons 1, 2, and 3. The sum of their weights is 601, and the average weight between them is 200.3. \nIs there a way to program something to do the above, other than brute force, preferably using python? I'm not even sure where to start researching this so any help or guidance is appreciated.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":35,"Q_Id":54894635,"Users Score":1,"Answer":"Start by translating the desired range to 0, just for convenience.  I'll translate to the lower bound, although the midpoint is also a good choice.\nThis makes your data set [10, 1, -10, 20, -12].  The set sum is 9; you need it to be in the range 0 to upper_bound * len(data).\nThis gives you a tractable variation of the \"target sum\" problem: find a subset of the list that satisfies the sum constraint.  In this case, you have two solutions: [10, 1, -10] and [10, 1, -12].  You can find this by enhancing the customary target-sum problems to include the changing sum: the \"remaining amount\" will include the change from the mean calculation.\nCan you finish from there?","Q_Score":1,"Tags":"python,optimization,weighted-average","A_Id":54894944,"CreationDate":"2019-02-26T21:46:00.000","Title":"Finding Largest Subset of Data where Average Matches Criteria","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm learning tensorflow, and the tf.data API confuses me. It is apparently better when dealing with large datasets, but when using the dataset, it has to be converted back into a tensor. But why not just use a tensor in the first place? Why and when should we use tf.data?\nWhy isn't it possible to have tf.data return the entire dataset, instead of processing it through a for loop? When just minimizing a function of the dataset (using something like tf.losses.mean_squared_error), I usually input the data through a tensor or a numpy array, and I don't know how to input data through a for loop. How would I do this?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1075,"Q_Id":54894799,"Users Score":3,"Answer":"The tf.data module has specific tools which help in building a input pipeline for your ML model. A input pipeline takes in the raw data, processes it and then feeds it to the model.\n\n\nWhen should I use tf.data module?\n\nThe tf.data module is useful when you have a large dataset in the form of a file such as .csv or .tfrecord. tf.data.Dataset can perform shuffling and batching of samples efficiently. Useful for large datasets as well as small datasets. It could combine train and test datasets.\n\nHow can I create batches and iterate through them for training?\n\nI think you can efficiently do this with NumPy and np.reshape method. Pandas can read data files for you. Then, you just need a for ... in ... loop to get each batch amd pass it to your model.\n\nHow can I feed NumPy data to a TensorFlow model?\n\nThere are two options to use tf.placeholder() or tf.data.Dataset.\n\nThe tf.data.Dataset is a much easier implementation. I recommend to use it. Also, has some good set of methods.\nThe tf.placeholder creates a placeholder tensor which feeds the data to a TensorFlow graph. This process would consume more time feeding in the data.","Q_Score":3,"Tags":"python,numpy,tensorflow,machine-learning","A_Id":54897167,"CreationDate":"2019-02-26T21:57:00.000","Title":"Why should I use tf.data?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"This may seem like an odd question but I'm new to this so thought I'd ask anyway.\nI want to use this Google News model over various different files on my laptop. This means I will be running this line over and over again in different Jupyter notebooks: \nmodel=word2vec.KeyedVectors.load_word2vec_format(\"GoogleNews-vectors-negative300.bin\",binary=True)\nDoes this eat 1) Storage (I've noticed my storage filling up exponentially for no reason) \n2) Less memory than it would otherwise if I close the previous notebook before running the next.\nMy storage has gone down by 50GB in one day and the only thing I have done on this computer is run the Google News model (I didn't do most_similar()). Restarting and closing notebooks hasn't helped and there aren't any big files on the laptop. Any ideas?\nThanks.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":149,"Q_Id":54911712,"Users Score":1,"Answer":"Just loading a model won't usually use any more disk storage. (An exception: if load or use needs addressable memory beyond your RAM, you may start using virtual memory, which might show up as less disk space depending on your OS. But, with these sorts of models, you want to avoid relying on any virtual memory, as basic most_similar() operations cycle through the full model, & will be very slow if they're reading from disk each time.) \nLoading the model will use memory, then more when 1st doing most_similar(). (That requires unit-normalized vectors, which are calculated the 1st time needed then cached.) \nBut terminating a notebook should free that memory. (Note that closing a tab may not cleanly terminate a Jupyter notebook. If the notebook is still running at the notebook server, even with no browsers viewing it, it will still use\/hold memory.)","Q_Score":0,"Tags":"python,nlp,gensim,word2vec,word-embedding","A_Id":54920367,"CreationDate":"2019-02-27T17:56:00.000","Title":"Does the Google News Word2Vec model take up storage every time you run it?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I was wondering, what's the difference between importing keras from tensorflow using import tensorflow.keras or just pip installing keras alone and importing it using import keras as both seemed to work nicely so far, the only difference I noticed is that i get Using TensorFlow backend. in the command line every time I execute the one using keras.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1437,"Q_Id":54913830,"Users Score":12,"Answer":"Tensorflow.keras is an version of Keras API implemented specifically for use with Tensorflow. It is a part of Tensorflow repo and from TF version 2.0 will become main high level API replacing tf.layers and slim. \nThe only reason to use standalone keras is to maintain framework-agnostic code, i.e. use it with another backend.","Q_Score":4,"Tags":"python-3.x,tensorflow,keras","A_Id":54914559,"CreationDate":"2019-02-27T20:19:00.000","Title":"what's the difference between \"import keras\" and \"import tensorflow.keras\"","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I just have a graph.pbtxt file. I want to view the graph in tensorboard. But I am not aware of how to do that. Do I have to write any python script or can I do it from the terminal itself? Kindly help me to know the steps involved.","AnswerCount":2,"Available Count":1,"Score":0.4621171573,"is_accepted":false,"ViewCount":5032,"Q_Id":54917785,"Users Score":5,"Answer":"Open tensorboard and use the \"Upload\" button on the left to upload the pbtxt file will directly open the graph in tensorboard.","Q_Score":5,"Tags":"python,tensorflow,tensorboard","A_Id":60148794,"CreationDate":"2019-02-28T03:04:00.000","Title":"Viewing Graph from saved .pbtxt file on Tensorboard","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have developed a Tensorflow based machine learning model on local Machine. And I want to deploy it in Google Cloud Platform (Cloud ML Engine) for predictions.\nThe model reads input data from Google Bigquery and the output predictions has to be written in Google Bigquery only. \nThere are some data preparation scripts which has to be run before the model prediction is run. I have used Google Cloud Storage for model storage and used it for deployment, i have deployed it successfully. \nBut, instead of using Google Cloud Storage for saving a model (i.e. .pb or .pkl model file) can i store it on GCP VM (Or Local machine) and call it from Cloud ML Engine for prediction? Is it possible? or I have only a option to upload Model directory to a Cloud Storage bucket which i will use it for prediction?\nCould you please help me on this.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":147,"Q_Id":54923211,"Users Score":0,"Answer":"For model deployment you need a Google Cloud Storage using AI Platform.\nAnother option is to use AI platform training (local or in GCP), output the model (SavedModel format) to a local folder or Cloud Storage and from there using TF Serving in Compute Engine Instance.","Q_Score":2,"Tags":"python,google-cloud-ml","A_Id":56553227,"CreationDate":"2019-02-28T10:14:00.000","Title":"Model Deployment On GCP Without Cloud Storage Bucket","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":1},{"Question":"Maybe it is a little bit hard to describe my problem. I am searching for an algorithm in Python, to create wrinkled paper effect on a white image with some text. \nMy first try was adding some real wrinkled paper image (with transparency) to the image with text. This looks nice, but hast the side effect, that the text is not really wrinkled.\nSo I am looking for a better solution, any ideas? Thanks","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":408,"Q_Id":54928574,"Users Score":0,"Answer":"Instead of using transparency, assuming you have two images of the same dimensions, one bright with the wrinkled paper and one with a dark text on a white background, you could try to take for every pixel the minimum of the values of the corresponding pixels in the two images.\nIn this way you should succeed in merging the text (darker than the wrinkled paper) and the wrinkled paper (darker than the original text background).","Q_Score":1,"Tags":"python,image-processing","A_Id":54934592,"CreationDate":"2019-02-28T15:02:00.000","Title":"Python: Image processing create wrinkled paper effect","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have 2 cumulative distributions that I want to find the intersection of. To get an underlying function, I used the scipy interpol1d function. What I\u2019m trying to figure out now, is how to calculate their intersection. Not sure how I can do it. Tried fsolve, but I can\u2019t find how to restrict the range in which to search for a solution (domain is limited).","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":58,"Q_Id":54930134,"Users Score":0,"Answer":"Use scipy.optimize.brentq for bracketed root-finding:\nbrentq(lambda x: interp1d(xx, yy)(x) - interp1d(xxx, yyy)(x), -1, 1)","Q_Score":0,"Tags":"python,python-3.x,scipy","A_Id":54950068,"CreationDate":"2019-02-28T16:24:00.000","Title":"Intersection of interpol1d objects","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"ValueError                                Traceback (most recent call last)  in () ----> 1 prediction = model.predict(image_resized.reshape(1,50,50,3))       2 print('Prediction Score:\\n',prediction[0])  ValueError: cannot reshape array of size 2352 into shape (1,50,50,3)","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":30,"Q_Id":54930833,"Users Score":0,"Answer":"By just looking at what you have posted you should replace image_resized.reshape(1,50,50,3) with image_resized.reshape(1,28,28,3)","Q_Score":0,"Tags":"python-3.x,tensorflow,jupyter-notebook","A_Id":54930980,"CreationDate":"2019-02-28T17:06:00.000","Title":"model can predict new image","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've built a data pipeline. Pseudo code is as follows:\n\ndataset -> \ndataset = augment(dataset)\ndataset = dataset.batch(35).prefetch(1)\ndataset = set_from_generator(to_feed_dict(dataset)) # expensive op\ndataset = Cache('\/tmp', dataset)\ndataset = dataset.unbatch()\ndataset = dataset.shuffle(64).batch(256).prefetch(1)\nto_feed_dict(dataset)\n\n1 to 5 actions are required to generate the pretrained model outputs. I cache them as they do not change throughout epochs (pretrained model weights are not updated). 5 to 8 actions prepare the dataset for training.\nDifferent batch sizes have to be used, as the pretrained model inputs are of a much bigger dimensionality than the outputs.\nThe first epoch is slow, as it has to evaluate the pretrained model on every input item to generate templates and save them to the disk. Later epochs are faster, yet they're still quite slow - I suspect the bottleneck is reading the disk cache.\nWhat could be improved in this data pipeline to reduce the issue?\nThank you!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":257,"Q_Id":54934207,"Users Score":0,"Answer":"prefetch(1) means that there will be only one element prefetched, I think you may want to have it as big as the batch size or larger. \nAfter first cache you may try to put it second time but without providing a path, so it would cache some in the memory.\nMaybe your HDD is just slow? ;)\nAnother idea is you could just manually write to compressed TFRecord after steps 1-4 and then read it with another dataset. Compressed file has lower I\/O but causes higher CPU usage.","Q_Score":0,"Tags":"python,tensorflow,machine-learning,tensorflow-datasets","A_Id":54935427,"CreationDate":"2019-02-28T21:02:00.000","Title":"Tensorflow data pipeline: Slow with caching to disk - how to improve evaluation performance?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm really confused with the architecture of yolov3. I've read the documentation and paper about it. Some people say that it has 103 convolutional layers, some others say that it has 53 layers. But when you count the convolutional layers in the .cfg file (after downloading it) it comes to about 75! ...What is missed here? What should I do to find it? This question is important for us because we need to cite this architecture in a paper and we need to know the exact size of the layers...","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":5589,"Q_Id":54948738,"Users Score":0,"Answer":"YOLO v3 has 107 layers in total, you should also count shortcut layers, route layers, upsample layers, and YOLO layers(32 in total). So, there are 75+32=107 layers in total. When you see indexes in shortcut or route layers, you will find that we count from 0. Therefore, yolo layers are in 82,94,106 layers.","Q_Score":3,"Tags":"python,conv-neural-network,object-detection,yolo","A_Id":71724209,"CreationDate":"2019-03-01T16:32:00.000","Title":"What is the real number of CNN layers in yolov3?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm having troubles understanding how to manage and modify numpy matrices. I find it very difficult to \"picture\" the matrices in my head.\nI have a (4x2x1x1) matrix which I want to make into a (1x2x1x4) matrix, such that I can apply matrix multiplication with another matrix which have the shape (3x2x1x1). \nThanks in advance!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":130,"Q_Id":54950408,"Users Score":0,"Answer":"If your matrix is called matrix, matrix.shape = (1,2,1,4) (as in my example above) does the trick. NumPy will automatically notices if your new shape is \"out of bounds\", and automatically reorder the data correctly if it's not.\nEDIT: You can also use newMatrix = numpy.reshape(matrix, (1,2,1,4)) to create a new matrix as a reshape of your first matrix.","Q_Score":0,"Tags":"python,numpy,matrix-multiplication,numpy-ndarray,array-broadcasting","A_Id":54958225,"CreationDate":"2019-03-01T18:33:00.000","Title":"Reordering numpy 4D-array","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"My question is simple: I have an image and I want to add pixel wise independent noise to the image. The noise can be derived from from any distribution such as Gaussian. What are the available modules in numpy\/scikit-learn to do the same?\nI do not have any code but I am learning about modules such as numpy.random.normal, etc. and I needed more clarification.\nNone of the modules explicitly say that if I draw samples from a distribution multiple times, the draws will be independent. \nThank you for suggestions.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":561,"Q_Id":54953272,"Users Score":1,"Answer":"You have several options. If you want to take random samples with replacement, just use one of the numpy's builtin random modules (i.e., numpy.random.random). You could also use numpy.random.pareto for more dramatic\/bursty noise. These methods generate independent samples.\nIf you have a distribution in the form of a set or array that you want to pull samples from without repetition (for instance you have an array [0.1, 0.3, 0.9] and want to generate noise with ONLY these values), you use python's builtin random.random.choice([0.1, 0.3, 0.9]) to draw independent samples from your custom distribution. You can also specify replace=False.","Q_Score":0,"Tags":"python,numpy,image-processing,scikit-learn,noise-generator","A_Id":54953548,"CreationDate":"2019-03-01T22:38:00.000","Title":"Add pixel wise independent noise to image","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm new to Pandas.\nI've got a dataframe where I want to group by user and then find their lowest score up until that date in the their speed column.\nSo I can't just use df.groupby(['user'])['speed'].transform('min) as this would give the min of all values not just form the current row to the first.\nWhat can I use to get what I need?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":43,"Q_Id":54958144,"Users Score":0,"Answer":"Without seeing your dataset it's hard to help you directly. The problem does boil down to the following. You need to select the range of data you want to work with (so select rows for the date range and columns for the user\/speed).  \nThat would look something like x = df.loc[[\"2-4-2018\",\"2-4-2019\"], ['users', 'speed']]\nFrom there you could do a simple x['users'].min() for the value or x['users'].idxmin() for the index of the value.\nI haven't played around for a bit with Dataframes, but you're looking for how to slice Dataframes.","Q_Score":0,"Tags":"python,pandas","A_Id":54958224,"CreationDate":"2019-03-02T11:44:00.000","Title":"Pandas - select lowest value to date","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Sorry, I feel like this may be a basic question, but I did not find any \"solutions\" for this. \nI am filling a python list with a lot of data, and finally want to convert it to a numpy.array for further processing.\nHowever, when I call numpy.asarray(my_list), I get an out of memory error. Why does that happen? Is it because numpy.array objects are stored in consecutive memory blocks, and there is not enough memory space for that?\nHow do I best treat such great data volumes then? I guess numpy is definitely the way to go, so I am a bit curious, that I can handle such volumes with simple list objects but not with my current numpy approach.\nAgain, repeating my most important question: How can I best handle data, which fits into python lists (so I guess overall it somehow still fits in my memory), but cannot be converted to a numpy.array?\nThanks!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":85,"Q_Id":54961068,"Users Score":0,"Answer":"Allocate the memory for a numpy array and never create a list in the first place.\nmemmap should not be neccessary as the original list fits in memory.","Q_Score":1,"Tags":"python,arrays,numpy,memory,out-of-memory","A_Id":54962810,"CreationDate":"2019-03-02T17:22:00.000","Title":"Why can python lists hold more data than numpy arrays?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a large CSV file and I just want to take a 1% sample from it.  Is there a good way to read the samples directly into pandas data frame without having to read the whole file and then discard 99% of the data?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1045,"Q_Id":54964173,"Users Score":0,"Answer":"Use skiprows with a complex selector:\nExample would be \nrows=1000\nprob=.01\nprint len(pd.read_csv(filename, usecols=[0], skiprows=lambda x: x in random.sample(xrange(0,num_rows), int((1.0-prob)*num_rows ))))\n\nThere are also other ways of doing it by reading in chunks and then just skipping rows.\ne.g. you could use chunksize=100\nskiprows=99","Q_Score":3,"Tags":"python,pandas,csv","A_Id":54964640,"CreationDate":"2019-03-02T23:40:00.000","Title":"pandas read csv by taking samples","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"From my understanding, RandomHorizontalFlip etc. replace image rather than adding new images to dataset. How do I increase my dataset size by adding augmented images to dataset using PyTorch?\nI have gone through the links posted & haven't found a solution. I want to increase the data size by adding flipped\/rotated images - but the post addresses the in-place processing of images.\nThanks.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2045,"Q_Id":54969705,"Users Score":1,"Answer":"Why do you want it? Generally speaking, it is enough to increase the number of epochs over the dataset, and your model will see the original and the augmented version of every image at least once (assuming a relatively high number of epochs).\nExplanation:\nFor instance, if your augmentation has a chance of 50% to be applied, after 100 epochs, for every sample you will get ~50 samples of the original image and ~50 augmented samples. So, increasing the dataset size is equivalent to add epochs but (maybe) less efficient in terms of memory (need to store the images in memory to have high performances).","Q_Score":3,"Tags":"python,pytorch","A_Id":58200454,"CreationDate":"2019-03-03T14:12:00.000","Title":"How to add augmented images to original dataset using Pytorch?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm a newbie at deep learning. I started with face recognition example and I found that there are 2 types of model base on data for pre-trained.\n1. One-shot learning with siamese network: Which we can use few data for train the model.\n2. Convolutional neural network: Need numerous data for train the model.\nCould we combine these methods is using one-shot learning with CNN in tensorflow?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":420,"Q_Id":54969832,"Users Score":0,"Answer":"As my knowledge, CNN needs lots of data for training the model.So we cant implement one shot learning features on CNN","Q_Score":0,"Tags":"python,tensorflow,conv-neural-network","A_Id":56727042,"CreationDate":"2019-03-03T14:26:00.000","Title":"Face Recognition with one shot learning","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Just like list in python where [1,\"hello\", {\"python\": 10}] it can have all different types within, can numpy array have this as well?\nwhen numpyarray.dtype => dtype('float64') is it implying all elements are of type float?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":2473,"Q_Id":54971408,"Users Score":1,"Answer":"From the docs:\n\ndtype : data-type, optional\nThe desired data-type for the array. If not given, then the type will\n  be determined as the minimum type required to hold the objects in the\n  sequence. This argument can only be used to \u2018upcast\u2019 the array. For\n  downcasting, use the .astype(t) method.\n\nSo if you set dtype as float64, everything needs to be a float. You can mix types, but then you can't set it as a mismatching type. It will use a type that will fit all data, like a string for example in the case of array(['1', 'Foo', '3.123']).","Q_Score":0,"Tags":"python,numpy","A_Id":54971460,"CreationDate":"2019-03-03T17:06:00.000","Title":"different element data types within numpy array?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a question about using weights in keras. I have some data as events and for each of them there is an associated weight. Therefore, when I do the training of my keras model I was using the sample_weight argument to pass that information.\nI then notice that if I want to use the model.predict method there isn't an argument to pass the weights... and now I'm not sure if the type of weights I have are the ones I'm supposed to use in the fit method in the sample_weight.\nMy question is, what type of weights is the fit method supposed to recieve? Also, is it understood that the predict method doesn't need any weight for the data?\nThanks!","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":1419,"Q_Id":54973519,"Users Score":1,"Answer":"I think there might be some confusion with what \"sample_weight\" means in the context of model.fit. When you call model.fit, you are minimizing a loss function. That loss function measures the error between your model prediction and the true values. Some of the samples in your dataset might be more important to you, so you would weigh the loss function more heavily on those samples. So, the \"sample_weights\" are only used to weigh certain samples in your dataset during training to \"better fit\" those some samples relative to others. They are an optional argument to model.fit (default just weights each sample equal - what you should do if you don't have a good reason to do otherwise). And (hopefully my explanation was clear enough) do not make any sense in the context of model.predict.","Q_Score":3,"Tags":"python,tensorflow,keras","A_Id":54974116,"CreationDate":"2019-03-03T20:40:00.000","Title":"Keras: using weights when fitting","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"After doing  \n\npip install tensorflow-gpu \n\nI am trying to \n\nimport gradients_util from tensorflow.python.ops  \n\nbut I get \n\ncannot import name 'gradients_util'  \n\nerror. How can I fix this?","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":1486,"Q_Id":54981982,"Users Score":2,"Answer":"You must use version of benchmark compatible with your TensorFlow version.\nDetermine it using pip show tensorflow.\nThen go to cloned repository of benchmarks, and checkout to required branch, for example git checkout cnn_tf_v1.13_compatible.\nPrevious comments suggested to use TensorFlow 2.0, but I don't think author of question needs it, as long as it's still unreleased and has a lot of API-breaking changes.\nP. S. You should've say that you are trying to launch benchmarks from tensorflow\/benchmarks in your question.","Q_Score":0,"Tags":"python,tensorflow","A_Id":54983029,"CreationDate":"2019-03-04T11:07:00.000","Title":"Tensorflow cannon import name 'gradients_util'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I trained, tested and still using a model in \"Pytorch 0.4.1\". It was, and is still working fine (output is what it should be) if I use pitch 0.4.1.\nBut as i upgrade to version 1.0.1, every time, I try to evaluate same input image, I get different output (Its a regression).\nI tried to see what has been changed in those versions, but since i am not getting any errors, warnings anything, i am not sure what should I look for specifically.\nPS: I checked the weights, they are also same when I load the model","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":58,"Q_Id":54984345,"Users Score":0,"Answer":"It was because of drop out layer. Model.eval disables the dropout layer. Pretty simple.\nBut Now in Pytorh upgrade, if Dropout is not defined specifically in model init function, it will not get disable during eval. \nAtleast this was reason for my case.","Q_Score":1,"Tags":"python,machine-learning,pytorch,torchvision","A_Id":55593835,"CreationDate":"2019-03-04T13:30:00.000","Title":"After Pytorch Upgrade , my model is giving almost random output","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Does anyone have an estimate of the number of generations one should search before concluding that the NEAT-algorithm is not able to reach the minima?\nI am running NEAT on a very small dataset of cancer patients (~5K rows). And after 5000 generations, the concordance index for prediction of survival index is not improving. \nDoes anyone have any experience of how many generations should one try before you deem this as not efficient for the given problem?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":210,"Q_Id":54993604,"Users Score":0,"Answer":"There are a couple other hyperparameters to consider before deciding NEAT cannot produce a usable neural network for your problem. You will have to make sure that your population is also large enough. Obviously a larger dataset is more helpful, but that is limited. Finally, changes such as mutation rates, aggregation options, activation functions, and your fitness function will all affect the training process for each genome. Feel free to PM if you want suggestions on them.","Q_Score":2,"Tags":"python-3.x,es-hyperneat","A_Id":55231544,"CreationDate":"2019-03-05T00:20:00.000","Title":"Typical Number of generations for NEAT (Neuro Evolution of Augmenting Topologies)?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a regression model where my target variable (days) quantitative values ranges between 2 to 30. My RMSE is 2.5 and all the other X variables(nominal) are categorical and hence I have dummy encoded them. \nI want to know what would be a good value of RMSE? I want to get something within 1-1.5 or even lesser but I am unaware what I should do to achieve the same.\nNote# I have already tried feature selection and removing features will less importance.\nAny ideas would be appreciated.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":53,"Q_Id":54993739,"Users Score":0,"Answer":"If your x values are categorical then it does not necessarily make much sense binding them to a uniform grid. Who's to say category A and B should be spaced apart the same as B and C. Assuming that they are will only lead to incorrect representation of your results.\nAs your choice of scale is the unknowns, you would be better in terms of visualisation to set your uniform x grid as being the day number and then seeing where the categories would place on the y scale if given a linear relationship.\nRMS Error doesn't come into it at all if you don't have quantitative data for x and y.","Q_Score":0,"Tags":"python,machine-learning,sklearn-pandas","A_Id":54993847,"CreationDate":"2019-03-05T00:37:00.000","Title":"Regression analysis for linear regression","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I recently download the PISA 2012 Student database from PISA. I follow the instructions and successfully read it on SAS. Then I exported as CSV to read it in Python 3, using proc export, but I keep getting this error when trying to read it in python pandas: UnicodeDecodeError: 'utf-8' codec can't decode byte 0xc1 in position 24: invalid start byte. What can I do?\npisa2012_Col=pd.read_csv('Pisasubset2012Col.csv')","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":183,"Q_Id":54995240,"Users Score":0,"Answer":"Thank you both. It seems to work. I read it successfully and until know, variables are responding fine to my calculations. Seems SOLVED.","Q_Score":1,"Tags":"python-3.x","A_Id":55007697,"CreationDate":"2019-03-05T04:09:00.000","Title":"error UnicodeDecodeError: 'utf-8' codec when reading CSV","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataframe with a column of user ids converted from int to string\ndf['uid'] = df['uid'].astype(str)\nHowever when I write to csv, the column got rounded to the nearest integer in format 1E+12 (the value is still correct when you select the cell).\nBut to_excel outputs the column correctly, can someone explain a bit?\nThank you!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":419,"Q_Id":55010853,"Users Score":3,"Answer":"CSV doesn't have data types.  Excel has no way of knowing what you want, so it tries to interpret it.  If you are using Excel, click the data tab and 'from csv' and you can specify dtypes on reading it.\nOtherwise open the csv file in notepad and you'll see that the data is there.","Q_Score":1,"Tags":"python,pandas","A_Id":55010897,"CreationDate":"2019-03-05T20:15:00.000","Title":"Pandas `to_csv` String column got converted","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Using the stackoverflow data dump, I am analyzing SO posts that are tagged with pytorch or keras. Specifically, I count how many times each co tag occurs (ie the tags that aren't pytorch in a pytorch tagged post). \nI'd like to filter out the tags that are so common they've lost real meaning for my analysis (like the python tag).\nI am looking into Tf-idf\nTF reprensents the frequency of word for each document. However, each co-tag can only occur once for a given post (ie you can't tag your post 'html' five times). So the tf for most words would be 1\/5, and others less (because post only has 4 tags for instance). Is it still possible to do Tf-Idf given this context?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":29,"Q_Id":55012475,"Users Score":0,"Answer":"If you want to filter out tags that are so common, you can use conditional probability. eg: python is so common on posts taged pytorch, so P(python|pytorch) will be hight, likes:0.9. You can find a threshold to filter those tags. \nAssociation rule learning is more suitable and more complex than the above.","Q_Score":0,"Tags":"python,nlp,tf-idf","A_Id":55014376,"CreationDate":"2019-03-05T22:16:00.000","Title":"Tf-idf for SO posts (where tag can only occur once )","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have installed tensorflow-gpu and keras on my gpu machine for deep learning training. The tensorflow version is 1.12. However, nyoka (pmml converter package of python) has conflict because of tensorflow dependencies. I think it uses tensorflow 1.2. Can there be any workaround for it?","AnswerCount":1,"Available Count":1,"Score":-0.1973753202,"is_accepted":false,"ViewCount":171,"Q_Id":55015742,"Users Score":-1,"Answer":"Which version of Nyoka are you using? The issue was there prior to version 3.0.1. The latest version of nyoka (3.0.6) does not have tensorflow as a dependency. Could you try with the latest version?","Q_Score":0,"Tags":"python-3.x,tensorflow,keras,pmml","A_Id":55375736,"CreationDate":"2019-03-06T04:46:00.000","Title":"nyoka package install conflict with keras\/tensorflow version","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I recently ran into a bug using python (v3.6.8) and pandas (v0.23.4) where I was trying to subtract a date offset. However, I accidentally typed two -- signs and it ended up adding the date offset instead. I did some more experimenting and found that 2--1 will return 3. This makes sense since you could interpret that as 2-(-1), but you can go even farther and string a bunch of negatives together 2----1 will return 3. I also replicated this in R and it does the same thing. Can anyone help me understand what's happening here?","AnswerCount":4,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":115,"Q_Id":55028071,"Users Score":5,"Answer":"2 - - - - 1 is the same as 2 - ( - ( - ( - 1))) what is the same as \n2 - ( - (1)) = 2 + 1 = 3\nAs soon as number of minuses is even you actually do \"+\".","Q_Score":1,"Tags":"python","A_Id":55028215,"CreationDate":"2019-03-06T16:39:00.000","Title":"Why does 2- - - -1=3?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I recently ran into a bug using python (v3.6.8) and pandas (v0.23.4) where I was trying to subtract a date offset. However, I accidentally typed two -- signs and it ended up adding the date offset instead. I did some more experimenting and found that 2--1 will return 3. This makes sense since you could interpret that as 2-(-1), but you can go even farther and string a bunch of negatives together 2----1 will return 3. I also replicated this in R and it does the same thing. Can anyone help me understand what's happening here?","AnswerCount":4,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":115,"Q_Id":55028071,"Users Score":2,"Answer":"mathematically, it's correct. by why would a programming language allow that? maybe i just lack imagination, but i can't think of any reason why you would want to explicitly string together plus or minus signs. and if you did do that, it is likely a typo as in the original post. if it's done through variables, then it should definitely be allowed (ie, a = -1;  2 -a should be 3). some languages allow for i++ to increment i. and python allows i += 1 to increment i. not throwing a syntax error just seems confusing to me, even if it is mathematically correct.","Q_Score":1,"Tags":"python","A_Id":55028501,"CreationDate":"2019-03-06T16:39:00.000","Title":"Why does 2- - - -1=3?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it possible to train convolutional autoencoder (CAE) with non-square (rectangular) input matrix? All the tutorials and resources I have studied on CAE seems to use squared images. The data I am working with is not image. I have hundreds of single cells and for each cell there is a matrix (genomic data) with thousands of genes in rows and  hundreds of bins in columns (genomic region of interest for each gene divided into the bins of equal size).\nI have tried some models with Keras, but size of input in the encoder part of the model is always different than the size of output matrix in the decoder. So it is giving error. Can someone help me how to solve this problem?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":469,"Q_Id":55031885,"Users Score":0,"Answer":"it is hard to tell what is the issue here since no sample code is provided . However most propably your Matrix is diminsions are odd ( example 9\u00d79 )  or becomes odd while pooling. tofix this issue you need to either pad your input to make the matrix diminsions even . our crop the decoder lsyers of your autoencoder to have a matching output size","Q_Score":1,"Tags":"python,keras,deep-learning,conv-neural-network,autoencoder","A_Id":55385124,"CreationDate":"2019-03-06T20:47:00.000","Title":"Using non-square input matrix for convolutional autoencoder","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am tuning hyperparameters for a neural network via gridsearch on google colab. I got a \"transport endpoint is not connected\" error after my code executed for 3 4 hours. I found out that this is because google colab doesn't want people to use the platform for a long time period(not quite sure though).\nHowever, funnily, after the exception was thrown when I reopened the browser, the cell was still running. I am not sure what happens to the process once this exception is thrown.\nThank you","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":4543,"Q_Id":55034469,"Users Score":0,"Answer":"In google colab, you can use their GPU service for upto 12 hours, after that it will halt your execution, if you ran it for 3-4 hours, it will just stop displaying Data continuously on your browser window(if left Idle), and refreshing the window will restore that connection.\nIn case you ran it for 34 hours, then it will definitely be terminated(Hyphens matter), this is apparently done to discourage people from mining cryptocurrency on their platform, in case you have to run your training for more than 12 hours, all you need to do is enable checkpoints on your google drive, and then you can restart the training, once a session is terminated, if you are good enough with the requests library in python, you can automate it.","Q_Score":0,"Tags":"python,neural-network,jupyter-notebook,jupyter,google-colaboratory","A_Id":55154500,"CreationDate":"2019-03-07T00:56:00.000","Title":"Google colab Transport endpoint is not connected","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":1,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am implementing a program to sample integers from a categorical distribution, where each integer is associated with a probability. I need to ensure that this program is differentiable, so that back propagation can be applied. I found tf.contrib.distributions.RelaxedOneHotCategorical which is very close to what I am trying to achieve.\nHowever, the sample method of this class returns a one-hot vector, instead of an integer. How to write a program that is both differentiable and returns an integer\/scalar instead of a vector?","AnswerCount":3,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":768,"Q_Id":55037810,"Users Score":0,"Answer":"You can do a dot product of the relaxed one hot vector with a vector of [1 2 3 4 ... n]. The result is going to give you the desired scalar.\nFor instance if your one hot vector is [0 0 0 1], then dot([0 0 0 1],[1 2 3 4]) will give you 4 which is what you are looking for.","Q_Score":0,"Tags":"python,tensorflow,tensorflow-probability","A_Id":56431054,"CreationDate":"2019-03-07T06:59:00.000","Title":"TensorFlow: Sample Integers from Gumbel Softmax","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am implementing a program to sample integers from a categorical distribution, where each integer is associated with a probability. I need to ensure that this program is differentiable, so that back propagation can be applied. I found tf.contrib.distributions.RelaxedOneHotCategorical which is very close to what I am trying to achieve.\nHowever, the sample method of this class returns a one-hot vector, instead of an integer. How to write a program that is both differentiable and returns an integer\/scalar instead of a vector?","AnswerCount":3,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":768,"Q_Id":55037810,"Users Score":0,"Answer":"You can't get what you want in a differentiable manner because argmax isn't differentiable, which is why the Gumbel-Softmax distribution was created in the first place. This allows you, for instance, to use the outputs of a language model as inputs to a discriminator in a generative adversarial network because the activation approaches a one-hot vector as the temperature changes.\nIf you simply need to retrieve the maximal element at inference or testing time, you can use tf.math.argmax. But there's no way to do that in a differentiable manner.","Q_Score":0,"Tags":"python,tensorflow,tensorflow-probability","A_Id":67752253,"CreationDate":"2019-03-07T06:59:00.000","Title":"TensorFlow: Sample Integers from Gumbel Softmax","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am implementing a program to sample integers from a categorical distribution, where each integer is associated with a probability. I need to ensure that this program is differentiable, so that back propagation can be applied. I found tf.contrib.distributions.RelaxedOneHotCategorical which is very close to what I am trying to achieve.\nHowever, the sample method of this class returns a one-hot vector, instead of an integer. How to write a program that is both differentiable and returns an integer\/scalar instead of a vector?","AnswerCount":3,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":768,"Q_Id":55037810,"Users Score":0,"Answer":"The reason that RelaxedOneHotCategorical is actually differentiable is connected to the fact that it returns a softmax vector of floats instead of the argmax int index. If all you want is the index of the maximal element, you might as well use Categorical.","Q_Score":0,"Tags":"python,tensorflow,tensorflow-probability","A_Id":55052713,"CreationDate":"2019-03-07T06:59:00.000","Title":"TensorFlow: Sample Integers from Gumbel Softmax","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dependent variable y and 6 independent variables. I want to make a linear regression out of it. I use sklearn library to do it. \nThe problem is some of my independent variables have correlation more than 0.5. So I can't have them in my model at the same time \nI searched throw internet but didn't find any solution to select best set of independent variables to draw linear regression and output the variables that had been selected.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":1223,"Q_Id":55039091,"Users Score":1,"Answer":"You are probably looking for a k-fold validation model.\nThe idea is to randomly select your features, and have a way to validate them against each other.\nThe idea is to train your model with your feature selection on (k-1) partitions of your data. And validate it against the last partition. You do it for each partition and take the average of your score (MAE \/ RMSE for instance)\nYour score is an objectif figure to compare your models aka your features selections","Q_Score":0,"Tags":"python,linear-regression","A_Id":55039173,"CreationDate":"2019-03-07T08:21:00.000","Title":"Python Select variables in multiple linear regression","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a Question \ni do have large amount of Unstructured  text data , which i want to classify into different -different sectors .\ni am using a Naive Bayes classifier for it \nNow, my question is what should i pass in Y?? because i don't have a Target values \nand as per the syntax i have to pass it .\nmnb = MultinomialNB()\nmnb.fit(X,y)\nTypeError: fit() missing 1 required positional argument: 'y'\nAs i said i don't have target value.\nHow can i do that?\nHelp will be appreciated","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":68,"Q_Id":55040536,"Users Score":0,"Answer":"The Naive Bayes classifier is a supervised learning method and requires you to train it using labelled data in which you know the targets in advance. You can then use it on unlabelled data to predict future values but you can't train it on data with no target values.\nIt's hard to recommend a different method without knowing more about your task but it sounds like you want to look into unsupervised clustering algorithms. k-means is a relatively simple one to start with.","Q_Score":0,"Tags":"python,classification,naivebayes","A_Id":55041022,"CreationDate":"2019-03-07T09:41:00.000","Title":"Text analysis for unstructured data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"glpsol.exe --help provides the following options: \nOptions specific to the MIP solver:\n--nomip consider all integer variables as continuous (allows solving MIP as pure LP)\n--first branch on first integer variable\n--last branch on last integer variable\n--mostf branch on most fractional variable\n--drtom branch using heuristic by Driebeck and Tomlin\n(default)","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":215,"Q_Id":55040678,"Users Score":0,"Answer":"To use --first as branch method\nimport pyomo.environ as pe\nopt_solver = pe.SolverFactory(\"glpk\")\nopt_solver.options[\"first\"] = \"\"","Q_Score":0,"Tags":"python,pyomo,glpk","A_Id":55043641,"CreationDate":"2019-03-07T09:48:00.000","Title":"How can I set the branch method used by glpk solver through pyomo?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm programming a game in python3.6. There is some pawns on the board which are instance of the class 'pawn'. There is also boulders on the board which are instance of the class boulder. I just want to store these pawns and boulders in an array, like a numpy.array.\nI have 2 problems : \n\nThere is different type of object in the array, which is not possible with a numpy.array\nHow can I represent an empty cell on my board, because I can't use an object, which has not the same type as the others.\n\nHow can I solve these two problems ? Is there already an object which can represent grid, an array and which accepts different type ?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":861,"Q_Id":55050445,"Users Score":2,"Answer":"Use np.dtype(object): np.array(board, dtype=np.dtype(object))\nAs for empty cells: just set them to None.\n\nEdit: as some people have suggested, you might not need a numpy array at all. A list of lists or a dict with tuple indexes might solve your problem just fine and remove the overhead of using numpy.","Q_Score":1,"Tags":"python,arrays,numpy,types,storage","A_Id":55050551,"CreationDate":"2019-03-07T18:21:00.000","Title":"Multi-type storage array in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Can I run a google colab (free edition) script and then shut down my computer?\nI am training several deeplearning models with crossvalidation, and therefore I would like to know if I can close the window or the computer with the training running at the same time in the cloud.","AnswerCount":4,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":96447,"Q_Id":55050988,"Users Score":2,"Answer":"use multiprocess in python make one other function and start while loop there! that while loop won't let it sleep!","Q_Score":103,"Tags":"python,google-colaboratory","A_Id":67356441,"CreationDate":"2019-03-07T18:58:00.000","Title":"Can I run a Google Colab (free edition) script and then shut down my computer?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a CSV file with two columns. \nOne has values like size XL, size L, size M and size S. In the other I only have XL and L. \nWhat I want to do is that when my loop finds XL in the first column it overwrites that cell value with XL and when it doesn't find XL in the next cell it should just skip it. \nIn the next iteration, it should do the same with L. The file should look something like this after the loop: XL, L, size M, size S.\nCan you assist me with python code to implement something like this?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":61,"Q_Id":55064953,"Users Score":0,"Answer":"I don't understand the question, and would be very confusing for others too.\nHowever, it seems like a simple loop and condition code. Try using PANDAS with Python, the library to process the CSV in case this helps you.","Q_Score":0,"Tags":"python,numpy-slicing","A_Id":55065070,"CreationDate":"2019-03-08T14:10:00.000","Title":"comparing two columns in the same csv file","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to sort an RDD that I have, which contains a key range of 0-49995 such that (0, value), ... , (49995, value).\nI want to sort it in ascending order, and I am using the SortByKey() function, but it seems like it is not working properly because this is the result that I am getting:\n\n\n\ntest0.sortByKey(True).take(5)\n\n\n\n[(u'0', [u'38737', u'18591', u'27383', u'34211', u'337', u'352', u'1532', u'12143', u'12561', u'17880']), (u'1', [u'35621', u'44891', u'14150', u'15356', u'35630', u'13801', u'13889', u'14078', u'25228', u'13805']), (u'10', [u'83', u'18', u'38', u'89', u'3', u'11', u'29', u'41', u'53', u'55']), (u'100', [u'42704', u'122', u'125', u'128', u'131', u'2501', u'11200', u'12049', u'12576', u'18583']), (u'1000', [u'8671', u'955', u'1012', u'1020', u'1378', u'2413', u'7699', u'10276', u'12625', u'12667'])]\nIt started at key 0, 1, but then skipped to 10 and jumped to 100, then 1000. It should ascending from 0-5. Can someone please tell me what I am doing wrong here?\nThank you!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":493,"Q_Id":55085647,"Users Score":0,"Answer":"You are sorting strings - as shown by the u prefix on the output (for Unicode, not that it really matters). Therefore it is working as 10 comes before 2 when tested as a string.\nMap all your values to integers before working with them and you should be fine.","Q_Score":0,"Tags":"python,python-2.7,apache-spark,pyspark","A_Id":55085727,"CreationDate":"2019-03-10T07:57:00.000","Title":"PySpark RDD SortByKey() Not Working Properly","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on the following Python codes.\nI am hoping to accomplish the following:\n\nCreate a total_fold_array which will hold 5 items (folds)\nFor each fold, create an array of data from a larger dataset based off of the logic (which I know is correct) inside of my for...zip loop\n\nTo help you understand:\nThe classses and class_weights returns:\n[0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0] and [0.14285714 0.14285714 0.14285714 0.14285714 0.14285714 0.14285714\n 0.14285714]\nThe while count !=6 is not working properly. In short, what I am trying to accomplish is to populate the total_fold_array with 5 individual folds, which each contain a number of rows from a dataset.\nAn example of the current_fold_array might be [A,B,C,D], so then ultimately, I have a total_fold_array which has 5 of those individual folds, which would look like [[A,B,C,D,],[A,B,B,C],[A,A,A,A],[B,C,D,D],[B,B,B,C]]\nHowever, this loop does not do that. Instead, it creates total_fold_array with the length of whatever the length of classes is (in this case 7), instead of having 5 folds within.\nMy code is below:\nI am currently getting a total_fold_array containing 7 items, when instead, it should contain 5. Each item within can have as many items as needed, but the total_fold_array should be 5 items long. I believe there is a logic bug in my code, and I am looking for some help. If I were to use a dataset with 5 classes, this works appropriately. \nPlease let me know if I need to make this clearer.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":910,"Q_Id":55093027,"Users Score":1,"Answer":"Just before for a_class,a_class_weight in zip(classes, class_weights):, you're initializing total_fold_array to [].\nThat loop executes for exactly as many times as there are elements in classes.\nEach iteration of that loop appends a curr_fold_array to total_fold_array.\nThat is why, at the end of that loop, you have as many elements in total_fold_array, as there are in classes.\nYou've enclosed all of this in while count != 6:. That seems totally unnecessary -- I think that while loop will execute exactly once. You are returning from that function before the second iteration of that while loop can happen. My guess is that you introduced that while loop hoping that it would somehow limit the number of elements in total_fold_array to 5. But that's not going to happen, because, inside that while loop, the for loop grows total_fold_array to have 7 elements, and this happens in the very first iteration of the while loop.","Q_Score":0,"Tags":"python,python-3.x,numpy,for-loop","A_Id":55093212,"CreationDate":"2019-03-10T22:26:00.000","Title":"While Loop in Python - Array Manipulation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I know this is an elementary question, but I'm not a python programmer. I have an app that is using the sklearn kit to run regressions on a python server.\nIs there a simple command which will return the predictions or the residuals for each and every data record in the sample?","AnswerCount":2,"Available Count":1,"Score":0.2913126125,"is_accepted":false,"ViewCount":12667,"Q_Id":55095437,"Users Score":3,"Answer":"One option is to use fit() to get predictions and residual is simply the difference between the actual value and predictions.","Q_Score":9,"Tags":"python,scikit-learn","A_Id":55095505,"CreationDate":"2019-03-11T04:56:00.000","Title":"In sklearn regression, is there a command to return residuals for all records?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have trained a single layer neural network model in python (a simple model without keras and tensorflow).\nHow canI save it after training along with weights in python, and how to load it later?","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":10072,"Q_Id":55102781,"Users Score":1,"Answer":"So you write it down yourself. You need some simple steps:\n\nIn your code for neural network, store weights in a variable. It could be simply done by using self.weights.weights are numpy ndarrays. for example if weights are between layer with 10 neurons to layer with 100 neurons, it is a 10 * 100(or 100* 10) nd array.\nUse numpy.save to save the ndarray.\nFor next use of your network, use numpy.load to load weights\nIn the first initialization of your network, use weights you've loaded.\nDon't forget, if your network is trained, weights should be frozen. It can be done by zeroing learning rate.","Q_Score":6,"Tags":"python,scikit-learn,pickle","A_Id":55103222,"CreationDate":"2019-03-11T13:21:00.000","Title":"How to save and load my neural network model after training along with weights in python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have made a custom NER model using spaCy by loading the training data from a text file in the prescribed format and the model is working fine, However If I am trying to load training data from excel file we get the output model but does not getting the entities(no output and also no error).\nModel from the training data in text file is working perfectly giving proper outputs but not getting results if loading training data from xlsx file.\nNo problem in datatypes(same for both the cases).\nEven If I am writing the same into a text file and then loading it , facing same issue","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":371,"Q_Id":55117481,"Users Score":0,"Answer":"Please write the code after 4 space you will get proper format in stack overflow or ctrl+k then write the code.","Q_Score":1,"Tags":"python,xlrd,spacy","A_Id":55223274,"CreationDate":"2019-03-12T08:57:00.000","Title":"spaCy 2.0: Loading Training Data from excel file Custom NER Model issues","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I try to calculate noise for input data using the gradient of the loss function from the input-data:\nmy_grad = tf.gradients(loss, input)\nloss is an array of size (n x 1) where n is the number of datasets, m is the size of the dataset, input is an array of (n x m) where m is the size of a single dataset.\nI need my_grad to be of size (n x m) - so for each dataset the gradient is calulated. But by definition the gradients where i!=j are zero - but tf.gradients allocates huge amount of memory and runs for prettymuch ever...\nA version, which calulates the gradients only where i=j would be great - any Idea how to get there?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":132,"Q_Id":55121377,"Users Score":0,"Answer":"I suppose I have found a solution:\nmy_grad = tf.gradients(tf.reduce_sum(loss), input)\nensures, that the cross dependencies i!=j are ignored - that works really nicely and fast..","Q_Score":1,"Tags":"python,tensorflow,diagonal,gradient","A_Id":55122132,"CreationDate":"2019-03-12T12:23:00.000","Title":"tf.gradient acting like tfp.math.diag_jacobian","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a pre-trained XGBoost model read from a pickle file. When I was trying to make predictions on a new dataset with some columns outside of the feature set of the model, I received the error message:\n\ntraining data did not have the following fields: column1, column2,...\n\nI am okay with excluding these columns not existing in training data. Instead of hard-coding the column names (there are many), I would like to just find the intersection between columns of the training and the prediction datasets. \nIs there a way I can extract the feature names from the trained model (apparently the model recorded the field names) without having to go back to my training dataset?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1723,"Q_Id":55126903,"Users Score":0,"Answer":"Its mandatory that prediction dataset should contain only those columns which are present in training dataset. It even makes sense not to include extra columns because the weights are learnt based on your training dataset. Including any extra column apart from training dataset doesn't provide any value or improve your accuracy, because when you are predicting all you do is multiply the learnt weights of model with new values. Make sure not to inlcude any extra feature for predicting.","Q_Score":2,"Tags":"python,scikit-learn,xgboost","A_Id":55127590,"CreationDate":"2019-03-12T16:58:00.000","Title":"extract feature names from trained model","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have installed Dask on OSX Mojave. Does it execute computations in parallel by default? Or do I need to change some settings?\nI am using the DataFrame API. Does that make a difference to the answer?\nI installed it with pip. Does that make a difference to the answer?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":136,"Q_Id":55133058,"Users Score":1,"Answer":"Yes, Dask is parallel by default. \nUnless you specify otherwise, or create a distributed Client, execution will happen with the \"threaded\" scheduler, in a number of threads equal to your number of cores. Note, however, that because of the python GIL (only one python instruction executed at a time), you may not get as much parallelism as available, depending on how good your specific tasks are at releasing the GIL. That is why you have a choice of schedulers.\nBeing on OSX, installing with pip: these make no difference. Using dataframes makes a difference in that it dictates the sorts of tasks you're likely running. Pandas is good at releasing the GIL for many operations.","Q_Score":0,"Tags":"python,dask,dask-distributed","A_Id":55143748,"CreationDate":"2019-03-13T01:30:00.000","Title":"Dask on single OSX machine - is it parallel by default?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"For the purpose of this exercise, let's consider a matrix where the element m_{i, j} is given by the rule m_{i, j} = i*j if i == j and 0 else.\nIs there an easy \"numpy\" way of calculating such a matrix without having to resort to if statements checking for the indices?","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1790,"Q_Id":55141674,"Users Score":0,"Answer":"You could use the identity matrix given by numpy.identity(n) and then multiply it by a n dimensional vector.","Q_Score":0,"Tags":"python,numpy,matrix","A_Id":55141944,"CreationDate":"2019-03-13T12:14:00.000","Title":"How to multiply diagonal elements by each other using numpy?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"For the purpose of this exercise, let's consider a matrix where the element m_{i, j} is given by the rule m_{i, j} = i*j if i == j and 0 else.\nIs there an easy \"numpy\" way of calculating such a matrix without having to resort to if statements checking for the indices?","AnswerCount":3,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":1790,"Q_Id":55141674,"Users Score":1,"Answer":"You can use the numpy function diag to construct a diagonal matrix if you give it the intended diagonal as a 1D array as input. \nSo you just need to create that, like [i**2 for i in range (N)] with N the dimension of the matrix.","Q_Score":0,"Tags":"python,numpy,matrix","A_Id":55141958,"CreationDate":"2019-03-13T12:14:00.000","Title":"How to multiply diagonal elements by each other using numpy?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have download a code for ocr using Keras, which applied the CRNN network and use the CTC loss as the loss function. \nHowever, I'm really new to CTC loss and just have trouble with the usage of K.ctc_batch_cost(), especially the meaning of input_length. In the document of keras, \n\nArguments of tf.keras.backend.ctc_batch_cost(\n      y_true,\n      y_pred,\n      input_length,\n      label_length\n  ) \n\n\ny_true: tensor (samples, max_string_length) containing the truth labels.\ny_pred: tensor (samples, time_steps, num_categories) containing the prediction, or output of the softmax.\ninput_length: tensor (samples, 1) containing the sequence length for each batch item in y_pred.\nlabel_length: tensor (samples, 1) containing the sequence length for each batch item in y_true.\nHowever, my problem is just what's the meaning of input_length? is that the dimensional of the output of LSTM?","AnswerCount":1,"Available Count":1,"Score":-0.1973753202,"is_accepted":false,"ViewCount":1540,"Q_Id":55160939,"Users Score":-1,"Answer":"CTC loss for one example is calculated on a 2D array (T,C). C must be equal to number of character + 1(blank characters). C contains the probability distribution of the characters at a time stamps. T will be the number of time stamps. \nT should be of length 2* max_string_length. All possible encoding of y_true with length T will be used in negative log loss calculation.\nIt is usually the shape of previous layer output.","Q_Score":5,"Tags":"python,tensorflow,keras","A_Id":55161676,"CreationDate":"2019-03-14T11:07:00.000","Title":"what's the mean of input_length in K.ctc_batch_cost()","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have the following problem, I have many files of 3D volumes that I open to extract a bunch of numpy arrays.\nI want to get those arrays randomly, i.e. in the worst case I open as many 3D volumes as numpy arrays I want to get, if all those arrays are in separate files.\nThe IO here isn't great, I open a big file only to get a small numpy array from it.\nAny idea how I can store all these arrays so that the IO is better?\nI can't pre-read all the arrays and save them all in one file because then that file would be too big to open for RAM.\nI looked up LMDB but it all seems to be about Caffe.\nAny idea how I can achieve this?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1082,"Q_Id":55166874,"Users Score":0,"Answer":"One trivial solution can be pre-processing your dataset and saving multiple smaller crops of the original 3D volumes separately. This way you sacrifice some disk space for more efficient IO.\nNote that you can make a trade-off with the crop size here: saving bigger crops than you need for input allows you to still do random crop augmentation on the fly. If you save overlapping crops in the pre-processing step, then you can ensure that still all possible random crops of the original dataset can be produced.\nAlternatively you may try using a custom data loader that retains the full volumes for a few batch. Be careful, this might create some correlation between batches. Since many machine learning algorithms relies on i.i.d samples (e.g. Stochastic Gradient Descent), correlated batches can easily cause some serious mess.","Q_Score":0,"Tags":"python,machine-learning,dataset,pytorch,lmdb","A_Id":55169008,"CreationDate":"2019-03-14T15:52:00.000","Title":"Faster pytorch dataset file","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Can one make Altair plots fit the screen size, rather than have a pixel-defined width and height? I've read things about autosize \"fit\", but I am unsure about where to specify these.","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2245,"Q_Id":55169344,"Users Score":5,"Answer":"There is no way to do this. The dimensions of Altair\/Vega-Lite charts are pre-determined by the chart specification and data, and cannot be made to scale with the size of the browser window.","Q_Score":7,"Tags":"python,vega-lite,altair","A_Id":55169600,"CreationDate":"2019-03-14T18:08:00.000","Title":"How to make Altair plots responsive","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"(base) C:\\Users\\Kevin>pip install scipy Requirement already satisfied:\n  scipy in c:\\programdata\\anaconda3\\lib\\site-packages (1.1.0)\n\netc\nSuddenly my jupyter notebook denies to import several packages. pandas and numpy work, but all the other packages do not  (spacy, nltk, scipy, requests)\nI tried reinstall packages, but it says I already have installed them.\nWhy is this happening?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":279,"Q_Id":55172651,"Users Score":0,"Answer":"Did you install Anaconda + Python ? Python doesn't come with package, maybe you're using Python path instead of Anaconda to run jupyter","Q_Score":1,"Tags":"python,pip,jupyter-notebook","A_Id":55172773,"CreationDate":"2019-03-14T21:59:00.000","Title":"No module named scipy, spacy, nltk","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a variable X, it contains a list (Python list), of 10 Numpy 1-D arrays (basically vectors).\nIf I ask for X[100], it throws an error saying: IndexError: list index out of range\nWhich makes total sense, but, when I ask for X[:100], it doesn't throw an error and it returns the entire list! \nWhy is that?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":156,"Q_Id":55189686,"Users Score":1,"Answer":"X[:100] means slice X from 0 to 100 or the end (whichever comes first)\nBut X[100] means the 100th element of X, and if it doesn't exist it throws an index out of range error","Q_Score":0,"Tags":"python,numpy","A_Id":55189726,"CreationDate":"2019-03-15T19:42:00.000","Title":"Weird Indexing by Python and Numpy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Objects cannot be serialised to json so therefore need to be converted or parsed through a custom JsonEncoder class.\npandas Dataframe has a number of methods, like from_records to read json data. Yet when you read that json data back it is returned as int64 instead of timestamp.\nThere are many ways to skin a cat in pandas. What is the best way to preserve data structures when reading and writing json?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":2822,"Q_Id":55205436,"Users Score":1,"Answer":"If I have correctly understood your problem, you are looking for a serialization way preserving the data types of a dataframe.\nThe problem is that the interchange formats internally use few types: only strings for csv, strings and numbers for json. Of course there are ways to give formatting hints at read time (date format for date columns in csv), and it is generaly easy to convert back to the proper type after extraction, by I think that you would hope a more natural way. As suggested by Attack68 you could use a database but for example a SQLite database would be off because it has no internal date type.\nIMHO a simple way would be to rely on the good old pickle module. After all, a dataframe is a Python object that contains other Python objects, so pickle is good at serializing that. The only point to remember is that, at deserialization time, pandas will have to be imported before calling pickle.load.\nBut I have just tested with a (tiny) dataframe containing various datatypes, and pickle was great as correctly saving and restoring them.","Q_Score":2,"Tags":"python,json,pandas,numpy","A_Id":55225618,"CreationDate":"2019-03-17T09:02:00.000","Title":"Pandas timestamp to and from json","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using the Mask RCNN library which is based on tenserflow and I can't seem to get it to run on my GPU (1080TI). The inference time is 4-5 seconds, during which I see a usage spike on my cpu but not my gpu. Any possible fixes for this?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":3990,"Q_Id":55211277,"Users Score":5,"Answer":"It is either because that GPU_COUNT is set to 0 in config.py or you don't have tensorflow-gpu installed (which is required for tensorflow to run on GPU)","Q_Score":2,"Tags":"python,tensorflow,machine-learning,keras","A_Id":55211330,"CreationDate":"2019-03-17T19:55:00.000","Title":"Mask RCNN uses CPU instead of GPU","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am implementing a server for recognizing objects in photos using tensorflow-gpu in \"semi-real\" time.  It will listen for new photos on a websocket connection, then enqueue it into a list for the detector run when it is free.  Would it be simpler to use asyncio or threading to handle the websocket listener and the recognition queue?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":924,"Q_Id":55229009,"Users Score":0,"Answer":"I am not an expert in threading\/asyncio but maybe it would be easier to spawn an instance of Kafka and have a piece of code that would listen to a Kafka topic? To this topic you would push images or paths to images if you already store them locally. Moreover, using consumer-groups you would get a load balancing like thing for free as it is a part of Kafka.","Q_Score":1,"Tags":"python,tensorflow,websocket,python-asyncio,python-multithreading","A_Id":55231076,"CreationDate":"2019-03-18T19:47:00.000","Title":"Python tensorflow: asyncio or threading","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am implementing a server for recognizing objects in photos using tensorflow-gpu in \"semi-real\" time.  It will listen for new photos on a websocket connection, then enqueue it into a list for the detector run when it is free.  Would it be simpler to use asyncio or threading to handle the websocket listener and the recognition queue?","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":924,"Q_Id":55229009,"Users Score":0,"Answer":"Ultimately I used asyncio to handle the websocket connection, enqueuing incoming images to a queue.  I used threading which had a thread to read the image into RAM, extracted some metadata, and queued it for the object detector. The detector, running in another thread, tagged the images and queued the tags in the database handler (yet another thread).","Q_Score":1,"Tags":"python,tensorflow,websocket,python-asyncio,python-multithreading","A_Id":61762278,"CreationDate":"2019-03-18T19:47:00.000","Title":"Python tensorflow: asyncio or threading","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"File \"C:\\ProgramData\\Anaconda3\\Scripts\\tensorboard-script.py\", line 10, in \n    sys.exit(run_main())\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorboard\\main.py\", line 57, in run_main\n    app.run(tensorboard.main, flags_parser=tensorboard.configure)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\absl\\app.py\", line 300, in run\n    _run_main(main, args)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\absl\\app.py\", line 251, in _run_main\n    sys.exit(main(argv))\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorboard\\program.py\", line 228, in main\n    self._register_info(server)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorboard\\program.py\", line 274, in _register_info\n    manager.write_info_file(info)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorboard\\manager.py\", line 269, in write_info_file\n    payload = \"%s\\n\" % _info_to_string(tensorboard_info)\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorboard\\manager.py\", line 129, in _info_to_string\n    for k in _TENSORBOARD_INFO_FIELDS\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorboard\\manager.py\", line 129, in \n    for k in _TENSORBOARD_INFO_FIELDS\n  File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorboard\\manager.py\", line 51, in \n    (dt - datetime.datetime.fromtimestamp(0)).total_seconds()),\nOSError: [Errno 22] Invalid argument","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":669,"Q_Id":55229123,"Users Score":0,"Answer":"is your tensorboard 1.13.1?\nif so,make it come to 1.12.1,solved the problem.\nbut i can't find out the reason.","Q_Score":0,"Tags":"python,tensorflow","A_Id":55440002,"CreationDate":"2019-03-18T19:56:00.000","Title":"Get this error when trying to run tensorboard?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How is quantization applied\/simulated in software in practice? Suppose for example that I'd like to compute how much error in an output of some function I will get if instead of using 16 bit floating point values I were to use 6 bit integer values in the parameters of the function. If it matters for this question, I am interested in applying quantization to neural networks and the like.\nMy naive thoughts about this: either somehow force the machine to use reduced bit precision (doesn't seem feasible or easy to do on general purpose OS like Linux, but I'd be interested to know if it is done in practice), or artificially simulate the quantization by mapping ranges of floats to a single integer value, where the integer value represents one quantized value.\nI put C and python as a tags because I can only understand those languages if you'd like to answer with code.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":77,"Q_Id":55229311,"Users Score":0,"Answer":"In general there are three approaches:\n\nAnalysis\nSimulation\nTesting\n\nTo analyze you must, of course, understand the calculation, and be a skilled mathematician.\nTo simulate you must still understand the calculation since you need to re-write it in the simulation language, but you don't need to be so good at math ;-)\nTesting is the easiest since you need neither understand of the calculation nor deep math skills. In your case this should be pretty trivial: since there are only 16-bit parameters, you can test all combinations of 2 arguments with 2^16 x 2^16 = 2^32 iterations of your test... a blink of an eye on a modern processor. Compare the result using 16-bit floats with 6-bit ints and keep some simple stats (mean error, max error, etc.). If you have more than two arguments you can save time over an exhaustive test by trying a large number of random inputs, but otherwise the same approach.","Q_Score":0,"Tags":"python,c,approximation","A_Id":55229729,"CreationDate":"2019-03-18T20:10:00.000","Title":"How is quantization applied\/simulated in software?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to use pre-trained word embeddings in my machine learning model. The word embedings file I have is about 4GB. I currently read the entire file into memory in a dictionary and whenever I want to map a word to its vector representation I perform a lookup in that dictionary.\nThe memory usage is very high and I would like to know if there is another way of using word embeddings without loading the entire data into memory.\nI have recently come across generators in Python. Could they help me reduce the memory usage?\nThank you!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":237,"Q_Id":55230575,"Users Score":1,"Answer":"What task do you have in mind? If this is a similarity based task, you could simply use the load_word2vec_format method in gensim, this allows you to pass in a limit to the number of vectors loaded. The vectors in something like the Googlenews set are ordered by frequency, this will give you the critical vectors. \nThis also makes sense theoretically as the words with low frequency will usually have relatively bad representations.","Q_Score":1,"Tags":"python,machine-learning,memory-management,nlp,word-embedding","A_Id":55230793,"CreationDate":"2019-03-18T21:53:00.000","Title":"Is there an alternative to fully loading pre-trained word embeddings in memory?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"say I have a matrix : a = [[1,2,3],[4,5,6],[7,8,9]]. How can I combine it to b = [1,2,3,4,5,6,7,8,9]?\nMany thanks","AnswerCount":5,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":61,"Q_Id":55230862,"Users Score":0,"Answer":"using numpy: \nlist(np.array(a).flatten())","Q_Score":1,"Tags":"python,numpy","A_Id":55238524,"CreationDate":"2019-03-18T22:14:00.000","Title":"Python, how to combine integer matrix to a list","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"is it possible to generate loss curve for MLPregressor with lbfgs solver? it has been specified that it can be generated only for 'adam' solver.\nif it can be done, kindly help me in this regard.","AnswerCount":1,"Available Count":1,"Score":-0.1973753202,"is_accepted":false,"ViewCount":286,"Q_Id":55241237,"Users Score":-1,"Answer":"You can plot model.loss_curve_ on your dataframe, and you're good to go!","Q_Score":1,"Tags":"python,regression","A_Id":55241306,"CreationDate":"2019-03-19T12:35:00.000","Title":"can we generate loss curve for mlpregressor with lbfgs solver","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using spyder and trying to import pandas as pd and its giving me the following error:\nimport pandas as pd\nTraceback (most recent call last):\nFile \"\", line 1, in \n    import pandas as pd\nFile \"C:\\Users\\omer qureshi\\AppData\\Roaming\\Python\\Python37\\site-packages\\pandas__init__.py\", line 13, in \n    import(dependency)\nFile \"C:\\Users\\omer qureshi\\AppData\\Roaming\\Python\\Python37\\site-packages\\numpy__init__.py\", line 142, in \n    from . import core\nFile \"C:\\Users\\omer qureshi\\AppData\\Roaming\\Python\\Python37\\site-packages\\numpy\\core__init__.py\", line 23, in \n    WinDLL(os.path.abspath(filename))\nFile \"C:\\Users\\omer qureshi\\Anaconda3\\lib\\ctypes__init__.py\", line 356, in init\n    self._handle = _dlopen(self._name, mode)\nOSError: [WinError 193] %1 is not a valid Win32 application\nCan someone explain what's wrong?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1376,"Q_Id":55241360,"Users Score":0,"Answer":"Another thing might have happened. VS code automatically searches for the numpy and other packages from predefined OS locations. It might have found out 32 bit version of numpy instead of a 64 bit version. \nTo fix this Uninstall numpy from all OS locations.\n* In VS code terminal Type pip uninstall numpy or conda uninstall numpy (If you use Anaconda) \n* Restart VS code \n* Voila! (Reinstall numpy if the problem persists)","Q_Score":1,"Tags":"pandas,python-3.7","A_Id":57216578,"CreationDate":"2019-03-19T12:43:00.000","Title":"WinError 193] %1 is not a valid Win32 application","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"This is something that has been answered and re-answered time and time again because the answer keeps changing with updates to pandas. I tried some of the solutions I found here and elsewhere online and none of them have worked for me on the current version of pandas. Does anyone know the current, March 2019, pandas 0.24.2, fix for removing the default styling that a DataFrame gives to its header when converting it to an excel sheet? Simply using xlsxwriter to overwrite the styling does not work because of an issue with precedence.","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":11224,"Q_Id":55246202,"Users Score":0,"Answer":"The key explanation is that: pandas writes a df's header with set_cell(). A cell format (in xlsxwriter speak, a \"format\" is a FormatObject that you have to add to the worksheetObject) can NOT be overridden with set_row(). If you are using set_row() to your header row, it will not work, you have to use set_cell().","Q_Score":8,"Tags":"python,excel,pandas,dataframe,xlsxwriter","A_Id":55662072,"CreationDate":"2019-03-19T16:50:00.000","Title":"Remove default formatting in header when converting pandas DataFrame to excel sheet","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've a MIP to solve with Pyomo and I want to set an initial solution for cplex.\nSo googling I find that I can set some variable of instance to some value and then execute this:\nsolver.solve(instance,warmstart=True,tee=True)\nBut when I run cplex it seem that it doesn't use the warm start, because for example i pass a solution with value 16, but in 5 seconds it return a solution with value 60.\nSo I don't know there is some error or other stuff that doesn't work.\nP.S.\nI don't know if is a problem but my warm start solution set only some variale to a value, but not all. Could be a problem?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1303,"Q_Id":55250019,"Users Score":1,"Answer":"Make sure that the solution you give to CPLEX is feasible. Otherwise, CPLEX will reject it and start from scratch. \nIf your solution is feasible, it is possible that CPLEX simply found a better solution than yours, since, after all, it is CPLEX's job, and in my own experience, CPLEX is very good at it. Is this a maximization problem? If so, in your example, CPLEX found a better solution (objective=60) than yours (objective=16), which is the expected behavior.\nSadly, CPLEX is often greedy in term of verbose, so it is hard to know from the solver log if warmstart was used or not (unlike its competitor GUROBI where it is clearly written in the log). However, it seems like you started the warmstart correctly, using the warmstart=True parameter. \nIf, however, your problem isn't a maximization problem, it is possible that CPLEX will not make a differenciation between the variables that you gave a value and the variable that still holds a solution from last solve. Plus, giving values to only a fraction of your variables might make the problem infeasible, considering that all values not manually specified are the values previously found by CPLEX. ex: contraint x<=2y. The solver found x=2, y=1 as a feasible solution. You define x:=3, then your constraint is not respected (y is still =1 for CPLEX, so the constraint x<=2y is 3<=2, which is false). CPLEX will see it as infeasible and will reject your solution. \nOne alternative that I can give you, if you absolutely want to use your own values in the final solution, is instead of defining values for your variables, create a constraint that explicitly defines your variable value. This constraint can afterward be \"deactivated\" if needed. But be careful, as this does not necessarily yield the optimal solution, but the \"optimal solution when some variables have the specific value\".","Q_Score":0,"Tags":"python,cplex,pyomo","A_Id":55250331,"CreationDate":"2019-03-19T21:08:00.000","Title":"Pyomo warm start","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I see many examples with either MonitoredTrainingSession or tf.Estimator as the training framework. However it's not clear why I would use one over the other. Both are configurable with SessionRunHooks. Both integrate with tf.data.Dataset iterators and can feed training\/val datasets. I'm not sure what the benefits of one setup would be.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":283,"Q_Id":55252406,"Users Score":0,"Answer":"Short answer is that MonitoredTrainingSession allows user to access Graph and Session objects, and training loop, while Estimator hides the details of graphs and sessions from the user, and generally, makes it easier to run training, especially, with train_and_evaluate, if you need to evaluate periodically. \nMonitoredTrainingSession different from plain tf.Session() in a way that it handles variables initialization, setting up file writers and also incorporates functionality for distributed training.\nEstimator API, on the other hand, is a high-level construct just like Keras. It's maybe used less in the examples because it was introduced later. It also allows to distribute training\/evaluation with DistibutedStrategy, and it has several canned estimators which allow rapid prototyping. \nIn terms of model definition they are pretty equal, both allow to use either keras.layers, or define completely custom model from the ground up.  So, if, for whatever reason, you need to access graph construction or customize training loop, use MonitoredTrainingSession. If you just want to define model, train it, run validation and prediction without additional complexity and boilerplate code, use Estimator","Q_Score":1,"Tags":"python,tensorflow,machine-learning,tensorflow-estimator","A_Id":55257099,"CreationDate":"2019-03-20T01:46:00.000","Title":"What are the reasons to use MonitoredTrainingSession vs Estimator in TensorFlow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Problem:\nThe Staked Auto Encoder is being applied to a dataset with 25K rows and 18 columns, all float values.\nSAE is used for feature extraction with encoding & decoding. \nWhen I train the model without feature scaling, the loss is around 50K, even after 200 epochs. But, when scaling is applied the loss is around 3 from the first epoch.\nMy questions:\n\nIs it recommended to apply feature scaling when SAE is used for feature extraction \nDoes it impact accuracy during decoding?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":535,"Q_Id":55253587,"Users Score":0,"Answer":"With a few exceptions, you should always apply feature scaling in machine learning, especially when working with gradient descent as in your SAE. Scaling your features will ensure a much smoother cost function and thus faster convergence to global (hopefully) minima. \n\nAlso worth noting that your much smaller loss after 1 epoch with scaling should be a result of much smaller values used to compute the loss.\n\nNo","Q_Score":1,"Tags":"python,neural-network,deep-learning,pytorch,autoencoder","A_Id":55256209,"CreationDate":"2019-03-20T04:25:00.000","Title":"Is Feature Scaling recommended for AutoEncoder?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a nested r-tree like datastructure in Python (list of lists). The key is a large number (about 10 digits). On each level there are about x number of items (eg:10) in the list. Then within each list, it recurses and has x items and so on. The height of the tree is h levels (eg: 5). Each level also has an indication of what range of keys it contains (like r-tree).\nFor a given key, I need to locate the corresponding entry in the tree. This can be trivially done by scanning through each level, check if the given key lies within the range. If so, then step into that layer and recurse till it reaches the leaf.\nThis can also be done by successively dividing the key by x and taking the quotient as list index. \nSo the question is, what is more effecient : walking through list sequentially (complexity = depth * x (eg:50)) or successively dividing the large number by x to get the actual list indices (complexity = h divisions (eg: 5 divisions)). \n(ie) 50 range checks or 5 divisions ?\nThis needs to be scalable. So if this code is being accessed in cloud by very large number of users, what is efficient ? May be division is more expensive to perform at scale than range checks  ?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":24,"Q_Id":55259486,"Users Score":1,"Answer":"You need to benchmark the code in somewhat realistic scenario.\nThe reason why it's so hard to say is that you are not just comparing division (by the way, modern compilers avoid divisions with a large number of tricks). On modern CPUs you have large caches so likely the list will fit into L2 or L3 which decreases the run-time dramatically. There's also the fancy vector\/SIMD instructions that might be used to speed up all the checks in the linear case.\nI would guess that going through the list sequentially will be faster, in addition the code will be simpler.\nBut don't take my word for it, take a real example and benchmark the two versions and pick based on the results. Especially if this is critical for your system's performance.","Q_Score":0,"Tags":"python,algorithm,optimization","A_Id":55263805,"CreationDate":"2019-03-20T11:16:00.000","Title":"Hash computation vs bucket walkthrough","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"If we have 4 parameters of X_train, y_train, X_test, and y_test, how can we calculate the bias and variance of a machine learning algorithm like linear regression?\nI have searched a lot but I could not find a single code for this.","AnswerCount":5,"Available Count":3,"Score":0.0399786803,"is_accepted":false,"ViewCount":4892,"Q_Id":55266588,"Users Score":1,"Answer":"So in terms of a function to approximate your population, high bias means underfit, high variance overfit. To detect which, partition dataset into training, cross validation and test sets.\nA low training error but high cross validation error means its overfit. \nA high training error means its underfit. \nHigh Bias: add polynomial features, get more samples. High Variance: increase regularisation (squeeze polynomial parameters small), or gather more data so it trains better","Q_Score":0,"Tags":"python,python-3.x,linear-regression","A_Id":55267001,"CreationDate":"2019-03-20T17:13:00.000","Title":"bias and variance calculation for linear regression","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"If we have 4 parameters of X_train, y_train, X_test, and y_test, how can we calculate the bias and variance of a machine learning algorithm like linear regression?\nI have searched a lot but I could not find a single code for this.","AnswerCount":5,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":4892,"Q_Id":55266588,"Users Score":0,"Answer":"In real life, we cannot calculate bias & variance. Recap: Bias measures how much the estimator (can be any machine learning algorithm) is wrong with respect to varying samples, and similarly variance measures how much the estimator fluctuate around the expected value of the estimator. To calculate the bias & variance, we need to generate a number of datasets from some known function by adding noise and train a separate model (estimator) using each dataset. Since we don't know neither the above mentioned known function nor the added noise, we cannot do it. In practise, we can only calculate the overall error. In order to combat with bias\/variance dilemma, we do cross-validation.","Q_Score":0,"Tags":"python,python-3.x,linear-regression","A_Id":62696336,"CreationDate":"2019-03-20T17:13:00.000","Title":"bias and variance calculation for linear regression","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"If we have 4 parameters of X_train, y_train, X_test, and y_test, how can we calculate the bias and variance of a machine learning algorithm like linear regression?\nI have searched a lot but I could not find a single code for this.","AnswerCount":5,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":4892,"Q_Id":55266588,"Users Score":0,"Answer":"Evaluation of Variance:\nVariance = np.var(Prediction) # Where Prediction is a vector variable obtained post the \n                              # predict() function of any Classifier.\nSSE = np.mean((np.mean(Prediction) - Y)** 2) # Where Y is your dependent variable.\n                                             # SSE : Sum of squared errors. \nBias = SSE - Variance","Q_Score":0,"Tags":"python,python-3.x,linear-regression","A_Id":61523772,"CreationDate":"2019-03-20T17:13:00.000","Title":"bias and variance calculation for linear regression","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to implement the decision-tree learning alogorithm.\nI am pretty new to coding so I know it's not the best code, but I just want it to work. Unfortunately i get the error: e2 = b(pk2\/(pk2 + nk2))\nZeroDivisionError: division by zero\nCan someone explain to me what I am doing wrong?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":104,"Q_Id":55268748,"Users Score":0,"Answer":"Lets assume after some splits you are left with two records with 3 features\/attributes (last column being the truth label)\n1   1   1   2\n2   2   2    1\nNow you are about to select the next best feature to split on, so you call this method remainder(examples, attribute) as part of selection which internally calls nk1, pk1 = pk_nk(1, examples, attribute). \nThe value returned by pk_nk for the above mentioned rows and features will be 0, 0 which will result in divide by zero exception for e1 = b(pk1\/(pk1 + nk1)). This is a valid scenario based on how you coded the DT and you should be handling the case.","Q_Score":0,"Tags":"python,decision-tree","A_Id":55269360,"CreationDate":"2019-03-20T19:26:00.000","Title":"Decision Tree Learning","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Does anyone has experience with mixed-precision training using the tensorflow estimator api? \nI tried casting my inputs to tf.float16 and the results of the network back to tf.float32. For scaling the loss I used tf.contrib.mixed_precision.LossScaleOptimizer.\nThe error messages I get are relatively uninformative: \"Tried to convert 'x' to a tensor and failed. Error: None values not supported\",","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":293,"Q_Id":55282603,"Users Score":0,"Answer":"I found the issue: I used tf.get_variable to store the learning rate. This variable has no gradient. Normal optimizers do not care, but tf.contrib.mixed_precision.LossScaleOptimizer crashes. Therefore, make sure these variables are not added to tf.GraphKeys.TRAINABLE_VARIABLES.","Q_Score":1,"Tags":"python,tensorflow,tensorflow-estimator","A_Id":55297814,"CreationDate":"2019-03-21T14:22:00.000","Title":"Training with mixed-precision using the tensorflow estimator api","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to use the python Dataframe to_dict() method without generating timestamps.\nMy problem: I have a dataframe with cells containing dates such as this: \"2019-06-01\". When I call the dataframe method \"to-dict()\" to generate a dictionnary, it converts the datevalue into something like: \"Timestamp('2019-06-01 00:00:00')\".\nThis is not what I would like. I would just like it to return a simple string such as: \"2019-06-01\"\nIs it possible with optional parameters ?\nThank you","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":252,"Q_Id":55282891,"Users Score":0,"Answer":"Simply convert those columns to object type with .astype(str) before calling to_dict().","Q_Score":0,"Tags":"python,dataframe,dictionary,timestamp","A_Id":55283054,"CreationDate":"2019-03-21T14:35:00.000","Title":"How to prevent Dataframe.to_dict() to generate timestamps","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to predict customer retention with a variety of features.\nOne of these is org_id which represents the organization the customer belongs to. It is currently a float column with numbers ranging from 0.0 to 416.0 and 417 unique values. \nI am wondering what the best way of preprocessing this column is before feeding it to a scikit-learn RandomForestClassifier. Generally, I would one-hot-encode categorical features, but there are so many values here so it would radically increase my data dimensionality. I have 12,000 rows of data, so I might be OK though, and only about 10 other features.\nThe alternatives are to leave the column with float values, convert the float values to int values, or convert the floats to pandas' categorical objects. \nAny tips are much appreciated.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":257,"Q_Id":55285986,"Users Score":2,"Answer":"org_id does not seem to be a feature that brings any info for the classification, you should drop this value and not pass it into the classifier.\nIn a classifier you only want to pass features that are discriminative for the task that you are trying to perform: here the elements that can impact the retention or churn. The ID of a company does not bring any valuable information in this context therefore it should not be used.\nEdit following OP's comment:\nBefore going further let's state something: with respect to the number of samples (12000) and the relative simplicity of the model, one can make multiple attempts to try different configurations of features easily.\nSo, As a baseline, I would do as I said before, drop this feature all together. Here is your baseline score i.e., a score you can compare your other combinations of features against.\nI think it cost nothing to try to hot-encode org_id, whichever result you observe is going to add up to your experience and knowledge of how the Random Forest behaves in such cases. As you only have 10 more features, the Boolean features is_org_id_1, is_org_id_2, ... will be highly preponderant and the classification results may be highly influenced by these features.\nThen I would try to reduce the number of Boolean features by finding new features that can \"describe\" these 400+ organizations. For instance, if they are only US organizations, their state which is ~50 features, or their number of users (which would be a single numerical feature), their years of existence (another single numerical feature). Let's note that these are only examples to illustrate the process of creating new features, only someone knowing the full problematic can design these features in a smart way.\nAlso, I would find interesting that, once you solve your problem, you come back here and write another answer to your question as I believe, many people run into such problems when working with real data :)","Q_Score":0,"Tags":"python,pandas,scikit-learn,categorical-data","A_Id":55286759,"CreationDate":"2019-03-21T17:23:00.000","Title":"Handling Categorical Data with Many Values in sklearn","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm a newbie in image processing and python in general. For an image recognition project, I want to compare every pixel with one another. For that, I need to create a program that iterates through every pixel, takes it's value (for example \"[28, 78, 72]\") and creates some kind of values through comparing it to every other pixel. I did manage to access one single number in an array element \/pixel (output: 28) through a bunch of for loops, but I just couldn't figure out how to access every number in every pixel, in every row. Does anyone know a good algorithm to solve my problem? I use OpenCV for reading in the image by the way.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":63,"Q_Id":55288421,"Users Score":0,"Answer":"Comparing every pixel with a \"pattern\" can be done with convolution. You should take a look at Haar cascade algorithm.","Q_Score":0,"Tags":"python-3.x,algorithm,image-recognition","A_Id":55295961,"CreationDate":"2019-03-21T20:01:00.000","Title":"Python: Iterate through every pixel in an image for image recognition","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using numpy.savetxt() to save an array, but its rounding my values to the first decimal point, which is a problem. anyone have any clue how to change this?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":789,"Q_Id":55288883,"Users Score":1,"Answer":"You can set the precision through changing fmt parameter. For example np.savetxt('tmp.txt',a, fmt='%1.3f') would leave you with an output with the precision of first three decimal points","Q_Score":0,"Tags":"python,save","A_Id":55289028,"CreationDate":"2019-03-21T20:38:00.000","Title":"numpy.savetxt() rounding values","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have basic knowledge of SVM, but now I am working with images.  I have images in 5 folders, each folder, for example, has images for letters a, b, c, d, e.  The folder 'a' has images of handwriting letters for 'a, folder 'b' has images of handwriting letters for 'b' and so on.\nNow how can I use the images as my training data in SVM in Python.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":72,"Q_Id":55292341,"Users Score":0,"Answer":"as far i understood you want to train your svm to classify these images into the classes named a,b,c,d . For that you can use any of the good image processing techniques to extract features (such as HOG which is nicely implemented in opencv) from your image and then use these features , and the label as the input to your SVM training (the corresponding label for those would be the name of the folders i.e a,b,c,d) you can train your SVM using the features only and during the inference time , you can simply calculate the HOG feature of the image and feed it to your SVM and it will give you the desired output.","Q_Score":1,"Tags":"python,svm","A_Id":55294058,"CreationDate":"2019-03-22T03:06:00.000","Title":"Training SVM in Python with pictures","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to add two vectors, one of which is (n,1) and the other (n,) such that the type is (n,)\nJust adding them with + gives the type (n,1). \nWhat is the function to convert it to a vector (same type as np.zeros(n))?\nOr to compute the sum directly into this format?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":65,"Q_Id":55297902,"Users Score":1,"Answer":"with .squeeze you can convert a (n,1) vector into an (n,) vector, then adding should work","Q_Score":0,"Tags":"python,numpy","A_Id":55298046,"CreationDate":"2019-03-22T10:44:00.000","Title":"Adding numpy arrays of different shape","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Im building a parser algorithm on images. tesseract not giving accuracy. so im thinking to build a CNN+LSTM based model for image to text conversion. is my approach is the right one? can we extract only the required string directly from CNN_LSTM model instead of NLP? or you see any other ways to improve tesseract accuracy?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":39,"Q_Id":55298360,"Users Score":0,"Answer":"NLP is used to allow the network to try and \"understand\" text. I think what you want here is to see if a picture contains text. For this, NLP would not be required, since you are not trying to get the network to analyze or understand the text. Instead, this should be more of an object detection type problem.\nThere are many models that do object detection.\nSome off the top of my head are YOLO, R-CNN, and Mask R-CNN.","Q_Score":0,"Tags":"deep-learning,lstm,python-tesseract","A_Id":55310781,"CreationDate":"2019-03-22T11:09:00.000","Title":"can we find the required string in image using CNN\/LSTM? or do we need to apply NLP after extracting text using CNN\/LSTM. can someone please clarify?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I just started fiddling with opencv and python using pycharm. I followed a tutorial on how to create a Haar Cascade file, but when I reached the step where I had to use 'opencv_createsamples' command, it returned:\n\n\"is not recognized as an internal or external command\"\n\nI searched for a solution. Most of them said to add opencv to path in the enviroment variables, so I downloaded opencv, extracted it in the C directory and added it to the path, but it still did not work. Could someone help me?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1017,"Q_Id":55301045,"Users Score":1,"Answer":"I tried this a quickie and I got the same message. I also use PyCharm. Are you sure you're using the right version of opencv? I'm on 2.4 which is quite old. Maybe this is a method that's been added in a later version. If you can import cv2 it shouldn't be the pythonpath.","Q_Score":3,"Tags":"python,opencv,pycharm","A_Id":55303353,"CreationDate":"2019-03-22T13:48:00.000","Title":"opencv_createsamples command it is not recognized in pycharm","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to run cv2, but when I try to import it, I get the following error:\nImportError: libGL.so.1: cannot open shared object file: No such file or directory\nThe suggested solution online is installing\napt install libgl1-mesa-glx\nbut this is already installed and the latest version.\nNB: I am actually running this on Docker, and I am not able to check the OpenCV version. I tried importing matplotlib and that imports fine.","AnswerCount":14,"Available Count":2,"Score":0.0285636566,"is_accepted":false,"ViewCount":205048,"Q_Id":55313610,"Users Score":2,"Answer":"had the same issue on centos 8 after using pip3 install opencv on a non gui server which is lacking all sorts of graphics libraries.\ndnf install opencv\npulls in all needed dependencies.","Q_Score":203,"Tags":"python,ubuntu-14.04","A_Id":67337028,"CreationDate":"2019-03-23T12:14:00.000","Title":"ImportError: libGL.so.1: cannot open shared object file: No such file or directory","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to run cv2, but when I try to import it, I get the following error:\nImportError: libGL.so.1: cannot open shared object file: No such file or directory\nThe suggested solution online is installing\napt install libgl1-mesa-glx\nbut this is already installed and the latest version.\nNB: I am actually running this on Docker, and I am not able to check the OpenCV version. I tried importing matplotlib and that imports fine.","AnswerCount":14,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":205048,"Q_Id":55313610,"Users Score":0,"Answer":"For me, the problem was related to proxy setting. For pypi, I was using nexus mirror to pypi, for opencv nothing worked. Until I connected to a different network.","Q_Score":203,"Tags":"python,ubuntu-14.04","A_Id":71321056,"CreationDate":"2019-03-23T12:14:00.000","Title":"ImportError: libGL.so.1: cannot open shared object file: No such file or directory","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I looked at the data and it seemed numeric?. I wrote a little loop and it displays values like 84 as not int, or 214.56 as not float. It just seems broken. Do Pandas Data Frames just have a randomness to them?\nMy data set has this shape:\n(622380, 45)\nWhen I isolate the column it still has a problem. But when I shorten the column it seems to be OK. \nIs there a length at which the data frame becomes unstable? Can I force the data types?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":349,"Q_Id":55328568,"Users Score":0,"Answer":"After digging around some more..  I found a better way to debug the data.\n    pd.to_numeric(model_data['Value2SPY'])\nDid the trick because when it bombed out it told me the line item..\nValueError: Unable to parse string \"#DIV\/0!\" at position 241396\nThe code I was using before \"if not isinstance(val, int):\" just was a bad method to debug the data...","Q_Score":0,"Tags":"python","A_Id":55328727,"CreationDate":"2019-03-24T21:05:00.000","Title":"Pandas Data Frame Data Type Recognized as object and not Numeric","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataframe with 5632 columns, and I only want to keep 500 of them. I have the columns names (that I wanna keep) in a dataframe as well, with the names as the row index. Is there any way to do this?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":84,"Q_Id":55330844,"Users Score":0,"Answer":"Let us assume your DataFrame is named as df and you have a list cols of column indices you want to retain. Then you should use:\ndf1 = df.iloc[:, cols]\nThis statement will drop all the columns other than the ones whose indices have been specified in cols. Use df1 as your new DataFrame.","Q_Score":0,"Tags":"python-3.x,pandas,dataframe","A_Id":55332549,"CreationDate":"2019-03-25T03:15:00.000","Title":"how to drop multiple (~5000) columns in the pandas dataframe?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on NLP project so I am using spacy, the problem is when I import nlp=spacy.load('fr_core_news_md'), it doesn't work for me and I get this error:\n\nOSError: [E050] Can't find model 'fr_core_news_md'. It doesn't seem to\nbe a shortcut link, a Python package or a valid path to a data\ndirectory.\"\n\nDespite the use of python -m spacy download fr_core_news_md","AnswerCount":4,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":7779,"Q_Id":55338972,"Users Score":0,"Answer":"first install the package then import it (but not vice versa)\nfirst : !python3 -m spacy download fr_core_news_md\nthen : nlp = spacy.load(\"fr_core_news_md\")","Q_Score":3,"Tags":"python,spacy","A_Id":72260042,"CreationDate":"2019-03-25T13:31:00.000","Title":"OSError: [E050] Can't find model 'fr_core_web_md'. It doesn't seem to be a shortcut link, a Python package or a valid path to a data directory","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I am working on NLP project so I am using spacy, the problem is when I import nlp=spacy.load('fr_core_news_md'), it doesn't work for me and I get this error:\n\nOSError: [E050] Can't find model 'fr_core_news_md'. It doesn't seem to\nbe a shortcut link, a Python package or a valid path to a data\ndirectory.\"\n\nDespite the use of python -m spacy download fr_core_news_md","AnswerCount":4,"Available Count":2,"Score":0.049958375,"is_accepted":false,"ViewCount":7779,"Q_Id":55338972,"Users Score":1,"Answer":"It is worth mentioning the bug I had recently.\n\nI installed the fr_core_news_md, and then I tried to load the fr_core_news_sm.\n\nIt was around 2:00 AM I wasn't able to find it.\nI slept and came back in the morning and found the solution.","Q_Score":3,"Tags":"python,spacy","A_Id":69374319,"CreationDate":"2019-03-25T13:31:00.000","Title":"OSError: [E050] Can't find model 'fr_core_web_md'. It doesn't seem to be a shortcut link, a Python package or a valid path to a data directory","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I'm trying to count the Customer's Name in my data. \nFor example, if there are [\"Jacob Lee\", \"Jacob Lee 30220\", \"30220\"] in the column, I want to count these cases as a same person. Because 30220 is Jacob Lee's account number. \nI'm not sure how to code this function. \nFYI: I'm using python 3.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":53,"Q_Id":55343651,"Users Score":0,"Answer":"Courtesy of @JohnGordon:\nUse if val == 'Jacob Lee' or val.startswith('Jacob Lee ') or val == '30220' or val.startswith('30220 '):","Q_Score":0,"Tags":"python","A_Id":55343775,"CreationDate":"2019-03-25T17:42:00.000","Title":"Count Name list (mixed with Number)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am transitioning to new version of TF for stability reasons (I was using a nightly docker build on Ubuntu 18.04 from before mainline switched to CUDA 10).  When I attempt to run my models in the new version I get the following error, which I assume to mean that there is an incompatibility with the models trained on the older version.\nFile \"\/usr\/local\/lib\/python3.5\/dist-packages\/tensorflow\/python\/framework\/importer.py\", line 426, in import_graph_def\n    graph._c_graph, serialized, options)  # pylint: disable=protected-access\ntensorflow.python.framework.errors_impl.InvalidArgumentError: NodeDef mentions attr 'explicit_paddings' not in Op<name=Conv2D; signature=input:T, filter:T -> output:T; attr=T:type,allowed=[DT_HALF, DT_BFLOAT16, DT_FLOAT, DT_DOUBLE]; attr=strides:list(int); attr=use_cudnn_on_gpu:bool,default=true; attr=padding:string,allowed=[\"SAME\", \"VALID\"]; attr=data_format:string,default=\"NHWC\",allowed=[\"NHWC\", \"NCHW\"]; attr=dilations:list(int),default=[1, 1, 1, 1]>; NodeDef: {{node FirstStageFeatureExtractor\/resnet_v1_101\/resnet_v1_101\/conv1\/Conv2D}}. (Check whether your GraphDef-interpreting binary is up to date with your GraphDef-generating binary.).\n\nWhat do I need to do to update the previously trained models to work with the new version of TF or do I need to continue running that version until my next training session?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1299,"Q_Id":55358952,"Users Score":0,"Answer":"After doing some looking, the graph has to be updated.  Since I did not still have the training checkpoints, I was successful in updating the graph by exporting from the previously frozen graph as the checkpoint.\npython3 export_inference_graph.py  --input_type image_tensor --pipeline_config_path FROZENGRAPHDIRECTORY\/pipeline.config --trained_checkpoint_prefix FROZENGRAPHDIRECTORY\/model.ckpt --output_directory FROZENGRAPHDIRECTORY_tfNEWTFVERSION","Q_Score":0,"Tags":"python,tensorflow,object-detection-api","A_Id":55377993,"CreationDate":"2019-03-26T13:57:00.000","Title":"Update trained object detection Models to correspond to TF updates","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've started to use the TextBlob library; for sentiment analysis. \nI have run a few tests on a few phrases and I have the polarity and subjectivity score - fine. \nWhat sentence would return the highest polarity value within TextBlob?\nFor instance \n\"I really, really, really love and admire your beauty, my good friend\" \nreturns a polarity score of 0.6. \nI understand that +1.0 is the highest score (-1.0) is the least.\nWhat sentence, have you found which returns a score closer to +1.0?\n\n\n\nTextBlob(\"I really, really, really love and admire your beauty my good friend\").sentiment\n      Sentiment(polarity=0.6, subjectivity=0.6000000000000001)\nTextBlob(\"I really, really, really love my place of work\").sentiment \n      Sentiment(polarity=0.5, subjectivity=0.6)\nTextBlob(\"I really love my place of work\").sentiment\n      Sentiment(polarity=0.5, subjectivity=0.6)\n\n\n\n\nI expect that the \"really\" should increase the sentiment score, at least a bit.  (i.e. really, really like = at least 0.9)\nI expect that the score overall, without the really (I really like my work) should return a score closer to 1.0.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":247,"Q_Id":55362335,"Users Score":0,"Answer":"You can use .sentiment_assessments to get some more idea of how your sentence is being evaluated.\nSentiment(polarity=0.6, subjectivity=0.6000000000000001, assessments=[(['really', 'really', 'really', 'love'], 0.5, 0.6, None), (['good'], 0.7, 0.6000000000000001, None)])","Q_Score":0,"Tags":"python,textblob","A_Id":58124093,"CreationDate":"2019-03-26T16:48:00.000","Title":"Highest Polarity Score (Sentiment Analysis) using the TextBlob library","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Consider a set of n cubes with colored facets (each one with a specific color\nout of 4 possible ones - red, blue, green and yellow). Form the highest possible tower of k cubes ( k \u2264 n ) properly rotated (12 positions of a cube), so the lateral faces of the tower will have the same color, using and evolutionary algorithm.\nWhat I did so far:\nI thought that the following representation would be suitable: an Individual could be an array of n integers, each number having a value between 1 and 12, indicating the current position of the cube (an input file contains n lines, each line shows information about the color of each face of the cube). \nThen, the Population consists of multiple Individuals.\nThe Crossover method should create a new child(Individual), containing information from its parents (approximately half from each parent).\nNow, my biggest issue is related to the Mutate and Fitness methods.\nIn Mutate method, if the probability of mutation (say 0.01), I should change the position of a random cube with other random position (for example, the third cube can have its position(rotation) changed from 5 to 12).\nIn Fitness method, I thought that I could compare, two by two, the cubes from an Individual, to see if they have common faces. If they have a common face, a \"count\" variable will be incremented with the number of common faces and if all the 4 lateral faces will be the same for these 2 cubes, the count will increase with another number of points. After comparing all the adjacent cubes, the count variable is returned. Our goal is to obtain as many adjacent cubes having the same lateral faces as we can, i.e. to maximize the Fitness method.\nMy question is the following:\nHow can be a rotation implemented? I mean, if a cube changes its position(rotation) from 3, to 10, how do we know the new arrangement of the faces? Or, if I perform a mutation on a cube, what is the process of rotating this cube if a random rotation number is selected?\nI think that I should create a vector of 6 elements (the colors of each face) for each cube, but when the rotation value of a cube is modified, I don't know in what manner the elements of its vector of faces should be rearranged.\nShuffling them is not correct, because by doing this, two opposite faces could become adjacent, meaning that the vector doesn't represent that particular cube anymore (obviously, two opposite faces cannot be adjacent).","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":227,"Q_Id":55367429,"Users Score":0,"Answer":"First, I'm not sure how you get 12 rotations; I get 24: 4 orientations with each of the 6 faces on the bottom.  Use a standard D6 (6-sided die) and see how many different layouts you get.\nApparently, the first thing you need to build is a something (a class?) that accurately represents a cube in any of the available orientations.  I suggest that you use a simple structure that can return the four faces in order -- say, front-right-back-left -- given a cube and the rotation number.\nI think you can effectively represent a cube as three pairs of opposing sides.  Once you've represented that opposition, the remaining organization is arbitrary numbering: any valid choice is isomorphic to any other.  Each rotation will produce an interleaved sequence of two opposing pairs.  For instance, a standard D6 has opposing pairs [(1, 6), (2, 5), (3, 4)].  The first 8 rotations would put 1 and 6 on the hidden faces (top and bottom), giving you the sequence 2354 in each of its 4 rotations and their reverses.\nThat class is one large subsystem of your problem; the other, the genetic algorithm, you seem to have well in hand.  Stack all of your cubes randomly; \"fitness\" is a count of the most prevalent 4-show (sequence of 4 sides) in the stack.  At the start, this will generally be 1, as nothing will match.\nFrom there, you seem to have an appropriate handle on mutation.  You might give a higher chance of mutating a non-matching cube, or perhaps see if some cube is a half-match: two opposite faces match the \"best fit\" 4-show, so you merely rotate it along that axis, preserving those two faces, and swapping the other pair for the top-bottom pair (note: two directions to do that).\nDoes that get you moving?","Q_Score":1,"Tags":"python,artificial-intelligence,evolutionary-algorithm","A_Id":55368146,"CreationDate":"2019-03-26T23:03:00.000","Title":"Tower of colored cubes","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"From what I understand from keras labeling, one hot encoding does not permit the values to be [0 0]? is this assumption correct?\nWe are trying to classify 2 classes and we want to be able to detect garbage when a garbage image is fed. However, it always detects either \n[0 1] or [1 0]. Is it possible to get [0 0] as a label without introducing a class the will handle the garbage or no?\nSo basically, can the CNN predict it to be something else if its not the 2 classes?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":74,"Q_Id":55367994,"Users Score":1,"Answer":"That should not be possible.  Your \"garbage\" would be a third class, requiring labels of [1 0 0], [0 1 0], and [0 0 1].\nVery simply, the model you've described will return one of two categories, whichever has a higher rating in your final layer.  This happens whether the input values are 0.501 and 0.499, or 0.011 and 0.010 with a large \"not sure\" portion.  If you don't explicitly code \"not sure\" into your model, then that portion of the decision will not be considered in the classification.","Q_Score":1,"Tags":"python,keras,conv-neural-network","A_Id":55368249,"CreationDate":"2019-03-27T00:15:00.000","Title":"Can I get [0 0] from Categorical Labeling in CNN's?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"What's the difference between shape(150,) and shape (150,1)?\nI think they are the same, I mean they both represent a column vector.","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":331,"Q_Id":55374185,"Users Score":0,"Answer":"Although they both occupy same space and positions in memory,\n\nI think they are the same, I mean they both represent a column vector.\n\nNo they are not and certainly not according to NumPy (ndarrays).\nThe main difference is that the \nshape (150,) => is a 1D array,  whereas\nshape (150,1) => is a 2D array","Q_Score":1,"Tags":"python,numpy","A_Id":55376254,"CreationDate":"2019-03-27T09:49:00.000","Title":"What's the difference between shape(150,) and shape (150,1)?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working in training a CNN in fourier domain. To speed up training, I thought of taking the fft of the entire dataset before training and feeding this data to the sequential model. But inside the first layer of the model, which is a custom Keras layer, the training data is shown to have float32 data type. Does the sequential model take in only real input data?\nThanks.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":127,"Q_Id":55398697,"Users Score":1,"Answer":"Adding an InputLayer(... dtype='complex64') layer, i.e. an InputLayer() with data type specified as 'complex64' as the first layer of the sequential model allowed me to pass complex64 data to the model.","Q_Score":1,"Tags":"python,keras,sequential","A_Id":55629323,"CreationDate":"2019-03-28T13:20:00.000","Title":"Feed complex64 data into Keras sequential model","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a flow graph with lower and upper bounds and my task is to find any feasible solution as fast as possible. I found many algorithms and approaches to maximum\/minimum flow and so on (also many times uses feasible solution as start point) but nothing specific for any feasible solution. Is there any algorithm\/approach that is specific for it and fast?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":861,"Q_Id":55400911,"Users Score":1,"Answer":"So I finally got time to sum this up. The solution I used is to take the initial graph and transform it in these steps.\n(Weights are in this order: lower bound, current flow, upper bound.)\n1. Connect t to s by edge of (0, 0, infinity).\n2. To each node of the\n        initial graph add balance value equal to: (sum of lower bound of\n        incoming edges - sum of lower bound of outgoing edges).\n3. Set upper\n            bound of every edge to (upper bound - lower bound). Set lower bound\n            and current flow of each edge to 0.\n4. Now make new s (s') and new t (t') which will be our new start and end (DO NOT DELETE s and t already in the graph, they just became\n    normal nodes).\n5. Create edge from s' to every vertex with positive balance with (0,\n    0, vertex.balance) bounds.\n6. Create edge from every vertex with negative balance to t' with (0,\n    0, abs(vertex.balance)).\n7. Run Ford-Fulkerson (or other maximum flow algorithm of your choice)\n    on the new graph.\n8. For every edge of initial graph sum value of the\n        edge with the initial old lower bound before transformation and you have your\n        initial flows for every edge of the initial graph.\nThis problem is actually a bit harder than to maximize flow when feasible flow is provided.","Q_Score":1,"Tags":"python,graph,network-flow","A_Id":55557061,"CreationDate":"2019-03-28T15:05:00.000","Title":"Finding any feasible flow in graph as fast as possible","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using Keras (with Tensorflow backend) for an image classification project. I have a total of almost 40 000 hi-resolution (1920x1080) images that I use as training input data. Training takes about 45 minutes and this is becoming a problem so I was thinking that I might be able to speed things up by lowering the resolution of the image files. Looking at the code (I didn't write it myself) it seems all images are re-sized to 30x30 pixels anyway before processing\nI have two general questions about this.\n\nIs it reasonable to expect this to improve the training speed?\nWould resizing the input image files affect the accuracy of the image classification?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":404,"Q_Id":55401716,"Users Score":1,"Answer":"1- Of course it will affect the training speed as the spatial dimensions is one of the most important key of the model speed performance.\n2- We can say sure it'll affect the accuracy, but how much exactly that depends on many of other aspects like what objects are you classifying and what dataset are you working with.","Q_Score":1,"Tags":"python-2.7,tensorflow,keras","A_Id":55402030,"CreationDate":"2019-03-28T15:43:00.000","Title":"How does image resolution affect result and accuracy in Keras?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been looking for a way to vectorize categorical variable and then I've come across category_encoders. It supports multiple ways to categorize.\nI tried TargetEncoder and BinaryEncoder but the docs doesn't explain much about the working of it?\nI really appreciate if anyone could explain how target encoder and binary encoder work and how they are different from one hot encoding?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":754,"Q_Id":55402010,"Users Score":1,"Answer":"Target encoding maps the categorical variable to the mean of the target variable. As it uses the target, steps must be taken to avoid overfitting (usually done with smoothing). \nBinary encoding converts each integer into binary digits with each binary digit having its one column. It is essentially a form of feature hashing.\nBoth help with lowering the cardinality of categorical variables which helps improve some model performance, most notably with tree-based models.","Q_Score":0,"Tags":"python,python-3.x,scikit-learn,categorical-data","A_Id":55794331,"CreationDate":"2019-03-28T15:58:00.000","Title":"What is TargetEncoder and BinaryEncoder in sklearn category_encoders?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I do side work writing\/improving a research project web application for some political scientists. This application collects articles pertaining to the U.S. Supreme Court and runs analysis on them, and after nearly a year and half, we have a database of around 10,000 articles (and growing) to work with.\nOne of the primary challenges of the project is being able to determine the \"relevancy\" of an article - that is, the primary focus is the federal U.S. Supreme Court (and\/or its justices), and not a local or foreign supreme court. Since its inception, the way we've addressed it is to primarily parse the title for various explicit references to the federal court, as well as to verify that \"supreme\" and \"court\" are keywords collected from the article text. Basic and sloppy, but it actually works fairly well. That being said, irrelevant articles can find their way into the database - usually ones with headlines that don't explicitly mention a state or foreign country (the Indian Supreme Court is the usual offender).\nI've reached a point in development where I can focus on this aspect of the project more, but I'm not quite sure where to start. All I know is that I'm looking for a method of analyzing article text to determine its relevance to the federal court, and nothing else. I imagine this will entail some machine learning, but I've basically got no experience in the field. I've done a little reading into things like tf-idf weighting, vector space modeling, and word2vec (+ CBOW and Skip-Gram models), but I'm not quite seeing a \"big picture\" yet that shows me how just how applicable these concepts can be to my problem. Can anyone point me in the right direction?","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":336,"Q_Id":55403920,"Users Score":1,"Answer":"There are many many ways to do this, and the best method changes depending on the project. Perhaps the easiest way to do this is to keyword search in your articles and then empirically choose a cut off score. Although simple, this actually works pretty well, especially in a topic like this one where you can think of a small list of words that are highly likely to appear somewhere in a relevant article. \nWhen a topic is more broad with something like 'business' or 'sports', keyword search can be prohibitive and lacking. This is when a machine learning approach might start to become the better idea. If machine learning is the way you want to go, then there are two steps:\n\nEmbed your articles into feature vectors\nTrain your model\n\nStep 1 can be something simple like a TFIDF vector. However, embedding your documents can also be deep learning on its own. This is where CBOW and Skip-Gram come into play. A popular way to do this is Doc2Vec (PV-DM). A fine implementation is in the Python Gensim library. Modern and more complicated character, word, and document embeddings are much more of a challenge to start with, but are very rewarding. Examples of these are ELMo embeddings or BERT. \nStep 2 can be a typical model, as it is now just binary classification. You can try a multilayer neural network, either fully-connected or convolutional, or you can try simpler things like logistic regression or Naive Bayes.\nMy personal suggestion would be to stick with TFIDF vectors and Naive Bayes. From experience, I can say that this works very well, is by far the easiest to implement, and can even outperform approaches like CBOW or Doc2Vec depending on your data.","Q_Score":1,"Tags":"python,machine-learning,nlp,data-science","A_Id":55404739,"CreationDate":"2019-03-28T17:45:00.000","Title":"Concepts to measure text \"relevancy\" to a subject?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to ask a question related to a DataFrame. I tried to add screenshots before but I got -3 reputation and it says I am not allowed to upload the image. What is the best way then. I am new to stack overflow. Please help.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":531,"Q_Id":55405730,"Users Score":1,"Answer":"You could either provide the code the generate sample data or you could do print(df) and paste the result with code format as a part of your question. For us it is possible to copy a dataframe as text a load it into a proper dataframe. Usually you can provide less than 20 rows of sample data and that should be enough to replicate the desired output","Q_Score":1,"Tags":"python,pandas,dataframe","A_Id":55405781,"CreationDate":"2019-03-28T19:44:00.000","Title":"Sometimes it is necessary to show my dataframe to properly ask the question. How can I do that?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I can use this torch.cuda.device_count() to check the number of GPUs. I was wondering if there was something equivalent to check the number of CPUs.","AnswerCount":2,"Available Count":1,"Score":0.4621171573,"is_accepted":false,"ViewCount":6996,"Q_Id":55411921,"Users Score":5,"Answer":"At present pytorch doesn't support multiple cpu cluster in DistributedDataParallel implementation. So, I am assuming you mean number of cpu cores.\nThere's no direct equivalent for the gpu count method but you can get the number of threads which are available for computation in pytorch by using\n\ntorch.get_num_threads()","Q_Score":8,"Tags":"python,neural-network,deep-learning,pytorch","A_Id":55413219,"CreationDate":"2019-03-29T06:50:00.000","Title":"Is there a pytorch method to check the number of cpus?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"A lot of people have issues installing tensorflow-gpu on their computers and I have read a lot of them and tried out a lot of them as well. So I'm not coming for an easy answer without searching the web beforehand. \nI'm running W10 with an NVIDIA Quadro P600 which can supposedly run CUDA. \nThe thing is whenever I'm trying to install CUDA (10.0 as suggested from tensorflow) the installation breaks with no clear indication of the error (\"The download wasn't successfully completed. Try again\") \nI have the feeling it breaks because my Laptop has two GPU's. The mentioned NVIDIA and the onboard Intel UHD Graphics 630 card.\nDoes anyone have a clue? Please share your workflow if you have installed tensorflow-gpu on your laptop!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":235,"Q_Id":55413488,"Users Score":0,"Answer":"At what phase are you getting the \"Download wasn't...\" message? did\n  you try manually downloading the wheel file and installing it directly\n  and locally? \u2013 Ido_f\n\nDownloading the local CUDA installation solved my issues.","Q_Score":0,"Tags":"python,tensorflow,installation","A_Id":55742886,"CreationDate":"2019-03-29T08:44:00.000","Title":"Installing tensorflow-gpu on a Laptop with two graphic cards","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have 10 different matrix of size (60, 100). I want to put them along the third dimension inside a for loop, so that the final shape is (10, 60, 100).\nI tried with concatenate and end up with size (600, 100).","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":402,"Q_Id":55415158,"Users Score":0,"Answer":"Try creating a new array that you fill with your 2D arrays\n\nnew3DArray = numpy.empty(10, 60, 100)","Q_Score":1,"Tags":"python,arrays,numpy,append","A_Id":55415405,"CreationDate":"2019-03-29T10:17:00.000","Title":"Number of 2D array to 3D array, extending third dimension","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm currently using an Android device (of Samsung), Pydroid 3.\nI tried to see any graphs, but it doesn't works.\nWhen I run the code, it just shows me a black-blank screen temporarily and then goes back to the source code editing window.\n(means that i can't see even terminal screen, which always showed me [Program Finished])  \nWell, even the basic sample code which Pydroid gives me doesn't show me the  graph :(\nI've seen many tutorials which successfully showed graphs, but well, mine can't do that things.\nUnfortunately, cannot grab any errors.\nUsing same code which worked at Windows, so don't think the code has problem.\nOf course, matplotlib is installed, numpy is also installed.\nIf there's any possible problems, please let me know.","AnswerCount":8,"Available Count":3,"Score":0.049958375,"is_accepted":false,"ViewCount":8119,"Q_Id":55434255,"Users Score":2,"Answer":"I also had this problem a while back, and managed to fix it by using plt.show()\nat the end of your code. With matplotlib.pyplot as plt.","Q_Score":4,"Tags":"android,python,matplotlib,pydroid","A_Id":56324221,"CreationDate":"2019-03-30T18:02:00.000","Title":"Matplotlib with Pydroid 3 on Android: how to see graph?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":1,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm currently using an Android device (of Samsung), Pydroid 3.\nI tried to see any graphs, but it doesn't works.\nWhen I run the code, it just shows me a black-blank screen temporarily and then goes back to the source code editing window.\n(means that i can't see even terminal screen, which always showed me [Program Finished])  \nWell, even the basic sample code which Pydroid gives me doesn't show me the  graph :(\nI've seen many tutorials which successfully showed graphs, but well, mine can't do that things.\nUnfortunately, cannot grab any errors.\nUsing same code which worked at Windows, so don't think the code has problem.\nOf course, matplotlib is installed, numpy is also installed.\nIf there's any possible problems, please let me know.","AnswerCount":8,"Available Count":3,"Score":1.2,"is_accepted":true,"ViewCount":8119,"Q_Id":55434255,"Users Score":0,"Answer":"After reinstalling it worked.\nThe problem was that I forced Pydroid to update matplotlib via Terminal, not the official PIP tab.\nThe version of matplotlib was too high for pydroid","Q_Score":4,"Tags":"android,python,matplotlib,pydroid","A_Id":60702515,"CreationDate":"2019-03-30T18:02:00.000","Title":"Matplotlib with Pydroid 3 on Android: how to see graph?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":1,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm currently using an Android device (of Samsung), Pydroid 3.\nI tried to see any graphs, but it doesn't works.\nWhen I run the code, it just shows me a black-blank screen temporarily and then goes back to the source code editing window.\n(means that i can't see even terminal screen, which always showed me [Program Finished])  \nWell, even the basic sample code which Pydroid gives me doesn't show me the  graph :(\nI've seen many tutorials which successfully showed graphs, but well, mine can't do that things.\nUnfortunately, cannot grab any errors.\nUsing same code which worked at Windows, so don't think the code has problem.\nOf course, matplotlib is installed, numpy is also installed.\nIf there's any possible problems, please let me know.","AnswerCount":8,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":8119,"Q_Id":55434255,"Users Score":0,"Answer":"You just need to add a line\nplt.show()\nThen it will work. You can also save the file before showing\nplt.savefig(\"*imageName*.png\")","Q_Score":4,"Tags":"android,python,matplotlib,pydroid","A_Id":66386763,"CreationDate":"2019-03-30T18:02:00.000","Title":"Matplotlib with Pydroid 3 on Android: how to see graph?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":1,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have already trained a model to recognise animal and it is working, deployed into android application. I'm finding for a solution to make the image classifier to only classify the trained categories. I'm not sure whether to do this through the model training or any code to be added to solve this.\nExample, if a picture of a cup is sent for classification, the result shows as Dog or some other animal name. How to make only classify the given category, anything else than that show it as \"not a animal\".\nIm using Tensorflow 1.12, MobileNet Model","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":115,"Q_Id":55441123,"Users Score":0,"Answer":"what about checking probability score? Eventhough a cup is classifed as Dog , it will have less score. so you can set your threshold. If the probability score > threshold value, then it will be displayed as animal otherwise not.","Q_Score":2,"Tags":"java,android,python-3.x,tensorflow","A_Id":55441333,"CreationDate":"2019-03-31T12:57:00.000","Title":"My trained image classifer model classify all the images that are not even in the category","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a set of 2000 points which are basically x,y coordinates of pass origins from association football. I want to run a k-means clustering algorithm on it to just classify it to get which 10 passes are the most common (k=10). However, I don't want to predict any points for future values. I simply want to work with the existing data. Do I still need to split it into testing-training sets? I assume they're only done when we want to train the model on a particular set to calculate for future values (?)\nI'm new to clustering (and Python as a whole) so any help would be appreciated.","AnswerCount":2,"Available Count":1,"Score":-0.0996679946,"is_accepted":false,"ViewCount":8449,"Q_Id":55450949,"Users Score":-1,"Answer":"No, in clustering (i.e unsupervised learning ) you do not need to split the data","Q_Score":0,"Tags":"python-3.x,machine-learning,cluster-analysis,k-means","A_Id":55451075,"CreationDate":"2019-04-01T08:34:00.000","Title":"Do I need a test-train split for K-means clustering even if I'm not looking to predict anything?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to reshape an array of size (14,14,3) to (None, 14,14,3). I have seen that the output of each layer in convolutional neural network has shape in the format(None, n, n, m). \nConsider that the name of my array is arr\nI tried arr[None,:,:] but it converts it to a dimension of (1,14,14,3).\nHow should I do it?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":163,"Q_Id":55462718,"Users Score":1,"Answer":"The shape (None, 14,14,3) represent ,(batch_size,imgH,imgW,imgChannel) now imgH and imgW can be use interchangeably depends on the network and the problem. \nBut the batchsize is given as \"None\" in the neural network because we don't want to restrict our batchsize to some specific value as our batchsize depends on a lot of factors like memory available for our model to run etc.\nSo lets say you have 4 images of size 14x14x3 then you can append each image into the array say L1, and now the L1 will have the shape 4x14x14x3 i.e you made a batch of 4 images and now you can feed this to your neural network. \nNOTE here None will be replaced by 4 and for the whole training process it will be 4. Similarly when you feed your network only one image it assumes the batchsize of 1 and set None equal to 1 giving you the shape (1X14X14X3)","Q_Score":0,"Tags":"python,arrays,numpy,conv-neural-network,reshape","A_Id":55471123,"CreationDate":"2019-04-01T20:01:00.000","Title":"converting an array of size (n,n,m) to (None,n,n,m)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have categorical variables such as Gender,Anxiety,Alcoholic and when i convert these categorical variables into numerical values using encoder techniques then all these variables resembles same in values and then multi co linearity is existing. How i can convert these variables to number so that multi co linearity doesn't exist. All three variables are important for prediction of target variable.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":35,"Q_Id":55469874,"Users Score":1,"Answer":"You don't need to transform the data.Instead you can change the way that you are calculating correlation between variables. As these are categorical features, you have to use Chi-Squared test of independence.Then, you won't be facing this issue.","Q_Score":0,"Tags":"python,machine-learning","A_Id":55472980,"CreationDate":"2019-04-02T08:12:00.000","Title":"I need to remove multi co-linearity between features","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm setting up a new algorithm which combines an object detector(bounding box detector) which is in python 3 and a mask generator which is in python 2. The problem here is I have several python 2 files which is required for the mask generation algorithm. So I tried 2to3 to convert all my python 2 files to python 3. The script seemed like working but as it was a deep learning algorithm(for mask generation when bounding box coordinates are given as input) which needs some pytorch weights to be loaded, while testing the model in python 3 the program was throwing out error like\n\n\"RuntimeError: Expected object of type torch.FloatTensor but found\n  type torch.cuda.FloatTensor for argument #2 \u2018weight\u2019\"\n\nI have searched in PyTorch forums but none of the posts were useful to me. Is it because my mask generation code is trained in python 2 ?\nDoes that means while loading the weights and testing the model I should use python 2 not python 3 ? It would be great if someone can shed some light on this. As a work around I can still use the object detector code downgraded to python 2. But still I want to know why it was throwing the error.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":174,"Q_Id":55471459,"Users Score":0,"Answer":"The problem is(can) that cpu object is expected but it gpu object. Try to put the object to cpu: \nmask.cpu()","Q_Score":0,"Tags":"python-2.7,pytorch","A_Id":55488118,"CreationDate":"2019-04-02T09:36:00.000","Title":"Code conversion from python 2 to python 3","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to create a matrix of dimension nxn in Sage. But every element in the matrix has to be 1\/n. The size of n is around 7000.\nFirst I tried using creating a matrix of ones with the build in sagemethod, and then multiplying the matrix with 1\/n. This is very slow and crashes my jupyter notebook kernel.\nT  =matrix.ones(7000) * 1\/n\nA second thing I tried is creating all the elements by list comprehension.\nT = matrix(RDF,[[1\/l for x in range(l)] for row in range(l)])\nThis also seems to be something my pc can't handle.","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":140,"Q_Id":55475303,"Users Score":3,"Answer":"@JamesKPolk gave me a working solution.\nT = matrix(RDF, 6000, 6000, lambda i,j: 1\/6000)","Q_Score":3,"Tags":"python,matrix,sage","A_Id":55475569,"CreationDate":"2019-04-02T12:52:00.000","Title":"Creating a big matrix where every element is the same","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Say I have a rectangle [a,b]x[c,d], where a,b,c,d are reals.\nI would like to produce k points (x,y) sampled uniformly from this rectangle, i.e. a <= x <= c and b <= y <= d.\nObviously, if sampling from [0,1]x[0,1] is possible, then\nthe problem is solved. How to achieve any of the two goals, in python?\nOr, another tool such as R, for example?","AnswerCount":2,"Available Count":1,"Score":-0.0996679946,"is_accepted":false,"ViewCount":577,"Q_Id":55482837,"Users Score":-1,"Answer":"I think modulo operator (%) is your friend to check if x and y are in [a,c] and [b,d]\nIf you can't use random between 2 numbers (others 0 and 1), you can try to make x = (random() *(c-a)+a)\nSame with y :)\nEDIT : Oh, i send it just after Merig","Q_Score":0,"Tags":"python,random","A_Id":55482982,"CreationDate":"2019-04-02T20:11:00.000","Title":"Python: (uniform) sampling from a rectangle","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"As the title says, I want to know whether input shape affects the accuracy of the deep learning model.\nAlso, can pre-trained models (like Xception) be used on grayscale images?\nP.S. : I recently started learning deep learning so if possible please explain in simple terms.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":54,"Q_Id":55487087,"Users Score":0,"Answer":"Usually, with convolutional neural networks, differences in the image shape (the width\/height of an image) will not matter. However, differences in the # of channels in the image (equivalently the depth of the image), will affect the performance. In fact, there will usually be dimension mismatch errors you get if the model was trained for greyscale\/colour and you put in the other type.","Q_Score":0,"Tags":"python,keras,conv-neural-network,pre-trained-model,transfer-learning","A_Id":55487120,"CreationDate":"2019-04-03T04:28:00.000","Title":"Does the accuracy of the deep learning program drop if I do not put in the default input shape into the pretrained model?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am attempting to pass my custom dataset which is loaded in from a CSV file using \npanda.readcsv() through sklearns MLPRegressor. \nMy initial error was my 1D array needed to become a 2D array. Expected 2D array, got 1D array instead: array=[0. 0. 1. ... 0. 0. 1.].\nSo I used panda.reshape(x_test, (-1, 1)) on both the x_test and y_train to solve this issue. However this now presents me with the following error.\nShape of passed values is (16209258, 1), indices imply (207811, 78)\nHave looked around a few other posts without success.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":44,"Q_Id":55495746,"Users Score":0,"Answer":"So I after implementing @Quang Hoang's suggestion  panda.reshape(array_name, (-1, 78)) on x_test, y_test and x_train this finally converted all nessasary arrays into the required 2D format.","Q_Score":0,"Tags":"python-3.x,pandas,scikit-learn","A_Id":55496284,"CreationDate":"2019-04-03T12:55:00.000","Title":"Shape of passed values blah indices imply blah","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a spreadsheet with 10 columns and 727 obs. Col 5 is 0\/1 whether a student is economically disadvantaged or not. I need to find the mean of Col 6 (test score) based on whether the student is economically disadvantaged or not. Help!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":16,"Q_Id":55506207,"Users Score":0,"Answer":"If I understand your question correctly: \n\n\nFirst you need to import your spreadsheet into python with csv module. \nThen you need \"for\" loop to sum all your col per person. \nCalculate mean of each obs. \nIf result greater than half of total number, get to your student 1 ;else get them 0 .","Q_Score":0,"Tags":"python","A_Id":55506296,"CreationDate":"2019-04-04T00:37:00.000","Title":"Need to find the MEAN of Col 6 based on the value of Col 5 (Col 5 is 0\/1)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Im beginner and this is my first steps.\nIm already learning about different Neural Network architecture and i have a question:\nWhich model i should choice for Rasberry PI \/ android?\nIm already tried \"ResNet\" with 98x98 resolution of images and that model requires almost full power of my PC. Exactly:\nModel takes 2 GB of video card memory, 1.4~2 GB of RAM.\nThis model is not suitable for the android \/ Rasberry (low power).\nWhich model i should choice for my task?\nP.S I expect at least 5~10 FPS on Rasberry and 10~15 on Android.","AnswerCount":2,"Available Count":2,"Score":0.1973753202,"is_accepted":false,"ViewCount":78,"Q_Id":55533997,"Users Score":2,"Answer":"TinyYOLO is a smaller version of the original YOLO network. You could try that one.","Q_Score":1,"Tags":"python,machine-learning,neural-network","A_Id":55534189,"CreationDate":"2019-04-05T11:04:00.000","Title":"\u0421hoosing the right NN model (speed \/ performance)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Im beginner and this is my first steps.\nIm already learning about different Neural Network architecture and i have a question:\nWhich model i should choice for Rasberry PI \/ android?\nIm already tried \"ResNet\" with 98x98 resolution of images and that model requires almost full power of my PC. Exactly:\nModel takes 2 GB of video card memory, 1.4~2 GB of RAM.\nThis model is not suitable for the android \/ Rasberry (low power).\nWhich model i should choice for my task?\nP.S I expect at least 5~10 FPS on Rasberry and 10~15 on Android.","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":78,"Q_Id":55533997,"Users Score":3,"Answer":"Object Detection on Raspberry Pi with 5-10FPS is highly unrealistic.\nYou can have a look at YOLO or SSD, for example YOLO has also a smaller implementation which can run on RPI but you will be happy with 1FPS.","Q_Score":1,"Tags":"python,machine-learning,neural-network","A_Id":55534144,"CreationDate":"2019-04-05T11:04:00.000","Title":"\u0421hoosing the right NN model (speed \/ performance)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have successfully used Q-learning to solve some classic reinforcement learning environments from OpenAI Gym (i.e. Taxi, CartPole). These environments allow for a single action to be taken at each time step. However I cannot find a way to solve problems where multiple actions are taken simultaneously at each time step. For example in the Roboschool Reacher environment, 2 torque values - one for each axis - must be specify at each time step. The problem is that the Q matrix is built from (state, action) pairs. However, if more than one action are taken simultaneously, it is not straightforward to build the Q matrix.\nThe book \"Deep Reinforcement Learning Hands-On\" by Maxim Lapan mentions this but does not give a clear answer, see quotation below.\n\nOf course, we're not limited to a single action to perform, and the environment could have multiple actions, such as pushing multiple buttons simultaneously or steering the wheel and pressing two pedals (brake and accelerator). To support such cases, Gym defines a special container class that allows the nesting of several action spaces into one unified action.\n\nDoes anybody know how to deal with multiple actions in Q learning?\nPS: I'm not talking about the issue \"continuous vs discrete action space\", which can be tackled with DDPG.","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":937,"Q_Id":55539820,"Users Score":2,"Answer":"You can take one of two approaches - depend on the problem:\n\nThink of the set of actions you need to pass to the environment as independent and make the network output actions values for each one (make softmax separately) - so if you need to pass two actions, the network will have two heads, one for each axis.\nThink of them as dependent and look on the Cartesian product of the sets of actions, and then make the network to output value for each product - so if you have two actions that you need to pass and 5 options for each, the size of output layer will be 2*5=10, and you just use softmax on that.","Q_Score":4,"Tags":"python,reinforcement-learning,openai-gym,q-learning","A_Id":55540865,"CreationDate":"2019-04-05T16:28:00.000","Title":"How do apply Q-learning to an OpenAI-gym environment where multiple actions are taken at each time step?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have just learned how to work with DataFrame in python's Pandas through an online course and there is this question:\n\"What is the difference between deleting and popping column?\"\nI thought they work the same way but most of the answers are \n\"You can store a popped column\"\nWhat does that mean?\nI saw from the documentation and it tells me that there's only a deleting a single column use out of the pop function so Im kind of confused here","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":939,"Q_Id":55548010,"Users Score":0,"Answer":"The difference between deleting and popping a column is that pop will return the deleted column back to you while del method won't.","Q_Score":1,"Tags":"python,python-3.x,pandas,dataframe,del","A_Id":61877221,"CreationDate":"2019-04-06T10:08:00.000","Title":"Is there any difference between del and pop for column in python pandas dataframe?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using eval and pybrain to make neural networks. Here's it stripped down. Using python 3.6\n\n\n    from pybrain import *\n\n    numnn = 100\n\n    eval(\"neuralNetwork\" + chr(numnn) + \" = buildNetwork(2, 3, 1, bias=True)\")","AnswerCount":1,"Available Count":1,"Score":0.537049567,"is_accepted":false,"ViewCount":127,"Q_Id":55552402,"Users Score":3,"Answer":"eval evaluates expressions.  Assignment in Python is not an expression, it's a statement.\nBut you don't need this anyway.  Make a list or dict to hold all of your values.","Q_Score":0,"Tags":"python","A_Id":55552428,"CreationDate":"2019-04-06T18:52:00.000","Title":"Python - Error at equals sign for no reason?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"The COCO dataset is very large for me to upload it to google colab. Is there any way I can directly download the dataset to google colab?","AnswerCount":5,"Available Count":1,"Score":0.0399786803,"is_accepted":false,"ViewCount":10788,"Q_Id":55556965,"Users Score":1,"Answer":"Using drive is better for further use. Also unzip the zip with using colab ( !unzip ) because using zip extractor on drive takes longer. I've tried :D","Q_Score":5,"Tags":"python,computer-vision,google-colaboratory,semantic-segmentation","A_Id":66080521,"CreationDate":"2019-04-07T08:18:00.000","Title":"Importing COCO datasets to google colaboratory","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In python how can I write subsets of an array to disk, without holding the entire array in memory?\nThe xarray input\/output docs note that xarray does not support incremental writes, only incremental reads except by streaming through dask.array. (Also that modifying a dataset only affects the in-memory copy, not the connected file.) The dask docs suggest it might be necessary to save the entire array after each manipulation?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":100,"Q_Id":55567542,"Users Score":0,"Answer":"This can be done using netCDF4 (the python library of low level NetCDF bindings). Simply assign to a slice of a dataset variable, and optionally call the dataset .sync() method afterward to ensure no delay before those changes are flushed to the file. \nNote this approach also provides the opportunity to progressively grow a dimension of the array (by calling createDimension with size None, making it the first dimension of a variable, and iteratively assigning to incrementally larger indices along that dimension of the variable).\nAlthough random-access window (i.e. subset) writes appear to require the lower level package, more systematic subset writes (eventually covering the entire array) can be done incrementally with xarray (by specifying a chunk size parameter to trigger use of the dask.array backend), and provided that your algorithm is refactored so that the main loop occurs in the dask\/xarray store-to-file call. This means you will not have explicit control over the sequence in which chunks are generated and written.","Q_Score":1,"Tags":"python,large-data,netcdf","A_Id":55602989,"CreationDate":"2019-04-08T06:39:00.000","Title":"Windowed writes in python, e.g. to NetCDF","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am applying a Savitzky-Golay filter to a signal, using the scipy function.\nI need to calculate the lag of the filtered signal, and how much is it behind the original signal.\nCould someone shed some light on this matter? How could I calculate it with scipy? How should I interpret the result correctly?\nI would be very grateful!","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":1092,"Q_Id":55572128,"Users Score":1,"Answer":"You are asking about lag\/latency of a digital filter: the only possible answer for a real-time filter is that the latency is determined entirely by the window size of the filter.\nNon-realtime filters (e.g, where the full set of samples is provided to the filter, as for e.g. the scipy Savitsky-Golay filter) can pretend\/simulate filtering at the \u2018time\u2019 of the current sample, but only by looking ahead at the full window.\nSome might protest that this is demonstrably how e.g. the scipy Savitzky-Goay filter works, and that\u2019s entirely correct, but nevertheless if you are asking about latency of a filter, which can only mean the delay that a real-time real-world filter will apply to real-time samples, the only possible answer is: this is only and undeniably\/incontrovertibly determined by the window size.","Q_Score":0,"Tags":"python,scipy,filtering,signal-processing","A_Id":55602759,"CreationDate":"2019-04-08T11:11:00.000","Title":"Delay in Savitzky-Golay filtering","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When running a linear regression, like y=a*x+b, the summary gives me the p-values of whether the parameters equal to zero, what if I would like to see the p-value of whether the parameter a equals to 2 or something different from zero?\nI expect the OLS summary gives me the p value of whether a is different from 2.","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":3001,"Q_Id":55576097,"Users Score":0,"Answer":"The p-value corresponds to the probability of observing this value of a under the null hypothesis (which is typically 0 as this is the case when there is no effect of the covariate x on the outcome y).\nThis is under the assumptions of linear regression which among other things state that a follows a normal distribution. Therefore, if you really want to change your null hypothesis to be a=2 then just transform a such that a_ = a - 2 now when a=2, a_ will be 0 as per the usual assumption.\nSo you can achieve this by solving for y+2x = a_*x + b and you will have a p-value for the probability that a=2 would occur by chance. As I said though this is a fairly unusual test...","Q_Score":0,"Tags":"python,statsmodels","A_Id":55577000,"CreationDate":"2019-04-08T14:47:00.000","Title":"How to check the p values of parameters in OLS","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a task where I need to run the same function on many different pandas dataframes. I load all the dataframes into a list then pass it to Pool.map using the multiprocessing module. The function code itself has been vectorized as much as possible, contains a few if\/else clauses and no matrix operations.\nI'm currently using a 10-core xeon and would like to speed things up, ideally passing from Pool(10) to Pool(xxx). I see two possibilities:\n\nGPU processing. From what I have read though I'm not sure if I can achieve what I want and would in any case need lots of code modification.\nXeon-Phi. I know it's being discontinued, but supposedly code adaptation is easier and if thats really the case I'd happily get one.\n\nWhich path should I concentrate on? Any other alternatives?\nSoftware: Ubuntu 18.04, Python 3.7. Hardware: X99 chipset, 10-core xeon (no HT)","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":428,"Q_Id":55576373,"Users Score":1,"Answer":"Took a while, but after changing it all to numpy and achieving a little more vectorization I managed to get a speed increase of over 20x - so thanks Paul.\nmax9111 thanks too, I'll have a look into numba.","Q_Score":1,"Tags":"python,pandas,multiprocessing,gpu,xeon-phi","A_Id":55655008,"CreationDate":"2019-04-08T15:01:00.000","Title":"Hardware for python multiprocessing","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm currently using a custom version of YOLO v2 from pjreddie.com written with Tensorflow and Keras. I've successfully got the model to start and finish training over 100 epochs with 10000 training images and 2400 testing images which I randomly generated along with the associated JSON files all on some Titan X gpus with CUDA. I only wish to detect two classes. However, after leaving the training going, the loss function decreases but the test accuracy hovers at below 3%. All the images appear to be getting converted to black and white. The model seems to perform reasonably on one of the classes when using the training data, so the model appears overfitted. What can I do to my code to get the model to become accurate?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":260,"Q_Id":55595512,"Users Score":0,"Answer":"Okay, so it turned out that YOLOv2 was performing very well on unseen data except that the unseen data has to be the same size of images as the ones it's trained on. Don't feed Yolo with 800x800 images if it's been trained on 400x400and 300x400 images. Also the Keras accuracy measure is meaningless for detection. It might say 2% accuracy and actually be detecting all objects. Passing unseen data of the same size solved the problem.","Q_Score":0,"Tags":"python,tensorflow,keras,computer-vision,artificial-intelligence","A_Id":55679352,"CreationDate":"2019-04-09T14:44:00.000","Title":"YOLO v2 bad accuracy in Tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to set up a non-conventional neural network using keras, and am having trouble efficiently setting this up. \nThe first few layers are standard convolutional layers, and the output of these have d channels, which each have image shapes of n x n.\nWhat I want to do is use a single dense layer to map this d x n x n tensor onto a single image of size n x n. I want to define a single dense layer, with input size d, and output size 1, and apply this function to each \"pixel\" on the input (with the inputs taken depthwise across channels). \nSo far, I have not found a efficient solution to this. I have tried first defining a fully connected layer, then looping over each \"pixel\" in the input, however this takes many hours to initialize the model, and I am worried that it will slow down backprop, as the computations are likely not properly parallelized.\nIs there an efficient way to do this?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":234,"Q_Id":55598702,"Users Score":1,"Answer":"What you're describing is a 1x1 convolution with output depth 1. You can implement it just as you implement the rest of the convolution layers. You might want to apply tf.squeeze afterwards to remove the depth, which should have size 1.","Q_Score":0,"Tags":"python,tensorflow,machine-learning,keras","A_Id":55598802,"CreationDate":"2019-04-09T18:02:00.000","Title":"How to apply a single fully connected layer to each point in an image","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need SSIM as a loss function in my network, but my network has 2 outputs. I need to use SSIM for first output and cross-entropy for the next. The loss function is a combination of them. However, I need to have a higher SSIM and lower cross-entropy, so I think the combination of them isn't true.  Another problem is that I could not find an implementation of SSIM in keras.\nTensorflow has tf.image.ssim, but it accepts the image and I do not think I can use it in loss function, right? Could you please tell me what should I do? I am a beginner in keras and deep learning and I do not know how can I make SSIM as a custom loss function in keras.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":3145,"Q_Id":55600106,"Users Score":0,"Answer":"other choice would be\nssim_loss = 1 - tf.reduce_mean(tf.image.ssim(target, output, max_val=self.max_val))\nthen\ncombine_loss = mae (or mse) + ssim_loss\nIn this way, you are minimizing both of them.","Q_Score":3,"Tags":"python,tensorflow,keras,loss-function","A_Id":70400820,"CreationDate":"2019-04-09T19:37:00.000","Title":"how do I implement ssim for loss function in keras?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am designing a program which will run continuously on a ROC64. It includes the usage of BackgroudsubtractorMOG2(a background-subtracting algorithm implemented in opencv). Opencv seems to use multithreading optimization in this algorithm and it eats up all the CPU resources. I understand that in C++ we can limit the number of threads by using setNumThreads().Is there a similar thing in Python or I must find another way to work around it?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":3919,"Q_Id":55604373,"Users Score":1,"Answer":"You can use cv2.setNumThreads(n) (Where n = number of threads)\nBut it didn't work for me, its still using all the CPU.","Q_Score":3,"Tags":"python,multithreading,opencv","A_Id":69070357,"CreationDate":"2019-04-10T03:32:00.000","Title":"How to limit the number of threads that opencv uses in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to do a filter on a dataframe like so:\nfiltered_df2 = filtered_df.select(\"li\", \"result.li\", \"fw\").orderBy(\"fw\")\nHowever, the nested column, result.li has the same name as li and this poses a problem. I get the following error:\nAnalysisException: 'Found duplicate column(s) when inserting into hdfs:\/\/...: `li`;'\nHow can I filter both fields successfully ?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2453,"Q_Id":55612813,"Users Score":0,"Answer":"I've got the solution now. What I needed to use was:\nAdd from pyspark.sql.functions import * at the file header\nSimply use col()'s alias function like so:\nfiltered_df2 = filtered_df.select(col(\"li\"),col(\"result.li\").alias(\"result_li\"), col(\"fw\")).orderBy(\"fw\")","Q_Score":0,"Tags":"python,apache-spark,dataframe,pyspark,apache-spark-sql","A_Id":55613124,"CreationDate":"2019-04-10T12:40:00.000","Title":"Found duplicate column when trying to query with Spark SQL","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Let\u2019s say I have 100 data points, consisting of two values (x,y or V1, V2).  \nRight now I am defining a  bunch of functions (like log, exp, poly, sigmoid etc.) with a bunch of parameters to scale the data and\/or adapt the base-equation. Then I use scipy.optimize.minimize to fit them to the data. After that I compare the fits visually  and by their rms to choose the best one.\nIs there a python module which does that?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":65,"Q_Id":55619624,"Users Score":0,"Answer":"In scipy there is curve_fit but I believe you have to define the curve that is going into it.","Q_Score":0,"Tags":"python,curve-fitting,data-fitting","A_Id":55619709,"CreationDate":"2019-04-10T19:02:00.000","Title":"Is there a module which tries to fit different functions to set of data points?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"For classification algorithm KNeighborsClassifier what does fitting on a sparse input mean?\nDoes it mean if I have x_train and x_test as sparse csr matrix and If I fit on x_train and don't specify algorithm it will automatically choose brute? can anyone clear this confusion.\n\nalgorithm : {\u2018auto\u2019, \u2018ball_tree\u2019, \u2018kd_tree\u2019, \u2018brute\u2019}, optional\nAlgorithm used to compute the nearest neighbors:\nNote: fitting on sparse input will override the setting of this parameter,\nusing brute force.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":133,"Q_Id":55620365,"Users Score":1,"Answer":"No, it means that if the input is sparse, whichever value passed to the argument algorithm will be ignored and brute force algorithm will be used (which is equivalent to algorithm='brute')","Q_Score":2,"Tags":"python,scikit-learn","A_Id":55621089,"CreationDate":"2019-04-10T19:53:00.000","Title":"Algorithm used in KNeighborsClassifier with sparse input?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am having a problem during feature engineering. Looking for some suggestions.   Problem statement: I have usage data of multiple customers for 3 days. Some have just 1 day usage some 2 and some 3. Data is related to number of emails sent \/ contacts added on each day etc.\nI am converting this time series data to column-wise ie., number of emails sent by a customer on day1 as one feature, number of emails sent by a customer on day2 as one feature and so on.\nBut problem is that, the usage can be of either increasing order or decreasing order for different customers.\nie., example 1:  customer 'A' --> 'number of emails sent on 1st . day' = 100 .  ' number of emails sent on 2nd day'=0\nexample 2: customer 'B' --> 'number of emails sent on 1st . day' = 0 .  ' number of emails sent on 2nd day'=100\nexample 3: customer 'C' --> 'number of emails sent on 1st . day' = 0 .  ' number of emails sent on 2nd day'=0\nexample 4: customer 'D' --> 'number of emails sent on 1st . day' = 100 .  ' number of emails sent on 2nd day'=100\nIn the first two cases => My new feature will have \"-100\" and \"100\" as values. Which I guess is good for differentiating.\nBut the problem arises for 3rd and 4th columns when the new feature value will be \"0\" in both scenarios\nCan anyone suggest a way to handle this","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":106,"Q_Id":55623095,"Users Score":1,"Answer":"You can extract the following features:\n\nSimple Moving Averages for day 2 and day 3 respectively. This means you now have two extra columns.\nPercentage Change from previous day\nPercentage Change from day 1 to 3","Q_Score":2,"Tags":"python,machine-learning,data-science,feature-engineering","A_Id":58826866,"CreationDate":"2019-04-11T00:35:00.000","Title":"Suggestions for feature engineering","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to remove the last several columns from a data frame.  However I get a syntax error when I do this:\ndb = db.drop(db.columns[[12:22]], axis = 1)\nThis works but it seems clumsy...\ndb = db.drop(db.columns[[12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22]], axis = 1)\nHow do I refer to a range of columns?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":99,"Q_Id":55623798,"Users Score":0,"Answer":"Steven Burnap's explanation is correct, but the solution can be simplified - just remove the internal parentheses:\ndb = db.drop(db.columns[12:22], axis = 1)\nthis way, db.columns[12:22] is a 'slice' of the columns array (actually index, but doesn't matter here), which goes into the drop method.","Q_Score":0,"Tags":"python,pandas","A_Id":55625183,"CreationDate":"2019-04-11T02:25:00.000","Title":"Syntax Error In Python When Trying To Refer To Range Of Columns","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to remove the last several columns from a data frame.  However I get a syntax error when I do this:\ndb = db.drop(db.columns[[12:22]], axis = 1)\nThis works but it seems clumsy...\ndb = db.drop(db.columns[[12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22]], axis = 1)\nHow do I refer to a range of columns?","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":99,"Q_Id":55623798,"Users Score":1,"Answer":"The first example uses [12:22] is a \"slice\" of nothing.  It's not a meaningful statement, so as you say, it gives a syntax error.  It seems that what you want is a list containing the numbers 12 through 22.  You need to either write it out fully as you did, or use some generator function to create it.\nThe simplest is range, which is a generator that creates a list of sequential values.  So you can rewrite your example like:\ndb = db.drop(db.columns[list(range(12,23)]], axis = 1)\nThough it looks like you are using some sort of library.  If you want more detailed control, you need to look the documentation of that library.  It seems that db.columns is an object of a class that has defined an array operator.  Perhaps that class's documentation shows a way of specifying ranges in a way other than a list.","Q_Score":0,"Tags":"python,pandas","A_Id":55623865,"CreationDate":"2019-04-11T02:25:00.000","Title":"Syntax Error In Python When Trying To Refer To Range Of Columns","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When I want to evaluate the performance of my model on the validation set, is it preferred to use with torch.no_grad: or model.eval()?","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":16936,"Q_Id":55627780,"Users Score":1,"Answer":"If you're reading this post because you've been encountering RuntimeError: CUDA out of memory, then with torch.no grad(): will likely to help save the memory. Using only model.eval() is unlikely to help with the OOM error.\nThe reason for this is that torch.no grad() disables autograd completely (you can no longer backpropagate), reducing memory consumption and speeding up computations.\nHowever, you will still be able to call the gardients when using model.eval(). Personally, I find this design decision intriguing. So, what is the purpose of .eval()? It seems its main functionality is to deactivate the Dropout during the evaluation time.\nTo summarize, if you use torch.no grad(), no intermediate tensors are saved, and you can possibly increase the batch size in your inference.","Q_Score":53,"Tags":"python,machine-learning,deep-learning,pytorch,autograd","A_Id":70600860,"CreationDate":"2019-04-11T08:16:00.000","Title":"Evaluating pytorch models: `with torch.no_grad` vs `model.eval()`","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have Memory Error when I run np.arange() with large number like 1e10.\nhow can I fix Memory Error on np.arange(0.01*1e10,100*1e10,0.5)","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":195,"Q_Id":55628673,"Users Score":0,"Answer":"You are trying to create an array of roughtly 2e12 elements. If every element was to be a byte, you would need approximately 2Tb of free memory to allocate it. Not sure you have so much ram available, that is why you have the memory error.\nNote: the array you are trying to allocate contains floats, so it is even bigger. Do you really need so much elements?\nHope it helps,","Q_Score":0,"Tags":"python,numpy,memo","A_Id":55628823,"CreationDate":"2019-04-11T09:06:00.000","Title":"how can I fix Memory Error on np.arange(0.01*1e10,100*1e10,0.5)?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I came across this piece of code\n(x_train, y_train), (x_test, y_test) = mnist.load_data()\nprint(\"Shape of x_train: \" + str(x_train.shape))\nprint(\"Shape of y_train: \" + str(y_train.shape))\nAnd found that the output looks like this\n(60000, 28, 28)\n(60000,)\nFor the first line of output\nSo far my understanding, does it means that in 1st dimension it can hold 60k items, then in next dimension, it can hold 28 \"array of 60k items\"\nand finally, in the last dimension, it can hold 28 \"array of 28 \"array of 60k items\"\"\nWhat I want to clarify is, Is this 60k samples of 28x28 data or something else?\nFor the second line of output, it seems like its just a 1d array of 60k items. So what does it actually represents? (i know that in x_train it was handwritten numbers and each number represents the intensity of grey in that cell)\nPlease note I have taken this code from some online example(i don't remember and won't mind if you want your credit to be added to this) and public dataset\ntf.keras.datasets.mnist","AnswerCount":3,"Available Count":2,"Score":0.0665680765,"is_accepted":false,"ViewCount":67,"Q_Id":55629163,"Users Score":1,"Answer":"Your understanding of the shapes is correct. From the context probably the x_train are 60k images of handwritten numbers (with resolution 28x28 pixel) and the y_train are simply the 60k true number, which the images show.","Q_Score":2,"Tags":"python,tensorflow,machine-learning,pytorch","A_Id":55629272,"CreationDate":"2019-04-11T09:30:00.000","Title":"Shape of tensor","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I came across this piece of code\n(x_train, y_train), (x_test, y_test) = mnist.load_data()\nprint(\"Shape of x_train: \" + str(x_train.shape))\nprint(\"Shape of y_train: \" + str(y_train.shape))\nAnd found that the output looks like this\n(60000, 28, 28)\n(60000,)\nFor the first line of output\nSo far my understanding, does it means that in 1st dimension it can hold 60k items, then in next dimension, it can hold 28 \"array of 60k items\"\nand finally, in the last dimension, it can hold 28 \"array of 28 \"array of 60k items\"\"\nWhat I want to clarify is, Is this 60k samples of 28x28 data or something else?\nFor the second line of output, it seems like its just a 1d array of 60k items. So what does it actually represents? (i know that in x_train it was handwritten numbers and each number represents the intensity of grey in that cell)\nPlease note I have taken this code from some online example(i don't remember and won't mind if you want your credit to be added to this) and public dataset\ntf.keras.datasets.mnist","AnswerCount":3,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":67,"Q_Id":55629163,"Users Score":1,"Answer":"You are right the first line gives 60K items of 28x28 size data thus (60000, 28, 28).\nThe y_train are labels of the x_train. Thus they are a one dimensional and 60k in number.\nFor example: If the first item of the x_train is a handwritten image of 3, then the first item of y_train will be '3' which is the label.","Q_Score":2,"Tags":"python,tensorflow,machine-learning,pytorch","A_Id":55629310,"CreationDate":"2019-04-11T09:30:00.000","Title":"Shape of tensor","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have created a 4-cluster k-means customer segmentation in scikit learn (Python). The idea is that every month, the business gets an overview of the shifts in size of our customers in each cluster.\nMy question is how to make these clusters 'durable'. If I rerun my script with updated data, the 'boundaries' of the clusters may slightly shift, but I want to keep the old clusters (even though they fit the data slightly worse).\nMy guess is that there should be a way to extract the paramaters that decides which case goes to their respective cluster, but I haven't found the solution yet.\nI would appreciate any help","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":454,"Q_Id":55631944,"Users Score":1,"Answer":"Got the answer in a different topic: \nJust record the cluster means. Then when new data comes in, compare it to each mean and put it in the one with the closest mean.","Q_Score":1,"Tags":"python,scikit-learn,k-means","A_Id":55648613,"CreationDate":"2019-04-11T11:57:00.000","Title":"KMeans: Extracting the parameters\/rules that fill up the clusters","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How to permute \"dimensions\" along a single axis of a tensor?\nSomething akin to tf.transpose, but at the level of \"dimensions\" along an axis, instead of at the level of axes.\nTo permute them randomly (along the first axis), there it tf.random.shuffle, and to shift them, there is tf.roll. But I can't find a more general function that would apply any given permutation.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":321,"Q_Id":55637345,"Users Score":2,"Answer":"tf.gather can be used to that end. In fact, it is even more general, as the indices it takes as one of its inputs don't need to represent a permutation.","Q_Score":1,"Tags":"python,tensorflow","A_Id":55637346,"CreationDate":"2019-04-11T16:35:00.000","Title":"Applying a permutation along one axis in TensorFlow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Levenshtein distance is an approach for measuring the difference between words, but not so for phrases.\nIs there a good distance metric for measuring differences between phrases?\nFor example, if phrase 1 is made of n words x1 x2 x_n, and phrase 2 is made of m words y1 y2 y_m. I'd think they should be fuzzy aligned by words, then the aligned words should have a score about how similar they are, and some kind of gap penalty should be applied for non aligned words. These positive scores and negative scores should be aggregated in some way. There seem to be some heuristics involved.\nIs there an existing solution for measuring the similarity between phrases? Python is preferred but other solution is also fine. Thanks.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":574,"Q_Id":55638949,"Users Score":1,"Answer":"You can also measure the similarity between two phrases using Levenshtein distance, threating each word as a single element. When you have strings of unequal sizes you can use the Smith-Waterman or the Needleman-Wunsch algorithm. Those algorithms are widely used in bioinformatics and the implementation can be found in the biopython package. \nYou can also tokenize the words in the phrases and measure the frequency of each token in each phrase, that will result in an array of frequencies for each phrase. From that array you can measure the pairwise similarity using any vector distance such as euclidean distance or cosine similarity. The tokenization of the phrases can be done with the nltk package, and the distances can be measured with scipy. \nHope it helps.","Q_Score":0,"Tags":"python,similarity,levenshtein-distance,sentence-similarity","A_Id":55645447,"CreationDate":"2019-04-11T18:22:00.000","Title":"similarity score between phrases","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I tried to install Pandas in Project Interpreter under my Project -> Clicked on '+' .. but it says \"Time out\" and shows nothing. So I installed it using \"py -m pip install pandas\" in cmd, but I dont see it under Project Interpreter - there is only pip and setuptools.\nWhat should I do to make it work ?\nI am still getting an error:\n    import pandas as pd\n    ModuleNotFoundError: No module named 'pandas'\nThank you","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":130,"Q_Id":55657228,"Users Score":1,"Answer":"It simply means that the pandas is not installed properly or not even installed at all.\nThe TimeOut error is generally for a connection problem, retry again after some time or try resetting your connection.","Q_Score":1,"Tags":"python,pandas,pycharm","A_Id":55657266,"CreationDate":"2019-04-12T17:54:00.000","Title":"How to use Pandas in Pycharm","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am writing a user defined function in python to extract specific chunks of columns from a matrix efficiently.\nMy matrix is 48 by 16240. The data is organised in some pattern column wise.\nMy objective is to make 4 matrices out of it. The first matrix is extracted by selecting the first 70 columns, skip the next 210, select the next 70, skip the next 210, till the end of the matrix. \nThe second matrix is extracted by selecting the second 70 columns, skip the next 210, select the next 70, skip the next 210, till the end of the matrix.\nThe third and the fourth matrix are extracted by selecting the third and the fourth 70 columns respectively, in the same manner as described above.\nAs can be observed, 16240 is dividable by 70. \nIs there a way to have this efficiently done?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":82,"Q_Id":55660812,"Users Score":1,"Answer":"The column index i should satisfy 0 =< i modulo (210+70) <= 70-1","Q_Score":0,"Tags":"python,numpy-slicing","A_Id":55660859,"CreationDate":"2019-04-13T00:02:00.000","Title":"How to select columns from a matrix with an algorithm","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In gensim I have a trained doc2vec model, if I have a document and either a single word or two-three words, what would be the best way to calculate the similarity of the words to the document? \nDo I just do the standard cosine similarity between them as if they were 2 documents? Or is there a better approach for comparing small strings to documents?\nOn first thought I could get the cosine similarity from each word in the 1-3 word string and every word in the document taking the averages, but I dont know how effective this would be.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":343,"Q_Id":55665180,"Users Score":3,"Answer":"There's a number of possible approaches, and what's best will likely depend on the kind\/quality of your training data and ultimate goals. \nWith any Doc2Vec model, you can infer a vector for a new text that contains known words \u2013 even a single-word text \u2013 via the infer_vector() method. However, like Doc2Vec in general, this tends to work better with documents of at least dozens, and preferably hundreds, of words. (Tiny 1-3 word documents seem especially likely to get somewhat peculiar\/extreme inferred-vectors, especially if the model\/training-data was underpowered to begin with.) \nBeware that unknown words are ignored by infer_vector(), so if you feed it a 3-word documents for which two words are unknown, it's really just inferring based on the one known word. And if you feed it only unknown words, it will return a random, mild initialization vector that's undergone no inference tuning. (All inference\/training always starts with such a random vector, and if there are no known words, you just get that back.)\nStill, this may be worth trying, and you can directly compare via cosine-similarity the inferred vectors from tiny and giant documents alike. \nMany Doc2Vec modes train both doc-vectors and compatible word-vectors. The default PV-DM mode (dm=1) does this, or PV-DBOW (dm=0) if you add the optional interleaved word-vector training (dbow_words=1). (If you use dm=0, dbow_words=0, you'll get fast training, and often quite-good doc-vectors, but the word-vectors won't have been trained at all - so you wouldn't want to look up such a model's word-vectors directly for any purposes.)\nWith such a Doc2Vec model that includes valid word-vectors, you could also analyze your short 1-3 word docs via their individual words' vectors. You might check each word individually against a full document's vector, or use the average of the short document's words against a full document's vector. \nAgain, which is best will likely depend on other particulars of your need. For example, if the short doc is a query, and you're listing multiple results, it may be the case that query result variety \u2013 via showing some hits that are really close to single words in the query, even when not close to the full query \u2013 is as valuable to users as documents close to the full query. \nAnother measure worth looking at is \"Word Mover's Distance\", which works just with the word-vectors for a text's words, as if they were \"piles of meaning\" for longer texts. It's a bit like the word-against-every-word approach you entertained \u2013 but working to match words with their nearest analogues in a comparison text. It can be quite expensive to calculate (especially on longer texts) \u2013 but can sometimes give impressive results in correlating alternate texts that use varied words to similar effect.","Q_Score":1,"Tags":"python,gensim,doc2vec","A_Id":55670156,"CreationDate":"2019-04-13T11:57:00.000","Title":"How do I calculate the similarity of a word or couple of words compared to a document using a doc2vec model?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'd like to compare ORB, SIFT, BRISK, AKAZE, etc. to find which works best for my specific image set. I'm interested in the final alignment of images.\nIs there a standard way to do it?\nI'm considering this solution: take each algorithm, extract the features, compute the homography and transform the image.\nNow I need to check which transformed image is closer to the target template.\nMaybe I can repeat the process with the target template and the transformed image and look for the homography matrix closest to the identity but I'm not sure how to compute this closeness exactly. And I'm not sure which algorithm should I use for this check, I suppose a fixed one.\nOr I could do some pixel level comparison between the images using a perceptual difference hash (dHash). But I suspect the the following hamming distance may not be very good for images that will be nearly identical.\nI could blur them and do a simple subtraction but sounds quite weak.\nThanks for any suggestions.\nEDIT: I have thousands of images to test. These are real world pictures. Images are of documents of different kinds, some with a lot of graphics, others mostly geometrical. I have about 30 different templates. I suspect different templates works best with different algorithms (I know in advance the template so I could pick the best one).\nRight now I use cv2.matchTemplate to find some reference patches in the transformed images and I compare their locations to the reference ones. It works but I'd like to improve over this.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":89,"Q_Id":55679644,"Users Score":0,"Answer":"From your question, it seems like the task is not to compare the feature extractors themselves, but rather to find which type of feature extractor leads to the best alignment.\nFor this, you need two things:\n\na way to perform the alignment using the features from different extractors\na way to check the accuracy of the alignment\n\nThe algorithm you suggested is a good approach for doing the alignment. To check if accuracy, you need to know what is a good alignment.\nYou may start with an alignment you already know. And the easiest way to know the alignment between two images is if you made the inverse operation yourself. For example, starting with one image, you rotate it some amount, you translate\/crop\/scale or combine all this operations. Knowing how you obtained the image, you can obtain your ideal alignment (the one that undoes your operations).\nThen, having the ideal alignment and the alignment generated by your algorithm, you can use one metric to evaluate its accuracy, depending on your definition of \"good alignment\".","Q_Score":0,"Tags":"python,opencv,computer-vision","A_Id":55680593,"CreationDate":"2019-04-14T20:15:00.000","Title":"Comparing feature extractors (or comparing aligned images)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm currently designing the architecture of a neural network for the colorization of grayscale images. Later on it should be able to colorize images with different sizes and different aspect ratios. I read that this would not be possible with a common CNN. I also read that the only options are downscaling the images to one specific size or to use a big fixed size (like 3000x3000px) and to fill the remaining space black. Both of these options don't seem to be that elegant. The first one is the opposite of what I want and the second would make the neural network slower.\nThen I read about Fully Convolutional Networks and that this problem would not exist there. This would be great if it really works. I would like to know why this special network can deal with different input shapes. And maybe you could show me some tensorflow code of such a network.\nBy the way, I thought about an Autoencoder combined with a GAN for the architecture.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":863,"Q_Id":55689510,"Users Score":0,"Answer":"In my experience you cannot train any network with different sample size on the same batch. \nFully convolutional network is similar to a fully connected network with fully connected layers at the end. As such any input image in the batch must have the same dims (w,h,d). \nThe difference is that a fully connected layers output a single output vector for every sample in the input batch while the fully convolutional net outputs a map of probability for each class.\nIt has a deeper meaning than just the image size, when trying to fit any data the size of it must be absolute and cannot be changed while training. I guess you can do it in different batches as I stated but I've never tried it.\nAn encoder\\decoder could help \"rebuild\" the image in a specific size.\nThis tip, again, is from my experience in object detection, I could be wrong :)","Q_Score":0,"Tags":"python,tensorflow,machine-learning,deep-learning,computer-vision","A_Id":55689959,"CreationDate":"2019-04-15T12:45:00.000","Title":"Neural Network with different input shapes","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I recently needed to store large array-like data (sometimes numpy, sometimes key-value indexed) whose values would be changed over time (t=1 one element changes, t=2 another element changes, etc.). This history needed to be accessible (some time in the future, I want to be able to see what t=2\u2019s array looked like). \nAn easy solution was to keep a list of arrays for all timesteps, but this became too memory intensive. I ended up writing a small class that handled this by keeping all data \u201celements\u201d in a dict with each element represented by a list of (this_value, timestamp_for_this_value). that let me recreate things for arbitrary timestamps by looking for the last change before some time t, but it was surely not as efficient as it could have been. \nAre there data structures available for python that have these properties natively? Or some sort of class of data structure meant for this kind of thing?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":272,"Q_Id":55691861,"Users Score":1,"Answer":"Have you considered writing a log file?  A good use of memory would be to have the arrays contain only the current relevant values but build in a procedure where the update statement could trigger a logging function.  This function could write to a text file, database or an array\/dictionary of some sort.  These types of audit trails are pretty common in the database world.","Q_Score":0,"Tags":"python,data-structures","A_Id":55692831,"CreationDate":"2019-04-15T14:53:00.000","Title":"Python Array Data Structure with History","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I was doing some quick tests for handling missing values and came across this weird behavior. When looking at ~pd.isnull(np.nan), I expect it to return False, but instead it returns -2. Why is this?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":59,"Q_Id":55694800,"Users Score":0,"Answer":"It's because you used an arithmetic, bitwie negation operator instead of a logical negation.","Q_Score":1,"Tags":"python","A_Id":55694836,"CreationDate":"2019-04-15T17:53:00.000","Title":"Why does ~pd.isnull() return -2?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Lets say I am trying to compute the average distance between a word and a document using distances() or compute cosine similarity between two documents using n_similarity(). However, lets say these new documents contain words that the original model did not. How does gensim deal with that?\nI have been reading through the documentation and cannot find what gensim does with unfound words.\nI would prefer gensim to not count those in towards the average. So, in the case of distances(), it should simply not return anything or something I can easily delete later before I compute the mean using numpy. In the case of n_similarity, gensim of course has to do it by itself....\nI am asking because the documents and words that my program will have to classify will in some instances contain unknown words, names, brands etc that I do not want to be taken into consideration during classification. So, I want to know if I'll have to preprocess every document that I am trying to classify.","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":179,"Q_Id":55710967,"Users Score":1,"Answer":"The models are defined on vectors, which, by default setting, depend only on old words so I do not expect them depend on new words. \nIt is still possible, depending on code, for new words to affect results. To be on safe side I recommend to test your particular model and\/or metrics on a small text (with and without a bunch of new words).","Q_Score":1,"Tags":"python,nlp,gensim","A_Id":55711407,"CreationDate":"2019-04-16T14:48:00.000","Title":"Dealing with new words in gensim not found in model","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Lets say I am trying to compute the average distance between a word and a document using distances() or compute cosine similarity between two documents using n_similarity(). However, lets say these new documents contain words that the original model did not. How does gensim deal with that?\nI have been reading through the documentation and cannot find what gensim does with unfound words.\nI would prefer gensim to not count those in towards the average. So, in the case of distances(), it should simply not return anything or something I can easily delete later before I compute the mean using numpy. In the case of n_similarity, gensim of course has to do it by itself....\nI am asking because the documents and words that my program will have to classify will in some instances contain unknown words, names, brands etc that I do not want to be taken into consideration during classification. So, I want to know if I'll have to preprocess every document that I am trying to classify.","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":179,"Q_Id":55710967,"Users Score":2,"Answer":"Depending on the context, Gensim will usually either ignore unknown words, or throw an error like KeyError when an exact-word lookup fails. (Also, some word-vector models, like FastText, can synthesize better-than-nothing guesswork vectors for unknown words based on word-fragments observed during training.)\nYou should try your desired operations with the specific models\/method of interest to observe the results.\nIf operation-interrupting errors are thrown and a problem for your code, you could pre-filter your lists-of-words to remove those not also present in the model.","Q_Score":1,"Tags":"python,nlp,gensim","A_Id":55716394,"CreationDate":"2019-04-16T14:48:00.000","Title":"Dealing with new words in gensim not found in model","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to solve the linear equation Ax = b, for the unknown matrix x. A and b are both large and sparse, and have shapes (when converted to dense) of 30,000 x 25 and 30,000 x 100,000, respectively.\nI have tried using both scipy.sparse.linalg.lsqr and scipy.sparse.linalg.lsmr, but they both require that b be dense, which is computationally very expensive and prohibitive.\nHow can I do this?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":222,"Q_Id":55712254,"Users Score":0,"Answer":"You could to use numpy.linalg.pinv to find \"x\" values","Q_Score":1,"Tags":"python,scipy,regression,sparse-matrix,least-squares","A_Id":55712434,"CreationDate":"2019-04-16T15:57:00.000","Title":"Linear algebra with large, sparse matrices","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a large number of pandas dataframe > 5000 of shape 3000x3000 float values with density of 60% (i.e. 40% values are NaNs). These frames have identical index and columns. \nI'd like to operate on these frames e.g. addition of all of them. If I do this sequentially, it takes more than 20 mins. Is there efficient way I could operate on them (e.g. sum them)?\nHow can I make this process memory efficient knowing that these dataframes are not dense?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":88,"Q_Id":55712366,"Users Score":0,"Answer":"If all of your data has the same shape, then I don't see the point of using lists of pandas DataFrames for this.\nThe most performance you could get out of Python with the least work is just stacking the dataframes into a 3D Numpy array of dimensions (3000, 3000, 5000) and then doing an sum over the last axis.\nAs this requires > 360 GB of RAM (at least 180 GB for the loaded dataframes, 180 GB for the stacked Numpy array), this is probably beyond a usual Desktop workload, and you may want to check out big data tools as mentioned in the comments.","Q_Score":0,"Tags":"python,pandas,bigdata,data-science","A_Id":55725242,"CreationDate":"2019-04-16T16:04:00.000","Title":"Operating on large number of dataframes","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to save the output dataframe to a csv file while using pyinstaller to create an exe, but my code freezes and generate \"[Errno 13] Permission denied: '.\\Output.csv' \" error. My question is. what wrong using df.to_csv to save the output file in the same exe directory ? \nThanks in adcance","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":281,"Q_Id":55730679,"Users Score":0,"Answer":"Look, if you use Innodb compiler then you will face a problem of permission denied error in the setup. So , I have tried to solve that by using temporary file but It is getting deleted after generation. But if you really want to solve this problem then use xlsxwriter and save it to a specific file location.","Q_Score":0,"Tags":"python,pyinstaller","A_Id":70164527,"CreationDate":"2019-04-17T15:01:00.000","Title":"pandas df to_csv freezes when using pyinstaller to save the df in the exe directory","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an RGB image in the format of a 3D array with the shape of (m, n, 3). I would like to create a SimpleITK image. Using the GetImageFromArray() function results in creation of an image in 3D which is not what I am looking for. How can I create a 2D RGB image instead?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":443,"Q_Id":55730852,"Users Score":1,"Answer":"The documentation reads:\n\nSignature: sitk.GetImageFromArray(arr, isVector=None)\nDocstring: Get a SimpleITK Image from a numpy array. If isVector is True, then the Image will have a Vector pixel type, and the last dimension of the array will be considered the component index. By default when isVector is None, 4D images are automatically considered 3D vector images.\n\nHave you tried passing isVector=True?","Q_Score":0,"Tags":"python,image,simpleitk","A_Id":55731210,"CreationDate":"2019-04-17T15:10:00.000","Title":"2D RGB image construction from 3D array in SimpleITK","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to write a function that when given two cameras, their rotation, translation matrices, focal point, and the coordinates of a point for each camera, will be able to triangulate the point into 3D space. Basically, given all the extrinsic\/intrinsic values needed\nI'm familiar with the general idea: to somehow create two rays and find the closest point that satisfies the least squares problem, however, I don't know exactly how to translate the given information to a series of equations to the coordinate point in 3D.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1933,"Q_Id":55740284,"Users Score":0,"Answer":"Assume you have two cameras -- camera 1 and camera 2. \nFor each camera j = 1, 2  you are given:\n\nThe distance hj between it's center Oj, (is \"focal point\" the right term? Basically the point Oj from which the camera is looking at its screen) and the camera's screen. The camera's coordinate system is centered at Oj, the Oj--->x and Oj--->y axes are parallel to the screen, while the Oj--->z axis is perpendicular to the screen.   \nThe 3 x 3 rotation matrix Uj and the 3 x 1 translation vector Tj which transforms the Cartesian 3D coordinates with respect to the system of camera j (see point 1) to the world-coordinates, i.e. the coordinates with respect to a third coordinate system from which all points in the 3D world are described.\nOn the screen of camera j, which is the plane parallel to the plane Oj-x-y and at a distance hj from the origin Oj, you have the 2D coordinates (let's say the x,y coordinates only) of point pj, where the two points p1 and p2 are in fact the projected images of the same point P, somewhere in 3D, onto the screens of camera 1 and 2 respectively. The projection is obtained by drawing the 3D line between point Oj and point P and defining point pj as the unique intersection point of this line with with the screen of camera j. The equation of the screen in camera j's 3D coordinate system is z = hj , so the coordinates of point pj with respect to the 3D coordinate system of camera j look like pj = (xj, yj, hj) and so the 2D screen coordinates are simply pj = (xj, yj) .  \n\nInput: You are given the 2D points p1 = (x1, y1),  p2 = (x2, y2) , the twp cameras' focal distances h1,  h2 , two 3 x 3 rotation matrices U1 and U2, two translation 3 x 1 vector columns T1 and T2 . \nOutput: The coordinates P = (x0, y0, z0) of point P in the world coordinate system.  \nOne somewhat simple way to do this, avoiding homogeneous coordinates and projection matrices (which is fine too and more or less equivalent), is the following algorithm:\n\nForm Q1 = [x1; y1; h1] and Q2 = [x2; y2; h2] , where they are interpreted as 3 x 1 vector columns;\nTransform P1 = U1*Q1 + T1  and P2 = U1*Q2 + T1 , where * is matrix multiplication, here it is a 3 x 3 matrix multiplied by a 3 x 1 column, givin a 3 x 1 column;\nForm the lines X = T1 + t1*(P1 - T1) and  X = T2 + t2*(P2 - T2) ;\nThe two lines from the preceding step 3 either intersect at a common point, which is the point P or they are skew lines, i.e. they do not intersect but are not parallel (not coplanar).     \nIf the lines are skew lines, find the unique point X1 on the first line and the uniqe point X2 on the second line such that the vector X2 - X1 is perpendicular to both lines, i.e. X2 - X1 is perpendicular to both vectors  P1 - T1 and P2 - T2. These two point X1 and X2 are the closest points on the two lines. Then point P = (X1 + X2)\/2 can be taken as the midpoint of the segment X1 X2.  \n\nIn general, the two lines should pass very close to each other, so the two points X1 and X2 should be very close to each other.","Q_Score":2,"Tags":"python,numpy,triangulation,vision","A_Id":56049754,"CreationDate":"2019-04-18T06:31:00.000","Title":"How to triangulate a point in 3D space, given coordinate points in 2 image and extrinsic values of the camera","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm working on a machine learning classification task in which I have trained many models with different algorithms in scikit-learn and Random Forest Classifier performed the best. Now I want to train the model further with new examples but if I train the same model by calling the fit method on new examples then it will start training the model from beginning by erasing the old parameters. \nSo, how can I train the trained model by training it with new examples in scikit-learn?\nI got some idea by reading online to pickle and unpickle the model but how would it help I don't know.","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":845,"Q_Id":55751844,"Users Score":0,"Answer":"Append the new data to your existing dataset, and train over the whole thing. Might want to reserve some of the new data for your testset.","Q_Score":1,"Tags":"python,python-3.x,machine-learning,scikit-learn","A_Id":55767754,"CreationDate":"2019-04-18T18:25:00.000","Title":"How to train a trained model with new examples in scikit-learn?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Primary Question\nWhen solving a NLP in Pyomo, using IPOPT as the solver, how can I tell IPOPT what the gradient of the objective function and\/or constraints are? I have to pass a callable function that returns objective values--can I likewise pass a callable function that evaluates the gradient as well?\nSecondary Question\nHow does Pyomo+IPOPT handle this by default? When I solve a simple NLP with Pyomo+IPOPT, part of the IPOPT output includes \"number of objective gradient evaluations\"...but how is it evaluating the gradient? Numerically with finite differences, or something?\nI'm using Pyomo 5.6 with Python 3.6 and IPOPT 3.7.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":561,"Q_Id":55753497,"Users Score":1,"Answer":"Pyomo provides first and second derivative information using the automatic differentiation features in the Ampl Solver Library (ASL). When calling IPOPT, Pyomo outputs your model using the '.nl' file format which is read by the ASL and linked to IPOPT. So you don't have to do anything to provide gradient information, this is done automatically.","Q_Score":1,"Tags":"python-3.x,pyomo,ipopt","A_Id":55795347,"CreationDate":"2019-04-18T20:43:00.000","Title":"How to specify gradient in Pyomo with IPOPT","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Am struggling with training wikipedia dump on doc2vec model, not experienced in setting up a server as a local machine is out of question due to the ram it requires to do the training. I couldnt find a pre trained model except outdated copies for python 2.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":200,"Q_Id":55756841,"Users Score":1,"Answer":"I'm not aware of any publicly-available standard gensim Doc2Vec models trained on Wikipedia.","Q_Score":0,"Tags":"python,nlp,gensim,word2vec,doc2vec","A_Id":55779081,"CreationDate":"2019-04-19T05:02:00.000","Title":"Where to find a pretrained doc2vec model on Wikipedia or large article dataset like Google news?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to build a image classifier using cnn. My images are of (256,256) pixel size. \nWhat will happen if i train the cnn by setting the input shape as (64,64) or (128,128), since (256,256) will take a lot of time to process?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":443,"Q_Id":55762873,"Users Score":0,"Answer":"Make sure your image size is same as the size your Input layer is expecting. Classification architectures, in general, are not flexible to the spatial dimensions of your input. So, that is important. Otherwise you will get a shape mismatch error.\nIn case you want to change the input shape of your model, that is possible to do. It's hard to say exactly how it will affect your classification. You have to, probably, also tune your CNN filters so that the filters are not bigger than your feature maps. Otherwise that might downgrade your performance. But you can try that out and see what happens.","Q_Score":0,"Tags":"python,keras,classification,conv-neural-network","A_Id":55763023,"CreationDate":"2019-04-19T13:46:00.000","Title":"input shape of convolutional neural network in keras","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to build a image classifier using cnn. My images are of (256,256) pixel size. \nWhat will happen if i train the cnn by setting the input shape as (64,64) or (128,128), since (256,256) will take a lot of time to process?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":443,"Q_Id":55762873,"Users Score":0,"Answer":"It will throw an error. You can resize you images with cv2.resize() or you can put the right input shape in your cnn layer and then put a maxpooling layer to reduce the number of parameters.","Q_Score":0,"Tags":"python,keras,classification,conv-neural-network","A_Id":55762953,"CreationDate":"2019-04-19T13:46:00.000","Title":"input shape of convolutional neural network in keras","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to use \"math.sqrt\" and my output should have 4 decimal after point even for numbers like \"4\". is there any func or way?!\nI used \"round(num_sqrt, 4)\" but it didn't work.\nmy input is like:\n1\n2\n3\n19\noutput must be:\n1.0000\n1.4142\n1.7320\n4.3588\nand my output is:\n1.0\n1.4142\n1.7320\n4.3588","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":65,"Q_Id":55763795,"Users Score":0,"Answer":"Try this if you really need unnecessary zeros:\ndef format_floats(reference, values):\n    formatted_values = []\n    for i in range(len(values)):\n        length = len(str(reference)[str(reference).find(\".\"):])-1\n        new_float = str(round(values[i], length))\n        new_float += \"0\"*(len(str(reference))-len(new_float))\n        formatted_values.append(new_float)\n\n    return formatted_values\n\nif __name__ == '__main__':\n\n    reference = 0.12345\n    values = [1.04, 2.045, 2.0]\n\n    print(format_floats(reference, values))\n\n\noutput: ['1.04000', '2.04500', '2.00000']","Q_Score":0,"Tags":"python","A_Id":55763943,"CreationDate":"2019-04-19T14:59:00.000","Title":"How to round number to constant decimal","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a Nvidia Jetson tx2 with the orbitty shield on it. \nI got it from a friend who worked on it last year. It came with ubuntu 16.04. I updated everything on it and i installed the latest python3.7 and pip.\nI tried checking the version of opencv to see what i have but when i do import cv2 it gives me :\nTraceback (most recent call last):\n  File \"\", line 1, in \nModuleNotFoundError: No module named 'cv2'\nSomehow besides python3.7 i have python2.7 and python3.5 installed. If i try to import cv2 on python2.7 and 3.5 it works, but in 3.7 it doesn't. \nCan u tell me how can i install opencv in python3.7 and the latest version?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1498,"Q_Id":55764829,"Users Score":0,"Answer":"Does python-3.7 -m pip install opencv-python work? You may have to change the python-3.7 to whatever path\/alias you use to open your own python 3.7.","Q_Score":2,"Tags":"python,opencv,ubuntu","A_Id":55764936,"CreationDate":"2019-04-19T16:25:00.000","Title":"How can i install opencv in python3.7 on ubuntu?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I am using CatBoost for ranking task. I am using QueryRMSE as my loss function. I notice for some features, the feature importance values are negative and I don't know how to interpret them.\nIt says in the documentation, the i-th feature importance is calculated as the difference between loss(model with i-th feature excluded) - loss(model).\nSo a negative feature importance value means that feature makes my loss go up?\nWhat does that suggest then?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2295,"Q_Id":55777986,"Users Score":0,"Answer":"Negative feature importance value means that feature makes the loss go up. This means that your model is not getting good use of this feature. This might mean that your model is underfit (not enough iteration and it has not used the feature enough) or that the feature is not good and you can try removing it to improve final quality.","Q_Score":0,"Tags":"python,machine-learning,catboost","A_Id":56173568,"CreationDate":"2019-04-20T21:40:00.000","Title":"Negative Feature Importance Value in CatBoost LossFunctionChange","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Looking for suggestions on how to store Price data using MAN AHL's Arctic Library for 5000 stocks EOD data as well as 1 minute data. Separate solutions for EOD and 1-minute data are also welcome. Once the data is stored, I want to perform the following operations:\n\nFetch data for a subset of stocks (lets say around 500-1000 out of the entire universe of 5000 stocks) between a given datetime range.\nAny update to historical data (data once stored in database) should have versioning. Data prior to the update should not be discarded. I should be able to fetch data as of a particular version\/timestamp.\n\nExample format of data:\n\n        Date        Stock        Price\n0       d1          s1            100\n1       d2          s1            110\n2       d3          s1            105\n3       d1          s2            50\n4       d2          s2            45\n5       d3          s2            40","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":105,"Q_Id":55785898,"Users Score":0,"Answer":"Arctic supports a few different storage engines. The only one that will do what you're looking for is VersionStore. It keeps versions of data, so any update you make to the data will be versioned, and you can retrieve data by timestamp ranges and by version. \nHowever it does not let you do a subsetting of stock like you want to do. I'd recommend subsetting your universe (say into US, EMEA, EUR, etc) or into whatever other organization makes sense for your use case.","Q_Score":0,"Tags":"python,pandas,finance","A_Id":60627318,"CreationDate":"2019-04-21T19:14:00.000","Title":"Storing and fetching multiple stocks in Arctic Library","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I am trying to train a model using SciKit Learn's SVM module. For the scoring, I could not find the mean_absolute_error(MAE), however, negative_mean_absolute_error(NMAE) does exist. What is the difference between these 2 metrics? Lets say I get the following results for 2 models:\nmodel 1 (NMAE = -2.6), model 2(NMAE = -3.0)\nWhich model is better? Is it model 1?\nMoreover, how does the negative compare to the positive? Say the following:\nmodel 1 (NMAE = -1.7), model 2(MAE = 1.4)\nHere, which model is better?","AnswerCount":2,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":9738,"Q_Id":55786121,"Users Score":6,"Answer":"I would like to add here, that this negative error is also helpful in finding best algorithm when you are comparing multiple algorithms through GridSearchCV().\nThis is because after training, GridSearchCV() ranks all the algorithms(estimators) and tells you which one is the best. Now when you use an error function, estimator with higher score will be ranked higher by sklearn, which is not true in the case of MAE (along with MSE and a few others).\nTo deal with this, the library flips the sign of error, so the highest MAE will be ranked lowest and vice versa.\nSo to answer your question: -2.6 is better than -3.0 because the actual MAE is 2.6 and 3.0 respectively.","Q_Score":9,"Tags":"python,machine-learning,scikit-learn,regression","A_Id":57091975,"CreationDate":"2019-04-21T19:40:00.000","Title":"What is the negative mean absolute error in scikit-learn?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a time-series dataframe with a cumulative data column. Data drops at night-time leaving me with NaN values, and picks up with first data read in the morning. \nI would like to interpolate the data so that all NaN values take on the value of the last known float\/valid number. Is this readily possible with .interpolate()?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":40,"Q_Id":55799207,"Users Score":0,"Answer":"Answer taken from @Quang Hoang above wiht ffill()","Q_Score":0,"Tags":"python,pandas,interpolation,nan","A_Id":55799292,"CreationDate":"2019-04-22T18:28:00.000","Title":"Interpolating missing data in cumulative timeseries data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Scikit-learn's documentation says there are two arguments to the function: X(data) and y(Target Values). Do we remove the target variable from our data and provide it separately as y? Or do we keep target variable in X and also provide it separately as y? I have come across both approaches and was wondering which was correct.","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":443,"Q_Id":55799226,"Users Score":3,"Answer":"The X should not contain the target as one of the columns. If you include it your linear model will produce no coding errors, but to predict the target y it will just use the feature y.","Q_Score":0,"Tags":"python,scikit-learn,linear-regression","A_Id":55799321,"CreationDate":"2019-04-22T18:29:00.000","Title":"Do we have to remove target variable from data in Scikit-learn's linearmodel.fit()?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Scikit-learn's documentation says there are two arguments to the function: X(data) and y(Target Values). Do we remove the target variable from our data and provide it separately as y? Or do we keep target variable in X and also provide it separately as y? I have come across both approaches and was wondering which was correct.","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":443,"Q_Id":55799226,"Users Score":1,"Answer":"To my understand, you shouldn't predict tomorrow's weather by tomorrow's weather. If you already know what's the correct value, it is pointless to predict one. \nHowever, you don't need to remove target variable in your dataset either, just don't include it in your X-axis. \nWhat we are trying to do with a predictive model? Based on past records(both x and y), we trained our model to find their relationships. In future, we may no longer have y, but we still have x in our hands, assuming their relationship doesn't change, we predict what is the y for the future.","Q_Score":0,"Tags":"python,scikit-learn,linear-regression","A_Id":55801233,"CreationDate":"2019-04-22T18:29:00.000","Title":"Do we have to remove target variable from data in Scikit-learn's linearmodel.fit()?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Using python and OpenCV, is it possible to display the same image on multiple users?\nI am using cv2.imshow but it only displays the image for the user that runs the code.\nThanks","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":46,"Q_Id":55804120,"Users Score":0,"Answer":"I was able to display the images on another user\/host by setting the DISPLAY environment variable of the X server to match the desired user's DISPLAY.","Q_Score":0,"Tags":"python,cv2,multi-user","A_Id":55821709,"CreationDate":"2019-04-23T04:00:00.000","Title":"cv2 - multi-user image display","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have huge size data (in TBs or PBs) in my HDFS which is located at remote PC. Now instead of taking Data to the Transformation Logic (which is not correct and efficient), I want to run my python Transformation Logic itself on the location where my Data is stored.\nSeeking some useful ideas about the technologies which can be used to fulfill this requirement.\nThings which I tried till now:\n1) Approach 1\n\nTook SSH Connection of Remote PC (where HDFS data is available), Copied my python Transformation Logic there and executed after fetching the data from HDFS.\n\n2) Approach 2\n\nLoaded HDFS data to Apache Spark RDDs which is on Remote PC where HDFS data is available and executed Spark Job from another PC.\n\nPlease suggest other technologies which can be used for Logic Execution remotely.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":42,"Q_Id":55822254,"Users Score":0,"Answer":"I would suggest setting up a Spark Cluster in the same local network where you have the data and running spark transformations in the cluster remotely (SSH or Remote Desktop). The advantages of the setup are:\n\nNetwork Latency will be minimised as the data is transferred in the\nsame network locally.\nRunning the transformations with distributed and in-memory processing engines like Apache Spark is fast.\n\nNote: Please ignore if the response is in line with your second approach","Q_Score":0,"Tags":"python,apache-spark,hdfs,remote-access","A_Id":55825408,"CreationDate":"2019-04-24T04:04:00.000","Title":"How to run any Transformation Logic on HDFS data which is at Remote PC","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I have a neural network(ALEXnet or VGG16) written with Keras for Image Classification and I would like to calculate the Number of floating point operations for a network. The size of the images in the dataset could varry.\nCan a generlized code be written in python which could calculate flops automatically ? or is there any libraray avaialble.\nI am working with spyderAnaconda and the defined network is a sequential model.\nThank you.","AnswerCount":3,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":3688,"Q_Id":55831235,"Users Score":0,"Answer":"There is no such code because the quantity of FLOPs is dependent on the hardware and software implementations.  You can certainly derive a typical quantity from expanding the layer-by-layer operations for each parameter and weight. and making reasonable implementation assumptions for each activation function.\nInput dimensions will proportionately affect the computations for the first layer.\nI'm not sure what you intend for \"generalized code in Python\"; do you envision using a form of the Keras model as the input?  This is possible, but you need to write modules that will extract the kernel characteristics and connection logic from the Keras representation.\nYour quantity of operations will vary from one implementation to another.  Hardware architectures now directly support parallel operations and short-cuts for sparse matrices.  Some have extra functionality for adjusting floating-point representations for greater training speed.  Software platforms include control and data flow analysis to optimize the functional flow.  Any of these will change the FLOPs computation.","Q_Score":2,"Tags":"python-3.x,deep-learning,conv-neural-network","A_Id":55839963,"CreationDate":"2019-04-24T13:28:00.000","Title":"Calculating the Number of flops for a given Neural Network?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a neural network(ALEXnet or VGG16) written with Keras for Image Classification and I would like to calculate the Number of floating point operations for a network. The size of the images in the dataset could varry.\nCan a generlized code be written in python which could calculate flops automatically ? or is there any libraray avaialble.\nI am working with spyderAnaconda and the defined network is a sequential model.\nThank you.","AnswerCount":3,"Available Count":3,"Score":0.1325487884,"is_accepted":false,"ViewCount":3688,"Q_Id":55831235,"Users Score":2,"Answer":"FLOPs are the floating-point operations performed by a model. It is usually calculated using the number of multiply-add operations that a model performs. Multiply-add operations, as the name suggests, are operations involving multiplication and addition of 2 or more variables. For example, the expression, a * b + c * d, has 2 flops while a * b + c * d + e * f + f * h has 4 flops.\nLet's now take a simple linear regression model for example. Assume this model has 4 parameters w1, w2, w3, and w4 and a bias b0. Inference on an input data, X = [x1, x2, x3, x4] results in output = x1 * h1 + x2 * h2 + x3 * h3 + x4 * h4 + b0. This operation has 4 flops\nThe FLOPs measurement in CNNs involves knowing the size of the input tensor, filters and output tensor for each layer. Using this information, flops are calculated for each layer and added together to obtain the total flops. Let's look at the first layer in VGG16 with input tensor of size 224x224x3, 64 filters of size 3x3x3 and output size of 224x224x64. Each element in the output results from an operation involving (3x3x3) multiply-add between the filter and the input tensor. Hence the number of flops for the first layer of VGG16 is (3x3x3)x(224x224x64)= 86,704,128","Q_Score":2,"Tags":"python-3.x,deep-learning,conv-neural-network","A_Id":64795470,"CreationDate":"2019-04-24T13:28:00.000","Title":"Calculating the Number of flops for a given Neural Network?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a neural network(ALEXnet or VGG16) written with Keras for Image Classification and I would like to calculate the Number of floating point operations for a network. The size of the images in the dataset could varry.\nCan a generlized code be written in python which could calculate flops automatically ? or is there any libraray avaialble.\nI am working with spyderAnaconda and the defined network is a sequential model.\nThank you.","AnswerCount":3,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":3688,"Q_Id":55831235,"Users Score":0,"Answer":"many papers using their own flops counting code.\nit is made by entering the input size of certain operation's tensor.\nso they manually calculate it's flops\nyou can find it with keyword like 'flops constraint' or 'flops counter' in github.\nor there are 'torchstat' tool which counts the flops and memory usage etc.","Q_Score":2,"Tags":"python-3.x,deep-learning,conv-neural-network","A_Id":66452856,"CreationDate":"2019-04-24T13:28:00.000","Title":"Calculating the Number of flops for a given Neural Network?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have two data frame both having same number of columns but the first data frame has multiple rows and the second one has only one row but same number of columns as the first one. I need to multiply the entries of the first data frame with the second by column name.\nDF:1\n  A   B   C\n0  34  54  56\n1  12  87  78\n2  78  35   0\n3  84  25  14\n4  26  82  13\nDF:2\nA  B  C\n0  2  3  1\nResult\nA     B    C\n68   162  56\n24   261  78\n156  105   0\n168   75  14\n52   246  13","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":45,"Q_Id":55831908,"Users Score":1,"Answer":"This will work. Here we are manipulating numpy array inside the DataFrame.\npd.DataFrame(df1.values*df2.values, columns=df1.columns, index=df1.index)","Q_Score":0,"Tags":"python,data-manipulation","A_Id":55832347,"CreationDate":"2019-04-24T14:00:00.000","Title":"Multiply each element of a column by each element of a different dataframe","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm implementing my first neural network for images classification.\nI would like to know if i should start to find best hyperparameters first and then try to modify my neural network architecture (e.g number of layer, dropout...) or architecture then hyperameters?","AnswerCount":2,"Available Count":2,"Score":0.1973753202,"is_accepted":false,"ViewCount":80,"Q_Id":55846870,"Users Score":2,"Answer":"First you should decide for an architecture and then play around with the hyperparameters. To compare different hyperparameters it is important to have the same base (architecture). \nOf course you can also play around with the architecture (layers, nodes,...).But I think here it is easier to search for an architecture online, because often the same or a similar problem yet have been solved or described in a tutorial\/blog.\nThe dropout is also a (training-)hyperparameter and not part of the architecture!","Q_Score":0,"Tags":"python,tensorflow,neural-network","A_Id":55847433,"CreationDate":"2019-04-25T10:13:00.000","Title":"neural network find best hyperameters or architecture first","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm implementing my first neural network for images classification.\nI would like to know if i should start to find best hyperparameters first and then try to modify my neural network architecture (e.g number of layer, dropout...) or architecture then hyperameters?","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":80,"Q_Id":55846870,"Users Score":1,"Answer":"The answer is as always : it depends\nWhat are you trying to achieve?\nIf you're hoping to make the worlds best image classifier by trial and error then you might want to ask yourself if you think you have more compute available than the people who have already done this. For a really good classifier there are several ones that come with tensorflow\/keras and can be easily implemented. If you're goofing around and learning the coding then I'd recommend different architectures because that's going to teach you more functions. If you have a dataset you don't think existing solutions will be good at analysing and genuinely need the best network to solve classify them then unfortunately it still depends...\nHow to decide:\nFirstly decide on the rough order of magnitude for your overall parameter count (the literal number of parameters your model has). For a given number of parameters, architecture is likely to produce the biggest difference in results between representative hyperparameter choices (don't choke your network down to a single neuron in the middle and expect it to be representative of that architecture).\nIts important to compare the rough performance per parameter so you're not giving an edge to the networks with greater overfitting capacity. You don't need to use all your training data or even train to completion, mostly you'll find the better networks learn faster and finish better (mostly). In the past I've done grid searches with multiple trials at each point using significantly reduced data then optimised the architecture with the most potential by considering the gradients of the grid search. Fun fact: with sufficient time you can use gradient descent methods on hyperparameters to find local minima. You might well find that there are many similarly top performing models, all of which should you can tune until a clear winner emerges.","Q_Score":0,"Tags":"python,tensorflow,neural-network","A_Id":55852346,"CreationDate":"2019-04-25T10:13:00.000","Title":"neural network find best hyperameters or architecture first","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataset with columns like ['station_id', 'feature1', 'feature2',...]\nEach row is a time step. And it is sorted by station_id.\nThe main problem is that station_ids have different number of timesteps ...\nI want to shape it for an LSTM layer, like (NumberOfExamples, TimeSteps, FeaturesPerStep). \nCan someone help me to use np.reshape() in this case please ?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":35,"Q_Id":55848195,"Users Score":0,"Answer":"You cannot easily use reshape when you have a different number of temporal steps for each example. What you typically do with LSTMs is that you have batches of examples and each batch is padded to the same length, usually with zeros. Use np.zeros(shape) and then iteratively assign to respective rows.","Q_Score":0,"Tags":"python,pandas,numpy,lstm","A_Id":55848378,"CreationDate":"2019-04-25T11:27:00.000","Title":"How to reshape spatiotemporal data as lstm input?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Which one among Gridsearchcv and Bayesian optimization works better for optimizing hyper parameters?","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":2664,"Q_Id":55849512,"Users Score":1,"Answer":"Grid search is known to be worse than random search for optimizing hyperparameters [1], both in theory and in practice. Never use grid search unless you are optimizing one parameter only.\nOn the other hand, Bayesian optimization is stated to outperform random search on various problems, also for optimizing hyperparameters [2]. However, this does not take into account several things: the generalization capabilities of models that use those hyperparameters, the effort to use Bayesian optimization compared to the much simpler random search, and the possibility to use random search in parallel.\nSo in conclusion, my recommendation is: never use grid search, use random search if you just want to try a few hyperparameters and can try them in parallel (or if you want the hyperparameters to generalize to different problems), and use Bayesian optimization if you want the best results and are willing to use a more advanced method.\n[1] Random Search for Hyper-Parameter Optimization, Bergstra & Bengio 2012.\n[2] Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020, Turner et al. 2021.","Q_Score":8,"Tags":"python-3.x,machine-learning,gridsearchcv","A_Id":70753506,"CreationDate":"2019-04-25T12:39:00.000","Title":"Gridsearchcv vs Bayesian optimization","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Which one among Gridsearchcv and Bayesian optimization works better for optimizing hyper parameters?","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":2664,"Q_Id":55849512,"Users Score":15,"Answer":"There is no better here, they are different approaches. \nIn Grid Search you try all the possible hyperparameters combinations within some ranges. \nIn Bayesian you don't try all the combinations, you search along the space of hyperparameters learning as you try them. This enables to avoid trying ALL the combinations.\nSo the pro of Grid Search is that you are exhaustive and the pro of Bayesian is that you don't need to be, basically if you can in terms of computing power go for Grid Search but if the space to search is too big go for Bayesian.","Q_Score":8,"Tags":"python-3.x,machine-learning,gridsearchcv","A_Id":55850059,"CreationDate":"2019-04-25T12:39:00.000","Title":"Gridsearchcv vs Bayesian optimization","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataframe containing variables of different scales (age, income, days as customer, percentage spent in each kind of product sold (values from 0 to 1), etc). I believe it's necessary to scale these variables for using in a neural network algorithm, for example. \nMy question is: The variables that are in percentage, are somehow already scaled, can I apply MinMax in my whole dataset or should I not consider these percentage variables in Min Max scaling and keep them with original values?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1082,"Q_Id":55851021,"Users Score":2,"Answer":"I think is not necessary. If the variables that are in percentage is between 0 and 1, you don't need scaled them because they are scaled already.","Q_Score":0,"Tags":"python,machine-learning,neural-network","A_Id":55851082,"CreationDate":"2019-04-25T13:56:00.000","Title":"Should I scale a percentage variable?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to optimize and convert a tensorflow model to OpenVINO IR. It hasn't been very successful because of the problems I'm facing with input shapes. So I'm planning to remodel the whole model with static shapes. The model I'm trying to work on is Tacotron by keithito.\nHow do I ensure all the nodes in my model will have static shapes?\nWill just setting the input placeholder nodes to a fixed shape allow tensorflow to infer and fix the shapes of all other nodes?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":342,"Q_Id":55852212,"Users Score":0,"Answer":"You can have dynamic shapes in TF model and provide static shape while cnverting model with ModelOptimizer. Example for input data of size 256x256 with 3 channels.\npython mo_tf.py --input_shape [1,256,256,3] --input_model model.pb","Q_Score":0,"Tags":"python,tensorflow,speech,openvino","A_Id":56388008,"CreationDate":"2019-04-25T14:54:00.000","Title":"How to ensure static shapes in Tensorflow model for easy OpenVINO conversion?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to find a better way to push data to sql db using python. I have tried \ndataframe.to_sql() method and cursor.fast_executemany()\nbut they don't seem to increase the speed with that data(the data is in csv files) i'm working with right now. Someone suggested that i could use named tuples and generators to load data much faster than pandas can do. \n[Generally the csv files are atleast 1GB in size and it takes around 10-17 minutes to push one file]\nI'm fairly new to much of concepts of python,so please suggest some method or atleast a reference any article that shows any info. Thanks in advance","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":678,"Q_Id":55852550,"Users Score":0,"Answer":"If you are trying to insert the csv as is into the database (i.e. without doing any processing in pandas), you could use sqlalchemy in python to execute a \"BULK INSERT [params, file, etc.]\". Alternatively, I've found that reading the csvs, processing, writing to csv, and then bulk inserting can be an option.\nOtherwise, feel free to specify a bit more what you want to accomplish, how you need to process the data before inserting to the db, etc.","Q_Score":1,"Tags":"python,python-3.x,pandas,sqlalchemy,pyodbc","A_Id":55852914,"CreationDate":"2019-04-25T15:12:00.000","Title":"How to improve the write speed to sql database using python","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"What would the most efficient way of flashing images be in python?\nCurrently I have an infinite while loop that calls sleep at the end, then uses matplotlib to display an image. However I can't get matplotlib to replace the current image, I instead have to close then show again which is slow. I'd like to flash sequences of images as precisely as possible at a set frequency.\nI'm opens to solutions that use other libraries to do the replacement in place.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":571,"Q_Id":55858199,"Users Score":0,"Answer":"Using a library called PsychoPy. It can guarantee that everything is drawn and allows you to control when a window is drawn (a frame) with the window.frame() function.","Q_Score":0,"Tags":"python,image,matplotlib,timer,frequency","A_Id":56177448,"CreationDate":"2019-04-25T22:00:00.000","Title":"Efficient way of flashing images in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to extract numerical entities like temperature and duration mentioned in unstructured formats of texts using neural models like CRF using python. I would like to know how to proceed for numerical extraction as most of the examples available on the internet are for specific words or strings extraction. \nInput: 'For 5 minutes there, I felt like baking in an oven at 350 degrees F'\nOutput: temperature: 350\n        duration: 5 minutes","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":110,"Q_Id":55862614,"Users Score":0,"Answer":"So far my research shows that you can treat numbers as words. \nThis raises an issue : learning 5 will be ok, but 19684 will be to rare to be learned.\nOne proposal is to convert into words. \"nineteen thousands six hundred eighty four\" and embedding each word. The inconvenient is that you are now learning a (minimum) 6 dimensional vector (one dimension per word)\nBased on your usage, you can also embed 0 to 3000 with distinct ids, and say 3001 to 10000 will map id 3001 in your dictionary, and then add one id in your dictionary for each 10x.","Q_Score":0,"Tags":"python-3.x,nlp,named-entity-recognition","A_Id":58901069,"CreationDate":"2019-04-26T07:18:00.000","Title":"numerical entity extraction from unstructured texts using python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am training a neural network for Neural Machine Traslation on Google Colaboratory. I know that the limit before disconnection is 12 hrs, but I am frequently disconnected before (4 or 6 hrs). The amount of time required for the training is more then 12 hrs, so I add some savings each 5000 epochs.\nI don't understand if when I am disconnected from Runtime (GPU is used) the code is still execute by Google on the VM? I ask it because I can easily save the intermediate models on Drive, and so continue the train also if I am disconnected.\nDoes anyone know it?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":6010,"Q_Id":55874473,"Users Score":1,"Answer":"Yes, for ~1.5 hours after you close the browser window. \nTo keep things running longer, you'll need an active tab.","Q_Score":1,"Tags":"python,neural-network,pytorch,google-colaboratory","A_Id":55874689,"CreationDate":"2019-04-26T20:21:00.000","Title":"Does Google-Colab continue running the script when \"Runtime disconnected\"?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataset with number of columns. I need to select some columns by their position. for example, I want to select columns 0,3,6,7,15 (by position) from the dataset. I tried using iloc but it seems it is applicable in the range of position, ( I may be wrong?) If there are any better ideas?","AnswerCount":2,"Available Count":1,"Score":-0.0996679946,"is_accepted":false,"ViewCount":54,"Q_Id":55881531,"Users Score":-1,"Answer":"Use this syntax: data.iloc[:, [0,1,20,22]]\nWhere 0,1,20 and 22 is the column index.","Q_Score":0,"Tags":"python,pandas","A_Id":55881586,"CreationDate":"2019-04-27T14:24:00.000","Title":"select costume column by position pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I was using simple logistic regression to predict  a problem and trying to plot the precision_recall_curve and the roc_curve with predict_proba(X_test). I checked the docstring of predict_proba but hadn't had much details on how it works. I was having bad input every time and checked that y_test, predict_proba(X_test) doesn't match. Finally discovered predict_proba() produces 2 columns and people use the second.\nIt would be really helpful if someone can give an explanation how it produces two columns and their significance. TIA.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":4584,"Q_Id":55882873,"Users Score":1,"Answer":"We can distinguish between the classifiers using the classifier classes. if the classifier name is model then model.classes_ will give the distinct classes.","Q_Score":6,"Tags":"python,machine-learning,scikit-learn,precision-recall","A_Id":65195428,"CreationDate":"2019-04-27T17:00:00.000","Title":"How predict_proba in sklearn produces two columns? what are their significance?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have just upgraded from TF 1.13 to TF 2.0, and my interpreter is complaining because tf.set_random_seed does no longer exist.\nWhat is the equivalent functionality in TF 2.0 ?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1162,"Q_Id":55890834,"Users Score":7,"Answer":"Found it: tf.random.set_seed is what I was looking for","Q_Score":7,"Tags":"python,tensorflow","A_Id":55891000,"CreationDate":"2019-04-28T13:41:00.000","Title":"Setting a random seed on TF 2.0","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have been studying convolution neural network architecture. I am horrendously confused on the part, where, a 3d kernel acts upon the 3d input image (well, it's 4d given we have stack of those images, but just to make explanation a bit easier). I know internet is full of stuffs like this. but i can't find exact answer to that matrix multiplication part. \nTo be easier for everyone to understand, Can someone show me an actual multiplication on how convolution of (5,5,3) matrix (our kernel) over (28,28,3) matrix (our RGB image ) takes place, outputting a 2d array.  \nAlso, please also show, (with a detailed picture) , how those numerous 2d arrays gets flattened and gets connected to a single fully connected layer. \ni know that, final layer of pooled 2d arrays are flattened. but, since there are like 64 2d arrays (just consider), .. so even if we flatten each one, we will have 64 flattened 1D array. so, how does this end up connecting to next fully connected layer ? (Picture please)","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":574,"Q_Id":55892792,"Users Score":1,"Answer":"You have multiple questions in one. I will answer the about the \"how the convolution takes place\". Short answer: it is not a matrix multiplication.\nStep 1) You slide a window of size (5,5,3) over your RGB image carving out subimages of that size. Incidentally these subimages have exactly the same dimension as that of the kernel.\nStep 2) You multiply each subimage values with the values of the convolution component wise. The output of that is again (5,5,3) subimage \"scaled\" by the values of the kernel.\nStep 3) You add all the values of the \"scaled\" (5,5,3) subimage together (effectively squishing the dimensions) into a single value -- that is our final output.","Q_Score":1,"Tags":"python,arrays,matrix,conv-neural-network,convolution","A_Id":55892936,"CreationDate":"2019-04-28T17:24:00.000","Title":"How exactly does matrix multiplication of 3d kernel and 3d image ( Say RGB) takes place to give 2d output?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to import numpy.  I do not have it as a module so I attempted to download it as a .whl file.  I successfully downloaded it to my computer but am having trouble with installing into python 3.7.\nI know I have to install numpy onto my computer and then Python.  I downloaded the .whl file but am having trouble transferring it into my cmd prompt.\n\n\n\nimport numpy as np\n      Traceback (most recent call last):\n        File \"stdin\", line 1, in module\n      ModuleNotFoundError: No module named 'numpy'\n\n\n\nI want to \"import numpy as np\" without errors","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1651,"Q_Id":55894852,"Users Score":0,"Answer":"The easiest way to download and manage modules is using python's built-in pip command.\npip install numpy will install numpy in your site-packages directory, where it needs to be to use it with Python 2\npip3 install numpy will do the same thing for Python 3","Q_Score":0,"Tags":"python,numpy,import","A_Id":55895064,"CreationDate":"2019-04-28T21:46:00.000","Title":"Trouble importing numpy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"These days i'm trying to teach myself machine learning and i'm going though some issues with my dataset.\nSome of my rows (i work with csv files that i create with some js script, i feel more confident doing that in js) are empty wich is normal as i'm trying to build some guessing model but the issue is that it results in having nan values on my training set.\nMy NN was not training so i added a piece of code to remove them from my set but now i have some issues where my model can't work with input from different size..\nSo my question is: how do i handle missing data ? (i basically have 2 rows and can only have the value from 1 and can't merge them as it will not give good results)\ni can remove it from my set, wich would reduce the accuracy of my model in the end.\nPS: if needed i'll post some code when i come back home.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":3144,"Q_Id":55903882,"Users Score":6,"Answer":"You need to have the same input size during training and inference. If you have a few missing values (a few %), you can always choose to replace the missing values by a 0 or by the average of the column. If you have more missing values (more than 50%) you are probably better off ignoring the column completely. Note that this theoretical, the best way to make it work is to try different strategies on your data.","Q_Score":3,"Tags":"python,machine-learning,keras,neural-network","A_Id":55904338,"CreationDate":"2019-04-29T12:55:00.000","Title":"Keras \/ NN - Handling NaN, missing input","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Cant' install 'matplotlib.pyplot' on Windows 10, Python 3.7\nI tried 'pip install matplotlib.pyplot' and received an error\nHere's the exact error code:\nCould not find a version that satisfies the requirement matplotlib.pyplot (from versions: )\nNo matching distribution found for matplotlib.pyplot","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":699,"Q_Id":55906943,"Users Score":2,"Answer":"Don't use pip install matplotlib.pyplot, use pip install matplotlib\nmatplotlib.pyplot is calling pyplot from the module matplotlib. What you want is the module, matplotlib. Then from idle or wherever you are running this, you can call matplotlib.pyplot","Q_Score":1,"Tags":"python,python-3.x","A_Id":55906967,"CreationDate":"2019-04-29T15:58:00.000","Title":"Trouble installing 'matplotlib'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Cant' install 'matplotlib.pyplot' on Windows 10, Python 3.7\nI tried 'pip install matplotlib.pyplot' and received an error\nHere's the exact error code:\nCould not find a version that satisfies the requirement matplotlib.pyplot (from versions: )\nNo matching distribution found for matplotlib.pyplot","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":699,"Q_Id":55906943,"Users Score":0,"Answer":"Try just using pip install on your command line (or terminal):\n'pip install matplotlib'\nI hope it helps.\nBR","Q_Score":1,"Tags":"python,python-3.x","A_Id":55906978,"CreationDate":"2019-04-29T15:58:00.000","Title":"Trouble installing 'matplotlib'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a short series of ~60 values. What I need to do is find the largest sum of 24 consecutive values in the series. \ne.g. I would need to be able to find the sums of the groups [0:23],[1:24],[2:25],[3:26], ... , [37:60] and determine which group has the largest sum.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":38,"Q_Id":55912004,"Users Score":0,"Answer":"You can use the rolling method on a serie :\nserie.rolling(24).sum()\nTo get directly the max \nmax_idx = serie.rolling(24).sum().idxmax()\nYou range of interest is [max_idx-24+1:max_idx] (from index max_idx - 24 + 1 to index max_idx (both included) , so be careful if you want to retrieve these elements, with .loc should be fine but if going back to the original list or if using iloc then you'll have to go to max_idx + 1.","Q_Score":1,"Tags":"python,python-3.x,pandas,dataframe,series","A_Id":55912036,"CreationDate":"2019-04-29T23:23:00.000","Title":"How can I find the sum of multiple groups within a series?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to train a doc2vec model using training data, then finding the similarity of every document in the test data for a specific document in the test data using the trained doc2vec model. However, I am unable to determine how to do this.\nI currently using model.docvecs.most_similar(...). However, this function only finds the similarity of every document in the training data for a specific document in the test data. \nI have tried manually comparing the inferred vector of a specific document in the test data with the inferred vectors of every other document in the test data using model.docvecs.n_similarity(inferred_vector.tolist(), testvectors[i].tolist()) but this returns KeyError: \"tag '-0.3502606451511383' not seen in training corpus\/invalid\" as there are vectors not in the dictionary.","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":1786,"Q_Id":55924378,"Users Score":1,"Answer":"The act of training-up a Doc2Vec model leaves it with a record of the doc-vectors learned from the training data, and yes, most_similar() just looks among those vectors. \nGenerally, doing any operations on new documents that weren't part of training will require the use of infer_vector(). Note that such inference:\n\nignores any unknown words in the new document\nmay benefit from parameter tuning, especially for short documents\nis currently done just one document at time in a single thread \u2013 so, acquiring inferred-vectors for a large batch of N-thousand docs can actually be slower than training a fresh model on the same N-thousand docs\nisn't necessarily deterministic, unless you take extra steps, because the underlying algorithms use random initialization and randomized selection processes during training\/inference\njust gives you the vector, without loading it into any convenient storage-object for performing further most_similar()-like comparisons\n\nOn the other hand, such inference from a \"frozen\" model can be parallelized across processes or machines. \nThe n_similarity() method you mention isn't really appropriate for your needs: it's expecting lists of lookup-keys ('tags') for existing doc-vectors, not raw vectors like you're supplying.\nThe similarity_unseen_docs() method you mention in your answer is somewhat appropriate, but just takes a pair of docs, re-calculating their vectors each time \u2013 somewhat wasteful if a single new document's doc-vector needs to be compared against many other new documents' doc-vectors.  \nYou may just want to train an all-new model, with both your \"training documents\" and your \"test documents\". Then all the \"test documents\" get their doc-vectors calculated, and stored inside the model, as part of the bulk training. This is an appropriate choice for many possible applications, and indeed could learn interesting relationships based on words that only appear in the \"test docs\" in a totally unsupervised way. And there's not yet any part of your question that gives reasons why it couldn't be considered here.\nAlternatively, you'd want to infer_vector() all the new \"test docs\", and put them into a structure like the various KeyedVectors utility classes in gensim - remembering all the vectors in one array, remembering the mapping from doc-key to vector-index, and providing an efficient bulk most_similar() over the set of vectors.","Q_Score":0,"Tags":"python,machine-learning,gensim,doc2vec","A_Id":55927699,"CreationDate":"2019-04-30T15:36:00.000","Title":"Doc2Vec - Finding document similarity in test data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to train a doc2vec model using training data, then finding the similarity of every document in the test data for a specific document in the test data using the trained doc2vec model. However, I am unable to determine how to do this.\nI currently using model.docvecs.most_similar(...). However, this function only finds the similarity of every document in the training data for a specific document in the test data. \nI have tried manually comparing the inferred vector of a specific document in the test data with the inferred vectors of every other document in the test data using model.docvecs.n_similarity(inferred_vector.tolist(), testvectors[i].tolist()) but this returns KeyError: \"tag '-0.3502606451511383' not seen in training corpus\/invalid\" as there are vectors not in the dictionary.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1786,"Q_Id":55924378,"Users Score":0,"Answer":"It turns out there is a function called similarity_unseen_docs(...) which can be used to find the similarity of 2 documents in the test data. \nHowever, I will leave the question unsolved for now as it is not very optimal since I would need manually compare the specific document with every other document in the test data. Also, it compares the words in the documents instead of the vectors which could affect accuracy.","Q_Score":0,"Tags":"python,machine-learning,gensim,doc2vec","A_Id":55924682,"CreationDate":"2019-04-30T15:36:00.000","Title":"Doc2Vec - Finding document similarity in test data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am getting strings ex. \"one hundred twenty three\", or \"nine hundred ninety nine\", and encoding it into a sequence of word tokens of length 4 using the Keras text preprocessing tokenizer and using it as my input with 4 nodes, and having many integer classes as my output ex. 0 1 2 ... 1000 with 1001 output nodes with a tensorflow backend.\nI'm using an embedding input layer and then a flatten layer and then a dense output layer with softmax activation to classify the input sequence to a number. \nThis approach works well for numbers from 0-1000 etc. but scaling up to 100,000 numbers with strings like \"eighty seven thousand four hundred twenty three\" proves to be a problem with very long training times as there's 100,000 output neurons.\nIs there a better way to structure the NN for possibly millions of numbers without sacrificing efficiency?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":91,"Q_Id":55931671,"Users Score":0,"Answer":"Maybe you can formulate it as sequence prediction problem using RNN or regression problem with N digit output nodes.","Q_Score":1,"Tags":"python,tensorflow,machine-learning,keras,neural-network","A_Id":55932064,"CreationDate":"2019-05-01T04:38:00.000","Title":"Text sequence to integer with many integer classes in Keras","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using a python script within ROS. Ros uses python 2.7 and the version of scipy that I'm using is 0.19.1.\nThe following error is reported:\nfrom scipy.spatial.transform import Rotation as R\n    ImportError: No module named transform","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":11819,"Q_Id":55941290,"Users Score":-2,"Answer":"Working on python 3.7.3. Make sure it's available on 2.7","Q_Score":0,"Tags":"python,scipy,scipy-spatial","A_Id":55941809,"CreationDate":"2019-05-01T19:02:00.000","Title":"Scipy ImportError: No module named transform","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a pretrained keras model that has output with dimesion of [n, 4000] (It makes the classification on 4000 classes).\nI need to make a prediction on the test data (300K observations).\nBut when I call method model.predict(X_train), I get an run-out memory error, because I don't have enough RAM to store matrix with shape (300K , 4000).\nTherefore, it would be logical to convert the model output to a sparse matrix.\nBut wrapping the predict method into scipy function sparse.csr_matrix does not work (sparse.csr_matrix(model.predict(X_train))), because it first allocates space in the RAM for the prediction, and only then it converts into the sparse matrix.\nI can also make a prediction on a specific batch of test data, and then convert it using forloop.\nBut it seems to me that this is not optimal and very resource-consuming way.\nPlease give me advice, can there be any other methods for converting the model output into a sparse matrix?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":261,"Q_Id":55950909,"Users Score":0,"Answer":"Isn't there a batch_size parameter in the predict()?\nIf I get it correct the n means number of sample right?\nAssume that you system ram is enough to hold the entire data but the VRAM is not.","Q_Score":3,"Tags":"python,tensorflow,machine-learning,keras","A_Id":55953034,"CreationDate":"2019-05-02T10:56:00.000","Title":"Convert Keras model output into sparse matrix without forloop","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to use Microsoft's Machine Learning Services in SQL SERVER 2016, specifically to leverage Python, NOT R.\nIs it possible?","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":73,"Q_Id":55956728,"Users Score":2,"Answer":"To add to @DMellons answer; Java is supported in SQL 2019 and up. So:\n\nSQL 2016: R \nSQL 2017: R, Python \nSQL 2019: R, Python, Java, more languages may come.","Q_Score":0,"Tags":"python,machine-learning,sql-server-2016","A_Id":55957245,"CreationDate":"2019-05-02T16:32:00.000","Title":"Can you use Python with MS Machine Learning in SQL SERVER 2016","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"is there a way to refer to a specific column relative to a specific data frame in python like there is in R (data.frame$data)?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":74,"Q_Id":55962891,"Users Score":0,"Answer":"You can generally use pandas to mimic R. You can use [] as below.\nmy_column = df['columnName']","Q_Score":1,"Tags":"r,python-3.x","A_Id":55963728,"CreationDate":"2019-05-03T03:22:00.000","Title":"$ in R... What is the equivalent in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"is there a way to refer to a specific column relative to a specific data frame in python like there is in R (data.frame$data)?","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":74,"Q_Id":55962891,"Users Score":1,"Answer":"Usually with [] => data.frame[\"data\"]\nOr for object like with . => data.frame.data","Q_Score":1,"Tags":"r,python-3.x","A_Id":55962898,"CreationDate":"2019-05-03T03:22:00.000","Title":"$ in R... What is the equivalent in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have come across a peculiar situation when preprocessing data.\nLet's say I have a dataset A. I split the dataset into A_train and A_test. I fit the A_train using any of the given scalers (sci-kit learn) and transform A_test with that scaler. Now training the neural network with A_train and validating on A_test works well. No overfitting and performance is good.\nLet's say I have dataset B with the same features as in A, but with different ranges of values for the features. A simple example of A and B could be Boston and Paris housing datasets respectively (This is just an analogy to say that features ranges like the cost, crime rate, etc vary significantly ). To test the performance of the above trained model on B, we transform B according to scaling attributes of A_train and then validate. This usually degrades performance, as this model is never shown the data from B.\nThe peculiar thing is if I fit and transform on B directly instead of using scaling attributes of A_train, the performance is a lot better. Usually, this reduces performance if I test this on A_test. In this scenario, it seems to work, although it's not right.\nSince I work mostly on climate datasets, training on every dataset is not feasible. Therefore I would like to know the best way to scale such different datasets with the same features to get better performance.\nAny ideas, please.\nPS: I know training my model with more data can improve performance, but I am more interested in the right way of scaling. I tried removing outliers from datasets and applied QuantileTransformer, it improved performance but could be better.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1123,"Q_Id":55969460,"Users Score":0,"Answer":"One possible solution could be like this.\n\nNormalize (pre-process) the dataset A such that the range of each features is within a fixed interval, e.g., between [-1, 1]. \nTrain your model on the normalized set A.\nWhenever you are given a new dataset like B: \n\n(3.1.) Normalize the new dataset such that the feature have the same range as they have in A ([-1, 1]).\n(3.2) Apply your trained model (step 2) on the normalized new set (3.1).\n\nAs you have a one-to-one mapping between set B and its normalized version, then you can see what is the prediction on set B, based on predictions on normalized set B.\n\nNote you do not need to have access to set B in advance (or such sets if they are hundreds of them). You normalize them, as soon as you are given one and you want to test your trained model on it.","Q_Score":0,"Tags":"python,scikit-learn,neural-network,preprocessor,feature-scaling","A_Id":55970210,"CreationDate":"2019-05-03T12:05:00.000","Title":"Best way to scale across different datasets","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to run tensorboard in jupyter using the latest tensorflow 2.0.0a0. \nWith the tensorboard version 1.13.1, and python 3.6. \nusing \n...\n%tensorboard --logdir {logs_base_dir}\nI get the error :\nUsageError: Line magic function %tensorboard not found\nDo you have an idea what the problem could be? It seems that all versions are up to date and the command seems correct too. \nThanks","AnswerCount":5,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":25713,"Q_Id":55970686,"Users Score":0,"Answer":"extension loading is required before. You can try -> %load_ext tensorboard \n.It worked for me. I am using TensorFlow 1.>","Q_Score":35,"Tags":"python,tensorflow,tensorflow2.0,tensorboard,tensorflow2.x","A_Id":61309357,"CreationDate":"2019-05-03T13:20:00.000","Title":"Tensorboard not found as magic function in jupyter","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to run tensorboard in jupyter using the latest tensorflow 2.0.0a0. \nWith the tensorboard version 1.13.1, and python 3.6. \nusing \n...\n%tensorboard --logdir {logs_base_dir}\nI get the error :\nUsageError: Line magic function %tensorboard not found\nDo you have an idea what the problem could be? It seems that all versions are up to date and the command seems correct too. \nThanks","AnswerCount":5,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":25713,"Q_Id":55970686,"Users Score":0,"Answer":"That's how I solved it\n%load_ext tensorboard\n%tensorboard --logdir \/content\/drive\/MyDrive\/Dog\\ Vision\/logs\nAfter --logdir, this is my path directory \/content\/drive\/MyDrive\/Dog\\ Vision\/logs. It should be different for you.","Q_Score":35,"Tags":"python,tensorflow,tensorflow2.0,tensorboard,tensorflow2.x","A_Id":72496748,"CreationDate":"2019-05-03T13:20:00.000","Title":"Tensorboard not found as magic function in jupyter","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am wondering how to go about visualization of my frozen graph def. I need it to figure out my tensorflow networks input and output nodes. I have already tried several methods to no avail, like the summarize graph tool. Does anyone have an answer for some things that I can try? I am open to clarifying questions, thanks in advance.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":221,"Q_Id":55977680,"Users Score":0,"Answer":"You can try to use TensorBoard. It is on the Tensorflow website...","Q_Score":0,"Tags":"python,tensorflow,tensorboard","A_Id":55977698,"CreationDate":"2019-05-03T22:13:00.000","Title":"Visualizing a frozen graph_def.pb","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need a recommendation from gurus out there on how to go about setting up a modeling application. I have thousands of scenarios to run on thousands for contracts for cash flow projections. Assuming I have 1000 scenarios and 1000 contracts I would need to run 1,000,000 projections (1000x1000). I'd like to do this in parallel using dask, ray or some other method. My data are in dataframes but I'm open to better suggestions. I can create 2 loops (scenario,contract) for each run but this would be sequential.\nScenario1 w Contract1\nScenario1 w Contract2\nScenario1 w Contract3\n.\n.\n.\nScenario1000 w Contract1000\nI'd like to distribute compute to multiple processor and multiple servers.\nI'll save my question on the inner loop projections where I have to run 100 scenario projections at each time step of the 1,000,000 runs for next time.\nAny suggestion to point me in the right direction would help.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":37,"Q_Id":55977829,"Users Score":0,"Answer":"From a simple conceptual perspective:\n\nWrite yourself a function that takes a contract and a scenario as parameters and performs the desired calculation\nUse Python's multiprocessing to set up a worker pool\nCreate a Queue (from multiprocessing package) that is to be shared across workers\nFill the queue with all combinations (might be a good idea to use fixed indices and only push a tuple of the contract\/scenario indices (C, S) to the queue to reduce required space\nMap your function to the worker pool given the queue\n\nThere are more elaborate ways to do this (including amqp\/celery\/...) depending whether you want to distribute tasks across multiple machines or just to all of your locally available cores. This simple concept should contain all required keywords to build your first local multiprocessing on your own!","Q_Score":0,"Tags":"python,pandas,parallel-processing,dask","A_Id":55978184,"CreationDate":"2019-05-03T22:33:00.000","Title":"Need setup recommendation for parallel processing many contracts through many scenarios","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using the library node2vec, which is based on gensim word2vec model to encode nodes in an embedding space, but when i want to fit the word2vec object I get this warning:\n\nC:\\Users\\lenovo\\Anaconda3\\lib\\site-packages\\gensim\\models\\base_any2vec.py:743:\n  UserWarning: C extension not loaded, training will be slow. Install a\n  C compiler and reinstall gensim for fast training.\n\nCan any one help me to fix this issue please ?","AnswerCount":4,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":8850,"Q_Id":55978013,"Users Score":0,"Answer":"I've faced this issue for a long time when I was running W2V Models which requires 'gensim'.\nFirst of all I've installed Anaconda Navigator and then installed required packages using pip.\nI've installed gensim manually using pip in cmd. When I run the W2V model, it took 40 min to train and give the result, which made me annoying and wasted a lot of time.\nThis problem got solved now. I just did what the warning showed. I've uninstalled gensim from my computer. Prior to that I've already created a system path of mingw-w64 in the environment variable which is an environment for c,c++ etc., programs. Later, I've reinstalled gensim using 'pip install gensim'.\nNow the program is running within seconds which made a drastic change in the execution time.\nHope this helps...","Q_Score":5,"Tags":"python-3.x,jupyter-notebook,anaconda,gensim,word2vec","A_Id":58553910,"CreationDate":"2019-05-03T23:01:00.000","Title":"How to fix 'C extension not loaded, training will be slow. Install a C compiler and reinstall gensim for fast training.'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using the library node2vec, which is based on gensim word2vec model to encode nodes in an embedding space, but when i want to fit the word2vec object I get this warning:\n\nC:\\Users\\lenovo\\Anaconda3\\lib\\site-packages\\gensim\\models\\base_any2vec.py:743:\n  UserWarning: C extension not loaded, training will be slow. Install a\n  C compiler and reinstall gensim for fast training.\n\nCan any one help me to fix this issue please ?","AnswerCount":4,"Available Count":3,"Score":0.049958375,"is_accepted":false,"ViewCount":8850,"Q_Id":55978013,"Users Score":1,"Answer":"anaconda prompt\nconda update conda-build\n==\nwindows 7 (32bit)\npython 3.7.3\nconda-build 3.18.5\ngensim 3.4.0","Q_Score":5,"Tags":"python-3.x,jupyter-notebook,anaconda,gensim,word2vec","A_Id":56800565,"CreationDate":"2019-05-03T23:01:00.000","Title":"How to fix 'C extension not loaded, training will be slow. Install a C compiler and reinstall gensim for fast training.'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using the library node2vec, which is based on gensim word2vec model to encode nodes in an embedding space, but when i want to fit the word2vec object I get this warning:\n\nC:\\Users\\lenovo\\Anaconda3\\lib\\site-packages\\gensim\\models\\base_any2vec.py:743:\n  UserWarning: C extension not loaded, training will be slow. Install a\n  C compiler and reinstall gensim for fast training.\n\nCan any one help me to fix this issue please ?","AnswerCount":4,"Available Count":3,"Score":0.049958375,"is_accepted":false,"ViewCount":8850,"Q_Id":55978013,"Users Score":1,"Answer":"For me, degrading back to Gensim version 3.7.1 from 3.7.3 worked.","Q_Score":5,"Tags":"python-3.x,jupyter-notebook,anaconda,gensim,word2vec","A_Id":56666561,"CreationDate":"2019-05-03T23:01:00.000","Title":"How to fix 'C extension not loaded, training will be slow. Install a C compiler and reinstall gensim for fast training.'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"For example when importing CIFAR-10 from Keras (using from keras.datasets import cifar10\n(x_train, y_train), (x_test, y_test) = cifar10.load_data())\n or temporarily installing a package like HAZM (Persian form of NLTK) using !pip install hazm which is not pre-installed on Google Colab, the cell containing the import statement starts to download the material it needs. I want to know if my internet traffic is used in the downloading process, or it happens on Google cloud servers and my internet connection is used only to run the code?\nThanks.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2643,"Q_Id":55980568,"Users Score":7,"Answer":"It happens on Google cloud servers and your internet connection is used only to run the code.\nI tried downloading some huge dataset using wget and my internet data wasnt affected by it.","Q_Score":3,"Tags":"python,python-3.x,google-colaboratory,python-module","A_Id":61169238,"CreationDate":"2019-05-04T07:45:00.000","Title":"Does Google Colab use my internet traffic while downloading a dataset or importing a new package into colab notebook?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to do stereo vision and finally find the real distance to the objects from cameras. I have done image rectification.Now I want to calculate disparity. My question is, to do disparity, do I need to rectify images first? Thank you!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":165,"Q_Id":55982564,"Users Score":0,"Answer":"Yes, disparity needs rectified images. Since the stereo matching is done with epipolar lines, rectified images ensure that all the distortions are rectified and hence the algorithm can search blocks in a straight line. For a basic level you can try out StereoBM provided by openCV using the recitified stereo image pair.\nRaw frames from camera -> Rectification -> Disparity map -> Depth perception. \nThis will be the pipeline for any passive stereo camera.","Q_Score":0,"Tags":"python-3.x,opencv,stereo-3d,disparity-mapping","A_Id":55984404,"CreationDate":"2019-05-04T12:35:00.000","Title":"previous steps before to calculate disparity? Is rectification needed?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have some trubble reading with pandas a csv file which include the special character '\u0153'.\nI've done some reseach and it appears that this character has been added to the ISO 8859-15 encoding standard.\nI've tried to specify this encoding standard to the pandas read_csv methods but it doesn't properly get this special character (I got instead a '\u2610') in the result dataframe :\ndf= pd.read_csv(my_csv_path, \";\", header=None, encoding=\"ISO-8859-15\")\nDoes someone know how could I get the right '\u0153' character (or eaven better the string 'oe') instead of this ?\nThank's a lot :)","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":167,"Q_Id":55987923,"Users Score":0,"Answer":"Anyone have a clue ? I've manage the problem by manually rewrite this special character before reading my csv with pandas but that doesn't answer my question :(","Q_Score":0,"Tags":"python-3.x,pandas,encoding","A_Id":55993149,"CreationDate":"2019-05-05T00:30:00.000","Title":"Pandas read_csv method can't get '\u0153' character properly while using encoding ISO 8859-15","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using sklearn's random forests module to predict a binary target variable based on 166 features.\nWhen I increase the number of dimensions to 175 the accuracy of the model decreases (from accuracy = 0.86 to 0.81 and from recall = 0.37 to 0.32) .\nI would expect more data to only make the model more accurate, especially when the added features were with business value.\nI built the model using sklearn in python.\nWhy the new features did not get weight 0 and left the accuracy as it was ?","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":2477,"Q_Id":55990255,"Users Score":1,"Answer":"Basically, you may be \"confusing\" your model with useless features. MORE FEATURES or MORE DATA WILL NOT ALWAYS MAKE YOUR MODEL BETTER. The new features will also not get weight zero because the model will try hard to use them! Because there are so many (175!), RF is just not able to come back to the previous \"pristine\" model with better accuracy and recall (maybe these 9 features are really not adding anything useful). \nThink about how a decision tree essentially works. These new features will cause some new splits that can worsen the results. Try to work it out from the basics and slowly adding new information always checking the performance. In addition, pay attention to for example the number of features used per split (mtry). For so many features, you would need to have a very high mtry (to allow for a big sample to be considered for every split). Have you considered adding 1 or 2 more and checking how the accuracy responds? Also, don't forget mtry!","Q_Score":1,"Tags":"python,machine-learning,random-forest","A_Id":56062610,"CreationDate":"2019-05-05T08:46:00.000","Title":"additional of features decrease the accuracy- random forest","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using sklearn's random forests module to predict a binary target variable based on 166 features.\nWhen I increase the number of dimensions to 175 the accuracy of the model decreases (from accuracy = 0.86 to 0.81 and from recall = 0.37 to 0.32) .\nI would expect more data to only make the model more accurate, especially when the added features were with business value.\nI built the model using sklearn in python.\nWhy the new features did not get weight 0 and left the accuracy as it was ?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":2477,"Q_Id":55990255,"Users Score":0,"Answer":"More data does not always make the model more accurate. Random forest is a traditional machine learning method where the programmer has to do the feature selection. If the model is given a lot of data but it is bad, then the model will try to make sense out of that bad data too and will end up messing things up. More data is better for neural networks as those networks select the best possible features out of the data on their own.\nAlso, 175 features is too much and you should definitely look into dimensionality reduction techniques and select the features which are highly correlated with the target. there are several methods in sklearn to do that. You can try PCA if your data is numerical or RFE to remove bad features, etc.","Q_Score":1,"Tags":"python,machine-learning,random-forest","A_Id":55993920,"CreationDate":"2019-05-05T08:46:00.000","Title":"additional of features decrease the accuracy- random forest","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm looking for a way to download surveys that are still open on Qualtrics so that I can create a report on how many surveys are completed and how many are still in progress. I was able to follow their API documentation to download the completed surveys to a csv file but I couldn't find a way to do the same for the In Progress surveys. Thanks in advance for your help.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":156,"Q_Id":55997128,"Users Score":0,"Answer":"Not through the API.  You can do it manually through the Qualtrics interface.\nIf you need to use the API and the survey is invite only, an alternative would be to download the distribution history for all the distributions. That will tell you the status of each invitee.","Q_Score":0,"Tags":"python,qualtrics","A_Id":56006150,"CreationDate":"2019-05-05T22:56:00.000","Title":"Is there a way to download \"Responses In Progress\" survey from Qualtrics?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":1,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to solve a simple binary classification problem using LSTM. I am trying to figure out the correct loss function for the network. The issue is, when I use the binary cross-entropy as loss function, the loss value for training and testing is relatively high as compared to using the mean squared error (MSE) function.\nUpon research, I came across justifications that binary cross-entropy should be used for classification problems and MSE for the regression problem. However, in my case, I am getting better accuracies and lesser loss value with MSE for binary classification. \nI am not sure how to justify these obtained results. Why not use mean squared error for classification problems?","AnswerCount":4,"Available Count":3,"Score":0.049958375,"is_accepted":false,"ViewCount":8441,"Q_Id":56013688,"Users Score":1,"Answer":"The answer is right there in your question. Value of binary cross entropy loss is higher than rmse loss.\nCase 1 (Large Error):\nLets say your model predicted 1e-7 and the actual label is 1.\nBinary Cross Entropy loss will be -log(1e-7) = 16.11.\nRoot mean square error will be (1-1e-7)^2 = 0.99.\nCase 2 (Small Error)\nLets say your model predicted 0.94 and the actual label is 1.\nBinary Cross Entropy loss will be -log(0.94) = 0.06.\nRoot mean square error will be (1-1e-7)^2 = 0.06.\nIn Case 1 when prediction is far off from reality, BCELoss has larger value compared to RMSE. When you have large value of loss you'll have large value of gradients, thus optimizer will take a larger step in direction opposite to gradient. Which will result in relatively more reduction in loss.","Q_Score":9,"Tags":"python,keras,lstm,cross-entropy,mean-square-error","A_Id":67363684,"CreationDate":"2019-05-06T23:44:00.000","Title":"Why not use mean squared error for classification problems?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to solve a simple binary classification problem using LSTM. I am trying to figure out the correct loss function for the network. The issue is, when I use the binary cross-entropy as loss function, the loss value for training and testing is relatively high as compared to using the mean squared error (MSE) function.\nUpon research, I came across justifications that binary cross-entropy should be used for classification problems and MSE for the regression problem. However, in my case, I am getting better accuracies and lesser loss value with MSE for binary classification. \nI am not sure how to justify these obtained results. Why not use mean squared error for classification problems?","AnswerCount":4,"Available Count":3,"Score":1.0,"is_accepted":false,"ViewCount":8441,"Q_Id":56013688,"Users Score":6,"Answer":"I would like to show it using an example.\nAssume a 6 class classification problem.\nAssume, \n     True probabilities = [1, 0, 0, 0, 0, 0]\nCase 1:\nPredicted probabilities = [0.2, 0.16, 0.16, 0.16, 0.16, 0.16]\nCase 2:\nPredicted probabilities = [0.4, 0.5, 0.1, 0, 0, 0]\nThe MSE in the Case1 and Case 2 is 0.128 and 0.1033 respectively.\nAlthough, Case 1 is correctly predicting class 1 for the instance, the loss in Case 1 is higher than the loss in Case 2.","Q_Score":9,"Tags":"python,keras,lstm,cross-entropy,mean-square-error","A_Id":58903890,"CreationDate":"2019-05-06T23:44:00.000","Title":"Why not use mean squared error for classification problems?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to solve a simple binary classification problem using LSTM. I am trying to figure out the correct loss function for the network. The issue is, when I use the binary cross-entropy as loss function, the loss value for training and testing is relatively high as compared to using the mean squared error (MSE) function.\nUpon research, I came across justifications that binary cross-entropy should be used for classification problems and MSE for the regression problem. However, in my case, I am getting better accuracies and lesser loss value with MSE for binary classification. \nI am not sure how to justify these obtained results. Why not use mean squared error for classification problems?","AnswerCount":4,"Available Count":3,"Score":1.2,"is_accepted":true,"ViewCount":8441,"Q_Id":56013688,"Users Score":-1,"Answer":"I'd like to share my understanding of the MSE and binary cross-entropy functions.\nIn the case of classification, we take the argmax of the probability of each training instance.\nNow, consider an example of a binary classifier where model predicts the probability as [0.49, 0.51]. In this case, the model will return 1 as the prediction.\nNow, assume that the actual label is also 1.\nIn such a case, if MSE is used, it will return 0 as a loss value, whereas the binary cross-entropy will return some \"tangible\" value. \nAnd, if somehow with all data samples, the trained model predicts a similar type of probability, then binary cross-entropy effectively return a big accumulative loss value, whereas MSE will return a 0.\nAccording to the MSE, it's a perfect model, but, actually, it's not that good model, that's why we should not use MSE for classification.","Q_Score":9,"Tags":"python,keras,lstm,cross-entropy,mean-square-error","A_Id":56045324,"CreationDate":"2019-05-06T23:44:00.000","Title":"Why not use mean squared error for classification problems?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Summary : Concern is related to UDF creation in Hive.\nDear friends, As I am new in creating UDFs in Hive (I have read about this via google but not getting very clear idea), my first thing here is to determine which would be the best possible way like Java\/Python or any other to write hive UDFs.\nAnother thing is on what basis I should analyse? What all parameter I should look for ?\nPlease not that I have few functions as given below for which UDFs needs to be written.\n1. To select and group by clauses required for another function when \"no aggregation\" is needed.\n2. To return the select and group by clauses required when \"aggregation\" is needed.\n3. For vector_indexes are SUM, LISTAGG strings for the data collection query \n4. To return the WHERE clause used by other function.\n5 To return the nth item in a comma separated string.\n6. Percentile Value function for Narrow data.\n7. To calculates percentile for a given counter name. Along with the percentile, it also outputs the number of samples used in the calculation, the peak and average.\nThank you very much in advance,","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":38,"Q_Id":56014157,"Users Score":0,"Answer":"This question probably isnt within guidelines because you are asking for an opinion.\nHaving said that i would propose that:\nA) you pick a language that you know.\nB) if you know both, then pick based upon the features you need.\nC) consider performance - i believe (but cannot confifm) that a compiled Java Jar will run without launching a java runtime just to support that Java module (it will run inside the hive java instance). To run a Python module a new python interpreter will need to be instantiated and data transferred via interprocess communication. Thus java is possibly slightly more perfofmant - especially if the algorithm is simple. However unless you are processing huge data sets you probably would not even notice.\nFinally, you could probably do all of the functions you asked with Hive query language.","Q_Score":0,"Tags":"java,python,hive,user-defined-functions","A_Id":56014229,"CreationDate":"2019-05-07T00:57:00.000","Title":"Determine the language for UDF creation in Hive","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a more or less standard implementation of DQN solving the Atari \"Breakout\" (from Coursera Reinforcement learning course), that behaves totally different on different computers:\n\non my Laptop it converges each time I run it\non Coursera and Google Colab servers it never converges!\n\nI use\n\nPython3\nTensorflow\nKerass (only for Conv2D, Flatten and Dense layers)\n\nI already spend some two weeks on the issue without any progress :(\nI already checked:\n\nThe versions:\n\n\nPython: same (3.6.7)\nTensorflow: same (tested with 1.4.0 and 1.5.0)\nnumpy: same up the bugfix number (1.16.2 vs 1.16.3)\n\nRandom seeds\nfloat32 vs float64: I always pass dtype=np.float32 to each np.array and tf.placeholder call.\nCPU\/GPU\n\n\nMy laptop, that converges, uses old CPU (that limits Tensorflow to <= 1.5.0)\nOn Coursera server, that never converges: CPU?\nOn Google Colab server, that never converges: GPU\n\n\nMy questions here are:\n\nWhat may be cause of the different behavior?\nHow such problems get debugged?\nWhat can I also do\/check to finally find the problem?\n\nUpdate: All the code (incl. hyperparameters, env, ...) is exactly the same.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":98,"Q_Id":56025783,"Users Score":1,"Answer":"I assume you run a certain version of your code with a given hyper-parameter values. Then, you need to fix random seed in the beginning of your code for tensorflow (e.g. tf.set_random_seed(1)), for numpy (e.g. np.random.seed(1)) and for random, if you use it. \nAdditionally, you have to have same version of tensorflow on all you machines. I had a experience that even the forward pass between 1.3 and 1.8 resulted in two different outputs. Same check is required for gym. \nFinally, you have to check with either cpu or gpu. You cannot compare the results of a cpu run with a gpu run. \nIf neither of these checks worked, I can check your colab code if you want to share it.","Q_Score":2,"Tags":"python,python-3.x,tensorflow,keras,reinforcement-learning","A_Id":56063874,"CreationDate":"2019-05-07T15:18:00.000","Title":"DQN behaves differently on different computers","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am having an issue getting OpenCV to work with python.  I compiled from source using CMake in order to gain access to the SIFT module.  Whenever I try to use openCV however, python returns the \"No module named 'cv2'\" error.  It works fine when I install using pip but then I have no SIFT.  My build directory is set as an environment variable and my bin directory is in my system path.  There were no build issues and the applications that came with the build run fine.  Is there another step that I have to perform, such as installing from the compiled project using pip?  How do I get my openCV library, compiled from source, to be importable by python?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":407,"Q_Id":56027199,"Users Score":0,"Answer":"The solution ended up being both simpler and sloppier than I would have liked.  I just installed the regular distribution using pip install opencv-contrib-python, then went into the cv2 folder in Lib\/site-packages, replaced the python extension (cv2.cp36-win32.pyd in my case.  may be different for others) with the .pyd file from my CMake build (build\/lib\/python3\/Release) and copied everything from build\/bin\/Release into the Lib\/site-packages\/cv2 folder.  It doesn't look pretty or organized but python can find everything now.  If anyone has a cleaner way to do this I'd love to hear it.","Q_Score":1,"Tags":"python,python-3.x,opencv","A_Id":56027872,"CreationDate":"2019-05-07T16:50:00.000","Title":"Getting OpenCV to work with python after compiling from source","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have created a word2vec file and I want to extract only the line at position [0]\nthis is the word2vec file\n`36 16\nActivity 0.013954502 0.009596351 -0.0002082094 -0.029975398 -0.0244055 -0.001624907 0.01995442 0.0050479663 -0.011549354 -0.020344704 -0.0113901375 -0.010574887 0.02007604 -0.008582828 0.030914625 -0.009170294\nDATABASED%GWC%5 0.022193532 0.011890317 -0.018219836 0.02621059 0.0029900416 0.01779779 -0.026217759 0.0070709535 -0.021979155 0.02609082 0.009237218 -0.0065825963 -0.019650755 0.024096865 -0.022521153 0.014374277\nDATABASED%GWC%7 0.021235622 -0.00062567473 -0.0045315344 0.028400827 0.016763352 0.02893731 -0.013499333 -0.0037113864 -0.016281538 0.004078895 0.015604254 -0.029257657 0.026601797 0.013721668 0.016954066 -0.026421601`","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":12,"Q_Id":56039771,"Users Score":0,"Answer":"glove_model[\"Activity\"] should get you its vector representation from the loaded model. This is because glove_model is an object of type  KeyedVectors and you can use key value to index into it.","Q_Score":0,"Tags":"python,pycharm","A_Id":56039845,"CreationDate":"2019-05-08T11:29:00.000","Title":"how to extract line from a word2vec file?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Let's say I have a set of images of passports. I am working on a project where I have to identify the name on each passport and eventually transform that object into text.\nFor the very first part of labeling (or classification (I think. beginner here)) where the name is on each passport, how would I go about that?\nWhat techniques \/ software can I use to accomplish this?\nin great detail or any links would be great. I'm trying to figure out how this is done exactly so I can began coding\nI know training a model is involved possibly but I'm just not sure\nI'm using Python if that matters. \nthanks","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":91,"Q_Id":56047785,"Users Score":0,"Answer":"There's two routes you can take, one where you have labeled data (or you want to label data yourseld), and one where you don't have that. \nLet's start with the latter. Say you have an image of a passport. You want to detect where the text in the image is, and what that text says. You can achieve this using a library called pytessaract. It's an AI that does exactly this for you. It works well because it has been trained on a lot of other images, so it's good in detecting text in any image.\nIf you have labels you might be able to improve your model you could make with pytessaract, but this is a lot harder. If you want to learn it anyway, I would recommend with learning \u0167ensorflow, and use \"transfer learning\" to improve your model.","Q_Score":1,"Tags":"python,machine-learning,deep-learning,classification","A_Id":56047915,"CreationDate":"2019-05-08T19:28:00.000","Title":"How would I go about image labeling\/Classification?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am plotting plots on one figure using matplotlib from csv files however, I want the plots in order. I want to somehow use the read_csv method to read the csv files from a directory in the order they are listed in so that they are outputted in the same fashion.\nI want the plots listed under each other the same way the csv files are listed in the directory.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":47,"Q_Id":56048345,"Users Score":2,"Answer":"you could use os.listdir() to get all the files in the folder and then sort them out in a certain way, for example by name(it would be enough using the python built in sorted() ). Instead if you want more fancy ordering you could retrieve both the name and last modified date and store them in a dictionary, order the keys and retrieve the values. So as @Fausto Morales said it all only depends on which order you would like them to be sorted.","Q_Score":1,"Tags":"python,pandas,csv,matplotlib,python-3.7","A_Id":56048584,"CreationDate":"2019-05-08T20:10:00.000","Title":"Is there a way to use the \"read_csv\" method to read the csv files in order they are listed in a directory?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have about 1200 tv show categories .. like Drama, News, Sports, Sports-non event, Drama Medical, Drama Crime.. etc\nHow do I use NLP so that I get groups such that Drama, Drama medical and Drama Crime group together and Sports, Sports-non event etc group together and so on... basically the end goal is to reduce the 1200 categories to very few broad categories.\nTill now I have used bag of words to build a dictionary with 146 words..","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":204,"Q_Id":56049055,"Users Score":0,"Answer":"Use a pre trained model to generate embeddings, and from there you can cluster the embeddings using a clustering algorithm like t-SNE or UMAP. I recommend fasttext or spacy, with spacey being the easiest to use.","Q_Score":1,"Tags":"python,python-3.x,nlp,classification,text-classification","A_Id":56049286,"CreationDate":"2019-05-08T21:06:00.000","Title":"How do I group similar categories?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am combining a Monte-Carlo Tree Search with a convolutional neural network as the rollout policy. I've identified the Keras model.predict function as being very slow. After experimentation, I found that surprisingly model parameter size and prediction sample size don't affect the speed significantly. For reference:\n\n0.00135549 s for 3 samples with batch_size = 3\n0.00303991 s for 3 samples with batch_size = 1\n0.00115528 s for 1 sample with batch_size = 1\n0.00136132 s for 10 samples with batch_size = 10\n\nas you can see I can predict 10 samples at about the same speed as 1 sample. The change is also very minimal though noticeable if I decrease parameter size by 100X but I'd rather not change parameter size by that much anyway. In addition, the predict function is very slow the first time run through (~0.2s) though I don't think that's the problem here since the same model is predicting multiple times. \nI wonder if there is some workaround because clearly the 10 samples can be evaluated very quickly, all I want to be able to do is predict the samples at different times and not all at once since I need to update the Tree Search before making a new prediction. Perhaps should I work with tensorflow instead?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":2440,"Q_Id":56052206,"Users Score":1,"Answer":"The batch size controls parallelism when predicting, so it is expected that increasing the batch size will have better performance, as you can use more cores and use GPU more efficiently.\nYou cannot really workaround, there is nothing really to work around, using a batch size of one is the worst case for performance. Maybe you should look into a smaller network that is faster to predict, or predict on the CPU if your experiments are done in a GPU, to minimize overhead due to transfer.\nDon't forget that model.predict does a full forward pass of the network, so its speed completely depends on the network architecture.","Q_Score":2,"Tags":"python,performance,keras","A_Id":56053083,"CreationDate":"2019-05-09T04:27:00.000","Title":"Keras tf backend predict speed slow for batch size of 1","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to convert a numpy ndarray with a shape of (2200,) to numpy ndarray with a shape of (2200,250,250,1). every single row contains an image (shape: 250,250,1)\nThis is my object:\ntype(x_train_left) prints numpy.ndarray\nx_train_left.shape prints (2200,)\ntype(x_train_left[0]) prints numpy.ndarray\nx_train_left[0].shape prints (250, 250, 1)\nBut for some reason when i try to reshape x_train_left to (2200,250,250,1)  i get the following error:\nValueError: cannot reshape array of size 2200 into shape (2200,250,250,1)\nThank for any help, iv'e searched for duplicated subjects, but they all have different problems.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1841,"Q_Id":56055571,"Users Score":0,"Answer":"I found the very simple solution:\nnp.stack(x_train_left)\nand then when i try:\nx_train_left.shape prints (2200, 250, 250, 1)","Q_Score":1,"Tags":"python,numpy,reshape,shapes,numpy-ndarray","A_Id":56055689,"CreationDate":"2019-05-09T08:46:00.000","Title":"cannot reshape array of size (x,) into shape (x,y,z,1)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have several trained Keras models, weights stored in h5 files using keras.models.save_model. They do not have the same architecture.\nMy goal is to load all of them in separate processes and be able to predict. I currently try doing this using a class which stores a TensorFlow session and graph object. I then use with statements at loading time and prediction time to prevent interference with any global variables.\nI can create my (empty) Keras Sequential model without problems, but when I call its load_weights function, the process just freezes.\nSetups with Graph and Session objects I tried:\n\nspecific Graph and Session -> process freezes on load_weights\nspecific Graph only -> \"TypeError: Cannot interpret feed_dict key as Tensor\"\nspecific Session only -> process freezes on load_weights\n\nI have been through most of the related answers on SO but have not been able to find a solution or even someone with the same problem.\nThanks for your help!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":713,"Q_Id":56055769,"Users Score":0,"Answer":"I still don't know the exact cause of the problem. However, I found out that my main process was loading a keras model and removing that solved my problem. I can now have multiple models running in parallel.","Q_Score":0,"Tags":"python,tensorflow,keras,multiprocessing,python-multiprocessing","A_Id":56058663,"CreationDate":"2019-05-09T08:57:00.000","Title":"Load multiple Keras models in different processes","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a problem I am trying to solve:\n- imbalanced dataset with 2 classes\n- one class dwarfs the other one (923 vs 38)\n- f1_macro score when the dataset is used as-is to train RandomForestClassifier stays for TRAIN and TEST in 0.6 - 0.65 range\nWhile doing research on the topic yesterday, I educated myself in resampling and especially SMOTE algorithm. It seems to have worked wonders for my TRAIN score, as after balancing the dataset with them, my score went from ~0.6 up to ~0.97. The way that I have applied it was as follows:\n\nI have splited away my TEST set away from the rest of data in the beginning (10% of the whole data)\nI have applied SMOTE on TRAIN set only (class balance 618 vs 618)\nI have trained a RandomForestClassifier on TRAIN set, and achieved f1_macro = 0.97\nwhen testing with TEST set, f1_macro score remained in ~0.6 - 0.65 range\n\nWhat I would assume happened, is that the holdout data in TEST set held observations, which were vastly different from pre-SMOTE observations of the minority class in TRAIN set, which ended up teaching the model to recognize cases in TRAIN set really well, but threw the model off-balance with these few outliers in the TEST set.\nWhat are the common strategies to deal with this problem? Common sense would dictate that I should try and capture a very representative sample of minority class in the TRAIN set, but I do not think that sklearn has any automated tools which allow that to happen?","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":162,"Q_Id":56069657,"Users Score":2,"Answer":"Your assumption is correct. Your machine learning model is basically overfitting on your training data which has the same pattern repeated for one class and thus, the model learns that pattern and misses the rest of the patterns, that is there in test data. This means that the model will not perform well in the wild world.\nIf SMOTE is not working, you can experiment by testing different machine learning models. Random forest generally performs well on this type of datasets, so try to tune your rf model by pruning it or tuning the hyperparameters. Another way is to assign the class weights when training the model. You can also try penalized models which imposes an additional cost on the model when the misclassify the minority class.\nYou can also try undersampling since you have already tested oversampling. But most probably your undersampling will also suffer from the same problem. Please try simple oversampling as well instead of SMOTE to see how your results change.\nAnother more advanced method that you should experiment is batching. Take all of your minority class and an equal number of entries from the majority class and train a model. Keep doing this for all the batches of your majority class and in the end you will have multiple machine learning models, which you can then use together to vote.","Q_Score":0,"Tags":"python,scikit-learn,dataset,resampling,oversampling","A_Id":56082079,"CreationDate":"2019-05-10T01:43:00.000","Title":"ML with imbalanced binary dataset","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to find the minimum of a function which is piecewise linear, convex and differentiable at all but a finite number of points. What scipy.optimize.minimize method is appropriate to find a fast solution to my problem?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":146,"Q_Id":56090155,"Users Score":2,"Answer":"If the function is piecewise linear and convex, the minimum must be at one of the points where the linear pieces are connected. There is no need for a derivative, you should be able to use a binary search.","Q_Score":0,"Tags":"python,scipy","A_Id":56090218,"CreationDate":"2019-05-11T12:02:00.000","Title":"Minimize a piecewise linear, convex function with scipy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Suppose I have 2 dataframe:\nDF1:\nCol1 | Col2 | Col3\nXCN000370\/17-18C | XCN0003711718C | 0003971718\nDF2\nCol1 | Col2 | Col3\nXCN0003711718C | XCN0003711718C | 0003971718\nI want them to merge like this:\n\nFirst Match Col1 (DF1) and Col1 (DF2)\nIn Remaining Unmatched, Match Col1 (DF1) with Col2 (DF2)\nIn remaining Unmatched, Match Col1 (DF1) with Col3 (DF2)\n\nNow repeat this by exchanging DF1 and DF2 with remaining unmatched\n\nIn Remaining Unmatched Match Col1 (DF2) and Col1 (DF1)\nIn Remaining Unmatched, Match Col1 (DF2) with Col2 (DF1)\nIn remaining Unmatched, Match Col1 (DF2) with Col3 (DF1)\n\nAny ideas?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":147,"Q_Id":56090979,"Users Score":0,"Answer":"I don't think that there is one line code to do this. So follow the steps.\n1) First, create a list:\ndfs = []\n2) Merge for each condition on dataframe:\ndfs.append(pd.merge(df1,df2,left_on='col1',right_on='col1',how='outer')).dropna()\ndfs.append(pd.merge(df1,df2,left_on='col1',right_on='col2',how='outer')).dropna()\ndfs.append(pd.merge(df1,df2,left_on='col1',right_on='col3',how='outer')).dropna()\n^ repeat\n3) Now concatenate them:\npd.concat(dfs)","Q_Score":0,"Tags":"python,pandas,dataframe","A_Id":56091221,"CreationDate":"2019-05-11T13:53:00.000","Title":"Pandas Merge Dataframes Sequentially on Conditions","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How do I get a list of row lables in pandas?\nI have a table with column labels and row labels. To return the column lables I use the dataframe \"column\" attribute.\nIt is possible to return the list of column labels with the attribute columns, but i couldn't find similiar attributes for rows.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":10189,"Q_Id":56092914,"Users Score":4,"Answer":"This should work. df.index.values\nThis returns index in the form of numpy array numpy.ndarray, run this type(df.index.values) to check.","Q_Score":3,"Tags":"python-3.x,pandas","A_Id":56092947,"CreationDate":"2019-05-11T17:53:00.000","Title":"How to get list of rows of pandas dataframe in python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I used gridsearchcv to determine which hyperparameters in the mlpclassifier can make the accuracy from my neural network higher. I figured out that the amount of layers and nodes makes a difference but I'm trying to figure out which other configurations can make a difference in accuracy (F1 score actualy). But from my experience it lookes like parameters like \"activation\", \"learning_rate\", \"solver\" don't really change anything. \nI need to do a research on which other hyperparameters can make a difference in the accuracy from predictions via the neural network. \nDoes someone have some tips\/ideas on which parameters different from the amount of layers \/ nodes that can make a difference in the accuracy from my neural network predictions?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":490,"Q_Id":56094779,"Users Score":1,"Answer":"It all depends on your dataset. Neural network are not magical tools that can learn everything and also they require a lot of data compared to traditional machine learning models. In case of MLP, making a model extremely complex by adding a lot of layers is never a good idea as it makes the model more complex, slow and can lead to overfitting as well. Learning rate is an important factor as it is used to find the best solution for the model. A model makes mistakes and learns from it and the speed of learning is controlled by learning rate. If learning rate is too small, your model will take a long time to reach the best possible stage but if it is too high the model might just skip the best stage. The choice of activation function is again dependent on the use case and the data but for simpler datasets, activation function will not make a huge differnece.\nIn traditional deep learning models, a neural network is build up of several layers which might not always be dense. All the layers in MLP as dense i.e. feed forward. To improve your model, you can try a combination of dense layers along with cnn, rnn, lstm, gru or other layers. Which layer to use depends completely on your dataset. If you are using a very simple dataset for a school project, then experiment with traditional machine learning methods like random forest as you might get better results.\nIf you want to stick to neural nets, read about other types of layers, dropout, regularization, pooling, etc.","Q_Score":0,"Tags":"python,machine-learning,scikit-learn,neural-network,classification","A_Id":56096067,"CreationDate":"2019-05-11T22:33:00.000","Title":"How to evaluate\/improve the accuracy from the prediction from a neural network with an unbalanced dataset?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have created a simple feed forward Neural Network library in Java - and I need a benchmark to compare and troubleshoot my library. \nComputer specs: \n\nAMD Ryzen 7 2700X Eight-Core Processor \nRAM 16.0 GB\nWINDOWS 10 OS  \nJVM args: -Xms1024m -Xmx8192m\n\nNote that I am not using a GPU. \nPlease list the following specs:\n\nComputer specs?\nGPU or CPU (CPU is proffered but GPU is good info) \nNumber of inputs 784 (this is fixed)\nFor each layer: \n\n\nHow many nodes? \nWhat activation function?\n\nOutput layer:\n\n\nHow many nodes? (10 if classification or 1 as regression)\nWhat activation function?\n\nWhat loss function?\nWhat gradient descent algorithm (i.e.: vanilla) \nWhat batch size?\nHow many epochs? (not iterations)\nAnd finally, what is the training time and accuracy? \n\nThank you so much\nEdit \nJust to give an idea of what I am dealing with. I created a network with \n\n784 input nodes\n784 in hidden layer 0\n256 in hidden layer 1\n128 in hidden layer 2\n1 output nodes\nmini-batch size 5\n16 threads for backprop\nAnd it has been training for ~8 hours and has only completed 694 iterations - that is not even 20% of one epoch. \n\nHow is this done in minutes as I've seen some claims?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1145,"Q_Id":56095288,"Users Score":0,"Answer":"am not really clear about the benchmark you're looking for, there is it performance from training perspective, or accuracy? for accuracy, there are some tools that can do the comparison between the predictions and actuals so you can measure the performance","Q_Score":0,"Tags":"python,tensorflow,neural-network,mnist","A_Id":63011554,"CreationDate":"2019-05-12T00:28:00.000","Title":"MNIST training time in CPU","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm currently learning gensim doc2model in Python3.6 to see similarity between sentences.\nI created a model but it returns KeyError: \"word 'WORD' not in vocabulary\" when I input a word which obviously exists in the training dataset, to find a similar word\/sentence.\nDoes it automatically skip some words not very important to define sentences? or is that simply a bug or something?\nVery appreciated if I could have any way out to cover all the appearing words in the dataset. thanks.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":358,"Q_Id":56106821,"Users Score":0,"Answer":"If a word you expected to be learned in the model isn't in the model, the most likely causes are:\n\nit wasn't really there, in the version the model saw, perhaps because your tokenization\/preprocessing is broken. Enable logging at INFO level, and examine your corpus as presented to the model, to ensure it's tokenized as intended\nit wasn't part of the surviving vocabulary after the 1st vocabulary-survey of the corpus. The default min_count=5 discards words with fewer than 5 occurrences, as such words both fail to get good vectors for themselves, and effectively serve as 'noise' interfering with the improvement of other vectors. \n\nYou can set min_count=1 to retain all words, but it's more likely to hurt than help your overall vector quality. Word2Vec & Doc2Vec require large, varied corpuses \u2013 if you want a good vector for a word, find more diverse examples of its usage in an expanded corpus. \n(Also note: one of the simple & fast Doc2Vec modes, that's also often a top-performer, especially on shorter texts, is plain PV-DBOW mode: dm=0. This mode will allocate\/randomly-initialize word-vectors, but then ignores them for training, only training the doc-vectors. If you use that mode, you can still request word-vectors from the model at the end \u2013 but they'll just be random nonsense.)","Q_Score":0,"Tags":"python,gensim,doc2vec","A_Id":56117031,"CreationDate":"2019-05-13T06:29:00.000","Title":"gensim doc2vec Model doesn't learn some words","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to be able to find which of my samples were wrongly classified by KNN, or which weren't classified at all. \nI have used sckit-learn to run KNN. I have a df that has ~280000 samples split into four groups, I have 13 features by which to classify by. My precsion per group ranges from 0.30-0.90. \nI expect the output to say which group each sample belongs to and which group it was classified into. \nthanks!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":43,"Q_Id":56111669,"Users Score":0,"Answer":"So as I'm understanding the question right you have the true group classification for your data.\nIn that case you can predict your whole dataset with your trained model and identify the outliers.","Q_Score":0,"Tags":"python,machine-learning,knn,nearest-neighbor","A_Id":56430302,"CreationDate":"2019-05-13T11:47:00.000","Title":"Identify what group KNN classified a sample in","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to solve an equation of the form yB = c, where y is my unknown (possibly a matrix). However the B matrix is not well conditioned, and I would like to have a method similar to numpy.linalg.solve in order to maintain the numerical accuracy of the solution.  \nI have tried to simply use the inverse of B, with numpy.linalg.inv, to find the solution y = cB^-1 as well as using the pseudo-inverse (numpy.linalg.pinv), but they prooved to be not accurate enough...\nI have also looked into the QR decomposition, since numpy provides the method for it, in order to adapt it to the right inverse case, but here I struggle with the algebra.\nIs there an accurate way to solve this equation ? Or is there an equivalent to numpy.linalg.solve for the right inverse ?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":104,"Q_Id":56113772,"Users Score":1,"Answer":"You can transpose the equation and then use linalg.solve.","Q_Score":2,"Tags":"python,numpy","A_Id":56113906,"CreationDate":"2019-05-13T13:48:00.000","Title":"Is there a way to solve yB = c without computing the right inverse?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a graph with 240k nodes and 550k edges with five attributes per node coming out of an autoencoder from a sparse dataset. I'm looking to partition the graph into n clusters, such that intra-partition attribute similarity is maximized, the partitions are connected, and the sum of one of the attributes doesn't exceed a threshold for any given cluster. \nI've tried poking around with an autoencoder but had issues making a loss function that would get the results I needed. I've also looked at heirarchical clustering with connectivity constraints but can't find a way to enforce my sum constraint optimally. Same issue with community detection algorithms on graphs like Louvain. \nIf anyone knows of any approaches to solving this I'd love to hear it, ideally something implemented in Python already but I can probably implement whatever algorithm I need should it not be. Thanks!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":350,"Q_Id":56127111,"Users Score":0,"Answer":"First of all, the problem is most likely NP-hard, so the best you can do is some greedy optimization. It will definitely help to first break the graph into subsets that cannot be connected ever (remove links of nodes that are not similar enough, then compute the connected components). Then for each component (which hopefully are much smaller than 250k, otherwise tough luck!) run a classic optimizer that allows you to specify the cost function. It is probably a good idea to use an integer linear program, and consider the Lagrange dual version of the problem.","Q_Score":2,"Tags":"python,constraints,cluster-analysis,graph-theory","A_Id":56161443,"CreationDate":"2019-05-14T09:28:00.000","Title":"Algorithms for constrained clustering on attributed graphs with some cluster-level constraints on their attributes","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to extract the names of Institutes from the given data. Institues names will look similar ( Anna University, Mashsa Institute of Techology , Banglore School of Engineering, Model Engineering College). It will be a lot of similar data. I want to extract these from text. How can I create a model to extract these names from data(I need to extract from resumes-C.V)\nI tried adding new NER in spacy but even after training, the loss doesnt decrease and predictions are wrong. That is why I want to make a new model just for this.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":472,"Q_Id":56127781,"Users Score":0,"Answer":"The problem you face is solved by specialized text search and text analysis tools. Using phonetic analysis and indexes.\nOne of the popular text analysis tools is Elasticsearch.\nYou index your documents and search them, using REST api.\nGoogle also provide such tools for text analysis and indexing.\nAlso modern RDBMS tools like Oracle and PostgresSQL provide such features.\nGood luck.","Q_Score":1,"Tags":"python,nlp,information-extraction","A_Id":56189877,"CreationDate":"2019-05-14T10:00:00.000","Title":"Extracting a particular type of data from unstructured text namely Institutes","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Currently our system is in live proving phase. So, we need to check whether the set of tables populated in production are matching with the tables populated in sandbox (test). At the moment we have written a query for each table comparison and then run it in sql client to check it. There will be few more tables to check in future. I thought of automating the process in python by supplying the table names to a function which can then load the two tables in dataframes and then do a comparison which could highlight the differences.\nSome of the tables have 2.7 millions rows for a day and are wide having 400 columns. When I tried to load the data (2.7 m rows * 400 columns) into dataframe, I get an error as it runs out of memory as I run my query in Jupyter where I have only 20 GB limit. what are the options here? Is Pandas dataframes only way to compare this large dataset? or are there any other library to achieve the same?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":218,"Q_Id":56129032,"Users Score":0,"Answer":"For handling this kind of data I would recommend using something like Hadoop rather than pandas\/python.  This isn't much of an answer but I can't comment yet.","Q_Score":0,"Tags":"python,python-3.x,pandas,pandasql","A_Id":56129689,"CreationDate":"2019-05-14T11:08:00.000","Title":"Python comparing millions of rows and hundreds of columns between two tables from relational DB","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to use Python to call my Modelica models using Dymola and BuildingsPy. I read the BuildingsPy tutorial, I understand in general how it goes, but I admit that the examples are not too intuitive for me. Could someone help me with a simple example using for example an existing model in the Modelica library. \nThank you","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":404,"Q_Id":56138688,"Users Score":0,"Answer":"Thank you for your explanation, it's really clear and it helped me a lot. I tested one of my models but by launching the code, dymola opens but it does not load the library or my model exists. That's the message I got: \nError: Simulation failed in 'C:\\Temp\\tmp-simulator-wwuvls\\BEE'\n   Exception: File C:\\Temp\\tmp-simulator-wwuvls\\BEE\\simulator.log does not exist.\n   You need to delete the directory manually.","Q_Score":1,"Tags":"python,dymola","A_Id":56263416,"CreationDate":"2019-05-14T21:14:00.000","Title":"Simple example on using BuildingsPy with Dymola","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a python program that takes in a list of objects of different types, and for each type, the program will output a dictionary of key\/value attributes where the key is some property of the given object's type, and value is its computed result.\nTo make it more concrete, my program takes in a list of 2000 objects, of 3 unique types: Cars, Planes, Ships. And for a car, it produces: \n\n{\"ID\": , \"Horsepower\":120.5, \"Fuel Efficiency\": 19,\n  \"Turning Radius\":20, \"Weight\":500}\n\nFor ship, it's \n\n{\"ID\": , \"Displacement\": 1000.5, \"Fuel Efficiency\": 8,\n  \"Weight\": 2000}\n\nFor plane, it's \n\n{\"ID\": , \"Engine Size\": 200.5, \"Fuel Efficiency\": 8,\n  \"Weight\": 2000}\n\nSo you can see, for each type, the number and content of its dictionary output is different, while they may all share some common fields such as \"ID\" (unique across different objects), \"Weight\", etc.\nAnd tomorrow there could be a new type that needs to be supported by the program with a similar output structure.\nThe question is what is the best way to persist these outputs, for easy querying\/aggregation later on. Such as: give me all planes with a weight >= 1000, or give me all weights of all cars whose horse power is between 200 and 300.\nLets say we use pandas dataframe as our storage format, I am faced with 2 choices:\nTake a union of all keys of all product types, and create a pandas df with those keys as column, and each row represents each product's output, and it may have None in a given column depending on product. This essentially creates a sparse matrix. And the column names can grow because new product types can have new keys as outputs.\nCreate a pandas df with 3 columns: ID, Key,  Value.\nWhich one do you recommend or is there an obvious third option I'm missing?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":94,"Q_Id":56141069,"Users Score":0,"Answer":"I would recommend persisting as json using pandas and reloading again as needed with pandas. Pandas will make the reading and writing really easy for you. This allows you to have the superset of columns in the dataframe with nulls in the spots that are missing data. \nThis saves you from needing to do a key value pair storage option as well. \nPlus, it looks like your data is already in a format close to json. Once you have it loaded back into the dataframe, querying as you need to will also be simple.","Q_Score":0,"Tags":"python,pandas","A_Id":56141111,"CreationDate":"2019-05-15T02:42:00.000","Title":"How to persist a python dictionary?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a python program that takes in a list of objects of different types, and for each type, the program will output a dictionary of key\/value attributes where the key is some property of the given object's type, and value is its computed result.\nTo make it more concrete, my program takes in a list of 2000 objects, of 3 unique types: Cars, Planes, Ships. And for a car, it produces: \n\n{\"ID\": , \"Horsepower\":120.5, \"Fuel Efficiency\": 19,\n  \"Turning Radius\":20, \"Weight\":500}\n\nFor ship, it's \n\n{\"ID\": , \"Displacement\": 1000.5, \"Fuel Efficiency\": 8,\n  \"Weight\": 2000}\n\nFor plane, it's \n\n{\"ID\": , \"Engine Size\": 200.5, \"Fuel Efficiency\": 8,\n  \"Weight\": 2000}\n\nSo you can see, for each type, the number and content of its dictionary output is different, while they may all share some common fields such as \"ID\" (unique across different objects), \"Weight\", etc.\nAnd tomorrow there could be a new type that needs to be supported by the program with a similar output structure.\nThe question is what is the best way to persist these outputs, for easy querying\/aggregation later on. Such as: give me all planes with a weight >= 1000, or give me all weights of all cars whose horse power is between 200 and 300.\nLets say we use pandas dataframe as our storage format, I am faced with 2 choices:\nTake a union of all keys of all product types, and create a pandas df with those keys as column, and each row represents each product's output, and it may have None in a given column depending on product. This essentially creates a sparse matrix. And the column names can grow because new product types can have new keys as outputs.\nCreate a pandas df with 3 columns: ID, Key,  Value.\nWhich one do you recommend or is there an obvious third option I'm missing?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":94,"Q_Id":56141069,"Users Score":0,"Answer":"json.dumps and json.loads are your friends. To translate your structure to be persisted, dumps creates a unique string that can be written to any file-like object and loads can reload it from a string-like object. Hope that helps!","Q_Score":0,"Tags":"python,pandas","A_Id":56141206,"CreationDate":"2019-05-15T02:42:00.000","Title":"How to persist a python dictionary?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am building a logistic regression classificator.\nI start form a set of 500.000 record and I want to use only a sample of them.\nwhat do you recommend:\n1) scaling the population and then sampling \n2) scaling the sample \n3) scaling just the X_TRAIN split of the sample?\nand why?\nmy considerations are:\n1) this may have sense if the sample is representative of the population (should I test it?) \n2) this is not convincing because I would go for several sample in order to see the generalization level of the classificator, and having everytime a slightly different scaler does not sounds good. plus it will bias the X_train, X_test split\n3) This will not bias the X_train, X_test split but has the same doubt of the point2)\nWhat  would you recommend and why?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":48,"Q_Id":56148094,"Users Score":1,"Answer":"Wonderful question. I had similar questions in my mind when I had started out few years ago. Let me try and give my two cents on this.\nI suggest to go with creating a scaler for scaling X_train, store the scaler and see if use it to transform X_test. According to centrality theorem, if you have done random sampling, you should have a mean and variance similar to the population attributes. The scaler works based on these two parameters in most cases. If it is representative of the population parameters, as long as the test data is coming from same population the scaler should work. If it is not working, you need more samples for training or another sampling attempt to get X_train representative of population. \nBy doing this, you are sure the model is going to work on new samples also as long as it is generated by same process. After all, the model is not built to be tested it is meant to be in production doing some useful work.\nMy recommendation would be to go with 3) scaling X_train and use the scaler to transform X_test.","Q_Score":1,"Tags":"python,data-science,sampling","A_Id":56158265,"CreationDate":"2019-05-15T11:17:00.000","Title":"classificator using sample of a population: scaling the population and then sampling \/ scaling the sample \/ scaling the X_TRAIN split of the sample?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to Synthia dataset. I would like to read the label file from this datset. I expect to have one channel matrix with size of my RGB image, but when I load the data I got 3x760x1280 and it is full of zeros.\nI tried to read as belows:\nlabel = np.asarray(imread(label_path)) \nCan anyone help to read these labels file correctly?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":335,"Q_Id":56148891,"Users Score":2,"Answer":"I found the right way to read it as below:\nlabel = np.asarray(imageio.imread(label_path, format='PNG-FI'))[:,:,0]","Q_Score":0,"Tags":"python,deep-learning,dataset,semantic-segmentation","A_Id":56169770,"CreationDate":"2019-05-15T12:00:00.000","Title":"How to read the label(annotation) file from Synthia Dataset?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am running pyspark scripts in jupyter notebook but the kernel is not starting. upon selecting pyspark from the dropdown the kernel loads and remains busy for some time and then shows \"no kernel\".\nCan someone help me?\nNote: upon running \"$Jupyter kernelspec list\" i can see pyspark kernel in the list.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":113,"Q_Id":56161339,"Users Score":0,"Answer":"The issue was resolved only by re configuring the Jupyter notebook.","Q_Score":0,"Tags":"pyspark,jupyter-notebook,kernel,ipython","A_Id":56224394,"CreationDate":"2019-05-16T05:21:00.000","Title":"Jupyter Notebook is showing No pyspark kernel upon startup","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"is there any way to set seed on train_test_split on python sklearn. I have set the parameter random_state to an integer, but I still can not reproduce the result.\nThanks in advance.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":12708,"Q_Id":56166130,"Users Score":0,"Answer":"simply in train_test_split, specify the parameter random_state=some_number_you_wan to use, like random_state=42","Q_Score":10,"Tags":"python-3.x,scikit-learn,jupyter-notebook,train-test-split","A_Id":71233630,"CreationDate":"2019-05-16T10:12:00.000","Title":"Setting seed on train_test_split sklearn python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to cluster a data set containing mixed data(nominal and ordinal) using k_prototype clustering based on Huang, Z.: Clustering large data sets with mixed numeric and categorical values.\nmy question is how to find the optimal number of clusters?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":121,"Q_Id":56166439,"Users Score":0,"Answer":"There is not one optimal number of clusters. But dozens. Every heuristic will suggest a different \"optimal\" number for another poorly defined notion of what is \"optimal\" that likely has no relevancy for the problem that you are trying to solve in the first place.\nRather than being overly concerned with \"optimality\", rather explore and experiment more. Study what you are actually trying to achieve, and how to get this into mathematical form to be able to compute what is solving your problem, and what is solving someone else's...","Q_Score":0,"Tags":"python,cluster-analysis","A_Id":56202424,"CreationDate":"2019-05-16T10:29:00.000","Title":"the clustering of mixed data using python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a fixed number of partitions of a topic. Producers produce data at varying rate in different hours of the day. \nI want to add consumers dynamically based on hours of the day for the processing so that I can process records as fast as I can.\nFor example I have 10 partitions of a topic. I want to deploy 5 consumers for non peak hours and 20 consumers for peak hours.\nMy problem is that when I will have 20 consumers, each consumer will be receiving duplicate records, which I want to avoid. I want to process unique records only to speed-up records processing.\nIs there any mechanism to do this?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":210,"Q_Id":56174516,"Users Score":0,"Answer":"If you have N partitions, then you can have up to N consumers within the same consumer group each of which reading from a single partition. When you have less consumers than partitions, then some of the consumers will read from more than one partition. Also, if you have more consumers than partitions then some of the consumers will be inactive and will receive no messages at all.\nTherefore, if you want to kick off 20 consumers, you need to increase the number of partitions of the topic to -at least- 20 otherwise, 10 of your consumers will be inactive. \nWith regards to the duplicates that you've mentioned, if all of your consumers belong to the same group, then each message will be consumed only once. \nTo summarise, \n\nIncrease the number of partitions of your topic to 20.\nCreate the mechanism that will be creating and killing consumers based on peak\/off-peak hours and make sure that when you kick of a consumer, it belongs to the existing consumer group so that the messages are consumed only once.","Q_Score":1,"Tags":"java,python,apache-kafka,kafka-consumer-api","A_Id":56175288,"CreationDate":"2019-05-16T18:21:00.000","Title":"How to scale Kafka stream processing dynamically?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"We typically use powerpoint to facilitate our experiments. We use \"sections\" in powerpoint to keep groups of slides together for each experimental task. Moving the sections to counterbalance the task order of the experiment has been a lot of work! \nI thought we might be able to predefine a counterbalance order (using a string of numbers representing the order of the sections) in a CSV which could be read from python. Then using python-pptx move the sections and save the file for each order. The problem I am having is understanding how to read sections from the python-pptx. If anyone has a better solution than python please let me know. \nThanks for your help!","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2247,"Q_Id":56175678,"Users Score":0,"Answer":"Would it be feasible - if all we're doing is reordering - to read the XML and rewrite it with the slide elements permuted?\nFurther - for the \"delete\" case - is it feasible to simply delete a slide element in the XML? (I realise this could leave dangling objects such as images in the file.)\nThe process of extracting the XML and rewriting it to a copy of the file is probably not too onerous.","Q_Score":1,"Tags":"python,powerpoint,python-pptx","A_Id":57013231,"CreationDate":"2019-05-16T19:49:00.000","Title":"Rearrange powerpoint slides automatically using python-pptx","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"If I am trying to cluster my data using DBSCAN, is there a way to assign a maximum number of clusters? I know I can set the minimum distance between points to be considered a cluster, but my data changes case by case and I would prefer to not allow more than 4 clusters. Any suggestions?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":281,"Q_Id":56175928,"Users Score":1,"Answer":"Not with DBSCAN itself. Connected components are connected components, there is no ambiguity at this point.\nYou could write your own rules to extract the X most significant costs from an OPTICS plot though. OPTICS is the more variable formulation of DBSCAN.","Q_Score":0,"Tags":"python,cluster-analysis,dbscan","A_Id":56178539,"CreationDate":"2019-05-16T20:10:00.000","Title":"Is there a way to assign a maximum number of clusters using DBSCAN?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a vector A = [0,1,2,3,0,0,1,1,2,2,3,3]. I need to sort it in an increasing matter such that it is listed in an ordered fashion and from that extract the argsort. To better explain this I need to sort A to such that it returns B = [0,4,5,1,6,7,2,8,9,3,10,11]. However, when I use pyotrch's torch.argsort(A) it returns B = [4,5,0,1,6,7,2,8,9,3,10,11]. \nI'm assuming the algorithm that does so cannot be controlled on my end. Is there anyway to approach this without introducing for loops? Such operation are part of my NN model and will cause performance issues if not done efficiently. Thanks!","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":2541,"Q_Id":56176439,"Users Score":1,"Answer":"Here is one way:\n\nsort the numpy array using numpy.argsort()\nconvert the result into tensor using torch.from_numpy()\nimport torch\n import numpy as np\n A = [0,1,2,3,0,0,1,1,2,2,3,3]\n x = np.array(A)\n y = torch.from_numpy(np.argsort(x, kind='mergesort'))\n print(y)","Q_Score":3,"Tags":"python,sorting,machine-learning,pytorch,tensor","A_Id":56179295,"CreationDate":"2019-05-16T20:52:00.000","Title":"Pytorch argsort ordered, with duplicate elements in the tensor","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to run my model on multiple gpus using DataParallel by setting model = nn.DataParallel(model).cuda(), but everytime getting this error - \n\nRuntimeError: chunk expects at least a 1-dimensional tensor (chunk at\n  \/pytorch\/aten\/src\/ATen\/native\/TensorShape.cpp:184).\n\nMy code is correct. Does anyone know what's wrong? \nI have tried setting device_ids=[0,1] parameter and also CUDA_VISIBLE_DEVICES on the terminal. Also tried different batch sizes.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1864,"Q_Id":56177305,"Users Score":1,"Answer":"To identify the problem, you should check the shape of your input data for each mini-batch. The documentation says, nn.DataParallel splits the input tensor in dim0 and sends each chunk to the specified GPUs. From the error message, it seems you are trying to pass a 0-dimensional tensor.\nOne possible reason can be if you have a mini-batch with n examples and you are running your program on more than n GPUs, then you will get this error.\nLet's consider the following scenario.\n\nTotal training examples = 161, Batch size = 80, total mini-batches = 3\nNumber of GPUs specified for DataParallel = 3\n\nNow, in the above scenario, in the 3rd mini-batch, there will be 1 example. So, it is not possible to send chunks to all the specific GPUs and you will receive the error message. So, please check if you are not a victim of this issue.","Q_Score":0,"Tags":"python,pytorch,multi-gpu","A_Id":56195251,"CreationDate":"2019-05-16T22:21:00.000","Title":"DataParallel multi-gpu RuntimeError: chunk expects at least a 1-dimensional tensor","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I was training a neural network with images of an eye that are shaped 36x60. So I can only predict the result using a 36x60 image? But in my application I have a video stream, this stream is divided into frames, for each frame 68 points of landmarks are predicted. In the eye range, I can select the eye point, and using the 'boundingrect' function from OpenCV, it is very easy to get a cropped image. But this image has no form 36x60. What is the correct way to get 36x60 data that can be used for forecasting? Or how to use a neural network for data of another form?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":81,"Q_Id":56181395,"Users Score":2,"Answer":"Neural networks (insofar as I've encountered) have a fixed input shape, freedom permitted only to batch size. This (probably) goes for every amazing neural network you've ever seen. Don't be too afraid of reshaping your image with off-the-shelf sampling to the network's expected input size. Robust computer-vision networks are generally trained on augmented data; randomly scaled, skewed, and otherwise transformed in order to---among other things---broaden the network's ability to handle this unavoidable scaling situation.\nThere are caveats, of course. An input for prediction should be as similar to the dataset it was trained on as possible, which is to say that a model should be applied to the data for which it was designed. For example, consider an object detection network made for satellite applications. If that same network is then applied to drone imagery, the relative size of objects may be substantially larger than the objects for which the network (specifically its anchor-box sizes) was designed.  \nTl;dr: Assuming you're using the right network for the job, don't be afraid to scale your images\/frames to fit the network's inputs.","Q_Score":5,"Tags":"python-3.x,opencv,keras,neural-network,data-science","A_Id":56182355,"CreationDate":"2019-05-17T07:14:00.000","Title":"How to predict different data via neural network, which is trained on the data with 36x60 size?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataset that I would like to order by date but second order with 'pass' value lowest inside of highest. The reason I don't have any code is because, I just have no idea where to begin.\ndataframe input:\n\nindex   date    pass\n0   11\/14\/2014  1\n1   3\/13\/2015   1\n2   3\/20\/2015   1\n3   5\/1\/2015    2\n4   5\/1\/2015    1\n5   5\/22\/2015   3\n6   5\/22\/2015   1\n7   5\/22\/2015   2\n8   9\/25\/2015   1\n9   9\/25\/2015   2\n10  9\/25\/2015   3\n11  12\/4\/2015   2\n12  12\/4\/2015   1\n13  2\/12\/2016   2\n14  2\/12\/2016   1\n15  5\/27\/2016   1\n16  6\/10\/2016   1\n17  9\/23\/2016   1\n18  12\/23\/2016  1\n19  11\/24\/2017  1\n20  12\/29\/2017  1\n21  1\/26\/2018   2\n22  1\/26\/2018   1\n23  2\/9\/2018    1\n24  3\/16\/2018   1\n25  4\/6\/2018    2\n26  4\/6\/2018    1\n27  6\/15\/2018   1\n28  6\/15\/2018   2\n29  10\/26\/2018  1\n30  11\/30\/2018  1\n31  12\/21\/2018  1\n\n\n** Expected Output **\nindex   date    pass\n0   11\/14\/2014  1\n1   3\/13\/2015   1\n2   3\/20\/2015   1\n3   5\/1\/2015    2\n4   5\/1\/2015    1\n\n5   5\/22\/2015   3\n6   5\/22\/2015   2\n7   5\/22\/2015   1\n\n8   9\/25\/2015   3\n9   9\/25\/2015   2\n10  9\/25\/2015   1\n11  12\/4\/2015   2\n12  12\/4\/2015   1\n13  2\/12\/2016   2\n14  2\/12\/2016   1\n15  5\/27\/2016   1\n16  6\/10\/2016   1\n17  9\/23\/2016   1\n18  12\/23\/2016  1\n19  11\/24\/2017  1\n20  12\/29\/2017  1\n\n21  1\/26\/2018   1\n22  1\/26\/2018   2\n\n23  2\/9\/2018    1\n24  3\/16\/2018   1\n\n25  4\/6\/2018    1\n26  4\/6\/2018    2\n\n27  6\/15\/2018   1\n28  6\/15\/2018   2\n29  10\/26\/2018  1\n30  11\/30\/2018  1\n31  12\/21\/2018  1\n\nI have spaced out the results that would change. index 5,6,7 and 21,21 and 25,26.  So all the bigger pass numbers should be inside the lower pass number if the dates are same.\nSo if you look at INDEX 5,6,7 the pass for it is changed to 3,2,1 and if you look at INDEX 25,26 the pass is changed to 1,2. Hope you understand.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":52,"Q_Id":56188801,"Users Score":0,"Answer":"Order first by pass then do it by date. This way you will be sure to have your df the way you want it","Q_Score":0,"Tags":"python,pandas,dataframe","A_Id":56188894,"CreationDate":"2019-05-17T14:46:00.000","Title":"Order data set using pandas dataframe based on lowest value inside","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am attempting a rather large unsupervised learning project and am not sure how to properly utilize word2vec. We're trying to cluster groups of customers based on some stats about them and what actions they take on our website. Someone recommended I use word2vec and treat each action a user takes as a word in a \"sentence\". The reason this step is necessary is because a single customer can create multiple rows in the database (roughly same stats, but new row for each action on the website in chronological order). In order to perform kmeans on this data we need to get that down to one row per customer ID. Hence the previous idea to collapse down the actions as words in a sentence \"describing the user's actions\"\nMy question is I've come across countless tutorials and resources online that show you how to use word2vec (combined with kmeans) to cluster words on their own, but none of them show how to use the word2vec output as part of a larger kmeans model. I need to be able to use the word2vec model along side other values about the customer. How should I go about this? I'm using python for the clustering if you want to be specific with coding examples, but I could also just be missing something super obvious and high level. It seems the word2vec outputs vectors, but kmeans needs straight numbers to work, no? Any guidance is appreciated.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":71,"Q_Id":56188976,"Users Score":0,"Answer":"There are two common approaches.\n\nTaking the average of all words. That is easy, but the resulting vectors tend to be, well, average. They are not similar to the keywords of the document, but rather similar to the most average and least informative words... My experiences with this approach are pretty disappointing, despite this being the most mentioned approach.\npar2vec\/doc2vec. You add a \"word\" for each user to all it's contexts, in addition to the neighbor words, during training. This way you get a \"predictive\" vector for each paragraph\/document\/user the same way you get a word in the first word2vec. These are supposedly more informative but require much more effort to train - you can't download a pretrained model because they are computed during training.","Q_Score":0,"Tags":"python,cluster-analysis,k-means,word2vec,unsupervised-learning","A_Id":56202144,"CreationDate":"2019-05-17T14:56:00.000","Title":"User word2vec model output in larger kmeans project","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using openCV to process an image and use houghcircles to detect the circles in the image under test, and also calculating the distance between their centers using euclidean distance.\nSince this would be in pixels, I need the absolute distances in mm or inches, can anyone let me know how this can be done\nThanks in advance.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1149,"Q_Id":56191574,"Users Score":0,"Answer":"The image formation process implies taking a 2D projection of the real, 3D world, through a lens. In this process, a lot of information is lost (e.g. the third dimension), and the transformation is dependent on lens properties (e.g. focal distance).\nThe transformation between the distance in pixels and the physical distance depends on the depth (distance between the camera and the object) and the lens. The complex, but more general way, is to estimate the depth (there are specialized algorithms which can do this under certain conditions, but require multiple cameras\/perspectives) or use a depth camera which can measure the depth. Once the depth is known, after taking into account the effects of the lens projection, an estimation can be made.\nYou do not give much information about your setup, but the transformation can be measured experimentally. You simply take a picture of an object of known dimensions and you determine the physical dimension of one pixel (e.g. if the object is 10x10 cm and in the picture it has 100x100px, then 10px is 1mm). This is strongly dependent on the distance to the camera from the object.\nAn approach a bit more automated is to use a certain pattern (e.g. checkerboard) of known dimensions. It can be automatically detected in the image and the same transformation can be performed.","Q_Score":0,"Tags":"python,opencv,image-processing","A_Id":56192030,"CreationDate":"2019-05-17T18:10:00.000","Title":"Conversion from pixel to general Metric(mm, in)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm doing dialect text classification and I'm using countVectorizer with naive bayes. The number of features are too many, I have collected 20k tweets with 4 dialects. every dialect have 5000 tweets. And the total number of features are 43K. I was thinking maybe that's why I could be having overfitting. Because the accuracy has dropped a lot when I tested on new data. So how can I fix the number of features to avoid overfitting the data?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":471,"Q_Id":56211670,"Users Score":2,"Answer":"You can set the parameter max_features to 5000 for instance, It might help with overfitting. You could also tinker with max_df (for instance set it to 0.95)","Q_Score":0,"Tags":"python,nlp,text-classification,naivebayes,countvectorizer","A_Id":56211743,"CreationDate":"2019-05-19T20:18:00.000","Title":"How to reduce the number of features in text classification?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"There are 2 huge (5-6 GB) each csv files. Now the objective is to compare both these files. how many rows are matching and how many rows are not matching? \nLets say file1.csv contains 5 similar lines, we need to count it as 1 but not 5.\nSimilarly, for file2.csv if there are redundant data, we need to count it as 1.\nI expect the output to display the number of rows that are matching and the no. of rows that are different.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":196,"Q_Id":56216081,"Users Score":0,"Answer":"I hope this algorithm work\n\ncreate hash of every line in both file\nnow create set of that hash\ndifference and intersection of that set.","Q_Score":2,"Tags":"python,python-3.x,python-2.7","A_Id":56238216,"CreationDate":"2019-05-20T07:23:00.000","Title":"Comparing 2 huge (5-6 GB) csv files and count the number of matching and unmatched no. of rows","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a tf.data.TFRecordDataset and a (computationally expensive) function, which I want to map to it. I use TensorFlow 1.12 and eager execution, and the function uses NumPy ndarray interpretations of the tensors in my dataset using EagerTensor.numpy(). However, code inside functions that are given to tf.Dataset.map() are not executed eagerly, which is why the .numpy() conversion doesn't work there and .map() is not an option anymore. Is it possible to for-loop through a dataset and modify the examples in it? Simply assigning to them doesn't seem to work.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":582,"Q_Id":56220696,"Users Score":1,"Answer":"No, not exactly. \nA Dataset is inherently lazily evaluated and cannot be assigned to in that way - conceptually try to think of it as a pipeline rather than a variable: each value is read, passed through any map() operations, batch() ops, etc and surfaced to the model as needed. To \"assign\" a value would be to write it to disk in the .tfrecord file and just isn't likely to ever be supported (these files are specifically designed to be fast-read not random-accessed).\nYou could, instead, use TensorFlow to do your pre-processing and use TfRecordWriter to write to a NEW tfrecord with the expensive pre-processing completed then use this new dataset as the input to your model. If you have the disk space avilable this might well be your best option.","Q_Score":1,"Tags":"python,tensorflow,tensor,map-function","A_Id":56221192,"CreationDate":"2019-05-20T12:05:00.000","Title":"TensorFlow: Is it possible to map a function to a dataset using a for-loop?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Can I use cross_validate in sklearn with cv=10 to instead of using Kfold with n_splits=10? Does they work as same?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":171,"Q_Id":56221694,"Users Score":0,"Answer":"I believe that KFold will simply carve your training data into 10 splits.\ncross_validate, however, will also carve the data into 10 splits (with the cv=10 parameter) but it will also actually perform the cross-validation. In other words, it will run your model 10x and you will be able to report on the performance of your model, which KFold does not do.\nIn other words, KFold is 1 small step in cross_validation.","Q_Score":0,"Tags":"python,scikit-learn","A_Id":56222077,"CreationDate":"2019-05-20T13:10:00.000","Title":"Differences between sklearn.model_selection.KFold and sklearn.model_selection.cross_validate with 'cv' parameter?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a bokeh plot with multiple y axes. I want to be able to zoom in one y axis while having the other one's displayed range stay the same. Is this possible in bokeh, and if it is, how can I accomplish that?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":27,"Q_Id":56225582,"Users Score":0,"Answer":"Bokeh does not support this, twin axes are always linked to maintain their original relative scale.","Q_Score":0,"Tags":"python,bokeh","A_Id":56226834,"CreationDate":"2019-05-20T17:25:00.000","Title":"How to make multiple y axes zoomable individually","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am programmatically creating csv files using Python. Many end users open and interact with those files using excel. The problem is that Excel by default mutates many of the string values within the file. For example, Excel converts 0123 > 123.\nThe values being written to the csv are correct and display correctly if I open them with some other program, such as Notepad. If I open a file with Excel, save it, then open it with Notepad, the file now contains incorrect values.\nI know that there are ways for an end user to change their Excel settings to disable this behavior, but asking every single user to do so is not possible for my situation.\nIs there a way to generate a csv file using Python that a default copy of Excel will NOT mutate the values of?\nEdit: Although these files are often opened in Excel, they are not only opened in Excel and must be output as .csv, not .xlsx.","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":190,"Q_Id":56227867,"Users Score":1,"Answer":"Have you tried expressly formatting the relevant column(s) to 'str' before exporting?\ndf['column_ex'] = df['column_ex'].astype('str')\ndf.to_csv('df_ex.csv')\nAnother workaround may be to open Excel program (not file), go to Data menu, then Import form Text. Excel's import utility will give you options to define each column's data type. I believe Apache's Liibre office defaults to keep the leading 0s but Excel doesn't.","Q_Score":1,"Tags":"python,excel,python-3.x,string,csv","A_Id":56228359,"CreationDate":"2019-05-20T20:37:00.000","Title":"Create a csv file that Excel will not mutate the data of when opening","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like go implement  a hierarchical resnet architecture. However, I could not find any solution for this. For example, my data structure is like:\n\nclass A\n\n\nSubclass 1\nSubclass 2\n....\n\nclass B\n\n\nsubclass 6\n........\n\n\nSo i would like to train and predict the main class and then the subclass of the chosen\/predicted mainclass. Can someone provide a simple example how to do this with generators?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":286,"Q_Id":56235267,"Users Score":0,"Answer":"The easiest way to do so would be to train multiple classifiers and build a hierarchical system by yourself.\nOne classifier detecting class A, B etc. After that make a new prediction for subclasses.\nIf you want only one single classifier:\nWhat about just killing the first hierarchy of parent classes? Should be also quite easy. If you really want a model, where the hierarchy is learned take a look at Hierarchical Multi-Label Classification Networks.","Q_Score":0,"Tags":"python,keras,conv-neural-network,hierarchical,deep-residual-networks","A_Id":56241130,"CreationDate":"2019-05-21T09:29:00.000","Title":"How to build a resnet with Keras that trains and predicts the subclass from the main class?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Could not find a version that satisfies the requirement torch>=1.0.0 \nNo matching distribution found for torch>=1.0.0 (from stanfordnlp)","AnswerCount":12,"Available Count":3,"Score":0.0831409664,"is_accepted":false,"ViewCount":95229,"Q_Id":56239310,"Users Score":5,"Answer":"I finally managed to solve this problem thanks to John Red' comment and serg06 answer. Here's what I've done :\n\nInstall Python 3.7.9 and not newer.\nBUT make sure to install 64bits python\n\nEvery other combination failed for me.","Q_Score":40,"Tags":"python,torch","A_Id":70215508,"CreationDate":"2019-05-21T13:23:00.000","Title":"Could not find a version that satisfies the requirement torch>=1.0.0?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Could not find a version that satisfies the requirement torch>=1.0.0 \nNo matching distribution found for torch>=1.0.0 (from stanfordnlp)","AnswerCount":12,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":95229,"Q_Id":56239310,"Users Score":0,"Answer":"I tried every possible command for Windows, but nothing worked. I also tried using Pycharm package installation, everything throws the same error.\nFinally installed Pytorch using Anaconda.","Q_Score":40,"Tags":"python,torch","A_Id":63728120,"CreationDate":"2019-05-21T13:23:00.000","Title":"Could not find a version that satisfies the requirement torch>=1.0.0?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Could not find a version that satisfies the requirement torch>=1.0.0 \nNo matching distribution found for torch>=1.0.0 (from stanfordnlp)","AnswerCount":12,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":95229,"Q_Id":56239310,"Users Score":0,"Answer":"I want to pip install  \" torch>=1.4.0,  torchvision>=0.5.0 \",  but in a conda env with python=3.0, this is not right.\nI tried create a new conda env with python=3.7, and pip install  \" torch>=1.4.0,  torchvision>=0.5.0 \" again, it is ok.","Q_Score":40,"Tags":"python,torch","A_Id":71016393,"CreationDate":"2019-05-21T13:23:00.000","Title":"Could not find a version that satisfies the requirement torch>=1.0.0?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"On the limited Azure Machine Learning Studio, one can import data from an On-Premises SQL Server Database.\nWhat about the ability to do the exact same thing on a python jupyter notebook on a virtual machine from the Azure Machine Learning Services workspace ?\nIt does not seem possible from what I've found in the documentation.\nData sources would be limited in Azure ML Services : \"Currently, the list of supported Azure storage services that can be registered as datastores are Azure Blob Container, Azure File Share, Azure Data Lake, Azure Data Lake Gen2, Azure SQL Database, Azure PostgreSQL, and Databricks File System\"\nThank you in advance for your assistance","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1048,"Q_Id":56240481,"Users Score":0,"Answer":"You can always push the data to a supported source using a data movement\/orchestration service.  Remember that all Azure services are not going to have every source option like Power BI, Logic Apps or Data Factory...this is why data orchestration\/movement services exist.","Q_Score":0,"Tags":"python,sql,azure,jupyter-notebook,azure-machine-learning-service","A_Id":56327491,"CreationDate":"2019-05-21T14:26:00.000","Title":"Can I import data from On-Premises SQL Server Database to Azure Machine Learning virtual machine?","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to import numpy with python3 on MacOS mojave. I am getting this error. I don't know if it has something to do with a virtual environment or something like that.\nError:\nPyCapsule_Import could not import module \"datetime\"\nI have tried reinstalling python3 and reinstalling numpy","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2527,"Q_Id":56252250,"Users Score":5,"Answer":"In my case I had this problem, because my script was called math.py, which caused module import problems. Make sure your own python files do not share name with some of common module names. After I renamed my script to something else, I could run script normally.","Q_Score":3,"Tags":"python-3.x,numpy,pip","A_Id":60404903,"CreationDate":"2019-05-22T08:17:00.000","Title":"Python3 numpy import error PyCapsule_Import could not import module \"datetime\"","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am having some text files in S3 location. I am trying to compress and zip each text files in it. I was able to zip and compress it in Jupyter notebook by selecting the file from my local. While trying the same code in S3, its throwing error as file is missing. Could someone please help","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":40,"Q_Id":56252619,"Users Score":0,"Answer":"Amazon S3 does not have a zip\/compress function.\nYou will need to download the files, zip them on an Amazon EC2 instance or your own computer, then upload the result.","Q_Score":0,"Tags":"python,amazon-web-services,amazon-s3,databricks","A_Id":56254221,"CreationDate":"2019-05-22T08:37:00.000","Title":"Zipping the files in S3","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When we are writing a pyspark dataframe to s3 from EC2 instance using pyspark code the time taken to complete write operation is longer than usual time. Earlier it used to  take 30 min to complete the write operation for 1000 records, but now it is taking more than an hour. Also after completion of the write operation the context switch to next lines of code is taking longer time(20-30min). We are not sure whether this is AWS-s3 issue or else because of lazy computation of Pyspark. Could anybody throw some light on this quesion.\nThanking in advance","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":75,"Q_Id":56256999,"Users Score":1,"Answer":"It seems an issue with the cloud environment. Four things coming to my mind, which you may check:\n\nSpark version: For some older version of spark, one gets S3 issues.\nData size being written in S3, and also the format of data while storing\nMemory\/Computation issue: The memory or CPU might be getting utilized to maximum levels.\nTemporary memory storage issue- Spark stores some intermediate data in temporary storage, and that might be getting full. \n\nSo, with more details, it may become clear on the solution.","Q_Score":0,"Tags":"python,amazon-web-services,amazon-s3,amazon-ec2,pyspark","A_Id":56291641,"CreationDate":"2019-05-22T12:38:00.000","Title":"writing a pyspark dataframe to AWS - s3 from EC2 instance using pyspark code the time taken to complete write operation is longer than usual time","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"Is it possible to train a tensorflow model, then export it as something accessible without tensorflow? I want to apply some machine learning to a school project in which the code is submitted on an online portal - it doesn\u2019t have tensorflow installed though, only standard libraries. I am able to upload additional files, but any tensorflow file would require tensorflow to make sense of... Will I have to write my ML code from scratch?","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":735,"Q_Id":56260192,"Users Score":1,"Answer":"Pretty much, unless you brought tensorflow and all of it's files with your application. Other than that, no, you cannot import tensorflow or have any tensorflow dependent modules or code.","Q_Score":2,"Tags":"python,tensorflow","A_Id":56260405,"CreationDate":"2019-05-22T15:23:00.000","Title":"Load tensorflow model without importing tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I Have Serious Doubt Between Them. Can Anyone Please Elaborate With Examples and Some Ideas.","AnswerCount":1,"Available Count":1,"Score":0.761594156,"is_accepted":false,"ViewCount":2581,"Q_Id":56278769,"Users Score":5,"Answer":"Flatten as the name implies, converts your multidimensional matrices (Batch.Size x Img.W x Img.H x Kernel.Size) to a nice single 2-dimensional matrix: (Batch.Size x (Img.W x Img.H x Kernel.Size)). During backpropagation it also converts back your delta of size (Batch.Size x (Img.W x Img.H x Kernel.Size)) to the original (Batch.Size x Img.W x Img.H x Kernel.Size).\nDense layer is of course the standard fully connected layer.","Q_Score":1,"Tags":"python,machine-learning,neural-network,deep-learning,conv-neural-network","A_Id":56278851,"CreationDate":"2019-05-23T15:41:00.000","Title":"What is Difference Between Flatten() and Dense() Layers in Convolutional Neural Network?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I trained a model in keras and I'm thinking of pruning my fully connected network. I'm little bit lost on how to prune the layers. \nAuthor of 'Learning both Weights and Connections for Efficient\nNeural Networks', say that they add a mask to threshold weights of a layer. I can try to do the same and fine tune the trained model. But, how does it reduce model size and # of computations?","AnswerCount":2,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":2526,"Q_Id":56299034,"Users Score":4,"Answer":"If you add a mask, then only a subset of your weights will contribute to the computation, hence your model will be pruned. For instance, autoregressive models use a mask to mask out the weights that refer to future data so that the output at time step t only depends on time steps 0, 1, ..., t-1.\nIn your case, since you have a simple fully connected layer, it is better to use dropout. It randomly turns off some neurons at each iteration step so it reduces the computation complexity. However, the main reason dropout was invented is to tackle overfitting: by having some neurons turned off randomly, you reduce neurons' co-dependencies, i.e. you avoid that some neurons rely on others. Moreover, at each iteration, your model will be different (different number of active neurons and different connections between them), hence your final model can be interpreted as an ensamble (collection) of several diifferent models, each specialized (we hope) in the understanding of a specific subset of the input space.","Q_Score":11,"Tags":"python,tensorflow,optimization,deep-learning,inference","A_Id":56299140,"CreationDate":"2019-05-24T20:15:00.000","Title":"How to implement neural network pruning?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"read the csv file flle using pd.read_csv() method. on displaying , it still in semicolon separated data. I expected table structure    \nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n%matplotlib inline\nimport random\nimport os\ndf=pd.read_csv(\"E:\\Python\\data_full.csv\")\ndf.head()\nActual result :\n56;\"housemaid\";\"married\";\"basic.4y\";\"no\";\"no\";...\n1\n57;\"services\";\"married\";\"high.school\";\"unknown...\n2\n37;\"services\";\"married\";\"high.school\";\"no\";\"ye...\n3\n40;\"admin.\";\"married\";\"basic.6y\";\"no\";\"no\";\"no...\n4\n56;\"services\";\"married\";\"high.school\";\"no\";\"no...\nIn [ ]:\n\u200b","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":83,"Q_Id":56302744,"Users Score":0,"Answer":"When I opened the csv file, each row was shown as single cell. Noticed that the delimiter was ;(semicolon ). I have changed the delimiter to ,(comma) and then the each value in the csv file was displayed in each cell.\nNow, the head() method is displaying the results in table structure as expected :)\nIs there any limitation to use semicolon as a delimiter in csv file?","Q_Score":0,"Tags":"python-2.x","A_Id":56303121,"CreationDate":"2019-05-25T07:28:00.000","Title":"Why Head() function showing semicolon separated data in my jupyter note book?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I run CNN code on Colab notebook, and it takes long time. However, the connection always breaks after I ran 2 or 3 hours and cannot be reconnect back. I was told Colab virture machine would break connection after 12 hours without any operates, so how can I avoid restart my code after connection broken, or any easier way to controll Colab?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":86,"Q_Id":56302763,"Users Score":0,"Answer":"Colab restarts on inactivity in 2-3 hours, you need to reconnect it. To simply avoid this show some activity on runtime.","Q_Score":0,"Tags":"python","A_Id":57820210,"CreationDate":"2019-05-25T07:31:00.000","Title":"Colab connection always broken","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am currently doing research at a university and wanted to create custom code that would be able to analyze hours worth of images of neurons and determine if the neurons are on or off. I want to write the code myself and was just wondering where I can get started. For example, what kinds of things can I import into python to read light in tiff files and what kind of things do I need to import to extract that data into a table or graph?\nSome clarification:\nThe neurons are always in the same place in all the images.\nIt is a black and white image. \nThe images are hours worth of neuron activity, where an image is taken every second or so. \nI do not want someone to write the whole code for me, but for people to please tell me what kind of things are necessary to read light in tiff files and export that data into some sort of table or graph.\nThank you!\nI am not allowed to show a photo as an example, but I am allowed to describe what it looks like. The background is all black. There are white dots on the images scattered all around. Some are neurons and I am able to locate where they are. In all of the photos, the location of the neurons are the same and some of the neurons are lit up and others are dark. Which neurons are lit and which are dark change from photo to photo. \nI do not have any code done yet, as I am here seeking for a place to start in this project.\nThe expected output of the code looking at the neurons and seeing which ones are lit up is data in the form of a table, graph, or even a text file if a table or graph is impossible. The table would include either a 1 or 0, 1 indicating that the neuron is lit up, and 0, indicating the neuron is dark. For the graph, it would have a number representing a neuron (1, 2, 3 4, ...) on the y-axis and on the x-axis would be a 1 or 0. Each column would be one image and you would see how the activity of each neuron would change over time. I am open to any other form of data.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":34,"Q_Id":56304143,"Users Score":0,"Answer":"You can read your file as an image and convert it to a black and white image. then if you can specify which pixels are located at each neurons in image you can check pixel's values to check if the neuron is on or off.\nany way I suggest to search in image processing packages of python the solution for your problem is easy to find.\nand in the case you can not specify which pixels are located at each neurons in image maybe search in \"image processing dilation and erosion\" would help.\nGood luck!","Q_Score":0,"Tags":"python-3.x,image,graph,tiff","A_Id":56305461,"CreationDate":"2019-05-25T10:52:00.000","Title":"I have many tiff files of neurons. I was wondering if there is a way to read the strength of light where neurons are and import that data into a file","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am running a LSTM model on a multivariate time series data set with 24 features. I have ran feature extraction using a few different methods (variance testing, random forest extraction, and Extra Tree Classifier). Different methods have resulted in a slightly different subset of features. I now want to test my LSTM model on all subsets to see which gives the best results.\nMy problem is that the test\/train RMSE scores for my 3 models are all very similar, and every time I run my model I get slightly different answers. This question is coming from a person who is naive and still learning the intricacies of neural nets, so please help me understand: in a case like this, how do you go about determining which model is best? Can you do seeding for neural nets? Or some type of averaging over a certain amount of trials?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":20,"Q_Id":56310122,"Users Score":2,"Answer":"Since you have mentioned that using the different feature extraction methods, you are only getting slightly different feature sets, so the results are also similar. Also since your LSTM model is then also getting almost similar RMSE values, the models are able to generalize well and learn similarly and extract important information from all the datasets.\nThe best model depends on your future data, the computation time and load of different methods and how well they will last in production. Setting a seed is not really a good idea in neural nets. The basic idea is that your model should be able to reach the optimal weights no matter how they start. If your models are always getting similar results, in most cases, it is a good thing.","Q_Score":1,"Tags":"python,neural-network,lstm,data-science,feature-extraction","A_Id":56315167,"CreationDate":"2019-05-26T02:09:00.000","Title":"Comparing results of neural net on two subsets of features","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have been working with a dataset which contains information about houses that have been sold on a particular market. There are two columns, 'price', and 'date'. \nI would like to make a line plot to show how the prices of this market have chaged over time.\nThe problem is, I see that some houses have been sold at the same date but with a diferent price.\nSo ideally i would need to get the mean\/average price of the house sold on each date before plotting.\nSo for example, if I had something like this:\nDATE  \/                PRICE\n02\/05\/2015      \/     $100\n02\/05\/2015      \/     $200\nI would need to get a new row with the following average:\nDATE          \/      PRICE\n02\/05\/2015      \/      $150\nI just havent been able to figure it out yet. I would appreciate anyone who could guide me in this matter. Thanks in advance.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":14,"Q_Id":56332208,"Users Score":0,"Answer":"Assuming you're using pandas:\npd.groupby('DATE')['PRICE'].mean()","Q_Score":0,"Tags":"python,datetime,plot","A_Id":56332236,"CreationDate":"2019-05-27T20:37:00.000","Title":"I have repeated date values in my x axis. How do i create a different row with a single average of those values?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using openpyxl to create charts. For some reason, I do not want to insert row names when adding data. So, I want to edit the legend entries manually. I am wondering if anyone know how to do this.\nMore specifically \nclass openpyxl.chart.legend.Legend(legendPos='r', legendEntry=(), \n     layout=None, overlay=None, spPr=None, txPr=None, extLst=None). I want to edit the legendEntry field","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":621,"Q_Id":56333244,"Users Score":0,"Answer":"You cannot do that. You need to set the rows when creating the plots. That will create the titles for your charts","Q_Score":0,"Tags":"python,excel,openpyxl","A_Id":56371107,"CreationDate":"2019-05-27T23:00:00.000","Title":"Setting legend entries manually","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"There's this event that my organization runs, and we have the ticket sales historic data from 2016, 2017, 2018. This data contains the quantity of tickets selled by day, considering all the sales period.\nTo the 2019 edition of this event, I was asked to make a prediction of the quantity of tickets selled by day, considering all the sales period, sort of to guide us through this period, meaning we would have the information if we are above or below the expected sales average.  \nThe problem is that the historic data has a different size of sales period in days:\nIn 2016, the total sales period was 46 days.\nIn 2017, 77 days.\nIn 2018, 113 days.\nIn 2019 we are planning 85 days. So how do I  ajust those historical data, in a logic\/statistical way, so I could use them as inputs to a statistical predictive model (such as ARIMA model)?\nAlso, I'm planning to do this on Python, so if you have any suggestions about that, I would love to hear them too!\nThank you!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":101,"Q_Id":56333832,"Users Score":0,"Answer":"Based on what I understand after reading your question, I would approach this problem in the following way.\n\nFor each day, find how far out the event is from that day. The max\nvalue for this number is 46 in 2016, 77 in 2017 etc. Scale this value\nby the max day.\nUse the above variable, along with day of the month, day of the week\netc as extraneous variable\nAdditionally, use lag information from ticket sales. You can try one\nday lag, one week lag etc.\nYou would be able to generate all this data from the sale start until\nend.\nUse the generated variables as predictor for each day and use ticket\nsales as target variable and generate a machine learning model\ninstead of forecasting.\nUse the machine learning model along with generated variables to predict future sales.","Q_Score":1,"Tags":"python,statistics,time-series,prediction","A_Id":56335423,"CreationDate":"2019-05-28T00:51:00.000","Title":"Time series prediction: need help using series with different periods of days","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm writing some code in PyTorch and I came across the gather function. Checking the documentation I saw that the index argument takes in a LongTensor, why is that? Why does it need to take in a LongTensor instead of another type such as IntTensor? What are the benefits?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":39,"Q_Id":56335215,"Users Score":1,"Answer":"By default all indices in pytorch are represented as long tensors - allowing for indexing very large tensors beyond just 4GB elements (maximal value of \"regular\" int).","Q_Score":0,"Tags":"python,pytorch","A_Id":56335363,"CreationDate":"2019-05-28T04:57:00.000","Title":"Why does PyTorch gather function require index argument to be of type LongTensor?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have gone through multiple questions that help divide your dataframe into train and test, with scikit, without etc.\nBut my question is I have 2 different csvs ( 2 different dataframes from different years). I want to use one as train and other as test?\nHow to do so for LinearRegression \/ any model?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1436,"Q_Id":56343657,"Users Score":0,"Answer":"please skillsmuggler what about the X_train and X_Test how I can define it because when I try to do that it said NameError: name 'X_train' is not defined","Q_Score":4,"Tags":"python,scikit-learn,linear-regression,data-science,training-data","A_Id":70738720,"CreationDate":"2019-05-28T13:45:00.000","Title":"How to pass different set of data to train and test without splitting a dataframe. (python)?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a small set of aerial images where different terrains visible in the image have been have been labelled by human experts. For example, an image may contain vegetation, river, rocky mountains, farmland etc. Each image may have one or more of these labelled regions. Using this small labeled dataset, I would like to fit a gaussian mixture model for each of the known terrain types. After this is complete, I would have N number of GMMs for each N types of terrains that I might encounter in an image.\nNow, given a new image, I would like to determine for each pixel, which terrain it belongs to by assigning the pixel to the most probable GMM. \nIs this the correct line of thought ? And if yes, how can I go about clustering an image using GMMs","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":353,"Q_Id":56344581,"Users Score":-1,"Answer":"Its not clustering if you use labeled training data!\nYou can, however, use the labeling function of GMM clustering easily.\nFor this, compute the prior probabilities, mean and covariance matrixes, invert them. Then classify each pixel of the new image by the maximum probability density (weighted by prior probabilities) using the multivariate Gaussians from the training data.","Q_Score":1,"Tags":"python-3.x,scikit-learn,cluster-analysis,gmm","A_Id":56390413,"CreationDate":"2019-05-28T14:35:00.000","Title":"Gaussian Mixture Models for pixel clustering","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I created a neural network in Python for a regression problem. I would like to have a prediction intervals for each value. How would I go about approaching this since neural networks are nonlinear?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":416,"Q_Id":56355244,"Users Score":1,"Answer":"One approach is caliculate residual for the validation set, it will be having a distribution, calculate mean, variance of the residual distribution and if you are looking for 95% add +,- 2sigma to your prediction and that should be your prediction interval.","Q_Score":0,"Tags":"python,machine-learning,neural-network,prediction,confidence-interval","A_Id":58932030,"CreationDate":"2019-05-29T07:31:00.000","Title":"Neural Network Prediction Interval","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am attempting to use some sort of clustering method on a set of datapoint vectors which have 45 dimensions. I'm fairly new to clustering data points and was wondering if anyone could point out appropriate methods to utilize? I was attempted using K-Means Clustering but was wondering if the dimensionality of my data might be too large for this?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":323,"Q_Id":56355551,"Users Score":0,"Answer":"45 dimensions is not particularly high. It's at best \"medium\" dimensionality, so most algorithms could work.\nUsually it's not so much a matter of the number of dimensions, but rather how well they are preprocessed. With bad preprocessing, 2 dimensions can be a problem if the signal in one attribute is drowned by the noise in the other.\nThere is no automatic way to get this right, otherwise it would be in all the libraries. Scaling can help, but can also harm. It's up to the user to prepare the data and choose parameters (such as distance functions and algorithms) to achieve the desired effect, because there is no computable equation fot \"desirable\".","Q_Score":1,"Tags":"python,machine-learning,cluster-analysis","A_Id":56373034,"CreationDate":"2019-05-29T07:51:00.000","Title":"Best Clustering Algorithm for High Dimensional Vectors","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm working on an assignment where i have to assign 1 up to 10 distribution centers  to all US states. I have made a model in excel to calculate all the costs, and clearly the goal of the assignment is to find the cheapest way. I have 50 rows (for each state) and 10 columns (for all possible DC locations). My model is based on this matrix, and if I change the matrix, the costs will instantly display. The only constraint is that each state is supplied by exactly 1 DC.\nIts clear I cant make all possible combinations by hand, I have tried to translate my model into an optimization program (AIMMS), but it'l take allot of time witch I already put in the excel model. I was thinking if I had all possible matrices (generated in R, Matlab, or Python, dont care witch one) i can loop over my spreadsheet them and let a program read the cost, to determine the best choice. Its theoretically possible to supply all state by 1 DC, and at most 10, so every possible  1x50, 2x50, 3x50 ... 10x50 matrix is needed to determine the best one.\nSo again in short, is it possible to generate every nxm binary matrix with a sum total of 1 on each row in preferably R, or otherwise in Matlab or Python?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":149,"Q_Id":56358243,"Users Score":2,"Answer":"TLDR: No.\n\nLet's look at the simpliest example: 2 DC. Your possible rows will be:\n\n(1,0)\n(0,1)\n\nNow you want to construct all possible 2x50 matrices. Their number is 2^50 (2 possible rows in 50 rows). It is equal to:\n1125899906842624\nWe suppose that each matrix stores 100 bytes. That all 2x50 matrices will store:\n(2**50) * 100 \/ 1024 \/ 1024 \/ 1024 \/ 1024 = 102400 terabytes of data.\nAnd to process all of them (in the most optimistic results for normal computers) will spend time equal to:\n(2**50) \/ 10**9 \/ 60 \/ 60 = 312 hours.\nAnd 10x50 will be even more...","Q_Score":1,"Tags":"python,r,matlab,matrix","A_Id":56358796,"CreationDate":"2019-05-29T10:18:00.000","Title":"How to generate all posible binary nxm matrices, where the sum of each row is 1","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on a knowledge based chatbot creation on ibm watson and I have trained my custom model on ibm watson knowledge studio for agricultural database. Now if someone asked about any information that is not available in our dataset then how can we retrained that model\/improve the model with that new data ? \nI am using knowledge graph to retrieve the information. Knowledge graph is made in Neo4J.\nIf any new data comes or admin wants to retrain the model with new data than the model should retrained without going to knowledge studio like rasa is ibm watson assistant provides feedback system.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":161,"Q_Id":56361575,"Users Score":0,"Answer":"IBM Watson Knowledge Studio does not support online training to retrain the existing model with new data. To adapt to new data, you need to train a brand new model with both the new data and the existing data.","Q_Score":0,"Tags":"python-3.x,ibm-watson,watson-nlu,watson-knowledge-studio","A_Id":56368801,"CreationDate":"2019-05-29T13:17:00.000","Title":"Which are the possible ways to retrain a model made in IBM Watson Knowledge Studio?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to produce a 0 or 1 with numpy's random.rand.\nnp.random.rand() produces a random float between 0 and 1 but not just a 0 or a 1. \nThank you.","AnswerCount":6,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":6029,"Q_Id":56372240,"Users Score":0,"Answer":"Is there a reason to specifically use np.random.rand? This function outputs a float as noted in the question and previous answers, and you would need thresholding to obtain an int.\nscipy.stats.bernoulli(p) directly outputs a 1 with probability p and 0 with probability 1-p.","Q_Score":3,"Tags":"python,numpy","A_Id":68811985,"CreationDate":"2019-05-30T05:38:00.000","Title":"How do you produce a random 0 or 1 with random.rand","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have been given the task of clustering our customers base on products they bought together. My data contains 500,000 rows related to each customer and 8,000 variables (product ids). Each variable is a one hot encode vector that shows if a customer bought that product or not. \nI have tried to reduce the dimensions of my data with MCA (multiple correspondence algorithm) and then use k-means and dbscan for cluster analysis, but my results were not satisfying. \nWhat are some proper algorithms for cluster analysis of large datasets with high dimensions and their python implementation?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":452,"Q_Id":56380999,"Users Score":1,"Answer":"Instead of clustering, what you should likely be using is frequent pattern mining.\nOne-hot encoding variables often does more harm than good. Either use a well-chosen distance for such data (could be as simple as Hamming or Jaccard on some data sets) with a suitable clustering algorithm (e.g., hierarchical, DBSCAN, but not k-means). Alternatively, try k-modes. But most likely, frequent itemsets is the more meaningful analysis onnsuvh data.","Q_Score":0,"Tags":"python,cluster-analysis,large-data","A_Id":56383864,"CreationDate":"2019-05-30T15:14:00.000","Title":"Cluster analysis of large dataset containing only categorical variables","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it possible to run ipython with some packages already imported?\nalmost every time when I run ipython I do import numpy as np, is it possible to automate this process? i.e. just after I run ipython I want to be able to write something like np.array([0,1]). Is it possible?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":50,"Q_Id":56382641,"Users Score":0,"Answer":"In the case of Mac add a script like load_numpy.py under ~\/.ipython\/profile_default\/startup\/ directory and add the import statements you need to that script. Every time you run ipython, all the scripts in the startup directory will be executed first and so the imports will be there. in case of Ubuntu add the file to ~\/.config\/ipython\/profile_default\/startup\/","Q_Score":1,"Tags":"python,numpy,ipython","A_Id":56382933,"CreationDate":"2019-05-30T17:06:00.000","Title":"Is it possible to run ipython with some packages already imported?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to generate requirements\/dev.txt and prod.txt files for my python project. I'm using pip-compile-multi to generate them from base.in dev.in and prod.in files. Everything works great until I add tensorflow-gpu==2.0.0a0 into the prod.in file. I get this error when I do: RuntimeError: Failed to pip-compile requirements\/prod.in.\nI believe this is because tensorflow-gpu is only available on Linux, and my dev machine is a Mac. (If I run pip install tensorflow-gpu==2.0.0a0 I am told there is no distribution for my platform.) Is there a way I can generate a requirements.txt file for pip for a package that is not available on my platform? To be clear, my goal is to generate a requirements.txt file using something like pip-compile-multi (because that will version dependencies) that will only install on Linux, but I want to be able to actually generate the file on any platform.","AnswerCount":4,"Available Count":1,"Score":0.049958375,"is_accepted":false,"ViewCount":663,"Q_Id":56383379,"Users Score":1,"Answer":"You could run pip-compile-multi in a Docker container. That way you'd be running it under Linux, and you could do that on your Mac or other dev machines. As a one-liner, it might look something like this:\ndocker run --rm --mount type=bind,src=$(pwd),dst=\/code -w \/code python:3.8 bash -c \"pip install pip-compile-multi && pip-compile-multi\"\nI haven't used pip-compile-multi, so I'm not exactly sure how you call it. Maybe you'd need to add some arguments to that command. Depending on how complicated your setup is, you could consider writing a Dockerfile and simplifying the one-liner a bit.","Q_Score":3,"Tags":"python,pip","A_Id":62975319,"CreationDate":"2019-05-30T18:01:00.000","Title":"How can I generate a requirements.txt file for a package not available on my development platform?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to cluster images based on their similarities with SIFT and Affinity Propagation, I did the clustering but I just don't want to visualize the results. How can I test with a random image from the obtained labels? Or maybe there's more to it?\nOther than data visualization, I just don't know what follows after clustering. How do I verify the 'clustering'","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":38,"Q_Id":56385518,"Users Score":1,"Answer":"since clustering is unsupervised, there isn't an objective way to evaluate it. Typically, you just observe and see if there is some features for a certain cluster.","Q_Score":0,"Tags":"python,deep-learning,cluster-analysis,k-means,sklearn-pandas","A_Id":56388545,"CreationDate":"2019-05-30T20:56:00.000","Title":"what follows after clustering","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to cluster images based on their similarities with SIFT and Affinity Propagation, I did the clustering but I just don't want to visualize the results. How can I test with a random image from the obtained labels? Or maybe there's more to it?\nOther than data visualization, I just don't know what follows after clustering. How do I verify the 'clustering'","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":38,"Q_Id":56385518,"Users Score":0,"Answer":"If you have ground-truth cluster labels, you can measure Jacquad-Index or something in that line to get an error score. Then, you can tweak your distance measure or parameters etc. to minimize the error score. \nYou can also do some clustering in order to group your data as the divide step in divide-and-conquer algorithms\/applications.","Q_Score":0,"Tags":"python,deep-learning,cluster-analysis,k-means,sklearn-pandas","A_Id":56388587,"CreationDate":"2019-05-30T20:56:00.000","Title":"what follows after clustering","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Traceback (most recent call last):   File\n  \"AutoFC_AlexNet_randomsearch_CalTech101_v2.py\", line 112, in \n      X = layers.Dense(neurons, activation=activation, kernel_initializer=weight_init)(X)   File\n  \"\/home\/shabbeer\/NAS\/lib\/python3.5\/site-packages\/keras\/legacy\/interfaces.py\",\n  line 91, in wrapper\n      return func(*args, **kwargs)   File \"\/home\/shabbeer\/NAS\/lib\/python3.5\/site-packages\/keras\/layers\/core.py\",\n  line 824, in init\n      self.kernel_initializer = initializers.get(kernel_initializer)   File\n  \"\/home\/shabbeer\/NAS\/lib\/python3.5\/site-packages\/keras\/initializers.py\",\n  line 503, in get\n      identifier) ValueError: ('Could not interpret initializer identifier:', 0.2)\n\nI am getting the above error when running the code using tensorflow-gpu version 1.4.0 and keras version 2.1.3","AnswerCount":1,"Available Count":1,"Score":0.6640367703,"is_accepted":false,"ViewCount":3852,"Q_Id":56388245,"Users Score":4,"Answer":"you should change it to X = layers.Dense(neurons, activation=activation, kernel_initializer=keras.initializers.Constant(weight_init))(X)","Q_Score":0,"Tags":"tensorflow,keras,python-3.5","A_Id":60986471,"CreationDate":"2019-05-31T03:19:00.000","Title":"ValueError: ('Could not interpret initializer identifier:', 0.2)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"The problem I encountered when I ran the official code of maskrcnn-benchmark for facebookresearch,which was wrong when loading the pre-training model.\nThe code runs on a remote server at the school and the graphics card is an NVIDIA P100.\ncheckpointer = DetectronCheckpointer(\n        cfg, model, optimizer, scheduler, output_dir, save_to_disk)\nextra_checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT)\narguments.update(extra_checkpoint_data)\nI expect to run the code correctly and understand why this is the problem.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":7056,"Q_Id":56391392,"Users Score":1,"Answer":"The reason about the problem is that the previous download was not finished. So when I deleted the original file and re-downloaded it, the problem was solved.","Q_Score":0,"Tags":"python-3.x,pytorch","A_Id":56556679,"CreationDate":"2019-05-31T08:28:00.000","Title":"How to fix '_pickle.UnpicklingError: invalid load key, '<' ' error in Pytorch","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Some preliminary testing shows that a project I'm working on could potentially benefit from the use of a Support-Vector-Machine to solve a tricky problem. The concern that I have is that there will be major memory constraints. Prototyping and testing is being done in python with scikit-learn. The final version will be custom written in C. The model would be pre-trained and only the decision function would be stored on the final product. There would be <= 10 training features, and <= 5000 training data-points. I've been reading mixed things regarding SVM memory, and I know the default sklearn memory cache is 200 MB. (Much larger than what I have available) Is this feasible? I know there are multiple different types of SVM kernel and that the kernel's can also be custom written. What kernel types could this potentially work with, if any?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":158,"Q_Id":56402429,"Users Score":2,"Answer":"If you're that strapped for space, you'll probably want to skip scikit and simply implement the math yourself.  That way, you can cycle through the data in structures of your own choosing.  Memory requirements depend on the class of SVM you're using; a two-class linear SVM can be done with a single pass through the data, considering only one observation at a time as you accumulate sum-of-products, so your command logic would take far more space than the data requirements.\nIf you need to keep the entire data set in memory for multiple passes, that's \"only\" 5000*10*8 bytes for floats, or 400k of your 1Mb, which might be enough room to do your manipulations.  Also consider a slow training process, re-reading the data on each pass, as this reduces the 400k to a triviality at the cost of wall-clock time.\nAll of this is under your control if you look up a usable SVM implementation and alter the I\/O portions as needed.\nDoes that help?","Q_Score":1,"Tags":"python,c,performance,memory-management,svm","A_Id":56402618,"CreationDate":"2019-05-31T22:41:00.000","Title":"Is it feasible to run a Support Vector Machine Kernel on a device with <= 1 MB RAM and <= 10 MB ROM?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to sample around 1000 points from a 3-D ellipsoid, uniformly. Is there some way to code it such that we can get points starting from the equation of the ellipsoid?\nI want points on the surface of the ellipsoid.","AnswerCount":6,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":2545,"Q_Id":56404399,"Users Score":3,"Answer":"Consider using Monte-Carlo simulation: generate a random 3D point; check if the point is inside the ellipsoid; if it is, keep it. Repeat until you get 1,000 points. \nP.S. Since the OP changed their question, this answer is no longer valid.","Q_Score":5,"Tags":"python,math,random,geometry,ellipse","A_Id":56404614,"CreationDate":"2019-06-01T06:29:00.000","Title":"How to generate a random sample of points from a 3-D ellipsoid using Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to sample around 1000 points from a 3-D ellipsoid, uniformly. Is there some way to code it such that we can get points starting from the equation of the ellipsoid?\nI want points on the surface of the ellipsoid.","AnswerCount":6,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":2545,"Q_Id":56404399,"Users Score":0,"Answer":"One way of doing this whch generalises for any shape or surface is to convert the surface to a voxel representation at arbitrarily high resolution (the higher the resolution the better but also the slower). Then you can easily select the voxels randomly however you want, and then you can select a point on the surface within the voxel using the parametric equation. The voxel selection should be completely unbiased, and the selection of the point within the voxel will suffer the same biases that come from using the parametric equation but if there are enough voxels then the size of these biases will be very small.\nYou need a high quality cube intersection code but with something like an elipsoid that can optimised quite easily. I'd suggest stepping through the bounding box subdivided into voxels. A quick distance check will eliminate most cubes and you can do a proper intersection check for the ones where an intersection is possible. For the point within the cube I'd be tempted to do something simple like a random XYZ distance from the centre and then cast a ray from the centre of the elipsoid and the selected point is where the ray intersects the surface. As I said above, it will be biased but with small voxels, the bias will probably be small enough.\nThere are libraries that do convex shape intersection very efficiently and cube\/elipsoid will be one of the options. They will be highly optimised but I think the distance culling would probably be worth doing by hand whatever. And you will need a library that differentiates between a surface intersection and one object being totally inside the other.\nAnd if you know your elipsoid is aligned to an axis then you can do the voxel\/edge intersection very easily as a stack of 2D square intersection elipse problems with the set of squares to be tested defined as those that are adjacent to those in the layer above. That might be quicker.\nOne of the things that makes this approach more managable is that you do not need to write all the code for edge cases (it is a lot of work to get around issues with floating point inaccuracies that can lead to missing or doubled voxels at the intersection). That's because these will be very rare so they won't affect your sampling.\nIt might even be quicker to simply find all the voxels inside the elipse and then throw away all the voxels with 6 neighbours... Lots of options. It all depends how important performance is. This will be much slower than the opther suggestions but if you want ~1000 points then ~100,000 voxels feels about the minimum for the surface, so you probably need ~1,000,000 voxels in your bounding box. However even testing 1,000,000 intersections is pretty fast on modern computers.","Q_Score":5,"Tags":"python,math,random,geometry,ellipse","A_Id":72498276,"CreationDate":"2019-06-01T06:29:00.000","Title":"How to generate a random sample of points from a 3-D ellipsoid using Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am currently implementing decision tree algorithm. If I have a continous featured data how do i decide a splitting point. I came across few resources which say to choose mid points between every two points but considering I have 8000 rows of data this would be very time consuming. The output\/feature label is having category data. Is any approach where I can perform this operation quicker","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":89,"Q_Id":56406338,"Users Score":0,"Answer":"Decision tree works calculating entropy and information gain to determine the most important feature. Indeed, 8000 row is not too much for decision tree. But generally, Random forest is similar to decision tree. It is working as ensemble. You can review and try it.Moreover,  maybe being slowly is related to another thing.","Q_Score":0,"Tags":"python,machine-learning,artificial-intelligence,decision-tree,machine-learning-model","A_Id":56410719,"CreationDate":"2019-06-01T11:32:00.000","Title":"How to choose a split variables for continous features for decision tree","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I found a solution to this:\ndf['Name']=df['Name'].str.lstrip\ndf['Parent']=df['Name'].str.lstrip\n\nI have this DataFrame df (there is a white space at the left of \"A\" and \"C\" in the second row (which doesn't show well here). I would like to remove that space.\nMark    Name    Parent  age\n10  A   C   1\n12   A   C  2\n13  B   D   3\nI tried \ndf['Name'].str.lstrip()\ndf['Parent'].str.lstrip()\nthen tried \ndf.to_excel('test.xlsx')\nbut the result in excel didn't remove the white spaces\nI then tried defining another variable\nx=df['Name'].str.lstrip\nx.to_excel('test.xlsx')\nthat worked fine in Excel, but this is a new dataFrame, and only had the x column\nI then tried repeating the same for 'Parent', and to play around with joining multiple dataframes to the original dataframe, but I still couldnt' get it to work, and that seems too convoluted anyway\nFinally, even if my first attempt had worked, I would like to be able to replace the white spaces in one go, without having to do a separate call for each column name","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":70,"Q_Id":56406907,"Users Score":0,"Answer":"You could try using\n\n\n\ndf['Name'].replace(\" \", \"\")\n\n\n\nthis would delete all whitespaces though.","Q_Score":0,"Tags":"python,pandas,strip","A_Id":56409739,"CreationDate":"2019-06-01T12:48:00.000","Title":"Removing white space at the beginning of values in multiple columns","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have trained a Machine Learnig Model using Sklearn and looked at different scores for the traing, testing (dev) and validation set. \nHere are the scores:\n\nAccuracy on Train: 94.5468% \nAccuracy on Test: 74.4646% \nAccuracy on Validation: 65.6548% \nPrecision on Train: 96.7002% \nPrecision on Test: 85.2289% \nPrecision on Validation: 79.7178% \nF1-Score on Train: 96.9761% \nF1-Score on Test: 85.6203% \nF1-Score on Validation: 79.6747%\nI am having some problems with Interpretation of the scores. Is it normal, that the Model has a much worse result on the validation set?\nDo you have thoughts on those results?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":330,"Q_Id":56406988,"Users Score":0,"Answer":"As you explained in the comments, your test set is the set you used to tune your parameters and the validation set is the set that your model didn't use for training.\n\nConsidering that, it's natural that your Validation scores are lower than other scores.\n\nWhen you're training a machine learning model, you show the training set to your model, that's why your model get's the best scores on training set, i.e. samples it has already seen and knows the answer for.\n\nYou use validation set to tune your parameters (e.g. degree of complexity in regression and so on) so your parameters are fine tuned for your validation sets but your model has not been trained on them. (for this you used the term test set, and to be fair they are sometimes used that way)\n\nfinally you have the least score on your test set which is natural since the parameters are not exactly tuned for the test set and the model has never seen them before.\n\nif there is a huge hap between your training and test results, your model might have become overfit and there are ways to avoid that.\n\n\nhope this helped ;)","Q_Score":0,"Tags":"python,validation,machine-learning,scikit-learn,data-science","A_Id":56407510,"CreationDate":"2019-06-01T13:01:00.000","Title":"Interpretation of Training, Testing (Dev) and Validation Score in Machine Learning","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I already trained a word2vec model with gensim library. For example, my model contains vectors for 2 words: \"new\" and \"york\". However, I also want to train a vector for the word \"new york\", so I transform \"new york\" into \"new_york\" and train a new vector model. Finally, I want to combine 3 vectors: vector of the word \"new\", \"york\" and \"new_york\" into one vector representation for the word \"new york\".\nHow can I save the new vector value to the model?\nI try to assign the new vector to the model but gensim did not allow to assign the new value for vector model.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":103,"Q_Id":56408959,"Users Score":1,"Answer":"Word-vectors are generally only comparable to each other if they were trained together.\nSo, if you want to have vectors for all of 'new', 'york', and 'new_york', you should prepare a corpus which includes them all, in a variety of uses, and train a Word2Vec model from that.","Q_Score":1,"Tags":"python,vector,gensim,word2vec","A_Id":56411523,"CreationDate":"2019-06-01T17:31:00.000","Title":"Create a new vector model in gensim","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"My model requires to run many epochs in order to get decent result, and it takes few hours using v100 on Google Cloud.\nSince I'm on a preemptible instance, it kicks me off in the middle of training. I would like to be able to resume from where it left off.\nIn my custom CallBack, I run self.model.save(...) in on_epoch_end. Also it stops the training if the score hasn't improved in last 50 epochs.\nHere are the steps I tried:\n\nI ran model.fit until the early stops kicked in after epoch 250 (best score was at epoch 200)\nI loaded the model saved after 100th epoch.\nI ran model.fit with initial_epoch=100. (It starts with Epoch 101.)\n\nHowever, it takes while to catch up with the first run. Also the accuracy score of each epoch gets kind of close to the first run, but it's lower. Finally the early stop kicked in at like 300, and the final score is lower than the first run. Only way I can get the same final score is to create the model from scratch and run fit from the epoch 1.\nI also tried to utilize float(K.get_value(self.model.optimizer.lr)) and K.set_value(self.model.optimizer.lr, new_lr).\nHowever, self.model.optimizer.lr always returned the same number. I assume it's because the adam optimizer calculates the real lr from the initial lr that I set with Adam(lr=1e-4).\nI'm wondering what's the right approach to resume training using Adam optimizer?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":6918,"Q_Id":56414605,"Users Score":0,"Answer":"What about model.load('saved.h5'). It should also load the optimizer if you save it with model.save() though.","Q_Score":5,"Tags":"python,tensorflow,machine-learning,keras","A_Id":56416556,"CreationDate":"2019-06-02T11:32:00.000","Title":"Keras How To Resume Training With Adam Optimizer","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Ist it possible to formulate a min-max-optimization problem of the following form in pyomo:\nmin(max(g_m(x)) s.t. L\nwhere g_m are nonlinear functions (actually constrains of another model) and L is a set of linear constrains?\nHow would I create the expression for the objective function of the model?\nThe problem is that using max() on a list of constraint-objects returns only the constraint possessesing the maximum value at a given point.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":966,"Q_Id":56416916,"Users Score":0,"Answer":"I think yes, but unless you find a clever way to reformulate your model, it might not be very efficent. \nYou could solve all possiblity of max(g_m(x)), then select the solution with the lowest objective function value. \nI fear that the max operation is not something you can add to a minimization model, since it is not a mathematical operation, but a solver operation. This operation is on the problems level. Keep in mind that when solving a model, Pyomo requires as argument only one sense of optimization (min or max), thus making it unable to understand min-max sense. Even if it did, how could it knows what to maximize or minimize? This is why I suggest you to break your problem in two, unless you work on its formulation.","Q_Score":0,"Tags":"python,pyomo","A_Id":56464549,"CreationDate":"2019-06-02T16:35:00.000","Title":"Is it possible to model a min-max-problem using pyomo","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"The inputs to Dijkstra's algorithm are a directed and weighted graph, generally represented by an adjacency (distance) matrix and a start node.\nI have two different distance matrices to be used as inputs, representing two different infrastructure (e.g., roads and cycle ways). Any ideas how modify Dijkstra's algorithm to be use these two inputs? I want to implement in Python. \nThanks!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":130,"Q_Id":56418597,"Users Score":0,"Answer":"Does your requirement have a function through which the both matrices are related \nIf yes then on the basis of that function find a new weight matrix. Use this matrix on the flow path\nIf no, then try running matrix one first and then two and vice-versa and choose the one with corresponding cost output to your requirement","Q_Score":0,"Tags":"python,algorithm,routing,navigation,dijkstra","A_Id":56421074,"CreationDate":"2019-06-02T20:10:00.000","Title":"Multiple inputs for Dijkstra's algorithm","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Why is my pipenv stuck in the \"Locking...\" stage when installing [numpy|opencv|pandas]? \nWhen running pipenv install pandas or pipenv update it hangs for a really long time with a message and loading screen that says it's still locking. Why? What do I need to do?","AnswerCount":5,"Available Count":2,"Score":0.0399786803,"is_accepted":false,"ViewCount":24826,"Q_Id":56440090,"Users Score":1,"Answer":"try doing pipenv --rm - removes virtual environment\nthen pipenv shell - this will again initiate virtual env\nthen pipenv install installs all the packages again\nworked for me","Q_Score":38,"Tags":"python,pip,pipenv","A_Id":62840501,"CreationDate":"2019-06-04T08:28:00.000","Title":"Pipenv stuck \"\u280b Locking...\"","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Why is my pipenv stuck in the \"Locking...\" stage when installing [numpy|opencv|pandas]? \nWhen running pipenv install pandas or pipenv update it hangs for a really long time with a message and loading screen that says it's still locking. Why? What do I need to do?","AnswerCount":5,"Available Count":2,"Score":0.0399786803,"is_accepted":false,"ViewCount":24826,"Q_Id":56440090,"Users Score":1,"Answer":"I had this happen to me just now. Pipenv got stuck locking forever, 20+ minutes with no end in sight, and pipenv --rm didn't help.\nIn the end, the problem was that I had run pipenv install \"boto3~=1.21.14\" to upgrade boto3 from boto3 = \"==1.17.105\". But I had other conflicting requirements (in my case, botocore = \"==1.20.105\" and s3transfer = \"==0.4.2\") which are boto3 dependencies. The new version of boto3 required newer versions of these two packages, but the == requirements didn't allow that. Pipenv didn't explain this, and just spun \"Locking\u2026\" forever.\nSo if you run into this, I would advise you to carefully look at your Pipenv packages, see if there are any obvious conflicts, and loosen or remove package requirements where possible.\nIn my case, I was able to just remove the s3transfer and botocore packages from the list entirely, and rely on boto3 to fetch the necessary versions.","Q_Score":38,"Tags":"python,pip,pipenv","A_Id":71402278,"CreationDate":"2019-06-04T08:28:00.000","Title":"Pipenv stuck \"\u280b Locking...\"","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've upgrade my tensorflow and keras with this code:\n!pip install tf-nightly-gpu-2.0-preview\nNow every epoch of model learning cost 22 min which was 17 sec before this upgrade!!!\nI did downgrade tensorflo and keras but it did not help!","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1597,"Q_Id":56443694,"Users Score":0,"Answer":"You can reset your backend using the Runtime -> Reset all runtimes... menu item. (This is much faster than kill -9 -1, which will take some time to reconnect.)","Q_Score":0,"Tags":"python,tensorflow,keras,google-colaboratory","A_Id":56446962,"CreationDate":"2019-06-04T12:14:00.000","Title":"google colab GPU processing become very slow after keras and tensorflow upgrade","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I understand that in scipy.integrate.trapz(y, x=None, dx=1.0, axis=-1) or simps, the min and max values of x (if specified) are taken to be the limits of the integral, but what happens when x=None? It has dx to figure out the spacing in the x values but at what value does it start x? \nI tried it with and without x, from which I understand that it starts x values from 0.0 when x is not specified.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":608,"Q_Id":56456248,"Users Score":0,"Answer":"but at what value does it start x\n\nIt doesn't matter for integration. You just specify the values of y and tell scipy how far the xs are apart. Whether they start at -5 or +26 doesn't influence the value of the integral.","Q_Score":0,"Tags":"python,python-3.x,scipy,numerical-integration","A_Id":56461379,"CreationDate":"2019-06-05T07:50:00.000","Title":"How to specify integration limits in scipy.integrate.trapz or simps?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there a difference between the parallelization that takes place between these two options? I\u2019m assuming num_workers is solely concerned with the parallelizing the data loading. But is setting torch.set_num_threads for training in general? Trying to understand the difference between these options. Thanks!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1236,"Q_Id":56463317,"Users Score":2,"Answer":"The num_workers for the DataLoader specifies how many parallel workers to use to load the data and run all the transformations. If you are loading large images or have expensive transformations then you can be in situation where GPU is fast to process your data and your DataLoader is too slow to continuously feed the GPU. In that case setting higher number of workers helps. I typically increase this number until my epoch step is fast enough. Also, a side tip: if you are using docker, usually you want to set shm to 1X to 2X number of workers in GB for large dataset like ImageNet.\nThe torch.set_num_threads specifies how many threads to use for parallelizing CPU-bound tensor operations. If you are using GPU for most of your tensor operations then this setting doesn't matter too much. However, if you have tensors that you keep on cpu and you are doing lot of operations on them then you might benefit from setting this. Pytorch docs, unfortunately, don't specify which operations will benefit from this so see your CPU utilization and adjust this number until you can max it out.","Q_Score":3,"Tags":"python,machine-learning,pytorch","A_Id":61623908,"CreationDate":"2019-06-05T15:11:00.000","Title":"DataLoader num_workers vs torch.set_num_threads","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"As described in Keras documentation, the verbose=1 asks the keras to print out results in a progress bar. But sometimes keras prints out the results of every batch, which makes a very messy printout report (see below). I wonder why is that? I mean, the only setup is the parameter of verbose, isn't it?\nMy code is simple:\n\nhistory = model.fit(X_shuffle, y_scores_one_hot, \n                      validation_split=0.2, verbose = 1,\n                      epochs = 100, batch_size = 50) \n\nWrong printout:\n\nEpoch 1\/100\n  5750\/8107 [====================>.........] - ETA: 5:03 - loss: 1.3690 - acc: 0.520 - ETA: 1:42 - loss: 1.3600 - acc: 0.533 - ETA: 1:02 - loss: 1.3994 - acc: 0.500 - ETA: 39s - loss: 1.4173 - acc: 0.482 - ETA: 29s - loss: 1.4189 - acc: 0.47 - ETA: 23s - loss: 1.4320 - acc: 0.46 - ETA: 19s - loss: 1.4432 - acc: 0.46 - ETA: 16s - loss: 1.4373 - acc: 0.46 - ETA: 14s - loss: 1.4318 - acc: 0.46 - ETA: 12s - loss: 1.4322 - acc: 0.46 - ETA: 11s - loss: 1.4314 - acc: 0.46 - ETA: 10s - loss: 1.4342 - acc: 0.46 - ETA: 10s - loss: 1.4386 - acc: 0.45 - ETA: 9s - loss: 1.4399 - acc: 0.4557 - ETA: 8s - loss: 1.4373 - acc: 0.458 - ETA: 7s - loss: 1.4418 - acc: 0.453 - ETA: 7s - loss: 1.4419 - acc: 0.454 - ETA: 6s - loss: 1.4435 - acc: 0.453 - ETA: 6s - loss: 1.4421 - acc: 0.453 - ETA: 6s - loss: 1.4439 - acc: 0.451 - ETA: 5s - loss: 1.4437 - acc: 0.452 - ETA: 5s - loss: 1.4388 - acc: 0.456 - ETA: 5s - loss: 1.4430 - acc: 0.453 - ETA: 4s - loss: 1.4440 - acc: 0.452 - ETA: 4s - loss: 1.4428 - acc: 0.452 - ETA: 4s - loss: 1.4469 - acc: 0.449 - ETA: 4s - loss: 1.4471 - acc: 0.450 - ETA: 3s - loss: 1.4517 - acc: 0.445 - ETA: 3s - loss: 1.4489\n\nI expected something like:\n\nEpoch 1\/100\n  3009\/3009 [==============================] - 30s 10ms\/step - loss: 1.5875 - acc: 0.2795 - val_loss: 1.5542 - val_acc: 0.4130\nEpoch 2\/100\n  3009\/3009 [==============================] - 27s 9ms\/step - loss: 1.5049 - acc: 0.4403 - val_loss: 1.4963 - val_acc: 0.4130","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":738,"Q_Id":56467912,"Users Score":0,"Answer":"That looks like an interaction with a notebook\/kernel environment.\nYou may prefer the results if you change verbose=1 to verbose=2.","Q_Score":2,"Tags":"python,keras","A_Id":56469920,"CreationDate":"2019-06-05T20:58:00.000","Title":"Keras prints out result of every batch in a single epoch, why is that?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to create a program that can read and store the data from a qr scanning device but i don't know how to get the input from the barcode scanner as an image or save it in a variable to read it after with openCV","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":601,"Q_Id":56469264,"Users Score":0,"Answer":"Typically a barcode scanner automatically outputs to the screen, just like a keyboard (except really quickly), and there is an end of line character at the end (like and enter). \nUsing a python script all you need to do is start the script, connect a scanner, scan something, and get the input (STDIN) of the script. If you built a script that was just always receiving input and storing or processing them, you could do whatever you please with the data. \nA QR code is read in the same way that a barcode scanner works, immediately outputting the encoded data as text. Just collect this using the STDIN of a python script and you're good to go!","Q_Score":0,"Tags":"python,input,qr-code,barcode-scanner","A_Id":56469292,"CreationDate":"2019-06-05T23:27:00.000","Title":"How to use python with qr scanner devices?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":1,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to know briefly about all the available estimators like logisticregression or multinomial regression or SVMs which can be used for classification problems.\nThese are the three I know. Are there any others like these? and relatively how long they run or how accurate can they get than these?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":26,"Q_Id":56471908,"Users Score":1,"Answer":"The following can be used for classification problems:\n\nLogistic Regression\nSVM\nRandomForest Classifier\nNeural Networks","Q_Score":0,"Tags":"python-3.x,scikit-learn","A_Id":56472750,"CreationDate":"2019-06-06T06:24:00.000","Title":"What are the available estimators which we can use as estimator in onevsrest classifier?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to fit a model using keras but I get the following error - \nWARNING:tensorflow:From \/anaconda3\/lib\/python3.7\/site-packages\/tensorflow\/python\/ops\/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\nInstructions for updating:\nUse tf.cast instead.\nEpoch 1\/100\nclassifier.fit(X_train,y_train,epochs=100,batch_size=10)","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":359,"Q_Id":56472233,"Users Score":0,"Answer":"There could be many reasons for the Kernel dying, the most common one I encounter is because I have ran out of memory. \nIf you are training a particularly large model try temporarily reducing it and bringing the batch_size down to 1\n(I don't think the warning message is related - this is just giving advance warning of a future change)","Q_Score":0,"Tags":"python,tensorflow,jupyter","A_Id":56472341,"CreationDate":"2019-06-06T06:51:00.000","Title":"Getting error while trying to fit model - The kernel appears to have died. It will restart automatically","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"i'm using conv net for image classification.\nThere is something I dont understand theoretically\nFor training I split my data 60%train\/20%validation\/20% test\nI save weight when metric on validation set is the best (I have same performance on training and validation set).\nNow, I do a new split. Some data from training set will be on test set. I load the weight and I classify new test set.\nSince weight have been computed on a part of the new test set, are we agree to says this is a bad procedure and I should retrain my model with my new training\/validation set?","AnswerCount":2,"Available Count":2,"Score":0.1973753202,"is_accepted":false,"ViewCount":26,"Q_Id":56473760,"Users Score":2,"Answer":"yes, for fair evaluation no sample in the test set should be seen during training","Q_Score":0,"Tags":"python,machine-learning,keras,conv-neural-network","A_Id":56473975,"CreationDate":"2019-06-06T08:34:00.000","Title":"conv net save weight and new test set","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"i'm using conv net for image classification.\nThere is something I dont understand theoretically\nFor training I split my data 60%train\/20%validation\/20% test\nI save weight when metric on validation set is the best (I have same performance on training and validation set).\nNow, I do a new split. Some data from training set will be on test set. I load the weight and I classify new test set.\nSince weight have been computed on a part of the new test set, are we agree to says this is a bad procedure and I should retrain my model with my new training\/validation set?","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":26,"Q_Id":56473760,"Users Score":1,"Answer":"The all purpose of having a test set is that the model must never see it until the very last moment.\nSo if your model trained on some of the data in your test set, it becomes useless and the results it will gives you will have no meaning.\nSo basicly:\n\n1.Train on your train set\n2.Validate on your validation set\n3.Repeat 1 and 2 until you are happy with the results\n4.At the very end, finally test your model on the test set","Q_Score":0,"Tags":"python,machine-learning,keras,conv-neural-network","A_Id":56474029,"CreationDate":"2019-06-06T08:34:00.000","Title":"conv net save weight and new test set","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have two clients (separate docker containers) both writing to a Cassandra cluster.\nThe first is writing real-time data, which is ingested at a rate that the cluster can handle, albeit with little spare capacity. This is regarded as high-priority data and we don't want to drop any. The ingestion rate varies quite a lot from minute to minute. Sometimes data backs up in the queue from which the client reads and at other times the client has cleared the queue and is (briefly) waiting for more data.\nThe second is a bulk data dump from an online store. We want to write it to Cassandra as fast as possible at a rate that soaks up whatever spare capacity there is after the real-time data is written, but without causing the cluster to start issuing timeouts.\nUsing the DataStax Python driver and keeping the two clients separate (i.e. they shouldn't have to know about or interact with each other), how can I throttle writes from the second client such that it maximises write throughput subject to the constraint of not impacting the write throughput of the first client?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":246,"Q_Id":56474650,"Users Score":0,"Answer":"The solution I came up with was to make both data producers write to the same queue.\nTo meet the requirement that the low-priority bulk data doesn't interfere with the high-priority live data, I made the producer of the low-priority data check the queue length and then add a record to the queue only if the queue length is below a suitable threshold (in my case 5 messages).\nThe result is that no live data message can have more than 5 bulk data messages in front of it in the queue. If messages start backing up on the queue then the bulk data producer stops queuing more data until the queue length falls below the threshold.\nI also split the bulk data into many small messages so that they are relatively quick to process by the consumer.\nThere are three disadvantages of this approach:\n\nThere is no visibility of how many queued messages are low priority and how many are high priority. However we know that there can't be more than 5 low priority messages.\nThe producer of low-priority messages has to poll the queue to get the current length, which generates a small extra load on the queue server.\nThe threshold isn't applied strictly because there is a race between the two producers from checking the queue length to queuing a message. It's not serious because the low-priority producer queues only a single message when it loses the race and next time it will know the queue is too long and wait.","Q_Score":0,"Tags":"python,cassandra,datastax-python-driver","A_Id":56577125,"CreationDate":"2019-06-06T09:28:00.000","Title":"Cassandra write throttling with multiple clients","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"Just confused as to the differences between keras.sequential train_on_batch and fit. Is the only difference that, with train_on_batch, you automatically pass over the data only once whereas with fit you specify this with the no. of epochs?\nIf I do model.fit(x, y, epochs=5) is this the same as \nfor i in range(5)\n     model.train_on_batch(x, y)?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":49,"Q_Id":56476357,"Users Score":1,"Answer":"If I do model.fit(x, y, epochs=5) is this the same as\nfor i in range(5)    model.train_on_batch(x, y)?\n\nYes.\nYour understanding is correct.\nThere are a few more bells and whistles to .fit() (we, can for example, artificially control the number of batches to consider an epoch rather than exhausting the whole dataset) but, fundamentally, you are correct.","Q_Score":0,"Tags":"python,tensorflow,keras","A_Id":56476394,"CreationDate":"2019-06-06T11:10:00.000","Title":"Is Keras Sequential fit the same as several train_on_batch calls?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it somehow possible to determine the array length of the arrays in the tck tuple returned by scipy.interpolate.splprep before computing the values?\nI have to fit a spline interpolation to noisy data with 5 million data points (or less, can be varying). \nMy observation is that the interpolation at an array length of ~ 90 is pretty good, while it takes a long time to compute the interpolation for higher array lengths (it sometimes also directly jumps from ~ 90 to ~ 1000 while making s one step smaller and the interpolation also becomes noisy) and it is not appropriate enough, if the array length is far less (<50)...\nActually, this array length depends on the smoothing factor s provided to the splprep function, but for different measurement data, s varies a lot to get a consistent array length of around 90. E.g. for data1 s has a value of around 1000 to get len(cfk[0]) equals to 90, for data2 s has a value of around 100 to get len(cfk[0]) equals to 90 at same lengths of data1 and data2. It might be dependent on the noise of the data...\nI have thought about a loop where s starts at some point and decreases through the loop while len(cfk[0]) is constantly being checked - but this takes ages, especially if len(cfk[0]) gets closer to 90.\nTherefore, it would be useful to somehow know the smoothing factor to get the desired array length before computing the cfk tuple.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":161,"Q_Id":56477397,"Users Score":0,"Answer":"Short answer: no, not easily. Dierckx Fortran library, which splrep wraps, uses some fairly non-trivial logic for determining the knot vector, and it's all baked into the Fortran code. So, the only way is to carefully trace the latter. It's available from netlib, also scipy\/interpolate\/fitpack","Q_Score":0,"Tags":"python,arrays,scipy,spline","A_Id":56484661,"CreationDate":"2019-06-06T12:14:00.000","Title":"Scipy spline interpolation: Determine array length of vector of knots \/ B-spline coefficients in tck before actual computation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have implemented a convolutional neural network by transfer learning using VGG19 to classify 5 different traffic signs. It works well with new test images, but when I apply the model upon video streaming it doesn't classify them correctly.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":30,"Q_Id":56477793,"Users Score":0,"Answer":"Assuming the neural network works well on images, it should work the same on frames of a video stream. In the end, a video stream is a sequence of images.\nThe problem is not that it doesn't work on video stream, it simply does not work on the type of images similar to the ones you have in the video stream.\nIt is hard to exactly find the problem, since the question does not have enough detail. However, some considerations are:\n\nObviously, there is a problem with the network's ability to generalize. Was the testing performed well? For example, is there a train-validation split of the data?\nDoes the training error and the validation error indicate any possible issues, such as overfitting?\nIs the data used to train the image similar enough to the video frames?\nIs the training dataset large enough? Data augmentation might help if there is not enough data.","Q_Score":0,"Tags":"python,image-processing,video-streaming,conv-neural-network,video-processing","A_Id":56499818,"CreationDate":"2019-06-06T12:39:00.000","Title":"Why a convolution neural network model which works well on new test images fails on video stream?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I came across some code where tf.Variable(... trainable=False) was used and I wondered whether there was any difference between using tf.constant(...) and tf.Variable(with the trainable argument set to False)\nIt seems a bit redundant to have the trainable argument option available with tf.constant is available.","AnswerCount":3,"Available Count":3,"Score":0.0665680765,"is_accepted":false,"ViewCount":739,"Q_Id":56479870,"Users Score":1,"Answer":"If you declare something with tf.constant() you won't be able to change the value in future. But, tf.Variable() let's you change the variable in future. You can assign some other value to it. If it is not trainable, then the gradient won't flow through it.","Q_Score":2,"Tags":"python,tensorflow","A_Id":56480077,"CreationDate":"2019-06-06T14:39:00.000","Title":"Difference between tf.constant and tf.Variable (trainable= False)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I came across some code where tf.Variable(... trainable=False) was used and I wondered whether there was any difference between using tf.constant(...) and tf.Variable(with the trainable argument set to False)\nIt seems a bit redundant to have the trainable argument option available with tf.constant is available.","AnswerCount":3,"Available Count":3,"Score":0.0665680765,"is_accepted":false,"ViewCount":739,"Q_Id":56479870,"Users Score":1,"Answer":"There may be other differences, but one that comes to mind is that, for some TF graphs, you want a variable to be trainable sometimes and frozen other times.  For example, for transfer learning with convnets you want to freeze layers closer to the inputs and only train layers closer to the output.  It would be inconvenient, I suppose, if you had to swap out all the tf.Variable layers for tf.constant layers.","Q_Score":2,"Tags":"python,tensorflow","A_Id":56480023,"CreationDate":"2019-06-06T14:39:00.000","Title":"Difference between tf.constant and tf.Variable (trainable= False)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I came across some code where tf.Variable(... trainable=False) was used and I wondered whether there was any difference between using tf.constant(...) and tf.Variable(with the trainable argument set to False)\nIt seems a bit redundant to have the trainable argument option available with tf.constant is available.","AnswerCount":3,"Available Count":3,"Score":1.2,"is_accepted":true,"ViewCount":739,"Q_Id":56479870,"Users Score":2,"Answer":"Few reasons I can tell you off the top of my head:\n\nIf you declare a tf.Variable, you can change it's value later on if you want to. On the other hand, tf.constant is immutable, meaning that once you define it you can't change its value.\nLet's assume that you have a neural network with multiple weight matrices, for the first few epochs you can to train the last layer, while keeping the all the rest frozen. After that, for the last few epochs, you want to fine-tune the whole model. If the first layers are defined as tf.constant, you can't do that.","Q_Score":2,"Tags":"python,tensorflow","A_Id":56480281,"CreationDate":"2019-06-06T14:39:00.000","Title":"Difference between tf.constant and tf.Variable (trainable= False)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Currently I'm using the spectral clustering method from sklearn for my dense 7000x7000 matrix which performs very slowly and exceeds an execution time of 6 hours. Is there a faster implementation of spectral clustering in python?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":244,"Q_Id":56482181,"Users Score":0,"Answer":"I'd recommend performing PCA to project the data to a lower dimensionality , and then utilize mini batch k-means","Q_Score":0,"Tags":"python,bigdata","A_Id":56482246,"CreationDate":"2019-06-06T17:09:00.000","Title":"Is there an efficient python implementation of spectral clustering for large, dense matrices?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to build a model to predict the outcome (win or loose) of a tennis match, as an exercise. I am using Python, Pandas and scikit-learn.\nThe dataset I have has the two players ID and the result of the match, among other quantities.\nIn my case, the way the database is organized, has always the Player1 as the winner and the Player2 as the looser. So, if I have to label the data, it will always be the same label (1, for instance).\nWhat do you think is better: \n\nto try to train a model with a single-valued training ser (for instance a 1-label SVM) \nto randomly shuffle the data, in order to place some of the Player2 as the Player1 and viceversa, and so to have another label (0, for instance)?\n\nThanks a lot!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":460,"Q_Id":56495100,"Users Score":0,"Answer":"Sounds to me you need to shuffle that.  The dataset you have have inherent information coded in the structure of the data ( Player 1 wins).  You have no way to recreate this information at runtime.  \nWhat you want is a dataset where the order of the player information is not important , and a label 0\/1 determining if player 1 or player 2 will win.","Q_Score":0,"Tags":"python,machine-learning,supervised-learning","A_Id":56495157,"CreationDate":"2019-06-07T13:15:00.000","Title":"How to train a model with data with only one label","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to save a dataset using partitionBy on S3 using pyspark. I am partitioning by on a date column. Spark job is taking more than hour to execute it. If i run the code without partitionBy it just takes 3-4 mints. \nCould somebody help me in fining tune the parititonby?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":1717,"Q_Id":56496387,"Users Score":1,"Answer":"Use version 2 of the FileOutputCommiter\n.set(\"mapreduce.fileoutputcommitter.algorithm.version\", \"2\")","Q_Score":0,"Tags":"python,apache-spark,amazon-s3,pyspark,amazon-emr","A_Id":57731033,"CreationDate":"2019-06-07T14:37:00.000","Title":"partitionBy taking too long while saving a dataset on S3 using Pyspark","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I know with pytorch you can turn off training by calling eval() on your model. \nAlso you can set requires_grad=False. \nHow can you ensure that a TensorFlow element is not modified during training?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":670,"Q_Id":56501260,"Users Score":0,"Answer":"If you don't want to train certain Variables in TensorFlow you can achieve this behaviour by adding trainable=False to Variables.","Q_Score":1,"Tags":"python,tensorflow,pytorch","A_Id":59047316,"CreationDate":"2019-06-07T21:11:00.000","Title":"Tensorflow no_grad concept","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"using pd.read_csv(\"my.csv\"), I have certain rows that appear as either TRUE or FALSE. read_csv is changing these rows in the dataframe as \"True\" and \"False\".  Is there any way to keep case sensitivity when reading a CSV for true and false values?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":162,"Q_Id":56501959,"Users Score":0,"Answer":"I think the output dataframe of read_csv already convert the columns to boolean values. You can verify it by calling df.info(). If you want to keep the columns as string values you need to pass a dict to the dtype parameter to specify it explicitly.","Q_Score":1,"Tags":"python,pandas","A_Id":56501994,"CreationDate":"2019-06-07T22:45:00.000","Title":"Pandas is replacing rows with FALSE and TRUE with False and True","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I use multigpu to train a model with pytorch. One gpu uses more memory than others, causing \"out-of-memory\". Why would one gpu use more memory? Is it possible to make the usage more balanced? Is there other ways to reduce memory usage? (Deleting variables that will not be used anymore...?) The batch size is already 1. Thanks.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":186,"Q_Id":56503303,"Users Score":0,"Answer":"DataParallel splits the batch and sends each split to a different GPU, each GPU has a copy of the model, then the forward pass is computed independently and then the outputs of each GPU are collected back to one GPU instead of computing loss independently in each GPU.\nIf you want to mitigate this issue you can include the loss computation in the DataParallel module.\nIf doing this is still an issue, then you might want model parallelism instead of data parallelism: move different parts of your model to different GPUs using .cuda(gpu_id). This is useful when the weights of your model are pretty large.","Q_Score":0,"Tags":"python,memory,gpu,pytorch,multi-gpu","A_Id":56651186,"CreationDate":"2019-06-08T04:00:00.000","Title":"One gpu uses more memory than others during training","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to compute a 3x4 matrix transformation, in homogeneous coordinates, that transforms 3d world points to 2d image points. My problem is that in the documentation and tutorials of the function getPerspectiveTransformation the default matrices are either 3x3 for perspective or 2x3 in affine transformations. \nIs there any built in function to compute the 3x4 matrix?\nI have read the tutorials, some books on computer vision and I know the matrix that transforms 3d to 2d is 3x4. It is necessary to input 6 points to get this matrix and in the examples I find for opencv they are using 4, so I guess this is a 2d to 2d transformation not what I need. \ncv2.getPerspectiveTransform\ncv2.getAffineTransform\nI used these functions but it didn't work, I used cv2.circle function to plot what was supposed to be a straigh line on the image and I got curved lines. Obviously I was not using the proper matrix transformation.","AnswerCount":2,"Available Count":1,"Score":-1.0,"is_accepted":false,"ViewCount":304,"Q_Id":56506815,"Users Score":-5,"Answer":"Yes of course there is. Just look for the computeThreeByFourMatrix() function in the OpenCV library documentation. It is all there","Q_Score":1,"Tags":"python,opencv,computational-geometry","A_Id":56506873,"CreationDate":"2019-06-08T13:22:00.000","Title":"Does Opencv allow you to compute the 3x4 perspective transformation using 6 points?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to create a W2V model and then generate train and test data to be used for my model.My question is how can I generate test data after I am done with creating a W2V model with my train data.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1283,"Q_Id":56508631,"Users Score":0,"Answer":"Word2Vec is considered an 'unsupervised' algorithm, so at least during its training, it is not typical to hold back any 'test' data for later evaluation. \nA Word2Vec model is usually then evaluated on how well it helps some other process - such as the analogy-solving highlighted by the original paper. In gensim, the method [evaluate_word_analogies()][1] can repeat that process. But note: word-vectors that perform best on word-analogies my not be best for other purposes, like classification or info-retrieval. It's always best to evaluate & tune your word-vectors in a repeatable way that's related to your actual underlying use. \n(If you're using the Word2Vec model's outputs - word-vectors specific to your domain \u2013 as part of a larger system, where some steps should be evaluated with held-back data, the decision of whether to train the Word2Vec component on all data could go either way, depending on other considerations.)","Q_Score":1,"Tags":"python,gensim,word2vec","A_Id":56517433,"CreationDate":"2019-06-08T17:25:00.000","Title":"Creating train,test data for Word2Vec model","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I make a Graph (not Digraph) from a data frame (Huge network) with networkx.\nI used this code to creat my graph:\nnx.from_pandas_edgelist(R,source='A',target='B',create_using=nx.Graph())\nHowever, in the output when I check the edge list, my source node and the target node has been changed based on the sort and I don't know how to keep it as the way it was in the dataframe (Need the source and target node stay as the way it was in dataframe).","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":309,"Q_Id":56509345,"Users Score":0,"Answer":"If you mean the order has changed, check out nx.OrderedGraph","Q_Score":1,"Tags":"python,pandas,networkx","A_Id":56509709,"CreationDate":"2019-06-08T19:04:00.000","Title":"How can I stop networkx to change the source and the target node?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to use Tensorflow 2.0(TF2.0) and Keras but I don't know if it's necessary to install both seperately or just TF2.0 (assuming TF2.0 has Keras bundled inside it). If I need to install TF2.0 only, will installing in a Python 3.7.1 be acceptable?\nThis is for Ubuntu 16.04 64 bit.","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":176,"Q_Id":56509459,"Users Score":2,"Answer":"In Tensorflow 2.0 there is strong integration between TensorFlow and the Keras API specification (TF ships its own Keras implementation, that respects the Keras standard), therefore you don't have to install Keras separately since Keras already comes with TF in the tf.keras package.","Q_Score":0,"Tags":"python-3.x,tensorflow2.0,tf.keras","A_Id":56521630,"CreationDate":"2019-06-08T19:22:00.000","Title":"After installing Tensorflow 2.0 in a python 3.7.1 env, do I need to install Keras, or does Keras come bundled with TF2.0?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"For example, Why use tf.int32? Why not just use Python builtin integers?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":41,"Q_Id":56518982,"Users Score":0,"Answer":"In short because TensorFlow is not executed by the python interpreter (at least not in general).\nPython provides but one possible API to interact with TensorFlow. The core of TensorFlow itself is compiled (written mostly in C++) where python datatypes are not available. Also, (despite recent advances allowing eager execution) we need to be able to first create an execution graph, then to pass data through it. That execution graph needs to be aware of datatypes but is language agnostic","Q_Score":2,"Tags":"python,tensorflow","A_Id":56524739,"CreationDate":"2019-06-09T23:02:00.000","Title":"What's the purpose of TensorFlow specific data types?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on a problem in which my response variable is a relative power whose value cannot go beyond 100%. When I use linear regression or any other machine-learning algorithms, the predicted value goes beyond 100% and I want to limit that to 100%. Is there any way we can achieve that?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":36,"Q_Id":56519098,"Users Score":0,"Answer":"It depends on the meaning of your response variable, considering you are using linear regression. But in a general function y=f(x), you can add a Softmax function y=Softmax(f(x)) to make sure y in (0, 1). If you replace Softmax with sigmoid and use it for regression, then you get a Logistic regression, they you can limit the response value to (0, 1).","Q_Score":1,"Tags":"python,machine-learning","A_Id":56520260,"CreationDate":"2019-06-09T23:29:00.000","Title":"Can we limit the value of a response variable in any machine learning algorithm?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have some audio files recorded from wind turbines, and I'm trying to do anomaly detection. The general idea is if a blade has a fault (e.g. cracking), the sound of this blade will differ with other two blades, so we can basically find a way to extract each blade's sound signal and compare the similarity \/ distance between them, if one of this signals has a significant difference, we can say the turbine is going to fail.\nI only have some faulty samples, labels are lacking.\nHowever, there seems to be no one doing this kind of work, and I met lots of troubles while attempting.\nI've tried using stft to convert the signal to power spectrum, and some spikes show. How to identify each blade from the raw data? (Some related work use AutoEncoders to detect anomaly from audio, but in this task we want to use some similarity-based method.)\nAnyone has good idea? Have some related work \/ paper to recommend?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":48,"Q_Id":56520227,"Users Score":1,"Answer":"Well...\nIf your shaft is rotating at, say 1200 RPM or 20 Hz, then all the significant sound produced by that rotation should be at harmonics of 20Hz.\nIf the turbine has 3 perfect blades, however, then it will be in exactly the same configuration 3 times for every rotation, so all of the sound produced by the rotation should be confined to multiples of 60 Hz.\nEnergy at the other harmonics of 20 Hz -- 20, 40, 80, 100, etc. -- that is above the noise floor would generally result from differences between the blades.\nThis of course ignores noise from other sources that are also synchronized to the shaft, which can mess up the analysis.","Q_Score":0,"Tags":"python,machine-learning,signal-processing","A_Id":56543927,"CreationDate":"2019-06-10T03:37:00.000","Title":"How to Identify Each Components from Audio Signal?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Why might the values between df.size and len(df.to_dict(\"records\")) be different? I find the value of df.size=58151429 while my len(df.to_dict(\"records\"))=2528323 which is quite a big difference. Why can that be?","AnswerCount":2,"Available Count":1,"Score":0.2913126125,"is_accepted":false,"ViewCount":696,"Q_Id":56520381,"Users Score":3,"Answer":"Size will display total number of values while len display length of Data Frame\nEx: if you have 3*2(3 rows and 2 columns)\n    size will be \"6\", len will be \"3\"","Q_Score":2,"Tags":"python,python-3.x,pandas","A_Id":56520450,"CreationDate":"2019-06-10T04:05:00.000","Title":"Different values between pandas df.size and len(df.to_dict(\"records\"))","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am doing a zebra crossing detection problem, and now I've already known the vertices of each zebra stripe, as a list of points. How can I efficiently calculate the coordinates of the vertices of the outline rectangle containing those zebra stripes?\nI am doing it in 3D\nI've been thinking this question for days, and cannot figure out a solution rather than brutal force...\nThat's a different problem from finding the bounding box of a given list of points. For this task, the return would be four of those zebra stripes' vertices. I just need to find them out.\nAny help or pointers would be valuable!\nUPDATE: I finally sorted those zebra-crossings by orientation and found the terminal zebra strips easily. The rest of the work is trivial","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":85,"Q_Id":56525947,"Users Score":0,"Answer":"As you told, you know the coordinates of each individual stripe of the zebra crossing. So now you can determine the first and last stripes by looking at max and min coordinates of all vertices(By considering a reference axis from which you can measure distance). then you know coordinates of terminal stripes and hence you can make the outline by considering the coordinates and hence making a larger rectangle from those four coordinates determining the whole zebra crossing.","Q_Score":0,"Tags":"python,computer-vision,computational-geometry","A_Id":56526165,"CreationDate":"2019-06-10T11:57:00.000","Title":"How can I calculate the coordinates of vertices of an zebra crossing area from the coordinates of vertices of zebra stripe?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am doing a zebra crossing detection problem, and now I've already known the vertices of each zebra stripe, as a list of points. How can I efficiently calculate the coordinates of the vertices of the outline rectangle containing those zebra stripes?\nI am doing it in 3D\nI've been thinking this question for days, and cannot figure out a solution rather than brutal force...\nThat's a different problem from finding the bounding box of a given list of points. For this task, the return would be four of those zebra stripes' vertices. I just need to find them out.\nAny help or pointers would be valuable!\nUPDATE: I finally sorted those zebra-crossings by orientation and found the terminal zebra strips easily. The rest of the work is trivial","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":85,"Q_Id":56525947,"Users Score":0,"Answer":"From what you say, it seems that you have the 3D coordinates of the outline of a rectangle. I will assume Cartesian coordinates and undistorted geometry.\nThe points belong to a plane, which you can determine by 3D plane fitting. Then by an orthogonal change of variables, you can project the points onto that plane.\nFor resonably good accuracy, you can\n\nfind the centroid of the points;\nfind the point the most distant from the centroid;\nsplit the point set by means of the line from the centroid to that point;\non both halves, find the most distant points from the centroid;\nthe line that joints them allows you to further split in four quadrants;\nin every quadrant, apply line fitting to find the edges.\n\n\nIf what you are after is the bounding box of several stripes, you can proceed as above to find the directions of the sides. Then apply a change of coordinates to bring those sides axis-aligned. Finding the bounding box is now straightforward.\nUndo the transforms to obtain the3D coordinates of the rectangle in 3D.","Q_Score":0,"Tags":"python,computer-vision,computational-geometry","A_Id":56526776,"CreationDate":"2019-06-10T11:57:00.000","Title":"How can I calculate the coordinates of vertices of an zebra crossing area from the coordinates of vertices of zebra stripe?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have created my tensorflow model which will act as a serve. Code will be hosted on client's local server. I don't want to give them my code but give them a wheel file. But after following python's package distribution my tensorflow files become corrupted.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":120,"Q_Id":56526497,"Users Score":0,"Answer":"You can send only the frozen inference graph in .pb format.","Q_Score":0,"Tags":"python,tensorflow,keras,setup.py,python-wheel","A_Id":56526556,"CreationDate":"2019-06-10T12:31:00.000","Title":"Packaging tensorflow models as wheel files","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I have a very expensive data pipeline. I want to use tf.data.Dataset.cache to cache the first epoch dataset to disk. Then speed up the process. The reason I'm doing this instead of saving the dataset into tfrecords is\n1) I change many parameters doing the processing every time, it is more convenient for me to cache it on the fly\n2) I'm doing cross-validation so I don't know which files to process\nI have a naive solution - to create a pipeline for each fold of the training files, but that takes a lot of space to cache (I'm doing 10 fold) that's equivalent of 1TB in total. \nIs there any other way to do this more efficiently both in space and in time?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":218,"Q_Id":56531580,"Users Score":0,"Answer":"Answering my own question, to do this, I can create a pipeline for each file, cache each pipeline on disk, put them into deque, then use tf.data.experimental.sample_from_datasets.","Q_Score":0,"Tags":"python-3.x,tensorflow,tensorflow-datasets","A_Id":56531971,"CreationDate":"2019-06-10T18:18:00.000","Title":"tensorflow dataset cache cross validation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have unstructured (taken in no regular order) point cloud data (x,y,z) for a surface. This surface has bulges (+z) and depressions (-z) scattered around in an irregular fashion. I would like to generate some surface that is a function of the original data points and then be able to input a specific (x,y) and get the surface roughness value from it (z value). How would I go about doing this? \nI've looked at scipy's interpolation functions, but I don't know if creating a single function for the entire surface is the correct approach? Is there a technical name for what I am trying to do? I would appreciate any suggestions\/direction.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":847,"Q_Id":56532588,"Users Score":0,"Answer":"I don't know if creating a single function for the entire surface is the correct approach?\n\nI guess this depends on your data. Let's assume the base form of your surface is spherical. Then you can model it as such. \nIf your surface is more complex then a sphere you might can still model the neighborhood of (x,y) as such. Maybe you could even consider your surface as plain in the near neighborhood of (x,y).","Q_Score":2,"Tags":"python,python-3.x,mesh,point-clouds,surface","A_Id":56533007,"CreationDate":"2019-06-10T19:39:00.000","Title":"Python point cloud data to surface fit\/function","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have unstructured (taken in no regular order) point cloud data (x,y,z) for a surface. This surface has bulges (+z) and depressions (-z) scattered around in an irregular fashion. I would like to generate some surface that is a function of the original data points and then be able to input a specific (x,y) and get the surface roughness value from it (z value). How would I go about doing this? \nI've looked at scipy's interpolation functions, but I don't know if creating a single function for the entire surface is the correct approach? Is there a technical name for what I am trying to do? I would appreciate any suggestions\/direction.","AnswerCount":2,"Available Count":2,"Score":-0.0996679946,"is_accepted":false,"ViewCount":847,"Q_Id":56532588,"Users Score":-1,"Answer":"What you are trying to do, can be called surface fitting, or two-dimensional curve fitting. You would be able to find lots of available algorithms by searching for those terms. Now, the choice of the particular algorithm\/method should be dictated:\n\nby the origin of your data (there are specialized algorithms or variations of more common ones that are tailored for certain application areas)\nby the future use of your data (depending on what you are going to do with it, maybe you need to be able to calculate derivatives easily, etc)\n\nIt is not easy to represent complicated data (especially the noisy one) using a single function. Thus there is a lot of research about it. However, in a lot of applications curve-fitting is very successful and very widely used.","Q_Score":2,"Tags":"python,python-3.x,mesh,point-clouds,surface","A_Id":56534865,"CreationDate":"2019-06-10T19:39:00.000","Title":"Python point cloud data to surface fit\/function","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"We are working on early action prediction but we are unable to understand the dataset itself NTU rgbd dataset is 1.3 tb.my laptop Hard disk is 931 GB\n .first problem : how to deal with such a big dataset?\nSecond problem : how to understand dataset?\nThird problem: how to load dataset ?\nThanks for the help","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":128,"Q_Id":56535700,"Users Score":0,"Answer":"The overall size of the dataset is 1.3 TB and this size will decrease after processing the data and converting it into numpy arrays or something else.\nBut I do not think you will work on the entire dataset, what is the part you want to work on it in the dataset?","Q_Score":0,"Tags":"python,machine-learning","A_Id":56695079,"CreationDate":"2019-06-11T02:31:00.000","Title":"How to load NTU rgbd dataset?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"That's it. It installs, I can import statsmodels, but statsmodels.api doesn't import.\nI've tried installing with pip and conda, both give me version 0.9.0 and everything is fine.\nI've installed all the dependencies, statsmodels works, but statsmodels.api doesn't.\n\n\n\n\nimport statsmodels.api Traceback (most recent call last):   File \"\", line 1, in    File\n        \"C:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python37-32\\lib\\site-packages\\statsmodels\\api.py\",\n        line 16, in \n            from .discrete.discrete_model import (Poisson, Logit, Probit,   File\n        \"C:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python37-32\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py\",\n        line 45, in \n            from statsmodels.distributions import genpoisson_p   File \"C:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python37-32\\lib\\site-packages\\statsmodels\\distributions__init__.py\",\n        line 2, in \n            from .edgeworth import ExpandedNormal   File \"C:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python37-32\\lib\\site-packages\\statsmodels\\distributions\\edgeworth.py\",\n        line 7, in \n            from scipy.misc import factorial ImportError: cannot import name 'factorial' from 'scipy.misc'\n        (C:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python37-32\\lib\\site-packages\\scipy\\misc__init__.py)","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":178,"Q_Id":56546431,"Users Score":1,"Answer":"From the error it looks as though there is not a function called factorial within the misc directory of the scipy package. \nHave you tried opening up the __init__.py file specified in the error and looking through the misc directory to find the factorial function?","Q_Score":1,"Tags":"python-3.x,statsmodels","A_Id":56546713,"CreationDate":"2019-06-11T14:52:00.000","Title":"Statsmodels.api doesn't import","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have 5 csv files that I am trying to put into one graph in python. In the first column of each csv file, all of the numbers are the same, and I want to treat these as the x values for each csv file in the graph. However, there are two more columns in each csv file (to make 3 columns total), but I just want to graph the second column as the 'y-values' for each csv file on the same graph, and ideally get 5 different lines, one for each file. Does anyone have any ideas on how I could do this?\nI have already uploaded my files to the variable file_list","AnswerCount":4,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":932,"Q_Id":56547899,"Users Score":0,"Answer":"Read the first file and create a list of lists in which each list filled by two columns of this file. Then read the other files one by one and append y column of them to the correspond index of this list.","Q_Score":0,"Tags":"python,pandas,csv,matplotlib,graph","A_Id":56548032,"CreationDate":"2019-06-11T16:14:00.000","Title":"Graphing multiple csv lists into one graph in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I\u2019m setting up a new invoice extraction method using AI, I able to recognize \"Total\"\/\"Company Details\" from invoice images but need help with extracting data from that particular region recognized in the invoice image by specifying an area in the image(Xmin, Xmax, Ymin, Ymax)?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":950,"Q_Id":56561357,"Users Score":1,"Answer":"Looks like you are newbird,so let me help you quick walkthrough of understanding of terms used in your keyword.\nOCR is optical character recognition a concept\nTesseract is special library handling for OCR.\nOpenCV helps in image processing library helping in object detection and recognition.\nYes, you can extract the text from image if its more than 300dpi by using tesseract library \nbut before that you should train the tesseract model with that font, if font of text is very new or unknown to system.\nAlso keep in mind, if you are able to box-image the text prior calling to tesseract, it will work more accurately.\nCertain word - box image, dpi will create alert, but these are pivot concepts to your work.\nMy suggestion, if you want to extract the digits from image, go in step by step.\n\nProcess the image by enhancing its quality.\nDetect the region which want to be extracted. \nFind the contour and area.\nPass it to box-image editor and tune the parameters \nFinally give it to Tesseract.","Q_Score":1,"Tags":"python,artificial-intelligence,ocr,tesseract,text-extraction","A_Id":56561928,"CreationDate":"2019-06-12T11:39:00.000","Title":"Is there any way in OCR\/tesseract\/OpenCV for extracting text from a particular region of an image?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been working on image classification with deep learning models (CNN with keras and tensorflow as back end) such as AlexNet and ResNet. I learned a lot about the whole dataset, learning, testing processes.\nI'm now shifting to object detection and have done a lot of research. I came across R-CNN, Fast R-CNN, Faster R-CNN, Mask R-CNN and different versions of YOLO networks. I noticed that these object detection networks require dataset annotation instead of the former simply needing to have cropped images stored in corresponding files.\nIs there any way to accomplish object detection without having to annotate dataset?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":872,"Q_Id":56562536,"Users Score":0,"Answer":"No, not really, Deep Learning is not magic, you need bounding box annotations. The field of research that deals with this problem is Weakly Supervised Object Detection, and its a research field, there are no solutions that perform as well as using an annotated dataset.","Q_Score":0,"Tags":"python,tensorflow,deep-learning,dataset,object-detection-api","A_Id":56563697,"CreationDate":"2019-06-12T12:43:00.000","Title":"Can I avoid annotating my dataset for object recognition?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a list of points that represent a needle\/catheter in a 3D volume. This volume is voxalized. I want to get all the voxels that the line that connects the point intersects. The line needs to go through all the points. \nIdeally, since the round needle\/catheter has a width I would like to be able to get the voxels that intersect the actual three dimensional object that is the needle\/catheter. (I imagine this is much harder so if I could get an answer to the first problem I would be very happy!)\nI am using the latest version of Anaconda (Python 3.7). I have seen some similar problems, but the code is always in C++ and none of it seems to be what I'm looking for. I am fairly certain that I need to use raycasting or a 3D Bresenham algorithm, but I don't know how. \nI would appreciate your help!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":628,"Q_Id":56565073,"Users Score":0,"Answer":"I ended up solving this problem myself. For anyone who is wondering how, I'll explain it briefly. \nFirst, since all the catheters point in the general direction of the z-axis, I got the thickness of the slices along that axis. Both input points land on a slice. I then got the coordinates of every intersection between the line between the two input points and the z-slices. Next, since I know the radius of the catheter and I can calculate the angle between the two points, I was able to draw ellipse paths on each slice around the points I had previously found (when you cut a cone at and angle, the cross-section is an ellipse). Then I got the coordinates of all the voxels on every slice along the z-axis and checked which voxels where within my ellipse paths. Those voxels are the ones that describe the volume of the catheter. If you would like to see my code please let me know.","Q_Score":0,"Tags":"python,3d,line,voxel,bresenham","A_Id":56839312,"CreationDate":"2019-06-12T14:54:00.000","Title":"How to get a voxel array from a list of 3D points that make up a line in a voxalized volume?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to read the records from mainframe file and apply the some filters on record values.\nSo I am looking for a solution to convert the mainframe file to csv or text or Excel workbook so that I can easily perform the operations on the file.\nI also need to validate the records count.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1297,"Q_Id":56568561,"Users Score":0,"Answer":"Who said anything about EBCDIC? The OP didn't.\nIf it is all text then FTP'ing with EBCDIC to ASCII translation is doable, including within Python.\nIf not then either:\n\nThe extraction and conversion to CSV needs to happen on z\/OS. Perhaps with a COBOL program. Then the CSV can be FTP'ed down with\n\nor\n\nThe data has to be FTP'ed BINARY and then parsed and bits of it translated.\n\nBut, as so often is the case, we need more information.","Q_Score":0,"Tags":"excel,python-3.x,export-to-csv,mainframe,ebcdic","A_Id":56583582,"CreationDate":"2019-06-12T18:44:00.000","Title":"We have many mainframe files which are in EBCDIC format, is there a way in Python to parse or convert the mainframe file into csv file or text file?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have two dataframes :\n1st dataframe\n\nCountrey    2000    2001    2002    2003    2004    2005\n\ncanada      779      771     754     740     760     747\nmexico      1311.2  1285.2  1271.2  1276.5  1270.6  1281\nusa         836     814     810     800     802     799\nIndia       914     892     888     878     880     877\nChina       992     970     966    956       958    955\n\n2nd dataframe\n\nyear        2000    2001    2002    2003    2004    2005\ndata        1.1     1.2     1.3    1.4       1.5    1.6\n\ni would like to merge above these two dataframe in following way?is it possible?\n\ndata        1.1      1.2    1.3     1.4      1.5    1.6\nCountrey    2000    2001    2002    2003    2004    2005\ncanada      779     771      754    740      760    747\nmexico      1311.2  1285.2  1271.2  1276.5  1270.6  1281\nusa         836      814    810     800      802    799\nIndia       914      892    888     878      880    877\nChina       992      970    966     956      958    955\nSum        4832.2   4732.2  4689.2  4650.5  4670.6  4659\n\nalso i wants the sum of each column","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":561,"Q_Id":56591367,"Users Score":0,"Answer":"You could use pandas.concat() or DataFrame.append() , check pandas document its easily available with examples.","Q_Score":0,"Tags":"python,python-3.x","A_Id":56592187,"CreationDate":"2019-06-14T04:26:00.000","Title":"Stack Data Frames on top of one another dataframe","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Let's say I have a numpy array where I would like to swap all the 1's to 0 and all the 0's to 1 (the array will have other values, and there is nothing special about the 0's and 1's). Of course, I can loop through the array and change the values one by one.\nIs there an efficient method you can recommend using? Does the np.where() method have an option for this operation?","AnswerCount":7,"Available Count":1,"Score":0.057080742,"is_accepted":false,"ViewCount":14516,"Q_Id":56594598,"Users Score":2,"Answer":"iverted = ~arr + 2  should do the work","Q_Score":9,"Tags":"python,numpy","A_Id":68484466,"CreationDate":"2019-06-14T08:46:00.000","Title":"Change 1's to 0 and 0's to 1 in numpy array without looping","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Can I train the object detection model with large images size 1600*1216 and obtain good accuracy . Thanks","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":84,"Q_Id":56594824,"Users Score":0,"Answer":"Yes, you can train the object detection model with large images size 1600*1216 and obtain good accuracy.","Q_Score":0,"Tags":"python-3.x,object-detection","A_Id":56815637,"CreationDate":"2019-06-14T09:02:00.000","Title":"Train object detection with large images","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have two dataframes :\n1st dataframe\n\ncat         2000    2001    2002    2003    2004    2005\n1            779     771     754     740     760     747\n2          1311.2   1285.2  1271.2  1276.5  1270.6  1281\n3           836      814    810      800     802     799\n4           914      892    888      878     880     877\n5           992      970    966      956     958     955\n\n2nd dataframe\n\n\nyear  data\n2000  1\n2001  4\n2002  7\n2003  10\n2004  6\n2005  3    \n\n\ni would like to merge above these two dataframe in following way?is it possible?\n\ncat         2000    2001    2002    2003    2004    2005\n1           779     771     754      740     760    747\n2          1311.2  1285.2   1271.2   1276.5 1270.6  1281\n3           836      814    810      800     802    799\n4           914      892    888      878     880    877\n5           992      970    966      956     958    955\n6(merge entry)1         4    7        10      6      3","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":38,"Q_Id":56596244,"Users Score":0,"Answer":"It's not hard at all. You have to transpose the second dataframe and add its values in the rows of the first dataframe. Use the .T command.","Q_Score":0,"Tags":"python,python-3.x","A_Id":56596649,"CreationDate":"2019-06-14T10:25:00.000","Title":"Merge column data into row in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have made a object recognition and detection model using tensorflow. It identifies the images which are clearly visible but its unable to identify if the same object is at a large distance. I am using Faster RCNN model.  the model is able to identify the same object when it is closer but not when it is at a far distance. It has been trained already for the same object. How can i make the model identify objects at a distance?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":32,"Q_Id":56602594,"Users Score":0,"Answer":"You can resize and add padding using data augmentation to images with objects that are clearly visible so that they look like they are in a big distance and train your model further with those images","Q_Score":0,"Tags":"python-3.x,tensorflow,object-detection-api","A_Id":56603296,"CreationDate":"2019-06-14T17:11:00.000","Title":"Model unable to identify distant objects","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am comparing design choice of wrapping either each \"vector\" into Object, or each whole \"matrix\" of vectors into Object. \nI realize there will be more overhead if I try to wrap up each path by a class object, but it would make the system a lot easier to understand and implement.\nHowever, I thought this might cost us the performance.\nWhat would be a convention when it comes to loading big data as attributes?\nI appreciate your thoughts in advance,\nq","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":20,"Q_Id":56605145,"Users Score":1,"Answer":"It seems you want to \"wrap paths\" and store many attributes for those paths, and there are lots of \"paths\".\nInstead of defining classes that require lots of custom objects creation just for the paths themselves, store the paths (strings I suppose) as keys in a dict, and the attributes in any appropriate form inside.","Q_Score":0,"Tags":"python,performance,oop","A_Id":57694779,"CreationDate":"2019-06-14T21:09:00.000","Title":"Design Choice: how to wrap up large files with 100k+ vectors","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Data similar to the images of 1000 x 1 pixels come from the equipment. Somewhere in the image may be 1, 2 or more objects. \nI'm going to build a neural network to detect objects. I want to make 1,000 outputs. Each output will indicate whether there is an object in that output or not. \nAdvise me which loss function to use.\nIt seems to me that \"categorical crossentropy\" is not suitable, because for example: in the training data, I will indicate that the objects are at 10 and 90 pixels. And the neural network will predict that the objects are at 11 and 89 pixels. It's not a big loss. But for the network, it will be the same loss as if it predict objects at 500 and 900 pixels. \nWhat loss function is suitable for such a case ?\nI'm using Keras","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":469,"Q_Id":56608876,"Users Score":0,"Answer":"You can use binary cross entropy loss and set the nearest n-bins to the ground truth as labels.\nFor example, you have 10 pixels and ground truth label is 3 and you selected 3 neighbours.\nIn typical categorical cross entropy, you would set label as follow using one-hot encoded vector.\n[0 0 1 0 0 0 0 0 0 0]\nIn the solution I suggested, you would use this\n[0 1 1 1 0 0 0 0 0 0]\nOr it can be this, basically imposing a Gaussian instead of flat labels.\n[0 0.5 1 0.5 0 0 0 0 0 0]\n\nObject detection architectures as suggested in the comments also essentially behave the same way I described. Except that they use a quantized scheme\n[0 1 0 0 0 0 0 0 0] (actual pixels)\n[- - 1 - - - - 0 - -] (group into 2 groups of 5. Your network only has two outputs now. Think of this as binning stage, as the actual pixel belong to group 1. this subnetwork uses binary cross entropy).\n[1 0] (first classification network output)\n[-1 0] (this second stage can be thought of as delta network, it takes the classified bin value from first stage and outputs a correction value, as the first bin is anchored at index 2, you need to predict -1 to move it to index 1. this network is trained using smoothed l1 loss).\nNow there is immediately a problem, what if there are two objects in  group 1? This is an unfortunate problem which also exists in object detection architecture. The way to workaround with this is to define slightly shifted and scaled bin(or anchor) positions. This way you can detect at one pixel maximum of N objects where N is the number of anchors defined at that pixel.","Q_Score":0,"Tags":"python,keras,neural-network,loss-function","A_Id":56609093,"CreationDate":"2019-06-15T08:50:00.000","Title":"Which loss function to use when there are many outputs?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Tensorflow library was working on my system, I have installed other library now tensorflow is not working. I am using window 10. python 3.7, tensorflow 1.13.1.\nI am getting following error:\nFile \"\", line 1, in \n    runfile('C:\/Users\/Yogesh\/.spyder-py3\/MNISTBasicClassification.py', wdir='C:\/Users\/Yogesh\/.spyder-py3')\nFile \"C:\\Users\\Yogesh\\Anaconda3\\lib\\site-packages\\spyder_kernels\\customize\\spydercustomize.py\", line 704, in runfile\n    execfile(filename, namespace)\nFile \"C:\\Users\\Yogesh\\Anaconda3\\lib\\site-packages\\spyder_kernels\\customize\\spydercustomize.py\", line 108, in execfile\n    exec(compile(f.read(), filename, 'exec'), namespace)\nFile \"C:\/Users\/Yogesh\/.spyder-py3\/MNISTBasicClassification.py\", line 58, in \n    model.fit(train_images, train_labels, epochs=5)\nFile \"C:\\Users\\Yogesh\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py\", line 880, in fit\n    validation_steps=validation_steps)\nFile \"C:\\Users\\Yogesh\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training_arrays.py\", line 251, in model_iteration\n    model.reset_metrics()\nFile \"C:\\Users\\Yogesh\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py\", line 1119, in reset_metrics\n    m.reset_states()\nFile \"C:\\Users\\Yogesh\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\keras\\metrics.py\", line 460, in reset_states\n    K.set_value(v, 0)\nFile \"C:\\Users\\Yogesh\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\keras\\backend.py\", line 2847, in set_value\n    get_session().run(assign_op, feed_dict={assign_placeholder: value})\nFile \"C:\\Users\\Yogesh\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\keras\\backend.py\", line 479, in get_session\n    session = _get_session()\nFile \"C:\\Users\\Yogesh\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\keras\\backend.py\", line 457, in _get_session\n    config=get_default_session_config())\nFile \"C:\\Users\\Yogesh\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\", line 1551, in init\n    super(Session, self).init(target, graph, config=config)\nFile \"C:\\Users\\Yogesh\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\", line 676, in init\n    self._session = tf_session.TF_NewSessionRef(self._graph._c_graph, opts)\nInternalError: cudaGetDevice() failed. Status: CUDA driver version is insufficient for CUDA runtime version","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":154,"Q_Id":56609576,"Users Score":0,"Answer":"I uninstalled the tensorflow and then created a new environment and then reinstalled the tensorflow then the issue was resolved.","Q_Score":0,"Tags":"python,tensorflow","A_Id":56632620,"CreationDate":"2019-06-15T10:41:00.000","Title":"tensor flow is not working in anaconda gettting CUDA runtime version error on window system","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"i used in my code from sklearn.cross_validation import KFold and it was working without no issue .\nnow i got error ModuleNotFoundError: No module named 'sklearn.cross_validation'\ni google it and i found that the package function is renamed to model_selection instead of cross_validation but i have to use KFold function from  sklearn.cross_validation import KFold.\nthe sklearn version i have is 0.20.1 with Python 3.7.1 \nusing KFold which is a method already implemented in  sklearn.cross_validation\nfrom sklearn.cross_validation import KFold\nexpected to run sucessfully as before but i got below error:\nModuleNotFoundError: No module named 'sklearn.cross_validation'","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":815,"Q_Id":56613045,"Users Score":0,"Answer":"Issue resolved by importing the supported package which is from sklearn.model_selection import KFold\nthen modifying KFold method with correct parameters as below\nKFold(n_splits=2, random_state=None, shuffle=False)","Q_Score":0,"Tags":"python-3.7","A_Id":56613628,"CreationDate":"2019-06-15T18:22:00.000","Title":"from sklearn.cross_validation import KFold renaming and depreaction of cross_validation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Recently, I read a book on Numpy which mentions different types of ufuncs, where I was encountered with two different ufuncs, namely 'modulus', denoted by % symbol and 'floor division' \/\/. Can someone explain the difference between them and why two operators are provided to do the the same thing (display reminder of division, according to me)? Please correct, if I am wrong.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":8915,"Q_Id":56627393,"Users Score":0,"Answer":"Assume a= 10, b = 6\na%b will give you the remainder, that is 4\na\/\/b will give you the quotient, that is 1","Q_Score":0,"Tags":"python,numpy,integer-division","A_Id":56627542,"CreationDate":"2019-06-17T08:20:00.000","Title":"Difference between modulus (%) and floor division(\/\/) in NumPy?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"thanks for hearing me out.\nI have a dataset that is a matrix of shape 75000x10000 filled with float values. Think of it like heatmap\/correlation matrix. I want to store this in a SQLite database (SQLite because I am modifying an existing Django project). The source data file is 8 GB in size and I am trying to use python to carry out my task.\nI have tried to use pandas chunking to read the file into python and transform it into unstacked pairwise indexed data and write it out onto a json file. But this method is eating up my computational cost. For a chunk of size 100x10000 it generates a 200 MB json file.\nThis json file will be used as a fixture to form the SQLite database in Django backend.\nIs there a better way to do this? Faster\/Smarter way. I don't think a 90 GB odd json file written out taking a full day is the way to go. Not even sure if Django databases can take this load.\nAny help is appreciated!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":124,"Q_Id":56633576,"Users Score":2,"Answer":"SQLite is quite impressive for what it is, but it's probably not going to give you the performance you are looking for at that scale, so even though your existing project is Django on SQLite I would recommend simply writing a Python wrapper for a different data backend and just using that from within Django.\nMore importantly, forget about using Django models for something like this; they are an abstraction layer built for convenience (mapping database records to Python objects), not for performance. Django would very quickly choke trying to build 100s of millions of objects since it doesn't understand what you're trying to achieve.\nInstead, you'll want to use a database type \/ engine that's suited to the type of queries you want to make; if a typical query consists of a hundred point queries to get the data in particular 'cells', a key-value store might be ideal; if you're typically pulling ranges of values in individual 'rows' or 'columns' then that's something to optimize for; if your queries typically involve taking sub-matrices and performing predictable operations on them then you might improve the performance significantly by precalculating certain cumulative values; and if you want to use the full dataset to train machine learning models, you're probably better off not using a database for your primary storage at all (since databases by nature sacrifice fast-retrieval-of-full-raw-data for fast-calculations-on-interesting-subsets), especially if your ML models can be parallelised using something like Spark.\nNo DB will handle everything well, so it would be useful if you could elaborate on the workload you'll be running on top of that data -- the kind of questions you want to ask of it?","Q_Score":0,"Tags":"python,sql,django,pandas,bigdata","A_Id":56656088,"CreationDate":"2019-06-17T14:39:00.000","Title":"I want to write a 75000x10000 matrix with float values effectively into a database","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"Azure Datascience VM with Nvidia Gpu extension\nUbuntu 16.04.6 LTS\nPython 3.5.5\nNVCC V10.0.130\nTensorflow-gpu 1.13.1\n\nWhen runnning an op, I get\n\nE tensorflow\/stream_executor\/cuda\/cuda_driver.cc:300] failed call to\n  cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":101,"Q_Id":56642210,"Users Score":0,"Answer":"what is the size of the VM you are using on Azure ?","Q_Score":0,"Tags":"python,azure,tensorflow","A_Id":56644886,"CreationDate":"2019-06-18T05:24:00.000","Title":"Enabling Cuda on Azure Datascience VM","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"For the sake of curiosity is there any practical difference between getting the column names of a DataFrame (let's say df) by using df.columns or df.keys()? \nI've checked the outs by type and it seems to be exactly the same. Am I missing something or these two methods are just as redundant as it seems? Is one more appropriate to use than the other?\nThanks.","AnswerCount":2,"Available Count":1,"Score":0.2913126125,"is_accepted":false,"ViewCount":1291,"Q_Id":56649500,"Users Score":3,"Answer":"one difference I noticed That you can Use .keys() with Series but You can not use .columns with Series .","Q_Score":3,"Tags":"python-3.x,pandas,dataframe","A_Id":62995790,"CreationDate":"2019-06-18T12:45:00.000","Title":"Is there any difference between using Dataframe.columns and Dataframe.keys() to obtain the column names?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"i am currently in the process of pre-processing my data and I understand that i have to use the same scaling parameters I have used on my training set, on my test set. However, when i applied the transform method from sklearn library, i noticed something weird.\nI first used preprocessing.MinMaxScaler(feature_range=(0,1)) on my training set which sets the maximum to be 1 and minimum to be 0. Next, i used minmax_scaler.transform(data) on my test set and I've noticed when i printed out the data-frame, I have values that are greater than 1. What can this possibly mean?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1278,"Q_Id":56665409,"Users Score":2,"Answer":"For a given feature x, your minmax scaling to (0,1) will effectively map:\nx to (x- min_train_x)\/(max_train_x - min_train_x)\nwhere min_train_x and max_train_x are the minimum and maximum value of x in the training set.\nIf a value of x in the testing set is larger than the max_train_x the scaling transformation will return a value > 1.\nIt usually is not a big problem except if the input has to be in the (0,1) range.","Q_Score":1,"Tags":"python,scikit-learn,sklearn-pandas","A_Id":56665504,"CreationDate":"2019-06-19T10:20:00.000","Title":"What happens when you transform the test set using MinMaxScaler","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using Tensorflow Object detection api. What I understood reading the faster_rcnn_inception_v2_pets.config file is that num_steps mean the total number of steps and not the epochs. But then what is the point of specifying batch_size?? Lets say I have 500 images in my training data and I set batch size = 5 and num_steps = 20k. Does that mean number of epochs are equal to 200 ??\nWhen I run model_main.py it shows only the global_steps loss. So if these global steps are not the epochs then how should I change the code to display train loss and val loss after each step and also after each epoch.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":2207,"Q_Id":56686630,"Users Score":1,"Answer":"So you are right with your assumption, that you have 200 epochs.\nI had a similar problem with the not showing of loss.\nmy solution was to go to the model_main.py file and then insert\ntf.logging.set_verbosity(tf.logging.INFO)\nafter the import stuff.\nthen it shows you the loss after each 100 steps.\nyou could change the set_verbosity if you want to have it after every epoch ;)","Q_Score":1,"Tags":"python,tensorflow,object-detection-api","A_Id":57010899,"CreationDate":"2019-06-20T13:04:00.000","Title":"How to display number of epochs in tensorflow object detection api with Faster Rcnn?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am getting very high RMSE and MAE for MLPRegressor , ForestRegression and Linear regression with only input variables scaled (30,000+) however when i scale target values aswell i get RMSE (0.2) , i will like to know if that is acceptable thing to do.\nSecondly is it normal to have better R squared values for Test (ie. 0.98 and 0.85 for train)\nThank You","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":4385,"Q_Id":56689243,"Users Score":0,"Answer":"It is actually a common practice to scale target values in many cases.\nFor example a highly skewed target may give better results if it is applied log or log1p transforms. I don't know the characteristics of your data, but there could a transformation that might decrease your RMSE.\nSecondly, Test set is meant to be a sample of unseen data, to give a final estimate of your model's performance. When you see the unseen data and tune to perform better on it, it becomes a cross validation set.\nYou should try to split your data into three parts, Train, Cross-validation and test sets. Train on your data and tune parameters according to it's performance on cross validation and then after you are done tuning, run it on the test set to get a prediction of how it works on unseen data and mark it as the accuracy of your model.","Q_Score":0,"Tags":"python-3.x,regression,data-science,prediction","A_Id":56690623,"CreationDate":"2019-06-20T15:23:00.000","Title":"Is it acceptable to scale target values for regressors?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Found existing installation: wrapt 1.10.11\nCannot uninstall 'wrapt'. It is a distutils installed project and thus we cannot accurately determine which files belong to it which would lead to only a partial uninstall.","AnswerCount":4,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":5569,"Q_Id":56705686,"Users Score":0,"Answer":"I was getting this error while installing from conda environment. Always upgrade conda or pip before a new installation.\nFollowing worked for me:\n\n[Optional] If installing in conda environment, then temporarily remove the conda env:\nconda remove --name myenv --all\nUpdate all conda packages:\nconda update --all\nCreate conda env again:\nconda create -n myenv\nActivate conda env:\nconda activate\nInstall tensorflow:\npip install tensorflow","Q_Score":2,"Tags":"python-3.x,tensorflow,anaconda","A_Id":60075464,"CreationDate":"2019-06-21T14:46:00.000","Title":"i tried installing tensorflow using 'pip install tensorflow ' in anaconda prompt and command prompt. its showing following output","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've got a partially trained model in Keras, and before training it any further I'd like to change the parameters for the dropout, l2 regularizer, gaussian noise etc. I have the model saved as a .h5 file, but when I load it, I don't know how to remove these regularizing layers or change their parameters. Any clue as to how I can do this?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":356,"Q_Id":56706219,"Users Score":0,"Answer":"Create a model with your required hyper-parameters and load the parameters to the model using load_weight().","Q_Score":1,"Tags":"python,python-3.x,keras,regularized","A_Id":56706524,"CreationDate":"2019-06-21T15:20:00.000","Title":"How to remove regularisation from pre-trained model?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I know that you can convert a spark dataframe df into a pandas dataframe with \n\ndf.toPandas()\n\nHowever, this is taking very long, so I found out about a koala package in databricks that could enable me to use the data as a pandas dataframe (for instance, being able to use scikit learn) without having a pandas dataframe. I already have the spark dataframe, but I cannot find a way to make it into a Koalas one.","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":8396,"Q_Id":56706860,"Users Score":22,"Answer":"To go straight from a pyspark dataframe (I am assuming that is what you are working with) to a koalas dataframe you can use:\nkoalas_df = ks.DataFrame(your_pyspark_df)\nHere I've imported koalas as ks.","Q_Score":10,"Tags":"python-3.x,dataframe,databricks","A_Id":56855229,"CreationDate":"2019-06-21T15:58:00.000","Title":"How to convert a spark dataframe into a databrick koalas dataframe?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I know that you can convert a spark dataframe df into a pandas dataframe with \n\ndf.toPandas()\n\nHowever, this is taking very long, so I found out about a koala package in databricks that could enable me to use the data as a pandas dataframe (for instance, being able to use scikit learn) without having a pandas dataframe. I already have the spark dataframe, but I cannot find a way to make it into a Koalas one.","AnswerCount":2,"Available Count":2,"Score":0.2913126125,"is_accepted":false,"ViewCount":8396,"Q_Id":56706860,"Users Score":3,"Answer":"Well. First of all, you have to understand the reason why toPandas() takes so long :  \n\nSpark dataframe are distributed in different nodes and when you run toPandas()\nIt will pull the distributed dataframe back to the driver node (that's the reason it takes long time)\nyou are then able to use pandas, or Scikit-learn in the single(Driver) node for faster analysis and modeling, because it's like your modeling on your own PC\nKoalas is the pandas API in spark and when you convert it to koalas dataframe : It's still distributed, so it will not shuffle data between different nodes, so you can use pandas' similar syntax for distributed dataframe transformation","Q_Score":10,"Tags":"python-3.x,dataframe,databricks","A_Id":58230046,"CreationDate":"2019-06-21T15:58:00.000","Title":"How to convert a spark dataframe into a databrick koalas dataframe?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"These are related to the TfidfVectorizer of sklearn.\nCould some explain please the similarities and differences between these two and when each one is useful.\nIt is quite confusing because they look very similar to each other but also quite different.\nAlso the rather limited sklearn documentation does not help much in this case either.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":509,"Q_Id":56707957,"Users Score":0,"Answer":"Basically, I think that they contain exactly the same information.\nHowever, if you have the name of the term and you look for the column position of it at the tf-idf matrix then you go for the .vocabulary_.\nThe .vocabulary_ has as keys the names of the terms and as values their column position at the tf-idf matrix.\nWhereas, if you know the column position of the term at the tf-idf matrix and you look for its name then you go for the .get_feature_names().\nThe position of the terms in the .get_feature_names() correspond to the column position of the elements to the tf-idf matrix.","Q_Score":0,"Tags":"python,python-3.x,scikit-learn,tfidfvectorizer","A_Id":56708040,"CreationDate":"2019-06-21T17:21:00.000","Title":".vocabulary_ vs .get_feature_names()","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have data in my Redshift cluster. I need to find the best and efficient way to delete the previously stored data when I re run the job.\nI have these two column to determine previous data previous_key (column that corresponds to run_dt) and creat_ts (time when we load the data)\nI found two approaches so far but they don't work in efficient way:\n\nUse sql DELETE command \u2013 might be slow, eventually requires Vacuum the table to reclaim storage space and resort rows\nUnload the data from a table into a file on S3 and then load table back (truncate and insert) with max clndr_key filtered out. Not really good either, might be risky.\n\nPlease suggest any good approach to rerun jobs on Redshift cluster.\nNote: partitions functionality is not available.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":6918,"Q_Id":56730152,"Users Score":1,"Answer":"Deleting data stored in Redshift with DELETE command will take time. The reason is that you are doing a soft delete, I mean you mark existing rows as deleted and then insert new row representing updated form of the data.\nSo one way is executing DELETE for junks of data. Instead of deleting one by one you should try to address multiple rows. Since each write takes place in 1 MB chunks of data, we should be minimizing those data read and writes eventually. \nIf you have a good information about the topology of the data stored in Redshift compute nodes and slices, addition to that information about distribution key and sort key, you can separate your DELETE command into multiple statements. (Any how we are expecting Redshift SQL Engine to do this for the SQL developer)","Q_Score":1,"Tags":"python,amazon-web-services,pyspark,bigdata,amazon-redshift","A_Id":56730467,"CreationDate":"2019-06-24T05:10:00.000","Title":"How to efficiently delete data from Redshift?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a list FFT data which I want to plot in a single heat map. Each data set has its own X and Y. Usually I make use of Seaborn and Panda dataframe to plot the heatmap as the Frequency will be common for all the sets, but now the frequency column is unique for each data set. How can we plot a heatmap from this kind of data?\nthat is , I have a number of data sets as\nX1,Y1\nX2,Y2\nX3,Y3\n...\n...\nXn,Yn\neach X is unique and I want to plot these N graphs as a heatmap.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":497,"Q_Id":56738558,"Users Score":0,"Answer":"The problem was caused due to the fact that the Data was of non-uniform grid, now I created a common domain (x-axis value) for all the functions and then interpolated all the Data set to the new X-value.\nI used the numpy.interpol() function to achieve this.","Q_Score":0,"Tags":"python,heatmap","A_Id":56775827,"CreationDate":"2019-06-24T14:19:00.000","Title":"Plotting heat-map in python with multiple data sets","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"If we apply object detection on an image containing a playing card, then it will make bounding box around that card. Now my question is this that is there any way to find the distance between the center of the card(bounding box) and the center of the whole image?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":580,"Q_Id":56747903,"Users Score":0,"Answer":"If the bounding box has bottom-left coordinate (x1, y1) and top-right (x2, y2), then its center'll be ((x1+x2)\/2, (y1+y2)\/2). Same in the case of the whole image. \nNow, determine the distance by using sqrt((c1x-c2x)^2 + (c1y-c2y)^2)\nHere, (c1x, c1y) is the center of the image and (c2x, c2y) is that of the bounding box","Q_Score":0,"Tags":"python,opencv,image-processing","A_Id":56748008,"CreationDate":"2019-06-25T06:18:00.000","Title":"Distance of the centre of the object from the centre of the image","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using windows with Python 3.7.3, I installed NumPy via command prompt with \"pip install NumPy\", and it installed NumPy 1.16.4 perfectly. However, when I run \"import numpy as np\" in a program, it says \"ModuleNotFoundError: No module named 'numpy'\"\nI only have one version of python installed, and I don't know how I can fix this. How do I fix this?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":696,"Q_Id":56755112,"Users Score":0,"Answer":"python3 is not supported under NumPy 1.16.4. Try to install a more recent version of NumPy:\npip uninstall numpy\npip install numpy","Q_Score":0,"Tags":"python,numpy,import,package","A_Id":56755246,"CreationDate":"2019-06-25T13:23:00.000","Title":"No module named 'numpy' Even When Installed","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm doing a project on custom object detection. I have to use a Bbox labelling tool and yolov3 weights to train my dataset. Still I'm confused about where to start and proceed with the same.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":25,"Q_Id":56757346,"Users Score":0,"Answer":"Short answer: You will probably have to do a lot of it manually, or maybe hire an intern. \nIf you already have a way to automatically annotate your data, you don't have to build another. \nYou can try to use another way as an initial Solution, which you still need to go through by hand afterwards and finetune or correct the solutions. Depending on the size of the project it might be worth it though. \nWays to do this could be things like using annotated data for similar classes, using a non-deep-learning way that kind of works, using video footage where you know consecutive frames having similar objects and bounding boxes so annotating is faster.","Q_Score":0,"Tags":"python,tensorflow","A_Id":56757810,"CreationDate":"2019-06-25T15:22:00.000","Title":"Does the BBox labelling for object detection has to be done manually on Images or is there any ways to automate it","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a folder with hundreds of csv files with the filenames in the format of file_nameddmmyyyy (e.g. data25062019.csv).\nI am using python\/pandas to read the files and manipulating the dataframes. I want to be able to pull multiple csv files based on the dates on the file names. \ne.g. I want to open the daily files from the previous one week and append them to a dataframe. \nThe problem is there are not files for everyday of the week, so there may only be 4-5 files for the last week. \nAny suggestions on best way to approach this?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":141,"Q_Id":56759396,"Users Score":0,"Answer":"Just put the read command in a try except statement:\ntry:\n    read_file()\n    append_file()\n except:\n     pass","Q_Score":0,"Tags":"python,pandas","A_Id":56760620,"CreationDate":"2019-06-25T17:41:00.000","Title":"Python\/Pandas - Read multiple files in a folder based on dates in filenames","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"This is a question on a conceptual level. \nI'm building a piece of small-scale algorithmic trading software, and I am wondering how I should set up the data collection\/retrieval within that system. The system should be fully autonomous. \nCurrently my algorithm that I want to trade live is doing so on a very low frequency, however I would like to be able to trade with higher frequency in the future and therefore I think that it would be a good idea to set up the data collection using a websocket to get real time trades straight away. I can aggregate these later if need be. \nMy first question is: considering the fact that the data will be real time, can I use a CSV-file for storage in the beginning, or would you recommend something more substantial?\nIn any case, the data collection would proceed as a daemon in my application. \nMy second question is: are there any frameworks available to handle real-time incoming data to keep the database constant while the rest of the software is querying it to avoid conflicts?\nMy third and final question is: do you believe it is a wise approach to use a websocket in this case or would it be better to query every time data is needed for the application?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":88,"Q_Id":56769559,"Users Score":0,"Answer":"CSV is a nice exchange format, but as it is based on a text file, it is not good for real-time updates. Only my opinion but I cannot imagine a reason to prefere that to database.\nIn order to handle real time conflicts, you will later need a professional grade database. PostgreSQL has the reputation of being robust, MariaDB is probably a correct choice too. You could use a liter database in development mode like SQLite, but beware of the slight differences: it is easy to write something that will work on one database and will break on another one. On another hand, if portability across databases is important, you should use at least 2 databases: one at development time and a different one at integration time.\nA question to ask yourself immediately is whether you want a relational database or a noSQL one. Former ensures ACID (Atomicity, Consistency, Isolation, Durability) transations, the latter offers greater scalability.","Q_Score":0,"Tags":"python,database,algorithmic-trading","A_Id":56769956,"CreationDate":"2019-06-26T09:33:00.000","Title":"How to set up data collection for small-scale algorithmic trading software","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I'm looking at some machine learning\/forecasting code using Keras, and the input data sets are stored in npz files instead of the usual csv format. \nWhy would the authors go with this format instead of csv? What advantages does it have?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":817,"Q_Id":56778614,"Users Score":1,"Answer":"It depends of the expected usage. If a file is expected to have broad use cases including direct access from an ordinary client machines, then csv is fine because it can be directly loaded in Excel or LibreOffice calc which are widely deployed. But it is just an good old text file with no indexes nor any additional feature.\nOn the other hand is a file is only expected to be used by data scientists or generally speaking numpy aware users, then npz is a much better choice because of the additional features (compression, lazy loading, etc.)\nLong story made short, you exchange a larger audience for higher features.","Q_Score":0,"Tags":"python,csv,numpy","A_Id":56778812,"CreationDate":"2019-06-26T17:58:00.000","Title":"When is it better to use npz files instead of csv?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I use LDAvis library to visualize my LDA topics. It works fine before, but it gets me this error when I download the saved model files from Sagemaker to the local computer. I don't know why does this happen? Does that relate to Sagemaker?\nIf I run from the local, and saved the model from local, and then run LDAviz library, it works fine.\n\nKeyError                                  Traceback (most recent call last)\n in ()\n~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\pyLDAvis\\gensim.py in prepare(topic_model, corpus, dictionary, doc_topic_dist, **kwargs)\n    116     See pyLDAvis.prepare for **kwargs.\n    117     \"\"\"\n--> 118     opts = fp.merge(_extract_data(topic_model, corpus, dictionary, doc_topic_dist), kwargs)\n    119     return vis_prepare(**opts)\n~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\pyLDAvis\\gensim.py in _extract_data(topic_model, corpus, dictionary, doc_topic_dists)\n     46           gamma = topic_model.inference(corpus)\n     47       else:\n---> 48           gamma, _ = topic_model.inference(corpus)\n     49       doc_topic_dists = gamma \/ gamma.sum(axis=1)[:, None]\n     50    else:\n~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\gensim\\models\\ldamodel.py in inference(self, chunk, collect_sstats)\n    665             # phinorm is the normalizer.\n    666             # TODO treat zeros explicitly, instead of adding epsilon?\n--> 667             eps = DTYPE_TO_EPS[self.dtype]\n    668             phinorm = np.dot(expElogthetad, expElogbetad) + eps\n    669 \nKeyError: dtype('float32')","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":520,"Q_Id":56779011,"Users Score":0,"Answer":"I know this is late but I just fixed a similar problem by updating my gensim library from 3.4 to the current version which for me is 3.8.","Q_Score":2,"Tags":"python,lda,amazon-sagemaker","A_Id":59459541,"CreationDate":"2019-06-26T18:26:00.000","Title":"How to fix 'KeyError: dtype('float32')' in LDAviz","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a 4 dimensional Numpy array, of (8, 1, 1, 102). Now, for instance, I simply want to ignore the middle two dimensions and have an array of shape (8,102), what may be the suitable way to accomplish this?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":358,"Q_Id":56781237,"Users Score":1,"Answer":"np.squeeze will collapse all the dimensions having length 1, or you can use the reshape function","Q_Score":0,"Tags":"python,numpy","A_Id":56781883,"CreationDate":"2019-06-26T21:37:00.000","Title":"How to reshape a 4 dimensional Numpy array into different dimensions?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm building a Keras sequential model to do a binary image classification. Now when I use like 70 to 80 epochs I start getting good validation accuracy (81%). But I was told that this is a very big number to be used for epochs which would affect the performance of the network. \nMy question is: is there a limited number of epochs that I shouldn't exceed, note that I have 2000 training images and 800 validation images.","AnswerCount":3,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":2256,"Q_Id":56781680,"Users Score":3,"Answer":"If the number of epochs are very high, your model may overfit and your training accuracy will reach 100%. In that approach you plot the error rate on training and validation data. The horizontal axis is the number of epochs and the vertical axis is the error rate. You should stop training when the error rate of validation data is minimum.\nYou need to have a trade-off between your regularization parameters. Major problem in Deep Learning is overfitting model. Various regularization techniques are used,as\ni) Reducing batch-size\nii) Data Augmentation(only if your data is not diverse)\niii) Batch Normalization\niv) Reducing complexity in architecture(mainly convolutional layers)\nv) Introducing dropout layer(only if you are using any dense layer)\nvi) Reduced learning rate.\nvii) Transfer learning\nBatch-size vs epoch tradeoff is quite important. Also it is dependent on your data and varies from application to application. In that case, you have to play with your data a little bit to know the exact figure. Normally a batch size of 32 medium size images requires 10 epochs for good feature extraction from the convolutional layers. Again, it is relative","Q_Score":2,"Tags":"python,keras,conv-neural-network","A_Id":56785434,"CreationDate":"2019-06-26T22:26:00.000","Title":"Number of epochs to be used in a Keras sequential model","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to export my DataFrame to Excel. Everything is good but I need two \"rows\" of headers in my output file. That mean I need two columns headers. I don't know how to export it and make double headers in DataFrame. My DataFrame is created with dictionary but I need to add extra header above.\nI tried few dumb things but nothing gave me a good result. I want to have on first level header for every three columns and on second level header for each column. They must be different.\nI expect output with two headers above columns.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1093,"Q_Id":56787460,"Users Score":0,"Answer":"Had a similar issue. Solved by persisting cell-by-cell using worksheet.write(i, j, df.iloc[i,j]), with i starting after the header rows.","Q_Score":0,"Tags":"python,pandas,export-to-excel","A_Id":59754774,"CreationDate":"2019-06-27T08:57:00.000","Title":"Multiple header in Pandas DataFrame to_excel","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a numpy array as:\ngroups=np.array([('Species1',), ('Species2', 'Species3')], dtype=object).\nWhen I ask np.where(groups == ('Species2', 'Species3')) or even np.where(groups == groups[1]) I get an empty reply: (array([], dtype=int64),)\nWhy is this and how can I get the indexes for such an element?","AnswerCount":4,"Available Count":1,"Score":0.049958375,"is_accepted":false,"ViewCount":1220,"Q_Id":56788435,"Users Score":1,"Answer":"It's not means search a tuple('Species2', 'Species3') from groups when you use \nnp.where(groups == ('Species2', 'Species3'))\nit means search 'Species2' and 'Species3' separately if you have a Complete array like this \ngroups=np.array([('Species1',''), ('Species2', 'Species3')], dtype=object)","Q_Score":1,"Tags":"python,numpy","A_Id":56789899,"CreationDate":"2019-06-27T09:51:00.000","Title":"How can I find the index of a tuple inside a numpy array?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have constructed 2 graphs and calculated the eigenvector centrality of each node. Each node can be considered as an individual project contributor. Consider 2 different rankings of project contributors. They are ranked based on the eigenvector of the node. \nRanking #1:\nRank 1 - A\nRank 2 - B\nRank 3 - C\nRanking #2:\nRank 1 - B\nRank 2 - C\nRank 3 - A\nThis is a very small example but in my case, I have almost 400 contributors and 4 different rankings. My question is how can I merge all the rankings and get an aggregate ranking. Now I can't just simply add the eigenvector centralities and divide it by the number of rankings. I was thinking to use the Khatri-Rao product or Kronecker Product to get the result. \nCan anyone suggest me how can I achieve this?\nThanks in advance.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":83,"Q_Id":56790761,"Users Score":0,"Answer":"Rank both graphs separately each node gets a rank in both graphs then do simple matrix addition. Now normalize the rank. This should keep the relationship like rank1>rank2>rank3>rank4 true and relationships like rank1+rank1>rank1+rank2 true. I don't know how it would help you taking the Khatri-Rao product of the matrix. That would make you end up with more than 400 nodes. Then you would need to compress them back to 400 nodes in-order to have 400 ranked nodes at the end. Who told you to use Khatri-Rao product?","Q_Score":0,"Tags":"python,data-mining,matrix-multiplication,data-analysis,ranking","A_Id":58783825,"CreationDate":"2019-06-27T12:15:00.000","Title":"Aggregate Ranking using Khatri-Rao product","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a lines dataset where each lines have several points (minimum 2). All the coordinates are known and on the same metric reference.\nI would like to merge the lines with the same azimut +\/- 10\u00b0 and a maximal distance of 5cm between lines.\nI think a clustering algorithm can do that, I found on web some clustering algorithms working with points and I would like to know if there is any existing function\/algorithm to do that with lines ?\nIf no, I will try to adapt a code myself.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":577,"Q_Id":56794801,"Users Score":0,"Answer":"Yes, almost all can be used. It's just probably not the most efficient way. Depending on how you'd formalize your problem, which is not clear enough. Do the points pairwise have to fulfill this property, or is it enough if they \"mostly\" do, or if there are some that do?\nIn many cases it will be easiest (but slow) to make a graph where you connect any two lines that have the desired property, then search for desirable structures such as cliques (and thenyou'll quickly see why it's not clear which solution you are looking for).","Q_Score":0,"Tags":"python,cluster-analysis","A_Id":56797251,"CreationDate":"2019-06-27T15:56:00.000","Title":"How to get cluster of lines using python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am currently working on Object detection. I am using Amazon Workspace for training my model. I am using the model to detect cars and bikes. I have used a pre-trained model (which is Faster-RCNN-Inception-V2 model) and customized it with my own dataset for 2 labels namely car and bike. It took me 5 hours to complete the training. Now I want to modify my model for 2 more labels (keeping to old ones) namely bus and auto. But I don't want to do the training from scratch as my model is already trained for cars and bikes. So is there any way that I can train my model only with the dataset of bus and auto, and after training it will detect all 4 objects(car, bike, bus, and auto)?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":469,"Q_Id":56802475,"Users Score":0,"Answer":"Load the saved model with pretrained weights. Remove last dense and 2 class softmax layer and add a new dense with 4 class softmax as your low level features are already trained. Now train this model with modified data.","Q_Score":0,"Tags":"python,deep-learning,conv-neural-network,transfer-learning","A_Id":56815137,"CreationDate":"2019-06-28T07:14:00.000","Title":"How to customize a pre-trained model with our own dataset?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to add a numpy array x = [1,2,3,4] to \"the end\" of a numpy array of shape (n,4) (i.e. \"push\" x onto the array) while removing the first element (i.e. \"shift\" the array). So basically, after adding my array x, the (4) shaped array that was added last should be removed.\nHow does one do this in the computationally most efficient manner?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":140,"Q_Id":56817603,"Users Score":0,"Answer":"You're describing a ring buffer.  Just use a normal NumPy array plus an integer which tells you which row is the \"top.\"  To push, simply replace the \"top\" row with a new row and increment the top index.","Q_Score":0,"Tags":"python,numpy","A_Id":56817681,"CreationDate":"2019-06-29T12:40:00.000","Title":"How to \"push and shift\" an element in a numpy array most efficiently?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to size a box classification system where boxes get on a queue until the packaging quantity is reached. Then they all leave the queue at a fixed rate.\nThis is done real time with production. I can estimate the volume for each SKU but I can't predict the order in which they will arrive at the classification\/sorting facility. However I can look at previous manufacturing data to test the algorithm.\nThe key point is how would you estimate the needed bins\/queues to accomplish the sorting (minimizing the \"all queues used\" condition)\nI thought of queue theory but I want to run some simulations with the known data (data is not totally random) and most of what I searched assumes random entry to queues.\nI'm starting to write a python script to model the queue behavior myself with given fixed times for queue evacuation.\nAny suggestions?\nThanks in advance.\nIdeally should be python based\nThe expected output should be the used queues vs time and, in case of a limited number of queues, the number of boxes \"discarded\" vs time","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":35,"Q_Id":56821368,"Users Score":0,"Answer":"Your question is frustratingly vague. I think I understand what you're asking for, but a little more detail would help.\nAs I understand it, you have something like an order processing facility, where each order is a queue in which you hold items until all the items for the order arrive. Then you release that queue at some rate (x seconds per item, or something like that).\nThe number of queues you need will depend on:\n\nThe expected delivery rate: how many orders per minute do you want to complete? Call this OPM: Orders Per Minute.\nThe average amount of time a queue lives. That is, time from when the first item arrives in the queue until the last item leaves. QLT: Queue Lifetime\n\nThe number of queues you need is OPM * QLT. If you want to deliver 100 orders per minute, and the average queue lifetime is 3 minutes. You will need 300 queues. If the average queue lifetime is 30 seconds, then you only need 50 queues.\nThe queue lifetime is a combination of how long it takes to fill the queue, and how long it takes to empty it. Call those QFT and QET. QET is easy: the average number of items in an order, divided by the queue empty rate. You said that items are released from the queue at a fixed rate. If the average order size is 5 items, and you empty the queue at a rate of 12 items per minute, then it will take 5\/12 minutes (25 seconds) to empty the queue.\nQFT (queue fill time) depends on the average order size and the average time it takes an item to be picked and delivered to the queue. If you can't get that from your manufacturing data, then you'll have to estimate it yourself.\nDoing those calculations gives you a good estimate of how your system should react on average. You can then build a simple simulation using those numbers, and begin varying one or more of the parameters. For example, what happens if the average number of items per order gets larger or smaller for a period. If it gets larger, then QLT will probably increase. If it gets smaller, QLT will decrease, but you'll probably have more concurrent orders (and thus need more queues).","Q_Score":0,"Tags":"python,queue,classification,bins","A_Id":56822417,"CreationDate":"2019-06-29T22:55:00.000","Title":"How to calculate the number of queues needed in a box classification\/packaging problem?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Let say I have fold1, fold2 , fold3.\nI trained fold1,fold2,fold3 with  modelA. \n\n\nA) modelA(fold1) -> modelA(fold2) -> modelA(fold3) \nB) modelA(fold1) -> saved weight modelA(fold1) -> modelA(fold2)->\n  saved weight modelA(fold2) -> modelA(fold3)-> saved weight\n  modelA(fold3) -> ensemble 3 weight \n\nwhich way is the right way to do the k-fold cross validation and why?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":33,"Q_Id":56825542,"Users Score":0,"Answer":"It depends what is your end goal. K-Fold CV is used for finding model hyperparameters.\nAfter this phase you may change your validation dataset and train your model with it.\nIf you want to harness as much data as you can (performing predictions) it might be a good idea to train N models on N different folds and ensemble their predictions. This one is similar to boostrap, all in all your ensemble saw all the data yet it didn't overfit. This approach is N times more computationally intensive though, so it still comes down to your goals.\nFinally, you should get better results with fitting different models to your folds instead of a single one, but this would require separate hyperparameter space for each algorithm.","Q_Score":0,"Tags":"python,cross-validation,k-fold","A_Id":56825630,"CreationDate":"2019-06-30T14:14:00.000","Title":"the way to do the cross validation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Numpy has great help function that allows me to search through the numpy documentation from terminal.\nIs there something similar to numpy.lookfor function that works for pandas library?\nI know that numpy.info works with pandas functions and methods but I have to know the name of the method beforehand.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":79,"Q_Id":56851264,"Users Score":0,"Answer":"There is probably no such thing for Pandas library as of today. However useful tip I found out is that you can press tab while typing pandas method in Jupyter Notebook, so it will suggest some possible methods. \nAlso by pressing shift and tab you can get information about the method within jupyter notebook. Pressing shift and 4 times tab will conveniently open a simple window that contains all the information about specific method.\nNot exactly as numpy.lookfor but stil useful.","Q_Score":3,"Tags":"python,pandas,numpy","A_Id":57441048,"CreationDate":"2019-07-02T11:17:00.000","Title":"Pandas numpy.lookfor equivalent","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am building an NLP pipeline and I am trying to get my head around in regards to the optimal structure. My understanding at the moment is the following: \n\nStep1 - Text Pre-processing [a. Lowercasing, b. Stopwords removal, c. stemming, d. lemmatisation,] \nStep 2 - Feature extraction \nStep 3 - Classification - using the different types of classifier(linearSvC etc) \n\nFrom what I read online there are several approaches in regard to feature extraction but there isn't a solid example\/answer. \n\na. Is there a solid strategy for feature extraction ? \nI read online that you can do [a. Vectorising usin ScikitLearn b. TF-IDF] \nbut also I read that you can use Part of Speech or word2Vec or other embedding and Name entity recognition. \nb. What is the optimal process\/structure of using these? \nc. On the text pre-processing I am ding the processing on a text column on a df and the last modified version of it is what I use as an input in my classifier. If you do feature extraction do you do that in the same column or you create a new one and you only send to the classifier the features from that column? \n\nThanks so much in advance","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":52,"Q_Id":56851945,"Users Score":1,"Answer":"The preprocessing pipeline depends mainly upon your problem which you are trying to solve. The use of TF-IDF, word embeddings etc. have their own restrictions and advantages.\n\nYou need to understand the problem and also the data associated with it. In order to make the best use of the data, we need to implement the proper pipeline.\n\nSpecifically for text related problems, you will find word embeddings to be very useful. TF-IDF is useful when the problem needs to be solved emphasising the words with lesser frequency. Word embeddings, on the other hand, convert the text to a N-dimensional vector which may show up similarity with some other vector. This could bring a sense of association in your data and the model can learn the best features possible.\nIn simple cases, we can use a bag of words representation to tokenize the texts.\nSo, you need to discover the best approach for your problem. If you are  solving a problems which closely resembles the famous NLP problems like IMDB review classification, sentiment analysis on Twitter data, then you can find a number of approaches on the internet.","Q_Score":0,"Tags":"python-3.x,pandas,nlp,jupyter-notebook,spacy","A_Id":56852299,"CreationDate":"2019-07-02T11:56:00.000","Title":"NLP Structure Question (best way for doing feature extraction)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have been working on a stereo imaging project for a few months now. The goal is to track a defined object, compute its position (by using triangulation with stereo camera) and its orientation (by using an IMU). I get really good results, but there is something I don't understand and it's bothering me : the minimum depth in the disparity map obtained after using the WLS filter function available in OpenCV is not the same as the minimum depth in the disparity map obtained with the stereo SGBM algorithm. Why ?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":151,"Q_Id":56867814,"Users Score":0,"Answer":"Anyone who might have an idea please?","Q_Score":0,"Tags":"python-3.x","A_Id":56921855,"CreationDate":"2019-07-03T10:13:00.000","Title":"Disparity value difference in Stereo SGBM vs. WLS disparity maps","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm writing a python program to perform certain processing on data for couple of millions (N) users. Outcome of the process would be several 1D arrays for each user (the process would apply on each user separately). I need set and get functions for each user output data.\nI have two options for implementing this:\n1. Create a class with attributes of size N by column_size, so one object that contains big arrays\n2. Create a class for each user and store instances of this class in a list, so a list of N objects\nMy question is that what are pros and cons of each approach in terms of speed and memory consumption?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":90,"Q_Id":56868707,"Users Score":0,"Answer":"The question is rather broad, so I will not go beyond generalities.\nIf you intend to process user by user, then it makes sense to have one object per user.\nOn the other hand, if you mainly process all users at the same time and attribute by attribute, then it makes sense to have classes for attributes each object containing the attribute for all users. That way, if memory become scarce, you can save everything to disk and only have one user (resp. attribute) in memory.","Q_Score":0,"Tags":"python,list,dataframe,object","A_Id":56869092,"CreationDate":"2019-07-03T11:04:00.000","Title":"Pros and cons of an object with big dataframe vs a list of lots of class objects in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'd like to apply lstm in my speech emotion datasets (dataset of features in numeric values with one column of targets). \nI've done split_train_test. Do I need some other transformation to do in the data set before the model?\nI ask this question because when I compile and fit the model I've got one error in the last dense layer.\n\nError when checking model target: expected activation_2 to have shape (8,) but got array with shape (1,).\n\nThanks.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":26,"Q_Id":56876204,"Users Score":0,"Answer":"After my internship I learn how to fix out this error and where to look.\nHere's what you have to take care.\nUnexpected error input form\nIf the reported layer is the first it is a cause of the input data for the train of a model as a same shape for a create your model.\nIf this is the last layer that bug then it is the labels that are well coded\nEither you put a sigmoid but the labels are not binary either you put softmax and the labels are in one-hot format [0,1,0]: example 3 classes, this element is of class 2. So, the labels are badly encoded or you are deceived in the function (sigmoid \/ softmax) of your output layer.\nHope this help","Q_Score":0,"Tags":"python,speech-recognition,lstm","A_Id":58441617,"CreationDate":"2019-07-03T18:40:00.000","Title":"How to apply lstm in speech emotion feature","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using Databricks to connect to an Eventhub, where each message comming from the EventHub may be very different from another.\nIn the message, I have a body and an id.\nI am looking for performance, so I am avoiding collecting data or doing unecessary processings, also I want to do the saving in parallel by partition. However I am not sure on how to do this in a proper way. \nI want to append the body of each ID in a different AND SPECIFIC table in batches, the ID will give me the information I need to save in the right table. So in order to do that I have been trying 2 approachs:\n\nPartitioning: Repartition(numPartitions, ID) -> ForeachPartition\nGrouping: groupBy('ID').apply(myFunction) #@pandas_udf GROUPED_MAP\n\nThe approach 1 doens't look very attracting to me, the repartition process looks kind unecessary and I saw in the docs that even if I set a column as a partition, it may save many ids of that column in a single partition. It only garantees that all data related to that id is in the partition and not splitted\nThe approach 2 forces me to output from the pandas_udf, a dataframe with the same schema of the input, which is not going to happen since I am transforming the eventhub message from CSV to dataframe in order to save it to the table. I could return the same dataframe that I received, but it sounds weird.\nIs there any nice approach I am not seeing?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":134,"Q_Id":56878553,"Users Score":1,"Answer":"If your Id has distinct number of values (kind of type\/country column) you can use partitionBy to store and thereby saving them to different table will be faster.\nOtherwise create a derive column(using withColumn) from you id column by using the logic same as you want to use while deviding data across tables. Then you can use that derive column as a partition column in order to have faster load.","Q_Score":0,"Tags":"python-3.x,pyspark,azure-databricks","A_Id":56882432,"CreationDate":"2019-07-03T22:16:00.000","Title":"How to write each dataframe partition into different tables","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have data. Based on the data (time, latitude, longitude, altitude) determine what are the typical routes that device makes during a full week.\nAfter determining the baseline routes or typical area frequented by device we can start determining an anomaly based on the device traveling outside it\u2019s frequent route\/area.\nAction: The process will then send an \u201calert\u201d to the system is traveling outside it\u2019s frequent area route\nPlease suggest which machine learning algorithm is useful. I am going to start clustering algorithm. Also tell me which python libraries is useful to use machine learning algorithm.","AnswerCount":1,"Available Count":1,"Score":-0.1973753202,"is_accepted":false,"ViewCount":493,"Q_Id":56881873,"Users Score":-1,"Answer":"First of all, if you use Python, then use scikit-learn.\nFor this problem, there is multiple possibilities.\nOne way is indeed to use a clustering algorithm. For this purpose to get the anomaly too, you can use DBSCAN. It is an algorithm designed to get cluster and the outliers.\nAnother way would be (assuming you have for each device all their position) to use more funny way like a clustering algorithm on all the positions to get the important place, and after an LDA (latent dirichlet allocation) to get the main topics (here the words would be the index of the cluster, the document would be the list of position of each device and so the topics would be the main \"routes\").","Q_Score":1,"Tags":"python-3.x,machine-learning,data-science","A_Id":56925320,"CreationDate":"2019-07-04T06:33:00.000","Title":"Find the outliers or anomaly in gps data (time, latitude, longitude, altitude)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a training dataset of shape(90000,50) and I trying to fit this in model(Gaussian process regression). This errors out with memory error. I do understand the computation, but is there way to pass data in batches using scikit? I am using the scikit implementation of the GPR algorithm.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":350,"Q_Id":56887202,"Users Score":0,"Answer":"The Gaussian process implementation(Regression\/classification) from scikit is'nt capable of handling big dataset. It can run only upto 15000 rows of data. So I decided to use a different algorithm instead as this seems to be a problem with algorithm.","Q_Score":1,"Tags":"python,machine-learning,keras,scikit-learn,training-data","A_Id":57056265,"CreationDate":"2019-07-04T11:42:00.000","Title":"Is there an option like generator in keras with scikit to process large records of data?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to learn pandas and I haven't been able to import it into my code. I have looked at other answers on this site and none of them have worked.\njupyter notebook is not importing my module.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":76,"Q_Id":56888145,"Users Score":0,"Answer":"let's try:\nin jupiter\n\n!pip install pandas --upgrade\nimport pandas as pd\nprint(pd.__version__)\n\nwhat do you see","Q_Score":0,"Tags":"python,pandas,numpy,jupyter-notebook,anaconda","A_Id":56888637,"CreationDate":"2019-07-04T12:38:00.000","Title":"jupyter notebook - problem no module named 'pandas'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"For semantic image segmentation, I understand that you often have a folder with your images and a folder with the corresponding masks. In my case, I have gray-scale images with the dimensions (32, 32, 32). The masks naturally have the same dimensions. The labels are saved as intensity values (value 1 = label 1, value 2 = label 2 etc.). 4 classes in total. Imagine I have found a model that was built with the keras model API. How do I know how to prepare my label data for it to be accepted by the model? Does it depend on the loss function? Is it defined in the model (Input parameter). Do I just add another dimension (4, 32, 32, 32) in which the 4 represents the 4 different classes and one-hot code it? \nI want to build a 3D convolutional neural network for semantic segmentation but I fail to understand how to feed in the data correctly in keras. The predicted output is supposed to be a 4-channel 3D image, each channel showing the probability values of each pixel to belong to a certain class.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":207,"Q_Id":56888245,"Users Score":0,"Answer":"The Input() function defines the shape of the input tensor of a given model. For 3D images, often a 5D Tensor is expected, e.g. (None, 32, 32, 32, 1), where None refers to the batch size. Therefore the training images and labels have to be reshaped. Keras offers the to_categorical function to one-hot encode the label data (which is necessary). The use of generators helps to feed in the data. In this case, I cannot use the ImageDataGenerator from keras as it can only deal with RGB and grayscale images and therefore have to write a custom script.","Q_Score":1,"Tags":"python,image,keras,deep-learning,conv-neural-network","A_Id":56973276,"CreationDate":"2019-07-04T12:44:00.000","Title":"Keras preprocessing for 3D semantic segmentation task","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to train a deep learning (CNN) model on a dataset containing around 100000 images. Since the dataset is huge (approx 82 Gb), I want to use Google colab since it's GPU supported. How do I upload this full image folder into my notebook and use it?\nI can not use google drive or git hub since my dataset is too large.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":308,"Q_Id":56891871,"Users Score":0,"Answer":"You can try zipping and the unzip on collab\nStep 1: Zip the whole folder\nStep 2:Upload the folder\nStep 3: !unzip myfoldername.zip    \nStep 4:type ls and see folder names to see if successful\nIt would be better if you compress or resize the images to reduce file size using opencv or something","Q_Score":0,"Tags":"python,deep-learning,dataset,google-colaboratory","A_Id":56893072,"CreationDate":"2019-07-04T16:46:00.000","Title":"Uploading images folders from my PC to Google Colab","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using Keras. I am training my Neural Network and using Early Stopping. My patience is 10 and the epoch with the lowest validation loss is 15. My network runs til 25 epochs and stops however my model is the one with 25 epochs not 15 if I understand correctly\nIs there an easy way to revert to the 15 epoch model or do I need to re-instantiate the model and run 15 epochs?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1076,"Q_Id":56896221,"Users Score":0,"Answer":"Yes, you get the model (weights) corresponding to the epoch when it stops. A commonly used strategy is to save the model whenever the validation loss\/acc improves.","Q_Score":1,"Tags":"python-3.x,tensorflow,keras,neural-network","A_Id":56897110,"CreationDate":"2019-07-05T03:31:00.000","Title":"Early Stopping, Model has gone through how many epochs?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to label images with the various categories that they belong to with a convolutional neural net. For my problem, the image can be in a single category, multiple categories, or zero categories. Is it standard practice to set the zero category as all zeroes or should I add an additional null class neuron to the final layer?\nAs an example, let's say there are 5 categories (not including the null class). Currently, I'm representing that with [0,0,0,0,0]. The alternative is adding a null category, which would look like [0,0,0,0,0,1]. Won't there also be some additional unnecessary parameters in this second case or will this allow the model to perform better?\nI've looked on Stackoverflow for similar questions, but they pertain to Multiclass Classification, which uses the Categorical Crossentropy with softmax output instead of Binary Crossentropy with Sigmoid output, so the obvious choice there is to add the null class (or to do thresholding).","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":791,"Q_Id":56896712,"Users Score":0,"Answer":"The model outputs a probability to each class, and we assign the input to the class which has the highest probability during prediction. The last layer is normally a softmax layer for multiclass classification and sigmoid for binary classification. Both of them squash the input into a range of 0 to 1. We can interpret them as probabilities. So no you cannot have all zeros as a one class (Null), because their values will sum up to 1 (probability). You have to define a new class for Null.","Q_Score":2,"Tags":"python,tensorflow,machine-learning,keras","A_Id":56896809,"CreationDate":"2019-07-05T04:50:00.000","Title":"How to represent the Null class in Multilabel Classification with Convolutional Neural Nets?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to label images with the various categories that they belong to with a convolutional neural net. For my problem, the image can be in a single category, multiple categories, or zero categories. Is it standard practice to set the zero category as all zeroes or should I add an additional null class neuron to the final layer?\nAs an example, let's say there are 5 categories (not including the null class). Currently, I'm representing that with [0,0,0,0,0]. The alternative is adding a null category, which would look like [0,0,0,0,0,1]. Won't there also be some additional unnecessary parameters in this second case or will this allow the model to perform better?\nI've looked on Stackoverflow for similar questions, but they pertain to Multiclass Classification, which uses the Categorical Crossentropy with softmax output instead of Binary Crossentropy with Sigmoid output, so the obvious choice there is to add the null class (or to do thresholding).","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":791,"Q_Id":56896712,"Users Score":5,"Answer":"Yes, the \"null\" category should be represented as just zeros. In the end multi-label classification is a set of C binary classification problems, where C is the number of classes, and if all C problems output \"no class\", then you get a vector of just zeros.","Q_Score":2,"Tags":"python,tensorflow,machine-learning,keras","A_Id":56897025,"CreationDate":"2019-07-05T04:50:00.000","Title":"How to represent the Null class in Multilabel Classification with Convolutional Neural Nets?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I tried training an AutoEnsembleEstimator with two DNNEstimators (with hidden units of 1000,500, 100) on a dataset with around 1850 features (after feature engineering), and I kept running out of memory (even on larger 400G+ high-mem gcp vms). \nI'm using the above for binary classification. Initially I had trained various models and combined them by training a traditional ensemble classifier over the trained models. I was hoping that Adanet would simplify the generated model graph that would make the inference easier, rather than having separate graphs\/pickles for various scalers\/scikit models\/keras models.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":39,"Q_Id":56916504,"Users Score":1,"Answer":"Three hypotheses:\n\nYou might have too many DNNs in your ensemble, which can happen if max_iteration_steps is too small and max_iterations is not set (both of those are constructor arguments to AutoEnsembleEstimator). If you want to train each DNN for N steps, and you want an ensemble with 2 DNNs, you should set max_iteration_steps=N, set max_iterations=2, and train the AutoEnsembleEstimator for 2N steps.\nYou might have been on adanet-0.6.0-dev, which had a memory leak. To fix this, try updating to the latest release and seeing if this problem still arises.\nYour batch size might have been too large. Try lowering your batch size.","Q_Score":1,"Tags":"python,tensorflow,tensorflow-estimator,adanet","A_Id":59382368,"CreationDate":"2019-07-06T18:05:00.000","Title":"Adanet running out of memory","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am a beginner for python programming, specifically for scientific computing. In python, the index of a np.array, by default, starts from 0. Is it possible to change the index starting from any number, such as -1 ..., like what can do in fortran? thanks a lot.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":36,"Q_Id":56920278,"Users Score":0,"Answer":"No.\nFeel free to write a wrapper class that adjusts slices by such an offset.","Q_Score":0,"Tags":"python-3.x","A_Id":56926165,"CreationDate":"2019-07-07T07:51:00.000","Title":"Is there a pre-defined custom indexed array in python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"In general, the CNN model uses a square filter. I would like to use a cross filter or X filter. The function of creating a square filter in tensorflow is provided, but information on other filter-types is not available.\nHow can I make a cross filter and\/or an X-filter?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":35,"Q_Id":56921148,"Users Score":0,"Answer":"If I understand your question correctly, you want to use a Cross Filter, like the one whose shape is3*4 rather than a Square Filter, whose shape is 3*3.\nIf that is the case, that is allowed using the Function, tf.keras.layers.Conv2D.\nIn the argument, kernel_size, you need to provide (3,4) instead of just 3 or (3,3).","Q_Score":1,"Tags":"python,tensorflow,filter,conv-neural-network","A_Id":59574175,"CreationDate":"2019-07-07T10:05:00.000","Title":"How to create a CNN cross filter for Tensorflow in python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am currently working on a dataset that provides information over several years about employees at a big company. Information includes whether or not the employee quit that year (True or False for every year), what Tax Bracket they're in and what age they are. Based on the dataset, I have plottet the percentage of people quitting against their age, and the percetange of people quitting against their tax bracket. Assuming those numbers can be considered as the probability of someone quitting given their age, and the probability of someone quitting given their tax bracket, I would like to find a way to predict the probability of someone quitting given both age and tax bracket.\nI cannot use our dataset for that, because it is too small and most combinations do not occur in it (so I simply get 0% for everything). Is there a way to predict it, using some kind of model?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":33,"Q_Id":56931584,"Users Score":0,"Answer":"A direct and simple approach would be to use the mean of the percentage you get from the age and tax bracket that they are in. The downsides is that you consider that both those variables are independant and have the same weight in deciding wether an employee will quit or not.\nA better approach would be to use a classifier to give you a more accurate prediction of the probability of the employee quitting based on his tax bracket and age. You could start with common classifiers like Random Forest.","Q_Score":0,"Tags":"python,dataframe,statistics,probability","A_Id":56931866,"CreationDate":"2019-07-08T09:04:00.000","Title":"Predict the probability of someone leaving based on two conditions","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using regression to analyze server data to find feature importance.\nSome of my IVs (independent variables) or Xs are in percentages like % of time, % of cores, % of resource used, while others are in numbers like number of bytes, etc.\nI standardized all my Xs with (X-X_mean)\/X_stddev. (Am I wrong in doing so?)\nWhich algorithm should I use in Python in case my IVs are a mix of numeric and %s and I predict Y in the following cases:\n\nCase 1: Predict a continuous valued Y\na.Will using a Lasso regression suffice?\nb. How do I interpret the X-coefficient if X is standardized and is a\n  numeric value?\nc. How do I interpret the X-coefficient if X is standardized and is a\n  %?\nCase 2: Predict a %-ed valued Y, like \"% resource used\".\na. Should I use Beta-Regression? If so which package in Python offers\n  this?\nb. How do I interpret the X-coefficient if X is standardized and is a\n  numeric value?\nc. How do I interpret the X-coefficient if X is standardized and is a\n  %?\n\nIf I am wrong in standardizing the Xs which are % already, is it fine to use these numbers as 0.30 for 30% so that they fall within the range 0-1? So that means I do not standardize them, I will still standardize the other numeric IVs.\n\nFinal Aim for both Cases 1 and 2:\nTo find the % of impact of IVs on Y. \n  e.g.: When X1 increases by 1 unit, Y increases by 21% \n\nI understand from other posts that we can NEVER add up all coefficients to a total of 100 to assess the % of impact of each and every IV on the DV. I hope I am correct in this regard.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":173,"Q_Id":56933059,"Users Score":1,"Answer":"Having a mix of predictors doesn't matter for any form of regression, this will only change how you interpret the coefficients. What does matter, however, is the type\/distribution of your Y variable\n\nCase 1: Predict a continuous valued Y\na.Will using a Lasso regression suffice?\n\nRegular OLS regression will work fine for this\n\nb. How do I interpret the X-coefficient if X is standardized and is a\nnumeric value?\n\nThe interpretation of coefficients always follows a format like \"for a 1 unit change in X, we expect an x-coefficient amount of change in Y, holding the other predictors constant\"\nBecause you have standardized X, your unit is a standard deviation. So the interpretation will be \"for a 1 standard deviation change in X, we expect an X-coefficient amount of change in Y...\"\n\nc. How do I interpret the X-coefficient if X is standardized and is a\n%?\n\nSame as above. You units are still standard deviations, despite it originally coming from a percentage\n\nCase 2: Predict a %-ed valued Y, like % resource used.\na. Should I use Beta-Regression? If so which package in Python offers\nthis?\n\nThis is tricky. The typical recommendation is to use something like binomial logistic regression when your Y outcome is a percentage.\n\nb. How do I interpret the X-coefficient if X is standardized and is a\nnumeric value?\nc. How do I interpret the X-coefficient if X is standardized and is a\n%?\n\nSame as interpretations above. But if you use logistic regression, they are in the units of log odds. I would recommend reading up on logistic regression to get a deeper sense of how this works\n\nIf I am wrong in standardizing the Xs which are a % already , is it\nfine to use these numbers as 0.30 for 30% so that they fall within the\nrange 0-1? So that means I do not standardize them, I will still\nstandardize the other numeric IVs.\n\nStandardizing is perfectly fine for variables in regression, but like I said, it changes your interpretation as your unit is now a standard deviation\n\nFinal Aim for both cases 1 & 2:\nTo find the % of impact of IVs on Y. Eg: When X1 increases by 1 unit,\nY increases by 21%\n\nIf your Y is a percentage and you use something like OLS regression, then that is exactly how you would interpret the coefficients (for a 1 unit change in X1, Y changes by some percent)","Q_Score":0,"Tags":"python,statistics,regression,percentage,feature-extraction","A_Id":56950836,"CreationDate":"2019-07-08T10:31:00.000","Title":"Which algorithm to use for percentage features in my DV and IV, in regression?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to remotely stream videos and images from the cameras of the Pepper and NAO robots to my laptop. \nFirst, I used a while loop to repeatedly capture images from the NAO's camera and processed the images through opencv. However, as you can imagine, this only provided me with a framerate of about 1 fps. \nThen I tried to access the camera through opencv's videocapture, but it is not working properly. \nNext, I attempted to use gstreamer 1.0 for python on Windows, but the Windows version seems to be missing a number of elements, even though I have all of the required plugins (base, good, bad, ugly). \nAlso, I am trying to avoid using ROS because I am having issues using it with the python 2.7 naoqi SDK of the Pepper and NAO robots. \nAny help would be greatly appreciated. Thanks","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1257,"Q_Id":56940339,"Users Score":2,"Answer":"gstreamer should already be installed on the robot, so you could run it on the robot with a command like this:\n\ngst-launch-0.10 -v v4l2src device=\/dev\/video-top ! video\/x-raw-yuv,width=640,height=480,framerate=30\/1 ! ffmpegcolorspace ! jpegenc ! multipartmux! tcpserversink port=3000 \n\n... and then you can open the stream from your computer, for example with vlc:\n\nvlc tcp:\/\/ip.of.the.robot:3000","Q_Score":2,"Tags":"python,opencv,gstreamer,nao-robot,pepper","A_Id":56952479,"CreationDate":"2019-07-08T18:02:00.000","Title":"Remote video streaming with Pepper and NAO robots","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I have an array with size (4,4) that can have values 0 and 1, so I can have 65536 different arrays. I need to produce all these arrays without repeating. I use wt_random=np.random.randint(2, size=(65536,4,4)) but I am worried they are not unique. could you please tell me this code is correct or not and what should I do to produce all possible arrays? Thank you.","AnswerCount":4,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":452,"Q_Id":56969477,"Users Score":0,"Answer":"If you need all possible arrays in random order, consider enumerating them in any arbitrary deterministic order and then shuffling them to randomize the order. If you don't want all arrays in memory, you could write a function to generate the array at a given position in the deterministic list, then shuffle the positions. Note that Fisher-Yates may not even need a dense representation of the list to shuffle... if you keep track of where the already shuffled entries end up you should have enough.","Q_Score":2,"Tags":"python,arrays,numpy,random","A_Id":56969631,"CreationDate":"2019-07-10T11:12:00.000","Title":"how do I produce unique random numbers as an array in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a file contain some columns which the second column is time. Like what I show below. I need to add a column of time which all are in seconds like this: \"2.13266      2.21784      2.20719      2.02499      2.16543\", to the time column in the first file (below). My question is how to add these two time to each other. And maybe in some cases when I add these times, then it goes to next day, and in this case how to change the date in related row.\n2014-08-26 19:49:32  0\n2014-08-28 05:43:21  0\n2014-08-30 11:47:54  0\n2014-08-30 03:26:10  0","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":673,"Q_Id":56970023,"Users Score":0,"Answer":"Probably the easiest way is to read your file into a pandas data-frame and parse each row as a datetime object. Then you create a datetime.timedelta object passing the fractional seconds.\nA datetime object + a timedelta handles wrapping around for days quite nicely so this should work without any additional code. Finally, write back your updated dataframe to a file.","Q_Score":0,"Tags":"python,datetime,time,add,timedelta","A_Id":56970162,"CreationDate":"2019-07-10T11:43:00.000","Title":"How to add a column of seconds to a column of times in python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a file contain some columns which the second column is time. Like what I show below. I need to add a column of time which all are in seconds like this: \"2.13266      2.21784      2.20719      2.02499      2.16543\", to the time column in the first file (below). My question is how to add these two time to each other. And maybe in some cases when I add these times, then it goes to next day, and in this case how to change the date in related row.\n2014-08-26 19:49:32  0\n2014-08-28 05:43:21  0\n2014-08-30 11:47:54  0\n2014-08-30 03:26:10  0","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":673,"Q_Id":56970023,"Users Score":0,"Answer":"Ok. Finally it is done via this code:\n    d= 2.13266\ndd= pd.to_timedelta (int(d), unit='s')\ndf= pd.Timestamp('2014-08-26 19:49:32')\nnew = df + dd","Q_Score":0,"Tags":"python,datetime,time,add,timedelta","A_Id":56973402,"CreationDate":"2019-07-10T11:43:00.000","Title":"How to add a column of seconds to a column of times in python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a matrix called POS which has form (10,132) and I need to average those first 10 elements in such a way that my averaged matrix has the form of (1,132)\nI have tried doing \nmeans = pos.mean (axis = 1)\nor\nmenas = np.mean(pos)\nbut the result in the first case is a matrix of (10,) and in the second it is a simple number\ni expect the ouput a matrix of shape (1,132)","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":95,"Q_Id":56974114,"Users Score":0,"Answer":"The solution is to specify the correct axis and use keepdims=True which is noted by several commenters (If you add your answer I will delete mine).\nThis can be done with either pos.mean(axis = 0,keepdims=True) or np.mean(pos,axis=0,keepdims=True)","Q_Score":0,"Tags":"python,numpy","A_Id":56981888,"CreationDate":"2019-07-10T15:34:00.000","Title":"how to average in a specific dimension with numpy.mean?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working with text data and at the moment I have put my data into a term document matrix and calculated the TF, term frequency and TF-IDF, term frequency inverse document frequency. From here my matrix looks like:\ncolumns = document names\nrownames = words\nfilled with their TF and TF-IDF scores. \nI have been using the tm package in R for much of my current analysis but to take it further I have started playing around with the gensim library in Python.\nIts not clear to me if I have the word embeddings as in the TF and TF-IDF. I am hopeing to use Word2Vec\/Doc2Vec and obtain a matrix similar to what I currently have and then calculate the cosine similarity between document. Is this one of the outputs of the models?\nI basically have about 6000 documents I want to calculate the cosine similarity between them and then rank these cosine similarity scores.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":1708,"Q_Id":56976941,"Users Score":1,"Answer":"Yes, you could train a Word2Vec or Doc2Vec model on your texts. (Though, your data is a bit small for these algorithms.)\nAfterwards, with a Word2Vec model (or some modes of Doc2Vec), you would have word-vectors for all the words in your texts. One simple way to then create a vector for a longer text is to average together all the vectors for the text's individual words. Then, with a vector for each text, you can compare texts by calculating the cosine-similarity of their vectors. \nAlternatively, with a Doc2Vec model, you can either (a) look up the learned doc-vectors for texts that were in the training set; or (b) use infer_vector() to feed in new text, which should be tokenized the same way as the training data, and get a model-compatible vector for that new text.","Q_Score":0,"Tags":"python,gensim,word2vec,doc2vec","A_Id":56978241,"CreationDate":"2019-07-10T18:50:00.000","Title":"Can I obtain Word2Vec and Doc2Vec matrices to calculate a cosine similarity?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using sklearn.feature_extraction.text.TfidfVectorizer. I'm processing text. It seems standard to remove stop words. However, it seems to me that if I already have a ceiling on document frequency, meaning I will not include tokens that are in a large percent of the document (eg max_df=0.8), dropping stop words doesn't seem necessary. Theoretically, stop words are words that appear often and should be excluded. This way, we don't have to debate on what to include in our list of stop words, right? It's my understanding that there is disagreement over what words are used often enough that they should be considered stop words, right? For example, scikit-learn includes \"whereby\" in its built-in list of English stop words.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":40,"Q_Id":56979185,"Users Score":1,"Answer":"You are right. It could be the definition of stop words. However, do not forget that one reason to remove the stop words in the first phase, is to prevent counting them and reduce the computation time. \nNotice that your intuition behind stop words is correct.","Q_Score":1,"Tags":"python,scikit-learn,nlp,text-mining,text-processing","A_Id":56980001,"CreationDate":"2019-07-10T21:59:00.000","Title":"Is it bad to not remove stopwords when I've already set a ceiling on document frequency?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it possible to use \"reinforcement learning\" or a feedback loop on a supervised model?\nI have worked on a machine learning problem using a supervised learning model, more precisely a linear regression model, but I would like to improve the results by creating a feedback loop on the outputs of the prediction, i.e, tell the algorithm if it made mistakes on some examples.\nAs I know, this is basically how reinforcement learning works: the model learns from positive and negative feedbacks.\nI found out that we can implement supervised learning and reinforcement learning algorithms using PyBrain, but I couldn't find a way to relate between both.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":361,"Q_Id":56986663,"Users Score":0,"Answer":"Most (or maybe all) iterative supervised learning methods already use a feedback loop on the outputs of the prediction. If fact, this feedback is very informative since it provides information with the exact amount of error in each sample. Think for example in stochastic gradient descent, where you compute the error of each sample to update the model parameters.\nIn reinforcement learning the feedback signal (i.e., reward) is much more limited than in supervised learning. Therefore, in the typical setup of adjusting some model parameters, if you have a set of input-output (i.e., a training data set), probably it has no sense to apply reinforcement learning.\nIf you are thinking on a more specific case\/problem, you should be more specific in your question.","Q_Score":0,"Tags":"python,linear-regression,reinforcement-learning,supervised-learning","A_Id":56988032,"CreationDate":"2019-07-11T10:03:00.000","Title":"Applying \"reinforcement learning\" on a supervised learning model","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it possible to use \"reinforcement learning\" or a feedback loop on a supervised model?\nI have worked on a machine learning problem using a supervised learning model, more precisely a linear regression model, but I would like to improve the results by creating a feedback loop on the outputs of the prediction, i.e, tell the algorithm if it made mistakes on some examples.\nAs I know, this is basically how reinforcement learning works: the model learns from positive and negative feedbacks.\nI found out that we can implement supervised learning and reinforcement learning algorithms using PyBrain, but I couldn't find a way to relate between both.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":361,"Q_Id":56986663,"Users Score":0,"Answer":"Reinforcement Learning has been used to tune hyper-parameters and\/or select optimal Supervised Learning Models. There's also a paper on it: \"Learning to optimize with Reinforcement Learning\".\nReading Pablo's answer you may want to read up on \"back propagation\". It may be what you are looking for.","Q_Score":0,"Tags":"python,linear-regression,reinforcement-learning,supervised-learning","A_Id":57064604,"CreationDate":"2019-07-11T10:03:00.000","Title":"Applying \"reinforcement learning\" on a supervised learning model","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using Tensorflow 2.0 and trying to write a tf.keras.callbacks.Callback that reads both the inputs and outputs of my model for the batch. \nI expected to be able to override on_batch_end and access model.inputs and model.outputs but they are not EagerTensor with a value that I could access. Is there anyway to access the actual tensors values that were involved in a batch? \nThis has many practical uses such as outputting these tensors to Tensorboard for debugging, or serializing them for other purposes. I am aware that I could just run the whole model again using model.predict but that would force me to run every input twice through the network (and I might also have non-deterministic data generator). Any idea on how to achieve this?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":354,"Q_Id":56988498,"Users Score":1,"Answer":"No, there is no way to access the actual values for input and output in a callback. That's not just part of the design goal of callbacks. Callbacks only have access to model, args to fit, the epoch number and some metrics values. As you found, model.input and model.output only points to the symbolic KerasTensors, not actual values.\nTo do what you want, you could take the input, stack it (maybe with RaggedTensor) with the output you care about, and then make it an extra output of your model. Then implement your functionality as a custom metric that only reads y_pred. Inside your metric, unstack the y_pred to get the input and output, and then visualize \/ serialize \/ etc. Metrics\nAnother way might be to implement a custom Layer that uses py_function to call a function back in python. This will be super slow during serious training but may be enough for use during diagnostic \/ debugging.","Q_Score":10,"Tags":"python,tensorflow,keras,tensorflow2.0,tf.keras","A_Id":68777974,"CreationDate":"2019-07-11T11:48:00.000","Title":"Tensorflow 2.0: Accessing a batch's tensors from a callback","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to check the collision between a set of points to a point cloud (contain around 1M pts). I actually want to know which parts of the point cloud is collided with those outside points, and store those collided pts in the point cloud.\nI loop through each outside point and use the Obbtree.IntersectWithLine to check the collision with each pt in point cloud, while it is too slow... I enabled the CPU parallel computing but the improvement is limited. I was wondering if any GPU-accelerated API that is compatible with the VTK objects, or there is a better-efficient way to check the collision?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":173,"Q_Id":56993004,"Users Score":0,"Answer":"IntersectWithLine only intersects a segment to a polygonal mesh afaik.\nYou can maybe make a convex hull of the first cloud and then ask for the internal points of the second (?)\nIn this case one would use vtkSelectEnclosedPoints.","Q_Score":0,"Tags":"python,gpu,vtk","A_Id":57031365,"CreationDate":"2019-07-11T15:48:00.000","Title":"VTK ObbTree.IntersectWIthLine too slow for large-scale collision detection","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to plot streamtraces from CFD analysis with Python, over a 2D contour plot. My problem is that I'm dealing with 4 large 1D arrays (x,y coordinates and u,v velocity components), say over 100k points, arising from an external CFD simulation (so I cannot manipulate them). Creating 2D arrays from them (e.g. with scipy.interpolate.griddata as I found) causes my computer to crash due to excessive memory usage.\nI've also tried with quiver but I can't get a size for the arrows that scales with the dimensions of the plot: they are either too big or too small and anyway too many.\nSince I've looked at all the solutions I've found but none worked.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":48,"Q_Id":56993521,"Users Score":0,"Answer":"try to vectorize your loops that may help greatly with large scale datas.","Q_Score":0,"Tags":"python,arrays,python-3.x,matplotlib,plot","A_Id":57639340,"CreationDate":"2019-07-11T16:21:00.000","Title":"Python Streamplot from large 1D arrays","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using google colab but I can not save the results directly from colab to my google drive or even in my computer. Can anybody give me a hand?\nRegards","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":67,"Q_Id":56994495,"Users Score":0,"Answer":"After mounting your Google drive. You can do the following to save your work on the drive:\n1 Click File.\n2 Click Save File in Drive.","Q_Score":0,"Tags":"python-2.7","A_Id":58046297,"CreationDate":"2019-07-11T17:26:00.000","Title":"Save colab results","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"There are several similar questions around that suggest using any() with sets, however mine is a bit different in a way that I want to get the most effective and Pythonic way of forming a new array out of existing arrays based on membership.\nI have the following array [[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 4, 5, 6], [2, 4, 5, 6, 9, 1, 4, 5, 6, 4, 5, 6], [1, 2, 9, 4, 5, 6, 7, 7, 8, 5]]\nWhat I need is a way to form a new object {[4, 5, 6] : 6} where [4, 5, 6] is the key, and 6 is the number of times the key sequence appears in the array above.\nI have achieved the result by making a function with simple for loops, but I don't think it is the most Pythonic and efficient way of achieving it. I was thinking of using map() and filter() as well.\nWhat would be the most efficient and Pythonic way of achieving the result?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":54,"Q_Id":56997410,"Users Score":1,"Answer":"best way to maintain a mapping of elements in an array to a value is a hash table.\nas @JacobIRR mentioned {[4, 5, 6] : 6} is not valid because you cant have multiple keys associated to a value. i cant give you an exact solution to your problem as you didnt mention how your function works.\nif your algorithm dosent depend on a list as a key,you can try  {6 :[4, 5, 6]} will be a valid solution as HashMap doesn't allow duplicate keys but allows duplicate values.","Q_Score":0,"Tags":"python,arrays","A_Id":56997616,"CreationDate":"2019-07-11T21:17:00.000","Title":"check for consecutive values membership in arrays","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to find out how fsolve in scipy works. So far, all I have found is that it is a numerical solver that finds the root of non linear sets of equations. But I can't find what method it uses to find the roots anywhere. Does anyone know how the roots are found?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":122,"Q_Id":57008554,"Users Score":0,"Answer":"So, after looking up the source code it seems that the solver \"fsolve\" uses the powell method to find the roots.","Q_Score":0,"Tags":"python,scipy","A_Id":57009082,"CreationDate":"2019-07-12T14:00:00.000","Title":"How does fsolve in scipy work? What methods does it use to find the roots?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to force scipy.stats.cauchy probability density function to only generate values between -1 and 1.\nCurrently I am doing the hacky way by running y=cauchy.rvs(center,sigma) inside a while loop and while its lower than -1 or higher than 1 it recomputes it and after it enters the desired interval it returns y. So essentially I am drawing a new random variable until the conditions are not met.\nI am wondering whether it's possible to do this in a simpler way, the scipy documentation is not very helpful and it's very ambiguous. Is there any way to specify the min\/max range of the random variables inside the function arguments, like via **kwargs or something?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":507,"Q_Id":57011662,"Users Score":1,"Answer":"Short answer: Not directly, no.\nThe issue is that a distribution is normalized, the integral of the pdf over the support is unity. When you change the support, you are effectively changing the distribution.\nFor the truncated Cauchy distribution, you can easily roll your own little generator, using the inverse function transform of a uniform random variate.","Q_Score":1,"Tags":"python,python-3.x,scipy","A_Id":57012785,"CreationDate":"2019-07-12T17:34:00.000","Title":"Force scipy.stats.cauchy between an interval","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a numpy array ids = np.array([1,1,1,1,2,2,2,3,4,4])\nand another array of equal length vals = np.array([1,2,3,4,5,6,7,8,9,10])\nNote: the ids array is sorted by ascending order\nI would like to insert 4 zeros before the beginning of each new id - i.e. \nnew array = np.array([0,0,0,0,1,2,3,4,0,0,0,0,5,6,7,0,0,0,0,8,0,0,0,0,9,10])\nOnly, way I am able to produce this is by iterating through the array which is very slow - and I am not quite sure how to do this using insert, pad, or expand_dim ...","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":126,"Q_Id":57015429,"Users Score":0,"Answer":"u can use np.zeros and append it to your existing array like\nnewid=np.append(np.zeros((4,), dtype=int),ids)\nGood Luck!","Q_Score":0,"Tags":"python-3.x,numpy","A_Id":57016163,"CreationDate":"2019-07-13T01:06:00.000","Title":"Quickest way to insert zeros into numpy array","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm working with NetCDF files from NCAR and I'm trying to plot sea-ice thickness. This variable is on a curvilinear (TLAT,TLON) grid. What is the best way to plot this data on a map projection? Do I need to re-grid it to a regular grid or is there a way to plot it directly? I'm fairly new to Python so any help would be appreciated. Please let me know if you need any more information. Thank you! \nI've tried libraries like iris, scipy, and basemap, but I couldn't really get a clear explanation on how to implement them for my case.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":276,"Q_Id":57023555,"Users Score":0,"Answer":"I am pretty sure you can already use methods like contour, contourf, pcolormesh from Python's matplotlib without re-gridding the data. The same methods work for Basemap.","Q_Score":1,"Tags":"python,interpolation,netcdf","A_Id":57053572,"CreationDate":"2019-07-13T23:37:00.000","Title":"How can I put my curvilinear coordinate data on a map projection?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Basically the idea is to automate some workflows without using Qgis.\nI'm failing to achieve similar results to the Qgis feature 'Add coordinates to points' in Geopandas, which allows you to grab the x,y coordinates of points in its current projection and create new attributes to the table.\nSo I have a set of points with which I played with. The original shapefile's CRS is epsg 2154 (Lambert 93). I need to get the latitude and longitude in a format compatible with Google Maps.\nGoogle uses epsg 3857 for Google Maps.\npoints = pd.DataFrame({'Id': ['001', '002', '003'],'geometry':         ['POINT (909149.3986619939 6881986.232659903)', 'POINT (909649.3986619939 6882486.232659903)', 'POINT (909149.3986619939 6882486.232659903)']})\nThe idea is to switch to epsg 3857 (wgs84) and from there, create lat\/long columns which would be filled with wgs84 coordinates, such as 47.357955,1.7317783.\nSo what I did was obvisouly changing the CRS:\npointswgs84 = points.to_crs(espg=3857)\nAnd then\npointswgs84['lon'] = pointswgs84.geometry.x\npointswgs84['lat'] = pointswgs84.geometry.y\nBut my lat\/long columns get then filled with coordinates corresponding to the original points dataframe:\npoints = pd.DataFrame({'Id': ['001', '002', '003'],'geometry':['POINT (909149.3986619939 6881986.232659903)', 'POINT (909649.3986619939 6882486.232659903)', 'POINT (909149.3986619939 6882486.232659903)'],'long': ['6881986.232659903', '6882486.232659903', '6882486.232659903'], 'lat': ['909149.3986619939', '909649.3986619939', '909149.3986619939']})\nLooks like I'm missing something here but since I'm rather new to Python & Geopandas in general I'm not sure about what... \nThank you for your help.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":659,"Q_Id":57027276,"Users Score":0,"Answer":"Alright, so problem solved. It was a simple espg issue. Here's the solution if that can help any other newbie like me:\nEven if Google uses espg 3857, if you need to convert the X,Y of your point to something which could be exploitable (such as a url parameter), you need to use espg 4326.\nYou'll then get your long lat the right way. Like 5.859162,49.003710.\nSo:\nStep 1: convert your original shapefile to espg 4326\npointswgs84 = points.to_crs({'init': 'epsg:4326'})\nStep 2: simply extract x and y and add them in new columns\npointswgs84['lon'] = pointswgs84.geometry.x\npointswgs84['lat'] = pointswgs84.geometry.y","Q_Score":0,"Tags":"python,pandas,gis,qgis,geopandas","A_Id":57029334,"CreationDate":"2019-07-14T12:11:00.000","Title":"Add coordinates to points after changing a df CRS in Geopandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to create a non-hdfs .csv file using a spark DataFrame. How can do it ? \nThe purpose of this non-hdfs .csv file is to use read_csv() on it and load it back to a pandas DataFrame.\nI tried using toPandas() but I have 45 million records in my spark DataFrame and its very slow.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":438,"Q_Id":57028445,"Users Score":0,"Answer":"If you have 45 million records you will likely need to create a set of csv files which spark will do automatically.  Depending on where you want to save the data the path will vary.  For example if you wanted to write to S3 you would provide a path like this.\n\ndf.write.csv(\"s3:\/\/my-bucket\/path\/to\/folder\/\")\n\nYou may also want to manually repartition the data before writing to get an exact number of output files.","Q_Score":1,"Tags":"python-3.x,pandas,apache-spark,pyspark,pyspark-sql","A_Id":57031293,"CreationDate":"2019-07-14T14:49:00.000","Title":"Create a non-hdfs csv from spark dataframe","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Does the grid search cv stops when my computer is in sleep mode? Should I turn off sleep mode when running it?\nRegards\nVikrant","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":68,"Q_Id":57028814,"Users Score":1,"Answer":"Sleep mode suspends all processor activities and places RAM in a low power state only enough to retain the state. So, yes grid search or any process for that matter is suspended.","Q_Score":1,"Tags":"python,machine-learning,grid-search,gridsearchcv","A_Id":57029035,"CreationDate":"2019-07-14T15:37:00.000","Title":"Grid Search and computer in sleep mode","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"take a input as a csv file and generate text\/sentence using nlg. I have tried with pynlg and markov chain.But nothing worked .What else I can use?","AnswerCount":1,"Available Count":1,"Score":-0.1973753202,"is_accepted":false,"ViewCount":311,"Q_Id":57035069,"Users Score":-1,"Answer":"There are not much python libraries for NLG!!. Try out nlglib a python wrapper around SimpleNLG. For tutorial purposes, you could read Building Natural Language Generation systems by e.reiter.","Q_Score":0,"Tags":"python,nlp","A_Id":57142523,"CreationDate":"2019-07-15T07:29:00.000","Title":"how to use natural language generation from a csv file input .which python module we should use.can any one share a sample tutorial?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"This is a problem given in HackWithInfy2019 in hackerrank.\nI am stuck with this problem since yesterday.\nQuestion:\nYou are given array of N integers.You have to find a pair (i,j) \nwhich maximizes the value of GCD(a[i],a[j])+(j - i)\nand 1<=i< j<=n\nConstraints are:\n2<= N <= 10^5\n1<= a[i] <= 10^5\nI've tried this problem using python","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":289,"Q_Id":57045356,"Users Score":0,"Answer":"Here is one way of doing it.\nCreate a mutable class MinMax for storing the min. and max. index.\nCreate a Map<Integer, MinMax> for storing the min. and max. index for a particular divisor.\nFor each value in a, find all divisors for a[i], and update the map accordingly, such that the MinMax object stores the min. and max. i of the number with that particular divisor.\nWhen done, iterate the map and find the entry with largest result of calculating key + value.max - value.min.\nThe min. and max. values of that entry is your answer.","Q_Score":0,"Tags":"java,c++,python-3.x,python-2.7,greatest-common-divisor","A_Id":57045925,"CreationDate":"2019-07-15T18:30:00.000","Title":"Maximizing GCD with some condition","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have five decision variables, each having a specific range. I need to find a combination of these variables so as to maximize one of my objectives while minimizing the other at the same time. I have prepared a datasheet of randomly generated variables with respective values of the 2 objective functions. Please suggest me how to approach the solution using neural networks. \nMy objective function involves thermodynamic calculations. If interested you can have a look at the objective functions here :","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":244,"Q_Id":57045756,"Users Score":0,"Answer":"There are many methods, but the easiest way is \"linear scalarization\".\nYou can add objectives to make single objective.\nWhile doing this, you can weight objectives considering priority.\n(Making linear combination of multiple objectives)\nSee examples:\nVariational AE loss (regularization loss + reconstruction loss)\nAssociative DA loss (classification loss + walker loss + visit loss)","Q_Score":0,"Tags":"python,matlab,neural-network,artificial-intelligence","A_Id":57048809,"CreationDate":"2019-07-15T19:03:00.000","Title":"How to do multi-objective optimization using neural networks?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a monthly time series which I want to forecast using Prophet. I also have external regressors which are only available on a quarterly basis. \nI have thought of following possibilities - \n\nrepeat the quarterly values to make it monthly and then include \nlinearly interpolate for the months\n\nWhat other options I can evaluate?\nWhich would be the most sensible thing to do in this situation?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":189,"Q_Id":57053017,"Users Score":0,"Answer":"You have to evaluate based on your business problem, but there are some questions you can ask yourself.\n\nHow are the external regressors making their predictions? Are they trained on completely different data?\n\nIf not, are they worth including?\n\nHow quickly do we expect those regressors to get \"stale\"? How far in the future are their predictions available? How well do they perform more than one quarter into the future?\n\nInterpolation can be reasonable based on these factors...but don't leak information about the future to your model at training time.\n\nDo they relate to subsets of your features?\n\nIf so, some feature engineering could but fun - combine the external regressor's output with your other data in meaningful ways.","Q_Score":0,"Tags":"python,forecasting,facebook-prophet","A_Id":57053491,"CreationDate":"2019-07-16T08:24:00.000","Title":"How to include quarterly regressor in Prophet for monthly time series?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"my GPU is NVIDIA RTX 2080 TI\nKeras 2.2.4\nTensorflow-gpu 1.12.0\nCUDA 10.0\nOnce I load build a model ( before compilation ), I found that GPU memory is fully allocated\n[0] GeForce RTX 2080 Ti | 50'C,  15 % | 10759 \/ 10989 MB | issd\/8067(10749M)\nWhat could be the reason, how can i debug it?\nI don't have spare memory to load the data even if I load via generators\nI have tried to monitor the GPUs memory usage found out it is full just after building the layers (before compiling model)","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":502,"Q_Id":57058071,"Users Score":0,"Answer":"I meet a similar problem when I load pre-trained ResNet50. The GPU memory usage just surges to 11GB while ResNet50 usually only consumes less than 150MB.\nThe problem in my case is that I also import PyTorch without actually used it in my code. After commented it, everything works fine.\nBut I have another PC with the same code that works just fine. So I uninstall and reinstall the Tensorflow and PyTorch with the correct version. Then everything works fine even if I import PyTorch.","Q_Score":0,"Tags":"python-3.x,tensorflow,keras","A_Id":67595461,"CreationDate":"2019-07-16T13:11:00.000","Title":"Keras, Tensorflow are reserving all GPU memory on model build","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"New to pandas - I have been trying to use the pandas.merge_asof to join two datasets together by shared ID first, then merge by nearest timestamp to the timestamp in df1. \nThe issue is that I have discovered is that both left_on and right_on must be int. I have one column that contains NaNs and they must remain. Floats was also ineffective. From my research on Stackoverflow, I found out that the latest version of Pandas, 24.02 has this functionality where you simply convert the column to Int64. However, the version of pandas I have available at work is 23.xx and cannot be upgraded at this time. \nWhat is my easiest option? If I were to simply remove the rows associated with the NaNs values in the one column, could I simply add them back later, and then change the dtype back from int to object? Would this disrupt anything?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":486,"Q_Id":57064741,"Users Score":0,"Answer":"I did two methods:\n1) I set the Nan's to -1. (there was no id that had -1 in the other dataset). Then put them back to Nan after.\n2) I removed the records associated with the Nan's for that column, and put the records back in after.\nI tried to compare results (and reset the indices, sort by timestamp), but I kept getting false. Both should give the same result regardless.","Q_Score":0,"Tags":"python,pandas,merge","A_Id":57084810,"CreationDate":"2019-07-16T20:08:00.000","Title":"Using pandas.merge_asof when one merge column contains NaNs","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I trained a model on size 512x512 images. I currently have size 982x982 (or any other size) images that I need to have the model predict on. When I run on these images, I get errors about unexpected input array shape.\nDoes Tensorflow provide any way to conveniently deploy a model on images of size\/shape larger than what the model was trained on?\nMore Details:\nSpecifically, this is a model used for image segmentation.\nI assume one workaround to the issue is creating my own sliding-window script that, instead, inputs into the model windows of the expected size taken at different intervals from the original image, and then somehow pasting all those output windows back into a single image after they have gone through the model. However, I want to know if there is a more standard approach.\nI am brand new to Tensorflow (and image segmentation) so it is very possible I just missed something, or that perhaps my question is unclear.\nThank you for any advice :)","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":234,"Q_Id":57066263,"Users Score":0,"Answer":"I once have the same question about how to deal with larger or smaller images. According to my experience, a possible method is to resize images to the input size of the network. For example, if your current image size is 982x982 and the network input size is 512x512, then you can just use some libraries like Pillow or OpenCV to resize the original image from 982x982 to 512x512.\nYour method is a possible solution. I would say that there are many possible solutions other than the resizing operation, but you should better try this simplest method to see if your network works well. What I have learned from my projects is that we can always try the simplest solution, and in most cases, it works perfectly.\nGenerally speaking, there is not a general perfect way to do it. You can start with the simplest method, and find a more complicated one if it does not work.","Q_Score":0,"Tags":"python,python-2.7,tensorflow","A_Id":57066365,"CreationDate":"2019-07-16T22:30:00.000","Title":"How to apply trained model on images of shape\/size larger than what the model was trained on (in Tensorflow)?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm having more than 100 String Columns which I need to be converting into enum so that the ML model identifies theses columns as categories. \nIn Pyspark, there is no Category type (as in Pandas) and hence I casted all categories as 'String'. I don't want to click 'convert to enum' > 100 times and I'm sure there is an easier way to perform this task. Any help would be greatly appreciated.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":228,"Q_Id":57068435,"Users Score":0,"Answer":"I was not able to find a coding solution. But found an easier way - saved the pyspark df as a parquet in hdfs and imported it into h2o. All string columns were auto-recognised as Enum.","Q_Score":0,"Tags":"python,pyspark,h2o","A_Id":57085156,"CreationDate":"2019-07-17T04:15:00.000","Title":"Converting multiple string columns to enum in H2o Flow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using Keras on Python to train a CNN autoencoder. In the fit() method I have to provide validation_split or validation_data. First, I would like to use  80% of my data as training data and 20% as validation data (random split). As soon as I have found the best parameters, I would like to train the autoencoder on all the data, i.e. no more using a validation set.\nIs it possible to train a Keras model without using a validation set, i.e. using all data to train?\nMoreover, the pixels in my images are all in the range [0, -0.04]. Is it still recommended to normalize the values of all pixels in all images in the training and validation set to the range [0,1] or to [-1,1] or to standardize it (zero mean, unit variance)? If so, which method is prefered? By the way, my images are actually 2D heat maps (one color channel).","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":3996,"Q_Id":57075037,"Users Score":2,"Answer":"Yes, you can train a keras model without validation data, but its not a good practice, because then you would not know if the model can generalize or not. The same applies for autoencoders, they can overfit to the training set.\nIt is always recommended to normalize your inputs, specially if the ranges are large or small. There is no preferred method, any normalization generally works the same.","Q_Score":0,"Tags":"python,keras,conv-neural-network,keras-2,standardized","A_Id":57077151,"CreationDate":"2019-07-17T11:43:00.000","Title":"Training Keras model without validation set and normalization of images","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Everytime I try to display a complete DataFrame in my jupyter notebook, the note book crashes. The file wont start up so I had to make a new jupyter notebook file. When i do display(df) it only shows a couple of the rows, when I need to show 57623 rows. I need to show results for all of these rows and put them into an html file.\nI tried setting the max rows and max columns, but the entire dataframe would not print out without the notebook crashing\n''' python\npd.set_option('display.max_columns', 24)\n    \"pd.set_option('display.max_rows', 57623)\n'''\nThe expected results were for the entire DataFrame to print out, but instead the notebook would have an hourglass next to it and nothing would load.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":534,"Q_Id":57083727,"Users Score":1,"Answer":"I think your machine is not powerful enough to handle so much data. I can display 57623 rows for my data (with 16 columns) without any problem. But my machine has 252GB memory, and it still took about 1 minute to display these data in jupyter notebook. \nIf I tried to scroll through the data, it's slow, and sometimes stuck for a while.\nOn the other hand, what do you want to achieve by displaying all data here? There's definitely another way to achieve the same thing as you are doing now.","Q_Score":0,"Tags":"python,pandas,numpy","A_Id":57209941,"CreationDate":"2019-07-17T20:35:00.000","Title":"How to display a DataFrame without Jupyter Notebook crashing?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using Google Colab to train an object detection model, using the tensorflow object detection api. When I run the cell train.py, it keeps printing diegnostic output. After 30 minutes or so the browser crashes, because of the high number of lines printed in the cell's output.\nIs there any script which one can use to clear the output periodically (say every 30 min) instead of manually pressing the clear output button?","AnswerCount":5,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":8171,"Q_Id":57086398,"Users Score":0,"Answer":"This is a partial solution, but you could also specify your verbose parameter in your fit method to 0 to get no output, or to 2 to get a reduced output.\nAnd you can use TensorBoard to still see what is going on.","Q_Score":3,"Tags":"python,tensorflow,google-colaboratory,object-detection-api","A_Id":62209904,"CreationDate":"2019-07-18T02:38:00.000","Title":"How do you clear Google Colab's output periodically","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"So I have this model written by the subclassing API, the call signature looks like call(x, training), where training argument is needed to differentiate between training and non-training when doing batchnorm and dropout. How do I make the model forward pass know I am in training mode or eval mode when I use model.fit?\nThanks!","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1713,"Q_Id":57088786,"Users Score":0,"Answer":"As far as i know, there is no argument  for this. Model.fit simply trains the model on whatever training data provided, and at the end of each epoch evaluates the training on either provided validation data, OR by the use of validation_split.","Q_Score":3,"Tags":"python,tensorflow,keras","A_Id":57088849,"CreationDate":"2019-07-18T07:01:00.000","Title":"how to pass in 'training' argument to tf,keras.Model when using model.fit","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"i have created a model for classification of two types of shoes \nnow how to deploy it in OpenCv (videoObject detection)??\nthanks in advance","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":5883,"Q_Id":57093345,"Users Score":0,"Answer":"You would save the model to H5 file model.save(\"modelname.h5\") , then load it in OpenCV code load_model(\"modelname.h5\"). Then in a loop detect the objects you find via model.predict(ImageROI)","Q_Score":6,"Tags":"python,opencv,keras","A_Id":57093852,"CreationDate":"2019-07-18T11:24:00.000","Title":"how to add custom Keras model in OpenCv in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to use a recurrent neural network for a regression problem. I have 50 samples of 600 timesteps each. I am planning to use 40 for training and 10 for validation. \nAfter my network gets trained, can I use it to predict for a timeseries of a smaller length (200 timesteps)? \nThe input and output dimensions (i.e. features) will remain same, just the length (i.e. number of timesteps) is smaller","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":182,"Q_Id":57101619,"Users Score":0,"Answer":"You have 2 options:\n\nFirst option, as @stormzhou commented above, you can pad you test data with zeros (not recommended) \nSecond option, you can use for training and validation only 200 timesteps.","Q_Score":2,"Tags":"python,keras,neural-network,time-series,lstm","A_Id":57110360,"CreationDate":"2019-07-18T19:41:00.000","Title":"Can my training and testing data for neural network be of different lengths?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to create a basic model for stock price prediction and some of the features I want to include come from the companies quarterly earnings report (every 3 months)  so for example; if my data features are Date, OpenPrice, Close Price, Volume, LastQrtrRevenue how do I include LastQrtrRevenue if I only have a value for it every 3 months?  Do I leave the other days blank (or null) or should I just include a constant of the LastQrtrRevenue and just update it on the day the new figures are released?  Please if anyone has any feedback on dealing with data that is released infrequently but is important to include please share.... Thank you in advance.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":161,"Q_Id":57103601,"Users Score":1,"Answer":"I would be tempted to put the last quarter revenue in a separate table, with a date field representing when that quarter began (or ended, it doesn't really matter). Then you can write queries to work the way that most suits your application. You could certainly reconstitute the view you mention above using that table, as long as you can relate it to the main table.\nYou would just need to join the main table by company name, while selected the max() of the last quarter revenue table.","Q_Score":1,"Tags":"python,deep-learning,dataset,missing-data","A_Id":57103655,"CreationDate":"2019-07-18T22:58:00.000","Title":"How to deal with infrequent data in a time series prediction model","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Trying to use 'plotly.graph_objects' but I get this error\n\nModuleNotFoundError: No module named 'plotly.graph_objects'\n\nHow do I download module and apply using anaconda navigator","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":24260,"Q_Id":57105747,"Users Score":28,"Answer":"You should use instead:\nfrom plotly import graph_objs as go","Q_Score":13,"Tags":"python,plotly,plotly-python","A_Id":57112843,"CreationDate":"2019-07-19T04:54:00.000","Title":"ModuleNotFoundError: No module named 'plotly.graph_objects'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to plot some data with Bokeh which has distance as the x-axis. However the distance data I have been provided with is in the format \"7 Miles 100 Meters, 7 Miles 200m\" etc.\nWhat's the best way to do this?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":123,"Q_Id":57108242,"Users Score":0,"Answer":"I figured it out in the end, I just converted the Miles(e.g. 7.5) and Meters(e.g. 400m) into Miles (e.g. 7.75miles). Then I used the tickformatter to display the specific points as meters. Thanks for the help","Q_Score":0,"Tags":"python,pandas,bokeh","A_Id":57165757,"CreationDate":"2019-07-19T08:13:00.000","Title":"How to Bokeh plot a combination of Miles and Metres on axis (e.g. 7.5 Miles 100m, 7.5 Miles 200m, 8 Miles 100m....)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When I run the code in kaggle kernel, it is working fine, but when executing the code on my machine, it throws this error. please help","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":53,"Q_Id":57110876,"Users Score":0,"Answer":"so in my case, I was running the code in python 2.7 in which Python 2.7 will give different ceil value as the answer, Python 3.4 reports different value.","Q_Score":0,"Tags":"python,deep-learning,valueerror","A_Id":57139784,"CreationDate":"2019-07-19T10:48:00.000","Title":"ValueError: could not broadcast input array from shape (848,837,8) into shape (800,800,8)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"On Ubuntu 18.04 with python 3.7.3, I'm attempting to import pandas but this fails because it can't find _lzma.  \nI've verified that _lzma is installed with dpkg:\n\n\/usr\/lib\/python3.7\/lib-dynload\/_lzma.cpython-37m-x86_64-linux-gnu.so. Oddly, _lzma is not a dependency of pandas (as specified by pip3).","AnswerCount":3,"Available Count":1,"Score":-0.0665680765,"is_accepted":false,"ViewCount":19345,"Q_Id":57113269,"Users Score":-1,"Answer":"just upgraded to version 0.25.1 and works well","Q_Score":13,"Tags":"pandas,python-3.7,ubuntu-18.04,lzma","A_Id":58452764,"CreationDate":"2019-07-19T13:13:00.000","Title":"import pandas results in ModuleNotFoundError :_lzma","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have to manually upload the training data label CSV and click on 'train' to train the model. I want to automate all these preferably with python.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":109,"Q_Id":57115839,"Users Score":0,"Answer":"I used AutoML RESTapi for creating datasets to training the model. Although If I want to retrain a model on more data, I have to delete the previously trained model and create and train a new one.","Q_Score":1,"Tags":"python,google-cloud-platform,automl,google-cloud-automl","A_Id":57150251,"CreationDate":"2019-07-19T15:48:00.000","Title":"Automating both Uploading the training data label csv and Training processed the model in AutoML Vision Image classification","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In Pandas with Python I could use:\nfor item in read_csv(csv_file, header=1)\nAnd in Spark I only have the option of true\/false?\ndf = spark.read.format(\"csv\").option(\"header\", \"true\").load('myfile.csv')\nHow can I read starting from the second row in Spark?\nThe suggested duplicate post is an outdated version of Spark. I am using the latest, 2.4.3.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1631,"Q_Id":57117577,"Users Score":1,"Answer":"Looks like there's no option in spark csv to specify how many lines to skip. Here are some alternatives you can try:\n\nRead with option(\"header\", \"true\"), and rename the column names using withColumnRenamed.\nRead with option(\"header\", \"false\"), and select rows from 2nd line using select.\nIf the first character of the first line is different from all other lines, you can use comment option to skip it. For example, if the first character of line #1 is D, you set comment='D'. Just be careful, comment will skip any line that starts with D here.\n\nHope this helps.","Q_Score":1,"Tags":"python,csv,apache-spark,pyspark,apache-spark-sql","A_Id":57118303,"CreationDate":"2019-07-19T18:00:00.000","Title":"Spark read from second row like Pandas header=1","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Often during debugging Spark Jobs on failure we can find the appropriate Stage and task responsible for the failure such as String Index Out of Bounds exception but it becomes difficult to understand which transformation is responsible for this failure.The UI shows information such as Exchange\/HashAggregate\/Aggregate but finding the actual transformation responsible for this failure becomes really difficult in 500+ lines of code, so how should it be possible to debug Spark task failures and tracing the transformation responsible for the same?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":117,"Q_Id":57117739,"Users Score":0,"Answer":"Break your execution down. It's the easiest way to understand where the error might be coming from. Running a 500+ line of code for the first time is never a good idea. You want to have the intermediate results while you are working with it. Another way is to use an IDE and walk through the code. This can help you understand where the error originated from. I prefer PyCharm (Community Edition is free), but VS Code might be a good alternative too.","Q_Score":2,"Tags":"python,scala,apache-spark,pyspark,apache-spark-sql","A_Id":57124598,"CreationDate":"2019-07-19T18:13:00.000","Title":"How does log in spark stage\/tasks help in understanding actual spark transformation it corresponds to","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on a problem which requires me to build a deep learning model that based on certain input image it has to output another image. It is worth noting that these two images are conceptually related but they don't have the same dimensions. \nAt first I thought that a classical CNN with a final dense layer whose argument is the multiplication of the height and width of the output image would suit this case, but when training it was giving strange figures such as accuracy of 0. \nWhile looking for some answers on the Internet I discovered the concepts of CNN autoencoders and I was wondering if this approach could help me solve my problem. Among all the examples I saw, the input and output of an autoencoder had the same size and dimensions. \nAt this point I wanted to ask if there was a type of CNN autoencoders that produce an output image that has different dimension compared to input image.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1346,"Q_Id":57126626,"Users Score":3,"Answer":"Auto-encoder (AE) is an architecture that tries to encode your image into a lower-dimensional representation by learning to reconstruct the data from such representation simultaniously. Therefore AE rely on a unsupervised (don't need labels) data that is used both as an input and as the target (used in the loss).\nYou can try using a U-net based architecture for your usecase. A U-net would forward intermediate data representations to later layers of the network which should assist with faster learning\/mapping of the inputs into a new domain..\nYou can also experiment with a simple architecture containing a few ResNet blocks without any downsampling layers, which might or might not be enough for your use-case.\nIf you want to dig a little deeper you can look into Disco-GAN and related methods.They explicitly try to map image into a new domain while maintaining image information.","Q_Score":3,"Tags":"python,tensorflow,keras,deep-learning","A_Id":57139328,"CreationDate":"2019-07-20T16:32:00.000","Title":"Can CNN autoencoders have different input and output dimensions?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"This is a general question about making real future predictions with an LSTM model using keras & tensorflow in Python (optional R). \nFor example stock prices. I know there is a train\/test split to measure the accuracy\/performance of the model comparing my results with the test prices. But I want to make real future predictions\/out of sample predictions. Does anyone has an idea & would like to share some thoughts on it?\nMe only came to mind to use a rolling window but that didn't work at all. So I'm glad about every tip you guys have.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":279,"Q_Id":57134812,"Users Score":0,"Answer":"There are two main ways to create train\/validation set for a time series situation: \n\nsplitting your samples ( taking for example 80 % of the time series for training and 20 % for validation) \nsplitting your time series ( training your model on the first n-k values of the time series and validation on the k other values)","Q_Score":0,"Tags":"python,tensorflow,keras,deep-learning,time-series","A_Id":57135408,"CreationDate":"2019-07-21T15:59:00.000","Title":"Out of sample predictions with LSTM","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"My laptop had a problem with training big a dataset but not for predicting. Can I use Google Cloud Platform for training, only then export and download some sort of weights or model of that machine learning, so I can use it on my own laptop, and if so how to do it?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":35,"Q_Id":57139636,"Users Score":0,"Answer":"Decide if you want to use Tensorflow or Keras etc. Prepare scripts to train and save model, and another script to use it for prediction. \nIt should be simple enough to use GCP for training and download the model to use on your machine. You can choose to use a high end machine (lot of memory, cores, GPU) on GCP. Training in distributed mode may be more complex. Then download the model and use it on local machine.\nIf you run into issues, post your scripts and ask another question.","Q_Score":0,"Tags":"python,machine-learning,google-cloud-platform","A_Id":57139854,"CreationDate":"2019-07-22T05:27:00.000","Title":"Can I use GCP for training only but predict with my own AI machine?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a use case where in I need to cluster N transactions but with a constraint that a particular column value in the resultant clusters should be same for individual clusters. I have been using NearestNeighbors - NN from sklearn for this purpose and it seems to workout to an extend. The distance metric chosen is Cosine and the the type of data is categorical - one hot encoding is done before actual clustering.\nNow if I have columns c1, c2...cn, which is used along with NN for clustering and if I want to enforce the criteria that for a particular cluster derived, Gi, there should be a single unique value for column cx with in Gi. How would I enforce this?\nI went through a couple of documents and some of the techniques indirectly suggests to groupby the column cx and then do the clustering or duplicate the column cx in data and cluster. Are these valid approached to tackle the problem?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":36,"Q_Id":57146403,"Users Score":0,"Answer":"If you have an inverted index, enforcing a certain value to be required, while having other values optional and only used for similarity should be straightforward. Just think of the full-text search example with required and optional terms.\nDepending on how many queries you do, linear search as well as a \"group by\" approach may be fine.","Q_Score":0,"Tags":"python,scikit-learn,cluster-analysis","A_Id":57216482,"CreationDate":"2019-07-22T12:48:00.000","Title":"Weighted clustering using NearestNeighbors","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using train_test_split to train the model and check the results using predict. How do I proceed to predict the labels of additional data, for example, from a test set or from user inputs?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":35,"Q_Id":57147986,"Users Score":1,"Answer":"You can use the custom data for prediction as long as it has the same number, order and type of features as your training data into an array type, not in list. If you meet these conditions, you can send that array to the model for prediction with the normal predict() method.","Q_Score":0,"Tags":"python,machine-learning,scikit-learn","A_Id":57148940,"CreationDate":"2019-07-22T14:15:00.000","Title":"Custom data prediction using decision trees","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to load a mesh from an .obj file (from ShapeNet dataset) using Trimesh, and then use the repair.fix_winding(mesh) function.\nBut when I load the mesh, via \ntrimesh.load('\/path\/to\/file.obj') or trimesh.load_mesh('\/path\/to\/file.obj'), \nthe object class returned is Scene, which is incompatible with repair.fix_winding(mesh), only Trimesh object are accepted.\nHow can I force it to load and return a Trimesh object or parse the Scene object to Trimesh object? Or any other way to fix winding of the triangles?\nUsing:\nPython 3.6.5\nTrimesh 3.0.14\nMacOS 10.14.5","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2337,"Q_Id":57155089,"Users Score":0,"Answer":"I have the same problem. You can access the \"geometry\" member of the scene, but I have no idea how the original mesh is split into multiple ones. Following.","Q_Score":0,"Tags":"python,trimesh","A_Id":57183592,"CreationDate":"2019-07-22T23:56:00.000","Title":"obj file loaded as Scene object instead of Trimesh object","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to generate random division problems for an educational game that I am building. Generating random addition, subtraction, and multiplication problems is not too difficult. But I want my division problems to not have any remainders. \nWith addition, subtraction and multiplication, I could just do [random number] times or plus or subtract [random number].\nIt is not so easy if I want to do division problems. Can anyone help?\nThanks in advance","AnswerCount":6,"Available Count":5,"Score":0.0665680765,"is_accepted":false,"ViewCount":766,"Q_Id":57155636,"Users Score":2,"Answer":"1) Take any non-zero randomized Divisor (x).  \/\/ say 5\n2) Take any randomized temporary Dividend (D).  \/\/ say 24\n3) Calculate R = D % x; \/\/ => 4\n4) return Dividend as (D -x ) \/\/ return 20\nNow, your dividend will always be perfectly divisible by the divisor.","Q_Score":0,"Tags":"python,math","A_Id":57162164,"CreationDate":"2019-07-23T01:45:00.000","Title":"Generating random division problems in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to generate random division problems for an educational game that I am building. Generating random addition, subtraction, and multiplication problems is not too difficult. But I want my division problems to not have any remainders. \nWith addition, subtraction and multiplication, I could just do [random number] times or plus or subtract [random number].\nIt is not so easy if I want to do division problems. Can anyone help?\nThanks in advance","AnswerCount":6,"Available Count":5,"Score":1.2,"is_accepted":true,"ViewCount":766,"Q_Id":57155636,"Users Score":2,"Answer":"x\/y = z\ny*z = x\nGenerate y and z as integers, then calculate x.","Q_Score":0,"Tags":"python,math","A_Id":57155682,"CreationDate":"2019-07-23T01:45:00.000","Title":"Generating random division problems in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to generate random division problems for an educational game that I am building. Generating random addition, subtraction, and multiplication problems is not too difficult. But I want my division problems to not have any remainders. \nWith addition, subtraction and multiplication, I could just do [random number] times or plus or subtract [random number].\nIt is not so easy if I want to do division problems. Can anyone help?\nThanks in advance","AnswerCount":6,"Available Count":5,"Score":0.0,"is_accepted":false,"ViewCount":766,"Q_Id":57155636,"Users Score":0,"Answer":"I think that @Vira has the right idea.\nIf you want to generate a and b such that a = b * q + r with r=0, the good way to do it is : \n\nGenerate b randomly\nGenerate q randomly\nCompute a = b * q\nAsk to compute the division : a divided by b. The answer is q.","Q_Score":0,"Tags":"python,math","A_Id":57165189,"CreationDate":"2019-07-23T01:45:00.000","Title":"Generating random division problems in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to generate random division problems for an educational game that I am building. Generating random addition, subtraction, and multiplication problems is not too difficult. But I want my division problems to not have any remainders. \nWith addition, subtraction and multiplication, I could just do [random number] times or plus or subtract [random number].\nIt is not so easy if I want to do division problems. Can anyone help?\nThanks in advance","AnswerCount":6,"Available Count":5,"Score":0.0333209931,"is_accepted":false,"ViewCount":766,"Q_Id":57155636,"Users Score":1,"Answer":"You can simply generate the divisor and quotient randomly and then compute the dividend. Note that the divisor must be nonzero (thanks to @o11c's remind).","Q_Score":0,"Tags":"python,math","A_Id":57155658,"CreationDate":"2019-07-23T01:45:00.000","Title":"Generating random division problems in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to generate random division problems for an educational game that I am building. Generating random addition, subtraction, and multiplication problems is not too difficult. But I want my division problems to not have any remainders. \nWith addition, subtraction and multiplication, I could just do [random number] times or plus or subtract [random number].\nIt is not so easy if I want to do division problems. Can anyone help?\nThanks in advance","AnswerCount":6,"Available Count":5,"Score":-0.0333209931,"is_accepted":false,"ViewCount":766,"Q_Id":57155636,"Users Score":-1,"Answer":"you can generate a number to be divided as [random number1]x[random number2]\n. The problem will then be [random number1]x[random number2] divide by [random number1]","Q_Score":0,"Tags":"python,math","A_Id":57155672,"CreationDate":"2019-07-23T01:45:00.000","Title":"Generating random division problems in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have created a Bamboo task which runs the python code from a BitBucket Repo. \nBamboo config:\nI am running the script as a file. \nI have selected interpreter as Shell and given this in the Script Body to execute the script python create_issue.py -c conf.yml\nAfter I click on 'Run Plan', the build fails with ImportError: No module named pandas. The rest of the libraries are working fine, like, requests, itertools, etc.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":485,"Q_Id":57156974,"Users Score":1,"Answer":"It sounds like you are running this Bamboo plan using the Agent Host and not a Docker Container.  As such you will need to:\n\nRemote\/Log into the Bamboo server\nUse pip or some other package tool to install requests, itertools, and any other missing imports\n\nAlternatively, you could set-up an isolated Docker image that has all these dependencies and build within that.","Q_Score":0,"Tags":"python,bamboo","A_Id":57165277,"CreationDate":"2019-07-23T05:02:00.000","Title":"Bamboo error - ImportError: no module named pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I started learning Linear Regression and I was solving this problem. When i draw scatter plot between independent variable and dependent variable, i get vertical lines. I have 0.5M sample data. X-axis data is given within range of let say 0-20. In this case I am getting multiple target value for same x-axis point hence it draws vertical line.\nMy question is, Is there any way i can transform the data in such a way that it doesn't perform vertical line and i can get my model working. There are 5-6 such  independent variable that draw the same pattern. \nThanks in advance.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":850,"Q_Id":57157943,"Users Score":0,"Answer":"Instead of fitting y as a function of x, in this case you should fit x as a function of y.","Q_Score":0,"Tags":"python,linear-regression","A_Id":57157989,"CreationDate":"2019-07-23T06:31:00.000","Title":"how to get best fit line when we have data on vertical line?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am counting the total no. of vehicles in a video, but I want to detect only the vehicles which are travelling up(roads have a divider) so my point is, Can i use yolo only on a rectangle where vehicles are moving up? I dont want to detect vehicles that are on the other side of the road.\nis there a way like i can draw a rectangle and only detect objects on that specific rectangle?\nThe best I can think of is for every frame, i'll have to crop the frame, perform all the operations and stitch it back to the original frame. I am expecting an easier alternative for the same\nAny help is appreciated. Thanks","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":955,"Q_Id":57164149,"Users Score":0,"Answer":"You can perform yolo on the entire image as usual, but add an if condition to only draw boxes the center of which falls in a specific region. Or you can add this condition (position) next to the conditions of IoU (where detected boxes are filtered). Also you can separate counting based on the direction of moving vehicles and use two different counters for the two directions. \nIf you don't mind me asking, how are you tracking the vehicles?","Q_Score":0,"Tags":"python,opencv,image-processing,yolo","A_Id":57170037,"CreationDate":"2019-07-23T12:23:00.000","Title":"detect objects in only specific region of the frame-yolo-opencv","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am counting the total no. of vehicles in a video, but I want to detect only the vehicles which are travelling up(roads have a divider) so my point is, Can i use yolo only on a rectangle where vehicles are moving up? I dont want to detect vehicles that are on the other side of the road.\nis there a way like i can draw a rectangle and only detect objects on that specific rectangle?\nThe best I can think of is for every frame, i'll have to crop the frame, perform all the operations and stitch it back to the original frame. I am expecting an easier alternative for the same\nAny help is appreciated. Thanks","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":955,"Q_Id":57164149,"Users Score":0,"Answer":"i'm doing a similar thing...\nif your product is going to be fixed on like a light poll then clearly you can either detect the road and zebra crossing by training a model.\nor\nmanually enter these values...\nlater run your object detection and object tracking on only these parts of the frames i.e, use\nframe[ymax:ymin, xmax:xmin]\nThis reduces the image size so your processing speed increases.\nbut why do you need the full image again after your work? still if you do need it then you just have to add the values of xmin and ymin of your object detection box on the road to the bounding box of the vehicle detected in that object detection box to get its bounding box values in uncropped image.","Q_Score":0,"Tags":"python,opencv,image-processing,yolo","A_Id":71621543,"CreationDate":"2019-07-23T12:23:00.000","Title":"detect objects in only specific region of the frame-yolo-opencv","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"My data is in a large multi-indexed pandas DataFrame. I re-index to flatten the DataFrame and then feed it through ColumnDataSource, but I need to group my data row wise in order to plot it correctly (think bunch of torque curves corresponding to a bunch of gears for a car). If I just plot the dictionary output of ColumnDataSource, it's a mess. \nI've tried converting the ColumnDataSource output back to DataFrame, but then I lose the update functionality, the callback won't touch the DataFrame, and the plots won't change. Anyone have any ideas?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":109,"Q_Id":57169954,"Users Score":0,"Answer":"The short answer to the question in the title is \"Yes\". The ColumnDataSource is the special, central data structure of Bokeh. It provides the data for all the glyphs in a plot, or content in data tables, and automatically keeps that data synchronized on the Python and JavaScript sides, so that you don't have to, e.g write a bunch of low-level websocket code yourself. To update things like glyphs in a plot, you update the CDS that drives them. \nIt's possible there are improvements that could be made in your approach to updating the CDS, but it is impossibe to speculate without seeing actual code for what you have tried.","Q_Score":0,"Tags":"python-3.x,dataframe,callback,bokeh","A_Id":57170266,"CreationDate":"2019-07-23T18:09:00.000","Title":"Is ColumnDataSource() the only way to get plots updated in a bokeh web app?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Loading the excel file using read_excel takes quite long. Each Excel file has several sheets. The first sheet is pretty small and is the sheet I'm interested in but the other sheets are quite large and have graphs in them. Generally this wouldn't be a problem if it was one file, but I need to do this for potentially thousands of files and pick and combine the necessary data together to analyze. If somebody knows a way to efficiently load in the file directly or somehow quickly make a copy of the Excel data as text that would be helpful!","AnswerCount":2,"Available Count":1,"Score":-0.0996679946,"is_accepted":false,"ViewCount":768,"Q_Id":57173573,"Users Score":-1,"Answer":"The method read_excel() reads the data into a Pandas Data Frame, where the first parameter is the filename and the second parameter is the sheet.\ndf = pd.read_excel('File.xlsx', sheetname='Sheet1')","Q_Score":0,"Tags":"python,pandas,python-2.7","A_Id":61889824,"CreationDate":"2019-07-23T23:56:00.000","Title":"How to best(most efficiently) read the first sheet in Excel file into Pandas Dataframe?","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"If not, why not? Sometimes I will have an epoch that gets 95ish % and then finish with an epoch that has 10% or so less accuracy. I just never can tell whether it reverts back to that best epoch.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":98,"Q_Id":57174825,"Users Score":0,"Answer":"keras.callbacks.ModelCheckpoint(filepath, monitor='val_loss', verbose=0, save_best_only=True, save_weights_only=False, mode='auto', period=1)","Q_Score":0,"Tags":"python,tensorflow,machine-learning,keras,epoch","A_Id":57176551,"CreationDate":"2019-07-24T03:29:00.000","Title":"When training neural networks, does Tensorflow automatically revert back to the best epoch after finishing?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to load a file of size around 100mb using pandas. I know we can load but I want to know does the file size affects the efficiency of the program. And is there any way to load the file efficiently?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":96,"Q_Id":57179262,"Users Score":0,"Answer":"Yes the performance is affected and sometimes system gets slow. \nYou can try to read the data in the form of table or you can also use chunksize. This will improve the efficiency","Q_Score":2,"Tags":"python,pandas,csv,data-science","A_Id":57181431,"CreationDate":"2019-07-24T09:11:00.000","Title":"Does pandas read the full data file and stores it in a data frame? Is it efficient to load a 100mb file in pandas?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to fit one function to another function by adjusting two parameters. But I dont know the form of this function. I have only cost function because for computation of this function is used LAMMPS (molecular dynamics). I need some tool which i can give only cost function and my guess and then it would minimize it.\nI was looking on SciPy optimization but it looks like it needs the original function which i dont have.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":54,"Q_Id":57180431,"Users Score":0,"Answer":"A function approximation algorithm needs you to make a few assumptions about how your mathematical model behaves.\nIf you see things from a black box point of view, three scenarios can occur - \nX -> MODEL -> Y\n\nYou have the X and MODEL, but you dont have the Y; This is simulation\nYou have the MODEL and Y, but you dont have the X; This is Optimization\nYou have the X and Y, but you dont have the MODEL; This is mathematical modelling\n\nHowever there is a catch. You can NEVER do 3. directly. Instead you use a trick to reframe the 3. as a 2. (optimization problem). The trick is to say that you assume your model to be something like y=mx+c, and then instead of finding the model you find new inputs m and c. Thus, we can instead say - \n\nYou have the (MODEL, X) and Y but you dont have M,C (New inputs); This is optimization as well.\n\n(M,C) -> (MODEL + X) -> Y\nThis means, that even if you dont know the input function, you have to assume some model and then estimate the parameters which when tuned, let the model behave as the close to the input function as possible.\nBasically, what you need is machine learning. You have the inputs, you have the outputs (or you can get them but running your first function with a large sample of outputs), you have the cost function. Assume a model, and train it to approximate your input function.\nIf you are not sure what to use, then use a generalized function approximator AKA neural networks. But beware, it needs a lot more data to train.","Q_Score":0,"Tags":"python,scipy,minimization,scipy-optimize","A_Id":57180683,"CreationDate":"2019-07-24T10:10:00.000","Title":"Minimization of known cost function without knowing original function","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I wanted to use pandas-profiling to do some eda on a dataset but I'm getting an error : AttributeError: 'DataFrame' object has no attribute 'profile_report'\nI have created a python script on spyder with the following code : \nimport pandas as pd\nimport pandas_profiling\ndata_abc = pd.read_csv('abc.csv')\nprofile = data_abc.profile_report(title='Pandas Profiling Report')\nprofile.to_file(output_file=\"abc_pandas_profiling.html\")\nAttributeError: 'DataFrame' object has no attribute 'profile_report'","AnswerCount":8,"Available Count":2,"Score":0.024994793,"is_accepted":false,"ViewCount":14039,"Q_Id":57193292,"Users Score":1,"Answer":"This should work for those who want to use the latest version:\n\nRun pip uninstall pandas_profiling from anaconda prompt (given you're using Spyder, I'd guess this would be your case) \/ or command prompt\nRun pip install https:\/\/github.com\/pandas-profiling\/pandas-profiling\/archive\/master.zip\n\nIf you're using something like a Jupyter Notebook\/Jupyter Lab, be sure to restart your kernel and re-import your packages. \nI hope this helps.","Q_Score":4,"Tags":"python-3.x,pandas,pandas-profiling","A_Id":58828414,"CreationDate":"2019-07-25T02:01:00.000","Title":"Pandas-profiling error AttributeError: 'DataFrame' object has no attribute 'profile_report'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I wanted to use pandas-profiling to do some eda on a dataset but I'm getting an error : AttributeError: 'DataFrame' object has no attribute 'profile_report'\nI have created a python script on spyder with the following code : \nimport pandas as pd\nimport pandas_profiling\ndata_abc = pd.read_csv('abc.csv')\nprofile = data_abc.profile_report(title='Pandas Profiling Report')\nprofile.to_file(output_file=\"abc_pandas_profiling.html\")\nAttributeError: 'DataFrame' object has no attribute 'profile_report'","AnswerCount":8,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":14039,"Q_Id":57193292,"Users Score":0,"Answer":"The only workaround I found was that the python script I made is getting executed from the command prompt and giving the correct output but the code is still giving an error in Spyder.","Q_Score":4,"Tags":"python-3.x,pandas,pandas-profiling","A_Id":57193306,"CreationDate":"2019-07-25T02:01:00.000","Title":"Pandas-profiling error AttributeError: 'DataFrame' object has no attribute 'profile_report'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working with one project in python using Tensorflow. But I am very beginner in Tensorflow and OpenCV. Last day I tried to custom objects. But while training I am always getting one status. \n\"I0725 10:26:31.453798  5176 supervisor.py:1117] Saving checkpoint to path traini\nng\/model.ckpt\".\nI don't know I what exactly happening, Is this error or not?\nI already waited around 10 hours. But now also getting this same status.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":352,"Q_Id":57194634,"Users Score":0,"Answer":"This message is informative. It's just telling you, that the checkpoint of the model you're training was saved. In case you find the model \"getting worse\" from that checkpoint, you can cancel the training with the latest best version of the model saved.\nLook for some tutorials for how to find out when the model is not getting better. Get familiar with terms like overfitting\/overtraining, etc. to understand the process itself.","Q_Score":0,"Tags":"python,opencv,tensorflow,deep-learning,artificial-intelligence","A_Id":57195148,"CreationDate":"2019-07-25T05:01:00.000","Title":"While train the model always getting \"Saving checkpoint to path training\/model.ckpt\"","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to find ways to improve performance of machine learning models either binary classification, regression or multinomial classification.\nI'm now looking at the topic categorical variables and trying to combine low occuring levels together. Let's say a categorical variable has 10 levels where 5 levels account for 85% of the total frequency count and the 5 levels remaining account for the 15% remaining.\nI'm currently trying different thresholds (30%, 20%, 10%) to combine levels together. This means I combine together the levels which represent either 30%, 20% or 10% of the remaining counts.\nI was wondering if grouping these \"low frequency groups\" into a new level called \"others\" would have any benefit in improving the performance.\nI further use a random forest for feature selection and I know that having fewer levels than orignally may create a loss of information and therefore not improve my performance.\nAlso, I tried discretizing numeric variables but noticed that my performance was weaker because random forests benefit from having the hability to split on their preferred split point rather than being forced to split on an engineered split point that I would have created by discretizing.\nIn your experience, would grouping low occuring levels together have a positive impact on performance ? If yes, would you recommend any techniques ?\nThank you for your help !","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":392,"Q_Id":57203915,"Users Score":0,"Answer":"This isn't a programming question... By having fewer classes, you inherently increase the chance of randomly predicting the correct class. \nConsider a stacked model (two models) where you have a primary model to classify between the overrepresented classes and the 'other' class, and then have a secondary model to classify between the classes within the 'other' class if the primary model predicts the 'other' class.","Q_Score":0,"Tags":"python,machine-learning","A_Id":57204226,"CreationDate":"2019-07-25T14:04:00.000","Title":"Grouping low frequency levels of categorical variables to improve machine learning performance","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I trained a Forward Neural Net for binary classification and I got an accuracy of 83%, which (I hope)I'm going to improve later by changing parameters in inputs.  But some tests make me feel confused :\n\nMy dataset length is 671 so I divide it as 513 train set, 58 Validation set and 100 test set \n\n\nWhen I change the size of my sets (Train, Validation, Test), the accuracy score can decrease to some very low scores like 40% \nThe neural net is supposed to learn from the train set but when I test, after the training, with the same Train set and not the Test set, I thought that the model should score 100 %, because he just learned from it, watched it, but it only improves a few with 87% ...\n\nI'm a beginner in ML so I don't know if it's normal or not, I'm just curious and wanna catch all the small things to know, to understand perfectly what I'm doing. I guess it's maybe the normalization of my vectors sets, but I don't know very much about it. I can share you my full code if you want to, but as every neural net, it's quite long but easy to read.","AnswerCount":3,"Available Count":2,"Score":0.0665680765,"is_accepted":false,"ViewCount":380,"Q_Id":57205718,"Users Score":1,"Answer":"OP: Your questions are very good for someone that's just getting started in machine learning.\n\nHave you ensured that the distribution of your training and test dataset are similar? I would try to keep the number of samples per class (label) about equal if possible. For instance, if your training set is severely imbalanced then your prediction algorithm might tend to favor the label that shows up more often.\nI think you are on the right track to overfit your model to ensure your neural net architecture, training and whatever else is setup correctly. Are you using regularization? If so, I think you might want to remove that to see if your model can fit to your training dataset. I understand that this goes against what the accepted answer's #2 suggests but this is a useful way to debug your setup\nHow good are the labels for your dataset? If you have any noise in your labels then that would affect the accuracy of your classifier\nYou could also try transfer learning if you cannot get more training data","Q_Score":0,"Tags":"python,machine-learning,neural-network,dataset,normalization","A_Id":57207926,"CreationDate":"2019-07-25T15:37:00.000","Title":"How can we be sure of the efficiency of a neural network","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I trained a Forward Neural Net for binary classification and I got an accuracy of 83%, which (I hope)I'm going to improve later by changing parameters in inputs.  But some tests make me feel confused :\n\nMy dataset length is 671 so I divide it as 513 train set, 58 Validation set and 100 test set \n\n\nWhen I change the size of my sets (Train, Validation, Test), the accuracy score can decrease to some very low scores like 40% \nThe neural net is supposed to learn from the train set but when I test, after the training, with the same Train set and not the Test set, I thought that the model should score 100 %, because he just learned from it, watched it, but it only improves a few with 87% ...\n\nI'm a beginner in ML so I don't know if it's normal or not, I'm just curious and wanna catch all the small things to know, to understand perfectly what I'm doing. I guess it's maybe the normalization of my vectors sets, but I don't know very much about it. I can share you my full code if you want to, but as every neural net, it's quite long but easy to read.","AnswerCount":3,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":380,"Q_Id":57205718,"Users Score":1,"Answer":"As suggested by many people 3:1:1 (60:20:20 = train-validate-test) ratio is a thumb rule to split data, if you are playing with small data set it better to stick with 80:20 or 70:30 just train-test,I usually go for 90:10 ratio for better results.\nBefore you start with classification, first check whether your data set is balanced or imbalanced ( there should not be less example belongs to one class as compared to other ) because even though it gives you good accuracy it will mislead the results.\nIf data set is imbalanced, pre-processed data set with sampling algorithm  (for e.g SMOTE) and re-sample it. It will create equal sets of examples for class based on neighbors.\nAs correctly mentioned in other answer, use cross validation classification such as K-fold. The concept of cross validation is done to tweak the parameters used for training in order to optimize its accuracy and to nullify the effect of over-fitting on the training data, it also remove the noise in data set. I usually go for 10-fold cross validation where data set divided in 10 partition and in each iteration 1\/10 partition use as test and rest as training. Take the average of the 10 computations to get a good estimate of the performance of your classifier.","Q_Score":0,"Tags":"python,machine-learning,neural-network,dataset,normalization","A_Id":57211888,"CreationDate":"2019-07-25T15:37:00.000","Title":"How can we be sure of the efficiency of a neural network","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am training using the Tensorflow Object Detection API in Google Colab. I want to suppress printing the loss at each step as the web page crashes after 30 minutes due to a large amount of text being printed as the output of the cell. I have to manually clear the output of the cell every 30 minutes or so to avoid this issue. Is there any way to modify the train.py code so that Tensorflow stops printing the loss at every step. I have tried changing the code in line 57 of the research\/object_detection\/legacy\/train.py from tf.logging.set_verbosity(tf.logging.INFO) to tf.logging.set_verbosity(tf.logging.WARN) but it did not seem to work. Any suggestions\/workarounds?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":290,"Q_Id":57207729,"Users Score":2,"Answer":"add ; if thats the print statement at the end of the print statement\nAdd %%capture in the first line of the cell for no print for the cell\nand for speficic function\nfrom IPython.utils import io\nwith io.capture_output() as captured:\n    function()","Q_Score":0,"Tags":"python,tensorflow,deep-learning,google-colaboratory,object-detection-api","A_Id":57208228,"CreationDate":"2019-07-25T17:54:00.000","Title":"How to prevent printing loss at each step while using the tensorflow object detection api?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a flight delay dataset and try to split the set to train and test set before sampling. On-time cases are about 80% of total data and delayed cases are about 20% of that. \nNormally in machine learning ratio of train and test set size is 8:2. But the data is too imbalanced. So considering extreme case, most of train data are on-time cases and most of test data are delayed cases and   accuracy will be poor. \nSo my question is How can I properly split imbalanced dataset to train and test set??","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1879,"Q_Id":57229775,"Users Score":0,"Answer":"Start from 50\/50 and go on changing the sets as 60\/40, 70\/30, 80\/20, 90\/10. declare all the results and come to some conclusion. In one of my work on Flight delays prediction project, I used 60\/40 database and got 86.8 % accuracy using MLP NN.","Q_Score":1,"Tags":"python,machine-learning,train-test-split,imbalanced-data","A_Id":57230509,"CreationDate":"2019-07-27T06:34:00.000","Title":"How can I properly split imbalanced dataset to train and test set?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I know when the weight of model updated while using gradient descent(in all three types of GD) but in my case I am using adam optimization with custom loss(triplet loss), when the weight get updated in the model in this case? Is it after every sample,every batch or every epochs?\nThanks in advance.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":140,"Q_Id":57230643,"Users Score":1,"Answer":"Adam just changes how the gradient update is performed in gradient descent, it does not change when that happens, so its literally the same as in normal gradient descent.\nWhen using mini-batch gradient descent (the current standard), weight updates happen after every batch.","Q_Score":1,"Tags":"python-3.x,optimization,keras,deep-learning","A_Id":57231506,"CreationDate":"2019-07-27T08:53:00.000","Title":"When the weight of model get updated if I am using adam optimization?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Do we have any way to get points on the forehead of a face image?\nI am using 68 points landmarks shape_predictor to get other points on the face but for this particular problem I need\npoints that are from the hairline to the center of the forehead.\nAny suggestions would be helpful.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":922,"Q_Id":57235066,"Users Score":0,"Answer":"you can use the tool provided with dlib called \"imgLab\" to train your own shape detector by performing landmark annotations","Q_Score":1,"Tags":"python,opencv,image-processing,deep-learning,dlib","A_Id":57388681,"CreationDate":"2019-07-27T18:39:00.000","Title":"Detect forehead points using Dlib\/python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am analyzing a real-estate dataset. While all regression assumptions fail, my XGBoosting model thrives. Am I missing something? Is XGBoost just the superior model in this case? The dataset is around 67.000 observations and 30 variables.","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":947,"Q_Id":57244326,"Users Score":0,"Answer":"There are a number of reasons for this, in my opinion.\n\nLinear regression assumes your y to be linearly related to variables where as tree-based models are considered non-linear models. (So the linearity assumption goes out the window)\nBreaking some of the linear regression assumptions may not inherently decrease the predictive ability of your model,  but WILL bias the coefficients. Often times, when building a regression model you are trying to determine the effect of an X variable on the Y. In this case you mainly only care about this weight and don't care as much about how well your model predicts. If you are aiming only for predictive ability you can break some assumptions.\n\nThese are the main 2 thoughts that come to mind. I would be interested to hear other people's take as well.","Q_Score":0,"Tags":"python,r","A_Id":57244533,"CreationDate":"2019-07-28T19:32:00.000","Title":"How come multiple regression has so many assumptions, while advanced machine learning algorithms have next to none?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to return the (average) contrast value of an image.\nIs this possible and if so, please show how?\nMore than just the code, (obviously the code is welcome), in what color space should I work? Is HSV appropriate or best? Can the contrast value of a single pixel be computed?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":3574,"Q_Id":57256159,"Users Score":1,"Answer":"Contrast is usually understood as intensity contrast and can be computed on the Luminance component (Y). It is a measure of spread of the histogram, such as the standard deviation.","Q_Score":2,"Tags":"python,opencv,image-processing,colors,contrast","A_Id":57256900,"CreationDate":"2019-07-29T14:42:00.000","Title":"How extract contrast level of a photo - opencv","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have my own TensorFlow serving server for multiple neural networks. Now I want to estimate the load on it. Does somebody know how to get the current number of requests in a queue in TensorFlow serving? I tried using Prometheus, but there is no such option.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1082,"Q_Id":57256298,"Users Score":0,"Answer":"what 's more ,you can assign the number of threads by the --rest_api_num_threads or let it empty and automatically configured by tf serivng","Q_Score":2,"Tags":"python-3.x,tensorflow,prometheus,tensorflow-serving","A_Id":57549978,"CreationDate":"2019-07-29T14:50:00.000","Title":"Tensorflow Serving number of requests in queue","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have my own TensorFlow serving server for multiple neural networks. Now I want to estimate the load on it. Does somebody know how to get the current number of requests in a queue in TensorFlow serving? I tried using Prometheus, but there is no such option.","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":1082,"Q_Id":57256298,"Users Score":1,"Answer":"Actually ,the tf serving doesn't have requests queue , which means that the tf serving would't rank the requests, if there are too many requests. \nThe only thing that tf serving would  do is allocating  a threads pool, when the server is  initialized.\nwhen  a request coming , the tf serving will use a unused thread to deal with the request , if there are no free threads, the tf serving will return a  unavailable error.and the client shoule retry again later.\nyou can find the these information in the comments of tensorflow_serving\/batching\/streaming_batch_schedulor.h","Q_Score":2,"Tags":"python-3.x,tensorflow,prometheus,tensorflow-serving","A_Id":57549954,"CreationDate":"2019-07-29T14:50:00.000","Title":"Tensorflow Serving number of requests in queue","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to set up a machine learning model predicting a continuous variable y on the basis of a feature vector (x1, x2, ..., xn). I know from elsewhere that y follows a normal distribution. Can I somehow specify this to the model and enhance its predictions this way? Is there a specific model that allows me to do this?\nI have used linear models, k-nearest neighbour models and random forest models (in python). All of them give some predictions but I was wondering whether they can be outperformed by some model that would know the distribution of the predicted variable.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":65,"Q_Id":57267855,"Users Score":0,"Answer":"One way you could do this is by creating a custom objective function that penalizes predictions that are not normally distributed.","Q_Score":0,"Tags":"python,machine-learning,scikit-learn,regression,supervised-learning","A_Id":57268592,"CreationDate":"2019-07-30T08:55:00.000","Title":"Can I tell a machine learning model that the dependent variable is normally distributed?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I already trained ssd_mobilenet_v2_coco with my custom data set on tensorflow. Also I trained YOLO with my data set too. I solved all problems and they work.\nI encounter a problem with both models. When my data set includes images with more size than 400kb, the trained models do not work. Some times \"allocation of memory\" problem occurs. I solved them with changing parameters(batch size etc.). But I still don't know whether there is a limit of image size when we are preparing data set?\nWhy more than 400kb images are problem for my system? My question is not about pixel size,it's about image file size.\nThanks...\nSystem Info\n\nNvidia RTX 2060 6G\nAMD Ryzen 7\n16GB DDR4 2600Mhz Ram\nCuda:10.0\nCudNN:7.4.2(I also try diffrent versions and same results occur)\nTensorflow:2","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":84,"Q_Id":57269090,"Users Score":1,"Answer":"Providing the solution here (Answer Section), even though it is present in the comment section (Thanks dasmehdix for the update) for the benefit of the community.\nNo, there is no a limit of image size that we use to train our model.","Q_Score":1,"Tags":"python,tensorflow,detection,yolo","A_Id":62347635,"CreationDate":"2019-07-30T10:01:00.000","Title":"Is there a limit about image size when we train custom object with already trained models?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"So I've recently updated my Anaconda environment, and now I can't import tensorflow anymore. Everytime I run a script containing it, the Spyder Console runs for a while, then just stops and resets to ln[1]. \nI tried to see how far the script compiles, and it does everything fine, until the import statement. Weirdly enough, the autocomplete still works for tf, which means that my installation should be fine. Reinstalling tensorflow also didn't do anything.\nThere are no error messages, because the compiler dies a silent death everytime I run the script. I've seen others describe a similar problem on a Jupyter, but their fixes didn't work. (Running the script without Spyder just freezes Python) \nI'd greatly appreciate help","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":316,"Q_Id":57270586,"Users Score":0,"Answer":"Ok so I did some black mathmagic and fixed it. What I did was reducing the tensorflow version via pip (to 1.14, but I don't think that matters) and then upgrading my entire conda set up again with conda update --all. I have no idea why, and the anaconda console screamed like it was tortured during the entire update, but now it works and I don't think I'll touch it again. If I see better fix, or if I encounter problems with this one, I'll update this post again.","Q_Score":0,"Tags":"python,tensorflow","A_Id":57325381,"CreationDate":"2019-07-30T11:27:00.000","Title":"Kernel Restarts upon failing(?) to import tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to build an algorithm for product master data purposes and I'm not sure about the best NLP approach for this. The scenario is:\n- I have Product golden records;\n- I have many others Product catalogs that need to be harmonized;\nExample:\n- Product Golden Record: Coke and Coke Zero;\n- Products description that need to be hamonized: Coke 300ml, Coke Zero 300ml, Cke zero.\nI need an algorithm that harmonize by similarity, since I have to consider typos and, sometimes, piece of a product in a sentence. Example: Coke zero JS MKT (JS and MKT are garbage, but the sentence is more similar to Coke Zero).\nI've been testing some NLP for sentence similarity such as Bag of words as well as reading some other approaches such as Cosine Similarity and Levenshtein distance. However, I don't know what is the best option for my case.\nCould you please help me to understand the best way to achieve what I need?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":104,"Q_Id":57282671,"Users Score":1,"Answer":"I have found two great solutions, by using Cosine similarity and Levenshtein distance. Im my case, Cosine similarity worked better, because I easily found part of the brand name into the text, so getting a score of 100% of accuracy. Matrix replacing (Levenshtein) was also good, but I good some errors due to very similar words in the dataset.","Q_Score":0,"Tags":"python,nlp","A_Id":60646118,"CreationDate":"2019-07-31T03:16:00.000","Title":"Word\/Sentence similarity. What is the best approach?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a large pandas DataFrame consisting of some 100k rows and ~100 columns with different dtypes and arbitrary content.\nI need to assert that it does not contain a certain value, let's say -1.\nUsing assert( not (any(test1.isin([-1]).sum()>0))) results in processing time of some seconds. \nAny idea how to speed it up?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":43,"Q_Id":57288507,"Users Score":1,"Answer":"Just to make a full answer out of my comment:\nWith -1 not in test1.values you can check if -1 is in your DataFrame.\nRegarding the performance, this still needs to check every single value, which is in your case \n10^5*10^2 = 10^7.\nYou only save with this the performance cost for summation and an additional comparison of these results.","Q_Score":1,"Tags":"python-3.x,pandas","A_Id":57290376,"CreationDate":"2019-07-31T10:20:00.000","Title":"speed up pandas search for a certain value not in the whole df","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using read_sql function to pull data from a postgresql table. As I store that data in a dataframe, I could find that some integer dtype column is automatically getting converted to float, is there any way to prevent that while using read_sql functiononly","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":45,"Q_Id":57290281,"Users Score":0,"Answer":"Since your column contains NaN values, which are floating point numbers, I don't think you can avoid this 'issue' loading from the Database without changing the query. \nIf you wish to change the query, you can insert a WHERE clause that would exclude None values, or check if the row contains such a column value.\nWhat I suggest would be to use .fillna(), and then to cast as integers using .astype('int')\nEdit : Just in case, your question is wrong, you are saying \n\nIs there any way to change columns datatype that should be int became a float while using read_sql from table\n\nBut since it includes NaN, it is not expected to be an int, but a float.","Q_Score":0,"Tags":"python,pandas","A_Id":57290466,"CreationDate":"2019-07-31T12:01:00.000","Title":"Is there any way to change columns datatype that should be int became a float while using read_sql from table","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When working with TensorFlow (image classification for example), sometimes images are loaded using cv2.imread(image) and other times they are loaded using tf.gfile.GFile(image, 'rb').read().  \nAre there any differences between cv2.imread(image) and tf.gfile.GFile(image, 'rb').read() when using them with TensorFlow?  \nEdit:\nMy question here is about performances and maintaining image accuracy (since both of them do the job).","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":452,"Q_Id":57293678,"Users Score":0,"Answer":"tf.gfile is basicly to use a less-convensional filesystem such as HDFS etc...\ncv2.imread(image) is for local filesystem usage.","Q_Score":1,"Tags":"python,opencv,tensorflow","A_Id":57293794,"CreationDate":"2019-07-31T14:56:00.000","Title":"what's the difference between tf.gfile.GFile().read() and cv2.imread() when it is about reading images","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to get the variances from the eigen vectors.\nWhat is the difference between explained_variance_ratio_ and explained_variance_ in PCA?","AnswerCount":3,"Available Count":1,"Score":0.1325487884,"is_accepted":false,"ViewCount":10300,"Q_Id":57293716,"Users Score":2,"Answer":"It's just normalization to see how each principal component important. You can say:\nexplained_variance_ratio_ = explained_variance_\/np.sum(explained_variance_)","Q_Score":7,"Tags":"python,scikit-learn,pca,covariance","A_Id":57294007,"CreationDate":"2019-07-31T14:58:00.000","Title":"Sklearn PCA explained variance and explained variance ratio difference","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to extract \"Twitter for iPhone\" part from this string.\nBut I have different values in the place of \"Twitter for iPhone\" in 1000s of columns in a dataframe. I only need the values after \">\" and before \"<\" from the following set of strings.\nI tried df.col.str.extract('(Twitter for iPhone|Twitter for Samsung|Twitter for others)') which extracts only those 'Twitter for iPhone' values but not the others and the rest are filled with NaNs.","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":259,"Q_Id":57297369,"Users Score":1,"Answer":"Try df.col.str.extract(pat = '(Twitter for (iPhone|Samsung|others))')","Q_Score":1,"Tags":"python,regex,pandas,dataframe","A_Id":57297560,"CreationDate":"2019-07-31T19:03:00.000","Title":"I want to extract a particular pattern from a content string: \"Twitter for iPhone\"","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I'm working on a univariate problem which involves aggregating payment data on a customer level - so that I have one row per customer, and the total amount they've spent with us.\nUsing this distribution of payment data, I fit an appropriate probability distribution and calculated the maximum likelihood estimates for the parameters of the pdf. \nNow I want to find the 90th percentile of the distribution. If I was to do this by hand I would set .10 equal to the integral from x to infinity of my pdf and then solve for x.\nIs there a package in python\/scipy\/statsmodels that allows me to do this?\nThanks in advance!\nCheers","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":666,"Q_Id":57299971,"Users Score":1,"Answer":"Eureka! It's the .interval() method for a rv_continuous object in scipy.stats - just pass in your parameters and it will give you end points that contain that percentage of the distribution.","Q_Score":2,"Tags":"python,scipy,statsmodels,calculus,probability-density","A_Id":57310189,"CreationDate":"2019-07-31T23:21:00.000","Title":"Finding the area under the curve of a probability distribution function in SciPy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have come across various articles online, some of which suggest that CountVectorizer should be fit on both the train and test sets, and some suggest that it should be fit only on the train set.\nWhich approach is generally better for text classification?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":96,"Q_Id":57306519,"Users Score":2,"Answer":"Generally the test_set should be kept unobserved, so the CountVectorizer should be only fitted on train_set","Q_Score":0,"Tags":"python,python-3.x,scikit-learn,countvectorizer","A_Id":57308378,"CreationDate":"2019-08-01T10:01:00.000","Title":"Should CountVectorizer be fit on both the train and test sets?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When opening an HDF5 file with h5py you can pass in a python file-like object. I have done so, where the file-like object is a custom implementation of my own network-based transport layer. \nThis works great, I can slice large HDF5 files over a high latency transport layer. However HDF5 appears to provide its own file locking functionality, so that if you open multiple files for read-only within the same process (threading model) it will still only run the operations, effectively, in series.\nThere are drivers in HDF5 that support parallel operations, such as h5py.File(f, driver='mpio'), but this doesn't appear to apply to python file-like objects which use h5py.File(f, driver='fileobj').\nThe only solution I see is to use multiprocessing. However the scalability is very limited, you can only realistically open 10's of processes because of overhead. My transport layer uses asyncio and is capable of parallel operations on the scale of 1,000's or 10,000's, allowing me to build a longer queue of slow file-read operations which boost my total throughput. \nI can achieve 1.5 GB\/sec of large-file, random-seek, binary reads with my transport layer against a local S3 interface when I queue 10k IO ops in parallel (requiring 50GB of RAM to service the requests, an acceptable trade-off for the throughput).\n\nIs there any way I can disable the h5py file locking when using driver='fileobj'?","AnswerCount":1,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":4420,"Q_Id":57310333,"Users Score":6,"Answer":"You just need to set the value to FALSE for the environment variable HDF5_USE_FILE_LOCKING.\nExamples are as follows:\nIn Linux or MacOS via Terminal: export HDF5_USE_FILE_LOCKING=FALSE\nIn Windows via Command Prompts (CMD): set HDF5_USE_FILE_LOCKING=FALSE","Q_Score":8,"Tags":"python,python-3.6,hdf5,h5py","A_Id":57445839,"CreationDate":"2019-08-01T13:37:00.000","Title":"Can we disable h5py file locking for python file-like object?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"Some people find it confusing with whiskers and outliers in a Boxplot.\nIs it possible to remove those from the Boxplot in Seaborn?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1271,"Q_Id":57314544,"Users Score":0,"Answer":"You can remove the outliers by setting showfliers=False and remove whiskers by setting whis=0.","Q_Score":1,"Tags":"python,seaborn,boxplot","A_Id":57314963,"CreationDate":"2019-08-01T17:53:00.000","Title":"Can I remove whiskers and outliers from Boxplot?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on an object detection model. I have annotated images whose values are stored in a data frame with columns (filename,x,y,w,h, class). I have my images inside \/drive\/mydrive\/images\/ directory. I have saved the data frame into a CSV file in the same directory. So, now I have annotations in a CSV file and images in the images\/ directory. \nI want to feed this CSV file as the ground truth along with the image so that when the bounding boxes are recognized by the model and it learns contents of the bounding box.\nHow do I feed this CSV file with the images to the model so that I can train my model to detect and later on use the same to predict bounding boxes of similar images?\nI have no idea how to proceed.\nI do not get an error. I just want to know how to feed the images with bounding boxes so that the network can learn those bounding boxes.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":75,"Q_Id":57315783,"Users Score":0,"Answer":"We need to feed the bounding boxes to the loss function. We need to design a custom loss function, preprocess the bounding boxes and feed it back during back propagation.","Q_Score":1,"Tags":"python,tensorflow,keras,computer-vision,object-detection","A_Id":57368538,"CreationDate":"2019-08-01T19:24:00.000","Title":"feeding annotations as ground truth along with the images to the model","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need some python advice to implement an algorithm.\nWhat I need is to detect which words from text 1 are in text 2:\n\nText 1: \"Mary had a dog. The dog's name was Ethan. He used to run down\n  the meadow, enjoying the flower's scent.\"\nText 2: \"Mary had a cat. The cat's name was Coco. He used to run down\n  the street, enjoying the blue sky.\"\n\nI'm thinking I could use some pandas datatype to check repetitions, but I'm not sure.\nAny ideas on how to implement this would be very helpful. Thank you very much in advance.","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":77,"Q_Id":57328345,"Users Score":0,"Answer":"You can use dictionary to first store words from first text and than just simply look up while iterating the second text. But this will take space.\nSo best way is to use regular expressions.","Q_Score":0,"Tags":"python,algorithm","A_Id":57335390,"CreationDate":"2019-08-02T14:20:00.000","Title":"Detecting which words are the same between two pieces of text","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need some python advice to implement an algorithm.\nWhat I need is to detect which words from text 1 are in text 2:\n\nText 1: \"Mary had a dog. The dog's name was Ethan. He used to run down\n  the meadow, enjoying the flower's scent.\"\nText 2: \"Mary had a cat. The cat's name was Coco. He used to run down\n  the street, enjoying the blue sky.\"\n\nI'm thinking I could use some pandas datatype to check repetitions, but I'm not sure.\nAny ideas on how to implement this would be very helpful. Thank you very much in advance.","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":77,"Q_Id":57328345,"Users Score":0,"Answer":"Since you do not show any work of your own, I'll just give an overall algorithm.\nFirst, split each text into its words. This can be done in several ways. You could remove any punctuation then split on spaces. You need to decide if an apostrophe as in dog's is part of the word--you probably want to leave apostrophes in. But remove periods, commas, and so forth.\nSecond, place the words for each text into a set.\nThird, use the built-in set operations to find which words are in both sets.\nThis will answer your actual question. If you want a different question that involves the counts or positions of the words, you should make that clear.","Q_Score":0,"Tags":"python,algorithm","A_Id":57328491,"CreationDate":"2019-08-02T14:20:00.000","Title":"Detecting which words are the same between two pieces of text","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"1) Is normalization necessary for Random Forests? \n2) Should all the features be normalized or only numerical ones? \n3) Does it matter whether I normalize before or after splitting into train and test data?\n4) Do I need to pre-process features of the future object that will be classified as well? (after accepting the model, not during the testing)","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1234,"Q_Id":57339104,"Users Score":2,"Answer":"1) No! Feature normalization isn't necessary for any tree-based classifier.\n2) Generally speaking, normalization should be done on all features not just numerical ones.\n3) In practice it doesn't make much difference. However, the correct practice is to identify the min and max values of each feature from the training set and then normalize the features of both sets according to those values.\n4) Yes, afterwards any sample that needs to be classified should be processed with exactly the same way as you did during training.","Q_Score":2,"Tags":"python,data-science,normalization,preprocessor,feature-engineering","A_Id":57339152,"CreationDate":"2019-08-03T14:13:00.000","Title":"Is normalization necessary for RandomForest?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am training a model and want to use the mAP metric. For some reason the tensorflow mean_average_precision_at_k does not work for me, but the sklearn average_precision_score works.\nHow can I have access to the keras's model outputs to perform the sklearn metrics?\nCan I compile the model one time and fit for 10 epochs, perform the metric and fit again for 10 epochs? Or do I need to save the model and reload it every time?\nThank you","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":258,"Q_Id":57340285,"Users Score":0,"Answer":"For any one who might have the same Issue. It seems that in Tensorflow 1.14, the implementation of Keras keeps the model weights, but restarts the optimizer which leads to bad results over many repetitions of the .fit() function. My loss is about 800 when using .fit() once and about 2800 when fitting for 5 epochs each time.","Q_Score":1,"Tags":"python,tensorflow,keras,metrics","A_Id":57507773,"CreationDate":"2019-08-03T16:40:00.000","Title":"Keras: Is there a need to reload the model if I train for 10 epochs multiple times?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am training a model and want to use the mAP metric. For some reason the tensorflow mean_average_precision_at_k does not work for me, but the sklearn average_precision_score works.\nHow can I have access to the keras's model outputs to perform the sklearn metrics?\nCan I compile the model one time and fit for 10 epochs, perform the metric and fit again for 10 epochs? Or do I need to save the model and reload it every time?\nThank you","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":258,"Q_Id":57340285,"Users Score":1,"Answer":"Can I compile the model one time and fit for 10 epochs, perform the metric and fit again for 10 epochs\n\nYes, absolutely.\nThe model will keep the training weights between calls to fit(). You can call this as many times as you please.","Q_Score":1,"Tags":"python,tensorflow,keras,metrics","A_Id":57344741,"CreationDate":"2019-08-03T16:40:00.000","Title":"Keras: Is there a need to reload the model if I train for 10 epochs multiple times?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am training a model with 107850 samples and validating on 26963 samples.\nHow much should batch size and number of epochs be when fitting a model in Keras to optimize the validation accuracy? Is there any sort of rule of thumb to use based on data input size? Does it overfit a model if an increased number of epochs? \nThank You.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":456,"Q_Id":57345714,"Users Score":0,"Answer":"No! Their is not any rule of thumb for selecting the batch size of the data. Its a trade off between better accuracy and time. So we have to take the batch size which will process our data fast and give good accuracy too. Now what happens when you take too large batch size. Actually after every batch your model is going to update their all the weights. Large batch size large error than according to the error your model will adjust weight. Now After processing a large number of batch and update the weight take less time than taking smaller batch and updating the weights after every batch. But when you take small batch size your model will update weights after every batch i.e(16,32,64) etc whatever you want than your model will be able to learn your data more accurately but its take time to update all the weights after every batch.\nNow According to the research papers most of the researchers use batch size\n(16,32,64) may be researcher use larger batch sizes but I haven't seen yet.\nI hope that answer is helpful for you.\nand if you want number of epochs to be optimized use callbacks for your neural network your neural network will automatically stop learning if your model is not learning for more than 4 or 5 epochs depends on you.","Q_Score":3,"Tags":"python,machine-learning,keras,neural-network","A_Id":57345858,"CreationDate":"2019-08-04T10:18:00.000","Title":"How much should batch size and number of epochs be when fitting a model in Keras?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm a bit clueless. I have a csv file with these columns: name - picture url\nI would like to bulk download the 70k images into a folder, rename the images with the name in the first column and number them if there is more than one per name.\nSome are jpegs some are pngs. \nI'm guessing I need to use pandas to get the data from the csv but I don't know how to make the downloading\/renaming part without starting  all the downloads at the same time, which will for sure crash my computer (It did, I wasn't even mad).\nThanks in advance for any light you can shed on this.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":642,"Q_Id":57346966,"Users Score":1,"Answer":"Try downloading in batches like 500 images...then sleep for some 1 seconds and loop it....quite time consuming...but sure fire method....for the coding reference you can explore packges like urllib (for downloading) and as soon as u download the file use os.rename() to change the name....As u already know for that csv file use pandas...","Q_Score":0,"Tags":"python,image,url,download,bulk","A_Id":57347119,"CreationDate":"2019-08-04T13:31:00.000","Title":"How do I bulk download images (70k) from urls with a restriction on the simultaneous downloads?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an image stored in a 2D array called data. I know how to calculate the standard deviation of the entire array using numpy that outputs one number quantifying how much the data is spread. However, how can I made a standard deviation map (of the same size as my image array) and each element in this array is the standard deviation of the corresponding pixel in the image array (i.e, data).","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":5088,"Q_Id":57351759,"Users Score":1,"Answer":"Use slicing, given images[num, width, height] you may calculate std. deviation of a single image using images[n].std() or for a single pixel: images[:, x, y].std()","Q_Score":0,"Tags":"python,arrays,numpy,standard-deviation","A_Id":57351808,"CreationDate":"2019-08-05T03:07:00.000","Title":"Standard Deviation of every pixel in an image in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using PySpark and I need to convert each row in a DataFrame to a JSON file (in s3), preferably naming the file using the value of a selected column.\nCouldn't find how to do that. Any help will be very appreciated.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":166,"Q_Id":57353211,"Users Score":0,"Answer":"I think directly we can't store for each row as a JSON based file. Instead of that we can do like iterate for each partition of dataframe and connect to S3 using AWS S3 based library's (to connect to S3 on the partition level). Then, On each partition with the help of iterator, we can convert the row into JSON based file and push to S3.","Q_Score":0,"Tags":"python,apache-spark,amazon-s3,pyspark,pyspark-sql","A_Id":57353337,"CreationDate":"2019-08-05T06:28:00.000","Title":"Convert each row in a PySpark DataFrame to a file in s3","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have some .xls datas in my Google Cloud Storage and want to use airflow to store it to GCP. Can I export it directly to BigQuery or can i use additional library (such a pandas and xlrd) to convert the files and store it into BigQuery? \nThanks","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":542,"Q_Id":57367921,"Users Score":2,"Answer":"Bigquery don't support xls format. The easiest way is to transform the file in CSV and to load it into big query.\nHowever, I don't know your xls format. If it's multisheet you have to work on the file.","Q_Score":0,"Tags":"excel,google-cloud-storage,airflow,xls,python-bigquery","A_Id":57369728,"CreationDate":"2019-08-06T01:50:00.000","Title":"Import XLS file from GCS to BigQuery","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"in a standard neural network, I'm trying to understand, intuitively, what the values of a hidden layer mean in the model.\nI understand the calculation steps, but I still dont know how to think about the hidden layers and how interpret the results (of the hidden layer)\nSo for example, given the standard MNIST datset that is used to train and predict handwritten digits between 0 to 9, a model would look like this:\n\nAn image of a handwritten digit will have 784 pixels.\nSince there are 784 pixels,  there would be 784 input nodes and the value of each node is the pixel intensity(0-255)\neach node branches out and these branches are the weights.\nMy next layer is my hidden layer, and the value of a given node in the hidden layer is the weighted sum of my input nodes (pixels*weights).\nWhatever value I get, I squash it with a sigmoid function and I get a value between 0 and 1.\n\nThat number that I get from the sigmoid.  What does it represent exactly and why is it relevant?  My understanding is that if I want to build more hidden layers, I'll be using the values of my initial hidden layer, but at this point, i'm stuck as to what the values of the first hidden layer mean exactly.\nThank you!","AnswerCount":3,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":395,"Q_Id":57369148,"Users Score":0,"Answer":"AFAIK, for this digit recognition case, one way to think about it is each level of the hidden layers represents the level of abstraction.\nFor now, imagine the neural network for digit recognition has only 3 layers which is 1 input layer, 1 hidden layer and 1 output layer.\nLet's take a look at a number. To recognise that it is a number we can break the picture of the number to a few more abstract concepts such as lines, circles and arcs. If we want to recognise 6, we can first recognise the more abstract concept that is exists in the picture. for 6 it would be an arc and a circle for this example. For 8 it would be 2 circles. For 1 it would be a line.\nIt is the same for a neural network. We can think of layer 1 for pixels, layer 2 for recognising the abstract concept we talked earlier such as lines, circles and arcs and finally in layer 3 we determine which number it is.\nHere we can see that the input goes through a series of layers from the most abstract layer to the less abstract layer (pixels -> line, circle, arcs -> number). In this example we only have 1 hidden layer but in real implementation it would be better to have more hidden layer that 1 depending on your interpretation of the neural network. Sometime we don't even have to think about what each layer represents and let the training do it fo us. That is the purpose of the training anyway.","Q_Score":0,"Tags":"python,tensorflow,neural-network","A_Id":57369487,"CreationDate":"2019-08-06T04:52:00.000","Title":"what do hidden layers mean in a neural network?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"in a standard neural network, I'm trying to understand, intuitively, what the values of a hidden layer mean in the model.\nI understand the calculation steps, but I still dont know how to think about the hidden layers and how interpret the results (of the hidden layer)\nSo for example, given the standard MNIST datset that is used to train and predict handwritten digits between 0 to 9, a model would look like this:\n\nAn image of a handwritten digit will have 784 pixels.\nSince there are 784 pixels,  there would be 784 input nodes and the value of each node is the pixel intensity(0-255)\neach node branches out and these branches are the weights.\nMy next layer is my hidden layer, and the value of a given node in the hidden layer is the weighted sum of my input nodes (pixels*weights).\nWhatever value I get, I squash it with a sigmoid function and I get a value between 0 and 1.\n\nThat number that I get from the sigmoid.  What does it represent exactly and why is it relevant?  My understanding is that if I want to build more hidden layers, I'll be using the values of my initial hidden layer, but at this point, i'm stuck as to what the values of the first hidden layer mean exactly.\nThank you!","AnswerCount":3,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":395,"Q_Id":57369148,"Users Score":0,"Answer":"Consider a very basic example of AND, OR, NOT and XOR functions.\nYou may already know that a single neuron is only suitable when the problem is linearly separable.\nHere in this case, AND, OR and NOT functions are linearly separable and so they can be easy handled using a single neuron.\nBut consider the XOR function. It is not linearly separable. So a single neuron will not be able to predict the value of XOR function.\nNow, XOR function is a combination of AND, OR and NOT. Below equation is the relation between them:\n\na XOR b = (a AND (NOT b)) OR ((NOT a) AND b)\n\nSo, for XOR, we can use a network which contain three layers.\nFirst layer will act as NOT function, second layer will act as AND of the output of first layer and finally the output layer will act as OR of the 2nd hidden layer.\nNote: This is just a example to explain why it is needed, XOR can be implemented in various other combination of neurons.","Q_Score":0,"Tags":"python,tensorflow,neural-network","A_Id":57369294,"CreationDate":"2019-08-06T04:52:00.000","Title":"what do hidden layers mean in a neural network?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"in a standard neural network, I'm trying to understand, intuitively, what the values of a hidden layer mean in the model.\nI understand the calculation steps, but I still dont know how to think about the hidden layers and how interpret the results (of the hidden layer)\nSo for example, given the standard MNIST datset that is used to train and predict handwritten digits between 0 to 9, a model would look like this:\n\nAn image of a handwritten digit will have 784 pixels.\nSince there are 784 pixels,  there would be 784 input nodes and the value of each node is the pixel intensity(0-255)\neach node branches out and these branches are the weights.\nMy next layer is my hidden layer, and the value of a given node in the hidden layer is the weighted sum of my input nodes (pixels*weights).\nWhatever value I get, I squash it with a sigmoid function and I get a value between 0 and 1.\n\nThat number that I get from the sigmoid.  What does it represent exactly and why is it relevant?  My understanding is that if I want to build more hidden layers, I'll be using the values of my initial hidden layer, but at this point, i'm stuck as to what the values of the first hidden layer mean exactly.\nThank you!","AnswerCount":3,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":395,"Q_Id":57369148,"Users Score":0,"Answer":"A hidden layer in a neural network may be understood as a layer that is neither an input nor an output, but instead is an intermediate step in the network's computation.\nIn your MNIST case, the network's state in the hidden layer is a processed version of the inputs, a reduction from full digits to abstract information about those digits.\nThis idea extends to all other hidden layer cases you'll encounter in machine learning -- a second hidden layer is an even more abstract version of the input data, a recurrent neural network's hidden layer is an interpretation of the inputs that happens to collect information over time, or the hidden state in a convolutional neural network is an interpreted version of the input with certain features isolated through the process of convolution.\nTo reiterate, a hidden layer is an intermediate step in your neural network's process.  The information in that layer is an abstraction of the input, and holds information required to solve the problem at the output.","Q_Score":0,"Tags":"python,tensorflow,neural-network","A_Id":57369365,"CreationDate":"2019-08-06T04:52:00.000","Title":"what do hidden layers mean in a neural network?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm currently trying to use HDBSCAN to cluster movie data. The goal is to cluster similar movies together (based on movie info like keywords, genres, actor names, etc) and then apply LDA to each cluster and get the representative topics. However, I'm having a hard time evaluating the results (apart from visual analysis, which is not great as the data grows). With LDA, although it's hard to evaluate it, i've been using the coherence measure. However, does anyone have any idea on how to evaluate the clusters made by HDBSCAN? I haven't been able to find much info on it, so if anyone has any idea, I'd very much appreciate!","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2279,"Q_Id":57377594,"Users Score":2,"Answer":"Its the same problem everywhere in unsupervised learning.\nIt is unsupervised, you are trying to discover something new and interesting. There is no way for the computer to decide whether something is actually interesting or new. It can decide and trivial cases when the prior knowledge is coded in machine processable form already, and you can compute some heuristics values as a proxy for interestingness. But such measures (including density-based measures such as DBCV are actually in no way better to judge this than the clustering algorithm itself is choosing the \"best\" solution).\nBut in the end, there is no way around manually looking at the data, and doing the next steps - try to put into use what you learned of the data. Supposedly you are not invory tower academic just doing this because of trying to make up yet another useless method... So use it, don't fake using it.","Q_Score":2,"Tags":"python,cluster-analysis,evaluation,hdbscan","A_Id":57383549,"CreationDate":"2019-08-06T13:48:00.000","Title":"How to evaluate HDBSCAN text clusters?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to setup a simple process to modify my CSV file and ingest it to the elasticsearch DB using Apache Nifi. I don't want to stream my CSV file on Stdout, while passing my file from one processor to another.\nI've already made two flows.\nMyfirst flow get my CSV file using GetFile processor, customizes it using ExecuteStreamCommand in which i  run my python script to read,modify and save my CSV file locally. \nMy second flow again read that modified CSV file using GetFile processor and ingest it directly to my ElasticSearch DB.\nNow, for getting this task accomplished, i run these two flows seperatly. \nCan i connect the ExecuteStreamCommand of my first flow and GetFile of my second flow together, so that i can run them together as one single flow.\nIs there any other option to read a file written by ExecuteStreamCommand locally without streaming ??","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":312,"Q_Id":57378012,"Users Score":1,"Answer":"The output of the command executed by ExecuteStreamCommand will be written to a flow file that is transferred to the \"output stream\" relationship. You should be able to connect ExecuteStreamCommand \"output stream\" directly to PutElasticSearch.","Q_Score":0,"Tags":"python,csv,elasticsearch,apache-nifi","A_Id":57378682,"CreationDate":"2019-08-06T14:09:00.000","Title":"Apache Nifi : I want to ingest my Data CSV to Elasticsearch without streaming it to some other processor using apache nifi","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an array like w=[0.854,0,0.66,0.245,0,0,0,0] and want to apply 100\/sqrt(x) on each value. As I can't divide by 0, I'm using this trick :\nw=np.where(w==0,0,100\/np.sqrt(w))\nAs I'm not dividing by 0, I shouldn't have any warning.\nSo why does numpy keep raising RuntimeWarning: divide by zero encountered in true_divide ?","AnswerCount":4,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":615,"Q_Id":57393768,"Users Score":2,"Answer":"Since it takes advantages of vectorization , it will execute 100\/np.sqrt(w)for  every element of w , so the division by 0 will happen , but then you are not taking the results associated with these entries. So basically with your trick you are still dividing by 0 but not using the entries where you divided by 0.","Q_Score":2,"Tags":"python,numpy,where-clause","A_Id":57393865,"CreationDate":"2019-08-07T11:48:00.000","Title":"why is numpy where keep raising divide by zero encountered?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an array like w=[0.854,0,0.66,0.245,0,0,0,0] and want to apply 100\/sqrt(x) on each value. As I can't divide by 0, I'm using this trick :\nw=np.where(w==0,0,100\/np.sqrt(w))\nAs I'm not dividing by 0, I shouldn't have any warning.\nSo why does numpy keep raising RuntimeWarning: divide by zero encountered in true_divide ?","AnswerCount":4,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":615,"Q_Id":57393768,"Users Score":2,"Answer":"100\/np.sqrt(w) still uses the w with zeros because function arguments are evaluated before executing a function. The square root of zero is zero, so you end up dividing 100 by an array that contains zeros, which in turn attempts to divide 100 by each element of this array and at some point tries to divide it by an element that's equal to zero.","Q_Score":2,"Tags":"python,numpy,where-clause","A_Id":57393853,"CreationDate":"2019-08-07T11:48:00.000","Title":"why is numpy where keep raising divide by zero encountered?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have built a list comprehension that takes in a list of lists [actual, predicted] and then classifies the contained lists. I want to produce output lists that have 1 if the original element is > .1, -1 if the original output is < -.1 and 0 if the original element is < .1 and > -.1. For example [[2, 0, -2],[0, 0, 0]] would be mapped to [[1,0,-1], [0,0,0]]. \nI am using this code to perform this:\nclassified = [list(map(lambda x: 1 if x > .1 else (-1 if x < .1 else 0), i)) for i in inputs].\nHowever, when my code should classify an element as 0, it classifies it as -1. For example [-.15, -.05, 0] is mapped to [-1, -1, -1] instead of [-1, 0, 0]. It does classify points that map to 1 and -1 correctly.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":42,"Q_Id":57400902,"Users Score":4,"Answer":"-1 if x < .1 else 0 should be -1 if x < -.1 else 0","Q_Score":0,"Tags":"python","A_Id":57400930,"CreationDate":"2019-08-07T19:10:00.000","Title":"Classification List Comprehension Not Behaving as Expected","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"In plotly_express.line the only options I see to modify hover settings are hover_name and hover_data. A few issues I'm facing with modifying hover are:\n\nIt seems that even if I set hover_data=None it still shows the values for name,x, and y. How can I set it to only show the hover info I select without adding defaults?\nI can't find a setting to modify the opacity for hover boxes. I'm displaying a lot of hover info so my hover box is large, which makes it difficult to know where I am on the plot behind the box.\nHow can I make it so hovering on one line displays hover info corresponding to a linked data column value on all other lines?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2985,"Q_Id":57402050,"Users Score":2,"Answer":"1 and 2 are not possible yet.\nFor 3 there is a hovermode attribute in layout that you can set to show one hover label per trace per y-value.","Q_Score":5,"Tags":"python,hover,plotly,plotly-dash,plotly-express","A_Id":57402233,"CreationDate":"2019-08-07T20:42:00.000","Title":"Plotly Express hover options","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataset containing both RGB and Segmented images as ground truth , the readme.txt included in the annotated dataset stated this :\nGT_color :folder containing the groundtruth masks for semantic segmentation \nAnnotations are given using a color representation, where each corresponds to a specific class. This is primairly provided for visualization. For training, create a corresponding ID image by assigning the colors to a specific class ID as given below.\n\nClass       R   G   B   ID\n\nVoid            -   -   -   0\n\nRoad            170     170     170 1\n\nGrass           0   255     0   2\n\nVegetation      102     102     51  3\n\nTree            0   60  0   3\n\nSky             0   120     255 4\n\nObstacle        0   0   0   5\n\nI don't understand what's meant by \"creating corresponding ID image\" , aren't the segmented images labelled already by each area's color ? meaning that the rgb are the labels ?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":83,"Q_Id":57410686,"Users Score":0,"Answer":"From what I understand, your segmented images come with 3 channels, where the color of each pixel corresponds to its GT label.\nWhen you train your image segmentation model, there is no need for an output of 3 channels (its redundant), so they recommend that you create a new annotated image, where you replace each color with the provided i.d.\nThis recommendation is simply there to make the job of the model a little bit easier.","Q_Score":0,"Tags":"python,machine-learning,computer-vision,dataset,image-segmentation","A_Id":57410794,"CreationDate":"2019-08-08T10:39:00.000","Title":"Create an ID segmented images","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am making a KNN model. The target variable is divided in 2 categories, and the features are 3 categorical variables (country, language and company). The model says the optimal is 5 clusters, so I did it with 5.\nI need to know how can I see the records in each of the 5 clusters (I mean, the countries, languages and companies that the model is grouping in each of them). Is there a way to add the labels of the clusters to the dataframe?\nI tried:\npredictions = knn.predict(features)\nBut that is only returning the estimations for the 2 labels of the target variable\nI did some research and found:\nkm.labels_\nBut that only applies for KMeans, and I am using KNN\nI hope somebody can tell me the equivalent for that or how to solve the problem for KNN Model please","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":112,"Q_Id":57424338,"Users Score":0,"Answer":"KNN is not clustering, but classification.\nThe parameter k is not the k of k-means; it is the number of neighbors not the number of clusters...\nHence, setting k to 5 dors not suddenly produce 5 labels. Your training data has 2 labels, hence you get 2 labels.\nKNN = k-nearest neighbors classification. For k=5 this means 5 nearest neighbors.\nK-means clustering = approximate the data with k center vectors. An entirely different k.","Q_Score":0,"Tags":"python,scikit-learn,label,knn","A_Id":57426426,"CreationDate":"2019-08-09T06:13:00.000","Title":"How can I identify the records inside each cluster in a KNN model in SciKit-Learn Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a CSV file in S3. I want to run a python script using data present in S3. The S3 file will change once in a week. I need to pass an input argument to my python script which loads my S3 file into Pandas and do some calculation to return the result.\nCurrently I am loading this S3 file using Boto3 in my server for each input argument. This process takes more time to return the result, and my nginx returns with 504 Gateway timeout.\nI am expecting some AWS service to do it in cloud. Can anyone point me in a right direction which AWS service is suitable to use here","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":389,"Q_Id":57426946,"Users Score":0,"Answer":"You have several options:\n\nUse AWS Lambda, but Lambda has limited local storage (500mb) and memory (3gb) with 15 run time.\nSince you mentioned Pandas I recommend using AWS Glue which has ability:\n\n\nDetect new file\nLarge Mem, CPU supported\nVisual data flow\nSupport Spark DF\nAbility to query data from your CSV files\nConnect to different database engines.\n\n\nWe currently use AWS Glue for our data parser processes","Q_Score":0,"Tags":"python-3.x,amazon-s3,aws-lambda,job-scheduling","A_Id":57427988,"CreationDate":"2019-08-09T09:16:00.000","Title":"How to run python script using S3 data in AWS","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to understand Distribute by clause and how it could be used in Spark-SQL to optimize Sort-Merge Joins. \nAs per my understanding, the Spark Sql optimizer will distribute the datasets of both the participating tables (of the join) based on the join keys (shuffle phase) to co-locate the same keys in the same partition. If that is the case, then if we use the distribute by in the sql, then also we are doing the same thing.\nSo in what way can distribute by could be used ameliorate join performance ? Or is it that it is better to use distribute by while writing the data to disk by the load process, so that subsequent queries using this data will benefit from it by not having to shuffle it ?\nCan you please explain with a real-world example to tune join using distribute by\/cluster by in Spark-SQL ?","AnswerCount":1,"Available Count":1,"Score":0.537049567,"is_accepted":false,"ViewCount":2866,"Q_Id":57429479,"Users Score":3,"Answer":"Let me try to answer each part of your question:\n\nAs per my understanding, the Spark Sql optimizer will distribute the datasets of both the participating tables (of the join) based on the join keys (shuffle phase) to co-locate the same keys in the same partition. If that is the case, then if we use the distribute by in the sql, then also we are doing the same thing.\n\nYes that is correct.\n\nSo in what way can distribute by could be used ameliorate join performance ?\n\nSometimes one of your tables is already distributed, for example the table was bucketed or the data was aggregated before the join by the same key. In this case if you explicitly repartition also the second table (distribute by) you will achieve the same partitioning in both branches of the join and Spark will not induce any more shuffle in the first branch (sometimes this is referenced to as one-side shuffle-free join because the shuffle will occur only in one branch of the join - the one in which you call repartition \/ distribute by). On the other hand if you don't repartition explicitly the other table, Spark will see that each branch of the join has different partitioning and thus it will shuffle both branches. So in some special cases calling repartition (distribute by) can save you one shuffle. \nNotice that to make this work you need to achieve also the same number of partitions in both branches. So if you have two tables that you want to join on the key user_id and if the first table is bucketed into 10 buckets with this key  then you need to repartition the other table also into 10 partitions by the same key and then the join will have only one shuffle (in the physical plan you can see that there will be Exchange operator only in one brach of the join).\n\nOr is it that it is better to use distribute by while writing the data to disk by the load process, so that subsequent queries using this data will benefit from it by not having to shuffle it ?\n\nWell, this is actually called bucketing (cluster by) and it will allow you to pre-shuffle the data once and then each time you read the data and join it (or aggregate) by the same key by which you bucketed, it will be free of shuffle. So yes, this is very common technique that you pay the cost only once when saving the data and then leverage that each time you read it.","Q_Score":2,"Tags":"python,apache-spark,apache-spark-sql,pyspark-sql","A_Id":57456535,"CreationDate":"2019-08-09T11:49:00.000","Title":"Difference between DISTRIBUTE BY and Shuffle in Spark-SQL","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm new to using KNN and in my train set I have velocity vector. Since the directions 359\u00b0 and 0\u00b0 are completely different I was thinking of transforming the direction so that the vector in test data it points to 180\u00b0.\nI could make this transformation before using KNeighborsClassifier if I predict from one data point, but when I tune the hyperparameters with GridSearchCV the transformation should be done between every comparison. Is there some way to do that?\nOr some alternative way that I'm missing?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":68,"Q_Id":57440714,"Users Score":0,"Answer":"You can break direction column, theta into 2 columns, sin(theta) and cos(theta), both of which would have continuity.","Q_Score":0,"Tags":"python,scikit-learn,knn,gridsearchcv","A_Id":57441200,"CreationDate":"2019-08-10T08:49:00.000","Title":"How to use directional data as feature using scikit-learn knn?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In matplotlib, matplotlib.pyplot.savefig saves the current figure. If I have multiple figures as variables in my workspace, e.g. fig1, fig2, and fig3, is it possible to save any of these figures based on their variable name, without first bringing them up as the current figure? E.g., I'd like to do something like:\nsave('fig2', 'fig2_file.png')","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":209,"Q_Id":57444490,"Users Score":1,"Answer":"You can save individual figures by: \n\nfigX.savefig('figX_file.png')\n\n.","Q_Score":0,"Tags":"python,matplotlib","A_Id":57444559,"CreationDate":"2019-08-10T17:59:00.000","Title":"How to save a figure in matplotlib by its variable name?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"If I need to backpropagate through a neural network twice and I don't use retain_graph=True, I get an error. \nWhy? I realize it is nice to keep the intermediate variables used for the first backpropagation to be reused for the second backpropagation. However, why aren't they simply recalculated, like they were originally calculated in the first backpropagation?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":55,"Q_Id":57447736,"Users Score":0,"Answer":"By default, PyTorch doesn't store intermediate gradients, because the PyTorch's main feature is Dynamic Computational Graphs, so after backpropagation the graph will be freed all the intermediate buffers will be destroyed.","Q_Score":0,"Tags":"python,neural-network,deep-learning,pytorch","A_Id":57450598,"CreationDate":"2019-08-11T06:13:00.000","Title":"Why does not using retain_graph=True result in error?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to access an excel file using python for my physics class. I have to generates data that follows a function but creates variance so it doesn\u2019t line up perfectly to the function(simulating the error experienced in experiments). I did this by using the rand() function. We need to generate a lot of data sets so that we can average them together and eliminate the error\/noise creates by the rand() function. I tried to do this by loading the excel file and recording the data I need, but then I can\u2019t figure out how to get the rand() function to rerun and create a new data set. In excel it reruns when i change the value of any cell on the excel sheet, but I don\u2019t know how to do this when I\u2019m accessing the file with Python. Can someone help me figure out how to do this? Thank You.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":193,"Q_Id":57454186,"Users Score":0,"Answer":"Excel formulas like RAND(), or any other formula, will only refresh when Excel is actually running and recalculating the worksheet.\nSo, even though you may be access the data in an Excel workbook with Python, you won't be able to run Excel calculations that way. You will need to find a different approach.","Q_Score":0,"Tags":"excel,python-3.x,pandas,xlrd,xlwt","A_Id":57454549,"CreationDate":"2019-08-11T22:56:00.000","Title":"How to get the rand() function in excel to rerun when accessing an excel file through python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I try train ssd-mobilenet in my own dataset :\ntraining image : 3400 with size :1600*1200\ntest set :800 with size :1600 *1200\ntensorflow -gpu :1.13.1  gpu :4GB\ncuda 10.0 cudnn 7\nobject: road damage like aligator crack\nbut after  197000 step my training loss cannot go down 2:\nI need helps.Thanks in advance","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":274,"Q_Id":57459492,"Users Score":0,"Answer":"I would first try with images of size 300 x 300 first, or use a library which will downscale your images and the bounding boxes.","Q_Score":1,"Tags":"python,tensorflow,object-detection-api","A_Id":57469658,"CreationDate":"2019-08-12T10:25:00.000","Title":"SSd_mobilenet loss cannot go down","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm very new to Computer Vision, I'm tryind to build a CV model which will detect and recognize price tags and extract info from it. I've already trained model that can detect price tags using YOLO. But I also want to teach my system to detect and recognize text which only written inside these price tags. Than parse this info into different parts, for example: price, product name, product description. Or mayby I firstly need to parse detected blocks (price block on the left side of the price tag, product name on the right side, etc.) then read it. Any ideas would be appriciated.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":71,"Q_Id":57460861,"Users Score":2,"Answer":"Well, the first one that pops into my mind would be to crop the objects detected with YOLO and then run the OCR on that image. After running OCR, you'll have to do some postprocessing to classify each line of text to a specific category (price, name etc.)","Q_Score":0,"Tags":"python,opencv,yolo","A_Id":57460928,"CreationDate":"2019-08-12T12:04:00.000","Title":"Detect text only inside detected objects","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a problem. I am trying to use my model with Rasa core, but it gives me this error:\n\nrasa_nlu.model.UnsupportedModelError: The model version is to old to\n  be loaded by this Rasa NLU instance. Either retrain the model, or run\n  withan older version. Model version: 0.14.6 Instance version: 0.15.1\n\nDoes someone know which version I need to use then and how I can install that version?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":879,"Q_Id":57465038,"Users Score":0,"Answer":"I believe you trained this model on the previous version of Rasa NLU and updated Rasa NLU to a new version (Rasa NLU is a dependency for Rasa Core, so changes were made in requirenments.txt file).\nIf this is a case, there are 2 ways to fix it:\n\nRecommended solution. If you have data and parameters, train your NLU model again using current dependencies (this one that you have running now). So you have a new model which is compatible with your current version of Rasa\nIf you don't have a data or can not retrain a model for some reason, then downgrade Rasa NLU to version 0.14.6. I'm not sure if your current Rasa core is compatible with NLU 0.14.6, so you might also need to downgrade Rasa core if you see errors.\n\nGood luck!","Q_Score":0,"Tags":"python,anaconda,rasa-nlu,rasa-core","A_Id":57510905,"CreationDate":"2019-08-12T16:37:00.000","Title":"Rasa NLU model to old","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I used to use keras and the image format it followed is [Height x Width x Channels x Samples]. i decided to switch to PyTorch. But i didn\u2019t switch out my data loading schemes. So now i have numpy arrays of shape HxWxCxS, instead of SxCxHxW which is required for PyTorch. Does anyone have any idea to convert this ?","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2309,"Q_Id":57467707,"Users Score":6,"Answer":"First, Keras format is (samples, height, width, channels).\nAll you need to do is a moved = numpy.moveaxis(data, -1,1)\nIf by luck you were using the non-default config \"channels_first\", then the config is identical to that of PyTorch, which is (samples, channels, height, width).  \nAnd when transforming to torch: data = torch.from_numpy(moved)","Q_Score":2,"Tags":"python,image,opencv,keras,pytorch","A_Id":57467836,"CreationDate":"2019-08-12T20:13:00.000","Title":"Reshaping Image for PyTorch","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have attempted to run a FDR correction on an array of p-values using both statsmodels.stats.multitest's multipletests(method='fdr_bh') and fdrcorrection. In both instances I receive an array of NaN's as the corrected p-values. I do not understand why the corrected p-value is returning as NaN. Could someone please help explain?","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":694,"Q_Id":57468030,"Users Score":2,"Answer":"Check for NaNs in your p-value array. using multipletests on an array with any NaNs will return all NaNs, as you describe.","Q_Score":1,"Tags":"python,statsmodels","A_Id":62476273,"CreationDate":"2019-08-12T20:45:00.000","Title":"Corrected P-value returned as NaN","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to build a word cloud containing multiple word structures (not just one word). In any given text we will have bigger frequencies for unigrams than bigrams. Actually, the n-gram frequency decreases when n increases for the same text.\nI want to find a magic number or a method to obtain comparative results between unigrams and bigrams, trigrams, n-grams. \nThere is any magic number as a multiplier for n-gram frequency in order to be comparable with a unigram? \nA solution that I have now in mind is to make a top for any n-gram (1, 2, 3, ...) and use the first z positions for any category of n-grams.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":442,"Q_Id":57482331,"Users Score":1,"Answer":"As you've asked this, there is no simple linear multiplier.  You can make a general estimate by the size of your set of units.  Consider the English alphabet of 26 letters: you have 26 possible unigrams, 26^2 digrams, 26^3 trigrams, ... Simple treatment suggests that you would multiply a digram's frequency by 26 to compare it with unigrams; trigram frequencies would get a 26^2 boost.\nI don't know whether that achieves the comparison you want, as the actual distribution of n-grams is not according to any mathematically tractable function.  For instance, letter-trigram distribution is a good way to differentiate the language in use: English, French, Spanish, German, Romanian, etc. have readily differential distributions.\nAnother possibility is to normalize the data: convert each value into a z-score, the amount of standard deviations above\/below the mean of the distribution.  The resulting list of z-scores has a mean of 0 and a standev of 1.0\nDoes either of those get you the results you need?","Q_Score":0,"Tags":"python,text,nlp,nltk","A_Id":57482532,"CreationDate":"2019-08-13T17:03:00.000","Title":"How to compare frequency of unigrams with frequencies of bigrams, trigrams, etc?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm preparing my dataset to be preprocessed before training with CNN model but i couldn't generate data from this type of file which contain several signals.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":198,"Q_Id":57485659,"Users Score":1,"Answer":"I recommend using the gdflib library. It'll allow you to process your .gdf files by organizing your data into nodes for further processing.\nIt would also help if you could please provide a minimal reproducible example of what you have tried.","Q_Score":0,"Tags":"python","A_Id":57485755,"CreationDate":"2019-08-13T21:41:00.000","Title":"How can i generate Data from .gdf files using Jupyter notebook?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"The dataset I have is a set of quotations that were presented to various customers in order to sell a commodity. Prices of commodities are sensitive and standardized on a daily basis and therefore negotiations are pretty tricky around their prices. I'm trying to build a classification model that had to understand if a given quotation will be accepted by a customer or rejected by a customer.\nI made use of most classifiers I knew about and XGBClassifier was performing the best with ~95% accuracy. Basically, when I fed an unseen dataset it was able to perform well. I wanted to test how sensitive is the model to variation in prices, in order to do that, I synthetically recreated quotations with various prices, for example, if a quote was being presented for $30, I presented the same quote at $5, $10, $15, $20, $25, $35, $40, $45,..\nI expected the classifier to give high probabilities of success as the prices were lower and low probabilities of success as the prices were higher, but this did not happen. Upon further investigation, I found out that some of the features were overshadowing the importance of price in the model and thus had to be dealt with. Even though I dealt with most features by either removing them or feature engineering them to better represent them I was still stuck with a few features that I just cannot remove (client-side requirements)\nWhen I checked the results, it turned out the model was sensitive to 30% of the test data and was showing promising results, but for the rest of the 70% it wasn't sensitive at all. \nThis is when the idea struck my mind to feed only that segment of the training data where price sensitivity can be clearly captured or where the success of the quote is inversely related to the price being quoted. This created a loss of about 85% of the data, however the relationship that I wanted the model to learn was being captured perfectly well.\nThis is going to be an incremental learning process for the model, so each time a new dataset comes, I'm thinking of first evaluating it for the price sensitivity and then feeding in only that segment of the data for training which is price sensitive. \nHaving given some context to the problem, some of the questions I had were:\n\nDoes it make sense to filter out the dataset for segments where the kind of relationship I'm looking for is being exhibited?\nPost training the model on the smaller segment of the data and reducing the number of features from 21 to 8, the model accuracy went down to ~87%, however it seems to have captured the price sensitivity bit perfectly. The way I evaluated price sensitivity is by taking the test dataset and artificially adding 10 rows for each quotation with varying prices to see how the success probability changes in the model. Is this a viable approach to such a problem?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":53,"Q_Id":57487124,"Users Score":1,"Answer":"To answer your first question, deleting the part of the dataset that doesn't work is not a good idea because then your model will overfit on the data that gives better numbers. This means that the accuracy will be higher, but when presented with something that is slightly different from the dataset, the probability of the network adapting is lower.\nTo answer the second question, it seems like that's a good approach, but again I'd recommend keeping the full dataset.","Q_Score":0,"Tags":"python,machine-learning,xgbclassifier","A_Id":57488153,"CreationDate":"2019-08-14T01:33:00.000","Title":"Does it make sense to use a part of the dataset to train my model?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am currently doing a group project about Semantic Segmentation and need to train model with own data set. The problem is  data set is not available in any pre-trained model since the objective is to detect each part of sneakers(eg. lace, outsole, front patch, logos etc). None of our team members had never studied deep learning but had studied in computer science.\nAlso, there's another question about Mask-RCNN. What is the exact meaning of weights of pre-trained model? Is it the weights calculated from DL model?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":103,"Q_Id":57491013,"Users Score":1,"Answer":"A pre-trained model is a model that has been trained usually with a big dataset using big computers and can be fine-tuned for a given problem using small amounts of computation. This is possible to do with Deep Learning, where training a model consists in adjusting some matrices of weights. When we refer to pre-trained weights we mean that we train a model with big datasets and then we store the weights in order to be used by other' tasks.","Q_Score":0,"Tags":"python,python-3.x,deep-learning","A_Id":57491074,"CreationDate":"2019-08-14T08:34:00.000","Title":"Mask-RCNN project","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"All of my previous code runs well. \nIt is only when I try to set the index to a particular column as the code below shows, that I run into an error. \nHonestly - this same method has worked before and I have not been able to find any other method to do the same thing. \ndf.set_index=(\"Neighbourhood\",inplace=True)\n\u200b\nError message: \nFile \"\", line 1\n    df.set_index=(\"Neighbourhood\",inplace=True)\n                                         ^\nSyntaxError: invalid syntax","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":499,"Q_Id":57493320,"Users Score":1,"Answer":"set_index is a function, you need to call it.\ntry df.set_index(\"Neighbourhood\",inplace=True) (without the =)","Q_Score":1,"Tags":"python,python-3.x,indexing,methods","A_Id":57493372,"CreationDate":"2019-08-14T10:55:00.000","Title":"df.set_index=(\"Neighbourhood\",inplace=True) giving me SyntaxError: invalid syntax","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I attempted to do import segmentation_models as sm, but I got an error saying efficientnet was not found. So I then did pip install efficientnet and tried it again. I now get ModuleNotFoundError: no module named efficientnet.tfkeras, even though Keras is installed as I'm able to do from keras.models import * or anything else with Keras\nhow can I get rid of this error?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":10143,"Q_Id":57503473,"Users Score":6,"Answer":"To install segmentation-models use the following command: pip install git+https:\/\/github.com\/qubvel\/segmentation_models","Q_Score":7,"Tags":"python,keras","A_Id":57539079,"CreationDate":"2019-08-15T00:11:00.000","Title":"ModuleNotFoundError: no module named efficientnet.tfkeras","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to set up Keras in order to run models using my GPU. I have a Radeon RX580 and am running Windows 10.\nI saw realized that CUDA only supports NVIDIA GPUs and was having difficulty finding a way to get my code to run on the GPU. I tried downloading and setting up plaidml but afterwards from tensorflow.python.client import device_lib\nprint(device_lib.list_local_devices()) \nonly printed that I was running on a CPU and there was not a GPU available even though the plaidml setup was a success. I have read that PyOpenCl is needed but have not gotten a  clear answer as to why or to what capacity. Does anyone know how to set up this AMD GPU to work properly? any help would be much appreciated. Thank you!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":435,"Q_Id":57504746,"Users Score":0,"Answer":"To the best of my knowledge, PlaidML was not working because I did not have the required prerequisites such as OpenCL. Once I downloaded the Visual Studio C++ build tools in order to install PyopenCL from a .whl file. This seemed to resolve the issue","Q_Score":1,"Tags":"python-3.x,tensorflow,keras,gpu,amd","A_Id":57529064,"CreationDate":"2019-08-15T04:06:00.000","Title":"Setting up keras and tensoflow to operate with AMD GPU","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I get a simple task request to perform a classification to predict classes i.e between 'Good' and 'Bad' customers. The problem is, recommendation is needed on suitable variables' values for those customers with 'Bad' prediction, so that they can take action to improve their profile. Examples of variable are 'Purchase Score' and 'Purchase Frequency'. Means that, these customers need to improve on these scores so that the prediction output can be obtained as 'Good' customers.\nIn this problem, when the optimized variables are input back into the classification problem, it needs to output 'Good' label instead of 'Bad' label.\nI've searched through the optimization methods such as scipy.optimize and Genetic Algorithms, but from my understanding, the optimized variables are for a set continuous value target instead of a class target. What technique can I use to achieve the optimization and recommendation part for a class prediction?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":28,"Q_Id":57507740,"Users Score":0,"Answer":"What I would do in this case is to use a finite-differences gradient approach to solve the problem. For that, you can follow the next steps:\n1) Select a customer with \"Bad\" prediction, increase and decrease their variables one by one a little bit and check the prediction. \n2) That way you'll which sign in each variable causes the probability of \"Good\" to go up. \n3) Once you have it, move a bit all the variables in the direction of improvement\n4) Repeat from 1) until convergence.\nBTW this is the way Excel Solver works.","Q_Score":0,"Tags":"python,optimization,classification","A_Id":57507841,"CreationDate":"2019-08-15T09:40:00.000","Title":"What is the suitable technique for discrete classification and variable optimization problem?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to use CP-SAT solver with some variables: x,y. I want to maximise an objective function of the form x**2-y*x with some constraints. I'm getting\n\nTypeError: unsupported operand type(s) for ** or pow(): 'IntVar' and\n  'int'\n\nerror messages. Am I correct in assuming I cannot use nonlinear objective function for CP-SAT, as I couldn't find any documentation or examples that did employ nonlinear objectives? Or is there some way to do this?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":671,"Q_Id":57524316,"Users Score":3,"Answer":"You have to create an intermediate variable using AddMultiplicationEquality(x2, [x, x])","Q_Score":1,"Tags":"python,or-tools,cp-sat-solver","A_Id":57524523,"CreationDate":"2019-08-16T12:02:00.000","Title":"Using CP-SAT Solver for non-linear objective function","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I see that the hovermode attribute in layout has options for x or y, but is it possible to use an arbitrary dataframe column? instead?\nFor example, I'm plotting precision-recall curves. The x-axis is recall, and the y-axis is precision. The independent variable is a detection threshold value (with range of np.linspace(0,1.0,101)) with a column in my dataframe called threshold. When I hover over a precision-recall point, what I'm interested in is points on other curves with the same detection threshold value. So can I instead hover on this column?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":544,"Q_Id":57525950,"Users Score":0,"Answer":"No, there\u2019s no feature for that at the moment. You could consider a second set of \u201ciso-threshold\u201d curves to see this kind of thing perhaps?","Q_Score":0,"Tags":"python,hover,plotly,plotly-python,plotly-express","A_Id":57528483,"CreationDate":"2019-08-16T13:58:00.000","Title":"Plotly Express hovermode with arbitrary column","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am having an image dataset each image is of dimensions=(2048,1536).In ImageDataGenerator to fetch data from the directory, I have used the same target size i.e (2048,1536) but while making Sequential model first layer, what input shape should I have to use?? Will it be same as (2048,1536) or I can take any random shape like (224,224).","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":347,"Q_Id":57538810,"Users Score":1,"Answer":"I would resize first the images with cv2.resize(). Do you really need all the information from such a big image?\nFor a sequential Model it follows for example:\n\nmodel = models.Sequential()\nmodel.add(layers.Conv2D(32,(3,3), activation='relu', input_shape = (height,width, ndim)))\n...,\nwhere height and width denote your input image dimensions and ndim = 1 for greyscale and ndim = 3 for colored images.","Q_Score":2,"Tags":"python,keras,deep-learning,dataset","A_Id":57539279,"CreationDate":"2019-08-17T18:15:00.000","Title":"What input shape should I take in first layer of Sequential model when the dimensions of the images are (2048*1536)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"My tensorflow  only prints out the line:\nI tensorflow\/stream_executor\/dso_loader.cc:152] successfully opened CUDA library libcublas.so.10.0 locally when running.\nTensorflow logs on the net has lots of other libraries being loaded like libcudnn.\nAs I think my installation performance is not optimal, I am trying to find out if it is because of this. Any help will be appreciated!\nmy tf is 1.13.1\nNVIDIA Driver Version: 418.67\nCUDA Version: 10.1  (I have also 10.0 installed. can this be the problem?)","AnswerCount":2,"Available Count":1,"Score":-0.0996679946,"is_accepted":false,"ViewCount":3774,"Q_Id":57541567,"Users Score":-1,"Answer":"Actually i always rely on a stable setup. And i tried most of the tf - cuda - cudnn versions. But most stable was tf 1.9.0 , CUDA 9.0, Cudnn 7 for me. Used it for too long without a problem. You should give it a try if it suits you.","Q_Score":6,"Tags":"python,tensorflow","A_Id":57784170,"CreationDate":"2019-08-18T04:05:00.000","Title":"Tensorflow shows only \"successfully opened CUDA library libcublas.so.10.0 locally\" and nothing about cudnn","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"A NaN of type decimal.Decimal causes:\n\nmath.isnan to return True\nnumpy.isnan to throw a TypeError exception.\npandas.isnull to return False\n\nWhat is the difference between math.isnan, numpy.isnan and pandas.isnull?","AnswerCount":1,"Available Count":1,"Score":0.537049567,"is_accepted":false,"ViewCount":1748,"Q_Id":57588107,"Users Score":3,"Answer":"The only difference between math.isnan and numpy.isnan is that \n\nnumpy.isnan can handle lists, arrays, tuples whereas \nmath.isnan can ONLY handle single integers or floats.\n\nHowever, I suggest using math.isnan when you just want to check if a number is nan because \n\nnumpy takes approximately 15MB of memory when importing it while\nmath takes only 0,2M of memory\n\nAs for pandas.isnull it returns True not only for nan but also for None python types and as numpy it can handle every structure of numbers. However, it is even more \"heavy\" than numpy.","Q_Score":2,"Tags":"python-3.x,pandas,numpy","A_Id":57588916,"CreationDate":"2019-08-21T09:05:00.000","Title":"What is the difference between math.isnan ,numpy.isnan and pandas.isnull in python 3?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I use GridSearchCV to find the best parameters in the inner loop of my nested cross-validation. The 'inner winner' is found using GridSearchCV(scorer='balanced_accuracy'), so as I understand the documentation the model with the highest balanced accuracy on average in the inner folds is the 'best_estimator'. I don't understand what the different arguments for refit in GridSearchCV do in combination with the scorer argument. If refit is True, what scoring function will be used to estimate the performance of that 'inner winner' when refitted to the dataset? The same scoring function that was passed to scorer (so in my case 'balanced_accuracy')? Why can you pass also a string to refit? Does that mean that you can use different functions for 1.) finding the 'inner winner' and 2.) to estimate the performance of that 'inner winner' on the whole dataset?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":3919,"Q_Id":57591311,"Users Score":7,"Answer":"When refit=True, sklearn uses entire training set to refit the model. So, there is no test data left to estimate the performance using any scorer function.\nIf you use multiple scorer in GridSearchCV, maybe f1_score or precision along with your balanced_accuracy, sklearn needs to know which one of those scorer to use to find the \"inner winner\" as you say. For example with KNN, f1_score might have best result with K=5, but accuracy might be highest for K=10. There is no way for sklearn to know which value of hyper-parameter K is the best.\nTo resolve that, you can pass one string scorer to refit to specify which of those scorer should ultimately decide best hyper-parameter. This best value will then be used to retrain or refit the model using full dataset. So, when you've got just one scorer, as your case seems to be, you don't have to worry about this. Simply refit=True will suffice.","Q_Score":5,"Tags":"python,scikit-learn,grid-search","A_Id":57593244,"CreationDate":"2019-08-21T12:09:00.000","Title":"Combination of GridSearchCV's refit and scorer unclear","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have survey data with about 100 columns for every individual. Based on certain criteria, for eg. a column contains info of whether a person reads comics and another column contains info of whether a person reads comics.\nI want to validate if the user has created clusters\/groups that are mutually exclusive.\neg. Group 1: Males with age 0-25 reading comics, Group 2: Males with age 20-25 reading comics as well as newspaper. In this case, I want to generate a warning that the groups are not mutually exclusive.\nOne (inefficient) way of doing is creating a list of individuals for every group and then finding intersection for every combination of groups. If there is intersection, the groups are not mutually exclusive and hence incorrect.\nWhat is an efficient way of doing this?\nOne (inefficient) way of doing is creating a list of individuals for every group and then finding intersection for every combination of groups. If there is intersection, the groups are not mutually exclusive and hence incorrect.\nExpected result:\nThe created groups are mutually exclusive.\nor\nThe created groups are not mutually exclusive.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":44,"Q_Id":57591831,"Users Score":0,"Answer":"Solve it the opposite way.\nRather than trying all combinations and then checking which conflict, first find all conflicts.\nSo if a record is in groups A, B, and O, then mark AB, AO, and BO as incompatible. When going through combinations, you can easily check that adding B is impossible if you chose to use A etc.","Q_Score":0,"Tags":"python-3.x,logic,cluster-analysis","A_Id":57599130,"CreationDate":"2019-08-21T12:38:00.000","Title":"Logic for creating mutually exclusive groups of individuals (clusters)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a Dash app with a dcc.Graph object and a legend for multiple deselectable traces. How can I pass the list of traces selected in the legend as an input to a callback?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":542,"Q_Id":57602189,"Users Score":0,"Answer":"Use restyleData input in the callback: Input(\"graph-id\", \"restyleData\")","Q_Score":3,"Tags":"python,callback,plotly-dash,legend-properties,plotly-python","A_Id":63844295,"CreationDate":"2019-08-22T04:46:00.000","Title":"Dash graph selected legend items as input to callback","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to fit a simple Bayesian regression model to some right-skewed data. Thought I'd try setting family to a log-normal distribution. I'm using pymc3 wrapper BAMBI. Is there a way to build a custom family with a log-normal distribution?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":162,"Q_Id":57606946,"Users Score":0,"Answer":"Unless I'm misunderstanding something, I think all you need to do is specify link='log' in the fit() call. If your assumption is correct, the exponentiated linear prediction will be normally distributed, and the default error distribution is gaussian, so I don't think you need to build a custom family for this\u2014the default gaussian family with a log link should work fine. But feel free to clarify if this doesn't address your question.","Q_Score":0,"Tags":"python-3.x,bambi","A_Id":57614302,"CreationDate":"2019-08-22T10:10:00.000","Title":"Specify log-normal family in BAMBI model","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a piece of DASK code run on local machine which work 90% of time but will stuck sometimes.  Stuck mean.  No crash, no error print out not cpu usage.  never end.    \nI google and think it maybe due to some worker dead.  I will be very useful if I can see the worker log and figure out why.\nBut I cannot find my worker log.  I go to edit config.yaml to add loging but still see nothing from stderr. \nThen I go to dashboard --> info --> logs and see blank page.\nThe code it stuck is \nX_test = df_test.to_dask_array(lengths=True)\nor \nproba = y_pred_proba_train[:, 1].compute()\nand my ~\/.config\/dask\/config.yaml or ~.dask\/config.yaml look like\nlogging:\n  distributed: info\n  distributed.client: warning\n  distributed.worker: debug\n  bokeh: error \nI am using \npython 3.6\ndask 1.1.4 \nAll I need is a way to see the log so that I can try to figure out what goes wrong.\nThanks\nJoseph","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":149,"Q_Id":57618323,"Users Score":1,"Answer":"Worker logs are usually managed by whatever system you use to set up Dask.  \nPerhaps you used something like Kubernetes or Yarn or SLURM?  \nThese systems all have ways to get logs back.  \nUnfortunately, once a Dask worker is no longer running, Dask itself has no ability to collect logs for you.  You need to use the system that you use to launch Dask.","Q_Score":2,"Tags":"python,dask","A_Id":57712280,"CreationDate":"2019-08-23T00:00:00.000","Title":"Dask worker seem die but cannot find the worker log to figure out why","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I am trying to find similarity score between two documents (containing around 15000 records).\nI am using two methods in python:\n1. TFIDF (Scikit learn) 2. Word2Vec (gensim, google pre-trained vectors)\nExample1\nDoc1- Click on \"Bills\" tab\nDoc2- Click on \"CHAPS\" tab\nFirst method gives 0.9 score.\nSecond method gives 1 score\nExample2\nDoc1- See following requirements:\nDoc2- See following requirements\nFirst method gives 1 score.\nSecond method gives 0.98 score\nCan anyone tell me:\nwhy in Example1 Word2Vec is giving 1 though they are very different\nand in Example2 Word2Vec is giving 0.98 though they are having difference of only \":\"","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2587,"Q_Id":57626276,"Users Score":0,"Answer":"In example one, the word2vec maybe doesn't have the words Bills and CHAPS into its bag of words. That been said, taking out these words the sentences are the same*. \nIn Example 2, maybe in the tokenization of the word2vec algorithm, it took the \"requirements:\" as one token and the \"requirements\" as a different one, That's why their vector it's a bit different so they aren't exactly the same.\n*Word2vec computes the sentence vector by taking the average of its word vectors. If a word arent in the word2vec's bag of words, it will have vector=[0,0,...0].","Q_Score":0,"Tags":"python,machine-learning,data-science,word2vec,tf-idf","A_Id":57627460,"CreationDate":"2019-08-23T12:22:00.000","Title":"TFIDF vs Word2Vec","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to fine turn my model when using Keras, and I want to change my training data and learning rate to train when the epochs arrive 10, So how to get a callback when the specified epoch number is over.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":484,"Q_Id":57636091,"Users Score":0,"Answer":"Actually, the way keras works this is probably not the best way to go, it would be much better to treat this as fine tuning, meaning that you finish the 10 epochs, save the model and then load the model (from another script) and continue training with the lr and data you fancy.\nThere are several reasons for this.\n\nIt is much clearer and easier to debug. You check you model properly after the 10 epochs, verify that it works properly and carry on\nIt is much better to do several experiments this way, starting from epoch 10.\n\nGood luck!","Q_Score":0,"Tags":"python,keras","A_Id":57636395,"CreationDate":"2019-08-24T07:50:00.000","Title":"How to get a callback when the specified epoch number is over?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have some SIFT features in two stereo images, and I'm trying to place them in 3D space. I've found triangulatePoints, which seems to be what I want, however, I'm having trouble with the arguments.\ntriangulatePoints takes 4 arguments, projMatr1 and projMatr2, which is where my issues start, and projPoints1 and projPoints2, which are my feature points. The OpenCV docs suggest using stereoRectify to find the projection matrices.\nstereoRectify takes the intrinsic camera matrices (which I've calculated prior with calibrateCamera) and the image size from calibration. As well as two arguments R (rotation matrix) and T (translation vector), which can be found with stereoCalibrate.\nHowever, stereoCalibrate takes \"object points\", which I'm pretty sure I can't calculate for images without a reference, which is a bit of a roadblock.\nIs this the best way to be calculating 3D positions from pairs of features? If so, how can I calculate projMatr1 and projMatr2 without stereoCalibrate?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":135,"Q_Id":57637608,"Users Score":0,"Answer":"As you say, you have no calibration, so let\u2019s forget about rectification. What you want is the depth of the points, so you can project them into 3D (which then uses just the intrinsic calibration of one camera, mainly the focal length).\nSince you have no rectification, you cannot expect exact results, so let\u2019s try to get as close as possible:\nDepth is focal length times baseline divided by disparity, disparity and focal length being in pixels, and depth and baseline in (recommendation) meters.\nFor accurate disparity you need a rectified camera and correspondences between your features in both images. Since without calibration, you have no hope of rectification, you could try to just use the original images instead. It will work fine the more parallel the cameras are. If they are not parallel, you will introduce an error here and your results will become less accurate. If this becomes bad you must find a way to calibrate your camera.\nBut most importantly, you need correspondences between your features in both images. Running SIFT in both images won\u2018t do. A better approach would be running SIFT in just one image and then finding the corresponding pixels for each of the features in the other image. There are plenty of methods for that, I believe OpenCv has some simple block matching builtin.","Q_Score":0,"Tags":"python,opencv","A_Id":57638917,"CreationDate":"2019-08-24T11:40:00.000","Title":"OpenCV feature pairs to point cloud","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have about 15 million pairs that consist of a single int, paired with a batch of (2 to 100) other ints. \nIf it makes a difference, the ints themselve range from 0 to 15 million. \nI have considered using: \nPandas, storing the batches as python lists\nNumpy, where the batch is stored as it's own numpy array (since numpy doesn't allow variable length rows in it's 2D data structures)\nPython List of Lists. \nI also looked at Tensorflow tfrecords but not too sure about this one. \nI only have about 12 gbs of RAM. I will also be using to train over a machine learning algorithm so","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":71,"Q_Id":57640595,"Users Score":1,"Answer":"Use numpy. It us the most efficient and you can use it easily with a machine learning model.","Q_Score":0,"Tags":"python,numpy","A_Id":57641090,"CreationDate":"2019-08-24T18:30:00.000","Title":"Best data type (in terms of speed\/RAM) for millions of pairs of a single int paired with a batch (2 to 100) of ints","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a project where I need to analyze a text to extract some information if the user who post this text need help in something or not, I tried to use sentiment analysis but it didn't work as expected, my idea was to get the negative post and extract the main words in the post and suggest to him some articles about that subject, if there is another way that can help me please post it below and thanks.\nfor the dataset i useed, it was a dataset for sentiment analyze, but now I found that it's not working and I need a dataset use for this subject.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":578,"Q_Id":57641504,"Users Score":0,"Answer":"Please use the NLP methods before processing the sentiment analysis. Use the TFIDF, Word2Vector to create vectors on the given dataset. And them try the sentiment analysis. You may also need glove vector for the conducting analysis.","Q_Score":1,"Tags":"python-3.x,machine-learning,text,nlp,analytics","A_Id":67599276,"CreationDate":"2019-08-24T20:59:00.000","Title":"Detecting questions in text","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I go to the PyTorch website and select the following options\nPyTorch Build: Stable (1.2)\nYour OS: Windows\nPackage: pip\nLanguage: Python 3.7\nCUDA: None\n(All of these are correct)\nThan it displays a command to run\npip3 install torch==1.2.0+cpu torchvision==0.4.0+cpu -f https:\/\/download.pytorch.org\/whl\/torch_stable.html\nI have already tried to mix around the the different options but none of them has worked.\n\nERROR: ERROR: Could not find a version that satisfies the requirement torch==1.2.0+cpu (from versions: 0.1.2, 0.1.2.post1, 0.1.2.post2)\nERROR: No matching distribution found for torch==1.2.0+cpu\nI tried to do pip install pytorch but pytorch doesn't support pypi","AnswerCount":3,"Available Count":3,"Score":0.0665680765,"is_accepted":false,"ViewCount":2971,"Q_Id":57642019,"Users Score":1,"Answer":"I've been in same situation. \nMy prob was,  the python version... I mean, in the 'bit' way.\nIt was 32 bit that the python I'd installed.\nYou should check which bit of python you installed. \nyou can check in the app in setting, search python, then you will see the which bit you've installed.\nAfter I installed the 64 bit of Python, it solved.\nI hope you figure it out! \nenvironment : win 10","Q_Score":1,"Tags":"python,pip,pytorch","A_Id":57797731,"CreationDate":"2019-08-24T22:45:00.000","Title":"PyTorch not downloading","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I go to the PyTorch website and select the following options\nPyTorch Build: Stable (1.2)\nYour OS: Windows\nPackage: pip\nLanguage: Python 3.7\nCUDA: None\n(All of these are correct)\nThan it displays a command to run\npip3 install torch==1.2.0+cpu torchvision==0.4.0+cpu -f https:\/\/download.pytorch.org\/whl\/torch_stable.html\nI have already tried to mix around the the different options but none of them has worked.\n\nERROR: ERROR: Could not find a version that satisfies the requirement torch==1.2.0+cpu (from versions: 0.1.2, 0.1.2.post1, 0.1.2.post2)\nERROR: No matching distribution found for torch==1.2.0+cpu\nI tried to do pip install pytorch but pytorch doesn't support pypi","AnswerCount":3,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":2971,"Q_Id":57642019,"Users Score":0,"Answer":"It looks like it can't find a version called \"1.2.0+cpu\" from it's list of versions that it can find (0.1.2, 0.1.2.post1, 0.1.2.post2). Try looking for one of those versions on the PyTorch website.","Q_Score":1,"Tags":"python,pip,pytorch","A_Id":57642037,"CreationDate":"2019-08-24T22:45:00.000","Title":"PyTorch not downloading","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I go to the PyTorch website and select the following options\nPyTorch Build: Stable (1.2)\nYour OS: Windows\nPackage: pip\nLanguage: Python 3.7\nCUDA: None\n(All of these are correct)\nThan it displays a command to run\npip3 install torch==1.2.0+cpu torchvision==0.4.0+cpu -f https:\/\/download.pytorch.org\/whl\/torch_stable.html\nI have already tried to mix around the the different options but none of them has worked.\n\nERROR: ERROR: Could not find a version that satisfies the requirement torch==1.2.0+cpu (from versions: 0.1.2, 0.1.2.post1, 0.1.2.post2)\nERROR: No matching distribution found for torch==1.2.0+cpu\nI tried to do pip install pytorch but pytorch doesn't support pypi","AnswerCount":3,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":2971,"Q_Id":57642019,"Users Score":0,"Answer":"So it looks like I cant install PyTorch because I am running python 32-bit. This may or may not be the problem but this is the only possible error that I could see this being the cause.","Q_Score":1,"Tags":"python,pip,pytorch","A_Id":57648698,"CreationDate":"2019-08-24T22:45:00.000","Title":"PyTorch not downloading","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am very new to the python keras. And with the understanding of the Keras, I'm confusing about the input shape of Keras. I feel under different neural network, I need to reconstruct my data into different shapes.\nFor example, if I'm building a simple ANN, my train data should be a matrix like [m, n], the m is the number of samples and n is the number of feature. But recently I'm learning 1D convolutional neural network. I found the tutorial construct the training data as [a, b, c], where the a is the number of sample, b is the number of timestep, c is the number of feature (equals to 1). But why I can't simply reshape the data into [a, b]? since c will always be 1 for a 1D convolutional neural network.\nI'm not sure if I understand the above right. I am just wondering is there a summarize of the training_data shape of different neural networks? Or is there any logic behind the shape of data? So I can always make sure my training data has the right format.\nThe different neural networks mean like ANN, 1D CNN, 2d CNN, RNN and so on.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":54,"Q_Id":57651278,"Users Score":0,"Answer":"Generally, CNN takes 4 dimensions as input data. Whenever you train a CNN the model in Keras it will automatically convert the input data into 4D. If you want to predict using your CNN model you have to make sure that even your output data\/ or the data you want to run the inference on should have the same dimensions as input data. You can simply add 0 or None at the output data by using numpy expand_dims function.","Q_Score":0,"Tags":"python,keras","A_Id":57658971,"CreationDate":"2019-08-26T02:56:00.000","Title":"Can someone explain or summarize the input shape of keras under different type of neural networks?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to generate a single report that integrates different objects as my analysis inlcudes plots (matplotlib, seaborn and bokeh) and pandas data frames (tables) and paragraphs (markdowns) into an HTML report in python","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":16,"Q_Id":57659156,"Users Score":0,"Answer":"Use Jupyter or Zeppelin notebooks. They provide all of the functionality you described and can export to PDF. Reports can even be run\/emailed on a predetermined schedule.","Q_Score":0,"Tags":"python,html,reporting","A_Id":57660353,"CreationDate":"2019-08-26T13:46:00.000","Title":"combined different objects contains plots and data frames (tables) and paragraphs (markdowns) in to single html report","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"During hyper-parameter tuning do the parameters (weights already learned during model training) are also optimized or are they fixed and only optimal values are found for the hyper-parameters? Please explain.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":34,"Q_Id":57661188,"Users Score":1,"Answer":"Short is NO, they are not fixed.\nBecause, Hyper-parameters directly influence your simple parameters.   So for a neural network, no of hidden layers to use is a hyper-parameter, while weights and biases in each layer can be called simple parameter. Of course, you can't make weights of individual layers constant when the number of layers of the network (hyper-parameter) itself is variable . Similarly in linear regression, your regularization hyper-parameter directly impacts the weights learned. \nSo goal of tuning hyper-parameter is to get a value that leads to best set of those simple parameters. Those simple parameters are the ones you actually care about, and they are the ones to be used in final prediction\/deployment. So tuning hyper-parameter while keeping them fixed is meaningless.","Q_Score":0,"Tags":"python,machine-learning,cross-validation","A_Id":57661488,"CreationDate":"2019-08-26T15:52:00.000","Title":"Does the simple parameters also change during Hyper-parameter tuning","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a corpus of free text medical narratives, for which I am going to use for a classification task, right now for about 4200 records. \nTo begin, I wish to create word embeddings using w2v, but I have a question about a train-test split for this task. \nWhen I train the w2v model, is it appropriate to use all of the data for the model creation? Or should I only use the train data for creating the model? \nReally, my question sort of comes down to: do I take the whole dataset, create the w2v model, transform the narratives with the model, and then split, or should I split, create w2v, and then transform the two sets independently?\nThanks!\nEDIT\nI found an internal project at my place of work which was built by a vendor; they create the split, and create the the w2v model on ONLY the train data, then transform the two sets independently in different jobs; so it's the latter of the two options that I specified above. This is what I thought would be the case, as I wouldn't want to contaminate the w2v model on any of the test data.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1011,"Q_Id":57662405,"Users Score":2,"Answer":"The answer to most questions like these in NLP is \"try both\" :-) \nContamination of test vs train data is not relevant or a problem in generating word vectors. That is a relevant issue in the model you use the vectors with. I found performance to be better with whole corpus vectors in my use cases.\nWord vectors improve in quality with more data. If you don't use test corpus, you will need to have a method for initializing out-of-vocabulary vectors and understanding the impact they may have on your model performance.","Q_Score":1,"Tags":"python,machine-learning,nlp,word2vec","A_Id":57663266,"CreationDate":"2019-08-26T17:30:00.000","Title":"Is it appropriate to train W2V model on entire corpus?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataset that contains 'tag' and 'date'. I need to group the data by 'tag' (this is pretty easy), then within each group count the number of row that the date for them is smaller than the date in that specific row. I basically need to loop over the rows after grouping the data. I don't know how to write a UDF which takes care of that in PySpark. I appreciate your help.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":62,"Q_Id":57665530,"Users Score":0,"Answer":"you need an aggregation ? \ndf.groupBy(\"tag\").agg({\"date\":\"min\"})\nwhat about that ?","Q_Score":0,"Tags":"python,pyspark","A_Id":57681370,"CreationDate":"2019-08-26T22:11:00.000","Title":"PySpark Group and apply UDF row by row operation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to implement a DQN which makes many calls to Estimator.train() followed by Estimator.predict() on the same model with a small number of examples each. But each call takes a minimum of a few hundred milliseconds to over a second which is independent of the number of examples for small numbers like 1-20.\nI think these delays are caused by rebuilding the graph and saving checkpoints on each call. Is there are way to keep the same graph and parameters in memory for fast train-predict iterations or otherwise speed it up?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":148,"Q_Id":57670160,"Users Score":0,"Answer":"Convert to a tf.keras.Model instead of an Estimator, and use tf.keras.Model.fit() instead of Estimator.train(). fit() doesn't have the fixed delay that train() does. The Keras predict() doesn't either.","Q_Score":0,"Tags":"python,tensorflow-estimator","A_Id":58252317,"CreationDate":"2019-08-27T08:01:00.000","Title":"Estimator.train() and .predict() are too slow for small data sets","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataset of 11k images labeled for semantic segmentation. About 8.8k belong to 'group 1' and the rest to 'group 2'\nI am trying to simulate what would happen if we lost access to 'group 1' imagery but not a network trained from them.\nSo I trained ResNet50 on group 1 only. Then used that network as a starting point for training group 2 only. \nResults are essentially slightly better than not training with group 2 imagery (3% in average per class accuracy) but less than 1% better than if I just started with imagenet weights. I tested freezing blocks of resnet50 and a range of learning rates.\nGroup 1 and 2 are part of the same problem domain but are a bit different. They are taken at different regions (in fact the whole set covers a bunch of areas but group 1 and 2 are disjoint in this regard) and a different camera\/resolution. They are resized to a fixed size though this fixed size is closer to group 1 average size.\nThey are very different to imagenet images. They are monochrome, rectangular and are essentially one type of object that I'm segmenting.\nI'm not seeking to get the same result as training on all the images at once but surely there must be a bump in doing this over just training from imagenet.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":150,"Q_Id":57674274,"Users Score":0,"Answer":"I have read a few articles about the same topic - i have 12k jpeg images from 3 classes and after 3 epochs the accuracy dropped to 0.  I am awaiting delivery of a new graphics card to improve performance (it's currently taking 90 - 120 minutes per epoch) and hope to give more feedback.  I am just wondering if the face that this model was designed for ImageNet and its 21k classes might be part of the problem - its too wide and deep, therefore too sensitive to changes to weights....... would be interested in others views","Q_Score":0,"Tags":"python,tensorflow,keras,deep-learning","A_Id":62469494,"CreationDate":"2019-08-27T12:03:00.000","Title":"Poor performance transfer learning ResNet50","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an array with ~1,000,000 rows, each of which is a numpy array of 4,800 float32 numbers.\nI need to save this as a csv file, however using numpy.savetxt has been running for 30 minutes and I don't know how much longer it will run for.\nIs there a faster method of saving the large array as a csv?\nMany thanks,\nJosh","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":266,"Q_Id":57679863,"Users Score":2,"Answer":"As pointed out in the comments, 1e6 rows * 4800 columns * 4 bytes per float32 is 18GiB. Writing a float to text takes ~9 bytes of text (estimating 1 for integer, 1 for decimal, 5 for mantissa and 2 for separator), which comes out to 40GiB. This takes a long time to do, since just the conversion to text itself is non-trivial, and disk I\/O will be a huge bottle-neck.\nOne way to optimize this process may be to convert the entire array to text on your own terms, and write it in blocks using Python's binary I\/O. I doubt that will give you too much benefit though.\nA much better solution would be to write the binary data to a file instead of text. Aside from the obvious advantages of space and speed, binary has the advantage of being searchable and not requiring transformation after loading. You know where every individual element is in the file, if you are clever, you can access portions of the file without loading the entire thing. Finally, a binary file is more likely to be highly compressible than a relatively low-entropy text file.\nDisadvantages of binary are that it is not human-readable, and not as portable as text. The latter is not a problem, since transforming into an acceptable format will be trivial. The former is likely a non-issue given the amount of data you are attempting to process anyway.\nKeep in mind that human readability is a relative term. A human can not read 40iGB of numerical data with understanding. A human can process A) a graphical representation of the data, or B) scan through relatively small portions of the data. Both cases are suitable for binary representations. Case A) is straightforward: load, transform and plot the data. This will be much faster if the data is already in a binary format that you can pass directly to the analysis and plotting routines. Case B) can be handled with something like a memory mapped file. You only ever need to load a small portion of the file, since you can't really show more than say a thousand elements on screen at one time anyway. Any reasonable modern platform should be able to keep upI\/O and binary-to-text conversion associated  with a user scrolling around a table widget or similar. In fact, binary makes it easier since you know exactly where each element belongs in the file.","Q_Score":2,"Tags":"python,numpy","A_Id":57680106,"CreationDate":"2019-08-27T17:48:00.000","Title":"Saving large numpy 2d arrays","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a homebrew binary fixed-point arithmetic support library and would like to add numpy array support. Specifically I would like to be able to pass around 2D arrays of fixed-point binary numbers and do various operations on them such as addition, subtraction, multiplication, rounding, changing of fixed point format, etc.\nThe fixed-point support under the hood works on integers, and separate tracking of fixed-point format data (number of integer and fractional bits) for range checking and type conversion.\nI have been reading the numpy documentation on ndarray subclassing and dtype, it seems like I might want at least a custom dtype, or separate dtype object for every unique range\/precision configuration of fixed-point numbers. I tried subclassing numpy.dtype in Python but that is not allowed.\nI'm not sure if I can write something to interoperate with numpy in the way I want without writing C level code - everything so far is pure Python, I have avoided looking under the covers at how to work on the C-based layer of numpy.","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":2338,"Q_Id":57702835,"Users Score":2,"Answer":"For anyone interested, this turned out to be too hard to do in Python extended Numpy, or just didn't fit the data model. I ended up writing a separate Python library of types implementing the behaviours I wanted, that use Numpy arrays of integers under the hood for speed.\nIt works OK and does the strict binary range calculation and checking that I wanted, but suffers Python code speed overhead especially with small arrays. If I had time I'm sure it could be done much better\/faster as a C library.","Q_Score":2,"Tags":"python,numpy-ndarray","A_Id":59026590,"CreationDate":"2019-08-29T04:06:00.000","Title":"How to implement fixed-point binary support in numpy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Tensorflow has tf.data.Dataset.repeat(x) that iterates through the data x number of times. It also has iterator.initializer which when iterator.get_next() is exhausted, iterator.initializer can be used to restart the iteration. My question is is there difference when using tf.data.Dataset.repeat(x) technique vs iterator.initializer?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1504,"Q_Id":57711103,"Users Score":8,"Answer":"As we know, each epoch in the training process of a model takes in the whole dataset and breaks it into batches. This happens on every epoch.\nSuppose, we have a dataset with 100 samples. On every epoch, the 100 samples are broken into 5 batches ( of 20 each ) for feeding them to the model. But, if I have to train the model for say 5 epochs then, I need to repeat the dataset 5 times. Meaning, the total elements in the repeated dataset will have 500 samples ( 100 samples multipled 5 times ).\nNow, this job is done by the tf.data.Dataset.repeat() method. Usually we pass the num_epochs argument to the method.\nThe iterator.get_next() is just a way of getting the next batch of data from the tf.data.Dataset. You are iterating the dataset batch by batch.\nThat's the difference. The tf.data.Dataset.repeat() repeats the samples in the dataset whereas iterator.get_next() one-by-one fetches the data in the form of batches.","Q_Score":3,"Tags":"python,tensorflow,repeat","A_Id":57712269,"CreationDate":"2019-08-29T13:17:00.000","Title":"Difference between tf.data.Dataset.repeat() vs iterator.initializer","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"What does toPandas() actually do when using arrows optimization?\nIs the resulting pandas dataframe safe for wide transformations (that requires data shuffling) on the pandas dataframe eg..merge operations? what about group and aggregate? What kind of performance limitation should I expect?\nI am trying to standardize to Pandas dataframe where possible, due to ease of unit testing and swapability with in-memory objects without starting the monstrous spark instance.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":279,"Q_Id":57714161,"Users Score":0,"Answer":"toPandas() takes your spark dataframe object and pulls all partitions on the client driver machine as a pandas dataframe.  Any operations on this new object (pandas dataframe) will be running on a single machine with python therefore no wide transformations will be possible because you aren't using spark cluster distributed computing anymore (i.e. no partitions\/worker node interaction).","Q_Score":0,"Tags":"python,pandas,apache-spark,apache-arrow","A_Id":57732477,"CreationDate":"2019-08-29T16:15:00.000","Title":"Spark Arrow, toPandas() and wide transformation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using Altair package when I use following objects I have following error message. \nAttributeError: 'Chart' object has no attribute 'configure_facet_cell'\nIn order to use attribute above, what should I install or add?\nThank you in advance.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":549,"Q_Id":57717942,"Users Score":0,"Answer":"As per the comments this was created on an early version of the software and is no longer reproducible on current Altair versions.","Q_Score":2,"Tags":"python,bar-chart,configure,facet,altair","A_Id":66201863,"CreationDate":"2019-08-29T21:30:00.000","Title":"'Chart' object has no attribute 'configure_facet_cell'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have some legacy Keras\/Tensorflow code, which is unstable using latest Tensorflow versions (1.13+). It works just fine with previous versions. However i want to use Pytorch's Tensorboard support which requires it to be 1.14+. I've installed all Tensorflow-related packages to 1.10 and wanted to do just conda install tensorboard=1.14 but it removes tensorflow=1.10 as a requirement. I know that these packages are generally independent. How to upgrade tensorboard while keeping tensorflow old? Preferably i would like to use a single conda environment.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1279,"Q_Id":57723038,"Users Score":0,"Answer":"as @jdehesa suggested in the comments, it's better to have a different conda environment for pytorch and then install just the tb there\n!pip install tb-nightly","Q_Score":1,"Tags":"python,tensorflow,conda,tensorboard","A_Id":57727108,"CreationDate":"2019-08-30T08:15:00.000","Title":"Update Tensorboard while keeping Tensorflow old with conda","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"When considering the problem of classifying an input to one of 2 classes, 99% of the examples I saw used a NN with a single output and sigmoid as their activation followed by a binary cross-entropy loss.  Another option that I thought of is having the last layer produce 2 outputs and use a categorical cross-entropy with C=2 classes, but I never saw it in any example.\nIs there any reason for that?\nThanks","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":636,"Q_Id":57726064,"Users Score":2,"Answer":"If you are using softmax on top of the two output network you get an output that is mathematically equivalent to using a single output with sigmoid on top.\nDo the math and you'll see.\nIn practice, from my experience, if you look at the raw \"logits\" of the two outputs net (before softmax) you'll see that one is exactly the negative of the other. This is a result of the gradients pulling exactly in the opposite direction each neuron.\nTherefore, since both approaches are equivalent, the single output configuration has less parameters and requires less computations, thus it is more advantageous to use a single output with a sigmoid ob top.","Q_Score":1,"Tags":"python,pytorch,cross-entropy","A_Id":57726399,"CreationDate":"2019-08-30T11:39:00.000","Title":"Binary cross entropy Vs categorical cross entropy with 2 classes","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"So i want to implement random search but there is no clear cut example as to how to do this. I am confused between the following methods:\n\ntune.randint()\nray.tune.suggest.BasicVariantGenerator()\ntune.sample_from(lambda spec: blah blah np.random.choice())\n\nCan someone please explain how and why these methods are same\/different for implementing random search.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":60,"Q_Id":57733690,"Users Score":0,"Answer":"Generally, you don't need to use ray.tune.suggest.BasicVariantGenerator(). \nFor the other two choices, it's up to what suits your need. tune.randint() is just a thin wrapper around tune.sample_from(lambda spec: np.random.randint(...)). You can do more expressive\/conditional searches with the latter, but the former is easier to use.","Q_Score":0,"Tags":"python,ray","A_Id":57743094,"CreationDate":"2019-08-30T22:08:00.000","Title":"what are the options to implement random search?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am setting up a new column as the day difference in Python (on Jupyter notebook). \nI carried out the day difference between the column date and current day. Also, I carried out that the day difference between the date column and newly created day via current day (Current day -\/+ input days with timedelta function). \nHowever, whenever I use max() of the same column and different column, the day difference column has NaN values. It does not make sense for me, maybe I am missing the date type. When I checked the types all of them seems datetime64 (already converted to datetime64 by me). \nI thought that the reason was having not big enough date. However, it happens with any specific date like max(datecolumn)+timedelta(days=i). \n\n\nt=data_signups[[\"date_joined\"]].max()\n\n\ndate_joined   2019-07-18 07:47:24.963450\ndtype: datetime64[ns]\n\n\nt = t + timedelta(30)\n\n\ndate_joined   2019-08-17 07:47:24.963450\ndtype: datetime64[ns]\n\n\ndata_signups['joined_to_today'] = (t - data_signups['date_joined']).dt.days\ndata_signups.head(2)\n\n\nshortened...\n\ndate_joined_______________\/\/  joined_to_today________\n2019-05-31 10:52:06.327341  \/\/  nan\n2019-04-02 09:20:26.520272  \/\/  nan\n\nHowever it worked on Current day task like below.\n\n\nCurrentdate = datetime.datetime.now()\n    print(Currentdate)\n    2019-09-01 17:05:48.934362\nbefore_days=int(input(\"Enter the number of days before today for analysis \")) \n30\n\n\nDone\n\n\nlast_day_for_analysis = Currentdate - timedelta(days=before_days)\nprint(last_day_for_analysis)\n\n\n2019-08-02 17:05:48.934362\n\n\ndata_signups['joined_to_today'] = (last_day_for_analysis - data_signups['date_joined']).dt.days\ndata_signups.head(2)\n\n\nshortened...\n\ndate_joined_______________\/\/  joined_to_today________\n2019-05-31 10:52:06.327341  \/\/  63\n2019-04-02 09:20:26.520272  \/\/  122\n\nI expect that there is datetype problem. However, I could not figure out since all of them are datetime64. There are no NaN values in the columns.\nThank you for your help. I am newbie and I try to learn everyday continuously.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":50,"Q_Id":57746737,"Users Score":0,"Answer":"Although I was busy with this question for 2 days, now I realized that I had a big mistake. Sorry to everyone. \nThe reason that can not take the maximum value as date comes from as below.\nExisting one: t=data_signups[[\"date_joined\"]].max()\nMust-be-One:  t=data_signups[\"date_joined\"].max()\nSo it works with as below.\ndata_signups['joined_to_today'] = (data_signups['date_joined'].max() - data_signups['date_joined']).dt.days\ndata_signups.head(3)\nThere will be no two brackets. So stupid mistake. Thank you.","Q_Score":0,"Tags":"python,pandas,datetime64","A_Id":57747241,"CreationDate":"2019-09-01T14:17:00.000","Title":"How to get the day difference between date-column and maximum date of same column or different column in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm new to machine learning but I'm trying to apply it to a project I have. I was able to train a model to convert words from one language to another using LSTM layers. Say I use A as input to my model and I get B as output. What I do is: \n\n'original word' -> word embedding -> one-hot encode (A) -> MODEL -> one-hot encoded output (B) -> word embedding -> 'translated word'\n\nThis is relatively simple as I'm using a character-level tokenizer to encode the words and that does not require much memory (small sequences, one for each word). \nHowever, I now have to train a model that takes B as input and gives me C (no longer a translation problem). C is later going to be used for different purposes. The difference is that C can have a length of say 315 numbers and each of them can be one of 5514 unique values i.e., shape(215, 5514). Generically what I want to do is, for example:\n\n'banana' -> (some processing, word embedding or one-hot) -> MODEL -> [434, 434, 410, 321, 225, 146, 86, 43, 13, -8, -23, -32, -38, -41, -13, 101, 227, 332, 411, 470, 515, 550, 577, 597, 611, 622, 628, 622, 608, 593, 580, 570, 561, 554, 549, 547, 548, 548, 549, 555, 564, 572, 579, 584, 587, 589, 590, 591, 591, 591, 590, 590, 584, 567, 550, 535, 524, 516, 511, 506, 503, 503, 507, 511, 518, 530, 543, 553, 561, 568, 573, 577, 580, 582, 584, 585, 586, 586, 587, 587, 588, 588, 588, 588, 588, 586]\n\nSo the problem is that I don't have enough memory to perform a one-hot encoding of the output sequences. I tried using generators to load each sequence from the disk instead of loading all of them from memory but It doesn't seem to be working.\nDo you have any suggestions as to how I should approach this problem?\nEDIT:\nThe dataset I'm using has the following format: n lines, each line contains 2 columns separated by a tab. The first column is the input word and the second column is the sequence I want to obtain if the input is that word.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":201,"Q_Id":57785752,"Users Score":0,"Answer":"One hot encoding increases number of columns according to unique categories in data set. I think you should check the performance of model with just using tokenizer not both. Because most of the time tokenizer alone performs very well.","Q_Score":0,"Tags":"python,tensorflow,machine-learning,keras","A_Id":57791481,"CreationDate":"2019-09-04T09:45:00.000","Title":"Creating a python algorithm to train a keras model to predict a large sequence of integers","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have tried to find out basic answers for this question, but none on Stack Overflow seems a best fit. \nI have a dataset with 40 columns and 55,000 rows. Only 8 out of these columns are numerical. The remaining 32 are categorical with string values in each.\nNow I wish to do an exploratory data analysis for a predictive model and I need to drop certain irrelevant columns that do not show high correlation with the target (variable to predict). But since all of these 32 variables are categorical what can I do to see their relevance with the target variable?\nWhat I am thinking to try:\n\nLabelEncoding all 32 columns then run a Dimensional Reduction via PCA, and then create a predictive model. (If I do this, then how can I clean my data by removing the irrelevant columns that have low corr() with target?)\nOne Hot Encoding all 32 columns and directly run a predictive model on it. \n(If I do this, then the concept of cleaning data is lost totally, and the number of columns will skyrocket and the model will consider all relevant and irrelevant variables for its prediction!)\n\nWhat should be the best practice in such a situation to make a predictive model in the end where you have many categorical columns?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":173,"Q_Id":57786660,"Users Score":0,"Answer":"You can have a look if your categorical variables are suitable for a Spearman rank correlation, which ranks the categorical variables and calculates the correlation coefficient. However, be careful for collinearity between the categorical variables.","Q_Score":0,"Tags":"python,pandas,machine-learning,data-analysis,preprocessor","A_Id":57786810,"CreationDate":"2019-09-04T10:38:00.000","Title":"Preprocessing Dataset with Large Categorical Variables","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have tried to find out basic answers for this question, but none on Stack Overflow seems a best fit. \nI have a dataset with 40 columns and 55,000 rows. Only 8 out of these columns are numerical. The remaining 32 are categorical with string values in each.\nNow I wish to do an exploratory data analysis for a predictive model and I need to drop certain irrelevant columns that do not show high correlation with the target (variable to predict). But since all of these 32 variables are categorical what can I do to see their relevance with the target variable?\nWhat I am thinking to try:\n\nLabelEncoding all 32 columns then run a Dimensional Reduction via PCA, and then create a predictive model. (If I do this, then how can I clean my data by removing the irrelevant columns that have low corr() with target?)\nOne Hot Encoding all 32 columns and directly run a predictive model on it. \n(If I do this, then the concept of cleaning data is lost totally, and the number of columns will skyrocket and the model will consider all relevant and irrelevant variables for its prediction!)\n\nWhat should be the best practice in such a situation to make a predictive model in the end where you have many categorical columns?","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":173,"Q_Id":57786660,"Users Score":0,"Answer":"you got to check the correlation.. There are two scenarios I can think of..\n\nif the target variable is continuous and independent variable is categorical, you can go with Kendall Tau correlation\nif both target and independent variable are categorical, you can go with CramersV correlation\n\nThere's a package in python which cam do all of these for you and you can select only columns that you need..\npip install ctrl4ai\nfrom ctrl4ai import automl\nautoml.preprocess(dataframe, learning type)\nuse help(automl.preprocess) to understand more about the hyper parameters and you can customise your preprocessing in the way you want to..\nplease check automl.master_correlation  which checks correlation based on the approach I explained above.","Q_Score":0,"Tags":"python,pandas,machine-learning,data-analysis,preprocessor","A_Id":62684892,"CreationDate":"2019-09-04T10:38:00.000","Title":"Preprocessing Dataset with Large Categorical Variables","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am creating an experiment in python. It includes the presentation of many mp4 videos, that include both image and sound. The sound is timed so that it appears at the exact same time as a certain visual image in the video. For the presentation of videos, I am using psychopy, namely the visual.MovieStim3 function.\nBecause I do not know much about technical sound issues, I am not sure if I should\/can take measures to improve possible latencies. I know that different sound settings make a difference for the presentation for sound stimuli alone in python, but is this also the case, if the sound is embedded in the video? And if so, can I improve this by choosing a different sound library?\nThank you for any input.\nJuliane","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":82,"Q_Id":57790029,"Users Score":0,"Answer":"Ultimately, yes, the issues are the same for audio-visual sync whether or not they are embedded in a movie file. By the time the computer plays them they are simply visual images on a graphics card and an audio stream on a sound card. The streams just happen to be bundled into a single (mp4) file.","Q_Score":0,"Tags":"python,audio,video,latency,psychopy","A_Id":57807337,"CreationDate":"2019-09-04T13:56:00.000","Title":"Improving sound latencies for video presentation in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to understand interpolation in pandas and I  don't seem to understand if the method 'cubic' is a polynomial  interpolation  of  order 3 or a spline. Does anybody know  what  pandas uses behind that method?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":180,"Q_Id":57796327,"Users Score":1,"Answer":"In interpolation methods, 'polynomial' generally means that you generate a polynomial with the same number of coefficients as you have data points. So, for 10 data points you would get an order 9 polynomial.\n'cubic' generally means piecewise 3rd order polynomials. A sliding window of 4 data points is used to generate these cubic polynomials.","Q_Score":0,"Tags":"python-3.x,pandas,interpolation","A_Id":57849959,"CreationDate":"2019-09-04T22:29:00.000","Title":"Pandas Interpolation Method 'Cubic' - spline or polynomial?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I used python-2.7 version to run the PyTorch with GPU support. I used this command to train the dataset using multi-GPU. \nCan someone please tell me how can I fix this error with PyTorch in OpenNMT-py or is there a way to take pytorch support for multi-GPU using python 2.7?\nHere is the command that I tried.\n\n\nCUDA_VISIBLE_DEVICES=1,2\n    python train.py -data data\/demo -save_model demo-model -world_size 2 -gpu_ranks 0 1\n\n\nThis is the error:\n\nTraceback (most recent call last):\n   File \"train.py\", line 200, in \n    main(opt)\n  File \"train.py\", line 60, in main\n  mp = torch.multiprocessing.get_context('spawn')\n  AttributeError: 'module' object has no attribute 'get_context'","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":166,"Q_Id":57798219,"Users Score":0,"Answer":"Maybe you can check whether your torch and python versions fit the openmt requiremen.\nI remember their torch is 1.0 or 1.2 (1.0 is better). You have to lower your latest of version of torch. Hope that would work","Q_Score":0,"Tags":"python-2.7,pytorch,opennmt","A_Id":60118279,"CreationDate":"2019-09-05T03:55:00.000","Title":"How to take multi-GPU support to the OpenNMT-py (pytorch)?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on pose estimation using OpenPose. For that I installed TensorFlow GPU and installed all the requirements including CUDA development kit.\nWhile running the Python script: \nC:\\Users\\abhi\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\resource_variable_ops.py, I encountered the following error:\n\nImportError: cannot import name '_pywrap_utils' from\n  'tensorflow.python'\n  (C:\\Users\\abhi\\Anaconda3\\lib\\site-packages\\tensorflow\\python__init__.py)\n\nI tried searching for _pywrap_utils file but there was no such file.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1170,"Q_Id":57806063,"Users Score":0,"Answer":"Try pip3 install pywrap and pip3 install tensorflow pywrap utils should be included with tensorflow. If it is not found, that means TF was not installed correctly.","Q_Score":0,"Tags":"python,python-3.x,tensorflow,tensorrt","A_Id":57807062,"CreationDate":"2019-09-05T12:59:00.000","Title":"cannot import name '_pywrap_utils' from 'tensorflow.python'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataset with daily activities for online students (time spent, videos watched etc). based on this data I want to predict if each student will pass or not. Until this point I have been treating it as a classification problem, training a model for each week with the student activity to date and their final outcomes. \nThis model works pretty well, but it ignores behavior over time. I am interested in doing some kind of time series analysis where the model takes into account all datapoints for each student over time to make the final prediction. \nThe time series models I've been looking at aim to forecast a specific metric for a population (demand, revenue etc) at future time steps. In my case I am less interested in the aggregated timestep metrics and more interested in the final outcome by individual. \nIn other words, mine is more of a classification or regression problem, but I am hoping to be able to leverage each individual students usage patterns over time for this. Is there a way to combine the two? Basically build a better classifier that understands patterns over time.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":217,"Q_Id":57812231,"Users Score":1,"Answer":"Look at the fbprophet module. This can separate a time series into components such as trend, seasonality and noise. The module was originally developed for web traffic.\nYou can incorporate this into your regression model in a number of ways by constructing additional variables, for example:\n\nRatio of trend at start of term to end of term\nThe magnitude of the weekly seasonal pattern\nThe variance of the white noise series.\netc.\n\nNot to say any of these constructed variables will be significant in your model, but it is the type of things I would try. You could feasibly construct some of these variables without doing any complex time series model at all, for instance the ratio of time spent watching videos at the start of the course vs the end of the course could be calculated in excel.","Q_Score":0,"Tags":"python,pandas,scikit-learn,time-series,forecasting","A_Id":57814580,"CreationDate":"2019-09-05T19:56:00.000","Title":"Time Series model for predicting online student grades?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been working with Tensorflow for quite a while now, had some issues, but they never remained unresolved. Today i wanted to train a new model, when things got interesting. At first, the training stopped after one step without any reason. It happend before, opening a new cmd window solved it. Not this time tough. After i tried again, train.py started to throw this:\n\nWindows fatal exception: access violation\nCurrent thread 0x000018d4 (most recent call first):\nFile\n  \"C:\\windows\\system32\\venv\\lib\\site-packages\\tensorflow\\python\\lib\\io\\file_io.py\",\n  line 84 in _preread_check   File\n  \"C:\\windows\\system32\\venv\\lib\\site-packages\\tensorflow\\python\\lib\\io\\file_io.py\",\n  line 122 in read   File\n  \"C:\\Users\\xx\\source\\TensorFlow\\models\\research\\object_detection\\utils\\label_map_util.py\",\n  line 133 in load_labelmap   File\n  \"C:\\Users\\xx\\source\\TensorFlow\\models\\research\\object_detection\\utils\\label_map_util.py\",\n  line 164 in get_label_map_dict   File\n  \"C:\\Users\\xx\\source\\TensorFlow\\models\\research\\object_detection\\data_decoders\\tf_example_decoder.py\",\n  line 59 in init   File\n  \"C:\\Users\\xx\\source\\TensorFlow\\models\\research\\object_detection\\data_decoders\\tf_example_decoder.py\",\n  line 314 in init   File\n  \"C:\\Users\\xx\\source\\TensorFlow\\models\\research\\object_detection\\builders\\dataset_builder.py\",\n  line 130 in build   File \"train.py\", line 121 in get_next   File\n  \"C:\\Users\\xx\\source\\TensorFlow\\models\\research\\object_detection\\legacy\\trainer.py\",\n  line 59 in create_input_queue   File\n  \"C:\\Users\\xx\\source\\TensorFlow\\models\\research\\object_detection\\legacy\\trainer.py\",\n  line 280 in train   File \"train.py\", line 180 in main   File\n  \"C:\\windows\\system32\\venv\\lib\\site-packages\\tensorflow\\python\\util\\deprecation.py\",\n  line 324 in new_func   File \"C:\\Program Files (x86)\\Microsoft Visual\n  Studio\\Shared\\Python37_64\\lib\\site-packages\\absl\\app.py\", line 251 in\n  _run_main File \"C:\\Program Files (x86)\\Microsoft Visual Studio\\Shared\\Python37_64\\lib\\site-packages\\absl\\app.py\", line 300 in\n  run File\n  \"C:\\windows\\system32\\venv\\lib\\site-packages\\tensorflow\\python\\platform\\app.py\",\n  line 40 in run   File \"train.py\", line 184 in \n\nLast time i saw this issue, it was because i was using data downloaded from the internet, and there was one particular picture that TF did not like, but removing that one from the dataset solved the issue. I was wondering if this was the case, but no. I couldnt start it with previously tried datasets either... i decided to reinstall tensorflow, set up a new virtual environment, but still nothing. Been looking for hours what the problem could be, both on the internet, and on my own trying different things, but nothing worked, same exception each time. Did anybody encounter anything similar?","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":3049,"Q_Id":57825630,"Users Score":2,"Answer":"I decided to share what solved my problem, might help the others. I reinstalled Tensorflow itself in a virtual environment, and upgraded it to version 1.8 (Requires Python 3.6, it is not compatible with higher versions (mine is 3.6.5 in particular)), make sure your PYTHONPATH variable is pointing to the right folder. Also, on Windows, this error message on can occur when you use generate_tfrecord.py, i ran into it many times, it usually happend, because i had image(s), that Tensorflow did not like (i'm not completly sure about the cause), at first, try removing .webp, .gif, etc.(non .png\/.jpg) files. I even had the exception for renaming an image downloaded from the internet, and TF did not stand it anymore.","Q_Score":3,"Tags":"python,tensorflow,machine-learning,tensorflow-datasets","A_Id":58536847,"CreationDate":"2019-09-06T16:27:00.000","Title":"Tensorflow train.py throws Windows fatal exception","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"OpenCV provides methods to calibrate a camera. I want to know if it also has a way to simply generate a view projection matrix if and when the parameters are known.\ni.e I know the camera position, rotation, up, FOV... and whatever else is needed, then call MagicOpenCVCamera(parameters) and obtain a 4x4 transformation matrix.\nI have searched this up but I can only find information about calibrating the camera, not about creating one if you already know the parameters.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2898,"Q_Id":57826605,"Users Score":1,"Answer":"The projection matrix is simply a 3x4 matrix whose [0:3,0:3] left square is occupied by the product K.dot(R) of the camera intrinsic calibration matrix K and its camera-from-world rotation matrix R, and the last column is K.dot(t), where t is the camera-from-world translation. To clarify, R is the matrix that brings into camera coordinates a vector decomposed in world coordinates, and t is the vector whose tail is at the camera center, and whose tip is at the world origin.\nThe OpenCV calibration routines produce the camera orientations as rotation vectors, not matrices, but you can use cv.Rodrigues to convert them.","Q_Score":0,"Tags":"python,opencv,camera,computer-vision,projection-matrix","A_Id":57835318,"CreationDate":"2019-09-06T17:54:00.000","Title":"Calculate camera matrix with KNOWN parameters (Python)?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have this strange but when I'm using a LightGBM model to calculate some predictions.\nI trained a LightGBM model inside of jupyter and dumped it into a file using pickle. This model is used in an external class.\nMy problem is when I call my prediction function from this external class outside of jupyter it always predicts an output of 0.5 (on all rows). When I use the exact same class inside of jupyter I get the expected output. In both cases the exact same model is used with the exact same data.\nHow can this behavior be explained and how can I achieve to get the same results outside of jupyter? Has it something to do with the fact I trained the model inside of jupyter? (I can't imagine why it would, but atm have no clue where this bug is coming from)\nEdit: Used versions:\nBoth times the same lgb version is used (2.2.3), I also checked the python version which are equal (3.6.8) and all system paths (sys.path output). The paths are equal except of  '\/home\/xxx\/.local\/lib\/python3.6\/site-packages\/IPython\/extensions' and '\/home\/xxx\/.ipython'.\nEdit 2: I copied the code I used inside of my jupyter and ran it as a normal python file. The model made this way works now inside of jupyter and outside of it. I still wonder why this bug accrued.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":100,"Q_Id":57833411,"Users Score":1,"Answer":"It can't be a jupyter problem since jupyter is just an interface to communicate with python. The problem could be that you are using different python environment and different version of lgbm... Check import lightgbm as lgb and lgb.__version__ on both jupyter and your python terminal and make sure there are the same (or check if there has been some major changements between these versions)","Q_Score":0,"Tags":"python,jupyter-notebook,lightgbm","A_Id":57833508,"CreationDate":"2019-09-07T11:50:00.000","Title":"LightGBM unexpected behaviour outside of jupyter","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am running a Flask application using AWS Elastic Beanstalk. The application deploys successfully, but there is a task in my code where I use pandas read_csv to pull data out of a csv file. The code line is:\nform1 = pd.read_csv('\/opt\/python\/current\/app\/application\/model\/static2\/form1.csv')\nWhen I try to execute that task in the application, I receive a FileNotFoundError:\nFileNotFoundError: [Errno 2] File b'\/opt\/python\/current\/app\/application\/model\/static2\/form1.csv' does not exist: b'\/opt\/python\/current\/app\/application\/model\/static2\/form1.csv'\nThe problem does not occur when I execute the program locally, but only if I use the full, absolute path to the file. This is due to the way my dependencies are set up.\nWhen I first deployed the application, I received errors because I was still using the local path to the file, and so I changed it to the one you see above, which is what I think is the absolute path to the file uploaded on Beanstalk. I think this because I copied it from a static image that I was having an issue with earlier. \nI should note that I cannot verify the absolute path because I am unable to remote into Elastic Beanstalk using EB CLI. I have been trying to get EB CLI set up on my machine for days and repeatedly failed, I think because of weird version and file issues on my machine. So I can't obtain information or fix the problem using command line. \nSo, is the path that I am using above consistent with an EB absolute path? Can this be solved by adding to my static file configurations? If so, how?Is there anything I could add to the .config file?\nAny help is greatly appreciated.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":432,"Q_Id":57838423,"Users Score":2,"Answer":"Problem solved! My process was being carried out by rq workers. The rq workers are running from my local machine and I did not realize that it would be the workers looking for the file path. I figured this out by printing os.getcwd() and noticing that the current working directory was still my local path. So, I threw an exception on a FileNotFoundError for the workers to use a local path instead as necessary.","Q_Score":2,"Tags":"python,flask,amazon-elastic-beanstalk","A_Id":57839165,"CreationDate":"2019-09-08T00:50:00.000","Title":"Elastic Beanstalk won't recognize absolute path to file, returns FileNotFoundError","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I am attempting to implement an audio classifier in my mobile app. When training the data, I used melspectrogram extracted from the raw audio. I am using Tensorflow Lite to integrate the model into the app.  \nThe problem is that I need to perform the same feature extraction on the input audio from the mic before passing into the tflite model. Python's Librosa library implements all of the functions that I need. My initial idea was run Python in flutter (there is the starflut Flutter package but I couldn't get it to work). \nAm I going about this in the wrong way? If so, what should I be doing? I could potentially rewrite the Librosa functions in dart lang, but I don't particularly want to do that.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":552,"Q_Id":57838465,"Users Score":1,"Answer":"If you are targeting mobile, check the integration with \u201cnative\u201d code. E.g. look for a java\/kotlin library that can do the same on android. And a swift\/objC one for iOS.\nThen, you could wrap that functionality  in a platform-specific module.","Q_Score":0,"Tags":"python,android,tensorflow,machine-learning,flutter","A_Id":57839378,"CreationDate":"2019-09-08T00:59:00.000","Title":"Is it feasible to perform feature extraction in a Flutter app?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a \"Date\" column in my df and I wish to use it as the index of df\nDate values in 'Date' column are in the correct format as per DateTime (yyyy-mm-dd)","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":40,"Q_Id":57846743,"Users Score":0,"Answer":"Using set_index we can a column index of the df\ndf.set_index('Date')","Q_Score":0,"Tags":"python-3.x,date,dataframe,indexing","A_Id":57859057,"CreationDate":"2019-09-09T00:45:00.000","Title":"How can i change column of a df to index of the df?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am doing a fit in python with lmfit and after I define my model (i.e. the functio I want to use for the fit) I do out = model.fit(...) and in order to visualize the result I do plt.plot(x, out.best_fit). This works fine, however this computes the value of the function only at the points used for the fit. How can I apply the parameters of the fit to any x vector (to get a smoother curve), something like x_1 = np.arange(xi,xf,i), plt.plot(x_1,out.best_fit(x_1))? Thank you!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":114,"Q_Id":57848079,"Users Score":0,"Answer":"Including a complete, minimal example that shows what you are doing is always a good idea.  In addition, your subject is not a good reflection of your question. You have now asked enough questions about lmfit here on SO to know better. \nYou probably want to use ModelResult.eval() to evaluate the ModelResult (probably your out) for a given independent variable.  If you need more help, ask an answerable question that might be useful to others...","Q_Score":1,"Tags":"python,lmfit","A_Id":57863047,"CreationDate":"2019-09-09T04:58:00.000","Title":"Getting the parameters from lmfit","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I\u2019m using pandas 0.25.1 in Jupyter Lab and the maximum number of rows I can display is 10, regardless of what pd.options.display.max_rows is set to. \nHowever, if pd.options.display.max_rows is set to less than 10 it takes effect and if pd.options.display.max_rows = None then all rows show.\nAny idea how I can get a pd.options.display.max_rows of more than 10 to take effect?","AnswerCount":2,"Available Count":1,"Score":-0.0996679946,"is_accepted":false,"ViewCount":32532,"Q_Id":57860775,"Users Score":-1,"Answer":"min_rows displays the number of rows to be displayed from the top (head) and from the bottom (tail) it will be evenly split..despite putting in an odd number. If you only want a set number of rows to be displayed without reading it into the memory,\nanother way is to use nrows = 'putnumberhere'.\ne.g. results = pd.read_csv('ex6.csv', nrows = 5) # display 5 rows from the top 0 - 4\nIf the dataframe has about 100 rows and you want to display only the first 5 rows from the top...NO TAIL use .nrows","Q_Score":38,"Tags":"python,pandas","A_Id":66486493,"CreationDate":"2019-09-09T20:26:00.000","Title":"pandas pd.options.display.max_rows not working as expected","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a Dataframe which columns is ['a','b','c'] and a Series param contain three values which is params of Dataframe. The param.index is ['a','b','c']. I want to realize df['a'] * param['a'] + df['b'] * param['b'] + df['c'] * param['c']. Because there are too many columns and params in my code. So is there any concise and elegant code can realize this?","AnswerCount":3,"Available Count":2,"Score":0.0665680765,"is_accepted":false,"ViewCount":41,"Q_Id":57863464,"Users Score":1,"Answer":"I think df * param.to_list() is good.","Q_Score":1,"Tags":"python,pandas,dataframe","A_Id":57863569,"CreationDate":"2019-09-10T02:59:00.000","Title":"DataFrame each column miltiply param then sum","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a Dataframe which columns is ['a','b','c'] and a Series param contain three values which is params of Dataframe. The param.index is ['a','b','c']. I want to realize df['a'] * param['a'] + df['b'] * param['b'] + df['c'] * param['c']. Because there are too many columns and params in my code. So is there any concise and elegant code can realize this?","AnswerCount":3,"Available Count":2,"Score":0.0665680765,"is_accepted":false,"ViewCount":41,"Q_Id":57863464,"Users Score":1,"Answer":"df*param is enough, it will auto determine according to the index.\nYou can change series indexes to ['b','c','a'] for testing","Q_Score":1,"Tags":"python,pandas,dataframe","A_Id":57864150,"CreationDate":"2019-09-10T02:59:00.000","Title":"DataFrame each column miltiply param then sum","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"We have set up a bokeh server in our institute, which works properly. We also have a python-based code to analyse fMRI data which at the moment uses matplotlib to plot and save. But I want to transfer the code to bokeh server and allow everybody to upload files into the server from the client and when the analysis is done in the server, save the output plots in their local HDD. This transfer file procedure seems to be lacking in bokeh atm. I saw a new feature recently added in github to upload json files, but my problem is fMRI files come in various formats, and asking (not necessarily tech-savvy) users to convert the files into a certain format beats the purpose. Also, I do not know any JS or the like, hence I do not know what solutions people usually use for such web-based applications. \nIf anybody has any solutions to get around this issue, it'd be happy to hear it. Even if it is a solution independent of bokeh (which would mean users need to open a separate page to upload the files, a page to run the analysis, and a page to save the output) please let me know. It won't be ideal, but at least better than no solution, which is the case in bokeh right now. Thanks!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":184,"Q_Id":57868893,"Users Score":1,"Answer":"I'm not sure where you are getting your information. The FileInput widget added in Bokeh 1.3.0 can upload any file the user chooses, not just JSON.","Q_Score":0,"Tags":"javascript,python,webserver,bokeh,bokehjs","A_Id":57882320,"CreationDate":"2019-09-10T10:29:00.000","Title":"Uploading a file from client to server in python bokeh","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I'm currently using flair for sentiment analysis and it's datasets. The datasets for flair are quite large in size and are currently installed on my quite small SSD in my user folder. Is there anyway that I can move the .flair folder from my user folder on my SSD to my other drive without breaking anything.\nThanks in advance","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":90,"Q_Id":57874651,"Users Score":0,"Answer":"When importing datasets into flair, one can specify a custom path to import from. Copy the flair datasets to a folder you choose on your larger harddrive and then specify that path when loading a dataset.\nflair.datasets.WASSA_FEAR(data_folder=\"E:\/flair_datasets\/\")","Q_Score":0,"Tags":"python,python-3.x","A_Id":62965796,"CreationDate":"2019-09-10T16:13:00.000","Title":"How to change location of .flair directory","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using tf.nn.sparse_softmax_cross_entropy_with_logits and when I pass through the labels and logits I get the following error\n\ntensorflow.python.framework.errors_impl.InvalidArgumentError: labels\n  must be 1-D, but got shape [50,1]\n\nI don't understnad how having a shape [50,1] is not the same as being 1D","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":108,"Q_Id":57878623,"Users Score":1,"Answer":"I don't understand how having a shape [50,1] is not the same as being 1D.\n\nWhile you can reshape a [50, 1] 2D matrix into a [50] 1D matrix just with a simple squeeze, Tensorflow will never do that automatically. \nThe only heuristic the tf.nn.sparse_softmax_cross_entropy_with_logits uses to check if the input shape is correct is to check the number of dimensions it has. If it's not 1D, it fails without trying other heuristics like checking if the input could be squeezed. This is a security feature.","Q_Score":0,"Tags":"python,tensorflow,neural-network,entropy","A_Id":57887051,"CreationDate":"2019-09-10T21:47:00.000","Title":"Error with tf.nn.sparse_softmax_cross_entropy_with_logits","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have used Tensorflow object detection for quite awhile now. I am more of a user, I dont really know how it works. I am wondering is it possible to train it to recognize an object is something and not something? For example, I want to detect cracks on the tiles. Can i use object detection to do so where i show an image of a tile and it can tell me if there is a crack (and also show the location), or it will tell me if there is no crack on the tile? \nI have tried to train using pictures with and without defect, using 2 classes (1 for defect and 1 for no defect). But the results keep showing both (if the picture have defect) in 1 picture. Is there a way to show only the one with defect?\nBasically i would like to do defect checking. This is a simplistic case of 1 defect. but the actual case will have a few defects.\nThank you.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":92,"Q_Id":57879708,"Users Score":0,"Answer":"In case you're only expecting input images of tiles, either with defects or not, you don't need a class for no defect.\nThe API adds a background class for everything which is not the other classes.\nSo you simply need to state one class - defect, and tiles which are not detected as such are not defected. \nSo in your training set - simply give bounding boxes of defects, and no bounding box in case of no defect, and then your model should learn to detect the defects as mentioned above.","Q_Score":0,"Tags":"python-3.x,tensorflow,object-detection","A_Id":57888733,"CreationDate":"2019-09-11T00:46:00.000","Title":"Using tensorflow object detection for either or detection","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've created a neural network with a sigmoid activation function in the last layer, so I get results between 0 and 1. I want to classify things in 2 classes, so I check \"is the number > 0.5, then class 1 else class 0\". All basic.\nHowever, I would like to say \"the probability of it being in class 0 is x and in class 1 is y\".\nHow can I do this?\n\nDoes a number like 0.73 tell me it's 73% sure to be in class 1? And then 1-0.73 = 0.27 so 27% in class 0?\nWhen it's 0.27, does that mean it's 27% sure in class 0, 73% in class 1? Makes no sense.\n\nShould I work with the 0.5 and look \"how far away from the center is the number, and then that's the percentage\"?\nOr am I misunderstanding the result of the NN?","AnswerCount":2,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":1575,"Q_Id":57903518,"Users Score":4,"Answer":"As pointed out by Teja, the short answer is no, however, depending on the loss you use, it may be closer to truth than you may think.\nImagine you try to train your network to differentiate numbers into two arbitrary categories that are beautiful and ugly. Say your input number are either 0 or 1 and 0s have a 0.2 probability of being labelled ugly whereas 1s have o 0.6probability of being ugly.\nImagine that your neural network takes as inputs 0s and 1s, passes them into some layers, and ends in a softmax function. If your loss is binary cross-entropy, then the optimal solution for your network is to output 0.2 when it sees a 0 in input and 0.6 when it sees a 1 in input (this is a property of the cross-entropy which is minimized when you output the true probabilities of each label). Therefore, you can interpret these numbers as probabilities.\nOf course, real world examples are not that easy and are generally deterministic so the interpretation is a little bit tricky. However, I believe that it is not entirely false to think of your results as probabilities as long as you use the cross-entropy as a loss.\nI'm sorry, this answer is not black or white, but reality is sometimes complex ;)","Q_Score":3,"Tags":"python,tensorflow,sigmoid","A_Id":57907511,"CreationDate":"2019-09-12T09:18:00.000","Title":"Interpreting a sigmoid result as probability in neural networks","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using the isolation forest algorithm from sklearn to do some unsupervised anomaly detection. \nI need to explained the predictions and I was wondering if there is any way to get the paths that lead to the decision for each sample.\nI usually used SHAP or ELI5 but i'd like to do something more custom. So i need the exact path.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2005,"Q_Id":57904088,"Users Score":0,"Answer":"You are creating an ensemble of trees so and the path of a given instance will be different for each tree in the ensemble. To detect an anomaly the isolation forest takes the average path length (number of splits to isolate a sample) of all the trees for a given instance and uses this to determine if it is an anomaly (average shorter path lengths indicate anomalies). As you are looking at the average of a set of trees there is no 'exact' path.\nWithin my knowledge, your best bet would be to use something like SHAP, as you mentioned, but you could also train only a few estimators and look at the path taken for a given instance in these trees to get an insight into the decisions.","Q_Score":0,"Tags":"python,unsupervised-learning,anomaly-detection","A_Id":57905788,"CreationDate":"2019-09-12T09:51:00.000","Title":"Is it possible to explain sklearn isolation forest prediction?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to take x number of columns from an existing df and convert them into a dictionary.\nMy questions are:\n\nThe method shown below is considered a good practice? I think it's repetitive and I'm sure it can be a more elegant code.\nShould I convert from df to dictionary if my idea is to build a plot? Or it's an unnecessary step?\n\nI've tried the code below:\nfamiliarity_dic = familiarity[{'Question':'Question','SCORE':'SCORE'}]\nfamiliarity_dic\nExpected result is correct but I want to know if it's the best practice for Pandas.\nQuestion    SCORE\n36  Invesco        100\n35  Schroders   96\n34  Fidelity    96\n31  M&G         95\n0   BlackRock   95","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":52,"Q_Id":57909996,"Users Score":0,"Answer":"I think easier to plot from pandas than a dict.  Try using df.plot().  You can subset your df as required to only plot the information you're interested it.","Q_Score":0,"Tags":"python,pandas,dataframe,dictionary","A_Id":57910171,"CreationDate":"2019-09-12T15:26:00.000","Title":"Best practice convert dataframe to dictionary?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a numpy array of shape (20000, 600, 768). I need to store it, so later I could load it back to my code.\nThe main problem is memory usage when you load it back.\nI have just 16GB RAM.\nFor example, I tried pickle. When it loads it all I almost have no memory left to do anything else. Especially to train the model.\nI tried write and load back with hdf5 (h5py). Just a small piece (1000, 600, 768). But it seems like it \"eats\" even more memory.\nAlso tried csv.. That's just a no-no. Takes TOO much time to write data in.\nWould be grateful for any suggestions how I could store my array so when I would load it back it wouldn't take that much memory.\nP.S. The data I store is vector representation of texts which I later use for training my model.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":947,"Q_Id":57921092,"Users Score":0,"Answer":"I think that you can do a lot of things. \nFirst of all you can change the data format to be stored in different ways:\n\nin a file in your secondary memory to be read iteratively (dumping a python object on secondary memory is not efficient. You need to find a better format. For example a text file in which the lines are the rows of the matrix)\nor in a database. Always to make the data readable in an iterative manner.\n\nSecond, and most important, you need to change your algorithm. If you cannot fit all the data in memory, you need to use other kinds of methods, in which you use batchs of data instead of all the data.\nFor machine learning for example, there are a lot of methods in which you do incremental updates of the model with batchs of data\nThird, there are method in which you can reduce the dimensionality of your training set. For example using methods like PCA, feature selection etc","Q_Score":2,"Tags":"python,numpy,ram","A_Id":57921590,"CreationDate":"2019-09-13T09:40:00.000","Title":"Storing\/Loading huge numpy array with less memory","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am looking to train a large model (resnet or vgg) for face identification.\nIs it valid strategy to train on few faces (1..3) to validate a model?\nIn other words - if a model learns one face well - is it evidence that the model is good for the task?\npoint here is that I don't want to spend a week of GPU expensive time only to find out that my model is no good or data has errors or my TF coding has a bug","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":221,"Q_Id":57937097,"Users Score":2,"Answer":"Short answer: No, because Deep Learning works well on huge amount of data.\nLong answer: No. The problem is that learning only one face could overfit your model on that specific face, without learning features not present in your examples. Because for example, the model has learn to detect your face thanks to a specific, very simple, pattern in that face (that's called overfitting). \nMaking a stupid simple example, your model has learn to detect that face because there is a mole on your right cheek, and it has learn to identify it\nTo make your model perform well on the general case, you need an huge amount of data, making your model capable to learn different kind of patterns\nSuggestion:\nBecause the training of a deep neural network is a time consuming task, usually one does not train one single neural network at time, but many neural network are trained in parallel, with different hyperparameters (layers, nodes, activation functions, learning rate, etc).\nEdit because of the discussion below:\nIf your dataset is small is quite impossible to have a good performance on the general case, because the neural network will learn the easiest pattern, which is usually not the general\/better one. \nAdding data you force the neural network to extract good patterns, that work on the general case. \nIt's a tradeoff, but usually a training on a small dataset would not lead to a good classifier on the general case\nedit2: refrasing everything to make it more clear. A good performance on a small dataset don't tell you if your model when trained on all the dataset is a good model. That's why you train to \nthe majority of your dataset and test\/validate on a smaller dataset","Q_Score":2,"Tags":"python,tensorflow,keras,resnet,vgg-net","A_Id":57937143,"CreationDate":"2019-09-14T15:55:00.000","Title":"Is it possible to validate a deep learning model by training small data subset?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a structured numpy ndarray la = {'val1':0,'val2':1} and I would like to return the vals using the 0 and 1 as keys, so I wish to return val1 when I have 0 and val2 when I have 1 which should have been straightforward however my attempts have failed, as I am not familiar with this structure.\nHow do I return only the corresponding val, or an array of all  vals so that I can read in order?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2969,"Q_Id":57948331,"Users Score":0,"Answer":"Just found out that I can use la.tolist() and it returns a dictionary, somehow? when I wanted a list, alas from there on I was able to solve my problem.","Q_Score":0,"Tags":"python,numpy,dictionary,key-value,numpy-ndarray","A_Id":57948589,"CreationDate":"2019-09-15T21:33:00.000","Title":"structured numpy ndarray, how to get values","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am developing an application for Android with Kivy, and package it with Buildozer. The core of my application is using pandas and specially the DataFrame function. It failed when I tried to package it with Buildozer even if I had put pandas in the requirements. So I want to use another library that can be used with Buildozer. So does anyone know about a great alternative to the pandas.DataFrame function with the numpy library for example or another one ?\nThanks a lot for your help. :)","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2855,"Q_Id":57956382,"Users Score":1,"Answer":"Similar to Pandas.DataFrame.\nAs database you likely know SQLite (in python see SQLAlchemy and SQLite3).\nOn the raw tables (i.e., pure matrix-like) Numpy (Numpy.ndarray), it lacks of some database functionalities compared to Pandas but it is fast and you could easily implement what you need. You can find many comparisons between Pandas and Numpy.\nFinally,depending on your needs, some simple python dictionaries, maybe OrderedDict.","Q_Score":1,"Tags":"python-3.x,pandas,numpy,kivy,buildozer","A_Id":57962750,"CreationDate":"2019-09-16T11:58:00.000","Title":"Is there any alternative for pandas.DataFrame function for Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have created a chatbot using RASA to work with free text and it is working fine. As per my new requirement i need to build button based chatbot which should follow flowchart kind of structure. I don't know how to do that what i thought is to convert the flowchart into graph data structure using networkx but i am not sure whether it has that capability. I did search but most of the examples are using dialogue or chat fuel. Can i do it using networkx.\nPlease help.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":614,"Q_Id":57961205,"Users Score":1,"Answer":"Sure, you can.\nYou just need each button to point to another intent. The payload of each button should point have the \/intent_value as its payload and this will cause the NLU to skip evaluation and simply predict the intent. Then you can just bind a trigger to the intent or use the utter_ method.\nHope that helps.","Q_Score":0,"Tags":"python,networkx,flowchart,rasa","A_Id":58013231,"CreationDate":"2019-09-16T16:45:00.000","Title":"How to create button based chatbot","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":1,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Say I have a function that accepts a row and a column as parameters, or returns a tuple of a row and a column as its return value. I know it doesn't actually make a difference, but is there a convention as to whether put the row first or the column first? Coming from math, if I think of the pair as coordinates into the table, I would intuitively put the column first, as in a cartesian point (x,y). But if I think of it as a whole matrix, I would put row first, as in MxN size of a matrix.\nIf there are different conventions for different languages, I would be interested especially in c++, c# and python.\nBy \"convention\" I mean preferably that that language's standard library does it a certain way, and if not that then second choice would be only if it were so universal that all major third-party libraries for that language would do it that way,preferably with an explanation why.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":75,"Q_Id":57964943,"Users Score":0,"Answer":"The convention is to address (column, row) like (x, y).\nColumn refers to the type of value and row indicates the value for the column.\nColumn is a key and with Row you have the value.\nBut using (row, column) is fine too.\nIt depends of what you're doing.\nIn SQL\/Linq, you always refers to Columns to get rows.","Q_Score":1,"Tags":"c#,python,c++,conventions","A_Id":57965103,"CreationDate":"2019-09-16T22:04:00.000","Title":"Should a function accept\/return values in (row,col) or (col,row) order?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Say I have a function that accepts a row and a column as parameters, or returns a tuple of a row and a column as its return value. I know it doesn't actually make a difference, but is there a convention as to whether put the row first or the column first? Coming from math, if I think of the pair as coordinates into the table, I would intuitively put the column first, as in a cartesian point (x,y). But if I think of it as a whole matrix, I would put row first, as in MxN size of a matrix.\nIf there are different conventions for different languages, I would be interested especially in c++, c# and python.\nBy \"convention\" I mean preferably that that language's standard library does it a certain way, and if not that then second choice would be only if it were so universal that all major third-party libraries for that language would do it that way,preferably with an explanation why.","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":75,"Q_Id":57964943,"Users Score":1,"Answer":"You shouldn't think of what is \"horizontal\" and what is \"vertical\". You should think of which convention is widely used not to introduce a lot of surprise to the developers who would use your code. The same is true for naming the parameters: use (x, y, z) for coordinates, (i, j) for indexes in matrix and (row, column) for the cells of the table (in this order).\nI agree with @ParalysisByAnalysis that sometimes it depends, but the rule of thumb is to follow the conventions of the subject.","Q_Score":1,"Tags":"c#,python,c++,conventions","A_Id":57965076,"CreationDate":"2019-09-16T22:04:00.000","Title":"Should a function accept\/return values in (row,col) or (col,row) order?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In gensim's word2vec python, I want to get the list of cosine similarity for \"price\".\nI read the document of gensim word2vec, but document it describes most_similar and n_similarity function)()\nI want the whole list of similarity between price and all others.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":204,"Q_Id":57969707,"Users Score":0,"Answer":"If you call wv.most_similar('price', topn=len(wv)), with a topn argument of the full vocabulary count of your model, you'll get back a ranked list of every word's similarity to 'price'. \nIf you call with topn=0, you'll get the raw similarities with all model words, unsorted (in the order the words appear inside wv.index2entity).","Q_Score":0,"Tags":"python,gensim,word2vec,similarity,cosine-similarity","A_Id":57980615,"CreationDate":"2019-09-17T07:53:00.000","Title":"Python3, word2vec, How can I get the list of similarity rank about \"price\" in my model","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have two columns of having high cardinal categorical values, one column(area_id) has 21878 unique values and other has(page_entry) 800 unique values. I am building a predictive ML model to predict the hits on a webpage.\ncolumn information:\narea_id: all the locations that were visited during the session. (has location code number of different areas of a webpage)\npage_entry: describes the landing page of the session.\nhow to change these two columns into numerical apart from one_hot encoding?\nthank you.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":150,"Q_Id":57975387,"Users Score":0,"Answer":"One approach could be to group your categorical levels into smaller buckets using business rules. In your case for the feature area_id you could simply group them based on their geographical location, say all area_ids from a single district (or for that matter any other level of aggregation) will be replaced by a single id. Similarly, for page_entry you could group similar pages based on some attributes like nature of the web page like sports, travel, etc.  In this way you could significantly reduce the number dimensions of your variables.\nHope this helps!","Q_Score":0,"Tags":"python,machine-learning,data-science,data-cleaning,data-processing","A_Id":57975781,"CreationDate":"2019-09-17T13:31:00.000","Title":"how to deal with high cardinal categorical feature into numeric for predictive machine learning model?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying optimize hiperparameters for classifiers and regression methods in sklearn. And I have a question. Why when you evaluate the results, you choose for example the best train accuracy, instead of evaluate this result over the test, and iterate others values with others train accuracys to obtain the best test accuracy? Because clearly the parameters for the best train accuracy are not the same that the parameters for the best test accuracy.\nThanks!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":266,"Q_Id":57978263,"Users Score":0,"Answer":"when we are training a model we usually divide data into train, validation and test sets. Lets look at what are the propose of each set\nTrain Set: It is used by model to learn its parameters. Usually model reduces its cost on the train set and selects parameters that gives minimum cost.\nValidation Set: By name, validation set is used to validate that model will also performs well on the data that it haven't seen yet. That gives us confidence that model is not memorizing the training data and performing very well on training data but not performing good on new data. If the model is complex enough then there is risk of model memorizing training data to improve its performance on training set but not well on validation data.\nUsually we use cross validation in which we divide training set into n equal parts, then for each iteration we select one part as validation and remaining parts as training set.\nTest Set: Test Set is set aside and only used at last when we are satisfied with our model to estimate how well your final model will perform on new data in wild. One main difference with validation set is that it is not used in any way to improve or change model or improve model but validation set help us select us final model. We do this because we don't want a model that is biased towards test data but will not perform well with data in wild.","Q_Score":0,"Tags":"python,optimization,hyperparameters,gridsearchcv","A_Id":57978592,"CreationDate":"2019-09-17T16:17:00.000","Title":"Why not evaluate over test fit results in RandomizedSearchCV?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"So, this might be an utterly dumb question, but I have just started working with python and it's data science libs, and I would like to see seaborn plots displayed, but I prefer to work with editors I have experience with, like VS Code or PyCharm instead of Jupyter notebook. Of course, when I run the python code, the console does not display the plots as those are images. So how do I get to display and see the plots when not using jupyter?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":427,"Q_Id":57999071,"Users Score":0,"Answer":"You can try to run an matplotlib example code with python console or ipython console. They will show you a window with your plot. \nAlso, you can use Spyder instead of those consoles. It is free, and works well with python libraries for data science. Of course, you can check your plots in Spyder.","Q_Score":0,"Tags":"python,jupyter-notebook","A_Id":57999219,"CreationDate":"2019-09-18T18:44:00.000","Title":"how to display plot images outside of jupyter notebook?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've coded a simple neural network for XOR in python. While there is loads of information online about how to program this, there isn't much on how to feed the data through it. I've tested the change in weights after one cycle for inputs [1,1] to compare my results with my lecture slides and it's 100% the same, so I believe the code works. I can train the network for that same input, but when I change the input (and corresponding target) every cycle the error doesn't go down.\nShould I allow changing the weights and inputs after every cycle or should I run through all the possible inputs first, get an average error and then change the weights? (But changing weights are dependent on the output, so what output would I use then) \nI can share my code, if needed, but I'm pretty certain it's correct.\nPlease give me some advice? Thank you in advance.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":704,"Q_Id":58010363,"Users Score":1,"Answer":"So, you're saying you implemented a neural network on your own ?\nwell in this case, basically each neuron on the input layer must be assigned with a feature of a certain row, than just iterate through each layer and each neuron in that layer and calculate as instructed.\nI'm sure you are familiar with the back-propagation algorithm so you'll know when to stop.\nonce you're done with that row, do it again to the next row, assign each feature to each of the input neurons and start the iterations again.\nonce youre done with all records, thats an Epoch.\nI hope that answers your question.\nalso, I would recommend you to try out Keras, its easy to use and a good tool to be experienced in.","Q_Score":2,"Tags":"python,neural-network,backpropagation","A_Id":58011558,"CreationDate":"2019-09-19T11:49:00.000","Title":"How do I feed data into my neural network?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Good day!\nI have a celebrity dataset on which I want to fine-tune a keras built-in model. SO far what I have explored and done, we remove the top layers of the original model (or preferably, pass the include_top=False) and add our own layers, and then train our newly added layers while keeping the previous layers frozen. This whole thing is pretty much like intuitive.\nNow what I require is, that my model learns to identify the celebrity faces, while also being able to detect all the other objects it has been trained on before. Originally, the models trained on imagenet come with an output layer of 1000 neurons, each representing a separate class. I'm confused about how it should be able to detect the new classes? All the transfer learning and fine-tuning articles and blogs tell us to replace the original 1000-neuron output layer with a different N-neuron layer (N=number of new classes). In my case, I have two celebrities, so if I have a new layer with 2 neurons, I don't know how the model is going to classify the original 1000 imagenet objects.\nI need a pointer on this whole thing, that how exactly can I have a pre-trained model taught two new celebrity faces while also maintaining its ability to recognize all the 1000 imagenet objects as well.\nThanks!","AnswerCount":2,"Available Count":1,"Score":0.2913126125,"is_accepted":false,"ViewCount":1312,"Q_Id":58027839,"Users Score":3,"Answer":"With transfer learning, you can make the trained model classify among the new classes on which you just trained using the features learned from the new dataset and the features learned by the model from the dataset on which it was trained in the first place. Unfortunately, you can not make the model to classify between all the classes (original dataset classes + second time used dataset classes), because when you add the new classes, it keeps their weights only for classification.\nBut, let's say for experimentation you change the number of output neurons (equal to the number of old + new classes) in the last layer, then it will now give random weights to these neurons which on prediction will not give you meaningful result. \nThis whole thing of making the model to classify among old + new classes experimentation is still in research area. \nHowever, one way you can achieve it is to train your model from scratch on the whole data (old + new).","Q_Score":4,"Tags":"python,tensorflow,keras,deep-learning,classification","A_Id":58028414,"CreationDate":"2019-09-20T11:50:00.000","Title":"How to fine-tune a keras model with existing plus newer classes?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using Imageio, the python library that wraps around ffmpeg to do hardware encoding via nvenc. My issue is that I can't get more than 2 sessions to launch (I am using non-quadro GPUs). Even using multiple GPUs. I looked over NVIDIA's support matrix and they state only 2 sessions per gpu, but it seems to be per system.\nFor example I have 2 GPUs in a system. I can either use the env variable CUDA_VISIBLE_DEVICES or set the ffmpeg flag -gpu to select the GPU. I've verified gpu usage using Nvidia-smi cli. I can get 2 encoding sessions working on a single gpu. Or 1 session working on 2 separate gpus each. But I can't get 2 encoding sessions working on 2 gpus. \nEven more strangely if I add more gpus I am still stuck at 2 sessions. I can't launch a third encoding session on a 3rd gpu. I am always stuck at 2 regardless of the # of gpus. Any ideas on how to fix this?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":967,"Q_Id":58029589,"Users Score":2,"Answer":"Nvidia limits it 2 per system Not 2 per GPU. The limitation is in the driver, not the hardware. There have been unofficially drivers posted to github which remove the limitation","Q_Score":0,"Tags":"python,ffmpeg,python-imageio,nvenc","A_Id":58042103,"CreationDate":"2019-09-20T13:43:00.000","Title":"Nvenc session limit per GPU","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am struggling with a vlookup in python.\nI have two datasets.\nFirst is called \"output_apu_stock1\". Here i have quantities and prices, that should be update the second dataset.\nSecond is called \"Angebote_Master_File\".\nNow, if i run my code, the new dataset \"results\" contains only the values, that matches. Leads to the problem, that my \"Angebote_Master_File\" that has originally around 1600 observations, shrinks to around 400 observations. \nimport pandas as pd\ndf1 = pd.read_csv(\"C:\/Users\/Desktop\/output_apu_stock1.csv\")\ndf2 = pd.read_csv(\"C:\/Users\/Desktop\/Angebote_Master_File.csv\")\nresults = df2.merge(df1,on=\"sku\")\nI got the point, that the final dataset contains only the matched observations (identifier is the column \"sku\") and drop the others...\nI need the merged file containing all observations from the \"Angebote_Master_File\" without any losses.\nThanks for your help!\nBest\nMichael","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":85,"Q_Id":58030255,"Users Score":0,"Answer":"results = df2.merge(df1,on=\"sku\", how=\"outer\")","Q_Score":0,"Tags":"python,pandas,replace,vlookup","A_Id":58031057,"CreationDate":"2019-09-20T14:25:00.000","Title":"Do a vlookup with pandas in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I don't know that much about vectorization, but I am interested in understanding why a language like python can not provide vectorization on iterables through a library interface, much like it provides threading support. I am aware that many numpy methods are vectorized, but it can be limiting to have to work with numpy for generic computations.\nMy current understanding is that python is not capable of vectorizing functions even if they match the \"SIMD\" pattern. For instance, in theory shouldn't any list comprehension or use of the map() function be vectorizable because they output a list which is the result of running the same function on independent inputs from an input list? \nWith my niave understanding, it seems that anytime I use map(), in theory, I should be able to create an instruction set that represents the function; then each element in the input just needs to be run through the same function that was compiled. What is the technical challenge to designing a tool that provides simd_map(func, iterable), which attempts to compile func \"just in time\" and then grabs batches of input from iterable and utilizes the processor's simd capabilities to run those batches through func()?\nThanks!","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":637,"Q_Id":58035479,"Users Score":0,"Answer":"You want vectorization or JIT compilation use numba, pypy or cython but be warned the speed comes at the cost of flexibility. \nnumba is a python module that will jit compile certain functions for you but it does not support many kinds of input and barfs on some (many) python constructs. It is really fast when it works but can be difficult to wrangle. It is also very targeted at working with numpy arrays.\npypy is a complete replacement for the cpython interpreter that is a JIT. It supports the entire python spec but does not integrate with extensions well so some libraries will not work.\ncython is an extension of python which compiles to a binary which will behave like a python module. However it does require you to use special syntax to take advantage of the speed gains and requires you to explicitly declare things as ctypes to real get any advantage.\nMy recommendation is:\nuse pypy if you are pure python. (if it works for you it's basically effortless)\nUse numba if you need to speed up numeric calculations that numpy doesn't have a good way to do.\nUse cython if you need the speed and the other 2 don't work.","Q_Score":2,"Tags":"python,parallel-processing,vectorization,python-multiprocessing,simd","A_Id":58035832,"CreationDate":"2019-09-20T21:44:00.000","Title":"Why can't python vectorize map() or list comprehensions","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a CNN with 2 hidden layers. When i use keras on cpu with 8GB RAM, sometimes i have \"Memory Error\" or sometimes precision class was 0 but some classes at the same time were 1.00. If i use keras on GPU,will it solve my problem?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":72,"Q_Id":58037171,"Users Score":0,"Answer":"You probably don't have enough memory to fit all the images in the CPU during training. Using a GPU will only help if it has more memory. If this is happening because you have too many images or they're resolution is too high, you can try using keras' ImageDataGenerator and any of the flow methods to feed your data in batches.","Q_Score":0,"Tags":"python-3.x,keras,gpu,conv-neural-network","A_Id":58045304,"CreationDate":"2019-09-21T03:59:00.000","Title":"What is difference between the result of using GPU or not?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am aware of this parameter var_smoothing and how to tune it, but I'd like an explanation from a math\/stats aspect that explains what tuning it actually does - I haven't been able to find any good ones online.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":4054,"Q_Id":58046129,"Users Score":12,"Answer":"A Gaussian curve can serve as a \"low pass\" filter, allowing only the samples close to its mean to \"pass.\" In the context of Naive Bayes, assuming a Gaussian distribution is essentially giving more weights to the samples closer to the distribution mean. This might or might not be appropriate depending if what you want to predict follows a normal distribution.\nThe variable, var_smoothing, artificially adds a user-defined value to the distribution's variance (whose default value is derived from the training data set). This essentially widens (or \"smooths\") the curve and accounts for more samples that are further away from the distribution mean.","Q_Score":3,"Tags":"python,machine-learning,scikit-learn,gaussian","A_Id":62098032,"CreationDate":"2019-09-22T04:50:00.000","Title":"Can someone give a good math\/stats explanation as to what the parameter var_smoothing does for GaussianNB in scikit learn?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to compare the two sentences. As a example,\nsentence1=\"football is good,cricket is bad\"\nsentence2=\"cricket is good,football is bad\"\nGenerally these senteces have no relationship that means they are different meaning. But when I compare with python nltk tools it will give 100% similarity. How can I fix this Issue? I need Help.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":566,"Q_Id":58046570,"Users Score":1,"Answer":"Semantic Similarity is a bit tricky this way, since even if you use context counts (which would be n-grams > 5) you cannot cope with antonyms (e.g. black and white) well enough. Before using different methods, you could try using a shallow parser or dependency parser for extracting subject-verb or subject-verb-object relations (e.g. ), which you can use as dimensions. If this does not give you the expected similarity (or values adequate for your application), use word embeddings trained on really large data.","Q_Score":0,"Tags":"python,nlp,nltk","A_Id":58234695,"CreationDate":"2019-09-22T06:19:00.000","Title":"How to Compare Sentences with an idea of the positions of keywords?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Could anyone tell me what's the meaning of  '10' and '49' in the following log of tensorflow?\nMuch Thanks\nINFO:tensorflow:Started compiling\nINFO:tensorflow:Finished compiling. Time elapsed: 5.899410247802734 secs\n10\/10 [==============================] - 23s 2s\/step - loss: 2.6726 - acc: 0.1459\n49\/49 [==============================] - 108s 2s\/step - loss: 2.3035 - acc: 0.2845 - val_loss: 2.6726 - val_acc: 0.1459\nEpoch 2\/100\n10\/10 [==============================] - 1s 133ms\/step - loss: 2.8799 - acc: 0.1693\n49\/49 [==============================] - 17s 337ms\/step - loss: 1.9664 - acc: 0.4042 - val_loss: 2.8799 - val_acc: 0.1693","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":173,"Q_Id":58047736,"Users Score":1,"Answer":"10 and 49 corresponds to the number of batches which your dataset has been divided into in each epoch. \nFor example, in your train dataset, there are totally 10000 images and your batch size is 64, then there will be totally math.ceil(10000\/64) = 157 batches possible in each epoch.","Q_Score":1,"Tags":"python-3.x,tensorflow,tensor,tensorflow-estimator","A_Id":58048268,"CreationDate":"2019-09-22T09:08:00.000","Title":"What's the meaning of the number before the progress bar when tensorflow is training","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"for my current requirement, I'm having a dataset of 10k+ faces from 100 different people from which I have trained a model for recognizing the face(s). The model was trained by getting the 128 vectors from the facenet_keras.h5 model and feeding those vector value to the Dense layer for classifying the faces.\nBut the issue I'm facing currently is\n\nif want to train one person face, I have to retrain the whole model once again.\n\nHow should I get on with this challenge? I have read about a concept called transfer learning but I have no clues about how to implement it. Please give your suggestion on this issue. What can be the possible solutions to it?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":675,"Q_Id":58049090,"Users Score":1,"Answer":"With transfer learning you would copy an existing pre-trained model and use it for a different, but similar, dataset from the original one. In your case this would be what you need to do if you want to train the model to recognize your specific 100 people.\nIf you already did this and you want to add another person to the database without having to retrain the complete model, then I would freeze all layers (set layer.trainable = False for all layers) except for the final fully-connected layer (or the final few layers). Then I would replace the last layer (which had 100 nodes) to a layer with 101 nodes. You could even copy the weights to the first 100 nodes and maybe freeze those too (I'm not sure if this is possible in Keras). In this case you would re-use all the trained convolutional layers etc. and teach the model to recognise this new face.","Q_Score":1,"Tags":"python-3.x,tensorflow,keras,deep-learning,face-recognition","A_Id":58049249,"CreationDate":"2019-09-22T12:12:00.000","Title":"How do i retrain the model without losing the earlier model data with new set of data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Consider two linear models:\nL1: y = 39.76x + 32.648628\nAnd\nL2: y = 43.2x + 19.8\nGiven the fact that both the models perform equally well on the test data set, which one would you prefer and why?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":183,"Q_Id":58049729,"Users Score":0,"Answer":"I'd probably go with the second one, just because the numbers in the second one are rounded more, and if they still do equally well, the extra digits in the first one are unnecessary and just make it look worse.\n(As a side note, this question doesn't seem related to programming so you may want to post it in a different community.)","Q_Score":1,"Tags":"python,linear-regression","A_Id":58049979,"CreationDate":"2019-09-22T13:36:00.000","Title":"Linear models : Given the fact that both the models perform equally well on the test data set, which one would you prefer and why?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I`m new to python. I have a csv file. I need to check whether the inputs are correct or not. The ode should scan through each rows. \nAll columns for a particular row should contain values of same type: Eg:\nAll columns of second row should contain only string, \nAll columns of third row should contain only numbers... etc\nI tried the following approach, (it may seem blunder):\nI have only 15 rows, but no idea on number of columns(Its user choice)\ndf.iloc[1].str.isalpha()\nThis checks  for string. I don`t know how to check ??","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":59,"Q_Id":58056352,"Users Score":1,"Answer":"Simple approach that can be modified:\n\nOpen df using df = pandas.from_csv(<path_to_csv>)\nFor each column, use df['<column_name>'] = df['<column_name>'].astype(str) (str = string, int = integer, float = float64, ..etc).\n\nYou can check column types using df.dtypes","Q_Score":1,"Tags":"python,pandas","A_Id":58056413,"CreationDate":"2019-09-23T05:52:00.000","Title":"Check inputs in csv file","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"So, I have a pandas dataframe with a lot of variables including start\/end date of loans. \nI subtract these two in order to get their difference in days.\nThe result I get is of the type i.e. 349 days 00:00:00.\nHow can I keep only for example the number 349 from this column?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1164,"Q_Id":58059278,"Users Score":0,"Answer":"Check this format,\ndf['date'] = pd.to_timedelta(df['date'], errors='coerce').days\nalso, check .normalize() function in pandas.","Q_Score":1,"Tags":"python,pandas,dataframe,days","A_Id":58059345,"CreationDate":"2019-09-23T09:20:00.000","Title":"Remove \"days 00:00:00\"from dataframe","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to onehot-encode some positions with TensorFlow.\nHowever, the length of the input sequences (and therefore the depth-argument in tf.one_hot) is None as I work with variable sized inputs.\nThis throws the following error: \n\n\"ValueError: Tried to convert 'depth' to a tensor and failed. Error: None values not supported.\".\n\nIs there a workaround for this?\nI have already tried to set the depth to the correct sequence length before each individual call (through a variable that has some arbitrary initialization value) for a given sequence but as the computational graph is already built, the changes do not come into effect and the depth is stuck at the initialization value.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":129,"Q_Id":58067427,"Users Score":0,"Answer":"This is how I solved the issue: The problem was that I was using someone else's code and the depth argument in tf.one_hot was derived from someTensor.get_shape().as_list()[1]. The problem here is that if the shape of someTensor is unknown, the argument is a Python-None which is not a valid argument for tf.one_hot. However, using tf.shape(someTensor)[1] solved that problem as it returns a Dimension with unknown shape instead of a Python-None. A Dimension with unknown shape is a valid depth-argument for tf.one_hot.","Q_Score":0,"Tags":"python,tensorflow,machine-learning,one-hot-encoding","A_Id":58080853,"CreationDate":"2019-09-23T17:28:00.000","Title":"Tensorflow: OneHot-encoding with variable sized length","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a pre-trained word2vec bin file by using skipgram. The file is pretty big (vector dimension of 200 ), over 2GB. I am thinking some methods to make the file size smaller. This bin file contains vectors for punctuation, some stop words. So, I want to know what are the options to decrease the file size for this word2vec. Is it safe to delete those punctuation and stop words rows and what would be the most effective way ?","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2014,"Q_Id":58069421,"Users Score":1,"Answer":"The size of a full Word2Vec model is chiefly determined by the chosen vector-size, and the size of the vocabulary. \nSo your main options for big savings is to train smaller vectors, or a smaller vocabulary. \nDiscarding a few hundred stop-words or punctuation-tokens won't make a noticeable dent in the model size. \nDiscarding many of the least-frequent words can make a big difference in model size \u2013 and often those less-frequent words aren't as important as you might think. (While there are a lot of them in total, each only appears rarely. And because they're rare in the training data, they often tend not to have very good vectors, anyway \u2013 based on few examples, and their training influence is swamped by the influence of more-frequent words.)\nThe easiest way to limit the vocabulary size is to use a higher min_count value during training (ignoring all words with fewer occurrences), or a  fixed max_final_vocab cap (which will keep only that many of the most-frequent words). \nNote also that if you've been saving\/reloading full Word2Vec models (via the gensim-internal .save()\/.load() methods), you're retaining model internal weights that are only needed for continued training, and will nearly double the model-size on disk or re-load. \nYou may want to save just the raw word-vectors in the .wv property instead (via either the gensim-internal .save() or the .save_word2vec_format() methods).","Q_Score":1,"Tags":"python,gensim,word2vec","A_Id":58072232,"CreationDate":"2019-09-23T20:00:00.000","Title":"gensim word2vec extremely big and what are the methods to make file size smaller?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have two data from.\ndf1 with columns: id,x1,x2,x3,x4,....xn\ndf2 with columns: id,y.\ndf3 =pd.concat([df1,df2],axis=1)\nwhen I use pandas concat to combine them, it became\nid,y,id,x1,x2,x3...xn.\nthere are two id here.How can I get rid of one.\nI have tried :\ndf3=pd.concat([df1,df2],axis=1).drop_duplicates().reset_index(drop=True).\nbut not work.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1418,"Q_Id":58070840,"Users Score":0,"Answer":"drop_duplicates() only removes rows that are completely identical.\nwhat you're looking for is pd.merge().\npd.merge(df1, df2, on='id)","Q_Score":1,"Tags":"python,pandas,concat","A_Id":58070875,"CreationDate":"2019-09-23T22:18:00.000","Title":"how to remove duplicates when using pandas concat to combine two dataframe","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to Machine Learning\nI have a dataset which has highly unbalanced classes(dominated by negative class) and contains more than 2K numeric features and the target is [0,1]. I have trained a logistics regression though I am getting an accuracy of 89% but from confusion matrix, it was found the model True positive is very low. Below are the scores of my model\nAccuracy Score : 0.8965989500114129\nPrecision Score : 0.3333333333333333\nRecall Score : 0.029545454545454545\nF1 Score : 0.05427974947807933\nHow I can increase my True Positives? Should I be using a different classification model?\nI have tried the PCA and represented my data in 2 components, it increased the model accuracy up to 90%(approx) however True Positives was decreased again","AnswerCount":4,"Available Count":4,"Score":0.0,"is_accepted":false,"ViewCount":4004,"Q_Id":58074203,"Users Score":0,"Answer":"What is the size of your dataset?How many rows are we talking here? \nYour dataset is not balanced and so its kind of normal for a simple classification algorithm to predict the 'majority-class' most of the times and give you an accuracy of 90%. Can you collect more data that will have more positive examples in it.\nOr, just try oversampling\/ under-sampling. see if that helps. \nYou can also use penalized version of the algorithm to impose penalty, whenever a wrong class is predicted. That may help.","Q_Score":1,"Tags":"python,machine-learning,statistics,data-science","A_Id":58075290,"CreationDate":"2019-09-24T06:23:00.000","Title":"How to increase true positive in your classification Machine Learning model?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to Machine Learning\nI have a dataset which has highly unbalanced classes(dominated by negative class) and contains more than 2K numeric features and the target is [0,1]. I have trained a logistics regression though I am getting an accuracy of 89% but from confusion matrix, it was found the model True positive is very low. Below are the scores of my model\nAccuracy Score : 0.8965989500114129\nPrecision Score : 0.3333333333333333\nRecall Score : 0.029545454545454545\nF1 Score : 0.05427974947807933\nHow I can increase my True Positives? Should I be using a different classification model?\nI have tried the PCA and represented my data in 2 components, it increased the model accuracy up to 90%(approx) however True Positives was decreased again","AnswerCount":4,"Available Count":4,"Score":0.0,"is_accepted":false,"ViewCount":4004,"Q_Id":58074203,"Users Score":0,"Answer":"You can try many different solutions.\nIf you have quite a lot data points. For instance you have 2k 1s and 20k 0s. You can try just dump those extra 0s only keep 2k 0s. Then train it. And also you can try to use different set of 2k 0s and same set of 2k 1s. To train multiple models. And make decision based on multiple models.\nYou also can try adding weights at the output layer. For instance, you have 10 times 0s than 1s. Try to multiply 10 at the 1s prediction value. \nProbably you also can try to increase dropout? \nAnd so on.","Q_Score":1,"Tags":"python,machine-learning,statistics,data-science","A_Id":58082997,"CreationDate":"2019-09-24T06:23:00.000","Title":"How to increase true positive in your classification Machine Learning model?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to Machine Learning\nI have a dataset which has highly unbalanced classes(dominated by negative class) and contains more than 2K numeric features and the target is [0,1]. I have trained a logistics regression though I am getting an accuracy of 89% but from confusion matrix, it was found the model True positive is very low. Below are the scores of my model\nAccuracy Score : 0.8965989500114129\nPrecision Score : 0.3333333333333333\nRecall Score : 0.029545454545454545\nF1 Score : 0.05427974947807933\nHow I can increase my True Positives? Should I be using a different classification model?\nI have tried the PCA and represented my data in 2 components, it increased the model accuracy up to 90%(approx) however True Positives was decreased again","AnswerCount":4,"Available Count":4,"Score":0.0,"is_accepted":false,"ViewCount":4004,"Q_Id":58074203,"Users Score":0,"Answer":"I'm assuming that your purpose is to obtain a model with good classification accuracy on some test set, regardless of the form of that model. \nIn that case, if you have access to the computational resources, try Gradient-Boosted Trees. That's a ensemble classifier using multiple decision trees on subsets of your data, then a voting ensemble to make predictions. As far as I know, it can give good results with unbalanced class counts.\nSciKitLearn has the function sklearn.ensemble.GradientBoostingClassifier for this. I have not used that particular one, but I use the regression version often and it seems good. I'm pretty sure MATLAB has this as a package too, if you have access.\n2k features might be difficult for the SKL algorithm - I don't know I've never tried.","Q_Score":1,"Tags":"python,machine-learning,statistics,data-science","A_Id":58074603,"CreationDate":"2019-09-24T06:23:00.000","Title":"How to increase true positive in your classification Machine Learning model?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to Machine Learning\nI have a dataset which has highly unbalanced classes(dominated by negative class) and contains more than 2K numeric features and the target is [0,1]. I have trained a logistics regression though I am getting an accuracy of 89% but from confusion matrix, it was found the model True positive is very low. Below are the scores of my model\nAccuracy Score : 0.8965989500114129\nPrecision Score : 0.3333333333333333\nRecall Score : 0.029545454545454545\nF1 Score : 0.05427974947807933\nHow I can increase my True Positives? Should I be using a different classification model?\nI have tried the PCA and represented my data in 2 components, it increased the model accuracy up to 90%(approx) however True Positives was decreased again","AnswerCount":4,"Available Count":4,"Score":0.1973753202,"is_accepted":false,"ViewCount":4004,"Q_Id":58074203,"Users Score":4,"Answer":"There are several ways to do this :\n\nYou can change your model and test whether it performs better or not\nYou can Fix a different prediction threshold : here I guess you predict 0 if the output of your regression is <0.5, you could change the 0.5 into 0.25 for example. It would increase your True Positive rate, but of course, at the price of some more False Positives.\nYou can duplicate every positive example in your training set so that your classifier has the feeling that classes are actually balanced.\nYou could change the loss of the classifier in order to penalize more False Negatives (this is actually pretty close to duplicating your positive examples in the dataset)\n\nI'm sure many other tricks could apply, here is just my favorite short-list.","Q_Score":1,"Tags":"python,machine-learning,statistics,data-science","A_Id":58074754,"CreationDate":"2019-09-24T06:23:00.000","Title":"How to increase true positive in your classification Machine Learning model?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Are numpy structured arrays an alternative to Python dict?\nI would like to save memory and I cannot affort much of a performance decline.\nIn my case, the keys are str and the values are int.\nCan you give a quick conversion line in case they actually are an alternative?\nI also don't mind if you can suggest a different alternative.\nI need to save memory, because some dictionaries get larger than 50Gb in memory and I need to open multiple at a time with 'only' 192 GB RAM available.","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":1173,"Q_Id":58077373,"Users Score":1,"Answer":"numpy array use contiguous block of memory and can store only one type of object like int, float, string or other object. Where each item are allocated fixed bytes in memory. \nNumpy also provide set of functions for operation like traversing array, arithmetic operation, some string operation on those stored items which are implemented using c. As these operation doesn't have overhead of python they are normally more efficient in terms of both memory and processing power\nAs you need key value pair you can also store that in numpy array similar to c-struct but it won't have features like dict like looking of item, checking if key existing filtering etc. you have do do those your self using array functionality\nbetter option for you may be pandas series, which also use numpy array to store its data for provides you lots of functionality on top of it","Q_Score":1,"Tags":"python,numpy,dictionary,time-complexity,structured-array","A_Id":58078010,"CreationDate":"2019-09-24T09:39:00.000","Title":"Use numpy structured array instead of dict to save space and keep speed","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a binary classification text data in which there are 10 text features. \nI use various techniques like Bag of words, TFIDF etc. to convert them to numerical.\nI use hstack() to stack all those features together again after processing them.\nAfter converting them to numerical feature, each feature now has large number of columns hence after conversion, my dataset has around 3000 columns.\nMy question is when I fit this dataset into decision tree classifier (sklearn), how does the classifier recognizes the columns which belong to  a particular feature? \nFor example first 51 column out of 3000 belong to US_states Bag of words.\nNow, how will the DT recognize it?\nPS: Data before processing is in pandas Dataframe.\nAfter processing, it is a stacked numpy array being input in the classifier.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":145,"Q_Id":58079493,"Users Score":0,"Answer":"The Decision Tree won't recognize from which features the attributes are coming.","Q_Score":0,"Tags":"python,machine-learning,scikit-learn,decision-tree,text-processing","A_Id":58079908,"CreationDate":"2019-09-24T11:39:00.000","Title":"How does decision tree recognize the features from a given text dataset?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Basically, I'm working on a robot arm that will play checkers.\nThere is a camera attached above the board supplying pictures (or even videomaterial but I guess that is just a series of images and since checkers is not really a fast paced game I can just take a picture every few seconds and go from there)\nI need to find a way to translate the visual board into a e.g a 2d array to feed into the A.I to compute the robots moves.\nI have a line detection working which draws lines on along the edges of the squares (and also returns edges in canny as a prior step). Moreover I detect green and red (the squares of my board are green and red) and return these both as a mask each. \nI also have a sphere detection in place to detect the position of the pieces and some black and white color detection returning a mask each with the black or white detected areas.\nMy question is how I can now combine these things I have and as a result get some type of array out of which I can deduct information over in which squares my pieces are ? \nLike how would i build the 2d array (or connect any 8x8) array to the image of the board with the lines and\/or the masks of the red\/green tiles ? I guess I have to do some type of calibration ?\nAnd secondly is there a way to somehow overlay the masks so that I then know which pieces are in which squares ?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":424,"Q_Id":58087263,"Users Score":0,"Answer":"Based on either the edge detector or the red\/green square detector, calculate the center coordinates of each square on the game board.  For example, average the x-coordinate of the left and right edge of a square to get the x-coordinate of the square's center.  Similarly, average the y-coordinate of the top and bottom edge to get the y-coordinate of the center.  \nIt might also be possible to find the top, left, bottom and right edge of the board and then interpolate to find the centers of all the squares.  The sides of each square are probably more than a hundred pixels in length, so the calculations don't need to be that accurate.\nTo determine where the pieces are, iterate of a list of the center coordinates and look at the color of the pixel.  If it is red or green, the square is empty.  If it is black or white, the square has a corresponding piece in it.  Use the information to fill an array with the information for the AI.\nIf the images are noisy, it might be necessary to average several pixels near the center or to average the  center pixel over several frames.\nIt would work best if the camera is above the center of the board.  If it is off to the side, the edges wouldn't be parallel\/orthogonal in the picture, which might complicate the math for finding the centers.","Q_Score":0,"Tags":"python,opencv,computer-vision","A_Id":58088639,"CreationDate":"2019-09-24T19:49:00.000","Title":"Create array from image of chessboard","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Basically, I'm working on a robot arm that will play checkers.\nThere is a camera attached above the board supplying pictures (or even videomaterial but I guess that is just a series of images and since checkers is not really a fast paced game I can just take a picture every few seconds and go from there)\nI need to find a way to translate the visual board into a e.g a 2d array to feed into the A.I to compute the robots moves.\nI have a line detection working which draws lines on along the edges of the squares (and also returns edges in canny as a prior step). Moreover I detect green and red (the squares of my board are green and red) and return these both as a mask each. \nI also have a sphere detection in place to detect the position of the pieces and some black and white color detection returning a mask each with the black or white detected areas.\nMy question is how I can now combine these things I have and as a result get some type of array out of which I can deduct information over in which squares my pieces are ? \nLike how would i build the 2d array (or connect any 8x8) array to the image of the board with the lines and\/or the masks of the red\/green tiles ? I guess I have to do some type of calibration ?\nAnd secondly is there a way to somehow overlay the masks so that I then know which pieces are in which squares ?","AnswerCount":2,"Available Count":2,"Score":0.1973753202,"is_accepted":false,"ViewCount":424,"Q_Id":58087263,"Users Score":2,"Answer":"Well, first of all remember that chess always starts with the same pieces on the same positions e.g. black knight starts at 8-B which can be [1][7] in your 2D array. If I were you I would start with a 2D array with the begin positions of all the chess pieces. \nAs to knowing which pieces are where: you do not need to recognize the pieces themselves. What I would do if I were you is detect the empty spots on the chessboard which is actually quite easy in comparison to really recognizing the different chess pieces. \nOnce your detection system detects that one of the previously empty spots is now no longer empty you know that a chess piece was moved there. Since you can also detect a new open spot(the spot where the chess piece came from) you also know the exact chess piece which was moved. If you keep track of this list during the whole game you can always know which pieces are moved and which pieces are where.\nEdit:\nAs noted in the comments my answer was based on chess instead of checkers. The idea is however still the same but instead of chess pieces you can now put men and kings in the 2D array.","Q_Score":0,"Tags":"python,opencv,computer-vision","A_Id":58087392,"CreationDate":"2019-09-24T19:49:00.000","Title":"Create array from image of chessboard","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to make a classifier for uncertain data (e.g ranged data) using python. in certain dataset, the list is a 2D array or array of record (contains float numbers for data and a string for labels), where in uncertain dataset the list is a 3D array (contains range of float numbers for data and a string for labels). i managed to manipulate a certain dataset to be uncertain using uniform probability distribution. A research paper says that i have to use supremum distance metric. how do i implement this metric in python? note that in uncertain dataset, both test set and training set is uncertain","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":119,"Q_Id":58089636,"Users Score":0,"Answer":"I found out using scipy spatial distance and tweaking for-loops in standard knn helps a lot","Q_Score":0,"Tags":"python,knn","A_Id":58090124,"CreationDate":"2019-09-25T00:25:00.000","Title":"Supremum Metric in Python for Knn with Uncertain Data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I used OpenCV's camera calibration function for calibrating my camera. I captured around 50 images with different angles and pattern near image borders. \nCx and Cy value in intrinsic matrix is around 300 px off. Is it alright? My average reprojection error is around 0.08 though.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":476,"Q_Id":58091445,"Users Score":0,"Answer":"Cx and Cy are the coordinates (in pixels) of the principal point in your image. Usually a good approximation is (image_width\/2, image_height\/2).\nAn average reprojection error of 0.08 pixel seems quite good.","Q_Score":0,"Tags":"python-3.x,opencv,camera,camera-calibration","A_Id":58096338,"CreationDate":"2019-09-25T05:01:00.000","Title":"OpenCV camera calibration - Intrinsic matrix values are off","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Imagine I have a fasttext model that had been trained thanks to the Wikipedia articles (like explained on the official website).\nWould it be possible to train it again with another corpus (scientific documents) that could add new \/ more pertinent links between words? especially for the scientific ones ?\nTo summarize, I would need the classic links that exist between all the English words coming from Wikipedia. But I would like to enhance this model with new documents about specific sectors. Is there a way to do that ? And if yes, is there a way to maybe 'ponderate' the trainings so relations coming from my custom documents would be 'more important'.\nMy final wish is to compute cosine similarity between documents that can be very scientific (that's why to have better results I thought about adding more scientific documents)","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":254,"Q_Id":58099559,"Users Score":2,"Answer":"Adjusting more-generic models with your specific domain training data is often called \"fine-tuning\". \nThe gensim implementation of FastText allows an existing model to expand its known-vocabulary via what's seen in new training data (via build_vocab(..., update=True)) and then for further training cycles including that new vocabulary to occur (through train()). \nBut, doing this particular form of updating introduces murky issues of balance between older and newer training data, with no clear best practices. \nAs just one example, to the extent there are tokens\/ngrams in the original model that don't recur in the new data, new training is pulling those in the new data into new positions that are optimal for the new data... but potentially arbitrarily far from comparable compatibility with the older tokens\/ngrams.)\nFurther, it's likely some model modes (like negative-sampling versus hierarchical-softmax), and some mixes of data, have a better chance of net-benefiting from this approach than others \u2013 but you pretty much have to hammer out the tradeoffs yourself, without general rules to rely upon. \n(There may be better fine-tuning strategies for other kinds models; this is just speaking to the ability of the gensim FastText to update-vocabulary and repeat-train.)\nBut perhaps, your domain of interest is scientific texts. And maybe you also have a lot of representative texts \u2013 perhaps even, at training time, the complete universe of papers you'll want to compare. \nIn that case, are you sure you want to deal with the complexity of starting with a more-generic word-model? Why would you want to contaminate your analysis with any of the dominant word-senses in generic reference material, like Wikipedia, if in fact you already have sufficiently-varied and representative examples of your domain words in your domain contexts?\nSo I would recommend 1st trying to train your own model, from your own representative data. And only if you then fear you're missing important words\/senses, try mixing in Wikipedia-derived senses. (At that point, another way to mix in that influence would be to mix Wikipedia texts with your other corpus. And  you should also be ready to test whether that really helps or hurts \u2013 because it could be either.)\nAlso, to the extent your real goal is comparing full papers, you might want to look into other document-modeling strategies, including bag-of-words representations, the Doc2Vec ('Paragraph Vector') implementation in gensim, or others. Those approaches will not necessarily require per-word vectors as an input, but might still work well for quantifying text-to-text similarities.","Q_Score":2,"Tags":"python,artificial-intelligence,gensim,training-data,fasttext","A_Id":58106916,"CreationDate":"2019-09-25T13:28:00.000","Title":"Training a model from multiple corpus","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have nearly 200 000 lines of tuples in my Pandas Dataframe. I injected that data into elastic search. Now, when I run the program It should check whether the present data already there in elastic search if not present insert into it.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":59,"Q_Id":58114367,"Users Score":0,"Answer":"I'd recommend to not worry about it and just load everything into Elasticsearch. As long as your _ids are consistent the existing documents will be overwritten instead of duplicated. So just be sure to specify an _id for each document and you are fine, the bulk helpers in the elasticsearch-py client all support you setting an _id value for each document alredy.","Q_Score":0,"Tags":"python-3.x,pandas,elasticsearch,elasticsearch-py","A_Id":58140537,"CreationDate":"2019-09-26T10:05:00.000","Title":"How to get data from elastic search, if new data came then update it, and again inject it?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Does anyone know of a quick way to close all open variable explorer windows in Spyder? (i.e. the windows that open when you click on a variable).\nIn Matlab, you can close all pop-up windows with close all. Does anything like that exist for Spyder?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":603,"Q_Id":58116958,"Users Score":1,"Answer":"(Spyder maintainer here) We don't have a command to do that, sorry.","Q_Score":3,"Tags":"python,window,spyder","A_Id":58118967,"CreationDate":"2019-09-26T12:35:00.000","Title":"Close all variable explorer windows in Spyder","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"i'm using a script ScriptGlobal.py that will call and execute 2 other scripts script1.py and script2.py exec(open(\".\/script2.py\").read()) AND exec(open(\".\/script1.py\").read())\nThe output of my script1 is the creation of csv file.\ndf1.to_csv('file1.csv',index=False)\nThe output of my script2 is the creation of another csv file.\ndf2.to_csv('file2.csv',index=False)\nIn my ScriptGlobal.py i want to read the 2 files file1.csv and file2.csv and then i got this error.\nParserError: Error tokenizing data. C error: Expected 1 fields in line 16, saw 3\nIs there solution to do it without doing manuallyu the manipulation in EXCEL ?\nThank you","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":160,"Q_Id":58117142,"Users Score":0,"Answer":"Did you try save these two .csv files as ANSI? I had problem with .csv when they were saved as UTF-8.","Q_Score":0,"Tags":"python-3.x,csv,dataframe","A_Id":58117742,"CreationDate":"2019-09-26T12:46:00.000","Title":"ParserError: Error tokenizing data. C error","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I imported a table using pandas and I was able to set independent variables (features) and my dependent variable (target). Two of my independent variables are \"object type\" and my others are int64 and float64. Do I need to convert my \"object\" type features to \"class\" or another type? How can I handle these in Sci-kit learn decision trees?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":346,"Q_Id":58126842,"Users Score":0,"Answer":"I used one hot encoding to convert my categorical data because the scikit-learn decision tree packages do not support categorical data.","Q_Score":0,"Tags":"python,types,scikit-learn,decision-tree","A_Id":58326018,"CreationDate":"2019-09-27T02:10:00.000","Title":"What object types can be used for fetures in decision trees? Do I need to convert my \"object\" type to another type?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Beginner python,\nI want to create a method like: max(mat,i)= the row with the maximum value in the column i of matrix mat.\nFor example, I have a matrix a=[[1,2,3],[4,5,6],[7,8,9]], then the largest value of the i=3 column is 9 and so max(a,3)=[7,8,9].\nI'm wondering if there is a builtin function in python?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":43,"Q_Id":58137114,"Users Score":0,"Answer":"To my knowledge, there is not a builtin function in python itself. I would recommend just building the utility yourself, since it's basically just a max over specified list out of matrix, which isn't hard to implement.","Q_Score":1,"Tags":"python","A_Id":58137213,"CreationDate":"2019-09-27T15:05:00.000","Title":"find the row of a matrix with the largest element in column i","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataset with the first column as date in the format: 2011-01-01 and type(data_raw['pandas_date']) gives me pandas.core.series.Series\nI want to convert the whole column into date time object so I can extract and process year\/month\/day from each row as required.\nI used pd.to_datetime(data_raw['pandas_date']) and it printed output with dtype: datetime64[ns] in the last line of the output. I assume that values were converted to datetime.\nbut when I run type(data_raw['pandas_date']) again, it still says pandas.core.series.Series and anytime I try to run .dt function on it, it gives me an error saying this is not a datetime object.\nSo, my question is - it looks like to_datetime function changed my data into datetime object, but how to I apply\/save it to the pandas_date column? I tried \ndata_raw['pandas_date'] = pd.to_datetime(data_raw['pandas_date'])\nbut this doesn't work either, I get the same result when I check the type. Sorry if this is too basic.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":384,"Q_Id":58138314,"Users Score":0,"Answer":"type(data_raw['pandas_date']) will always return pandas.core.series.Series, because the object data_raw['pandas_date'] is of type pandas.core.series.Series. What you want is to get the dtype, so you could just do data_raw['pandas_date'].dtype.\n\ndata_raw['pandas_date'] = pd.to_datetime(data_raw['pandas_date'])\n\nThis is correct, and if you do data_raw['pandas_date'].dtype again afterwards, you will see that it is datetime[64].","Q_Score":1,"Tags":"python,pandas,datetime","A_Id":58138420,"CreationDate":"2019-09-27T16:26:00.000","Title":"Change column from Pandas date object to python datetime","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I installed ndimage with sudo pip3 install scipy\nthen i'm importing it as import scipy.ndimage\nthen i'm doing the following line b=scipy.ndimage.filter.gaussian_filter(i,sigma=10)\nand I get AttributeError: module 'scipy.ndimage' has no attribute 'filter'\nAnyone encountered this before?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":384,"Q_Id":58139189,"Users Score":1,"Answer":"Found it!\nIt should have been b=scipy.ndimage.filters instead of filter","Q_Score":1,"Tags":"python","A_Id":58139262,"CreationDate":"2019-09-27T17:37:00.000","Title":"scipy ndimage has no attribute filter?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"My system has two NUMA nodes and two GTX 1080 Ti attached to NUMA node 1 (XEON E5).\nThe NN models are trained via single-machine multi-GPU data parallelism using Keras' multi_gpu_model.\nHow can TF be instructed to allocate memory and execute the TF workers (merging weights) only on NUMA node 1? For performance reasons I'd like to prevent accessing memory through the QPI.\ntf.device():\n1) Does tf.device('\/cpu:0') refer to a physical CPU or a physical core or is it simply a 'logical device' (thread|pool?) that is moved between all physical cores that are online?\n2) How can the TF scheduler be influenced to map the logical device to a set of physical cores?\n3) In the case of memory allocation on NUMA systems - does TF support allocating memory on specific nodes? Or do I have to fall back to set_mempolicy()\/numactl (LINUX)?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":366,"Q_Id":58162375,"Users Score":1,"Answer":"no, answer ...\nI'm using numactl --cpunodebind=1 --membind=1 - binds execution and memory allocation to NUMA node 1.","Q_Score":1,"Tags":"python,tensorflow,numa","A_Id":58364117,"CreationDate":"2019-09-30T06:16:00.000","Title":"pin and allocate tensorflow on specific NUMA node","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am relatively new to python and it seems to me (probably because I don't understand) that the syntax is sometimes slightly inconsistent.\nSuppose we are working with the pandas package import pandas as pd. Then any method within this package can be accessed by pd.method, i.e. pd.DataFrame(...). Now, there are certain objects within the pandas package that have certain methods, i.e. df.reindex() (notice circular brackets), or certain attributes, i.e. df.columns (notice no brackets).\nMy question is two fold:\nFirst of all, is what I have said above correct?\nSecondly, why does the iloc method not maintain the above syntax? If it is a method then surely I should use df.iloc(0,0) instead of df.iloc[0,0] to obtain the top left value of a data frame...\nThanks","AnswerCount":2,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":363,"Q_Id":58166876,"Users Score":4,"Answer":"import pandas as pd here it is python module \npd.DataFrame(...) if you pay attention to naming convection DataFrame is a class here.\ndf.reindex() is a method called on instance itself.\ndf.columns has no bracket because it is an attribute of the object not a method\ndf.iloc is meant to get item by index so to show it's index-able nature [] makes more sense here.","Q_Score":2,"Tags":"python,pandas","A_Id":58167125,"CreationDate":"2019-09-30T11:20:00.000","Title":"Why does iloc use [] and not ()?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Replace function failed to work even with inplace=True.\ndata:\n\n0      245778 others\n  1      245778 others\n  2      245778 others\n  4      245778 others\n\ncode:\ndf.likes=df.likes.astype('str')\ndf.likes.replace('others','',inplace=True)\nResult:\n\n0      245778 others\n  1      245778 others\n  2      245778 others\n  4      245778 others\n\nExpected Result:\n\n0      245778 \n  1      245778 \n  2      245778 \n  4      245778","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":43,"Q_Id":58176409,"Users Score":1,"Answer":"Without setting the regex flag to True, replace will look for an exact match.\nTo get a partial match, just use df.likes = df.likes.replace(' others', '', regex=True).","Q_Score":0,"Tags":"python,pandas","A_Id":58176536,"CreationDate":"2019-09-30T23:30:00.000","Title":"replace do not work even with inplace=True","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm on macOS 10.15 Beta, running a .py script that requires pandas, which is not installed.\nWhen I run sudo python -m pip install --upgrade pandas I receive:\n\nERROR: Cannot uninstall 'numpy'. It is a distutils installed project\n  and thus we cannot accurately determine which files belong to it which\n  would lead to only a partial uninstall.\n\nIf I run sudo pip install pandas I receive the same error.\nHelp appreciated.","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1097,"Q_Id":58178508,"Users Score":0,"Answer":"I found an alternative method to install pandas, by installing minicondas, and running conda install pandas.","Q_Score":1,"Tags":"python,pandas,macos","A_Id":58178725,"CreationDate":"2019-10-01T05:24:00.000","Title":"can't install pandas (ERROR: Cannot uninstall 'numpy')","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm on macOS 10.15 Beta, running a .py script that requires pandas, which is not installed.\nWhen I run sudo python -m pip install --upgrade pandas I receive:\n\nERROR: Cannot uninstall 'numpy'. It is a distutils installed project\n  and thus we cannot accurately determine which files belong to it which\n  would lead to only a partial uninstall.\n\nIf I run sudo pip install pandas I receive the same error.\nHelp appreciated.","AnswerCount":3,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":1097,"Q_Id":58178508,"Users Score":0,"Answer":"I tried the solutions presented in the other answers, but what worked for me was installing pandas using conda.","Q_Score":1,"Tags":"python,pandas,macos","A_Id":59296658,"CreationDate":"2019-10-01T05:24:00.000","Title":"can't install pandas (ERROR: Cannot uninstall 'numpy')","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to select all values bigger than 8000 within a pandas dataframe. \nnew_df = df.loc[df['GM'] > 8000]\nHowever, it is not working. I think the problem is, that the value comes from an Excel file and the number is interpreted as string e.g. \"1.111,52\". Do you know how I can convert such a string to float \/ int in order to compare it properly?","AnswerCount":4,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":93,"Q_Id":58179925,"Users Score":2,"Answer":"You can see value of df.dtypes to see what is the type of each column. Then, if the column type is not as you want to, you can change it by df['GM'].astype(float), and then new_df = df.loc[df['GM'].astype(float) > 8000] should work as you want to.","Q_Score":1,"Tags":"python,pandas","A_Id":58179988,"CreationDate":"2019-10-01T07:26:00.000","Title":"String problem \/ Select all values > 8000 in pandas dataframe","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I ran Python 3.6.6 Deep Learning with Pycharm 2019.1.3. The process was set at maximum 651 episode and it stopped at episode 368 with this message \"Process finished with exit code -2147483645\". \nI searched through Google but there's not even a result. Anyone knows about the code? Please help!","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2679,"Q_Id":58197666,"Users Score":0,"Answer":"Mmmm... I dont know about the error. But given the fact that it starts and works well for 368 episodes... I would aim that is some lack of memory related problem.\nI would run it several times, if it crash after a similar number of episodes I'd try with more memory.\nHope this helps even just a little bit.","Q_Score":3,"Tags":"python,pycharm,exit-code","A_Id":58198069,"CreationDate":"2019-10-02T08:15:00.000","Title":"\"Process finished with exit code -2147483645\" Pycharm","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to convert models trained in TensorFlow 1.12.0 Python into that of TensorFlow.js. What version of tf.js and tf.js converter is compatible with it?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":259,"Q_Id":58197749,"Users Score":1,"Answer":"You have not mentioned in which format you are saving your model in TensorFlow 1.12. I would recommend to make use of saved model format to save your model. If you use saved models, you can use the latest versions of tf.js and tf.js converters. Same is the case for keras h5 model as well.\nHowever, if you save it in form of pb files, you will have to use tf.js version of 0.15 and tf.js converter of 0.8.6","Q_Score":2,"Tags":"python,tensorflow,tensorflow.js","A_Id":58202111,"CreationDate":"2019-10-02T08:20:00.000","Title":"Which version of TensorFlow.js is compatible with models trained in TensorFlow 1.12.0 (Python)?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am pretty new to machine learning and I am currently dealing with a dataset in the format of a csv file comprised of categorical data. As a means of preprocessing, I One Hot Encoded all the variables in my dataset. \nAt the moment I am trying to apply a random forest algorithm to classify the entries into one of the 4 classes. My problem is that I do not understand exactly what happens to these One Hot Encoded variables. How do I feed them to the algorithm? Is it able to make the difference between buying_price_high, buying_price_low (One Hot Encoded from buying_price)? \nI One Hot Encoded the response variable as well.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":170,"Q_Id":58201116,"Users Score":0,"Answer":"Method of (One Hot Encoder) applies to category variables, and category variables have no size relationship.For the price variable,I suggest you use OrinalEncoder.Sklearn is a good package for machine.like, sklearn learning.preprocessing.OneHotEncoder or sklearn.preprocessing.OrdinalEncoder","Q_Score":0,"Tags":"python,machine-learning,scikit-learn,random-forest,multiclass-classification","A_Id":58201684,"CreationDate":"2019-10-02T12:02:00.000","Title":"Single-label multiclass classification random forest python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm running into problems using tensorflow 2 in VS Code. The code executes without a problem, the errors are just related to pylint in VS Code. \nFor example this import from tensorflow.keras.layers import Dense gives a warning \"Unable to import 'tensorflow.keras.layers'pylint(import-error)\". Importing tensorflow and using tf.keras.layers.Dense does not produce an error. I'm just using a global python environment (3.7.2) on Windows 10, tensorflow is installed via Pip.","AnswerCount":5,"Available Count":1,"Score":0.1586485043,"is_accepted":false,"ViewCount":11921,"Q_Id":58202095,"Users Score":4,"Answer":"I too faced the same issue. I solved it by installing keras as a new package and then I changed all packages name removing the prefix tensorflow.. So in your case after installing keras you should replace tensorflow.keras.layers with keras.layers","Q_Score":10,"Tags":"python,tensorflow,keras,visual-studio-code","A_Id":59021516,"CreationDate":"2019-10-02T13:02:00.000","Title":"Can't import tensorflow.keras in VS Code","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to visualize music into an image by using sounddevice to input the sound and then converting it to a numpy array. \nThe array is 2D and so I convert it to 3D (otherwise I only get a single thin vertical line in the image). \nHowever when I use PIL to show the image it says 'Cannot handle this datatype'\nThe code is mentioned below:\nimport sounddevice as sd \nfrom scipy.io.wavfile import write\nimport soundfile as sf\nimport numpy\nfrom numpy import zeros, newaxis\nfrom PIL import Image\nfs = 44100  # Sample rate\nseconds = 3 # Duration of recording\nmyrecording = sd.rec(int(seconds * fs), samplerate=fs, channels=2)\nsd.wait()  # Wait until recording is finished\nprint(myrecording)\nprint(numpy.shape(myrecording))\nwrite('output.wav', fs, myrecording)  # Save as WAV file\nfilename = 'output.wav'\nA=myrecording[:,:,newaxis]\nprint(A)\nim = Image.fromarray((A * 255).astype(numpy.uint8))\nim.show()\nI expect to get an image which shows colours corresponding to the sound being inputted in","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":62,"Q_Id":58223453,"Users Score":0,"Answer":"This depends a little bit on what you want to do.\nYou have two channels with n-samples ((nsamples, 2) ndarray); do you want each channel to be a column of the image where the color varies depending on what the value is? That is why you were getting a very narrow image when you just plot myrecording.\nYou do not really have the data to create a full 2D image, unless you reshape the time series data to be something more like a square (so it actually looks like an image), but then you sort of lose the time dependence nature that I think you are going for.","Q_Score":0,"Tags":"python-3.x,numpy,python-imaging-library","A_Id":58225796,"CreationDate":"2019-10-03T16:36:00.000","Title":"How to fix 'Cannot handle this data type' while trying to convert a numpy array into an image using PIL","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a  caltech101 dataset for object detection. Can we detect multiple objects in single image using model trained on caltech101 dataset?\nThis dataset contains only folders (label-wise) and in each folder, some images label wise.\nI have trained model on caltech101 dataset using keras and it predicts single object in image. Results are satisfactory but is it possible to detect multiple objects in single image?\nAs I know some how regarding this. for detecting multiple objects in single image, we should have dataset containing images and bounding boxes with name of objects in images.\nThanks in advance","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":233,"Q_Id":58225543,"Users Score":0,"Answer":"The dataset can be used for detecting multiple objects but with below steps to be followed:\n\nThe dataset has to be annotated with bounding boxes on the object present in the image\nAfter the annotations are done, you can use any of the Object detectors to do transfer learning and train on the annotated caltech 101 dataset\n\nNote: - Without annotations, with just the caltech 101 dataset, detecting multiple objects in a single image is not possible","Q_Score":0,"Tags":"python,keras,deep-learning,object-detection,tensorflow-datasets","A_Id":58337978,"CreationDate":"2019-10-03T19:17:00.000","Title":"Can we detect multiple objects in image using caltech101 dataset containing label wise images?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a python script and used it to create a dataframe in Power BI. \nNow I want to edit that dataframe in Power BI but don\u00b4t enter from scratch as new data because I want to keep all the charts inside my Power BI model.\nFor example in my old dataframe i specified some dates inside my script so the information was limited to those dates. Now i want to change the dates to new ones but dont want to lose all the model.\ndf = df","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":3747,"Q_Id":58226050,"Users Score":0,"Answer":"You can edit the python scripts doing the following steps:\n\nOpen Query Editor\nAt 'Applied steps', the first one, source, contains a small gear symbol just on the right side, click on it.\n\nYou can change the script direct into Power Query.","Q_Score":2,"Tags":"python,powerbi","A_Id":60456032,"CreationDate":"2019-10-03T19:53:00.000","Title":"Edit python script used as Data entry in Power BI","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to use the concept of spam classification and apply it to a business problem where we identify if a vision statement for a company is good or not. Here's a rough outline of what I've come up with for the project. Does this seem feasible?\n\nPrepare dataset by collecting vision statements from top leading companies (i.e. Fortune 5000)\nLet features = most frequent words (excluding non-alphanumerics, to, the, etc)\nCreate feature vector (dictionary) x of all words listed above\nUse supervised learning algorithm (logistic regression) to train and test data\nLet y = good vision statement and return the value 1; y = 0 if not good","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":29,"Q_Id":58229976,"Users Score":0,"Answer":"I'm skeptical. The reason simple Bayesian filtering works for spam is that spam messages typically use a quite different vocabulary than legitimate messages.\nAnecdotally, people who sell pharmaceuticals use the same words and phrases in their legitimate business correspondence as in some types of spam; so they get bad filtering results on pharma spam, while the spam filter quickly learns to correctly discard dating, Nigerian fraud, stock scams etc. (Pharma spam might still contain various hyperbolic phrases etc which set them apart even from non-spam marketing messaging, though.)\nBusiness bullshit lingo tends to look the same whether the underlying plan is sound or not. You may be able to filter out the worst gibberish, but word-token level analysis is simply not a good indicator of whether actual sound thought went into composing those words into a particular arrangement.","Q_Score":0,"Tags":"python,nlp,classification,text-classification","A_Id":58232793,"CreationDate":"2019-10-04T04:37:00.000","Title":"Using spam classification in a different application?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to loadthis file.\nBut, Python3 said \"OSError: Failed to interpret file 'D:\/USER\/Downloads\/wine.data' as a pickle\".\nHow to load this file?\nThe code I used following this\ndata = np.load(\"D:\/USER\/Downloads\/wine.data\")","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":150,"Q_Id":58237039,"Users Score":0,"Answer":"I had the same problem. In my case it was the newer version of numpy that caused the problem.\nTry installing numpy version 1.12.0\npip install numpy==1.12.0","Q_Score":0,"Tags":"python-3.x,compiler-errors","A_Id":58237280,"CreationDate":"2019-10-04T13:04:00.000","Title":"OSError: Failed to interpret file 'name.data' as a pickle","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am writing a serial data logger in Python and am wondering which data type would be best suited for this. Every few milliseconds a new value is read from the serial interface and is saved into my variable along with the current time. I don't know how long the logger is going to run, so I can't preallocate for a known size.\nIntuitively I would use an numpy array for this, but appending \/ concatenating elements creates a new array each time from what I've read.\nSo what would be the appropriate data type to use for this?\nAlso, what would be the proper vocabulary to describe this problem?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":139,"Q_Id":58237574,"Users Score":0,"Answer":"Python doesn't have arrays as you think of them in most languages. It has \"lists\", which use the standard array syntax myList[0] but unlike arrays, lists can change size as needed. using myList.append(newItem) you can add more data to the list without any trouble on your part.\nSince you asked for proper vocabulary in a useful concept to you would be \"linked lists\" which is a way of implementing array like things with varying lengths in other languages.","Q_Score":1,"Tags":"python,types,data-acquisition","A_Id":58237688,"CreationDate":"2019-10-04T13:40:00.000","Title":"Data type to save expanding data for data logging in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"tf.boolean_mask(tensor, mask) => returns (?, 4)\nHow do I check if the returned tensor by boolean_mask is empty or not?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":422,"Q_Id":58245630,"Users Score":1,"Answer":"There are multiple ways to solve this, basically you are trying to identify a null tensor. \nPossible solutions can be:\n\nis_empty = tf.equal(tf.size(boolean_tensor), 0). If not empty it will give false\nCount non zeros number using tf.count_nonzero(boolean_tensor)\nBy simply printing the tensor and checking the vaules","Q_Score":0,"Tags":"python,tensorflow","A_Id":58316577,"CreationDate":"2019-10-05T05:32:00.000","Title":"How to know if dynamic tensor returned by \"tf.boolean_mask\" is empty or not?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"One is a pandas.core.series.Series and another is a dataframe pandas.core.frame.DataFrame. \nI have seen codes using them both. Is there a guideline on when to use which?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":70,"Q_Id":58246700,"Users Score":1,"Answer":"to be more specific, df['column'] returns only one column, but when you use df[['column']] you can call more than one column.\nfor example df[['column1','column2']] returns column1 and column2 from df","Q_Score":0,"Tags":"python,pandas","A_Id":58246743,"CreationDate":"2019-10-05T08:41:00.000","Title":"What is the difference between single bracket df[\"column\"] and double bracket df[[\"column\"]]","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"So I have a trained model, that was trained on a standardized dataset. When I try to use the model for testing on new data, that isn't in a dataset and that isn't standardized, it returns ridiculous values, because I can standardize the inputs, but I can't inverse transform the output as I did during training. What should I do?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":277,"Q_Id":58248367,"Users Score":0,"Answer":"Use sc.inverse_transform(predicted)","Q_Score":0,"Tags":"python-3.x,scikit-learn,neural-network,deep-learning,sklearn-pandas","A_Id":59757240,"CreationDate":"2019-10-05T12:38:00.000","Title":"How to inverse transform models output?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to fetch location based on lat and long. I have a data of 600K in my csv and I am trying to run my for loop on it . My notebook is taking very long time to process the data. ( 40min to complete 2percent)\nI have decent laptop  Core i7-8550U quad-core 1.8GHz and 16GB DDR4 RAM . \nI am not sure how to optain the result for my data quickly .Pls help","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":10,"Q_Id":58253000,"Users Score":0,"Answer":"Do you have sample code of what you are doing ? Are you reading your file every time you fetch a latitude and longitude  If this is the case this is why it takes so long. First load the file in your memory as a pandas object for example and then you should be able to look for your data much faster.","Q_Score":0,"Tags":"python,dataframe,for-loop,data-analysis","A_Id":58253014,"CreationDate":"2019-10-05T23:06:00.000","Title":"File progress for long duration","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"for example i have a image dataset. I used these images for train my model, and I am using another image for test. How can i recognize the test image is on the dataset? How do I determine what the similarity percentage is?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":32,"Q_Id":58255567,"Users Score":0,"Answer":"you can try correlation on both images if there exactly the same should get 1","Q_Score":0,"Tags":"python-3.x,keras,conv-neural-network","A_Id":58255664,"CreationDate":"2019-10-06T08:35:00.000","Title":"How can i recognize two picture are same?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Are there any method where I can upsert into a SQL datawarehouse table ?\nSuppose I have a Azure SQL datawarehouse table :\ncol1   col2    col3 \n2019   09       10\n2019   10       15\nI have a dataframe \ncol1  col2   col3\n2019   10     20\n2019   11     30\nThen merge into the original table of Azure data warehouse table \ncol1 col2 col3\n2019 09    10 \n2019 10    20 \n2019 11    30 \nThanks for everyone idea","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":782,"Q_Id":58255818,"Users Score":0,"Answer":"you can save the output in a file and then use the stored procedure activity from azure data factory for the upsert. Just a small procedure which will upsert the values from the file.  I am assuming that you are using the Azure data factory here.","Q_Score":1,"Tags":"python,databricks","A_Id":58305335,"CreationDate":"2019-10-06T09:13:00.000","Title":"Databricks: merge dataframe into sql datawarehouse table","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"If I freeze my base_model with trainable=false, I get strange numbers with trainable_weights.\nBefore freezing my model has 162 trainable_weights. After freezing, the model only has 2. I tied 2 layers to the pre-trained network. Does trainable_weights show me the layers to train? I find the number weird, when I see 2,253,335 Trainable params.","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":3554,"Q_Id":58258312,"Users Score":1,"Answer":"Trainable weights are the weights that will be learnt during the training process. If you do trainable=False then those weights are kept as it is and are not changed because they are not learnt. You might see some \"strange numbers\" because either you are using a pre-trained network that has its weights already learnt or you might be using random initialization when defining the model. When using transfer learning with pre-trained models a common practice is to freeze the weights of base model (pre-trained) and only train the extra layers that you add at the end.","Q_Score":1,"Tags":"python,keras,deep-learning,conv-neural-network,transfer-learning","A_Id":58258675,"CreationDate":"2019-10-06T14:38:00.000","Title":"What is the meaning of \"trainable_weights\" in Keras?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Does a \"sparse matrix\" mean that it contains *more than 50% 0's?\nI can't seem to locate that information.\nedit - more","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":480,"Q_Id":58277617,"Users Score":1,"Answer":"You can't locate a definition because there isn't one.  \"Sparse\" is whatever relation makes a different algorithm more efficient.  It may be a particular proportion of elements; it may be a function of the matrix side (e.g. n element in a nxn matrix); it may require zero rows or diagonals.\nIt depends critically on how you plan to alter your handling of a \"sparse\" matrix.\nWhen we learned the basics of sparse representations, we used a heuristic of 10% non-zero elements.  However, that was a particular family of OS data storage and retrieval.","Q_Score":0,"Tags":"python,matrix,sparse-matrix","A_Id":58277773,"CreationDate":"2019-10-07T21:54:00.000","Title":"What is the threshold for sparse matrices? Is it a matrix that contain less than 50% 0's?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"So I'm initializing a model as:\nmodel = tf.keras.utils.multi_gpu_model(model, gpus=NUM_GPUS) and when I do model.compile() it runs perfectly fine.\nBut when I do history = model.fit(tf.cast(X_train, tf.float32), tf.cast(Y_train, tf.float32), validation_split=0.25, batch_size = 16, verbose=1, epochs=100), it gives me error: \n\nOOM when allocating tensor with shape[4760,256,256,3] and type float\n  on \/job:localhost\/replica:0\/task:0\/device:GPU:0 by allocator GPU_0_bfc\n  [Op:Cast] name: Cast\/\n\nThis code worked perfectly fine previously but not anymore with Tensorflow 2.0. I have 4760 samples in my training set. I don't know why it's taking the entire set instead of the batch size.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":451,"Q_Id":58277991,"Users Score":1,"Answer":"model.compile() only does configure the model for training and it doesn't have any memory allocation. \nYour bug is self-explained, you directly feed a large numpy array into the model. I would suggest coding a new data generator or keras.utils.Sequence to feed your input data. If so, you do not need specify the batch_size in fit method again, because your own generator or Sequence  will generate batches.","Q_Score":0,"Tags":"python,tensorflow,keras","A_Id":58278374,"CreationDate":"2019-10-07T22:38:00.000","Title":"Tensorflow 2.0 fit() is not recognizing batch_size","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a python script that loops through a dataset of videos and applies a face and lip detector function to each video. The function returns a 3D numpy array of pixel data centered on the human lips in each frame. \nThe dataset is quite large (70GB total, ~500,000 videos each about 1 second in duration) and executing on a normal CPU would take days. I have a Nvidia 2080 Ti that I would like to use to execute code. Is it possible to include some code that executes my entire script on the available GPU? Or am I oversimplifying a complex problem?\nSo far I have been trying to implement using numba and pycuda and havent made any progress as the examples provided don't really fit my problem well.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":81,"Q_Id":58278145,"Users Score":1,"Answer":"Your first problem is actually getting your Python code to run on all CPU cores!\nPython is not fast, and this is pretty much by design. More accurately, the design of Python emphasizes other qualities. Multi-threading is fairly hard in general, and Python can't make it easy due to those design constraints. A pity, because modern CPU's are highly parallel. In your case, there's a lucky escape. Your problem is also highly parallel. You can just divide those 500,000 video's over CPU cores. Each core runs a copy of the Python script over its own input. Even a quad-core would process 4x125.000 files using that strategy.\nAs for the GPU, that's not going to help much with Python code. Python simply doesn't know how to send data to the GPU, send commands to the CPU, or get results back. Some Pythons extensions can use the GPU, such as Tensorflow. But they use the GPU for their own internal purposes, not to run Python code.","Q_Score":0,"Tags":"python,gpu,face-detection,numba","A_Id":58915896,"CreationDate":"2019-10-07T22:56:00.000","Title":"How to execute python script (face detection on very large dataset) on Nvidia GPU","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm having trouble connecting the mathematical concept of spline interpolation with the application of a spline filter in python. My very basic understanding of spline interpolation is that it's fitting the data in a piece-wise fashion, and the piece-wise polynomials fitted are called splines. But its applications in image processing involve pre-filtering the image and then performing interpolation, which I'm having trouble understanding.\nTo give an example, I want to interpolate an image using scipy.ndimage.map_coordinates(input, coordinates, prefilter=True), and the keyword prefilter according to the documentation:\n\nDetermines if the input array is prefiltered with spline_filter before interpolation \n\nAnd the documentation for scipy.ndimage.interpolation.spline_filter simply says the input is filtered by a spline filter. So what exactly is a spline filter and how does it alter the input data to allow spline interpolation?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":389,"Q_Id":58278604,"Users Score":1,"Answer":"I'm guessing a bit here. In order to calculate a 2nd order spline, you need the 1st derivative of the data. To calculate a 3rd order spline, you need the second derivative. I've not implemented an interpolation motor beyond 3rd order, but I suppose the 4th and 5th order splines will require at least the 3rd and 4th derivatives.\nRather than recalculating these derivatives every time you want to perform an interpolation, it is best to calculate them just once. My guess is that spline_filter is doing this pre-calculation of the derivatives which then get used later for the interpolation calculations.","Q_Score":2,"Tags":"python,interpolation,spline","A_Id":58283603,"CreationDate":"2019-10-08T00:10:00.000","Title":"What is the difference between spline filtering and spline interpolation?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to understand why when we were using pandas to_csv(), a number 3189069486778499 has been output as \"0.\\x103189069486778499\". And this is the only case happened within a huge amount of data.\nWhen using to_csv(), we have already used encoding='utf8', normally that would solve some unicode problems...\nSo, I'm trying to understand what is \"\\x10\", so that I may know why...\nSince the whole process was running in luigi pipeline, sometimes luigi will generate weird output. I tried the same thing in IPython, same version of pandas and everything works fine....","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2069,"Q_Id":58279018,"Users Score":0,"Answer":"Because it's the likely answer, even if the details aren't provide in your question:\nIt's highly likely something in your pipeline is intentionally producing fields with length prefixed text, rather than the raw unstructured text. \\x103189069486778499 is a binary byte with the value 16 (0x10), followed by precisely 16 characters. The 0. before it may be from a previous output, or some other part of whatever custom data serialization format it's using.\nThis design is usually intended to make parsing more efficient; if you use a delimiter character between fields (e.g. a comma, like CSV), you're stuck coming up with ways to escape or quote the delimiter when it occurs in your actual data, and parsers have to scan character by character, statefully, to figure out where a field begins and ends. With length prefixed text, the parser finds a field length and knows exactly how many characters to read to slurp the field, or how many to skip to find the next field, no quoting or escaping required, no matter what the field contains.\nAs for what's doing this: You're going to have to check the commands in your pipeline. Your question provides no meaningful way to determine the cause of this problem.","Q_Score":0,"Tags":"python,pandas,unicode,luigi","A_Id":58279203,"CreationDate":"2019-10-08T01:19:00.000","Title":"unicode vs character: What is '\\x10'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm looking for a method to implement word embedding network with LSTM layers in Pytorch such that the input to the nn.Embedding layer has a different form than vectors of words IDs.\nEach word in my case has a corresponding vector and the sentence in my corpus is consequently a vector of vectors. So, for example, I may have the word \"King\" with vector [500, 3, 18] where 500 is the Word ID, 3 is the word color, and 18 is the font size, etc. The embedding layer role here is to do some automatic feature reduction\/extraction.\nHow can I feed the embedding layer with such form data? Or do you have any better suggestions?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1159,"Q_Id":58281876,"Users Score":2,"Answer":"I am not sure what do you mean by word2vec algorithm with LSTM because the original word2vec algorithm does not use LSTMs and uses directly embeddings to predict surrounding words.\nAnyway, it seems you have multiple categorical variables to embed. In the example, it is word ID, color ID, and font size (if you round it to integer values). You have two option:\n\nYou can create new IDs for all possible combinations of your features and use nn.Embedding for them. There is however a risk that most of the IDs will appear too sparsely in the data to learn reliable embeddings.\nHave separate embedding for each of the features. Then, you will need to combine the embeddings for the features together. You have basically three options how to do it:\n\nJust concatenate the embeddings and let the following layers of the network to resolve the combination.\nChoose the same embedding dimension for all features and average them. (I would start with this one probably.)\nAdd a nn.Dense layer (or two, the first one with ReLU activation and the second without activation) that will explicitly combine the embeddings for your features.\n\n\nIf you need to include continuous features that cannot be discretized,  you can always take the continuous features, apply a layer or two on top of them and combine them with the embeddings of the discrete features.","Q_Score":0,"Tags":"python,python-3.x,pytorch,word-embedding","A_Id":58283102,"CreationDate":"2019-10-08T07:27:00.000","Title":"Word embeddings with multiple categorial features for a single word","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a numpy array and I flatten it by np.ravel() and I am confused when i tried to learn the size of the both array\n\n\n\narray =np.arange(15).reshape(3,5)\nsys.getsizeof(array)\n      112\nsys.getsizeof(array.ravel())\n      96\narray.size\n      15\narray.ravel().size\n      15\n\n\n\n \n\n\n\narray = np.arange(30).reshape(5,6)\nsys.getsizeof(array)\n      112\nsys.getsizeof(array.ravel())\n      96\narray.size\n      30\n\n\n\nAs seen above two different arrays have the same memory size but each has different amount of element. Why does it happen?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":344,"Q_Id":58302190,"Users Score":0,"Answer":"As Derte mentioned, sys.getsizeof doesn't say the size of the array. The 96 you got is holding information about the array (if it's 1-Dimensional) and the 112 if it's multi dimensional. Any additional element will increase the size with 8 bytes assuming you are using a dtype=int64.","Q_Score":2,"Tags":"python,numpy,reshape","A_Id":58302623,"CreationDate":"2019-10-09T10:32:00.000","Title":"why numpy array has size of 112 byte and when I do flatten it, it has 96 byte of memory?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"May I ask what's the difference between df.head() and df.head in python's syntax nature? Could I interpret as the former one is for calling a method and the later one is just trying to obtain the DataFrame's attribute, which is the head? I am so confused why sometimes there is a parenthesis at the end but sometimes not... Thank you so much.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":44,"Q_Id":58315096,"Users Score":0,"Answer":"You understand it well, in general parenthesis call class method, and without you call an attribute.\nIn your exemple you don't have an error, because df.head is bound to NDFrame.head who is a method as well. If df.head was only a method, calling it without parenthesis will raise an AttributeError.","Q_Score":1,"Tags":"python,pandas,dataframe","A_Id":58315161,"CreationDate":"2019-10-10T03:36:00.000","Title":"DataFrame issue (parenthesis)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using Keras for the layers, optimizer, and model and my model is Sequential\nI've got two DQN networks and I'm making them duel each other in a simulated environment however after about 35 episodes (different each time) the script just stops without any errors. I've isolated my issue to be somewhere around when the agent runs the prediction model for the current state to get the action. The process is called but never completed and the script just stops without any error. How can I debug this issue?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":46,"Q_Id":58332232,"Users Score":1,"Answer":"The way I was looping my function needed to be a for loop instead of directly calling the function as a loop method. And my error was a stack overflow","Q_Score":0,"Tags":"python,python-3.x,machine-learning,artificial-intelligence","A_Id":58334583,"CreationDate":"2019-10-10T23:11:00.000","Title":"Python script closes after a while","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to improve mobilenet_v2's detection of boats with about 400 images I have annotated myself, but keep on getting an underfitted model when I freeze the graphs, (detections are random does not actually seem to be detecting rather just randomly placing an inference). I performed 20,000 steps and had a loss of 2.3.\nI was wondering how TF knows that what I am training it on with my custom label map\nID:1\nName: 'boat'\nIs the same as what it regards as a boat ( with an ID of 9) in the mscoco label map.\nOr whether, by using an ID of 1, I am training the models' idea of what a person looks like to be a boat?\nThank you in advance for any advice.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":51,"Q_Id":58332687,"Users Score":0,"Answer":"The model works with the category labels (numbers) you give it.  The string \"boat\" is only a translation for human convenience in reading the output.\nIf you have a model that has learned to identify a set of 40 images as class 9, then giving it a very similar image that you insist is class 1 will confuse it.  Doing so prompts the model to elevate the importance of differences between the 9 boats and the new 1 boats.  If there are no significant differences, then the change in weights will find unintended features that you don't care about.\nThe result is a model that is much less effective.","Q_Score":0,"Tags":"python,tensorflow,deep-learning,conv-neural-network,object-detection","A_Id":58345624,"CreationDate":"2019-10-11T00:23:00.000","Title":"How does TF know what object you are finetuning for","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to improve mobilenet_v2's detection of boats with about 400 images I have annotated myself, but keep on getting an underfitted model when I freeze the graphs, (detections are random does not actually seem to be detecting rather just randomly placing an inference). I performed 20,000 steps and had a loss of 2.3.\nI was wondering how TF knows that what I am training it on with my custom label map\nID:1\nName: 'boat'\nIs the same as what it regards as a boat ( with an ID of 9) in the mscoco label map.\nOr whether, by using an ID of 1, I am training the models' idea of what a person looks like to be a boat?\nThank you in advance for any advice.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":51,"Q_Id":58332687,"Users Score":0,"Answer":"so I managed to figure out the issue.\nWe created the annotation tool from scratch and the issue that was causing underfitting whenever we trained regardless of the number of steps or various fixes I tried to implement was that When creating bounding boxes there was no check to identify whether the xmin and ymin coordinates were less than the xmax and ymax I did not realize this would be such a large issue but after creating a very simple check to ensure the coordinates are correct training ran smoothly.","Q_Score":0,"Tags":"python,tensorflow,deep-learning,conv-neural-network,object-detection","A_Id":58513059,"CreationDate":"2019-10-11T00:23:00.000","Title":"How does TF know what object you are finetuning for","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I trained my unsupervised model using fasttext.train_unsupervised() function in python. I want to save it as vec file since I will use this file for pretrainedVectors parameter in fasttext.train_supervised() function. pretrainedVectors only accepts vec file but I am having troubles to creating this vec file. Can someone help me?\nPs. I am able to save it in bin format. It would be also helpful if you suggest me a way to convert bin file to vec file.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":6401,"Q_Id":58337469,"Users Score":1,"Answer":"you should add words num and dimension at first line of your vec file, than use -preTrainedVectors para","Q_Score":9,"Tags":"python,word-embedding,fasttext","A_Id":59558292,"CreationDate":"2019-10-11T08:46:00.000","Title":"How to save fasttext model in vec format?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When trying to import numpy in spyder i get the following error message:\nImportError:\nIMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE!\nImporting the numpy c-extensions failed.\n- Try uninstalling and reinstalling numpy.\n- If you have already done that, then:\n1. Check that you expected to use Python3.7 from \"\/home\/sltzgs\/anaconda3\/bin\/python\",and that you have no directories in your PATH or PYTHONPATH that can interfere with the Python and numpy version \"1.17.2\" you're trying to use.\n2. If (1) looks fine, you can open a new issue at\n     https:\/\/github.com\/numpy\/numpy\/issues.  Please include details on:\n     - how you installed Python\n     - how you installed numpy\n     - your operating system\n     - whether or not you have multiple versions of Python installed\n     - if you built from source, your compiler versions and ideally a build log\n- If you're working with a numpy git repository, trygit clean -xdf(removes all files not under version control) and rebuild numpy.\nNote: this error has many possible causes, so please don't comment on\nan existing issue about this - open a new one instead.\nOriginal error was: No module named 'numpy.core._multiarray_umath'\nHowever, importing in a jupyter notebook works fine. How is that possible? \nI have uninstalled\/installed numpy a few times by now and also made sure, that the sys.paths are identical.\nAny help appreciated. In case any additional information is required I would happily help out. Thanks.\nSome essentials:\npython 3.7\nSpyder 3.3.6 \nnumpy 1.17.2","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1431,"Q_Id":58339023,"Users Score":1,"Answer":"Problem did not occur again after re-installing Anaconda. Thanks @CarlosCordoba.","Q_Score":0,"Tags":"python,numpy,spyder","A_Id":61201527,"CreationDate":"2019-10-11T10:11:00.000","Title":"Numpy can't be imported in Spyder","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have used OpenCVs stereocalibrate function to get the relative rotation and translation from one camera to another. What I'd like to do is change the origin of the world space and update the extrinsics of both cameras accordingly. I can easily do this with cameras that have a shared view with SolvePnP but I'd like to do this with cameras in which each is defined by it's pose relative to an adjacent camera where all of their fields don't overlap - like daisy chaining their relative poses.\nI've determined the pose of the cameras relative to where I'd like the world origin and orientation to be using SolvePnP so that I know what the final extrinsics 'should be'. I've then tried combining the rotation matrices and translation vectors from the stereocalibration with the SolvePnP from the primary camera to get the same value both with ComposeRT and manually but to no avail.\nEdit: So it turns out that for whatever reason the StereoCalibration and SolvePnP functions produce mirrored versions of the poses as the StereoCalibration appears to produce poses with a 180 degree rotation around the Y-axis. So by applying that rotation to the produced relative rotation matrix and translation vector everything works!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":554,"Q_Id":58339628,"Users Score":0,"Answer":"If you know relative poses between all the cameras via a chain (relative poses between cameras a, b and b, c), you can combine the translations and rotations from camera a to c via b by\nR ac = Rab R bc\nt ac = t ab + R ab t bc\nIn other words, the new rotation from ac is rotating first from a to b and then from b to c. Translation is calculated in the same way, in addition the second translation vector has to be rotated by R ab.\nSome amount of error is expected, depending how accurate your pairwise calibration is. Errors in camera pose accumulate over the chain. If you have camera poses making full circle you generally don't get the exact same poses for the starting\/ending camera.","Q_Score":0,"Tags":"python,opencv,camera-calibration","A_Id":58340103,"CreationDate":"2019-10-11T10:47:00.000","Title":"Combining relative camera rotations & translations with known pose","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have the following error when running a CNN made in keras\n\nFile\n  \"venv\/lib\/python3.6\/site-packages\/keras\/backend\/tensorflow_backend.py\",\n  line 4185, in truncated_normal\n      return tf.truncated_normal(shape, mean, stddev, dtype=dtype, seed=seed) AttributeError: module 'tensorflow' has no attribute\n  'truncated_normal'\n\nI have already installed and reinstalled Tensorflow 2.0 several times. What could be happening?","AnswerCount":3,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":8138,"Q_Id":58341433,"Users Score":3,"Answer":"Keras 2.2.4 does not support TensorFlow 2.0 (it was released much before TF 2.0), so you can either downgrade TensorFlow to version 1.x, or upgrade Keras to version 2.3, which does support TensorFlow 2.0.","Q_Score":3,"Tags":"python,tensorflow,keras","A_Id":58342047,"CreationDate":"2019-10-11T12:36:00.000","Title":"Error when running tensorflow in virtualenv: module 'tensorflow' has no attribute 'truncated_normal'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have the following error when running a CNN made in keras\n\nFile\n  \"venv\/lib\/python3.6\/site-packages\/keras\/backend\/tensorflow_backend.py\",\n  line 4185, in truncated_normal\n      return tf.truncated_normal(shape, mean, stddev, dtype=dtype, seed=seed) AttributeError: module 'tensorflow' has no attribute\n  'truncated_normal'\n\nI have already installed and reinstalled Tensorflow 2.0 several times. What could be happening?","AnswerCount":3,"Available Count":2,"Score":0.3215127375,"is_accepted":false,"ViewCount":8138,"Q_Id":58341433,"Users Score":5,"Answer":"In Tensorflow v2.0 and above, \"tf.truncated_normal\" replaced with \"tf.random.truncated_normal\"","Q_Score":3,"Tags":"python,tensorflow,keras","A_Id":67832940,"CreationDate":"2019-10-11T12:36:00.000","Title":"Error when running tensorflow in virtualenv: module 'tensorflow' has no attribute 'truncated_normal'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I understand how it works when you have one column output but could not understand how it is done for 4 column outputs.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":49,"Q_Id":58342612,"Users Score":1,"Answer":"It\u2019s not advised to calculate accuracy for continuous values. For such values you would want to calculate a measure of how close the predicted values are to the true values. This task of prediction of continuous values is known as regression. And generally R-squared value is used to measure the performance of the model.\nIf the predicted output is of continuous values then mean square error is the right option \nFor example:\nPredicted o\/p vector1-----> [2,4,8] and\nActual o\/p vector1 -------> [2,3.5,6]\n1.Mean square error is  sqrt((2-2)^2+(4-3.5)^2+(8-6)^2 )\n2.Mean absolute error..etc.\n(2)if the output is of classes then accuracy is the right metric to decide on model performance\nPredicted o\/p vector1-----> [0,1,1]\nActual o\/p vector1 -------> [1,0,1]\nThen accuracy calculation can be done with following:\n1.Classification Accuracy\n2.Logarithmic Loss\n3.Confusion Matrix\n4.Area under Curve\n5.F1 Score","Q_Score":0,"Tags":"python-3.x,tensorflow,neural-network,conv-neural-network,recurrent-neural-network","A_Id":60562288,"CreationDate":"2019-10-11T13:46:00.000","Title":"I have a network with 3 features and 4 vector outputs. How is MSE and accuracy metric calculated?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"During inference, when the models are being loaded, Cuda throws InternalError: CUDA runtime implicit initialization on GPU:0 failed. Status: out of memory. \nI am performing inference on a machine with 6GB of VRAM. A few days back, the machine was able to perform the tasks, but now I am frequently getting these messages. Restarting the device sometimes does help, but is not a viable solution. I have checked through nvidia-smi, but it is also showing only about 500 MB of VRam being used and I was not able to see any spike in memory usage when tensorflow was trying to load the models.\nI am currently using tensorflow 1.14.0 and python 3.7.4","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":2542,"Q_Id":58350456,"Users Score":2,"Answer":"I am using Tensorflow 2.3.0 on a remote server. My code was working fine, but suddenly the server gets disconnected from the network, and my training stopped. When I re-run the code I got the same issue you got. So I guess this problem is related to GPU being busy in something not existing anymore. Clearing the session as the comment said is enough to solve the problem (I also believe restarting the machine can also fix the problem but I did not get the chance to try this solution).\nfor tensorflow 2.3 use tf.kerasbackend.clear_session() it solve the issue","Q_Score":0,"Tags":"python,tensorflow","A_Id":68995590,"CreationDate":"2019-10-12T02:55:00.000","Title":"CUDA goes out of memory during inference and gives InternalError: CUDA runtime implicit initialization on GPU:0 failed. Status: out of memory","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"If I  am given a large data set with many variables is it possible to determine whether any two of them are independent or dependent? Lets assume I know nothing else about the data other than a statistical study.\nWould looking at the correlation\/covariance be able to determine this?\nThe purpose of this is to determine which variables would be the best to use in machine learning to predict a specific outcome. I have some variables with a correlation of 0.40 - 0.50 with one another but I'm not sure if a high correlation == dependence.\nThanks","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":75,"Q_Id":58358757,"Users Score":0,"Answer":"Dependence and correlation are different. if 2 variables are dependent, then they are correlated. However, if they are correlated, it is not sure that they are dependent, we need domain knowledge to consider more. To check the correlation, we can use the Correlation Coefficient. For the dependence test, we can use the Chi-Square Test.","Q_Score":0,"Tags":"python,statistics,data-science,cross-validation","A_Id":58358854,"CreationDate":"2019-10-12T21:35:00.000","Title":"Dependent vs Independent Variables","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to use tf.contrib.layers.fully_connected() in one of my projects, and it's been deprecated in tensorflow 2.0. Is there an equivalent function, or should I just keep tensorflow v1.x in my virtual environment for this projcet?","AnswerCount":4,"Available Count":3,"Score":1.2,"is_accepted":true,"ViewCount":9478,"Q_Id":58359881,"Users Score":4,"Answer":"In TensorFlow 2.0 the package tf.contrib has been removed (and this was a good choice since the whole package was a huge mix of different projects all placed inside the same box), so you can't use it.\nIn TensorFlow 2.0 we need to use tf.keras.layers.Dense to create a fully connected layer, but more importantly, you have to migrate your codebase to Keras. In fact, you can't define a layer and use it, without creating a tf.keras.Model object that uses it.","Q_Score":4,"Tags":"python,python-3.x,tensorflow,tensorflow2.0","A_Id":58361716,"CreationDate":"2019-10-13T01:25:00.000","Title":"tf.contrib.layers.fully_connected() in Tensorflow 2?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to use tf.contrib.layers.fully_connected() in one of my projects, and it's been deprecated in tensorflow 2.0. Is there an equivalent function, or should I just keep tensorflow v1.x in my virtual environment for this projcet?","AnswerCount":4,"Available Count":3,"Score":0.2449186624,"is_accepted":false,"ViewCount":9478,"Q_Id":58359881,"Users Score":5,"Answer":"tf-slim, as a standalone package, already included tf.contrib.layers.you can install by pip install tf-slim,call it by from tf_slim.layers import layers as _layers;  _layers.fully_conntected(..).The same as the original, easy to replace","Q_Score":4,"Tags":"python,python-3.x,tensorflow,tensorflow2.0","A_Id":61319962,"CreationDate":"2019-10-13T01:25:00.000","Title":"tf.contrib.layers.fully_connected() in Tensorflow 2?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to use tf.contrib.layers.fully_connected() in one of my projects, and it's been deprecated in tensorflow 2.0. Is there an equivalent function, or should I just keep tensorflow v1.x in my virtual environment for this projcet?","AnswerCount":4,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":9478,"Q_Id":58359881,"Users Score":0,"Answer":"tf.contrib.layers.fully_connected() is a perfect mess. It is a very old historical mark(or a prehistory DNN legacy). Google has completely deprecated the function since Google hated it. There is no any direct function in TensoFlow 2.x to replace tf.contrib.layers.fully_connected(). Therefore, it is not worth inquiring and getting to know the function.","Q_Score":4,"Tags":"python,python-3.x,tensorflow,tensorflow2.0","A_Id":64493808,"CreationDate":"2019-10-13T01:25:00.000","Title":"tf.contrib.layers.fully_connected() in Tensorflow 2?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to deep learning, I was wondering if there is a way to extract parts of images containing the different label and then feed those parts to different model for further processing?\nFor example,consider the dog vs cat classification.\nSuppose the image contains both cat and dog.\nWe successfully classify that the image contains both, but how can we classify the breed of the dog and cat present?\nThe approach I thought of was,extracting\/cutting out the parts of the image containing dog and cat.And then feed those parts to the respective dog breed classification model and cat breed classification model separately.\nBut I have no clue on how to do this.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":170,"Q_Id":58362763,"Users Score":1,"Answer":"Your thinking is correct, you can have multiple pipelines based on the number of classes.\n\nTraining:\nMain model will be an object detection and localization model like Faster RCNN, YOLO, SSD etc trained to classify at a high level like cat and dog. This pipeline provides you bounding box details (left, bottom, right, top) along with the labels.\nSub models will be multiple models trained on a lover level. For example a model that is trained to classify breed. This can be done by using models like vgg, resnet, inception etc. You can utilize transfer learning here.\nInference: Pass the image through Main model, crop out the detection objects using bounding box details (left, bottom, right, top) and based on the label information, feed it appropriate sub model and extract the results.","Q_Score":0,"Tags":"python,tensorflow,machine-learning,keras,deep-learning","A_Id":58363469,"CreationDate":"2019-10-13T10:37:00.000","Title":"How to extract\/cut out parts of images classified by the model?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm new to dask and trying to use it in our cluster which uses NC job scheduler (from Runtime Design Automation, similar to LSF). I'm trying to create an NCCluster class similar to LSFCluster to keep things simple. \nWhat are the steps involved in creating a job scheduler for custom clusters?\nIs there any other way to interface dask to custom clusters without using JobQueueCluster?\nI could find info on how to use the LSFCluster\/PBSCluster\/..., but couldn't find much information on creating one for a different HPC.\nAny links to material\/examples\/docs will help\nThanks","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":60,"Q_Id":58364733,"Users Score":0,"Answer":"Got it working after going through the source code.\nTips for anyone trying:\n\nCreate a customCluster & customJob class similar to LSFCluster & LSFJob.\nOverride the following\n\n\nsubmit_command\ncancel_command\nconfig_name (you'll have to define it in the jobqueue.yaml)\nDepending on the cluster, you may need to override the _submit_job, _job_id_from_submit_ouput and other functions.\n\n\nHope this helps.","Q_Score":0,"Tags":"python,python-3.x,dask,dask-distributed","A_Id":58399685,"CreationDate":"2019-10-13T14:56:00.000","Title":"Creating dask_jobqueue schedulers to launch on a custom HPC","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to implement for Keras a new optimization function that would not be based on the partial derivatives of the parameters, but also on the derivatives of these partial derivatives. How can I proceed?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":87,"Q_Id":58365610,"Users Score":0,"Answer":"In fact, after having looked at the Keras code of the Optimizer 2-3 times, not only did I quickly give up trying to understand everything, but it seemed to me that the get_updates function simply returns the gradients already calculated, where I seek to directly access the partial derivation functions of the parameters in order to use the derivatives of these derivatives. So the gradients are useless ...","Q_Score":0,"Tags":"python,tensorflow,math,keras","A_Id":58468050,"CreationDate":"2019-10-13T16:35:00.000","Title":"How to define a new optimization function for Keras","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"May I know if we can use variable\/array in the arrays_zip function ??\nFor example I declare and array \narray1=[\"col1\",\"col2\"]\nthen in the dataframe. I write the following :\ndf.withColumn(\"zipped\",arrays_zip(array1))\nthen it tells me it's not a valid argument not a string or column\nany one has the idea ?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":376,"Q_Id":58369843,"Users Score":0,"Answer":"using the following:\narray=[\"col1\",\"col2\"]\ndf.select(arrays_zip(*[c for c in array]]).show()\nThanks","Q_Score":0,"Tags":"python,databricks","A_Id":58385939,"CreationDate":"2019-10-14T03:42:00.000","Title":"DataBricks: using variable in arrays_zip function","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it possible to add a new face features into trained face recognition model, without retraining it with previous faces?\nCurrently am using facenet architecture,","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":718,"Q_Id":58372751,"Users Score":0,"Answer":"basically, by the mathematics theory behind the machine learning models, you basically need to do another train iteration with only this new data...\nbut, in practice, those models, especially the sophisticated ones, rely on multiple iterations for training and a various technics of suffering and nose reductions\na good approach can be train of the model from previous state with a subset of the data that include the new data, for a couple of iterations","Q_Score":0,"Tags":"python-3.x,machine-learning,computer-vision,face-recognition,object-recognition","A_Id":58375333,"CreationDate":"2019-10-14T08:24:00.000","Title":"How to we add a new face into trained face recognition model(inception\/resnet\/vgg) without retraining complete model?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it possible to add a new face features into trained face recognition model, without retraining it with previous faces?\nCurrently am using facenet architecture,","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":718,"Q_Id":58372751,"Users Score":1,"Answer":"Take a look in Siamese Neural Network.\nActually if you use such approach you don't need to retrain the model.\nBasically you train a model to generate an embedding (a vector) that maps similar images near and different ones far.\nAfter you have this model trainned, when you add a new face it will be far from the others but near of the samples of the same person.","Q_Score":0,"Tags":"python-3.x,machine-learning,computer-vision,face-recognition,object-recognition","A_Id":59680250,"CreationDate":"2019-10-14T08:24:00.000","Title":"How to we add a new face into trained face recognition model(inception\/resnet\/vgg) without retraining complete model?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using Windows 10, CPU: Intel(R) Core(TM) 2 Duo CPU T6600 @ 2.2GHz (2 CPUs) ~2.2GHz. RAM: 4GB. Video card: ATI Mobility Radeon HD 3400 Series.\nI uninstalled everything and then\nI installed Python 3.6 and Tensorflow==1.10.0. When I do import tensorflow, I get this error.\n\nmodulenotfounderror no module named '_pywrap_tensorflow_internal'\n\nI can install whichever Python\/Tensorflow version you want. I just want to use Tensorflow. I saw similar issues, but none of them seems to be solving my issue.\nI know there are many similar questions on github\/stackoverflow, but none of them seems to help me.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":242,"Q_Id":58374635,"Users Score":1,"Answer":"Mentioning the Answer here for the benefit of the Community.\nIssue is resolved by using Python==3.6, Tensorflow==1.5, protobuf==3.6.0.","Q_Score":4,"Tags":"python,tensorflow","A_Id":59246432,"CreationDate":"2019-10-14T10:15:00.000","Title":"modulenotfounderror no module named '_pywrap_tensorflow_internal'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to build a recommender system using collaborative filtering.\n\nI am having user-item dataset. I am unable to find similarity between similar user, since i cannot use  Euclidean \/ Cosine distance will not work here. \nIf i convert categorical variable into 0, 1 then will not able to calculate distance.\nCan you please suggest any recommendation algorithm in python which handles categorical data.","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":2649,"Q_Id":58376140,"Users Score":1,"Answer":"One good example to calculate distance between categorical features is Hamming Distance where we calculate the number of different instances.\nOn the other hand, you can still calculate Cosine Similarity for user-item data set.\nAs an example; \nuser 1 buys item 1, item 2\nuser 2 buys item 2, item 3\nThen, user vectors are;\nuser 1 = [1, 1, 0]\nuser 2 = [0, 1, 1]\nAnd cosine similarity will be 0.5\nSame rules apply for items.","Q_Score":0,"Tags":"python,recommendation-engine,collaborative-filtering","A_Id":58376285,"CreationDate":"2019-10-14T11:48:00.000","Title":"How to calculate similarity between categorical variables in collaborative filtering","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to build a recommender system using collaborative filtering.\n\nI am having user-item dataset. I am unable to find similarity between similar user, since i cannot use  Euclidean \/ Cosine distance will not work here. \nIf i convert categorical variable into 0, 1 then will not able to calculate distance.\nCan you please suggest any recommendation algorithm in python which handles categorical data.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":2649,"Q_Id":58376140,"Users Score":0,"Answer":"Cosine similarity will handle the problem as a whole vector which includes all values of the variable. And may not give the answer for correlation. So when you received a good score from cosine similarity, it will not make sure they are also correlated.","Q_Score":0,"Tags":"python,recommendation-engine,collaborative-filtering","A_Id":62181010,"CreationDate":"2019-10-14T11:48:00.000","Title":"How to calculate similarity between categorical variables in collaborative filtering","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a sequence tagging model that predicts a tag for every word in an input sequence (essentially named entity recognition). Model structure: Embeddings layer \u2192 BiLSTM  \u2192 CRF\nSo essentially the BiLSTM learns non-linear combinations of features based on the token embeddings and uses these to output the unnormalized scores for every possible tag at every timestep. The CRF classifier then learns how to choose the best tag sequence given this information.\nMy CRF is an instance of the keras_contrib crf, which implements a linear chain CRF (as does tensorflow.contrib.crf). Thus it considers tag transition probabilities from one tag to the next but doesn't maximize the global tag sequence (which a general CRF would).\nThe default activation function is 'linear'. My question is, why is it linear, and what difference would other activations make?\nI.e., is it linear because it's decisions are essentially being reduced to predicting the likelihood of tag yt given tag y-1 (which could possibly be framed as a linear regression problem)? Or is it linear for some other reason, e.g. giving the user flexibility to apply the CRF wherever they like and choose the most appropriate activation function themselves?\nFor my problem, should I actually be using softmax activation? I already have a separate model with a similar but different structure: Embeddings  \u2192  BiLSTM  \u2192 Dense with softmax. So I if I were to use softmax activation in the linear chain CRF (i.e. in the Embeddings layer \u2192 BiLSTM  \u2192 CRF I mentioned at the start of this post), it sounds like it would be nearly identical to that separate model except for being able to use transition probabilities from yt-1 to yt.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":458,"Q_Id":58377983,"Users Score":1,"Answer":"When using Embeddings \u2192 BiLSTM \u2192 Dense + softmax, you implicitly assume that the likelihood of the tags is conditionally independent given the RNN states. This can lead to the label bias problem. The distribution over the tags always needs to sum up to one. There is no way to express that the model is not certain about the particular tag does an independent prediction for that.\nIn a CRF, this can get fixed using the transition scores that the CRF learns in addition to scoring the hidden states. The score for the tag can be an arbitrary real number. If the model is uncertain about a tag, all scores can be low (because they do not have to sum up to one) and predictions from the neighboring tags might help in choosing what tag to chose via the transition scores. The likelihood of the tags is not factorized over the sequence but computed for the entire sequence of tags using a dynamic programming algorithm.\nIf you used an activation function with a limited range, it would limit what scores can be assigned to the tags and might the CRF not efficient. If you think you need a non-linearity after the RNN, you can add one dense layer with activation of your choice and then do the linear projection.","Q_Score":2,"Tags":"python,tensorflow,keras,neural-network,crf","A_Id":58378980,"CreationDate":"2019-10-14T13:35:00.000","Title":"Which actvation function to use for linear-chain CRF classifier?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In rivescript, if a user asks me the price of a certain item, I want the bot to look for that items' price in the csv file. Im new to to rivescript so any kind of help would be appreciated.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":89,"Q_Id":58378873,"Users Score":0,"Answer":"you can use macro for doing this. \n> object read_from_csv python\n   #code to read from CSV\n    return \"\"\n< object\nFrom rive response\n-<call>read_from_csv <star><\/call>","Q_Score":1,"Tags":"python,csv,rivescript","A_Id":59798103,"CreationDate":"2019-10-14T14:28:00.000","Title":"How to bring variable values from csv to rivescript?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"In data mining, I use a machine learning algorithm to solve the binary classification.\nHowever, the distribution of data samples is extremely imbalanced.\nThe ratio between good samples and bad samples is as high as 500:1.\nWhich methods can be used to solve the binary classification for imbalanced data?","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":104,"Q_Id":58393565,"Users Score":0,"Answer":"You can use Synthetic Minority Oversampling Techniques(SMOTE) or Adasyn to tackle this. Try both methods and finalize based on your desired results.","Q_Score":0,"Tags":"python","A_Id":58399690,"CreationDate":"2019-10-15T11:24:00.000","Title":"binary classification for imbalanced data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In data mining, I use a machine learning algorithm to solve the binary classification.\nHowever, the distribution of data samples is extremely imbalanced.\nThe ratio between good samples and bad samples is as high as 500:1.\nWhich methods can be used to solve the binary classification for imbalanced data?","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":104,"Q_Id":58393565,"Users Score":0,"Answer":"Also you can use asymmetric loss functions which penalize models differently based on the label of data. In your case the loss function should penalize errors in \"bad\" samples much more than errors in \"good\" samples. In this way the model pays more attentions to the rare data points.","Q_Score":0,"Tags":"python","A_Id":66716747,"CreationDate":"2019-10-15T11:24:00.000","Title":"binary classification for imbalanced data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using scipy.cluster.hierarchy.fclusterdata function to cluster a list of vectors (vectors with 384 components).\nIt works nice, but when I try to cluster large amounts of data I run out of memory and the program crashes.\nHow can I perform the same task without running out of memory?\nMy machine has 32GB RAM, Windows 10 x64, python 3.6 (64 bit)","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":593,"Q_Id":58396826,"Users Score":0,"Answer":"You'll need to choose a different algorithm.\nHierarchical clustering needs O(n\u00b2) memory and the textbook algorithm O(n\u00b3) time. This cannot scale well to large data.","Q_Score":2,"Tags":"python,python-3.x,scipy,cluster-analysis,data-analysis","A_Id":58406856,"CreationDate":"2019-10-15T14:18:00.000","Title":"How to cluster large amounts of data with minimal memory usage","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"If the sum is 1, I could just divide the values by their sum. However, this approach is not applicable when the sum is 0. \nMaybe I could compute the opposite of each value I sample, so I would always have a pair of numbers, such that their sum is 0. However this approach reduces the \"randomness\" I would like to have in my random array.\nAre there better approaches?\nEdit: the array length can vary (from 3 to few hundreds), but it has to be fixed before sampling.","AnswerCount":5,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":437,"Q_Id":58407760,"Users Score":0,"Answer":"Since you are fine with the approach of generating lots of numbers and dividing by the sum, why not generate n\/2 positive numbers divide by sum. Then generate n\/2 negative numbers and divide by sum?\nWant a random positive to negative mix? Randomly generate that mix randomly first then continue.","Q_Score":1,"Tags":"python,random","A_Id":58408874,"CreationDate":"2019-10-16T07:16:00.000","Title":"How to generate random values in range (-1, 1) such that the total sum is 0?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have created a machine learning software that detects objects(duh!), processes the objects based on some computer vision parameters and then triggers some hardware that puts the object in the respective bin. The objects are placed on a conveyer belt and a camera is mounted at a point to snap pictures of objects(one object at a time) when they pass beneath the camera. I don't have control over the speed of the belt.\nNow, the challenge is that I have to configure a ton of things to make the machine work properly.\nThe first problem is the time model takes to create segmentation masks, it varies from one object to another.\nAnother issue is how do I maintain signals that are generated after computer vision processing, send them to actuators in a manner that it won't get misaligned with the computer vision-based inferencing.\nMy initial design includes creating processes responsible for a specific task and then make them communicate with one other as per the necessity. However, the problem of synchronization still persists.\nAs of now, I am thinking of treating the software stack as a group of services as we usually do in backend and make them communicate using something like celery and Redis queue.\nI am a kind of noob in system design, come from a background of data science. I have explored python's multithreading module and found it unusable for my purpose(all threads run on single core). I am concerned if I used multiprocessing, there could be additional delays in individual processes due to messaging and thus, that would add another uncertainty to the program.\nAdditional Details:\n\nProgramming Frameworks and Library: Tensorflow, OpenCV and python\nCamera Resolution: 1920P\nMaximum Accutuation Speed: 3 triggers\/second\nDeep Learning Models: MaskRCNN\/UNet\n\nP.S: You can also comment on the technologies or the keywords I should search for because a vanilla search yields nothing good.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":73,"Q_Id":58409257,"Users Score":1,"Answer":"Let me summarize everything first. \n\nWhat you want to do\n\nThe \"object\" is on the conveyer belt\nThe camera will take pictures of the object\nMaskRCNN will run to do the analyzing\n\nHere are some problems you're facing\n\n\"The first problem is the time model takes to create segmentation masks, it varies from one object to another.\"   \n\n-> if you want to reduce the processing time for each image, then an accelerator (FPGA, Chip, etc) or some acceleration technique is needed. Intel OpenVino and Intel DL stick is a good start. \n-> if there are too many pictures to process then you'll have 2 choices: 1) put a lot of machines so all the job can be done or 2) select only the important job and discard others. The fact that you set the \"Maximum Accutuation\" to a fixed number (3\/sec) made me think that this is the problem you're facing. A background subtractor is a good start for creating images capture triggers. \n\n\"Another issue is how do I maintain signals that are generated after computer vision processing, send them to actuators in a manner that it won't get misaligned with the computer vision-based inferencing.\"\n\n-> a \"job distributor\" like Celery is good choice here. If the message is stacked inside the broker (Redis), then some tasks will have to wait. But this can easily by scaling up your computer.\n\nJust a few advice here:\n\na vision system also includes the hardware parts, so a hardware specification is a must. \nClarify the requirements \nImpossible things do exist, so sometimes you could reduce some factors (reliable, cost) of your project.","Q_Score":0,"Tags":"python,tensorflow,deep-learning,robotics","A_Id":58479863,"CreationDate":"2019-10-16T08:45:00.000","Title":"How to design realtime deeplearnig application for robotics using python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"When I re-create a model, keras always makes a new name for a layer (conv2d_2 and so on) even if I override the model. How to make keras using the same name every time I run it without restarting the kernel.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":305,"Q_Id":58413230,"Users Score":1,"Answer":"When using Tensorflow (1.X) as a backend, whenever you add a new layer to any model, the name of the layer -unless manually set- will be set to the default name for that layer, plus an incremental index at the end.\nDefining a new model is not enough to reset the incrementing index, because all models end up on the same underlying graph. To reset the index, you must reset the underlying graph.\nIn TF 1.X, this is done via tf.reset_default_graph(). In TF 2.0, you can do this via the v1 compatibility API: tf.compat.v1.reset_default_graph() (the latter will also solve some deprecation warnings you might get with the latest versions of TF 1.X)","Q_Score":2,"Tags":"python,tensorflow,keras,neural-network,jupyter-notebook","A_Id":58428014,"CreationDate":"2019-10-16T12:21:00.000","Title":"How to prevent keras from renaming layers","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"As the title says, that's basically it. I have tried to install matplotlib already but:\n\nI am on Windows and \"sudo\" doesn't work\nEvery solution and answers on Stack Overflow regarding matplotlib (or some other package) not being able to be installed doesn't work for me...\nI get \"Error Code 1\"\n\nSo! Is there any other way to plot a graph in python without matplotlib? If not, can I have help with how to install matplotlib, successfully?","AnswerCount":1,"Available Count":1,"Score":-0.1973753202,"is_accepted":false,"ViewCount":4146,"Q_Id":58414797,"Users Score":-1,"Answer":"in cmd (coammand prompt) type pip install matplotlib","Q_Score":2,"Tags":"python,matplotlib,graph","A_Id":68501575,"CreationDate":"2019-10-16T13:41:00.000","Title":"Is there another way to plot a graph in python without matplotlib?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a problem that I have been treating as a classification problem. I am trying to predict whether a machine will pass or fail a particular test based on a number of input features. \nWhat I am really interested in is actually whether a new machine is predicted to pass or fail the test. It can pass or fail the test by having certain signatures (such as speed, vibration etc) go out of range.\nTherefore, I could either:\n1) Treat it as a pure regression problem; try to predict the actual values of speed, vibration etc\n2) Treat it as a pure classification problem; for each observation, feed in whether it passed or failed on the labels, and try to predict this in the tool I am making\n3) Treat it as a pseudo problem; where I predict the actual value, and come up with some measure of how confident I am that it is a pass or fail based on distance from the threshold of pass\/fail\nTo be clear; I am working on a real problem. I am not interested in getting a super precise prediction of a certain value, just whether a machine is predicted to pass or fail (and bonus extension; how likely that it is to be true).\nI have been working with classification model as I only have a couple hundred observations and some previous research showed that this might be the best way to treat the problem. However I am wondering now whether this is the right thing to do.\nWhat would you do!?\nMany thanks.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":42,"Q_Id":58416636,"Users Score":0,"Answer":"Without having the data and running classification or regression, a comparison would be hard because of the metric you use for each family is different. \nFor example, comparing RMSE of a regression with F1 score (or accuracy) of a classification problem would be apple to orange comparison.\nIt would be ideal if you can train a good regression model (low RMSE) because that would give you information more than the original pass\/fail question. From my past experiences with industrial customers, \nFirst, train all 3 models you have mentioned and then present the outcome to your customer and let them give you more direction on which models\/outputs are more meaningful for them.","Q_Score":1,"Tags":"python,machine-learning,regression,classification","A_Id":58421378,"CreationDate":"2019-10-16T15:15:00.000","Title":"Regression vs Classification for a problem that could be solved by both","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to clean a csv file for data analysis. How do I convert TRUE FALSE into 1 and 0?\nWhen I search Google, they suggested df.somecolumn=df.somecolumn.astype(int). However this csv file has 100 columns and not every column is true false(some are categorical, some are numerical). How do I do a sweeping code that allows us to convert any column with TRUE FALSE to 1 and 0 without typing 50 lines of df.somecolumn=df.somecolumn.astype(int)","AnswerCount":3,"Available Count":1,"Score":0.2605204458,"is_accepted":false,"ViewCount":87,"Q_Id":58417900,"Users Score":4,"Answer":"you can use:\ndf.select_dtypes(include='bool')=df.select_dtypes(include='bool').astype(int)","Q_Score":0,"Tags":"python-3.x,pandas,data-cleaning","A_Id":58418351,"CreationDate":"2019-10-16T16:30:00.000","Title":"Data Cleaning with Pandas in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How do I create a 2D array of zeros that will be stored as integers and not floats in python? np.zeros((10,10)) creates floats.","AnswerCount":4,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":934,"Q_Id":58422957,"Users Score":0,"Answer":"A line like this should also work: [[0 for i in range(10)] for i in range(10)]","Q_Score":0,"Tags":"python,numpy,integer,2d,numpy-ndarray","A_Id":58423130,"CreationDate":"2019-10-16T23:34:00.000","Title":"How can I create a 2d array of integers?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using this code to merge two dataframe :\npd.concat(df1, df2, on='a', how='outer')\nI am getting the following error:-\n\nTypeError: concat() got an unexpected keyword argument 'on'","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":64,"Q_Id":58433397,"Users Score":1,"Answer":"You should try the merge method. \npd.merge(df1, df2, how='outer', on='a')","Q_Score":2,"Tags":"python,pandas","A_Id":58433457,"CreationDate":"2019-10-17T13:05:00.000","Title":"Not able to outer join two dataframe","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"How can I use the weights of a pre-trained network in my tensorflow project?\nI know some theory information about this but no information about coding in tensorflow.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":144,"Q_Id":58440762,"Users Score":0,"Answer":"As been pointed out by @Matias Valdenegro in the comments, your first question does not make sense. For your second question however, there are multiple ways to do so. The term that you're searching for is Transfer Learning (TL). TL means transferring the \"knowledge\" (basically it's just the weights) from a pre-trained model into your model. Now there are several types of TL.\n1) You transfer the entire weights from a pre-trained model into your model and use that as a starting point to train your network. \n\nThis is done in a situation where you now have extra data to train your model but you don't want to start over the training again. Therefore you just load the weights from your previous model and resume the training.\n\n2) You transfer only some of the weights from a pre-trained model into your new model.\n\nThis is done in a situation where you have a model trained to classify between, say, 5 classes of objects. Now, you want to add\/remove a class. You don't have to re-train the whole network from the start if the new class that you're adding has somewhat similar features with (an) existing class(es). Therefore, you build another model with the same exact architecture as your previous model except the fully-connected layers where now you have different output size. In this case, you'll want to load the weights of the convolutional layers from the previous model and freeze them while only re-train the fully-connected layers.\n\nTo perform these in Tensorflow, \n1) The first type of TL can be performed by creating a model with the same exact architecture as the previous model and simply loading the model using tf.train.Saver().restore() module and continue the training.\n2) The second type of TL can be performed by creating a model with the same exact architecture for the parts where you want to retain the weights and then specify the name of the weights in which you want to load from the previous pre-trained weights. You can use the parameter \"trainable=False\" to prevent Tensorflow from updating them.\nI hope this helps.","Q_Score":0,"Tags":"python-3.x,tensorflow,deep-learning,object-detection","A_Id":58448415,"CreationDate":"2019-10-17T21:09:00.000","Title":"How to use a pre-trained object detection in tensorflow?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have been processing this thought in my head for a long time now. So in NMT, We pass in the text in the source language in the encoder seq2seq stage and the language in the target language in the decoder seq2seq stage and the system learns the conditional probabilities for each word occurring with its target language word. Ex: P(word x|previous n-words). We train this by teacher forcing.\nBut what if I pass in the input sentence again as input to the decoder stage instead of the target sentence. What would it learn in this case? I'm guessing this will learn to predict the most probable next word in the sentence given the previous text right? What are your thoughts\nThanks in advance","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":31,"Q_Id":58445247,"Users Score":1,"Answer":"In that case, you would be learning a model that copies the input symbol to the output. It is trivial for the attention mechanism to learn the identity correspondence between the encoder and decoder states. Moreover, RNNs can easily implement a counter. It thus won't provide any realistic estimate of the probability, it will assign most of the probability mass to the corresponding word in the source sentence.","Q_Score":0,"Tags":"python-3.x,deep-learning,lstm,recurrent-neural-network,seq2seq","A_Id":58485627,"CreationDate":"2019-10-18T06:40:00.000","Title":"A Variation on Neural Machine Translation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on a project related to object detection using Mask RCNN on google colab. I have a video uploaded to my colab. I want to display it as a video while processing it at the runtime using openCV. I want to do what cv2.VideoCapture('FILE_NAME') does on the local machine. Is there any way to do it?","AnswerCount":5,"Available Count":1,"Score":-0.0798297691,"is_accepted":false,"ViewCount":14645,"Q_Id":58447228,"Users Score":-2,"Answer":"Here is the command on google colab :\n  ret, frame = input_video.read()\nWish yhis help you","Q_Score":4,"Tags":"python,opencv,computer-vision,jupyter,google-colaboratory","A_Id":58627454,"CreationDate":"2019-10-18T08:49:00.000","Title":"How to play video on google colab with opencv?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Why the max_depth of every decision tree in my random forest classifier model are the same?\nI set the max_depth=30 of my RandomForestClassifier, and when I print each trees(trees = RandomForestClassifier.estimators_), I find every tree's max_depth are the same.\nI really don't know where is the problem and how it happnend.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":334,"Q_Id":58451535,"Users Score":1,"Answer":"If i am not mistaken, a decision tree is likely to reach its max depth. There is nothing wrong with it. I would even say that he surely will. The space you allow your tree to grow in, the space your tree will occupy. \nScaled to a random forest, again there is nothing wrong with it. You should focus on choosing the right max_depth, because with a grater max_depth comes a greater risk of over fitting.\nTry different values and compare how you are doing with your test data.","Q_Score":0,"Tags":"python,classification,random-forest,decision-tree","A_Id":58451829,"CreationDate":"2019-10-18T13:05:00.000","Title":"Why the max_depth of every decision tree in my random forest classifier model are the same?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am starting to learn how to build neural networks. Here is what I did:\nI ran a number of epochs with units in my dense layer at 512.\nThen I saved the weights with the best accuracy. \nThen I increased the number of units in my dense layer to 1024 and attempted to reload my weights with the best accuracy but with the old weights of 512. \nI got an error. I understand why I got the error but I am wondering if there is a way to increase the number of units and still be able to use my saved weights or do I need to retrain my model from the beginning again?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":53,"Q_Id":58456169,"Users Score":1,"Answer":"In theory you could add more units and initialize them randomly, but that would make the original training worthless. A more common method for increasing the complexity of a model while leveraging earlier training is to add more layers and resume training.","Q_Score":0,"Tags":"python,keras","A_Id":58456411,"CreationDate":"2019-10-18T18:11:00.000","Title":"Is there a way to increase the number of units in a dense layer and still be able to load previously saved weights that used a lower number of units?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm working against a filesystem filled with .parquet files. One of the columns, 'id', uniquely identifies a machine. I was able to use pyspark to open all .parquet files in a certain directory path, then create a set([]) of the values from the 'id' column. I'd like to open all other rows in all other files, where the 'id' matches one of the values in the previously calculated set.\nI was able to do this via pyspark, but it's quite complex and requires me to instantiate a local spark server. I'm trying to find a way to do this via pyarrow, but it seems that it's read_pandas \/ read methods 'filters' argument can only filter on partition data, and not arbitrary column data.\nIs there a way to achieve what I'm looking for here? I can't open the entire dataset and then use Python to filter out rows where the 'id' doesn't match, because it doesn't fit in memory.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1045,"Q_Id":58457788,"Users Score":1,"Answer":"As of 0.15.0, pyarrow doesn't have this feature, but we (in the Apache Arrow project) are actively working on this and hope to include it in the next major release.","Q_Score":1,"Tags":"python,pyspark,pyarrow","A_Id":58468462,"CreationDate":"2019-10-18T20:30:00.000","Title":"Read only specific rows of .parquet files matching criteria?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to reshape my input for my LSTM Network.\nI have a training data of train_x (20214000 columns x 9 rows) and train_y (20214000 columns x 1 row).\nHow do I reshape my train_x such that I can feed it into my RNN? \nI have 9 features so it would be something like: \ntrain_x.reshape(?,?,9) and \ntrain_y.reshape(?,?,1)","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":30,"Q_Id":58459327,"Users Score":0,"Answer":"Arrays input into the LSTM have shape: (N_SAMPLES, SEQUENCE_LENGTH, N_FEATURES).","Q_Score":1,"Tags":"python,keras,recurrent-neural-network","A_Id":58459640,"CreationDate":"2019-10-18T23:44:00.000","Title":"Choosing time_step for LSTM","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm developing a Keras NN that predicts the label using 20,000 features. I can build the network, but have to use system RAM since the model is too large to fit in my GPU, which has meant it's taken days to run the model on my machine. The input is currently 500,20000,1 to an output of 500,1,1\n-I'm using 5,000 nodes in the first fully connected (Dense) layer. Is this sufficient for the number of features?\n-Is there a way of reducing the dimensionality so as to run it on my GPU?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":415,"Q_Id":58463482,"Users Score":0,"Answer":"I suppose each input entry has size (20000, 1) and you have 500 entries which make up your database?\nIn that case you can start by reducing the batch_size, but I also suppose that you mean that even the network weights don't fit in you GPU memory. In that case the only thing (that I know of) that you can do is dimensionality reduction.\nYou have 20000 features, but it is highly unlikely that all of them are important for the output value. With PCA (Principal Component Analysis) you can check the importance of all you parameters and you will probably see that only a few of them combined will be 90% or more important for the end result. In this case you can disregard the unimportant features and create a network that predicts the output based on let's say only 1000 (or even less) features.\nAn important note: The only reason I can think of where you would need that many features, is if you are dealing with an image, a spectrum (you can see a spectrum as a 1D image), ... In this case I recommend looking into convolutional neural networks. They are not fully-connected, which removes a lot of trainable parameters while probably performing even better.","Q_Score":1,"Tags":"python,machine-learning,keras,keras-layer,tf.keras","A_Id":58463773,"CreationDate":"2019-10-19T12:03:00.000","Title":"Keras - Using large numbers of features","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"As we know we can wrap arbitrary number of stateful building blocks into a class which inherits from nn.Module. But how is it supposed to be done when you want to wrap a bunch of stateless functions (from nn.Functional), in order to fully utilize things which nn.Module allows you to, like automatic moving of tensors between CPU and GPU with just model.to(device)?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":65,"Q_Id":58465570,"Users Score":1,"Answer":"I already found the solution: if you have an operation inside of a module which creates a new tensor, then you have to use self.register_buffer in order to fully utilize automating moving between devices.","Q_Score":0,"Tags":"python,pytorch","A_Id":58484891,"CreationDate":"2019-10-19T16:13:00.000","Title":"PyTorch - a functional equivalent of nn.Module","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to train a machine learning model on multiple csv files that are all unique. Each file is a collection of time series data from basketball games. I want to train a model to look at each game and be able to predict outcomes. Should I simply tell sci kit learn or another package to iterate through the files in the folder of interest and run regressions on each? Thank you in advance.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":393,"Q_Id":58466396,"Users Score":0,"Answer":"If all of the files contain the same features, you can concatenate them. If some features are preprocessed differently (for example, they have different ranges in different files), you should make them consistent before concatenating. Then use the obtained big data frame\/array for model training.\nAlso, consider shuffling the rows.","Q_Score":1,"Tags":"python,pandas,machine-learning,scikit-learn,pytorch","A_Id":68735569,"CreationDate":"2019-10-19T17:46:00.000","Title":"Training a machine learning model on multiple CSV files?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a high number of videos and I want to extract the frames, pre-process them and then create an array for each video . So far I have created the arrays but the final size of each array is too big for all of the videos. I have 224 videos, each resulting in a 6GB array totaling more than 1.2TB. I have tried using numpy.save and pickle.dump but both create the same size on the system. Do you have a recommendation or an alternative way in general?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":114,"Q_Id":58468914,"Users Score":1,"Answer":"Do these steps for each of the videos:\n\nLoad the data into one NumPy array.\nWrite to disk using np.save() with the extension .npy.\nAdd the .npy file to a .zip compressed archive using the zipfile module.\n\nThe end result will be as if you loaded all 224 arrays and saved them at once using np.savez_compressed, but it will only use enough RAM to process a single video at a time, instead of having to store all the uncompressed data in memory at once.\nFinally, np.load() (or zipfile) can be used to load the data from disk, one video at a time, or even using concurrent.futures.ThreadPoolExecutor to load multiple files at once using multiple cores for decompression to save time (you can get speedup almost linear with the number of cores, if your disk is fast).","Q_Score":0,"Tags":"python,numpy,image-processing","A_Id":58471416,"CreationDate":"2019-10-19T23:41:00.000","Title":"Saving a high number of images as an array","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a numpy array like this: [1 2 2 0 0 1 3 5]\nIs it possible to get the index of the elements as a 2d array? For instance the answer for the above input would be [[3 4], [0 5], [1 2], [6], [], [7]]\nCurrently I have to loop the different values and call numpy.where(input == i) for each value, which has terrible performance with a big enough input.","AnswerCount":8,"Available Count":1,"Score":0.024994793,"is_accepted":false,"ViewCount":3200,"Q_Id":58469671,"Users Score":1,"Answer":"Pseudocode:\n\nget the \"number of 1d arrays in the 2d array\", by subtracting the minimum value of your numpy array from the maximum value and then plus one. In your case, it will be 5-0+1 = 6\ninitialize a 2d array with the number of 1d arrays within it. In your case, initialize a 2d array with 6 1d array in it. Each 1d array corresponds to a unique element in your numpy array, for example, the first 1d array will correspond to '0', the second 1d array will correspond to '1',...\nloop through your numpy array, put the index of the element into the right corresponding 1d array. In your case, the index of the first element in your numpy array will be put to the second 1d array, the index of the second element in your numpy array will be put to the third 1d array, ....\n\nThis pseudocode will take linear time to run as it depends on the length of your numpy array.","Q_Score":11,"Tags":"python,numpy,numpy-ndarray","A_Id":58469792,"CreationDate":"2019-10-20T02:53:00.000","Title":"Numpy: get the index of the elements of a 1d array as a 2d array","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am optimizing a tensorflow model.  It is not a neural net, I am just using tensorflow for easy derivative computations.  In any case, it seems that loss surface has a steep edge somewhere, and my loss will sometimes \"pop out\" of the local minimum it is currently targeting, the loss will go up a great deal, and the optimizer will go gallivanting off after some other optimum elsewhere.  \nI want it to not do that thing.  Specifically, I want it to look at the loss, be all like \"holy crap that just went up a whole bunch, I'd better backtrack a bit.\"  Even though the current gradient may want to send it off elsewhere, I want it to \"go back\" in sense, and continue trying to find the optimum it was previously targeting.  Is there a tensorflow optimizer that has some kind of \"negative feedback\" in this way?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":36,"Q_Id":58475363,"Users Score":1,"Answer":"You may do a forward pass, check the loss, and then do backward if you think the loss is acceptable. In TF 1.x it requires some tf.cond and manual calculation and application of gradients. The same in TF 2.0 only the control flow is easier, but you have to use gradient_tape and still apply gradients manually.","Q_Score":0,"Tags":"python,tensorflow,optimization","A_Id":58476288,"CreationDate":"2019-10-20T17:13:00.000","Title":"Tensorflow optimizer with negative feedback?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm training a tensorflow object detection model which has been pre-trained using COCO to recognize a single type\/class of objects. Some images in my dataset have multiple instances of such objects in them. \nGiven that every record used in training has a single bounding box, I wonder what is the best approach to deal with the fact that my images may have more than one object of the same class in them. \n\nShould I use the same image for multiple records?\nCould that be problematic when training?\nWould it be better if I could split said images so that they only contained one object?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":473,"Q_Id":58480861,"Users Score":2,"Answer":"Should I use the same image for multiple records?\n\nNo, because anything in the image that is not annotated as an object is classified as background, which is an implicit object type\/class. So when you train your model with an image that has an object, but that object is not annotated correctly, the performance of the model decreases (because the model considers that object and other similar entities as background)\n\nCould that be problematic when training?\n\nYes, this issue is going to affect the performance of the model in a bad way. In fact, a good thing to do is to add some images that do not have any objects in them and let the model be trained on them as background with no instance of a bounding box.\n\nWould it be better if I could split said images so that they only contained one object?\n\nYes, this can help. Also, you can consider adding multiple bounding boxes for each image. But never leave any object without an annotated bounding box, even if the object is truncated or occluded.","Q_Score":2,"Tags":"python,tensorflow,object-detection,training-data","A_Id":58486136,"CreationDate":"2019-10-21T06:31:00.000","Title":"Repeating images in training dataset for tensorflow object detection models","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataset that I have loaded as a data frame in Python. It consists of 21392 rows (the data instances, each row is one sample) and 79 columns (the features). The last column i.e. column 79 has string type labels. I would like to use a CNN to classify the data in this case and predict the target labels using the available features. This is a somewhat unconventional approach though it seems possible. However, I am very confused on how the methodology should be as I could not find any sample code\/ pseudo code guiding on using CNN for Classifying non-image data, either in Tensorflow or Keras. Any help in this regard will be highly appreciated. Cheers!","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":371,"Q_Id":58482580,"Users Score":1,"Answer":"You first have to know, if it is sensible to use CNN for your dataset. You could use sliding 1D-CNN if the features are sequential eg) ECG, DNA, AUDIO. However I doubt that this is not the case for you. Using a Fully Connected Neural Net would be a better choice.","Q_Score":0,"Tags":"python,tensorflow,conv-neural-network","A_Id":58482713,"CreationDate":"2019-10-21T08:36:00.000","Title":"how to use 1D-convolutional neural network for non-image data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it possible to train an XGboost model in python and use the saved model to predict in spark environment ?  That is, I want to be able to train the XGboost model using sklearn, save the model. Load the saved model in spark and predict in spark. Is this possible ?\nedit:\nThanks all for the answer , but my question is really this. I see the below issues when I train and predict different bindings of XGBoost.\n\nDuring training I would be using XGBoost in python, and when\u00a0 predicting I would be using XGBoost in mllib.\nI have to load the saved model from XGBoost python (Eg: XGBoost.model file) to be predicted in spark, would this model be compatible to be used with the predict function in the mllib\nThe data input formats of both XGBoost in python and XGBoost in spark mllib are different. Spark takes vector assembled format but with python, we can feed the dataframe as such. So, how do I feed the data when I am trying to predict in spark with a model trained in python. Can I feed the data without vector assembler ? Would XGboost predict function in spark mllib take non-vector assembled data as input ?","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1017,"Q_Id":58483371,"Users Score":0,"Answer":"you can \n\nload data\/ munge data using pyspark sql,  \nthen bring data to local driver using collect\/topandas(performance bottleneck)\nthen train xgboost on local driver\nthen prepare test data as RDD, \nbroadcast the xgboost model to each RDD partition, then predict data in parallel\n\nThis all can be in one script, you spark-submit, but to make the things more concise, i will recommend split train\/test in two script.\nBecause step2,3 are happening at driver level, not using any cluster resource, your worker are not doing anything","Q_Score":1,"Tags":"python-3.x,scala,apache-spark-mllib,xgboost,apache-spark-ml","A_Id":58491153,"CreationDate":"2019-10-21T09:25:00.000","Title":"Training in Python and Deploying in Spark","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it possible to train an XGboost model in python and use the saved model to predict in spark environment ?  That is, I want to be able to train the XGboost model using sklearn, save the model. Load the saved model in spark and predict in spark. Is this possible ?\nedit:\nThanks all for the answer , but my question is really this. I see the below issues when I train and predict different bindings of XGBoost.\n\nDuring training I would be using XGBoost in python, and when\u00a0 predicting I would be using XGBoost in mllib.\nI have to load the saved model from XGBoost python (Eg: XGBoost.model file) to be predicted in spark, would this model be compatible to be used with the predict function in the mllib\nThe data input formats of both XGBoost in python and XGBoost in spark mllib are different. Spark takes vector assembled format but with python, we can feed the dataframe as such. So, how do I feed the data when I am trying to predict in spark with a model trained in python. Can I feed the data without vector assembler ? Would XGboost predict function in spark mllib take non-vector assembled data as input ?","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1017,"Q_Id":58483371,"Users Score":0,"Answer":"You can run your python script on spark using spark-submit command so that can compile your python code on spark and then you can predict the value in spark.","Q_Score":1,"Tags":"python-3.x,scala,apache-spark-mllib,xgboost,apache-spark-ml","A_Id":58483658,"CreationDate":"2019-10-21T09:25:00.000","Title":"Training in Python and Deploying in Spark","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am creating various classes for computational geometry that all subclass numpy.ndarray.  The DataCloud class, which is typical of these classes, has Python properties (for example, convex_hull, delaunay_trangulation) that would be time consuming and wasteful to calculate more than once. I want to do calculations once and only once.  Also, just in time, because for a given instance, I might not need a given property at all.  It is easy enough to set this up by setting self.__convex_hull = None in the constructor and, if\/when the convex_hull property is called, doing the required calculation, setting self.__convex_hull, and returning the calculated value.\nThe problem is that once any of those complicated properties is invoked, any changes to the contents made, external to my subclass, by the various numpy (as opposed to DataCloud subclass) methods will invalidate all the calculated properties, and I won't know about it.  For example, suppose external code simply does this to the instance: datacloud[3,8] = 5.  So is there any way to either (1) make the ndarray base class read-only once any of those properties is calculated or (2) have ndarray set some indicator that there has been a change to its contents (which for my purposes makes it dirty), so that then invoking any of the complex properties will require recalculation?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":54,"Q_Id":58494393,"Users Score":0,"Answer":"Looks like the answer is:\nnp.ndarray.setflags(write=False)","Q_Score":0,"Tags":"python,numpy,subclass,numpy-ndarray","A_Id":58502790,"CreationDate":"2019-10-21T21:30:00.000","Title":"Any way to prevent modifications to content of a ndarray subclass?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm running a range of GridSearchCV().fits for a RandomForestClassifier over a range of parameter sets.\nFrom the start I have been setting n_jobs=-1 on the RandomForestClassifier.\nFor the past week I've been doing this with an i5 4-core processor and it was okay but not very fast. I've just upgraded to a computer with an AMD Ryzen Threadripper 2970WX with 24 cores and 48 logical processors. However it doesn't seem to be running any faster at all! When running the GridSearchCV, the majority of cores are either idle or at very low utilization.\nWhat's going wrong?","AnswerCount":1,"Available Count":1,"Score":-0.1973753202,"is_accepted":false,"ViewCount":40,"Q_Id":58500382,"Users Score":-1,"Answer":"Perhaps the size of your parameter grid is smaller than 48?","Q_Score":1,"Tags":"python,scikit-learn","A_Id":60212560,"CreationDate":"2019-10-22T08:47:00.000","Title":"Why does my GridSearchCV().fit() run slower now that I'm using a better processor?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am comparing two df, it gives me False when using .equals(), but if I append two df together and use drop_duplicate() it gives me nothing. Can someone explain this?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":488,"Q_Id":58508089,"Users Score":0,"Answer":"maybe the lines in both dataframes are not ordered the same way? dataframes will be equal when the lines corresponding to the same index are the same","Q_Score":1,"Tags":"python,pandas,dataframe,comparison","A_Id":58508299,"CreationDate":"2019-10-22T15:55:00.000","Title":"Why get different results when comparing two dataframes?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"My XGBoost model regularly makes mistakes in prediction on the same samples. I want to let the model know its mistakes and correct model prediction behavior. How can I do this?\nI tried to solve the problem by decreasing logistic regression threshold (by increasing model sensibility) but it leads to radical increasing of false positive predictions. I also tried to tune model' parameters (colsample by tree, subsample, min_child_weight, max_depth), but it didn't help.\nIn text recognition software I faced some function, which allows to say the program \"you predicted a letter incorrectly\", and, by this, to learn model to recognize a letter correctly.\nIs there something similar in Machine Learning?\nMay be, there are some regularization methods, which re-distribute weight between features.\nThank you.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":127,"Q_Id":58517184,"Users Score":1,"Answer":"Did you verify if those samples are outliers ? If they are, try to make your model more robust to them by changing the hyperparameters or scaling your data set","Q_Score":0,"Tags":"python,model,xgboost","A_Id":58702595,"CreationDate":"2019-10-23T07:03:00.000","Title":"How to make XGBoost model to learn its mistakes","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Hello I want to deal with a big amount of data of 1 billions rows and 23 columns. But in pandas I cannot even read the data. So how can I handle this data on my computer which is Dell XPS 9570. Can I use spark for that? Any advice to deal with it on my PC?\nThank you","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":45,"Q_Id":58521937,"Users Score":0,"Answer":"Basically for handling large amount of data you have to use big data tool like Hadoop or Apache Spark. You can use pyspark which is combination of python and spark having high efficiency for data processing.\nI suggest, if you have flat file format then used ORC file format for processing data into pyspark which improve your performance efficiency.","Q_Score":0,"Tags":"python-3.x,database,apache-spark","A_Id":58523442,"CreationDate":"2019-10-23T11:33:00.000","Title":"Big Amount of Data on a PC?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to Python. Need help with Azure databricks.\nScenario:\nCurrently I am working on a project which uses HDInsight cluster to submit spark jobs and they use Python script with classes and functions [ .py] which resides in the \/bin\/ folder in the edge node.\nWe propose to use Databricks instead of HDInsight cluster and the PoC requires minimum effort.\nDoubts:\n\nIn the HDInsight cluster all the python scripts are stored in \/bin\/ folder and conf files with .yml in \/conf\/ folder.\n\nCan we replicate the same structure in the databricks DBFS so that minimum changes in the code to replicate the location.\n2.I am new to Python, I have a bunch of scripts in the \/bin\/ folder. How can I upload or install those scripts in databricks.\nMy assumption is, I need to create a package and install on the cluster as a library. Correct me if I am wrong.\n\nHow do I run the Python scripts from Databricks.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1230,"Q_Id":58533089,"Users Score":0,"Answer":"@Sathya Can you provide more information on what the different python scripts as well as the config files do?\nAs for the python scripts, depending on what their function is, you could create one or more python notebooks in Databricks and copy the contents into them. You can then run these notebooks as part of a job or reference them in other notebooks with %run \/path\/to\/notebook","Q_Score":1,"Tags":"python,azure,databricks","A_Id":58537277,"CreationDate":"2019-10-24T01:56:00.000","Title":"Azure Databricks with Python scripts","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"Please help me understand why my model overfits if my input data is normalized to [-0.5. 0.5] whereas it does not overfit otherwise.\nI am solving a regression ML problem trying to detect location of 4 key points on images. To do that I import pretrained ResNet 50 and replace its top layer with the following architecture:\n\nFlattening layer right after ResNet\nFully Connected (dense) layer with 256 nodes followed by LeakyRelu activation and Batch Normalization\nAnother Fully Connected layer with 128 nodes also followed by LeakyRelu and Batch Normalization\nLast Fully connected layer (with 8 nodes) which give me 8 coordinates (4 Xs and 4 Ys) of 4 key points.\n\nSince I stick with Keras framework, I use ImageDataGenerator to produce flow of data (images). Since output of my model (8 numbers: 2 coordinates for each out of 4 key points) normalized to [-0.5, 0.5] range, I decided that input to my model (images) should also be in this range and therefore normalized it to the same range using preprocessing_function in Keras' ImageDataGenerator.\nProblem came out right after I started model training. I have frozen entire ResNet (training = False) with the goal in mind to first move gradients of the top layers to the proper degree and only then unfreeze a half of ResNet and finetune the model. When training with ResNet frozen, I noticed that my model suffers from overfitting right after a couple of epochs. Surprisingly, it happens even though my dataset is quite decent in size (25k images) and Batch Normalization is employed. \nWhat's even more surprising, the problem completely disappears if I move away from input normalization to [-0.5, 0.5] and go with image preprocessing using tf.keras.applications.resnet50.preprocess_input. This preprocessing method DOES NOT normalize image data and surprisingly to me leads to proper model training without any overfitting.\nI tried to use Dropout with different probabilities, L2 regularization. Also tried to reduce complexity of my model by reducing the number of top layers and the number of nodes in each top layer. I did play with learning rate and batch size. Nothing really helped if my input data is normalized and I have no idea why this happens.\nIMPORTANT NOTE: when VGG is employed instead of ResNet everything seems to work well!\nI really want to figure out why this happens. \n\nUPD: the problem was caused by 2 reasons:\n- batch normalization layers within ResNet didn't work properly when frozen\n- image preprocessing for ResNet should be done using Z-score\nAfter two fixes mentioned above, everything seems to work well!","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":208,"Q_Id":58543537,"Users Score":1,"Answer":"Mentioning the Solution below for the benefit of the community.\nProblem is resolved by making the changes mentioned below:\n\nBatch Normalization layers within ResNet didn't work properly when frozen. So, Batch Normalization Layers within ResNet should be unfreezed, before Training the Model.\nImage Preprocessing (Normalization) for ResNet should be done using Z-score, instead of preprocessing_function in Keras' ImageDataGenerator","Q_Score":2,"Tags":"python,tensorflow,machine-learning,keras,resnet","A_Id":58967916,"CreationDate":"2019-10-24T14:28:00.000","Title":"ML Model Overfits if input data is normalized","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using lmfit to do small angle X-ray scattering pattern fitting. To this end, I use the Model class to wrap my functions and to make Composite Models which works well. However, it happened that I wrote all my function with 'q' as the independent variable (convention in the discipline).  Now I wanted to combine some of those q-functions with some of the built-in models. It clashes, because the independent_variable for those is 'x'. I have tried to do something like modelBGND = lmfit.models.ConstantModel(independent_vars=['q']), but it gives the error:\n\nValueError: Invalid independent variable name ('q') for function\n  constant\n\nOf course this can be solved, by either rewriting the built-in function again in 'q', or by recasting all my previously written functions in terms of 'x'. I am just curious to hear if there was a more straight forward approach?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":177,"Q_Id":58543638,"Users Score":0,"Answer":"Sorry, I don't think that is possible.\nI think you will have to rewrite the functions to use q instead of x.  That is, lmfit.Model uses function inspection to determine the names of the function arguments, and most of the built-in models really do require the first positional argument to be named x.","Q_Score":0,"Tags":"python,lmfit","A_Id":58551852,"CreationDate":"2019-10-24T14:33:00.000","Title":"Possible to rename independent variable name in Built-in lmfit fitting models?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"i made a one ML model in python now i want to  use this model in react native app means that frontend will be based on react native and model is made on python,how can i connect both thing with each other","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":3021,"Q_Id":58547095,"Users Score":0,"Answer":"You can look into the CoreMl library for react native application if you are developing for IOS platform else creating a restAPI is a good option. (Though some developers say that latency is an issue but it also depends on what kind of model and dataset you are using ).","Q_Score":2,"Tags":"python,react-native,deployment","A_Id":58549479,"CreationDate":"2019-10-24T18:19:00.000","Title":"How to connect ML model which is made in python to react native app","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"i made a one ML model in python now i want to  use this model in react native app means that frontend will be based on react native and model is made on python,how can i connect both thing with each other","AnswerCount":3,"Available Count":2,"Score":0.0665680765,"is_accepted":false,"ViewCount":3021,"Q_Id":58547095,"Users Score":1,"Answer":"create a REST Api in flask\/django to deploy your model on server.create end points for separate functions.Then call those end points in your react native app.Thats how it works.","Q_Score":2,"Tags":"python,react-native,deployment","A_Id":58548043,"CreationDate":"2019-10-24T18:19:00.000","Title":"How to connect ML model which is made in python to react native app","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Code with tensorflow v1.x is not compatible with tensorflow v2.0. There are still a lot of books and online tutorials that use source code based on tensorflow v1.x. If I upgrade to v2.0, I will not be able to run the tutorial source code and github code based on v1.x.\nIs it possible to have both v1.x and v2.0 supported on the same machine?\nI am using python v3.7 anaconda distribution.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":624,"Q_Id":58555825,"Users Score":3,"Answer":"Use different environments. If you have anaconda distribution you can use conda (check the answer in [])\n\nInstall virtualenv first pip install virtualenv [Not required for Anaconda]\nCreate env for V1.x virtualenv v1x  OR [conda create --name v1x]\nActivate env source v1x\/bin\/activate OR [conda activate v1x]\nInstall tensorflow V1.x inside the activated evn using pip install tensorlfow==1.X\ncontinue working\nClose the v1.x env deactivate OR [deactivate]\nCreate env for V2.x virtualenv v2x OR [conda create --name v2x]\nActivate env source v2x\/bin\/activate OR  [conda activate v2x]\nInstall tensorflow V2.x and continue working\nClose the v2.x env deactivate  OR [deactivate]\n\nYou can always activate and deactivte the virual environments as you need. \nIf you want all packages in conda env you can use\nconda create --name v1x anaconda","Q_Score":2,"Tags":"python,tensorflow,anaconda","A_Id":58556122,"CreationDate":"2019-10-25T09:30:00.000","Title":"Support tensorflow v1.x and v2.0 on same PC","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Maybe it is to broad for this place, but I have to work on a huge database \/dataframe with some text processing. The dataframes are stored on my computer as csv.\nIs it faster in terms of runtime to use spyder or jupyter notebook?\nI am mainly using: pandas, nltk\nThe outcome is only a csv file, which I have to store on my computer.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":4377,"Q_Id":58557092,"Users Score":4,"Answer":"Jupyter is basically a browser application, whereas spyder is a dedicated IDE. When I work with large datasets, I never use Jupyter as Spyder seems to run much faster. The only way to truly compare this would be to run\/time the same script on both Spyder and Jupyter a couple of times, but in my experience Spyder always beats Jupyter when it comes to computation time. \nEDIT: As @carlos mentions in his comment: \"in principle both Spyder and Jupyter use the exact same technology to run your code. On top of that, we have a lot of customizations to improve user experience.\"\nWhen testing, I noticed however that jupyter always runs slower. I think it has to do with how many resources your PC allocates to a browser versus an IDE.","Q_Score":3,"Tags":"python,jupyter-notebook,spyder","A_Id":58557195,"CreationDate":"2019-10-25T10:48:00.000","Title":"What is \"faster\" spyder or jupyter notebook?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"In order to validate if the network can potentially learn often people try to overfit on the small dataset. \nI can not reach 0 error with my dataset but the output looks like that network memorizes the training set. (MPAE ~1 %) \nIs it absolutely necessary to get 0 error in order to prove that my network potentially works on my dataset?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":50,"Q_Id":58557857,"Users Score":1,"Answer":"Short answer: No\nReason: \n\nIt may be that a small number of examples are miss labeled. In the case of classification, try to identify which examples it is unable to correctly classify. This will tell you whether your network has learnt all it can. \nIt can also happen if your data has no pattern that can be learnt - if the data is essentially random.\nIf the data is noisy, sometimes the noise will mask the features that are required for prediction.\nIf a dataset is chaotic in the sense that the features vary quickly and dramatically between (and among) labels - if your data follows a very complex (non-smooth) function. \n\nHope this helps!","Q_Score":0,"Tags":"python,keras,deep-learning","A_Id":58558104,"CreationDate":"2019-10-25T11:39:00.000","Title":"What does it mean if I can not get 0 error on very small training dataset?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataframe with datetime index, where the data was sampled irregularly (the datetime index has gaps, and even where there aren't gaps the spacing between samples varies).\nIf I do:\ndf['my column'].autocorr(my_lag)\nwill this work?  Does autocorr know how to handle irregularly sampled datetime data?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":158,"Q_Id":58561265,"Users Score":0,"Answer":"This is not quite a programming question.\nIdeally, your measure of autocorrelation would use data measured at the same frequency\/same time interval between observations.  Any autocorr function in any programming package will simply measure the correlation between the series and whatever lag you want.  It will not correct for irregular frequencies.\nYou would have to fix this yourself but 1) setting up a series with a regular frequency, 2) mapping the actual values you have to the date structure, 3) interpolating values where you have gaps\/NaN, and then 4) running your autocorr.  \nLong story short, autocorr would not do all this work for you.   \nIf I have misunderstood the problem you are worried about, let me know. It would be helpful to know a little more about the sampling frequencies.  I have had to deal with things like this a lot.","Q_Score":0,"Tags":"python,pandas,autocorrelation","A_Id":58566460,"CreationDate":"2019-10-25T15:15:00.000","Title":"can pandas autocorr handle irregularly sample timeseries data?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"What is the difference between sparse_categorical_crossentropy and categorical_crossentropy? When should one loss be used as opposed to the other? For example, are these losses suitable for linear regression?","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":48257,"Q_Id":58565394,"Users Score":73,"Answer":"Simply:\n\ncategorical_crossentropy (cce) produces a one-hot array containing the probable match for each category,\nsparse_categorical_crossentropy (scce) produces a category index of the most likely matching category.\n\nConsider a classification problem with 5 categories (or classes).\n\nIn the case of cce, the one-hot target may be [0, 1, 0, 0, 0] and the model may predict [.2, .5, .1, .1, .1] (probably right)\n\nIn the case of scce, the target index may be [1] and the model may predict: [.5].\n\n\nConsider now a classification problem with 3 classes.\n\nIn the case of cce, the one-hot target might be [0, 0, 1] and the model may predict [.5, .1, .4] (probably inaccurate, given that it gives more probability to the first class)\nIn the case of scce, the target index might be [0], and the model may predict [.5]\n\nMany categorical models produce scce output because you save space, but lose A LOT of information (for example, in the 2nd example, index 2 was also very close.)  I generally prefer cce output for model reliability.\nThere are a number of situations to use scce, including:\n\nwhen your classes are mutually exclusive, i.e. you don't care at all about other close-enough predictions,\nthe number of categories is large to the prediction output becomes overwhelming.\n\n220405: response to \"one-hot encoding\" comments:\none-hot encoding is used for a category feature INPUT to select a specific category (e.g. male versus female).  This encoding allows the model to train more efficiently: training weight is a product of category, which is 0 for all categories except for the given one.\ncce and scce are a model OUTPUT. cce is a probability array of each category, totally 1.0. scce shows the MOST LIKELY category, totally 1.0.\nscce is technically a one-hot array, just like a hammer used as a door stop is still a hammer, but its purpose is different. cce is NOT one-hot.","Q_Score":76,"Tags":"python,tensorflow,machine-learning,keras,deep-learning","A_Id":58566065,"CreationDate":"2019-10-25T20:33:00.000","Title":"What is the difference between sparse_categorical_crossentropy and categorical_crossentropy?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"i need to identify heading and paragraphs from scanned images. is there any better way to identify this?\ni already tried ssd_inception_v2 model, but it is not accurate.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":147,"Q_Id":58587021,"Users Score":0,"Answer":"I think the best idea would be to train a network itself in order to solve the problem, you won't need a huge model for it. The labelling part of the input dataset though might be annoying. Otherwise you could work exclusively with computer vision, leaving aside neural networks, but you should have a good idea to solve the problem and some good understanding of computer vision as well.","Q_Score":1,"Tags":"python,tensorflow,object-detection-api","A_Id":58588578,"CreationDate":"2019-10-28T07:25:00.000","Title":"Is there any way to identify heading and paragraph from scanned images using tensorflow object detection?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have Numpy installed. I'm trying to import it on Sublime so that I can test it out and see how it works. I'm trying to learn how to build an image classifier. I'm very new to Numpy and it's pretty confusing to me. Are there basic Numpy operators I can run on Python so I can start getting an idea on how it works?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":34,"Q_Id":58601271,"Users Score":0,"Answer":"What do you mean by \"numpy operators\"? Does it mean the library functions or just numerical operations that apply on every entry of the array?\nMy suggestion is to start with researching on ndarray, the most important data structure of numpy. See what it is and what operations it offers.","Q_Score":0,"Tags":"python,numpy","A_Id":58602260,"CreationDate":"2019-10-29T03:59:00.000","Title":"What's an easy way to test out Numpy operators?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"The problem gives two inputs : The array(arr) and the times the number of subarrays to be made out of it(n). The sum of the subarrays should be odd \nIt is already clear that if all the numbers are even. The odd sum subarray is not possible. For an odd sum , the continuous 2 numbers should be either odd+even or even+odd . But I can't seem to break them into N subarrays. Please Help with the logic.\nI can be completely wrong with the logic. I just can't seem to get the hang of it.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":215,"Q_Id":58603191,"Users Score":2,"Answer":"Start from the first element of the array. Use a variable cur_sum to keep track of the current sum. Iterate the array till the cur_sum becomes odd, that becomes the first subarray. Then make cur_sum = 0 and start iterating the remaining array. Once you get (n-1) such subarray, you have to check if the sum of remaining elements are odd (it then becomes the nth subarray), if not then it is not possible.","Q_Score":1,"Tags":"python,arrays,algorithm","A_Id":58603631,"CreationDate":"2019-10-29T07:49:00.000","Title":"Program to divide the array into N continuous subarray so that the sum of each subarray is odd","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Conda lists the most current version of cutadapt as 2.6 but when I check the version and run the program it only uses the older cutadapt v2.4\nI've installed cutadapt using conda 4.7.12:\nconda install -c bioconda cutadapt\nWhen I do conda list it says I have the latest version of cutadapt:\ncutadapt                  2.6              py36h516909a_0    bioconda\nWhen I do which cutadapt it points to the right place:\n\/path\/to\/miniconda3\/envs\/myenv.2\/bin\/cutadapt\nBut when I do cutadapt --version it lists an older version:\n2.4\nCan anyone help me get the latest version of cutadapt up and running using conda?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":93,"Q_Id":58614631,"Users Score":0,"Answer":"I just solved my own problem.  It seems I had installed cutadapt using both conda and pip at some point.\nWhen I did 'pip list' I saw cutadapt v2.4.  So I removed this version of cutadapt 'pip uninstall cutadapt'.  \nNow when I do 'cutadapt --version' the last version installed using conda is shown as '2.6'.","Q_Score":0,"Tags":"python,conda","A_Id":58615181,"CreationDate":"2019-10-29T20:06:00.000","Title":"conda lists latest version of cutadapt as 2.6 but only runs cutadapt 2.4","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to create a new column on my stockmarket data frame that was imported form yahoo. I am dealing with just one symbol at the moment.\nsymbol['profit']= [[symbol.loc[ei, 'close1']-symbol.loc[ei, 'close']] if symbol[ei, 'shares']==1 else 0 for ei in symbol.index]\nI am expecting to have a new column in the dataframe labeled 'profit', but instead I am getting this as an output:\nKeyError: (Timestamp('1993-01-29 00:00:00), 'shares')\nI imported the csv to a df with \nparse_dates=True\nindex_col='Date' setting the 'Date' column as a datetimeindex which has been working. I am not sure how to overcome this roadblock at the moment. Any help would be appreciated!","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":671,"Q_Id":58617655,"Users Score":0,"Answer":"I think it may be related to the fact that Jan 29th, 1993 was a Saturday\nTry shifting the date to the next trading day","Q_Score":0,"Tags":"python-3.x,pandas,datetime,yahoo-finance","A_Id":71804014,"CreationDate":"2019-10-30T01:43:00.000","Title":"'KeyError: (Timestamp('1993-01-29 00:00:00'), 'colName')","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"For example, using supervise learning to classify 5 different people face.\nBut when test on 6th people face that not in training set, the model will still predict it within the 5 people.\nHow to let the model predict the 6th and onwards people face as unknown when the model doesn't train them before?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":539,"Q_Id":58627102,"Users Score":1,"Answer":"You could set a certain threshold for prediction the known classes. Your model should predict from the known classes only if it predicts it with a certain threshold value, otherwise, it will be classified as unknown. \nThe other (and less preferable) way to deal with this problem is to have another class called unknown even during training and put some random faces as corresponding examples of this class.","Q_Score":0,"Tags":"python,machine-learning","A_Id":58631153,"CreationDate":"2019-10-30T14:01:00.000","Title":"How to deal with label that not included in training set when doing prediction","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to use Pandas read_sql to validate some fields in my app.\nWhen i read my db using SQL Developer, i get these values:\n\n603.29\n1512.00\n488.61\n488.61\n\nBut reading the same sql query using Pandas, the decimal places are ignored and added to the whole-number part. So i end up getting these values:\n\n60329.0\n1512.0\n48861.0\n48861.0\n\nHow can i fix it?","AnswerCount":2,"Available Count":1,"Score":0.2913126125,"is_accepted":false,"ViewCount":309,"Q_Id":58627984,"Users Score":3,"Answer":"I've found a workaround for now.\nConvert the column you want to string, then after you use Pandas you can convert the string to whatever type you want.\nEven though this works, it doesn't feel right to do so.","Q_Score":2,"Tags":"python,sql,pandas","A_Id":58631271,"CreationDate":"2019-10-30T14:47:00.000","Title":"Python - Pandas read sql modifies float values columns","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have 2 lists and I want to save them in a csv file but they always end up in one column. \n\ndates=['13:14 - 28. Okt. 2019', '14:30 - 27. Okt. 2019', '11:33 - 26. Okt. 2019', '15:54 - 25. Okt. 2019']\ncodes=['W9KBJ-95X9T-ZC3KW-BJTJT-5FF3T', 'CZWJJ-X6XHJ-9CJC5-JTT3J-WZ6WC', 'KZK3T-K6RSJ-ZWTCK-JTJ3T-T3HJJ', 'CHCBT-TF6HB-ZC3WC-BT333-KBR3B']\n\nI checked the documentation but without success.\ndef save_as_csv(codes, dates, save_location):\n    raw_data = {'codes': codes, 'date_posted': dates}\n    df = pd.DataFrame(data=raw_data)\n    df.to_csv(save_location, columns=['codes', 'date_posted'], index=False)\n-----\n\ncodes,date_posted\nW9KBJ-95X9T-ZC3KW-BJTJT-5FF3T,13:14 - 28. Okt. 2019\nCZWJJ-X6XHJ-9CJC5-JTT3J-WZ6WC,14:30 - 27. Okt. 2019\nKZK3T-K6RSJ-ZWTCK-JTJ3T-T3HJJ,11:33 - 26. Okt. 2019\nCHCBT-TF6HB-ZC3WC-BT333-KBR3B,15:54 - 25. Okt. 2019\n\nThis is my result, but they are all in one column.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":141,"Q_Id":58649100,"Users Score":0,"Answer":"It seems they are two columns already, as you gave example in your question\n\ncodes[W9KBJ-95X9T-ZC3KW-BJTJT-5FF3T]\ndate_posted[13:14 - 28. Okt. 2019]\n\nread this file in pandas again, with explicit \",\" as a delimiter, you'll be able to read this file in CSV format.\nLet me know if you are still not clear. \nThanks.","Q_Score":0,"Tags":"python,pandas,csv","A_Id":58649241,"CreationDate":"2019-10-31T18:10:00.000","Title":"pandas saves my data only into one column in a csv-file","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an audio file sampled at 44 kbps and it has a few hours of recording. I would like to view the raw waveform in a plot (figure) with something like matplotlib (or GR in Julia) and then to save the figure to disk. Currently this takes a considerable amount of time and would like to reduce that time. \nWhat are some common strategies to do so? Are there any special circumstances to consider on approaches of reducing the number of points in the figure? I expect that some type of subsampling of the time points will be needed and that some interpolation or smoothing will be used. (Python or Julia solutions would be ideal but other languages like R or MATLAB are similar enough to understand the approach.)","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":144,"Q_Id":58667844,"Users Score":1,"Answer":"Assuming that your audio file has a sample rate of 44 kHz (which is the most common sampling rate), then there are 60*60*44_000 = 158400000 samples per hour. This number should be compared to a high-resolution screen which is ~4000 pixels wide (4k resolution). If you would print time series with a 600 dpi printer, 1 hour would be 60*60*44_000 \/ (600 * 2.54 * 100) = 1039 meters long if every sample should be resolved. (so please don't print this :-))\nInstead have a look at PyPlot.jl functions psd (power spectral density) and specgram (spectrogram) which are often used to visualize frequencies present in an audio recording.","Q_Score":0,"Tags":"python,matplotlib,audio,plot,julia","A_Id":58717599,"CreationDate":"2019-11-02T02:40:00.000","Title":"How to plot a very large audio file with low latency and time to save file?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a huge master data dump excel file. I have to append data to it on a regular basis. The data to be appended is stored as a pandas dataframe. Is there a way to append this data to the master dump file without having to read its contents. \nThe dump file is huge and takes a considerable amount of time for the program to load the file (using pandas).\nI have already tried openpyxl and XlsxWriter but it didn't work.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":152,"Q_Id":58669599,"Users Score":2,"Answer":"It isn't possible to just append to an xlsx file like a text file. An xlsx file is a collection of XML files in a Zip container so to append data you would need to unzip the file, read the XML data, add the new data, rewrite the XML file(s) and then rezip them.\nThis is effectively what OpenPyXL does.","Q_Score":2,"Tags":"python,excel,pandas","A_Id":58670829,"CreationDate":"2019-11-02T08:54:00.000","Title":"Is there a way to append data to an excel file without reading its contents, in python?","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Facing this issue while running the code to load ML model pickle file.,, \nAttributeError: Can't get attribute 'DeprecationDict' on","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":404,"Q_Id":58676350,"Users Score":0,"Answer":"You used a new version of scikit-learn to load a model that was trained by an older version of scikit-learn.\nTherefore, the options are:\n\nRetrain the model with the current version of scikit-learn if you have a training text and data.\nOr go back to the lower version of the scikit-learn reported in the warning message","Q_Score":1,"Tags":"python,scikit-learn","A_Id":63735920,"CreationDate":"2019-11-03T00:04:00.000","Title":"Facing this error : AttributeError: Can't get attribute 'DeprecationDict' on <module 'sklearn.utils.deprecation'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Are pandas iterrows fast compare to np.where on a smaller dataset? I heard numpy is always efficient compared to pandas?\nI was surprised to see that when I used iterrow in my code vs numpy's np.where on a small dataset, iterrows execution was fast.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":31,"Q_Id":58681323,"Users Score":0,"Answer":"Yes, there is a speed difference.\nFeel free to post your timeit benchmark figures.","Q_Score":0,"Tags":"python-3.x","A_Id":58684334,"CreationDate":"2019-11-03T14:51:00.000","Title":"pandas vs numpy packages in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using Tensorflow 1.14 and the tf.keras API to build a number (>10) of differnet neural networks. (I'm also interested in the answers to this question using Tensorflow 2). I'm wondering how I should organize my project.\nI convert the keras models into estimators using tf.keras.estimator.model_to_estimator and Tensorboard for visualization. I'm also sometimes using model.summary(). Each of my models has a number (>20) of hyperparameters and takes as input one of three types of input data. I sometimes use hyperparameter optimization, such that I often manually delete models and use tf.keras.backend.clear_session() before trying the next set of hyperparameters. \nCurrently I'm using functions that take hyperparameters as arguments and return the respective compiled keras model to be turned into an estimator. I use three different \"Main_Datatype.py\" scripts to train models for the three different input data types. All data is loaded from .tfrecord files and there is an input function for each data type, which is used by all estimators taking that type of data as input. I switch between models (i.e. functions returning a model) in the Main scripts. I also have some building blocks that are part of more than one model, for which I use helper functions returning them, piecing together the final result using the Keras functional API.\nThe slight incompatibilities of the different models are begining to confuse me and I've decided to organise the project using classes. I'm planing to make a class for each model that keeps track of hyperparameters and correct naming of each model and its model directory. However, I'm wondering if there are established or recomended ways to do this in Tensorflow.\nQuestion: Should I be subclassing tf.keras.Model instead of using functions to build models or python classes that encapsulate them? Would subclassing keras.Model break (or require much work to enable) any of the functionality that I use with keras estimators and tensorboard? I've seen many issues people have with using custom Model classes and am somewhat reluctant to put in the work only to find that it doesn't work for me. Do you have other suggestions how to better organize my project?\nThank you very much in advance.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":349,"Q_Id":58685407,"Users Score":3,"Answer":"Subclass only if you absolutely need to. I personally prefer following the following order of implementation. If the complexity of the model you are designing, can not be achieved using the first two options, then of course subclassing is the only option left.  \n\ntf.keras Sequential API\ntf.keras Functional API\nSubclass tf.keras.Model","Q_Score":2,"Tags":"python,tensorflow,tensorflow-estimator,tf.keras","A_Id":58685507,"CreationDate":"2019-11-03T23:03:00.000","Title":"Project organization with Tensorflow.keras. Should one subclass tf.keras.Model?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Now, the official TensorFlow on Anaconda is 2.0. My question is how to force Anaconda to install an earlier version of TensorFlow instead. So, for example, I would like Anaconda to install TensorFlow 1.14 as plenty of my projects are depending on this version.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":51822,"Q_Id":58688481,"Users Score":1,"Answer":"first find the python version of tensorflow==1.14.0, then find the Anaconda version by python version.\ne.g. tensorflow 1.14.0 can work well on python36, and Anaconda 3.5.1 has python36. So install the Anaconda 3.5.1, then install tensorflow==1.14.0 by pip","Q_Score":28,"Tags":"python,python-3.x,tensorflow,anaconda,version","A_Id":69961017,"CreationDate":"2019-11-04T06:52:00.000","Title":"Force Anaconda to install tensorflow 1.14","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am currently working in python with spacy and there are different pre-trained models like the en_core_web_sm or the en_core_web_md. One of them is using words vectors to find word similarity and the other one is using context-sensitive tensors. \nWhat is the difference between using context-sensitive tensors and using word vectors? And what is context-senstiive tensors exactly?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":492,"Q_Id":58688938,"Users Score":2,"Answer":"Word vectors are stored in a big table in the model and when you look up cat, you always get the same vector from this table.\nThe context-sensitive tensors are dense feature vectors computed by the models in the pipeline while analyzing the text. You will get different vectors for cat in different texts. If you use en_core_web_sm, the token cat in I have a cat will not have the same vector as in The cat is black. Having the context-sensitive tensors available when the model doesn't include word vectors lets the similarity functions work to some degree, but the results are very different than with word vectors.\nFor most purposes, you probably want to use the _md or _lg model with word vectors.","Q_Score":2,"Tags":"python,nlp,spacy","A_Id":58690132,"CreationDate":"2019-11-04T07:33:00.000","Title":"Difference between context-sensitive tensors and word vectors","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm training a keras deep learning model with 3 fold cross validation. For every fold I'm receiving a best performing model and in the end my algorithm is giving out the combined score of the three best models.\nMy question now is, if there is a possibility to combine the 3 models in the end or if it would be a legit solution to just take the best performing model of those 3 models?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":415,"Q_Id":58691535,"Users Score":1,"Answer":"A (more) correct reflection of your performance on your dataset would be to average the N fold-results on your validation set.\nAs per the three resulting models, you can have an average prediction (voting ensemble) for a new data point. In other words, whenever a new data point arrives, predict with all your three models and average the results.\nPlease note a very important thing: The purpose of K-fold cross-validation is model checking, not model building. By using K-fold cross-validation you ensure that when you randomly split your data, say in an 80-20 percent fashion, you do not create a very easy test set. Creating a very easy test set would lead the developer to consider that he\/she has a very good model, and when subjected to test data the model would perform much worse.\nIn essence and eventually, what you would want to do is to take all the data that you are using for both train and test and using it only for training.","Q_Score":1,"Tags":"python,keras,scikit-learn,deep-learning,cross-validation","A_Id":58692931,"CreationDate":"2019-11-04T10:30:00.000","Title":"How can deep learning models found via cross validation be combined?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"At the end of my code I sum by dataframe below, then export to csv:\nsumbyname = d5.groupby(['Name'])['Value'].agg('sum')\nI sum the value of each person by name, Now if I sum this column in excel using SUM then I get +12\nNow if i do d5['Value'].sum()) in my code to find the total sum, I get -11.\nIs there a difference in the way i'm summing these 2 values? I thought they should be the same.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":658,"Q_Id":58701025,"Users Score":1,"Answer":"The value in column d5['Name'] might contains null values. \nGroupby will ignore those rows with None in d5['Name'].","Q_Score":0,"Tags":"python,pandas","A_Id":58701942,"CreationDate":"2019-11-04T21:03:00.000","Title":"Pandas, .agg('sum') vs .sum()","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a Pandas dataFrame object train_df with say a column called \"ColA\" and a column \"ColB\". It has been loaded from a csv file with columns header using read_csv\nI obtain the same results when I code:\npd.crosstab(train_df['ColA'], train_df['ColB'])\nor \npd.crosstab(train_df.ColA, train_df.ColB)\nIs there any difference in these 2 ways of selecting columns? \nWhen I request to print the type it's the same : pandas.core.series.Series","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":65,"Q_Id":58704076,"Users Score":0,"Answer":"No difference\npd.crosstab(train_df['ColA'], train_df['ColB']) is recommended to prevent possible errors. \nFor example, if you have a column named count and if you type train_df.count it will give an error. train_df['count'] won't give an error.","Q_Score":0,"Tags":"python,pandas,dataframe","A_Id":58704122,"CreationDate":"2019-11-05T03:30:00.000","Title":"Python Pandas dataFrame - Columns selection","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a Pandas dataFrame object train_df with say a column called \"ColA\" and a column \"ColB\". It has been loaded from a csv file with columns header using read_csv\nI obtain the same results when I code:\npd.crosstab(train_df['ColA'], train_df['ColB'])\nor \npd.crosstab(train_df.ColA, train_df.ColB)\nIs there any difference in these 2 ways of selecting columns? \nWhen I request to print the type it's the same : pandas.core.series.Series","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":65,"Q_Id":58704076,"Users Score":0,"Answer":"If you only want to select a single column, there is no difference between the two ways.\nHowever, the dot notation doesn't allow you to select multiple columns, whereas you can use dataframe[['col1', 'col2']] to select multiple columns (which returns a pandas.core.frame.DataFrame instead of a pandas.core.series.Series).","Q_Score":0,"Tags":"python,pandas,dataframe","A_Id":58704174,"CreationDate":"2019-11-05T03:30:00.000","Title":"Python Pandas dataFrame - Columns selection","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"This might be a dumb question, but would like someone to tell me yes or no. \nSay I have an LSTM network in Tensorflow, and am training it using the Adam Optimizer to minimize a cost function by feeding X and Y variables a set of X and Y dict's during training, and then IN THE SAME SESSION, feeding the variables new X and Y dict's for testing, does Tensorflow automatically use the best model found during it's training (i.e. using the weights that brought about the lowest cost value during training), or just the most recent one in it's run (i.e. the latest epoch)? \nWondering if I need to set up a model.saver function to capture the best model as a new lower cost value is reached, close the current session, and re-open a new one using that saved model, OR if I can just assume that when I test in the same session as training, it will use the best model.\nThanks!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":30,"Q_Id":58704575,"Users Score":1,"Answer":"It doesn't.\nThe model relies on a single set of weights, that are variables. You can store the best model with a saver and save the training progress as a separate checkpoint.\nOther option would be to have a duplicate set of variables and copy weights once a better model is found.\nYet, the it is normally uncommon to judge if a model at epoch X is better than at epoch Y, since training accuracy might be misleading (read: overfitting). Therefore, one usually evaluates model after every epoch and saves the checkpoint if performance got better during evaluation. This way there is no need to maintain multiple copies of the same model.","Q_Score":1,"Tags":"python-3.x,tensorflow","A_Id":58704657,"CreationDate":"2019-11-05T04:35:00.000","Title":"Does Tensorflow Use the Best Weights or Most Recent Weights When Testing in the Same Session?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Everything was working fine, but suddenly running a python task which imports gym and from gym imports spaces leads to an error(though it was working fine before): \nImportError: cannot import name 'spaces'\nI have tried reinstalling gym but then my tensorflow needs bleach version to be 1.5 while gym requires a upgraded version.\nI tried upgrading tensoflow from 1.8.0 to 1.12.0, this again throws an error:\nImportError: libcublas.so.10.0: cannot open shared object file: No such file or directory","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":796,"Q_Id":58705609,"Users Score":0,"Answer":"There are probably multiple reasons for this error message. On Windows 10, this can be due to access permissions to gym-related folder. Make sure your Windows user account is granted access to gym and\/or python libraries more broadly.","Q_Score":1,"Tags":"python-3.x,python-import,importerror,openai-gym","A_Id":63646557,"CreationDate":"2019-11-05T06:27:00.000","Title":"Cannot Import name 'spaces' from gym","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am applying the pruning function from tensorflow_model_optimization,    tfmot.sparsity.keras.prune_low_magnitude() to MobileNetV2.\nIs there any way to set only some layers of the model to be prunable? For training, there is a method \"set_trainable\", but I haven't found any equivalent for pruning.\nAny ideas or comments will be appreciated! :)","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":321,"Q_Id":58711222,"Users Score":0,"Answer":"In the end I found that you can also apply prune_low_magnitude() per layer.\nSo the workaround would be to define a list containing the names or types of the layers that shall be pruned, and iterate the layer-wise pruning over all layers in this list.","Q_Score":0,"Tags":"python,machine-learning,keras,tensorflow2.0,pruning","A_Id":59372216,"CreationDate":"2019-11-05T12:18:00.000","Title":"How to set prunable layers for tfmot.sparsity.keras.prune_low_magnitude?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataframe containing the coordinates of millions of particles which I want to use to train a Neural network. These particles build individual clusters which are already identified and labeled; meaning that every particle is already assigned to its correct cluster (this assignment is done by a density estimation but for my purpose not that relevant).\nthe challenge is now to build a network which does this clustering after learning from the huge data. there are also a few more features in the dataframe like clustersize, amount of particles in a cluster etc. \nsince this is not a classification problem but more a identification of clusters-challenge what kind of neural network should i use? I have also problems to build this network: for example a CNN which classifies wheather there is a dog or cat in the picture, the output is obviously binary. so also the last layer just consists of two outputs which represent the probability for being 1 or 0. But how can I implement the last layer when I want to identify clusters? \nduring my research I heard about self organizing maps. would these networks do the job?\nthank you","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":97,"Q_Id":58711675,"Users Score":1,"Answer":"If you want to treat clustering as a classification problem, then you can try to train the network to predict whether two points belong to the same clusters or to different clusters.\nThis does not ultimately solve your problems, though - to cluster the data, this labeling needs to be transitive (which it likely will not be) and you have to label n\u00b2 pairs, which is expensive.\nFurthermore, because your clustering is density-based, your network may need to know about further data points to judge which ones should be connected...","Q_Score":0,"Tags":"python,tensorflow,neural-network,cluster-analysis","A_Id":58724694,"CreationDate":"2019-11-05T12:45:00.000","Title":"Cluster identification with NN","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataframe containing the coordinates of millions of particles which I want to use to train a Neural network. These particles build individual clusters which are already identified and labeled; meaning that every particle is already assigned to its correct cluster (this assignment is done by a density estimation but for my purpose not that relevant).\nthe challenge is now to build a network which does this clustering after learning from the huge data. there are also a few more features in the dataframe like clustersize, amount of particles in a cluster etc. \nsince this is not a classification problem but more a identification of clusters-challenge what kind of neural network should i use? I have also problems to build this network: for example a CNN which classifies wheather there is a dog or cat in the picture, the output is obviously binary. so also the last layer just consists of two outputs which represent the probability for being 1 or 0. But how can I implement the last layer when I want to identify clusters? \nduring my research I heard about self organizing maps. would these networks do the job?\nthank you","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":97,"Q_Id":58711675,"Users Score":0,"Answer":"These particles build individual clusters which are already identified\n  and labeled; meaning that every particle is already assigned to its\n  correct cluster (this assignment is done by a density estimation but\n  for my purpose not that relevant).\n  the challenge is now to build a network which does this clustering\n  after learning from the huge data.\n\nSounds pretty much like a classification problem to me. Images themselves can build clusters in their image space (e.g. a vector space of dimension width * height * RGB).\n\nsince this is not a classification problem but more a identification\n  of clusters-challenge what kind of neural network should i use?\n\nYou have data of coordinates, you have labels. Start with a simple fully connected single\/multi-layer-perceptron i.e. vanilla NN, with as many outputs as number of clusters and softmax-activation function.\nThere are tons of blogs and tutorials for Deep Learning libraries like keras out there in the internet.","Q_Score":0,"Tags":"python,tensorflow,neural-network,cluster-analysis","A_Id":58712729,"CreationDate":"2019-11-05T12:45:00.000","Title":"Cluster identification with NN","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to do a linear regression using Matrix multiplication. \nX is the feature matrix, and I have 100 data points. As per the normal equation, the dot product of X and of the transpose of X is required. \nHaving added a column of ones as required, the shape of X is 100\u00d72 while for the transpose of X it is 2\u00d7100.\nHowever, when I am doing the dot product, the result (which is given in the book) comes accordingly, a 2\u00d72 matrix. Shouldn't it be a 100\u00d7100 matrix as per laws of matrix multiplication using dot product?\nConceptually, where am I going wrong?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":48,"Q_Id":58715034,"Users Score":0,"Answer":"You are feeding them in the wrong order\nInstead of feeding (100,2) * (2,100), you are feeding (2,100) * (100,2)","Q_Score":1,"Tags":"python,numpy,regression","A_Id":58715086,"CreationDate":"2019-11-05T15:56:00.000","Title":"Matrix multiplication using numpy array","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an image with image.shape=(20,10)and I want to resize this image so that new image size would be image.size = 90.\nI want to use np.resize(image,(new_width, new_height)), but how can I calculate new_width and new_height, so that it maintains aspect_ratio as same as in original image.","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":52,"Q_Id":58717150,"Users Score":0,"Answer":"Well, you choose which dimension you want to enforce and then you adjust the other one by calculating either new_width = new_height*aspect_ratio or new_height = new_width\/aspect_ratio.\nYou might want to round those numbers and convert them to int too.","Q_Score":0,"Tags":"python-3.x,numpy","A_Id":58717243,"CreationDate":"2019-11-05T18:11:00.000","Title":"How to resize image by mainitaining aspect ratio in python3?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to denoise my signal using discrete wavelet transform in python using pywt package. But i cannot define what is threshold value that i should set in pywt.threshold() function\nI have no idea what the best threshold value that should be set in order to reconstruct a signal with minimal noise\nI used ordinary code:\npywt.threshold(mysignal, threshold, 'soft')\nyes i am intended to do soft thresholding\nI want to know if the threshold value could be determined by looking to my signal or from the other way","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":956,"Q_Id":58725295,"Users Score":2,"Answer":"There are some helpful graphics on pywt webpage that help visualize what these thresholds are and what they do.\nThe threshold applies to the coefficients as opposed to your raw signal.  So for denoising, this will typically be the last couple of entries returned by pywt.wavedec that will need to be zeroed\/thresholded.\nI could initial guess is the 0.5*np.std of each coefficeint level you want to threshold.","Q_Score":3,"Tags":"python,wavelet","A_Id":58730741,"CreationDate":"2019-11-06T07:45:00.000","Title":"How to set a threshold value from signal to be processed in wavelet thresholding in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In the code provided by tensorlfow tutorial for CycleGAN, they have trained discriminator and generator simultaneously.\n\n\n    def train_step(real_x, real_y):\n      # persistent is set to True because the tape is used more than\n      # once to calculate the gradients.\n      with tf.GradientTape(persistent=True) as tape:\n        # Generator G translates X -> Y\n        # Generator F translates Y -> X.\n\n        fake_y = generator_g(real_x, training=True)\n        cycled_x = generator_f(fake_y, training=True)\n\n        fake_x = generator_f(real_y, training=True)\n        cycled_y = generator_g(fake_x, training=True)\n\n        # same_x and same_y are used for identity loss.\n        same_x = generator_f(real_x, training=True)\n        same_y = generator_g(real_y, training=True)\n\n        disc_real_x = discriminator_x(real_x, training=True)\n        disc_real_y = discriminator_y(real_y, training=True)\n\n        disc_fake_x = discriminator_x(fake_x, training=True)\n        disc_fake_y = discriminator_y(fake_y, training=True)\n\n        # calculate the loss\n        gen_g_loss = generator_loss(disc_fake_y)\n        gen_f_loss = generator_loss(disc_fake_x)\n\n        total_cycle_loss = calc_cycle_loss(real_x, cycled_x) + calc_cycle_loss(real_y, cycled_y)\n\n        # Total generator loss = adversarial loss + cycle loss\n        total_gen_g_loss = gen_g_loss + total_cycle_loss + identity_loss(real_y, same_y)\n        total_gen_f_loss = gen_f_loss + total_cycle_loss + identity_loss(real_x, same_x)\n\n        disc_x_loss = discriminator_loss(disc_real_x, disc_fake_x)\n        disc_y_loss = discriminator_loss(disc_real_y, disc_fake_y)\n\n      # Calculate the gradients for generator and discriminator\n      generator_g_gradients = tape.gradient(total_gen_g_loss, \n                                            generator_g.trainable_variables)\n      generator_f_gradients = tape.gradient(total_gen_f_loss, \n                                            generator_f.trainable_variables)\n\n      discriminator_x_gradients = tape.gradient(disc_x_loss, \n                                                discriminator_x.trainable_variables)\n      discriminator_y_gradients = tape.gradient(disc_y_loss, \n                                                discriminator_y.trainable_variables)\n\n      # Apply the gradients to the optimizer\n      generator_g_optimizer.apply_gradients(zip(generator_g_gradients, \n                                                generator_g.trainable_variables))\n\n      generator_f_optimizer.apply_gradients(zip(generator_f_gradients, \n                                                generator_f.trainable_variables))\n\n      discriminator_x_optimizer.apply_gradients(zip(discriminator_x_gradients,\n                                                    discriminator_x.trainable_variables))\n\n      discriminator_y_optimizer.apply_gradients(zip(discriminator_y_gradients,\n                                                    discriminator_y.trainable_variables))\n\n\nBut while training a GAN network we need to stop training discriminator when we are training generator network.\nWhat's the benefit of using it?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":413,"Q_Id":58726483,"Users Score":0,"Answer":"For the training to happen in an adversarial way the gradients of the discriminator and generator networks should be updated separately. The discriminator becomes stronger because generator produces more realistic samples and vise versa. If you update these networks together the \"adversarial\" training is not happening - to the best of my knowledge you are unlikely to obtain pleasing synthetic samples this way.","Q_Score":0,"Tags":"python,tensorflow,deep-learning,generative-adversarial-network","A_Id":65612208,"CreationDate":"2019-11-06T09:06:00.000","Title":"Should we stop training discriminator while training generator in CycleGAN tutorial?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In the code provided by tensorlfow tutorial for CycleGAN, they have trained discriminator and generator simultaneously.\n\n\n    def train_step(real_x, real_y):\n      # persistent is set to True because the tape is used more than\n      # once to calculate the gradients.\n      with tf.GradientTape(persistent=True) as tape:\n        # Generator G translates X -> Y\n        # Generator F translates Y -> X.\n\n        fake_y = generator_g(real_x, training=True)\n        cycled_x = generator_f(fake_y, training=True)\n\n        fake_x = generator_f(real_y, training=True)\n        cycled_y = generator_g(fake_x, training=True)\n\n        # same_x and same_y are used for identity loss.\n        same_x = generator_f(real_x, training=True)\n        same_y = generator_g(real_y, training=True)\n\n        disc_real_x = discriminator_x(real_x, training=True)\n        disc_real_y = discriminator_y(real_y, training=True)\n\n        disc_fake_x = discriminator_x(fake_x, training=True)\n        disc_fake_y = discriminator_y(fake_y, training=True)\n\n        # calculate the loss\n        gen_g_loss = generator_loss(disc_fake_y)\n        gen_f_loss = generator_loss(disc_fake_x)\n\n        total_cycle_loss = calc_cycle_loss(real_x, cycled_x) + calc_cycle_loss(real_y, cycled_y)\n\n        # Total generator loss = adversarial loss + cycle loss\n        total_gen_g_loss = gen_g_loss + total_cycle_loss + identity_loss(real_y, same_y)\n        total_gen_f_loss = gen_f_loss + total_cycle_loss + identity_loss(real_x, same_x)\n\n        disc_x_loss = discriminator_loss(disc_real_x, disc_fake_x)\n        disc_y_loss = discriminator_loss(disc_real_y, disc_fake_y)\n\n      # Calculate the gradients for generator and discriminator\n      generator_g_gradients = tape.gradient(total_gen_g_loss, \n                                            generator_g.trainable_variables)\n      generator_f_gradients = tape.gradient(total_gen_f_loss, \n                                            generator_f.trainable_variables)\n\n      discriminator_x_gradients = tape.gradient(disc_x_loss, \n                                                discriminator_x.trainable_variables)\n      discriminator_y_gradients = tape.gradient(disc_y_loss, \n                                                discriminator_y.trainable_variables)\n\n      # Apply the gradients to the optimizer\n      generator_g_optimizer.apply_gradients(zip(generator_g_gradients, \n                                                generator_g.trainable_variables))\n\n      generator_f_optimizer.apply_gradients(zip(generator_f_gradients, \n                                                generator_f.trainable_variables))\n\n      discriminator_x_optimizer.apply_gradients(zip(discriminator_x_gradients,\n                                                    discriminator_x.trainable_variables))\n\n      discriminator_y_optimizer.apply_gradients(zip(discriminator_y_gradients,\n                                                    discriminator_y.trainable_variables))\n\n\nBut while training a GAN network we need to stop training discriminator when we are training generator network.\nWhat's the benefit of using it?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":413,"Q_Id":58726483,"Users Score":0,"Answer":"In the GANs, you don't stop training D or G. They are trained simultaneously.\nHere they first calculate the gradient values for each network (not to change D or G before calculating the current loss), then update the weights using those.\nIt's not clear in your question, what's the benefit of what?","Q_Score":0,"Tags":"python,tensorflow,deep-learning,generative-adversarial-network","A_Id":58727960,"CreationDate":"2019-11-06T09:06:00.000","Title":"Should we stop training discriminator while training generator in CycleGAN tutorial?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I just learned that you can handle missing data\/ NaN with imputation and interpolation, what i just found is interpolation is a type of estimation, a method of constructing new data points within the range of a discrete set of known data points while imputation is replacing the missing data of the mean of the column. But is there any differences more than that? When is the best practice to use each of them?","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":4779,"Q_Id":58731044,"Users Score":2,"Answer":"I will answer the second part of your question i.e. when to use what.\nWe use both techniques depending upon the use case. \nImputation:\nIf you are given a dataset of patients with a disease (say Pneumonia) and there is a feature called body temperature. So, if there are null values for this feature then you can replace it by average value i.e. Imputation.\nInterpolation:\nIf you are given a dataset of the share price of a company, you know that every Saturday and Sunday are off. So those are missing values. Now, these values can be filled by the average of Friday value and Monday value i.e. Interpolation.\nSo, you can choose the technique depending upon the use case.","Q_Score":6,"Tags":"python-3.x,pandas","A_Id":62236017,"CreationDate":"2019-11-06T13:14:00.000","Title":"What is the difference between interpolation and imputation?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I trained a object detection model through vision in GCP, How can i use that model in normal tensorflow object detection api provided by google in GitHub?\nIt gives 3 options for exporting the model which one to use & how?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":51,"Q_Id":58731323,"Users Score":0,"Answer":"You need to save the model in the local drive from the cloud and load this model into the object detection api, the function load_model usually downloads pre-trained model from URL, you need to give the path of the local saved model here. The API that you are looking for is tf.saved_model.load, also update the path for labels correctly in PATH_TO_LABELS.","Q_Score":0,"Tags":"python-3.x,tensorflow,google-cloud-platform,object-detection-api","A_Id":58952487,"CreationDate":"2019-11-06T13:30:00.000","Title":"How to use GCP vision trained model in Object Detection API","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to create a neuroevolution project using python and tensorflow\/keras but I couldn't find any good way of mutating the neural network.\nI am aware that there are librarys like NEAT, but I wanted to try and code it myself.\nWould appreciate it if anyone can tell me something.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":181,"Q_Id":58737140,"Users Score":0,"Answer":"Your question is a little bit too vague, but I would assume that coding your own evolutionary algorithm shouldn\u2019t be too difficult for you given what you have done with neural networks so far.\nA good starting point for you would be to research the following EA concepts\u2026\n\nEncoding.\nFitness.\nCrossover and\/or Mutation.","Q_Score":0,"Tags":"python,tensorflow,keras,evolutionary-algorithm","A_Id":58738193,"CreationDate":"2019-11-06T19:18:00.000","Title":"Is there a way to mutate a neural network in tensorflow\/keras?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a multitude of mature curves (days are plotted on X axis and data is >= 90 days old so the curve is well developed).\nOnce a week I get a new set of data that is anywhere between 0 and 14 days old.\nAll of the data (old and new), when plotted, follows a log curve (in shape) but with different slopes. So some weeks have a higher slope, curve goes higher, some smaller slope, curve is lower. At 90 days all curves flatten. \nFrom the set of \"mature curves\" I need to select the one whose slope matches the best the slope of my newly received date. Also, from the mature curve I then select the Y-value at 90 days and associate it with my \"immature\"\/new curve.\nAny suggestions how to do this? I can seem to find any info. \nThanks much!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":53,"Q_Id":58767395,"Users Score":0,"Answer":"This seems more like a mathematical problem than a coding problem, but I do have a solution.\nIf you want to find how similar two curves are, you can use box-differences or just differences.\nYou calculate or take the y-values of the two curves for each x value shared by both the curves (or, if they share no x-values because, say, one has even and the other odd values, you can interpolate those values).\nThen you take the difference of the two y-values for every x-value.\nThen you sum up those differences for all x-values.\nThe resulting number represents how different the two curves are.\nOptionally, you can square all the values before summing up, but that depends on what definition of \"likeness\" you are using.","Q_Score":0,"Tags":"python","A_Id":58767741,"CreationDate":"2019-11-08T13:21:00.000","Title":"Select a \"mature\" curve that best matches the slope of a new \"immature\" curve","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'd like to use my own tensorflow 2 \/ keras model with opencv (cv.dnn.readNetFromTensorflow(    bufferModel[, bufferConfig] ). But, I didn't manage to generate the required files : \nbufferModel :   buffer containing the content of the pb file (frozen_inference_graphe)\nbufferConfig : buffer containing the content of the pbtxt file (model configuration file)\nEverything I've found rely on \"freeze_graph.py\" or other solution that only work with tensorflow 1.x. How should I do with tensorflow 2 ?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":243,"Q_Id":58780057,"Users Score":0,"Answer":"The .pb file gets generated when using keras.callbacks.ModelCheckpoint().\nHowever, I don't know how to create the .pbtxt file.","Q_Score":2,"Tags":"python,opencv,tensorflow,keras,tensorflow2.0","A_Id":59092212,"CreationDate":"2019-11-09T14:07:00.000","Title":"How to generate frozen_inference_graphe.pb and .pbtxt files with tensorflow 2","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Let's say we have a string R of length 20000 (or another arbitrary length). I want to get 8 random non-overlapping sub strings of length k from string R.\nI tried to partition string R into 8 equal length partitions and get the [:k] of each partition but that's not random enough to be used in my application, and the condition of the method to work can not easily be met.\nI wonder if I could use the built-in random package to accomplish the job, but I can't think of a way to do it, how can I do it?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":284,"Q_Id":58784258,"Users Score":0,"Answer":"You could simply run a loop, and inside the loop use the random package to pick a starting index, and extract the substring starting at that index. Keep track of the starting indices that you have used so that you can check that each substring is non-overlapping. As long as k isn't too large, this should work quickly and easily.\nThe reason I mention the size of k is because if it is large enough, then it could be possible to select substrings that don't allow you to find 8 non-overlapping ones. But that only needs to be considered if k is quite large with respect to the length of the original string.","Q_Score":2,"Tags":"python,string,random","A_Id":58784283,"CreationDate":"2019-11-09T22:53:00.000","Title":"Get N random non-overlapping substrings of length K","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I m trying to create an application that captures the feed of one camera, detects the faces in the feed, then takes pictures of them and adds them to the image database. Simultaneously another camera feed will be captured and another neural network will compare the faces in the second camera feed with the face images in the database and then will display the name of the person.\nIdeally, the new face images should be loaded into the neural network model without it completely retraining.\nRight now I'm trying to achieve that with TensorFlow and OpenCV.\nWould a dynamic neural network be possible with TensorFlow?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":40,"Q_Id":58784730,"Users Score":0,"Answer":"As for step two(getting the name of the person), I don't think you would need any retraining to achieve this. \nYou could use Convolutional LSTM or a similar nn. input shape  could be (None,image_dimension_x,y,3) (3 is the color channel, for RGB)\nwhere None would be the current total number of images in the database. It passes all the images in the database into the nn and returns a number as an index.\nOr alternatively, you could use a normal convolution (without the None)and make it output the confidence it has for each image in the database to be the person on camera right now. Then choose the person with the highest confidence.\nI would say the second one is easier and probably better, that's my suggestion anyway.\nHope it helps :)","Q_Score":1,"Tags":"python,tensorflow,deep-learning,image-recognition","A_Id":58798707,"CreationDate":"2019-11-10T00:17:00.000","Title":"Retrain Tensorflow Model on the go","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Some of my plots have several million lines. I dynamically adjust the alpha value, by the number of lines, so that the outliers more or less disappear, while the most prominent features appear clear. But for some alpha's, the lines just disappear.\nWhat is the smallest valid alpha value for line plots in in matplotlib? And why is there a lower limit?","AnswerCount":2,"Available Count":1,"Score":-0.0996679946,"is_accepted":false,"ViewCount":418,"Q_Id":58788958,"Users Score":-1,"Answer":"There's no lower limit; the lines just appear to be invisible for very small alpha values.\nIf you draw one line with alpha=0.01 the difference in color is too small for your screen \/ eyes to discern. If you draw 100 lines with a=0.01 on top of each other, you will see them.\nAs for your problem, you can just add a small number to the alpha value of each draw call so that lines that would otherwise have alpha < 0.1 still appear.","Q_Score":0,"Tags":"python,matplotlib","A_Id":58789056,"CreationDate":"2019-11-10T13:06:00.000","Title":"The smallest valid alpha value in matplotlib?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am quite new with pandas (couple of months) and I am starting building up a project that will be based on a pandas data array.\nSuch pandas data array will consist on a table including different kind of words present in a collection of texts (around 100k docs, and around 200 key-words). \nimagine for instance the words \"car\" and the word \"motorbike\" and documents numbered doc1, doc2 etc.\nhow should I go about the arrangement? \na) The name of every column is the doc number and the index the words \"car\" and \"motorbike\" or \nb) the other way around; the index being the docs numbers and the columns head the words?\nI don't have enough insights of pandas in order to be able to foreseen what will the consequences of such choice. And all the code will be based on that decision.\nAs a side note there array is not static, there will be more documents and more words being added to the array every now and again.\nwhat would you recommend? a or b? and why?\nthanks.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":28,"Q_Id":58789312,"Users Score":1,"Answer":"Generally in pandas, we follow a practice that instances are columns (here doc number) and features are columns (here words). So, prefer to use the approach 'b'.","Q_Score":0,"Tags":"python,pandas,indexing,row,multiple-columns","A_Id":58789387,"CreationDate":"2019-11-10T13:51:00.000","Title":"How to choose python pandas arrangement columns vs rows","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I  have a table with 4 columns filled with integer.  Some of the rows have a value \"null\" as its more than 1000 records with this \"null\" value, how can I delete these rows all at once? I tried the delete method but it requires the index of the row its theres over 1000 rows. Is there as faster way to do it?\nThanks","AnswerCount":5,"Available Count":4,"Score":0.0,"is_accepted":false,"ViewCount":1193,"Q_Id":58789936,"Users Score":0,"Answer":"use the 'drop.isnull()' function.","Q_Score":1,"Tags":"python-3.x,jupyter-notebook","A_Id":58818221,"CreationDate":"2019-11-10T15:08:00.000","Title":"How to remove rows from a datascience table in python","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I  have a table with 4 columns filled with integer.  Some of the rows have a value \"null\" as its more than 1000 records with this \"null\" value, how can I delete these rows all at once? I tried the delete method but it requires the index of the row its theres over 1000 rows. Is there as faster way to do it?\nThanks","AnswerCount":5,"Available Count":4,"Score":0.0,"is_accepted":false,"ViewCount":1193,"Q_Id":58789936,"Users Score":0,"Answer":"To remove a row in a datascience package:\nname_of_your_table.remove() # number of the row in the bracket","Q_Score":1,"Tags":"python-3.x,jupyter-notebook","A_Id":63076734,"CreationDate":"2019-11-10T15:08:00.000","Title":"How to remove rows from a datascience table in python","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I  have a table with 4 columns filled with integer.  Some of the rows have a value \"null\" as its more than 1000 records with this \"null\" value, how can I delete these rows all at once? I tried the delete method but it requires the index of the row its theres over 1000 rows. Is there as faster way to do it?\nThanks","AnswerCount":5,"Available Count":4,"Score":0.0,"is_accepted":false,"ViewCount":1193,"Q_Id":58789936,"Users Score":0,"Answer":"#df is the original dataframe#\n#The '-' operator removes the null values and re-assigns the remaining ones to df#\ndf=idf[-(df['Column'].isnull())]","Q_Score":1,"Tags":"python-3.x,jupyter-notebook","A_Id":66193032,"CreationDate":"2019-11-10T15:08:00.000","Title":"How to remove rows from a datascience table in python","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I  have a table with 4 columns filled with integer.  Some of the rows have a value \"null\" as its more than 1000 records with this \"null\" value, how can I delete these rows all at once? I tried the delete method but it requires the index of the row its theres over 1000 rows. Is there as faster way to do it?\nThanks","AnswerCount":5,"Available Count":4,"Score":0.0,"is_accepted":false,"ViewCount":1193,"Q_Id":58789936,"Users Score":0,"Answer":"use dataframe_name.isnull() #To check the is there any missing values in your table.\nuse dataframe_name.isnull.sum() #To get the total number of missing values.\nuse dataframe_name.dropna() # To drop or delete the missing values.","Q_Score":1,"Tags":"python-3.x,jupyter-notebook","A_Id":68397031,"CreationDate":"2019-11-10T15:08:00.000","Title":"How to remove rows from a datascience table in python","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an image, where four corner points are defined. Now I want to get the pixel values of the region, that is defined by the 4 corners. Problem is, although it is a rectangle, it has a \"slope\", which means neither the two upper corner points nor the lower one are at the same height. How can I still solve this issue?\nI have not found anything for this yet.. I'd appreciate any kind of support! :)","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2611,"Q_Id":58790535,"Users Score":0,"Answer":"It is not very easy to iterate through a slanted rectangle. Therefore, what you can do is to rotate the whole image such that the rectangle is parallel to the sides again. \nFor this, you can compute the slope of one side as difference in the y coordinate over the difference in the x coordinate of the corners. The value you get is the slope. The arctangent of the slope is the angle to the horizontal. You need to rotate the image with the opposite (negative) of this value. \nTo make it more efficient, you can crop a bit the image.","Q_Score":2,"Tags":"python,image,opencv","A_Id":58790592,"CreationDate":"2019-11-10T16:14:00.000","Title":"How to get pixel values inside of a rectangle within an image","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":1,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a keras model, which uses custom function for metrics:\nmodel.compile(optimizer = tf.keras.optimizers.Adam(), loss = 'binary_crossentropy', metrics = ['accuracy', roc_auc])\nThe function works fine and model behaves as expected. However, when saving the model via model.save() and then loading it via model.load_model(), I get ValueError: Unknown metric function:roc_auc when running following code: model.predict(X). Interestingly this error does not appear when I run the same command again, through command shell, it only occurs during first run. Is this a bug?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":24,"Q_Id":58801078,"Users Score":1,"Answer":"Do you mean keras.models.load_model(path)? It sounds very strange to have model.load_model(). \nYou are probably missing the argument custom_objects = {'roc_auc': roc_auc} in load_model. Keras cannot create a model if it doesn't know what roc_auc means.","Q_Score":1,"Tags":"python,keras","A_Id":58801419,"CreationDate":"2019-11-11T12:09:00.000","Title":"Error when using loaded Keras classifier with custom metrics function","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"i am working on a project where i have to present the Chart \/graph created using matplotlib with python3 into a PDF format. The PDF must carry the data, custom titles along with the chart\/graph. PDF can be multiple page report as well. I know that we can store the matplotlib charts in PDF. But i am looking for any solution if we can achieve Data, chart and custom text in PDF format.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":284,"Q_Id":58802543,"Users Score":0,"Answer":"Even though the question is not very clear. If I have to do what I understand from your question, I will use Jupyter Notebook and save it as PDF. In this notebook, I will have:\n\nExploratory analysis of the data (What data scientists call EDA)\nDiscussion and other mathematical formulas at they may apply to your case\nThe plots\n\nYou can save jupyter notebooks to PDF using nbconvert module in Python.\nIf you don't have it installed on your computer, do so with this command: \npip install nbconvert\nTo save your notebook as a PDF, go to the folder containing your Jupyter notebook file and run the following command:\njupyter nbconvert --to pdf MyNotebook.ipynb","Q_Score":0,"Tags":"python,matplotlib","A_Id":58802847,"CreationDate":"2019-11-11T13:42:00.000","Title":"How to create custom report in PDF using matplotlib and python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Problem description\nIntroductory remark: For the code have a look below\nLet's say we have a pandas dataframe consisting of 3 columns and 2 rows.\nI'd like to add a 4th column called 'Max_LF' that will consist of an array. The value of the cell is retrieved by having a look at the column 'Max_WD'. For the first row that would be 0.35 which will than be compared to the values in the column 'WD' where 0.35 can be found at the third position. Therefore, the third value of the column 'LF' should be written into the column 'Max_LF'. If the value of 'Max_WD' occures multiple times in 'WD', then all corresponding items of 'LF' should be written into 'Max_LF'.\nFailed attempt\nSo far I had various attemps on first retrieving the index of the item in 'Max_WD' in 'WD'. After potentially retrieving the index the idea was to then get the items of 'LF' via their index:\ndf4['temp_indices'] = [i for i, x in enumerate(df4['WD']) if x == df4['Max_WD']]\nHowever, a ValueError occured:\nraise ValueError('Lengths must match to compare')\nValueError: Lengths must match to compare\nThis is what the example dateframe looks like\ndf = pd.DataFrame(data={'LF': [[1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4]] , 'WD': [[0.28, 0.34, 0.35, 0.18], [0.42, 0.45, 0.45, 0.18], [0.31, 0.21, 0.41, 0.41]], 'Max_WD': [0.35, 0.45, 0.41]})\nThe expected outcome should look like\ndf=pd.DataFrame(data={'LF': [[1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4]] , 'WD': [[0.28, 0.34, 0.35, 0.18], [0.42, 0.45, 0.45, 0.18], [0.31, 0.21, 0.41, 0.41]], 'Max_WD': [0.35, 0.45, 0.41], 'Max_LF': [[3] ,[2,3], [3,4]]})","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":1805,"Q_Id":58805531,"Users Score":1,"Answer":"Thanks guys! With your help I was able to solve my problem.\nLike Prince Francis suggested I first did\ndf['temp'] = df.apply(lambda x : [i for i, e in enumerate(x['WD']) if e == x['Max_WD']], axis=1)\nto get the indicees of the 'WD'-values in 'LF'. In a second stept I then could add the actual column 'Max_LF' by doing\ndf['LF_Max'] = df.apply(lambda x: [x['LF'][e] for e in (x['temp'])],axis=1)\nThanks a lot guys!","Q_Score":2,"Tags":"python,pandas,list-comprehension","A_Id":58808296,"CreationDate":"2019-11-11T16:51:00.000","Title":"Adding a column to a pandas dataframe based on other columns","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I currently have a pipeline running on GCP. The entire thing is written using pandas to manipulate CSVs and do some transformations, as well as side inputs from external sources. (It makes use of bigquery and storage APIs). The thing is, it runs on a 32vCPUs\/120GB RAM Compute Engine instance (VM) and it does simple parallel processing with python's multiprocessing library. We are currently thinking about switching to Dataflow, and what I'd like to know is: if I were to implement the same pipeline using Beam's DirectRunner, how should I expect the performance to compare to that of the current implementation? Would it be faster or slower and why? Will the DirectRunner use well all the machine resources or is it limited somehow?","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":564,"Q_Id":58809283,"Users Score":2,"Answer":"You question is broad. However, I will try to provide you some inputs. It's hard to compare a DirectRunner and a DataflowRunner. \n\nDirectRunner launches your pipeline on your current VM and use the capability of this only VM. It's your VM, you have to set it up, patch it, take care to free disk\/partition\/logs file, (...)   \nDataflowRunner launches the pipeline to a managed platform. Dataflow, according with its metrics and \"prediction\" (no ML here!) chooses to scale up or down the number of VM to execute as quickly as possible your pipeline. You can set small VM (1 vCPU for example) and Dataflow will spawn a lot of them, or bigger VM and, maybe that dataflow will spawn only 1 because it's enough for the pipeline.\n\nPro tips: the VM bandwidth is limited to 2Gbs per vCPU up to 8 vCPU. Take care of the network bottleneck and choose wisely the VM size (I recommend VM with 4 or 8 vCPU usually) \nOn one side, you have only one VM to manage, on the other side, you only have to set parameters and let Dataflow managing and scaling your pipeline.\nI don't know your growth perspective, but vertical scalability (adding more vCPU\/memory on your single VM) can reach a limit a day. With Dataflow, it's elastic and you don't worry about this; in addition of server management and patching.\nFinally, answer to your question \"faster or slower\", too hard to answer... Dataflow, if it run on several VM, will add network latency, dataflow internal management overhead, but can scale to use more vCPU in parallel at some point of time compare to your current VM. Is your pipeline can leverage of this parallelism or not? Is it solve some of your current bottleneck? Too hard to answer on my side.","Q_Score":1,"Tags":"python,google-cloud-platform,cloud,apache-beam,dataflow","A_Id":58812577,"CreationDate":"2019-11-11T22:02:00.000","Title":"Apache beam DirectRunner vs \"normal\" parallel processes","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I'm running Anaconda 64 bit on Windows 10 and I've encountered a CondaVerificationError when I try installing TensorFlow 2.0 on one of my computers. I believe the error stems from mixing pip installations with conda installations for the same package. I originally installed then uninstalled TensorFlow with pip and then tried installing with conda and now I'm stuck trying to resolve this issue.\n    I've tried reinstalling Anaconda but the issue persists. Thoughts?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":74,"Q_Id":58812244,"Users Score":0,"Answer":"Update:\nI reinstalled TensorFlow 2.0 with pip and even though it successfully installed, I was still facing issues when I tried to run my code. A file called cudart64_100.dll could not be located. I eventually managed to get my TF 2.0 code to run successfully by installing an older version of CUDA. I'm still not sure what the issue is with conda, but I'll settle for the current working version of anaconda.","Q_Score":0,"Tags":"python,windows,tensorflow,pip,conda","A_Id":58844839,"CreationDate":"2019-11-12T04:53:00.000","Title":"TensorFlow CondaVerificationError - Mixing Pip with Conda","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to install the matplotlib package for Python with pip install matplotlib in the command prompt but suddenly the lines get red and the next error appears:\nERROR: Command errored out with exit status 1: 'c:\\users\\pol\\appdata\\local\\programs\\python\\python38\\python.exe' -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '\"'\"'C:\\\\Users\\\\Pol\\\\AppData\\\\Local\\\\Temp\\\\pip-install-v44y041t\\\\matplotlib\\\\setup.py'\"'\"'; __file__='\"'\"'C:\\\\Users\\\\Pol\\\\AppData\\\\Local\\\\Temp\\\\pip-install-v44y041t\\\\matplotlib\\\\setup.py'\"'\"';f=getattr(tokenize, '\"'\"'open'\"'\"', open)(__file__);code=f.read().replace('\"'\"'\\r\\n'\"'\"', '\"'\"'\\n'\"'\"');f.close();exec(compile(code, __file__, '\"'\"'exec'\"'\"'))' install --record 'C:\\Users\\Pol\\AppData\\Local\\Temp\\pip-record-d5re6a86\\install-record.txt' --single-version-externally-managed --compile Check the logs for full command output.\nI'm using Windows and my Python version is 3.8.0. I have already tried python -m pip install matplotlib but it doesn't work.","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":11426,"Q_Id":58814671,"Users Score":0,"Answer":"pip install --pre -U scikit-learn\nthis command is work for me\nI have found because this error come for the duplication of same libraries.","Q_Score":4,"Tags":"python,python-3.x,matplotlib,pip,python-packaging","A_Id":69829396,"CreationDate":"2019-11-12T08:24:00.000","Title":"Matplotlib: Command errored out with exit status 1","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to install the matplotlib package for Python with pip install matplotlib in the command prompt but suddenly the lines get red and the next error appears:\nERROR: Command errored out with exit status 1: 'c:\\users\\pol\\appdata\\local\\programs\\python\\python38\\python.exe' -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '\"'\"'C:\\\\Users\\\\Pol\\\\AppData\\\\Local\\\\Temp\\\\pip-install-v44y041t\\\\matplotlib\\\\setup.py'\"'\"'; __file__='\"'\"'C:\\\\Users\\\\Pol\\\\AppData\\\\Local\\\\Temp\\\\pip-install-v44y041t\\\\matplotlib\\\\setup.py'\"'\"';f=getattr(tokenize, '\"'\"'open'\"'\"', open)(__file__);code=f.read().replace('\"'\"'\\r\\n'\"'\"', '\"'\"'\\n'\"'\"');f.close();exec(compile(code, __file__, '\"'\"'exec'\"'\"'))' install --record 'C:\\Users\\Pol\\AppData\\Local\\Temp\\pip-record-d5re6a86\\install-record.txt' --single-version-externally-managed --compile Check the logs for full command output.\nI'm using Windows and my Python version is 3.8.0. I have already tried python -m pip install matplotlib but it doesn't work.","AnswerCount":3,"Available Count":2,"Score":-0.0665680765,"is_accepted":false,"ViewCount":11426,"Q_Id":58814671,"Users Score":-1,"Answer":"Try running your command prompt with administrator privileges\nIf the problem further persists try reinstalling pip","Q_Score":4,"Tags":"python,python-3.x,matplotlib,pip,python-packaging","A_Id":58814829,"CreationDate":"2019-11-12T08:24:00.000","Title":"Matplotlib: Command errored out with exit status 1","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I can't read a pickle file saved with a different version of Python pandas. I know this has been asked here before, but the solutions offered, using pd.read_pickle(\"my_file.pkl\") is not working either. I think (but I am not sure) that these pickle files were created with an newer version of pandas than that of the machine I am working now.\nUnfortunately, I am not the administrator and I cannot change the version of pandas. How can I read my files? Are they irrecoverable?","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":4370,"Q_Id":58822129,"Users Score":2,"Answer":"You will need the same version (or a later one) of pandas as the one used to_pickle.\nWhen pandas converts a dataframe to pickle the compress process is specific to that version.\nI advise to contact your administrator and have them convert the pickle to csv that way you can open it with any version of pandas.\nUnless the dataframe contains objects csv should be fine","Q_Score":2,"Tags":"python,pandas","A_Id":58822290,"CreationDate":"2019-11-12T15:49:00.000","Title":"Read a pickle with a different version of pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Let's say, for the sake of this question, I have a column titled Blah filled with the following data points (I will give it in a list for clarity):\nValues = ['one', 'two', 'four', 'six', 'nine']\nHow could I choose to sort by specific values in this column? For example, I would like to sort this column, Blah, filled with the values above into the following: ['nine', 'four', 'two', 'six', 'one'].\nUnfortunately, it is not as easy as just sort_values and choose alphabetical!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":55,"Q_Id":58825887,"Users Score":0,"Answer":"What are you sorting by? Alphabetical would be ['four', 'nine', 'one', 'six', 'two']","Q_Score":1,"Tags":"python,python-3.x,pandas,sorting","A_Id":58825987,"CreationDate":"2019-11-12T20:02:00.000","Title":"How to Sort a Pandas Column by specific values","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm working on converting our model from tensorflow 1.8.0 to 2.0 but using sequential api's is quite difficult for our current model.So if there any support for functional api's in 2.0 as it is not easy to use sequential api's.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":35,"Q_Id":58836772,"Users Score":1,"Answer":"Tensorflow 2.0 is more or less made around the keras apis. You can use the tf.keras.Model for creating both sequential as well as functional apis.","Q_Score":0,"Tags":"python-3.x,tensorflow,tensorflow2.0","A_Id":58841007,"CreationDate":"2019-11-13T12:06:00.000","Title":"Is there support for functional layers api support in tensorflow 2.0?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have two text files containing parallel text in two languages (potentially millions of lines).  I am trying to generate random train\/validate\/test files from that single file, as train_test_split does in sklearn.  However when I try to import it into pandas using read_csv I get errors from many of the lines because of erroneous data in there and it would be way too much work to try and fix the broken lines.  If I try and set the error_bad_lines=false then it will skip some lines in one of the files and possibly not the other which would ruin the alignment.  If I split it manually using unix split it works fine for my needs though so I'm not concerned with cleaning it, but the data that is returned is not random.\nHow should I go about splitting this dataset into train\/validate\/test sets?\nI'm using python but I can also use linux commands if that would be easier.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":85,"Q_Id":58840145,"Users Score":0,"Answer":"I found that I can use the shuf command on the file with the random-source parameter, like this shuf tgt-full.txt -o tgt-fullshuf.txt --random-source=tgt-full.txt.","Q_Score":0,"Tags":"python,pandas,unix,scikit-learn,dataset","A_Id":58842847,"CreationDate":"2019-11-13T15:15:00.000","Title":"How to split parallel corpora while keeping alignment?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I was trying to compare the accuracy results of CNN model and combined CNN-SVM model for classification. However I found that CNN model have better accuracy than combined CNN-SVM model. Is That correct or it can happen?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":144,"Q_Id":58844965,"Users Score":1,"Answer":"It depends on a large number of factors , but yes if the underlying data is image - cnn have proven to deliver better results.","Q_Score":1,"Tags":"python,classification,svm","A_Id":58847337,"CreationDate":"2019-11-13T20:18:00.000","Title":"CNN model have better accuracy than combined CNN-SVM model","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Whenever I try to plot a figure in a Google Colab notebook using matplotlib, a plot is displayed whenever I use %matplotlib inline but is not displayed when I do %matplotlib ipympl or %matplotlib widget. How can I resolve this issue. My goal is to get the plot to be interactive.\n\nClarification: when I run %matplotlib --list I get the following output\nAvailable matplotlib backends: ['tk', 'gtk', 'gtk3', 'wx', 'qt4', 'qt5', 'qt', 'osx', 'nbagg', 'notebook', 'agg', 'inline', 'ipympl', 'widget']\n\nThanks for your help!","AnswerCount":2,"Available Count":1,"Score":-0.0996679946,"is_accepted":false,"ViewCount":1694,"Q_Id":58845278,"Users Score":-1,"Answer":"Available matplotlib backends: ['tk', 'gtk', 'gtk3', 'wx', 'qt4', 'qt5', 'qt', 'osx', 'nbagg', 'notebook', 'agg', 'inline', 'ipympl', 'widget']","Q_Score":2,"Tags":"python,matplotlib,google-colaboratory","A_Id":62375398,"CreationDate":"2019-11-13T20:39:00.000","Title":"Matplotlib and Google Colab: Using ipympl","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Supposing that I have following signal:\ny = np.sin(50.0 * 2.0*np.pi*x) + 0.5*np.sin(100.0 * 2.0*np.pi*x) + 0.2*np.sin(200 * 2.0*np.pi*x)\nhow can I filter out in example 100Hz using Band-stop filter in Python? In this signal there are peaks at 50Hz, 100Hz and 200Hz. It would be helpful it it could be visualized using FFT in order to confirm that this frequency has been filtered correctly.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":978,"Q_Id":58846626,"Users Score":0,"Answer":"Why magnitude for frequency 50 Hz decreased from 1 to 0.7 after Fast Fourier Transform?","Q_Score":0,"Tags":"python,numpy,scipy,fft","A_Id":58858660,"CreationDate":"2019-11-13T22:30:00.000","Title":"Filter out range of frequencies using band stop filter in Python and confirm it using Fourier Transform FFT","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I made a simple CNN that classifies dogs and cats and I want this CNN to detect images that aren't cats or dogs and this must be a third different class. How to implement this? should I use R-CNN or something else?\nP.S I use Keras for CNN","AnswerCount":3,"Available Count":2,"Score":0.0665680765,"is_accepted":false,"ViewCount":125,"Q_Id":58850711,"Users Score":1,"Answer":"What you want to do is called \"transfer learning\" using the learned weights of a net to solve a new problem.\nPlease note that this is very hard and acts under many constraints i.e. using a CNN that can detect cars to detect trucks is simpler than using a CNN trained to detect people to also detect cats.\nIn any case you would use your pre-trained model, load the weights and continue to train it with new data and examples.\nWhether this is faster or indeed even better than simply training a new model on all desired classes depends on the actual implementation and problem.\nTl:Dr\nTransfer learning is hard! Unless you know what you are doing or have a specific reason, just train a new model on all classes.","Q_Score":0,"Tags":"python,tensorflow,keras","A_Id":58852959,"CreationDate":"2019-11-14T06:33:00.000","Title":"CNN on python with Keras","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I made a simple CNN that classifies dogs and cats and I want this CNN to detect images that aren't cats or dogs and this must be a third different class. How to implement this? should I use R-CNN or something else?\nP.S I use Keras for CNN","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":125,"Q_Id":58850711,"Users Score":0,"Answer":"You can train that almost with the same architecture (of course it depends on this architecture, if it is already bad then it will not be useful on more classes too. I would suggest to use the state of the art model architecture for dogs and cats classification) but you will also need the dogs and cats dataset in addition to this third class dataset. Unfortunately, it is not possible to use pre-trained for making predictions between all 3 classes by only training on the third class later. \nSo, cut to short, you will need to have all three datasets and train the model from scratch if you want to make predictions between these three classes otherwise use the pre-trained and after training it on third class it can predict if some image belongs to this third class of not.","Q_Score":0,"Tags":"python,tensorflow,keras","A_Id":58852922,"CreationDate":"2019-11-14T06:33:00.000","Title":"CNN on python with Keras","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a table in sql I'm looking to read into a pandas dataframe. I can read the table in but all column dtypes are being read in as objects. When I write the table to a csv then re-read it back in using read_csv, the correct data types are assumed. Obviously this intermediate step is inefficient and I just want to be able to read the data directly from sql with the correct data types assumed.\nI have 650 columns in the df so obviously manually specifying the data types is not possible.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":651,"Q_Id":58855925,"Users Score":0,"Answer":"So it turns out all the data types in the database are defined as varchar.\nIt seems read_sql reads the schema and assumes data types based off this. What's strange is then I couldn't convert those data types using infer_objects().\nThe only way to do it was to write to a csv then read than csv using pd.read_csv().","Q_Score":0,"Tags":"python,sql,pandas","A_Id":58861981,"CreationDate":"2019-11-14T11:38:00.000","Title":"Pandas not assuming dtypes when using read_sql?","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Assume I want to fit a random forest, RFC, and grid search using sklearns GridSearchCV.\nWe can get the best parameters using RFC.best_params_ but if I then want to create a random forest I need manually to write those parameters in e.g RFC(n_estimators=12,max_depth=7) afterwards. Is there a way,something like RFC_opt=RFC(best_params_) to do it automatically?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":75,"Q_Id":58857069,"Users Score":0,"Answer":"I figured out, if you unpack the parameters it is doable, i.e if\nbest_par=RFC_grid_search.best_params_ then you can create the optimal RFC with the parameters in best_params_ by\nrfc_opt=RFC(**best_part)","Q_Score":0,"Tags":"python,scikit-learn","A_Id":58863619,"CreationDate":"2019-11-14T12:42:00.000","Title":"Automatically create a new object from GridSearchCV best_params_","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using VGG16 model and fine tuned them on my data. I am predicting ethnicity of images (faces) .i have 5 output classes like white, black,Asian, Sub-continent and others. Should i use softmax or sigmoid. And why??","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":403,"Q_Id":58857899,"Users Score":0,"Answer":"In general cases, if you are dealing with multi-class clasification problems, you should use a Softmax because you are guaranted that the sum of probabilities of all clases will sum 1, by weighting them individually and computing the join distribution, whereas with a Sigmoid, you'd be predicting the probability of each class individually, but not necesarilly weighted. If not careful and aware of the difference you can run into some issues with your output.","Q_Score":0,"Tags":"python,image-processing,deep-learning,data-science","A_Id":58858073,"CreationDate":"2019-11-14T13:27:00.000","Title":"Softmax or sigmoid for multiclass problem","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an intel D415 depth camera and want to identify obstacles in the path of my robot.\nI want to reduce the points from the cam pc=(102720,3) to only a rectangular area where the robot has to pass through\nI came up with this list comprehension, p[0] is the x-axis, p[2] the distance and the values are in meters, the robot needs around a 1 meter \"door\" and I limit the distance to 2 meters.\ninPathPoints = np.asarray([p for p in pc if p[0] > -0.5 and p[0] < 0.5 and p[2] > 0.2 and p[2] < 2])\nOn my laptop cProfile shows a runtime of 0.25 seconds for this evaluation. \nAs the robot needs to check for obstacles while moving I wanted to repeat this check about 5..10 times a \nsecond. Any hints what I could try to speed it up?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":34,"Q_Id":58861669,"Users Score":0,"Answer":"Instead of list comprehension, is used numpy and came up with this statement:\ninPathPoints = pc[(pc[:, 0] > -0.5) & (pc[:, 0] < 0.5) & (pc[:, 2] > 0.2) & (pc[:, 2] < 2)]\nThis is so fast it does not even show up in the profile output.","Q_Score":1,"Tags":"python-3.x,list-comprehension,point-clouds","A_Id":58877449,"CreationDate":"2019-11-14T16:37:00.000","Title":"cut out part of a point cloud","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I just got my 2080 ti today and hooked it right up to experiment with Keras on my models. But for some reason, when I train on a dense model the 2080 ti is 2 times slower than my CPU (an i7 4790k) and definitely slower than my old GTX 1070 (don't have exact numbers to compare it to).\nTo train one epoch on my CPU it takes 27 seconds while the 2080 ti is taking 67 seconds with nothing about the model or data changing. Same batch size of 128, etc. This is also significantly slower than my 1070 I just had in the machine last night. I checked the GPU usage while training and the memory usage goes up to max, and the GPU usage goes up to about 20%, while idle is 4%. I have CUDA 10, and the latest CuDNN on NVIDIA's site: v7.6.5. TensorFlow is 1.15\nDoes anyone have any clue what is going on here? If any more details are needed, just comment I can add them.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":884,"Q_Id":58867071,"Users Score":2,"Answer":"I figured it out! Thanks to the suggestion of a friend who got a 2060 recently, he noted that the default power mode is maximum power savings in the Nvidia Control Panel, or P8 power mode according to nvidia-smi (which is half clock speeds). After setting to prefer maximum performance in 3D settings, training times have significantly been reduced.","Q_Score":1,"Tags":"python,tensorflow,keras","A_Id":58885009,"CreationDate":"2019-11-14T21:51:00.000","Title":"Keras - RTX 2080 ti training slower than both CPU-only and GTX 1070?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using SMOP version 0.41 to translate my MATLAB code to anaconda python however\nwhenever there is a statement with a greater or equal to statment for example:\nif numFFT >= 2\nI get the following error\nSyntaxError: Unexpected \"=\" (parser)\nHas anyone experienced this?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":246,"Q_Id":58867582,"Users Score":0,"Answer":"you can simply edit lexer.py for greater or equal by putting \"\\\" in front of \"<\" or \">\". \nexample: \nfrom \"<=\"\nto \"\\<=\" or \"<\\=\", both of them works the same on your converted python code.","Q_Score":0,"Tags":"python,matlab","A_Id":59451889,"CreationDate":"2019-11-14T22:38:00.000","Title":"smop has issues when translating a >= statment in MATLAB","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm connecting to an oracle database and trying to bring across a table with roughly 77 million rows. At first I tried using chunksize in pandas but I always got a memory error no matter what chunksize I set. I then tried using Dask since I know its better for large amounts of data. However, there're some columns that need to be made NULL, is there away to do this within read_sql_table query like there is in pandas when you can write out your sql query?\nCheers","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":83,"Q_Id":58868931,"Users Score":0,"Answer":"If possible, I recommend setting this up on the oracle side, making a view with the correct data types, and using read_sql_table with that.\nYou might be able to do it directly, since read_sql_table accepts sqlalchemy expressions. If you can phrase it as such, it ought to work.","Q_Score":0,"Tags":"python,pandas,dask","A_Id":58879617,"CreationDate":"2019-11-15T01:16:00.000","Title":"Is there a way to set columns to null within dask read_sql_table?","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a large pickle file and I want to load the data from pickle file to train a deep learning model. Is there any way if I can use a generator to load the data for each key? The data is in the form of a dictionary in the pickle file. I am using pickle.load(filename), but I am afraid that it will occupy too much RAM while running the model. I used pickle.HIGHEST_PROTOCOL to dump the data to the pickle file initially.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":513,"Q_Id":58869572,"Users Score":3,"Answer":"Nope. The pickle file format isn't like JSON or something else where you can just read part of it and decode it incrementally. A pickle file is a list of instructions for building a Python object, and just like following half the instructions to bake a cake won't bake half a cake, reading half a pickle won't give you half the pickled object.","Q_Score":1,"Tags":"python,python-3.x,generator,pickle","A_Id":58869681,"CreationDate":"2019-11-15T02:38:00.000","Title":"Can we read the data in a pickle file with python generators","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to do sentiment analysis using machine learning (text classification) approach. For example nltk Naive Bayes Classifier.\nBut the issue is that a small amount of my data is labeled. (For example, 100 articles are labeled positive or negative) and 500 articles are not labeled.\nI was thinking that I train the classifier with labeled data and then try to predict sentiments of unlabeled data. \nIs it possible? \nI am a beginner in machine learning and don't know much about it. \nI am using python 3.7. \nThank you in advance.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":299,"Q_Id":58869955,"Users Score":1,"Answer":"Is it possible to train the sentiment classification model with the labeled data and then use it to predict sentiment on data that is not labeled?\n\nYes. This is basically the definition of what supervised learning is.\nI.e. you train on data that has labels, so that you can then put it into production on categorizing your data that does not have labels.\n(Any book on supervised learning will have code examples.)\nI wonder if your question might really be: can I use supervised learning to make a model, assign labels to another 500 articles, then do further machine learning on all 600 articles? Well the answer is still yes, but the quality will fall somewhere between these two extremes:\n\nAssign random labels to the 500. Bad results.\nGet a domain expert assign correct labels to those 500. Good results.\n\nYour model could fall anywhere between those two extremes. It is useful to know where it is, so know if it is worth using the data. You can get an estimate of that by taking a sample, say 25 records, and have them also assigned by a domain expert. If all 25 match, there is a reasonable chance your other 475 records also have been given good labels. If  e.g. only 10 of the 25 match, the model is much closer to the random end of the spectrum, and using the other 475 records is probably a bad idea.\n(\"10\", \"25\", etc. are arbitrary examples; choose based on the number of different labels, and your desired confidence in the results.)","Q_Score":0,"Tags":"nltk,python-3.7,sentiment-analysis,text-classification,training-data","A_Id":58874622,"CreationDate":"2019-11-15T03:34:00.000","Title":"Is it possible to train the sentiment classification model with the labeled data and then use it to predict sentiment on data that is not labeled?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm reading these days about deep learning and its utilization and the methods we can use it. I had a general question regarding the image verification or let's say a simple matrix. \nSuppose I have a matrix of size X = (4,4) and a vector of size Y = (1,4), I multiplied the the vector Y by only one column from X, let's say the second column. Hence, Z = Y.*X(:,2). Suppose I know the matrix X and resulted vector Z, can I use the deep learning to verify which column from X was multiplied based on Z and X ? \nI know, we can all simply use Maximum likehood decoder, or by divided X\/Z ; In reality I need to avoid these conventional ways and go to deep learning. Can we do that using deep learning? which algorithm can be used in that case ?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":30,"Q_Id":58870076,"Users Score":1,"Answer":"You can all simply use DNN, however the results are not that good compared with ML but there is no way to solve that.","Q_Score":0,"Tags":"python,algorithm,machine-learning,deep-learning","A_Id":58950331,"CreationDate":"2019-11-15T03:50:00.000","Title":"Which algorithm in Deep learning can verfity the relationship of column into a matrix","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I launched Jupyter Notebook, created a new notebook in python, imported the necessary libraries and tried to access a .xlsx file on the desktop with this code:\nhaber = pd.read_csv('filename.xlsx') \nbut error keeps popping up. Want a reliable way of accessing this file on my desktop without incurring any error response","AnswerCount":3,"Available Count":1,"Score":0.1325487884,"is_accepted":false,"ViewCount":3030,"Q_Id":58872437,"Users Score":2,"Answer":"This is an obvious path problem, because your notebook is not booted on the desktop path, you must indicate the absolute path to the desktop file, or the relative path relative to the jupyter boot directory.","Q_Score":0,"Tags":"python,python-3.x,pandas","A_Id":58872562,"CreationDate":"2019-11-15T07:47:00.000","Title":"How do you read files on desktop with jupyter notebook?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm looking for the time complexity of these methods as a function of the number of rows in a dataframe, n. \nAnother way of asking this question is: Are indexes for dataframes in pandas btrees (with log(n) time look ups) or hash tables (with constant time lookups)?\nAsking this question because I'd like a way to do constant time look ups for rows in a dataframe based on a custom index.","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":2034,"Q_Id":58876676,"Users Score":2,"Answer":"Alright so it would appear that:\n1) You can build your own index on a dataframe with .set_index in O(n) time where n is the number of rows in the dataframe\n2) The index is lazily initialized and built (in O(n) time) the first time you try to access a row using that index. So accessing a row for the first time using that index takes O(n) time\n3) All subsequent row access takes constant time. \nSo it looks like the indexes are hash tables and not btrees.","Q_Score":4,"Tags":"python,pandas,performance,data-structures,time-complexity","A_Id":58882586,"CreationDate":"2019-11-15T12:03:00.000","Title":"What is the time complexity of .at and .loc in pandas?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I\u2019m training linear model on MNIST dataset, but I wanted to train only on one digit that is 4. How do I choose my X_test,X_train, y_test, y_train?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":157,"Q_Id":58896645,"Users Score":0,"Answer":"If you only need to recognize 4s it's a binary classification problem, so you just need to create a new target variable: Y=1 if class is 4, Y=0 if class is not 4.\n\nTrain_X will be unchanged \nTrain_Y will be your new target variable related to Train_X\nTest_X will be unchanged \nTest_Y will be your new target variable related to Test_X.\n<\\ul>\n\nData will be a bit unbalanced but it should not be an issue!","Q_Score":0,"Tags":"python,mnist,sklearn-pandas","A_Id":59198937,"CreationDate":"2019-11-17T01:08:00.000","Title":"MNIST dataset with Sklearn","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I\u2019m training linear model on MNIST dataset, but I wanted to train only on one digit that is 4. How do I choose my X_test,X_train, y_test, y_train?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":157,"Q_Id":58896645,"Users Score":0,"Answer":"Your classifier needs to learn to discriminate between sets of different classes.\nIf you only care about digit 4, you should split your training and testing set into:\n\nClass 4 instances\nNot class 4 instances: union of all other digits\n\nOtherwise the train\/test split is still the typical one, where you want to have no overlap.","Q_Score":0,"Tags":"python,mnist,sklearn-pandas","A_Id":58896802,"CreationDate":"2019-11-17T01:08:00.000","Title":"MNIST dataset with Sklearn","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have tried to train my model using ssd_mobilenet_v1_coco_11_06_2017 ,ssd_mobilenet_v1_coco_2018_01_28 and faster_rcnn_inception_v2_coco_2018_01_28 and did it successfully however when i tried to run object_detection_tutorial.ipynb and test my test_images all i get is images without bounding boxes, i trained my model using model_main.py and also tried train.py and i aquired a loss of < 1 in both. i am using tensorflow = 1.14 and i tried it on tensorflow = 2.0. im stuck in this final step. i am positive i create my tfrecords correctly. and also when i run the models(ssd_mobilenet_v1_coco_11_06_2017 ,ssd_mobilenet_v1_coco_2018_01_28 and faster_rcnn_inception_v2_coco_2018_01_28) that i trained them on they worked perfectly, so i suspect that there is something wrong with my model","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":46,"Q_Id":58897297,"Users Score":0,"Answer":"Did you update the path of your model in the object_detection_tutorial.ipynb file, \"tf.saved_model.load\" is the API where you have to give the path of your trained model.","Q_Score":0,"Tags":"python,python-3.x,tensorflow,object-detection,object-detection-api","A_Id":58917762,"CreationDate":"2019-11-17T03:41:00.000","Title":"No bouding boxes create when running my trained model","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to Python. Just working with OSMnx and wanted to open a map as an svg in Illustrator. This was posted in the GitHub documentation: \n# you can also plot\/save figures as SVGs to work with in Illustrator later\nfig, ax = ox.plot_graph(G_projected, save=True, file_format='svg')\nI tried it in JupyterLab and it downloaded to my files but when I open it, it is just text. How can I correctly download it and open as SVG? - Thanks!","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":548,"Q_Id":58904416,"Users Score":0,"Answer":"Instead of\n\nfilename='image', file_format='svg'\n\nUse:\n\nfilepath='image.svg'","Q_Score":0,"Tags":"python,svg,jupyter-notebook,openstreetmap,osmnx","A_Id":72333300,"CreationDate":"2019-11-17T19:35:00.000","Title":"How do I correctly download a map from osmnx as .svg?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have been using Fortran for my computational physics related work for a long time, and recently started learning and playing around with Python. I am aware of the fact that being an interpreted language Python is generally slower than Fortran for primarily CPU-intensive computational work. But then I thought using numpy would significantly improve the performance for a simple task like sorting.\nSo my test case was sorting an array\/a list of size 10,000 containing random floats using bubble sort (just a test case with many array operations, so no need to comment on the performance of the algorithm itself). My timing results are as follows (all functions use identical algorithm):\nPython3 (using numpy array, but my own function instead of numpy.sort): 33.115s\nPython3 (using list): 9.927s \nFortran (gfortran) : 0.291s\nPython3 (using numpy.sort): 0.269s (not a fair comparison, since it uses a different algorithm)\nI was surprised that operating with numpy array is ~3 times slower than with python list, and ~100 times slower than Fortran. So at this point my questions are:\n\nWhy operating with numpy array is significantly slower than python list for this test case?\nIn case an algorithm that I need is not already implemented in scipy\/numpy, and I need to write my own function within Python framework with best performance in mind, which data type I should operate with: numpy array or list?\nIf my applications are performance oriented, and I want to write functions with equivalent performance as in-built numpy functions (e.g. np.sort), what tools\/framework I should learn\/use?","AnswerCount":2,"Available Count":1,"Score":1.0,"is_accepted":false,"ViewCount":3448,"Q_Id":58910042,"Users Score":7,"Answer":"You seem to be misunderstanding what NumPy does to speed up computations.\nThe speedup you get in NumPy does not come from NumPy using some smart way of saving data. Or compiling your Python code to C automatically.\nInstead, NumPy implements many useful algorithms in C or Fortran, numpy.sort() being one of them. These functions understand np.ndarrays as input and loop over the data in a C\/Fortran loop.\nIf you want to write fast NumPy code there are really three ways to do that:\n\nBreak down your code into NumPy operations (multiplications, dot-product, sort, broadcasting etc.)\nWrite the algorithm you want to implement in C\/Fortran and also write bindings to Python that accept np.ndarrays (internally they're a contiguous array of the type you've chosen).\nUse Numba to speed up your function by having Python Just-In-Time compile your code to machine code (with some limitations)","Q_Score":5,"Tags":"python,performance,numpy","A_Id":58910192,"CreationDate":"2019-11-18T07:50:00.000","Title":"Sorting performance comparison between numpy array, python list, and Fortran","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Hope you are doing well.\nI am trying to build a following robot which follows a person.\nI have a raspberry pi and and a calibrated stereo camera setup.Using the camera setup,i can find depth value of any pixel with respect to the reference frame of the camera. \nMy plan is to use feed from the camera to detect person and then using the stereo camera to find the average depth value thus calculating distance and from that calculate the position of the person with respect to the camera and run the motors of my robot accordingly using PID.\nNow i have the robot running and person detection using HOGdescriptor that comes opencv.But the problem is,even with nomax suppression, the detector is not stable to implement on a robot as too many false positives and loss of tracking occurs pretty often.\nSo my question is,can u guys suggest a good way to track only people. Mayb a light NN of some sort,as i plan to run it on a raspberry pi 3b+.\nI am using intel d435 as my depth camera. \nTIA","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":527,"Q_Id":58934308,"Users Score":1,"Answer":"Raspberry pi does not have the computational capacity to perform object detection and realsense driver support, check out the processor load once you start the realsense application. One of the simplest models for person detection is opencv's HOGdescripto that you have used.","Q_Score":0,"Tags":"python,computer-vision,raspberry-pi3,object-detection,robotics","A_Id":58939946,"CreationDate":"2019-11-19T12:37:00.000","Title":"fast light and accurate person-detection algorithm to run on raspberry pi","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a ssh dask.distributed cluster with a main computer containing all modules for my script and another one with only a few, including dask itself of course. \nIs it possible to change the syspath of the other computer so that it also looks for modules in the main one? Of course, I could simply upload them via sftp but since I keep making a lot of modules that would be very annoying to do repeatedly.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":43,"Q_Id":58934528,"Users Score":1,"Answer":"Such things are possible with networking solutions such as NFS or SSH remote mounts, but that's a pretty big field and beyond the scope of Dask itself. If you are lucky, other answers will appear here, others have solved similar problems, but more likely copying is the simpler solution.","Q_Score":0,"Tags":"python,dask","A_Id":58935738,"CreationDate":"2019-11-19T12:48:00.000","Title":"Share python modules across computers in a dask.distributed cluster","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Let's say there are two sub images of a large image. I am trying to detect the overlapping area of two sub images. I know that template matching can help to find the templates. But i'm not sure how to find the intersected area and remove them in either one of the sub images. Please help me out.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":420,"Q_Id":58937483,"Users Score":1,"Answer":"MatchTemplate returns the most probable position of a template inside a picture. You could do the following steps:\n\nFind the (x,y) origin, width and height of each picture inside the larger one\nSave them as rectangles with that data(cv::Rect r1, cv::Rect r2)\nUsing the & operator, find the overlap area between both rectangles (r1&r2)","Q_Score":0,"Tags":"python,opencv","A_Id":58937944,"CreationDate":"2019-11-19T15:26:00.000","Title":"How to find the intersecting area of two sub images using OpenCv?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have this numpy matrix:\nx = np.random.randn(700,2)\nWhat I wanna do is scale the values of the first column between that range: 1.5 and 11 and the values of the second column between `-0.5 and 5.0. Does anyone have an idea how I could achieve this? Thanks in advance","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":4738,"Q_Id":58941935,"Users Score":2,"Answer":"subtract each column's minimum from itself\nfor each column of the result divide by its maximum\nfor column 0 of that result multiply by 11-1.5\nfor column 1 of that result multiply by 5--0.5\nadd 1.5 to column zero of that result\nadd -0.5 to column one of that result\n\nYou could probably combine some of those steps.","Q_Score":1,"Tags":"python,numpy,machine-learning,scaling,numpy-ndarray","A_Id":58942213,"CreationDate":"2019-11-19T19:57:00.000","Title":"How to scale numpy matrix in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I hope there will be some code where the Convolutional Neural Network will be implemented without Tensorflow OR theano OR Scikit etc. I searched over the google, but google is so crazy some time :), if i write \"CNN without Tensorflow\" it just grab the tesorflow part and show me all the results with tesorflow :( and if i skip the tensorflow, it again shows me some how similar results. any help please.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1274,"Q_Id":58959547,"Users Score":0,"Answer":"A lot of Deep Learning courses will ask the student to implement a CNN in Python with just numpy, then teach them to achieve the same result with Tensorflow etc. You can just search on Github for \"Deep-Learning-Coursera\" and you will probably find something like this https:\/\/github.com\/enggen\/Deep-Learning-Coursera\/blob\/master\/Convolutional%20Neural%20Networks\/Week1\/Convolution%20model%20-%20Step%20by%20Step%20-%20v2.ipynb, where the CNN functions are implemented without Tensorflow.","Q_Score":0,"Tags":"python,tensorflow,deep-learning,conv-neural-network","A_Id":58959742,"CreationDate":"2019-11-20T16:53:00.000","Title":"Is there any Python code for Convolutional Neural Network, but without Tensorflow\/Theano\/Scikit etc?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using pytorch and I wonder if I should use torch.backends.cudnn.benchmark = True. I find on google that I should use it when computation graph does not change. What is computation graph in pytorch?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":9045,"Q_Id":58961768,"Users Score":16,"Answer":"If your model does not change and your input sizes remain the same - then you may benefit from setting torch.backends.cudnn.benchmark = True.\nHowever, if your model changes: for instance, if you have layers that are only \"activated\" when certain conditions are met, or you have layers inside a loop that can be iterated a different number of times, then setting torch.backends.cudnn.benchmark = True might stall your execution.","Q_Score":13,"Tags":"python,pytorch","A_Id":58965640,"CreationDate":"2019-11-20T19:15:00.000","Title":"set `torch.backends.cudnn.benchmark = True` or not?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have training sample X_train, and Y_train to train and X_estimated.\nI got task to make my classificator learn as accurate as it can, and then predict vector of results over X_estimated to get close results to Y_estimated (which i have now, and I have to be as much precise as it can). If I split my training data to like 75\/25 to train and test it, I can get accuracy using sklearn.metrics.accuracy_score and confusion matrix. But I am losing that 25% of samples, that would make my predictions more accurate. \nIs there any way, I could learn by using 100% of the data, and still be able to see accuracy score (or percentage), so I can predict it many times, and save best (%) result?\nI am using random forest with 500 estimators, and usually get like 90% accuracy. I want to save best prediction vector as possible for my task, without splitting any data (not wasting anything), but still be able to calculate accuracy (so I can save best prediction vector) from multiple attempts (random forest always shows different results)\nThank you","AnswerCount":5,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":2055,"Q_Id":58964954,"Users Score":2,"Answer":"Splitting your data is critical for evaluation. \nThere is no way that you could train your model on 100% of the data and be able to get a correct evaluation accuracy unless you expand your dataset. I mean, you could change your train\/test split, or try to optimize your model in other ways, but i guess the simple answer to your question would be no.","Q_Score":2,"Tags":"python,machine-learning,scikit-learn,neural-network,random-forest","A_Id":58965054,"CreationDate":"2019-11-20T23:26:00.000","Title":"How to check machine learning accuracy without cross validation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have training sample X_train, and Y_train to train and X_estimated.\nI got task to make my classificator learn as accurate as it can, and then predict vector of results over X_estimated to get close results to Y_estimated (which i have now, and I have to be as much precise as it can). If I split my training data to like 75\/25 to train and test it, I can get accuracy using sklearn.metrics.accuracy_score and confusion matrix. But I am losing that 25% of samples, that would make my predictions more accurate. \nIs there any way, I could learn by using 100% of the data, and still be able to see accuracy score (or percentage), so I can predict it many times, and save best (%) result?\nI am using random forest with 500 estimators, and usually get like 90% accuracy. I want to save best prediction vector as possible for my task, without splitting any data (not wasting anything), but still be able to calculate accuracy (so I can save best prediction vector) from multiple attempts (random forest always shows different results)\nThank you","AnswerCount":5,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":2055,"Q_Id":58964954,"Users Score":0,"Answer":"It is not necessary to do 75|25 split of your data all the time. 75\n|25 is kind of old school now. It greatly depends on the amount of data that you have. For example, if you have 1 billion sentences for training a language model, it is not necessary to reserve 25% for testing. \nAlso, I second the previous answer of trying K-fold cross-validation. As a side note, you could consider looking at the other metrics like precision and recall as well.","Q_Score":2,"Tags":"python,machine-learning,scikit-learn,neural-network,random-forest","A_Id":58975977,"CreationDate":"2019-11-20T23:26:00.000","Title":"How to check machine learning accuracy without cross validation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to improve my ResNet model by creating an ensemble of X number of this model, taking the X best one I have trained. For what I've seen, a technique like bagging will take X time longer to classify an image, which is really not an option in my case. \nIs there a way to create an ensemble without increasing the required classifying time? Note that I don't care about increasing the training time, because it only needs to be done one time, compared to the classification which could be made a very large number of time.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":293,"Q_Id":58966086,"Users Score":1,"Answer":"There is no magic pill for doing what you want. Extra computation cannot come free.\nSo one way this can be achieved is by using multiple worker machines to run inference in parallel.\nEach model could run on a different machine using tensorflow serving.  \nFor every new inference do the following:  \n\nHave a primary machine which takes up the job of running the inference\nThis primary machine, submits requests to different workers (all of which can run in parallel)\nThe primary machine collects results from each individual worker, and creates the final output by combining them based upon your ensemble logic.","Q_Score":2,"Tags":"python,deep-learning,ensemble-learning","A_Id":58966156,"CreationDate":"2019-11-21T01:53:00.000","Title":"Can we create an ensemble of deep learning models without increasing the classification time?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an integer programming problem with a decision variable X_i_j_k_t that is 1 if job i was assigned to worker j for day k and shift t. I am maximizing the benefit of assigning orders to my workers. I have an additional binary variable Y_i_k_t that is 1 if the job was executed and a given day and shift (jobs might require more than one worker). How can I add this variable in CPLEX? So as to form, for example, sum(i, k, t)(Y_i_k_t) <= 1 (the order can\u00b4t be done more than once).\nThank you in advance","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1334,"Q_Id":58966188,"Users Score":1,"Answer":"You did not say whether you use the CPLEX Python API or docplex. But in either case, you can call the functions that create variables multiple times.\nSo in the CPLEX Python API call Cplex.variables.add() again to add another set of variables.\nIn docplex just call Model.binary_var_dict() (or whatever method you used to create X) again for the Y variables.","Q_Score":1,"Tags":"python,mathematical-optimization,linear-programming,cplex","A_Id":58969593,"CreationDate":"2019-11-21T02:08:00.000","Title":"How to add an additional binary variable with CPLEX and Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm involved in a research project that is looking at using Neural Networks in a safety critical environment. Part of the regulatory framework this research is targeted towards states that there must be no dormant code within the system. There must be a pathway through every part of the system and that pathway must be testable\/verifiable.\nObviously the neural network is comprised of many nodes. The input\/output nodes are easy to test for activation, but does anyone know of a method of testing activation of hidden layer nodes? \nObviously the activation is dependent of the node's input values and activation function and there may be a mathematical approach to this.\nUltimately the code will be in C\/C++, but we're doing the NN development in Python. So any ideas involving related toolsets would be gratefully received. I could also export\/import the NN structure and matrices to another package or environment if that helps with this testing.\nHopefully you'll all be overflowing with ideas, because Google didn't offer anything. :(\nThanks.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":108,"Q_Id":58976062,"Users Score":1,"Answer":"Somehow techytushar's comment nudged my brain into a new line of reasoning, which I think has been very helpful:\nSo the problem I'm addressing is: 'There can be no dormant code.' Be that lines of C or array elements that are never, and can never, be accessed.\nSo when the trained NN runs as a compiled C application, the application will calculate the value for each neuron and evaluate its activation function irrespective of the node's input value(s). So actually there is no such thing as dormant code or array elements in this regard. Just a true\/false output for that node's activation at that moment. It might change in the next moment. It'll all be re calculated, even if mathmatically the result is always for no activation.\nSo the question then moves away from this subject, to ensuring that no combinations of node activation can result in the system being in a dangerous state. That's off topic of the original question, so I think I can draw a line under this...?","Q_Score":1,"Tags":"python,neural-network,artificial-intelligence","A_Id":58976553,"CreationDate":"2019-11-21T13:21:00.000","Title":"Neural Networks - Checking for node activation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using opencv in python (cv2) to do some processing on images with format jpg, png and jpeg, JPG. I am doing a test that write image to disk using \"cv2.imwrite\" right after reading from \"cv2.imread\". I found part of the image get rotated, some of them rotate 90d, some rotate 180d. But most image keep the right orientation. I cannot conclude a pattern that causes this rotation. Anyone knows more details? Thanks!","AnswerCount":3,"Available Count":1,"Score":0.0665680765,"is_accepted":false,"ViewCount":2287,"Q_Id":58985183,"Users Score":1,"Answer":"One possible solution is to change cv2.IMREAD_UNCHANGED to cv2.IMREAD_COLOR while loading image with imdecode. From some reason \"unchanged\" is not able to read EXIF metadata correctly","Q_Score":2,"Tags":"python,image,opencv,png,jpeg","A_Id":66943729,"CreationDate":"2019-11-21T23:17:00.000","Title":"opencv imwrite, image get rotated","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Let's say I have a numpy array a = numpy.array([1,2,3,4]). Now \nstr(a) will give me \"[1 2 3 4]\". How do I convert the string \"[1 2 3 4]\" back to a numpy.array([1,2,3,4])?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":77,"Q_Id":58993231,"Users Score":2,"Answer":"Try numpy.array([int(v) for v in your_str[1:-1].split()])","Q_Score":1,"Tags":"python,numpy","A_Id":58993348,"CreationDate":"2019-11-22T11:22:00.000","Title":"Convert a str(numpy array) representaion to a numpy array - Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to create an application using python, In which I would like to able to read a .csv or .xlsx file and display its contents on my application, I believe there should be some packages which helps to do this in python, can I have some suggestions?\nRegards,\nRam","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":36,"Q_Id":58994269,"Users Score":1,"Answer":"I think working with PyQt for large application is the best option ( for large applications ) but tkinter is the secondary option for fast small apps.","Q_Score":1,"Tags":"python","A_Id":58994326,"CreationDate":"2019-11-22T12:25:00.000","Title":"How to integrate spreadsheet\/excel kind of view to my application using python?","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I implemented a bloom filter with 3 hash functions, and now I should calculate the exact number of false positives (not possibility) in that filter. Is there an efficient way to calculate that? The number of items in the filter is 200 million and the size of bit array is 400 million","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":308,"Q_Id":58999182,"Users Score":0,"Answer":"Yes, and it is very simple.\nCount the number of bits that are 'on' and divide that by the total number of bits. This will give you your fill-rate. \nWhen querying, all elements that were inserted earlier will hit 'on' bits and return positive. For elements which were not inserted into the filter, the probability of hitting an 'on' bit is your fill-rate. Therefore, with 3 hash functions, your error-rate will be (fill_rate^3).\nThough 0.5 is the optimal fill-rate that maximizes space vs. error-rate, any other fill rate is possible but it will either take too much space or has a higher error-rate than required. So you may be better off using 4 hash functions with less space. It really depends on your use case. What is your requirement? what error-rate are you looking for?","Q_Score":0,"Tags":"python,dataframe,hash,bloom-filter","A_Id":59466483,"CreationDate":"2019-11-22T17:36:00.000","Title":"False positive number Bloom filter","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need to read a sheet in excel file. But the number of columns(approx 100 to 150), column names and column position may change everyday in the sheet. Will pandas.read_excel return a dataframe with columns in the same order as they are in my daily excel sheet ? I'm using pandas 0.25.3","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1802,"Q_Id":59006318,"Users Score":1,"Answer":"pandas will return to you the column order exactly as in the original file. If the order changes in the file, the order of columns in the dataframe will change too. \nYou can define the column order yourself when reading in the data. Sometimes you'd also load the data, check what columns are present (with dataframe.columns.values) and then apply certain heuristic to preprocess them.","Q_Score":1,"Tags":"python,python-3.x,pandas","A_Id":59011075,"CreationDate":"2019-11-23T09:38:00.000","Title":"Will pandas.read_excel preserve column order?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have been trying to install the sklearn_pandas package. I tried two methods which I found online:\n1) By running 'pip install sklearn-pandas' in the Windows command line in the same location as my Python working directory:\nThis resulted in the error ''pip' is not recognized as an internal or external command, operable program or batch file.' So I tried 'python -m pip install sklearn-pandas'. This got executed but showed nothing (no message\/warning etc) in terms of output.\nAfter this I attempted to import a function from sklearn_pandas in a code (using Spyder IDE), but got an error saying 'No module named 'sklearn_pandas''.\n2) After the above, I attempted another suggestion which was to execute 'easy_install sklearn_pandas'. I ran this in the Spyder IDE and got an error saying invalid syntax.\nCould someone help me out with this? Thanks","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":225,"Q_Id":59016428,"Users Score":0,"Answer":"Your pip doesn't recognized and constantly showing this message while executing: 'pip' is not recognized as an internal or external command, operable program or batch file. If your python version is 3.x.x format then you use pip3 not pip anymore. The usage is pip3 is exactly the same as pip","Q_Score":1,"Tags":"python","A_Id":59016457,"CreationDate":"2019-11-24T09:49:00.000","Title":"Issues installing sklearn_pandas package","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"i am working on cryptography.The data set i am using is numerical data to resolve the dimensional reduction issue i want to apply auto encoder and decoder neural network. is it possible to apply Auto Encoder on numerical data set if yes then how?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":226,"Q_Id":59026939,"Users Score":0,"Answer":"Sure you can apply classical Autoencoders to numerical data. In its simplest form its just a matrix multiplication to a lower dimensional space and then a matrix multiplication to the original space and a L2-Loss based on the reconstruction and the input. You could start from there and add layers if your performance is insufficient.","Q_Score":0,"Tags":"python,neural-network,deep-learning,cryptography,autoencoder","A_Id":59034403,"CreationDate":"2019-11-25T07:23:00.000","Title":"Auto encoder and decoder on numerical data-set","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I try to set my CUDA Tensorflow on my windows 10. \nI would like to know what is the newest version of Python that works without bugs with the CUDA tensorflow and in which version.\nAlso what version of cuDNN should i use?\nThanks a lot.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":357,"Q_Id":59027575,"Users Score":0,"Answer":"you can use any \nbut it's better to use python version 3 with pip install 19.0 version \nas for the Cuda version you need to check what cuda version can be rub for your GPU \nand for cuDnn it will be desided on your cuda version","Q_Score":0,"Tags":"python,tensorflow,gpu","A_Id":59027703,"CreationDate":"2019-11-25T08:18:00.000","Title":"Which Python version should I download to run tensorflow GPU","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have some pandas series with the type \"pandas.core.series.Series\". I know that I can see its datetimeindex when I add \".index\" to the end of it. \nBut what if I want to get the element of the series at this time? and whats if I have a \"pandas._libs.tslibs.timestamps.Timestamp\" and want to get the element of the series at this time?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":135,"Q_Id":59032213,"Users Score":0,"Answer":"If your dataframe is indexed by date, you can:\n\ndf[date] to access all the rows indexed by such date (e.g. df['2019-01-01']);\ndf[date1:date2] to access all the rows with date index between date1 and date2 (e.g. df['2019-01-01': '2019-11-25']); \ndf[:date] to access all the rows with index before date value (e.g. df[:'2019-01-01']);\ndf[date:] to access all the rows with index after date value (e.g. df['2019-01-01':]).","Q_Score":0,"Tags":"python-3.x,pandas,numpy,time-series","A_Id":59032616,"CreationDate":"2019-11-25T12:48:00.000","Title":"Finding an element of pandas series at a certain time (date)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to implement a classifier which can have 1 of 10 possible classes. I am trying to use the MultiClass Softmax Loss Function to do this. Going through the documentation I'm not clear with what input is required for the function. \nThe documentation says it needs two matrices of [N, C] of which one is input and the other is target. As much as I understand, input matrix would be the one my Neural Network would calculate, which would have probabilities given by the neural network to each of the 10 classes. The target is the one that I have from my dataset. \nThe documentation says - \"Target(N, C) - label targets padded by -1 ensuring same shape as the input.\" What does this mean? Do I pass zeros in incorrect classes and -1 for the correct one? \nIt would be great if someone could elaborate on this and show even a sample 2d matrix which could be passed as a target matrix.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":3994,"Q_Id":59040237,"Users Score":0,"Answer":"In pytorch 1.8.1, I think the right way to do is fill the front part of the target with labels and pad the rest part of the target with -1. It is the same as the MultiLabelMarginLoss, and I got that from the example of MultiLabelMarginLoss.","Q_Score":2,"Tags":"python,pytorch,loss-function,softmax","A_Id":66849256,"CreationDate":"2019-11-25T21:18:00.000","Title":"MultiLabel Soft Margin Loss in PyTorch","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to deploy my function on AWS Lambda. I need the following packages for my code to function:\n\nkeras-tensorflow\nPillow\nscipy\nnumpy\npandas\n\nI tried installing using docker and uploading the zip file, but it exceeds the file size.\nIs there a get around for this? How to use these packages for my Lambda function?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":273,"Q_Id":59040958,"Users Score":1,"Answer":"when the zip file size is bigger than 49 mb, You can upload the zip file to Amazon S3 and use it to update the function code. \n\naws lambda update-function-code --function-name calculateMath --region us-east-1 --s3-bucket calculate-math-bucket --s3-key 100MBFile.zip","Q_Score":0,"Tags":"python,amazon-web-services,numpy,tensorflow,aws-lambda","A_Id":59041168,"CreationDate":"2019-11-25T22:22:00.000","Title":"Deploy function in AWS Lamda (package size exceeds)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"GPU: Nvidia GTX 2070\nPython Version: 3.5 \nTensorflow: 1.13.1\nCUDA: 10\ncuDNN: 7.4\nModel: Faster-RCNN-Inception-V2\nI am using the legacy method of training my model (trian.py) and when I run it as such\npython train.py --logtostderr --train_dir=training\/ --pipeline_config_path=training\/faster_rcnn_inception_v2_pets.config\nThe training runs for some random amount of time (it usually gets stuck around the 150th step, but it will often make it up to 300-700 sometimes when I try it) and then will get stuck attempting to save a checkpoint. I reach the point where it just says \nINFO:tensorflow:global step 864: loss = 0.4430 (0.996 sec\/step)\nINFO:tensorflow:Saving checkpoint to path training\/model.ckpt\nINFO:tensorflow:Saving checkpoint to path training\/model.ckpt\nAnd does not move past that point. Once it reaches this point, I also become incapable of killing the program no matter which methods I try and am forced to simply close the terminal window if I want the process to stop. \nAdditionally, based on what I have read, the program should theoretically be using up close to 100% of my GPU while it trains but it only ends up using about 10%. I'm not sure if those two things are related but I feel it is probably worth mentioning, especially considering I would like to have it train as fast as possible if I do manage to get it working. \nI've seen others post about similar issues in the past but none seem to have any answers. If anyone has any idea please let me know! Thanks.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":778,"Q_Id":59042621,"Users Score":0,"Answer":"Had the same issues till I upgradet the Nvidia driver from Version 441.28 to the newest Version.\nAfter this, the training runs without stops or freezes.","Q_Score":1,"Tags":"python,tensorflow","A_Id":59877757,"CreationDate":"2019-11-26T01:56:00.000","Title":"Tensorflow-GPU getting stuck saving checkpoint during training - also not using entire GPU, not sure why","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a Pandas DataFrame with a bunch of rows and labeled columns.\nI also have an excel file which I prepared with one sheet which contains no data but only\nlabeled columns in row 1 and each column is formatted as it should be: for example if I\nexpect percentages in one column then that column will automatically convert a raw number to percentage.\nWhat I want to do is fill the raw data from my DataFrame into that Excel sheet in such a way\nthat row 1 remains intact so the column names remain. The data from the DataFrame should fill\nthe excel rows starting from row 2 and the pre-formatted columns should take care of converting\nthe raw numbers to their appropriate type, hence filling the data should not override the column format.\nI tried using openpyxl but it ended up creating a new sheet and overriding everything.\nAny help?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1888,"Q_Id":59050052,"Users Score":0,"Answer":"If your # of columns and order is same then you may try xlsxwriter and also mention the sheet name to want to refresh: \ndf.to_excel('filename.xlsx', engine='xlsxwriter', sheet_name='sheetname', index=False)","Q_Score":1,"Tags":"python,python-3.x,pandas,dataframe","A_Id":59050279,"CreationDate":"2019-11-26T11:34:00.000","Title":"Fill an existing Excel file with data from a Pandas DataFrame","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataframe with 5000 items(rows) and 2048 features(columns). \nShape of my dataframe is (5000, 2048).\nwhen I calculate cosine matrix using pairwise distance in sklearn, I get (5000,5000) matrix. \nHere I can compare each other. \nBut now If I have a new vector shape of (1,2048), how can find cosine similarity of this item with early dataframe which I had, using (5000,5000) cosine matrix which I have already calculated? \nEDIT \nPS: I can append this new vector to my dataframe and calculate again cosine similarity. But for large amount of data it gets slow. Or is there any other fast and accurate distance metrics?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":241,"Q_Id":59052818,"Users Score":0,"Answer":"The initial (5000,5000) matrix encodes the similarity values of all your 5000 items in pairs (i.e. symmetric matrix).\nTo have the similarities in case of a new item, concatenate and make a (5001, 2048) matrix and then estimate similarity again to get (5001,5001)\nIn other words, you can not directly use the (5000,5000) precomputed matrix to get the similarity with the new (1,2048) vector.","Q_Score":0,"Tags":"python,pandas,machine-learning,scikit-learn,computer-vision","A_Id":59053007,"CreationDate":"2019-11-26T14:11:00.000","Title":"how to find cosine similarity in a pre-computed matrix with a new vector?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataframe with 5000 items(rows) and 2048 features(columns). \nShape of my dataframe is (5000, 2048).\nwhen I calculate cosine matrix using pairwise distance in sklearn, I get (5000,5000) matrix. \nHere I can compare each other. \nBut now If I have a new vector shape of (1,2048), how can find cosine similarity of this item with early dataframe which I had, using (5000,5000) cosine matrix which I have already calculated? \nEDIT \nPS: I can append this new vector to my dataframe and calculate again cosine similarity. But for large amount of data it gets slow. Or is there any other fast and accurate distance metrics?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":241,"Q_Id":59052818,"Users Score":0,"Answer":"Since cosine similarity is symmetric. You can compute the similarity meassure with the old data matrix, that is similarity between the new sample (1,2048) and old matrix (5000,2048) this will give you a vector of (5000,1) you can append this vector into the column dimension of the pre-computed cosine matrix making it (5000,5001) now since you know the cosine similarity of the new sample to itself. you can append this similarity to itself, back into the previously computed vector making it of size (5001,1), this vector you can append in the row dimension of the new cosine matrix that will make it (5001,5001)","Q_Score":0,"Tags":"python,pandas,machine-learning,scikit-learn,computer-vision","A_Id":59053865,"CreationDate":"2019-11-26T14:11:00.000","Title":"how to find cosine similarity in a pre-computed matrix with a new vector?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a set of elements, which is  for example \nx= [250,255,273,180,400,309,257,368,349,248,401,178,149,189,46,277,293,149,298,223]\nI want to group these into n number of groups A,B,C... such that sum of all group variances is minimized. Each group need not have same number of elements.\nI would like a optimization approach in python or R.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":343,"Q_Id":59063240,"Users Score":0,"Answer":"I would sort the numbers into increasing order and then use dynamic programming to work out where to place the boundaries between groups of contiguous elements. For example, if the only constraint is that every number must be in exactly one group, work from left to right. At each stage, for i=1..n work out the set of boundaries that produces minimum variance computed among the elements seen so far for i groups. For i=1 there is no choice. For i>1 consider every possible location for the boundary of the last group, and look up the previously computed answer for the best allocation of items before this boundary into i-1 groups, and use the figure previously computed here to work out the contribution of the variance of the previous i-1 groups.\n(I haven't done the algebra, but I believe that if you have groups A and B where mean(A) < mean(B) but there are elements a in A and b in B such that a > b, you can reduce the variance by swapping these between groups. So the lower variance must come from groups that are contiguous when the elements are written out in sorted order).","Q_Score":1,"Tags":"python,r,algorithm,optimization,minimization","A_Id":59063566,"CreationDate":"2019-11-27T04:57:00.000","Title":"How should I group these elements such that overall variance is minimized?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have two tensors of dimension 1000 * 1. I want to check how many of the 1000 elements are equal in the two tensors. I think I should be able to do this in one line like Numpy but couldn't find a similar function.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":4942,"Q_Id":59078318,"Users Score":1,"Answer":"Something like\nequal_count = len((tensor_1.flatten() == tensor_2.flatten()).nonzero().flatten())\nshould work.","Q_Score":4,"Tags":"python,pytorch,equality,tensor","A_Id":59095464,"CreationDate":"2019-11-27T20:57:00.000","Title":"Compare Number of Equal Elements in Tensors","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have time series data set which contain TimeStamp[hour base] and wind sensor value. I need to find anomalies from this data set.\nWhat are the techniques to find out anomalies ?\nHow to find anomalies with only these two features ( TimeStamp, sensor-value ) ?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":50,"Q_Id":59083825,"Users Score":0,"Answer":"Very broad question so this will be a generic\/broad answer:\nTo define anomalies you'll need to think and define what you consider normal. \nUsually we consider two things in terms of (time series) data; \n\ndata availability:\nis the data there that you expect? Usually monitorred by looking at a row count over time (are you inserting more or less data than expected)\nalso counting null values could be used here, but this already leads into the question of data quality:\ndata quality: \nare the values in ranges you expect? are they in the type\/format you expect, etc. \nyou can use standard deviations\/variance\/normal distribution to monitor this. Or hard limit and define which values you accept\/expect (min, max for instance)","Q_Score":0,"Tags":"python,machine-learning,deep-learning,time-series,data-science-experience","A_Id":59083949,"CreationDate":"2019-11-28T07:38:00.000","Title":"How to find anomalies in wind-sensor TimeSeries data?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I need some help in R Machine Learning model execution by a Python REST API.\nI have 2 executable ML model, developed in R Language by my colleague.\nNow I need to deploy those models as a REST API, as we need to pass some input parameter and take output from those Models as a return statement.\nI found, we can do this with Python, using RPY2 or Plumber Libraries. So I tried to implement Flask REST API for R model deployment, but not getting the exact reference from google for my challenges and for learning. I am new in R-Language, just in last 2 weeks, I have explored some basis of R.\nCan someone please share me some reference to my query or any other approach or code reference to implement PYTHON REST API to execute R model by passing some input field.\nThanks in advance","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":331,"Q_Id":59084012,"Users Score":0,"Answer":"You will need a Python based REST framework like Flask(Django or Pyramid will also do) to do this. You need to understand how to write a REST API by going through their respective docs. You can basically hide the R model behind a REST Resource. This resource will be responsible in receiving the inputs for the model. Using the RPY library you can invoke the model by passing required parameters and return a response to the API client.","Q_Score":0,"Tags":"python,r,rest,api,rpy2","A_Id":59084811,"CreationDate":"2019-11-28T07:51:00.000","Title":"How to Execute R Machine Learning Model using Python REST API?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to build a simple NN for regression purposes; the dimension of my input data reads (100000,3): meaning I have 1mio particles and their corresponding x,y,z coordinates. Out of these particles I want to predict centers which the particles correspond to where the data of the centers read (1000,3).\nmy question is: since the input array should have the same number of samples as target arrays how can I solve this problem?\nactually my mapping is from (100000,3) -> (1000,3) because on average about 100 particles belong to one center.\nto train the model i will use many of those datasets with the right centers as output; after that i want to predict out of one new set of particle coordinates the corresponding centers.","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":68,"Q_Id":59089002,"Users Score":1,"Answer":"This is the answer to a question regarding unknown labels.\nYou have to know labels before using any supervised algorithm. Otherwise, there is no way you can train a model. You need to think of solving this problem by employing one of the unsupervised techniques, such as k-means algorithm, Gaussian Mixture Models, or Classification And Regression Trees, etc.\nFor example, one of the suggestions I would give is to try k-means with a fixed number of k, which is 1000 in your case, run the algorithm a couple of times and see if centroids are any close to elements in your output. Then, you could classify your output based on how close they are to one of the nearest centroids. Then all elements of input belonging to individual centroids would be classified as such.\n\nEDIT.\nAfter reconsidering your example, I think that perhaps k-NN would be much more helpful to your problem. In k-NN, outputs are considered as neighbours. Each point from the input is assigned to the closest neighbour. In the end, you already have the output but you don't know how to map all elements in both the input and the output. I have just realised your problem is \"the mapping\". It is a good chance k-NN would solve that meaning it would create labels for all elements in the input that correspond to elements in the output. Once it is done, the Neural Network model can be trained.","Q_Score":0,"Tags":"python,tensorflow,keras,neural-network,regression","A_Id":59116976,"CreationDate":"2019-11-28T12:32:00.000","Title":"NN multidim regression with matrix as output","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to build a simple NN for regression purposes; the dimension of my input data reads (100000,3): meaning I have 1mio particles and their corresponding x,y,z coordinates. Out of these particles I want to predict centers which the particles correspond to where the data of the centers read (1000,3).\nmy question is: since the input array should have the same number of samples as target arrays how can I solve this problem?\nactually my mapping is from (100000,3) -> (1000,3) because on average about 100 particles belong to one center.\nto train the model i will use many of those datasets with the right centers as output; after that i want to predict out of one new set of particle coordinates the corresponding centers.","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":68,"Q_Id":59089002,"Users Score":1,"Answer":"All you have to do is to match the sizes. Assuming you know what particle belongs to what center that shouldn't be too hard.\nSo in your case you should have a matrix of (1000000,3)(atoms) and a vector of (1000000,)(centers) as their labels. This means that each entry in the vector corresponds to one row in the atom matrix.","Q_Score":0,"Tags":"python,tensorflow,keras,neural-network,regression","A_Id":59106465,"CreationDate":"2019-11-28T12:32:00.000","Title":"NN multidim regression with matrix as output","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In my case I have a dataset of letters and symbols, detected in an image. The detected items are represented by their coordinates, type (letter, number etc), value, orientation and not the actual bounding box of the image. My goal is, using this dataset, to group them into different \"words\" or contextual groups in general.\nSo far I achieved ok-ish results by applying classic unsupervised clustering, using DBSCAN algorithm, but still this is way t\u03bfo limited on the geometric distance of the samples and so the resulting groups cannot resemble the \"words\" I am aiming for. So I am searching for a way to influence the results of the clustering algorithm by using the knowledge I have about the \"word-like\" nature of the clusters needed.\nMy possible approach that I thought was to create a dataset of true and false clusters and train an SVM model (or any classifier) to detect whether a proposed cluster is correct or not. But still for this, I have no solid proof that I can train a model well enough to discriminate between good and bad clusters, plus I find it difficult to efficiently and consistently represent the clusters, based on the features of their members. Moreover, since my \"testing data\" will be a big amount of all possible combinations of the letters and symbols I have, the whole approach seems a bit too complicated to attempt implementing it without any proof or indications that it's going to work in the end.\nTo conclude, my question is, if someone has any prior experience with that kind of task (in my mind sounds rather simple task, but apparently it is not). Do you know of any supervised clustering algorithm and if so, which is the proper way to represent clusters of data so that you can efficiently train a model with them? \nAny idea\/suggestion or even hint towards where I can research about it will be much appreciated.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1973,"Q_Id":59093163,"Users Score":0,"Answer":"A standard approach would be to use the dendrogram.\nThen merge branches only if they agree with your positive examples and don't violate any of your negative examples.","Q_Score":2,"Tags":"python,machine-learning,cluster-analysis,unsupervised-learning,supervised-learning","A_Id":59095070,"CreationDate":"2019-11-28T16:34:00.000","Title":"Is there any supervised clustering algorithm or a way to apply prior knowledge to your clustering?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"More of a theoretical question: \nUse case: Create an API that takes json input, triggers ML algorithm inside of it and returns result to the user. \nI know that in case of python ML model, I could just pack whole thing into pickle and use it easily inside of my web app. The problem is that all our algorithms are currently written in R and I would rather avoid re-writing them to python. I have checked a few libraries that allow to run R code within python but I cannot find a way to pack it \"in a pickle way\" and then just utilize. \nIt may be stupid but I have not had much to do with R so far.\nThank you in advance for any suggestions!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":60,"Q_Id":59101644,"Users Score":0,"Answer":"Not sure what calling R code from Python has to do with ML models.\nIf you have a trained model, you can try converting it into ONNX format (emerging standard), and try using the result from Python.","Q_Score":0,"Tags":"python,r,machine-learning,web-applications","A_Id":59107037,"CreationDate":"2019-11-29T08:58:00.000","Title":"Is there a way to use pre-trained R ML model in python web app?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a data set of around 15k observations. This observations are city names from all over the world. This Data set has been populated by people from many different countries which means that i have several duplicates of the same city in different languages. see below DF extract:\ncity_name\nbruselas\nbrussel\nbrussels\nbrussels\nbrussels  auderghem \nbruxelles\nbruxelles belgium\nmunchen\nmunchenstein\nmunchwilen\nmunderkingen\nmundolsheim\nmungia\nmunguia\nmunich\nmunich\nmunich\nmunich  germany\nmunich  munchen \nmunich rupert mayer strasse\nThe task is to map all cities in the DF to its english name but, becaue the cities are in different format and in different languages i am finding it very difficult to come up with a solution other than perform this task manually which is not productive as we have 15,000+ observations to go through. The final data set should look something like this(using a few of the observations above only):\ncity_name              mapped_city\nbrussels  auderghem     Brusels\nbruxelles               Brusels\nbruxelles belgium       Brusels\nmunchen                 Munich\nmunich  germany         Munich\nAny help would be greatly appreciated","AnswerCount":3,"Available Count":1,"Score":-0.0665680765,"is_accepted":false,"ViewCount":333,"Q_Id":59104543,"Users Score":-1,"Answer":"Try mapping by first letters of a city that will reduce your work load","Q_Score":3,"Tags":"python,algorithm,sorting,dataframe","A_Id":59104620,"CreationDate":"2019-11-29T12:10:00.000","Title":"Effective way to map 15k cities in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Everything was ok around a week ago.\nEven though I am running on a server, I really don't think much has changed. \nWonder what could have caused it.\nTensorflow has version 2.1.0-dev20191015\nAnyway, here is the GPU status:\nNVIDIA-SMI 430.50\nDriver Version: 430.50\nCUDA Version: 10.1 \nEpoch 1\/5\n2019-11-29 22:08:00.334979: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10.0\n2019-11-29 22:08:00.644569: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n2019-11-29 22:08:00.647191: E tensorflow\/stream_executor\/cuda\/cuda_dnn.cc:329] Could not create cudnn handle: CUDNN_STATUS_NOT_INITIALIZED\n2019-11-29 22:08:00.647309: E tensorflow\/stream_executor\/cuda\/cuda_dnn.cc:337] Possibly insufficient driver version: 430.50.0\n2019-11-29 22:08:00.647347: W tensorflow\/core\/framework\/op_kernel.cc:1655] OP_REQUIRES failed at cudnn_rnn_ops.cc:1510 : Unknown: Fail to find the dnn implementation.\n2019-11-29 22:08:00.647393: W tensorflow\/core\/common_runtime\/base_collective_executor.cc:217] BaseCollectiveExecutor::StartAbort Unknown: Fail to find the dnn implementation.\nAt the end, I get:\nUnknownError:  [_Derived_]  Fail to find the dnn implementation.\n     [[{{node CudnnRNN}}]]\n     [[sequential\/bidirectional\/forward_lstm\/StatefulPartitionedCall]] [Op:__inference_distributed_function_18158]\nFunction call stack:\ndistributed_function -> distributed_function -> distributed_function\nThe code gets traced back to here:\n174         history = model.fit(training_input, training_output,  epochs=EPOCHES,\n    175                             batch_size=BATCH_SIZE,\n--> 176                             validation_split=0.1)\nThank you.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":389,"Q_Id":59112898,"Users Score":1,"Answer":"There was indeed a system-wide upgrade.\nUpdating cuda to cuda 10.2 and nvidia-driver to 440 and making libcudnn7 7.6.5 fixed the problem.","Q_Score":4,"Tags":"python,tensorflow","A_Id":59178620,"CreationDate":"2019-11-30T03:20:00.000","Title":"Encountering \"Could not create cudnn handle: CUDNN_STATUS_NOT_INITIALIZED\" on a previously working system","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"I have been using Tensorflow on Anaconda for a while now, but recently I have been getting the mentioned error when trying to import Tensorflow. \nThis has been asked here multiple times, so I tried suggested solutions but nothing worked so far (reinstalling tensorflow (both normal and gpu versions), reinstalling Anaconda). When running help('modules') tensorflow appears in module list. But even after I run pip uninstall tensorflow and pip uninstall tensorflow-gpu tensorflow still remains in module list when running help('modules'). \nWhat can I do to fix this?","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1414,"Q_Id":59115365,"Users Score":1,"Answer":"Apparently the reason was the Python version (which is strange as according to documentation Tensorflow supports Python 3.7). I downgraded to 3.6 and I am able to import Tensorflow again","Q_Score":0,"Tags":"python,tensorflow,anaconda","A_Id":59117449,"CreationDate":"2019-11-30T10:43:00.000","Title":"ModuleNotFoundError: No module named 'tensorflow.python' Anaconda","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Everything was working smoothly until I started getting the following error:\n\nTraceback (most recent call last):\n    File \"\", line 1, in \n    File \"\/home\/user\/Workspace\/Practices\/Tensorflow\/tensorflow2\/venv\/lib\/python3.7\/site-packages\/tensorflow\/init.py\", line 98, in \n      from tensorflow_core import *\n    File \"\/home\/user\/Workspace\/Practices\/Tensorflow\/tensorflow2\/venv\/lib\/python3.7\/site-packages\/tensorflow_core\/init.py\", line 40, in \n      from tensorflow.python.tools import module_util as _modle_util\n  ModuleNotFoundError: No module named 'tensorflow.python.tools'; 'tensorflow.python' is not a package\n\nMy environment setup:\n\npython-3.7\nUsing venv module to create virtual environment\ntensorflow 2.0.0\npip 19.0.3\nManjaro Linux\n\nNow, I even can't import tensorflow module as well. It gives same above error. Tried reinstalling with cache and without cache as well, but no luck. Recreated virtual environment as well, no luck. This is really weird and have no clue where to start troubleshooting as well. Looking at virtual environment site packages, everything seems fine.","AnswerCount":4,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":12652,"Q_Id":59116456,"Users Score":0,"Answer":"Found a noob problem. I was using my file name as csv.py which already exist in python library, which I think was messing up the paths. But don't know how yet.","Q_Score":2,"Tags":"python,tensorflow","A_Id":59123027,"CreationDate":"2019-11-30T13:06:00.000","Title":"No module named 'tensorflow.python.tools'; 'tensorflow.python' is not a package","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Everything was working smoothly until I started getting the following error:\n\nTraceback (most recent call last):\n    File \"\", line 1, in \n    File \"\/home\/user\/Workspace\/Practices\/Tensorflow\/tensorflow2\/venv\/lib\/python3.7\/site-packages\/tensorflow\/init.py\", line 98, in \n      from tensorflow_core import *\n    File \"\/home\/user\/Workspace\/Practices\/Tensorflow\/tensorflow2\/venv\/lib\/python3.7\/site-packages\/tensorflow_core\/init.py\", line 40, in \n      from tensorflow.python.tools import module_util as _modle_util\n  ModuleNotFoundError: No module named 'tensorflow.python.tools'; 'tensorflow.python' is not a package\n\nMy environment setup:\n\npython-3.7\nUsing venv module to create virtual environment\ntensorflow 2.0.0\npip 19.0.3\nManjaro Linux\n\nNow, I even can't import tensorflow module as well. It gives same above error. Tried reinstalling with cache and without cache as well, but no luck. Recreated virtual environment as well, no luck. This is really weird and have no clue where to start troubleshooting as well. Looking at virtual environment site packages, everything seems fine.","AnswerCount":4,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":12652,"Q_Id":59116456,"Users Score":0,"Answer":"You don't need to uninstall tensorflow what version you have because it will take time to reinstall. You can fix this issue just by installing tensorflow==2.0.\npip install tensorflow==2.0","Q_Score":2,"Tags":"python,tensorflow","A_Id":68268774,"CreationDate":"2019-11-30T13:06:00.000","Title":"No module named 'tensorflow.python.tools'; 'tensorflow.python' is not a package","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Everything was working smoothly until I started getting the following error:\n\nTraceback (most recent call last):\n    File \"\", line 1, in \n    File \"\/home\/user\/Workspace\/Practices\/Tensorflow\/tensorflow2\/venv\/lib\/python3.7\/site-packages\/tensorflow\/init.py\", line 98, in \n      from tensorflow_core import *\n    File \"\/home\/user\/Workspace\/Practices\/Tensorflow\/tensorflow2\/venv\/lib\/python3.7\/site-packages\/tensorflow_core\/init.py\", line 40, in \n      from tensorflow.python.tools import module_util as _modle_util\n  ModuleNotFoundError: No module named 'tensorflow.python.tools'; 'tensorflow.python' is not a package\n\nMy environment setup:\n\npython-3.7\nUsing venv module to create virtual environment\ntensorflow 2.0.0\npip 19.0.3\nManjaro Linux\n\nNow, I even can't import tensorflow module as well. It gives same above error. Tried reinstalling with cache and without cache as well, but no luck. Recreated virtual environment as well, no luck. This is really weird and have no clue where to start troubleshooting as well. Looking at virtual environment site packages, everything seems fine.","AnswerCount":4,"Available Count":3,"Score":0.049958375,"is_accepted":false,"ViewCount":12652,"Q_Id":59116456,"Users Score":1,"Answer":"I just faced this problem right now. I ran the source code on another computer and it showed the same error. I went ahead and compared the version of TensorFlow and turns out that the other computer was running tensorflow==2.1.0 and mine was running tensorflow==1.14.0.\n\nIn short, downgrade your tensorflow installation (pip install tensorflow==1.14.0)","Q_Score":2,"Tags":"python,tensorflow","A_Id":60205686,"CreationDate":"2019-11-30T13:06:00.000","Title":"No module named 'tensorflow.python.tools'; 'tensorflow.python' is not a package","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"What is the performance difference between two JSON loading methods into BigQuery:\nload_table_from_file(io.StringIO(json_data) vs create_rows_json\nThe first one loads the file as a whole and the second one streams the data. Does it mean that the first method will be faster to complete, but binary, and the second one slower, but discretionary?\nAny other concerns?\nThanks!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":88,"Q_Id":59143310,"Users Score":3,"Answer":"It's for two different logics and they have their own limits.\n\nLoad from file is great if you can have your data placed in files. A file can be up to 5TB in size. This load is free. You can query data immediately after completion. \nThe streaming insert, is great if you have your data in form of events that you can stream to BigQuery. While a streaming insert single request is limited up to 10MB, it can be super parallelized up to 1 Million rows per second, that's a big scale. Streaming rows to BigQuery has it's own cost. You can query data immediately after streaming, but for some copy and export jobs data can be available later up to 90 minutes.","Q_Score":0,"Tags":"python,google-bigquery","A_Id":59144477,"CreationDate":"2019-12-02T16:47:00.000","Title":"Performance difference in json data into BigQuery loading methods","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Using Keras in Python to create a CNN that pumps out the angle of rotation and zoom of an image. I am working on create the training data. I have a few questions though. \nI plan on using Keras Preprocessing as the tool to manipulate before I train, but is there a way to save what angle and zoom is used so that I can use those as the trainable parameters?\nIf not, is there something easier to use?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":44,"Q_Id":59148940,"Users Score":0,"Answer":"You can use the method apply_transform of the ImageDataGenerator in which you can specify the parameters of the transformation you want while for example saving these parameters in a list or another structure, using them later as features.","Q_Score":0,"Tags":"python,machine-learning,keras","A_Id":59149250,"CreationDate":"2019-12-03T01:23:00.000","Title":"Multiple Labels as Training data for ML","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a tabular pytorch model that takes in cities and zipcodes as a categorical embeddings.  However, I can't stratify effectively based on those columns. \nHow can I get pytorch to run if it's missing a categorical value in the test set that's not in the train set, or has a categorical value in the holdout set that was not in the train\/test set?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":39,"Q_Id":59159578,"Users Score":0,"Answer":"u can try to use one hot encoding instead\nPS: this is a suggestion not an answer","Q_Score":0,"Tags":"python,pytorch","A_Id":59160973,"CreationDate":"2019-12-03T14:42:00.000","Title":"How to deal with different categories in pytorch train, test, and holdout set","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a lot of strings in a pandas dataframe, I want to assign every string a number for keras.\nthe string represent a location:\nCwmyNiVcURtyAf+o\/6wbAg==\nI want to turn it into a number and back again. I'm using keras, tensorflow and pandas. Does one of the modules contain a function which does that? Or do I have to write a hashtable?\nLike this:\nCwmyNiVcURtyAf+o\/6wbAg== => 1\nCwmyUSVcbBtiBQEkAN4bVbA= =>  2\nCwmypSVdCRNYBv4MAFUTSRY= =>  3\nCwnBoiVCjRNPBAAJ\/ysTHw== =>  4\nCwnBoiVCjRNfBv5QAEITCA== =>  5\nCwmyUSVcbBtiBQEkAN4bVbA= =>  2\nI have ~8000 locations and each location is 15 times in the Dataframe","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":373,"Q_Id":59160301,"Users Score":2,"Answer":"You should consider a one-hot encoding, which can be done easily with pandas via the get_dummies function. This will create binary columns for each \"category\" (i.e. unique string).","Q_Score":0,"Tags":"python,pandas,keras","A_Id":59160453,"CreationDate":"2019-12-03T15:24:00.000","Title":"Is there a function to convert a string into a number and back for machine learning","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have some 360 odd features on which I am training my neural network model.\nThe accuracy I am getting is abysmally bad. There is one feature amongst the 360 that is more important than the others.\nRight now, it does not enjoy any special status amongst the other features.\nIs there a way to lay emphasis on one of the features while training the model? I believe this could improve my model's accuracy.\nI am using Python 3.5 with Keras and Scikit-learn.\nEDIT: I am attempting a regression problem\nAny help would be appreciated","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":28,"Q_Id":59172607,"Users Score":0,"Answer":"First of all, I would make sure that this feature alone has a decent prediction probability, but I am assuming that you already made sure of it.\nThen, one approach that you could take, is to \"embed\" your 359 other features in a first layer, and only feed in your special feature once you have compressed the remaining information.\nContrary to what most tutorials make you believe, you do not have to add in all features already in the first layer, but can technically insert them at any point in time (or even multiple times).\nThe first layer that captures your other inputs is then some form of \"PCA approximator\", where you are embedding a high-dimensional feature space (359 dimensions) into something that is less dominant over your other feature (maybe 20-50 dimensions as a starting point?)\nOf course there is no guarantee that this will work, but you might have a much better chance of getting attention on your special feature, although I am fairly certain that in general you should still see an increase in performance if the single feature is strongly enough correlated with your output.\nThe other question that is still open is the kind of task you are training for, i.e., whether you are doing some form of classification (if so, how many classes?), or regression. This might also influence architectural choices, and the amount of focus you can\/should put on a single feature.","Q_Score":0,"Tags":"python-3.x,machine-learning,scikit-learn,neural-network,hyperparameters","A_Id":59172700,"CreationDate":"2019-12-04T09:22:00.000","Title":"Emphasis on a feature while training a vanilla nn","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataset where each row represents the number of occurrences of certain behaviour. The columns represent a window of a set amount of time. It looks like this:\n\n+----------+----------+----------+----------+-----------+------+\n| Episode1 | Episode2 | Episode3 | Episode4 |  Episode5 |  ... |\n+----------+----------+----------+----------+-----------+------+\n|        2 |        0 |        1 |        3 |           |      |\n|        1 |        2 |        4 |        2 |         3 |      |\n|        0 |          |          |          |           |      |\n+----------+----------+----------+----------+-----------+------+\n\nThere are over 150 episodes. I want to find a way to represent each row as a trend, whether the occurrences are exhibiting more\/less.\nI have tried to first calculate the average\/median\/sum of every 3\/5\/10 cells of each row (because each row has different length and many 0 values), and use these to correlate with a horizontal line (which represent the time), the coefficients of these correlations should tell the trend (<0 means downward, >0 upward). The trends will be used in further analysis.\nI'm wondering if there is a better way to do this. Thanks.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":192,"Q_Id":59180323,"Users Score":1,"Answer":"If you expect the trend to be linear, you could fit a linear regression to each row separately, using time to predict number of occurences of a behavior. Then store the slopes.\nThis slope represents the effect of increasing time by 1 episode on the behavior. It also naturally accounts for the difference in length of the time series.","Q_Score":1,"Tags":"python,pandas,math,regression,data-science","A_Id":59182082,"CreationDate":"2019-12-04T16:14:00.000","Title":"How to represent the trend (upward\/downward\/no change) of data?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am dealing with different datasets that have only Categorical variables\/features with only two values such as (temperature = 'low' and 'high') or (light = 'on' and 'off' or '0' and '1'). \nI am not really sure whether to use \"one-hot encoding\" or \"Label Encoding\" method to train my models. \nI am working on a classification problem and using some supervised machine learning algorithms. \nI used \"Label Encoding\" and I got a pretty good result. I feel there could be something that I did was wrong. I am not sure if I should use \"one-hot encoding\" or not.\nIn case of Categorical variables with only two values which method should I use to convert the variables?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":239,"Q_Id":59188318,"Users Score":0,"Answer":"There are only a few cases where LabelEncoder is useful because of the ordinality issue. If your categorial features are ordinal then use LabelEncoder otherwise use One-hot encoding. But, One-hot encoding increases dimension. In this case, I typically employ One-hot encoding followed by PCA for dimensionality reduction.","Q_Score":0,"Tags":"python-3.x,encoding,categorical-data,one-hot-encoding,labeling","A_Id":59188878,"CreationDate":"2019-12-05T04:34:00.000","Title":"Categorical variables with only two values","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Given an array of integers, I need to reduce it to a single number by repeatedly replacing any two numbers with their difference, to produce the maximum possible result.\nExample1 - If I have array of [0,-1,-1,-1] then performing (0-(-1)) then (1-(-1)) and then (2-(-1)) will give 3 as maximum possible output\nExample2- [3,2,1,1] we can get maximum output as 5 { first (1-1) then (0-2) then (3-(-2)}\nCan someone tell me how to solve this question?","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":113,"Q_Id":59189207,"Users Score":0,"Answer":"The other answers are fine, but here's another way to think about it:\nIf you expand the result into individual terms, you want all the positive numbers to end up as additive terms, and all the negative numbers to end up as subtractive terms.\nIf you have both signs available, then this is easy:\n\nSubtract all but one of the positive numbers from a negative number\nSubtract all of the negative numbers from the remaining positive number\n\nIf all your numbers have the same sign, then pick the one with the smallest absolute value at treat it as having the opposite sign in the above procedure.  That works out to:\n\nIf you have only negative numbers, then subtract them all from the least negative one; or\nIf you have only positive numbers, then subtract all but one from the smallest, and then subtract the result from the remaining one.","Q_Score":3,"Tags":"python,arrays,algorithm","A_Id":59196692,"CreationDate":"2019-12-05T06:08:00.000","Title":"Find the maximum result after collapsing an array with subtractions","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"SHAP values seem to be additive and e.g. the overall feature importance plot simply adds the absolute SHAP values per feature and compares them.  This allows us to use SHAP for global importance aswell as local importance. We could also get feature importance for a particular subset of data records the same way.  \nBy the same token, is it valid to get aggregate SHAP values for sets of variables? e.g. \"Height\", \"Weight\" and \"Eye Colour\" into \"HumanDescription\" or \"Temperature\", \"Humidity\" and \"Air-Pressure\" into \"Weather\"and rank them accordingly.\nTheoretically, I can't see why not but would appreciate feedback on this in case of any gotchas.","AnswerCount":1,"Available Count":1,"Score":0.6640367703,"is_accepted":false,"ViewCount":1514,"Q_Id":59193277,"Users Score":4,"Answer":"From Lundberg, package author: \"The short answer is yes, you can add up SHAP values across the columns to get the importance of a whole group of features (just make sure you don't take the absolute value like we do when going across rows for global feature importance).\nThe long answer is that when Shapley values \"fairly\" allocate credit for interaction effects between features, they assume each feature in an interaction effect should get equal credit for the interaction. This means that for high order interaction terms you might get slightly different results when running Shapley values before (and summing) vs. after grouping features (since the new group just gets one chunk of the interaction pie so to speak, as opposed to multiple chunks when it was several features). These differences are typically small though so I wouldn't sweat it much since both ways are reasonable.\"","Q_Score":2,"Tags":"python,shap","A_Id":59281571,"CreationDate":"2019-12-05T10:37:00.000","Title":"Is it Valid to Aggregate SHAP values to Sets of of Features?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In my dataset X I have two continuous variables a, b and two boolean variables c, d, making a total of 4 columns. \nI have a multidimensional target y consisting of two continuous variables A, B and one boolean variable C. \nI would like to train a model on the columns of X to predict the columns of y. However, having tried LinearRegression on X it didn't perform so well (my variables vary several orders of magnitude and I have to apply suitable transforms to get the logarithms, I won't go into too much detail here). \nI think I need to use LogisticRegression on the boolean columns. \nWhat I'd really like to do is combine both LinearRegression on the continuous variables and LogisticRegression on the boolean variables into a single pipeline. Note that all the columns of y depend on all the columns of X, so I can't simply train the continuous and boolean variables independently.\nIs this even possible, and if so how do I do it?","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":458,"Q_Id":59200594,"Users Score":0,"Answer":"If your target data Y has multiple columns you need to use multi-task learning approach. Scikit-learn contains some multi-task learning algorithms for regression like multi-task elastic-net but you cannot combine logistic regression with linear regression because these algorithms use different loss functions to optimize. Also, you may try neural networks for your problem.","Q_Score":2,"Tags":"python,machine-learning,scikit-learn,regression","A_Id":59200836,"CreationDate":"2019-12-05T17:41:00.000","Title":"Combining logistic and continuous regression with scikit-learn","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"In my dataset X I have two continuous variables a, b and two boolean variables c, d, making a total of 4 columns. \nI have a multidimensional target y consisting of two continuous variables A, B and one boolean variable C. \nI would like to train a model on the columns of X to predict the columns of y. However, having tried LinearRegression on X it didn't perform so well (my variables vary several orders of magnitude and I have to apply suitable transforms to get the logarithms, I won't go into too much detail here). \nI think I need to use LogisticRegression on the boolean columns. \nWhat I'd really like to do is combine both LinearRegression on the continuous variables and LogisticRegression on the boolean variables into a single pipeline. Note that all the columns of y depend on all the columns of X, so I can't simply train the continuous and boolean variables independently.\nIs this even possible, and if so how do I do it?","AnswerCount":3,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":458,"Q_Id":59200594,"Users Score":0,"Answer":"What i understand you want to do is to is to train a single model that both predicts a continuous variable and a class. You would need to combine both loses into one single loss to be able to do that which I don't think is possible in scikit-learn. However I suggest you use a deep learning framework (tensorflow, pytorch, etc) to implement your own model with the required properties you need which would be more flexible. In addition you can also tinker with solving the above problem using neural networks which would improve your results.","Q_Score":2,"Tags":"python,machine-learning,scikit-learn,regression","A_Id":59203141,"CreationDate":"2019-12-05T17:41:00.000","Title":"Combining logistic and continuous regression with scikit-learn","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"So I have trained a RandomForest model on a fairly simple customer data. The prediction is either 1 or 0 telling if a customer will churn or not. \nLet's say I have 10 features called 'f1', 'f2', 'f3' and so on... As the model has already been trained I took another period of the similar data to see how the model performs. But in this data the features could be shuffled in a different way. (for example 'f3', 'f10', 'f1', ...). Will the model look at the name of the features or it won't matter for it and it will think that 'f1' is 'f3'? Let's say the type of the data is the same in each column.\nThe reason I am asking this is because to check this theory I renamed 'f3' column name to 'a' and to my astonishment the model worked anyways. What are your thoughts?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":110,"Q_Id":59209196,"Users Score":0,"Answer":"The algorithm works independent from your column names. You can name your columns whatever you want in most algorithms(except fbprophet etc.) \nBut there is one important point here:\nWhen you want to predict a dataset result you need to give your dataset columns respect to training model columns' order. \nIn your case you can rename your columns f1, f2, f3.. to abc1, abc2, def3..\nbut you cannot shuffle their order.","Q_Score":0,"Tags":"python,python-3.x,machine-learning,data-science,random-forest","A_Id":59209501,"CreationDate":"2019-12-06T08:07:00.000","Title":"The naming and the sorting of the trained RF model's features in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using opencv3.4 to stitch images with a lot of customization, but cv.detail_MultiBandBlender seems only avaiable in opencv 4.x but with 4.x surf is \"patented and is excluded\". Is there any hack so that I can use blender with opencv-python3.4?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":427,"Q_Id":59209586,"Users Score":0,"Answer":"Patented stuff is usually just moved to the contrib repository, so you have to clone the original OpenCV repo, then add the contrib over and maybe modify a few compile options to get all your required things back and running.","Q_Score":0,"Tags":"python,python-3.x,opencv","A_Id":59209729,"CreationDate":"2019-12-06T08:38:00.000","Title":"How to create blender with opencv-python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"One of the columns in my dataset has the same value for all observations\/rows. \nShould I remove that column while building a machine learning model?\nWill removing this column affect my model\/performance metric?\nIf I replace all the values with a different constant value, will it change the model\/performance metric?","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":1263,"Q_Id":59209830,"Users Score":1,"Answer":"A Machine Learning Model is nothing but a mathematical equation i.e.\ny = f(x)\nin which\ny = Target\/Dependent Variable\nf(x) = Independent Variables(In our case a DataFrame containing the Train\/Test Data)\nSo technically, ML models quantifies and estimates about for what value of X, what will the probable output y.\nAssuming a single whole column is constant. So, a relationship between y and f(x=constant) is meaningless because for whatever value of y, that x will remain same. No mathematical relationship is possible except for the only option that y is also an constant. Which we can safely assume isn't the case, else why else you will build a model to get a constant value.\nHence, we can safely drop any constant column, which doesn't add any variation in data to the DataFrame to save computational time, as that column won't affect y in any sense.","Q_Score":1,"Tags":"python,r,pandas,machine-learning,data-science","A_Id":59212992,"CreationDate":"2019-12-06T08:56:00.000","Title":"Will removing a column having same values for all observations affect my model?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"One of the columns in my dataset has the same value for all observations\/rows. \nShould I remove that column while building a machine learning model?\nWill removing this column affect my model\/performance metric?\nIf I replace all the values with a different constant value, will it change the model\/performance metric?","AnswerCount":2,"Available Count":2,"Score":0.1973753202,"is_accepted":false,"ViewCount":1263,"Q_Id":59209830,"Users Score":2,"Answer":"If one of your column in the dataset is having the same values, you can drop this column as it will not do any help to your model to differentiate between two different labels while on the other hand, it can even negatively affect your model by creating a bias in the data.\nFor Example: Consider you have two different fruits, like one is Green Apple and one is Guava. Then, both of these fruits will have the same color i.e. \"Green\", so that basically means that you just can not differentiate both these fruits on the basis of their color, but if they have been two different colored fruits, you could have used this feature to differentiate between them.\nHope it helps clarifying what you should do with such a column with same set of observations.\nThanks.","Q_Score":1,"Tags":"python,r,pandas,machine-learning,data-science","A_Id":59210087,"CreationDate":"2019-12-06T08:56:00.000","Title":"Will removing a column having same values for all observations affect my model?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to transform two datasets: x_train and x_test using tsne. I assume the way to do this is to fit tsne to x_train, and then transform x_test and x_train. But, I am not able to transform any of the datasets.\ntsne = TSNE(random_state = 420, n_components=2, verbose=1, perplexity=5, n_iter=350).fit(x_train)\nI assume that tsne has been fitted to x_train.\nBut, when I do this:\nx_train_tse = tsne.transform(x_subset)\nI get:\nAttributeError: 'TSNE' object has no attribute 'transform'\nAny help will be appreciated. (I know I could do fit_transform, but wouldn't I get the same error on x_test?)","AnswerCount":3,"Available Count":1,"Score":0.3215127375,"is_accepted":false,"ViewCount":6221,"Q_Id":59214232,"Users Score":5,"Answer":"As the accepted answer says, there is no separate transform method and it probably wouldn't work in a a train\/test setting.\nHowever, you can still use TSNE without information leakage.\nTraining Time\nCalculate the TSNE per record on the training set and use it as a feature in classification algorithm.\nTesting Time\nAppend your training and testing data and fit_transform the TSNE.  Now continue on processing your test set, using the TSNE as a feature on those records.\nDoes this cause information leakage? No.\nInference Time\nNew records arrive e.g. as images or table rows.\nAdd the new row(s) to the training table, calculate TSNE (i.e. where the new sample sits in the space relative to your trained samples). Perform any other processing and run your prediction against the row.\nIt works fine.  Sometimes, we worry too much about train\/test split because of Kaggle etc. But the main thing is can your method be replicated at inference time and with the same expected accuracy for live use.  In this case, yes it can!\nOnly drawback is you need your training database available at inference time and depending on size, the preprocessing might be costly.","Q_Score":12,"Tags":"python,machine-learning","A_Id":64245990,"CreationDate":"2019-12-06T13:36:00.000","Title":"python tsne.transform does not exist?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Hi I have a built up Python 2.7 environment with Ubuntu 19.10. \nI would like to build a whl package for pandas.\nI pip installed the pandas but do not know how to pack it into whl package.\nMay I ask what I should do to pack it.\nThanks","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":139,"Q_Id":59214739,"Users Score":0,"Answer":"You cannot pack back an installed wheel. Either you download a ready-made wheel with pip download or build from sources: python setup.py bdist_wheel (need to download the sources first).","Q_Score":0,"Tags":"python,pandas,python-wheel","A_Id":59246265,"CreationDate":"2019-12-06T14:08:00.000","Title":"How to build whl package for pandas?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Fairly new to ROS, but haven't been able to find this information searching around. \nWe're building an instrument where we need to transfer large data streams over the network on a 100Mbit limited cable. Preferably we need to transfer RAW images (~10MB a piece) or we can do some lossless compression resulting in about 5MB a piece. \nIs this perfectly fine for ROS with native image topics, or would a separate protocol such as ZeroMQ be better for this task? What are the limitations in ROS on large data streams?\nHope that someone with the knowledge could take a second to share some experience.\nThanks!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1161,"Q_Id":59224453,"Users Score":6,"Answer":"10E6 [B] over a private, 100% free 100E6 [b\/s] channel takes no less ~0.8 [s]\n_5E6 [B] over a private, 100% free 100E6 [b\/s] channel takes no less ~0.4 [s]\n\nQ : What are the limitations in <something> on large data streams?\n\nHere we always fight a three-fold Devil mix of:\nPower( for data processing, a 10[MB]->5[MB] compression + RAM-I\/O not being excluded )\n+\nTime( latency + jitter of the E2E data-transport across the series of transport-channels )\n+\nErrors( uncertainties of content delivery, completeness and authenticity over the E2E data-transport path )\nIn the ROS domain, being a system for coordinated control-loops' constrained sub-systems, there is one more problem - not meeting the \"in-time-ness\" causes the control to fail into principally unstable territory.\n\n Countless examples of what happens when this border has been crossed - from production line falling into panic, resulting in an immediate emergency total-stop state, to damaged tools, products, equipment and continued colliding, still crashing during still continued operations ( when collision-detection and emergency total-stops were not implemented safe ).\n\n\n\nQ : would a separate protocol such as ZeroMQ be better for this task?\n\nZeroMQ has excellent performance ( does not add much on the Time leg of the Devil-mix, yet it always depends on having (in-)sufficient resources ( the Power to smoothly process )\nZeroMQ has excellent performance scalability, sure, if the Power leg of the Devil-mix permits.\nZeroMQ has excellent properties for the Errors leg of the Devil-mix - we get warranty of Zero-Errors - it either delivers the message (the payload) as a bit-to-bit identical copy of the original content, or nothing at all. This warranty may look strange, a sure overkill for blurred or noisy images - yet, it is a fair strategy for not having additional Power and Time-uncertainty issues due to error-detection\/limited-recovery. Yet it leaves us free with choices, how to handle (if needed), within a given, constrained, Time- and Power-domains, the main duty - the ROS control-loops' stability - with missing or re-transmit requested payloads, given Errors were indirectly detected from time-stamping or monotonic-ordinal indexing et al.\nROS Topics, on the contrary, are limited to a single PUB\/SUB formal communication-pattern archetype only and fixed to use either a TCPROS transport-class ( ZeroMQ may use a faster, L3+protocol stack-less { inproc:\/\/ | ipc:\/\/ } or, if needed, even extended to a mil-std guaranteed-delivery or a distributed grid-computing tipc:\/\/ or hypervisor-orchestrated vmci:\/\/ transports ) or UDPROS, which is currently available in roscpp only and lossy, but having lower latency, compared to TCPROS.","Q_Score":3,"Tags":"python,zeromq,ros","A_Id":59226070,"CreationDate":"2019-12-07T08:56:00.000","Title":"ROS: Is ZeroMQ better for large data streams, e.g. raw images, than native image topic?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I wanna run my opencv3.1 programs, but when i try to read a file using cv2.VideoCapture shows me the error:\nerror: (-5:Bad argument) CAP_IMAGES: can't find starting number (in the name of file): .\/..\/images\/walking.avi in function 'icvExtractPattern'\nBut, when i using the camera with cv2.VideoCapture(0) it works perfectly. I verify the file path and using the relative and the absolute path, but still not working.\nI gonna wait for your answers.\nThanks a lot","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1168,"Q_Id":59230366,"Users Score":0,"Answer":"Originally I used cv2.VideoWriter_fourcc(*'XVID') getting the same error\nswitch the (*'XVID') to (*'MJPG')\nI am using a raspberry pi Gen. 4 (4GB) with image: Raspbian Buster Lite","Q_Score":0,"Tags":"python-3.x,opencv,artificial-intelligence,opencv3.1,opencv4","A_Id":60907594,"CreationDate":"2019-12-07T21:25:00.000","Title":"Can't read .avi files using Python OpenCV 4.1.2-dev","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a list of very large numbers I need to round. \nFor example:\n6.624147027484989e+25 I need to round to 6.62e25.\nHowever, np.around, math.ceiling, round(), etc... are not working. I'm thinking because instead of round 6.624147027484989e+25 to 6.62e25, it's just making it an integer while I actually need to make the entire number much smaller... if that makes sense.","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":179,"Q_Id":59231931,"Users Score":5,"Answer":"I think, if I understand your problem correctly, you could use float(\"%.2e\" % x)\nThis just converts the value to text, in exponential format, with two fractional places (so 'pi' would become \"3.14e+00\"), and then converts that back to float. It will work with your example, and with small numbers like 5.42242344e-30\nFor python 3.6+, it's better to use float(f\"{x:.2e}\") - thanks @gabriel-jablonski","Q_Score":1,"Tags":"python,python-3.x,rounding","A_Id":59231944,"CreationDate":"2019-12-08T02:25:00.000","Title":"Rounding large exponential numbers e.g. (6.624147...e+25 to 6.62e+25)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I trained a deep learning model to classify the given images into three classes. Now I want to add one more class to my model. I tried to check out \"Online learning\", but it seems to train on new data for existing classes. Do I need to train my whole model again on all four classes or is there any way I can just train my model on new class?","AnswerCount":3,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":5204,"Q_Id":59236502,"Users Score":3,"Answer":"You probably have used a softmax after 3 neuron dense layer at the end of the architecture to classify into 3 classes. Adding a class will lead to doing a softmax over 4 neuron dense layer so there will be no way to accommodate that extra neuron in your current graph with frozen weights, basically you're modifying the graph and hence you'll have to train the whole model from scratch \n-----or-----\none way would be loading the model and removing the last layer , changing it to 4 neurons and training the network again! This will basically train the weights of the last layer from scratch . I don't think there is anyway to keep these(weights of the last layer) weights intact while adding a new class .","Q_Score":6,"Tags":"python,keras,deep-learning,multiclass-classification,online-machine-learning","A_Id":59236950,"CreationDate":"2019-12-08T14:39:00.000","Title":"How to add a new class to an existing classifier in deep learning?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I trained a deep learning model to classify the given images into three classes. Now I want to add one more class to my model. I tried to check out \"Online learning\", but it seems to train on new data for existing classes. Do I need to train my whole model again on all four classes or is there any way I can just train my model on new class?","AnswerCount":3,"Available Count":2,"Score":0.1973753202,"is_accepted":false,"ViewCount":5204,"Q_Id":59236502,"Users Score":3,"Answer":"You have to remove the final fully-connected layer, freeze the weights in the feature extraction layers, add a new fully-connected layer with four outputs and retrain the model with images of the original three classes and the new fourth class.","Q_Score":6,"Tags":"python,keras,deep-learning,multiclass-classification,online-machine-learning","A_Id":60471776,"CreationDate":"2019-12-08T14:39:00.000","Title":"How to add a new class to an existing classifier in deep learning?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a big dataset containing almost 0.5 billions of tweets. I'm doing some research about how firms are engaged in activism and so far, I have labelled tweets which can be clustered in an activism category according to the presence of certain hashtags within the tweets.\nNow, let's suppose firms are tweeting about an activism topic without inserting any hashtag in the tweet. My code won't categorized it and my idea was to run a SVM classifier with only one class.\nThis lead to the following question:\n\nIs this solution data-scientifically feasible?\nDoes exists any other one-class classifier?\n(Most important of all) Are there any other ways to find if a tweet is similar to the ensable of tweets containing activism hashtags?\n\nThanks in advance for your help!","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":540,"Q_Id":59238140,"Users Score":1,"Answer":"Sam H has a great answer about using your dataset as-is, but I would strongly recommend annotating data so you have a few hundred negative examples, which should take less than an hour. Depending on how broad your definition of \"activism\" is that should be plenty to make a good classifier using standard methods.","Q_Score":1,"Tags":"python,twitter,nlp,classification,text-classification","A_Id":59395618,"CreationDate":"2019-12-08T17:44:00.000","Title":"Find how similar a text is - One Class Classifier (NLP)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I made a TensorFlow model without using CUDA, but it is very slow. Fortunately, I gained access to a Linux server (Ubuntu 18.04.3 LTS), which has a Geforce 1060, also the necessary components are installed - I could test it, the CUDA acceleration is working.\nThe tensorflow-gpu package is installed (only 1.14.0 is working due to my code) in my virtual environment.\nMy code does not contain any CUDA-related snippets. I was assuming that if I run it in a pc with CUDA-enabled environment, it will automatically use it.\nI tried the with tf.device('\/GPU:0'): then reorganizing my code below it, didn't work. I got a strange error, which said only XLA_CPU, CPU and XLA_GPU is there. I tried it with XLA_GPU but didn't work.\nIs there any guide about how to change existing code to take advantage of CUDA?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":38,"Q_Id":59246985,"Users Score":0,"Answer":"Not enough to give exact answer.\nHave you installed tensorflow-gpu separately? Check using pip list.\nCause, initially, you were using tensorflow (default for CPU).\nOnce you use want to use Nvidia, make sure to install tensorflow-gpu. \nSometimes, I had problem having both installed at the same time. It would always go for the CPU. But, once I deleted the tensorflow using \"pip uninstall tensorflow\" and I kept only the GPU version, it worked for me.","Q_Score":0,"Tags":"python,tensorflow","A_Id":59262244,"CreationDate":"2019-12-09T10:34:00.000","Title":"Existing Tensorflow model to use GPU","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When using tensorflow to train a neural network I can set the loss function arbitrarily. Is there a way to do the same in sklearn when training a SVM? Let's say I want my classifier to only optimize sensitivity (regardless of the sense of it), how would I do that?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":396,"Q_Id":59248882,"Users Score":1,"Answer":"This is not possible with Support Vector Machines, as far as I know. With other models you might either change the loss that is optimized, or change the classification threshold on the predicted probability. \nSVMs however minimize the hinge loss, and they do not model the probability of classes but rather their separating hyperplane, so there is not much room for manual adjustements.\nIf you need to focus on Sensitivity or Specificity, use a different model that allows maximizing that function directly, or that allows predicting the class probabilities (thinking Logistic Regressions, Tree based methods, for example)","Q_Score":0,"Tags":"python,machine-learning,optimization,scikit-learn","A_Id":59253034,"CreationDate":"2019-12-09T12:23:00.000","Title":"how to select the metric to optimize in sklearn's fit function?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have two images - the images are identical but of different sizes.\nCurrently I complete a Canny analysis of the smaller image using track bars in an interactive environment. \nI want to have this output created on the second (larger) image - when I apply the same parameters the output is different\nI've tried to use cv.resize however the output is blurred significantly\nAny help is appreciated\nThanks in advance","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":26,"Q_Id":59249664,"Users Score":0,"Answer":"To archieve comparable results you should resize the bigger image to the size of the smaller one. Image upscaling is \"creating\" information which isn't contained in your image, that's why you see the blur. Using interpolation=cv2.INTER_AREA should deliver good results, if you used a camera for images acquisition.","Q_Score":0,"Tags":"python,opencv,canny-operator","A_Id":59252280,"CreationDate":"2019-12-09T13:11:00.000","Title":"Applying identical Canny to two different images","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on the project with the group and we have decided to make the project on the ' Automatic Attendance System by AI '\nI have learned the CNNs to categorize the objects i.e dogs and cats.\nWith that knowledge, we have decided to make the attendance system based on CNN. ( Please tell me if we shouldn't take this path or the technology if you find something bad here... )\nBut continuing it with CNN, let's say we have trained the model with 2 students, and on the last layer we put the two neurons as they are just two, right...?\nNow the third comes, now to train his face to the NN, I have to change the model's structure and retrain every faces again...\nIf we apply the project to the big institute, where hundreds of students are there and if we want to train the model for each individual student, the nthis is not the feasible solution to recreate the model..\nSo we thought, we will fix the model's output layer size to let's say 50.\nSo only 50 faces can be trained per model.\nBut it is not always possible that there will always be 50.\nThey may 40 or if one gets in with ne admission, then 41.\nSo how to re-train the network with existing weights ?\n( The same question is asked somewhere I know, but please direct me with my situation )\n\nOr is there any other technology to use... ?\n\nPlease Direct me...","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":99,"Q_Id":59250070,"Users Score":2,"Answer":"You don't need classification. Classification is not the solution for everywhere problem.\nYou should look into these: \n\nCosine Similarity\nSiamese Network\n\nYou can use existing models from FaceNet or OpenCV. \nSince they are already trained on a huge dataset of faces, you can extract feature vector easily. \nStore the feature vector for every new student. \nThen compute similarities(existing image, current image) based on distance or similarity score mark attendance.\nThis is scable and much faster approach. No training or retraining.","Q_Score":1,"Tags":"python,keras,neural-network,artificial-intelligence,conv-neural-network","A_Id":59268457,"CreationDate":"2019-12-09T13:36:00.000","Title":"How to add new Category in the CNN for Attendance by AI","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I use ubuntu (through Windows Subsystem For Linux) and I created a new conda environment, I activated it and I installed a library in it (opencv). However, I couldn't import opencv in Jupyter lab till I created a new kernel that it uses the path of my new conda environment. So, my questions are:\n\nDo I need to create a new kernel every time I create a new conda environment in order for it to work? I read that in general we should use kernels for using different versions of python, but if this is the case, then how can I use a specific conda environment in jupyter lab? Note that browsing from Jupyter lab to my new env folder or using os.chdir to set up the directory didn't work.\nUsing the new kernel that it's connected to the path of my new environment, I couldn't import matplotlib and I had to activate the new env and install there again the matplotlib. However, matplotlib could be imported when I was using the default kernel Python3. \nIs it possible to have some standard libraries to use them with all my conda environments (i.e. install some libraries out of my conda environments, like matplotlib and use them in all my enviroments) and then have specific libraries in each of my environments? I have installed some libraries through the base environment in ubuntu but I can't import these in my new conda environment.\n\nThanks in advance!","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":1076,"Q_Id":59252973,"Users Score":1,"Answer":"To my best understanding:\nYou need ipykernel in each of the environments so that jupyter can import the other library.\nIn my case, I have a new environment called TensorFlow, then I activate it and install the ipykernel, and then add it to jupyter kernelspec. Finally I can access it in jupyter no matter the environment is activated or not.","Q_Score":3,"Tags":"python,anaconda,conda,windows-subsystem-for-linux,jupyter-lab","A_Id":68878419,"CreationDate":"2019-12-09T16:29:00.000","Title":"conda environment: does each new conda environment needs a new kernel to work? How can I have specific libraries for all my environments?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on school's project about Outlier detecttion. I think i will create my own small dataset and use DBSCAN to work with it. I think i will try to create a dataset that about a click on ads on a website is cheat or not. Below is detail information of the dataset that i am gona create.\nDataset Name: Cheat Ads Click detection.\n\nColumn:value\nsource: \u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0 (categorical) url: 0, redirect: 1, search: 2 \nvisited_before:\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0(categorical) no:1, few_time: 1, fan: 2 \ntime_on_site(seconds):\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 (numerical) time user working on the site before leaving by seconds.\nactive_type:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 (categorical) fake_active: 0 (like they just open website but don't do anythings but click ads), normal_active: 1, real_acive: 2 (Maybe i will let it become score of active: float value from 0 to 10.)\nCheat (label): \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0(categorical) no: 0, yes: 1\nMaybe i will have some more other columns like number of times user click on ads,...\nMy question is do you think that DBSCAN can work well on this dataset? If yes, can you please give me some tips to make a great dataset or to create dataset faster? And if no, please suggest me some other datasets that DBSCAN can work well with theme.\nThank you so much.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1315,"Q_Id":59257864,"Users Score":0,"Answer":"Are describing a classification problem, not a clustering problem.\nAlso that data does not have a bottom of density, does it?\nLast but not least, (A) click fraud is heavily clustered, not outliers, (B) noise (low density) is not the same as outlier (rare) and (C) first get the data, then speculate about possible algorithms, because what if you can't get the data?","Q_Score":0,"Tags":"python,machine-learning,dataset,outliers,dbscan","A_Id":59259166,"CreationDate":"2019-12-09T22:58:00.000","Title":"Outlier detection DBSCAN","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there a way to do a numpy array comprehension in Python? The only way I have seen it does is by using list comprehension and then casting the results as a numpy array, e.g. np.array(list comprehension). I would have expected there to be a way to do it directly using numpy arrays, without using lists as an intermediate step.\nAlso, is it possible to overload list operators, i.e. [ and ], so that the results is a numpy array, not a list.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":3420,"Q_Id":59265151,"Users Score":0,"Answer":"A fundamental problem here is that numpy arrays are of static size whereas python lists are dynamic. Since the list comprehension doesn't know a priori how long the returned list is going to be, one necessarily needs to maintain a dynamic list throughout the generation process.","Q_Score":2,"Tags":"python,numpy,list-comprehension","A_Id":59265331,"CreationDate":"2019-12-10T10:30:00.000","Title":"Numpy array comprehension","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm doing stock prediction using keras.While prediction i get only one possible result.I need to view all the probability value for example,\n    input 100 120 100 120, target while training 100\n    while prediction if i give the same input it returns 120 as a output\n    So that,is there any possibility of view prediction probability value?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":36,"Q_Id":59266375,"Users Score":0,"Answer":"What i've done is include a randomized feature. This way the network won't be purely deterministic","Q_Score":0,"Tags":"python,keras","A_Id":59268849,"CreationDate":"2019-12-10T11:39:00.000","Title":"How can i get all the prediction probability value?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Hello I have a dataframe I sorted so the index is not in order so I want to reorder the index so that sorted values have an index that is sequential I have not been able to figure this out should I remove the index or is there a way to set the index? When I reindex it should sorts by the index which unsorts by index.","AnswerCount":2,"Available Count":1,"Score":-0.0996679946,"is_accepted":false,"ViewCount":762,"Q_Id":59276899,"Users Score":-1,"Answer":"df=df.reset_index(drop=True)? \u2013 ansev 1 min ago","Q_Score":0,"Tags":"python,pandas","A_Id":59276925,"CreationDate":"2019-12-10T23:36:00.000","Title":"Pandas set index or reindex without changing the order of the data frame","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to build a code that checks whether a given object is an np.array() in python. \nif isinstance(obj,np.array()) doesn't seem to work.\nI would truly appreciate any help.","AnswerCount":4,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":3934,"Q_Id":59279803,"Users Score":8,"Answer":"isinstance(obj, numpy.ndarray) may work","Q_Score":2,"Tags":"python,arrays","A_Id":59279941,"CreationDate":"2019-12-11T06:13:00.000","Title":"How to check if an object is an np.array()?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to build a code that checks whether a given object is an np.array() in python. \nif isinstance(obj,np.array()) doesn't seem to work.\nI would truly appreciate any help.","AnswerCount":4,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":3934,"Q_Id":59279803,"Users Score":0,"Answer":"The type of what numpy.array returns is numpy.ndarray.  You can determine that in the repl by calling type(numpy.array([])).  Note that this trick works even for things where the raw class is not publicly accessible.  It's generally better to use the direct reference, but storing the return from type(someobj) for later comparison does have its place.","Q_Score":2,"Tags":"python,arrays","A_Id":59279975,"CreationDate":"2019-12-11T06:13:00.000","Title":"How to check if an object is an np.array()?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Today I was trying to implement an object detection API in Tensorflow. After carrying out the training process, I was trying to run the program to detect objects in webcam. As I was running it, the following message was printed in the terminal:\n\nAllocator (GPU_0_bfc) ran out of memory trying to allocate 2.05GiB\n  with freed_by_count=0. The caller indicates that this is not a\n  failure, but may mean that there could be performance gains if more\n  memory were available\n\nDue to the performace issue it seems I am getting a lot of false positives.\nHow can we calculate beforehand the memory required to run this program, or any program?\nI am not asking how much memory it is using, which we can find out. I am using Python.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":2441,"Q_Id":59282135,"Users Score":3,"Answer":"In Object Detection, most of the Layers used will be CNNs and the Calculation of Memory Consumption for CNN is explained below. You can follow the same approach for other layers of the Model.\n\nFor example,\n  consider a convolutional layer with 5 \u00d7 5 filters, outputting 200\n  feature maps of size 150 \u00d7 100, with stride 1 and SAME padding. If the\n  input is a 150 \u00d7 100 RGB image (three channels), then the number of\n  parameters is (5 \u00d7 5 \u00d7 3 + 1) \u00d7 200\n  = 15,200 (the +1 corresponds to the bias terms), which is fairly small compared to a fully connected layer.7 However, each of the 200 feature\n  maps contains 150 \u00d7 100 neurons, and each of these neurons needs to\n  compute a weighted sum of its 5 \u00d7 5 \u00d7 3 = 75 inputs: that\u2019s a total of\n  225 million float multiplications. Not as bad as a fully con\u2010nected\n  layer, but still quite computationally intensive. Moreover, if the\n  feature maps are represented using 32-bit floats, then the\n  convolutional layer\u2019s output will occupy 200 \u00d7 150 \u00d7 100 \u00d7 32 = 96\n  million bits (about 11.4 MB) of RAM.8  And that\u2019s just for one\n  instance! If a training batch contains 100 instances, then this layer\n  will use up over 1 GB of RAM!\n\nMore understanding about Memory Consumption can be found from the below Question and the respective Answer:\nQuestion: \n\nConsider a CNN composed of three convolutional layers, each with 3 \u00d7 3\n  kernels, a stride of 2, and SAME padding. The lowest layer outputs 100\n  feature maps, the middle one outputs 200, and the top one outputs 400.\n  The input images are RGB images of 200 \u00d7 300 pixels. What is the total\n  number of parameters in the CNN? If we are using 32-bit floats, at\n  least how much RAM will this network require when making a prediction\n  for a single instance? What about when training on a mini-batch of 50\n  images?\n\nAnswer is mentioned below:\n\nLet\u2019s compute how many parameters the CNN has. Since its first\n  convolutional layer has 3 \u00d7 3 kernels, and the input has three\n  channels (red, green, and blue), then each feature map has 3 \u00d7 3 \u00d7 3\n  weights, plus a bias term. That\u2019s 28 parame\u2010 ters per feature map.\n  Since this first convolutional layer has 100 feature maps, it has a\n  total of 2,800 parameters. The second convolutional layer has 3 \u00d7 3\n  kernels, and its input is the set of 100 feature maps of the previous\n  layer, so each feature map has 3 \u00d7 3 \u00d7 100 = 900 weights, plus a bias\n  term. Since it has 200 feature maps, this layer has 901 \u00d7 200 =\n  180,200 parameters. Finally, the third and last convolutional layer\n  also has 3 \u00d7 3 kernels, and its input is the set of 200 feature maps\n  of the previous layers, so each feature map has 3 \u00d7 3 \u00d7 200 = 1,800\n  weights, plus a bias term. Since it has 400 feature maps, this layer\n  has a total of 1,801 \u00d7 400\n  = 720,400 parameters. All in all, the CNN has 2,800 + 180,200 + 720,400 = 903,400 parameters. Now let\u2019s compute how much RAM this\n  neural network will require (at least) when making a prediction for a\n  single instance. First let\u2019s compute the feature map size for each\n  layer. Since we are using a stride of 2 and SAME padding, the\n  horizontal and vertical size of the feature maps are divided by 2 at\n  each layer (rounding up if necessary), so as the input channels are\n  200 \u00d7 300 pixels, the first layer\u2019s feature maps are 100 \u00d7 150, the\n  second layer\u2019s feature maps are 50 \u00d7 75, and the third layer\u2019s feature\n  maps are 25 \u00d7 38. Since 32 bits is 4 bytes and the first convolutional\n  layer has 100 feature maps, this first layer takes up 4 x 100 \u00d7 150 \u00d7\n  100 = 6 million bytes (about 5.7 MB, considering that 1 MB = 1,024 KB\n  and 1 KB\n  = 1,024 bytes). The second layer takes up 4 \u00d7 50 \u00d7 75 \u00d7 200 = 3 million bytes (about 2.9 MB). Finally, the third layer takes up 4 \u00d7 25\n  \u00d7 38 \u00d7 400 = 1,520,000 bytes (about 1.4 MB). However, once a layer has\n  been computed, the memory occupied by the previous layer can be\n  released, so if everything is well optimized, only 6 + 9 = 15 million\n  bytes (about 14.3 MB) of RAM will be required (when the second layer\n  has just been computed, but the memory occupied by the first layer is\n  not released yet). But wait, you also need to add the memory occupied\n  by the CNN\u2019s parameters. We computed earlier that it has 903,400\n  parameters, each using up 4 bytes, so this adds 3,613,600 bytes (about\n  3.4 MB). The total RAM required is (at least) 18,613,600 bytes (about 17.8 MB).\n\nFor more information, refer \"Memory Requirements\" Section of \"Chapter 13, Convolutional Neural Networks\" of the Book, \"Hands on Machine Learning with Scikit-Learn and Tensorflow\" (pdfs are availble online).","Q_Score":0,"Tags":"python,tensorflow,memory,memory-management","A_Id":59893011,"CreationDate":"2019-12-11T09:06:00.000","Title":"How do we approximately calculate how much memory is required to run a program?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have been trying to install Pandas on my Azure App Service (running Flask) for a long time now but nothing seems to work.\nI tried to use wheel, created a wheelhouse directory manually and tried to install the relevant Pandas .whl file (along with its dependent packages) but it still doesn't work. This approach gives me the following error - \n\"Could not find a version that satisfies the requirement\"\n\"No matching distribution found for ...\"\nA simple \"pip install pandas\" also doesn't work - when I do this, in my Kudu Bash client the command gets stuck on \"Cleaning up..\" and nothing gets installed.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":537,"Q_Id":59287420,"Users Score":0,"Answer":"I solved this by using the SSH terminal instead of the Kudu terminal. I find no reason why it was not working in the Kudu Remote Execution terminal, but using \"pip install pandas\" in Azure's SSH terminal solved it.","Q_Score":0,"Tags":"python,azure,azure-web-app-service","A_Id":59301763,"CreationDate":"2019-12-11T13:58:00.000","Title":"Azure Installing Pandas Module","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":1},{"Question":"I am not sure how can I describe all the steps that I am taking but basically my question is simple:\nI use same code, same data from text file, gather some statistics about that data and then use umap for 2D reduction.\nIs it normal to have different graphs when I plot the result? \nI use scikit-learn, umap-learn, ggplot2.\nThe continuation of the problem is when I use hdbscan. Because every time I run the code, the plot is different, then cluster size and clusters become different and so on. I am wondering if this is something expected or not, basically.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1264,"Q_Id":59290251,"Users Score":3,"Answer":"Yes it is. Dimensions reduction algorithms like tSNE and uMAP are stochastic, so every time you run the clustering and values will be different. If you want to keep the same graph you need to set a common seed. You can achieve that in R by setting the seed (e.g. set.seed(123)) before calling uMAP (or set flag if the function allows that). np.random.seed(123) should work in python scikit.","Q_Score":1,"Tags":"python,r,ggplot2,scikit-learn","A_Id":59291330,"CreationDate":"2019-12-11T16:34:00.000","Title":"Is it normal to get different graphs for same data after umap","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a tensorflow model with my truth data in the shape (N, 32, 32, 5) ie. 32x32 images with 5 channels.\nInside the loss function I would like to calculate, for each pixel, the sum of the values of the neighboring pixels for each channel, generating a new (N, 32, 32, 5) tensor.\nThe tf.nn.pool function does something similar but not exactly what I need. I was trying to see if tf.nn.conv2d could get me there but I'm not sure what I'd need to use as the filter parameter in this case.\nIs there a specific function for this? Or can I use conv2d somehow?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":188,"Q_Id":59290785,"Users Score":0,"Answer":"A new convolutional layer with the filter size of 3x3 and filters initialized to 1 will do the job. Just be careful to declare this special filter as an untrainable variable otherwise your optimizer would change its contents. Additionally, set padding to \"SAME\" to get the same size output from that convolutional layer. The pixels at the edges will have zero neigbors in that case.","Q_Score":1,"Tags":"python,tensorflow","A_Id":59290915,"CreationDate":"2019-12-11T17:07:00.000","Title":"Sum of neighbors in tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have N samples of 2D features with variable dimensions along one axis. For example:\n\nSample 1 : (100,20)\n\nSample 2 : (150,20)\n\nSample 3 : (90,20)\nIs there a way to combine all N samples into a 3D array so that the first dimension (N,?,?) denotes the sample number?\n\n\nPS: I wish to avoid padding and reshaping, and want to find a way to input the features with their dimensions intact into an LSTM network in Keras. Any other suggestions to achieve the same are welcome.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":124,"Q_Id":59314807,"Users Score":1,"Answer":"Keras does allow for variable length input to an LSTM but within a single batch all inputs must have the same length. A way to reduce the padding needed would be to batch your input sequences together based on their length and only pad up to the maximum length within each batch. For example you could have one batch with sequence length 100 and another with sequence length 150. But I'm afraid there is no way to completely avoid padding. During inference you can use any sequence length.","Q_Score":0,"Tags":"python,numpy,keras,numpy-ndarray","A_Id":59318510,"CreationDate":"2019-12-13T01:06:00.000","Title":"Concatenating 'N' 2D arrays in NumPy with varying dimensions into one 3D array","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I trained a XGBClassifier for my classification problem and did Hyper-parameter tuning over huge grid(probably tuned every possible parameter) using optuna. While testing, change of random_state changes model performance metrics (roc_auc\/recall\/precision), feature_importance and even model predictions (predict_prob).   \n\nWhat does this tell me about my data?\n\nSince I have to take this model in production, how should I tackle this for model to be more robust?\n\nStay with one random_state (say default 0) which we use during cross_validation and use it on out-of-sample as well.\nDuring cross_validation, on top of each param_combination, run few random_state(say 10) and take avg model performance.","AnswerCount":3,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":729,"Q_Id":59318853,"Users Score":0,"Answer":"random_state parameter just helps in replicating results every time you run your model.\nSince you are using cross_validation, assuming it is k-fold, then all your data will go into train and test and the CV score will be anyways average of the number of folds you decide. I believe you can set on any random_state and quote the results from CV.","Q_Score":1,"Tags":"python,xgboost,feature-selection,xgbclassifier","A_Id":59319017,"CreationDate":"2019-12-13T08:39:00.000","Title":"Changes to model performance by changing random_state of XGBClassifier","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I trained a XGBClassifier for my classification problem and did Hyper-parameter tuning over huge grid(probably tuned every possible parameter) using optuna. While testing, change of random_state changes model performance metrics (roc_auc\/recall\/precision), feature_importance and even model predictions (predict_prob).   \n\nWhat does this tell me about my data?\n\nSince I have to take this model in production, how should I tackle this for model to be more robust?\n\nStay with one random_state (say default 0) which we use during cross_validation and use it on out-of-sample as well.\nDuring cross_validation, on top of each param_combination, run few random_state(say 10) and take avg model performance.","AnswerCount":3,"Available Count":3,"Score":0.0665680765,"is_accepted":false,"ViewCount":729,"Q_Id":59318853,"Users Score":1,"Answer":"These are my two cents. Take the answer with a grain of salt.\nThe XGB classifier is a boosting algorithm, which naturally depends on randomness (so is a Random Forest for example).\nHence, changing seed will inherently change the training of the model and its output.\nDifferent seeds will also change the CV splits and alter further the results.\nFurther, boosting aims to reduce variance as it uses multiple models (bagging) and at the same time it reduces bias as it trains each subsequent model based on the previous models' errors (the boosting part).\nHowever, boosting models can, in principle, overfit.\nIn fact, if your base learner is not weak it will easily overfits the data and there won't be any residuals or errors for the subsequent models to build upon.\nNow, for your problem, you should first verify that you are not overfitting your model to the data.\nThen you might want to fix a certain number of seeds (you still want to be able to reproduce the results so it's important to fix them) and average the results obtained across the seeds.","Q_Score":1,"Tags":"python,xgboost,feature-selection,xgbclassifier","A_Id":59320839,"CreationDate":"2019-12-13T08:39:00.000","Title":"Changes to model performance by changing random_state of XGBClassifier","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I trained a XGBClassifier for my classification problem and did Hyper-parameter tuning over huge grid(probably tuned every possible parameter) using optuna. While testing, change of random_state changes model performance metrics (roc_auc\/recall\/precision), feature_importance and even model predictions (predict_prob).   \n\nWhat does this tell me about my data?\n\nSince I have to take this model in production, how should I tackle this for model to be more robust?\n\nStay with one random_state (say default 0) which we use during cross_validation and use it on out-of-sample as well.\nDuring cross_validation, on top of each param_combination, run few random_state(say 10) and take avg model performance.","AnswerCount":3,"Available Count":3,"Score":0.0665680765,"is_accepted":false,"ViewCount":729,"Q_Id":59318853,"Users Score":1,"Answer":"I tend to think if the model is sensitive to the random seed, it isn't a very good model. With XGB can try and add more estimators - that can help make it more stable.\nFor any model with a random seed, for each candidate set of parameter options (usually already filtered to a shortlist of candidate), I tend to run a bunch of repeats on the same data for different random seeds and measure the difference in the output. I expect the evaluation metric standard deviation to be small (relative to mean), and the overlap of the predictions in each class to be very high. If either of these is not the case I don't accept the model. If it is the case, I simply pick one of the candidate models at random - it should not matter what the random seed is! \nI still record the random seed used - this is still needed to recreate results!","Q_Score":1,"Tags":"python,xgboost,feature-selection,xgbclassifier","A_Id":59321064,"CreationDate":"2019-12-13T08:39:00.000","Title":"Changes to model performance by changing random_state of XGBClassifier","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I just trained my model successfully and I have some checkpoints from the training process. Can you explain to me how to use this data to recognize the objects live with the help of a webcam?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":37,"Q_Id":59324845,"Users Score":0,"Answer":"Congratulations :) \nFirst of all, you use the model to recognize the objects, the model learned from the data, minor detail.\nIt really depends on what you are aiming for, as the comment suggest, you should probably provide a bit more information. \nThe simplest setup would probably be to take an image with your webcam, read the file, pass it to the model and get the predictions. If you want to do it live, you are gonna have the stream from the webcam and then pass the images to the model.","Q_Score":0,"Tags":"python,tensorflow","A_Id":59324955,"CreationDate":"2019-12-13T14:48:00.000","Title":"Implementing trained-model on camera","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using lambda function and agg() in python to perform some function on each element of the dataframe.\nI have following cases\n\nlambda x: (x==0).sum() - Question: Does this logically compute (x==0) as 1, if true, and 0, if false and then adds all ones and zeros? or is it doing something else?\nlambda x: x.sum() - Question: This is apparent, but still I'll ask. This adds all the elements or x passed to it. Is this correct?","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":592,"Q_Id":59326882,"Users Score":2,"Answer":"(x == 0).sum() counts the number of rows where the condition x == 0 is true. x.sum() just computes the \"sum\" of x (the actual result depends on the type).","Q_Score":0,"Tags":"python,pandas","A_Id":59326907,"CreationDate":"2019-12-13T16:59:00.000","Title":"Pandas agg how to count rows where a condition is true","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm reading a file with numerical values. \ndata = pd.read_csv('data.dat', sep=' ', header=None)\nIn the text file, each row end with a space, So pandas wait for a value that is not there and add a \"nan\" at the end of each row.\nFor example:\n2.343 4.234 \nis read as: \n[2.343, 4.234, nan]\nI can avoid it using , usecols = [0 1] but I would prefer a more general solution","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":3252,"Q_Id":59327525,"Users Score":0,"Answer":"Specifying which columns to read using usecols will be a cleaner approach or you can drop the column once you have read the data but this comes with an overhead of reading data that you don't need. The generic approach will require you the create a regex parser which will be more time consuming and more messy.","Q_Score":3,"Tags":"python,pandas,file","A_Id":59327644,"CreationDate":"2019-12-13T17:48:00.000","Title":"pandas read_csv. How to ignore delimiter before line break","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have 3D medical images and wanted to know if I have a CNN that uses Conv2D can I just change the Conv2D to a Conv3D? If not what would I need to change?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":721,"Q_Id":59327819,"Users Score":1,"Answer":"Yes, you can, but there are a few things to change. \nYour kernel will now need to be in 3D, so the argument kernel_size must be a 3 integer tuple. Same thing for strides. Note that the CNN you will modify will probably be in 3D already (e.g., 60, 60, 3) if it's designed to train on colored images. The only difference is that you want the neural net to not only detect features in 3 separate 60x60 windows, but through the three windows. In other words, not 3 times 2D, but 3D. \ntl;dr yes you can, just change kernel_size and strides. The default values of the keras.layers.Conv3D are adjusted accordingly anyway.","Q_Score":0,"Tags":"python,conv-neural-network,dicom,medical","A_Id":59327905,"CreationDate":"2019-12-13T18:10:00.000","Title":"Conv2D to Conv3D","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"What is the best Python GloVe word embedding package that I can use? I want a package that can help modify the co-occurrence matrix weights. If someone can provide an example, I would really appreciate that.\nThanks,\nMohammed","AnswerCount":1,"Available Count":1,"Score":-0.1973753202,"is_accepted":false,"ViewCount":213,"Q_Id":59328248,"Users Score":-1,"Answer":"If you are using python3, gensim would be the best choice.\nfor example:\nfrom gensim.scripts.glove2word2vec import glove2word2vec\nwill fetch the gloVe module.\nSaul","Q_Score":0,"Tags":"python-3.x,word-embedding,glove","A_Id":60968553,"CreationDate":"2019-12-13T18:48:00.000","Title":"Best Python GloVe word embedding package","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Every tutorial I have found about machine learning includes testing an algorithm on a dataset that has target values and then it finds how accurate the algorithm is by testing its predictions on the test set.\nWhat if you then receive all of the data except for the target value and you want to make target value predictions to see if they come true in the future?Every tutorial I have seen has been with data that they already know the future target value predictions.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":27,"Q_Id":59332410,"Users Score":3,"Answer":"Decision tree is a supervised algorithm. That means you must use some target value(or lable) to build the tree(dividing node's value based on information gain rule).","Q_Score":0,"Tags":"python","A_Id":59332455,"CreationDate":"2019-12-14T04:54:00.000","Title":"How to make predictions with a decision tree on a dataset without a target value?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have been working to create an OMR bubble sheet scanner using OpenCV from Python.\nI have created the bubble-sheets (using OpenCV) in PNG format. \nThen I am using OpenCV to read those files. OpenCV does a perfect job on PNG images, it works perfectly.... but when I use this on JPG files, it simply doesn't! Lists running out of indexes because it cannot detect all the proper contours.\nI know this doesn't make sense, but I tried saving the same PNG image directly into JPG (using Paint), and the same results. It works on PNG version, but doesn't work on JPG version. It also shows a different number of contours in the images even the images are exact same.\nAny help regarding this would be greatly appreciated, thanks!","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1643,"Q_Id":59333332,"Users Score":1,"Answer":"The problem is inherent to the image format you are using. There are majorly two type of compression techniques(all image formats: jpeg, png, webp are compression techniques):\n\nLossless Compression\nLossy compression\n\nAs the name suggests, Lossless compression technique do not change the underlying matrix data while compression, for example: PNG. And the Lossy compression technique may substitute the underlying data with some nearest round off values to save some space, for example: JPEG.\nIn your case while you are using JPEG format to save the image, it may distort some RGB pixel information, so if your threshold range are really tight then you will have different results as compared to PNG.","Q_Score":0,"Tags":"python-3.x,image,opencv,image-processing,computer-vision","A_Id":59333699,"CreationDate":"2019-12-14T07:57:00.000","Title":"OpenCV from Python shows different results for JPG and PNG images?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am training a multi-label classification model for detecting attributes of clothes. I am using transfer learning in Keras, retraining the last few layers of the vgg-19 model.\nThe total number of attributes is 1000 and about 99% of them are 0s. Metrics like accuracy, precision, recall, etc., all fail, as the model can predict all zeroes and still achieve a very high score. Binary cross-entropy, hamming loss, etc., haven't worked in the case of loss functions.\nI am using the deep fashion dataset. \nSo, which metrics and loss functions can I use to measure my model correctly?","AnswerCount":5,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":23020,"Q_Id":59336899,"Users Score":24,"Answer":"What hassan has suggested is not correct - \nCategorical Cross-Entropy loss or Softmax Loss is a Softmax activation plus a Cross-Entropy loss. If we use this loss, we will train a CNN to output a probability over the C classes for each image. It is used for multi-class classification.\nWhat you want is multi-label classification, so you will use Binary Cross-Entropy Loss or Sigmoid Cross-Entropy loss. It is a Sigmoid activation plus a Cross-Entropy loss. Unlike Softmax loss it is independent for each vector component (class), meaning that the loss computed for every CNN output vector component is not affected by other component values. That\u2019s why it is used for multi-label classification, where the insight of an element belonging to a certain class should not influence the decision for another class.\nNow for handling class imbalance, you can use weighted Sigmoid Cross-Entropy loss. So you will penalize for wrong prediction based on the number\/ratio of positive examples.","Q_Score":17,"Tags":"python,machine-learning,keras,multilabel-classification,vgg-net","A_Id":59433454,"CreationDate":"2019-12-14T16:15:00.000","Title":"Which loss function and metrics to use for multi-label classification with very high ratio of negatives to positives?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am training a multi-label classification model for detecting attributes of clothes. I am using transfer learning in Keras, retraining the last few layers of the vgg-19 model.\nThe total number of attributes is 1000 and about 99% of them are 0s. Metrics like accuracy, precision, recall, etc., all fail, as the model can predict all zeroes and still achieve a very high score. Binary cross-entropy, hamming loss, etc., haven't worked in the case of loss functions.\nI am using the deep fashion dataset. \nSo, which metrics and loss functions can I use to measure my model correctly?","AnswerCount":5,"Available Count":2,"Score":1.0,"is_accepted":false,"ViewCount":23020,"Q_Id":59336899,"Users Score":7,"Answer":"Actually you should use tf.nn.weighted_cross_entropy_with_logits.\nIt not only for multi label classification and also has a pos_weight can pay much attention at the positive classes as you would expected.","Q_Score":17,"Tags":"python,machine-learning,keras,multilabel-classification,vgg-net","A_Id":63974451,"CreationDate":"2019-12-14T16:15:00.000","Title":"Which loss function and metrics to use for multi-label classification with very high ratio of negatives to positives?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm running an heavy pytorch task on this VM(n1-standard, 2vCpu, 7.5 GB) and the statistics show that the cpu % is at 50%. On my PC(i7-8700) the cpu utilization is about 90\/100% when I run this script (deep learning model).\nI don't understand if there is some limit for the n1-standard machine(I have read in the documentation that only the f1 obtain 20% of cpu usage and the g1 the 50%).\nMaybe if I increase the max cpu usage, my script runs faster.\nIs there any setting I should change?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":75,"Q_Id":59339838,"Users Score":2,"Answer":"In this case, the task utilizes only one of the two processors that you have available so that's why you see only 50% of the CPU getting used. \nIf you allow pytorch to use all the CPUs of your VM by setting the number of threads, then it will see that the usage goes to 100%","Q_Score":1,"Tags":"python,multithreading","A_Id":59349161,"CreationDate":"2019-12-14T22:53:00.000","Title":"Compute Engine n1-standard only use 50% of CPU","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm developing a 8 Puzzle game solver in python lately and I need a bit of help\nSo far I finished coding the A* algorithm using Manhattan distance as a heuristic function.\nThe solver runs and find ~60% of the solutions in less than 2 seconds\nHowever, for the other ~40%, my solver can take up to 20-30 minutes, like it was running without heuristic.\nI started troubleshooting, and it seems that the openset I use is causing some problems :\n\nMy open set is an array\nEach iteration, I loop through the openset to find the lowest f(n) (complexity : O(n) )\n\nI have the feeling that O(n) is way too much to run a decent A* algorithm with such memory used so I wanted to know how should I manage to make the openset less \"time eater\"\nThank you for your help ! Have a good day\nEDIT: FIXED\nI solved my problem which was in fact a double problem.\nI tried to use a dictionary instead of an array, in which I stored the nodes by their f(n) value and that allowed me to run the solver and the ~181000 possibilities of the game in a few seconds\nThe second problem (I didn't know about it because of the first), is that I didn't know about the solvability of a puzzle game and as I randomised the initial node, 50% of the puzzles couldn't be solved. That's why it took so long with the openset as the array.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":164,"Q_Id":59340795,"Users Score":0,"Answer":"The open set should be a priority queue. Typically these are implemented using a binary heap, though other implementations exist.\nNeither an array-list nor a dictionary would be efficient.\n\nThe closed set should be an efficient set, so usually a hash table or binary search tree, depending on what your language's standard library defaults to.\nA dictionary (aka \"map\") would technically work, but it's conceptually the wrong data-structure because you're not mapping to anything. An array-list would not be efficient.","Q_Score":0,"Tags":"python,algorithm,complexity-theory,a-star,sliding-tile-puzzle","A_Id":59342082,"CreationDate":"2019-12-15T02:32:00.000","Title":"8Puzzle game with A* : What structure for the open set?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm a newbie in programming and python (only 30 days). I've installed Anaconda and working in the Spyder IDE. Everything was going fine and have been adding packages as necessary while I was learning different things until now. Now, I'm getting an error when I'm trying to import Matplotlib. Can anyone advise me what to do in simple terms, please?\n\nError processing line 1 of\n  C:\\Anaconda\\lib\\site-packages\\matplotlib-3.1.2-py3.7-nspkg.pth:\nTraceback (most recent call last):\n      File \"C:\\Anaconda\\lib\\site.py\", line 168, in addpackage\n        exec(line)\n      File \"\", line 1, in \n      File \"\", line 580, in module_from_spec   AttributeError: 'NoneType' object has no attribute\n  'loader'\nRemainder of file ignored","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":270,"Q_Id":59345149,"Users Score":0,"Answer":"It seems like your package uninstall did not finish properly and something of that google package has been left behind.\nYou need to either move some of your source files at correct destination or uninstall anaconda and reinstall again.","Q_Score":0,"Tags":"python,matplotlib","A_Id":59346258,"CreationDate":"2019-12-15T15:05:00.000","Title":"Matplotlib throw errors and doesn't work when I try to import it","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I made models with sklearn, something like this:\nclf = SGDClassifier(loss=\"log\")\nclf.fit(X, Y)\nAnd then now I would like to add data to learn for this model, but with more important weight. I tried to use partial_fit with sample_weight bigger but not working. Maybe I don't use fit and partial_fit as good, sorry I'm beginner...\nIf someone know how to add new data I could be happy to know it :)\nThanks for help.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":141,"Q_Id":59347375,"Users Score":0,"Answer":"Do you think it has other way to do a first learning and then add new data more important for the model? Keras?\nThanks guys","Q_Score":0,"Tags":"python,scikit-learn","A_Id":59348501,"CreationDate":"2019-12-15T19:29:00.000","Title":"Add new data to model sklearn: SGD","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I built a neural network of the dimensions Layers = [203,100,100,100,2]. So I have 203 features and get two classes as a Result. I think, in my case, it would not be necessary to have two classes. My result is the prediction of a customer quitting his contract. So I guess one class would be sufficient (And 1 being quit, 0 being stay). I built the network with two classes to keep it flexible if I want to add more output-classes in the future.\nI put dropout,batch_normalization, and weight-decay. I am training with an Adam-optimizer. At the end of the day, I come up with\n\nprecision: 0.7826087, recall: 0.6624 on test-data.\nprecision: 0.8418698, recall: 0.72445 on training-data\n\nThis means if I predict a customer to quit, I can be 78% confident that he really quits.\nOn the opposite, if he quits his contract, I predicted with 66% that he will do so.\nSo my classifier doesn\u00b4t work too bad. One thing keeps nagging at me: How do I know if there is any chance to do better still? In other words: Is there a possibility to calculate the Bayes-error my setup determines? Or to say it clearer: If the difference of my training-error and test-error is high like this, can I conclude for sure, that I am having a variance problem? Or is it possible that I must cope with the fact the test-accuracy cannot be improved?\nWhat else can I try to train better?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":45,"Q_Id":59353398,"Users Score":0,"Answer":"I put more training data. Now I use 70000 records instead of 45000.\nMy results:\n\nprecision: 0.81765974, recall: 0.65085715 on test-data\nprecision: 0.83833283, recall: 0.708 on training-data\n\nI am pretty confident that this result is as good as possible. Thanks for reading","Q_Score":0,"Tags":"python,tensorflow,neural-network,statistics,precision-recall","A_Id":59377401,"CreationDate":"2019-12-16T09:06:00.000","Title":"cope with high variance or keep training","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to know how back-end automation is possible through RPA.\nI'd be interested in solving this scenario relative to an Incident Management Application, in which authentication is required. The app provide:\n\nAn option useful to download\/export the report to a csv file\nSort the csv as per the requirement\nSend an email with the updated csv to the team\n\nPlease let me know how this possible through RPA and what are those tools \navailable in RPA to automate this kind of scenario?","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":442,"Q_Id":59360594,"Users Score":-1,"Answer":"There are several ways to do it. It is especially useful when your backed are 3rd party applications where you do not have lot of control. Many RPA products like Softomotive WinAutomation, Automation Anywhere, UiPath etc. provide file utilities, excel utilities, db utilities, ability to call apis, OCR capabilities etc., which you can use for backed automation.","Q_Score":1,"Tags":"python-3.x,rpa,automationanywhere","A_Id":60356275,"CreationDate":"2019-12-16T16:28:00.000","Title":"RPA : How to do back-end automation using RPA tools?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I would like to know how back-end automation is possible through RPA.\nI'd be interested in solving this scenario relative to an Incident Management Application, in which authentication is required. The app provide:\n\nAn option useful to download\/export the report to a csv file\nSort the csv as per the requirement\nSend an email with the updated csv to the team\n\nPlease let me know how this possible through RPA and what are those tools \navailable in RPA to automate this kind of scenario?","AnswerCount":2,"Available Count":2,"Score":0.1973753202,"is_accepted":false,"ViewCount":442,"Q_Id":59360594,"Users Score":2,"Answer":"RPA tools are designed to automate mainly front-end activities by mimicing human actions. It can be done easily using any RPA tool.\nHowever, if you are interested in back-end automation the first question would be, if specific application has an option to interact in the way you want through the back-end\/API?\nIf yes, in theory you could develop RPA robot to run pre-developed back-end script. However, if all you need would be to run this script, creating robot for this case may be redundant.","Q_Score":1,"Tags":"python-3.x,rpa,automationanywhere","A_Id":59442056,"CreationDate":"2019-12-16T16:28:00.000","Title":"RPA : How to do back-end automation using RPA tools?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm building a model and I have two dataframes in Pandas. One is the training data and the other the testing data. One of the variables is the country. I was thinking about using OrdinalEncoder() to convert the country column to a categorical column. E.g.: \"USA\" will be 1 in the new column, \"Brazil\" will be 2 and so on. However, I want the same conversion for the two dataframes. If \"USA\" in the training data becomes 1 as a categorical column, I want that \"USA\" in the testing data also becomes 1.\nIs that possible? How so?\nThanks in advance","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":38,"Q_Id":59362334,"Users Score":0,"Answer":"In general, it is recommended to use the OrdinalEncoder when you are sure or know that there exists an 'ordered' relationship between the categories. For example, the grades F, B-, B, A- and A : for each of these it makes sense to have the encoding as 1,2,3,4,5 where higher the grade, higher is the weight ( in the form of the encoded category). \nIn your current case, it would be better to use a OneHot encoder for the Country column before splitting into train\/test datasets.","Q_Score":0,"Tags":"python,pandas,scikit-learn","A_Id":59362487,"CreationDate":"2019-12-16T18:26:00.000","Title":"How can I transform a string variable to a categorical variable in two different datasets, keeping the same conversion?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"The matplotlib.pyplot.hist() documentation describes the parameter \"density\" (its deprecated name was \"normed\") as:\n\n\ndensity : bool, optional\n\nIf True, the first element of the return tuple will be the counts normalized to form a probability density, i.e., the area (or integral) under the histogram will sum to 1. This is achieved by dividing the count by the number of observations times the bin width and not dividing by the total number of observations.\n\n\n\nWith the first element of the tuple it refers to the y-axis values. It says that it manages to get the area under the histogram to be 1 by: dividing the count by the number of observations times the bin width.\nWhat is the difference between count and number of observations? In my head they are the same thing: the number of instances (or number of counts or number of observations) the variable value falls into a certain bin. However, this would mean that the transformed number of counts for each bin is just one over the bin width (since # \/ #*bin_width = 1\/bin_width) which does not make any sense.\nCould someone clarify this for me? Thank you for your help and sorry for the probably stupid question.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1174,"Q_Id":59362968,"Users Score":2,"Answer":"I think the wording in the documentation is a bit confusing. The count is the number of entries in a given bin (height of the bin) and the number of observation is the total number of events that go into the histogram.\nThe documentation makes the distinction about how they normalized because there are generally two ways to do the normalization:\n\ncount \/ number of observations - in this case if you add up all the entries of the output array you would get 1.\ncount \/ (number of observations * bin width) - in this case the integral of the output array is 1 so it is a true probability density. This is what matplotlib does, and they just want to be clear in this distinction.","Q_Score":1,"Tags":"python,matplotlib,histogram","A_Id":59363215,"CreationDate":"2019-12-16T19:16:00.000","Title":"Difference between \"counts\" and \"number of observations\" in matplotlib histogram","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"We are doing a POC to see how to access SAS data sets from Anaconda \nAll documentation i find says only SASpy works with SAS 9.4 or higher\nOur SAS version is 9.04.01M3P062415\nCan this be done? If yes any documentation in this regard will be highly appreciated\nMany thanks in Advance!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":181,"Q_Id":59365941,"Users Score":0,"Answer":"SAS datasets are ODBC compliant. SasPy is for running SAS code. If the goal is to read SAS datasets, only, use ODBC or OleDb. I do not have Python code but SAS has a lot of documentation on doing this using C#. Install the free SAS ODBC drivers and read the sas7bdat. The drivers are on the SAS website.\nWriting it is different but reading should be fine. You will lose some aspects of the dataset but data will come through.","Q_Score":0,"Tags":"python,sas,anaconda,saspy","A_Id":59369038,"CreationDate":"2019-12-17T00:12:00.000","Title":"Accessing SAS(9.04) from Anaconda","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I wonder what does .build_vocab_from_freq() function from gensim actually do? What is the difference when I'm not using it? Thank you!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":397,"Q_Id":59368232,"Users Score":0,"Answer":"It \"builds a vocabulary from a dictionary of word frequencies\". You need a vocabulary for your gensim models. Usually you build it from your corpus. This is basically an alternative option to build your vocabulary from a word frequencies dictionary. Word frequencies for example are usually used to filter low or high frequent words which are meaningless for your model.","Q_Score":0,"Tags":"python,gensim,word2vec,fasttext","A_Id":59368454,"CreationDate":"2019-12-17T05:48:00.000","Title":"Gensim Word2Vec or FastText build vocab from frequency","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am solving a system of non-linear equations using the Newton Raphson Method in Python. This involves using the solve(Ax,b) function (spsolve in my case, which is for sparse matrices) iteratively until the error or update reduces below a certain threshold. My specific problem involves calculating functions such as x\/(e^x - 1) , which are badly calculated for small x by Python, even using np.expm1().\nDespite these difficulties, it seems like my solution converges, because the error becomes of the order of 10^-16. However, the dependent quantities, do not behave physically, and I suspect this is due to the precision of these calculations. For example, I am trying to calculate the current due to a small potential difference. When this potential difference becomes really small, this current begins to oscillate, which is wrong, because currents must be conserved.\nI would like to globally increase the precision of my code, but I'm not sure if that's a useful thing to do since I am not sure whether this increased precision would be reflected in functions such as spsolve. I feel the same about using the Decimal library, which would also be quite cumbersome. Can someone give me some general advice on how to go about this or point me towards a relevant post?\nThank you!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":43,"Q_Id":59372579,"Users Score":0,"Answer":"You can try using mpmath, but YMMV. generally scipy uses double precision. For a vast majority of cases, analyzing the sources of numerical errors is more productive than just trying to reimplement everything with higher widths floats.","Q_Score":1,"Tags":"python,numpy,scipy","A_Id":59381955,"CreationDate":"2019-12-17T10:48:00.000","Title":"Can you change the precision globally of a piece of code in Python, as a way of debugging it?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"import sklearn\nversion--3.8.0 64-bit\n\n\n\nTraceback (most recent call last):\n  File \"\", line 1, in \n    import sklearn\n  File \"C:\\Users\\SAI-PC\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn__init__.py\", line 75, in \n    from .utils._show_versions import show_versions\n  File \"C:\\Users\\SAI-PC\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\utils_show_versions.py\", line 12, in \n    from ._openmp_helpers import _openmp_parallelism_enabled\nImportError: DLL load failed while importing _openmp_helpers: The specified module could not be found.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1319,"Q_Id":59377573,"Users Score":0,"Answer":"Tried hard to solve it in IDLE but it didn't got rectified. Finally overcame it by installing anaconda IDE and using jupyter notebook.","Q_Score":1,"Tags":"python-3.x,scikit-learn,openmp,dllimport,sklearn-pandas","A_Id":59410610,"CreationDate":"2019-12-17T15:38:00.000","Title":"ImportError: dll load failed while importing _openmp_helpers: The specified module could not be found while importing sklearn package","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a multiclass, single label classifier which predicts some samples as \"-1\", which means that it is not confident enough to assign the sample a label. I would like to use sklearn.metrics.precision_recall_fscore_support to calculate the metrics for the model, however I am unable to prevent the \"-1\" classifications from being considered as false positives. \nThe only thing I can think of is to do this on a \"per class\" basis for the metrics then do a weighted-average over the metrics excluding the \"-1\" class (i.e. the micro option in precision_recall_fscore_support while excluding \"-1\" false positives).\nIs there any standardized way to do this in sklearn without having to compute the averages myself?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":20,"Q_Id":59381435,"Users Score":0,"Answer":"I believe I figured it out. The labels parameter of precision_recall_fscore_support allows you to specify which labels you desire to use. Therefore, by using labels=list(set(y_true).union(set(y_pred)).difference(set([\"-1\"])))\nI am able to obtain the desired behavior.","Q_Score":0,"Tags":"python,machine-learning,scikit-learn","A_Id":59381734,"CreationDate":"2019-12-17T20:11:00.000","Title":"sklearn.metrics Prevent unlabeled predictions from being classified as false positives","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have been working on image processing problem and I have preprocessed a bunch of images to find the most prominent horizontal lines in those images. Based on this data, I want to classify if the image has a good perspective angle or a bad angle. \nThe data points are angles of lines I was able to detect in a sequence of images. Based on the perspective of the image, I know this data sometimes represents a \"good-angle\" image, and in some other cases, it represents a \"bad-angle\" image.\nI tried np.polyfit, finding slopes of lines, finding derivatives of slopes, and several other methods but unable to find a simple metric that is so obvious by just looking at this data.\nThese are examples of \"Good angles\". You can notice they start from positive ones, and later ones are are negative. \nGood angle data\n[7.97, 7.99, 9.01, 5.07, 5.01, 14.81, 8.86, -2.11, -0.86, 1.06, 0.86, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.97, 0.92, -0.95, -2.05, -2.2, -2.78, -2.93, -2.8, -2.99, -2.88, -2.94, -2.81, -3.04, -3.07, -3.0]\n[3.96, 4.12, 6.04, 6.03, 6.08, 5.99, 6.99, 6.81, 6.81, 6.1, 6.1, 4.06, 3.98, 4.03, 3.92, 3.95, 3.84, 3.94, 4.07, 3.95, 3.87, 2.65, 1.88, 0.0, 0.0, -0.94, -1.06, -1.81, -1.81, -3.95, -4.09, -4.0, -3.93]\n[8.75, 10.06, 9.02, 9.96, 9.89, 10.08, 9.99, 10.0, 10.02, 9.95, 4.04, 4.03, 3.93, -1.18, -0.95, -1.12, -1.02, -1.76, -1.92, -2.06, -5.99, -5.83, -6.01, -4.96, -7.84, -7.67]\nThese are examples of \"Bad Angle\" images. You can notice they start from negative numbers, and later ones are positive. You can also notice that these are significantly larger numbers than 0.\nBad Angle Data\n[-13.92, -14.93, -4.11, -4.04, -2.18, 17.12, 18.01, 16.91, 15.95, 16.75, 14.16, 14.04]\n[-14.93, -14.93, -7.92, -4.04, -5.91, -4.98, 16.08, 16.26, 16.24]\n[11.81, -9.77, -10.2, -9.96, -10.09, -6.81, 2.13, 3.02, 2.77, 3.01, 2.78, 5.92, 5.96, 5.93, 2.96, 3.06, 1.03, 2.94, 6.2, 5.81, 5.04, 7.13, 5.89, 5.09, 4.89, 3.91, 4.15, 17.99, 6.04, 5.67, 7.24, 16.34, 17.02, 16.92, 15.99, 16.93, 15.76]\nAs this is based off of data captured from real images, we do have some irregularities in the dataset. I would like to avoid any glitches and use a metric that can classify my arrays as Good angle or bad angles.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":64,"Q_Id":59385064,"Users Score":0,"Answer":"If my assumptions are true I don't see a reason for any complex classifier. I'd simply check if the angle always gets larger or always gets smaller. Everytime this rule is followed you add 1 to a quality counter. If the rule is broken you reduce the quality counter by 1. In the end you divide the quality counter by the total amount of measured angles -> and then you decide a threshold for a good quality ratio.\nSorry if I don't understand the issue any better - an actual image could help a lot.","Q_Score":1,"Tags":"python,numpy,opencv,statistics,regression","A_Id":59387758,"CreationDate":"2019-12-18T03:24:00.000","Title":"Best way to classify a series of data in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I made a c# application that draw random point on the panel. I need to cluster this points according to euclidian distance. I already implement kruskal algorithm. Normally, there must be number of minimum spanning tree up to written number. For instance, when the user want to clusters drawn point for 3 clusters , end of the kruskal algorithm there must be 3 huge MST. \nBut I did it in a different way. I made a one huge  MST, now I have to divide this MST into written number of clusters.\nFor example, point number = 5 , cluster number 2  my kruskal output is = 0-3:57  1-2:99  1-4:102 \nfrom-to:euclidian distance \nProblem is I don't know in where I should cut these MST for create clusters","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":233,"Q_Id":59389090,"Users Score":2,"Answer":"In Kruskal's algorithm, MST edges are added in order of increasing weight.\nIf you're starting with an MST and you want to get the same effect as stopping Kruskal's algorithm when there are N connected components, then just delete the N-1 highest-weight edges in the MST.","Q_Score":0,"Tags":"python,algorithm,cluster-analysis","A_Id":59394538,"CreationDate":"2019-12-18T09:34:00.000","Title":"Dividing MST for kruskal clustering","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on jupyter notebook on a python assignment and \nI am trying to import  KNearestNeighbor from sklearn but I am getting the error:\n\nImportError: cannot import name 'KNearestNeighbor' from 'sklearn'\n  (C:\\Users\\michaelconway\\Anaconda3\\lib\\site-packages\\sklearn__init__.py)\n\nI have checked and I do have sklearn installed: version 0.22\nAny ideas please?","AnswerCount":2,"Available Count":1,"Score":0.2913126125,"is_accepted":false,"ViewCount":1512,"Q_Id":59393468,"Users Score":3,"Answer":"I have never seen KNearestNeighbor in sklearn. There is two thing you can do instead of KNearestNeighbor\n\nfrom sklearn.neighbors import KNeighborsClassifier\n\nor     \n\nfrom sklearn.neighbors import NearestNeighbors\n\nI think 1st option is the option which you want now","Q_Score":1,"Tags":"python,scikit-learn","A_Id":59394913,"CreationDate":"2019-12-18T13:50:00.000","Title":"Cannot import from sklearn import c","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataset with many different samples (numpy arrays). It is rather impractical to store everything in only one file, so I store many different 'npz' files (numpy arrays compressed in zip).\nNow I feel that if I could somehow exploit the fact that all the files are similar to one another I could achieve a much higher compression factor, meaning a much smaller footprint on my disk.\nIs it possible to store separately a 'zip basis'? I mean something which is calculated for all the files together and embodies their statistical features and is needed for decompression, but is shared between all the files.\nI would have said 'zip basis' file and a separate list of compressed files, which would be much smaller in size than each file zipped alone, and to decompress I would use the share 'zip basis' every time for each file.\nIs it technically possible? Is there something that works like this?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":71,"Q_Id":59397512,"Users Score":0,"Answer":"A 'zip-basis' is interesting but problematic. \nYou could preprocess the files instead. Take one file as a template and calculate the diff of each file compared to the template. Then compress the diffs.","Q_Score":1,"Tags":"python,zip,compression","A_Id":59445732,"CreationDate":"2019-12-18T17:43:00.000","Title":"I must compress many similar files, can I exploit the fact they are similar?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"To be very honest, I've played with matplotlib a little but iam new to django.\nI have wandered all over google to search to plot a line graph in django using csv.\nUnfortunately i couldnt find anything apart from 'bokeh' and 'chartit'. Although they arent very useful to help me make a start.\nMy Goal: I need to plot a line graph, where x-axis have dates, y-axis have some numbers.\nNow, what should my views.py look like? What thing should i include in template?\nAnyone please help me out, or send me some provide some video tutorial to start with","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":139,"Q_Id":59398219,"Users Score":0,"Answer":"as pointed out by @roganjosh, you'll render the graph using a js library.\nSo in the views.py you'll have to add your data to the context, and then render it in the template using a js library. I personally like plotly.js, they have a neat and easy to use interface. D3.js is also a very popular data visualisation library.","Q_Score":0,"Tags":"python,django","A_Id":59398539,"CreationDate":"2019-12-18T18:35:00.000","Title":"Line Graph in django template","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I have just upgraded python and I cannot get pandas to run properly, please see below. Nothing appears to work.\n\nTraceback (most recent call last):   File\n  \"\/Library\/Frameworks\/Python.framework\/Versions\/3.8\/lib\/python3.8\/site-packages\/tqdm\/_tqdm.py\",\n  line 613, in pandas\n      from pandas.core.groupby.groupby import DataFrameGroupBy, \\ ImportError: cannot import name 'DataFrameGroupBy' from\n  'pandas.core.groupby.groupby'\n  (\/Library\/Frameworks\/Python.framework\/Versions\/3.8\/lib\/python3.8\/site-packages\/pandas\/core\/groupby\/groupby.py)\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):   File\n  \"code\/analysis\/get_cost_matrix.py\", line 23, in \n      tqdm.pandas() # Gives us nice progress bars   File \"\/Library\/Frameworks\/Python.framework\/Versions\/3.8\/lib\/python3.8\/site-packages\/tqdm\/_tqdm.py\",\n  line 616, in pandas\n      from pandas.core.groupby import DataFrameGroupBy, \\ ImportError: cannot import name 'PanelGroupBy' from 'pandas.core.groupby'\n  (\/Library\/Frameworks\/Python.framework\/Versions\/3.8\/lib\/python3.8\/site-packages\/pandas\/core\/groupby\/init.py)","AnswerCount":2,"Available Count":1,"Score":0.2913126125,"is_accepted":false,"ViewCount":2138,"Q_Id":59403256,"Users Score":3,"Answer":"I guess you are using an older version of tqdm. Try using a version above tqdm>=4.23.4. \nThe command using pip would be,\npip install tqdm --upgrade","Q_Score":3,"Tags":"python-3.x,pandas","A_Id":59403299,"CreationDate":"2019-12-19T04:20:00.000","Title":"Pandas not working: DataFrameGroupBy ; PanelGroupBy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have successfully trained a predictor model - so with no labels using ModelTraining class.\nCurrently, I can use CustomImagePrediction.predictImage() to return a value of what it thinks is in the picture.\nI want to be able to detect the location of the object in the image, not just what it thinks it is. This functionality is in CustomObjectDetection but this is obviously a different class (gives a no label error as it requires the other training method, with the labels).\nIs it possible to achieve this with a predictor model?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":135,"Q_Id":59409085,"Users Score":1,"Answer":"No, location of objects is only possible with detection because it works on coordinates (bounding box), label error means you have to annotate your dataset.","Q_Score":0,"Tags":"python,tensorflow,keras,imageai","A_Id":63578193,"CreationDate":"2019-12-19T11:42:00.000","Title":"ImageAI Object detection with prediction training","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"What statistical techniques should I adopt when trying to determine how much my independent variables explain the variance of my dependent variable?\nFor further context - I have been asked to develop a model in Python with the aim of examining the extent to which the predictor variables impact upon the response variable. Having usually focused on developing models for predictive purposes, I am unsure of where to start here.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":105,"Q_Id":59426564,"Users Score":0,"Answer":"I would begin analysis by finding the R-squared (R2) value of a model with all predictor variables, and then determine the change in R-squared when iteratively leaving out each predictor variable one at a time. Such an analysis should weed out the predictors with minimal impact on the regression, and give you a good idea of the impact for the remaining predictor variables. I would choose the R-squared fit statistic for this analysis as it is generally held to explain the amount of dependent data variance explained by the model, and I calculate R-squared by using numpy as \"R2 = 1.0 - (numpy.var(regression_error) \/ numpy.var(dependent_data))\"","Q_Score":1,"Tags":"python,machine-learning,statistics,regression","A_Id":59428792,"CreationDate":"2019-12-20T14:07:00.000","Title":"Examining explained and unexplained variance of the DV","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"This might be a silly question to ask, however, it is for a specific task in a multi-step process to clean up some data. \nBasically, each column label is a location represented as a series of long numbers. Each column contains measurement values in each subsequent row for those locations. I do not need the measurements, only the locations (hence why I just need the column labels only). \nThe reason I need this is because I need to replace some mixed up column labels in one CSV file with the correct column labels from another CSV file. \nI cannot do this in Excel since there are too many columns to read in (over 300,000 columns). I am essentially looking for a way to do a coded \"Copy\" and \"Paste\" from one file to another using Pandas if it can be done. \nI had a considered just dropping the columns I do not need, however, because the columns are labelled as numbers, I'd be filtering based on a multiple set of a conditions. I thought this method would be easier. \nThank you for your help.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":702,"Q_Id":59429705,"Users Score":1,"Answer":"Just dodataframe.columns to get all column names","Q_Score":2,"Tags":"python,pandas","A_Id":59429840,"CreationDate":"2019-12-20T18:14:00.000","Title":"Is there a way to select only column labels using Pyhon Pandas library without any rows?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I was doing numerical method in R and Python\nI have applied leapfrog method in python and it worked perfectly but I want to do the slimier thing in R. Here you can see my code \nPossibly I have tried doing u[2,2]=beta*(u[1,1]-2*u[2,1]+u[3,1]) this works, here I can see that the error is due to the bold statement means due to u[2,0] does not exist. But the same code worked in python, Please help to resolve the error while executing the loop\nu[i, j - 1] evaluates to u[2, 0], or numeric(0). This is what produces the error.\nIs there any solution.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":81,"Q_Id":59430331,"Users Score":0,"Answer":"Keep in mind that R indexes are 1-based, while in Python they are 0-based. In your code, the first time through the for loop, u[i, j - 1] evaluates to u[2, 0], or numeric(0). This is what produces the error.","Q_Score":1,"Tags":"python,r,numerical-methods","A_Id":59431088,"CreationDate":"2019-12-20T19:18:00.000","Title":"replacement has length zero in R from a python code","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a PyTorch video feature tensor of shape [66,7,7,1024] and I need to convert it to [1024,66,7,7]. How to rearrange a tensor shape? Also, how to perform mean across dimension=1? i.e., after performing mean of the dimension with size 66, I need the tensor to be [1024,1,7,7].\nI have tried to calculate the mean of dimension=1 but I failed to replace it with the mean value. And I could not imagine a 4D tensor in which one dimension is replaced by its mean. \nEdit:\n I tried torch.mean(my_tensor, dim=1). But this returns me a tensor of shape [1024,7,7]. The 4D tensor is being converted to 3D. But I want it to remain 4D with shape [1024,1,7,7].\nThank you very much.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":4489,"Q_Id":59435653,"Users Score":2,"Answer":"The first part of the question has been answered in the comments section. So we can use tensor.transpose([3,0,1,2]) to convert the tensor to the shape [1024,66,7,7].\nNow mean over the temporal dimension can be taken by \ntorch.mean(my_tensor, dim=1)\nThis will give a 3D tensor of shape [1024,7,7].\nTo obtain a tensor of shape [1024,1,7,7], I had to unsqueeze in dimension=1:\ntensor = tensor.unsqueeze(1)","Q_Score":1,"Tags":"python,numpy,computer-vision,pytorch,tensor","A_Id":59441386,"CreationDate":"2019-12-21T11:10:00.000","Title":"Calculate mean across one specific dimension of a 4D tensor in Pytorch","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I was trying to manually calculate TPR and FPR for the given data. But unfortunately I dont have any false positive cases in my dataset and even no true positive cases. \nSo I am getting divided by zero error in pandas. So I have an intuition that fpr=1-tpr. Please let me know my intuition is correct if not let know how to fix this issue.\nThank you","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":650,"Q_Id":59438262,"Users Score":0,"Answer":"It is possible to have FPR = 1 with TPR = 1 if your prediction is always positive no matter what your inputs are.\nTPR = 1 means we predict correctly all the positives. FPR = 1 is equivalent to predicting always positively when the condition is negative.\nAs a reminder:\n\nFPR = 1 - TNR = [False Positives] \/ [Negatives]\nTPR = 1 - FNR = [True Positives] \/ [Positives]","Q_Score":0,"Tags":"python,pandas","A_Id":59439160,"CreationDate":"2019-12-21T17:16:00.000","Title":"False Positive Rate in Confusion Matrix","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Import yfinance as yf\nShould run normally on conda but get this message\n\nModuleNotFoundError Traceback (most recent call\n  last)  in \n        1 import pandas as pd\n  ----> 2 import yfinance as yf\n        3 import matplotlib.pyplot as plt\nModuleNotFoundError: No module named 'yfinance'\n\nStrange? As should be simple to install?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":6906,"Q_Id":59440380,"Users Score":0,"Answer":"The best way to install a python library is to:\n\nOpen a terminal or cmd or powershell on your system.\nIf using a virtual environment, activate the environment in your terminal or cmd or powershell.\n\nFor e.g., A virtual environment named virtual_environment created using virtualenv in C:\/\/Users\/Admin\/VirtualEnvironments\/ directory can be activated by opening a terminal or cmd or powershell in that directory and running python -m virtualenv virtual_environment\/Scripts\/activate. For ubuntu users, python -m virtualenv virtual_environment\/bin\/activate.\n\nTo install a library named python-library, Run pip install python-library.\nTo install a specific version of that library, like a library named python-library with version 2.0.1 can be installed by Running the command pip install python-library==2.0.1\n\nSimilarly, you simply have to run pip install yfinance\nIf you want to install a specific version, like 0.1.70, then run command pip install yfinance==0.1.70.\nIf you have a .whl file of that library like python-library.whl, and want to install that wheel, then run command pip install python-library.whl.","Q_Score":0,"Tags":"python,conda,yahoo,yfinance","A_Id":70914645,"CreationDate":"2019-12-21T22:42:00.000","Title":"Import yfinance as yf","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Import yfinance as yf\nShould run normally on conda but get this message\n\nModuleNotFoundError Traceback (most recent call\n  last)  in \n        1 import pandas as pd\n  ----> 2 import yfinance as yf\n        3 import matplotlib.pyplot as plt\nModuleNotFoundError: No module named 'yfinance'\n\nStrange? As should be simple to install?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":6906,"Q_Id":59440380,"Users Score":0,"Answer":"If you using anaconda, try downloan yfinance using Powershell Prompt.","Q_Score":0,"Tags":"python,conda,yahoo,yfinance","A_Id":68372729,"CreationDate":"2019-12-21T22:42:00.000","Title":"Import yfinance as yf","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I don't understand this message \/opt\/conda\/envs\/Python36\/lib\/python3.6\/site-packages\/sklearn\/preprocessing\/data.py:645: DataConversionWarning: Data with input dtype uint8, int64 were all converted to float64 by StandardScaler.\n  return self.partial_fit(X, y)\n\/opt\/conda\/envs\/Python36\/lib\/python3.6\/site-packages\/ipykernel\/__main__.py:2: DataConversionWarning: Data with input dtype uint8, int64 were all converted to float64 by StandardScaler.\n  from ipykernel import kernelapp as app \nMy code is :\nX = Feature_test\ntestX= preprocessing.StandardScaler().fit(X).transform(X)\nWhat does it mean??\nAnd how can I fix it?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1496,"Q_Id":59441173,"Users Score":0,"Answer":"It means that you have columns in your dataset of type integer. It's a warning, so you are good to go if you need to scale your features for a regression or a neural network.","Q_Score":1,"Tags":"python-3.x","A_Id":59991016,"CreationDate":"2019-12-22T02:02:00.000","Title":"python: DataConversionWarning: Data with input dtype uint8, int64 were all converted to float64 by StandardScaler","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to know if i retrain my saved model which i ran with 4 epochs, be faster with same image set at 10 epochs?\nMy data set consists of 2 folders of training and validation with 5 classes and 3000 training and 1000 validation images","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":150,"Q_Id":59447567,"Users Score":0,"Answer":"If you are using keras, basically calling fit will start from pre-existing weights and therefore the first epoch will consist of the 5th epochs. Therefore, the error will already be lower. However, beware that the processing time for each epoch will be equivalent. You will only start from a model which do not have random weights.","Q_Score":0,"Tags":"python,tensorflow,machine-learning,keras,deep-learning","A_Id":59447620,"CreationDate":"2019-12-22T19:49:00.000","Title":"Re train a saved model with same Dataset","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm getting this error when i use the tensorboard callback while training. \nI tried looking for answers from posts related to tensorboard errors but this exact error was not found in any stackoverflow posts or github issues.\nPlease let know.\nThe following versions are installed in my pc:\nTensorflow and Tensorflow GPU : 2.0.0\nTensorboard: 2.0.0","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":5342,"Q_Id":59452858,"Users Score":0,"Answer":"This error mostly happens because of mixed imports from keras and tf.keras. Make sure that throughout the code exact referencing of libraries is maintained. For example instead of model.add(Conv2d()) try model.add(tf.keras.layers.Conv2D()) , applying this for all layers solved the problem for me.","Q_Score":8,"Tags":"python-3.x,tensorflow,deep-learning,tensorboard,tensorflow2.0","A_Id":61602101,"CreationDate":"2019-12-23T09:26:00.000","Title":"Tensorboard: AttributeError: 'Model' object has no attribute '_get_distribution_strategy'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I got array list looks like  \n\n[array(99.75142857), array(99.79928571), array(99.82238095),\n  array(99.83857143), array(99.85), array(99.85738095),\n  array(99.86285714), array(99.86767857)]\n\nI'm not sure what is this array but I just want to ge a numbers \n[99.75142857,99.79928571....]\nthis array() means numpy array","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":226,"Q_Id":59466258,"Users Score":0,"Answer":"A numpy array of rank 0 is a scalar (it's got shape ())  and will behave like a scalar everyone. You can treat it like that.\nYou're perhaps mixing it up with an array of rank 1, e.g., np.array([99.79928571]).\nYou can also wrap your list into np.array to get an array of float64. Perhaps that looks nicer to your eye.","Q_Score":1,"Tags":"python,numpy","A_Id":59467879,"CreationDate":"2019-12-24T08:58:00.000","Title":"numpy array to the just number in the array","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I know that the recommendation is not to use linalg.inv and use linalg.solve when inverting matrices. This makes sense when I have situation like Ax = b and I want to get x, but is there a way to compute something like: A - B * D^{-1} * C without using linalg.inv? Or what is the most numerically stable way to deal with the inverse in the expression?\nThanks!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":416,"Q_Id":59473735,"Users Score":0,"Answer":"Please don't inv\u2014it's not as bad as most people think, but there's easier ways: you mentioned how np.linalg.solve(A, b) equals A^{-1} . b, but there's no requirement on what b is. You can use solve to solve your question, A - np.dot(B, np.linalg.solve(D, C)).\n(Note, if you're doing blockwise matrix inversion, C is likely B.transpose(), right?)","Q_Score":0,"Tags":"python,linear-algebra,matrix-inverse","A_Id":59480515,"CreationDate":"2019-12-24T22:06:00.000","Title":"Numerically stable way to compute conditional covariance matrix using linalg.solve","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"As part of a class project, I'm trying to write a word2vec implementation in Python and train it on a corpus of ~6GB. I'm trying to code a reasonably optimized solution so I don't have to let my PC sit for days.\nGoing through the C word2vec source code, I notice that there, each thread reads words from a file, and takes the time to look up the index of every word. At the end, it stores a \"sentence\" of word indexes.\nWouldn't it be logical to translate the whole corpus into one containing integer indexes of the appropriate words? That way, time isn't lost during training on hash-table lookups, while the translation process is a one-time expense.\nI understand that for extremely large corpuses, you are effectively doubling the amount it takes on disk, which you might want to avoid.\nHowever, if you do have the memory, wouldn't this offer a noticeable increase in efficiency? Or am I just overestimating the impact of a table lookup?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":47,"Q_Id":59473926,"Users Score":1,"Answer":"Hashtable lookups can be very fast, and repeated lookups may not contribute much to the overall runtime.\nBut the only way to really know the potential speedup of your proposed optimization is to implement it, and profile it in comparison to the prior behavior. \nAlso, as you note, to be able to re-use a single-pass token-lookup, you'd need to store those results somewhere. Google's word2vec.c code, like many other implementations, seeks to work well with input corpuses that are far larger than addressable memory. Writing the interim tokenization to disk would require extra code complication, and extra working space on disk, compared to the baseline of repeated lookups. So: even if it did speed things a little, implementors might consider the extra complexity undesirable.","Q_Score":0,"Tags":"python,machine-learning,nlp,word2vec","A_Id":59474328,"CreationDate":"2019-12-24T22:42:00.000","Title":"Processing a Corpus For a word2vec Implementation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Let's say I have a matrix M of size 10x5, and a set of indices ix of size 4x3. I want to do tf.reduce_sum(tf.gather(M,ix),axis=1) which would give me a result of size 4x5. However, to do this, it creates an intermediate gather matrix of size 4x3x5. While at these small sizes this isn't a problem, if these sizes grow large enough, I get an OOM error. However, since I'm simply doing a sum over the 1st dimension, I never need to calculate the full matrix. So my question is, is there a way to calculate the end 4x5 matrix without going through the intermediate 4x3x5 matrix?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":145,"Q_Id":59474657,"Users Score":0,"Answer":"I think you can just multiply by sparse matrix -- I was searching if the two are internally equivalent then I landed on your post","Q_Score":1,"Tags":"python,tensorflow,neural-network","A_Id":71549947,"CreationDate":"2019-12-25T02:06:00.000","Title":"tensorflow gather then reduce_sum","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have 120 samples and the shape of features for each sample is matrix of 15*17. how to use SVM to classify? Is it simply to reshape the matrix to long vector?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":28,"Q_Id":59475835,"Users Score":0,"Answer":"Yes, that would be the approach I would recommend. It is essentially the same procedure that is used when utilizing images in image classification tasks, since each image can be seen as a matrix. \nSo what people do is to write the matrix as a long vector, consisting of every column concatenated to one another.\nSo you can do the same here.","Q_Score":1,"Tags":"python,svm","A_Id":60563891,"CreationDate":"2019-12-25T06:36:00.000","Title":"how to use SVM to classify if the shape of features for each sample is matrix? Is it simply to reshape the matrix to long vector?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a (540, 960, 1) shaped image with values ranging from [0..255] which is black and white. I need to convert it to a \"heatmap\" representation. As an example, pixels with 255 should be of most heat and pixels with 0 should be with least heat. Others in-between. I also need to return the heat maps as Numpy arrays so I can later merge them to a video. Is there a way to achieve this?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":20273,"Q_Id":59478962,"Users Score":0,"Answer":"You need to convert the image to a proper grayscale representation. This can be done a few ways, particularly with imread(filename, cv2.IMREAD_GRAYSCALE). This reduces the shape of the image to (54,960) (hint, no third dimension).","Q_Score":10,"Tags":"python,image,opencv,image-processing,computer-vision","A_Id":59479125,"CreationDate":"2019-12-25T13:43:00.000","Title":"How to convert a grayscale image to heatmap image with Python OpenCV","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to know let's say I have 10 custom entities to recognize how much annotated training sentences should I give (Any rough idea) ??\nThank You, in Advance!! :)\nI am new to this, please help","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":1690,"Q_Id":59488470,"Users Score":1,"Answer":"For developing custom ner model at least 50-100 occurrences of each entity will be required along with their proper context. Otherwise if you have less data than your custom model will overfit on that. So, depending upon your data you will require atleast 200 to 300 sentences.","Q_Score":0,"Tags":"python,machine-learning,spacy,named-entity-recognition","A_Id":59488669,"CreationDate":"2019-12-26T12:25:00.000","Title":"How many training data(sentences) are required for custom NER using spacy python?[Just rought idea]","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to know let's say I have 10 custom entities to recognize how much annotated training sentences should I give (Any rough idea) ??\nThank You, in Advance!! :)\nI am new to this, please help","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":1690,"Q_Id":59488470,"Users Score":1,"Answer":"For the custom NER model from Spacy, you will definitely require around 100 samples for each entity that too without any biases in your dataset.\nAll this is as per my experience.\nSuggestion -:\nSpacy Custom model you can explore, but for production level or some good project, you can't be totally dependent on that only, You have to do some NLP\/ Relation Extraction, etc. along with this.\nHope this helps.","Q_Score":0,"Tags":"python,machine-learning,spacy,named-entity-recognition","A_Id":59490154,"CreationDate":"2019-12-26T12:25:00.000","Title":"How many training data(sentences) are required for custom NER using spacy python?[Just rought idea]","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using a custom training loop. The loss that is returned by tf.keras.losses.categorical_crossentropy is an array of I'm assuming (1,batch_size). Is this what it is supposed to return or a single value?\nIn the latter case, any idea what I could be doing wrong?","AnswerCount":2,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":747,"Q_Id":59493127,"Users Score":2,"Answer":"Most usual losses return the original shape minus the last axis.\nSo, if your original y_pred shape was (samples, ..., ..., classes), then your resulting shape will be (samples, ..., ...).\nThis is probably because Keras may use this tensor in further calculations, for sample weights and maybe other things. \nIn a custom loop, if these dimensions are useless, you can simply take a K.mean(loss_result) before calculating the gradients. (Where K is either keras.backend or tensorflow.keras.backend)","Q_Score":3,"Tags":"python,tensorflow,keras,loss-function","A_Id":59493316,"CreationDate":"2019-12-26T20:22:00.000","Title":"Does tf.keras.losses.categorical_crossentropy return an array or a single value?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have CNN that I have built using on Tensor-flow 2.0. I need to access outputs of the intermediate layers. I was going over other stackoverflow questions that were similar but all had solutions involving Keras sequential model. \nI have tried using model.layers[index].output but I get \n\nLayer conv2d has no inbound nodes.\n\nI can post my code here (which is super long) but I am sure even without that someone can point to me how it can be done using just Tensorflow 2.0 in eager mode.","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":3974,"Q_Id":59493222,"Users Score":0,"Answer":"The most straightforward solution would go like this:\nmid_layer = model.get_layer(\"layer_name\")\nyou can now treat the \"mid_layer\" as a model, and for instance:\nmid_layer.predict(X)\nOh, also, to get the name of a hidden layer, you can use this:\nmodel.summary() \nthis will give you some insights about the layer input\/output as well.","Q_Score":2,"Tags":"python,tensorflow,deep-learning,tensorflow2.0","A_Id":63206321,"CreationDate":"2019-12-26T20:34:00.000","Title":"Access output of intermediate layers in Tensor-flow 2.0 in eager mode","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working with the dataset of size (500, 33). \nIn particular the data set contains 9 features say\n[X_High, X_medium, X_low, Y_High, Y_medium, Y_low, Z_High, Z_medium, Z_low]\nBoth visually & after correlation matrix calculation I observed that \n[X_High, Y_High, Z_High] & [ X_medium, Y_medium,  Z_medium ] & [X_low, Y_low, Z_low] are highly correlated (above 85%).\nI would like to perform a Clustering algorithm (say K means or GMM or DBSCAN).\nIn that case,\nIs it necessary to remove the correlated features for Unsupervised learning ?\nWhether removing correlation or modifying features creates any impact ?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1456,"Q_Id":59494747,"Users Score":3,"Answer":"My assumption here is that you're asking this question because in cases of linear modeling, highly collinear variables can cause issues.\nThe short answer is no, you don't need to remove highly correlated variables from clustering for collinearity concerns.  Clustering doesn't rely on linear assumptions, and so collinearity wouldn't cause issues.\nThat doesn't mean that using a bunch of highly correlated variables is a good thing.  Your features may be overly redundant and you may be using more data than you need to reach the same patterns.  With your data size\/feature set that's probably not an issue, but for large data you could leverage the correlated variables via PCA\/dimensionality reduction to reduce your computation overhead.","Q_Score":1,"Tags":"python,machine-learning,correlation,unsupervised-learning,feature-engineering","A_Id":59494808,"CreationDate":"2019-12-27T00:23:00.000","Title":"Does correlation important factor in Unsupervised learning (Clustering)?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am doing a small project on sentiment analysis using TextBlob. I understand there are are 2 ways to check the sentiment of tweet:\n\nTweet polarity: Using it I can tell whether the tweet is positive, negative or neutral\nTraining a classifier: I am using this method where I am training a TextBlob Naive Bayes classifier on positive and negative tweets and using the classifier to classify tweet either as 'positive' or 'negative'.\n\nMy question is, using the Naive bayes classifier, can I also classify the tweet as 'neutral' ? In other words, can the 'sentiment polarity' defined in option 1 can somehow be used in option 2 ?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":304,"Q_Id":59498416,"Users Score":0,"Answer":"If you have only two classes, Positive and Negative, and you want to predict if a tweet is Neutral, you can do so by predicting class probabilities.\nFor example, a tweet predicted as 80% Positive remains Postive. However, a tweet predicting as 50% Postive could be Neutral instead.","Q_Score":0,"Tags":"python,nltk,sentiment-analysis,naivebayes,textblob","A_Id":59678549,"CreationDate":"2019-12-27T09:10:00.000","Title":"TextBlob Naive Bayes classifier for neutral tweets","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":1,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am confused as to how I can use Doc2Vec(using Gensim) for IMDB sentiment classification dataset. I have got the Doc2Vec embeddings after training on my corpus and built my Logistic Regression model using it. How do I use it to make predictions for new reviews? sklearn TF-IDF has a transform method that can be used on test data after training on training data, what is its equivalent in Gensim Doc2Vec?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":217,"Q_Id":59501121,"Users Score":1,"Answer":"To get a vector for an unseen document, use vector = model.infer_vector([\"new\", \"document\"])\nThen feed vectorinto your classifier: preds = clf.predict([vector]).","Q_Score":0,"Tags":"python,nlp,gensim,doc2vec","A_Id":59501847,"CreationDate":"2019-12-27T12:55:00.000","Title":"Sentiment Classification using Doc2Vec","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have very sparse and huge rating data which I should find top k neighbors for each session. I need to compare approximate and exact nearest neighbor algorithms but since the data is very big and sparse the computation of the exact method is taking days to compute with brute force. I want to use KD Trees or Ball Trees but they are not supporting cosine distance. Is there a way to convert other distance measures to cosine similarity by math or is there any other way to compute exact neighborhood?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":302,"Q_Id":59503600,"Users Score":1,"Answer":"try to normalize your matrix and use euclidian metric.","Q_Score":0,"Tags":"python,scikit-learn,knn,nearest-neighbor,cosine-similarity","A_Id":59694232,"CreationDate":"2019-12-27T16:40:00.000","Title":"Is there a way find nearest neighbors with BallTree or KDTree using cosine similarity?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am currently working on a project where get the top 10 most relevant words of set of document using tfidf in python. However, there are results where are get the same word and its plurial or adverb or so. To go around this problem, I decided to use stemming, but this leads to a problem where words and their antonyms can have the same root or by reducing a word to its root does not enable to go back and find that specific word in the document if a user was to search for it. Is there a nlp that might be better in this context than nlp? Any hint or link will be useful. I working on something that is very similar to youtube.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":281,"Q_Id":59505444,"Users Score":0,"Answer":"First you'd want to make a choice between Stems and Lemmas (neither are Roots, mind you). Google the difference for more on that. \nYou mention antonyms, but most are determined by prefix (e.g. important vs (un)important). So the Stemmer should leave most antonyms unchanged. \nAs for synonyms, let's assume you're thinking only about words with the exact same Stem, because if you want to relate synonyms with completely unrelated roots, you'd be thinking about semantics and something like wordnet but that would likely complicate your problem beyond reasonable...\nFrom your question, you already have a Stemmer working in Python...The simplest solution would be using two dictionaries: One dictionary mapping stems\/lemmas to the set\/list of inflected\/derived complete words (and\/or their frequency). And a second dictionary mapping those complete words to their various positions in the documents you are indexing.\nThat way you can stem the user input word, check for it in the top-k tf-idf\/stem dictionary, and afterwards map the complete word with the second dictionary to its occurrences in the document set.\n(It's hard to elaborate further given your question.)","Q_Score":1,"Tags":"python,nlp,tf-idf,cosine-similarity","A_Id":59507255,"CreationDate":"2019-12-27T19:50:00.000","Title":"Remove synonyms of TFIDF results in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"using PCA (principle component analysis) to extract features from a set of 4K images giving me the memory error\nFile \"\/home\/paul90\/.local\/lib\/python3.6\/site-packages\/sklearn\/decomposition\/_pca.py\", line 369, in fit_transform\n    U, S, V = self._fit(X)\nMemoryError: Unable to allocate array with shape (23339520, 40) and data type float32\nI am trying to extract 30 features (# of components) from 4K images and getting this error.\n\nPseudocode:\n\nimmatrix = np.array([np.array(Image.open(im, 'r')).flatten() for im in file_list], 'f')\nx = StandardScaler().fit_transform(immatrix)\npc_train = pca.fit_transform(x)\nFilelist is list of images (currently I have 600 images)\nI can't reduce the number of images in the list and can't reduce the initial 4K resolution. In this context,how can I solve this memory allocation issue?\nIt will be a great help if anyone can tell me the steps to avoid the memory issues.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":323,"Q_Id":59506774,"Users Score":0,"Answer":"As this problem stated, you have to reconfigure your system to make more RAM available to the run-time system.\nHowever, I suspect that you can finesse the problem by iterating through the images, rather than loading all of them into one large NumPy array.  Rather than using the monolithic processing tools, you'll need to write code to perform the same computations serially.","Q_Score":0,"Tags":"python,image,pca,feature-extraction","A_Id":59506950,"CreationDate":"2019-12-27T22:57:00.000","Title":"PCA analysis incites memory allocation problem. How to solve this without reducing image resolution or number of images","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am attempting to verify version of my Sklearn to verify the proper installation, earlier I was having an issue where I had to change my registry value of Computer\\HKEY_LOCAL_MACHINE\\SYSTEM\\CurrentControlSet\\Control\\FileSystem@LongPathEnabled value to 1.\nThen I was able to install the files how ever, now when I attempt to check the version of my skilearn I get the following:\nTraceback (most recent call last):\n  File \"C:\/Users\/terry\/pyversions.py\", line 11, in <module>\n    import sklearn\n  File \"C:\\Users\\terry\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.7_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python37\\site-packages\\sklearn\\__init__.py\", line 74, in <module>\n    from .base import clone\n  File \"C:\\Users\\terry\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.7_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python37\\site-packages\\sklearn\\base.py\", line 20, in <module>\n    from .utils import _IS_32BIT\n  File \"C:\\Users\\terry\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.7_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python37\\site-packages\\sklearn\\utils\\__init__.py\", line 25, in <module>\n    from .fixes import np_version\n  File \"C:\\Users\\terry\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.7_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python37\\site-packages\\sklearn\\utils\\fixes.py\", line 18, in <module>\n    import scipy.stats\n  File \"C:\\Users\\terry\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.7_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python37\\site-packages\\scipy\\stats\\__init__.py\", line 384, in <module>\n    from .stats import *\n  File \"C:\\Users\\terry\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.7_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python37\\site-packages\\scipy\\stats\\stats.py\", line 179, in <module>\n    from scipy.spatial.distance import cdist\n  File \"C:\\Users\\terry\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.7_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python37\\site-packages\\scipy\\spatial\\__init__.py\", line 102, in <module>\n    from ._procrustes import procrustes\n  File \"C:\\Users\\terry\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.7_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python37\\site-packages\\scipy\\spatial\\_procrustes.py\", line 11, in <module>\n    from scipy.linalg import orthogonal_procrustes\n  File \"C:\\Users\\terry\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.7_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python37\\site-packages\\scipy\\linalg\\__init__.py\", line 195, in <module>\n    from .misc import *\n  File \"C:\\Users\\terry\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.7_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python37\\site-packages\\scipy\\linalg\\misc.py\", line 5, in <module>\n    from .blas import get_blas_funcs\n  File \"C:\\Users\\terry\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.7_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python37\\site-packages\\scipy\\linalg\\blas.py\", line 215, in <module>\n    from scipy.linalg import _fblas\nImportError: DLL load failed: The specified module could not be found.\nMy versions are as follows:\nPython: 3.7.6 (tags\/v3.7.6:43364a7ae0, Dec 19 2019, 01:54:44) [MSC v.1916 64 bit (AMD64)]\nscipy: 1.4.1\nnumpy: 1.18.0\nmatplotlib: 3.1.2\npandas: 0.25.3\nPlease what do I need to do to fix my errors?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":112,"Q_Id":59512003,"Users Score":0,"Answer":"I am leaving this up to help others, I found the solution, had to unistall completely, reinstall and changing the reg file to allow long path download may have played a role in the beginning.","Q_Score":0,"Tags":"python,scikit-learn,scipy","A_Id":59512325,"CreationDate":"2019-12-28T14:59:00.000","Title":"Skilearn ImportError: DLL load failed: The specified module could not be found","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to build a client-Server Application where client is an android device and server is a windows Pc.it would be easy to do socket programming in java for building the complete application.At first, I tried to build the server side completely in python because of the image classifier I wrote in python. but it got me into problems when I started working on the socket.Now, I want to use python trained model in java.\nplease help me out.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":60,"Q_Id":59514643,"Users Score":0,"Answer":"python trained model server serve http api ,accept the image , return classification result","Q_Score":0,"Tags":"java,python,android,sockets,tensorflow","A_Id":59518465,"CreationDate":"2019-12-28T20:33:00.000","Title":"How can i train a Image classifier model with tensorflow in Python and use the trained model in Java application?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I am working with Colab, I have a very large column and I want to see the possible values that are in this column.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":164,"Q_Id":59529170,"Users Score":0,"Answer":"for numeric columns use: describe()\nfor categorical columns use : value_counts()","Q_Score":0,"Tags":"python,pandas,data-analysis","A_Id":59529321,"CreationDate":"2019-12-30T10:18:00.000","Title":"How can I see the possible values on a column","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"If we perform value_counts function on a column of a Data Frame, it gives us a Series which contains unique values' counts.\nThe type operation gives pandas.core.series.Series as a result. My question is that what is the basic difference between a Series & a Data Frame?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":819,"Q_Id":59534544,"Users Score":2,"Answer":"You can think of Series as a column in a DataFrame while the actual DataFrame is the table if you think of it in terms of sql","Q_Score":1,"Tags":"python,pandas,dataframe,series","A_Id":59534703,"CreationDate":"2019-12-30T17:22:00.000","Title":"Difference between Series & Data Frame","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Can anyone tell me what is the difference between the pearson correlation method and the normal corr() method? I expect it to be the same output, is that right?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":41,"Q_Id":59546892,"Users Score":0,"Answer":"no difference, if method='pearson'","Q_Score":0,"Tags":"python","A_Id":59546917,"CreationDate":"2019-12-31T17:17:00.000","Title":"Python data correlation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on lake ice thickness in the northern hemisphere. My final data set is an xarray with dimensions [365,360,720] - (days,lat,lon) and a data varibale 'icethickness'. This data variable has 3 kinds of values. A finite value for ice thickness, zero for water and 'nan' for oceans.\nI want to convert all the finite values of this xarray to 1 and keep the zeros and nan as they are.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":73,"Q_Id":59554967,"Users Score":1,"Answer":"You can use the xr.where function.\nxr.where(data > 0, 1, data)","Q_Score":0,"Tags":"gis,python-xarray","A_Id":59571636,"CreationDate":"2020-01-01T18:18:00.000","Title":"Replace finite values with '1' in a 3 dimension xarray","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a list of n functions: f1(),f2()..fn() etc and need to select n randomly and apply one\/more of those in sequence to an object. What's the Pythonic way to do this?  \nThe use-case is to generate augmentations for images (for training an ML model) from a set of images, and apply (one\/more) augmentation functions to an image.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":45,"Q_Id":59555146,"Users Score":1,"Answer":"How about this functools.reduce(lambda acc, f: f(acc), random.sample(funs, n), image)?","Q_Score":0,"Tags":"python,function","A_Id":59555564,"CreationDate":"2020-01-01T18:42:00.000","Title":"Randomly select one\/more functions from collection and apply combinations","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I pulled a (seemingly popular, supported by Jupyter) jupyter-tensorflow Docker image using\ndocker pull jupyter\/tensorflow-notebook\nand started it successfully with \ndocker run -p 8888:8888 jupyter\/tensorflow-notebook\nHowever, upon navigating to http:\/\/127.0.0.1:8888\/?token=177a...., I am prompted for a password or token (despite the token already being present in the URL). \nI know of no password to use, and the token 177a... does not work. \nAny suggestions?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1754,"Q_Id":59556013,"Users Score":5,"Answer":"The answer turned out to be quite easy: there was an other Jupyter notebook server I wasn't aware of running. Closing it did the trick.","Q_Score":0,"Tags":"python,docker,jupyter-notebook","A_Id":59556331,"CreationDate":"2020-01-01T20:51:00.000","Title":"Invalid token when trying to login into Jupyter notebook with Docker jupyter\/tensorflow-notebook","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"we are training machine learning models offline and persist them in python pickle-files.\nWe were wondering about the best way to embedd those pickeled-models into a stream (e.g. sensorInputStream > PredictionJob > OutputStream.\nApache Flink ML seems to be the right choice to train a model with stream-data but not to reference an existing model. \nThanks for you response. \nKind Regards \nLomungo","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":825,"Q_Id":59563265,"Users Score":3,"Answer":"There are two possible solutions depending on the model You are using:\n\nPossibly the simples idea is to create external service that will call the model and return the results and then simply call the service with AsyncFunction\nUse some library, again depending on Your model to load the pre-trained model inside a ProcessFunction's open method. And then simply calling the model for each data that arrived. \n\nThe second solution has two disadvantages, first You need to have the Java version of the specific library available and the other is that You need to somehow externalize the metadata of the model if You want to be able to update it over time.","Q_Score":1,"Tags":"python,machine-learning,apache-flink,flinkml","A_Id":59563487,"CreationDate":"2020-01-02T12:21:00.000","Title":"Embedd existing ML model in apache flink","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I solved most of this assignment using Python, but not sure how to input (or even understand) the remaining information. Aren't you supposed to find the eigenvector given some eigenvalue, thus it appears in reverse in the following: \nLet the symmetric 3 \u00d7 3 matrix A be A = ((1 2 1), (2 5 0), (1 0 5))\nIf A has the following three eigenvectors, then find three corresponding eigenvalues:\nv1 = ((-5), (2), (1)); v2 = ((0), (-1), (2)); v3 = ((1), (2), (1))","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":196,"Q_Id":59568960,"Users Score":1,"Answer":"Think about the definition of an eigenvector. An eigenvector v of a linear transformation represented by matrix A is a vector that only changes in magnitude, not direction, when that linear transformation is applied to it. The scalar change in magnitude of the eigenvector is its eigenvalue. You have a linear transformation, and you've been given its eigenvectors - to find the eigenvalues, all you need to do is apply the transformation to the eigenvectors and determine by what scalar each eigenvector got scaled.","Q_Score":0,"Tags":"python,matrix,eigenvalue,eigenvector","A_Id":59569070,"CreationDate":"2020-01-02T19:29:00.000","Title":"Finding corresponding eigenvalues to a set of eigenvector","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"The server currently has CentOS 6.9 with GLIBC 2.12 and tensorflow 1.14.\ntensorflow is throwing error saying that it needs GLIBC 2.15.\nNow I want to upgrade tensorflow to 2.0 but I want to know what's a working GLIBC version for tensorflow 2.0 on CentOS 6.9 before doing so.\nI didn't seem to find any source to state this compatibility issue. Does anyone here know?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":308,"Q_Id":59569722,"Users Score":0,"Answer":"Just tested and tensorflow 2.0 works fine with GLIBC 2.12.\nIt was tensorflow 1.14 that's not compatible with GLIBC 2.12.","Q_Score":0,"Tags":"python,tensorflow,python-3.6,centos6,tensorflow2.0","A_Id":59652333,"CreationDate":"2020-01-02T20:40:00.000","Title":"What's a compatible GLIBC version for tensorflow 2.0 for python3.6.8 on CentOS 6.9?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":1,"Web Development":0},{"Question":"Assume you have lot of text sentences which may have (or not) similarities. Now you want to cluster similar sentences for finding centroids of each cluster. Which method is the prefered way for doing this kind of clustering? K-means with TF-IDF sounds promising. Nevertheless, are there more sophisticated algorithms or better ones? Data structure is tokenized and in a one-hot encoded format.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":51,"Q_Id":59571031,"Users Score":0,"Answer":"in my view, You can use LDA(latent Dirichlet allocation, it is more flexible in comparison to other clustering techniques because of having Alpha and Beta vectors that can adjust to the contribution of each topic in a document and word in a topic. It can help if the documents are not of similar length or quality.","Q_Score":0,"Tags":"python,cluster-analysis,text-mining","A_Id":59575035,"CreationDate":"2020-01-02T22:49:00.000","Title":"Which clustering method is the standard way to go for text analytics?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I just started learning TF and realized that if I create a model of a NN, and then if I were to create a seession to do something with the NN such as getting an output value given the input value,\nand then after the session close, if I were to do something else with the NN again, then create a session for that, then it re-initializes the weights into random, which makes it pointless.\nSo then is it a common practice to, say in the first session, I save the model, and then in the second session, I would load the weights do something else with the NN?\nI understand that TF is intended to be used with 1 session but when dealing with something more complex than a \"simple\" supervised classification problem, such as reinforcement learning, then I need to use the NN for various way for various reasons.\nSo coding all of these such that i can execute all the functionalities within 1 session is very tiring and confusing at times. where I would rather create 1 session each to do something with the NN. But is it a poor practice to do so?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":126,"Q_Id":59572172,"Users Score":0,"Answer":"Yes, of course this is common practice. If you're trying to work with a large net that takes hours to train, it would be totally impractical to do anything other than save weights for future use. In fact, if you're at all concerned about reproducibility of your results, you should save any noteworthy iterations of your net.","Q_Score":0,"Tags":"python,tensorflow,oop,neural-network","A_Id":59572241,"CreationDate":"2020-01-03T01:31:00.000","Title":"Is it a common practice to save and load NN models for multiple sessions in practice?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to do some ML on my computer with Python, I'm facing problem with the installation of tensorflow and I found that tensorflow could work with GPU, which is CUDA enabled. I've got a GPU Geforce gtx 1650, will tensorflow work on that.\nIf yes, then, how could I do so?","AnswerCount":4,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":2593,"Q_Id":59572687,"Users Score":0,"Answer":"Here are the steps for installation of tensorflow:\n\nDownload and install the Visual Studio.\nInstall CUDA 10.1\nAdd lib, include and extras\/lib64 directory to the PATH variable.\nInstall cuDNN\nInstall tensorflow by pip install tensorflow","Q_Score":1,"Tags":"python,python-3.x,tensorflow","A_Id":59953057,"CreationDate":"2020-01-03T03:03:00.000","Title":"How could I run tensorflow on windows 10? I have the gpu Geforce gtx 1650. Can I run tensorflow on it? if yes, then how?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I tried importing numpy to carry out some array operations in Python:\nimport numpy *\nBut I got this error message:\n\nModuleNotFoundError: No module named 'numpy'\n\nWhat do i do??","AnswerCount":5,"Available Count":1,"Score":0.1194272985,"is_accepted":false,"ViewCount":14026,"Q_Id":59575113,"Users Score":3,"Answer":"If you are using PyCharm, open Pycharm Go to File->Setting->Project->Python Interpreter->Click on '+'->search and select required package->install package","Q_Score":1,"Tags":"python-3.x,numpy","A_Id":67939485,"CreationDate":"2020-01-03T08:08:00.000","Title":"ModuleNotFoundError: No module named 'numpy'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a task in NLP to do a sentence segmentation, but I wonder, what are the advantages of using built-in NLP sentence segmentation algorithms, such as Spacy, NLTK, BERT etc, over Python '.' separator or similar algorithm?\nIs it the speed? or accuracy? or less line of code?\nHow different or strong these algorithms over the ones that we can build ourselves in Python?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":164,"Q_Id":59578665,"Users Score":1,"Answer":"The sentence segmentation routines from the NLP libraries like SpaCy, NLTK, etc. handle edge cases much better and are more robust to handling punctuation and context. For example, if you choose to split sentences by treating a '.' as a sentence boundary, how would you handle a sentence like - \"There are 0.5 liters of water in this bottle.\"?","Q_Score":1,"Tags":"python-3.x,nlp","A_Id":59582216,"CreationDate":"2020-01-03T12:28:00.000","Title":"What is the benefit of NLP sentence segmentation over Python algorithm?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"GPT2 is an excellent OpenAI project for NLP. The developer requirement stated we need to use tensor Flow and GPU. I only want to use (not to train) the existing trained parameters. Is there any way to use GPT2 without the expensive hardware with GPU and without the need to install of Tensor Flow?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1003,"Q_Id":59588423,"Users Score":0,"Answer":"What I would do : \n\nsess.run([var for var in tf.trainable_variables]) to get trained parameters and save them as numpy array.\nrebuild a numpy only version of gpt2.  \n\nBut : \n\nThis will take a substantial amount of work to rebuild the model\nYou need to install tensorflow at least once to get the trained variables. \n\nAs for the gpu, you don't need it actually, the model will run with a cpu version of tensorflow (gpu are especially needed to speed up training time).","Q_Score":2,"Tags":"python,tensorflow,nlp,gpu","A_Id":59590347,"CreationDate":"2020-01-04T06:18:00.000","Title":"Is there anyway to run GPT2 without GPU and TensorFlow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have an image loaded with opencv with img.shape = (208, 117, 3). I also have a boolean numpy array with mask.shape = (208, 117). How to I make all pixels in the img (0,0,0) wherever the mask has False, otherwise leave the pixels as they are?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":696,"Q_Id":59593132,"Users Score":1,"Answer":"Answer is\nimg[~mask,:] = [0,0,0]\nThat ,: takes care of the other dimension so you don't get mismatch issues.","Q_Score":0,"Tags":"python,numpy,opencv","A_Id":59593303,"CreationDate":"2020-01-04T17:28:00.000","Title":"Most efficient way to mask an opencv bgr with a boolean array","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've created a csv file with the column names and saved it using pandas library. This file will be used to create a historic record where the rows will be charged one by one in different moments... what I'm doing to add rows to this csv previously created is transform the record to a DataFrame and then using to_csv() I choose mode = 'a' as a parameter in order to append this record to the existing file. The problem here is that I would like to see and index automatically generated in the file everytime I add a new row. I already know when I import this file as a DF, an index is generated automatically, but this is within the idle interface...when I open the csv with Excel for example...the file doesn't have an index.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":81,"Q_Id":59602713,"Users Score":0,"Answer":"While writing your files to csv, you can use set index = True in the to_csv method. This ensures that the index of your dataframe is written explicitly to the csv file","Q_Score":0,"Tags":"python,pandas,dataframe,indexing","A_Id":59603091,"CreationDate":"2020-01-05T18:01:00.000","Title":"exporting and indexing csv file with pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I updated to Spyder 4 (4.0.0?) to get the latest and greatest features. At this time I found out that Spyder now has the option of using Kite for autocompletion. After trying Kite for a bit I wanted to revert back to the previous autocompletion setup. I thought this was possible by unchecking to use Kite in the preferences, but then autocompletion seemed to be completely broken.\nIn conda I noticed my current version of Jedi was out of date, and I upgraded to the newest version. In the process I noticed that conda said it needed to downgrade Spyder for the Jedi upgrade. This gives me the impression that Spyder 4 just doesn't support Jedi. Is that the case?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1717,"Q_Id":59604624,"Users Score":2,"Answer":"(Spyder maintainer here) Spyder 4.0.0 and 4.0.1 only work with Jedi 0.14.1. Newer nor older versions are supported, so be sure to have that exact version installed.\nBy the way, although not required, Kite completions are better for scientific packages than Jedi ones.","Q_Score":1,"Tags":"python,spyder","A_Id":59613792,"CreationDate":"2020-01-05T22:07:00.000","Title":"jedi support in spyder 4","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using tensorflow 2.0 to train a model. I am deciding about whether I should put multiple examples in a batch along the rows or columns of a matrix. Obviously this will affect how I design the model as well. Is there any practical advice on which is better?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":56,"Q_Id":59613281,"Users Score":1,"Answer":"Quoting @ShanqingCai\n\nPutting examples in a batch along the rows of a matrix (i.e., the first axis of the input tensor) is the prevailing way of training deep learning models. Virtually any tensorflow2 \/ keras example you can find follows that pattern. Putting them along any non-first axis is much rarer.\n\nAs said, the row dimension is the preferred way of storing samples. I can think of two reasons why this is the case,\n\nTF does a lot of matrix multiplications involving batches of data. Therefore, by keeping batch dimension as the first dimension, you are able to continuously produce tensors using matrix multiplication, which also has the batch dimension as the first dimension. (e.g. [batch size, 10] . [10, 2] produces [batch size, 2])\nThe other reason is that the row dimension is the slowest changing dimension. Therefore by you can access an individual sample by taking a single contiguous chunk of memory which is always preferred when it comes to disk reading\/ memory reading.","Q_Score":0,"Tags":"python-3.x,tensorflow2.0","A_Id":59617838,"CreationDate":"2020-01-06T13:48:00.000","Title":"Placing examples in a mini-batch on the rows or columns of a matrix?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I've been doing Applied Machine Learing in Python course on coursera and on Assignment of week 4 I`ve found something interesting. During my first attempt to complete the assignment I tried using RandomForestClassifier from sklearn to predict labels, but the model was overfitting and was showing poor test accuracy results. As an experiment I switched to RandomForestRegressor and, guess what, not only did it not overfit, but test accurary was also a lot higher. So, why does RandomForestRegressor perform a lot better on a binary classification problem?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":78,"Q_Id":59614227,"Users Score":2,"Answer":"The Random Forest regressor does differ somewhat from the Random Forest classifier when it comes to ensembling the decision trees:\n\nThe classifier uses the mode of the predicted classes of the decision trees\nThe regressor uses the mean of the predicted values of the decision trees\n\nDue to this difference the models can have different results. And in some cases this might result in the regressor performing better than the classifier. \nIn addition to that I would say that if you tune your hyperparameters correctly, the classifier should perform better on a classification problem than the regressor.","Q_Score":0,"Tags":"python,scikit-learn,data-science,random-forest","A_Id":59617330,"CreationDate":"2020-01-06T14:54:00.000","Title":"RandomForestRegressor for classification problems","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am new to Tensorflow and I have used tensorflow.placeholder() in tensorflow 1.0. But is there any replacement of placeholder.","AnswerCount":2,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":4307,"Q_Id":59625668,"Users Score":4,"Answer":"There is no replacement for placeholder in Tf2 as its default mode is eager execution , if want to use placeholder in tf2 than use tf.compat.v1 syntax and disable v2 behavior","Q_Score":1,"Tags":"python,tensorflow2.0","A_Id":59625798,"CreationDate":"2020-01-07T09:36:00.000","Title":"What is the replacement of Placeholder in Tensorflow 2.0","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to send images (4000, 3000, 3) between two processes.\nMy first process acquires the image with a camera, attaches to it some metadata, another image and then sends the whole thing to the second process, which processes it.\nI want to have a maximal delay of 0.2 seconds between the moment the image is acquired and the moment the processing is over.\nLet's assume the way I acquire and process the image is optimal.\nI tried 2 methods to send the image, with a queue (mp.Queue) and with a shared array (mp.Array('i', 4000*3000*3))\nBoth took to much time. \nThe Queue.put() method takes about 0.5 seconds to send the package.\nCopying the image in a shared array like this:\nshared_array[:] = img.copy()\nTakes about 2 seconds.\nSo my question is, is anyone aware of a faster way to transit two images between two processes?\nThanks for your time!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":282,"Q_Id":59631595,"Users Score":0,"Answer":"EDIT: I found this question, which has been answered, and addressed my problem using sharedmem module","Q_Score":0,"Tags":"python-3.x,ipc,python-multiprocessing,opencv4","A_Id":59661270,"CreationDate":"2020-01-07T15:42:00.000","Title":"Image transmission between processes too slow in python's multiprocessing","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I tried applying tesseract ocr on image but before applying OCR I want to improve the quality of the image so that OCR efficiency increase,\n\nHow to detect image brightness and increase or decrease the brightness of the image as per requirement.\n\nHow to detect image sharpness","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":328,"Q_Id":59639187,"Users Score":0,"Answer":"*Basic recommendation, using the gray images is better on OCR\nYou can use the BRISQUE image quality assessment for scoring the image quality, it's available as a library. check it out, a smaller score means good quality.","Q_Score":1,"Tags":"python-3.x,image-processing,ocr,python-tesseract","A_Id":65552040,"CreationDate":"2020-01-08T03:52:00.000","Title":"How to detect image brightness and sharpness in python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am migrating a piece of software from Python 2.7 to Python 3.\nOne problem that arises is:\n\nThe sklearn.neighbors.kde module is  deprecated in version 0.22 and\n  will be removed in version 0.24. The corresponding classes \/ functions\n  should instead be imported from sklearn.neighbors. Anything that\n  cannot be imported from sklearn.neighbors is now part of the private\n  API.\n\nI am not sure which line causes this, and not sure if it is an error or a warning, and what are the implications.\nOn python 2.7 everything works fine.\nHow do I get rid of this?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":8393,"Q_Id":59643694,"Users Score":1,"Answer":"It's just a warning, for now -- until you upgrade sklearn to version 0.24. Then your code will need to be modified before it works. It's giving you a heads-up about this, so you can fix your code ahead of time. The modifications described below should work with your current version; you don't need to wait to upgrade before changing your code (at least, that's how these deprecation warnings usually work).\n\nThe corresponding classes \/ functions should instead be imported from sklearn.neighbors.\n\nIf I read this message correctly, it's saying that if you're using a function like sklearn.neighbours.kde.some_function() in your code now, you need to change it to sklearn.neighbours.some_function(). \n\nAnything that cannot be imported from sklearn.neighbors is now part of the private API.\n\nThis seems to be saying that there may be some functions that will no longer be available to you, even using the modification above.","Q_Score":5,"Tags":"python,scikit-learn,deprecation-warning","A_Id":59644182,"CreationDate":"2020-01-08T10:17:00.000","Title":"The sklearn.* module is deprecated in version 0.22 and will be removed in version 0.24","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"The question is simple, however I cannot find solution:\nHow to recognise cats and dogs and filter out everything other?\nAnother words: I have a big database with images of cats and dogs and all other photos mixed together, is there a way to say in output: cat -or- dog -or- something else?\nEither CNNs are not constructed to do such things efficiently?\nI'm using python \/ Keras \/ Tensorflow; solution with finding cats \/ dogs when I provide images of cats and dogs only - works fine.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":39,"Q_Id":59653108,"Users Score":1,"Answer":"Labelling next class as 'other' or something like that is the simplest way to do that.\nWhat I found, that because of many different types of images in 'other' class, training sample should be much bigger than just when differing between cats and dogs.","Q_Score":0,"Tags":"python-3.x,tensorflow,keras,conv-neural-network","A_Id":59683042,"CreationDate":"2020-01-08T20:05:00.000","Title":"Neural network filtering out everything else than cats\/dogs","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When I train a classification model using lightgbm, I usually use validation set and early stopping to determine the number of iterations. \nNow I want to combine training and validation set to train a model (so I have more training examples), and use the model to predict the test data, should I change the number of iterations derived from the validation process? \nThanks!","AnswerCount":3,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":764,"Q_Id":59658070,"Users Score":0,"Answer":"In a similar problem in deep learning with Keras: I do it by using an early stopper and cross validation with train and validation data, and let the model optimize itself using validation data during trainings. \nAfter each training, I test the model with test data and examine the mean accuracies. In the mean time after each training I save the stopped_epoch from EarlyStopper. If CV scores are satisfying, I take the mean of stopped epochs and do a full training (including all data I have) with the number of mean stopped epochs, and save the model.","Q_Score":0,"Tags":"python,lightgbm","A_Id":59658376,"CreationDate":"2020-01-09T06:04:00.000","Title":"Is there any rules of thumb for the relation of number of iterations and training size for lightgbm?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When I train a classification model using lightgbm, I usually use validation set and early stopping to determine the number of iterations. \nNow I want to combine training and validation set to train a model (so I have more training examples), and use the model to predict the test data, should I change the number of iterations derived from the validation process? \nThanks!","AnswerCount":3,"Available Count":3,"Score":0.0665680765,"is_accepted":false,"ViewCount":764,"Q_Id":59658070,"Users Score":1,"Answer":"As you said in your comment, this is not comparable to the Deep Learning number of epochs because deep learning is usually stochastic.\nWith LGBM, all parameters and features being equals, by adding 10% up to 15% more training points, we can expect the trees to look alike: as you have more information your split values will be better, but it is unlikely to drastically change your model (this is less true if you use parameters such as bagging_fraction or if the added points are from a different distribution).\nI saw people multiplying the number of iterations by 1.1 (can't find my sources sorry).  Intuitively this makes sense to add some trees as you potentially add information. Experimentally this value worked well but the optimal value will be dependent of your model and data.","Q_Score":0,"Tags":"python,lightgbm","A_Id":59661123,"CreationDate":"2020-01-09T06:04:00.000","Title":"Is there any rules of thumb for the relation of number of iterations and training size for lightgbm?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"When I train a classification model using lightgbm, I usually use validation set and early stopping to determine the number of iterations. \nNow I want to combine training and validation set to train a model (so I have more training examples), and use the model to predict the test data, should I change the number of iterations derived from the validation process? \nThanks!","AnswerCount":3,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":764,"Q_Id":59658070,"Users Score":0,"Answer":"I'm not aware of a well-established rule of thumb to do such estimate. As Florian has pointed out, sometimes people rescale the number of iterations obtained from early stopping by a factor. If i remember correctly, typically the factor assumes a linear dependence of the data size and the optimal number of trees. I.e. in the 10-fold cv this would be a rescaling 1.1 factor. But there is no solid justification for this. As Florian also pointed out, the dependence around the optimum is typically reasonably flat, so +- a bit of trees will not have a dramatic effect.\nTwo suggestions:\n\ndo k-fold validation instead of a single train-validation split. This will allow to evaluate how stable the estimate of the optimal number of trees is. If this fluctuates a lot between folds- do not rely on such estimate :)\nfix the size of the validation sample and re-train your model with early stopping using gradually increasing training set. This will allow to evaluae the dependence of the number of trees on the sample size and approximate it to the full sample size.","Q_Score":0,"Tags":"python,lightgbm","A_Id":59810976,"CreationDate":"2020-01-09T06:04:00.000","Title":"Is there any rules of thumb for the relation of number of iterations and training size for lightgbm?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using Holoviews to construct a dashboard of charts. Some of these charts have percentages in the y axis where as others have sums\/counts etc. When I try to output all the charts I have created to a html file, all the charts change their y axis to match the axis of the first chart of my chart list. \nFor example:\n\nChart 1 is a sum, values go from 0 to 1000\nChart 2 is a %\nChart 3 is a %\n\nwhen I combine these charts in holoviews using:\n\nCharts = Chart 1 + Chart 2 + Chart 3\n\nThe y axis of charts 2 and 3 become the same as chart 1.\nDoes anyone know why this is happening and how I can fix it so all the charts keep their individual axis pertinent to what they are trying to represent. \nThank you!","AnswerCount":3,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1953,"Q_Id":59661074,"Users Score":3,"Answer":"Sander's response is correct and will solve your specific problem, but in this case it may not be addressing the root cause. HoloViews only links axes that are the same, and it sounds like you're plotting different quantities on the y axis in each plot. In that case, the real fix is to put in a real name for the y axis of each plot, something that distinguishes it from other things that you might want to plot on the y axis in some other plot you're showing. Then not only will HoloViews no longer link the axes inappropriately, the viewer of your plot will be able to tell that each plot is showing different things.","Q_Score":8,"Tags":"python,holoviews,holoviz","A_Id":59674053,"CreationDate":"2020-01-09T09:41:00.000","Title":"Holoviews charts sharing axis when combined and outputted","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I have a xarray dataset, with data variables of:\nData variables:\n    hid      (particle) float32 ...\n    d        (particle) float32 ...\n    x        (particle) float32 ...\n    y        (particle) float32 ...\n    z        (particle) float32 ...\n    image    (hologram_number, xsize, ysize) uint8 ...\nI am wondering if there is a way to take all of the x values for my 10,000 data points, and pass them into a new one dimensional numpy array? Any sort of direction would be amazing. I have been reading the xarray.Dataset API and I'm not really getting anywhere.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":46,"Q_Id":59671677,"Users Score":0,"Answer":"Try ds.x.values, where ds is the name of your dataset. .x (which is equivalent to ds[\"x\"]) gets the x dataarray from your dataset and .values returns the numpy array with its values.","Q_Score":0,"Tags":"python,numpy,python-xarray","A_Id":59682913,"CreationDate":"2020-01-09T20:33:00.000","Title":"Passing a specific Xarry Data Variable into a New Numpy Array","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using Tensorflow 2.0 for Python 3.\nSuppose I have two tensor variables, x and y, and I want to compute their element-wise sum x + y. Should I just write x + y, or tf.add(x, y)? If they are not equivalent, when should I use one or the other?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1972,"Q_Id":59673839,"Users Score":5,"Answer":"In my understanding they are equivalent and just executes the __add__ magic function.","Q_Score":3,"Tags":"python-3.x,tensorflow2.0","A_Id":59743781,"CreationDate":"2020-01-10T00:03:00.000","Title":"Should I use tf.add or + to add two tensors in Tensorflow?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"im trying to get the font size of text in an image, is there any library in python or can it be done using opencv? Thanks in advance","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1072,"Q_Id":59676639,"Users Score":1,"Answer":"You can first use a text detector or a contour detector to get the height of one line of text. That can be used to get the size of text in pixels. But after that, you need some reference to convert it to font size (which is usually defined in points). \nAll of this can be done in OpenCV. If you post an image with text, some of us will be able to provide more detailed answers.","Q_Score":1,"Tags":"python,opencv,image-processing","A_Id":59691150,"CreationDate":"2020-01-10T06:40:00.000","Title":"Get text size using opencv python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have imported excel file into python pandas. but when I display customer numbers I get in float64 format i.e \n\n7.500505e+09 , 7.503004e+09\n  how do convert the column containing these numbers","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":497,"Q_Id":59677058,"Users Score":0,"Answer":"int(yourVariable) will cast your float64 to a integer number.\nIs this what you are looking for?","Q_Score":1,"Tags":"python,pandas","A_Id":59677083,"CreationDate":"2020-01-10T07:19:00.000","Title":"convert float64 to int (excel to pandas)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am starting out with computer vision and opencv. I would like to try camera calibration for the images that I have to see how it works. I have a very basic doubt.\nShould I use the same camera from which the distorted images were captured or I can use any camera to perform my camera calibration?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":67,"Q_Id":59678398,"Users Score":1,"Answer":"Camera calibration is supposed to do for the same camera. Purpose of calibrating a camera is to understand how much distortion the image has and to correct it before we use it to take actual pics. Even if you do not have the original camera, If you have the checkerboard images taken from that camera it is sufficient. Otherwise, look for a similar camera with features as similar as possible (focal length etc.) to take checker board images for calibration and this will somewhat serve your purpose.","Q_Score":0,"Tags":"python,opencv,image-processing,camera,camera-calibration","A_Id":59678540,"CreationDate":"2020-01-10T09:01:00.000","Title":"Camera Calibration basic doubts","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I want to create a binary 16*15 matrix with certain conditions. I use binary strings to make the matrix. I want my matrix to be as described:\n-The first and last two elements of each row must be alternative.\n-the sum of each row must be 8 or 7.\n-in each row, there should not be consecutive 1s or 0s. (one couple(00 or 11) is allowed in each row) .\n-the sum of the columns must be 8.\nthere are 26 possible strings that can fulfill the first 3 conditions.how can I fulfill the last conditions? \nI have a code but it is not working because it takes so much time and it is almost impossible.is there any other way?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":122,"Q_Id":59683287,"Users Score":1,"Answer":"I don't think you need any constraint to fulfill the last conditions. Columns = 8, which is just half of 16. You can just simply copy the first 8 rows to the last 8 rows and reverse all the 0 and 1, then the column sum would be 8 and the first three conditions are met.","Q_Score":0,"Tags":"python,constraints,binary-matrix","A_Id":59684408,"CreationDate":"2020-01-10T14:04:00.000","Title":"random binary matrix with restrictions","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working on a cyber security project wherein we have to prioritize vulnerabilities based on the existing features which are mostly categorical variables (also including couple of ordinal variables). \nThe objective here is to detect vulnerability that is most likely to be exploited, and thereby prioritizing it. Hence we have to predict a score of 0-10 . Whichever is the highest rating that we predict (in this case 10), will be the most critical vulnerability that needs immediate attention.\nAll that we have are the categorical variables (as input features).\nOnce again summarizing the problem here :\nCurrent Input features : All categorical variables (with couple of ordinal variables)\nCurrent Output feature : DOES NOT EXIST\nExpected Output : Predict a score in the range 0-10, with 10 being most critical vulnerability \nNever came across this kind of problem. It definitely looks like Regression is not the answer. Can you please share your thoughts on the same.","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":95,"Q_Id":59687810,"Users Score":0,"Answer":"I may be misunderstanding but it appears that you don't have the necessary information to make the prediction.\nMy understanding is that you have category information but no other associations. For some categories you might be able to hard code your prediction based on expert opinion. Predicting a ping sweep is basically benign, for example, just by knowing what it's called. For anything more dynamic you're going to need more information than you listed.","Q_Score":0,"Tags":"python,machine-learning,predict,continuous","A_Id":59687911,"CreationDate":"2020-01-10T19:21:00.000","Title":"How to predict a continuous variable without any output data ? All i have is Input data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to install scikit learn on Pycharm but it always shows an error:\n\" numpy.distutils.system_info.NotFoundError: No lapack\/blas resources found. \"\nAlso, pycharm is not able to recognize pre-installed libraries(basically I installed some libraries  eg. numpy scikit learn using pip in cmd)\nit is not able to install other libraries such as matplotlib. \nThe only library it installed is numpy, pandas.\nthe project interpreter is correct, but still, i am getting an error while installing these libs.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1024,"Q_Id":59691800,"Users Score":0,"Answer":"In PyCharm go to File -> Settings -> Project -> Project Interpreter. Click on the + sign on the right hand side of the window. Type the name of the library you want to install and PyCharm will add it for you.\nWhen PyCharm is unable to make that installation, it usually has to do with your virtual environment not being set up correctly.","Q_Score":1,"Tags":"python,scikit-learn,pycharm","A_Id":59691992,"CreationDate":"2020-01-11T05:01:00.000","Title":"not able to install scikit-learn on pycharm on windows","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataset of dialogues with various parameters (like if it is a question, an action, what emotion it conveys etc ). I have 4 different \"informations\" per sentence.\nlet s say A replys to B\nA has an additive parameter in a different list for its possible emotions (1.0.0.0) (angry.happy.sad.bored) - an another list for it s possible actions (1.0.0.0) (question.answer.inpulse.ending)\nI know how to build a regular RNN model (from the tutorials and papers I have seen here and there), but I can t seem to find a \"parameters\" architecture.\nShould I train multiple models ? (like sentence A --> emotions, then sentence B -->actions) then train the main RNN separately and predicting the result through all models ?\nor is there a way to build one single model with all the information stored right at the beginning ?\nI apologize for my approximate English, witch makes my search for answers even more difficult.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":61,"Q_Id":59692913,"Users Score":0,"Answer":"From the way I understand your question, you want to find emotions\/actions based on a particular sentence. Sentence A has emotions as labels and Sentence B has actions as labels. Each of the labels has 4 different values with a total of 8 values. And you are confused about how to implement labels as input.\nNow, you can give all these labels their separate classes. Like emotions will have labels (1.2.3.4) and actions will have labels (5.6.7.8). Then concat both the datasets and run Classification through RNN.\nIf you need to pass emotions\/actions as input, then add them to vectorized matrix. Suppose you have Sentence A stating \"Today's environment is very good\" with happy emotion. Add the emotion with it's matrix row, like this:\nToday | Environment | very | good | health\n1     | 1           | 1    | 1    | 0  \nNow add emotion such that:\nToday | Environment | very | good | health | emotion\n1     | 1           | 1    | 1    | 0      | 2(for happy)\nI hope this answers your question.","Q_Score":2,"Tags":"python,tensorflow,keras,deep-learning,artificial-intelligence","A_Id":59693366,"CreationDate":"2020-01-11T08:50:00.000","Title":"NLP AI logic - dialogue sequences with multiple parameters per sequence architecture","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have some data {x_i,y_i} and I want to fit a model function y=f(x,a,b,c) to find the best fitting values of the parameters (a,b,c); however, the three of them are not totally independent but constraints to 1<b , 0<=c<1 and g(a,b,c)>0, where g is a \"good\" function. How could I implement this in Python since with curve_fit one cannot put the parametric constraints directly?\nI have been reading with lmfit but I only see numerical constraints like 1<b, 0<=c<1 and not the one with g(a,b,c)>0, which is the most important.","AnswerCount":2,"Available Count":1,"Score":-0.0996679946,"is_accepted":false,"ViewCount":672,"Q_Id":59694481,"Users Score":-1,"Answer":"Like James Phillips, I was going to suggest SciPy's curve_fit.  But the way that you have defined your function, one of the constraints is on the function itself, and SciPy's bounds are defined only in terms of input variables.\nWhat, exactly, are the forms of your functions?  Can you transform them so that you can use a standard definition of bounds, and then reverse the transformation to give a function in the original form that you wanted?\nI have encountered a related problem when trying to fit exponential regressions using SciPy's curve_fit.  The parameter search algorithms vary in a linear fashion, and it's really easy to fail to establish a gradient.  If I write a function which fits the logarithm of the function I want, it's much easier to make curve_fit work.  Then, for my final work, I take the exponent of my fitted function.\nThis same strategy could work for you.  Predict ln(y).  The value of that function can be unbounded.  Then for your final result, output exp(ln(y)) = y.","Q_Score":0,"Tags":"python,curve-fitting,non-linear-regression","A_Id":59707606,"CreationDate":"2020-01-11T12:41:00.000","Title":"Fitting with funtional parameter constraints in Python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"tf 2.0.0-gpu\nCUDA 10.0\nRTX2070super\nhi. i got a problem regarding allocating gmemory. The initial allocation of memory is 7GB like this.\nCreated TensorFlow device (\/job:localhost\/replica:0\/task:0\/device:GPU:0 with 6994 MB memory)\n2020-01-11 22:19:22.983048: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n2020-01-11 22:19:23.786225: I tensorflow\/stream_executor\/cuda\/cuda_driver.cc:830] failed to allocate 2.78G (2989634304 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory\n2020-01-11 22:19:24.159338: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10.0\nLimit:                  7333884724\nInUse:                  5888382720\nMaxInUse:               6255411968\nNumAllocs:                    1264\nMaxAllocSize:           2372141056\nbut i can only use 5900MB memory and the rest of memory always fails to be allocated.\ni guess that if whole gpu memory is used in rtx 2070s, i use 2 types data typse(float16, float32). so i got a policy by using this codes\nopt = tf.keras.optimizers.Adam(1e-4)\nopt = tf.train.experimental.enable_mixed_precision_graph_rewrite(opt)\nStill, the allocation always fails.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":1026,"Q_Id":59694874,"Users Score":0,"Answer":"Tensorflow memory management can be frustrating.\nMain takeaway: whenever you see OOM there is actually not enough memory and you either have to reduce your model size or batch size. TF would throw OOM when it tries to allocate sufficient memory, regardless of how much memory has been allocated before.\nOn the start, TF would try to allocate a reasonably large chunk of memory which would be equivalent to about 90-98% of the whole memory available - 5900MB in your case. Then, when actual data starts to take more than that, TF would additionally try to allocate sufficient amount of memory or a bit more -  2.78G. And if that does not fit it would throw OOM, like in your case. Your GPU could not fit 5.9+2.8Gb. The last chunk of 2.78G might actually be a little more than TF needs, but it would anyhow be used later if you have multiple training steps because maximum required memory can fluctuate a bit between identical Session.run's.","Q_Score":0,"Tags":"python,tensorflow","A_Id":59699915,"CreationDate":"2020-01-11T13:32:00.000","Title":"rtx 2070s failed to allocate gpu memory from device:CUDA_ERROR_OUT_OF_MEMORY: out of memory","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I launched a notebook with GCP AI Platform. Then, I tried to install tensorflow by:\nimport tensorflow as tf\nThere is an error message of \n\nNo module named 'tensorflow' \n\nI tried to install it by:\n!pip install -U --user tensorflow==1.14.0\nBut the same error message appeared. As it is a GCP platform, I wonder why I need to install tensorflow. During Coursera training, I can import tensorflow directly without installation. I wonder if I missed anything.\nGrateful if you can help.\nThank you","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":358,"Q_Id":59697085,"Users Score":0,"Answer":"You have probably selected an instance type that doesn't have tensorflow pre-installed. \nAfter you install a Python dependency you will have to restart the Python Kernel for updates to take effect by clicking on Kernel->Restart Kernel....","Q_Score":0,"Tags":"tensorflow,google-cloud-platform,installation,python-import,gcp-ai-platform-notebook","A_Id":59703393,"CreationDate":"2020-01-11T17:50:00.000","Title":"error message of No module named 'tensorflow' in GCP AI Platform Notebook","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to build a Speech Recognition System, which is a squence-to-sequence model. But I got confused about how to feed the extracted feature(fbank with the dimension of 40) to LSTM. As far as I have found, there are different methods to feed the data as input into LSTM. However, I have a doubt to fully understand them. I would be so thankful if someone tells me whether or not I am correct in the following cases.\nCase 1:\nIn the convenient format [Batch_Size, Time_Step, Feature_Dim], If I select [1, None, 40], the length of each sequence(utterance) can be varied? if so, in this case I do not need to pad each sequence, am I right?\nCase 2: \nIf all input sequences are padded to the same length, the Batch_Size can be any value like 64, 128 and etc?\nFinally, one more question, do I notice that the Time_Step in each Batch should be the same?\nI would be so thankful if someone can help me to get rid of my doubts or give me some suggestions.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":327,"Q_Id":59697342,"Users Score":0,"Answer":"it depends on how your system built is it end-to-end training or did u use hand-engineering features such MFCC? one more note the main use of RNN's is to have a variable-length input.","Q_Score":0,"Tags":"python,speech-recognition,lstm,recurrent-neural-network,speech-to-text","A_Id":59744476,"CreationDate":"2020-01-11T18:22:00.000","Title":"How to feed variable-length of speech feature to RNN(LSTM) for Speech Recognition?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Let X be a variable I have stored in multiple files. In each files, X has dimension of (time, depth, latitude, longitude). I wish to do a yearly average, and each file store a year of data.\nTo be efficient, I open the data with X = xarray.open_mfdataset('path'+'year_*_X.nc').\nI do not want to average all the time axis, i.e.: X.mean(dim='time').\nI observe that X store 'chunks' since it gives the 'chunksize', so maybe it can be used to average the time dimension of each chunks?\nIn [1] : X\nOut[1] : <xarray.DataArray 'ty_trans_submeso' (time: 240, st_ocean: 50, yu_ocean: 200, xt_ocean: 360)>\ndask.array<shape=(240, 50, 200, 360), dtype=float32, chunksize=(12, 50, 200, 360)>","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":139,"Q_Id":59699168,"Users Score":1,"Answer":"X.groupby('time.year').mean(dim='time') should do the trick as long as time is a datetime64 object.","Q_Score":0,"Tags":"python-xarray","A_Id":59707182,"CreationDate":"2020-01-11T22:23:00.000","Title":"Average a dimension of a chunk (xarray)","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm sorry, i know that this is a very basic question but since i'm still a beginner in machine learning, determining what model suits best for my problem is still confusing to me, lately i used linear regression model (causing the r2_score is so low) and a user mentioned i could use certain model according to the curve of the plot of my data and when i see another coder use random forest regressor (causing the r2_score 30% better than the linear regression model) and i do not know how the heck he\/she knows better model since he\/she doesn't mention about it. I mean in most sites that i read, they shoved the data to some models that they think would suit best for the problem (example: for regression problem, the models could be using linear regression or random forest regressor) but in some sites and some people said firstly we need to plot the data so we can predict what exact one of the models that suit the best. I really don't know which part of the data should i plot? I thought using seaborn pairplot would give me insight of the shape of the curve but i doubt that it is the right way, what should i actually plot? only the label itself or the features itself or both? and how can i get the insight of the curve to know the possible best model after that?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":56,"Q_Id":59705218,"Users Score":0,"Answer":"If you are using off-the-shelf packages like sklearn, then many simple models like SVM, RF, etc, are just one-liners, so in practice, we usually try several such models at the same time.","Q_Score":0,"Tags":"python,machine-learning,plot,prediction","A_Id":59709870,"CreationDate":"2020-01-12T15:18:00.000","Title":"Which data to plot to know what model suits best for the problem?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm sorry, i know that this is a very basic question but since i'm still a beginner in machine learning, determining what model suits best for my problem is still confusing to me, lately i used linear regression model (causing the r2_score is so low) and a user mentioned i could use certain model according to the curve of the plot of my data and when i see another coder use random forest regressor (causing the r2_score 30% better than the linear regression model) and i do not know how the heck he\/she knows better model since he\/she doesn't mention about it. I mean in most sites that i read, they shoved the data to some models that they think would suit best for the problem (example: for regression problem, the models could be using linear regression or random forest regressor) but in some sites and some people said firstly we need to plot the data so we can predict what exact one of the models that suit the best. I really don't know which part of the data should i plot? I thought using seaborn pairplot would give me insight of the shape of the curve but i doubt that it is the right way, what should i actually plot? only the label itself or the features itself or both? and how can i get the insight of the curve to know the possible best model after that?","AnswerCount":2,"Available Count":2,"Score":0.0996679946,"is_accepted":false,"ViewCount":56,"Q_Id":59705218,"Users Score":1,"Answer":"This question is too general, but I will try to give an overview of how to choose the model. First of all you should that there is no general rule to choose the family of models to use, it is more a choosen by experiminting different model and looking to which one gives better results. You should also now that in general you have multi-dimensional features, thus plotting the data will not give you a full insight of the dependance of your features with the target, however to check if you want to fit a linear model or not, you can start plotting the target vs each dimension of the input, and look if there is some kind of linear relation. However I would recommand that you to fit a linear model, and check if if this is relvant from a statistical point of view (student test, smirnov test, check the residuals...). Note that in real life applications, it is not likeley that linear regression will be the best model, unless you do a lot of featue engineering. So I would recommand you to use more advanced methods (RandomForests, XGboost...)","Q_Score":0,"Tags":"python,machine-learning,plot,prediction","A_Id":59706610,"CreationDate":"2020-01-12T15:18:00.000","Title":"Which data to plot to know what model suits best for the problem?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"my project run local on pycharm without error but when i deployed it on heroku\n error message appears :Importing the multiarray numpy extension module failed.\n i changed numpy version more than one but it does not work\nDjango Version:     2.2.5\nException Type:     ImportError\nException Value: Importing the multiarray numpy extension module failed.  Most\nlikely you are trying to import a failed build of numpy.\nIf you're working with a numpy git repo, try git clean -xdf (removes all\nfiles not under version control).  Otherwise reinstall numpy.\nOriginal error was: libpython3.6m.so.1.0: cannot open shared object file: No such file or directory\nException Location:     \/app\/.heroku\/python\/lib\/python3.6\/site-packages\/numpy\/core\/init.py in , line 26\nPython Executable:  \/app\/.heroku\/python\/bin\/python\nPython Version:     3.6.10\nnumpy version : 1.18.1\nany solution?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":322,"Q_Id":59709490,"Users Score":0,"Answer":"i solve it \nmy solution was\nRun locally   1- python -m pip install python-dev-tools\nthen  2- pip freeze > requirements.txt\n3-git add .\n4-git commit -m \"v5\"\n5- heroku git:remote -a \"your appname on heroku\"\n6-git push heroku master","Q_Score":0,"Tags":"python,numpy,heroku","A_Id":59742768,"CreationDate":"2020-01-13T00:59:00.000","Title":"Importing the multiarray numpy extension module failed. Most likely you are trying to import a failed build of numpy","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":1},{"Question":"I have to predict the winner of the Australian Open 2020. My dataset has these features: Location \/ Tournament \/ Date \/ Series \/ Court \/ Surface \/ Round \/ Winner \/ Loser etc. \nI trained my model using just these features 'Victory','Series','Court','Surface','WinRank','LoseRank','WPts','LPts','Wsets','Lsets','Weather' and I have a 0.93 accuracy but now I have to predict the name of the winner and I don't have any idea how to do it based on the model that I trained. \nExample:  If I have Dimitrov G. vs Simion G using random forest the model has to give me one of them as the winner of the match. \nI transformed the names of the players in dummy variables but after that, I don't know what to do? \nCan anyone give me just an idea of how could I predict the winner? so I can create a Tournament, please?","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":30,"Q_Id":59722197,"Users Score":0,"Answer":"To address such a problem, I would suggest creation of a custom target variable. \nFirstly, the transformation of names of players into dummy variables seems reasonable (Just make sure, the unique player is identified with the same first and last name combinations thereby, avoiding duplications and thus, having the correct dummy code for the player name).\nNow, to create the target variable \"wins\" - \n\nUse the two player names - P1, P2 of the match as input features for your model.\nDefine the \"wins\" as 1 if P1 wins and 0 if P2 wins.\nRun your model with this set up. \nWhen you want to create a tournament and predict the winner, the inputs will be your 2 players and other match features. If, \"wins\" is close to 1, it means your P1 wins and output that player name.","Q_Score":0,"Tags":"python,machine-learning","A_Id":59722344,"CreationDate":"2020-01-13T18:30:00.000","Title":"How to predict the player using random forest ML","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have to deploy a Deep Learning model on AWS Lambda which does object detection. It is triggered on addition of image in the S3 bucket.\nThe issue that I'm facing is that the Lambda function code uses a lot of libraries like Tensorflow, PIL, Numpy, Matplotlib, etc. and if I try adding all of them in the function code or as layers, it exceeds the 250 MB size limit. \nIs there any way I can deploy the libraries zip file on S3 bucket and use them from there in the function code (written in Python 3.6) instead of directly having them as a part of the code?\nI can also try some entirely different approach for this.","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":1106,"Q_Id":59729056,"Users Score":0,"Answer":"Ultimately, I changed my approach from using Lambda to using EC2.\nI deployed the whole code with libraries on an EC2 instance and then triggered it using Lambda. On EC2, it can also be deployed on Apache server to change port mapping.","Q_Score":2,"Tags":"python,amazon-web-services,amazon-s3,deep-learning,aws-lambda","A_Id":59902346,"CreationDate":"2020-01-14T07:18:00.000","Title":"Importing libraries in AWS Lambda function code from S3 bucket","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm currently testing out various denoising algorithms on noisy images. Is there a way of measuring the standard deviation of the noise in the images that I'm denoising (before and after) with OpenCV(python) or any easier method?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":565,"Q_Id":59742157,"Users Score":0,"Answer":"I would try to pursue this by taking the image before denoise and applying a bitwise_and to it from the denoised image. This would give you just the noise. Then you could take the standard deviation and compare between the denoising algorithms.","Q_Score":0,"Tags":"python,image,opencv,image-processing,noise","A_Id":59742877,"CreationDate":"2020-01-14T21:39:00.000","Title":"How to calculate the standard deviation of noise for an image?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using catboost classifier for my binary classification model where I have a highly imbalance dataset of 0 -> 115000 & 1 -> 10000.\nCan someone please guide me in how to use the following parameters in catboostclassifier:\n1. class_weights \n2. scale_pos_weight ? \nFrom the documentation, I am under the impression that I can use\nRatio of sum of negative class by sum of positive class i.e. 115000\/10000=11.5 as the input for scale_pos_weight but I am not sure .\nPlease let me know what exact values to use for these two parameters and method to derive that value?\nThanks","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":3731,"Q_Id":59746304,"Users Score":2,"Answer":"For scale_pos_weight you would use negative class \/\/ positive class. in your case it would be 11 (I prefer to use whole numbers).\nFor class weight you would provide a tuple of the class imbalance. in your case it would be: class_weights = (1, 11)\nclass_weights is more flexible so you could define it for multi-class targets. for example if you have 4 classes you can set it: class_weights = (0.5,1,5,25)\nand you need to use only one of the parameters. for a binary classification problem I would stick with scale_pos_weight.","Q_Score":1,"Tags":"python,classification,catboost","A_Id":59773204,"CreationDate":"2020-01-15T06:45:00.000","Title":"catboost classifier for class imbalance?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I trained a neural network with google colab.\nI saved the neural network using joblib.dump()\nI then loaded the model on my PC using joblib.load()\nI made a prediction on the exact same sample, using the same model, on both colab and my PC.  On colab, it has an output of [[0.51]].  On my pc, it has an output of [[nan]].\nThe model summary reports that the architecture of the model is the same.\nI checked the weights of the model I loaded on my PC, and the model on colab, and the weights are the exact same.\nAny ideas as to what I can do?  Thank you.\nQuick update:  even if I change all of my inputs to zero, the prediction is still nan.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":358,"Q_Id":59760569,"Users Score":0,"Answer":"As far as I know keras has its own function to save the model such as model.save('file.h5'), and the joblib library is used to save sklearn models.","Q_Score":0,"Tags":"python,tensorflow,keras,google-colaboratory","A_Id":59761188,"CreationDate":"2020-01-15T22:40:00.000","Title":"keras model prediction is nan after saving and loading","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have trained tensorflow object detection model (for num_steps:50000) using SSD (mobilenet-v1) on custom dataset. I got mAP@.50IOU ~0.98 and loss ~1.17. The dataset consist of uno playing card images (skip, reverse, and draw four). On all these cards, model performs pretty well as I have trained model only on these 3 card (around 278 images with 829 bounding boxes (25% bounding box used for testing i.e. validation) collected using mobile phone).\nHowever, I haven\u2019t trained model on any other card but still it detects other cards (inference using webcam).\nHow can I fix this? Should I also collect other class images (anything other than skip, reverse and draw four cards) and ignore this class in operation? So that model sees this class i.e. Label: Other images during training and doesn\u2019t put any label during inference.\nI am not sure how to inform tensorflow object detection API that it should ignore images from Other class.\nCan anyone please provide pointer?\nPlease share your views!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":687,"Q_Id":59769698,"Users Score":1,"Answer":"Yes, you need to have another Class which is the object you don't want to detect. \nIf you don't have this Other Class which includes everything that is not to be detected. The model will compare it to the existing class which is almost identical to the cards of interest.\nSome of the factors are:\n\nSimilarity of Shape\nSimilarity of Color \nSimilarity of Symbols\n\nThis is why even though it is not the card of interest (Skip, Reverse, and Draw 4), it would somehow have high \"belongingness\" to these three classes.\nHaving another Class to dump all of these can significantly lessen the \"belongingness\" to the three classes of interest and as much as possible provide A LOT of Data during Training.\nIf you don't want to have another class.\nYou could overfit Skip, Reverse, and Draw 4 cards (close to 100%), then increase your threshold value of detection to (70-90%).\nHope this will help you.","Q_Score":2,"Tags":"python,tensorflow,object-detection,object-detection-api","A_Id":61222708,"CreationDate":"2020-01-16T12:26:00.000","Title":"How to use \"ignore\" class with tensorflow object detection API?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Hi guys was wondering if anyone could help me with what I would actually answer for this question on a upcoming exam I have on AI in Python. The question confuses me as I thought I would usually need more info to answer but it is not provided. the question asked is\n\nA Python class is used to represent a neural network and the feed forward operation is called as indicated below:\n' y_hat = NN.forward(X) '\nwhere y_hat is the output and X is the input matrix.\nThe neural network has an input size of 80, one hidden layer of size 40 and an output layer of size 1.\nWhat size will be the W1 and W2 matrices?\n\nIf anyone could help me with this as my lecturer is not replying to the classes emails.\nMany Thanks!","AnswerCount":3,"Available Count":2,"Score":0.0665680765,"is_accepted":false,"ViewCount":1373,"Q_Id":59770747,"Users Score":1,"Answer":"By your description I suppose that you have two layers, where the first layer outpits a tensor of batch x 40 and the second layer a batch x 1 tensor, meanwhile the input is a tensor of batch x 80.\nThen the weights dimentions are:\nW1: 80x40 -> first layer\nW2: 40x1-> out layer","Q_Score":0,"Tags":"python,neural-network","A_Id":59771196,"CreationDate":"2020-01-16T13:30:00.000","Title":"Neural Networks Weight matrices explained","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Hi guys was wondering if anyone could help me with what I would actually answer for this question on a upcoming exam I have on AI in Python. The question confuses me as I thought I would usually need more info to answer but it is not provided. the question asked is\n\nA Python class is used to represent a neural network and the feed forward operation is called as indicated below:\n' y_hat = NN.forward(X) '\nwhere y_hat is the output and X is the input matrix.\nThe neural network has an input size of 80, one hidden layer of size 40 and an output layer of size 1.\nWhat size will be the W1 and W2 matrices?\n\nIf anyone could help me with this as my lecturer is not replying to the classes emails.\nMany Thanks!","AnswerCount":3,"Available Count":2,"Score":0.1325487884,"is_accepted":false,"ViewCount":1373,"Q_Id":59770747,"Users Score":2,"Answer":"Let's say :\n\nX input vector, (size 80*1)\nH hidden layer vector, (size 40*1)\nY output vector, (size 1*1)\n\nYou have :\n\nH = W1 * X\nY = W2 * H\n\nSo :\n\nW1 has size (40*80)\nW2 has size (1*40)\n\nNote : size (m*n) means m rows, n columns","Q_Score":0,"Tags":"python,neural-network","A_Id":59771216,"CreationDate":"2020-01-16T13:30:00.000","Title":"Neural Networks Weight matrices explained","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am having the following error when importing TensorFlow:\n\nERROR:root:Internal Python error in the inspect module.\n      Below is the traceback from this internal error.\n\nI believe it is related to .dll \n\nImportError: DLL load failed: The specified module could not be found.\n\nI am using Python version 3.7.4. \nAny advice? Thanks","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":146,"Q_Id":59771753,"Users Score":1,"Answer":"try:\n$ conda install tensorflow==2.0.0","Q_Score":0,"Tags":"python,anaconda","A_Id":59771829,"CreationDate":"2020-01-16T14:25:00.000","Title":"Anaconda Jupyter shows eroor when importing tensorflow","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a dataset with 200000 rows and 201 columns. I want to have descriptive statistics of all the variables.\nI tried:\n        '''train.describe()'''\nBut this is only giving the output for the first and last 8 variables. This there any method I can use to get the statistics for all of the columns.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":678,"Q_Id":59773763,"Users Score":1,"Answer":"probably, some of your columns where in some  type other than numerical. Try train.apply(pd.to_numeric) then train.describe()","Q_Score":4,"Tags":"python-3.x,pandas,data-analysis","A_Id":59773876,"CreationDate":"2020-01-16T16:15:00.000","Title":"How to get descriptive statistics of all columns in python","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am training a neural network, but the feature vectors do not have the same size. \nThis problem may be fixed by adding some zeros or removing some values, but the greater problem would be data loss or generating meaningless data.\nSo, is there any approach to make them equal size, without mentioned weaknesses? Maybe transformation to other dimensions?\nI do not want to use random values or \"NA\".","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":160,"Q_Id":59776583,"Users Score":0,"Answer":"Adding zeros or zero padding is the most common method of making very short audio signals longer as well as it can be used to match the lengths of audio data before feature extraction. \nIn my understanding, this does not affect the outcome of the analysis, specially as you are using a neural network.","Q_Score":0,"Tags":"python-3.x,wav,feature-extraction,mfcc","A_Id":59810718,"CreationDate":"2020-01-16T19:31:00.000","Title":"How to make feature vectors size equal for training neural networks?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a data frame with columns X Y temperature Label\nlabel is an integer between 1 and 9 \nI want to add an additional column my_label_mean_temperature which will contain for each row the mean of the temperatures of the rows that has the same label.\nI'm pretty sure i need to start with my_df.groupby('label') but not sure how to calculate the mean on temperature and propagate the values on all the rows of my original data frame","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":38,"Q_Id":59803001,"Users Score":0,"Answer":"df['my_label_mean_temperature']= df.groupby('label', as_index=False)['temperature'].mean()","Q_Score":0,"Tags":"python,pandas","A_Id":59803137,"CreationDate":"2020-01-18T17:16:00.000","Title":"Set column value as the mean of a group in pandas","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Hello I have a time series data that basically behaves in a sawtooth manner. After each maintenance period, the signal always goes up before going down until a maintenance happens which will cause the signal to increase again. I am trying to predict the signal and see what happens to the signal if I schedule future maintenance. \nI am new to time series and I am not sure which model should I use to predict the data. I have looked at cross correlation but it doesnt seem to take into account any events that will influence the signal like my problem.\nI just what what happens after each maintenance event and the signal follws a similar trend all the time after each maintenance period where it goes up and down. Any suggestions?","AnswerCount":1,"Available Count":1,"Score":0.3799489623,"is_accepted":false,"ViewCount":58,"Q_Id":59807263,"Users Score":2,"Answer":"You are looking for a ML model for time-series data. \nThis is a huge field but I`ll try to write a few important notes:\n\nTry to generate a dataframe where each row is a timestamp and each column is a feature. \nNow you can generate rolling features - for example rolling mean\/std of your features, using a few different time windows.\nsplit your data to train and test - this is a very tricky part in time series data. You should be very careful with this. You have to split the data by time (not randomly), in order to simulate the real world where you learn from the past and predict the future. You must verify that you haven't a leakage - for example, if you use as a feature \"rolling mean of the last week\", you must verify that you didn't calculated your signal for validation using data from the train set. \nTrain a baseline model using classic ML methods - for example boosting trees etc.\nIn the next steps you can improve your baseline and then continue with more advanced models (LSTM etc)","Q_Score":1,"Tags":"python,machine-learning,time-series,prediction","A_Id":59807451,"CreationDate":"2020-01-19T04:55:00.000","Title":"How can I use machine learning for time series problem","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have a data frame which contains multiple number sequences, i.e.:\n1324123\n1235324\n12342212\n4313423\n221231;\n...\nthese numbers met the following requirement: the number of each digit is from 1 - 4.\nWhat I want to do is find all unique sequences and their reads. Regarding the unique sequence, two-digit differences are allowed.\nFor example:\n12344\n12344\n12334\n1234\n123444\nare considered as the same sequence and the original sequence is 1234 and the associated read is 5.\nI want to accomplish this in python and only basic python packages are allowed: numpy, pandas, etc.\nEDIT\nthe real case is DNA sequence. For a simple DNA sequence ATGCTAGC, due to reading errors, the output of this actual sequence might be:\nATGCTAG(deleted), ATGCTAGG(altered), ATGCTAGCG(insertion), ATGCTAGC(unchanged).\nThese four sequences are considered the same sequence, and read is the time of appearance.","AnswerCount":2,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":122,"Q_Id":59807333,"Users Score":0,"Answer":"As it is, the problem isn't defined well enough - it is underconstrained.\n(I'm going to be using case-sensitive sequences of [A-Za-z] for examples, since using unique characters makes the reasoning easier, but the same things apply to [1-4] and [ACGT] as well; For the same reason, I'm allowing only single-character differences in the examples. When I include a number in parenthesis after a sequence, it denotes the read)\nJust a few examples off the top of my head:\n\nFor {ABCD, ABCE}, which one should be selected as the real sequence? By random?\nWhat about {ABCD, ABCE, ABCE}? Is random still okay?\nFor {ABCD, ABCE, ABED}, should ABCD(3) be selected, since there's a single-letter difference between it and the other two, even though there's a two-letter difference between ABCE and ABED?\nFor {ABCE, ABED}, should ABCD(2) be selected, since there's a single-letter difference between it and the other two, even though the sequence doesn't exist in the input itself?\nFor {ABCD, ABCZ, ABYZ}, should ABCZ(3) be selected? Why not {ABCD(2), ABYZ(2)}?\nFor {ABCD, ABCZ, ABYZ, AXYZ}, should {ABCD(2), AXYZ(2)} be selected? Why not {ABCZ(3), ABYZ(3)}? (Or maybe you want it to chain, so you'd get a read of 4, even though the maximum difference is already 3 letters?)\n\nIn the comments, you said:\n\nI am just listing a very simple example, the real case is much longer.\n\nHow long? What's the minimum length? (What's the maximum?) It's relevant information.\nAnd finally - before I get to the meat of the problem - what are you doing this for? If it's just for learning - as a personal exercise - that's fine. But if you're actually doing some real research: For all that is good and holy, please research existing tools\/libraries for dealing with DNA sequences and\/or enlist the help of someone who is familiar with those. I'm sure there are heaps of tools available that can do better and faster, than what I'm about to present. That being said...\nLet's look at this logically. If you have a big collection of strings, and you want to quickly find if it contains a specific string, you'd use a set (or a dictionary, if there's associated data). The problem, of course, is that you don't want to find only exact matches. But since the number of allowed errors is constrained and extremely small, there are some easy workarounds.\nFor one, you could just generate all the possible sequences with the allowable amount of error, and try to lookup each of them - but that really only makes sense if the if the strings are short and there's only one allowable error, since the amount of possible error combinations scales up really fast.\nIf the strings are long enough, and aren't expected to generally share large chunks (unless they're within the allowable error, so the strings are considered same), you can make the observation that if there's a maximum of two modifications, and you cut a string into 3 parts (it doesn't matter if there's leftovers), then one of the parts must match the corresponding part of the original string. This can be extended to insertions and deletions by generating the 3 parts for 3 different shifts of the string (and choosing\/dealing with the part lengths suitably). So by generating 9 keys for each sequence, and using a dictionary, you can quickly find all sequences that are capable of matching the sequence with 2 errors. (Of course, as I said at the start, this doesn't work if a large part of unrelated strings share big chunks: If all of your strings only have differences at the beginning, and have the same end, you'll just end up with all the strings grouped together, and no closer to solving the problem) (Also: If the sequence you want to select doesn't necessarily exist in the input, like described in the 4th example, you need 5 parts with 5 shifts to guarantee a matching key, since the difference between the existing sequences can be up to 4)\nAn example:\n\nOriginal sequence:\nABCDEFGHIJKLMNOP\n\nGenerated parts: (Divided into 3 parts (of size 4), with 3 different shifts)\n[ABCD][EFGH][IJKL]MNOP\nA[BCDE][FGHI][JKLM]NOP\nAB[CDEF][GHIJ][KLMN]OP\n\nIf you now make any two modifications to the original sequence, and generate parts for it in the same manner, at least one of the parts will always match. If the sequences are all approximately the same size, the part size can just be statically set to a suitable value (there must be at least 2 characters left over after the shift, as shown here, so a string with two deletions can still generate the same keys). If not, eg. powers of two can be used, taking care to generate keys for both sides when the string length is such that matching sequences could fall into a neighbouring size bucket.\nBut those are in essence just examples of how you could approach coming up with solutions when presented with this kind of a problem; just random ad hoc methods. For a smarter, more general solution, you could look at eg. generalized suffix trees - they should allow you to find matching sequences with mismatches allowed very fast, though I'm not sure if that includes insertions\/deletions, or how easy that would be to do.","Q_Score":0,"Tags":"python","A_Id":60538738,"CreationDate":"2020-01-19T05:09:00.000","Title":"Find unique number of sequence and reads","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Whenever I try to install TensorFlow with pip on Python 3.8, I get the error that TensorFlow is not found. I have realized later on that it is not supported by Python 3.8.\nHow can I install TensorFlow on Python 3.8?","AnswerCount":6,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":55356,"Q_Id":59809495,"Users Score":0,"Answer":"Currently it does support python 3.8 all we need to do is create a new environment ,select 'update index' , select uninstalled and one can find tensorflow for installing","Q_Score":13,"Tags":"python,python-3.x,tensorflow,python-3.8","A_Id":67048383,"CreationDate":"2020-01-19T11:16:00.000","Title":"How to install TensorFlow with Python 3.8","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Whenever I try to install TensorFlow with pip on Python 3.8, I get the error that TensorFlow is not found. I have realized later on that it is not supported by Python 3.8.\nHow can I install TensorFlow on Python 3.8?","AnswerCount":6,"Available Count":3,"Score":0.0996679946,"is_accepted":false,"ViewCount":55356,"Q_Id":59809495,"Users Score":3,"Answer":"Tensorflow does not support Python 3.8 at the moment. The latest supported Python version is 3.7. A solution is to install Python 3.7, this will not affect your codes since Python 3.7 and 3.8 are very similar. Right now Python 3.7 is supported by more frameworks like TensorFlow. Soon Python 3.8 will have more supported frameworks, and that\u00b4s when you can install TensorFlow for Python 3.8.","Q_Score":13,"Tags":"python,python-3.x,tensorflow,python-3.8","A_Id":61622264,"CreationDate":"2020-01-19T11:16:00.000","Title":"How to install TensorFlow with Python 3.8","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Whenever I try to install TensorFlow with pip on Python 3.8, I get the error that TensorFlow is not found. I have realized later on that it is not supported by Python 3.8.\nHow can I install TensorFlow on Python 3.8?","AnswerCount":6,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":55356,"Q_Id":59809495,"Users Score":0,"Answer":"Instead of pip or conda command, I used pip3 command and it worked.","Q_Score":13,"Tags":"python,python-3.x,tensorflow,python-3.8","A_Id":69379579,"CreationDate":"2020-01-19T11:16:00.000","Title":"How to install TensorFlow with Python 3.8","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have limited knowledge about sample_weights in the sklearn library, but from what I gather, it's generally used to help balance imbalanced datasets during training. What I'm wondering is, if I already have a perfectly balanced binary classification dataset (i.e. equal amounts of 1's and 0's in the label\/Y\/class column), could one add a sample weight to the 0's in order to put more importance on predicting the 1's correctly?\nFor example, let's say I really want my model to predict 1's well, and it's ok to predict 0's even though they turn out to be 1's. Would setting a sample_weight of 2 for 0's, and 1 for the 1's be the correct thing to do here in order to put more importance on correctly predicting the 1's? Or does that matter? And then I guess during training, is the f1 scoring function generally accepted as the best metric to use?\nThanks for the input!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":81,"Q_Id":59816484,"Users Score":0,"Answer":"ANSWER\nAfter a couple rounds of testing and more research, I've discovered that yes, it does make sense to add more weight to the 0's with a balanced binary classification dataset, if your goal is to decrease the chance of over-predicting the 1's. I ran two separate training sessions using a weight of 2 for 0's and 1 for the 1's, and then again vice versa, and found that my model predicted less 1's when the weight was applied to the 0's, which was my ultimate goal.\nIn case that helps anyone. \nAlso, I'm using SKLearn's Balanced Accuracy scoring function for those tests, which takes an average of each separate class's accuracy.","Q_Score":1,"Tags":"python-3.x,scikit-learn,classification","A_Id":59871447,"CreationDate":"2020-01-20T02:58:00.000","Title":"Does it make sense to use sample_weights for balanced datasets?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"My dataset has a column where upon printing the dataframe each entry in the column is like so:\n{\"Wireless Internet\",\"Air conditioning\",Kitchen}\nThere are multiple things wrong with this that I would like to correct\n\nUpon printing this in the console, python is printing this:'{\"Wireless Internet\",\"Air conditioning\",Kitchen}' Notice the quotations around the curly brackets, since python is printing a string. \nIdeally, I would like to find a way to convert this to a list like: [\"Wireless Internet\",\"Air conditioning\",\"Kitchen\"] but I do not know how. Further, notice how some words so not have quotations, such as Kitchen. I do not know how to go about correcting this. \n\nThanks","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":182,"Q_Id":59817518,"Users Score":0,"Answer":"what you have is a set of words, curly brackets are for Dictionary use such as {'Alex,'19',Marry','20'} its linking it as a key and value which in my case it name and age, rather than that you can use to_list command in python maybe it suits your needs.","Q_Score":3,"Tags":"python,string,list,dictionary,type-conversion","A_Id":59817648,"CreationDate":"2020-01-20T05:37:00.000","Title":"My Dataset is showing a string when it should be a curly bracket set\/dictionary","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I just installed the latest version of Tensorflow via pip install tensorflow and whenever I run a program, I get the log message:\n\nW tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:55] Could not load dynamic library 'cudart64_101.dll'; dlerror: cudart64_101.dll not found\n\nIs this bad? How do I fix the error?","AnswerCount":16,"Available Count":5,"Score":0.024994793,"is_accepted":false,"ViewCount":357438,"Q_Id":59823283,"Users Score":2,"Answer":"download CUDA Toolkit 11.0 RC\nTo solve the issue,\nI just find cudart64_101.dll on my disk\n( C:\\Program Files\\NVIDIA Corporation\\NvStreamSrv) and add it as variable environment that is add value (C:\\Program Files\\NVIDIA\\Corporation\\NvStreamSrv)cudart64_101.dll to user's environment variable Path).","Q_Score":143,"Tags":"python,python-3.x,tensorflow,keras,tensorflow2.0","A_Id":68297432,"CreationDate":"2020-01-20T12:26:00.000","Title":"Could not load dynamic library 'cudart64_101.dll' on tensorflow CPU-only installation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I just installed the latest version of Tensorflow via pip install tensorflow and whenever I run a program, I get the log message:\n\nW tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:55] Could not load dynamic library 'cudart64_101.dll'; dlerror: cudart64_101.dll not found\n\nIs this bad? How do I fix the error?","AnswerCount":16,"Available Count":5,"Score":0.0,"is_accepted":false,"ViewCount":357438,"Q_Id":59823283,"Users Score":0,"Answer":"This could be caused by the version of python you are running as well, I was using the python 3.7 from the microsoft store and I run into this error, switching to python 3.10 fixed it.","Q_Score":143,"Tags":"python,python-3.x,tensorflow,keras,tensorflow2.0","A_Id":72103706,"CreationDate":"2020-01-20T12:26:00.000","Title":"Could not load dynamic library 'cudart64_101.dll' on tensorflow CPU-only installation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I just installed the latest version of Tensorflow via pip install tensorflow and whenever I run a program, I get the log message:\n\nW tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:55] Could not load dynamic library 'cudart64_101.dll'; dlerror: cudart64_101.dll not found\n\nIs this bad? How do I fix the error?","AnswerCount":16,"Available Count":5,"Score":0.024994793,"is_accepted":false,"ViewCount":357438,"Q_Id":59823283,"Users Score":2,"Answer":"Tensorflow gpu 2.2 and 2.3 nightly\n(along CUDA Toolkit 11.0 RC)\nTo solve the same issue as OP, I just had to find cudart64_101.dll on my disk (in my case C:\\Program Files\\NVIDIA Corporation\\NvStreamSrv) and add it as variable environment (that is add value C:\\Program Files\\NVIDIA\\Corporation\\NvStreamSrv)cudart64_101.dll to user's environment variable Path).","Q_Score":143,"Tags":"python,python-3.x,tensorflow,keras,tensorflow2.0","A_Id":62364379,"CreationDate":"2020-01-20T12:26:00.000","Title":"Could not load dynamic library 'cudart64_101.dll' on tensorflow CPU-only installation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I just installed the latest version of Tensorflow via pip install tensorflow and whenever I run a program, I get the log message:\n\nW tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:55] Could not load dynamic library 'cudart64_101.dll'; dlerror: cudart64_101.dll not found\n\nIs this bad? How do I fix the error?","AnswerCount":16,"Available Count":5,"Score":0.0374824318,"is_accepted":false,"ViewCount":357438,"Q_Id":59823283,"Users Score":3,"Answer":"I installed cudatoolkit 11 and copy dll\nC:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.1\\bin to C:\\Windows\\System32.\nIt fixed for PyCharm but not for Anaconda jupyter:\n\n[name: \"\/device:CPU:0\" device_type: \"CPU\" memory_limit: 268435456\nlocality { } incarnation: 6812190123916921346 , name: \"\/device:GPU:0\"\ndevice_type: \"GPU\" memory_limit: 13429637120 locality {   bus_id: 1\nlinks {   } } incarnation: 18025633343883307728 physical_device_desc:\n\"device: 0, name: Quadro P5000, pci bus id: 0000:02:00.0, compute\ncapability: 6.1\" ]","Q_Score":143,"Tags":"python,python-3.x,tensorflow,keras,tensorflow2.0","A_Id":65183422,"CreationDate":"2020-01-20T12:26:00.000","Title":"Could not load dynamic library 'cudart64_101.dll' on tensorflow CPU-only installation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I just installed the latest version of Tensorflow via pip install tensorflow and whenever I run a program, I get the log message:\n\nW tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:55] Could not load dynamic library 'cudart64_101.dll'; dlerror: cudart64_101.dll not found\n\nIs this bad? How do I fix the error?","AnswerCount":16,"Available Count":5,"Score":-1.0,"is_accepted":false,"ViewCount":357438,"Q_Id":59823283,"Users Score":-4,"Answer":"A simpler way would be to create a link called cudart64_101.dll to point to cudart64_102.dll. This is not very orthodox but since TensorFlow is looking for cudart64_101.dll exported symbols and the nvidia folks are not amateurs, they would most likely not remove symbols from 101 to 102. It works, based on this assumption (mileage may vary).","Q_Score":143,"Tags":"python,python-3.x,tensorflow,keras,tensorflow2.0","A_Id":60424108,"CreationDate":"2020-01-20T12:26:00.000","Title":"Could not load dynamic library 'cudart64_101.dll' on tensorflow CPU-only installation","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am having problems with uninstalling TensorFlow.\nI have a Python script that uses TensorFlow. I want it to use TensorFlow1.15, but it is currently using TensorFlow 2.1.0. I deleted TensorFlow via my cmd: pip uninstall tensorflow and pip uninstall tensorflow-gpu. When I run these commands again it says that TensorFlow is not installed. However, I can see that my script says it is using TensorFlow 2.1.0 (I added the line:print(tf.__version__) in my script). Does anyone know where this TensorFlow 2.1.0 is installed and how I can delete it from my PC?","AnswerCount":4,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":7509,"Q_Id":59824224,"Users Score":0,"Answer":"This problem means that you use different pythons in your terminal and your script.\nYou mentioned PyCharm. By default, it creates a virtual environment for a new project. It also can use your global python. In any case, open a terminal in PyCharm (it should be in the bottom of the window or View -> Tool Windows -> Terminal or Option + F12 on Mac, Alt + F12 on Windows). From there you should run pip uninstall tensorflow. PyCharm automatically activates the correct python environment in the terminal.","Q_Score":0,"Tags":"python,tensorflow","A_Id":59824522,"CreationDate":"2020-01-20T13:22:00.000","Title":"Uninstall tensorflow 2.1.0","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm starting to work with the MapReduce paradigm with Pyspark, I'm stuck with a problem and I don't know if it a programming error or that I shouldn't do it this way.\nI have data from which I am extracting the following information with map for each line:\n(id, (date, length, counter)), I did it this way to extract all the info I need from the raw data file and filtering the noisy lines so I don't have to use the raw data file again. Btw: Counter is originally 1, and it is intended for addition in future reduceByKey.\nNow the data looks like this:\ndata = [('45', ('28\/5\/2010', 0.63, 1)), ('43', ('21\/2\/2012', 2.166, 1)), ('9', ('12\/1\/2009', 2.33, 1))]\nFirst, I am trying to count the number of key-value pairs, so it is a simple reduceByKey adding the counters, I tried to do it this way: data.reduceByKey(lambda a,b: a[2] + b[2]) which provides the following error TypeError: 'int' object is not subscriptable. \nIf a and b are supposed to get the value of the pair, the element 2 is supposed to have the counter of it, I cannot get my head around it. Is it better to map several times the raw data file extracting every-time a different needed value? Should I map this data variable extracting (key, value) pairs each time with the value needed from the tuple? Is it just that I'm making a programming error? \nAny guidance is welcome, thanks!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":158,"Q_Id":59837355,"Users Score":0,"Answer":"Ok, I found the issue. \nWhen reducing for example (id, (x, x, 1)), (id, (y, y, 1)), (id, (z, z, 1)), in the first reduce you would obtain (id, 2), (id, (z, z, 1)) so when trying to reducing again, the number 2 in the first element is non subscriptable, I must keep the data structure during the process.","Q_Score":0,"Tags":"python,pyspark,mapreduce","A_Id":59837712,"CreationDate":"2020-01-21T08:57:00.000","Title":"Reducing tuple values with reduceByKey in Pyspark","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to classify images of cells into N classes (\"cell 1\", \"cell 2\" ...) but some of the images are just noise and I would like to either not classify them or put them into a \"garbage\" class.\nI have tried the latter, but it is not very successful and I suspect it is because my \"garbage\" class is very heterogenous.\nAny suggestions on how to allow excluding some data from classification, or classify it as noise?\nI am using python and sklearn, but I would welcome either specific python\/sklearn tips or generic machine learning algorithms.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":205,"Q_Id":59838018,"Users Score":0,"Answer":"I would recommend to \n\nmake a binary classifier that classifies into\n\"garbage\"\/\"non-garbage\" classes\nmake a regular N-classes classifier that classifies \"non-garbage\"\ninputs","Q_Score":0,"Tags":"python,machine-learning,scikit-learn,classification,noise","A_Id":59838258,"CreationDate":"2020-01-21T09:34:00.000","Title":"How to classify data into N classes + one \"garbage\" class (or leave some data out)?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I plan to use NLTK, Gensim and Scikit Learn for some NLP\/text mining. But i will be using these libraries to work with my org data. The question is while using these libraries 'do they make API calls to process the data' or is the data taken out of the python shell to be processed. It is a security question, so was wondering if someone has any documentation for reference.\nAppreciate any help on this.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":67,"Q_Id":59845191,"Users Score":0,"Answer":"Generally with NLTK, gensim, and scikit-learn, algorithms are implemented in their source code, and run locally on your data, without sending data elsehwere for processing. \nI've never noticed any documentation\/functionality of these packages mentioning a reliance on an remote\/cloud service, nor seen users discussing the same. \nHowever, they're each large libraries, with many functions I've never reviewed, and with many contributors adding new options. And I don't know if the project leads have stated an explicit commitment to never rely on external services. \nSo a definitive, permanent answer may not be possible. To the extent such security is a concern for your project, you should carefully review the documentation, and even source code, for those functions\/classes\/methods you're using. (None of these projects would intentionally hide a reliance on outside services.)\nYou could also develop, test, and deploy the code on systems whose ability to contact outside services is limited by firewalls \u2013 so that you could detect and block any undisclosed or inadvertent communication with outside machines. \nNote also that each of these libraries in turn relies on other public libraries. If your concern also extends to the potential for either careless or intentionally, maliciously-inserted methods of private data exfiltration, you would want to do a deeper analysis of these libraries & all other libraries they bring-in. (Simply trusting the top-level documentation could be insufficient.) \nAlso, each of these libraries have utility functions which, on explicit user demand, download example datasets or shared non-code resources (like lists of stopwords or lexicons). Using such functions doesn't upload any of your data elsewhere, but may leak that you're using specific functionality. The firewall-based approach mentioned above could interfere with such download steps. Under a situation of maximum vigilance\/paranoia, you might want pay special attention to the use & behavior of such extra-download methods, to be sure they're not doing any more than they should to change the local environment or execute\/replace other library code. \nFinally, by sticking to widely-used packages\/functions, and somewhat older versions that have remained continuously available, you may benefit from a bit of \"community assurance\" that a package's behavior is well-understood, without surprising dependencies or vulnerabilities. That is, many other users will have already given those code-paths some attention, analysis, & real-usage \u2013 so any problems may have already been discovered, disclosed, and fixed.","Q_Score":1,"Tags":"python,api,nlp,nltk,gensim","A_Id":59847592,"CreationDate":"2020-01-21T16:17:00.000","Title":"API calls from NLTK, Gensim, Scikit Learn","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to implement Speech-to-text using wav2letter. As far as I have researched the model uses the Arrayfire tensor library with dependency on flashlight ML library.\nNow, the flashlight library is built for Linux based system.\nIs there any way to run this model on the Windows-based system.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":626,"Q_Id":59857456,"Users Score":1,"Answer":"Wav2Letter has different train- and inference-time dependencies.\nI am assuming you will be performing training on CUDA backend. If so, you need ArrayFire and Flashlight.\nFor inference, besides basic dependancies (such as cereal for serialization, etc.) you don't need either. The FAIR team has provided their own implementations of neural network layers (linear, conv1d, etc.) based on FBGEMM (FB General Matrix Multiplication) backend. FBGEMM can both be compiled for CPU and CUDA backend - on intel-based CPUs, it can further be accelerated using intel's optimized MKL math library and on CUDA backend, using cuDNN.\nYou are free to add your own implementations of backend based on say, LibTorch or C++ TensorFlow and submit a PR.","Q_Score":1,"Tags":"python-3.x,deep-learning,speech-recognition,speech-to-text","A_Id":64513424,"CreationDate":"2020-01-22T10:21:00.000","Title":"Alternative way to run wav2letter Facebook AI Research Speech to Text model on Windows Machine","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm running the API over a series of video frames to track objects through a scene and I'm extracting the bounding box coordinates for each object in order to calculate the centre of each bounding box. It would appear, however, that there is an offset in the ymin and ymax coordinates. \nThe scene is of a person walking the field of view with the bottom of the frame matched up to the persons feet (which would infer a very small value for ymin and a ymax value that would not extend to the stop of the frame. However the API gives the following normalised box coordinates [452.26962089538574, 197.93473720550537, 1073.7505388259888, 639.3438720703125]. The absolute coordinates are [0.41876816749572754, 0.10309100896120071, 0.9942134618759155, 0.3329916000366211] For reference the video is 1920 x 1080. \nThe same frame put into MATLABs video labler app (when translated into [ymin xmin ymax xmax]) returns [8.396575927734375, 57.50376892089844, 722.7988586425781, 431.51695251464844]. I'm aware they won't exactly match because I've manually drawn in the box as ground truth (this is especially true for the x coordinates), however the ymin and ymax should be pretty close and these results seem much more realistic.\nHas anyone come across this before? The bounding boxes are drawn correctly onto the image when the API runs the inference, so I'm at bit of a loss as to what's happening. As I'm taking data directly from boxes = detection_graph.get_tensor_by_name('detection_boxes:0') and storing it on each iteration.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":141,"Q_Id":59858497,"Users Score":0,"Answer":"It turns out I'd over looked the fact that python's coordinate system has 0,0 in the top left and the API accounts for this by making top = ymin and bottom = ymax within visualization_utils.py. So a ymax = 1073 would indeed be near the bottom of the frame.","Q_Score":0,"Tags":"python,tensorflow,object-detection,bounding-box","A_Id":59859213,"CreationDate":"2020-01-22T11:17:00.000","Title":"Is Tensorflows object detection API producing incorrect ymin and ymax bounding box coordinates?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I\u2019m way new to ML so I have a really rudimentary question. I would appreciate it if one clarifies it for me.\nSuppose I have a set of tweets which labeled as negative and positive. I want to perform some sentiment analysis. \nI extracted 3 basic features: \n\nEmotion icons \nExclamation marks \nIntensity words(very, really etc.). \n\nHow should I use these features with SVM or other ML algorithms? \nIn other words, how should I deploy the extracted features in SVM algorithm?\nI'm working with python and already know how should I run SVM or other algorithms, but I don't have any idea about the relation between extracted features and role of them in each algorithm!\nBased on the responses of some experts I update my question:\nAt first, I wanna appreciate your time and worthy explanations. I think my problem is solving\u2026 So in line with what you said, each ML algorithm may need some vectorized features and I should find a way to represent my features as vectors. I want to explain what I got from your explanation via a rudimentary example.\nSay I have emoticon icons (for example 3 icons) as one feature:\n1-Hence, I should represent this feature by a vector with 3 values. \n2-The vectorized feature can initial in this way : [0,0,0] (each value represents an icon = :) and :( and :P ). \n3-Next I should go through each tweet and check whether the tweet has an icon or not. For example [2,1,0] shows that the tweet has: :) 2 times, and :( 1 time, and :p no time.\n4-After I check all the tweets I will have a big vector with the size of n*3 (n is the total number of my tweets). \n5-Stages 1-4 should be done for other features.\n6-Then I should merge all those features by using m models of SVM (m is the number of my features) and then classify by majority vote or some other method.\nOr should create a long vector by concatenating all of the vectors, and feed it to the SVM.\nCould you please correct me if there is any misunderstanding? If it is not correct I will delete it otherwise I should let it stay cause It can be practical for any beginners such as me...\nThanks a bunch\u2026","AnswerCount":1,"Available Count":1,"Score":0.6640367703,"is_accepted":false,"ViewCount":83,"Q_Id":59858819,"Users Score":4,"Answer":"basically, to make things very \"simple\" and \"shallow\", all algorithm takes some sort of a numeric vector represent the features\nthe real work is to find how to represent the features as vector which yield the best result, this depends by the feature itself and on the algorithm using\nfor example to use SVM which basically find a separator plane, you need to project the features on some vectors set which yield a good enough separation, so for instance you can treat your features like this:\n\nEmotion icons - create a vector which represent all the icons present in that tweet, define each icon to an index from 1 to n so tweet represented by [0,0,0,2,1] means the 4th and 5th icons are appearing in his body 2 and 1 times respectively\nExclamation marks - you can simply count the number of occurrences (a better approach will be to represent some more information about it like the place in a sentence and such...)\nIntensity words - you can use the same approach as the Emotion icons\n\nbasically each feature can be used alone in the SVM model to classify good and bad\nyou can merge all those features by using 3 models of SVM and then classify by majority vote or some other method\nor\nyou can create a long vector by concatenating all of the vectors, and feed it to the SVM\n\nthis is just a one approach, you might tweak it or use some other one to fit your data, model and goal better","Q_Score":2,"Tags":"python,machine-learning,sentiment-analysis,feature-selection","A_Id":59859426,"CreationDate":"2020-01-22T11:33:00.000","Title":"How can I deploy my features in a Machine Learning algorithm?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using dataframes and CSV files to manipulate data. Most of the time, my dataframes, or the one provided by the API I'm using don't have indexes. If they have indexes, especially when writing and reading CSV files, I just remove them by using the name of the column, \"unnamed:0\".\nBut this time, to_CSV places indexes in my CSV file without naming the column. So I must use df.drop(df.columns[0], axis=1, inplace=True). But for pandas, the first column is the first named, not the real first one.\nI already used index=False, but it just removed an important column instead of not adding indexes.\nHow can I remove the first column which isn't named and haven't index to find it ?","AnswerCount":3,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":1352,"Q_Id":59858908,"Users Score":0,"Answer":"Use this : df = pd.read_csv(\"nameOfFile.csv\", index_col=\"nameOfColToUseAsIndex\") and put the name of your first column in \"nameOfColToUseAsIndex\".","Q_Score":0,"Tags":"python,pandas,dataframe,indexing","A_Id":59859181,"CreationDate":"2020-01-22T11:37:00.000","Title":"How to avoid pandas from adding indexes in dataframes when using CSV files","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm using dataframes and CSV files to manipulate data. Most of the time, my dataframes, or the one provided by the API I'm using don't have indexes. If they have indexes, especially when writing and reading CSV files, I just remove them by using the name of the column, \"unnamed:0\".\nBut this time, to_CSV places indexes in my CSV file without naming the column. So I must use df.drop(df.columns[0], axis=1, inplace=True). But for pandas, the first column is the first named, not the real first one.\nI already used index=False, but it just removed an important column instead of not adding indexes.\nHow can I remove the first column which isn't named and haven't index to find it ?","AnswerCount":3,"Available Count":2,"Score":0.1325487884,"is_accepted":false,"ViewCount":1352,"Q_Id":59858908,"Users Score":2,"Answer":"Writing pandas dataframes into file via to_csv() method has optional parameter index, which you can set to false to prevent it from writing its own index:\ndf.to_csv('filename.csv', index=False)","Q_Score":0,"Tags":"python,pandas,dataframe,indexing","A_Id":59859039,"CreationDate":"2020-01-22T11:37:00.000","Title":"How to avoid pandas from adding indexes in dataframes when using CSV files","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to try an object detection model on a custom data set. I want it to recognize a specififc piece of metal from my garage. I took like 32 photos and labelled them. The training goes well, but up to 10% loss. After that it goes very slow, so I need to stop it. After that, I implemented the model on camera, but it has no accuracy. Could it be because of the fact that I have only 32 images of the object? I have tried with YoloV2 and Faster RCNN.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":42,"Q_Id":59860042,"Users Score":0,"Answer":"It has low probability that your model implemented to a camera has no accuracy because you have only 32 images. \nAnyway before you've had about up to 10% loss (It seems to be about 90% accuracy), so it should work  I think the problem is not in the amount of images.\nAfter training your model you need to save the coefficients of model trained. \nMake sure that you implemented model trained, and not you don't use model from scratch","Q_Score":1,"Tags":"python,tensorflow","A_Id":59860211,"CreationDate":"2020-01-22T12:43:00.000","Title":"Training model on custom data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to try an object detection model on a custom data set. I want it to recognize a specififc piece of metal from my garage. I took like 32 photos and labelled them. The training goes well, but up to 10% loss. After that it goes very slow, so I need to stop it. After that, I implemented the model on camera, but it has no accuracy. Could it be because of the fact that I have only 32 images of the object? I have tried with YoloV2 and Faster RCNN.","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":42,"Q_Id":59860042,"Users Score":0,"Answer":"Just labeling will not help in object detection. What you are doing is image classification but expecting results of object detection. \nObject detection requires bounding box annotations and changes in the loss function which is to be fed to the model during each backpropagation step.\nYou need some tools to do data annotations first, then manipulate your Yolov2\/Fast-RCNN codes along with the loss function. Train it well and try using Image Augmentations to generate little more images because 32 images are less. In that case, you might end up in a pitfall of getting higher training accuracy but less test accuracy. Training models in fewer images sometimes lead to unexpected overfitting.\nOnly then you should try to implement using the camera.","Q_Score":1,"Tags":"python,tensorflow","A_Id":59860321,"CreationDate":"2020-01-22T12:43:00.000","Title":"Training model on custom data","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to run a TensorFlow model that I estimate will take roughly 11 hours. As such, I would like to use nohup so I can exit my terminal keep the process running. I use the following command to do so:\nnohup python3 trainModel.py > log.txt &\nMy model appears to be running as normal, but gets hung up with the last message outputted being:\n2020-01-22 19:06:24.669183: I tensorflow\/core\/common_runtime\/gpu\/gpu_device.cc:1697] Adding visible gpu devices: 0\nIt is normal for my model to output this, however when I am not using nohup, the rest of the code still executes. What do I need to do to have this command run as it does when I am not using nohup?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":89,"Q_Id":59866803,"Users Score":0,"Answer":"Try to use absolute paths in your execution instead of relative paths","Q_Score":1,"Tags":"python,tensorflow,google-compute-engine","A_Id":59940960,"CreationDate":"2020-01-22T19:16:00.000","Title":"Tensorflow Hanging on Google Compute Engine Using nohup","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am trying to run a TensorFlow model that I estimate will take roughly 11 hours. As such, I would like to use nohup so I can exit my terminal keep the process running. I use the following command to do so:\nnohup python3 trainModel.py > log.txt &\nMy model appears to be running as normal, but gets hung up with the last message outputted being:\n2020-01-22 19:06:24.669183: I tensorflow\/core\/common_runtime\/gpu\/gpu_device.cc:1697] Adding visible gpu devices: 0\nIt is normal for my model to output this, however when I am not using nohup, the rest of the code still executes. What do I need to do to have this command run as it does when I am not using nohup?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":89,"Q_Id":59866803,"Users Score":0,"Answer":"could you send the exit status code of the execution?  echo $?\nThis will help to get an exact idea of the error\nFurthermore, can you try to send the standard error output to the log.txt file, for instance\nnohup python3 trainModel.py  2> log.txt &\nStandard output will be redirected to the nohup file and log.txt will contain the standard output error\nI Hope this helps","Q_Score":1,"Tags":"python,tensorflow,google-compute-engine","A_Id":59869881,"CreationDate":"2020-01-22T19:16:00.000","Title":"Tensorflow Hanging on Google Compute Engine Using nohup","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Say I have an trained RandomForestClassifier model from sklearn.  We're using gRPC to serve that model and provide predictions in real-time in a high traffic situation.  From what I understand, gRPC can support multiple threads run concurrently.  Can they all simultaneously make calls to RF's predict method without running into concurrency issues?  Is sklearn's predict thread-safe?\nIf this isn't the case, it sounds like you'd have to load individual copies of the model in each worker thread.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":127,"Q_Id":59870409,"Users Score":0,"Answer":"I don't know anything about sklearn specifically, but in general, gRPC is thread-safe, and it's fine to make many concurrent calls to the same method.","Q_Score":0,"Tags":"python,scikit-learn,thread-safety,grpc,grpc-python","A_Id":59973866,"CreationDate":"2020-01-23T01:16:00.000","Title":"Model Serving with sklearn and gRPC","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there any pre-trained doc2Vec models, trained in python 3 on news articles data. \nMost of the models that I am finding are in older versions. And, it's difficult to implement in python 3.7.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":54,"Q_Id":59874635,"Users Score":1,"Answer":"The answer is, I'm afraid, no. Not at this time. Also, I think the python 2 models that are available are word2vec, not doc2vec","Q_Score":0,"Tags":"python,python-3.7","A_Id":59874911,"CreationDate":"2020-01-23T08:55:00.000","Title":"Is there pre-trained doc2vec model in Python 3.7?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am training a gan and I am the accuracy doesn't change over epoch meanwhile the loss is deacresing.\nIs there something wrong or it is normal because it's a gan?\nThank you in advance.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":88,"Q_Id":59882584,"Users Score":0,"Answer":"In order to fully answer this question (specific to your case) we'd need to know what loss function you are using and how you measure accuracy.\nIn general, this can certainly happen for a variety of reasons.  The easiest reason to illustrate is with a simple classifier.  Suppose you have a 2-class classification problem (for simplicity) and an input $x$ and label (1, 0), i.e. the label says it belongs to class 1 and not class 2.  When you feed $x$ through your network you get an output: $y=(p_1, p_2)$.  If $p_1 > p_2$ then the prediction is correct (i.e. you chose the right class).  The loss function can continue to go down until $p_1=1$ and $p_2=0$ (the target).  So, you can have lots of correct predictions (high accuracy) but still have room to improve the output to better match the labels (room for improved loss).","Q_Score":0,"Tags":"python,tensorflow,machine-learning,deep-learning,artificial-intelligence","A_Id":59884342,"CreationDate":"2020-01-23T16:00:00.000","Title":"Constant accuracy over epochs","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I previously created metrics for a TensorFlow Classifier referencing predictions['logits'] to calculate the metrics. I have changed the model from a Classifier to an Estimator in order to enable multi-objective learning (using MultiHead). However, this has caused Python to throw an error since now the elements of predictions are keyed by pairs of the head name and original key, e.g. ('label1','logits') for a head with name 'label1'.\nI'd like to allow for dynamic generation of metrics based on a configuration file in order to more easily train and test a variety of models with different label combinations. The problem now is that the metric_fn parameter to tf.estimator.add_metrics does not take any additional parameters to allow for dynamically determined or constructed metrics.\nHow can I generate an estimator with multiple heads and custom metrics for each head?","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":373,"Q_Id":59888227,"Users Score":1,"Answer":"Build a class around the model creation that holds the model configuration and use a member function for the metric_fn parameter.\nclass ModelBuilder:\n  # constructor storing configuration options in self\n  def __init__(self, labels, other_config_args):\n    self.labels = labels\n    ...\n  # Function for building the estimator with multiple heads (multi-objective)\n  def build_estimator(self, func_args):\n    heads = []\n    for label in self.labels:\n      heads.append(tf.estimator.MultiClassHead(n_classes=self.nclasses[label], name=label))\n    head = tf.estimator.MultiHead(heads)\n    estimator = tf.estimator.DNNEstimator(head=heads,...) # or whatever type of estimator you want\n    estimator = tf.estimator.add_metrics(estimator, self.model_metrics)\n    return estimator\n  # Member function that adds metrics to the estimator based on the model configuration params\n  def model_metrics(self, labels, predictions, features):\n    metrics = {}\n    for label in self.labels: # generate a metric for each head name\n      metrics['metric_name'] = metric_func(features,labels,predictions[(label,'logits')])\n    return metrics","Q_Score":0,"Tags":"python,tensorflow,machine-learning,metrics","A_Id":59888228,"CreationDate":"2020-01-23T22:58:00.000","Title":"How should metrics be added to a multi-headed TensorFlow estimator?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am having trouble importing and installing tensorflow. I can't install it via that Thonny manage package option nor via the command window for windows operators. I get the same error for both ways:\nERROR: Could not find a version that satisfies the requirement tensorflow (from versions: none) Error: No matching distribution found for tensorflow\nI tried to convert back to python 3.6 but the issue still arises. This is annoying me because I cannot implement Machine Learning, which is something I am strongly passionate on. \nAny reasons or solutions would be appreciated","AnswerCount":4,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":1448,"Q_Id":59908131,"Users Score":0,"Answer":"Tensorflow can be installed in Thonny by\nTools -> Open System Shell\npip install --upgrade tensorflow","Q_Score":0,"Tags":"python,tensorflow,machine-learning,data-science,thonny","A_Id":61153120,"CreationDate":"2020-01-25T09:49:00.000","Title":"Thonny : installing tensorflow and importing it","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am having trouble importing and installing tensorflow. I can't install it via that Thonny manage package option nor via the command window for windows operators. I get the same error for both ways:\nERROR: Could not find a version that satisfies the requirement tensorflow (from versions: none) Error: No matching distribution found for tensorflow\nI tried to convert back to python 3.6 but the issue still arises. This is annoying me because I cannot implement Machine Learning, which is something I am strongly passionate on. \nAny reasons or solutions would be appreciated","AnswerCount":4,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":1448,"Q_Id":59908131,"Users Score":0,"Answer":"I use Thonny and the way to install it is\ntool>>Open system shell\nThen type in \"pip3.6 install --upgrade TensorFlow\"","Q_Score":0,"Tags":"python,tensorflow,machine-learning,data-science,thonny","A_Id":61500169,"CreationDate":"2020-01-25T09:49:00.000","Title":"Thonny : installing tensorflow and importing it","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am having trouble importing and installing tensorflow. I can't install it via that Thonny manage package option nor via the command window for windows operators. I get the same error for both ways:\nERROR: Could not find a version that satisfies the requirement tensorflow (from versions: none) Error: No matching distribution found for tensorflow\nI tried to convert back to python 3.6 but the issue still arises. This is annoying me because I cannot implement Machine Learning, which is something I am strongly passionate on. \nAny reasons or solutions would be appreciated","AnswerCount":4,"Available Count":3,"Score":0.0,"is_accepted":false,"ViewCount":1448,"Q_Id":59908131,"Users Score":0,"Answer":"Here is how I got Tensorflow version 2.5.0 to install and import using python version 3.6.8, with Thonny version 3.3.7, on a windows 10 laptop;-\nWhen I installed Thonny, the default python interpreter was set to python 3.7.9. I need to change that, as follows;-\nUsing Thonnys menus and options , use tools \/ options \/ interpreter . Use the pull down option selections to find the python 3.6.8 that must have been previously installed on your machine. On my machine it is located at C:\\Users\\XXXX\\ApplicationData\\Local\\Programs\\Python\\Python36\\python.exe\nHit Ok and Thonny will show that it is now using python 3.6.8 in the lower shell !\nThen install tensorflow using the Thonny menu tools \/ manage packages.\nI installed tensorflow version 2.5.0 without any problems !\nThen, in the interactive shell, I tested it ;-\nPython 3.6.8 (C:\\Users\\con_o\\AppData\\Local\\Programs\\Python\\Python36\\python.exe)\n\n\n\nimport tensorflow\n2021-05-20 09:06:01.231885: W tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:64] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found\n2021-05-20 09:06:01.258437: I tensorflow\/stream_executor\/cuda\/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n\n\n\n\n\n\nprint(tensorflow.version)\n<module 'tensorflow._api.v2.version' from 'C:\\Users\\XXX\\AppData\\Local\\Programs\\Python\\Python36\\lib\\site-packages\\tensorflow\\_api\\v2\\version\\init.py'>\n\n\n\n\n\n\nprint(tensorflow.version)\n\n\n\n2.5.0\nNote;- The last command print(tensorflow.version) does NOT work without the previous command print(tensorflow.version) being run first !!\nNote;- I could only install version 2.5.o of tensorflow. Thonny could not install any earlier version of Tensorflow!\nI hope this helps the many folk struggling with the very difficult Tensorflow installations.","Q_Score":0,"Tags":"python,tensorflow,machine-learning,data-science,thonny","A_Id":67617331,"CreationDate":"2020-01-25T09:49:00.000","Title":"Thonny : installing tensorflow and importing it","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm aware of Decimal, however I am working with a lot of code written by someone else, and I don't want to go through a large amount of code to change every initialization of a floating point number to Decimal.  It would be more convenient if there was some kind of package where I could put SetPrecision(128) or such at the top of my scripts and be off to the races.  I suspect no such thing exists but I figured I would ask just in case I'm wrong.\nTo head off XY Problem comments, I'm solving differential equations which are supposed to be positive invariant, and one quantity which has an equilibrium on the order of 1e-12 goes negative regardless of the error tolerance I specify (using scipy's interface to LSODA).","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":384,"Q_Id":59914993,"Users Score":0,"Answer":"LSODA exposed through scipy.integrate is double precision only.\nYou might want to look into some rescaling of variables, so that that thing which is 1e-12 becomes closer to unity.\nEDIT. In the comments, you indicated\n\nAs I've stated three times, I am open to rewriting to avoid LSODA\n\nThen what you can try doing is to look over the code of solve_ivp, which is pure python. Feed it with decimals or mpmath high-precision floats. Observe where it fails, look for where it assumes double precision. Rewrite, remove this assumption. Rinse and repeat. Whether it'll work in the end, I don't know. Whether it's worth it, I suspect not, but YMMV.","Q_Score":0,"Tags":"python,scipy,precision","A_Id":59921768,"CreationDate":"2020-01-26T02:03:00.000","Title":"Is there a way of setting a default precision that differs from double in Python?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to make a function that takes a picture of a table and converts that into a csv. The picture of the table could contain commas if it was say a bank statement i.e. $3,000. So what separator should I use when creating arbitrary csv document since the standard comma seperator would be confused with the commas in the contents of the csv when excel tries to read it.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":17,"Q_Id":59915542,"Users Score":0,"Answer":"As Tim already stated, a correct CSV file should quote any field that contains characters which are used as field (mostly comma or semikolon) or line separaters (mostly \\n or \\r\\n) in the CSV file.\nIf you don't want to check the contens you can just quote all fields. Any good CSV import engine should be alright with that.","Q_Score":0,"Tags":"excel,python-3.x,csv,ocr","A_Id":60014615,"CreationDate":"2020-01-26T04:25:00.000","Title":"What seperator to use when creating csv of unknown contents","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"Hi so basically my question is I have a matrix which I've SVD decomposed and have it in the variables u, s, and v. I've made some alterations to the s matrix to make it diagonal, as well as altered some of the numbers. Now I'm basically trying to reconstruct it into a regular matrix from the 3 matrices back into the original matrix. Does anyone know of any functions that do this? I can't seem to find any examples of this within numpy.","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":3020,"Q_Id":59916150,"Users Score":1,"Answer":"I figured it out, just using the np.matmul() function and then just multiplying the 3 matrices of u s and v together was enough to get them back into an original matrix.","Q_Score":0,"Tags":"python-3.x,numpy,matrix,linear-algebra,svd","A_Id":59939869,"CreationDate":"2020-01-26T06:43:00.000","Title":"Reconstructing a matrix from an SVD in python 3","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"i try do cluster words into the same category. Therfore i wanted to use Spacy Word2Vec.\nIts already working with easy words like banana apple and car. It shows the nearly same word.\nIf the words gets more specific like car, battery, accumulator, accu and so on, if the were more technical, Spacy sends Zero vectos. So these words were not included into the bibliothek. \nDo you have some input for me?\nFurthermore, i have to do it in german. \nThank you very much\nJokulema","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":80,"Q_Id":59916393,"Users Score":0,"Answer":"If you need word-vectors for words not in the model you're using, you'll have to either:\n\nfind & use a different model that contains those words\ntrain your own model from your own training data, that contains many examples of those words' usages in context","Q_Score":1,"Tags":"python,spacy,word2vec","A_Id":59926374,"CreationDate":"2020-01-26T07:28:00.000","Title":"Word To Vec with Spacy, words into same categorie","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm trying to write text that was scraped from a German website into a CSV file. I tried using UTF-8 encoding as such:\nwith open('\/Users\/filepath\/result.csv', 'a', encoding='utf8') as f:\nf.write(text)\nBut this is what appeared in the CSV file instead of with an \u00fc:\nProjekt f\u00c3\u00bcr Alleinerziehende mit mehreren (behinderten) Kindern","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":201,"Q_Id":59921723,"Users Score":1,"Answer":"Try using ISO-8859-15 encoding. Also make sure that when you open the file in an editor, it's encoding is set to same encoding.","Q_Score":0,"Tags":"python,selenium,file,web-scraping,utf","A_Id":59921966,"CreationDate":"2020-01-26T19:05:00.000","Title":"How do you write German text into a CSV file?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":1},{"Question":"I am trying to classify text data into a few categories. But in the data set, there can be data that does not belong to any of the defined categories. \nAnd after deploying the final product, the product should be deal with text data that does not belong to the predefined category.\nTo implement that solution I am currently using the SVM text classifier. And I am planning to define another category as \n\n\"non\"\n\nto deal with the data that does not belong to predefined categories.\nIs this a correct approach?","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":106,"Q_Id":59922367,"Users Score":0,"Answer":"Yes, that would work. It is essentially an additional class called \"non\", for which the classifier will learn to classify all the documents into, that are labeled as that class.\nSo when you use your final product, it will try to classify the new text data into the classes, including \"non\".","Q_Score":1,"Tags":"python,svm,text-classification","A_Id":60563844,"CreationDate":"2020-01-26T20:20:00.000","Title":"Text classification into predefined categories","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have to make 2 business-like reports about media. Therefore I have to analyze data and give colleagues an excel file with multiple custom formatted tables. \nIs there a way to make custom formatted tables in R or python and export them to excel? \nThis way I can automate formatting the 3000+ tables :) \nThanks in advance!","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":178,"Q_Id":59931829,"Users Score":0,"Answer":"Have you tried using pandas ? In python.","Q_Score":0,"Tags":"python,r,excel,python-3.x","A_Id":59931906,"CreationDate":"2020-01-27T13:09:00.000","Title":"How to export custom tables from R\/Python to Excel?","Data Science and Machine Learning":1,"Database and SQL":1,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Can I use scipy.optimize.minimize to only restrict its answer to for example 2, 3, 4, 6, 8, 10? Just those values and not float values?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":76,"Q_Id":59932915,"Users Score":0,"Answer":"If the question is, if you can use discrete\/integer\/categorical design variables in scipy.optimize.minimize, the answer is no. It is mainly focused on gradient-based optimization, and it assumes continues design variables and continuous objective function. You can implement some continuous approximation, or branch-and-bound or similar methods to solve for discrete design variables.","Q_Score":1,"Tags":"python,scipy-optimize-minimize","A_Id":60004958,"CreationDate":"2020-01-27T14:17:00.000","Title":"Using scipy.optimize.minimize with a quantized integer range","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have 4.5 million rows to process, so I need to speed this up so bad. \nI don't understand how regex works pretty well, so other answers are so hard for me to understand.\nI have a column that contains IDs, e.g.:\n\nELI-123456789\n\nThis numeric part of this ID is contained in this string in a columnm bk_name started with a \"@\":\n\nAAAAA@123456789_BBBBB;CCCCC;\n\nNow my goal is to change that string into this string, throw the ID at the end, started with a \"#\", save it in new_name:\n\nAAAAA_BBBBB;CCCCC;#123456789\n\nHere's what I tried:\n\nTake the ID and replace \"ELI-\" with \"@\", save as ID2:\n\ndf[\"ID2\"] = df[\"ID\"].str.replace(\"ELI-\", \"@\")\n\nFind ID2 in the string of bk_name and replace it with \"\":\n\ndf[\"new_name\"] = df[\"bk_name\"].replace(regex = r'(?i)' + df[\"ID2\"].astype(str), value = \"\")\n\nTake the ID again, replace \"ELI-\" with \"#\", add it at the end of the string of new_name:\n\ndf[\"new_name\"] = df[\"new_name\"] + df[\"ID\"].str.replace(\"ELI-\", \"#\") \nNow the problem is that step 2 is the line that takes most of the time, it took 6.5 second to process 6700 rows.\nBut now I have 4.6 millions rows to process, it's been 7 hours and it's still running and I have no idea why.\nIn my opinions, regex slows down my code. But I have no deeper understanding.\nSo thanks for your help in advance, any suggestions would be appreciated :)","AnswerCount":3,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":116,"Q_Id":59935416,"Users Score":0,"Answer":"I don't understand what caused the problem but I solved it.\nThe only thing I changed is in step 2. I used apply\/lambda function and it suddenly works and I have no idea why.\n\nTake the ID and replace \"ELI-\" with \"@\", save as ID2:\n\ndf[\"ID2\"] = df[\"ID\"].str.replace(\"ELI-\", \"@\")\n\nFind ID2 in the string of bk_name and replace it with \"\":\n\ndf[\"new_name\"] = df[[\"bk_name\", \"ID2\"]].astype(str).apply(lambda x: x[\"bk_name\"].replace(x[\"ID2\"], \"\"), axis=1)\n\nTake the ID again, replace \"ELI-\" with \"#\", add it at the end of the string of new_name:\n\ndf[\"new_name\"] = df[\"new_name\"] + df[\"ID\"].str.replace(\"ELI-\", \"#\")","Q_Score":0,"Tags":"python,regex,replace","A_Id":59953332,"CreationDate":"2020-01-27T16:43:00.000","Title":"How to speed up this Regular Expression in .Replace function?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I was wondering if it were possible to iterate over a pandas column and replace strings if a particular condition was met.  Essentialy I have a dataframe column with 100s of strings all in the general format GCA_XXXXX.X_MMXXXX.X, although some are in the XXXX_MMXXXX.X format, and I need to remove one of that dashes and replace with '|' and only if it is followed by MM, if it is AFTER GCA then I need it to be replaced form '_' to ''.  Is there any way in python that I can set this conditions and iterate a function over the column? thanks!","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":20,"Q_Id":59937062,"Users Score":0,"Answer":"Maybe try something like this:\ndf['column'] = df['column'].astype(str).replace('_MM','|MM')\ndf['column'] = df['column'].astype(str).replace('GCA_','GCA')","Q_Score":0,"Tags":"python-3.x,pandas","A_Id":59937589,"CreationDate":"2020-01-27T18:44:00.000","Title":"Specific conditions for string replacement?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am working with the python pandas_dedupe package, specifically with pandas_dedupe.dedupe_dataframe.\nI have trained the dedupe_dataframe module via the interactive prompts. But now I need to retrain the dedupe_dataframe module. How can I erase the training set and start from scratch?\nI have tried deleting the dedupe_dataframe_learned_settings and dedupe_dataframe_training.json files, but then then the python script throws an error.\nI work with PyCharm as my IDE.\nAny hint would be much appreciated.\nThanks!","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":258,"Q_Id":59937320,"Users Score":0,"Answer":"pandas-dedupe v1.3.1, you simply need to do the following:\n\ndelete dedupe_dataframe_learned_settings and dedupe_dataframe_training.json;\nrun dedupe_dataframe setting update_model=False [note: this is the default].\n\nThis is the standard procedure.\nIf it does not work, please provide more info related to the error you get.","Q_Score":1,"Tags":"python,pandas,python-dedupe","A_Id":62744734,"CreationDate":"2020-01-27T19:02:00.000","Title":"How to erase pandas_dedupe.dedupe_dataframe training set?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am building a package that uses the Google Analytics API for Python.\nBut, in severous cases when I have multiple dimensions the extraction by day is sampled.\nI know that if I use sampling_level = LARGE will use a sample more accurate.\nBut, somebody knows if has a way to reduce a request that you can extract one day without sampling?\nGrateful","AnswerCount":3,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":840,"Q_Id":59969386,"Users Score":0,"Answer":"setting sampling to LARGE is the only method we have to decide the amount of sampling but as you already know this doesn't prevent it.\nThe only way to reduce the chances of sampling is to request less data. A reduced number of dimensions and metrics as well as a shorter date range are the best ways to ensure that you dont get sampled data","Q_Score":0,"Tags":"python-3.x,google-analytics,google-analytics-api","A_Id":59969860,"CreationDate":"2020-01-29T14:33:00.000","Title":"How to get unsampled data from Google Analytics API in a specific day","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":1,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I am using the MLPClassifier using the lbfgs solver.\nIf I calculate the expected value using the predict_proba() method and the classes_ attribute, it does not match what the predict() method returns.\nWhich one is more accurate?\nDoes the value returned by predict() have to be one of the classes? Will it not interpolate between the classes? I have a continuously varying variable I want to predict.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":251,"Q_Id":59980207,"Users Score":1,"Answer":"Definition\npredict - classifies your input into a label. \npredict_proba - returns the predicted probability for each class in your model.\nExample\nIf this were a binary problem, and your classes labelled 0 and 1. Then for some input you are testing, predict will return a class, let's say 1.\nHowever, predict_proba is going to give you the predicted probability that this input maps to class 1, which could, in this example, be 0.8. This is why their values do not match.\nWhich is more accurate?\nYou can't really compare their accuracy. However, you can treat the predict_proba as the confidence of your model for a particular class. For example, if you have three classes, and you tested one sample. Then you would receive an output of three real numbers:\n[0.11, 0.01, 0.88]\nYou could treat this as your model having a high confidence that this input maps to the third class, as it has the highest probability of 0.88. \nIn contrast, for some other input value, your model might spit out the following:\n[0.33, 0.32, 0.34]\nYour model still predicts the third class, as this has the highest probability. However, there is a low confidence that the third class is the true class.","Q_Score":0,"Tags":"python,scikit-learn,neural-network,classification","A_Id":59980630,"CreationDate":"2020-01-30T06:26:00.000","Title":"MLPClassfier predict and predict_proba seem inconsistent","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is there any option to load a pickle file in chunks?\nI know we can save the data in CSV and load it in chunks.\nBut other than CSV, is there any option to load a pickle file or any python native file in chunks?","AnswerCount":2,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":3192,"Q_Id":59983073,"Users Score":0,"Answer":"I had a similar issue, where I wrote a barrel file descriptor pool, and noticed that my pickle files were getting corrupt when I closed a file descriptor. Although you may do multiple dump() operations to an open file descriptor, it's not possible to subsequently do an open('file', 'ab') to start saving a new set of objects.\nI got around this by doing a pickler.dump(None) as a session terminator right before I had to close the file descriptor, and upon re-opening, I instantiated a new Pickler instance to resume writing to the file.\nWhen loading from this file, a None object signified an end-of-session, at which point I instantiated a new Pickler instance with the file descriptor to continue reading the remainder of the multi-session pickle file.\nThis only applies if for some reason you have to close the file descriptor, though. Otherwise, any number of dump() calls can be performed for load() later.","Q_Score":10,"Tags":"python,file,csv,pickle,chunks","A_Id":67877414,"CreationDate":"2020-01-30T09:45:00.000","Title":"How to load Pickle file in chunks?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I just started looking into deep learning and started to build a CNN with Keras. \nSo I've noticed that oftentimes when the Dataset MNIST is used, after importing the images, they are getting converted to float32-Datatype. \nSo my question is, why is that the case? It seems like it should work fine with uint8-Data. What am I missing here? Why is float32 needed?","AnswerCount":2,"Available Count":2,"Score":1.2,"is_accepted":true,"ViewCount":1654,"Q_Id":59986353,"Users Score":7,"Answer":"Well the reason is simple, the whole math for neural networks is continuous, not discrete, and this is best approximated with floating point numbers. The inputs, outputs, and weights of a neural network are continuous numbers.\nIf you had integer outputs, they will still be converted to floating point at some point in the pipeline, in order to have compatible types where operations can be made. This might happen explicitly or implicitly, its better to be explicit about types.\nIn some frameworks you might get errors if you do not cast the inputs to the expected types.","Q_Score":2,"Tags":"python,keras,deep-learning,mnist","A_Id":59986740,"CreationDate":"2020-01-30T12:45:00.000","Title":"Why do i have to convert \"uint8\" into \"float32\"","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I just started looking into deep learning and started to build a CNN with Keras. \nSo I've noticed that oftentimes when the Dataset MNIST is used, after importing the images, they are getting converted to float32-Datatype. \nSo my question is, why is that the case? It seems like it should work fine with uint8-Data. What am I missing here? Why is float32 needed?","AnswerCount":2,"Available Count":2,"Score":0.0,"is_accepted":false,"ViewCount":1654,"Q_Id":59986353,"Users Score":0,"Answer":"The answer is:\nWe should perform a lot of data augmentation and training as well in CNN. This will lead to a faster training experience!","Q_Score":2,"Tags":"python,keras,deep-learning,mnist","A_Id":70925908,"CreationDate":"2020-01-30T12:45:00.000","Title":"Why do i have to convert \"uint8\" into \"float32\"","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I have implemented python code that is listening my db and continuously stores data in pandas DataFrame (inserts new rows and updates old as infinite loop). The data comes to python as it appears in db (updates come on average every 1-5 seconds). \nThe second part of my python code should do some stuff with this DataFrame. So what I want to do is to split my code into separate threads. \nThe first thread will store data in DataFrame and the other thread (or more than one) will use the DataFrame to do some stuff and return results via plots, variables and so on. \nI have done some reaserch on threading, but I have not found the solution yet. Any help on this issue and\/or example code is highly appreciated.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":67,"Q_Id":59990330,"Users Score":0,"Answer":"After all I have created a table in my db that is countinuously updated via trigger-function functionality. On python side I have done Threads that call to this table asynchronously, get data and do their stuff.\nPerhaps, this is not the ideal solution, but it works and covers my current needs.","Q_Score":1,"Tags":"python,multithreading","A_Id":60012708,"CreationDate":"2020-01-30T16:24:00.000","Title":"Python getting\/posting data in threads","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm new to Pandas and Dask, Dask dataframes wrap pandas dataframes and share most of the same function calls.\nI using Dask to sort(set_index) a largeish csv file ~1,000,000 rows ~100columns.\nOnce it's sorted I use itertuples() to grab each dataframe row, to compare with a row from a database with ~1,000,000 rows ~100 columns. \nBut it's running slowly (takes around 8 hours), is there a faster way to do this? \nI used dask because it can sort very large csv files and has a flexible csv parsing engine. It'll also let me run more advanced operations on the dataset, and parse more data formats in the future\nI could presort the csv but I want to see if Dask can be fast enough for my use case, it would make things alot more hands off in the long run.","AnswerCount":1,"Available Count":1,"Score":1.2,"is_accepted":true,"ViewCount":412,"Q_Id":60007843,"Users Score":1,"Answer":"By using iter_tuples, you are bringing each row back to the client, one by one. Please read up on map_partitions or map to see how you can apply function to rows or blocks of the dataframe without pulling data to the client. \nNote that each worker should write to a different file, since they operate in parallel.","Q_Score":0,"Tags":"python-3.x,pandas,dask","A_Id":60028390,"CreationDate":"2020-01-31T16:34:00.000","Title":"Whats the fastest way to loop through sorted dask dataframe?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"I tried installing numpy using the command 'pip install numpy' and ran 'import numpy' but I received the error ModuleNotFoundError: No module named 'numpy' when I tried to re-run the code. After reading similar questions on SO, I ran pip3 install numpy, but since i already installed it, I received the message Requirement already satisfied.\nNOTE: I have read other similar questions on SO, but none of them solve my problem.\n\nPossible solution for future readers:\nLike @Sampath mentioned in his answer, I had two versions of python installed. To solve the issue, I manually deleted all the installation files of the older version.","AnswerCount":1,"Available Count":1,"Score":0.1973753202,"is_accepted":false,"ViewCount":1294,"Q_Id":60012193,"Users Score":1,"Answer":"Welcome to SO!\n\nIt is likely that there are 2 python versions(Python2.X and Python3.X) are installed in your system.\npip is likely pointing to your Python2.X and so if you want to used libraries installed in this version ===> use python to run\npip3 is pointing to your Python3.X, so use Python3 in your terminal to use this.\n\nNote:\n\nTo know installed libraries in your python2 use pip freeze or pip3 freeze for Python3\nIn case, if you are getting error for Python3 as not then this is like to be system path issues.\n\nIf you are still having trouble, you learn more about Anaconda-Python which has a curated list of steps and guidelines that are easy for beginners too. Hope this helps!","Q_Score":1,"Tags":"python,numpy,pip","A_Id":60019415,"CreationDate":"2020-01-31T22:37:00.000","Title":"ModuleNotFoundError: No module named 'numpy' after using 'pip install numpy'","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0},{"Question":"I'm currently trying to implement a neural network in Python to play the game of Snake that is trained using a genetic algorithm (although that's a separate matter right now).\nEvery network that plays the game does the same movement over and over (e.g. continues in a straight line, keeps turning left). There are 5 inputs to the network: the distance to an object (food, a boundary, its own tail) in all four directions, and the angle between the food and the direction the snake is facing. The three outputs represent turning left, continuing straight, and turning right.\nI've never worked with anything like this before so I have a fairly basic understanding at this point. The number of hidden layers and the number of nodes per layer is variable, and is something I have been altering a lot to test, but the snakes continue to each repeat the exact same motion.\nAny advice on why this is happening would be greatly appreciated, and how to fix it. I can show my code if it's useful.","AnswerCount":1,"Available Count":1,"Score":0.0,"is_accepted":false,"ViewCount":77,"Q_Id":60022144,"Users Score":0,"Answer":"You may have weights initialized to zero and if they aren't trained properly and stay zero for some reason, neural network will be producing bias as an output always.","Q_Score":0,"Tags":"python,machine-learning,neural-network","A_Id":60022224,"CreationDate":"2020-02-01T23:55:00.000","Title":"Neural network produing same output regardless of input","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":0,"System Administration and DevOps":0,"Web Development":0},{"Question":"Is it in memory?\nIf so, then it doesn't matter if I import chunk by chunk or not because eventually, when I concatenate them, they'll all be stored in memory. \nDoes that mean for a large data set, there is no way to use pandas?","AnswerCount":2,"Available Count":1,"Score":0.0996679946,"is_accepted":false,"ViewCount":114,"Q_Id":60023966,"Users Score":1,"Answer":"Yes it is in memory, and yes when the dataset gets too large you have to use other tools.\nOf course you can load data in chucks, process one chunk at a time and write down the results (and so free memory for the next chunk).\nThat works fine for some type of process like filtering and annotating while if you need sorting or grouping you need to use some other tool, personally I like bigquery from google cloud.","Q_Score":1,"Tags":"python,pandas","A_Id":60024009,"CreationDate":"2020-02-02T07:08:00.000","Title":"Where does pandas store the DataFrame while the program is running?","Data Science and Machine Learning":1,"Database and SQL":0,"GUI and Desktop Applications":0,"Networking and APIs":0,"Other":0,"Python Basics and Environment":1,"System Administration and DevOps":0,"Web Development":0}]